From e8d0dadeecf865bd5cb5487b28ef6e027c5027bd Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Wed, 7 Oct 2020 10:29:00 +0200
Subject: [PATCH 01/46] Team_4 Regression Skeleton python notebook

Hey Team, I am adding the skeleton notebook that Thiya created for our team to collaborate on.
---
 team4_regression.ipynb | 282 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 282 insertions(+)
 create mode 100644 team4_regression.ipynb

diff --git a/team4_regression.ipynb b/team4_regression.ipynb
new file mode 100644
index 0000000..2be4f33
--- /dev/null
+++ b/team4_regression.ipynb
@@ -0,0 +1,282 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "l_LulNCC8z96"
+   },
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xpXdowrE9DxW"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nhKd4hWx9GFt"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "6JhpWJi59J1p"
+   },
+   "source": [
+    "## Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "f8GfYDZ59O18"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "AyhQaTwP9RzG"
+   },
+   "source": [
+    "## Splitting the dataset into the Training set and Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "bxOOauiN9VpC"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZijQwFMQ9itx"
+   },
+   "source": [
+    "## Training the Simple Linear Regression model on the Training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 882,
+     "status": "ok",
+     "timestamp": 1586352495803,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "B4Aj_8YJ9l7J",
+    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wa2T1Lq89o5H"
+   },
+   "source": [
+    "## Predicting the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Rn_sTJ2o9smm"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "-zSoMZ-P9v8t"
+   },
+   "source": [
+    "## Visualising the Training set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 763,
+     "status": "ok",
+     "timestamp": 1586352502760,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "IAePn_u-93tI",
+    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "EUX1Vhsv97ZT"
+   },
+   "source": [
+    "## Visualising the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1097,
+     "status": "ok",
+     "timestamp": 1586352506966,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "Ze9vpBTf-Bol",
+    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
+   "name": "Simple Linear Regression",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 0f2fe6395b3085dfc573a5d5b1e706b052fe13e9 Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Thu, 8 Oct 2020 11:35:38 +0200
Subject: [PATCH 02/46] Created using Colaboratory

---
 team4_regression.ipynb | 2164 ++++++++++++++++++++++++++++++++++------
 1 file changed, 1885 insertions(+), 279 deletions(-)

diff --git a/team4_regression.ipynb b/team4_regression.ipynb
index 2be4f33..8795644 100644
--- a/team4_regression.ipynb
+++ b/team4_regression.ipynb
@@ -1,282 +1,1888 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "l_LulNCC8z96"
-   },
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xpXdowrE9DxW"
-   },
-   "source": [
-    "## Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nhKd4hWx9GFt"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6JhpWJi59J1p"
-   },
-   "source": [
-    "## Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "f8GfYDZ59O18"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exploritory Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "AyhQaTwP9RzG"
-   },
-   "source": [
-    "## Splitting the dataset into the Training set and Test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "bxOOauiN9VpC"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ZijQwFMQ9itx"
-   },
-   "source": [
-    "## Training the Simple Linear Regression model on the Training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 882,
-     "status": "ok",
-     "timestamp": 1586352495803,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "B4Aj_8YJ9l7J",
-    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wa2T1Lq89o5H"
-   },
-   "source": [
-    "## Predicting the Test set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Rn_sTJ2o9smm"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-zSoMZ-P9v8t"
-   },
-   "source": [
-    "## Visualising the Training set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 295
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 763,
-     "status": "ok",
-     "timestamp": 1586352502760,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "IAePn_u-93tI",
-    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "EUX1Vhsv97ZT"
-   },
-   "source": [
-    "## Visualising the Test set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 295
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 1097,
-     "status": "ok",
-     "timestamp": 1586352506966,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "Ze9vpBTf-Bol",
-    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
-   "name": "Simple Linear Regression",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+      "name": "team4_regression.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.9"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mokai1701/Team_4_Regression_Predict_API_Instructions/blob/master/team4_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uzo7rtQ5OeJD"
+      },
+      "source": [
+        "# Regression"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l_LulNCC8z96"
+      },
+      "source": [
+        "# Introduction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JAp7-5iTOeJS"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xpXdowrE9DxW"
+      },
+      "source": [
+        "## Importing the libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nhKd4hWx9GFt"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6JhpWJi59J1p"
+      },
+      "source": [
+        "## Importing the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "f8GfYDZ59O18",
+        "outputId": "451c154c-c0e5-4861-c2da-e5c882edf913",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 426
+        }
+      },
+      "source": [
+        "train = pd.read_csv('Train.csv')\n",
+        "test = pd.read_csv('Test.csv')\n",
+        "df_rider = pd.read_csv('Riders.csv') \n",
+        "df_variable = pd.read_csv('VariableDefinitions.csv')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "FileNotFoundError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-4-619d5bd3a32c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_rider\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Riders.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'VariableDefinitions.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1169\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Train.csv'"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oUB5_xjSAelw",
+        "outputId": "e0f02965-8a04-41a2-d98e-561f390ac26b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 181
+        }
+      },
+      "source": [
+        "# Viewing the first five rows of the dataframe\n",
+        "df_rider.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-1-cfee79f0987f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Viewing the first five rows of the dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_rider\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'df_rider' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3zKj25KPFeH4",
+        "outputId": "d8caffb7-cc88-4cf2-bb82-4c2f30ed964b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 411
+        }
+      },
+      "source": [
+        "test.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Order No</th>\n",
+              "      <th>User Id</th>\n",
+              "      <th>Vehicle Type</th>\n",
+              "      <th>Platform Type</th>\n",
+              "      <th>Personal or Business</th>\n",
+              "      <th>Placement - Day of Month</th>\n",
+              "      <th>Placement - Weekday (Mo = 1)</th>\n",
+              "      <th>Placement - Time</th>\n",
+              "      <th>Confirmation - Day of Month</th>\n",
+              "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+              "      <th>Confirmation - Time</th>\n",
+              "      <th>Arrival at Pickup - Day of Month</th>\n",
+              "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+              "      <th>Arrival at Pickup - Time</th>\n",
+              "      <th>Pickup - Day of Month</th>\n",
+              "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+              "      <th>Pickup - Time</th>\n",
+              "      <th>Distance (KM)</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Precipitation in millimeters</th>\n",
+              "      <th>Pickup Lat</th>\n",
+              "      <th>Pickup Long</th>\n",
+              "      <th>Destination Lat</th>\n",
+              "      <th>Destination Long</th>\n",
+              "      <th>Rider Id</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Order_No_19248</td>\n",
+              "      <td>User_Id_3355</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4:44:10 PM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4:44:29 PM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4:53:04 PM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5:06:47 PM</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-1.333275</td>\n",
+              "      <td>36.870815</td>\n",
+              "      <td>-1.305249</td>\n",
+              "      <td>36.822390</td>\n",
+              "      <td>Rider_Id_192</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Order_No_12736</td>\n",
+              "      <td>User_Id_3647</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>17</td>\n",
+              "      <td>5</td>\n",
+              "      <td>12:57:35 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>5</td>\n",
+              "      <td>12:59:17 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1:20:27 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1:25:37 PM</td>\n",
+              "      <td>5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-1.272639</td>\n",
+              "      <td>36.794723</td>\n",
+              "      <td>-1.277007</td>\n",
+              "      <td>36.823907</td>\n",
+              "      <td>Rider_Id_868</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Order_No_768</td>\n",
+              "      <td>User_Id_2154</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>27</td>\n",
+              "      <td>4</td>\n",
+              "      <td>11:08:14 AM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>4</td>\n",
+              "      <td>11:25:05 AM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>4</td>\n",
+              "      <td>11:33:20 AM</td>\n",
+              "      <td>27</td>\n",
+              "      <td>4</td>\n",
+              "      <td>11:57:54 AM</td>\n",
+              "      <td>5</td>\n",
+              "      <td>22.8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-1.290894</td>\n",
+              "      <td>36.822971</td>\n",
+              "      <td>-1.276574</td>\n",
+              "      <td>36.851365</td>\n",
+              "      <td>Rider_Id_26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Order_No_15332</td>\n",
+              "      <td>User_Id_2910</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1:51:35 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1:53:27 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2:02:41 PM</td>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2:16:52 PM</td>\n",
+              "      <td>5</td>\n",
+              "      <td>24.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-1.290503</td>\n",
+              "      <td>36.809646</td>\n",
+              "      <td>-1.303382</td>\n",
+              "      <td>36.790658</td>\n",
+              "      <td>Rider_Id_685</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Order_No_21373</td>\n",
+              "      <td>User_Id_1205</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>11:30:28 AM</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>11:34:45 AM</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>11:47:19 AM</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>11:56:04 AM</td>\n",
+              "      <td>6</td>\n",
+              "      <td>24.4</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>-1.281081</td>\n",
+              "      <td>36.814423</td>\n",
+              "      <td>-1.266467</td>\n",
+              "      <td>36.792161</td>\n",
+              "      <td>Rider_Id_858</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Order No       User Id  ... Destination Long      Rider Id\n",
+              "0  Order_No_19248  User_Id_3355  ...        36.822390  Rider_Id_192\n",
+              "1  Order_No_12736  User_Id_3647  ...        36.823907  Rider_Id_868\n",
+              "2    Order_No_768  User_Id_2154  ...        36.851365   Rider_Id_26\n",
+              "3  Order_No_15332  User_Id_2910  ...        36.790658  Rider_Id_685\n",
+              "4  Order_No_21373  User_Id_1205  ...        36.792161  Rider_Id_858\n",
+              "\n",
+              "[5 rows x 25 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wkMPC7F_CoLv"
+      },
+      "source": [
+        ""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fvYXNJtOCqZM"
+      },
+      "source": [
+        "# Exploritory Data Analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gvnMRXLABPAQ",
+        "outputId": "27037dc7-f99e-456f-9539-20c128a9cfb5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "# viewing the summary of the datatypes in the dataframe, the total row and column number, and checking for null values\n",
+        "train.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 21201 entries, 0 to 21200\n",
+            "Data columns (total 29 columns):\n",
+            " #   Column                                     Non-Null Count  Dtype  \n",
+            "---  ------                                     --------------  -----  \n",
+            " 0   Order No                                   21201 non-null  object \n",
+            " 1   User Id                                    21201 non-null  object \n",
+            " 2   Vehicle Type                               21201 non-null  object \n",
+            " 3   Platform Type                              21201 non-null  int64  \n",
+            " 4   Personal or Business                       21201 non-null  object \n",
+            " 5   Placement - Day of Month                   21201 non-null  int64  \n",
+            " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
+            " 7   Placement - Time                           21201 non-null  object \n",
+            " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
+            " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
+            " 10  Confirmation - Time                        21201 non-null  object \n",
+            " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
+            " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
+            " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
+            " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
+            " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
+            " 16  Pickup - Time                              21201 non-null  object \n",
+            " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
+            " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
+            " 19  Arrival at Destination - Time              21201 non-null  object \n",
+            " 20  Distance (KM)                              21201 non-null  int64  \n",
+            " 21  Temperature                                16835 non-null  float64\n",
+            " 22  Precipitation in millimeters               552 non-null    float64\n",
+            " 23  Pickup Lat                                 21201 non-null  float64\n",
+            " 24  Pickup Long                                21201 non-null  float64\n",
+            " 25  Destination Lat                            21201 non-null  float64\n",
+            " 26  Destination Long                           21201 non-null  float64\n",
+            " 27  Rider Id                                   21201 non-null  object \n",
+            " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
+            "dtypes: float64(6), int64(13), object(10)\n",
+            "memory usage: 4.7+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TC271nnYFxCk",
+        "outputId": "5b604349-057c-40c4-cf4c-8eb086e8e9f9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "test.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7068 entries, 0 to 7067\n",
+            "Data columns (total 25 columns):\n",
+            " #   Column                                Non-Null Count  Dtype  \n",
+            "---  ------                                --------------  -----  \n",
+            " 0   Order No                              7068 non-null   object \n",
+            " 1   User Id                               7068 non-null   object \n",
+            " 2   Vehicle Type                          7068 non-null   object \n",
+            " 3   Platform Type                         7068 non-null   int64  \n",
+            " 4   Personal or Business                  7068 non-null   object \n",
+            " 5   Placement - Day of Month              7068 non-null   int64  \n",
+            " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
+            " 7   Placement - Time                      7068 non-null   object \n",
+            " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
+            " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
+            " 10  Confirmation - Time                   7068 non-null   object \n",
+            " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
+            " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
+            " 13  Arrival at Pickup - Time              7068 non-null   object \n",
+            " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
+            " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
+            " 16  Pickup - Time                         7068 non-null   object \n",
+            " 17  Distance (KM)                         7068 non-null   int64  \n",
+            " 18  Temperature                           5631 non-null   float64\n",
+            " 19  Precipitation in millimeters          199 non-null    float64\n",
+            " 20  Pickup Lat                            7068 non-null   float64\n",
+            " 21  Pickup Long                           7068 non-null   float64\n",
+            " 22  Destination Lat                       7068 non-null   float64\n",
+            " 23  Destination Long                      7068 non-null   float64\n",
+            " 24  Rider Id                              7068 non-null   object \n",
+            "dtypes: float64(6), int64(10), object(9)\n",
+            "memory usage: 1.3+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aZS3BWJKOeKR"
+      },
+      "source": [
+        "# Data Cleaning and Formatting \n",
+        "\n",
+        "### Taking care of missing data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hFMTFhW8OeKT"
+      },
+      "source": [
+        "# Filling in the null values with zeros for both the Temperature and Precipititation on millimeters columns for both the test and train dataframes\n",
+        "train1 = train.fillna(0, axis = 1)\n",
+        "test1 = test.fillna(0, axis = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nGrxpYVujkU3",
+        "outputId": "99e7d55c-00f5-4da5-9403-6430b76575ef",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "# checking that the null values have been populated\n",
+        "train1.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 21201 entries, 0 to 21200\n",
+            "Data columns (total 29 columns):\n",
+            " #   Column                                     Non-Null Count  Dtype  \n",
+            "---  ------                                     --------------  -----  \n",
+            " 0   Order No                                   21201 non-null  object \n",
+            " 1   User Id                                    21201 non-null  object \n",
+            " 2   Vehicle Type                               21201 non-null  object \n",
+            " 3   Platform Type                              21201 non-null  int64  \n",
+            " 4   Personal or Business                       21201 non-null  object \n",
+            " 5   Placement - Day of Month                   21201 non-null  int64  \n",
+            " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
+            " 7   Placement - Time                           21201 non-null  object \n",
+            " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
+            " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
+            " 10  Confirmation - Time                        21201 non-null  object \n",
+            " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
+            " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
+            " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
+            " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
+            " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
+            " 16  Pickup - Time                              21201 non-null  object \n",
+            " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
+            " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
+            " 19  Arrival at Destination - Time              21201 non-null  object \n",
+            " 20  Distance (KM)                              21201 non-null  int64  \n",
+            " 21  Temperature                                21201 non-null  float64\n",
+            " 22  Precipitation in millimeters               21201 non-null  float64\n",
+            " 23  Pickup Lat                                 21201 non-null  float64\n",
+            " 24  Pickup Long                                21201 non-null  float64\n",
+            " 25  Destination Lat                            21201 non-null  float64\n",
+            " 26  Destination Long                           21201 non-null  float64\n",
+            " 27  Rider Id                                   21201 non-null  object \n",
+            " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
+            "dtypes: float64(6), int64(13), object(10)\n",
+            "memory usage: 4.7+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0LyGyNc_jkmK",
+        "outputId": "25c33465-5ba6-4786-addd-700409432804",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "# checking that the null values have been populated\n",
+        "test1.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7068 entries, 0 to 7067\n",
+            "Data columns (total 25 columns):\n",
+            " #   Column                                Non-Null Count  Dtype  \n",
+            "---  ------                                --------------  -----  \n",
+            " 0   Order No                              7068 non-null   object \n",
+            " 1   User Id                               7068 non-null   object \n",
+            " 2   Vehicle Type                          7068 non-null   object \n",
+            " 3   Platform Type                         7068 non-null   int64  \n",
+            " 4   Personal or Business                  7068 non-null   object \n",
+            " 5   Placement - Day of Month              7068 non-null   int64  \n",
+            " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
+            " 7   Placement - Time                      7068 non-null   object \n",
+            " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
+            " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
+            " 10  Confirmation - Time                   7068 non-null   object \n",
+            " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
+            " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
+            " 13  Arrival at Pickup - Time              7068 non-null   object \n",
+            " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
+            " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
+            " 16  Pickup - Time                         7068 non-null   object \n",
+            " 17  Distance (KM)                         7068 non-null   int64  \n",
+            " 18  Temperature                           7068 non-null   float64\n",
+            " 19  Precipitation in millimeters          7068 non-null   float64\n",
+            " 20  Pickup Lat                            7068 non-null   float64\n",
+            " 21  Pickup Long                           7068 non-null   float64\n",
+            " 22  Destination Lat                       7068 non-null   float64\n",
+            " 23  Destination Long                      7068 non-null   float64\n",
+            " 24  Rider Id                              7068 non-null   object \n",
+            "dtypes: float64(6), int64(10), object(9)\n",
+            "memory usage: 1.3+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p-bqcIkymnfr"
+      },
+      "source": [
+        "### Match the number of train columns to the number of test columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PH-VOqrGmw8N"
+      },
+      "source": [
+        "# save the columns from the test dataframe into a new list called testcols\n",
+        "testcols = test1.columns\n",
+        "\n",
+        "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
+        "newtrain = train1[testcols]\n",
+        "\n",
+        "# Seperate the target variable while at it\n",
+        "y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GhRD75OsnSAK"
+      },
+      "source": [
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "h4_1f_-BnZ5e",
+        "outputId": "c48a8081-4857-49d4-ea9f-8457ebe044b0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "# print the new columns of the train and test data to show that they are the same\n",
+        "print(newtrain.columns)\n",
+        "print(test1.columns)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+            "       'Personal or Business', 'Placement - Day of Month',\n",
+            "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+            "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+            "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+            "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+            "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+            "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+            "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+            "       'Rider Id'],\n",
+            "      dtype='object')\n",
+            "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+            "       'Personal or Business', 'Placement - Day of Month',\n",
+            "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+            "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+            "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+            "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+            "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+            "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+            "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+            "       'Rider Id'],\n",
+            "      dtype='object')\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lpZbfzfHnrFy"
+      },
+      "source": [
+        "### Combining the dataset for the base model\n",
+        "\n",
+        "#### Proceed with caution: Only do this and only this for the base model, doing this for the actual model will return errors and it's not allowed. This is only done for base model submission purposes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fqEDMYUUoAbD",
+        "outputId": "947a96c1-c184-4555-9549-1f7310334a2b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 411
+        }
+      },
+      "source": [
+        "# creating a new dataframe from the concatenated newtrain and test1 dataframes\n",
+        "df = pd.concat([newtrain, test1])\n",
+        "\n",
+        "# view the first five rows\n",
+        "df.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Order No</th>\n",
+              "      <th>User Id</th>\n",
+              "      <th>Vehicle Type</th>\n",
+              "      <th>Platform Type</th>\n",
+              "      <th>Personal or Business</th>\n",
+              "      <th>Placement - Day of Month</th>\n",
+              "      <th>Placement - Weekday (Mo = 1)</th>\n",
+              "      <th>Placement - Time</th>\n",
+              "      <th>Confirmation - Day of Month</th>\n",
+              "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+              "      <th>Confirmation - Time</th>\n",
+              "      <th>Arrival at Pickup - Day of Month</th>\n",
+              "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+              "      <th>Arrival at Pickup - Time</th>\n",
+              "      <th>Pickup - Day of Month</th>\n",
+              "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+              "      <th>Pickup - Time</th>\n",
+              "      <th>Distance (KM)</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Precipitation in millimeters</th>\n",
+              "      <th>Pickup Lat</th>\n",
+              "      <th>Pickup Long</th>\n",
+              "      <th>Destination Lat</th>\n",
+              "      <th>Destination Long</th>\n",
+              "      <th>Rider Id</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Order_No_4211</td>\n",
+              "      <td>User_Id_633</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>9</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:35:46 AM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:40:10 AM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>5</td>\n",
+              "      <td>10:04:47 AM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>5</td>\n",
+              "      <td>10:27:30 AM</td>\n",
+              "      <td>4</td>\n",
+              "      <td>20.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.317755</td>\n",
+              "      <td>36.830370</td>\n",
+              "      <td>-1.300406</td>\n",
+              "      <td>36.829741</td>\n",
+              "      <td>Rider_Id_432</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Order_No_25375</td>\n",
+              "      <td>User_Id_2285</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Personal</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11:16:16 AM</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11:23:21 AM</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11:40:22 AM</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "      <td>11:44:09 AM</td>\n",
+              "      <td>16</td>\n",
+              "      <td>26.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.351453</td>\n",
+              "      <td>36.899315</td>\n",
+              "      <td>-1.295004</td>\n",
+              "      <td>36.814358</td>\n",
+              "      <td>Rider_Id_856</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Order_No_1899</td>\n",
+              "      <td>User_Id_265</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>30</td>\n",
+              "      <td>2</td>\n",
+              "      <td>12:39:25 PM</td>\n",
+              "      <td>30</td>\n",
+              "      <td>2</td>\n",
+              "      <td>12:42:44 PM</td>\n",
+              "      <td>30</td>\n",
+              "      <td>2</td>\n",
+              "      <td>12:49:34 PM</td>\n",
+              "      <td>30</td>\n",
+              "      <td>2</td>\n",
+              "      <td>12:53:03 PM</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.308284</td>\n",
+              "      <td>36.843419</td>\n",
+              "      <td>-1.300921</td>\n",
+              "      <td>36.828195</td>\n",
+              "      <td>Rider_Id_155</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Order_No_9336</td>\n",
+              "      <td>User_Id_1402</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>15</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:25:34 AM</td>\n",
+              "      <td>15</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:26:05 AM</td>\n",
+              "      <td>15</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:37:56 AM</td>\n",
+              "      <td>15</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9:43:06 AM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>19.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.281301</td>\n",
+              "      <td>36.832396</td>\n",
+              "      <td>-1.257147</td>\n",
+              "      <td>36.795063</td>\n",
+              "      <td>Rider_Id_855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Order_No_27883</td>\n",
+              "      <td>User_Id_1737</td>\n",
+              "      <td>Bike</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9:55:18 AM</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9:56:18 AM</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10:03:53 AM</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10:05:23 AM</td>\n",
+              "      <td>9</td>\n",
+              "      <td>15.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.266597</td>\n",
+              "      <td>36.792118</td>\n",
+              "      <td>-1.295041</td>\n",
+              "      <td>36.809817</td>\n",
+              "      <td>Rider_Id_770</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Order No       User Id  ... Destination Long      Rider Id\n",
+              "0   Order_No_4211   User_Id_633  ...        36.829741  Rider_Id_432\n",
+              "1  Order_No_25375  User_Id_2285  ...        36.814358  Rider_Id_856\n",
+              "2   Order_No_1899   User_Id_265  ...        36.828195  Rider_Id_155\n",
+              "3   Order_No_9336  User_Id_1402  ...        36.795063  Rider_Id_855\n",
+              "4  Order_No_27883  User_Id_1737  ...        36.809817  Rider_Id_770\n",
+              "\n",
+              "[5 rows x 25 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "H00GbTXEoVrZ",
+        "outputId": "6cde2784-079c-4b5b-efd8-130c35bfcaf1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "# viewing the datatypes of the concatenated dataframe, for the sake of the base model, we want to do away with as many object type columns as possible\n",
+        "df.dtypes"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Order No                                 object\n",
+              "User Id                                  object\n",
+              "Vehicle Type                             object\n",
+              "Platform Type                             int64\n",
+              "Personal or Business                     object\n",
+              "Placement - Day of Month                  int64\n",
+              "Placement - Weekday (Mo = 1)              int64\n",
+              "Placement - Time                         object\n",
+              "Confirmation - Day of Month               int64\n",
+              "Confirmation - Weekday (Mo = 1)           int64\n",
+              "Confirmation - Time                      object\n",
+              "Arrival at Pickup - Day of Month          int64\n",
+              "Arrival at Pickup - Weekday (Mo = 1)      int64\n",
+              "Arrival at Pickup - Time                 object\n",
+              "Pickup - Day of Month                     int64\n",
+              "Pickup - Weekday (Mo = 1)                 int64\n",
+              "Pickup - Time                            object\n",
+              "Distance (KM)                             int64\n",
+              "Temperature                             float64\n",
+              "Precipitation in millimeters            float64\n",
+              "Pickup Lat                              float64\n",
+              "Pickup Long                             float64\n",
+              "Destination Lat                         float64\n",
+              "Destination Long                        float64\n",
+              "Rider Id                                 object\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AZA9_5xIomwb"
+      },
+      "source": [
+        "Using a correlation heatmap to check for multicollinearity between predictor variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YyIhjbAmopVU",
+        "outputId": "0068320a-99cf-418e-b8a3-389110b33ea2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        }
+      },
+      "source": [
+        "corr = df.corr()\n",
+        "sns.heatmap(corr)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1d8ca29b0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OOn68P3wow6Z"
+      },
+      "source": [
+        "Dropping co-linear variables as well as time variables in order to reduce memory usage"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I8iDNgZopBPX"
+      },
+      "source": [
+        "### Team below is the error I am talking about, the Plan B I am having is to create a new df similar to the one that came from dropping the columns and see if I won't be able to progress to modelling.\n",
+        "\n",
+        "### If anyone spots what keeps causing the error, please advise"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "11QYR_40ozF2"
+      },
+      "source": [
+        "df = df.drop(['Placement - Weekday (Mo = 1)', 'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)', 'Arrival at Pickup - Day of Month', 'Arrival at Pickup - Weekday (Mo = 1)', 'Pickup - Day of Month','Pickup - Weekday (Mo = 1)', 'Placement - Time', 'Confirmation - Time', 'Arrival at Pickup - Time', 'Pickup - Time'], axis = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GgEpClBbH8vQ"
+      },
+      "source": [
+        "df_final = df.drop(['Order No', 'User Id', 'Vehicle Type', 'Rider Id'], axis=1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S0s-PYf9GQ2m",
+        "outputId": "72bfabaf-ac2d-4c6e-e772-efa8e4c469cc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9teXExYbJEBz",
+        "outputId": "423d7f9f-1855-45c4-e86a-2a22a3e95dab",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        }
+      },
+      "source": [
+        "df_final.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Platform Type</th>\n",
+              "      <th>Personal or Business</th>\n",
+              "      <th>Placement - Day of Month</th>\n",
+              "      <th>Distance (KM)</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Precipitation in millimeters</th>\n",
+              "      <th>Pickup Lat</th>\n",
+              "      <th>Pickup Long</th>\n",
+              "      <th>Destination Lat</th>\n",
+              "      <th>Destination Long</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>9</td>\n",
+              "      <td>4</td>\n",
+              "      <td>20.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.317755</td>\n",
+              "      <td>36.830370</td>\n",
+              "      <td>-1.300406</td>\n",
+              "      <td>36.829741</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Personal</td>\n",
+              "      <td>12</td>\n",
+              "      <td>16</td>\n",
+              "      <td>26.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.351453</td>\n",
+              "      <td>36.899315</td>\n",
+              "      <td>-1.295004</td>\n",
+              "      <td>36.814358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>30</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.308284</td>\n",
+              "      <td>36.843419</td>\n",
+              "      <td>-1.300921</td>\n",
+              "      <td>36.828195</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Business</td>\n",
+              "      <td>15</td>\n",
+              "      <td>9</td>\n",
+              "      <td>19.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.281301</td>\n",
+              "      <td>36.832396</td>\n",
+              "      <td>-1.257147</td>\n",
+              "      <td>36.795063</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal</td>\n",
+              "      <td>13</td>\n",
+              "      <td>9</td>\n",
+              "      <td>15.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.266597</td>\n",
+              "      <td>36.792118</td>\n",
+              "      <td>-1.295041</td>\n",
+              "      <td>36.809817</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Platform Type Personal or Business  ...  Destination Lat  Destination Long\n",
+              "0              3             Business  ...        -1.300406         36.829741\n",
+              "1              3             Personal  ...        -1.295004         36.814358\n",
+              "2              3             Business  ...        -1.300921         36.828195\n",
+              "3              3             Business  ...        -1.257147         36.795063\n",
+              "4              1             Personal  ...        -1.295041         36.809817\n",
+              "\n",
+              "[5 rows x 10 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CDj9j_lxJQKv",
+        "outputId": "946722cf-cac6-4bfd-85df-f95d367d8aec",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "df_final.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 28269 entries, 0 to 7067\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column                        Non-Null Count  Dtype  \n",
+            "---  ------                        --------------  -----  \n",
+            " 0   Platform Type                 28269 non-null  int64  \n",
+            " 1   Personal or Business          28269 non-null  object \n",
+            " 2   Placement - Day of Month      28269 non-null  int64  \n",
+            " 3   Distance (KM)                 28269 non-null  int64  \n",
+            " 4   Temperature                   28269 non-null  float64\n",
+            " 5   Precipitation in millimeters  28269 non-null  float64\n",
+            " 6   Pickup Lat                    28269 non-null  float64\n",
+            " 7   Pickup Long                   28269 non-null  float64\n",
+            " 8   Destination Lat               28269 non-null  float64\n",
+            " 9   Destination Long              28269 non-null  float64\n",
+            "dtypes: float64(6), int64(3), object(1)\n",
+            "memory usage: 2.4+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "D3RXDBrvJZym",
+        "outputId": "17647a4d-8585-44a8-ef97-1a1e16671c68",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 258
+        }
+      },
+      "source": [
+        "df_final = pd.get_dummies(df_final, drop_first=True)\n",
+        "df_final.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Platform Type</th>\n",
+              "      <th>Placement - Day of Month</th>\n",
+              "      <th>Distance (KM)</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Precipitation in millimeters</th>\n",
+              "      <th>Pickup Lat</th>\n",
+              "      <th>Pickup Long</th>\n",
+              "      <th>Destination Lat</th>\n",
+              "      <th>Destination Long</th>\n",
+              "      <th>Personal or Business_Business</th>\n",
+              "      <th>Personal or Business_Personal</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3</td>\n",
+              "      <td>9</td>\n",
+              "      <td>4</td>\n",
+              "      <td>20.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.317755</td>\n",
+              "      <td>36.830370</td>\n",
+              "      <td>-1.300406</td>\n",
+              "      <td>36.829741</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>16</td>\n",
+              "      <td>26.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.351453</td>\n",
+              "      <td>36.899315</td>\n",
+              "      <td>-1.295004</td>\n",
+              "      <td>36.814358</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>30</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.308284</td>\n",
+              "      <td>36.843419</td>\n",
+              "      <td>-1.300921</td>\n",
+              "      <td>36.828195</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>15</td>\n",
+              "      <td>9</td>\n",
+              "      <td>19.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.281301</td>\n",
+              "      <td>36.832396</td>\n",
+              "      <td>-1.257147</td>\n",
+              "      <td>36.795063</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13</td>\n",
+              "      <td>9</td>\n",
+              "      <td>15.4</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.266597</td>\n",
+              "      <td>36.792118</td>\n",
+              "      <td>-1.295041</td>\n",
+              "      <td>36.809817</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Platform Type  ...  Personal or Business_Personal\n",
+              "0              3  ...                              0\n",
+              "1              3  ...                              1\n",
+              "2              3  ...                              0\n",
+              "3              3  ...                              0\n",
+              "4              1  ...                              1\n",
+              "\n",
+              "[5 rows x 11 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cBshEqtwJbMM"
+      },
+      "source": [
+        "#y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)\n",
+        "finaltrain = df_final[:len(train)]\n",
+        "finaltest = df_final[len(train):]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3HuO-M1MJrJX"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_val, y_train, y_val = train_test_split(finaltrain, y, test_size=0.3)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VAFRGIpsKy3N",
+        "outputId": "615300fe-16cd-46da-c7ca-9ea83c379351",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(x_train, y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6_slUwxdKzp8"
+      },
+      "source": [
+        "val_preds = lr.predict(x_val)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TtBhydw4Kz5g"
+      },
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "def rmse(y_test, y_predict):\n",
+        "  return np.sqrt(mean_squared_error(y_test, y_predict))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "152-nZYJL9Il",
+        "outputId": "dc25e6c4-32db-43fa-842e-832baa6e654e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "rmse(y_val, val_preds)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "797.0468983875842"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FvJskkBtL9WP"
+      },
+      "source": [
+        "y_pred = lr.predict(finaltest)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hZ_CF7ukL9iP",
+        "outputId": "84172ee3-3ee1-4772-e3bc-d9d286921819",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "submission_df = test1[['Order No']]\n",
+        "submission_df['Time from Pickup to Arrival'] = y_pred"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  \n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wNL5aolXNo_i",
+        "outputId": "7c7ee066-b292-495f-d630-201c29c243ee",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        }
+      },
+      "source": [
+        "submission_df"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Order No</th>\n",
+              "      <th>Time from Pickup to Arrival</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Order_No_19248</td>\n",
+              "      <td>1290.862912</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Order_No_12736</td>\n",
+              "      <td>1081.300485</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Order_No_768</td>\n",
+              "      <td>1058.000881</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Order_No_15332</td>\n",
+              "      <td>1105.787069</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Order_No_21373</td>\n",
+              "      <td>1194.660281</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7063</th>\n",
+              "      <td>Order_No_3612</td>\n",
+              "      <td>1122.848121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7064</th>\n",
+              "      <td>Order_No_7657</td>\n",
+              "      <td>2894.321995</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7065</th>\n",
+              "      <td>Order_No_1969</td>\n",
+              "      <td>1649.879452</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7066</th>\n",
+              "      <td>Order_No_10591</td>\n",
+              "      <td>2611.831283</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7067</th>\n",
+              "      <td>Order_No_1603</td>\n",
+              "      <td>1458.138944</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>7068 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Order No  Time from Pickup to Arrival\n",
+              "0     Order_No_19248                  1290.862912\n",
+              "1     Order_No_12736                  1081.300485\n",
+              "2       Order_No_768                  1058.000881\n",
+              "3     Order_No_15332                  1105.787069\n",
+              "4     Order_No_21373                  1194.660281\n",
+              "...              ...                          ...\n",
+              "7063   Order_No_3612                  1122.848121\n",
+              "7064   Order_No_7657                  2894.321995\n",
+              "7065   Order_No_1969                  1649.879452\n",
+              "7066  Order_No_10591                  2611.831283\n",
+              "7067   Order_No_1603                  1458.138944\n",
+              "\n",
+              "[7068 rows x 2 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wbxVKI6FNtC3"
+      },
+      "source": [
+        "submission_df.to_csv('base_pred.csv', index=False)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AyhQaTwP9RzG"
+      },
+      "source": [
+        "## Splitting the dataset into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G7k8xA1VJPV8"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bxOOauiN9VpC"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZijQwFMQ9itx"
+      },
+      "source": [
+        "## Training the Simple Linear Regression model on the Training set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "B4Aj_8YJ9l7J"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wa2T1Lq89o5H"
+      },
+      "source": [
+        "## Predicting the Test set results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Rn_sTJ2o9smm"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-zSoMZ-P9v8t"
+      },
+      "source": [
+        "## Visualising the Training set results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IAePn_u-93tI"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EUX1Vhsv97ZT"
+      },
+      "source": [
+        "## Visualising the Test set results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ze9vpBTf-Bol"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WjZAkvukOeL8"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 90d69b79db260850703ed85bf3bfee3252524b69 Mon Sep 17 00:00:00 2001
From: Manko Mofokeng <60190943+MankoMofokeng@users.noreply.github.com>
Date: Thu, 8 Oct 2020 16:28:29 +0200
Subject: [PATCH 03/46] Created using Colaboratory


From 9e2921a32cea6a18e4429a42bad0556488959cda Mon Sep 17 00:00:00 2001
From: Manko Mofokeng <60190943+MankoMofokeng@users.noreply.github.com>
Date: Fri, 9 Oct 2020 01:36:43 +0200
Subject: [PATCH 04/46] Add files via upload

---
 team4_regression.ipynb | 3713 ++++++++++++++++++++--------------------
 1 file changed, 1858 insertions(+), 1855 deletions(-)

diff --git a/team4_regression.ipynb b/team4_regression.ipynb
index 8795644..dde81f2 100644
--- a/team4_regression.ipynb
+++ b/team4_regression.ipynb
@@ -1,1888 +1,1891 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "team4_regression.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.9"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regression"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mokai1701/Team_4_Regression_Predict_API_Instructions/blob/master/team4_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Uzo7rtQ5OeJD"
-      },
-      "source": [
-        "# Regression"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "l_LulNCC8z96"
-      },
-      "source": [
-        "# Introduction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "JAp7-5iTOeJS"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xpXdowrE9DxW"
-      },
-      "source": [
-        "## Importing the libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "nhKd4hWx9GFt"
-      },
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6JhpWJi59J1p"
-      },
-      "source": [
-        "## Importing the dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "f8GfYDZ59O18",
-        "outputId": "451c154c-c0e5-4861-c2da-e5c882edf913",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 426
-        }
-      },
-      "source": [
-        "train = pd.read_csv('Train.csv')\n",
-        "test = pd.read_csv('Test.csv')\n",
-        "df_rider = pd.read_csv('Riders.csv') \n",
-        "df_variable = pd.read_csv('VariableDefinitions.csv')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "FileNotFoundError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-4-619d5bd3a32c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_rider\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Riders.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'VariableDefinitions.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1169\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Train.csv'"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "oUB5_xjSAelw",
-        "outputId": "e0f02965-8a04-41a2-d98e-561f390ac26b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 181
-        }
-      },
-      "source": [
-        "# Viewing the first five rows of the dataframe\n",
-        "df_rider.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "NameError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-1-cfee79f0987f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Viewing the first five rows of the dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_rider\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m: name 'df_rider' is not defined"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "3zKj25KPFeH4",
-        "outputId": "d8caffb7-cc88-4cf2-bb82-4c2f30ed964b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 411
-        }
-      },
-      "source": [
-        "test.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Order No</th>\n",
-              "      <th>User Id</th>\n",
-              "      <th>Vehicle Type</th>\n",
-              "      <th>Platform Type</th>\n",
-              "      <th>Personal or Business</th>\n",
-              "      <th>Placement - Day of Month</th>\n",
-              "      <th>Placement - Weekday (Mo = 1)</th>\n",
-              "      <th>Placement - Time</th>\n",
-              "      <th>Confirmation - Day of Month</th>\n",
-              "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-              "      <th>Confirmation - Time</th>\n",
-              "      <th>Arrival at Pickup - Day of Month</th>\n",
-              "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-              "      <th>Arrival at Pickup - Time</th>\n",
-              "      <th>Pickup - Day of Month</th>\n",
-              "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-              "      <th>Pickup - Time</th>\n",
-              "      <th>Distance (KM)</th>\n",
-              "      <th>Temperature</th>\n",
-              "      <th>Precipitation in millimeters</th>\n",
-              "      <th>Pickup Lat</th>\n",
-              "      <th>Pickup Long</th>\n",
-              "      <th>Destination Lat</th>\n",
-              "      <th>Destination Long</th>\n",
-              "      <th>Rider Id</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Order_No_19248</td>\n",
-              "      <td>User_Id_3355</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>27</td>\n",
-              "      <td>3</td>\n",
-              "      <td>4:44:10 PM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>3</td>\n",
-              "      <td>4:44:29 PM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>3</td>\n",
-              "      <td>4:53:04 PM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>3</td>\n",
-              "      <td>5:06:47 PM</td>\n",
-              "      <td>8</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-1.333275</td>\n",
-              "      <td>36.870815</td>\n",
-              "      <td>-1.305249</td>\n",
-              "      <td>36.822390</td>\n",
-              "      <td>Rider_Id_192</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Order_No_12736</td>\n",
-              "      <td>User_Id_3647</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>17</td>\n",
-              "      <td>5</td>\n",
-              "      <td>12:57:35 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>5</td>\n",
-              "      <td>12:59:17 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1:20:27 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1:25:37 PM</td>\n",
-              "      <td>5</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-1.272639</td>\n",
-              "      <td>36.794723</td>\n",
-              "      <td>-1.277007</td>\n",
-              "      <td>36.823907</td>\n",
-              "      <td>Rider_Id_868</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Order_No_768</td>\n",
-              "      <td>User_Id_2154</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>27</td>\n",
-              "      <td>4</td>\n",
-              "      <td>11:08:14 AM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>4</td>\n",
-              "      <td>11:25:05 AM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>4</td>\n",
-              "      <td>11:33:20 AM</td>\n",
-              "      <td>27</td>\n",
-              "      <td>4</td>\n",
-              "      <td>11:57:54 AM</td>\n",
-              "      <td>5</td>\n",
-              "      <td>22.8</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-1.290894</td>\n",
-              "      <td>36.822971</td>\n",
-              "      <td>-1.276574</td>\n",
-              "      <td>36.851365</td>\n",
-              "      <td>Rider_Id_26</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Order_No_15332</td>\n",
-              "      <td>User_Id_2910</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>17</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1:51:35 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1:53:27 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2:02:41 PM</td>\n",
-              "      <td>17</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2:16:52 PM</td>\n",
-              "      <td>5</td>\n",
-              "      <td>24.5</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-1.290503</td>\n",
-              "      <td>36.809646</td>\n",
-              "      <td>-1.303382</td>\n",
-              "      <td>36.790658</td>\n",
-              "      <td>Rider_Id_685</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Order_No_21373</td>\n",
-              "      <td>User_Id_1205</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>11</td>\n",
-              "      <td>2</td>\n",
-              "      <td>11:30:28 AM</td>\n",
-              "      <td>11</td>\n",
-              "      <td>2</td>\n",
-              "      <td>11:34:45 AM</td>\n",
-              "      <td>11</td>\n",
-              "      <td>2</td>\n",
-              "      <td>11:47:19 AM</td>\n",
-              "      <td>11</td>\n",
-              "      <td>2</td>\n",
-              "      <td>11:56:04 AM</td>\n",
-              "      <td>6</td>\n",
-              "      <td>24.4</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>-1.281081</td>\n",
-              "      <td>36.814423</td>\n",
-              "      <td>-1.266467</td>\n",
-              "      <td>36.792161</td>\n",
-              "      <td>Rider_Id_858</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         Order No       User Id  ... Destination Long      Rider Id\n",
-              "0  Order_No_19248  User_Id_3355  ...        36.822390  Rider_Id_192\n",
-              "1  Order_No_12736  User_Id_3647  ...        36.823907  Rider_Id_868\n",
-              "2    Order_No_768  User_Id_2154  ...        36.851365   Rider_Id_26\n",
-              "3  Order_No_15332  User_Id_2910  ...        36.790658  Rider_Id_685\n",
-              "4  Order_No_21373  User_Id_1205  ...        36.792161  Rider_Id_858\n",
-              "\n",
-              "[5 rows x 25 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wkMPC7F_CoLv"
-      },
-      "source": [
-        ""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fvYXNJtOCqZM"
-      },
-      "source": [
-        "# Exploritory Data Analysis"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "gvnMRXLABPAQ",
-        "outputId": "27037dc7-f99e-456f-9539-20c128a9cfb5",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "# viewing the summary of the datatypes in the dataframe, the total row and column number, and checking for null values\n",
-        "train.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 21201 entries, 0 to 21200\n",
-            "Data columns (total 29 columns):\n",
-            " #   Column                                     Non-Null Count  Dtype  \n",
-            "---  ------                                     --------------  -----  \n",
-            " 0   Order No                                   21201 non-null  object \n",
-            " 1   User Id                                    21201 non-null  object \n",
-            " 2   Vehicle Type                               21201 non-null  object \n",
-            " 3   Platform Type                              21201 non-null  int64  \n",
-            " 4   Personal or Business                       21201 non-null  object \n",
-            " 5   Placement - Day of Month                   21201 non-null  int64  \n",
-            " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
-            " 7   Placement - Time                           21201 non-null  object \n",
-            " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
-            " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
-            " 10  Confirmation - Time                        21201 non-null  object \n",
-            " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
-            " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
-            " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
-            " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
-            " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
-            " 16  Pickup - Time                              21201 non-null  object \n",
-            " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
-            " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
-            " 19  Arrival at Destination - Time              21201 non-null  object \n",
-            " 20  Distance (KM)                              21201 non-null  int64  \n",
-            " 21  Temperature                                16835 non-null  float64\n",
-            " 22  Precipitation in millimeters               552 non-null    float64\n",
-            " 23  Pickup Lat                                 21201 non-null  float64\n",
-            " 24  Pickup Long                                21201 non-null  float64\n",
-            " 25  Destination Lat                            21201 non-null  float64\n",
-            " 26  Destination Long                           21201 non-null  float64\n",
-            " 27  Rider Id                                   21201 non-null  object \n",
-            " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
-            "dtypes: float64(6), int64(13), object(10)\n",
-            "memory usage: 4.7+ MB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TC271nnYFxCk",
-        "outputId": "5b604349-057c-40c4-cf4c-8eb086e8e9f9",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "test.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 7068 entries, 0 to 7067\n",
-            "Data columns (total 25 columns):\n",
-            " #   Column                                Non-Null Count  Dtype  \n",
-            "---  ------                                --------------  -----  \n",
-            " 0   Order No                              7068 non-null   object \n",
-            " 1   User Id                               7068 non-null   object \n",
-            " 2   Vehicle Type                          7068 non-null   object \n",
-            " 3   Platform Type                         7068 non-null   int64  \n",
-            " 4   Personal or Business                  7068 non-null   object \n",
-            " 5   Placement - Day of Month              7068 non-null   int64  \n",
-            " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
-            " 7   Placement - Time                      7068 non-null   object \n",
-            " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
-            " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
-            " 10  Confirmation - Time                   7068 non-null   object \n",
-            " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
-            " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
-            " 13  Arrival at Pickup - Time              7068 non-null   object \n",
-            " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
-            " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
-            " 16  Pickup - Time                         7068 non-null   object \n",
-            " 17  Distance (KM)                         7068 non-null   int64  \n",
-            " 18  Temperature                           5631 non-null   float64\n",
-            " 19  Precipitation in millimeters          199 non-null    float64\n",
-            " 20  Pickup Lat                            7068 non-null   float64\n",
-            " 21  Pickup Long                           7068 non-null   float64\n",
-            " 22  Destination Lat                       7068 non-null   float64\n",
-            " 23  Destination Long                      7068 non-null   float64\n",
-            " 24  Rider Id                              7068 non-null   object \n",
-            "dtypes: float64(6), int64(10), object(9)\n",
-            "memory usage: 1.3+ MB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aZS3BWJKOeKR"
-      },
-      "source": [
-        "# Data Cleaning and Formatting \n",
-        "\n",
-        "### Taking care of missing data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hFMTFhW8OeKT"
-      },
-      "source": [
-        "# Filling in the null values with zeros for both the Temperature and Precipititation on millimeters columns for both the test and train dataframes\n",
-        "train1 = train.fillna(0, axis = 1)\n",
-        "test1 = test.fillna(0, axis = 1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "nGrxpYVujkU3",
-        "outputId": "99e7d55c-00f5-4da5-9403-6430b76575ef",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "# checking that the null values have been populated\n",
-        "train1.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 21201 entries, 0 to 21200\n",
-            "Data columns (total 29 columns):\n",
-            " #   Column                                     Non-Null Count  Dtype  \n",
-            "---  ------                                     --------------  -----  \n",
-            " 0   Order No                                   21201 non-null  object \n",
-            " 1   User Id                                    21201 non-null  object \n",
-            " 2   Vehicle Type                               21201 non-null  object \n",
-            " 3   Platform Type                              21201 non-null  int64  \n",
-            " 4   Personal or Business                       21201 non-null  object \n",
-            " 5   Placement - Day of Month                   21201 non-null  int64  \n",
-            " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
-            " 7   Placement - Time                           21201 non-null  object \n",
-            " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
-            " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
-            " 10  Confirmation - Time                        21201 non-null  object \n",
-            " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
-            " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
-            " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
-            " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
-            " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
-            " 16  Pickup - Time                              21201 non-null  object \n",
-            " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
-            " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
-            " 19  Arrival at Destination - Time              21201 non-null  object \n",
-            " 20  Distance (KM)                              21201 non-null  int64  \n",
-            " 21  Temperature                                21201 non-null  float64\n",
-            " 22  Precipitation in millimeters               21201 non-null  float64\n",
-            " 23  Pickup Lat                                 21201 non-null  float64\n",
-            " 24  Pickup Long                                21201 non-null  float64\n",
-            " 25  Destination Lat                            21201 non-null  float64\n",
-            " 26  Destination Long                           21201 non-null  float64\n",
-            " 27  Rider Id                                   21201 non-null  object \n",
-            " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
-            "dtypes: float64(6), int64(13), object(10)\n",
-            "memory usage: 4.7+ MB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "0LyGyNc_jkmK",
-        "outputId": "25c33465-5ba6-4786-addd-700409432804",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "# checking that the null values have been populated\n",
-        "test1.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 7068 entries, 0 to 7067\n",
-            "Data columns (total 25 columns):\n",
-            " #   Column                                Non-Null Count  Dtype  \n",
-            "---  ------                                --------------  -----  \n",
-            " 0   Order No                              7068 non-null   object \n",
-            " 1   User Id                               7068 non-null   object \n",
-            " 2   Vehicle Type                          7068 non-null   object \n",
-            " 3   Platform Type                         7068 non-null   int64  \n",
-            " 4   Personal or Business                  7068 non-null   object \n",
-            " 5   Placement - Day of Month              7068 non-null   int64  \n",
-            " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
-            " 7   Placement - Time                      7068 non-null   object \n",
-            " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
-            " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
-            " 10  Confirmation - Time                   7068 non-null   object \n",
-            " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
-            " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
-            " 13  Arrival at Pickup - Time              7068 non-null   object \n",
-            " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
-            " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
-            " 16  Pickup - Time                         7068 non-null   object \n",
-            " 17  Distance (KM)                         7068 non-null   int64  \n",
-            " 18  Temperature                           7068 non-null   float64\n",
-            " 19  Precipitation in millimeters          7068 non-null   float64\n",
-            " 20  Pickup Lat                            7068 non-null   float64\n",
-            " 21  Pickup Long                           7068 non-null   float64\n",
-            " 22  Destination Lat                       7068 non-null   float64\n",
-            " 23  Destination Long                      7068 non-null   float64\n",
-            " 24  Rider Id                              7068 non-null   object \n",
-            "dtypes: float64(6), int64(10), object(9)\n",
-            "memory usage: 1.3+ MB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "p-bqcIkymnfr"
-      },
-      "source": [
-        "### Match the number of train columns to the number of test columns"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "PH-VOqrGmw8N"
-      },
-      "source": [
-        "# save the columns from the test dataframe into a new list called testcols\n",
-        "testcols = test1.columns\n",
-        "\n",
-        "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
-        "newtrain = train1[testcols]\n",
-        "\n",
-        "# Seperate the target variable while at it\n",
-        "y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GhRD75OsnSAK"
-      },
-      "source": [
-        ""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "h4_1f_-BnZ5e",
-        "outputId": "c48a8081-4857-49d4-ea9f-8457ebe044b0",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "# print the new columns of the train and test data to show that they are the same\n",
-        "print(newtrain.columns)\n",
-        "print(test1.columns)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-            "       'Personal or Business', 'Placement - Day of Month',\n",
-            "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-            "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-            "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-            "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-            "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-            "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-            "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-            "       'Rider Id'],\n",
-            "      dtype='object')\n",
-            "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-            "       'Personal or Business', 'Placement - Day of Month',\n",
-            "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-            "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-            "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-            "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-            "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-            "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-            "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-            "       'Rider Id'],\n",
-            "      dtype='object')\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lpZbfzfHnrFy"
-      },
-      "source": [
-        "### Combining the dataset for the base model\n",
-        "\n",
-        "#### Proceed with caution: Only do this and only this for the base model, doing this for the actual model will return errors and it's not allowed. This is only done for base model submission purposes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fqEDMYUUoAbD",
-        "outputId": "947a96c1-c184-4555-9549-1f7310334a2b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 411
-        }
-      },
-      "source": [
-        "# creating a new dataframe from the concatenated newtrain and test1 dataframes\n",
-        "df = pd.concat([newtrain, test1])\n",
-        "\n",
-        "# view the first five rows\n",
-        "df.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Order No</th>\n",
-              "      <th>User Id</th>\n",
-              "      <th>Vehicle Type</th>\n",
-              "      <th>Platform Type</th>\n",
-              "      <th>Personal or Business</th>\n",
-              "      <th>Placement - Day of Month</th>\n",
-              "      <th>Placement - Weekday (Mo = 1)</th>\n",
-              "      <th>Placement - Time</th>\n",
-              "      <th>Confirmation - Day of Month</th>\n",
-              "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-              "      <th>Confirmation - Time</th>\n",
-              "      <th>Arrival at Pickup - Day of Month</th>\n",
-              "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-              "      <th>Arrival at Pickup - Time</th>\n",
-              "      <th>Pickup - Day of Month</th>\n",
-              "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-              "      <th>Pickup - Time</th>\n",
-              "      <th>Distance (KM)</th>\n",
-              "      <th>Temperature</th>\n",
-              "      <th>Precipitation in millimeters</th>\n",
-              "      <th>Pickup Lat</th>\n",
-              "      <th>Pickup Long</th>\n",
-              "      <th>Destination Lat</th>\n",
-              "      <th>Destination Long</th>\n",
-              "      <th>Rider Id</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Order_No_4211</td>\n",
-              "      <td>User_Id_633</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>9</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:35:46 AM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:40:10 AM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>5</td>\n",
-              "      <td>10:04:47 AM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>5</td>\n",
-              "      <td>10:27:30 AM</td>\n",
-              "      <td>4</td>\n",
-              "      <td>20.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.317755</td>\n",
-              "      <td>36.830370</td>\n",
-              "      <td>-1.300406</td>\n",
-              "      <td>36.829741</td>\n",
-              "      <td>Rider_Id_432</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Order_No_25375</td>\n",
-              "      <td>User_Id_2285</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Personal</td>\n",
-              "      <td>12</td>\n",
-              "      <td>5</td>\n",
-              "      <td>11:16:16 AM</td>\n",
-              "      <td>12</td>\n",
-              "      <td>5</td>\n",
-              "      <td>11:23:21 AM</td>\n",
-              "      <td>12</td>\n",
-              "      <td>5</td>\n",
-              "      <td>11:40:22 AM</td>\n",
-              "      <td>12</td>\n",
-              "      <td>5</td>\n",
-              "      <td>11:44:09 AM</td>\n",
-              "      <td>16</td>\n",
-              "      <td>26.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.351453</td>\n",
-              "      <td>36.899315</td>\n",
-              "      <td>-1.295004</td>\n",
-              "      <td>36.814358</td>\n",
-              "      <td>Rider_Id_856</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Order_No_1899</td>\n",
-              "      <td>User_Id_265</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>30</td>\n",
-              "      <td>2</td>\n",
-              "      <td>12:39:25 PM</td>\n",
-              "      <td>30</td>\n",
-              "      <td>2</td>\n",
-              "      <td>12:42:44 PM</td>\n",
-              "      <td>30</td>\n",
-              "      <td>2</td>\n",
-              "      <td>12:49:34 PM</td>\n",
-              "      <td>30</td>\n",
-              "      <td>2</td>\n",
-              "      <td>12:53:03 PM</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.308284</td>\n",
-              "      <td>36.843419</td>\n",
-              "      <td>-1.300921</td>\n",
-              "      <td>36.828195</td>\n",
-              "      <td>Rider_Id_155</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Order_No_9336</td>\n",
-              "      <td>User_Id_1402</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>15</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:25:34 AM</td>\n",
-              "      <td>15</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:26:05 AM</td>\n",
-              "      <td>15</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:37:56 AM</td>\n",
-              "      <td>15</td>\n",
-              "      <td>5</td>\n",
-              "      <td>9:43:06 AM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>19.2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.281301</td>\n",
-              "      <td>36.832396</td>\n",
-              "      <td>-1.257147</td>\n",
-              "      <td>36.795063</td>\n",
-              "      <td>Rider_Id_855</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Order_No_27883</td>\n",
-              "      <td>User_Id_1737</td>\n",
-              "      <td>Bike</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Personal</td>\n",
-              "      <td>13</td>\n",
-              "      <td>1</td>\n",
-              "      <td>9:55:18 AM</td>\n",
-              "      <td>13</td>\n",
-              "      <td>1</td>\n",
-              "      <td>9:56:18 AM</td>\n",
-              "      <td>13</td>\n",
-              "      <td>1</td>\n",
-              "      <td>10:03:53 AM</td>\n",
-              "      <td>13</td>\n",
-              "      <td>1</td>\n",
-              "      <td>10:05:23 AM</td>\n",
-              "      <td>9</td>\n",
-              "      <td>15.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.266597</td>\n",
-              "      <td>36.792118</td>\n",
-              "      <td>-1.295041</td>\n",
-              "      <td>36.809817</td>\n",
-              "      <td>Rider_Id_770</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         Order No       User Id  ... Destination Long      Rider Id\n",
-              "0   Order_No_4211   User_Id_633  ...        36.829741  Rider_Id_432\n",
-              "1  Order_No_25375  User_Id_2285  ...        36.814358  Rider_Id_856\n",
-              "2   Order_No_1899   User_Id_265  ...        36.828195  Rider_Id_155\n",
-              "3   Order_No_9336  User_Id_1402  ...        36.795063  Rider_Id_855\n",
-              "4  Order_No_27883  User_Id_1737  ...        36.809817  Rider_Id_770\n",
-              "\n",
-              "[5 rows x 25 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "H00GbTXEoVrZ",
-        "outputId": "6cde2784-079c-4b5b-efd8-130c35bfcaf1",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "# viewing the datatypes of the concatenated dataframe, for the sake of the base model, we want to do away with as many object type columns as possible\n",
-        "df.dtypes"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Order No                                 object\n",
-              "User Id                                  object\n",
-              "Vehicle Type                             object\n",
-              "Platform Type                             int64\n",
-              "Personal or Business                     object\n",
-              "Placement - Day of Month                  int64\n",
-              "Placement - Weekday (Mo = 1)              int64\n",
-              "Placement - Time                         object\n",
-              "Confirmation - Day of Month               int64\n",
-              "Confirmation - Weekday (Mo = 1)           int64\n",
-              "Confirmation - Time                      object\n",
-              "Arrival at Pickup - Day of Month          int64\n",
-              "Arrival at Pickup - Weekday (Mo = 1)      int64\n",
-              "Arrival at Pickup - Time                 object\n",
-              "Pickup - Day of Month                     int64\n",
-              "Pickup - Weekday (Mo = 1)                 int64\n",
-              "Pickup - Time                            object\n",
-              "Distance (KM)                             int64\n",
-              "Temperature                             float64\n",
-              "Precipitation in millimeters            float64\n",
-              "Pickup Lat                              float64\n",
-              "Pickup Long                             float64\n",
-              "Destination Lat                         float64\n",
-              "Destination Long                        float64\n",
-              "Rider Id                                 object\n",
-              "dtype: object"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AZA9_5xIomwb"
-      },
-      "source": [
-        "Using a correlation heatmap to check for multicollinearity between predictor variables"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "YyIhjbAmopVU",
-        "outputId": "0068320a-99cf-418e-b8a3-389110b33ea2",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 460
-        }
-      },
-      "source": [
-        "corr = df.corr()\n",
-        "sns.heatmap(corr)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1d8ca29b0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OOn68P3wow6Z"
-      },
-      "source": [
-        "Dropping co-linear variables as well as time variables in order to reduce memory usage"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I8iDNgZopBPX"
-      },
-      "source": [
-        "### Team below is the error I am talking about, the Plan B I am having is to create a new df similar to the one that came from dropping the columns and see if I won't be able to progress to modelling.\n",
-        "\n",
-        "### If anyone spots what keeps causing the error, please advise"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "11QYR_40ozF2"
-      },
-      "source": [
-        "df = df.drop(['Placement - Weekday (Mo = 1)', 'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)', 'Arrival at Pickup - Day of Month', 'Arrival at Pickup - Weekday (Mo = 1)', 'Pickup - Day of Month','Pickup - Weekday (Mo = 1)', 'Placement - Time', 'Confirmation - Time', 'Arrival at Pickup - Time', 'Pickup - Time'], axis = 1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "GgEpClBbH8vQ"
-      },
-      "source": [
-        "df_final = df.drop(['Order No', 'User Id', 'Vehicle Type', 'Rider Id'], axis=1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "S0s-PYf9GQ2m",
-        "outputId": "72bfabaf-ac2d-4c6e-e772-efa8e4c469cc",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mounted at /content/drive\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "l_LulNCC8z96"
+   },
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xpXdowrE9DxW"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nhKd4hWx9GFt"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np #Why?\n",
+    "import pandas as pd #Why?\n",
+    "import matplotlib.pyplot as plt #Why?\n",
+    "from matplotlib import rc #Why?\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "6JhpWJi59J1p"
+   },
+   "source": [
+    "## Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "f8GfYDZ59O18"
+   },
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('Train.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "9teXExYbJEBz",
-        "outputId": "423d7f9f-1855-45c4-e86a-2a22a3e95dab",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 238
-        }
-      },
-      "source": [
-        "df_final.head()"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Platform Type</th>\n",
-              "      <th>Personal or Business</th>\n",
-              "      <th>Placement - Day of Month</th>\n",
-              "      <th>Distance (KM)</th>\n",
-              "      <th>Temperature</th>\n",
-              "      <th>Precipitation in millimeters</th>\n",
-              "      <th>Pickup Lat</th>\n",
-              "      <th>Pickup Long</th>\n",
-              "      <th>Destination Lat</th>\n",
-              "      <th>Destination Long</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>9</td>\n",
-              "      <td>4</td>\n",
-              "      <td>20.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.317755</td>\n",
-              "      <td>36.830370</td>\n",
-              "      <td>-1.300406</td>\n",
-              "      <td>36.829741</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>3</td>\n",
-              "      <td>Personal</td>\n",
-              "      <td>12</td>\n",
-              "      <td>16</td>\n",
-              "      <td>26.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.351453</td>\n",
-              "      <td>36.899315</td>\n",
-              "      <td>-1.295004</td>\n",
-              "      <td>36.814358</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>30</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.308284</td>\n",
-              "      <td>36.843419</td>\n",
-              "      <td>-1.300921</td>\n",
-              "      <td>36.828195</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>3</td>\n",
-              "      <td>Business</td>\n",
-              "      <td>15</td>\n",
-              "      <td>9</td>\n",
-              "      <td>19.2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.281301</td>\n",
-              "      <td>36.832396</td>\n",
-              "      <td>-1.257147</td>\n",
-              "      <td>36.795063</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1</td>\n",
-              "      <td>Personal</td>\n",
-              "      <td>13</td>\n",
-              "      <td>9</td>\n",
-              "      <td>15.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.266597</td>\n",
-              "      <td>36.792118</td>\n",
-              "      <td>-1.295041</td>\n",
-              "      <td>36.809817</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Platform Type Personal or Business  ...  Destination Lat  Destination Long\n",
-              "0              3             Business  ...        -1.300406         36.829741\n",
-              "1              3             Personal  ...        -1.295004         36.814358\n",
-              "2              3             Business  ...        -1.300921         36.828195\n",
-              "3              3             Business  ...        -1.257147         36.795063\n",
-              "4              1             Personal  ...        -1.295041         36.809817\n",
-              "\n",
-              "[5 rows x 10 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 23
-        }
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
       ]
-    },
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "CDj9j_lxJQKv",
-        "outputId": "946722cf-cac6-4bfd-85df-f95d367d8aec",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "df_final.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 28269 entries, 0 to 7067\n",
-            "Data columns (total 10 columns):\n",
-            " #   Column                        Non-Null Count  Dtype  \n",
-            "---  ------                        --------------  -----  \n",
-            " 0   Platform Type                 28269 non-null  int64  \n",
-            " 1   Personal or Business          28269 non-null  object \n",
-            " 2   Placement - Day of Month      28269 non-null  int64  \n",
-            " 3   Distance (KM)                 28269 non-null  int64  \n",
-            " 4   Temperature                   28269 non-null  float64\n",
-            " 5   Precipitation in millimeters  28269 non-null  float64\n",
-            " 6   Pickup Lat                    28269 non-null  float64\n",
-            " 7   Pickup Long                   28269 non-null  float64\n",
-            " 8   Destination Lat               28269 non-null  float64\n",
-            " 9   Destination Long              28269 non-null  float64\n",
-            "dtypes: float64(6), int64(3), object(1)\n",
-            "memory usage: 2.4+ MB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21201 entries, 0 to 21200\n",
+      "Data columns (total 29 columns):\n",
+      "Order No                                     21201 non-null object\n",
+      "User Id                                      21201 non-null object\n",
+      "Vehicle Type                                 21201 non-null object\n",
+      "Platform Type                                21201 non-null int64\n",
+      "Personal or Business                         21201 non-null object\n",
+      "Placement - Day of Month                     21201 non-null int64\n",
+      "Placement - Weekday (Mo = 1)                 21201 non-null int64\n",
+      "Placement - Time                             21201 non-null object\n",
+      "Confirmation - Day of Month                  21201 non-null int64\n",
+      "Confirmation - Weekday (Mo = 1)              21201 non-null int64\n",
+      "Confirmation - Time                          21201 non-null object\n",
+      "Arrival at Pickup - Day of Month             21201 non-null int64\n",
+      "Arrival at Pickup - Weekday (Mo = 1)         21201 non-null int64\n",
+      "Arrival at Pickup - Time                     21201 non-null object\n",
+      "Pickup - Day of Month                        21201 non-null int64\n",
+      "Pickup - Weekday (Mo = 1)                    21201 non-null int64\n",
+      "Pickup - Time                                21201 non-null object\n",
+      "Arrival at Destination - Day of Month        21201 non-null int64\n",
+      "Arrival at Destination - Weekday (Mo = 1)    21201 non-null int64\n",
+      "Arrival at Destination - Time                21201 non-null object\n",
+      "Distance (KM)                                21201 non-null int64\n",
+      "Temperature                                  16835 non-null float64\n",
+      "Precipitation in millimeters                 552 non-null float64\n",
+      "Pickup Lat                                   21201 non-null float64\n",
+      "Pickup Long                                  21201 non-null float64\n",
+      "Destination Lat                              21201 non-null float64\n",
+      "Destination Long                             21201 non-null float64\n",
+      "Rider Id                                     21201 non-null object\n",
+      "Time from Pickup to Arrival                  21201 non-null int64\n",
+      "dtypes: float64(6), int64(13), object(10)\n",
+      "memory usage: 4.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = pd.read_csv('Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "D3RXDBrvJZym",
-        "outputId": "17647a4d-8585-44a8-ef97-1a1e16671c68",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 258
-        }
-      },
-      "source": [
-        "df_final = pd.get_dummies(df_final, drop_first=True)\n",
-        "df_final.head()"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_19248</td>\n",
+       "      <td>User_Id_3355</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4:44:10 PM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5:06:47 PM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.333275</td>\n",
+       "      <td>36.870815</td>\n",
+       "      <td>-1.305249</td>\n",
+       "      <td>36.822390</td>\n",
+       "      <td>Rider_Id_192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_12736</td>\n",
+       "      <td>User_Id_3647</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12:57:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1:25:37 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.272639</td>\n",
+       "      <td>36.794723</td>\n",
+       "      <td>-1.277007</td>\n",
+       "      <td>36.823907</td>\n",
+       "      <td>Rider_Id_868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Order_No_768</td>\n",
+       "      <td>User_Id_2154</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:08:14 AM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:57:54 AM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290894</td>\n",
+       "      <td>36.822971</td>\n",
+       "      <td>-1.276574</td>\n",
+       "      <td>36.851365</td>\n",
+       "      <td>Rider_Id_26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Order_No_15332</td>\n",
+       "      <td>User_Id_2910</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1:51:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2:16:52 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290503</td>\n",
+       "      <td>36.809646</td>\n",
+       "      <td>-1.303382</td>\n",
+       "      <td>36.790658</td>\n",
+       "      <td>Rider_Id_685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Order_No_21373</td>\n",
+       "      <td>User_Id_1205</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:30:28 AM</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:56:04 AM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281081</td>\n",
+       "      <td>36.814423</td>\n",
+       "      <td>-1.266467</td>\n",
+       "      <td>36.792161</td>\n",
+       "      <td>Rider_Id_858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Platform Type</th>\n",
-              "      <th>Placement - Day of Month</th>\n",
-              "      <th>Distance (KM)</th>\n",
-              "      <th>Temperature</th>\n",
-              "      <th>Precipitation in millimeters</th>\n",
-              "      <th>Pickup Lat</th>\n",
-              "      <th>Pickup Long</th>\n",
-              "      <th>Destination Lat</th>\n",
-              "      <th>Destination Long</th>\n",
-              "      <th>Personal or Business_Business</th>\n",
-              "      <th>Personal or Business_Personal</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>3</td>\n",
-              "      <td>9</td>\n",
-              "      <td>4</td>\n",
-              "      <td>20.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.317755</td>\n",
-              "      <td>36.830370</td>\n",
-              "      <td>-1.300406</td>\n",
-              "      <td>36.829741</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>3</td>\n",
-              "      <td>12</td>\n",
-              "      <td>16</td>\n",
-              "      <td>26.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.351453</td>\n",
-              "      <td>36.899315</td>\n",
-              "      <td>-1.295004</td>\n",
-              "      <td>36.814358</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>3</td>\n",
-              "      <td>30</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.308284</td>\n",
-              "      <td>36.843419</td>\n",
-              "      <td>-1.300921</td>\n",
-              "      <td>36.828195</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>3</td>\n",
-              "      <td>15</td>\n",
-              "      <td>9</td>\n",
-              "      <td>19.2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.281301</td>\n",
-              "      <td>36.832396</td>\n",
-              "      <td>-1.257147</td>\n",
-              "      <td>36.795063</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1</td>\n",
-              "      <td>13</td>\n",
-              "      <td>9</td>\n",
-              "      <td>15.4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.266597</td>\n",
-              "      <td>36.792118</td>\n",
-              "      <td>-1.295041</td>\n",
-              "      <td>36.809817</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Platform Type  ...  Personal or Business_Personal\n",
-              "0              3  ...                              0\n",
-              "1              3  ...                              1\n",
-              "2              3  ...                              0\n",
-              "3              3  ...                              0\n",
-              "4              1  ...                              1\n",
-              "\n",
-              "[5 rows x 11 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 26
-        }
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
+       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
+       "2    Order_No_768  User_Id_2154         Bike              3   \n",
+       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
+       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                        27   \n",
+       "1             Business                        17   \n",
+       "2             Business                        27   \n",
+       "3             Business                        17   \n",
+       "4             Business                        11   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             3       4:44:10 PM                           27   \n",
+       "1                             5      12:57:35 PM                           17   \n",
+       "2                             4      11:08:14 AM                           27   \n",
+       "3                             1       1:51:35 PM                           17   \n",
+       "4                             2      11:30:28 AM                           11   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                3  ...                         3   \n",
+       "1                                5  ...                         5   \n",
+       "2                                4  ...                         4   \n",
+       "3                                1  ...                         1   \n",
+       "4                                2  ...                         2   \n",
+       "\n",
+       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
+       "0     5:06:47 PM              8         NaN                           NaN   \n",
+       "1     1:25:37 PM              5         NaN                           NaN   \n",
+       "2    11:57:54 AM              5        22.8                           NaN   \n",
+       "3     2:16:52 PM              5        24.5                           NaN   \n",
+       "4    11:56:04 AM              6        24.4                           NaN   \n",
+       "\n",
+       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
+       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
+       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
+       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
+       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
+       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
+       "\n",
+       "[5 rows x 25 columns]"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "cBshEqtwJbMM"
-      },
-      "source": [
-        "#y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)\n",
-        "finaltrain = df_final[:len(train)]\n",
-        "finaltest = df_final[len(train):]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "3HuO-M1MJrJX"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_val, y_train, y_val = train_test_split(finaltrain, y, test_size=0.3)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "VAFRGIpsKy3N",
-        "outputId": "615300fe-16cd-46da-c7ca-9ea83c379351",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "lr = LinearRegression()\n",
-        "lr.fit(x_train, y_train)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 31
-        }
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "6_slUwxdKzp8"
-      },
-      "source": [
-        "val_preds = lr.predict(x_val)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TtBhydw4Kz5g"
-      },
-      "source": [
-        "from sklearn.metrics import mean_squared_error\n",
-        "def rmse(y_test, y_predict):\n",
-        "  return np.sqrt(mean_squared_error(y_test, y_predict))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "152-nZYJL9Il",
-        "outputId": "dc25e6c4-32db-43fa-842e-832baa6e654e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "rmse(y_val, val_preds)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "797.0468983875842"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 36
-        }
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7068 entries, 0 to 7067\n",
+      "Data columns (total 25 columns):\n",
+      "Order No                                7068 non-null object\n",
+      "User Id                                 7068 non-null object\n",
+      "Vehicle Type                            7068 non-null object\n",
+      "Platform Type                           7068 non-null int64\n",
+      "Personal or Business                    7068 non-null object\n",
+      "Placement - Day of Month                7068 non-null int64\n",
+      "Placement - Weekday (Mo = 1)            7068 non-null int64\n",
+      "Placement - Time                        7068 non-null object\n",
+      "Confirmation - Day of Month             7068 non-null int64\n",
+      "Confirmation - Weekday (Mo = 1)         7068 non-null int64\n",
+      "Confirmation - Time                     7068 non-null object\n",
+      "Arrival at Pickup - Day of Month        7068 non-null int64\n",
+      "Arrival at Pickup - Weekday (Mo = 1)    7068 non-null int64\n",
+      "Arrival at Pickup - Time                7068 non-null object\n",
+      "Pickup - Day of Month                   7068 non-null int64\n",
+      "Pickup - Weekday (Mo = 1)               7068 non-null int64\n",
+      "Pickup - Time                           7068 non-null object\n",
+      "Distance (KM)                           7068 non-null int64\n",
+      "Temperature                             5631 non-null float64\n",
+      "Precipitation in millimeters            199 non-null float64\n",
+      "Pickup Lat                              7068 non-null float64\n",
+      "Pickup Long                             7068 non-null float64\n",
+      "Destination Lat                         7068 non-null float64\n",
+      "Destination Long                        7068 non-null float64\n",
+      "Rider Id                                7068 non-null object\n",
+      "dtypes: float64(6), int64(10), object(9)\n",
+      "memory usage: 1.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Fill null values with 0, axis=1 means you are doing it per column, axis=0 means you are doing it per row\n",
+    "\n",
+    "ftrain = train.fillna(0, axis=1)\n",
+    "ftest = test.fillna(0, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#drop most of the object dtype columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The columns in test and train do not match 100% lets fix that\n",
+    "testcols = ftest.columns  #getting a list of the columns in test set\n",
+    "newtrain = ftrain[testcols]  #redefining the training set to only include those columns\n",
+    "y = np.array(ftrain['Time from Pickup to Arrival']).reshape(-1, 1) #assigning the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FvJskkBtL9WP"
-      },
-      "source": [
-        "y_pred = lr.predict(finaltest)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n",
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(newtrain.columns)\n",
+    "print(ftest.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hZ_CF7ukL9iP",
-        "outputId": "84172ee3-3ee1-4772-e3bc-d9d286921819",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "submission_df = test1[['Order No']]\n",
-        "submission_df['Time from Pickup to Arrival'] = y_pred"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10:27:30 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:44:09 AM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:53:03 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:43:06 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10:05:23 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
-            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-            "Try using .loc[row_indexer,col_indexer] = value instead\n",
-            "\n",
-            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-            "  \n"
-          ],
-          "name": "stderr"
-        }
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                5  ...                         5   \n",
+       "1                                5  ...                         5   \n",
+       "2                                2  ...                         2   \n",
+       "3                                5  ...                         5   \n",
+       "4                                1  ...                         1   \n",
+       "\n",
+       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
+       "0    10:27:30 AM              4        20.4                           0.0   \n",
+       "1    11:44:09 AM             16        26.4                           0.0   \n",
+       "2    12:53:03 PM              3         0.0                           0.0   \n",
+       "3     9:43:06 AM              9        19.2                           0.0   \n",
+       "4    10:05:23 AM              9        15.4                           0.0   \n",
+       "\n",
+       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
+       "0   -1.317755   36.830370        -1.300406         36.829741  Rider_Id_432  \n",
+       "1   -1.351453   36.899315        -1.295004         36.814358  Rider_Id_856  \n",
+       "2   -1.308284   36.843419        -1.300921         36.828195  Rider_Id_155  \n",
+       "3   -1.281301   36.832396        -1.257147         36.795063  Rider_Id_855  \n",
+       "4   -1.266597   36.792118        -1.295041         36.809817  Rider_Id_770  \n",
+       "\n",
+       "[5 rows x 25 columns]"
       ]
-    },
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Combining the datasets \n",
+    "df = pd.concat([newtrain, ftest])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wNL5aolXNo_i",
-        "outputId": "7c7ee066-b292-495f-d630-201c29c243ee",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 419
-        }
-      },
-      "source": [
-        "submission_df"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Order No</th>\n",
-              "      <th>Time from Pickup to Arrival</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Order_No_19248</td>\n",
-              "      <td>1290.862912</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Order_No_12736</td>\n",
-              "      <td>1081.300485</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Order_No_768</td>\n",
-              "      <td>1058.000881</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Order_No_15332</td>\n",
-              "      <td>1105.787069</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Order_No_21373</td>\n",
-              "      <td>1194.660281</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7063</th>\n",
-              "      <td>Order_No_3612</td>\n",
-              "      <td>1122.848121</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7064</th>\n",
-              "      <td>Order_No_7657</td>\n",
-              "      <td>2894.321995</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7065</th>\n",
-              "      <td>Order_No_1969</td>\n",
-              "      <td>1649.879452</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7066</th>\n",
-              "      <td>Order_No_10591</td>\n",
-              "      <td>2611.831283</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7067</th>\n",
-              "      <td>Order_No_1603</td>\n",
-              "      <td>1458.138944</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>7068 rows × 2 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "            Order No  Time from Pickup to Arrival\n",
-              "0     Order_No_19248                  1290.862912\n",
-              "1     Order_No_12736                  1081.300485\n",
-              "2       Order_No_768                  1058.000881\n",
-              "3     Order_No_15332                  1105.787069\n",
-              "4     Order_No_21373                  1194.660281\n",
-              "...              ...                          ...\n",
-              "7063   Order_No_3612                  1122.848121\n",
-              "7064   Order_No_7657                  2894.321995\n",
-              "7065   Order_No_1969                  1649.879452\n",
-              "7066  Order_No_10591                  2611.831283\n",
-              "7067   Order_No_1603                  1458.138944\n",
-              "\n",
-              "[7068 rows x 2 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 40
-        }
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x27844e4fa48>"
       ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wbxVKI6FNtC3"
-      },
-      "source": [
-        "submission_df.to_csv('base_pred.csv', index=False)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AyhQaTwP9RzG"
-      },
-      "source": [
-        "## Splitting the dataset into the Training set and Test set"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "G7k8xA1VJPV8"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#machine learning model cannot read an object type, convert objects , encode (dummy them)\n",
+    "correlation = df.corr()\n",
+    "sns.heatmap(correlation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#drop all co-linear variables as well time variables as they will cause us to run into memory problems\n",
+    "#df = df.drop(['Placement - Weekday (Mo = 1)', 'Confirmation - Day of Month', 'Confirmation - Weekday', 'Arrival at Pick up Day of Month', 'Arrival at pickup weekday', 'pick up day of month', 'pickup weekday', 'placement time', 'confirmation time', 'arrival at pickup time', 'pickup time'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df_final = df.drop(['order no(Too variant, not adding value to model)', 'user id(Too variant, not adding value to model)', 'vehicle type(only a single value, pointless)', 'rider id()'], axis=1) #dropping IDs\n",
+    "#df_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "bxOOauiN9VpC"
-      },
-      "source": [
-        ""
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Rider Id_Rider_Id_954</th>\n",
+       "      <th>Rider Id_Rider_Id_955</th>\n",
+       "      <th>Rider Id_Rider_Id_956</th>\n",
+       "      <th>Rider Id_Rider_Id_957</th>\n",
+       "      <th>Rider Id_Rider_Id_958</th>\n",
+       "      <th>Rider Id_Rider_Id_959</th>\n",
+       "      <th>Rider Id_Rider_Id_96</th>\n",
+       "      <th>Rider Id_Rider_Id_97</th>\n",
+       "      <th>Rider Id_Rider_Id_98</th>\n",
+       "      <th>Rider Id_Rider_Id_99</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 109614 columns</p>\n",
+       "</div>"
       ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZijQwFMQ9itx"
-      },
-      "source": [
-        "## Training the Simple Linear Regression model on the Training set"
+      "text/plain": [
+       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "0              3                         9                             5   \n",
+       "1              3                        12                             5   \n",
+       "2              3                        30                             2   \n",
+       "3              3                        15                             5   \n",
+       "4              1                        13                             1   \n",
+       "\n",
+       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "0                            9                                5   \n",
+       "1                           12                                5   \n",
+       "2                           30                                2   \n",
+       "3                           15                                5   \n",
+       "4                           13                                1   \n",
+       "\n",
+       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                 9                                     5   \n",
+       "1                                12                                     5   \n",
+       "2                                30                                     2   \n",
+       "3                                15                                     5   \n",
+       "4                                13                                     1   \n",
+       "\n",
+       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  ...  \\\n",
+       "0                      9                          5              4  ...   \n",
+       "1                     12                          5             16  ...   \n",
+       "2                     30                          2              3  ...   \n",
+       "3                     15                          5              9  ...   \n",
+       "4                     13                          1              9  ...   \n",
+       "\n",
+       "   Rider Id_Rider_Id_954  Rider Id_Rider_Id_955  Rider Id_Rider_Id_956  \\\n",
+       "0                      0                      0                      0   \n",
+       "1                      0                      0                      0   \n",
+       "2                      0                      0                      0   \n",
+       "3                      0                      0                      0   \n",
+       "4                      0                      0                      0   \n",
+       "\n",
+       "   Rider Id_Rider_Id_957  Rider Id_Rider_Id_958  Rider Id_Rider_Id_959  \\\n",
+       "0                      0                      0                      0   \n",
+       "1                      0                      0                      0   \n",
+       "2                      0                      0                      0   \n",
+       "3                      0                      0                      0   \n",
+       "4                      0                      0                      0   \n",
+       "\n",
+       "   Rider Id_Rider_Id_96  Rider Id_Rider_Id_97  Rider Id_Rider_Id_98  \\\n",
+       "0                     0                     0                     0   \n",
+       "1                     0                     0                     0   \n",
+       "2                     0                     0                     0   \n",
+       "3                     0                     0                     0   \n",
+       "4                     0                     0                     0   \n",
+       "\n",
+       "   Rider Id_Rider_Id_99  \n",
+       "0                     0  \n",
+       "1                     0  \n",
+       "2                     0  \n",
+       "3                     0  \n",
+       "4                     0  \n",
+       "\n",
+       "[5 rows x 109614 columns]"
       ]
-    },
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Dummying out the last categorical column\n",
+    "#df_final = pd.get_dummies(df)\n",
+    "#df_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "B4Aj_8YJ9l7J"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
+     "ename": "KeyError",
+     "evalue": "21201",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 21201",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-55-4746259f765f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mftrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfinaltrain\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mfinaltest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 21201"
+     ]
+    }
+   ],
+   "source": [
+    "#splitting our dataset again\n",
+    "y = np.array(ftrain['Time from Pickup to Arrival']).reshape(-1, 1)\n",
+    "finaltrain = df_final[:len(train)]\n",
+    "finaltest = df_final[len(train)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Train test split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_val, y_train, y_val = train_test_split(finaltrain, y, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define our model, training and making predictions on the validation set\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_preds = lr.predict(x_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Checking the RMSE of our validation predictions\n",
+    "#def rmse(y_test, y_predict):\n",
+    "    #return np.sqrt(mean_squared_error(y_test, y_predict))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#rmse(y_val, val_preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#making predictions on the test set and sending the result to csv for submission\n",
+    "#y_pred = lr.predict(finaltest) #retraining model - no need to do this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = ftest[['Order_No']] - renaming the dataset, use identifyer\n",
+    "submission_df['Time from Pickup to arrival'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(submission_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#send df to csv\n",
+    "submission_df.to_csv('base prediction', index=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Create a few plots for the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Advanced plotting? plot both dependent data series on the same graph, assign two separate y-axxes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "AyhQaTwP9RzG"
+   },
+   "source": [
+    "## Splitting the dataset into the Training set and Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "bxOOauiN9VpC"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wa2T1Lq89o5H"
-      },
-      "source": [
-        "## Predicting the Test set results"
-      ]
+     "ename": "NameError",
+     "evalue": "name 'x' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-7e414ed48a8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZijQwFMQ9itx"
+   },
+   "source": [
+    "## Training the Simple Linear Regression model on the Training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find the best fit line = y=a + bx, estimate a and b\n",
+    "#define a function to generate values of y from a list of x given a and b\n",
+    "# calculate the error of each generated value of y e = y - f(x) errors = np.array(df.Y - y_gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Rn_sTJ2o9smm"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 882,
+     "status": "ok",
+     "timestamp": 1586352495803,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-zSoMZ-P9v8t"
-      },
-      "source": [
-        "## Visualising the Training set results"
-      ]
+    "id": "B4Aj_8YJ9l7J",
+    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "regressor = LinearRegression()\n",
+    "regressor.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wa2T1Lq89o5H"
+   },
+   "source": [
+    "## Predicting the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Rn_sTJ2o9smm"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = regressor.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "-zSoMZ-P9v8t"
+   },
+   "source": [
+    "## Visualising the Training set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "IAePn_u-93tI"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 763,
+     "status": "ok",
+     "timestamp": 1586352502760,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EUX1Vhsv97ZT"
-      },
-      "source": [
-        "## Visualising the Test set results"
-      ]
+    "id": "IAePn_u-93tI",
+    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(x_train, y_train, color = 'red')\n",
+    "plt.plot(x_train, regressor.predict(x_train), color = 'blue')\n",
+    "plt.title('Salary vs Experience (Training set)')\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "EUX1Vhsv97ZT"
+   },
+   "source": [
+    "## Visualising the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ze9vpBTf-Bol"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1097,
+     "status": "ok",
+     "timestamp": 1586352506966,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "WjZAkvukOeL8"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
+    "id": "Ze9vpBTf-Bol",
+    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(x_test, y_test, color = 'red')\n",
+    "plt.plot(y_train, regressor.predict(x_train), color = 'blue')\n",
+    "plt.title('Salary vs Experience (Test set)')\n",
+    "plt.xlabel('Years of Experience')\n",
+    "plt.ylabel('Salary')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
+   "name": "Simple Linear Regression",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 4b413b411f5d30989945668452899272f895b450 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Mon, 12 Oct 2020 22:57:39 +0200
Subject: [PATCH 05/46] Add files via upload

Temp Notebook
---
 Sendy_Team4-Copy2.ipynb | 2941 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 2941 insertions(+)
 create mode 100644 Sendy_Team4-Copy2.ipynb

diff --git a/Sendy_Team4-Copy2.ipynb b/Sendy_Team4-Copy2.ipynb
new file mode 100644
index 0000000..8c3b4fd
--- /dev/null
+++ b/Sendy_Team4-Copy2.ipynb
@@ -0,0 +1,2941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting lightgbm\n",
+      "  Downloading lightgbm-3.0.0-py2.py3-none-win_amd64.whl (737 kB)\n",
+      "Requirement already satisfied: numpy in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (1.18.1)\n",
+      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (0.22.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (1.4.1)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)\n",
+      "Installing collected packages: lightgbm\n",
+      "Successfully installed lightgbm-3.0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install lightgbm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting xgboost\n",
+      "  Downloading xgboost-1.2.0-py3-none-win_amd64.whl (86.5 MB)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Exception:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 425, in _error_catcher\n",
+      "    yield\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 507, in read\n",
+      "    data = self._fp.read(amt) if not fp_closed else b\"\"\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\cachecontrol\\filewrapper.py\", line 62, in read\n",
+      "    data = self.__fp.read(amt)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\http\\client.py\", line 457, in read\n",
+      "    n = self.readinto(b)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\http\\client.py\", line 501, in readinto\n",
+      "    n = self.fp.readinto(b)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\socket.py\", line 589, in readinto\n",
+      "    return self._sock.recv_into(b)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\ssl.py\", line 1071, in recv_into\n",
+      "    return self.read(nbytes, buffer)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\ssl.py\", line 929, in read\n",
+      "    return self._sslobj.read(len, buffer)\n",
+      "socket.timeout: The read operation timed out\n",
+      "\n",
+      "During handling of the above exception, another exception occurred:\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\cli\\base_command.py\", line 186, in _main\n",
+      "    status = self.run(options, args)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\commands\\install.py\", line 331, in run\n",
+      "    resolver.resolve(requirement_set)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 177, in resolve\n",
+      "    discovered_reqs.extend(self._resolve_one(requirement_set, req))\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 333, in _resolve_one\n",
+      "    abstract_dist = self._get_abstract_dist_for(req_to_install)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 282, in _get_abstract_dist_for\n",
+      "    abstract_dist = self.preparer.prepare_linked_requirement(req)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 482, in prepare_linked_requirement\n",
+      "    hashes=hashes,\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 287, in unpack_url\n",
+      "    hashes=hashes,\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 159, in unpack_http_url\n",
+      "    link, downloader, temp_dir.path, hashes\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 303, in _download_http_url\n",
+      "    for chunk in download.chunks:\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\utils\\ui.py\", line 160, in iter\n",
+      "    for x in it:\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\network\\utils.py\", line 39, in response_chunks\n",
+      "    decode_content=False,\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 564, in stream\n",
+      "    data = self.read(amt=amt, decode_content=decode_content)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 529, in read\n",
+      "    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\contextlib.py\", line 130, in __exit__\n",
+      "    self.gen.throw(type, value, traceback)\n",
+      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 430, in _error_catcher\n",
+      "    raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\n",
+      "pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install xgboost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "#import xgboost as xgb\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2) Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(21201, 29) (7068, 25) (960, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
+    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
+    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "\n",
+    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Data Manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Full_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((21201, 26), (7068, 26), (28269, 26))"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train_df.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train_df.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Renaming columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
+       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
+       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
+       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
+       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
+       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
+       "       'Rider_Id', 'Time_Pic_Arr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15:07:35</td>\n",
+       "      <td>15:08:57</td>\n",
+       "      <td>15:21:36</td>\n",
+       "      <td>15:30:30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
+       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
+       "5  15:07:35  15:08:57     15:21:36    15:30:30"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filling Missing Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filling Missing Values for temperatures and humidity\n",
+    "\n",
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
+       "\n",
+       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
+       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
+       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
+       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
+       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
+       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
+       "\n",
+       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
+       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
+       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
+       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
+       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
+       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
+       "\n",
+       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
+       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
+       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
+       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
+       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
+       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
+    "\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
+    "\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  "
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
+    "\n",
+    "full_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
+       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
+       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
+       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
+       "       'Day_of_Month', 'Day_of_Week'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
+       "0   36.830370        -1.300406        36.829741             9            5  \n",
+       "1   36.899315        -1.295004        36.814358            12            5  \n",
+       "2   36.843419        -1.300921        36.828195            30            2  "
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)\n",
+    "full_df[numeric_cols].head(3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
+       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
+       "2  12:39:25  12:42:44     12:49:34    12:53:03"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[time_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Personal_Business      Rider_Id\n",
+       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
+       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
+       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[object_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert an object to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "Name: Personal_Business, dtype: int32"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "3              3             9    19.200000           7.573502   -1.281301   \n",
+       "4              1             9    15.400000           7.573502   -1.266597   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
+       "0   36.830370        -1.300406        36.829741             9            5   \n",
+       "1   36.899315        -1.295004        36.814358            12            5   \n",
+       "2   36.843419        -1.300921        36.828195            30            2   \n",
+       "3   36.832396        -1.257147        36.795063            15            5   \n",
+       "4   36.792118        -1.295041        36.809817            13            1   \n",
+       "\n",
+       "   Personal_Business  \n",
+       "0                  0  \n",
+       "1                  1  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  1  "
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = numeric_cols + ['Personal_Business']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28269, 19) (28269, 11) (21201, 11) (7068, 11) (21201,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21196</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28.600000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.275285</td>\n",
+       "      <td>36.802702</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21197</th>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.307143</td>\n",
+       "      <td>36.825009</td>\n",
+       "      <td>-1.331619</td>\n",
+       "      <td>36.847976</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21198</th>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>29.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.286018</td>\n",
+       "      <td>36.897534</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21199</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.250030</td>\n",
+       "      <td>36.874167</td>\n",
+       "      <td>-1.279209</td>\n",
+       "      <td>36.794872</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21200</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>30.900000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.320157</td>\n",
+       "      <td>36.830887</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21201 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
+       "0                  3             4    20.400000           7.573502   \n",
+       "1                  3            16    26.400000           7.573502   \n",
+       "2                  3             3    23.255689           7.573502   \n",
+       "3                  3             9    19.200000           7.573502   \n",
+       "4                  1             9    15.400000           7.573502   \n",
+       "...              ...           ...          ...                ...   \n",
+       "21196              3             3    28.600000           7.573502   \n",
+       "21197              3             7    26.000000           7.573502   \n",
+       "21198              3            20    29.200000           7.573502   \n",
+       "21199              1            13    15.000000           7.573502   \n",
+       "21200              3            12    30.900000           7.573502   \n",
+       "\n",
+       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
+       "0       -1.317755   36.830370        -1.300406        36.829741             9   \n",
+       "1       -1.351453   36.899315        -1.295004        36.814358            12   \n",
+       "2       -1.308284   36.843419        -1.300921        36.828195            30   \n",
+       "3       -1.281301   36.832396        -1.257147        36.795063            15   \n",
+       "4       -1.266597   36.792118        -1.295041        36.809817            13   \n",
+       "...           ...         ...              ...              ...           ...   \n",
+       "21196   -1.258414   36.804800        -1.275285        36.802702            20   \n",
+       "21197   -1.307143   36.825009        -1.331619        36.847976            13   \n",
+       "21198   -1.286018   36.897534        -1.258414        36.804800             7   \n",
+       "21199   -1.250030   36.874167        -1.279209        36.794872             4   \n",
+       "21200   -1.255189   36.782203        -1.320157        36.830887            26   \n",
+       "\n",
+       "       Day_of_Week  Personal_Business  \n",
+       "0                5                  0  \n",
+       "1                5                  1  \n",
+       "2                2                  0  \n",
+       "3                5                  0  \n",
+       "4                1                  1  \n",
+       "...            ...                ...  \n",
+       "21196            3                  1  \n",
+       "21197            6                  0  \n",
+       "21198            4                  0  \n",
+       "21199            3                  1  \n",
+       "21200            2                  0  \n",
+       "\n",
+       "[21201 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21201</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.333275</td>\n",
+       "      <td>36.870815</td>\n",
+       "      <td>-1.305249</td>\n",
+       "      <td>36.822390</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21202</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.272639</td>\n",
+       "      <td>36.794723</td>\n",
+       "      <td>-1.277007</td>\n",
+       "      <td>36.823907</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21203</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22.800000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.290894</td>\n",
+       "      <td>36.822971</td>\n",
+       "      <td>-1.276574</td>\n",
+       "      <td>36.851365</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21204</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.500000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.290503</td>\n",
+       "      <td>36.809646</td>\n",
+       "      <td>-1.303382</td>\n",
+       "      <td>36.790658</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21205</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281081</td>\n",
+       "      <td>36.814423</td>\n",
+       "      <td>-1.266467</td>\n",
+       "      <td>36.792161</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28264</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.800000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.288780</td>\n",
+       "      <td>36.816831</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28265</th>\n",
+       "      <td>3</td>\n",
+       "      <td>22</td>\n",
+       "      <td>30.700000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.276141</td>\n",
+       "      <td>36.771084</td>\n",
+       "      <td>-1.316098</td>\n",
+       "      <td>36.913164</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28266</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25.100000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.301446</td>\n",
+       "      <td>36.766138</td>\n",
+       "      <td>-1.264960</td>\n",
+       "      <td>36.798178</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28267</th>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>23.600000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.248404</td>\n",
+       "      <td>36.678276</td>\n",
+       "      <td>-1.272027</td>\n",
+       "      <td>36.817411</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28268</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>22.300000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7068 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
+       "21201              3             8    23.255689           7.573502   \n",
+       "21202              3             5    23.255689           7.573502   \n",
+       "21203              3             5    22.800000           7.573502   \n",
+       "21204              3             5    24.500000           7.573502   \n",
+       "21205              3             6    24.400000           7.573502   \n",
+       "...              ...           ...          ...                ...   \n",
+       "28264              3             5    24.800000           7.573502   \n",
+       "28265              3            22    30.700000           7.573502   \n",
+       "28266              3            10    25.100000           7.573502   \n",
+       "28267              3            18    23.600000           7.573502   \n",
+       "28268              3             8    22.300000           7.573502   \n",
+       "\n",
+       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
+       "21201   -1.333275   36.870815        -1.305249        36.822390            27   \n",
+       "21202   -1.272639   36.794723        -1.277007        36.823907            17   \n",
+       "21203   -1.290894   36.822971        -1.276574        36.851365            27   \n",
+       "21204   -1.290503   36.809646        -1.303382        36.790658            17   \n",
+       "21205   -1.281081   36.814423        -1.266467        36.792161            11   \n",
+       "...           ...         ...              ...              ...           ...   \n",
+       "28264   -1.258414   36.804800        -1.288780        36.816831             7   \n",
+       "28265   -1.276141   36.771084        -1.316098        36.913164            10   \n",
+       "28266   -1.301446   36.766138        -1.264960        36.798178             5   \n",
+       "28267   -1.248404   36.678276        -1.272027        36.817411            29   \n",
+       "28268   -1.255189   36.782203        -1.300406        36.829741             4   \n",
+       "\n",
+       "       Day_of_Week  Personal_Business  \n",
+       "21201            3                  0  \n",
+       "21202            5                  0  \n",
+       "21203            4                  0  \n",
+       "21204            1                  0  \n",
+       "21205            2                  0  \n",
+       "...            ...                ...  \n",
+       "28264            1                  0  \n",
+       "28265            3                  0  \n",
+       "28266            3                  0  \n",
+       "28267            2                  1  \n",
+       "28268            2                  0  \n",
+       "\n",
+       "[7068 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         745.0\n",
+       "1        1993.0\n",
+       "2         455.0\n",
+       "3        1341.0\n",
+       "4        1214.0\n",
+       "          ...  \n",
+       "21196       9.0\n",
+       "21197     770.0\n",
+       "21198    2953.0\n",
+       "21199    1380.0\n",
+       "21200    2128.0\n",
+       "Name: Time_Pic_Arr, Length: 21201, dtype: float64"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modeling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Algorithm  CrossValMeans  CrossValErrors\n",
+      "4      LGBM     774.614768       18.715030\n",
+      "1       GBR     778.134629       17.888758\n",
+      "3       RFR     800.179967       16.876306\n",
+      "0       SVR     914.156364       19.688177\n",
+      "2       EXR     934.162660       18.913009\n"
+     ]
+    }
+   ],
+   "source": [
+    "rs = 3\n",
+    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True)\n",
+    "\n",
+    "regressors = []\n",
+    "regressors.append(SVR())\n",
+    "regressors.append(GradientBoostingRegressor(random_state=rs))\n",
+    "regressors.append(ExtraTreesRegressor(n_estimators=rs))\n",
+    "regressors.append(RandomForestRegressor(random_state=rs))\n",
+    "#regressors.append(xgb.XGBRegressor(random_state=rs, objective=\"reg:squarederror\"))\n",
+    "regressors.append(lgb.LGBMRegressor(random_state=rs))\n",
+    "\n",
+    "cv_results = []\n",
+    "for regressor in regressors:     #scores to be minimised are negated (neg)\n",
+    "    cv_results.append(np.sqrt(abs(cross_val_score(regressor, X_train, y=y_train, scoring='neg_mean_squared_error', cv=kfold))))\n",
+    "\n",
+    "cv_means = []\n",
+    "cv_stds = []\n",
+    "for cv_result in cv_results:\n",
+    "    cv_means.append(cv_result.mean())\n",
+    "    cv_stds.append(cv_result.std())\n",
+    "    \n",
+    "#cv_res = pd.DataFrame({ \n",
+    "#    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"XGBR\", \"LGBM\"],\n",
+    "#    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "#                       })\n",
+    "cv_res = pd.DataFrame({ \n",
+    "    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"LGBM\"],\n",
+    "    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "                       })\n",
+    "\n",
+    "cv_res = cv_res.sort_values(\"CrossValMeans\", ascending=True)\n",
+    "print(cv_res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "773.5171026354723 {'max_depth': None, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 300}\n"
+     ]
+    }
+   ],
+   "source": [
+    "RFC = RandomForestRegressor(random_state=rs)\n",
+    "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
+    "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
+    "rsearch = GridSearchCV(RFC, cv=kfold, scoring='neg_mean_squared_error',param_grid=rf_param)\n",
+    "rfm = rsearch.fit(X_train, y_train)\n",
+    "\n",
+    "r_score = np.sqrt(abs(rfm.best_score_))\n",
+    "r_params = rfm.best_params_\n",
+    "print(r_score, r_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Prediction = predict(fit, test)\n",
+    "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
+    "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = lr.predict(test_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = test_df1[['Order No']]\n",
+    "submission_df['Time_Pic_Arr'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### lightgbm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
+      "{'learning_rate': 0.1, 'min_data_in_leaf': 300, 'n_estimators': 75, 'num_leaves': 15, 'objective': 'regression', 'reg_alpha': 0.02} 772.4197981914269\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'n_estimators':[75], # [75, 95],\n",
+    "    'num_leaves': [15], #[12,15, 17],\n",
+    "    'reg_alpha': [0.02], #[0.02, 0.05],\n",
+    "    'min_data_in_leaf': [300],  #[250, 280, 300]\n",
+    "    'learning_rate': [0.1], #[0.05, 0.1, 0.25],\n",
+    "    'objective': ['regression'] #['regression', None]\n",
+    "    }\n",
+    "\n",
+    "lsearch = GridSearchCV(estimator = lgb.LGBMRegressor(random_state=rs), cv=kfold,scoring='neg_mean_squared_error', param_grid=params)\n",
+    "lgbm = lsearch.fit(X_train, y_train)\n",
+    "\n",
+    "l_params = lgbm.best_params_\n",
+    "l_score = np.sqrt(abs(lgbm.best_score_))\n",
+    "print(lgbm.best_params_, np.sqrt(abs(lgbm.best_score_)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Training and making a prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001396 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 1336\n",
+      "[LightGBM] [Info] Number of data points in the train set: 16960, number of used features: 11\n",
+      "[LightGBM] [Info] Start training from score 1555.150236\n",
+      "[1]\tvalid_0's l2: 918188\n",
+      "Training until validation scores don't improve for 20 rounds\n",
+      "[2]\tvalid_0's l2: 866539\n",
+      "[3]\tvalid_0's l2: 824476\n",
+      "[4]\tvalid_0's l2: 789734\n",
+      "[5]\tvalid_0's l2: 761691\n",
+      "[6]\tvalid_0's l2: 738958\n",
+      "[7]\tvalid_0's l2: 720292\n",
+      "[8]\tvalid_0's l2: 704931\n",
+      "[9]\tvalid_0's l2: 691675\n",
+      "[10]\tvalid_0's l2: 681557\n",
+      "[11]\tvalid_0's l2: 673080\n",
+      "[12]\tvalid_0's l2: 665960\n",
+      "[13]\tvalid_0's l2: 660426\n",
+      "[14]\tvalid_0's l2: 654948\n",
+      "[15]\tvalid_0's l2: 650506\n",
+      "[16]\tvalid_0's l2: 647193\n",
+      "[17]\tvalid_0's l2: 644222\n",
+      "[18]\tvalid_0's l2: 641821\n",
+      "[19]\tvalid_0's l2: 638942\n",
+      "[20]\tvalid_0's l2: 637037\n",
+      "[21]\tvalid_0's l2: 635290\n",
+      "[22]\tvalid_0's l2: 634152\n",
+      "[23]\tvalid_0's l2: 633216\n",
+      "[24]\tvalid_0's l2: 632033\n",
+      "[25]\tvalid_0's l2: 630848\n",
+      "[26]\tvalid_0's l2: 630171\n",
+      "[27]\tvalid_0's l2: 629561\n",
+      "[28]\tvalid_0's l2: 629029\n",
+      "[29]\tvalid_0's l2: 628206\n",
+      "[30]\tvalid_0's l2: 628106\n",
+      "[31]\tvalid_0's l2: 627302\n",
+      "[32]\tvalid_0's l2: 627181\n",
+      "[33]\tvalid_0's l2: 625904\n",
+      "[34]\tvalid_0's l2: 625357\n",
+      "[35]\tvalid_0's l2: 625171\n",
+      "[36]\tvalid_0's l2: 624768\n",
+      "[37]\tvalid_0's l2: 624364\n",
+      "[38]\tvalid_0's l2: 624260\n",
+      "[39]\tvalid_0's l2: 623846\n",
+      "[40]\tvalid_0's l2: 623815\n",
+      "[41]\tvalid_0's l2: 623591\n",
+      "[42]\tvalid_0's l2: 622871\n",
+      "[43]\tvalid_0's l2: 622676\n",
+      "[44]\tvalid_0's l2: 622707\n",
+      "[45]\tvalid_0's l2: 622054\n",
+      "[46]\tvalid_0's l2: 621485\n",
+      "[47]\tvalid_0's l2: 621052\n",
+      "[48]\tvalid_0's l2: 620831\n",
+      "[49]\tvalid_0's l2: 620571\n",
+      "[50]\tvalid_0's l2: 620528\n",
+      "[51]\tvalid_0's l2: 620492\n",
+      "[52]\tvalid_0's l2: 620317\n",
+      "[53]\tvalid_0's l2: 620496\n",
+      "[54]\tvalid_0's l2: 620237\n",
+      "[55]\tvalid_0's l2: 620022\n",
+      "[56]\tvalid_0's l2: 619893\n",
+      "[57]\tvalid_0's l2: 619697\n",
+      "[58]\tvalid_0's l2: 619534\n",
+      "[59]\tvalid_0's l2: 619475\n",
+      "[60]\tvalid_0's l2: 619321\n",
+      "[61]\tvalid_0's l2: 619248\n",
+      "[62]\tvalid_0's l2: 619262\n",
+      "[63]\tvalid_0's l2: 619191\n",
+      "[64]\tvalid_0's l2: 619172\n",
+      "[65]\tvalid_0's l2: 619077\n",
+      "[66]\tvalid_0's l2: 619023\n",
+      "[67]\tvalid_0's l2: 619158\n",
+      "[68]\tvalid_0's l2: 619221\n",
+      "[69]\tvalid_0's l2: 619360\n",
+      "[70]\tvalid_0's l2: 619396\n",
+      "[71]\tvalid_0's l2: 619116\n",
+      "[72]\tvalid_0's l2: 618896\n",
+      "[73]\tvalid_0's l2: 618973\n",
+      "[74]\tvalid_0's l2: 618808\n",
+      "[75]\tvalid_0's l2: 618729\n",
+      "Did not meet early stopping. Best iteration is:\n",
+      "[75]\tvalid_0's l2: 618729\n",
+      "The RMSE of prediction is  786.5930821174317\n"
+     ]
+    }
+   ],
+   "source": [
+    "lgb_train = lgb.Dataset(X_train, y_train)\n",
+    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
+    "\n",
+    "lparams = {\n",
+    "           'learning_rate': 0.1, 'min_data_in_leaf': 300, \n",
+    "           'n_estimators': 75, 'num_leaves': 20, 'random_state':rs,\n",
+    "           'objective': 'regression', 'reg_alpha': 0.02,\n",
+    "          'feature_fraction': 0.9, 'bagging_fraction':0.9}\n",
+    "\n",
+    "\n",
+    "lgbm = lgb.train(lparams, lgb_train, valid_sets=lgb_eval, num_boost_round=20, early_stopping_rounds=20)\n",
+    "\n",
+    "lpred = lgbm.predict(X_test, num_iteration=lgbm.best_iteration)\n",
+    "\n",
+    "print(\"The RMSE of prediction is \", mean_squared_error(y_test, lpred)**0.5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"D:\\Temp\\submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 38e4cd10790152c21f9d1beab2b46d73deef496d Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Mon, 12 Oct 2020 23:00:31 +0200
Subject: [PATCH 06/46] Add files via upload

---
 Sendy_Team4-Copy3.ipynb | 2384 +++++++++++++++++++++++++++++++++++++++
 Sendy_Team4.ipynb       | 1957 ++++++++++++++++++++++++++++++++
 2 files changed, 4341 insertions(+)
 create mode 100644 Sendy_Team4-Copy3.ipynb
 create mode 100644 Sendy_Team4.ipynb

diff --git a/Sendy_Team4-Copy3.ipynb b/Sendy_Team4-Copy3.ipynb
new file mode 100644
index 0000000..c7862f8
--- /dev/null
+++ b/Sendy_Team4-Copy3.ipynb
@@ -0,0 +1,2384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "#import xgboost as xgb\n",
+    "#import lightgbm as lgb\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2) Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(21201, 29) (7068, 25) (960, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
+    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
+    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "\n",
+    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Data Manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Full_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((21201, 26), (7068, 26), (28269, 26))"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train_df.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train_df.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Renaming columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
+       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
+       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
+       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
+       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
+       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
+       "       'Rider_Id', 'Time_Pic_Arr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15:07:35</td>\n",
+       "      <td>15:08:57</td>\n",
+       "      <td>15:21:36</td>\n",
+       "      <td>15:30:30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
+       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
+       "5  15:07:35  15:08:57     15:21:36    15:30:30"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filling Missing Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filling Missing Values for temperatures and humidity\n",
+    "\n",
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
+       "\n",
+       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
+       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
+       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
+       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
+       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
+       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
+       "\n",
+       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
+       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
+       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
+       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
+       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
+       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
+       "\n",
+       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
+       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
+       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
+       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
+       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
+       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
+    "\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
+    "\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  "
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
+    "\n",
+    "full_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
+       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
+       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
+       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
+       "       'Day_of_Month', 'Day_of_Week'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
+       "0   36.830370        -1.300406        36.829741             9            5  \n",
+       "1   36.899315        -1.295004        36.814358            12            5  \n",
+       "2   36.843419        -1.300921        36.828195            30            2  "
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)\n",
+    "full_df[numeric_cols].head(3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
+       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
+       "2  12:39:25  12:42:44     12:49:34    12:53:03"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[time_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Personal_Business      Rider_Id\n",
+       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
+       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
+       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[object_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert an object to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "Name: Personal_Business, dtype: int32"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Order_No</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_4211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_25375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_1899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Order_No_9336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Order_No_27883</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "3              3             9    19.200000           7.573502   -1.281301   \n",
+       "4              1             9    15.400000           7.573502   -1.266597   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
+       "0   36.830370        -1.300406        36.829741             9            5   \n",
+       "1   36.899315        -1.295004        36.814358            12            5   \n",
+       "2   36.843419        -1.300921        36.828195            30            2   \n",
+       "3   36.832396        -1.257147        36.795063            15            5   \n",
+       "4   36.792118        -1.295041        36.809817            13            1   \n",
+       "\n",
+       "   Personal_Business        Order_No  \n",
+       "0                  0   Order_No_4211  \n",
+       "1                  1  Order_No_25375  \n",
+       "2                  0   Order_No_1899  \n",
+       "3                  0   Order_No_9336  \n",
+       "4                  1  Order_No_27883  "
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = numeric_cols + ['Personal_Business'] + ['Order_No']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28269, 19) (28269, 12) (21201, 12) (7068, 12) (21201,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Prep for split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21201, 11)"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_new = train.drop('Order_No',axis=1)\n",
+    "test_new = test.drop('Order_No',axis=1)\n",
+    "train_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7068, 11)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train_new, y, test_size=0.2, random_state = 1, shuffle=False)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "778.5641538562762"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LinearRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "test_pred = lr.predict(X_test)\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "def rmse(y_test,y_predict):\n",
+    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
+    "rmse(y_test,test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = lr.predict(test_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = test_df1[['Order No']]\n",
+    "submission_df['Time_Pic_Arr'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LR with Scailing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.396406</td>\n",
+       "      <td>-0.971370</td>\n",
+       "      <td>-0.887117</td>\n",
+       "      <td>-0.00314</td>\n",
+       "      <td>-1.189424</td>\n",
+       "      <td>0.509897</td>\n",
+       "      <td>-0.511888</td>\n",
+       "      <td>0.414159</td>\n",
+       "      <td>-0.756213</td>\n",
+       "      <td>1.122927</td>\n",
+       "      <td>-0.468583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.396406</td>\n",
+       "      <td>1.145469</td>\n",
+       "      <td>0.975120</td>\n",
+       "      <td>-0.00314</td>\n",
+       "      <td>-2.294056</td>\n",
+       "      <td>2.349813</td>\n",
+       "      <td>-0.356757</td>\n",
+       "      <td>0.070177</td>\n",
+       "      <td>-0.415254</td>\n",
+       "      <td>1.122927</td>\n",
+       "      <td>2.134095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.396406</td>\n",
+       "      <td>-1.147774</td>\n",
+       "      <td>-0.000789</td>\n",
+       "      <td>-0.00314</td>\n",
+       "      <td>-0.878981</td>\n",
+       "      <td>0.858136</td>\n",
+       "      <td>-0.526672</td>\n",
+       "      <td>0.379588</td>\n",
+       "      <td>1.630501</td>\n",
+       "      <td>-0.791244</td>\n",
+       "      <td>-0.468583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.396406</td>\n",
+       "      <td>-0.089354</td>\n",
+       "      <td>-1.259565</td>\n",
+       "      <td>-0.00314</td>\n",
+       "      <td>0.005523</td>\n",
+       "      <td>0.563962</td>\n",
+       "      <td>0.730374</td>\n",
+       "      <td>-0.361289</td>\n",
+       "      <td>-0.074294</td>\n",
+       "      <td>1.122927</td>\n",
+       "      <td>-0.468583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-2.802759</td>\n",
+       "      <td>-0.089354</td>\n",
+       "      <td>-2.438982</td>\n",
+       "      <td>-0.00314</td>\n",
+       "      <td>0.487515</td>\n",
+       "      <td>-0.510932</td>\n",
+       "      <td>-0.357811</td>\n",
+       "      <td>-0.031369</td>\n",
+       "      <td>-0.301601</td>\n",
+       "      <td>-1.429301</td>\n",
+       "      <td>2.134095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0       0.396406     -0.971370    -0.887117           -0.00314   -1.189424   \n",
+       "1       0.396406      1.145469     0.975120           -0.00314   -2.294056   \n",
+       "2       0.396406     -1.147774    -0.000789           -0.00314   -0.878981   \n",
+       "3       0.396406     -0.089354    -1.259565           -0.00314    0.005523   \n",
+       "4      -2.802759     -0.089354    -2.438982           -0.00314    0.487515   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
+       "0    0.509897        -0.511888         0.414159     -0.756213     1.122927   \n",
+       "1    2.349813        -0.356757         0.070177     -0.415254     1.122927   \n",
+       "2    0.858136        -0.526672         0.379588      1.630501    -0.791244   \n",
+       "3    0.563962         0.730374        -0.361289     -0.074294     1.122927   \n",
+       "4   -0.510932        -0.357811        -0.031369     -0.301601    -1.429301   \n",
+       "\n",
+       "   Personal_Business  \n",
+       "0          -0.468583  \n",
+       "1           2.134095  \n",
+       "2          -0.468583  \n",
+       "3          -0.468583  \n",
+       "4           2.134095  "
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "train_new_scaled = scaler.fit_transform(train_new)\n",
+    "train_new_standardise = pd.DataFrame(train_new_scaled,columns=train_new.columns)\n",
+    "train_new_standardise.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, y, test_size=0.2, random_state = 1, shuffle=False)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "778.564153856276"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LinearRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "test_pred = lr.predict(X_test)\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "def rmse(y_test,y_predict):\n",
+    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
+    "rmse(y_test,test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = lr.predict(test_new)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "778.564153856276"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, \n",
+    "                                                    y, \n",
+    "                                                    test_size=0.2, \n",
+    "                                                    shuffle=False)\n",
+    "from sklearn.linear_model import Ridge\n",
+    "ridge = Ridge()\n",
+    "ridge.fit(X_train, y_train)\n",
+    "test_pred = lr.predict(X_test)\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "def rmse(y_test,y_predict):\n",
+    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
+    "rmse(y_test,test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = lr.predict(test_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = test_df1[['Order No']]\n",
+    "submission_df['Time_Pic_Arr'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df.to_csv('D:/Temp/LRRidge.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "778.564153856276"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# split dataset into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, \n",
+    "                                                    y, \n",
+    "                                                    test_size=0.20,\n",
+    "                                                    random_state=1,\n",
+    "                                                    shuffle=False)\n",
+    "from sklearn.linear_model import Lasso\n",
+    "lasso = Lasso(alpha=0.01)\n",
+    "lasso.fit(X_train, y_train)\n",
+    "test_pred = lr.predict(X_test)\n",
+    "def rmse(y_test,y_predict):\n",
+    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
+    "rmse(y_test,test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Sendy_Team4.ipynb b/Sendy_Team4.ipynb
new file mode 100644
index 0000000..a2f6cb0
--- /dev/null
+++ b/Sendy_Team4.ipynb
@@ -0,0 +1,1957 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'xgboost'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-39-fb1e6551696f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mxgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlightgbm\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mlgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "import xgboost as xgb\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2) Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(21201, 29) (7068, 25) (960, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
+    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
+    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "\n",
+    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Data Manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Full_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((21201, 26), (7068, 26), (28269, 26))"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train_df.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train_df.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Renaming columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
+       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
+       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
+       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
+       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
+       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
+       "       'Rider_Id', 'Time_Pic_Arr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15:07:35</td>\n",
+       "      <td>15:08:57</td>\n",
+       "      <td>15:21:36</td>\n",
+       "      <td>15:30:30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
+       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
+       "5  15:07:35  15:08:57     15:21:36    15:30:30"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filling Missing Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filling Missing Values for temperatures and humidity\n",
+    "\n",
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
+       "\n",
+       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
+       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
+       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
+       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
+       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
+       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
+       "\n",
+       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
+       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
+       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
+       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
+       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
+       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
+       "\n",
+       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
+       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
+       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
+       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
+       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
+       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
+    "\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
+    "\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
+    "\n",
+    "full_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
+       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
+       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
+       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
+       "       'Day_of_Month', 'Day_of_Week'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
+       "0   36.830370        -1.300406        36.829741             9            5  \n",
+       "1   36.899315        -1.295004        36.814358            12            5  \n",
+       "2   36.843419        -1.300921        36.828195            30            2  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)\n",
+    "full_df[numeric_cols].head(3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
+       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
+       "2  12:39:25  12:42:44     12:49:34    12:53:03"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[time_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Personal_Business      Rider_Id\n",
+       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
+       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
+       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[object_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convert an object to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "Name: Personal_Business, dtype: int32"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
+       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
+       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
+       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
+       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "3              3             9    19.200000           7.573502   -1.281301   \n",
+       "4              1             9    15.400000           7.573502   -1.266597   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
+       "0   36.830370        -1.300406        36.829741             9            5   \n",
+       "1   36.899315        -1.295004        36.814358            12            5   \n",
+       "2   36.843419        -1.300921        36.828195            30            2   \n",
+       "3   36.832396        -1.257147        36.795063            15            5   \n",
+       "4   36.792118        -1.295041        36.809817            13            1   \n",
+       "\n",
+       "   Personal_Business  \n",
+       "0                  0  \n",
+       "1                  1  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  1  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = numeric_cols + ['Personal_Business']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28269, 19) (28269, 11) (21201, 11) (7068, 11) (21201,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 017b7787debafe1b8b2beb6f4600661954c92259 Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Thu, 15 Oct 2020 12:24:11 +0200
Subject: [PATCH 07/46] uploaded .pkl file

---
 assets/trained-models/lgbm_model.pkl | Bin 0 -> 145747 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 assets/trained-models/lgbm_model.pkl

diff --git a/assets/trained-models/lgbm_model.pkl b/assets/trained-models/lgbm_model.pkl
new file mode 100644
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HcmV?d00001


From 44eb5875698cd79b97da7059d8aa69716de577ec Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 15:02:53 +0200
Subject: [PATCH 08/46] Modified model.py file by adding data preprocessing
 code

---
 model.py | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 81 insertions(+), 2 deletions(-)

diff --git a/model.py b/model.py
index f18e699..b116166 100644
--- a/model.py
+++ b/model.py
@@ -59,8 +59,87 @@ def _preprocess_data(data):
     # ---------------------------------------------------------------
 
     # ----------- Replace this code with your own preprocessing steps --------
-    predict_vector = feature_vector_df[['Pickup Lat','Pickup Long',
-                                        'Destination Lat','Destination Long']]
+    #predict_vector = feature_vector_df[['Pickup Lat','Pickup Long',
+    #                                    'Destination Lat','Destination Long']]
+    
+    #Drop data not available in test, Pickup Time + label = Arrival times
+    feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
+
+    #Renaming columns (shorten, remove space, standardize)
+    new_names = {"Order No": "Order_No", "User Id": "User_Id", "Vehicle Type": "Vehicle_Type",
+    "Personal or Business": "Personal_Business", "Placement - Day of Month": "Pla_Mon",
+    "Placement - Weekday (Mo = 1)": "Pla_Weekday", "Placement - Time": "Pla_Time", 
+    "Confirmation - Day of Month":"Con_Day_Mon", "Confirmation - Weekday (Mo = 1)": "Con_Weekday","Confirmation - Time": "Con_Time", 
+    "Arrival at Pickup - Day of Month": "Arr_Pic_Mon", "Arrival at Pickup - Weekday (Mo = 1)": "Arr_Pic_Weekday", 
+                "Arrival at Pickup - Time": "Arr_Pic_Time", "Platform Type": "Platform_Type",
+     "Pickup - Day of Month": "Pickup_Mon", "Pickup - Weekday (Mo = 1)": "Pickup_Weekday",           
+    "Pickup - Time": "Pickup_Time",  "Distance (KM)": "Distance(km)",
+    "Precipitation in millimeters": "Precipitation(mm)", "Pickup Lat": "Pickup_Lat", "Pickup Long": "Pickup_Lon", 
+    "Destination Lat": "Destination_Lat", "Destination Long":"Destination_Lon", "Rider Id": "Rider_Id",
+                            "Time from Pickup to Arrival": "Time_Pic_Arr"
+                           }
+    feature_vector_df = feature_vector_df.rename(columns=new_names)
+    
+    #Convert Time from 12H to 24H
+    def convert_to_24hrs(feature_vector_df):
+        
+        for col in feature_vector_df.columns:
+            if col.endswith("Time"):
+                feature_vector_df[col] = pd.to_datetime(feature_vector_df[col], format='%I:%M:%S %p').dt.strftime("%H:%M:%S")
+        return feature_vector_df
+
+    feature_vector_df = convert_to_24hrs(feature_vector_df)
+
+    feature_vector_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]
+    
+    #Filling Missing Values for temperatures and humidity
+    feature_vector_df['Temperature'] = feature_vector_df['Temperature'].fillna(feature_vector_df['Temperature'].mean())
+    feature_vector_df['Precipitation(mm)'].fillna(feature_vector_df['Precipitation(mm)'].mean(), inplace=True)
+    
+    #Since, we have not been given the actual dates & bikes (same day) 
+    month_cols = [col for col in full_df.columns if col.endswith("Mon")]
+    weekday_cols = [col for col in full_df.columns if col.endswith("Weekday")]
+
+    count = 0
+    instances_of_different_days = [];
+    for i, row in feature_vector_df.iterrows():
+        if len(set(row[month_cols].values)) > 1:
+            print(count+1, end="\r")
+            count = count + 1
+            instances_of_different_days.append(list(row[month_cols].values))
+    instances_of_different_days
+    
+    #Creating Month and Weekday columns
+    feature_vector_df['Day_of_Month'] = feature_vector_df[month_cols[0]]
+    feature_vector_df['Day_of_Week'] = feature_vector_df[weekday_cols[0]]
+    
+    #Dropping redundant columns
+    feature_vector_df.drop(month_cols+weekday_cols, axis=1, inplace=True)
+    feature_vector_df.drop('Vehicle_Type', axis=1, inplace=True)
+    
+    #Variable Datatypes
+    numeric_cols = []
+    object_cols = []
+    time_cols = []
+    for k, v in feature_vector_df.dtypes.items():
+        if (v != object):
+            if (k != "Time_Pic_Arr"):
+                numeric_cols.append(k)
+    elif k.endswith("Time"):
+        time_cols.append(k)
+    else:
+        object_cols.append(k)
+        
+    #Convert an object to numeric (encoding)
+    le = LabelEncoder()
+    le.fit(feature_vector_df['Personal_Business'])
+    feature_vector_df['Personal_Business'] = le.transform(feature_vector_df['Personal_Business'])
+    feature_vector_df['Personal_Business'][:2]
+
+    #Feature Selection
+    features = numeric_cols + ['Personal_Business']
+    predict_vector = feature_vector_df[features]
+    
     # ------------------------------------------------------------------------
 
     return predict_vector

From 5f02b22c30cb331672ce443af694ecd878b3d30b Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 17:18:03 +0200
Subject: [PATCH 09/46] Fixed error

---
 model.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/model.py b/model.py
index b116166..90bd359 100644
--- a/model.py
+++ b/model.py
@@ -125,10 +125,10 @@ def convert_to_24hrs(feature_vector_df):
         if (v != object):
             if (k != "Time_Pic_Arr"):
                 numeric_cols.append(k)
-    elif k.endswith("Time"):
-        time_cols.append(k)
-    else:
-        object_cols.append(k)
+        elif k.endswith("Time"):
+            time_cols.append(k)
+        else:
+            object_cols.append(k)
         
     #Convert an object to numeric (encoding)
     le = LabelEncoder()

From a0f5bbe8eb57c00168d017b696c3ed4b7a539476 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 18:14:27 +0200
Subject: [PATCH 10/46] Testing API

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index b4d0390..9802e85 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://127.0.0.1:5000/api_v0.1'
+url = 'http://54.170.181.212:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From c55aa0daa113247f1b50eda19251793cad078ba6 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 18:14:41 +0200
Subject: [PATCH 11/46] Testing API


From 4bde2513c3381c5b5b5bc2a54d114bb78f036689 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 21:15:30 +0200
Subject: [PATCH 12/46] rollback

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index 9802e85..b4d0390 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://54.170.181.212:5000/api_v0.1'
+url = 'http://127.0.0.1:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From c2bd53db6ff579145aace4d85a9c6518e80fba88 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Thu, 15 Oct 2020 21:23:25 +0200
Subject: [PATCH 13/46] ReUpdated for 2.4

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index b4d0390..9802e85 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://127.0.0.1:5000/api_v0.1'
+url = 'http://54.170.181.212:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From f65f4f4fe617bdbd21e5c68f91ec128a0466065c Mon Sep 17 00:00:00 2001
From: Grewies <68637353+Grewies@users.noreply.github.com>
Date: Fri, 16 Oct 2020 11:30:32 +0200
Subject: [PATCH 14/46] Add files via upload

---
 team4_regression-EDA.ipynb | 2563 ++++++++++++++++++++++++++++++++++++
 1 file changed, 2563 insertions(+)
 create mode 100644 team4_regression-EDA.ipynb

diff --git a/team4_regression-EDA.ipynb b/team4_regression-EDA.ipynb
new file mode 100644
index 0000000..cdd034a
--- /dev/null
+++ b/team4_regression-EDA.ipynb
@@ -0,0 +1,2563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "l_LulNCC8z96"
+   },
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xpXdowrE9DxW"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nhKd4hWx9GFt"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "#import missingno as mnso\n",
+    "import matplotlib.style as style"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "6JhpWJi59J1p"
+   },
+   "source": [
+    "## Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "f8GfYDZ59O18"
+   },
+   "outputs": [],
+   "source": [
+    "df_train = pd.read_csv('Train.csv') \n",
+    "df_test = pd.read_csv('Test.csv') \n",
+    "df_rider = pd.read_csv('Riders.csv') \n",
+    "df_variable = pd.read_csv('VariableDefinitions.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variable Definitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35, 2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_variable.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>Unique number identifying the order</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>User Id</td>\n",
+       "      <td>Unique number identifying the customer on a pl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Vehicle Type</td>\n",
+       "      <td>For this competition limited to bikes, however...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Platform Type</td>\n",
+       "      <td>Platform used to place the order, there are 4 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal or Business</td>\n",
+       "      <td>Customer type</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Placement - Day of Month</td>\n",
+       "      <td>Placement - Day of Month i.e 1-31</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Order No                Unique number identifying the order\n",
+       "0                   User Id  Unique number identifying the customer on a pl...\n",
+       "1              Vehicle Type  For this competition limited to bikes, however...\n",
+       "2             Platform Type  Platform used to place the order, there are 4 ...\n",
+       "3      Personal or Business                                      Customer type\n",
+       "4  Placement - Day of Month                  Placement - Day of Month i.e 1-31"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_variable.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21201"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21201, 29)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+       "       'Personal or Business', 'Placement - Day of Month',\n",
+       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+       "       'Arrival at Destination - Day of Month',\n",
+       "       'Arrival at Destination - Weekday (Mo = 1)',\n",
+       "       'Arrival at Destination - Time', 'Distance (KM)', 'Temperature',\n",
+       "       'Precipitation in millimeters', 'Pickup Lat', 'Pickup Long',\n",
+       "       'Destination Lat', 'Destination Long', 'Rider Id',\n",
+       "       'Time from Pickup to Arrival'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>16835.000000</td>\n",
+       "      <td>552.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.752182</td>\n",
+       "      <td>15.653696</td>\n",
+       "      <td>3.240083</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>9.506533</td>\n",
+       "      <td>23.258889</td>\n",
+       "      <td>7.905797</td>\n",
+       "      <td>-1.281470</td>\n",
+       "      <td>36.811264</td>\n",
+       "      <td>-1.282581</td>\n",
+       "      <td>36.811220</td>\n",
+       "      <td>1556.920947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.625178</td>\n",
+       "      <td>8.798916</td>\n",
+       "      <td>1.567295</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>5.668963</td>\n",
+       "      <td>3.615768</td>\n",
+       "      <td>17.089971</td>\n",
+       "      <td>0.030507</td>\n",
+       "      <td>0.037473</td>\n",
+       "      <td>0.034824</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>987.270788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>-1.438302</td>\n",
+       "      <td>36.653621</td>\n",
+       "      <td>-1.430298</td>\n",
+       "      <td>36.606594</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>1.075000</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.784605</td>\n",
+       "      <td>-1.301201</td>\n",
+       "      <td>36.785661</td>\n",
+       "      <td>882.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>23.500000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>-1.279395</td>\n",
+       "      <td>36.807040</td>\n",
+       "      <td>-1.284382</td>\n",
+       "      <td>36.808002</td>\n",
+       "      <td>1369.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>-1.261177</td>\n",
+       "      <td>36.829477</td>\n",
+       "      <td>2040.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>32.100000</td>\n",
+       "      <td>99.100000</td>\n",
+       "      <td>-1.147170</td>\n",
+       "      <td>36.991046</td>\n",
+       "      <td>-1.030225</td>\n",
+       "      <td>37.016779</td>\n",
+       "      <td>7883.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "count   21201.000000              21201.000000                  21201.000000   \n",
+       "mean        2.752182                 15.653696                      3.240083   \n",
+       "std         0.625178                  8.798916                      1.567295   \n",
+       "min         1.000000                  1.000000                      1.000000   \n",
+       "25%         3.000000                  8.000000                      2.000000   \n",
+       "50%         3.000000                 15.000000                      3.000000   \n",
+       "75%         3.000000                 23.000000                      5.000000   \n",
+       "max         4.000000                 31.000000                      7.000000   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "count                 21201.000000                     21201.000000   \n",
+       "mean                     15.653837                         3.240225   \n",
+       "std                       8.798886                         1.567228   \n",
+       "min                       1.000000                         1.000000   \n",
+       "25%                       8.000000                         2.000000   \n",
+       "50%                      15.000000                         3.000000   \n",
+       "75%                      23.000000                         5.000000   \n",
+       "max                      31.000000                         7.000000   \n",
+       "\n",
+       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "count                      21201.000000                          21201.000000   \n",
+       "mean                          15.653837                              3.240225   \n",
+       "std                            8.798886                              1.567228   \n",
+       "min                            1.000000                              1.000000   \n",
+       "25%                            8.000000                              2.000000   \n",
+       "50%                           15.000000                              3.000000   \n",
+       "75%                           23.000000                              5.000000   \n",
+       "max                           31.000000                              7.000000   \n",
+       "\n",
+       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
+       "count           21201.000000               21201.000000   \n",
+       "mean               15.653837                   3.240225   \n",
+       "std                 8.798886                   1.567228   \n",
+       "min                 1.000000                   1.000000   \n",
+       "25%                 8.000000                   2.000000   \n",
+       "50%                15.000000                   3.000000   \n",
+       "75%                23.000000                   5.000000   \n",
+       "max                31.000000                   7.000000   \n",
+       "\n",
+       "       Arrival at Destination - Day of Month  \\\n",
+       "count                           21201.000000   \n",
+       "mean                               15.653837   \n",
+       "std                                 8.798886   \n",
+       "min                                 1.000000   \n",
+       "25%                                 8.000000   \n",
+       "50%                                15.000000   \n",
+       "75%                                23.000000   \n",
+       "max                                31.000000   \n",
+       "\n",
+       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
+       "count                               21201.000000   21201.000000  16835.000000   \n",
+       "mean                                    3.240225       9.506533     23.258889   \n",
+       "std                                     1.567228       5.668963      3.615768   \n",
+       "min                                     1.000000       1.000000     11.200000   \n",
+       "25%                                     2.000000       5.000000     20.600000   \n",
+       "50%                                     3.000000       8.000000     23.500000   \n",
+       "75%                                     5.000000      13.000000     26.000000   \n",
+       "max                                     7.000000      49.000000     32.100000   \n",
+       "\n",
+       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
+       "count                    552.000000  21201.000000  21201.000000   \n",
+       "mean                       7.905797     -1.281470     36.811264   \n",
+       "std                       17.089971      0.030507      0.037473   \n",
+       "min                        0.100000     -1.438302     36.653621   \n",
+       "25%                        1.075000     -1.300921     36.784605   \n",
+       "50%                        2.900000     -1.279395     36.807040   \n",
+       "75%                        4.900000     -1.257147     36.829741   \n",
+       "max                       99.100000     -1.147170     36.991046   \n",
+       "\n",
+       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
+       "count     21201.000000      21201.000000                 21201.000000  \n",
+       "mean         -1.282581         36.811220                  1556.920947  \n",
+       "std           0.034824          0.044721                   987.270788  \n",
+       "min          -1.430298         36.606594                     1.000000  \n",
+       "25%          -1.301201         36.785661                   882.000000  \n",
+       "50%          -1.284382         36.808002                  1369.000000  \n",
+       "75%          -1.261177         36.829477                  2040.000000  \n",
+       "max          -1.030225         37.016779                  7883.000000  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separating by data type\n",
+    "numeric_columns = []\n",
+    "object_columns = []\n",
+    "time_columns = []\n",
+    "for k, v in df_train.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_columns.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_columns.append(k)\n",
+    "    else:\n",
+    "        object_columns.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Time</th>\n",
+       "      <th>Arrival at Pickup - Time</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9:40:10 AM</td>\n",
+       "      <td>10:04:47 AM</td>\n",
+       "      <td>10:27:30 AM</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>11:23:21 AM</td>\n",
+       "      <td>11:40:22 AM</td>\n",
+       "      <td>11:44:09 AM</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>12:42:44 PM</td>\n",
+       "      <td>12:49:34 PM</td>\n",
+       "      <td>12:53:03 PM</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>9:26:05 AM</td>\n",
+       "      <td>9:37:56 AM</td>\n",
+       "      <td>9:43:06 AM</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>9:56:18 AM</td>\n",
+       "      <td>10:03:53 AM</td>\n",
+       "      <td>10:05:23 AM</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Placement - Time Confirmation - Time Arrival at Pickup - Time Pickup - Time  \\\n",
+       "0       9:35:46 AM          9:40:10 AM              10:04:47 AM   10:27:30 AM   \n",
+       "1      11:16:16 AM         11:23:21 AM              11:40:22 AM   11:44:09 AM   \n",
+       "2      12:39:25 PM         12:42:44 PM              12:49:34 PM   12:53:03 PM   \n",
+       "3       9:25:34 AM          9:26:05 AM               9:37:56 AM    9:43:06 AM   \n",
+       "4       9:55:18 AM          9:56:18 AM              10:03:53 AM   10:05:23 AM   \n",
+       "\n",
+       "  Arrival at Destination - Time  \n",
+       "0                   10:39:55 AM  \n",
+       "1                   12:17:22 PM  \n",
+       "2                    1:00:38 PM  \n",
+       "3                   10:05:27 AM  \n",
+       "4                   10:25:37 AM  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train[time_columns].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type Personal or Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike             Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike             Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike             Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike             Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike             Personal   \n",
+       "\n",
+       "       Rider Id  \n",
+       "0  Rider_Id_432  \n",
+       "1  Rider_Id_856  \n",
+       "2  Rider_Id_155  \n",
+       "3  Rider_Id_855  \n",
+       "4  Rider_Id_770  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train[object_columns].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "0              3                         9                             5   \n",
+       "1              3                        12                             5   \n",
+       "2              3                        30                             2   \n",
+       "3              3                        15                             5   \n",
+       "4              1                        13                             1   \n",
+       "\n",
+       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "0                            9                                5   \n",
+       "1                           12                                5   \n",
+       "2                           30                                2   \n",
+       "3                           15                                5   \n",
+       "4                           13                                1   \n",
+       "\n",
+       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                 9                                     5   \n",
+       "1                                12                                     5   \n",
+       "2                                30                                     2   \n",
+       "3                                15                                     5   \n",
+       "4                                13                                     1   \n",
+       "\n",
+       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
+       "0                      9                          5   \n",
+       "1                     12                          5   \n",
+       "2                     30                          2   \n",
+       "3                     15                          5   \n",
+       "4                     13                          1   \n",
+       "\n",
+       "   Arrival at Destination - Day of Month  \\\n",
+       "0                                      9   \n",
+       "1                                     12   \n",
+       "2                                     30   \n",
+       "3                                     15   \n",
+       "4                                     13   \n",
+       "\n",
+       "   Arrival at Destination - Weekday (Mo = 1)  Distance (KM)  Temperature  \\\n",
+       "0                                          5              4         20.4   \n",
+       "1                                          5             16         26.4   \n",
+       "2                                          2              3          NaN   \n",
+       "3                                          5              9         19.2   \n",
+       "4                                          1              9         15.4   \n",
+       "\n",
+       "   Precipitation in millimeters  Pickup Lat  Pickup Long  Destination Lat  \\\n",
+       "0                           NaN   -1.317755    36.830370        -1.300406   \n",
+       "1                           NaN   -1.351453    36.899315        -1.295004   \n",
+       "2                           NaN   -1.308284    36.843419        -1.300921   \n",
+       "3                           NaN   -1.281301    36.832396        -1.257147   \n",
+       "4                           NaN   -1.266597    36.792118        -1.295041   \n",
+       "\n",
+       "   Destination Long  Time from Pickup to Arrival  \n",
+       "0         36.829741                          745  \n",
+       "1         36.814358                         1993  \n",
+       "2         36.828195                          455  \n",
+       "3         36.795063                         1341  \n",
+       "4         36.809817                         1214  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train[numeric_columns].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train: (21201, 29) \n",
+      "  test: (7068, 25) \n",
+      " riders: (960, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'train: {df_train.shape} \\n  test: {df_test.shape} \\n riders: {df_rider.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing Values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2141233c8>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(df_train.isnull(), cmap=cmap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                         0\n",
+       "User Id                                          0\n",
+       "Vehicle Type                                     0\n",
+       "Platform Type                                    0\n",
+       "Personal or Business                             0\n",
+       "Placement - Day of Month                         0\n",
+       "Placement - Weekday (Mo = 1)                     0\n",
+       "Placement - Time                                 0\n",
+       "Confirmation - Day of Month                      0\n",
+       "Confirmation - Weekday (Mo = 1)                  0\n",
+       "Confirmation - Time                              0\n",
+       "Arrival at Pickup - Day of Month                 0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)             0\n",
+       "Arrival at Pickup - Time                         0\n",
+       "Pickup - Day of Month                            0\n",
+       "Pickup - Weekday (Mo = 1)                        0\n",
+       "Pickup - Time                                    0\n",
+       "Arrival at Destination - Day of Month            0\n",
+       "Arrival at Destination - Weekday (Mo = 1)        0\n",
+       "Arrival at Destination - Time                    0\n",
+       "Distance (KM)                                    0\n",
+       "Temperature                                   4366\n",
+       "Precipitation in millimeters                 20649\n",
+       "Pickup Lat                                       0\n",
+       "Pickup Long                                      0\n",
+       "Destination Lat                                  0\n",
+       "Destination Long                                 0\n",
+       "Rider Id                                         0\n",
+       "Time from Pickup to Arrival                      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "THE % OF MISSING VALUES IN THE DATASET: Order No                                      0.00\n",
+      "User Id                                       0.00\n",
+      "Vehicle Type                                  0.00\n",
+      "Platform Type                                 0.00\n",
+      "Personal or Business                          0.00\n",
+      "Placement - Day of Month                      0.00\n",
+      "Placement - Weekday (Mo = 1)                  0.00\n",
+      "Placement - Time                              0.00\n",
+      "Confirmation - Day of Month                   0.00\n",
+      "Confirmation - Weekday (Mo = 1)               0.00\n",
+      "Confirmation - Time                           0.00\n",
+      "Arrival at Pickup - Day of Month              0.00\n",
+      "Arrival at Pickup - Weekday (Mo = 1)          0.00\n",
+      "Arrival at Pickup - Time                      0.00\n",
+      "Pickup - Day of Month                         0.00\n",
+      "Pickup - Weekday (Mo = 1)                     0.00\n",
+      "Pickup - Time                                 0.00\n",
+      "Arrival at Destination - Day of Month         0.00\n",
+      "Arrival at Destination - Weekday (Mo = 1)     0.00\n",
+      "Arrival at Destination - Time                 0.00\n",
+      "Distance (KM)                                 0.00\n",
+      "Temperature                                  20.59\n",
+      "Precipitation in millimeters                 97.40\n",
+      "Pickup Lat                                    0.00\n",
+      "Pickup Long                                   0.00\n",
+      "Destination Lat                               0.00\n",
+      "Destination Long                              0.00\n",
+      "Rider Id                                      0.00\n",
+      "Time from Pickup to Arrival                   0.00\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The Percentage of missing values in the df_train dataset\n",
+    "MP_df_train = df_train.isna().mean().round(4)*100\n",
+    "print('THE % OF MISSING VALUES IN THE DATASET:', MP_df_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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79++HnZ0dTp48iWPHjuHYsWM4evQocnNzdf4L9/DhwxEZGYnWrVvjpZdeAgAkJCRg2rRp8PLyAgBcvHixwvsOpaSkwN7eHu+//36J+/eoVCpYWlpCpVJpXRht374daWlpGDhwIBwcHGBtbS0tfqSnp2Pw4MFISUnBpk2bMGvWLLi4uCAoKAguLi64cuUKgCfJSpdCsaL91dSXs7MzfvvtN1y6dAkA8Msvv+DcuXPo1q2b1mN+8skn0oO3tCmsHz16hMjISIwZMwZWVlZISEjA0KFDMXz4cDg4OODYsWNSzAqFAqNHj8bs2bMxePBgWFlZlemvT58+2Llzp/SXwdjYWHTt2hV169bVKv6OHTsiLy8P//73vzF06NAS7+k7P7p+bkTGwhxb83NsgwYN8Oqrr2LTpk1wd3eX2t3c3LB69Wq4ublJbS4uLti4cSMKCgqgVqsRFhaGmJgY2NrawtnZWXqIx6NHjzBq1CjExcUBAOrWrYvOnTtj4cKFiIiIwL1799C7d2+cOnUKd+/eBfDkXnBPz9OECRPg6+uL5557DomJidI81atXD0OGDEFoaGiZJ30Xc3BwwK1btzS+5+vriy+//BJZWVlo1apViff69OmDzZs3o7CwEGq1Ghs3bkTv3r0rm2K9CSFw584d6QocInNSk/O/Jh07dsSvv/6KX375BcCTh9wFBQUB0JzDGjZsiMLCQly/fh0AcPDgwRL9nTlzRron6JYtW0rk12KsgctiDUy1BXOs6XMsa+D/YQ1sWryC2Uxs3rwZkyZNKvGXj4YNG2LcuHH46quvdOrL19cXMTExiImJkdo+/PBDTJs2DfXr14etrS26du2KP/74o9w+evfujR07dsDT0xMKhQLdunWDvb09bt68iWbNmsHJyQmDBg3Cxo0b8eyzz5b42W+++Qbnz5+X/urk4OCA//73v1JBuGrVKnzyySf4/PPPUVRUhA8++ACdO3eGo6Mjzp49Cy8vL9SrVw8vvfQSxo0bBwDw9PTEuHHjsHz5cq3moKL97du3L2JiYkr8Jcve3h5Lly5FZGQk8vLyoFAoEBUVBQcHB/z000/aTbwWPv74Y1hbW0OpVEKlUuHNN9/Ee++9BwAICAhAeHi49EAuZ2dnXLt2TfrZoUOHIjo6usRXT542bNgwpKenY/jw4VCr1WjWrBmWLFmiU3w+Pj7YuHEj+vTpU6Jd3/np0aMHPv74Y0RGRqJdu3Y6xUJkSMyxtSPHurq64rPPPivxlGw3NzcsXrwYYWFhUtv777+P6OhoDB06FCqVCo6OjggJCQEALFmyBJGRkfD29kZBQQEGDx6MIUOGSFfTAUD37t0xaNAghIaGYt26dQgKCsKECRNgY2MDJycnabtp06bh008/xdKlS1GnTh106tSpxHHh5+eHbdu2wdfXV+P+9OrVC7Nnz8ajR4/QsGHDEu/1798fc+fOxYcffljm56ZOnYro6Gj4+vqiqKgITk5OJfbf0JKTk9G0aVPp6hgic1KT8r82GjVqhCVLliA4OBgqlQq2trbSg+aKacph77zzDuzt7eHp6Vli2xdffBFBQUG4d+8eWrRogfnz5+scE8AamKimYo41jxzLGvgJ1sCmpRD8fg6RLBw8eBC7d++W7vFERETyJYTAunXrcPv2bcybN6/c7VavXg2lUol33nmnGqPTTUhICDw9PUtcIUNE8rdr1y589913WLNmjUnjYA1MRDWRueTY6sYauObiFcxEMjBu3DhkZmZi1apVpg6FiIgMoF+/fnjhhRcqzesBAQGYOnUqfH19pYeomJOUlBQoFAoW1kRkFKyBiYhqFtbANRevYCYiIiIiIiIiIiIivfAhf0RERERERERERESkFy4wExEREREREREREZFezO4ezBcuXICVlVWJtvz8/DJtciLn+Bm76cg5fjnHDhg3/vz8fDg7Oxulb2PSlJu1wWPBdOQcOyDv+Bm76egbf03JzbX18zMHco4dkHf8co4dkHf8xo5djrmZNbP8yDl2QN7xyzl2QN7xG6NmNrsFZisrKzg6OpZoS01NLdMmJ3KOn7Gbjpzjl3PsgHHjT01NNUq/xqYpN2uDx4LpyDl2QN7xM3bT0Tf+mpKba+vnZw7kHDsg7/jlHDsg7/iNHbscczNrZvmRc+yAvOOXc+yAvOM3Rs3MW2QQERERERERERERkV64wExEREREREREREREeuECMxERERERERERERHphQvMNUBeoUqndiIiInPX9NXmZdp4XiOiqmDNTERENQ1rZjIXZveQP9KddR0lXg05WKb990WDTBANERFR1dnUsypzbuN5jYiqgjUzERHVNKyZyVzwCmYiIiIiIiIiIiIi0oteVzAXFhYiJCQEt2/fhoWFBSIjI2FpaYmQkBAoFAq0bNkSc+fOhYWFBVasWIETJ07A0tISoaGhcHJyMvQ+1Bp5hSpY11GaOgwiMhMPHjyAn58f1q9fr1MOvnnzpsZtiYjIsFgzmw7rZiIqxpqZiMj49Fpg/v7771FUVIQtW7bg1KlT+M9//oPCwkLMmDED3bt3R3h4OOLi4tC4cWOcPXsW27dvR3p6OgIDA7Fz505D70OtURu+1lfeLwP8JYGopMLCQoSHh8Pa2hoAEBUVpXUO1rTtgAEDTLxHREQ1D2tm09FUN9ekmhnQXB+zZiYqiTUzEVH10GuB2cHBASqVCmq1GtnZ2bC0tMSFCxfQrVs3AICrqytOnToFBwcHuLi4QKFQoHHjxlCpVMjMzIS9vb1Bd4JqjvIW0X+O9NS4PYtoqq2io6Ph7++PtWvXAgAuX76sdQ7WtG1lxXJ+fj5SU1N1jjMvL0+vnzMXco5fzrEDgKOjo8Z2OeyTnOdezrED5hc/a2YyJk11M2tmopJYM1cPOccv59gB1symJOf4jRG7XgvM9evXx+3btzFw4ED89ddfWL16Nc6dOweFQgEAsLGxQVZWFrKzs2FnZyf9XHF7RcWypoQs5w8NqDj+pq82h009qzLtOY/z8cfvv5VoKy9xVKSq81bdc1/ePlZ09XZ58dXk48bcyTl2wPzj37VrF+zt7dGnTx+pWBZCaJ2DNW1bGSsrK71zkD4/Z2o14aowuc59Zcx5n2rCt3DkftzoG7+xcr4xa2agbN1s7uevyhiqZgZ0zxVyq5kBzfuoT80MyPvYkXPsgLzjN/fYWTMbH2sf82XO+1QTjhtA3seOMWpmvRaYv/rqK7i4uOCf//wn0tPTMWHCBBQWFkrv5+TkoGHDhrC1tUVOTk6J9gYNGlTYt6aELOcPDag8/vKKQEPsc1X7kMPcV/QXO3OPvSJyjl/OsQPGjd8QRfjOnTuhUChw+vRppKamIjg4GJmZmdL7leXgp+8dV7wtlVQbvlpNhlcbbmVFujFmzQyUrZtr+vmXNXPVVBSfHOIvj5xjB+Qdv7Fjr2rdzJrZ+Fj7kD543NRMet2hvmHDhlLR+8wzz6CoqAht27ZFUlISACA+Ph5dunRBp06dkJCQALVajTt37kCtVvOrfkRkFHmFKq3aaoKNGzdiw4YNiI2NhaOjI6Kjo+Hq6qp1DtaUr4mIyPBYMxORuSmvPq6JdTNrZiKi6qPXFcwTJ05EaGgoRo8ejcLCQnz44Ydo3749wsLCEBMTg+bNm8PDwwNKpRJdunTByJEjoVarER4ebuj4TaqmXNZPVcdjwfRq+xWnwcHBWudgTduS/JXON8VXFDEPEZkOa+YnWCdRMR4LplfbrxxkzUysmYmMQ68FZhsbGyxdurRM+4YNG8q0BQYGIjAwUJ9hzF5tPzmbs7xCVZmvaxnzhMFjgUwlNjZW+n9tc7CDg4PGbY2h6avNNbazgDM85iEi88Oa+QnmJ/NVfD5+um5mzUw1EWtmKsY8RGQcei0wE5n7A7Bq+9WsRObCpp4VCzgiIqq1zP2KXdbMROaBNTMRyR0XmI1A01cuzKWINBQWo0RERERUFbXha8q8Uo6IiIhqAy4wGwEXX4mIiIiIKsbFVyIiIqKawcLUARARERERERERERGRPHGBuZS8QpVWbURU8xT/W9f0gEgiIiL6n/LOjTxnEtV8rJmJiKg03iKjFN7eoqTynmZLVBPxq7pERETa4TmzJNbMVJvw3z8REZXGBWaqEJ9mS0RERERUMdbMREREVJvxFhlEZHDlXcXDr80REREREf2PprqZNTMREckNr2AmIoMzl6t48gpVsK6j1LqdiIiIiKg6aaqbTXHlu6b6mDUzERFpiwvMRGSWDLE4zPvDEREREVFNZ4jFYT6LiIiIqoILzLVQdf91mleRkj64OExERESmxJqZ5IKLw0REZGpcYK6FqrsA4UIhEREREckNa2YiIiIi7fAhf0QyVN6DPwzVTkRERERUE2iqd1kzExERGRavYCYyE6W//ujo6KixHdD9ChdzuSKGX/0kIiIioqrQpWYGdLsSnTUzERGRfrjATGQmzKWgNabasI9EREREZDy1oZ6sDftIREQ1i94LzGvWrMGxY8dQWFiIUaNGoVu3bggJCYFCoUDLli0xd+5cWFhYYMWKFThx4gQsLS0RGhoKJycnQ8ZPRFTrqFQqzJkzBzdu3IBSqURUVBSEEFrn4Js3b2rcloiIDI81MxGRabBmJiKqPnplx6SkJPz000/YvHkzYmNjcffuXURFRWHGjBnYtGkThBCIi4vD5cuXcfbsWWzfvh0xMTGYN2+eoeMnIqp1jh8/DgDYsmULpk+fjqioKJ1ysKZtiYjI8FgzExGZDmtmIqLqo9cVzAkJCWjVqhWmTZuG7OxszJw5E9u2bUO3bt0AAK6urjh16hQcHBzg4uIChUKBxo0bQ6VSITMzE/b29uX2nZ+fj9TU1BJteXl5Zdq01fTV5rCpZ1WmPedxPv74/bcy7cX38CpN0/jlbVseXfsovb2u4+k6Zm3dR0PQZR8N0Yeu4xkiPl0Y4nM05D4aog9jHTv66N+/P9zc3AAAd+7cQaNGjXDixAmtc/Dly5fLbDtgwIAKx9SUm7UhlzktrbpziCHIda7Lw8/ANKpSc5kDc4vfmDUzUDY3m0PNDBinvjOnusAQ42nqx9z30RCqu2auaHtzqQXN/Vg1l3nSB2tm45Nj3HKMuSKsmU3H3OpOXRgjdr0WmP/66y/cuXMHq1evRlpaGqZOnQohBBQKBQDAxsYGWVlZyM7Ohp2dnfRzxe0VFctWVlZlDrbU1FS9irZi5d2/Spc+qzK+vn1U95jcx5I0PURDnwdrmPM+6rK9sR82UhuOVcBwJ0xLS0sEBwfjyJEjWLZsGY4fP651DtaUryujKTdXlaH7qw5yjBmQb9yayHVf5BJ3VWsuU9M3fmP9cmDMmhkom5vNoWYGzL/2MYfxTDEma+aqjceaufrGLGaI3Mya2XTkGLccYy6PXPdFTnHLuW42Rs2s1wKznZ0dmjdvjrp166J58+awsrLC3bt3pfdzcnLQsGFD2NraIicnp0R7gwYN9BmSqNrp8sRpXcj16c982Ij5iY6Oxscff4wRI0YgPz9faq8sBz9977jibYmIyPBYM1NtYKyaGZBn3cya2fywZiYiMj697sHcuXNnnDx5EkIIZGRk4PHjx+jZsyeSkpIAAPHx8ejSpQs6deqEhIQEqNVq3LlzB2q1utIrMYhquuKis/R/RNras2cP1qxZAwCoV68eFAoF2rdvr3UObtu2bZltiYjI8FgzE1WNprqZSFusmYmIqo9eVzC7u7vj3LlzGDZsGIQQCA8PR5MmTRAWFoaYmBg0b94cHh4eUCqV6NKlC0aOHAm1Wo3w8HBDx09UZXK8MoJqtzfffBOzZs3CmDFjUFRUhNDQULz22mta5+Dg4OAy2xIRkeGxZqaahDUzyQ1rZiKi6qPXAjMAzJw5s0zbhg0byrQFBgYiMDBQ32GIjK42f43NUPfMo+pVv359LF26tEy7tjnYwcFB47ZERGR4rJmppmDNzJpZblgzExFVH70XmIlI/ox5zzwiIiIiopqANTMREVHF9LoHMxERERERERERERERF5iJiIiIiIiIiIiISC9cYCYiIiIiIiIiIiIivXCBmYiIiIiIiIiIiIj0wgVmIiIiIiIiIiIiItILF5iJiIiIiIiIiIiISC9cYCYiIiIiIiIiIqv2ZJYAACAASURBVCIivdSoBea8QpWpQyAiIiIiMmusmYmIiIjIkCxNHYAhWddR4tWQgyXafl80yETREBERERGZH9bMRERERGRINeoKZiIiIiIiIiIiIiKqPlxgJiKqhKavEvPrxURERERE/8OamYio9qpRt8ggIjIGfpWYiIiIiKhirJmJiGovXsFMRERERERERERERHqp0hXMDx48gJ+fH9avXw9LS0uEhIRAoVCgZcuWmDt3LiwsLLBixQqcOHEClpaWCA0NhZOTk6FiJyKqlQoLCxEaGorbt2+joKAAU6dORYsWLbTOwTdv3tS4LRERGQdrZiKi6seamYio+uidHQsLCxEeHg5ra2sAQFRUFGbMmIFNmzZBCIG4uDhcvnwZZ8+exfbt2xETE4N58+YZLHAiotpq3759sLOzw6ZNm7Bu3TpERkbqlIM1bUtERMbBmpmIyDRYMxMRVR+9F5ijo6Ph7++PF154AQBw+fJldOvWDQDg6uqKxMREnD9/Hi4uLlAoFGjcuDFUKhUyMzMNEzkRUS3l6emJDz74QHqtVCp1ysGatiUiIuNgzUxEZBqsmYmIqo9et8jYtWsX7O3t0adPH6xduxYAIISAQqEAANjY2CArKwvZ2dmws7OTfq643d7evty+8/PzkZqaWqItLy+vTJsmjo6OOu2Hpj7L60OXbQ0xnqbtdR1P1zG5j4YfT9P2hthHXcbTZ0zuo/59VAcbGxsAQHZ2NqZPn44ZM2YgOjpa6xysKV9XRlNu1oaun6O50CWHmAu5znV5+BmYhrY1l7kyt/iNWTMDZXOzOdTMmravaXWBIcbT1E9N28eqHjfGHpP7aPjxdOmjOrBmNj45xi3HmCvCmtl0zK3u1IUxYtdrgXnnzp1QKBQ4ffo0UlNTERwcXOIqi5ycHDRs2BC2trbIyckp0d6gQYMK+7aysipzsKWmpupVtFVGlz4NMb6ufVT3mNxH4/TBfTR8H6bYR0P1Yagknp6ejmnTpmH06NHw9vbG4sWLpfcqy8FP3zuueNvKaMrNVWWMvG5scowZkG/cmsh1X+QSt7Fqruqib/zG+uXAmDUzUDY3m0PNrM/2Ve1DjuOZYkxzHs8UY3Ifzb8PQ+Rm1symI8e45RhzeeS6L3KKW851szFqZr1ukbFx40Zs2LABsbGxcHR0RHR0NFxdXZGUlAQAiI+PR5cuXdCpUyckJCRArVbjzp07UKvVlV6JQUREFbt//z4CAgIQFBSEYcOGAQDatm2rdQ7WtC0RERkea2YiItNhzUxEVH30uoJZk+DgYISFhSEmJgbNmzeHh4cHlEolunTpgpEjR0KtViM8PNxQwxER1VqrV6/Go0ePsGrVKqxatQoAMHv2bCxYsECrHKwpXxMRUfVgzUxEVD1YMxMRVZ8qLzDHxsZK/79hw4Yy7wcGBiIwMLCqwxAR0f83Z84czJkzp0y7tjnYwcFB47ZERGQ8rJmJiKoXa2Yiouqj1y0yiIiIiIiIiIiIiIi4wExEREREREREREREeuECMxERERERERERERHphQvMRERERERERERERKQXLjATERERERERERERkV64wExEREREREREREREeuECMxERERERERERERHphQvMRERERERERERERKQXLjATERERERERERERkV64wExEREREREREREREeuECMxERERERERERERHphQvMRERERERERERERKQXLjATERERERERERERkV64wExERERUi+UVqqT/d3R0LNNGRERERFTbla6PHR0dWTM/xdLUARARERGR6VjXUeLVkIMl2n5fNMhE0RARERERmR/WzBXTa4G5sLAQoaGhuH37NgoKCjB16lS0aNECISEhUCgUaNmyJebOnQsLCwusWLECJ06cgKWlJUJDQ+Hk5GTofSAiqpUuXryIJUuWIDY2Fjdv3tQ6B5e3LRERGRZrZiIi02PNTERkfHplx3379sHOzg6bNm3CunXrEBkZiaioKMyYMQObNm2CEAJxcXG4fPkyzp49i+3btyMmJgbz5s0zdPxERLXSunXrMGfOHOTn5wOATjlY07ZERGR4rJmJiEyLNTMRUfXQa4HZ09MTH3zwgfRaqVTi8uXL6NatGwDA1dUViYmJOH/+PFxcXKBQKNC4cWOoVCpkZmYaJnIiolqsadOmWL58ufRalxysaVsiIjI81sxERKbFmpmIqHrodYsMGxsbAEB2djamT5+OGTNmIDo6GgqFQno/KysL2dnZsLOzK/FzWVlZsLe3L7fv/Px8pKamlmjLy8sr06ZJ8YNptKWpz/L60GVbQ4ynaXtdx9N1TO6j4cfTtL0h9lGX8fQZk/uofx/VxcPDA2lpadJrIYTWOVjTtpXRlJu1oevnaC50ySHmQq5zXR5+BtVHjnNdHm1rxupizJoZKJubzaFm1rR9TasLDDGepn5q2j5W9bgx9pjcR8OPp0sf1YU1s3HJMW45xlwROdZxcv0M5DjX5TFGzaz3Q/7S09Mxbdo0jB49Gt7e3li8eLH0Xk5ODho2bAhbW1vk5OSUaG/QoEGF/VpZWZX50FJTU/Uq2iqjS5+GGF/XPqp7TO6jcfrgPhq+D1Pso6H6MNbJ5+n7wVWWgzVtWxlNubmqjJHXjU2OMQPyjVsTue6LHOOWY8z61ozG/MXAWDUzUDY3m0PNrM/2Ve1DjuOZYkxzHs8UY3Ifzb8PY+Rm1szVR45xyzHm8sh1X+QYtxxjNkbNrNctMu7fv4+AgAAEBQVh2LBhAIC2bdsiKSkJABAfH48uXbqgU6dOSEhIgFqtxp07d6BWqyu9EoOIiHSnSw7WtC0RERkea2YiIvPCmpmIyDj0uoJ59erVePToEVatWoVVq1YBAGbPno0FCxYgJiYGzZs3h4eHB5RKJbp06YKRI0dCrVYjPDzcoMETEdETwcHBCAsL0yoHa9qWiIgMjzUzEZF5Yc1MRGQcei0wz5kzB3PmzCnTvmHDhjJtgYGBCAwM1GcYIiKqQJMmTbBt2zYAgIODg9Y5uLxtiYjIsFgzExGZHmtmIiLj0+sWGUREREREREREREREXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIFZiIiIiIiIiIiIiLSCxeYiYiIiIiIiIiIiEgvXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIFZiIiIiIiIiIiIiLSCxeYiYiIiIiIiIiIiEgvXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIFZiIiIiIiIiIiIiLSCxeYiYiIiIiIiIiIiEgvXGAmIiIiIlnJK1RpbG/6avNqjoSIiIiIyDxVZ81safAeiYiIiIiMyLqOEq+GHCzT/vuiQSaIhoiIiIjI/FRnzWz0BWa1Wo2IiAhcvXoVdevWxYIFC9CsWTNjD0tERBVgbiYiMi/My0RE5oe5mYhIO0a/RcbRo0dRUFCArVu34p///CcWLVpk7CGJiKgSzM1EROaFeZmIyPwwNxMRaUchhBDGHCAqKgpOTk4YNOjJ5dd9+vTByZMny93+woULsLKyMmZIREQmk5+fD2dnZ1OHwdxMRPQUc8jNuuZlgLmZiGo2OeZm5mUiqskqystGv0VGdnY2bG1tpddKpRJFRUWwtNQ8tKlPIEREtQFzMxGRedE1LwPMzURExsaamYhIO0a/RYatrS1ycnKk12q1usJCmYiIjI+5mYjIvDAvExGZH+ZmIiLtGH2BuVOnToiPjwfw5OsirVq1MvaQRERUCeZmIiLzwrxMRGR+mJuJiLRj9HswFz919dq1axBCYOHChXjttdeMOSQREVWCuZmIyLwwLxMRmR/mZiIi7Rh9gZmIiIiIiIiIiIiIaiaj3yKDiIiIiIiIiIiIiGomLjATERERERERERERkV7M+vGnxfc7unr1KurWrYsFCxagWbNmpg5La76+vmjQoAEAoEmTJoiKijJxRNq5ePEilixZgtjYWNy8eRMhISFQKBRo2bIl5s6dCwsL8/27xNOxX758Ge+99x5effVVAMCoUaPg5eVl2gDLUVhYiNDQUNy+fRsFBQWYOnUqWrRoIYu51xT7P/7xD9nMvUqlwpw5c3Djxg0olUpERUVBCCGLuTd3T/97lBNNx3S/fv1MHZbWNB3TTZs2NXVYOnnw4AH8/Pywfv162d3nUK7nfgBYs2YNjh07hsLCQowaNQrDhw83dUha27VrF3bv3g0AyM/PR2pqKk6dOoWGDRuaOLLqwZrZNFgzVz/WzKbDmtm45Fg3s2Y2PdbMpsGaWTOzXmA+evQoCgoKsHXrVly4cAGLFi3CZ599ZuqwtJKfnw8AsjpBAMC6deuwb98+1KtXDwAQFRWFGTNmoHv37ggPD0dcXBwGDBhg4ig1Kx37lStXMGnSJAQEBJg4ssrt27cPdnZ2WLx4Mf766y8MHToUbdq0kcXca4p92rRpspn748ePAwC2bNmCpKQkqViWw9ybs9L/HuVE0zEtp2JZ0zEtl3Mn8OSXlfDwcFhbW5s6FJ3J9dwPAElJSfjpp5+wefNmPH78GOvXrzd1SDrx8/ODn58fAGDevHl46623as3iMsCa2RRYM5sGa2bTYc1sPHKtm1kzmxZrZtNgzVw+s/7z4vnz59GnTx8AgLOzM1JSUkwckfZ+/vlnPH78GAEBARg/fjwuXLhg6pC00rRpUyxfvlx6ffnyZXTr1g0A4OrqisTERFOFVqnSsaekpODEiRMYM2YMQkNDkZ2dbcLoKubp6YkPPvhAeq1UKmUz95pil9Pc9+/fH5GRkQCAO3fuoFGjRrKZe3NW+t+jnGg6puVE0zEtJ9HR0fD398cLL7xg6lB0JtdzPwAkJCSgVatWmDZtGt577z24ubmZOiS9JCcn4/r16xg5cqSpQ6lWrJmrH2tm02DNbDqsmY1HrnUza2bTYs1sGqyZy2fWC8zZ2dmwtbWVXiuVShQVFZkwIu1ZW1vj7bffxhdffIF58+bh448/lkXsHh4esLT834XtQggoFAoAgI2NDbKyskwVWqVKx+7k5ISZM2di48aNeOWVV7By5UoTRlcxGxsb2NraIjs7G9OnT8eMGTNkM/eaYpfT3AOApaUlgoODERkZCQ8PD9nMvTkr/e9RTjQd03JT+piWi127dsHe3l5aKJMbuZ77AeCvv/5CSkoKli5dKsUuhDB1WDpbs2YNpk2bZuowqh1r5urHmtk0WDObFmtm45Br3cya2XRYM5sOa+bymfUCs62tLXJycqTXarVaNonXwcEBQ4YMgUKhgIODA+zs7HDv3j1Th6Wzp++hlZOTI6uvmw4YMADt27eX/v/KlSsmjqhi6enpGD9+PHx8fODt7S2ruS8du9zmHnjyF+DvvvsOYWFh0ld2APOfezKO0se0HD19TOfm5po6HK3s3LkTiYmJGDduHFJTUxEcHCyrc6ecz/12dnZwcXFB3bp10bx5c1hZWSEzM9PUYenk0aNH+O2339CjRw9Th1LtWDObnpzqttLkVrexZjYt1sz0NNbMpsGa2XRYM5fPrBeYO3XqhPj4eADAhQsX0KpVKxNHpL0dO3Zg0aJFAICMjAxkZ2fj+eefN3FUumvbti2SkpIAAPHx8ejSpYuJI9Le22+/jUuXLgEATp8+jXbt2pk4ovLdv38fAQEBCAoKwrBhwwDIZ+41xS6nud+zZw/WrFkDAKhXrx4UCgXat28vi7kn49B0TMuJpmNaLl9Z3LhxIzZs2IDY2Fg4OjoiOjpaVudOOZ/7O3fujJMnT0IIgYyMDDx+/Bh2dnamDksn586dQ69evUwdhkmwZjY9udRtmsipbmPNbDqsmak01symw5rZdFgzl8+sL20YMGAATp06BX9/fwghsHDhQlOHpLVhw4Zh1qxZGDVqFBQKBRYuXCibK0meFhwcjLCwMMTExKB58+ay+tpIREQEIiMjUadOHTRq1Ei6v5I5Wr16NR49eoRVq1Zh1apVAIDZs2djwYIFZj/3mmIPCQnBwoULZTH3b775JmbNmoUxY8agqKgIoaGheO2112R73FPVaTqm161bJ5sHaGg6pq2srEwdVq0g53O/u7s7zp07h2HDhkEIgfDwcNn8klXsxo0baNKkianDMAnWzKbHmrl6sGY2HdbMVBprZtKXnM/9rJnLpxByvFkIEREREREREREREZmcWd8ig4iIiIiIiIiIiIjMFxeYiYiIiIiIiIiIiEgvXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIFZiIiIiIiIiIiIiLSCxeYiYiIiIiIiIiIiEgvXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIFZiIiIiIiIiIiIiLSCxeYiYiIiIiIiIiIiEgvXGAmIiIiIiIiIiIiIr1wgZmIiIiIiIiIiIiI9MIF5hpMpVLhyy+/hJ+fH3x8fODl5YXFixejoKCgSn1OnToVHh4e2LBhA3x8fPDo0SMDRv0/J06cwNKlSwEAcXFxWLBggVHGKS0kJAR9+vSBj4+PNG/h4eG4d++e0cfOzs6Gv78/Bg0ahMOHD5eJq3Xr1jhz5kyJ9rS0NLRp0wbz58/Xe9xbt24hMDBQ6u/111/Xuy+i2oI5VncXLlxA9+7doVarpbaPPvoI7du3R3Z2ttQWERGBxYsX6z1O3759kZycXOl2r7/+OtLS0vQep7TFixcjISEBaWlpaN26NcaOHVtmm+JcnpmZabBxNUlISICPj4/0Ojs7G5MnT0ZeXp5RxyUi/aWlpcHR0VGqQb29veHv749vvvmmSv1u374dGzduBABs3rwZa9eu1buvS5cuITw8HACQnJyM6dOnVym2p2mbu5/29LmMiKiqmId1z8NExSxNHQAZT0REBB4+fIivv/4aDRo0QG5uLj7++GPMnj1b71/cMzIykJCQgAsXLkCpVGr85dlQkpOT8fDhQwBAv3790K9fP6ONVdrEiRPx9ttvAwCEEFizZg0mT56MXbt2QalUGm3c1NRUPHjwAEeOHNH4fuPGjbF371706NFDatuzZw+ee+65Ko17584d3Lhxo0p9ENU2zLG6c3JyAgBcvXoVjo6OKCoqQlJSErp3746TJ09i4MCBAIAzZ84gMjLS6PEY0oULF/Drr78iKCgIaWlpsLKywo0bN3D79m28/PLLAIDc3Fz8+OOPRo0jLy8Pn332GTZt2oQXX3xRare1tcXgwYOxdOlSBAcHGzUGItKftbU19u7dK72+ffs2Jk6cCKVSCQ8PD736PH/+PFq2bAkAGDVqVJXiu379OjIyMgAAHTp0wLJly6rUX1U9fS4jIjIE5mEi/XCBuYZKS0vD/v37kZCQAFtbWwBA/fr1MW/ePOmX26ysLMybNw8///wzFAoF+vTpg48++giWlpbo0KEDpkyZglOnTuHPP//E5MmTMWTIEEyePBlFRUXw8/PD8uXLMWDAAJw+fRonTpzAjh078PjxY9ja2mLo0KE4fPgw1Go17ty5gxdffBEjRozAhg0b8Pvvv2PSpEkICAhAbm4uIiIicPPmTfz999+wsbHBkiVLkJWVhS1btkClUqFBgwZo1qwZvvvuO6xZswZ3795FREQEbt++DSEEfH19MXnyZKSlpWHixIl44403cPHiRTx69AhBQUEYMGBAleZSoVDgvffew+7du3Hq1Cm4urpi9erViIuLQ15eHh4/fozg4GD0798fnp6eCA8PR+/evQEAs2fPRqtWrTBhwoQSfR49ehQrVqyAWq2GjY0NZs2aBVtbW4SGhiIjIwM+Pj7YunUrrK2tS/ycl5cXduzYgby8POm9b7/9FgMHDpSuCNR1fvr27Ys5c+YgIyMDb7/9NubNmweVSoXw8HAkJycjKysLQUFBep9MiWoi5lj9cqyFhQVcXFyQlJQER0dHnD9/Hq1bt4anpyeOHTuGgQMHIiMjAw8ePJC+SbF9+3Zs3rwZarUadnZ2CAsLw2uvvYaCggIsWbIE586dg0qlQtu2bTFnzhzp8wCAnJwcTJkyBc7OzggKCsIPP/yAyMhIKBQKdOjQQcqbarUaCxcuxMWLF5GTkwMhBBYsWABHR0e88cYb2LZtGxwcHAA8+QPk2LFj0b9//xL7tnz58hJ/EFAqlRg4cCD279+P9957DwBw+PBh9OvXD+vXr5e227p1K2JjY2FhYYFGjRohLCxMGqtYYmIioqOjy8znxx9/jD59+pRoS0hIwOPHj7Fo0SL8+9//LvHewIEDsWTJErz99tto1KiRdh8aEZnUyy+/jOnTp+OLL76Ah4dHhblv06ZN2LJlC+rUqQMrKyvMnz8fN27cwLFjx3Dq1ClYW1sjMzMTf/31F8LDw9G3b18MHToUp0+fRnp6Onx8fDBjxoxyc2Ljxo2xbNkyZGVlYdasWfD19UVkZCQOHDig8zlv9OjRWs+BtueyDz/80IifBBHVVszDT6xcuRIHDx6EUqmEg4MDwsLC8Pzzz2PcuHFwdnbGjz/+iPT0dPTs2RORkZGwsLDArl27sHbtWlhbW6NHjx7473//iytXrhjpkyKTE1QjHTp0SLz11lsVbjNz5kwRGRkp1Gq1yM/PFwEBAWLNmjVCCCFatWolYmNjhRBCJCcni/bt24u8vDxx69Yt4ezsLPXRqlUr8eDBA7Fz507RtWtXkZWVJYQQYufOnaJz587izp07QqVSCS8vLxEYGChUKpVITU0VHTp0ECqVSnz77bciMjJS6i8sLEzMnz9fCCHEsmXLxLx586T+pkyZIoQQYsyYMWL9+vVCCCEePXokvL29xYEDB8StW7dEq1atxLFjx6Q5cHNz03nugoODxeeff16mPTAwUKxbt06kpaWJcePGicePHwshhDhw4IAYPHiwEEKIL7/8UkyfPl0IIURWVpbo0aOHePjwYYl+rl+/Lnr16iX++OMPIYQQiYmJonfv3iIrK0ucOXNGDBo0qMK43n33XXHw4EEhhBDnzp0TgYGBJeZKn/l5etzi7Q4dOiSEEOLw4cOiX79+Os8jUU3GHKt/jt29e7eYOnWqEEKIqKgo8fXXX4uMjAzRrVs3UVRUJHbv3i1mzJghhBAiKSlJjB49WuTm5gohhDh58qTw9PQUQgixfPlysWjRIqFWq4UQQvzrX/8Sc+fOFUII4e7uLhITE8XIkSOlOc/Pzxe9evUSiYmJQggh9u/fL1q1aiVu3bolfvzxR2n+hBBizZo14t133xVCCLFgwQIRHR0thBDi5s2b4o033hBFRUUl9unhw4eiY8eOIj8/XwghpM8xOTlZilcIISZMmCCuXr0qfa6JiYmif//+4sGDB9LnMHDgQGmfqqK888m7774rduzYUeX+icjwSp8Dil27dk107NhRCFF+7isqKhLt2rUTGRkZQognuXbLli1CiJK17dO5393dXSxatEgIIcTdu3dFhw4dxB9//FFhTnz6fPF0ntHnnFeau7u7uHTpUpl2bc9lRERVxTysOQ/v2LFDjBw5UuTk5Ej7EBAQIIQQYuzYsWL69OlCpVKJrKws4eLiIk6fPi1++eUX0bNnT5Geni7NW6tWrbT6HEieeAVzDWVhYVHiHpeaxMfHY/PmzVAoFKhbty78/f3x9ddfY8qUKQAgfV26Xbt2KCgoQG5uboX9tW7dusSVYx06dMBLL70EAGjSpAlcXFxgYWGBV155Bfn5+Xj8+DE8PT3xyiuvIDY2Fjdv3sTZs2crvP9v8deLi6/+atCgAfz8/BAfH4+OHTuiTp06eOONNwAAbdu2xd9//13JTGlPoVCgXr16ePnll/Hpp59i//79uHnzpvRXRQDw8/PDypUrkZmZiUOHDsHNzQ0NGzYs0c+ZM2fQo0cPvPLKKwCAnj17wt7eHikpKVAoFJXG4ePjg71798LLywt79uzB0KFDkZKSYtD5qVOnjnTFcps2bfDgwQM9Zoyo5mKO1T/Hurq6IioqCmq1GsePH8fnn3+OF154AS+//DJSUlJw5swZaYwTJ07g5s2b8Pf3l37+0aNH+Pvvv3HixAlkZWUhMTERAFBYWFjidkFBQUGwtLTE+PHjAQDXrl2DpaUlevbsCQAYPHiwdP+6119/Hc888wy2bNmCW7duISkpCTY2NgCA0aNHY+zYsfjwww+xdetWDBs2rMytkm7evInnn38edevWLdHevn17KJVKpKSk4LnnnkNOTg5atWolvX/y5El4eXnB3t4ewJNzyCeffIK0tDTpHAHodgVzZZo0acJbIhHJjEKhkL65Vl7uUyqV8PT0hL+/P9zc3ODi4iLl0ooUn4tefPFFPPfcc3j48GGFObE8+pzzrKystNp/Xc9lRESGVtvzcHx8PPz8/FC/fn0AwPjx47F69Wrp2TPu7u6wsLCAra0tmjVrhocPH+Lnn39G79698Y9//AMAMHbsWCxfvlyr8UieuMBcQzk5OeG3335DdnZ2iQWJjIwMhIWFYdmyZVCr1SUWNNVqNYqKiqTXxcmmeBshRIVjFiebYqV/0ba0LHu4bdq0Cdu2bcOYMWPg7e0NOzu7Ch+4pFary8TxdNx16tSBhYVFibhL27x5M7Zs2QLgyS//n3zySYX7BTzZ98uXL2Ps2LG4fPky3n//fUycOBG9e/dG165dMW/ePABAw4YN4enpiX379mH//v2YO3euxn0oHZsQAkVFRahTp06lsfTr1w/z589Heno6zp07h4iICGmB2RDzU7xdMW0WvYlqG+ZY/XOsvb09mjRpgsOHD0OpVEoLqW5ubjh//jzOnj2LmTNnSmP7+PggKChIev3nn3/imWeegVqtRmhoqFS45+TkID8/Xxpn6tSpSEpKwuLFixEWFgag7BwXz9mJEyfwySefYNKkSejXrx+aN2+Offv2AQAcHBzQunVrxMXF4cCBA9i2bVuZfVYoFOX+wWHIkCHYt28f7O3tSzx0r3h/Sis+HzytV69eJe4FWBV16tQx6rMEiMjwkpOTpT9OVZT7lixZ1yPq8QAAIABJREFUgmvXriExMRFr167F3r17K30A3tOLCwqFAkKICnNieQx9znuarucyIiJDq+15uLK+n761Z/E+KJXKEmOw/qz5LEwdABnHiy++CG9vb4SGhiI7OxvAkyfIR0REwM7ODtbW1nBxccGGDRsghEBBQQG2bduGXr16VWucCQkJGDp0KIYPHw4HBwccO3YMKpUKwJMEVPqXbFtbW3Ts2FF6AmtWVhb27NmjU9yjRo3C3r17sXfvXq0Wl1UqFVauXIlnn30WXbt2xblz59C+fXtMmjQJ3bp1Q1xcnBQzAIwZMwb//e9/IYSQHmj1tJ49eyIhIQG3bt0CAOl+Sx07dtQq/rp162LAgAGYOXMm+vbtW2JRSd/5USqVKCws1Gp8ImKOrYg2OdbV1RWrVq2Cm5ub1Obm5oa9e/fi+eefl67odXFxwcGDB/Hnn38CeLJ4XXxPexcXF2zcuBEFBQVQq9UICwtDTEyM1J+TkxMiIiJw6NAhJCQkoHXr1hBC4PvvvwcAxMXFSQ+GOnXqFNzd3TF69Gi0b98eR48eLZHXR48ejU8//RROTk4lHpxXrGnTpnjw4EGJBe5iPj4+OHToEL755hsMHjy4xHt9+vTBN998g8zMTADAzp07YWdnh2bNmlU8yVWQlpZW5h7PRGS+bty4gVWrViEgIABA+bkvMzMTb7zxBuzs7DBx4kTMmDEDycnJADTn+4pUlBPL68uY5zxdz2VERIbEPPykZt25c6f0jcvY2Fh07dq1zAUvpeM5ffq09EDC7du3GyQWMl+8grkGmzt3LlatWgV/f38olUoUFBSgf//+CAwMBADMmTMHCxYsgLe3NwoLC9GnTx/pQUTVJSAgAOHh4dixYwcAwNnZGdeuXQMA9OjRAx9//DEiIyPRrl076WeWLFmC+fPnY9euXSgoKIC3tzf8/Pxw+/Ztg8X11VdfYd++fVAoFFCpVOjQoQPWrl0L4MnXqg8fPiw9WM/d3R0PHz6UrmRs06YNnnnmmRJf6X5aixYtMHfuXPzf//0fVCoVrK2tsXr1ajRo0EDr+Hx8fDB69Gjpqryn6TM/LVq0gJWVFYYNG1bmoVBEpBlzrP6KF5ifzmEdOnTA/fv3SzxwxMXFBe+88w4CAgKgUChga2uLFStWQKFQ4P3330d0dDSGDh0KlUoFR0dHhISElBjH3t4ec+fORWhoKPbv34+VK1ciIiICMTExcHT8f+zdeVxU9f4/8NcwICBLRNvNFANX1Eum5nJFzIVAE7dcADMNS3NBKRcUEfViIlHuIeq9XQs0BfUqVl+vhguJhKbiQqjXDQURUyyFZJ35/eGPcxlngJlhhjlneD0fjx6P5jOfOed9Phw/58x7Pp/PcReW1PD398fs2bPh5+eHiooK9O7dW3iIooWFBfr164fw8PAa+3VHR0d07dpVZXmPKi+99BJatWoFBwcHODk5qbzXu3dvTJw4ERMmTIBCoYCzszM2btwojBI3tLKyMmRmZmr14yoRmUZJSYkw28HCwgLW1tb45JNPhB/kaur77O3tMXXqVEycOBE2NjaQy+VYtmwZgCd97ooVK7SOobY+sXPnzvjyyy8xY8YMjB8/XviMoa557777rkofOGfOHK2vZZrui4mIdMV+WL0fDggIQH5+PkaPHg2FQoGWLVvi888/r3U7rq6uWLBgASZNmoQmTZrA3d0dtra2OsdD0iFT6jIunojqdPPmTYwfPx779+9nB0pEZAbOnDmD8PBwfPfddzUuDXL69GnExcUJP0aK0e7du/Hf//4XoaGhpg6FiIiIiMzYrVu3sHfvXkybNg0WFhY4cOAANm/ezJHMZowjmIkMaM2aNUhMTMTSpUuZXCYiMgOhoaE4ceIEVq1aVeua9F26dIGrqytSU1Ph5eXVgBFqp7i4GN999x3Wr19v6lCIiIiIyMz95S9/wd27d+Hn5we5XA4HBwcsX77c1GGREXEEMxERERERERERERHphQ/5IyIiIiIiIiIiIiK9MMFMRERERERERERERHoR3RrMmZmZsLa2VikrLS1VK5MSKcfP2E1HyvFLOXbAuPGXlpaic+fORtm2MWnqm7XBc8F0pBw7IO34Gbvp6Bu/VPvm06dPi+aZD2I7d8QUj5hiARhPbcQUCyCueEwRixT7Zm3vmcX0txULtolmbBd1bBN1DdUmtfXLokswW1tbw93dXaUsOztbrUxKpBw/YzcdKccv5dgB48afnZ1tlO0am6a+WRs8F0xHyrED0o6fsZuOvvFLtW+WyWSi+XuJ7dwRUzxiigVgPLURUyyAuOIxRSxS7Ju1vWcW099WLNgmmrFd1LFN1DVUm9TWL4suwUxEREREJDWVlZUIDw/H9evXIZfLERUVBaVSifnz50Mmk6FNmzZYvHgxLCwssH79ehw5cgSWlpYICwuDh4cHcnJyNNYlIiIiIhI73rUSEREREdXT4cOHAQDbt2/HzJkzERUVhaioKISEhGDbtm1QKpVISUlBVlYWTpw4gaSkJKxcuRJLly4FAI11iYiIiIikgAlmIiIiIqJ6GjhwICIjIwEAt2/fxvPPP4+srCx0794dAODl5YXjx4/j1KlT8PT0hEwmQ7NmzVBZWYnCwkKNdYmIiIiIpIBLZJiBkvJK2FjJtS4nIiISO5dX3dTKeF0jsbO0tERoaCgOHjyItWvX4vDhw5DJZAAAOzs7PHr0CEVFRXBychI+U1WuVCrV6tZFoVCIZo3SkpIS0cQCiCuehozF5VU32NmqP+Sn+HEpbt641uDxaENM8YgpFkBc8YgpFiJzoeneVtf8Du+PSSyYYDYDNlZyvDr/e7XyGyveNkE0RERE9Wdna612beN1jaQgOjoac+bMwZgxY1BaWiqUFxcXw9HREfb29iguLlYpd3BwUFlvuapuXSwsLETzkBuxPXBHTPE0dCw1fS+oikFMbQOIKx4xxQKIKx4+5I/I8DTlcmq632Xeh8SOS2QQEREREdXTnj17sHHjRgCAra0tZDIZOnXqhIyMDABAamoqunXrhi5duuDYsWNQKBS4ffs2FAoFnJ2d0aFDB7W6RERERERSoNcI5vLycsyfPx95eXmwsLBAZGQkLC0ttX5KNumHUx+IqLr79+9j5MiR+Oqrr3Tqg3NycjTWJSIi/b311ltYsGABxo0bh4qKCoSFhaFVq1ZYtGgRVq5cCTc3N/j4+EAul6Nbt24YO3YsFAoFIiIiAAChoaFqdUn6SsorNY765H09ERERmRO9EsxHjx5FRUUFtm/fjrS0NKxevRrl5eUICQlBjx49EBERgZSUFDRr1kx4SnZ+fj6Cg4Oxa9cuQx9Do9EYpkRwXSEi7ZSXlyMiIgI2NjYAgKioKK37YE11vb29TXxERETS1rRpU6xZs0atPCEhQa0sODgYwcHBKmWurq4a65K0NYb7dyIiIiK9Esyurq6orKyEQqFAUVERLC0tkZmZqfLk67S0NLi6ump8Srazs7NBD4LMR0034RcjfTXWZ+KZGqvo6Gj4+/tj06ZNAICsrCyt+2BNdetKMJeWluq1Dp7UHwgj5filHDuAGtd5FPMxVT1c6+nYqz9cS+ykft5IPX4iIiKi+uLAPTIFvRLMTZs2RV5eHgYNGoQHDx4gLi4OJ0+e1Pop2bUlmDUlMaT+ZaG2+LV50nMVfR6qUN92a+i2r+kYaxv9UVN85nzeiJ2UYwfEH//u3bvh7OyMPn36CAlmpVKpdR+sqW5drK3Vk2baENPDaXShyxOdxUqqbV8XsR9TXQ/XEjupnzf6xi/mPp+IiPRTXl6OsLAw5OXloaysDFOnTkXr1q25rByZPc6eIVPQK8G8ZcsWeHp6Yvbs2cjPz8eECRNQXl4uvF/XU7JroymJYe5fdoz5ZbS+25BC29c2yk3ssddGyvGbInZDJgSNGb8hkhi7du2CTCZDeno6srOzERoaisLCQuH9uvrg6jfGVXVJlS5PdCYiIiIi8UlOToaTkxNiYmLw4MEDjBgxAu3bt+eyckRERqBXgtnR0RFWVlYAgGeeeQYVFRXCk6979OiB1NRU9OzZEy4uLoiJicGkSZNw584d4SnZRESG1pgSglu3bhX+f/z48ViyZAliYmK07oM19ddERERERObE19dX5YGpcrlcNMvKiX3GpCk0xjbRZkm4qnapbQDU0+2mS10paoznSl3E0CZ6JZgnTpyIsLAwBAYGory8HB9//DE6deqk9VOyzQXXtaEqPBfI1EJDQ7XugzXVJel7ur+purFkP0RERESNkZ2dHQCgqKgIM2fOREhICKKjo0WxrJyUZ6saC9vkf6q3gzbtoku7mUMb81xR11BtUlsSW68Es52dXb2ekm0uuK6NeJWUV6r94zJmkoXnAplKfHy88P/a9sGurq4a6xqDy6tuGsuZ9DQ89kNEREREqvLz8zF9+nQEBgbCz88PMTExwntcVo6IyHD0SjATif0BWI1puQQiMbOztWbSk4iIiIga3L179xAUFISIiAj06tULAHRa2pPLyhERaY8JZiPQNE1ZTMlXQ2ACl4iIiIiIiMQqLi4ODx8+RGxsLGJjYwEACxcuxLJly7isHIlWTcveEYkdE8xGwOQrERERERERkemEh4cjPDxcrVyMy8oRVeGydyRVFnVXISIiIiIiIjEpKa80dQhEREREADiCWY3Y1xYmIuOp+rfekA+IJCIiItIHZ00SERGRWDDB/BTeqKlyedXN1CEQNRhORyIiIiIiIiIi0g0TzFQrO1trJtyIiIiIiIiIiIhII67BTEQGV9PId64VSERERERERERkXjiCmYgMTiwj32taO5lrKhMREREREVFDMOb3T363JbFggpmIRMkQyWGuqUxERERERESmZMzvpXyOGIkFE8yNkKYEnSl+UeMvbVQbJoeJiIiIiIiIiMSPCeZGqKF/4WKikIiIiIiIiIiIyDzxIX9EIvH0A/Dc3d01ltdUZshyIiIiIiIiIiIibXAEM5FI6DLSW9dR4WIZRc7lUoiIiIiIiIiIzAsTzETUYMSS6Ja6yspKhIeH4/r165DL5YiKioJSqcT8+fMhk8nQpk0bLF68GBYWFli/fj2OHDkCS0tLhIWFwcPDAzk5ORrrEhERERERkXnigC8yJr0TzBs3bsShQ4dQXl6OgIAAdO/eXevkBhER6e/w4cMAgO3btyMjI0NIMIeEhKBHjx6IiIhASkoKmjVrhhMnTiApKQn5+fkIDg7Grl27EBUVpVbX29vbxEdFRERERERExsIBX2RMeg1Zy8jIwJkzZ/Dtt98iPj4ed+7cERIW27Ztg1KpREpKCrKysoTkxsqVK7F06VJDx09E1OgMHDgQkZGRAIDbt2/j+eefR1ZWFrp37w4A8PLywvHjx3Hq1Cl4enpCJpOhWbNmqKysRGFhoca6RERE1HB0ecYGERERkdjpNYL52LFjaNu2LaZPn46ioiLMmzcPiYmJKgmLtLQ0uLq6akxuODs717jt0tJSZGdnq5SVlJSolWnL5VU32Nlaq5UXPy7FzRvX1MqrHqz2NE37r6luTXTdxtP1dd2frvtsrMdoCLocoyG2oev+DBGfLgzxdzTkMRpiG8Y6d/RlaWmJ0NBQHDx4EGvXrsXhw4chk8kAAHZ2dnj06BGKiorg5OQkfKaqXKlUqtWti6a+WRtSatPqGroPMQSptnVN+Dcwjfrcc4mB1OOnxkPTKDKOICMyjrNnz+Lzzz9HfHw8srKy8NFHH+HVV18FAAQEBGDw4MFcVo6IqJ70SjA/ePAAt2/fRlxcHHJzczF16lSNCYuakhu1JZitra3VvqBlZ2frlbCqUtMUAF22WZ/967uNht4nj1GVpnWI9Fmb6Ol9GmIbhqqrS31jr9fUGM5VwLBJpujoaMyZMwdjxoxBaWmpUF5cXAxHR0fY29ujuLhYpdzBwUHlxriqbl009c31ZejtNQQpxgxIN25NpHosUom7vvdcpqZv/ExKExGZp82bNyM5ORm2trYAgF9//RXvv/8+goKChDrVZ15zWTkiIv3olWB2cnKCm5sbmjRpAjc3N1hbW+POnTvC+3UlN4ikwFgjS6S67pFU4zZHe/bsQUFBAaZMmQJbW1vIZDJ06tQJGRkZ6NGjB1JTU9GzZ0+4uLggJiYGkyZNwp07d6BQKODs7IwOHTqo1SUiovopLy9HWFgY8vLyUFZWhqlTp6J169Z8ACsRkQm5uLhg3bp1mDdvHgDgwoULuH79OlJSUtCyZUuEhYVpvaxcWlpanQlmbWf9ccaNOnNuEyn8eC+ltjfnc0VfYmgTvRLMXbt2xTfffIP3338fd+/exePHj9GrVy+tkxtERKS/t956CwsWLMC4ceNQUVGBsLAwtGrVCosWLcLKlSvh5uYGHx8fyOVydOvWDWPHjoVCoUBERAQAIDQ0VK0uERHVT3JyMpycnBATE4MHDx5gxIgRaN++PR/ASkRkQj4+PsjNzRVee3h4YPTo0ejUqRM2bNiAL7/8Eg4ODgZbVk7bWX9SnzFkDGwT05JS2/NcUddQbVJbEluvBHO/fv1w8uRJjBo1CkqlEhEREWjevLnWyQ0iMTHUEg9SZKhlQKhhNW3aFGvWrFErT0hIUCsLDg5GcHCwSpmrq6vGukREpD9fX1+VH+zkcrnG0W81PaNEn5FyCoXC5KNVqohh5Ex1YonHEM+IMPS2xdI2VcQUj5hiAcQVj5hiqQ9vb29heThvb29ERkZiwIABBltWjoiosdIrwQxAmGJSnbbJDSIxacxLP/ABM0RERIZhZ2cHACgqKsLMmTMREhKC6Ohooz6A1cLCQjQjeMQ2mkhs8WjLmDFXbVtsbSOmeMQUCyCueEwRizES2pMmTcKiRYvg4eGB9PR0dOzYEV26dOGyckRE9aR3gpmIiIiIiP4nPz8f06dPR2BgIPz8/BATEyO8Z4wHsBIRkW6WLFmCyMhIWFlZ4fnnn0dkZCTs7e25rBwRUT0xwUxEREREVE/37t1DUFAQIiIi0KtXLwDQOPqND2AlImpYzZs3R2JiIgCgY8eO2L59u1odLitHRFQ/TDATEREREdVTXFwcHj58iNjYWMTGxgIAFi5ciGXLlvEBrERERERk1phgJiIiIiKqp/DwcISHh6uV8wGsRERERGTuLOquQkRERERERERERESkjglmIiIiIiIiIiIiItILE8xEREREREREREREpBcmmImIiIiIiIiIiIhIL2aVYC4przR1CERERERERERERESNhqWpAzAkGys5Xp3/vUrZjRVvmygaIiIiIiIiIiIyNyXllbCxktdZRtRYmFWCmYiIiIiIiIiIyJg4wJFIlVktkUFEZAyalt/hkjxERERERERERBzBTERUJ/46TURERMZW09RqTrkmIiIisWOCmYiIiIiIqB4MkQTW9IM2wB+1iYiISPzqlWC+f/8+Ro4cia+++gqWlpaYP38+ZDIZ2rRpg8WLF8PCwgLr16/HkSNHYGlpibCwMHh4eBgqdiKiRqm8vBxhYWHIy8tDWVkZpk6ditatW2vdB+fk5GisS0RERPphcpiIiIgaM70zCuXl5YiIiICNjQ0AICoqCiEhIdi2bRuUSiVSUlKQlZWFEydOICkpCStXrsTSpUsNFjgRUWOVnJwMJycnbNu2DZs3b0ZkZKROfbCmukRERERERERE+tA7wRwdHQ1/f3+8+OKLAICsrCx0794dAODl5YXjx4/j1KlT8PT0hEwmQ7NmzVBZWYnCwkLDRE5E1Ej5+vpi1qxZwmu5XK5TH6ypLhERERGROTp79izGjx8PAMjJyUFAQAACAwOxePFiKBQKAMD69esxatQo+Pv749y5c7XWJSIidXotkbF79244OzujT58+2LRpEwBAqVRCJpMBAOzs7PDo0SMUFRXByclJ+FxVubOzc43bLi0tRXZ2tkpZSUmJWpkm7u7uOh2Hpm3WtA1d6hpif5rq67o/XffJYzT8/jTVN8Qx6rI/ffbJY9R/Gw3Bzs4OAFBUVISZM2ciJCQE0dHRWvfBmvrrumjqm7Wh699RLHTpQ8RCqm1dE/4NTEPbey6xknr8RERkWJs3b0ZycjJsbW0B/G8mX48ePRAREYGUlBQ0a9ZMmPWXn5+P4OBg7Nq1S2Ndb29vEx8REZE46ZVg3rVrF2QyGdLT05GdnY3Q0FCVkcnFxcVwdHSEvb09iouLVcodHBxq3ba1tbXaF7Ts7Gy9ElZ10WWbhti/rtto6H3yGI2zDR6j4bdhimM01DYMlfjIz8/H9OnTERgYCD8/P8TExAjv1dUHV19vuapuXTT1zfVljH7d2KQYMyDduDWR6rFIJW5j3XM1FH3jZ1KaxMwQDxAkaqxcXFywbt06zJs3D4D6zOu0tDS4urpqNesvLS2tzgSztoMy+IOoOqm1iTEHtZmClNpeaudKQxBDm+iVYN66davw/+PHj8eSJUsQExODjIwM9OjRA6mpqejZsydcXFwQExODSZMm4c6dO1AoFLWOXiYiorrdu3cPQUFBiIiIQK9evQAAHTp00LoP1lSXiIiIxIkPECTSn4+PD3Jzc4XXusy81mfWn7aDMqT+g64xmEubSPUYpBS3uZwrhtRQbVJbEluvBLMmoaGhWLRoEVauXAk3Nzf4+PhALpejW7duGDt2LBQKBSIiIgy1OyKiRisuLg4PHz5EbGwsYmNjAQALFy7EsmXLtOqDNfXXRERERETmTtNMPkPO+iMiaqzqnWCOj48X/j8hIUHt/eDgYAQHB9d3N0RE9P+Fh4cjPDxcrVzbPtjV1VVjXSIiIiIic8ZZf0RExmGwEcxERERERERERGKly8xrzvojItIeE8xEREREREREZJaaN2+OxMREADXP5OOsPyKi+rGouwoRERERERERERERkTommImIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxEREREZCBnz57F+PHjAQA5OTkICAhAYGAgFi9eDIVCAQBYv349Ro0aBX9/f5w7d67WukREREREYscEMxERERGRAWzevBnh4eEoLS0FAERFRSEkJATbtm2DUqlESkoKsrKycOLECSQlJWHlypVYunRpjXWJiIiIiKTA0tQBEBERERGZAxcXF6xbtw7z5s0DAGRlZaF79+4AAC8vL6SlpcHV1RWenp6QyWRo1qwZKisrUVhYqLGut7d3rftTKBTIzs427kFpqaSkRDSxAA0fj7u7e4PtS19V7dHY/1a1EVMsgLjiEVMsREQkPkwwExEREREZgI+PD3Jzc4XXSqUSMpkMAGBnZ4dHjx6hqKgITk5OQp2qck1162JhYSGaxGZ2drZoYgHEF48YVLWH2NpGTPGIKRZAXPGYIhYmtImIpINLZBARERERGYGFxf9utYuLi+Ho6Ah7e3sUFxerlDs4OGisS0REREQkBUwwExEREREZQYcOHZCRkQEASE1NRbdu3dClSxccO3YMCoUCt2/fhkKhgLOzs8a6ZFol5ZValRERERE1dlwig4iIiKgRKymvhI2VHMD/ptBXLyP9hYaGYtGiRVi5ciXc3Nzg4+MDuVyObt26YezYsVAoFIiIiKixLpmWjZUcr87/XqXsxoq3TRQNERERkXgxwUxERETUiDGJZljNmzdHYmIiAMDV1RUJCQlqdYKDgxEcHKxSVlNdIiIiMr6aflznj+5E2mGCmYiIiIiIiIiIGi1NP7gDuv3ozmQ0NWZ6JZjLy8sRFhaGvLw8lJWVYerUqWjdujXmz58PmUyGNm3aYPHixbCwsMD69etx5MgRWFpaIiwsDB4eHoY+BiKiRuns2bP4/PPPER8fj5ycHK374JrqEhERERERkX4MkaRuDDha3DzplWBOTk6Gk5MTYmJi8ODBA4wYMQLt27dHSEgIevTogYiICKSkpKBZs2Y4ceIEkpKSkJ+fj+DgYOzatcvQx0BE1Ohs3rwZycnJsLW1BQBERUVp3Qdrquvt7W3iIyIiIiIiahjDhw+Hg4MDgCdLG40dOxaffvop5HI5PD09MWPGDCgUCixZsgSXLl1CkyZNsGzZMrRs2dLEkRNJHxPx5kmvBLOvr6/Kg0fkcjmysrLQvXt3AICXlxfS0tLg6uoKT09PyGQyNGvWDJWVlSgsLISzs7NhoiciaqRcXFywbt06zJs3DwB06oM11WWCmYiIqG6NeXQVR5yRuSgtLQUAxMfHC2XDhg3DunXr0KJFC0yePBlZWVnCjO0dO3YgMzMTK1aswIYNG0wVNhGRqOmVYLazswMAFBUVYebMmQgJCUF0dDRkMpnw/qNHj1BUVAQnJyeVzz169KjWBHNpaSmys7NVykpKStTKNKl68rm2NG2zpm3oUtcQ+9NUX9f96bpPHqPh96epviGOUZf96bNPHqP+22goPj4+yM3NFV4rlUqt+2BNdeuiqW/Whq5/R7HQpQ8RC6m2dU34N2g4Umzrmmh7z0hUnS4J0sY86qoxHzuZl4sXL+Lx48cICgpCRUUFgoODUVZWBhcXFwCAp6cn0tPT8dtvv6FPnz4AgM6dO+PChQt1blvbe2Zer9SZuk2M/f1T7Bqy7et7z2zqc0WMxNAmej/kLz8/H9OnT0dgYCD8/PwQExMjvFdcXAxHR0fY29ujuLhYpbxqGkpNrK2t1U627Oxso/zj1WWbhti/rtto6H3yGI2zDR6j4bdhimM01DaM1elXX0O5rj5YU926aOqb60uKN2VSjBmQbtyaSPVYpBi3FGPW957R1DfkZFpMnBI1LjY2Npg0aRJGjx6NGzdu4MMPP1S5H7azs8OtW7dQVFQEe3t7oVwul6OiogKWljWnUbS9ZzZWjkPKxNwmYo3LkMRyjPz3o5+GapPa7pn1eqrTvXv3EBQUhLlz52LUqFEAgA4dOiAjIwMAkJqaim7duqFLly44duwYFAoFbt++DYVCweUxiIiMQJc+WFNdIiIiIqLGwNXVFUMYk+n+AAAgAElEQVSHDoVMJoOrqyscHBzw+++/C+/XNFhDoVDUmlwmMicl5ZU6lRPp1TvGxcXh4cOHiI2NRWxsLABg4cKFWLZsGVauXAk3Nzf4+PhALpejW7duGDt2LBQKBSIiIgwaPBERPREaGopFixZp1QdrqktERERE1Bjs3LkTly9fxpIlS1BQUIDHjx+jadOmuHnzJlq0aIFjx45hxowZuHPnDg4fPozBgwcjMzMTbdu2NXXoRA2Gs3tIV3olmMPDwxEeHq5WnpCQoFYWHByM4OBgfXZDRES1aN68ORITEwE8GYmhbR9cU10iIiIyT9XXma4+hZYP6KPGaNSoUViwYAECAgIgk8mwfPlyWFhYYM6cOaisrISnpydee+01/PWvf0VaWhr8/f2hVCqxfPlyU4dOZBRiuRbwYbLSxvkdREREREREZowj0Yj+p0mTJvjiiy/UyqsGblSxsLDA3//+94YKi8hkNF0jTHF94LVK2vRag5mIiIiIiIiIiMiccc1hIu1wBDMREREREREREdFTOKq2fgyxvMXT23B3d+eyGSLEBDMREREREREREREZlCGW3xDLEh5UOy6RQURERERERERERHrhUiLEEcxERERERERERESkFy4lQhzBTERERERERERERER6YYKZiIiIiIjMisurbmplnL5LRERUP2K/ltYUn9jjNgdcIoOIiIiIiMyKna01HwjUQErKK2FjJa+zjIiIpE8sS2HUdJ0RS3yNERPMREREREREBED35LCmL/P8Ik9E9cUfr6g2uiaSeT4ZHxPMREREREREjZCmL9cc/UVEYsAfr8iQeD4ZHxPMREREREQkGhxl1HD4hZuIGhteT4iMgwlmIiIiIiISDWMlPZlUaDg1tTX/BkT0tKp+wd3dvd7b0KacszSIjIMJZiIiIiKSlJq+SLq86maCaKguYkk2MqnQcNjWRKQtXfoLXR/sdjHS1zBBElGdmGAmIiIiIklh8kpaDPH3EkuSmoiITEfX6wmXAaLa8N7CsIyeYFYoFFiyZAkuXbqEJk2aYNmyZWjZsqWxd0tERLVg30xEJC7sl2tXW1KBPzZIh6Yv7e7u7jp9mWdCgBoS+2Yi86XryHddnhHRGK9JRk8w//jjjygrK8OOHTuQmZmJFStWYMOGDcbeLRER1YJ9MxGRuJhLv2yIRCGZL0N8mTfENoi0ZS59c33pmkTjw1pJynS5zvCa9D8ypVKpNOYOoqKi4OHhgbfffjKKoE+fPvjpp59qrJ+ZmQlra2tjhkREZDKlpaXo3LmzqcNg30xEVI0Y+mZd+2WAfTMRmTcp9s3sl4nInNXWLxt9BHNRURHs7e2F13K5HBUVFbC01LxrU19AiIgaA/bNRETiomu/DLBvJiIyNt4zExFpx8LYO7C3t0dxcbHwWqFQ1HqjTERExse+mYhIXNgvExGJD/tmIiLtGD3B3KVLF6SmpgJ4Ml2kbdu2xt4lERHVgX0zEZG4sF8mIhIf9s1ERNox+hrMVU9dvXz5MpRKJZYvX45WrVoZc5dERFQH9s1EROLCfpmISHzYNxMRacfoCWYiIiIiIiIiIiIiMk9GXyKDiIiIiIiIiIiIiMwTE8xEREREREREREREpBdRP/60ar2jS5cuoUmTJli2bBlatmxp6rC0Nnz4cDg4OAAAmjdvjqioKBNHpJ2zZ8/i888/R3x8PHJycjB//nzIZDK0adMGixcvhoWFeH+XqB57VlYWPvroI7z66qsAgICAAAwePNi0AdagvLwcYWFhyMvLQ1lZGaZOnYrWrVtLou01xf6Xv/xFMm1fWVmJ8PBwXL9+HXK5HFFRUVAqlZJoe7Gr/u9RSjSd0wMGDDB1WFrTdE67uLiYOiyd3L9/HyNHjsRXX30luXUOpXrtB4CNGzfi0KFDKC8vR0BAAEaPHm3qkLS2e/du/Pvf/wYAlJaWIjs7G2lpaXB0dDRxZIZz8OBB7N+/H1988YXG9xUKBSZPnowBAwYgICAASqUSXl5ewrW4c+fOmD17tkliKSkpwdy5c3H//n3Y2dkhOjoazs7OBomlrni2bt2K3bt3QyaTYfr06ejXr5/J2kZTLKZsmy1btuD7778HAPTt2xczZswwWdtoisWUbQMAhYWF8Pf3x759+2BtbW3UttEnHmO2T22xJCYmYvv27bC0tMTUqVPRr18//P777/Dx8REegDdw4EBMmDDBILFIjab7MDs7O4SHh+Phw4eorKzEZ599pnZvJuVrsDb0aRdzv7br0ybl5eWYP38+8vLyYGFhgcjISMndK9dFn3YpKyvDggULcOvWLdjb2yMiIkLoq82BpjZZtWoV7t27BwDIy8vDa6+9hlWrVgmfMfY1VBNRJ5h//PFHlJWVYceOHcjMzMSKFSuwYcMGU4elldLSUgCQXGJl8+bNSE5Ohq2tLQAgKioKISEh6NGjByIiIpCSkgJvb28TR6nZ07H/+uuveP/99xEUFGTiyOqWnJwMJycnxMTE4MGDBxgxYgTat28vibbXFPv06dMl0/aHDx8GAGzfvh0ZGRlCglkKbS9mT/97lBJN57SUEsyazmmpXDuBJzfOERERsLGxMXUoOpPqtR8AMjIycObMGXz77bd4/PgxvvrqK1OHpJORI0di5MiRAIClS5finXfeMZsvoACwbNkyHDt2DO7u7jXWWb16Nf744w/h9c2bN9GxY0fExcWZPJZvv/0Wbdu2RXBwML7//nvExsYiPDzc6PEUFhZi27Zt2LNnD0pLS/H222/jzTffNEnb1BSLqdrm1q1bSE5ORlJSEmQyGQIDAzFw4EDY2to2eNvUFEt6erpJ2gYAfvrpJ3zxxRfCl3fAeP+m9I3HWOdObbH89ttviI+Px65du1BaWorAwED07t0bv/76K4YMGYJFixbVe/9Sp+k+7JlnnoGfnx8GDx6Mn3/+GdeuXVNJjkn9GqwNfdrF3K/t+rTJ0aNHUVFRge3btyMtLQ2rV6/GunXrTHUIRqFPuyQmJqJp06ZITEzEtWvXEBkZiX/+85+mOgSDq+373R9//IH33nsPCxYsUPmMMe8vaiLqIXmnTp1Cnz59ADz5hfjChQsmjkh7Fy9exOPHjxEUFIT33nsPmZmZpg5JKy4uLiodVFZWFrp37w4A8PLywvHjx00VWp2ejv3ChQs4cuQIxo0bh7CwMBQVFZkwutr5+vpi1qxZwmu5XC6ZttcUu5TafuDAgYiMjAQA3L59G88//7xk2l7Mnv73KCWazmkp0XROS0l0dDT8/f3x4osvmjoUnUn12g8Ax44dQ9u2bTF9+nR89NFHePPNN00dkl7Onz+PK1euYOzYsaYOxaC6dOmCJUuW1Pj+/v37IZPJ4OXlJZRlZWWhoKAA48ePx4cffohr166ZLJbq9/ReXl5IT083SCx1xePs7Iy9e/fCysoK9+7dg6OjI2QymUnapqZYTNU2f/nLX/CPf/wDcrkcFhYWqKiogLW1tUnapqZYTNU2AGBhYYF//etfcHJyEsqM1Tb6xmOs9qktlnPnzuH1119HkyZN4ODgABcXF1y8eBEXLlxAVlYW3n33XcycORN37941SCxSpOk+7PTp0ygoKMDEiROxb98+4XtGFXO5BtdGn3apYq7Xdn3axNXVFZWVlVAoFCgqKoKlpajHjOpFn3a5cuWKcN/h5uaGq1evNnjcxlTb97t169bh3XffVfvuZMxraE1EnWAuKiqCvb298Foul6OiosKEEWnPxsYGkyZNwj//+U8sXboUc+bMkUTsPj4+Kp2UUqmETCYDANjZ2eHRo0emCq1OT8fu4eGBefPmYevWrWjRogW+/PJLE0ZXOzs7O9jb26OoqAgzZ85ESEiIZNpeU+xSansAsLS0RGhoKCIjI+Hj4yOZthezp/89Sommc1pqnj6npWL37t1wdnYWboakRqrXfgB48OABLly4gDVr1gixK5VKU4els40bN2L69OmmDkNvSUlJGDJkiMp/586dw+DBg4Xr0tMuX76M7777TuWHMQB44YUXMHnyZMTHx2PKlCmYO3euyWIpKioSlo7R97qqTzzAk/4wISEBY8eOFfpDU7RNTbGYqm2srKzg7OwMpVKJ6OhodOjQAa6uriZpm5piMeV507t3bzz77LMqZfVtG0PHU9/20SeW6vus2m9RURHc3Nwwc+ZMJCQkYODAgVi2bJlOsZibp+/D8vLy4OjoiC1btuDll1/G5s2bVeqbyzW4Lrq2SxWpX9tro2ubNG3aFHl5eRg0aBAWLVqE8ePHmyhy49K1Xdzd3XH48GEolUpkZmaioKAAlZWVJoreODR9v7t//z7S09OFkf7VGeIaqnOMRt9DPdjb26O4uFh4rVAoJJOwcHV1RcuWLSGTyeDq6gonJyf89ttvePnll00dmk6qrztbXFwsqSkp3t7eQrze3t7CLz5ilZ+fj+nTpyMwMBB+fn6IiYkR3hN72z8d+8OHDyXV9sCTUZNz5szBmDFjhGnugPjbnozj6XNaiqqf099//z2aNm1q6pDqtGvXLshkMqSnpyM7OxuhoaHYsGEDXnjhBVOHphUpX/udnJzg5uaGJk2awM3NDdbW1igsLMRzzz1n6tC09vDhQ1y7dg09e/Y0dSh6Gz16tM7rbu7ZswcFBQWYMGEC8vLyYGVlhVdeeQVvvPGGMAOjW7duKCgoUPkBtSFjqX5Pr+91VZ94qrz77rsYM2YMPvzwQ/z888947bXXGrxtaorFlG1TWlqKsLAw2NnZYfHixQCATp06maRtNMVi6vPmafVtG0PHU9/20SeWp7+fFxcXw8HBAR4eHsKyaN7e3li7dq1O2zVH1e/DHBwc0L9/fwBA//79VdZJBczjGqwtXdoFMI9re110aZMtW7bA09MTs2fPRn5+PiZMmCCsy25udGmXd955B1evXsV7772HLl26oGPHjpKbhaqNp7/f7d+/H0OGDNF4rIa4hupK1COYu3TpgtTUVABAZmam8NAAKdi5cydWrFgBACgoKEBRUZFkviBX16FDB2RkZAAAUlNT0a1bNxNHpL1Jkybh3LlzAID09HR07NjRxBHV7N69ewgKCsLcuXMxatQoANJpe02xS6nt9+zZg40bNwIAbG1tIZPJ0KlTJ0m0PRmHpnNaSjSd01K5wdq6dSsSEhIQHx8Pd3d3REdHS+raKeVrf9euXfHTTz9BqVSioKAAjx8/VpmKLQUnT57E3/72N1OH0eDmzZuHpKQkxMfHY8SIEZg4cSK8vLywfv16fP311wCeLN/SrFkznRJhhoylS5cuOHr0KIAn19WuXbsaNY4q165dEx5cZ2VlhSZNmsDCwsIkbVNTLKZqG6VSiWnTpqFdu3b4+9//LlwnTNE2NcViqrapiSnapjamaB8PDw+cOnUKpaWlePToEa5evYq2bdsiPDwc//nPfwCI/97f2DTdh3Xv3l34W508eRKtW7dW+Yw5XIProk+7VJWb67VdnzZxdHQURqU+88wzqKioMLuRuvq0y/nz59G1a1fEx8dj4MCBaNGiRYPHbUw1fb9LT09XWZKsOlNcI0Q9HNjb2xtpaWnw9/eHUqnE8uXLTR2S1kaNGoUFCxYgICAAMpkMy5cvl8zo6+pCQ0OxaNEirFy5Em5ubpKaar1kyRJERkbCysoKzz//vKhH0cbFxeHhw4eIjY1FbGwsAGDhwoVYtmyZ6NteU+zz58/H8uXLJdH2b731FhYsWIBx48ahoqICYWFhaNWqlWTPe6o/Tef05s2bJfPQOU3ntDmOahAjKV/7+/Xrh5MnT2LUqFFQKpWIiIiQzA8TVa5fv47mzZubOowG869//QsuLi41PoR08uTJmDt3Lo4ePSo8cdxUsQQEBCA0NBQBAQGwsrLCF198YbRYno6nffv2GDt2LGQyGfr06YPu3bujXbt2JmkbTbH89a9/NUnbKBQKnDhxAmVlZfjpp58AAJ988olJzpuaYjHleaNJQ7aNNvE0ZPtUj2X8+PEIDAyEUqnExx9/DGtra8yePRthYWH49ttvYWtr26iXyNB0H+bu7o7w8HBs374d9vb2wt9q3rx5CAkJMYtrcF30aZdmzZqZ9bVdnzaZOHEiwsLCEBgYiPLycnz88ceSmKWoC33apWXLllizZg2++uorODg44NNPPzXxURhWTd/vrl+/rpZMDwoKQlxcXINfQwFApjTHxX2IiIiIiIiIiIiIyOhEvUQGEREREREREREREYkXE8xEREREREREREREpBcmmImIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxEREREREREREREemGCmYiIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkFyaYiYiIiIiIiIiIiEgvTDATERERERERERERkV6YYKYGk5ubC3d3dwwbNgzDhg2Dn58f/P398cMPPwh11qxZgz179tS6nfXr1+PHH380drh1iomJwbFjx5Cbm4vXX39d5b0ffvgBPXr0QHp6OnJzc9GuXTu8++67atuYP38+2rVrh8LCQhQVFeGDDz5ASUlJQx0CEUnM0/3osGHDMHToUOzcuRMAkJKSgmXLltW6jfHjx2P//v0NEa6KdevWoWfPnkLcb7/9Nj755BPcuHHD6PuurKzE1KlT4ePjg4SEBLW42rVrh127dqmU//nnn3j99dcxZcoUvff76NEjvPfee8Lrqv6eiMRJqn3s3bt30aFDB/z+++9C2RdffIF27drh2rVrQtnmzZsREhKi9360PbYhQ4YgIyND7/08bdu2bdixYweAJ/1o//79oVQqVepU9eXnz5832H41uXjxIjw9PYXXlZWVmDJlCu7fv2/U/RKRdIj9WtK/f3+d+8ojR45gzZo1RomHzIelqQOgxsXGxgZ79+4VXufl5WHixImQy+Xw8fHBrFmz6txGRkYGWrdubcww65SZmYmrV69i7ty5yM3NVXlv+/btiI2NxZYtW+Du7o7c3FxYW1vj+vXryMvLwyuvvALgSfLi9OnTwufs7e0xZMgQrFmzBqGhoQ16PEQkHU/3owUFBRgyZAg6deqEAQMGYMCAASaMrnaDBw9GRESE8HrPnj2YMGECvv/+e9jb2xttvwUFBTh27BgyMzMhl8vV3m/WrBn27t2Ld955Ryg7cOAAmjZtWq/9/vHHH0ZPdhCRYUmxj33xxRfRtm1b/PLLLxg4cCCAJ8mAfv364dChQ3BzcwMA/Pzzz/Dz8zNlqDrLy8vDv//9byQmJgplSqUSv/zyC9544w3h9f/93//hmWeeMVocFRUVSEhIwObNm/Hnn38K5XK5HB988AGWLl2KtWvXGm3/RCQtUryW1Ob8+fP4448/TB0GiRxHMJNJvfLKK5g5cyb++c9/Angyorfq/9euXQs/Pz+MHDkSkyZNwt27d7F161ZcuHABn332GQ4ePIjr16/j/fffx5gxY9CvXz9MnToVpaWlAIC//vWvWLduHfz9/dG/f39s27ZN2O/GjRvh6+uLIUOGYPr06Xj06BEAICkpCSNHjsTw4cMxceJEXL16VWPc69atw9ixY9XKN23ahC1btmDbtm1wd3cXyuVyOQYNGoR9+/YJZQcOHFC7sFTVuXfvnj7NSUSN0EsvvYSWLVvixo0b2L17tzDi9rfffsO0adPg6+uLwYMH45tvvlH5XEVFBWbNmoXZs2ejoqJCbaRE9dcdOnTAqlWrMHLkSPj6+uLAgQMGiX348OFo1aqV0Dfu3LkTo0ePxvDhw9GvXz+h337//fdVkguxsbFYvny52vZ++eUXjBkzRrh2pKamCrNDKioqMHLkSNy8eVPtc3369MGVK1dw584doezf//43hg4dKrx+9OgR5syZgyFDhsDPzw+fffYZKioqANR8vVmwYAFKSkowbNgwVFZWAnhy/Rg5ciT69++PrVu31rcJicjIpNLHenl5CaOGc3NzUVZWhgkTJuDQoUMAgLKyMpw5cwZ9+vQBABw6dEjob/39/XHmzBlhWxs2bMCIESMwbNgwTJs2DQUFBbUe25UrV4S+d9asWSoJ2Li4OIwePRp+fn4YOHAgDh48CKVSCR8fH6SlpQn1Fi5ciK+//lrtuDZu3Ihhw4ZBJpMJZUOHDkVycrLw+tSpU2jdurXKD5U//vgjhg8fjqFDhyIgIADnzp1T2/aVK1dURhhW/ff0jBYA+PXXX3Hp0iWsX79e7b033ngDV65cQXZ2ttp7RESANK4lf/75J+bNm4exY8fCx8cHI0eOxLVr13D27Fls374dP/zwA1atWlXPliBzxgQzmVz79u1x+fJllbL8/Hx8/fXX2LVrF3bv3o3evXvj3LlzGDduHDp16oR58+bB29sbiYmJGD58OBITE3HgwAHk5ubiyJEjAJ7cSD/77LPYvn071q5di6ioKJSWliIlJQW7d+/Gjh078N1336F58+ZISEjAiRMnsGfPHmzduhV79uzBBx98gBkzZqjF+/DhQ5w6dQq9e/dWKf/ss8/wxRdfYPz48WjevLna54YPH67yK+aePXswYsQIlTrW1tbo1KkTjh49qm9zElEjc+bMGdy8eROvvfaaSvnSpUvx6quvYv/+/dixYwcSExORk5MDACgvL8esWbPw3HPP4fPPP4elZe0TmiorK2Fra4vdu3dj9erVCAsLM9hSD+3atcPly5dRXFyMpKQkbNq0CXv27MGqVasQExMDABg3bpyQYFYoFNi5cyf8/f1VtvPgwQPMnDkTCxcuxL59+xAdHY25c+fiwYMH2LRpkzCSxMXFRS0GS0tLDBo0SEhY3L59G8XFxWjTpo1QZ9myZXBycsK+ffuwa9cuXLp0CV999RWAmq83UVFRwn6rRk63aNECu3fvxvr167FixQqUl5cbpB2JyDik0sd6eXnhxIkTAIDDhw/jzTffRLdu3XDlyhUUFhbi7NmzaN26NZ577jncuHEDq1atEvrbyMhIBAcH488//8SePXtw+fJlJCUlYe/evejbty/Cw8OF/Wg6tjlz5mD06NHYt28f3nvvPdy+fRvAk9HHx48fR3x8PPbt24ePP/4Ya9euhUwmQ0BAgNCvFxUV4dChQ2r3xUqlEgcOHMCbb76pUj5kyBAcPHgQZWVlAJ78IFj9s1evXsXixYuxbt06JCcnY+bMmZg2bRqKiopUttO6dWvs3btX7b/qs1mqeHh4ICoqCi+88ILG9v/b3/6GgwcPavOnIqJGSArXktTUVDg6OmLHjh34z3/+g06dOmHr1q147bXX4O/vj8GDB+Pjjz/W/eCp0eASGWRyMpkMNjY2KmUvvfQS2rdvjxEjRsDLywteXl7o1auX2mfnzp2LtLQ0bN68GTdu3MDdu3dVRk1UjRDu2LEjysrK8OeffyI9PR2+vr7CNLoFCxYAeJIgzsnJUUlaPHz4EL///jucnJyEspycHLzwwgto0qSJUPbnn3/i8uXL2LRpEz7++GO8/vrr6NChg0qsnTp1glwux4ULF/Dcc8+huLgYbdu2VTum5s2b4/r161q3HxE1LlUjYoEnN5LPPvssYmJi8PLLL6vUO378OObOnQsAcHBwwHfffSe8Fx0djeLiYhw8eFBlVFhtqtaRb9++Pdq2bYuTJ0/Cx8en3sdTdQ2ws7NDXFwcjh49ihs3buDixYtCf96vXz98+umnuHjxIgoKCtC8eXNhyneVc+fOwcXFRbhxb9OmDbp06YITJ06gR48edcYxbNgwLFy4EJMnT8bevXsxfPhwlfdTU1Px7bffQiaToUmTJvD398fXX3+NyZMnA9B8vdFkyJAhAAB3d3eUlZWhqKgIzz77rA4tRkTGJNU+tnPnzsjPz8fvv/+Ow4cP48MPP4SVlRV69uyJn3/+GVevXkXfvn0BAGlpabh79y4mTpwofF4mk+HmzZs4fPgwzp8/LyRZFQoFHj9+XOOxPXjwAJcuXRL6zK5duwo/zr3yyiv47LPPsG/fPuTk5ODs2bMoLi4GAIwcORJffvklCgsLsX//frz55ptwdHRUOaYHDx7g0aNHagM3nnvuOXh4eODw4cPo27cvfvnlFyxdulR4/+eff0bPnj3RokULAECvXr3g7OyMCxcuoGfPnkK9K1euYPbs2Wpt+d5772lMMtemefPmOHv2rE6fISLzJcVria+vL1q0aIH4+Hjk5OTgxIkTas+aIqoNE8xkcufPn1dLtFpYWCAhIQHnz59Heno6li9fjj59+mDevHkq9T755BNUVlZi0KBBePPNN5Gfn6/y0A9ra2sAEDpkpVIJuVyu0kE/fPgQDx8+hEKhwLBhw4QOXqFQ4O7du2rruclkMigUCpUyGxsbbNiwAVZWVpgyZQpmzJiB3bt3qySmgf9N6XN2dhYuOE+zsrLSuEYoERGgvqZbTSwtLVX6ulu3bgmJzKFDh0KpVCI8PBxxcXFCner959Mja6v3SwqFQq2fSklJEdaffPHFF7F582atjqcqkXHnzh2MHTsWY8aMQdeuXeHr64vDhw8L+x47dix27tyJu3fvqo1eBp7cvD99861UKoVlLOri4eGByspKZGdn44cffkB8fLwwtbzqmKtvX6FQqGxb0/VGk6rRJ3XVIyLTkGofa2lpiZ49eyI1NRXZ2dno1q0bAKBv3744deoULl68iLCwMGH7vXr1wurVq4XP5+fn48UXX4RCocAHH3yAwMBAAE9maFRfd1ObY6vq57KysjBt2jRMnDgRvXv3xhtvvCEkgh0dHeHr64vk5GTs27cPixcvVmtjmUwGpVIJhUIBCwvVibfDhw9HcnIyysrK0L9/f5WRfU/311XxPX09qBrBbAiWlpZqMRJR4yXWa0lttm3bhsTERIwbNw5+fn5wcnJSe94UUW14FSSTun79OmJjYxEUFKRSfvHiRQwZMgStWrXClClTMHHiROFBSXK5XLhBPHbsGE2AnDIAACAASURBVKZPn47BgwcDAM6ePSusc1mTqilsVdPk1q1bhy1btsDT0xPff/897t69CwD49ttvMWHCBLXPu7i44P79+8Jaz8CThLiVlRUAYPLkyWjdujVmz56tlogeNmwY9u/fjx9++EEYxfa03NxcuLq61noMRER16dWrl7CO5KNHjzBhwgTcuHEDwJNkakhICG7evClMUa4a3QU8GdV16dIlle3t2bMHwJOEwfXr14WHK1UZMGCAML1Y2+RyUlIScnNzMWjQIFy4cAHOzs6YNm0aPD09heRyVZ8+evRo/Pjjj8jKyoK3t7fatjp37oxr164J62z+97//xcmTJ9G9e3etYgGe9NHLly+Hq6ur2g+Enp6eSEhIgFKpRFlZGRITE/G3v/2t1u1ZWlqisrKSSWQiMyTGPtbLywv/+Mc/0L17d+G+tG/fvkhPT0d+fr4wu65Xr15IS0sTnjVy9OhRDB06FCUlJfD09MTOnTuF++Q1a9aoDPB4+tieffZZdOzYEUlJSUL8VUvfnTx5Ep06dcL777+P7t27IyUlReU+fdy4cfjmm2+gVCrh4eGhdjzPPvssHB0dkZeXp/begAEDcObMGWzdulVtaY1evXrh2LFjuHXrFgAIx//01HRDys3NVZtZQ0RUl4a+ltTm2LFjGDFiBEaPHg1XV1ccOnRI6LOr52CIasIRzNSgqk8VsbCwgLW1NT755BO1tdXat2+PQYMG4Z133kHTpk1hY2MjrP/Wv39/rFy5EuXl5fj4448xffp0NG3aFPb29njjjTc0PsCpur59++LKlSsICAgA8GT0QmRkJOzt7fHhhx8iKCgIMpkM9vb2WL9+vdoICEdHR3Tt2hU///yzMNWwOplMhujoaIwYMQKrV6/GmDFjhPdeeukltGrVCg4ODmrJC+DJKJHMzEx8+umndTcmEVEtIiIisGTJEvj5+UGpVGLKlCno1KmT8L61tTVWrFiBoKAg9OzZE1OnTsX8+fNx9OhRuLm5CaPfqpw+fRqJiYlQKBRYtWqV2uwObfzwww84deqUMBPE1dUV33zzDaytrdG7d2/s3LkTvr6+kMlk6N69O5ydnZGTkwM3Nzc899xz6NSpE1q1aiUkTqpzdnbGmjVrEBkZiZKSEshkMkRFRcHV1VXr0RdDhw7F6tWrERsbq/ZeeHg4li1bBj8/P5SXl6NPnz746KOPat3eCy+8AA8PD7z99tt8oB+RmRFjH+vl5YWFCxeqDNx4/vnn0bRpU3Tu3Fm4p23dujX+/ve/45NPPoFSqYSlpSU2bNgAOzs7jB49GgUFBRgzZgxkMhlefvllrFixQmU/Tx/bypUrsWDBAmzfvh0uLi5ConXIkCE4cOAABg0aBIVCgX79+uGPP/5AUVER7O3t0b59ezzzzDMaZ6VUeeutt/DTTz8JI6qrx9C/f3/8+uuvajMhW7dujcWLF2PGjBmorKyEjY0N4uLi4ODgoHObaistLU1lRDgRkTZMdS159913VWZdzJkzB0FBQYiIiMDOnTsBPBm8UfWDYc+ePTFnzhxERkZi0aJFhm4GMhMyJYfVEOns9OnTiIuLw6ZNmwy63d27d+O///0vQkNDDbpdIqL6aNeuHdLT0+Hs7GyyGAoLCzFq1Chs3bpVbf06IiIpE0Mfawo3b97E+PHjsX//ftja2mqsc+vWLcyaNQu7du3Seg3ShpaRkYGtW7cKS5gQEZlCY72WkHhwiQwiPXTp0gWurq5ITU012DaLi4vx3XffITg42GDbJCIyB4mJiRg8eDAmTZrE5DIRkRlYs2YNAgICsGjRohqTywDQokULDB8+HNu3b2/A6LRXWVmJf/zjH8JMSyIiosaKI5iJiIiIiIiIiIiISC8cwUxEREREREREZun+/fvo27cvrl69ipycHAQEBCAwMBCLFy8WHsq+fv16jBo1Cv7+/sIDg2uqS0RE6phgJiIiIiIiIiKzU15ejoiICNjY2AAAoqKiEBISgm3btkGpVCIlJQVZWVk4ceIEkpKSsHLlSixdurTGukREpJmlqQN4WmZmJqytrWutU1paWmcdc8FjNV+N6Xh5rKrvd+7cuQEjMgxNfbOU/65Sjh2QdvyM3XSkHL+xYzenvlkbPBdMR8rxSzl2QNrxN9bYDdE3R0dHw9/fX3g4e1ZWFrp37w4A8PLyQlpaGlxdXeHp6QmZTIZmzZqhsrIShYWFGut6e3vXur/Tp08LD6RUKBSwsJD2mD4egzhI/RikHj/AY6gik8lq7JdrTTCXl5cjLCwMeXl5KCsrw9SpU9G6dWvMnz8fMpkMbdq0weLFi2FhYYH169fjyJEjsLS0RFhYGDw8PJCTk6Oxbm2sra3h7u5ea53s7Ow665gLHqv5akzHy2NVfV+KNPXNUv67Sjl2QNrxM3bTkXL8xo7dnPpmbfBcMB0pxy/l2AFpx99YY69v37x79244OzujT58+QoJZqVQKCWA7Ozs8evQIRUVFcHJyEj5XVa6pbl1sbW2F45Xy360Kj0EcpH4MUo8f4DFU30ZNak0wJycnw8nJCTExMXjw4AFGjBiB9u3bIyQkBD169EBERARSUlLQrFkzYUpJfn4+goODsWvXLmFKSfW6df3iR0RERERERERUH7t27YJMJkN6ejqys7MRGhqKwsJC4f3i4mI4OjrC3t4excXFKuUODg4qg+Oq6hIRkWa1Dif29fXFrFmzhNdyuVxtmsjx48dx6tQpraaUHD9+3IiHQkREREREREQEbN26FQkJCYiPj4e7uzuio6Ph5eWFjIwMAEBqaiq6deuGLl264NixY1AoFLh9+zYUCgWcnZ3RoUMHtbpERKRZrSOY7ezsAABFRUWYOXMmQkJCEB0dbdQpJaWlpXVOhSkpKZHsVEZd8VjNV2M6Xh4rERERERGZWmhoKBYtWoSVK1fCzc0NPj4+kMvl6NatG8aOHQuFQoGIiIga6xIRkWZ1PuQvPz8f06f/P/buPSyqav0D+HdmQEDAOGSnIsXARDEij5l4AdIUMZXj5ahclEo8aqYU/VJBRMDEC2KWiop5ss7B1FRMLTtdvOA9NA0vSJqp5AVNRY9CXIaZ9fvDhwmcAWZwhpk9fD/P4/M4axZ7v2vvPWvPvHvvtSYhIiICISEhSEtL07xnikdKrHUM5jKlCva2Cr3Lq0ixrQ3VlNoKNK32sq0136emQVf/Xl+fT0REZKncn/bUWc5zG0lBZmam5v9r1qzRej86OhrR0dE1yjw8PHTWJSJtdeW8qGmoM8F88+ZNREVFITExEd27dwcAzWMifn5+2Lt3L7p16wZ3d3ekpaVh7NixuHbtmtYjJdXrNlX2tgo8Hbddq/zi/IFmiIaIiExNV7/PPp+IiKTK0cGOv2eIiEgn5ryozgRzRkYG7t69i+XLl2P58uUAgBkzZiAlJaVJPVLS0LuPiYiIiKhpuXXrFoYNG4bVq1fDxsYGcXFxkMlkaNeuHZKSkiCXy5Geno7s7GzY2NggPj4evr6+KCgo0FmXiIiIiMjS1ZlgTkhIQEJCglZ5U3ukhFdiiEgKlEol4uLicOXKFcjlcsyePdug5AY1DC82ElEVpVKJxMRE2NvbAwDmzZuHmJgY+Pn5ITExETt37oSbmxsOHz6MjRs3orCwENHR0cjKytJZNygoyMwtIiIiIiKqX71jMBMRkTTs2bMHlZWVWL9+PQ4cOIAPP/wQSqVS7+QGNUxTuAjJJ3mI9JOamoqwsDB89NFHAIC8vDx07doVABAYGIgDBw7Aw8MD/v7+kMlkcHNzg0qlQlFRkc66TDATERERkRQwwUxEZCU8PDygUqmgVqtRXFwMGxsb5Obm6p3ccHV1rXXZ5eXlWhMUlpWVSXbSQmPGbugElsZYb2Nve29vb51J9J9n99cqKyktx28Xz9e6LB435iPl+KUQ++bNm+Hq6oqAgABNglkIAZlMBgBwdHTEvXv3UFxcDBcXF83fVZXrqlsfXX2zPqSwPWsj5dgBaccv1UmM3Z/2hKNDzYnk6ztXWRopHzdSjp2IiPTHBDMRkZVo3rw5rly5gldeeQW3b99GRkYGjhw5ondyo64Es52dndYPy/z8fIOTq5aivth13ZlrrLt1jbHNLGXb1zaRYX1JCEuIvSGkHDsg7fhNHbsxkh9ZWVmQyWQ4dOgQ8vPzERsbi6KiIs37JSUlaNGiBZycnFBSUlKj3NnZucZ4y1V166Orb9aHFI8Fa3mSQorbXh+W3iZDz1WWRsrHzcPEzsQ0EZF0MMFMRGQlPv30U/j7++Pdd99FYWEhXnvtNSiVSs379SU36E+1JU6JSH/WkhDU12effab5f2RkJJKTk5GWloacnBz4+flh79696NatG9zd3ZGWloaxY8fi2rVrUKvVcHV1RceOHbXq0p+awnBERERERFLFBDMRkZVo0aIFbG1tAQCPPPIIKisrdSYsaktuEBEZExOCQGxsLGbOnIlFixbB09MTwcHBUCgU6NKlC0JDQ6FWq5GYmFhrXSIiIiIiKWCCmYjISrz++uuIj49HREQElEol3nnnHfj4+Oid3LAWphzegqSFxwKZS2Zmpub/a9as0Xo/Ojoa0dHRNco8PDx01iVpe7DPqRoqgH0RERERWRMmmImIrISjoyMWL16sVa5vcsNacHgLy1SVTHlwHEZTJll4LBDp5v60p1YZE56mwTv5iYiIqClggpmIiKxeU7iDzNLHu2WShchyODrY8eILERERERkNE8wPwdJ/zBMR0X1NIbnZFNpIRERERERElocJ5ofAH/NERERERERERETUlMnNHYClKVOqzB0CERERERERERERkSTwDuYHGGNCIA6RQURkPByOiKjpMsfkkEREREREZBgmmE2As9YTERkPhyPS5v60p7lDIGoU/PwTEREREVk+JpiJiIgkxtHBjhcyiYiIiIiIyCIwwUxERETUBNR253tjDzfBYW+IiKgxqFQqJCQk4MKFC1AoFJg3bx6EEIiLi4NMJkO7du2QlJQEuVyO9PR0ZGdnw8bGBvHx8fD19UVBQYHOukREpI0JZiIiIqImQNed70Dj3/3OYS+IiKgx7N69GwCwfv165OTkaBLMMTEx8PPzQ2JiInbu3Ak3NzccPnwYGzduRGFhIaKjo5GVlYV58+Zp1Q0KCjJzq4iILJNel9+OHz+OyMhIAEBeXh4CAgIQGRmJyMhIfP311wCA9PR0DB8+HGFhYThx4gQAoKCgAOHh4YiIiEBSUhLUarWJmkFERCRdZUqVQeVE5sJjlYiIpKJv376YPXs2AODq1ato2bIl8vLy0LVrVwBAYGAgDh48iKNHj8Lf3x8ymQxubm5QqVQoKirSWZeIiHSr9w7mVatWYdu2bXBwcAAAnD59GmPGjEFUVJSmTl5eHq/4ERERNZA57ujUNRwBhyig+vDuYyIikhIbGxvExsbi+++/x5IlS7B7927IZDIAgKOjI+7du4fi4mK4uLho/qaqXAihVbc+5eXlyM/PBwCUlZVp/i9VbINlkEIbvL29a31PCvHXh22oX70JZnd3dyxduhTTpk0DAJw6dQoXLlzAzp070aZNG8THx+t9xe/AgQNMMBMREVkAXYlCJgkbD8chJiIiahypqamYMmUKRo4cifLyck15SUkJWrRoAScnJ5SUlNQod3Z2rjHeclXd+tjZ2WkSbfn5+XUm3aSAbbAMUm+Dvb29pOMHpL8PAOO0oa4Edb0J5uDgYFy+fFnz2tfXFyNGjICPjw9WrFiBZcuWwdnZ2SRX/Gpjyqy7OQ6YutpiDVdJ9NWU2go0rfayrUQkVdUTvlXfEQxNDusq553AREREprVlyxZcv34dEyZMgIODA2QyGXx8fJCTkwM/Pz/s3bsX3bp1g7u7O9LS0jB27Fhcu3YNarUarq6u6Nixo1ZdIiLSzeBJ/oKCgjRX7oKCgjB79mz06dPHJFf8amMNVw6qq6st1tbWujSltgJNq71sa833iUg6DLnTW6pJYw6XQkRE1qhfv36YPn06Ro0ahcrKSsTHx6Nt27aYOXMmFi1aBE9PTwQHB0OhUKBLly4IDQ2FWq1GYmIiACA2NlarLhER6WZwgnns2LGYOXMmfH19cejQITz77LPo3Lkzr/g1EB+RJSJjWrlyJXbt2gWlUonw8HB07doVcXFxkMlkaNeuHZKSkiCXy5Geno7s7GzY2NggPj4evr6+5g6diMyEw6UYh0qlQkJCAi5cuACFQoF58+ZBCKF3H1xQUKCzLhERNUzz5s2xePFirfI1a9ZolUVHRyM6OrpGmYeHh866RESkzeAEc3JyMmbPng1bW1u0bNkSs2fPhpOTE6/4NZBU73YiIsuTk5ODn376CevWrUNpaSlWr16tc7JVNzc3nROzEhFRw+3evRsAsH79euTk5GgSzPr2wZwcm4iIiIikSq8Ec6tWrbBhwwYAwLPPPov169dr1eEVPyIi89q/fz+8vLwwadIkFBcXY9q0adiwYYPWZKseHh46J2Z1dXU1cwuIiKSrb9++6NWrFwDg6tWraNmyJbKzs/Xugzk5NhERERFJlcF3MBMRkWW6ffs2rl69ioyMDFy+fBkTJ07UOdlqcXGxzolZ60ow65qA9WEmNHR/2hOODnZa5SWl5fjt4vkaZYaOaW3oeN+GLKOx12eOdVpKG43BkDYaYxmGtNGStochcRirjcZYhiWOaW9jY4PY2Fh8//33WLJkCXbv3q13H2yqybF1McZno7FJ7VioItW4dZFqW6R4vD9IypNISzl2IiLSHxPMRERWwsXFBZ6enmjWrBk8PT1hZ2eHa9euad6vmmzVyclJ58SsddE1AevDTt5Y2/BAhizTGJNHWvL6zLFOS26jseYt0LU+YyzDWPUNqWvKCfqawrFaxZjJj9TUVEyZMgUjR45EeXm5pry+PthUk2MbQqoT8jJu85NiW6QUs5QnzH6Y2JmYJiKSDiaYiYisxAsvvID//Oc/GDNmDH7//XeUlpaie/fuWpOturu765yYlcjSmXLeAqnOicAJ+izHli1bcP36dUyYMAEODg6QyWTw8fHRuw/m5NhEREREJFVMMBMRWYnevXvjyJEjGD58OIQQSExMRKtWrbQmW1UoFDonZiUioobr168fpk+fjlGjRqGyshLx8fFo27at3n0wJ8cmIiIiIqligpmIyIpMmzZNq0zXZKu6JmYlsiTGGuZBaow1DAg1vubNm2Px4sVa5fr2wZwcm4iIiIikiglmIiIisjhNdegHqQ7VQURERERETZe8/ipERERERERERERERNqYYCYiIiIiIiIiIiKiBmGCmYiIiIiIiIiIiIgahAlmIiIiIiIiIiIiMytTqrTKvL29dZYTWRJO8kdERERERERERGRmnPCZpIp3MFuoqqtT3t7eWmVEREREREREREREloB3MFsoXVeteMWKiIiIiIiIiIiILAnvYCYiIpPhkxdERERERERE1q3J3sFcplTB3lZh7jCIiKwaxxAjIiIiIiIism5NNsHMpAcRERERERGRdVIqlYiPj8eVK1dQUVGBiRMn4plnnkFcXBxkMhnatWuHpKQkyOVypKenIzs7GzY2NoiPj4evry8KCgp01iUiIm3sHYmIiIiIiIjIqmzbtg0uLi5Yu3YtVq1ahdmzZ2PevHmIiYnB2rVrIYTAzp07kZeXh8OHD2Pjxo1YtGgRZs2aBQA66xIRkW56JZiPHz+OyMhIAEBBQQHCw8MRERGBpKQkqNVqAEB6ejqGDx+OsLAwnDhxos66REREREQNVdv47hz3nYiIqvTv3x9vv/225rVCoUBeXh66du0KAAgMDMTBgwdx9OhR+Pv7QyaTwc3NDSqVCkVFRTrrEhGRbvUOkbFq1Sps27YNDg4OAP68iufn54fExETs3LkTbm5umit+hYWFiI6ORlZWls66QUFBJm+Utapt3GiOJ01ERERNCYc6IyKi+jg6OgIAiouL8dZbbyEmJgapqamQyWSa9+/du4fi4mK4uLjU+Lt79+5BCKFVtz7l5eXIz88HAJSVlWn+L1VsQ+Pz9vau9T1LbkddcUttH+jCNtSv3gSzu7s7li5dimnTpgGA1lW8AwcOwMPDQ68rfgcOHKg3wVy9Q66NMTZKXQe/parrx5TUD3TAOj6whmhK7WVbiYiIiIiosRUWFmLSpEmIiIhASEgI0tLSNO+VlJSgRYsWcHJyQklJSY1yZ2fnGuMtV9Wtj52dnSbXkJ+fL8m8Q3Vsg2WRajvs7e0lG3sVaziOjNGGunId9SaYg4ODcfnyZc1rXVfxjHnFr3qHXBtr2LHGZg3bo6nt16bUXra15vumdOvWLQwbNgyrV6+GjY2N3pOYEBHRw+FkUkREluXmzZuIiopCYmIiunfvDgDo2LEjcnJy4Ofnh71796Jbt25wd3dHWloaxo4di2vXrkGtVsPV1VVnXSIi0s3gb626ruIZ84ofERE1jFKpRGJiIuzt7QHonpiktklMiIjo4XAyKSIiy5KRkYG7d+9i+fLliIyMRGRkJGJiYrB06VKEhoZCqVQiODgYPj4+6NKlC0JDQxEdHY3ExEQAQGxsrFZdIiLSrd47mB/EK37Sp2vMZo7jTCR9qampCAsLw0cffQTAsCGNXF1dzRk6EZHk9e/fv0byQddkUsYeWo6IiGqXkJCAhIQErfI1a9ZolUVHRyM6OrpGmYeHh866RESkzeAEc2xsLGbOnIlFixbB09MTwcHBUCgUmit+arW6xhW/B+uS+ekay5mT4hBJ2+bNm+Hq6oqAgABNgtmQIY3qSzDrGh9fn/GmGzI0yoPLNHTIEUPXacgyGnt95lgn22j89emqb4zPRl3LsZY2Gms7NQZzTyZlCEOOG0thjZMeWXLcuki1LVI83h8k5Tk+pBw7ERHpT68Ec6tWrbBhwwYAtV/F4xU/IiLzycrKgkwmw6FDh5Cfn4/Y2FgUFRVp3q9vSKP66Bof31RjaxuyTGOs35LXZ451so2mWQbbaNnLMFbyw5yTSRmDVOdLYNzmJ8W2SClmKc9n8jCxMzFNZHn49DvVhjOHWIEypcqgciKyPp999hnWrFmDzMxMeHt7IzU1FYGBgcjJyQEA7N27F126dEHnzp2xf/9+qNVqXL16VTOkERERPZyqyaSmTp2K4cOHA/hzaDmg/n5YV10iIiIiS1L1RPyD/4gMHiKDLI+uIS8ADntB1NQZMqQRERE9nOqTSS1fvhwAMGPGDKSkpHBoOSIiIiKyakwwExFZmczMTM3/9R3SiIiIHg4nkyIiIiKipopDZBARERERERERERFRgzDBTEREREREREREREQNwgQzERERERERERERETWI1SeYy5Qqc4dAREREREREREREZJWsfpI/e1sFno7brlV+cf5AM0RDREREREREREREZD2s/g5mIiIiIiIiIiIiIjINJpitGIcHISIiIiIiIiIiIlOy+iEymjIOD0JEREREREREZHplShXsbRV6l5tqnaZcH1FtmGAmAObpCImIiIiIiIiIrIE5bvLTtc6fZ/fXWdeQvA9zQWQoJpgJAO92JiIiIiIiIiKSOkPzO7rqX5w/kDkiMgjHYCYiIiIiIiIiIiKiBmGCmYiIiIiIiIiIyIqVKVXmDoGsGIfIICIiIiIiIrIQD4596u3trbOc9HP8+HEsXLgQmZmZKCgoQFxcHGQyGdq1a4ekpCTI5XKkp6cjOzsbNjY2iI+Ph6+vb611iaSKQ6OSKTU4wTxkyBA4OzsDAFq1aoXQ0FDMmTMHCoUC/v7+mDx5MtRqNZKTk3HmzBk0a9YMKSkpaNOmjdGCJyIiIiIiIrImTAIZz6pVq7Bt2zY4ODgAAObNm4eYmBj4+fkhMTERO3fuhJubGw4fPoyNGzeisLAQ0dHRyMrK0lk3KCjIzC0iIrJMDUowl5eXAwAyMzM1ZYMHD8bSpUvRunVrjB8/Hnl5ebhy5QoqKirw+eefIzc3F/Pnz8eKFSuMEzk1CkNmGSUiIiIiIiKyFO7u7li6dCmmTZsGAMjLy0PXrl0BAIGBgThw4AA8PDzg7+8PmUwGNzc3qFQqFBUV6azLBDMRkW4NSjD//PPPKC0tRVRUFCorKxEdHY2Kigq4u7sDAPz9/XHo0CHcuHEDAQEBAIBOnTrh1KlTxoucGkVtV89/nt1fq4xJZyLzUiqViI+P11zcmzhxIp555hm9HwMkIqKHx0exiYgsR3BwMC5fvqx5LYSATCYDADg6OuLevXsoLi6Gi4uLpk5Vua669SkvL0d+fj4AoKysTPN/qWIbDFM1nI0u+sZQ2zKknG/hcWQZTN2GBiWY7e3tMXbsWIwYMQIXL17EuHHj0KJFC837jo6OuHTpEoqLi+Hk5KQpVygUqKyshI1N7aut3iHXxpCNUtcHnBpOV+L54vyBD3WwWsMH1hBNqb1sa+PYtm0bXFxckJaWhtu3b2Po0KHo0KGD3o8BEhHRw+Gj2ERElq36hbuSkhK0aNECTk5OKCkpqVHu7Oyss2597OzsNDmI/Px8yecj2AbjedgYpDx0jr29vUXsg4dhKcfRwzBGG+rKdTQowezh4YE2bdpAJpPBw8MDzs7OuHPnjub9qs63rKysRketVqvrTC4DNTvk2ljDjrVWD7Nfmtp+bUrtZVtrvm8q/fv3R3BwsOa1QqEw6DFAV1fXWpet6+KfPsn0huz3B5dp6PY0dJ2GLKOx12eOdbKNxl+frvrG+GzUtRxraaOxtlNjaexHsfW5MUMXQ44bS2Guc+vDkmrcuki1LTzezcvSbjTp2LEjcnJy4Ofnh71796Jbt25wd3dHWloaxo4di2vXrkGtVsPV1VVnXSIi0q1BCeZNmzbh7NmzSE5OxvXr11FaWormzZvjt99+Q+vWrbF//35MnjwZ165dw+7duzFgwADk5ubCy8vL2PGTBeF4zUTm5ejoCAAoLi7GW2+9hZiYGKSmpur9GGBdCWZdF/9MqOGMNgAAIABJREFUdeHAkGUaY/2WvD5zrJNtNM0y2EbLXoaxkh+N/Si2PjdmGEKqF4MZt/lJsS1SjBmQXtwP833RFInp2NhYzJw5E4sWLYKnpyeCg4OhUCjQpUsXhIaGQq1WIzExsda6RMbEXAlZkwYlmIcPH47p06cjPDwcMpkMc+fOhVwux5QpU6BSqeDv74/nn38ezz33HA4cOICwsDAIITB37lxjx08WRMqPbBBZi8LCQkyaNAkREREICQlBWlqa5r36HgMkIiLjMvWj2EREVL9WrVphw4YNAO4/jb1mzRqtOtHR0YiOjq5RVltdImOpbehRIilqUIK5WbNmeP/997XKqzrtKnK5HO+9917DIiMiIoPcvHkTUVFRSExMRPfu3QEY9hggEREZFx/FJiIiIqKmoEEJZiIisjwZGRm4e/culi9fjuXLlwMAZsyYgZSUFL0eAyQiIuPio9hERERE1BQwwUxEZCUSEhKQkJCgVa7vY4BERPTw+Cg2ERERETU18vqrEBERERERERERERFpY4KZzKZMqarx2tvbW6uMiIiIiIiIiMiSMHdBVJNVDZFRplTB3lZh7jDoAbXtF86YSkRERERERERSw3wGUU1WlWDmB9wy6dovAPcNERERERERERGR1HGIDLIotT1mwsdPiIiIiIiIiKgxMRdBpB+ruoOZpI93OxMRERERERGRJWCOgkg/vIOZiIiIiIiIiIiIiBqECWYiIiIiIiIiIiKSBA6vank4RAZJQplSBXtbxUOXExERERERERGRdHHoEsvDBDNJQl2dBzsVIiIiIiIiIiLrwxsIpYEJZiIiIiIiIiIiIhNggvTh6LrhkDcVWh4mmImIiIiIiIiIiEygKQ/nUKZUwdvbW6uMCXfrwwQzERERERERERERGZWl3H2sK6nNRLdxMcFMVomdBxERERERUePR9XvL/WlPM0VDUlHb73T+fidjspREtzUzeYJZrVYjOTkZZ86cQbNmzZCSkoI2bdqYerXUxDV258GTIkkN+2YiIsvCfpmIpM4aEzjsm03PlMNHGDI8A3+7Ez0ckyeYd+zYgYqKCnz++efIzc3F/PnzsWLFClOvlkhvhpxgaqvblMdUImli30xEZFnYLxMRWR72zdJgyO/0i/MH1vrbnb/pG4elJPktJQ5rYfIE89GjRxEQEAAA6NSpE06dOmXqVRIZpK7ksCEnI13qSlJzoHsyJ/bNRESWhf0yEZHlYd9cN1Mm6HjDl/UyJAdTVa4vQ4692uL4eXZ/rTJvb2/mbOohE0IIU65gxowZ6NevH1566SUAQK9evbBjxw7Y2OjObefm5sLOzs6UIRERmU15eTk6depk7jDYNxMRVWMJfbOh/TLAvpmIrJsU+2b2y0Rkzerql01+B7OTkxNKSko0r9VqdZ1flM19AiEiagrYNxMRWRZD+2WAfTMRkanxOzMRkX7kpl5B586dsXfvXgD3r+Z5eXmZepVERFQP9s1ERJaF/TIRkeVh30xEpB+TD5FRNevq2bNnIYTA3Llz0bZtW1OukoiI6sG+mYjIsrBfJiKyPOybiYj0Y/IEMxERERERERERERFZJ5MPkUFERERERERERERE1okJZiIiIiIiIiIiIiJqECaYiYiIiIiIiIiIiKhBbMwdgL6qBtc/c+YMmjVrhpSUFLRp08bcYZnE8ePHsXDhQmRmZqKgoABxcXGQyWRo164dkpKSIJdL/7qAUqlEfHw8rly5goqKCkycOBHPPPOMVbZVpVIhISEBFy5cgEKhwLx58yCEsMq2Vrl16xaGDRuG1atXw8bGxqrbOmTIEDg7OwMAWrVqhdDQUMyZMwcKhQL+/v6YPHmymSM0Lmvoix/cZ/PmzTNzRPWT+nmhevx5eXl444038PTTTwMAwsPDMWDAAPMGqIPUz1O64n/iiSckse2b4nmzMVX/PEqJrmO6T58+5g5LL7qOaXd3d3OHZZDq3+2kNsGZFM/71a1cuRK7du2CUqlEeHg4RowYYe6Q9LJ582Z88cUXAIDy8nLk5+fjwIEDaNGihZkjM43vv/8e33zzDd5//32d76vVaowfPx59+vRBeHg4hBAIDAzUnJM7deqEd999txEj1mZoG8rKyjB16lTcunULjo6OSE1NhaurayNHXVNdbfjss8+wefNmyGQyTJo0Cb1797a4/WBo/FLbB59++im2b98OAHjppZcwefJki9sHgOFtkNp+AICioiKEhYXhyy+/hJ2dnVH2g2QSzDt27EBFRQU+//xz5ObmYv78+VixYoW5wzK6VatWYdu2bXBwcAAAzJs3DzExMfDz80NiYiJ27tyJoKAgM0f58LZt2wYXFxekpaXh9u3bGDp0KDp06GCVbd29ezcAYP369cjJydH8ULbGtgL3fwAmJibC3t4egPUew8D9L8sAavxIHzx4MJYuXYrWrVtj/PjxyMvLw7PPPmuuEI1O6n2xrn1m6aR+Xngw/tOnT2PMmDGIiooyc2R1k/p5Slf8kyZNksS2b2rnzcb04OdRSnQd01JJMOs6pqV07nzwu52USPG8X11OTg5++uknrFu3DqWlpVi9erW5Q9LbsGHDMGzYMADArFmz8I9//MNqk8spKSnYv38/vL29a63z4Ycf4n//+5/m9W+//YZnn30WGRkZjRFivRrShnXr1sHLywvR0dHYvn07li9fjoSEhMYIV6e62lBUVIS1a9diy5YtKC8vx8CBA9GrVy+L2g8NiV9K++DSpUvYtm0bNm7cCJlMhoiICPTt2xcODg4Wsw+AhrXh0KFDktkPALBv3z68//77uHnzpqbMGJ8Fydz6cfToUQQEBAC4n0k/deqUmSMyDXd3dyxdulTzOi8vD127dgUABAYG4uDBg+YKzaj69++Pt99+W/NaoVBYbVv79u2L2bNnAwCuXr2Kli1bWm1bASA1NRVhYWH461//CsB6j2EA+Pnnn1FaWoqoqCi8+uqrOHLkCCoqKuDu7g6ZTAZ/f38cOnTI3GEaldT74gf3WW5urrlDqpfUzwsPxn/q1ClkZ2dj1KhRiI+PR3FxsRmjq53Uz1O64pfKtm9q583G9ODnUUp0HdNSoeuYlpIHv9tJiRTP+9Xt378fXl5emDRpEt544w306tXL3CEZ7OTJkzh37hxCQ0PNHYrJdO7cGcnJybW+/80330AmkyEwMFBTlpeXh+vXryMyMhLjxo3D+fPnGyHS2jWkDdV/FwQGBpr9d09dbXB1dcXWrVtha2uLmzdvokWLFpDJZBa1HxoSv5T2wRNPPIF//etfUCgUkMvlqKyshJ2dnUXtA6BhbZDSfgAAuVyOTz75BC4uLpoyY+wHySSYi4uL4eTkpHmtUChQWVlpxohMIzg4GDY2f95YLoSATCYDADg6OuLevXvmCs2oHB0d4eTkhOLiYrz11luIiYmx2rYCgI2NDWJjYzF79mwEBwdbbVs3b94MV1dXTecKWO8xDAD29vYYO3YsPv74Y8yaNQvTp0+vcVeYtbUXkH5f/OA+mzJlisXHL/XzwoPx+/r6Ytq0afjss8/QunVrLFu2zIzR1U7q5yld8Utl2wNN57zZ2B78PEqJrmNaSh48pqVC13c7KZHieb+627dv49SpU1i8eLEmfiGEucMyyMqVKzFp0iRzh2EUGzduxKBBg2r8O3HiBAYMGKA5Rz3o7Nmz+Oqrr2pcIAOAxx57DOPHj0dmZiYmTJiAqVOnNkYTjNqG4uJizfAzjXlubkgbgPv98Jo1axAaGqrph82xH4wZv5T2ga2tLVxdXSGEQGpqKjp27AgPDw9JfRZqa4OU9gMA9OzZE3/5y19qlBljP0jmG6aTkxNKSko0r9VqtWS/IBui+viCJSUlVvVYUWFhISZNmoSIiAiEhIQgLS1N8561tRW4f/fHlClTMHLkSM3jeoB1tTUrKwsymQyHDh1Cfn4+YmNjUVRUpHnfmtoKAB4eHmjTpg1kMhk8PDzg7OyMO3fuaN63tvYC0u+LH9xnLi4uuHHjBp588klzh6Y3qZ8XgoKCNDEHBQVp7uqzRFI/Tz0Y/927dyWz7YGmcd4kwzx4TEtN9WN6+/btaN68ublDqpeu73YrVqzAY489Zu7Q9CL1876Liws8PT3RrFkzeHp6ws7ODkVFRXj00UfNHZpe7t69i/Pnz6Nbt27mDsUoRowYYfAY2Fu2bMH169fx2muv4cqVK7C1tcVTTz2FF198UfMkRpcuXXD9+vUaF1NNxZhtqP67oDHPzQ1pQ5XRo0dj5MiRGDduHH744Qc8//zzjb4fjBm/1PZBeXk54uPj4ejoiKSkJACAj4+PZD4LgO42SG0/6GKM/SCZO5g7d+6MvXv3AgByc3Ph5eVl5ogaR8eOHZGTkwMA2Lt3L7p06WLmiIzj5s2biIqKwtSpUzF8+HAA1tvWLVu2YOXKlQAABwcHyGQy+Pj4WGVbP/vsM6xZswaZmZnw9vZGamoqAgMDrbKtALBp0ybMnz8fAHD9+nWUlpaiefPm+O233yCEwP79+62qvYD0++IH91lxcbFkfiRXkXpfOXbsWJw4cQIAcOjQIYsdo1zq5yld8Utl2zel8ybpT9cxLRW6jmmpDPGh67udlM6bUj/vv/DCC9i3bx+EEJrvmtUfabZ0R44cQY8ePcwdhllNmzYNGzduRGZmJoYOHYrXX38dgYGBSE9Px7///W8A94dycXNzM3lCraFqa0Pnzp2xZ88eAPfPzS+88IKZI63d+fPnNRPK2draolmzZpDL5ZLZD7XFL6V9IITAm2++ifbt2+O9997TnAelsg+A2tsgpf1QG2PsB8ncdhYUFIQDBw4gLCwMQgjMnTvX3CE1itjYWMycOROLFi2Cp6enpB6pq0tGRgbu3r2L5cuXY/ny5QCAGTNmICUlxera2q9fP0yfPh2jRo1CZWUl4uPj0bZtW6vcr7pY6zEMAMOHD8f06dMRHh4OmUyGuXPnQi6XY8qUKVCpVPD398fzzz9v7jCNSup9sa59JqU7sAHpf6aSk5Mxe/Zs2NraomXLlhZ7F63Uz1O64o+Li8PcuXMtfts39fMm6abrmF61apUkJp7TdUzb2dmZO6wmQern/d69e+PIkSMYPnw4hBBITEyUzMUJALhw4QJatWpl7jDM4pNPPoG7u3utk5GOHz8eU6dOxZ49e6BQKDBv3rxGjrB+9bUhPDwcsbGxCA8Ph62tLd5///1GjrB+1dvQoUMHhIaGQiaTISAgAF27dkX79u0tej/UF/9zzz0nmX2gVqtx+PBhVFRUYN++fQCA//u//5PUZ6G2Nkjts6CLMfaDTEhtECciIiIiIiIiIiIisgiSGSKDiIiIiIiIiIiIiCwLE8xERERERERERERE1CBMMBMRERERERERERFRgzDBTEREREREREREREQNwgQzERERERERERERETUIE8xERERERERERERE1CBMMBMRERERERERERFRgzDBTEREREREREREREQNwgQzERERERERERERETUIE8xERERERERERERE1CBMMBMRERERERERERFRgzDBTEREREREREREREQNwgQzmdXly5fh7e2NwYMHa/79/e9/x6ZNmwAAO3fuREpKSp3LiIyMxDfffGOS+F5++WWcPHnSJMsmIgLq7wc3b96MCRMmmDlKw6Wnp2PHjh0G/11kZCRefvllzbYYOHAgFixYgOLiYhNEWVNhYSEGDRqEwYMH46efftKKq3379rh06VKN8pycHLRv3x4ff/xxg9d74sQJJCYmapY3aNCgBi+LiGpiH/un7du3Y8iQITXKQkNDERAQACGEpmzcuHFYu3Ztg2Nr3749ioqK6qxTVFSE9u3bN3gdukydOhVnz57V9MuxsbFadSIjI/G3v/3NqOvVZdOmTXjjjTc0rwsLCzF58mSo1WqTr5uITMNY55OEhAScOnUKAJCfn4++ffti2LBhuHz5sknjf+uttzRxt2/fHiEhIRg8eDAiIyNNul5qOmzMHQCRvb09tm7dqnl9/fp1DBo0CD4+PujTpw/69OljxuiIiEyvrn5QqnJycvDMM8806G+nTZuG/v37AwCUSiVSUlIwZcoUZGRkGDNELTk5OWjZsiU+/fRTne+7ublh69atmDx5sqZsy5YtaNmy5UOt99y5c7h+/fpDLYOIasc+9r6ePXsiNjYWd+7cgYuLC4qKivD777/j0UcfxcmTJ+Hr64vKykocPXoUycnJpgncRL7++ms4OzvDy8sLOTk5eOyxx7B7926UlpbCwcEBAHDlyhVcuHDBpHHcuXMHixYtwpdffomuXbtqyp988kl06NABa9euxejRo00aAxGZjjHOJwcPHkRoaCiA+zfU+fn5Yc6cOUaP9UFLlizR/L99+/b497//DVdXV5Ovl5oOJpjJ4jz++ONo06YNLl68iNOnT+Pbb7/FypUrcePGDSQlJeH8+fOQy+UICwvDq6++qvm7yspKvPvuu7CxsUFqairGjBmDUaNGaZIUkZGRmtcdO3bEuHHjsG/fPvzxxx/4v//7P/Tr18+gOJctW4bt27dDoVDAw8MDM2fOxGOPPYbIyEh06tQJx44dQ2FhIbp3747Zs2dDLpdj8+bN+Oijj2Bvb49u3brhP//5D06fPm3U7UdE0le9H6wuNzcXaWlpqKiowI0bN9CjRw/MnTsXALB79258+OGHUKvVaN68OWbNmoUOHTrg2LFjWLhwIUpLSyGXyzF58mT07t0bmzdvxnfffQe1Wo2rV6/i8ccfx8iRI7FmzRpcvHgRY8aMQVRUFABg48aNWLduHdRqNVxcXDBz5ky0bdsWcXFxcHJywpkzZ3Dt2jW0b98eqamp2LJlC06dOoUFCxZAoVAgKCiowdvC1tYW06dPR8+ePfHrr7/Cw8MDc+fOxfHjx1FSUgIhBFJSUuDt7Y2XXnoJGzZsgIeHBwDg9ddfx+jRo9G3b98ay/z888+RmZkJuVyOli1bYubMmbh+/To+/PBD3Lt3D5GRkcjMzNSK5e9//zu+/PJLTYK5tLQUx44dQ/fu3TV1fvnlF7z33nu4c+cOZDIZoqKiMGTIEOTk5OCDDz5A69at8csvv6CyshKzZs2Cm5sblixZgnv37mH69OkYMmQI/vjjD7zzzjs4f/48ysvLkZKSgi5dujR4GxJRTU21j3VxcYGPjw9+/PFH9O3bF9nZ2fD398ejjz6KXbt2wdfXF8ePH8dTTz2Fp556CgCwYsUKTTueeuopJCUl4fHHH8e9e/cwZ84cnD17FkqlEt27d8e0adNgY/Pnz8sbN25gzJgxCA8Px6hRo/Ddd9/hgw8+gIODQ41kzB9//IHk5GQUFBTgzp07cHR0xMKFC2Fvb49BgwZhz549cHZ2hhAC/fv3x+LFi9GhQ4cabVu6dCkWL15co62tW7fGjh07EBISAuD+BcGQkBCsX79eU6+27/PVbdmyBZ988onW9lywYIHWXdj//e9/8de//hWxsbHYvXt3jfdGjBiB4cOHY+TIkWjWrJle+4yILJuh55MPPvgAv//+O6ZMmYKQkBCsW7cOKpUKZWVleP/99+vMMTzyyCM4f/48wsPD8d133+HZZ59Fbm4uioqKMHLkSNy8eROHDx9GaWkpPvzwQ4OeElmxYgXOnTuH999/HwDw448/IiUlBenp6YiMjERAQACOHz8OIQQSExM130trO0dQEyWIzOjSpUuiU6dONcqOHTsmXnzxRXH16lWRlZUlxo8fL4QQYtKkSSI1NVUIIcTdu3fFwIEDxcWLF8Xo0aPFtm3bxJtvvilmzZol1Gq1EEKI0aNHi//+97+a5VZ/7eXlJVasWCGEECI/P1+88MIL4tatW1rx9e7dW5w4cUKrfNOmTSI0NFSUlJQIIYRYsmSJiIqK0qznrbfeEiqVSty7d0/4+/uLQ4cOiV9++UV0795dFBYWCiGEWLp0qfDy8mr4xiMiq2BIP/jOO++IH374QQghRHFxsfDz8xMnT54UN27cEC+88ILIy8sTQgjx7bffirFjx4o7d+6Ifv36iUuXLgkhhLh27ZoIDAwUV65cEVlZWeKFF14QV69eFSqVSgwYMEBER0cLlUol8vPzxXPPPSdUKpXIyckRERER4o8//hBCCLFv3z7Rv39/IYQQsbGxIjQ0VJSXl4uKigoxZMgQsWnTJiGEdh+sr9r+btiwYeLrr78Wx44d08QphBArV64UEyZMEEIIkZKSojlPFBQUiJdeeklUVlbWWM7BgwdF3759NX1+VlaWeOWVV4Rara6xrWuLa9CgQSI3N1cIIcSWLVvE/PnzRWxsrPjXv/4llEql6NOnj/j222812zsgIEAcO3ZM/PDDD8Lb21ucPn1aCCHExx9/LEaNGqWJoWq9VfWq1vHJJ5+IV1991eDtSET3sY+tKT09XaSkpAghhJg8ebLYsWOH+Omnn0RISIgQ4v7307S0NCGEEF988YWIiYkRSqVSCCHE+vXrxT//+U8hhBBxcXHiP//5jxBCiMrKSjFlyhTx0UcfCSHuf88+ffq0GDBggNi6dasQQmi24S+//CKEECIjI0PzPfi///2vmD17tibGmTNnivfee08IIcTEiRPFmjVrhBD3+++RI0dqtenMmTOid+/emtc//PCDGDhwoPjmm2/E2LFjNeUDBw4Up06d0hwPdX2ff1i1nU8GDRokDh06ZJR1EFHjMsb5RIiaOYYlS5aIWbNmCSHqzzFMnz5ds97Ro0eLyZMnCyGEyM3NFV5eXmLnzp1CCCHmzJkjEhIS6myLl5dXjfzHzZs3RefOncXt27eFEEJMnTpVrFu3Tly6dEl4eXmJbdu2CSGEyM7OFj179hQVFRV1niOoaeIdzGR2ZWVlGDx4MABApVLhL3/5C9LS0vDkk0/WqHfw4EFMnToVAODs7IyvvvpK815qaipKSkrw/fffQyaT6bXeqsfTOnToAC8vLxw5cgTBwcF6/e3evXsxbNgwNG/eHADw6quvIiMjAxUVFQCA3r17Qy6Xw8nJCW3atMH//vc//Pzzz+jZsyeeeOIJzfqXLl2q1/qIyLrp2w/Onz8fe/fuRUZGhubu1j/++APHjh1Du3bt0LFjRwBAv3790K9fP+zZswc3btzApEmTNMuQyWQ4c+YMAOC5557TrKNVq1bw9/eHXC5H69atUV5ejtLSUmRnZ6OgoABhYWGaZdy9exd37twBAAQEBGjuxPLy8sL//vc/k2wjmUwGBwcH/O1vf8MjjzyC9evX49KlS8jJyYGjoyMAICIiAqNHj8Y777yDzz//HMOHD4dCoaixnH379mHAgAGaRwKHDRuGOXPm6D3u3eDBg7Ft2zY8//zz2LJlC6ZPn47Vq1cDAC5evIjy8nLNEzGPP/44+vXrh3379sHPzw9ubm7w9vYGAHTs2BFffPGFznW0bt0azz//PID756isrCwDtxYRVcc+9k+BgYFISEhARUUFfvzxRyxYsAB2dna4efMmbt68iZycHLz99tsA7t+1ffLkSfzjH/8AAKjVapSWlgIAsrOzcfLkSc3Yo2VlZTXWM27cODzxxBOau4ePHj0KLy8vzbAeoaGhWLRoEQCgf//+aN26NTIzM1FQUIDDhw9rxkkeNWoU0tLSMGrUKHz++ecIDw/XatP58+fh7u6uVd67d28kJyfj5s2bKCgogKenJx555BHN+3V9n69+h7EhdzDXp1WrVrhw4QK6detm0N8RkWV42PNJXerLMTz4NFvV0yutW7cGcP98AQDu7u44fPiwQe169NFH0atXL2zduhVDhgzB/v37kZSUhNu3b+ORRx7R9OUvvfQSFAoFzpw5U+c5gpomJpjJ7B4cx6g2NjY2NZLHly5dwl/+8hcA9x9bFkIgISGhxhidotqEJUqlssbyqicd1Gq1VhKiLmq1ukYsarUalZWVNdpURSaTQQgBhUJRIx5D1kdE1k3ffnD06NFo3749AgIC8Morr2geVVMoFDX6JCEEzpw5A5VKhbZt22Ljxo2a965fvw5XV1d8+eWXWo/oVn+0uYparcbgwYM1F/jUajV+//13zY90Xf1dXRYvXoxdu3YBuD+RalUioy6lpaX49ddf8cwzzyA7Oxtz5szBmDFj0KdPH3h6emLbtm0AAA8PD7Rv3x47d+7EV199hQ0bNuhsz4OEEDX68LqEhITgH//4B15//XUUFxfDy8tL855KpdK6yFl92fpuK1tbW73qEZF+2Mf+ycfHB7du3cKOHTvg4+OjGZ84ICAABw4cwIULF9CpUydNLP/85z8REREBAKioqNAkuNVqNRYvXoy2bdsCuJ8Ur76N3nvvPWRkZOCTTz7RDAVSPfbq22Lt2rXYsGEDRo0ahZCQELi4uGgu+vXo0QOlpaU4dOgQfvzxR6Smpmq1WSaT6ezbmzVrhn79+mH79u04d+4chg4dWuP9+r7PVxkyZIjW5IgNZWtry98ARBL2sOeTutTXJ1Ulnqs8eI6p/v2xIUaNGoXk5GTY2NigX79+cHR0xO3bt7X6rKrcSV3nCGqa5OYOgEhf3bt319zFde/ePbz22muasY58fX0RExOD3377TZNQcHV11czOeu7cOc3dJFW2bNkCAMjLy8OFCxfw4osv6h1LQEAAsrKyNFchMzMz8eKLL9Y5npq/vz8OHTqkmcip+o8RIqL63L17FydPnsSUKVPQr18/XLt2Db/99hvUajWef/55/Prrr/jll18A3J8wZOrUqejUqRMKCgpw5MgRAPdnqg4ODjZoQjl/f39s374dv//+OwBg3bp1eO211+r9O4VCofOH+ttvv42tW7di69ateiWXy8rKMHfuXAQGBqJVq1Y4cOAAevfujYiICPj4+GDHjh1QqVSa+hEREViwYAF8fX11jgEXEBCAr7/+GkVFRQCArKwsuLi4oE2bNvXGAty/K7l9+/aIj4/X3MFSxdPTEzY2Nvjuu+8A3E80ffvtt+jRo0edy6xkwTEQAAAgAElEQVRtWxFR42kqfaxMJkOPHj2QkZGBXr16acp79eqF1atXo2vXrprkr7+/PzZt2oTi4mIA95PX06ZN07z36aefQgiBiooKTJw4EWvWrNEsr1OnTpg/fz5WrFiBs2fP4sUXX8S5c+fw888/AwA2b96sqbt//34MHToUI0aMgIeHB3bt2qXp12UyGSIiIjBjxgwMGjQIdnZ2Wm3y8PDApUuXdG6nIUOG4IsvvsCRI0c0d/dVacj3+Yd1+fJleHp6mmz5RGR+dZ1PgNr7b3P0SdV17twZcrkcH3/8cY2naoqKirB3714AwK5du2BrawsvL686zxHUNPEOZpKMxMREJCcnIyQkBEIITJgwocYEIXZ2dpg/fz6ioqLQrVs3TJw4EXFxcdizZw88PT21Hik5duwYNmzYALVajQ8++KDGI3PVjR49GnL5n9dipkyZgvDwcBQWFmLEiBFQq9Vo06YNFi5cWGf8Hh4emD59OsaOHYtmzZrB29tbc9cIEVF9WrRogfHjx2Po0KFo3rw5Hn/8cXTu3BkFBQXo3r07Fi5ciNjYWKhUKjg5OeGDDz6Aq6srlixZggULFqC8vBxCCCxYsACtWrXS+9E5f39/jBs3DlFRUZDJZHByckJ6enq9wxG9/PLLWLRoEZRKpdZdY/VZsGABVqxYAblcjsrKSvTo0QMzZswAAISFheHdd99FSEgIKisr0bNnT83kInK5HL1790ZCQkKNL8bV9ezZE6+//jpee+01qNVquLq6YuXKlTX6+foMHjwY8fHxWsMc2draYvny5UhJScHSpUuhUqkwadIkdOvWDTk5ObUur1OnTli2bBkmT56MyMhIveMgIuNpSn1sYGAgtm7dit69e9eIY+rUqRgzZoymbMSIEbh+/TpGjhwJmUyGJ598EvPnzwcAzJgxA3PmzEFISAiUSiV69OiBf/7znzXW4+npiTfffBNTp07Fxo0bsXDhQkyZMgW2trY1buyIiopCYmKiZriNTp064ezZs5r3hw4ditTUVISGhupsj5eXF+zs7PDrr79q7qiu8re//Q2lpaV4+eWXte4gHz58uMHf5x/GzZs3cevWLXTu3Nlk6yAi86vvfBIUFISpU6ciOTm5xt81dp+ky7Bhw/D111/XmEjVzs4OW7du1Uy+umzZMigUijrPEdQ0yQSfu6QmqH379jh06JBmDM7GcOnSJWzduhVvvvkm5HI5vvvuO6xatYp3MhMRGdFPP/2EhIQEfPXVV3qPyU9ERJZr+/bt+OKLL/Cvf/2r1jpffvkljh49qpWwsSRLly6Fq6srRo0aZe5QiIi0VFZWYvLkyfj73/+OAQMGALj/1EVISAh++uknM0dHUsA7mIkayRNPPIHff/8dISEhUCgUcHZ2xty5c80dFhGR1YiNjcXhw4fxwQcfMLlMRGQFIiMjUVRUhOXLl9dZLyQkBLt27cKZM2cMnnivMRQWFiIvLw/Lli0zdyhERFrOnTuH8PBw9O3bF/379zd3OCRRvIOZiIiIiIiIiIiIiBqEk/wRERERERERERERUYMwwUxEREREREREREREDWJxYzDn5ubCzs5O7/rl5eUG1TcXxmlcjNO4pBInIJ1Ya4uzvLwcnTp1MkNED0dX3yyVfaGLlGMHpB0/YzcfKcdv6titqW/WB48F85Fy/FKOHZB2/E01din2zU2xXwakHb+UYwekHb+UYwekHX9DY6+rX7a4BLOdnR28vb31rp+fn29QfXNhnMbFOI1LKnEC0om1tjjz8/PNEM3D09U3S2Vf6CLl2AFpx8/YzUfK8Zs6dmvqm/XBY8F8pBy/lGMHpB1/U41din1zU+yXAWnHL+XYAWnHL+XYAWnH39DY6+qXOUQGERERERERERERETUIE8xERERERERERERE1CBMMBMRERERERERERFRgzDBTGZTplTpVUZEJEXs44iIyJq4P+2ps5znNiIiMkTVeePBMYB5PpE2i5vkj5oOe1sFno7bXqPs4vyBZoqGiMi42McREZE1cXSw0zqvATy3ERGRYXT9TgJ4PpE63sFMRERERGQkt27dwksvvYRff/0VBQUFCA8PR0REBJKSkqBWqwEA6enpGD58OMLCwnDixAkAqLUuEREREZGlY4KZiMhKKJVKvPvuuwgLC0NERITByQ1qGD7KRURVlEolEhMTYW9vDwCYN28eYmJisHbtWgghsHPnTuTl5eHw4cPYuHEjFi1ahFmzZtVal4iIiIhICjhEBhGRldizZw8qKyuxfv16HDhwAB9++CGUSiViYmLg5+eHxMRE7Ny5E25ubprkRmFhIaKjo5GVlWXu8CWrKTziVaZUwd5WoXc5UVOVmpqKsLAwfPTRRwCAvLw8dO3aFQAQGBiIAwcOwMPDA/7+/pDJZHBzc4NKpUJRUZHOukFBQWZrCxERERGRvphgJiKyEh4eHlCpVFCr1SguLoaNjQ1yc3P1Tm64urrWuuzy8nLk5+fXKCsrK9Mqkwpjxv7g5BT1McZ6G3vbe3t760yi/zy7v1ZZSWk5frt4vtZl8bgxHynHL4XYN2/eDFdXVwQEBGgSzEIIyGQyAICjoyPu3buH4uJiuLi4aP6uqlxX3fro6pv1IYXtWRspxw5IO/66zneW3Cb3pz3h6GBXI/76zlWWRsrHjZRjJyIi/THBTERkJZo3b44rV67glVdewe3bt5GRkYEjR47ondyoK8FsZ2en9cMyPz/f4OSqpagvdl135hrrbl1jbDNL2fa1TWRYXxLCEmJvCCnHDkg7flPHbozkR1ZWFmQyGQ4dOoT8/HzExsaiqKhI835JSQlatGgBJycnlJSU1Ch3dnaGXC7XqlsfXX2zPqR4LFjLkxRS3Pb6sPQ2GXqusjRSPm4eJnYmpomIpKPeBLNKpUJCQgIuXLgAhUKBefPmQQiBuLg4yGQytGvXDklJSZDL5UhPT0d2djZsbGwQHx8PX19fFBQU6KxLRETG9emnn8Lf3x/vvvsuCgsL8dprr0GpVGrery+5QX+qLXFKRPqzloSgvj777DPN/yMjI5GcnIy0tDTk5OTAz88Pe/fuRbdu3eDu7o60tDSMHTsW165dg1qthqurKzp27KhVl/7UFIYjIiIiIpKqehPMu3fvBgCsX78eOTk5mgSzvmN6Vk1YUr0ux5MjIjK+Fi1awNbWFgDwyCOPoLKyUmfCorbkBhGRMTEhCMTGxmLmzJlYtGgRPD09ERwcDIVCgS5duiA0NBRqtRqJiYm11iUiIiIikoJ6E8x9+/ZFr169AABXr15Fy5YtkZ2dbbIJSwwdS04qYzoxTm21PSqlz/q5PY1LKnEC0onVHHG+/vrriI+PR0REBJRKJd555x34+PjondywFqYc3oKkhccCmUtmZqbm/2vWrNF6Pzo6GtHR0TXKPDw8dNYlaXuwz6n6/su+iIiIiKyJXmMw29jYIDY2Ft9//z2WLFmC3bt3m2zCEkPHkpPKeFSMU3/6rN8S4tQH4zQ+qcRaW5ymTDo7Ojpi8eLFWuX6JjesBYe3sExVyZQHPxemTLLwWCDSzf1pT60yJjxNg3fyExERUVOg9yR/qampmDJlCkaOHIny8nJNuSkmLCEiIjKmpnAHmaWPd8skC5HlcHSw48UXIiIiIjKaehPMW7ZswfXr1zFhwgQ4ODhAJpPBx8eHE5YQEZFkNIXkZlNoIxEREREREVmeehPM/fr1w/Tp0zFq1ChUVlYiPj4ebdu25YQlRERERERERGQxjh8/joULFyIzMxN5eXl444038PTTTwMAwsPDMWDAAKSnpyM7Oxs2NjaIj4+Hr68vCgoKEBcXB5lMhnbt2iEpKQlyuVxnXSIi0lZvgrl58+YPNaYnJywhIiIiIiIiIlNatWoVtm3bBgcHBwDA6dOnMWbMGERFRWnq5OXl4fDhw9i4cSMKCwsRHR2NrKwszJs3DzExMfDz80NiYiJ27twJNzc3nXWJiEibvP4qRERE5lOmVBlUTkTWo+pzrmtySCIiourc3d2xdOlSzetTp04hOzsbo0aNQnx8PIqLi3H06FH4+/tDJpPBzc0NKpUKRUVFyMvLQ9euXQEAgYGBOHjwYK11iYhIm96T/BEREZkDxxbW5v60p7lDIGoU/PwTEZG+goODcfnyZc1rX19fjBgxAj4+PlixYgWWLVsGZ2dnuLi4aOo4Ojri3r1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rMgxDRUVFNkeKRFm5cqXOnTsnSTp16pS+/OUv2xyRtTLhnBSLSPWRyW3kT3/6k1588UVJ0uTJk+VyuTR79uyMbR+pKBXGzLGcOxPdv0Tbh1kVT7THjFXxRDsmtyoeSTp06JB27NghSeHx85QpU/TBBx/INE11dXWFY7q9befl5WnChAl37BuPaMfzVtbRW2+9pa9//euSNGqZraqfaK8FrKwfAIB1XKZpmnYHEY+bT8V9++23ZZqmtm3bpunTp9sdVkK0trbqL3/5i8rKysLbNmzYoNbWVgWDQZWVlam1tVVut9vGKBOnrq5OW7ZsUVZWljZt2uS4Mv785z9Xd3e3TNPUs88+q6lTpzqqnIFAQE1NTfr4448VDAa1fPlyzZ4921FlvHTpkn784x/rwIEDeu+99yKWra2tTZ2dnTIMQ+vXr2dwnOZu/c59Pp9aWlo0YcIE3XPPPWppaRnxE0+ny7Rz0t1Eqo/6+nrt2rUrI9vIf//7X61fv17Xrl3TjRs39NRTT2n69OmOOgekO7vGzOM9d46273jF0odZEU8sx4wV8dzqbmNyq+IZHh7W+vXrdeXKFblcLq1bt05ZWVnatm2bQqGQPB6Pnn322VHbdk9Pzx37xiva8bxVdfS73/1O2dnZ+t73vidJEctsVf3Eci1gdZsGACRf2ieYAQAAAAAAAAD2SPslMgAAAAAAAAAA9iDBDAAAAAAAAAAYFxLMAAAAAAAAAIBxIcEMAAAAAAAAABgXEswAAAAAAAAAgHEhwQwAAAAAAAAAGBcSzAAAAAAAAACAcfl/BdDV4qIXmvgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Distribution review for train data\n",
+    "df_train[[column for column in df_train.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Column Skewness of the testing dataset\n",
+    "df_train.skew().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Univariate Anaysis\n",
+    "# Calculating Skewness in the columns of our Training dataset\n",
+    "df_train.kurt().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Platform Type</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.005528</td>\n",
+       "      <td>0.225060</td>\n",
+       "      <td>-0.055948</td>\n",
+       "      <td>-0.002742</td>\n",
+       "      <td>0.021327</td>\n",
+       "      <td>0.006319</td>\n",
+       "      <td>0.043924</td>\n",
+       "      <td>-0.003827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>-0.007585</td>\n",
+       "      <td>0.013097</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004929</td>\n",
+       "      <td>-0.005453</td>\n",
+       "      <td>0.007541</td>\n",
+       "      <td>0.002077</td>\n",
+       "      <td>-0.014710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.003582</td>\n",
+       "      <td>0.031064</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000918</td>\n",
+       "      <td>0.011008</td>\n",
+       "      <td>-0.004299</td>\n",
+       "      <td>-0.000875</td>\n",
+       "      <td>0.009693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>-0.007587</td>\n",
+       "      <td>0.013099</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004923</td>\n",
+       "      <td>-0.005448</td>\n",
+       "      <td>0.007550</td>\n",
+       "      <td>0.002073</td>\n",
+       "      <td>-0.014701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.003574</td>\n",
+       "      <td>0.031074</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000883</td>\n",
+       "      <td>0.011036</td>\n",
+       "      <td>-0.004251</td>\n",
+       "      <td>-0.000900</td>\n",
+       "      <td>0.009744</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Platform Type  Placement - Day of Month  \\\n",
+       "Platform Type                         1.000000                  0.012764   \n",
+       "Placement - Day of Month              0.012764                  1.000000   \n",
+       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
+       "Confirmation - Day of Month           0.012770                  0.999998   \n",
+       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
+       "\n",
+       "                                 Placement - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                       -0.058038   \n",
+       "Placement - Day of Month                             0.026148   \n",
+       "Placement - Weekday (Mo = 1)                         1.000000   \n",
+       "Confirmation - Day of Month                          0.026132   \n",
+       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
+       "\n",
+       "                                 Confirmation - Day of Month  \\\n",
+       "Platform Type                                       0.012770   \n",
+       "Placement - Day of Month                            0.999998   \n",
+       "Placement - Weekday (Mo = 1)                        0.026132   \n",
+       "Confirmation - Day of Month                         1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
+       "\n",
+       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                          -0.058005   \n",
+       "Placement - Day of Month                                0.026122   \n",
+       "Placement - Weekday (Mo = 1)                            0.999952   \n",
+       "Confirmation - Day of Month                             0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
+       "\n",
+       "                                 Arrival at Pickup - Day of Month  \\\n",
+       "Platform Type                                            0.012770   \n",
+       "Placement - Day of Month                                 0.999998   \n",
+       "Placement - Weekday (Mo = 1)                             0.026132   \n",
+       "Confirmation - Day of Month                              1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
+       "\n",
+       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                               -0.058005   \n",
+       "Placement - Day of Month                                     0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
+       "Confirmation - Day of Month                                  0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
+       "\n",
+       "                                 Pickup - Day of Month  \\\n",
+       "Platform Type                                 0.012770   \n",
+       "Placement - Day of Month                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                  0.026132   \n",
+       "Confirmation - Day of Month                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
+       "\n",
+       "                                 Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                    -0.058005   \n",
+       "Placement - Day of Month                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                      0.999952   \n",
+       "Confirmation - Day of Month                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
+       "\n",
+       "                                 Arrival at Destination - Day of Month  \\\n",
+       "Platform Type                                                 0.012770   \n",
+       "Placement - Day of Month                                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
+       "Confirmation - Day of Month                                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
+       "\n",
+       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                                    -0.058005   \n",
+       "Placement - Day of Month                                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
+       "Confirmation - Day of Month                                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
+       "\n",
+       "                                 Distance (KM)  Temperature  \\\n",
+       "Platform Type                         0.005528     0.225060   \n",
+       "Placement - Day of Month             -0.007585     0.013097   \n",
+       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
+       "Confirmation - Day of Month          -0.007587     0.013099   \n",
+       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
+       "\n",
+       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
+       "Platform Type                                       -0.055948   -0.002742   \n",
+       "Placement - Day of Month                            -0.204143   -0.004929   \n",
+       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
+       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
+       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
+       "\n",
+       "                                 Pickup Long  Destination Lat  \\\n",
+       "Platform Type                       0.021327         0.006319   \n",
+       "Placement - Day of Month           -0.005453         0.007541   \n",
+       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
+       "Confirmation - Day of Month        -0.005448         0.007550   \n",
+       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
+       "\n",
+       "                                 Destination Long  Time from Pickup to Arrival  \n",
+       "Platform Type                            0.043924                    -0.003827  \n",
+       "Placement - Day of Month                 0.002077                    -0.014710  \n",
+       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
+       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
+       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CorrMatrix_train = df_train.corr()\n",
+    "CorrMatrix_train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Pearson Correlation of Features')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.style as style\n",
+    "style.use('ggplot')\n",
+    "cm = CorrMatrix_train.corr()\n",
+    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
+    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
+    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Personal or Business"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9d35d088>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Platform Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9de9fec8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vehicle Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9eee35c8>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(df_train['Temperature'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    12.416667\n",
+       "1    33.216667\n",
+       "2     7.583333\n",
+       "3    22.350000\n",
+       "4    20.233333\n",
+       "Name: Time from Pickup to Arrival, dtype: float64"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numeric_Training = df_train._get_numeric_data()\n",
+    "Numeric_Training.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x222671c3288>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-ddf5308ac5e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whitegrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m   2063\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2064\u001b[0m                         \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2065\u001b[1;33m                         **kwargs)\n\u001b[0m\u001b[0;32m   2066\u001b[0m                     \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2067\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 527\u001b[1;33m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mDraw\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfigure\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m--> 386\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    397\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 864x7.63233e+06 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l = df_train.values\n",
+    "number_of_columns= 12\n",
+    "number_of_rows = len(l)-1/number_of_columns\n",
+    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.set_style('whitegrid')\n",
+    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cannot do label indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [12.416666666666666] of <class 'numpy.float64'>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-151-706b1053bd53>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mchart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n\u001b[0;32m      6\u001b[0m                           horizontalalignment='right')\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4401\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4403\u001b[1;33m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4404\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4405\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\numeric.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_wrap_joined_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoined\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m   2867\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ix\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2868\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2869\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"label\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2871\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"loc\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m   3075\u001b[0m         \"\"\"\n\u001b[0;32m   3076\u001b[0m         raise TypeError(\n\u001b[1;32m-> 3077\u001b[1;33m             \u001b[1;34mf\"cannot do {form} indexing on {type(self)} with these \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3078\u001b[0m             \u001b[1;34mf\"indexers [{key}] of {type(key)}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m         )\n",
+      "\u001b[1;31mTypeError\u001b[0m: cannot do label indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [12.416666666666666] of <class 'numpy.float64'>"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 360x12211660 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m   2063\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2064\u001b[0m                         \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2065\u001b[1;33m                         **kwargs)\n\u001b[0m\u001b[0;32m   2066\u001b[0m                     \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2067\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 527\u001b[1;33m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mDraw\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfigure\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m--> 386\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    397\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 360x12211660 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 360x1.22117e+07 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
+    "for i in range(0,len(nu_tra)):\n",
+    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
+    "    chart=sns.distplot(df_train[nu_tra[i]],kde=True) \n",
+    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+    "                          horizontalalignment='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    21201.000000\n",
+       "mean      1556.920947\n",
+       "std        987.270788\n",
+       "min          1.000000\n",
+       "25%        882.000000\n",
+       "50%       1369.000000\n",
+       "75%       2040.000000\n",
+       "max       7883.000000\n",
+       "Name: Time from Pickup to Arrival, dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Useful statistics about our target column\n",
+    "\n",
+    "df_train['Time from Pickup to Arrival'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        12.416667\n",
+       "1        33.216667\n",
+       "2         7.583333\n",
+       "3        22.350000\n",
+       "4        20.233333\n",
+       "           ...    \n",
+       "21196     0.150000\n",
+       "21197    12.833333\n",
+       "21198    49.216667\n",
+       "21199    23.000000\n",
+       "21200    35.466667\n",
+       "Name: Time from Pickup to Arrival, Length: 21201, dtype: float64"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-142-4ae327d448f7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time (minutes)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4403\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4404\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4405\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4406\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4407\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
+   "source": [
+    "(df_train['Time from Pickup to Arrival']/60).hist(bins=50)\n",
+    "plt.title(\"Delivery time distribution\")\n",
+    "plt.xlabel(\"Delivery time (minutes)\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Neither the `x` nor `y` variable appears to be numeric.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-14-3ea73a4708e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Outliers Overview for numeric features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2239\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2240\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2241\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2243\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    441\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[0;32m    442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 443\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    444\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[0morient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_orient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Option 2a:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(self, x, y, orient)\u001b[0m\n\u001b[0;32m    360\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    361\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mno_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    363\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Neither the `x` nor `y` variable appears to be numeric."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1440x576 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(df_test))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.distplot(df[l[i]],kde=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "AyhQaTwP9RzG"
+   },
+   "source": [
+    "## Splitting the dataset into the Training set and Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "bxOOauiN9VpC"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZijQwFMQ9itx"
+   },
+   "source": [
+    "## Training the Simple Linear Regression model on the Training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 882,
+     "status": "ok",
+     "timestamp": 1586352495803,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "B4Aj_8YJ9l7J",
+    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wa2T1Lq89o5H"
+   },
+   "source": [
+    "## Predicting the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Rn_sTJ2o9smm"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "-zSoMZ-P9v8t"
+   },
+   "source": [
+    "## Visualising the Training set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 763,
+     "status": "ok",
+     "timestamp": 1586352502760,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "IAePn_u-93tI",
+    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "EUX1Vhsv97ZT"
+   },
+   "source": [
+    "## Visualising the Test set results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1097,
+     "status": "ok",
+     "timestamp": 1586352506966,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "Ze9vpBTf-Bol",
+    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
+   "name": "Simple Linear Regression",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 96e3c290741a9828c0afce7e28f74602ea278617 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Fri, 16 Oct 2020 11:35:31 +0200
Subject: [PATCH 15/46] Rollback to default

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index 9802e85..b4d0390 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://54.170.181.212:5000/api_v0.1'
+url = 'http://127.0.0.1:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 33a8ed498444d0964fe145f5eddb4acba608eb0e Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Fri, 16 Oct 2020 11:43:24 +0200
Subject: [PATCH 16/46] Fixing traceback error

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index b4d0390..5ea59fc 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -51,6 +51,6 @@
 # Display the prediction result
 print("Received POST response:")
 print("*"*50)
-print(f"API prediction result: {api_response.json()[0]}")
+print(f"API prediction result: {api_response.json()}")
 print(f"The response took: {api_response.elapsed.total_seconds()} seconds")
 print("*"*50)

From a2fcbce15dbc9c3fdaa3692f2c18a6b315293deb Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Fri, 16 Oct 2020 12:36:34 +0200
Subject: [PATCH 17/46] using 3.250.220.203 IP address

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index 5ea59fc..331609c 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://127.0.0.1:5000/api_v0.1'
+url = 'http://3.250.220.203:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 1f109aa17e33d30c62c6aca1acbf926ce31d2729 Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Sat, 17 Oct 2020 15:07:39 +0200
Subject: [PATCH 18/46] testing url for remote ip address

---
 utils/request.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index 331609c..cd9c849 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,7 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://3.250.220.203:5000/api_v0.1'
+url = 'http://34.240.251.83:5000/api_v0.1'
+#url = 'http://127.0.0.1:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From b47c37d17452559f94b0011a56f69f41f54dbd71 Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Sat, 17 Oct 2020 15:15:22 +0200
Subject: [PATCH 19/46] testing with [0] at line 55

---
 utils/request.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/request.py b/utils/request.py
index cd9c849..55da296 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -52,6 +52,6 @@
 # Display the prediction result
 print("Received POST response:")
 print("*"*50)
-print(f"API prediction result: {api_response.json()}")
+print(f"API prediction result: {api_response.json()[0]}")
 print(f"The response took: {api_response.elapsed.total_seconds()} seconds")
 print("*"*50)

From e3c71e3852e423f80821687cb350087131d328b7 Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Sat, 17 Oct 2020 15:21:50 +0200
Subject: [PATCH 20/46] commenting out line 66

---
 model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/model.py b/model.py
index 90bd359..e584773 100644
--- a/model.py
+++ b/model.py
@@ -63,7 +63,7 @@ def _preprocess_data(data):
     #                                    'Destination Lat','Destination Long']]
     
     #Drop data not available in test, Pickup Time + label = Arrival times
-    feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
+    #feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
 
     #Renaming columns (shorten, remove space, standardize)
     new_names = {"Order No": "Order_No", "User Id": "User_Id", "Vehicle Type": "Vehicle_Type",

From b41140e3c38b0c93f2dda847aa85fb7c7a8f7413 Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Sat, 17 Oct 2020 15:26:20 +0200
Subject: [PATCH 21/46] code line 66

---
 model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/model.py b/model.py
index e584773..90bd359 100644
--- a/model.py
+++ b/model.py
@@ -63,7 +63,7 @@ def _preprocess_data(data):
     #                                    'Destination Lat','Destination Long']]
     
     #Drop data not available in test, Pickup Time + label = Arrival times
-    #feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
+    feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
 
     #Renaming columns (shorten, remove space, standardize)
     new_names = {"Order No": "Order_No", "User Id": "User_Id", "Vehicle Type": "Vehicle_Type",

From ba1f8404f1446686dfe7ae4fab8a86c1360c98fc Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Sun, 18 Oct 2020 12:42:57 +0200
Subject: [PATCH 22/46] comment out line 41 to test line 42 locally , remove
 [0] line 55

---
 utils/request.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 55da296..2e697db 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://34.240.251.83:5000/api_v0.1'
-#url = 'http://127.0.0.1:5000/api_v0.1'
+#url = 'http://34.240.251.83:5000/api_v0.1'
+url = 'http://127.0.0.1:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")
@@ -52,6 +52,6 @@
 # Display the prediction result
 print("Received POST response:")
 print("*"*50)
-print(f"API prediction result: {api_response.json()[0]}")
+print(f"API prediction result: {api_response.json()}")
 print(f"The response took: {api_response.elapsed.total_seconds()} seconds")
 print("*"*50)

From 6c82ed5bf7d8668fa1558f01d469a1f3283df68d Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 14:14:15 +0200
Subject: [PATCH 23/46] Update Sendy_Team4-Copy2.ipynb

Intro and table of contents
---
 Sendy_Team4-Copy2.ipynb | 58 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 54 insertions(+), 4 deletions(-)

diff --git a/Sendy_Team4-Copy2.ipynb b/Sendy_Team4-Copy2.ipynb
index 8c3b4fd..bfea1a0 100644
--- a/Sendy_Team4-Copy2.ipynb
+++ b/Sendy_Team4-Copy2.ipynb
@@ -1,10 +1,44 @@
 {
  "cells": [
+  {
+   "source": [
+    "# EDSA 2021: Sendy Logistics Challenge\n",
+    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Overview\n",
+    "\n",
+    "### - Importing libraries and data\n",
+    "### - Data Exploration Analysis\n",
+    "### - Data Cleaning and Formatting\n",
+    "### - Feature Engineering\n",
+    "### - Data Preprocessing for Model\n",
+    "### - Basic Model Building\n",
+    "### - Model Tuning\n",
+    "### - Ensemble Model Building\n",
+    "### - Results"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1) Import Libraries"
+    "## Importing Python libraries"
    ]
   },
   {
@@ -115,9 +149,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "#Linear algebra\n",
     "import numpy as np\n",
+    "\n",
+    "#Data processing\n",
+    "import pandas as pd\n",
+    "\n",
+    "#Date library\n",
     "import datetime as dt\n",
+    "\n",
+    "#Data visualization\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import seaborn as sns\n",
@@ -125,11 +166,13 @@
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
     "\n",
+    "#Metrics\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
     "from sklearn.model_selection import cross_val_score, learning_curve\n",
     "from sklearn.metrics import mean_squared_error\n",
     "\n",
+    "#Algorithms\n",
     "from sklearn.svm import SVR\n",
     "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
     "from sklearn.linear_model import LinearRegression\n",
@@ -144,7 +187,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2) Import Data"
+    "## Importing the dataset"
    ]
   },
   {
@@ -398,6 +441,13 @@
     "train_df.head()"
    ]
   },
+  {
+   "source": [
+    "# Exploritory Data Analysis"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2938,4 +2988,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file

From 3a8ffffcea474a36acb3c099b97491610e0c4084 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 14:16:55 +0200
Subject: [PATCH 24/46] Name change

---
 Sendy_Team4-Copy2.ipynb => Sendy_Team4_Final.ipynb | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Sendy_Team4-Copy2.ipynb => Sendy_Team4_Final.ipynb (100%)

diff --git a/Sendy_Team4-Copy2.ipynb b/Sendy_Team4_Final.ipynb
similarity index 100%
rename from Sendy_Team4-Copy2.ipynb
rename to Sendy_Team4_Final.ipynb

From b20ac63740aa05fcf7559bc4b4a008143e830778 Mon Sep 17 00:00:00 2001
From: Manko Mofokeng <60190943+MankoMofokeng@users.noreply.github.com>
Date: Sun, 18 Oct 2020 15:01:48 +0200
Subject: [PATCH 25/46] Delete team4_regression.ipynb

---
 team4_regression.ipynb | 1891 ----------------------------------------
 1 file changed, 1891 deletions(-)
 delete mode 100644 team4_regression.ipynb

diff --git a/team4_regression.ipynb b/team4_regression.ipynb
deleted file mode 100644
index dde81f2..0000000
--- a/team4_regression.ipynb
+++ /dev/null
@@ -1,1891 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "l_LulNCC8z96"
-   },
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xpXdowrE9DxW"
-   },
-   "source": [
-    "## Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nhKd4hWx9GFt"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np #Why?\n",
-    "import pandas as pd #Why?\n",
-    "import matplotlib.pyplot as plt #Why?\n",
-    "from matplotlib import rc #Why?\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6JhpWJi59J1p"
-   },
-   "source": [
-    "## Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "f8GfYDZ59O18"
-   },
-   "outputs": [],
-   "source": [
-    "train = pd.read_csv('Train.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21201 entries, 0 to 21200\n",
-      "Data columns (total 29 columns):\n",
-      "Order No                                     21201 non-null object\n",
-      "User Id                                      21201 non-null object\n",
-      "Vehicle Type                                 21201 non-null object\n",
-      "Platform Type                                21201 non-null int64\n",
-      "Personal or Business                         21201 non-null object\n",
-      "Placement - Day of Month                     21201 non-null int64\n",
-      "Placement - Weekday (Mo = 1)                 21201 non-null int64\n",
-      "Placement - Time                             21201 non-null object\n",
-      "Confirmation - Day of Month                  21201 non-null int64\n",
-      "Confirmation - Weekday (Mo = 1)              21201 non-null int64\n",
-      "Confirmation - Time                          21201 non-null object\n",
-      "Arrival at Pickup - Day of Month             21201 non-null int64\n",
-      "Arrival at Pickup - Weekday (Mo = 1)         21201 non-null int64\n",
-      "Arrival at Pickup - Time                     21201 non-null object\n",
-      "Pickup - Day of Month                        21201 non-null int64\n",
-      "Pickup - Weekday (Mo = 1)                    21201 non-null int64\n",
-      "Pickup - Time                                21201 non-null object\n",
-      "Arrival at Destination - Day of Month        21201 non-null int64\n",
-      "Arrival at Destination - Weekday (Mo = 1)    21201 non-null int64\n",
-      "Arrival at Destination - Time                21201 non-null object\n",
-      "Distance (KM)                                21201 non-null int64\n",
-      "Temperature                                  16835 non-null float64\n",
-      "Precipitation in millimeters                 552 non-null float64\n",
-      "Pickup Lat                                   21201 non-null float64\n",
-      "Pickup Long                                  21201 non-null float64\n",
-      "Destination Lat                              21201 non-null float64\n",
-      "Destination Long                             21201 non-null float64\n",
-      "Rider Id                                     21201 non-null object\n",
-      "Time from Pickup to Arrival                  21201 non-null int64\n",
-      "dtypes: float64(6), int64(13), object(10)\n",
-      "memory usage: 4.7+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "train.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = pd.read_csv('Test.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_19248</td>\n",
-       "      <td>User_Id_3355</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>27</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4:44:10 PM</td>\n",
-       "      <td>27</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5:06:47 PM</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.333275</td>\n",
-       "      <td>36.870815</td>\n",
-       "      <td>-1.305249</td>\n",
-       "      <td>36.822390</td>\n",
-       "      <td>Rider_Id_192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_12736</td>\n",
-       "      <td>User_Id_3647</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>17</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12:57:35 PM</td>\n",
-       "      <td>17</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1:25:37 PM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.272639</td>\n",
-       "      <td>36.794723</td>\n",
-       "      <td>-1.277007</td>\n",
-       "      <td>36.823907</td>\n",
-       "      <td>Rider_Id_868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Order_No_768</td>\n",
-       "      <td>User_Id_2154</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>27</td>\n",
-       "      <td>4</td>\n",
-       "      <td>11:08:14 AM</td>\n",
-       "      <td>27</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>11:57:54 AM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>22.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.290894</td>\n",
-       "      <td>36.822971</td>\n",
-       "      <td>-1.276574</td>\n",
-       "      <td>36.851365</td>\n",
-       "      <td>Rider_Id_26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Order_No_15332</td>\n",
-       "      <td>User_Id_2910</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>17</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1:51:35 PM</td>\n",
-       "      <td>17</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2:16:52 PM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.290503</td>\n",
-       "      <td>36.809646</td>\n",
-       "      <td>-1.303382</td>\n",
-       "      <td>36.790658</td>\n",
-       "      <td>Rider_Id_685</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Order_No_21373</td>\n",
-       "      <td>User_Id_1205</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>11:30:28 AM</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>11:56:04 AM</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281081</td>\n",
-       "      <td>36.814423</td>\n",
-       "      <td>-1.266467</td>\n",
-       "      <td>36.792161</td>\n",
-       "      <td>Rider_Id_858</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
-       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
-       "2    Order_No_768  User_Id_2154         Bike              3   \n",
-       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
-       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                        27   \n",
-       "1             Business                        17   \n",
-       "2             Business                        27   \n",
-       "3             Business                        17   \n",
-       "4             Business                        11   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             3       4:44:10 PM                           27   \n",
-       "1                             5      12:57:35 PM                           17   \n",
-       "2                             4      11:08:14 AM                           27   \n",
-       "3                             1       1:51:35 PM                           17   \n",
-       "4                             2      11:30:28 AM                           11   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                3  ...                         3   \n",
-       "1                                5  ...                         5   \n",
-       "2                                4  ...                         4   \n",
-       "3                                1  ...                         1   \n",
-       "4                                2  ...                         2   \n",
-       "\n",
-       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
-       "0     5:06:47 PM              8         NaN                           NaN   \n",
-       "1     1:25:37 PM              5         NaN                           NaN   \n",
-       "2    11:57:54 AM              5        22.8                           NaN   \n",
-       "3     2:16:52 PM              5        24.5                           NaN   \n",
-       "4    11:56:04 AM              6        24.4                           NaN   \n",
-       "\n",
-       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
-       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
-       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
-       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
-       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
-       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 7068 entries, 0 to 7067\n",
-      "Data columns (total 25 columns):\n",
-      "Order No                                7068 non-null object\n",
-      "User Id                                 7068 non-null object\n",
-      "Vehicle Type                            7068 non-null object\n",
-      "Platform Type                           7068 non-null int64\n",
-      "Personal or Business                    7068 non-null object\n",
-      "Placement - Day of Month                7068 non-null int64\n",
-      "Placement - Weekday (Mo = 1)            7068 non-null int64\n",
-      "Placement - Time                        7068 non-null object\n",
-      "Confirmation - Day of Month             7068 non-null int64\n",
-      "Confirmation - Weekday (Mo = 1)         7068 non-null int64\n",
-      "Confirmation - Time                     7068 non-null object\n",
-      "Arrival at Pickup - Day of Month        7068 non-null int64\n",
-      "Arrival at Pickup - Weekday (Mo = 1)    7068 non-null int64\n",
-      "Arrival at Pickup - Time                7068 non-null object\n",
-      "Pickup - Day of Month                   7068 non-null int64\n",
-      "Pickup - Weekday (Mo = 1)               7068 non-null int64\n",
-      "Pickup - Time                           7068 non-null object\n",
-      "Distance (KM)                           7068 non-null int64\n",
-      "Temperature                             5631 non-null float64\n",
-      "Precipitation in millimeters            199 non-null float64\n",
-      "Pickup Lat                              7068 non-null float64\n",
-      "Pickup Long                             7068 non-null float64\n",
-      "Destination Lat                         7068 non-null float64\n",
-      "Destination Long                        7068 non-null float64\n",
-      "Rider Id                                7068 non-null object\n",
-      "dtypes: float64(6), int64(10), object(9)\n",
-      "memory usage: 1.3+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "test.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Fill null values with 0, axis=1 means you are doing it per column, axis=0 means you are doing it per row\n",
-    "\n",
-    "ftrain = train.fillna(0, axis=1)\n",
-    "ftest = test.fillna(0, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#drop most of the object dtype columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exploritory Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#The columns in test and train do not match 100% lets fix that\n",
-    "testcols = ftest.columns  #getting a list of the columns in test set\n",
-    "newtrain = ftrain[testcols]  #redefining the training set to only include those columns\n",
-    "y = np.array(ftrain['Time from Pickup to Arrival']).reshape(-1, 1) #assigning the target variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-      "       'Personal or Business', 'Placement - Day of Month',\n",
-      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-      "       'Rider Id'],\n",
-      "      dtype='object')\n",
-      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-      "       'Personal or Business', 'Placement - Day of Month',\n",
-      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-      "       'Rider Id'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(newtrain.columns)\n",
-    "print(ftest.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10:27:30 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:44:09 AM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:53:03 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:43:06 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10:05:23 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                5  ...                         5   \n",
-       "1                                5  ...                         5   \n",
-       "2                                2  ...                         2   \n",
-       "3                                5  ...                         5   \n",
-       "4                                1  ...                         1   \n",
-       "\n",
-       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
-       "0    10:27:30 AM              4        20.4                           0.0   \n",
-       "1    11:44:09 AM             16        26.4                           0.0   \n",
-       "2    12:53:03 PM              3         0.0                           0.0   \n",
-       "3     9:43:06 AM              9        19.2                           0.0   \n",
-       "4    10:05:23 AM              9        15.4                           0.0   \n",
-       "\n",
-       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
-       "0   -1.317755   36.830370        -1.300406         36.829741  Rider_Id_432  \n",
-       "1   -1.351453   36.899315        -1.295004         36.814358  Rider_Id_856  \n",
-       "2   -1.308284   36.843419        -1.300921         36.828195  Rider_Id_155  \n",
-       "3   -1.281301   36.832396        -1.257147         36.795063  Rider_Id_855  \n",
-       "4   -1.266597   36.792118        -1.295041         36.809817  Rider_Id_770  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Combining the datasets \n",
-    "df = pd.concat([newtrain, ftest])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x27844e4fa48>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#machine learning model cannot read an object type, convert objects , encode (dummy them)\n",
-    "correlation = df.corr()\n",
-    "sns.heatmap(correlation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#drop all co-linear variables as well time variables as they will cause us to run into memory problems\n",
-    "#df = df.drop(['Placement - Weekday (Mo = 1)', 'Confirmation - Day of Month', 'Confirmation - Weekday', 'Arrival at Pick up Day of Month', 'Arrival at pickup weekday', 'pick up day of month', 'pickup weekday', 'placement time', 'confirmation time', 'arrival at pickup time', 'pickup time'], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df_final = df.drop(['order no(Too variant, not adding value to model)', 'user id(Too variant, not adding value to model)', 'vehicle type(only a single value, pointless)', 'rider id()'], axis=1) #dropping IDs\n",
-    "#df_final.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Rider Id_Rider_Id_954</th>\n",
-       "      <th>Rider Id_Rider_Id_955</th>\n",
-       "      <th>Rider Id_Rider_Id_956</th>\n",
-       "      <th>Rider Id_Rider_Id_957</th>\n",
-       "      <th>Rider Id_Rider_Id_958</th>\n",
-       "      <th>Rider Id_Rider_Id_959</th>\n",
-       "      <th>Rider Id_Rider_Id_96</th>\n",
-       "      <th>Rider Id_Rider_Id_97</th>\n",
-       "      <th>Rider Id_Rider_Id_98</th>\n",
-       "      <th>Rider Id_Rider_Id_99</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>16</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 109614 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "0              3                         9                             5   \n",
-       "1              3                        12                             5   \n",
-       "2              3                        30                             2   \n",
-       "3              3                        15                             5   \n",
-       "4              1                        13                             1   \n",
-       "\n",
-       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "0                            9                                5   \n",
-       "1                           12                                5   \n",
-       "2                           30                                2   \n",
-       "3                           15                                5   \n",
-       "4                           13                                1   \n",
-       "\n",
-       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                 9                                     5   \n",
-       "1                                12                                     5   \n",
-       "2                                30                                     2   \n",
-       "3                                15                                     5   \n",
-       "4                                13                                     1   \n",
-       "\n",
-       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  ...  \\\n",
-       "0                      9                          5              4  ...   \n",
-       "1                     12                          5             16  ...   \n",
-       "2                     30                          2              3  ...   \n",
-       "3                     15                          5              9  ...   \n",
-       "4                     13                          1              9  ...   \n",
-       "\n",
-       "   Rider Id_Rider_Id_954  Rider Id_Rider_Id_955  Rider Id_Rider_Id_956  \\\n",
-       "0                      0                      0                      0   \n",
-       "1                      0                      0                      0   \n",
-       "2                      0                      0                      0   \n",
-       "3                      0                      0                      0   \n",
-       "4                      0                      0                      0   \n",
-       "\n",
-       "   Rider Id_Rider_Id_957  Rider Id_Rider_Id_958  Rider Id_Rider_Id_959  \\\n",
-       "0                      0                      0                      0   \n",
-       "1                      0                      0                      0   \n",
-       "2                      0                      0                      0   \n",
-       "3                      0                      0                      0   \n",
-       "4                      0                      0                      0   \n",
-       "\n",
-       "   Rider Id_Rider_Id_96  Rider Id_Rider_Id_97  Rider Id_Rider_Id_98  \\\n",
-       "0                     0                     0                     0   \n",
-       "1                     0                     0                     0   \n",
-       "2                     0                     0                     0   \n",
-       "3                     0                     0                     0   \n",
-       "4                     0                     0                     0   \n",
-       "\n",
-       "   Rider Id_Rider_Id_99  \n",
-       "0                     0  \n",
-       "1                     0  \n",
-       "2                     0  \n",
-       "3                     0  \n",
-       "4                     0  \n",
-       "\n",
-       "[5 rows x 109614 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Dummying out the last categorical column\n",
-    "#df_final = pd.get_dummies(df)\n",
-    "#df_final.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "21201",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 21201",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-55-4746259f765f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mftrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfinaltrain\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mfinaltest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 21201"
-     ]
-    }
-   ],
-   "source": [
-    "#splitting our dataset again\n",
-    "y = np.array(ftrain['Time from Pickup to Arrival']).reshape(-1, 1)\n",
-    "finaltrain = df_final[:len(train)]\n",
-    "finaltest = df_final[len(train)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Train test split\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "x_train, x_val, y_train, y_val = train_test_split(finaltrain, y, test_size=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#define our model, training and making predictions on the validation set\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "lr = LinearRegression()\n",
-    "lr.fit(x_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_preds = lr.predict(x_val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Checking the RMSE of our validation predictions\n",
-    "#def rmse(y_test, y_predict):\n",
-    "    #return np.sqrt(mean_squared_error(y_test, y_predict))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#rmse(y_val, val_preds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#making predictions on the test set and sending the result to csv for submission\n",
-    "#y_pred = lr.predict(finaltest) #retraining model - no need to do this"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df = ftest[['Order_No']] - renaming the dataset, use identifyer\n",
-    "submission_df['Time from Pickup to arrival'] = y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(submission_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#send df to csv\n",
-    "submission_df.to_csv('base prediction', index=false)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##Create a few plots for the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Advanced plotting? plot both dependent data series on the same graph, assign two separate y-axxes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "AyhQaTwP9RzG"
-   },
-   "source": [
-    "## Splitting the dataset into the Training set and Test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "bxOOauiN9VpC"
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'x' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-1-7e414ed48a8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ZijQwFMQ9itx"
-   },
-   "source": [
-    "## Training the Simple Linear Regression model on the Training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Find the best fit line = y=a + bx, estimate a and b\n",
-    "#define a function to generate values of y from a list of x given a and b\n",
-    "# calculate the error of each generated value of y e = y - f(x) errors = np.array(df.Y - y_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 882,
-     "status": "ok",
-     "timestamp": 1586352495803,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "B4Aj_8YJ9l7J",
-    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "regressor = LinearRegression()\n",
-    "regressor.fit(x_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wa2T1Lq89o5H"
-   },
-   "source": [
-    "## Predicting the Test set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Rn_sTJ2o9smm"
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = regressor.predict(x_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-zSoMZ-P9v8t"
-   },
-   "source": [
-    "## Visualising the Training set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 295
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 763,
-     "status": "ok",
-     "timestamp": 1586352502760,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "IAePn_u-93tI",
-    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
-   },
-   "outputs": [],
-   "source": [
-    "plt.scatter(x_train, y_train, color = 'red')\n",
-    "plt.plot(x_train, regressor.predict(x_train), color = 'blue')\n",
-    "plt.title('Salary vs Experience (Training set)')\n",
-    "plt.xlabel('Years of Experience')\n",
-    "plt.ylabel('Salary')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "EUX1Vhsv97ZT"
-   },
-   "source": [
-    "## Visualising the Test set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 295
-    },
-    "colab_type": "code",
-    "executionInfo": {
-     "elapsed": 1097,
-     "status": "ok",
-     "timestamp": 1586352506966,
-     "user": {
-      "displayName": "Hadelin de Ponteves",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
-      "userId": "15047218817161520419"
-     },
-     "user_tz": -240
-    },
-    "id": "Ze9vpBTf-Bol",
-    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
-   },
-   "outputs": [],
-   "source": [
-    "plt.scatter(x_test, y_test, color = 'red')\n",
-    "plt.plot(y_train, regressor.predict(x_train), color = 'blue')\n",
-    "plt.title('Salary vs Experience (Test set)')\n",
-    "plt.xlabel('Years of Experience')\n",
-    "plt.ylabel('Salary')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
-   "name": "Simple Linear Regression",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

From b69f3c734ca1d57735f98cecec6f0ae545424515 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 15:11:22 +0200
Subject: [PATCH 26/46] update EDA

---
 Sendy_Team4_Final.ipynb | 298 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 292 insertions(+), 6 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index bfea1a0..a4df757 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -22,7 +22,7 @@
     "# Overview\n",
     "\n",
     "### - Importing libraries and data\n",
-    "### - Data Exploration Analysis\n",
+    "### - Exploratory Data Analysis\n",
     "### - Data Cleaning and Formatting\n",
     "### - Feature Engineering\n",
     "### - Data Preprocessing for Model\n",
@@ -436,9 +436,34 @@
     "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
     "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
     "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "df_variable = pd.read_csv('VariableDefinitions.csv')\n",
     "\n",
-    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
-    "train_df.head()"
+    "print(train_df.shape, test_df.shape, riders_df.shape)"
+   ]
+  },
+  {
+   "source": [
+    "### Variable Definitions"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_variable.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_variable.head()"
    ]
   },
   {
@@ -449,10 +474,173 @@
    "metadata": {}
   },
   {
+   "source": [
+    "## Understanding the train and test datasets"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### Train vs Test Dataset"
+   ],
    "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### 3) Data Manipulation"
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting"
+   ]
+  },
+  {
+   "source": [
+    "### Separate the data by datatypes"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separating by data type\n",
+    "numeric_columns = []\n",
+    "object_columns = []\n",
+    "time_columns = []\n",
+    "for k, v in df_train.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_columns.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_columns.append(k)\n",
+    "    else:\n",
+    "        object_columns.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train[time_columns].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train[object_columns].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train[numeric_columns].head()"
+   ]
+  },
+  {
+   "source": [
+    "### Check which cols are in train and not in test data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in train.columns:\n",
+    "    if col not in test.columns:\n",
+    "        print(col)"
+   ]
+  },
+  {
+   "source": [
+    "### Match the number of train columns to the number of test columns and also separate the target variable"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the columns from the test dataframe into a new list called testcols\n",
+    "testcols = test1.columns\n",
+    "\n",
+    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
+    "newtrain = train1[testcols]\n",
+    "\n",
+    "# Seperate the target variable from the predictor variable\n",
+    "y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)"
    ]
   },
   {
@@ -463,14 +651,112 @@
    "source": [
     "#Drop data not available in test, Pickup Time + label = Arrival times\n",
     "\n",
-    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the new columns of the train and test data to show that they are the same\n",
+    "print(newtrain.columns)\n",
+    "print(test1.columns)"
+   ]
+  },
+  {
+   "source": [
+    "## Missing values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(df_train.isnull(), cmap=cmap)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Percentage of the missing values\n",
+    "def missing_values(df, threshold=0):\n",
+    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
+    "\n",
+    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
+    "             index=['% of Missing values']\n",
+    "             ).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_values(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_values(test)"
+   ]
+  },
+  {
+   "source": [
+    "### Filling missing Values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Creating Full_df"
+    "## Creating Full_df"
    ]
   },
   {

From 72893087ad232a9bb8dbba3ed93b6e47551a66fb Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 15:25:12 +0200
Subject: [PATCH 27/46] Update Sendy_Team4_Final.ipynb

---
 Sendy_Team4_Final.ipynb | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index a4df757..ac0915e 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -735,6 +735,13 @@
     "missing_values(test)"
    ]
   },
+  {
+   "source": [
+    "##Distribution"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "source": [
     "### Filling missing Values"
@@ -2156,6 +2163,13 @@
     "full_df.head()"
    ]
   },
+  {
+   "source": [
+    "# Feature Engineering"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2163,6 +2177,13 @@
     "#### Feature Selection"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 144,

From 03977d2cc75155a70388bb511576d529de3102e3 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 15:56:10 +0200
Subject: [PATCH 28/46] Complete EDA

---
 Sendy_Team4_Final.ipynb | 244 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 240 insertions(+), 4 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index ac0915e..6e5a048 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -161,6 +161,7 @@
     "#Data visualization\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
+    "import matplotlib.style as style\n",
     "import seaborn as sns\n",
     "\n",
     "import warnings\n",
@@ -737,14 +738,74 @@
   },
   {
    "source": [
-    "##Distribution"
+    "## Distribution"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "### Filling missing Values"
+    "#Distribution review for train data\n",
+    "df_train[[column for column in df_train.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Column Skewness of the testing dataset\n",
+    "df_train.skew().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Univariate Anaysis\n",
+    "# Calculating Skewness in the columns of our Training dataset\n",
+    "df_train.kurt().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CorrMatrix_train = df_train.corr()\n",
+    "CorrMatrix_train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "style.use('ggplot')\n",
+    "cm = CorrMatrix_train.corr()\n",
+    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
+    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
+    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
+   ]
+  },
+  {
+   "source": [
+    "## Personal or Business"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -755,8 +816,166 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+    "df_train['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "source": [
+    "## Platform type"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "source": [
+    "## Vehicle Type"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(df_train['Temperature'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numeric_Training = df_train._get_numeric_data()\n",
+    "Numeric_Training.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = df_train.values\n",
+    "number_of_columns= 12\n",
+    "number_of_rows = len(l)-1/number_of_columns\n",
+    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.set_style('whitegrid')\n",
+    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
+    "for i in range(0,len(nu_tra)):\n",
+    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
+    "    chart=sns.distplot(df_train[nu_tra[i]],kde=True) \n",
+    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+    "                          horizontalalignment='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "# Target"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Useful statistics about our target column\n",
+    "\n",
+    "df_train['Time from Pickup to Arrival'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(df_train['Time from Pickup to Arrival']/60).hist(bins=50)\n",
+    "plt.title(\"Delivery time distribution\")\n",
+    "plt.xlabel(\"Delivery time (minutes)\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(df_test))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.distplot(df[l[i]],kde=True)"
    ]
   },
   {
@@ -794,6 +1013,23 @@
     "train_df.shape, test_df.shape, full_df.shape"
    ]
   },
+  {
+   "source": [
+    "### Filling missing Values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From ab5244ee65997d869c3a9cf5ee3456f42a0ed9e4 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 16:15:03 +0200
Subject: [PATCH 29/46] Data cleaning and formatting

---
 Sendy_Team4_Final.ipynb | 55 ++++++++++++++---------------------------
 1 file changed, 19 insertions(+), 36 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 6e5a048..4cc0521 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -542,13 +542,6 @@
     "df_test.describe()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting"
-   ]
-  },
   {
    "source": [
     "### Separate the data by datatypes"
@@ -805,7 +798,7 @@
   },
   {
    "source": [
-    "## Personal or Business"
+    "### Personal or Business"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -821,7 +814,7 @@
   },
   {
    "source": [
-    "## Platform type"
+    "### Platform type"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -837,7 +830,7 @@
   },
   {
    "source": [
-    "## Vehicle Type"
+    "### Vehicle Type"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -916,7 +909,7 @@
   },
   {
    "source": [
-    "# Target"
+    "### Target"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -978,6 +971,13 @@
     "    sns.distplot(df[l[i]],kde=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1013,28 +1013,11 @@
     "train_df.shape, test_df.shape, full_df.shape"
    ]
   },
-  {
-   "source": [
-    "### Filling missing Values"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Renaming columns"
+    "### Renaming columns"
    ]
   },
   {
@@ -1083,7 +1066,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Convert Time"
+    "### Convert Time"
    ]
   },
   {
@@ -1174,7 +1157,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Filling Missing Values"
+    "### Filling Missing Values"
    ]
   },
   {
@@ -1409,7 +1392,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Traversing Month and Weekday"
+    "### Traversing Month and Weekday"
    ]
   },
   {
@@ -1495,7 +1478,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Creating Month and Weekday columns"
+    "### Creating Month and Weekday columns"
    ]
   },
   {
@@ -1512,7 +1495,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Dropping redundant columns"
+    "### Dropping redundant columns"
    ]
   },
   {
@@ -1899,7 +1882,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Variable Datatypes"
+    "### Variable Datatypes"
    ]
   },
   {
@@ -2168,7 +2151,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Convert an object to numeric"
+    "### Convert object types to numeric"
    ]
   },
   {

From 390c40e6701ac495c0e672c8f53e7e6bc7f387b3 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 16:23:42 +0200
Subject: [PATCH 30/46] Feature Engineering

---
 Sendy_Team4_Final.ipynb | 32 +++++++++++++-------------------
 1 file changed, 13 insertions(+), 19 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 4cc0521..63687b5 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -2393,16 +2393,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Feature Selection"
+    "### Feature Selection"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 144,
@@ -3111,7 +3104,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### train_test_split"
+    "### train_test_split"
    ]
   },
   {
@@ -3136,14 +3129,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Modeling"
+    "## Modeling"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Cross validation"
+    "### Cross validation"
    ]
   },
   {
@@ -3203,7 +3196,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Random Forest"
+    "## Random Forest"
    ]
   },
   {
@@ -3227,7 +3220,8 @@
     "rfm = rsearch.fit(X_train, y_train)\n",
     "\n",
     "r_score = np.sqrt(abs(rfm.best_score_))\n",
-    "r_params = rfm.best_params_\n",
+    "r_params = rfm.best_p\n",
+    "arams_\n",
     "print(r_score, r_params)"
    ]
   },
@@ -3235,7 +3229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Submission"
+    "# Submission"
    ]
   },
   {
@@ -3286,21 +3280,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
+    "#submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### lightgbm"
+    "## Lightgbm"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Parameter Tuning"
+    "### Parameter Tuning"
    ]
   },
   {
@@ -3349,7 +3343,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Training and making a prediction"
+    "### Training and making a prediction"
    ]
   },
   {
@@ -3470,7 +3464,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Submission"
+    "# Submission"
    ]
   },
   {

From 44293717d8f3e57eb55f7bb4b1af14fd702a40e8 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 17:15:41 +0200
Subject: [PATCH 31/46] complete notebook

---
 Sendy_Team4_Final.ipynb | 222 +++++++++++++---------------------------
 1 file changed, 72 insertions(+), 150 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 63687b5..3dec775 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -10,26 +10,26 @@
   },
   {
    "source": [
-    "# Introduction\n",
+    "# Overview\n",
     "\n",
-    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
+    "### - Introduction\n",
+    "### - Importing libraries and data\n",
+    "### - Exploratory Data Analysis\n",
+    "### - Data Cleaning and Formatting\n",
+    "### - Feature Engineering\n",
+    "### - Train/Test Split\n",
+    "### - Modeling\n",
+    "### - Making Predictions\n",
+    "### - Submission"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
    "source": [
-    "# Overview\n",
+    "# Introduction\n",
     "\n",
-    "### - Importing libraries and data\n",
-    "### - Exploratory Data Analysis\n",
-    "### - Data Cleaning and Formatting\n",
-    "### - Feature Engineering\n",
-    "### - Data Preprocessing for Model\n",
-    "### - Basic Model Building\n",
-    "### - Model Tuning\n",
-    "### - Ensemble Model Building\n",
-    "### - Results"
+    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -38,7 +38,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Importing Python libraries"
+    "# Importing Python libraries"
    ]
   },
   {
@@ -188,7 +188,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Importing the dataset"
+    "# Importing the dataset"
    ]
   },
   {
@@ -436,35 +436,10 @@
     "\n",
     "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
     "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
-    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
-    "df_variable = pd.read_csv('VariableDefinitions.csv')\n",
+    "rider_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
     "\n",
-    "print(train_df.shape, test_df.shape, riders_df.shape)"
-   ]
-  },
-  {
-   "source": [
-    "### Variable Definitions"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_variable.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_variable.head()"
+    "print(train.shape, test.shape, rider_df.shape, variable_df.shape)"
    ]
   },
   {
@@ -481,20 +456,13 @@
    "cell_type": "markdown",
    "metadata": {}
   },
-  {
-   "source": [
-    "### Train vs Test Dataset"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train.head()"
+    "train_df.head()"
    ]
   },
   {
@@ -503,7 +471,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_test.head()"
+    "test_df.head()"
    ]
   },
   {
@@ -512,7 +480,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train.info()"
+    "train_df.info()"
    ]
   },
   {
@@ -521,7 +489,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_test.info()"
+    "test_df.info()"
    ]
   },
   {
@@ -530,7 +498,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train.describe()"
+    "train_df.describe()"
    ]
   },
   {
@@ -539,61 +507,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_test.describe()"
-   ]
-  },
-  {
-   "source": [
-    "### Separate the data by datatypes"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Separating by data type\n",
-    "numeric_columns = []\n",
-    "object_columns = []\n",
-    "time_columns = []\n",
-    "for k, v in df_train.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_columns.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_columns.append(k)\n",
-    "    else:\n",
-    "        object_columns.append(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_train[time_columns].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_train[object_columns].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_train[numeric_columns].head()"
+    "test_df.describe()"
    ]
   },
   {
@@ -628,13 +542,13 @@
    "outputs": [],
    "source": [
     "# save the columns from the test dataframe into a new list called testcols\n",
-    "testcols = test1.columns\n",
+    "testcols = test_df.columns\n",
     "\n",
     "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
-    "newtrain = train1[testcols]\n",
+    "train1 = train_df[testcols]\n",
     "\n",
     "# Seperate the target variable from the predictor variable\n",
-    "y = np.array(train1['Time from Pickup to Arrival']).reshape(-1, 1)"
+    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
    ]
   },
   {
@@ -655,8 +569,8 @@
    "outputs": [],
    "source": [
     "# print the new columns of the train and test data to show that they are the same\n",
-    "print(newtrain.columns)\n",
-    "print(test1.columns)"
+    "print(train1.columns)\n",
+    "print(test_df.columns)"
    ]
   },
   {
@@ -672,10 +586,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#\n",
+    "#plot cmap\n",
     "plt.figure(figsize=(20, 10))\n",
     "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
-    "sns.heatmap(df_train.isnull(), cmap=cmap)\n"
+    "sns.heatmap(train1.isnull(), cmap=cmap)\n"
    ]
   },
   {
@@ -684,7 +598,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train.isnull().sum()"
+    "train1.isnull().sum()"
    ]
   },
   {
@@ -693,7 +607,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_test.isnull().sum()"
+    "test_df.isnull().sum()"
    ]
   },
   {
@@ -717,7 +631,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "missing_values(train)"
+    "missing_values(train1)"
    ]
   },
   {
@@ -726,7 +640,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "missing_values(test)"
+    "missing_values(test_df)"
    ]
   },
   {
@@ -743,7 +657,7 @@
    "outputs": [],
    "source": [
     "#Distribution review for train data\n",
-    "df_train[[column for column in df_train.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "train1[[col for col in train1.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -755,7 +669,7 @@
    "outputs": [],
    "source": [
     "#Column Skewness of the testing dataset\n",
-    "df_train.skew().plot.bar(figsize =(10,5))\n",
+    "train1.skew().plot.bar(figsize =(10,5))\n",
     "plt.show()"
    ]
   },
@@ -767,7 +681,7 @@
    "source": [
     "## Univariate Anaysis\n",
     "# Calculating Skewness in the columns of our Training dataset\n",
-    "df_train.kurt().plot.bar(figsize =(10,5))\n",
+    "train1.kurt().plot.bar(figsize =(10,5))\n",
     "plt.show()"
    ]
   },
@@ -777,7 +691,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "CorrMatrix_train = df_train.corr()\n",
+    "CorrMatrix_train = train1.corr()\n",
     "CorrMatrix_train.head(5)"
    ]
   },
@@ -809,7 +723,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
    ]
   },
   {
@@ -825,7 +739,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
    ]
   },
   {
@@ -841,7 +755,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "train1['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
    ]
   },
   {
@@ -852,7 +766,7 @@
    "source": [
     "#Outliers Overview for numeric features\n",
     "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(df_train['Temperature'])\n",
+    "sns.boxplot(train1['Temperature'])\n",
     "plt.show()"
    ]
   },
@@ -862,7 +776,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Numeric_Training = df_train._get_numeric_data()\n",
+    "Numeric_Training = train1._get_numeric_data()\n",
     "Numeric_Training.head()"
    ]
   },
@@ -872,7 +786,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
    ]
   },
   {
@@ -881,7 +795,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "l = df_train.values\n",
+    "l = train1.values\n",
     "number_of_columns= 12\n",
     "number_of_rows = len(l)-1/number_of_columns\n",
     "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
@@ -901,7 +815,7 @@
     "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
     "for i in range(0,len(nu_tra)):\n",
     "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
-    "    chart=sns.distplot(df_train[nu_tra[i]],kde=True) \n",
+    "    chart=sns.distplot(train1[nu_tra[i]],kde=True) \n",
     "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
     "                          horizontalalignment='right')\n",
     "plt.show()"
@@ -922,7 +836,7 @@
    "source": [
     "#Useful statistics about our target column\n",
     "\n",
-    "df_train['Time from Pickup to Arrival'].describe()"
+    "train1['Time from Pickup to Arrival'].describe()"
    ]
   },
   {
@@ -931,7 +845,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train"
+    "train1"
    ]
   },
   {
@@ -940,7 +854,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(df_train['Time from Pickup to Arrival']/60).hist(bins=50)\n",
+    "(train1['Time from Pickup to Arrival']/60).hist(bins=50)\n",
     "plt.title(\"Delivery time distribution\")\n",
     "plt.xlabel(\"Delivery time (minutes)\")\n",
     "plt.ylabel(\"Frequency\")\n",
@@ -955,7 +869,7 @@
    "source": [
     "#Outliers Overview for numeric features\n",
     "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(df_test))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df))\n",
     "plt.show()"
    ]
   },
@@ -1006,11 +920,11 @@
     "#explore training, make (column) changes to full, later we use the border to separate\n",
     "#Be careful of information leakage\n",
     "\n",
-    "border = train_df.shape[0]\n",
+    "border = train1.shape[0]\n",
     "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
-    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
+    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
     "\n",
-    "train_df.shape, test_df.shape, full_df.shape"
+    "train1.shape, test_df.shape, full_df.shape"
    ]
   },
   {
@@ -1995,8 +1909,16 @@
     "    elif k.endswith(\"Time\"):\n",
     "        time_cols.append(k)\n",
     "    else:\n",
-    "        object_cols.append(k)\n",
-    "full_df[numeric_cols].head(3) "
+    "        object_cols.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df[numeric_cols].head(3)"
    ]
   },
   {
@@ -3104,7 +3026,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### train_test_split"
+    "# Train/Test split"
    ]
   },
   {
@@ -3192,6 +3114,13 @@
     "print(cv_res)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Making predictions"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3225,13 +3154,6 @@
     "print(r_score, r_params)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Submission"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3343,7 +3265,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Training and making a prediction"
+    "## Training and making a prediction"
    ]
   },
   {

From edd0cc2e608f90c26ae8c2c064dcf5e92e6d573b Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Sun, 18 Oct 2020 19:07:38 +0200
Subject: [PATCH 32/46] Update Sendy_Team4_Final.ipynb

---
 Sendy_Team4_Final.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 3dec775..4dc936c 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -3051,7 +3051,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Modeling"
+    "# Modeling"
    ]
   },
   {

From 353c29ef7bf66fa759844a8f786e864f3268a71b Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Mon, 19 Oct 2020 11:27:04 +0200
Subject: [PATCH 33/46] testing ec2 url

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 2e697db..2bf66f7 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-#url = 'http://34.240.251.83:5000/api_v0.1'
-url = 'http://127.0.0.1:5000/api_v0.1'
+url = 'http://34.240.251.83:5000/api_v0.1'
+#url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 6fbb75364870aa98283a529f38e1da707ddc6a86 Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Mon, 19 Oct 2020 11:33:43 +0200
Subject: [PATCH 34/46] default url line 42

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 2bf66f7..5048604 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://34.240.251.83:5000/api_v0.1'
-#url = 'http://0.0.0.0:5000/api_v0.1'
+#url = 'http://34.240.251.83:5000/api_v0.1'
+url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From f219339a5da9d55369adb07f440e5cbc70c0b9a9 Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Mon, 19 Oct 2020 16:35:56 +0200
Subject: [PATCH 35/46] Fixed full_df on line 100

---
 model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/model.py b/model.py
index 90bd359..1d0672c 100644
--- a/model.py
+++ b/model.py
@@ -97,8 +97,8 @@ def convert_to_24hrs(feature_vector_df):
     feature_vector_df['Precipitation(mm)'].fillna(feature_vector_df['Precipitation(mm)'].mean(), inplace=True)
     
     #Since, we have not been given the actual dates & bikes (same day) 
-    month_cols = [col for col in full_df.columns if col.endswith("Mon")]
-    weekday_cols = [col for col in full_df.columns if col.endswith("Weekday")]
+    month_cols = [col for col in feature_vector_df.columns if col.endswith("Mon")]
+    weekday_cols = [col for col in feature_vector_df.columns if col.endswith("Weekday")]
 
     count = 0
     instances_of_different_days = [];

From e4f4959ea19494f279a699540761aa3ddd69b7fa Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Mon, 19 Oct 2020 16:39:20 +0200
Subject: [PATCH 36/46] Comment out line 66

---
 model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/model.py b/model.py
index 1d0672c..e9ae7ce 100644
--- a/model.py
+++ b/model.py
@@ -63,7 +63,7 @@ def _preprocess_data(data):
     #                                    'Destination Lat','Destination Long']]
     
     #Drop data not available in test, Pickup Time + label = Arrival times
-    feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
+    #feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
 
     #Renaming columns (shorten, remove space, standardize)
     new_names = {"Order No": "Order_No", "User Id": "User_Id", "Vehicle Type": "Vehicle_Type",

From 9450619f11e6fb1812bfe1232cdf474e3527c04e Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Mon, 19 Oct 2020 16:55:06 +0200
Subject: [PATCH 37/46] testing with vscode

---
 .../Sendy_Team4_Final-checkpoint.ipynb        | 1248 ++++++
 .vscode/settings.json                         |    3 +
 Sendy_Team4_Final.ipynb                       | 3457 ++++++++++++++---
 3 files changed, 4182 insertions(+), 526 deletions(-)
 create mode 100644 .ipynb_checkpoints/Sendy_Team4_Final-checkpoint.ipynb
 create mode 100644 .vscode/settings.json

diff --git a/.ipynb_checkpoints/Sendy_Team4_Final-checkpoint.ipynb b/.ipynb_checkpoints/Sendy_Team4_Final-checkpoint.ipynb
new file mode 100644
index 0000000..e2902c1
--- /dev/null
+++ b/.ipynb_checkpoints/Sendy_Team4_Final-checkpoint.ipynb
@@ -0,0 +1,1248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EDSA 2021: Sendy Logistics Challenge\n",
+    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overview\n",
+    "\n",
+    "### - Introduction\n",
+    "### - Importing libraries and data\n",
+    "### - Exploratory Data Analysis\n",
+    "### - Data Cleaning and Formatting\n",
+    "### - Feature Engineering\n",
+    "### - Train/Test Split\n",
+    "### - Modeling\n",
+    "### - Making Predictions\n",
+    "### - Submission"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing Python libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: lightgbm in c:\\users\\jen\\anaconda3\\lib\\site-packages (3.0.0)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.5.2)\n",
+      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (0.23.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.19.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install lightgbm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: xgboost in c:\\users\\jen\\anaconda3\\lib\\site-packages (1.2.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install xgboost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Linear algebra\n",
+    "import numpy as np\n",
+    "\n",
+    "#Data processing\n",
+    "import pandas as pd\n",
+    "\n",
+    "#Date library\n",
+    "import datetime as dt\n",
+    "\n",
+    "#Data visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import matplotlib.style as style\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "#Metrics\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "#Algorithms\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "#import xgboost as xgb\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'D:\\\\Temp\\\\Train.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-5e050ad09f6c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Train_Masked has extra columns: Delivery destination (day, month, time)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtrain_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\Train.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\Test.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mrider_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\Riders.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    684\u001b[0m     )\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 452\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    945\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 946\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    948\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1176\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1177\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1179\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1180\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   2006\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2008\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2009\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2010\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\Temp\\\\Train.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
+    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
+    "rider_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
+    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
+    "\n",
+    "print(train.shape, test.shape, rider_df.shape, variable_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the train and test datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check which cols are in train and not in test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in train.columns:\n",
+    "    if col not in test.columns:\n",
+    "        print(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Match the number of train columns to the number of test columns and also separate the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the columns from the test dataframe into a new list called testcols\n",
+    "testcols = test_df.columns\n",
+    "\n",
+    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
+    "train1 = train_df[testcols]\n",
+    "\n",
+    "# Seperate the target variable from the predictor variable\n",
+    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the new columns of the train and test data to show that they are the same\n",
+    "print(train1.columns)\n",
+    "print(test_df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot cmap\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(train1.isnull(), cmap=cmap)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Percentage of the missing values\n",
+    "def missing_values(df, threshold=0):\n",
+    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
+    "\n",
+    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
+    "             index=['% of Missing values']\n",
+    "             ).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_values(train1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_values(test_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Distribution review for train data\n",
+    "train1[[col for col in train1.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Column Skewness of the testing dataset\n",
+    "train1.skew().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Univariate Anaysis\n",
+    "# Calculating Skewness in the columns of our Training dataset\n",
+    "train1.kurt().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CorrMatrix_train = train1.corr()\n",
+    "CorrMatrix_train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "style.use('ggplot')\n",
+    "cm = CorrMatrix_train.corr()\n",
+    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
+    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
+    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Personal or Business"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Platform type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vehicle Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(train1['Temperature'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numeric_Training = train1._get_numeric_data()\n",
+    "Numeric_Training.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = train1.values\n",
+    "number_of_columns= 12\n",
+    "number_of_rows = len(l)-1/number_of_columns\n",
+    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.set_style('whitegrid')\n",
+    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
+    "for i in range(0,len(nu_tra)):\n",
+    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
+    "    chart=sns.distplot(train1[nu_tra[i]],kde=True) \n",
+    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+    "                          horizontalalignment='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Useful statistics about our target column\n",
+    "\n",
+    "train1['Time from Pickup to Arrival'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(train1['Time from Pickup to Arrival']/60).hist(bins=50)\n",
+    "plt.title(\"Delivery time distribution\")\n",
+    "plt.xlabel(\"Delivery time (minutes)\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.distplot(df[l[i]],kde=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Full_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train1.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train1.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Renaming columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filling Missing Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filling Missing Values for temperatures and humidity\n",
+    "\n",
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
+    "\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
+    "\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
+    "\n",
+    "full_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df[numeric_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df[time_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df[object_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert object types to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = numeric_cols + ['Personal_Business']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train/Test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rs = 3\n",
+    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True)\n",
+    "\n",
+    "regressors = []\n",
+    "regressors.append(SVR())\n",
+    "regressors.append(GradientBoostingRegressor(random_state=rs))\n",
+    "regressors.append(ExtraTreesRegressor(n_estimators=rs))\n",
+    "regressors.append(RandomForestRegressor(random_state=rs))\n",
+    "#regressors.append(xgb.XGBRegressor(random_state=rs, objective=\"reg:squarederror\"))\n",
+    "regressors.append(lgb.LGBMRegressor(random_state=rs))\n",
+    "\n",
+    "cv_results = []\n",
+    "for regressor in regressors:     #scores to be minimised are negated (neg)\n",
+    "    cv_results.append(np.sqrt(abs(cross_val_score(regressor, X_train, y=y_train, scoring='neg_mean_squared_error', cv=kfold))))\n",
+    "\n",
+    "cv_means = []\n",
+    "cv_stds = []\n",
+    "for cv_result in cv_results:\n",
+    "    cv_means.append(cv_result.mean())\n",
+    "    cv_stds.append(cv_result.std())\n",
+    "    \n",
+    "#cv_res = pd.DataFrame({ \n",
+    "#    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"XGBR\", \"LGBM\"],\n",
+    "#    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "#                       })\n",
+    "cv_res = pd.DataFrame({ \n",
+    "    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"LGBM\"],\n",
+    "    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "                       })\n",
+    "\n",
+    "cv_res = cv_res.sort_values(\"CrossValMeans\", ascending=True)\n",
+    "print(cv_res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Making predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RFC = RandomForestRegressor(random_state=rs)\n",
+    "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
+    "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
+    "rsearch = GridSearchCV(RFC, cv=kfold, scoring='neg_mean_squared_error',param_grid=rf_param)\n",
+    "rfm = rsearch.fit(X_train, y_train)\n",
+    "\n",
+    "r_score = np.sqrt(abs(rfm.best_score_))\n",
+    "r_params = rfm.best_p\n",
+    "arams_\n",
+    "print(r_score, r_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Prediction = predict(fit, test)\n",
+    "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
+    "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = lr.predict(test_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = test_df1[['Order No']]\n",
+    "submission_df['Time_Pic_Arr'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lightgbm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    'n_estimators':[75], # [75, 95],\n",
+    "    'num_leaves': [15], #[12,15, 17],\n",
+    "    'reg_alpha': [0.02], #[0.02, 0.05],\n",
+    "    'min_data_in_leaf': [300],  #[250, 280, 300]\n",
+    "    'learning_rate': [0.1], #[0.05, 0.1, 0.25],\n",
+    "    'objective': ['regression'] #['regression', None]\n",
+    "    }\n",
+    "\n",
+    "lsearch = GridSearchCV(estimator = lgb.LGBMRegressor(random_state=rs), cv=kfold,scoring='neg_mean_squared_error', param_grid=params)\n",
+    "lgbm = lsearch.fit(X_train, y_train)\n",
+    "\n",
+    "l_params = lgbm.best_params_\n",
+    "l_score = np.sqrt(abs(lgbm.best_score_))\n",
+    "print(lgbm.best_params_, np.sqrt(abs(lgbm.best_score_)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and making a prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgb_train = lgb.Dataset(X_train, y_train)\n",
+    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
+    "\n",
+    "lparams = {\n",
+    "           'learning_rate': 0.1, 'min_data_in_leaf': 300, \n",
+    "           'n_estimators': 75, 'num_leaves': 20, 'random_state':rs,\n",
+    "           'objective': 'regression', 'reg_alpha': 0.02,\n",
+    "          'feature_fraction': 0.9, 'bagging_fraction':0.9}\n",
+    "\n",
+    "\n",
+    "lgbm = lgb.train(lparams, lgb_train, valid_sets=lgb_eval, num_boost_round=20, early_stopping_rounds=20)\n",
+    "\n",
+    "lpred = lgbm.predict(X_test, num_iteration=lgbm.best_iteration)\n",
+    "\n",
+    "print(\"The RMSE of prediction is \", mean_squared_error(y_test, lpred)**0.5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"D:\\Temp\\submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..ea8ac03
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\jen\\anaconda3\\python.exe"
+}
\ No newline at end of file
diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 4dc936c..b878a92 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -1,14 +1,16 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# EDSA 2021: Sendy Logistics Challenge\n",
     "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Overview\n",
     "\n",
@@ -21,18 +23,16 @@
     "### - Modeling\n",
     "### - Making Predictions\n",
     "### - Submission"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Introduction\n",
     "\n",
     "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
@@ -43,21 +43,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting lightgbm\n",
-      "  Downloading lightgbm-3.0.0-py2.py3-none-win_amd64.whl (737 kB)\n",
-      "Requirement already satisfied: numpy in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (1.18.1)\n",
-      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (0.22.1)\n",
-      "Requirement already satisfied: scipy in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from lightgbm) (1.4.1)\n",
-      "Requirement already satisfied: joblib>=0.11 in c:\\users\\maakw\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)\n",
-      "Installing collected packages: lightgbm\n",
-      "Successfully installed lightgbm-3.0.0\n"
+      "Requirement already satisfied: lightgbm in c:\\users\\jen\\anaconda3\\lib\\site-packages (3.0.0)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.5.2)\n",
+      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (0.23.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.19.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n"
      ]
     }
    ],
@@ -67,75 +65,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting xgboost\n",
-      "  Downloading xgboost-1.2.0-py3-none-win_amd64.whl (86.5 MB)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Exception:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 425, in _error_catcher\n",
-      "    yield\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 507, in read\n",
-      "    data = self._fp.read(amt) if not fp_closed else b\"\"\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\cachecontrol\\filewrapper.py\", line 62, in read\n",
-      "    data = self.__fp.read(amt)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\http\\client.py\", line 457, in read\n",
-      "    n = self.readinto(b)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\http\\client.py\", line 501, in readinto\n",
-      "    n = self.fp.readinto(b)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\socket.py\", line 589, in readinto\n",
-      "    return self._sock.recv_into(b)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\ssl.py\", line 1071, in recv_into\n",
-      "    return self.read(nbytes, buffer)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\ssl.py\", line 929, in read\n",
-      "    return self._sslobj.read(len, buffer)\n",
-      "socket.timeout: The read operation timed out\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\cli\\base_command.py\", line 186, in _main\n",
-      "    status = self.run(options, args)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\commands\\install.py\", line 331, in run\n",
-      "    resolver.resolve(requirement_set)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 177, in resolve\n",
-      "    discovered_reqs.extend(self._resolve_one(requirement_set, req))\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 333, in _resolve_one\n",
-      "    abstract_dist = self._get_abstract_dist_for(req_to_install)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\legacy_resolve.py\", line 282, in _get_abstract_dist_for\n",
-      "    abstract_dist = self.preparer.prepare_linked_requirement(req)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 482, in prepare_linked_requirement\n",
-      "    hashes=hashes,\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 287, in unpack_url\n",
-      "    hashes=hashes,\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 159, in unpack_http_url\n",
-      "    link, downloader, temp_dir.path, hashes\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 303, in _download_http_url\n",
-      "    for chunk in download.chunks:\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\utils\\ui.py\", line 160, in iter\n",
-      "    for x in it:\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_internal\\network\\utils.py\", line 39, in response_chunks\n",
-      "    decode_content=False,\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 564, in stream\n",
-      "    data = self.read(amt=amt, decode_content=decode_content)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 529, in read\n",
-      "    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\contextlib.py\", line 130, in __exit__\n",
-      "    self.gen.throw(type, value, traceback)\n",
-      "  File \"C:\\Users\\maakw\\anaconda3\\lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 430, in _error_catcher\n",
-      "    raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\n",
-      "pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.\n"
+      "Requirement already satisfied: xgboost in c:\\users\\jen\\anaconda3\\lib\\site-packages (1.2.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n"
      ]
     }
    ],
@@ -145,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,16 +132,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(21201, 29) (7068, 25) (960, 5)\n"
+      "(21201, 29) (7068, 25) (960, 5) (35, 2)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"Train.csv\")\n",
+    "test_df = pd.read_csv(\"Test.csv\")\n",
+    "rider_df = pd.read_csv(\"Riders.csv\")\n",
+    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
+    "\n",
+    "print(train_df.shape, test_df.shape, rider_df.shape, variable_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the train and test datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
     {
      "data": {
       "text/html": [
@@ -426,118 +398,987 @@
        "[5 rows x 29 columns]"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
-    "\n",
-    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
-    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
-    "rider_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
-    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
-    "\n",
-    "print(train.shape, test.shape, rider_df.shape, variable_df.shape)"
-   ]
-  },
-  {
-   "source": [
-    "# Exploritory Data Analysis"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "source": [
-    "## Understanding the train and test datasets"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "train_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_19248</td>\n",
+       "      <td>User_Id_3355</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4:44:10 PM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5:06:47 PM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.333275</td>\n",
+       "      <td>36.870815</td>\n",
+       "      <td>-1.305249</td>\n",
+       "      <td>36.822390</td>\n",
+       "      <td>Rider_Id_192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_12736</td>\n",
+       "      <td>User_Id_3647</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12:57:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1:25:37 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.272639</td>\n",
+       "      <td>36.794723</td>\n",
+       "      <td>-1.277007</td>\n",
+       "      <td>36.823907</td>\n",
+       "      <td>Rider_Id_868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_768</td>\n",
+       "      <td>User_Id_2154</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:08:14 AM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:57:54 AM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290894</td>\n",
+       "      <td>36.822971</td>\n",
+       "      <td>-1.276574</td>\n",
+       "      <td>36.851365</td>\n",
+       "      <td>Rider_Id_26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_15332</td>\n",
+       "      <td>User_Id_2910</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1:51:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2:16:52 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290503</td>\n",
+       "      <td>36.809646</td>\n",
+       "      <td>-1.303382</td>\n",
+       "      <td>36.790658</td>\n",
+       "      <td>Rider_Id_685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_21373</td>\n",
+       "      <td>User_Id_1205</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:30:28 AM</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:56:04 AM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281081</td>\n",
+       "      <td>36.814423</td>\n",
+       "      <td>-1.266467</td>\n",
+       "      <td>36.792161</td>\n",
+       "      <td>Rider_Id_858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
+       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
+       "2    Order_No_768  User_Id_2154         Bike              3   \n",
+       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
+       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                        27   \n",
+       "1             Business                        17   \n",
+       "2             Business                        27   \n",
+       "3             Business                        17   \n",
+       "4             Business                        11   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             3       4:44:10 PM                           27   \n",
+       "1                             5      12:57:35 PM                           17   \n",
+       "2                             4      11:08:14 AM                           27   \n",
+       "3                             1       1:51:35 PM                           17   \n",
+       "4                             2      11:30:28 AM                           11   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                3  ...                         3   \n",
+       "1                                5  ...                         5   \n",
+       "2                                4  ...                         4   \n",
+       "3                                1  ...                         1   \n",
+       "4                                2  ...                         2   \n",
+       "\n",
+       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
+       "0     5:06:47 PM              8         NaN                           NaN   \n",
+       "1     1:25:37 PM              5         NaN                           NaN   \n",
+       "2    11:57:54 AM              5        22.8                           NaN   \n",
+       "3     2:16:52 PM              5        24.5                           NaN   \n",
+       "4    11:56:04 AM              6        24.4                           NaN   \n",
+       "\n",
+       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
+       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
+       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
+       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
+       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
+       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "test_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21201 entries, 0 to 21200\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                                     Non-Null Count  Dtype  \n",
+      "---  ------                                     --------------  -----  \n",
+      " 0   Order No                                   21201 non-null  object \n",
+      " 1   User Id                                    21201 non-null  object \n",
+      " 2   Vehicle Type                               21201 non-null  object \n",
+      " 3   Platform Type                              21201 non-null  int64  \n",
+      " 4   Personal or Business                       21201 non-null  object \n",
+      " 5   Placement - Day of Month                   21201 non-null  int64  \n",
+      " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
+      " 7   Placement - Time                           21201 non-null  object \n",
+      " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
+      " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
+      " 10  Confirmation - Time                        21201 non-null  object \n",
+      " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
+      " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
+      " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
+      " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
+      " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
+      " 16  Pickup - Time                              21201 non-null  object \n",
+      " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
+      " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
+      " 19  Arrival at Destination - Time              21201 non-null  object \n",
+      " 20  Distance (KM)                              21201 non-null  int64  \n",
+      " 21  Temperature                                16835 non-null  float64\n",
+      " 22  Precipitation in millimeters               552 non-null    float64\n",
+      " 23  Pickup Lat                                 21201 non-null  float64\n",
+      " 24  Pickup Long                                21201 non-null  float64\n",
+      " 25  Destination Lat                            21201 non-null  float64\n",
+      " 26  Destination Long                           21201 non-null  float64\n",
+      " 27  Rider Id                                   21201 non-null  object \n",
+      " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
+      "dtypes: float64(6), int64(13), object(10)\n",
+      "memory usage: 4.7+ MB\n"
+     ]
+    }
+   ],
    "source": [
     "train_df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7068 entries, 0 to 7067\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                                Non-Null Count  Dtype  \n",
+      "---  ------                                --------------  -----  \n",
+      " 0   Order No                              7068 non-null   object \n",
+      " 1   User Id                               7068 non-null   object \n",
+      " 2   Vehicle Type                          7068 non-null   object \n",
+      " 3   Platform Type                         7068 non-null   int64  \n",
+      " 4   Personal or Business                  7068 non-null   object \n",
+      " 5   Placement - Day of Month              7068 non-null   int64  \n",
+      " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
+      " 7   Placement - Time                      7068 non-null   object \n",
+      " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
+      " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
+      " 10  Confirmation - Time                   7068 non-null   object \n",
+      " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
+      " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
+      " 13  Arrival at Pickup - Time              7068 non-null   object \n",
+      " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
+      " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
+      " 16  Pickup - Time                         7068 non-null   object \n",
+      " 17  Distance (KM)                         7068 non-null   int64  \n",
+      " 18  Temperature                           5631 non-null   float64\n",
+      " 19  Precipitation in millimeters          199 non-null    float64\n",
+      " 20  Pickup Lat                            7068 non-null   float64\n",
+      " 21  Pickup Long                           7068 non-null   float64\n",
+      " 22  Destination Lat                       7068 non-null   float64\n",
+      " 23  Destination Long                      7068 non-null   float64\n",
+      " 24  Rider Id                              7068 non-null   object \n",
+      "dtypes: float64(6), int64(10), object(9)\n",
+      "memory usage: 1.3+ MB\n"
+     ]
+    }
+   ],
    "source": [
     "test_df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>16835.000000</td>\n",
+       "      <td>552.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.752182</td>\n",
+       "      <td>15.653696</td>\n",
+       "      <td>3.240083</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>9.506533</td>\n",
+       "      <td>23.258889</td>\n",
+       "      <td>7.905797</td>\n",
+       "      <td>-1.281470</td>\n",
+       "      <td>36.811264</td>\n",
+       "      <td>-1.282581</td>\n",
+       "      <td>36.811220</td>\n",
+       "      <td>1556.920947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.625178</td>\n",
+       "      <td>8.798916</td>\n",
+       "      <td>1.567295</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>5.668963</td>\n",
+       "      <td>3.615768</td>\n",
+       "      <td>17.089971</td>\n",
+       "      <td>0.030507</td>\n",
+       "      <td>0.037473</td>\n",
+       "      <td>0.034824</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>987.270788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>-1.438302</td>\n",
+       "      <td>36.653621</td>\n",
+       "      <td>-1.430298</td>\n",
+       "      <td>36.606594</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>1.075000</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.784605</td>\n",
+       "      <td>-1.301201</td>\n",
+       "      <td>36.785661</td>\n",
+       "      <td>882.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>23.500000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>-1.279395</td>\n",
+       "      <td>36.807040</td>\n",
+       "      <td>-1.284382</td>\n",
+       "      <td>36.808002</td>\n",
+       "      <td>1369.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>-1.261177</td>\n",
+       "      <td>36.829477</td>\n",
+       "      <td>2040.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>32.100000</td>\n",
+       "      <td>99.100000</td>\n",
+       "      <td>-1.147170</td>\n",
+       "      <td>36.991046</td>\n",
+       "      <td>-1.030225</td>\n",
+       "      <td>37.016779</td>\n",
+       "      <td>7883.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "count   21201.000000              21201.000000                  21201.000000   \n",
+       "mean        2.752182                 15.653696                      3.240083   \n",
+       "std         0.625178                  8.798916                      1.567295   \n",
+       "min         1.000000                  1.000000                      1.000000   \n",
+       "25%         3.000000                  8.000000                      2.000000   \n",
+       "50%         3.000000                 15.000000                      3.000000   \n",
+       "75%         3.000000                 23.000000                      5.000000   \n",
+       "max         4.000000                 31.000000                      7.000000   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "count                 21201.000000                     21201.000000   \n",
+       "mean                     15.653837                         3.240225   \n",
+       "std                       8.798886                         1.567228   \n",
+       "min                       1.000000                         1.000000   \n",
+       "25%                       8.000000                         2.000000   \n",
+       "50%                      15.000000                         3.000000   \n",
+       "75%                      23.000000                         5.000000   \n",
+       "max                      31.000000                         7.000000   \n",
+       "\n",
+       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "count                      21201.000000                          21201.000000   \n",
+       "mean                          15.653837                              3.240225   \n",
+       "std                            8.798886                              1.567228   \n",
+       "min                            1.000000                              1.000000   \n",
+       "25%                            8.000000                              2.000000   \n",
+       "50%                           15.000000                              3.000000   \n",
+       "75%                           23.000000                              5.000000   \n",
+       "max                           31.000000                              7.000000   \n",
+       "\n",
+       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
+       "count           21201.000000               21201.000000   \n",
+       "mean               15.653837                   3.240225   \n",
+       "std                 8.798886                   1.567228   \n",
+       "min                 1.000000                   1.000000   \n",
+       "25%                 8.000000                   2.000000   \n",
+       "50%                15.000000                   3.000000   \n",
+       "75%                23.000000                   5.000000   \n",
+       "max                31.000000                   7.000000   \n",
+       "\n",
+       "       Arrival at Destination - Day of Month  \\\n",
+       "count                           21201.000000   \n",
+       "mean                               15.653837   \n",
+       "std                                 8.798886   \n",
+       "min                                 1.000000   \n",
+       "25%                                 8.000000   \n",
+       "50%                                15.000000   \n",
+       "75%                                23.000000   \n",
+       "max                                31.000000   \n",
+       "\n",
+       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
+       "count                               21201.000000   21201.000000  16835.000000   \n",
+       "mean                                    3.240225       9.506533     23.258889   \n",
+       "std                                     1.567228       5.668963      3.615768   \n",
+       "min                                     1.000000       1.000000     11.200000   \n",
+       "25%                                     2.000000       5.000000     20.600000   \n",
+       "50%                                     3.000000       8.000000     23.500000   \n",
+       "75%                                     5.000000      13.000000     26.000000   \n",
+       "max                                     7.000000      49.000000     32.100000   \n",
+       "\n",
+       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
+       "count                    552.000000  21201.000000  21201.000000   \n",
+       "mean                       7.905797     -1.281470     36.811264   \n",
+       "std                       17.089971      0.030507      0.037473   \n",
+       "min                        0.100000     -1.438302     36.653621   \n",
+       "25%                        1.075000     -1.300921     36.784605   \n",
+       "50%                        2.900000     -1.279395     36.807040   \n",
+       "75%                        4.900000     -1.257147     36.829741   \n",
+       "max                       99.100000     -1.147170     36.991046   \n",
+       "\n",
+       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
+       "count     21201.000000      21201.000000                 21201.000000  \n",
+       "mean         -1.282581         36.811220                  1556.920947  \n",
+       "std           0.034824          0.044721                   987.270788  \n",
+       "min          -1.430298         36.606594                     1.000000  \n",
+       "25%          -1.301201         36.785661                   882.000000  \n",
+       "50%          -1.284382         36.808002                  1369.000000  \n",
+       "75%          -1.261177         36.829477                  2040.000000  \n",
+       "max          -1.030225         37.016779                  7883.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train_df.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>5631.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.752122</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>9.458970</td>\n",
+       "      <td>23.246120</td>\n",
+       "      <td>6.651759</td>\n",
+       "      <td>-1.282043</td>\n",
+       "      <td>36.811148</td>\n",
+       "      <td>-1.282255</td>\n",
+       "      <td>36.811326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.624000</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>5.575689</td>\n",
+       "      <td>3.591161</td>\n",
+       "      <td>14.085439</td>\n",
+       "      <td>0.030044</td>\n",
+       "      <td>0.037962</td>\n",
+       "      <td>0.034236</td>\n",
+       "      <td>0.043507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.200000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>-1.433512</td>\n",
+       "      <td>36.653828</td>\n",
+       "      <td>-1.422652</td>\n",
+       "      <td>36.639195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>-1.301201</td>\n",
+       "      <td>36.784605</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.786546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>23.400000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.807219</td>\n",
+       "      <td>-1.283219</td>\n",
+       "      <td>36.807927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>25.900000</td>\n",
+       "      <td>4.950000</td>\n",
+       "      <td>-1.257274</td>\n",
+       "      <td>36.829716</td>\n",
+       "      <td>-1.261256</td>\n",
+       "      <td>36.828195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>32.100000</td>\n",
+       "      <td>99.100000</td>\n",
+       "      <td>-1.148352</td>\n",
+       "      <td>36.991046</td>\n",
+       "      <td>-1.030225</td>\n",
+       "      <td>37.018958</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "count    7068.000000               7068.000000                   7068.000000   \n",
+       "mean        2.752122                 15.621817                      3.249859   \n",
+       "std         0.624000                  8.776650                      1.580926   \n",
+       "min         1.000000                  1.000000                      1.000000   \n",
+       "25%         3.000000                  8.000000                      2.000000   \n",
+       "50%         3.000000                 15.000000                      3.000000   \n",
+       "75%         3.000000                 23.000000                      5.000000   \n",
+       "max         4.000000                 31.000000                      7.000000   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "count                  7068.000000                      7068.000000   \n",
+       "mean                     15.621817                         3.249859   \n",
+       "std                       8.776650                         1.580926   \n",
+       "min                       1.000000                         1.000000   \n",
+       "25%                       8.000000                         2.000000   \n",
+       "50%                      15.000000                         3.000000   \n",
+       "75%                      23.000000                         5.000000   \n",
+       "max                      31.000000                         7.000000   \n",
+       "\n",
+       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "count                       7068.000000                           7068.000000   \n",
+       "mean                          15.621817                              3.249859   \n",
+       "std                            8.776650                              1.580926   \n",
+       "min                            1.000000                              1.000000   \n",
+       "25%                            8.000000                              2.000000   \n",
+       "50%                           15.000000                              3.000000   \n",
+       "75%                           23.000000                              5.000000   \n",
+       "max                           31.000000                              7.000000   \n",
+       "\n",
+       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "count            7068.000000                7068.000000    7068.000000   \n",
+       "mean               15.621817                   3.249859       9.458970   \n",
+       "std                 8.776650                   1.580926       5.575689   \n",
+       "min                 1.000000                   1.000000       1.000000   \n",
+       "25%                 8.000000                   2.000000       5.000000   \n",
+       "50%                15.000000                   3.000000       8.000000   \n",
+       "75%                23.000000                   5.000000      13.000000   \n",
+       "max                31.000000                   7.000000      47.000000   \n",
+       "\n",
+       "       Temperature  Precipitation in millimeters   Pickup Lat  Pickup Long  \\\n",
+       "count  5631.000000                    199.000000  7068.000000  7068.000000   \n",
+       "mean     23.246120                      6.651759    -1.282043    36.811148   \n",
+       "std       3.591161                     14.085439     0.030044     0.037962   \n",
+       "min      13.200000                      0.100000    -1.433512    36.653828   \n",
+       "25%      20.600000                      1.100000    -1.301201    36.784605   \n",
+       "50%      23.400000                      2.900000    -1.281301    36.807219   \n",
+       "75%      25.900000                      4.950000    -1.257274    36.829716   \n",
+       "max      32.100000                     99.100000    -1.148352    36.991046   \n",
+       "\n",
+       "       Destination Lat  Destination Long  \n",
+       "count      7068.000000       7068.000000  \n",
+       "mean         -1.282255         36.811326  \n",
+       "std           0.034236          0.043507  \n",
+       "min          -1.422652         36.639195  \n",
+       "25%          -1.300921         36.786546  \n",
+       "50%          -1.283219         36.807927  \n",
+       "75%          -1.261256         36.828195  \n",
+       "max          -1.030225         37.018958  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "test_df.describe()"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Check which cols are in train and not in test data"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arrival at Destination - Day of Month\n",
+      "Arrival at Destination - Weekday (Mo = 1)\n",
+      "Arrival at Destination - Time\n",
+      "Time from Pickup to Arrival\n"
+     ]
+    }
+   ],
    "source": [
-    "for col in train.columns:\n",
-    "    if col not in test.columns:\n",
+    "for col in train_df.columns:\n",
+    "    if col not in test_df.columns:\n",
     "        print(col)"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Match the number of train columns to the number of test columns and also separate the target variable"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,7 +1394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,9 +1405,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n",
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
    "source": [
     "# print the new columns of the train and test data to show that they are the same\n",
     "print(train1.columns)\n",
@@ -574,17 +1446,40 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Missing values"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Hx1ckJBERERERERGxMmYnH/bv3096ejrJycls3bqVb7/9lt27d5uSDF9//bVprNFoZMKECYwZM4Zt27bRtm1bEhMTATh27BhhYWGkpKSQkpJCSEhIxVclIiIiIiIiIlbD7G0XTk5OhIWFYW9vD8Af/vAHzp8/z9dff01wcDB///vfTWO//fZbHBwc6NatGwDjx48nNzcXgG+++YbMzEzeffdd3N3dmTVrFg0bNqzImkRERETkAWBNDR5FROT3mZ18+OMf/2j6OzMzk08++YQPP/yQVq1aAdySfPjXv/5F06ZNCQ8P58SJEzz66KPMmjULuJnECAoKolOnTixatIjo6Gjeekv/QyIiIiIiv8/aTnRQMkREyq1MPR/u2ffff09QUBDTpk0zJR7+U0lJCYcOHWLo0KEkJyfj6upKbGwsAMuWLeOJJ57AYDAQHBzMF198UdGQRERERERERMSKVCj5cOTIEV566SVee+01BgwY8JvjnJyccHNzo3379gD4+fmRkZFBXl4eq1evNo0zGo3Y2NhUJCQRERERERGRasFoNFbZZWlmb7vIysrilVdeIT4+Hi8vr98d27FjR3Jycjh58iQeHh7s27ePdu3a4eDgwHvvvUfHjh3p0KEDa9asoWfPnuaGJCIiIiIPEG1zEBGpPsxOPrz//vvcuHHDtH0CIDAwkKFDh942tk6dOixbtoyIiAgKCgpwcXFhwYIF2NjYsHjxYqKioigsLKRVq1a3HNkpIiIiIvJb1PNBRKo9K6hIqCoGozXUX5jJ083b0iGIiIiIiIXox/5vs7bEjIi5Ms6kWTqE++rnXbuqbK4WvXtX2Vx3Ynblg4iIiIiIJVnbD2wlQ0REfluFT7sQEREREREREfk9Fap8SEhI4JNPPgHA29ubadOmMWPGDI4cOULdunUBmDhxIj179iQtLY2FCxcC0KZNG6Kjo6lXrx7nzp1j+vTp5Ofn4+joSGxsLC1atKjgskRERERERETEWphd+bB//37S09NJTk5m69atfPvtt+zevZtjx46xZs0aUlJSSElJoWfPnuTm5hIWFkZ8fDypqal4eHgQHx8PwNtvv42vry8pKSn06tXL9FxERERERESkJjOWGavssjSzKx+cnJwICwvD3t4egD/84Q+cP3+e8+fPEx4eTnZ2Nj179mTixIlkZmbSvHlzWrduDUD37t0JDg4mIiKCsrIy8vPzASgoKKBOnTqVsCwRERERqenUY0FEpPowO/nwxz/+0fR3ZmYmn3zyCWvXruXQoUNERkbSoEEDxo0bx6ZNm+jTpw+//PILJ0+exMPDg08++YRLly4BMGXKFAIDA0lKSqK4uJj169dXfFUiIiIiIiIi1s7yBQlVpsINJ7///nuCgoKYNm0ajz76KMuWLcPZ2Zm6desyYsQI0tLScHR0JC4ujlmzZjFo0CCcnZ2xs7MDYPr06URHR/PFF18we/ZsJk6cSDU+/VNERERERERE/kOFGk4eOXKEyZMnEx4ejq+vL6dOnSIzM5Pe///5oUajEVtbW0pLS3FxcWHjxo0AZGRk4OrqSk5ODqdPn6ZHjx4A9O7dm8jISK5cuUKTJk0quDQRERERqcl01KaISPVhduVDVlYWr7zyCgsXLsTX1xe4mWyYN28e165dM22h6NmzJwaDgaCgILKzszEajaxevRofHx8aN25M7dq1OXz4MHAzmVGvXj0lHkRERERERERqELMrH95//31u3LhBbGys6VlgYCBjx45l6NChlJSU0KtXL/z8/ACIjo4mODiYoqIivLy8GD16NAaDgYSEBObMmUNhYSH16tVj6dKlFV+ViIiIiIiIiJV7kFoOGIzVeLWebt6WDkFEREREBLCubRfWtiVFxFwZZ9IsHcJ9dXb7J1U2l6tv3yqb604q1PNBRERERERERMxUfWsByq3Cp12IiIiIiIiIiPyeCiUf3n77bXx8fPD19WXVqlUA7N+/H39/f3r16kV8fLxp7O7du/H398fX15ewsDCKiopuedfixYvV70FERERERESkBjI7+XDo0CH+8Y9/sG3bNjZv3kxSUhInT54kPDyc5cuXs2PHDo4dO0ZaWhq//vor0dHRrFq1iu3bt3Pjxg2Sk5MByMvLIzw83JS8EBEREREREZGaxezkw1/+8hc++OADbG1tuXz5MqWlpeTm5uLm5oarqyu2trb4+/uzc+dOHBwc2LdvH02bNqWgoIDLly/j6OgIwN69e2nVqhWjRo2qtEWJiIiIiIiIWDtjmbHKLkur0LYLOzs7lixZgq+vL15eXly4cAEnJyfT587OzmRnZ5vGpqWl8cwzz3DlyhW6du0KQP/+/Rk7diw2NjYVCUVERERERERErFSFG05OnjyZAwcOkJWVRWZmJgaDwfSZ0Wi85d7b25uDBw/SvXt3oqKiKjq1iIiIiIiISLVlNBqr7LI0s5MPP/74IydOnACgbt269OrVi4MHD3Lx4kXTmIsXL+Ls7MzVq1dJT083Pff39+fUqVMVCFtEREREREREqguzkw/nzp0jIiKCoqIiioqK2Lt3L4GBgfz000+cOXOG0tJSPv74Y7p164bRaCQ0NJTz588DsHPnTjp16lRpixARERERERER62Vr7he9vb3JyMigf//+2NjY0KtXL3x9fWnSpAmTJk3ixo0beHt706dPHwwGA3PmzGHcuHEYDAZat27N7NmzK3MdIiIiIiIiItWL5XdDVBmD0Ro2f5jJ083b0iGIiIiIiADweepblg7B5Bn/1ywdgkilyDiTZukQ7qszWz+usrnc+vtV2Vx3UuGGkyIiIiIiIiIiv8fsbRciIiIiIpZkTZUGIiLy+yqUfHj77bfZtWsXBoOBwYMHM2rUKNNna9asYdeuXSQlJQFw+vRpIiMjuXbtGk5OTixatIiGDRuaxh8/fpwhQ4Zw7NixioQkIiIiIg8Ia9taoGSIiJRXNe6CUG5mb7s4dOgQ//jHP9i2bRubN28mKSmJ06dPA/DDDz+QmJhoGms0GpkwYQJjxoxh27ZttG3b9pbPCwoKmDNnDsXFxRVYioiIiIiIiIhYI7OTD3/5y1/44IMPsLW15fLly5SWluLg4EBRURFvvPEGkydPNo399ttvcXBwoFu3bgCMHz+e4cOHmz6PjY1l5MiRFViGiIiIiIiISDVTZqy6y8Iq1HDSzs6OJUuW4Ovri5eXF82aNeOtt95i0KBBuLq6msb961//omnTpoSHhzNgwAAiIyNxcHAAYO/evRQWFtKnT5+KrURERERERERErFKFT7uYPHkyBw4cICsri/Xr15OVlcWgQYNuGVNSUsKhQ4cYOnQoycnJuLq6Ehsby8WLF1mxYgWzZs2qaBgiIiIiIiIiYqXMTj78+OOPnDhxAoC6devSq1cvjh49yvfff09AQAAREREcO3aMqVOn4uTkhJubG+3btwfAz8+PjIwMPv/8c65evcrw4cMJCAgAICAggPz8/EpYmoiIiIiIiIhYA7NPuzh37hxLlizhww8/BG5unxg0aBDz588H4ODBgyQkJLB48WIKCwvJycnh5MmTeHh4sG/fPtq1a8fzzz/P888/b3qnu7s7KSkpFVySiIiIiIiIiPV7kE67MDv54O3tTUZGBv3798fGxoZevXrh6+t7x7F16tRh2bJlREREUFBQgIuLCwsWLDA7aBERERERERGpPgzGapxq8XTztnQIIiIiImIhn6e+ZekQrNYz/q9ZOgSRSpFxJs3SIdxXpzdWXeX/o88HVNlcd2J25YOIiIiIiCVZ2w9sJUNERH6bkg8iIiIiIiIillB9NyKUW4WSD2+//Ta7du3CYDAwePBgRo0aRXp6OgsWLKCsrIw//elPzJ07F3t7e9LS0li4cCEAbdq0ITo6msLCQoKCgkzvy8vL48qVK3z99dcVW5WIiIiIiIiIWA2zj9o8dOgQ//jHP9i2bRubN28mKSmJ06dPM3PmTOLj4/n4448pLCwkJSWF3NxcwsLCiI+PJzU1FQ8PD+Lj43nooYdISUkhJSWF5ORkWrRoQXR0dGWuT0REREREREQszOzkw1/+8hc++OADbG1tuXz5MqWlpTg4OFBaWkp+fj6lpaXcuHGD2rVrk5mZSfPmzWndujUA3bt3Z8+ePbe8b/PmzdStWxd/f/+KrUhERERERERErIrZyQcAOzs7lixZgq+vL15eXjRr1oyoqChGjBjB008/zZUrV+jTpw+tWrXil19+4eTJkwB88sknXLp0yfSe0tJS3nnnHV57zbqaBomIiIiIiIhIxVW44eTkyZMZM2YM48ePZ9myZXz88cd8/PHHtGzZkvnz5zN//nwiIyOJi4tj1qxZlJWVMWTIEOzs7Ezv+OKLL2jVqhXu7u4VDUdEREREHhA6XUJEqjtjmRpO3tWPP/5IUVERbdu2pW7duvTq1Yt169bRpk0b/uu//guAIUOGMHXqVEpLS3FxcWHjxo0AZGRk4OrqanrXnj178PHxqeBSRERERORBoqM2RUSqD7O3XZw7d46IiAiKioooKipi79699OvXj4yMDNOWir1799K+fXsMBgNBQUFkZ2djNBpZvXr1LcmGf/7zn3Tu3LniqxERERERERGpLoxVeFmY2ZUP3t7eZGRk0L9/f2xsbOjVqxfjxo3D2dmZF198ERsbG9zc3IiOjqZWrVpER0cTHBxMUVERXl5ejB492vSus2fP4uLiUikLEhERERERERHrYjAajVaQAzGPp5u3pUMQEREREQGsa9uFtW1JETFXxpk0S4dwX/34YXKVzfWHoQOqbK47qdBpFyIiIiIiIiJSs6SmpuLj40OvXr1Yu3btbZ9/++23DBo0iH79+jFu3Dhyc3Pv+k4lH0REREREREQswGg0Vtl1r7Kzs4mPj2fdunVs3bqV9evX88MPP9wyJiYmhsmTJ7Nt2zYeeeQR3n///bu+t1KSD3FxcYSFhQGwbt06fH198fHxIS4uzrTI3bt34+/vj6+vL2FhYRQVFQFw/vx5hg8fTp8+fZgwYQLXr1+vjJBERERERERE5P+Xm5vLuXPnbrv+s2ph//79dOnShUaNGuHg4EDv3r3ZuXPnLWPKyspMv90LCgqoU6fOXec3u+Hk/zpw4ADJyck888wznD17ltWrV7N161Zq167N8OHD+fLLL+nUqRPR0dEkJyfTtGlTQkJCSE5O5oUXXmD27NkMGzYMX19fli1bxvLlywkNDa1oWCIiIiIiVUp9FkSkvKqyBePf//53EhISbns+ceJEJk2aZLq/cOECTk5OpntnZ2cyMjJu+U5YWBhBQUHMmzePunXrsmHDhrvOX6Hkw9WrV4mPj2f8+PGcPHkSV1dXtm/fjp2dHVeuXCE/Px9HR0ccHBzYt28fdnZ2FBQUcPnyZRwdHSkuLuarr75i2bJlAAwcOJD//u//VvJBRERERKodNZwUEWs2cuRIBgy4vemko6PjLfdlZWUYDAbTvdFovOW+sLCQmTNnsnr1ajw9PVm1ahXTp08nMTHxd+evUPLhjTfeICQkhKysLNMzOzs7NmzYQFxcHJ6ennh4eJiep6WlMW3aNJydnenatStXrlyhfv362NreDMPJyYns7OyKhCQiIiIiDwhr+rEvImLtHB0db0s03ImLiwuHDx823V+8eBFnZ2fT/XfffUft2rXx9PQE4IUXXuDtt9++63vNTj5s3LiRhx9+GC8vL7Zs2XLLZ0OGDGHgwIHMmDGDhIQEXn31VQC8vb05ePAgixYtIioqimnTpt2SQQFuuxcRERERuRNr+9d9JUNEpCb461//ytKlS8nJyaFu3bp8+umnzJkzx/S5m5sbv/zyC6dPn+bRRx9l7969tG/f/q7vNTv5sGPHDi5evEhAQADXrl3j119/ZcaMGQwePJgnnngCW1tbfH19+fDDD7l69SrHjh2ja9euAPj7+xMSEkKTJk3Iy8ujtLQUGxub2zIqIiIiIiIiIjVWmaUDuF2zZs0ICQnhxRdfpLi4mMGDB+Pp6cmYMWOYPHky7du3Z/78+UydOhWj0chDDz3EvHnz7vpes5MPq1atMv29ZcsWDh06xKhRoxg/fjxbt26lQYMG7Nq1iyeeeAKj0UhoaCibN2+mefPm7Ny5k06dOmFnZ0fnzp3ZsWMH/v7+bN26lW7dupkbkoiIiIiIiIhUkL+/P/7+/rc8W7lypelvb29vvL29y/XOCp928e/atGnD2LFjCQwMxMbGhs6dOzNq1Cjs7OyYM2cO48aNw2Aw0Lp1a2bPng1AZGQkYWFhrFixgocffphFixZVZkgiIiIiUkNpm4OIVHdVedqFpRmM1Xi1nm7ly7SIiIiIiNwv1pQMsbZ+GCLmyjiTZukQ7qvvPthUZXO1eXFwlc11J7UsOruIiIiIiIiI1HiVuu1CRERERERERO5NNd6IUG6VknyIi4vjypUr9O3b95aeDdnZ2XTo0IF3332X06dPExkZybVr13BycmLRokU0bNiQw4cPM2/ePIqLi2nRogVxcXE0bNiwMsISEREREREREStQ4W0XBw4cIDk5GbjZ8TIlJYWUlBTee+896tevz4wZMzAajUyYMIExY8awbds22rZtS2JiIgAzZsxgwYIFpKam0rp1a95///2KhiQiIiIiIiIiVqRClQ9Xr14lPj6e8ePHc/LkyVs+W7BgAYGBgbRq1Ypjx47h4OBgOkZz/Pjx5ObmArBjxw7s7OwoLi4mOzsbd3f3ioQkIiIiIg8Ia2rwKCIiv69CyYc33niDkJAQsrKybnmemZnJoUOHiImJAeBf//oXTZs2JTw8nBMnTvDoo48ya9YsAOzs7Dh16hSjRo3C1taWV199tSIhiYiIiMgDwtpOdFAyRETK7cFp+WD+touNGzfy8MMP4+Xlddtn69evZ9iwYdjb2wNQUlLCoUOHGDp0KMnJybi6uhIbG2sa7+7uzv79+3n55ZcJCQkxNyQRERERERERsUJmJx927NjBl19+SUBAAEuWLGHfvn3MmzcPgL179+Lj42Ma6+TkhJubG+3btwfAz8+PjIwMbty4wZ49e0zj+vXrx6lTp8wNSURERERERKTaMBqNVXZZmtnbLlatWmX6e8uWLRw6dIjw8HBycnIoLCzE1dXV9HnHjh3Jycnh5MmTeHh4sG/fPtq1a4etrS2zZ8/GxcWFxx57jE8++YROnTpVbEUiIiIiIiIiYlUq5ajNf3fu3DlcXFxueVanTh2WLVtGREQEBQUFuLi4sGDBAmxsbIiPj+eNN96gtLSUZs2amfpEiIiIiIj8HvVYEBGpPgxGa6i/MJOnm7elQxARERERAawrGWJtzThFzJVxJs3SIdxXJ1dtqLK5PEYNqbK57qTSKx9ERERERERE5O6MZdW2FqDclHwQERERkWrJmioNRETk91Uo+TBixAhycnKwtb35mujoaDp06EBxcTHBwcG8/PLLPPnkkwCkpaWxcOFCANq0aUN0dDT16tXjwoULREREcOHCBerUqcPChQtp2bJlBZclIiIiIjWdtW0tUDJERMqr+jZBKD+zj9o0Go1kZmaSkpJiujp06MDp06cZMWIEX3/9tWlsbm4uYWFhxMfHk5qaioeHB/Hx8QBMmzaN7t27s3XrVgICAkwJChERERERERGpGcxOPpw+fRqAoKAg+vXrx5o1awDYtGkTwcHBdOjQwTQ2MzOT5s2b07p1awC6d+/Onj17TMdvBgYGAjBo0CCmTp1qbkgiIiIiIiIiYoXM3naRm5uLl5cXs2bNori4mBdffJFHHnmEadOmAfD3v//dNLZVq1b88ssvnDx5Eg8PDz755BMuXbrE2bNnad68ObGxsRw+fBgnJydmzZpV8VWJiIiIiIiIiNUwO/nQsWNHOnbsaLofPHgwaWlpPPXUU7eNdXR0JC4ujlmzZlFWVsaQIUOws7OjpKSE48ePM2nSJGbMmMHGjRsJCwsjKSnJ3LBERERE5AGhHgsiItWH2cmHw4cPU1xcjJeXF3CzB8T/Np78T6Wlpbi4uLBx40YAMjIycHV1xcnJiXr16tG9e3cA/Pz8mDt3rrkhiYiIiMgDRA0nRaTae4A6TpqdfMjLy2PJkiV89NFHFBcXk5yczOzZs+841mAwEBQUxMaNG3F2dmb16tX4+PjwX//1X7i4uJCWloa3tzefffYZ7dq1M3sxIiIiIvLg0I99EZHqw+zkQ/fu3Tl69Cj9+/enrKyMYcOG3bIN49/VqlWL6OhogoODKSoqwsvLi9GjRwOwdOlSIiMjefPNN6lfvz6xsbHmhiQiIiIiDxBVPohIdfcAFT5gMBqr73I93bwtHYKIiIiICGBdyQdrS8yImCvjTJqlQ7ivvk38qMrmajc2sMrmuhOzKx9ERERERERExHzGsmpbC1ButSwdgIiIiIiIiIjUbKp8EBEREZFqyZq2OYiIyO+rUPJhxIgR5OTkmI7YjI6OpqysjPnz53P9+nXc3d2JjY3F3t6ePXv2sHTpUoxGIy1btmT+/Pk0bNiQjIwMZs+eTVFREc2bN2fu3Lk4OTlVyuJEREREpOaytr4GSoaIiPw2s7ddGI1GMjMzSUlJMV1/+MMfmDRpEtHR0Wzfvh2ATZs2kZ+fT1RUFImJiWzbtg13d3dTImLy5MmEhoaSmppKQEAAs2bNqrTFiYiIiIiIiFgto7HqLgszu/Lh9OnTAAQFBXH16lWGDBmCk5MTjz/+OB4eHgBERERQWlpKcXExkZGRNGvWDAB3d3dSU1O5cuUKhYWFdOnSBbh5fOe0adMoKirC3t6+omsTEREREREREStgdvIhNzcXLy8vZs2aRXFxMS+++CL9+vXDwcGBkJAQTp8+TadOnQgLC6N27dr07NkTgMLCQhITExkxYgSNGzfGwcGB9PR0unbtyvbt2ykuLubKlSumRIWIiIiIyJ1om4OIVHdWUJBQZczedtGxY0cWLFhAgwYNaNKkCYMHD+att94iPT2dV199lS1btlBQUEBiYqLpO3l5eYwdOxYPDw8GDBiAwWBgyZIlvPvuu/Tv35+8vDwaNWqEnZ1dpSxORERERERERCzP7MqHw4cPU1xcjJeXF3CzB0Tjxo3p0KEDrq6uAPTt25c1a9YAcOHCBUaPHk2XLl0IDw//vwBsbUlKSgLg8uXLLF++nEaNGpkbloiIiIg8INRwUkSk+jA7+ZCXl8eSJUv46KOPKC4uJjk5mYSEBF599VWysrJ4+OGH+eyzz2jXrh2lpaWMHz+evn378vLLL9/ynvDwcKKiovD09GTVqlX06dOHWrXMLsgQERERkQeEfuyLiFQfZicfunfvztGjR+nfvz9lZWUMGzaMzp07Ex0dzfjx47lx4wZt27Zl+vTp7Nu3j+PHj1NaWsquXbsAeOyxx4iJiSEqKorIyEgKCgpwd3cnJiam0hYnIiIiIjWXKh9EpLozPkBNHwzGarxaTzdvS4cgIiIiIgJYV/LB2hIzIubKOJNm6RDuq4xla6tsLs9XhlfZXHei/Q0iIiIiIiIicl+Zve1CRERERERERMxXffchlF+FKh/27dvHwIED6du3L3PnzgVg//79+Pv706tXL+Lj401jT58+zYgRI+jXrx+jR4/m2rVrABw5coTBgwcTEBDAyJEj+fnnnysSkoiIiIiIiIhYGbOTD2fPniUyMpLly5ezbds2jh8/TlpaGuHh4SxfvpwdO3Zw7Ngx0tLSMBqNTJgwgTFjxrBt2zbatm1LYmIiAKGhocydO5eUlBT8/f1NSQwRERERERERqRnM3naxe/dufHx8cHFxASA+Pp4zZ87g5uaGq6srAP7+/uzcuZOHHnoIBwcHunXrBsD48ePJzc2lqKiIKVOm4OHhAYC7uztr1qyp6JpERERERERExIqYnXw4c+YMdnZ2jB8/nqysLJ555hn++Mc/4uTkZBrj7OxMdnY2//rXv2jatCnh4eGcOHGCRx99lFmzZmFvb09AQAAAZWVlJCQk0KNHj4qvSkRERERERMTKGcsenKYPZm+7KC0t5cCBA8ybN4/169eTkZHB2bNnMRgMpjFGoxGDwUBJSQmHDh1i6NChJCcn4+rqSmxsrGlcUVERr7/+OiUlJYwbN65iKxIRERERERERq2J28qFp06Z4eXnRpEkT6tSpQ48ePdi/fz8XL140jbl48SLOzs44OTnh5uZG+/btAfDz8yMjIwOA69evExwcTElJCStWrMDOzq6CSxIRERERERGpBozGqrsszOzkQ/fu3UlPTyc3N5fS0lK++OIL+vTpw08//cSZM2coLS3l448/plu3bnTs2JGcnBxOnjwJ3Dwlo127dsDNhpNubm4sXrwYe3v7ylmViIiIiIiIiFgNs3s+dOjQgeDgYIYNG0ZxcTFPPfUUQ4cO5dFHH2XSpEncuHEDb29v+vTpg8FgYNmyZURERFBQUICLiwsLFizg+PHj7N27l9atWzNgwADgZp+IlStXVtoCRURERKRm+jz1LUuHICJSIVZQkFBlDEZj9V2up5u3pUMQEREREQtR8uG3PeP/mqVDEKkUGWfSLB3CffX14qQqm6vj1BFVNtedmF35ICIiIiJiSdb2A1vJEBGR32Z2zwcRERERERERkXtRoeTDvn37GDhwIH379mXu3Lm3fLZmzRpGjPi/so60tDT8/f3x9/fntdde4/r16wAkJyfTtWtXAgICCAgIID4+viIhiYiIiIiIiFQLRqOxyi5LM3vbxdmzZ4mMjGTjxo089NBDjBw5krS0NLy9vfnhhx9ITEzEzc0NgNzcXMLCwkhKSqJ169asXLmS+Ph4IiIiOHbsGGFhYfj5+VXaokRERERERETEepidfNi9ezc+Pj64uLgAEB8fT+3atSkqKuKNN95g8uTJpKSkAJCZmUnz5s1p3bo1cPOYzuDgYCIiIvjmm2/IzMzk3Xffxd3dnVmzZtGwYcNKWJqIiIiI1GTqsSAi1Z0VFCRUGbOTD2fOnMHOzo7x48eTlZXFM888w9SpU4mNjWXQoEG0bNnSNLZVq1b88ssvnDx5Eg8PDz755BMuXboEgJOTE0FBQXTq1IlFixYRHR3NW2/pf0hERERE5Pep4aSISPVhdvKhtLSUw4cPk5SUhIODAxMmTGDjxo1kZWUxY8YMDh48aBrr6OhIXFwcs2bNoqysjCFDhmBnZwfAsmXLTOOCg4Pp2bNnBZYjIiIiIiIiItbG7ORD06ZN8fLyokmTJgD06NGDr7/+mu+//56AgAB+/fVXLl26xNSpU3nrrbdwcXFh48aNAGRkZODq6kpeXh6bN2/mpZdeAm4227Cxsan4qkRERERERETEaph92kX37t1JT08nNzeX0tJSvvjiCzp16sQnn3xCSkoKc+fO5bHHHmPx4sUYDAaCgoLIzs7GaDSyevVqfHx8cHBw4L333uPo0aPAzRMyVPkgIiIiIiIiUrOYXfnQoUMHgoODGTZsGMXFxTz11FMMGjTojmNr1apFdHQ0wcHBFBUV4eXlxejRo7GxsWHx4sVERUVRWFhIq1atWLBggdmLEREREREREakurOEIzKpiMFbj1Xq6eVs6BBERERERwLoaTlpbM04Rc2WcSbN0CPfV4bf+XmVzdX5tZJXNdSdmVz6IiIiIiIiISAWUWTqAqmN2zwcRERERERERkXtRocqHffv2kZCQQEFBAU899RQRERFs2bKF9957DxsbG5588knCwsKwtbVlz549LF26FKPRSMuWLZk/fz4NGzbk3LlzTJ8+nfz8fBwdHYmNjaVFixaVtT4RERERkSqhrQ4iUl7VuAtCuZnd8+Hs2bMMGzaMjRs38tBDDzFy5Eh69+7N+++/z6ZNm3B2diYqKgo3Nzeef/55+vTpw+bNm2nWrBlvv/02eXl5REREEBoaSseOHRk2bBhJSUkcPXqUhQsX3lMM6vkgIiIiItZCPR9EKl9N7/nw1YLVVTbXn6e9VGVz3YnZ2y52796Nj48PLi4u2NnZER8fj5OTE48//jjOzs7AzeM49+zZQ3FxMZGRkTRr1gwAd3d3srKyACgrKyM/Px+AgoIC6tSpU9E1iYiIiIiIiIgVMXvbxZkzZ7Czs2P8+PFkZWXxzDPPEBAQQFxcHFlZWTg7O7Nz504uXbpE48aN6dmzJwCFhYUkJiYyYsQIAKZMmUJgYCBJSUkUFxezfv36ylmZiIiIiIiIiFgFs5MPpaWlHD58mKSkJBwcHJgwYQJubm689tprTJgwgTp16tCnTx+++eYb03fy8vJ45ZVX8PDwYMCAAQBMnz6d6OhoevTowa5du5g4cSLbtm3DYDBUfHUiIiIiIiIiVuoBavlg/raLpk2b4uXlRZMmTahTpw49evTgq6++wtPTk61bt/LRRx/RrFkzXF1dAbhw4QLDhg3D3d2dmJgYAHJycjh9+jQ9evQAoHfv3ly8eJErV65UwtJERERERERExBqYnXzo3r076enp5ObmUlpayhdffMEf//hHXnrpJfLz8ykqKmLNmjX4+PhQWlrK+PHj6du3LzNnzjRVNTRu3JjatWtz+PBhAI4cOUK9evVo0qRJ5axORERERERExEoZjcYquyzN7G0XHTp0IDg4mGHDhlFcXMxTTz3FSy+9RIMGDXjhhRcoKSnBz88Pf39/du/ezfHjxyktLWXXrl0APPbYY8TExJCQkMCcOXMoLCykXr16LF26tNIWJyIiIiIiIiKWZ/ZRm9ZAR22KiIiIiLXQUZsila+mH7V5MHZVlc31ZNioKpvrTszediEiIiIiIiIici+UfBARERERERGR+8rsng8bN25kzZo1pvtz584REBBA69atWbt2LUajEW9vb6ZNm4bBYOD06dNERkZy7do1nJycWLRoEQ0bNjR9f/HixdjY2DBp0qSKrUhEREREHgjWtM1BRMQc1bcJQvmZnXx4/vnnef755wH4/vvveeWVV+jfvz+vv/46W7dupXbt2gwfPpwvv/ySp556igkTJjBz5ky6devGwoULSUxMJDQ0lLy8PObPn8/27dsJDg6utIWJiIiISM1mbX0NlAwREfltZicf/l1UVBQhISF4enqyfft27OzsuHLlCvn5+Tg6OvLtt9/i4OBAt27dABg/fjy5ubkA7N27l1atWjFqlGWbX4iIiIiIiIhUpWp8/kO5Vbjnw/79+yksLKRv374A2NnZsWHDBnr06IGTkxMeHh7861//omnTpoSHhzNgwAAiIyNxcHAAoH///owdOxYbG5uKhiIiIiIiIiIiVqjCyYePPvrotqqFIUOGcPDgQZo2bUpCQgIlJSUcOnSIoUOHkpycjKurK7GxsRWdWkRERERERESqgQolH4qKivjqq6949tlnAcjKyuLIkSMA2Nra4uvry6lTp3BycsLNzY327dsD4OfnR0ZGRgVDFxEREREREZHqoELJh1OnTtGqVSvTFoq8vDxCQ0PJzc3FaDSya9cunnjiCTp27EhOTg4nT54EYN++fbRr167i0YuIiIiIiIhUU0Zj1V2WVqGGk2fPnsXFxcV036ZNG8aOHUtgYCA2NjZ07tyZUaNGYWdnx7Jly4iIiKCgoAAXFxcWLFhQ4eBFRERERERExPoZjNW4vaanm7elQxARERERC9HRlr/N2o4hFTFXxpk0S4dwX3055/0qm+upWaOrbK47qXDDSRERERERERGR31OhbRciIiIiIpZibf+6r0oMESmv6rsPofzMTj5s3LiRNWvWmO7PnTtHo0aNqF+/vulZdnY2HTp04N133yUtLY2FCxcCN3tDREdHU69ePdPY48ePM2TIEI4dO2ZuSCIiIiLyANGPfRGR6sPs5MPzzz/P888/D8D333/PK6+8wkcffUSTJk0AuHjxIkOHDmXGjBnk5uYSFhZGUlISrVu3ZuXKlcTHxxMREQFAQUEBc+bMobi4uBKWJCIiIiIiIiLWpFK2XURFRRESEmJKPAAsWLCAwMBAWrVqRUZGBs2bN6d169YAdO/eneDgYFPyITY2lpEjR/L//t//q4xwREREROQBoG0XIiLVR4UbTu7fv5/CwkL69u1repaZmcmhQ4d48cUXAWjVqhW//PILJ0+eBOCTTz7h0qVLAOzdu5fCwkL69OlT0VBEREREREREqg2j0Vhll6VVuPLho48+YtSoUbc8W79+PcOGDcPe3h4AR0dH4uLimDVrFmVlZQwZMgQ7OzsuXrzIihUrWL16dUXDEBERERERERErVaHKh6KiIr766iueffbZW57v3bsXHx8f031paSkuLi5s3LiRzZs307ZtW1xdXfn888+5evUqw4cPJyAgAICAgADy8/MrEpaIiIiIiIiIWJEKVT6cOnWKVq1a4eDgYHqWk5NDYWEhrq6upmcGg4GgoCA2btyIs7Mzq1evxsfH55amlQDu7u6kpKRUJCQRERERERGR6sEKtkNUlQolH86ePYuLi8stz86dO3fbs1q1ahEdHU1wcDBFRUV4eXkxevToikwtIiIiIiIiItVEhZIPPj4+t2yvAPD09GTDhg23jX3mmWd45plnfvd9p06dqkg4IiIiIiIiImKFKnzahYiIiIiIiIjI76nwaRciIiIiIiIiUn4PUMuHiiUfUlJSSExMBKBbt25Mnz6d9evXk5SUhMFg4LHHHmP27NnY29uzZ88eli5ditFopGXLlsyfP5+SkhKCgoJM78vLy+PKlSt8/fXXFVuViIiIiIiIiFgNs7ddFBQUEBMTQ1JSEikpKRw+fJj169fz/vvv89FHH7Ft2zbKyspYt24d+fn5REVFkZiYyLZt23B3d2fp0qU89NBDpKSkkJKSQnJyMi1atCA6Oroy1yciIiIiIiJilYxGY5VdlmZ28qG0tJSysjIKCgooKSmhpKSERx55hMjISOrXr4/BYKBNmzacP3+e4uJiIiMjadasGXDzSM2srKxb3rd582bq1q2Lv79/xVYkIiIiIiIiIlbF7G0X9evXZ8qUKfTt25e6devy5z//mT//+c8YDAYAcnJyWLt2LfPnz6dx48b07NkTgMLCQhITExkxYoTpXaWlpbzzzjssX768gssREREREREREWtjduXDyZMn2bx5M5999hlffPEFtWrV4v333wcgOzubkSNHMmjQIJ588knTd/Ly8hg7diweHh4MGDDA9PyLL76gVatWuLu7V2ApIiIiIiIiImKNzK58SE9Px8vLi4ceegiAgQMHsm7dOrp3705wcDAjRoy4pZnkhQsXGD16NF26dCE8PPyWd+3ZswcfHx9zQxERERGRB9DnqW9ZOgQRkQqxglYMVcbs5IOHhwdvvvkmv/76K3Xr1mXfvn08+uijjB49mqlTp9K/f3/T2NLSUsaPH0/fvn15+eWXb3vXP//5T8aMGWNuKCIiIiLyAHrG/zVLh3ALJUNERH6b2cmHrl27cvz4cQYOHIidnR3t27encePGXLp0iVWrVrFq1SoAnn32Wf70pz9x/PhxSktL2bVrFwCPPfYYMTExAJw9exYXF5dKWI6IiIiIPCj0Y19EqjtrOIXiTlJTU1mxYgUlJSWMHDmS4cOH3/L56dOniYyM5Nq1azg5ObFo0SIaNmz4u+80GK11tffA083b0iGIiIiIiADWlQyxtqoQEXNlnEmzdAj31WcR71bZXN3njruncdnZ2QwdOpQtW7Zgb29PYGAgixYtonXr1sDNhEmfPn2YOXMm3bp1Y+HChRiNRkJDQ3/3vWZXPoiIiIiIiIiI+aqyFCA3N5fc3Nzbnjs6OuLo6Gi6379/P126dKFRo0YA9O7dm507dzJx4kQAvv32WxwcHOjWrRsA48ePv+N7/5OSDyIiIiIilUDVBiJizf7+97+TkJBw2/OJEycyadIk0/2FCxdwcnIy3Ts7O5ORkWG6/9e//kXTpk0JDw/nxIkTPProo8yaNeuu81co+ZCSkkJiYiIA3bp1Y/r06Xz99dfMnz+f69ev4+7uTmxsLPb29r+5J+T8+fOEhoZy+fJlHnnkERYuXEi9evUqEpaIiIiISJXTtgsRsWYjR45kwIABtz3/96oHgLKyMgwGg+neaDTecl9SUsKhQ4dYs2YN7du3Z/HixcTGxhIbG/u789cyN/CCggJiYmJISkoiJSWFw4cPs3fvXiZNmkR0dDTbt28HYNOmTRiNRiZMmMCYMWPYtm0bbdu2NSUtZs+ezbBhw9i5cyePPfYYy5cvNzckEREREREREbkDR0dHWrZsedv1n8kHFxcXLl68aLq/ePEizs7OpnsnJyfc3Nxo3749AH5+frdURvwWsysfSktLKSsro6CgAAcHB0pKSvjuu+94/PHH8fDwACAiIoLS0tLf3BNSXFzMV199xbJlywAYOHAg//3f/33XRhUiIiIiItZUaSAiUlP89a9/ZenSpeTk5FC3bl0+/fRT5syZY/q8Y8eO5OTkcPLkSTw8PNi3bx/t2rW763vNTj7Ur1+fKVOm0LdvX+rWrcuf//xnbGxscHBwICQkhNOnT9OpUyfCwsL46quv7rgn5MqVK9SvXx9b25thODk5kZ2dbW5IIiIiIvIAsbatBUqGiEh5Gcus7/DJZs2aERISwosvvkhxcTGDBw/G09OTMWPGMHnyZNq3b8+yZcuIiIigoKAAFxcXFixYcNf3mp18OHnyJJs3b+azzz6jQYMGvP766xQVFZGens769etp3rw5M2fOJDExETc3tzvuCQkJCbll7whw272IiIiIiIiIVB1/f3/8/f1vebZy5UrT3x06dGDTpk3leqfZyYf09HS8vLx46KGHgJtbJiZPnsxTTz2Fq6srAH379mXNmjV07tz5tj0hkydPpkmTJuTl5VFaWoqNjc1te0lERERERH6LKg1EpLqryqM2Lc3s5IOHhwdvvvkmv/76K3Xr1mXfvn34+fmRnp5OVlYWDz/8MJ999hnt2rX7zT0hdnZ2dO7cmR07duDv78/WrVtNfSFERERERH6Ptl2IiFQfZicfunbtyvHjxxk4cCB2dna0b9+eyMhIDhw4wPjx47lx4wZt27Zl+vTp1KlT5zf3hERGRhIWFsaKFSt4+OGHWbRoUaUtTkRERERqLv3YFxGpPgxGY/Ut9PB087Z0CCIiIiIigHUlQ6ytKkTEXBln0iwdwn21J+ydKpurR+z4KpvrTsyufBARERERERER81XjWoByU/JBRERERKola6o0EBGR31eh5ENiYiKbN2/G3t4eHx8fJkyYwP79+5k/fz43btygb9++hISEAJCWlsbChQsBaNOmDdHR0dSrV4/Dhw8zb948iouLadGiBXFxcTRs2LDiKxMRERGRGs3athYoGSIi5fUAFT5Qy9wv7t+/n9TUVDZv3szWrVs5evQo27ZtIzw8nOXLl7Njxw6OHTtGWloaubm5hIWFER8fT2pqKh4eHsTHxwMwY8YMFixYQGpqKq1bt+b999+vtMWJiIiIiIiIiOWZnXw4fvw4Xbt2pX79+tjY2PD000+zceNG3NzccHV1xdbWFn9/f3bu3ElmZibNmzendevWAHTv3p09e/YAsGPHDlq3bk1xcTHZ2dk4OjpWzspERERERERExCqYnXxo164d6enpXL16lRs3brBv3z7+3//7fzg5OZnGODs7k52dTatWrfjll184efIkAJ988gmXLl0CwM7OjlOnTuHt7c3Bgwfx9fWt4JJERERERERExJqY3fPBy8uLgQMHMmLECBo1aoSXlxfp6ekYDAbTGKPRiMFgwNHRkbi4OGbNmkVZWRlDhgzBzs7ONM7d3Z39+/fz0UcfERISwkcffVSxVYmIiIiIiIhYubIHqOmD2ZUP+fn59OrVi9TUVJKSkrC3t+cvf/kLFy9eNI25ePEizs7OlJaW4uLiwsaNG9m8eTNt27bF1dWVGzdumLZfAPTr149Tp05VbEUiIiIiIiIiYlXMTj6cO3eOl19+mZKSEvLy8ti0aRNTp07lp59+4syZM5SWlvLxxx/TrVs3DAYDQUFBZGdnYzQaWb16NT4+Ptja2jJ79myOHTsG3NyO0alTp0pbnIiIiIiIiIhYntnbLjw8POjVqxf9+vWjtLSUl156iSeeeILY2FgmTZrEjRs38Pb2pk+fPhgMBqKjowkODqaoqAgvLy9Gjx6NjY0N8fHxvPHGG5SWltKsWTNiYmIqc30iIiIiIiIiVukB2nWBwWisvsv1dPO2dAgiIiIiIgB8nvqWpUMwecb/NUuHIFIpMs6kWTqE++qT15dX2Vx9F75cZXPdidmVDyIiIiIiIiJivmpcC1BuSj6IiIiISLVkTZUGIiLy+yqUfEhMTGTz5s3Y29vj4+PDhAkTmDFjBkeOHKFu3boATJw4kZ49e7Jnzx6WLl2K0WikZcuWzJ8/n4YNG3LhwgUiIiK4cOECderUYeHChbRs2bJSFiciIiIiNZe1bS1QMkRE5LeZnXzYv38/qampbN68mbp16/LKK6/w6aefcuzYMdasWYOzs7NpbH5+PlFRUWzevJlmzZrx9ttvs3TpUiIiIpg2bRq9e/dm6NChfPjhhyxcuJDFixdXxtpEREREpAbTj30RkerD7OTD8ePH6dq1K/Xr1wfg6aefZvv27Zw/f57w8HCys7Pp2bMnEydOpLi4mMjISJo1awaAu7s7qamp5OTkcPLkSVatWgXAoEGD8PLyqoRliYiIiEhNp8oHEanuHqCWD+YnH9q1a8e8efMYN24cdevWZd++fRiNRrp06UJkZCQNGjRg3LhxbNq0iSFDhtCzZ08ACgsLSUxMZMSIEZw9e5bmzZsTGxvL4cOHcXJyYtasWZW2OBERERGpufRjX0Sk+qhl7he9vLwYOHAgI0aMIDg4mCeeeAI7OzuWLVuGs7MzdevWZcSIEaSl/d/RKHl5eYwdOxYPDw8GDBhASUkJx48fp0uXLmzevJnnnnuOsLCwSlmYiIiIiIiIiDUzGo1Vdlma2ZUP+fn59OrVi1GjRgHw3nvvUVhYyK5du+jduzdw8z9IW9ubU1y4cIHRo0fTpUsXwsPDAXBycqJevXp0794dAD8/P+bOnVuhBYmIiIjIg0HbLkREqg+zKx/OnTvHyy+/TElJCXl5eWzatIlXXnmFefPmce3aNYqLi1m/fj09e/aktLSU8ePH07dvX2bOnInBYADgv/7rv3BxcTFVR3z22We0a9euclYmIiIiIiIiIlbB7MoHDw8PevXqRb9+/SgtLeWll17iL3/5C2PHjmXo0KGUlJTQq1cv/Pz82L17N8ePH6e0tJRdu3YB8NhjjxETE8PSpUuJjIzkzTffpH79+sTGxlba4kRERERERETE8gxGa9j8YSZPN29LhyAiIiIiAljXtgtr25IiYq6MM2l3H1SNpU5NqLK5/BdPrLK57sTsbRciIiIiIiIiIvfC7G0XIiIiIiIiImK+arwRodxU+SAiIiIiIiIi91WFKh8SExPZvHkz9vb2+Pj4MGHCBLZs2cJ7772HjY0NTz75JGFhYdja2pKQkMDmzZtxdHQEYMiQIQwfPpyMjAxmz55NUVERzZs3Z+7cuTg5OVXK4kRERESk5rKmHgsiIvL7zE4+7N+/n9TUVDZv3kzdunV55ZVXSExMZM2aNWzatAlnZ2eioqJISkpi1KhRHDt2jEWLFtGxY0fTO4xGI5MnTyY2NpYuXbqwY8cOZs2axTvvvFMpixMRERGRmsvamioqGSIi5fUA7bowf9vF8ePH6dq1K/Xr18fGxoann36ahIQEHn/8cZydnQHo3r07e/bsAeDYsWO8++67+Pv7Ex0dzY0bN7hy5QqFhYV06dLFND49PZ2ioqJKWJqIiIiIiIiIWAOzkw/t2rUjPT2dq1evcuPGDfbt28fjjz/O0aNHycrKorS0lJ07d3Lp0iWuX79O27ZtCQ0NJTk5mdzcXJYvX07jxo1xcHAgPT0dgO3bt1NcXMyVK1cqbYEiIiIiIiIiYllmJx+8vLwYOHAgI0aMIDg4mCeeeIK6devy2muvMWHCBIYPH467uzt2dnbUq1ePlStX8oc//AFbW1uCgoJIS0vDYDCwZMkS3n33Xfr3709eXh6NGjXCzs6uMtcoIiIiIiIiIhZkds+H/Px8evXqxahRowB47733aNasGZ6enmzduhWATz75BFdXV86fP8/+/fsZPHgwcLPXg63tzaltbW1JSkoC4PLlyyxfvpxGjRpVYEkiIiIiIiIi1u9BOmrT7OTDuXPnmD59Ops3b6agoIBNmzYRExPDSy+9xMcff4y9vT1r1qwhMDCQOnXq8Oabb/Lkk0/SsmVL1q5dS8+ePQEIDw8nKioKT09PVq1aRZ8+fahVSyeAioiIiMjvU4NHEZHqw+zkg4eHB7169aJfv36Ulpby0ksv8cQTT/DKK6/wwgsvUFJSgp+fH/7+/gBER0czYcIEiouL6dSpk6liIioqisjISAoKCnB3dycmJqZyViYiIiIiNZpOuxCR6u4BKnzAYKzGdR6ebt6WDkFEREREBLCu5IO1JWZEzJVxJs3SIdxXyROXVNlcAxImV9lcd2J25YOIiIiIiCVZ0499ERH5fUo+iIiIiEi1ZG3/uq9kiIjIb7unzo75+fn4+flx7tw5APbv34+/vz+9evUiPj7eNG737t34+/vj6+tLWFgYRUVFAGRkZDBo0CD8/f0ZN24cFy9eBODHH39k+PDhBAQE8MILL3DixInKXp+IiIiIiIiIVSozGqvssrS7Vj4cPXqUiIgIMjMzASgsLCQ8PJykpCQefvhhxo0bR1paGn/+85+Jjo4mOTmZpk2bEhISQnJyMkOGDGHy5MnExsbSpUsXduzYwaxZs3jnnXeIiIhg3LhxPPPMMxw4cIDp06ezbdu2+71mEREREZFKZ22VGCIi1uSuyYcNGzYQGRnJtGnTgJtVDG5ubri6ugLg7+/Pzp078fb2Zt++fdjZ2VFQUMDly5dxdHTkypUrFBYW0qVLFwC6d+/OtGnTKCoq4vnnn+fpp58GwN3dnaysrPu1ThERERGpYbTN4bcpESJSPVhBQUKVuWvy4T+Pvrxw4QJOTk6me2dnZ7KzswGws7MjLS2NadOm4ezsTNeuXalfvz4ODg6kp6fTtWtXtm/fTnFxMVeuXGHgwIGm9yxZsoQePXpU1rpEREREpIazth/YSoaIiPy2e+r58O/KysowGAyme6PReMu9t7c3Bw8epHv37kRFRWEwGFiyZAnvvvsu/fv3Jy8vj0aNGmFnZ2f6flxcHEePHiU8PLwSliQiIiIiIiIi1qTcyQcXFxdTw0iAixcv4uzszNWrV0lPTzc99/f359SpUwDY2tqSlJTE1q1b6devH2VlZTRq1IiSkhJef/11vvnmGz744AMaNGhQCUsSEREREREREWtS7uRDhw4d+Omnnzhz5gylpaV8/PHHdOvWDaPRSGhoKOfPnwdg586ddOrUCYDw8HAyMjIAWLVqFX369KFWrVrExcWRn5/P3/72NyUeRERERERE5IFiNBqr7LK0u/Z8+E+1a9cmNjaWSZMmcePGDby9venTpw8Gg4E5c+Ywbtw4DAYDrVu3Zvbs2QBERUURGRlJQUEB7u7uxMTEkJOTw9q1a2nZsiXPP/+86f0pKSmVtzoRERERERERsTiD0RpSIGbydPO2dAgiIiIiIoB1NZy0tmacIubKOJNm6RDuq40TFlfZXM+vmFplc91JuSsfRERERERERKTiqm8pQPkp+SAiIiIi1ZI1VRqIiMjvu6fkQ35+PoGBgbzzzju0bNmS/fv3M3/+fG7cuEHfvn0JCQkB4Ntvv+WNN96guLiYhx9+mDfffBNHR0eOHDnC/PnzKS4uplGjRsybN48WLVpw6NAhJk2ahIuLCwB/+tOfmD9//v1brYiIiIjUGNa2tUDJEBEpr2rcBaHc7nraxdGjRxk6dCiZmZkAFBYWEh4ezvLly9mxYwfHjh0jLe3mPpyYmBgmT57Mtm3beOSRR3j//fcBCA0NZe7cuaSkpODv78/cuXMBOHbsGEFBQaSkpJCSkqLEg4iIiIiIiEgNdNfkw4YNG4iMjMTZ2RmAjIwM3NzccHV1xdbWFn9/f3bu3AlAWVkZ169fB6CgoIA6depQVFTElClT8PDwAMDd3Z2srCwAvvnmG9LT0/H392f8+PGm5yIiIiIiIiJSc9w1+RATE0Pnzp1N9xcuXMDJycl07+zsTHZ2NgBhYWFERETQtWtX9u/fT2BgIPb29gQEBAA3kxMJCQn06NEDgAYNGjBixAhSU1Px9vY2bd8QERERERERkZqj3A0ny8rKMBgMpnuj0YjBYKCwsJCZM2eyevVqPD09WbVqFdOnTycxMRGAoqIiwsLCKCkpYdy4cQBER0eb3jN06FDeeust8vLyaNCgQUXXJSIiIiI1nHosiEh19wC1fCh/8sHFxYWLFy+a7i9evIizszPfffcdtWvXxtPTE4AXXniBt99+G4Dr168zYcIEGjVqxIoVK7Czs6OsrIx3332XsWPHYmNjY3rfv/8tIiIiIvJb1HBSRKT6KHfyoUOHDvz000+cOXOGli1b8vHHHzNo0CDc3Nz45ZdfOH36NI8++ih79+6lffv2wM2Gk25ubsyePZtatW7u9KhVqxa7d+/Gzc0NHx8ftm7dSocOHXBwcKjcFYqIiIhIjaQf+yJS3ZU9QKUP5U4+1K5dm9jYWCZNmsSNGzfw9vamT58+GAwG5s+fz9SpUzEajTz00EPMmzeP48ePs3fvXlq3bs2AAQOAm30iVq5cSVxcHLNmzWLZsmU0adKEBQsWVPoCRURERKRmUuWDiEj1YTBW44NFPd28LR2CiIiIiAhgXckHa0vMiJgr40yapUO4r9aNWVRlcw1b+WqVzXUndz3tQkRERERERESkIsq97UJEREREREREKq767kMov3uufMjPz8fPz49z586Znk2bNo0tW7aY7o8cOcLgwYMJCAhg5MiR/PzzzwAcOnSIJ598koCAAAICApgxYwZw8/jN1157DX9/fwICAti/f39lrUtERERERERErMQ9VT4cPXqUiIgIMjMzAcjOziYyMpIDBw7QpUsX07jQ0FCWL1+Oh4cHmzZtYu7cuaxYsYJjx44RFBTEuHHjbnlvSkoKZWVlpKamcurUKcaMGcP//M//VN7qRERERERERKxUNW7BWG73lHzYsGEDkZGRTJs2DYDU1FSee+45GjVqZBpTVFTElClT8PDwAMDd3Z01a9YA8M0333Dp0iU+/vhjWrRoQWRkJA8//DBlZWUUFBRQWlpKQUEBderUqeTliYiIiEhNZU0NHkVE5PfdU/IhJibmlvvg4GDg5jaL/2Vvb09AQAAAZWVlJCQk0KNHDwAaNGhA37596dWrFx9++CEhISF89NFHDBgwgOTkZJ5++mlyc3NZtKjqOn2KiIiIiIiISNWo9IaTRUVFhIWFUVJSYtpmER0dbfp86NChvPXWW+Tl5bFy5Uoef/xxPvzwQzIzM3nppZdo164dLVq0qOywRERERKSGsbbjJFWJISLy2yr1qM3r168THBxMSUkJK1aswM7OjrKyMlasWEFpaektY21sbNi7dy8DBw7EYDDwyCOP0KFDBzIyMiozJBERERERERGxsEpNPoSGhuLm5sbixYuxt7e/OUGtWuzevZtdu3YBsHXrVjp06ICDgwMeHh7s2bMHgJycHI4dO0bbtm0rMyQRERERERERq2Q0Vt1laZW27eL48ePs3buX1q1bM2DAAACcnZ1ZuXIlcXFxzJo1i2XLltGkSRMWLFgAwIwZM5g1axa+vr7UqlWLV199lVatWlVWSCIiIiJSg2mbg4hI9WEwVuOzPTzdvC0dgoiIiIiIiNwnGWfSLB3CffXBqIVVNteLq16vsrnupNIbToqIiIiIPIisqRLD2ppxiogo+SAiIiIiIiJiAWXVdyNCud1z8iE/P5/AwEDeeecdWrZsCcC0adPo0qULAwcOBODChQtERERw4cIF6tSpw8KFC2nZsiWZmZlERERw7do1GjVqRHR0NI888ojp3V9++SWJiYn8/e9/r+TliYiIiEhNZU2VBiIi8vvu6bSLo0ePMnToUDIzMwHIzs5m/PjxphMs/te0adPo3r07W7duJSAggIULb+5fmTFjBgMHDiQ1NZXXXnuNqVOnAlBWVsbf/vY3Xn31VcrKyipvVSIiIiIiIiJiNe4p+bBhwwYiIyNxdnYGIDU1leeee46+ffuaxuTk5HDy5EkCAwMBGDRokCnJcOLECfr06QPA448/zoULFzh79iw//vgjP/74I3PmzKnMNYmIiIiIiIiIFbmn5ENMTAydO3c23QcHB/P888/fMubs2bM0b96c2NhYBg0axOTJk7GzswPgT3/6E9u3bwfgwIEDXL16lYsXL/LHP/6RmJgYGjZsWFnrEREREREREakWjMaquyyt0hpOlpSUcPz4cSZNmsSMGTPYuHEjYWFhJCUlERsby5w5c0hKSqJbt254eHiYEhMiIiIiIuawthMd1INCROS3VVrywcnJiXr16tG9e3cA/Pz8mDt3LnAzMbFs2TLs7e0pLi5m/fr1pqaVIiIiIiIiIg8iozWUJFSRe9p2cS/+67/+CxcXF9LS0gD47LPPaNeuHQDx8fHs3bsXgE2bNtG+fXsaN25cWVOLiIiIiIiIiBWrtMoHgKVLlxIZGcmbb75J/fr1iY2NBeD1119n+vTpJCQk0KxZM+bPn1+Z04qIiIjIA0jbHEREqg+DsRrXeXi6eVs6BBERERERwLqSIdbWD0PEXBln0iwdwn31txcXVNlcQR9Mq7K57qTStl2IiIiIiIiIiNxJpW67EBEREREREZF7U1Zt9yGU3z1VPuTn5+Pn58e5c+cAWL9+PX5+fvj7+zNjxgyKiopuGT9t2jS2bNliuj9y5AiDBw8mICCAkSNH8vPPP98y/pdffuEvf/mL6f0iIiIiIiIiUnPcNflw9OhRhg4dSmZmJgA//fQT77//Ph999BHbtm2jrKyMdevWAZCdnc348ePZtWvXLe8IDQ1l7ty5pKSk4O/vbzqCE6CsrIyZM2dSXFxcicsSERERERERsW5Go7HKLku7a/Jhw4YNREZG4uzsDIC9vT2RkZHUr18fg8FAmzZtOH/+PACpqak899xz9O3b1/T9oqIipkyZgoeHBwDu7u5kZWWZPn/vvff461//qqM3RURERERERGqouyYfYmJi6Ny5s+m+RYsWPPXUUwDk5OSwdu1annvuOQCCg4N5/vnnb/m+vb09AQEBwM0qh4SEBHr06AHAsWPH+Mc//sGoUaMqZzUiIiIiIiIi1YTRWHWXpZndcDI7O5vg4GAGDRrEk08+edfxRUVFhIWFUVJSwrhx4ygoKGD27Nm8/fbb1KqlQzdEREREREREaiqzfvX/+OOPBAYGMmDAAF555ZW7jr9+/TrBwcGUlJSwYsUK7OzsOHz4MJcvX2bChAkEBARw4cIFxo4dy+nTp80JSURERERERESsVLkrH/Lz8xk9ejRTp06lf//+9/Sd0NBQ3NzcmD17tqnK4emnn2bfvn2mMc8++yyJiYm0bNmyvCGJiIiIiIiIiBUrd/Jh06ZNXLp0iVWrVrFq1SrgZuJgypQpdxx//Phx9u7dS+vWrRkwYAAAzs7OrFy5sgJhi4iIiIiIiFRv1nAKRVUxGKvxaj3dvC0dgoiIiIgIAJ+nvmXpEEye8X/N0iGIVIqMM2mWDuG+endYbJXNNW5dWJXNdSdmN5wUEREREREREfOVVd9agHLTMRMiIiIiIiIicl/dU+VDfn4+gYGBvPPOO7Rs2ZL169eTlJSEwWDgscceY/bs2djb25OQkMDmzZtxdHQEYMiQIQwfPpzk5GTeeustHnroIQCeeeYZQkJCGDhwIKWlpQAUFhZy9uxZ/ud//oemTZvep+WKiIiIiIiISFW7a/Lh6NGjREREkJmZCcBPP/3E+++/z5YtW6hXrx5hYWGsW7eOl156iWPHjrFo0SI6dux4yzuOHTtGWFgYfn5+tzzfsmWL6e9p06YxYMAAJR5EREREREREapi7brvYsGEDkZGRODs7A2Bvb09kZCT169fHYDDQpk0bzp8/D9xMMrz77rv4+/sTHR3NjRs3APjmm29ITk7G39+f119/nWvXrt0yx4EDBzh58iRjxoyp7PWJiIiIiIiIWCWjseouS7tr5UNMTMwt9y1atKBFixYA5OTksHbtWubPn8/169dp27YtoaGhuLm5ERYWxvLlywkJCcHJyYmgoCA6derEokWLiI6O5q23/q8b8JIlSwgJCcHGxqaSlyciIiIiNZU1nS4hIiK/z+zTLrKzswkODmbQoEE8+eSTAKxcudL0eVBQEOHh4YSEhLBs2TLT8+DgYHr27Gm6//7777ly5Qrdu3c3NxQREREReQBZ23GSSoaISE2RmprKihUrKCkpYeTIkQwfPvyO4z7//HOio6PZt2/fXd9p1mkXP/74I4GBgQwYMIBXXnkFgPPnz7Np0ybTGKPRiK2tLXl5eaxevfqW5/9e4bBnzx58fHzMCUNERERERESk2jJW4f/dq+zsbOLj41m3bh1bt25l/fr1/PDDD7eNu3TpEnFxcff83nInH/Lz8xk9ejRTpkwhKCjI9LxOnTq8+eabnD17FqPRyNq1a+nZsycODg689957HD16FIA1a9bcUvnwz3/+k86dO5c3DBERERERERG5R7m5uZw7d+62Kzc395Zx+/fvp0uXLjRq1AgHBwd69+7Nzp07b3tfREQEEydOvOf5y73tYtOmTVy6dIlVq1axatUqAJ599lmmTJlCdHQ0EyZMoLi4mE6dOjFq1ChsbGxYvHgxUVFRFBYW0qpVKxYsWGB639mzZ2nWrFl5wxARERERERGRe/T3v/+dhISE255PnDiRSZMmme4vXLiAk5OT6d7Z2ZmMjIxbvvPBBx/wpz/9iQ4dOtzz/PecfPjfPRwvvfQSL7300h3H9O7dm969e9/2vHPnziQnJ9/xOzt27LjXEERERERERETEDCNHjmTAgAG3PXd0dLzlvqysDIPBYLo3Go233H/33Xd8+umnrF69ml9++eWe5ze74aSIiIiIiCWpwaOIVHdlVXgGpqOj422JhjtxcXHh8OHDpvuLFy/i7Oxsut+5cycXL15k0KBBFBcXc+HCBYYNG8a6det+971KPoiIiIhItaTTLkREKt9f//pXli5dSk5ODnXr1uXTTz9lzpw5ps8nT57M5MmTATh37hwvvvjiXRMPcI8NJ/Pz8/Hz8+PcuXMArF+/Hj8/P/z9/ZkxYwZFRUUApKWl4e/vj7+/P6+99hrXr18HICMjg0GDBuHv78+4ceO4ePEicLPhxdixY+nbty/Dhw83PRcRERERERGp6YzGqrvuVbNmzQgJCeHFF1+kf//++Pn54enpyZgxY/jmm2/MXqvBaPz9MI4ePUpERAQ//fQTO3fupLi4mHHjxrFlyxbq1atHWFgYbdu2ZeDAgfTu3ZukpCRat27NypUryc7OZubMmXTv3p3Y2Fi6dOnCjh072LZtG++88w7R0dG4uLgwduxYtm7dyueff87ixYvvOXhPN2+zFy4iIiIi1ZsqDX6btVWFiJgr40yapUO4r5YMiamyuSZvmFllc93JXbddbNiwgcjISKZNmwaAvb09kZGR1K9fH4A2bdpw/vx5MjMzad68Oa1btwage/fuBAcH8/LLL1NYWEiXLl1Mz6dNm0ZRURGff/45a9euBcDPz4/o6GiKi4uxs7O7L4sVERERkZrD2n5gKxkiIuV1l1qAGuWuyYeYmFszMS1atKBFixYA5OTksHbtWubPn0+rVq345ZdfOHnyJB4eHnzyySdcunSJxo0b4+DgQHp6Ol27dmX79u0UFxdz5cqVW47wsLW1pX79+uTk5OjoTRERERG5K/3YFxGpPsxuOJmdnU1wcDCDBg3iySefBCAuLo5Zs2ZRVlbGkCFDsLOzw2AwsGTJEuLi4li4cCEBAQE0atTojtUNRqORWrXuqQ2FiIiIiDzgVPkgIlJ9mJV8+PHHHwkODmbEiBEEBQUBUFpaiouLCxs3bgRuNpl0dXW9OYmtLUlJSQBcvnyZ5cuX06hRI5ydnbl06RIuLi6UlJRw/fp1GjVqVAnLEhERERGpWtaWDBERsSblTj7k5+czevRopk6dSv/+/U3PDQYDQUFBbNy4EWdnZ1avXo2Pjw8A4eHhREVF4enpyapVq+jTpw+1atXC29ubrVu3Mn78eHbs2EHnzp3V70FEREREqiVrqnxQIkSkeih7cFo+lD/5sGnTJi5dusSqVatYtWoVAM8++yxTpkwhOjqa4OBgioqK8PLyYvTo0QBERUURGRlJQUEB7u7upj4SU6ZMISwsDF9fXxo0aMDChQsrcWkiIiIiIiIiYg3uetSmNdNRmyIiIiJiLVT5IFL5avpRm4sGzamyuV7dPKvK5roTdXcUERERERERkftKyQcRERERERERua/uKfmQn5+Pn58f586dA2DdunX4+vri4+NDXFwc/7lz4/PPP+fZZ5813R85coTBgwcTEBDAyJEj+fnnnwH44YcfCAwMpF+/fowYMcL0XERERERERERqjrsmH44ePcrQoUPJzMwE4OzZs6xevZqNGzeSmprK119/zZdffmkaf+nSJeLi4m55R2hoKHPnziUlJQV/f3/mzp0LwOzZs3n55ZfZtm0bPj4+LFq0qBKXJiIiIiIiIiLW4K7Jhw0bNhAZGYmzszMArq6ubN++HQcHB3Jzc8nPz8fR0dE0PiIigokTJ5rui4qKmDJlCh4eHgC4u7uTlZUFwKpVq+jWrRtlZWWcP3/+lveIiIiIiIiI1GRGo7HKLku761Gb/3ss5r+zs7Njw4YNxMXF4enpaUosfPDBB/zpT3+iQ4cOprH29vYEBAQAUFZWRkJCAj169Lg5ua0tubm5+Pj4UFhYSFJSUqUsSkRERERERESsh9kNJ4cMGcLBgwdp2rQpCQkJfPfdd3z66ae8/PLLdxxfVFTE66+/TklJCePGjTM9d3R0JD09nUWLFjFhwgRKS0vNDUlERERERESk2igzVt1laeVOPmRlZXHkyBHgZuWCr68vp06dYufOnVy8eJFBgwYxduxYLly4wLBhwwC4fv06wcHBlJSUsGLFCuzs7ADYsWOHqfyjW7duFBYWcu3atcpam4iIiIiIiIhYgXInH/Ly8ggNDSU3Nxej0ciuXbt44oknmDx5Mrt27SIlJYXExEScnZ1Zt24dcLPhpJubG4sXL8be3t70rr/97W/s3r0bgH/84x80btyYJk2aVNLSRERERERERMQa3LXnw39q06YNY8eOJTAwEBsbGzp37syoUaN+c/zx48fZu3cvrVu3ZsCAAQA4OzuzcuVKYmNjmTVrFsuWLaNBgwYsWbLE/JWIiIiIiIiIiFUyGK2h7aWZPN28LR2CiIiIiAgAn6e+ZekQTJ7xf83SIYhUiowzaZYO4b5a0H92lc01bWtklc11J2Y3nBQRERERERERuRfl3nYhIiIiIiIiIhVnpNpuRCi3e6p8yM/Px8/Pj3PnzgGwbt06fH198fHxIS4uznRixbfffsugQYPo168f48aNIzc3F4Dk5GS6du1KQEAAAQEBxMfHm9772muv0b9/f/r378+33357P9YoIiIiIiIiIhZ01+TD0aNHGTp0KJmZmQCcPXuW1atXs3HjRlJTU/n666/58ssvAYiJiWHy5Mls27aNRx55hPfffx+AY8eOERYWRkpKCikpKYSEhAAwf/58Hn74YbZu3cqrr75KVFTU/VmliIiIiIiIiJUpMxqr7LK0u2672LBhA5GRkUybNg0AV1dXtm/fjp2dHVeuXCE/Px9HR0cAysrKuH79OgAFBQU0bNgQgG+++YbMzEzeffdd3N3dmTVrFo6Ojnz66afs3bsXgG7duvHwww/fl0WKiIiIiIiIiOXcNfkQExNz2zM7Ozs2bNhAXFwcnp6eeHh4ABAWFkZQUBDz5s2jbt26bNiwAQAnJyeCgoLo1KkTixYtIjo6mhkzZmBvb8+6dev47LPPqF27NuHh4ZW8PBERERGRqqETJkREfpvZDSeHDBnCwIEDmTFjBgkJCbz88svMnDmT1atX4+npyapVq5g+fTqJiYksW7bM9L3g4GB69uzJtGnTuHTpEg0aNGD9+vV8+eWXvPLKK6ZKCBERERGR6kRHbYqI/LZyH7WZlZXFkSNHALC1tcXX15dTp07x3XffUbt2bTw9PQF44YUXOHToEHl5eaxevdr0faPRiI2NDY0bN8bW1hY/Pz8AnnrqKX799VcuX75cCcsSEREREREREWtR7uRDXl4eoaGh5ObmYjQa2bVrF0888QRubm788ssvnD59GoC9e/fSvn17HBwceO+99zh69CgAa9asoWfPntjb2/PXv/6V7du3A/DPf/6TunXr0rhx40pcnoiIiIiIiIh1Mhqr7rK0cm+7aNOmDWPHjiUwMBAbGxs6d+7MqFGjsLOzY/78+UydOhWj0chDDz3EvHnzsLGxYfHixURFRVFYWEirVq1YsGABcLOfxBtvvMG6deuwtbUlPj6eWrXKnQ8REREREREREStmMBqtIQdiHk83b0uHICIiIiICqOeDyP2QcSbN0iHcV3P936iyuSJSo6tsrjtRmYGIiIiIiIiI3Fdmn3YhIiIiImJJ1lRpICIiv0+VDyIiIiIiIiJyX91T5UN+fj6BgYG88847tGzZ0vR8zZo17Nq1i6SkJAD27NnD0qVLMRqNtGzZkvnz59OwYUPOnTvH9OnTyc/Px9HRkdjYWFq0aEFmZiYRERFcu3aNRo0aER0dzSOPPHJ/VioiIiIiNYq19TVQJYaIlFc1bsFYbnetfDh69ChDhw4lMzPzluc//PADiYmJpvv8/HyioqJITExk27ZtuLu7s3TpUgDefvttfH19SUlJoVevXsTHxwMwY8YMBg4cSGpqKq+99hpTp06tvJWJiIiIiIiIiFW4a/Jhw4YNREZG4uzsbHpWVFTEG2+8weTJk03PiouLiYyMpFmzZgC4u7uTlZUFQFlZGfn5+QAUFBRQp04dAE6cOEGfPn0AePzxx7lw4QJnz56tpKWJiIiIiIiIWC+jseouS7vrtouYmJjbnr311lsMGjToli0YjRs3pmfPngAUFhaSmJjIiBEjAJgyZQqBgYEkJSVRXFzM+vXrAfjTn/7E9u3bef755zlw4ABXr17l4sWLuLq6VsriRERERKTm0jYHEZHqo9ynXXz55ZdkZWUxY8YMDh48eNvneXl5vPLKK3h4eDBgwAAApk+fTnR0ND169GDXrl1MnDiRbdu2ERsby5w5c0hKSqJbt254eHhgZ2dX8VWJiIiISI2nng8iItVHuZMPH3/8Md9//z0BAQH8+uuvXLp0ialTp7J48WIuXLjA6NGj6dKlC+Hh4QDk5ORw+vRpevToAUDv3r2JjIzkypUrlJSUsGzZMuzt7U0VEf9eTSEiIiIiIiIi1V+5kw/z5883/X3w4EESEhJYvHgxpaWljB8/nr59+/Lyyy+bxjRu3JjatWtz+PBhOnfuzJEjR6hXrx5NmjQhMjISHx8f+vbty6ZNm2jfvj2NGzeunJWJiIiIiIiIWLEya2jGUEXKnXz4Lfv27eP48eOUlpaya9cuAB577DFiYmJISEhgzpw5FBYWUq9ePdMpGK+//jrTp08nISGBZs2a3ZLYEBEREREREZGawWCsxgeLerp5WzoEERERERHAuno+WFs/DBFzZZxJs3QI91Wkz8wqm2v2jtsPk6hKdz1qU0RERERERESkIpR8EBEREREREZH76p6SD/n5+fj5+XHu3Llbnq9Zs4YRI0YAcOLECQICAkzX008/jZ+fHwBHjhxh8ODBBAQEMHLkSH7++WcAfvzxR4YPH05AQAAvvPACJ06cqMy1iYiIiIiIiFitMqOxyi5Lu2vy4ejRowwdOpTMzMxbnv/www8kJiaa7tu2bUtKSgopKSl89NFHNGzYkKioKABCQ0OZO3cuKSkp+Pv7M3fuXAAiIiIYM2YMKSkpTJ06lenTp1feykRERERERETEKtw1+bBhwwYiIyNxdnY2PSsqKuKNN95g8uTJd/zOu+++y5///Gc6d+5MUVERU6ZMwcPDAwB3d3eysrIAeP7553n66advey4iIiIiIiIiNcddj9qMibm9I+Zbb73FoEGDaNmy5W2f5eXlsWHDBlJTUwGwt7cnICAAgLKyMhISEujRowcAAwcONH1vyZIlpuciIiIiIiIiUnOUu+Hkl19+SVZWFoMGDbrj59u2baNHjx489NBDtzwvKiri9ddfp6SkhHHjxpmeG41G4uLiOHr0KOHh4eUNR0RERERERKRaMhqr7rK0cicfPv74Y77//nsCAgKIiIjg2LFjTJ061fT5nj178PHxueU7169fJzg4mJKSElasWIGdnR0AJSUlvP7663zzzTd88MEHNGjQoGKrERERERERERGrc9dtF/9p/vz5pr8PHjxIQkICixcvBm5WMXz77bd07Njxlu+Ehobi5ubG7NmzqVXr//IdcXFx5Ofn87e//Q17e3szlyAiIiIiIiJS/VjDKRRVpdzJh9+Tk5ODnZ0dtWvXNj07fvw4e/fupXXr1gwYMAAAZ2dn4uLiWLt2LS1btuT55583jU9JSanMkERERESkhvo89S1LhyAiIvfIYDRW31SLp5u3pUMQEREREQGsKxnyjP9rlg5BpFJknEmzdAj3VXjvGVU217xd8+8+6D4qd88HEREREREREZHyqNRtFyIiIiIiIiJyb6rxRoRyu6fKh/z8fPz8/Dh37hwAM2bMoFevXgQEBBAQEMDu3btvGT9t2jS2bNliuk9OTqZr166m8fHx8QAcOnSIJ5980vR8xoyqKzkRERERERERkapx18qHo0ePEhERQWZmpunZsWPHWLNmDc7OzreMzc7OJjIykgMHDtClS5dbxoeFheHn53fL+GPHjhEUFMS4ceMquAwRERERERGR6sWIKh9MNmzYQGRkpCnRUFBQwPnz5wkPD8ff358lS5ZQVlYGQGpqKs899xx9+/a95R3ffPMNycnJ+Pv78/rrr3Pt2jXT8/T0dPz9/Rk/fjxZWVmVvT4RERERERERsbC7Jh9iYmLo3Lmz6f7SpUt06dKFefPmsWHDBg4fPsymTZsACA4OvuXYzP/l5OTEyy+/zLZt23j44YeJjo4GoEGDBowYMYLU1FS8vb0JCQmprHWJiIiIiIiIWLUyo7HKLksr92kXrq6uLFu2DGdnZ+rWrcuIESNIS/v940+WLVvGE088gcFgIDg4mC+++AKA6OhoevXqBcDQoUP54YcfyMvLM2MZIiIiIiIiImKtyp18OHXqFLt27TLdG41GbG1/u3VEXl4eq1evvmW8jY0NZWVlrFixgtLS0lvG29jYlDckEREREREREbFi5U4+GI1G5s2bx7Vr1yguLmb9+vX07NnzN8c7ODjw3nvvcfToUQDWrFlDz549qVWrFrt37zYlMrZu3UqHDh1wcHAwcykiIiIiIiIiYo3uetrFf/Lw8GDs2LEMHTqUkpISevXqddspFv/OxsaGxYsXExUVRWFhIa1atWLBggUAxMXFMWvWLJYtW0aTJk1Mz0VERERERESk5jAYjVbQecJMnm7elg5BRERERASAz1PfsnQIJs/4v2bpEEQqRcaZ3+8vWN2F9phWZXO9ucey/9hf7soHERERERFrYE0/9kVE5Pcp+SAiIiIi1ZK1/eu+kiEiUl7WcARmVbmnhpP5+fn4+flx7tw5AGbMmEGvXr0ICAggICCA3bt3A5CQkED37t1Nz9euXQvAuXPnGD58OAEBAYwYMYKff/4ZgGvXrjFmzBj69evH4MGDOXHixP1Yo4iIiIiIiIhY0F0rH44ePUpERASZmZmmZ8eOHWPNmjU4OzvfMvbYsWMsWrSIjh073vL87bffxtfXl2HDhpGUlER8fDwLFy5k1apVtGnThpUrV7Jv3z6io6P58MMPK2dlIiIiIiIiImIV7lr5sGHDBiIjI02JhoKCAs6fP094eDj+/v4sWbKEsrIy4Gby4d1338Xf35/o6Ghu3LgBQFlZGfn5+abv16lTx/T8+vXrtz0XERERERERkZrjrpUPMTExt9xfunSJLl26EBkZSYMGDRg3bhybNm3C19eXtm3bEhoaipubG2FhYSxfvpyQkBCmTJlCYGAgSUlJFBcXs379egCCgoJ44YUX6Nq1K9evX+dvf/vb/VmliIiIiNQ46rEgItVdNT58stzuqefDv3N1dWXZsmU4OztTt25dRowYQVpaGvXq1WPlypX84Q9/wNbWlqCgINLSbh6LMn36dKKjo/niiy+YPXs2EydOxGg0MmfOHIYPH056ejp/+9vfCAkJMVVCiIiIiIiIiEjNUO7TLk6dOkVmZia9e/cGbmZqbG1tOX/+PPv372fw4MG3PM/JyeH06dP06NEDgN69exMZGcmVK1fYu3cv0dHRAHTs2JGHHnqIH3/8EU9Pz8pan4iIiIjUUDrtQkSquzJU+fCbjEYj8+bN49q1a6YtFD179qROnTq8+eabnD17FqPRyNq1a+nZsyeNGzemdu3aHD58GIAjR45Qr149mjRpgoeHB3v27AEgMzOTCxcu8Mgjj1TuCkVERERERETEospd+eDh4cHYsWMZOnQoJSUl9OrVCz8/PwCio6OZMGECxcXFdOrUiVGjRmEwGEhISGDOnDkUFhZSr149li5dCkBsbCxvvPEGK1euxN7enri4OBo0aFC5KxQRERGRGkmVBiIi1YfBWI07XHi6eVs6BBERERERwLqSIda2JUXEXBln0iwdwn019dmq++/q4n2W/f9R5a58EBEREREREZGKq8a1AOVW7p4PIiIiIiIiIiLlcU+VD/n5+QQGBvLOO+/QsmVLvv76a+bPn8/169dxd3cnNjYWe3t7Tpw4wcyZM7l+/TqdO3dm9uzZ2NracuTIEebPn09xcTGNGjVi3rx5tGjRwvT+jRs3cuTIEWJjY+/bQkVERESkZrGmbQ4iIvL77pp8OHr0KBEREWRmZgI3ExGTJk3ivffew8PDg1dffZVNmzYxbNgwQkNDmTt3Lo8//jjh4eFs2LDB9Hz58uV4eHiwadMm5s6dy4oVK7hx4wZLly5l7dq1pqM7RURERETuhbX1NVAyRETKq+wB2nZx1+TDhg0biIyMZNq0aQB8+eWXPP7443h4eAAQERFBaWkpP//8M4WFhTz++OMADBw4kCVLljB48GCmTJliGu/u7s6aNWsA+OqrrygrKyM0NJSMjIz7sT4RERERqaH0Y19EpPq4a/IhJibmlvszZ87g4OBASEgIp0+fplOnToSFhXH8+HGcnJxM45ycnMjOzsbe3p6AgAAAysrKSEhIoEePHgB07dqVrl27smXLlspck4iIiIg8AFT5ICLVnRpO/o7S0lLS09P5/9i777gq6/fx4y8EV+6Fe5Q7TdRIScmBpiAgIO5Vaa7cmuJAUZDcaY4cmWJqCi5QUzRcaa60nIlSiooLcKAoyDjn9wc/zocj4Oib9/s2r2cPH3ruczr3dXifc7jv636/r2vEiBFs2rSJhIQEli5disFgwMLCwvQ4o9FodjspKYkvv/ySlJQU+vXr9+9EL4QQQgghhBBCCN176eRD8eLFsbGxoXz58lhaWuLk5MTp06cpVaoUMTExpsfFxsZibW0NwKNHj/j8889JSUlh0aJF5MyZ8997BUIIIYQQQgghhNC1F+p2kZG9vT3z58/n5s2blC5dmr1791KrVi3Kli1L7ty5OXHiBO+//z4hISE0adIEgFGjRlGxYkUmT55MjhzS3VMIIYQQQvzfyTIHIYR4fbx08qF06dL4+vrSv39/njx5Qs2aNfHy8gJg1qxZeHt7Ex8fT61atejZsyd//vknu3fvpkqVKnh4eABgbW3Nd9999+++EiGEEEII8UaRmg9CiNed4c0p+YCF8TWucFGnYlPVIQghhBBCCAHoK/mgt8SMEP/U6Sv7VYfwSn3RZJhm+/r2l7ma7SsrLz3zQQghhBBCCCGEEP93Rl7buQAvTQowCCGEEEIIIYQQ4pV6oeRDfHw8Li4uREVFAfDHH3/QsWNHnJ2dGTFiBElJSWaP37dvHw4ODqbbmzdvxt7eHjc3N9zc3JgzZ47Z42/dukWDBg1Mzy+EEEIIIYQQQoj/jucuuzh16hTe3t5ERkYCaYmIwYMHs2zZMmrUqMGIESPYsGEDXbt2BdJabE6fPt3sOc6ePcuYMWNwcXHJ9PwGg4Hx48eTnJz8L7wcIYQQQgghhBBC6M1zZz4EBQXh4+ODtbU1AL/++it169alRo0aAHh7e/Pxxx+bHu/t7c2gQYPMnuPMmTNs3rwZV1dXvvzyS+Li4kz3LVu2jEaNGlGkSJF/5QUJIYQQQgghhBCvA6PRqNkf1Z4788Hf39/s9pUrV3jrrbcYPnw4ly5don79+owZMwaAH374gXfffRcbGxuz/6dEiRL06tWL+vXr8/XXX+Pr68vs2bM5e/YsR44cYdmyZaxZs+ZffFlCCCGEEOK/Tk/dJYQQQjzbS3e7SE1N5eDBgwQGBlKmTBnGjx/P0qVLad26Nbt27SIgIIBbt26Z/T8LFy40/fvzzz/n448/JiEhgcmTJ/PNN9+QI4fUvRRCCCGEEC9Hb+0kJRkihHhZBh3MSNDKS5/1Fy9eHBsbG8qXL4+lpSVOTk6cPn2a0NBQYmJi8PT0pG/fvkRHR9O1a1cePnxIQECA6f83Go1YWlpy/Phx7ty5w4ABA3BzcyM6Opq+ffty6dKlf/P1CSGEEEIIIYQQQrGXnvlgb2/P/PnzuXnzJqVLl2bv3r3UqlWLIUOGMGTIEACioqLo2bMnP/74I6mpqSxbtox69ephY2PD6tWr+fjjj/noo4/Ys2eP6XkdHBxYunQp5cqV+/denRBCCCGEEEIIIZR76eRD6dKl8fX1pX///jx58oSaNWvi5eWV7eMtLS2ZO3cukyZNIjExkUqVKjFjxoz/U9BCCCGEEEIIIYR4fVgY9VD28h+qU7Gp6hCEEEIIIYQQQrwip6/sVx3CK9XXfrBm+1p6cL5m+8rKS898EEIIIYQQQmSmp4KTeivGKYTI2ms8F+ClSfJBCCGEEEK8lvR0si+EEOLZXij5EB8fT+fOnVm8eDHlypXjjz/+YOrUqTx69Ijq1aszbdo0cuXKxblz55g4cSLJycmULl2amTNnUrBgQaKiovDy8iI+Pp6CBQsybdo0ypYtS7t27UhNTQUgMTGRa9eu8csvv1C8ePFX+qKFEEIIIYQQQgjV3qRWm89NPpw6dQpvb28iIyOBtETE4MGDWbZsGTVq1GDEiBFs2LCBrl274u/vz5AhQ2jatCnTpk3j+++/Z/jw4XzzzTc4OzvTtWtXVq1axZw5c5g1axabNm0y7Wf06NF4eHhI4kEIIYQQQrwQvS0tkJkYQgiRvecmH4KCgvDx8WH06NEA/Prrr9StW5caNWoA4O3tbZq9YDAYePToEQAJCQkUKlTItD0+Pt60PU+ePGb7OHz4MOHh4UydOvVfellCCCGEEEJoS2/JECGE/hnR58yHrVu3smjRIlJSUvjkk0/o1q2b2f1hYWHMnz8fo9FIuXLlmDp1qun8Pzsv3O3CwcGBH374ge3bt/PXX3+RnJzMpUuXqF+/PmPGjCF37tycPHmSXr168dZbb5E3b16CgoIoUqQIV69epXPnzlhaWpKcnExgYCAVK1Y0PXeXLl3o27cvzZs3f6kfiHS7EEIIIYQQeqGnmQ+SCBH/Ff/1bhe9Gn2h2b6WH/r2hR53+/ZtunTpwqZNm8iVKxedO3fm66+/pkqVKkDaaghHR0c2btxIyZIl+eabb3j48CHe3t7PfN6XLjiZmprKwYMHCQwMpEyZMowfP56lS5fSp08fxo8fT0BAAHXq1GHFihV4eXmxdOlSvLy88PX1pWXLluzcuZNBgwaxZcsWLCwsiIiI4N69ey+deBBCCCGEEG82PZ3sCyGE3j148IAHDx5k2l6wYEEKFixoun3o0CHs7OwoXLgwAK1btyY0NJRBgwYBkJycjI+PDyVLlgSgevXqbN269bn7f+nkQ/HixbGxsaF8+fIAODk5sXr1ai5evEju3LmpU6cOAJ06deKbb77h7t27XLp0iZYtW5oC9/Hx4d69exQtWpSwsDDatGnzsmEIIYQQQog3nN6u7ksyRAihZytXrmTBggWZtg8aNIjBgwebbkdHR1OiRAnTbWtra06fPm26XaRIET7++GMgrXHE0qVL6dGjx3P3/9LJB3t7e+bPn8/NmzcpXbo0e/fupVatWlSsWJFbt25x6dIl3nnnHXbv3s17771HkSJFyJ07N8ePH8fW1pYTJ06QL18+ihYtCsDJkyf55JNPXjYMIYQQQgghhBDitaZlt4tPPvkEDw+PTNszznqAtJqNFhYWpttGo9HsdrqHDx8ycOBAatSokeXzPu2lkw+lS5fG19eX/v378+TJE2rWrImXlxd58+Zl6tSpDBs2DKPRSLFixfjqq6+wsLBgwYIF+Pn5kZiYSL58+Zg/f77p+a5du2aariGEEEIIIcSLkpkGQgjx4p5eXpGdUqVKcfz4cdPtmJgYrK2tzR4THR1N7969sbOzY9y4cS+0/xcuOKlHUnBSCCGEEELohZ6SIXpbkiLEP/VfLzj5iV1/zfa18sjiF3pcesHJDRs2kDdvXjp37oyfn5+pxEJqaiodOnSgZcuWfPHFixfMfOmZD0IIIYQQQuiBnk72hRDiv6JkyZIMHz6cnj17kpycTPv27alTpw59+vRhyJAh3Lp1iz///JPU1FR27twJQO3atfH393/m88rMByGEEEIIIf4FekqGyMwH8V8hMx/+PS868+FVeaGZD/Hx8XTu3JnFixfz999/8/XXX5vuu337NjY2NixZsoSff/6ZefPmYTAYeO+99/D19SVXrlycOHGCqVOnkpycTOHChfnqq68oW7YsDx484Msvv+TatWsULVqUuXPnmlXVFEIIIYQQQgghxOsvx/MecOrUKbp06UJkZCQATZs2JSQkhJCQEJYtW0b+/PkZO3Ysjx8/xtfXlxUrVvDTTz/x5MkTNm/eDMCoUaOYMmUKISEhuLq6MmXKFADmzp2Lra0tO3bsoEOHDs+dpiGEEEIIIYQQQvxXGIxGzf6o9tzkQ1BQED4+PpmqWwLMmDGDzp07U6lSJd566y327NlD8eLFSUhI4M6dOxQsWJCkpCSGDh1KjRo1AKhevTo3b94EYN++fbi6ugLg4uLCL7/8QnJy8r/5+oQQQgghhBBCCKHYc5ddZDcbITIykmPHjpndnzNnTvbv38/o0aOxtrbG3t6eXLly4ebmBqT1C12wYAEtW7YE0tpzpC+zsLKyIn/+/Ny9e1dabwohhBBCiOfSU40FIYQQz/aPu10EBgbStWtXcuXKZba9adOmHD16lK+//ppJkyYxe3baL4WkpCTGjBlDSkoK/fr1y/I5jUYjOXI8dzKGEEIIIYQQuiuqKMkQIcTLMqJ+OYRW/vGZ/u7du2nTpo3p9v379zl48KDptqurKxcuXADg0aNHfP7556SkpLBo0SJy5swJgLW1NbGxsQCkpKTw6NEjChcu/E9DEkIIIYQQQgghhA79o+TD3bt3SUxMpHz58qZtRqORUaNGcePGDQBCQ0OpX78+kFZwsmLFisydO9dspkTTpk0JDg4GYPv27dja2poSE0IIIYQQQgghhPhv+EfLLqKioihVqpTZtiJFiuDn50e/fv2wsLCgSpUqTJ48mT///JPdu3dTpUoVPDw8gLQZD9999x1Dhw5lzJgxODs7U6BAAWbNmvV/f0VCCCGEEEIIIYTQFQujUQc9N/6hOhWbqg5BCCGEEEIIQF81H/RWD0OIf+r0lf2qQ3ilujboo9m+fjz2nWb7yopUdxRCCCGEEEIIIcQr9Y+7XQghhBBCCCGEEOKfe40XIry0F0o+xMfH07lzZxYvXszff//N119/bbrv9u3b2NjYMGzYMMaMGWPafvfuXQoVKsS2bdvYvHkzs2fPplixYgA0a9aM4cOH89dff+Ht7c3jx48pVKgQ06ZNo2zZsv/ySxRCCCGEEEIIIYRKz00+nDp1Cm9vbyIjI4G0DhVNm6bVWoiJiaFLly6MHTuWSpUqERISAkBCQgIdOnRg0qRJAJw9e5YxY8bg4uJi9tyTJ0/miy++oEmTJqxdu5avv/6a2bP1s1ZOCCGEEEIIIYR4VQwy8+F/goKC8PHxYfTo0ZnumzFjBp07d6ZSpUpm25csWcIHH3yAra0tAGfOnCEyMpIlS5ZQvXp1JkyYQKFChVixYgVWVlYYDAZu3LhBwYIF/51XJYQQQggh/vP0VOBRCCHEsz03+eDv75/l9sjISI4dO5bp/ocPHxIUFMTWrVtN20qUKEGvXr2oX78+X3/9Nb6+vsyePRsrKysePHhAmzZtSExMZNWqVf/HlyOEEEIIId4UeuvoIMkQIYTI3j8uOBkYGEjXrl3JlSuX2fYtW7bQsmVLU30HgIULF5r+/fnnn/Pxxx+bbhcsWJCDBw/yyy+/MGDAAHbv3o2lpeU/DUsIIYQQQrwh5GRfCCFeH/+41ebu3btp06ZNpu1hYWFm2x8+fEhAQIDpttFoNCUXtm/fbqru2aRJExITE4mLi/unIQkhhBBCCCGEEK8No4b/qfaPZj7cvXuXxMREypcvb7bdaDRy7tw56tWrZ9r21ltvsWzZMurVq4eNjQ2rV682zXxYvnw5VlZWtGrViiNHjlCkSBGKFi36f3g5QgghhBDiTSHLLoQQ4vXxj5IPUVFRlCpVKtP2u3fvkjNnTnLnzm3aZmlpydy5c5k0aRKJiYlUqlSJGTNmADBt2jQmTJjAwoULKVCgAPPmzfuHL0MIIYQQQgghhHi9vEndLiyMxtf31dap2FR1CEIIIYQQQgD6mvmgt1khQvxTp6/sVx3CK9X+/c8029eGEys021dW/nHNByGEEEIIIYQQQogXIckHIYQQQgghhBBCvFKSfBBCCCGEEEIIIcQr9ULJh/j4eFxcXIiKigLg4MGDtG3bFhcXF0aPHk1SUpLZ40ePHs2mTZtMt6OioujWrRtubm706NGD69evm5535MiRuLu74+7uzrlz5/6t1yWEEEIIIYQQQuia0WjU7I9qz00+nDp1ii5duhAZGWnaNn78eObMmcO2bdtITEwkJCQEgNu3b9O/f3927txp9hzffPMNzs7OhISE0KpVK+bMmQPA1KlTKV26NMHBwYwYMYJJkyb9e69MCCGEEEIIIYQQuvDcVptBQUH4+PgwevRo07bU1FTi4+NJTU3lyZMnptaaW7dupUWLFhQuXNjsOQwGA/Hx8QAkJCSQJ08ejEYju3btYvfu3QA0adKE0qVL/1uvSwghhBBCCE1JhwkhxMsyGA2qQ9DMc5MP/v7+mbZNmjSJHj16kD9/fsqVK4ejoyMAn3/+OQAnTpwwe/zQoUPp3Lkzq1atIjk5mcDAQO7cuUOuXLn48ccf2bt3L7lz52bcuHH/xmsSQgghhBBCc9JqUwghsvfSBSdjYmKYNWsW27Zt4+DBg9jY2DB16tRn/j9eXl74+vpy4MABJk+ezKBBg0hNTSU2NpYCBQoQGBhIv379GDhw4D9+IUIIIYQQQgghxOvEqOF/qr108uH48eNUq1aNChUqkCNHDjp27MixY8eyffzdu3e5dOkSLVu2BKB169bExMRgaWmJlZUVLi4uADRu3JjHjx9z586df/hShBBCCCGEEEIIoUcvnXyoVq0ap0+fJjY2FoDdu3fz3nvvZfv4IkWKkDt3bo4fPw6kLcnIly8fxYsXp1GjRvz0008AnDx5krx581KkSJF/8jqEEEIIIYQQQgihU8+t+fC0ypUrM3ToUHr27ImlpSUVK1bE19c328dbWFiwYMEC/Pz8SExMJF++fMyfPx9IqycxceJEfvzxR6ysrJgzZw45crx0PkQIIYQQQgghhBA6ZmHUQ8PPf6hOxaaqQxBCCCGEEAKQgpNCvAqnr+xXHcIr1bZud832teXkas32lZWXnvkghBBCCCGEHujpZF8IIcSzSfJBCCGEEEK8lvR2dV+SIUKIl/UaL0R4aS9UYCE+Ph4XFxeioqIAOHjwIG3btsXFxYXRo0eTlJQEwKVLl+jRowdt27ald+/exMXFmT3P3LlzTfUeACIjI+nevTuurq706NGDy5cv/1uvSwghhBBCCCGEEDrx3OTDqVOn6NKlC5GRkaZt48ePZ86cOWzbto3ExERCQkIwGo0MGDCAPn36sGXLFmrWrMnSpUsBePjwIePGjWPFihVmzz127FjatWvH1q1bGTlyJMOGDftXX5wQQgghhBBCCCHUe27yISgoCB8fH6ytrU3bUlNTiY+PJzU1lSdPnpA7d27OnTvHW2+9RZMmTQDo378/3bp1A9LacVaqVInPPvvM7LnPnz+Po6MjAHXr1iU6Oppr1679ay9OCCGEEEIIIYQQ6j03+eDv74+tra3ZtkmTJtGjRw8++ugj7t27h6OjI1evXqV48eKMGzcODw8PfHx8eOuttwBwd3enb9++WFpamj3Pu+++y08//QTA4cOHuX//PjExMf/WaxNCCCGEEEIIIYQOvFDNh4xiYmKYNWsW27Zt4+DBg9jY2DB16lRSUlI4duwYXbp0YfPmzZQvX55p06Y987mmTZvGrl27aNu2Lb/++is1atQgZ86c//jFCCGEEEIIIYQQrwujhv+p9tLJh+PHj1OtWjUqVKhAjhw56NixI8eOHaNEiRJUrFiR9957DwAXFxdOnz79zOdKSUlh4cKFbNmyhaFDhxIVFUW5cuX+2SsRQgghhBBCCCGELr108qFatWqcPn2a2NhYIK2ew3vvvUe9evW4e/cu4eHhAOzZs4datWo987nmzJnD7t27AdiwYQPvvfceRYoUedmQhBBCCCGEEEKI147BaNTsj2pWL/s/VK5cmaFDh9KzZ08sLS2pWLEivr6+5MmTh4ULF+Lt7U1CQgKlSpVixowZz3yuL7/8Ei8vLxYsWEDJkiWZOnXqP34hQgghhBDizbJv62zVIQghhHhBFkajDlIg/1Cdik1VhyCEEEIIIQSgr2RIM9eRqkMQ4l9x+sp+1SG8Um3qdNFsX9tPr9VsX1l56WUXQgghhBBCCCGEEC/jpZddCCGEEEIIIYQQ4v/uNV6I8NJeKPkQHx9P586dWbx4MeXKlWPTpk0sW7YMS0tLGjZsyJgxY7CysuL8+fOMHz+eR48eYWtry+TJk7GysmLz5s3Mnj2bYsWKAdCsWTOGDx/O33//zcSJE4mPjydPnjxMmjSJmjVrvtIXLIQQQggh/hv0tMxBCCHEsz132cWpU6fo0qULkZGRAFy6dIm5c+cSEBDA1q1bSUlJYdWqVQCMGjWKiRMnsnPnToxGI0FBQQCcPXuWMWPGEBISQkhICMOHDwfA29ubPn36EBISwrBhw/Dy8npFL1MIIYQQQgghhNAXg9Gg2R/VnjvzISgoCB8fH0aPHg3AhQsXqFu3LtbW1gA0b96cpUuX0qpVKxITE6lbty4A7dq1Y968eXTt2pUzZ84QGRnJkiVLqF69OhMmTKBQoUJ06NCBjz76CIDq1atz8+bNV/QyhRBCCCHEf43eiirKTAwhhMjec5MP/v7+Zrdr1KjBtGnTuHnzJtbW1oSGhhIbG0t0dDQlSpQwPa5EiRLcvn3b9O9evXpRv359vv76a3x9fZk9ezbt2rUzPX7evHm0bNny33pdQgghhBDiP05O9oUQrzsjUvMhW2+//TYjR45kwIAB5MmTB0dHR86cOYPBYMDCwsL0OKPRaLq9cOFC0/bPP/+cjz/+2OxxM2bM4NSpU/zwww//l9cihBBCCCHeIHqb+SCEECJ7L518ePLkCXXq1CE4OBiAHTt2UL58eUqVKkVMTIzpcbGxsVhbW/Pw4UM2btzIp59+CqQlGywtLQFISUnBy8uL27dv88MPP1CgQIH/+ysSQgghhBBCAT3NxJDEjBBCb55bcPJpjx8/5tNPPyU+Pp6kpCRWr15NmzZtKFu2LLlz5+bEiRMAhISE0KRJE9566y2WLVvGqVOnAFi9erVp5sP06dOJj49n+fLlkngQQgghhBBCCCH+o1565kORIkUYOHAgnTp1IiUlBRcXF1xdXQGYNWsW3t7exMfHU6tWLXr27ImlpSVz585l0qRJJCYmUqlSJWbMmMHdu3dZs2YN5cqVo0OHDqbnDwkJ+fdenRBCCCGEEEIIoVMG45tT88HCaHx9X22dik1VhyCEEEIIIQQgyy6EeBVOX9mvOoRXqmWt9prtK+zcBs32lZWXnvkghBBCCCGEHujpZF8IIcSzSfJBCCGEEEIIIYRQ4DVeiPDSXij5EB8fT+fOnVm8eDHlypVj06ZNLFu2DEtLSxo2bMiYMWOwsvrfU+3btw9fX1/27NkDQFRUFF5eXsTHx1OwYEGmTZtG2bJlOXbsGIMHD6ZUqVIAvPvuu0ydOvUVvEwhhBBCCPFfo7elBTITQwghsvfcbhenTp2iS5cuREZGAnDp0iXmzp1LQEAAW7duJSUlhVWrVpkeHxsby/Tp082e45tvvsHZ2ZmQkBBatWrFnDlzADh79iy9evUiJCSEkJAQSTwIIYQQQgghhBD/Qc9NPgQFBeHj44O1tTUAFy5coG7duqbbzZs3JywszPR4b29vBg0aZPYcBoOB+Ph4ABISEsiTJw8AZ86c4eDBg7i6utK/f39u3rz577wqIYQQQgghhBBC6MZzkw/+/v7Y2tqabteoUYNTp05x8+ZNUlNTCQ0NJTY2FoAffviBd999FxsbG7PnGDp0KAEBAXz00UcsX76cPn36AFCgQAF69OjB1q1badq0KcOHD/83X5sQQgghhBBCCKFbBqNBsz+qvXTBybfffpuRI0cyYMAA8uTJg6OjI2fOnOHixYvs2rWLgIAAbt26Zfb/eHl54evrS8uWLdm5cyeDBg1iy5Yt+Pr6mh7TpUsXZs+ezcOHDylQoMD//ZUJIYQQQoj/NKmxIIQQr4+XTj48efKEOnXqEBwcDMCOHTsoX748oaGhxMTE4OnpSXJyMtHR0XTt2pUFCxZw6dIlWrZsCUDr1q3x8fHhzp07rF+/nr59+2JpaWl6/oz/FkIIIYQQIjtScFII8boz8uZ0u3jusounPX78mE8//ZT4+HiSkpJYvXo1bdq0YciQIezcuZOQkBCWLl2KtbU1P/74I0WKFCF37twcP34cgBMnTpAvXz6KFy/Ozz//zM6dOwEIDg7GxsaGt9566999hUIIIYQQQgghhFDqpWc+FClShIEDB9KpUydSUlJwcXHB1dU128dbWFiwYMEC/Pz8SExMJF++fMyfPx+A6dOnM2HCBBYuXEjRokWZMWPGP38lQgghhBBCCCGE0CULo9H42s7zqFOxqeoQhBBCCCGEAPS17EJvS1KE+KdOX9mvOoRXqllNd832te98sGb7yspLz3wQQgghhBBCCCHE/53h9Z0L8NJeuuaDEEIIIYQQQgghxMt4oeRDfHw8Li4uREVFAbBp0ybatGmDq6srU6ZMISUlBYD9+/fj6uqKq6srI0eO5NGjR2bPM3fuXFO9B4C4uDj69OlD27Ztad++PefPn/+3XpcQQgghhBBCCKFrRqNRsz+qPTf5cOrUKbp06UJkZCQAly5dYu7cuQQEBLB161ZSUlJYtWoVDx48YMyYMcyZM4etW7dSo0YN5syZA8DDhw8ZN24cK1asMHvuFStWUK1aNbZs2cIXX3yBr6/vv/8KhRBCCCGEEEIIodRzkw9BQUH4+PhgbW0NwIULF6hbt67pdvPmzQkLCyMyMpIyZcpQpUoVs+0Au3fvplKlSnz22Wdmz20wGEyzIxISEsiTJ8+/98qEEEIIIYQQQggdM2LQ7I9qzy046e/vb3a7Ro0aTJs2jZs3b2JtbU1oaCixsbFUqlSJW7duER4eTo0aNdixYwexsbEAuLu7A5gtuQDo1asXnTp1wt7enkePHrF8+fJ/6WUJIYQQQgghhBBCL1664OTbb7/NyJEjGTBgAN26daN69erkzJmTggULMn36dCZMmICnpyfW1tbkzJnzmc/l5+dHt27dOHjwIMuXL2f48OGZ6kQIIYQQQgghhBDi9fbSrTafPHlCnTp1CA4OBmDHjh2UL1+e1NRUSpUqxfr16wE4ffo05cuXf+Zz7d6921TnoV69ehQrVoy///6bOnXqvGxYQgghhBBCCCGE0KmXTj48fvyYTz/9lG3btpErVy5Wr15N586dsbCwoFevXqxfvx5ra2sCAgJo06bNM5+rRo0ahIWF4ebmRmRkJNHR0bz99tv/+MUIIYQQQgihSjPXkapDEEII3Xrp5EORIkUYOHAgnTp1IiUlBRcXF1xdXQHw9fXl888/JykpiQ8//JDevXs/87mmTZvGxIkT+e6778iVKxfTp0+nQIEC/+yVCCGEEEIIodC+rbNVh2AiiRAhXg8GHbTA1IqFUQ8NP/+hOhWbqg5BCCGEEEIooqeTfb2R5IP4rzh9Zb/qEF6pRtWcNdvXoYs/abavrLz0zAchhBBCCCH0QG8n2JIMEUK8rNd4LsBLe+luF0IIIYQQQgghhBAv44WSDwsWLMDZ2RlnZ2dmzJgBwKFDh3B1daVVq1bMmTPH9Niff/4ZV1dXnJ2dGTNmDElJSQBs3rwZe3t73NzccHNzM/t/ANavX8+YMWP+rdclhBBCCCGEEEIInXjusotDhw5x8OBBNm/ejIWFBZ9//jnbtm1j1qxZrFq1itKlS9OvXz/279/PBx98gK+vL5s3b6Z48eIMHz6czZs306lTJ86ePcuYMWNwcXExe/4nT54wf/581qxZQ+vWrV/ZCxVCCCGEEP8tssxBCCFeH89NPpQoUYIxY8aQK1cuACpXrkxkZCQVK1akfPnyALi6uhIaGkrTpk3Zs2cPOXPmJCEhgTt37lCwYEEAzpw5Q2RkJEuWLKF69epMmDCBQoUK8dtvv2EwGBg1ahSnT59+hS9VCCGEEEL8l0jNByHE685gNKgOQTPPXXZRtWpV6tatC0BkZCQ7duzAwsKCEiVKmB5jbW3N7du3AciZMyf79++nWbNm3Lt3D3t7eyAtifHFF1+wZcsWSpcuja+vLwD29vaMHj2aPHny/NuvTQghhBBCCCGEEDrwwgUnIyIi6NWrF6NHj6Z8+fJYWFiY7jMajWa3mzZtytGjR2nevDmTJk0CYOHChbz//vumpRsHDhz4916FEEIIIYQQQgjxmjFq+J9qL9Rq88SJEwwZMoRx48bh7OzMsWPHiImJMd0fExODtbU19+/f5+zZs6bZDq6urgwfPpyHDx+yceNGPv30UyAtWWFpafnvvxohhBBCCPHGkGUOQgjx+njuzIebN28ycOBAZs2ahbOzMwA2NjZcvnyZK1eukJqayrZt22jSpAlGo5FRo0Zx48YNAEJDQ6lfvz5vvfUWy5Yt49SpUwCsXr2ajz/++BW+LCGEEEIIIYQQQujFc2c+fP/99zx58oRp06aZtnXu3Jlp06YxePBgnjx5QtOmTXF0dMTCwgI/Pz/69euHhYUFVapUYfLkyVhaWjJ37lwmTZpEYmIilSpVMrXsFEIIIYQQ4p+QgpNCCPH6sDAajeoXf/xDdSo2VR2CEEIIIYQQgL6SD3pLzAjxT52+sl91CK/UB5Vbabav3/7epdm+svLCBSeFEEIIIYQQQggh/glJPgghhBBCCCGEEOKVeqFuFwsWLGDHjh1AWhvN0aNHc+jQIaZOncqTJ09wcnJi+PDhnD9/njFjxpj+v7t371KoUCG2bdtGVFQUXl5exMfHU7BgQaZNm0bZsmVNj7116xZt27Zl06ZNlCtX7l9+mUIIIYQQQgghhL4YjAbVIWjmuTMfDh06xMGDB9m8eTPBwcGcO3eObdu2MW7cOL799lu2b9/O2bNn2b9/PzVr1iQkJISQkBDWrVtHoUKFmDRpEgDffPMNzs7OhISE0KpVK+bMmWPah8FgYPz48SQnJ7+yFyqEEEIIIYQQQgg1npt8KFGiBGPGjCFXrlzkzJmTypUrExkZScWKFSlfvjxWVla4uroSGhpq9v8tWbKEDz74AFtbWyAtwRAfHw9AQkICefLkMT122bJlNGrUiCJFivybr00IIYQQQgghhNAto9Go2R/Vnpt8qFq1KnXr1gUgMjKSHTt2YGFhQYkSJUyPsba25vbt26bbDx8+JCgoiEGDBpm2DR06lICAAD766COWL19Onz59ADh79ixHjhzhs88++7dekxBCCCGEEEIIIXTkhQtORkRE0KtXL0aPHk358uWxsLAw3Wc0Gs1ub9myhZYtW1KsWDHTNi8vL3x9fTlw4ACTJ09m0KBBPH78mMmTJzNlyhRy5JDal0IIIYQQQgghxH/RC53xnzhxgk8//ZSRI0fi4eFBqVKliImJMd0fExODtbW16XZYWBht2rQx3b579y6XLl2iZcuWALRu3ZqYmBj27dvHnTt3GDBgAG5ubkRHR9O3b18uXbr0b70+IYQQQgghhBBCKPbcbhc3b95k4MCBzJkzhw8//BAAGxsbLl++zJUrVyhXrhzbtm3D09MTSJsFce7cOerVq2d6jiJFipA7d26OHz+Ora0tJ06cIF++fLRp08YsSeHg4MDSpUul24UQQgghhBBCiP88A+prMWjlucmH77//nidPnjBt2jTTts6dOzNt2jQGDx7MkydPaNq0KY6OjkDaLIecOXOSO3du0+MtLCxYsGABfn5+JCYmki9fPubPn/8KXo4QQgghhBBCCCH0xsKoh7KX/1Cdik1VhyCEEEIIIQQA+7bOVh2CSTPXkapDEOJfcfrKftUhvFI2lZpptq9Tkfs021dWpMqjEEIIIYQQQgghXilJPgghhBBCCCGEEOKVem7NByGEEEIIIfRIT8schBDiv2Tr1q0sWrSIlJQUPvnkE7p162Z2//nz5xk/fjyPHj3C1taWyZMnY2X17PSCJB+EEEIIIcRrSW91DSQZIoR4WXoswXj79m3mzJnDpk2byJUrF507d6Zhw4ZUqVLF9JhRo0YxZcoU6taty7hx4wgKCqJr167PfF5ZdiGEEEIIIcT/kSQehBB69+DBA6KiojL9efDggdnjDh06hJ2dHYULF+att96idevWhIaGmu6/fv06iYmJ1K1bF4B27dqZ3Z+d13rmw3+98qkQQgghhBD/hBwnC/F60PKzOn/+fBYsWJBp+6BBgxg8eLDpdnR0NCVKlDDdtra25vTp09neX6JECW7fvv3c/b/WyQchhBBCCCGEEEI83yeffIKHh0em7QULFjS7bTAYsLCwMN02Go1mt593f3Yk+SCEEEIIIYQQQvzHFSxYMFOiISulSpXi+PHjptsxMTFYW1ub3R8TE2O6HRsba3Z/dqTmgxBCCCGEEEIIIQBo1KgRhw8f5u7duyQkJLBr1y6aNGliur9s2bLkzp2bEydOABASEmJ2f3YsjHosrymEEEIIIYQQQggltm7dypIlS0hOTqZ9+/b06dOHPn36MGTIEN577z3Cw8Px9vYmPj6eWrVqMXXqVHLlyvXM55TkgxBCCCGEEEIIIV4pWXYhhBBCCCGEEEKIV0qSD0IIIYQQQgghhHilJPkghBBCCCGEEEKIV0qSD0IIIYQQQgghhHilJPkghBBCCCGEEEKIV0qSD0L3Tpw4wdq1a0lKSuK3335THQ5JSUkAXLlyhX379mEwGBRHpB8XLlxg165dhIWFcfHiRdXh6O69I54tLi5OdQjiBclYZe/BgwecO3eO8+fP8/DhQ9XhEBUVxb59+0hNTeXatWtKY9Hbz0av4uPjiYiIULb/rD7f169fVxCJPmU1NidPntQ+ECFeQ29kq827d+8yefJkjhw5QmpqKg0bNmTy5MkUL15c0zgWLFjwzPsHDRqkUSTmkpKS+P7777l8+TITJ04kICCAvn37Prdv66uwcuVKwsLCiI6OZt26dXTt2pX27dvTu3dvzWOBtDG7dOkSX375JR07dqRKlSpUqVIFb29vJfHs27ePBQsWcP/+fYxGI0ajEQsLC3bv3q1ZDEajkbVr17Jy5Ury5ctHmTJlsLS05Pr168THx9OzZ086d+5Mjhza5jr19t6Ji4vjp59+4t69e2T82lXxOb979y5r1qxhz549XLlyhRw5clChQgVatGhBly5dKFq0qKbxnD9/nuHDh5OYmEhgYCDdu3dn7ty51KpVS9M40slYZU9vY3X69GmWL1+eaax++OEHzWPZv38/y5Yt46+//qJUqVJYWlpy69YtKleuTK9evWjatKnmMW3fvp1FixaRkJBAYGAgbdu2ZfTo0bi5uWkahx5/NpDWw/6vv/6if//+7Ny5E3d3dyVxAKxfv54TJ04wevRo3N3dyZcvH25ubvTv31+zGG7evInRaKRv37589913ps9Uamoqffr0ITQ0VLNYMurRowcWFham2xYWFuTJk4d33nmH/v37U6hQIU3iOHHiBAaDAW9vb/z9/U0/n5SUFCZNmsTOnTs1ieNprVq1IjU11XQ748/Hy8uLsmXLvvIYHBwczMboaVoelwp9eyOTD4MGDaJevXp06tQJg8FAYGAgx48fZ8mSJZrGkZ58OH36NLdu3cLR0RErKyt+/vlnypYty9y5czWNJ523tzdFixZlz549rF+/Hh8fHwwGA7NmzdI8Fnd3d4KCgujYsSPBwcE8evSIDh06sH37ds1jAWjXrh0//vgjP/zwA/fv32f06NG0a9eOTZs2KYnn448/Zvz48VSpUsXsS1+LXzTpBg8eTKNGjXB2dqZgwYJm9z18+JDNmzdz+PBhFi1apFlMoL/3Tvfu3SlatChVq1Y1GyutT2jXrFnDrl27aNWqFba2tpQtWxYrKyuioqI4evQoP/30E46OjvTs2VOzmLp164avry8jR44kODiYX3/9lTlz5rBhwwbNYshIxip7ehsrJycnunfvnuk7sEGDBprGMWbMGIoXL46bmxtVq1Y1uy8iIoINGzYQGxvL7NmzNY3Lw8ODVatW0b17d4KDg4mOjuazzz7jp59+0iwGvf5sZs2axa1btzh37hzr169nwIAB1KpVizFjxmgaR7p27dqxePFiQkNDuXz5MuPHj6djx46aHl+MHTuWo0ePEh0djbW1tWm7lZUVzZo1Y9y4cZrFktHkyZOxsrLC09MTgG3btnHr1i1q167N8ePHn3sx798yf/58jh07xtmzZ6ldu7Zpu5WVFR999BG9evXSJI6nTZkyhXLlytG+fXsAtmzZwpkzZ3BwcGDNmjUEBAS88hiuX7+O0Whk4cKFlC9fnnbt2mFpacnWrVuJiopi4sSJrzwG8ZowvoHatm2baZuLi4uCSNJ06tTJ+PjxY9PtxMREY8eOHZXF4+7ubjQajUY3Nzej0Wg0GgwGo7Ozs5JYPDw8zGJJTk5WOlbpcXTu3Nl45MgRY2pqqtHR0VFZPOk/H5UePXr0rzzm36a3947KfWe0a9eu5z4mNDRUg0j+5+mxMhqNRldXV01jyEjGKnt6G6v031eq3bp167mPuXnzpgaRmGvXrp3RaDQfL63f33r92bi5uRkNBoPZ7wgnJyfN40iX/tnq1auXcd++fUaj0Whs06aNkliWLFmiZL/ZyepYJ6v3tlY2b96s+T6fJavvwfSfmdbfkVmNlR6OVYV+WKlOfqhgYWHBzZs3KV26NAA3btzAykrdj+LevXtmV2ySk5O5f/++sngsLCxISkoyxfR0fFpq0KAB06dPJyEhgbCwMAIDA7Gzs1MSC8CHH36Ii4sLefLk4YMPPqB79+44ODhoHkd6/YIqVaowZcoUWrRoYfYe/uCDDzSL5a233vpXHvNv09t7p1q1apmulqjw8ccfP/cxrVu31iCS/ylcuDDh4eGm75ktW7ZoNo02KzJW2dPLWN24cQOAmjVrEhAQQIsWLbC0tDTdX6ZMGU3jKVmy5HMfU6pUKQ0iMVe1alVWr15NSkoK58+f58cff6RGjRqaxpCammoar6yUKVNGyc8mfSlg+ns5KSlJ8+WBGVWpUoV+/foRFRXFhx9+yLBhw6hTp46SWDw9PQkICODRo0cYjUYMBgNRUVHMmDFDSTzJyclERESYZs5ERERgMBhITEwkOTlZ83jq1q3LlClTePz4sdnPZ82aNZrHAmnv5QMHDvDRRx8BcODAAXLlykVsbCwpKSmax3P48GE+/PBDIG3JVcbvZiHeyGUXe/fuxcfHBxsbG4xGI6dOncLPz49mzZopiWfZsmVs3ryZJk2aALBnzx4++eQTunbtqiSe4OBg1q9fz5UrV3ByciIsLIyBAweapnNpyWAwEBQUxKFDhzAajTRs2JDOnTsrTRbduHGDkiVLYmlpSXh4uOYHcpC2/jE7FhYWmq53ftZBJWh/EpAu43vHYDBgZ2en5L2Tvg4yMTGRu3fvmt47RgX1OfTq6tWreHl5cebMGfLkyUPFihWZOXMm77zzjqZxyFg9n97GKqtDGBmr/3n8+DGLFi0y+x4cOHAg+fPn1yyGGjVqUKhQIdM+M46ZyrFaunQp586d48yZM/Ts2ZMtW7bQqlUrTWssZHTmzBkSExOpWrUqhQsXZs+ePTRp0kTJ8U7Pnj0pXbo0J0+epGXLluzbt4/33nuPadOmaR4LwNGjR/Hy8qJYsWIYDAYePHjAjBkz2LNnD4UKFaJv376axtOuXTuaNWvG3r178fDw4Oeff6Zy5cpMmjRJ0zjSXbx4kTFjxpiKglaoUIFp06YRGhpKmTJl8PDw0CyWP//8Ey8vL2JiYjAajZQtW5YZM2ZQpUoVzWIQ+vZGJh8grYjX6dOnMRgM2NjYUKxYMaXxnD17lmPHjmFhYcGHH36o5IQ2o7/++oujR49iMBj44IMPlMZz/vx5Dh8+jJWVFY0bN6Zy5crKYjl9+jQnTpygW7du9O/fnz///JMZM2aYEkday3glIN3JkyepW7euZjG4uroSGRmJtbV1phMB1ScB8fHxPHjwwGyb1smQ51UI17I+B8Cnn376zA4tKgr1pXv8+DEGg0HTE6OMZKxenOqxSnf//n0KFy5sti0qKopy5cppGoe9vT137tzJtD09cXX+/HlN49GTgIAAwsLCyJcvH05OTrRs2VL5+wbSZmQcOnTILDHTvHlzZfE4OTmxY8cOZfvPyNHRkdDQUKZPn46joyMVKlTgk08+YcuWLcpiSklJ4eLFi+TIkYPKlSuTM2dO0+dLa66urmzdupWvv/6aJk2aULt2bTw9PTWtpZKVuLg4LC0tdfH5Sp81/fT3sxBv1LKL4ODgLLcfOHAAQPMqxxmvGBctWhRHR0ez+1RdMU5OTubgwYMcOXIEKysrcufOTfXq1ZV8wX///fcEBgbSokULUlNTGTBgAP369TMVHdLalClTGDJkCDt37iRPnjxs3ryZQYMGaZ580FPF5bVr19K1a1d8fHx4//33Ndvv80yfPp2goCDTLz5VV6/TT1gHDx7M/Pnzze775JNPWLlypabx9OnThxEjRuDv75+pQKgqx48fZ+XKlZnau2l9ci1j9Xx6GSu9VeXfuHEjPXv2ZOHChbq6whcQEMC3335ramupIhny6aef8umnn3Lz5k22b99Onz59KFq0KM7Ozjg4OJAnTx7NYsmoffv2bN682TRVXbUqVaqwYMECbGxszH4mWi6jTJe+lOrtt98mPDwcGxsbzWPI6Pr166xevZq4uDizixxTp05VEk/evHlJSkqiUqVKnDt3DltbWyVxpPvzzz9ZvHhxpp+Plt/Leu3gJ/TnjUo+HD16NNO25ORkdu7cSb58+TRPPnTv3t1s2mj6yb3qKb7e3t4kJibSsWNHDAYDISEhREREMH78eM1jCQoKYtOmTaYs7sCBA+nSpYuy5IPBYMDe3p6RI0fSqlUrSpcubdbeSCuHDh3i2LFjREdH880335i2W1lZ0alTJ01jyZ8/P1OmTGH9+vW6Sj7s3r2bX375hXz58imNY9CgQZw/f57o6GhatGhh2p6amqpknXPjxo3p168f+/fvx8/PT/P9Z2XMmDEMGjRIWcI1nYzV8+llrObNm2eqyt+tWzfT9vSq/ForWbIk48aNY968ecybN0/z/Wfnhx9+IDg4WPl4AZQuXZrevXvTu3dvIiIimDBhAuPHj+ePP/5QEk/x4sU5fvw4derUUdJK/Gn379/n6NGjZseqWi+jTGdnZ8eQIUPw8vKiV69enDt3TlmSCGDYsGHY2tpia2urrAZZRm3btqV///7MmjWLTp06ceDAgReq+/KqeHl50alTp0wdmoTQJc1KW+rQ2bNnjS4uLsYvvvjCGBMTozoc3WjdurXZ7dTUVGUVoDt27GhMSEgw3X7y5Imxffv2SmIxGo3G7t27G7///ntjo0aNjPfv3zeuXLnS2LVrV2Xx6K3isp4MGjTIePfuXdVhGB8+fGi8du2asX///saoqCjTn1u3bhmTk5OVxGQwGIwRERFK9p0VlZ+hjGSsnk8vY5VOb1X59aZ3797GJ0+eqA7DaDQajQkJCcbQ0FDjsGHDjC1atDBOmDDBeOjQIWXxNGzY0Fi9enWzPzVq1FAWj95cuXLFaDSmHSuvWLHCePv2bWWx6KWrTUYPHz40Go1pnVp27dpl1rVOayqPi4V4WW9kzYeUlBQWLFjAhg0bGDNmDC4uLqpD0pXPP/+cCRMmULFiRQCio6Px8vJixYoVmscyduxY/vzzT5ydnbGysuLnn38mT548pivsWk/jun37NuvXr6dx48bUq1ePmTNn0qNHDyVXRUF/UxH1JCwsjLFjx1KtWjWzSssq18lHRERkGisVU2r1JjQ0lLCwMOzs7MyKq2k9Gy0jGaus6W2s4uLi2LZtG/fv3zcbK5nim+aXX37B398fGxsbs+9BLX9HbN++ndDQUM6ePYu9vT1OTk40bNhQaWcJPbp+/Tre3t5cv36dNWvWMHLkSL766ivN65dkp2/fvixdulTJvqdMmUKjRo2wt7fXxSyVrKTXgVDhm2++oWjRotjb25M7d27Tdj3MeBLiaW/Usgv4XxXWihUrEhwcTPHixVWHpDspKSm4ublha2uLlZUVJ06coESJEvTs2RPQ9uStbNmylC1blqSkJJKSkmjcuLFm+85KyZIlsbOzIzw8nFq1atGsWTNliQfQ31REPZkzZw7jx4/XzS9fX19f9uzZQ/ny5U3bVE2p1ZuNGzfy5MkTTpw4YbZd1QmtjFX29DZWw4YNo0CBAjLdOBuzZ8/G1dVV82KpGY0YMYLSpUtja2tLcnIyW7ZsMStcqCpZnt0adVWJq4kTJ9K7d29mzZpF8eLFcXFxwcvLS1n7xqcdP35c2b5DQ0NZvXq12Ta9FXKNiopStu+QkBAAs4uEqgt+C5GdNyr5MHfuXFauXEn//v1xdXUlKSnJrOijqpOUu3fvUrRoUSX7zsoXX3xhdrtXr16KIoE8efLg5uZGiRIllMWQ0cqVKwkLCyM6OhpHR0cmTpxI+/bt6d27t5J4UlJS8PLyUrJvvStQoIDSK+dPO3jwIKGhoUrXzepVbGwsmzdvVh2GiYxV9vQ2VrGxsUpm5b0ucuXKpXwWyOswEy85OZkDBw4oLax479497O3tmTVrFhYWFnTs2FE3iQfVDh48qDqE51KZ/NyzZ4+yfT9t3bp1dO7cWXUYQsfeqOTDli1bKFKkCIGBgQQFBemm13S3bt10014J4LvvvqNdu3a0aNFC+fS2xMREevToQYUKFfDw8KBly5bkzJlTWTybN28mKCiIjh07UqRIETZs2ECHDh2UJR/ef/999uzZo4upiNu2bcPFxcX0t2rvvvsugwcPpkmTJmbvGVUJifLly2dqRarS48ePeeutt0x/q1SnTh327t1LkyZNzKaGqyJjlT29jVXNmjUJDw9X3p463bFjx2jQoIHpb9Xef/99pk2blul7UMslRPXq1aNSpUpZ3qeyNeHTSZmBAwcqv9hy69Yt00ns8ePHlf9e14uEhAQWLFjA4cOHSU1Nxc7OjqFDhyr/PtSLu3fv4uvra/bzmTRpkpLZ3atXr5bkg3imN7Lmg94MHz6cpk2bUqdOHbMrbapmYhw7dozg4GCOHDlC06ZN8fDwoE6dOkpiSXf8+HG2bdvGsWPHsLOzo0OHDtSsWVPzONq1a8emTZtwd3cnODiYlJQUPDw8lK3zs7e3JzY21mybqqmIHh4ebN682fS3amPHjs1yu6qrcCNGjODkyZPUq1fP7IBSVTx6Gi89vY9BxupZ9DZWHh4ehIeHU6xYMXLnzq28W5SexgqgR48embZpvYTIwcGBlStXmi1jSkhIYPLkyRw4cIBff/1Vs1ie5d69e3h6eiq7inz69GkmTJjA1atXqVChAnFxcXzzzTeazsZwcHDI8gq+0Wjk1q1b/Pnnn5rFktHYsWPJmzcvHTt2BNI6oT18+JCZM2dqGkeNGjWy/fmo/B4cNGgQ9erVo1OnThgMBgIDAzl+/DhLlizRPJbPP/+cpKQkbGxszOpPqJ6BJfTjjZr5oFenTp3i1KlTZttUHjw1aNCABg0akJiYSGhoKIMHD6ZAgQK0b9+erl27ap6Jf/z4MVFRUVy7do0cOXJQqFAh/P39qVevHiNHjtQ0lgYNGjB9+nQSEhIICwsjMDAQOzs7TWPISI9TEfWSz9TbVN+PPvpIN/3kM9LDeOntfSxjlT29jdXzesurooexAli1apXqEBgxYgSfffYZK1eupGzZspw7d44RI0ZQuXJlZYl7MD/RNhqNxMXFKZvFCFCxYkU2bNhAZGQkqampvPPOO8TExGgagx7eL1k5d+6cWZ2QiRMn0qZNG83jCA8P13yfL+LatWtm34V9+vQx+3lpqW7dukr2K14fknzQAT2t1Up39OhRQkJC+PXXX2nSpAlt2rTh0KFDDBgwgO+//16zOL788kuOHDlCkyZNGDBgALa2tgAkJSVhb2+vefJh9OjRBAUFUb16dYKDg2natKnS6WUyFTGzfv36sWTJkmyv4Ki8Inrx4kWOHTtGSkoKDRs2VDJ7R08CAwPp1KmT7gq/yVhlptexKlOmDGvXruXIkSOkpKRgZ2dH9+7dlcSiJxMmTMDPz48ePXpk+T2o5cwHFxcXLC0t6dWrF66urqxatYovv/ySDh06aBZDVjKeaFtYWFCwYEHy58+veRw3b97EaDTSt29fvvvuO/Llywekddfq06cPoaGhmsWisjDpsxiNRh48eEDBggUBePDggS6WfemFhYUFN2/epHTp0gDcuHHDrBuRlgYNGsTjx4+5evUq1apVIzEx8Y0+JhWZvZHJBz2tD4W0VmEzZ87k6tWrzJs3j+nTpzN27FjTl6zWmjdvTrly5fD09GTixImmpSANGzbE09NTkxiWLFlCv379sLOzw9fXN9MXV65cuZSsFc2RIwcuLi40bdrUdGUrOjpa2RIZX19f8ubNy1dffQWkTUX08fHRfCqinnz++eeA/q7gBAcHs2DBAlq2bInBYGDQoEEMGDCA9u3bK41LOgRkJmP1+pgxYwZXrlzB09MTo9HIpk2buHbtGuPHj1cdmlLpJ/aDBw9WHEkaJycnLC0tGTFiBMuXL9dFPYxp06Yxf/58s22ffPIJK1eu1DSOefPmcfToUaKjo+nWrZtpu5WVFc2aNdM0Fr369NNP6dChA82bNwfSLtr16dNHcVT6MXToUDp16oSNjQ1Go5FTp07h6+urJJbDhw8zceJEUlNTCQwMxMXFhdmzZ2Nvb68kHqFDxjeQo6Oj6hDMDB482Lhu3Tqjq6ur8cmTJ8avv/7a2KdPH83jGDRokNFoNBqvXLmi+b6f5u7urjqELC1atMhYr149Y/PmzY0ODg6mv1VxdXXNtM3JyUlBJEbjmDFjzP5WRa/vnbZt2xrv3r1run3nzh2js7Ozsnjc3NzM/lYh/TtHb2SsMtPrWLm6uhpTU1NNt5OTk5X+ju/Vq5fZ36ro6Xvw2LFjpj9fffWV8eOPPzYeOnTItE1rAwcONDo4OBhr165tdHBwMP1p0qSJsVOnTprHk27JkiXK9v06uHDhgnH16tXGH374wRgeHq46HN25c+eOce/evcbdu3cbY2NjlcXRvn17Y3R0tOn3VURERJbHquLN9UbOfKhSpQoLFizAxsbGrMCjltWfM4qKiqJTp06sXbuWXLlyMXz4cNq2baskDoAKFSpovu/XxYYNGwgLC9NNa1SjjqYiptdYUF1rwaiTtdZPMxgMFClSxHS7aNGiSq9kL1682OxvFVT2RX8WGavM9DpWqamppKSkmGoRpaamKp2Onb4sUcvliVnR0/fgvHnzzG6XLFmSb7/9FtC++CWkzXi4f/8+/v7+eHt7m7ZbWVlRrFgxTWPJ6NNPP2Xx4sVcvnyZCRMmEBAQQN++fZV0vEhOTmbNmjUcOXIEKysrmjRpQocOHZR+D1arVo1q1aqZbvft25elS5cqieXBgwds3bqV+/fvm33WVBZVLFq0qNlMGVdXVyU1VQwGAyVKlDDdrlKliuYxCH17I5MP9+/f5+jRoxw9etS0TcUvwHSWlpY8fPjQ9KUeGRlJjhw5NI/j0aNHHD9+PNuDFi2TM+fPn89yjbVRcUXh0qVLU6hQISX7zsqnn35K+/btcXBwwGg0snfvXvr27as6LKVu3ryZbacLUJccqV69Ov7+/qap+xs2bFC6/KtUqVJmf6ugp++cjGSsMtPrWLm6utKzZ0+cnZ2BtNaNemj1q1psbOwzi3FqeZKktyVw+fPnJ3/+/CxatIg///yTx48fYzQaSU1N5cCBA8qWV/n6+lK0aFHOnTuHpaUlV69eZdy4ccyaNUvzWLy9vUlMTKRjx44YDAZCQkKIiIjQ1XKm48ePK9v30KFDKVCgAFWrVtXtcjhVCeNSpUqxd+9eLCwsePDgAWvWrFG2NFnok7Ta1IEDBw4we/Zsbt68yfvvv8/Jkyf56quvNF/rV69ePd57770sDy61Ts6kt7LUmwkTJnDx4kUaNmxodjVCZbb74sWL/PbbbxgMBho0aED16tWVxaIHzZs3Z8iQIdne7+HhoWE0/5OYmGha22s0GmnYsCEDBw5UUuBML/T0nZORjFVmeh0rgF9++YXDhw9jNBqxs7OTdfKktUR9VjFkLX9nDRkyhM6dO9OoUaMs79+3bx8bN27MVH/hVfP29ubYsWPExcXxzjvvEB4eTv369ZXNWklvz5p+/GM0GnF1dWXbtm2ax+Lo6GhW6NJgMODi4sL27ds1jyU79evX5/fff1eyb1WzCl6Gqp/PnTt38Pf359ChQxgMBuzs7PD29sba2lrzWIQ+vZEzH65fv463tzfXr19nzZo1jBw5kq+++opy5copieejjz6iVq1anD59mtTUVPz8/JRM/atYsaLSA8jXQcmSJSlZsqTqMPjtt9/MbqdPRXzw4AG//fabsquQycnJXL58mdTUVKpWraqk2nLhwoWVJRieJU+ePIwePVp1GLqi1+8cGavM9DZWN27cMP27SpUqZlN7b9y4ofxKW3x8PAaDQVnh6BIlSihNimc0depUFixYgJ+fHzVq1KBUqVJYWVkRFRXF2bNnadmypZIZaYcOHWLnzp34+fnRs2dPEhISmDZtmuZxpLOwsCApKcl0Jf3evXvKrqqXK1eOK1euULFiRSBtJo0ejn30ombNmrorXq8XxYoV4+uvv1YdhtCxNzL5MHHiRHr37s2sWbMoXrw4Li4ueHl5sWbNGiXxXL16lZMnT+Li4oKPjw/ffvstkydPpnbt2kri0QNHR0fVIWRJLwdzPXr0oFixYlSuXBkwX9+r6irkmTNnGDp0KIULF8ZgMBAbG8vChQuxsbHRNI6cOXNqur/nya7lZzpVrT8TEhKYP38+R44cITU1lYYNGzJs2LA3uiWWjNXrw8HBgUKFCplmozz9HahqrK5evcqIESO4evUqRqORsmXLMmfOHN5++21N49DTpNZ8+fLh5eXFwIEDOXLkCFeuXMHCwgIbGxv8/f2VvY+tra3JmTMnlStX5sKFCzg7O/Pw4UMlsQD07NmTzz77jJiYGPz9/QkLC2PgwIFKYklJScHNzQ1bW1ssLS05ceIE1tbW9OzZE9CuVWt238lGo5HExERNYshKREQEHh4eFCtWjNy5c5uWBGv9vVOjRo1sfz5aJ670+vtT6M8bueyiXbt2bNq0yWxqv5ubGyEhIUri6datGx06dCB//vysXLmSoUOHMmvWLNatW6dpHAcPHpRWONlInw759Be9qhoUYWFh7NixgytXrtC8eXPatGmj+cHt0zp37szYsWNNyYaTJ08yZcoUNmzYoDQu1b744gvOnz9Ps2bNaNOmTaYrsqr6qo8dO5a8efPSsWNHIK1N68OHDzVt06q37xwZq+zpbawCAgIICwsjX758ODk50bJlS10si/nss8/o1KmTKYG+fft21q5dq3ndg/v371O4cGFN9/m6GTp0KO+++y4ffvghM2fOpHPnzsyfP99suYHW/vrrL44ePUpqaioNGjRQdmX92LFjz7xfq1ap169ff+b9qr6Ts4tLVTx6cP36dYxGIwsXLqR8+fK0a9cOS0tLtm7dSlRUFBMnTlQdotCJNzL50LVrV77++msGDBjA5s2bOX78ONOnT2f9+vVK4mnfvj0bNmxg/Pjx2NjY0LFjR1OCRIhnefLkCXv37mX79u1ER0fj4OBAmzZtlCwhatu2LVu2bDHb9jqsi9RCfHw8YWFhhIaG8ujRI1q2bImjo6PSaaxZjVebNm10taZXBRmr18vNmzfZvn27qQuRs7MzDg4OZp2stJRVvSL5HtSn+Ph49u/fj7OzM6tWreLQoUN88skn2NnZKYknKSmJgwcP8uDBA7Pt7u7umsfy9NLOdKqWdOqN0Whk7dq1HDlyhJSUFOzs7OjevbuSYvF6k9X5i5zTiIzeyGUXY8aMoV+/fly9ehU3Nzfi4uKYO3eusngsLS3ZuXMn+/btY+jQoYSFhckXmE6lL5FxdXXFx8eHc+fOKV0ikzt3bhwdHXF0dOTvv/9m/PjxzJkzR0k3kEKFChEWFkbLli2BtNkZcuUtTf78+XF3d8fd3Z0HDx7w888/M3ToUKysrFi9erWSmPTUplVPZKxeL6VLl6Z379707t2biIgIJkyYwPjx4/njjz+UxJMrVy7OnTtHrVq1ADh79ix58+ZVEot4tqFDh5qKS/bo0YMePXoojadPnz6mpToZqUg+ZGyPmpKSwoULF7C1tZXkw/83Y8YMrly5gqenJ0ajkU2bNnHt2jVddQNR6fDhw3z44YcA7N+/X35fCTNvZPKhTp06bNiwgcjISFJTU3nnnXeU9FFON3nyZFauXMnEiROxtrbmp59+YsqUKcriSRcXF6e8reSlS5cICgoiLi7ObLuqdoljx46lQ4cO7N69m8uXLzN27FimTJmi+RKZdNevXyc0NJRdu3aRnJyMo6OjplOxM/L19WX06NGmX77ly5dnxowZSmJ52vz58xk8eLDqMLh79y67du0iNDSU+Ph4Pv74Y2Wx6LVNa8YTN5VkrJ5PD2OVmJjI/v37CQ0N5cyZMzRq1IihQ4cqi2fcuHEMHjyYwoULYzQaiYuLY86cOcriySgwMJBOnTopjSEqKoq//vqLjz76iBs3blC+fHllsSQkJHDz5k1Kly6tLIaM7t27l2mGkypPLxO6du2asuMuPfr1118JDg42XShs1qwZrq6uiqPShylTpuDl5UVMTIwpmaaXY0GhD2/UsouxY8c+8375Yk1z/vx5hg8fTmJiIoGBgXTv3p25c+cqOchs06YNbdq0yXQlQFU3A70skVm6dCm7du3CYDDg6OiIk5OT0oO4jB4/fozBYNDF+ut06TU7VIiOjubnn38mNDSUu3fv0qpVK1q3bq2LKtl6bNMqY5U1GStz27dvJzQ0lLNnz2Jvb4+TkxMNGzbUxazB5ORkIiMjMRgMvP3220ovbmSkcrwgbcwWLVpEQkICgYGBtG3bltGjR+Pm5qYkHicnJyIjI5UXDUyX3mLdzs5OF+/jpz3dflNLycnJrFmzhiNHjmBlZUWTJk3o0KGDsm4gzs7ObN682fTZfvLkCZ6enkraokLabLitW7dy//59s2KzKoukp3drkRmw4mlv1MyH9AI5e/fu5dGjR7Rt2xYrKyu2b99OgQIFNI/n6eKFFhYWFCxYkMaNGzNhwgRlH9gpU6awcOFCRo4cScmSJZk0aRI+Pj5KCgcWLFhQNx0mQD9LZL7++mtKlixJhQoVOHDgAAcPHjS7X8tuF69DUk9ljrVp06aUKlWKVq1amT7z4eHhhIeHA9pPqdVrm9Z0Mlb/I2OVvREjRlC6dGlsbW1JTk5my5YtZleNtf7eWbBgwTPv18PvMdXXmr777jvWrl1L9+7dKVasGJs3b+azzz5TlnxYtmyZkv1mp0yZMvTq1ct0XKiqoDVk/r3+999/m75/VPD29iYxMZGOHTtiMBgICQkhIiJC2TIHV1dXevbsibOzMwA//fST6d8qDB06lAIFClC1alVlCZkJEybg5+dHjx49soxBT62ahVpvVPIh/Wr5jz/+SGBgoOmk0cnJyVRFXEvpB7QZxcbGEhQUhK+vr7I+uQkJCaYWjgCNGzdm+vTpSmLx8PBgzpw52NnZYWX1v7erqoNuX19fAgIClC+R0dOX+N69e7G0tKR169bUqVNH+QFuVoYMGaJs325ublhYWPDgwYMsK4hrfUKrxzatGalsMSxj9XJUjpUekpoZLV68mEKFCtGyZUtKlCihOpwsde7cWen+c+TIYTYjztraWukV/rJly7J161b++usv+vfvz86dO5XUV0gXFBTEnj17MnXZUSFjNwsLCwscHR1p1KiRsnhOnTplNuvCwcEBFxcXZfH079+fd999l8OHD2M0Gunfvz/NmjVTFk9sbCwrVqxQtn/AtKRLD0tchb69UcmHdA8fPuT+/fsULVoUSPvQPn78WHFUaYoXL84XX3yhNINauHBhwsPDTZnLLVu2KKv98Mcff/D777/z+++/m7apPOguUKCA6Yv1xo0bjBo1SkkcWrW5ehG//vorhw8fZvv27fzwww/Y29vTpk0bXUxVT+fg4KBs39OmTVO276wsWLBAd21aM1JZ70bG6uWoHCtVS++yc+DAAXbu3MnOnTu5evUqjo6OtGrViiJFiqgOzUR18qFq1aqsXr2alJQUzp8/z48//qj098SsWbO4desW586do0+fPmzcuJHw8HDGjBmjJJ4SJUroZop65cqVqVOnjul2YmIis2fPVvazKVeuHFeuXKFixYpA2nG7ig5E6XVufvvtN/LmzWt2bKFyNlrNmjUJDw9X+nmqXbs2ly5domLFimZjc+fOHb755htdHbcKtd6omg/pgoODmTVrFvXr18doNHLy5EkmTJhAq1atVIdmklW7Lq1cvXoVLy8vzpw5Q548eahYsSIzZ87knXfe0TwWvbUoc3BwwMLCAqPRSEpKCrGxsdSsWZONGzeqDk0XkpOT+fXXX9mxYweXLl2iSZMmkgXXKT21aRXPJmP1erlz546pEHCuXLlwcnKiXbt2qsNS7vHjxyxatIhDhw5hNBpp2LAhAwcOVFYfyN3dnc2bN+Ph4UFwcDApKSm0bdtWWQvboUOHcubMGerXr0/OnDlN21XM8mndujXTpk2jXr167N+/n8mTJ2NnZ8dXX32leSyQVnT35MmT2NraYmlpyYkTJ7C2tqZ48eKAdrNBvb29mTJlSpadUVReGPPw8CA8PFxp/ZIFCxaYuscsXLiQRo0a8f3337Nw4ULq1atnuk+INzL5EB4eTtGiRfnjjz+wsLDg/fffp1ixYqrDMtm1axc//vgjAQEBSuPQQ+HAESNG0LdvX11dRc/o9OnTrFmzRvNlKRmvAOjNpUuXCA0NZceOHRQqVEhZe8KsJCUl6ab4m56kt2k9deqUkvXF4sXJWKV5/Pgxb731luowshUfH8/u3bv58ccfiYmJYc+ePapDUi41NZV9+/bRokUL7t69y549e/D09FS2Rr1du3Zs3LiRdu3asXnzZh4/fkzHjh2VFQ3Mrhioilk+ly9fZvDgwZQvX56oqCh8fHywtbXVPI50WS2Dy0jrq+oXL17MVAPj5MmT1K1bV9M40l2/fj3L7U8Xa3+VWrRowdq1a4mOjmbevHkYDAZu377N6NGj+eijjzSLQ+jfG5l8cHJyYseOHarDMF1Fzyg+Pt4000Drk8vsisSkU5HRdXd358KFC5QoUYKcOXMqr0adFRcXF80PVtJnhHzxxRd8++23mu47KxEREaYrfQULFsTR0ZHWrVtjbW2tLKZOnToRGBhoum0wGHBzc9N8Js2XX37JrFmz2LBhA+3bt9d038+SVZtWlV1T4uPjefjwoVldA63XPstYvZjz589z5MgRLC0tady4sVmNIK2kd26YNGkSkyZN0nz/WXnw4AFhYWHs3LmTyMhImjdvjqOjo7ITEtBXl4CxY8diMBiYPn06d+/eZerUqeTNmxdfX1/NY4G0rlHnzp3jzJkz9OzZky1bttCqVSv69++vaRwxMTGUKFGCGzduZHm/lt+DGWO4ceMGw4YNw9vb27QEQ1U9iqeL76bTepnDiRMnMBgMeHt74+/vb/p9lZKSwqRJk9i5c6em8aQzGo2sXbuWI0eOkJKSgp2dHd27d9e0pkrbtm1NRX/t7Oxwd3dn1KhRWFpaahaDeD28kcmHwYMHU716dWxsbMiTJ49pu9ZfYk9nKnPkyEHBggXJly+fpnGk01tmGdJmFmQ1K0XLbG5GT1c0j4iI4P79+6xcuVLTONq1a0fu3Lm5cOFClkXftEwUOTk5kZiYSKtWrXB0dMy0DlPrg5WePXtm+V62srLCwcGBefPmaRpP+tKTRYsWZVn4UusCZ3ps07p48WKWLl1qtt5ZRZJRxur5vv/+ewIDA3FwcMBgMLB371769++Pp6enpnE4OjpSu3ZtDhw4kGVNF62nqn/++edERkbSokULnJyclCYcMvLy8iIxMRE3NzdTl4BSpUop6RKQ1TJK1UsrDxw4YLYMpHnz5prH0K9fP5YsWWK2rDOd1t+DWcWgKpaMMi5zSElJ4cKFC9ja2rJ06VJN45g/fz7Hjh3j7NmzZsdeVlZWfPTRR/Tq1UvTeNJNnz6dK1eu4OnpidFoZNOmTZQtW1bTz3nG5eJ6ucgr9OmNTD7oba2W3ty+fZsffviBUaNGce3aNebPn8/o0aNNa+u0pLcvsKeTD0WKFMHZ2VnzIlHx8fGcP3+e8ePHZ1n0TctEUcYD/4xX01TPUpkyZQre3t5K9p3R/v372blzJ7t379bFSVKNGjVMbVqBTFdAVXwPtmzZkqCgIFMRYFVkrJ6vdevWbNy40bQcLy4uji5dumi+Tv7mzZscPXqUb775JstEkdZT1Z9eGqiHdomQlqTJ2CXAYDDg4uKipK6Bs7MzK1asMM2Iu3PnDr169SIkJETzWNKFhYVx+PBh06yQxo0bK4tFb5KTk8mZMyfJyckkJSUpuzCWlWvXrjF16lRlMz+Dg4OVdkZ5Wtu2bQkODjbNdEhJScHV1VXT4+f02WhP/1uIp72R3S5WrVqlOgRd+/LLL03dNkqWLImtrS2jR49m+fLlmsdSo0YNgoODqVOnjtksFVVT/zL2ar9//76yytT58+fngw8+YN26dUBaG6rU1FTq1q2reZJIr2uZvby82LdvH/fv3zfbrvUBQ9OmTWnatCnr16+nQ4cOmu47K3pMspYuXVpZR52MZKyer3DhwmZtj/PmzavkpKR06dK4u7tTo0YNKleuzOXLl0lNTaVq1apm8Wklq9bZeqCXLgGQ1p7Qw8OD999/H0j7vaViBka6adOmcfLkSZydnTEYDHzzzTecOXNG82UXY8eOfeb9KgpO7tixg2+//ZatW7dy8+ZNevTowYQJE2jZsqXmsWSlfPnyXLp0Sdn+69aty5QpU3j8+DFGoxGDwUBUVBRr1qxREk9qaiopKSmmmlapqamaL3eIiIigRYsWQNpFzPR/q74QJfTnjUs+/Pbbb3z77becOXMGCwsLateuzcCBA5UW0tGbuLg4U0uuXLly0bFjR9auXaskllOnTnHq1CmzbSq+xB4+fMiSJUsoXrw4jo6O9OrVi8uXL1O6dGnmzJmDjY2NpvGkO3fuHOPGjaNu3boYDAYmTpyIv7+/plNHZ8+eTd++fSlQoECW99+/f5/vvvtO87akX375JTdu3KBy5cpmV4xVXa1wcHBg6NChHDlyhNTUVBo2bMjkyZM1TxYVLFjwuQVcz58/T82aNTWKCCpVqkTXrl1p2LChWUHQjMk+LclYZe+dd96hU6dOODs7Y2Vlxc8//0z+/PlNs8K0HrPk5GRat25N4cKFMRgMxMbGsnDhQs2/k1evXk3Xrl2zXWOdmprKjz/+mOXMy1cpJSUFNze3TF0CevbsCWib4HJ1daVBgwacPHkSKysrvL29ldYF2rt3Lz/99JMpWdW5c2fc3d01Tz7osQXht99+y4oVKwCoUKECmzZtolevXsqSD08naP7+++9MBR+1NGLECJo1a8aJEyfw8PDg559/pmrVqsricXV1pWfPnqYLhz/99JPp31pRVe9CvH7eqOTD4cOHGT16NAMGDGDcuHEkJyfzxx9/MHz4cGbNmkXDhg1Vh6gLefLkYf/+/TRt2hSAQ4cOkTdvXiWx6OWq+vjx4ylVqhQRERGsXLmSnj170qFDBw4dOoS/vz9BQUFK4pozZw4//vijaR34tWvXGDRokKbJBycnJ7744gusra2xtbWlVKlSWFlZcf36dY4cOUJ0dDTjxo3TLJ50Fy5cMJturJqPjw/16tXD398fg8FAYGAg48ePZ8mSJZrGERISwvLly2nbti22tramGUUJCQn89ttvbNy4kdKlS2t6QluyZEllV2OzImOVvbJly1K2bFmSkpJISkpSPk3d39/fLAF88uRJ/Pz82LBhg6ZxlClThq5du9KgQYMsvwePHj2q+UktwBdffGF2u3fv3prHkC69IOf9+/cxGo2mpSiqkowlSpTgwYMHpuVeycnJFClSRPM49HjsmZycbJZsLVasWJZ1ILSSMUFjYWGBo6MjjRo1UhZPcnIyQ4YMISUlhXfffZeOHTtqXvcmo/79+/Puu+9y+PBhjEYj/fv3p1mzZprGoKoWm3j9vFE1H7p378748eMzHaidPXuWqVOnKpsupTfnz59n1KhRxMTEAGnTW2fMmKEky3z37l18fX05fPgwqamp2NnZMWnSJM2vQKZ3tDAYDDRt2pQDBw6Y7mvXrh2bNm3SNJ50GasLp1NVwOvIkSPs2bOHK1euYGFhQYUKFWjevDkffvih5rEADBw4EB8fH6VX1jJyc3PLtLZZ1ViFh4ezYsUK9u3bB0DOnDlJTU2lSZMmfPbZZ0pa2969e1fp8qGMZKxeH3r6DkxKSmLr1q1m34MVK1akWbNmtG3bVkmbX710CQD47LPPKFCgAFWrVjWbjaYq+TBkyBB+//13WrRogZWVFQcOHKBo0aK8/fbbgHbLHfRY5HH8+PEkJCTg6uqKhYUF27dvJ0+ePMo6k5w+fdrUcQMgMTGRuXPnMmbMGCXxdOzYkdWrV7Nt2zYePnzIJ598grOzMz/99JOmcZw7d45atWrp6nMuxPO8UTMf4uPjs7xCVLt2beLi4hREpE81a9Zk27Zt3Lt3j5w5c5oKi6kwceJE6tWrx5QpU5RegUyflpkjR45MJ0Uq83dlypQhICDA1BZww4YNyrLPdnZ22NnZKdl3VhITE3F0dKRatWpmB/2q1tFbWFhw8+ZNSpcuDaS1MVOxNh3SaqlMnz4dSDvpt7CwUHLFL92BAweULx/KSMbq2fE8XfjS2tqa/fv3K4mnUKFChIWFmaaDh4WFKavFkytXLjw9PZVeAX1axu4+GbsEqDgpiY2NNU3l14PmzZubfcdk1TlKC3qZ4ZmRj48Pq1atIjAwECsrK2xtbenatauyeEaNGsW0adOoV68e+/fvZ/LkyUqPN9q2bUv//v2ZNWsWnTp14sCBA0pm761du5YpU6Zk2cVLCukLvXqjkg+PHz8mJSUl00FkSkoKKSkpiqLSjwkTJuDn50ePHj2y7AGu4kvs2rVrZh0m+vTpk+kqlxZSUlK4efMmBoOB5ORkbt68aUo6JCcnax5POn9/f/z8/Fi8eDFGoxE7OztlVyb0pl+/fqpDMDN06FA6deqEjY0NRqORU6dO4efnpzos5R0mQB/LhzKSscpexsKKycnJhIWFcfLkSWXx+Pn5MWrUKFPhwvLlyzNz5kxl8ejN0wW207sEqFCzZk3Cw8N1M1vHw8OD+Ph4Hjx4YLZd64LW8+fPZ/DgwdkWntRyvGJiYihRogSxsbE4OTnh5ORkui82NlZZse/FixczePBgypcvT1RUFDNmzFBaq6179+64u7uTP39+Vq1axZkzZ7C3t9c8jvRuZxMmTMg0O1nl97IQz/JGLbvw9fUlV65cZtO0UlNT+eqrr8iZM6ey6Vt6kd63+NixY1ner6Iokru7O4sWLTK7Ajlw4EDNW/jocVqkeL4TJ05w8eJFPD09OXXqlPIpiHfv3uX06dMYDAZsbGwoVqyY0nj0Qk9T59PJWL24rJapaO3x48cYDAalM/VeF0+339SKh4cH4eHhFCtWjNy5cyuvgj99+nSCgoJMM2VUxbNnzx4cHByyPa7Rsm1sv379WLJkiemYJ52qn82NGzfM/j1s2DC8vb1NSzBUJUMePHjA1q1bTfVL0mm9hOjEiRMYDAa8vb3x9/c3xZKSksKkSZOkCKTQpTcq+fD48WP69+/PzZs3qV27NqmpqZw9e5YqVaqwYMECJesx9ervv//m3r17Zl+qKk7c9u7di4+PT6YrkFoX0hHPt2PHDlq0aKGbz9HKlSsJCwsjOjqadevW0bVrV9q3b6+04JqeJCUl6Was+vfvj52dndnyoSNHjrB48WLFkemDnsYqODjY9G+j0UhERARHjx5l48aN6oLSkafXpquWVZeAUqVKZTlN+1W7fv16lttVLRVs1aoVmzdvVtIqNjt6mImhJ3q98KOX+iXz58/n2LFjpouH6aysrPjoo4/o1auXpvEI8SLeqORDumPHjplabdapU0fabD5l4sSJ7N+/nwoVKpi2ab12LCAggE8//ZTw8HCsra3lCuRrYOzYsRw9epSmTZvi4eGh/ADc3d2doKAgOnbsSHBwMI8ePaJDhw5s375daVx60axZM5o3b66Lsbpz5w5+fn4cOXLEtHxo/PjxuikWqpqexurpk9kiRYrQpUsX05KZN12PHj24f/8+bm5uuLm5UaJECaXxZLyanl4vpFGjRuTMmVPzWJKSkti/fz+PHj0C0maeRkVFMXToUM1jARg8eDC+vr5Ka6hkpIeZGLdv32bGjBlERERQr149Ro4cScGCBTXb/7MkJyeTM2dOkpOTSUpKUpo0Uj0z72nBwcHK2ogL8bLeyOSDeLaWLVuyfft2pVfaHBwcWLFiBQMHDuS7777LlPV+k68EZKS32QaJiYns3LmTbdu2cefOHZydnXF3d1eSMErvQuLu7k5wcDApKSl4eHgoO2DQ09VrSGvXuHPnToKDg7lz5w7u7u60bdtW+cmSHshYZe/evXuZTtbSp42roLeZBpB2hT8kJIQdO3ZQpkwZPDw8aNGihZITfj11CRg0aBBxcXFcvXoVW1tbjh49Sv369ZXMwoC04qRjx46lWrVqWFpamrarKtKnh5kYvXv3plq1ajRs2NA0ZV9VjZCMduzYwbfffsvWrVu5evUqPXr0YMKECaZCs1obPXo0vXr10k39ksjISFavXs3jx48xGo0YDAaioqKki5/QJUk+iEx69OjBt99+S4ECBZTFMG/ePLZs2cKtW7cyXf1UOdVu3759ulryobfZBgDHjx9ny5YtHDlyhLp163L+/Hk6depE9+7dNY1j2rRpWFhYsGfPHkaNGkVgYCCVKlUyFabTmp6uXj/t559/ZsqUKTx48IAPP/wQLy8vKlas+Mr3m9364nSqPucyVtlzd3dn+fLlFC1alJiYGPz8/Pjrr7+UzSjS20yDdDdu3GDbtm2sW7eO0qVLExsby5dffsnHH3+saRytW7fOskvAV199pWkcAB9//DG7du3C398fT09P8ufPz7Bhw5Qt2XF2dqZPnz6ZLmaoqG8F+piJkd5WHNJmGri7u2vePjIrrq6urFixwtRt7M6dO/Tq1UtZrRm91S9p164dzZo1Y+/evXh4ePDzzz9TuXJlJk2apCQeIZ5Fkg/CJH067ZUrV7h16xa2trZmVwNUZL99fHyYPHmy5vvNjoo+zs+jl9kGc+bMYdu2bZQrVw5PT09at25N7ty5iY+Pp0WLFhw9elTTeAwGA0FBQRw6dAiDwYCdnR2dO3dW1jJRT1evIe1zvmXLFrZt20aZMmVo164drVq14siRI/j7+7Nr165XHkN0dDTW1ta6WwsuY5W9nTt3smjRItzd3Vm2bBldunShb9++Sq7qp9PTTIP169cTEhJCTEwM7u7ueHh4UKpUKW7fvo2HhweHDh3SNJ7Lly+bdQnw8fFRttS0c+fOrFu3jjVr1pAvXz7T50pFB6uM8eiFHmZieHh4mC3VSZ85qFpWRVJVvnf09jsrfRnI119/TZMmTahduzaenp66O14VAt6wVpvi2dKz/aqy/lm5e/dupm2ffPIJK1euVBBNWhu3sWPHYmNjQ548eUzbVa61y5MnD2XLlqV06dJcuXKFCxcu8Omnn2o+2yBHjhwEBARkWvudP39+vvvuO83iyBiPi4sLTZo0MW2Ljo5WtmQnb968uLu74+7ubrp6vWDBAiVXryGtYFa7du1Yvny52QFT06ZN+fXXXzWJIX1W07Rp05g/f77ZfSo/5zJW2WvdujX58+dn8ODBLFq0iIYNG2q6/6yULVsWd3d3rKysWLduHatWrWLOnDlKZhr89ttvDB48ONPPpWTJkvj4+GgWR3qXgNy5czNp0iRTl4AyZcpw48YNJd+DVatWxc/Pjy5duvDll18SHR2dZSFBrbz77rsMHjyYJk2amCWqVP0+nzNnDuPHj9fVstKsZqSp8P777zNixAhcXV2xsLBg+/bt1K1bV/M49u7dS/Pmzfntt9+yvF9V8iFv3rwkJSVRqVIlzp07J7XshK7JzAeRpaioKP766y/s7e25efOm5sXEBg0axJ9//klMTIzZsouUlBRKly6t7GqFHvpwZ6Sn2QZPnjzhl19+0U0xMT0U78pIT1ev4X8/j4y3o6KiNP2sDxo0iPPnz5tmQKRLTU2lVKlSyj7nMlaZZVwaYzQauXfvHpaWlhQsWFDp50pvMw0A/vzzT9Pa6/TvwfROLlrRY5eA1NRU/vjjD2xtbdm9ezeHDx+mU6dOVK1aVfNYQH+/z/UwE6N27dqULFnSdPv27duULFlS+e/PpKQkVq1axW+//YaVlRW2trZ07dpV89o88+bNY8iQIbp776xevZo9e/Ywa9YsOnXqRMWKFTEYDCxfvlxJPEI8iyQfRCbbt29n0aJFJCYmsm7dOtq2bcvo0aNxc3PTLIb4+Hju37+Pv78/3t7epu1WVlYUK1ZM2dR5SFsHefnyZVJTU6latarSWL755hvatWuX5UmI1oXY9FZMTA/FuzJycHCgXbt2eHh4ZLo68tVXXzFu3DhN4wkMDGT69OkkJCSYtpUtW5awsDDNYtDr51zGKrPsphmnU3XFb/To0Xh6emY5A2Pnzp20bt1a03i8vb05duwYcXFxvPPOO4SHh1O/fn2+//57TePISC9dAvz8/JgwYYLZNi8vL6ZPn64knnRxcXEUKlRIaQwAvr6+xMTEKJ2JobfPeUxMDCVKlDDN5Hmaqlkiv/76K40bNzbbtmvXLlq1aqUkHkj7fZo/f35u3brFmTNnsLe3J2/evMriESI7knwQmXh4eLBq1Sq6d+9OcHAw0dHRfPbZZ8rWjunhKlK6s2fPMmTIEAoXLozBYCA2NpaFCxdiY2OjJB49zTbQWzExPRTvykgPV68zcnBwYOXKlcydO5fhw4ezf/9+fv/9d2bPnq0kHj19zmWssnfx4kUWLVrEnDlz+Pvvv5k4cSJ+fn688847mseSTk/vHQcHB3bu3Imfnx89e/YkISGBadOmKas6r4cuAePHj+fatWucPXuW2rVrm7anpqby4MEDZR2IwsPDGTZsGImJiQQGBtK9e3fmzp1LrVq1lMSjt6vpepBdUWJVMzG2b99OUlKSaQZEupSUFJYsWcLPP/+saTzp0j9H9+/fN5vtNGjQICXxCPEsUvNBZJIjRw7y589vum1tbU2OHDmUxJLdVSRVB5ZTpkxhzpw5pmTDyZMn8fPzY8OGDUriGTlyZJazDVQoVqwYFhYWvP3221y4cAF3d3eSk5OVxALg5uZGq1atdNNGLSgoSPnV64yKFStG+fLlqV69OhcvXqRbt26sXbtWSSx6+5zLWGXP29ubgQMHAlC5cmW++OILxo8fL++d/8/a2pqcOXNSuXJlLly4gLOzMw8fPlQSC8C3337LihUrAKhQoQKbNm2iV69emiYfBgwYwPXr1/H39zc7GbK0tKRy5cqaxfE0Pz8/Fi5cyMiRIylZsiSTJk3Cx8dH2e/zNznJkJ0lS5YAae189eDRo0f8/vvvPHr0yGxZq6WlJcOHD1cW19ChQylQoABVq1bVTZ0OIbIjyQeRSdWqVVm9ejUpKSmcP3+eH3/8UVkv40OHDmV5FUmVx48fm81yqFu3Lk+ePFEWz4ULF8xmGwwbNoxhw4YpiUVvxcT0VrxryZIlhISEZLp6rUrevHk5cuQI1atXJywsjPfee4/ExEQlsejtcy5jlb2EhASaNm1qut24cWNmzpypJBbQ33unZMmSLFmyhA8//ND0c0lKSlIWT3Jysqk9IaQlsrT+Xi5XrhzlypVjy5Ytpvoux48fJzw8XNksA0h7L2dMfjRu3Fj5EhBh7vbt28yYMYOIiAjq1avHyJEjKViwoLJ4OnToQIcOHTh8+DAffvihaXv6kgdVYmNjTUlGIfROzeVsoUv3798HYOLEidy+fZvcuXMzbtw48ufPr2mV7oyevor03nvvKb2KVKhQIbOrn2FhYaaChio8PdugfPnyymYbTJo0CScnJ6pUqcKQIUOIjo5WNoUfoECBAri7u9OgQQOzP6pkdfX6woULyuLx9vZmz549fPTRR9y/fx9HR0dNu6NkpLfPuYxV9ooWLcratWt59OgRjx49Yv369Zq39c1Ib+8df39/ypUrR506dWjVqhXbtm1j0qRJyuJJ7xKwd+9e9u3bx+jRo5V0CYC01tlz587lr7/+YuTIkZw7d86s1otW0ltJFi5cmPDwcNOV4i1btuii9oP4n3HjxmFtbc2IESNISkrSzeyQhIQEZs6cyaNHj3BycqJFixZs2rRJWTw1a9YkPDxc2f6FeBky80GYtG7dmg8//BBPT09GjBjByJEjVYeku6tIfn5+jBo1ivHjxwNprTdVXvXTw2yDp1tO/fbbbxQoUIDWrVsTFxenaSwZ6a2Nmp6uXgNUq1bNVDjx6TaXWtPb51zGKntTp05l8uTJzJgxg5w5c/LBBx/g7++vLB69vHcyFsSrV68eN27coEWLFrRo0ULzWDLy8fFh1apVBAYGmnUJUOHMmTNs3LiRBQsW0L59ewYPHoynp6fmcfzwww94eHjg4+PDmDFjiIiI4P3336dSpUpKf5+LzG7fvm0q1tq4cWOlbc0zWrhwIf7+/mzfvp06deowceJEevToQbt27ZTEExERgYeHB8WKFSN37tzKu5MI8SySfBAm+/btY9euXQQEBODj44Obmxuenp6UK1dOWUz+/v7s379fN1eRKlWqxPr163n8+DEGg0HpNDtIm23wxx9/UKVKFQYPHszhw4c1n22Q3s3i/v37XLt2jXr16pEjRw7++OMPqlWrpqx1WEJCAvnz5880XV7VwYu3tzcbNmxgzJgxbNiwAScnJyXFoJ4u3PU0FQcrevucy1hlr0yZMqZ12Hqgl/dO9+7dsbCw4MmTJ9y5c4fy5cuTI0cOrl27Rrly5di5c6em8aR3CYiNjcXJyQknJyfTfbGxsUqWo6WmpmIwGNi9ezeTJ08mISHBrK6K1ipWrMjatWt18/v8wIEDzJkzhwcPHmA0GuUEEswuHOTMmdPstmo1atRg/vz5tG3blnz58imtcbVgwQJl+xbiZUm3C5Gl27dvs23bNrZs2ULhwoVp3749rq6umu0/u7ZK6fSyjl+1ixcvcunSJfLkyUPlypWVVeMH6NOnD97e3lSsWBFIa9k1ceJEpS3mQD9t1PTi+vXrGI1GFi5cSPny5WnXrh2WlpZs3bqVqKgoJk6cqCSu+Ph4Hjx4YLbtTf+c62mssqs6n07VCVJ8fDyXL18mT548VKhQgdy5cyuJI93w4cPp1q0btra2QFrL42XLlmneclhvXQIAVqxYwdKlS6lfvz4LFy6kTZs2dOrUiU8++UTTOGrXrk3JkiWzvV/Ve7l169aMGTMmU9FAVW1s9cDDw8O0TCar26r069ePcuXKERYWxo4dO5g3bx6XL1/WPDG7d+9emjdvTnBwcJb362WmiBAZSfJBPNOtW7dYtGgRGzdu5OzZs5rt18HBIdM2CwsLuRLw/925c4chQ4YQERFBxYoVsbCw4PLly9SrV4/Zs2dToEABzWNydnY2a8dqNBpp06YNO3bs0DwW0FcbtY0bN7JmzRouX75M7ty5qVKlCt26dTO7Gqm1du3aZVqjmtU2LUyfPp2goCBT/RSVn3MZq6ylFwq8fv16lvdrfYL0+PFjJk2axPbt203Jxfj4eNq1a8e4ceOUXSFt27YtW7ZsMdvm6uqqrJ2k3hgMBlP3rLt371K0aFHNY3B2dmbp0qXZ3q/qZL9z587KZgrq1dOJotu3b1OyZEnlx4Lx8fGEhYVRv359KlSowJo1a3Bzc9N89kx6y09p0ypeJ7LsQmTy4MEDQkND2bp1K7Gxsbi7u2v+Ba+Xtkp6NXv2bN5//30CAgJMB9lJSUnMnz8ff39/JdXea9WqhZeXF05OThiNRrZu3Wq6+qeCXtqorVmzhnXr1vHFF19QrVo1IK1LyeLFi4mLi6Nz586axpNRxord+/fvN2tJqqXdu3fzyy+/kC9fPiX7Tydjlb3ixYsTFBTExYsXqV+/Pm3atNF0/09Ln0mwb98+UzeH2NhYZs6cyYwZM0x1ebRWqlQpvvnmG9q0aYPRaCQkJIRKlSppHoeeugRMmDABPz8/evTokeWsGa3bH+fMmVOXswnef/99pk6dykcffWQ2g+eDDz5QGJVaWi9XelH58+cnR44cbNy4kf79+5MvXz4ly3aGDBkCgIuLC40bNza7b9euXZrHI8SLkJkPwmT79u1s2bKFP/74gxYtWtCuXTtlJ496OnACsj1oSqf1wZOTk1OWMwqMRiNubm6ZrrxpISkpidWrV3Ps2DEAGjVqRLdu3ZSd0KZfGXZ3dzdNSczqquSr5urqyg8//ECRIkXMtkdHR9OvXz9lU0j//PNPvLy8iImJwWg0UrZsWWbOnGnWek4rgwcPxtfXN9PPSGsyVtmbOHEi4eHhvP/++/z666+0atVKSR2MdE5OTmzbti3T90tSUhKenp7KZhrExcUxb948s+/BIUOGaJ5Y6927N9WqVaNhw4amEzhVV0HPnj1L7dq1TT+Tp2ndhcjX11fZ8rJn6dGjR6ZtFhYWmh9fiOebNWsWt27d4ty5c6xfv54BAwZQq1YtxowZo2kc27dvJykpyTQDIl1KSgpLlizh559/1jQeIV6EzHwQJqtXr8bT05Ovv/6at956S2ks48aNo1q1ari6urJz506mTp2qdPrY4MGDle07K9mta7awsDBNadVaeHg4vXr1olevXsD/WlFp/cs4nV7aqOXIkSPLk2pra2vNY8mocuXKbN26lXv37mFhYUHhwoU5f/68kljc3Nxo1aoV1apVMzuZ1PqgW8Yqe7/99hvbt2/HwsKCe/fu8cknnyhNPuTKlSvLxGZ227Xy4MEDJkyYYLptNBpZvXp1lieWr5KeugTUrl0bSPtO1kONIj0mHgBWrVqlOgTxgg4ePMjmzZvx8PAgf/78rFixgrZt22p+vPPo0SN+//13Hj16xNGjR03bLS0tGT58uKaxCPGiJPkgTH788UfVIZjo6cAJzK/M/Pnnnzx+/Bij0UhqaipRUVGaX7l51iyMZ933Ko0aNYpp06ZRr1499u/fz+TJk7Gzs1MSC6R1AvHy8iIiIgJbW1sqVqyopI2aqmTQ8/Tp04elS5dSpEgREhMTmT59Olu3buXgwYOaxzJnzhzGjx+vvMCkjFX2cufObfpuKVKkiLLvmXTPGiuVsX3++ecsXbqUihUrcuHCBby9vcmXL5/myQc9dQnIrkZR3bp1+frrr5XUKNITvS1LEc+X/v2TPl5JSUlKfn906NCBDh06mC3Lg7SaFKq7twiRHUk+CF3S04FTRt7e3hw7doy4uDjeeecdwsPDqV+/Pu3bt9c0joiIiCz7xxuNRmJiYjSNJd3ixYsZPHgw5cuXJyoqihkzZiit+VChQgXmz5/PW2+9hcFg4M6dO6ZOHFqKiYnJtg2WqrECaNGiBX369KFnz55Mnz6dhg0bsm3bNiWxFChQQHmCEWSsnuXpkyLViZrIyEh69uyZabvRaOTKlSsKIkozdepUBgwYQKNGjdi1axcjRozQxXtbZUJGjzWK9KRTp06A/mZYiuw5OjoybNgw4uLiCAgIYMuWLbi4uCiLJ32m6RdffEH79u25e/cuXl5etGvXTllMQmRHaj4IXdJreyUHBwd27tyJn58fPXv2JCEhgWnTprFmzRpN48iu4nw6LYtpZWyLeuPGDYYNG4a3tzd16tQB1LVL/OGHH9i8eTObN2/m+vXrfP7553z66aemAz2tPK//tsqp65s3b2bChAksWLCAZs2aKYvD19eXmJgYmjRpYpZo1PqkTcYqew0bNjTrQrRnzx6z21ovi8uufkA6rWejZRQeHs7nn3/O7NmzadiwoZIY9NQlQI81ioT4vzpw4ACHDh3CYDBgZ2dH8+bNlcXi6emJv78/Z86c4fjx40ycOJEePXoo6V4lxPNI8kHokp4OnDJKb4W1cuVKihcvjrOzs5IihnqS3kc+q68SlWPl4uJCUFCQqX5JQkICHTt2fONb3mWc2ms0GomIiCB//vymJJGKKb7SJixrehqr5yV/PTw8NIpEn2rUqGE2VmDeHlrrGh16SlBnLPr7MvcJoVcXL17URf2SdJ6enmzcuJGBAwfStm1bWrduLS1+hW7JsguhS3ptr1SyZEmWLFnChx9+aKofkJSUpDgqtfTaFjU5OZlcuXKZbutl6Y5qepzaa21tLcWxsqCnsXrTkwvPEx4erjoEM3pqJanHGkVC/BPZ1S+pV68es2fPVla/pHjx4vj5+XH27FlmzpzJtGnTlNdQEiI7MvNBiJcQHx/P/v37cXZ2ZtWqVRw6dIhPPvlEaWFFvYiLi2PmzJlcvXqVefPmMX36dMaOHausRerMmTM5efIkTk5OWFhYsHPnTurVqycnuRmcOHGCixcv4unpyalTp5T1k2/bti0hISFyIvIMehkr8WxJSUksX76cy5cvM2HCBAICAujbt69ZIvRN8/RMxnTpNYrOnDmjICp9ioiIIC4uzmwmoXzW9WPcuHEUL16cwYMHZ6pfEhMTo6x+SXx8PGFhYdSvX58KFSqwZs0a3NzcpOik0CVJPgjxkuLj43n48KHZwYEeMszz589XeqV0yJAhNG7cmDVr1rBhwwYWLlzI+fPnWbp0qbKYQkND+e2337CysuKDDz6gZcuWymLJ6Ny5c9SqVUtpDCtXriQsLIzo6GjWrVtH165dad++Pb1799Y8lp49e3L79m1q1apl1kZWD8suZKxeH4GBgZrXdHmat7c3RYsWZc+ePaxfv56JEydiNBqZNWuW0rhU0tMSED2bPHkye/fuNZvCb2FhId0udETP9Uu2bNnC33//Tf/+/dm5c6cuCt0KkRVZdiHES1i8eDFLly6lcOHCZut5VdU1yGjPnj1Kkw9RUVF06tSJtWvXkitXLoYPH07btm2VxQNpHS+KFy9uaou6YcMGzTuTZMXb21t5AdXNmzcTFBREx44dKVKkCBs2bKBDhw5KTmj1PKVfxipzLE+P15o1a+jWrZvmsTxt3bp1ypMP586dY/Pmzfzyyy/kzZuXGTNm4OrqqjQm1SS58GJ+/fVXQkNDyZMnj+pQRDYyJsczsrCwUNoBaNasWdy6dYtz587Rp08fNm7cSHh4OGPGjFEWkxDZkeSDEC9hw4YNhIWFUbRoUdWhZKJ6EpOlpSUPHz40TZ2PjIxU+stYL21Rs6J6rCCtVWLGqeC5c+fG0tJS0xhiYmIoUaKEso4AL0LGKk1AQADx8fGsW7fO7Ep2SkoK27Zt00XyQQ9jZWFhQVJSkul78N69e7KcSLyQ8uXL6+I9LLKn1/olBw8eNCWG8+fPz4oVK2jbtq0kH4QuSfJBiJdQunRpChUqpDqMLA0ZMkT5/nv06MHNmzf54osvOHnyJF999ZWyeA4dOpRlW1Q9qF27tuoQaNCgAdOnTychIYGwsDACAwM1r13i7e3NkiVL6N69e6aOKXqZUSRjlaZSpUqcPXs20/bcuXPr5nPVuXNn1SHQs2dPPvvsM2JiYvD39ycsLIyBAweqDku8BgoVKoSzszP16tUzSzbqYfmZSBMREUGLFi0ybU+vX6JK+oWe9ARIUlKS0os/QjyL1HwQ4iVMmDCBixcv0rBhQ7ODg0GDBimMSj/u3r3L6dOnSU1NxcbGhuLFiyuLRdqiPpvBYCAoKMisT3nnzp2xstIuJz1z5kxGjRrF/v37adq0qWb7fd3oYazS/f3331SuXNlsW2JiokwVz+Cvv/7i6NGjpKam0qBBA2rUqKE6JPEayG55l56Xpb1p9Fq/ZOnSpZw7d44zZ87Qs2dPtmzZQqtWrejfv7+SeIR4Fpn5IMRLKFmyZJZVu0Vapj0oKIhLly4xYcIEVq5cqbTKu7RFfbYcOXJgb29PqVKlsLe35+bNm5qfzG7dupXGjRvz1VdfkS9fvkxTjqXKexo9jFW6K1euMGzYMBISEjAajRgMBhISEjhy5IiSePTo/PnzREdH069fP3bt2iXJB/FCPDw8uHjxIseOHSMlJYWGDRtSs2ZN1WGJDPRav6Rv374cOHCAMmXKcPPmTQYPHkzz5s1VhyVElmTmgxAv6e7du5w6dYrU1FTq1q2r9Op+VpKSkpSc8D9d5d3HxweDwaCsyru0RX227du3s2jRIhITE1m3bh1t27Zl9OjRuLm5aRZDUFAQP/30E6dPn860vEGqvP+PHsYq3ccff4yfnx8rVqygf//+hIWFkZCQwMSJEzWPRY8yFn5bv349AwYMoFatWrL2WjxXcHAwCxYsoGXLlhgMBnbv3s2AAQN0UadI6NvFixe5dOkSefLkoXLlymYdU4TQG0k+CPESDhw4wLhx46hbty4Gg4E//vgDf39/ZRnmTp06ERgYaLptMBhwc3Nj69atmsfi4eHB5s2bcXd3Jzg4GKPRiKurK9u2bdM0jhs3bjzzfpVtUfXUptXDw4NVq1bRvXt3goODiY6O5rPPPuOnn37SPJaFCxfqbl38+fPnOXLkCJaWljRu3DjTUgMt6Wms2rVrx6ZNm/j222+pXbs2TZo0oU2bNmzfvl3zWACSk5NZs2YNR44cwcrKiiZNmtChQwdlxd/c3d1Nhd+Cg4NJSUmhbdu2yn4+4vXh5uZGQEAARYoUAdIudPTs2VPz36Hi9XHnzh2GDBlCREQEFStWxMLCgsuXL1OvXj1mz55NgQIFVIcoRCay7EKIlzBnzhx+/PFHU1b52rVrDBo0SPPkQ8+ePTl27BiA2ZReKysrHBwcNI0lnV6qvHfv3j3TNj20Rc3YpjVjXKriyZEjB/nz5zfdtra2Vlagqnv37qxZs4b79++bJWZU1VL5/vvvCQwMxMHBAYPBQP/+/enfvz+enp5K4tHTWOXJk4fLly9TuXJljh07hp2dHcnJyUpigbQZV4mJiXTs2BGDwUBISAgRERGMHz9eSTxS+E38UwaDwZR4AChatKh0ShHPNHv2bN5//30CAgLImTMnkPadM3/+fPz9/XVTDFiIjCT5IMRLSElJMZvOVr58eQwGg+ZxpE9HnzJlCt7e3prvPyt6qfK+Z88ezff5IvTWprVq1aqsXr2alJQUzp8/z48//qhsbfqwYcMoUKAAVatW1cXBdlBQEJs2bTKd8A8cOJAuXbooSz7obazmzp3LzJkzWbp0KYGBgUqnhZ86dYrQ0FDTbQcHB1xcXJTF4+joyLBhw4iLiyMgIIAtW7YojUe8PqpXr46/v7/p87RhwwapFyKe6Y8//mDHjh1m23LlysWIESOULMsT4kVI8kGIl1CmTBkCAgLMDg5UFiDy8vJi37593L9/32y7u7u7ZjHcuHGDMmXK4O7uTu3atU1V3hctWqTkwOn27dvMmDGDiIgI6tWrx8iRIylYsKDmcTxNb21aJ06cyKJFi8idOzfjxo3Dzs4OLy8vJbHExsayYsUKJfvOSuHChc0KOubNm5d8+fIpi0dPY9WgQQMaNGgAwMaNG4mLi1P6vi5XrhxXrlyhYsWKQNp7SUVR4PRaO1L4TfxTU6ZMYd68eYwbNw6j0UjDhg3x8fFRHZbQsdy5c2e53cLCQmZcCd2Smg9CvIQ7d+7g5+fHkSNHMBqN2NnZMX78eKytrZXEM3ToUG7cuEHlypXNrhhr2Rf8o48+4q233sLe3p5GjRphZ2en9EStd+/eVKtWjYYNG7Jz505AH33S9dKmtUePHtjb29O4ceNMRR5VGT16NL169dLNVb6xY8fy559/4uzsjJWVFT///DN58uTh/fffB7QbMz2Old58+umnnDx5EltbWywtLTlx4gTW1tamQsBaFS2tX78+9erVo3HjxjRq1Eg372UhxH9Xeq2tl71PCJUk+SDEP/Tw4UNu3bpF1apVlcXg6OhoNuVYlatXr3L8+HF+++03Tp48SdGiRWnUqBGNGzembt26msbi4uJiKtCVnJyMu7u7ksJ8T1uwYEGW27VOPhw8eJDjx49z4sQJrl69Sv369WnUqBH29vaULl1a01jSeXh4EB4eTrFixcidO7fy+hzZjVU6rcZMj2OlN+m1b7KTPkvjVUtISODkyZOm78EbN26YJSNUJaiF/qWfJNaoUcPsIkL69+D58+cVRif0rHbt2lnO9DIajcTExHDmzBkFUQnxbJJ8EOIlrF+/nhMnTjB69Gjc3d3Jly8fbm5u9O/fX0k8AwcOxMfHR1cHtg8ePGD37t0sX76cy5cvc/bsWU33/3S2P737hh7orU1rUlISZ86c4cSJE2zbto2kpCQlyazr169nuV2vPdVV0MtY6c1vv/2W5fYPPvhA40jMJSUl8dNPP7F8+XL++usvOYEU/4iq1tni9ZDd78508jtU6JHUfBDiJaxdu5bFixezbds2WrRowfjx4+nYsaOy5ENiYiKOjo5Uq1bN7ABFq6nGkFaE88SJExw4cICDBw+SmJhIo0aNGDp0KHZ2dprFkR09FDCEzG1aJ06cqLRN66VLlzh48CBHjx7l77//5p133qFx48ZKYilTpgxr167lyJEjpKSkYGdnl2XXEq08fQUS0jpM7N+/X0k8ehirHj16PPOzpOV3Tkbz5s0z/TslJYULFy5ga2urJPkQFRXFwYMHOXjwIGfPnqV69ep4enpib2+veSzi9ZNV62xPT08lrbPF60GSC+J1JMkHIV5S+klIz549sbKy4smTJ8pi6devn7J9p/vggw+oX78+rVu3ZsGCBZQrV05pPBEREbRo0cJ0+/bt27Ro0UL5VH69tGmdOHEihw8fplixYjRu3JhevXpRt25dLC0tNY0joxkzZnDlyhU8PT0xGo1s2rSJa9euKWuXGB4ebvp3cnIyYWFhnDx5UvM49DRWgwcP1nyfL2LVqlVmt69du6akxkvr1q3Jnz8/9vb29OjRg/r165ta3wnxLHpsnS2EEK+KJB+EeAlVqlShX79+REVF8eGHHzJs2DDq1KmjLJ4GDRpw4sQJLl68iKenJ6dOndL8il/nzp05fPgwGzdu5NatWzRu3Jh69eopq7ScXmRSb/TSpjUsLIzq1avTqlUr7O3tzWJS5ddffyU4ONj0nmnWrBmurq6Ko0qTM2dOnJycWLx4seb71tNYZayd8Oeff/L48WOMRiOpqalERUVpVlvhecqXL8+lS5c032/16tW5cOECV69epUKFClSqVElJ1w3x+tFj62whhHhVpOaDEC/hzJkzJCYmUrVqVQoXLsyePXto0qSJWVs+La1cuZKwsDCio6NZt24dXbt2pX379vTu3VvzWKKjo01Tjs+cOUO1atWwt7enS5cumseiR/3798fOzs6sTeuRI0eUnNSeO3fONFZ37tyhYcOGNG7cGDs7O/Lnz695PM7OzmzevNm0dOjJkyd4enqaCodqLWONEKPRSEREBEePHmXjxo2ax6K3sfL27SqDVQAAKLJJREFU9ubYsWPExcXxzjvvEB4eTv369fn+++81jwXSOpNk9Pfff1OqVCmz5RhaMRgMnDp1yjRejx8/5sMPP8Te3p4mTZpoHo94vTx58oRffvmFR48eAZgSe0OHDlUcmRBC/Hsk+SDES3BycmLHjh2qwzBxd3cnKCiIjh07EhwczKNHj+jQoQPbt29XEk96Ubzff/+dkJAQ7t+/z8GDB5XEojd6a9Oa7tGjR+zYsYOAgAAiIyM1LxAKsHjxYvbt24ezszMAP/30E82aNVNWS+XpE9oiRYrQpUsX5bNE9DBWDg4O7Ny5Ez8/P3r27ElCQgLTpk1jzZo1mscCmBWXtbCwoEiRIjRq1Ej5kofbt2+zf/9+fvjhB2VjJV4vgwYNIi4ujqtXr2Jra8vRo0epX7++kkSaEEK8KrLsQoiXUKVKFRYsWICNjQ158uQxbVdVWT1HjhxmhSZz586t+Xrw3bt38//au/eoqK6zDeDPIIj1FgUDjURtJSFUqUbDTWmQAhJhRCBaRCQQtUunKg2NiUbBeKEoeFmYhRFJQqISilA7jDeUQqjoAhGbiyYihERrVAxS0Qk0Q4GZ+f7gY8qIJF5w9pn4/NZyLc4+f8yzcoBw3r33uz/55BN8/PHHuHLlCsaPHw9PT0+kpqYKPYZUamxtbbFt2zbRMQB0zA5/8skn+OSTT/Dpp5+if//+eP7557F8+XIheRQKBcaMGYOTJ09Cr9dDoVDAx8dHSBYAWL58OYYOHWo0VlJSIqT4ILVnZWdnBysrKzg6OqKmpgZyuRxNTU1CsgCAo6Oj0da3lpYWbN26FW+88YZJc1RXVxs9p0GDBsHT0xOvvfaa8JM3yDzU1NTg73//O5KSkjBz5kzExcUhLi5OdCwiol7F4gPRPbh16xZOnTqFU6dOGcZkMpmwTu/u7u5ISUmBRqNBcXExcnNzTX7CRHZ2Njw9PbFq1Sq4uLgI6/UgVYsWLUJGRgZ8fX3veFqAqRtgenh4wMbGBp6envDx8cGKFSswZMgQk2boSq1WQ6vVwtvbG97e3jh16pTwotW8efPw/vvvw8bGBg0NDUhMTMRXX31l8uZvUntWAGBvb4+MjAxMmjQJmzdvBtCx4kmU119/HcnJyZgwYQJKS0uxbt06IafsdJ7u4+fnh1WrVsHGxsbkGci82draQiaT4Ze//CVqamoQGhqKtrY20bGIiHoVt10QmTGdToe8vDyUl5dDp9PB09MTERERwnpQUHfXr1+HnZ1dj+dxm/qorPr6esk0wquqqsLChQuxYcMGw5741NRUKJVKvPvuu0ad302psLAQ6enpCA0NxXvvvYc5c+Zg4cKFJl/KL6Vn1am5uRmlpaWQy+XIyspCeXk5YmJihB2re/HiRcTGxmLEiBG4cuUK1qxZA1dXVyFZiB7E6tWr0bdvX8yZMwevvfYagoKCcPDgQR61SUQ/KSw+EN2Dq1evIiEhAVevXkV2djaWLVuGDRs2CD1esrm5Gd99953R2PDhwwWloZ7ExsYiLS3NaCwmJga7d+8WlEi8mJgYLF68GB4eHkbjJ06cQGZmJnbt2iUmGDpO4IiNjUV6enq3fI+yDRs2YMaMGXBxcRGao66uzujruLg4JCQkGLZg8HcgmRutVotPP/0Urq6u+Oijj3Dy5EnMnj1b+EowIqLexOID0T1YsGAB5s2bhy1btiA/Px9//etfsX//fmHN1lJSUpCXl2dYiq3X6yGTyUy+lJ96tnTpUpw/f96wAqKTVqvFz3/+c+zdu1dgOrHCwsKMGgZ2FRISgv3795s0T9etMXq9Hjdv3kSfPn0wePBg/lz9vw8//BCHDx+GWq1GcHAwgoODhRRfO5/Vnf6E4bMic5SYmIjVq1cbja1YsQIpKSmCEhER9T6uzSa6Bzdv3sRvfvMbbNmyBTKZDOHh4cIKD0BHv4Djx49jwIABwjLcSVpaGmJjY0XHkITk5GTcunULSUlJRme4W1pawtbWVmCy/zl37hzGjh1r8s9tb2+HTqfr1idEp9MJ2euclZVl8s+8V6KeVaeoqChERUXh2rVrKCgowJIlSzBgwAD85S9/MWmOkpISw9dtbW2wsrJCW1sbWltbJfP7MDc3F7NnzxYdgyQuPj4ely9fxhdffIHa2lrDuFar7baqkYjI3LEzHNE96NevH7799lvD7Og///lPo9MmTO2ZZ54R2uytJ11fDB51AwcOxJNPPon09HSo1Wpcu3YNdXV1uHjxIlQqleh4AGBUFDElNzc3bN++vdv4jh07hCzrd3BwgIODA/7zn/9gy5YtcHBwQEtLC5YvX47//ve/Js9zJ6KeVVdNTU0oKytDWVkZtFotvLy8hGU5cuQIXnzxRQDAtWvXEBQUhOLiYmF5unqUVzXR3fvDH/6AJUuW4Mknn8TSpUsN/1599VWzKIgSEd0LrnwgugdvvPEGFi1ahG+++QYhISFQq9V46623hOUJCQlBQEAAnJycjI7YFHX6Rifu5uouISEBlZWVUKvVGD16NKqrqzFx4kTMmjVLdDRhz+vVV1/FwoULoVKp4OzsDGtra1RVVcHGxgbp6elCMgEdz2rJkiUAOo5yXLx4MeLj45GTkyMsUyfRP1sKhQLnzp1DQEAAXnnlFYwfP15onh07duCDDz4AAIwcORJKpRLz58+Hv7+/0FyA+GdF5sHa2hoeHh7YuXNnt3vff/+98BNuiIh6E4sPRPdg3Lhx2LdvH/71r39Bq9Vi9OjRQlc+pKamIj4+XnLN1f74xz+KjiA55eXlKCwsRGJiIqKjo6HRaJCcnCw6FgAIax44cOBAZGdno6KiAufPn4eFhQXmzp0r/LQCjUaDKVOmGK69vLwMx0qKJrrRY3h4OLy9vSVzok5bWxuGDRtmuLa1tZXMS39ERIToCGQGEhISkJGRgaioqG732L+EiH5q2HCS6C7U19dj06ZNqK2txYQJE7Bs2TIMHjxYdCxERERwaa+Z6HxWu3fvxrBhwyCXyzFjxgwcOHBAdDS6TUxMDKZNm4YZM2YAAAoKCnD06FFkZmYKTiZOZx+XlStX3vH+xo0bTZyoQ3x8PDQaDYKDgyGTyVBQUIB+/fph/fr1QvIQERFRz6QxdUEkcatWrYKTkxOCg4NRWFiIjRs3Cvtju6sxY8YgNjYW3t7esLKyMoyHhoaKC0V3ZG9vj4yMDEyaNMkwiy7Ffh3U8SK9bt06bNq0CVZWVnBzc0NSUpLoWEJ1Nrl0d3cXnMTYmjVrkJWVhdzcXFhaWsLV1RWRkZGiYxHds7q6Ovz5z39GRUUFLC0t4e3tjVWrVsHGxkZ0NCKiXsOVD0R3Yfr06Th06BCAjmW+oaGhOHz4sOBUkNws5O1aW1uFbkuRkubmZpSWlkIulyMrKwvl5eWIiYmBp6en6GhEd23BggWSWAHS0NCAxx9/HHV1dXe8L7WtaEQ/JjIyEkFBQQgNDYVOp4NSqURZWRneffdd0dGIiHoNVz4Q3YWuqwqsrKyMrkXqLDKo1Wo89thjQrPMnj0bubm5hmudToeZM2fi4MGDAlNJx8CBAzFlyhTU1dXBz88Pfn5+oiOhubkZTU1NRnvkH+WXtkWLFiEjIwO+vr6GE226Ern3+vz586ioqECfPn3g5eUFR0dHITlaWlpw7do1PPHEE0I+v1PXffJdn5Verxe6T76trc3Qx6Rz9vp3v/vdHb+fiLpqbm426vvw8ssvQ6lUCkxERNT7WHwgug9S+UOyuroacXFxaGlpQW5uLqKiorBt2zbDEmlTiI6ORmVlJQDA2dnZMG5paQlfX1+T5ZC6lJQU5OXlGTqXi35J2rlzJ9555x2jTuqPenOzxMREAJDc8XaZmZnIzc2Fr68vdDodFAoFFAoFZs6cafIsN27cgK+vL2xtbWFtbS3s+zgjIwOA9I71TUhIQEtLC8LDw6HT6bB//37U1tYiPj5edDSSuAkTJmD//v0ICQkBABw7dgxjxowRnIqIqHdx2wXRXXBxcYG9vb3hur6+Hvb29sJfIOfOnYv169dj2bJlUKlUKCsrQ2pqKvbt22fyLH/+85+RkJBg8s81FwEBAcjPz8eAAQNERwEA+Pv7Iy8vj/uJu9DpdNi3bx++/PJLTJw4EUFBQaIjAQBeeOEF/O1vf8PAgQMBdKx0mjNnDgoKCkye5ezZs7C1te027uDgYNIcUm0CPG3aNBw9etRwrdPpMH36dCHPiszL5MmT0djYiH79+kEmk0Gj0RjuyWQynD9/XmA6IqLewZUPRHehsLBQdIQ70mg0Rsuvvby8kJKSIiTLihUrcOzYMdy6dctonM0vOzzzzDNobW2VTPHhiSeeEL5VR2rWrl2L6upqPPfcc9i5cycuXLiApUuXio6FIUOGGB1t+bOf/UzY99GKFStw5MgRIZ/dlVSbAD/55JO4dOkSRo0aBQD497//bVS4JupJeXm56AhERA8diw9Ed8HUs3p3a8iQIaiurjZsAzlw4ICwF8rXXnsNdXV1cHR0NNqWwuJDh5CQEAQEBMDJyQl9+vQxjO/Zs0dInl/84heIjIyEh4eHUVNQKbxsi3L69GkUFBRAJpPh5s2biImJkcR/j9GjR2P27NmQy+WwtLREUVERBg4ciO3btwMw7TNzdnaGSqXCuHHj0K9fP8O4qXuF1NfXGxpfenl5Seb3THt7O0JCQuDq6oo+ffrg448/hp2dHaKjowGI+3kn6crNzcXs2bMNP8+3k8LvICKi3sLiA5EZW7t2LVasWIHa2lq4urpi1KhRhmMcTa2mpsZouTEZS01NRXx8vGQaOtrb23NG9jbW1taGwtnQoUMl09vFwcEBDg4OaG1tRWtrK7y8vIRlOXPmDM6cOQPgf71vWltbceLECZPmkGoT4MWLFxtdL1iwQFASMhfc/UxEjxIWH4jM2MiRI5GWlob+/ftDp9Phxo0bhuW+pubo6Ijr16/Dzs5OyOdL3aBBgyQzOwt0zKY1NjbizJkz0Gq1ePbZZzFs2DDRsYS6vdhgYWEhKIkxKc18djZ4bGtrQ1FREXJycvD5558LTiWdJsA95XBzczNxEjIXERERAACFQoHS0lL4+fmhsbERJSUlQprKEhE9TGw4SWTG9uzZg/z8fOTn5+Pq1av4/e9/j5dffhmzZ882eZYFCxbg008/hZOTk9Eyfi4z7rB+/Xo0NDTA29vbaJZWVEHixIkTWLVqFZ599lnodDp8+umnSEpKwm9/+1sheaTAw8PD6ISWkpISo2tRPQWcnZ27vdTa2dmhtLTU5FkuX76MvLw8KJVKqNVqKBQKREZGmrxxqVSbAL/00kuGr9vb21FTUwNXV1e88847QvKQ+Vi5ciV0Oh1SUlLQ2NiIjRs34mc/+xnWr18vOhoRUa9h8YHIjE2fPh15eXno378/gI4GlOHh4Th48KDJs3Qet3k7d3d3EyeRppUrV95xXNQL7Ysvvoi33noLI0aMANDxUrl06VLs379fSB4pyM/P/8H7YWFhJkrSs7a2NhQXF+Ozzz7r8XvqYSgqKsLevXtx7tw5TJ06FdOmTcPq1auFHXV59erVH7wvlT49ly9fxsaNG7Fjxw7RUUjigoODu/2/+05jRETmjNsuiMxYW1ub0SoDkfue3d3d8fHHH+PLL7/EzJkzcebMGS417sLOzg5/+tOfRMcwaG9vNxQeAGDEiBHQ6XQCE4knheLCj7GyskJgYCB27txp0s+NjY1FYGAgcnNzDVu7RG51kEpx4ceMGDECFy5cEB2DzIBOpzPaunjjxg3JbP0iIuotLD4QmTF/f3/ExMQgMDAQMpkMhYWFRsvETWn37t0oLi7G9evXMW3aNLz55puYNWsWG679v3/84x+Ii4uTzN704cOHY9euXZg1axYAYN++fWbzQveoUalUhq/1ej1qa2uNjt40hQMHDkCpVCIyMhIODg6Qy+XQarUmzWAObl+N8vXXX8PJyUlQGjInCoUCYWFheO655wB0NHeNj48XnIqIqHdx2wWRmTt69ChOnz4NS0tLuLm5wd/fX0iO0NBQ5OXlITw8HCqVCv/5z3/wu9/9DgUFBULySE10dDTq6+sxduxYWFtbG8ZFbbu4ceMGEhMTUVFRAb1eD09PT8THx7NhqATd/kI7dOhQzJkzx2jliqm0t7fj2LFjUCqVOH78OCZPnoy5c+diypQpJs8iRV237shkMgwdOhSTJ0+WzGkcJG319fX47LPPYGlpiV//+tf8fUxEPzlc+UBk5kaOHIlhw4ZBr9dDq9Vi3759htlsU7KwsDDaAmJtbY0+ffqYPIdUSW1Jv62tLbZt2yY6hiTl5+d3e17Z2dmYO3eukDzLly/H0KFDjcZKSkqEFB8sLS3h7+8Pf39/NDY2QqVSYevWrSw+/D9HR0eMGzfOcN3S0oKtW7fijTfeEJiKzEFrayvy8/Nx4cIFrF69Grt378bChQuN/r9KRGTuWHwgMmMJCQmorKyEWq3G6NGjUV1djYkTJwopPri7uyMlJQUajQbFxcXIzc2Fp6enyXNITUNDAx5//HF4eHiIjgIAWLRoETIyMuDr63vHLSCiTgmQgl27dqG5uRl79+41amjY3t6OQ4cOCSs+zJs3D++//z5sbGzQ0NCAxMREfPXVV8K2WHWysbHB/PnzMX/+fKE5pOT1119HcnIyJkyYgNLSUqxbt46/B+murF+/HjY2NqiqqoKlpSW++eYbrFq1Clu2bBEdjYio13DbBZEZ8/X1RWFhIRITExEdHQ2NRoPk5GRkZ2ebPItOp0NeXh7Ky8uh0+ng6emJiIgIk+9Nl5rbX/a7/soVcSRgZ0Oznk4LeJT7Phw7dgxffPEF9u7di4iICMN4nz594ObmBldXVyG5CgsLkZ6ejtDQULz33nuYM2cOFi5cyKX8EnTx4kXExsZixIgRuHLlCtasWSPs+4bMS1hYGPLz8xEaGgqVSgW9Xo/g4GAcOnRIdDQiol7zaL8VEJk5Ozs7WFlZwdHRETU1NZDL5WhqahKSxcLCAtOnT4e3t7dh7Pr16xg+fLiQPFLx1FNPAQDWrFkjiaXpnXuIk5OTkZaWZnQvJiYGu3fvFhFLEnx8fODj44PAwEA4Ojoa3WtpaRGUCnjhhRcwcOBAxMbGIj09XTKraOh/6urqAHRsN1u7di3i4uKQkJCA4cOHo66u7pH/PUg/TiaTobW11bAi7ebNm5JpUExE1FtYfCAyY/b29sjIyMCkSZOwefNmAB37RkVISUlBXl4ehgwZAqCjK7+ImX2pOXjwILy8vLBhwwYMGDAAty82M/VxpEuXLsX58+dx/fp1+Pn5Gca1Wi1+/vOfmzSLVF26dAlxcXHQaDTQ6/XQ6XTQaDSoqKgwaY6uW2P0ej30ej2WLFmCwYMH82dLYqKiooxWNvXt2xebNm0CIGaFE5mf6OhozJs3Dw0NDUhKSkJxcTGWLFkiOhYRUa/itgsiM9bc3IzS0lLI5XJkZWWhvLwcMTExQvYYBwQEID8/HwMGDDD5Z0tZXl4eDh8+jLNnz8LFxcXonkwmw549e0yap7m5Gbdu3UJSUhISEhIM45aWlrC1tX3kt8kAwNSpU5GYmIgPPvgACoUCxcXF0Gg0ePPNN02ao6etMZ0e5S0yUtbW1gYrKyu0tbWhtbWVvxPprjQ2NqKxsRGnTp2CVquFu7s7nJ2dRcciIupVLD4QmaHOJb49EbHENzY2FuvXr+/WlZ86vP3225KbxaqqqsL3339vOCnlypUrQpqVSs2LL74IpVKJHTt2wMXFBd7e3ggKChJ2bOyXX36J9PR0pKam4uuvv8abb76JxMREjB49Wkge6tmRI0ewY8cOHDx4EN988w1eeuklrF69WtgRyGQ+AgMDceTIEdExiIgeKk5xEZmhqKiobmOdS35FLfENCQlBQEAAnJycjI7YNPXMvlRFRUUhOzsbt27dMtp6sXTpUiF5pHRSitT069cPFy9ehKOjIyorK+Hp6Ym2tjZheRISEgyFK0dHRyxevBjx8fHIyckRlonubMeOHfjggw8AdByDrFQqMX/+fBYf6Ec5OztDpVJh3Lhx6Nevn2Gc/UKI6KeExQciM1RSUiI6QjepqamIj4/nH0o9iIuLw6BBg/D0009LoolYeXn5HU9KoY5ntW3bNmzevBnvvPMOcnNzhRZlNBqNUbNSLy8vQ48Xkpa2tjYMGzbMcG1ra9utzwvRnZw5cwZnzpwxGmO/ECL6qWHxgcgM1dfXY9OmTaitrcWECROwbNkyDB48WGimQYMGITQ0VGgGKfv3v/9tmBGVAimdlCI17u7ucHd3BwD87W9/g1qtxmOPPSYsj42NDXJycjBjxgwAQEFBAWxtbYXloZ4999xzePXVVxEcHAyZTIaCggI8++yzomORGZDipAIRUW9jzwciM7RgwQI4OTnBw8MDhYWFAICNGzcKzbR+/Xo0NDTA29sbVlZWhnEWJDosX74c8+fPl0wDsVdeeQVjxowxnJQSERGBtLQ0HD16VHQ0uk1dXR3WrVuHyspKWFlZwc3NDatXr+bpJBLU2tqKrKwsnD59GpaWlnB1dUVkZCT69u0rOhpJlBQnE4iIHhYWH4jM0PTp03Ho0CEAHct8Q0NDcfjwYaGZVq5cecdx0UURqQgLC0N1dTVsbW1hbW0t/ChSKZ2UQmTuGhoa8Pjjj/fYDJjb0agnUpxMICJ6WFh8IDJDYWFhyM/PN1yHhoZCpVKJC9SF6CXqUtXTsYkij0tsbm7Gd999ZzTGlyTpWLRoETIyMuDr63vHPiHcCy4dPT0r0UVGkj4pTiYQET0s7PlA9BMghQaG1dXViIuLQ0tLC3JzcxEVFYVt27Zh7NixoqNJwvDhw5GTk4OKigq0t7fD09PzjqeWmEpKSgry8vIwZMgQAHxJAoCXXnrpB3+WTH1yS2JiIgAgKyvLpJ9L9y4jIwMA9+3Tveu6TdHKysromojop4bFByIzVFtbCz8/P8N1fX09/Pz8hL5AJiYm4u2338ayZctgb2+PtWvXYs2aNdi3b5/Js0jRpk2bcOnSJcycORN6vR5KpRKXL19GfHy8kDwfffQRjh8/jgEDBgj5fCmKjY0VHcHIsGHDkJeXhy+//BITJ05EUFCQ6EjUA+7bp94ihckEIqKHhcUHIjPUuS9USjQaDRwdHQ3XXl5eSElJEZhIWsrKyqBSqWBhYQEA8PHxQXBwsLA8zzzzDFpbW1l86KLzhAsAqKqqwvfffw+9Xg+tVosrV64Y3TeFtWvXorq6Gs899xx27tyJCxcuYOnSpSbNQHdn1apVcHJyQnBwMAoLC7Fx40bu26e7IsXJBCKih4XFByIzJLJPQE+GDBmC6upqw6zNgQMH2PuhC61Wi/b2dkPXe61Wiz59+gjLExISgoCAADg5ORnlMPXWAilKSEhAZWUl1Go1Ro8ejerqakycOBGzZs0yaY7Tp0+joKAAMpkMN2/eRExMDIsPElVfX4/MzEwAHYVXnvJDd0uKkwlERA8Liw9E1CvWrl2LFStWoLa2Fq6urhg1ahQ2b94sOpZkBAcHIzo6GnK5HABw+PBhTJ8+XVie1NRUxMfHs8HkHZSXl6OwsBCJiYmIjo6GRqNBcnKyyXNYW1sbinlDhw7lcmwJ4759ul9SnEwgInpYWHwgol4xcuRIpKWloX///tDpdLhx4wZGjRolOpZkKBQKjBkzBidPnoRer4dCoYCPj4+wPIMGDeLsbA/s7OxgZWUFR0dH1NTUQC6Xo6mpyeQ5bi82dG7ZIeljoYiIiKg7Fh+IqFfs2bMH+fn5yM/Px9WrV6FQKPDyyy9j9uzZoqMJp1arodVq4e3tDW9vb5w6dQpPP/200ExjxoxBbGwsvL29jWZpWZAA7O3tkZGRgUmTJhlW77S2tpo8R11dHVauXNnjNXsKSAf37RMREf04mV6v14sOQUTmb/r06cjLy0P//v0BdDSgDA8Px8GDBwUnE6uqqgoLFy7Ehg0b4O3tDaBjy4NSqcS7774LZ2dnIbm6vsR2xRdaoLm5GaWlpZDL5cjKykJ5eTliYmLg6elp0hz5+fk/eD8sLMxESejHXL169Qfvc2k9ERERiw9E1EteeOEFHD58GJaWHQuq2tvbERYW9sgXH2JiYrB48WJ4eHgYjZ84cQKZmZnYtWuXkFypqan405/+JOSzpW7Dhg2YMWMGXFxcREchIiIi+sngBlIi6hX+/v6IiYnBhx9+iOzsbMyfPx++vr6iYwn33XffdSs8AMDzzz+PmzdvCkjU4R//+AdYe76zkSNHIikpCUFBQUhPT8eVK1dERyIiIiIye1z5QES95ujRozh9+jQsLS3h5uYGf39/0ZGECw4Oxv79+7s1C9TpdJg+fToKCgqE5IqOjkZ9fT3Gjh0La2trwzi3XfzPtWvXUFBQgAMHDmDAgAH4y1/+IjoSERERkdliw0ki6jUjR47EsGHDoNfrodVqsW/fPsyaNUt0LKHc3Nywfft2/PGPfzQa37Fjh9Bl/ewX8MOamppQVlaGsrIyaLVaeHl5CcuSn5/f7XllZ2dj7ty5ghIRERER3TuufCCiXpGQkIDKykqo1WqMHj0a1dXVmDhxIjIzM0VHE6q5uRkLFy7Et99+C2dnZ1hbW6Oqqgo2NjZIT0/HkCFDTJqnoaEBjz/+OOrq6u54f/jw4SbNI0UKhQLnzp1DQEAAZsyYgfHjxwvJsWvXLjQ3N2Pv3r2IiIgwjLe3t+PQoUMoLi4WkouIiIjofnDlAxH1ivLychQWFiIxMRHR0dHQaDRITk4WHUu4gQMHIjs7GxUVFTh//jwsLCwwd+5cuLq6CsmTkJCAjIwMREVFQSaTGfV94JGAHcLDw+Ht7W1onirKL37xC3zxxRfdxq2trfmzRURERGaHxQci6hV2dnawsrKCo6MjampqIJfL0dTUJDqWJMhkMkyaNAmTJk0SHQVPPfUUAGDNmjWYMmWK4DTSkpaWhtjYWBQVFaGoqKjbfVP3w/Dx8YGPjw8CAwPh6OhodK+lpcWkWYiIiIgeFIsPRNQr7O3tkZGRgUmTJmHz5s0AgNbWVsGp6HYHDx6El5cXNmzYgAEDBnQ78cLNzU1QMvHGjh0LAHB3dxecxNilS5cQFxcHjUYDvV4PnU4HjUaDiooK0dGIiIiI7hp7PhBRr2hubkZpaSnkcjmysrJQXl6OmJgYeHp6io5GXeTl5eHw4cM4e/Zst4aXMpkMe/bsEZRMOhYsWCCpXiVTp05FYmIiPvjgAygUChQXF0Oj0eDNN98UHY2IiIjornHlAxE9kK6NCydMmIC6ujr4+fnBz89PYCrqSXh4OMLDw/H2229jyZIlouNIUktLC65du4YnnnhCdBQAwKBBg+Dp6YlPPvkETU1NeP311xEUFCQ6FhEREdE9YfGBiB5IVFRUt7HORoZsYChdUVFRyM7Oxq1bt4y2XixdulRgKmm4ceMGfH19YWtrC2tra+Hfy/369cPFixfh6OiIyspKeHp6oq2tTUgWIiIiovvFbRdERI+gefPmYdCgQXj66achk8kM4yw+AGfPnoWtrW23cQcHBwFpgMrKSmRnZ2Pz5s2YM2cOvvnmG8yaNQsrVqwQkoeIiIjofrD4QEQPpL6+Hps2bUJtbS0mTJiAZcuWYfDgwaJj0Y8IDg7GwYMHRceQpMDAQBw5ckR0jB6p1Wo89thjomMQERER3RML0QGIyLytWrUKdnZ2ePXVV9Ha2mry4wjp/vzqV79CdXW16BiS5OzsDJVKhQsXLqCurs7wTypYeCAiIiJzxJ4PRPRA6uvrDScDeHl5ITQ0VGwguiu1tbUICwuTTF8DKTlz5gzOnDkDAIYtKa2trThx4oTIWERERERmjcUHInogVlZWRl93vSbp2r59u+gIklVSUgIAaGtrQ1FREXJycvD5558LTkVERERk3lh8IKJe1bV5IUnX8OHDkZOTg4qKCrS3t8PT0/OOJ5c8ii5fvoy8vDwolUqo1WooFAq89dZbJs/x0ksv/eDP0549e0yYhoiIiOjBsOEkET0QFxcX2NvbG67r6+thb2/PZfwSl5KSgkuXLmHmzJnQ6/VQKpVwcHBAfHy86GjCFBUVYe/evTh37hymTp2KadOmYfXq1YaVEKZWWVn5g/fd3d1NlISIiIjowXHlAxE9kMLCQtER6D6UlZVBpVLBwqKj77CPjw+Cg4MFpxIrNjYWgYGByM3NxahRowCIXcnTtbhQVVWF77//Hnq9HlqtFleuXGHxgYiIiMwKiw9E9EAcHBxER6D7oNVq0d7ejr59+xqu+/TpIziVWAcOHIBSqURkZCQcHBwgl8uh1WpFx0JCQgIqKyuhVqsxevRoVFdXY+LEiZg1a5boaERERER3jdsuiIgeQTt37sSxY8cgl8sBAIcPH4aPjw8UCoXgZOK1t7fj2LFjUCqVOH78OCZPnoy5c+diypQpQvL4+vqisLAQiYmJiI6OhkajQXJyMrKzs4XkISIiIrofXPlARPQIUigUGDNmDE6ePAm9Xg+FQgEfHx/RsSTB0tIS/v7+8Pf3R2NjI1QqFbZu3Sqs+GBnZwcrKys4OjqipqYGcrkcTU1NQrIQERER3S+ufCAiesSo1WpotVrY2NgAAE6dOoWnn37acE3S8sorr2DMmDGYNGkSNm/ejIiICKSlpeHo0aOioxERERHdNQvRAYiIyHSqqqogl8vxxRdfGMbKy8sREhKC6upqgcmoJ0lJSXjyyScxbtw4BAQE4NChQ1i7dq3oWERERET3hCsfiIgeITExMVi8eDE8PDyMxk+cOIHMzEzs2rVLTDDq0YYNGzBjxgy4uLiIjkJERER037jygYjoEfLdd991KzwAwPPPP4+bN28KSEQ/ZuTIkUhKSkJQUBDS09Nx5coV0ZGIiIiI7hmLD0REj5D29nbodLpu4zqdDm1tbQIS0Y+JiopCTk4OMjMz0bdvXyxZsgSRkZGiYxERERHdExYfiIgeIW5ubti+fXu38R07dnBZv4Q1NTWhrKwMZWVl0Gq18PLyEh2JiIiI6J6w5wMR0SOkubkZCxcuxLfffgtnZ2dYW1ujqqoKNjY2SE9Px5AhQ0RHpNsoFAqcO3cOAQEBmDFjBsaPHy86EhEREdE9Y/GBiOgRo9frUVFRgfPnz8PCwgIuLi5wdXUVHYt6UFJSAm9vb1haWoqOQkRERHTfWHwgIiKSoLS0NMTGxmLlypV3vL9x40YTJyIiIiK6f5xGISIikqCxY8cCANzd3QUnISIiInpwXPlAREQkYQsWLEBmZqboGEREREQPhKddEBERSVhLSwuuXbsmOgYRERHRA+G2CyIiIgm7ceMGfH19YWtrC2tra+j1eshkMnz00UeioxERERHdNW67ICIikrCzZ8/C1ta227iDg4OANERERET3h8UHIiIiCQsMDMSRI0dExyAiIiJ6INx2QUREJGHOzs5QqVQYN24c+vXrZxgfPny4wFRERERE94YrH4iIiCTM19fX8LVMJgMAtLa24sSJE6IiEREREd0zFh+IiIjMQFtbG4qKipCTk4PPP/8cn332mehIRERERHeNxQciIiIJu3z5MvLy8qBUKqFWq6FQKBAZGQkbGxvR0YiIiIjuGosPREREElRUVIS9e/fi3LlzmDp1KqZNm4bVq1ejpKREdDQiIiKie8aGk0RERBIUGxuLwMBA5ObmYtSoUQD+1/OBiIiIyNyw+EBERCRBBw4cgFKpRGRkJBwcHCCXy6HVakXHIiIiIrov3HZBREQkYe3t7Th27BiUSiWOHz+OyZMnY+7cuZgyZYroaERERER3jcUHIiIiM9HY2AiVSgWVSoUDBw6IjkNERER011h8ICIiIiIiIqKHykJ0ACIiIiIiIiL6aWPxgYiIiIiIiIgeKhYfiIiIiIiIiOihYvGBiIiIiIiIiB4qFh+IiIiIiIiI6KH6P/F7zyOj2nd3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#plot cmap\n",
     "plt.figure(figsize=(20, 10))\n",
@@ -594,25 +1489,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                    0\n",
+       "User Id                                     0\n",
+       "Vehicle Type                                0\n",
+       "Platform Type                               0\n",
+       "Personal or Business                        0\n",
+       "Placement - Day of Month                    0\n",
+       "Placement - Weekday (Mo = 1)                0\n",
+       "Placement - Time                            0\n",
+       "Confirmation - Day of Month                 0\n",
+       "Confirmation - Weekday (Mo = 1)             0\n",
+       "Confirmation - Time                         0\n",
+       "Arrival at Pickup - Day of Month            0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)        0\n",
+       "Arrival at Pickup - Time                    0\n",
+       "Pickup - Day of Month                       0\n",
+       "Pickup - Weekday (Mo = 1)                   0\n",
+       "Pickup - Time                               0\n",
+       "Distance (KM)                               0\n",
+       "Temperature                              4366\n",
+       "Precipitation in millimeters            20649\n",
+       "Pickup Lat                                  0\n",
+       "Pickup Long                                 0\n",
+       "Destination Lat                             0\n",
+       "Destination Long                            0\n",
+       "Rider Id                                    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train1.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                   0\n",
+       "User Id                                    0\n",
+       "Vehicle Type                               0\n",
+       "Platform Type                              0\n",
+       "Personal or Business                       0\n",
+       "Placement - Day of Month                   0\n",
+       "Placement - Weekday (Mo = 1)               0\n",
+       "Placement - Time                           0\n",
+       "Confirmation - Day of Month                0\n",
+       "Confirmation - Weekday (Mo = 1)            0\n",
+       "Confirmation - Time                        0\n",
+       "Arrival at Pickup - Day of Month           0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)       0\n",
+       "Arrival at Pickup - Time                   0\n",
+       "Pickup - Day of Month                      0\n",
+       "Pickup - Weekday (Mo = 1)                  0\n",
+       "Pickup - Time                              0\n",
+       "Distance (KM)                              0\n",
+       "Temperature                             1437\n",
+       "Precipitation in millimeters            6869\n",
+       "Pickup Lat                                 0\n",
+       "Pickup Long                                0\n",
+       "Destination Lat                            0\n",
+       "Destination Long                           0\n",
+       "Rider Id                                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "test_df.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,34 +1598,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% of Missing values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.205934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.973963</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.205934\n",
+       "Precipitation in millimeters             0.973963"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing_values(train1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% of Missing values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.203311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.971845</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.203311\n",
+       "Precipitation in millimeters             0.971845"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing_values(test_df)"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Distribution"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Distribution review for train data\n",
     "train1[[col for col in train1.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
@@ -664,9 +1748,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Column Skewness of the testing dataset\n",
     "train1.skew().plot.bar(figsize =(10,5))\n",
@@ -675,9 +1772,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "## Univariate Anaysis\n",
     "# Calculating Skewness in the columns of our Training dataset\n",
@@ -687,9 +1797,234 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Platform Type</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.005528</td>\n",
+       "      <td>0.225060</td>\n",
+       "      <td>-0.055948</td>\n",
+       "      <td>-0.002742</td>\n",
+       "      <td>0.021327</td>\n",
+       "      <td>0.006319</td>\n",
+       "      <td>0.043924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>-0.007585</td>\n",
+       "      <td>0.013097</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004929</td>\n",
+       "      <td>-0.005453</td>\n",
+       "      <td>0.007541</td>\n",
+       "      <td>0.002077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.003582</td>\n",
+       "      <td>0.031064</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000918</td>\n",
+       "      <td>0.011008</td>\n",
+       "      <td>-0.004299</td>\n",
+       "      <td>-0.000875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>-0.007587</td>\n",
+       "      <td>0.013099</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004923</td>\n",
+       "      <td>-0.005448</td>\n",
+       "      <td>0.007550</td>\n",
+       "      <td>0.002073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.003574</td>\n",
+       "      <td>0.031074</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000883</td>\n",
+       "      <td>0.011036</td>\n",
+       "      <td>-0.004251</td>\n",
+       "      <td>-0.000900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Platform Type  Placement - Day of Month  \\\n",
+       "Platform Type                         1.000000                  0.012764   \n",
+       "Placement - Day of Month              0.012764                  1.000000   \n",
+       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
+       "Confirmation - Day of Month           0.012770                  0.999998   \n",
+       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
+       "\n",
+       "                                 Placement - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                       -0.058038   \n",
+       "Placement - Day of Month                             0.026148   \n",
+       "Placement - Weekday (Mo = 1)                         1.000000   \n",
+       "Confirmation - Day of Month                          0.026132   \n",
+       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
+       "\n",
+       "                                 Confirmation - Day of Month  \\\n",
+       "Platform Type                                       0.012770   \n",
+       "Placement - Day of Month                            0.999998   \n",
+       "Placement - Weekday (Mo = 1)                        0.026132   \n",
+       "Confirmation - Day of Month                         1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
+       "\n",
+       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                          -0.058005   \n",
+       "Placement - Day of Month                                0.026122   \n",
+       "Placement - Weekday (Mo = 1)                            0.999952   \n",
+       "Confirmation - Day of Month                             0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
+       "\n",
+       "                                 Arrival at Pickup - Day of Month  \\\n",
+       "Platform Type                                            0.012770   \n",
+       "Placement - Day of Month                                 0.999998   \n",
+       "Placement - Weekday (Mo = 1)                             0.026132   \n",
+       "Confirmation - Day of Month                              1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
+       "\n",
+       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                               -0.058005   \n",
+       "Placement - Day of Month                                     0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
+       "Confirmation - Day of Month                                  0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
+       "\n",
+       "                                 Pickup - Day of Month  \\\n",
+       "Platform Type                                 0.012770   \n",
+       "Placement - Day of Month                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                  0.026132   \n",
+       "Confirmation - Day of Month                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
+       "\n",
+       "                                 Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "Platform Type                                    -0.058005       0.005528   \n",
+       "Placement - Day of Month                          0.026122      -0.007585   \n",
+       "Placement - Weekday (Mo = 1)                      0.999952       0.003582   \n",
+       "Confirmation - Day of Month                       0.026123      -0.007587   \n",
+       "Confirmation - Weekday (Mo = 1)                   1.000000       0.003574   \n",
+       "\n",
+       "                                 Temperature  Precipitation in millimeters  \\\n",
+       "Platform Type                       0.225060                     -0.055948   \n",
+       "Placement - Day of Month            0.013097                     -0.204143   \n",
+       "Placement - Weekday (Mo = 1)        0.031064                     -0.050592   \n",
+       "Confirmation - Day of Month         0.013099                     -0.204143   \n",
+       "Confirmation - Weekday (Mo = 1)     0.031074                     -0.050592   \n",
+       "\n",
+       "                                 Pickup Lat  Pickup Long  Destination Lat  \\\n",
+       "Platform Type                     -0.002742     0.021327         0.006319   \n",
+       "Placement - Day of Month          -0.004929    -0.005453         0.007541   \n",
+       "Placement - Weekday (Mo = 1)      -0.000918     0.011008        -0.004299   \n",
+       "Confirmation - Day of Month       -0.004923    -0.005448         0.007550   \n",
+       "Confirmation - Weekday (Mo = 1)   -0.000883     0.011036        -0.004251   \n",
+       "\n",
+       "                                 Destination Long  \n",
+       "Platform Type                            0.043924  \n",
+       "Placement - Day of Month                 0.002077  \n",
+       "Placement - Weekday (Mo = 1)            -0.000875  \n",
+       "Confirmation - Day of Month              0.002073  \n",
+       "Confirmation - Weekday (Mo = 1)         -0.000900  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "CorrMatrix_train = train1.corr()\n",
     "CorrMatrix_train.head(5)"
@@ -697,9 +2032,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Pearson Correlation of Features')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "style.use('ggplot')\n",
     "cm = CorrMatrix_train.corr()\n",
@@ -711,58 +2069,140 @@
    ]
   },
   {
-   "source": [
-    "### Personal or Business"
-   ],
    "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "### Personal or Business"
    ]
   },
-  {
-   "source": [
-    "### Platform type"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "### Vehicle Type"
+    "### Platform type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
    ],
+   "source": [
+    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
    "cell_type": "markdown",
-   "metadata": {}
+   "metadata": {},
+   "source": [
+    "### Vehicle Type"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "train1['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Outliers Overview for numeric features\n",
     "plt.figure(figsize=(20, 8))\n",
@@ -772,9 +2212,199 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "0              3                         9                             5   \n",
+       "1              3                        12                             5   \n",
+       "2              3                        30                             2   \n",
+       "3              3                        15                             5   \n",
+       "4              1                        13                             1   \n",
+       "\n",
+       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "0                            9                                5   \n",
+       "1                           12                                5   \n",
+       "2                           30                                2   \n",
+       "3                           15                                5   \n",
+       "4                           13                                1   \n",
+       "\n",
+       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                 9                                     5   \n",
+       "1                                12                                     5   \n",
+       "2                                30                                     2   \n",
+       "3                                15                                     5   \n",
+       "4                                13                                     1   \n",
+       "\n",
+       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "0                      9                          5              4   \n",
+       "1                     12                          5             16   \n",
+       "2                     30                          2              3   \n",
+       "3                     15                          5              9   \n",
+       "4                     13                          1              9   \n",
+       "\n",
+       "   Temperature  Precipitation in millimeters  Pickup Lat  Pickup Long  \\\n",
+       "0         20.4                           NaN   -1.317755    36.830370   \n",
+       "1         26.4                           NaN   -1.351453    36.899315   \n",
+       "2          NaN                           NaN   -1.308284    36.843419   \n",
+       "3         19.2                           NaN   -1.281301    36.832396   \n",
+       "4         15.4                           NaN   -1.266597    36.792118   \n",
+       "\n",
+       "   Destination Lat  Destination Long  \n",
+       "0        -1.300406         36.829741  \n",
+       "1        -1.295004         36.814358  \n",
+       "2        -1.300921         36.828195  \n",
+       "3        -1.257147         36.795063  \n",
+       "4        -1.295041         36.809817  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Numeric_Training = train1._get_numeric_data()\n",
     "Numeric_Training.head()"
@@ -782,21 +2412,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-37-67d706ebc61e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'bar'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
    "source": [
-    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5]) #returns error"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l = train1.values\n",
-    "number_of_columns= 12\n",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-38-a40df607a66f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whitegrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 864x7.63233e+06 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l = train1.values  #returns error\n",
+    "number_of_columns= 12\n",
     "number_of_rows = len(l)-1/number_of_columns\n",
     "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
     "for i in range(0,len(l)):\n",
@@ -808,11 +2505,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'number_of_columns_Test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-39-2a23d4201dc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+      "\u001b[1;31mNameError\u001b[0m: name 'number_of_columns_Test' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
+    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test)) #returns error\n",
     "for i in range(0,len(nu_tra)):\n",
     "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
     "    chart=sns.distplot(train1[nu_tra[i]],kde=True) \n",
@@ -822,40 +2533,504 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-9f95e1fbe116>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Useful statistics about our target column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
+   "source": [
+    "#Useful statistics about our target column\n",
+    "\n",
+    "train1['Time from Pickup to Arrival'].describe() #returns error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10:27:30 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:44:09 AM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:53:03 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:43:06 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10:05:23 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21196</th>\n",
+       "      <td>Order_No_8834</td>\n",
+       "      <td>User_Id_2001</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3:54:38 PM</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4:20:08 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.275285</td>\n",
+       "      <td>36.802702</td>\n",
+       "      <td>Rider_Id_953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21197</th>\n",
+       "      <td>Order_No_22892</td>\n",
+       "      <td>User_Id_1796</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10:13:34 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10:33:27 AM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.307143</td>\n",
+       "      <td>36.825009</td>\n",
+       "      <td>-1.331619</td>\n",
+       "      <td>36.847976</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21198</th>\n",
+       "      <td>Order_No_2831</td>\n",
+       "      <td>User_Id_2956</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5:06:16 PM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5:50:52 PM</td>\n",
+       "      <td>20</td>\n",
+       "      <td>29.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.286018</td>\n",
+       "      <td>36.897534</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>Rider_Id_697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21199</th>\n",
+       "      <td>Order_No_6174</td>\n",
+       "      <td>User_Id_2524</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9:31:39 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9:45:15 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.250030</td>\n",
+       "      <td>36.874167</td>\n",
+       "      <td>-1.279209</td>\n",
+       "      <td>36.794872</td>\n",
+       "      <td>Rider_Id_347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21200</th>\n",
+       "      <td>Order_No_9836</td>\n",
+       "      <td>User_Id_718</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2:19:47 PM</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2:41:55 PM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>30.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.320157</td>\n",
+       "      <td>36.830887</td>\n",
+       "      <td>Rider_Id_177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21201 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0       Order_No_4211   User_Id_633         Bike              3   \n",
+       "1      Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2       Order_No_1899   User_Id_265         Bike              3   \n",
+       "3       Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4      Order_No_27883  User_Id_1737         Bike              1   \n",
+       "...               ...           ...          ...            ...   \n",
+       "21196   Order_No_8834  User_Id_2001         Bike              3   \n",
+       "21197  Order_No_22892  User_Id_1796         Bike              3   \n",
+       "21198   Order_No_2831  User_Id_2956         Bike              3   \n",
+       "21199   Order_No_6174  User_Id_2524         Bike              1   \n",
+       "21200   Order_No_9836   User_Id_718         Bike              3   \n",
+       "\n",
+       "      Personal or Business  Placement - Day of Month  \\\n",
+       "0                 Business                         9   \n",
+       "1                 Personal                        12   \n",
+       "2                 Business                        30   \n",
+       "3                 Business                        15   \n",
+       "4                 Personal                        13   \n",
+       "...                    ...                       ...   \n",
+       "21196             Personal                        20   \n",
+       "21197             Business                        13   \n",
+       "21198             Business                         7   \n",
+       "21199             Personal                         4   \n",
+       "21200             Business                        26   \n",
+       "\n",
+       "       Placement - Weekday (Mo = 1) Placement - Time  \\\n",
+       "0                                 5       9:35:46 AM   \n",
+       "1                                 5      11:16:16 AM   \n",
+       "2                                 2      12:39:25 PM   \n",
+       "3                                 5       9:25:34 AM   \n",
+       "4                                 1       9:55:18 AM   \n",
+       "...                             ...              ...   \n",
+       "21196                             3       3:54:38 PM   \n",
+       "21197                             6      10:13:34 AM   \n",
+       "21198                             4       5:06:16 PM   \n",
+       "21199                             3       9:31:39 AM   \n",
+       "21200                             2       2:19:47 PM   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  ...  \\\n",
+       "0                                9                                5  ...   \n",
+       "1                               12                                5  ...   \n",
+       "2                               30                                2  ...   \n",
+       "3                               15                                5  ...   \n",
+       "4                               13                                1  ...   \n",
+       "...                            ...                              ...  ...   \n",
+       "21196                           20                                3  ...   \n",
+       "21197                           13                                6  ...   \n",
+       "21198                            7                                4  ...   \n",
+       "21199                            4                                3  ...   \n",
+       "21200                           26                                2  ...   \n",
+       "\n",
+       "      Pickup - Weekday (Mo = 1)  Pickup - Time  Distance (KM) Temperature  \\\n",
+       "0                             5    10:27:30 AM              4        20.4   \n",
+       "1                             5    11:44:09 AM             16        26.4   \n",
+       "2                             2    12:53:03 PM              3         NaN   \n",
+       "3                             5     9:43:06 AM              9        19.2   \n",
+       "4                             1    10:05:23 AM              9        15.4   \n",
+       "...                         ...            ...            ...         ...   \n",
+       "21196                         3     4:20:08 PM              3        28.6   \n",
+       "21197                         6    10:33:27 AM              7        26.0   \n",
+       "21198                         4     5:50:52 PM             20        29.2   \n",
+       "21199                         3     9:45:15 AM             13        15.0   \n",
+       "21200                         2     2:41:55 PM             12        30.9   \n",
+       "\n",
+       "       Precipitation in millimeters  Pickup Lat Pickup Long  Destination Lat  \\\n",
+       "0                               NaN   -1.317755   36.830370        -1.300406   \n",
+       "1                               NaN   -1.351453   36.899315        -1.295004   \n",
+       "2                               NaN   -1.308284   36.843419        -1.300921   \n",
+       "3                               NaN   -1.281301   36.832396        -1.257147   \n",
+       "4                               NaN   -1.266597   36.792118        -1.295041   \n",
+       "...                             ...         ...         ...              ...   \n",
+       "21196                           NaN   -1.258414   36.804800        -1.275285   \n",
+       "21197                           NaN   -1.307143   36.825009        -1.331619   \n",
+       "21198                           NaN   -1.286018   36.897534        -1.258414   \n",
+       "21199                           NaN   -1.250030   36.874167        -1.279209   \n",
+       "21200                           NaN   -1.255189   36.782203        -1.320157   \n",
+       "\n",
+       "       Destination Long      Rider Id  \n",
+       "0             36.829741  Rider_Id_432  \n",
+       "1             36.814358  Rider_Id_856  \n",
+       "2             36.828195  Rider_Id_155  \n",
+       "3             36.795063  Rider_Id_855  \n",
+       "4             36.809817  Rider_Id_770  \n",
+       "...                 ...           ...  \n",
+       "21196         36.802702  Rider_Id_953  \n",
+       "21197         36.847976  Rider_Id_155  \n",
+       "21198         36.804800  Rider_Id_697  \n",
+       "21199         36.794872  Rider_Id_347  \n",
+       "21200         36.830887  Rider_Id_177  \n",
+       "\n",
+       "[21201 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Useful statistics about our target column\n",
-    "\n",
-    "train1['Time from Pickup to Arrival'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "train1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-42-0671fade3f72>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time (minutes)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
    "source": [
-    "(train1['Time from Pickup to Arrival']/60).hist(bins=50)\n",
-    "plt.title(\"Delivery time distribution\")\n",
+    "(train1['Time from Pickup to Arrival']/60).hist(bins=50) #returns error\n",
+    "plt.title(\"Delivery time distribution\") \n",
     "plt.xlabel(\"Delivery time (minutes)\")\n",
     "plt.ylabel(\"Frequency\")\n",
     "plt.show()"
@@ -863,23 +3038,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Neither the `x` nor `y` variable appears to be numeric.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-43-278ac3dc675f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Outliers Overview for numeric features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2233\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2234\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2235\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    434\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[0morient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_orient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Option 2a:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(self, x, y, orient)\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 354\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mno_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    355\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Neither the `x` nor `y` variable appears to be numeric."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1440x576 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Outliers Overview for numeric features\n",
     "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df)) #returns error\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-44-7701a3d72f00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1728x7.63233e+06 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
+    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows)) #returns error\n",
     "for i in range(0,len(l)):\n",
     "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
     "    sns.distplot(df[l[i]],kde=True)"
@@ -901,16 +3146,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((21201, 26), (7068, 26), (28269, 26))"
+       "((21201, 25), (7068, 26), (28269, 26))"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,8 +3181,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -952,7 +3199,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -985,8 +3232,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1048,7 +3297,7 @@
        "5  15:07:35  15:08:57     15:21:36    15:30:30"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +3325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1088,8 +3337,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1158,7 +3409,7 @@
        "      <td>-1.300406</td>\n",
        "      <td>36.829741</td>\n",
        "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1182,7 +3433,7 @@
        "      <td>-1.295004</td>\n",
        "      <td>36.814358</td>\n",
        "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1206,7 +3457,7 @@
        "      <td>-1.300921</td>\n",
        "      <td>36.828195</td>\n",
        "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1230,7 +3481,7 @@
        "      <td>-1.257147</td>\n",
        "      <td>36.795063</td>\n",
        "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1254,7 +3505,7 @@
        "      <td>-1.295041</td>\n",
        "      <td>36.809817</td>\n",
        "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1284,16 +3535,16 @@
        "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
        "\n",
        "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
-       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
-       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
-       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
-       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
-       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
+       "0       -1.300406        36.829741  Rider_Id_432           NaN  \n",
+       "1       -1.295004        36.814358  Rider_Id_856           NaN  \n",
+       "2       -1.300921        36.828195  Rider_Id_155           NaN  \n",
+       "3       -1.257147        36.795063  Rider_Id_855           NaN  \n",
+       "4       -1.295041        36.809817  Rider_Id_770           NaN  \n",
        "\n",
        "[5 rows x 26 columns]"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1311,7 +3562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1327,7 +3578,7 @@
        "[[17, 18, 18, 18], [11, 13, 13, 13]]"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1350,7 +3601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1359,7 +3610,7 @@
        "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1370,7 +3621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1379,7 +3630,7 @@
        "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1397,7 +3648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1414,8 +3665,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1478,7 +3731,7 @@
        "      <td>-1.300406</td>\n",
        "      <td>36.829741</td>\n",
        "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>9</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1500,7 +3753,7 @@
        "      <td>-1.295004</td>\n",
        "      <td>36.814358</td>\n",
        "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>12</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1522,7 +3775,7 @@
        "      <td>-1.300921</td>\n",
        "      <td>36.828195</td>\n",
        "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>30</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
@@ -1547,12 +3800,12 @@
        "2           7.573502   -1.308284   36.843419        -1.300921   \n",
        "\n",
        "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  "
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  "
       ]
      },
-     "execution_count": 136,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1568,8 +3821,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1632,7 +3887,7 @@
        "      <td>-1.300406</td>\n",
        "      <td>36.829741</td>\n",
        "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>9</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1654,7 +3909,7 @@
        "      <td>-1.295004</td>\n",
        "      <td>36.814358</td>\n",
        "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>12</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1676,7 +3931,7 @@
        "      <td>-1.300921</td>\n",
        "      <td>36.828195</td>\n",
        "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>30</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
@@ -1698,7 +3953,7 @@
        "      <td>-1.257147</td>\n",
        "      <td>36.795063</td>\n",
        "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>15</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1720,7 +3975,7 @@
        "      <td>-1.295041</td>\n",
        "      <td>36.809817</td>\n",
        "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>13</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -1751,14 +4006,14 @@
        "4           7.573502   -1.266597   36.792118        -1.295041   \n",
        "\n",
        "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
+       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
+       "4        36.809817  Rider_Id_770           NaN            13            1  "
       ]
      },
-     "execution_count": 137,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1769,8 +4024,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1783,7 +4040,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1801,8 +4058,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 57,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1893,38 +4171,21 @@
        "2   36.843419        -1.300921        36.828195            30            2  "
       ]
      },
-     "execution_count": 139,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "numeric_cols = []\n",
-    "object_cols = []\n",
-    "time_cols = []\n",
-    "for k, v in full_df.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_cols.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_cols.append(k)\n",
-    "    else:\n",
-    "        object_cols.append(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "full_df[numeric_cols].head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -1986,7 +4247,7 @@
        "2  12:39:25  12:42:44     12:49:34    12:53:03"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1997,8 +4258,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2060,7 +4323,7 @@
        "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2078,8 +4341,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2089,7 +4354,7 @@
        "Name: Personal_Business, dtype: int32"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2105,8 +4370,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2169,7 +4436,7 @@
        "      <td>-1.300406</td>\n",
        "      <td>36.829741</td>\n",
        "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>9</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -2191,7 +4458,7 @@
        "      <td>-1.295004</td>\n",
        "      <td>36.814358</td>\n",
        "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>12</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -2213,7 +4480,7 @@
        "      <td>-1.300921</td>\n",
        "      <td>36.828195</td>\n",
        "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>30</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
@@ -2235,7 +4502,7 @@
        "      <td>-1.257147</td>\n",
        "      <td>36.795063</td>\n",
        "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>15</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -2257,7 +4524,7 @@
        "      <td>-1.295041</td>\n",
        "      <td>36.809817</td>\n",
        "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>13</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -2288,14 +4555,14 @@
        "4           7.573502   -1.266597   36.792118        -1.295041   \n",
        "\n",
        "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
+       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
+       "4        36.809817  Rider_Id_770           NaN            13            1  "
       ]
      },
-     "execution_count": 143,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2305,11 +4572,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Feature Engineering"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2320,8 +4587,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2455,7 +4724,7 @@
        "4                  1  "
       ]
      },
-     "execution_count": 144,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2474,8 +4743,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2491,8 +4762,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2731,7 +5004,7 @@
        "[21201 rows x 11 columns]"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2742,8 +5015,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -2982,7 +5257,7 @@
        "[7068 rows x 11 columns]"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2993,27 +5268,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0         745.0\n",
-       "1        1993.0\n",
-       "2         455.0\n",
-       "3        1341.0\n",
-       "4        1214.0\n",
-       "          ...  \n",
-       "21196       9.0\n",
-       "21197     770.0\n",
-       "21198    2953.0\n",
-       "21199    1380.0\n",
-       "21200    2128.0\n",
+       "0       NaN\n",
+       "1       NaN\n",
+       "2       NaN\n",
+       "3       NaN\n",
+       "4       NaN\n",
+       "         ..\n",
+       "21196   NaN\n",
+       "21197   NaN\n",
+       "21198   NaN\n",
+       "21199   NaN\n",
+       "21200   NaN\n",
        "Name: Time_Pic_Arr, Length: 21201, dtype: float64"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3031,8 +5308,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -3063,25 +5342,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Algorithm  CrossValMeans  CrossValErrors\n",
-      "4      LGBM     774.614768       18.715030\n",
-      "1       GBR     778.134629       17.888758\n",
-      "3       RFR     800.179967       16.876306\n",
-      "0       SVR     914.156364       19.688177\n",
-      "2       EXR     934.162660       18.913009\n"
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-69-6a76cfe783d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mcv_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mregressor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregressors\u001b[0m\u001b[1;33m:\u001b[0m     \u001b[1;31m#scores to be minimised are negated (neg)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0mcv_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mcv_means\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    404\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    407\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 248\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
      ]
     }
    ],
    "source": [
     "rs = 3\n",
-    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True)\n",
+    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True) #returns error\n",
     "\n",
     "regressors = []\n",
     "regressors.append(SVR())\n",
@@ -3130,18 +5431,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "773.5171026354723 {'max_depth': None, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 300}\n"
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-81-e8fb8588c79a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m            \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n\u001b[0;32m      5\u001b[0m \u001b[0mrsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRFC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrf_param\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mrfm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mr_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    763\u001b[0m             \u001b[0mrefit_start_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    302\u001b[0m             )\n\u001b[0;32m    303\u001b[0m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[1;32m--> 304\u001b[1;33m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0m\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    803\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    804\u001b[0m         y = check_array(y, accept_sparse='csr', force_all_finite=True,\n\u001b[1;32m--> 805\u001b[1;33m                         ensure_2d=False, dtype=None)\n\u001b[0m\u001b[0;32m    806\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    807\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
      ]
     }
    ],
    "source": [
+    "rs = 3 #retuns errors\n",
     "RFC = RandomForestRegressor(random_state=rs)\n",
     "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
     "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
@@ -3156,22 +5473,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-71-f5926b27e7ad>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-71-f5926b27e7ad>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
-    "Prediction = predict(fit, test)\n",
+    "Prediction = predict(fit, test) #returns errors\n",
     "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
     "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'lgbm' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-72-bce2b5ba7327>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlgbm_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[0;32m      4\u001b[0m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'lgbm' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration) #returns errors, lgb not defined\n",
     "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
     "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
     "lgbm_output.to_csv(\"submission.csv\", index=False)"
@@ -3179,26 +5521,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'lr' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-73-acbae1ec0c43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'lr' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "y_pred = lr.predict(test_new)"
+    "y_pred = lr.predict(test_new) #lr not defined"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'test_df1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-74-fc2ddc35a87f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msubmission_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_df1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Order No'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msubmission_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time_Pic_Arr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'test_df1' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "submission_df = test_df1[['Order No']]\n",
+    "submission_df = test_df1[['Order No']] #test_df1 not found error\n",
     "submission_df['Time_Pic_Arr'] = y_pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3221,25 +5587,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n",
-      "{'learning_rate': 0.1, 'min_data_in_leaf': 300, 'n_estimators': 75, 'num_leaves': 15, 'objective': 'regression', 'reg_alpha': 0.02} 772.4197981914269\n"
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-76-0f04e4b0aaeb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mlsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLGBMRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mlgbm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0ml_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    734\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1188\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    713\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 715\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
      ]
     }
    ],
@@ -3270,97 +5657,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001396 seconds.\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003107 seconds.\n",
       "You can set `force_col_wise=true` to remove the overhead.\n",
-      "[LightGBM] [Info] Total Bins 1336\n",
+      "[LightGBM] [Info] Total Bins 1334\n",
       "[LightGBM] [Info] Number of data points in the train set: 16960, number of used features: 11\n",
-      "[LightGBM] [Info] Start training from score 1555.150236\n",
-      "[1]\tvalid_0's l2: 918188\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[1]\tvalid_0's l2: 0\n",
       "Training until validation scores don't improve for 20 rounds\n",
-      "[2]\tvalid_0's l2: 866539\n",
-      "[3]\tvalid_0's l2: 824476\n",
-      "[4]\tvalid_0's l2: 789734\n",
-      "[5]\tvalid_0's l2: 761691\n",
-      "[6]\tvalid_0's l2: 738958\n",
-      "[7]\tvalid_0's l2: 720292\n",
-      "[8]\tvalid_0's l2: 704931\n",
-      "[9]\tvalid_0's l2: 691675\n",
-      "[10]\tvalid_0's l2: 681557\n",
-      "[11]\tvalid_0's l2: 673080\n",
-      "[12]\tvalid_0's l2: 665960\n",
-      "[13]\tvalid_0's l2: 660426\n",
-      "[14]\tvalid_0's l2: 654948\n",
-      "[15]\tvalid_0's l2: 650506\n",
-      "[16]\tvalid_0's l2: 647193\n",
-      "[17]\tvalid_0's l2: 644222\n",
-      "[18]\tvalid_0's l2: 641821\n",
-      "[19]\tvalid_0's l2: 638942\n",
-      "[20]\tvalid_0's l2: 637037\n",
-      "[21]\tvalid_0's l2: 635290\n",
-      "[22]\tvalid_0's l2: 634152\n",
-      "[23]\tvalid_0's l2: 633216\n",
-      "[24]\tvalid_0's l2: 632033\n",
-      "[25]\tvalid_0's l2: 630848\n",
-      "[26]\tvalid_0's l2: 630171\n",
-      "[27]\tvalid_0's l2: 629561\n",
-      "[28]\tvalid_0's l2: 629029\n",
-      "[29]\tvalid_0's l2: 628206\n",
-      "[30]\tvalid_0's l2: 628106\n",
-      "[31]\tvalid_0's l2: 627302\n",
-      "[32]\tvalid_0's l2: 627181\n",
-      "[33]\tvalid_0's l2: 625904\n",
-      "[34]\tvalid_0's l2: 625357\n",
-      "[35]\tvalid_0's l2: 625171\n",
-      "[36]\tvalid_0's l2: 624768\n",
-      "[37]\tvalid_0's l2: 624364\n",
-      "[38]\tvalid_0's l2: 624260\n",
-      "[39]\tvalid_0's l2: 623846\n",
-      "[40]\tvalid_0's l2: 623815\n",
-      "[41]\tvalid_0's l2: 623591\n",
-      "[42]\tvalid_0's l2: 622871\n",
-      "[43]\tvalid_0's l2: 622676\n",
-      "[44]\tvalid_0's l2: 622707\n",
-      "[45]\tvalid_0's l2: 622054\n",
-      "[46]\tvalid_0's l2: 621485\n",
-      "[47]\tvalid_0's l2: 621052\n",
-      "[48]\tvalid_0's l2: 620831\n",
-      "[49]\tvalid_0's l2: 620571\n",
-      "[50]\tvalid_0's l2: 620528\n",
-      "[51]\tvalid_0's l2: 620492\n",
-      "[52]\tvalid_0's l2: 620317\n",
-      "[53]\tvalid_0's l2: 620496\n",
-      "[54]\tvalid_0's l2: 620237\n",
-      "[55]\tvalid_0's l2: 620022\n",
-      "[56]\tvalid_0's l2: 619893\n",
-      "[57]\tvalid_0's l2: 619697\n",
-      "[58]\tvalid_0's l2: 619534\n",
-      "[59]\tvalid_0's l2: 619475\n",
-      "[60]\tvalid_0's l2: 619321\n",
-      "[61]\tvalid_0's l2: 619248\n",
-      "[62]\tvalid_0's l2: 619262\n",
-      "[63]\tvalid_0's l2: 619191\n",
-      "[64]\tvalid_0's l2: 619172\n",
-      "[65]\tvalid_0's l2: 619077\n",
-      "[66]\tvalid_0's l2: 619023\n",
-      "[67]\tvalid_0's l2: 619158\n",
-      "[68]\tvalid_0's l2: 619221\n",
-      "[69]\tvalid_0's l2: 619360\n",
-      "[70]\tvalid_0's l2: 619396\n",
-      "[71]\tvalid_0's l2: 619116\n",
-      "[72]\tvalid_0's l2: 618896\n",
-      "[73]\tvalid_0's l2: 618973\n",
-      "[74]\tvalid_0's l2: 618808\n",
-      "[75]\tvalid_0's l2: 618729\n",
-      "Did not meet early stopping. Best iteration is:\n",
-      "[75]\tvalid_0's l2: 618729\n",
-      "The RMSE of prediction is  786.5930821174317\n"
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[2]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[3]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[4]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[5]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[6]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[7]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[8]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[9]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[10]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[11]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[12]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[13]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[14]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[15]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[16]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[17]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[18]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[19]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[20]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[21]\tvalid_0's l2: 0\n",
+      "Early stopping, best iteration is:\n",
+      "[1]\tvalid_0's l2: 0\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-77-21f5823a14a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mlpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The RMSE of prediction is \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
      ]
     }
    ],
@@ -3391,14 +5781,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 78,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-78-a6078aba1174>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors)\u001b[0m\n\u001b[0;32m   3168\u001b[0m             \u001b[0mdecimal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3169\u001b[0m         )\n\u001b[1;32m-> 3170\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3172\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    188\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m                 \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m                 \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    191\u001b[0m             )\n\u001b[0;32m    192\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors)\u001b[0m\n\u001b[0;32m    491\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    492\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    494\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m             \u001b[1;31m# No explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'"
+     ]
+    }
+   ],
    "source": [
     "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
     "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
     "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
-    "lgbm_output.to_csv(\"D:\\Temp\\submission.csv\", index=False)"
+    "lgbm_output.to_csv(\"submission.csv\", index=False)"
    ]
   },
   {
@@ -3425,9 +5830,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}

From dd8b088450c7f9c764ea472770fbd0fb5688e5e3 Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Mon, 19 Oct 2020 18:31:04 +0200
Subject: [PATCH 38/46] testing Sendy_4_final

---
 .ipynb_checkpoints/testing-checkpoint.ipynb | 5838 +++++++++++++++++++
 1 file changed, 5838 insertions(+)
 create mode 100644 .ipynb_checkpoints/testing-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/testing-checkpoint.ipynb b/.ipynb_checkpoints/testing-checkpoint.ipynb
new file mode 100644
index 0000000..b878a92
--- /dev/null
+++ b/.ipynb_checkpoints/testing-checkpoint.ipynb
@@ -0,0 +1,5838 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EDSA 2021: Sendy Logistics Challenge\n",
+    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overview\n",
+    "\n",
+    "### - Introduction\n",
+    "### - Importing libraries and data\n",
+    "### - Exploratory Data Analysis\n",
+    "### - Data Cleaning and Formatting\n",
+    "### - Feature Engineering\n",
+    "### - Train/Test Split\n",
+    "### - Modeling\n",
+    "### - Making Predictions\n",
+    "### - Submission"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing Python libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: lightgbm in c:\\users\\jen\\anaconda3\\lib\\site-packages (3.0.0)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.5.2)\n",
+      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (0.23.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.19.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install lightgbm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: xgboost in c:\\users\\jen\\anaconda3\\lib\\site-packages (1.2.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install xgboost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Linear algebra\n",
+    "import numpy as np\n",
+    "\n",
+    "#Data processing\n",
+    "import pandas as pd\n",
+    "\n",
+    "#Date library\n",
+    "import datetime as dt\n",
+    "\n",
+    "#Data visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import matplotlib.style as style\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "#Metrics\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
+    "from sklearn.model_selection import cross_val_score, learning_curve\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "#Algorithms\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "#import xgboost as xgb\n",
+    "import lightgbm as lgb\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(21201, 29) (7068, 25) (960, 5) (35, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
+    "\n",
+    "train_df = pd.read_csv(\"Train.csv\")\n",
+    "test_df = pd.read_csv(\"Test.csv\")\n",
+    "rider_df = pd.read_csv(\"Riders.csv\")\n",
+    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
+    "\n",
+    "print(train_df.shape, test_df.shape, rider_df.shape, variable_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploritory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the train and test datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Arrival at Destination - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:39:55 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:17:22 PM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>1993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:00:38 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:27 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>1341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:25:37 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>1214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                         9   \n",
+       "1             Personal                        12   \n",
+       "2             Business                        30   \n",
+       "3             Business                        15   \n",
+       "4             Personal                        13   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             5       9:35:46 AM                            9   \n",
+       "1                             5      11:16:16 AM                           12   \n",
+       "2                             2      12:39:25 PM                           30   \n",
+       "3                             5       9:25:34 AM                           15   \n",
+       "4                             1       9:55:18 AM                           13   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
+       "0                                5  ...                   10:39:55 AM   \n",
+       "1                                5  ...                   12:17:22 PM   \n",
+       "2                                2  ...                    1:00:38 PM   \n",
+       "3                                5  ...                   10:05:27 AM   \n",
+       "4                                1  ...                   10:25:37 AM   \n",
+       "\n",
+       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
+       "0              4         20.4                          NaN   -1.317755   \n",
+       "1             16         26.4                          NaN   -1.351453   \n",
+       "2              3          NaN                          NaN   -1.308284   \n",
+       "3              9         19.2                          NaN   -1.281301   \n",
+       "4              9         15.4                          NaN   -1.266597   \n",
+       "\n",
+       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
+       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
+       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
+       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
+       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
+       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
+       "\n",
+       "  Time from Pickup to Arrival  \n",
+       "0                         745  \n",
+       "1                        1993  \n",
+       "2                         455  \n",
+       "3                        1341  \n",
+       "4                        1214  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
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+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_19248</td>\n",
+       "      <td>User_Id_3355</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4:44:10 PM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5:06:47 PM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.333275</td>\n",
+       "      <td>36.870815</td>\n",
+       "      <td>-1.305249</td>\n",
+       "      <td>36.822390</td>\n",
+       "      <td>Rider_Id_192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_12736</td>\n",
+       "      <td>User_Id_3647</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12:57:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1:25:37 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.272639</td>\n",
+       "      <td>36.794723</td>\n",
+       "      <td>-1.277007</td>\n",
+       "      <td>36.823907</td>\n",
+       "      <td>Rider_Id_868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_768</td>\n",
+       "      <td>User_Id_2154</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:08:14 AM</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11:57:54 AM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290894</td>\n",
+       "      <td>36.822971</td>\n",
+       "      <td>-1.276574</td>\n",
+       "      <td>36.851365</td>\n",
+       "      <td>Rider_Id_26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_15332</td>\n",
+       "      <td>User_Id_2910</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1:51:35 PM</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2:16:52 PM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.290503</td>\n",
+       "      <td>36.809646</td>\n",
+       "      <td>-1.303382</td>\n",
+       "      <td>36.790658</td>\n",
+       "      <td>Rider_Id_685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_21373</td>\n",
+       "      <td>User_Id_1205</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:30:28 AM</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11:56:04 AM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281081</td>\n",
+       "      <td>36.814423</td>\n",
+       "      <td>-1.266467</td>\n",
+       "      <td>36.792161</td>\n",
+       "      <td>Rider_Id_858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
+       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
+       "2    Order_No_768  User_Id_2154         Bike              3   \n",
+       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
+       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
+       "\n",
+       "  Personal or Business  Placement - Day of Month  \\\n",
+       "0             Business                        27   \n",
+       "1             Business                        17   \n",
+       "2             Business                        27   \n",
+       "3             Business                        17   \n",
+       "4             Business                        11   \n",
+       "\n",
+       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
+       "0                             3       4:44:10 PM                           27   \n",
+       "1                             5      12:57:35 PM                           17   \n",
+       "2                             4      11:08:14 AM                           27   \n",
+       "3                             1       1:51:35 PM                           17   \n",
+       "4                             2      11:30:28 AM                           11   \n",
+       "\n",
+       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                3  ...                         3   \n",
+       "1                                5  ...                         5   \n",
+       "2                                4  ...                         4   \n",
+       "3                                1  ...                         1   \n",
+       "4                                2  ...                         2   \n",
+       "\n",
+       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
+       "0     5:06:47 PM              8         NaN                           NaN   \n",
+       "1     1:25:37 PM              5         NaN                           NaN   \n",
+       "2    11:57:54 AM              5        22.8                           NaN   \n",
+       "3     2:16:52 PM              5        24.5                           NaN   \n",
+       "4    11:56:04 AM              6        24.4                           NaN   \n",
+       "\n",
+       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
+       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
+       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
+       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
+       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
+       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21201 entries, 0 to 21200\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                                     Non-Null Count  Dtype  \n",
+      "---  ------                                     --------------  -----  \n",
+      " 0   Order No                                   21201 non-null  object \n",
+      " 1   User Id                                    21201 non-null  object \n",
+      " 2   Vehicle Type                               21201 non-null  object \n",
+      " 3   Platform Type                              21201 non-null  int64  \n",
+      " 4   Personal or Business                       21201 non-null  object \n",
+      " 5   Placement - Day of Month                   21201 non-null  int64  \n",
+      " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
+      " 7   Placement - Time                           21201 non-null  object \n",
+      " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
+      " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
+      " 10  Confirmation - Time                        21201 non-null  object \n",
+      " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
+      " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
+      " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
+      " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
+      " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
+      " 16  Pickup - Time                              21201 non-null  object \n",
+      " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
+      " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
+      " 19  Arrival at Destination - Time              21201 non-null  object \n",
+      " 20  Distance (KM)                              21201 non-null  int64  \n",
+      " 21  Temperature                                16835 non-null  float64\n",
+      " 22  Precipitation in millimeters               552 non-null    float64\n",
+      " 23  Pickup Lat                                 21201 non-null  float64\n",
+      " 24  Pickup Long                                21201 non-null  float64\n",
+      " 25  Destination Lat                            21201 non-null  float64\n",
+      " 26  Destination Long                           21201 non-null  float64\n",
+      " 27  Rider Id                                   21201 non-null  object \n",
+      " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
+      "dtypes: float64(6), int64(13), object(10)\n",
+      "memory usage: 4.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7068 entries, 0 to 7067\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                                Non-Null Count  Dtype  \n",
+      "---  ------                                --------------  -----  \n",
+      " 0   Order No                              7068 non-null   object \n",
+      " 1   User Id                               7068 non-null   object \n",
+      " 2   Vehicle Type                          7068 non-null   object \n",
+      " 3   Platform Type                         7068 non-null   int64  \n",
+      " 4   Personal or Business                  7068 non-null   object \n",
+      " 5   Placement - Day of Month              7068 non-null   int64  \n",
+      " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
+      " 7   Placement - Time                      7068 non-null   object \n",
+      " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
+      " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
+      " 10  Confirmation - Time                   7068 non-null   object \n",
+      " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
+      " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
+      " 13  Arrival at Pickup - Time              7068 non-null   object \n",
+      " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
+      " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
+      " 16  Pickup - Time                         7068 non-null   object \n",
+      " 17  Distance (KM)                         7068 non-null   int64  \n",
+      " 18  Temperature                           5631 non-null   float64\n",
+      " 19  Precipitation in millimeters          199 non-null    float64\n",
+      " 20  Pickup Lat                            7068 non-null   float64\n",
+      " 21  Pickup Long                           7068 non-null   float64\n",
+      " 22  Destination Lat                       7068 non-null   float64\n",
+      " 23  Destination Long                      7068 non-null   float64\n",
+      " 24  Rider Id                              7068 non-null   object \n",
+      "dtypes: float64(6), int64(10), object(9)\n",
+      "memory usage: 1.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>16835.000000</td>\n",
+       "      <td>552.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.752182</td>\n",
+       "      <td>15.653696</td>\n",
+       "      <td>3.240083</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>15.653837</td>\n",
+       "      <td>3.240225</td>\n",
+       "      <td>9.506533</td>\n",
+       "      <td>23.258889</td>\n",
+       "      <td>7.905797</td>\n",
+       "      <td>-1.281470</td>\n",
+       "      <td>36.811264</td>\n",
+       "      <td>-1.282581</td>\n",
+       "      <td>36.811220</td>\n",
+       "      <td>1556.920947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.625178</td>\n",
+       "      <td>8.798916</td>\n",
+       "      <td>1.567295</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>8.798886</td>\n",
+       "      <td>1.567228</td>\n",
+       "      <td>5.668963</td>\n",
+       "      <td>3.615768</td>\n",
+       "      <td>17.089971</td>\n",
+       "      <td>0.030507</td>\n",
+       "      <td>0.037473</td>\n",
+       "      <td>0.034824</td>\n",
+       "      <td>0.044721</td>\n",
+       "      <td>987.270788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>-1.438302</td>\n",
+       "      <td>36.653621</td>\n",
+       "      <td>-1.430298</td>\n",
+       "      <td>36.606594</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>1.075000</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.784605</td>\n",
+       "      <td>-1.301201</td>\n",
+       "      <td>36.785661</td>\n",
+       "      <td>882.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>23.500000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>-1.279395</td>\n",
+       "      <td>36.807040</td>\n",
+       "      <td>-1.284382</td>\n",
+       "      <td>36.808002</td>\n",
+       "      <td>1369.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>-1.261177</td>\n",
+       "      <td>36.829477</td>\n",
+       "      <td>2040.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>32.100000</td>\n",
+       "      <td>99.100000</td>\n",
+       "      <td>-1.147170</td>\n",
+       "      <td>36.991046</td>\n",
+       "      <td>-1.030225</td>\n",
+       "      <td>37.016779</td>\n",
+       "      <td>7883.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "count   21201.000000              21201.000000                  21201.000000   \n",
+       "mean        2.752182                 15.653696                      3.240083   \n",
+       "std         0.625178                  8.798916                      1.567295   \n",
+       "min         1.000000                  1.000000                      1.000000   \n",
+       "25%         3.000000                  8.000000                      2.000000   \n",
+       "50%         3.000000                 15.000000                      3.000000   \n",
+       "75%         3.000000                 23.000000                      5.000000   \n",
+       "max         4.000000                 31.000000                      7.000000   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "count                 21201.000000                     21201.000000   \n",
+       "mean                     15.653837                         3.240225   \n",
+       "std                       8.798886                         1.567228   \n",
+       "min                       1.000000                         1.000000   \n",
+       "25%                       8.000000                         2.000000   \n",
+       "50%                      15.000000                         3.000000   \n",
+       "75%                      23.000000                         5.000000   \n",
+       "max                      31.000000                         7.000000   \n",
+       "\n",
+       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "count                      21201.000000                          21201.000000   \n",
+       "mean                          15.653837                              3.240225   \n",
+       "std                            8.798886                              1.567228   \n",
+       "min                            1.000000                              1.000000   \n",
+       "25%                            8.000000                              2.000000   \n",
+       "50%                           15.000000                              3.000000   \n",
+       "75%                           23.000000                              5.000000   \n",
+       "max                           31.000000                              7.000000   \n",
+       "\n",
+       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
+       "count           21201.000000               21201.000000   \n",
+       "mean               15.653837                   3.240225   \n",
+       "std                 8.798886                   1.567228   \n",
+       "min                 1.000000                   1.000000   \n",
+       "25%                 8.000000                   2.000000   \n",
+       "50%                15.000000                   3.000000   \n",
+       "75%                23.000000                   5.000000   \n",
+       "max                31.000000                   7.000000   \n",
+       "\n",
+       "       Arrival at Destination - Day of Month  \\\n",
+       "count                           21201.000000   \n",
+       "mean                               15.653837   \n",
+       "std                                 8.798886   \n",
+       "min                                 1.000000   \n",
+       "25%                                 8.000000   \n",
+       "50%                                15.000000   \n",
+       "75%                                23.000000   \n",
+       "max                                31.000000   \n",
+       "\n",
+       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
+       "count                               21201.000000   21201.000000  16835.000000   \n",
+       "mean                                    3.240225       9.506533     23.258889   \n",
+       "std                                     1.567228       5.668963      3.615768   \n",
+       "min                                     1.000000       1.000000     11.200000   \n",
+       "25%                                     2.000000       5.000000     20.600000   \n",
+       "50%                                     3.000000       8.000000     23.500000   \n",
+       "75%                                     5.000000      13.000000     26.000000   \n",
+       "max                                     7.000000      49.000000     32.100000   \n",
+       "\n",
+       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
+       "count                    552.000000  21201.000000  21201.000000   \n",
+       "mean                       7.905797     -1.281470     36.811264   \n",
+       "std                       17.089971      0.030507      0.037473   \n",
+       "min                        0.100000     -1.438302     36.653621   \n",
+       "25%                        1.075000     -1.300921     36.784605   \n",
+       "50%                        2.900000     -1.279395     36.807040   \n",
+       "75%                        4.900000     -1.257147     36.829741   \n",
+       "max                       99.100000     -1.147170     36.991046   \n",
+       "\n",
+       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
+       "count     21201.000000      21201.000000                 21201.000000  \n",
+       "mean         -1.282581         36.811220                  1556.920947  \n",
+       "std           0.034824          0.044721                   987.270788  \n",
+       "min          -1.430298         36.606594                     1.000000  \n",
+       "25%          -1.301201         36.785661                   882.000000  \n",
+       "50%          -1.284382         36.808002                  1369.000000  \n",
+       "75%          -1.261177         36.829477                  2040.000000  \n",
+       "max          -1.030225         37.016779                  7883.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>5631.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "      <td>7068.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.752122</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>15.621817</td>\n",
+       "      <td>3.249859</td>\n",
+       "      <td>9.458970</td>\n",
+       "      <td>23.246120</td>\n",
+       "      <td>6.651759</td>\n",
+       "      <td>-1.282043</td>\n",
+       "      <td>36.811148</td>\n",
+       "      <td>-1.282255</td>\n",
+       "      <td>36.811326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.624000</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>8.776650</td>\n",
+       "      <td>1.580926</td>\n",
+       "      <td>5.575689</td>\n",
+       "      <td>3.591161</td>\n",
+       "      <td>14.085439</td>\n",
+       "      <td>0.030044</td>\n",
+       "      <td>0.037962</td>\n",
+       "      <td>0.034236</td>\n",
+       "      <td>0.043507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.200000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>-1.433512</td>\n",
+       "      <td>36.653828</td>\n",
+       "      <td>-1.422652</td>\n",
+       "      <td>36.639195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>-1.301201</td>\n",
+       "      <td>36.784605</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.786546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>23.400000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.807219</td>\n",
+       "      <td>-1.283219</td>\n",
+       "      <td>36.807927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>25.900000</td>\n",
+       "      <td>4.950000</td>\n",
+       "      <td>-1.257274</td>\n",
+       "      <td>36.829716</td>\n",
+       "      <td>-1.261256</td>\n",
+       "      <td>36.828195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>32.100000</td>\n",
+       "      <td>99.100000</td>\n",
+       "      <td>-1.148352</td>\n",
+       "      <td>36.991046</td>\n",
+       "      <td>-1.030225</td>\n",
+       "      <td>37.018958</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "count    7068.000000               7068.000000                   7068.000000   \n",
+       "mean        2.752122                 15.621817                      3.249859   \n",
+       "std         0.624000                  8.776650                      1.580926   \n",
+       "min         1.000000                  1.000000                      1.000000   \n",
+       "25%         3.000000                  8.000000                      2.000000   \n",
+       "50%         3.000000                 15.000000                      3.000000   \n",
+       "75%         3.000000                 23.000000                      5.000000   \n",
+       "max         4.000000                 31.000000                      7.000000   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "count                  7068.000000                      7068.000000   \n",
+       "mean                     15.621817                         3.249859   \n",
+       "std                       8.776650                         1.580926   \n",
+       "min                       1.000000                         1.000000   \n",
+       "25%                       8.000000                         2.000000   \n",
+       "50%                      15.000000                         3.000000   \n",
+       "75%                      23.000000                         5.000000   \n",
+       "max                      31.000000                         7.000000   \n",
+       "\n",
+       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "count                       7068.000000                           7068.000000   \n",
+       "mean                          15.621817                              3.249859   \n",
+       "std                            8.776650                              1.580926   \n",
+       "min                            1.000000                              1.000000   \n",
+       "25%                            8.000000                              2.000000   \n",
+       "50%                           15.000000                              3.000000   \n",
+       "75%                           23.000000                              5.000000   \n",
+       "max                           31.000000                              7.000000   \n",
+       "\n",
+       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "count            7068.000000                7068.000000    7068.000000   \n",
+       "mean               15.621817                   3.249859       9.458970   \n",
+       "std                 8.776650                   1.580926       5.575689   \n",
+       "min                 1.000000                   1.000000       1.000000   \n",
+       "25%                 8.000000                   2.000000       5.000000   \n",
+       "50%                15.000000                   3.000000       8.000000   \n",
+       "75%                23.000000                   5.000000      13.000000   \n",
+       "max                31.000000                   7.000000      47.000000   \n",
+       "\n",
+       "       Temperature  Precipitation in millimeters   Pickup Lat  Pickup Long  \\\n",
+       "count  5631.000000                    199.000000  7068.000000  7068.000000   \n",
+       "mean     23.246120                      6.651759    -1.282043    36.811148   \n",
+       "std       3.591161                     14.085439     0.030044     0.037962   \n",
+       "min      13.200000                      0.100000    -1.433512    36.653828   \n",
+       "25%      20.600000                      1.100000    -1.301201    36.784605   \n",
+       "50%      23.400000                      2.900000    -1.281301    36.807219   \n",
+       "75%      25.900000                      4.950000    -1.257274    36.829716   \n",
+       "max      32.100000                     99.100000    -1.148352    36.991046   \n",
+       "\n",
+       "       Destination Lat  Destination Long  \n",
+       "count      7068.000000       7068.000000  \n",
+       "mean         -1.282255         36.811326  \n",
+       "std           0.034236          0.043507  \n",
+       "min          -1.422652         36.639195  \n",
+       "25%          -1.300921         36.786546  \n",
+       "50%          -1.283219         36.807927  \n",
+       "75%          -1.261256         36.828195  \n",
+       "max          -1.030225         37.018958  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check which cols are in train and not in test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arrival at Destination - Day of Month\n",
+      "Arrival at Destination - Weekday (Mo = 1)\n",
+      "Arrival at Destination - Time\n",
+      "Time from Pickup to Arrival\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in train_df.columns:\n",
+    "    if col not in test_df.columns:\n",
+    "        print(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Match the number of train columns to the number of test columns and also separate the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the columns from the test dataframe into a new list called testcols\n",
+    "testcols = test_df.columns\n",
+    "\n",
+    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
+    "train1 = train_df[testcols]\n",
+    "\n",
+    "# Seperate the target variable from the predictor variable\n",
+    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n",
+      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+      "       'Personal or Business', 'Placement - Day of Month',\n",
+      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+      "       'Rider Id'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print the new columns of the train and test data to show that they are the same\n",
+    "print(train1.columns)\n",
+    "print(test_df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot cmap\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(train1.isnull(), cmap=cmap)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                    0\n",
+       "User Id                                     0\n",
+       "Vehicle Type                                0\n",
+       "Platform Type                               0\n",
+       "Personal or Business                        0\n",
+       "Placement - Day of Month                    0\n",
+       "Placement - Weekday (Mo = 1)                0\n",
+       "Placement - Time                            0\n",
+       "Confirmation - Day of Month                 0\n",
+       "Confirmation - Weekday (Mo = 1)             0\n",
+       "Confirmation - Time                         0\n",
+       "Arrival at Pickup - Day of Month            0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)        0\n",
+       "Arrival at Pickup - Time                    0\n",
+       "Pickup - Day of Month                       0\n",
+       "Pickup - Weekday (Mo = 1)                   0\n",
+       "Pickup - Time                               0\n",
+       "Distance (KM)                               0\n",
+       "Temperature                              4366\n",
+       "Precipitation in millimeters            20649\n",
+       "Pickup Lat                                  0\n",
+       "Pickup Long                                 0\n",
+       "Destination Lat                             0\n",
+       "Destination Long                            0\n",
+       "Rider Id                                    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train1.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                   0\n",
+       "User Id                                    0\n",
+       "Vehicle Type                               0\n",
+       "Platform Type                              0\n",
+       "Personal or Business                       0\n",
+       "Placement - Day of Month                   0\n",
+       "Placement - Weekday (Mo = 1)               0\n",
+       "Placement - Time                           0\n",
+       "Confirmation - Day of Month                0\n",
+       "Confirmation - Weekday (Mo = 1)            0\n",
+       "Confirmation - Time                        0\n",
+       "Arrival at Pickup - Day of Month           0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)       0\n",
+       "Arrival at Pickup - Time                   0\n",
+       "Pickup - Day of Month                      0\n",
+       "Pickup - Weekday (Mo = 1)                  0\n",
+       "Pickup - Time                              0\n",
+       "Distance (KM)                              0\n",
+       "Temperature                             1437\n",
+       "Precipitation in millimeters            6869\n",
+       "Pickup Lat                                 0\n",
+       "Pickup Long                                0\n",
+       "Destination Lat                            0\n",
+       "Destination Long                           0\n",
+       "Rider Id                                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Percentage of the missing values\n",
+    "def missing_values(df, threshold=0):\n",
+    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
+    "\n",
+    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
+    "             index=['% of Missing values']\n",
+    "             ).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% of Missing values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.205934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.973963</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.205934\n",
+       "Precipitation in millimeters             0.973963"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values(train1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% of Missing values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.203311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.971845</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.203311\n",
+       "Precipitation in millimeters             0.971845"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values(test_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Distribution review for train data\n",
+    "train1[[col for col in train1.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Column Skewness of the testing dataset\n",
+    "train1.skew().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Univariate Anaysis\n",
+    "# Calculating Skewness in the columns of our Training dataset\n",
+    "train1.kurt().plot.bar(figsize =(10,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Platform Type</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.005528</td>\n",
+       "      <td>0.225060</td>\n",
+       "      <td>-0.055948</td>\n",
+       "      <td>-0.002742</td>\n",
+       "      <td>0.021327</td>\n",
+       "      <td>0.006319</td>\n",
+       "      <td>0.043924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>-0.007585</td>\n",
+       "      <td>0.013097</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004929</td>\n",
+       "      <td>-0.005453</td>\n",
+       "      <td>0.007541</td>\n",
+       "      <td>0.002077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.003582</td>\n",
+       "      <td>0.031064</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000918</td>\n",
+       "      <td>0.011008</td>\n",
+       "      <td>-0.004299</td>\n",
+       "      <td>-0.000875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>-0.007587</td>\n",
+       "      <td>0.013099</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004923</td>\n",
+       "      <td>-0.005448</td>\n",
+       "      <td>0.007550</td>\n",
+       "      <td>0.002073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.003574</td>\n",
+       "      <td>0.031074</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000883</td>\n",
+       "      <td>0.011036</td>\n",
+       "      <td>-0.004251</td>\n",
+       "      <td>-0.000900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Platform Type  Placement - Day of Month  \\\n",
+       "Platform Type                         1.000000                  0.012764   \n",
+       "Placement - Day of Month              0.012764                  1.000000   \n",
+       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
+       "Confirmation - Day of Month           0.012770                  0.999998   \n",
+       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
+       "\n",
+       "                                 Placement - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                       -0.058038   \n",
+       "Placement - Day of Month                             0.026148   \n",
+       "Placement - Weekday (Mo = 1)                         1.000000   \n",
+       "Confirmation - Day of Month                          0.026132   \n",
+       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
+       "\n",
+       "                                 Confirmation - Day of Month  \\\n",
+       "Platform Type                                       0.012770   \n",
+       "Placement - Day of Month                            0.999998   \n",
+       "Placement - Weekday (Mo = 1)                        0.026132   \n",
+       "Confirmation - Day of Month                         1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
+       "\n",
+       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                          -0.058005   \n",
+       "Placement - Day of Month                                0.026122   \n",
+       "Placement - Weekday (Mo = 1)                            0.999952   \n",
+       "Confirmation - Day of Month                             0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
+       "\n",
+       "                                 Arrival at Pickup - Day of Month  \\\n",
+       "Platform Type                                            0.012770   \n",
+       "Placement - Day of Month                                 0.999998   \n",
+       "Placement - Weekday (Mo = 1)                             0.026132   \n",
+       "Confirmation - Day of Month                              1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
+       "\n",
+       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                               -0.058005   \n",
+       "Placement - Day of Month                                     0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
+       "Confirmation - Day of Month                                  0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
+       "\n",
+       "                                 Pickup - Day of Month  \\\n",
+       "Platform Type                                 0.012770   \n",
+       "Placement - Day of Month                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                  0.026132   \n",
+       "Confirmation - Day of Month                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
+       "\n",
+       "                                 Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "Platform Type                                    -0.058005       0.005528   \n",
+       "Placement - Day of Month                          0.026122      -0.007585   \n",
+       "Placement - Weekday (Mo = 1)                      0.999952       0.003582   \n",
+       "Confirmation - Day of Month                       0.026123      -0.007587   \n",
+       "Confirmation - Weekday (Mo = 1)                   1.000000       0.003574   \n",
+       "\n",
+       "                                 Temperature  Precipitation in millimeters  \\\n",
+       "Platform Type                       0.225060                     -0.055948   \n",
+       "Placement - Day of Month            0.013097                     -0.204143   \n",
+       "Placement - Weekday (Mo = 1)        0.031064                     -0.050592   \n",
+       "Confirmation - Day of Month         0.013099                     -0.204143   \n",
+       "Confirmation - Weekday (Mo = 1)     0.031074                     -0.050592   \n",
+       "\n",
+       "                                 Pickup Lat  Pickup Long  Destination Lat  \\\n",
+       "Platform Type                     -0.002742     0.021327         0.006319   \n",
+       "Placement - Day of Month          -0.004929    -0.005453         0.007541   \n",
+       "Placement - Weekday (Mo = 1)      -0.000918     0.011008        -0.004299   \n",
+       "Confirmation - Day of Month       -0.004923    -0.005448         0.007550   \n",
+       "Confirmation - Weekday (Mo = 1)   -0.000883     0.011036        -0.004251   \n",
+       "\n",
+       "                                 Destination Long  \n",
+       "Platform Type                            0.043924  \n",
+       "Placement - Day of Month                 0.002077  \n",
+       "Placement - Weekday (Mo = 1)            -0.000875  \n",
+       "Confirmation - Day of Month              0.002073  \n",
+       "Confirmation - Weekday (Mo = 1)         -0.000900  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CorrMatrix_train = train1.corr()\n",
+    "CorrMatrix_train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Pearson Correlation of Features')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "style.use('ggplot')\n",
+    "cm = CorrMatrix_train.corr()\n",
+    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
+    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
+    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Personal or Business"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Platform type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vehicle Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train1['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(train1['Temperature'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "0              3                         9                             5   \n",
+       "1              3                        12                             5   \n",
+       "2              3                        30                             2   \n",
+       "3              3                        15                             5   \n",
+       "4              1                        13                             1   \n",
+       "\n",
+       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "0                            9                                5   \n",
+       "1                           12                                5   \n",
+       "2                           30                                2   \n",
+       "3                           15                                5   \n",
+       "4                           13                                1   \n",
+       "\n",
+       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                 9                                     5   \n",
+       "1                                12                                     5   \n",
+       "2                                30                                     2   \n",
+       "3                                15                                     5   \n",
+       "4                                13                                     1   \n",
+       "\n",
+       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
+       "0                      9                          5              4   \n",
+       "1                     12                          5             16   \n",
+       "2                     30                          2              3   \n",
+       "3                     15                          5              9   \n",
+       "4                     13                          1              9   \n",
+       "\n",
+       "   Temperature  Precipitation in millimeters  Pickup Lat  Pickup Long  \\\n",
+       "0         20.4                           NaN   -1.317755    36.830370   \n",
+       "1         26.4                           NaN   -1.351453    36.899315   \n",
+       "2          NaN                           NaN   -1.308284    36.843419   \n",
+       "3         19.2                           NaN   -1.281301    36.832396   \n",
+       "4         15.4                           NaN   -1.266597    36.792118   \n",
+       "\n",
+       "   Destination Lat  Destination Long  \n",
+       "0        -1.300406         36.829741  \n",
+       "1        -1.295004         36.814358  \n",
+       "2        -1.300921         36.828195  \n",
+       "3        -1.257147         36.795063  \n",
+       "4        -1.295041         36.809817  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numeric_Training = train1._get_numeric_data()\n",
+    "Numeric_Training.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-37-67d706ebc61e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'bar'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
+   "source": [
+    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5]) #returns error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-38-a40df607a66f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whitegrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 864x7.63233e+06 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l = train1.values  #returns error\n",
+    "number_of_columns= 12\n",
+    "number_of_rows = len(l)-1/number_of_columns\n",
+    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.set_style('whitegrid')\n",
+    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'number_of_columns_Test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-39-2a23d4201dc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+      "\u001b[1;31mNameError\u001b[0m: name 'number_of_columns_Test' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test)) #returns error\n",
+    "for i in range(0,len(nu_tra)):\n",
+    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
+    "    chart=sns.distplot(train1[nu_tra[i]],kde=True) \n",
+    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
+    "                          horizontalalignment='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-9f95e1fbe116>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Useful statistics about our target column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
+   "source": [
+    "#Useful statistics about our target column\n",
+    "\n",
+    "train1['Time from Pickup to Arrival'].describe() #returns error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Time</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:35:46 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10:27:30 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16 AM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:44:09 AM</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25 PM</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:53:03 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:25:34 AM</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9:43:06 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9:55:18 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10:05:23 AM</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21196</th>\n",
+       "      <td>Order_No_8834</td>\n",
+       "      <td>User_Id_2001</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3:54:38 PM</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4:20:08 PM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.275285</td>\n",
+       "      <td>36.802702</td>\n",
+       "      <td>Rider_Id_953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21197</th>\n",
+       "      <td>Order_No_22892</td>\n",
+       "      <td>User_Id_1796</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10:13:34 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10:33:27 AM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.307143</td>\n",
+       "      <td>36.825009</td>\n",
+       "      <td>-1.331619</td>\n",
+       "      <td>36.847976</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21198</th>\n",
+       "      <td>Order_No_2831</td>\n",
+       "      <td>User_Id_2956</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5:06:16 PM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5:50:52 PM</td>\n",
+       "      <td>20</td>\n",
+       "      <td>29.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.286018</td>\n",
+       "      <td>36.897534</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>Rider_Id_697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21199</th>\n",
+       "      <td>Order_No_6174</td>\n",
+       "      <td>User_Id_2524</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9:31:39 AM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9:45:15 AM</td>\n",
+       "      <td>13</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.250030</td>\n",
+       "      <td>36.874167</td>\n",
+       "      <td>-1.279209</td>\n",
+       "      <td>36.794872</td>\n",
+       "      <td>Rider_Id_347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21200</th>\n",
+       "      <td>Order_No_9836</td>\n",
+       "      <td>User_Id_718</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2:19:47 PM</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2:41:55 PM</td>\n",
+       "      <td>12</td>\n",
+       "      <td>30.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.320157</td>\n",
+       "      <td>36.830887</td>\n",
+       "      <td>Rider_Id_177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21201 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Order No       User Id Vehicle Type  Platform Type  \\\n",
+       "0       Order_No_4211   User_Id_633         Bike              3   \n",
+       "1      Order_No_25375  User_Id_2285         Bike              3   \n",
+       "2       Order_No_1899   User_Id_265         Bike              3   \n",
+       "3       Order_No_9336  User_Id_1402         Bike              3   \n",
+       "4      Order_No_27883  User_Id_1737         Bike              1   \n",
+       "...               ...           ...          ...            ...   \n",
+       "21196   Order_No_8834  User_Id_2001         Bike              3   \n",
+       "21197  Order_No_22892  User_Id_1796         Bike              3   \n",
+       "21198   Order_No_2831  User_Id_2956         Bike              3   \n",
+       "21199   Order_No_6174  User_Id_2524         Bike              1   \n",
+       "21200   Order_No_9836   User_Id_718         Bike              3   \n",
+       "\n",
+       "      Personal or Business  Placement - Day of Month  \\\n",
+       "0                 Business                         9   \n",
+       "1                 Personal                        12   \n",
+       "2                 Business                        30   \n",
+       "3                 Business                        15   \n",
+       "4                 Personal                        13   \n",
+       "...                    ...                       ...   \n",
+       "21196             Personal                        20   \n",
+       "21197             Business                        13   \n",
+       "21198             Business                         7   \n",
+       "21199             Personal                         4   \n",
+       "21200             Business                        26   \n",
+       "\n",
+       "       Placement - Weekday (Mo = 1) Placement - Time  \\\n",
+       "0                                 5       9:35:46 AM   \n",
+       "1                                 5      11:16:16 AM   \n",
+       "2                                 2      12:39:25 PM   \n",
+       "3                                 5       9:25:34 AM   \n",
+       "4                                 1       9:55:18 AM   \n",
+       "...                             ...              ...   \n",
+       "21196                             3       3:54:38 PM   \n",
+       "21197                             6      10:13:34 AM   \n",
+       "21198                             4       5:06:16 PM   \n",
+       "21199                             3       9:31:39 AM   \n",
+       "21200                             2       2:19:47 PM   \n",
+       "\n",
+       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  ...  \\\n",
+       "0                                9                                5  ...   \n",
+       "1                               12                                5  ...   \n",
+       "2                               30                                2  ...   \n",
+       "3                               15                                5  ...   \n",
+       "4                               13                                1  ...   \n",
+       "...                            ...                              ...  ...   \n",
+       "21196                           20                                3  ...   \n",
+       "21197                           13                                6  ...   \n",
+       "21198                            7                                4  ...   \n",
+       "21199                            4                                3  ...   \n",
+       "21200                           26                                2  ...   \n",
+       "\n",
+       "      Pickup - Weekday (Mo = 1)  Pickup - Time  Distance (KM) Temperature  \\\n",
+       "0                             5    10:27:30 AM              4        20.4   \n",
+       "1                             5    11:44:09 AM             16        26.4   \n",
+       "2                             2    12:53:03 PM              3         NaN   \n",
+       "3                             5     9:43:06 AM              9        19.2   \n",
+       "4                             1    10:05:23 AM              9        15.4   \n",
+       "...                         ...            ...            ...         ...   \n",
+       "21196                         3     4:20:08 PM              3        28.6   \n",
+       "21197                         6    10:33:27 AM              7        26.0   \n",
+       "21198                         4     5:50:52 PM             20        29.2   \n",
+       "21199                         3     9:45:15 AM             13        15.0   \n",
+       "21200                         2     2:41:55 PM             12        30.9   \n",
+       "\n",
+       "       Precipitation in millimeters  Pickup Lat Pickup Long  Destination Lat  \\\n",
+       "0                               NaN   -1.317755   36.830370        -1.300406   \n",
+       "1                               NaN   -1.351453   36.899315        -1.295004   \n",
+       "2                               NaN   -1.308284   36.843419        -1.300921   \n",
+       "3                               NaN   -1.281301   36.832396        -1.257147   \n",
+       "4                               NaN   -1.266597   36.792118        -1.295041   \n",
+       "...                             ...         ...         ...              ...   \n",
+       "21196                           NaN   -1.258414   36.804800        -1.275285   \n",
+       "21197                           NaN   -1.307143   36.825009        -1.331619   \n",
+       "21198                           NaN   -1.286018   36.897534        -1.258414   \n",
+       "21199                           NaN   -1.250030   36.874167        -1.279209   \n",
+       "21200                           NaN   -1.255189   36.782203        -1.320157   \n",
+       "\n",
+       "       Destination Long      Rider Id  \n",
+       "0             36.829741  Rider_Id_432  \n",
+       "1             36.814358  Rider_Id_856  \n",
+       "2             36.828195  Rider_Id_155  \n",
+       "3             36.795063  Rider_Id_855  \n",
+       "4             36.809817  Rider_Id_770  \n",
+       "...                 ...           ...  \n",
+       "21196         36.802702  Rider_Id_953  \n",
+       "21197         36.847976  Rider_Id_155  \n",
+       "21198         36.804800  Rider_Id_697  \n",
+       "21199         36.794872  Rider_Id_347  \n",
+       "21200         36.830887  Rider_Id_177  \n",
+       "\n",
+       "[21201 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Time from Pickup to Arrival'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-42-0671fade3f72>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time (minutes)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+     ]
+    }
+   ],
+   "source": [
+    "(train1['Time from Pickup to Arrival']/60).hist(bins=50) #returns error\n",
+    "plt.title(\"Delivery time distribution\") \n",
+    "plt.xlabel(\"Delivery time (minutes)\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Neither the `x` nor `y` variable appears to be numeric.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-43-278ac3dc675f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Outliers Overview for numeric features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2233\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2234\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2235\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    434\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[0morient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_orient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Option 2a:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(self, x, y, orient)\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 354\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mno_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    355\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Neither the `x` nor `y` variable appears to be numeric."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1440x576 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Outliers Overview for numeric features\n",
+    "plt.figure(figsize=(20, 8))\n",
+    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df)) #returns error\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-44-7701a3d72f00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1728x7.63233e+06 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows)) #returns error\n",
+    "for i in range(0,len(l)):\n",
+    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
+    "    sns.distplot(df[l[i]],kde=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Full_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((21201, 25), (7068, 26), (28269, 26))"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train1.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train1.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Renaming columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
+       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
+       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
+       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
+       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
+       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
+       "       'Rider_Id', 'Time_Pic_Arr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15:07:35</td>\n",
+       "      <td>15:08:57</td>\n",
+       "      <td>15:21:36</td>\n",
+       "      <td>15:30:30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
+       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
+       "5  15:07:35  15:08:57     15:21:36    15:30:30"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filling Missing Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Filling Missing Values for temperatures and humidity\n",
+    "\n",
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
+       "\n",
+       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
+       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
+       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
+       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
+       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
+       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
+       "\n",
+       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
+       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
+       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
+       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
+       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
+       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
+       "\n",
+       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
+       "0       -1.300406        36.829741  Rider_Id_432           NaN  \n",
+       "1       -1.295004        36.814358  Rider_Id_856           NaN  \n",
+       "2       -1.300921        36.828195  Rider_Id_155           NaN  \n",
+       "3       -1.257147        36.795063  Rider_Id_855           NaN  \n",
+       "4       -1.295041        36.809817  Rider_Id_770           NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
+    "\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
+    "\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
+    "\n",
+    "full_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
+       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
+       "4        36.809817  Rider_Id_770           NaN            13            1  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
+       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
+       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
+       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
+       "       'Day_of_Month', 'Day_of_Week'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
+       "0   36.830370        -1.300406        36.829741             9            5  \n",
+       "1   36.899315        -1.295004        36.814358            12            5  \n",
+       "2   36.843419        -1.300921        36.828195            30            2  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[numeric_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
+       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
+       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
+       "2  12:39:25  12:42:44     12:49:34    12:53:03"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[time_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id Personal_Business      Rider_Id\n",
+       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
+       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
+       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[object_cols].head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert object types to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "Name: Personal_Business, dtype: int32"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Rider_Id</th>\n",
+       "      <th>Time_Pic_Arr</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>10:27:30</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>Rider_Id_432</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>11:44:09</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>Rider_Id_856</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>12:53:03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>Rider_Id_155</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>09:43:06</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>Rider_Id_855</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>10:05:23</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>Rider_Id_770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
+       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
+       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
+       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
+       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
+       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
+       "\n",
+       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
+       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
+       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
+       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
+       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
+       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "\n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "\n",
+       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
+       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
+       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
+       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
+       "4        36.809817  Rider_Id_770           NaN            13            1  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
+       "0              3             4    20.400000           7.573502   -1.317755   \n",
+       "1              3            16    26.400000           7.573502   -1.351453   \n",
+       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "3              3             9    19.200000           7.573502   -1.281301   \n",
+       "4              1             9    15.400000           7.573502   -1.266597   \n",
+       "\n",
+       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
+       "0   36.830370        -1.300406        36.829741             9            5   \n",
+       "1   36.899315        -1.295004        36.814358            12            5   \n",
+       "2   36.843419        -1.300921        36.828195            30            2   \n",
+       "3   36.832396        -1.257147        36.795063            15            5   \n",
+       "4   36.792118        -1.295041        36.809817            13            1   \n",
+       "\n",
+       "   Personal_Business  \n",
+       "0                  0  \n",
+       "1                  1  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  1  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = numeric_cols + ['Personal_Business']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28269, 19) (28269, 11) (21201, 11) (7068, 11) (21201,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.317755</td>\n",
+       "      <td>36.830370</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21196</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28.600000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.275285</td>\n",
+       "      <td>36.802702</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21197</th>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.307143</td>\n",
+       "      <td>36.825009</td>\n",
+       "      <td>-1.331619</td>\n",
+       "      <td>36.847976</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21198</th>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>29.200000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.286018</td>\n",
+       "      <td>36.897534</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21199</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.250030</td>\n",
+       "      <td>36.874167</td>\n",
+       "      <td>-1.279209</td>\n",
+       "      <td>36.794872</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21200</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>30.900000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.320157</td>\n",
+       "      <td>36.830887</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21201 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
+       "0                  3             4    20.400000           7.573502   \n",
+       "1                  3            16    26.400000           7.573502   \n",
+       "2                  3             3    23.255689           7.573502   \n",
+       "3                  3             9    19.200000           7.573502   \n",
+       "4                  1             9    15.400000           7.573502   \n",
+       "...              ...           ...          ...                ...   \n",
+       "21196              3             3    28.600000           7.573502   \n",
+       "21197              3             7    26.000000           7.573502   \n",
+       "21198              3            20    29.200000           7.573502   \n",
+       "21199              1            13    15.000000           7.573502   \n",
+       "21200              3            12    30.900000           7.573502   \n",
+       "\n",
+       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
+       "0       -1.317755   36.830370        -1.300406        36.829741             9   \n",
+       "1       -1.351453   36.899315        -1.295004        36.814358            12   \n",
+       "2       -1.308284   36.843419        -1.300921        36.828195            30   \n",
+       "3       -1.281301   36.832396        -1.257147        36.795063            15   \n",
+       "4       -1.266597   36.792118        -1.295041        36.809817            13   \n",
+       "...           ...         ...              ...              ...           ...   \n",
+       "21196   -1.258414   36.804800        -1.275285        36.802702            20   \n",
+       "21197   -1.307143   36.825009        -1.331619        36.847976            13   \n",
+       "21198   -1.286018   36.897534        -1.258414        36.804800             7   \n",
+       "21199   -1.250030   36.874167        -1.279209        36.794872             4   \n",
+       "21200   -1.255189   36.782203        -1.320157        36.830887            26   \n",
+       "\n",
+       "       Day_of_Week  Personal_Business  \n",
+       "0                5                  0  \n",
+       "1                5                  1  \n",
+       "2                2                  0  \n",
+       "3                5                  0  \n",
+       "4                1                  1  \n",
+       "...            ...                ...  \n",
+       "21196            3                  1  \n",
+       "21197            6                  0  \n",
+       "21198            4                  0  \n",
+       "21199            3                  1  \n",
+       "21200            2                  0  \n",
+       "\n",
+       "[21201 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform_Type</th>\n",
+       "      <th>Distance(km)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation(mm)</th>\n",
+       "      <th>Pickup_Lat</th>\n",
+       "      <th>Pickup_Lon</th>\n",
+       "      <th>Destination_Lat</th>\n",
+       "      <th>Destination_Lon</th>\n",
+       "      <th>Day_of_Month</th>\n",
+       "      <th>Day_of_Week</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21201</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.333275</td>\n",
+       "      <td>36.870815</td>\n",
+       "      <td>-1.305249</td>\n",
+       "      <td>36.822390</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21202</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>23.255689</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.272639</td>\n",
+       "      <td>36.794723</td>\n",
+       "      <td>-1.277007</td>\n",
+       "      <td>36.823907</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21203</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22.800000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.290894</td>\n",
+       "      <td>36.822971</td>\n",
+       "      <td>-1.276574</td>\n",
+       "      <td>36.851365</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21204</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.500000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.290503</td>\n",
+       "      <td>36.809646</td>\n",
+       "      <td>-1.303382</td>\n",
+       "      <td>36.790658</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21205</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24.400000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.281081</td>\n",
+       "      <td>36.814423</td>\n",
+       "      <td>-1.266467</td>\n",
+       "      <td>36.792161</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28264</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.800000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.258414</td>\n",
+       "      <td>36.804800</td>\n",
+       "      <td>-1.288780</td>\n",
+       "      <td>36.816831</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28265</th>\n",
+       "      <td>3</td>\n",
+       "      <td>22</td>\n",
+       "      <td>30.700000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.276141</td>\n",
+       "      <td>36.771084</td>\n",
+       "      <td>-1.316098</td>\n",
+       "      <td>36.913164</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28266</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25.100000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.301446</td>\n",
+       "      <td>36.766138</td>\n",
+       "      <td>-1.264960</td>\n",
+       "      <td>36.798178</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28267</th>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>23.600000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.248404</td>\n",
+       "      <td>36.678276</td>\n",
+       "      <td>-1.272027</td>\n",
+       "      <td>36.817411</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28268</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>22.300000</td>\n",
+       "      <td>7.573502</td>\n",
+       "      <td>-1.255189</td>\n",
+       "      <td>36.782203</td>\n",
+       "      <td>-1.300406</td>\n",
+       "      <td>36.829741</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7068 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
+       "21201              3             8    23.255689           7.573502   \n",
+       "21202              3             5    23.255689           7.573502   \n",
+       "21203              3             5    22.800000           7.573502   \n",
+       "21204              3             5    24.500000           7.573502   \n",
+       "21205              3             6    24.400000           7.573502   \n",
+       "...              ...           ...          ...                ...   \n",
+       "28264              3             5    24.800000           7.573502   \n",
+       "28265              3            22    30.700000           7.573502   \n",
+       "28266              3            10    25.100000           7.573502   \n",
+       "28267              3            18    23.600000           7.573502   \n",
+       "28268              3             8    22.300000           7.573502   \n",
+       "\n",
+       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
+       "21201   -1.333275   36.870815        -1.305249        36.822390            27   \n",
+       "21202   -1.272639   36.794723        -1.277007        36.823907            17   \n",
+       "21203   -1.290894   36.822971        -1.276574        36.851365            27   \n",
+       "21204   -1.290503   36.809646        -1.303382        36.790658            17   \n",
+       "21205   -1.281081   36.814423        -1.266467        36.792161            11   \n",
+       "...           ...         ...              ...              ...           ...   \n",
+       "28264   -1.258414   36.804800        -1.288780        36.816831             7   \n",
+       "28265   -1.276141   36.771084        -1.316098        36.913164            10   \n",
+       "28266   -1.301446   36.766138        -1.264960        36.798178             5   \n",
+       "28267   -1.248404   36.678276        -1.272027        36.817411            29   \n",
+       "28268   -1.255189   36.782203        -1.300406        36.829741             4   \n",
+       "\n",
+       "       Day_of_Week  Personal_Business  \n",
+       "21201            3                  0  \n",
+       "21202            5                  0  \n",
+       "21203            4                  0  \n",
+       "21204            1                  0  \n",
+       "21205            2                  0  \n",
+       "...            ...                ...  \n",
+       "28264            1                  0  \n",
+       "28265            3                  0  \n",
+       "28266            3                  0  \n",
+       "28267            2                  1  \n",
+       "28268            2                  0  \n",
+       "\n",
+       "[7068 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       NaN\n",
+       "1       NaN\n",
+       "2       NaN\n",
+       "3       NaN\n",
+       "4       NaN\n",
+       "         ..\n",
+       "21196   NaN\n",
+       "21197   NaN\n",
+       "21198   NaN\n",
+       "21199   NaN\n",
+       "21200   NaN\n",
+       "Name: Time_Pic_Arr, Length: 21201, dtype: float64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train/Test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
+    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-69-6a76cfe783d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mcv_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mregressor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregressors\u001b[0m\u001b[1;33m:\u001b[0m     \u001b[1;31m#scores to be minimised are negated (neg)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0mcv_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mcv_means\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    404\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    407\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 248\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "rs = 3\n",
+    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True) #returns error\n",
+    "\n",
+    "regressors = []\n",
+    "regressors.append(SVR())\n",
+    "regressors.append(GradientBoostingRegressor(random_state=rs))\n",
+    "regressors.append(ExtraTreesRegressor(n_estimators=rs))\n",
+    "regressors.append(RandomForestRegressor(random_state=rs))\n",
+    "#regressors.append(xgb.XGBRegressor(random_state=rs, objective=\"reg:squarederror\"))\n",
+    "regressors.append(lgb.LGBMRegressor(random_state=rs))\n",
+    "\n",
+    "cv_results = []\n",
+    "for regressor in regressors:     #scores to be minimised are negated (neg)\n",
+    "    cv_results.append(np.sqrt(abs(cross_val_score(regressor, X_train, y=y_train, scoring='neg_mean_squared_error', cv=kfold))))\n",
+    "\n",
+    "cv_means = []\n",
+    "cv_stds = []\n",
+    "for cv_result in cv_results:\n",
+    "    cv_means.append(cv_result.mean())\n",
+    "    cv_stds.append(cv_result.std())\n",
+    "    \n",
+    "#cv_res = pd.DataFrame({ \n",
+    "#    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"XGBR\", \"LGBM\"],\n",
+    "#    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "#                       })\n",
+    "cv_res = pd.DataFrame({ \n",
+    "    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"LGBM\"],\n",
+    "    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
+    "                       })\n",
+    "\n",
+    "cv_res = cv_res.sort_values(\"CrossValMeans\", ascending=True)\n",
+    "print(cv_res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Making predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-81-e8fb8588c79a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m            \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n\u001b[0;32m      5\u001b[0m \u001b[0mrsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRFC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrf_param\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mrfm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mr_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    763\u001b[0m             \u001b[0mrefit_start_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    302\u001b[0m             )\n\u001b[0;32m    303\u001b[0m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[1;32m--> 304\u001b[1;33m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0m\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    803\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    804\u001b[0m         y = check_array(y, accept_sparse='csr', force_all_finite=True,\n\u001b[1;32m--> 805\u001b[1;33m                         ensure_2d=False, dtype=None)\n\u001b[0m\u001b[0;32m    806\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    807\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "rs = 3 #retuns errors\n",
+    "RFC = RandomForestRegressor(random_state=rs)\n",
+    "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
+    "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
+    "rsearch = GridSearchCV(RFC, cv=kfold, scoring='neg_mean_squared_error',param_grid=rf_param)\n",
+    "rfm = rsearch.fit(X_train, y_train)\n",
+    "\n",
+    "r_score = np.sqrt(abs(rfm.best_score_))\n",
+    "r_params = rfm.best_p\n",
+    "arams_\n",
+    "print(r_score, r_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-71-f5926b27e7ad>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-71-f5926b27e7ad>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "Prediction = predict(fit, test) #returns errors\n",
+    "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
+    "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'lgbm' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-72-bce2b5ba7327>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlgbm_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[0;32m      4\u001b[0m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'lgbm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration) #returns errors, lgb not defined\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'lr' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-73-acbae1ec0c43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'lr' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = lr.predict(test_new) #lr not defined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'test_df1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-74-fc2ddc35a87f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msubmission_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_df1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Order No'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msubmission_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time_Pic_Arr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'test_df1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "submission_df = test_df1[['Order No']] #test_df1 not found error\n",
+    "submission_df['Time_Pic_Arr'] = y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lightgbm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-76-0f04e4b0aaeb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mlsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLGBMRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mlgbm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0ml_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    734\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1188\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    713\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 715\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'n_estimators':[75], # [75, 95],\n",
+    "    'num_leaves': [15], #[12,15, 17],\n",
+    "    'reg_alpha': [0.02], #[0.02, 0.05],\n",
+    "    'min_data_in_leaf': [300],  #[250, 280, 300]\n",
+    "    'learning_rate': [0.1], #[0.05, 0.1, 0.25],\n",
+    "    'objective': ['regression'] #['regression', None]\n",
+    "    }\n",
+    "\n",
+    "lsearch = GridSearchCV(estimator = lgb.LGBMRegressor(random_state=rs), cv=kfold,scoring='neg_mean_squared_error', param_grid=params)\n",
+    "lgbm = lsearch.fit(X_train, y_train)\n",
+    "\n",
+    "l_params = lgbm.best_params_\n",
+    "l_score = np.sqrt(abs(lgbm.best_score_))\n",
+    "print(lgbm.best_params_, np.sqrt(abs(lgbm.best_score_)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and making a prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003107 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 1334\n",
+      "[LightGBM] [Info] Number of data points in the train set: 16960, number of used features: 11\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[1]\tvalid_0's l2: 0\n",
+      "Training until validation scores don't improve for 20 rounds\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[2]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[3]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[4]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[5]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[6]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[7]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[8]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[9]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[10]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[11]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[12]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[13]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[14]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[15]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[16]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[17]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[18]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[19]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[20]\tvalid_0's l2: 0\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
+      "[21]\tvalid_0's l2: 0\n",
+      "Early stopping, best iteration is:\n",
+      "[1]\tvalid_0's l2: 0\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-77-21f5823a14a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mlpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The RMSE of prediction is \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
+     ]
+    }
+   ],
+   "source": [
+    "lgb_train = lgb.Dataset(X_train, y_train)\n",
+    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
+    "\n",
+    "lparams = {\n",
+    "           'learning_rate': 0.1, 'min_data_in_leaf': 300, \n",
+    "           'n_estimators': 75, 'num_leaves': 20, 'random_state':rs,\n",
+    "           'objective': 'regression', 'reg_alpha': 0.02,\n",
+    "          'feature_fraction': 0.9, 'bagging_fraction':0.9}\n",
+    "\n",
+    "\n",
+    "lgbm = lgb.train(lparams, lgb_train, valid_sets=lgb_eval, num_boost_round=20, early_stopping_rounds=20)\n",
+    "\n",
+    "lpred = lgbm.predict(X_test, num_iteration=lgbm.best_iteration)\n",
+    "\n",
+    "print(\"The RMSE of prediction is \", mean_squared_error(y_test, lpred)**0.5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-78-a6078aba1174>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors)\u001b[0m\n\u001b[0;32m   3168\u001b[0m             \u001b[0mdecimal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3169\u001b[0m         )\n\u001b[1;32m-> 3170\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3172\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    188\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m                 \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m                 \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    191\u001b[0m             )\n\u001b[0;32m    192\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors)\u001b[0m\n\u001b[0;32m    491\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    492\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    494\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m             \u001b[1;31m# No explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
+    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
+    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
+    "lgbm_output.to_csv(\"submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 9c2b3204c776cb62eedeaa0cf325aa6c0377ff4c Mon Sep 17 00:00:00 2001
From: MoKai1701 <mj.maakekai@gmail.com>
Date: Mon, 19 Oct 2020 18:45:34 +0200
Subject: [PATCH 39/46] test with url again

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 5048604..2bf66f7 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-#url = 'http://34.240.251.83:5000/api_v0.1'
-url = 'http://0.0.0.0:5000/api_v0.1'
+url = 'http://34.240.251.83:5000/api_v0.1'
+#url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 67b4e3cacb7eeaa55886f8c8c148725dc155c43c Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Mon, 19 Oct 2020 19:14:24 +0200
Subject: [PATCH 40/46] back to default url

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 2bf66f7..5048604 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://34.240.251.83:5000/api_v0.1'
-#url = 'http://0.0.0.0:5000/api_v0.1'
+#url = 'http://34.240.251.83:5000/api_v0.1'
+url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From a2423e9d749ab751bbb8f55ad246e58f5d65050c Mon Sep 17 00:00:00 2001
From: exclusivedollar <31101097+exclusivedollar@users.noreply.github.com>
Date: Mon, 19 Oct 2020 19:40:22 +0200
Subject: [PATCH 41/46] Fixed NameError

name 'LabelEncoder' is not defined
---
 model.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/model.py b/model.py
index e9ae7ce..5552ae6 100644
--- a/model.py
+++ b/model.py
@@ -131,6 +131,7 @@ def convert_to_24hrs(feature_vector_df):
             object_cols.append(k)
         
     #Convert an object to numeric (encoding)
+    from sklearn.preprocessing import LabelEncoder
     le = LabelEncoder()
     le.fit(feature_vector_df['Personal_Business'])
     feature_vector_df['Personal_Business'] = le.transform(feature_vector_df['Personal_Business'])

From a5b07feb35e397955ca917ff3714f6fdb6d4be5b Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Mon, 19 Oct 2020 19:51:44 +0200
Subject: [PATCH 42/46] test Team_renaming_to_3 with ec2 url

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 5048604..2bf66f7 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-#url = 'http://34.240.251.83:5000/api_v0.1'
-url = 'http://0.0.0.0:5000/api_v0.1'
+url = 'http://34.240.251.83:5000/api_v0.1'
+#url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 8bba35e1b3bb33161261ed310b3996dea692cc3f Mon Sep 17 00:00:00 2001
From: mokai1701 <68580767+mokai1701@users.noreply.github.com>
Date: Mon, 19 Oct 2020 19:59:19 +0200
Subject: [PATCH 43/46] request.py still returns errors, roll back to default

---
 utils/request.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/request.py b/utils/request.py
index 2bf66f7..5048604 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -38,8 +38,8 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://34.240.251.83:5000/api_v0.1'
-#url = 'http://0.0.0.0:5000/api_v0.1'
+#url = 'http://34.240.251.83:5000/api_v0.1'
+url = 'http://0.0.0.0:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")

From 05643f3e8c496d6d3f6500766925bd3be18966d3 Mon Sep 17 00:00:00 2001
From: MankoMofokeng <mankomofokeng1@gmail.com>
Date: Mon, 19 Oct 2020 23:27:14 +0200
Subject: [PATCH 44/46] Update Sendy_Team4_Final.ipynb

---
 Sendy_Team4_Final.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index b878a92..88a051c 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -118,7 +118,7 @@
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "#import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
+    "import lightgbm as lgbm\n",
     "\n",
     "sns.set(style='white', context='notebook', palette='deep')"
    ]
@@ -5835,4 +5835,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file

From f35aa04d60c5bde5151759b5cf94e62718faf1a7 Mon Sep 17 00:00:00 2001
From: Grewies <68637353+Grewies@users.noreply.github.com>
Date: Tue, 20 Oct 2020 09:33:34 +0200
Subject: [PATCH 45/46] Add files via upload

---
 Sendy_Team4_Final.ipynb | 5702 ++++++++++++++++-----------------------
 1 file changed, 2270 insertions(+), 3432 deletions(-)

diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
index 88a051c..81c9296 100644
--- a/Sendy_Team4_Final.ipynb
+++ b/Sendy_Team4_Final.ipynb
@@ -50,12 +50,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: lightgbm in c:\\users\\jen\\anaconda3\\lib\\site-packages (3.0.0)\n",
-      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.5.2)\n",
-      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (0.23.2)\n",
-      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from lightgbm) (1.19.2)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
-      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jen\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n"
+      "Requirement already satisfied: lightgbm in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (3.0.0)\n",
+      "Requirement already satisfied: numpy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (1.18.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (0.22.1)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)\n"
      ]
     }
    ],
@@ -72,9 +71,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: xgboost in c:\\users\\jen\\anaconda3\\lib\\site-packages (1.2.1)\n",
-      "Requirement already satisfied: scipy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
-      "Requirement already satisfied: numpy in c:\\users\\jen\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n"
+      "Requirement already satisfied: xgboost in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (1.2.1)\n",
+      "Requirement already satisfied: numpy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from xgboost) (1.18.1)\n",
+      "Requirement already satisfied: scipy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from xgboost) (1.4.1)\n"
      ]
     }
    ],
@@ -84,7 +83,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,6 +103,8 @@
     "#Date library\n",
     "import datetime as dt\n",
     "\n",
+    "\n",
+    "\n",
     "#Data visualization\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
@@ -118,7 +126,7 @@
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "#import xgboost as xgb\n",
-    "import lightgbm as lgbm\n",
+    "import lightgbm as lgb\n",
     "\n",
     "sns.set(style='white', context='notebook', palette='deep')"
    ]
@@ -132,26 +140,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(21201, 29) (7068, 25) (960, 5) (35, 2)\n"
-     ]
-    }
-   ],
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
-    "\n",
-    "train_df = pd.read_csv(\"Train.csv\")\n",
-    "test_df = pd.read_csv(\"Test.csv\")\n",
-    "rider_df = pd.read_csv(\"Riders.csv\")\n",
-    "variable_df = pd.read_csv('VariableDefinitions.csv')\n",
-    "\n",
-    "print(train_df.shape, test_df.shape, rider_df.shape, variable_df.shape)"
+    "train_df = pd.read_csv('Train.csv') \n",
+    "test_df = pd.read_csv('Test.csv') \n",
+    "rider_df = pd.read_csv('Riders.csv') \n",
+    "variable_df = pd.read_csv('VariableDefinitions.csv')"
    ]
   },
   {
@@ -170,9 +169,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": true
+    "scrolled": true
    },
    "outputs": [
     {
@@ -398,7 +397,7 @@
        "[5 rows x 29 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,10 +408,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 159,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -446,7 +443,6 @@
        "      <th>Confirmation - Day of Month</th>\n",
        "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
        "      <th>...</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
        "      <th>Pickup - Time</th>\n",
        "      <th>Distance (KM)</th>\n",
        "      <th>Temperature</th>\n",
@@ -456,6 +452,7 @@
        "      <th>Destination Lat</th>\n",
        "      <th>Destination Long</th>\n",
        "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -472,7 +469,6 @@
        "      <td>27</td>\n",
        "      <td>3</td>\n",
        "      <td>...</td>\n",
-       "      <td>3</td>\n",
        "      <td>5:06:47 PM</td>\n",
        "      <td>8</td>\n",
        "      <td>NaN</td>\n",
@@ -482,6 +478,7 @@
        "      <td>-1.305249</td>\n",
        "      <td>36.822390</td>\n",
        "      <td>Rider_Id_192</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -496,7 +493,6 @@
        "      <td>17</td>\n",
        "      <td>5</td>\n",
        "      <td>...</td>\n",
-       "      <td>5</td>\n",
        "      <td>1:25:37 PM</td>\n",
        "      <td>5</td>\n",
        "      <td>NaN</td>\n",
@@ -506,6 +502,7 @@
        "      <td>-1.277007</td>\n",
        "      <td>36.823907</td>\n",
        "      <td>Rider_Id_868</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -520,7 +517,6 @@
        "      <td>27</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
-       "      <td>4</td>\n",
        "      <td>11:57:54 AM</td>\n",
        "      <td>5</td>\n",
        "      <td>22.8</td>\n",
@@ -530,6 +526,7 @@
        "      <td>-1.276574</td>\n",
        "      <td>36.851365</td>\n",
        "      <td>Rider_Id_26</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -544,7 +541,6 @@
        "      <td>17</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
        "      <td>2:16:52 PM</td>\n",
        "      <td>5</td>\n",
        "      <td>24.5</td>\n",
@@ -554,6 +550,7 @@
        "      <td>-1.303382</td>\n",
        "      <td>36.790658</td>\n",
        "      <td>Rider_Id_685</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -568,7 +565,6 @@
        "      <td>11</td>\n",
        "      <td>2</td>\n",
        "      <td>...</td>\n",
-       "      <td>2</td>\n",
        "      <td>11:56:04 AM</td>\n",
        "      <td>6</td>\n",
        "      <td>24.4</td>\n",
@@ -578,10 +574,11 @@
        "      <td>-1.266467</td>\n",
        "      <td>36.792161</td>\n",
        "      <td>Rider_Id_858</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
+       "<p>5 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -606,31 +603,31 @@
        "3                             1       1:51:35 PM                           17   \n",
        "4                             2      11:30:28 AM                           11   \n",
        "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                3  ...                         3   \n",
-       "1                                5  ...                         5   \n",
-       "2                                4  ...                         4   \n",
-       "3                                1  ...                         1   \n",
-       "4                                2  ...                         2   \n",
+       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Time  Distance (KM)  \\\n",
+       "0                                3  ...    5:06:47 PM              8   \n",
+       "1                                5  ...    1:25:37 PM              5   \n",
+       "2                                4  ...   11:57:54 AM              5   \n",
+       "3                                1  ...    2:16:52 PM              5   \n",
+       "4                                2  ...   11:56:04 AM              6   \n",
        "\n",
-       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
-       "0     5:06:47 PM              8         NaN                           NaN   \n",
-       "1     1:25:37 PM              5         NaN                           NaN   \n",
-       "2    11:57:54 AM              5        22.8                           NaN   \n",
-       "3     2:16:52 PM              5        24.5                           NaN   \n",
-       "4    11:56:04 AM              6        24.4                           NaN   \n",
+       "   Temperature Precipitation in millimeters  Pickup Lat  Pickup Long  \\\n",
+       "0          NaN                          NaN   -1.333275    36.870815   \n",
+       "1          NaN                          NaN   -1.272639    36.794723   \n",
+       "2         22.8                          NaN   -1.290894    36.822971   \n",
+       "3         24.5                          NaN   -1.290503    36.809646   \n",
+       "4         24.4                          NaN   -1.281081    36.814423   \n",
        "\n",
-       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
-       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
-       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
-       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
-       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
-       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
+       "  Destination Lat  Destination Long      Rider Id  Time from Pickup to Arrival  \n",
+       "0       -1.305249         36.822390  Rider_Id_192                          NaN  \n",
+       "1       -1.277007         36.823907  Rider_Id_868                          NaN  \n",
+       "2       -1.276574         36.851365   Rider_Id_26                          NaN  \n",
+       "3       -1.303382         36.790658  Rider_Id_685                          NaN  \n",
+       "4       -1.266467         36.792161  Rider_Id_858                          NaN  \n",
        "\n",
-       "[5 rows x 25 columns]"
+       "[5 rows x 26 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -641,10 +638,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train: (21201, 32) \n",
+      "  test: (7068, 26) \n",
+      " riders: (960, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'train: {train_df.shape} \\n  test: {test_df.shape} \\n riders: {rider_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 21 201, 7 068 and 960 data entries in the train, test and riders dataset respectevily.\n",
+    "Also, there are 29, 25 and 5 columns in the train, test and riders dataset respectevily. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+       "       'Personal or Business', 'Placement - Day of Month',\n",
+       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+       "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+       "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+       "       'Rider Id', 'Time from Pickup to Arrival', 'Pickup_Hour',\n",
+       "       'Pickup_Minutes', 'Arrival_Destination_Hour',\n",
+       "       'Arrival_Destination_Minutes', 'Time from Pickup to Arrival_Hour',\n",
+       "       'Time from Pickup to Arrival_Minutes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
+       "       'Personal or Business', 'Placement - Day of Month',\n",
+       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
+       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
+       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
+       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
+       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
+       "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
+       "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
+       "       'Rider Id'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arrival at Destination - Day of Month\n",
+      "Arrival at Destination - Weekday (Mo = 1)\n",
+      "Arrival at Destination - Time\n",
+      "Time from Pickup to Arrival\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in train_df.columns:\n",
+    "    if col not in test_df.columns:\n",
+    "        print(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are four data columns in the train dataset that are not in the test dataset \n",
+    "\n",
+    "* Arrival at Destination - Day of Month\n",
+    "* Arrival at Destination - Weekday (Mo = 1)\n",
+    "* Arrival at Destination - Time\n",
+    "* Time from Pickup to Arrival"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -695,10 +814,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -745,10 +862,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                         0\n",
+       "User Id                                          0\n",
+       "Vehicle Type                                     0\n",
+       "Platform Type                                    0\n",
+       "Personal or Business                             0\n",
+       "Placement - Day of Month                         0\n",
+       "Placement - Weekday (Mo = 1)                     0\n",
+       "Placement - Time                                 0\n",
+       "Confirmation - Day of Month                      0\n",
+       "Confirmation - Weekday (Mo = 1)                  0\n",
+       "Confirmation - Time                              0\n",
+       "Arrival at Pickup - Day of Month                 0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)             0\n",
+       "Arrival at Pickup - Time                         0\n",
+       "Pickup - Day of Month                            0\n",
+       "Pickup - Weekday (Mo = 1)                        0\n",
+       "Pickup - Time                                    0\n",
+       "Arrival at Destination - Day of Month            0\n",
+       "Arrival at Destination - Weekday (Mo = 1)        0\n",
+       "Arrival at Destination - Time                    0\n",
+       "Distance (KM)                                    0\n",
+       "Temperature                                   4366\n",
+       "Precipitation in millimeters                 20649\n",
+       "Pickup Lat                                       0\n",
+       "Pickup Long                                      0\n",
+       "Destination Lat                                  0\n",
+       "Destination Long                                 0\n",
+       "Rider Id                                         0\n",
+       "Time from Pickup to Arrival                      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order No                                   0\n",
+       "User Id                                    0\n",
+       "Vehicle Type                               0\n",
+       "Platform Type                              0\n",
+       "Personal or Business                       0\n",
+       "Placement - Day of Month                   0\n",
+       "Placement - Weekday (Mo = 1)               0\n",
+       "Placement - Time                           0\n",
+       "Confirmation - Day of Month                0\n",
+       "Confirmation - Weekday (Mo = 1)            0\n",
+       "Confirmation - Time                        0\n",
+       "Arrival at Pickup - Day of Month           0\n",
+       "Arrival at Pickup - Weekday (Mo = 1)       0\n",
+       "Arrival at Pickup - Time                   0\n",
+       "Pickup - Day of Month                      0\n",
+       "Pickup - Weekday (Mo = 1)                  0\n",
+       "Pickup - Time                              0\n",
+       "Distance (KM)                              0\n",
+       "Temperature                             1437\n",
+       "Precipitation in millimeters            6869\n",
+       "Pickup Lat                                 0\n",
+       "Pickup Long                                0\n",
+       "Destination Lat                            0\n",
+       "Destination Long                           0\n",
+       "Rider Id                                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing_values(df, threshold=0):\n",
+    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
+    "\n",
+    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
+    "             index=['% of Missing values']).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -771,52 +993,180 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
+       "      <th>% of Missing values</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>16835.000000</td>\n",
-       "      <td>552.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.205934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>mean</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.973963</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.205934\n",
+       "Precipitation in millimeters             0.973963"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values(train_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The train dataset has \"Temperature\" and \"Precipitation in millimeters\" data columns with 20.59 % and 97.4 % of data missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% of Missing values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Temperature</th>\n",
+       "      <td>0.203311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <td>0.971845</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              % of Missing values\n",
+       "Temperature                              0.203311\n",
+       "Precipitation in millimeters             0.971845"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values(test_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The test dataset has \"Temperature\" and \"Precipitation in millimeters\" data columns with 20.33 % and 97.18% of data missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>16835.000000</td>\n",
+       "      <td>552.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "      <td>21201.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
        "      <td>2.752182</td>\n",
        "      <td>15.653696</td>\n",
        "      <td>3.240083</td>\n",
@@ -1055,7 +1405,7 @@
        "max          -1.030225         37.016779                  7883.000000  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1066,10 +1416,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1329,7 +1677,7 @@
        "max          -1.030225         37.018958  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1342,420 +1690,181 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Check which cols are in train and not in test data"
+    "# Visualization"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 93,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arrival at Destination - Day of Month\n",
-      "Arrival at Destination - Weekday (Mo = 1)\n",
-      "Arrival at Destination - Time\n",
-      "Time from Pickup to Arrival\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7f93c3908>"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "for col in train_df.columns:\n",
-    "    if col not in test_df.columns:\n",
-    "        print(col)"
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(train_df.isnull(), cmap=cmap)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Match the number of train columns to the number of test columns and also separate the target variable"
+    "Visuals from train dataset depicting data columns( \"Temperature\" and \" precipitation in millimetrs\") with missing values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 92,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7f909f208>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# save the columns from the test dataframe into a new list called testcols\n",
-    "testcols = test_df.columns\n",
-    "\n",
-    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
-    "train1 = train_df[testcols]\n",
-    "\n",
-    "# Seperate the target variable from the predictor variable\n",
-    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
+    "sns.heatmap(test_df.isnull(), cmap=cmap)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
-    "\n",
-    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
+    "Visuals from test dataset depicting data columns( \"Temperature\" and \" precipitation in millimetrs\") with missing values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-      "       'Personal or Business', 'Placement - Day of Month',\n",
-      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-      "       'Rider Id'],\n",
-      "      dtype='object')\n",
-      "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-      "       'Personal or Business', 'Placement - Day of Month',\n",
-      "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-      "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-      "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-      "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-      "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-      "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-      "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-      "       'Rider Id'],\n",
-      "      dtype='object')\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# print the new columns of the train and test data to show that they are the same\n",
-    "print(train1.columns)\n",
-    "print(test_df.columns)"
+    "#Distribution review for train data\n",
+    "train_df[[column for column in train_df.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Missing values"
+    "Deductions from the train dataset data column distribution.\n",
+    "\n",
+    "* The most common temperature is 25 degrees Celsius.\n",
+    "* Platform 3 is the most used platform type\n",
+    "* Most of the orders delievered are from Business clients.\n",
+    "* Most orders are dealt with during the weekdays than on the weekend.\n",
+    "* Most of the trips covered between 8KM and 15KM."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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LY8bIJ9tZoU0czXE/UBVcYITcLcpQ90Orhzdm5oGj7K/xmhmUYvCJEfFsSqHoBEpPtu9QXYSr2vIvUYrBe1NuZNtqrDx1LF8FTqqKF8+nXEBufOd0fX4q5sDSyGxfJ9i+ZuaiiLidku8+2LjoRvnNsB2lI8W7q2UzgXOzulFgVaB+KvB/jJwfN3yE0nnjWODt1bLWeB+sXvtySse34yht+61UF/Sy3HT8Zkphe29Kp7ZWbXPgyrnVthZV/242k+Xb/Bm05MFZ7kGzBZPDHHiC7Nk8PPahDDt5amaun5nrU4ZtrE6Zt6gTp1N6St2cmXdVy15CGe52YfV4G8qHfSRbUhqIj1CSyl3hkTvGPkiZ76ibJKzxhbVJ9Xh3yrBvgH9mGcb8ZuCUKDeO+jOwcpSh8FDmPW62U/VlB6Wxv6x1h1luntUYgjGeQvOalPN4YjUE4yWUaS/OoMzJvBvVOcwyjPBkyrk/P9vP1fp14M1V4QvKXEXfzXHevbfqtbwqZS6is1qevh7YqCqCUZ3X51Pm8BzNg7QpckhDxva1z+1r1VPkV5Q56JqH4l1O6dndfFOUbwCHRcTKVZJ9GvDRqnfLDynF20Yb/YPq+EY6tquAF1cFfoD9m/bzEspw6nMpPexexNI2/R/VOTuT0jOnnQT+ZYTnzqHMT/fYlh4dUL4L3hIRK1XH9zZKMX5KVO+l9ap4paGVZS75zwNnRsQaANX/T6ZMb3YfS9uXGRHxKEpRYso+fyMYMZ+kTV5WFXZ+SFXEjTIv6hvpIO7MPLWpjR5PoXkmZaqLP1QX255PKaIcTyk0v5Jlv+dOouS1K2Tmj9ps8hrgJRHxjGr7L6T0wLthnPH/kzKU/Bzgssx8sOm5iZ4fc2BpnGxfRzbO9vVK4AMsnwPvC9yTS6dq+wbw+lg6deahlPaz8dxy+XHT9n5EuRC3Z0S8mDICckZEvKw6tldQ5p2GkvNeluV+MTfSvk3/BGXUc7v7tPwGeEpErNLmufMo93tpHYkNJQd+XTUSpjHN6B9pP1JwsjydpvtcafwsNg+Pt1Amtn/kqks1BO0zlBsbdeJsypWg5jvDvg+4JCJ+Trkh0ncowx9GchVlcvikzIu2LqU4sgFlfrUfAb+Y4HBeskzQvg9wdjUM5D9ouRFSZn6bclOQM6tCxXuAKyPixyzbU5cq1nMj4peUYRj/NpG4gM9XQzTmUgq1Pwb+X/XcJylDTW4GvkcZStN8Ds+mJM7/PcK2z6D0gP5RFeezWTrP83idSxkG0jz/J5n5B0qj/tnqb3w+Zb7WXy2/iWVcS0n+P9thHFKd2L4ORvt6JeUYr21adgVlTr+vNS37MGVo4U8pN7ybwdIbi+wNPKc61zcAF2SZ936kY/t5dWzXRMSNLNsD5Bjgk1Wb/lVKL/Pmv9vnKMOoz6G9qykX+dZs89xXquNq7dEB5SLDPZRh2r+k/PB55wj7mAxbUuYl/d2Ya0r191ZKu9EYgnxD9bhRAHgH5XP98+q/BP6zxzGOlk+OlJftQ7nw93PKd8SXWb7jQbf+vcqBf0ppf9dl6TyXFwBPrL4HbqH0OHx8lDmayXKfkv9jhItzmXkL5W/z5YiYRxm1sVv1/TNe51CGxJ/V5rmJnJ9bgPsj4kfYi1kaD9vXibuSMtd0c7H5Rsr9Xx7JgTPzKspNAL9ZHcPewKurjm2j5ceN1/+B8nc6k9Kp5pUsnX7u1ZTOFVDa6h2rY/4JpXj89KqITRXn6ozcpv+Zcn5f0Oa531Py29sy808tz32TMkLm2oj4BWUE4K5NvccnVZQp7Z5Dm44yGtuMJUtaRyRJ00uUO2W/JjPHOwx9quJ4HbBfLp0bVZJqbVDa116regO/F1gvM98yynrvowyJHNgbSUXEWcCXMvOKsdaVpImIchOpbwPRmOZIklRPEbEtpWPN7KrQ3W6d5wLvz8wRb4reb9XvmE0y891jravl2bNZGgAR8W3K0Ji3j7GqJGnw/RZ4BWUY+Wg+CbywmrJj4ETEHGCJhWZJUyUijqFMafR2C82SVG8RcTZlhOABIxWaATLzOiAj4qU9C64D1ZzWewNH9TmU2rJnsyRJkiRJkiSpa/ZsliRJkiRJkiR1bcV+BzCW6k6kW1FuevTQGKtLUt3MBNYGflzdLX3g2S5LGmK2yZI0OGyTJWmwjKtdHvhiM6Wh/l6/g5CkKbY98P1+BzFOtsuShp1tsiQNDttkSRoso7bLXRWbqxs6vAZYApyRmcdHxM7A8cCqwIWZeWS17haUO1KuAXwXODQzHxzHbhYAfP7zn2ettZbeP2fevHnMnj27m/D7ps6xQ73jN/b+qXP8Uxn7Pffcwz777ANVW1cTbdvl8fB90B91jh3qHb+x989E4p/MNrlHefIjsZorD4Y6xw71jt/Y+2eq4jdPrpc6x2/s/VPn+Kdj7ONtlydcbI6IHYAXApsBKwG3RMQ1wJnADsBdwBURsUtmXgmcBxyYmT+MiDOAg4BTxrGrhwDWWmstZs2a9cjChQsXLvO4TuocO9Q7fmPvnzrH36PY6zTMrm27PB6+D/qjzrFDveM39v7pMv6u2uQe5smPxGquPBjqHDvUO35j758exG+eXAN1jt/Y+6fO8U/z2Edtlyd8g8DM/A7wgqrXxZMphes1gdsy8/Zq+XnAnhGxHrBqZv6wevlZwJ4T3bckSZI0qMyTJUmSNF11NY1GZi6OiKOBdwFfAp7Ksl2pFwCzRlk+bvPmzWPhwoXLLJs7d+4Eoh4MdY4d6h2/sfdPneOfqtgXLVo0JduVJPVXL/NkMFceJHWOHeodv7H3z1TEb54sSfXU9Q0CM/NDEfFx4DJgQ8q8dA0zgIcpPajbLR+32bNnL9PFe+7cucyZM2eiYfdVnWOHesdv7P1T5/inMvb58+dPyXYlSf3XqzwZzJUHRZ1jh3rHb+z9M1XxmydLUj1NeBqNiNioupkJmfkP4MvAjsDaTautBdwNzB9huSRJkjRUzJMlSZI0XU242Aw8AzgtIh4VESsDuwP/DUREbBARM4G9gSsz807g/oh4XvXafYEruwlcxQOL28/JPdJySZLqZqONN1lumd9zGnDmyQPCXFmSNMza5cng95z6a8LTaGTm1yJia+CnlLsQXpyZX4iIRcDFwCrA14CLqpfsQ0m61wB+Anymq8gFwMorzWS3wy9dbvllx+3eh2gkSZp8qz16leW+6/ye0yAzTx4c5sqSpGHWLk8Gv+fUX93eIPAo4KiWZdcAm7dZ92fA1t3sT5IkSaoD82RJkiRNR91MoyFJkqQ2GkMXW2+Y5JBGSZIkTXfmysOtq57NkiRJWp5D9yVJkqT2zJWHmz2bNfC8sYskSZLUXruc2DxZkiT1iz2bh9QDix9i5ZVmjnv5IPOKlyRJkiZTu5y4jnkytM+VzZMlSVK/WGweUhZoJWlyNIoP7eYTq2NRQpJkgVaSJou5sqRWFpslSRqFF+8kSZKk9syVJbWy2KxJMUzTdkh1EBFrANcBu2bmHRFxMPAOYAlwI3BIZj4QER8C3gz8X/XS0zLzpIjYAjgdWAP4LnBoZj441XFvtPEmyy2znZAkDTtzZam36pgrt8uTwXZCUv1YbNak8Gqm1DsRsQ1wGrBh9XhD4N3AHOBvwFnA24ATgC2B12Xm9S2bOQ84MDN/GBFnAAcBp0x17Ks9ehWHLUuSph1zZal36port8uTwXZCUv1YbJak+jmIkiCfWz3+J/DWzPwrQET8HFi3em5L4H0RsR6lV8a7gKcAq2bmD6t1zgKOpgfFZkmSJGmKmStLUh9ZbJakmsnMAwEiovH4TuDOatmTgMOA/SNideCnlJ4cv6Ykyh8ALgcWNG1yATCr0zjmzZvHwoULO3pN641DGubOndvp7ntmpJhhsOPeaONNWO3RqwBLj+Hef9zPrb/8RT/DmhDfN/1Tp1jb6TT+RYsWTVEkkqReGYRceTLzZBjs7+M6xt2cJ0O9c+U6nn+ob9yt6hRrq4nEPt5c2WJzD7TOsdT4UDn3kqTJFBHrAFcCZ2Tmt6vFL2t6/jjgTOBrlPnqGmYAD3e6v9mzZzNrVsc16rZGSzYG2aDH3W7KkkGPuRN1PZa6xD137tzaxNrOROKfP3/+FEWj0ZgrS+qFXubKk5knQ31yh1aDHPdIU5YMcsydquux1CXuOufKE419vLmyxeYecI42SVMtIjYCvgF8JjOPq5atC+ycmWdWq80AFgPzgbWbXr4WcHcPw5Uk6RHmypKmmrmyJPXOCv0OQJLUnYh4DHAVcGQjea7cBxwbEU+PiBmUuesuqYYS3h8Rz6vW25fSy0OSJEkaKubKktRbFpvH8MDihzpaLmkwTLPP7oGUG5kcHhE3Vf8dk5mLgEOAy4Ck9NZoJNj7ACdExK3A6sBn+hC3JKnmptn3rTQ02n1Gh/hza64sST3kNBpj6HRYn3PLSYNhOgzJzcz1q3+eUP3Xbp2LgYvbLP8ZsPWUBSdJmhY6+b41T5YGR7vP7jDlyWCuLEn9YrF5kk2HL21JkiSpU9PhQrAkSdJ05zQa6otpNmxLkiRJGhenJpEkSXVmz2b1hT3AJUmSpOXZA1ySJNWZPZslSZIkSZIkSV2z2CxJkiRJkiRJ6prFZkmSJEmSJElS1yw2S1PAG7sMLm9OKUmS1F/myoPJv4skaTJ4g0BpCnhjl8HlzSklSZL6y1x5MPl3kSRNBns2S5IkSZIkSZK6ZrFZ00Jj6NecOXPaLh8GDnubfNPhfSNJktQu5xm2fMep1CbfdHjfSJI65zQamhYGfUjYA4sfYuWVZo65bDSDfox15DmVJEnTwSBPMzZSTjwZufKgHGNdeU4lSe1YbJYGgImaJEmStDwv/kuSVC9OozGAnA5BsPzfuzE8zfeBJEmazpwOQWCuLEnSoLJn8wDy6v34TMbUE4PM94EkSdLyHBE2tsmaemKQmStLkjSYLDartvyhUS8bbbzJcsuG6QdPr0XEGsB1wK6ZeUdE7AwcD6wKXJiZR1brbQGcDqwBfBc4NDMfjIh1gfOAJwMJ7JOZf+/DoUiSpElmIbZe2uXJYK48UebJktRfTqMhjZPTm3RntUevwm6HX7rMf50mzw6bLSJiG+D7wIbV41WBM4HdgY2BrSJil2r184DDMnNDYAZwULX8ZODkzNwIuBH4QO+OQJIkDRvztIlrlyd3miv7W6UwT5ak/rNnszo20pX3YTcdeogM+pBLe7M/4iDgbcC51eOtgdsy83aAiDgP2DMibgFWzcwfVuudBRwdEacDzwde2bT8O8B7exK9JElDarrmyTA98rRBnsZvOvxWGSfzZEnqM4vN6ljjynuzaZjEDKVOktRBL0wPs8w8ECAiGoueCixoWmUBMGuU5U8E/pqZD7Ys78i8efNYuHBhR69p3Lyn1dy5czvdfc+MFDPUM+5BjnkkdTyWur5vWtUp1nY6jX/RokVTFImmi3Z5MpgrD4tOCurmyv0xjHkyDPb3cR3jrmPMI6nrsdQ17lZ1irXVRGIfb65ssVmahiYjybX3xEBZAVjS9HgG8HAHy6mWd2T27NnMmtVx7t3WaMnGIKtj3HWMeSR1PZa6xD137tzaxNrOROKfP3/+FEUjqU7MlYdK7fNkqE/u0KqOcdcx5pHU9VjqEnedc+WJxj7eXLmrYnNEfAh4bfXwisx8T6eT73ezf00Or7pPP4OS/PremzTzgbWbHq8F3D3K8v8FHhsRMzPzoWqdu3sUqyRNC+bJw8N8ZfoxVx4q5smS1GMTLjZXyfKLgWdRrvx9PSJeD3wc2AG4C7giInbJzCspk+8fmJk/jIgzKHMpndLtAah7g5JMafrxvTdpbgAiIjYAbgf2Bs7MzDsj4v6IeF5m/gDYF7gyMxdHxPeAvYDzgTcCV/YreEkaNubJw8V8Rf3ie29SmCdLUo+t0MVrFwCHZ+YDmbkY+CXljq+3ZebtVW+MxuT767H85Pt7drHvrninXknDJDPvB/YHLgZuAW4FLqqe3gc4ISJuBVYHPlMtfytwcHVzlO2BI3sZsyQNudrmydA+JzZPllRH5smS1HsT7tmcmb9o/Dsi/pUyTPCzdDb5/ri1m2B/ohNxz5kzZ8QrxK3b7HTS8k7nPOl0G53ENxnb6PUxDtew8bYAACAASURBVMI5Hclk/V0G4Rh933S/jcm6EUA3N6PKzPWb/n0NsHmbdX5GuQt36/I7gR0nvHNJ0oh6nSfD1OfK7fLkxrojmYr8Y7TtDEI+MB1yrJFM1t/F983E9lnX981ozJMlqZ66vkFgRGwCXAG8G3iQ0mujYazJ98etdYL9qZqIu5NtTsb+O91Gr/c57Pvrxz49xvrvb7L2Cd6MSpKGWa/yZOhNrmz+0f99Dvv++rFPj3Ew9wfmyZJUV91Mo0FEPA+4BjgiM8+m88n3pYHjMFFJktQt82QNK3NlSZI0mm5uEPg04CvAXpl5bbW4o8n3u4xdmhLeiEOSJHXDPFnDzFxZkiSNpptpNN4FrAIcHxGNZaeydPL9VYCvsezk+6dFxBrAT1g6+b4kSZI0TMyTJUmSNC11c4PAdwLvHOHpcU++L0mSJA0T82RJkiRNV13N2SxJkiRJkiRJElhsliRJkiRJkiRNAovNkiRJkiRJkqSuWWyWJEmSJEmSJHXNYrMkSZIkSZIkqWsWmyVJkiRJkiRJXbPYLEmSJEmSJEnqmsVmSZIkSZIkSVLXhrrY/MDih/odgiRJkjSQzJUlSZI02VbsdwBTaeWVZrLb4Zcut/yy43bvQzSSNHUi4kDgsKZFTwfOBVYDtgPurZYfnZmXRMTOwPHAqsCFmXlkL+OVJPWfubKk6cJcWZJ6Z6iLzZI0XWTm6cDpABGxCfAV4CjgW8DzM3NBY92IWBU4E9gBuAu4IiJ2ycwrex23JEmSNNXMlSWpdyw2S9LwOQV4H/APYF3gzIhYB7gEOBrYGrgtM28HiIjzgD0BE2hJkiQNO3NlSZpCFpslaYhUQ/5WzcwvRcQzgGuBtwJ/AS4HDgD+DixoetkCYFan+5o3bx4LFy7s6DVz5sxpu3zu3Lmd7r5nRooZ6hn3IMc8kjoeS13fN63qFGs7nca/aNGiKYpEkjQIepUrT2aeDIP9fVzHuOsY80jqeix1jbtVnWJtNZHYx5srW2yWpOFyCGV+OTLzt8CrGk9ExGeBNwIXAUuaXjMDeLjTHc2ePZtZszquUbc1WrIxyOoYdx1jHkldj6Uucc+dO7c2sbYzkfjnz58/RdFIkgZET3LlycyToT65Q6s6xl3HmEdS12OpS9x1zpUnGvt4c+UVOt6yJGkgRcTKlLnlvlo93jQi9mhaZQawGJgPrN20fC3g7l7FKUmSJPWaubIk9YY9myVpeGwG/CozG3fTngF8KiKupQwHPBg4G7gBiIjYALgd2JtyExRJkiRpWJkrS1IP2LNZkobHMyg9MQDIzJuBjwI/AG4BbsrMCzLzfmB/4OJq+a2U4YKSJEnSsDJXlqQesGezJA2JzPwi8MWWZScDJ7dZ9xpg8x6FJkmSJPWVubIk9YY9myVJkiRJkiRJXbPYLEmSJEmSJEnqmsVmSZIkSZIkSVLXLDZLkiRJkiRJkrpmsVmSJEmSJEmS1DWLzZLUoQcWPzSuZZIkSdJ0MlJObK4sSdPHiv0OQJLqZuWVZrLb4Zcus+yy43bvUzSSJEnSYGiXJ4O5siRNJ/ZsliRJkiRJkiR1zWKzJEmSJEmSJKlrFpslSZIkSZIkSV2z2CxJkiRJkiRJ6prFZkmSJEmSJElS1yw2S5IkSZIkSZK6ZrFZkiRJkiRJktS1FfsdgCRpckTEt4AnA4urRYcA/wIcCawEfCozT6rW3Rk4HlgVuDAzj+x9xJIkSVJvmCtLUm9YbJakIRARM4ANgfUy88Fq2TrAF4A5wD+B66ok+3bgTGAH4C7giojYJTOv7EvwkiRJ0hQyV5ak3um62BwRawDXAbtm5h0jXQGMiC2A04E1gO8ChzYaeUlS16L6/1UR8QTgNOBvwLWZ+SeAiLgIeA3wHeC2zLy9Wn4esCdgAi1Jk8g8WZIGhrmyJPVIV8XmiNiG0khvWD1elZGvAJ4HHJiZP4yIM4CDgFO62b8k6RGPA64B3k4ZBvht4EJgQdM6C4Ctgae2WT6r0x3OmzePhQsXdvSaOXPmtF0+d+7cTnffMyPFDPWMe5BjHkkdj6Wu75tWdYq1nU7jX7Ro0aTt2zxZkgZKT3PlycyTYbC/j+sYdx1jHkldj6WucbeqU6ytJhL7eHPlbns2HwS8DTi3erw1ba4ARsQtwKqZ+cNqvbOAozGJlqRJkZnXA9c3HlfFiuOBjzStNgN4mHJz2CVtlndk9uzZzJrVcY26rdGSjUFWx7jrGPNI6nosdYl77ty5tYm1nYnEP3/+/MkMwTxZkgZEr3PlycyToT65Q6s6xl3HmEdS12OpS9x1zpUnGvt4c+Wuis2ZeSBARGNEyohXAKfkyuBYVfiJnLjWbXZ6taXTfXa6jU7im4xt9PoYPacT21+n+xy2Y+zHOR3vNsYyWb3oImI74FGZeU21aAZwB7B202prAXcD80dYLkmaJL3Mk6E3ufIg5B+jbccca2L7G20bw3JOR9vOsBzjIL1vOtnGaCZ5tIm5siT1yGTfIHCkK4CTfmVwqq4gdLLNydh/p9vo9T6HfX/92KfHWP/9TeY2JrEX3ZrAMRHxXMrQwP2ANwDnRcSTgHuBPYCDgZuBiIgNKDdA2ZsytFuSNHWmLE+G3uTK5h/93+ew768f+/QYB3cbkzzaxFxZknpkhUne3khXAL0yKElTKDMvB64AfgrMBc7MzB8A7we+BdwEnJ+ZP8rM+4H9gYuBW4BbgYv6EbckTSPmyZLUJ+bKktQ7k92z+QbaXAHMzDsj4v6IeF7VoO+Ld3KVpEmVmR8APtCy7Hzg/DbrXgNs3qPQJEnmyZLUV+bKktQbk9qzeYwrgPsAJ0TErcDqwGcmc9+SJEnqzgOLH3rk343hzs3LNHHmyZIkSfXVmhObK49sUno2Z+b6Tf9uewUwM39GuQu3JEmSBtDKK81kt8MvXWbZZcft3qdohoN5siRJUv21y5PBXLmdyZ6zWZIkSZIkSZI0DVlsliRJkiRJkiR1zWKzJEmSJEmSJKlrFpslSZIkSZIkSV2z2CxJkiRJkiRJ6prFZkmSJEmSJElS1yw2S5IkSZIkSZK6ZrFZkiRJkiRJktQ1i82SJEmSJEmSpK5ZbJYkSZIkSZIkdc1isyRJkiRJkiSpayv2OwBJ0uSIiA8Br60eXpGZ74mIzwHbAfdWy4/OzEsiYmfgeGBV4MLMPLL3EUuSJEm9Ya4sSb1hsVmShkCVEL8YeBawBPh6RLwK2BJ4fmYuaFp3VeBMYAfgLuCKiNglM6/sfeSSJEnS1DJXlqTesdgsScNhAXB4Zj4AEBG/BNat/jszItYBLgGOBrYGbsvM26t1zwP2BEygJUmSNIzMlSWpRyw2S9IQyMxfNP4dEf9KGSK4PbAj8FbgL8DlwAHA3ykJd8MCYFavYpUkSZJ6yVxZknrHYrMkDZGI2AS4Anh3ZibwqqbnPgu8EbiIMnywYQbwcKf7mjdvHgsXLuzoNXPmzGm7fO7cuZ3uvmdGihnqGfcgxzySOh6L75vB0GncixYtmqJIJEmDoFe58mTmyTDY38N1jLuOMY+krsdSx7jrGPNoJhLzeHNli82SNCQi4nnAxcC/ZeYXImJTYMPMvLhaZQawGJgPrN300rWAuzvd3+zZs5k1a3I6eYz2xT3I6hh3HWMeSV2PpY5x1zHmuXPndhz3/PnzpygaSVK/9TJXnsw8Ger5PQz1jLuOMY+krsdSx7jrFvNE8mQYf65ssVmShkBEPA34CrBXZl5bLZ4BfCoirqUMBzwYOBu4obwkNgBuB/am3ARFkiRJGjrmypLUOxabJWk4vAtYBTg+IhrLTgU+CvwAWAm4ODMvAIiI/Sk9O1YBvkYZLihJkiQNI3NlSeoRi82SNAQy853AO0d4+uQ2618DbD6lQUmSJEkDwFxZknpnhX4HIEmSJEmSJEmqP4vNkiRJkiRJkqSuWWyWJEmSJEmSJHXNYrMkSZIkSZIkqWsWmyVJkiRJkiRJXbPYLEmSJEmSJEnqmsVmSZIkSZIkSVLXLDZLkiRJkiRJkrpmsVmSJEmSJEmS1DWLzZIkSZIkSZKkrllsliRJkiRJkiR1zWKzJEmSauuBxQ+1Xb7Rxpv0OBJJkiRpsLTLlac6T15xSrcuSZIkTaGVV5rJbodfutzyy47bvQ/RSJIkSYOjXa481XmyPZslSZIkSZIkSV3rac/miNgbOBJYCfhUZp7Uy/1LkpayTZakwWK7LEmDwzZZkiamZz2bI2Id4D+B7YAtgIMj4pm92r8kaSnbZEkaLLbLkjQ4bJMlaeJ62bN5Z+DazPwTQERcBLwGOGaM180EuOeee5ZZuGjRIubPnz/mThf/40/LLZs/f/6IyzvZxnj32en+Ot3nsBzjRM7HZGyjm/1NZBvjjc/3zfi3Md51R9rnVG9jNE1t28wJbWDiJtomwwjt8nhN1rnrpU7fp4Oijud6JHU8Ft83vTNZ57qPbTIMeK48SPlHu+2YRw5PjtXN/jrd57Ad46C8b2yTJy9Phun1PdxLdYx5JHU9ljrGXceYYfLy+/G2yzOWLFkyoR10KiL+H7BaZh5ZPT4Q2DozDx7jddsB3+tBiJLUT9tn5vd7tbOJtsnVurbLkoZdT9tkMFeWpFHYJkvSYBm1Xe5lz+YVgObK9gzg4XG87sfA9sAC4KEpiEuS+mkmsDalreulibbJYLssaXj1q00Gc2VJamWbLEmDZVztci+LzfMpjW7DWsDdY70oM/8J9PQqpiT12G/6sM8Jtclguyxp6PWjTQZzZUlqxzZZkgbLmO1yL4vNVwNHRcSTgHuBPYAxh2tLkqaEbbIkDRbbZUkaHLbJkjRBK/RqR5n5e+D9wLeAm4DzM/NHvdq/JGkp22RJGiy2y5I0OGyTJWnienaDQEmSJEmSJEnS8OpZz2ZJkiRJkiRJ0vCy2CxJkiRJkiRJ6prFZkmSJEmSJElS1yw2S5IkSZIkSZK6ZrFZkiRJkiRJktS1FfsdQKciYm/gSGAl4FOZeVKfQ+pIRHwLeDKwuFp0SGbe0MeQxhQRawDXAbtm5h0RsTNwPLAqcGFmHtnXAEfRJvbPAdsB91arHJ2Zl/QtwBFExIeA11YPr8jM99TsvLeLvy7n/hjgNcAS4IzMPL5O536QtX4e+xzOuLV7P/cznk61e0/3OaSORcQngSdm5v79jqUTdfzOb4iI3YAPAasBV2XmO/sc0rhExIHAYU2Lng6cm5mHjfCSoVPnXLmOn5k658lgrtwv5spqx1y598yT+6eO3/kNdc2ToXe58owlS5ZM5vamVESsA3wfmAP8k9IQvz4zb+lrYOMUETOA+cB6mflgv+MZj4jYBjgN2AjYEFgIJLADcBdwBeWHzJV9C3IErbFXCfTPgRdn5oL+RjeyKlk7GngB5Uvv68DpwMepx3lvF/+JwDEM/rnfAfhPYEfKj/RbgFcCl1GDcz/I2n0e+xvR+Iz0fh7EH3/tjPCefmlmZj/j6kRE7AR8gfLjZf8+hzNudfzOb4iIZwDfA7ahfO9fC/xX3dq9iNgE+AqwbWb+od/x9EKdc+U6fmbqnCeDuXK/mCurHXPl3jNP7p86fuc3DEueDFObK9dtGo2dgWsz80+ZeS9wEeUqVF1E9f+rIuJnEVGHXjYHAW8D7q4ebw3clpm3V43CecCe/QpuDMvEHhGPBtYFzoyImyPi6IgYxM/AAuDwzHwgMxcDv6T8gKnLeW8X/7rU4Nxn5neAF1Tn+MmU0R9rUp9zP8ha25K6GOn9XAsjvKfvHf1VgyMiHk/5EfBf/Y5lAur4nd/wKkrPtPnV+34voBY9TVqcArxvuhSaK3XOlev4malzngzmyv1irqx2zJV7zDy5r+r4nd8wLHkyTGGuXLdpNJ5KacwaFlCSurp4HHAN8HbKlbNvR0Rm5jf7G9bIMvNAgIhGW9D2bzCrx2GNS5vY16JcdXor8BfgcuAAyhXkgZGZv2j8OyL+lTIk6bPU57y3i397yhXjgT73AJm5OCKOBt4FfIkavecHWZvPYy2M8H5+Xv8i6lyb9/Tv+xxSJ/4beD/wtH4HMgG1+85vsgHwQER8lfKD8XLgA/0NqTNVT6tVM/NL/Y6lx+qcK9fuM1PnPBnMlfvFXFntmCv3h3ly39TuO79J7fNkmPpceeCulo5hBcrQjIYZwMN9iqVjmXl9Zr4xM/9SXTk4A3hZv+PqUG3/Bpn528x8VWYuyMx/UJLSgT3/1ZCGbwLvBn5Lzc57c/xZ1ObcZ+aHgCdRvrg3pGbnXpOv5f18W7/j6VTLe/qgPoczLtV8Yndl5jX9jmUiav6dvyKlh+wBwLaUYYL79TWizh1CmT90uqlznlbnz0xDbc8/mCv3mrmyhkmdc2Xz5N6r+Xf+MOTJMMW5ct2KzfOBtZser0WNhplExHbVnDoNM1g6GXpd1PZvEBGbRsQeTYsG9vxHxPMoV/qOyMyzqdl5b42/Luc+IjaKiC0AqkT/y5ReJrU595p8bT6PtTHCe3qz/kY1bnsBL46ImyjzWL4iIk7oc0zjVvPv/HuAqzNzUWbeB1xCfXrHEhErU+YO/Wq/Y+mDWuULzWr+mWmo7fkHc+VeMlfWMKlrrmye3D81/86vdZ4MvcmV6zaNxtXAURHxJMpcOnsAB/c3pI6sCRwTEc+lDBXYDzi0vyF17AYgImID4HZgb+DM/oY0bjOAT0XEtcDfKe+dgfsyjIinUSZp3yszr60W1+a8jxB/Lc498Azg6IjYjtJDY3fK8KRP1OHca/KN8H6uk3bv6Vq8fzPzRY1/R8T+wI6Z+e/9i6hjdf7Ovxw4OyLWBP4G7EL5HNTFZsCvqjmLp5s658p1/sw01CZfG0Et8jVz5b4yV9Yyap4rmyf3T52/8+ueJ0MPcuVa9WzOzN9T5qT5FnATcH5m/qi/UY1fZl5OuTvvT4G5wJmZeX1/o+pMZt4P7A9cTLlb662Um88MvMy8Gfgo8ANK7Ddl5gX9jaqtdwGrAMdHxE3V1cr9qc95bxf/c6nBuc/Mr7HsZ/S6zPwC9Tn3mnzLvZ8joi6J0GjvaU2xOn/nZ+YNwLHA9ynt3p3A5/oaVGeeQenlOO3UOVeu82emoc55Mpgr95C5soZJbXNl8+T+qfN3/hDkydCDXHnGkiVLxl5LkiRJkiRJkqRR1KpnsyRJkiRJkiRpMFlsliRJkiRJkiR1zWKzJEmSJEmSJKlrFpslSZIkSZIkSV2z2CxJkiRJkiRJ6prFZkmSJEmSJElS1yw2S5IkSZIkSZK6ZrFZkiRJkiRJktQ1i82SJEmSJEmSpK5ZbJYkSZIkSZIkdc1isyRJkiRJkiSpaxabJUmSJEmSJEldW7HfAWj6iYj1gd8AP68WrQD8HfhUZn6xi+0eCKycmSdHxKHAmpn5sQluayvggMw8NCK2BI7IzNdMNLaWbd8BvCYzb+zgNS8HtsnMD05GDJKmL9vguIMO22BJ6ofxtNcRcQzw68w8Z5TtfBD4WWZeOrURjy4iPg5cA/wKmJeZqzc9txdwErAX5ZhvB76bmTu0bOMsYD/gScA/gS8Cr87M+3pxDJLqoU37CTAD+HRmnhkRrwB2zsx3jLKNbwMnZuZFUxlrm/0eBbwN+H21aCXgZuCDmXnbFO97JvBlYGPgM5l5YktcHwLenJmfa1q+GrAQ+HZm7jrB/T4WuCQzX1g9XgI8KTP/MNFjUX9ZbFa/3JeZWzQeRMR6wDUR8VBmXjzBbW4HzAPIzFO7jG8TYFa1rRuBSSlydGEr4PF9jkHS8LANlqR6GLW9HmdHhBcCt0xZhOMQEc8BNs7M91ZFoObnDgE+QCn83FQ9f395KtbLzDur9VYDntd4XWb+LSIuAD4MvKs3RyKpRlrbz3WAeRFxY2Z+Ffhq/0Ib04WZeVjjQUTsC1wbEZtk5l+ncL/rAC8BVsvMh9o8/ztgX+BzTcv2oFwI7cbjgK273IYGiMVmDYTMvLPqdfFu4OKIWBn4OLADMBP4KfCOzPxrRLwFOBR4gJKIHgIE8ArgRRFxH6W3wxMz87CqF9tZwE7AusA5mfmBiFgBOAF4DvAYypXOAykN6DHAYyPic8DZlCuas6srbicBWwBLgCuB92XmgxFxP/Ax4MXA2sCxmXnKeM9BlUCfAvwr8ATgb8DewJrV8c6MiL9k5vvHf2YlaWy2wUVEfAB4PfAgpefdYZl5T9Wz5XpKkWNd4Grg4Mx8OCL2B44A7gOuBd6ZmeZXkqZEm/b6LEov4U9GxNHAqyjt8x+B/YFXA1sCn4iIh4BfUNrRx1DaypuAvTLz/tHa0Yj4f5QexQ8CtwH7Z+ZfIuIA4K2UXtd/pLSbt7YJ/SjgxNaFEXFEFed2mXlH01MPARcC+wD/VS17NXApcHjTel8EPh4Rn8jMhWOdP0nTV2b+PiJuAzaMiGdTRrrtGhFrAacCGwEPA6dm5mcar4uIFYHzgcWUdvBqmno8N/eAjogHKe3oLsBqlDz1y5MQ+7lVwXlv4NSIeDMlB1+Z0intY5l5SkR8E/hiZp5WxXYk8ITM/Pfm7UXE9sAngEdTvjOOBH4AfJ3Sk3puROyRmb9pCeXrwCsjYlZmzq+W7QecRzl/TDBf/xywakTcBMyptnt0daHyCcAnMvOkbs6hess5mzVIfgZsWv37CEoyOyczNwfuBj5WDev4FPDSzNwK+B9KcnoJ5crkCSM0Qqtn5vbAc4F3RcTTgW2ApwLbZuYzKQWNIzLzLuCDwPcy800t2/kMJZHelJK4b87SnhSPAv6Qmc+l9MI7ISJW6eD4dwH+nJnbZuaGwI8pCfsNlC+/Cy00S5pC07oNjog3UdrhrTJzM0ov7bOaVvkXYEdgs2q9HSLimZSi/M6Z+Szgr5TivCRNpeb2GoCIeBrwb5Q2bEvgKsoUbCcBNwLvrtrqg4CzM/M5wAbA04GXV5tp245Ww833p7TXsylTXBwWETtQigzbV23gscAlrcFGxJrA9lVMzcuPBT5KGap9R5vjPIfSg65hP5Ztl8nM+yk588vanilJqkTEtpR274aWp04GfpWZGwHbAgdHxAbVcysDXwL+F3hDZj44xm5mAv/IzDnAa4EzI+JJk3QIPwM2jYjVKW35y6q2dy9K+wulyHsQQNWx4wBKLeEREfEE4CJKB4nNWFosfiKlLb0vM7doU2iGUnD/IuVCIBGxLuXi5bymdSaSr7+pab+NHtW/rc7jq4DjImKlTk6W+stiswbJEuAf1b93BXYHflpd3Xol8Myq4fkScF1EnAj8GThjHNu+FMrVTMoXxeMz83rKFbxDIuKTlMZu9ZE3AZQCw4mZuSQz/0lpuHdp3Q/wE0pDuto4YqOK7SLgrIh4e0R8mlLUGCseSZos07oNrrbzucy8t3r8aWCnqpc3wGWZ+XA1dPHXlF4kLwGuaurZ8dkO9idJE9XcXjf8nlKI+EnVpt6UmV9p89r3Aosi4j2UEXVPZdm2t107ujPwpcz8P4DM/I/M/E9KkXoDynfCTZRix+MionXqtw2ABZn5QNOy1SiFiJdRLmY+qzXQzJwLPBQRc6pi+mMyc17repTid7RZLml6WzUibqr+m0e5uLVP1bGh2c6UDhRk5l8yc3Zm/rp67jjgRcCHM3PJOPd7YrWtmylzRj+/2wOpLKEUsv9OydVfHhEfBt7P0nb8MuApEbE5JU+9PTOzZTvbUOb6v6GK8xeUXs07jjOOc4A3VP/et3rcbLLy9fOr/99UrbfGOOPTALDYrEGyFUsn8J9JudK2RZZ5lrammrMzM98A7Eb5sX8EcME4tt1805AlwIwoN927olp2KaURnDHGdlaoXt/8uPkK231VjI11xtreI6qh6WdQfjycTzmucb9ekro0rdtgyjG3bnvFpm0sdwyU3t/N+2g3t50kTbbm9hqAzHyYMvXR/pQeZSdUPYdbXQAcDNxJmcroJyzbjrVrRx+kqX2MiDWreZVnAuc2fVc8m9KL7f9a9rmE5X933ge8IjOvpBSAvtymSA1wLqWosW/173YWY/sraXmNnrJbVAXkHas2p1VrG/eMiGgUNs+lXJg7rWn9Rh7YsDLLau79vAIt7VNEvKKpCP61Do5nK+DnETGLUoBdD/g+pfMGAFXHkP8G3lz91+4+Kq05byPOcfUczswfU6b43ILSq/r8llUmK19fPM71NIAsNmsgRMSGlBuDHFct+gZleN7K1fCP04CPRsQTI+Iu4I+Z+SlKw7pV9ZoHGWcDWXkRpafaKZThha9k6fDnkbbViGtGRDyKkqx/s4N9juYlwFmZeQaQlGLOWPFIUtdsg4EyB92bq/nzAd4BfLfqkTGSbwA7R7nhDJQ5pyVpyrRprxvLN6cMY/5lZn6UUkhu1z6/BDgmMy+sHm/D2NP/XA28uqn4chTwH5Q28PURsXa1/FDgmjav/w2lp13z1EYPZ+bi6t8fo9zA8ILqO6fZecCetC9oNDwdaDdPtCSNx9WUaRwa8w1fQ7mPEsCPKG3uBhFxULVsEeXCGtWUapu1bO+N1XPPpsxj/J3mJzPzq/+fvTsPk6wqDz/+HUaWEUQloIADRIO8ECYy2oIaFFFxQcEdiYwsKiAxKCbuAURwRRQVE5YIOCgajOCCwBg2FURBaQQckFejiLQMw6A/F0Sc9ffHuQVFU91T3bVXfz/Pw8PUqVv3vvdU1bm33zpLXRK8qSmAqvnxn0CZwuKpVQwfpExPtFe1Ta0tP50y9cQIDaY2oqxDsn1E7FK9bkdK7+vvNBNL5QuU68zPMvN3456bzv36SkoC24TykHABG/VKbfJ3KJPw3we8NzNrvdw+AHycsijVbMovd2/PsjjVBymrcP+F0ijVGv1FwIkRTY+iO5VyU/sTynfhYuBV1U3u1cAxEfFVypxDNW+lDJP+CeUXzG8BH5rSmRdXRMTqusfvopzvf1UXklmUi0BtPr7LgS9FxGcy8y3TOJ4k1bMNfmgbfCqwFfDDKob/o5qPbiKZ+bOI+Ffg/6a20gAAIABJREFUf6sFT67noUPbJakVa2uvAcjMGyLif4BrI+IeSs+xt1ZPn0/5wXA94N+Br0XEn4E/UJIg2zKJzLyoSqhcVbXxNwGHZOafIuJ44JKqTf0j8MrxQ80z8/cRcSXwHMq1Yvz+10TEAZRrzgephrNXz/0mIn4K/KFBQoPqnJ5OmZdUkqbjcOCUiLiR0iHzI5k5WrunzbKA6kHAxRFxOaWdOqsapXcLcMW4/e0aEYdW+9q3NgXRFO0bEc/kgZEhCexexXIxpddyUq4L36Ukn7ct4eZdEXEt5cfHFeN3nJl3R8Q+wGci4uHVPl5f3df+bZPxnU2ph5c1eG469+tLKIn9m6IsXqgBN2vNmmannZEkSVJNlIUOD6DM47c6Il4JvDszn9bj0CSpr0TEPwJHZuZL1rrx1PZ7ELBjZr6znfuVpOmIiDXAZpl5dw9j2JSycOpuDeanlrrCaTQkSZKmZ4yyuNZPIuIG4G2UniaSpDqZ+X0gI+JF7dpnRGwE7EeZ1kOSZrxqqo+fAieYaFYv2bNZkiRJkiRJktQyezZLkiRJkiRJklrW9wsEVqtX7kyZMHxVj8ORpHabDWwB/Cgz/9rKjiLidcB7q4eLMvMdETGfsiLxxpTFKw7LzJURsTVlYYfHUBaXWJCZ9zR5HNtlScOqbW1yt9gmSxpitsmS1F+aapf7PtlMaaiv7HUQktRhzwK+N90XVysJnwRsB/yesmL8HsCngIMz8+qIOAM4BDgFOBk4OTPPiYijgaOBdzd5ONtlScOupTa5y2yTJQ27trTJEXEc8GpgDXBGZp5Y3S+fCMwBvpyZR1XbNuyw0cRhbJMlzQSTtsuDkGxeAvDFL36RzTffvOsHX7x4MfPmzev6cSdiPJPrt3ig/2Iynsl1O54777yTBQsWQNXWtWA2ZWqkDYE/A+sCK4A5mXl1tc1C4NiIOB3YDXh5Xfl3aT7Z3LF2ud8+D9CfMUF/xmVMzevHuPoxJuhuXG1sk7tprW1yv763nTYTz3smnjN43sOqnW1yRDwbeC7wJMp98s0RcRlwJvBs4HbgwojYMzMXUUYANuqwsTYN2+RBfq8GOXYY7PiNvXcGOf5Oxt5suzwIyeZVAJtvvjlz587t+sGXLl3ak+NOxHgm12/xQP/FZDyT62E8LQ2zy8w/VT2UbwHupSSPl/Pgi8ASYC6wKfDHut4ZtfIpxXr33Xcza9asVsJ+iM0224ylS5e2dZ+t6seYoD/jMqbm9WNc/RgTdDeuu+++u/bPQRr6vNZ75X671nbLTDzvmXjO4HnPAC23yZn53Yh4TjWd3OMouZBHAT/PzFsBIuJsYJ+IuJkGHTZoLtncsE0e5PdqkGOHwY7f2HtnkOPvUuyTtsuDkGyWJK1FRDwJeAOwDfAHSm+MF1CGCdbMAlZTekCvGbeL1VM95rx589p+ERsdHWVkZKSt+2xVP8YE/RmXMTWvH+Pqx5igu3GNjY115TiSpO7LzBURcSzwDuArwJY07pgxUXnTFi9e/JAfSkdHR6cRdX8Y5NhhsOM39t4Z5Pg7FfuyZcua2s5ksyQNhxcCl2XmXQARsZByI71F3TabA3cAdwGPjIjZmbmq2uaO7oYrSZIkdVdmHhMRxwPfpKx10kzHjFp508Z3yujXH3SbMcixw2DHb+y9M8jxdzL2ZjtmrNORo0uSuu0GYI+I2DAiZgF7U6bSuC8idq222R9YlJkrKAuX7FuVHwAs6nbAkiRJUjdExPbVon9k5r3AV4HdadwxY2yCcklSE0w2q+8tX9F4KpiJyqWZKDMvBv4bGAVupCx88lFgAfDJiLgF2Ag4qXrJm4FDqznpngUc1fWgpXFs7yXVs02Q1EZPAD4bEetHxHrAy4DTgIiIbSNiNrAfpWPGbTTosNGTqIeM7bo0MziNhqZs+YpVrLfubID7u+bXl7XbeuvOZu+3f+Mh5d/8xMs6cjxpUGXm8cDx44pvAHZpsO1tlN4cUt+wvZdUzzZBUrtk5kURsQvwY8rCVudl5jkRsQw4D9gAuAg4t3rJAkpyemPgOh7osKEW2K5LM4PJZk1ZowuEFwdJkiRJUr/KzPcD7x9XdhmwU4NtG3bYkCStndNoSJIkSZIkSZJaZrJZkiRJkiRJktQyk82SJEmSJElSi1wEUXLOZkmSJEmSJKllLoIo2bNZkiRJkiRJPWJvYGm42LNZkiRJkiRJPWFvYGm42LNZkiRJkiRJktQyezZLkiRJbRYRewPHABsCF2fmERGxB3AiMAf4cmYeVW07Hzgd2Bi4AjgsM1f2JvL2WL5iFeutO3utZZIkddtE1yOvU1J7mGyWJElNm+zmXFIREU8ATgWeBiwFLo+IPYHTgGcDtwMXRsSembkIOBs4ODOvjogzgEOAU3oTfXs0GhLtcGhJUj9w2g6ps0w2S5KkpnlzLjXlFZSey2MAEbEv8ETg55l5a1V2NrBPRNwMzMnMq6vXLgSOZcCTzZIkSZqZWko2R8TrgPdWDxdl5jsmGgYYEVtTem08BkhgQWbe08rxJUmSpD60LbA8Is4HtgYuAG4CltRtswSYC2w5QfmULF68mKVLl074/Ojo6FR32ZKRkZGW45hoH1PZT7fPux/MxHMGz3sYLVu2rNchzEjjR7HV2mKnmJDUrGknmyPi4cBJwHbA74GrqnnoPkXjYYAnAydn5jkRcTRwNPDuVk9AkiRJ6jMPA3YDdgfuAc4H/gKsqdtmFrCasmB3o/IpmTdvHnPnNs5Rj46OTpq47ZblK1Y1jOOvK1ax/hQTGM2cT7+cdzfNxHMGz3tYjY2N9TqEGclRbJJa1UrP5tmUm+MNgT8D6wIraDAMMCJOp9xwv7yu/LuYbJYkSdLwuRO4NDOXAUTE14B9gPrJzTcH7gDGgC0alA+dyRIYJjYkSZKGw7STzZn5p6qH8i3AvZTk8XIaDwPcFPhj3araUx4euLahgZ3Ub0OTeh1PO4ZFtuN4Ex2z1/XTSL/FZDyT62Y8Dg/UsGs05NJhmJoBLgDOiohHAX8C9gTOBd4TEdsCtwL7AWdm5m0RcV9E7JqZVwH7A4t6FbgkaXBNtpCz915S/xq2724r02g8CXgDsA3wB8p8zC+gueGBMMXhgZMNDeykfhua1G/x1OtFXOOP2Y/1028xGc/kuh2PwwM17Br1ZLS3ooZdZl4TER8DvkcZ/XcJZVq5W4DzgA2AiygJaIAFwGcjYmPgOspUdZIkTclUp8AY1ESWNGyGbfqaVqbReCFwWWbeBRARC4F30HgY4F3AIyNidmauqrYZyuGBkiRJUmaeCZw5rvgyYKcG294A7NKNuCRJqrFTgKROWKeF194A7BERG0bELGBvylQa90XErtU2+wOLMnMFcCWwb1V+AA4PlCRJkiRJUh9bvmJVU2WSilbmbL44Ip4MjFIWBvwh8FHgazQeBvhmytx1RwG/Bl7bSuCSJEmSJElSJ9kDXJqaVqbRIDOPB44fV9xwGGBm3gbs3srxJEmSJEmSJEn9qZVpNCRJkiRJkiRJAkw2S5IkSZIkSZLawGSz2mKiyfGdNF+SZoblK1YxMjLS6zAkdZn3epIkDTbzOf1rUBenbGnOZqmm0YT54KT5UjdFxN7AMcCGwMWZeURE7AGcCMwBvpyZR1XbzgdOBzYGrgAOy8yVvYlcw8DrgDQz+d2XJGmweS3vX4O6OKU9myVpCETEE4BTgZcDTwKeEhF7AmcCLwN2AHauygDOBg7PzO2AWcAh3Y9akiRJkiQNE5PNkjQcXkHpuTyWmSuAfYF7gZ9n5q1Vr+WzgX0iYhtgTmZeXb12IbBPL4KWJEmSJD2gNk3C+CnqBmH6hGY5dUf79dPnxmk0JGk4bAssj4jzga2BC4CbgCV12ywB5gJbTlAuSVLfW75iFeutO3utZZIkDaKZMK3FTDjHbuunOjXZrIHV6I+K7XfYsUfRSD33MGA3YHfgHuB84C/AmrptZgGrKaNaGpVPyeLFi1m6dOk0w53Y6Oho2/fZqn6MCXoTV7sWAWwU+2T7buVcff+a148xQffiWrZsWVeOo9YM6vyFkiQNA3/01dqYbNbA8g8N6UHuBC7NzGUAEfE1ytQY9WNmNgfuAMaALRqUT8m8efOYO7e9HaJHR0fblsxsl36MCfo3rmZNNfbpnmu/1lM/xtWPMUF34xobG+vKcSRJGhYTJRlNPg4vczFaG5PNkjQcLgDOiohHAX8C9gTOBd4TEdsCtwL7AWdm5m0RcV9E7JqZVwH7A4t6FbgkSZKkwdRPQ/f72bD3Bh5/LrWOAsN0jmqeyWZJGgKZeU1EfAz4HrAucAlwCnALcB6wAXARJQENsAD4bERsDFwHnNT1oNU37JEiSZIkdc6w9wYeth8dGk3R6t9GzTPZLElDIjPPBM4cV3wZsFODbW8AdulGXOp/w3ZzKEmSJM1kdiZpzYYP32CofxzoNJPNkiRJkiRJ0pCwM0nvDfvUKZMx2SxJkiRJkqSh02g6BA2Hfu+9PexTp0zGZLMkSZIkSZKGjtMhDL6JksdT6b3d74npYWOyWR3lF1qSJEmS1GsRcQzwmurhhZn5rojYAzgRmAN8OTOPqradD5wObAxcARyWmSt7ELbGMccw87RjSpB27MPPXvNMNqujnCdIkiRJktRLVVL5BcCTgTXAtyLitcDxwLOB24ELI2LPzFwEnA0cnJlXR8QZwCHAKb2JXvXMMahX/Ow1b51eByBJkiRJktRBS4C3Z+byzFwB/BTYDvh5Zt5a9Vo+G9gnIrYB5mTm1dVrFwL79CJoKL0mp1IuSb3WUs/miNgbOAbYELg4M49wGIokSZIkSeoXmXlT7d8R8UTKdBqfoSSha5YAc4EtJyhv2uLFi1m6dOmDykZHR6cWdGVkZGTC3pTj9zkyMjLhfhodf7Lt27GPqcTXjn10+xz7oU4n0q73pR/O0c9N6/uYbvsz3rJly5rabtrJ5oh4AnAq8DRgKXB5ROwJnIbDUCRJkiRJUh+JiB2BC4F3AispvZtrZgGrKSPA1zQob9q8efOYO/eB/PTo6Oi0EmZrM5V9tuP4U91Ht4857MfrxTE9x8E/XruOCTA2NtbUdq1Mo/EKSs/lsWoYyr7AvQzAMBRJkiRJkjRzRMSuwGXAezLzLGAM2KJuk82BOyYpl/qO06moH7Uyjca2wPKIOB/YGrgAuIkuDkPplnZ1N2+XXsfTrl9E2jH8o5X9dlO/xWQ8k+tmPM0OQ5EkSZI0PRGxFfB1YN/MvLwqvqY8FdsCtwL7AWdm5m0RcV9E7JqZVwH7A4t6Eri0Fi5ap37USrL5YcBuwO7APcD5wF9oPNyk7cNQuqVTw12mq9/iaUWnzqPf6qff3jPjmVy342l2GIo0TJavWMV6687udRiSJGnmeAewAXBiRNTKTgUOAs6rnrsIOLd6bgHw2YjYGLgOOKmbwUrSIGsl2XwncGlmLgOIiK9Rpsao78PvMBRJkvQg9sCQJEndlJlHAEdM8PRODba/Adilo0FJ0pBqZc7mC4AXRsSjImI2sCflV8CIiG2rsv2ARZl5G3BfNUcSOAxFDTjXkCRJkiRJkjS4pt2zOTOviYiPAd8D1gUuAU4BbsFhKJoGe7pJkiRJkiRJg6uVaTTIzDOBM8cVX4bDUCRJkiQi4uPAppl5UETMB04HNgauAA7LzJURsTVwNvAYIIEFmXlPz4KWJEmSpqmVaTQkSZIkTSAingccWFd0NnB4Zm5HWTD7kKr8ZODkzNweuBY4uquBSpIkSW1islmSJElqs4jYBPgQ8OHq8TbAnMy8utpkIbBPRKwL7MYDU88tpCy6LUmSJA2clqbRkCRJktTQacCRwFbV4y2BJXXPLwHmApsCf8zMlePKp2Tx4sUsXbp0wudHR0enusumjIyMdGS/09HoHDt13v1sJp4zeN7DaNmyZb0OQZI0DSab1RPLV6xivXVn9zoMSdIAaHTN8DqifhYRBwO3Z+ZlEXFQVbwOsKZus1nA6gblVOVTMm/ePObObZyjHh0d7aukcKeMP8eZct71ZuI5g+c9rMbGxnodgiRpGkw2qyfWW3c2e7/9Gw8q++YnXtajaCRJ/cxrhgbQvsAWEXE9sAmwESWhvEXdNpsDdwB3AY+MiNmZuara5o4uxytJkiS1hXM2S5IkSW2Umc/PzHmZOR94H3B+Zr4euC8idq022x9YlJkrgCspCWqAA4BFXQ9aktRTy1es6nUIktQW9myWJEmSumMB8NmI2Bi4DjipKn8zcFZEHAX8Gnhtj+IbWI2m1hkZGXHKHUkDo9FILnA0l6TBY7JZkiRJ6pDMXAgsrP59A7BLg21uA3bvZlzDxiSNJElSf3AaDUmSJEmSJElSy+zZLElDJiI+DmyamQdFxHzgdGBj4ArgsMxcGRFbA2cDjwESWJCZ9/QsaEmSJEmSNPDs2SxJQyQingccWFd0NnB4Zm4HzAIOqcpPBk7OzO2Ba4GjuxqoBoIL1UiSJEmSpsKezZI0JCJiE+BDwIeBnSJiG2BOZl5dbbIQODYiTgd2A15eV/5d4N1dDVh9r9EcqM5/Ks1MLrQnSZKkZphslqThcRpwJLBV9XhLYEnd80uAucCmwB8zc+W48ilZvHgxS5cunX60ExgdHW37PlvVjzFB++IaGRlpy366rdnzH/b3r536MSboXlzLli3rynEG0aAuwDdRktzkuSRJUmeYbJakIRARBwO3Z+ZlEXFQVbwOsKZus1nA6gblVOVTMm/ePObOnXKOelKjo6N9l/jsx5igf+PqpmbOv1/rqR/j6seYoLtxjY2NdeU46p5BTZJLkiQNKpPNkjQc9gW2iIjrgU2AjSgJ5S3qttkcuAO4C3hkRMzOzFXVNnd0OV5JkiRJkjRkXCBQkoZAZj4/M+dl5nzgfcD5mfl64L6I2LXabH9gUWauAK6kJKgBDgAWdT1oSZIkSZI0VEw2S9JwWwB8MiJuofR2PqkqfzNwaETcDDwLOKpH8UmSJEmSpCHhNBqSNGQycyGwsPr3DcAuDba5Ddi9m3FJkiRJkqThZs9mSZIkSZIkSVLLWu7ZHBEfBzbNzIMiYj5wOrAxcAVwWGaujIitgbOBxwAJLMjMe1o9tiRJkiRJkiSpP7TUszkingccWFd0NnB4Zm4HzAIOqcpPBk7OzO2Ba4GjWzmuJEmSJPXS8hWrmiqTJEnqlV7cr0y7Z3NEbAJ8CPgwsFNEbAPMycyrq00WAsdGxOnAbsDL68q/C7x7useWJEmSpF5ab93Z7P32bzyo7JufeFmPopEkSXqoXtyvtDKNxmnAkcBW1eMtgSV1zy8B5gKbAn/MzJXjyqdk8eLFLF26dPrRtmB0dLQnx51Ir+MZGRnp6fHXptf100i/xWQ8k+tmPMuWLevasSRJkiRJkjppWsnmiDgYuD0zL4uIg6ridYA1dZvNAlY3KKcqn5J58+Yxd+6Uc9QtGx0d7avkar/F04/6rX767T0znsl1O56xsbGuHUuSJEmSJKmTptuzeV9gi4i4HtgE2IiSUN6ibpvNgTuAu4BHRsTszFxVbXPH9EOWJEmSJEmSJPWbaS0QmJnPz8x5mTkfeB9wfma+HrgvInatNtsfWJSZK4ArKQlqgAOARS3GLUmSJEnT4uJ+kiRJndHKnM2NLAA+GxEbA9cBJ1XlbwbOioijgF8Dr23zcSVJkiSpKS7uJ0mS1BktJ5szcyGwsPr3DcAuDba5Ddi91WNJkiRJkiRJkvrTtKbRkCRJkiRJkiSpnslmTch56yRJkiRJkiQ1q91zNmuINJrLDpzPTpIkSWpk+YpVrLfu7KbLJUmSho3JZkmSJEn3MzE6fXbWkPpbRGwMfB/YKzN/FRF7ACcCc4AvZ+ZR1XbzgdOBjYErgMMyc2WPwpakgeI0GpIkSZLuV0uY1v83kzm1nDQcIuJpwPeA7arHc4AzgZcBOwA7R8Se1eZnA4dn5nbALOCQ7kcsSYPJns2SJEmSNAF7K0tD4xDgX4AvVI93AX6embcCRMTZwD4RcTMwJzOvrrZbCBwLnNLdcCVpMJlsliRphnBovCRJmqky82CAiKgVbQksqdtkCTB3kvKmLV68mKVLlz6obHR0dNLXjIyMTOUQDfc52T4aHX+qx5zqPqYSXzv20e1ztE6nd7ypHnPYzrEXddrsPtZm2bJlTW1nslmSpBnC3nmSJEn3WwdYU/d4FrB6kvKmzZs3j7lzH8hPj46OTithtjZT2Wc7jj/VfXT7mMN+vF4c03Mc/OO1cx9jY2NNbeeczZIkSZIkaaYZA7aoe7w5cMck5ZKkJtizWZKkIeSUGVJvRcQxwGuqhxdm5rsiYg/gRGAO8OXMPKradj5wOrAxcAVwWGau7EHYkjSTXANERGwL3ArsB5yZmbdFxH0RsWtmXgXsDyzqZaCSNEjs2SxJ0hCqTZlR/5+k7qiSyi8AngzMB0Yi4rXAmcDLgB2AnSNiz+olZwOHZ+Z2lOHah3Q/akmaWTLzPuAg4DzgZuAW4Nzq6QXAJyPiFmAj4KRexChJg8iezTNMo55u9n6TJElqqyXA2zNzOUBE/BTYDvh5Zt5alZ0N7BMRNwNzMvPq6rULgWOBU7oetSTNAJn5t3X/vgzYqcE2NwC7dDEsSRoaJptnmEaLQ7kwlCRJUvtk5k21f0fEEynTaXyGkoSuWQLMBbacoHxKFi9ezNKlSyd8fiorjndiEatB0Y7V3aey707o1nH6jec9fJYtW9brECRJ02CyWZIkSeqAiNgRuBB4J7CS0ru5ZhawmjKt3ZoG5VMyb9485s5tnKMeHR2d0QnkqehkPXXjPZip77XnPZzGxsZ6HYIkaRpMNkvSkHAxKknqHxGxK2Ue0Ldl5jkR8Wxgi7pNNgfuAMYmKJckSZIGjgsEStIQcDEqSeofEbEV8HVgv8w8pyq+pjwV20bEbGA/YFFm3gbcVyWnAfYHFnU9aHXU8hWrplQuSZI0qOzZLEnDwcWoJKl/vAPYADgxImplpwIHUXo7bwBcBJxbPbcA+GxEbAxcB5zUzWDVeY3WTQHXTpEkScPHZLNYvmIV6607u9dhSGpBPy5GNV39uNBNP8YEk8c1zHM41jT7vgzi+9cr/RgTdC+udi1GlZlHAEdM8PRODba/AdilLQeXJEmSeshks+xpIQ2RflmMarr6caGbfowJ+jeubmrm/Pu1nvoxrn6MCbobl4tRSZIkSa1pKdnsYlSS1D9cjEqSpME30ahDRyNKkqRBMO1k87jFqNYA36oWozoeeDZwO3BhROyZmYsoi1EdnJlXR8QZlMWonB9UktqgbjGqfTPz8qr4/sWogFspi1GdmZm3RcR9EbFrZl6Fi1FJktQTjRLIjjqUJEmDrJWezS5GJUn9w8WoJEkaMI0SyyaVJUnSIJt2srnbi1F1aiGqZvTbYjmtxNOPczG2W7+9X9B/MRnP5LoZj4tRSe01vpdg7brn8HNJkiRJ6ryWFwjs1mJUnViIqhn9tlhOv8XTj6ZSP42SD+1OSPTbe2Y8k+t2PC5GJbWXw88lSZIkqXdaXSDQxag00By6KGlQ2GO397rxA6UkSZIkDbJWFgh0MSpJkrrEHru95w+UkiRJkjS5Vno2uxiVJEmSJEmSJAlobYFAF6PqYw7rlSRJkiRJktRNLS8QqP7kcGtJkiRJkiRJ3bROrwOQJEmS1H3LV6zqdQiSJEkaMvZsliRJ6pLaNFcjIyMNy6VuciTcYJmonbD9kCRJ/cRksyRJUpeY3JM0XRO1H+d9dK/7/137IcsEtCRJ6hWTzZIkSZI0oBolof0BS5Ik9YpzNkuSNMBm6pyrnTzvdux7pr4vkiRJkmY2ezZLkjTAZuq0DJ0873bse6a+L5IkSZJmNns2S+NM1BvNXmqSNFzswSxJkiRJ7WXPZmkce6NJ0szQ7z2YJ1rgy4W/JK2N7YckSeoVk81Sk7xpl6TBNYhttT9+Spou2w9JktQrJps1I7QjyeBNu6R6/gA1WBq14bbfkiRJktReJps1VCZK8pgoltRutitqJ3+kkNRLjdog2yVJkjQdJpu7oF2935avWMXIyEhL+xh2Jn8kSYPInteSumEqHTNsgyRJ0nSYbO6CdiVAvQmUJEmSNF12zJAkSZ22Tq8DkCRJkiRJkiQNPpPNA275ilW9DkGShtr2O+zY6xAkSZIkSRoITqMxTe2ah3kq+27EoXCS1FkbPnyDjrWzE7X3f12xivWdj1+SJEmSNGBMNk9TJ5O8JpAHi6t3S6rX6Ps/UfJ4svbeOfolSf2ok51uJEnS4DPZ3EPekA2HTi3c6I28NJgmahP8EVHt4vVBUi9N9EPpeR/d6yFlE/3YOlG57ZgkSYOvq8nmiNgPOApYF/hUZv5nN4/fb+zBrMn4+VCn2SZLg2kqiZ5OJm5Meref7bIG2VR/bPU+V/3ONlmSpqdryeaIeBzwIWAE+Cvw/Yj4dmbe3K0YpsOFoTQd9X9oj4yMNCyfaPup7Hu6+5AGtU1uF6e/0TBqlOhplICGxr0KR0ZGGpZP9N3wR9H2muntsgRTm4qqvrz+ftte02oH22RJmr5u9mzeA7g8M38HEBHnAq8GjlvL62YD3HnnnVM+4IqVq1n3Yes8pHz5ytWs16B8ou1f9fZLHlJ2+pHPZ8W9v3tI+a2/+nXDfTTadmxsrOVy99Gf+7hr6RIO/tBDPzenvPt5DT8fwEO2n+gzNtV9j/+8P/axj+WXv/p1w+/ARN+NRuUTfV+m8r1bsXI1y5YtY2xsrKV9t1OjeDqprm3r9l8/022Toc3tcv1n47GPfez99T+Vz+NUtq0Z/z2a7PvZD+2K+xjefXRy3xNdM04/8vlNl0/nu/GQ7Vq4J6u1y+26r5tMD9tk6OC98mTXNr8vDy6byKDV06DWR6M2ayrt1WTljdqyqbYTjbbvxT5qun3f2m3D1CY3+15N9Xs7lX00e8ypHm+qxxyWc2xXGznVfbRyvOnso9nezJb1AAAgAElEQVT4/Nw0v49mt53omJ3ex2SabZdnrVmzZloHmKqIeC+wYWYeVT0+GNglMw9dy+ueCVzZhRAlqZeelZnf69bBptsmV9vaLksadl1tk8F7ZUmahG2yJPWXSdvlbvZsXgeoz2zPAlY38bofAc8ClgCrOhCXJPXSbGALSlvXTdNtk8F2WdLw6lWbDN4rS9J4tsmS1F+aape7mWweozS6NZsDd6ztRZn5V6Crv2JKUpf9ogfHnFabDLbLkoZeL9pk8F5ZkhqxTZak/rLWdrmbyeZLgfdHxGbAn4FXAWsdri1J6gjbZEnqL7bLktQ/bJMlaZo6u9JWncz8DXAk8G3geuBLmfnDbh1fkvQA22RJ6i+2y5LUP2yTJWn6urZAoCRJkiRJkiRpeHWtZ7MkSZIkSZIkaXiZbJYkSZIkSZIktcxksyRJkiRJkiSpZSabJUmSJEmSJEkte1ivA+gXEfEBYFVmvn+SbR5BWYn2jZn5nars7cAhlMT9ezLzq92IKSK2AL4AbAbcB7wpM6+PiHWB3wK/rNt8JDNX9SieWcAJwF7AauCQzLyq1ViajOdzwObVcd+RmZf3uH4axdOT+qnb5vmUz+3zqscdq58WYurVZ2g94AzgqcBfgP0y85ZO19GwiYjjgFcDa4AzMvPEiHgG8EngEcCNwIGZuXzc67YATge2BO4FFmTmryLi2cBXgdurTX+cma/vdEwR8Rjg4rrdPBLYLDM3iohHAV8EngAsA16TmXf2OKaW62m6cVWv+1vg88DGwO+rbW6b6HvV45i2ARYDv6g2XZqZL+xSTLsA/wmsD/waODgz72xHPXUorrbXFXAL8OG6TR4HXJOZe4173dbA2cBjgKS0Cfe04/s3zCJiP+AoYF3gU5n5nz0OqaMiYmPg+8Be1TVjD+BEYA7w5cw8qqcBdkBEHAO8pnp4YWa+a9jPe4K2bajPuV5EfBzYNDMPioj5lPuljYErgMMyc2VPA9SDDHo7HBHfplx7V1RFb8rMa3oY0loN8rWgQeyfA54J/Lna5NjM/FrPApzAoF+LJoh/UOq+L6+JM75nc0Q8MiLOAN7exOb/ATy67rU7A68D5lM+hCdExCZdiunDwLmZuRNwDHByVf4k4AeZOb/uv5aSYC3G8ypgB+DvgZcDCyOipR85moznBOCbmTkfeC3wpYiYTe/qZ6J4elI/EbFO9UPJOcDsuqfaXj9tiKlXn6G3An/OzB2AtwELq/KO1NEwqhKez6XU2VOBt0TETpQk6KGZuWO16RsbvPwLlO/Mk6t/H1+VPxX4eF3dTzXRPK2YMvOu2jGBpwC/Ag6tnv4gcGX1Wfks8Ok+iKmlemolrsoHgP+uYjsP+FBVPtH3qpcxPRX4Ul1dTTV5Oq2Yqh/SzgXelZlPoiTC/6t6uqV66mBcba8r4Bd1n+MXAX8E/rXBy08GTs7M7YFrgaOr8pa+f8MsIh5H+Zw/k3KvemhE/H1vo+qciHga8D1gu+rxHOBM4GWU+4idI2LP3kXYftUfky8Ankx5j0ci4rUM8XlP0rYN7TnXi4jnAQfWFZ0NHJ6Z2wGzKJ2g1CcGvR2u7gm2A3aqu/b3e6J5YK8F42OvPBXYra7++zHZOdDXognifwWDUfd9e02c8clmyhvwc+ATk20UEfsCf6L0Aqp5MfDVzLwvM+8CvkPpfdmNmN7IA3/8PR74f9W/dwY2i4hrI+Lq6sPXy3heApyTmasz82eUHlL/2IV4vgZ8qfr3/wEbABvRu/qZKJ5e1c8O1X/jb0g7UT+txtSrOnoJpbccmXkFpV62pnN1NHQy87vAc6oeNo+hjKaZT0nW19rSt1C+H/eLiE2BnYDTqqLPUXqEQKn/F0TEjRFxfkRs1Y2Yxnk9cG9m1r7T939WgP8G9ozSA76XMbVUT22IazallxXAhpTeuTDx96qXMe0MzIuI6yPi8oj4h2bjaTGmTYE5mfnt6vEFwIsiYn1arKcOxtWJuvpz3SYnAKdm5s/rX1d9n3ajJMGhJN/3qf7d0vdvyO0BXJ6Zv8vMP1Pq79U9jqmTDgH+BbijerwL8PPMvLX6zJ3NA5+bYbEEeHtmLs/MFcBPKUmKoT3vCdqRRzHE51xTdWr6ENVokCijTeZk5tXVJgsZwvMecIPeDkf1/4sj4oaIOLyn0TRnkK8FD4o9Ih4ObA2cWd3THxsR/ZjDG/RrUaP4t2YA6r6fr4l9V1ndlpmfz8yPAhP2TKz+wHsb8M5xT21J+WDWLAHmdiOmKvG2OiJuoQyHPal6ag3wdeAZwD8DX64SN72Kp+111GQ852VmLeH9DsoQ8j/Qu/qZKJ5e1c9NmXkw8LtxT7W9ftoQU0/qaJLjdqSOhlVmroiIY4GbgcsoU8ncExHnRMT1wLGUKQ3q/R3lR4VPRMSPKDfmtWH+vwc+U/W4vIjSE74bMQFQjUg4EnhPXfH9n5Xqgv5HypRCvYyp5XpqMa6jgX+LiN9QRhDUeqa3/H3uQEz3UW7CngJ8HPh6lGksOh3T3cCfI+IF1eN/ogyx/Rva1O51IK5O1NVvACLiicDuPHD/UG9T4I/5wNDw+vpo+fs3xDpyn9qvMvPgzLyyrmjoz7+6d7oa7v8OvYYy5diwn/f4dmTo3+vKaZTrfe1viply3oNs0N+jR1O+Y68AngccFmXKw741yNeCBrFvDlwOvAF4OvAsGo/e66lBvxZNEP+3GIC6h/69Js6YZHNE7BMRY+P+u7SJ161DmVPw8Mz8y7in16EknmpmUb5UHY2pXpbhpE8HvhARm2TmaZl5XGauyMwfA9cAu/YqHlqoo3bEExFvA94EHFDF19P6GR8PPa6f8Vqpn07FRO/qqOFxW62jmSgzj6Ekf7ai9Op/IfBeYITSw/Q9417yMMowpsszc2fgG8BZ1b4Oy2pu/Mw8FdgxIh7ZhZhqXkT5pfgndWWzxm0zpWtBJ2JqVz21ENdZlKkaHgccBnwtylDMlq6bnYgpM9+fmadUP5peBNxDGWXR0Zgycw1lmqB/j4gfU3oh/Jbyw0pb6qndcXWormqjWQ6lTJPx1wYvGV8f8EB9tOX7N6Ta9jkaUDPm/CNiR+ASSseYXzIDzntcO7IdQ37OEXEwcHtmXlZXPGM+4wNsoN+jzPxBZh6QmX/IzLspeZEX9zquKRrY9yAzf5mZr8jMJZl5L/AZ+rj+B/1aVB9/FgNT9/14TZwxCwRm5leAr0zjpdtX/50REQDbAqdHxCHAGLBF3babUxat6XRMRMRLgO9m5j1ZFuK7DXhCVf79zKwt3jOLBybz73o8NK6jOxrto53xVDF9jDK8drfMHKvK9qcH9TNRPPSwfiaIcdr106mY6F0d1Y5bq4vNgTtaraOZJCK2BzbIzOsz896I+CqlN+TFmXlrtc3/AOOH5N0J/CkzL6gefwk4qfrx773AR/PB82Q3vRBOCzHVvJyH9hL+DeXzMRZlPvFHUJJzPYmpHfXUSlwRsRmwfWZ+A8rIjog4ldI7teH3qpcxRcQ/UeYhrr1nU/pOt/j+rcjM3attHkPpff07WqynTsUVEW+h/XX1pOrpl1Pmy2vkLuCRETG7+kxvwQP10dL3b8iNUXri1Ez5czTgpn3/MEgiYlfKPPRvy8xzokzvNbTnPUE78moePFptqM65si+wRTUqZRPKdHxrGOL3ekgMdDscEc8E1q/7kWMQ/+4Z2GtBlOnKtsvM86qivq3/Qb8WNYh/IOq+n6+JM6Zn83Rl5s2ZuVU+sHjNtZRV2b8NLAJeFREPr/6QfR6l23o3HEi1EFSURQY2p6zovhPVomdRsuNPBq6cYB/diOciYEFEzI6IbSm/svyo08FUPYifA+xal9iFHtXPJPH0pH4m0avPz2R6VUcXUfVAr2607svMX9OfddSvngB8NiLWjzLU/mWUdmIkHphDeC9gtP5FVSJ/LB5YyGBvYDQzV1OG8b0KICIOAK7JMgdeR2Oq8wwe+n7f/1mh/DF4ZZb5vnoSU5vqqZW47gbui4hnVcfflfLjwTIm/l71MqZnUw2Lq26MZ1OuX52OCeBzURYbBvg34CvV+9dqPXUqrk7U1feiTEU0p5YEH6/6Pl1J+X5BqZtF1b9b/f4Ns0uB50XEZlHmfXwVZVjoTHEN5VK9bZTphvbjgc/NUKi+y18H9svM2o+Ow37ejdqR0xjucyYzn5+Z86q/R98HnJ9l8d/7qmsawP4M2XkPgUFvhx8FnBARG0TEIyh/8/fdImlrMcht4izgUxHx6CjrURxKH9b/oF+LJoh/IOqePr4mmmyeQEQcFhHHTbZNZv6QMnfhjyirhh6dmb/pUkxvA14YETdQFs96bWbeAxwHPCYiFlPmOT0gM//Uw3jOBW6iLKz4DeCN+dDpSNoaT5Sh2sdQJkj/TpSFjK6PiC3pQf2sJZ6u189aNuta/Uwhpl7V0WeA9SPiJsocovtX5V2to0FWDbO/EPgxJaH1/cz8AmUqmW9GmeN9E+AjABFxekS8tHr5K4F3V/V8BGW+LCg3uW+r3pfXAwd3MSZ4YMRGvaOBp1cxvZmysEevY2qpnlqJK8s0DK8EPh4RNwIfo0p8M/H3qpcxHQE8v/qsfZxy/Wp6qFmL798/A6dV2zyB6oesVuupg3F1oq7OofFneHxMbwYOjYibKb3EaouGtvT9G2bVPemRwLeB6ym90n/Y26i6JzPvAw6i9FS6mfLDyLmTvWYAvYMyPc6JtftLyjkfxJCe9yTtyEEM6TmvxQLgk1V7vRGN571Xjwx6O1yNMqz/vp2ZmT/obVRTM8jXgiwLOn8EuIoS+/WZ+d+9jaqhQb8WNYr/HxmAuu/na+KsNWvGT4EnSZIkSZIkSdLU2LNZkiRJkiRJktQyk82SJEmSJEmSpJaZbJYkSZIkSZIktcxksyRJkiRJkiSpZSabJUmSJEmSJEktM9ksSZIkSZIkSWqZyWZJkiRJkiRJUstMNkuSJEmSJEmSWmayWZIkSZIkSZLUMpPNkiRJkiRJkqSWmWyWJEmSJEmSJLXMZLMkSZIkSZIkqWUP63UA6r6I+FvgF8BP6opnAZ/OzDMj4qXAHpn51kn28R3gPzLz3E7GOu6YWwC3A4/JzN9VZR8B3gNsn5lZlb0LGMnMfad5nO/QxLlFxGLg8Mz8znSO02B//wysysz/iog1wG3A4zNzTd027weOAXbOzGvbcdwJYtkJ+FZmblE9ng18A3hDZt7VqeNKGgz9fh2JiF8Br55KOxkRLwGelpnva3c8ktQuw9j+SlKzmmgDD6K0QXv1ILxpi4j3ATdk5jem+LrvANsAf6iK1gUuBD6QmX9qa5APPfZWwCJgJfDPmfmDcXE9G/i7zPxlXfnuwLeBd2bmx6d53J2BN2bmYdX+/iMz5033PNQZJptnrr9k5vzag4h4HLA4Iq7NzPOB83sXWmOZuSQifgLsBny9Kt4L+CbwUuCEquy5wJe6H+H0RcQ2wEHA0+uKZwHPAq6otpkFvAb4fx2M42HAW4B3AxvVyjNzVUR8DDgZeHWnji9poAzcdWQtdgY26XUQktSEYWt/JWkqJmwDexhTq54L3DzN176z9uNhRKwLnETJh+zdptgm8hzgzszcY4Lnfw28DjiuruwAYGmLx90RmNviPtRhJpsFQGb+JiJ+DmwXEU+h+jUwIjYHTgW2B1YDp2bmSbXXVcnJLwErgAOBS6nrKVHfcyIiVgIfBfYENgT+PTO/OsVQFwG7A1+vftVcH/g08H7ghIhYD/hHSiNGROwNHAWsB9wLvKP2i1tEHAm8ijKdzK+AN2fmHZOc23bAmcDDgVuqc6ht++/Ay4A5Vfk7KAnxWyi9ny+ptjsd+Elmfnrceb0X+EJ9L2bgbErjfEX1+JmUC9DD6477ckpP53WAPwH/lpk/rN9xRPw9jZPvn87Mz40rewrwD8ArgEvqn8jMKyLi1IiYn5nXN9ifpBlsEK4jEbEhcArwROBvKO3mfsCjgMOA2RHxh8w8soWqkKSuGoT2t9rf0cBrKb3gfka5R76zOs4PgF2Bras4Ds3M1VUvxfcAfwEuB47ITP+GlXS/+jawvjwing58jJIz2AK4JDPfWD23F/BByt/RfwYOy8wbIuIfgeMp7dwq4NjMvKBqi2q5g22AMeCzwOHVcU/MzE9U+34j8OZq299S2rpbImIh8EfK39tbATdS8hYHAk+l5DNWZebXWqiLFRHxb8CdEbE9pa39JKVT2yMoHdoOBq6vzuFpmfmzKu5Lgc+M710dEYcCb63qY2l1zo+r6u+REfHtzHxOg3Bq+Yzjqv08nJLTuLRu3zsC/0G5L18DfCIzP1/1WP4Q8EtgHqXH9psoCezjquN+DjgL2CgizqFc6zYADsnMK6dVgWob52wWABHxDGBb4JpxT50M/CwztweeARwaEdtWz60HfAW4C3hdZq5cy2FmA/dm5gilh+6ZEbHZFEOtJZuh/FJ3ASUZu2NEbEppRG/OzLsi4onAh4EXZ+aTgUOBr0bEhhFxAKWR36X6VfQi4PS64zQ6ty8Cn83MJ1ES3NvA/b2S9wB2r547EjiuShyfAhxSbfcISg/ss+pPqOqx/KrqXOp9CXhFRKxfPT4QWFj3uu0pf0C8KjN3At4HfCMiNq7fSWbenJnzG/w3PtFMZv4wM98ALBn/XOVSSiJakh5kQK4jewK/z8xnZOZ2wI8ofwBcQ2lPv2yiWdKgGYT2NyJeT2mDd67ulxdTd18L/B3lHv9J1XbPrjpMHE+ZFuTJlCTN7GaPKWlmmKQNPAJ4X2Y+Dfh74KURMRIRj6UkQl9ftUcnAB+NiEcDnwP2z8ynUDqTnRIRW1f7exalc8KTKMnifwKeB7wY+GBErBMRz6b83f6sqt36GFCfPB4BXgTsAPwtsE9m/idwLaWH8rQTzTWZ+RdKkvkfgKcBWwLPyMy/p+Qi3pOZf67+fTBARPwdJWn+oJxERDwXeBfwnCrn8CVKp7rvUPIPV06QaAb4MXBfRDytevxKysibldW+H1Y9/kz1PuwJfLh6P6li/0RVj58DPpyZt9cd9/XVdnOBT1Z5ndMoHRHVY/4qPHPNiYha79SHAXcDCzLz9oio324PSuNCZv6B8qsS1TafoPw69nfjeuRO5j+qfd1YNyXGeVOI+wfA1hGxCSXZ/NHq17vLKQ39DpQ5igCeT/kF87K6c1pNuRDtBewCXFs9N5u6HsPjzy0i/oZyUfl8Ff9VUeZsJjNvq5LXC6ob+KfzwBQUC4FjqpvxVwMXZObvx53T3wCPysxfjStfCvwQ2CsiLuKBi1vNc4HLanMgZeblEXEX5QL27dpGU+zZvDa3Uhp9SRq460jVO++XEfEWyrVgd8p1RZIGycC1v5QkwueqBAeUjhtHVqMSAb6ZmauBP0bE/1GmNZoPXJyZY9U2n8EkgqTm28ADgRdXo5C3p4xC3ogyimJxZv4YoBql8dWIeDElf/D1uv2soeQBAH5UJTuJiFsp7dPqiPgFpUftw4GXUO4xv1+3j0dX+Qso6yL9tdrHT+jcFG5rKD8Q/iAijgLeVCWUd6eM7IPyg+QV1YjvQ4HTM3PVuP28iNIZYxlAZi6MiE9TEuXN+AKld/M1lPfjXymjwKEktzeojZLJzDsi4rzqmN8GbqsbUX0dZdrRRn5RdRyB0mP7DU3Gpg4y2TxzPWieo0mspDRUAETEEyiNOZSGYxZl+MhLq7I1VVnNejxYfa+JdShDMe4XZVGT2pw+d2Tmi+ufz8yVEXEZ5YZ1PlAbHnEhZUjGTsDbqrLZlGTs/QsFRpnE/o7queMz85SqfH3g0XWHanRujDu32i9yT6EsnvdJ4GLgu5QezWTm7yPiK5QGdj/gX3ioNcCsiFinusmu9/nqtetTbsJX1l20ZlP33lTWoQwxuV9m3kypq3ZYwbj3TNKM1ZfXkclEWYj1UErC5EvA74DHN/t6SeoTA9f+8tD71nUof4vWjveXuudqcawcF4/3oJKg+TbwCspUFd8C/ofSaarWttS3jbMovYBnAz+tekLXntsSWAYsAP46bv8rGhxzNmV6zHdXr1+H0rO4tu5So7ZuQhFxHA+00ednEwtZV9NV7ADcFGUB7E9TfmD8BmWaz9cBZObPIuJGSg/u/WjcqWw2sHxc2SzG5Rwm8UVgNCJOBDbOzMVTyGc0W1crmtxOXeQ0GlqbS4HXA0TEI4HLKHNdQul1ezSwbUQcUpUto8w3VOtR+yQerDaX8lMovy5+t/7JzDy/bpqHF9PYIkovje9kZq1huYjSs3krynANqlhfUE03QfVL5Y2UXzT/Fzi4bsqJ4yg33TUPOrfM/C0wygPDTGpzG0Pp1XFtZp5Ync/LefAQv/+kzHG0To6bT7k6599SLj7bNDjXb1DmoD6cBw81rJ3fC6s/HGpDXLbiocOH2unxlAuUJDWrq9eRtXghsDAzzwCSMkKm1l6vpPkbZ0kaBP3U/n4LeEOUufOh3BtfUevhN4H/BfaIsvgXVPfhkrQ2EfEoyuLP7656zs6l9DieTfl7eYdqvmAoydazgauBJ0bEbtU+5gM/p8xP3Kz/BV4bEVtUjw+jtL1r0/A+NDPfV5cfaSbRPAf4FLCoGjn9fEqntVMoU3U0ylWcAPww69avqvMt4J9q0yZVUyL9Fvi/Js6Jap83Uta++sK4p28BVkTEK6t9b0mZXvQSJuc9+wAw2ay1OZzSEN8IXAV8JDNHa09m5n2U4QwnVMMyPkhJ8C6mJHCvGLe/XSPiOkpjs29m/j+mbhHl5vf++YQycylwD3B5bShg1aP3UOCciLgB+ADw0sy8hzI/8wXA1RFxU7W/g+oP0uDcXktpaH9CuTn/abXpfwObRsRPKQv43QNsEmWOZjLzBkoy+dRJzqk2XORBqhjOB9bLzMXjnruZsvDAV6v6/iiwdzVMslNeAJzbwf1LGj69uo5cERH31P33ZuDjlGGEN1JGxlxH+cMDysJTL4yIz7ThnCWpH/RT+3sGJfn9w+qe+SmUnoITyrJo1b8C/xsR11J66t07hfOXNENVU1d+BLiuatPeQ2kHt61yBwuAs6rpOP4N+KdqqohXUdrEGyjJ0f0bTHc52XEvpsw1f0nV9u4HvLKJ6YrOBz4SEQdO5TwrJ0TE9VX7fA0lH1Hbz6nA7lUO4zrgF8Djqx7XUHIiGzFBriIzL6GM4L68ypscCOzVYET2ZD5P6UD3oKk9q46DLweOqOrqUsraV99+6C4e5GrgCRExpUVq1V2z1qxpdoouqTURsQbYLDPvXuvGQ6S6ef8OEJnZ8AY5Ih5PSeI+dQrz5nVVlBVh/yUz9+l1LJJmppl6HZGkXutF+1vdHx8AfKCaF/WVlF6Krh8iSW0QZTG+04F5/ZqH0GCyZ7PUQdUcS1cBb5ko0QyQmbdSVoN9U7dim4qImE2ZuuStvY5FkiRJM8IYZa7Tn1S9DN+GCz9JUltExFnAOcAbTTSr3ezZLEmSJEmSJElqmT2bJUmSJEmSJEkte1ivA1ibiFifsoroEmBVj8ORpHabDWwB/GgtK7L3DdtlSUPMNlmS+odtsiT1l6ba5b5PNlMa6it7HYQkddizgO/1Oogm2S5LGna2yZLUP9rSJlfr6bwaWAOckZknRsQewInw/9m793DHyurw499hHGDU4qWoXEa8lLKgjIIewVauVtTSQqlFpIIgrYCU0uqvaG0FqtBa72ixgpaLoCOKBfHCxVoBBS+gHgUdkVWqgB4Zh2ltUcBxLvD7491hMpnkzMlJTpKd+X6eZ545ebOzs/ZO8mZn7Xevl4XAJZl5arXs7pSJ07YCrgdOyMw1M3ga+2RJm4Jp++U6JJuXAXz0ox9lm2226eqBS5cuZfHixXMS1CDUOX5jH546x78pxv7Tn/6UI488Eqq+riZm3S+Pgjq/z5q5HaNlHLZjHLYBetuOTaFPrsvrbJz9ZZz9ZZz91y7WfvbJEbEf8LvAM4EFwK0RcQ1wAbAf8GPgyog4MDOvBpYAx2bmjRFxPnAccM4Mnqptn1yn16JVnWOHesdv7MNT5/jnMvaZ9st1SDavBdhmm21YtGhRVw9cvnx5148ZJXWO39iHp87xb+Kx1+kyu1n3y6Ogzu+zZm7HaBmH7RiHbYC+bcfY9sl1eZ2Ns7+Ms7+Ms/82EmvPfXJmfikinp+ZayJie0ou5LHA7Zl5B0BELAEOi4hbgYWZeWP18AuB05lZsrltn1yn16JVnWOHesdv7MNT5/gHFPu0/XIdks2SJEmSJEmzlpmrI+J04HXAvwHbsf7ovGXAomnaZ2zp0qUsX758vbbJyclZRD0a6hw71Dt+Yx+eOsc/V7GvWLFiRsuZbJakGoqIrYCvAgdl5p3d1puLiB0olwc+EUjgyMy8bwibIkmSJA1EZr4pIt4OfBbYiVK/uWEe8CCwWYf2GVu8ePF6IwsnJyeZmJiYbdhDVefYod7xG/vw1Dn+uYx9ampqRsttNifPLkmaMxHxXEox/p2q2wsp9eYOAXYB9oiIA6vFlwAnZeZOlAPl46r2s4GzM3Nn4JvAaYPbAkmSJGlwImLnahAGmfkA8Elgf2DbpsW2Ae4Gpjq0S5JmwGSz1GLnXXZt275qdZ3KN2rMHQf8BesOevekqjdXzZLdqDf3FDasN3dYRCwA9gUubW4fUOxSbbX7HvC7QdKgdOpv7IekGXk6cG5EbBERm1MGaXwQiIjYMSLmA0cAV2fmXcDKiNireuxRwNVDiXrM2I9JmwbLaEgtHvXILTn45E9v0P7Zdx8yhGikDWXmsQAR0Wjqtt7c1sDPq8R0c3tX2tWiq4s6199q5nYM1sTExAbfD5999yEPx1+X7ZjOOGwDzH47ZlqHThqGzRfM9xhVmqXMvCoi9gS+TZnY6rLM/HhErAAuA7YErmLdYIwjKcnprYBvAWcNIeyxYz8mbRpMNktS/XWqKzfTduiyDh1sWIuuLupcf6uZ2zE6JiYmxmI7xmEboLBKoPQAACAASURBVLftmGkdOklS/WTmm4E3t7RdA+zWZtlbKFcPSpK6ZBkNSaq/TnXlOrXfAzymulyQahnr0EmSJEmSpJ6YbJbGRKPOVetoLutfbRJuoot6c5m5GrgBOLxqPxrr0EmSJEmSpB5ZRkMaE9a/2nRl5sqIOIbu6s2dCFwUEacCPwJePtCgJUmSJEnS2DHZLEk1lZlPbfq7q3pz1ajn/ecwPEmSJEnapKxavZbNF8yfcbs0jkw2S5IkSZIkaSjGKUHrFceSyeaR1OhQ29XerVtHK0mSJEmS1IkJWmm8mGweQXa0kiRJkiRJkupmxsnmanKprwIHZeadEXE88FfAQ8A3gVdn5qqIeBPwZ8D/Vg89NzPfHxG7A+cBWwHXAydk5po+bssGdt5l17btjhCWJEmSJEna9IxT2Q5pFM0o2RwRzwXOBXaqbu8EvB6YAH4BXAj8BfAe4DnAn2Tm11pWswQ4NjNvjIjzgeOAc/qwDR096pFbOkJYkiRJkiRJgFeTS3NtpiObj6Mkkz9S3f4VcGJm/hwgIr4L7FDd9xzgjRHxFMoI5tcBTwIWZuaN1TIXAqczx8lmDU7rGcBGvWnPDEqSJEmSJEmbhhklmzPzWICIaNy+C7iransCcBJwTEQ8Gvg2ZdTzf1GSyqcBVwDLmla5DFjUTaBLly5l+fLl3Txkgwn2mk1OTna1rkGqY9wTExMdzwyOasyd1HH/Q33jblWnWFvNJvYVK1bMQSSSJEmSJEmD19MEgRGxPXA1cH5mfrFq/v2m+98NXABcRant3DAPeLCb51q8eDGLFnWVn57WdIm5UVbHuOsYcyd13Za6xD05OVmbWFvNNvapqak5iEaSJEmSuudVy5J6Netkc0TsDPw7cFZmvrtq2wE4IDMvqBabB6wGpoBtmx6+DXD3bJ9bkiRJGhVtJtI+ADgTWAhckpmnVsu1nTC7OoZeAjwRSODIzLxvCJsiSdrEWc9YUq82m82DIuLXgM8DpzYSzZVfAu+IiKdFxDxKnefLq7IbKyNir2q5oygjoiVJkqTaqibS/jLrJtJeSLmy7xBgF2CPiDiwWnwJcFJm7kQZlHFc1X42cHZm7gx8k1KGTpIkSaqdWSWbgWMpk/6dHBE3V//OyMwVwKuBz1JGZcwDGsnoI4H3RMRtwKOBs3oLXZIkSRq6xkTajav29gRuz8w7MnMNJcF8WDV5duuE2YdFxAJgX+DS5vYBxS5JGiOrVq/tS7ukwRq3z2hXZTQy86nVn++p/rVb5jLgsjbtt1AOviVJkqSx0DqRNrAd7SfG7tS+NfDzKjHd3N6VbibTrstkvMa5vl4ng3Z/9pdx9l9rrE6k3b1uS2C0W95yGdLgjVv5mp4mCJQkSZK0ns1oPzH2TNuhy4m0YeaTaddlMl7j7M7GYhiVODfGOPurLnFC+1idSFujot3kiE6YKHVmslmSJEnqn04TY3dqvwd4TETMz8y11TJOpC1J0ohwBLjUndnWbJYkSZK0oZuAiIgdI2I+cARwdacJszNzNXADcHjVfjROpC1JkqSaMtksSZIk9UlmrgSOocxhcitwG+sm/+s0YfaJwPERcSuwD3DqIGOWJEmS+sUyGpI0JiLiFcDfVTevzszXRcTuwHnAVsD1wAmZuSYidgCWAE8EEjgyM+8bRtySNA6aJtImM68BdmuzTNsJs6tRz/vPYXiSJEnSQDiyWZLGQEQ8kjJCbj9KgmOfiDiAklA+KTN3okxGdVz1kLOBszNzZ+CbwGmDj1qSJEmSNEyrVq/tql2D0+41qMPr4shmSRoP8yknEB8F3A8sAFYDCzPzxmqZC4HTI+I8YF/gj5ravwS8YYDxSpIkSZKGrN0EiOAkiKOgrpNTOrJZksZAZv6CMjr5NmAKuBNYBSxrWmwZsAjYGvh5Zq5paZckSZIkSZo1RzZL0hiIiGcCfwY8BbiXUj7jRcBDTYvNAx6knGh8qGUVD3b7nEuXLmX58uWzinfYJicnhx1CX7gdgzUxMdG2vRF/XbZjOuOwDTD77VixYkWfI5EkSerOqtVr2XzB/A2OPRvt0qgz2SxJ4+HFwDWZeQ9ARFwIvA7YtmmZbYC7gXuAx0TE/MxcWy1zd7dPuHjxYhYtqt+A6MnJyY5JwzpxO0bHxMTEWGzHOGwD9LYdU1NTfY5GkiSpO5tCWYtOiXMT6rM3SicpTDZL0ni4BXhHRDwKeAA4mFKH+aURsVdmfgU4Crg6M1dHxA3A4cDFwNHA1UOKW5IkSZJUE+2Sl90mNDeFhPqgjdI+NdksSWMgMz8fEc8CJikTA34deBtwOXBuRGwFfAs4q3rIicBFEXEq8CPg5YOPWpIkSZJUJ3WdtE6DY7JZksZEZr4deHtL8y3Anm2WvQvYfwBhSZIkSRpTlkOYmX6MBh5lrdvSKOUwTtuomZtRsrkaEfdV4KDMvDMiDgDOBBYCl2TmqdVyuwPnAVsB1wMnZOaaiNiBMlnVE4EEjszM+/q+NZIkSZIkSRqIUbp0f5SN+2jgcXsf7LzLrhu0mTifuc02tkBEPBf4MrBTdXshcAFwCLALsEdEHFgtvgQ4KTN3AuYBx1XtZwNnZ+bOwDeB0/q5EZIkSZIkSZLUq0c9cksOPvnT6/0z0TxzG002UxLGfwHcXd3eE7g9M+/IzDWUBPNhEfEUYGFm3lgtd2HVvgDYF7i0ub0/4UuSJEmSJElqWLV6bVft6r92+3pT2f8bLaORmccCRESjaTtgWdMiy4BF07RvDfy8Skw3t0uSJEmSJElzol05hE3BuJW1aGfU64WPe+mU6cxmgsDNgIeabs8DHuyinaq9K0uXLmX58uVdPaZRkLydycnJbkMYmDrGXceYO6nrttQ17lZ1irXVbGJfsWLFHEQijbbmA8DmvmtUDgxHJQ5JkiT1plEOodmmkvAbd90k1Ec9MT1uZpNsngK2bbq9DaXERqf2e4DHRMT8zFxbLXM3XVq8eDGLFvVvQPR0iblRVse46xhzJ3XdlrrEPTk5WZtYW8029qmpqTmIRhptoz7SYtTjkyRJ3YuINwEvq25emZl/ExEHAGcCC4FLMvPUatndgfOArYDrgROartaWNED9SAh7fD9YM6nZ3OomICJix4iYDxwBXJ2ZdwErI2KvarmjqvbVwA3A4VX70cDVPcYtSZIkSZK0UVVS+UXAs4DdgYmIeDlwAXAIsAuwR0QcWD1kCXBSZu5EuWr7uMFHrXasRbzpaSSKW/8Nmu+9met6ZHNmroyIY4DLgC2Bq1g3+d+RwLkRsRXwLeCsqv1E4KKIOBX4EfDyHuOWJEmSJEmaiWXAyZm5CiAivg/sBNyemXdUbUuAwyLiVmBhZt5YPfZC4HTgnIFHjZf/t3KEqobF997MzTjZnJlPbfr7GmC3NsvcAuzZpv0uYP9ZRShJkiRJkjRLmfm9xt8R8ZuUchrvoyShG5YBi4DtOrQPhQkuSXUzm5rNkiRJkiRJtRIRuwJXAq8H1lBGNzfMAx6klBt9qE37jC1dupTly5ev1zbbidC7mQi+20nju51zptt1dBNfP9Yx6G0chX3aie+b0d3GUdins7VixYoZLWeyWZIkSeqziDgWOKmp6WnAR4BHAXsD91ftp2fm5Z0mqZIk9Uc1v9RlwGsz8+MRsR+wbdMi2wB3A1Md2mds8eLFLFq0bjD0XE2E3s06+/H83a5j0M857s/XbvnZlFOp2zbO9XOO+/P16zkBpqamZrScyWZJkiSpzzLzPOA8eHgk3aeANwPXAftm5sOXaEfEQsokVfsBPwaujIgDM9NJtSWpDyLiyZR++PDMvLZqvqncFTsCdwBHABdk5l0RsTIi9srMrwBHAfbHGkmWWdEoMtksSZIkza1zgDcCDwA7ABdExPbA5ZRJp/akzSRVmNyQpH55HbAlcGZENNo+ABxDGe28JXAVcGl135HAuRGxFfAt4KxBBitJdWayWZIkSZojVXmMhZn5bxHxdOBa4ETgXuAK4FXAffQ4GVW7+qCd9Ktu31wzzvX1WovR/dlfxtl/rbHOtDboTGTma4DXdLh7tzbL30I5EShJ6pLJZkmSJGnuvJpSi5nM/CHwksYdEfE+4GjKSLqeJqNqrQ/ayVzVDe034+zOxmIYlTg3xjj7qy5xQvtYZ1obVJI0WjYbdgCSJEnSOIqIzSl1mD9T3X5GRBzatMg8YDV9mIxKkiRJGgWObJakMRERBwNvAh4FfD4zX1Ndvn0msBC4JDNPrZbdnTJx1VbA9cAJmblmOJFL02s3y/ZsZt6WhuCZwH9m5v3V7XnAeyPiWkrpjOOBi+gwSdUQ4pUkSZJ64shmSRoDVR3QDwB/REluPDsiDqQkKw4BdgH2qNoAlgAnZeZOlOTHcYOPWpqZxizbzf9MNKsmnk4ZtQxAZn4HeCvwFeBW4ObM/FhmrmTdJFW3ArexbpIqaaSsWr22q3ZJkrRpcWSzJI2Hl1BGLk8BRMThwG8Ct2fmHVXbEuCwiLiVMlnVjdVjLwROB84ZeNSSNMYy8xPAJ1razgbObrPsNbSZpEoaNY0TgK0+++5DhhCNJEkaNSabJWk87AisiojPADsAVwDfA5Y1LbMMWARs16FdkiRJkiRp1kw2S9J4eASwL7A/pQ7oZ4BfAg81LTMPeJBSQqlde1eWLl3K8uXLZxnucE1OTg47hL6o63a0zjbfrN02dVp+rrZ/uvimi6Our0ezcdgGmP12rFixos+RSJIkSZsWk82SNB5+CnwhM1cARMTlwGFAcwHFbYC7KfVDt23T3pXFixezaFH9BkRPTk52nUwcReOyHa262aZR2f6JiYmxeD3GYRugt+2Ympra+ELSiHESVUmSNEpMNkvSeLgCuCgiHgv8AjiQMrnU30bEjsAdwBHABZl5V0SsjIi9MvMrwFHA1cMKXJIkzV67GsrWT5bqx5NEksbFrJPNEXEscFJT09OAjwCPAvYG7q/aT8/MyyPiAOBMYCFlEqtTZ/vckqT1ZeZNEfEO4MvAAuA/KBP+3QZcBmwJXEVJQAMcCZwbEVsB3wLOGnjQkiRpbDjCWuqNk29KGhezTjZn5nnAeQARsSvwKeDNwHXAvpn58ORTEbEQuADYD/gxcGVEHJiZjqSTpD7JzAsofW2za4Dd2ix7C7DnIOKSJEnjzxHWkiQJ+ldG4xzgjcADwA7ABRGxPXA5cDoloXF7Zt4BEBFLKLVETTZLkiRJkiRJ0hjoOdlclcdYmJn/FhFPB64FTgTupdQQfRVwH7Cs6WHLgK5mlVq6dCnLly/vKrZuZ7sfFXWMu44xd1LXbalr3K3qFGur2cS+YsWKOYhEkiRJkiRp8PoxsvnVlFrMZOYPgZc07oiI9wFHU2qEPtT0mHnAg908yeLFi1m0qKv89LTqOtt6HeOuY8yd1HVb6hL35ORkbWJtNdvYp6am5iAaSZIkSZKkwduslwdHxOaUOsyfqW4/IyIObVpkHrAamAK2bWrfBri7l+eWJEmSJEmSJI2OXkc2PxP4z8y8v7o9D3hvRFxLKZ1xPHARcBMQEbEjcAdwBBtOYiVJkiRJkiRJqqmeRjYDT6eMWgYgM78DvBX4CnArcHNmfiwzVwLHAJdV7bdRSmtIkiRJkiRJksZATyObM/MTwCda2s4Gzm6z7DXAbr08nyRJkiRJkiRpNPU6slmSJEmSJEmSJJPNkiRJkiRJkqTemWyWJEmSJEmSJPXMZLMkSZIkSZIkqWcmmyVJ0iZp1eq1ww5BkiRJksbKI4YdgCRJ0jBsvmA+B5/86Q3aP/vuQ4YQjcZRRFwHPBFYXTW9GvgN4FRgAfDezHx/tewBwJnAQuCSzDx18BFL66xavZbNF8wfdhiSJKlmTDZLkiRJfRYR84CdgKdk5pqqbXvg48AE8Cvgq1VC+g7gAmA/4MfAlRFxYGZePZTgJTwhJ0nSOGh38niuTyibbJYkSZL6L6r/Px8Rvw6cC/wCuDYzfwYQEZcCLwW+BNyemXdU7UuAwwCTzZIkSZq1dieP5/rEsTWbJUmSpP57HHAN8BLgBcAJwA7AsqZllgGLgO06tEuSJEm14shmSZIkqc8y82vA1xq3I+J8Sk3mf2xabB7wIGUAyENt2mds6dKlLF++fEbLTk5OdrPqoTHO9U1MTMx42dbLYxuPvf+Bldz2/e/1/fmm0+3+8XXvr7rECRvGumLFiiFFIknqhclmSRozEfEuYOvMPCYidgfOA7YCrgdOyMw1EbEDsIQycVUCR2bmfUMLWpLGTETsDWyRmddUTfOAO4FtmxbbBrgbmOrQPmOLFy9m0aKND4aenJzsWxJxLhlnbzrVW77sbQe1jXcuazd2s39GdX+2Ms7+axfr1NTUkKKRJPXCZLMkjZGIeAHwSuDKqmkJcGxm3liNqjsOOAc4Gzg7Mz8eEacBpwFvGEbMkjSmHgucERHPAxZQ+uZXAEsi4gnA/cChwPHAd4CIiB0pkwUeQZkwUOorJ/2TJElzzZrNkjQmIuLxwFuAf6puPwVYmJk3VotcCBwWEQuAfYFLm9sHGqw2GatWrx12CMDoxKFNR2ZeQTnx921gErggM78CnAJcB9wMXJyZX8/MlcAxwGXArcBtrOujJUmSpNpwZLMkjY8PUpIYT65ud5pwamvg55m5pqW9K93UBx01dapfOJ06bMfExERXsx+326ZOlwB3s/3dxtGNRhx1eD02Zhy2AWa/Hf2uD5qZjStHmtsuBi5us+w1wG59DUCSJEkasJ6SzRFxHaXe5+qq6dXAbwCnUi4XfG9mvr9a9gDKpCgLgUsy89RenluStE5EHAv8ODOviYhjquZOE061tkOXE1HBzOuDjpo61S+czrC2o1Ndz37V++xmm0bldZyYmBiL99U4bAP0th3WB5UkSZJ6M+tkc0TMA3YCntIYHRcR2wMfByaAXwFfrRLSd1Dqzu0H/Bi4MiIOzMyre4xfklQcDmwbETcDjwceTUkot5tw6h7gMRExPzPXVst0NRGVNl3W+5QkSZIkddJLzeao/v98RNwSEScBBwDXZubPMvN+Sq25lwJ7Ardn5h1VYnoJ1geVpL7JzBdm5uLM3B34e+AzmfmnwMqI2Kta7Cjg6sxcDdxASVADHA148k+SJEmSJPWklzIajwOuAf6SUjLji8AlbFgfdE861w2dsdnUBp3uEspRrklYx7jrGHMndd2Wusbdqk6xtppN7P2uD9rGkcC5EbEV8C3grKr9ROCiiDgV+BHw8rkORJIkSRqm6pj4q8BBmXlnp3KfEbE7cB6wFXA9cELTfCeSpGnMOtmcmV8Dvta4HRHnUzrpf2xarFN90Eb7jPW7NmhdaxLWMe46xtxJXbelLnHXuV7obGOfi/qgmXkhcGH19y2Uk36ty9wF7N/3J5ckSZJGUEQ8FziXUg6UiFhI53KfS4BjM/PGKtdxHHDOcCKXpHqZdRmNiNg7Il7Q1DQPuJP29UGnOrRLkiRJkiTNteOAv2BdLqJtuc+IeAqwMDNvrJa7EMuAStKM9VJG47HAGRHxPEoZjVcCrwCWRMQTgPuBQ4Hjge8AERE7UiYLPIJyBlGSJKlvVq1ey+YL5g87DEmSNGIy81iAiMb0Ux3Lfc5JGdCNldybzVWSrevstrRit8/Z7Tq6ia8f6xj0NrpPZ/d83T7nuG3jMPbpTNexMTMtA9pLGY0rqstQvg3MB96fmV+JiFOA64DNgfMy8+sAEXEMcBmwJXAVZfJASZKkvtl8wXwOPvnTG7R/9t2HDCEaSZI0wjqV++x7GdC5KhfYzTr78fzdrmPQzznuzzeM53Qb6/98/VzHTMuA9jKymcw8DTitpe1i4OI2y14D7NbL80mSJEmSJPVBp3KflgGVpB7MumazJElSP61avXbYIUiSpE3HTVTlPiNiPqXc59XVRNorI2KvarmjgKuHFaQk1U1PI5slqRettVUbl3FYc1XaNFkCQ5IkDUpmrpym3OeRwLkRsRXwLeCsoQQpSTVkslnS0JhYkjRbnpSSJEmzkZlPbfq7bbnPzLwF2HOAYUnS2DDZLEmSaseTVZIkSZI0eqzZLEmSJEmSJEnqmclmSZIkSdqEOUGrJEnqF8toSJIkSdImrF1pIssSSZKk2XBksyRJkiRJkiSpZyabJUmSJEmSJEk9M9ksSZIkSZIkSeqZNZslSZKkPouINwEvq25emZl/ExEfAvYG7q/aT8/MyyPiAOBMYCFwSWaeOviIJUmSpN6ZbJYkSZL6qEoevwh4FvAQ8LmIeAnwHGDfzFzWtOxC4AJgP+DHwJURcWBmXj34yCVJkqTemGyWpDHRYRRd29FyEbE7cB6wFXA9cEJmrhlC2JI0jpYBJ2fmKoCI+D6wQ/XvgojYHrgcOB3YE7g9M++oll0CHAaYbJYkSVLtmGyWpDHQYRTdy4G303603BLg2My8MSLOB44DzhlO9JI0XjLze42/I+I3KScC9wH2B04E7gWuAF4F3EdJTjcsAxZ1+5xLly5l+fLlM1p2cnKy29UPhXGub2JiYiDP02/d7h9f9/6qS5ywYawrVqwYUiSSpF70lGy2Fp0kjYx2o+h2os1ouYi4FViYmTdWj72QMrrOZLPWs2r1WjZfMH/YYUi1FRG7AlcCr8/MBF7SdN/7gKOBSyknCRvmAQ92+1yLFy9m0aKN56gnJydrkbQ0zvHRzf6py/40zv5rF+vU1NSQopEk9WLWyWZr0UnS6Ogwiu59tB8tt12H9q50M4pu1NRplM905no7JiYmOPjkT6/X9tl3H9JVPHX5kduLxnaPw/tqHLYBZr8d/RxFFxF7AZcBr83Mj0fEM4CdMvOyapF5wGpgCti26aHbAHf3LRBJkiRpgHoZ2WwtOkkaMc2j6IA1lNHNDY3RcpsxwFF0o6ZOo3ymM4rbMWrxDMrExMRIvh7dGodtgN62o1+j6CLiycCngMMz89qqeR7w3oi4llI643jgIuCm8pDYEbgDOIIySEOSJEmqnVknmwddi242I+im+6ExyiN36hh3HWPupK7bUse46xjzdGYT8xyPotuP9qPlHEUnSXPrdcCWwJkR0Wj7APBW4CvAAuCyzPwYQEQcQ+m/twSuopTW0CbKEkaSJKnOep4gcFC16Po9gq6uI3fqGHcdY+6krttSx7jrFvNsR9LN8Si6tqPlMvOuiFgZEXtl5leAo/BKE0nqm8x8DfCaDnef3Wb5a4Dd5jQo1cbmC+ZvUMIIpi9jJEmSNCp6nSDQWnSSNBo6jaI7hvaj5Y4Ezo2IrYBvAWcNMlhJkiRJkjR+epkg0Fp0kjQiNjKKboPRcpl5C6WeviRJmmOdSmNYMkOSJI2bXkY2W4tOkiSpD9olnExCSePD0hjr69S/7bzLrkOIRpIk9VMvEwRai06SpBrp9OP+V6vXsoVJzaFql4jaVJNQ0qZkUz2pZPJdkqTx1fMEgZIkqR6m+3Hf64/+TTVhIkm98ERT/1myRJKk4TLZLEmSeuYoNUnSKPD7SJKk4dps2AFIkiRJkiRJkurPZLMkSZIkSZIkqWcmmyVJkiRJY23V6rUzapMkSb2xZrMkSdIIcpIrSeofJ2OUJGkwTDZLkiSNICe5kqTR0jjZNzExsUGbJEkqTDZLkiTNUrvEQ3O7JGl8ODpakqSNM9ksSZI0S44+lqT+aXeibhgn7zxhKEnS7JlsliSpxqzrq4ZRSdJIm4pVq9d6VcNGdLs/uhk5PJf72hOJkiTNnslmSZLm2FwmhP1BrAYv75YGy8/cxs3ld1Q/1u3JAUmS+s9ksyR1qd0Pk5132XVI0agOTAhL0qbB5GW9DOP72atQJEnjzmSzJHXJkUyqA3+4StLgeXKx/0b9+6zbq5c8jpQkjTuTzZIkjSF/zI6mUU+aSMPQ7nMxMTHRl8+Lde3rb9QT+KMenyRJgzbQZHNEHAGcCiwA3puZ7x/k80uS1rFPHl1eYju+Bp2UMNFWL5tqvzwqdX39vEhqtqn2yZLUq4ElmyNie+AtwATwK+CrEXFdZt46qBgkSYV98mhzVHL9zWVyqpt1O+KuPuyXN9Tpvf6r1WvZoqW9XVu3/LyonzxJUW/2yZI0e4Mc2XwAcG1m/gwgIi4FXgqcsZHHzQf46U9/OqsnXf3AzzZom5qamtW6BqmOcdcx5k7qui11jLuOMcOGcc825qa+bdC/RmbbJ0OP/fKwrVixou3rtXrNgyx4xGYbtK9a8yCbt2nvtHzH9jbv9Tvu/FHbZdst37rsk570pIe3o9PnqN37dKbLuo7e1nHP8mUc+5b/2KD9vFNeONB1d1pHu/fek570JH545482eL93ek930m75btfRi06f8ZkYYp8MAzpW7mX/9Gq690G37/XW9nZtjfZu+t9e++pR75s21XUMI75u++q5MMzPe7faxTpOffJMX4tu34/drGOmz9nt83X7nOOyjbPZH/1YRy/PN5t1zDQ+3zczX8dMl+30nHO9junMtF+e99BDD83qCboVEX8HPCozT61uHwvsmZnHb+RxewM3DCBESRqmfTLzy4N6stn2ydWy9suSxt1A+2TwWFmSpmGfLEmjZdp+eZAjmzcDmjPb84AHZ/C4bwD7AMuAtXMQlyQN03xgW0pfN0iz7ZPBflnS+BpWnwweK0tSK/tkSRotM+qXB5lsnqJ0ug3bAHdv7EGZ+StgoGcxJWnAfjCE55xVnwz2y5LG3jD6ZPBYWZLasU+WpNGy0X55kMnmLwBvjognAPcDhwIbvVxbkjQn7JMlabTYL0vS6LBPlqRZGsxMLUBm/gQ4BbgOuBm4ODO/PqjnlyStY58sSaPFflmSRod9siTN3sAmCJQkSZIkSZIkja+BjWyWJEmSJEmSJI0vk82SJEmSJEmSpJ6ZbJYkSZIkSZIk9cxksyRJkiRJkiSpZ48YdgBzKSK2Ar4KHJSZdw45nBmLiDcBL6tuXpmZfzPMeLoREWcALwUeAs7PzDOHHFLXIuJdwNaZecywY+lGRN8QIwAAIABJREFURFwHPBFYXTW9OjNvGmJIMxYRBwNvAh4FfD4zXzPkkGYsIo4FTmpqehrwkcw8qcNDNEAR8Q/A2sx88zTL/BpllvFXZeYXq7aTgeMoJ2X/NjM/OffRdjbddkTEtsBHgCcAKymf/ZsjYgHwP8APmxafyMy1Awh5A7PchnnAO4GDgAeB4zLzK4OLekMz2I4PAdtQ4n1dZl47aq8FzHo7avV6NC3zQsrn+AXV7ZF7PXrR7tgrIn4HeA/wa8B3gFdm5qqWx20LnAdsBzwAHJmZd0bEfsAngR9Xi347M/90GHFGxBOBzzet5jHAEzLz0RHxWOCjwNOBFcDLMvOnIxjnyOzP6nFPBT4MbAX8X7XMXRGxOXA+8Bzgl8ARmXnbCMb5FGAp8INq0eWZ+eIhxrkn8H5gC+BHwLGZ+dMR3J+d4hzI/gRuA/6paZHtgZsy86CWx+0ALKH8pklKv3TfXH3eByEijgBOBRYA783M9w85pK7U8Tdmax4oIg4AzgQWApdk5qlDDXAabWL/ELA3cH+1yOmZefnQAuygXQ6rZvu9Xfx12fftvheGvu/HdmRzRDwX+DKw07Bj6Ub1pngR8Cxgd2AiIl4y3KhmpjqY/l3gmZQDq7+MiBhuVN2JiBcArxx2HN2qEgA7Abtl5u7Vv5E+CGiIiKcDHwD+iPLeeXZEHDjcqGYuM89r7HPgSOAe4M3DjUoR8ZiIOB84eQaL/wvwuKbH7gG8gtIH7w28MyIePyeBbsQMt+OfgEszczfKSZuzq/ZnAl9r6hN2H0YyrcdtOBTYBfgtSh9xYUQM5UT5DLfjncBnq/7g5cDFETGfEXktoOftqNXrERGbVSeOPg7Mb7prZF6PXnU49tqNktw8PjN3rRZ9VZuHf4TyOj+r+vvtVftzgHc17Zt+JEZnFWdm3tP0Hfts4E7g+OrufwRuyMxdgHOBfx7ROEdmf1b+AfhYFetlwFuq9r8C7q/252uBC0c0zucAFzftz34kRmcVZ3X8fSnwN5n5TEpy/F+ru0dmf24kzoHsT+AHTZ+R3wN+Dvy/Ng8/Gzg7M3cGvgmcVrX3/fM+CBGxPeW9uzfluPL4iPit4UY1c3X8jdmaB4qIhcAFwCGUY5g9RvW3Zocc1nOAfZv2/ygmO9vlsF5OffZ7pxxcHfZ9p++Foe/7sU02U0ak/QVw97AD6dIy4OTMXJWZq4HvAzsMOaYZycwvAc/PzDWUs5+PYN1ZoJFXJZPewvpn3euikdT/fETcEhF1GlX7EsrZtqnqPX84MNIHMdM4B3hjZv73sAMRhwC3A++ebqGIOBz4BWUkUMPvA5/MzJWZeQ/wRcpIzmGYyXa8inU/Gp8G/G/19x7AEyLimxFxY3UwMgy9bMMfAB/PzAcz8z8po7GeN1eBbsRMtuNy4OLq7/8CtgQezei8FtDbdtTt9dil+ndcS/sovR496XDstTslmd7o1/6S8po+LCK2BnYDPlg1fYgy8g7K/nlRRHwnIj4TEU8eVpwt/hR4IDMb780/oIx0BPgYcGA1an3U4hy1/TmfMloYyhVlv6z+fnh/Zub1lM9IT79B5ijOPYDFEXFzRFwbEc/oJcYe49waWJiZ11W3rwB+LyK2YLT253RxDmp/Nv8mfCfwgcy8vflx1ed3X0piHEqC/rDq775/3gfkAODazPxZZt5P2baXDjmmbtTxN2ZrHmhP4PbMvKN6Ty5h3ftq1KwXe0Q8kpILuqD6Djk9IkYxh9cuh7UT9dnvnXJwI7/vO/S3j2UE9v3I7ax+ycxjM/OGYcfRrcz8XmbeCBARv0kZyn/VcKOaucxcHRGnA7cC1wA/GXJI3fggcArrkhx18jjK/n4J8ALghCiXDdfBjsD86gfYzcCJ1PA1qM6ILszMfxt2LILM/HBmvg3oOFqx+tH3WuD1LXdtRznoaFgGLOp7kDMwk+2oEn8PRsRtlMtoz6ruegj4FPA7wJ8Dl1QJpoHqcRvq9lpclpmN/ut1lMvl72VEXosqxl62o26vx/cy81jgZy13jczr0Q9tjr22Ae6LiI9X36unU8oQNPsNysmCd0fENygJkMZl9/8HvK8aAXkVZWT4sOIEoBpZfwrwt03ND78fqx9TP6eU4hm1OEdtf54G/HVE/IRyZUBjRPucfL7nIM6VlB/OzwbeBXwqSsmKYcT538D9EfGi6vafUEol/DqjtT+ni3NQ+/Mn8PDv2/1Z9z3fbGvg59XnGdbfZ3PyeR+AkfnenKXa/cZskweqzWvQJvZtgGuBPwN+G9iH9leCDFWHHNaD1Ge/t4v/c9Rg30Pb/nYk3vNjm2yuu4jYFfgP4PWtZ31HXWa+ifLl/2Q2HE00kqLU3f1xZl4z7FhmIzO/lplHZ+a91aja8ymjM+vgEZSz/q+i/PB/LjUsZQK8mlIXSQMUEYdFxFTLvy/M4HGbUT4nJ2XmL1vu3oySjGqYRzlgmjOz3Y5m1SWnvw18JCIen5kfzMwzMnN1Zn6bcsXAXnMRP8zNNlDT1yIiXkvpE44GGPRrUcXQ9+2gpq9Hq2G8HnOt5dhrS+DFwN8BE5RRoX/b8pBHUC4XvTYz9wA+DVxUreuErOrUZ+YHgF0j4jFDirPh9yijdL7b1DavZZm+vR/7GecI7s+LKCUXtgdOAC6Pcqn8nH2++xlnZr45M8+pTlJeBdxHuYJh4HFm5kOU8kJvjIhvU0aT/Q/lxM3I7M/p4hzg/mz8JjyeUibjV20e0rrPYN0+m7PP+xwb+PdmP9X8N2ZDbV+DzPxhZr4kM5dl5gPA+xjh/d+cw6LMi1Gr/d6Sg8s67fuW/nYnRmDfj/UEgXUVEXtRapO9NjP7MvphECJiZ2DLzLw5Mx+IiE9SasfUweHAttWIgMcDj46I92Rmu1piIyci9ga2aEqWz2PdJA6j7qfAFzJzBUBEXE653OnCYQbVjWoEyH7AMUMOZZNTjSSfzWjynat/50cpLb8jcF5EHAdMAds2LbsNZZKaOdPDdhARfwB8KTPvyzKp3l3A06v2r2ZmY9KfOe0X5mIbaP9azGl5rF62AyAi3kG53HffzJyq2o5igK8FzM12UMPXo51hvB5zpcOx17sok+3eUS3zCdafyBbKd+8vMvOK6vbFwFnVibi/A96W69exXkMPeoiz4Y/YcETwTyjvwakotcN/jZJAG5k4R21/RsQTgJ0z89NQrmKIiA9QRpQ2Pt+Nz0XPn++5iDMi/oRSY7jxWvf8+e3xdV+dmftXyzyRMiL7Z4zQ/pwuzoj4SwazPxu/Cf+IUhu1nXuAx0TE/Orzsi3r9lnfP+8DMkUZEdkw59+b/VTz35gNAz926ZcoZW12yszLqqaR3f+tOawoJcpqs9/bxF+Lfd+hv30p61/5N5R978jmEROljtunKDMW1ybRXHk6cG5EbFEl3w6hFLgfeZn5wsxcnGXSir8HPlOXRHPlsZRJzLaMiF+jjAweuQL2HVwBvDgiHhvl8tMDgckhx9StZwL/maUWm2ogM2/NzCfnuslqvkmZmf064Grg0Ih4ZPWD9wWUS5JG1SupJqKKMunMNpRZ33ejmjwtSkb9WcColpfqtA1XAUdGxPyI2JFypv4bQ4tyI6qRwM8H9mpK0EK9XovptqNWr8c0avV6bES7Y6/jKZPbNGoDH0TL92qVaJ+KdRPGHAxMZuaDlMulDwWIiKOBm/rw/TarOJv8Dhu+RlexbtT94ZTJw3r9IdjXOEdwf/43sDIi9qni2Yty0mEFTfuzSjCtzMwfjWCc+1FdylwlM+ZTvi+GESfAh6JMLAzw18C/Va/7KO3P6eIc1P78cpRyRQsbifFW1ef3BsrnGcr+u7r6ey4+74PwBeAFEfGEKPV3D6Vcnl8Xdf6N2XAT5et+x+q35hGse1+NunnAeyPicVFqlB/PCO7/Djms2uz3DvHXYt/Tvr/9ICOw7002j57XUS6HOjPKRA03R8QJww5qJqpLr64Evk05yPlqDRPmtVSNTGre9xdk5teGG9XMZJnR+B2UExO3AndRJiqqk8YITI24iDghIs6YbpnM/DqlfuE3KO/L0zJzpOrPt2zHayknbG6hfHZenpn3AWcAT4yIpZR6rEdn5i+GE/GGZrgNlwLfo0zg+GngVblh2ZOhamxHlMvQ30SZnOOLTd/h2zHirwXMeDtq83psZLGRfz1mqsOx10co5U8+G6UO+uOBtwJExHkR8YfVw/8YeEO1H15DqUsIJZnw2oj4HmWyu2OHHCe0/549DfjtKs4TKZMqjWKcI7M/s5RT+GPgXRHxHcrx16HVat8HbFHFeRZw1IjG+RrghdX79l2U74ueLg/u8XX/c+CD1TJPpzqRxQjtz43EOaj9+XE6HC+3xHkicHxE3EoZEdyYuLTvn/dBqI4fTwGuA26mjCL/+nCjmrk6/8ZsyMyVlKtPL6P81ryNdZNQjrQsE3++FfgKJfabM/Njw42qrQ1yWJR9fgz12O/t4n8eNdj30/S3xzDkfT/voYdayyJJkiRJkiRJktQdRzZLkiRJkiRJknpmslmSJEmSJEmS1DOTzZIkSZIkSZKknplsliRJkiRJkiT1zGSzJEmSJEmSJKlnJpslSZIkSZIkST0z2SxJkiRJkiRJ6pnJZkmSJEmSJElSz0w2S5IkSZIkSZJ6ZrJZkiRJkiRJktQzk82SJEmSJEmSpJ6ZbJYkSZIkSZIk9ewRww5A/RERTwV+AHy3qXke8M+ZeUFEHAO8NDMPGkJ4sxYRfw/ckpmf7uIxfwK8ITOf1dT2NWAHYFFmPlS1XQV8NjPPmWVsDwFPyMz/nmaZrYEVmTlvNs/RYZ0fAd4ObA1cB3w4M1/ZsswXgedk5qP79bwdYvkz4CWZeXB1+8nAP1Peaw/O5XNL6r9+fZdExLnABzJzMiJ2By4D/g84NDPvnIvYq+e9FNixurkbsBRYC/xvZj5/rp5X0vjaWL/Yp+c4A/ivzPzwNMv8IXBAZv5VRPwB8NzM/PuNrPfh5Zof34d4twMuzczn9bquNus+AXhsZr4tIu4EXgo8GviXzFw8k33V5fN9HjhiuuN5SWonIs4C9q1u/hZwB/DL6vbvZOYv2z5wBETEPOA/KMf1/zfseDR+TDaPl19m5u6NGxGxPbA0Ir45xJh69bvArV0+5vPAhyPi8Zn5s4h4ArAdcA/wHOAbEfEIYG/gz/sa7RyLiJcB92bm0ojYH1gGHBwRj8zMB6plngLsNMdxPB74J+BI4EuN9sz8cUTcDJwI/MtcxiBpzvTju+SFwAerv/8QuC4zj+1jjG1l5ksbf1cnBJ9vAkFSH3TsFzPzO72ufGNJ42qZzwCfqW7uATx+Bqt+eLmWx/ckM+8G+p5ortb9gY3cv9F91aUX9nl9kjYRzSfvqpNjR2ZmXXIv84EXDDsIjS+TzWMsM38SEbfTkniMiN8G3gFsAWwL/Edmvqq67yDgHyklVu4HTsjMWyLieZTRtI+ijBI7PTOvqEa5HVot/xRgCjgXOKl63jMz893Vul9FSUJuBvwPcFJm3hYRFwI/B54BPBn4DnA08EpKcvidEbE2My+f4Xb/rEqK7At8CvgD4N+B5ZSkxzeA5wJ3ZeZdVWynNG3HncCJmXl3RDyGMlL3GcAC4Brg9Zm5pml/bgN8ATgnM98fEX8MvAV4oHquxnKPAs4BfhP4deAXwBGUs59LgSdn5r3VWcYEDsvMW1o273TgsKbbP6OMtvkj4OKq7ejq7xOanvs04OXAGuA/Kfv+p80rjoijgb9us0uPyszvtrS9DLgbeB1wcMt951ES+v+amavarE9SjXT7XRIRb6Gc4PtoRFxMOak3PyIWZuaRnfqj6oqMnwE7U/rKQ4FJ4LeBJwL/CmwD7Ef5LnpZm76po6qf/63MPLK6vTfwPuAllJNmn6N8N8yrYrqh6XEbfD/M9HkljZ/mfjEing28itIv3ZuZz5/mmPfRlH5nL0of+CngFOBDwNLMfFdErAHeBhxYrfONmfnJxpUlwD9QjvHmR8S9lJP/7Y4vH9uy3O1UV6ZExKLqMU+l9HkXZeY7q1Hc1wBXUfrDxwF/03oMXi23NDMfHRFvrtazLeW3wE+AV2TmspbHvBn4DWD7atlJyhV6rwSeVj3Px6rlts7Mk9rt++p3Q2NfrQTOBA6gjH5+M+U4+RmU49SDM/P+iNiFcjz/65QEy1nV1TofqlZ7XUT8PvAgZbDEDpTj/o9n5j9V23sD8P1qW18AvLF6HVcDPwT+NDPvaxezpE1PROxK6XceR+l33pOZF0XEAZS+ahml376Pcjz9V5Rj7U9k5uuq5c6g9GVRLXdMZmZEbFE9Zp9q3ZPAazLzFxExBXyZcoXf31C+h95AOV5/AnBBZr6Z8r0DcENEvBj4OnBQZt5cxT8FHFQ97xeA/6Lkavap4nwb8EhKbuhNmXlVX3egas+azWMsIn6HcjnxTS13vQb4+8x8LuVyjz+MiImIeBKwhHKw9EzgncDbIuJxlM7oqMx8NnAIcE5E7FCtbx/KwewzKR3Qn1AOwn4f+MeI2Cwi9qMcTO5Tlbd4B9B84DoB/B6wC+Ug7rDMfD/wTUpyd0aJ5iZXA/tXfx8MXAFcSUk2U8V3ZbWfjqYclO5ZjVq5ipIwBXgPMJmZE8CzKKUrmhOyiygH5W+tEs1PAi6gXCo+AdzVtOyBwP9l5u9k5k6URPRJmfkj4FrKKGGA5wP/05pojojFwMLMXNqyrR8Gjmq6fTjrEs9ExJ9Wz71H9bouBS5s3WGZ+eHM3L3Nvw2SOZn5gcw8A/hVm/vupvyw2qv1Pkn10+13SWaeQjkwPjIzTwc+AFxSJZo31h/9b2b+Vma+r7r91MzcC3gF5Xvji5n5HEpi+C+73JRzgYOqKzMAjq9ig5JY+FL1HfC3wCURsWAj3w+SNlFt+sVdgf2rRPN0x7xnAFtSjnd3pxwr7dey+vnAA9Vx5MuAC6qr9ADIzJtY16+eQufjy9blmn2UcsXJM6oYXlGVoQN4OvDvmbknpT987wx2yT6UY/edqQardFhub8oJvmdTfif8VmbuSxmkcvoMnqfVFsBPq1gvovTPr6V8Jz0GOKS6mvFS4G+rfbof8LqI+O3M/NNqPc/PzB8DH6EkYiaAPYEDqqsKoRzz/0O1j59K+Z2xW7XsDym/gySJiFgA/BtwctVH7A/8XUQ8p1rkuZT+ZHfKwLPXU/ry5wCvjYgnNi13ZtVXf5TSz0E5SflLYCIzdwP+mzLYreGWzNyFku84mXICcILS359W5Xca/d8+MxhE8RTgtMwMSnL5fEr5oWdT+vR/rU5iSg9zZPN4WViVMIDy2v435cf+jyOieblXAr8fEW+kjCBbSBkNsBdlpMC3ATLzk8AnqzP92wKfalrPQ6w7qPpGdYBGRNwBfD4zH4yIH1AOqB9JGV28I/DVpnU8rulH/+cy81fVOr7LzC4NnM7VwHkRsTnlAPgoYCWwTZUQ/l3gtGrZgygHlN+sYptfxfzwfdUIFSj7qtlVlNHcjeTu3sB3M7NR+uODlBEnZOalEfHDiPhLyr7YH/hatdz7KT9GzgZeTRlt0mpnyhnFVp+lJP+fVK33NsrowIYDgQ9l5v3V7X8GTomIzZtHHnc5snlj7qCcgb2uy8dJGr5ev0um07E/qm7f0LL8J6v/f1D9/7mm2/vPbHOKzLwnIq4AjoqIDwMvpow83JqS5L64Wu7qiFhL+Y6b7vtB0qZjY/3idzLz59X90x3zHgD8dWaupfxg3w+gGrXc7F8AMvM71XHxvnSwkePLDVRX2u0FvKh6/L3VaOEDgRspI3UbI9S+xcyOyb/YtP3fnuYxX8jMe6s47mb9Pn22x/6XNa3ju5n5k2r9d1Tr3IkyovqCptdjIWUQyY2Nhmq/7Ac8PiL+oWp+NOWkwNcpI9Eb+/W7lNfvpoj4d+CyzPz6LOOXNH52oZy4u6ip39mC0u/cQak73yjB9ANgeWauBu6JiPtY1x9OZuZXq7/PA86qrrw+iNI//V61/s0pAz0abgCocjJ/QBlscRTlRNw8yrHsL7rYnlWsO7m6FyU39JmmbXsQWEzJi0iAyeZxs149uWlcTylV8TngE6y7bHgNJYkMPFw0/hmUH9ffr0avNe7bDlhBGY3bOrp1dZvnnA98JDPfUD1+M8pl1v/biL1p2YeqeDqKMjlIY5TyZ3LD+m2TwJMo5SW+mevqGX+OUpstWHfAOB94e1YTBVaXpTyu6b7DMvP71X2PpWkfURLDp1CStO+u2ppjby638eeUkXT/QklO/4xy2SCUS1MeGREvoPygWG/Cv8pDtLkaITNXRcRllBHlu7LhqOX5LTFvRvnsr7ePs0y00pfJVijvgbV9Wpekwer1u2Q6G+uPWi9BXu/7pToQ78X7KSfz1lCSA/dFmch1Tctym1H6sOm+HyRtOjbWLzb3XdMd87Yeaz+ZUnatVXOf1OiP2trI8WU7m7FhX70ZpWwEwKpcN8nzRo/JKzM9jp/Jb4ZuNa+z02+Qe3P9mttPAu5ts9w84HlNvxu2pgxW2Rr4VVZl9DLz/yJiN0rS5XcpV8O8MzPP7sP2SKq/+ZQrlZv7nW0ok2Xvzcz7wtbvAlh3fPoXmfkf1bp/jZJwbrivqf3blKs7vky5AvuPad9Ht/bdzev7ZdP3wnzKib2Hr2KOMo/BPR22QZsoy2hsYqpk6R7AG6qRy4sooyDmU85W7VLVF4JSLmMJ5az/b0bEvtU6dqfUfdu+i6f+d+DlEbFtdfsESvmJjVnDuoPfh2Xm3zeVedhgopDMfIgyu+oplBIaDVdQ6gx/MdfVXf534NiI2Kq6fQblMrrGff8vIuZVSYbPUC71a/gaJTF8alXm4npg1+oAFOCYpmVfDFyYmedTajIfTNnvjXjPppyxvDgzV7bZF0kZmdHOh6vn2pd1o0QaPgf8WTViA0o9qOsbI8nnyNMoI6wljaGNfJdAh76b4fRHD/v/7d19mCR1leD7b9s2doODLytOoy04LnJAWkFLce+Awr2iDrMyyFVkBFFmbNBFHOYu6uwMzTC4o46OoFdX0IcX4dq+sAOLIg0u2qDyIrhTCtoIZ907gNbQtLXXWRUUu2n6/hFRkFRnVuVLZUZE1vfzPP105S8jM88vIuoXkad+caKcHfIIxXGg9SZUu0XEHwBExBEUJ/0/ZO7jgyS1M9c57zeAt5Ul5p5EkQCYXUYDivtvEEU96H1ouRlzqXWM7Xh+SZuxODN/RXFu/67yM55Sft7Xe+5pMyTwm4h4Czya4N9IUcIPisTNsnJm9i2UV/mVx7mbKL4PPU4U97jZANycRe3T/4fimChJAD8CHpkpTxQRewJ3UNRR7sVES27mHRQl3x6gOM68uyz5tpQiify3bV4fFLOYz8jMqyhKiT6R4hixjSLBPHOMmKYo40EU9aJ32+HdCjcDL4iIg8plX0JxD5bf7bFvGnMmmxeZzPxfwIeA70XERopabDcBe2XmZoqZypeUlwr+e+CPM3Oa4uZIfx8Rt1N80T4+M+/p4XOvpbjB4Ncj4gcUNy75P8sk61yuBD4UEe1m+s7nGorLoFuTzddSXD7SWsD+gnKZWyLijvI1J5TP/RnFzVl+SDGD74cU5S4elZlJcbOWdRSzJI6luDHW93j8zJKPAu8o+38DxaWJe7U8fwlFzevPtOtMFrWafxPFTU5mP/edMs6rWpLoMy6k+HLz3Yi4k6JO3nEMSTlb5JkU+5WkMTTXsaRc5L8A6yLiNbNeOtLxqIPPAve1XL4Ixcy148tj3OnA68vL3Oc6PkjSDuY55z2L4nLk2ylmm11d/sFutoPK88iLgGMy819mPX8d8NqI+CRzn1+2LtfqOOBVZYmO71KM2RcP0O3aKkvGHUnxh8MfUHwXOCMzZ85T/wH4Vjlp5Fjg35Tr5Vbgi5n5+TZvew1F4mhjFDcl/336qzktaQyVkyj+CPh35bjzNYq68bPvfzKfTcCHy3PtP+Sxq5//hqJkxW0UY9FWirrPs32fYsy7qzzv/gOKCWF7lceky4Eby/zC+yjq2d9GcQ+o29q8H2XO6I3Ax8rz5osp6jdbQkOPs2T79vlyfZJGofzL59sy8/A5ljkWODgzTx5dZL2J4i7i01nc4FGSaiOKG0VdAazLzEvLtudS3K9gvnrTkjR0EbEd2C0z/2fVsUiSqlHOLv5ol6XtpNpxZrNUAxHxTYobFr57ruXKG1g9PSJeOIq4elVemvgSHn95uiRVLiJeQHGJ4DTFTDZJkiRJ0gJzZrMkSZIkSZIkaWDObJYkSZIkSZIkDeyJVQcwn/JOzS+jKI6+reJwJGmhLQV2B/5beTOJ2nNcljTGHJMlqT4ckyWpXroal2ufbKYYqG+oOghJGrJXADdWHUSXHJcljTvHZEmqD8dkSaqXOcflJiSbNwF8/vOfZ+XKlT29cOPGjaxevXooQY1Ck+M39uo0Of7FGPv999/PcccdB+VY1xCLclw29uo0OX5jr04/8Tsmj5/F0EdYHP20j+Oj236Oy5hcx+1ax5jAuHpRx5ignnHVMSaoZ1zzxdTtuNyEZPM2gJUrV7Jq1aqeXrh58+aeX1MnTY7f2KvT5PgXeexNusxuUY7Lxl6dJsdv7NUZMH7H5DGxGPoIi6Of9nF89NHPRo/JddyudYwJjKsXdYwJ6hlXHWOCesbVQ0xzjsveIFCSJEmSJEmSNLAmzGyWJLWIiCOAM4FdgGsz89SIOAw4B1gBXJqZa8tlDwAuAHYFvg28MzMfjog9gHXAM4EEjsvMB0bfG0mSJEmSNC6c2SxJDRIRzwM+DbweeBHwkog4HLgIOBLYF3hZ2QZFQvmUzNwbWAKcWLafC5ybmfsA/wicMbpeSJIkSZKkcWSyWZKa5SiKmctTmbkVOAb4NfDjzLw7Mx+mSDAfHRF7Aisy85bytReX7cuAVwKXtbaPsA+SJEmSJGkMWUZDmmWfffdr275l6zZ2WrbJVlaOAAAgAElEQVR0xNFIO9gL2BIRVwJ7AFcBd/D4u8FuAlYBz+rQ/gzgl2ViurW9Jxs3bmTz5s09d2BycrLn19SFsVenafHvs+9+7LLzciYmJh7X/uCvH+KuO++oKKreNW29z9Zr/NPT00OKpJ4855EkqTlaj88z55ges1VHJpulWXbZeTlHnPaVHdq/evaRFUQj7eCJFLOSDwUeAK4EfgNsb1lmCfAIxdUr3bRTtvdk9erVPd89d3JycofkW1MYe3WaGn+nY0lT+tLU9T6jn/inpqaGFM1jIuItwF+WD6/JzPd0qq8/7Fg855EkqTl2WrZ0h+O2x2zVkWU0JKlZ7ge+kZnTmfkb4ArgMGD3lmVWAvcBUx3afwY8JSJm/gS+e9kuSRqiiNgZ+ARwCLA/8IryBq+d6utLkiRJjWKyWRoTW7ZuA9hhFtdMu8bGVcBrI+KpZbL4cIrayxERe5Vtx1LMlrsXeCgiDipfe3zZvhW4gaLeM8BbgWtG2gtJWpyWUpx/7wIsK/9tpU19/UqikyRJkgZkGQ1pTLS7pAa8rGbcZOatEfER4EaKJMXXgfOAu4DLgeXA1Tx287/jgPMjYlfgexQz6gBOBi6JiLXAT4A3j6wTkrRIZeavIuIMijH718C3gC20r6/fk37q6M9VZqTp9bpbjVNf5rIY+mkfx0c3/VxsdfQlaVyYbJakhsnMi4CLZjVvoLgke/aytwMHtmm/l6LusyRpRCLiRcCfAnsCv6Aon/Ea2tfX70k/dfTn0uR63a2aXnu8W4uhn/ZxfHTbz1HU0ZckLTzLaEiSJEmj8VpgQ2b+LDN/S1Ey41Da19eXJEmSGsdkcw1Ze1eSJGks3Q4cFhG7RMQS4AiKUho71NevKkBJkiRpEJbRqCFr70qSJI2fzLw2Il4MTFLcGPC7wN8BV9C+vr4kaYFExJnAm8qH6zPzfRHxWeBg4MGy/azMvCIiDgPOAVYAl2bm2tFHLEnN1HWyuTz5vRl4XWbeExEnAX9GUWPuH4F3ZOaWcgD/U+Bfypeen5mfiogDgAuAXYFvA+/MzIcXsC872Gff/dq2b9m6jZ2WLR3mR0uSJEk7yMwPAx+e1dy2vr4kaWGUyePXAC+myGF8LSKOAl4KvDIzN7Usu4Li/iiHAD8F1kfE4ZnpVSeS1IWuks0R8XLgfGDv8vHewHuBCeBXFPXm3gV8jGKw/uPM/M6st1kHrMnMWyLiQuBE4LwF6ENHu+y83BnCkiRJkiQtbpuA0zJzC0BE3AnsUf67KCKeTXGVyVkUf/z7cWbeXS67DjgaSxxJUle6ndl8IkUy+XPl498CJ2fmLwEi4ocUgzQUyea/iog9KWYwvwf4XWBFZt5SLnMxxSA+1GSzRmf2bPGZetPOIpckSZIkVSkz75j5OSKeT1FO4xUUN2k9GfgFcBXwduABiuT0jE3Aql4+b+PGjWzevPnRx5OTk31GPjx1jAmMay6z7+s1ow6xtapbPFDPmKCecc0V0/T0dFfv0VWyOTPXAETEzON7gXvLtt2AU4ATIuLJwPcpZj3/D4qk8hkUg/aCDtbd6PSLCPXcoDOaGPfExETHWeR1jbmTJq5/aG7cszUp1tn6ib3bwVqSJEnSYCJiP2A98N7MTOColuc+CbwVuIyi1MaMJcAjvXzO6tWrWbWqSHlMTk7O+V2tCnWMCYyrX3WKrY7rqo4xQT3jmi+mqamprt5noBsElpeaXANcmJnfLJv/sOX5sylqHV3NAg7WC6FuG7RbTYy7iTF30tS+NCXuOg623eo39m4Ha0mSJEn9i4iDgMuBP8/ML0XEC4G9M/PycpElFDdvnQJ2b3npSuC+kQYrSQ3Wd7I5IvYB/ivwicw8u2zbAzgsMy8qF3OwliRJkiRJlYmI5wBfBo7JzOvK5iXAxyPiOorSGScBlwC3Fi+JvYC7gWMpJtFJi0qnsqiWS9V8+ko2R8TvANcCp2fm51qe+g3wkYi4HriHos7zFZl5b0Q8FBEHZeZNwPFYXF+SJEmSJA3fe4DlwDkz5UGBTwMfAm4ClgGXZ+YXASLiBIpZ0MsprtS+bMTxSpXbadnSjuVSpbn0O7N5DcVN/06LiNPKtisz868j4h3AV4GdgBuBs8vnjwPOj4hdge8Bn+g/bEmSJEmSpPll5qnAqR2ePrfN8huA/YcalCSNqZ6SzZn53PLHj5X/2i1zOcVfAGe33w4c2GN8kiRJkiRJkqQGeELVAUiSJEmSJEmSms9ksyRJkiRJkiRpYCabJUmSJEmSJEkDM9ksSZIkSZIkaUFt2bqtp3aNh55uEChJkiRJkiRJ89lp2VKOOO0rO7R/9ewjK4hGo+LMZkmSJEmSJEnSwEw2S5IkSZIkSZIGZrJZkiRJkiRJkjQwk82SJEmSJEmSpIGZbJYkSZIkSZIkDcxksyRJkiRJkqR5bdm6ras2LV5PrDoASZIkSZIkSfW307KlHHHaVx7X9tWzj6woGtWRM5slSZIkSZIkSQNzZrMkSZIkSRprEXEm8Kby4frMfF9EHAacA6wALs3MteWyBwAXALsC3wbemZkPVxC2xsSWrdvYadnSeduG+XlVxKHFqatkc0TsCtwMvC4z7+l1QI6IPYB1wDOBBI7LzAcWvDeSJElSjUXEEcCZwC7AtZl5aqdza0nSwijH2dcALwa2A1+LiDcDHwYOAX4KrI+IwzPzGor8xZrMvCUiLgROBM6rJnqNg1GXnmj3eZ0+s5dlm2B28nxiYsKE+ojNW0YjIl4O3AjsXT5eAVwEHAnsC7wsIg4vF18HnJKZewNLKAZkgHOBczNzH+AfgTMWshOSJElS3UXE84BPA68HXgS8pDyP7nRuLUlaGJuA0zJzS2ZuBe6kyHH8ODPvLmctrwOOjog9gRWZeUv52ouBo6sIWlLvZpLnrf9MNI9WNzObTwTeBXyufHwg5YAMEBEzA/KP2HFAPisiLgBeSXFSPdP+LeAvFqIDkiRJUkMcRTFzeQogIo4Bnk+bc2vgmsqilKQxk5l3zPwcEc+nKKfxSYok9IxNwCrgWR3au7Zx40Y2b9786OPJycnegx6yOsYE4xvXxMTEwO/by3t0Wrbd8nMt24uZ9219/17iWCgLsa5HpWkxTU9Pd/Ue8yabM3MNQETMNHUaeDu1PwP4ZUt9o54HathxsO5GFTv1Qmhi3E2MuZOm9qWpcc/WpFhn6yf2bgdrSdJY2AvYEhFXAnsAVwF3MGBSAxbXuXKvxqkvc1kM/bSP46Obfg7jPDki9gPWA+8FHqa8gru0BHiE4grw7W3au7Z69WpWrSqG8snJyQVL5i2UOsYEizOuhXjfXt5jy9ZtQ+1LL+tq1Nu6bvtWHff3+WKamprq6n36uUFgp4G323bocaCGxw/WC6FuG7RbTYy7iTF30tS+NCXuOg623eo39m4Ha0nSWHgixRV/hwIPAFcCv2HApAZ4rtxJk88terEY+mkfx0e3/Vzo8+SIOAi4HPjzzPxSRBwC7N6yyErgPmCqQ7s0r7rXBh517WgtTv0kmzsNvJ3afwY8JSKWZua2chkHakmSJC029wPfyMxpgIi4gqJkxraWZUxqSNICi4jnAF8GjsnM68rmW4unYi/gbuBY4KLMvDciHoqIgzLzJuB4LG2kLo3bzfakfsx7g8A2Hh2QI2IpxYB8TWbeCzxU/rUQygG5LL5/A3BM2f5WHKglSZK0+FwFvDYinlqeRx8OXEabc+sqg5SkMfQeYDlwTkTcFhG3ASeU/y4HfgTcRTEmAxwHfCwi7gKeDHxi1AFL3diyddv8C0kj1vPM5sx8KCJOoBiQlwNX8/gB+fyI2BX4Ho8NyCcDl0TEWuAnwJsHjFuSFr2I+CjwjMw8ISIOAC4AdgW+DbwzMx+OiD0o7qz9TCCB4zLzgYh4KvB54HnANPCmzLy/ko5I0iKRmbdGxEeAG4FlwNeB8ygSHO3OrSVJCyAzTwVO7fD0/m2Wvx04cKhBSQtg3GZStytDUvfSJNpR18nmzHxuy88b6GFALmc9H9pXhJKkHUTEq4C3UdzgBIqE8prMvCUiLgROpEhgnAucW9alOwM4A/gL4G+BGzLz30bE8cD/zWNXoEiShiQzLwIumtXc9txakiRpMbGm9Hjop4yGJKlCEfF04APAB8vHewIrMvOWcpGLgaMjYhnFjagua20vf/63FDObAb4IHF4uL0mSJEmS1Jd+bhAoSarWZ4DTgeeUj58FbGp5fhOwCngG8MvMfHhW++NeU5bb+CWwGz3clGrjxo1s3ry55+AnJyd7fk1dGHt1mhb/xMREx+ea1JcmxdpOr/FPT08PKRJJkiRpcTDZLEkNEhFrgJ9m5oayfj4UV6lsb1lsCfBIm3bK9pllWi1pea4rq1evZtWqVfMv2GJycnLOJFydGXt1mh7/bE3pS9PXez/xT01NDSkaSZKk7lijWE1nslmSmuUYYPfyDtpPp7g79nZg95ZlVlLMUP4Z8JSIWJqZ28plZmYu/3O53FREPBH4HeD/G00XJEl15s15JEmqzrjd9E+Lj8lmSWqQzHz1zM/lzOZDM/NPImJjRByUmTcBxwPXZObWiLiBIkH9BeCtwDXly68uH3+wfP6GzNw6wq5IkmrKm/NIkqRh8o/Y481ksySNh+OA8yNiV+B7wCfK9pOBSyJiLfAT4M1l+xnAxRFxB/C/ytdLkiRJkjRUzt4ebyabJamhMvNi4OLy59uBA9sscy9waJv2nwN/NNQAJUmSJEnSovKEqgOQJEmSJEmSJDWfyWZJkiRJkiSpS1u2bqs6BKm2LKMhSZIkSZIkdcmaw1JnzmyWJEmSJEnS2Gk3A9lZydJwObNZkiRJkiRJY6fdDGRnH0vD5cxmSZIkSZIkSdLAnNksSZIkSZLGXkTsCtwMvC4z74mIzwIHAw+Wi5yVmVdExGHAOcAK4NLMXFtNxBqGLVu3sdOypV23S+pN38nmiFgDnNLS9HvA54BdcLCWJEmSJEk1EREvB84H9m5pfinwyszc1LLcCuAi4BDgp8D6iDg8M68ZZbwaHm/uJw1X38nmzLwAuAAgIvYDvgz8DXA9DtaSJEmSJKk+TgTeRTFJjojYGdgDuCging1cAZwFHAj8ODPvLpdbBxwNmL+QKuBM9OZZqDIa5wF/BfwaB2tJkiRJklQjmbkGICJmmlYC1wEnA78ArgLeDjwAbGp56SZgVS+ftXHjRjZv3vzo48nJyX7DHpo6xgQLH9fExMSCfP7s9l7ft5fPW6j3HrWZvrT2aSH6MtdM9F7WXx33+abFND093dV7DJxsLstjrMjMf4iI5zGiwbobc+3UddygM5oYdxNj7qSpfWlq3LM1KdbZ+om928FakiRJ0sLJzH8Cjpp5HBGfBN4KXAZsb1l0CfBIL++9evVqVq0qUh6Tk5O1Sx7WMSaoPq4tW7e1/fwHf/0Qu+y8fCifWcft0K+JiYmRb8NePqtu67rq/b2d+WKamprq6n0WYmbzOyhqMY9ssF4Iddug3Wpi3E2MuZOm9qUpcddxsO1Wv7F3O1hLkiRJWjgR8UJg78y8vGxaAmwFpoDdWxZdCdw34vBUAWs5SwtjoGRzROxEUYf5hPKxg7UkSZIkSaq7JcDHI+I6iquxTwIuAW4FIiL2Au4GjqW4B5UkqQtPGPD1LwL+e2Y+WD6eGayfFhHLKAbrK2gZrCNiKcVgbb1mSZIkSZI0cpn5A+BDwE3Aj4DbMvOLmfkQxYS6y8v2uyiu1tYitWXrtqpDUBtul/oatIzG8yhmLQPFYB0RM4P1MuDyzPwiQEScQDFYLweuxsFakiRJi1REfBR4RmaeEBEHABcAuwLfBt6ZmQ9XGqAkjanMfG7Lz+cC57ZZZgOw/wjDUo21K69haY3qWfakvgZKNmfmfwb+86w2B2tJkiSpg4h4FfA2YH3ZtA5Yk5m3RMSFwInAeVXFJ0mS1IstW7ex07Kltb0H00x8s/126zae1Ka90/LqzkLcIFCSJElSFyLi6cAHgA8C+0fEnsCKzLylXORi4CxMNkuSpIao+yzjueKrc9xNZbJZkiRJGp3PAKcDzykfPwvY1PL8JmBVr2+6ceNGNm/e3NNrep19NDk52dPyddHUuHu1GPppH8dHN/2cnp4eQSQaF85ElerDZLMkSZI0AhGxBvhpZm4o72cCxQ27t7cstgR4pNf3Xr16NatW9Zyj7kldL42dy+TkZCPj7tVi6Kd9HB/d9nNqamreZaQZdZ9ZKy0mJpslSZKk0TgG2D0ibgOeDjyZItG8e8syK4H7KohNkiRJGtgTqg5AkiRJWgwy89WZuTozDwD+GrgyM/8EeCgiDioXOx64prIgJUnSSGzZuq3qEKShcGazJEmSVK3jgPMjYlfge8AnKo5HkqRaGqfazJb+0Lgy2SxJkiSNWGZeDFxc/nw7cGCV8UiS1AQmaKX6s4yGJEmSJEmSJGlgJpslSZIkSZIkSQMz2SxJkiRJkqRa8QZ6UjNZs1mSJEmSJEm10q4+s7WZpfpzZrMkSZIkSZIkaWAmmyVJkiRJkiRJAxuojEZEXA88E9haNr0D+NfAWmAZ8PHM/FS57GHAOcAK4NLMXDvIZ0uSJEmSJHUrInYFbgZel5n3dMpTRMQBwAXArsC3gXdm5sMVhS1JjdL3zOaIWALsDeyfmQdk5gHAFPAB4GDgAOCkiHhBRKwALgKOBPYFXhYRhw8cvSRJkiRJ0jwi4uXAjRR5DObJU6wDTsnMvYElwImjj1iSmmmQMhpR/n9tRNweEacAhwHXZebPM/NB4DLgjcCBwI8z8+7yr4HrgKMHCVySJEmSJKlLJwLvAu4rH7fNU0TEnsCKzLylXO5izF9IUtcGKaPxNGAD8G6KkhnfBC4FNrUss4liAH9Wm/ZVA3y2JEmSJElSVzJzDUDEzLy5jnmKgfMXGzduZPPmzY8+npyc7D3gIatjTPD4uCYmJiqMRIvdKH5H6vh7OFdM09PTXb1H38nmzPwO8J2ZxxFxIUWto79tWWwJ8AjFDOrtbdq7Nnuw7sZcA1MdN+iMJsbdxJg7aWpfmhr3bE2KdbZ+Yu92sG4VEWcCbyofrs/M9/Vaby4i9qCYvfFMIIHjMvOBnoORJEmSmqlTnmLg/MXq1atZtarIT09OTtYuaVrHmKC+cWlxGva+WMf9fb6YpqamunqfvpPNEXEw8KTM3FA2LQHuAXZvWWwlxSUqUx3au9Y6WC+Eum3QbjUx7ibG3ElT+9KUuOs42Har39i7HaxnlEnl1wAvpjgJ/lpEvBn4MHAI8FNgfUQcnpnXUCSU12TmLeUfBU8EzgPOBc7NzC9FxBnAGcBf9NwBSZIkqZk65SkGzl9I0mI2SM3mpwJ/HxHLI+J3gLcBbwFeFRG7RcTOwBuArwG3AhERe0XEUuBY4JoBY5ekxWgTcFpmbsnMrcCdFDc56breXEQsA15JUVf/0fYR9kGSJEmqWts8RWbeCzwUEQeVyx2P+QtJ6togZTSuKu/m+n1gKfCpzLwpIk4Hrgd2Ai7IzO8CRMQJwOXAcuBqHktySJK6lJl3zPwcEc+nKKfxSXqrN/cM4JdlYrq1vSf9lDeCxVcqpS6aHDs0L35LG9VDr/H3U9pIktRMmfnQHHmK44DzI2JX4HvAJyoJUlIltmzdxk7Lls7bpvYGuUEgmTlz6XVr2xeAL7RZdgOw/yCfJ0kqRMR+wHrgvcDDFLObZ8xXb252O/RYhw76K2+0GEul1EGTY4fmxz9bU/rS9PXeT/y9ljaSJDVPZj635ee2eYrMvB04cIRhSaqRnZYt5YjTvvK4tq+efWRF0TTPIGU0JEkVKC/p2wD8h8y8hN7rzf0MeEp5uSDlMtahkyRJkiRJAzHZLKkyW7Zue9zjmRlos9v1mIh4DvBl4NjM/FLZ3FO9ubLW8w3AMWX7W7EOnSRJkiRJGtBAZTQkaRDtLk0BL0+Zx3soasqdExEzbZ8GTqC3enMnA5dExFrgJ8CbRxG8JEmSJEkaXyabJalBMvNU4NQOT3ddb66c9XzoggYnSZIkSZIWNctoSJIkSZIkSVKPOpUBXczlQZ3ZLEmSJEmSJEk9sjzojpzZLEmSJEmSJEkamDObJUmSpBGJiDOBN5UP12fm+yLiMOAcYAVwaWaurSxASZIkaQDObJYkSZJGoEwqvwZ4MXAAMBERbwYuAo4E9gVeFhGHVxelJEmS1D+TzZIkSdJobAJOy8wtmbkVuBPYG/hxZt6dmQ8D64CjqwxSkiRJ6pdlNCRJkqQRyMw7Zn6OiOdTlNP4JEUSesYmYFWv771x40Y2b97c02smJiZ6Wn5ycrKn5euiqXH3ajH00z6Oj276OT09PYJIJEkLzWSzJEmSNEIRsR+wHngv8DDF7OYZS4BHen3P1atXs2pVzznqnvSanK6DycnJRsbdq8XQT/s4Prrt59TU1Aii0Sht2bqNnZYt7bpdUjOZbJYkSZJGJCIOAi4H/jwzvxQRhwC7tyyyErivkuAkSRqinZYt5YjTvrJD+1fPPrKCaCQNi8lmSZIkaQQi4jnAl4FjMvO6svnW4qnYC7gbOJbihoGSJElS4wyUbI6IMylqzQGsz8z3RcRngYOBB8v2szLzivLu2+cAK4BLM3PtIJ8tSZIkNcx7gOXAOREx0/Zp4ASK2c7LgauBy6oITpIWo4i4HngmsLVsegfwr4G1wDLg45n5qYrCWxRmymgshjIy0mLQd7K5TB6/BngxsB34WkQcBbwUeGVmbmpZdgXFDI1DgJ8C6yPi8My8ZpDgJUmSpKbIzFOBUzs8vf8oY5EkQUQsoaibv2dmPly2PRv4EjAB/Ba4OSKuz8wfVRfpeLO8hjReBpnZvAk4LTO3AETEncAe5b+LygH6CuAs4EDgx5l5d7nsOuBowGSzJEmSVHPe1EnSmJq5zOTaiPhXwPnAr4DrMvPnABFxGfBG4P3VhCipidqdIy2W86a+k82ZecfMzxHxfIpyGq8ADgVOBn4BXAW8HXiAIjk9YxPQ0+2yN27cyObNm3uKca5LMCYnJ3t6r1FqYtxNjLmTpvaliXE3Mea59BPz9PT0ECKRJGlhOetM0ph6GrABeDdFyYxvApeyY/7iwF7edHb+oo7fbRYqpn323Y9ddl6+Q/uDv36Iu+6843FtlslQ07X7vem0X7c7d/rq2Uc+7j2aNjZ0m78Y+AaBEbEfsB54b2YmcFTLc58E3kpRd257y8uWAI/08jmrV69m1aqe8tNzauog18S4mxhzJ03tSxPjblrMk5OTfcU8NTU1hGgkSZIkzSczvwN8Z+ZxRFxIca+pv21ZbKD8Rb/fE4ZpoWPq9MfIuvVbGsSWrdsWZJ+eeY8mjg3d5i8GvUHgQRQ3M/nzzPxSRLwQ2DszLy8XWUJRZH8K2L3lpSuB+wb5bEmSJEmSpH5FxMHAkzJzQ9m0BLgH8xeSZvEqr+4NcoPA5wBfBo7JzOvK5iXAxyPiOorSGScBlwC3Fi+JvYC7gWMpbhgoSZIkSZJUhacC74+I36coo/E24C3AuojYDXgQeANFbkOS1IVBZja/B1gOnBMxU1OfTwMfAm6iGKgvz8wvAkTECRSzoJcDV1OU1pAkSZIkSRq5zLwqIl4OfB9YCnwqM2+KiNOB64GdgAsy87tVxilpcWrqTQYHuUHgqcCpHZ4+t83yG4D9+/08SZIkSc3V6ctRE740SRpfmXkGcMasti8AX6gmIkkqdLrJYN0NfINASZIkSZqPtQ4lSdJi1voH9tYb8Y3bH95NNkuSJEmSJEnSEC2WP7w/oeoAJEmLxz777rdD25at2yqIRJIkSZIkLTRnNkuSRmaXnZc3suaUJEmStBgsVH39cSsLINVFE+6BYbJZkiRJkiRJC3aZf1NvbCbVXRNKcVhGQ5IkSdKCGmaJpHbvbUkmSeqdY6ekYXBmsyRJkqQFNcwZbc6Wk6SF4XgqaRic2SxJkiRJkiRJFRi3qwyc2SxJkiRJktRwTbhxmKQdNaEOcy9MNkuSJEmSJDVcp4TV5X/3usc9npiY6Pm9TVhL6pbJZkmSxlDrF4KZLxR+SZDUJI5ZkrQwFmLW5LjNvJQ0PCabJalH7b787rPvfhVFI7XnDV8kNZ2JDUmSpOYx2SxJPTKJJ0mSJKlKXv0hqa5MNkuSJEnqi8kOzWi3L7h/LD7eoG50nAAjqa5GmmyOiGOBtcAy4OOZ+alRfr4k6TGOyZJUL00clxei1IVJqIVXRcLPxJdgvMrfNHFMlqQ6GFmyOSKeDXwAmAB+C9wcEddn5o9GFYMkqeCYLEn1spjH5XFKTtWF61QaTBVjsrPCJQ1DFVcejXJm82HAdZn5c4CIuAx4I/D+eV63FOD+++/v60O3/vrnO7RNTU319V6j1MS4mxhzJ03tSxPjbmLMsGPc/cbcMraN+gyy3zEZFnhcbsL2Bpienm5MrDOauq5na+K6h+aObzOaut5n9BN/hWMy1Oxcud340e2yw3yPud67nabvx91q188qxqBhHncWw7Yclz7Ot+91289xGZN72a5rPvD1HdouOP3VHZdv4ljte/geTY2v7u9x9z0/YdkTn7BDO+w4tnQaV+Ybr7odl5ds3759rucXTET8JbBLZq4tH68BDszMk+Z53cHADSMIUZKq9IrMvHFUH9bvmFwu67gsadyNdEwGz5UlaQ6OyZJUL3OOy6Oc2fwEoDWzvQR4pIvX/TfgFcAmYNsQ4pKkKi0FdqcY60ap3zEZHJclja+qxmTwXFmSZnNMlqR66WpcHmWyeYpi0J2xErhvvhdl5m+Bkf4VU5JG7P+t4DP7GpPBcVnS2KtiTAbPlSWpHcdkSaqXecflUSabvwH8TUTsBjwIvAGY93JtSdJQOCZLUr04LktSfTgmS1Kf2leOHoLM/GfgdOB64DbgC5n53VF9viTpMY7JklQvjsuSVB+OyZLUv5HdIFCSJEmSJEmSNL5GNrNZkiRJkiRJkjS+TDZLkh5TtjcAAArmSURBVCRJkiRJkgZmslmSJEmSJEmSNDCTzZIkSZIkSZKkgZlsliRJkiRJkiQN7IlVBzBMEbErcDPwusy8p+JwuhYRZwJvKh+uz8z3VRlPLyLi/cAbge3AhZl5TsUh9SwiPgo8IzNPqDqWXkTE9cAzga1l0zsy89YKQ+paRBwBnAnsAlybmadWHFLXImINcEpL0+8Bn8vMUzq8ZFGYa/yNiAOAC4BdgW8D78zMh0ce5Bzmif9M4E+Bfymbzs/MT402wvbmO37Ued13EXtt1zvMf/yr+bqfL/Zar3vofOyOiD2AdRTHxwSOy8wHRh9hvUTEscBaYBnw8bptz361G0ci4jDgHGAFcGlmrq0swAXWut/XeYzpR7tzw3HclhHxFuAvy4fXZOZ7xmVbzj6X6rT9mt7fNv08CfgzimPqP1J8J9sy6zVvA/4O2Fw2rc/M04cc12eBg4EHy0XOyswrZr1m6NuiNS7gBcAHW55+NnBrZr5u1muGtr76PW4M+/yiQ1yV7lsdYqp8v5odF/BNKt6vyvff4fy66n2rQ0xD2a/GdmZzRLwcuBHYu+pYelHufK8BXgwcAExExFHVRtWdiDgE+D+AFwEvBd4dEVFtVL2JiFcBb6s6jl5FxBKKfX3/zDyg/NeURPPzgE8Dr6fYd14SEYdXG1X3MvOCmXUOHAf8DPibaqOqVhfj7zrglMzcG1gCnDiq2LrRRfwvBf645XetFkmaLo8ftVz3XcZey/UOXR//6rruu4m9tuse5j12nwucm5n7UJxAnzGywGoqIp4NfIDiy+EBwEkR8YJqoxpch3HkzcBFwJHAvsDLmnSOMZc2+30tx5h+zHFuOFbbMiJ2Bj4BHALsD7yi3I8bvy1nn0tFxAo6b7/G9rdNP/cG3gv8PsW++wTgXW1e+lLg37ccVxc60dzuXPalwCtbPvOKNi8d6raYHVdmXt3yPeoPgF8C/1eblw5lfQ143Bja+UWHuP6CCvetOc7VK92v2sUFPKnK/aqMq9359f5UuG/Ncc4/lP1qbJPNFDvwu4D7qg6kR5uA0zJzS2ZuBe4E9qg4pq5k5reA/738K9UzKWbOPzj3q+ojIp5O8eXrg/MtW0MziYFrI+L2iGjSrNqjKP6qN1Xu88cAjUiUt3Ee8FeZ+T+rDqRiHcffiNgTWJGZt5RNFwNHjy60rsx3/Hgp8FcR8YOI+E8RsXx0oc1pzuNHzdd9N8e+uq73eY9/dV73XR67a7vu5zp2R8Qy4JXAZWXTxdRkvVfsMOC6zPx5Zj5IsX7eWHFMC6HdOLI38OPMvLvcx9cxBvvA7P2+zmNMn9qdG/6a8duWSym+j+9CcZXBMoorFMdhW84+lzqQNttvDPbd2f38LXByZv4yM7cDP6T9d/mXAW+LiB9GxLqIeNow4yr/sLEHcFF5LD8rIh6XCxrRtpjrHPvvgU9n5o/bPDes9dXXcWME5xft4lpOtftWp3P1qver+b5DVLFfdTq/fioV7lsdYnqIIe1XY5tszsw1mXlD1XH0KjPvmPlFjIjnU1wOcHW1UXUvM7dGxFnAj4ANwD9XHFIvPgOczmOXCTfJ0yjW91HAq4B3RsSrqw2pa3sBSyPiyoi4DTiZBm6D8q+qKzLzH6qOpWrzjL/PojgpmLEJWDX8qLo3V/wR8WTg+xR/AX4JxUlDLWZKdnH8qO26ny/2Oq/3GfMc/2q77mHu2Buw7uc6dj8D+GU+dqlmrdZ7hWq9P/arwzjyCGPYV3bc78dtm7Y7Nxy3PpKZv6IYT+8CpoB7gC2MQT/bnEt12n6N3q6z+5mZ92bm1wEiYjeKUntfafPSTcB/pJhJ+FPgPw0zLmAlcB1FSax/A7wCePuslw19W3Q6xy7H7EMpZvq3M5T1NcBxY6jnFx3i+kKV+1aHmL5GxfvVXN8hqtqvWmKbfX7dzboY9r41O6afDGu/Gttkc9NFxH7A14H3dvgrTG1l5pnAbsBzaMhlUFHU3f1pZm6oOpZ+ZOZ3MvOtmfmLclbthcAfVh1Xl55IMcvq7cD/BrycBpYyAd5BUX9Jc3sCRT2oGUsoTuoaITMfyMw/zMy7ypOAs6nZ79ocx4/ar/tOsTdhvcOcx7/ar/tOsdd53Xdx7J693qFm670itd8fB9E6jgD/xJj1tcN+P27btN254fMYrz4SES+iSNLsSZGE2EZxOfhY9bPUaR8dt30XeLRc0QaKmqjfnP18Zh6VmTeVMwk/Agy1JExm/lP5mZsy89fAJ9nxWF7ltjiJomzAb9s9Oez11cdxYyTnF+3Oi6vet2bFlHXZrzp8h6h0vyo/o/X8em9qsG+1O+cfxn5lsrmGIuIgig39HzLzkqrj6VZE7BNF8XfKwea/UPzlowmOAV5Tzp54P/BHEfGximPqWkQcXNbum7GEx24UWHf3A9/IzOnM/A1wBcWldo0RETtR1Nu7supYGmAK2L3l8UoaVO4oIvaIiD9taarV79o8x49ar/u5Ym/Aep/v+FfbdT9f7DVf9/Mdu38GPCUilpaPd6cm671itd0fB9VmHBnHvu6w3wNrGK9+tjs3PIzx6iPAa4ENmfmzMhlyMcUsvHHrJ3T+XRy739GI2IfiBniXZOZ/bPP8UyKitX7sEmCoN0SMiBdGxBtmfebsY3mV2+L1wJfaPTHs9dXncWPo5xftzour3rdmx1SX/WqO7xBV7lftzq8PpcJ9q9M5/7D2K5PNNRMRzwG+DBybmW1/MWrsecD5EfGkMvl2JMUNAGovM1+dmauzKCL/18CVmdmuiHxdPRX4+4hYHhG/QzEzuF1x/jq6CnhtRDy1HFQPByYrjqlXLwL+exa1LzWHzLwXeKg8KQA4HrimwpB69RvgIxHxe1HcmPNd1OR3bb7jR53XfRfHvtqu99Kcx786r3vmP3bXdt3Pd+zOonbfDRTJOYC3Up/1XqVvAK+KiN2iqOP5BopLYRutwzhya/FU7FWeYxxLw/eBDvv9n1DfMaYf7c4NL2PMtiVwO3BYROxSjq9HAN9ivLbljLa/izU/Pvas/B52LbA2M8/usNgDwPuiuFkeFJetD/u4ugT4eEQ8LYqasCfN/syqtkVEPIOiFOHdHRYZ2vrq97gx7POLdnFVvW91WFeV71edvkNUuV+V2p1ff4Zq9612Md3KkPYrk8318x6K4u/nRMRt5b93Vh1UNzLzamA9RW3HSeDmBibMGykzr+Lx6/6izPxOtVF1JzNvpbgU40aK2kH3Ap+tNKjePY/ir7bqICKujoiXlg+PAz4WEXcBT6ZzHa3amIk/M6cpSqZ8FUiKk6xOB+ZRa3v8aMi6nzP2mq/3jse/Jqz7+WKv+7pvJyIuiIg/Kh+eDJwUET+iqCW4trrI6iEz/5mi3u/1wG0UdSC/W21UC2KHcQQ4ofx3OcU5xl08dtOdcVPLMaYfHc4Nz2PMtmVmXgt8kWLs/QHFDQL/jjHaljMy8yE6b79x6u8a4HeB01rOZ94Pjx2bMnMbRV3Z8yLiTmACeN8wg8rMHwAfAm6iWP+3ZeYXy7iqPldp+z1qROurp+PGCM8v2sV1CtXuW+1i+n2q36865c+q3K/myo2dQEX7VruYKGpED2W/WrJ9++xyIJIkSZIkSZIk9caZzZIkSZIkSZKkgZlsliRJkiRJkiQNzGSzJEmSJEmSJGlgJpslSZIkSZIkSQMz2SxJkiRJkiRJGpjJZkmSJEmSJEnSwEw2S5IkSZIkSZIG9v8DuSPVaiEL9KcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Figure size 1440x720 with 16 Axes>"
       ]
      },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "#plot cmap\n",
-    "plt.figure(figsize=(20, 10))\n",
-    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
-    "sns.heatmap(train1.isnull(), cmap=cmap)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order No                                    0\n",
-       "User Id                                     0\n",
-       "Vehicle Type                                0\n",
-       "Platform Type                               0\n",
-       "Personal or Business                        0\n",
-       "Placement - Day of Month                    0\n",
-       "Placement - Weekday (Mo = 1)                0\n",
-       "Placement - Time                            0\n",
-       "Confirmation - Day of Month                 0\n",
-       "Confirmation - Weekday (Mo = 1)             0\n",
-       "Confirmation - Time                         0\n",
-       "Arrival at Pickup - Day of Month            0\n",
-       "Arrival at Pickup - Weekday (Mo = 1)        0\n",
-       "Arrival at Pickup - Time                    0\n",
-       "Pickup - Day of Month                       0\n",
-       "Pickup - Weekday (Mo = 1)                   0\n",
-       "Pickup - Time                               0\n",
-       "Distance (KM)                               0\n",
-       "Temperature                              4366\n",
-       "Precipitation in millimeters            20649\n",
-       "Pickup Lat                                  0\n",
-       "Pickup Long                                 0\n",
-       "Destination Lat                             0\n",
-       "Destination Long                            0\n",
-       "Rider Id                                    0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train1.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order No                                   0\n",
-       "User Id                                    0\n",
-       "Vehicle Type                               0\n",
-       "Platform Type                              0\n",
-       "Personal or Business                       0\n",
-       "Placement - Day of Month                   0\n",
-       "Placement - Weekday (Mo = 1)               0\n",
-       "Placement - Time                           0\n",
-       "Confirmation - Day of Month                0\n",
-       "Confirmation - Weekday (Mo = 1)            0\n",
-       "Confirmation - Time                        0\n",
-       "Arrival at Pickup - Day of Month           0\n",
-       "Arrival at Pickup - Weekday (Mo = 1)       0\n",
-       "Arrival at Pickup - Time                   0\n",
-       "Pickup - Day of Month                      0\n",
-       "Pickup - Weekday (Mo = 1)                  0\n",
-       "Pickup - Time                              0\n",
-       "Distance (KM)                              0\n",
-       "Temperature                             1437\n",
-       "Precipitation in millimeters            6869\n",
-       "Pickup Lat                                 0\n",
-       "Pickup Long                                0\n",
-       "Destination Lat                            0\n",
-       "Destination Long                           0\n",
-       "Rider Id                                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df.isnull().sum()"
+    "#Distribution review for test data\n",
+    "test_df[[column for column in test_df.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 22,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#Percentage of the missing values\n",
-    "def missing_values(df, threshold=0):\n",
-    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
+    "Deductions from the test dataset data column distribution.\n",
     "\n",
-    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
-    "             index=['% of Missing values']\n",
-    "             ).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>% of Missing values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Temperature</th>\n",
-       "      <td>0.205934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <td>0.973963</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              % of Missing values\n",
-       "Temperature                              0.205934\n",
-       "Precipitation in millimeters             0.973963"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing_values(train1)"
+    "* The most common temperature is 22,5 degrees Celsius.\n",
+    "* Platform 3 is the most used platform type\n",
+    "* Most of the orders delievered are from Business clients.\n",
+    "* Most orders are dealt with during the weekdays than on the weekend.\n",
+    "* Most of the trips covered between 4KM and 10KM."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>% of Missing values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Temperature</th>\n",
-       "      <td>0.203311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <td>0.971845</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              % of Missing values\n",
-       "Temperature                              0.203311\n",
-       "Precipitation in millimeters             0.971845"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing_values(test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 105,
    "metadata": {},
-   "source": [
-    "## Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x720 with 16 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Distribution review for train data\n",
-    "train1[[col for col in train1.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1766,41 +1875,46 @@
    ],
    "source": [
     "#Column Skewness of the testing dataset\n",
-    "train1.skew().plot.bar(figsize =(10,5))\n",
+    "train_df.skew().plot.bar(figsize =(10,5), color = 'Blue')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "## Univariate Anaysis\n",
     "# Calculating Skewness in the columns of our Training dataset\n",
-    "train1.kurt().plot.bar(figsize =(10,5))\n",
+    "train_df.kurt().plot.bar(figsize =(10,5))\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1832,6 +1946,8 @@
        "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
        "      <th>Pickup - Day of Month</th>\n",
        "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
        "      <th>Distance (KM)</th>\n",
        "      <th>Temperature</th>\n",
        "      <th>Precipitation in millimeters</th>\n",
@@ -1839,6 +1955,7 @@
        "      <th>Pickup Long</th>\n",
        "      <th>Destination Lat</th>\n",
        "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1853,6 +1970,8 @@
        "      <td>-0.058005</td>\n",
        "      <td>0.012770</td>\n",
        "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
        "      <td>0.005528</td>\n",
        "      <td>0.225060</td>\n",
        "      <td>-0.055948</td>\n",
@@ -1860,6 +1979,7 @@
        "      <td>0.021327</td>\n",
        "      <td>0.006319</td>\n",
        "      <td>0.043924</td>\n",
+       "      <td>-0.003827</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Placement - Day of Month</th>\n",
@@ -1872,6 +1992,8 @@
        "      <td>0.026122</td>\n",
        "      <td>0.999998</td>\n",
        "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
        "      <td>-0.007585</td>\n",
        "      <td>0.013097</td>\n",
        "      <td>-0.204143</td>\n",
@@ -1879,6 +2001,7 @@
        "      <td>-0.005453</td>\n",
        "      <td>0.007541</td>\n",
        "      <td>0.002077</td>\n",
+       "      <td>-0.014710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Placement - Weekday (Mo = 1)</th>\n",
@@ -1891,6 +2014,8 @@
        "      <td>0.999952</td>\n",
        "      <td>0.026132</td>\n",
        "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
        "      <td>0.003582</td>\n",
        "      <td>0.031064</td>\n",
        "      <td>-0.050592</td>\n",
@@ -1898,6 +2023,7 @@
        "      <td>0.011008</td>\n",
        "      <td>-0.004299</td>\n",
        "      <td>-0.000875</td>\n",
+       "      <td>0.009693</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Confirmation - Day of Month</th>\n",
@@ -1910,6 +2036,8 @@
        "      <td>0.026123</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
        "      <td>-0.007587</td>\n",
        "      <td>0.013099</td>\n",
        "      <td>-0.204143</td>\n",
@@ -1917,6 +2045,7 @@
        "      <td>-0.005448</td>\n",
        "      <td>0.007550</td>\n",
        "      <td>0.002073</td>\n",
+       "      <td>-0.014701</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
@@ -1929,6 +2058,8 @@
        "      <td>1.000000</td>\n",
        "      <td>0.026123</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>0.003574</td>\n",
        "      <td>0.031074</td>\n",
        "      <td>-0.050592</td>\n",
@@ -1936,6 +2067,7 @@
        "      <td>0.011036</td>\n",
        "      <td>-0.004251</td>\n",
        "      <td>-0.000900</td>\n",
+       "      <td>0.009744</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1991,65 +2123,356 @@
        "Confirmation - Day of Month                   1.000000   \n",
        "Confirmation - Weekday (Mo = 1)               0.026123   \n",
        "\n",
-       "                                 Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
-       "Platform Type                                    -0.058005       0.005528   \n",
-       "Placement - Day of Month                          0.026122      -0.007585   \n",
-       "Placement - Weekday (Mo = 1)                      0.999952       0.003582   \n",
-       "Confirmation - Day of Month                       0.026123      -0.007587   \n",
-       "Confirmation - Weekday (Mo = 1)                   1.000000       0.003574   \n",
+       "                                 Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                    -0.058005   \n",
+       "Placement - Day of Month                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                      0.999952   \n",
+       "Confirmation - Day of Month                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
+       "\n",
+       "                                 Arrival at Destination - Day of Month  \\\n",
+       "Platform Type                                                 0.012770   \n",
+       "Placement - Day of Month                                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
+       "Confirmation - Day of Month                                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
+       "\n",
+       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                                    -0.058005   \n",
+       "Placement - Day of Month                                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
+       "Confirmation - Day of Month                                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
        "\n",
-       "                                 Temperature  Precipitation in millimeters  \\\n",
-       "Platform Type                       0.225060                     -0.055948   \n",
-       "Placement - Day of Month            0.013097                     -0.204143   \n",
-       "Placement - Weekday (Mo = 1)        0.031064                     -0.050592   \n",
-       "Confirmation - Day of Month         0.013099                     -0.204143   \n",
-       "Confirmation - Weekday (Mo = 1)     0.031074                     -0.050592   \n",
+       "                                 Distance (KM)  Temperature  \\\n",
+       "Platform Type                         0.005528     0.225060   \n",
+       "Placement - Day of Month             -0.007585     0.013097   \n",
+       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
+       "Confirmation - Day of Month          -0.007587     0.013099   \n",
+       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
        "\n",
-       "                                 Pickup Lat  Pickup Long  Destination Lat  \\\n",
-       "Platform Type                     -0.002742     0.021327         0.006319   \n",
-       "Placement - Day of Month          -0.004929    -0.005453         0.007541   \n",
-       "Placement - Weekday (Mo = 1)      -0.000918     0.011008        -0.004299   \n",
-       "Confirmation - Day of Month       -0.004923    -0.005448         0.007550   \n",
-       "Confirmation - Weekday (Mo = 1)   -0.000883     0.011036        -0.004251   \n",
+       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
+       "Platform Type                                       -0.055948   -0.002742   \n",
+       "Placement - Day of Month                            -0.204143   -0.004929   \n",
+       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
+       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
+       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
        "\n",
-       "                                 Destination Long  \n",
-       "Platform Type                            0.043924  \n",
-       "Placement - Day of Month                 0.002077  \n",
-       "Placement - Weekday (Mo = 1)            -0.000875  \n",
-       "Confirmation - Day of Month              0.002073  \n",
-       "Confirmation - Weekday (Mo = 1)         -0.000900  "
+       "                                 Pickup Long  Destination Lat  \\\n",
+       "Platform Type                       0.021327         0.006319   \n",
+       "Placement - Day of Month           -0.005453         0.007541   \n",
+       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
+       "Confirmation - Day of Month        -0.005448         0.007550   \n",
+       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
+       "\n",
+       "                                 Destination Long  Time from Pickup to Arrival  \n",
+       "Platform Type                            0.043924                    -0.003827  \n",
+       "Placement - Day of Month                 0.002077                    -0.014710  \n",
+       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
+       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
+       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "CorrMatrix_train = train1.corr()\n",
-    "CorrMatrix_train.head(5)"
+    "CorrMatrix_train = train_df.corr()\n",
+    "CorrMatrix_train.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
+       "      <th>Distance (KM)</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Platform Type</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.005528</td>\n",
+       "      <td>0.225060</td>\n",
+       "      <td>-0.055948</td>\n",
+       "      <td>-0.002742</td>\n",
+       "      <td>0.021327</td>\n",
+       "      <td>0.006319</td>\n",
+       "      <td>0.043924</td>\n",
+       "      <td>-0.003827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <td>0.012764</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>-0.007585</td>\n",
+       "      <td>0.013097</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004929</td>\n",
+       "      <td>-0.005453</td>\n",
+       "      <td>0.007541</td>\n",
+       "      <td>0.002077</td>\n",
+       "      <td>-0.014710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058038</td>\n",
+       "      <td>0.026148</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.003582</td>\n",
+       "      <td>0.031064</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000918</td>\n",
+       "      <td>0.011008</td>\n",
+       "      <td>-0.004299</td>\n",
+       "      <td>-0.000875</td>\n",
+       "      <td>0.009693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <td>0.012770</td>\n",
+       "      <td>0.999998</td>\n",
+       "      <td>0.026132</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>-0.007587</td>\n",
+       "      <td>0.013099</td>\n",
+       "      <td>-0.204143</td>\n",
+       "      <td>-0.004923</td>\n",
+       "      <td>-0.005448</td>\n",
+       "      <td>0.007550</td>\n",
+       "      <td>0.002073</td>\n",
+       "      <td>-0.014701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <td>-0.058005</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.999952</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.026123</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.003574</td>\n",
+       "      <td>0.031074</td>\n",
+       "      <td>-0.050592</td>\n",
+       "      <td>-0.000883</td>\n",
+       "      <td>0.011036</td>\n",
+       "      <td>-0.004251</td>\n",
+       "      <td>-0.000900</td>\n",
+       "      <td>0.009744</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Platform Type  Placement - Day of Month  \\\n",
+       "Platform Type                         1.000000                  0.012764   \n",
+       "Placement - Day of Month              0.012764                  1.000000   \n",
+       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
+       "Confirmation - Day of Month           0.012770                  0.999998   \n",
+       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
+       "\n",
+       "                                 Placement - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                       -0.058038   \n",
+       "Placement - Day of Month                             0.026148   \n",
+       "Placement - Weekday (Mo = 1)                         1.000000   \n",
+       "Confirmation - Day of Month                          0.026132   \n",
+       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
+       "\n",
+       "                                 Confirmation - Day of Month  \\\n",
+       "Platform Type                                       0.012770   \n",
+       "Placement - Day of Month                            0.999998   \n",
+       "Placement - Weekday (Mo = 1)                        0.026132   \n",
+       "Confirmation - Day of Month                         1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
+       "\n",
+       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                          -0.058005   \n",
+       "Placement - Day of Month                                0.026122   \n",
+       "Placement - Weekday (Mo = 1)                            0.999952   \n",
+       "Confirmation - Day of Month                             0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
+       "\n",
+       "                                 Arrival at Pickup - Day of Month  \\\n",
+       "Platform Type                                            0.012770   \n",
+       "Placement - Day of Month                                 0.999998   \n",
+       "Placement - Weekday (Mo = 1)                             0.026132   \n",
+       "Confirmation - Day of Month                              1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
+       "\n",
+       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                               -0.058005   \n",
+       "Placement - Day of Month                                     0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
+       "Confirmation - Day of Month                                  0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
+       "\n",
+       "                                 Pickup - Day of Month  \\\n",
+       "Platform Type                                 0.012770   \n",
+       "Placement - Day of Month                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                  0.026132   \n",
+       "Confirmation - Day of Month                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
+       "\n",
+       "                                 Pickup - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                    -0.058005   \n",
+       "Placement - Day of Month                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                      0.999952   \n",
+       "Confirmation - Day of Month                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
+       "\n",
+       "                                 Arrival at Destination - Day of Month  \\\n",
+       "Platform Type                                                 0.012770   \n",
+       "Placement - Day of Month                                      0.999998   \n",
+       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
+       "Confirmation - Day of Month                                   1.000000   \n",
+       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
+       "\n",
+       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
+       "Platform Type                                                    -0.058005   \n",
+       "Placement - Day of Month                                          0.026122   \n",
+       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
+       "Confirmation - Day of Month                                       0.026123   \n",
+       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
+       "\n",
+       "                                 Distance (KM)  Temperature  \\\n",
+       "Platform Type                         0.005528     0.225060   \n",
+       "Placement - Day of Month             -0.007585     0.013097   \n",
+       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
+       "Confirmation - Day of Month          -0.007587     0.013099   \n",
+       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
+       "\n",
+       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
+       "Platform Type                                       -0.055948   -0.002742   \n",
+       "Placement - Day of Month                            -0.204143   -0.004929   \n",
+       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
+       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
+       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
+       "\n",
+       "                                 Pickup Long  Destination Lat  \\\n",
+       "Platform Type                       0.021327         0.006319   \n",
+       "Placement - Day of Month           -0.005453         0.007541   \n",
+       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
+       "Confirmation - Day of Month        -0.005448         0.007550   \n",
+       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
+       "\n",
+       "                                 Destination Long  Time from Pickup to Arrival  \n",
+       "Platform Type                            0.043924                    -0.003827  \n",
+       "Placement - Day of Month                 0.002077                    -0.014710  \n",
+       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
+       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
+       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CorrMatrix_test = test_df.corr()\n",
+    "CorrMatrix_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.05, 'Pearson Correlation of Features')"
+       "Text(0.5, 1.05, 'Train Dataset Pearson Correlation of Features')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 2 Axes>"
       ]
@@ -2059,44 +2482,36 @@
     }
    ],
    "source": [
+    "import matplotlib.style as style\n",
     "style.use('ggplot')\n",
     "cm = CorrMatrix_train.corr()\n",
     "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
     "plt.figure(figsize=(20,10))\n",
     "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
     "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
-    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Personal or Business"
+    "plt.title('Train Dataset Pearson Correlation of Features', y=1.05, size=40)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 101,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "Text(0.5, 1.05, 'Test Dataset Pearson Correlation of Features')"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1440x720 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -2104,118 +2519,67 @@
     }
    ],
    "source": [
-    "train1['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Platform type"
+    "import matplotlib.style as style\n",
+    "style.use('ggplot')\n",
+    "cm = CorrMatrix_test.corr()\n",
+    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
+    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
+    "plt.title('Test Dataset Pearson Correlation of Features', y=1.05, size=40)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 147,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "array([5, 2, 1, 3, 4, 6, 7], dtype=int64)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAEtCAYAAACmrnyFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAedElEQVR4nO3df2xV9f3H8dftudy2QJre/kC5MgziajvZ0qtLQYuujlnpNqJZJEMDbpEihKRUCKyQWA2GuCI6la6ULnUENyNK65JpJmYU0oa03fwOCWy0CZQwZVcst70tP1p6vb3n+4fxxlqE/sLPudvz8Q+7n3tL3mfn0/jMOafFZdu2LQAAABiTYHoAAACA/3UEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGFu0wOMVyAQMD2C4/h8Pv5/wYixXzBS7BWMBvtlOJ/P97XvcYUMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAMIIMAADAsLj/tyydwgoFpe5zpseQJIXOnJI1MGB6jM+lZWrQm2F6CgAAHI0gmyjd5xSuKDM9hSQpbHqAL/Fs3CoRZAAAXBW3LAEAAAwjyAAAAAwjyAAAAAwb0TNkfX19Ki8vV1lZmc6cOaM33ngj9l53d7e+/e1va+PGjdq7d68OHjyoKVOmSJIWLFighQsXKhgMqrKyUr29vfL5fFqzZo2SkpJ06dIlbd++XZ2dnUpJSdHatWuVmpp6XQ4UAADAqa4ZZCdOnFBNTY0CgYAk6Y477tAdd9whSerp6VF5ebl+8YtfSJI6Ojr05JNPKisra8jfUVtbq8LCQuXn56uurk51dXVaunSp9uzZo5ycHG3atElNTU3atWuX1q5dO9HHCAAA4GjXvGXZ0NCg5cuXKy0tbdh7f/jDH3T//fdr+vTpkqRTp07p7bff1vr16/Xqq68qHA4rEomora1N8+bNkyQVFBSotbVVknT48GHNnz9fkpSfn68jR44oEolM2MEBAADEg2teIVu1atUV1z/55BMdP3489v7ly5c1a9YsLVu2TDfeeKOqqqpUX1+vhQsXKjk5WZZlSZK8Xq+6urokSaFQSF6vV5JkWZaSk5N1/vz5K8bf1/H5fCP+7PUUOnPKUb9uwik8iYnyOuQc4es55fsIzsdewWiwX0ZuzL+HbP/+/SosLNSkSZMkSUlJSdq0aVPs/UWLFqm6uloPPPCAXC7XkK9NSPj8wpxt20PWbduOvTdSX9xKNc0xv4jVYcIDA445R7gyn8/HOcKIsFcwGuyX4a4WqGP+KcsPPvhA+fn5sdfBYFAHDhyIvbZtW5ZlKSUlRX19fYpGo5KGXhVLS0tTT0+PJGlwcFCXL1/W1KlTxzoSAABAXBpTkJ0/f17hcFjTpk2LrXk8Hr3++uvq7OyUbdt6//33lZeXJ7fbrezsbDU3N0uSmpqalJubK0ny+/1qbGyUJDU3Nys7O1tuN/94AAAA+N8ypvrp7OxUenr6kLWUlBStWLFCW7duVSQS0W233aZFixZJkoqLi2PPlGVkZKi0tFSStGTJElVVVWndunWaMmWKSkpKxnk4AAAA8cdlf/VBrjjjlPvTVkebY/4tSyfxbNyqwdk5psfAVfCcB0aKvYLRYL8Md12eIQMAAMDEIMgAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMI8gAAAAMc4/kQ319fSovL1dZWZmmTZumHTt2qL29XYmJiZKkxYsXKy8vT6dPn9bOnTvV39+vnJwcrVixQpZlKRgMqrKyUr29vfL5fFqzZo2SkpJ06dIlbd++XZ2dnUpJSdHatWuVmpp6PY8XAADAca55hezEiRN6+umnFQgEYmsdHR3avHmztm3bpm3btikvL0+SVFlZqccff1yvvPKKbNtWQ0ODJKm2tlaFhYV6+eWXdcstt6iurk6StGfPHuXk5Oill17SggULtGvXrutxjAAAAI52zSBraGjQ8uXLlZaWJkkaGBhQMBhUdXW11q9fr7feekvRaFTnzp1TOBxWVlaWJKmgoEAtLS2KRCJqa2vTvHnzYuutra2SpMOHD2v+/PmSpPz8fB05ckSRSOS6HCgAAIBTXfOW5apVq4a87unp0Zw5c1RcXKzJkyeroqJCBw4c0MyZM4fcbvR6veru7taFCxeUnJwsy7Ji611dXZKkUCgkr9crSbIsS8nJyTp//nws/kbC5/ON+LPXU+jMKYVND+FAnsREeR1yjvD1nPJ9BOdjr2A02C8jN6JnyL7shhtu0IYNG2Kvi4qK1NjYqBkzZsjlcsXWbduWy+WK/fllCQkJsc98mW3bsfdG6su3Uk2yBgZMj+BI4YEBx5wjXJnP5+McYUTYKxgN9stwVwvUUf+U5UcffRS75Sh9HlGWZSk9PV2hUCi23tPTI6/Xq5SUFPX19SkajUoaelUsLS1NPT09kqTBwUFdvnxZU6dOHe1IAAAAcW3UQWbbtnbv3q2LFy8qEolo//79ysvLU2Zmpjwej9rb2yVJTU1N8vv9crvdys7OVnNzc2w9NzdXkuT3+9XY2ChJam5uVnZ2ttzuUV+0AwAAiGujrp+bb75ZDz30kMrLyzU4OKi5c+fGHswvKSlRTU2N+vv7NWvWLBUVFUmSiouLVVVVpfr6emVkZKi0tFSStGTJElVVVWndunWaMmWKSkpKJvDQAAAA4oPL/uqDXHHGKfenrY42hSvKTI/hOJ6NWzU4O8f0GLgKnvPASLFXMBrsl+Em9BkyAAAATCyCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDCCDAAAwDD3SD7U19en8vJylZWVadq0adq/f7/ee+89SdLs2bP1xBNPyO12a+/evTp48KCmTJkiSVqwYIEWLlyoYDCoyspK9fb2yufzac2aNUpKStKlS5e0fft2dXZ2KiUlRWvXrlVqaup1O1gAAAAnumaQnThxQjU1NQoEApKkQCCgP//5z6qoqFBycrKqqqq0b98+/fSnP1VHR4eefPJJZWVlDfk7amtrVVhYqPz8fNXV1amurk5Lly7Vnj17lJOTo02bNqmpqUm7du3S2rVrr8+RAgAAONQ1b1k2NDRo+fLlSktLkyRNmjRJxcXFmjx5slwul2bOnKlgMChJOnXqlN5++22tX79er776qsLhsCKRiNra2jRv3jxJUkFBgVpbWyVJhw8f1vz58yVJ+fn5OnLkiCKRyHU5UAAAAKe65hWyVatWDXmdmZmpzMxMSdL58+f1/vvva/Xq1bp8+bJmzZqlZcuW6cYbb1RVVZXq6+u1cOFCJScny7IsSZLX61VXV5ckKRQKyev1SpIsy1JycrLOnz8fi7+R8Pl8I/7s9RQ6c0ph00M4kCcxUV6HnCN8Pad8H8H52CsYDfbLyI3oGbIr6e7u1nPPPaf77rtPt99+uyRp06ZNsfcXLVqk6upqPfDAA3K5XEO+NiHh8wtztm0PWbdtO/beSH1xK9U0a2DA9AiOFB4YcMw5wpX5fD7OEUaEvYLRYL8Md7VAHdNPWf7nP//RU089pR/84Ad6+OGHJUnBYFAHDhyIfca2bVmWpZSUFPX19SkajUoaelUsLS1NPT09kqTBwUFdvnxZU6dOHctIAAAAcWvUQdbf368tW7ZoyZIlWrRoUWzd4/Ho9ddfV2dnp2zb1vvvv6+8vDy53W5lZ2erublZktTU1KTc3FxJkt/vV2NjoySpublZ2dnZcrvHfNEOAAAgLo26fhoaGtTb26t33nlH77zzjiTp+9//vn7+859rxYoV2rp1qyKRiG677bZYsBUXF8eeKcvIyFBpaakkacmSJaqqqtK6des0ZcoUlZSUTOChAQAAxAeX/dUHueKMU+5PWx1tCleUmR7DcTwbt2pwdo7pMXAVPOeBkWKvYDTYL8NN+DNkAAAAmDgEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGHukXyor69P5eXlKisr07Rp03T06FG99tprCofDuvvuu7VkyRJJ0unTp7Vz50719/crJydHK1askGVZCgaDqqysVG9vr3w+n9asWaOkpCRdunRJ27dvV2dnp1JSUrR27VqlpqZez+MFAABwnGteITtx4oSefvppBQIBSVI4HFZ1dbV+9atf6aWXXlJHR4c+/PBDSVJlZaUef/xxvfLKK7JtWw0NDZKk2tpaFRYW6uWXX9Ytt9yiuro6SdKePXuUk5Ojl156SQsWLNCuXbuu13ECAAA41jWDrKGhQcuXL1daWpok6eTJk5o+fbqmTZsmy7J0zz33qKWlRefOnVM4HFZWVpYkqaCgQC0tLYpEImpra9O8efNi662trZKkw4cPa/78+ZKk/Px8HTlyRJFI5LocKAAAgFNd85blqlWrhrzu7u4eclsxNTVV3d3dCoVCQ9a9Xq+6u7t14cIFJScny7Ks2HpXV5ckKRQKyev1SpIsy1JycrLOnz8fi7+R8Pl8I/7s9RQ6c0ph00M4kCcxUV6HnCN8Pad8H8H52CsYDfbLyI3oGbIvs21bLpdryJrL5VI0Gh2y/sXnrvT5hISE2Ge++nd/8d5IfXEr1TRrYMD0CI4UHhhwzDnClfl8Ps4RRoS9gtFgvwx3tUAd9U9Zpqenq6enJ/a6p6dHXq9X6enpCoVCw9ZTUlLU19enaDQqaehVsbS0tNjfNTg4qMuXL2vq1KmjHQkAACCujTrIbr31VgUCAZ09e1bRaFSHDh2S3+9XZmamPB6P2tvbJUlNTU3y+/1yu93Kzs5Wc3NzbD03N1eS5Pf71djYKElqbm5Wdna23O5RX7QDAACIa6OuH4/Ho9WrV+vFF19UOByW3++PPbBfUlKimpoa9ff3a9asWSoqKpIkFRcXq6qqSvX19crIyFBpaakkacmSJaqqqtK6des0ZcoUlZSUTOChAQAAxAeX/dUHueKMU+5PWx1tCleUmR7DcTwbt2pwdo7pMXAVPOeBkWKvYDTYL8NN6DNkAAAAmFgEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGEEGQAAgGHusX5hQ0OD9u3bF3vd2dmpe++9VwMDA2pvb1diYqIkafHixcrLy9Pp06e1c+dO9ff3KycnRytWrJBlWQoGg6qsrFRvb698Pp/WrFmjpKSk8R8ZAABAnBhzkC1YsEALFiyQJH388cfatm2bFi9erM2bN2vz5s3yer1DPl9ZWamVK1cqKytL1dXVamhoUGFhoWpra1VYWKj8/HzV1dWprq5OS5cuHd9RAQAAxJEJuWVZW1urRx55RImJiQoGg6qurtb69ev11ltvKRqN6ty5cwqHw8rKypIkFRQUqKWlRZFIRG1tbZo3b15svbW1dSJGAgAAiBtjvkL2haNHjyocDuuuu+7Sp59+qjlz5qi4uFiTJ09WRUWFDhw4oJkzZyo1NTX2NV6vV93d3bpw4YKSk5NlWVZsvaura7wjAQAAxJVxB9n+/fv1k5/8RJJ0ww03aMOGDbH3ioqK1NjYqBkzZsjlcsXWbduWy+WK/fllCQmju2jn8/nGMf3ECZ05pbDpIRzIk5gor0POEb6eU76P4HzsFYwG+2XkxhVkkUhEx48f1+rVqyVJH330kQKBQOwWpG3bsixL6enpCoVCsa/r6emR1+tVSkqK+vr6FI1GlZCQoFAoNOzZs2sJBALjOYQJYw0MmB7BkcIDA445R7gyn8/HOcKIsFcwGuyX4a4WqON6huzf//63pk+fHvupSNu2tXv3bl28eFGRSET79+9XXl6eMjMz5fF41N7eLklqamqS3++X2+1Wdna2mpubY+u5ubnjGQkAACDujOsK2aeffqr09PTY65tvvlkPPfSQysvLNTg4qLlz52r+/PmSpJKSEtXU1Ki/v1+zZs1SUVGRJKm4uFhVVVWqr69XRkaGSktLxzMSAABA3HHZtm2bHmI8nHI51OpoU7iizPQYjuPZuFWDs3NMj4Gr4LYCRoq9gtFgvwx33W5ZAgAAYPwIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMMIMgAAAMPc4/nizZs3q7e3V5ZlSZKeeOIJ9ff367XXXlM4HNbdd9+tJUuWSJJOnz6tnTt3qr+/Xzk5OVqxYoUsy1IwGFRlZaV6e3vl8/m0Zs0aJSUljf/IAAAA4sSYg8y2bQUCAe3YsSMWZOFwWKWlpdq8ebPS09NVUVGhDz/8UH6/X5WVlVq5cqWysrJUXV2thoYGFRYWqra2VoWFhcrPz1ddXZ3q6uq0dOnSCTtAAAAApxvzLctAICBJ2rJlizZs2KB9+/bp5MmTmj59uqZNmybLsnTPPfeopaVF586dUzgcVlZWliSpoKBALS0tikQiamtr07x582Lrra2tE3BYAAAA8WPMV8guXbqk7373u3r88ccViUS0efNmPfjgg0pNTY19JjU1Vd3d3QqFQkPWvV6vuru7deHCBSUnJ8eusHm9XnV1dY1qDp/PN9ZDmFChM6cUNj2EA3kSE+V1yDnC13PK9xGcj72C0WC/jNyYgywrKyt2xUuS7rvvPr355pvKzs4e8jmXy6VoNCqXyxVbs21bLpcr9ueXJSSM7qLdF1fqTLMGBkyP4EjhgQHHnCNcmc/n4xxhRNgrGA32y3BXC9Qx37Jsb2/XsWPHhqxNmzZNPT09sdc9PT3yer1KT09XKBQatp6SkqK+vj5Fo1FJUigUktfrHetIAAAAcWnMQXbp0iX98Y9/VDgcVn9/vxobG/XII48oEAjo7NmzikajOnTokPx+vzIzM+XxeNTe3i5Jampqkt/vl9vtVnZ2tpqbm2Prubm5E3JgAAAA8WLMtyzvvPNOnThxQmVlZYpGo3rggQeUlZWl1atX68UXX1Q4HJbf7489sF9SUqKamhr19/dr1qxZKioqkiQVFxerqqpK9fX1ysjIUGlp6cQcGQAAQJxw2bZtmx5iPJxyf9rqaFO4osz0GI7j2bhVg7NzTI+Bq+A5D4wUewWjwX4Z7ro8QwYAAICJQZABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAYRpABAAAY5h7PF+/du1ctLS2SpDvuuENLly7Vjh071N7ersTEREnS4sWLlZeXp9OnT2vnzp3q7+9XTk6OVqxYIcuyFAwGVVlZqd7eXvl8Pq1Zs0ZJSUnjPzIAAIA4MeYgO3r0qI4eParnn39ekvTcc8/p73//uzo6OrR582Z5vd4hn6+srNTKlSuVlZWl6upqNTQ0qLCwULW1tSosLFR+fr7q6upUV1enpUuXju+oAAAA4siYb1l6vV4tW7ZMbrdbbrdbN910k4LBoILBoKqrq7V+/Xq99dZbikajOnfunMLhsLKysiRJBQUFamlpUSQSUVtbm+bNmxdbb21tnZgjAwAAiBNjvkL2rW99K/a/P/nkE7W0tOjZZ5/Vv/71LxUXF2vy5MmqqKjQgQMHNHPmTKWmpsY+7/V61d3drQsXLig5OVmWZcXWu7q6RjWHz+cb6yFMqNCZUwqbHsKBPImJ8jrkHOHrOeX7CM7HXsFosF9GblzPkEnSxx9/rIqKCi1dulQ+n08bNmyIvVdUVKTGxkbNmDFDLpcrtm7btlwuV+zPL0tIGN1Fu0AgML4DmCDWwIDpERwpPDDgmHOEK/P5fJwjjAh7BaPBfhnuaoE6rp+ybG9v17PPPqtHH31UBQUF+uijj4bccrRtW5ZlKT09XaFQKLbe09Mjr9erlJQU9fX1KRqNSpJCodCwZ88AAAD+2405yILBoLZt26bS0lLl5+dL+jzAdu/erYsXLyoSiWj//v3Ky8tTZmamPB6P2tvbJUlNTU3y+/1yu93Kzs5Wc3NzbD03N3f8RwUAABBHxnzL8p133tFnn32m3bt3x9buv/9+PfTQQyovL9fg4KDmzp2r+fPnS5JKSkpUU1Oj/v5+zZo1S0VFRZKk4uJiVVVVqb6+XhkZGSotLR3nIQEAAMQXl23btukhxsMp96etjjaFK8pMj+E4no1bNTg7x/QYjmOFglL3OdNjSPr8By/CTngGMi1Tg94M01PgKngmCKPBfhnuas+QjfuhfgBj0H3OMQHvlJ8O9mzcKhFkAP5H8U8nAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGEaQAQAAGOY2PQAA4OtZoaDUfc70GJKk0JlTsgYGTI/xubRMDXozTE8BTBiCDACcrPucwhVlpqeQJIVND/Alno1bJYIM/0W4ZQkAAGAYQQYAAGAYQQYAAGAYQQYAAGAYQQYAAGAYQQYAAGAYQQYAAGAYQQYAAGAYQQYAAGCYI35T/6FDh1RfX6/BwUH9+Mc/1sKFC02PBAAA8I0xHmTd3d164403tHXrVrndbpWXl2vOnDmaMWOG6dEAAAC+EcZvWR49elRz5szR1KlTlZSUpLlz56q1tdX0WAAAAN8Y41fIQqGQvF5v7LXX69XJkydH/PU+n+96jDV6Pp90z/+ZngLxgv2CkWKvII455r/RccD4FTLbtoe9drlchqYBAAD45hkPsrS0NPX09MRe9/T0KC0tzdxAAAAA3zDjQfa9731Px44d0/nz5zUwMKC//e1vys3NNT0WAADAN8Zlf/WeoQGHDh3Sn/70J0UiEf3whz/Ugw8+aHokAACAb4wjggwAAOB/mfFblgAAAP/rCDIAAADDCDIAAADDCDIAAADDCDIAAADDCDIAAADDCLL/Ap988om6u7slSQ0NDfr973+v5uZmw1MBiGcffPCB3nvvPZ09e3bI+v79+w1NhHjy2muvmR4h7hj/x8UxPu+++6727dunaDSqOXPmqKurS3l5eTp48KACgYAefvhh0yMCiDOvv/66Tp06pZtuuklvv/22li1bpnvvvVeS9Ne//lU/+tGPDE8IJ9mxY8ewtX/84x+6ePGiJGn16tXf9EhxiSCLcwcPHtRvfvMb9fb2at26dXr11Vfl8Xi0YMECbdq0iSDDEMFg8KrvZ2RkfEOTwMkOHz6s559/XpZlqaioSFu2bNGkSZN01113id8ljq+aOnWqmpqa9LOf/UyTJ0+WJP3zn//Ud77zHcOTxReCLM7Ztq1JkyYpMzNTixYtksfjib03ODhocDI40a9//WudPXtWXq932H9YXS6Xfvvb3xqaDE7jcrkkSdOnT9fGjRu1ZcsWpaSkxNaBLzz22GPy+/3as2ePHn30Ud1+++36y1/+ooKCAtOjxRX+6aQ49+abb+r48eN65plnlJDw+SOBp0+fVk1Nje68806ukGGIvr4+PfPMM1q+fLmys7NNjwOH2rt3r44dO6bHHntMt956qySpvb1dL7zwgj777DPt3r3b8IRwoosXL+p3v/udMjIydPToUb3wwgumR4orBNl/gePHjw+5NBwIBPTpp5/K7/cbnApOdfLkSTU0NGjlypWmR4GDHTt2TF6vVzNmzIitBYNBvfvuu/rlL39pbjA4XkNDg1paWvTUU0+ZHiWuEGQAAACG8WsvAAAADCPIAAAADCPIAAAADCPIAAAADPt/vWEQBDdgJZIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "train1['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Vehicle Type"
+    "train_df[\"Pickup - Weekday (Mo = 1)\"].unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 106,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "array([3, 5, 4, 1, 2, 6, 7], dtype=int64)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "train1['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
+    "test_df[\"Pickup - Weekday (Mo = 1)\"].unique()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "#Outliers Overview for numeric features\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(train1['Temperature'])\n",
-    "plt.show()"
+    "The company is running operation for 7 days a week."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 111,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2238,1849 +2602,528 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Order No</th>\n",
+       "      <th>User Id</th>\n",
+       "      <th>Vehicle Type</th>\n",
        "      <th>Platform Type</th>\n",
+       "      <th>Personal or Business</th>\n",
        "      <th>Placement - Day of Month</th>\n",
        "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Placement - Time</th>\n",
        "      <th>Confirmation - Day of Month</th>\n",
        "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
+       "      <th>...</th>\n",
        "      <th>Destination Lat</th>\n",
        "      <th>Destination Long</th>\n",
+       "      <th>Rider Id</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "      <th>Pickup_Hour</th>\n",
+       "      <th>Pickup_Minutes</th>\n",
+       "      <th>Arrival_Destination_Hour</th>\n",
+       "      <th>Arrival_Destination_Minutes</th>\n",
+       "      <th>Time from Pickup to Arrival_Hour</th>\n",
+       "      <th>Time from Pickup to Arrival_Minutes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>0 rows × 35 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "0              3                         9                             5   \n",
-       "1              3                        12                             5   \n",
-       "2              3                        30                             2   \n",
-       "3              3                        15                             5   \n",
-       "4              1                        13                             1   \n",
-       "\n",
-       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "0                            9                                5   \n",
-       "1                           12                                5   \n",
-       "2                           30                                2   \n",
-       "3                           15                                5   \n",
-       "4                           13                                1   \n",
-       "\n",
-       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                 9                                     5   \n",
-       "1                                12                                     5   \n",
-       "2                                30                                     2   \n",
-       "3                                15                                     5   \n",
-       "4                                13                                     1   \n",
-       "\n",
-       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
-       "0                      9                          5              4   \n",
-       "1                     12                          5             16   \n",
-       "2                     30                          2              3   \n",
-       "3                     15                          5              9   \n",
-       "4                     13                          1              9   \n",
+       "Empty DataFrame\n",
+       "Columns: [Order No, User Id, Vehicle Type, Platform Type, Personal or Business, Placement - Day of Month, Placement - Weekday (Mo = 1), Placement - Time, Confirmation - Day of Month, Confirmation - Weekday (Mo = 1), Confirmation - Time, Arrival at Pickup - Day of Month, Arrival at Pickup - Weekday (Mo = 1), Arrival at Pickup - Time, Pickup - Day of Month, Pickup - Weekday (Mo = 1), Pickup - Time, Arrival at Destination - Day of Month, Arrival at Destination - Weekday (Mo = 1), Arrival at Destination - Time, Distance (KM), Temperature, Precipitation in millimeters, Pickup Lat, Pickup Long, Destination Lat, Destination Long, Rider Id, Time from Pickup to Arrival, Pickup_Hour, Pickup_Minutes, Arrival_Destination_Hour, Arrival_Destination_Minutes, Time from Pickup to Arrival_Hour, Time from Pickup to Arrival_Minutes]\n",
+       "Index: []\n",
        "\n",
-       "   Temperature  Precipitation in millimeters  Pickup Lat  Pickup Long  \\\n",
-       "0         20.4                           NaN   -1.317755    36.830370   \n",
-       "1         26.4                           NaN   -1.351453    36.899315   \n",
-       "2          NaN                           NaN   -1.308284    36.843419   \n",
-       "3         19.2                           NaN   -1.281301    36.832396   \n",
-       "4         15.4                           NaN   -1.266597    36.792118   \n",
-       "\n",
-       "   Destination Lat  Destination Long  \n",
-       "0        -1.300406         36.829741  \n",
-       "1        -1.295004         36.814358  \n",
-       "2        -1.300921         36.828195  \n",
-       "3        -1.257147         36.795063  \n",
-       "4        -1.295041         36.809817  "
+       "[0 rows x 35 columns]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Numeric_Training = train1._get_numeric_data()\n",
-    "Numeric_Training.head()"
+    "train_df[train_df[\"Pickup - Weekday (Mo = 1)\"]!=train_df[\"Arrival at Destination - Weekday (Mo = 1)\"]]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Time from Pickup to Arrival'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-37-67d706ebc61e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'bar'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "train1['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5]) #returns error"
+    "All orders are delivered on the same day they are picked up, this is beacuse there are no orders where the day of the week of the pick-up is not the day of the week of the arrival.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-38-a40df607a66f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whitegrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 864x7.63233e+06 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "l = train1.values  #returns error\n",
-    "number_of_columns= 12\n",
-    "number_of_rows = len(l)-1/number_of_columns\n",
-    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
-    "for i in range(0,len(l)):\n",
-    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
-    "    sns.set_style('whitegrid')\n",
-    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
-    "    plt.tight_layout()"
+    "train_df['Pickup - Time'] = pd.to_datetime(train_df['Pickup - Time'])\n",
+    "train_df['Arrival at Destination - Time'] = pd.to_datetime(train_df['Arrival at Destination - Time'])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'number_of_columns_Test' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-39-2a23d4201dc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mchart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
-      "\u001b[1;31mNameError\u001b[0m: name 'number_of_columns_Test' is not defined"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test)) #returns error\n",
-    "for i in range(0,len(nu_tra)):\n",
-    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
-    "    chart=sns.distplot(train1[nu_tra[i]],kde=True) \n",
-    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
-    "                          horizontalalignment='right')\n",
-    "plt.show()"
+    "Converting time format provided to datatime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df['Pickup_Hour'] = train_df['Pickup - Time'].dt.hour\n",
+    "train_df['Arrival_Destination_Hour'] = train_df['Arrival at Destination - Time'].dt.hour\n",
+    "train_df['Time from Pickup to Arrival_Hour'] = train_df['Arrival at Destination - Time'].dt.hour"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Target"
+    "Converting the datatime to hours for more in depth time analysis."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7faa794c8>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_df['Pickup_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of pickup hours are between 9 AM in the morning to 17 PM in the afternoon.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fa96b788>"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_df['Arrival_Destination_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'Blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
    "metadata": {
-    "collapsed": true
+    "scrolled": true
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7824b0088>"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_df['Arrival at Pickup - Day of Month'].value_counts().plot(kind='bar',figsize=[15,5], color = 'Blue')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df['Arrival at Pickup - Day of Month'].value_counts().plot(kind='bar',figsize=[15,5], color = 'Blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the Destination arrival hours are between 9 AM in the morning to 17 PM in the afternoon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0       -1.317755\n",
+       " 1       -1.351453\n",
+       " 2       -1.308284\n",
+       " 3       -1.281301\n",
+       " 4       -1.266597\n",
+       "            ...   \n",
+       " 21196   -1.258414\n",
+       " 21197   -1.307143\n",
+       " 21198   -1.286018\n",
+       " 21199   -1.250030\n",
+       " 21200   -1.255189\n",
+       " Name: Pickup Lat, Length: 21201, dtype: float64,\n",
+       " 0        36.830370\n",
+       " 1        36.899315\n",
+       " 2        36.843419\n",
+       " 3        36.832396\n",
+       " 4        36.792118\n",
+       "            ...    \n",
+       " 21196    36.804800\n",
+       " 21197    36.825009\n",
+       " 21198    36.897534\n",
+       " 21199    36.874167\n",
+       " 21200    36.782203\n",
+       " Name: Pickup Long, Length: 21201, dtype: float64)"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df['Pickup Lat'], train_df['Pickup Long']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
    "outputs": [
     {
      "ename": "KeyError",
-     "evalue": "'Time from Pickup to Arrival'",
+     "evalue": "\"None of [Float64Index([-1.3177546999999998,           -1.351453,          -1.3082842,\\n                       -1.2813012, -1.2665973999999998,          -1.3025826,\\n                        -1.279395, -1.2527958000000001,          -1.2551895,\\n                        -1.225322,\\n              ...\\n              -1.2821421999999998, -1.3167113000000001,          -1.2865677,\\n                       -1.2602337,          -1.2384056,          -1.2584143,\\n                        -1.307143,          -1.2860183, -1.2500303999999998,\\n                       -1.2551895],\\n             dtype='float64', length=21201)] are in the [columns]\"",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-80-9f95e1fbe116>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Useful statistics about our target column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
+      "\u001b[1;32m<ipython-input-158-f8a369fa3c91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pickup Lat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pickup Long'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"better_visualization_plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    790\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 792\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    793\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    264\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    968\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m         scatter = ax.scatter(\n\u001b[1;32m--> 970\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    971\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m             \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2804\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2806\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2808\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m         self._validate_read_indexer(\n\u001b[1;32m-> 1552\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1553\u001b[0m         )\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: \"None of [Float64Index([-1.3177546999999998,           -1.351453,          -1.3082842,\\n                       -1.2813012, -1.2665973999999998,          -1.3025826,\\n                        -1.279395, -1.2527958000000001,          -1.2551895,\\n                        -1.225322,\\n              ...\\n              -1.2821421999999998, -1.3167113000000001,          -1.2865677,\\n                       -1.2602337,          -1.2384056,          -1.2584143,\\n                        -1.307143,          -1.2860183, -1.2500303999999998,\\n                       -1.2551895],\\n             dtype='float64', length=21201)] are in the [columns]\""
      ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "#Useful statistics about our target column\n",
-    "\n",
-    "train1['Time from Pickup to Arrival'].describe() #returns error"
+    "train_df.plot(kind=\"scatter\", x= train_df['Pickup Lat'], y=train_df['Pickup Long'], alpha=0.1)\n",
+    "save_fig(\"better_visualization_plot\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The \"Time from Pickup to Arrival\" does not have any missing values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10:27:30 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:44:09 AM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:53:03 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:43:06 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10:05:23 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21196</th>\n",
-       "      <td>Order_No_8834</td>\n",
-       "      <td>User_Id_2001</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>20</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3:54:38 PM</td>\n",
-       "      <td>20</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4:20:08 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>28.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.258414</td>\n",
-       "      <td>36.804800</td>\n",
-       "      <td>-1.275285</td>\n",
-       "      <td>36.802702</td>\n",
-       "      <td>Rider_Id_953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21197</th>\n",
-       "      <td>Order_No_22892</td>\n",
-       "      <td>User_Id_1796</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10:13:34 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10:33:27 AM</td>\n",
-       "      <td>7</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.307143</td>\n",
-       "      <td>36.825009</td>\n",
-       "      <td>-1.331619</td>\n",
-       "      <td>36.847976</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21198</th>\n",
-       "      <td>Order_No_2831</td>\n",
-       "      <td>User_Id_2956</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5:06:16 PM</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5:50:52 PM</td>\n",
-       "      <td>20</td>\n",
-       "      <td>29.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.286018</td>\n",
-       "      <td>36.897534</td>\n",
-       "      <td>-1.258414</td>\n",
-       "      <td>36.804800</td>\n",
-       "      <td>Rider_Id_697</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21199</th>\n",
-       "      <td>Order_No_6174</td>\n",
-       "      <td>User_Id_2524</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9:31:39 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9:45:15 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.250030</td>\n",
-       "      <td>36.874167</td>\n",
-       "      <td>-1.279209</td>\n",
-       "      <td>36.794872</td>\n",
-       "      <td>Rider_Id_347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21200</th>\n",
-       "      <td>Order_No_9836</td>\n",
-       "      <td>User_Id_718</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>26</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2:19:47 PM</td>\n",
-       "      <td>26</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2:41:55 PM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>30.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.255189</td>\n",
-       "      <td>36.782203</td>\n",
-       "      <td>-1.320157</td>\n",
-       "      <td>36.830887</td>\n",
-       "      <td>Rider_Id_177</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>21201 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0       Order_No_4211   User_Id_633         Bike              3   \n",
-       "1      Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2       Order_No_1899   User_Id_265         Bike              3   \n",
-       "3       Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4      Order_No_27883  User_Id_1737         Bike              1   \n",
-       "...               ...           ...          ...            ...   \n",
-       "21196   Order_No_8834  User_Id_2001         Bike              3   \n",
-       "21197  Order_No_22892  User_Id_1796         Bike              3   \n",
-       "21198   Order_No_2831  User_Id_2956         Bike              3   \n",
-       "21199   Order_No_6174  User_Id_2524         Bike              1   \n",
-       "21200   Order_No_9836   User_Id_718         Bike              3   \n",
-       "\n",
-       "      Personal or Business  Placement - Day of Month  \\\n",
-       "0                 Business                         9   \n",
-       "1                 Personal                        12   \n",
-       "2                 Business                        30   \n",
-       "3                 Business                        15   \n",
-       "4                 Personal                        13   \n",
-       "...                    ...                       ...   \n",
-       "21196             Personal                        20   \n",
-       "21197             Business                        13   \n",
-       "21198             Business                         7   \n",
-       "21199             Personal                         4   \n",
-       "21200             Business                        26   \n",
-       "\n",
-       "       Placement - Weekday (Mo = 1) Placement - Time  \\\n",
-       "0                                 5       9:35:46 AM   \n",
-       "1                                 5      11:16:16 AM   \n",
-       "2                                 2      12:39:25 PM   \n",
-       "3                                 5       9:25:34 AM   \n",
-       "4                                 1       9:55:18 AM   \n",
-       "...                             ...              ...   \n",
-       "21196                             3       3:54:38 PM   \n",
-       "21197                             6      10:13:34 AM   \n",
-       "21198                             4       5:06:16 PM   \n",
-       "21199                             3       9:31:39 AM   \n",
-       "21200                             2       2:19:47 PM   \n",
-       "\n",
-       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  ...  \\\n",
-       "0                                9                                5  ...   \n",
-       "1                               12                                5  ...   \n",
-       "2                               30                                2  ...   \n",
-       "3                               15                                5  ...   \n",
-       "4                               13                                1  ...   \n",
-       "...                            ...                              ...  ...   \n",
-       "21196                           20                                3  ...   \n",
-       "21197                           13                                6  ...   \n",
-       "21198                            7                                4  ...   \n",
-       "21199                            4                                3  ...   \n",
-       "21200                           26                                2  ...   \n",
-       "\n",
-       "      Pickup - Weekday (Mo = 1)  Pickup - Time  Distance (KM) Temperature  \\\n",
-       "0                             5    10:27:30 AM              4        20.4   \n",
-       "1                             5    11:44:09 AM             16        26.4   \n",
-       "2                             2    12:53:03 PM              3         NaN   \n",
-       "3                             5     9:43:06 AM              9        19.2   \n",
-       "4                             1    10:05:23 AM              9        15.4   \n",
-       "...                         ...            ...            ...         ...   \n",
-       "21196                         3     4:20:08 PM              3        28.6   \n",
-       "21197                         6    10:33:27 AM              7        26.0   \n",
-       "21198                         4     5:50:52 PM             20        29.2   \n",
-       "21199                         3     9:45:15 AM             13        15.0   \n",
-       "21200                         2     2:41:55 PM             12        30.9   \n",
-       "\n",
-       "       Precipitation in millimeters  Pickup Lat Pickup Long  Destination Lat  \\\n",
-       "0                               NaN   -1.317755   36.830370        -1.300406   \n",
-       "1                               NaN   -1.351453   36.899315        -1.295004   \n",
-       "2                               NaN   -1.308284   36.843419        -1.300921   \n",
-       "3                               NaN   -1.281301   36.832396        -1.257147   \n",
-       "4                               NaN   -1.266597   36.792118        -1.295041   \n",
-       "...                             ...         ...         ...              ...   \n",
-       "21196                           NaN   -1.258414   36.804800        -1.275285   \n",
-       "21197                           NaN   -1.307143   36.825009        -1.331619   \n",
-       "21198                           NaN   -1.286018   36.897534        -1.258414   \n",
-       "21199                           NaN   -1.250030   36.874167        -1.279209   \n",
-       "21200                           NaN   -1.255189   36.782203        -1.320157   \n",
-       "\n",
-       "       Destination Long      Rider Id  \n",
-       "0             36.829741  Rider_Id_432  \n",
-       "1             36.814358  Rider_Id_856  \n",
-       "2             36.828195  Rider_Id_155  \n",
-       "3             36.795063  Rider_Id_855  \n",
-       "4             36.809817  Rider_Id_770  \n",
-       "...                 ...           ...  \n",
-       "21196         36.802702  Rider_Id_953  \n",
-       "21197         36.847976  Rider_Id_155  \n",
-       "21198         36.804800  Rider_Id_697  \n",
-       "21199         36.794872  Rider_Id_347  \n",
-       "21200         36.830887  Rider_Id_177  \n",
-       "\n",
-       "[21201 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Time from Pickup to Arrival'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-42-0671fade3f72>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time (minutes)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
-     ]
-    }
-   ],
-   "source": [
-    "(train1['Time from Pickup to Arrival']/60).hist(bins=50) #returns error\n",
-    "plt.title(\"Delivery time distribution\") \n",
-    "plt.xlabel(\"Delivery time (minutes)\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Neither the `x` nor `y` variable appears to be numeric.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-43-278ac3dc675f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Outliers Overview for numeric features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2233\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2234\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2235\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    434\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[0morient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_orient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Option 2a:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(self, x, y, orient)\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 354\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mno_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    355\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Neither the `x` nor `y` variable appears to be numeric."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1440x576 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Outliers Overview for numeric features\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(test_df)) #returns error\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-44-7701a3d72f00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2185\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                         functools.partial(\n\u001b[1;32m-> 2187\u001b[1;33m                             print_method, orientation=orientation)\n\u001b[0m\u001b[0;32m   2188\u001b[0m                     )\n\u001b[0;32m   2189\u001b[0m                     ctx = (renderer._draw_disabled()\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1553\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    400\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;31m# docstring inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m         \u001b[1;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    416\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    417\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 418\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Image size of 1728x7632330 pixels is too large. It must be less than 2^16 in each direction."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1728x7.63233e+06 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows)) #returns error\n",
-    "for i in range(0,len(l)):\n",
-    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
-    "    sns.distplot(df[l[i]],kde=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Full_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 117,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((21201, 25), (7068, 26), (28269, 26))"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
-    "#explore training, make (column) changes to full, later we use the border to separate\n",
-    "#Be careful of information leakage\n",
-    "\n",
-    "border = train1.shape[0]\n",
-    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
-    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
-    "\n",
-    "train1.shape, test_df.shape, full_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Renaming columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
-       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
-       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
-       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
-       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
-       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
-       "       'Rider_Id', 'Time_Pic_Arr'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Renaming columns (shorten, remove space, standardize)\n",
-    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
-    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
-    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
-    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
-    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
-    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
-    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
-    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
-    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
-    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
-    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
-    "                           }\n",
-    "\n",
-    "full_df = full_df.rename(columns=new_names)\n",
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Convert Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>15:07:35</td>\n",
-       "      <td>15:08:57</td>\n",
-       "      <td>15:21:36</td>\n",
-       "      <td>15:30:30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
-       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
-       "5  15:07:35  15:08:57     15:21:36    15:30:30"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Convert Time from 12H to 24H\n",
-    "\n",
-    "def convert_to_24hrs(fulldf):\n",
-    "    for col in fulldf.columns:\n",
-    "        if col.endswith(\"Time\"):\n",
-    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
-    "    return fulldf\n",
-    "\n",
-    "full_df = convert_to_24hrs(full_df)\n",
-    "\n",
-    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Filling Missing Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Filling Missing Values for temperatures and humidity\n",
-    "\n",
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Vehicle_Type</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Mon</th>\n",
-       "      <th>Pla_Weekday</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Day_Mon</th>\n",
-       "      <th>Con_Weekday</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
-       "\n",
-       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
-       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
-       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
-       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
-       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
-       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
-       "\n",
-       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
-       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
-       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
-       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
-       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
-       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
-       "\n",
-       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
-       "0       -1.300406        36.829741  Rider_Id_432           NaN  \n",
-       "1       -1.295004        36.814358  Rider_Id_856           NaN  \n",
-       "2       -1.300921        36.828195  Rider_Id_155           NaN  \n",
-       "3       -1.257147        36.795063  Rider_Id_855           NaN  \n",
-       "4       -1.295041        36.809817  Rider_Id_770           NaN  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Traversing Month and Weekday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
-    "\n",
-    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
-    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
-    "\n",
-    "count = 0\n",
-    "instances_of_different_days = [];\n",
-    "for i, row in full_df.iterrows():\n",
-    "    if len(set(row[month_cols].values)) > 1:\n",
-    "        print(count+1, end=\"\\r\")\n",
-    "        count = count + 1\n",
-    "        instances_of_different_days.append(list(row[month_cols].values))\n",
-    "instances_of_different_days"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "month_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weekday_cols"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating Month and Weekday columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
-    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping redundant columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
-       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
-       "2        36.828195  Rider_Id_155           NaN            30            2  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
-    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
-    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
-    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
-    "\n",
-    "full_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
-       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
-       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
-       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
-       "4        36.809817  Rider_Id_770           NaN            13            1  "
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fd157648>"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "full_df.head()"
+    "train_df['Time from Pickup to Arrival_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the Time from Pickup to Arrival_Hour are between 9 AM in the morning to 17 PM in the afternoon."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 119,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
-       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
-       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
-       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
-       "       'Day_of_Month', 'Day_of_Week'],\n",
-       "      dtype='object')"
+       "count    21201.000000\n",
+       "mean        13.303995\n",
+       "std          2.570458\n",
+       "min          0.000000\n",
+       "25%         11.000000\n",
+       "50%         13.000000\n",
+       "75%         15.000000\n",
+       "max         23.000000\n",
+       "Name: Time from Pickup to Arrival_Hour, dtype: float64"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "full_df.columns"
+    "train_df['Time from Pickup to Arrival_Hour'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13    2668\n",
+      "12    2636\n",
+      "11    2589\n",
+      "15    2586\n",
+      "16    2521\n",
+      "14    2411\n",
+      "10    2189\n",
+      "17    1445\n",
+      "9     1054\n",
+      "18     586\n",
+      "19     195\n",
+      "8      180\n",
+      "20      89\n",
+      "21      34\n",
+      "7        8\n",
+      "22       8\n",
+      "23       1\n",
+      "0        1\n",
+      "Name: Time from Pickup to Arrival_Hour, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "count = train_df['Time from Pickup to Arrival_Hour'].value_counts()\n",
+    "print(count)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Variable Datatypes"
+    "The peak for Time from Pick to Arrival_hour is taking place at 13:00 PM in the afternoon and lowest at 00:00 AM in the early hours of the morning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Match the number of train columns to the number of test columns and also separate the target variable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
-    "numeric_cols = []\n",
-    "object_cols = []\n",
-    "time_cols = []\n",
-    "for k, v in full_df.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_cols.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_cols.append(k)\n",
+    "# save the columns from the test dataframe into a new list called testcols\n",
+    "testcols = test_df.columns\n",
+    "\n",
+    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
+    "train1 = train_df[testcols]\n",
+    "\n",
+    "# Seperate the target variable from the predictor variable\n",
+    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
+    "\n",
+    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the new columns of the train and test data to show that they are the same\n",
+    "print(train1.columns)\n",
+    "print(test_df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# New Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_of_day(hour):\n",
+    "    if (hour > 0) and (hour <= 12 ):\n",
+    "        return 'AM'\n",
     "    else:\n",
-    "        object_cols.append(k)"
+    "        return'PM'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'PM'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_of_day(14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4103,277 +3146,387 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
+       "      <th>Platform Type</th>\n",
+       "      <th>Placement - Day of Month</th>\n",
+       "      <th>Placement - Weekday (Mo = 1)</th>\n",
+       "      <th>Confirmation - Day of Month</th>\n",
+       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Pickup - Day of Month</th>\n",
+       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Pickup - Day of Month</th>\n",
+       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
+       "      <th>Arrival at Destination - Day of Month</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Precipitation in millimeters</th>\n",
+       "      <th>Pickup Lat</th>\n",
+       "      <th>Pickup Long</th>\n",
+       "      <th>Destination Lat</th>\n",
+       "      <th>Destination Long</th>\n",
+       "      <th>Time from Pickup to Arrival</th>\n",
+       "      <th>Pickup_Hour</th>\n",
+       "      <th>Pickup_Minutes</th>\n",
+       "      <th>Arrival_Destination_Hour</th>\n",
+       "      <th>Arrival_Destination_Minutes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>-1.317755</td>\n",
        "      <td>36.830370</td>\n",
        "      <td>-1.300406</td>\n",
        "      <td>36.829741</td>\n",
+       "      <td>745</td>\n",
+       "      <td>10</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.351453</td>\n",
+       "      <td>36.899315</td>\n",
+       "      <td>-1.295004</td>\n",
+       "      <td>36.814358</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>11</td>\n",
+       "      <td>44</td>\n",
+       "      <td>12</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.308284</td>\n",
+       "      <td>36.843419</td>\n",
+       "      <td>-1.300921</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>455</td>\n",
+       "      <td>12</td>\n",
+       "      <td>53</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>1341</td>\n",
        "      <td>9</td>\n",
+       "      <td>43</td>\n",
+       "      <td>10</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>1214</td>\n",
+       "      <td>10</td>\n",
        "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
+       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
+       "0              3                         9                             5   \n",
+       "1              3                        12                             5   \n",
+       "2              3                        30                             2   \n",
+       "3              3                        15                             5   \n",
+       "4              1                        13                             1   \n",
+       "\n",
+       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
+       "0                            9                                5   \n",
+       "1                           12                                5   \n",
+       "2                           30                                2   \n",
+       "3                           15                                5   \n",
+       "4                           13                                1   \n",
+       "\n",
+       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
+       "0                                 9                                     5   \n",
+       "1                                12                                     5   \n",
+       "2                                30                                     2   \n",
+       "3                                15                                     5   \n",
+       "4                                13                                     1   \n",
+       "\n",
+       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
+       "0                      9                          5   \n",
+       "1                     12                          5   \n",
+       "2                     30                          2   \n",
+       "3                     15                          5   \n",
+       "4                     13                          1   \n",
+       "\n",
+       "   Arrival at Destination - Day of Month  ...  Precipitation in millimeters  \\\n",
+       "0                                      9  ...                           NaN   \n",
+       "1                                     12  ...                           NaN   \n",
+       "2                                     30  ...                           NaN   \n",
+       "3                                     15  ...                           NaN   \n",
+       "4                                     13  ...                           NaN   \n",
+       "\n",
+       "   Pickup Lat  Pickup Long  Destination Lat  Destination Long  \\\n",
+       "0   -1.317755    36.830370        -1.300406         36.829741   \n",
+       "1   -1.351453    36.899315        -1.295004         36.814358   \n",
+       "2   -1.308284    36.843419        -1.300921         36.828195   \n",
+       "3   -1.281301    36.832396        -1.257147         36.795063   \n",
+       "4   -1.266597    36.792118        -1.295041         36.809817   \n",
        "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
-       "0   36.830370        -1.300406        36.829741             9            5  \n",
-       "1   36.899315        -1.295004        36.814358            12            5  \n",
-       "2   36.843419        -1.300921        36.828195            30            2  "
+       "   Time from Pickup to Arrival  Pickup_Hour  Pickup_Minutes  \\\n",
+       "0                          745           10              27   \n",
+       "1                         1993           11              44   \n",
+       "2                          455           12              53   \n",
+       "3                         1341            9              43   \n",
+       "4                         1214           10               5   \n",
+       "\n",
+       "   Arrival_Destination_Hour  Arrival_Destination_Minutes  \n",
+       "0                        10                           39  \n",
+       "1                        12                           17  \n",
+       "2                        13                            0  \n",
+       "3                        10                            5  \n",
+       "4                        10                           25  \n",
+       "\n",
+       "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "full_df[numeric_cols].head(3)"
+    "Numeric_Training = train_df._get_numeric_data()\n",
+    "Numeric_Training.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning and Formatting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Full_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 137,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
-       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
-       "2  12:39:25  12:42:44     12:49:34    12:53:03"
+       "((21201, 25), (7068, 26), (28269, 26))"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "full_df[time_cols].head(3)"
+    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
+    "#explore training, make (column) changes to full, later we use the border to separate\n",
+    "#Be careful of information leakage\n",
+    "\n",
+    "border = train1.shape[0]\n",
+    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
+    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
+    "\n",
+    "train1.shape, test_df.shape, full_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Renaming columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 138,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "         Order_No       User_Id Personal_Business      Rider_Id\n",
-       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
-       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
-       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
+       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
+       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
+       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
+       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
+       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
+       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
+       "       'Rider_Id', 'Time_Pic_Arr'],\n",
+       "      dtype='object')"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "full_df[object_cols].head(3)"
+    "#Renaming columns (shorten, remove space, standardize)\n",
+    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
+    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
+    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
+    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
+    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
+    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
+    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
+    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
+    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
+    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
+    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
+    "                           }\n",
+    "\n",
+    "full_df = full_df.rename(columns=new_names)\n",
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "time data '2020-10-19 10:27:30' does not match format '%I:%M:%S %p' (match)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\tslibs\\conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-139-405ca5f988bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mfull_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_to_24hrs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mfull_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pla_Time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Con_Time'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Arr_Pic_Time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Pickup_Time'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-139-405ca5f988bc>\u001b[0m in \u001b[0;36mconvert_to_24hrs\u001b[1;34m(fulldf)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Time\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m             \u001b[0mfulldf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfulldf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%I:%M:%S %p'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%H:%M:%S\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m    726\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    433\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    398\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m                     result, timezones = array_strptime(\n\u001b[1;32m--> 400\u001b[1;33m                         \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    401\u001b[0m                     )\n\u001b[0;32m    402\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;34m\"%Z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformat\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"%z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\tslibs\\strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: time data '2020-10-19 10:27:30' does not match format '%I:%M:%S %p' (match)"
+     ]
+    }
+   ],
+   "source": [
+    "#Convert Time from 12H to 24H\n",
+    "\n",
+    "def convert_to_24hrs(fulldf):\n",
+    "    for col in fulldf.columns:\n",
+    "        if col.endswith(\"Time\"):\n",
+    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
+    "    return fulldf\n",
+    "\n",
+    "full_df = convert_to_24hrs(full_df)\n",
+    "\n",
+    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Convert object types to numeric"
+    "### Filling Missing Values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "Name: Personal_Business, dtype: int32"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "#Convert an object to numeric (encoding)\n",
+    "#Filling Missing Values for temperatures and humidity\n",
     "\n",
-    "le = LabelEncoder()\n",
-    "le.fit(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'][:2]\n"
+    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
+    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 142,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4398,11 +3551,15 @@
        "      <th></th>\n",
        "      <th>Order_No</th>\n",
        "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
        "      <th>Platform_Type</th>\n",
        "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
        "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>...</th>\n",
        "      <th>Pickup_Time</th>\n",
        "      <th>Distance(km)</th>\n",
        "      <th>Temperature</th>\n",
@@ -4413,8 +3570,6 @@
        "      <th>Destination_Lon</th>\n",
        "      <th>Rider_Id</th>\n",
        "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -4422,12 +3577,16 @@
        "      <th>0</th>\n",
        "      <td>Order_No_4211</td>\n",
        "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
        "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-10-19 10:27:30</td>\n",
        "      <td>4</td>\n",
        "      <td>20.400000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4437,19 +3596,21 @@
        "      <td>36.829741</td>\n",
        "      <td>Rider_Id_432</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Order_No_25375</td>\n",
        "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
        "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-10-19 11:44:09</td>\n",
        "      <td>16</td>\n",
        "      <td>26.400000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4459,19 +3620,21 @@
        "      <td>36.814358</td>\n",
        "      <td>Rider_Id_856</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Order_No_1899</td>\n",
        "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
        "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-10-19 12:53:03</td>\n",
        "      <td>3</td>\n",
        "      <td>23.255689</td>\n",
        "      <td>7.573502</td>\n",
@@ -4481,19 +3644,21 @@
        "      <td>36.828195</td>\n",
        "      <td>Rider_Id_155</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Order_No_9336</td>\n",
        "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
        "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-10-19 09:43:06</td>\n",
        "      <td>9</td>\n",
        "      <td>19.200000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4503,19 +3668,21 @@
        "      <td>36.795063</td>\n",
        "      <td>Rider_Id_855</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Order_No_27883</td>\n",
        "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
        "      <td>1</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>13</td>\n",
        "      <td>1</td>\n",
        "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2020-10-19 10:05:23</td>\n",
        "      <td>9</td>\n",
        "      <td>15.400000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4525,44 +3692,52 @@
        "      <td>36.809817</td>\n",
        "      <td>Rider_Id_770</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
+       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
+       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
+       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
+       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
+       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
+       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
        "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
+       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ...  \\\n",
+       "0        9            5  09:35:46            9            5  ...   \n",
+       "1       12            5  11:16:16           12            5  ...   \n",
+       "2       30            2  12:39:25           30            2  ...   \n",
+       "3       15            5  09:25:34           15            5  ...   \n",
+       "4       13            1  09:55:18           13            1  ...   \n",
        "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
+       "           Pickup_Time  Distance(km)  Temperature Precipitation(mm)  \\\n",
+       "0  2020-10-19 10:27:30             4    20.400000          7.573502   \n",
+       "1  2020-10-19 11:44:09            16    26.400000          7.573502   \n",
+       "2  2020-10-19 12:53:03             3    23.255689          7.573502   \n",
+       "3  2020-10-19 09:43:06             9    19.200000          7.573502   \n",
+       "4  2020-10-19 10:05:23             9    15.400000          7.573502   \n",
+       "\n",
+       "   Pickup_Lat  Pickup_Lon Destination_Lat  Destination_Lon      Rider_Id  \\\n",
+       "0   -1.317755   36.830370       -1.300406        36.829741  Rider_Id_432   \n",
+       "1   -1.351453   36.899315       -1.295004        36.814358  Rider_Id_856   \n",
+       "2   -1.308284   36.843419       -1.300921        36.828195  Rider_Id_155   \n",
+       "3   -1.281301   36.832396       -1.257147        36.795063  Rider_Id_855   \n",
+       "4   -1.266597   36.792118       -1.295041        36.809817  Rider_Id_770   \n",
        "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432           NaN             9            5  \n",
-       "1        36.814358  Rider_Id_856           NaN            12            5  \n",
-       "2        36.828195  Rider_Id_155           NaN            30            2  \n",
-       "3        36.795063  Rider_Id_855           NaN            15            5  \n",
-       "4        36.809817  Rider_Id_770           NaN            13            1  "
+       "   Time_Pic_Arr  \n",
+       "0           NaN  \n",
+       "1           NaN  \n",
+       "2           NaN  \n",
+       "3           NaN  \n",
+       "4           NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4570,202 +3745,191 @@
    "source": [
     "full_df.head()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Feature Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Traversing Month and Weekday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\r"
+     ]
+    },
+    {
+     "data": {
       "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
-       "3              3             9    19.200000           7.573502   -1.281301   \n",
-       "4              1             9    15.400000           7.573502   -1.266597   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
-       "0   36.830370        -1.300406        36.829741             9            5   \n",
-       "1   36.899315        -1.295004        36.814358            12            5   \n",
-       "2   36.843419        -1.300921        36.828195            30            2   \n",
-       "3   36.832396        -1.257147        36.795063            15            5   \n",
-       "4   36.792118        -1.295041        36.809817            13            1   \n",
-       "\n",
-       "   Personal_Business  \n",
-       "0                  0  \n",
-       "1                  1  \n",
-       "2                  0  \n",
-       "3                  0  \n",
-       "4                  1  "
+       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "features = numeric_cols + ['Personal_Business']\n",
+    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
     "\n",
-    "data_df = full_df[features]\n",
+    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
+    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
     "\n",
-    "y = full_df[:border]['Time_Pic_Arr']\n",
-    "train = data_df[:border]\n",
-    "test = data_df[border:]\n",
+    "count = 0\n",
+    "instances_of_different_days = [];\n",
+    "for i, row in full_df.iterrows():\n",
+    "    if len(set(row[month_cols].values)) > 1:\n",
+    "        print(count+1, end=\"\\r\")\n",
+    "        count = count + 1\n",
+    "        instances_of_different_days.append(list(row[month_cols].values))\n",
+    "instances_of_different_days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekday_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Month and Weekday columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
+    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping redundant columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
+    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
+    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
+    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
     "\n",
-    "train.head()"
+    "full_df.head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 135,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(28269, 19) (28269, 11) (21201, 11) (7068, 11) (21201,)\n"
+     "ename": "NameError",
+     "evalue": "name 'full_df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-135-3c7297abd118>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfull_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'full_df' is not defined"
      ]
     }
    ],
    "source": [
-    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+    "full_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variable Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_cols = []\n",
+    "object_cols = []\n",
+    "time_cols = []\n",
+    "for k, v in full_df.dtypes.items():\n",
+    "    if (v != object):\n",
+    "        if (k != \"Time_Pic_Arr\"):\n",
+    "            numeric_cols.append(k)\n",
+    "    elif k.endswith(\"Time\"):\n",
+    "        time_cols.append(k)\n",
+    "    else:\n",
+    "        object_cols.append(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4789,6 +3953,14 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Platform_Type</th>\n",
+       "      <th>Pla_Mon</th>\n",
+       "      <th>Pla_Weekday</th>\n",
+       "      <th>Con_Day_Mon</th>\n",
+       "      <th>Con_Weekday</th>\n",
+       "      <th>Arr_Pic_Mon</th>\n",
+       "      <th>Arr_Pic_Weekday</th>\n",
+       "      <th>Pickup_Mon</th>\n",
+       "      <th>Pickup_Weekday</th>\n",
        "      <th>Distance(km)</th>\n",
        "      <th>Temperature</th>\n",
        "      <th>Precipitation(mm)</th>\n",
@@ -4798,13 +3970,20 @@
        "      <th>Destination_Lon</th>\n",
        "      <th>Day_of_Month</th>\n",
        "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>20.400000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4814,11 +3993,18 @@
        "      <td>36.829741</td>\n",
        "      <td>9</td>\n",
        "      <td>5</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
        "      <td>16</td>\n",
        "      <td>26.400000</td>\n",
        "      <td>7.573502</td>\n",
@@ -4828,197 +4014,117 @@
        "      <td>36.814358</td>\n",
        "      <td>12</td>\n",
        "      <td>5</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>23.255689</td>\n",
        "      <td>7.573502</td>\n",
        "      <td>-1.308284</td>\n",
        "      <td>36.843419</td>\n",
        "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21196</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>28.600000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.258414</td>\n",
-       "      <td>36.804800</td>\n",
-       "      <td>-1.275285</td>\n",
-       "      <td>36.802702</td>\n",
-       "      <td>20</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21197</th>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.307143</td>\n",
-       "      <td>36.825009</td>\n",
-       "      <td>-1.331619</td>\n",
-       "      <td>36.847976</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
+       "      <td>36.828195</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21198</th>\n",
+       "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>20</td>\n",
-       "      <td>29.200000</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19.200000</td>\n",
        "      <td>7.573502</td>\n",
-       "      <td>-1.286018</td>\n",
-       "      <td>36.897534</td>\n",
-       "      <td>-1.258414</td>\n",
-       "      <td>36.804800</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>-1.281301</td>\n",
+       "      <td>36.832396</td>\n",
+       "      <td>-1.257147</td>\n",
+       "      <td>36.795063</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21199</th>\n",
+       "      <th>4</th>\n",
        "      <td>1</td>\n",
        "      <td>13</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.250030</td>\n",
-       "      <td>36.874167</td>\n",
-       "      <td>-1.279209</td>\n",
-       "      <td>36.794872</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
        "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21200</th>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>30.900000</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15.400000</td>\n",
        "      <td>7.573502</td>\n",
-       "      <td>-1.255189</td>\n",
-       "      <td>36.782203</td>\n",
-       "      <td>-1.320157</td>\n",
-       "      <td>36.830887</td>\n",
-       "      <td>26</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>-1.266597</td>\n",
+       "      <td>36.792118</td>\n",
+       "      <td>-1.295041</td>\n",
+       "      <td>36.809817</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>21201 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
-       "0                  3             4    20.400000           7.573502   \n",
-       "1                  3            16    26.400000           7.573502   \n",
-       "2                  3             3    23.255689           7.573502   \n",
-       "3                  3             9    19.200000           7.573502   \n",
-       "4                  1             9    15.400000           7.573502   \n",
-       "...              ...           ...          ...                ...   \n",
-       "21196              3             3    28.600000           7.573502   \n",
-       "21197              3             7    26.000000           7.573502   \n",
-       "21198              3            20    29.200000           7.573502   \n",
-       "21199              1            13    15.000000           7.573502   \n",
-       "21200              3            12    30.900000           7.573502   \n",
+       "   Platform_Type  Pla_Mon  Pla_Weekday  Con_Day_Mon  Con_Weekday  Arr_Pic_Mon  \\\n",
+       "0              3        9            5            9            5            9   \n",
+       "1              3       12            5           12            5           12   \n",
+       "2              3       30            2           30            2           30   \n",
+       "3              3       15            5           15            5           15   \n",
+       "4              1       13            1           13            1           13   \n",
        "\n",
-       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
-       "0       -1.317755   36.830370        -1.300406        36.829741             9   \n",
-       "1       -1.351453   36.899315        -1.295004        36.814358            12   \n",
-       "2       -1.308284   36.843419        -1.300921        36.828195            30   \n",
-       "3       -1.281301   36.832396        -1.257147        36.795063            15   \n",
-       "4       -1.266597   36.792118        -1.295041        36.809817            13   \n",
-       "...           ...         ...              ...              ...           ...   \n",
-       "21196   -1.258414   36.804800        -1.275285        36.802702            20   \n",
-       "21197   -1.307143   36.825009        -1.331619        36.847976            13   \n",
-       "21198   -1.286018   36.897534        -1.258414        36.804800             7   \n",
-       "21199   -1.250030   36.874167        -1.279209        36.794872             4   \n",
-       "21200   -1.255189   36.782203        -1.320157        36.830887            26   \n",
+       "   Arr_Pic_Weekday  Pickup_Mon  Pickup_Weekday  Distance(km)  Temperature  \\\n",
+       "0                5           9               5             4    20.400000   \n",
+       "1                5          12               5            16    26.400000   \n",
+       "2                2          30               2             3    23.255689   \n",
+       "3                5          15               5             9    19.200000   \n",
+       "4                1          13               1             9    15.400000   \n",
        "\n",
-       "       Day_of_Week  Personal_Business  \n",
-       "0                5                  0  \n",
-       "1                5                  1  \n",
-       "2                2                  0  \n",
-       "3                5                  0  \n",
-       "4                1                  1  \n",
-       "...            ...                ...  \n",
-       "21196            3                  1  \n",
-       "21197            6                  0  \n",
-       "21198            4                  0  \n",
-       "21199            3                  1  \n",
-       "21200            2                  0  \n",
+       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
+       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
+       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
+       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
+       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
+       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
        "\n",
-       "[21201 rows x 11 columns]"
+       "   Destination_Lon  Day_of_Month  Day_of_Week  \n",
+       "0        36.829741             9            5  \n",
+       "1        36.814358            12            5  \n",
+       "2        36.828195            30            2  \n",
+       "3        36.795063            15            5  \n",
+       "4        36.809817            13            1  "
       ]
      },
-     "execution_count": 65,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train"
+    "full_df[numeric_cols].head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 152,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5041,260 +4147,259 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
+       "      <th>Pla_Time</th>\n",
+       "      <th>Con_Time</th>\n",
+       "      <th>Arr_Pic_Time</th>\n",
+       "      <th>Pickup_Time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>21201</th>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.333275</td>\n",
-       "      <td>36.870815</td>\n",
-       "      <td>-1.305249</td>\n",
-       "      <td>36.822390</td>\n",
-       "      <td>27</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>09:35:46</td>\n",
+       "      <td>09:40:10</td>\n",
+       "      <td>10:04:47</td>\n",
+       "      <td>2020-10-19 10:27:30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21202</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.272639</td>\n",
-       "      <td>36.794723</td>\n",
-       "      <td>-1.277007</td>\n",
-       "      <td>36.823907</td>\n",
-       "      <td>17</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
+       "      <th>1</th>\n",
+       "      <td>11:16:16</td>\n",
+       "      <td>11:23:21</td>\n",
+       "      <td>11:40:22</td>\n",
+       "      <td>2020-10-19 11:44:09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21203</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>22.800000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.290894</td>\n",
-       "      <td>36.822971</td>\n",
-       "      <td>-1.276574</td>\n",
-       "      <td>36.851365</td>\n",
-       "      <td>27</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <th>2</th>\n",
+       "      <td>12:39:25</td>\n",
+       "      <td>12:42:44</td>\n",
+       "      <td>12:49:34</td>\n",
+       "      <td>2020-10-19 12:53:03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21204</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24.500000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.290503</td>\n",
-       "      <td>36.809646</td>\n",
-       "      <td>-1.303382</td>\n",
-       "      <td>36.790658</td>\n",
-       "      <td>17</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <th>3</th>\n",
+       "      <td>09:25:34</td>\n",
+       "      <td>09:26:05</td>\n",
+       "      <td>09:37:56</td>\n",
+       "      <td>2020-10-19 09:43:06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21205</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281081</td>\n",
-       "      <td>36.814423</td>\n",
-       "      <td>-1.266467</td>\n",
-       "      <td>36.792161</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
+       "      <th>4</th>\n",
+       "      <td>09:55:18</td>\n",
+       "      <td>09:56:18</td>\n",
+       "      <td>10:03:53</td>\n",
+       "      <td>2020-10-19 10:05:23</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pla_Time  Con_Time Arr_Pic_Time          Pickup_Time\n",
+       "0  09:35:46  09:40:10     10:04:47  2020-10-19 10:27:30\n",
+       "1  11:16:16  11:23:21     11:40:22  2020-10-19 11:44:09\n",
+       "2  12:39:25  12:42:44     12:49:34  2020-10-19 12:53:03\n",
+       "3  09:25:34  09:26:05     09:37:56  2020-10-19 09:43:06\n",
+       "4  09:55:18  09:56:18     10:03:53  2020-10-19 10:05:23"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_df[time_cols].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order_No</th>\n",
+       "      <th>User_Id</th>\n",
+       "      <th>Vehicle_Type</th>\n",
+       "      <th>Personal_Business</th>\n",
+       "      <th>Rider_Id</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>28264</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24.800000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.258414</td>\n",
-       "      <td>36.804800</td>\n",
-       "      <td>-1.288780</td>\n",
-       "      <td>36.816831</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>Order_No_4211</td>\n",
+       "      <td>User_Id_633</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28265</th>\n",
-       "      <td>3</td>\n",
-       "      <td>22</td>\n",
-       "      <td>30.700000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.276141</td>\n",
-       "      <td>36.771084</td>\n",
-       "      <td>-1.316098</td>\n",
-       "      <td>36.913164</td>\n",
-       "      <td>10</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <th>1</th>\n",
+       "      <td>Order_No_25375</td>\n",
+       "      <td>User_Id_2285</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_856</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28266</th>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>25.100000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.301446</td>\n",
-       "      <td>36.766138</td>\n",
-       "      <td>-1.264960</td>\n",
-       "      <td>36.798178</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <th>2</th>\n",
+       "      <td>Order_No_1899</td>\n",
+       "      <td>User_Id_265</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28267</th>\n",
-       "      <td>3</td>\n",
-       "      <td>18</td>\n",
-       "      <td>23.600000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.248404</td>\n",
-       "      <td>36.678276</td>\n",
-       "      <td>-1.272027</td>\n",
-       "      <td>36.817411</td>\n",
-       "      <td>29</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
+       "      <th>3</th>\n",
+       "      <td>Order_No_9336</td>\n",
+       "      <td>User_Id_1402</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>Rider_Id_855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28268</th>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>22.300000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.255189</td>\n",
-       "      <td>36.782203</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
+       "      <th>4</th>\n",
+       "      <td>Order_No_27883</td>\n",
+       "      <td>User_Id_1737</td>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Personal</td>\n",
+       "      <td>Rider_Id_770</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>7068 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Platform_Type  Distance(km)  Temperature  Precipitation(mm)  \\\n",
-       "21201              3             8    23.255689           7.573502   \n",
-       "21202              3             5    23.255689           7.573502   \n",
-       "21203              3             5    22.800000           7.573502   \n",
-       "21204              3             5    24.500000           7.573502   \n",
-       "21205              3             6    24.400000           7.573502   \n",
-       "...              ...           ...          ...                ...   \n",
-       "28264              3             5    24.800000           7.573502   \n",
-       "28265              3            22    30.700000           7.573502   \n",
-       "28266              3            10    25.100000           7.573502   \n",
-       "28267              3            18    23.600000           7.573502   \n",
-       "28268              3             8    22.300000           7.573502   \n",
-       "\n",
-       "       Pickup_Lat  Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  \\\n",
-       "21201   -1.333275   36.870815        -1.305249        36.822390            27   \n",
-       "21202   -1.272639   36.794723        -1.277007        36.823907            17   \n",
-       "21203   -1.290894   36.822971        -1.276574        36.851365            27   \n",
-       "21204   -1.290503   36.809646        -1.303382        36.790658            17   \n",
-       "21205   -1.281081   36.814423        -1.266467        36.792161            11   \n",
-       "...           ...         ...              ...              ...           ...   \n",
-       "28264   -1.258414   36.804800        -1.288780        36.816831             7   \n",
-       "28265   -1.276141   36.771084        -1.316098        36.913164            10   \n",
-       "28266   -1.301446   36.766138        -1.264960        36.798178             5   \n",
-       "28267   -1.248404   36.678276        -1.272027        36.817411            29   \n",
-       "28268   -1.255189   36.782203        -1.300406        36.829741             4   \n",
-       "\n",
-       "       Day_of_Week  Personal_Business  \n",
-       "21201            3                  0  \n",
-       "21202            5                  0  \n",
-       "21203            4                  0  \n",
-       "21204            1                  0  \n",
-       "21205            2                  0  \n",
-       "...            ...                ...  \n",
-       "28264            1                  0  \n",
-       "28265            3                  0  \n",
-       "28266            3                  0  \n",
-       "28267            2                  1  \n",
-       "28268            2                  0  \n",
-       "\n",
-       "[7068 rows x 11 columns]"
+       "         Order_No       User_Id Vehicle_Type Personal_Business      Rider_Id\n",
+       "0   Order_No_4211   User_Id_633         Bike          Business  Rider_Id_432\n",
+       "1  Order_No_25375  User_Id_2285         Bike          Personal  Rider_Id_856\n",
+       "2   Order_No_1899   User_Id_265         Bike          Business  Rider_Id_155\n",
+       "3   Order_No_9336  User_Id_1402         Bike          Business  Rider_Id_855\n",
+       "4  Order_No_27883  User_Id_1737         Bike          Personal  Rider_Id_770"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "full_df[object_cols].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert object types to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Convert an object to numeric (encoding)\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "le.fit(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
+    "full_df['Personal_Business'][:2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = numeric_cols + ['Personal_Business']\n",
+    "\n",
+    "data_df = full_df[features]\n",
+    "\n",
+    "y = full_df[:border]['Time_Pic_Arr']\n",
+    "train = data_df[:border]\n",
+    "test = data_df[border:]\n",
+    "\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "test"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       NaN\n",
-       "1       NaN\n",
-       "2       NaN\n",
-       "3       NaN\n",
-       "4       NaN\n",
-       "         ..\n",
-       "21196   NaN\n",
-       "21197   NaN\n",
-       "21198   NaN\n",
-       "21199   NaN\n",
-       "21200   NaN\n",
-       "Name: Time_Pic_Arr, Length: 21201, dtype: float64"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "y"
    ]
@@ -5308,19 +4413,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
     "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
@@ -5342,47 +4437,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-69-6a76cfe783d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mcv_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mregressor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregressors\u001b[0m\u001b[1;33m:\u001b[0m     \u001b[1;31m#scores to be minimised are negated (neg)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0mcv_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mcv_means\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    404\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    407\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 248\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "rs = 3\n",
-    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True) #returns error\n",
+    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True)\n",
     "\n",
     "regressors = []\n",
     "regressors.append(SVR())\n",
@@ -5431,34 +4491,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-81-e8fb8588c79a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m            \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n\u001b[0;32m      5\u001b[0m \u001b[0mrsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRFC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrf_param\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mrfm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mr_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    763\u001b[0m             \u001b[0mrefit_start_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    302\u001b[0m             )\n\u001b[0;32m    303\u001b[0m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[1;32m--> 304\u001b[1;33m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0m\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    803\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    804\u001b[0m         y = check_array(y, accept_sparse='csr', force_all_finite=True,\n\u001b[1;32m--> 805\u001b[1;33m                         ensure_2d=False, dtype=None)\n\u001b[0m\u001b[0;32m    806\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    807\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "rs = 3 #retuns errors\n",
     "RFC = RandomForestRegressor(random_state=rs)\n",
     "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
     "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
@@ -5473,47 +4509,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-71-f5926b27e7ad>, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-71-f5926b27e7ad>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "Prediction = predict(fit, test) #returns errors\n",
+    "Prediction = predict(fit, test)\n",
     "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
     "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'lgbm' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-72-bce2b5ba7327>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlgbm_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[0;32m      4\u001b[0m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'lgbm' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration) #returns errors, lgb not defined\n",
+    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
     "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
     "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
     "lgbm_output.to_csv(\"submission.csv\", index=False)"
@@ -5521,50 +4532,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'lr' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-73-acbae1ec0c43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'lr' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "y_pred = lr.predict(test_new) #lr not defined"
+    "y_pred = lr.predict(test_new)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'test_df1' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-74-fc2ddc35a87f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msubmission_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_df1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Order No'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msubmission_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time_Pic_Arr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'test_df1' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "submission_df = test_df1[['Order No']] #test_df1 not found error\n",
+    "submission_df = test_df1[['Order No']]\n",
     "submission_df['Time_Pic_Arr'] = y_pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5587,49 +4574,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Warning] min_data_in_leaf is set=300, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=300\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-76-0f04e4b0aaeb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mlsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLGBMRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mlgbm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0ml_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    734\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 736\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1188\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    713\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 715\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m         \u001b[0mtest_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m     error_msg = (\"scoring must return a number, got %s (%s) \"\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m                 score = scorer._score(cached_call, estimator,\n\u001b[1;32m---> 88\u001b[1;33m                                       *args, **kwargs)\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[1;32m--> 213\u001b[1;33m                                                  **self._kwargs)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "params = {\n",
     "    'n_estimators':[75], # [75, 95],\n",
@@ -5657,103 +4604,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003107 seconds.\n",
-      "You can set `force_col_wise=true` to remove the overhead.\n",
-      "[LightGBM] [Info] Total Bins 1334\n",
-      "[LightGBM] [Info] Number of data points in the train set: 16960, number of used features: 11\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[1]\tvalid_0's l2: 0\n",
-      "Training until validation scores don't improve for 20 rounds\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[2]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[3]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[4]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[5]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[6]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[7]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[8]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[9]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[10]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[11]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[12]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[13]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[14]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[15]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[16]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[17]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[18]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[19]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[20]\tvalid_0's l2: 0\n",
-      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
-      "[21]\tvalid_0's l2: 0\n",
-      "Early stopping, best iteration is:\n",
-      "[1]\tvalid_0's l2: 0\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-77-21f5823a14a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mlpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlgbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The RMSE of prediction is \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m    254\u001b[0m     \"\"\"\n\u001b[0;32m    255\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[1;32m--> 256\u001b[1;33m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[0;32m    257\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 645\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n\u001b[1;32m---> 99\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m    100\u001b[0m             )\n\u001b[0;32m    101\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "lgb_train = lgb.Dataset(X_train, y_train)\n",
     "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
@@ -5781,29 +4634,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-78-a6078aba1174>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n\u001b[0;32m      3\u001b[0m                            \"Time from Pickup to Arrival\": lgbm_y })\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mlgbm_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\Temp\\submission.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors)\u001b[0m\n\u001b[0;32m   3168\u001b[0m             \u001b[0mdecimal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3169\u001b[0m         )\n\u001b[1;32m-> 3170\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3172\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    188\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m                 \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m                 \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    191\u001b[0m             )\n\u001b[0;32m    192\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors)\u001b[0m\n\u001b[0;32m    491\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    492\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    494\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m             \u001b[1;31m# No explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\Temp\\\\submission.csv'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
     "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
     "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
-    "lgbm_output.to_csv(\"submission.csv\", index=False)"
+    "lgbm_output.to_csv(\"D:\\Temp\\submission.csv\", index=False)"
    ]
   },
   {
@@ -5830,9 +4668,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}

From 0d6d943e10ea03c9815b94b3ef453d5c8d9dfffb Mon Sep 17 00:00:00 2001
From: Thiyasizwe Kubeka <thiyasizwekubeka@gmail.com>
Date: Tue, 20 Oct 2020 11:44:08 +0200
Subject: [PATCH 46/46] API

---
 .DS_Store                                     |   Bin 0 -> 6148 bytes
 API                                           |     1 +
 README.md                                     |   263 -
 Sendy_Team4-Copy3.ipynb                       |  2384 --
 Sendy_Team4.ipynb                             |  1957 --
 Sendy_Team4_Final.ipynb                       |  4676 ----
 api.py                                        |    60 -
 assets/imgs/API.png                           |   Bin 91523 -> 0 bytes
 assets/imgs/EDSA_logo.png                     |   Bin 36548 -> 0 bytes
 assets/imgs/Flask_logo.png                    |   Bin 13445 -> 0 bytes
 assets/imgs/What_is_an_API.png                |   Bin 78899 -> 0 bytes
 assets/imgs/fork_repo.png                     |   Bin 79737 -> 0 bytes
 assets/trained-models/lgbm_model.pkl          |   Bin 145747 -> 0 bytes
 .../sendy_simple_lm_regression.pkl            |   Bin 585 -> 0 bytes
 model.py                                      |   187 -
 team4_regression-EDA.ipynb                    |  2563 ---
 utils/data/riders.csv                         |   961 -
 utils/data/test_data.csv                      |  3182 ---
 utils/data/train_data.csv                     | 18021 ----------------
 utils/request.py                              |    57 -
 utils/train_model.py                          |    34 -
 21 files changed, 1 insertion(+), 34345 deletions(-)
 create mode 100644 .DS_Store
 create mode 160000 API
 delete mode 100644 README.md
 delete mode 100644 Sendy_Team4-Copy3.ipynb
 delete mode 100644 Sendy_Team4.ipynb
 delete mode 100644 Sendy_Team4_Final.ipynb
 delete mode 100644 api.py
 delete mode 100644 assets/imgs/API.png
 delete mode 100644 assets/imgs/EDSA_logo.png
 delete mode 100644 assets/imgs/Flask_logo.png
 delete mode 100644 assets/imgs/What_is_an_API.png
 delete mode 100644 assets/imgs/fork_repo.png
 delete mode 100644 assets/trained-models/lgbm_model.pkl
 delete mode 100644 assets/trained-models/sendy_simple_lm_regression.pkl
 delete mode 100644 model.py
 delete mode 100644 team4_regression-EDA.ipynb
 delete mode 100644 utils/data/riders.csv
 delete mode 100644 utils/data/test_data.csv
 delete mode 100644 utils/data/train_data.csv
 delete mode 100644 utils/request.py
 delete mode 100644 utils/train_model.py

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diff --git a/API b/API
new file mode 160000
index 0000000..09b3cad
--- /dev/null
+++ b/API
@@ -0,0 +1 @@
+Subproject commit 09b3cad1b24144cbe5aea5f4cb205c7d831118fd
diff --git a/README.md b/README.md
deleted file mode 100644
index ce55349..0000000
--- a/README.md
+++ /dev/null
@@ -1,263 +0,0 @@
-# Flask-based Model API
-#### EXPLORE Data Science Academy Regression Predict
-
-### Table of Contents
-
-- [Flask-based Model API](#flask-based-model-api)
-      - [EXPLORE Data Science Academy Regression Predict](#explore-data-science-academy-regression-predict)
-  * [1) Overview](#1-overview)
-      - [1.1) Wait, what is an API again?](#11-wait-what-is-an-api-again)
-      - [1.2) How our API will work](#12-how-our-api-will-work)
-        * [Description of files](#description-of-files)
-  * [2) Usage Instructions](#2-usage-instructions)
-      - [2.1) Creating a copy of this repo](#21-creating-a-copy-of-this-repo)
-      - [2.2) Running the API on your local machine](#22-running-the-api-on-your-local-machine)
-      - [2.3) Updating the API to use your own model](#23-updating-the-api-to-use-your-own-model)
-        * [Prerequisites](#prerequisites)
-        * [Making the changes](#making-the-changes)
-      - [2.4) Running the API on a remote AWS EC2 instance](#24-running-the-api-on-a-remote-aws-ec2-instance)
-  * [3) FAQ](#3-faq)
-
-## 1) Overview
-
-This repository forms the basis of *Task 2* for the **Regression Predict** within EDSA's Data Science course. It hosts template code which will enable students to deploy their own developed models through a web server-based API.   
-
-#### 1.1) Wait, what is an API again?
-
-[![What is an API](assets/imgs/What_is_an_API.png)](https://youtu.be/s7wmiS2mSXY)
-
-An API - or Application Programming Interface - refers to a set of procedures and protocols that allows us to send and request information between ourselves and remote applications. You can think of this as a channel of communication to a remote server using specific commands that allow you to use their applications without needing to host that functionality yourself. Many types of API's exist, but for this predict task we are interested specifically in Web API's. These allow us to send and receive information using web development languages, such as HTML and JSON. The video above provides a simple and intuitive explanation of how API's operate.
-
-#### 1.2) How our API will work
-
-![Flask Web-server](assets/imgs/API.png)
-
-##### Description of files
-
-Several files within this repository enable the correct functioning of our API. We provide a high-level description of these salient files within the table below:
-
-| File Name              | Description                                                                          |
-| :--------------------- | :--------------------                                                                |
-| `api.py`               | Flask web server application definition and instantiation.                           |
-| `model.py`             | Contains helper functions to separate model specific code from our API definition.   |
-| `utils/request.py`     | Simple script to simulate a POST request sent to our API.                            |
-| `utils/train_model.py` | Code used to train the simple model used for demonstration of the API's functioning. |
-
-## 2) Usage Instructions
-
-#### 2.1) Creating a copy of this repo
-
-| :zap: WARNING :zap: |
-|:--------------------|
-|Do **NOT** *clone* this repository. Instead follow the instructions in this section to *fork* the repo.|
-
-As described within the Predict instructions for the Regression Sprint, this code represents a *template* from which you can base your own model's API. As such, in order to modify the template to serve your own model (and the associated code changes which are required for this), you will need to **[fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo)** this repository. Failing to do this will lead to complications when trying to work on the API remotely.
-
-![Fork Repo](assets/imgs/fork_repo.png)  
-
-To fork the repo, simply ensure that you are logged into your GitHub account, and then click on the 'fork' button at the top of this page as indicated within the figure above.
-
-
-#### 2.2) Running the API on your local machine
-
-As a first step to becoming familiar with our API's functioning, we recommend setting up a running instance on your own local machine.
-
-To do this, follow the steps below by running the given commands within a Git bash (Windows), or terminal (Mac/Linux):
-
- 1. Ensure that you have the prerequisite Python libraries installed on your local machine:
-
- ```bash
- pip install -U flask numpy pandas scikit-learn
- ```
-
- 2. Clone the *forked* repo to your local machine.
-
- ```bash
- git clone https://github.com/{your-account-name}/regression-predict-api-template.git
- ```  
-
- 3. Navigate to the base of the cloned repo, and run the API web-server initialisation script.
-
- ```bash
- cd regression-predict-api-template/
- python api.py
- ```
-
- If the web server was able to initialise successfully, the following message should be displayed within your bash/terminal session:
-
-```
-----------------------------------------
-Model succesfully loaded
-----------------------------------------
- * Serving Flask app "api" (lazy loading)
- * Environment: production
-   WARNING: This is a development server. Do not use it in a production deployment.
-   Use a production WSGI server instead.
- * Debug mode: off
- * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
-```
-
-4. Leave the web server script running within the current bash/terminal session. Open a new session, and navigate to the `utils` subfolder of the cloned repo.
-
-```
-cd {your/custom/path}/regression-predict-api-template/utils/
-```
-
-5. Run the `request.py` script located within the utils subfolder to simulate a POST request for our running API.
-
-```
-python request.py
-```
-
-If you receive an error at this point, please ensure that the web server is still running in your original bash/terminal session. If the script ran successfully, you should receive similar output to the message shown below:
-
-```
-Sending POST request to web server API at: http://127.0.0.1:5000/api_v0.1
-
-Querying API with the following data:
- ['Order_No_21660', 'User_Id_1329', 'Bike', 3, 'Business', 31, 5, '12:16:49 PM', 31, 5, '12:22:48 PM', 31, 5, '12:23:47 PM', 31, 5, '12:38:24 PM', 4, 21.8, nan, -1.2795183, 36.8238089, -1.273056, 36.811298, 'Rider_Id_812', 4402, 1090, 14.3, 1301]
-
-Received POST response:
-**************************************************
-API prediction result: 1547.3014476106036
-The response took: 0.004323 seconds
-**************************************************
-```
-
-Congratulations! You've now officially deployed your first web server API, and have successfully received a response from it.
-
-With these steps completed, we're now ready to both modify the template code to place our own model within the API, and to host this API within an AWS EC2 instance. These processes are outlined within the sections below.  
-
-#### 2.3) Updating the API to use your own model
-| :information_source: NOTE :information_source: |
-|:--------------------|
-|We strongly encourage you to be familiar with running the API as described in Section 2.2 before attempting to use your own model.|
-
-##### Prerequisites
-Before you can update the API code-base to use your own custom model, you will need to have the following:
-
- - Your own `sklearn` model, trained and saved as a `.pkl` file.
-
-    For a simple example of how to pickle your model, review the script found in `utils/train_model.py`. For further instructions, consult the *'Saving and Restoring Models in Python'* train in Athena.     
-
-    (Note: You are not limited to use only a single model within the API. Furthermore, other `sklearn` structures which have saved parameters may be required for your model to function as well. Obviously, you are expected to handle the loading of such structures in a similar way as described within this section.)
-
-  - Code for the data preprocessing pipeline used to train your model.
-
-      This code should cover aspects such as data cleaning, feature engineering, feature selection, and feature transformations.
-
-      The requirement of this code is vital as your API is built to provide a standard interface for POST requests. I.e. someone asking your API to make a prediction shouldn't have to worry about what specific features your model uses internally. Instead, anyone who sends a request with the standard features within the public dataset, should expect to receive a prediction result. This design principle makes it far easier to swap out an old model for a newer one, even if ends up using radically different features.
-
-##### Making the changes
-
-Once you've gathered the prerequisites from the above section, making the changes to API is relatively straight forward. It involves three steps:
-
-1. Place your `.pkl` file within the `assets/trained-models/` directory of the repo.
-
-2. Modify the `api.py` file by changing the `path_to_model` variable to reflect the new model `.pkl` file.
-
-3. Modify the `model.py` file by adding your data preprocessing code to the `_preprocess_data()` helper function.  
-
-If the following steps were carried out successfully, running the API should now produce a new prediction result.  
-
-#### 2.4) Running the API on a remote AWS EC2 instance
-| :information_source: NOTE :information_source: |
-|:--------------------|
-|You will only be able to work on this section of the API setup once you've completed the *'Introduction to Amazon AWS - Part I'* train on Athena.|
-
-The following steps will enable you to run your web server API on a remote EC2 instance, allowing it to the accessed by any device/application which has internet access.
-
-Within these setup steps, we will be using a remote EC2 instance, which we will refer to as the ***Host***, in addition to our local machine, which we will call the ***Client***. We use these designations for convenience, and to align our terminology with that of common web server practices. In cases where commands are provided, use Git bash (Windows) or Terminal (Mac/Linux) to enter these.
-
-1. Ensure that you have access to a running AWS EC2 instance with an assigned public IP address. Instructions for this process are found within the *'Introduction to Amazon AWS - Part I'* train on Athena.
-
-2. Install the prerequisite python libraries on both the Host (EC2 instance), and Client (local machine):
-
-```bash
-pip install -U flask numpy pandas scikit-learn
-```
-
-3. Clone your copy of the API repo onto both the Host and Client machines, then navigate to the base of the cloned repo:
-
-```bash
-git clone https://github.com/{your-account-name}/regression-predict-api-template.git
-cd regression-predict-api-template/
-```
-**[On the Host]:**
-
-4.  Run the API web-server initialisation script.
-
-```bash
-python api.py
-```
-
-If this command ran successfully, the following output should be observed on the Host:
-
-```
-----------------------------------------
-Model succesfully loaded
-----------------------------------------
- * Serving Flask app "api" (lazy loading)
- * Environment: production
-   WARNING: This is a development server. Do not use it in a production deployment.
-   Use a production WSGI server instead.
- * Debug mode: off
- * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
-```
-
-**[On the Client]:**
-
-5.  Navigate to the `utils` subdirectory within the repo.
-
-```bash
-cd utils/
-```
-
-6. Open the `request.py` file using your favourite text editor.
-
-    Change the value of the `url` variable to reflect the ***public IP address*** of the Host. (Instructions for getting the public IP address are provided within the *‘Introduction to Amazon AWS - Part I’* train on Athena.)
-
-```bash
-url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-```   
-
-7. Once the editing is completed, close the file and run it:
-
-```bash
-python request.py
-```
-
- If the command ran successfully, you should see output similar to the following:
-
-```
-Sending POST request to web server API at: http://54.229.152.221:5000/api_v0.1
-
-Querying API with the following data:
- ['Order_No_21660', 'User_Id_1329', 'Bike', 3, 'Business', 31, 5, '12:16:49 PM', 31, 5, '12:22:48 PM', 31, 5, '12:23:47 PM', 31, 5, '12:38:24 PM', 4, 21.8, nan, -1.2795183, 36.8238089, -1.273056, 36.811298, 'Rider_Id_812', 4402, 1090, 14.3, 1301]
-
-Received POST response:
-**************************************************
-API prediction result: 1547.3014476106036
-The response took: 0.406473 seconds
-**************************************************
-```
-
-If you have completed the steps in 2.3), then the prediction result should differ from the one given above.
-
-**[On the Host]**
-
-You should also see an update to the web server output, indicating that it was contacted by the Client (the values of this string will differ for your output):
-
-```
-102.165.194.240 - - [08/May/2020 07:31:31] "POST /api_v0.1 HTTP/1.1" 200 -
-```
-
-If you are able to see these messages on both the Host and Client, then your API has succesfully been deployed to the Web. Snap :zap:!
-
-## 3) FAQ
-
-This section of the repo will be periodically updated to represent common questions which may arise around its use. If you detect any problems/bugs, please [create an issue](https://help.github.com/en/github/managing-your-work-on-github/creating-an-issue) and we will do our best to resolve it as quickly as possible.
-
-We wish you all the best in your learning experience :rocket:
-
-![Explore Data Science Academy](assets/imgs/EDSA_logo.png)
diff --git a/Sendy_Team4-Copy3.ipynb b/Sendy_Team4-Copy3.ipynb
deleted file mode 100644
index c7862f8..0000000
--- a/Sendy_Team4-Copy3.ipynb
+++ /dev/null
@@ -1,2384 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1) Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
-    "from sklearn.model_selection import cross_val_score, learning_curve\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "from sklearn.svm import SVR\n",
-    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "#import xgboost as xgb\n",
-    "#import lightgbm as lgb\n",
-    "\n",
-    "sns.set(style='white', context='notebook', palette='deep')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2) Import Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(21201, 29) (7068, 25) (960, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
-    "\n",
-    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
-    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
-    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
-    "\n",
-    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
-    "train_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3) Data Manipulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
-    "\n",
-    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Creating Full_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((21201, 26), (7068, 26), (28269, 26))"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
-    "#explore training, make (column) changes to full, later we use the border to separate\n",
-    "#Be careful of information leakage\n",
-    "\n",
-    "border = train_df.shape[0]\n",
-    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
-    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
-    "\n",
-    "train_df.shape, test_df.shape, full_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Renaming columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
-       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
-       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
-       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
-       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
-       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
-       "       'Rider_Id', 'Time_Pic_Arr'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Renaming columns (shorten, remove space, standardize)\n",
-    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
-    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
-    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
-    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
-    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
-    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
-    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
-    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
-    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
-    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
-    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
-    "                           }\n",
-    "\n",
-    "full_df = full_df.rename(columns=new_names)\n",
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Convert Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>15:07:35</td>\n",
-       "      <td>15:08:57</td>\n",
-       "      <td>15:21:36</td>\n",
-       "      <td>15:30:30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
-       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
-       "5  15:07:35  15:08:57     15:21:36    15:30:30"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Convert Time from 12H to 24H\n",
-    "\n",
-    "def convert_to_24hrs(fulldf):\n",
-    "    for col in fulldf.columns:\n",
-    "        if col.endswith(\"Time\"):\n",
-    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
-    "    return fulldf\n",
-    "\n",
-    "full_df = convert_to_24hrs(full_df)\n",
-    "\n",
-    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Filling Missing Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Filling Missing Values for temperatures and humidity\n",
-    "\n",
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Vehicle_Type</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Mon</th>\n",
-       "      <th>Pla_Weekday</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Day_Mon</th>\n",
-       "      <th>Con_Weekday</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
-       "\n",
-       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
-       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
-       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
-       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
-       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
-       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
-       "\n",
-       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
-       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
-       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
-       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
-       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
-       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
-       "\n",
-       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
-       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
-       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
-       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
-       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
-       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Traversing Month and Weekday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
-    "\n",
-    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
-    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
-    "\n",
-    "count = 0\n",
-    "instances_of_different_days = [];\n",
-    "for i, row in full_df.iterrows():\n",
-    "    if len(set(row[month_cols].values)) > 1:\n",
-    "        print(count+1, end=\"\\r\")\n",
-    "        count = count + 1\n",
-    "        instances_of_different_days.append(list(row[month_cols].values))\n",
-    "instances_of_different_days"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "month_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weekday_cols"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Creating Month and Weekday columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
-    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Dropping redundant columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  "
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
-    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
-    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
-    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
-    "\n",
-    "full_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
-       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
-       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
-       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
-       "       'Day_of_Month', 'Day_of_Week'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Variable Datatypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
-       "0   36.830370        -1.300406        36.829741             9            5  \n",
-       "1   36.899315        -1.295004        36.814358            12            5  \n",
-       "2   36.843419        -1.300921        36.828195            30            2  "
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numeric_cols = []\n",
-    "object_cols = []\n",
-    "time_cols = []\n",
-    "for k, v in full_df.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_cols.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_cols.append(k)\n",
-    "    else:\n",
-    "        object_cols.append(k)\n",
-    "full_df[numeric_cols].head(3) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
-       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
-       "2  12:39:25  12:42:44     12:49:34    12:53:03"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[time_cols].head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Personal_Business      Rider_Id\n",
-       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
-       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
-       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[object_cols].head(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Convert an object to numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "Name: Personal_Business, dtype: int32"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Convert an object to numeric (encoding)\n",
-    "\n",
-    "le = LabelEncoder()\n",
-    "le.fit(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'][:2]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Feature Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Order_No</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_4211</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_25375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_1899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Order_No_9336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Order_No_27883</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
-       "3              3             9    19.200000           7.573502   -1.281301   \n",
-       "4              1             9    15.400000           7.573502   -1.266597   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
-       "0   36.830370        -1.300406        36.829741             9            5   \n",
-       "1   36.899315        -1.295004        36.814358            12            5   \n",
-       "2   36.843419        -1.300921        36.828195            30            2   \n",
-       "3   36.832396        -1.257147        36.795063            15            5   \n",
-       "4   36.792118        -1.295041        36.809817            13            1   \n",
-       "\n",
-       "   Personal_Business        Order_No  \n",
-       "0                  0   Order_No_4211  \n",
-       "1                  1  Order_No_25375  \n",
-       "2                  0   Order_No_1899  \n",
-       "3                  0   Order_No_9336  \n",
-       "4                  1  Order_No_27883  "
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = numeric_cols + ['Personal_Business'] + ['Order_No']\n",
-    "\n",
-    "data_df = full_df[features]\n",
-    "\n",
-    "y = full_df[:border]['Time_Pic_Arr']\n",
-    "train = data_df[:border]\n",
-    "test = data_df[border:]\n",
-    "\n",
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(28269, 19) (28269, 12) (21201, 12) (7068, 12) (21201,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Prep for split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(21201, 11)"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_new = train.drop('Order_No',axis=1)\n",
-    "test_new = test.drop('Order_No',axis=1)\n",
-    "train_new.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7068, 11)"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_new.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(train_new, y, test_size=0.2, random_state = 1, shuffle=False)\n",
-    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "778.5641538562762"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lr = LinearRegression()\n",
-    "lr.fit(X_train, y_train)\n",
-    "test_pred = lr.predict(X_test)\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "def rmse(y_test,y_predict):\n",
-    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
-    "rmse(y_test,test_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = lr.predict(test_new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df = test_df1[['Order No']]\n",
-    "submission_df['Time_Pic_Arr'] = y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### LR with Scailing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.396406</td>\n",
-       "      <td>-0.971370</td>\n",
-       "      <td>-0.887117</td>\n",
-       "      <td>-0.00314</td>\n",
-       "      <td>-1.189424</td>\n",
-       "      <td>0.509897</td>\n",
-       "      <td>-0.511888</td>\n",
-       "      <td>0.414159</td>\n",
-       "      <td>-0.756213</td>\n",
-       "      <td>1.122927</td>\n",
-       "      <td>-0.468583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.396406</td>\n",
-       "      <td>1.145469</td>\n",
-       "      <td>0.975120</td>\n",
-       "      <td>-0.00314</td>\n",
-       "      <td>-2.294056</td>\n",
-       "      <td>2.349813</td>\n",
-       "      <td>-0.356757</td>\n",
-       "      <td>0.070177</td>\n",
-       "      <td>-0.415254</td>\n",
-       "      <td>1.122927</td>\n",
-       "      <td>2.134095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.396406</td>\n",
-       "      <td>-1.147774</td>\n",
-       "      <td>-0.000789</td>\n",
-       "      <td>-0.00314</td>\n",
-       "      <td>-0.878981</td>\n",
-       "      <td>0.858136</td>\n",
-       "      <td>-0.526672</td>\n",
-       "      <td>0.379588</td>\n",
-       "      <td>1.630501</td>\n",
-       "      <td>-0.791244</td>\n",
-       "      <td>-0.468583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.396406</td>\n",
-       "      <td>-0.089354</td>\n",
-       "      <td>-1.259565</td>\n",
-       "      <td>-0.00314</td>\n",
-       "      <td>0.005523</td>\n",
-       "      <td>0.563962</td>\n",
-       "      <td>0.730374</td>\n",
-       "      <td>-0.361289</td>\n",
-       "      <td>-0.074294</td>\n",
-       "      <td>1.122927</td>\n",
-       "      <td>-0.468583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-2.802759</td>\n",
-       "      <td>-0.089354</td>\n",
-       "      <td>-2.438982</td>\n",
-       "      <td>-0.00314</td>\n",
-       "      <td>0.487515</td>\n",
-       "      <td>-0.510932</td>\n",
-       "      <td>-0.357811</td>\n",
-       "      <td>-0.031369</td>\n",
-       "      <td>-0.301601</td>\n",
-       "      <td>-1.429301</td>\n",
-       "      <td>2.134095</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0       0.396406     -0.971370    -0.887117           -0.00314   -1.189424   \n",
-       "1       0.396406      1.145469     0.975120           -0.00314   -2.294056   \n",
-       "2       0.396406     -1.147774    -0.000789           -0.00314   -0.878981   \n",
-       "3       0.396406     -0.089354    -1.259565           -0.00314    0.005523   \n",
-       "4      -2.802759     -0.089354    -2.438982           -0.00314    0.487515   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
-       "0    0.509897        -0.511888         0.414159     -0.756213     1.122927   \n",
-       "1    2.349813        -0.356757         0.070177     -0.415254     1.122927   \n",
-       "2    0.858136        -0.526672         0.379588      1.630501    -0.791244   \n",
-       "3    0.563962         0.730374        -0.361289     -0.074294     1.122927   \n",
-       "4   -0.510932        -0.357811        -0.031369     -0.301601    -1.429301   \n",
-       "\n",
-       "   Personal_Business  \n",
-       "0          -0.468583  \n",
-       "1           2.134095  \n",
-       "2          -0.468583  \n",
-       "3          -0.468583  \n",
-       "4           2.134095  "
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "train_new_scaled = scaler.fit_transform(train_new)\n",
-    "train_new_standardise = pd.DataFrame(train_new_scaled,columns=train_new.columns)\n",
-    "train_new_standardise.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16960, 11) (4241, 11) (16960,) (4241,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, y, test_size=0.2, random_state = 1, shuffle=False)\n",
-    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "778.564153856276"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lr = LinearRegression()\n",
-    "lr.fit(X_train, y_train)\n",
-    "test_pred = lr.predict(X_test)\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "def rmse(y_test,y_predict):\n",
-    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
-    "rmse(y_test,test_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = lr.predict(test_new)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Ridge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "778.564153856276"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.2, \n",
-    "                                                    shuffle=False)\n",
-    "from sklearn.linear_model import Ridge\n",
-    "ridge = Ridge()\n",
-    "ridge.fit(X_train, y_train)\n",
-    "test_pred = lr.predict(X_test)\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "def rmse(y_test,y_predict):\n",
-    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
-    "rmse(y_test,test_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = lr.predict(test_new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df = test_df1[['Order No']]\n",
-    "submission_df['Time_Pic_Arr'] = y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df.to_csv('D:/Temp/LRRidge.csv', index = False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Lasso"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "778.564153856276"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# split dataset into train and test sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(train_new_standardise, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.20,\n",
-    "                                                    random_state=1,\n",
-    "                                                    shuffle=False)\n",
-    "from sklearn.linear_model import Lasso\n",
-    "lasso = Lasso(alpha=0.01)\n",
-    "lasso.fit(X_train, y_train)\n",
-    "test_pred = lr.predict(X_test)\n",
-    "def rmse(y_test,y_predict):\n",
-    "    return np.sqrt(mean_squared_error(y_test,y_predict))\n",
-    "rmse(y_test,test_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Sendy_Team4.ipynb b/Sendy_Team4.ipynb
deleted file mode 100644
index a2f6cb0..0000000
--- a/Sendy_Team4.ipynb
+++ /dev/null
@@ -1,1957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1) Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'xgboost'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-39-fb1e6551696f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mxgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlightgbm\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mlgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
-    "from sklearn.model_selection import cross_val_score, learning_curve\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "from sklearn.svm import SVR\n",
-    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "sns.set(style='white', context='notebook', palette='deep')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2) Import Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(21201, 29) (7068, 25) (960, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
-    "\n",
-    "train_df = pd.read_csv(\"D:\\Temp\\Train.csv\")\n",
-    "test_df = pd.read_csv(\"D:\\Temp\\Test.csv\")\n",
-    "riders_df = pd.read_csv(\"D:\\Temp\\Riders.csv\")\n",
-    "\n",
-    "print(train_df.shape, test_df.shape, riders_df.shape)\n",
-    "train_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3) Data Manipulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
-    "\n",
-    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Creating Full_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((21201, 26), (7068, 26), (28269, 26))"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
-    "#explore training, make (column) changes to full, later we use the border to separate\n",
-    "#Be careful of information leakage\n",
-    "\n",
-    "border = train_df.shape[0]\n",
-    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
-    "full_df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n",
-    "\n",
-    "train_df.shape, test_df.shape, full_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Renaming columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
-       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
-       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
-       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
-       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
-       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
-       "       'Rider_Id', 'Time_Pic_Arr'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Renaming columns (shorten, remove space, standardize)\n",
-    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
-    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
-    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
-    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
-    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
-    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
-    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
-    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
-    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
-    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
-    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
-    "                           }\n",
-    "\n",
-    "full_df = full_df.rename(columns=new_names)\n",
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Convert Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>15:07:35</td>\n",
-       "      <td>15:08:57</td>\n",
-       "      <td>15:21:36</td>\n",
-       "      <td>15:30:30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "3  09:25:34  09:26:05     09:37:56    09:43:06\n",
-       "4  09:55:18  09:56:18     10:03:53    10:05:23\n",
-       "5  15:07:35  15:08:57     15:21:36    15:30:30"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Convert Time from 12H to 24H\n",
-    "\n",
-    "def convert_to_24hrs(fulldf):\n",
-    "    for col in fulldf.columns:\n",
-    "        if col.endswith(\"Time\"):\n",
-    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
-    "    return fulldf\n",
-    "\n",
-    "full_df = convert_to_24hrs(full_df)\n",
-    "\n",
-    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Filling Missing Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Filling Missing Values for temperatures and humidity\n",
-    "\n",
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Vehicle_Type</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Mon</th>\n",
-       "      <th>Pla_Weekday</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Day_Mon</th>\n",
-       "      <th>Con_Weekday</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
-       "\n",
-       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ... Pickup_Time  \\\n",
-       "0        9            5  09:35:46            9            5  ...    10:27:30   \n",
-       "1       12            5  11:16:16           12            5  ...    11:44:09   \n",
-       "2       30            2  12:39:25           30            2  ...    12:53:03   \n",
-       "3       15            5  09:25:34           15            5  ...    09:43:06   \n",
-       "4       13            1  09:55:18           13            1  ...    10:05:23   \n",
-       "\n",
-       "   Distance(km)  Temperature Precipitation(mm)  Pickup_Lat  Pickup_Lon  \\\n",
-       "0             4    20.400000          7.573502   -1.317755   36.830370   \n",
-       "1            16    26.400000          7.573502   -1.351453   36.899315   \n",
-       "2             3    23.255689          7.573502   -1.308284   36.843419   \n",
-       "3             9    19.200000          7.573502   -1.281301   36.832396   \n",
-       "4             9    15.400000          7.573502   -1.266597   36.792118   \n",
-       "\n",
-       "  Destination_Lat  Destination_Lon      Rider_Id  Time_Pic_Arr  \n",
-       "0       -1.300406        36.829741  Rider_Id_432         745.0  \n",
-       "1       -1.295004        36.814358  Rider_Id_856        1993.0  \n",
-       "2       -1.300921        36.828195  Rider_Id_155         455.0  \n",
-       "3       -1.257147        36.795063  Rider_Id_855        1341.0  \n",
-       "4       -1.295041        36.809817  Rider_Id_770        1214.0  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Traversing Month and Weekday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
-    "\n",
-    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
-    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
-    "\n",
-    "count = 0\n",
-    "instances_of_different_days = [];\n",
-    "for i, row in full_df.iterrows():\n",
-    "    if len(set(row[month_cols].values)) > 1:\n",
-    "        print(count+1, end=\"\\r\")\n",
-    "        count = count + 1\n",
-    "        instances_of_different_days.append(list(row[month_cols].values))\n",
-    "instances_of_different_days"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "month_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weekday_cols"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Creating Month and Weekday columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
-    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Dropping redundant columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  "
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
-    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
-    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
-    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
-    "\n",
-    "full_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3          Business  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3          Personal  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3          Business  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3          Business  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1          Personal  09:55:18   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Platform_Type', 'Personal_Business', 'Pla_Time',\n",
-       "       'Con_Time', 'Arr_Pic_Time', 'Pickup_Time', 'Distance(km)',\n",
-       "       'Temperature', 'Precipitation(mm)', 'Pickup_Lat', 'Pickup_Lon',\n",
-       "       'Destination_Lat', 'Destination_Lon', 'Rider_Id', 'Time_Pic_Arr',\n",
-       "       'Day_of_Month', 'Day_of_Week'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Variable Datatypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \n",
-       "0   36.830370        -1.300406        36.829741             9            5  \n",
-       "1   36.899315        -1.295004        36.814358            12            5  \n",
-       "2   36.843419        -1.300921        36.828195            30            2  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numeric_cols = []\n",
-    "object_cols = []\n",
-    "time_cols = []\n",
-    "for k, v in full_df.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_cols.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_cols.append(k)\n",
-    "    else:\n",
-    "        object_cols.append(k)\n",
-    "full_df[numeric_cols].head(3) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time Pickup_Time\n",
-       "0  09:35:46  09:40:10     10:04:47    10:27:30\n",
-       "1  11:16:16  11:23:21     11:40:22    11:44:09\n",
-       "2  12:39:25  12:42:44     12:49:34    12:53:03"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[time_cols].head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Personal_Business      Rider_Id\n",
-       "0   Order_No_4211   User_Id_633          Business  Rider_Id_432\n",
-       "1  Order_No_25375  User_Id_2285          Personal  Rider_Id_856\n",
-       "2   Order_No_1899   User_Id_265          Business  Rider_Id_155"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[object_cols].head(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Convert an object to numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "Name: Personal_Business, dtype: int32"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Convert an object to numeric (encoding)\n",
-    "\n",
-    "le = LabelEncoder()\n",
-    "le.fit(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'][:2]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id  Platform_Type  Personal_Business  Pla_Time  \\\n",
-       "0   Order_No_4211   User_Id_633              3                  0  09:35:46   \n",
-       "1  Order_No_25375  User_Id_2285              3                  1  11:16:16   \n",
-       "2   Order_No_1899   User_Id_265              3                  0  12:39:25   \n",
-       "3   Order_No_9336  User_Id_1402              3                  0  09:25:34   \n",
-       "4  Order_No_27883  User_Id_1737              1                  1  09:55:18   \n",
-       "\n",
-       "   Con_Time Arr_Pic_Time Pickup_Time  Distance(km)  Temperature  \\\n",
-       "0  09:40:10     10:04:47    10:27:30             4    20.400000   \n",
-       "1  11:23:21     11:40:22    11:44:09            16    26.400000   \n",
-       "2  12:42:44     12:49:34    12:53:03             3    23.255689   \n",
-       "3  09:26:05     09:37:56    09:43:06             9    19.200000   \n",
-       "4  09:56:18     10:03:53    10:05:23             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon      Rider_Id  Time_Pic_Arr  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741  Rider_Id_432         745.0             9            5  \n",
-       "1        36.814358  Rider_Id_856        1993.0            12            5  \n",
-       "2        36.828195  Rider_Id_155         455.0            30            2  \n",
-       "3        36.795063  Rider_Id_855        1341.0            15            5  \n",
-       "4        36.809817  Rider_Id_770        1214.0            13            1  "
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Feature Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Distance(km)  Temperature  Precipitation(mm)  Pickup_Lat  \\\n",
-       "0              3             4    20.400000           7.573502   -1.317755   \n",
-       "1              3            16    26.400000           7.573502   -1.351453   \n",
-       "2              3             3    23.255689           7.573502   -1.308284   \n",
-       "3              3             9    19.200000           7.573502   -1.281301   \n",
-       "4              1             9    15.400000           7.573502   -1.266597   \n",
-       "\n",
-       "   Pickup_Lon  Destination_Lat  Destination_Lon  Day_of_Month  Day_of_Week  \\\n",
-       "0   36.830370        -1.300406        36.829741             9            5   \n",
-       "1   36.899315        -1.295004        36.814358            12            5   \n",
-       "2   36.843419        -1.300921        36.828195            30            2   \n",
-       "3   36.832396        -1.257147        36.795063            15            5   \n",
-       "4   36.792118        -1.295041        36.809817            13            1   \n",
-       "\n",
-       "   Personal_Business  \n",
-       "0                  0  \n",
-       "1                  1  \n",
-       "2                  0  \n",
-       "3                  0  \n",
-       "4                  1  "
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = numeric_cols + ['Personal_Business']\n",
-    "\n",
-    "data_df = full_df[features]\n",
-    "\n",
-    "y = full_df[:border]['Time_Pic_Arr']\n",
-    "train = data_df[:border]\n",
-    "test = data_df[border:]\n",
-    "\n",
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(28269, 19) (28269, 11) (21201, 11) (7068, 11) (21201,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
-    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Sendy_Team4_Final.ipynb b/Sendy_Team4_Final.ipynb
deleted file mode 100644
index 81c9296..0000000
--- a/Sendy_Team4_Final.ipynb
+++ /dev/null
@@ -1,4676 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# EDSA 2021: Sendy Logistics Challenge\n",
-    "Predict the estimated time of arrival (ETA) for motorbike deliveries in Nairobi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Overview\n",
-    "\n",
-    "### - Introduction\n",
-    "### - Importing libraries and data\n",
-    "### - Exploratory Data Analysis\n",
-    "### - Data Cleaning and Formatting\n",
-    "### - Feature Engineering\n",
-    "### - Train/Test Split\n",
-    "### - Modeling\n",
-    "### - Making Predictions\n",
-    "### - Submission"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "In this notebook, based on historic data used to predict an accurate time for the arrival of the rider at the destination of a package, we will be building a machine learning model that predicts an accurate delivery time, from picking up a package to arriving at the final destination. An accurate arrival time prediction will help all businesses to improve their logistics and communicate an accurate time to their customers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Importing Python libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: lightgbm in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (3.0.0)\n",
-      "Requirement already satisfied: numpy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (1.18.1)\n",
-      "Requirement already satisfied: scipy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (1.4.1)\n",
-      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from lightgbm) (0.22.1)\n",
-      "Requirement already satisfied: joblib>=0.11 in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install lightgbm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: xgboost in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (1.2.1)\n",
-      "Requirement already satisfied: numpy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from xgboost) (1.18.1)\n",
-      "Requirement already satisfied: scipy in c:\\users\\chuene mokgokong\\anaconda3\\lib\\site-packages (from xgboost) (1.4.1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install xgboost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Linear algebra\n",
-    "import numpy as np\n",
-    "\n",
-    "#Data processing\n",
-    "import pandas as pd\n",
-    "\n",
-    "#Date library\n",
-    "import datetime as dt\n",
-    "\n",
-    "\n",
-    "\n",
-    "#Data visualization\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import matplotlib.style as style\n",
-    "import seaborn as sns\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "#Metrics\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
-    "from sklearn.model_selection import cross_val_score, learning_curve\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "#Algorithms\n",
-    "from sklearn.svm import SVR\n",
-    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "#import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "sns.set(style='white', context='notebook', palette='deep')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "#Train_Masked has extra columns: Delivery destination (day, month, time)\n",
-    "train_df = pd.read_csv('Train.csv') \n",
-    "test_df = pd.read_csv('Test.csv') \n",
-    "rider_df = pd.read_csv('Riders.csv') \n",
-    "variable_df = pd.read_csv('VariableDefinitions.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exploritory Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Understanding the train and test datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_19248</td>\n",
-       "      <td>User_Id_3355</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>27</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4:44:10 PM</td>\n",
-       "      <td>27</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5:06:47 PM</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.333275</td>\n",
-       "      <td>36.870815</td>\n",
-       "      <td>-1.305249</td>\n",
-       "      <td>36.822390</td>\n",
-       "      <td>Rider_Id_192</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_12736</td>\n",
-       "      <td>User_Id_3647</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>17</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12:57:35 PM</td>\n",
-       "      <td>17</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:25:37 PM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.272639</td>\n",
-       "      <td>36.794723</td>\n",
-       "      <td>-1.277007</td>\n",
-       "      <td>36.823907</td>\n",
-       "      <td>Rider_Id_868</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_768</td>\n",
-       "      <td>User_Id_2154</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>27</td>\n",
-       "      <td>4</td>\n",
-       "      <td>11:08:14 AM</td>\n",
-       "      <td>27</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11:57:54 AM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>22.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.290894</td>\n",
-       "      <td>36.822971</td>\n",
-       "      <td>-1.276574</td>\n",
-       "      <td>36.851365</td>\n",
-       "      <td>Rider_Id_26</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_15332</td>\n",
-       "      <td>User_Id_2910</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>17</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1:51:35 PM</td>\n",
-       "      <td>17</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2:16:52 PM</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.290503</td>\n",
-       "      <td>36.809646</td>\n",
-       "      <td>-1.303382</td>\n",
-       "      <td>36.790658</td>\n",
-       "      <td>Rider_Id_685</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_21373</td>\n",
-       "      <td>User_Id_1205</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>11:30:28 AM</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11:56:04 AM</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281081</td>\n",
-       "      <td>36.814423</td>\n",
-       "      <td>-1.266467</td>\n",
-       "      <td>36.792161</td>\n",
-       "      <td>Rider_Id_858</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
-       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
-       "2    Order_No_768  User_Id_2154         Bike              3   \n",
-       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
-       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                        27   \n",
-       "1             Business                        17   \n",
-       "2             Business                        27   \n",
-       "3             Business                        17   \n",
-       "4             Business                        11   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             3       4:44:10 PM                           27   \n",
-       "1                             5      12:57:35 PM                           17   \n",
-       "2                             4      11:08:14 AM                           27   \n",
-       "3                             1       1:51:35 PM                           17   \n",
-       "4                             2      11:30:28 AM                           11   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Time  Distance (KM)  \\\n",
-       "0                                3  ...    5:06:47 PM              8   \n",
-       "1                                5  ...    1:25:37 PM              5   \n",
-       "2                                4  ...   11:57:54 AM              5   \n",
-       "3                                1  ...    2:16:52 PM              5   \n",
-       "4                                2  ...   11:56:04 AM              6   \n",
-       "\n",
-       "   Temperature Precipitation in millimeters  Pickup Lat  Pickup Long  \\\n",
-       "0          NaN                          NaN   -1.333275    36.870815   \n",
-       "1          NaN                          NaN   -1.272639    36.794723   \n",
-       "2         22.8                          NaN   -1.290894    36.822971   \n",
-       "3         24.5                          NaN   -1.290503    36.809646   \n",
-       "4         24.4                          NaN   -1.281081    36.814423   \n",
-       "\n",
-       "  Destination Lat  Destination Long      Rider Id  Time from Pickup to Arrival  \n",
-       "0       -1.305249         36.822390  Rider_Id_192                          NaN  \n",
-       "1       -1.277007         36.823907  Rider_Id_868                          NaN  \n",
-       "2       -1.276574         36.851365   Rider_Id_26                          NaN  \n",
-       "3       -1.303382         36.790658  Rider_Id_685                          NaN  \n",
-       "4       -1.266467         36.792161  Rider_Id_858                          NaN  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: (21201, 32) \n",
-      "  test: (7068, 26) \n",
-      " riders: (960, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'train: {train_df.shape} \\n  test: {test_df.shape} \\n riders: {rider_df.shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are 21 201, 7 068 and 960 data entries in the train, test and riders dataset respectevily.\n",
-    "Also, there are 29, 25 and 5 columns in the train, test and riders dataset respectevily. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-       "       'Personal or Business', 'Placement - Day of Month',\n",
-       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-       "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-       "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-       "       'Rider Id', 'Time from Pickup to Arrival', 'Pickup_Hour',\n",
-       "       'Pickup_Minutes', 'Arrival_Destination_Hour',\n",
-       "       'Arrival_Destination_Minutes', 'Time from Pickup to Arrival_Hour',\n",
-       "       'Time from Pickup to Arrival_Minutes'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-       "       'Personal or Business', 'Placement - Day of Month',\n",
-       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-       "       'Distance (KM)', 'Temperature', 'Precipitation in millimeters',\n",
-       "       'Pickup Lat', 'Pickup Long', 'Destination Lat', 'Destination Long',\n",
-       "       'Rider Id'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arrival at Destination - Day of Month\n",
-      "Arrival at Destination - Weekday (Mo = 1)\n",
-      "Arrival at Destination - Time\n",
-      "Time from Pickup to Arrival\n"
-     ]
-    }
-   ],
-   "source": [
-    "for col in train_df.columns:\n",
-    "    if col not in test_df.columns:\n",
-    "        print(col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are four data columns in the train dataset that are not in the test dataset \n",
-    "\n",
-    "* Arrival at Destination - Day of Month\n",
-    "* Arrival at Destination - Weekday (Mo = 1)\n",
-    "* Arrival at Destination - Time\n",
-    "* Time from Pickup to Arrival"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21201 entries, 0 to 21200\n",
-      "Data columns (total 29 columns):\n",
-      " #   Column                                     Non-Null Count  Dtype  \n",
-      "---  ------                                     --------------  -----  \n",
-      " 0   Order No                                   21201 non-null  object \n",
-      " 1   User Id                                    21201 non-null  object \n",
-      " 2   Vehicle Type                               21201 non-null  object \n",
-      " 3   Platform Type                              21201 non-null  int64  \n",
-      " 4   Personal or Business                       21201 non-null  object \n",
-      " 5   Placement - Day of Month                   21201 non-null  int64  \n",
-      " 6   Placement - Weekday (Mo = 1)               21201 non-null  int64  \n",
-      " 7   Placement - Time                           21201 non-null  object \n",
-      " 8   Confirmation - Day of Month                21201 non-null  int64  \n",
-      " 9   Confirmation - Weekday (Mo = 1)            21201 non-null  int64  \n",
-      " 10  Confirmation - Time                        21201 non-null  object \n",
-      " 11  Arrival at Pickup - Day of Month           21201 non-null  int64  \n",
-      " 12  Arrival at Pickup - Weekday (Mo = 1)       21201 non-null  int64  \n",
-      " 13  Arrival at Pickup - Time                   21201 non-null  object \n",
-      " 14  Pickup - Day of Month                      21201 non-null  int64  \n",
-      " 15  Pickup - Weekday (Mo = 1)                  21201 non-null  int64  \n",
-      " 16  Pickup - Time                              21201 non-null  object \n",
-      " 17  Arrival at Destination - Day of Month      21201 non-null  int64  \n",
-      " 18  Arrival at Destination - Weekday (Mo = 1)  21201 non-null  int64  \n",
-      " 19  Arrival at Destination - Time              21201 non-null  object \n",
-      " 20  Distance (KM)                              21201 non-null  int64  \n",
-      " 21  Temperature                                16835 non-null  float64\n",
-      " 22  Precipitation in millimeters               552 non-null    float64\n",
-      " 23  Pickup Lat                                 21201 non-null  float64\n",
-      " 24  Pickup Long                                21201 non-null  float64\n",
-      " 25  Destination Lat                            21201 non-null  float64\n",
-      " 26  Destination Long                           21201 non-null  float64\n",
-      " 27  Rider Id                                   21201 non-null  object \n",
-      " 28  Time from Pickup to Arrival                21201 non-null  int64  \n",
-      "dtypes: float64(6), int64(13), object(10)\n",
-      "memory usage: 4.7+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 7068 entries, 0 to 7067\n",
-      "Data columns (total 25 columns):\n",
-      " #   Column                                Non-Null Count  Dtype  \n",
-      "---  ------                                --------------  -----  \n",
-      " 0   Order No                              7068 non-null   object \n",
-      " 1   User Id                               7068 non-null   object \n",
-      " 2   Vehicle Type                          7068 non-null   object \n",
-      " 3   Platform Type                         7068 non-null   int64  \n",
-      " 4   Personal or Business                  7068 non-null   object \n",
-      " 5   Placement - Day of Month              7068 non-null   int64  \n",
-      " 6   Placement - Weekday (Mo = 1)          7068 non-null   int64  \n",
-      " 7   Placement - Time                      7068 non-null   object \n",
-      " 8   Confirmation - Day of Month           7068 non-null   int64  \n",
-      " 9   Confirmation - Weekday (Mo = 1)       7068 non-null   int64  \n",
-      " 10  Confirmation - Time                   7068 non-null   object \n",
-      " 11  Arrival at Pickup - Day of Month      7068 non-null   int64  \n",
-      " 12  Arrival at Pickup - Weekday (Mo = 1)  7068 non-null   int64  \n",
-      " 13  Arrival at Pickup - Time              7068 non-null   object \n",
-      " 14  Pickup - Day of Month                 7068 non-null   int64  \n",
-      " 15  Pickup - Weekday (Mo = 1)             7068 non-null   int64  \n",
-      " 16  Pickup - Time                         7068 non-null   object \n",
-      " 17  Distance (KM)                         7068 non-null   int64  \n",
-      " 18  Temperature                           5631 non-null   float64\n",
-      " 19  Precipitation in millimeters          199 non-null    float64\n",
-      " 20  Pickup Lat                            7068 non-null   float64\n",
-      " 21  Pickup Long                           7068 non-null   float64\n",
-      " 22  Destination Lat                       7068 non-null   float64\n",
-      " 23  Destination Long                      7068 non-null   float64\n",
-      " 24  Rider Id                              7068 non-null   object \n",
-      "dtypes: float64(6), int64(10), object(9)\n",
-      "memory usage: 1.3+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order No                                         0\n",
-       "User Id                                          0\n",
-       "Vehicle Type                                     0\n",
-       "Platform Type                                    0\n",
-       "Personal or Business                             0\n",
-       "Placement - Day of Month                         0\n",
-       "Placement - Weekday (Mo = 1)                     0\n",
-       "Placement - Time                                 0\n",
-       "Confirmation - Day of Month                      0\n",
-       "Confirmation - Weekday (Mo = 1)                  0\n",
-       "Confirmation - Time                              0\n",
-       "Arrival at Pickup - Day of Month                 0\n",
-       "Arrival at Pickup - Weekday (Mo = 1)             0\n",
-       "Arrival at Pickup - Time                         0\n",
-       "Pickup - Day of Month                            0\n",
-       "Pickup - Weekday (Mo = 1)                        0\n",
-       "Pickup - Time                                    0\n",
-       "Arrival at Destination - Day of Month            0\n",
-       "Arrival at Destination - Weekday (Mo = 1)        0\n",
-       "Arrival at Destination - Time                    0\n",
-       "Distance (KM)                                    0\n",
-       "Temperature                                   4366\n",
-       "Precipitation in millimeters                 20649\n",
-       "Pickup Lat                                       0\n",
-       "Pickup Long                                      0\n",
-       "Destination Lat                                  0\n",
-       "Destination Long                                 0\n",
-       "Rider Id                                         0\n",
-       "Time from Pickup to Arrival                      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order No                                   0\n",
-       "User Id                                    0\n",
-       "Vehicle Type                               0\n",
-       "Platform Type                              0\n",
-       "Personal or Business                       0\n",
-       "Placement - Day of Month                   0\n",
-       "Placement - Weekday (Mo = 1)               0\n",
-       "Placement - Time                           0\n",
-       "Confirmation - Day of Month                0\n",
-       "Confirmation - Weekday (Mo = 1)            0\n",
-       "Confirmation - Time                        0\n",
-       "Arrival at Pickup - Day of Month           0\n",
-       "Arrival at Pickup - Weekday (Mo = 1)       0\n",
-       "Arrival at Pickup - Time                   0\n",
-       "Pickup - Day of Month                      0\n",
-       "Pickup - Weekday (Mo = 1)                  0\n",
-       "Pickup - Time                              0\n",
-       "Distance (KM)                              0\n",
-       "Temperature                             1437\n",
-       "Precipitation in millimeters            6869\n",
-       "Pickup Lat                                 0\n",
-       "Pickup Long                                0\n",
-       "Destination Lat                            0\n",
-       "Destination Long                           0\n",
-       "Rider Id                                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def missing_values(df, threshold=0):\n",
-    "    \"\"\"Returns the feature name and the % of  missing values as dataframe\"\"\"\n",
-    "\n",
-    "    return pd.DataFrame(data= {col: (df[col].isnull().mean()) for col in df.columns if df[col].isnull().mean() > threshold}, \n",
-    "             index=['% of Missing values']).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>% of Missing values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Temperature</th>\n",
-       "      <td>0.205934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <td>0.973963</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              % of Missing values\n",
-       "Temperature                              0.205934\n",
-       "Precipitation in millimeters             0.973963"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing_values(train_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The train dataset has \"Temperature\" and \"Precipitation in millimeters\" data columns with 20.59 % and 97.4 % of data missing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>% of Missing values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Temperature</th>\n",
-       "      <td>0.203311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <td>0.971845</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              % of Missing values\n",
-       "Temperature                              0.203311\n",
-       "Precipitation in millimeters             0.971845"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing_values(test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The test dataset has \"Temperature\" and \"Precipitation in millimeters\" data columns with 20.33 % and 97.18% of data missing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>16835.000000</td>\n",
-       "      <td>552.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.752182</td>\n",
-       "      <td>15.653696</td>\n",
-       "      <td>3.240083</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>9.506533</td>\n",
-       "      <td>23.258889</td>\n",
-       "      <td>7.905797</td>\n",
-       "      <td>-1.281470</td>\n",
-       "      <td>36.811264</td>\n",
-       "      <td>-1.282581</td>\n",
-       "      <td>36.811220</td>\n",
-       "      <td>1556.920947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.625178</td>\n",
-       "      <td>8.798916</td>\n",
-       "      <td>1.567295</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>5.668963</td>\n",
-       "      <td>3.615768</td>\n",
-       "      <td>17.089971</td>\n",
-       "      <td>0.030507</td>\n",
-       "      <td>0.037473</td>\n",
-       "      <td>0.034824</td>\n",
-       "      <td>0.044721</td>\n",
-       "      <td>987.270788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>11.200000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>-1.438302</td>\n",
-       "      <td>36.653621</td>\n",
-       "      <td>-1.430298</td>\n",
-       "      <td>36.606594</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>20.600000</td>\n",
-       "      <td>1.075000</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.784605</td>\n",
-       "      <td>-1.301201</td>\n",
-       "      <td>36.785661</td>\n",
-       "      <td>882.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>23.500000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>-1.279395</td>\n",
-       "      <td>36.807040</td>\n",
-       "      <td>-1.284382</td>\n",
-       "      <td>36.808002</td>\n",
-       "      <td>1369.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>4.900000</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>-1.261177</td>\n",
-       "      <td>36.829477</td>\n",
-       "      <td>2040.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>49.000000</td>\n",
-       "      <td>32.100000</td>\n",
-       "      <td>99.100000</td>\n",
-       "      <td>-1.147170</td>\n",
-       "      <td>36.991046</td>\n",
-       "      <td>-1.030225</td>\n",
-       "      <td>37.016779</td>\n",
-       "      <td>7883.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "count   21201.000000              21201.000000                  21201.000000   \n",
-       "mean        2.752182                 15.653696                      3.240083   \n",
-       "std         0.625178                  8.798916                      1.567295   \n",
-       "min         1.000000                  1.000000                      1.000000   \n",
-       "25%         3.000000                  8.000000                      2.000000   \n",
-       "50%         3.000000                 15.000000                      3.000000   \n",
-       "75%         3.000000                 23.000000                      5.000000   \n",
-       "max         4.000000                 31.000000                      7.000000   \n",
-       "\n",
-       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "count                 21201.000000                     21201.000000   \n",
-       "mean                     15.653837                         3.240225   \n",
-       "std                       8.798886                         1.567228   \n",
-       "min                       1.000000                         1.000000   \n",
-       "25%                       8.000000                         2.000000   \n",
-       "50%                      15.000000                         3.000000   \n",
-       "75%                      23.000000                         5.000000   \n",
-       "max                      31.000000                         7.000000   \n",
-       "\n",
-       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "count                      21201.000000                          21201.000000   \n",
-       "mean                          15.653837                              3.240225   \n",
-       "std                            8.798886                              1.567228   \n",
-       "min                            1.000000                              1.000000   \n",
-       "25%                            8.000000                              2.000000   \n",
-       "50%                           15.000000                              3.000000   \n",
-       "75%                           23.000000                              5.000000   \n",
-       "max                           31.000000                              7.000000   \n",
-       "\n",
-       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
-       "count           21201.000000               21201.000000   \n",
-       "mean               15.653837                   3.240225   \n",
-       "std                 8.798886                   1.567228   \n",
-       "min                 1.000000                   1.000000   \n",
-       "25%                 8.000000                   2.000000   \n",
-       "50%                15.000000                   3.000000   \n",
-       "75%                23.000000                   5.000000   \n",
-       "max                31.000000                   7.000000   \n",
-       "\n",
-       "       Arrival at Destination - Day of Month  \\\n",
-       "count                           21201.000000   \n",
-       "mean                               15.653837   \n",
-       "std                                 8.798886   \n",
-       "min                                 1.000000   \n",
-       "25%                                 8.000000   \n",
-       "50%                                15.000000   \n",
-       "75%                                23.000000   \n",
-       "max                                31.000000   \n",
-       "\n",
-       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
-       "count                               21201.000000   21201.000000  16835.000000   \n",
-       "mean                                    3.240225       9.506533     23.258889   \n",
-       "std                                     1.567228       5.668963      3.615768   \n",
-       "min                                     1.000000       1.000000     11.200000   \n",
-       "25%                                     2.000000       5.000000     20.600000   \n",
-       "50%                                     3.000000       8.000000     23.500000   \n",
-       "75%                                     5.000000      13.000000     26.000000   \n",
-       "max                                     7.000000      49.000000     32.100000   \n",
-       "\n",
-       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
-       "count                    552.000000  21201.000000  21201.000000   \n",
-       "mean                       7.905797     -1.281470     36.811264   \n",
-       "std                       17.089971      0.030507      0.037473   \n",
-       "min                        0.100000     -1.438302     36.653621   \n",
-       "25%                        1.075000     -1.300921     36.784605   \n",
-       "50%                        2.900000     -1.279395     36.807040   \n",
-       "75%                        4.900000     -1.257147     36.829741   \n",
-       "max                       99.100000     -1.147170     36.991046   \n",
-       "\n",
-       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
-       "count     21201.000000      21201.000000                 21201.000000  \n",
-       "mean         -1.282581         36.811220                  1556.920947  \n",
-       "std           0.034824          0.044721                   987.270788  \n",
-       "min          -1.430298         36.606594                     1.000000  \n",
-       "25%          -1.301201         36.785661                   882.000000  \n",
-       "50%          -1.284382         36.808002                  1369.000000  \n",
-       "75%          -1.261177         36.829477                  2040.000000  \n",
-       "max          -1.030225         37.016779                  7883.000000  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>5631.000000</td>\n",
-       "      <td>199.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "      <td>7068.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.752122</td>\n",
-       "      <td>15.621817</td>\n",
-       "      <td>3.249859</td>\n",
-       "      <td>15.621817</td>\n",
-       "      <td>3.249859</td>\n",
-       "      <td>15.621817</td>\n",
-       "      <td>3.249859</td>\n",
-       "      <td>15.621817</td>\n",
-       "      <td>3.249859</td>\n",
-       "      <td>9.458970</td>\n",
-       "      <td>23.246120</td>\n",
-       "      <td>6.651759</td>\n",
-       "      <td>-1.282043</td>\n",
-       "      <td>36.811148</td>\n",
-       "      <td>-1.282255</td>\n",
-       "      <td>36.811326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.624000</td>\n",
-       "      <td>8.776650</td>\n",
-       "      <td>1.580926</td>\n",
-       "      <td>8.776650</td>\n",
-       "      <td>1.580926</td>\n",
-       "      <td>8.776650</td>\n",
-       "      <td>1.580926</td>\n",
-       "      <td>8.776650</td>\n",
-       "      <td>1.580926</td>\n",
-       "      <td>5.575689</td>\n",
-       "      <td>3.591161</td>\n",
-       "      <td>14.085439</td>\n",
-       "      <td>0.030044</td>\n",
-       "      <td>0.037962</td>\n",
-       "      <td>0.034236</td>\n",
-       "      <td>0.043507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>13.200000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>-1.433512</td>\n",
-       "      <td>36.653828</td>\n",
-       "      <td>-1.422652</td>\n",
-       "      <td>36.639195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>20.600000</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>-1.301201</td>\n",
-       "      <td>36.784605</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.786546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>23.400000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.807219</td>\n",
-       "      <td>-1.283219</td>\n",
-       "      <td>36.807927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>25.900000</td>\n",
-       "      <td>4.950000</td>\n",
-       "      <td>-1.257274</td>\n",
-       "      <td>36.829716</td>\n",
-       "      <td>-1.261256</td>\n",
-       "      <td>36.828195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>32.100000</td>\n",
-       "      <td>99.100000</td>\n",
-       "      <td>-1.148352</td>\n",
-       "      <td>36.991046</td>\n",
-       "      <td>-1.030225</td>\n",
-       "      <td>37.018958</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "count    7068.000000               7068.000000                   7068.000000   \n",
-       "mean        2.752122                 15.621817                      3.249859   \n",
-       "std         0.624000                  8.776650                      1.580926   \n",
-       "min         1.000000                  1.000000                      1.000000   \n",
-       "25%         3.000000                  8.000000                      2.000000   \n",
-       "50%         3.000000                 15.000000                      3.000000   \n",
-       "75%         3.000000                 23.000000                      5.000000   \n",
-       "max         4.000000                 31.000000                      7.000000   \n",
-       "\n",
-       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "count                  7068.000000                      7068.000000   \n",
-       "mean                     15.621817                         3.249859   \n",
-       "std                       8.776650                         1.580926   \n",
-       "min                       1.000000                         1.000000   \n",
-       "25%                       8.000000                         2.000000   \n",
-       "50%                      15.000000                         3.000000   \n",
-       "75%                      23.000000                         5.000000   \n",
-       "max                      31.000000                         7.000000   \n",
-       "\n",
-       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "count                       7068.000000                           7068.000000   \n",
-       "mean                          15.621817                              3.249859   \n",
-       "std                            8.776650                              1.580926   \n",
-       "min                            1.000000                              1.000000   \n",
-       "25%                            8.000000                              2.000000   \n",
-       "50%                           15.000000                              3.000000   \n",
-       "75%                           23.000000                              5.000000   \n",
-       "max                           31.000000                              7.000000   \n",
-       "\n",
-       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  Distance (KM)  \\\n",
-       "count            7068.000000                7068.000000    7068.000000   \n",
-       "mean               15.621817                   3.249859       9.458970   \n",
-       "std                 8.776650                   1.580926       5.575689   \n",
-       "min                 1.000000                   1.000000       1.000000   \n",
-       "25%                 8.000000                   2.000000       5.000000   \n",
-       "50%                15.000000                   3.000000       8.000000   \n",
-       "75%                23.000000                   5.000000      13.000000   \n",
-       "max                31.000000                   7.000000      47.000000   \n",
-       "\n",
-       "       Temperature  Precipitation in millimeters   Pickup Lat  Pickup Long  \\\n",
-       "count  5631.000000                    199.000000  7068.000000  7068.000000   \n",
-       "mean     23.246120                      6.651759    -1.282043    36.811148   \n",
-       "std       3.591161                     14.085439     0.030044     0.037962   \n",
-       "min      13.200000                      0.100000    -1.433512    36.653828   \n",
-       "25%      20.600000                      1.100000    -1.301201    36.784605   \n",
-       "50%      23.400000                      2.900000    -1.281301    36.807219   \n",
-       "75%      25.900000                      4.950000    -1.257274    36.829716   \n",
-       "max      32.100000                     99.100000    -1.148352    36.991046   \n",
-       "\n",
-       "       Destination Lat  Destination Long  \n",
-       "count      7068.000000       7068.000000  \n",
-       "mean         -1.282255         36.811326  \n",
-       "std           0.034236          0.043507  \n",
-       "min          -1.422652         36.639195  \n",
-       "25%          -1.300921         36.786546  \n",
-       "50%          -1.283219         36.807927  \n",
-       "75%          -1.261256         36.828195  \n",
-       "max          -1.030225         37.018958  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7f93c3908>"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.figure(figsize=(15, 5))\n",
-    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
-    "sns.heatmap(train_df.isnull(), cmap=cmap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visuals from train dataset depicting data columns( \"Temperature\" and \" precipitation in millimetrs\") with missing values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7f909f208>"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.figure(figsize=(15, 5))\n",
-    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
-    "sns.heatmap(test_df.isnull(), cmap=cmap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visuals from test dataset depicting data columns( \"Temperature\" and \" precipitation in millimetrs\") with missing values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Distribution review for train data\n",
-    "train_df[[column for column in train_df.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deductions from the train dataset data column distribution.\n",
-    "\n",
-    "* The most common temperature is 25 degrees Celsius.\n",
-    "* Platform 3 is the most used platform type\n",
-    "* Most of the orders delievered are from Business clients.\n",
-    "* Most orders are dealt with during the weekdays than on the weekend.\n",
-    "* Most of the trips covered between 8KM and 15KM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Distribution review for test data\n",
-    "test_df[[column for column in test_df.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deductions from the test dataset data column distribution.\n",
-    "\n",
-    "* The most common temperature is 22,5 degrees Celsius.\n",
-    "* Platform 3 is the most used platform type\n",
-    "* Most of the orders delievered are from Business clients.\n",
-    "* Most orders are dealt with during the weekdays than on the weekend.\n",
-    "* Most of the trips covered between 4KM and 10KM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Column Skewness of the testing dataset\n",
-    "train_df.skew().plot.bar(figsize =(10,5), color = 'Blue')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Univariate Anaysis\n",
-    "# Calculating Skewness in the columns of our Training dataset\n",
-    "train_df.kurt().plot.bar(figsize =(10,5))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Platform Type</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.012764</td>\n",
-       "      <td>-0.058038</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.005528</td>\n",
-       "      <td>0.225060</td>\n",
-       "      <td>-0.055948</td>\n",
-       "      <td>-0.002742</td>\n",
-       "      <td>0.021327</td>\n",
-       "      <td>0.006319</td>\n",
-       "      <td>0.043924</td>\n",
-       "      <td>-0.003827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <td>0.012764</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026148</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>-0.007585</td>\n",
-       "      <td>0.013097</td>\n",
-       "      <td>-0.204143</td>\n",
-       "      <td>-0.004929</td>\n",
-       "      <td>-0.005453</td>\n",
-       "      <td>0.007541</td>\n",
-       "      <td>0.002077</td>\n",
-       "      <td>-0.014710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <td>-0.058038</td>\n",
-       "      <td>0.026148</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.003582</td>\n",
-       "      <td>0.031064</td>\n",
-       "      <td>-0.050592</td>\n",
-       "      <td>-0.000918</td>\n",
-       "      <td>0.011008</td>\n",
-       "      <td>-0.004299</td>\n",
-       "      <td>-0.000875</td>\n",
-       "      <td>0.009693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>-0.007587</td>\n",
-       "      <td>0.013099</td>\n",
-       "      <td>-0.204143</td>\n",
-       "      <td>-0.004923</td>\n",
-       "      <td>-0.005448</td>\n",
-       "      <td>0.007550</td>\n",
-       "      <td>0.002073</td>\n",
-       "      <td>-0.014701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.003574</td>\n",
-       "      <td>0.031074</td>\n",
-       "      <td>-0.050592</td>\n",
-       "      <td>-0.000883</td>\n",
-       "      <td>0.011036</td>\n",
-       "      <td>-0.004251</td>\n",
-       "      <td>-0.000900</td>\n",
-       "      <td>0.009744</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-       "                                 Platform Type  Placement - Day of Month  \\\n",
-       "Platform Type                         1.000000                  0.012764   \n",
-       "Placement - Day of Month              0.012764                  1.000000   \n",
-       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
-       "Confirmation - Day of Month           0.012770                  0.999998   \n",
-       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
-       "\n",
-       "                                 Placement - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                       -0.058038   \n",
-       "Placement - Day of Month                             0.026148   \n",
-       "Placement - Weekday (Mo = 1)                         1.000000   \n",
-       "Confirmation - Day of Month                          0.026132   \n",
-       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
-       "\n",
-       "                                 Confirmation - Day of Month  \\\n",
-       "Platform Type                                       0.012770   \n",
-       "Placement - Day of Month                            0.999998   \n",
-       "Placement - Weekday (Mo = 1)                        0.026132   \n",
-       "Confirmation - Day of Month                         1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
-       "\n",
-       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                          -0.058005   \n",
-       "Placement - Day of Month                                0.026122   \n",
-       "Placement - Weekday (Mo = 1)                            0.999952   \n",
-       "Confirmation - Day of Month                             0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
-       "\n",
-       "                                 Arrival at Pickup - Day of Month  \\\n",
-       "Platform Type                                            0.012770   \n",
-       "Placement - Day of Month                                 0.999998   \n",
-       "Placement - Weekday (Mo = 1)                             0.026132   \n",
-       "Confirmation - Day of Month                              1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
-       "\n",
-       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                               -0.058005   \n",
-       "Placement - Day of Month                                     0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
-       "Confirmation - Day of Month                                  0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
-       "\n",
-       "                                 Pickup - Day of Month  \\\n",
-       "Platform Type                                 0.012770   \n",
-       "Placement - Day of Month                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                  0.026132   \n",
-       "Confirmation - Day of Month                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
-       "\n",
-       "                                 Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                    -0.058005   \n",
-       "Placement - Day of Month                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                      0.999952   \n",
-       "Confirmation - Day of Month                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
-       "\n",
-       "                                 Arrival at Destination - Day of Month  \\\n",
-       "Platform Type                                                 0.012770   \n",
-       "Placement - Day of Month                                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
-       "Confirmation - Day of Month                                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
-       "\n",
-       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                                    -0.058005   \n",
-       "Placement - Day of Month                                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
-       "Confirmation - Day of Month                                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
-       "\n",
-       "                                 Distance (KM)  Temperature  \\\n",
-       "Platform Type                         0.005528     0.225060   \n",
-       "Placement - Day of Month             -0.007585     0.013097   \n",
-       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
-       "Confirmation - Day of Month          -0.007587     0.013099   \n",
-       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
-       "\n",
-       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
-       "Platform Type                                       -0.055948   -0.002742   \n",
-       "Placement - Day of Month                            -0.204143   -0.004929   \n",
-       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
-       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
-       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
-       "\n",
-       "                                 Pickup Long  Destination Lat  \\\n",
-       "Platform Type                       0.021327         0.006319   \n",
-       "Placement - Day of Month           -0.005453         0.007541   \n",
-       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
-       "Confirmation - Day of Month        -0.005448         0.007550   \n",
-       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
-       "\n",
-       "                                 Destination Long  Time from Pickup to Arrival  \n",
-       "Platform Type                            0.043924                    -0.003827  \n",
-       "Placement - Day of Month                 0.002077                    -0.014710  \n",
-       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
-       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
-       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CorrMatrix_train = train_df.corr()\n",
-    "CorrMatrix_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
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-       "      <th>Confirmation - Day of Month</th>\n",
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-       "      <th>Arrival at Pickup - Day of Month</th>\n",
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-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
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-       "      <td>-0.003827</td>\n",
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-       "    <tr>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <td>0.012764</td>\n",
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-       "      <td>0.026148</td>\n",
-       "      <td>0.999998</td>\n",
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-       "      <td>0.999998</td>\n",
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-       "      <td>-0.005453</td>\n",
-       "      <td>0.007541</td>\n",
-       "      <td>0.002077</td>\n",
-       "      <td>-0.014710</td>\n",
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-       "    <tr>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <td>-0.058038</td>\n",
-       "      <td>0.026148</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.003582</td>\n",
-       "      <td>0.031064</td>\n",
-       "      <td>-0.050592</td>\n",
-       "      <td>-0.000918</td>\n",
-       "      <td>0.011008</td>\n",
-       "      <td>-0.004299</td>\n",
-       "      <td>-0.000875</td>\n",
-       "      <td>0.009693</td>\n",
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-       "    <tr>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>1.000000</td>\n",
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-       "      <td>1.000000</td>\n",
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-       "      <td>1.000000</td>\n",
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-       "      <td>0.026123</td>\n",
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-       "      <td>0.013099</td>\n",
-       "      <td>-0.204143</td>\n",
-       "      <td>-0.004923</td>\n",
-       "      <td>-0.005448</td>\n",
-       "      <td>0.007550</td>\n",
-       "      <td>0.002073</td>\n",
-       "      <td>-0.014701</td>\n",
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-       "    <tr>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
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-       "      <td>0.999952</td>\n",
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-       "      <td>1.000000</td>\n",
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-       "                                 Platform Type  Placement - Day of Month  \\\n",
-       "Platform Type                         1.000000                  0.012764   \n",
-       "Placement - Day of Month              0.012764                  1.000000   \n",
-       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
-       "Confirmation - Day of Month           0.012770                  0.999998   \n",
-       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
-       "\n",
-       "                                 Placement - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                       -0.058038   \n",
-       "Placement - Day of Month                             0.026148   \n",
-       "Placement - Weekday (Mo = 1)                         1.000000   \n",
-       "Confirmation - Day of Month                          0.026132   \n",
-       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
-       "\n",
-       "                                 Confirmation - Day of Month  \\\n",
-       "Platform Type                                       0.012770   \n",
-       "Placement - Day of Month                            0.999998   \n",
-       "Placement - Weekday (Mo = 1)                        0.026132   \n",
-       "Confirmation - Day of Month                         1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
-       "\n",
-       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                          -0.058005   \n",
-       "Placement - Day of Month                                0.026122   \n",
-       "Placement - Weekday (Mo = 1)                            0.999952   \n",
-       "Confirmation - Day of Month                             0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
-       "\n",
-       "                                 Arrival at Pickup - Day of Month  \\\n",
-       "Platform Type                                            0.012770   \n",
-       "Placement - Day of Month                                 0.999998   \n",
-       "Placement - Weekday (Mo = 1)                             0.026132   \n",
-       "Confirmation - Day of Month                              1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
-       "\n",
-       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                               -0.058005   \n",
-       "Placement - Day of Month                                     0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
-       "Confirmation - Day of Month                                  0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
-       "\n",
-       "                                 Pickup - Day of Month  \\\n",
-       "Platform Type                                 0.012770   \n",
-       "Placement - Day of Month                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                  0.026132   \n",
-       "Confirmation - Day of Month                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
-       "\n",
-       "                                 Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                    -0.058005   \n",
-       "Placement - Day of Month                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                      0.999952   \n",
-       "Confirmation - Day of Month                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
-       "\n",
-       "                                 Arrival at Destination - Day of Month  \\\n",
-       "Platform Type                                                 0.012770   \n",
-       "Placement - Day of Month                                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
-       "Confirmation - Day of Month                                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
-       "\n",
-       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                                    -0.058005   \n",
-       "Placement - Day of Month                                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
-       "Confirmation - Day of Month                                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
-       "\n",
-       "                                 Distance (KM)  Temperature  \\\n",
-       "Platform Type                         0.005528     0.225060   \n",
-       "Placement - Day of Month             -0.007585     0.013097   \n",
-       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
-       "Confirmation - Day of Month          -0.007587     0.013099   \n",
-       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
-       "\n",
-       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
-       "Platform Type                                       -0.055948   -0.002742   \n",
-       "Placement - Day of Month                            -0.204143   -0.004929   \n",
-       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
-       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
-       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
-       "\n",
-       "                                 Pickup Long  Destination Lat  \\\n",
-       "Platform Type                       0.021327         0.006319   \n",
-       "Placement - Day of Month           -0.005453         0.007541   \n",
-       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
-       "Confirmation - Day of Month        -0.005448         0.007550   \n",
-       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
-       "\n",
-       "                                 Destination Long  Time from Pickup to Arrival  \n",
-       "Platform Type                            0.043924                    -0.003827  \n",
-       "Placement - Day of Month                 0.002077                    -0.014710  \n",
-       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
-       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
-       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CorrMatrix_test = test_df.corr()\n",
-    "CorrMatrix_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.05, 'Train Dataset Pearson Correlation of Features')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.style as style\n",
-    "style.use('ggplot')\n",
-    "cm = CorrMatrix_train.corr()\n",
-    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
-    "plt.figure(figsize=(20,10))\n",
-    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
-    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
-    "plt.title('Train Dataset Pearson Correlation of Features', y=1.05, size=40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.05, 'Test Dataset Pearson Correlation of Features')"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.style as style\n",
-    "style.use('ggplot')\n",
-    "cm = CorrMatrix_test.corr()\n",
-    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
-    "plt.figure(figsize=(20,10))\n",
-    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
-    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
-    "plt.title('Test Dataset Pearson Correlation of Features', y=1.05, size=40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5, 2, 1, 3, 4, 6, 7], dtype=int64)"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df[\"Pickup - Weekday (Mo = 1)\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 5, 4, 1, 2, 6, 7], dtype=int64)"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df[\"Pickup - Weekday (Mo = 1)\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The company is running operation for 7 days a week."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "      <th>Pickup_Hour</th>\n",
-       "      <th>Pickup_Minutes</th>\n",
-       "      <th>Arrival_Destination_Hour</th>\n",
-       "      <th>Arrival_Destination_Minutes</th>\n",
-       "      <th>Time from Pickup to Arrival_Hour</th>\n",
-       "      <th>Time from Pickup to Arrival_Minutes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>0 rows × 35 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [Order No, User Id, Vehicle Type, Platform Type, Personal or Business, Placement - Day of Month, Placement - Weekday (Mo = 1), Placement - Time, Confirmation - Day of Month, Confirmation - Weekday (Mo = 1), Confirmation - Time, Arrival at Pickup - Day of Month, Arrival at Pickup - Weekday (Mo = 1), Arrival at Pickup - Time, Pickup - Day of Month, Pickup - Weekday (Mo = 1), Pickup - Time, Arrival at Destination - Day of Month, Arrival at Destination - Weekday (Mo = 1), Arrival at Destination - Time, Distance (KM), Temperature, Precipitation in millimeters, Pickup Lat, Pickup Long, Destination Lat, Destination Long, Rider Id, Time from Pickup to Arrival, Pickup_Hour, Pickup_Minutes, Arrival_Destination_Hour, Arrival_Destination_Minutes, Time from Pickup to Arrival_Hour, Time from Pickup to Arrival_Minutes]\n",
-       "Index: []\n",
-       "\n",
-       "[0 rows x 35 columns]"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df[train_df[\"Pickup - Weekday (Mo = 1)\"]!=train_df[\"Arrival at Destination - Weekday (Mo = 1)\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All orders are delivered on the same day they are picked up, this is beacuse there are no orders where the day of the week of the pick-up is not the day of the week of the arrival.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df['Pickup - Time'] = pd.to_datetime(train_df['Pickup - Time'])\n",
-    "train_df['Arrival at Destination - Time'] = pd.to_datetime(train_df['Arrival at Destination - Time'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Converting time format provided to datatime."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df['Pickup_Hour'] = train_df['Pickup - Time'].dt.hour\n",
-    "train_df['Arrival_Destination_Hour'] = train_df['Arrival at Destination - Time'].dt.hour\n",
-    "train_df['Time from Pickup to Arrival_Hour'] = train_df['Arrival at Destination - Time'].dt.hour"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Converting the datatime to hours for more in depth time analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7faa794c8>"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_df['Pickup_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'blue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Most of pickup hours are between 9 AM in the morning to 17 PM in the afternoon.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fa96b788>"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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HgXXAq4H/DdwSEasyc9MMt1+SJGnemMker6j//1xEPAe4BmgDX8zMHwFExAbgjcCXgIcy8+F6fD1wCmDxkiRJi8ZM5ng9G9gMnAz8GnA2MARs71hnO3AIsLzLuCRJ0qIx7T1emfkV4CvjpyPir6jmcP3XjtUGgCepCt7YBOM9a7VaXZe128NM9rbuybTbbVqtB6d1XrPNNtvsprJ7MdnjYtPMNtvs6ZnJHK/jgH0yc3M9NAB8Fzi4Y7WDgEeBbV3GezYyMtJ1WbcPMOzF4ODgpJc9FbPNNtvsJrKn0mq1Gr18s802e/rZkxW2mczx+iXg/RHxKqp3ML4N+PfA+oh4LvA48AZgNfANICLiMKqJ9qdSTbaXJElaNKY9xyszPwPcAnwNaAHrMvMO4L3AFuDrwA2Z+dXM3AGcAWwE7gceADbMbNMlSZLmlxl9jldmXgRctNvYDcANE6y7GThyJnmSJEnzmZ9cL0mSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCrF4SZIkFWLxkiRJKsTiJUmSVMjSfm+AJOnptm6F0dHuy9vtYdrtiZcNDcGKFc1sl6SZsXhJ0hw0OgorV062xmDXJVu2zKx4Wfqk5li8JElP0c/SJy10zvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCvFzvCRJc4Yf3qqFzuIlSZoz/PBWLXQeapQkSSrE4iVJklSIxUuSJKmQonO8IuJUYA2wN3B5Zn6kZL4kSVI/FSteEfE84APACPAz4M6I2JKZ95faBkmSuvEdlSqh5B6vE4EvZuaPACJiA/BG4P0Ft0GSpAn5jkqVULJ4LQe2d5zeDhxTMF+SpDmpn3vb3NNX1sDY2FiRoIh4L7BvZl5Unz4LGMnMs6c6b6vVKrORkiRJs2BkZGRgovGSe7y2Acd3nD4IeLSXM3bbeEmSpPmkZPH6AnBxRDwXeBx4A7C6YL4kSVJfFfscr8z8J+C9wBbg68ANmfnVUvmSJEn9VmyOlyRJ0mLnJ9dLkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQkp+gKokzUhE/C7wu1TffPFz4DvA/8zMr/R1wxawiHgt8NPM/EpEnA+8Bvh74LLM/HlfN24BiogrgPdl5v/t97YsJhHxQuCNwCHAk1TfrPO3mXnPbGf5OV6S5oWI+GPg3wJ/C/wOcDtV+ToTWJuZ1/Rx8xakiPhvwAnA3sDDVE9I1wO/DSzNzLMazv8N4BSe+mS4KTM3NpnbTxHxz8D3gD/OzP9VOHsp8A5gCPhkZt7esezizLy44fwTgX+m+pD1i4GXAF8GPpSZTzSYew7VN+lsALYDA1Qv7t4ArM/MD81m3oLc49XHX15f77T9EhEnZean6p/PBH4L2AncnJk3NZy9lOqJ92aq3/l7gGOAFnBpZu5oMn+C7fl4Zr6lUFZfnpQi4tPAeZm5tcmcCbwZeGlmjkXEdcCtmfnaiLgGuAtotHjV97XTgZ9SPUB/GHg11d6fCzLzRw1m7wX8R56+t++mzLyxqVxgFXAksA8wCizPzJ0RsYnq8bUxEfF+qr/l9Tz1yfDMiHhlZl7QYPYJky3PzNuayqYquKcBfxER7wbWAp/OzJ82mDnufwBLgG8CH4uIazLzg/Wy36F6Pm1ERPwpcCxwANVj2feAq6n2Ql0OvKupbOBcqseWn+y2TWuBewGL12T6/Mvr5522nw8U7wM+FREXU30R+pVUD5KrI+IlmfneBrM/Wv+/geqP41nAR4DXA+uAU5sKjogtwO67jI+OiC8CZOZrG8zu25MS1V6nz0bE1cAVmbmzwaxO+wLPpPqu12XAc+rxx6iKZ9OuBfart+NdwN1UZfAk4C+pHmOa8iHgGcCf1jn3UT2+vSsiDs/MP2kod4DqsfRZVNd9f+CHVLf/MxrKHPdm4IjMfMrvNiI+DvwD0OR9/L8Ar6T6HQ/stmwMaOxvGxjLzPuBV9c7EVYD/z0iHgS2ZWZjj2nA0Zl5JEBEfAz4QkT8JDMv5+m3w2z7d8CLgV+melHxy5n5ZF3yv9Zw9i6qvbq7W0a1E2FWLbjiRX9/ef280/bzgWLcycArxvcyRcRnqB4gmyxeL8nMF9d5xwEvy8wxYFNE3N9gLsBG4N3ARVSvUgeo9rpc0nAu9PdJ6Z+A3wT+DPjHiLgKuDEzH2kwE6pDXHdExGeB3wCui4gh4FPADQ1nQ3XfeklELKF6AnxVPX5/RDS69wd4bcdjy2eB2zLzuHrv4zeAporXZcA/Ut23LwQ+HxFfAE6kemHTpB1Ue3NHdxs/FPhZw9mrqL5X+PLM/HTDWbv7/4/fmfkFqueRvamO3KxoOHuviNgvMx/PzB9ExG9R/c39gKe/yGzCPpn5w4i4oOOxbZCJS9Fs+gDwtYjYTPVCdgxYTvW8OevPXwv1XY37ZOYPqXb/l/zl7RUR+wFk5g+oDrmdFxGn0fyddhVVsbw8M1fu9q/p0rVfRPwrYBvVK+Jxz6R6JdGkxyLiRfXPDwDPB4iI59Hwg3Nm/jlVAXg7cGhm/h3QzswvZeaXmszmF09KuyvxpDSWmd/LzNOpnoCfQ/WEPBoRdzYVmpmXAecDPwD+c/2C5kfA6Zn5gaZyOzwZEcPAy4ADIuIFABHxXJp/bFkaEQfWPx9M9bcF1V6nxv7GMnM91f1sqL6/vw34PvDuzPyzpnJr5wO3R8TnI+JjEfHRiPg8VSH6T00G13tx3w68aqp1G/Dnuw9k5s7MbGXmJxrOvhK4NyJ+rc4df5H1QeCIhrM/AtwXEUsy81qAiHgV1d7dy5sMzswbgOOo5o3+hOox9Hbg+CYO5S+4yfUR8R+A84BfHZ/PVf/y/gb4YJMTcCNiNdWDxTmZubkeeyHwWeDAzFzWVHadNQy8PTPf02TOBLnXASNUc9s2Z+YbIuL3qObAXJqZVzeY/UqqPU93Uh2Ceh3VfJ8R4A8zc1NT2R3b8AzgUqrr/6uZ+aIpzjIbmScCfwU8yFNfoQ0DZ2Tmlgazv5aZL51g/DnAisz8+6ay+ykiXkd1uHEvqrmcl1FNKzgGuCgz/7rB7DOo9mrdSXWo9z1Uc8s2Axdn5nVNZfdTRPw+1X36Caq9ytuo9uq/LTP/suHsk6j+pm/NzO90jK9uOrufIuJCqjl9N4xf74gYBN5Rv/hpMvs8qt/1rZn5nYg4hOpQ97EL6TZfcMULICJekJnf7Th9CHBAZn6rQPbhwM8yc7RjbBA4s36FvmBFxDLgoMx8OCL+DTCQmd8skDsI/DpwGNXh8/8DfC4ztzWdvdt2/Drw+5l5ZqG8fame9JdTlYFtwN2Z2eger4hYVaLQznX1Xt7jgX/IzAcK5A1THW66LzMfioh9gP2anNTfTxFxGdULqAeANwHn13vgiIh7M/NlDWcfDXyb6g0sF5TK7qd+Xu/FdJsvuOJVz/noqrMQmW222fMru5+8zcuKiG9SvdNsV/2C9nPAhZn5iW57XRdCdj/V1/uozHzC27w5C3Fy/S3A4VTv+JloknmTkxPNnhvZY/XPiy173ELO7idv87LG/46o9/C9nmouYYmJ3v3M7qfOif3e5g1ZiMXrWKpJcedk5h1mm232gsruJ2/zsj4B/F1EnJ+ZX83Mb0XEKVSf2bfPAs7uJ2/zAhbcuxoz88fAWVTvvjHbbLMXUHY/eZuXlZmXUH32Ybtj7A6qeV+Nvpmgn9n95G1exoKb4yVJkjRXLbg9XpIkSXOVxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmF/D/jZLtfDP3xQQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_df['Arrival_Destination_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'Blue')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7824b0088>"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_df['Arrival at Pickup - Day of Month'].value_counts().plot(kind='bar',figsize=[15,5], color = 'Blue')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df['Arrival at Pickup - Day of Month'].value_counts().plot(kind='bar',figsize=[15,5], color = 'Blue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Most of the Destination arrival hours are between 9 AM in the morning to 17 PM in the afternoon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0       -1.317755\n",
-       " 1       -1.351453\n",
-       " 2       -1.308284\n",
-       " 3       -1.281301\n",
-       " 4       -1.266597\n",
-       "            ...   \n",
-       " 21196   -1.258414\n",
-       " 21197   -1.307143\n",
-       " 21198   -1.286018\n",
-       " 21199   -1.250030\n",
-       " 21200   -1.255189\n",
-       " Name: Pickup Lat, Length: 21201, dtype: float64,\n",
-       " 0        36.830370\n",
-       " 1        36.899315\n",
-       " 2        36.843419\n",
-       " 3        36.832396\n",
-       " 4        36.792118\n",
-       "            ...    \n",
-       " 21196    36.804800\n",
-       " 21197    36.825009\n",
-       " 21198    36.897534\n",
-       " 21199    36.874167\n",
-       " 21200    36.782203\n",
-       " Name: Pickup Long, Length: 21201, dtype: float64)"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df['Pickup Lat'], train_df['Pickup Long']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "\"None of [Float64Index([-1.3177546999999998,           -1.351453,          -1.3082842,\\n                       -1.2813012, -1.2665973999999998,          -1.3025826,\\n                        -1.279395, -1.2527958000000001,          -1.2551895,\\n                        -1.225322,\\n              ...\\n              -1.2821421999999998, -1.3167113000000001,          -1.2865677,\\n                       -1.2602337,          -1.2384056,          -1.2584143,\\n                        -1.307143,          -1.2860183, -1.2500303999999998,\\n                       -1.2551895],\\n             dtype='float64', length=21201)] are in the [columns]\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-158-f8a369fa3c91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pickup Lat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pickup Long'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"better_visualization_plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    790\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 792\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    793\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    264\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    968\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m         scatter = ax.scatter(\n\u001b[1;32m--> 970\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    971\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m             \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2804\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2806\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2808\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m         self._validate_read_indexer(\n\u001b[1;32m-> 1552\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1553\u001b[0m         )\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: \"None of [Float64Index([-1.3177546999999998,           -1.351453,          -1.3082842,\\n                       -1.2813012, -1.2665973999999998,          -1.3025826,\\n                        -1.279395, -1.2527958000000001,          -1.2551895,\\n                        -1.225322,\\n              ...\\n              -1.2821421999999998, -1.3167113000000001,          -1.2865677,\\n                       -1.2602337,          -1.2384056,          -1.2584143,\\n                        -1.307143,          -1.2860183, -1.2500303999999998,\\n                       -1.2551895],\\n             dtype='float64', length=21201)] are in the [columns]\""
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_df.plot(kind=\"scatter\", x= train_df['Pickup Lat'], y=train_df['Pickup Long'], alpha=0.1)\n",
-    "save_fig(\"better_visualization_plot\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The \"Time from Pickup to Arrival\" does not have any missing values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fd157648>"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_df['Time from Pickup to Arrival_Hour'].value_counts().plot(kind='bar',figsize=[10,5], color = 'blue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Most of the Time from Pickup to Arrival_Hour are between 9 AM in the morning to 17 PM in the afternoon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    21201.000000\n",
-       "mean        13.303995\n",
-       "std          2.570458\n",
-       "min          0.000000\n",
-       "25%         11.000000\n",
-       "50%         13.000000\n",
-       "75%         15.000000\n",
-       "max         23.000000\n",
-       "Name: Time from Pickup to Arrival_Hour, dtype: float64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df['Time from Pickup to Arrival_Hour'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13    2668\n",
-      "12    2636\n",
-      "11    2589\n",
-      "15    2586\n",
-      "16    2521\n",
-      "14    2411\n",
-      "10    2189\n",
-      "17    1445\n",
-      "9     1054\n",
-      "18     586\n",
-      "19     195\n",
-      "8      180\n",
-      "20      89\n",
-      "21      34\n",
-      "7        8\n",
-      "22       8\n",
-      "23       1\n",
-      "0        1\n",
-      "Name: Time from Pickup to Arrival_Hour, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "count = train_df['Time from Pickup to Arrival_Hour'].value_counts()\n",
-    "print(count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The peak for Time from Pick to Arrival_hour is taking place at 13:00 PM in the afternoon and lowest at 00:00 AM in the early hours of the morning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Match the number of train columns to the number of test columns and also separate the target variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save the columns from the test dataframe into a new list called testcols\n",
-    "testcols = test_df.columns\n",
-    "\n",
-    "# reassign the number of the train dataframe to match that of the test by saving them to an updated dataframe, newtrain\n",
-    "train1 = train_df[testcols]\n",
-    "\n",
-    "# Seperate the target variable from the predictor variable\n",
-    "y = np.array(train_df['Time from Pickup to Arrival']).reshape(-1, 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Drop data not available in test, Pickup Time + label = Arrival times\n",
-    "\n",
-    "train_df = train_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print the new columns of the train and test data to show that they are the same\n",
-    "print(train1.columns)\n",
-    "print(test_df.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# New Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_of_day(hour):\n",
-    "    if (hour > 0) and (hour <= 12 ):\n",
-    "        return 'AM'\n",
-    "    else:\n",
-    "        return'PM'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'PM'"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "time_of_day(14)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "      <th>Pickup_Hour</th>\n",
-       "      <th>Pickup_Minutes</th>\n",
-       "      <th>Arrival_Destination_Hour</th>\n",
-       "      <th>Arrival_Destination_Minutes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>745</td>\n",
-       "      <td>10</td>\n",
-       "      <td>27</td>\n",
-       "      <td>10</td>\n",
-       "      <td>39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>11</td>\n",
-       "      <td>44</td>\n",
-       "      <td>12</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>455</td>\n",
-       "      <td>12</td>\n",
-       "      <td>53</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>1341</td>\n",
-       "      <td>9</td>\n",
-       "      <td>43</td>\n",
-       "      <td>10</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>1214</td>\n",
-       "      <td>10</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "0              3                         9                             5   \n",
-       "1              3                        12                             5   \n",
-       "2              3                        30                             2   \n",
-       "3              3                        15                             5   \n",
-       "4              1                        13                             1   \n",
-       "\n",
-       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "0                            9                                5   \n",
-       "1                           12                                5   \n",
-       "2                           30                                2   \n",
-       "3                           15                                5   \n",
-       "4                           13                                1   \n",
-       "\n",
-       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                 9                                     5   \n",
-       "1                                12                                     5   \n",
-       "2                                30                                     2   \n",
-       "3                                15                                     5   \n",
-       "4                                13                                     1   \n",
-       "\n",
-       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
-       "0                      9                          5   \n",
-       "1                     12                          5   \n",
-       "2                     30                          2   \n",
-       "3                     15                          5   \n",
-       "4                     13                          1   \n",
-       "\n",
-       "   Arrival at Destination - Day of Month  ...  Precipitation in millimeters  \\\n",
-       "0                                      9  ...                           NaN   \n",
-       "1                                     12  ...                           NaN   \n",
-       "2                                     30  ...                           NaN   \n",
-       "3                                     15  ...                           NaN   \n",
-       "4                                     13  ...                           NaN   \n",
-       "\n",
-       "   Pickup Lat  Pickup Long  Destination Lat  Destination Long  \\\n",
-       "0   -1.317755    36.830370        -1.300406         36.829741   \n",
-       "1   -1.351453    36.899315        -1.295004         36.814358   \n",
-       "2   -1.308284    36.843419        -1.300921         36.828195   \n",
-       "3   -1.281301    36.832396        -1.257147         36.795063   \n",
-       "4   -1.266597    36.792118        -1.295041         36.809817   \n",
-       "\n",
-       "   Time from Pickup to Arrival  Pickup_Hour  Pickup_Minutes  \\\n",
-       "0                          745           10              27   \n",
-       "1                         1993           11              44   \n",
-       "2                          455           12              53   \n",
-       "3                         1341            9              43   \n",
-       "4                         1214           10               5   \n",
-       "\n",
-       "   Arrival_Destination_Hour  Arrival_Destination_Minutes  \n",
-       "0                        10                           39  \n",
-       "1                        12                           17  \n",
-       "2                        13                            0  \n",
-       "3                        10                            5  \n",
-       "4                        10                           25  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Numeric_Training = train_df._get_numeric_data()\n",
-    "Numeric_Training.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Full_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((21201, 25), (7068, 26), (28269, 26))"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Create (full_df = train + test) ** caution (dont shuffle, avoid drop/adding rows)\n",
-    "#explore training, make (column) changes to full, later we use the border to separate\n",
-    "#Be careful of information leakage\n",
-    "\n",
-    "border = train1.shape[0]\n",
-    "test_df['Time from Pickup to Arrival'] = [np.nan]* test_df.shape[0]\n",
-    "full_df = pd.concat([train1, test_df], axis=0, ignore_index=True)\n",
-    "\n",
-    "train1.shape, test_df.shape, full_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Renaming columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order_No', 'User_Id', 'Vehicle_Type', 'Platform_Type',\n",
-       "       'Personal_Business', 'Pla_Mon', 'Pla_Weekday', 'Pla_Time',\n",
-       "       'Con_Day_Mon', 'Con_Weekday', 'Con_Time', 'Arr_Pic_Mon',\n",
-       "       'Arr_Pic_Weekday', 'Arr_Pic_Time', 'Pickup_Mon', 'Pickup_Weekday',\n",
-       "       'Pickup_Time', 'Distance(km)', 'Temperature', 'Precipitation(mm)',\n",
-       "       'Pickup_Lat', 'Pickup_Lon', 'Destination_Lat', 'Destination_Lon',\n",
-       "       'Rider_Id', 'Time_Pic_Arr'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Renaming columns (shorten, remove space, standardize)\n",
-    "new_names = {\"Order No\": \"Order_No\", \"User Id\": \"User_Id\", \"Vehicle Type\": \"Vehicle_Type\",\n",
-    "    \"Personal or Business\": \"Personal_Business\", \"Placement - Day of Month\": \"Pla_Mon\",\n",
-    "    \"Placement - Weekday (Mo = 1)\": \"Pla_Weekday\", \"Placement - Time\": \"Pla_Time\", \n",
-    "    \"Confirmation - Day of Month\":\"Con_Day_Mon\", \"Confirmation - Weekday (Mo = 1)\": \"Con_Weekday\",\"Confirmation - Time\": \"Con_Time\", \n",
-    "    \"Arrival at Pickup - Day of Month\": \"Arr_Pic_Mon\", \"Arrival at Pickup - Weekday (Mo = 1)\": \"Arr_Pic_Weekday\", \n",
-    "                \"Arrival at Pickup - Time\": \"Arr_Pic_Time\", \"Platform Type\": \"Platform_Type\",\n",
-    "     \"Pickup - Day of Month\": \"Pickup_Mon\", \"Pickup - Weekday (Mo = 1)\": \"Pickup_Weekday\",           \n",
-    "    \"Pickup - Time\": \"Pickup_Time\",  \"Distance (KM)\": \"Distance(km)\",\n",
-    "    \"Precipitation in millimeters\": \"Precipitation(mm)\", \"Pickup Lat\": \"Pickup_Lat\", \"Pickup Long\": \"Pickup_Lon\", \n",
-    "    \"Destination Lat\": \"Destination_Lat\", \"Destination Long\":\"Destination_Lon\", \"Rider Id\": \"Rider_Id\",\n",
-    "                            \"Time from Pickup to Arrival\": \"Time_Pic_Arr\"\n",
-    "                           }\n",
-    "\n",
-    "full_df = full_df.rename(columns=new_names)\n",
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Convert Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "time data '2020-10-19 10:27:30' does not match format '%I:%M:%S %p' (match)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\tslibs\\conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-139-405ca5f988bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mfull_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_to_24hrs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mfull_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Pla_Time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Con_Time'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Arr_Pic_Time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Pickup_Time'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-139-405ca5f988bc>\u001b[0m in \u001b[0;36mconvert_to_24hrs\u001b[1;34m(fulldf)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Time\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m             \u001b[0mfulldf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfulldf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%I:%M:%S %p'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%H:%M:%S\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfulldf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m    726\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    433\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    398\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m                     result, timezones = array_strptime(\n\u001b[1;32m--> 400\u001b[1;33m                         \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    401\u001b[0m                     )\n\u001b[0;32m    402\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;34m\"%Z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformat\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"%z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\tslibs\\strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: time data '2020-10-19 10:27:30' does not match format '%I:%M:%S %p' (match)"
-     ]
-    }
-   ],
-   "source": [
-    "#Convert Time from 12H to 24H\n",
-    "\n",
-    "def convert_to_24hrs(fulldf):\n",
-    "    for col in fulldf.columns:\n",
-    "        if col.endswith(\"Time\"):\n",
-    "            fulldf[col] = pd.to_datetime(fulldf[col], format='%I:%M:%S %p').dt.strftime(\"%H:%M:%S\")\n",
-    "    return fulldf\n",
-    "\n",
-    "full_df = convert_to_24hrs(full_df)\n",
-    "\n",
-    "full_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Filling Missing Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Filling Missing Values for temperatures and humidity\n",
-    "\n",
-    "full_df['Temperature'] = full_df['Temperature'].fillna(full_df['Temperature'].mean())\n",
-    "full_df['Precipitation(mm)'].fillna(full_df['Precipitation(mm)'].mean(), inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Vehicle_Type</th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Pla_Mon</th>\n",
-       "      <th>Pla_Weekday</th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Day_Mon</th>\n",
-       "      <th>Con_Weekday</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "      <th>Time_Pic_Arr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020-10-19 10:27:30</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020-10-19 11:44:09</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020-10-19 12:53:03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020-10-19 09:43:06</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020-10-19 10:05:23</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Vehicle_Type  Platform_Type Personal_Business  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3          Business   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3          Personal   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3          Business   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3          Business   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1          Personal   \n",
-       "\n",
-       "   Pla_Mon  Pla_Weekday  Pla_Time  Con_Day_Mon  Con_Weekday  ...  \\\n",
-       "0        9            5  09:35:46            9            5  ...   \n",
-       "1       12            5  11:16:16           12            5  ...   \n",
-       "2       30            2  12:39:25           30            2  ...   \n",
-       "3       15            5  09:25:34           15            5  ...   \n",
-       "4       13            1  09:55:18           13            1  ...   \n",
-       "\n",
-       "           Pickup_Time  Distance(km)  Temperature Precipitation(mm)  \\\n",
-       "0  2020-10-19 10:27:30             4    20.400000          7.573502   \n",
-       "1  2020-10-19 11:44:09            16    26.400000          7.573502   \n",
-       "2  2020-10-19 12:53:03             3    23.255689          7.573502   \n",
-       "3  2020-10-19 09:43:06             9    19.200000          7.573502   \n",
-       "4  2020-10-19 10:05:23             9    15.400000          7.573502   \n",
-       "\n",
-       "   Pickup_Lat  Pickup_Lon Destination_Lat  Destination_Lon      Rider_Id  \\\n",
-       "0   -1.317755   36.830370       -1.300406        36.829741  Rider_Id_432   \n",
-       "1   -1.351453   36.899315       -1.295004        36.814358  Rider_Id_856   \n",
-       "2   -1.308284   36.843419       -1.300921        36.828195  Rider_Id_155   \n",
-       "3   -1.281301   36.832396       -1.257147        36.795063  Rider_Id_855   \n",
-       "4   -1.266597   36.792118       -1.295041        36.809817  Rider_Id_770   \n",
-       "\n",
-       "   Time_Pic_Arr  \n",
-       "0           NaN  \n",
-       "1           NaN  \n",
-       "2           NaN  \n",
-       "3           NaN  \n",
-       "4           NaN  \n",
-       "\n",
-       "[5 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Traversing Month and Weekday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[[17, 18, 18, 18], [11, 13, 13, 13]]"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Since, we have not been given the actual dates & bikes (same day) were used, is Pick, Arrival date not the same?\n",
-    "\n",
-    "month_cols = [col for col in full_df.columns if col.endswith(\"Mon\")]\n",
-    "weekday_cols = [col for col in full_df.columns if col.endswith(\"Weekday\")]\n",
-    "\n",
-    "count = 0\n",
-    "instances_of_different_days = [];\n",
-    "for i, row in full_df.iterrows():\n",
-    "    if len(set(row[month_cols].values)) > 1:\n",
-    "        print(count+1, end=\"\\r\")\n",
-    "        count = count + 1\n",
-    "        instances_of_different_days.append(list(row[month_cols].values))\n",
-    "instances_of_different_days"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Mon', 'Con_Day_Mon', 'Arr_Pic_Mon', 'Pickup_Mon']"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "month_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Pla_Weekday', 'Con_Weekday', 'Arr_Pic_Weekday', 'Pickup_Weekday']"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weekday_cols"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating Month and Weekday columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df['Day_of_Month'] = full_df[month_cols[0]]\n",
-    "full_df['Day_of_Week'] = full_df[weekday_cols[0]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping redundant columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#All Vehicle types are Bikes, Vehicle Type is not necessary.\n",
-    "#Day & Weekday values are repeated in all rows except 2, we retain only one\n",
-    "full_df.drop(month_cols+weekday_cols, axis=1, inplace=True)\n",
-    "full_df.drop('Vehicle_Type', axis=1, inplace=True)\n",
-    "\n",
-    "full_df.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'full_df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-135-3c7297abd118>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfull_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'full_df' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Variable Datatypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numeric_cols = []\n",
-    "object_cols = []\n",
-    "time_cols = []\n",
-    "for k, v in full_df.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_cols.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_cols.append(k)\n",
-    "    else:\n",
-    "        object_cols.append(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform_Type</th>\n",
-       "      <th>Pla_Mon</th>\n",
-       "      <th>Pla_Weekday</th>\n",
-       "      <th>Con_Day_Mon</th>\n",
-       "      <th>Con_Weekday</th>\n",
-       "      <th>Arr_Pic_Mon</th>\n",
-       "      <th>Arr_Pic_Weekday</th>\n",
-       "      <th>Pickup_Mon</th>\n",
-       "      <th>Pickup_Weekday</th>\n",
-       "      <th>Distance(km)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation(mm)</th>\n",
-       "      <th>Pickup_Lat</th>\n",
-       "      <th>Pickup_Lon</th>\n",
-       "      <th>Destination_Lat</th>\n",
-       "      <th>Destination_Lon</th>\n",
-       "      <th>Day_of_Month</th>\n",
-       "      <th>Day_of_Week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23.255689</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.200000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.400000</td>\n",
-       "      <td>7.573502</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform_Type  Pla_Mon  Pla_Weekday  Con_Day_Mon  Con_Weekday  Arr_Pic_Mon  \\\n",
-       "0              3        9            5            9            5            9   \n",
-       "1              3       12            5           12            5           12   \n",
-       "2              3       30            2           30            2           30   \n",
-       "3              3       15            5           15            5           15   \n",
-       "4              1       13            1           13            1           13   \n",
-       "\n",
-       "   Arr_Pic_Weekday  Pickup_Mon  Pickup_Weekday  Distance(km)  Temperature  \\\n",
-       "0                5           9               5             4    20.400000   \n",
-       "1                5          12               5            16    26.400000   \n",
-       "2                2          30               2             3    23.255689   \n",
-       "3                5          15               5             9    19.200000   \n",
-       "4                1          13               1             9    15.400000   \n",
-       "\n",
-       "   Precipitation(mm)  Pickup_Lat  Pickup_Lon  Destination_Lat  \\\n",
-       "0           7.573502   -1.317755   36.830370        -1.300406   \n",
-       "1           7.573502   -1.351453   36.899315        -1.295004   \n",
-       "2           7.573502   -1.308284   36.843419        -1.300921   \n",
-       "3           7.573502   -1.281301   36.832396        -1.257147   \n",
-       "4           7.573502   -1.266597   36.792118        -1.295041   \n",
-       "\n",
-       "   Destination_Lon  Day_of_Month  Day_of_Week  \n",
-       "0        36.829741             9            5  \n",
-       "1        36.814358            12            5  \n",
-       "2        36.828195            30            2  \n",
-       "3        36.795063            15            5  \n",
-       "4        36.809817            13            1  "
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[numeric_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pla_Time</th>\n",
-       "      <th>Con_Time</th>\n",
-       "      <th>Arr_Pic_Time</th>\n",
-       "      <th>Pickup_Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>09:35:46</td>\n",
-       "      <td>09:40:10</td>\n",
-       "      <td>10:04:47</td>\n",
-       "      <td>2020-10-19 10:27:30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:16:16</td>\n",
-       "      <td>11:23:21</td>\n",
-       "      <td>11:40:22</td>\n",
-       "      <td>2020-10-19 11:44:09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12:39:25</td>\n",
-       "      <td>12:42:44</td>\n",
-       "      <td>12:49:34</td>\n",
-       "      <td>2020-10-19 12:53:03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>09:25:34</td>\n",
-       "      <td>09:26:05</td>\n",
-       "      <td>09:37:56</td>\n",
-       "      <td>2020-10-19 09:43:06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>09:55:18</td>\n",
-       "      <td>09:56:18</td>\n",
-       "      <td>10:03:53</td>\n",
-       "      <td>2020-10-19 10:05:23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Pla_Time  Con_Time Arr_Pic_Time          Pickup_Time\n",
-       "0  09:35:46  09:40:10     10:04:47  2020-10-19 10:27:30\n",
-       "1  11:16:16  11:23:21     11:40:22  2020-10-19 11:44:09\n",
-       "2  12:39:25  12:42:44     12:49:34  2020-10-19 12:53:03\n",
-       "3  09:25:34  09:26:05     09:37:56  2020-10-19 09:43:06\n",
-       "4  09:55:18  09:56:18     10:03:53  2020-10-19 10:05:23"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[time_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order_No</th>\n",
-       "      <th>User_Id</th>\n",
-       "      <th>Vehicle_Type</th>\n",
-       "      <th>Personal_Business</th>\n",
-       "      <th>Rider_Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order_No       User_Id Vehicle_Type Personal_Business      Rider_Id\n",
-       "0   Order_No_4211   User_Id_633         Bike          Business  Rider_Id_432\n",
-       "1  Order_No_25375  User_Id_2285         Bike          Personal  Rider_Id_856\n",
-       "2   Order_No_1899   User_Id_265         Bike          Business  Rider_Id_155\n",
-       "3   Order_No_9336  User_Id_1402         Bike          Business  Rider_Id_855\n",
-       "4  Order_No_27883  User_Id_1737         Bike          Personal  Rider_Id_770"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_df[object_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Convert object types to numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Convert an object to numeric (encoding)\n",
-    "\n",
-    "le = LabelEncoder()\n",
-    "le.fit(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'] = le.transform(full_df['Personal_Business'])\n",
-    "full_df['Personal_Business'][:2]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Feature Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = numeric_cols + ['Personal_Business']\n",
-    "\n",
-    "data_df = full_df[features]\n",
-    "\n",
-    "y = full_df[:border]['Time_Pic_Arr']\n",
-    "train = data_df[:border]\n",
-    "test = data_df[border:]\n",
-    "\n",
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(full_df.shape,data_df.shape,train.shape,test.shape,y.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Train/Test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, shuffle=True)\n",
-    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cross validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rs = 3\n",
-    "kfold = KFold(n_splits=10, random_state=rs, shuffle=True)\n",
-    "\n",
-    "regressors = []\n",
-    "regressors.append(SVR())\n",
-    "regressors.append(GradientBoostingRegressor(random_state=rs))\n",
-    "regressors.append(ExtraTreesRegressor(n_estimators=rs))\n",
-    "regressors.append(RandomForestRegressor(random_state=rs))\n",
-    "#regressors.append(xgb.XGBRegressor(random_state=rs, objective=\"reg:squarederror\"))\n",
-    "regressors.append(lgb.LGBMRegressor(random_state=rs))\n",
-    "\n",
-    "cv_results = []\n",
-    "for regressor in regressors:     #scores to be minimised are negated (neg)\n",
-    "    cv_results.append(np.sqrt(abs(cross_val_score(regressor, X_train, y=y_train, scoring='neg_mean_squared_error', cv=kfold))))\n",
-    "\n",
-    "cv_means = []\n",
-    "cv_stds = []\n",
-    "for cv_result in cv_results:\n",
-    "    cv_means.append(cv_result.mean())\n",
-    "    cv_stds.append(cv_result.std())\n",
-    "    \n",
-    "#cv_res = pd.DataFrame({ \n",
-    "#    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"XGBR\", \"LGBM\"],\n",
-    "#    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
-    "#                       })\n",
-    "cv_res = pd.DataFrame({ \n",
-    "    \"Algorithm\": [\"SVR\", \"GBR\", \"EXR\", \"RFR\", \"LGBM\"],\n",
-    "    \"CrossValMeans\": cv_means, \"CrossValErrors\": cv_stds\n",
-    "                       })\n",
-    "\n",
-    "cv_res = cv_res.sort_values(\"CrossValMeans\", ascending=True)\n",
-    "print(cv_res)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Making predictions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Random Forest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "RFC = RandomForestRegressor(random_state=rs)\n",
-    "rf_param = {\"max_depth\":[None], \"max_features\":[3], \"min_samples_split\":[10],\n",
-    "           \"min_samples_leaf\": [3], \"n_estimators\":[300]}\n",
-    "rsearch = GridSearchCV(RFC, cv=kfold, scoring='neg_mean_squared_error',param_grid=rf_param)\n",
-    "rfm = rsearch.fit(X_train, y_train)\n",
-    "\n",
-    "r_score = np.sqrt(abs(rfm.best_score_))\n",
-    "r_params = rfm.best_p\n",
-    "arams_\n",
-    "print(r_score, r_params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Prediction = predict(fit, test)\n",
-    "submit = data.frame(PassengerId = test$PassengerId, Survived = Prediction)\n",
-    "write.csv(submit, file = “firstforest.csv”, row.names = FALSE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
-    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
-    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
-    "lgbm_output.to_csv(\"submission.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = lr.predict(test_new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "submission_df = test_df1[['Order No']]\n",
-    "submission_df['Time_Pic_Arr'] = y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#submission_df.to_csv('D:/Temp/LRImproved.csv', index = False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lightgbm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameter Tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = {\n",
-    "    'n_estimators':[75], # [75, 95],\n",
-    "    'num_leaves': [15], #[12,15, 17],\n",
-    "    'reg_alpha': [0.02], #[0.02, 0.05],\n",
-    "    'min_data_in_leaf': [300],  #[250, 280, 300]\n",
-    "    'learning_rate': [0.1], #[0.05, 0.1, 0.25],\n",
-    "    'objective': ['regression'] #['regression', None]\n",
-    "    }\n",
-    "\n",
-    "lsearch = GridSearchCV(estimator = lgb.LGBMRegressor(random_state=rs), cv=kfold,scoring='neg_mean_squared_error', param_grid=params)\n",
-    "lgbm = lsearch.fit(X_train, y_train)\n",
-    "\n",
-    "l_params = lgbm.best_params_\n",
-    "l_score = np.sqrt(abs(lgbm.best_score_))\n",
-    "print(lgbm.best_params_, np.sqrt(abs(lgbm.best_score_)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training and making a prediction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lgb_train = lgb.Dataset(X_train, y_train)\n",
-    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n",
-    "\n",
-    "lparams = {\n",
-    "           'learning_rate': 0.1, 'min_data_in_leaf': 300, \n",
-    "           'n_estimators': 75, 'num_leaves': 20, 'random_state':rs,\n",
-    "           'objective': 'regression', 'reg_alpha': 0.02,\n",
-    "          'feature_fraction': 0.9, 'bagging_fraction':0.9}\n",
-    "\n",
-    "\n",
-    "lgbm = lgb.train(lparams, lgb_train, valid_sets=lgb_eval, num_boost_round=20, early_stopping_rounds=20)\n",
-    "\n",
-    "lpred = lgbm.predict(X_test, num_iteration=lgbm.best_iteration)\n",
-    "\n",
-    "print(\"The RMSE of prediction is \", mean_squared_error(y_test, lpred)**0.5)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Submission"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lgbm_y = lgbm.predict(test, num_iteration=lgbm.best_iteration)\n",
-    "lgbm_output = pd.DataFrame({\"Order No\":test_df['Order No'], \n",
-    "                           \"Time from Pickup to Arrival\": lgbm_y })\n",
-    "lgbm_output.to_csv(\"D:\\Temp\\submission.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api.py b/api.py
deleted file mode 100644
index b438b2d..0000000
--- a/api.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""
-
-    Simple Flask-based API for Serving an Sklearn Model.
-
-    Author: Explore Data Science Academy.
-
-    Note:
-    ---------------------------------------------------------------------
-    Plase follow the instructions provided within the README.md file
-    located within this directory for guidance on how to use this script
-    correctly.
-    ---------------------------------------------------------------------
-
-    Description: This file instantiates a Flask webserver
-    as a means to create a simple API used to deploy models trained within
-    the sklearn framework.
-
-"""
-
-# API Dependencies
-import pickle
-import json
-import numpy as np
-from model import load_model, make_prediction
-from flask import Flask, request, jsonify
-
-# Application definition
-app = Flask(__name__)
-
-# Load our model into memory.
-# Please update this path to reflect your own trained model.
-static_model = load_model(
-    path_to_model='assets/trained-models/sendy_simple_lm_regression.pkl')
-
-print ('-'*40)
-print ('Model succesfully loaded')
-print ('-'*40)
-
-# Define the API's interface.
-# Here the 'model_prediction()' function will be called when a POST request
-# is sent to our interface located at:
-# http:{Host-machine-ip-address}:5000/api_v0.1
-@app.route('/api_v0.1', methods=['POST'])
-def model_prediction():
-    # We retrieve the data payload of the POST request
-    data = request.get_json(force=True)
-    # We then preprocess our data, and use our pretrained model to make a
-    # prediction.
-    output = make_prediction(data, static_model)
-    # We finally package this prediction as a JSON object to deliver a valid
-    # response with our API.
-    return jsonify(output)
-
-# Configure Server Startup properties.
-# Note:
-# When developing your API, set `debug=True`
-# This will allow Flask to automatically restart itself everytime you
-# update your API code.
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=False)
diff --git a/assets/imgs/API.png b/assets/imgs/API.png
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diff --git a/assets/trained-models/sendy_simple_lm_regression.pkl b/assets/trained-models/sendy_simple_lm_regression.pkl
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diff --git a/model.py b/model.py
deleted file mode 100644
index 5552ae6..0000000
--- a/model.py
+++ /dev/null
@@ -1,187 +0,0 @@
-"""
-
-    Helper functions for the pretrained model to be used within our API.
-
-    Author: Explore Data Science Academy.
-
-    Note:
-    ---------------------------------------------------------------------
-    Plase follow the instructions provided within the README.md file
-    located within this directory for guidance on how to use this script
-    correctly.
-
-    Importantly, you will need to modify this file by adding
-    your own data preprocessing steps within the `_preprocess_data()`
-    function.
-    ----------------------------------------------------------------------
-
-    Description: This file contains several functions used to abstract aspects
-    of model interaction within the API. This includes loading a model from
-    file, data preprocessing, and model prediction.  
-
-"""
-
-# Helper Dependencies
-import numpy as np
-import pandas as pd
-import pickle
-import json
-
-def _preprocess_data(data):
-    """Private helper function to preprocess data for model prediction.
-
-    NB: If you have utilised feature engineering/selection in order to create
-    your final model you will need to define the code here.
-
-
-    Parameters
-    ----------
-    data : str
-        The data payload received within POST requests sent to our API.
-
-    Returns
-    -------
-    Pandas DataFrame : <class 'pandas.core.frame.DataFrame'>
-        The preprocessed data, ready to be used our model for prediction.
-
-    """
-    # Convert the json string to a python dictionary object
-    feature_vector_dict = json.loads(data)
-    # Load the dictionary as a Pandas DataFrame.
-    feature_vector_df = pd.DataFrame.from_dict([feature_vector_dict])
-
-    # ---------------------------------------------------------------
-    # NOTE: You will need to swap the lines below for your own data
-    # preprocessing methods.
-    #
-    # The code below is for demonstration purposes only. You will not
-    # receive marks for submitting this code in an unchanged state.
-    # ---------------------------------------------------------------
-
-    # ----------- Replace this code with your own preprocessing steps --------
-    #predict_vector = feature_vector_df[['Pickup Lat','Pickup Long',
-    #                                    'Destination Lat','Destination Long']]
-    
-    #Drop data not available in test, Pickup Time + label = Arrival times
-    #feature_vector_df = feature_vector_df.drop(['Arrival at Destination - Day of Month', 'Arrival at Destination - Weekday (Mo = 1)', 'Arrival at Destination - Time'], axis=1)
-
-    #Renaming columns (shorten, remove space, standardize)
-    new_names = {"Order No": "Order_No", "User Id": "User_Id", "Vehicle Type": "Vehicle_Type",
-    "Personal or Business": "Personal_Business", "Placement - Day of Month": "Pla_Mon",
-    "Placement - Weekday (Mo = 1)": "Pla_Weekday", "Placement - Time": "Pla_Time", 
-    "Confirmation - Day of Month":"Con_Day_Mon", "Confirmation - Weekday (Mo = 1)": "Con_Weekday","Confirmation - Time": "Con_Time", 
-    "Arrival at Pickup - Day of Month": "Arr_Pic_Mon", "Arrival at Pickup - Weekday (Mo = 1)": "Arr_Pic_Weekday", 
-                "Arrival at Pickup - Time": "Arr_Pic_Time", "Platform Type": "Platform_Type",
-     "Pickup - Day of Month": "Pickup_Mon", "Pickup - Weekday (Mo = 1)": "Pickup_Weekday",           
-    "Pickup - Time": "Pickup_Time",  "Distance (KM)": "Distance(km)",
-    "Precipitation in millimeters": "Precipitation(mm)", "Pickup Lat": "Pickup_Lat", "Pickup Long": "Pickup_Lon", 
-    "Destination Lat": "Destination_Lat", "Destination Long":"Destination_Lon", "Rider Id": "Rider_Id",
-                            "Time from Pickup to Arrival": "Time_Pic_Arr"
-                           }
-    feature_vector_df = feature_vector_df.rename(columns=new_names)
-    
-    #Convert Time from 12H to 24H
-    def convert_to_24hrs(feature_vector_df):
-        
-        for col in feature_vector_df.columns:
-            if col.endswith("Time"):
-                feature_vector_df[col] = pd.to_datetime(feature_vector_df[col], format='%I:%M:%S %p').dt.strftime("%H:%M:%S")
-        return feature_vector_df
-
-    feature_vector_df = convert_to_24hrs(feature_vector_df)
-
-    feature_vector_df[['Pla_Time', 'Con_Time' , 'Arr_Pic_Time', 'Pickup_Time']][3:6]
-    
-    #Filling Missing Values for temperatures and humidity
-    feature_vector_df['Temperature'] = feature_vector_df['Temperature'].fillna(feature_vector_df['Temperature'].mean())
-    feature_vector_df['Precipitation(mm)'].fillna(feature_vector_df['Precipitation(mm)'].mean(), inplace=True)
-    
-    #Since, we have not been given the actual dates & bikes (same day) 
-    month_cols = [col for col in feature_vector_df.columns if col.endswith("Mon")]
-    weekday_cols = [col for col in feature_vector_df.columns if col.endswith("Weekday")]
-
-    count = 0
-    instances_of_different_days = [];
-    for i, row in feature_vector_df.iterrows():
-        if len(set(row[month_cols].values)) > 1:
-            print(count+1, end="\r")
-            count = count + 1
-            instances_of_different_days.append(list(row[month_cols].values))
-    instances_of_different_days
-    
-    #Creating Month and Weekday columns
-    feature_vector_df['Day_of_Month'] = feature_vector_df[month_cols[0]]
-    feature_vector_df['Day_of_Week'] = feature_vector_df[weekday_cols[0]]
-    
-    #Dropping redundant columns
-    feature_vector_df.drop(month_cols+weekday_cols, axis=1, inplace=True)
-    feature_vector_df.drop('Vehicle_Type', axis=1, inplace=True)
-    
-    #Variable Datatypes
-    numeric_cols = []
-    object_cols = []
-    time_cols = []
-    for k, v in feature_vector_df.dtypes.items():
-        if (v != object):
-            if (k != "Time_Pic_Arr"):
-                numeric_cols.append(k)
-        elif k.endswith("Time"):
-            time_cols.append(k)
-        else:
-            object_cols.append(k)
-        
-    #Convert an object to numeric (encoding)
-    from sklearn.preprocessing import LabelEncoder
-    le = LabelEncoder()
-    le.fit(feature_vector_df['Personal_Business'])
-    feature_vector_df['Personal_Business'] = le.transform(feature_vector_df['Personal_Business'])
-    feature_vector_df['Personal_Business'][:2]
-
-    #Feature Selection
-    features = numeric_cols + ['Personal_Business']
-    predict_vector = feature_vector_df[features]
-    
-    # ------------------------------------------------------------------------
-
-    return predict_vector
-
-def load_model(path_to_model:str):
-    """Adapter function to load our pretrained model into memory.
-
-    Parameters
-    ----------
-    path_to_model : str
-        The relative path to the model weights/schema to load.
-        Note that unless another file format is used, this needs to be a
-        .pkl file.
-
-    Returns
-    -------
-    <class: sklearn.estimator>
-        The pretrained model loaded into memory.
-
-    """
-    return pickle.load(open(path_to_model, 'rb'))
-
-def make_prediction(data, model):
-    """Prepare request data for model prediciton.
-
-    Parameters
-    ----------
-    data : str
-        The data payload received within POST requests sent to our API.
-    model : <class: sklearn.estimator>
-        An sklearn model object.
-
-    Returns
-    -------
-    list
-        A 1-D python list containing the model prediction.
-
-    """
-    # Data preprocessing.
-    prep_data = _preprocess_data(data)
-    # Perform prediction with model and preprocessed data.
-    prediction = model.predict(prep_data)
-    # Format as list for output standerdisation.
-    return prediction[0].tolist()
diff --git a/team4_regression-EDA.ipynb b/team4_regression-EDA.ipynb
deleted file mode 100644
index cdd034a..0000000
--- a/team4_regression-EDA.ipynb
+++ /dev/null
@@ -1,2563 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "l_LulNCC8z96"
-   },
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xpXdowrE9DxW"
-   },
-   "source": [
-    "## Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nhKd4hWx9GFt"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "#import missingno as mnso\n",
-    "import matplotlib.style as style"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV \n",
-    "from sklearn.model_selection import cross_val_score, learning_curve\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "from sklearn.svm import SVR\n",
-    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.linear_model import LinearRegression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6JhpWJi59J1p"
-   },
-   "source": [
-    "## Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "f8GfYDZ59O18"
-   },
-   "outputs": [],
-   "source": [
-    "df_train = pd.read_csv('Train.csv') \n",
-    "df_test = pd.read_csv('Test.csv') \n",
-    "df_rider = pd.read_csv('Riders.csv') \n",
-    "df_variable = pd.read_csv('VariableDefinitions.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Variable Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(35, 2)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_variable.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>Unique number identifying the order</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>User Id</td>\n",
-       "      <td>Unique number identifying the customer on a pl...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Vehicle Type</td>\n",
-       "      <td>For this competition limited to bikes, however...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Platform Type</td>\n",
-       "      <td>Platform used to place the order, there are 4 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Personal or Business</td>\n",
-       "      <td>Customer type</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Placement - Day of Month</td>\n",
-       "      <td>Placement - Day of Month i.e 1-31</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   Order No                Unique number identifying the order\n",
-       "0                   User Id  Unique number identifying the customer on a pl...\n",
-       "1              Vehicle Type  For this competition limited to bikes, however...\n",
-       "2             Platform Type  Platform used to place the order, there are 4 ...\n",
-       "3      Personal or Business                                      Customer type\n",
-       "4  Placement - Day of Month                  Placement - Day of Month i.e 1-31"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_variable.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exploritory Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Rider Id</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike              3   \n",
-       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
-       "2   Order_No_1899   User_Id_265         Bike              3   \n",
-       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
-       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
-       "\n",
-       "  Personal or Business  Placement - Day of Month  \\\n",
-       "0             Business                         9   \n",
-       "1             Personal                        12   \n",
-       "2             Business                        30   \n",
-       "3             Business                        15   \n",
-       "4             Personal                        13   \n",
-       "\n",
-       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
-       "0                             5       9:35:46 AM                            9   \n",
-       "1                             5      11:16:16 AM                           12   \n",
-       "2                             2      12:39:25 PM                           30   \n",
-       "3                             5       9:25:34 AM                           15   \n",
-       "4                             1       9:55:18 AM                           13   \n",
-       "\n",
-       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
-       "0                                5  ...                   10:39:55 AM   \n",
-       "1                                5  ...                   12:17:22 PM   \n",
-       "2                                2  ...                    1:00:38 PM   \n",
-       "3                                5  ...                   10:05:27 AM   \n",
-       "4                                1  ...                   10:25:37 AM   \n",
-       "\n",
-       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
-       "0              4         20.4                          NaN   -1.317755   \n",
-       "1             16         26.4                          NaN   -1.351453   \n",
-       "2              3          NaN                          NaN   -1.308284   \n",
-       "3              9         19.2                          NaN   -1.281301   \n",
-       "4              9         15.4                          NaN   -1.266597   \n",
-       "\n",
-       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
-       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
-       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
-       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
-       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
-       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
-       "\n",
-       "  Time from Pickup to Arrival  \n",
-       "0                         745  \n",
-       "1                        1993  \n",
-       "2                         455  \n",
-       "3                        1341  \n",
-       "4                        1214  \n",
-       "\n",
-       "[5 rows x 29 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21201"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(21201, 29)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Order No', 'User Id', 'Vehicle Type', 'Platform Type',\n",
-       "       'Personal or Business', 'Placement - Day of Month',\n",
-       "       'Placement - Weekday (Mo = 1)', 'Placement - Time',\n",
-       "       'Confirmation - Day of Month', 'Confirmation - Weekday (Mo = 1)',\n",
-       "       'Confirmation - Time', 'Arrival at Pickup - Day of Month',\n",
-       "       'Arrival at Pickup - Weekday (Mo = 1)', 'Arrival at Pickup - Time',\n",
-       "       'Pickup - Day of Month', 'Pickup - Weekday (Mo = 1)', 'Pickup - Time',\n",
-       "       'Arrival at Destination - Day of Month',\n",
-       "       'Arrival at Destination - Weekday (Mo = 1)',\n",
-       "       'Arrival at Destination - Time', 'Distance (KM)', 'Temperature',\n",
-       "       'Precipitation in millimeters', 'Pickup Lat', 'Pickup Long',\n",
-       "       'Destination Lat', 'Destination Long', 'Rider Id',\n",
-       "       'Time from Pickup to Arrival'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>16835.000000</td>\n",
-       "      <td>552.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "      <td>21201.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.752182</td>\n",
-       "      <td>15.653696</td>\n",
-       "      <td>3.240083</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>15.653837</td>\n",
-       "      <td>3.240225</td>\n",
-       "      <td>9.506533</td>\n",
-       "      <td>23.258889</td>\n",
-       "      <td>7.905797</td>\n",
-       "      <td>-1.281470</td>\n",
-       "      <td>36.811264</td>\n",
-       "      <td>-1.282581</td>\n",
-       "      <td>36.811220</td>\n",
-       "      <td>1556.920947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.625178</td>\n",
-       "      <td>8.798916</td>\n",
-       "      <td>1.567295</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>8.798886</td>\n",
-       "      <td>1.567228</td>\n",
-       "      <td>5.668963</td>\n",
-       "      <td>3.615768</td>\n",
-       "      <td>17.089971</td>\n",
-       "      <td>0.030507</td>\n",
-       "      <td>0.037473</td>\n",
-       "      <td>0.034824</td>\n",
-       "      <td>0.044721</td>\n",
-       "      <td>987.270788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>11.200000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>-1.438302</td>\n",
-       "      <td>36.653621</td>\n",
-       "      <td>-1.430298</td>\n",
-       "      <td>36.606594</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>20.600000</td>\n",
-       "      <td>1.075000</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.784605</td>\n",
-       "      <td>-1.301201</td>\n",
-       "      <td>36.785661</td>\n",
-       "      <td>882.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>23.500000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>-1.279395</td>\n",
-       "      <td>36.807040</td>\n",
-       "      <td>-1.284382</td>\n",
-       "      <td>36.808002</td>\n",
-       "      <td>1369.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>4.900000</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>-1.261177</td>\n",
-       "      <td>36.829477</td>\n",
-       "      <td>2040.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>49.000000</td>\n",
-       "      <td>32.100000</td>\n",
-       "      <td>99.100000</td>\n",
-       "      <td>-1.147170</td>\n",
-       "      <td>36.991046</td>\n",
-       "      <td>-1.030225</td>\n",
-       "      <td>37.016779</td>\n",
-       "      <td>7883.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "count   21201.000000              21201.000000                  21201.000000   \n",
-       "mean        2.752182                 15.653696                      3.240083   \n",
-       "std         0.625178                  8.798916                      1.567295   \n",
-       "min         1.000000                  1.000000                      1.000000   \n",
-       "25%         3.000000                  8.000000                      2.000000   \n",
-       "50%         3.000000                 15.000000                      3.000000   \n",
-       "75%         3.000000                 23.000000                      5.000000   \n",
-       "max         4.000000                 31.000000                      7.000000   \n",
-       "\n",
-       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "count                 21201.000000                     21201.000000   \n",
-       "mean                     15.653837                         3.240225   \n",
-       "std                       8.798886                         1.567228   \n",
-       "min                       1.000000                         1.000000   \n",
-       "25%                       8.000000                         2.000000   \n",
-       "50%                      15.000000                         3.000000   \n",
-       "75%                      23.000000                         5.000000   \n",
-       "max                      31.000000                         7.000000   \n",
-       "\n",
-       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "count                      21201.000000                          21201.000000   \n",
-       "mean                          15.653837                              3.240225   \n",
-       "std                            8.798886                              1.567228   \n",
-       "min                            1.000000                              1.000000   \n",
-       "25%                            8.000000                              2.000000   \n",
-       "50%                           15.000000                              3.000000   \n",
-       "75%                           23.000000                              5.000000   \n",
-       "max                           31.000000                              7.000000   \n",
-       "\n",
-       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
-       "count           21201.000000               21201.000000   \n",
-       "mean               15.653837                   3.240225   \n",
-       "std                 8.798886                   1.567228   \n",
-       "min                 1.000000                   1.000000   \n",
-       "25%                 8.000000                   2.000000   \n",
-       "50%                15.000000                   3.000000   \n",
-       "75%                23.000000                   5.000000   \n",
-       "max                31.000000                   7.000000   \n",
-       "\n",
-       "       Arrival at Destination - Day of Month  \\\n",
-       "count                           21201.000000   \n",
-       "mean                               15.653837   \n",
-       "std                                 8.798886   \n",
-       "min                                 1.000000   \n",
-       "25%                                 8.000000   \n",
-       "50%                                15.000000   \n",
-       "75%                                23.000000   \n",
-       "max                                31.000000   \n",
-       "\n",
-       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
-       "count                               21201.000000   21201.000000  16835.000000   \n",
-       "mean                                    3.240225       9.506533     23.258889   \n",
-       "std                                     1.567228       5.668963      3.615768   \n",
-       "min                                     1.000000       1.000000     11.200000   \n",
-       "25%                                     2.000000       5.000000     20.600000   \n",
-       "50%                                     3.000000       8.000000     23.500000   \n",
-       "75%                                     5.000000      13.000000     26.000000   \n",
-       "max                                     7.000000      49.000000     32.100000   \n",
-       "\n",
-       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
-       "count                    552.000000  21201.000000  21201.000000   \n",
-       "mean                       7.905797     -1.281470     36.811264   \n",
-       "std                       17.089971      0.030507      0.037473   \n",
-       "min                        0.100000     -1.438302     36.653621   \n",
-       "25%                        1.075000     -1.300921     36.784605   \n",
-       "50%                        2.900000     -1.279395     36.807040   \n",
-       "75%                        4.900000     -1.257147     36.829741   \n",
-       "max                       99.100000     -1.147170     36.991046   \n",
-       "\n",
-       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
-       "count     21201.000000      21201.000000                 21201.000000  \n",
-       "mean         -1.282581         36.811220                  1556.920947  \n",
-       "std           0.034824          0.044721                   987.270788  \n",
-       "min          -1.430298         36.606594                     1.000000  \n",
-       "25%          -1.301201         36.785661                   882.000000  \n",
-       "50%          -1.284382         36.808002                  1369.000000  \n",
-       "75%          -1.261177         36.829477                  2040.000000  \n",
-       "max          -1.030225         37.016779                  7883.000000  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Separating by data type\n",
-    "numeric_columns = []\n",
-    "object_columns = []\n",
-    "time_columns = []\n",
-    "for k, v in df_train.dtypes.items():\n",
-    "    if (v != object):\n",
-    "        if (k != \"Time_Pic_Arr\"):\n",
-    "            numeric_columns.append(k)\n",
-    "    elif k.endswith(\"Time\"):\n",
-    "        time_columns.append(k)\n",
-    "    else:\n",
-    "        object_columns.append(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Placement - Time</th>\n",
-       "      <th>Confirmation - Time</th>\n",
-       "      <th>Arrival at Pickup - Time</th>\n",
-       "      <th>Pickup - Time</th>\n",
-       "      <th>Arrival at Destination - Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9:35:46 AM</td>\n",
-       "      <td>9:40:10 AM</td>\n",
-       "      <td>10:04:47 AM</td>\n",
-       "      <td>10:27:30 AM</td>\n",
-       "      <td>10:39:55 AM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:16:16 AM</td>\n",
-       "      <td>11:23:21 AM</td>\n",
-       "      <td>11:40:22 AM</td>\n",
-       "      <td>11:44:09 AM</td>\n",
-       "      <td>12:17:22 PM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12:39:25 PM</td>\n",
-       "      <td>12:42:44 PM</td>\n",
-       "      <td>12:49:34 PM</td>\n",
-       "      <td>12:53:03 PM</td>\n",
-       "      <td>1:00:38 PM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9:25:34 AM</td>\n",
-       "      <td>9:26:05 AM</td>\n",
-       "      <td>9:37:56 AM</td>\n",
-       "      <td>9:43:06 AM</td>\n",
-       "      <td>10:05:27 AM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9:55:18 AM</td>\n",
-       "      <td>9:56:18 AM</td>\n",
-       "      <td>10:03:53 AM</td>\n",
-       "      <td>10:05:23 AM</td>\n",
-       "      <td>10:25:37 AM</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Placement - Time Confirmation - Time Arrival at Pickup - Time Pickup - Time  \\\n",
-       "0       9:35:46 AM          9:40:10 AM              10:04:47 AM   10:27:30 AM   \n",
-       "1      11:16:16 AM         11:23:21 AM              11:40:22 AM   11:44:09 AM   \n",
-       "2      12:39:25 PM         12:42:44 PM              12:49:34 PM   12:53:03 PM   \n",
-       "3       9:25:34 AM          9:26:05 AM               9:37:56 AM    9:43:06 AM   \n",
-       "4       9:55:18 AM          9:56:18 AM              10:03:53 AM   10:05:23 AM   \n",
-       "\n",
-       "  Arrival at Destination - Time  \n",
-       "0                   10:39:55 AM  \n",
-       "1                   12:17:22 PM  \n",
-       "2                    1:00:38 PM  \n",
-       "3                   10:05:27 AM  \n",
-       "4                   10:25:37 AM  "
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train[time_columns].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Order No</th>\n",
-       "      <th>User Id</th>\n",
-       "      <th>Vehicle Type</th>\n",
-       "      <th>Personal or Business</th>\n",
-       "      <th>Rider Id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Order_No_4211</td>\n",
-       "      <td>User_Id_633</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Order_No_25375</td>\n",
-       "      <td>User_Id_2285</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Order_No_1899</td>\n",
-       "      <td>User_Id_265</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Order_No_9336</td>\n",
-       "      <td>User_Id_1402</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Business</td>\n",
-       "      <td>Rider_Id_855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Order_No_27883</td>\n",
-       "      <td>User_Id_1737</td>\n",
-       "      <td>Bike</td>\n",
-       "      <td>Personal</td>\n",
-       "      <td>Rider_Id_770</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Order No       User Id Vehicle Type Personal or Business  \\\n",
-       "0   Order_No_4211   User_Id_633         Bike             Business   \n",
-       "1  Order_No_25375  User_Id_2285         Bike             Personal   \n",
-       "2   Order_No_1899   User_Id_265         Bike             Business   \n",
-       "3   Order_No_9336  User_Id_1402         Bike             Business   \n",
-       "4  Order_No_27883  User_Id_1737         Bike             Personal   \n",
-       "\n",
-       "       Rider Id  \n",
-       "0  Rider_Id_432  \n",
-       "1  Rider_Id_856  \n",
-       "2  Rider_Id_155  \n",
-       "3  Rider_Id_855  \n",
-       "4  Rider_Id_770  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train[object_columns].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.317755</td>\n",
-       "      <td>36.830370</td>\n",
-       "      <td>-1.300406</td>\n",
-       "      <td>36.829741</td>\n",
-       "      <td>745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.351453</td>\n",
-       "      <td>36.899315</td>\n",
-       "      <td>-1.295004</td>\n",
-       "      <td>36.814358</td>\n",
-       "      <td>1993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.308284</td>\n",
-       "      <td>36.843419</td>\n",
-       "      <td>-1.300921</td>\n",
-       "      <td>36.828195</td>\n",
-       "      <td>455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.281301</td>\n",
-       "      <td>36.832396</td>\n",
-       "      <td>-1.257147</td>\n",
-       "      <td>36.795063</td>\n",
-       "      <td>1341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.266597</td>\n",
-       "      <td>36.792118</td>\n",
-       "      <td>-1.295041</td>\n",
-       "      <td>36.809817</td>\n",
-       "      <td>1214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
-       "0              3                         9                             5   \n",
-       "1              3                        12                             5   \n",
-       "2              3                        30                             2   \n",
-       "3              3                        15                             5   \n",
-       "4              1                        13                             1   \n",
-       "\n",
-       "   Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
-       "0                            9                                5   \n",
-       "1                           12                                5   \n",
-       "2                           30                                2   \n",
-       "3                           15                                5   \n",
-       "4                           13                                1   \n",
-       "\n",
-       "   Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "0                                 9                                     5   \n",
-       "1                                12                                     5   \n",
-       "2                                30                                     2   \n",
-       "3                                15                                     5   \n",
-       "4                                13                                     1   \n",
-       "\n",
-       "   Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
-       "0                      9                          5   \n",
-       "1                     12                          5   \n",
-       "2                     30                          2   \n",
-       "3                     15                          5   \n",
-       "4                     13                          1   \n",
-       "\n",
-       "   Arrival at Destination - Day of Month  \\\n",
-       "0                                      9   \n",
-       "1                                     12   \n",
-       "2                                     30   \n",
-       "3                                     15   \n",
-       "4                                     13   \n",
-       "\n",
-       "   Arrival at Destination - Weekday (Mo = 1)  Distance (KM)  Temperature  \\\n",
-       "0                                          5              4         20.4   \n",
-       "1                                          5             16         26.4   \n",
-       "2                                          2              3          NaN   \n",
-       "3                                          5              9         19.2   \n",
-       "4                                          1              9         15.4   \n",
-       "\n",
-       "   Precipitation in millimeters  Pickup Lat  Pickup Long  Destination Lat  \\\n",
-       "0                           NaN   -1.317755    36.830370        -1.300406   \n",
-       "1                           NaN   -1.351453    36.899315        -1.295004   \n",
-       "2                           NaN   -1.308284    36.843419        -1.300921   \n",
-       "3                           NaN   -1.281301    36.832396        -1.257147   \n",
-       "4                           NaN   -1.266597    36.792118        -1.295041   \n",
-       "\n",
-       "   Destination Long  Time from Pickup to Arrival  \n",
-       "0         36.829741                          745  \n",
-       "1         36.814358                         1993  \n",
-       "2         36.828195                          455  \n",
-       "3         36.795063                         1341  \n",
-       "4         36.809817                         1214  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train[numeric_columns].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: (21201, 29) \n",
-      "  test: (7068, 25) \n",
-      " riders: (960, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'train: {df_train.shape} \\n  test: {df_test.shape} \\n riders: {df_rider.shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Missing Values "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2141233c8>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.figure(figsize=(20, 10))\n",
-    "cmap = sns.cubehelix_palette(as_cmap=True, reverse=True)\n",
-    "sns.heatmap(df_train.isnull(), cmap=cmap)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order No                                         0\n",
-       "User Id                                          0\n",
-       "Vehicle Type                                     0\n",
-       "Platform Type                                    0\n",
-       "Personal or Business                             0\n",
-       "Placement - Day of Month                         0\n",
-       "Placement - Weekday (Mo = 1)                     0\n",
-       "Placement - Time                                 0\n",
-       "Confirmation - Day of Month                      0\n",
-       "Confirmation - Weekday (Mo = 1)                  0\n",
-       "Confirmation - Time                              0\n",
-       "Arrival at Pickup - Day of Month                 0\n",
-       "Arrival at Pickup - Weekday (Mo = 1)             0\n",
-       "Arrival at Pickup - Time                         0\n",
-       "Pickup - Day of Month                            0\n",
-       "Pickup - Weekday (Mo = 1)                        0\n",
-       "Pickup - Time                                    0\n",
-       "Arrival at Destination - Day of Month            0\n",
-       "Arrival at Destination - Weekday (Mo = 1)        0\n",
-       "Arrival at Destination - Time                    0\n",
-       "Distance (KM)                                    0\n",
-       "Temperature                                   4366\n",
-       "Precipitation in millimeters                 20649\n",
-       "Pickup Lat                                       0\n",
-       "Pickup Long                                      0\n",
-       "Destination Lat                                  0\n",
-       "Destination Long                                 0\n",
-       "Rider Id                                         0\n",
-       "Time from Pickup to Arrival                      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "THE % OF MISSING VALUES IN THE DATASET: Order No                                      0.00\n",
-      "User Id                                       0.00\n",
-      "Vehicle Type                                  0.00\n",
-      "Platform Type                                 0.00\n",
-      "Personal or Business                          0.00\n",
-      "Placement - Day of Month                      0.00\n",
-      "Placement - Weekday (Mo = 1)                  0.00\n",
-      "Placement - Time                              0.00\n",
-      "Confirmation - Day of Month                   0.00\n",
-      "Confirmation - Weekday (Mo = 1)               0.00\n",
-      "Confirmation - Time                           0.00\n",
-      "Arrival at Pickup - Day of Month              0.00\n",
-      "Arrival at Pickup - Weekday (Mo = 1)          0.00\n",
-      "Arrival at Pickup - Time                      0.00\n",
-      "Pickup - Day of Month                         0.00\n",
-      "Pickup - Weekday (Mo = 1)                     0.00\n",
-      "Pickup - Time                                 0.00\n",
-      "Arrival at Destination - Day of Month         0.00\n",
-      "Arrival at Destination - Weekday (Mo = 1)     0.00\n",
-      "Arrival at Destination - Time                 0.00\n",
-      "Distance (KM)                                 0.00\n",
-      "Temperature                                  20.59\n",
-      "Precipitation in millimeters                 97.40\n",
-      "Pickup Lat                                    0.00\n",
-      "Pickup Long                                   0.00\n",
-      "Destination Lat                               0.00\n",
-      "Destination Long                              0.00\n",
-      "Rider Id                                      0.00\n",
-      "Time from Pickup to Arrival                   0.00\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The Percentage of missing values in the df_train dataset\n",
-    "MP_df_train = df_train.isna().mean().round(4)*100\n",
-    "print('THE % OF MISSING VALUES IN THE DATASET:', MP_df_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x720 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Distribution review for train data\n",
-    "df_train[[column for column in df_train.select_dtypes(include='number')]].hist(bins=50, figsize=(20,10))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Column Skewness of the testing dataset\n",
-    "df_train.skew().plot.bar(figsize =(10,5))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Univariate Anaysis\n",
-    "# Calculating Skewness in the columns of our Training dataset\n",
-    "df_train.kurt().plot.bar(figsize =(10,5))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Platform Type</th>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Pickup - Day of Month</th>\n",
-       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Pickup - Day of Month</th>\n",
-       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
-       "      <th>Arrival at Destination - Day of Month</th>\n",
-       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
-       "      <th>Distance (KM)</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Precipitation in millimeters</th>\n",
-       "      <th>Pickup Lat</th>\n",
-       "      <th>Pickup Long</th>\n",
-       "      <th>Destination Lat</th>\n",
-       "      <th>Destination Long</th>\n",
-       "      <th>Time from Pickup to Arrival</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Platform Type</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.012764</td>\n",
-       "      <td>-0.058038</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.005528</td>\n",
-       "      <td>0.225060</td>\n",
-       "      <td>-0.055948</td>\n",
-       "      <td>-0.002742</td>\n",
-       "      <td>0.021327</td>\n",
-       "      <td>0.006319</td>\n",
-       "      <td>0.043924</td>\n",
-       "      <td>-0.003827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Placement - Day of Month</th>\n",
-       "      <td>0.012764</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026148</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>-0.007585</td>\n",
-       "      <td>0.013097</td>\n",
-       "      <td>-0.204143</td>\n",
-       "      <td>-0.004929</td>\n",
-       "      <td>-0.005453</td>\n",
-       "      <td>0.007541</td>\n",
-       "      <td>0.002077</td>\n",
-       "      <td>-0.014710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Placement - Weekday (Mo = 1)</th>\n",
-       "      <td>-0.058038</td>\n",
-       "      <td>0.026148</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.003582</td>\n",
-       "      <td>0.031064</td>\n",
-       "      <td>-0.050592</td>\n",
-       "      <td>-0.000918</td>\n",
-       "      <td>0.011008</td>\n",
-       "      <td>-0.004299</td>\n",
-       "      <td>-0.000875</td>\n",
-       "      <td>0.009693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Confirmation - Day of Month</th>\n",
-       "      <td>0.012770</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.026132</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>-0.007587</td>\n",
-       "      <td>0.013099</td>\n",
-       "      <td>-0.204143</td>\n",
-       "      <td>-0.004923</td>\n",
-       "      <td>-0.005448</td>\n",
-       "      <td>0.007550</td>\n",
-       "      <td>0.002073</td>\n",
-       "      <td>-0.014701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
-       "      <td>-0.058005</td>\n",
-       "      <td>0.026122</td>\n",
-       "      <td>0.999952</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.026123</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.003574</td>\n",
-       "      <td>0.031074</td>\n",
-       "      <td>-0.050592</td>\n",
-       "      <td>-0.000883</td>\n",
-       "      <td>0.011036</td>\n",
-       "      <td>-0.004251</td>\n",
-       "      <td>-0.000900</td>\n",
-       "      <td>0.009744</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Platform Type  Placement - Day of Month  \\\n",
-       "Platform Type                         1.000000                  0.012764   \n",
-       "Placement - Day of Month              0.012764                  1.000000   \n",
-       "Placement - Weekday (Mo = 1)         -0.058038                  0.026148   \n",
-       "Confirmation - Day of Month           0.012770                  0.999998   \n",
-       "Confirmation - Weekday (Mo = 1)      -0.058005                  0.026122   \n",
-       "\n",
-       "                                 Placement - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                       -0.058038   \n",
-       "Placement - Day of Month                             0.026148   \n",
-       "Placement - Weekday (Mo = 1)                         1.000000   \n",
-       "Confirmation - Day of Month                          0.026132   \n",
-       "Confirmation - Weekday (Mo = 1)                      0.999952   \n",
-       "\n",
-       "                                 Confirmation - Day of Month  \\\n",
-       "Platform Type                                       0.012770   \n",
-       "Placement - Day of Month                            0.999998   \n",
-       "Placement - Weekday (Mo = 1)                        0.026132   \n",
-       "Confirmation - Day of Month                         1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                     0.026123   \n",
-       "\n",
-       "                                 Confirmation - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                          -0.058005   \n",
-       "Placement - Day of Month                                0.026122   \n",
-       "Placement - Weekday (Mo = 1)                            0.999952   \n",
-       "Confirmation - Day of Month                             0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                         1.000000   \n",
-       "\n",
-       "                                 Arrival at Pickup - Day of Month  \\\n",
-       "Platform Type                                            0.012770   \n",
-       "Placement - Day of Month                                 0.999998   \n",
-       "Placement - Weekday (Mo = 1)                             0.026132   \n",
-       "Confirmation - Day of Month                              1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                          0.026123   \n",
-       "\n",
-       "                                 Arrival at Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                               -0.058005   \n",
-       "Placement - Day of Month                                     0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                 0.999952   \n",
-       "Confirmation - Day of Month                                  0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                              1.000000   \n",
-       "\n",
-       "                                 Pickup - Day of Month  \\\n",
-       "Platform Type                                 0.012770   \n",
-       "Placement - Day of Month                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                  0.026132   \n",
-       "Confirmation - Day of Month                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)               0.026123   \n",
-       "\n",
-       "                                 Pickup - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                    -0.058005   \n",
-       "Placement - Day of Month                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                      0.999952   \n",
-       "Confirmation - Day of Month                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                   1.000000   \n",
-       "\n",
-       "                                 Arrival at Destination - Day of Month  \\\n",
-       "Platform Type                                                 0.012770   \n",
-       "Placement - Day of Month                                      0.999998   \n",
-       "Placement - Weekday (Mo = 1)                                  0.026132   \n",
-       "Confirmation - Day of Month                                   1.000000   \n",
-       "Confirmation - Weekday (Mo = 1)                               0.026123   \n",
-       "\n",
-       "                                 Arrival at Destination - Weekday (Mo = 1)  \\\n",
-       "Platform Type                                                    -0.058005   \n",
-       "Placement - Day of Month                                          0.026122   \n",
-       "Placement - Weekday (Mo = 1)                                      0.999952   \n",
-       "Confirmation - Day of Month                                       0.026123   \n",
-       "Confirmation - Weekday (Mo = 1)                                   1.000000   \n",
-       "\n",
-       "                                 Distance (KM)  Temperature  \\\n",
-       "Platform Type                         0.005528     0.225060   \n",
-       "Placement - Day of Month             -0.007585     0.013097   \n",
-       "Placement - Weekday (Mo = 1)          0.003582     0.031064   \n",
-       "Confirmation - Day of Month          -0.007587     0.013099   \n",
-       "Confirmation - Weekday (Mo = 1)       0.003574     0.031074   \n",
-       "\n",
-       "                                 Precipitation in millimeters  Pickup Lat  \\\n",
-       "Platform Type                                       -0.055948   -0.002742   \n",
-       "Placement - Day of Month                            -0.204143   -0.004929   \n",
-       "Placement - Weekday (Mo = 1)                        -0.050592   -0.000918   \n",
-       "Confirmation - Day of Month                         -0.204143   -0.004923   \n",
-       "Confirmation - Weekday (Mo = 1)                     -0.050592   -0.000883   \n",
-       "\n",
-       "                                 Pickup Long  Destination Lat  \\\n",
-       "Platform Type                       0.021327         0.006319   \n",
-       "Placement - Day of Month           -0.005453         0.007541   \n",
-       "Placement - Weekday (Mo = 1)        0.011008        -0.004299   \n",
-       "Confirmation - Day of Month        -0.005448         0.007550   \n",
-       "Confirmation - Weekday (Mo = 1)     0.011036        -0.004251   \n",
-       "\n",
-       "                                 Destination Long  Time from Pickup to Arrival  \n",
-       "Platform Type                            0.043924                    -0.003827  \n",
-       "Placement - Day of Month                 0.002077                    -0.014710  \n",
-       "Placement - Weekday (Mo = 1)            -0.000875                     0.009693  \n",
-       "Confirmation - Day of Month              0.002073                    -0.014701  \n",
-       "Confirmation - Weekday (Mo = 1)         -0.000900                     0.009744  "
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CorrMatrix_train = df_train.corr()\n",
-    "CorrMatrix_train.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.05, 'Pearson Correlation of Features')"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.style as style\n",
-    "style.use('ggplot')\n",
-    "cm = CorrMatrix_train.corr()\n",
-    "colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
-    "plt.figure(figsize=(20,10))\n",
-    "sns.heatmap(cm, annot=True, cmap = colormap,cbar_kws={'shrink':.9 },\n",
-    "           linewidths=0.5,vmax=2.0, linecolor='white',annot_kws={'fontsize':12 })\n",
-    "plt.title('Pearson Correlation of Features', y=1.05, size=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Personal or Business"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9d35d088>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train['Personal or Business'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Platform Type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9de9fec8>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train['Platform Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vehicle Type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x20d9eee35c8>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train['Vehicle Type'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Outliers Overview for numeric features\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(df_train['Temperature'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    12.416667\n",
-       "1    33.216667\n",
-       "2     7.583333\n",
-       "3    22.350000\n",
-       "4    20.233333\n",
-       "Name: Time from Pickup to Arrival, dtype: float64"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Numeric_Training = df_train._get_numeric_data()\n",
-    "Numeric_Training.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x222671c3288>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train['Time from Pickup to Arrival'].value_counts().plot(kind= 'bar' , figsize = [10,5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-ddf5308ac5e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whitegrid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m   2063\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2064\u001b[0m                         \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2065\u001b[1;33m                         **kwargs)\n\u001b[0m\u001b[0;32m   2066\u001b[0m                     \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2067\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 527\u001b[1;33m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mDraw\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfigure\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m--> 386\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    397\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Image size of 864x7632330 pixels is too large. It must be less than 2^16 in each direction."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 864x7.63233e+06 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "l = df_train.values\n",
-    "number_of_columns= 12\n",
-    "number_of_rows = len(l)-1/number_of_columns\n",
-    "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
-    "for i in range(0,len(l)):\n",
-    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
-    "    sns.set_style('whitegrid')\n",
-    "    sns.boxplot(df[l[i]],color='green',orient='v')\n",
-    "    plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "cannot do label indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [12.416666666666666] of <class 'numpy.float64'>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-151-706b1053bd53>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_rows_Test\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber_of_columns_Test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mchart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnu_tra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n\u001b[0;32m      6\u001b[0m                           horizontalalignment='right')\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4401\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4403\u001b[1;33m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4404\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4405\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\numeric.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_wrap_joined_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoined\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m   2867\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ix\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2868\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2869\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"label\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2871\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"loc\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m   3075\u001b[0m         \"\"\"\n\u001b[0;32m   3076\u001b[0m         raise TypeError(\n\u001b[1;32m-> 3077\u001b[1;33m             \u001b[1;34mf\"cannot do {form} indexing on {type(self)} with these \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3078\u001b[0m             \u001b[1;34mf\"indexers [{key}] of {type(key)}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m         )\n",
-      "\u001b[1;31mTypeError\u001b[0m: cannot do label indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [12.416666666666666] of <class 'numpy.float64'>"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Image size of 360x12211660 pixels is too large. It must be less than 2^16 in each direction.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m   2063\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2064\u001b[0m                         \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2065\u001b[1;33m                         **kwargs)\n\u001b[0m\u001b[0;32m   2066\u001b[0m                     \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2067\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 527\u001b[1;33m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mDraw\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfigure\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m--> 386\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[1;34m(self, cleared)\u001b[0m\n\u001b[0;32m    397\u001b[0m                           and getattr(self, \"_lastKey\", None) == key)\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, width, height, dpi)\u001b[0m\n\u001b[0;32m     84\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Image size of 360x12211660 pixels is too large. It must be less than 2^16 in each direction."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 360x1.22117e+07 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(number_of_columns_Test,8*number_of_rows_Test))\n",
-    "for i in range(0,len(nu_tra)):\n",
-    "    plt.subplot(number_of_rows_Test + 1,number_of_columns_Test,i+1)\n",
-    "    chart=sns.distplot(df_train[nu_tra[i]],kde=True) \n",
-    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=-180, \n",
-    "                          horizontalalignment='right')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    21201.000000\n",
-       "mean      1556.920947\n",
-       "std        987.270788\n",
-       "min          1.000000\n",
-       "25%        882.000000\n",
-       "50%       1369.000000\n",
-       "75%       2040.000000\n",
-       "max       7883.000000\n",
-       "Name: Time from Pickup to Arrival, dtype: float64"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Useful statistics about our target column\n",
-    "\n",
-    "df_train['Time from Pickup to Arrival'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0        12.416667\n",
-       "1        33.216667\n",
-       "2         7.583333\n",
-       "3        22.350000\n",
-       "4        20.233333\n",
-       "           ...    \n",
-       "21196     0.150000\n",
-       "21197    12.833333\n",
-       "21198    49.216667\n",
-       "21199    23.000000\n",
-       "21200    35.466667\n",
-       "Name: Time from Pickup to Arrival, Length: 21201, dtype: float64"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Time from Pickup to Arrival'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-142-4ae327d448f7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Time from Pickup to Arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time distribution\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Delivery time (minutes)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4403\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4404\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4405\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4406\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4407\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Time from Pickup to Arrival'"
-     ]
-    }
-   ],
-   "source": [
-    "(df_train['Time from Pickup to Arrival']/60).hist(bins=50)\n",
-    "plt.title(\"Delivery time distribution\")\n",
-    "plt.xlabel(\"Delivery time (minutes)\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Neither the `x` nor `y` variable appears to be numeric.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-14-3ea73a4708e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Outliers Overview for numeric features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"variable\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2239\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2240\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2241\u001b[1;33m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[0;32m   2242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2243\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    441\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[0;32m    442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 443\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    444\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[0morient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_orient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Option 2a:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(self, x, y, orient)\u001b[0m\n\u001b[0;32m    360\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    361\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_not_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mno_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    363\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Neither the `x` nor `y` variable appears to be numeric."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1440x576 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Outliers Overview for numeric features\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(df_test))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
-    "for i in range(0,len(l)):\n",
-    "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
-    "    sns.distplot(df[l[i]],kde=True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning and Formatting "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
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-   "metadata": {
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-    "id": "AyhQaTwP9RzG"
-   },
-   "source": [
-    "## Splitting the dataset into the Training set and Test set"
-   ]
-  },
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-    "id": "ZijQwFMQ9itx"
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-    "## Training the Simple Linear Regression model on the Training set"
-   ]
-  },
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-   "execution_count": null,
-   "metadata": {
-    "colab": {
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-    "executionInfo": {
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-     "status": "ok",
-     "timestamp": 1586352495803,
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-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
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-    "id": "B4Aj_8YJ9l7J",
-    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wa2T1Lq89o5H"
-   },
-   "source": [
-    "## Predicting the Test set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
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-   "metadata": {
-    "colab_type": "text",
-    "id": "-zSoMZ-P9v8t"
-   },
-   "source": [
-    "## Visualising the Training set results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
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-    "id": "EUX1Vhsv97ZT"
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-   "source": [
-    "## Visualising the Test set results"
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-   "execution_count": null,
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diff --git a/utils/data/riders.csv b/utils/data/riders.csv
deleted file mode 100644
index 27741ea..0000000
--- a/utils/data/riders.csv
+++ /dev/null
@@ -1,961 +0,0 @@
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-Rider_Id_479,360,951,13.5,176
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-Rider_Id_865,2178,1010,14.1,746
-Rider_Id_791,688,1021,13.9,216
-Rider_Id_408,2212,1447,14.2,542
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-Rider_Id_188,3837,2018,14.1,786
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-Rider_Id_691,249,399,14.8,23
-Rider_Id_346,2396,1860,14.4,594
-Rider_Id_296,3264,2005,13.9,1278
-Rider_Id_197,3067,1988,13.8,1039
-Rider_Id_530,1947,1531,14.3,663
-Rider_Id_513,178,490,13,24
-Rider_Id_686,799,410,13.2,46
-Rider_Id_758,81,385,15,18
-Rider_Id_377,484,381,13.5,59
-Rider_Id_104,215,2012,15,6
-Rider_Id_34,20,378,15,5
-Rider_Id_556,4,378,0,1
-Rider_Id_277,148,376,14.6,14
-Rider_Id_601,27,377,15,4
-Rider_Id_772,99,550,15,8
-Rider_Id_540,134,378,13.7,22
-Rider_Id_869,1972,1111,12.9,654
-Rider_Id_696,37,370,13.5,8
-Rider_Id_468,355,371,14.1,36
-Rider_Id_860,5,448,15,2
-Rider_Id_311,192,368,14.4,11
-Rider_Id_953,1270,367,14.4,131
-Rider_Id_225,28,366,11.1,7
-Rider_Id_825,2009,1760,14,668
-Rider_Id_433,27,362,13.5,5
-Rider_Id_426,2113,1875,14,668
-Rider_Id_411,903,356,14.2,108
-Rider_Id_590,202,355,14.8,30
-Rider_Id_605,57,379,14.3,8
-Rider_Id_0,3048,2570,14,512
-Rider_Id_959,275,379,13.7,26
-Rider_Id_947,65,379,3,6
-Rider_Id_645,4317,2061,13.7,1094
-Rider_Id_863,913,354,13.6,89
-Rider_Id_168,708,357,12.9,59
-Rider_Id_887,5,352,15,3
-Rider_Id_776,2158,1891,14.1,694
-Rider_Id_91,2069,2103,14,649
-Rider_Id_198,3009,1922,13.8,884
-Rider_Id_883,282,353,14,23
-Rider_Id_681,2572,1684,14.2,818
-Rider_Id_678,292,349,15,39
-Rider_Id_767,239,2699,11.1,6
-Rider_Id_488,2439,1985,14.4,656
-Rider_Id_603,88,338,11.1,6
-Rider_Id_483,53,339,15,13
-Rider_Id_152,63,338,15,5
-Rider_Id_339,20,338,15,3
-Rider_Id_856,396,339,13.6,69
-Rider_Id_724,41,340,15,7
-Rider_Id_448,1220,339,14.6,119
-Rider_Id_378,298,336,14.1,31
-Rider_Id_545,130,339,14.4,10
-Rider_Id_8,497,341,14.1,103
-Rider_Id_824,55,338,12.9,14
-Rider_Id_169,13,329,15,2
-Rider_Id_461,60,339,15,14
-Rider_Id_262,99,2282,15,5
-Rider_Id_385,2440,2190,14.4,544
-Rider_Id_430,2825,1990,13.8,655
-Rider_Id_154,197,328,13.7,19
-Rider_Id_372,77,328,13.5,16
-Rider_Id_955,2595,1943,13.8,746
-Rider_Id_127,264,338,12.6,21
-Rider_Id_389,3219,2111,14,1230
-Rider_Id_459,326,321,13.3,39
-Rider_Id_797,1898,2386,14.7,431
-Rider_Id_341,467,315,13.3,58
-Rider_Id_61,419,312,13.3,61
-Rider_Id_922,7717,2642,14.9,83
-Rider_Id_348,3074,2166,13.8,894
-Rider_Id_688,28,307,15,7
-Rider_Id_830,903,1992,14.6,254
-Rider_Id_716,20,306,15,6
-Rider_Id_885,192,308,13.2,21
-Rider_Id_17,24,307,15,4
-Rider_Id_529,3,307,0,1
-Rider_Id_599,3117,2255,13.7,878
-Rider_Id_44,9,307,0,1
-Rider_Id_291,1209,305,14.1,145
-Rider_Id_59,32,336,0,1
-Rider_Id_245,643,307,13.9,58
-Rider_Id_564,626,307,13.5,54
-Rider_Id_318,293,305,14.5,36
-Rider_Id_563,15,309,15,5
-Rider_Id_604,13,300,0,0
-Rider_Id_657,18,308,0,0
-Rider_Id_763,235,307,13.7,27
-Rider_Id_278,36,296,12,3
-Rider_Id_814,4767,1943,14.1,1451
-Rider_Id_106,958,307,13.2,99
-Rider_Id_119,1208,300,14.3,139
-Rider_Id_118,150,296,14.7,30
-Rider_Id_653,576,1822,12.7,159
-Rider_Id_76,847,297,14,108
-Rider_Id_373,2639,2173,13.8,663
-Rider_Id_701,19,298,12,3
-Rider_Id_787,389,297,13.6,30
-Rider_Id_99,435,301,13.5,73
-Rider_Id_349,615,341,14.7,30
-Rider_Id_181,34,291,15,5
-Rider_Id_392,2011,2350,14.7,374
-Rider_Id_690,505,580,14.1,40
-Rider_Id_132,104,302,9.8,9
-Rider_Id_305,6,294,0,1
-Rider_Id_309,274,293,14.9,36
-Rider_Id_495,62,297,15,6
-Rider_Id_709,1023,294,14.2,145
-Rider_Id_463,38,292,7.8,10
-Rider_Id_314,201,306,12.6,21
-Rider_Id_177,526,286,13.6,70
-Rider_Id_58,2527,1908,14.1,909
-Rider_Id_367,621,2196,11.1,21
-Rider_Id_732,317,1821,13.5,97
-Rider_Id_682,132,291,13,19
-Rider_Id_855,886,283,14.5,113
-Rider_Id_600,1875,1817,14,822
-Rider_Id_233,333,286,13.7,46
-Rider_Id_218,187,285,14.2,15
-Rider_Id_628,117,278,14.4,18
-Rider_Id_652,129,272,11.8,23
-Rider_Id_759,15,337,15,2
-Rider_Id_170,4628,2255,14,1506
-Rider_Id_954,11,276,0,0
-Rider_Id_683,17,273,15,6
-Rider_Id_613,365,269,13.8,40
-Rider_Id_376,30,292,0,1
-Rider_Id_484,432,271,13.8,62
-Rider_Id_126,261,270,13.5,29
-Rider_Id_816,2466,3114,15.1,142
-Rider_Id_506,191,278,14.7,19
-Rider_Id_780,4,269,0,0
-Rider_Id_907,582,273,13.2,42
-Rider_Id_129,25,278,15,6
-Rider_Id_943,41,269,12,10
-Rider_Id_868,488,273,14.4,45
-Rider_Id_841,2204,2871,15.1,31
-Rider_Id_135,2447,2786,15.1,198
-Rider_Id_20,201,271,13.7,19
-Rider_Id_306,228,268,15,30
-Rider_Id_910,398,266,14,29
-Rider_Id_784,2311,2824,15.2,16
-Rider_Id_460,9,259,13.5,5
-Rider_Id_94,3120,2671,13.2,1125
-Rider_Id_77,25,256,13,6
-Rider_Id_292,49,255,15,5
-Rider_Id_166,173,253,13.5,16
-Rider_Id_524,48,256,15,5
-Rider_Id_507,209,254,14.5,22
-Rider_Id_500,15,251,13.5,5
-Rider_Id_7,339,250,14.3,46
-Rider_Id_843,583,252,15,58
-Rider_Id_435,343,251,14.7,30
-Rider_Id_508,172,252,13.7,22
-Rider_Id_740,97,250,13.8,10
-Rider_Id_850,32,242,12,3
-Rider_Id_92,563,251,14.7,41
-Rider_Id_760,76,252,15,7
-Rider_Id_693,24,240,0,0
-Rider_Id_204,456,240,14.5,42
-Rider_Id_155,1023,242,12.5,114
-Rider_Id_161,718,237,14,83
-Rider_Id_695,89,236,13.5,8
-Rider_Id_438,557,239,14.6,55
-Rider_Id_394,162,237,15,14
-Rider_Id_570,222,238,13.2,26
-Rider_Id_790,271,236,14.7,32
-Rider_Id_525,10,307,0,1
-Rider_Id_324,68,231,0,0
-Rider_Id_344,396,231,13.9,38
-Rider_Id_316,3,231,0,1
-Rider_Id_151,38,230,15,3
-Rider_Id_710,154,225,15,9
-Rider_Id_835,3001,2195,13.7,942
-Rider_Id_251,9,226,0,1
-Rider_Id_363,44,220,13.5,8
-Rider_Id_193,385,225,14.8,48
-Rider_Id_641,44,220,15,3
-Rider_Id_572,262,222,12.9,20
-Rider_Id_815,534,218,14.1,49
-Rider_Id_422,312,222,13.2,37
-Rider_Id_68,311,214,14.8,29
-Rider_Id_924,27,216,15,5
-Rider_Id_388,16,220,15,2
-Rider_Id_705,49,221,14.3,8
-Rider_Id_214,226,2189,15,74
-Rider_Id_211,731,198,13.3,54
-Rider_Id_921,510,198,14.3,56
-Rider_Id_13,3108,3237,14.2,469
-Rider_Id_950,8,210,15,3
-Rider_Id_3,32,194,9,3
-Rider_Id_792,18,195,3,2
-Rider_Id_864,393,207,14.8,28
-Rider_Id_141,2988,3543,14.2,553
-Rider_Id_624,314,184,14.1,24
-Rider_Id_930,282,184,12.8,17
-Rider_Id_538,308,182,14.1,25
-Rider_Id_591,679,183,13.5,61
-Rider_Id_9,2265,3101,15.1,31
-Rider_Id_745,3,172,0,0
-Rider_Id_78,33,183,0,1
-Rider_Id_165,487,1579,13.4,188
-Rider_Id_298,82,182,13.5,17
-Rider_Id_694,31,212,15,2
-Rider_Id_502,85,178,14.3,8
-Rider_Id_256,28,169,11.1,7
-Rider_Id_567,116,175,12.6,10
-Rider_Id_689,130,167,15,9
-Rider_Id_491,220,167,14.7,30
-Rider_Id_582,369,170,13.2,40
-Rider_Id_945,1795,2542,14.7,442
-Rider_Id_183,213,167,15,27
-Rider_Id_27,166,160,14.9,32
-Rider_Id_617,42,159,15,4
-Rider_Id_610,36,150,15,7
-Rider_Id_158,536,157,13.7,55
-Rider_Id_821,94,155,14.6,15
-Rider_Id_369,200,156,13.3,15
-Rider_Id_2,455,152,13.6,51
-Rider_Id_602,62,149,6,4
-Rider_Id_321,17,152,0,0
-Rider_Id_163,889,2817,0,0
-Rider_Id_139,676,153,14.4,66
-Rider_Id_221,22,142,15,5
-Rider_Id_673,314,152,9.6,11
-Rider_Id_826,167,138,15,19
-Rider_Id_203,8,141,0,0
-Rider_Id_542,18,132,12,5
-Rider_Id_310,282,326,14.7,46
-Rider_Id_706,124,133,15,8
-Rider_Id_838,45,138,0,0
-Rider_Id_115,10,2214,15,3
-Rider_Id_55,267,138,14.7,31
-Rider_Id_788,231,139,14.5,25
-Rider_Id_146,191,130,13.5,31
-Rider_Id_429,123,130,14.8,24
-Rider_Id_176,417,136,12.8,34
-Rider_Id_162,1574,2273,13.6,209
-Rider_Id_42,389,130,14.9,46
-Rider_Id_255,24,130,13.5,4
-Rider_Id_261,27,129,15,5
-Rider_Id_243,273,125,13.9,38
-Rider_Id_944,238,127,14,23
-Rider_Id_643,265,139,14.4,31
-Rider_Id_739,425,129,14.2,49
-Rider_Id_894,476,116,14.9,48
-Rider_Id_827,59,118,12,4
-Rider_Id_109,297,136,14.8,22
-Rider_Id_475,42,138,15,2
-Rider_Id_36,304,127,12.9,22
-Rider_Id_271,301,114,12,24
-Rider_Id_35,35,113,15,2
-Rider_Id_60,89,111,13.5,9
-Rider_Id_133,82,110,15,5
-Rider_Id_834,336,111,14.3,35
-Rider_Id_481,42,119,15,13
-Rider_Id_577,591,300,13.4,47
-Rider_Id_766,78,125,14.1,6
-Rider_Id_371,250,115,14.8,25
-Rider_Id_637,10,106,0,0
-Rider_Id_773,5,105,0,0
-Rider_Id_891,489,103,13.3,35
-Rider_Id_67,55,99,13,6
-Rider_Id_492,185,103,15,12
-Rider_Id_896,152,99,12.4,18
-Rider_Id_149,69,101,10.2,10
-Rider_Id_270,338,96,14.4,41
-Rider_Id_201,159,96,15,9
-Rider_Id_904,172,108,13.7,18
\ No newline at end of file
diff --git a/utils/data/test_data.csv b/utils/data/test_data.csv
deleted file mode 100644
index a62e08f..0000000
--- a/utils/data/test_data.csv
+++ /dev/null
@@ -1,3182 +0,0 @@
-Order No,User Id,Vehicle Type,Platform Type,Personal or Business,Placement - Day of Month,Placement - Weekday (Mo = 1),Placement - Time,Confirmation - Day of Month,Confirmation - Weekday (Mo = 1),Confirmation - Time,Arrival at Pickup - Day of Month,Arrival at Pickup - Weekday (Mo = 1),Arrival at Pickup - Time,Pickup - Day of Month,Pickup - Weekday (Mo = 1),Pickup - Time,Distance (KM),Temperature,Precipitation in millimeters,Pickup Lat,Pickup Long,Destination Lat,Destination Long,Rider Id
-Order_No_13098,User_Id_636,Bike,1,Personal,7,4,3:09:30 PM,7,4,3:14:41 PM,7,4,3:37:40 PM,7,4,3:54:43 PM,15,20.0,,-1.2245407,36.7583778,-1.3034735,36.7986213,Rider_Id_808
-Order_No_21660,User_Id_1329,Bike,3,Business,31,5,12:16:49 PM,31,5,12:22:48 PM,31,5,12:23:47 PM,31,5,12:38:24 PM,4,21.8,,-1.2795183,36.823808899999996,-1.273056,36.811298,Rider_Id_812
-Order_No_699,User_Id_1458,Bike,1,Personal,10,1,9:53:38 AM,10,1,9:55:39 AM,10,1,10:13:07 AM,10,1,10:18:19 AM,6,17.9,,-1.3043649,36.7816132,-1.2862984,36.8293971,Rider_Id_754
-Order_No_25334,User_Id_1245,Bike,3,Business,25,1,2:10:20 PM,25,1,2:11:04 PM,25,1,2:11:42 PM,25,1,2:42:20 PM,17,28.1,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_239
-Order_No_4367,User_Id_1966,Bike,3,Business,20,1,11:41:44 AM,20,1,11:43:36 AM,20,1,12:19:36 PM,20,1,12:52:22 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_863
-Order_No_10939,User_Id_1976,Bike,3,Business,7,2,11:05:53 AM,7,2,11:07:35 AM,7,2,11:11:54 AM,7,2,11:15:54 AM,12,,,-1.257028,36.7810435,-1.3073569,36.82021629999999,Rider_Id_647
-Order_No_26482,User_Id_2102,Bike,3,Business,29,2,4:11:05 PM,29,2,4:11:41 PM,29,2,4:26:58 PM,29,2,4:52:53 PM,13,,,-1.2613768,36.801941799999994,-1.3323107,36.8692616,Rider_Id_205
-Order_No_1989,User_Id_978,Bike,1,Personal,8,1,9:55:29 AM,8,1,9:55:47 AM,8,1,9:55:53 AM,8,1,10:09:22 AM,13,19.6,,-1.3084067,36.83450139999999,-1.3253106000000001,36.8903296,Rider_Id_595
-Order_No_19128,User_Id_417,Bike,1,Personal,2,6,12:08:53 PM,2,6,12:09:37 PM,2,6,12:10:16 PM,2,6,12:19:12 PM,13,23.7,,-1.267691,36.793649,-1.2108499,36.781936200000004,Rider_Id_379
-Order_No_2621,User_Id_3180,Bike,1,Personal,12,5,11:11:07 AM,12,5,11:11:34 AM,12,5,11:24:59 AM,12,5,12:14:00 PM,10,24.7,,-1.260232,36.807334000000004,-1.2994194,36.7647917,Rider_Id_651
-Order_No_1999,User_Id_3429,Bike,2,Personal,10,4,9:23:03 AM,10,4,9:36:39 AM,10,4,9:40:04 AM,10,4,9:41:37 AM,5,18.4,,-1.3043728,36.8044157,-1.2911753000000001,36.77539229999999,Rider_Id_105
-Order_No_8873,User_Id_1850,Bike,3,Business,18,1,12:21:08 PM,18,1,12:21:31 PM,18,1,12:21:43 PM,18,1,12:39:51 PM,3,26.9,,-1.2551895,36.7822034,-1.2636813999999998,36.802996500000006,Rider_Id_320
-Order_No_7985,User_Id_1759,Bike,3,Personal,30,6,10:01:16 AM,30,6,10:02:14 AM,30,6,10:11:25 AM,30,6,10:18:55 AM,16,,,-1.2251551999999999,36.878727000000005,-1.3011678,36.7914991,Rider_Id_8
-Order_No_26383,User_Id_3701,Bike,3,Business,7,5,8:56:18 AM,7,5,8:58:52 AM,7,5,9:16:35 AM,7,5,9:19:08 AM,8,20.7,1.1,-1.2684239,36.79524620000001,-1.2593606000000002,36.777789500000004,Rider_Id_362
-Order_No_11522,User_Id_302,Bike,3,Business,21,5,6:01:48 PM,21,5,6:09:59 PM,21,5,6:17:12 PM,21,5,6:19:51 PM,6,25.1,,-1.2649605,36.798177700000004,-1.283016,36.801959499999995,Rider_Id_279
-Order_No_9803,User_Id_995,Bike,2,Personal,9,2,12:07:18 PM,9,2,12:08:46 PM,9,2,12:17:59 PM,9,2,12:26:44 PM,9,22.2,,-1.293898,36.7583778,-1.2824535,36.8265153,Rider_Id_844
-Order_No_10146,User_Id_246,Bike,3,Business,8,6,1:41:32 PM,8,6,1:41:56 PM,8,6,1:50:46 PM,8,6,2:07:54 PM,1,24.5,,-1.260775,36.8013889,-1.2584143,36.8048002,Rider_Id_427
-Order_No_7710,User_Id_1341,Bike,3,Business,13,3,2:40:15 PM,13,3,2:40:44 PM,13,3,2:40:45 PM,13,3,3:18:38 PM,15,29.4,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_669
-Order_No_27637,User_Id_2112,Bike,3,Business,26,2,4:25:06 PM,26,2,4:28:02 PM,26,2,5:09:39 PM,26,2,5:15:33 PM,11,28.3,,-1.2765736,36.851364600000004,-1.2156006000000001,36.8916865,Rider_Id_187
-Order_No_5800,User_Id_2688,Bike,2,Personal,16,3,3:46:30 PM,16,3,3:47:05 PM,16,3,3:54:26 PM,16,3,4:01:32 PM,4,25.5,,-1.2962603000000001,36.7766164,-1.2897215,36.7945959,Rider_Id_898
-Order_No_28159,User_Id_2330,Bike,3,Business,21,4,2:20:49 PM,21,4,2:21:07 PM,21,4,2:34:20 PM,21,4,2:51:33 PM,6,26.4,,-1.2642903,36.80021370000001,-1.2641888000000001,36.7603984,Rider_Id_795
-Order_No_16307,User_Id_2044,Bike,1,Personal,24,4,8:30:57 AM,24,4,8:59:04 AM,24,4,9:18:51 AM,24,4,9:23:36 AM,5,17.1,,-1.284367,36.824186,-1.2517392,36.82496079999999,Rider_Id_103
-Order_No_19313,User_Id_970,Bike,3,Business,29,3,12:38:57 PM,29,3,12:39:43 PM,29,3,12:46:11 PM,29,3,12:48:26 PM,1,,,-1.2571472,36.795063299999995,-1.2629594,36.8077626,Rider_Id_90
-Order_No_1318,User_Id_635,Bike,3,Business,5,2,11:13:44 AM,5,2,11:14:09 AM,5,2,11:17:14 AM,5,2,11:25:32 AM,4,25.4,,-1.2602337,36.79905479999999,-1.277261,36.790856,Rider_Id_727
-Order_No_15975,User_Id_2484,Bike,1,Business,7,4,3:29:24 PM,7,4,3:29:31 PM,7,4,3:36:14 PM,7,4,4:03:06 PM,20,20.0,,-1.2962832,36.784968299999996,-1.3700383,36.9190174,Rider_Id_852
-Order_No_23259,User_Id_1484,Bike,3,Business,21,4,2:08:28 PM,21,4,2:08:40 PM,21,4,2:08:53 PM,21,4,2:13:52 PM,13,31.0,,-1.2551895,36.7822034,-1.3083777,36.85900770000001,Rider_Id_931
-Order_No_27913,User_Id_3517,Bike,3,Business,26,5,3:06:52 PM,26,5,3:22:09 PM,26,5,3:48:22 PM,26,5,3:56:03 PM,18,,,-1.3882008,36.7699445,-1.2867454,36.8259017,Rider_Id_532
-Order_No_11449,User_Id_3175,Bike,3,Business,5,2,11:56:06 AM,5,2,11:56:32 AM,5,2,12:12:46 PM,5,2,12:13:49 PM,15,21.5,,-1.2612417,36.7958868,-1.3038645,36.8684596,Rider_Id_574
-Order_No_18798,User_Id_1927,Bike,3,Business,8,3,1:58:16 PM,8,3,1:58:41 PM,8,3,1:58:50 PM,8,3,2:36:06 PM,19,,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_272
-Order_No_16118,User_Id_867,Bike,3,Business,29,5,1:41:23 PM,29,5,1:42:27 PM,29,5,1:55:31 PM,29,5,2:18:12 PM,10,29.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_709
-Order_No_13820,User_Id_3339,Bike,3,Business,22,5,12:14:26 PM,22,5,12:14:55 PM,22,5,12:15:16 PM,22,5,12:19:18 PM,2,,,-1.2551895,36.7822034,-1.2566888999999999,36.7930784,Rider_Id_926
-Order_No_22707,User_Id_2285,Bike,3,Personal,14,5,11:06:57 AM,14,5,11:08:13 AM,14,5,11:27:37 AM,14,5,11:29:12 AM,15,22.1,,-1.351453,36.899315,-1.2950041,36.814358299999995,Rider_Id_955
-Order_No_16294,User_Id_1873,Bike,3,Business,12,2,11:11:46 AM,12,2,11:14:25 AM,12,2,11:27:40 AM,12,2,12:07:06 PM,5,24.4,,-1.2571472,36.795063299999995,-1.2934656999999998,36.8212737,Rider_Id_678
-Order_No_10090,User_Id_2767,Bike,3,Business,8,3,2:48:23 PM,8,3,2:49:10 PM,8,3,3:04:57 PM,8,3,3:12:33 PM,9,25.8,,-1.257773,36.795486,-1.2248308,36.801257299999996,Rider_Id_116
-Order_No_13794,User_Id_517,Bike,3,Business,29,3,1:15:44 PM,29,3,1:17:03 PM,29,3,1:23:37 PM,29,3,1:37:04 PM,4,21.3,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_690
-Order_No_5089,User_Id_733,Bike,3,Business,4,2,2:01:54 PM,4,2,2:03:44 PM,4,2,2:43:08 PM,4,2,3:00:27 PM,20,22.6,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_536
-Order_No_22028,User_Id_264,Bike,1,Personal,26,6,4:23:06 PM,26,6,4:24:33 PM,26,6,4:52:00 PM,26,6,4:58:03 PM,8,21.9,,-1.2824079,36.8171287,-1.3203917,36.8334751,Rider_Id_480
-Order_No_22533,User_Id_3404,Bike,3,Business,31,4,4:31:14 PM,31,4,4:31:44 PM,31,4,4:44:53 PM,31,4,5:08:03 PM,15,27.6,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_895
-Order_No_20010,User_Id_1147,Bike,2,Personal,31,4,5:00:24 PM,31,4,5:18:23 PM,31,4,5:18:32 PM,31,4,5:40:31 PM,4,22.1,,-1.2781868,36.8236786,-1.26211,36.79646629999999,Rider_Id_100
-Order_No_18272,User_Id_3207,Bike,1,Personal,8,3,2:08:51 PM,8,3,2:11:06 PM,8,3,2:32:47 PM,8,3,2:47:59 PM,8,25.8,,-1.3014461000000002,36.7661381,-1.264945,36.784678,Rider_Id_131
-Order_No_11374,User_Id_2701,Bike,3,Business,22,4,9:53:50 AM,22,4,10:03:00 AM,22,4,10:32:11 AM,22,4,10:50:57 AM,5,22.4,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_247
-Order_No_474,User_Id_302,Bike,3,Business,30,7,1:07:28 PM,30,7,1:07:54 PM,30,7,1:27:23 PM,30,7,1:44:35 PM,3,26.6,,-1.254924,36.815159,-1.2649605,36.798177700000004,Rider_Id_795
-Order_No_16362,User_Id_1527,Bike,3,Business,30,4,10:01:02 AM,30,4,10:01:57 AM,30,4,10:03:41 AM,30,4,10:46:44 AM,16,,,-1.3004062,36.829741,-1.2197383999999998,36.890467799999996,Rider_Id_591
-Order_No_9056,User_Id_302,Bike,3,Business,21,5,3:31:44 PM,21,5,3:32:49 PM,21,5,3:50:14 PM,21,5,3:57:31 PM,4,24.8,,-1.2649605,36.798177700000004,-1.2838296999999999,36.810923200000005,Rider_Id_685
-Order_No_13984,User_Id_2532,Bike,3,Business,14,3,2:52:57 PM,14,3,2:56:20 PM,14,3,3:17:47 PM,14,3,3:18:50 PM,12,25.8,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_932
-Order_No_24019,User_Id_1340,Bike,3,Business,7,2,3:10:51 PM,7,2,3:17:44 PM,7,2,3:21:23 PM,7,2,3:23:36 PM,7,24.8,,-1.2707178999999997,36.7979713,-1.2819626,36.7540971,Rider_Id_937
-Order_No_13204,User_Id_1642,Bike,3,Business,25,4,4:43:46 PM,25,4,4:46:16 PM,25,4,4:54:14 PM,25,4,5:09:01 PM,5,,,-1.2855798999999999,36.817620899999994,-1.2728069,36.7962362,Rider_Id_889
-Order_No_26026,User_Id_1363,Bike,3,Business,10,1,9:47:25 AM,10,1,9:50:19 AM,10,1,10:04:06 AM,10,1,10:07:02 AM,6,16.7,,-1.3004062,36.829741,-1.290815,36.7969312,Rider_Id_271
-Order_No_20798,User_Id_3686,Bike,3,Business,23,3,11:45:14 AM,23,3,11:47:03 AM,23,3,11:52:43 AM,23,3,12:08:11 PM,5,,,-1.263717,36.823985,-1.2551895,36.7822034,Rider_Id_727
-Order_No_27676,User_Id_1934,Bike,2,Personal,20,6,11:04:29 AM,20,6,11:05:01 AM,20,6,11:07:53 AM,20,6,11:14:02 AM,9,25.4,,-1.304033,36.7848695,-1.2502923000000001,36.788756799999994,Rider_Id_917
-Order_No_8458,User_Id_1389,Bike,3,Business,28,5,12:26:59 PM,28,5,12:38:57 PM,28,5,12:40:08 PM,28,5,1:00:40 PM,12,23.7,,-1.2494503000000001,36.8531852,-1.207546,36.7913976,Rider_Id_664
-Order_No_8666,User_Id_3283,Bike,3,Business,21,4,3:19:53 PM,21,4,3:31:53 PM,21,4,3:46:31 PM,21,4,3:58:10 PM,13,26.3,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_801
-Order_No_24788,User_Id_81,Bike,3,Business,25,1,3:19:10 PM,25,1,3:22:34 PM,25,1,3:35:11 PM,25,1,3:40:42 PM,4,,,-1.2641496,36.791413899999995,-1.2555811000000001,36.802267,Rider_Id_101
-Order_No_15435,User_Id_136,Bike,3,Business,8,3,9:03:04 AM,8,3,9:04:00 AM,8,3,9:10:07 AM,8,3,9:11:36 AM,18,,,-1.3254522,36.721658399999995,-1.2461646999999998,36.7902361,Rider_Id_65
-Order_No_12740,User_Id_2030,Bike,3,Business,4,2,10:51:28 AM,4,2,11:21:33 AM,4,2,11:22:26 AM,4,2,11:27:42 AM,3,19.5,,-1.265715,36.823815,-1.2753108999999998,36.8130185,Rider_Id_878
-Order_No_22673,User_Id_1389,Bike,3,Business,6,1,4:06:54 PM,6,1,4:07:03 PM,6,1,4:19:32 PM,6,1,4:28:37 PM,14,,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_205
-Order_No_3260,User_Id_929,Bike,2,Personal,19,3,2:01:41 PM,19,3,2:01:57 PM,19,3,2:11:34 PM,19,3,2:21:04 PM,9,25.5,,-1.2563754999999999,36.8215762,-1.2873232,36.765857200000006,Rider_Id_263
-Order_No_1980,User_Id_194,Bike,1,Personal,2,3,12:50:38 PM,2,3,12:51:17 PM,2,3,12:59:21 PM,2,3,1:02:38 PM,2,21.9,,-1.27267,36.8124492,-1.2658373,36.8018598,Rider_Id_650
-Order_No_8345,User_Id_3194,Bike,3,Business,10,3,3:39:36 PM,10,3,3:39:49 PM,10,3,3:40:44 PM,10,3,3:47:56 PM,29,30.5,,-1.2551895,36.7822034,-1.1270934,36.648315999999994,Rider_Id_858
-Order_No_14534,User_Id_3751,Bike,1,Personal,15,2,2:01:24 PM,15,2,2:06:05 PM,15,2,2:12:57 PM,15,2,2:18:37 PM,7,24.3,,-1.2819541,36.7888036,-1.273043,36.826355,Rider_Id_102
-Order_No_20862,User_Id_2030,Bike,3,Business,1,5,2:23:40 PM,1,5,2:30:36 PM,1,5,2:50:32 PM,1,5,2:53:52 PM,5,31.8,,-1.3100586,36.850032899999995,-1.2965799999999998,36.823699,Rider_Id_590
-Order_No_13354,User_Id_3674,Bike,3,Business,24,5,2:53:45 PM,24,5,3:11:53 PM,24,5,3:23:01 PM,24,5,3:25:09 PM,12,23.2,,-1.2551895,36.7822034,-1.3200694,36.793130600000005,Rider_Id_205
-Order_No_16624,User_Id_2828,Bike,3,Business,20,1,2:21:01 PM,20,1,2:22:48 PM,20,1,2:39:58 PM,20,1,2:41:58 PM,4,21.2,,-1.2696183,36.8021778,-1.2564152,36.779280299999996,Rider_Id_452
-Order_No_23764,User_Id_1352,Bike,3,Personal,8,1,8:50:41 AM,8,1,9:00:08 AM,8,1,9:10:14 AM,8,1,9:17:57 AM,4,19.8,,-1.2655306,36.828148,-1.2753108999999998,36.8130185,Rider_Id_932
-Order_No_1870,User_Id_3283,Bike,3,Business,17,4,10:38:22 AM,17,4,10:39:48 AM,17,4,10:46:29 AM,17,4,10:54:09 AM,5,25.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_40
-Order_No_9147,User_Id_3365,Bike,2,Personal,31,2,12:31:23 PM,31,2,12:31:37 PM,31,2,12:37:41 PM,31,2,12:39:04 PM,5,18.6,,-1.2795796,36.819062,-1.304078,36.83014,Rider_Id_933
-Order_No_297,User_Id_2335,Bike,3,Business,14,3,2:44:47 PM,14,3,2:44:57 PM,14,3,2:48:14 PM,14,3,2:49:26 PM,21,25.6,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_833
-Order_No_17798,User_Id_2415,Bike,1,Personal,11,3,1:49:04 PM,11,3,1:50:29 PM,11,3,1:56:50 PM,11,3,2:07:22 PM,4,20.5,,-1.285521,36.816526,-1.2619982,36.813697499999996,Rider_Id_159
-Order_No_7667,User_Id_1568,Bike,3,Business,30,2,2:43:37 PM,30,2,2:54:33 PM,30,2,3:13:09 PM,30,2,3:21:26 PM,6,27.5,,-1.2897368,36.8226783,-1.2657756000000002,36.7926481,Rider_Id_281
-Order_No_27946,User_Id_1637,Bike,3,Business,26,5,2:16:35 PM,26,5,2:17:23 PM,26,5,2:23:39 PM,26,5,2:31:09 PM,8,,,-1.3014461000000002,36.7661381,-1.2615888000000002,36.7928732,Rider_Id_268
-Order_No_24570,User_Id_2523,Bike,3,Business,2,3,12:52:20 PM,2,3,12:54:42 PM,2,3,1:02:59 PM,2,3,1:13:07 PM,10,23.9,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_427
-Order_No_3237,User_Id_1346,Bike,3,Business,29,3,2:31:50 PM,29,3,2:33:51 PM,29,3,3:01:38 PM,29,3,3:04:41 PM,20,21.3,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_177
-Order_No_24244,User_Id_2344,Bike,3,Business,11,3,9:53:11 AM,11,3,9:54:30 AM,11,3,10:04:03 AM,11,3,10:14:33 AM,7,15.5,0.9,-1.2857684999999999,36.7866843,-1.2551895,36.7822034,Rider_Id_74
-Order_No_13167,User_Id_2066,Bike,3,Business,13,1,2:52:59 PM,13,1,2:53:56 PM,13,1,3:04:58 PM,13,1,3:30:47 PM,12,19.5,,-1.316961,36.820337,-1.2565338,36.870888,Rider_Id_234
-Order_No_25003,User_Id_3689,Bike,3,Business,6,4,4:01:12 PM,6,4,4:02:01 PM,6,4,4:02:21 PM,6,4,4:14:38 PM,2,,,-1.2527958000000001,36.8003131,-1.2610837,36.8021315,Rider_Id_829
-Order_No_1225,User_Id_2587,Bike,3,Business,13,3,11:21:56 AM,13,3,11:23:28 AM,13,3,11:38:27 AM,13,3,11:42:11 AM,5,24.9,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_362
-Order_No_20447,User_Id_1363,Bike,3,Business,7,5,10:56:36 AM,7,5,10:57:22 AM,7,5,10:59:50 AM,7,5,11:01:08 AM,13,,,-1.3004062,36.829741,-1.242902,36.870334,Rider_Id_740
-Order_No_15294,User_Id_2140,Bike,1,Personal,24,5,10:55:34 AM,24,5,10:55:57 AM,24,5,11:07:11 AM,24,5,11:08:36 AM,5,21.1,,-1.2545163000000001,36.8243717,-1.2653428999999998,36.799555600000005,Rider_Id_698
-Order_No_8121,User_Id_1874,Bike,3,Business,19,2,10:24:45 AM,19,2,10:26:12 AM,19,2,10:30:48 AM,19,2,10:39:51 AM,7,26.5,,-1.2571472,36.795063299999995,-1.3041018999999998,36.8105732,Rider_Id_31
-Order_No_13129,User_Id_1207,Bike,3,Business,14,2,6:37:03 PM,14,2,6:38:50 PM,14,2,6:50:08 PM,14,2,6:55:12 PM,8,,,-1.2629666000000002,36.8068369,-1.2745321000000003,36.765088,Rider_Id_538
-Order_No_22592,User_Id_187,Bike,3,Business,11,1,11:42:38 AM,11,1,11:42:43 AM,11,1,11:42:48 AM,11,1,12:18:57 PM,27,18.1,,-1.2859912,36.8756811,-1.3169591,36.7000029,Rider_Id_408
-Order_No_4955,User_Id_1874,Bike,3,Business,23,5,11:14:41 AM,23,5,11:18:44 AM,23,5,11:27:40 AM,23,5,11:34:10 AM,8,24.0,,-1.2571472,36.795063299999995,-1.3015435,36.7904968,Rider_Id_337
-Order_No_20552,User_Id_610,Bike,1,Personal,28,6,3:48:58 PM,28,6,3:50:37 PM,28,6,4:19:52 PM,28,6,4:30:04 PM,3,24.8,,-1.3090663,36.813686499999996,-1.3257299999999999,36.801356,Rider_Id_939
-Order_No_13068,User_Id_3070,Bike,3,Business,17,5,1:16:42 PM,17,5,1:17:17 PM,17,5,1:18:17 PM,17,5,1:32:54 PM,14,,,-1.2653676,36.79110120000001,-1.3127833999999998,36.869639899999996,Rider_Id_829
-Order_No_7163,User_Id_2710,Bike,3,Personal,25,1,12:50:01 PM,25,1,12:50:13 PM,25,1,1:05:58 PM,25,1,1:15:24 PM,11,25.5,,-1.2980228999999999,36.78865,-1.2535486,36.7933541,Rider_Id_411
-Order_No_20735,User_Id_3326,Bike,3,Business,31,4,3:37:25 PM,31,4,3:38:14 PM,31,4,3:43:22 PM,31,4,3:57:50 PM,9,28.7,,-1.2551895,36.7822034,-1.2987732,36.7932128,Rider_Id_225
-Order_No_22035,User_Id_346,Bike,3,Business,9,1,3:19:21 PM,9,1,3:20:31 PM,9,1,3:30:27 PM,9,1,3:50:59 PM,1,21.0,,-1.279395,36.825364,-1.2885528999999998,36.816014100000004,Rider_Id_421
-Order_No_15977,User_Id_190,Bike,3,Business,16,1,3:08:22 PM,16,1,3:13:30 PM,16,1,3:25:51 PM,16,1,3:36:17 PM,7,22.5,,-1.3385185,36.8725654,-1.3270347,36.866736200000005,Rider_Id_890
-Order_No_1645,User_Id_1217,Bike,3,Personal,11,5,2:22:21 PM,11,5,2:23:05 PM,11,5,2:45:30 PM,11,5,3:06:03 PM,32,26.3,,-1.2175583,36.884343,-1.398531,36.767365000000005,Rider_Id_268
-Order_No_21240,User_Id_1882,Bike,1,Business,6,1,3:45:33 PM,6,1,3:47:58 PM,6,1,4:17:33 PM,6,1,4:27:28 PM,7,24.7,,-1.2268871000000001,36.8073946,-1.2536041999999998,36.806489500000005,Rider_Id_882
-Order_No_22921,User_Id_1271,Bike,3,Business,19,2,2:33:31 PM,19,2,2:33:59 PM,19,2,2:43:52 PM,19,2,2:50:42 PM,17,30.2,,-1.2927792,36.9053492,-1.2649605,36.798177700000004,Rider_Id_543
-Order_No_17058,User_Id_426,Bike,3,Business,25,2,12:29:45 PM,25,2,12:29:52 PM,25,2,12:42:55 PM,25,2,12:47:31 PM,15,23.7,,-1.3323107,36.8692616,-1.2969741000000001,36.7856612,Rider_Id_543
-Order_No_24266,User_Id_867,Bike,3,Business,9,5,2:49:49 PM,9,5,2:50:32 PM,9,5,2:50:53 PM,9,5,2:55:33 PM,12,26.1,,-1.306378,36.7519845,-1.2996533000000001,36.829262899999996,Rider_Id_93
-Order_No_26590,User_Id_2789,Bike,3,Business,20,3,12:21:47 PM,20,3,12:22:47 PM,20,3,12:29:13 PM,20,3,12:33:36 PM,7,26.3,,-1.304735,36.784605400000004,-1.2668815,36.8017244,Rider_Id_24
-Order_No_23629,User_Id_478,Bike,3,Business,31,5,7:59:31 AM,31,5,8:06:54 AM,31,5,8:39:37 AM,31,5,9:24:49 AM,8,,,-1.3045333000000001,36.82280720000001,-1.2603403,36.7998848,Rider_Id_55
-Order_No_1900,User_Id_867,Bike,3,Business,26,5,1:47:49 PM,26,5,1:49:05 PM,26,5,2:16:17 PM,26,5,2:53:56 PM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_328
-Order_No_6253,User_Id_904,Bike,3,Business,7,4,1:32:34 PM,7,4,1:32:47 PM,7,4,2:06:43 PM,7,4,2:09:17 PM,12,27.7,,-1.304192,36.739457,-1.2593048999999998,36.802937299999996,Rider_Id_205
-Order_No_11699,User_Id_3647,Bike,3,Business,21,5,2:07:37 PM,21,5,2:08:39 PM,21,5,2:15:26 PM,21,5,2:21:11 PM,20,24.6,,-1.2726389999999999,36.794723,-1.2162985,36.906510499999996,Rider_Id_866
-Order_No_24715,User_Id_393,Bike,3,Business,15,1,12:19:51 PM,15,1,12:20:02 PM,15,1,12:20:06 PM,15,1,12:37:18 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619
-Order_No_13887,User_Id_3491,Bike,3,Business,19,2,10:40:16 AM,19,2,10:40:59 AM,19,2,10:46:58 AM,19,2,10:59:09 AM,9,27.5,,-1.2796151000000002,36.8910091,-1.2283403,36.8822756,Rider_Id_168
-Order_No_20207,User_Id_3368,Bike,2,Personal,5,5,1:17:27 PM,5,5,1:57:20 PM,5,5,2:09:35 PM,5,5,2:14:55 PM,9,26.8,,-1.362319,36.743983,-1.3187761,36.8069926,Rider_Id_536
-Order_No_20269,User_Id_1787,Bike,3,Business,11,2,11:01:05 AM,11,2,11:01:16 AM,11,2,11:04:48 AM,11,2,11:14:28 AM,15,24.0,,-1.2297202,36.881950700000004,-1.1605816,36.8977958,Rider_Id_45
-Order_No_8138,User_Id_2592,Bike,1,Personal,25,5,8:37:10 AM,25,5,8:54:35 AM,25,5,9:50:13 AM,25,5,10:15:37 AM,10,16.9,,-1.3359536,36.853478700000004,-1.28433,36.802593,Rider_Id_742
-Order_No_8046,User_Id_3283,Bike,3,Business,27,1,12:22:43 PM,27,1,12:23:10 PM,27,1,12:27:42 PM,27,1,12:37:36 PM,13,20.8,,-1.318062,36.849326,-1.2628473,36.781805,Rider_Id_675
-Order_No_5891,User_Id_868,Bike,3,Business,20,1,3:18:41 PM,20,1,3:21:01 PM,20,1,3:26:37 PM,20,1,3:41:19 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_61
-Order_No_26894,User_Id_2069,Bike,3,Business,21,4,10:17:04 AM,21,4,10:17:11 AM,21,4,10:26:13 AM,21,4,10:50:47 AM,6,28.0,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_291
-Order_No_18381,User_Id_3309,Bike,3,Personal,9,1,2:55:49 PM,9,1,3:03:58 PM,9,1,4:11:48 PM,9,1,4:14:44 PM,6,21.0,,-1.2995135,36.793363299999996,-1.2823851000000002,36.8186472,Rider_Id_844
-Order_No_4327,User_Id_1500,Bike,3,Business,18,2,10:26:46 AM,18,2,10:26:52 AM,18,2,10:26:56 AM,18,2,10:29:58 AM,10,19.8,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_518
-Order_No_24551,User_Id_393,Bike,3,Business,12,2,11:34:00 AM,12,2,11:34:25 AM,12,2,11:42:03 AM,12,2,11:45:18 AM,14,28.3,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_25335,User_Id_3064,Bike,3,Business,3,1,4:21:06 PM,3,1,4:31:39 PM,3,1,4:40:59 PM,3,1,4:48:38 PM,8,17.6,,-1.2908945,36.82465139999999,-1.2571472,36.795063299999995,Rider_Id_434
-Order_No_1358,User_Id_3698,Bike,3,Business,17,3,11:51:47 AM,17,3,11:53:04 AM,17,3,12:11:30 PM,17,3,12:41:13 PM,23,,,-1.3244885,36.897791999999995,-1.1933341,36.9271667,Rider_Id_863
-Order_No_8562,User_Id_878,Bike,1,Personal,2,3,1:42:20 PM,2,3,1:54:08 PM,2,3,2:16:39 PM,2,3,2:18:12 PM,12,23.3,,-1.2859912,36.8756811,-1.3041128999999998,36.810493900000004,Rider_Id_900
-Order_No_2818,User_Id_3404,Bike,3,Business,22,6,2:26:20 PM,22,6,2:31:50 PM,22,6,2:32:53 PM,22,6,2:57:05 PM,5,26.0,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_779
-Order_No_288,User_Id_1784,Bike,1,Personal,28,4,1:15:44 PM,28,4,1:23:17 PM,28,4,1:33:27 PM,28,4,1:37:40 PM,1,,,-1.294555,36.814462,-1.300722,36.816865,Rider_Id_620
-Order_No_25338,User_Id_868,Bike,3,Business,28,2,3:49:19 PM,28,2,3:49:39 PM,28,2,3:56:56 PM,28,2,4:09:55 PM,5,19.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_882
-Order_No_3317,User_Id_633,Bike,3,Business,26,2,3:03:34 PM,26,2,3:04:21 PM,26,2,3:19:40 PM,26,2,3:25:17 PM,12,30.8,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_306
-Order_No_5726,User_Id_53,Bike,3,Business,28,4,9:39:13 AM,28,4,9:47:22 AM,28,4,9:57:13 AM,28,4,10:29:32 AM,8,22.6,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_590
-Order_No_19560,User_Id_134,Bike,3,Business,20,3,1:45:31 PM,20,3,1:45:48 PM,20,3,1:46:21 PM,20,3,2:04:30 PM,16,28.6,,-1.2609745,36.7886626,-1.207546,36.7913976,Rider_Id_955
-Order_No_13849,User_Id_1500,Bike,3,Business,22,3,12:30:43 PM,22,3,12:31:26 PM,22,3,12:40:07 PM,22,3,12:42:06 PM,9,23.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161
-Order_No_21574,User_Id_3283,Bike,3,Business,16,6,12:36:27 PM,16,6,12:37:11 PM,16,6,12:52:55 PM,16,6,1:05:13 PM,6,25.5,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_855
-Order_No_20517,User_Id_2283,Bike,2,Personal,18,3,12:23:01 PM,18,3,12:23:10 PM,18,3,12:43:57 PM,18,3,12:54:35 PM,4,19.9,,-1.2595421999999998,36.7871178,-1.2562796999999999,36.8075858,Rider_Id_951
-Order_No_1760,User_Id_2216,Bike,3,Business,24,3,2:41:19 PM,24,3,2:41:55 PM,24,3,2:53:22 PM,24,3,2:59:28 PM,19,,,-1.2551895,36.7822034,-1.3239618,36.899082,Rider_Id_177
-Order_No_11880,User_Id_3217,Bike,1,Business,3,1,2:36:30 PM,3,1,2:37:49 PM,3,1,2:47:46 PM,3,1,3:16:03 PM,15,23.1,,-1.28878,36.8168312,-1.2143386999999999,36.7914548,Rider_Id_249
-Order_No_15400,User_Id_248,Bike,3,Business,3,3,10:27:41 AM,3,3,10:28:16 AM,3,3,11:50:40 AM,3,3,11:59:56 AM,3,27.2,,-1.2929747,36.76463579999999,-1.304735,36.784605400000004,Rider_Id_362
-Order_No_13730,User_Id_1342,Bike,3,Business,14,5,5:48:31 PM,14,5,5:48:49 PM,14,5,6:06:50 PM,14,5,6:10:01 PM,9,21.8,,-1.2597528999999998,36.806629,-1.3025826,36.7670807,Rider_Id_328
-Order_No_8798,User_Id_3051,Bike,3,Personal,6,3,10:52:39 AM,6,3,10:54:33 AM,6,3,11:11:52 AM,6,3,11:20:26 AM,6,19.9,,-1.2702333000000001,36.80065,-1.303664,36.818777000000004,Rider_Id_844
-Order_No_26605,User_Id_3647,Bike,3,Business,4,4,3:52:59 PM,4,4,3:53:10 PM,4,4,4:20:10 PM,4,4,4:47:30 PM,18,,,-1.2726389999999999,36.794723,-1.3390024,36.8746657,Rider_Id_818
-Order_No_23742,User_Id_1927,Bike,3,Business,13,3,5:04:06 PM,13,3,5:42:14 PM,13,3,5:56:19 PM,13,3,5:59:56 PM,10,27.3,,-1.3169591,36.7000029,-1.3472066,36.7692638,Rider_Id_419
-Order_No_14052,User_Id_503,Bike,3,Business,18,5,1:01:21 PM,18,5,1:01:31 PM,18,5,1:03:10 PM,18,5,1:50:18 PM,9,24.2,,-1.2859912,36.8756811,-1.2860649,36.8306325,Rider_Id_408
-Order_No_1702,User_Id_2200,Bike,3,Business,21,5,12:57:45 PM,21,5,1:01:39 PM,21,5,1:04:30 PM,21,5,1:17:58 PM,5,22.7,,-1.290894,36.822971,-1.2591018999999999,36.800577000000004,Rider_Id_554
-Order_No_24194,User_Id_1715,Bike,3,Business,29,2,8:53:15 AM,29,2,8:53:28 AM,29,2,9:09:47 AM,29,2,9:18:20 AM,6,16.7,,-1.253966,36.7987692,-1.28503,36.821897,Rider_Id_808
-Order_No_11967,User_Id_478,Bike,3,Business,30,2,10:45:34 AM,30,2,10:48:06 AM,30,2,10:56:11 AM,30,2,11:03:17 AM,3,,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_690
-Order_No_23486,User_Id_1809,Bike,3,Business,3,3,3:53:00 PM,3,3,3:53:22 PM,3,3,3:54:29 PM,3,3,4:11:07 PM,2,26.6,,-1.2642719999999998,36.7978791,-1.2646084,36.8028645,Rider_Id_446
-Order_No_19801,User_Id_3239,Bike,3,Personal,4,3,10:16:37 AM,4,3,10:34:13 AM,4,3,10:44:53 AM,4,3,10:53:34 AM,9,17.2,,-1.2835524999999999,36.829638200000005,-1.2323371,36.864816999999995,Rider_Id_208
-Order_No_26632,User_Id_3491,Bike,3,Business,27,3,1:04:39 PM,27,3,1:05:37 PM,27,3,1:31:00 PM,27,3,1:37:51 PM,14,29.3,,-1.225322,36.80855,-1.2283403,36.8822756,Rider_Id_445
-Order_No_477,User_Id_2289,Bike,3,Business,22,5,1:44:10 PM,22,5,2:02:05 PM,22,5,2:18:10 PM,22,5,2:20:39 PM,6,,,-1.2551895,36.7822034,-1.2877796000000001,36.8149258,Rider_Id_531
-Order_No_25324,User_Id_1849,Bike,3,Business,8,5,2:56:55 PM,8,5,2:57:19 PM,8,5,2:57:32 PM,8,5,3:21:29 PM,12,27.4,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_330
-Order_No_18312,User_Id_1259,Bike,3,Business,11,5,9:46:57 AM,11,5,9:47:24 AM,11,5,10:01:38 AM,11,5,10:29:02 AM,15,17.9,5.0,-1.2913193,36.8338181,-1.229351,36.7556406,Rider_Id_543
-Order_No_11728,User_Id_3517,Bike,3,Business,11,2,8:56:57 AM,11,2,8:57:09 AM,11,2,9:03:21 AM,11,2,9:29:12 AM,15,18.4,1.9,-1.300921,36.828195,-1.3429554,36.7262559,Rider_Id_674
-Order_No_2701,User_Id_265,Bike,3,Business,27,2,12:37:51 PM,27,2,12:45:58 PM,27,2,12:56:39 PM,27,2,1:01:00 PM,7,23.7,,-1.300921,36.828195,-1.2595466999999998,36.8113044,Rider_Id_685
-Order_No_26625,User_Id_2154,Bike,3,Business,8,5,4:10:13 PM,8,5,4:11:11 PM,8,5,4:21:57 PM,8,5,4:28:19 PM,9,30.3,,-1.2285834999999998,36.882646799999996,-1.2765736,36.851364600000004,Rider_Id_675
-Order_No_9153,User_Id_2532,Bike,3,Business,17,5,3:31:20 PM,17,5,3:32:03 PM,17,5,3:37:01 PM,17,5,3:44:26 PM,8,,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_868
-Order_No_2055,User_Id_2069,Bike,3,Business,30,3,3:29:39 PM,30,3,3:30:34 PM,30,3,3:45:18 PM,30,3,4:11:26 PM,10,29.2,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_75
-Order_No_17088,User_Id_3701,Bike,3,Business,10,3,10:21:56 AM,10,3,10:22:06 AM,10,3,10:30:36 AM,10,3,10:31:47 AM,6,22.0,,-1.2612573999999999,36.7879622,-1.2804664,36.7701111,Rider_Id_541
-Order_No_11174,User_Id_1245,Bike,3,Business,21,2,12:21:03 PM,21,2,12:21:34 PM,21,2,12:34:55 PM,21,2,12:52:19 PM,17,22.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_676
-Order_No_12828,User_Id_1280,Bike,3,Business,17,4,10:30:59 AM,17,4,10:31:18 AM,17,4,10:31:30 AM,17,4,10:35:22 AM,15,25.5,,-1.2551895,36.7822034,-1.3288106000000002,36.8697344,Rider_Id_630
-Order_No_16126,User_Id_2154,Bike,3,Business,19,6,10:26:17 AM,19,6,10:29:18 AM,19,6,10:38:22 AM,19,6,10:40:40 AM,4,25.4,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_15
-Order_No_26051,User_Id_517,Bike,3,Business,6,4,8:12:52 AM,6,4,8:13:11 AM,6,4,8:13:25 AM,6,4,8:25:18 AM,14,18.6,3.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523
-Order_No_898,User_Id_2066,Bike,3,Business,24,5,3:12:28 PM,24,5,3:12:37 PM,24,5,3:17:35 PM,24,5,3:36:59 PM,7,23.0,,-1.316961,36.820337,-1.303105,36.764577200000005,Rider_Id_857
-Order_No_26855,User_Id_1304,Bike,3,Business,13,2,11:14:18 AM,13,2,11:15:35 AM,13,2,11:17:32 AM,13,2,11:36:00 AM,5,22.4,,-1.3332749,36.870814700000004,-1.3083777,36.85900770000001,Rider_Id_487
-Order_No_24863,User_Id_990,Bike,1,Business,15,3,10:21:55 AM,15,3,10:22:04 AM,15,3,10:36:59 AM,15,3,10:53:40 AM,20,18.3,,-1.3164339,36.857888200000005,-1.3548775,36.721168299999995,Rider_Id_213
-Order_No_12817,User_Id_3283,Bike,3,Business,23,6,11:38:49 AM,23,6,11:39:00 AM,23,6,11:41:27 AM,23,6,11:55:59 AM,5,26.5,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_607
-Order_No_26802,User_Id_3201,Bike,3,Business,25,1,11:21:12 AM,25,1,11:21:53 AM,25,1,11:27:10 AM,25,1,11:42:23 AM,2,25.4,,-1.2584143,36.8048002,-1.2561334,36.7990458,Rider_Id_547
-Order_No_2762,User_Id_2900,Bike,3,Business,11,2,12:02:21 PM,11,2,12:18:17 PM,11,2,12:48:09 PM,11,2,1:08:37 PM,21,19.2,,-1.17595,36.83448,-1.2963097,36.7688221,Rider_Id_551
-Order_No_19155,User_Id_972,Bike,3,Business,13,4,10:31:32 AM,13,4,10:32:34 AM,13,4,11:20:44 AM,13,4,11:34:17 AM,13,22.1,,-1.2862033000000002,36.8152966,-1.3446058,36.760517799999995,Rider_Id_448
-Order_No_25883,User_Id_3520,Bike,1,Personal,18,4,11:35:06 AM,18,4,11:35:31 AM,18,4,12:22:41 PM,18,4,12:25:17 PM,9,25.3,,-1.2962862,36.7604041,-1.2793858999999999,36.821800200000006,Rider_Id_829
-Order_No_15742,User_Id_633,Bike,3,Business,27,3,1:26:02 PM,27,3,1:55:28 PM,27,3,2:08:05 PM,27,3,2:17:13 PM,4,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_571
-Order_No_10087,User_Id_1642,Bike,3,Business,6,3,2:37:06 PM,6,3,3:25:31 PM,6,3,3:42:53 PM,6,3,3:54:00 PM,5,,,-1.2797536,36.8223726,-1.2726506000000002,36.7960153,Rider_Id_436
-Order_No_16116,User_Id_71,Bike,3,Business,21,1,11:24:47 AM,21,1,11:33:07 AM,21,1,11:55:04 AM,21,1,12:01:00 PM,10,19.8,,-1.2248308,36.801257299999996,-1.2821108,36.8250555,Rider_Id_186
-Order_No_21057,User_Id_1850,Bike,3,Business,3,3,10:10:49 AM,3,3,10:12:03 AM,3,3,10:31:31 AM,3,3,10:58:30 AM,6,27.3,,-1.3173868999999998,36.8112044,-1.2816664,36.8226716,Rider_Id_507
-Order_No_1611,User_Id_2412,Bike,3,Business,3,4,8:32:31 AM,3,4,8:32:41 AM,3,4,8:33:59 AM,3,4,9:22:46 AM,14,20.3,,-1.2285834999999998,36.882646799999996,-1.2551895,36.7822034,Rider_Id_779
-Order_No_1446,User_Id_2521,Bike,3,Business,11,6,11:35:04 AM,11,6,11:38:20 AM,11,6,11:38:35 AM,11,6,11:45:26 AM,8,21.0,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_586
-Order_No_13603,User_Id_517,Bike,3,Business,6,5,1:01:28 PM,6,5,1:24:14 PM,6,5,2:03:23 PM,6,5,3:10:47 PM,8,22.6,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_333
-Order_No_7817,User_Id_1341,Bike,3,Business,6,4,12:57:58 PM,6,4,1:09:41 PM,6,4,1:27:32 PM,6,4,2:24:38 PM,20,21.2,,-1.3188358999999998,36.703852600000005,-1.3306113000000002,36.848052100000004,Rider_Id_364
-Order_No_16724,User_Id_2077,Bike,2,Personal,16,1,8:26:38 AM,16,1,8:28:15 AM,16,1,9:00:05 AM,16,1,9:07:15 AM,18,14.6,,-1.2701385,36.8235208,-1.2565206,36.6917289,Rider_Id_581
-Order_No_5417,User_Id_201,Bike,3,Business,25,4,11:45:43 AM,25,4,11:47:10 AM,25,4,11:59:02 AM,25,4,12:02:44 PM,8,20.2,,-1.2551895,36.7822034,-1.271672,36.783604,Rider_Id_50
-Order_No_26225,User_Id_3283,Bike,3,Business,30,2,10:19:18 AM,30,2,10:20:12 AM,30,2,10:28:13 AM,30,2,10:33:23 AM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_315
-Order_No_9293,User_Id_533,Bike,3,Business,4,1,1:28:53 PM,4,1,1:50:03 PM,4,1,2:08:34 PM,4,1,2:13:42 PM,8,,,-1.2765492,36.7669809,-1.2869955,36.8202749,Rider_Id_41
-Order_No_18905,User_Id_3383,Bike,1,Personal,6,6,2:48:08 PM,6,6,2:48:40 PM,6,6,2:57:54 PM,6,6,3:01:10 PM,6,27.8,,-1.2586742,36.801643799999994,-1.2848844,36.7893911,Rider_Id_407
-Order_No_16753,User_Id_393,Bike,3,Business,15,5,8:57:30 AM,15,5,8:57:43 AM,15,5,9:15:26 AM,15,5,9:43:05 AM,14,19.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_127
-Order_No_2356,User_Id_3439,Bike,3,Business,14,5,10:31:22 AM,14,5,10:31:22 AM,14,5,11:06:40 AM,14,5,11:21:51 AM,9,21.8,,-1.2854173999999998,36.7951152,-1.2393157,36.766887700000005,Rider_Id_709
-Order_No_24080,User_Id_2112,Bike,3,Business,3,5,11:36:40 AM,3,5,11:44:51 AM,3,5,12:14:21 PM,3,5,12:19:42 PM,12,,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_778
-Order_No_12164,User_Id_1329,Bike,3,Business,10,1,2:35:53 PM,10,1,2:57:33 PM,10,1,3:21:59 PM,10,1,3:23:13 PM,8,18.9,,-1.273056,36.811298,-1.324009,36.845882700000004,Rider_Id_267
-Order_No_18173,User_Id_2750,Bike,3,Business,28,3,9:25:52 AM,28,3,9:26:17 AM,28,3,9:33:34 AM,28,3,9:35:18 AM,13,20.6,,-1.275442,36.766020700000006,-1.3164106000000002,36.6952197,Rider_Id_470
-Order_No_5073,User_Id_2200,Bike,3,Business,25,4,4:18:56 PM,25,4,5:16:13 PM,25,4,5:25:04 PM,25,4,5:51:16 PM,8,,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_436
-Order_No_10931,User_Id_3283,Bike,3,Business,17,5,8:39:34 AM,17,5,8:41:45 AM,17,5,8:51:30 AM,17,5,8:59:06 AM,5,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_694
-Order_No_22953,User_Id_2112,Bike,3,Business,24,4,1:54:19 PM,24,4,1:54:38 PM,24,4,2:07:12 PM,24,4,2:12:10 PM,17,28.2,,-1.2232123,36.8932547,-1.2551895,36.7822034,Rider_Id_104
-Order_No_14094,User_Id_3201,Bike,3,Business,10,1,9:23:18 AM,10,1,9:25:54 AM,10,1,10:19:43 AM,10,1,10:51:09 AM,7,18.4,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_250
-Order_No_12721,User_Id_3517,Bike,3,Business,19,6,10:44:56 AM,19,6,10:45:26 AM,19,6,11:00:39 AM,19,6,12:09:50 PM,15,25.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_654
-Order_No_19770,User_Id_867,Bike,3,Business,14,2,12:59:15 PM,14,2,12:59:40 PM,14,2,12:59:46 PM,14,2,1:46:38 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_855
-Order_No_14371,User_Id_540,Bike,3,Business,25,4,12:16:58 PM,25,4,12:21:25 PM,25,4,12:45:43 PM,25,4,12:50:16 PM,8,20.2,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_946
-Order_No_9917,User_Id_53,Bike,3,Business,25,4,9:36:35 AM,25,4,9:36:55 AM,25,4,9:43:18 AM,25,4,9:52:44 AM,7,,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_76
-Order_No_14289,User_Id_3467,Bike,1,Personal,13,4,9:41:46 AM,13,4,10:03:29 AM,13,4,10:23:59 AM,13,4,10:27:35 AM,18,18.4,,-1.2388499,36.872726799999995,-1.3333155,36.8065341,Rider_Id_727
-Order_No_12038,User_Id_867,Bike,3,Business,4,4,10:27:05 AM,4,4,10:30:36 AM,4,4,10:41:46 AM,4,4,10:46:37 AM,10,25.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_263
-Order_No_5024,User_Id_186,Bike,3,Business,14,3,11:03:42 AM,14,3,11:03:57 AM,14,3,11:16:15 AM,14,3,11:39:57 AM,17,24.5,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_250
-Order_No_16071,User_Id_1281,Bike,3,Business,20,5,10:51:39 AM,20,5,10:52:04 AM,20,5,11:01:43 AM,20,5,11:22:42 AM,9,18.3,,-1.2869041,36.8240057,-1.2707742,36.7578609,Rider_Id_329
-Order_No_7203,User_Id_2411,Bike,3,Business,15,5,2:36:26 PM,15,5,2:42:14 PM,15,5,3:19:22 PM,15,5,3:23:01 PM,6,17.9,,-1.2864995000000001,36.812605,-1.2615888000000002,36.7928732,Rider_Id_634
-Order_No_19512,User_Id_393,Bike,3,Business,19,3,1:11:41 PM,19,3,2:13:19 PM,19,3,2:20:52 PM,19,3,2:41:02 PM,5,24.3,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_914
-Order_No_16165,User_Id_1642,Bike,3,Business,7,4,11:17:08 AM,7,4,11:23:09 AM,7,4,11:42:33 AM,7,4,11:49:54 AM,5,27.5,,-1.2887076000000002,36.825465799999996,-1.2726506000000002,36.7960153,Rider_Id_660
-Order_No_14087,User_Id_3331,Bike,3,Business,16,4,3:51:52 PM,16,4,3:52:29 PM,16,4,3:58:44 PM,16,4,4:00:37 PM,7,19.0,,-1.2551895,36.7822034,-1.2941988000000002,36.8148746,Rider_Id_415
-Order_No_20052,User_Id_187,Bike,3,Business,9,2,2:30:43 PM,9,2,2:30:51 PM,9,2,2:46:34 PM,9,2,3:11:18 PM,31,22.5,,-1.2413316,36.878552500000005,-1.3169591,36.7000029,Rider_Id_882
-Order_No_7179,User_Id_391,Bike,3,Business,1,4,12:04:21 PM,1,4,1:01:30 PM,1,4,1:19:12 PM,1,4,1:50:46 PM,5,,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_685
-Order_No_8683,User_Id_1427,Bike,1,Personal,18,4,9:32:36 AM,18,4,9:33:57 AM,18,4,9:45:47 AM,18,4,9:49:33 AM,8,20.4,,-1.3014461000000002,36.7661381,-1.3100608999999999,36.820536499999996,Rider_Id_65
-Order_No_26912,User_Id_265,Bike,3,Business,20,3,12:50:33 PM,20,3,12:55:24 PM,20,3,1:00:14 PM,20,3,1:20:14 PM,8,26.4,,-1.300921,36.828195,-1.277783,36.7816464,Rider_Id_206
-Order_No_7901,User_Id_246,Bike,3,Business,30,3,9:18:07 AM,30,3,9:18:49 AM,30,3,9:18:55 AM,30,3,9:38:36 AM,7,19.9,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_31
-Order_No_9006,User_Id_1849,Bike,3,Business,19,3,12:24:21 PM,19,3,12:30:13 PM,19,3,12:39:25 PM,19,3,12:44:15 PM,3,22.2,,-1.290894,36.822971,-1.2896431000000002,36.8086201,Rider_Id_725
-Order_No_10211,User_Id_3517,Bike,3,Business,25,1,11:52:00 AM,25,1,11:52:12 AM,25,1,12:30:15 PM,25,1,12:36:55 PM,15,27.3,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_715
-Order_No_16286,User_Id_2069,Bike,3,Business,24,5,11:25:14 AM,24,5,11:37:03 AM,24,5,11:39:51 AM,24,5,11:45:17 AM,12,21.6,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_240
-Order_No_22874,User_Id_3688,Bike,3,Business,19,3,2:52:32 PM,19,3,3:23:24 PM,19,3,3:37:37 PM,19,3,3:40:46 PM,8,24.5,,-1.2551895,36.7822034,-1.2962731,36.811186299999996,Rider_Id_899
-Order_No_11221,User_Id_3355,Bike,3,Business,24,3,3:08:18 PM,24,3,3:09:48 PM,24,3,3:10:15 PM,24,3,3:17:11 PM,4,18.5,,-1.3052491000000002,36.8223899,-1.2909536,36.8407162,Rider_Id_848
-Order_No_141,User_Id_893,Bike,3,Business,24,6,10:54:01 AM,24,6,10:55:31 AM,24,6,11:05:20 AM,24,6,11:22:22 AM,13,24.1,,-1.2664852,36.7919817,-1.2146739,36.8560568,Rider_Id_574
-Order_No_18702,User_Id_393,Bike,3,Business,20,3,10:13:44 AM,20,3,10:15:24 AM,20,3,10:30:42 AM,20,3,10:37:03 AM,8,26.3,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_448
-Order_No_27439,User_Id_3216,Bike,2,Personal,28,5,7:21:15 AM,28,5,7:21:35 AM,28,5,7:29:40 AM,28,5,7:37:14 AM,6,20.6,,-1.301093,36.7790314,-1.3175119,36.8058513,Rider_Id_722
-Order_No_4214,User_Id_3688,Bike,3,Business,17,5,10:40:54 AM,17,5,10:42:11 AM,17,5,10:46:54 AM,17,5,10:52:28 AM,15,,,-1.2551895,36.7822034,-1.3203209999999999,36.7195522,Rider_Id_868
-Order_No_6316,User_Id_3521,Bike,3,Business,27,4,2:24:37 PM,27,4,2:25:42 PM,27,4,2:45:51 PM,27,4,2:49:12 PM,5,25.1,,-1.2811504999999999,36.8145398,-1.2696908000000002,36.81519,Rider_Id_622
-Order_No_6546,User_Id_776,Bike,1,Personal,2,6,2:32:04 PM,2,6,2:32:36 PM,2,6,2:32:50 PM,2,6,2:49:59 PM,8,23.9,,-1.278967,36.8236745,-1.2907222,36.8843012,Rider_Id_669
-Order_No_8522,User_Id_1363,Bike,3,Business,12,2,3:16:21 PM,12,2,3:16:44 PM,12,2,3:24:28 PM,12,2,3:47:27 PM,5,27.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_523
-Order_No_25986,User_Id_3773,Bike,3,Personal,30,4,12:19:01 PM,30,4,12:19:18 PM,30,4,12:19:35 PM,30,4,12:31:51 PM,9,20.4,,-1.2962832,36.784968299999996,-1.270701,36.837875700000005,Rider_Id_207
-Order_No_17963,User_Id_552,Bike,3,Business,20,4,11:04:34 AM,20,4,11:05:22 AM,20,4,11:08:12 AM,20,4,11:31:30 AM,21,22.1,,-1.2551895,36.7822034,-1.3632162,36.9108253,Rider_Id_574
-Order_No_21885,User_Id_189,Bike,3,Personal,15,3,2:14:30 PM,15,3,2:15:07 PM,15,3,2:24:38 PM,15,3,2:32:43 PM,5,25.9,,-1.2859015,36.824532,-1.285355,36.795287,Rider_Id_845
-Order_No_21366,User_Id_1882,Bike,3,Business,11,5,12:25:15 PM,11,5,12:26:10 PM,11,5,12:34:49 PM,11,5,1:21:52 PM,7,21.2,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_862
-Order_No_18146,User_Id_268,Bike,3,Business,14,4,3:53:49 PM,14,4,4:04:28 PM,14,4,4:10:30 PM,14,4,4:14:50 PM,5,28.2,,-1.2551895,36.7822034,-1.2808188,36.810013700000006,Rider_Id_404
-Order_No_11819,User_Id_1075,Bike,3,Business,3,3,12:13:39 PM,3,3,12:16:02 PM,3,3,12:21:40 PM,3,3,12:28:42 PM,6,27.4,,-1.2813012,36.832396200000005,-1.2527958000000001,36.8003131,Rider_Id_709
-Order_No_607,User_Id_635,Bike,3,Business,4,5,11:40:55 AM,4,5,11:48:14 AM,4,5,12:31:13 PM,4,5,12:56:04 PM,7,22.7,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_589
-Order_No_5442,User_Id_2069,Bike,3,Business,28,4,1:55:51 PM,28,4,1:57:02 PM,28,4,2:05:55 PM,28,4,2:16:20 PM,12,30.1,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_101
-Order_No_27908,User_Id_2330,Bike,3,Business,14,5,1:37:29 PM,14,5,1:45:50 PM,14,5,2:02:40 PM,14,5,2:04:45 PM,12,23.6,,-1.3376825,36.7575489,-1.2915246999999999,36.805575700000006,Rider_Id_232
-Order_No_22248,User_Id_1557,Bike,3,Business,23,5,12:02:47 PM,23,5,12:41:17 PM,23,5,12:52:19 PM,23,5,12:54:18 PM,8,24.3,,-1.2551895,36.7822034,-1.3033012,36.8170864,Rider_Id_726
-Order_No_23163,User_Id_1963,Bike,1,Personal,28,5,11:21:50 AM,28,5,11:26:36 AM,28,5,11:38:55 AM,28,5,11:42:39 AM,4,25.2,,-1.2740396,36.7993518,-1.2560455,36.780086700000005,Rider_Id_453
-Order_No_9939,User_Id_2804,Bike,3,Business,11,1,2:34:28 PM,11,1,2:37:29 PM,11,1,2:42:30 PM,11,1,2:49:18 PM,5,29.1,,-1.2551895,36.7822034,-1.2686558000000001,36.7982618,Rider_Id_655
-Order_No_2672,User_Id_3291,Bike,3,Business,13,4,12:47:55 PM,13,4,12:50:35 PM,13,4,1:29:17 PM,13,4,1:32:16 PM,16,22.9,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_746
-Order_No_19231,User_Id_2854,Bike,3,Business,31,4,2:46:21 PM,31,4,3:53:21 PM,31,4,4:17:08 PM,31,4,4:37:35 PM,23,22.2,,-1.1983052,36.928971399999995,-1.2963097,36.7688221,Rider_Id_101
-Order_No_23146,User_Id_346,Bike,3,Business,5,2,11:30:20 AM,5,2,11:30:37 AM,5,2,11:36:20 AM,5,2,11:50:38 AM,20,23.5,,-1.3169591,36.7000029,-1.28878,36.8168312,Rider_Id_496
-Order_No_167,User_Id_2523,Bike,3,Business,31,4,12:45:46 PM,31,4,12:52:03 PM,31,4,1:06:02 PM,31,4,1:10:35 PM,11,26.5,,-1.2551895,36.7822034,-1.3233496,36.8139159,Rider_Id_862
-Order_No_2855,User_Id_496,Bike,3,Business,26,2,9:19:03 AM,26,2,9:19:40 AM,26,2,9:19:46 AM,26,2,9:31:08 AM,26,,,-1.2551895,36.7822034,-1.3916458999999999,36.93058920000001,Rider_Id_523
-Order_No_2429,User_Id_3578,Bike,3,Business,27,6,12:53:55 PM,27,6,12:54:12 PM,27,6,1:09:35 PM,27,6,1:15:37 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_641
-Order_No_19820,User_Id_496,Bike,3,Business,30,2,2:46:18 PM,30,2,2:57:59 PM,30,2,3:06:29 PM,30,2,3:09:57 PM,5,,,-1.2592947,36.7698085,-1.2551895,36.7822034,Rider_Id_245
-Order_No_20157,User_Id_788,Bike,3,Business,23,5,7:58:11 AM,23,5,7:58:33 AM,23,5,8:20:53 AM,23,5,8:36:39 AM,12,20.1,,-1.225322,36.80855,-1.2494503000000001,36.8531852,Rider_Id_101
-Order_No_12032,User_Id_3324,Bike,1,Business,11,1,1:41:16 PM,11,1,1:44:06 PM,11,1,1:59:18 PM,11,1,2:24:26 PM,5,23.1,,-1.2643993999999998,36.812919,-1.2730069,36.811159,Rider_Id_101
-Order_No_24221,User_Id_246,Bike,3,Business,3,4,4:07:22 PM,3,4,4:08:26 PM,3,4,4:49:59 PM,3,4,5:05:31 PM,3,18.1,,-1.2584143,36.8048002,-1.2559559999999999,36.789867,Rider_Id_103
-Order_No_11643,User_Id_2302,Bike,1,Personal,1,3,4:50:28 PM,1,3,4:51:00 PM,1,3,5:00:58 PM,1,3,5:06:42 PM,2,23.3,,-1.2913382,36.8018508,-1.2874290000000002,36.786357,Rider_Id_263
-Order_No_24423,User_Id_3149,Bike,3,Business,14,1,11:19:49 AM,14,1,11:20:38 AM,14,1,11:35:04 AM,14,1,11:50:37 AM,6,23.4,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_771
-Order_No_16053,User_Id_2847,Bike,3,Personal,25,6,4:13:28 PM,25,6,4:13:43 PM,25,6,4:38:00 PM,25,6,4:39:19 PM,13,,,-1.2822773,36.795312100000004,-1.2311903,36.8102105,Rider_Id_400
-Order_No_8691,User_Id_1482,Bike,3,Business,29,1,12:02:33 PM,29,1,12:03:02 PM,29,1,12:04:09 PM,29,1,12:53:31 PM,8,,,-1.3214137,36.8569475,-1.3385790000000002,36.89630379999999,Rider_Id_698
-Order_No_17787,User_Id_2828,Bike,3,Business,29,4,2:47:17 PM,29,4,2:47:46 PM,29,4,2:51:34 PM,29,4,3:00:31 PM,3,24.7,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_547
-Order_No_10600,User_Id_2877,Bike,1,Personal,22,2,10:30:46 AM,22,2,10:31:00 AM,22,2,10:44:32 AM,22,2,10:56:44 AM,21,19.5,,-1.3610501,36.7520889,-1.2612531999999999,36.7897977,Rider_Id_663
-Order_No_1486,User_Id_3291,Bike,3,Business,3,1,9:25:11 AM,3,1,9:26:03 AM,3,1,9:30:44 AM,3,1,9:35:47 AM,14,18.1,4.9,-1.3306113000000002,36.848052100000004,-1.2600926000000001,36.8088685,Rider_Id_839
-Order_No_4550,User_Id_3486,Bike,3,Business,17,1,2:52:08 PM,17,1,4:07:08 PM,17,1,4:13:37 PM,17,1,4:18:16 PM,8,24.7,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_646
-Order_No_25638,User_Id_3688,Bike,3,Business,28,1,3:20:48 PM,28,1,3:22:16 PM,28,1,3:27:54 PM,28,1,3:38:20 PM,16,26.2,,-1.2551895,36.7822034,-1.3326173000000001,36.71462329999999,Rider_Id_74
-Order_No_13365,User_Id_186,Bike,3,Business,18,2,11:42:34 AM,18,2,11:52:23 AM,18,2,12:06:40 PM,18,2,12:13:43 PM,6,,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_548
-Order_No_27208,User_Id_136,Bike,3,Business,9,5,10:08:37 AM,9,5,10:10:01 AM,9,5,10:13:31 AM,9,5,10:24:58 AM,10,20.0,,-1.2536041999999998,36.806489500000005,-1.2248308,36.801257299999996,Rider_Id_903
-Order_No_8655,User_Id_3462,Bike,1,Personal,25,2,1:52:25 PM,25,2,1:52:54 PM,25,2,2:32:44 PM,25,2,2:34:01 PM,6,27.2,,-1.2556829999999999,36.813771,-1.2817758,36.798025200000005,Rider_Id_434
-Order_No_2354,User_Id_391,Bike,3,Business,6,1,2:13:35 PM,6,1,2:15:13 PM,6,1,2:18:12 PM,6,1,2:25:25 PM,5,,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_953
-Order_No_24064,User_Id_1874,Bike,3,Business,21,5,2:57:22 PM,21,5,2:57:40 PM,21,5,2:58:20 PM,21,5,3:06:10 PM,3,27.1,,-1.2571472,36.795063299999995,-1.26312,36.78347,Rider_Id_167
-Order_No_259,User_Id_2376,Bike,2,Personal,19,5,11:44:31 AM,19,5,11:46:10 AM,19,5,12:02:53 PM,19,5,12:09:21 PM,9,24.9,,-1.2812014999999999,36.82612460000001,-1.2916953000000002,36.8855273,Rider_Id_477
-Order_No_20835,User_Id_1559,Bike,2,Personal,31,2,12:14:46 PM,31,2,12:22:22 PM,31,2,12:39:50 PM,31,2,12:53:56 PM,4,18.4,,-1.2423801,36.8478805,-1.221696,36.8270195,Rider_Id_229
-Order_No_18051,User_Id_2330,Bike,3,Business,13,2,3:27:22 PM,13,2,3:54:53 PM,13,2,3:57:04 PM,13,2,4:19:29 PM,3,26.1,,-1.3015197,36.7658456,-1.2868601,36.7738025,Rider_Id_45
-Order_No_19557,User_Id_2956,Bike,3,Business,3,5,12:29:16 PM,3,5,12:30:06 PM,3,5,12:43:54 PM,3,5,12:46:24 PM,9,21.4,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_140
-Order_No_17615,User_Id_580,Bike,1,Personal,8,2,11:56:51 AM,8,2,12:12:35 PM,8,2,12:46:50 PM,8,2,1:02:56 PM,17,21.2,,-1.2161475,36.8828534,-1.2972306999999998,36.8139073,Rider_Id_620
-Order_No_7377,User_Id_3372,Bike,3,Business,10,2,11:24:45 AM,10,2,11:25:35 AM,10,2,11:31:24 AM,10,2,11:41:44 AM,18,20.3,,-1.2969741000000001,36.7856612,-1.214328,36.7911903,Rider_Id_101
-Order_No_19333,User_Id_2227,Bike,3,Personal,17,4,11:19:12 AM,17,4,11:20:09 AM,17,4,11:36:04 AM,17,4,11:39:00 AM,5,25.6,,-1.2541275,36.8263055,-1.2546708999999998,36.798938,Rider_Id_589
-Order_No_635,User_Id_3491,Bike,3,Business,24,1,3:25:35 PM,24,1,3:27:27 PM,24,1,3:31:54 PM,24,1,3:37:42 PM,9,24.8,,-1.2283403,36.8822756,-1.2741556,36.869394299999996,Rider_Id_933
-Order_No_19492,User_Id_1209,Bike,3,Business,18,4,2:32:57 PM,18,4,2:50:58 PM,18,4,3:05:36 PM,18,4,3:13:28 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_515
-Order_No_20841,User_Id_635,Bike,3,Business,5,2,10:21:37 AM,5,2,10:21:47 AM,5,2,10:22:40 AM,5,2,10:23:59 AM,6,17.9,4.1,-1.2599563,36.799343900000004,-1.2824577,36.7967953,Rider_Id_100
-Order_No_3208,User_Id_794,Bike,2,Personal,16,6,9:26:35 AM,16,6,9:29:48 AM,16,6,9:30:18 AM,16,6,9:34:42 AM,4,15.5,,-1.2629944,36.7998816,-1.2811695,36.8200313,Rider_Id_379
-Order_No_7431,User_Id_892,Bike,2,Personal,29,5,3:59:08 PM,29,5,4:15:10 PM,29,5,4:39:01 PM,29,5,4:42:12 PM,9,20.5,,-1.306735,36.8256226,-1.2606445,36.8185586,Rider_Id_549
-Order_No_7776,User_Id_2956,Bike,3,Business,14,4,3:49:26 PM,14,4,3:51:31 PM,14,4,3:54:55 PM,14,4,3:58:13 PM,3,27.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_793
-Order_No_13036,User_Id_269,Bike,3,Business,25,4,3:15:21 PM,25,4,4:13:57 PM,25,4,4:17:32 PM,25,4,4:21:16 PM,10,20.8,,-1.3332749,36.870814700000004,-1.2810586000000002,36.883829399999996,Rider_Id_620
-Order_No_17432,User_Id_136,Bike,3,Business,22,2,9:26:40 AM,22,2,9:27:06 AM,22,2,9:31:30 AM,22,2,9:35:25 AM,8,17.3,,-1.3025826,36.7670807,-1.2560874,36.7931031,Rider_Id_65
-Order_No_621,User_Id_1245,Bike,3,Business,24,4,3:53:44 PM,24,4,3:53:54 PM,24,4,3:53:55 PM,24,4,4:00:38 PM,17,28.4,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519
-Order_No_7711,User_Id_391,Bike,3,Business,21,2,12:10:10 PM,21,2,12:14:52 PM,21,2,12:17:01 PM,21,2,1:10:26 PM,11,22.3,,-1.2784063,36.8222933,-1.2105983999999999,36.8109774,Rider_Id_564
-Order_No_17121,User_Id_1578,Bike,3,Business,7,3,12:25:48 PM,7,3,12:32:56 PM,7,3,12:55:12 PM,7,3,12:56:20 PM,5,25.3,,-1.2571472,36.795063299999995,-1.28878,36.8168312,Rider_Id_266
-Order_No_7415,User_Id_3669,Bike,3,Business,8,2,2:35:47 PM,8,2,2:40:20 PM,8,2,2:47:46 PM,8,2,2:54:15 PM,11,25.2,,-1.2615888000000002,36.7928732,-1.308819,36.8421201,Rider_Id_870
-Order_No_9161,User_Id_1578,Bike,3,Business,29,1,1:26:49 PM,29,1,2:14:08 PM,29,1,2:27:33 PM,29,1,2:40:12 PM,10,24.6,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_101
-Order_No_16019,User_Id_1919,Bike,3,Business,21,5,11:31:22 AM,21,5,11:33:18 AM,21,5,11:45:18 AM,21,5,11:58:56 AM,19,22.7,,-1.2658315,36.809049,-1.332624,36.913187,Rider_Id_30
-Order_No_11222,User_Id_1329,Bike,3,Business,30,6,11:07:36 AM,30,6,11:07:51 AM,30,6,11:11:51 AM,30,6,11:16:19 AM,3,26.6,,-1.273056,36.811298,-1.2534301,36.800098299999995,Rider_Id_751
-Order_No_7472,User_Id_1932,Bike,1,Business,16,1,9:12:02 AM,16,1,9:43:10 AM,16,1,9:57:08 AM,16,1,10:01:07 AM,7,14.5,,-1.2937193,36.8236214,-1.2985986999999999,36.7809119,Rider_Id_726
-Order_No_18960,User_Id_640,Bike,3,Business,27,3,2:57:32 PM,27,3,2:58:06 PM,27,3,3:09:06 PM,27,3,3:11:41 PM,6,29.1,,-1.2969741000000001,36.7856612,-1.28359,36.823091999999995,Rider_Id_812
-Order_No_20286,User_Id_3586,Bike,3,Business,6,2,2:06:48 PM,6,2,2:43:30 PM,6,2,3:04:04 PM,6,2,3:13:17 PM,9,27.3,,-1.2854453000000001,36.818836299999994,-1.275442,36.766020700000006,Rider_Id_548
-Order_No_22303,User_Id_242,Bike,3,Business,9,3,11:21:24 AM,9,3,11:22:07 AM,9,3,12:07:43 PM,9,3,12:17:41 PM,6,21.0,,-1.2559559999999999,36.789867,-1.2803861,36.825950899999995,Rider_Id_771
-Order_No_14994,User_Id_3724,Bike,1,Personal,30,3,9:20:35 AM,30,3,9:22:27 AM,30,3,9:45:51 AM,30,3,9:51:46 AM,10,17.4,,-1.3205294,36.819726200000005,-1.2571421999999999,36.801562700000005,Rider_Id_208
-Order_No_25585,User_Id_1513,Bike,1,Personal,27,4,12:11:58 PM,27,4,12:16:46 PM,27,4,12:28:54 PM,27,4,12:32:51 PM,7,22.9,,-1.2574219,36.7927073,-1.2929431999999998,36.821506799999995,Rider_Id_844
-Order_No_7153,User_Id_2956,Bike,3,Business,27,1,3:00:22 PM,27,1,3:01:13 PM,27,1,3:03:56 PM,27,1,3:38:42 PM,7,22.9,,-1.2584143,36.8048002,-1.3004062,36.829741,Rider_Id_624
-Order_No_21542,User_Id_2634,Bike,3,Business,29,1,3:20:02 PM,29,1,3:20:17 PM,29,1,3:24:24 PM,29,1,3:29:37 PM,21,,,-1.3039411,36.7695316,-1.3160976999999998,36.913164200000004,Rider_Id_854
-Order_No_24260,User_Id_1205,Bike,3,Business,1,4,10:40:12 AM,1,4,10:40:21 AM,1,4,10:44:04 AM,1,4,10:47:43 AM,8,,,-1.257309,36.806008399999996,-1.2839651,36.789170399999996,Rider_Id_726
-Order_No_28214,User_Id_3283,Bike,3,Business,27,1,1:06:13 PM,27,1,1:07:10 PM,27,1,1:17:47 PM,27,1,1:31:11 PM,13,20.7,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_16
-Order_No_8095,User_Id_3647,Bike,3,Business,6,3,4:21:08 PM,6,3,4:21:29 PM,6,3,4:31:31 PM,6,3,4:37:35 PM,20,,,-1.2726389999999999,36.794723,-1.1834466000000001,36.8887328,Rider_Id_520
-Order_No_8216,User_Id_2200,Bike,3,Business,26,5,3:31:23 PM,26,5,3:43:06 PM,26,5,3:51:58 PM,26,5,3:59:51 PM,6,,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_237
-Order_No_25983,User_Id_120,Bike,1,Personal,8,2,2:28:42 PM,8,2,2:29:47 PM,8,2,2:59:13 PM,8,2,3:01:12 PM,14,23.2,,-1.2905033000000001,36.8096462,-1.2927978999999998,36.89817,Rider_Id_654
-Order_No_7138,User_Id_2330,Bike,3,Business,14,4,4:44:20 PM,14,4,4:46:17 PM,14,4,5:05:06 PM,14,4,5:10:12 PM,15,26.0,,-1.2854537,36.9051024,-1.2291477,36.8440295,Rider_Id_588
-Order_No_24979,User_Id_3635,Bike,1,Business,9,2,12:44:18 PM,9,2,12:45:28 PM,9,2,12:50:33 PM,9,2,12:58:20 PM,20,22.0,,-1.2615888000000002,36.7928732,-1.3292721,36.6656534,Rider_Id_848
-Order_No_9778,User_Id_265,Bike,3,Business,4,5,2:53:12 PM,4,5,3:05:41 PM,4,5,3:42:39 PM,4,5,3:57:53 PM,5,,,-1.300921,36.828195,-1.3049875,36.86334179999999,Rider_Id_332
-Order_No_11969,User_Id_2523,Bike,3,Business,21,4,11:22:49 AM,21,4,11:24:11 AM,21,4,11:37:09 AM,21,4,11:38:37 AM,7,28.2,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_682
-Order_No_16911,User_Id_1568,Bike,3,Business,21,4,3:33:00 PM,21,4,3:45:53 PM,21,4,3:53:30 PM,21,4,4:01:08 PM,7,22.4,,-1.2897368,36.8226783,-1.2551895,36.7822034,Rider_Id_698
-Order_No_12226,User_Id_1341,Bike,3,Business,24,3,1:55:28 PM,24,3,2:20:42 PM,24,3,2:29:20 PM,24,3,2:39:27 PM,17,,,-1.2297799999999999,36.836215,-1.331619,36.847976,Rider_Id_75
-Order_No_2057,User_Id_2201,Bike,3,Personal,9,2,3:47:24 PM,9,2,3:49:51 PM,9,2,4:31:03 PM,9,2,5:01:19 PM,18,27.9,,-1.3195373,36.923047,-1.2556108,36.8400444,Rider_Id_300
-Order_No_6507,User_Id_136,Bike,3,Business,23,5,12:54:21 PM,23,5,1:18:16 PM,23,5,1:30:03 PM,23,5,1:32:06 PM,22,26.3,,-1.3326173000000001,36.71462329999999,-1.3332749,36.870814700000004,Rider_Id_219
-Order_No_11283,User_Id_2439,Bike,3,Business,20,3,1:56:58 PM,20,3,2:03:14 PM,20,3,2:24:43 PM,20,3,2:35:30 PM,7,29.4,,-1.2325706,36.808392299999994,-1.2534301,36.800098299999995,Rider_Id_245
-Order_No_4355,User_Id_2066,Bike,3,Business,27,3,9:55:42 AM,27,3,9:58:12 AM,27,3,10:12:51 AM,27,3,10:14:50 AM,7,,,-1.3210188999999999,36.841055100000005,-1.2850638,36.813096,Rider_Id_411
-Order_No_9035,User_Id_2801,Bike,3,Business,31,3,9:15:30 AM,31,3,9:15:51 AM,31,3,9:23:08 AM,31,3,9:25:00 AM,3,19.2,,-1.2595421999999998,36.7871178,-1.2566586000000002,36.792968200000004,Rider_Id_167
-Order_No_24683,User_Id_142,Bike,3,Business,9,2,1:58:53 PM,9,2,1:59:40 PM,9,2,2:09:07 PM,9,2,2:27:39 PM,8,30.6,,-1.2963097,36.7688221,-1.2630694,36.7743511,Rider_Id_269
-Order_No_24542,User_Id_1500,Bike,3,Business,21,1,1:18:56 PM,21,1,2:08:59 PM,21,1,2:18:05 PM,21,1,2:31:30 PM,10,21.6,,-1.3177546999999998,36.8303703,-1.2569006,36.7925141,Rider_Id_948
-Order_No_23034,User_Id_2987,Bike,3,Business,23,2,10:20:28 AM,23,2,11:38:22 AM,23,2,11:52:34 AM,23,2,12:01:17 PM,7,17.8,,-1.2702120000000001,36.82108770000001,-1.3064585,36.8119065,Rider_Id_518
-Order_No_20248,User_Id_718,Bike,3,Business,13,3,9:49:08 AM,13,3,9:49:37 AM,13,3,10:03:41 AM,13,3,10:07:35 AM,9,20.0,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_76
-Order_No_5484,User_Id_1148,Bike,3,Business,7,4,3:39:57 PM,7,4,3:40:11 PM,7,4,3:40:54 PM,7,4,4:51:47 PM,12,25.6,,-1.3100586,36.850032899999995,-1.2559559999999999,36.772901,Rider_Id_774
-Order_No_12603,User_Id_3647,Bike,3,Business,3,4,2:27:35 PM,3,4,2:28:37 PM,3,4,2:46:29 PM,3,4,2:52:42 PM,5,25.9,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_932
-Order_No_17946,User_Id_1246,Bike,3,Business,20,1,3:47:20 PM,20,1,3:47:53 PM,20,1,3:49:03 PM,20,1,4:16:32 PM,2,,,-1.2571472,36.795063299999995,-1.2665327,36.8108799,Rider_Id_536
-Order_No_5598,User_Id_2219,Bike,3,Business,30,2,1:34:40 PM,30,2,1:44:43 PM,30,2,1:56:05 PM,30,2,2:00:04 PM,6,,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_404
-Order_No_11653,User_Id_3283,Bike,3,Business,24,5,3:11:10 PM,24,5,3:11:24 PM,24,5,3:14:22 PM,24,5,3:20:34 PM,4,22.9,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_770
-Order_No_16802,User_Id_635,Bike,3,Business,28,4,2:01:03 PM,28,4,2:01:20 PM,28,4,2:06:03 PM,28,4,2:22:12 PM,14,27.5,,-1.3081376,36.83297720000001,-1.2171851999999999,36.8145508,Rider_Id_519
-Order_No_27764,User_Id_496,Bike,3,Business,26,5,4:52:58 PM,26,5,5:52:40 PM,26,5,6:00:36 PM,26,5,6:03:51 PM,9,,,-1.2551895,36.7822034,-1.2976558999999999,36.791414700000004,Rider_Id_336
-Order_No_13718,User_Id_3222,Bike,3,Business,26,1,12:47:05 PM,26,1,1:07:14 PM,26,1,1:21:44 PM,26,1,1:29:46 PM,5,22.9,,-1.2971153999999998,36.7963489,-1.2821378,36.7740428,Rider_Id_38
-Order_No_3501,User_Id_3222,Bike,3,Business,11,5,9:53:00 AM,11,5,9:54:54 AM,11,5,9:58:49 AM,11,5,10:16:35 AM,11,21.0,,-1.2551895,36.7822034,-1.3173868999999998,36.8112044,Rider_Id_205
-Order_No_22240,User_Id_2195,Bike,3,Business,17,3,12:56:20 PM,17,3,12:56:32 PM,17,3,1:11:23 PM,17,3,1:20:29 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_8
-Order_No_34,User_Id_426,Bike,3,Business,30,2,2:04:33 PM,30,2,2:05:23 PM,30,2,2:26:09 PM,30,2,2:36:16 PM,5,27.5,,-1.3323107,36.8692616,-1.3048663,36.8535754,Rider_Id_276
-Order_No_22815,User_Id_53,Bike,3,Business,15,6,9:41:46 AM,15,6,9:43:29 AM,15,6,9:50:16 AM,15,6,10:26:09 AM,11,16.7,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_161
-Order_No_23777,User_Id_867,Bike,3,Business,14,4,2:50:06 PM,14,4,2:51:31 PM,14,4,3:01:47 PM,14,4,3:40:09 PM,14,28.0,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_85
-Order_No_24022,User_Id_176,Bike,1,Personal,24,4,3:13:49 PM,24,4,5:04:50 PM,24,4,5:30:14 PM,24,4,5:47:19 PM,3,21.3,,-1.2864298,36.8224345,-1.2666695,36.8328283,Rider_Id_752
-Order_No_19865,User_Id_2174,Bike,3,Business,5,5,10:28:06 AM,5,5,10:28:33 AM,5,5,10:28:37 AM,5,5,11:29:00 AM,16,24.6,,-1.2482781,36.884091999999995,-1.1668055,36.830444799999995,Rider_Id_86
-Order_No_5685,User_Id_3434,Bike,3,Business,1,3,1:17:17 PM,1,3,1:18:02 PM,1,3,1:27:28 PM,1,3,2:01:17 PM,12,23.8,,-1.3049935,36.8572897,-1.2963097,36.7688221,Rider_Id_414
-Order_No_25281,User_Id_1940,Bike,1,Personal,21,4,10:43:03 AM,21,4,10:49:41 AM,21,4,10:57:46 AM,21,4,11:19:43 AM,10,21.8,,-1.2763120000000001,36.882742,-1.3157973,36.815148,Rider_Id_549
-Order_No_17440,User_Id_111,Bike,3,Business,10,4,11:18:09 AM,10,4,11:21:26 AM,10,4,12:12:46 PM,10,4,12:24:44 PM,7,24.6,,-1.2551895,36.7822034,-1.2871938,36.8195031,Rider_Id_831
-Order_No_23648,User_Id_893,Bike,3,Business,28,3,10:14:05 AM,28,3,10:14:28 AM,28,3,10:18:36 AM,28,3,10:21:59 AM,8,20.3,,-1.2663194,36.809901,-1.3019530000000001,36.788458,Rider_Id_727
-Order_No_26653,User_Id_1286,Bike,1,Personal,30,6,3:52:45 PM,30,6,4:03:37 PM,30,6,4:28:57 PM,30,6,4:45:18 PM,6,21.8,,-1.2819626,36.7540971,-1.2943360000000002,36.795332,Rider_Id_522
-Order_No_15940,User_Id_3647,Bike,3,Business,27,3,1:25:25 PM,27,3,1:26:07 PM,27,3,1:38:35 PM,27,3,1:45:45 PM,8,29.5,,-1.2726389999999999,36.794723,-1.2575690000000002,36.842164000000004,Rider_Id_907
-Order_No_20394,User_Id_3698,Bike,3,Business,4,2,3:05:36 PM,4,2,4:06:53 PM,4,2,4:18:31 PM,4,2,4:39:46 PM,22,22.5,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_883
-Order_No_11788,User_Id_3291,Bike,3,Business,17,4,10:07:22 AM,17,4,10:08:43 AM,17,4,10:30:47 AM,17,4,10:36:39 AM,6,25.2,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_899
-Order_No_25126,User_Id_391,Bike,3,Business,11,2,11:51:02 AM,11,2,1:31:21 PM,11,2,1:57:29 PM,11,2,2:06:30 PM,5,22.2,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_47
-Order_No_24569,User_Id_635,Bike,3,Business,19,1,1:23:12 PM,19,1,1:33:49 PM,19,1,1:41:35 PM,19,1,1:44:17 PM,6,25.1,,-1.2599563,36.799343900000004,-1.2951243000000001,36.828134399999996,Rider_Id_447
-Order_No_6733,User_Id_3718,Bike,3,Business,20,2,11:19:39 AM,20,2,11:19:49 AM,20,2,11:24:52 AM,20,2,11:33:20 AM,3,19.9,,-1.2561334,36.7990458,-1.2650027,36.8126242,Rider_Id_932
-Order_No_3555,User_Id_868,Bike,3,Business,7,5,3:30:24 PM,7,5,3:34:44 PM,7,5,3:37:54 PM,7,5,4:04:43 PM,6,,,-1.307143,36.825009,-1.2663631000000002,36.8163329,Rider_Id_2
-Order_No_27931,User_Id_2425,Bike,1,Personal,3,1,3:08:38 PM,3,1,3:10:26 PM,3,1,3:34:43 PM,3,1,3:36:34 PM,6,22.8,,-1.2840002000000001,36.829921999999996,-1.3195185,36.8180902,Rider_Id_857
-Order_No_7043,User_Id_598,Bike,2,Business,12,5,12:02:14 PM,12,5,12:02:44 PM,12,5,12:14:08 PM,12,5,12:29:46 PM,19,24.5,,-1.2138373,36.799857200000005,-1.2890253999999999,36.7990753,Rider_Id_45
-Order_No_11392,User_Id_1148,Bike,3,Business,5,1,12:21:58 PM,5,1,12:22:19 PM,5,1,12:28:13 PM,5,1,12:40:07 PM,10,24.4,,-1.3100586,36.850032899999995,-1.2568001,36.8215083,Rider_Id_848
-Order_No_23978,User_Id_2829,Bike,3,Business,9,4,5:23:11 PM,9,4,5:23:42 PM,9,4,5:32:10 PM,9,4,5:40:26 PM,6,23.2,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_898
-Order_No_24536,User_Id_3688,Bike,3,Business,13,3,4:45:17 PM,13,3,4:46:23 PM,13,3,4:52:05 PM,13,3,5:05:26 PM,3,25.8,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_764
-Order_No_14379,User_Id_393,Bike,3,Business,11,2,2:32:23 PM,11,2,2:34:12 PM,11,2,2:46:57 PM,11,2,2:50:19 PM,4,21.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_2
-Order_No_4298,User_Id_2123,Bike,1,Personal,15,1,9:22:26 AM,15,1,9:23:31 AM,15,1,9:58:53 AM,15,1,10:02:13 AM,3,,,-1.2833335,36.8260084,-1.2969035,36.80838060000001,Rider_Id_338
-Order_No_2114,User_Id_136,Bike,3,Business,2,4,11:02:04 AM,2,4,11:19:44 AM,2,4,11:27:10 AM,2,4,11:33:41 AM,16,,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_675
-Order_No_10951,User_Id_3175,Bike,3,Business,6,2,2:08:20 PM,6,2,2:10:07 PM,6,2,2:55:02 PM,6,2,2:57:39 PM,11,27.7,,-1.2681003999999998,36.7952081,-1.3104733999999998,36.7363698,Rider_Id_532
-Order_No_10512,User_Id_2330,Bike,3,Business,18,1,12:51:27 PM,18,1,12:51:51 PM,18,1,1:11:56 PM,18,1,1:18:44 PM,13,27.8,,-1.2680363000000001,36.799046600000004,-1.2524916000000001,36.8656196,Rider_Id_205
-Order_No_10640,User_Id_2066,Bike,3,Business,23,6,10:08:24 AM,23,6,10:09:18 AM,23,6,10:30:46 AM,23,6,10:36:09 AM,4,26.3,,-1.3210188999999999,36.841055100000005,-1.3112893,36.8201664,Rider_Id_448
-Order_No_19953,User_Id_2974,Bike,3,Business,24,4,12:42:47 PM,24,4,12:44:29 PM,24,4,1:30:21 PM,24,4,1:34:58 PM,4,22.9,,-1.2987748,36.8024593,-1.287428,36.7800821,Rider_Id_752
-Order_No_5679,User_Id_635,Bike,3,Business,23,5,1:55:59 PM,23,5,2:03:01 PM,23,5,2:10:19 PM,23,5,2:32:57 PM,8,26.5,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_237
-Order_No_13398,User_Id_2383,Bike,3,Business,28,3,8:33:07 AM,28,3,8:33:21 AM,28,3,8:37:38 AM,28,3,8:45:10 AM,1,20.5,,-1.2608993,36.799246000000004,-1.2631940000000002,36.797624,Rider_Id_407
-Order_No_10506,User_Id_248,Bike,3,Business,7,3,10:47:14 AM,7,3,10:47:28 AM,7,3,10:55:07 AM,7,3,11:03:18 AM,9,24.9,,-1.304735,36.784605400000004,-1.2635089,36.78873370000001,Rider_Id_88
-Order_No_7087,User_Id_111,Bike,3,Business,9,3,10:16:04 AM,9,3,10:16:33 AM,9,3,10:17:37 AM,9,3,10:27:38 AM,2,20.0,,-1.2551895,36.7822034,-1.2569503,36.78347279999999,Rider_Id_322
-Order_No_23911,User_Id_3404,Bike,3,Business,6,3,2:29:18 PM,6,3,2:30:33 PM,6,3,2:41:02 PM,6,3,2:48:57 PM,22,27.5,,-1.279395,36.825364,-1.159242,36.922571000000005,Rider_Id_812
-Order_No_7604,User_Id_2896,Bike,3,Business,22,4,3:11:24 PM,22,4,3:35:56 PM,22,4,4:13:43 PM,22,4,4:31:54 PM,12,26.6,,-1.3338005,36.863026,-1.2715686000000002,36.8023526,Rider_Id_882
-Order_No_2572,User_Id_2828,Bike,2,Business,7,6,9:30:41 AM,7,6,9:31:09 AM,7,6,9:31:35 AM,7,6,10:02:12 AM,3,14.3,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_51
-Order_No_974,User_Id_3149,Bike,3,Business,8,5,9:50:20 AM,8,5,9:51:22 AM,8,5,10:19:26 AM,8,5,10:36:35 AM,7,28.5,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_916
-Order_No_3086,User_Id_3361,Bike,3,Personal,16,4,12:48:45 PM,16,4,12:48:55 PM,16,4,1:09:43 PM,16,4,1:33:34 PM,19,19.0,,-1.2880351,36.828638899999994,-1.3297531,36.7063699,Rider_Id_648
-Order_No_20489,User_Id_3149,Bike,3,Business,15,4,3:04:54 PM,15,4,3:25:00 PM,15,4,3:25:55 PM,15,4,4:01:59 PM,6,27.7,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_623
-Order_No_8185,User_Id_3222,Bike,3,Business,29,1,2:57:00 PM,29,1,3:04:16 PM,29,1,3:20:53 PM,29,1,4:05:22 PM,8,24.2,,-1.2551895,36.7822034,-1.2939132,36.8081606,Rider_Id_606
-Order_No_20584,User_Id_1500,Bike,3,Business,2,1,1:51:06 PM,2,1,2:24:50 PM,2,1,2:25:16 PM,2,1,3:16:19 PM,8,19.9,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_656
-Order_No_5276,User_Id_867,Bike,3,Business,1,5,2:37:22 PM,1,5,2:37:49 PM,1,5,2:40:12 PM,1,5,2:47:28 PM,14,31.8,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_239
-Order_No_19966,User_Id_1146,Bike,3,Business,6,4,1:55:47 PM,6,4,2:03:57 PM,6,4,2:26:40 PM,6,4,2:39:12 PM,6,21.2,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_2
-Order_No_1304,User_Id_53,Bike,3,Business,11,4,10:36:09 AM,11,4,10:36:52 AM,11,4,11:03:14 AM,11,4,11:08:58 AM,11,25.7,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_448
-Order_No_14070,User_Id_909,Bike,3,Business,21,4,1:34:00 PM,21,4,1:37:16 PM,21,4,2:00:47 PM,21,4,2:04:29 PM,10,26.5,,-1.225322,36.80855,-1.2885315,36.8230419,Rider_Id_452
-Order_No_10083,User_Id_1029,Bike,2,Personal,28,4,3:07:00 PM,28,4,3:08:18 PM,28,4,3:30:15 PM,28,4,3:33:02 PM,8,,,-1.2507752,36.755662799999996,-1.2745187,36.787643599999996,Rider_Id_334
-Order_No_12950,User_Id_3201,Bike,3,Business,21,4,3:55:55 PM,21,4,3:56:08 PM,21,4,4:02:27 PM,21,4,4:11:58 PM,7,30.8,,-1.2584143,36.8048002,-1.2857671,36.7875614,Rider_Id_858
-Order_No_23948,User_Id_2763,Bike,3,Business,10,1,3:40:09 PM,10,1,4:35:07 PM,10,1,4:35:49 PM,10,1,4:45:52 PM,16,19.0,,-1.2551895,36.7822034,-1.332175,36.8819641,Rider_Id_108
-Order_No_7103,User_Id_2561,Bike,3,Business,19,2,10:46:57 AM,19,2,10:50:22 AM,19,2,11:03:35 AM,19,2,11:09:07 AM,8,,,-1.2551895,36.7822034,-1.2819711,36.835333299999995,Rider_Id_752
-Order_No_23108,User_Id_186,Bike,3,Business,20,2,10:17:04 AM,20,2,10:35:18 AM,20,2,10:42:49 AM,20,2,10:49:10 AM,4,19.7,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_259
-Order_No_6917,User_Id_3222,Bike,3,Business,24,3,3:05:04 PM,24,3,3:06:21 PM,24,3,3:13:30 PM,24,3,3:17:48 PM,8,18.5,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_775
-Order_No_23123,User_Id_2846,Bike,1,Personal,23,6,3:53:55 PM,23,6,3:56:37 PM,23,6,4:05:56 PM,23,6,4:28:34 PM,4,,,-1.2863906999999999,36.8194287,-1.2780823000000001,36.7960191,Rider_Id_340
-Order_No_4585,User_Id_1544,Bike,3,Business,3,4,1:12:50 PM,3,4,1:39:35 PM,3,4,2:21:29 PM,3,4,2:29:59 PM,8,24.8,,-1.2786917,36.8252217,-1.3232881,36.8537357,Rider_Id_196
-Order_No_12689,User_Id_2801,Bike,3,Business,22,5,12:03:12 PM,22,5,12:03:28 PM,22,5,12:18:52 PM,22,5,12:46:16 PM,15,,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_883
-Order_No_7151,User_Id_2066,Bike,3,Business,1,3,10:07:58 AM,1,3,10:08:15 AM,1,3,10:19:00 AM,1,3,10:23:02 AM,5,,,-1.316961,36.820337,-1.303711,36.81549329999999,Rider_Id_229
-Order_No_10071,User_Id_2386,Bike,1,Personal,18,3,11:57:30 AM,18,3,11:57:42 AM,18,3,12:00:27 PM,18,3,12:25:46 PM,20,20.1,,-1.1855066,36.825464600000004,-1.2924639,36.77957979999999,Rider_Id_334
-Order_No_2599,User_Id_3091,Bike,3,Business,14,1,3:19:04 PM,14,1,3:19:30 PM,14,1,3:35:39 PM,14,1,3:41:15 PM,8,25.7,,-1.3012008,36.764868,-1.2615888000000002,36.7928732,Rider_Id_328
-Order_No_200,User_Id_1594,Bike,3,Business,8,3,10:32:28 AM,8,3,10:33:01 AM,8,3,10:44:42 AM,8,3,11:08:03 AM,8,,,-1.2571472,36.795063299999995,-1.225322,36.80855,Rider_Id_819
-Order_No_25033,User_Id_3747,Bike,1,Personal,24,2,12:13:52 PM,24,2,12:14:16 PM,24,2,12:23:01 PM,24,2,12:24:18 PM,20,17.5,,-1.2602386,36.7503491,-1.2628423999999998,36.8950641,Rider_Id_735
-Order_No_19854,User_Id_393,Bike,3,Business,18,5,8:34:45 AM,18,5,8:35:35 AM,18,5,8:58:59 AM,18,5,9:41:51 AM,11,20.0,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_436
-Order_No_12343,User_Id_3565,Bike,3,Business,2,6,11:32:49 AM,2,6,11:33:43 AM,2,6,11:39:44 AM,2,6,11:50:23 AM,18,27.7,,-1.2844665000000002,36.7866113,-1.207546,36.7913976,Rider_Id_862
-Order_No_1338,User_Id_2764,Bike,3,Personal,14,2,2:27:29 PM,14,2,2:29:30 PM,14,2,3:18:44 PM,14,2,4:17:52 PM,13,,,-1.2810888999999999,36.8247135,-1.315194,36.9074428,Rider_Id_61
-Order_No_11805,User_Id_1724,Bike,3,Business,11,2,8:27:01 AM,11,2,8:38:18 AM,11,2,8:50:15 AM,11,2,8:53:38 AM,12,16.4,,-1.2852139,36.751683,-1.3325582,36.740669700000005,Rider_Id_576
-Order_No_21231,User_Id_2419,Bike,1,Personal,25,3,3:25:22 PM,25,3,3:25:51 PM,25,3,3:33:35 PM,25,3,3:43:10 PM,9,17.8,,-1.2551895,36.7822034,-1.3072409999999999,36.830236799999994,Rider_Id_519
-Order_No_25690,User_Id_2330,Bike,3,Business,17,3,6:42:49 PM,17,3,6:43:56 PM,17,3,6:53:42 PM,17,3,6:56:29 PM,10,,,-1.3040812,36.7924217,-1.3192788999999998,36.711299100000005,Rider_Id_269
-Order_No_18669,User_Id_1687,Bike,3,Business,30,3,10:54:25 AM,30,3,10:55:02 AM,30,3,11:10:33 AM,30,3,11:16:39 AM,3,24.8,,-1.2649605,36.798177700000004,-1.2729802,36.793734,Rider_Id_611
-Order_No_17031,User_Id_419,Bike,3,Business,7,2,2:25:53 PM,7,2,2:26:30 PM,7,2,2:29:59 PM,7,2,2:43:57 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_233
-Order_No_20637,User_Id_1329,Bike,3,Business,3,3,6:32:04 PM,3,3,6:32:35 PM,3,3,6:42:17 PM,3,3,6:53:37 PM,6,27.2,,-1.2882120000000001,36.778334,-1.273056,36.811298,Rider_Id_855
-Order_No_7093,User_Id_3203,Bike,3,Business,6,1,2:50:25 PM,6,1,2:51:09 PM,6,1,2:55:45 PM,6,1,3:06:53 PM,9,24.8,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_427
-Order_No_943,User_Id_2423,Bike,3,Business,18,2,10:14:07 AM,18,2,10:45:07 AM,18,2,10:58:56 AM,18,2,11:21:39 AM,8,,,-1.2551895,36.7822034,-1.294869,36.815327,Rider_Id_925
-Order_No_11030,User_Id_111,Bike,3,Business,3,1,11:06:47 AM,3,1,11:07:58 AM,3,1,11:08:39 AM,3,1,11:21:06 AM,12,20.7,,-1.2551895,36.7822034,-1.3077749,36.7747247,Rider_Id_107
-Order_No_6924,User_Id_3517,Bike,3,Business,31,5,10:32:14 AM,31,5,10:32:36 AM,31,5,10:37:39 AM,31,5,11:00:49 AM,19,21.9,,-1.3207708,36.8685919,-1.3882008,36.7699445,Rider_Id_243
-Order_No_15395,User_Id_2154,Bike,3,Business,31,3,11:48:29 AM,31,3,11:49:28 AM,31,3,11:55:32 AM,31,3,12:03:39 PM,5,24.8,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_878
-Order_No_26652,User_Id_1142,Bike,1,Personal,23,3,10:51:15 AM,23,3,10:52:37 AM,23,3,11:08:47 AM,23,3,11:21:25 AM,9,21.6,,-1.2850416999999998,36.8074973,-1.2463211,36.789487,Rider_Id_499
-Order_No_21624,User_Id_3560,Bike,3,Business,16,2,2:03:18 PM,16,2,2:03:34 PM,16,2,2:05:30 PM,16,2,2:50:12 PM,6,27.2,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_477
-Order_No_10565,User_Id_2096,Bike,1,Personal,27,4,11:20:10 AM,27,4,11:20:18 AM,27,4,11:23:55 AM,27,4,11:40:46 AM,20,22.8,,-1.2139708,36.882646,-1.2965,36.7721234,Rider_Id_704
-Order_No_22484,User_Id_235,Bike,1,Personal,17,3,4:15:37 PM,17,3,4:18:13 PM,17,3,4:34:19 PM,17,3,4:41:23 PM,13,24.7,,-1.291623,36.814209999999996,-1.2643343,36.7447423,Rider_Id_38
-Order_No_14863,User_Id_2956,Bike,3,Business,7,2,4:03:39 PM,7,2,4:04:03 PM,7,2,4:10:03 PM,7,2,4:14:33 PM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_177
-Order_No_22680,User_Id_2803,Bike,3,Business,9,5,9:33:45 AM,9,5,9:49:47 AM,9,5,9:53:12 AM,9,5,9:56:45 AM,5,20.3,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_114
-Order_No_3697,User_Id_3247,Bike,3,Business,24,3,11:56:18 AM,24,3,11:57:34 AM,24,3,12:02:17 PM,24,3,12:10:04 PM,12,19.8,,-1.2571472,36.795063299999995,-1.318524,36.85537670000001,Rider_Id_614
-Order_No_14773,User_Id_1500,Bike,3,Business,15,5,9:50:01 AM,15,5,9:50:49 AM,15,5,9:57:56 AM,15,5,10:09:03 AM,8,20.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_25
-Order_No_8113,User_Id_2200,Bike,3,Business,25,2,10:40:17 AM,25,2,10:59:32 AM,25,2,11:05:42 AM,25,2,11:36:26 AM,6,23.6,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_216
-Order_No_19974,User_Id_391,Bike,3,Business,25,4,11:49:39 AM,25,4,11:50:42 AM,25,4,12:04:09 PM,25,4,12:44:43 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_921
-Order_No_3691,User_Id_3698,Bike,3,Business,15,1,3:03:33 PM,15,1,3:32:26 PM,15,1,3:47:06 PM,15,1,4:00:26 PM,11,,,-1.3107918,36.761794,-1.2864733,36.8239937,Rider_Id_855
-Order_No_4489,User_Id_3283,Bike,3,Business,4,2,12:07:14 PM,4,2,12:07:41 PM,4,2,12:07:53 PM,4,2,12:21:04 PM,6,20.5,,-1.2663631000000002,36.8163329,-1.2628473,36.781805,Rider_Id_800
-Order_No_22188,User_Id_868,Bike,3,Business,9,4,3:41:41 PM,9,4,3:44:02 PM,9,4,4:12:50 PM,9,4,4:23:03 PM,7,25.6,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_85
-Order_No_9876,User_Id_3283,Bike,3,Business,28,5,11:55:54 AM,28,5,11:57:07 AM,28,5,11:59:53 AM,28,5,12:09:02 PM,18,23.8,,-1.2628473,36.781805,-1.2967066,36.886073700000004,Rider_Id_770
-Order_No_12897,User_Id_2627,Bike,3,Business,12,3,2:07:01 PM,12,3,2:09:18 PM,12,3,2:16:16 PM,12,3,2:22:36 PM,8,21.1,,-1.2571472,36.795063299999995,-1.3080696000000003,36.8237191,Rider_Id_709
-Order_No_7278,User_Id_136,Bike,3,Business,4,2,3:43:14 PM,4,2,3:43:52 PM,4,2,3:48:06 PM,4,2,3:53:06 PM,8,23.9,10.9,-1.2962832,36.784968299999996,-1.2649605,36.798177700000004,Rider_Id_454
-Order_No_16250,User_Id_2579,Bike,3,Business,6,2,10:23:26 AM,6,2,10:23:36 AM,6,2,10:28:38 AM,6,2,10:32:30 AM,7,24.8,,-1.287428,36.7800821,-1.3031315,36.8175661,Rider_Id_854
-Order_No_5808,User_Id_1604,Bike,3,Personal,3,5,6:02:32 PM,3,5,6:04:41 PM,3,5,6:21:09 PM,3,5,6:30:08 PM,6,22.8,,-1.2615584,36.814950599999996,-1.2670847,36.7858231,Rider_Id_174
-Order_No_9961,User_Id_2956,Bike,3,Business,1,1,3:12:30 PM,1,1,3:22:53 PM,1,1,3:34:47 PM,1,1,3:38:13 PM,4,,,-1.2584143,36.8048002,-1.2821421999999998,36.8161565,Rider_Id_506
-Order_No_6322,User_Id_3354,Bike,3,Business,24,4,1:03:01 PM,24,4,1:50:15 PM,24,4,1:51:27 PM,24,4,1:54:56 PM,2,28.1,,-1.3700383,36.9190174,-1.3708843000000002,36.921096,Rider_Id_746
-Order_No_16457,User_Id_868,Bike,3,Business,12,2,1:44:31 PM,12,2,1:45:29 PM,12,2,1:56:01 PM,12,2,2:00:26 PM,9,,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_728
-Order_No_7448,User_Id_718,Bike,3,Business,7,4,3:11:29 PM,7,4,3:11:40 PM,7,4,3:17:51 PM,7,4,3:19:27 PM,3,31.3,,-1.2551895,36.7822034,-1.2634530000000002,36.7955612,Rider_Id_25
-Order_No_9202,User_Id_3283,Bike,3,Business,9,6,1:42:40 PM,9,6,1:42:55 PM,9,6,1:49:43 PM,9,6,2:00:08 PM,9,19.4,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_844
-Order_No_16593,User_Id_2801,Bike,3,Business,12,2,1:23:55 PM,12,2,1:25:00 PM,12,2,1:29:08 PM,12,2,1:35:32 PM,9,,,-1.2674273999999999,36.787082700000006,-1.2864746,36.83024210000001,Rider_Id_674
-Order_No_14782,User_Id_2900,Bike,3,Business,18,3,7:36:49 PM,18,3,7:37:01 PM,18,3,7:38:01 PM,18,3,7:41:52 PM,14,16.9,,-1.296797,36.776452500000005,-1.3711733,36.7319722,Rider_Id_813
-Order_No_23260,User_Id_1142,Bike,1,Personal,22,2,6:15:34 PM,22,2,6:16:11 PM,22,2,6:42:59 PM,22,2,6:54:25 PM,14,18.1,,-1.255945,36.783509,-1.2772153999999998,36.6872972,Rider_Id_789
-Order_No_16973,User_Id_3599,Bike,3,Business,26,5,12:06:04 PM,26,5,1:06:32 PM,26,5,1:47:21 PM,26,5,1:48:48 PM,23,,,-1.3302996,36.8707935,-1.3237428,36.6890511,Rider_Id_482
-Order_No_14829,User_Id_3557,Bike,3,Business,7,5,1:03:34 PM,7,5,1:04:39 PM,7,5,1:05:02 PM,7,5,1:09:28 PM,6,25.0,,-1.2546732,36.808679600000005,-1.288167,36.8181484,Rider_Id_547
-Order_No_2377,User_Id_2330,Bike,3,Business,20,1,12:24:50 PM,20,1,12:25:24 PM,20,1,12:31:21 PM,20,1,12:35:37 PM,8,,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_692
-Order_No_2426,User_Id_3426,Bike,3,Business,5,2,3:40:33 PM,5,2,3:53:15 PM,5,2,4:08:16 PM,5,2,4:23:49 PM,9,27.7,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_540
-Order_No_4813,User_Id_738,Bike,3,Business,18,2,2:11:05 PM,18,2,2:11:13 PM,18,2,2:15:44 PM,18,2,2:20:48 PM,5,27.2,,-1.2881479999999998,36.7850276,-1.2869351,36.7425907,Rider_Id_612
-Order_No_5243,User_Id_2601,Bike,3,Personal,31,5,12:22:27 PM,31,5,12:28:50 PM,31,5,12:53:22 PM,31,5,1:08:00 PM,2,21.6,,-1.3014461000000002,36.7661381,-1.2963097,36.7688221,Rider_Id_255
-Order_No_9398,User_Id_867,Bike,3,Business,1,5,2:48:35 PM,1,5,2:48:59 PM,1,5,2:49:58 PM,1,5,3:01:03 PM,9,31.8,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_523
-Order_No_22214,User_Id_1341,Bike,3,Business,9,4,9:50:51 AM,9,4,9:51:19 AM,9,4,10:02:32 AM,9,4,10:11:01 AM,18,,,-1.228911,36.881876,-1.331619,36.847976,Rider_Id_8
-Order_No_23019,User_Id_3560,Bike,3,Business,20,3,12:51:34 PM,20,3,12:53:13 PM,20,3,1:05:06 PM,20,3,1:10:06 PM,2,26.2,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_448
-Order_No_5521,User_Id_296,Bike,3,Personal,13,3,11:57:20 AM,13,3,12:25:26 PM,13,3,12:35:06 PM,13,3,12:55:12 PM,7,19.9,,-1.2951243000000001,36.828134399999996,-1.2772632,36.792225099999996,Rider_Id_241
-Order_No_24303,User_Id_2330,Bike,3,Business,29,5,5:38:21 PM,29,5,5:42:58 PM,29,5,5:53:37 PM,29,5,6:03:03 PM,10,28.1,,-1.2642903,36.80021370000001,-1.2687407,36.7373043,Rider_Id_341
-Order_No_11179,User_Id_1162,Bike,3,Business,3,5,9:31:21 AM,3,5,9:32:28 AM,3,5,9:54:16 AM,3,5,10:15:29 AM,8,,,-1.3122610000000001,36.794658,-1.2665327,36.8108799,Rider_Id_328
-Order_No_18554,User_Id_1341,Bike,3,Business,27,3,4:53:23 PM,27,3,4:53:44 PM,27,3,5:03:11 PM,27,3,5:10:34 PM,15,27.6,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_177
-Order_No_9518,User_Id_2289,Bike,3,Business,25,5,11:47:28 AM,25,5,11:48:20 AM,25,5,11:58:05 AM,25,5,12:01:15 PM,13,21.0,,-1.2553615,36.789537599999996,-1.2990731000000002,36.848805799999994,Rider_Id_742
-Order_No_10772,User_Id_346,Bike,3,Business,2,2,3:18:47 PM,2,2,3:32:49 PM,2,2,3:56:19 PM,2,2,4:04:48 PM,5,29.3,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_779
-Order_No_1441,User_Id_3496,Bike,3,Business,21,1,3:20:07 PM,21,1,3:20:30 PM,21,1,3:30:01 PM,21,1,3:32:05 PM,17,21.6,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_620
-Order_No_23987,User_Id_2276,Bike,2,Personal,7,1,9:00:24 AM,7,1,9:02:37 AM,7,1,9:07:26 AM,7,1,9:17:09 AM,4,18.6,,-1.2832116000000002,36.788102,-1.2874711,36.764620799999996,Rider_Id_813
-Order_No_2719,User_Id_571,Bike,3,Business,2,2,6:12:55 PM,2,2,6:55:35 PM,2,2,7:17:51 PM,2,2,7:49:21 PM,27,23.4,,-1.3236332,36.701152,-1.2911179,36.8893013,Rider_Id_831
-Order_No_27928,User_Id_2330,Bike,3,Business,5,5,2:13:10 PM,5,5,2:14:59 PM,5,5,2:21:01 PM,5,5,2:37:18 PM,6,26.5,,-1.2267055,36.8013889,-1.2602688,36.7992556,Rider_Id_882
-Order_No_17951,User_Id_867,Bike,3,Business,28,4,9:00:06 AM,28,4,9:00:27 AM,28,4,9:07:55 AM,28,4,9:51:17 AM,11,19.7,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_41
-Order_No_15504,User_Id_816,Bike,3,Business,5,2,8:56:35 AM,5,2,8:58:24 AM,5,2,9:12:29 AM,5,2,9:34:18 AM,11,20.9,2.0,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_411
-Order_No_27494,User_Id_3686,Bike,3,Business,28,1,2:36:20 PM,28,1,2:43:23 PM,28,1,2:56:52 PM,28,1,2:58:20 PM,10,26.2,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_931
-Order_No_20939,User_Id_2212,Bike,3,Business,9,4,12:28:27 PM,9,4,12:31:43 PM,9,4,12:40:40 PM,9,4,12:47:00 PM,5,,,-1.2571472,36.795063299999995,-1.2425149,36.789013700000005,Rider_Id_937
-Order_No_24272,User_Id_393,Bike,3,Business,24,1,8:27:51 AM,24,1,8:53:43 AM,24,1,9:05:09 AM,24,1,9:47:09 AM,4,15.6,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_731
-Order_No_13221,User_Id_2066,Bike,3,Business,12,6,8:53:09 AM,12,6,8:53:16 AM,12,6,9:10:13 AM,12,6,9:20:28 AM,12,,,-1.3210188999999999,36.841055100000005,-1.3692201000000002,36.9252718,Rider_Id_26
-Order_No_17242,User_Id_59,Bike,3,Business,14,4,3:18:56 PM,14,4,3:27:37 PM,14,4,4:00:33 PM,14,4,4:16:36 PM,4,27.8,,-1.290894,36.822971,-1.3047026000000002,36.8172033,Rider_Id_61
-Order_No_23774,User_Id_2982,Bike,3,Business,22,6,9:16:54 AM,22,6,9:17:26 AM,22,6,9:47:27 AM,22,6,9:56:48 AM,18,20.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_725
-Order_No_16275,User_Id_2154,Bike,3,Business,4,4,12:36:07 PM,4,4,12:36:50 PM,4,4,12:55:21 PM,4,4,1:21:32 PM,6,25.4,,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_448
-Order_No_22723,User_Id_311,Bike,3,Business,16,2,2:35:13 PM,16,2,2:35:36 PM,16,2,2:52:36 PM,16,2,3:31:14 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_863
-Order_No_9388,User_Id_2112,Bike,3,Business,31,4,11:47:23 AM,31,4,11:47:32 AM,31,4,11:50:02 AM,31,4,12:23:41 PM,13,26.6,,-1.2527958000000001,36.8003131,-1.2156006000000001,36.8916865,Rider_Id_205
-Order_No_15205,User_Id_3291,Bike,3,Business,10,1,10:49:45 AM,10,1,10:50:08 AM,10,1,11:02:08 AM,10,1,11:09:00 AM,12,23.7,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_685
-Order_No_20257,User_Id_3599,Bike,3,Business,31,3,12:16:22 PM,31,3,12:30:57 PM,31,3,12:53:35 PM,31,3,1:46:59 PM,12,26.8,,-1.3302996,36.8707935,-1.2874367,36.880444700000005,Rider_Id_427
-Order_No_17699,User_Id_3283,Bike,3,Business,10,1,10:40:59 AM,10,1,11:19:30 AM,10,1,11:33:00 AM,10,1,12:18:36 PM,7,18.6,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_864
-Order_No_5691,User_Id_1245,Bike,3,Business,13,5,1:09:21 PM,13,5,1:10:42 PM,13,5,1:50:53 PM,13,5,2:06:03 PM,17,19.2,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_618
-Order_No_4292,User_Id_2801,Bike,3,Business,18,1,2:20:11 PM,18,1,2:21:58 PM,18,1,2:26:05 PM,18,1,2:42:44 PM,14,29.3,,-1.2595421999999998,36.7871178,-1.3104506,36.8687235,Rider_Id_242
-Order_No_6164,User_Id_1724,Bike,3,Business,2,4,9:40:44 AM,2,4,9:42:26 AM,2,4,9:59:25 AM,2,4,10:08:03 AM,10,,,-1.3044458,36.8233667,-1.2551895,36.7822034,Rider_Id_92
-Order_No_4218,User_Id_2565,Bike,3,Personal,17,3,10:17:40 AM,17,3,10:18:08 AM,17,3,10:18:12 AM,17,3,10:32:08 AM,13,25.2,,-1.2975066000000002,36.76894229999999,-1.330608,36.677485,Rider_Id_874
-Order_No_8794,User_Id_2330,Bike,3,Business,26,4,2:39:02 PM,26,4,2:39:39 PM,26,4,2:47:15 PM,26,4,2:48:58 PM,22,21.9,,-1.2614329,36.8094365,-1.380892,36.923378,Rider_Id_726
-Order_No_4494,User_Id_816,Bike,3,Business,15,4,9:40:00 AM,15,4,9:41:00 AM,15,4,9:53:43 AM,15,4,9:58:54 AM,5,19.7,,-1.290894,36.822971,-1.2596969999999998,36.800802000000004,Rider_Id_81
-Order_No_12634,User_Id_3291,Bike,3,Business,8,6,11:59:43 AM,8,6,11:59:57 AM,8,6,12:05:37 PM,8,6,12:19:12 PM,11,18.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_810
-Order_No_9785,User_Id_868,Bike,3,Business,30,5,4:55:16 PM,30,5,5:01:56 PM,30,5,5:10:27 PM,30,5,5:18:06 PM,4,22.2,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_279
-Order_No_3715,User_Id_1471,Bike,3,Business,26,1,1:02:59 PM,26,1,1:13:56 PM,26,1,1:16:47 PM,26,1,1:17:56 PM,10,23.1,,-1.2551895,36.7822034,-1.2967108,36.787113500000004,Rider_Id_674
-Order_No_4429,User_Id_893,Bike,3,Business,22,5,12:50:16 PM,22,5,12:50:41 PM,22,5,1:11:45 PM,22,5,1:13:35 PM,2,,,-1.2663194,36.809901,-1.2617028000000001,36.7983849,Rider_Id_411
-Order_No_5048,User_Id_510,Bike,3,Business,11,4,4:58:50 PM,11,4,5:03:55 PM,11,4,5:21:08 PM,11,4,5:29:06 PM,12,26.3,,-1.279395,36.825364,-1.2860183,36.8975338,Rider_Id_404
-Order_No_8880,User_Id_633,Bike,3,Business,8,2,9:56:18 AM,8,2,9:56:50 AM,8,2,9:56:55 AM,8,2,10:16:21 AM,4,19.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_239
-Order_No_622,User_Id_2330,Bike,3,Business,8,5,1:21:18 PM,8,5,1:25:16 PM,8,5,1:30:00 PM,8,5,1:31:05 PM,5,24.2,,-1.3143328,36.8137434,-1.3040812,36.7924217,Rider_Id_327
-Order_No_26748,User_Id_393,Bike,3,Business,16,2,3:59:46 PM,16,2,3:59:58 PM,16,2,4:00:02 PM,16,2,4:17:02 PM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_10
-Order_No_25304,User_Id_2987,Bike,3,Business,25,2,12:18:16 PM,25,2,12:31:00 PM,25,2,12:34:34 PM,25,2,12:36:39 PM,10,23.6,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_574
-Order_No_7948,User_Id_764,Bike,3,Personal,13,4,5:25:21 PM,13,4,5:25:58 PM,13,4,5:36:12 PM,13,4,5:42:18 PM,3,22.4,,-1.3039706,36.7688174,-1.2800978,36.776917100000006,Rider_Id_161
-Order_No_6196,User_Id_2200,Bike,3,Business,12,1,10:34:35 AM,12,1,10:53:27 AM,12,1,11:09:18 AM,12,1,11:45:27 AM,8,23.7,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_703
-Order_No_21963,User_Id_1809,Bike,3,Business,22,5,12:37:18 PM,22,5,12:58:10 PM,22,5,1:22:53 PM,22,5,1:48:32 PM,15,30.9,,-1.2285834999999998,36.882646799999996,-1.2571472,36.795063299999995,Rider_Id_75
-Order_No_15471,User_Id_2801,Bike,3,Business,7,5,2:02:29 PM,7,5,2:03:34 PM,7,5,2:17:20 PM,7,5,2:23:43 PM,3,,,-1.2658373,36.8018598,-1.2674273999999999,36.787082700000006,Rider_Id_690
-Order_No_23198,User_Id_3557,Bike,3,Business,21,5,10:00:16 AM,21,5,10:02:00 AM,21,5,10:20:09 AM,21,5,10:32:18 AM,6,20.1,,-1.2854173999999998,36.7951152,-1.2546732,36.808679600000005,Rider_Id_532
-Order_No_17444,User_Id_3291,Bike,3,Business,23,2,1:56:16 PM,23,2,1:56:27 PM,23,2,1:58:56 PM,23,2,2:08:26 PM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_155
-Order_No_24428,User_Id_1342,Bike,3,Business,15,1,12:29:43 PM,15,1,12:36:26 PM,15,1,12:47:37 PM,15,1,1:02:55 PM,11,,,-1.3025826,36.7670807,-1.2905978999999999,36.844258200000006,Rider_Id_86
-Order_No_5948,User_Id_2335,Bike,3,Business,3,3,10:33:47 AM,3,3,10:33:54 AM,3,3,10:40:11 AM,3,3,10:53:05 AM,22,27.3,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_831
-Order_No_10936,User_Id_3577,Bike,2,Personal,14,2,12:18:22 PM,14,2,12:18:39 PM,14,2,12:33:11 PM,14,2,12:36:53 PM,12,21.6,,-1.2731241000000002,36.799281799999996,-1.3314048,36.8229367,Rider_Id_857
-Order_No_25533,User_Id_3283,Bike,3,Business,11,4,12:18:19 PM,11,4,12:18:46 PM,11,4,12:24:48 PM,11,4,12:38:31 PM,7,25.7,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_154
-Order_No_2052,User_Id_2330,Bike,3,Business,20,3,11:49:53 AM,20,3,11:50:27 AM,20,3,12:27:20 PM,20,3,12:51:26 PM,11,27.4,,-1.2684123,36.8143348,-1.3015197,36.7658456,Rider_Id_132
-Order_No_25532,User_Id_3331,Bike,3,Business,18,3,3:47:46 PM,18,3,3:47:56 PM,18,3,3:57:04 PM,18,3,3:59:12 PM,9,20.6,,-1.2551895,36.7822034,-1.2359565000000001,36.76227279999999,Rider_Id_620
-Order_No_2650,User_Id_1575,Bike,3,Personal,26,4,3:57:28 PM,26,4,3:57:38 PM,26,4,4:01:45 PM,26,4,5:06:09 PM,12,19.4,,-1.263987,36.798891,-1.2646374,36.723567100000004,Rider_Id_74
-Order_No_15528,User_Id_2330,Bike,3,Business,13,4,1:06:25 PM,13,4,1:22:27 PM,13,4,1:41:50 PM,13,4,2:24:46 PM,8,22.2,,-1.2642903,36.80021370000001,-1.304735,36.784605400000004,Rider_Id_454
-Order_No_2145,User_Id_391,Bike,3,Business,10,1,11:19:44 AM,10,1,12:17:51 PM,10,1,12:29:06 PM,10,1,12:45:50 PM,5,23.4,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_48
-Order_No_7082,User_Id_343,Bike,3,Personal,25,5,3:46:25 PM,25,5,3:49:21 PM,25,5,4:05:57 PM,25,5,4:16:38 PM,5,23.3,,-1.2858511000000001,36.7946184,-1.2663778,36.8076024,Rider_Id_474
-Order_No_7972,User_Id_3264,Bike,3,Business,13,3,2:26:56 PM,13,3,2:27:28 PM,13,3,2:28:09 PM,13,3,2:36:22 PM,8,29.2,,-1.2551895,36.7822034,-1.293679,36.767255,Rider_Id_576
-Order_No_962,User_Id_970,Bike,3,Business,18,4,10:26:46 AM,18,4,10:31:51 AM,18,4,10:38:01 AM,18,4,10:42:12 AM,4,,,-1.2571472,36.795063299999995,-1.2850638,36.813096,Rider_Id_709
-Order_No_22141,User_Id_136,Bike,3,Business,16,2,10:26:03 AM,16,2,10:28:58 AM,16,2,11:08:03 AM,16,2,11:09:11 AM,15,,,-1.3254522,36.721658399999995,-1.2695287,36.798177700000004,Rider_Id_572
-Order_No_10885,User_Id_2801,Bike,3,Business,30,2,10:33:02 AM,30,2,10:33:42 AM,30,2,10:42:07 AM,30,2,10:43:29 AM,3,22.1,,-1.2595421999999998,36.7871178,-1.2566586000000002,36.792968200000004,Rider_Id_228
-Order_No_613,User_Id_1721,Bike,3,Business,10,1,8:40:58 AM,10,1,8:41:21 AM,10,1,8:50:51 AM,10,1,8:54:03 AM,3,18.5,1.9,-1.2551895,36.7822034,-1.2607765,36.8080021,Rider_Id_566
-Order_No_10344,User_Id_2216,Bike,3,Business,11,1,10:40:31 AM,11,1,10:52:59 AM,11,1,10:57:40 AM,11,1,11:00:48 AM,3,18.1,,-1.2551895,36.7822034,-1.2615299999999998,36.791109999999996,Rider_Id_322
-Order_No_9242,User_Id_426,Bike,3,Business,30,2,2:19:03 PM,30,2,3:30:50 PM,30,2,3:46:46 PM,30,2,4:09:56 PM,15,27.2,,-1.2696823999999998,36.8026164,-1.3323107,36.8692616,Rider_Id_403
-Order_No_11759,User_Id_2030,Bike,3,Business,21,5,11:15:22 AM,21,5,1:14:53 PM,21,5,1:33:55 PM,21,5,1:59:50 PM,8,27.1,,-1.3123562,36.9157558,-1.3333725,36.871761299999996,Rider_Id_584
-Order_No_1069,User_Id_1271,Bike,3,Business,19,2,4:24:26 PM,19,2,4:26:17 PM,19,2,4:37:09 PM,19,2,4:52:19 PM,29,28.2,,-1.3244885,36.897791999999995,-1.1838303,36.9543295,Rider_Id_675
-Order_No_24351,User_Id_2330,Bike,3,Business,7,5,12:19:30 PM,7,5,12:20:52 PM,7,5,12:29:00 PM,7,5,12:47:13 PM,14,,,-1.2890715,36.8157588,-1.2199518,36.803213299999996,Rider_Id_472
-Order_No_20238,User_Id_2412,Bike,3,Business,23,4,11:48:33 AM,23,4,11:48:48 AM,23,4,11:52:08 AM,23,4,12:30:57 PM,23,18.4,,-1.3562374,36.904295399999995,-1.2297202,36.881950700000004,Rider_Id_882
-Order_No_18078,User_Id_1694,Bike,2,Personal,31,4,9:41:58 AM,31,4,9:42:15 AM,31,4,9:45:14 AM,31,4,9:58:50 AM,12,17.6,,-1.2595466999999998,36.8113044,-1.3186521999999998,36.769988399999995,Rider_Id_103
-Order_No_22479,User_Id_3621,Bike,3,Business,24,5,8:25:09 AM,24,5,8:25:34 AM,24,5,8:47:39 AM,24,5,8:49:11 AM,7,18.2,,-1.2836583000000001,36.7886014,-1.31447,36.802046999999995,Rider_Id_468
-Order_No_440,User_Id_1469,Bike,3,Business,5,2,3:23:21 PM,5,2,3:26:22 PM,5,2,3:48:25 PM,5,2,4:03:15 PM,4,27.5,,-1.2551895,36.7822034,-1.2738521999999999,36.799281799999996,Rider_Id_607
-Order_No_4738,User_Id_2346,Bike,1,Personal,15,5,11:26:27 AM,15,5,11:26:57 AM,15,5,11:35:18 AM,15,5,11:37:46 AM,4,19.9,,-1.3054537,36.8284768,-1.3046069,36.807548499999996,Rider_Id_444
-Order_No_221,User_Id_2200,Bike,3,Business,1,4,12:24:16 PM,1,4,12:33:19 PM,1,4,12:43:17 PM,1,4,12:51:26 PM,6,,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_726
-Order_No_14309,User_Id_1792,Bike,3,Business,12,5,10:38:29 AM,12,5,10:39:10 AM,12,5,10:54:14 AM,12,5,10:59:24 AM,3,24.4,,-1.2809121,36.7852524,-1.2734311000000003,36.7863778,Rider_Id_396
-Order_No_18863,User_Id_1075,Bike,3,Business,31,4,1:10:07 PM,31,4,1:11:15 PM,31,4,1:16:54 PM,31,4,1:34:22 PM,13,28.7,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_75
-Order_No_13450,User_Id_666,Bike,2,Personal,8,5,1:06:32 PM,8,5,1:17:49 PM,8,5,1:31:10 PM,8,5,2:01:23 PM,9,21.6,,-1.2770071000000003,36.832167,-1.330692,36.84115,Rider_Id_360
-Order_No_18130,User_Id_478,Bike,3,Business,1,5,8:24:05 AM,1,5,8:25:27 AM,1,5,8:36:51 AM,1,5,8:56:17 AM,3,,,-1.2551895,36.7822034,-1.2686614,36.8031594,Rider_Id_247
-Order_No_9906,User_Id_2829,Bike,3,Business,31,2,3:23:53 PM,31,2,3:24:51 PM,31,2,3:34:51 PM,31,2,3:37:37 PM,4,22.1,,-1.2571472,36.795063299999995,-1.2885528999999998,36.816014100000004,Rider_Id_452
-Order_No_13110,User_Id_1075,Bike,3,Business,4,1,3:11:43 PM,4,1,3:13:09 PM,4,1,3:32:30 PM,4,1,3:39:41 PM,10,28.6,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_862
-Order_No_8685,User_Id_867,Bike,3,Business,19,3,3:30:40 PM,19,3,3:31:58 PM,19,3,3:32:07 PM,19,3,3:53:41 PM,6,24.3,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_518
-Order_No_3242,User_Id_3686,Bike,3,Business,8,5,11:42:32 AM,8,5,11:44:13 AM,8,5,11:55:12 AM,8,5,12:18:56 PM,8,28.4,,-1.2551895,36.7822034,-1.3012618999999999,36.821369700000005,Rider_Id_322
-Order_No_24421,User_Id_1085,Bike,3,Business,18,2,1:54:41 PM,18,2,1:54:53 PM,18,2,2:01:38 PM,18,2,2:05:00 PM,2,25.4,,-1.2628638,36.8070399,-1.2715686000000002,36.8023526,Rider_Id_658
-Order_No_16805,User_Id_3647,Bike,3,Business,18,4,9:46:15 AM,18,4,9:46:38 AM,18,4,9:53:18 AM,18,4,9:55:11 AM,6,20.5,,-1.2726389999999999,36.794723,-1.3004846,36.790159100000004,Rider_Id_674
-Order_No_12329,User_Id_972,Bike,3,Business,14,3,2:36:18 PM,14,3,2:46:50 PM,14,3,2:59:34 PM,14,3,3:02:04 PM,10,25.7,,-1.2862033000000002,36.8152966,-1.3263004999999999,36.8637914,Rider_Id_31
-Order_No_10023,User_Id_3194,Bike,3,Business,19,5,4:40:48 PM,19,5,4:41:42 PM,19,5,4:42:15 PM,19,5,4:51:22 PM,6,24.4,,-1.263987,36.798891,-1.2854173999999998,36.7951152,Rider_Id_875
-Order_No_10093,User_Id_1927,Bike,3,Business,19,2,11:21:04 AM,19,2,11:21:47 AM,19,2,11:54:09 AM,19,2,11:55:26 AM,13,27.4,,-1.2765736,36.851364600000004,-1.3472066,36.7692638,Rider_Id_61
-Order_No_11344,User_Id_1615,Bike,3,Personal,3,1,8:56:53 AM,3,1,8:58:01 AM,3,1,9:22:11 AM,3,1,9:25:35 AM,4,18.2,5.1,-1.2686893,36.8096032,-1.2793858999999999,36.821800200000006,Rider_Id_106
-Order_No_14958,User_Id_1831,Bike,3,Business,5,2,4:28:26 PM,5,2,4:33:14 PM,5,2,4:46:57 PM,5,2,4:53:16 PM,18,25.0,,-1.282543,36.824618,-1.3562374,36.904295399999995,Rider_Id_622
-Order_No_275,User_Id_186,Bike,3,Business,10,5,9:18:11 AM,10,5,9:18:47 AM,10,5,9:29:29 AM,10,5,9:36:06 AM,6,13.7,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_770
-Order_No_15431,User_Id_435,Bike,3,Business,16,4,9:55:09 AM,16,4,9:55:38 AM,16,4,10:15:02 AM,16,4,10:30:17 AM,11,,,-1.2333206,36.8304981,-1.300921,36.828195,Rider_Id_143
-Order_No_7874,User_Id_1999,Bike,1,Personal,2,1,8:32:28 AM,2,1,8:32:55 AM,2,1,8:43:15 AM,2,1,8:47:03 AM,4,16.5,,-1.2927600000000001,36.783799,-1.2771989,36.808975200000006,Rider_Id_88
-Order_No_21866,User_Id_3560,Bike,3,Business,11,1,12:33:11 PM,11,1,12:33:44 PM,11,1,12:51:35 PM,11,1,1:01:57 PM,5,24.1,,-1.2658424,36.8101804,-1.279395,36.825364,Rider_Id_365
-Order_No_10638,User_Id_2457,Bike,3,Business,26,5,12:38:38 PM,26,5,12:54:09 PM,26,5,1:00:05 PM,26,5,1:06:49 PM,9,,,-1.2551895,36.7822034,-1.2942,36.7777614,Rider_Id_726
-Order_No_26440,User_Id_3647,Bike,3,Business,7,4,2:10:56 PM,7,4,2:11:11 PM,7,4,2:22:23 PM,7,4,2:29:11 PM,5,31.0,,-1.2726389999999999,36.794723,-1.2625899999999999,36.77486,Rider_Id_315
-Order_No_6580,User_Id_2985,Bike,2,Personal,28,5,2:53:28 PM,28,5,3:02:43 PM,28,5,3:21:05 PM,28,5,3:27:31 PM,10,25.7,,-1.3019857,36.786008700000004,-1.333076,36.75697,Rider_Id_919
-Order_No_26980,User_Id_3599,Bike,3,Business,9,4,11:51:37 AM,9,4,11:52:09 AM,9,4,12:06:50 PM,9,4,12:15:49 PM,14,,,-1.3302996,36.8707935,-1.2976287,36.7744015,Rider_Id_675
-Order_No_3010,User_Id_1500,Bike,3,Business,20,3,10:50:54 AM,20,3,10:51:14 AM,20,3,10:54:03 AM,20,3,11:06:24 AM,8,26.5,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_411
-Order_No_21340,User_Id_1363,Bike,3,Business,24,3,10:49:43 AM,24,3,11:00:05 AM,24,3,11:08:00 AM,24,3,11:13:13 AM,3,20.1,,-1.3004062,36.829741,-1.3012173,36.8093987,Rider_Id_96
-Order_No_10416,User_Id_393,Bike,3,Business,24,5,10:22:51 AM,24,5,10:23:05 AM,24,5,10:23:10 AM,24,5,11:11:02 AM,11,21.2,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_619
-Order_No_5259,User_Id_2457,Bike,3,Business,18,2,11:05:08 AM,18,2,11:05:25 AM,18,2,11:06:38 AM,18,2,11:12:11 AM,2,,,-1.2551895,36.7822034,-1.2677753,36.7998955,Rider_Id_575
-Order_No_23422,User_Id_2453,Bike,1,Personal,9,2,12:53:08 PM,9,2,12:53:39 PM,9,2,1:16:31 PM,9,2,1:20:02 PM,21,21.8,,-1.2374348,36.9155704,-1.2861471000000002,36.7903723,Rider_Id_847
-Order_No_901,User_Id_1500,Bike,3,Business,10,1,2:52:34 PM,10,1,2:54:50 PM,10,1,3:06:07 PM,10,1,3:13:25 PM,9,18.5,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_42
-Order_No_4463,User_Id_3621,Bike,3,Business,5,3,11:53:04 AM,5,3,11:53:42 AM,5,3,12:00:00 PM,5,3,12:01:21 PM,16,16.8,,-1.2836583000000001,36.7886014,-1.2062428,36.8379106,Rider_Id_176
-Order_No_11838,User_Id_1133,Bike,3,Business,5,2,7:25:38 AM,5,2,7:46:56 AM,5,2,7:52:33 AM,5,2,8:01:21 AM,3,17.8,3.9,-1.2579303999999998,36.8019667,-1.2736094999999998,36.8011282,Rider_Id_116
-Order_No_13453,User_Id_982,Bike,3,Business,7,4,10:45:03 AM,7,4,10:59:23 AM,7,4,11:08:03 AM,7,4,11:15:31 AM,20,24.5,,-1.2517871,36.6795716,-1.2547659,36.8236557,Rider_Id_887
-Order_No_23453,User_Id_311,Bike,3,Business,5,3,2:29:07 PM,5,3,2:29:16 PM,5,3,2:35:27 PM,5,3,2:44:10 PM,21,23.7,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_74
-Order_No_18891,User_Id_1067,Bike,3,Personal,5,2,1:43:54 PM,5,2,1:45:25 PM,5,2,2:10:21 PM,5,2,2:27:11 PM,18,27.3,,-1.2696183,36.8021778,-1.2381178,36.6678333,Rider_Id_580
-Order_No_2924,User_Id_1681,Bike,1,Personal,25,5,3:50:38 PM,25,5,3:50:58 PM,25,5,4:13:13 PM,25,5,4:21:43 PM,14,23.6,,-1.2248308,36.801257299999996,-1.2924639,36.77957979999999,Rider_Id_103
-Order_No_20040,User_Id_704,Bike,1,Business,5,5,1:06:08 PM,5,5,1:06:13 PM,5,5,1:06:30 PM,5,5,1:08:21 PM,18,23.6,,-1.2860183,36.8975338,-1.2885010000000001,36.784742,Rider_Id_284
-Order_No_14744,User_Id_81,Bike,3,Business,21,4,4:52:02 PM,21,4,4:53:07 PM,21,4,5:04:51 PM,21,4,5:08:56 PM,5,20.1,,-1.2641496,36.791413899999995,-1.263732,36.81391,Rider_Id_427
-Order_No_25368,User_Id_2235,Bike,3,Personal,25,5,10:17:34 AM,25,5,10:17:40 AM,25,5,10:17:48 AM,25,5,10:25:20 AM,11,20.5,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_272
-Order_No_6279,User_Id_1363,Bike,3,Business,18,1,12:39:26 PM,18,1,12:40:07 PM,18,1,12:42:30 PM,18,1,12:44:47 PM,5,27.0,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_660
-Order_No_130,User_Id_3597,Bike,2,Personal,17,5,9:58:54 AM,17,5,9:59:28 AM,17,5,10:08:20 AM,17,5,10:24:30 AM,6,14.4,,-1.2538731,36.7850223,-1.2855798999999999,36.817620899999994,Rider_Id_754
-Order_No_19787,User_Id_375,Bike,3,Business,30,2,2:20:24 PM,30,2,4:02:18 PM,30,2,4:10:57 PM,30,2,4:15:06 PM,8,,,-1.2551895,36.7822034,-1.3025453000000002,36.832049700000006,Rider_Id_709
-Order_No_12456,User_Id_136,Bike,3,Business,7,1,10:09:21 AM,7,1,10:09:45 AM,7,1,10:33:06 AM,7,1,10:39:37 AM,11,25.0,,-1.2965045,36.7913036,-1.3165103999999999,36.707772600000006,Rider_Id_337
-Order_No_20369,User_Id_928,Bike,3,Business,30,2,4:23:45 PM,30,2,4:51:23 PM,30,2,5:03:52 PM,30,2,5:39:42 PM,7,,,-1.2551895,36.7822034,-1.2936941000000002,36.8156795,Rider_Id_953
-Order_No_18238,User_Id_2200,Bike,3,Business,4,4,2:11:35 PM,4,4,2:17:58 PM,4,4,2:32:37 PM,4,4,2:42:24 PM,9,24.6,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_622
-Order_No_20519,User_Id_633,Bike,3,Business,28,4,7:25:53 AM,28,4,7:31:23 AM,28,4,7:32:43 AM,28,4,8:07:49 AM,9,22.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_778
-Order_No_3978,User_Id_666,Bike,2,Personal,13,3,1:06:24 PM,13,3,1:19:49 PM,13,3,1:24:52 PM,13,3,1:41:18 PM,6,23.3,,-1.2801026000000002,36.7825749,-1.2883346000000002,36.8148489,Rider_Id_101
-Order_No_9962,User_Id_3091,Bike,3,Business,6,6,12:13:43 PM,6,6,12:15:34 PM,6,6,12:21:23 PM,6,6,12:30:07 PM,1,26.5,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_378
-Order_No_13141,User_Id_265,Bike,3,Business,12,2,9:14:43 AM,12,2,9:15:03 AM,12,2,9:32:38 AM,12,2,9:37:24 AM,8,18.2,,-1.2945798000000002,36.7737755,-1.300921,36.828195,Rider_Id_778
-Order_No_10025,User_Id_3647,Bike,3,Business,13,4,3:16:41 PM,13,4,3:17:31 PM,13,4,3:18:05 PM,13,4,3:29:41 PM,3,26.4,,-1.2726389999999999,36.794723,-1.2615983,36.8083096,Rider_Id_353
-Order_No_25951,User_Id_640,Bike,3,Business,28,4,2:51:14 PM,28,4,2:51:56 PM,28,4,3:06:44 PM,28,4,3:11:41 PM,3,27.2,,-1.2969741000000001,36.7856612,-1.287079,36.7811277,Rider_Id_205
-Order_No_2083,User_Id_1500,Bike,3,Business,6,3,9:35:10 AM,6,3,9:35:52 AM,6,3,9:35:59 AM,6,3,9:56:55 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_70
-Order_No_19978,User_Id_3160,Bike,3,Business,26,1,2:32:49 PM,26,1,3:13:49 PM,26,1,3:45:30 PM,26,1,4:15:09 PM,15,22.1,0.9,-1.2642197,36.716824700000004,-1.2844665000000002,36.7866113,Rider_Id_240
-Order_No_16241,User_Id_2523,Bike,3,Business,4,4,3:35:04 PM,4,4,3:35:45 PM,4,4,3:43:40 PM,4,4,4:04:13 PM,8,24.8,,-1.2551895,36.7822034,-1.2821378,36.7740428,Rider_Id_412
-Order_No_20027,User_Id_1202,Bike,3,Personal,1,1,2:44:40 PM,1,1,2:46:02 PM,1,1,3:07:57 PM,1,1,3:11:01 PM,7,25.6,,-1.2850643999999998,36.8343657,-1.3122610000000001,36.794658,Rider_Id_88
-Order_No_14720,User_Id_3372,Bike,3,Business,26,6,9:54:28 AM,26,6,9:54:50 AM,26,6,10:06:56 AM,26,6,10:09:43 AM,4,18.6,,-1.304033,36.7848695,-1.2872751,36.7889153,Rider_Id_735
-Order_No_3882,User_Id_186,Bike,3,Business,5,5,4:35:10 PM,5,5,4:36:12 PM,5,5,4:51:18 PM,5,5,5:00:11 PM,4,26.4,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_205
-Order_No_3744,User_Id_3647,Bike,3,Business,29,5,12:21:30 PM,29,5,12:22:37 PM,29,5,12:40:32 PM,29,5,12:56:09 PM,16,26.7,,-1.2726389999999999,36.794723,-1.3502295,36.7242597,Rider_Id_576
-Order_No_1296,User_Id_867,Bike,3,Business,28,3,9:55:40 AM,28,3,10:02:56 AM,28,3,10:08:50 AM,28,3,10:16:24 AM,14,20.2,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_523
-Order_No_9171,User_Id_706,Bike,3,Business,4,5,9:14:52 AM,4,5,9:14:58 AM,4,5,9:21:33 AM,4,5,9:23:51 AM,15,17.6,0.4,-1.3077869,36.844320700000004,-1.3559093,36.910045200000006,Rider_Id_26
-Order_No_20365,User_Id_685,Bike,3,Business,27,1,12:45:02 PM,27,1,12:47:26 PM,27,1,1:04:34 PM,27,1,1:09:31 PM,12,20.9,,-1.2489594,36.7358883,-1.265432,36.795034,Rider_Id_269
-Order_No_16124,User_Id_1925,Bike,2,Personal,19,2,3:12:25 PM,19,2,3:12:31 PM,19,2,3:12:37 PM,19,2,3:19:53 PM,17,18.7,,-1.2860183,36.8975338,-1.2661246000000002,36.7961775,Rider_Id_397
-Order_No_27829,User_Id_1850,Bike,3,Business,28,1,11:43:15 AM,28,1,11:43:52 AM,28,1,11:48:21 AM,28,1,11:55:38 AM,4,23.7,,-1.2551895,36.7822034,-1.2750247,36.7981039,Rider_Id_647
-Order_No_16866,User_Id_1500,Bike,3,Business,25,1,4:26:44 PM,25,1,4:27:19 PM,25,1,4:27:59 PM,25,1,4:53:33 PM,8,27.7,,-1.3080091999999999,36.8284543,-1.2638185,36.7930057,Rider_Id_206
-Order_No_14864,User_Id_2587,Bike,3,Business,10,3,3:34:39 PM,10,3,3:35:36 PM,10,3,3:53:56 PM,10,3,4:05:25 PM,16,30.5,,-1.323615,36.700475,-1.28878,36.8168312,Rider_Id_195
-Order_No_17596,User_Id_2330,Bike,3,Business,27,3,7:30:03 PM,27,3,7:47:59 PM,27,3,7:57:29 PM,27,3,8:03:19 PM,8,22.0,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_452
-Order_No_11615,User_Id_1482,Bike,3,Business,18,1,10:30:42 AM,18,1,10:32:53 AM,18,1,10:34:37 AM,18,1,10:56:03 AM,6,27.8,,-1.2990813,36.8034213,-1.2649605,36.798177700000004,Rider_Id_71
-Order_No_20907,User_Id_3017,Bike,3,Business,14,6,6:29:16 PM,14,6,6:33:02 PM,14,6,6:38:50 PM,14,6,6:41:26 PM,4,18.8,,-1.299313,36.78314879999999,-1.3004311000000002,36.7524273,Rider_Id_735
-Order_No_21399,User_Id_3355,Bike,3,Business,16,3,10:06:25 AM,16,3,10:06:53 AM,16,3,10:07:23 AM,16,3,10:18:32 AM,3,19.8,,-1.3052491000000002,36.8223899,-1.2918290000000001,36.829358,Rider_Id_272
-Order_No_7018,User_Id_3506,Bike,3,Personal,9,2,10:11:12 AM,9,2,10:11:56 AM,9,2,10:21:50 AM,9,2,10:26:42 AM,10,17.5,4.1,-1.2563531000000001,36.7896742,-1.2873232,36.765857200000006,Rider_Id_720
-Order_No_4449,User_Id_3517,Bike,3,Business,6,1,9:04:06 AM,6,1,9:04:46 AM,6,1,9:08:28 AM,6,1,10:40:13 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_591
-Order_No_11024,User_Id_2330,Bike,3,Business,9,4,1:55:24 PM,9,4,1:57:28 PM,9,4,2:07:21 PM,9,4,2:11:21 PM,5,,,-1.3015197,36.7658456,-1.2921463,36.8029626,Rider_Id_348
-Order_No_5027,User_Id_393,Bike,3,Business,25,1,2:54:05 PM,25,1,2:54:16 PM,25,1,2:54:24 PM,25,1,3:09:30 PM,6,30.3,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_239
-Order_No_3884,User_Id_2829,Bike,3,Business,6,4,10:03:07 AM,6,4,10:03:40 AM,6,4,10:25:36 AM,6,4,10:27:07 AM,6,16.1,3.9,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_573
-Order_No_18832,User_Id_2676,Bike,3,Business,17,4,2:16:29 PM,17,4,2:19:27 PM,17,4,3:06:26 PM,17,4,3:10:39 PM,8,28.0,,-1.2650042,36.790392700000005,-1.2963097,36.7688221,Rider_Id_764
-Order_No_7539,User_Id_1934,Bike,2,Personal,15,1,4:27:26 PM,15,1,4:28:08 PM,15,1,4:34:58 PM,15,1,4:58:34 PM,7,,,-1.264621,36.778904,-1.2563576,36.7881585,Rider_Id_754
-Order_No_17469,User_Id_2728,Bike,1,Personal,7,1,1:00:47 PM,7,1,1:01:39 PM,7,1,1:25:27 PM,7,1,1:26:35 PM,6,21.3,,-1.3101624,36.818915000000004,-1.3211103000000002,36.792948700000004,Rider_Id_742
-Order_No_3408,User_Id_496,Bike,3,Business,8,4,2:52:27 PM,8,4,3:06:55 PM,8,4,3:07:13 PM,8,4,3:37:26 PM,12,27.4,,-1.2551895,36.7822034,-1.3277531999999999,36.829790100000004,Rider_Id_684
-Order_No_12942,User_Id_868,Bike,3,Business,13,3,12:12:15 PM,13,3,1:45:02 PM,13,3,1:54:40 PM,13,3,2:09:57 PM,9,23.5,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_770
-Order_No_13742,User_Id_2999,Bike,1,Personal,3,5,1:44:59 PM,3,5,1:45:04 PM,3,5,1:53:13 PM,3,5,1:57:46 PM,12,25.3,,-1.2696183,36.8021778,-1.2489553,36.7695771,Rider_Id_857
-Order_No_28099,User_Id_3686,Bike,3,Business,12,2,11:00:18 AM,12,2,11:00:52 AM,12,2,12:40:11 PM,12,2,12:42:51 PM,7,28.0,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_268
-Order_No_24354,User_Id_1148,Bike,3,Business,7,4,3:34:48 PM,7,4,3:44:48 PM,7,4,4:25:29 PM,7,4,4:49:22 PM,3,29.4,,-1.3100586,36.850032899999995,-1.3120711999999999,36.8567865,Rider_Id_787
-Order_No_3223,User_Id_3647,Bike,3,Business,2,2,3:56:15 PM,2,2,3:57:07 PM,2,2,4:10:06 PM,2,2,4:22:10 PM,14,26.3,,-1.2726389999999999,36.794723,-1.2363091999999998,36.882361700000004,Rider_Id_754
-Order_No_24885,User_Id_3263,Bike,2,Personal,7,6,2:28:44 PM,7,6,2:29:10 PM,7,6,2:29:39 PM,7,6,2:39:10 PM,12,21.3,,-1.2559559999999999,36.772901,-1.2201106000000002,36.803634,Rider_Id_101
-Order_No_13645,User_Id_2677,Bike,3,Business,27,1,10:07:35 AM,27,1,10:28:03 AM,27,1,10:28:41 AM,27,1,11:13:37 AM,12,20.7,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_855
-Order_No_7689,User_Id_3291,Bike,3,Business,18,4,11:19:10 AM,18,4,11:19:42 AM,18,4,11:26:24 AM,18,4,11:31:57 AM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_7
-Order_No_14837,User_Id_2982,Bike,3,Business,4,1,1:08:34 PM,4,1,1:09:43 PM,4,1,1:56:56 PM,4,1,2:13:01 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_268
-Order_No_3913,User_Id_2085,Bike,3,Personal,14,2,1:53:58 PM,14,2,1:54:15 PM,14,2,2:00:04 PM,14,2,2:02:49 PM,6,23.8,,-1.296797,36.776452500000005,-1.2668611,36.795638700000005,Rider_Id_281
-Order_No_9964,User_Id_552,Bike,3,Business,25,4,4:21:41 PM,25,4,5:37:49 PM,25,4,5:51:32 PM,25,4,6:01:43 PM,9,,,-1.3226301999999999,36.8719901,-1.3632162,36.9108253,Rider_Id_532
-Order_No_13434,User_Id_135,Bike,1,Personal,16,2,12:02:04 PM,16,2,12:03:44 PM,16,2,12:29:05 PM,16,2,12:44:28 PM,7,23.6,,-1.2912772,36.7734495,-1.2661263999999999,36.8030359,Rider_Id_565
-Order_No_17740,User_Id_2423,Bike,3,Business,15,5,3:08:48 PM,15,5,3:09:13 PM,15,5,3:09:23 PM,15,5,3:13:12 PM,9,18.0,,-1.2551895,36.7822034,-1.294869,36.815327,Rider_Id_576
-Order_No_16699,User_Id_3355,Bike,3,Business,14,3,9:28:30 AM,14,3,9:33:20 AM,14,3,9:40:10 AM,14,3,9:49:17 AM,7,18.4,,-1.2584143,36.8048002,-1.3052491000000002,36.8223899,Rider_Id_383
-Order_No_27014,User_Id_2150,Bike,3,Business,4,6,10:37:51 AM,4,6,11:07:59 AM,4,6,11:16:43 AM,4,6,11:42:22 AM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_459
-Order_No_16336,User_Id_136,Bike,3,Business,26,2,9:11:37 AM,26,2,9:12:01 AM,26,2,9:16:18 AM,26,2,9:19:47 AM,5,22.0,,-1.3254522,36.721658399999995,-1.3287648,36.744573200000005,Rider_Id_628
-Order_No_5481,User_Id_2423,Bike,3,Business,12,2,10:09:44 AM,12,2,10:10:01 AM,12,2,10:10:21 AM,12,2,10:18:17 AM,5,17.8,,-1.2551895,36.7822034,-1.2596241000000001,36.762407,Rider_Id_649
-Order_No_5534,User_Id_1450,Bike,3,Personal,18,4,1:16:05 PM,18,4,1:16:33 PM,18,4,1:23:08 PM,18,4,1:27:52 PM,11,25.4,,-1.2583017,36.8023526,-1.3108386,36.7555239,Rider_Id_648
-Order_No_2164,User_Id_2183,Bike,3,Business,26,1,9:17:26 AM,26,1,9:42:03 AM,26,1,9:44:01 AM,26,1,10:02:52 AM,8,17.7,5.0,-1.2551895,36.7822034,-1.2939912,36.8161329,Rider_Id_74
-Order_No_5631,User_Id_1209,Bike,3,Business,18,4,10:24:48 AM,18,4,10:25:26 AM,18,4,10:31:23 AM,18,4,10:43:53 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_855
-Order_No_8521,User_Id_2008,Bike,3,Business,16,2,10:33:34 AM,16,2,10:34:04 AM,16,2,10:56:50 AM,16,2,11:00:42 AM,17,,,-1.2508229,36.7895263,-1.2482377,36.6673782,Rider_Id_690
-Order_No_12915,User_Id_3557,Bike,3,Business,14,4,8:04:29 AM,14,4,8:11:06 AM,14,4,8:21:42 AM,14,4,8:26:06 AM,9,17.7,,-1.2546732,36.808679600000005,-1.3141593,36.8101097,Rider_Id_101
-Order_No_19304,User_Id_314,Bike,2,Personal,28,5,1:32:35 PM,28,5,1:33:15 PM,28,5,1:37:34 PM,28,5,2:01:12 PM,10,25.7,,-1.25454,36.839220000000005,-1.3064585,36.8119065,Rider_Id_726
-Order_No_18788,User_Id_142,Bike,3,Business,29,5,1:41:11 PM,29,5,1:41:50 PM,29,5,1:41:55 PM,29,5,1:47:54 PM,4,28.8,,-1.2963097,36.7688221,-1.2977386999999998,36.803144700000004,Rider_Id_425
-Order_No_13877,User_Id_2743,Bike,1,Personal,13,1,3:18:42 PM,13,1,3:20:30 PM,13,1,3:29:27 PM,13,1,3:40:24 PM,9,19.7,,-1.2615048,36.8116724,-1.290059,36.7703475,Rider_Id_427
-Order_No_941,User_Id_2150,Bike,3,Business,11,2,4:15:41 PM,11,2,4:16:51 PM,11,2,4:22:01 PM,11,2,4:25:36 PM,15,22.4,,-1.3444459999999998,36.756597,-1.265715,36.823815,Rider_Id_257
-Order_No_23394,User_Id_2509,Bike,3,Business,3,2,12:20:02 PM,3,2,12:20:15 PM,3,2,12:26:28 PM,3,2,12:40:30 PM,20,18.5,,-1.2579303999999998,36.8019667,-1.3700383,36.9190174,Rider_Id_103
-Order_No_1439,User_Id_1509,Bike,3,Personal,25,3,2:39:49 PM,25,3,2:40:41 PM,25,3,2:49:54 PM,25,3,3:01:33 PM,4,17.9,,-1.2728058,36.8049903,-1.2709086,36.7929516,Rider_Id_100
-Order_No_26644,User_Id_346,Bike,3,Business,7,4,2:25:21 PM,7,4,2:25:32 PM,7,4,2:26:49 PM,7,4,2:32:10 PM,8,31.0,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_520
-Order_No_25396,User_Id_2777,Bike,3,Business,4,4,4:18:42 PM,4,4,4:21:14 PM,4,4,4:37:21 PM,4,4,4:42:46 PM,8,26.0,,-1.2551895,36.7822034,-1.2944081,36.7897046,Rider_Id_279
-Order_No_17843,User_Id_3283,Bike,3,Business,14,2,12:34:33 PM,14,2,12:35:18 PM,14,2,12:45:12 PM,14,2,12:56:28 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_959
-Order_No_14600,User_Id_3496,Bike,3,Business,21,5,10:19:48 AM,21,5,10:23:05 AM,21,5,10:39:52 AM,21,5,10:41:54 AM,7,22.7,,-1.2599563,36.799343900000004,-1.300921,36.828195,Rider_Id_728
-Order_No_19997,User_Id_2330,Bike,3,Business,11,1,12:22:44 PM,11,1,12:22:55 PM,11,1,12:36:38 PM,11,1,12:38:24 PM,18,23.9,,-1.3015197,36.7658456,-1.2284323,36.8757058,Rider_Id_933
-Order_No_13597,User_Id_3560,Bike,3,Business,14,1,8:57:02 AM,14,1,8:57:14 AM,14,1,9:19:49 AM,14,1,9:22:12 AM,11,20.4,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_456
-Order_No_6877,User_Id_2069,Bike,3,Business,7,3,4:03:29 PM,7,3,4:03:45 PM,7,3,4:04:09 PM,7,3,4:29:00 PM,17,28.2,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_699
-Order_No_15054,User_Id_3709,Bike,2,Personal,8,6,4:34:50 PM,8,6,4:38:06 PM,8,6,5:01:57 PM,8,6,5:08:21 PM,7,23.0,,-1.2854453000000001,36.818836299999994,-1.2882726999999998,36.770512200000006,Rider_Id_208
-Order_No_22757,User_Id_2190,Bike,3,Business,28,2,12:45:09 PM,28,2,12:46:15 PM,28,2,12:56:36 PM,28,2,1:07:44 PM,28,21.1,,-1.3019530000000001,36.788458,-1.1528493,36.9567638,Rider_Id_548
-Order_No_20425,User_Id_540,Bike,3,Business,14,5,3:19:01 PM,14,5,3:19:48 PM,14,5,3:29:12 PM,14,5,3:39:03 PM,4,23.6,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_50
-Order_No_1509,User_Id_2217,Bike,1,Personal,5,5,11:46:08 AM,5,5,12:06:51 PM,5,5,12:14:12 PM,5,5,12:17:52 PM,7,24.0,,-1.2792316000000001,36.8131524,-1.32589,36.831679,Rider_Id_722
-Order_No_958,User_Id_1687,Bike,3,Business,10,1,3:39:14 PM,10,1,3:43:37 PM,10,1,3:53:23 PM,10,1,4:03:34 PM,3,18.4,,-1.2626574,36.8014034,-1.2755629,36.813152200000005,Rider_Id_576
-Order_No_27730,User_Id_198,Bike,3,Business,18,3,1:39:42 PM,18,3,3:07:44 PM,18,3,3:19:15 PM,18,3,3:25:45 PM,11,20.9,,-1.3396256000000002,36.8863135,-1.3154651000000002,36.8648005,Rider_Id_397
-Order_No_12669,User_Id_3450,Bike,3,Business,15,1,2:59:12 PM,15,1,2:59:49 PM,15,1,3:04:07 PM,15,1,3:07:17 PM,18,,,-1.2657464,36.780114399999995,-1.2155671000000001,36.8898931,Rider_Id_120
-Order_No_15900,User_Id_867,Bike,3,Business,17,5,10:50:27 AM,17,5,10:51:05 AM,17,5,10:53:26 AM,17,5,11:30:01 AM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_690
-Order_No_13042,User_Id_1456,Bike,3,Business,24,5,3:42:46 PM,24,5,3:48:17 PM,24,5,3:57:07 PM,24,5,4:19:06 PM,6,23.0,,-1.2859912,36.8756811,-1.2860183,36.8975338,Rider_Id_126
-Order_No_1857,User_Id_435,Bike,3,Business,22,3,8:43:20 AM,22,3,8:47:18 AM,22,3,8:47:22 AM,22,3,9:12:35 AM,6,18.6,,-1.300921,36.828195,-1.281577,36.79382929999999,Rider_Id_61
-Order_No_14638,User_Id_3032,Bike,3,Business,18,5,2:38:17 PM,18,5,2:38:54 PM,18,5,2:49:56 PM,18,5,2:59:31 PM,11,27.2,,-1.2571472,36.795063299999995,-1.21488,36.8141238,Rider_Id_660
-Order_No_25289,User_Id_3229,Bike,3,Personal,24,3,10:51:49 AM,24,3,10:53:53 AM,24,3,11:11:37 AM,24,3,11:14:35 AM,10,,,-1.3068448000000001,36.8348641,-1.2561231000000002,36.794099700000004,Rider_Id_130
-Order_No_8189,User_Id_693,Bike,2,Personal,15,1,2:27:21 PM,15,1,2:27:32 PM,15,1,2:39:43 PM,15,1,2:52:29 PM,21,,,-1.3791465,36.7514836,-1.2580991000000001,36.794151899999996,Rider_Id_228
-Order_No_14542,User_Id_1899,Bike,3,Business,25,3,1:34:54 PM,25,3,1:35:20 PM,25,3,1:38:17 PM,25,3,1:57:41 PM,13,17.6,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_937
-Order_No_19418,User_Id_1252,Bike,1,Personal,17,1,1:20:11 PM,17,1,1:26:38 PM,17,1,1:49:06 PM,17,1,1:53:58 PM,14,28.4,,-1.2343087,36.84749,-1.319977,36.813866299999994,Rider_Id_328
-Order_No_24607,User_Id_1578,Bike,3,Business,11,2,1:08:08 PM,11,2,1:25:50 PM,11,2,1:26:28 PM,11,2,1:34:07 PM,5,22.3,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_150
-Order_No_9332,User_Id_3599,Bike,3,Business,26,5,2:02:51 PM,26,5,2:26:21 PM,26,5,2:50:44 PM,26,5,2:54:51 PM,14,,,-1.3302996,36.8707935,-1.2515637,36.8119508,Rider_Id_675
-Order_No_14193,User_Id_1500,Bike,3,Business,10,1,11:46:02 AM,10,1,11:47:23 AM,10,1,12:23:03 PM,10,1,12:38:03 PM,8,18.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_161
-Order_No_25776,User_Id_811,Bike,3,Personal,29,5,5:12:54 PM,29,5,5:17:33 PM,29,5,5:31:04 PM,29,5,5:39:22 PM,1,27.9,,-1.3043377,36.776354,-1.2963097,36.7688221,Rider_Id_197
-Order_No_3738,User_Id_868,Bike,3,Business,17,6,2:51:15 PM,17,6,2:53:52 PM,17,6,3:27:05 PM,17,6,4:12:37 PM,5,23.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_6
-Order_No_5634,User_Id_1500,Bike,3,Business,6,3,11:11:39 AM,6,3,11:12:54 AM,6,3,11:20:55 AM,6,3,11:27:01 AM,13,22.6,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_247
-Order_No_2053,User_Id_2344,Bike,3,Business,28,1,3:15:35 PM,28,1,3:15:46 PM,28,1,3:18:28 PM,28,1,3:21:53 PM,23,24.5,,-1.2553615,36.789537599999996,-1.3447079,36.91105,Rider_Id_103
-Order_No_6745,User_Id_3300,Bike,3,Business,22,3,9:16:31 AM,22,3,9:18:06 AM,22,3,9:26:44 AM,22,3,9:40:27 AM,11,18.9,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_709
-Order_No_645,User_Id_627,Bike,3,Business,4,4,4:21:34 PM,4,4,4:22:11 PM,4,4,4:43:25 PM,4,4,4:44:30 PM,19,,,-1.2551895,36.7822034,-1.32943,36.894242999999996,Rider_Id_751
-Order_No_20328,User_Id_136,Bike,3,Business,27,5,11:49:28 AM,27,5,11:49:32 AM,27,5,12:16:53 PM,27,5,12:24:30 PM,8,22.5,,-1.273425,36.84469,-1.260232,36.807334000000004,Rider_Id_229
-Order_No_2005,User_Id_3560,Bike,3,Business,20,3,3:51:49 PM,20,3,3:54:23 PM,20,3,4:23:59 PM,20,3,4:28:29 PM,5,28.5,,-1.2658424,36.8101804,-1.279395,36.825364,Rider_Id_411
-Order_No_19793,User_Id_2412,Bike,3,Business,8,3,12:23:02 PM,8,3,12:24:19 PM,8,3,1:02:53 PM,8,3,1:48:16 PM,19,,,-1.3352545,36.8659639,-1.2283403,36.8822756,Rider_Id_430
-Order_No_10651,User_Id_1271,Bike,3,Business,3,5,4:14:44 PM,3,5,4:30:13 PM,3,5,4:56:49 PM,3,5,5:09:16 PM,28,22.5,,-1.3244885,36.897791999999995,-1.1984413999999999,36.900920500000005,Rider_Id_415
-Order_No_26319,User_Id_1246,Bike,3,Business,2,4,4:20:28 PM,2,4,4:23:20 PM,2,4,4:38:46 PM,2,4,4:42:17 PM,2,23.9,,-1.2571472,36.795063299999995,-1.2682478000000001,36.8072852,Rider_Id_651
-Order_No_12987,User_Id_97,Bike,3,Business,23,3,9:15:57 AM,23,3,9:17:15 AM,23,3,9:35:50 AM,23,3,9:50:07 AM,3,,,-1.2934995,36.791600700000004,-1.2969741000000001,36.7856612,Rider_Id_200
-Order_No_14728,User_Id_608,Bike,3,Business,20,3,10:21:33 AM,20,3,10:22:26 AM,20,3,10:33:58 AM,20,3,10:41:33 AM,7,26.6,,-1.2658373,36.8018598,-1.2706041000000001,36.830314200000004,Rider_Id_276
-Order_No_16638,User_Id_3355,Bike,3,Business,2,1,3:09:17 PM,2,1,3:21:54 PM,2,1,3:23:18 PM,2,1,4:04:34 PM,9,20.0,,-1.2859912,36.8756811,-1.3052491000000002,36.8223899,Rider_Id_900
-Order_No_1241,User_Id_1245,Bike,3,Business,2,5,12:05:39 PM,2,5,12:15:09 PM,2,5,12:29:50 PM,2,5,1:00:42 PM,17,23.5,,-1.300921,36.828195,-1.3156678,36.713569,Rider_Id_859
-Order_No_1849,User_Id_1500,Bike,3,Business,14,4,3:23:17 PM,14,4,3:23:54 PM,14,4,3:28:24 PM,14,4,3:30:39 PM,12,27.4,,-1.2638185,36.7930057,-1.3167113000000001,36.8301563,Rider_Id_709
-Order_No_24701,User_Id_3470,Bike,3,Business,16,6,3:17:40 PM,16,6,3:18:56 PM,16,6,3:33:58 PM,16,6,3:45:54 PM,4,,,-1.2659913999999999,36.828365500000004,-1.28878,36.8168312,Rider_Id_795
-Order_No_11659,User_Id_3487,Bike,3,Business,10,3,1:32:13 PM,10,3,1:34:13 PM,10,3,2:05:49 PM,10,3,2:10:33 PM,5,30.8,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_341
-Order_No_5215,User_Id_540,Bike,3,Business,11,5,1:18:20 PM,11,5,1:20:13 PM,11,5,1:28:15 PM,11,5,1:30:49 PM,7,20.8,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_878
-Order_No_13356,User_Id_645,Bike,1,Personal,14,4,2:58:56 PM,14,4,3:02:28 PM,14,4,3:14:05 PM,14,4,3:29:15 PM,11,21.6,,-1.2655306,36.828148,-1.3275191000000002,36.7992817,Rider_Id_322
-Order_No_17023,User_Id_1115,Bike,2,Personal,19,4,12:15:53 PM,19,4,12:17:18 PM,19,4,12:28:16 PM,19,4,12:32:21 PM,5,20.0,,-1.2848216000000001,36.8097316,-1.2875842,36.7799696,Rider_Id_648
-Order_No_17142,User_Id_2246,Bike,1,Personal,18,4,2:58:37 PM,18,4,2:59:34 PM,18,4,3:16:10 PM,18,4,3:22:11 PM,10,26.8,,-1.2821883,36.826961100000005,-1.249526,36.7711579,Rider_Id_208
-Order_No_17574,User_Id_2552,Bike,3,Personal,13,3,9:37:46 AM,13,3,9:38:53 AM,13,3,9:48:40 AM,13,3,9:53:31 AM,5,20.0,,-1.3100608999999999,36.820536499999996,-1.2962966999999999,36.8020231,Rider_Id_154
-Order_No_15485,User_Id_20,Bike,3,Business,5,5,8:25:44 AM,5,5,8:26:14 AM,5,5,8:39:08 AM,5,5,8:45:29 AM,14,19.9,,-1.2629666000000002,36.8068369,-1.3278522,36.8711134,Rider_Id_99
-Order_No_19431,User_Id_2876,Bike,1,Personal,12,5,12:38:00 PM,12,5,12:39:09 PM,12,5,12:43:09 PM,12,5,12:44:44 PM,6,24.5,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_875
-Order_No_5283,User_Id_633,Bike,3,Business,22,5,9:51:19 AM,22,5,9:52:44 AM,22,5,10:13:35 AM,22,5,10:18:38 AM,5,22.0,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_378
-Order_No_8889,User_Id_3295,Bike,3,Business,25,4,2:58:40 PM,25,4,3:00:00 PM,25,4,3:19:34 PM,25,4,3:35:50 PM,2,,,-1.2571472,36.795063299999995,-1.2736646,36.814277399999995,Rider_Id_290
-Order_No_12000,User_Id_3632,Bike,1,Personal,25,2,10:48:20 AM,25,2,10:48:38 AM,25,2,10:55:06 AM,25,2,11:00:59 AM,8,23.9,,-1.288002,36.827379,-1.290821,36.774477000000005,Rider_Id_703
-Order_No_6892,User_Id_419,Bike,3,Business,7,1,4:11:54 PM,7,1,4:13:15 PM,7,1,4:16:12 PM,7,1,4:29:00 PM,6,26.9,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_436
-Order_No_4393,User_Id_2330,Bike,3,Business,25,4,3:54:08 PM,25,4,3:55:39 PM,25,4,4:01:07 PM,25,4,4:28:34 PM,2,,,-1.2612417,36.813863299999994,-1.2642903,36.80021370000001,Rider_Id_881
-Order_No_24094,User_Id_1715,Bike,3,Business,30,1,1:20:30 PM,30,1,1:20:51 PM,30,1,1:27:48 PM,30,1,1:34:42 PM,7,20.4,,-1.2608993,36.799246000000004,-1.300722,36.816865,Rider_Id_882
-Order_No_27880,User_Id_2298,Bike,3,Personal,4,4,8:15:02 AM,4,4,8:16:02 AM,4,4,8:36:44 AM,4,4,8:41:38 AM,14,21.8,,-1.3234104,36.7989459,-1.2665433000000001,36.7611081,Rider_Id_99
-Order_No_9922,User_Id_1336,Bike,3,Business,13,3,11:51:52 AM,13,3,11:52:04 AM,13,3,12:00:02 PM,13,3,12:24:26 PM,8,24.7,,-1.2615888000000002,36.7928732,-1.2885029,36.825604999999996,Rider_Id_462
-Order_No_5229,User_Id_53,Bike,3,Business,20,3,12:47:44 PM,20,3,12:54:35 PM,20,3,1:07:41 PM,20,3,1:08:52 PM,2,26.5,,-1.2770708,36.8231093,-1.279858,36.824597600000004,Rider_Id_747
-Order_No_21632,User_Id_2190,Bike,3,Business,29,3,4:02:08 PM,29,3,4:05:33 PM,29,3,4:14:57 PM,29,3,4:16:38 PM,13,21.1,,-1.3019530000000001,36.788458,-1.240218,36.766816,Rider_Id_24
-Order_No_17870,User_Id_2388,Bike,2,Personal,31,4,10:45:07 AM,31,4,11:44:20 AM,31,4,11:57:51 AM,31,4,12:10:39 PM,8,22.0,,-1.3598678,36.737584000000005,-1.3192808,36.7121888,Rider_Id_159
-Order_No_19588,User_Id_2523,Bike,3,Business,16,4,10:34:35 AM,16,4,10:35:37 AM,16,4,10:41:52 AM,16,4,10:47:48 AM,4,,,-1.2551895,36.7822034,-1.2566688999999998,36.779695200000006,Rider_Id_253
-Order_No_21308,User_Id_496,Bike,3,Business,10,1,4:10:55 PM,10,1,4:11:37 PM,10,1,4:17:25 PM,10,1,4:27:18 PM,7,18.5,,-1.2571472,36.795063299999995,-1.2944292,36.779356899999996,Rider_Id_709
-Order_No_10283,User_Id_3586,Bike,3,Business,22,2,1:39:00 PM,22,2,1:39:20 PM,22,2,1:46:30 PM,22,2,1:56:01 PM,9,25.6,,-1.2869041,36.8240057,-1.275442,36.766020700000006,Rider_Id_237
-Order_No_16196,User_Id_1134,Bike,2,Personal,31,2,9:34:28 AM,31,2,9:34:43 AM,31,2,9:36:36 AM,31,2,9:41:09 AM,13,16.9,1.1,-1.2560455,36.780086700000005,-1.2318301,36.7327062,Rider_Id_735
-Order_No_25728,User_Id_929,Bike,2,Personal,19,4,11:50:24 AM,19,4,11:50:42 AM,19,4,12:18:25 PM,19,4,12:25:50 PM,16,20.3,,-1.3616128,36.7393753,-1.2911739,36.7614223,Rider_Id_939
-Order_No_6561,User_Id_3291,Bike,3,Business,20,3,9:50:25 AM,20,3,9:50:37 AM,20,3,9:58:26 AM,20,3,11:20:32 AM,5,27.3,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_708
-Order_No_17815,User_Id_1746,Bike,3,Business,21,6,1:25:30 PM,21,6,1:25:57 PM,21,6,1:55:52 PM,21,6,2:11:28 PM,23,18.5,,-1.2029923,36.9066203,-1.296797,36.776452500000005,Rider_Id_102
-Order_No_11757,User_Id_3599,Bike,3,Business,10,3,11:42:10 AM,10,3,11:42:32 AM,10,3,11:47:43 AM,10,3,12:08:11 PM,18,28.0,,-1.3302996,36.8707935,-1.2323371,36.864816999999995,Rider_Id_855
-Order_No_24832,User_Id_2783,Bike,3,Personal,17,1,9:41:03 AM,17,1,9:41:58 AM,17,1,9:54:56 AM,17,1,9:59:27 AM,4,19.1,,-1.2983707,36.800875700000006,-1.2790232,36.7923526,Rider_Id_375
-Order_No_28135,User_Id_1966,Bike,3,Business,1,5,11:26:31 AM,1,5,11:27:02 AM,1,5,12:09:59 PM,1,5,12:16:02 PM,20,28.6,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_222
-Order_No_9258,User_Id_540,Bike,3,Business,13,1,3:47:02 PM,13,1,3:52:01 PM,13,1,4:08:33 PM,13,1,4:10:23 PM,5,,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_289
-Order_No_27348,User_Id_3487,Bike,3,Business,4,6,11:04:32 AM,4,6,11:05:21 AM,4,6,11:10:01 AM,4,6,11:16:12 AM,5,,,-1.2591018999999999,36.800577000000004,-1.279395,36.825364,Rider_Id_843
-Order_No_11711,User_Id_1450,Bike,3,Personal,28,4,12:27:06 PM,28,4,12:28:16 PM,28,4,12:41:14 PM,28,4,12:46:22 PM,3,,,-1.2995135,36.793363299999996,-1.2952784,36.7793271,Rider_Id_124
-Order_No_11034,User_Id_1075,Bike,3,Business,15,5,11:05:05 AM,15,5,11:05:31 AM,15,5,11:21:24 AM,15,5,11:29:48 AM,20,26.6,,-1.3277669,36.8975169,-1.2527958000000001,36.8003131,Rider_Id_675
-Order_No_19731,User_Id_2187,Bike,3,Business,24,5,1:10:42 PM,24,5,1:20:10 PM,24,5,1:22:25 PM,24,5,1:38:45 PM,5,22.9,,-1.28878,36.8168312,-1.312469,36.815177,Rider_Id_320
-Order_No_21341,User_Id_3635,Bike,1,Business,8,6,4:22:39 PM,8,6,4:22:58 PM,8,6,4:30:25 PM,8,6,5:35:21 PM,20,23.0,,-1.2615888000000002,36.7928732,-1.3103985,36.9114173,Rider_Id_647
-Order_No_15943,User_Id_2828,Bike,3,Business,29,5,3:36:13 PM,29,5,3:38:43 PM,29,5,3:44:36 PM,29,5,3:56:18 PM,17,21.3,,-1.2615888000000002,36.7928732,-1.2540898999999999,36.70427279999999,Rider_Id_100
-Order_No_27471,User_Id_865,Bike,3,Business,28,2,4:56:16 PM,28,2,4:56:32 PM,28,2,5:09:47 PM,28,2,5:12:50 PM,2,21.4,,-1.2456412000000001,36.7955298,-1.2576123,36.8054354,Rider_Id_139
-Order_No_23768,User_Id_393,Bike,3,Business,1,1,11:21:15 AM,1,1,11:22:45 AM,1,1,11:32:25 AM,1,1,11:35:56 AM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_507
-Order_No_13616,User_Id_422,Bike,2,Personal,29,5,10:55:39 AM,29,5,10:55:55 AM,29,5,11:00:57 AM,29,5,11:12:00 AM,11,19.8,,-1.2684319,36.81658529999999,-1.3271684,36.8173021,Rider_Id_589
-Order_No_27822,User_Id_2931,Bike,3,Business,17,5,12:22:29 PM,17,5,12:28:36 PM,17,5,12:48:41 PM,17,5,12:51:10 PM,7,,,-1.2862984,36.8293971,-1.2452664,36.802854100000005,Rider_Id_119
-Order_No_15734,User_Id_3647,Bike,3,Business,29,3,4:25:09 PM,29,3,4:28:20 PM,29,3,4:48:25 PM,29,3,4:57:37 PM,4,20.9,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_158
-Order_No_7155,User_Id_2330,Bike,3,Business,3,1,5:37:01 PM,3,1,6:38:10 PM,3,1,6:39:09 PM,3,1,6:48:14 PM,8,17.8,,-1.2687407,36.7373043,-1.3015197,36.7658456,Rider_Id_310
-Order_No_22136,User_Id_20,Bike,3,Business,18,4,1:28:55 PM,18,4,1:32:11 PM,18,4,1:40:20 PM,18,4,1:45:01 PM,6,,,-1.2680478000000002,36.8013518,-1.2965824,36.815302200000005,Rider_Id_484
-Order_No_27012,User_Id_982,Bike,3,Business,22,5,2:06:41 PM,22,5,2:23:23 PM,22,5,2:54:12 PM,22,5,3:07:59 PM,15,27.2,,-1.2517871,36.6795716,-1.2614987,36.7905804,Rider_Id_588
-Order_No_6780,User_Id_3686,Bike,3,Business,6,2,10:14:30 AM,6,2,10:15:04 AM,6,2,10:21:54 AM,6,2,10:27:24 AM,10,19.6,,-1.2551895,36.7822034,-1.2365703000000001,36.805436799999995,Rider_Id_770
-Order_No_400,User_Id_1984,Bike,2,Business,24,1,12:10:40 PM,24,1,12:10:57 PM,24,1,12:13:24 PM,24,1,12:15:32 PM,9,22.5,,-1.2758945,36.7648661,-1.2456412000000001,36.7955298,Rider_Id_449
-Order_No_7907,User_Id_393,Bike,3,Business,8,5,12:42:44 PM,8,5,12:45:59 PM,8,5,12:49:53 PM,8,5,12:57:48 PM,8,24.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_660
-Order_No_9548,User_Id_2448,Bike,2,Personal,9,3,12:11:41 PM,9,3,12:13:03 PM,9,3,12:30:31 PM,9,3,12:38:28 PM,4,21.3,,-1.302922,36.816383,-1.2814031000000001,36.82016470000001,Rider_Id_207
-Order_No_5991,User_Id_2069,Bike,3,Business,24,5,2:18:20 PM,24,5,2:20:08 PM,24,5,2:34:16 PM,24,5,2:36:44 PM,9,23.2,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_622
-Order_No_12419,User_Id_190,Bike,3,Business,11,2,2:59:11 PM,11,2,3:29:17 PM,11,2,3:36:14 PM,11,2,3:47:01 PM,11,22.4,,-1.2707373,36.805440999999995,-1.3270347,36.866736200000005,Rider_Id_878
-Order_No_11213,User_Id_867,Bike,3,Business,22,4,8:51:05 AM,22,4,9:09:14 AM,22,4,9:09:47 AM,22,4,9:31:17 AM,10,19.5,3.0,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_663
-Order_No_9959,User_Id_3222,Bike,3,Business,7,2,11:20:06 AM,7,2,11:22:01 AM,7,2,11:59:14 AM,7,2,12:06:28 PM,13,,,-1.2551895,36.7822034,-1.3184737,36.862645,Rider_Id_570
-Order_No_26404,User_Id_393,Bike,3,Business,24,3,10:28:54 AM,24,3,10:29:39 AM,24,3,10:29:46 AM,24,3,11:07:39 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515
-Order_No_23746,User_Id_2066,Bike,3,Business,24,6,2:19:59 PM,24,6,2:21:33 PM,24,6,2:34:46 PM,24,6,3:06:12 PM,11,26.9,,-1.3210188999999999,36.841055100000005,-1.250476,36.802013,Rider_Id_622
-Order_No_23860,User_Id_649,Bike,3,Business,12,5,10:10:36 AM,12,5,11:09:54 AM,12,5,11:12:44 AM,12,5,11:18:15 AM,6,24.4,,-1.2551895,36.7822034,-1.2890333,36.8234839,Rider_Id_726
-Order_No_1380,User_Id_3391,Bike,1,Personal,25,2,4:06:27 PM,25,2,4:12:04 PM,25,2,4:38:44 PM,25,2,4:42:03 PM,7,24.1,,-1.2998789,36.816896299999996,-1.2747045,36.840638299999995,Rider_Id_88
-Order_No_20928,User_Id_627,Bike,3,Business,23,5,3:00:44 PM,23,5,3:07:46 PM,23,5,3:20:20 PM,23,5,3:29:25 PM,12,26.6,,-1.2551895,36.7822034,-1.3056581,36.8445565,Rider_Id_708
-Order_No_22332,User_Id_1131,Bike,3,Business,30,1,11:23:16 AM,30,1,11:23:30 AM,30,1,11:30:29 AM,30,1,11:44:08 AM,16,22.7,,-1.2914308,36.801886700000004,-1.214328,36.7911903,Rider_Id_882
-Order_No_162,User_Id_3599,Bike,3,Business,12,5,12:40:01 PM,12,5,12:40:35 PM,12,5,12:53:44 PM,12,5,12:56:53 PM,24,26.3,,-1.3302996,36.8707935,-1.2835228,36.7437619,Rider_Id_233
-Order_No_27963,User_Id_2627,Bike,3,Business,13,3,9:51:51 AM,13,3,9:53:24 AM,13,3,10:17:38 AM,13,3,10:20:26 AM,8,20.2,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_660
-Order_No_5540,User_Id_3674,Bike,3,Business,30,2,9:37:19 AM,30,2,9:38:21 AM,30,2,9:48:08 AM,30,2,9:50:25 AM,8,17.3,,-1.2551895,36.7822034,-1.2976788000000001,36.802791600000006,Rider_Id_167
-Order_No_26524,User_Id_868,Bike,3,Business,2,2,4:15:12 PM,2,2,4:18:04 PM,2,2,4:26:09 PM,2,2,4:54:56 PM,6,28.3,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_519
-Order_No_25891,User_Id_2583,Bike,3,Personal,24,1,2:07:21 PM,24,1,2:08:28 PM,24,1,2:09:50 PM,24,1,2:20:59 PM,2,,,-1.2707178999999997,36.7979713,-1.2807,36.808066,Rider_Id_51
-Order_No_7228,User_Id_867,Bike,3,Business,19,5,8:29:48 AM,19,5,8:30:09 AM,19,5,8:40:37 AM,19,5,8:58:20 AM,14,20.2,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_731
-Order_No_19443,User_Id_1075,Bike,3,Business,9,6,9:20:21 AM,9,6,9:28:39 AM,9,6,9:45:14 AM,9,6,9:51:58 AM,5,20.8,,-1.2793686000000002,36.82592329999999,-1.2527958000000001,36.8003131,Rider_Id_576
-Order_No_6967,User_Id_1754,Bike,3,Business,11,5,9:23:29 AM,11,5,9:23:35 AM,11,5,9:23:41 AM,11,5,9:27:25 AM,10,18.1,4.9,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_900
-Order_No_11415,User_Id_2956,Bike,3,Business,12,2,9:14:50 AM,12,2,9:22:51 AM,12,2,9:48:30 AM,12,2,9:53:49 AM,7,17.8,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_452
-Order_No_17553,User_Id_1705,Bike,3,Business,7,5,12:06:12 PM,7,5,12:09:34 PM,7,5,12:10:57 PM,7,5,12:20:34 PM,30,25.3,,-1.2551895,36.7822034,-1.1427273,36.9371213,Rider_Id_574
-Order_No_4446,User_Id_1966,Bike,3,Business,26,5,3:53:21 PM,26,5,3:53:30 PM,26,5,3:53:44 PM,26,5,4:12:58 PM,14,,,-1.3700383,36.9190174,-1.3046598,36.85373870000001,Rider_Id_863
-Order_No_2370,User_Id_1882,Bike,3,Business,19,2,5:36:17 PM,19,2,5:36:44 PM,19,2,5:54:15 PM,19,2,5:57:14 PM,5,28.4,,-1.2877809999999998,36.817865999999995,-1.2591018999999999,36.800577000000004,Rider_Id_365
-Order_No_10758,User_Id_3339,Bike,3,Business,7,4,4:05:32 PM,7,4,4:06:26 PM,7,4,4:08:45 PM,7,4,4:12:14 PM,2,31.3,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_154
-Order_No_4935,User_Id_2069,Bike,3,Business,8,2,5:06:08 PM,8,2,5:08:11 PM,8,2,5:19:59 PM,8,2,5:34:01 PM,9,23.3,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_427
-Order_No_27654,User_Id_3599,Bike,3,Business,19,2,11:29:23 AM,19,2,11:56:21 AM,19,2,12:22:33 PM,19,2,12:32:41 PM,30,27.7,,-1.3302996,36.8707935,-1.2747526000000002,36.6514681,Rider_Id_218
-Order_No_16440,User_Id_2978,Bike,3,Business,12,1,10:23:26 AM,12,1,10:23:30 AM,12,1,10:29:48 AM,12,1,10:36:01 AM,3,23.6,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_620
-Order_No_982,User_Id_1500,Bike,3,Business,10,3,9:41:14 AM,10,3,9:41:43 AM,10,3,9:41:51 AM,10,3,10:40:13 AM,13,27.7,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_239
-Order_No_7136,User_Id_2869,Bike,3,Business,3,3,9:10:57 AM,3,3,9:15:47 AM,3,3,9:22:09 AM,3,3,9:25:26 AM,10,21.5,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_682
-Order_No_6570,User_Id_1471,Bike,3,Business,22,5,12:21:45 PM,22,5,12:22:20 PM,22,5,12:26:11 PM,22,5,12:44:56 PM,7,,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_404
-Order_No_1155,User_Id_393,Bike,3,Business,13,3,8:52:57 AM,13,3,8:53:25 AM,13,3,8:53:39 AM,13,3,10:00:13 AM,11,20.1,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_515
-Order_No_18864,User_Id_2340,Bike,2,Personal,13,3,9:21:28 AM,13,3,9:29:09 AM,13,3,10:00:26 AM,13,3,10:09:54 AM,10,16.0,,-1.2456774,36.7822414,-1.290059,36.7703475,Rider_Id_755
-Order_No_8766,User_Id_2436,Bike,3,Business,17,3,1:55:14 PM,17,3,1:56:20 PM,17,3,2:02:46 PM,17,3,2:09:41 PM,12,28.4,,-1.2571472,36.795063299999995,-1.2397503,36.87976810000001,Rider_Id_612
-Order_No_21984,User_Id_1397,Bike,3,Business,14,4,1:30:37 PM,14,4,1:45:53 PM,14,4,1:53:05 PM,14,4,1:58:27 PM,6,27.8,,-1.2616841,36.801999,-1.287079,36.7811277,Rider_Id_411
-Order_No_26655,User_Id_2956,Bike,3,Business,2,5,1:44:46 PM,2,5,1:45:39 PM,2,5,1:46:07 PM,2,5,2:24:35 PM,1,25.3,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_598
-Order_No_27467,User_Id_1526,Bike,3,Personal,31,4,4:30:10 PM,31,4,5:15:16 PM,31,4,5:45:09 PM,31,4,5:51:41 PM,29,27.7,,-1.1837692,36.9428521,-1.2886166000000001,36.78368029999999,Rider_Id_75
-Order_No_21877,User_Id_291,Bike,1,Personal,7,6,11:37:27 AM,7,6,11:39:42 AM,7,6,11:48:27 AM,7,6,12:10:23 PM,6,19.0,,-1.2556829999999999,36.813771,-1.2920972,36.8102988,Rider_Id_808
-Order_No_24996,User_Id_53,Bike,3,Business,6,3,2:31:27 PM,6,3,2:32:54 PM,6,3,2:44:11 PM,6,3,2:51:32 PM,9,27.6,,-1.2770708,36.8231093,-1.3167113000000001,36.8301563,Rider_Id_518
-Order_No_159,User_Id_2154,Bike,3,Business,23,4,3:14:18 PM,23,4,3:15:02 PM,23,4,3:29:39 PM,23,4,3:31:01 PM,17,24.6,,-1.2765736,36.851364600000004,-1.3705766000000001,36.9120742,Rider_Id_845
-Order_No_26819,User_Id_1066,Bike,3,Personal,15,3,3:09:40 PM,15,3,3:10:17 PM,15,3,3:24:09 PM,15,3,3:32:08 PM,20,21.5,,-1.2191053,36.8995881,-1.2885712999999999,36.764981799999994,Rider_Id_384
-Order_No_27094,User_Id_2066,Bike,3,Business,10,4,8:25:08 AM,10,4,8:26:01 AM,10,4,8:49:55 AM,10,4,9:02:45 AM,10,19.3,,-1.3210188999999999,36.841055100000005,-1.2795290000000001,36.8828522,Rider_Id_436
-Order_No_14288,User_Id_3560,Bike,3,Business,29,5,12:23:23 PM,29,5,12:40:29 PM,29,5,12:49:27 PM,29,5,12:54:09 PM,6,27.0,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_306
-Order_No_24043,User_Id_3771,Bike,3,Personal,4,2,3:21:42 PM,4,2,3:23:58 PM,4,2,3:34:00 PM,4,2,3:37:37 PM,4,22.6,,-1.2664491000000002,36.8102685,-1.2786221000000002,36.8240639,Rider_Id_584
-Order_No_7733,User_Id_372,Bike,1,Business,13,1,12:27:51 PM,13,1,12:28:07 PM,13,1,12:28:39 PM,13,1,12:58:57 PM,11,17.0,,-1.2765736,36.851364600000004,-1.2559559999999999,36.789867,Rider_Id_179
-Order_No_6161,User_Id_1271,Bike,3,Business,11,2,4:22:37 PM,11,2,4:56:23 PM,11,2,5:38:38 PM,11,2,5:55:43 PM,7,20.9,,-1.3226301999999999,36.8719901,-1.3244885,36.897791999999995,Rider_Id_646
-Order_No_19851,User_Id_1927,Bike,3,Business,14,2,12:18:58 PM,14,2,12:19:10 PM,14,2,12:22:56 PM,14,2,12:44:50 PM,19,,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_438
-Order_No_25284,User_Id_3222,Bike,3,Business,8,5,4:21:54 PM,8,5,5:06:33 PM,8,5,5:15:37 PM,8,5,5:22:41 PM,7,27.8,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_76
-Order_No_22319,User_Id_867,Bike,3,Business,14,4,11:59:28 AM,14,4,11:59:55 AM,14,4,11:59:59 AM,14,4,12:20:12 PM,10,25.2,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_901
-Order_No_17520,User_Id_3560,Bike,3,Business,18,1,12:51:47 PM,18,1,12:58:49 PM,18,1,1:12:01 PM,18,1,1:33:01 PM,17,31.0,,-1.279395,36.825364,-1.3705766000000001,36.9120742,Rider_Id_267
-Order_No_4650,User_Id_393,Bike,3,Business,15,5,1:05:25 PM,15,5,1:05:34 PM,15,5,1:06:09 PM,15,5,1:16:33 PM,14,25.7,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_2936,User_Id_2112,Bike,3,Business,27,2,3:08:37 PM,27,2,3:11:47 PM,27,2,3:58:03 PM,27,2,4:01:11 PM,12,25.5,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_622
-Order_No_2888,User_Id_1363,Bike,3,Business,22,4,11:24:52 AM,22,4,11:27:06 AM,22,4,11:35:30 AM,22,4,11:36:38 AM,2,22.4,,-1.3004062,36.829741,-1.2987440000000001,36.817823700000005,Rider_Id_867
-Order_No_20017,User_Id_393,Bike,3,Business,23,4,10:52:52 AM,23,4,10:53:06 AM,23,4,11:07:30 AM,23,4,11:19:28 AM,8,23.5,,-1.3228003000000002,36.8306435,-1.2770708,36.8231093,Rider_Id_438
-Order_No_18870,User_Id_60,Bike,3,Business,12,5,11:20:57 AM,12,5,11:23:53 AM,12,5,11:34:51 AM,12,5,11:41:58 AM,8,24.3,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_453
-Order_No_24510,User_Id_1469,Bike,3,Business,5,2,12:04:03 PM,5,2,12:04:49 PM,5,2,12:12:57 PM,5,2,12:17:33 PM,3,25.6,,-1.2551895,36.7822034,-1.2607765,36.8080021,Rider_Id_956
-Order_No_20083,User_Id_552,Bike,3,Business,11,5,9:40:01 AM,11,5,10:02:27 AM,11,5,10:32:47 AM,11,5,10:38:33 AM,17,21.2,,-1.3632162,36.9108253,-1.291768,36.8297208,Rider_Id_332
-Order_No_14112,User_Id_2718,Bike,3,Business,23,1,4:09:33 PM,23,1,4:10:24 PM,23,1,4:17:13 PM,23,1,4:21:04 PM,2,,,-1.2601,36.796528,-1.2635089,36.78873370000001,Rider_Id_519
-Order_No_4104,User_Id_136,Bike,3,Business,2,2,10:44:09 AM,2,2,10:46:24 AM,2,2,11:10:52 AM,2,2,11:17:20 AM,15,26.3,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_341
-Order_No_650,User_Id_3226,Bike,3,Personal,14,2,10:42:18 AM,14,2,10:42:35 AM,14,2,10:49:12 AM,14,2,11:02:15 AM,8,21.4,,-1.2669026,36.844527299999996,-1.2567464,36.7915273,Rider_Id_414
-Order_No_8801,User_Id_1482,Bike,3,Business,29,3,8:23:24 AM,29,3,8:23:24 AM,29,3,8:48:53 AM,29,3,8:50:45 AM,7,17.8,,-1.2649605,36.798177700000004,-1.3066126,36.8231944,Rider_Id_576
-Order_No_2188,User_Id_3470,Bike,3,Business,29,4,11:33:23 AM,29,4,12:24:00 PM,29,4,12:24:59 PM,29,4,12:36:09 PM,8,21.6,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_260
-Order_No_14820,User_Id_111,Bike,3,Business,10,4,10:56:22 AM,10,4,10:57:44 AM,10,4,10:59:05 AM,10,4,11:10:08 AM,7,24.8,,-1.2551895,36.7822034,-1.2804664,36.7701111,Rider_Id_536
-Order_No_24371,User_Id_867,Bike,3,Business,26,2,3:21:07 PM,26,2,3:28:10 PM,26,2,3:49:22 PM,26,2,4:03:22 PM,9,31.1,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_267
-Order_No_7345,User_Id_246,Bike,3,Business,13,2,3:26:35 PM,13,2,3:44:33 PM,13,2,3:55:10 PM,13,2,3:56:18 PM,5,26.4,,-1.2584143,36.8048002,-1.2820175,36.8223974,Rider_Id_88
-Order_No_24308,User_Id_265,Bike,3,Business,7,4,2:46:00 PM,7,4,2:46:12 PM,7,4,2:46:32 PM,7,4,3:01:02 PM,10,27.5,,-1.3014461000000002,36.7661381,-1.300921,36.828195,Rider_Id_623
-Order_No_27382,User_Id_391,Bike,3,Business,16,6,10:10:01 AM,16,6,10:11:43 AM,16,6,10:22:55 AM,16,6,10:36:22 AM,5,25.5,,-1.300921,36.828195,-1.3292148000000001,36.847480299999994,Rider_Id_576
-Order_No_918,User_Id_1363,Bike,3,Business,28,1,4:03:31 PM,28,1,4:07:45 PM,28,1,4:26:42 PM,28,1,4:35:55 PM,3,22.4,,-1.3077869,36.844320700000004,-1.3090727,36.831526200000006,Rider_Id_432
-Order_No_21876,User_Id_2289,Bike,3,Business,28,4,2:42:16 PM,28,4,2:42:29 PM,28,4,2:50:26 PM,28,4,2:53:12 PM,19,29.8,,-1.2551895,36.7822034,-1.3285517,36.9018,Rider_Id_328
-Order_No_9355,User_Id_3291,Bike,3,Business,13,3,9:03:33 AM,13,3,9:03:59 AM,13,3,9:08:57 AM,13,3,9:28:32 AM,4,20.1,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_63
-Order_No_1376,User_Id_3674,Bike,3,Business,25,1,2:10:38 PM,25,1,2:20:53 PM,25,1,2:44:04 PM,25,1,2:46:18 PM,7,30.2,,-1.2551895,36.7822034,-1.2880883,36.8227336,Rider_Id_709
-Order_No_4042,User_Id_1500,Bike,3,Business,18,1,12:12:43 PM,18,1,12:13:10 PM,18,1,12:26:32 PM,18,1,12:50:51 PM,8,27.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_61
-Order_No_22967,User_Id_302,Bike,3,Business,20,4,1:31:36 PM,20,4,1:32:28 PM,20,4,1:36:59 PM,20,4,1:51:13 PM,5,27.8,,-1.2649605,36.798177700000004,-1.2652741000000003,36.833439500000004,Rider_Id_906
-Order_No_13293,User_Id_1500,Bike,3,Business,6,3,1:07:28 PM,6,3,1:07:52 PM,6,3,1:09:25 PM,6,3,1:40:42 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_154
-Order_No_11248,User_Id_393,Bike,3,Business,17,4,2:13:57 PM,17,4,2:14:16 PM,17,4,2:14:40 PM,17,4,2:50:40 PM,14,23.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_702
-Order_No_17626,User_Id_635,Bike,3,Business,8,4,1:58:19 PM,8,4,1:58:49 PM,8,4,2:10:11 PM,8,4,2:26:51 PM,13,27.5,,-1.3016417,36.8271679,-1.2296129,36.7641955,Rider_Id_123
-Order_No_19756,User_Id_2066,Bike,3,Business,6,6,1:19:55 PM,6,6,1:33:27 PM,6,6,1:42:59 PM,6,6,2:00:18 PM,6,27.9,,-1.316961,36.820337,-1.2927298999999999,36.78836210000001,Rider_Id_328
-Order_No_21754,User_Id_3147,Bike,3,Business,11,1,8:35:13 AM,11,1,8:35:28 AM,11,1,8:42:00 AM,11,1,8:44:26 AM,17,20.9,,-1.3472066,36.7692638,-1.2571472,36.795063299999995,Rider_Id_490
-Order_No_2041,User_Id_963,Bike,2,Personal,11,1,12:26:11 PM,11,1,12:28:58 PM,11,1,12:39:19 PM,11,1,12:46:16 PM,9,18.2,,-1.2874127,36.82476629999999,-1.3332749,36.870814700000004,Rider_Id_820
-Order_No_6467,User_Id_1329,Bike,3,Business,1,5,3:11:37 PM,1,5,3:11:50 PM,1,5,3:15:12 PM,1,5,3:16:35 PM,3,28.7,,-1.273056,36.811298,-1.263499,36.801454799999995,Rider_Id_783
-Order_No_12363,User_Id_3647,Bike,3,Business,23,2,3:45:13 PM,23,2,3:49:44 PM,23,2,4:05:43 PM,23,2,4:10:12 PM,6,,,-1.2726389999999999,36.794723,-1.3020831,36.7573386,Rider_Id_161
-Order_No_5615,User_Id_2436,Bike,3,Business,27,2,3:49:04 PM,27,2,5:25:14 PM,27,2,5:38:40 PM,27,2,5:40:09 PM,7,22.1,,-1.2551895,36.7822034,-1.2854453000000001,36.818836299999994,Rider_Id_260
-Order_No_16667,User_Id_2763,Bike,3,Business,8,3,3:33:39 PM,8,3,3:34:51 PM,8,3,3:46:13 PM,8,3,3:48:50 PM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_337
-Order_No_20997,User_Id_3291,Bike,3,Business,4,1,2:19:01 PM,4,1,2:20:11 PM,4,1,2:24:14 PM,4,1,2:39:59 PM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_698
-Order_No_11553,User_Id_3300,Bike,3,Business,21,2,9:58:24 AM,21,2,9:59:00 AM,21,2,10:22:07 AM,21,2,10:27:27 AM,8,,,-1.2663631000000002,36.8163329,-1.303596,36.778378000000004,Rider_Id_624
-Order_No_7908,User_Id_136,Bike,3,Business,21,4,9:55:30 AM,21,4,9:56:15 AM,21,4,10:04:32 AM,21,4,10:13:49 AM,11,19.2,,-1.2560874,36.7931031,-1.2313147,36.80168629999999,Rider_Id_290
-Order_No_18989,User_Id_3062,Bike,3,Personal,8,2,5:48:52 PM,8,2,5:52:29 PM,8,2,6:11:27 PM,8,2,6:19:25 PM,7,23.7,,-1.2795819,36.8307141,-1.2765625,36.780930100000006,Rider_Id_698
-Order_No_15565,User_Id_893,Bike,3,Business,3,1,12:26:40 PM,3,1,12:29:38 PM,3,1,12:29:46 PM,3,1,12:43:41 PM,7,21.5,,-1.2663194,36.809901,-1.3001909,36.816362700000006,Rider_Id_876
-Order_No_9270,User_Id_2801,Bike,3,Business,5,2,3:02:57 PM,5,2,3:03:41 PM,5,2,3:04:08 PM,5,2,3:38:34 PM,8,,,-1.2595421999999998,36.7871178,-1.2987276,36.81763370000001,Rider_Id_876
-Order_No_20924,User_Id_1693,Bike,3,Business,28,3,3:41:51 PM,28,3,3:42:00 PM,28,3,3:42:26 PM,28,3,3:50:01 PM,17,,,-1.3385790000000002,36.89630379999999,-1.2574087999999999,36.799712,Rider_Id_539
-Order_No_9395,User_Id_3078,Bike,1,Personal,3,5,3:51:35 PM,3,5,3:54:49 PM,3,5,4:15:05 PM,3,5,4:29:18 PM,6,25.1,,-1.2555969999999999,36.830024,-1.2906502,36.814118,Rider_Id_263
-Order_No_22392,User_Id_1868,Bike,3,Business,3,1,11:05:14 AM,3,1,11:05:52 AM,3,1,11:09:01 AM,3,1,11:12:18 AM,6,21.4,,-1.2752846000000002,36.802702200000006,-1.2817325000000002,36.768154100000004,Rider_Id_564
-Order_No_12041,User_Id_307,Bike,2,Personal,10,2,3:07:10 PM,10,2,3:10:04 PM,10,2,3:32:57 PM,10,2,3:42:57 PM,4,20.9,,-1.2887458,36.786049,-1.2882462,36.81645379999999,Rider_Id_844
-Order_No_17810,User_Id_2582,Bike,3,Business,20,3,4:23:24 PM,20,3,4:25:13 PM,20,3,4:36:13 PM,20,3,4:39:30 PM,10,27.3,,-1.2551895,36.7822034,-1.2985642,36.7707474,Rider_Id_953
-Order_No_9097,User_Id_2102,Bike,3,Business,11,5,3:03:47 PM,11,5,3:03:55 PM,11,5,3:13:58 PM,11,5,3:17:20 PM,15,26.4,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_622
-Order_No_3676,User_Id_2306,Bike,3,Business,6,3,10:58:39 AM,6,3,10:59:54 AM,6,3,11:24:32 AM,6,3,11:30:58 AM,13,22.5,,-1.2271998,36.808490500000005,-1.2945128000000001,36.8396881,Rider_Id_937
-Order_No_14220,User_Id_3291,Bike,3,Business,9,6,12:21:36 PM,9,6,12:21:51 PM,9,6,12:25:12 PM,9,6,12:38:24 PM,9,27.4,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_607
-Order_No_23292,User_Id_635,Bike,3,Business,26,2,3:24:46 PM,26,2,3:25:59 PM,26,2,3:38:32 PM,26,2,4:04:50 PM,7,31.0,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_459
-Order_No_6528,User_Id_631,Bike,1,Personal,18,5,12:55:27 PM,18,5,12:56:03 PM,18,5,1:06:23 PM,18,5,1:09:56 PM,20,21.2,,-1.2124618999999999,36.8739332,-1.2954745,36.7841149,Rider_Id_148
-Order_No_1427,User_Id_2066,Bike,3,Business,28,4,5:44:01 PM,28,4,5:45:18 PM,28,4,6:07:52 PM,28,4,6:11:03 PM,9,28.0,,-1.3210188999999999,36.841055100000005,-1.2715686000000002,36.8023526,Rider_Id_328
-Order_No_4113,User_Id_1874,Bike,3,Business,18,2,10:48:00 AM,18,2,10:48:10 AM,18,2,10:54:23 AM,18,2,11:10:51 AM,3,,,-1.2571472,36.795063299999995,-1.2634530000000002,36.7955612,Rider_Id_259
-Order_No_16279,User_Id_131,Bike,2,Personal,8,1,2:46:24 PM,8,1,2:46:59 PM,8,1,2:54:54 PM,8,1,3:08:34 PM,9,24.8,,-1.322087,36.8402977,-1.3234801999999999,36.7938203,Rider_Id_449
-Order_No_10061,User_Id_561,Bike,3,Personal,14,5,3:05:30 PM,14,5,3:07:14 PM,14,5,3:10:30 PM,14,5,3:17:21 PM,11,27.1,,-1.2947536,36.8247182,-1.238146,36.764936600000006,Rider_Id_726
-Order_No_13226,User_Id_1205,Bike,3,Business,5,2,9:56:00 AM,5,2,11:22:40 AM,5,2,11:32:14 AM,5,2,11:33:25 AM,4,21.7,,-1.2588727,36.7761615,-1.2597482,36.7915646,Rider_Id_215
-Order_No_4608,User_Id_2767,Bike,3,Business,10,2,1:37:12 PM,10,2,1:39:20 PM,10,2,1:51:50 PM,10,2,1:53:37 PM,8,20.9,,-1.257773,36.795486,-1.2952784,36.7793271,Rider_Id_467
-Order_No_22358,User_Id_391,Bike,3,Business,29,5,10:57:41 AM,29,5,11:00:43 AM,29,5,11:10:05 AM,29,5,11:20:13 AM,5,26.7,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_96
-Order_No_25635,User_Id_5,Bike,1,Personal,16,1,3:31:33 PM,16,1,3:33:21 PM,16,1,3:39:18 PM,16,1,3:42:45 PM,5,22.2,,-1.2816351000000001,36.8117147,-1.2648622,36.8000216,Rider_Id_726
-Order_No_16208,User_Id_2701,Bike,3,Business,8,5,9:29:14 AM,8,5,9:30:20 AM,8,5,9:48:24 AM,8,5,9:50:24 AM,12,20.5,,-1.2559559999999999,36.772901,-1.2765736,36.851364600000004,Rider_Id_933
-Order_No_22469,User_Id_1148,Bike,3,Business,23,2,11:41:19 AM,23,2,11:41:57 AM,23,2,11:47:46 AM,23,2,12:01:23 PM,9,,,-1.3104867,36.8495593,-1.2664428,36.809453000000005,Rider_Id_855
-Order_No_12438,User_Id_3718,Bike,3,Business,11,2,10:08:53 AM,11,2,10:13:30 AM,11,2,10:21:47 AM,11,2,10:31:04 AM,12,16.3,43.1,-1.2650027,36.8126242,-1.2037968000000001,36.7802765,Rider_Id_427
-Order_No_15010,User_Id_1721,Bike,3,Business,30,2,1:03:45 PM,30,2,1:18:45 PM,30,2,1:45:20 PM,30,2,1:46:48 PM,4,,,-1.2551895,36.7822034,-1.2737314,36.807269399999996,Rider_Id_422
-Order_No_14940,User_Id_1443,Bike,1,Personal,20,4,10:01:34 AM,20,4,10:02:05 AM,20,4,10:30:16 AM,20,4,10:40:05 AM,15,24.4,,-1.2131531,36.8334735,-1.301894,36.830993,Rider_Id_532
-Order_No_2345,User_Id_1304,Bike,3,Business,5,5,4:02:50 PM,5,5,4:29:21 PM,5,5,4:43:04 PM,5,5,4:45:18 PM,9,24.9,,-1.2551895,36.7822034,-1.2977565,36.8025534,Rider_Id_107
-Order_No_23842,User_Id_2344,Bike,3,Business,25,5,9:03:40 AM,25,5,9:03:53 AM,25,5,9:35:54 AM,25,5,9:49:37 AM,8,20.9,,-1.290815,36.7969312,-1.2551895,36.7822034,Rider_Id_761
-Order_No_19962,User_Id_1927,Bike,3,Business,19,3,4:16:17 PM,19,3,4:33:11 PM,19,3,4:40:29 PM,19,3,4:51:51 PM,10,22.3,,-1.3165542,36.700067,-1.3472066,36.7692638,Rider_Id_279
-Order_No_20122,User_Id_1500,Bike,3,Business,27,2,1:02:37 PM,27,2,3:36:05 PM,27,2,3:58:49 PM,27,2,4:03:15 PM,10,25.8,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_958
-Order_No_20998,User_Id_81,Bike,3,Business,21,4,10:34:19 AM,21,4,10:35:28 AM,21,4,10:36:14 AM,21,4,11:11:26 AM,4,21.9,,-1.2641496,36.791413899999995,-1.261177,36.801916,Rider_Id_354
-Order_No_21087,User_Id_2658,Bike,3,Business,20,2,11:41:14 AM,20,2,12:15:00 PM,20,2,12:33:28 PM,20,2,12:35:13 PM,9,19.8,,-1.2891921999999998,36.825189,-1.3014461000000002,36.7661381,Rider_Id_253
-Order_No_20711,User_Id_3320,Bike,3,Business,17,5,8:24:37 AM,17,5,8:25:02 AM,17,5,8:29:14 AM,17,5,8:43:39 AM,7,14.2,,-1.3080091999999999,36.8284543,-1.2722716,36.800345,Rider_Id_179
-Order_No_14821,User_Id_2956,Bike,3,Business,8,3,10:50:39 AM,8,3,10:50:51 AM,8,3,10:51:03 AM,8,3,11:05:48 AM,2,22.2,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_51
-Order_No_7982,User_Id_53,Bike,3,Business,13,1,2:34:33 PM,13,1,2:38:53 PM,13,1,2:53:13 PM,13,1,3:27:53 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_921
-Order_No_11333,User_Id_1073,Bike,3,Business,20,3,4:34:57 PM,20,3,4:35:24 PM,20,3,4:35:43 PM,20,3,4:50:27 PM,5,28.2,,-1.2561964,36.7885001,-1.2753108999999998,36.8130185,Rider_Id_576
-Order_No_9481,User_Id_3491,Bike,3,Business,23,3,2:03:04 PM,23,3,2:04:56 PM,23,3,2:29:03 PM,23,3,2:31:34 PM,16,22.0,,-1.214328,36.7911903,-1.2297202,36.874550799999994,Rider_Id_581
-Order_No_24744,User_Id_1793,Bike,1,Personal,6,3,9:30:16 AM,6,3,9:32:28 AM,6,3,9:44:49 AM,6,3,9:49:39 AM,9,15.8,13.9,-1.3131471000000001,36.817656799999995,-1.2969275,36.78678789999999,Rider_Id_648
-Order_No_17543,User_Id_633,Bike,3,Business,26,1,11:10:20 AM,26,1,11:51:21 AM,26,1,11:53:17 AM,26,1,12:01:17 PM,9,22.8,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_878
-Order_No_9169,User_Id_598,Bike,1,Business,19,2,4:56:04 PM,19,2,4:58:35 PM,19,2,5:07:03 PM,19,2,5:11:38 PM,6,19.7,,-1.2536041999999998,36.806489500000005,-1.2668264999999999,36.7680484,Rider_Id_427
-Order_No_25401,User_Id_2066,Bike,3,Business,31,2,11:06:53 AM,31,2,11:09:13 AM,31,2,11:13:13 AM,31,2,11:15:02 AM,6,18.4,,-1.316961,36.820337,-1.2979147,36.785781400000005,Rider_Id_726
-Order_No_12849,User_Id_3661,Bike,3,Business,3,1,8:00:50 AM,3,1,8:02:01 AM,3,1,8:16:13 AM,3,1,8:19:41 AM,18,18.2,4.9,-1.2132866000000002,36.867827399999996,-1.3208162,36.8451209,Rider_Id_96
-Order_No_8056,User_Id_517,Bike,3,Business,31,1,1:45:05 PM,31,1,1:45:25 PM,31,1,1:48:04 PM,31,1,1:52:39 PM,14,24.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523
-Order_No_6750,User_Id_478,Bike,3,Business,27,1,10:20:50 AM,27,1,10:21:15 AM,27,1,10:24:53 AM,27,1,10:32:20 AM,15,20.7,,-1.2551895,36.7822034,-1.3255466,36.7139,Rider_Id_926
-Order_No_14124,User_Id_302,Bike,3,Business,1,5,2:24:25 PM,1,5,2:27:46 PM,1,5,2:34:21 PM,1,5,2:49:00 PM,4,32.0,,-1.2649605,36.798177700000004,-1.2684319,36.81658529999999,Rider_Id_383
-Order_No_13005,User_Id_932,Bike,1,Personal,3,3,12:29:37 PM,3,3,12:32:16 PM,3,3,12:38:45 PM,3,3,12:45:38 PM,3,23.9,,-1.2750247,36.7981039,-1.2671296,36.81257720000001,Rider_Id_268
-Order_No_1478,User_Id_2212,Bike,3,Business,9,4,4:24:08 PM,9,4,5:05:52 PM,9,4,5:05:55 PM,9,4,5:10:52 PM,5,,,-1.2571472,36.795063299999995,-1.2845341000000001,36.8252076,Rider_Id_564
-Order_No_3848,User_Id_393,Bike,3,Business,31,3,8:15:01 AM,31,3,8:15:48 AM,31,3,8:15:55 AM,31,3,8:57:13 AM,10,19.2,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_239
-Order_No_17726,User_Id_3239,Bike,3,Personal,29,2,9:12:34 AM,29,2,9:14:23 AM,29,2,9:21:37 AM,29,2,9:30:35 AM,8,16.5,,-1.2839322,36.8292426,-1.3197107,36.8054354,Rider_Id_845
-Order_No_8991,User_Id_136,Bike,3,Business,25,4,10:50:50 AM,25,4,12:06:35 PM,25,4,12:42:03 PM,25,4,12:47:26 PM,17,,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_427
-Order_No_11585,User_Id_1469,Bike,3,Business,21,4,3:53:33 PM,21,4,3:53:47 PM,21,4,3:56:22 PM,21,4,3:59:30 PM,3,31.2,,-1.2551895,36.7822034,-1.25896,36.8016691,Rider_Id_856
-Order_No_27619,User_Id_1245,Bike,3,Business,27,6,10:25:38 AM,27,6,10:41:41 AM,27,6,11:00:52 AM,27,6,11:25:35 AM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_427
-Order_No_11040,User_Id_72,Bike,1,Personal,7,5,9:28:33 AM,7,5,9:28:56 AM,7,5,9:41:16 AM,7,5,9:54:26 AM,12,18.1,,-1.3222489,36.786702500000004,-1.2918239,36.8235665,Rider_Id_328
-Order_No_22138,User_Id_3283,Bike,3,Business,15,4,1:05:56 PM,15,4,1:35:39 PM,15,4,1:47:57 PM,15,4,2:00:43 PM,18,27.5,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_702
-Order_No_16537,User_Id_1498,Bike,3,Business,13,4,12:47:53 PM,13,4,12:49:04 PM,13,4,1:20:56 PM,13,4,1:25:15 PM,2,22.1,,-1.2538731,36.7850223,-1.2648024,36.7966196,Rider_Id_155
-Order_No_1617,User_Id_1304,Bike,3,Business,25,2,3:40:12 PM,25,2,3:41:19 PM,25,2,3:49:03 PM,25,2,3:50:58 PM,8,27.3,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_338
-Order_No_27581,User_Id_1913,Bike,1,Personal,11,4,12:37:01 PM,11,4,12:37:57 PM,11,4,12:53:52 PM,11,4,12:57:25 PM,8,24.1,,-1.2652108,36.8056075,-1.304033,36.7848695,Rider_Id_38
-Order_No_23155,User_Id_1710,Bike,3,Personal,11,1,2:27:42 PM,11,1,2:27:51 PM,11,1,2:28:16 PM,11,1,2:39:57 PM,24,28.0,,-1.2963097,36.7688221,-1.1829533,36.718327,Rider_Id_786
-Order_No_27517,User_Id_53,Bike,3,Business,3,3,2:25:54 PM,3,3,2:26:25 PM,3,3,2:38:49 PM,3,3,2:58:07 PM,5,28.4,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_507
-Order_No_17287,User_Id_567,Bike,3,Business,25,4,4:33:18 PM,25,4,4:34:47 PM,25,4,4:40:44 PM,25,4,4:47:57 PM,5,,,-1.2799006000000002,36.7916973,-1.2963097,36.7688221,Rider_Id_484
-Order_No_22754,User_Id_1250,Bike,1,Personal,23,3,10:40:22 AM,23,3,10:41:05 AM,23,3,10:58:45 AM,23,3,11:07:04 AM,11,21.9,,-1.2620741000000002,36.815814700000004,-1.2743743,36.7657321,Rider_Id_633
-Order_No_26395,User_Id_1342,Bike,3,Business,20,4,6:09:24 PM,20,4,6:09:34 PM,20,4,6:09:49 PM,20,4,6:17:20 PM,18,25.1,,-1.3025826,36.7670807,-1.2248308,36.801257299999996,Rider_Id_105
-Order_No_2094,User_Id_1245,Bike,3,Business,14,5,12:15:58 PM,14,5,12:16:31 PM,14,5,12:25:25 PM,14,5,12:42:03 PM,25,21.7,,-1.300921,36.828195,-1.3526635,36.6646172,Rider_Id_654
-Order_No_12123,User_Id_393,Bike,3,Business,15,3,11:06:31 AM,15,3,11:07:07 AM,15,3,11:18:21 AM,15,3,11:26:11 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211
-Order_No_24801,User_Id_1271,Bike,3,Business,9,5,1:35:07 PM,9,5,1:35:44 PM,9,5,2:14:45 PM,9,5,2:23:28 PM,22,26.3,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_63
-Order_No_6626,User_Id_1178,Bike,3,Business,22,4,2:34:24 PM,22,4,2:39:30 PM,22,4,2:49:18 PM,22,4,2:59:40 PM,19,26.3,,-1.2868515,36.7844247,-1.3712481,36.746725899999994,Rider_Id_268
-Order_No_26613,User_Id_2803,Bike,3,Business,6,3,1:14:39 PM,6,3,2:58:32 PM,6,3,3:28:22 PM,6,3,3:31:56 PM,6,21.6,,-1.2765736,36.851364600000004,-1.2574976,36.8705638,Rider_Id_397
-Order_No_4262,User_Id_635,Bike,3,Business,15,5,11:56:13 AM,15,5,12:29:41 PM,15,5,1:17:43 PM,15,5,1:33:02 PM,8,17.6,,-1.2780878,36.846943599999996,-1.2602337,36.79905479999999,Rider_Id_459
-Order_No_21178,User_Id_3264,Bike,3,Business,5,3,4:23:17 PM,5,3,4:23:22 PM,5,3,4:24:31 PM,5,3,4:26:42 PM,3,24.8,,-1.2551895,36.7822034,-1.2642665000000002,36.778828600000004,Rider_Id_259
-Order_No_27627,User_Id_2676,Bike,3,Business,5,2,11:10:23 AM,5,2,11:10:58 AM,5,2,11:16:00 AM,5,2,11:27:07 AM,2,25.3,,-1.2963097,36.7688221,-1.3014461000000002,36.7661381,Rider_Id_365
-Order_No_281,User_Id_566,Bike,1,Personal,13,6,11:21:13 AM,13,6,11:30:30 AM,13,6,11:37:06 AM,13,6,11:47:49 AM,9,25.1,,-1.2540928,36.69782,-1.2924733,36.7560649,Rider_Id_731
-Order_No_20009,User_Id_2956,Bike,3,Business,29,3,4:51:47 PM,29,3,4:51:56 PM,29,3,5:06:26 PM,29,3,5:15:30 PM,15,18.9,,-1.2297202,36.881950700000004,-1.2584143,36.8048002,Rider_Id_384
-Order_No_14021,User_Id_1502,Bike,1,Business,1,5,12:25:08 PM,1,5,12:25:32 PM,1,5,12:48:11 PM,1,5,12:53:20 PM,20,22.3,,-1.285611,36.9047023,-1.3467388999999999,36.7690312,Rider_Id_397
-Order_No_9800,User_Id_393,Bike,3,Business,23,4,3:44:26 PM,23,4,3:45:28 PM,23,4,3:58:30 PM,23,4,4:06:50 PM,11,24.7,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_158
-Order_No_9619,User_Id_1271,Bike,3,Business,5,5,9:15:58 AM,5,5,9:16:36 AM,5,5,9:40:37 AM,5,5,9:44:42 AM,15,19.9,,-1.3244885,36.897791999999995,-1.2285834999999998,36.882646799999996,Rider_Id_411
-Order_No_27598,User_Id_238,Bike,3,Business,2,5,1:21:15 PM,2,5,1:23:04 PM,2,5,1:31:03 PM,2,5,1:49:27 PM,7,25.3,,-1.3077869,36.844320700000004,-1.2969356,36.8183395,Rider_Id_906
-Order_No_20742,User_Id_1461,Bike,2,Personal,13,3,10:50:24 AM,13,3,10:50:57 AM,13,3,10:57:32 AM,13,3,11:08:32 AM,13,20.1,,-1.2720768999999998,36.8180604,-1.2037823,36.7733347,Rider_Id_409
-Order_No_9704,User_Id_3582,Bike,3,Business,8,1,2:48:31 PM,8,1,2:53:10 PM,8,1,2:57:27 PM,8,1,3:12:41 PM,10,29.5,,-1.2384056,36.8718698,-1.2629818000000002,36.8135865,Rider_Id_75
-Order_No_10069,User_Id_1108,Bike,3,Personal,10,3,6:01:43 PM,10,3,6:01:51 PM,10,3,6:03:46 PM,10,3,6:27:27 PM,2,27.2,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_613
-Order_No_19255,User_Id_302,Bike,3,Business,19,1,5:43:33 PM,19,1,5:43:54 PM,19,1,5:44:13 PM,19,1,5:54:24 PM,1,23.8,,-1.2649605,36.798177700000004,-1.25841,36.804026,Rider_Id_720
-Order_No_12521,User_Id_3647,Bike,3,Business,5,5,9:35:39 AM,5,5,9:37:11 AM,5,5,9:51:08 AM,5,5,9:58:37 AM,4,19.6,,-1.2726389999999999,36.794723,-1.2760462,36.807469700000006,Rider_Id_155
-Order_No_19829,User_Id_257,Bike,1,Personal,8,1,8:58:56 AM,8,1,9:00:02 AM,8,1,9:15:49 AM,8,1,9:27:40 AM,9,19.8,,-1.2644443,36.7734547,-1.2872899,36.820434000000006,Rider_Id_692
-Order_No_10558,User_Id_2350,Bike,1,Personal,30,1,9:14:45 AM,30,1,9:15:18 AM,30,1,9:20:09 AM,30,1,9:35:44 AM,9,16.5,2.0,-1.2552938,36.7578876,-1.2745107,36.797450299999994,Rider_Id_574
-Order_No_21167,User_Id_733,Bike,3,Business,6,4,3:14:21 PM,6,4,3:15:17 PM,6,4,3:16:34 PM,6,4,3:21:12 PM,20,21.5,,-1.2551895,36.7822034,-1.3185307,36.9001089,Rider_Id_709
-Order_No_11008,User_Id_1500,Bike,3,Business,4,2,2:44:47 PM,4,2,2:46:13 PM,4,2,2:48:42 PM,4,2,3:07:05 PM,11,22.5,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_36
-Order_No_15295,User_Id_2195,Bike,3,Business,15,1,12:57:47 PM,15,1,12:58:12 PM,15,1,1:06:18 PM,15,1,1:18:58 PM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_154
-Order_No_12523,User_Id_935,Bike,1,Personal,18,3,9:32:45 AM,18,3,9:33:13 AM,18,3,10:24:43 AM,18,3,10:32:01 AM,6,20.0,,-1.2762383999999998,36.7929522,-1.2551895,36.7822034,Rider_Id_334
-Order_No_4073,User_Id_3380,Bike,3,Personal,13,1,1:09:56 PM,13,1,1:11:09 PM,13,1,1:22:15 PM,13,1,1:34:15 PM,7,,,-1.2781938999999998,36.8191922,-1.2963097,36.7688221,Rider_Id_800
-Order_No_15944,User_Id_1500,Bike,3,Business,3,1,8:33:04 AM,3,1,8:34:15 AM,3,1,8:41:16 AM,3,1,8:45:40 AM,13,18.2,5.0,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_582
-Order_No_5736,User_Id_3517,Bike,3,Business,17,4,8:25:18 AM,17,4,8:25:42 AM,17,4,8:29:27 AM,17,4,8:51:44 AM,12,,,-1.300921,36.828195,-1.361923,36.770045,Rider_Id_842
-Order_No_136,User_Id_3434,Bike,3,Business,20,2,2:04:42 PM,20,2,2:06:21 PM,20,2,2:22:30 PM,20,2,2:26:01 PM,20,24.0,,-1.2963097,36.7688221,-1.3123562,36.9157558,Rider_Id_941
-Order_No_17040,User_Id_3718,Bike,3,Business,18,4,2:40:08 PM,18,4,2:40:31 PM,18,4,3:13:38 PM,18,4,3:31:39 PM,14,26.9,,-1.2650027,36.8126242,-1.3178524,36.7139804,Rider_Id_755
-Order_No_26306,User_Id_1066,Bike,1,Personal,20,5,11:42:00 AM,20,5,11:44:30 AM,20,5,12:27:57 PM,20,5,12:29:14 PM,7,18.4,,-1.2794426,36.825373299999995,-1.2885712999999999,36.764981799999994,Rider_Id_951
-Order_No_27668,User_Id_1341,Bike,3,Business,30,6,10:42:28 AM,30,6,10:42:52 AM,30,6,10:53:41 AM,30,6,11:04:09 AM,19,26.8,,-1.228911,36.881876,-1.331619,36.847976,Rider_Id_75
-Order_No_5180,User_Id_186,Bike,3,Business,15,1,10:00:48 AM,15,1,10:01:58 AM,15,1,10:06:26 AM,15,1,10:15:05 AM,1,,,-1.2571472,36.795063299999995,-1.2606419,36.8055244,Rider_Id_648
-Order_No_26714,User_Id_2804,Bike,3,Business,29,1,2:36:55 PM,29,1,3:39:54 PM,29,1,3:46:13 PM,29,1,3:57:34 PM,28,24.5,,-1.2551895,36.7822034,-1.4136642,36.957045,Rider_Id_402
-Order_No_11194,User_Id_246,Bike,3,Business,24,4,10:57:05 AM,24,4,10:57:24 AM,24,4,11:00:32 AM,24,4,11:04:29 AM,3,24.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_714
-Order_No_8319,User_Id_830,Bike,3,Business,16,1,12:11:45 PM,16,1,12:18:51 PM,16,1,12:31:23 PM,16,1,12:38:34 PM,8,19.3,,-1.2551895,36.7822034,-1.2812845,36.8327127,Rider_Id_726
-Order_No_2502,User_Id_2216,Bike,3,Business,17,5,3:55:20 PM,17,5,3:57:19 PM,17,5,3:58:22 PM,17,5,4:26:22 PM,8,,,-1.2551895,36.7822034,-1.2824577,36.7967953,Rider_Id_709
-Order_No_5608,User_Id_346,Bike,3,Business,16,4,11:05:29 AM,16,4,12:08:25 PM,16,4,12:39:07 PM,16,4,12:40:10 PM,15,,,-1.3562374,36.904295399999995,-1.28878,36.8168312,Rider_Id_211
-Order_No_26122,User_Id_1882,Bike,3,Business,13,3,8:37:14 AM,13,3,8:38:03 AM,13,3,8:47:57 AM,13,3,9:24:38 AM,6,20.0,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_855
-Order_No_18120,User_Id_3095,Bike,3,Personal,13,3,3:22:48 PM,13,3,3:23:01 PM,13,3,3:30:35 PM,13,3,3:54:38 PM,10,29.1,,-1.3015700000000001,36.7871,-1.2633397,36.7454238,Rider_Id_678
-Order_No_13250,User_Id_1502,Bike,3,Business,6,1,2:04:38 PM,6,1,2:05:33 PM,6,1,2:12:44 PM,6,1,2:17:10 PM,6,,,-1.2232123,36.8932547,-1.2475028000000001,36.877469700000006,Rider_Id_341
-Order_No_22142,User_Id_540,Bike,3,Business,7,1,12:56:30 PM,7,1,12:57:39 PM,7,1,1:00:19 PM,7,1,1:05:19 PM,8,24.7,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_382
-Order_No_27089,User_Id_39,Bike,2,Personal,23,4,10:48:42 AM,23,4,10:53:10 AM,23,4,11:14:36 AM,23,4,11:17:23 AM,7,18.7,,-1.2696795,36.8640088,-1.2839568000000001,36.823310600000006,Rider_Id_754
-Order_No_3327,User_Id_510,Bike,3,Business,18,2,3:42:55 PM,18,2,5:47:19 PM,18,2,6:09:02 PM,18,2,6:10:45 PM,13,23.6,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_399
-Order_No_1058,User_Id_138,Bike,3,Business,27,3,11:52:32 AM,27,3,11:54:25 AM,27,3,12:13:24 PM,27,3,12:23:21 PM,8,26.0,,-1.2813012,36.832396200000005,-1.2615888000000002,36.7928732,Rider_Id_156
-Order_No_16660,User_Id_1075,Bike,3,Business,22,2,3:55:58 PM,22,2,4:07:02 PM,22,2,4:15:29 PM,22,2,4:19:56 PM,8,25.7,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_660
-Order_No_3105,User_Id_2265,Bike,1,Personal,14,1,12:59:32 PM,14,1,1:01:50 PM,14,1,1:27:26 PM,14,1,1:32:45 PM,13,25.8,,-1.3141805,36.7397786,-1.269534,36.80697,Rider_Id_520
-Order_No_5191,User_Id_136,Bike,3,Business,21,3,10:28:00 AM,21,3,10:29:02 AM,21,3,10:39:04 AM,21,3,10:44:28 AM,11,22.1,,-1.2962832,36.784968299999996,-1.3326173000000001,36.71462329999999,Rider_Id_958
-Order_No_23883,User_Id_2580,Bike,1,Personal,12,4,8:16:18 AM,12,4,8:18:36 AM,12,4,8:35:46 AM,12,4,8:43:22 AM,4,16.2,,-1.2555326,36.781296000000005,-1.260039,36.8159152,Rider_Id_726
-Order_No_22987,User_Id_827,Bike,1,Personal,3,3,9:57:09 AM,3,3,10:18:17 AM,3,3,10:37:18 AM,3,3,10:50:57 AM,5,24.0,,-1.2740396,36.7993518,-1.2940473,36.8270407,Rider_Id_88
-Order_No_5786,User_Id_3647,Bike,3,Business,21,5,4:18:41 PM,21,5,4:19:01 PM,21,5,4:28:41 PM,21,5,4:30:36 PM,22,24.6,,-1.2726389999999999,36.794723,-1.3673224,36.916197499999996,Rider_Id_958
-Order_No_3638,User_Id_1660,Bike,1,Personal,30,4,6:29:54 PM,30,4,6:30:02 PM,30,4,6:31:03 PM,30,4,7:02:51 PM,15,20.5,,-1.2694178,36.812692799999994,-1.3337097,36.7659945,Rider_Id_329
-Order_No_28072,User_Id_2853,Bike,3,Business,3,2,5:06:05 PM,3,2,5:09:14 PM,3,2,5:13:56 PM,3,2,5:18:15 PM,24,18.5,,-1.3700383,36.9190174,-1.2617341999999998,36.79272329999999,Rider_Id_379
-Order_No_25421,User_Id_972,Bike,3,Business,2,2,10:55:44 AM,2,2,11:07:42 AM,2,2,11:14:10 AM,2,2,11:18:32 AM,5,21.0,,-1.2862033000000002,36.8152966,-1.307774,36.840754100000005,Rider_Id_845
-Order_No_5428,User_Id_633,Bike,3,Business,13,3,4:20:42 PM,13,3,4:21:07 PM,13,3,4:33:49 PM,13,3,4:40:31 PM,4,27.4,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_728
-Order_No_495,User_Id_3283,Bike,3,Business,15,1,1:38:47 PM,15,1,1:39:28 PM,15,1,1:49:55 PM,15,1,2:14:54 PM,14,,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_96
-Order_No_13172,User_Id_1280,Bike,3,Business,30,2,10:12:02 AM,30,2,10:12:21 AM,30,2,10:16:33 AM,30,2,10:21:53 AM,8,,,-1.2551895,36.7822034,-1.2949438999999998,36.7604214,Rider_Id_291
-Order_No_5737,User_Id_1490,Bike,1,Personal,25,3,4:54:31 PM,25,3,4:55:03 PM,25,3,5:04:43 PM,25,3,5:13:22 PM,7,17.6,,-1.2975039,36.8110959,-1.2681003999999998,36.7952081,Rider_Id_229
-Order_No_1959,User_Id_3149,Bike,3,Business,10,1,11:18:03 AM,10,1,11:18:53 AM,10,1,11:20:37 AM,10,1,11:21:41 AM,5,18.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_161
-Order_No_21762,User_Id_190,Bike,3,Business,10,5,2:54:32 PM,10,5,2:58:10 PM,10,5,3:04:06 PM,10,5,3:14:50 PM,10,,,-1.3270347,36.866736200000005,-1.3456714,36.919369200000006,Rider_Id_349
-Order_No_24743,User_Id_190,Bike,3,Business,18,4,10:32:55 AM,18,4,11:10:03 AM,18,4,11:22:07 AM,18,4,11:25:08 AM,5,25.7,,-1.3382857,36.887071500000005,-1.3270347,36.866736200000005,Rider_Id_532
-Order_No_22301,User_Id_435,Bike,3,Business,29,3,12:44:25 PM,29,3,12:44:56 PM,29,3,1:31:52 PM,29,3,2:02:24 PM,14,21.3,,-1.242265,36.88087279999999,-1.300921,36.828195,Rider_Id_260
-Order_No_12857,User_Id_1236,Bike,3,Personal,26,5,3:00:34 PM,26,5,3:03:38 PM,26,5,3:21:25 PM,26,5,3:29:23 PM,4,,,-1.2813012,36.832396200000005,-1.2765736,36.851364600000004,Rider_Id_843
-Order_No_20485,User_Id_3688,Bike,3,Business,28,1,3:03:07 PM,28,1,3:10:17 PM,28,1,3:19:55 PM,28,1,3:30:38 PM,5,26.4,,-1.2551895,36.7822034,-1.2736466,36.8164042,Rider_Id_948
-Order_No_22876,User_Id_391,Bike,3,Business,4,2,9:27:09 AM,4,2,10:08:57 AM,4,2,10:29:07 AM,4,2,10:56:18 AM,5,20.5,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_158
-Order_No_23153,User_Id_635,Bike,3,Business,6,4,1:26:40 PM,6,4,1:43:08 PM,6,4,1:58:18 PM,6,4,2:00:04 PM,7,,,-1.2599563,36.799343900000004,-1.234958,36.8104235,Rider_Id_267
-Order_No_19981,User_Id_3560,Bike,3,Business,5,2,3:17:02 PM,5,2,3:25:46 PM,5,2,3:30:23 PM,5,2,3:37:53 PM,5,27.3,,-1.2588181,36.8106709,-1.279395,36.825364,Rider_Id_828
-Order_No_2219,User_Id_1927,Bike,3,Business,16,4,1:45:04 PM,16,4,1:45:51 PM,16,4,2:00:15 PM,16,4,2:09:16 PM,17,,,-1.34364,36.8925345,-1.3472066,36.7692638,Rider_Id_502
-Order_No_11617,User_Id_485,Bike,3,Personal,14,2,4:45:11 PM,14,2,4:46:36 PM,14,2,5:01:30 PM,14,2,5:07:56 PM,29,,,-1.3463428999999998,36.9069888,-1.1677948,36.9835042,Rider_Id_168
-Order_No_16576,User_Id_1724,Bike,3,Business,10,3,3:21:10 PM,10,3,3:22:08 PM,10,3,4:04:37 PM,10,3,4:20:25 PM,14,30.8,,-1.32189,36.8589727,-1.2907853999999999,36.7774108,Rider_Id_155
-Order_No_17693,User_Id_3647,Bike,3,Business,12,1,9:02:37 AM,12,1,9:14:31 AM,12,1,9:22:48 AM,12,1,9:28:33 AM,4,19.2,0.5,-1.2726389999999999,36.794723,-1.2707373,36.805440999999995,Rider_Id_881
-Order_No_10360,User_Id_517,Bike,3,Business,27,4,3:55:11 PM,27,4,3:55:22 PM,27,4,3:55:26 PM,27,4,3:57:39 PM,11,23.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523
-Order_No_9115,User_Id_1389,Bike,3,Business,28,4,4:17:55 PM,28,4,4:24:38 PM,28,4,4:25:16 PM,28,4,5:02:39 PM,15,27.9,,-1.290894,36.822971,-1.207546,36.7913976,Rider_Id_19
-Order_No_2540,User_Id_3647,Bike,3,Business,21,5,1:39:47 PM,21,5,1:39:58 PM,21,5,1:55:11 PM,21,5,1:57:49 PM,3,27.3,,-1.2726389999999999,36.794723,-1.2619756000000002,36.800139,Rider_Id_467
-Order_No_22486,User_Id_3718,Bike,3,Business,4,2,4:27:04 PM,4,2,4:27:29 PM,4,2,4:36:45 PM,4,2,4:39:22 PM,5,22.3,,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_762
-Order_No_21801,User_Id_1233,Bike,3,Personal,22,2,1:10:07 PM,22,2,1:24:07 PM,22,2,1:28:44 PM,22,2,1:47:30 PM,6,25.7,,-1.2825816,36.8092346,-1.25537,36.7756,Rider_Id_487
-Order_No_20252,User_Id_867,Bike,3,Business,26,2,8:25:35 AM,26,2,8:25:50 AM,26,2,8:26:47 AM,26,2,8:43:23 AM,9,,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_778
-Order_No_16376,User_Id_53,Bike,3,Business,13,4,12:16:26 PM,13,4,12:18:44 PM,13,4,12:39:26 PM,13,4,12:51:12 PM,8,21.9,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_766
-Order_No_7146,User_Id_1927,Bike,3,Business,3,3,11:33:01 AM,3,3,11:33:43 AM,3,3,11:39:01 AM,3,3,11:50:17 AM,16,27.2,,-1.2620912,36.8013562,-1.3472066,36.7692638,Rider_Id_937
-Order_No_27706,User_Id_268,Bike,3,Business,14,4,3:52:41 PM,14,4,4:04:42 PM,14,4,4:10:39 PM,14,4,4:14:57 PM,7,27.8,,-1.2551895,36.7822034,-1.290724,36.81947,Rider_Id_404
-Order_No_25302,User_Id_3300,Bike,3,Business,29,3,8:50:58 AM,29,3,8:51:20 AM,29,3,8:51:47 AM,29,3,9:05:36 AM,9,17.9,,-1.259478,36.801781299999995,-1.303596,36.778378000000004,Rider_Id_178
-Order_No_18423,User_Id_2263,Bike,1,Personal,18,2,10:52:17 AM,18,2,10:52:47 AM,18,2,11:11:23 AM,18,2,11:14:52 AM,16,22.5,,-1.2134646,36.8755266,-1.3081260000000001,36.8532641,Rider_Id_74
-Order_No_8369,User_Id_1473,Bike,3,Business,1,1,4:31:02 PM,1,1,4:33:31 PM,1,1,4:43:56 PM,1,1,5:16:59 PM,17,27.8,,-1.2848211999999999,36.822415899999996,-1.3700383,36.9190174,Rider_Id_718
-Order_No_15706,User_Id_2587,Bike,3,Business,6,3,3:35:32 PM,6,3,3:42:23 PM,6,3,3:56:22 PM,6,3,4:02:42 PM,6,,,-1.28878,36.8168312,-1.2561334,36.7990458,Rider_Id_291
-Order_No_20371,User_Id_1073,Bike,3,Business,20,3,4:05:53 PM,20,3,4:06:04 PM,20,3,4:06:21 PM,20,3,4:22:37 PM,9,29.6,,-1.2561964,36.7885001,-1.2846026000000001,36.7844073,Rider_Id_907
-Order_No_7758,User_Id_3491,Bike,3,Business,20,7,2:17:35 PM,20,7,3:34:21 PM,20,7,4:01:21 PM,20,7,4:05:52 PM,11,20.5,,-1.2649605,36.798177700000004,-1.2297202,36.874550799999994,Rider_Id_95
-Order_No_27003,User_Id_3147,Bike,3,Business,19,3,12:13:35 PM,19,3,12:37:47 PM,19,3,12:39:17 PM,19,3,12:54:19 PM,5,22.3,,-1.3472066,36.7692638,-1.3678944,36.7513257,Rider_Id_12
-Order_No_6501,User_Id_1809,Bike,3,Business,17,4,10:18:47 AM,17,4,10:19:58 AM,17,4,10:44:47 AM,17,4,10:49:43 AM,8,25.4,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_436
-Order_No_5169,User_Id_2330,Bike,3,Business,11,2,3:58:49 PM,11,2,4:05:50 PM,11,2,4:09:17 PM,11,2,4:18:03 PM,6,21.5,,-1.2915246999999999,36.805575700000006,-1.3015197,36.7658456,Rider_Id_826
-Order_No_4210,User_Id_1812,Bike,1,Personal,14,5,1:34:33 PM,14,5,1:35:18 PM,14,5,1:40:01 PM,14,5,1:48:20 PM,5,27.1,,-1.2560455,36.780086700000005,-1.2804664,36.7701111,Rider_Id_770
-Order_No_7254,User_Id_419,Bike,3,Business,18,5,3:00:29 PM,18,5,3:00:51 PM,18,5,3:08:38 PM,18,5,3:20:32 PM,5,27.0,,-1.2884146,36.810766799999996,-1.2571472,36.795063299999995,Rider_Id_137
-Order_No_11182,User_Id_3613,Bike,3,Business,21,4,2:45:30 PM,21,4,2:46:47 PM,21,4,2:56:01 PM,21,4,3:12:50 PM,7,31.1,,-1.2830057,36.81754779999999,-1.2733622,36.7704045,Rider_Id_336
-Order_No_24157,User_Id_2330,Bike,3,Business,30,3,3:23:25 PM,30,3,3:24:49 PM,30,3,3:38:35 PM,30,3,3:46:45 PM,8,29.2,,-1.2326041,36.8009029,-1.2341666,36.8443334,Rider_Id_436
-Order_No_17807,User_Id_2869,Bike,3,Business,15,5,11:44:42 AM,15,5,11:44:54 AM,15,5,11:46:17 AM,15,5,12:05:50 PM,10,25.9,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_345
-Order_No_10167,User_Id_2412,Bike,3,Business,15,5,6:39:38 PM,15,5,6:41:02 PM,15,5,6:48:12 PM,15,5,7:01:17 PM,11,27.6,,-1.290894,36.822971,-1.2283403,36.8822756,Rider_Id_448
-Order_No_11074,User_Id_1090,Bike,3,Personal,3,3,10:22:40 AM,3,3,10:23:23 AM,3,3,11:16:31 AM,3,3,11:19:24 AM,6,27.3,,-1.2652279,36.8020159,-1.2913246,36.8019522,Rider_Id_223
-Order_No_23562,User_Id_3573,Bike,3,Personal,24,1,2:56:08 PM,24,1,2:56:27 PM,24,1,3:05:31 PM,24,1,3:13:21 PM,2,,,-1.26836,36.809632,-1.2631940000000002,36.797624,Rider_Id_882
-Order_No_1604,User_Id_3291,Bike,3,Business,4,2,9:23:52 AM,4,2,9:59:54 AM,4,2,10:16:22 AM,4,2,10:22:44 AM,4,16.0,83.1,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_477
-Order_No_4617,User_Id_3051,Bike,3,Personal,27,3,11:59:33 AM,27,3,12:00:45 PM,27,3,12:08:34 PM,27,3,12:14:58 PM,3,26.2,,-1.2702333000000001,36.80065,-1.2736498,36.8061911,Rider_Id_228
-Order_No_7828,User_Id_3491,Bike,3,Business,25,4,12:08:07 PM,25,4,12:08:15 PM,25,4,12:21:37 PM,25,4,12:33:50 PM,24,19.8,,-1.3700383,36.9190174,-1.2297202,36.881950700000004,Rider_Id_222
-Order_No_27805,User_Id_28,Bike,3,Business,26,1,12:24:25 PM,26,1,12:24:44 PM,26,1,1:03:41 PM,26,1,1:34:00 PM,15,22.1,0.7,-1.2450667,36.88238270000001,-1.2917868000000001,36.7872675,Rider_Id_408
-Order_No_4903,User_Id_2978,Bike,3,Business,16,3,11:10:42 AM,16,3,11:10:49 AM,16,3,11:11:39 AM,16,3,11:25:57 AM,7,22.6,,-1.2551895,36.7822034,-1.276751,36.7776926,Rider_Id_56
-Order_No_7243,User_Id_2801,Bike,3,Business,4,4,9:25:05 AM,4,4,9:25:46 AM,4,4,9:38:20 AM,4,4,9:47:01 AM,9,18.4,,-1.2839049999999999,36.824927200000005,-1.2595421999999998,36.7871178,Rider_Id_532
-Order_No_16775,User_Id_2685,Bike,1,Personal,5,4,10:45:04 AM,5,4,10:45:37 AM,5,4,11:14:44 AM,5,4,11:19:28 AM,8,,,-1.2899,36.7794355,-1.2590031000000002,36.8078168,Rider_Id_140
-Order_No_23180,User_Id_3147,Bike,3,Business,10,5,12:51:03 PM,10,5,12:51:56 PM,10,5,1:10:26 PM,10,5,1:18:31 PM,12,,,-1.2831203000000002,36.8316065,-1.3472066,36.7692638,Rider_Id_591
-Order_No_18517,User_Id_788,Bike,3,Business,20,5,3:58:38 PM,20,5,3:59:11 PM,20,5,4:06:25 PM,20,5,4:09:40 PM,11,20.7,,-1.2605938,36.807323499999995,-1.225322,36.80855,Rider_Id_379
-Order_No_3113,User_Id_2801,Bike,3,Business,25,2,9:49:00 AM,25,2,9:49:06 AM,25,2,9:56:37 AM,25,2,10:03:31 AM,7,17.7,,-1.2879652,36.8203441,-1.2595421999999998,36.7871178,Rider_Id_622
-Order_No_21674,User_Id_393,Bike,3,Business,27,2,11:40:25 AM,27,2,11:40:51 AM,27,2,11:40:55 AM,27,2,11:58:58 AM,14,23.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_10
-Order_No_5293,User_Id_3404,Bike,3,Business,4,5,2:54:20 PM,4,5,4:38:04 PM,4,5,4:54:34 PM,4,5,5:07:30 PM,11,21.5,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_489
-Order_No_14867,User_Id_848,Bike,3,Personal,13,1,10:16:37 AM,13,1,10:16:55 AM,13,1,10:35:34 AM,13,1,10:40:48 AM,22,17.4,,-1.2449128,36.76975679,-1.3287648,36.744573200000005,Rider_Id_116
-Order_No_20706,User_Id_2069,Bike,3,Business,22,3,11:06:02 AM,22,3,11:06:54 AM,22,3,11:24:16 AM,22,3,11:42:31 AM,9,22.9,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_221
-Order_No_12644,User_Id_2509,Bike,3,Business,3,3,9:23:26 AM,3,3,9:23:52 AM,3,3,9:28:29 AM,3,3,9:33:42 AM,20,18.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_937
-Order_No_10540,User_Id_627,Bike,3,Business,7,4,2:59:34 PM,7,4,3:00:44 PM,7,4,3:34:56 PM,7,4,3:42:39 PM,11,30.9,,-1.2571472,36.795063299999995,-1.2416156,36.8736721,Rider_Id_216
-Order_No_11282,User_Id_1342,Bike,3,Business,17,3,12:57:35 PM,17,3,1:00:11 PM,17,3,1:29:52 PM,17,3,2:25:57 PM,11,28.5,,-1.3472066,36.7692638,-1.3025826,36.7670807,Rider_Id_65
-Order_No_19310,User_Id_3283,Bike,3,Business,8,2,9:34:01 AM,8,2,9:34:40 AM,8,2,10:21:43 AM,8,2,10:49:48 AM,19,23.5,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_237
-Order_No_14142,User_Id_286,Bike,3,Business,7,5,11:12:40 AM,7,5,11:13:02 AM,7,5,11:15:55 AM,7,5,11:27:32 AM,25,,,-1.2551895,36.7822034,-1.34364,36.8925345,Rider_Id_564
-Order_No_4150,User_Id_1927,Bike,3,Business,8,6,11:22:30 AM,8,6,11:22:48 AM,8,6,11:33:51 AM,8,6,12:07:26 PM,15,18.5,,-1.3025826,36.7670807,-1.3463041999999998,36.768164,Rider_Id_355
-Order_No_3194,User_Id_2828,Bike,3,Business,10,1,11:32:34 AM,10,1,12:36:57 PM,10,1,12:44:35 PM,10,1,12:53:37 PM,12,23.6,,-1.2615888000000002,36.7928732,-1.2095044,36.783206400000005,Rider_Id_268
-Order_No_23017,User_Id_944,Bike,1,Personal,2,4,11:25:59 AM,2,4,12:45:41 PM,2,4,12:54:07 PM,2,4,1:01:24 PM,10,22.3,,-1.2555969999999999,36.830024,-1.235348,36.760016,Rider_Id_409
-Order_No_9369,User_Id_393,Bike,3,Business,7,1,9:46:21 AM,7,1,9:47:24 AM,7,1,10:05:44 AM,7,1,10:41:23 AM,10,21.3,,-1.3177546999999998,36.8303703,-1.2569006,36.7925141,Rider_Id_358
-Order_No_99,User_Id_516,Bike,3,Business,22,5,10:43:35 AM,22,5,10:44:14 AM,22,5,10:45:04 AM,22,5,10:47:19 AM,9,,,-1.2551895,36.7822034,-1.2978456,36.7537369,Rider_Id_379
-Order_No_26117,User_Id_60,Bike,3,Business,27,3,1:29:29 PM,27,3,1:30:44 PM,27,3,1:56:02 PM,27,3,2:16:25 PM,6,29.3,,-1.228911,36.881876,-1.2296618000000001,36.84382720000001,Rider_Id_75
-Order_No_1348,User_Id_412,Bike,3,Business,5,2,3:24:30 PM,5,2,3:41:40 PM,5,2,4:11:03 PM,5,2,4:19:45 PM,15,27.5,,-1.3444459999999998,36.756597,-1.266837,36.799248999999996,Rider_Id_812
-Order_No_22901,User_Id_283,Bike,3,Business,28,3,1:01:33 PM,28,3,1:15:59 PM,28,3,1:30:32 PM,28,3,1:40:32 PM,5,,,-1.2804227,36.8250375,-1.2985749,36.8088,Rider_Id_222
-Order_No_19216,User_Id_2180,Bike,2,Personal,19,2,11:38:41 AM,19,2,11:42:31 AM,19,2,12:02:01 PM,19,2,12:04:37 PM,15,,,-1.3294489999999999,36.890451,-1.2659396,36.7903067,Rider_Id_735
-Order_No_22923,User_Id_1375,Bike,1,Personal,12,6,10:27:22 AM,12,6,10:27:45 AM,12,6,10:39:34 AM,12,6,10:46:10 AM,9,23.1,,-1.3043292,36.8036743,-1.3209614,36.78088270000001,Rider_Id_103
-Order_No_5509,User_Id_2532,Bike,3,Business,23,3,1:44:45 PM,23,3,1:52:59 PM,23,3,2:32:21 PM,23,3,2:53:20 PM,6,22.0,,-1.2579303999999998,36.8019667,-1.2824911,36.8218042,Rider_Id_116
-Order_No_7419,User_Id_2982,Bike,3,Business,12,6,11:44:52 AM,12,6,11:45:05 AM,12,6,11:47:51 AM,12,6,12:23:46 PM,32,,,-1.300921,36.828195,-1.1462238999999999,36.748540000000006,Rider_Id_26
-Order_No_19604,User_Id_302,Bike,3,Business,28,2,6:31:05 PM,28,2,6:32:07 PM,28,2,6:49:08 PM,28,2,6:55:07 PM,2,18.0,,-1.2649605,36.798177700000004,-1.2544458,36.7897277,Rider_Id_40
-Order_No_17324,User_Id_3283,Bike,3,Business,27,3,11:05:09 AM,27,3,11:05:58 AM,27,3,11:07:53 AM,27,3,11:19:17 AM,7,25.9,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_328
-Order_No_5018,User_Id_1902,Bike,1,Personal,25,1,1:39:23 PM,25,1,1:39:51 PM,25,1,1:48:33 PM,25,1,1:54:02 PM,5,,,-1.2854453000000001,36.818836299999994,-1.3038912,36.7961524,Rider_Id_89
-Order_No_3734,User_Id_1456,Bike,3,Business,29,3,9:57:17 AM,29,3,9:57:31 AM,29,3,10:03:27 AM,29,3,10:08:07 AM,16,17.9,,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_660
-Order_No_15274,User_Id_511,Bike,3,Personal,27,3,3:21:59 PM,27,3,3:28:02 PM,27,3,3:37:55 PM,27,3,3:51:15 PM,2,29.1,,-1.2878148999999999,36.8146245,-1.2885417,36.810052299999995,Rider_Id_320
-Order_No_20614,User_Id_2825,Bike,2,Personal,18,6,1:26:18 PM,18,6,1:26:25 PM,18,6,1:36:43 PM,18,6,1:47:56 PM,14,20.5,,-1.2156006000000001,36.8916865,-1.2695713,36.8053074,Rider_Id_384
-Order_No_10524,User_Id_627,Bike,3,Business,3,2,12:17:14 PM,3,2,12:18:22 PM,3,2,12:36:34 PM,3,2,12:40:20 PM,9,18.5,,-1.2551895,36.7822034,-1.2983462,36.8139996,Rider_Id_89
-Order_No_18734,User_Id_2587,Bike,3,Business,13,3,12:58:06 PM,13,3,12:58:37 PM,13,3,1:06:47 PM,13,3,1:22:00 PM,3,24.8,,-1.2770549999999998,36.813027000000005,-1.28878,36.8168312,Rider_Id_785
-Order_No_494,User_Id_2189,Bike,1,Personal,25,5,11:17:33 AM,25,5,11:18:56 AM,25,5,11:51:03 AM,25,5,12:12:33 PM,11,20.9,,-1.2777823999999998,36.8977804,-1.3226301999999999,36.8719901,Rider_Id_116
-Order_No_11378,User_Id_2030,Bike,3,Business,15,2,2:38:59 PM,15,2,2:39:08 PM,15,2,2:44:23 PM,15,2,2:49:31 PM,2,24.4,,-1.257309,36.806008399999996,-1.2601197,36.7985271,Rider_Id_453
-Order_No_3472,User_Id_314,Bike,2,Personal,13,6,5:37:31 PM,13,6,5:40:41 PM,13,6,5:51:36 PM,13,6,5:54:53 PM,20,26.3,,-1.2270789,36.8412311,-1.3209614,36.78088270000001,Rider_Id_383
-Order_No_23087,User_Id_346,Bike,3,Business,18,3,1:11:31 PM,18,3,1:12:08 PM,18,3,1:34:12 PM,18,3,1:37:14 PM,10,20.9,,-1.2885528999999998,36.816014100000004,-1.2390763,36.869159700000004,Rider_Id_174
-Order_No_8106,User_Id_2150,Bike,3,Business,1,3,12:14:50 PM,1,3,12:14:59 PM,1,3,12:18:15 PM,1,3,12:23:56 PM,12,20.1,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_414
-Order_No_1282,User_Id_2289,Bike,3,Business,27,2,2:09:14 PM,27,2,2:10:35 PM,27,2,2:40:45 PM,27,2,2:43:24 PM,19,25.4,,-1.2551895,36.7822034,-1.3285517,36.9018,Rider_Id_580
-Order_No_18128,User_Id_1469,Bike,3,Business,24,3,3:49:44 PM,24,3,3:51:30 PM,24,3,4:01:52 PM,24,3,4:03:14 PM,10,,,-1.2571472,36.795063299999995,-1.3127877000000001,36.848198700000005,Rider_Id_931
-Order_No_6239,User_Id_3283,Bike,3,Business,17,3,2:12:49 PM,17,3,2:12:57 PM,17,3,2:17:24 PM,17,3,2:27:57 PM,14,,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_276
-Order_No_14935,User_Id_3080,Bike,3,Business,8,1,1:00:24 PM,8,1,1:00:30 PM,8,1,1:01:13 PM,8,1,1:07:43 PM,8,24.3,,-1.2551895,36.7822034,-1.2886166000000001,36.78368029999999,Rider_Id_731
-Order_No_3288,User_Id_3147,Bike,3,Business,3,5,1:35:52 PM,3,5,1:37:58 PM,3,5,1:43:13 PM,3,5,1:59:31 PM,17,,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_695
-Order_No_21821,User_Id_635,Bike,3,Business,30,2,1:33:27 PM,30,2,1:34:06 PM,30,2,1:38:11 PM,30,2,2:52:35 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_206
-Order_No_16291,User_Id_3589,Bike,1,Personal,29,2,12:49:50 PM,29,2,12:51:30 PM,29,2,1:00:23 PM,29,2,1:01:37 PM,6,21.3,,-1.2579303999999998,36.8019667,-1.3051608000000001,36.828891399999996,Rider_Id_88
-Order_No_16880,User_Id_2801,Bike,3,Business,4,1,8:09:03 AM,4,1,8:36:08 AM,4,1,8:46:29 AM,4,1,8:47:31 AM,9,22.1,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_452
-Order_No_5890,User_Id_2424,Bike,3,Personal,11,4,1:16:46 PM,11,4,2:59:15 PM,11,4,3:00:07 PM,11,4,3:07:13 PM,4,26.9,,-1.304033,36.7848695,-1.2915246999999999,36.7929598,Rider_Id_393
-Order_No_26210,User_Id_3291,Bike,3,Business,15,5,12:19:33 PM,15,5,12:20:31 PM,15,5,12:45:04 PM,15,5,1:09:07 PM,18,26.0,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_883
-Order_No_20863,User_Id_3050,Bike,3,Business,1,5,2:53:22 PM,1,5,2:54:28 PM,1,5,3:05:00 PM,1,5,3:18:56 PM,14,31.8,,-1.2945073999999999,36.8083195,-1.3169591,36.7000029,Rider_Id_30
-Order_No_4307,User_Id_246,Bike,3,Business,16,5,2:35:27 PM,16,5,2:35:34 PM,16,5,2:41:04 PM,16,5,2:47:31 PM,15,,,-1.2232123,36.8932547,-1.2584143,36.8048002,Rider_Id_62
-Order_No_6934,User_Id_3647,Bike,3,Business,6,4,1:32:48 PM,6,4,2:13:52 PM,6,4,2:29:52 PM,6,4,2:33:23 PM,8,,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_447
-Order_No_1012,User_Id_3647,Bike,3,Business,27,4,9:30:19 AM,27,4,9:31:11 AM,27,4,9:39:34 AM,27,4,9:50:50 AM,6,19.0,3.0,-1.2951243000000001,36.828134399999996,-1.2726389999999999,36.794723,Rider_Id_726
-Order_No_13029,User_Id_1307,Bike,3,Business,3,7,11:40:59 AM,3,7,11:41:07 AM,3,7,11:55:18 AM,3,7,12:19:18 PM,21,21.8,,-1.2844665000000002,36.7866113,-1.3681253,36.915529799999995,Rider_Id_668
-Order_No_6722,User_Id_2509,Bike,3,Business,3,2,8:58:52 AM,3,2,8:59:08 AM,3,2,9:04:03 AM,3,2,9:09:42 AM,20,15.4,1.9,-1.2579303999999998,36.8019667,-1.3700383,36.9190174,Rider_Id_116
-Order_No_27154,User_Id_2277,Bike,3,Business,29,1,4:48:27 PM,29,1,4:48:35 PM,29,1,4:58:39 PM,29,1,5:00:11 PM,14,23.3,,-1.3036370000000002,36.816116799999996,-1.3204931,36.7043102,Rider_Id_257
-Order_No_13164,User_Id_3128,Bike,1,Personal,18,3,1:43:52 PM,18,3,1:56:48 PM,18,3,2:11:11 PM,18,3,2:15:21 PM,6,20.6,,-1.2604472,36.8365287,-1.2753108999999998,36.8130185,Rider_Id_212
-Order_No_1883,User_Id_2922,Bike,2,Personal,23,3,11:10:26 AM,23,3,11:13:13 AM,23,3,11:30:50 AM,23,3,11:38:51 AM,4,21.9,,-1.2697141,36.798177700000004,-1.270054,36.815164,Rider_Id_167
-Order_No_2937,User_Id_272,Bike,3,Personal,7,5,6:21:42 PM,7,5,6:23:10 PM,7,5,6:37:50 PM,7,5,6:39:45 PM,11,21.3,,-1.2979633999999998,36.7719936,-1.3428521999999998,36.7087888,Rider_Id_643
-Order_No_16713,User_Id_1109,Bike,3,Personal,30,2,10:31:16 AM,30,2,10:34:29 AM,30,2,10:48:19 AM,30,2,10:51:56 AM,9,,,-1.2658424,36.8101804,-1.3139995,36.8176693,Rider_Id_911
-Order_No_23417,User_Id_1484,Bike,3,Business,11,1,9:51:17 AM,11,1,9:54:40 AM,11,1,10:02:50 AM,11,1,10:17:44 AM,14,18.0,,-1.2551895,36.7822034,-1.2464947,36.8802085,Rider_Id_497
-Order_No_4126,User_Id_830,Bike,3,Business,5,1,4:06:24 PM,5,1,4:07:01 PM,5,1,4:07:44 PM,5,1,4:15:20 PM,10,25.9,,-1.2551895,36.7822034,-1.275931,36.8516227,Rider_Id_937
-Order_No_25888,User_Id_1442,Bike,3,Personal,16,3,11:22:40 AM,16,3,11:22:55 AM,16,3,12:02:06 PM,16,3,12:18:25 PM,11,23.9,,-1.3185034,36.69851292,-1.2963097,36.7688221,Rider_Id_478
-Order_No_27584,User_Id_1341,Bike,3,Business,24,3,2:02:21 PM,24,3,2:02:54 PM,24,3,2:09:48 PM,24,3,2:13:07 PM,13,,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_324
-Order_No_21837,User_Id_105,Bike,1,Personal,27,1,3:26:42 PM,27,1,3:26:58 PM,27,1,3:43:16 PM,27,1,3:48:55 PM,22,,,-1.2202661,36.7650838,-1.3291124,36.8701589,Rider_Id_361
-Order_No_2464,User_Id_868,Bike,3,Business,18,4,3:30:36 PM,18,4,3:33:37 PM,18,4,3:46:32 PM,18,4,4:03:42 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_126
-Order_No_11293,User_Id_2112,Bike,3,Business,12,3,1:04:35 PM,12,3,1:04:49 PM,12,3,1:42:42 PM,12,3,1:50:38 PM,6,26.7,,-1.2156006000000001,36.8916865,-1.2389424999999998,36.8775718,Rider_Id_384
-Order_No_7116,User_Id_3517,Bike,3,Business,14,4,11:07:45 AM,14,4,11:12:21 AM,14,4,11:23:42 AM,14,4,11:46:02 AM,3,23.7,,-1.300921,36.828195,-1.315089,36.833064,Rider_Id_856
-Order_No_13615,User_Id_393,Bike,3,Business,12,1,2:54:55 PM,12,1,2:55:10 PM,12,1,3:17:27 PM,12,1,3:25:10 PM,14,26.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_555
-Order_No_28086,User_Id_3234,Bike,1,Business,16,4,9:50:57 AM,16,4,9:51:14 AM,16,4,9:57:34 AM,16,4,10:08:31 AM,34,15.6,,-1.3025043,36.753532899999996,-1.3886344,36.9372718,Rider_Id_249
-Order_No_11401,User_Id_1976,Bike,3,Business,15,2,10:03:13 AM,15,2,10:03:28 AM,15,2,10:08:38 AM,15,2,10:10:10 AM,6,20.4,,-1.257028,36.7810435,-1.2800815,36.775352399999996,Rider_Id_576
-Order_No_3018,User_Id_1245,Bike,3,Business,25,5,2:25:23 PM,25,5,2:25:30 PM,25,5,2:38:23 PM,25,5,2:44:05 PM,20,29.3,,-1.300921,36.828195,-1.310453,36.6902954,Rider_Id_498
-Order_No_22270,User_Id_3647,Bike,3,Business,4,4,9:15:56 AM,4,4,9:16:11 AM,4,4,9:16:19 AM,4,4,9:42:57 AM,12,21.7,,-1.2726389999999999,36.794723,-1.3178263000000001,36.7141818,Rider_Id_828
-Order_No_22282,User_Id_2544,Bike,3,Personal,21,2,1:02:09 PM,21,2,1:03:03 PM,21,2,1:05:27 PM,21,2,1:10:06 PM,5,22.4,,-1.2664677,36.779325799999995,-1.2580991000000001,36.794151899999996,Rider_Id_269
-Order_No_20274,User_Id_393,Bike,3,Business,1,4,10:12:52 AM,1,4,10:13:43 AM,1,4,10:13:51 AM,1,4,10:22:50 AM,8,,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_128
-Order_No_5441,User_Id_1304,Bike,3,Business,3,1,1:14:36 PM,3,1,1:14:46 PM,3,1,1:42:37 PM,3,1,2:10:27 PM,17,22.8,,-1.2859912,36.8756811,-1.2646084,36.8028645,Rider_Id_179
-Order_No_6589,User_Id_157,Bike,3,Business,29,1,9:16:03 AM,29,1,9:16:15 AM,29,1,9:32:22 AM,29,1,9:37:12 AM,18,,,-1.225322,36.80855,-1.2257343,36.89335429999999,Rider_Id_953
-Order_No_25293,User_Id_53,Bike,3,Business,17,4,12:28:47 PM,17,4,1:11:08 PM,17,4,1:28:37 PM,17,4,1:36:27 PM,8,28.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_725
-Order_No_4236,User_Id_3404,Bike,3,Business,14,5,2:12:02 PM,14,5,2:15:28 PM,14,5,2:24:29 PM,14,5,2:27:11 PM,1,23.6,,-1.2793686000000002,36.82592329999999,-1.2857548,36.819062,Rider_Id_660
-Order_No_4103,User_Id_510,Bike,3,Business,1,1,3:11:25 PM,1,1,3:13:02 PM,1,1,3:27:38 PM,1,1,3:40:48 PM,20,25.7,,-1.3472066,36.7692638,-1.2860183,36.8975338,Rider_Id_648
-Order_No_16198,User_Id_3517,Bike,3,Business,17,3,3:12:12 PM,17,3,3:12:37 PM,17,3,3:29:33 PM,17,3,4:17:20 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_126
-Order_No_23031,User_Id_1073,Bike,3,Business,18,2,6:12:25 PM,18,2,6:13:17 PM,18,2,6:15:20 PM,18,2,6:18:04 PM,5,,,-1.2596969999999998,36.800664000000005,-1.2568188,36.79082877,Rider_Id_379
-Order_No_3034,User_Id_1927,Bike,3,Business,22,1,10:09:06 AM,22,1,10:10:46 AM,22,1,11:17:48 AM,22,1,12:54:42 PM,12,24.9,,-1.290894,36.822971,-1.3463041999999998,36.768164,Rider_Id_926
-Order_No_10486,User_Id_136,Bike,3,Business,25,1,3:45:33 PM,25,1,3:45:46 PM,25,1,3:54:45 PM,25,1,4:01:58 PM,18,28.0,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_785
-Order_No_25692,User_Id_186,Bike,3,Business,6,4,9:50:27 AM,6,4,9:51:08 AM,6,4,10:04:18 AM,6,4,10:14:02 AM,5,18.6,3.1,-1.2890399,36.8164942,-1.2571472,36.795063299999995,Rider_Id_477
-Order_No_10965,User_Id_1245,Bike,3,Business,28,4,2:57:29 PM,28,4,3:03:18 PM,28,4,3:04:23 PM,28,4,3:54:24 PM,21,30.0,,-1.300921,36.828195,-1.2733676,36.699383000000005,Rider_Id_661
-Order_No_14752,User_Id_2069,Bike,3,Business,10,5,4:34:07 PM,10,5,4:53:34 PM,10,5,5:00:16 PM,10,5,5:07:30 PM,12,,,-1.266837,36.799248999999996,-1.228911,36.881876,Rider_Id_787
-Order_No_11153,User_Id_2581,Bike,3,Personal,11,1,12:48:21 PM,11,1,1:26:09 PM,11,1,1:43:24 PM,11,1,1:49:55 PM,5,23.1,,-1.2961951999999999,36.7876396,-1.2814451999999998,36.8253735,Rider_Id_526
-Order_No_2953,User_Id_1927,Bike,3,Business,17,3,1:04:44 PM,17,3,1:04:59 PM,17,3,1:12:49 PM,17,3,1:18:04 PM,18,25.5,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_620
-Order_No_15186,User_Id_2216,Bike,3,Business,8,2,12:23:44 PM,8,2,12:24:05 PM,8,2,12:26:54 PM,8,2,12:45:54 PM,8,23.3,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_512
-Order_No_27340,User_Id_2532,Bike,3,Business,21,4,12:04:19 PM,21,4,12:04:40 PM,21,4,12:09:39 PM,21,4,12:21:02 PM,7,27.9,,-1.2824911,36.8218042,-1.2551895,36.7822034,Rider_Id_377
-Order_No_7988,User_Id_970,Bike,3,Business,11,1,11:16:04 AM,11,1,11:33:39 AM,11,1,11:48:03 AM,11,1,11:52:40 AM,7,23.8,,-1.2551895,36.7822034,-1.293082,36.8278578,Rider_Id_210
-Order_No_8515,User_Id_1280,Bike,3,Business,13,5,11:00:16 AM,13,5,11:00:23 AM,13,5,11:01:30 AM,13,5,11:10:06 AM,7,,,-1.2551895,36.7822034,-1.2779711,36.782234499999994,Rider_Id_103
-Order_No_7474,User_Id_3147,Bike,3,Business,12,1,8:12:14 AM,12,1,9:34:53 AM,12,1,9:57:20 AM,12,1,10:01:51 AM,10,19.5,0.3,-1.3472066,36.7692638,-1.3899636,36.748237700000004,Rider_Id_383
-Order_No_20529,User_Id_3752,Bike,3,Business,17,5,3:13:04 PM,17,5,3:13:40 PM,17,5,3:15:17 PM,17,5,3:23:46 PM,4,,,-1.265432,36.795034,-1.2753108999999998,36.8130185,Rider_Id_155
-Order_No_13791,User_Id_393,Bike,3,Business,11,5,4:03:03 PM,11,5,4:03:24 PM,11,5,4:05:52 PM,11,5,4:18:54 PM,11,26.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523
-Order_No_2212,User_Id_1271,Bike,3,Business,2,2,9:58:15 AM,2,2,9:58:27 AM,2,2,10:33:54 AM,2,2,10:46:08 AM,24,26.0,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_459
-Order_No_21766,User_Id_2183,Bike,3,Business,9,3,10:19:09 AM,9,3,10:20:21 AM,9,3,10:36:46 AM,9,3,10:40:00 AM,20,25.1,,-1.3138691999999998,36.909690600000005,-1.2584143,36.8048002,Rider_Id_418
-Order_No_14780,User_Id_868,Bike,3,Business,9,2,3:54:40 PM,9,2,3:54:57 PM,9,2,4:02:13 PM,9,2,4:17:21 PM,7,30.5,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_161
-Order_No_5986,User_Id_394,Bike,3,Personal,18,6,11:31:09 AM,18,6,11:32:40 AM,18,6,11:56:52 AM,18,6,12:12:15 PM,19,21.3,,-1.3175925,36.8742349,-1.2378425,36.7579108,Rider_Id_377
-Order_No_633,User_Id_3222,Bike,3,Business,29,4,10:13:26 AM,29,4,10:16:11 AM,29,4,10:58:39 AM,29,4,11:01:00 AM,10,21.7,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_257
-Order_No_2280,User_Id_3291,Bike,3,Business,3,5,9:37:26 AM,3,5,9:37:40 AM,3,5,9:56:26 AM,3,5,10:10:44 AM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_89
-Order_No_15334,User_Id_346,Bike,3,Business,5,5,11:49:46 AM,5,5,11:50:12 AM,5,5,12:12:54 PM,5,5,12:23:18 PM,1,24.7,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_448
-Order_No_25810,User_Id_552,Bike,3,Business,23,3,11:41:49 AM,23,3,11:42:13 AM,23,3,11:42:19 AM,23,3,11:46:49 AM,15,21.8,,-1.3051608000000001,36.828891399999996,-1.3562374,36.904295399999995,Rider_Id_408
-Order_No_15232,User_Id_1363,Bike,3,Business,26,2,10:46:18 AM,26,2,10:47:46 AM,26,2,10:54:41 AM,26,2,10:59:09 AM,7,26.3,,-1.3004062,36.829741,-1.2953162,36.7828041,Rider_Id_837
-Order_No_11196,User_Id_3326,Bike,3,Business,25,5,2:29:03 PM,25,5,2:31:02 PM,25,5,2:50:13 PM,25,5,2:52:53 PM,8,29.0,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_862
-Order_No_17176,User_Id_1245,Bike,3,Business,15,5,3:23:57 PM,15,5,3:24:39 PM,15,5,3:24:48 PM,15,5,4:00:48 PM,18,18.0,,-1.300921,36.828195,-1.3090488999999998,36.7065907,Rider_Id_267
-Order_No_10204,User_Id_53,Bike,3,Business,10,3,12:34:33 PM,10,3,12:35:02 PM,10,3,12:35:18 PM,10,3,1:33:04 PM,5,30.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_507
-Order_No_13248,User_Id_3686,Bike,3,Business,17,2,10:38:13 AM,17,2,10:38:23 AM,17,2,10:43:06 AM,17,2,10:57:04 AM,8,19.2,,-1.2551895,36.7822034,-1.2898353999999999,36.787270299999996,Rider_Id_752
-Order_No_7708,User_Id_1363,Bike,3,Business,5,2,9:09:44 AM,5,2,9:10:49 AM,5,2,9:14:16 AM,5,2,9:16:17 AM,11,20.8,2.0,-1.3004062,36.829741,-1.3302098999999998,36.8898999,Rider_Id_136
-Order_No_13127,User_Id_1329,Bike,3,Business,1,6,12:26:29 PM,1,6,12:26:53 PM,1,6,12:29:34 PM,1,6,1:05:54 PM,21,22.1,,-1.273056,36.811298,-1.2995252,36.6871615,Rider_Id_704
-Order_No_5714,User_Id_627,Bike,3,Business,3,5,11:14:04 AM,3,5,11:14:39 AM,3,5,11:17:12 AM,3,5,11:21:41 AM,10,,,-1.2551895,36.7822034,-1.2299678,36.81530789999999,Rider_Id_161
-Order_No_2112,User_Id_778,Bike,3,Business,31,3,2:00:33 PM,31,3,2:04:20 PM,31,3,2:12:54 PM,31,3,2:15:45 PM,7,27.2,,-1.2535486,36.7933541,-1.2717174,36.775388299999996,Rider_Id_237
-Order_No_9617,User_Id_1329,Bike,3,Business,24,5,3:38:41 PM,24,5,3:45:58 PM,24,5,3:57:13 PM,24,5,4:23:35 PM,5,22.9,,-1.273056,36.811298,-1.2871057,36.822679799999996,Rider_Id_139
-Order_No_911,User_Id_3517,Bike,3,Business,16,4,9:55:28 AM,16,4,9:57:19 AM,16,4,10:38:30 AM,16,4,10:59:44 AM,14,14.6,,-1.300921,36.828195,-1.3681253,36.915529799999995,Rider_Id_179
-Order_No_1034,User_Id_3647,Bike,3,Business,13,1,10:07:09 AM,13,1,10:07:50 AM,13,1,10:19:14 AM,13,1,10:24:56 AM,3,,,-1.2726389999999999,36.794723,-1.2634530000000002,36.7955612,Rider_Id_576
-Order_No_25441,User_Id_496,Bike,3,Business,5,5,1:21:17 PM,5,5,1:21:26 PM,5,5,1:27:02 PM,5,5,1:29:37 PM,5,24.4,,-1.2551895,36.7822034,-1.2677588,36.8169359,Rider_Id_76
-Order_No_20281,User_Id_2100,Bike,3,Business,31,5,11:20:34 AM,31,5,11:20:54 AM,31,5,11:22:19 AM,31,5,11:42:48 AM,6,17.3,,-1.2765736,36.851364600000004,-1.300722,36.816865,Rider_Id_179
-Order_No_14223,User_Id_1304,Bike,3,Business,9,4,12:35:24 PM,9,4,12:36:46 PM,9,4,12:49:19 PM,9,4,12:53:36 PM,14,23.6,,-1.2551895,36.7822034,-1.242902,36.870334,Rider_Id_116
-Order_No_7963,User_Id_1627,Bike,1,Personal,22,3,9:57:58 AM,22,3,10:23:20 AM,22,3,10:33:07 AM,22,3,10:37:47 AM,3,23.0,,-1.3339943,36.7263409,-1.3204931,36.7043102,Rider_Id_226
-Order_No_24535,User_Id_3222,Bike,3,Business,25,4,4:13:16 PM,25,4,4:14:51 PM,25,4,4:21:47 PM,25,4,4:28:57 PM,1,,,-1.2551895,36.7822034,-1.2549259,36.7919494,Rider_Id_205
-Order_No_4795,User_Id_1245,Bike,3,Business,27,4,9:16:09 AM,27,4,9:16:34 AM,27,4,9:40:05 AM,27,4,10:02:30 AM,20,17.9,,-1.300921,36.828195,-1.3340618,36.710894200000006,Rider_Id_654
-Order_No_16130,User_Id_1688,Bike,3,Business,4,2,8:48:00 AM,4,2,8:49:13 AM,4,2,8:50:38 AM,4,2,9:21:46 AM,5,16.4,99.1,-1.300921,36.828195,-1.3210621999999999,36.802019,Rider_Id_644
-Order_No_14741,User_Id_3674,Bike,3,Business,13,4,9:58:53 AM,13,4,9:59:52 AM,13,4,10:00:56 AM,13,4,10:08:39 AM,8,18.4,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_827
-Order_No_13949,User_Id_265,Bike,3,Business,16,6,12:11:24 PM,16,6,12:11:40 PM,16,6,12:17:55 PM,16,6,12:19:05 PM,6,25.7,,-1.300921,36.828195,-1.2666309,36.8358454,Rider_Id_206
-Order_No_28267,User_Id_2956,Bike,3,Business,1,1,4:13:51 PM,1,1,4:42:44 PM,1,1,4:50:38 PM,1,1,5:04:27 PM,10,27.4,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_218
-Order_No_15237,User_Id_2801,Bike,3,Business,10,3,10:52:23 AM,10,3,10:52:54 AM,10,3,11:11:16 AM,10,3,11:27:17 AM,15,27.9,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_218
-Order_No_17604,User_Id_2801,Bike,3,Business,25,1,2:02:57 PM,25,1,2:03:20 PM,25,1,2:13:00 PM,25,1,2:18:43 PM,16,27.8,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_466
-Order_No_10330,User_Id_412,Bike,3,Business,24,3,11:20:35 AM,24,3,11:20:53 AM,24,3,11:25:26 AM,24,3,11:35:57 AM,4,,,-1.2628473,36.781805,-1.266837,36.799248999999996,Rider_Id_709
-Order_No_4583,User_Id_1693,Bike,3,Business,20,3,9:11:15 AM,20,3,9:12:19 AM,20,3,9:44:54 AM,20,3,9:48:48 AM,9,21.1,,-1.2574087999999999,36.799712,-1.2983864,36.81666679999999,Rider_Id_205
-Order_No_11226,User_Id_171,Bike,3,Business,31,5,1:31:40 PM,31,5,1:32:21 PM,31,5,1:43:47 PM,31,5,1:51:06 PM,2,19.9,,-1.296797,36.776452500000005,-1.2973649999999999,36.785936,Rider_Id_812
-Order_No_21650,User_Id_2330,Bike,3,Business,6,4,10:08:05 AM,6,4,10:08:20 AM,6,4,11:01:37 AM,6,4,11:30:55 AM,18,24.0,,-1.2524916000000001,36.8656196,-1.3015197,36.7658456,Rider_Id_266
-Order_No_9294,User_Id_3109,Bike,2,Personal,31,4,10:59:42 AM,31,4,11:00:18 AM,31,4,11:15:31 AM,31,4,12:08:52 PM,11,22.0,,-1.3107918,36.761794,-1.2553615,36.789537599999996,Rider_Id_45
-Order_No_21953,User_Id_687,Bike,3,Business,27,2,10:07:56 AM,27,2,10:14:07 AM,27,2,10:29:06 AM,27,2,10:30:08 AM,20,18.5,2.0,-1.2339733999999998,36.674732899999995,-1.3109027,36.768945200000005,Rider_Id_240
-Order_No_22056,User_Id_1259,Bike,3,Business,5,1,11:27:21 AM,5,1,11:27:29 AM,5,1,12:09:05 PM,5,1,12:12:16 PM,25,24.6,,-1.2359565000000001,36.76227279999999,-1.3478563000000001,36.8935824,Rider_Id_870
-Order_No_5067,User_Id_1075,Bike,3,Business,7,2,1:44:24 PM,7,2,1:45:03 PM,7,2,1:49:11 PM,7,2,1:55:44 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_930
-Order_No_16595,User_Id_1119,Bike,1,Personal,27,4,3:09:20 PM,27,4,3:25:10 PM,27,4,3:53:55 PM,27,4,3:55:27 PM,18,25.0,,-1.3832336,36.764737200000006,-1.3208162,36.8451209,Rider_Id_676
-Order_No_6021,User_Id_2457,Bike,1,Business,18,4,4:36:44 PM,18,4,4:40:10 PM,18,4,4:48:50 PM,18,4,4:55:22 PM,3,23.5,,-1.2571472,36.795063299999995,-1.269534,36.80697,Rider_Id_646
-Order_No_1836,User_Id_2100,Bike,3,Business,31,4,10:23:19 AM,31,4,10:23:53 AM,31,4,10:58:38 AM,31,4,11:00:45 AM,8,26.6,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_660
-Order_No_4234,User_Id_2154,Bike,3,Business,24,4,4:41:45 PM,24,4,4:51:50 PM,24,4,5:19:21 PM,24,4,5:27:17 PM,10,26.2,,-1.2551895,36.7822034,-1.2765736,36.851364600000004,Rider_Id_755
-Order_No_5174,User_Id_3434,Bike,3,Business,20,1,1:10:01 PM,20,1,1:10:43 PM,20,1,1:17:46 PM,20,1,1:20:51 PM,28,20.2,,-1.2963097,36.7688221,-1.1612808000000001,36.9563278,Rider_Id_334
-Order_No_24323,User_Id_1984,Bike,3,Business,10,1,10:59:31 AM,10,1,11:00:00 AM,10,1,11:00:36 AM,10,1,12:10:07 PM,8,,,-1.2830989,36.8165739,-1.2758945,36.7648661,Rider_Id_402
-Order_No_10619,User_Id_3641,Bike,3,Business,23,5,11:00:50 AM,23,5,12:11:39 PM,23,5,12:11:42 PM,23,5,12:32:52 PM,4,24.3,,-1.2571472,36.795063299999995,-1.2787985,36.819832,Rider_Id_813
-Order_No_3383,User_Id_3127,Bike,3,Business,16,2,10:03:26 AM,16,2,10:03:53 AM,16,2,10:17:37 AM,16,2,10:20:06 AM,7,,,-1.2918246,36.801584999999996,-1.2760462,36.807469700000006,Rider_Id_559
-Order_No_4602,User_Id_3080,Bike,3,Business,31,5,10:46:10 AM,31,5,10:46:35 AM,31,5,10:51:52 AM,31,5,10:57:28 AM,13,17.2,,-1.2551895,36.7822034,-1.3025958,36.8614441,Rider_Id_611
-Order_No_7945,User_Id_788,Bike,3,Business,9,5,2:24:34 PM,9,5,2:39:49 PM,9,5,2:46:54 PM,9,5,2:50:29 PM,9,26.1,,-1.2653185,36.8358841,-1.225322,36.80855,Rider_Id_949
-Order_No_14521,User_Id_868,Bike,3,Business,12,3,9:50:48 AM,12,3,10:38:54 AM,12,3,11:00:32 AM,12,3,11:23:21 AM,9,19.3,,-1.331619,36.847976,-1.2663631000000002,36.8163329,Rider_Id_864
-Order_No_26163,User_Id_1271,Bike,3,Business,18,2,9:57:35 AM,18,2,9:57:41 AM,18,2,9:57:45 AM,18,2,10:04:32 AM,18,16.4,,-1.3244885,36.897791999999995,-1.254191,36.809913200000004,Rider_Id_874
-Order_No_23861,User_Id_1162,Bike,3,Business,20,1,8:47:07 AM,20,1,8:47:31 AM,20,1,8:56:34 AM,20,1,9:01:14 AM,10,18.9,,-1.3122610000000001,36.794658,-1.264788,36.7883897,Rider_Id_68
-Order_No_8829,User_Id_419,Bike,3,Business,20,1,1:45:17 PM,20,1,1:45:29 PM,20,1,1:46:52 PM,20,1,1:52:45 PM,6,21.5,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_45
-Order_No_22639,User_Id_3186,Bike,3,Business,29,5,12:16:10 PM,29,5,12:16:40 PM,29,5,12:20:20 PM,29,5,12:25:26 PM,5,19.8,,-1.2980228999999999,36.78865,-1.3014515,36.8120092,Rider_Id_919
-Order_No_26557,User_Id_567,Bike,3,Business,3,1,11:04:28 AM,3,1,11:07:40 AM,3,1,11:30:24 AM,3,1,11:37:42 AM,8,21.7,,-1.2766906,36.8202151,-1.2963097,36.7688221,Rider_Id_2
-Order_No_25943,User_Id_1918,Bike,1,Personal,27,4,10:39:23 AM,27,4,10:39:54 AM,27,4,10:47:55 AM,27,4,10:49:59 AM,2,23.0,,-1.253332,36.80636,-1.2572708000000001,36.812419399999996,Rider_Id_795
-Order_No_13822,User_Id_1051,Bike,3,Business,31,2,3:15:42 PM,31,2,3:16:23 PM,31,2,3:23:41 PM,31,2,3:26:09 PM,9,22.2,,-1.2963097,36.7688221,-1.2930128,36.8356869,Rider_Id_140
-Order_No_20262,User_Id_627,Bike,3,Business,12,2,11:54:57 AM,12,2,11:57:32 AM,12,2,12:08:23 PM,12,2,12:09:32 PM,10,24.7,,-1.2551895,36.7822034,-1.2380935,36.80729470000001,Rider_Id_928
-Order_No_11442,User_Id_2154,Bike,3,Business,7,4,11:03:42 AM,7,4,11:04:38 AM,7,4,11:17:46 AM,7,4,11:33:43 AM,4,27.5,,-1.3083777,36.85900770000001,-1.2765736,36.851364600000004,Rider_Id_697
-Order_No_25515,User_Id_1724,Bike,3,Business,27,3,10:52:51 AM,27,3,10:53:48 AM,27,3,11:10:31 AM,27,3,11:23:11 AM,14,26.2,,-1.32189,36.8589727,-1.2907853999999999,36.7774108,Rider_Id_25
-Order_No_19664,User_Id_1958,Bike,3,Business,6,4,3:15:50 PM,6,4,3:16:18 PM,6,4,3:26:08 PM,6,4,3:46:57 PM,22,21.3,,-1.2464366000000002,36.8170136,-1.3700383,36.9190174,Rider_Id_291
-Order_No_21229,User_Id_432,Bike,3,Business,24,5,2:04:19 PM,24,5,2:10:50 PM,24,5,2:30:42 PM,24,5,2:49:35 PM,8,22.9,,-1.2615888000000002,36.7928732,-1.3012008,36.764868,Rider_Id_567
-Order_No_27444,User_Id_661,Bike,3,Business,29,2,2:49:54 PM,29,2,3:01:25 PM,29,2,3:16:53 PM,29,2,3:38:59 PM,4,23.9,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_312
-Order_No_25438,User_Id_393,Bike,3,Business,28,4,3:35:02 PM,28,4,3:36:11 PM,28,4,3:40:44 PM,28,4,3:49:42 PM,4,30.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523
-Order_No_26071,User_Id_111,Bike,3,Business,25,5,4:12:28 PM,25,5,4:13:33 PM,25,5,4:29:49 PM,25,5,4:31:00 PM,4,29.3,,-1.2551895,36.7822034,-1.2727979999999999,36.8124195,Rider_Id_764
-Order_No_11726,User_Id_2149,Bike,1,Personal,29,6,12:24:07 PM,29,6,12:37:23 PM,29,6,12:44:19 PM,29,6,12:49:34 PM,6,25.2,,-1.2675332000000001,36.808561,-1.293393,36.790237,Rider_Id_38
-Order_No_15853,User_Id_2154,Bike,3,Business,27,4,2:11:17 PM,27,4,2:17:41 PM,27,4,2:30:20 PM,27,4,2:32:06 PM,4,23.6,,-1.2765736,36.851364600000004,-1.2979063,36.8373372,Rider_Id_263
-Order_No_22761,User_Id_3727,Bike,3,Business,16,2,3:04:33 PM,16,2,3:09:06 PM,16,2,3:12:59 PM,16,2,3:19:05 PM,6,,,-1.2963097,36.7688221,-1.2825816,36.8092346,Rider_Id_678
-Order_No_6172,User_Id_517,Bike,3,Business,27,4,8:38:15 AM,27,4,8:38:22 AM,27,4,8:40:52 AM,27,4,8:44:12 AM,14,17.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523
-Order_No_25141,User_Id_3718,Bike,3,Business,1,4,3:59:09 PM,1,4,3:59:27 PM,1,4,4:03:35 PM,1,4,4:08:58 PM,3,,,-1.2650027,36.8126242,-1.257309,36.806008399999996,Rider_Id_795
-Order_No_1909,User_Id_391,Bike,3,Business,5,1,12:39:13 PM,5,1,1:03:44 PM,5,1,1:35:12 PM,5,1,1:36:54 PM,5,25.9,,-1.2777923999999998,36.8231577,-1.3016417,36.8271679,Rider_Id_237
-Order_No_373,User_Id_3381,Bike,3,Business,23,5,12:54:45 PM,23,5,1:10:53 PM,23,5,1:18:24 PM,23,5,1:27:49 PM,9,24.3,,-1.256522,36.786413200000005,-1.279662,36.814902000000004,Rider_Id_114
-Order_No_16666,User_Id_3439,Bike,3,Business,20,1,3:35:36 PM,20,1,3:36:33 PM,20,1,3:56:20 PM,20,1,4:01:26 PM,11,,,-1.2393157,36.766887700000005,-1.2128248,36.8029667,Rider_Id_689
-Order_No_8732,User_Id_2155,Bike,1,Personal,21,5,9:49:04 AM,21,5,9:49:50 AM,21,5,9:50:05 AM,21,5,10:24:52 AM,8,20.2,,-1.2937193,36.8236214,-1.2656393999999997,36.804041999999995,Rider_Id_179
-Order_No_16197,User_Id_1805,Bike,2,Personal,7,5,6:27:59 PM,7,5,6:31:56 PM,7,5,6:45:40 PM,7,5,6:52:37 PM,6,22.2,,-1.2979147,36.785781400000005,-1.2855688,36.824538000000004,Rider_Id_312
-Order_No_13983,User_Id_496,Bike,3,Business,12,2,11:05:56 AM,12,2,11:06:50 AM,12,2,11:15:12 AM,12,2,11:31:01 AM,4,24.5,,-1.2551895,36.7822034,-1.2736498,36.8061911,Rider_Id_383
-Order_No_4076,User_Id_2853,Bike,3,Business,18,5,3:41:31 PM,18,5,3:52:27 PM,18,5,4:10:26 PM,18,5,4:18:48 PM,22,24.3,,-1.3700383,36.9190174,-1.2576006000000002,36.815763000000004,Rider_Id_900
-Order_No_15159,User_Id_391,Bike,3,Business,30,3,10:27:48 AM,30,3,10:29:41 AM,30,3,10:52:13 AM,30,3,10:59:59 AM,5,22.6,,-1.2777923999999998,36.8231577,-1.3016417,36.8271679,Rider_Id_844
-Order_No_9149,User_Id_2212,Bike,3,Business,22,5,11:45:03 AM,22,5,11:51:49 AM,22,5,12:04:03 PM,22,5,12:17:49 PM,8,28.5,,-1.2772989,36.8526035,-1.2571472,36.795063299999995,Rider_Id_291
-Order_No_5124,User_Id_1544,Bike,3,Business,18,6,9:29:20 AM,18,6,9:34:15 AM,18,6,9:44:25 AM,18,6,9:59:34 AM,6,,,-1.2924639,36.77957979999999,-1.2869955,36.8202749,Rider_Id_119
-Order_No_12984,User_Id_1500,Bike,3,Business,4,4,9:09:14 AM,4,4,9:09:31 AM,4,4,9:19:32 AM,4,4,9:36:10 AM,6,21.9,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_119
-Order_No_16074,User_Id_2069,Bike,3,Business,28,3,1:11:29 PM,28,3,1:11:52 PM,28,3,1:23:19 PM,28,3,1:56:11 PM,17,,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_237
-Order_No_4503,User_Id_3517,Bike,3,Business,2,4,1:01:08 PM,2,4,1:01:18 PM,2,4,1:02:23 PM,2,4,1:37:50 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_923
-Order_No_11138,User_Id_2869,Bike,3,Business,11,2,5:54:04 PM,11,2,5:55:00 PM,11,2,6:12:37 PM,11,2,6:19:32 PM,3,20.7,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_55
-Order_No_1224,User_Id_868,Bike,3,Business,21,2,9:09:34 AM,21,2,9:10:57 AM,21,2,9:22:49 AM,21,2,9:53:20 AM,11,,,-1.3306113000000002,36.848052100000004,-1.265715,36.823815,Rider_Id_709
-Order_No_16369,User_Id_487,Bike,2,Personal,18,4,11:56:34 AM,18,4,11:56:45 AM,18,4,12:06:45 PM,18,4,12:11:38 PM,12,25.3,,-1.2592809999999999,36.722127,-1.2844001,36.790587,Rider_Id_726
-Order_No_11064,User_Id_1976,Bike,3,Business,4,2,3:38:47 PM,4,2,3:39:44 PM,4,2,3:47:07 PM,4,2,3:50:34 PM,26,23.7,11.0,-1.257028,36.7810435,-1.3476332,36.908480499999996,Rider_Id_648
-Order_No_7592,User_Id_1329,Bike,3,Business,27,1,4:18:24 PM,27,1,4:19:28 PM,27,1,4:29:00 PM,27,1,4:33:31 PM,21,,,-1.273056,36.811298,-1.2995252,36.6871615,Rider_Id_829
-Order_No_21629,User_Id_1809,Bike,3,Business,15,1,3:28:00 PM,15,1,3:30:51 PM,15,1,3:31:04 PM,15,1,3:48:48 PM,2,,,-1.2642719999999998,36.7978791,-1.2646084,36.8028645,Rider_Id_51
-Order_No_25102,User_Id_348,Bike,3,Business,23,1,12:42:00 PM,23,1,12:44:55 PM,23,1,1:22:23 PM,23,1,1:31:43 PM,7,18.5,,-1.2922364,36.78955379999999,-1.2739208,36.8310153,Rider_Id_844
-Order_No_11066,User_Id_2448,Bike,2,Personal,2,4,11:20:39 AM,2,4,11:22:21 AM,2,4,11:38:48 AM,2,4,11:54:36 AM,7,22.3,,-1.296797,36.776452500000005,-1.2876365,36.816096,Rider_Id_229
-Order_No_14179,User_Id_1709,Bike,1,Personal,8,3,8:15:11 AM,8,3,8:33:32 AM,8,3,9:20:33 AM,8,3,9:23:50 AM,19,17.5,,-1.284497,36.8286605,-1.1941773999999998,36.928793299999995,Rider_Id_179
-Order_No_5448,User_Id_2150,Bike,3,Business,10,1,12:29:37 PM,10,1,12:31:13 PM,10,1,12:31:21 PM,10,1,12:39:18 PM,9,18.6,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_855
-Order_No_1438,User_Id_246,Bike,3,Business,10,4,10:46:45 AM,10,4,10:47:28 AM,10,4,11:11:55 AM,10,4,11:19:26 AM,3,,,-1.2584143,36.8048002,-1.2559559999999999,36.789867,Rider_Id_188
-Order_No_22558,User_Id_2064,Bike,2,Personal,4,1,11:11:52 AM,4,1,11:12:19 AM,4,1,11:30:01 AM,4,1,11:37:09 AM,8,20.9,,-1.2627823999999999,36.7964192,-1.2959613,36.7684114,Rider_Id_101
-Order_No_7538,User_Id_2285,Bike,3,Personal,22,3,11:06:39 AM,22,3,11:06:48 AM,22,3,11:07:04 AM,22,3,11:13:02 AM,19,23.3,,-1.3714436,36.9221125,-1.2950041,36.814358299999995,Rider_Id_595
-Order_No_23317,User_Id_1244,Bike,3,Business,19,6,12:26:14 PM,19,6,12:29:47 PM,19,6,12:40:30 PM,19,6,12:54:54 PM,9,21.5,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_327
-Order_No_14321,User_Id_136,Bike,3,Business,29,5,10:21:41 AM,29,5,10:22:39 AM,29,5,10:48:34 AM,29,5,10:53:26 AM,16,26.6,,-1.3254522,36.721658399999995,-1.281614,36.8275424,Rider_Id_448
-Order_No_21691,User_Id_3517,Bike,3,Business,19,1,11:17:37 AM,19,1,11:19:34 AM,19,1,11:49:02 AM,19,1,11:59:22 AM,15,24.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_280
-Order_No_27051,User_Id_1133,Bike,3,Business,28,5,10:28:29 AM,28,5,10:30:24 AM,28,5,10:34:05 AM,28,5,10:38:08 AM,9,25.0,,-1.2571472,36.795063299999995,-1.267694,36.757611499999996,Rider_Id_882
-Order_No_24671,User_Id_1921,Bike,1,Personal,29,6,1:12:11 PM,29,6,1:13:10 PM,29,6,1:25:25 PM,29,6,1:32:24 PM,3,28.0,,-1.2728947,36.807942600000004,-1.2784495,36.7981729,Rider_Id_584
-Order_No_12014,User_Id_3017,Bike,3,Business,15,7,5:50:55 PM,15,7,5:51:15 PM,15,7,5:59:06 PM,15,7,6:03:14 PM,3,20.5,,-1.299313,36.78314879999999,-1.2905113000000001,36.759113799999994,Rider_Id_45
-Order_No_7625,User_Id_1280,Bike,3,Business,16,2,2:39:41 PM,16,2,2:40:42 PM,16,2,2:49:31 PM,16,2,2:54:25 PM,10,,,-1.2551895,36.7822034,-1.3138211000000002,36.8257935,Rider_Id_80
-Order_No_6104,User_Id_2200,Bike,3,Business,13,3,12:47:58 PM,13,3,1:14:51 PM,13,3,1:22:32 PM,13,3,1:24:13 PM,4,19.9,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_444
-Order_No_25280,User_Id_3621,Bike,3,Business,30,4,1:41:05 PM,30,4,1:42:10 PM,30,4,1:47:52 PM,30,4,1:52:41 PM,14,,,-1.2836583000000001,36.7886014,-1.2206179,36.8488524,Rider_Id_676
-Order_No_1014,User_Id_2030,Bike,3,Business,29,2,1:14:22 PM,29,2,1:32:41 PM,29,2,1:34:30 PM,29,2,2:08:28 PM,6,27.1,,-1.2365885,36.8898127,-1.2156006000000001,36.8916865,Rider_Id_75
-Order_No_26374,User_Id_393,Bike,3,Business,29,1,10:52:47 AM,29,1,10:58:10 AM,29,1,10:58:13 AM,29,1,12:07:34 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_239
-Order_No_5590,User_Id_3669,Bike,3,Business,7,2,12:32:26 PM,7,2,12:32:46 PM,7,2,12:39:25 PM,7,2,1:02:50 PM,17,,,-1.2615888000000002,36.7928732,-1.2491258,36.689425299999996,Rider_Id_690
-Order_No_10085,User_Id_2956,Bike,3,Business,24,3,9:56:29 AM,24,3,9:56:43 AM,24,3,9:59:35 AM,24,3,10:13:05 AM,5,,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_953
-Order_No_7516,User_Id_3147,Bike,3,Business,5,3,1:27:26 PM,5,3,2:02:41 PM,5,3,2:14:55 PM,5,3,2:27:59 PM,13,24.9,,-1.2765736,36.851364600000004,-1.3472066,36.7692638,Rider_Id_933
-Order_No_7816,User_Id_1966,Bike,3,Business,28,4,3:54:54 PM,28,4,4:13:26 PM,28,4,4:24:47 PM,28,4,4:27:36 PM,4,27.3,,-1.3700383,36.9190174,-1.3462618,36.9071972,Rider_Id_863
-Order_No_28262,User_Id_391,Bike,3,Business,23,2,2:43:11 PM,23,2,2:44:21 PM,23,2,3:04:47 PM,23,2,3:09:28 PM,20,18.8,,-1.2857573999999998,36.8295667,-1.1923911,36.944733299999996,Rider_Id_449
-Order_No_3838,User_Id_735,Bike,1,Personal,14,6,4:39:05 PM,14,6,4:42:20 PM,14,6,4:56:20 PM,14,6,5:05:11 PM,10,19.1,,-1.2519787,36.8096802,-1.3174417,36.8198414,Rider_Id_539
-Order_No_2994,User_Id_1209,Bike,3,Business,23,2,4:18:41 PM,23,2,4:35:17 PM,23,2,4:48:21 PM,23,2,4:51:55 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_161
-Order_No_10677,User_Id_868,Bike,3,Business,2,3,1:18:56 PM,2,3,1:19:47 PM,2,3,1:25:44 PM,2,3,1:33:22 PM,7,26.1,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_521
-Order_No_23389,User_Id_3602,Bike,3,Business,13,3,7:18:36 PM,13,3,7:24:15 PM,13,3,7:45:41 PM,13,3,7:51:15 PM,6,20.7,,-1.2797013000000002,36.7299371,-1.2963097,36.7688221,Rider_Id_862
-Order_No_9429,User_Id_176,Bike,1,Personal,21,1,12:52:13 PM,21,1,12:53:54 PM,21,1,1:19:03 PM,21,1,1:30:15 PM,4,20.2,,-1.2854453000000001,36.818836299999994,-1.2666695,36.8328283,Rider_Id_116
-Order_No_8814,User_Id_475,Bike,3,Business,13,1,3:03:40 PM,13,1,3:04:02 PM,13,1,3:05:51 PM,13,1,3:19:30 PM,7,,,-1.2571472,36.795063299999995,-1.3024794,36.8228284,Rider_Id_438
-Order_No_17820,User_Id_3044,Bike,3,Business,27,3,2:16:08 PM,27,3,2:53:09 PM,27,3,3:42:04 PM,27,3,3:57:07 PM,20,29.3,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_96
-Order_No_10663,User_Id_1582,Bike,1,Business,25,2,3:34:55 PM,25,2,4:34:28 PM,25,2,4:45:34 PM,25,2,4:47:09 PM,6,23.8,,-1.285244,36.832159000000004,-1.2508229,36.7895263,Rider_Id_532
-Order_No_8298,User_Id_2066,Bike,3,Business,22,3,3:42:39 PM,22,3,3:50:16 PM,22,3,4:08:33 PM,22,3,4:10:00 PM,5,25.9,,-1.316961,36.820337,-1.3007714,36.8001722,Rider_Id_754
-Order_No_22720,User_Id_867,Bike,3,Business,28,1,9:13:29 AM,28,1,9:50:18 AM,28,1,9:50:23 AM,28,1,10:13:17 AM,10,19.5,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_523
-Order_No_1362,User_Id_1329,Bike,3,Business,15,5,10:53:42 AM,15,5,10:54:06 AM,15,5,11:02:57 AM,15,5,11:04:37 AM,9,26.1,,-1.273056,36.811298,-1.324658,36.834047999999996,Rider_Id_76
-Order_No_6723,User_Id_2423,Bike,3,Business,4,4,12:15:59 PM,4,4,12:19:09 PM,4,4,12:25:48 PM,4,4,12:58:03 PM,7,25.4,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_507
-Order_No_22444,User_Id_901,Bike,1,Personal,5,4,11:19:18 AM,5,4,11:20:28 AM,5,4,11:28:36 AM,5,4,11:42:48 AM,16,,,-1.3688433,36.7402114,-1.2854988999999999,36.8218183,Rider_Id_791
-Order_No_24802,User_Id_3635,Bike,1,Business,12,5,1:27:51 PM,12,5,1:28:53 PM,12,5,1:35:36 PM,12,5,1:49:22 PM,7,27.1,,-1.2615888000000002,36.7928732,-1.2896398999999998,36.812449799999996,Rider_Id_379
-Order_No_21146,User_Id_136,Bike,3,Business,25,1,5:07:40 PM,25,1,5:07:51 PM,25,1,5:24:49 PM,25,1,5:29:40 PM,20,,,-1.2962832,36.784968299999996,-1.3190102,36.923069299999995,Rider_Id_330
-Order_No_10487,User_Id_246,Bike,3,Business,3,6,1:20:00 PM,3,6,1:20:25 PM,3,6,1:20:39 PM,3,6,1:27:31 PM,4,23.5,,-1.268027,36.8037005,-1.2584143,36.8048002,Rider_Id_881
-Order_No_12544,User_Id_3517,Bike,3,Business,31,4,12:42:03 PM,31,4,12:43:55 PM,31,4,12:55:42 PM,31,4,1:37:19 PM,15,22.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_808
-Order_No_8088,User_Id_1357,Bike,1,Personal,28,5,12:15:38 PM,28,5,12:17:19 PM,28,5,12:38:26 PM,28,5,12:49:54 PM,5,25.1,,-1.2663525,36.812833700000006,-1.2897215,36.7945959,Rider_Id_537
-Order_No_20415,User_Id_2587,Bike,3,Business,18,1,11:17:21 AM,18,1,11:17:58 AM,18,1,11:26:54 AM,18,1,11:34:38 AM,6,26.8,,-1.2615888000000002,36.7928732,-1.28878,36.8168312,Rider_Id_778
-Order_No_12585,User_Id_2212,Bike,3,Business,13,3,11:28:49 AM,13,3,11:29:51 AM,13,3,11:37:16 AM,13,3,11:46:59 AM,6,24.6,,-1.2551895,36.7822034,-1.2850638,36.813096,Rider_Id_411
-Order_No_18534,User_Id_2343,Bike,3,Business,31,4,8:46:23 AM,31,4,8:50:23 AM,31,4,8:58:44 AM,31,4,9:08:23 AM,10,17.5,,-1.2959613,36.7684114,-1.2809708,36.834855,Rider_Id_46
-Order_No_3721,User_Id_323,Bike,1,Business,8,1,3:11:19 PM,8,1,3:21:11 PM,8,1,3:35:37 PM,8,1,3:44:17 PM,16,24.9,,-1.3861826000000002,36.9349923,-1.3123348999999997,36.9152001,Rider_Id_427
-Order_No_1504,User_Id_1051,Bike,3,Business,10,3,2:22:16 PM,10,3,2:23:11 PM,10,3,2:32:05 PM,10,3,2:37:44 PM,14,30.5,,-1.2963097,36.7688221,-1.3038645,36.8684596,Rider_Id_324
-Order_No_27291,User_Id_3203,Bike,3,Business,14,2,10:37:46 AM,14,2,10:37:57 AM,14,2,10:38:03 AM,14,2,11:06:36 AM,13,21.6,,-1.307143,36.825009,-1.228911,36.881876,Rider_Id_589
-Order_No_6152,User_Id_3185,Bike,1,Personal,25,3,9:45:02 AM,25,3,11:40:38 AM,25,3,11:41:05 AM,25,3,11:58:26 AM,16,,,-1.291216,36.8746657,-1.2295205,36.812042600000005,Rider_Id_618
-Order_No_19056,User_Id_2216,Bike,3,Business,10,1,2:33:39 PM,10,1,2:34:38 PM,10,1,2:40:50 PM,10,1,2:47:28 PM,1,18.4,,-1.2551895,36.7822034,-1.2549259,36.7919494,Rider_Id_205
-Order_No_28143,User_Id_2066,Bike,3,Business,29,3,3:40:56 PM,29,3,3:42:11 PM,29,3,3:55:31 PM,29,3,3:58:39 PM,1,18.8,,-1.316961,36.820337,-1.3108175,36.8221698,Rider_Id_676
-Order_No_2447,User_Id_3283,Bike,3,Business,22,3,2:58:04 PM,22,3,2:58:41 PM,22,3,3:09:38 PM,22,3,3:15:33 PM,6,25.5,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_857
-Order_No_6295,User_Id_3661,Bike,3,Business,6,4,9:27:40 AM,6,4,9:28:43 AM,6,4,9:51:42 AM,6,4,9:57:20 AM,15,16.0,4.1,-1.2132866000000002,36.867827399999996,-1.3090727,36.831526200000006,Rider_Id_291
-Order_No_27266,User_Id_2328,Bike,2,Personal,3,4,12:18:04 PM,3,4,12:18:26 PM,3,4,12:38:48 PM,3,4,12:43:20 PM,10,21.9,,-1.298529,36.816482,-1.2355548,36.84741939999999,Rider_Id_742
-Order_No_18091,User_Id_3515,Bike,1,Personal,3,7,4:40:41 PM,3,7,4:41:05 PM,3,7,4:49:01 PM,3,7,4:50:40 PM,6,20.8,,-1.3054651000000002,36.8845286,-1.2871568000000002,36.863819299999996,Rider_Id_890
-Order_No_4205,User_Id_552,Bike,3,Business,25,3,12:13:32 PM,25,3,12:13:42 PM,25,3,12:13:47 PM,25,3,12:36:36 PM,13,,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_589
-Order_No_18579,User_Id_3117,Bike,1,Personal,22,3,10:55:06 AM,22,3,10:56:42 AM,22,3,11:13:55 AM,22,3,11:16:16 AM,9,22.9,,-1.2850643999999998,36.8343657,-1.2295205,36.812042600000005,Rider_Id_754
-Order_No_23836,User_Id_1683,Bike,3,Business,22,4,2:05:28 PM,22,4,2:07:57 PM,22,4,2:26:28 PM,22,4,2:41:44 PM,7,26.4,,-1.2994194,36.7647917,-1.2765334,36.80150510000001,Rider_Id_375
-Order_No_16776,User_Id_627,Bike,3,Business,28,5,12:12:36 PM,28,5,12:14:21 PM,28,5,12:27:54 PM,28,5,12:33:41 PM,7,25.1,,-1.2571472,36.795063299999995,-1.2876155,36.78684439999999,Rider_Id_96
-Order_No_1806,User_Id_2987,Bike,3,Business,10,2,4:14:51 PM,10,2,4:42:07 PM,10,2,4:52:00 PM,10,2,4:58:50 PM,10,20.4,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_217
-Order_No_10456,User_Id_246,Bike,3,Business,8,2,5:00:52 PM,8,2,5:01:09 PM,8,2,5:14:43 PM,8,2,5:20:43 PM,2,23.6,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_566
-Order_No_9623,User_Id_1724,Bike,3,Business,14,4,11:41:11 AM,14,4,11:42:00 AM,14,4,11:43:12 AM,14,4,12:13:46 PM,8,24.8,,-1.2907853999999999,36.7774108,-1.2551895,36.7822034,Rider_Id_708
-Order_No_15324,User_Id_2523,Bike,3,Business,24,5,9:48:27 AM,24,5,9:48:45 AM,24,5,9:50:16 AM,24,5,10:10:11 AM,5,18.3,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_438
-Order_No_7119,User_Id_3750,Bike,3,Personal,17,7,1:54:27 PM,17,7,1:54:51 PM,17,7,2:22:53 PM,17,7,2:47:54 PM,13,29.0,,-1.3438225000000001,36.6664253,-1.3881391,36.7367636,Rider_Id_647
-Order_No_1562,User_Id_3404,Bike,3,Business,7,4,3:16:56 PM,7,4,3:24:21 PM,7,4,3:42:37 PM,7,4,3:49:27 PM,8,27.7,,-1.279395,36.825364,-1.2765492,36.7669809,Rider_Id_411
-Order_No_27717,User_Id_2314,Bike,2,Personal,4,1,10:58:39 AM,4,1,11:24:27 AM,4,1,11:45:54 AM,4,1,12:20:53 PM,10,21.1,,-1.3406072,36.887746,-1.3107959999999999,36.862829999999995,Rider_Id_844
-Order_No_19861,User_Id_627,Bike,3,Business,20,2,1:19:45 PM,20,2,1:46:47 PM,20,2,1:59:02 PM,20,2,2:01:53 PM,10,24.1,,-1.2551895,36.7822034,-1.2380935,36.80729470000001,Rider_Id_72
-Order_No_6347,User_Id_1882,Bike,3,Business,2,4,3:26:21 PM,2,4,3:43:46 PM,2,4,4:01:21 PM,2,4,4:14:31 PM,8,,,-1.3014461000000002,36.7661381,-1.323615,36.700475,Rider_Id_599
-Order_No_18715,User_Id_111,Bike,3,Business,10,3,9:54:32 AM,10,3,9:54:45 AM,10,3,10:27:45 AM,10,3,10:31:52 AM,23,21.7,,-1.3700383,36.9190174,-1.2551895,36.7822034,Rider_Id_488
-Order_No_23236,User_Id_1148,Bike,3,Business,12,2,10:06:01 AM,12,2,10:22:31 AM,12,2,10:50:42 AM,12,2,11:22:10 AM,14,21.0,,-1.3100586,36.850032899999995,-1.2294985,36.8775495,Rider_Id_549
-Order_No_18596,User_Id_1368,Bike,3,Business,28,1,2:32:31 PM,28,1,2:33:09 PM,28,1,2:42:58 PM,28,1,2:51:43 PM,7,26.1,,-1.2664227,36.7453671,-1.2571472,36.795063299999995,Rider_Id_337
-Order_No_16520,User_Id_1106,Bike,1,Personal,31,2,10:28:43 AM,31,2,10:29:02 AM,31,2,10:42:02 AM,31,2,10:45:42 AM,13,18.4,,-1.3025043,36.753532899999996,-1.3529387,36.664256200000004,Rider_Id_414
-Order_No_6276,User_Id_3275,Bike,1,Personal,6,3,3:26:16 PM,6,3,3:31:38 PM,6,3,3:49:55 PM,6,3,3:59:10 PM,8,22.0,,-1.2738167,36.8157821,-1.311943,36.833113399999995,Rider_Id_621
-Order_No_19411,User_Id_1808,Bike,1,Personal,30,6,9:15:18 AM,30,6,9:21:24 AM,30,6,9:48:25 AM,30,6,9:52:53 AM,12,17.6,,-1.3312488,36.8801409,-1.3092483999999998,36.822863899999994,Rider_Id_212
-Order_No_4409,User_Id_1458,Bike,1,Personal,28,2,12:52:04 PM,28,2,12:53:12 PM,28,2,1:45:37 PM,28,2,1:58:16 PM,7,19.5,,-1.2643343,36.7447423,-1.263499,36.801454799999995,Rider_Id_65
-Order_No_27367,User_Id_2066,Bike,3,Business,13,1,9:09:26 AM,13,1,9:09:34 AM,13,1,9:15:13 AM,13,1,9:26:28 AM,11,15.2,,-1.316961,36.820337,-1.2580991000000001,36.794151899999996,Rider_Id_329
-Order_No_14239,User_Id_3274,Bike,1,Personal,6,5,12:08:16 PM,6,5,12:08:40 PM,6,5,12:09:05 PM,6,5,12:31:00 PM,4,20.2,,-1.2682178999999998,36.8009289,-1.2738348,36.7852209,Rider_Id_813
-Order_No_26561,User_Id_2587,Bike,3,Business,23,3,1:59:25 PM,23,3,1:59:42 PM,23,3,2:04:15 PM,23,3,2:07:25 PM,18,27.3,,-1.28878,36.8168312,-1.3562374,36.904295399999995,Rider_Id_70
-Order_No_14508,User_Id_3781,Bike,3,Business,1,4,1:40:22 PM,1,4,1:40:39 PM,1,4,1:41:24 PM,1,4,2:13:53 PM,10,,,-1.2128018,36.83263710000001,-1.26071,36.8206332,Rider_Id_101
-Order_No_22085,User_Id_2330,Bike,3,Business,1,1,10:06:22 AM,1,1,10:08:40 AM,1,1,10:42:47 AM,1,1,10:47:16 AM,9,,,-1.2642903,36.80021370000001,-1.2124582,36.802894,Rider_Id_843
-Order_No_387,User_Id_3647,Bike,3,Business,12,5,5:28:35 PM,12,5,5:29:27 PM,12,5,5:38:53 PM,12,5,5:43:46 PM,15,26.7,,-1.2726389999999999,36.794723,-1.2156006000000001,36.8916865,Rider_Id_576
-Order_No_5313,User_Id_391,Bike,3,Business,19,1,12:18:03 PM,19,1,12:34:39 PM,19,1,12:58:11 PM,19,1,1:00:38 PM,20,24.5,,-1.319613,36.711055,-1.2867454,36.8259017,Rider_Id_250
-Order_No_21356,User_Id_1827,Bike,3,Business,17,2,4:43:14 PM,17,2,4:43:37 PM,17,2,4:50:10 PM,17,2,4:54:43 PM,12,24.0,,-1.318062,36.849326,-1.293515,36.897607,Rider_Id_951
-Order_No_9554,User_Id_517,Bike,3,Business,7,5,7:27:46 AM,7,5,9:47:28 AM,7,5,10:21:20 AM,7,5,10:30:05 AM,4,20.5,1.1,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_436
-Order_No_3308,User_Id_3123,Bike,3,Personal,21,5,9:34:00 AM,21,5,9:34:05 AM,21,5,10:00:02 AM,21,5,10:12:12 AM,20,19.5,,-1.315194,36.9074428,-1.2979147,36.785781400000005,Rider_Id_594
-Order_No_11050,User_Id_1646,Bike,3,Business,16,4,12:18:39 PM,16,4,12:19:01 PM,16,4,12:51:35 PM,16,4,12:57:12 PM,22,,,-1.2517448,36.7924971,-1.3662811000000001,36.75421420000001,Rider_Id_205
-Order_No_9966,User_Id_393,Bike,3,Business,10,5,10:43:34 AM,10,5,10:43:42 AM,10,5,10:43:47 AM,10,5,11:02:40 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_2032,User_Id_265,Bike,3,Business,15,1,11:27:29 AM,15,1,11:28:21 AM,15,1,11:39:53 AM,15,1,11:45:38 AM,7,,,-1.2636813999999998,36.802996500000006,-1.300921,36.828195,Rider_Id_264
-Order_No_22020,User_Id_822,Bike,1,Personal,18,5,8:50:46 AM,18,5,8:53:25 AM,18,5,9:03:49 AM,18,5,9:06:32 AM,8,17.8,,-1.3039425,36.7855361,-1.269534,36.80697,Rider_Id_449
-Order_No_17086,User_Id_3355,Bike,3,Business,7,3,10:16:06 AM,7,3,10:16:24 AM,7,3,10:16:36 AM,7,3,10:28:14 AM,12,,,-1.3052491000000002,36.8223899,-1.2413316,36.878552500000005,Rider_Id_120
-Order_No_26772,User_Id_867,Bike,3,Business,26,5,1:13:54 PM,26,5,1:38:49 PM,26,5,1:51:05 PM,26,5,1:52:28 PM,10,,,-1.306378,36.7519845,-1.339046,36.762596,Rider_Id_532
-Order_No_5542,User_Id_2330,Bike,3,Business,22,1,1:46:32 PM,22,1,1:47:35 PM,22,1,1:50:57 PM,22,1,3:02:31 PM,5,26.4,,-1.2891975,36.8225285,-1.2614329,36.8094365,Rider_Id_521
-Order_No_26685,User_Id_2587,Bike,3,Business,9,6,9:52:02 AM,9,6,9:53:43 AM,9,6,10:04:21 AM,9,6,10:13:15 AM,5,20.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_579
-Order_No_9476,User_Id_393,Bike,3,Business,11,4,10:50:20 AM,11,4,10:50:33 AM,11,4,10:57:23 AM,11,4,11:16:12 AM,4,25.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_177
-Order_No_24018,User_Id_210,Bike,3,Business,3,1,10:22:22 AM,3,1,10:24:04 AM,3,1,10:31:43 AM,3,1,10:41:05 AM,6,21.5,,-1.2728947,36.807942600000004,-1.2868736,36.780588200000004,Rider_Id_383
-Order_No_11220,User_Id_1469,Bike,3,Business,19,3,12:04:50 PM,19,3,12:05:56 PM,19,3,12:11:31 PM,19,3,12:16:33 PM,7,22.3,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_445
-Order_No_8452,User_Id_633,Bike,3,Business,4,1,9:59:18 AM,4,1,9:59:37 AM,4,1,10:02:27 AM,4,1,10:07:41 AM,9,21.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_245
-Order_No_26434,User_Id_393,Bike,3,Business,13,1,1:30:12 PM,13,1,1:30:29 PM,13,1,1:30:53 PM,13,1,1:44:40 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349
-Order_No_2244,User_Id_3291,Bike,3,Business,20,2,2:43:24 PM,20,2,2:43:52 PM,20,2,2:54:36 PM,20,2,3:14:55 PM,2,24.1,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_467
-Order_No_1893,User_Id_640,Bike,3,Business,20,1,4:00:47 PM,20,1,4:02:26 PM,20,1,4:12:18 PM,20,1,4:15:10 PM,5,,,-1.2969741000000001,36.7856612,-1.3047132,36.808955299999994,Rider_Id_576
-Order_No_6418,User_Id_2789,Bike,3,Business,11,1,7:15:31 AM,11,1,7:17:13 AM,11,1,7:28:51 AM,11,1,7:32:47 AM,41,18.1,,-1.304735,36.784605400000004,-1.0568483999999998,36.777982200000004,Rider_Id_932
-Order_No_15103,User_Id_3420,Bike,3,Business,17,5,3:44:50 PM,17,5,3:45:24 PM,17,5,4:00:17 PM,17,5,4:07:44 PM,8,20.0,,-1.3046007,36.821174299999996,-1.2575406999999998,36.7868197,Rider_Id_213
-Order_No_4099,User_Id_117,Bike,1,Personal,6,6,8:28:35 AM,6,6,8:31:29 AM,6,6,9:02:58 AM,6,6,9:13:22 AM,16,19.0,,-1.3206958,36.8769553,-1.276245,36.836329299999996,Rider_Id_858
-Order_No_2246,User_Id_53,Bike,3,Business,3,1,9:17:09 AM,3,1,9:20:07 AM,3,1,9:47:52 AM,3,1,9:54:11 AM,13,18.0,4.9,-1.2770708,36.8231093,-1.2257343,36.89335429999999,Rider_Id_92
-Order_No_16562,User_Id_136,Bike,3,Business,16,5,3:05:05 PM,16,5,3:43:58 PM,16,5,4:17:59 PM,16,5,4:42:20 PM,16,26.0,,-1.2768704,36.8251777,-1.3326173000000001,36.71462329999999,Rider_Id_287
-Order_No_13433,User_Id_2587,Bike,3,Business,21,5,10:48:40 AM,21,5,11:49:22 AM,21,5,11:58:31 AM,21,5,12:06:25 PM,8,22.7,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_456
-Order_No_20179,User_Id_2981,Bike,1,Personal,23,4,10:54:12 AM,23,4,10:55:09 AM,23,4,11:12:42 AM,23,4,11:13:47 AM,7,18.7,,-1.2872996,36.8310969,-1.2609186000000001,36.7938342,Rider_Id_849
-Order_No_489,User_Id_2291,Bike,2,Personal,23,3,3:33:15 PM,23,3,3:35:00 PM,23,3,3:50:09 PM,23,3,4:02:18 PM,8,22.1,,-1.2641723999999999,36.8014198,-1.2994194,36.7647917,Rider_Id_366
-Order_No_27347,User_Id_3647,Bike,3,Business,19,3,5:01:00 PM,19,3,5:04:01 PM,19,3,5:28:15 PM,19,3,5:32:21 PM,12,22.3,,-1.2726389999999999,36.794723,-1.3056581,36.8445565,Rider_Id_952
-Order_No_7295,User_Id_1207,Bike,3,Business,17,3,11:34:22 AM,17,3,11:35:12 AM,17,3,11:40:31 AM,17,3,11:55:35 AM,13,,,-1.2628638,36.8070399,-1.2156006000000001,36.8916865,Rider_Id_484
-Order_No_18099,User_Id_2330,Bike,3,Business,5,2,4:24:04 PM,5,2,4:53:03 PM,5,2,5:05:00 PM,5,2,5:08:29 PM,8,24.8,,-1.3015197,36.7658456,-1.293082,36.8278578,Rider_Id_75
-Order_No_12930,User_Id_3404,Bike,3,Business,16,6,3:22:44 PM,16,6,3:38:55 PM,16,6,4:05:46 PM,16,6,4:07:28 PM,6,,,-1.279395,36.825364,-1.3185736000000001,36.8258933,Rider_Id_448
-Order_No_5994,User_Id_1594,Bike,3,Business,22,1,11:57:24 AM,22,1,11:58:38 AM,22,1,12:00:07 PM,22,1,12:07:45 PM,13,,,-1.2584143,36.8048002,-1.2156006000000001,36.8916865,Rider_Id_245
-Order_No_10607,User_Id_1244,Bike,3,Business,19,2,3:45:24 PM,19,2,3:49:06 PM,19,2,4:03:19 PM,19,2,4:20:04 PM,16,29.5,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_75
-Order_No_10705,User_Id_868,Bike,3,Business,15,6,10:50:10 AM,15,6,10:51:54 AM,15,6,11:18:25 AM,15,6,11:30:24 AM,14,19.3,,-1.293515,36.897607,-1.2663631000000002,36.8163329,Rider_Id_709
-Order_No_14128,User_Id_393,Bike,3,Business,2,5,12:48:49 PM,2,5,1:11:34 PM,2,5,1:19:30 PM,2,5,2:09:28 PM,10,25.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_762
-Order_No_9125,User_Id_1271,Bike,3,Business,14,2,1:09:12 PM,14,2,1:24:56 PM,14,2,2:47:20 PM,14,2,3:41:12 PM,28,23.8,,-1.3899636,36.748237700000004,-1.3244885,36.897791999999995,Rider_Id_431
-Order_No_23983,User_Id_2314,Bike,2,Personal,4,6,7:50:40 AM,4,6,7:51:00 AM,4,6,8:02:27 AM,4,6,8:06:25 AM,12,15.8,,-1.2911123,36.7921078,-1.2482518,36.7550829,Rider_Id_851
-Order_No_18718,User_Id_821,Bike,1,Personal,18,5,7:55:44 AM,18,5,7:56:05 AM,18,5,8:11:44 AM,18,5,8:17:18 AM,11,17.9,,-1.2792155,36.830315,-1.328751,36.8818019,Rider_Id_680
-Order_No_13668,User_Id_6,Bike,3,Personal,30,6,9:40:07 AM,30,6,9:40:25 AM,30,6,9:45:43 AM,30,6,9:53:18 AM,12,,,-1.3216721000000002,36.8550496,-1.2675238000000002,36.8127148,Rider_Id_26
-Order_No_17864,User_Id_3629,Bike,3,Personal,1,1,8:19:29 AM,1,1,8:19:52 AM,1,1,8:34:12 AM,1,1,8:36:28 AM,11,18.4,,-1.2852951000000001,36.81338529999999,-1.22589,36.7822548,Rider_Id_761
-Order_No_25361,User_Id_3674,Bike,3,Business,14,1,3:19:30 PM,14,1,3:27:28 PM,14,1,3:37:12 PM,14,1,3:42:52 PM,9,25.6,,-1.2551895,36.7822034,-1.2747604,36.8259205,Rider_Id_62
-Order_No_467,User_Id_929,Bike,2,Personal,8,6,10:31:13 AM,8,6,10:31:28 AM,8,6,10:38:52 AM,8,6,10:51:24 AM,9,22.5,,-1.2563754999999999,36.8215762,-1.2873232,36.765857200000006,Rider_Id_427
-Order_No_23398,User_Id_3560,Bike,3,Business,24,1,1:48:32 PM,24,1,1:49:20 PM,24,1,2:08:07 PM,24,1,2:10:48 PM,2,24.7,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_362
-Order_No_13570,User_Id_2509,Bike,3,Business,1,4,11:06:47 AM,1,4,11:07:48 AM,1,4,11:07:55 AM,1,4,11:16:34 AM,6,,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_731
-Order_No_14989,User_Id_466,Bike,3,Personal,14,1,11:57:12 AM,14,1,11:57:24 AM,14,1,12:00:31 PM,14,1,12:03:48 PM,9,23.3,,-1.2768326,36.8073804,-1.2310607,36.803577600000004,Rider_Id_764
-Order_No_4519,User_Id_970,Bike,3,Business,25,1,2:23:14 PM,25,1,2:24:05 PM,25,1,2:24:20 PM,25,1,2:31:36 PM,8,28.2,,-1.2551895,36.7822034,-1.2935508,36.794894899999996,Rider_Id_518
-Order_No_16068,User_Id_1075,Bike,3,Business,2,2,12:38:56 PM,2,2,12:42:13 PM,2,2,12:59:27 PM,2,2,1:18:06 PM,6,26.3,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_740
-Order_No_16772,User_Id_149,Bike,3,Business,23,3,5:43:05 PM,23,3,5:43:31 PM,23,3,5:50:03 PM,23,3,5:55:16 PM,6,20.6,,-1.289498,36.818878000000005,-1.2745187,36.787643599999996,Rider_Id_956
-Order_No_24313,User_Id_3218,Bike,3,Business,11,1,9:18:59 AM,11,1,9:19:42 AM,11,1,9:26:55 AM,11,1,9:48:31 AM,6,14.4,,-1.2885528999999998,36.816014100000004,-1.2765736,36.851364600000004,Rider_Id_444
-Order_No_24907,User_Id_597,Bike,3,Business,29,3,11:43:17 AM,29,3,11:43:24 AM,29,3,11:58:11 AM,29,3,12:05:04 PM,16,18.4,,-1.2605600000000001,36.703846,-1.2893883999999998,36.828207,Rider_Id_229
-Order_No_12209,User_Id_1500,Bike,3,Business,24,3,9:37:29 AM,24,3,9:44:51 AM,24,3,10:07:43 AM,24,3,10:10:52 AM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_306
-Order_No_3282,User_Id_868,Bike,3,Business,18,2,10:56:21 AM,18,2,11:25:48 AM,18,2,11:25:48 AM,18,2,11:49:08 AM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_770
-Order_No_10318,User_Id_2659,Bike,3,Business,25,2,12:59:49 PM,25,2,1:00:31 PM,25,2,1:18:28 PM,25,2,1:26:50 PM,6,23.6,,-1.2917868000000001,36.7872675,-1.2935617,36.835057,Rider_Id_722
-Order_No_6969,User_Id_2587,Bike,3,Business,4,1,1:05:39 PM,4,1,1:09:07 PM,4,1,1:53:00 PM,4,1,1:55:05 PM,5,,,-1.2527958000000001,36.8003131,-1.28878,36.8168312,Rider_Id_885
-Order_No_21198,User_Id_20,Bike,3,Business,10,1,9:52:43 AM,10,1,9:53:12 AM,10,1,9:56:42 AM,10,1,10:08:19 AM,2,16.6,,-1.2653379999999999,36.8088155,-1.2603403,36.7998848,Rider_Id_96
-Order_No_66,User_Id_393,Bike,3,Business,3,5,1:59:39 PM,3,5,2:02:54 PM,3,5,2:02:57 PM,3,5,2:57:29 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619
-Order_No_16306,User_Id_1142,Bike,1,Personal,8,3,5:08:13 PM,8,3,5:09:40 PM,8,3,5:19:01 PM,8,3,5:25:32 PM,4,23.9,,-1.2652108,36.8056075,-1.2463211,36.789487,Rider_Id_726
-Order_No_4190,User_Id_1280,Bike,3,Business,8,5,12:39:41 PM,8,5,12:41:51 PM,8,5,12:51:24 PM,8,5,1:06:48 PM,4,28.3,,-1.2551895,36.7822034,-1.266294,36.8057971,Rider_Id_575
-Order_No_18840,User_Id_2154,Bike,3,Business,31,1,2:18:10 PM,31,1,2:22:56 PM,31,1,2:38:27 PM,31,1,2:40:09 PM,4,23.7,,-1.2765736,36.851364600000004,-1.2911905000000001,36.822654299999996,Rider_Id_26
-Order_No_6387,User_Id_1231,Bike,3,Business,18,2,10:49:07 AM,18,2,10:55:21 AM,18,2,11:00:19 AM,18,2,11:08:05 AM,13,,,-1.2821421999999998,36.8161565,-1.2156006000000001,36.8916865,Rider_Id_268
-Order_No_26484,User_Id_2169,Bike,2,Personal,28,4,1:54:16 PM,28,4,1:54:58 PM,28,4,2:01:19 PM,28,4,2:04:53 PM,14,,,-1.3205163999999998,36.8098882,-1.2287282,36.8115304,Rider_Id_421
-Order_No_27034,User_Id_3669,Bike,3,Business,8,2,2:55:18 PM,8,2,3:22:44 PM,8,2,3:31:36 PM,8,2,3:33:34 PM,13,25.3,,-1.2615888000000002,36.7928732,-1.3339166,36.8692482,Rider_Id_86
-Order_No_7763,User_Id_2083,Bike,3,Personal,22,5,2:15:24 PM,22,5,2:16:17 PM,22,5,2:23:55 PM,22,5,2:25:24 PM,8,27.5,,-1.291768,36.8297208,-1.2615888000000002,36.7928732,Rider_Id_456
-Order_No_6251,User_Id_2982,Bike,3,Business,4,1,8:46:56 AM,4,1,8:47:05 AM,4,1,8:54:00 AM,4,1,9:43:59 AM,18,22.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_148
-Order_No_23047,User_Id_11,Bike,1,Personal,31,2,7:06:14 PM,31,2,7:33:41 PM,31,2,7:45:01 PM,31,2,7:49:59 PM,13,17.6,,-1.2222024,36.8888065,-1.2800513999999998,36.829403000000006,Rider_Id_886
-Order_No_11045,User_Id_2180,Bike,2,Personal,14,4,8:43:06 AM,14,4,8:58:32 AM,14,4,9:07:45 AM,14,4,9:09:54 AM,3,15.4,,-1.2962832,36.784968299999996,-1.296797,36.776452500000005,Rider_Id_648
-Order_No_22131,User_Id_53,Bike,3,Business,6,4,12:47:14 PM,6,4,12:48:39 PM,6,4,1:24:02 PM,6,4,1:48:29 PM,9,21.5,,-1.2770708,36.8231093,-1.3167113000000001,36.8301563,Rider_Id_673
-Order_No_10835,User_Id_265,Bike,3,Business,10,4,12:59:20 PM,10,4,1:16:07 PM,10,4,1:27:25 PM,10,4,1:40:42 PM,12,25.1,,-1.300921,36.828195,-1.2503017,36.8624317,Rider_Id_237
-Order_No_14197,User_Id_3657,Bike,2,Personal,20,5,12:36:16 PM,20,5,12:38:17 PM,20,5,1:10:09 PM,20,5,1:22:37 PM,14,18.3,,-1.22606,36.80039,-1.278498,36.7731432,Rider_Id_159
-Order_No_21859,User_Id_2523,Bike,3,Business,19,3,11:03:47 AM,19,3,1:41:49 PM,19,3,1:42:14 PM,19,3,1:43:35 PM,8,24.5,,-1.2551895,36.7822034,-1.2901228,36.8234237,Rider_Id_237
-Order_No_1349,User_Id_2195,Bike,3,Business,17,3,12:29:45 PM,17,3,12:30:31 PM,17,3,12:35:22 PM,17,3,12:37:52 PM,16,,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_695
-Order_No_22410,User_Id_2753,Bike,3,Personal,14,1,11:29:33 AM,14,1,11:59:56 AM,14,1,12:26:43 PM,14,1,12:33:29 PM,2,22.9,,-1.279285,36.826057399999996,-1.2842286,36.8150609,Rider_Id_100
-Order_No_10175,User_Id_84,Bike,3,Business,10,5,1:55:35 PM,10,5,1:55:52 PM,10,5,1:56:20 PM,10,5,2:31:50 PM,22,21.9,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_116
-Order_No_1884,User_Id_2154,Bike,3,Business,17,1,2:28:50 PM,17,1,3:08:21 PM,17,1,3:20:20 PM,17,1,3:47:46 PM,10,24.4,,-1.2551895,36.7822034,-1.2765736,36.851364600000004,Rider_Id_150
-Order_No_2666,User_Id_2066,Bike,3,Business,7,2,2:46:41 PM,7,2,2:47:29 PM,7,2,2:52:16 PM,7,2,2:55:06 PM,5,24.6,,-1.316961,36.820337,-1.3012173,36.8093987,Rider_Id_726
-Order_No_1931,User_Id_1500,Bike,3,Business,23,2,10:24:40 AM,23,2,10:24:50 AM,23,2,10:25:03 AM,23,2,10:47:10 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_843
-Order_No_13121,User_Id_633,Bike,3,Business,7,4,3:51:12 PM,7,4,3:51:22 PM,7,4,3:51:37 PM,7,4,4:14:07 PM,9,27.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_10
-Order_No_17140,User_Id_1502,Bike,3,Business,12,2,10:50:47 AM,12,2,10:53:11 AM,12,2,11:13:26 AM,12,2,11:14:29 AM,11,24.3,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_408
-Order_No_23174,User_Id_3669,Bike,3,Business,12,1,1:49:21 PM,12,1,2:10:12 PM,12,1,2:10:40 PM,12,1,2:22:21 PM,6,27.0,,-1.279395,36.825364,-1.2615888000000002,36.7928732,Rider_Id_606
-Order_No_26226,User_Id_74,Bike,3,Business,12,3,1:06:18 PM,12,3,1:08:22 PM,12,3,1:32:42 PM,12,3,1:42:02 PM,13,21.2,,-1.2283403,36.8822756,-1.225322,36.80855,Rider_Id_8
-Order_No_22447,User_Id_1502,Bike,3,Business,15,5,1:30:26 PM,15,5,1:30:58 PM,15,5,1:43:43 PM,15,5,1:56:16 PM,12,27.9,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_339
-Order_No_10738,User_Id_2200,Bike,3,Business,18,4,8:08:56 AM,18,4,8:46:47 AM,18,4,8:58:11 AM,18,4,9:15:34 AM,2,20.8,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_266
-Order_No_20795,User_Id_53,Bike,3,Business,12,2,11:18:22 AM,12,2,11:49:31 AM,12,2,12:17:46 PM,12,2,12:31:52 PM,8,24.5,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_452
-Order_No_27571,User_Id_3283,Bike,3,Business,3,1,9:13:21 AM,3,1,9:13:54 AM,3,1,9:21:28 AM,3,1,9:31:28 AM,16,18.2,4.9,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_310
-Order_No_8656,User_Id_302,Bike,3,Business,27,7,1:19:22 PM,27,7,1:19:41 PM,27,7,1:21:58 PM,27,7,1:30:03 PM,3,,,-1.2649605,36.798177700000004,-1.2653906000000001,36.8134406,Rider_Id_611
-Order_No_7239,User_Id_868,Bike,3,Business,12,3,2:08:27 PM,12,3,2:10:09 PM,12,3,2:10:32 PM,12,3,2:20:28 PM,4,21.0,,-1.259478,36.801781299999995,-1.2663631000000002,36.8163329,Rider_Id_607
-Order_No_1354,User_Id_3191,Bike,3,Personal,6,1,10:50:10 AM,6,1,10:51:53 AM,6,1,10:55:46 AM,6,1,10:59:03 AM,5,20.5,,-1.284835,36.77601,-1.2946939,36.8043582,Rider_Id_754
-Order_No_893,User_Id_3621,Bike,3,Business,9,4,8:46:21 AM,9,4,8:47:07 AM,9,4,8:54:20 AM,9,4,8:56:07 AM,11,,,-1.2836583000000001,36.7886014,-1.3203209999999999,36.7195522,Rider_Id_99
-Order_No_5175,User_Id_635,Bike,3,Business,3,4,2:34:12 PM,3,4,2:34:55 PM,3,4,2:44:04 PM,3,4,2:50:00 PM,11,26.0,,-1.2599563,36.799343900000004,-1.2610117,36.7236153,Rider_Id_647
-Order_No_25260,User_Id_867,Bike,3,Business,1,4,2:06:36 PM,1,4,2:06:48 PM,1,4,2:08:25 PM,1,4,2:10:23 PM,9,,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_518
-Order_No_988,User_Id_2174,Bike,3,Business,13,4,2:09:26 PM,13,4,2:09:35 PM,13,4,2:21:18 PM,13,4,2:39:21 PM,10,26.6,,-1.2482781,36.884091999999995,-1.2782261000000001,36.822500899999994,Rider_Id_82
-Order_No_2419,User_Id_2580,Bike,1,Personal,4,5,2:23:14 PM,4,5,2:26:44 PM,4,5,2:40:19 PM,4,5,2:43:24 PM,9,24.2,,-1.2793148,36.8148394,-1.3051188999999999,36.7560948,Rider_Id_671
-Order_No_6150,User_Id_1271,Bike,3,Business,28,3,12:31:08 PM,28,3,12:35:44 PM,28,3,12:36:06 PM,28,3,12:47:34 PM,29,,,-1.3244885,36.897791999999995,-1.1404395,36.9668024,Rider_Id_345
-Order_No_4091,User_Id_238,Bike,3,Business,17,4,2:23:35 PM,17,4,2:23:43 PM,17,4,2:30:07 PM,17,4,2:32:01 PM,3,27.9,,-1.3077869,36.844320700000004,-1.3166811999999999,36.859460799999994,Rider_Id_858
-Order_No_9162,User_Id_1749,Bike,3,Personal,7,2,10:09:26 AM,7,2,10:11:06 AM,7,2,10:16:51 AM,7,2,10:22:02 AM,5,,,-1.2621576,36.8103473,-1.2892697,36.81545139999999,Rider_Id_96
-Order_No_4889,User_Id_2869,Bike,3,Business,24,3,4:57:28 PM,24,3,4:57:59 PM,24,3,5:16:11 PM,24,3,5:51:50 PM,16,,,-1.225322,36.80855,-1.3025826,36.7670807,Rider_Id_484
-Order_No_17223,User_Id_2030,Bike,3,Business,28,2,8:23:11 AM,28,2,10:22:24 AM,28,2,10:30:34 AM,28,2,10:45:48 AM,5,17.4,,-1.3100586,36.850032899999995,-1.2965799999999998,36.823699,Rider_Id_329
-Order_No_10482,User_Id_2216,Bike,3,Business,19,1,1:14:37 PM,19,1,1:19:41 PM,19,1,1:33:53 PM,19,1,1:50:37 PM,13,25.4,,-1.2551895,36.7822034,-1.2443697,36.869058100000004,Rider_Id_41
-Order_No_10531,User_Id_517,Bike,3,Business,31,5,3:57:41 PM,31,5,4:02:55 PM,31,5,4:25:02 PM,31,5,5:15:22 PM,14,23.6,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_436
-Order_No_24999,User_Id_836,Bike,1,Personal,22,2,11:56:02 AM,22,2,11:57:57 AM,22,2,12:32:18 PM,22,2,12:37:05 PM,7,19.2,,-1.2463211,36.789487,-1.286937,36.824674200000004,Rider_Id_844
-Order_No_24381,User_Id_2344,Bike,3,Business,21,3,12:35:05 PM,21,3,12:35:15 PM,21,3,12:42:10 PM,21,3,12:56:26 PM,7,21.8,,-1.2571472,36.795063299999995,-1.2881596000000002,36.789291999999996,Rider_Id_566
-Order_No_25398,User_Id_178,Bike,2,Personal,23,1,8:39:36 AM,23,1,8:40:02 AM,23,1,8:50:53 AM,23,1,8:59:38 AM,13,14.5,,-1.3157969999999999,36.8436497,-1.2997463,36.7536549,Rider_Id_858
-Order_No_5619,User_Id_1809,Bike,3,Business,23,5,2:17:35 PM,23,5,2:18:37 PM,23,5,2:44:30 PM,23,5,2:57:40 PM,15,26.4,,-1.2571472,36.795063299999995,-1.3463041999999998,36.768164,Rider_Id_383
-Order_No_27251,User_Id_3560,Bike,3,Business,5,2,4:11:50 PM,5,2,4:13:39 PM,5,2,4:30:22 PM,5,2,4:36:58 PM,8,,,-1.279395,36.825364,-1.2765492,36.7669809,Rider_Id_831
-Order_No_31,User_Id_496,Bike,3,Business,20,4,2:41:39 PM,20,4,2:42:06 PM,20,4,2:46:17 PM,20,4,2:52:28 PM,10,28.1,,-1.2551895,36.7822034,-1.3046156,36.765857200000006,Rider_Id_482
-Order_No_20913,User_Id_3560,Bike,3,Business,24,3,11:14:24 AM,24,3,11:32:46 AM,24,3,11:39:46 AM,24,3,11:56:40 AM,5,20.0,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_358
-Order_No_8194,User_Id_2620,Bike,2,Personal,28,1,5:17:14 PM,28,1,5:33:39 PM,28,1,5:56:06 PM,28,1,5:57:22 PM,12,22.3,,-1.3681253,36.915529799999995,-1.3326788,36.8790255,Rider_Id_494
-Order_No_22318,User_Id_2956,Bike,3,Business,5,5,12:02:48 PM,5,5,12:03:13 PM,5,5,12:38:57 PM,5,5,12:43:42 PM,3,24.5,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_547
-Order_No_15425,User_Id_3194,Bike,3,Business,11,1,10:05:08 AM,11,1,10:05:44 AM,11,1,10:18:13 AM,11,1,10:28:10 AM,22,20.8,,-1.2551895,36.7822034,-1.3835235000000001,36.764939700000006,Rider_Id_148
-Order_No_9587,User_Id_2763,Bike,3,Business,12,2,12:12:17 PM,12,2,12:13:02 PM,12,2,12:20:41 PM,12,2,12:24:15 PM,8,24.3,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_345
-Order_No_26115,User_Id_635,Bike,3,Business,10,6,12:21:03 PM,10,6,12:21:34 PM,10,6,12:26:29 PM,10,6,12:50:01 PM,14,23.7,,-1.2599563,36.799343900000004,-1.3164992,36.779313200000004,Rider_Id_620
-Order_No_11857,User_Id_286,Bike,3,Business,23,5,6:46:10 PM,23,5,7:10:45 PM,23,5,7:24:35 PM,23,5,7:45:47 PM,8,20.4,,-1.2962966999999999,36.8020231,-1.2551895,36.7822034,Rider_Id_19
-Order_No_9278,User_Id_1410,Bike,3,Business,6,2,10:13:07 AM,6,2,10:13:47 AM,6,2,10:13:54 AM,6,2,11:02:49 AM,20,24.8,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_69
-Order_No_13414,User_Id_685,Bike,3,Business,7,5,11:42:01 AM,7,5,11:44:04 AM,7,5,12:03:22 PM,7,5,12:06:23 PM,8,,,-1.265432,36.795034,-1.2960687,36.8167909,Rider_Id_177
-Order_No_22005,User_Id_3222,Bike,3,Business,29,3,1:57:05 PM,29,3,2:02:30 PM,29,3,2:10:02 PM,29,3,2:14:08 PM,11,21.2,,-1.2551895,36.7822034,-1.3038543,36.83810510000001,Rider_Id_183
-Order_No_16813,User_Id_3517,Bike,3,Business,11,1,3:59:31 PM,11,1,4:04:35 PM,11,1,4:15:19 PM,11,1,4:48:19 PM,15,27.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_931
-Order_No_23116,User_Id_1162,Bike,2,Business,18,4,8:31:27 AM,18,4,10:18:11 AM,18,4,10:36:40 AM,18,4,10:42:50 AM,9,25.7,,-1.2575391000000002,36.8055695,-1.3122610000000001,36.794658,Rider_Id_38
-Order_No_15609,User_Id_2521,Bike,3,Business,3,6,8:16:10 AM,3,6,9:03:31 AM,3,6,9:06:49 AM,3,6,10:32:15 AM,10,23.6,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_62
-Order_No_22918,User_Id_1363,Bike,3,Business,6,4,11:02:52 AM,6,4,11:03:38 AM,6,4,11:12:57 AM,6,4,11:14:18 AM,4,19.7,,-1.3004062,36.829741,-1.3185736000000001,36.8258933,Rider_Id_211
-Order_No_2626,User_Id_3674,Bike,3,Business,11,4,2:35:56 PM,11,4,2:36:47 PM,11,4,2:52:04 PM,11,4,3:20:07 PM,9,26.9,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_291
-Order_No_492,User_Id_53,Bike,3,Business,2,6,9:26:57 AM,2,6,9:57:48 AM,2,6,10:05:54 AM,2,6,10:21:36 AM,5,22.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_448
-Order_No_10472,User_Id_1280,Bike,3,Business,13,4,1:15:26 PM,13,4,1:15:53 PM,13,4,1:17:38 PM,13,4,1:21:13 PM,13,21.9,,-1.2551895,36.7822034,-1.3208221000000002,36.829573499999995,Rider_Id_168
-Order_No_25019,User_Id_198,Bike,3,Business,9,4,3:17:07 PM,9,4,3:18:49 PM,9,4,3:41:21 PM,9,4,3:44:45 PM,17,25.7,,-1.3396256000000002,36.8863135,-1.2682178999999998,36.8009289,Rider_Id_207
-Order_No_18311,User_Id_1527,Bike,3,Business,6,6,9:09:51 AM,6,6,9:10:04 AM,6,6,9:10:16 AM,6,6,9:43:39 AM,4,19.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206
-Order_No_20921,User_Id_136,Bike,3,Business,27,1,10:13:56 AM,27,1,10:36:54 AM,27,1,11:01:48 AM,27,1,11:08:30 AM,9,23.8,,-1.3169591,36.7000029,-1.3025826,36.7670807,Rider_Id_100
-Order_No_4859,User_Id_104,Bike,3,Personal,22,6,4:28:01 PM,22,6,4:36:15 PM,22,6,4:58:30 PM,22,6,5:00:15 PM,6,24.0,,-1.2804891,36.7865102,-1.2971245,36.753242799999995,Rider_Id_368
-Order_No_4705,User_Id_1724,Bike,3,Business,28,4,5:09:17 PM,28,4,5:09:53 PM,28,4,5:10:59 PM,28,4,5:15:09 PM,8,28.2,,-1.2551895,36.7822034,-1.2907853999999999,36.7774108,Rider_Id_89
-Order_No_4595,User_Id_635,Bike,3,Business,12,2,10:03:32 AM,12,2,10:05:41 AM,12,2,10:14:20 AM,12,2,10:45:11 AM,5,28.2,,-1.2857763999999998,36.8236622,-1.2602337,36.79905479999999,Rider_Id_119
-Order_No_26138,User_Id_444,Bike,2,Business,27,5,1:52:48 PM,27,5,1:52:58 PM,27,5,2:20:37 PM,27,5,2:22:33 PM,6,24.1,,-1.2763548,36.8218713,-1.2672426,36.7884223,Rider_Id_229
-Order_No_16741,User_Id_1724,Bike,3,Business,7,3,9:20:14 AM,7,3,9:33:09 AM,7,3,10:06:12 AM,7,3,10:16:02 AM,8,,,-1.2551895,36.7822034,-1.3037386999999998,36.8175661,Rider_Id_206
-Order_No_22292,User_Id_2100,Bike,3,Business,22,1,12:00:53 PM,22,1,12:01:06 PM,22,1,12:08:33 PM,22,1,12:12:22 PM,8,24.7,,-1.2765736,36.851364600000004,-1.2597528999999998,36.806629,Rider_Id_328
-Order_No_17010,User_Id_3621,Bike,3,Business,1,6,10:03:51 AM,1,6,10:04:07 AM,1,6,10:25:54 AM,1,6,10:31:09 AM,26,18.6,,-1.2836583000000001,36.7886014,-1.1567082,36.9618034,Rider_Id_227
-Order_No_18518,User_Id_393,Bike,3,Business,5,2,4:43:39 PM,5,2,4:52:50 PM,5,2,4:53:01 PM,5,2,5:29:04 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_515
-Order_No_11380,User_Id_2200,Bike,3,Business,16,3,1:03:37 PM,16,3,1:03:43 PM,16,3,1:06:29 PM,16,3,1:17:18 PM,7,24.2,,-1.279395,36.825364,-1.2626906,36.7827022,Rider_Id_742
-Order_No_20899,User_Id_1329,Bike,3,Business,18,1,1:21:54 PM,18,1,1:22:48 PM,18,1,1:27:38 PM,18,1,1:33:09 PM,6,30.7,,-1.273056,36.811298,-1.2944292,36.779356899999996,Rider_Id_812
-Order_No_22212,User_Id_552,Bike,3,Business,5,1,10:53:42 AM,5,1,10:57:03 AM,5,1,11:17:26 AM,5,1,11:31:56 AM,22,24.6,,-1.3472066,36.7692638,-1.3632162,36.9108253,Rider_Id_548
-Order_No_27164,User_Id_1123,Bike,1,Personal,12,4,8:45:19 AM,12,4,8:46:47 AM,12,4,8:54:00 AM,12,4,9:10:36 AM,4,15.9,,-1.2658334999999998,36.804286700000006,-1.2609186000000001,36.7938342,Rider_Id_752
-Order_No_15814,User_Id_3331,Bike,3,Business,7,1,10:04:38 AM,7,1,10:05:28 AM,7,1,10:20:47 AM,7,1,10:42:00 AM,7,24.9,,-1.2551895,36.7822034,-1.2897288999999998,36.8271257,Rider_Id_247
-Order_No_20428,User_Id_3355,Bike,3,Business,15,1,1:18:12 PM,15,1,1:19:24 PM,15,1,1:35:40 PM,15,1,1:53:15 PM,8,,,-1.2527958000000001,36.8003131,-1.3052491000000002,36.8223899,Rider_Id_22
-Order_No_17312,User_Id_2801,Bike,3,Business,31,4,4:00:59 PM,31,4,4:01:23 PM,31,4,4:09:45 PM,31,4,4:16:17 PM,2,28.6,,-1.2595421999999998,36.7871178,-1.2635744,36.781677,Rider_Id_598
-Order_No_13778,User_Id_1132,Bike,2,Personal,24,1,11:12:54 AM,24,1,11:27:17 AM,24,1,11:45:30 AM,24,1,11:47:01 AM,11,22.6,,-1.3173868999999998,36.8112044,-1.2793831,36.7617573,Rider_Id_532
-Order_No_15420,User_Id_1550,Bike,3,Personal,20,4,8:38:59 AM,20,4,8:39:49 AM,20,4,8:49:16 AM,20,4,9:00:29 AM,4,17.8,,-1.280753,36.8191175,-1.2985749,36.8088,Rider_Id_726
-Order_No_16147,User_Id_19,Bike,1,Personal,16,7,10:40:03 AM,16,7,11:12:09 AM,16,7,12:12:23 PM,16,7,12:39:10 PM,12,25.1,,-1.3056546999999998,36.818900899999996,-1.2975623,36.9025949,Rider_Id_101
-Order_No_9025,User_Id_835,Bike,3,Business,26,1,12:10:24 PM,26,1,12:11:51 PM,26,1,12:27:59 PM,26,1,12:37:13 PM,20,22.9,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_647
-Order_No_14526,User_Id_893,Bike,3,Business,13,3,12:06:58 PM,13,3,12:19:00 PM,13,3,12:39:39 PM,13,3,12:49:53 PM,5,,,-1.2732603999999998,36.823046000000005,-1.2594473999999998,36.8089122,Rider_Id_76
-Order_No_12367,User_Id_1527,Bike,3,Business,3,3,8:44:09 AM,3,3,8:44:57 AM,3,3,9:06:27 AM,3,3,9:11:57 AM,9,21.5,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_613
-Order_No_23020,User_Id_2627,Bike,3,Business,7,4,10:49:25 AM,7,4,10:49:38 AM,7,4,10:58:13 AM,7,4,11:58:17 AM,2,27.4,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_489
-Order_No_22100,User_Id_3606,Bike,3,Business,1,4,4:03:37 PM,1,4,4:04:25 PM,1,4,4:08:45 PM,1,4,4:13:44 PM,5,,,-1.2628638,36.8070399,-1.256522,36.786413200000005,Rider_Id_647
-Order_No_3797,User_Id_2982,Bike,3,Business,4,2,2:27:55 PM,4,2,2:44:16 PM,4,2,2:49:44 PM,4,2,3:36:59 PM,8,22.5,,-1.300921,36.828195,-1.2587114,36.8349398,Rider_Id_310
-Order_No_22375,User_Id_3293,Bike,1,Personal,19,3,10:31:47 AM,19,3,10:32:32 AM,19,3,10:41:45 AM,19,3,10:51:05 AM,10,,,-1.3273028,36.8739571,-1.2852868,36.87812879999999,Rider_Id_543
-Order_No_21275,User_Id_2789,Bike,3,Business,15,5,2:31:51 PM,15,5,2:32:20 PM,15,5,2:39:10 PM,15,5,2:40:47 PM,6,17.6,,-1.304735,36.784605400000004,-1.2896398999999998,36.812449799999996,Rider_Id_590
-Order_No_3961,User_Id_633,Bike,3,Business,25,1,12:55:25 PM,25,1,12:55:35 PM,25,1,1:09:34 PM,25,1,1:19:08 PM,4,27.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_910
-Order_No_17978,User_Id_3599,Bike,3,Business,23,5,1:58:11 PM,23,5,2:22:30 PM,23,5,2:40:27 PM,23,5,3:01:41 PM,20,26.2,,-1.3302996,36.8707935,-1.216212,36.841799,Rider_Id_519
-Order_No_19892,User_Id_635,Bike,3,Business,22,6,10:20:08 AM,22,6,10:20:26 AM,22,6,10:20:38 AM,22,6,10:33:49 AM,7,21.2,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_179
-Order_No_23316,User_Id_1205,Bike,3,Business,30,2,1:53:41 PM,30,2,2:04:34 PM,30,2,2:15:10 PM,30,2,2:20:24 PM,7,,,-1.2495931,36.80650870000001,-1.2958383,36.8073162,Rider_Id_484
-Order_No_18094,User_Id_3547,Bike,3,Business,13,2,10:59:36 AM,13,2,11:00:20 AM,13,2,11:10:08 AM,13,2,11:40:16 AM,13,22.5,,-1.3270716,36.8406371,-1.2800648000000001,36.762267,Rider_Id_31
-Order_No_2048,User_Id_1849,Bike,3,Business,30,5,10:59:32 AM,30,5,11:53:54 AM,30,5,12:07:19 PM,30,5,12:24:23 PM,5,23.8,,-1.290894,36.822971,-1.2836583000000001,36.7886014,Rider_Id_101
-Order_No_19363,User_Id_829,Bike,3,Business,15,1,10:38:27 AM,15,1,11:20:35 AM,15,1,11:40:40 AM,15,1,11:44:03 AM,16,,,-1.2967108,36.787113500000004,-1.293274,36.9044706,Rider_Id_692
-Order_No_12211,User_Id_1244,Bike,3,Business,1,5,9:03:35 AM,1,5,9:45:38 AM,1,5,10:03:58 AM,1,5,11:17:06 AM,8,28.5,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_61
-Order_No_10639,User_Id_2330,Bike,3,Business,15,4,4:56:10 PM,15,4,5:05:15 PM,15,4,5:25:44 PM,15,4,5:32:45 PM,15,25.5,,-1.2291477,36.8440295,-1.2868601,36.7738025,Rider_Id_237
-Order_No_12073,User_Id_3238,Bike,3,Business,29,3,1:21:35 PM,29,3,1:22:41 PM,29,3,1:37:09 PM,29,3,1:44:01 PM,28,21.2,,-1.2454537,36.7782792,-1.3976138,36.782965999999995,Rider_Id_815
-Order_No_1130,User_Id_2216,Bike,3,Business,2,3,2:07:12 PM,2,3,2:49:17 PM,2,3,3:11:00 PM,2,3,3:12:17 PM,15,26.0,,-1.2867627000000001,36.883446,-1.2854173999999998,36.7951152,Rider_Id_576
-Order_No_8618,User_Id_2923,Bike,1,Personal,11,3,9:22:04 AM,11,3,9:23:11 AM,11,3,10:21:18 AM,11,3,10:30:49 AM,9,15.4,1.0,-1.2390406999999999,36.7667702,-1.2549476000000002,36.8269576,Rider_Id_234
-Order_No_25705,User_Id_2651,Bike,3,Business,3,3,4:50:11 PM,3,3,5:21:48 PM,3,3,5:46:35 PM,3,3,5:48:37 PM,8,27.6,,-1.3402212,36.7696184,-1.2974014999999999,36.7772406,Rider_Id_843
-Order_No_24335,User_Id_868,Bike,3,Business,21,1,11:15:47 AM,21,1,11:17:05 AM,21,1,11:57:26 AM,21,1,12:21:53 PM,5,25.6,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_489
-Order_No_2988,User_Id_393,Bike,3,Business,8,1,8:35:38 AM,8,1,8:36:29 AM,8,1,9:01:18 AM,8,1,9:40:54 AM,4,21.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_728
-Order_No_23967,User_Id_1584,Bike,3,Business,30,5,9:28:47 AM,30,5,9:48:56 AM,30,5,9:55:00 AM,30,5,10:07:49 AM,7,19.2,,-1.2811695,36.8200313,-1.2969741000000001,36.7856612,Rider_Id_14
-Order_No_26534,User_Id_633,Bike,3,Business,25,1,12:11:42 PM,25,1,12:12:41 PM,25,1,12:25:57 PM,25,1,12:31:05 PM,12,25.7,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_761
-Order_No_11086,User_Id_186,Bike,3,Business,25,1,9:00:01 AM,25,1,9:00:10 AM,25,1,9:00:57 AM,25,1,9:20:01 AM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_881
-Order_No_8107,User_Id_203,Bike,3,Business,22,5,2:56:56 PM,22,5,2:57:31 PM,22,5,3:18:12 PM,22,5,3:32:26 PM,4,31.0,,-1.2852908,36.7557439,-1.295908,36.791433000000005,Rider_Id_118
-Order_No_20533,User_Id_393,Bike,3,Business,6,2,4:38:32 PM,6,2,5:00:19 PM,6,2,5:13:36 PM,6,2,5:15:11 PM,4,,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_333
-Order_No_16170,User_Id_653,Bike,3,Business,2,6,3:29:49 PM,2,6,3:32:37 PM,2,6,4:16:09 PM,2,6,4:19:39 PM,6,29.0,,-1.279395,36.825364,-1.316315,36.8268861,Rider_Id_686
-Order_No_4645,User_Id_3647,Bike,3,Business,11,1,2:04:44 PM,11,1,2:05:23 PM,11,1,2:10:54 PM,11,1,2:56:30 PM,19,27.8,,-1.2726389999999999,36.794723,-1.3443916999999999,36.6700333,Rider_Id_818
-Order_No_10602,User_Id_1675,Bike,1,Personal,13,1,9:37:34 AM,13,1,9:39:25 AM,13,1,9:53:01 AM,13,1,9:54:21 AM,7,15.4,,-1.296797,36.776452500000005,-1.2857094999999998,36.8188266,Rider_Id_571
-Order_No_5883,User_Id_718,Bike,3,Business,11,2,4:11:39 PM,11,2,4:14:56 PM,11,2,4:19:12 PM,11,2,4:22:31 PM,12,21.2,,-1.2551895,36.7822034,-1.3114499,36.7425836,Rider_Id_80
-Order_No_16647,User_Id_2454,Bike,3,Business,1,5,3:21:11 PM,1,5,3:21:32 PM,1,5,3:45:53 PM,1,5,4:14:21 PM,4,28.3,,-1.2921436000000002,36.7757259,-1.3031438999999998,36.805133000000005,Rider_Id_427
-Order_No_26801,User_Id_1753,Bike,2,Business,5,5,5:48:47 PM,5,5,5:48:55 PM,5,5,5:54:56 PM,5,5,6:01:54 PM,11,24.9,,-1.2551895,36.7822034,-1.3107918,36.761794,Rider_Id_648
-Order_No_5721,User_Id_3404,Bike,3,Business,2,2,1:18:13 PM,2,2,1:18:32 PM,2,2,1:25:50 PM,2,2,1:29:22 PM,8,21.0,,-1.3014461000000002,36.7661381,-1.2793686000000002,36.82592329999999,Rider_Id_88
-Order_No_17931,User_Id_1096,Bike,3,Personal,25,1,3:01:41 PM,25,1,3:01:57 PM,25,1,3:11:02 PM,25,1,3:15:09 PM,2,29.9,,-1.303711,36.81549329999999,-1.2997244,36.8158016,Rider_Id_351
-Order_No_7391,User_Id_2763,Bike,3,Business,9,4,3:08:41 PM,9,4,3:13:51 PM,9,4,3:23:17 PM,9,4,3:29:37 PM,6,,,-1.2571472,36.795063299999995,-1.2885417,36.810052299999995,Rider_Id_96
-Order_No_9826,User_Id_540,Bike,3,Business,22,1,10:30:42 AM,22,1,10:31:07 AM,22,1,10:36:18 AM,22,1,10:47:15 AM,6,24.7,,-1.2551895,36.7822034,-1.2877595,36.8230991,Rider_Id_726
-Order_No_3861,User_Id_830,Bike,3,Business,24,5,5:25:20 PM,24,5,5:26:47 PM,24,5,5:34:00 PM,24,5,5:38:07 PM,3,20.2,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_697
-Order_No_1608,User_Id_142,Bike,3,Business,29,1,3:18:15 PM,29,1,3:18:49 PM,29,1,3:42:01 PM,29,1,3:45:49 PM,8,,,-1.2570328000000002,36.7869744,-1.2963097,36.7688221,Rider_Id_710
-Order_No_21059,User_Id_2789,Bike,3,Business,15,2,5:37:05 PM,15,2,5:37:33 PM,15,2,5:45:02 PM,15,2,5:50:53 PM,11,23.0,,-1.304735,36.784605400000004,-1.3373344999999999,36.7118861,Rider_Id_246
-Order_No_14997,User_Id_1319,Bike,1,Personal,27,4,8:59:29 AM,27,4,8:59:41 AM,27,4,9:00:51 AM,27,4,9:13:06 AM,25,18.9,3.1,-1.2615888000000002,36.7928732,-1.3773235000000001,36.9365672,Rider_Id_875
-Order_No_22202,User_Id_3622,Bike,3,Personal,14,4,11:56:03 AM,14,4,11:56:43 AM,14,4,12:00:43 PM,14,4,12:28:12 PM,7,24.8,,-1.2882767,36.8201996,-1.271672,36.783604,Rider_Id_845
-Order_No_23252,User_Id_3291,Bike,3,Business,12,2,2:49:33 PM,12,2,2:50:35 PM,12,2,2:57:22 PM,12,2,3:37:25 PM,13,,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_403
-Order_No_10970,User_Id_1927,Bike,3,Business,27,3,3:02:09 PM,27,3,3:42:22 PM,27,3,3:56:29 PM,27,3,4:00:39 PM,22,29.6,,-1.3472066,36.7692638,-1.3632162,36.9108253,Rider_Id_519
-Order_No_17549,User_Id_1075,Bike,3,Business,8,5,12:34:54 PM,8,5,12:35:22 PM,8,5,12:36:06 PM,8,5,12:45:24 PM,3,24.2,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_539
-Order_No_23999,User_Id_1484,Bike,3,Business,11,1,9:49:42 AM,11,1,9:52:49 AM,11,1,10:15:36 AM,11,1,10:16:56 AM,4,18.1,,-1.2551895,36.7822034,-1.2662138,36.7997481,Rider_Id_275
-Order_No_4952,User_Id_1471,Bike,3,Business,30,2,11:18:23 AM,30,2,11:19:10 AM,30,2,11:26:14 AM,30,2,11:35:48 AM,21,,,-1.2551895,36.7822034,-1.3688433,36.7402114,Rider_Id_125
-Order_No_27254,User_Id_393,Bike,3,Business,23,2,7:54:18 AM,23,2,8:05:26 AM,23,2,8:37:46 AM,23,2,8:55:01 AM,4,15.0,7.1,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_748
-Order_No_28188,User_Id_59,Bike,3,Business,4,1,9:20:48 AM,4,1,9:22:10 AM,4,1,9:41:49 AM,4,1,10:06:51 AM,6,18.1,,-1.290894,36.822971,-1.2628638,36.8070399,Rider_Id_927
-Order_No_13059,User_Id_3295,Bike,3,Business,22,3,12:42:11 PM,22,3,12:43:22 PM,22,3,12:43:57 PM,22,3,1:01:31 PM,13,23.0,,-1.2571472,36.795063299999995,-1.3104733999999998,36.7363698,Rider_Id_564
-Order_No_2942,User_Id_2102,Bike,3,Business,28,2,2:24:12 PM,28,2,2:25:47 PM,28,2,2:40:12 PM,28,2,2:42:29 PM,16,23.4,,-1.3323107,36.8692616,-1.2581931999999998,36.8152763,Rider_Id_233
-Order_No_12043,User_Id_2871,Bike,1,Personal,17,5,12:06:47 PM,17,5,12:07:28 PM,17,5,12:31:51 PM,17,5,12:37:06 PM,8,17.6,,-1.288922,36.814826000000004,-1.2623805000000001,36.7805404,Rider_Id_213
-Order_No_18000,User_Id_3359,Bike,3,Business,18,1,11:50:53 AM,18,1,11:51:22 AM,18,1,11:55:25 AM,18,1,12:02:29 PM,7,27.1,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_124
-Order_No_14730,User_Id_3594,Bike,3,Personal,7,2,10:51:35 AM,7,2,11:07:16 AM,7,2,11:19:34 AM,7,2,11:31:30 AM,16,,,-1.2397552,36.879146899999995,-1.3246161,36.8703695,Rider_Id_541
-Order_No_10612,User_Id_868,Bike,3,Business,16,2,4:35:55 PM,16,2,4:36:06 PM,16,2,5:15:14 PM,16,2,5:20:26 PM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547
-Order_No_2405,User_Id_2876,Bike,1,Personal,20,3,9:03:35 AM,20,3,9:04:00 AM,20,3,9:13:45 AM,20,3,9:15:36 AM,6,16.6,0.4,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_808
-Order_No_28104,User_Id_3147,Bike,3,Business,2,2,1:48:17 PM,2,2,1:49:13 PM,2,2,2:39:03 PM,2,2,2:45:22 PM,15,26.0,,-1.3472066,36.7692638,-1.308106,36.86594,Rider_Id_846
-Order_No_5555,User_Id_3291,Bike,3,Business,8,5,1:34:00 PM,8,5,2:42:17 PM,8,5,2:59:02 PM,8,5,3:58:36 PM,22,30.4,,-1.335477,36.930829700000004,-1.2600926000000001,36.8088685,Rider_Id_99
-Order_No_23325,User_Id_1209,Bike,3,Business,12,5,12:34:28 PM,12,5,12:38:07 PM,12,5,12:41:59 PM,12,5,12:54:01 PM,6,26.6,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_459
-Order_No_487,User_Id_2587,Bike,3,Business,26,5,10:22:02 AM,26,5,10:35:56 AM,26,5,10:48:33 AM,26,5,10:56:57 AM,7,,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_171
-Order_No_23550,User_Id_3283,Bike,3,Business,15,3,9:42:58 AM,15,3,9:43:14 AM,15,3,9:46:43 AM,15,3,10:16:58 AM,5,16.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_179
-Order_No_10351,User_Id_1858,Bike,1,Personal,28,5,6:51:39 AM,28,5,7:06:59 AM,28,5,7:45:32 AM,28,5,7:48:13 AM,21,20.4,,-1.2536530000000001,36.684954,-1.304119,36.819649,Rider_Id_228
-Order_No_28097,User_Id_2360,Bike,3,Business,21,4,11:16:33 AM,21,4,11:16:57 AM,21,4,11:40:26 AM,21,4,11:58:31 AM,23,28.2,,-1.3696893,36.735470299999996,-1.2551895,36.7822034,Rider_Id_847
-Order_No_545,User_Id_2229,Bike,3,Business,10,1,9:10:46 AM,10,1,9:11:21 AM,10,1,9:58:44 AM,10,1,10:10:19 AM,20,16.4,,-1.3247683000000001,36.85803,-1.2367023000000001,36.8758725,Rider_Id_48
-Order_No_25040,User_Id_3560,Bike,3,Business,11,1,12:51:44 PM,11,1,1:16:17 PM,11,1,1:31:28 PM,11,1,1:50:41 PM,12,29.0,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_99
-Order_No_16867,User_Id_824,Bike,3,Business,14,3,4:35:23 PM,14,3,4:37:27 PM,14,3,4:58:12 PM,14,3,5:05:00 PM,14,23.3,,-1.2642719999999998,36.7978791,-1.207546,36.7913976,Rider_Id_793
-Order_No_18093,User_Id_1959,Bike,3,Business,23,4,11:50:41 AM,23,4,11:51:27 AM,23,4,11:52:39 AM,23,4,12:09:19 PM,3,23.2,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_107
-Order_No_14334,User_Id_2054,Bike,1,Personal,25,5,1:28:59 PM,25,5,1:29:11 PM,25,5,2:05:55 PM,25,5,2:07:26 PM,14,23.6,,-1.3507360000000002,36.8960004,-1.3016151000000002,36.811735399999996,Rider_Id_543
-Order_No_23190,User_Id_661,Bike,3,Business,7,3,7:47:28 PM,7,3,7:48:00 PM,7,3,7:56:51 PM,7,3,8:01:08 PM,3,21.2,,-1.2976558999999999,36.791414700000004,-1.2963097,36.7688221,Rider_Id_130
-Order_No_3275,User_Id_2115,Bike,1,Personal,18,5,11:18:36 AM,18,5,11:23:53 AM,18,5,11:34:05 AM,18,5,11:40:47 AM,9,21.5,,-1.3001926000000001,36.7659299,-1.3373344999999999,36.7118861,Rider_Id_543
-Order_No_3064,User_Id_516,Bike,3,Business,30,5,10:55:34 AM,30,5,10:55:45 AM,30,5,10:59:06 AM,30,5,11:16:21 AM,2,23.9,,-1.2571472,36.795063299999995,-1.2668071,36.8068157,Rider_Id_274
-Order_No_17503,User_Id_635,Bike,3,Business,1,3,2:25:47 PM,1,3,2:26:03 PM,1,3,2:37:43 PM,1,3,3:15:12 PM,13,23.9,,-1.3016417,36.8271679,-1.2297202,36.881950700000004,Rider_Id_549
-Order_No_21435,User_Id_136,Bike,3,Business,7,5,11:07:24 AM,7,5,11:08:39 AM,7,5,11:32:48 AM,7,5,11:34:16 AM,2,,,-1.2654716000000001,36.7983635,-1.2760462,36.807469700000006,Rider_Id_577
-Order_No_14397,User_Id_3517,Bike,3,Business,17,5,3:51:28 PM,17,5,3:51:47 PM,17,5,3:52:12 PM,17,5,4:08:53 PM,16,,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_332
-Order_No_17018,User_Id_1346,Bike,3,Business,8,5,2:57:19 PM,8,5,3:10:36 PM,8,5,3:34:53 PM,8,5,3:36:47 PM,6,27.1,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_26
-Order_No_25262,User_Id_2042,Bike,3,Business,9,4,1:05:33 PM,9,4,1:06:24 PM,9,4,1:17:37 PM,9,4,2:07:47 PM,18,25.7,,-1.2860183,36.8975338,-1.2592428,36.7894541,Rider_Id_874
-Order_No_6799,User_Id_3674,Bike,3,Business,27,3,9:06:32 AM,27,3,9:07:04 AM,27,3,9:18:29 AM,27,3,9:33:29 AM,22,21.5,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_177
-Order_No_12098,User_Id_1340,Bike,3,Business,10,4,1:08:16 PM,10,4,1:10:08 PM,10,4,1:31:49 PM,10,4,1:38:36 PM,9,21.9,1.1,-1.2819626,36.7540971,-1.2707178999999997,36.7979713,Rider_Id_749
-Order_No_7650,User_Id_1511,Bike,3,Personal,26,4,1:49:33 PM,26,4,1:50:16 PM,26,4,1:59:08 PM,26,4,2:00:39 PM,10,22.0,,-1.3053806000000001,36.7830636,-1.2654199,36.8008276,Rider_Id_898
-Order_No_4554,User_Id_3674,Bike,3,Business,14,4,9:29:38 AM,14,4,9:29:53 AM,14,4,9:30:52 AM,14,4,9:37:23 AM,22,19.1,,-1.2551895,36.7822034,-1.178201,36.9345516,Rider_Id_576
-Order_No_1351,User_Id_246,Bike,3,Business,15,6,10:09:06 AM,15,6,10:09:35 AM,15,6,10:14:22 AM,15,6,10:16:10 AM,9,17.8,,-1.3052491000000002,36.8223899,-1.2584143,36.8048002,Rider_Id_31
-Order_No_15960,User_Id_1363,Bike,3,Business,30,5,11:21:09 AM,30,5,11:28:41 AM,30,5,11:43:59 AM,30,5,11:53:07 AM,16,23.7,,-1.3004062,36.829741,-1.2216291000000001,36.8935547,Rider_Id_276
-Order_No_14133,User_Id_3283,Bike,3,Business,9,1,12:38:58 PM,9,1,12:39:49 PM,9,1,12:40:00 PM,9,1,1:04:38 PM,5,20.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_51
-Order_No_10296,User_Id_1700,Bike,1,Personal,18,1,3:23:12 PM,18,1,3:24:48 PM,18,1,3:28:33 PM,18,1,3:31:13 PM,9,17.0,,-1.2935303,36.7988142,-1.2557778999999998,36.7879648,Rider_Id_322
-Order_No_12287,User_Id_1402,Bike,3,Business,31,5,10:55:07 AM,31,5,10:57:06 AM,31,5,11:08:20 AM,31,5,11:16:14 AM,1,21.8,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_895
-Order_No_2865,User_Id_868,Bike,3,Business,4,6,1:42:41 PM,4,6,1:42:56 PM,4,6,1:51:35 PM,4,6,2:00:09 PM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_161
-Order_No_26995,User_Id_1363,Bike,3,Business,28,2,3:09:06 PM,28,2,3:09:34 PM,28,2,3:10:08 PM,28,2,3:21:58 PM,3,19.4,,-1.3004062,36.829741,-1.2996681,36.806841299999995,Rider_Id_672
-Order_No_19554,User_Id_1146,Bike,3,Business,30,2,1:17:18 PM,30,2,1:18:32 PM,30,2,1:25:19 PM,30,2,1:46:01 PM,7,,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_211
-Order_No_8998,User_Id_1250,Bike,1,Personal,31,2,4:39:58 PM,31,2,4:40:53 PM,31,2,5:00:02 PM,31,2,5:23:03 PM,12,19.8,,-1.2620741000000002,36.815814700000004,-1.3014461000000002,36.7661381,Rider_Id_882
-Order_No_11705,User_Id_3234,Bike,1,Business,16,4,11:01:38 AM,16,4,11:01:57 AM,16,4,11:16:25 AM,16,4,11:35:48 AM,6,14.8,,-1.2664428,36.809453000000005,-1.2854406,36.789409,Rider_Id_222
-Order_No_7700,User_Id_1250,Bike,1,Personal,5,6,12:04:01 PM,5,6,12:04:08 PM,5,6,12:11:58 PM,5,6,12:25:50 PM,5,22.6,,-1.2904078,36.8301231,-1.2620741000000002,36.815814700000004,Rider_Id_948
-Order_No_1479,User_Id_3201,Bike,3,Business,28,4,12:32:00 PM,28,4,12:33:57 PM,28,4,12:43:00 PM,28,4,1:05:53 PM,6,23.7,,-1.2886923000000001,36.8241519,-1.2584143,36.8048002,Rider_Id_506
-Order_No_7766,User_Id_1120,Bike,3,Personal,28,1,10:47:45 AM,28,1,10:48:34 AM,28,1,10:58:24 AM,28,1,11:01:00 AM,3,22.8,,-1.2638788,36.799846200000005,-1.2629818000000002,36.8135865,Rider_Id_103
-Order_No_24722,User_Id_635,Bike,3,Business,4,1,4:24:16 PM,4,1,4:24:42 PM,4,1,4:33:32 PM,4,1,4:37:06 PM,8,27.8,,-1.2602337,36.79905479999999,-1.2786123999999999,36.7678061,Rider_Id_786
-Order_No_22383,User_Id_1280,Bike,3,Business,13,4,12:51:28 PM,13,4,12:53:35 PM,13,4,12:59:47 PM,13,4,1:05:59 PM,2,22.3,,-1.2551895,36.7822034,-1.2567464,36.7915273,Rider_Id_50
-Order_No_8386,User_Id_20,Bike,3,Business,23,5,9:42:27 AM,23,5,10:42:58 AM,23,5,11:12:41 AM,23,5,11:15:03 AM,7,24.0,,-1.2648363,36.802375399999995,-1.3017611,36.8361973,Rider_Id_114
-Order_No_4215,User_Id_770,Bike,1,Personal,11,4,12:07:31 PM,11,4,12:09:47 PM,11,4,12:28:35 PM,11,4,12:34:33 PM,12,23.7,,-1.3145553,36.749384899999995,-1.3258649,36.8000584,Rider_Id_64
-Order_No_9871,User_Id_84,Bike,3,Business,22,2,12:36:14 PM,22,2,12:37:32 PM,22,2,12:49:03 PM,22,2,12:54:39 PM,11,23.8,,-1.2551895,36.7822034,-1.3141836999999998,36.8163275,Rider_Id_758
-Order_No_26344,User_Id_478,Bike,3,Business,22,3,10:26:17 AM,22,3,10:27:50 AM,22,3,10:38:43 AM,22,3,10:40:12 AM,2,23.3,,-1.2707177,36.8009828,-1.2571472,36.795063299999995,Rider_Id_167
-Order_No_22052,User_Id_734,Bike,1,Personal,9,2,6:37:15 PM,9,2,6:37:49 PM,9,2,6:45:52 PM,9,2,6:51:57 PM,5,18.8,,-1.2947442,36.7624888,-1.2654136,36.7825146,Rider_Id_648
-Order_No_337,User_Id_136,Bike,3,Business,20,3,10:50:04 AM,20,3,11:06:59 AM,20,3,11:11:13 AM,20,3,11:13:12 AM,8,27.1,,-1.3254522,36.721658399999995,-1.3009676000000001,36.7657189,Rider_Id_337
-Order_No_4444,User_Id_1207,Bike,3,Business,19,1,10:46:28 AM,19,1,10:46:50 AM,19,1,11:04:23 AM,19,1,11:28:23 AM,23,24.6,,-1.207546,36.7913976,-1.2745321000000003,36.765088,Rider_Id_940
-Order_No_4715,User_Id_201,Bike,3,Business,20,1,2:21:04 PM,20,1,2:23:20 PM,20,1,2:29:35 PM,20,1,2:40:14 PM,7,,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_659
-Order_No_9566,User_Id_2330,Bike,3,Business,22,5,3:07:27 PM,22,5,3:10:05 PM,22,5,3:22:47 PM,22,5,3:31:04 PM,8,27.1,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_328
-Order_No_1803,User_Id_3218,Bike,3,Business,18,5,9:14:28 AM,18,5,9:15:57 AM,18,5,9:49:24 AM,18,5,9:53:26 AM,9,17.6,,-1.2297202,36.874550799999994,-1.2765736,36.851364600000004,Rider_Id_408
-Order_No_16471,User_Id_700,Bike,3,Business,19,6,10:53:41 AM,19,6,12:19:09 PM,19,6,12:25:04 PM,19,6,12:26:57 PM,8,25.1,,-1.2786912,36.8265105,-1.3010773000000002,36.7752554,Rider_Id_235
-Order_No_12417,User_Id_2956,Bike,3,Business,29,3,5:09:16 PM,29,3,5:10:27 PM,29,3,5:12:49 PM,29,3,5:16:03 PM,3,21.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_849
-Order_No_22199,User_Id_1502,Bike,3,Business,4,1,3:52:59 PM,4,1,3:53:46 PM,4,1,3:57:05 PM,4,1,4:15:16 PM,11,,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_206
-Order_No_13851,User_Id_510,Bike,3,Business,6,3,11:25:56 AM,6,3,11:26:20 AM,6,3,11:39:47 AM,6,3,12:43:31 PM,20,,,-1.3472066,36.7692638,-1.2860183,36.8975338,Rider_Id_533
-Order_No_17447,User_Id_2528,Bike,3,Business,8,3,1:06:00 PM,8,3,1:06:30 PM,8,3,1:09:33 PM,8,3,1:46:20 PM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_427
-Order_No_586,User_Id_898,Bike,1,Personal,1,6,10:46:22 AM,1,6,10:46:32 AM,1,6,10:57:09 AM,1,6,11:46:27 AM,11,18.6,,-1.289498,36.818878000000005,-1.3206958,36.8769553,Rider_Id_449
-Order_No_50,User_Id_3218,Bike,3,Business,23,3,4:36:51 PM,23,3,5:12:04 PM,23,3,5:20:26 PM,23,3,5:54:34 PM,16,20.6,,-1.3404187,36.7614685,-1.2765736,36.851364600000004,Rider_Id_478
-Order_No_26635,User_Id_3291,Bike,3,Business,18,1,11:11:37 AM,18,1,11:30:03 AM,18,1,11:32:49 AM,18,1,11:36:07 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_726
-Order_No_16185,User_Id_2987,Bike,3,Business,2,5,10:46:04 AM,2,5,10:47:58 AM,2,5,11:10:01 AM,2,5,11:13:25 AM,12,23.4,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_937
-Order_No_816,User_Id_2330,Bike,3,Business,4,1,1:14:53 PM,4,1,1:18:09 PM,4,1,1:26:43 PM,4,1,1:33:26 PM,9,28.7,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_862
-Order_No_22883,User_Id_3404,Bike,3,Business,28,3,11:53:14 AM,28,3,1:06:21 PM,28,3,1:37:57 PM,28,3,1:56:42 PM,12,,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_554
-Order_No_12201,User_Id_3647,Bike,3,Business,7,4,12:25:27 PM,7,4,12:26:02 PM,7,4,12:30:25 PM,7,4,12:35:36 PM,5,24.3,,-1.2726389999999999,36.794723,-1.2935921000000001,36.7876166,Rider_Id_205
-Order_No_18015,User_Id_1781,Bike,3,Business,6,3,3:02:05 PM,6,3,3:12:00 PM,6,3,3:32:01 PM,6,3,3:42:43 PM,7,21.6,,-1.2826316999999998,36.8159429,-1.2579303999999998,36.8019667,Rider_Id_268
-Order_No_14079,User_Id_439,Bike,3,Business,6,4,9:17:08 AM,6,4,9:30:39 AM,6,4,9:55:34 AM,6,4,10:22:37 AM,5,18.9,3.1,-1.3270790000000001,36.881469,-1.3323107,36.8692616,Rider_Id_611
-Order_No_9207,User_Id_53,Bike,3,Business,16,4,1:34:37 PM,16,4,1:38:39 PM,16,4,1:45:56 PM,16,4,2:03:11 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_709
-Order_No_17826,User_Id_3503,Bike,2,Personal,3,5,1:29:48 PM,3,5,1:30:12 PM,3,5,1:34:45 PM,3,5,1:55:24 PM,14,25.4,,-1.3299841000000001,36.8466903,-1.2828204,36.78426210000001,Rider_Id_101
-Order_No_4227,User_Id_187,Bike,3,Business,31,1,12:47:27 PM,31,1,12:48:20 PM,31,1,12:57:27 PM,31,1,1:29:38 PM,30,21.1,,-1.3705766000000001,36.9120742,-1.3165542,36.700067,Rider_Id_670
-Order_No_20955,User_Id_3091,Bike,3,Business,6,4,12:18:46 PM,6,4,12:20:41 PM,6,4,12:22:52 PM,6,4,1:12:29 PM,18,19.8,,-1.290894,36.822971,-1.3190102,36.923069299999995,Rider_Id_462
-Order_No_11032,User_Id_2195,Bike,3,Business,8,6,5:11:48 PM,8,6,5:12:05 PM,8,6,5:22:29 PM,8,6,5:29:49 PM,9,22.0,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309
-Order_No_25742,User_Id_1280,Bike,3,Business,11,2,4:19:28 PM,11,2,4:23:31 PM,11,2,4:32:27 PM,11,2,4:38:21 PM,3,20.4,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_690
-Order_No_22805,User_Id_393,Bike,3,Business,21,2,10:52:57 AM,21,2,10:53:22 AM,21,2,11:01:48 AM,21,2,11:32:40 AM,4,22.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_183
-Order_No_16203,User_Id_2532,Bike,3,Business,17,5,3:05:40 PM,17,5,3:06:04 PM,17,5,3:07:55 PM,17,5,3:12:19 PM,6,,,-1.2551895,36.7822034,-1.2884146,36.810766799999996,Rider_Id_709
-Order_No_17561,User_Id_3560,Bike,3,Business,8,4,3:58:33 PM,8,4,4:13:31 PM,8,4,4:15:17 PM,8,4,5:42:45 PM,14,25.4,,-1.279395,36.825364,-1.3103985,36.9114173,Rider_Id_284
-Order_No_16577,User_Id_1133,Bike,3,Business,1,5,10:48:18 AM,1,5,11:06:58 AM,1,5,11:34:20 AM,1,5,11:35:49 AM,5,26.7,,-1.2571472,36.795063299999995,-1.2889024,36.8187402,Rider_Id_311
-Order_No_15165,User_Id_785,Bike,1,Personal,10,4,8:02:08 AM,10,4,8:41:38 AM,10,4,9:26:24 AM,10,4,9:31:12 AM,9,18.5,,-1.2592428,36.7894541,-1.3004846,36.790159100000004,Rider_Id_101
-Order_No_10664,User_Id_3557,Bike,3,Business,8,5,3:03:55 PM,8,5,3:04:43 PM,8,5,3:15:11 PM,8,5,3:58:50 PM,7,27.1,,-1.2662639999999998,36.762418,-1.2546732,36.808679600000005,Rider_Id_720
-Order_No_25242,User_Id_635,Bike,3,Business,27,6,11:08:31 AM,27,6,11:10:02 AM,27,6,11:21:41 AM,27,6,11:41:13 AM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_309
-Order_No_23013,User_Id_1731,Bike,1,Personal,15,5,11:59:21 AM,15,5,12:01:17 PM,15,5,12:45:25 PM,15,5,12:47:58 PM,10,19.9,,-1.3029819999999999,36.794579999999996,-1.3228367,36.854366600000006,Rider_Id_444
-Order_No_4639,User_Id_2407,Bike,2,Personal,10,3,5:01:40 PM,10,3,5:15:01 PM,10,3,5:18:20 PM,10,3,5:35:41 PM,5,22.9,,-1.3014461000000002,36.7661381,-1.2901382,36.784678,Rider_Id_578
-Order_No_18463,User_Id_3560,Bike,3,Business,7,2,10:55:24 AM,7,2,11:07:43 AM,7,2,11:14:57 AM,7,2,11:31:23 AM,1,23.1,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_670
-Order_No_10106,User_Id_2190,Bike,3,Business,6,2,3:51:59 PM,6,2,3:53:13 PM,6,2,3:53:33 PM,6,2,4:02:26 PM,2,27.5,,-1.3019530000000001,36.788458,-1.2919665,36.796108700000005,Rider_Id_390
-Order_No_6296,User_Id_2957,Bike,3,Personal,27,3,12:13:29 PM,27,3,12:13:41 PM,27,3,12:16:24 PM,27,3,12:30:44 PM,8,,,-1.3004311000000002,36.7524273,-1.2633493999999998,36.7959696,Rider_Id_327
-Order_No_9013,User_Id_1482,Bike,3,Business,15,5,9:20:28 AM,15,5,9:20:40 AM,15,5,9:33:15 AM,15,5,9:39:24 AM,8,20.3,,-1.3385790000000002,36.89630379999999,-1.3324865,36.8938303,Rider_Id_863
-Order_No_20288,User_Id_1363,Bike,3,Business,14,2,10:32:57 AM,14,2,10:33:33 AM,14,2,11:16:11 AM,14,2,11:45:10 AM,6,,,-1.3357287,36.8655463,-1.3004062,36.829741,Rider_Id_448
-Order_No_24881,User_Id_136,Bike,3,Business,4,2,2:46:25 PM,4,2,2:46:42 PM,4,2,3:04:57 PM,4,2,3:06:21 PM,16,23.7,11.0,-1.3233496,36.8139159,-1.3326173000000001,36.71462329999999,Rider_Id_263
-Order_No_22474,User_Id_3629,Bike,3,Personal,13,3,3:10:48 PM,13,3,3:13:59 PM,13,3,3:34:46 PM,13,3,3:39:40 PM,3,27.1,,-1.2857524,36.819680600000005,-1.2651643,36.807221399999996,Rider_Id_452
-Order_No_20421,User_Id_1318,Bike,1,Personal,4,4,12:14:45 PM,4,4,12:15:26 PM,4,4,12:20:03 PM,4,4,12:26:53 PM,11,,,-1.27871,36.819573,-1.319496,36.872668,Rider_Id_865
-Order_No_58,User_Id_2154,Bike,3,Business,16,2,1:07:15 PM,16,2,1:07:36 PM,16,2,1:22:41 PM,16,2,1:32:19 PM,11,,,-1.2156006000000001,36.8916865,-1.2765736,36.851364600000004,Rider_Id_660
-Order_No_3633,User_Id_868,Bike,3,Business,25,1,5:08:06 PM,25,1,5:08:59 PM,25,1,5:33:33 PM,25,1,5:35:27 PM,4,28.5,,-1.265715,36.823815,-1.2783848999999998,36.8163445,Rider_Id_118
-Order_No_16006,User_Id_635,Bike,3,Business,21,1,2:21:18 PM,21,1,2:22:12 PM,21,1,2:28:54 PM,21,1,2:30:32 PM,7,27.5,,-1.2599563,36.799343900000004,-1.3081376,36.83297720000001,Rider_Id_933
-Order_No_16320,User_Id_1500,Bike,3,Business,8,3,3:55:45 PM,8,3,4:07:30 PM,8,3,4:14:32 PM,8,3,4:22:12 PM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_168
-Order_No_26291,User_Id_1926,Bike,3,Business,25,1,2:28:19 PM,25,1,2:32:02 PM,25,1,2:45:46 PM,25,1,2:49:28 PM,6,,,-1.3332749,36.870814700000004,-1.3318059,36.8359596,Rider_Id_663
-Order_No_16980,User_Id_469,Bike,3,Personal,4,3,12:41:20 PM,4,3,12:41:54 PM,4,3,1:19:47 PM,4,3,1:25:31 PM,18,16.8,,-1.3253826000000002,36.8822315,-1.2730895,36.8374777,Rider_Id_494
-Order_No_22648,User_Id_2698,Bike,1,Personal,26,4,1:31:54 PM,26,4,1:32:05 PM,26,4,1:48:25 PM,26,4,2:06:17 PM,3,22.2,,-1.2998459999999998,36.7940155,-1.292278,36.797717999999996,Rider_Id_144
-Order_No_19716,User_Id_2985,Bike,2,Personal,4,5,8:36:13 AM,4,5,8:36:35 AM,4,5,9:11:02 AM,4,5,9:18:30 AM,18,19.3,52.1,-1.226855,36.8857626,-1.3018775,36.7856016,Rider_Id_771
-Order_No_4604,User_Id_635,Bike,3,Business,8,6,9:58:48 AM,8,6,9:59:52 AM,8,6,10:00:03 AM,8,6,10:27:38 AM,7,18.4,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_179
-Order_No_69,User_Id_3517,Bike,3,Business,17,1,10:05:15 AM,17,1,10:05:34 AM,17,1,10:05:41 AM,17,1,10:25:48 AM,15,18.3,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_450
-Order_No_8118,User_Id_1926,Bike,3,Business,24,3,2:20:40 PM,24,3,2:22:01 PM,24,3,2:54:03 PM,24,3,3:23:43 PM,16,18.4,,-1.3332749,36.870814700000004,-1.2571472,36.795063299999995,Rider_Id_520
-Order_No_13784,User_Id_893,Bike,3,Business,19,1,12:40:35 PM,19,1,12:41:45 PM,19,1,12:49:04 PM,19,1,12:55:31 PM,5,24.9,,-1.2594473999999998,36.8089122,-1.2850523,36.7859591,Rider_Id_675
-Order_No_15363,User_Id_1342,Bike,3,Business,27,5,8:47:49 AM,27,5,8:48:23 AM,27,5,8:55:14 AM,27,5,8:58:37 AM,10,16.0,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_857
-Order_No_15599,User_Id_2112,Bike,3,Business,29,5,1:08:18 PM,29,5,1:20:34 PM,29,5,1:23:22 PM,29,5,1:33:48 PM,11,21.3,,-1.2765736,36.851364600000004,-1.2156006000000001,36.8916865,Rider_Id_549
-Order_No_5709,User_Id_3661,Bike,3,Business,24,1,10:16:03 AM,24,1,10:16:25 AM,24,1,10:24:17 AM,24,1,10:29:25 AM,15,15.4,,-1.2134646,36.8755266,-1.3090727,36.831526200000006,Rider_Id_813
-Order_No_15227,User_Id_779,Bike,1,Personal,25,2,12:12:30 PM,25,2,12:13:57 PM,25,2,12:28:54 PM,25,2,1:00:31 PM,8,24.0,,-1.2658334999999998,36.804286700000006,-1.2905113000000001,36.759113799999994,Rider_Id_467
-Order_No_8965,User_Id_391,Bike,3,Business,18,1,11:48:57 AM,18,1,11:49:16 AM,18,1,12:06:58 PM,18,1,12:27:03 PM,16,27.8,,-1.300921,36.828195,-1.2121839,36.892427399999995,Rider_Id_95
-Order_No_7877,User_Id_1671,Bike,3,Personal,27,5,3:30:20 PM,27,5,3:31:57 PM,27,5,3:52:28 PM,27,5,4:08:02 PM,6,23.9,,-1.2733079999999999,36.802847,-1.300452,36.779103000000006,Rider_Id_836
-Order_No_4665,User_Id_1500,Bike,3,Business,24,3,8:31:36 AM,24,3,8:35:15 AM,24,3,9:08:26 AM,24,3,9:24:53 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_435
-Order_No_23839,User_Id_393,Bike,3,Business,8,5,3:32:53 PM,8,5,3:33:37 PM,8,5,3:47:41 PM,8,5,3:59:17 PM,14,27.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_267
-Order_No_12650,User_Id_2330,Bike,3,Business,7,4,5:53:44 PM,7,4,5:54:15 PM,7,4,5:59:31 PM,7,4,6:02:20 PM,8,29.3,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_649
-Order_No_11602,User_Id_1159,Bike,1,Personal,24,5,10:29:26 AM,24,5,10:31:18 AM,24,5,10:43:46 AM,24,5,10:59:19 AM,9,21.2,,-1.3068448000000001,36.8348641,-1.272517,36.877848,Rider_Id_519
-Order_No_24176,User_Id_3667,Bike,3,Personal,22,5,11:32:02 AM,22,5,11:33:58 AM,22,5,12:13:29 PM,22,5,12:17:05 PM,11,28.6,,-1.2612573999999999,36.7879622,-1.2295205,36.812042600000005,Rider_Id_452
-Order_No_21392,User_Id_1148,Bike,3,Business,20,5,12:44:46 PM,20,5,12:51:07 PM,20,5,1:04:36 PM,20,5,1:27:48 PM,10,18.4,,-1.3100586,36.850032899999995,-1.2615584,36.814950599999996,Rider_Id_616
-Order_No_25659,User_Id_758,Bike,3,Business,1,1,1:03:56 PM,1,1,1:05:03 PM,1,1,1:13:03 PM,1,1,1:22:14 PM,14,,,-1.2917868000000001,36.7872675,-1.3194518,36.869396,Rider_Id_498
-Order_No_2289,User_Id_3674,Bike,3,Business,18,2,10:39:27 AM,18,2,10:39:34 AM,18,2,10:46:14 AM,18,2,10:52:24 AM,3,,,-1.2551895,36.7822034,-1.257309,36.806008399999996,Rider_Id_446
-Order_No_15850,User_Id_825,Bike,1,Personal,23,7,4:47:57 PM,23,7,4:49:11 PM,23,7,5:17:46 PM,23,7,5:23:14 PM,7,24.1,,-1.2874711,36.764620799999996,-1.2590031000000002,36.8078168,Rider_Id_565
-Order_No_11390,User_Id_265,Bike,3,Business,13,3,1:22:38 PM,13,3,1:23:08 PM,13,3,1:26:52 PM,13,3,1:32:57 PM,14,29.0,,-1.300921,36.828195,-1.3614620000000002,36.746238899999994,Rider_Id_519
-Order_No_3089,User_Id_346,Bike,3,Business,19,2,9:09:02 AM,19,2,9:09:35 AM,19,2,9:24:29 AM,19,2,9:27:22 AM,1,21.3,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_583
-Order_No_17616,User_Id_2112,Bike,3,Business,23,3,11:56:00 AM,23,3,11:56:08 AM,23,3,12:03:14 PM,23,3,12:08:59 PM,13,,,-1.2584143,36.8048002,-1.2156006000000001,36.8916865,Rider_Id_804
-Order_No_13947,User_Id_1244,Bike,3,Business,4,3,1:57:32 PM,4,3,1:58:22 PM,4,3,2:09:48 PM,4,3,2:21:05 PM,8,18.1,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_312
-Order_No_3123,User_Id_393,Bike,3,Business,5,2,10:28:56 AM,5,2,10:30:01 AM,5,2,10:48:35 AM,5,2,10:58:03 AM,4,25.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_167
-Order_No_24172,User_Id_3186,Bike,3,Business,16,4,9:58:43 AM,16,4,9:59:10 AM,16,4,10:27:30 AM,16,4,10:29:56 AM,5,,,-1.2881004999999999,36.8184914,-1.2854406,36.789409,Rider_Id_548
-Order_No_27688,User_Id_3149,Bike,3,Business,15,3,11:53:19 AM,15,3,11:53:51 AM,15,3,12:01:25 PM,15,3,12:11:42 PM,12,,,-1.3167113000000001,36.8301563,-1.2769540000000001,36.765926,Rider_Id_690
-Order_No_3323,User_Id_2587,Bike,3,Business,18,4,2:30:43 PM,18,4,2:31:36 PM,18,4,2:55:52 PM,18,4,3:20:13 PM,8,,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_338
-Order_No_8846,User_Id_3127,Bike,3,Business,30,3,9:36:30 AM,30,3,9:38:13 AM,30,3,10:25:29 AM,30,3,10:31:43 AM,5,19.8,,-1.2760462,36.807469700000006,-1.2918246,36.801584999999996,Rider_Id_456
-Order_No_26601,User_Id_2987,Bike,3,Business,23,2,10:49:40 AM,23,2,11:30:13 AM,23,2,11:51:55 AM,23,2,12:14:37 PM,7,17.8,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_713
-Order_No_3028,User_Id_1544,Bike,3,Business,23,4,10:17:09 AM,23,4,10:17:54 AM,23,4,10:30:53 AM,23,4,10:36:29 AM,14,23.1,,-1.3184737,36.862645,-1.2748221000000002,36.7817865,Rider_Id_440
-Order_No_22312,User_Id_3127,Bike,3,Business,26,5,12:32:43 PM,26,5,12:33:58 PM,26,5,12:44:59 PM,26,5,12:48:32 PM,8,,,-1.2914681000000001,36.80128870000001,-1.2623805000000001,36.7805404,Rider_Id_722
-Order_No_27056,User_Id_700,Bike,3,Business,20,2,10:54:40 AM,20,2,12:38:34 PM,20,2,12:50:49 PM,20,2,1:01:27 PM,7,19.9,,-1.3205163999999998,36.8098882,-1.2872282,36.76750139999999,Rider_Id_726
-Order_No_14567,User_Id_176,Bike,1,Personal,12,4,4:52:20 PM,12,4,5:01:38 PM,12,4,5:05:31 PM,12,4,5:13:14 PM,4,20.5,,-1.2907672,36.8252167,-1.2666695,36.8328283,Rider_Id_629
-Order_No_19514,User_Id_3264,Bike,3,Business,9,2,10:03:56 AM,9,2,10:04:03 AM,9,2,10:04:43 AM,9,2,10:12:24 AM,24,20.7,,-1.2551895,36.7822034,-1.1923911,36.944733299999996,Rider_Id_291
-Order_No_4321,User_Id_2200,Bike,3,Business,24,3,11:40:29 AM,24,3,12:15:34 PM,24,3,12:23:07 PM,24,3,12:34:07 PM,6,19.7,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_726
-Order_No_22554,User_Id_2716,Bike,3,Business,24,1,12:10:30 PM,24,1,12:11:17 PM,24,1,12:33:36 PM,24,1,12:43:49 PM,2,22.6,,-1.2917868000000001,36.7872675,-1.2978908000000002,36.796321500000005,Rider_Id_222
-Order_No_27026,User_Id_2442,Bike,2,Personal,20,3,1:10:05 PM,20,3,1:10:45 PM,20,3,1:11:56 PM,20,3,1:19:35 PM,11,20.2,,-1.2695287,36.798177700000004,-1.22589,36.7822548,Rider_Id_379
-Order_No_27470,User_Id_1517,Bike,1,Personal,16,2,3:39:50 PM,16,2,3:47:44 PM,16,2,3:55:18 PM,16,2,4:04:10 PM,9,27.5,,-1.3099295,36.8436807,-1.2662716,36.815023700000005,Rider_Id_116
-Order_No_19911,User_Id_3091,Bike,3,Business,11,2,12:25:19 PM,11,2,1:05:32 PM,11,2,1:16:30 PM,11,2,1:30:30 PM,9,19.2,,-1.3052491000000002,36.8223899,-1.3025826,36.7670807,Rider_Id_665
-Order_No_26598,User_Id_2718,Bike,3,Business,3,3,11:32:54 AM,3,3,11:34:18 AM,3,3,11:44:57 AM,3,3,11:47:03 AM,3,23.8,,-1.2668815,36.8017244,-1.269534,36.80697,Rider_Id_755
-Order_No_475,User_Id_2008,Bike,3,Business,14,3,11:36:42 AM,14,3,11:37:20 AM,14,3,12:03:05 PM,14,3,12:07:06 PM,16,24.7,,-1.2508229,36.7895263,-1.33924,36.760736200000004,Rider_Id_878
-Order_No_2759,User_Id_3355,Bike,3,Business,4,2,5:07:37 PM,4,2,5:09:36 PM,4,2,6:09:31 PM,4,2,6:14:13 PM,11,22.3,,-1.3052491000000002,36.8223899,-1.238874,36.807889,Rider_Id_267
-Order_No_20204,User_Id_2212,Bike,3,Business,17,5,2:15:16 PM,17,5,2:15:56 PM,17,5,2:21:02 PM,17,5,2:27:11 PM,5,,,-1.2571472,36.795063299999995,-1.280753,36.8191175,Rider_Id_206
-Order_No_1866,User_Id_3686,Bike,3,Business,25,2,11:25:09 AM,25,2,11:26:31 AM,25,2,11:43:27 AM,25,2,11:47:44 AM,13,23.9,,-1.2551895,36.7822034,-1.3318059,36.8359596,Rider_Id_279
-Order_No_22768,User_Id_3525,Bike,3,Business,22,5,4:13:29 PM,22,5,4:18:00 PM,22,5,4:18:31 PM,22,5,5:54:33 PM,20,28.7,,-1.2649605,36.798177700000004,-1.3700383,36.9190174,Rider_Id_613
-Order_No_23411,User_Id_3436,Bike,1,Personal,31,5,4:57:21 PM,31,5,4:58:33 PM,31,5,5:11:16 PM,31,5,5:17:29 PM,7,20.1,,-1.2571472,36.795063299999995,-1.2918148,36.7693264,Rider_Id_536
-Order_No_303,User_Id_2468,Bike,3,Business,4,1,11:09:09 AM,4,1,11:09:51 AM,4,1,11:18:56 AM,4,1,11:25:46 AM,5,25.4,,-1.2857671,36.7875614,-1.2996681,36.806841299999995,Rider_Id_432
-Order_No_833,User_Id_3016,Bike,1,Personal,9,3,10:00:45 AM,9,3,10:18:08 AM,9,3,10:23:34 AM,9,3,10:26:16 AM,4,17.6,0.7,-1.269199,36.8236418,-1.273333,36.8122251,Rider_Id_421
-Order_No_9427,User_Id_3434,Bike,3,Business,13,6,9:14:05 AM,13,6,9:14:46 AM,13,6,9:17:30 AM,13,6,9:32:39 AM,5,19.9,,-1.2963097,36.7688221,-1.3043951999999999,36.766413799999995,Rider_Id_168
-Order_No_24418,User_Id_3355,Bike,3,Business,26,2,12:39:27 PM,26,2,12:42:06 PM,26,2,1:10:31 PM,26,2,1:17:17 PM,8,,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_101
-Order_No_3238,User_Id_3621,Bike,3,Business,25,1,1:50:09 PM,25,1,1:50:58 PM,25,1,1:59:43 PM,25,1,2:02:26 PM,6,29.9,,-1.2836583000000001,36.7886014,-1.2836832,36.8306719,Rider_Id_336
-Order_No_7713,User_Id_2016,Bike,1,Personal,13,5,6:42:26 PM,13,5,6:42:43 PM,13,5,6:53:29 PM,13,5,6:58:27 PM,19,19.0,,-1.2821569,36.7984426,-1.1871375,36.8480466,Rider_Id_806
-Order_No_5809,User_Id_868,Bike,3,Business,2,4,12:28:33 PM,2,4,12:29:27 PM,2,4,12:42:20 PM,2,4,12:53:47 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_269
-Order_No_15939,User_Id_635,Bike,3,Business,7,4,10:39:44 AM,7,4,10:40:04 AM,7,4,10:46:34 AM,7,4,11:08:12 AM,6,24.4,,-1.3019924999999999,36.7833825,-1.2602337,36.79905479999999,Rider_Id_933
-Order_No_19788,User_Id_1961,Bike,3,Business,29,3,1:16:04 PM,29,3,1:16:40 PM,29,3,1:24:38 PM,29,3,1:29:34 PM,9,,,-1.303596,36.778378000000004,-1.3188358999999998,36.703852600000005,Rider_Id_176
-Order_No_8966,User_Id_1721,Bike,3,Business,8,1,3:27:20 PM,8,1,3:27:27 PM,8,1,3:30:35 PM,8,1,3:35:53 PM,3,29.7,,-1.2551895,36.7822034,-1.2562796999999999,36.8075858,Rider_Id_649
-Order_No_6281,User_Id_187,Bike,3,Business,9,1,1:12:20 PM,9,1,2:10:55 PM,9,1,2:23:37 PM,9,1,2:34:44 PM,10,20.9,,-1.3169591,36.7000029,-1.3581286,36.6580851,Rider_Id_478
-Order_No_13560,User_Id_2066,Bike,3,Business,1,3,12:23:24 PM,1,3,12:23:38 PM,1,3,12:38:45 PM,1,3,12:42:58 PM,5,20.0,,-1.316961,36.820337,-1.3057159999999999,36.809882,Rider_Id_19
-Order_No_16742,User_Id_633,Bike,3,Business,24,4,12:56:17 PM,24,4,12:58:26 PM,24,4,1:00:17 PM,24,4,1:28:21 PM,4,24.5,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_496
-Order_No_14873,User_Id_474,Bike,3,Business,30,1,3:03:11 PM,30,1,3:03:53 PM,30,1,3:17:57 PM,30,1,3:19:28 PM,3,20.4,,-1.264945,36.784678,-1.2666153,36.8015124,Rider_Id_770
-Order_No_8435,User_Id_20,Bike,3,Business,21,3,1:30:19 PM,21,3,1:35:04 PM,21,3,1:47:56 PM,21,3,2:13:34 PM,27,23.0,2.9,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_449
-Order_No_1163,User_Id_1340,Bike,3,Business,30,5,10:05:27 AM,30,5,10:06:31 AM,30,5,10:17:56 AM,30,5,10:21:19 AM,4,19.2,,-1.2707178999999997,36.7979713,-1.2734594,36.8170558,Rider_Id_167
-Order_No_11908,User_Id_1835,Bike,1,Personal,31,4,11:47:28 AM,31,4,11:50:33 AM,31,4,12:03:36 PM,31,4,12:07:31 PM,5,21.8,,-1.2745107,36.797450299999994,-1.2966785,36.7896469,Rider_Id_322
-Order_No_6712,User_Id_1687,Bike,3,Business,12,1,12:51:35 PM,12,1,12:54:40 PM,12,1,1:07:17 PM,12,1,1:11:35 PM,6,23.8,,-1.2733003999999999,36.7943468,-1.3014732,36.7781201,Rider_Id_519
-Order_No_28014,User_Id_720,Bike,3,Personal,28,2,11:13:10 AM,28,2,11:13:45 AM,28,2,11:14:15 AM,28,2,11:27:04 AM,12,21.3,,-1.2372041999999999,36.760574299999995,-1.2979851999999998,36.8069651,Rider_Id_99
-Order_No_23547,User_Id_517,Bike,3,Business,17,6,9:55:40 AM,17,6,10:22:00 AM,17,6,10:28:19 AM,17,6,10:50:05 AM,4,24.0,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_708
-Order_No_3141,User_Id_3674,Bike,3,Business,18,5,4:14:23 PM,18,5,4:14:54 PM,18,5,4:18:38 PM,18,5,4:20:42 PM,5,27.2,,-1.2551895,36.7822034,-1.2615048,36.8116724,Rider_Id_326
-Order_No_19754,User_Id_302,Bike,3,Business,1,1,1:16:09 PM,1,1,1:16:29 PM,1,1,1:17:18 PM,1,1,1:45:33 PM,1,25.7,,-1.2649605,36.798177700000004,-1.256098,36.800655,Rider_Id_647
-Order_No_20026,User_Id_2869,Bike,3,Business,22,3,10:19:57 AM,22,3,10:20:13 AM,22,3,10:22:44 AM,22,3,10:32:55 AM,10,23.0,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_411
-Order_No_23429,User_Id_1329,Bike,3,Business,3,5,9:55:24 AM,3,5,9:55:36 AM,3,5,10:07:05 AM,3,5,10:09:43 AM,5,,,-1.273056,36.811298,-1.2844858000000001,36.79558839999999,Rider_Id_195
-Order_No_13145,User_Id_2956,Bike,3,Business,12,5,9:30:30 AM,12,5,9:30:48 AM,12,5,9:35:50 AM,12,5,9:39:09 AM,3,18.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_674
-Order_No_17500,User_Id_2828,Bike,3,Business,30,5,12:44:33 PM,30,5,12:45:16 PM,30,5,12:50:37 PM,30,5,12:55:57 PM,3,23.8,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_228
-Order_No_13125,User_Id_2212,Bike,3,Business,4,2,4:02:52 PM,4,2,4:13:35 PM,4,2,4:18:27 PM,4,2,4:27:11 PM,13,22.3,,-1.2571472,36.795063299999995,-1.302453,36.8668919,Rider_Id_162
-Order_No_15564,User_Id_1482,Bike,3,Business,17,1,10:54:21 AM,17,1,10:54:26 AM,17,1,10:54:34 AM,17,1,10:59:10 AM,18,23.7,,-1.3385790000000002,36.89630379999999,-1.2637056999999998,36.805170000000004,Rider_Id_345
-Order_No_15811,User_Id_3688,Bike,3,Business,12,2,10:46:05 AM,12,2,10:46:17 AM,12,2,10:46:23 AM,12,2,11:00:38 AM,4,24.7,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_952
-Order_No_11404,User_Id_3420,Bike,3,Business,28,4,3:48:26 PM,28,4,3:48:57 PM,28,4,3:55:31 PM,28,4,3:58:25 PM,7,27.3,,-1.3046007,36.821174299999996,-1.3038645,36.8684596,Rider_Id_206
-Order_No_21806,User_Id_246,Bike,3,Business,28,5,12:14:34 PM,28,5,12:39:23 PM,28,5,12:48:45 PM,28,5,12:54:24 PM,4,25.1,,-1.2584143,36.8048002,-1.2848173999999999,36.826163,Rider_Id_754
-Order_No_19291,User_Id_3109,Bike,2,Personal,13,5,7:56:32 AM,13,5,7:57:18 AM,13,5,8:01:52 AM,13,5,8:33:29 AM,5,15.5,,-1.2551895,36.7822034,-1.2693522,36.797399299999995,Rider_Id_116
-Order_No_9479,User_Id_2069,Bike,3,Business,27,6,4:53:27 PM,27,6,4:53:46 PM,27,6,4:59:41 PM,27,6,5:05:40 PM,9,,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_775
-Order_No_10855,User_Id_728,Bike,2,Personal,17,5,2:47:26 PM,17,5,2:47:33 PM,17,5,2:48:11 PM,17,5,3:03:19 PM,6,20.0,,-1.2853965,36.8183118,-1.3090663,36.813686499999996,Rider_Id_213
-Order_No_27630,User_Id_3517,Bike,3,Business,16,2,1:23:28 PM,16,2,1:23:43 PM,16,2,1:24:55 PM,16,2,2:00:01 PM,13,27.5,,-1.300921,36.828195,-1.2655495,36.910537,Rider_Id_622
-Order_No_16506,User_Id_867,Bike,3,Business,7,1,3:56:42 PM,7,1,3:56:50 PM,7,1,3:57:08 PM,7,1,4:00:07 PM,11,26.7,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_523
-Order_No_14715,User_Id_805,Bike,3,Personal,25,1,6:34:50 PM,25,1,7:04:13 PM,25,1,7:46:37 PM,25,1,8:21:54 PM,12,24.4,,-1.3987597999999999,36.767703600000004,-1.3165719,36.7871583,Rider_Id_226
-Order_No_14690,User_Id_1500,Bike,3,Business,12,5,11:06:45 AM,12,5,11:07:06 AM,12,5,11:14:38 AM,12,5,11:22:49 AM,9,26.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161
-Order_No_19651,User_Id_1527,Bike,3,Business,10,1,12:57:44 PM,10,1,12:58:52 PM,10,1,1:03:48 PM,10,1,1:21:25 PM,4,18.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_403
-Order_No_10890,User_Id_1865,Bike,1,Personal,20,6,11:52:40 AM,20,6,11:52:54 AM,20,6,12:03:03 PM,20,6,12:08:02 PM,14,25.6,,-1.2166078000000002,36.882501,-1.2851761,36.8936402,Rider_Id_343
-Order_No_1139,User_Id_3091,Bike,3,Business,10,4,4:59:35 PM,10,4,4:59:59 PM,10,4,5:03:53 PM,10,4,5:09:52 PM,3,23.5,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_436
-Order_No_14095,User_Id_3291,Bike,3,Business,6,4,9:47:02 AM,6,4,9:47:53 AM,6,4,10:02:07 AM,6,4,10:09:28 AM,5,19.0,3.0,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_337
-Order_No_18464,User_Id_733,Bike,3,Business,6,2,12:02:51 PM,6,2,12:03:27 PM,6,2,12:04:38 PM,6,2,12:17:20 PM,20,25.1,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_648
-Order_No_8649,User_Id_1535,Bike,1,Business,21,1,4:10:51 PM,21,1,4:13:44 PM,21,1,4:44:23 PM,21,1,4:49:16 PM,7,,,-1.2897368,36.8226783,-1.2579303999999998,36.8019667,Rider_Id_844
-Order_No_1911,User_Id_3688,Bike,3,Business,6,4,2:48:45 PM,6,4,2:52:11 PM,6,4,3:26:15 PM,6,4,3:28:02 PM,6,,,-1.2736466,36.8164042,-1.2551895,36.7822034,Rider_Id_857
-Order_No_22981,User_Id_624,Bike,3,Business,27,2,2:50:17 PM,27,2,2:50:42 PM,27,2,2:58:35 PM,27,2,3:36:19 PM,21,25.7,,-1.3444459999999998,36.756597,-1.2297799999999999,36.836215,Rider_Id_541
-Order_No_21952,User_Id_186,Bike,3,Business,31,5,4:33:25 PM,31,5,4:52:27 PM,31,5,5:07:38 PM,31,5,5:21:54 PM,4,23.7,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_582
-Order_No_20953,User_Id_718,Bike,3,Business,26,2,12:31:41 PM,26,2,12:32:21 PM,26,2,12:51:47 PM,26,2,12:55:16 PM,9,,,-1.2551895,36.7822034,-1.2956171,36.7888097,Rider_Id_832
-Order_No_18300,User_Id_450,Bike,1,Personal,11,4,10:53:09 AM,11,4,10:53:20 AM,11,4,11:01:44 AM,11,4,11:11:26 AM,9,23.8,,-1.2740396,36.7993518,-1.3205163999999998,36.8098882,Rider_Id_148
-Order_No_9965,User_Id_2627,Bike,3,Business,9,4,2:12:27 PM,9,4,2:20:15 PM,9,4,2:30:02 PM,9,4,2:38:21 PM,7,,,-1.2571472,36.795063299999995,-1.304344,36.812274,Rider_Id_538
-Order_No_14579,User_Id_893,Bike,3,Business,18,5,2:07:05 PM,18,5,2:07:22 PM,18,5,2:17:03 PM,18,5,2:18:52 PM,3,26.9,,-1.2663194,36.809901,-1.2574087999999999,36.799712,Rider_Id_611
-Order_No_28171,User_Id_516,Bike,3,Business,29,2,11:26:53 AM,29,2,11:28:50 AM,29,2,11:35:40 AM,29,2,11:54:29 AM,7,23.3,,-1.2571472,36.795063299999995,-1.2843046,36.774613200000005,Rider_Id_279
-Order_No_27196,User_Id_2587,Bike,3,Business,10,4,4:09:10 PM,10,4,4:10:50 PM,10,4,4:18:15 PM,10,4,4:22:03 PM,5,25.0,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_467
-Order_No_17803,User_Id_3283,Bike,3,Business,20,1,2:39:57 PM,20,1,2:40:53 PM,20,1,2:45:51 PM,20,1,2:49:48 PM,13,,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_48
-Order_No_14544,User_Id_302,Bike,3,Business,27,7,2:44:53 PM,27,7,2:45:24 PM,27,7,2:49:22 PM,27,7,3:02:53 PM,7,28.1,,-1.2649605,36.798177700000004,-1.291469,36.780261200000005,Rider_Id_112
-Order_No_13538,User_Id_2523,Bike,3,Business,19,2,9:48:10 AM,19,2,9:48:23 AM,19,2,10:21:53 AM,19,2,10:33:22 AM,19,,,-1.2551895,36.7822034,-1.2219026000000002,36.917669599999996,Rider_Id_100
-Order_No_2537,User_Id_3091,Bike,3,Business,11,4,1:58:55 PM,11,4,2:52:32 PM,11,4,3:36:46 PM,11,4,3:56:58 PM,6,27.0,,-1.3147073999999999,36.811721999999996,-1.290894,36.822971,Rider_Id_907
-Order_No_13912,User_Id_3283,Bike,3,Business,8,1,3:18:14 PM,8,1,3:18:29 PM,8,1,3:18:48 PM,8,1,3:25:45 PM,5,29.7,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_881
-Order_No_12384,User_Id_1927,Bike,3,Business,6,3,12:55:41 PM,6,3,12:56:23 PM,6,3,12:58:42 PM,6,3,1:05:59 PM,12,22.4,,-1.2880883,36.8227336,-1.3472066,36.7692638,Rider_Id_926
-Order_No_4703,User_Id_1927,Bike,3,Business,11,1,1:59:59 PM,11,1,2:01:23 PM,11,1,2:35:36 PM,11,1,2:55:09 PM,17,28.9,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_856
-Order_No_13894,User_Id_1927,Bike,3,Business,20,3,8:40:48 AM,20,3,8:41:06 AM,20,3,9:28:48 AM,20,3,9:55:42 AM,15,21.0,,-1.3263105,36.8622904,-1.3472066,36.7692638,Rider_Id_26
-Order_No_9647,User_Id_3633,Bike,3,Personal,9,6,11:39:57 AM,9,6,11:41:53 AM,9,6,12:06:47 PM,9,6,12:17:35 PM,14,17.4,,-1.2739208,36.8310153,-1.2797013000000002,36.7299371,Rider_Id_581
-Order_No_15388,User_Id_3404,Bike,3,Business,10,6,1:04:31 PM,10,6,1:09:17 PM,10,6,1:13:04 PM,10,6,1:21:35 PM,1,23.8,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_927
-Order_No_25605,User_Id_2066,Bike,3,Business,2,2,4:06:52 PM,2,2,4:08:08 PM,2,2,4:18:20 PM,2,2,4:24:41 PM,16,29.3,,-1.3210188999999999,36.841055100000005,-1.2397503,36.87976810000001,Rider_Id_299
-Order_No_3630,User_Id_2986,Bike,3,Personal,27,1,11:44:01 AM,27,1,11:45:41 AM,27,1,12:10:03 PM,27,1,12:13:16 PM,15,21.0,,-1.3245778999999998,36.7021035,-1.2903886,36.8063847,Rider_Id_690
-Order_No_6851,User_Id_393,Bike,3,Business,20,3,10:55:25 AM,20,3,10:55:59 AM,20,3,10:56:23 AM,20,3,11:06:44 AM,4,26.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_842
-Order_No_7507,User_Id_1846,Bike,2,Personal,19,5,10:25:23 AM,19,5,10:26:47 AM,19,5,10:34:44 AM,19,5,10:57:55 AM,4,24.8,,-1.2699049,36.8081403,-1.289498,36.818878000000005,Rider_Id_268
-Order_No_86,User_Id_1721,Bike,3,Business,30,2,1:10:44 PM,30,2,1:13:25 PM,30,2,1:18:12 PM,30,2,1:42:43 PM,6,,,-1.2551895,36.7822034,-1.2743751,36.8205442,Rider_Id_291
-Order_No_9699,User_Id_974,Bike,1,Personal,3,5,3:33:29 PM,3,5,3:33:45 PM,3,5,3:56:19 PM,3,5,4:37:07 PM,10,22.6,,-1.2795261,36.826018499999996,-1.3080097,36.7494062,Rider_Id_167
-Order_No_8832,User_Id_3547,Bike,3,Business,6,1,1:05:25 PM,6,1,1:07:05 PM,6,1,1:22:11 PM,6,1,1:31:13 PM,5,,,-1.2761408,36.7710839,-1.2651573999999999,36.7474347,Rider_Id_106
-Order_No_10732,User_Id_3369,Bike,3,Business,18,1,2:35:00 PM,18,1,2:35:58 PM,18,1,2:47:35 PM,18,1,2:50:47 PM,6,17.0,,-1.2696183,36.8021778,-1.260345,36.841598600000005,Rider_Id_434
-Order_No_18394,User_Id_706,Bike,3,Business,18,5,9:21:53 AM,18,5,9:22:03 AM,18,5,9:22:07 AM,18,5,9:44:44 AM,5,19.9,,-1.298331,36.822614,-1.3077869,36.844320700000004,Rider_Id_26
-Order_No_9899,User_Id_3426,Bike,3,Business,29,1,5:33:11 PM,29,1,5:33:38 PM,29,1,5:40:20 PM,29,1,5:48:38 PM,10,23.2,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_854
-Order_No_5545,User_Id_393,Bike,3,Business,31,3,11:43:37 AM,31,3,11:51:22 AM,31,3,12:03:04 PM,31,3,12:07:17 PM,7,24.9,,-1.3177546999999998,36.8303703,-1.3083266000000002,36.8494756,Rider_Id_731
-Order_No_10601,User_Id_3491,Bike,3,Business,13,2,1:49:41 PM,13,2,1:49:49 PM,13,2,2:15:20 PM,13,2,2:22:05 PM,15,26.4,,-1.3043095,36.8163115,-1.2297202,36.881950700000004,Rider_Id_419
-Order_No_17384,User_Id_393,Bike,3,Business,8,4,2:05:25 PM,8,4,2:15:04 PM,8,4,2:26:43 PM,8,4,2:33:05 PM,14,27.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_952
-Order_No_5362,User_Id_2064,Bike,2,Personal,6,1,4:18:57 PM,6,1,4:19:23 PM,6,1,4:22:05 PM,6,1,4:28:00 PM,3,24.7,,-1.2675132,36.8014826,-1.2622120000000001,36.7886506,Rider_Id_338
-Order_No_5893,User_Id_2237,Bike,3,Business,15,6,10:28:24 AM,15,6,10:30:16 AM,15,6,10:43:23 AM,15,6,10:59:17 AM,4,,,-1.266571,36.801406,-1.2772389,36.7855864,Rider_Id_882
-Order_No_12451,User_Id_237,Bike,3,Business,7,3,11:36:34 AM,7,3,11:38:35 AM,7,3,11:51:31 AM,7,3,11:56:34 AM,12,25.0,,-1.2551895,36.7822034,-1.3181687,36.8373675,Rider_Id_19
-Order_No_4643,User_Id_1500,Bike,3,Business,6,3,12:49:15 PM,6,3,12:50:00 PM,6,3,1:13:35 PM,6,3,1:22:11 PM,8,22.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_332
-Order_No_9251,User_Id_3201,Bike,3,Business,1,3,11:59:03 AM,1,3,11:59:12 AM,1,3,12:04:28 PM,1,3,12:07:38 PM,2,20.0,,-1.2649605,36.798177700000004,-1.2675613000000001,36.8133001,Rider_Id_882
-Order_No_21573,User_Id_2733,Bike,3,Business,12,5,12:52:03 PM,12,5,12:52:36 PM,12,5,1:11:22 PM,12,5,1:24:57 PM,2,26.5,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_324
-Order_No_27067,User_Id_2701,Bike,3,Business,23,5,11:06:47 AM,23,5,11:12:34 AM,23,5,12:00:24 PM,23,5,12:01:29 PM,6,24.0,,-1.2765736,36.851364600000004,-1.2885315,36.8230419,Rider_Id_928
-Order_No_19961,User_Id_909,Bike,3,Business,12,3,1:09:24 PM,12,3,1:10:57 PM,12,3,1:32:55 PM,12,3,1:34:51 PM,9,26.3,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_933
-Order_No_3414,User_Id_2801,Bike,3,Business,28,3,2:07:58 PM,28,3,2:08:25 PM,28,3,2:09:04 PM,28,3,2:32:56 PM,15,,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_595
-Order_No_4727,User_Id_2700,Bike,1,Business,16,6,3:23:31 PM,16,6,3:24:30 PM,16,6,3:30:27 PM,16,6,3:40:50 PM,8,23.1,,-1.2559559999999999,36.772901,-1.2780963,36.8274099,Rider_Id_925
-Order_No_12574,User_Id_2982,Bike,3,Business,7,3,9:28:52 AM,7,3,9:29:02 AM,7,3,9:29:15 AM,7,3,10:16:00 AM,16,,,-1.300921,36.828195,-1.2155671000000001,36.8898931,Rider_Id_70
-Order_No_21513,User_Id_3283,Bike,3,Business,1,5,1:17:16 PM,1,5,1:17:33 PM,1,5,1:25:42 PM,1,5,1:41:40 PM,13,31.7,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_663
-Order_No_14120,User_Id_540,Bike,3,Business,12,4,8:03:54 AM,12,4,8:10:37 AM,12,4,8:28:29 AM,12,4,8:35:34 AM,2,16.0,,-1.2551895,36.7822034,-1.256783,36.789915,Rider_Id_103
-Order_No_17115,User_Id_1798,Bike,3,Business,28,5,2:34:09 PM,28,5,2:42:10 PM,28,5,2:47:41 PM,28,5,2:54:15 PM,31,25.4,,-1.2615888000000002,36.7928732,-1.1055559,36.6402664,Rider_Id_449
-Order_No_13157,User_Id_104,Bike,3,Personal,24,1,11:15:16 AM,24,1,11:19:22 AM,24,1,12:02:54 PM,24,1,12:04:11 PM,5,22.6,,-1.2823746999999999,36.8258855,-1.2804891,36.7865102,Rider_Id_143
-Order_No_18705,User_Id_2200,Bike,3,Business,26,1,9:58:13 AM,26,1,11:27:45 AM,26,1,11:31:13 AM,26,1,11:49:05 AM,8,23.1,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_711
-Order_No_10014,User_Id_60,Bike,3,Business,14,5,5:17:57 PM,14,5,5:18:01 PM,14,5,5:18:22 PM,14,5,5:32:19 PM,9,21.3,,-1.2296618000000001,36.84382720000001,-1.2663631000000002,36.8163329,Rider_Id_564
-Order_No_4468,User_Id_918,Bike,1,Personal,3,1,2:59:22 PM,3,1,2:59:27 PM,3,1,3:23:22 PM,3,1,3:41:13 PM,23,23.0,,-1.3415373,36.7295377,-1.2344773999999998,36.7553163,Rider_Id_248
-Order_No_12553,User_Id_1075,Bike,3,Business,1,5,9:14:25 AM,1,5,9:15:01 AM,1,5,9:25:59 AM,1,5,9:42:49 AM,17,,,-1.2852569,36.8976121,-1.2527958000000001,36.8003131,Rider_Id_772
-Order_No_10409,User_Id_868,Bike,3,Business,31,2,11:29:55 AM,31,2,11:31:45 AM,31,2,11:46:42 AM,31,2,11:49:42 AM,8,18.7,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_726
-Order_No_26612,User_Id_3135,Bike,3,Personal,1,1,10:01:00 AM,1,1,10:01:05 AM,1,1,10:07:16 AM,1,1,10:17:04 AM,8,18.6,,-1.277585,36.77145,-1.3043951999999999,36.766413799999995,Rider_Id_785
-Order_No_12580,User_Id_3338,Bike,3,Business,14,5,2:43:26 PM,14,5,2:43:47 PM,14,5,2:55:08 PM,14,5,3:07:33 PM,7,23.2,,-1.2932786,36.7834311,-1.2551895,36.7822034,Rider_Id_315
-Order_No_19535,User_Id_1329,Bike,3,Business,11,2,10:05:01 AM,11,2,10:42:42 AM,11,2,11:01:42 AM,11,2,11:04:42 AM,4,19.6,,-1.273056,36.811298,-1.2720281999999998,36.784531,Rider_Id_112
-Order_No_24957,User_Id_3283,Bike,3,Business,20,4,10:08:06 AM,20,4,10:20:22 AM,20,4,10:35:06 AM,20,4,10:53:18 AM,19,22.1,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_496
-Order_No_18446,User_Id_71,Bike,3,Business,5,2,2:57:26 PM,5,2,2:58:16 PM,5,2,3:10:12 PM,5,2,3:23:45 PM,6,22.8,,-1.2601708999999999,36.8002238,-1.2821108,36.8250555,Rider_Id_742
-Order_No_23661,User_Id_2700,Bike,1,Business,4,3,9:31:24 AM,4,3,9:32:44 AM,4,3,9:54:22 AM,4,3,10:00:59 AM,23,15.1,,-1.2757858,36.788684100000005,-1.3678101999999999,36.9360455,Rider_Id_465
-Order_No_26914,User_Id_867,Bike,3,Business,5,3,3:48:28 PM,5,3,3:51:53 PM,5,3,4:02:21 PM,5,3,4:13:53 PM,9,25.1,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_878
-Order_No_3898,User_Id_3711,Bike,3,Business,7,1,11:13:28 AM,7,1,11:13:39 AM,7,1,11:13:56 AM,7,1,11:21:42 AM,9,25.0,,-1.2551895,36.7822034,-1.3012008,36.764868,Rider_Id_89
-Order_No_24493,User_Id_3291,Bike,3,Business,30,2,10:19:35 AM,30,2,10:20:29 AM,30,2,10:22:48 AM,30,2,10:30:42 AM,9,17.3,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_283
-Order_No_22985,User_Id_1637,Bike,3,Business,7,3,3:11:05 PM,7,3,3:11:18 PM,7,3,3:11:35 PM,7,3,3:21:48 PM,3,28.1,,-1.2615888000000002,36.7928732,-1.2657464,36.780114399999995,Rider_Id_517
-Order_No_15888,User_Id_2982,Bike,3,Business,8,4,12:08:47 PM,8,4,12:09:02 PM,8,4,12:11:14 PM,8,4,12:30:50 PM,23,26.2,,-1.300921,36.828195,-1.1956524,36.948806899999994,Rider_Id_519
-Order_No_21250,User_Id_868,Bike,3,Business,16,6,10:14:17 AM,16,6,10:32:54 AM,16,6,10:45:23 AM,16,6,11:08:35 AM,9,25.9,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_660
-Order_No_25563,User_Id_1307,Bike,3,Business,29,5,8:45:26 AM,29,5,8:48:03 AM,29,5,9:01:58 AM,29,5,9:17:51 AM,14,,,-1.2844665000000002,36.7866113,-1.2296416000000001,36.8422602,Rider_Id_247
-Order_No_24825,User_Id_3222,Bike,3,Business,30,4,1:16:14 PM,30,4,1:19:34 PM,30,4,1:28:53 PM,30,4,1:32:59 PM,15,,,-1.2551895,36.7822034,-1.3212161,36.869276899999996,Rider_Id_576
-Order_No_27448,User_Id_867,Bike,3,Business,19,2,12:13:41 PM,19,2,12:14:12 PM,19,2,12:19:18 PM,19,2,12:41:16 PM,10,26.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_831
-Order_No_2539,User_Id_3189,Bike,3,Personal,10,1,10:58:08 AM,10,1,10:58:35 AM,10,1,11:04:42 AM,10,1,11:19:47 AM,15,18.6,,-1.3087303999999997,36.83129879999999,-1.2513528999999999,36.752171999999995,Rider_Id_211
-Order_No_22708,User_Id_733,Bike,3,Business,9,2,3:51:05 PM,9,2,3:52:36 PM,9,2,4:11:44 PM,9,2,4:12:52 PM,8,30.7,,-1.2551895,36.7822034,-1.2915263000000001,36.795007500000004,Rider_Id_523
-Order_No_1568,User_Id_393,Bike,3,Business,8,2,3:39:56 PM,8,2,3:44:44 PM,8,2,3:45:50 PM,8,2,4:01:19 PM,14,25.2,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_188
-Order_No_25273,User_Id_393,Bike,3,Business,7,4,8:23:47 AM,7,4,8:23:53 AM,7,4,8:25:15 AM,7,4,8:34:22 AM,14,19.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523
-Order_No_26738,User_Id_3246,Bike,3,Business,28,4,4:28:25 PM,28,4,4:29:06 PM,28,4,4:29:49 PM,28,4,4:54:07 PM,28,,,-1.2156006000000001,36.8916865,-1.3232626,36.7077125,Rider_Id_661
-Order_No_2797,User_Id_65,Bike,1,Personal,16,2,2:23:11 PM,16,2,2:26:23 PM,16,2,2:34:37 PM,16,2,2:39:48 PM,7,27.4,,-1.2684319,36.81658529999999,-1.2943401,36.787266100000004,Rider_Id_22
-Order_No_4567,User_Id_1304,Bike,3,Business,18,5,4:45:48 PM,18,5,4:48:22 PM,18,5,4:57:49 PM,18,5,5:02:09 PM,6,22.5,,-1.2626906,36.7827022,-1.2839568000000001,36.823310600000006,Rider_Id_123
-Order_No_17855,User_Id_1500,Bike,3,Business,8,3,10:51:49 AM,8,3,10:52:11 AM,8,3,11:26:58 AM,8,3,11:46:10 AM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_668
-Order_No_21209,User_Id_1880,Bike,3,Business,5,5,9:46:25 AM,5,5,9:49:52 AM,5,5,9:59:48 AM,5,5,10:02:18 AM,12,17.7,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_383
-Order_No_26713,User_Id_3436,Bike,3,Personal,15,2,11:59:31 AM,15,2,11:59:50 AM,15,2,12:06:38 PM,15,2,12:12:51 PM,9,24.1,,-1.2915128999999999,36.769997100000005,-1.3034072,36.821803499999994,Rider_Id_246
-Order_No_12596,User_Id_2521,Bike,3,Business,25,5,3:48:01 PM,25,5,3:48:37 PM,25,5,3:51:49 PM,25,5,3:53:43 PM,14,29.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523
-Order_No_12901,User_Id_3355,Bike,3,Business,17,2,8:06:02 AM,17,2,8:07:13 AM,17,2,8:32:10 AM,17,2,8:34:56 AM,8,14.3,,-1.3052491000000002,36.8223899,-1.3103200000000002,36.87306829999999,Rider_Id_890
-Order_No_13287,User_Id_1402,Bike,3,Business,30,5,10:49:33 AM,30,5,11:13:01 AM,30,5,11:31:57 AM,30,5,11:45:25 AM,2,23.5,,-1.290894,36.822971,-1.2891921999999998,36.825189,Rider_Id_362
-Order_No_18872,User_Id_186,Bike,3,Business,5,2,2:51:12 PM,5,2,3:07:57 PM,5,2,3:29:26 PM,5,2,3:36:22 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_883
-Order_No_4868,User_Id_540,Bike,3,Business,23,5,12:10:42 PM,23,5,12:11:54 PM,23,5,12:25:56 PM,23,5,12:32:41 PM,3,24.2,,-1.2571472,36.795063299999995,-1.2753108999999998,36.8130185,Rider_Id_840
-Order_No_17063,User_Id_635,Bike,3,Business,8,3,8:29:12 AM,8,3,8:33:36 AM,8,3,8:33:58 AM,8,3,9:37:50 AM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_206
-Order_No_1238,User_Id_3232,Bike,3,Business,31,3,4:35:16 PM,31,3,4:35:48 PM,31,3,4:38:57 PM,31,3,4:40:39 PM,8,24.4,,-1.2551895,36.7822034,-1.3010058999999998,36.8216816,Rider_Id_199
-Order_No_21715,User_Id_1299,Bike,3,Business,2,1,11:49:11 AM,2,1,11:49:23 AM,2,1,12:01:41 PM,2,1,12:33:59 PM,3,19.3,,-1.2652093,36.8064216,-1.282543,36.824618,Rider_Id_616
-Order_No_9852,User_Id_733,Bike,3,Business,7,2,12:40:43 PM,7,2,12:41:13 PM,7,2,12:52:05 PM,7,2,12:57:55 PM,6,23.1,,-1.276245,36.836329299999996,-1.264865,36.810488,Rider_Id_174
-Order_No_10800,User_Id_3291,Bike,3,Business,6,3,10:49:02 AM,6,3,10:50:22 AM,6,3,11:06:49 AM,6,3,11:26:13 AM,5,,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_763
-Order_No_1459,User_Id_3674,Bike,3,Business,8,5,2:28:51 PM,8,5,2:30:24 PM,8,5,3:07:08 PM,8,5,3:31:27 PM,9,27.0,,-1.2551895,36.7822034,-1.2974988,36.7956306,Rider_Id_322
-Order_No_5119,User_Id_2563,Bike,1,Personal,18,4,2:06:05 PM,18,4,2:06:17 PM,18,4,2:06:30 PM,18,4,2:22:22 PM,12,26.9,,-1.3108386,36.7555239,-1.2598589,36.7470168,Rider_Id_105
-Order_No_14328,User_Id_3239,Bike,3,Personal,16,3,3:45:51 PM,16,3,3:48:08 PM,16,3,3:49:11 PM,16,3,3:58:54 PM,6,25.3,,-1.276154,36.822416499999996,-1.2982033000000002,36.809263200000004,Rider_Id_100
-Order_No_7679,User_Id_3647,Bike,3,Business,14,3,1:08:45 PM,14,3,1:51:55 PM,14,3,2:06:51 PM,14,3,2:08:18 PM,4,25.6,,-1.2726389999999999,36.794723,-1.2762965,36.8211047,Rider_Id_85
-Order_No_10021,User_Id_3149,Bike,3,Business,25,2,12:04:51 PM,25,2,12:22:32 PM,25,2,12:41:06 PM,25,2,1:04:16 PM,15,24.0,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_532
-Order_No_12792,User_Id_281,Bike,2,Personal,18,6,3:47:40 PM,18,6,3:47:50 PM,18,6,3:54:54 PM,18,6,4:01:53 PM,7,20.5,,-1.3002897,36.76016370000001,-1.3353385,36.7778749,Rider_Id_452
-Order_No_28145,User_Id_2801,Bike,3,Business,14,1,1:33:25 PM,14,1,1:34:11 PM,14,1,2:05:18 PM,14,1,2:06:25 PM,7,25.5,,-1.2879652,36.8203441,-1.2595421999999998,36.7871178,Rider_Id_432
-Order_No_21912,User_Id_295,Bike,3,Business,8,5,11:46:06 AM,8,5,12:08:12 PM,8,5,12:18:12 PM,8,5,12:23:26 PM,8,28.5,,-1.2967108,36.787113500000004,-1.261307,36.809549,Rider_Id_583
-Order_No_4238,User_Id_749,Bike,1,Personal,2,1,10:55:39 AM,2,1,10:56:59 AM,2,1,10:58:55 AM,2,1,11:05:51 AM,5,19.1,,-1.296613,36.819823,-1.2807268,36.786665899999996,Rider_Id_726
-Order_No_1401,User_Id_3194,Bike,3,Business,26,2,3:08:56 PM,26,2,3:16:14 PM,26,2,3:27:37 PM,26,2,3:29:41 PM,6,30.9,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_85
-Order_No_2267,User_Id_1345,Bike,2,Personal,7,2,10:20:04 AM,7,2,10:20:18 AM,7,2,10:24:47 AM,7,2,10:27:55 AM,15,16.7,,-1.311943,36.833113399999995,-1.225233,36.796890999999995,Rider_Id_16
-Order_No_418,User_Id_1792,Bike,3,Business,19,1,10:09:19 AM,19,1,10:10:10 AM,19,1,10:20:55 AM,19,1,10:23:41 AM,20,19.4,,-1.2809121,36.7852524,-1.3103985,36.9114173,Rider_Id_439
-Order_No_8906,User_Id_2358,Bike,4,Business,5,5,12:09:56 PM,5,5,12:11:44 PM,5,5,12:25:30 PM,5,5,12:49:23 PM,5,23.8,,-1.302222,36.75873,-1.2812549,36.7236622,Rider_Id_565
-Order_No_26452,User_Id_3303,Bike,3,Business,3,2,2:33:46 PM,3,2,2:37:18 PM,3,2,2:53:36 PM,3,2,2:56:29 PM,3,20.1,,-1.2934527,36.7755139,-1.271606,36.791095,Rider_Id_312
-Order_No_19599,User_Id_2143,Bike,3,Business,26,1,1:26:10 PM,26,1,1:26:34 PM,26,1,1:27:35 PM,26,1,1:31:42 PM,5,22.9,,-1.2975066000000002,36.76894229999999,-1.2945684,36.8154093,Rider_Id_829
-Order_No_1160,User_Id_733,Bike,3,Business,30,2,11:04:49 AM,30,2,11:10:04 AM,30,2,11:12:21 AM,30,2,11:24:07 AM,9,,,-1.2551895,36.7822034,-1.3031971000000002,36.786314600000004,Rider_Id_686
-Order_No_89,User_Id_393,Bike,3,Business,24,3,9:35:03 AM,24,3,10:13:07 AM,24,3,10:26:46 AM,24,3,10:37:44 AM,14,19.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_796
-Order_No_758,User_Id_2154,Bike,3,Business,11,2,12:51:58 PM,11,2,12:56:05 PM,11,2,1:14:07 PM,11,2,1:28:39 PM,12,19.3,,-1.303031,36.758577,-1.2765736,36.851364600000004,Rider_Id_412
-Order_No_25802,User_Id_302,Bike,3,Business,17,1,7:02:15 PM,17,1,7:11:55 PM,17,1,7:59:17 PM,17,1,8:02:39 PM,6,19.0,,-1.2649605,36.798177700000004,-1.2624806999999998,36.8368896,Rider_Id_178
-Order_No_27502,User_Id_3355,Bike,3,Business,7,5,4:48:03 PM,7,5,4:53:07 PM,7,5,5:44:18 PM,7,5,5:50:43 PM,4,23.0,,-1.290894,36.822971,-1.3052491000000002,36.8223899,Rider_Id_267
-Order_No_4081,User_Id_2536,Bike,3,Personal,2,6,11:15:15 AM,2,6,11:15:42 AM,2,6,11:37:46 AM,2,6,11:42:16 AM,6,24.0,,-1.2765492,36.7669809,-1.2705336,36.7943509,Rider_Id_215
-Order_No_4183,User_Id_3557,Bike,3,Business,25,1,3:14:55 PM,25,1,3:15:22 PM,25,1,3:18:41 PM,25,1,3:20:17 PM,5,30.3,,-1.2575406999999998,36.7868197,-1.2546732,36.808679600000005,Rider_Id_280
-Order_No_25353,User_Id_1920,Bike,3,Business,31,3,5:02:28 PM,31,3,5:02:44 PM,31,3,5:04:34 PM,31,3,5:07:49 PM,8,24.3,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_551
-Order_No_2928,User_Id_1882,Bike,3,Business,19,6,12:07:45 PM,19,6,12:08:37 PM,19,6,12:20:54 PM,19,6,12:24:03 PM,2,25.4,,-1.277018,36.820337,-1.2877809999999998,36.817865999999995,Rider_Id_360
-Order_No_12515,User_Id_224,Bike,3,Personal,9,4,2:46:01 PM,9,4,2:47:50 PM,9,4,2:49:21 PM,9,4,2:58:33 PM,5,,,-1.2886923000000001,36.8241519,-1.2991116000000003,36.7960658,Rider_Id_790
-Order_No_9109,User_Id_627,Bike,3,Business,3,3,3:53:46 PM,3,3,4:31:10 PM,3,3,4:36:33 PM,3,3,4:48:17 PM,18,23.6,,-1.2748998,36.886482,-1.2551895,36.7822034,Rider_Id_589
-Order_No_12050,User_Id_1075,Bike,3,Business,3,1,2:12:42 PM,3,1,2:35:47 PM,3,1,2:49:29 PM,3,1,3:30:47 PM,6,23.3,,-1.2886923000000001,36.8241519,-1.2527958000000001,36.8003131,Rider_Id_291
-Order_No_6270,User_Id_2573,Bike,1,Personal,26,3,11:12:00 AM,26,3,11:19:50 AM,26,3,11:24:45 AM,26,3,11:33:48 AM,6,23.8,,-1.2941992,36.790017,-1.273854,36.8003368,Rider_Id_722
-Order_No_18838,User_Id_2567,Bike,1,Personal,8,3,10:32:15 AM,8,3,10:32:40 AM,8,3,10:36:00 AM,8,3,10:40:45 AM,11,22.1,,-1.318165,36.828513,-1.2634125,36.778789,Rider_Id_449
-Order_No_9916,User_Id_3326,Bike,3,Business,12,2,2:37:33 PM,12,2,2:39:46 PM,12,2,2:58:26 PM,12,2,3:00:15 PM,7,26.8,,-1.2551895,36.7822034,-1.2862637,36.781301,Rider_Id_953
-Order_No_11910,User_Id_346,Bike,3,Business,24,4,2:46:41 PM,24,4,2:47:54 PM,24,4,2:55:18 PM,24,4,3:02:20 PM,2,28.5,,-1.28878,36.8168312,-1.2859116000000002,36.809803499999994,Rider_Id_819
-Order_No_4783,User_Id_2294,Bike,3,Business,16,2,12:41:29 PM,16,2,12:42:33 PM,16,2,1:15:55 PM,16,2,1:19:37 PM,11,,,-1.2977968999999998,36.8104085,-1.2788572,36.87943679999999,Rider_Id_831
-Order_No_6097,User_Id_2822,Bike,1,Personal,13,4,1:49:46 PM,13,4,2:25:16 PM,13,4,2:36:43 PM,13,4,2:44:09 PM,6,26.7,,-1.2863504,36.8277928,-1.2881479999999998,36.7850276,Rider_Id_713
-Order_No_21421,User_Id_265,Bike,3,Business,21,1,5:19:34 PM,21,1,5:19:49 PM,21,1,5:23:49 PM,21,1,5:34:59 PM,27,,,-1.300921,36.828195,-1.2500868,37.0109232,Rider_Id_116
-Order_No_3923,User_Id_3718,Bike,3,Business,17,1,3:05:08 PM,17,1,3:15:49 PM,17,1,3:23:41 PM,17,1,3:30:09 PM,3,24.6,,-1.2650027,36.8126242,-1.2726389999999999,36.794723,Rider_Id_125
-Order_No_2773,User_Id_2271,Bike,3,Business,2,3,12:30:49 PM,2,3,12:30:56 PM,2,3,12:31:02 PM,2,3,12:34:55 PM,12,21.8,,-1.318062,36.849326,-1.293515,36.897607,Rider_Id_408
-Order_No_14858,User_Id_510,Bike,3,Business,29,2,2:32:39 PM,29,2,2:32:51 PM,29,2,2:32:57 PM,29,2,2:37:48 PM,14,23.6,,-1.2860183,36.8975338,-1.3562374,36.904295399999995,Rider_Id_589
-Order_No_21741,User_Id_3222,Bike,3,Business,22,2,1:58:05 PM,22,2,1:59:17 PM,22,2,2:13:14 PM,22,2,2:20:02 PM,8,25.8,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_607
-Order_No_9344,User_Id_2423,Bike,3,Business,1,1,1:55:11 PM,1,1,1:57:12 PM,1,1,2:09:16 PM,1,1,2:24:49 PM,8,,,-1.2551895,36.7822034,-1.2950408000000002,36.809817200000005,Rider_Id_907
-Order_No_28247,User_Id_3677,Bike,1,Personal,11,5,10:51:14 AM,11,5,10:53:47 AM,11,5,11:12:13 AM,11,5,11:16:18 AM,6,20.9,,-1.2921983,36.7965431,-1.2717851,36.810536299999995,Rider_Id_782
-Order_No_17666,User_Id_2330,Bike,3,Business,25,5,3:06:16 PM,25,5,3:06:42 PM,25,5,3:24:31 PM,25,5,3:25:57 PM,7,29.4,,-1.2915246999999999,36.805575700000006,-1.2642903,36.80021370000001,Rider_Id_332
-Order_No_25494,User_Id_81,Bike,3,Business,28,3,9:54:33 AM,28,3,9:54:57 AM,28,3,9:59:44 AM,28,3,10:10:25 AM,4,20.6,,-1.2732506000000001,36.8132437,-1.2641496,36.791413899999995,Rider_Id_829
-Order_No_6644,User_Id_3451,Bike,1,Personal,16,3,1:29:42 PM,16,3,1:45:07 PM,16,3,2:05:24 PM,16,3,2:21:19 PM,15,25.5,,-1.3322116000000002,36.890378999999996,-1.276975,36.8231488,Rider_Id_777
-Order_No_6503,User_Id_1719,Bike,3,Business,29,1,4:44:33 PM,29,1,4:45:13 PM,29,1,4:53:36 PM,29,1,5:06:19 PM,14,23.4,,-1.3032946,36.8222045,-1.2330694,36.745451,Rider_Id_648
-Order_No_13772,User_Id_830,Bike,3,Business,6,4,12:43:16 PM,6,4,12:44:38 PM,6,4,12:52:52 PM,6,4,12:54:44 PM,9,19.8,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_770
-Order_No_27302,User_Id_3145,Bike,3,Business,27,2,11:38:09 AM,27,2,11:44:35 AM,27,2,11:54:29 AM,27,2,11:57:19 AM,7,23.9,,-1.2726389999999999,36.794723,-1.2981625,36.813014200000005,Rider_Id_726
-Order_No_14062,User_Id_3201,Bike,3,Business,21,5,12:15:07 PM,21,5,12:24:30 PM,21,5,12:39:47 PM,21,5,12:44:01 PM,9,22.6,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_847
-Order_No_24698,User_Id_2643,Bike,3,Personal,24,4,12:10:52 PM,24,4,12:11:00 PM,24,4,12:15:56 PM,24,4,12:17:08 PM,6,24.7,,-1.2844516000000001,36.8245932,-1.3008084999999998,36.8356717,Rider_Id_70
-Order_No_26171,User_Id_3426,Bike,3,Business,31,1,5:30:30 PM,31,1,5:31:13 PM,31,1,5:37:51 PM,31,1,5:44:23 PM,2,22.5,,-1.3012008,36.764868,-1.2881542,36.7616794,Rider_Id_952
-Order_No_24855,User_Id_3647,Bike,3,Business,30,2,2:46:42 PM,30,2,2:47:06 PM,30,2,2:57:19 PM,30,2,3:01:16 PM,4,27.3,,-1.265971,36.766806,-1.2726389999999999,36.794723,Rider_Id_727
-Order_No_9031,User_Id_3089,Bike,2,Personal,4,1,11:04:21 AM,4,1,11:05:05 AM,4,1,11:14:36 AM,4,1,11:19:17 AM,10,20.8,,-1.3102256,36.84323679999999,-1.2751858,36.820536499999996,Rider_Id_408
-Order_No_10331,User_Id_3635,Bike,1,Business,25,2,10:51:44 AM,25,2,10:52:06 AM,25,2,10:56:23 AM,25,2,11:11:33 AM,3,23.8,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_647
-Order_No_17091,User_Id_2142,Bike,2,Personal,23,3,3:21:15 PM,23,3,3:33:33 PM,23,3,3:44:15 PM,23,3,3:47:19 PM,3,22.1,,-1.2882411,36.827495500000005,-1.3047132,36.808955299999994,Rider_Id_948
-Order_No_13442,User_Id_3599,Bike,3,Business,1,1,12:01:30 PM,1,1,12:04:06 PM,1,1,12:14:10 PM,1,1,12:38:09 PM,13,23.8,,-1.3302996,36.8707935,-1.2735236,36.7992196,Rider_Id_519
-Order_No_17339,User_Id_3404,Bike,3,Business,2,3,2:24:04 PM,2,3,2:24:32 PM,2,3,2:27:47 PM,2,3,2:32:26 PM,5,25.9,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_134
-Order_No_4957,User_Id_3517,Bike,3,Business,21,3,11:03:46 AM,21,3,11:07:23 AM,21,3,11:17:26 AM,21,3,11:57:12 AM,15,21.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_715
-Order_No_15067,User_Id_265,Bike,3,Business,12,4,12:19:32 PM,12,4,12:19:44 PM,12,4,12:19:54 PM,12,4,12:35:27 PM,10,18.7,,-1.300921,36.828195,-1.2911739,36.7614223,Rider_Id_806
-Order_No_133,User_Id_562,Bike,3,Business,12,5,12:03:09 PM,12,5,12:25:38 PM,12,5,1:29:03 PM,12,5,1:31:55 PM,8,26.3,,-1.3227181000000001,36.8528097,-1.2811695,36.8200313,Rider_Id_155
-Order_No_10160,User_Id_1271,Bike,3,Business,12,2,11:05:44 AM,12,2,11:15:54 AM,12,2,11:36:52 AM,12,2,11:48:26 AM,23,24.7,,-1.3107918,36.761794,-1.3244885,36.897791999999995,Rider_Id_240
-Order_No_8486,User_Id_2047,Bike,1,Personal,18,4,2:34:53 PM,18,4,2:53:53 PM,18,4,3:10:49 PM,18,4,3:14:22 PM,7,26.8,,-1.3107959999999999,36.862829999999995,-1.3044313,36.818504,Rider_Id_365
-Order_No_26209,User_Id_3647,Bike,3,Business,14,4,4:44:16 PM,14,4,4:46:59 PM,14,4,4:56:17 PM,14,4,5:00:24 PM,4,26.2,,-1.2726389999999999,36.794723,-1.2886303,36.8026947,Rider_Id_953
-Order_No_23658,User_Id_2588,Bike,3,Personal,2,1,9:16:08 AM,2,1,9:16:43 AM,2,1,9:25:20 AM,2,1,9:36:14 AM,7,16.7,,-1.2607156000000002,36.814536700000005,-1.2983124,36.8104456,Rider_Id_103
-Order_No_13199,User_Id_1402,Bike,3,Business,14,5,3:32:25 PM,14,5,4:44:52 PM,14,5,4:56:39 PM,14,5,5:14:01 PM,1,23.0,,-1.290894,36.822971,-1.281754,36.8324229,Rider_Id_150
-Order_No_5433,User_Id_3688,Bike,3,Business,24,1,11:52:40 AM,24,1,11:53:24 AM,24,1,12:00:27 PM,24,1,12:10:24 PM,5,22.9,,-1.2551895,36.7822034,-1.2684319,36.81658529999999,Rider_Id_260
-Order_No_1466,User_Id_1838,Bike,3,Business,14,2,1:53:48 PM,14,2,1:54:23 PM,14,2,2:02:23 PM,14,2,2:07:18 PM,4,,,-1.2649605,36.798177700000004,-1.2899256000000001,36.815551,Rider_Id_341
-Order_No_18822,User_Id_3647,Bike,3,Business,3,4,3:31:42 PM,3,4,3:37:02 PM,3,4,3:50:38 PM,3,4,3:53:03 PM,9,24.9,,-1.2726389999999999,36.794723,-1.2930128,36.8356869,Rider_Id_56
-Order_No_15325,User_Id_785,Bike,1,Personal,10,4,12:36:04 PM,10,4,12:36:43 PM,10,4,12:51:35 PM,10,4,12:55:36 PM,3,,,-1.2672588,36.8000252,-1.2592428,36.7894541,Rider_Id_409
-Order_No_9349,User_Id_1648,Bike,3,Personal,6,3,2:24:48 PM,6,3,2:41:42 PM,6,3,3:30:20 PM,6,3,3:31:32 PM,32,27.7,,-1.3976138,36.782965999999995,-1.2096572,36.8049574,Rider_Id_883
-Order_No_11550,User_Id_53,Bike,3,Business,7,4,1:20:33 PM,7,4,1:22:23 PM,7,4,2:20:56 PM,7,4,2:27:55 PM,8,31.2,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_349
-Order_No_27875,User_Id_1280,Bike,3,Business,11,1,8:09:15 AM,11,1,8:12:26 AM,11,1,8:20:28 AM,11,1,8:25:41 AM,8,17.8,,-1.2551895,36.7822034,-1.2993556,36.8146776,Rider_Id_854
-Order_No_28133,User_Id_119,Bike,1,Personal,3,4,2:08:35 PM,3,4,2:08:59 PM,3,4,2:46:50 PM,3,4,2:58:24 PM,19,24.8,,-1.278027,36.909125,-1.2559559999999999,36.789867,Rider_Id_900
-Order_No_6495,User_Id_1500,Bike,3,Business,28,4,1:43:30 PM,28,4,1:43:52 PM,28,4,1:43:59 PM,28,4,2:10:19 PM,8,27.5,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_41
-Order_No_20147,User_Id_3001,Bike,3,Business,29,3,2:04:00 PM,29,3,2:10:37 PM,29,3,2:14:40 PM,29,3,2:19:44 PM,3,19.1,,-1.2846195,36.7958316,-1.2922688999999998,36.7760763,Rider_Id_670
-Order_No_27317,User_Id_867,Bike,3,Business,3,5,9:15:27 AM,3,5,9:19:13 AM,3,5,9:28:33 AM,3,5,9:36:49 AM,9,,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_783
-Order_No_19591,User_Id_375,Bike,3,Business,6,3,5:35:53 PM,6,3,5:36:01 PM,6,3,5:36:12 PM,6,3,5:40:00 PM,3,26.0,,-1.2551895,36.7822034,-1.2597367,36.7848482,Rider_Id_445
-Order_No_4344,User_Id_2756,Bike,3,Business,13,6,12:36:45 PM,13,6,12:37:56 PM,13,6,12:43:29 PM,13,6,12:54:29 PM,8,25.8,,-1.3266035,36.841236200000004,-1.2814609,36.826593200000005,Rider_Id_291
-Order_No_11337,User_Id_478,Bike,3,Business,8,5,12:39:45 PM,8,5,12:49:27 PM,8,5,1:41:20 PM,8,5,1:43:16 PM,8,27.1,,-1.2551895,36.7822034,-1.29752,36.793808299999995,Rider_Id_928
-Order_No_886,User_Id_3355,Bike,3,Business,8,3,5:15:13 PM,8,3,5:42:58 PM,8,3,5:53:27 PM,8,3,6:23:28 PM,9,,,-1.2879041000000002,36.8217926,-1.2859912,36.8756811,Rider_Id_61
-Order_No_26028,User_Id_1133,Bike,3,Business,14,5,12:58:25 PM,14,5,1:02:54 PM,14,5,1:05:36 PM,14,5,1:08:14 PM,4,24.5,,-1.2738936,36.8035805,-1.2571472,36.795063299999995,Rider_Id_85
-Order_No_5975,User_Id_2330,Bike,3,Business,23,4,3:13:16 PM,23,4,3:14:42 PM,23,4,3:30:03 PM,23,4,3:34:55 PM,9,24.4,,-1.2687407,36.7373043,-1.3015197,36.7658456,Rider_Id_240
-Order_No_7409,User_Id_1340,Bike,3,Business,17,2,1:56:43 PM,17,2,1:56:55 PM,17,2,2:16:37 PM,17,2,2:20:22 PM,16,24.4,,-1.2602386,36.7503491,-1.2644776000000002,36.870655,Rider_Id_74
-Order_No_11453,User_Id_2676,Bike,3,Business,29,2,11:33:15 AM,29,2,11:33:54 AM,29,2,11:57:55 AM,29,2,12:31:04 PM,2,21.5,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_890
-Order_No_3461,User_Id_811,Bike,2,Personal,23,1,11:00:29 AM,23,1,11:01:46 AM,23,1,11:10:25 AM,23,1,11:17:09 AM,7,18.7,,-1.2963097,36.7688221,-1.2864673999999998,36.820262,Rider_Id_229
-Order_No_5337,User_Id_2523,Bike,3,Business,13,4,3:38:57 PM,13,4,3:42:03 PM,13,4,3:51:14 PM,13,4,3:52:20 PM,9,23.1,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_409
-Order_No_17228,User_Id_972,Bike,3,Business,4,2,10:34:45 AM,4,2,11:03:50 AM,4,2,11:24:21 AM,4,2,11:25:28 AM,4,19.5,,-1.2862033000000002,36.8152966,-1.3084722,36.8224665,Rider_Id_88
-Order_No_19249,User_Id_107,Bike,1,Personal,6,1,3:20:20 PM,6,1,3:21:46 PM,6,1,3:37:44 PM,6,1,3:40:09 PM,9,24.7,,-1.2927600000000001,36.783799,-1.3134523999999999,36.8064495,Rider_Id_722
-Order_No_4763,User_Id_1363,Bike,3,Business,29,4,2:47:27 PM,29,4,2:48:45 PM,29,4,3:02:41 PM,29,4,3:08:50 PM,10,24.9,,-1.3004062,36.829741,-1.2592947,36.7698085,Rider_Id_279
-Order_No_1841,User_Id_496,Bike,3,Business,15,5,12:50:47 PM,15,5,12:51:12 PM,15,5,12:52:54 PM,15,5,1:01:22 PM,13,26.0,,-1.2551895,36.7822034,-1.3024388,36.859699,Rider_Id_926
-Order_No_8119,User_Id_1363,Bike,3,Business,13,3,11:20:33 AM,13,3,11:47:58 AM,13,3,12:03:28 PM,13,3,12:06:34 PM,3,24.6,,-1.3004062,36.829741,-1.2897368,36.8226783,Rider_Id_928
-Order_No_3370,User_Id_1724,Bike,3,Business,23,2,12:36:17 PM,23,2,12:36:57 PM,23,2,12:37:43 PM,23,2,12:44:06 PM,4,,,-1.2551895,36.7822034,-1.2728058,36.8049903,Rider_Id_422
-Order_No_874,User_Id_2052,Bike,2,Personal,27,4,4:11:51 PM,27,4,4:12:08 PM,27,4,4:26:55 PM,27,4,4:38:04 PM,9,23.2,,-1.3127149999999999,36.8077464,-1.2657464,36.780114399999995,Rider_Id_612
-Order_No_1290,User_Id_517,Bike,3,Business,30,3,3:25:12 PM,30,3,3:25:19 PM,30,3,3:27:59 PM,30,3,3:36:09 PM,4,29.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523
-Order_No_6188,User_Id_3727,Bike,3,Business,20,1,4:25:29 PM,20,1,4:27:28 PM,20,1,5:04:31 PM,20,1,5:07:07 PM,6,,,-1.2963097,36.7688221,-1.2825816,36.8092346,Rider_Id_277
-Order_No_17478,User_Id_3175,Bike,3,Business,26,5,9:27:23 AM,26,5,9:32:24 AM,26,5,9:56:13 AM,26,5,10:04:32 AM,7,,,-1.2681003999999998,36.7952081,-1.2735999,36.8218131,Rider_Id_171
-Order_No_3053,User_Id_700,Bike,3,Business,8,4,1:55:24 PM,8,4,2:45:04 PM,8,4,2:52:38 PM,8,4,3:00:01 PM,7,27.1,,-1.3134312,36.7220484,-1.3307406000000002,36.7533,Rider_Id_470
-Order_No_3696,User_Id_1487,Bike,1,Personal,1,1,12:16:17 PM,1,1,1:51:36 PM,1,1,1:54:52 PM,1,1,2:00:27 PM,3,25.8,,-1.2658334999999998,36.804286700000006,-1.2866011999999998,36.8195691,Rider_Id_857
-Order_No_2049,User_Id_2828,Bike,3,Business,22,5,9:21:16 AM,22,5,9:44:08 AM,22,5,10:03:57 AM,22,5,10:12:34 AM,13,,,-1.2615888000000002,36.7928732,-1.3263105,36.8622904,Rider_Id_772
-Order_No_12998,User_Id_2212,Bike,3,Business,11,2,11:49:53 AM,11,2,11:50:25 AM,11,2,11:57:15 AM,11,2,12:02:09 PM,2,19.4,,-1.2571472,36.795063299999995,-1.2682478000000001,36.8072852,Rider_Id_770
-Order_No_15081,User_Id_2048,Bike,3,Personal,21,5,7:26:58 AM,21,5,7:27:05 AM,21,5,7:40:34 AM,21,5,7:41:53 AM,18,19.8,,-1.2850643999999998,36.8343657,-1.2559913999999999,36.689566,Rider_Id_235
-Order_No_3072,User_Id_970,Bike,3,Business,7,3,1:18:10 PM,7,3,1:24:00 PM,7,3,1:34:02 PM,7,3,1:40:08 PM,9,28.2,,-1.2551895,36.7822034,-1.306431,36.812042,Rider_Id_952
-Order_No_19631,User_Id_3355,Bike,3,Business,15,3,12:54:34 PM,15,3,12:54:46 PM,15,3,1:07:04 PM,15,3,1:18:48 PM,4,18.3,,-1.291768,36.8297208,-1.3052491000000002,36.8223899,Rider_Id_234
-Order_No_3302,User_Id_2532,Bike,3,Business,31,3,2:29:54 PM,31,3,2:37:24 PM,31,3,2:45:16 PM,31,3,3:01:32 PM,6,27.1,,-1.2551895,36.7822034,-1.281606,36.8150086,Rider_Id_237
-Order_No_19190,User_Id_2330,Bike,3,Business,12,2,9:26:42 AM,12,2,9:27:09 AM,12,2,9:29:50 AM,12,2,9:35:31 AM,9,20.9,,-1.2649605,36.798177700000004,-1.3015197,36.7658456,Rider_Id_953
-Order_No_2427,User_Id_2344,Bike,3,Business,11,1,12:21:34 PM,11,1,12:24:43 PM,11,1,12:25:53 PM,11,1,12:41:26 PM,23,18.1,,-1.2551895,36.7822034,-1.3447079,36.91105,Rider_Id_379
-Order_No_262,User_Id_2200,Bike,3,Business,29,5,9:55:28 AM,29,5,9:57:49 AM,29,5,10:27:46 AM,29,5,11:11:29 AM,12,26.7,,-1.233018,36.8004477,-1.290894,36.822971,Rider_Id_177
-Order_No_7027,User_Id_3064,Bike,3,Business,26,5,2:19:13 PM,26,5,2:22:19 PM,26,5,2:50:49 PM,26,5,2:57:32 PM,7,,,-1.2571472,36.795063299999995,-1.3025299,36.7901963,Rider_Id_775
-Order_No_5699,User_Id_242,Bike,3,Business,28,4,3:37:15 PM,28,4,3:37:28 PM,28,4,3:52:54 PM,28,4,4:01:41 PM,17,,,-1.2551895,36.7822034,-1.3286928,36.8925243,Rider_Id_808
-Order_No_4289,User_Id_53,Bike,3,Business,4,2,1:57:21 PM,4,2,2:08:47 PM,4,2,2:36:20 PM,4,2,3:10:27 PM,5,22.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_42
-Order_No_4736,User_Id_2869,Bike,3,Business,11,4,9:31:58 AM,11,4,9:32:24 AM,11,4,9:33:43 AM,11,4,10:17:39 AM,9,22.2,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_61
-Order_No_13487,User_Id_2031,Bike,1,Personal,9,4,11:51:18 AM,9,4,11:52:54 AM,9,4,12:01:50 PM,9,4,12:10:59 PM,11,23.8,,-1.2802151000000002,36.8231386,-1.2317292,36.796820000000004,Rider_Id_865
-Order_No_8384,User_Id_1926,Bike,3,Business,2,5,1:29:27 PM,2,5,1:30:25 PM,2,5,1:35:32 PM,2,5,1:37:56 PM,14,25.0,,-1.3332749,36.870814700000004,-1.2959018,36.7948256,Rider_Id_807
-Order_No_23393,User_Id_1532,Bike,3,Personal,8,2,6:18:50 PM,8,2,6:19:35 PM,8,2,6:40:40 PM,8,2,6:41:42 PM,8,23.5,,-1.3105877000000001,36.817283,-1.2536041999999998,36.806489500000005,Rider_Id_235
-Order_No_22758,User_Id_393,Bike,3,Business,8,5,12:08:53 PM,8,5,12:11:06 PM,8,5,12:25:02 PM,8,5,12:27:15 PM,8,28.4,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_349
-Order_No_10903,User_Id_3718,Bike,3,Business,9,5,12:26:38 PM,9,5,12:38:16 PM,9,5,12:51:14 PM,9,5,12:53:18 PM,8,25.5,,-1.2650027,36.8126242,-1.2973168,36.7760143,Rider_Id_512
-Order_No_7581,User_Id_3149,Bike,3,Business,14,3,2:37:17 PM,14,3,2:45:12 PM,14,3,2:49:53 PM,14,3,3:30:01 PM,6,25.9,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_744
-Order_No_4820,User_Id_830,Bike,3,Business,26,5,4:51:04 PM,26,5,4:51:21 PM,26,5,5:16:09 PM,26,5,5:23:25 PM,17,,,-1.2551895,36.7822034,-1.2859912,36.8756811,Rider_Id_840
-Order_No_25222,User_Id_246,Bike,3,Business,13,3,2:27:26 PM,13,3,2:31:16 PM,13,3,3:06:08 PM,13,3,3:55:24 PM,6,23.5,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_431
-Order_No_4466,User_Id_467,Bike,3,Business,9,4,1:53:14 PM,9,4,1:55:14 PM,9,4,2:07:24 PM,9,4,2:12:19 PM,11,,,-1.304735,36.784605400000004,-1.3507171999999998,36.741369399999996,Rider_Id_343
-Order_No_12841,User_Id_2066,Bike,3,Business,24,5,3:54:50 PM,24,5,3:55:08 PM,24,5,3:56:55 PM,24,5,3:58:57 PM,9,23.0,,-1.3210188999999999,36.841055100000005,-1.2846195,36.7958316,Rider_Id_233
-Order_No_21549,User_Id_2205,Bike,2,Personal,18,4,12:59:28 PM,18,4,1:36:21 PM,18,4,1:36:50 PM,18,4,1:57:38 PM,6,26.6,,-1.2697858999999998,36.7946203,-1.2836183,36.8297208,Rider_Id_531
-Order_No_13243,User_Id_1244,Bike,3,Business,8,2,12:24:46 PM,8,2,12:25:04 PM,8,2,12:29:44 PM,8,2,12:44:47 PM,8,23.5,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_829
-Order_No_8929,User_Id_857,Bike,1,Personal,25,6,4:31:54 PM,25,6,4:32:05 PM,25,6,4:41:03 PM,25,6,5:23:50 PM,19,,,-1.2850643999999998,36.8343657,-1.1986098,36.949194299999995,Rider_Id_648
-Order_No_2243,User_Id_393,Bike,3,Business,22,5,2:04:25 PM,22,5,2:04:33 PM,22,5,2:05:00 PM,22,5,2:33:27 PM,11,27.5,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_10
-Order_No_23511,User_Id_2763,Bike,3,Business,18,3,12:07:23 PM,18,3,12:08:18 PM,18,3,12:17:14 PM,18,3,12:21:39 PM,5,19.9,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_415
-Order_No_25718,User_Id_1085,Bike,3,Business,29,1,11:51:30 AM,29,1,12:07:49 PM,29,1,1:03:53 PM,29,1,1:17:12 PM,25,,,-1.3869643,36.738795700000004,-1.2628638,36.8070399,Rider_Id_817
-Order_No_11151,User_Id_2618,Bike,1,Personal,17,5,5:24:30 PM,17,5,5:49:29 PM,17,5,6:25:15 PM,17,5,6:34:38 PM,15,19.0,,-1.2342433000000002,36.7317684,-1.2129211000000002,36.7963408,Rider_Id_144
-Order_No_5552,User_Id_625,Bike,3,Business,18,6,1:01:10 PM,18,6,1:01:32 PM,18,6,1:08:02 PM,18,6,1:20:57 PM,5,18.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_882
-Order_No_9555,User_Id_2537,Bike,2,Personal,6,6,8:49:59 AM,6,6,9:05:07 AM,6,6,9:54:54 AM,6,6,10:02:29 AM,30,18.9,,-1.3779324,36.6868613,-1.2664227,36.7453671,Rider_Id_565
-Order_No_22023,User_Id_2258,Bike,3,Business,8,2,2:49:40 PM,8,2,2:51:03 PM,8,2,2:58:28 PM,8,2,3:02:31 PM,6,25.5,,-1.2571472,36.795063299999995,-1.3011768,36.818364,Rider_Id_671
-Order_No_15800,User_Id_20,Bike,3,Business,7,4,12:35:09 PM,7,4,12:41:17 PM,7,4,12:52:39 PM,7,4,1:02:02 PM,15,27.8,,-1.2628638,36.8070399,-1.3324865,36.8938303,Rider_Id_267
-Order_No_13530,User_Id_3560,Bike,3,Business,9,3,1:01:53 PM,9,3,1:04:26 PM,9,3,1:32:36 PM,9,3,1:35:23 PM,9,25.5,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_436
-Order_No_215,User_Id_1245,Bike,3,Business,19,6,10:21:59 AM,19,6,10:22:32 AM,19,6,10:31:05 AM,19,6,10:53:08 AM,19,25.2,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_130
-Order_No_2562,User_Id_3669,Bike,3,Business,17,4,10:52:39 AM,17,4,10:53:42 AM,17,4,11:00:36 AM,17,4,11:20:21 AM,3,25.2,,-1.25373,36.787929999999996,-1.2615888000000002,36.7928732,Rider_Id_903
-Order_No_26283,User_Id_2763,Bike,3,Business,14,2,9:54:04 AM,14,2,9:54:25 AM,14,2,9:54:41 AM,14,2,10:01:27 AM,12,,,-1.2551895,36.7822034,-1.3200694,36.793130600000005,Rider_Id_99
-Order_No_24312,User_Id_3498,Bike,2,Personal,5,3,1:52:10 PM,5,3,1:52:50 PM,5,3,2:34:36 PM,5,3,2:44:32 PM,5,23.7,,-1.2753902,36.808223,-1.2551895,36.7822034,Rider_Id_795
-Order_No_26587,User_Id_186,Bike,3,Business,18,4,1:48:23 PM,18,4,1:56:08 PM,18,4,2:15:12 PM,18,4,2:19:22 PM,23,27.0,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_612
-Order_No_7705,User_Id_635,Bike,3,Business,25,6,10:49:15 AM,25,6,11:03:53 AM,25,6,11:19:08 AM,25,6,11:27:30 AM,13,,,-1.2602337,36.79905479999999,-1.3231725,36.864017700000005,Rider_Id_617
-Order_No_12124,User_Id_916,Bike,3,Business,18,6,8:53:46 AM,18,6,8:53:57 AM,18,6,9:00:35 AM,18,6,9:25:12 AM,10,15.6,,-1.301259,36.842244,-1.3321944,36.889232899999996,Rider_Id_857
-Order_No_4747,User_Id_258,Bike,2,Personal,17,3,1:19:59 PM,17,3,1:35:30 PM,17,3,1:43:46 PM,17,3,2:03:38 PM,39,28.3,,-1.4289319999999999,36.963932,-1.3192808,36.7121888,Rider_Id_692
-Order_No_27541,User_Id_2900,Bike,3,Business,21,4,11:44:14 AM,21,4,11:44:47 AM,21,4,11:58:32 AM,21,4,12:02:28 PM,9,25.2,,-1.2615888000000002,36.7928732,-1.2963097,36.7688221,Rider_Id_326
-Order_No_4934,User_Id_496,Bike,3,Business,20,4,2:31:30 PM,20,4,2:32:00 PM,20,4,2:38:43 PM,20,4,2:51:28 PM,7,27.8,,-1.2551895,36.7822034,-1.2853938,36.8095554,Rider_Id_848
-Order_No_9079,User_Id_238,Bike,3,Business,18,1,1:07:42 PM,18,1,1:08:42 PM,18,1,1:18:10 PM,18,1,1:39:33 PM,15,29.6,,-1.2991441000000001,36.7528804,-1.3077869,36.844320700000004,Rider_Id_831
-Order_No_26251,User_Id_3599,Bike,3,Business,25,6,11:09:21 AM,25,6,11:10:28 AM,25,6,11:44:27 AM,25,6,11:47:25 AM,13,,,-1.3302996,36.8707935,-1.2713370000000002,36.786712,Rider_Id_459
-Order_No_2272,User_Id_3710,Bike,3,Business,30,5,5:10:39 PM,30,5,5:18:37 PM,30,5,5:43:28 PM,30,5,5:46:50 PM,20,22.1,,-1.2551895,36.7822034,-1.3106315,36.9012422,Rider_Id_383
-Order_No_11851,User_Id_633,Bike,3,Business,1,4,11:27:09 AM,1,4,11:27:29 AM,1,4,11:31:39 AM,1,4,11:37:06 AM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_518
-Order_No_20956,User_Id_3647,Bike,3,Business,5,5,11:10:55 AM,5,5,11:11:39 AM,5,5,11:24:32 AM,5,5,11:28:12 AM,5,24.0,,-1.2726389999999999,36.794723,-1.2885417,36.810052299999995,Rider_Id_607
-Order_No_13547,User_Id_1075,Bike,3,Business,24,5,10:36:09 AM,24,5,10:48:37 AM,24,5,11:14:44 AM,24,5,11:33:11 AM,20,21.4,,-1.3562374,36.904295399999995,-1.2527958000000001,36.8003131,Rider_Id_379
-Order_No_6237,User_Id_2310,Bike,3,Business,29,2,3:12:44 PM,29,2,3:12:51 PM,29,2,3:30:02 PM,29,2,3:37:24 PM,14,23.6,,-1.2591018999999999,36.800577000000004,-1.2859912,36.8756811,Rider_Id_755
-Order_No_1102,User_Id_2008,Bike,3,Business,8,1,3:39:28 PM,8,1,3:43:20 PM,8,1,4:00:44 PM,8,1,4:06:05 PM,8,29.8,,-1.2508229,36.7895263,-1.2935303,36.7988142,Rider_Id_660
-Order_No_4356,User_Id_60,Bike,3,Business,28,4,9:31:23 AM,28,4,9:31:52 AM,28,4,9:32:15 AM,28,4,9:59:16 AM,8,19.3,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_900
-Order_No_21002,User_Id_1569,Bike,2,Personal,19,7,1:38:46 PM,19,7,1:38:57 PM,19,7,1:56:41 PM,19,7,2:08:36 PM,17,21.2,,-1.2724556,36.7931938,-1.1934853,36.84489310000001,Rider_Id_727
-Order_No_27232,User_Id_2383,Bike,3,Business,29,3,12:16:40 PM,29,3,12:17:56 PM,29,3,12:35:48 PM,29,3,12:40:04 PM,3,,,-1.2608993,36.799246000000004,-1.2633493999999998,36.7959696,Rider_Id_344
-Order_No_7798,User_Id_346,Bike,3,Business,3,2,3:16:25 PM,3,2,3:22:45 PM,3,2,3:36:35 PM,3,2,3:56:45 PM,2,20.2,,-1.2951620000000001,36.8115914,-1.2885528999999998,36.816014100000004,Rider_Id_890
-Order_No_10991,User_Id_3064,Bike,3,Business,22,5,10:22:00 AM,22,5,10:22:38 AM,22,5,10:48:59 AM,22,5,10:59:00 AM,4,,,-1.2571472,36.795063299999995,-1.2519787,36.780649100000005,Rider_Id_227
-Order_No_13070,User_Id_2423,Bike,3,Business,21,4,2:29:56 PM,21,4,2:30:10 PM,21,4,2:47:37 PM,21,4,2:57:00 PM,3,26.3,,-1.2551895,36.7822034,-1.2642665000000002,36.778828600000004,Rider_Id_269
-Order_No_27168,User_Id_1687,Bike,3,Business,30,3,1:33:16 PM,30,3,1:34:29 PM,30,3,1:38:03 PM,30,3,1:39:44 PM,7,29.0,,-1.28164,36.816866999999995,-1.3197323,36.8120386,Rider_Id_85
-Order_No_14721,User_Id_2174,Bike,3,Business,14,4,10:19:34 AM,14,4,10:19:52 AM,14,4,10:26:41 AM,14,4,10:42:03 AM,8,24.8,,-1.2482781,36.884091999999995,-1.2845996000000002,36.9018814,Rider_Id_307
-Order_No_22576,User_Id_3326,Bike,3,Business,26,2,11:33:12 AM,26,2,11:33:22 AM,26,2,11:36:43 AM,26,2,11:41:36 AM,4,27.6,,-1.2551895,36.7822034,-1.2585521000000002,36.7945765,Rider_Id_709
-Order_No_14005,User_Id_2457,Bike,3,Business,13,1,12:45:14 PM,13,1,12:45:29 PM,13,1,12:46:56 PM,13,1,12:52:00 PM,19,,,-1.2551895,36.7822034,-1.32943,36.894242999999996,Rider_Id_25
-Order_No_7888,User_Id_2030,Bike,3,Business,23,2,9:49:00 AM,23,2,9:57:54 AM,23,2,10:16:08 AM,23,2,10:38:31 AM,2,,,-1.3047482,36.784967200000004,-1.2963097,36.7688221,Rider_Id_933
-Order_No_9650,User_Id_1792,Bike,3,Business,21,1,11:48:28 AM,21,1,11:51:00 AM,21,1,12:11:20 PM,21,1,12:14:28 PM,6,25.5,,-1.2809121,36.7852524,-1.2791983,36.8237495,Rider_Id_332
-Order_No_19130,User_Id_868,Bike,3,Business,1,3,11:38:34 AM,1,3,11:54:07 AM,1,3,12:01:26 PM,1,3,12:25:01 PM,9,20.0,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_900
-Order_No_17301,User_Id_2987,Bike,3,Business,30,4,10:31:38 AM,30,4,10:32:13 AM,30,4,10:39:03 AM,30,4,10:43:30 AM,11,20.4,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_116
-Order_No_15838,User_Id_2216,Bike,3,Business,11,5,1:48:15 PM,11,5,1:48:57 PM,11,5,1:59:40 PM,11,5,2:02:19 PM,8,26.3,,-1.2551895,36.7822034,-1.2933017,36.8008304,Rider_Id_660
-Order_No_18121,User_Id_1081,Bike,3,Business,13,3,10:58:53 AM,13,3,10:59:32 AM,13,3,10:59:44 AM,13,3,11:35:57 AM,10,25.0,,-1.2551895,36.7822034,-1.2967108,36.787113500000004,Rider_Id_576
-Order_No_11548,User_Id_2154,Bike,3,Business,16,2,1:05:20 PM,16,2,1:09:38 PM,16,2,1:16:56 PM,16,2,1:18:46 PM,8,,,-1.2584143,36.8048002,-1.2765736,36.851364600000004,Rider_Id_933
-Order_No_7659,User_Id_3404,Bike,3,Business,6,4,10:40:21 AM,6,4,11:14:13 AM,6,4,11:23:14 AM,6,4,11:25:00 AM,4,24.0,,-1.279395,36.825364,-1.2565836000000001,36.812398200000004,Rider_Id_878
-Order_No_28195,User_Id_2974,Bike,3,Business,11,2,9:12:57 AM,11,2,9:14:24 AM,11,2,9:47:34 AM,11,2,9:50:20 AM,13,16.1,42.9,-1.287428,36.7800821,-1.3227037,36.847087200000004,Rider_Id_671
-Order_No_21326,User_Id_1340,Bike,3,Business,19,4,11:00:21 AM,19,4,11:00:51 AM,19,4,11:09:27 AM,19,4,11:19:40 AM,15,20.0,,-1.3301121,36.89331,-1.2707178999999997,36.7979713,Rider_Id_670
-Order_No_19992,User_Id_2787,Bike,1,Personal,24,4,1:58:55 PM,24,4,2:00:40 PM,24,4,2:39:15 PM,24,4,2:42:31 PM,6,22.8,,-1.2797311000000002,36.821852,-1.2553615,36.789537599999996,Rider_Id_844
-Order_No_4744,User_Id_190,Bike,3,Business,3,1,12:39:59 PM,3,1,12:44:58 PM,3,1,1:21:21 PM,3,1,1:32:32 PM,5,23.3,,-1.3270347,36.866736200000005,-1.3390024,36.8746657,Rider_Id_126
-Order_No_592,User_Id_778,Bike,3,Business,26,2,1:32:22 PM,26,2,1:32:37 PM,26,2,1:47:36 PM,26,2,2:04:43 PM,10,29.5,,-1.304127,36.805743,-1.2535486,36.7933541,Rider_Id_322
-Order_No_14633,User_Id_2982,Bike,3,Business,10,1,1:00:36 PM,10,1,1:08:50 PM,10,1,1:34:11 PM,10,1,1:58:23 PM,18,18.9,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_631
-Order_No_18952,User_Id_635,Bike,3,Business,21,2,9:46:54 AM,21,2,9:47:22 AM,21,2,9:47:30 AM,21,2,10:29:02 AM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_403
-Order_No_18309,User_Id_3149,Bike,3,Business,30,2,11:43:17 AM,30,2,11:43:54 AM,30,2,11:46:54 AM,30,2,11:53:43 AM,12,22.1,,-1.2574219,36.7927073,-1.3177546999999998,36.8303703,Rider_Id_551
-Order_No_14756,User_Id_552,Bike,3,Business,7,1,11:49:06 AM,7,1,11:50:20 AM,7,1,12:06:29 PM,7,1,12:09:28 PM,14,25.0,,-1.2852569,36.8976121,-1.3562374,36.904295399999995,Rider_Id_831
-Order_No_2444,User_Id_1850,Bike,3,Business,13,3,11:17:07 AM,13,3,11:18:28 AM,13,3,11:25:43 AM,13,3,11:27:51 AM,7,,,-1.2551895,36.7822034,-1.296613,36.819823,Rider_Id_674
-Order_No_23370,User_Id_3635,Bike,3,Business,1,4,4:05:53 PM,1,4,4:06:19 PM,1,4,4:08:36 PM,1,4,4:17:26 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_427
-Order_No_19326,User_Id_2611,Bike,3,Personal,16,2,4:10:39 PM,16,2,4:14:07 PM,16,2,4:15:26 PM,16,2,4:25:18 PM,6,27.3,,-1.3012475000000001,36.8295592,-1.3035983999999998,36.8035843,Rider_Id_89
-Order_No_28200,User_Id_1157,Bike,3,Business,4,5,10:59:00 AM,4,5,10:59:49 AM,4,5,11:22:23 AM,4,5,11:51:03 AM,8,22.7,,-1.3164474,36.857586100000006,-1.2817945,36.825805100000004,Rider_Id_285
-Order_No_19796,User_Id_867,Bike,3,Business,9,2,8:15:22 AM,9,2,8:16:12 AM,9,2,8:37:08 AM,9,2,8:52:45 AM,10,21.0,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_61
-Order_No_22110,User_Id_3647,Bike,3,Business,4,4,12:28:27 PM,4,4,12:28:56 PM,4,4,1:10:58 PM,4,4,1:14:21 PM,8,25.7,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_291
-Order_No_5136,User_Id_1740,Bike,3,Personal,28,4,3:55:47 PM,28,4,4:07:06 PM,28,4,4:32:43 PM,28,4,4:47:51 PM,12,,,-1.286837,36.823308000000004,-1.3385790000000002,36.89630379999999,Rider_Id_360
-Order_No_10336,User_Id_517,Bike,3,Business,30,3,10:12:00 AM,30,3,10:16:46 AM,30,3,10:31:54 AM,30,3,10:37:52 AM,12,24.9,,-1.3167113000000001,36.8301563,-1.2551895,36.7822034,Rider_Id_523
-Order_No_3158,User_Id_1584,Bike,3,Business,10,5,12:28:39 PM,10,5,12:34:50 PM,10,5,12:50:31 PM,10,5,1:01:37 PM,7,19.2,,-1.2767864,36.8213306,-1.2969741000000001,36.7856612,Rider_Id_88
-Order_No_24036,User_Id_2119,Bike,3,Personal,19,3,11:50:58 AM,19,3,12:02:08 PM,19,3,12:54:56 PM,19,3,12:56:24 PM,4,22.4,,-1.280572,36.826162,-1.2706041000000001,36.830314200000004,Rider_Id_456
-Order_No_15965,User_Id_2803,Bike,3,Business,5,5,9:11:21 AM,5,5,9:25:47 AM,5,5,9:35:14 AM,5,5,9:50:44 AM,3,17.7,,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_584
-Order_No_14369,User_Id_3239,Bike,3,Personal,10,4,10:02:13 AM,10,4,10:19:13 AM,10,4,10:38:01 AM,10,4,10:47:05 AM,8,,,-1.276154,36.822416499999996,-1.3104506,36.8687235,Rider_Id_618
-Order_No_16556,User_Id_2457,Bike,3,Business,26,5,12:32:57 PM,26,5,12:33:53 PM,26,5,12:47:09 PM,26,5,12:48:59 PM,3,,,-1.2551895,36.7822034,-1.2544973,36.8061844,Rider_Id_485
-Order_No_4200,User_Id_1346,Bike,3,Business,8,4,12:40:04 PM,8,4,12:53:12 PM,8,4,1:19:47 PM,8,4,1:23:04 PM,6,26.3,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_726
-Order_No_2102,User_Id_3222,Bike,3,Business,8,5,2:33:00 PM,8,5,2:34:20 PM,8,5,2:53:18 PM,8,5,3:02:22 PM,8,30.6,,-1.2551895,36.7822034,-1.2856664,36.755983799999996,Rider_Id_103
-Order_No_10775,User_Id_1271,Bike,3,Business,8,1,2:47:20 PM,8,1,2:47:57 PM,8,1,3:11:45 PM,8,1,3:30:52 PM,24,29.8,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_746
-Order_No_11973,User_Id_2898,Bike,1,Personal,21,2,1:08:27 PM,21,2,1:08:43 PM,21,2,1:31:17 PM,21,2,1:40:59 PM,4,21.1,,-1.2543163999999998,36.831860999999996,-1.2619538000000001,36.80876370000001,Rider_Id_400
-Order_No_27318,User_Id_3091,Bike,3,Business,28,5,12:59:18 PM,28,5,1:00:15 PM,28,5,1:11:41 PM,28,5,1:27:52 PM,8,25.3,,-1.2615888000000002,36.7928732,-1.3012008,36.764868,Rider_Id_770
-Order_No_2277,User_Id_1544,Bike,3,Business,10,1,11:40:11 AM,10,1,12:05:09 PM,10,1,12:19:33 PM,10,1,12:42:11 PM,7,23.4,,-1.2924639,36.77957979999999,-1.2885029,36.825604999999996,Rider_Id_853
-Order_No_18861,User_Id_635,Bike,3,Business,2,3,12:36:28 PM,2,3,12:41:25 PM,2,3,12:55:21 PM,2,3,1:04:06 PM,7,21.8,,-1.3007481,36.8341778,-1.2599563,36.799343900000004,Rider_Id_753
-Order_No_16524,User_Id_633,Bike,3,Business,5,2,4:43:12 PM,5,2,4:44:16 PM,5,2,4:44:23 PM,5,2,4:47:23 PM,9,25.2,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_311
-Order_No_24934,User_Id_604,Bike,3,Business,22,1,10:19:12 AM,22,1,10:25:22 AM,22,1,10:32:29 AM,22,1,10:35:42 AM,3,24.8,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_548
-Order_No_5566,User_Id_1461,Bike,2,Personal,3,5,8:46:27 AM,3,5,8:47:00 AM,3,5,8:55:24 AM,3,5,8:59:58 AM,13,17.3,,-1.2720768999999998,36.8180604,-1.3099584,36.732364399999994,Rider_Id_414
-Order_No_22428,User_Id_1500,Bike,3,Business,13,1,4:16:09 PM,13,1,4:30:00 PM,13,1,4:35:01 PM,13,1,4:36:37 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_381
-Order_No_8995,User_Id_989,Bike,2,Personal,7,5,4:21:18 PM,7,5,4:21:39 PM,7,5,4:30:14 PM,7,5,4:35:57 PM,14,22.0,,-1.2936372,36.7698422,-1.2950846,36.686068799999994,Rider_Id_644
-Order_No_16470,User_Id_909,Bike,3,Business,30,3,4:04:36 PM,30,3,4:04:50 PM,30,3,4:05:11 PM,30,3,4:12:59 PM,1,24.9,,-1.290894,36.822971,-1.2918290000000001,36.829358,Rider_Id_408
-Order_No_22962,User_Id_1754,Bike,3,Business,25,4,12:06:53 PM,25,4,12:07:09 PM,25,4,12:19:29 PM,25,4,12:24:11 PM,14,20.2,,-1.3562374,36.904295399999995,-1.2860183,36.8975338,Rider_Id_532
-Order_No_18229,User_Id_2139,Bike,3,Personal,10,3,7:14:16 PM,10,3,7:16:08 PM,10,3,7:31:03 PM,10,3,8:03:44 PM,9,24.3,,-1.2336611000000002,36.806181,-1.1926934999999999,36.7574155,Rider_Id_438
-Order_No_6245,User_Id_2463,Bike,2,Personal,13,6,9:30:25 AM,13,6,9:31:51 AM,13,6,9:51:31 AM,13,6,9:52:39 AM,9,,,-1.2433667,36.76094929999999,-1.2698167999999999,36.7421898,Rider_Id_38
-Order_No_13308,User_Id_1500,Bike,3,Business,16,4,9:30:21 AM,16,4,9:31:21 AM,16,4,9:31:37 AM,16,4,10:04:28 AM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_41
-Order_No_16119,User_Id_3752,Bike,3,Business,22,3,11:37:37 AM,22,3,11:39:08 AM,22,3,12:00:11 PM,22,3,12:28:29 PM,7,23.0,,-1.3379798999999999,36.883545299999994,-1.34364,36.8925345,Rider_Id_168
-Order_No_12554,User_Id_517,Bike,3,Business,22,4,10:22:21 AM,22,4,10:22:39 AM,22,4,10:22:42 AM,22,4,10:53:58 AM,14,22.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_515
-Order_No_14250,User_Id_2383,Bike,3,Business,24,5,8:35:19 AM,24,5,8:35:51 AM,24,5,8:38:36 AM,24,5,8:45:47 AM,2,18.3,,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_178
-Order_No_11087,User_Id_3295,Bike,3,Business,25,4,11:01:37 AM,25,4,11:02:42 AM,25,4,11:21:35 AM,25,4,11:38:27 AM,12,,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_435
-Order_No_12157,User_Id_1482,Bike,3,Business,15,4,9:48:55 AM,15,4,9:52:38 AM,15,4,10:07:53 AM,15,4,11:04:00 AM,8,24.3,,-1.3214137,36.8569475,-1.3385790000000002,36.89630379999999,Rider_Id_559
-Order_No_7575,User_Id_3283,Bike,3,Business,18,1,12:33:25 PM,18,1,12:35:54 PM,18,1,1:21:04 PM,18,1,1:32:45 PM,13,30.9,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_311
-Order_No_2090,User_Id_3634,Bike,2,Personal,11,2,5:09:57 PM,11,2,5:14:44 PM,11,2,5:26:16 PM,11,2,5:37:36 PM,14,23.5,,-1.3178263000000001,36.7141818,-1.2696341,36.791664000000004,Rider_Id_268
-Order_No_26719,User_Id_2330,Bike,3,Business,16,4,6:06:26 PM,16,4,6:06:52 PM,16,4,6:22:03 PM,16,4,6:37:26 PM,16,,,-1.2326041,36.8009029,-1.3015197,36.7658456,Rider_Id_205
-Order_No_4390,User_Id_1441,Bike,2,Personal,24,2,3:06:30 PM,24,2,3:08:10 PM,24,2,3:29:43 PM,24,2,4:00:49 PM,6,18.4,,-1.298529,36.816482,-1.2849763,36.7841767,Rider_Id_937
-Order_No_10628,User_Id_540,Bike,3,Business,20,3,9:26:05 AM,20,3,9:27:31 AM,20,3,9:40:22 AM,20,3,11:15:23 AM,8,27.0,,-1.2551895,36.7822034,-1.3027475,36.8178196,Rider_Id_868
-Order_No_27885,User_Id_2606,Bike,1,Personal,19,5,9:46:36 AM,19,5,9:47:13 AM,19,5,10:00:06 AM,19,5,10:14:06 AM,8,20.0,,-1.2528485,36.7953722,-1.2973948999999998,36.816038,Rider_Id_446
-Order_No_20192,User_Id_2330,Bike,3,Business,25,1,12:07:04 PM,25,1,12:08:36 PM,25,1,12:41:16 PM,25,1,12:45:15 PM,9,26.9,,-1.2291477,36.8440295,-1.2524916000000001,36.8656196,Rider_Id_307
-Order_No_21898,User_Id_296,Bike,3,Business,16,2,8:32:05 AM,16,2,8:32:57 AM,16,2,8:41:44 AM,16,2,8:56:31 AM,8,,,-1.2571472,36.795063299999995,-1.2976854,36.775358600000004,Rider_Id_96
-Order_No_919,User_Id_1770,Bike,3,Personal,10,4,12:34:28 PM,10,4,12:34:32 PM,10,4,12:50:18 PM,10,4,1:08:55 PM,14,,,-1.3223334999999998,36.8708169,-1.2603714,36.7893387,Rider_Id_543
-Order_No_24137,User_Id_923,Bike,1,Personal,17,5,1:34:47 PM,17,5,1:35:01 PM,17,5,1:38:01 PM,17,5,2:29:25 PM,8,20.4,,-1.2945178999999998,36.7685349,-1.2668071,36.8068157,Rider_Id_272
-Order_No_16920,User_Id_3201,Bike,3,Business,21,5,4:52:32 PM,21,5,4:55:01 PM,21,5,4:58:04 PM,21,5,5:09:33 PM,2,23.2,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_65
-Order_No_11041,User_Id_191,Bike,1,Personal,31,2,1:08:57 PM,31,2,1:09:56 PM,31,2,1:12:55 PM,31,2,1:21:20 PM,9,18.7,,-1.296797,36.776452500000005,-1.2668264999999999,36.7680484,Rider_Id_101
-Order_No_17830,User_Id_1500,Bike,3,Business,1,1,1:17:56 PM,1,1,1:18:27 PM,1,1,1:21:16 PM,1,1,1:28:21 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_177
-Order_No_13271,User_Id_2457,Bike,3,Business,8,3,3:12:19 PM,8,3,3:12:34 PM,8,3,3:21:14 PM,8,3,3:29:14 PM,4,25.6,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_636
-Order_No_7688,User_Id_627,Bike,3,Business,5,3,2:10:56 PM,5,3,2:19:46 PM,5,3,2:39:17 PM,5,3,2:42:52 PM,4,24.9,,-1.2527958000000001,36.8003131,-1.2764851000000002,36.8007126,Rider_Id_509
-Order_No_15210,User_Id_2216,Bike,3,Business,8,4,11:13:36 AM,8,4,12:26:01 PM,8,4,12:26:59 PM,8,4,12:45:01 PM,7,26.1,,-1.2551895,36.7822034,-1.2830276000000003,36.8297516,Rider_Id_477
-Order_No_14393,User_Id_3648,Bike,3,Personal,23,3,4:08:29 PM,23,3,4:08:39 PM,23,3,4:18:25 PM,23,3,4:27:07 PM,17,27.4,,-1.2038216999999998,36.87945070000001,-1.3002553999999997,36.8256566,Rider_Id_377
-Order_No_14547,User_Id_2895,Bike,3,Business,30,2,12:47:27 PM,30,2,12:52:27 PM,30,2,12:58:23 PM,30,2,1:01:28 PM,14,22.5,,-1.3700383,36.9190174,-1.307182,36.850321,Rider_Id_620
-Order_No_24700,User_Id_2587,Bike,3,Business,16,2,3:13:20 PM,16,2,3:14:40 PM,16,2,3:15:00 PM,16,2,3:43:13 PM,8,,,-1.28878,36.8168312,-1.3322618,36.8045412,Rider_Id_718
-Order_No_14345,User_Id_2523,Bike,3,Business,6,4,2:05:10 PM,6,4,2:06:20 PM,6,4,2:16:53 PM,6,4,2:23:30 PM,22,21.1,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_320
-Order_No_12680,User_Id_1500,Bike,3,Business,6,1,8:02:40 AM,6,1,8:14:11 AM,6,1,8:29:10 AM,6,1,8:44:18 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_92
-Order_No_22305,User_Id_3496,Bike,3,Business,27,3,11:24:01 AM,27,3,11:24:27 AM,27,3,11:34:19 AM,27,3,11:39:23 AM,4,26.1,,-1.3151629999999999,36.814073,-1.300921,36.828195,Rider_Id_829
-Order_No_5469,User_Id_188,Bike,1,Personal,28,5,5:06:17 PM,28,5,5:28:56 PM,28,5,5:48:55 PM,28,5,6:02:17 PM,9,,,-1.2994194,36.7647917,-1.2910023000000002,36.8310071,Rider_Id_674
-Order_No_7542,User_Id_1500,Bike,3,Business,4,4,10:16:05 AM,4,4,10:18:19 AM,4,4,10:24:02 AM,4,4,10:27:38 AM,6,21.6,,-1.2638185,36.7930057,-1.2944081,36.7897046,Rider_Id_855
-Order_No_19674,User_Id_533,Bike,3,Business,23,2,3:52:58 PM,23,2,3:56:44 PM,23,2,3:57:33 PM,23,2,4:08:26 PM,6,19.0,,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_107
-Order_No_6402,User_Id_2767,Bike,3,Business,12,3,9:56:48 AM,12,3,9:57:06 AM,12,3,9:58:14 AM,12,3,9:59:23 AM,4,21.5,,-1.257773,36.795486,-1.24757,36.7626328,Rider_Id_382
-Order_No_17011,User_Id_3291,Bike,3,Business,2,2,12:28:04 PM,2,2,12:28:37 PM,2,2,12:45:16 PM,2,2,12:59:12 PM,4,26.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_52
-Order_No_13492,User_Id_265,Bike,3,Business,9,3,2:33:02 PM,9,3,2:33:35 PM,9,3,2:36:56 PM,9,3,2:51:34 PM,8,25.3,,-1.2927600000000001,36.783799,-1.300921,36.828195,Rider_Id_862
-Order_No_2806,User_Id_186,Bike,3,Business,26,5,2:52:46 PM,26,5,3:07:32 PM,26,5,3:10:51 PM,26,5,3:13:55 PM,8,,,-1.2794780000000001,36.8304521,-1.2571472,36.795063299999995,Rider_Id_237
-Order_No_24976,User_Id_2982,Bike,3,Business,4,4,10:51:43 AM,4,4,10:51:50 AM,4,4,10:57:36 AM,4,4,11:18:34 AM,18,25.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_419
-Order_No_1704,User_Id_706,Bike,3,Business,9,3,10:37:51 AM,9,3,10:38:11 AM,9,3,10:38:27 AM,9,3,10:54:08 AM,4,25.3,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_518
-Order_No_26743,User_Id_2330,Bike,3,Business,4,2,3:18:06 PM,4,2,3:24:35 PM,4,2,3:33:02 PM,4,2,3:41:23 PM,8,22.5,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_155
-Order_No_8336,User_Id_1207,Bike,3,Business,22,6,10:33:13 AM,22,6,10:33:28 AM,22,6,10:37:01 AM,22,6,10:45:24 AM,13,23.1,,-1.2629666000000002,36.8068369,-1.2156006000000001,36.8916865,Rider_Id_685
-Order_No_14831,User_Id_63,Bike,2,Personal,18,5,12:48:48 PM,18,5,12:50:19 PM,18,5,12:58:22 PM,18,5,1:13:48 PM,11,21.3,,-1.3004846,36.790159100000004,-1.2477886999999999,36.7960085,Rider_Id_390
-Order_No_1065,User_Id_1148,Bike,3,Business,12,2,9:22:41 AM,12,2,9:23:48 AM,12,2,9:39:18 AM,12,2,9:57:44 AM,5,17.9,,-1.3100586,36.850032899999995,-1.2960687,36.8167909,Rider_Id_678
-Order_No_23321,User_Id_1544,Bike,3,Business,10,1,3:54:11 PM,10,1,4:21:35 PM,10,1,4:42:37 PM,10,1,4:47:57 PM,11,18.9,,-1.2791223999999999,36.8306878,-1.2805395,36.890386,Rider_Id_821
-Order_No_2778,User_Id_2964,Bike,3,Business,3,3,11:51:15 AM,3,3,11:54:01 AM,3,3,12:04:15 PM,3,3,12:32:55 PM,22,27.5,,-1.3124537,36.925365,-1.2551895,36.7822034,Rider_Id_126
-Order_No_4263,User_Id_419,Bike,3,Business,12,1,12:37:55 PM,12,1,12:40:11 PM,12,1,12:54:54 PM,12,1,1:11:39 PM,9,24.0,,-1.289882,36.8105888,-1.2571472,36.795063299999995,Rider_Id_726
-Order_No_3728,User_Id_393,Bike,3,Business,28,2,10:20:33 AM,28,2,10:20:56 AM,28,2,10:22:40 AM,28,2,10:34:18 AM,14,21.3,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_211
-Order_No_26671,User_Id_1721,Bike,3,Business,23,2,11:43:53 AM,23,2,11:44:10 AM,23,2,11:46:26 AM,23,2,11:53:50 AM,4,,,-1.2551895,36.7822034,-1.2728947,36.807942600000004,Rider_Id_438
-Order_No_25930,User_Id_393,Bike,3,Business,8,5,10:00:53 AM,8,5,10:01:50 AM,8,5,10:08:13 AM,8,5,10:35:14 AM,4,24.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_206
-Order_No_23421,User_Id_928,Bike,3,Business,2,4,4:58:04 PM,2,4,5:01:30 PM,2,4,5:16:22 PM,2,4,5:21:27 PM,11,,,-1.2571472,36.795063299999995,-1.3039222,36.8534707,Rider_Id_96
-Order_No_11998,User_Id_3565,Bike,3,Business,2,7,11:58:52 AM,2,7,11:59:03 AM,2,7,12:22:26 PM,2,7,12:50:04 PM,21,22.6,,-1.2844665000000002,36.7866113,-1.398531,36.767365000000005,Rider_Id_267
-Order_No_14084,User_Id_417,Bike,1,Personal,28,1,3:08:53 PM,28,1,3:17:36 PM,28,1,3:46:31 PM,28,1,3:48:17 PM,6,24.7,,-1.267691,36.793649,-1.2628892,36.8244802,Rider_Id_752
-Order_No_11146,User_Id_265,Bike,3,Business,29,1,8:19:20 AM,29,1,8:20:01 AM,29,1,8:39:30 AM,29,1,8:52:37 AM,12,,,-1.3251247,36.899592600000005,-1.300921,36.828195,Rider_Id_411
-Order_No_9677,User_Id_598,Bike,1,Business,1,3,10:06:42 AM,1,3,10:06:51 AM,1,3,10:11:28 AM,1,3,10:17:43 AM,17,,,-1.2750257,36.8157165,-1.3178263000000001,36.7141818,Rider_Id_857
-Order_No_11157,User_Id_1095,Bike,1,Personal,25,5,1:19:59 PM,25,5,1:21:48 PM,25,5,1:28:46 PM,25,5,1:37:44 PM,7,23.3,,-1.2624838,36.83963,-1.2658315,36.809049,Rider_Id_735
-Order_No_13504,User_Id_3283,Bike,3,Business,7,1,8:57:50 AM,7,1,8:59:34 AM,7,1,9:06:06 AM,7,1,9:15:30 AM,18,21.3,,-1.3444459999999998,36.756597,-1.2628473,36.781805,Rider_Id_768
-Order_No_10549,User_Id_1955,Bike,3,Business,27,3,11:11:59 AM,27,3,11:12:36 AM,27,3,11:35:37 AM,27,3,11:37:44 AM,15,25.9,,-1.2765492,36.7669809,-1.3263105,36.8622904,Rider_Id_348
-Order_No_12872,User_Id_1219,Bike,1,Personal,20,4,12:26:55 PM,20,4,12:27:31 PM,20,4,12:35:17 PM,20,4,1:00:52 PM,5,24.5,,-1.2943686,36.8077475,-1.2707833,36.7993825,Rider_Id_365
-Order_No_25106,User_Id_3291,Bike,3,Business,17,5,12:12:09 PM,17,5,12:13:08 PM,17,5,12:29:38 PM,17,5,12:34:46 PM,11,,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_591
-Order_No_24282,User_Id_911,Bike,3,Personal,11,6,5:27:45 PM,11,6,5:28:58 PM,11,6,5:50:32 PM,11,6,5:54:11 PM,28,,,-1.2872974,36.824174299999996,-1.364732,36.658943,Rider_Id_622
-Order_No_4128,User_Id_2330,Bike,3,Business,2,4,3:40:32 PM,2,4,3:41:57 PM,2,4,4:07:44 PM,2,4,4:14:33 PM,8,,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_548
-Order_No_8869,User_Id_2271,Bike,3,Business,8,1,12:54:08 PM,8,1,12:54:21 PM,8,1,1:19:24 PM,8,1,1:37:51 PM,12,29.5,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_155
-Order_No_5105,User_Id_3222,Bike,3,Business,13,4,2:41:40 PM,13,4,2:42:53 PM,13,4,3:05:00 PM,13,4,3:06:33 PM,13,22.3,,-1.2551895,36.7822034,-1.3069686000000003,36.8596454,Rider_Id_108
-Order_No_912,User_Id_2069,Bike,3,Business,11,1,10:57:47 AM,11,1,10:59:20 AM,11,1,11:45:23 AM,11,1,12:15:02 PM,9,26.8,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309
-Order_No_25391,User_Id_2212,Bike,3,Business,11,1,2:18:39 PM,11,1,2:20:28 PM,11,1,2:21:06 PM,11,1,2:28:10 PM,14,28.1,,-1.2571472,36.795063299999995,-1.3291155000000001,36.8861,Rider_Id_293
-Order_No_20234,User_Id_1719,Bike,3,Business,11,5,10:54:09 AM,11,5,11:13:53 AM,11,5,11:24:05 AM,11,5,11:28:58 AM,10,20.9,,-1.3339943,36.7263409,-1.2962603000000001,36.7766164,Rider_Id_100
-Order_No_21038,User_Id_1500,Bike,3,Business,18,4,9:07:01 AM,18,4,9:07:18 AM,18,4,9:20:03 AM,18,4,9:30:52 AM,10,,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_92
-Order_No_17955,User_Id_393,Bike,3,Business,18,5,10:28:41 AM,18,5,11:10:50 AM,18,5,11:19:40 AM,18,5,12:23:49 PM,14,21.5,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_927
-Order_No_8306,User_Id_788,Bike,3,Business,10,4,3:20:41 PM,10,4,3:21:22 PM,10,4,3:27:47 PM,10,4,3:33:30 PM,14,25.0,,-1.311173,36.827569,-1.225322,36.80855,Rider_Id_136
-Order_No_6369,User_Id_478,Bike,3,Business,29,5,1:04:31 PM,29,5,1:06:46 PM,29,5,1:12:56 PM,29,5,1:26:09 PM,8,19.9,,-1.2551895,36.7822034,-1.2900272,36.7935096,Rider_Id_101
-Order_No_5257,User_Id_2956,Bike,3,Business,17,2,4:07:46 PM,17,2,4:10:53 PM,17,2,4:27:44 PM,17,2,4:35:40 PM,20,23.9,,-1.2860183,36.8975338,-1.2584143,36.8048002,Rider_Id_698
-Order_No_20613,User_Id_2523,Bike,3,Business,22,3,9:44:08 AM,22,3,9:44:43 AM,22,3,9:49:20 AM,22,3,9:56:39 AM,16,18.6,,-1.2551895,36.7822034,-1.3312488,36.8801409,Rider_Id_205
-Order_No_4314,User_Id_2956,Bike,3,Business,10,5,9:35:44 AM,10,5,9:35:52 AM,10,5,9:37:30 AM,10,5,9:43:43 AM,3,13.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_865
-Order_No_1892,User_Id_1469,Bike,3,Business,21,4,2:46:43 PM,21,4,2:47:07 PM,21,4,2:48:29 PM,21,4,3:00:51 PM,7,31.2,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_764
-Order_No_240,User_Id_3674,Bike,3,Business,29,3,2:32:44 PM,29,3,3:12:23 PM,29,3,3:14:37 PM,29,3,3:25:02 PM,8,21.2,,-1.2551895,36.7822034,-1.2801883,36.8234728,Rider_Id_68
-Order_No_8468,User_Id_1934,Bike,2,Personal,21,5,12:44:37 PM,21,5,1:11:33 PM,21,5,1:16:03 PM,21,5,1:29:54 PM,6,20.2,,-1.2862252,36.8234218,-1.2502923000000001,36.788756799999994,Rider_Id_156
-Order_No_7991,User_Id_1246,Bike,3,Business,11,5,8:27:58 AM,11,5,8:28:06 AM,11,5,8:28:27 AM,11,5,8:45:41 AM,2,21.2,,-1.2571472,36.795063299999995,-1.2682478000000001,36.8072852,Rider_Id_409
-Order_No_1120,User_Id_201,Bike,3,Business,6,4,12:03:24 PM,6,4,12:04:30 PM,6,4,12:16:59 PM,6,4,12:23:50 PM,10,19.8,,-1.2762743,36.815278899999996,-1.2577288999999998,36.74107720000001,Rider_Id_72
-Order_No_13536,User_Id_261,Bike,2,Personal,31,5,4:57:12 PM,31,5,5:18:18 PM,31,5,5:32:27 PM,31,5,6:03:20 PM,8,20.0,,-1.2789683,36.8284768,-1.296797,36.776452500000005,Rider_Id_857
-Order_No_1233,User_Id_2330,Bike,3,Business,26,2,2:10:41 PM,26,2,2:10:54 PM,26,2,2:15:23 PM,26,2,2:18:33 PM,8,29.8,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_831
-Order_No_24406,User_Id_2563,Bike,1,Personal,1,1,10:40:58 AM,1,1,10:42:13 AM,1,1,10:42:33 AM,1,1,11:02:33 AM,12,24.0,,-1.3108386,36.7555239,-1.2665476999999998,36.7410165,Rider_Id_179
-Order_No_24621,User_Id_2233,Bike,2,Personal,28,5,1:27:17 PM,28,5,1:29:29 PM,28,5,1:29:39 PM,28,5,1:40:41 PM,8,25.4,,-1.2912247,36.7885452,-1.2835586,36.8125641,Rider_Id_123
-Order_No_27114,User_Id_3711,Bike,3,Business,14,5,3:25:35 PM,14,5,3:26:19 PM,14,5,3:35:18 PM,14,5,3:40:22 PM,8,23.5,,-1.3014461000000002,36.7661381,-1.2615888000000002,36.7928732,Rider_Id_550
-Order_No_23024,User_Id_496,Bike,3,Business,20,3,8:42:16 AM,20,3,8:44:06 AM,20,3,8:54:42 AM,20,3,8:58:18 AM,10,20.9,,-1.2551895,36.7822034,-1.2948533000000002,36.799161600000005,Rider_Id_953
-Order_No_26164,User_Id_1271,Bike,3,Business,5,4,4:37:25 PM,5,4,4:40:49 PM,5,4,4:55:27 PM,5,4,5:08:54 PM,19,20.5,,-1.3244885,36.897791999999995,-1.2905046,36.7828277,Rider_Id_621
-Order_No_23660,User_Id_1926,Bike,3,Business,24,3,3:42:31 PM,24,3,3:42:56 PM,24,3,3:54:12 PM,24,3,4:15:21 PM,17,,,-1.3700383,36.9190174,-1.3050321000000003,36.88432460000001,Rider_Id_863
-Order_No_19107,User_Id_3521,Bike,3,Business,19,2,4:57:29 PM,19,2,4:58:11 PM,19,2,5:15:59 PM,19,2,5:20:06 PM,5,28.5,,-1.2696908000000002,36.81519,-1.2472601,36.7859285,Rider_Id_291
-Order_No_7299,User_Id_3283,Bike,3,Business,22,2,4:07:14 PM,22,2,4:13:03 PM,22,2,4:36:27 PM,22,2,4:47:57 PM,6,18.4,,-1.2673934999999998,36.8030328,-1.2628473,36.781805,Rider_Id_726
-Order_No_4313,User_Id_3496,Bike,3,Business,28,5,3:28:56 PM,28,5,3:30:25 PM,28,5,3:39:45 PM,28,5,3:50:37 PM,12,25.5,,-1.300921,36.828195,-1.2925776000000002,36.745692600000005,Rider_Id_925
-Order_No_7122,User_Id_1584,Bike,3,Business,2,5,1:49:02 PM,2,5,2:33:47 PM,2,5,2:44:32 PM,2,5,2:56:25 PM,6,25.0,,-1.2857524,36.819680600000005,-1.2969741000000001,36.7856612,Rider_Id_237
-Order_No_19057,User_Id_2069,Bike,3,Business,28,4,10:50:38 AM,28,4,10:51:40 AM,28,4,10:55:54 AM,28,4,11:13:33 AM,12,27.8,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_148
-Order_No_10690,User_Id_2869,Bike,3,Business,21,2,1:35:49 PM,21,2,1:36:23 PM,21,2,1:51:21 PM,21,2,2:07:22 PM,10,25.0,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_831
-Order_No_21726,User_Id_3222,Bike,3,Business,18,2,9:03:58 AM,18,2,9:04:18 AM,18,2,9:04:33 AM,18,2,9:27:25 AM,8,16.3,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_116
-Order_No_25947,User_Id_3599,Bike,3,Business,22,3,12:50:25 PM,22,3,1:13:48 PM,22,3,1:31:47 PM,22,3,1:40:56 PM,14,24.2,,-1.3302996,36.8707935,-1.2886434,36.770986799999996,Rider_Id_211
-Order_No_10154,User_Id_2809,Bike,2,Personal,25,1,4:21:42 PM,25,1,4:22:11 PM,25,1,4:28:53 PM,25,1,4:33:27 PM,31,,,-1.2672212999999999,36.8275035,-1.1559779,36.640507899999996,Rider_Id_101
-Order_No_12261,User_Id_1880,Bike,3,Business,3,3,10:21:44 AM,3,3,10:27:09 AM,3,3,10:27:51 AM,3,3,10:31:59 AM,7,18.5,,-1.2861764999999998,36.8209086,-1.2571472,36.795063299999995,Rider_Id_726
-Order_No_1631,User_Id_635,Bike,3,Business,24,4,11:26:12 AM,24,4,11:28:04 AM,24,4,11:51:39 AM,24,4,12:22:44 PM,4,21.0,,-1.2599563,36.799343900000004,-1.2833620000000001,36.8260149,Rider_Id_844
-Order_No_7842,User_Id_1990,Bike,3,Business,23,4,2:37:42 PM,23,4,2:39:00 PM,23,4,2:49:27 PM,23,4,3:01:57 PM,10,24.6,,-1.2558721000000002,36.722296,-1.275442,36.766020700000006,Rider_Id_868
-Order_No_2130,User_Id_393,Bike,3,Business,11,5,10:00:58 AM,11,5,10:01:27 AM,11,5,10:01:30 AM,11,5,10:26:30 AM,11,20.8,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_914
-Order_No_6531,User_Id_1209,Bike,3,Business,3,3,10:56:18 AM,3,3,11:02:55 AM,3,3,11:30:56 AM,3,3,11:33:24 AM,4,27.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_740
-Order_No_3909,User_Id_2908,Bike,3,Personal,17,5,3:27:55 PM,17,5,3:28:27 PM,17,5,3:33:54 PM,17,5,3:50:20 PM,6,20.4,,-1.2902869,36.8085396,-1.2929747,36.76463579999999,Rider_Id_241
-Order_No_20964,User_Id_517,Bike,3,Business,29,3,4:36:07 PM,29,3,4:37:50 PM,29,3,4:38:00 PM,29,3,5:09:25 PM,11,21.0,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_515
-Order_No_5353,User_Id_1342,Bike,3,Business,6,2,11:46:39 AM,6,2,11:54:30 AM,6,2,12:07:08 PM,6,2,12:14:07 PM,9,24.9,,-1.3165542,36.700067,-1.3025826,36.7670807,Rider_Id_521
-Order_No_12614,User_Id_3398,Bike,3,Business,7,2,10:02:51 AM,7,2,10:03:05 AM,7,2,10:15:24 AM,7,2,10:21:08 AM,8,16.9,,-1.3472066,36.7692638,-1.3169591,36.7000029,Rider_Id_4
-Order_No_12552,User_Id_393,Bike,3,Business,10,1,1:18:43 PM,10,1,1:22:47 PM,10,1,1:29:39 PM,10,1,2:09:08 PM,8,18.5,,-1.3228003000000002,36.8306435,-1.2770708,36.8231093,Rider_Id_35
-Order_No_22900,User_Id_2066,Bike,3,Business,9,1,12:25:39 PM,9,1,12:27:59 PM,9,1,12:37:57 PM,9,1,12:47:12 PM,11,20.1,,-1.316961,36.820337,-1.362319,36.743983,Rider_Id_650
-Order_No_13296,User_Id_357,Bike,3,Business,30,3,10:02:11 AM,30,3,10:08:42 AM,30,3,10:23:34 AM,30,3,10:32:20 AM,5,25.1,,-1.3052605,36.8043094,-1.2945178999999998,36.7685349,Rider_Id_24
-Order_No_2218,User_Id_540,Bike,3,Business,21,5,5:15:04 PM,21,5,5:20:49 PM,21,5,5:33:47 PM,21,5,5:35:22 PM,4,23.1,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_108
-Order_No_15248,User_Id_928,Bike,3,Business,22,3,3:44:44 PM,22,3,3:45:48 PM,22,3,3:57:00 PM,22,3,4:04:05 PM,8,24.3,,-1.2551895,36.7822034,-1.2944702,36.80097070000001,Rider_Id_138
-Order_No_3042,User_Id_757,Bike,1,Personal,20,4,12:27:06 PM,20,4,12:27:28 PM,20,4,12:33:07 PM,20,4,12:53:54 PM,14,24.2,,-1.2248308,36.801257299999996,-1.2952784,36.7793271,Rider_Id_215
-Order_No_17757,User_Id_2200,Bike,3,Business,4,2,2:37:52 PM,4,2,3:02:05 PM,4,2,3:25:03 PM,4,2,3:26:14 PM,12,23.9,10.9,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_928
-Order_No_800,User_Id_2030,Bike,3,Business,21,4,11:44:53 AM,21,4,11:52:02 AM,21,4,12:07:36 PM,21,4,12:23:03 PM,1,21.6,,-1.2810807,36.8144229,-1.2814451999999998,36.8253735,Rider_Id_88
-Order_No_8493,User_Id_570,Bike,3,Business,30,5,3:57:33 PM,30,5,4:14:32 PM,30,5,4:18:30 PM,30,5,4:21:49 PM,3,24.8,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_913
-Order_No_16146,User_Id_3457,Bike,3,Business,11,2,10:26:44 AM,11,2,10:27:37 AM,11,2,10:32:28 AM,11,2,10:36:20 AM,1,19.4,,-1.2684666999999998,36.8002891,-1.266294,36.8057971,Rider_Id_50
-Order_No_10744,User_Id_3647,Bike,3,Business,21,4,4:44:51 PM,21,4,4:45:30 PM,21,4,4:56:08 PM,21,4,5:08:23 PM,3,28.6,,-1.2726389999999999,36.794723,-1.2634530000000002,36.7955612,Rider_Id_177
-Order_No_14716,User_Id_1304,Bike,3,Business,21,1,11:31:33 AM,21,1,11:33:36 AM,21,1,11:34:09 AM,21,1,11:41:52 AM,6,20.2,,-1.2626906,36.7827022,-1.2765492,36.7669809,Rider_Id_633
-Order_No_8166,User_Id_3331,Bike,3,Business,24,4,4:36:12 PM,24,4,4:36:29 PM,24,4,4:47:37 PM,24,4,4:58:51 PM,28,26.1,,-1.2551895,36.7822034,-1.162368,36.91076029999999,Rider_Id_531
-Order_No_11847,User_Id_2677,Bike,3,Business,15,1,1:27:57 PM,15,1,1:31:34 PM,15,1,1:39:33 PM,15,1,1:45:59 PM,11,,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_320
-Order_No_16023,User_Id_1346,Bike,3,Business,25,5,12:09:09 PM,25,5,12:09:17 PM,25,5,12:09:31 PM,25,5,12:15:09 PM,20,26.3,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_338
-Order_No_24579,User_Id_2885,Bike,1,Personal,25,2,4:24:38 PM,25,2,4:30:59 PM,25,2,4:55:23 PM,25,2,5:03:15 PM,6,24.1,,-1.2681732,36.8150364,-1.291412,36.8096125,Rider_Id_519
-Order_No_12203,User_Id_1787,Bike,3,Business,11,4,12:35:20 PM,11,4,12:36:59 PM,11,4,12:59:36 PM,11,4,1:42:55 PM,11,26.5,,-1.291768,36.8297208,-1.2297202,36.881950700000004,Rider_Id_402
-Order_No_4197,User_Id_1668,Bike,3,Personal,4,4,4:49:13 PM,4,4,4:49:24 PM,4,4,4:58:05 PM,4,4,5:04:18 PM,8,,,-1.2987976,36.829477000000004,-1.2551895,36.7822034,Rider_Id_532
-Order_No_26827,User_Id_633,Bike,3,Business,1,5,10:23:51 AM,1,5,10:24:40 AM,1,5,10:32:45 AM,1,5,10:40:28 AM,9,28.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_452
-Order_No_20242,User_Id_2330,Bike,3,Business,6,1,3:01:14 PM,6,1,3:06:41 PM,6,1,3:17:48 PM,6,1,3:21:14 PM,9,,,-1.3015197,36.7658456,-1.2645318,36.7981775,Rider_Id_678
-Order_No_3455,User_Id_527,Bike,2,Personal,17,3,9:55:55 AM,17,3,9:56:06 AM,17,3,10:12:13 AM,17,3,10:23:57 AM,12,21.0,,-1.2857524,36.819680600000005,-1.3319425,36.894470899999995,Rider_Id_268
-Order_No_24008,User_Id_3032,Bike,3,Business,26,5,12:40:06 PM,26,5,12:40:43 PM,26,5,12:44:41 PM,26,5,12:51:28 PM,2,,,-1.2571472,36.795063299999995,-1.2622145,36.8087391,Rider_Id_578
-Order_No_27086,User_Id_788,Bike,3,Business,28,3,12:23:07 PM,28,3,12:37:36 PM,28,3,12:52:37 PM,28,3,1:12:36 PM,10,,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_320
-Order_No_27176,User_Id_666,Bike,2,Personal,25,5,11:04:14 AM,25,5,11:13:45 AM,25,5,11:28:38 AM,25,5,11:32:25 AM,4,20.8,,-1.2770071000000003,36.832167,-1.2640292,36.8122027,Rider_Id_846
-Order_No_9534,User_Id_1393,Bike,1,Business,28,5,8:59:12 AM,28,5,8:59:37 AM,28,5,9:06:51 AM,28,5,9:10:11 AM,2,20.5,,-1.2527958000000001,36.8003131,-1.2685707,36.806496100000004,Rider_Id_881
-Order_No_642,User_Id_1927,Bike,3,Business,22,4,12:59:23 PM,22,4,1:26:14 PM,22,4,1:31:05 PM,22,4,1:38:49 PM,10,26.4,,-1.305399,36.822184899999996,-1.3472066,36.7692638,Rider_Id_810
-Order_No_14023,User_Id_758,Bike,3,Business,25,5,11:20:30 AM,25,5,11:21:43 AM,25,5,11:27:55 AM,25,5,12:03:15 PM,7,26.5,,-1.2917868000000001,36.7872675,-1.264917,36.8150651,Rider_Id_379
-Order_No_15884,User_Id_3462,Bike,1,Personal,8,5,3:08:40 PM,8,5,3:19:44 PM,8,5,3:36:27 PM,8,5,3:45:54 PM,13,21.7,,-1.2556829999999999,36.813771,-1.2137605,36.8788137,Rider_Id_844
-Order_No_21461,User_Id_391,Bike,3,Business,30,2,11:36:44 AM,30,2,11:38:42 AM,30,2,11:43:41 AM,30,2,12:22:57 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_411
-Order_No_15326,User_Id_84,Bike,3,Business,26,1,12:04:37 PM,26,1,12:14:19 PM,26,1,12:24:11 PM,26,1,12:35:34 PM,9,22.9,,-1.2551895,36.7822034,-1.3014461000000002,36.7661381,Rider_Id_607
-Order_No_11256,User_Id_1363,Bike,3,Business,26,5,10:43:38 AM,26,5,10:44:05 AM,26,5,10:49:46 AM,26,5,11:00:13 AM,5,,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_211
-Order_No_4160,User_Id_990,Bike,1,Business,14,4,4:49:10 PM,14,4,4:51:00 PM,14,4,4:55:19 PM,14,4,5:01:57 PM,16,21.0,,-1.263593,36.7882903,-1.3416856000000001,36.7180208,Rider_Id_101
-Order_No_19268,User_Id_2829,Bike,3,Business,24,5,2:01:43 PM,24,5,2:02:23 PM,24,5,2:14:26 PM,24,5,2:25:18 PM,21,22.9,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_564
-Order_No_2805,User_Id_1046,Bike,1,Personal,23,1,6:37:38 PM,23,1,6:37:45 PM,23,1,6:50:17 PM,23,1,6:57:18 PM,7,19.1,,-1.3054537,36.8284768,-1.290881,36.8559893,Rider_Id_144
-Order_No_14795,User_Id_286,Bike,3,Business,22,4,4:17:49 PM,22,4,4:20:03 PM,22,4,4:33:06 PM,22,4,4:34:29 PM,5,24.3,,-1.2970034,36.833321399999996,-1.2962966999999999,36.8020231,Rider_Id_216
-Order_No_24263,User_Id_3283,Bike,3,Business,9,2,9:44:34 AM,9,2,9:44:46 AM,9,2,9:46:54 AM,9,2,10:09:56 AM,6,20.7,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_79
-Order_No_3688,User_Id_3560,Bike,3,Business,24,1,11:13:16 AM,24,1,11:16:50 AM,24,1,11:29:05 AM,24,1,11:32:30 AM,2,22.7,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_275
-Order_No_3766,User_Id_3295,Bike,3,Business,15,4,12:34:12 PM,15,4,12:39:42 PM,15,4,12:48:52 PM,15,4,1:01:01 PM,7,24.6,,-1.2571472,36.795063299999995,-1.2942081,36.7820227,Rider_Id_72
-Order_No_24775,User_Id_657,Bike,1,Personal,20,3,5:16:18 PM,20,3,5:49:32 PM,20,3,6:06:47 PM,20,3,6:13:17 PM,12,21.7,,-1.2873119,36.8251255,-1.3095379999999999,36.7353407,Rider_Id_744
-Order_No_11319,User_Id_265,Bike,3,Business,8,5,1:38:57 PM,8,5,1:39:33 PM,8,5,2:24:56 PM,8,5,2:26:24 PM,23,21.7,,-1.300921,36.828195,-1.1554889,36.809043,Rider_Id_340
-Order_No_23753,User_Id_393,Bike,3,Business,12,5,10:08:41 AM,12,5,10:27:32 AM,12,5,10:30:05 AM,12,5,11:16:46 AM,10,24.4,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731
-Order_No_15985,User_Id_1363,Bike,3,Business,16,5,2:50:13 PM,16,5,2:52:27 PM,16,5,3:01:58 PM,16,5,3:47:28 PM,5,,,-1.3004062,36.829741,-1.3083777,36.85900770000001,Rider_Id_360
-Order_No_3817,User_Id_2174,Bike,3,Business,13,6,9:42:26 AM,13,6,9:42:54 AM,13,6,9:42:57 AM,13,6,10:45:12 AM,22,26.0,,-1.2482781,36.884091999999995,-1.3470008999999998,36.761945399999995,Rider_Id_86
-Order_No_16391,User_Id_1655,Bike,3,Personal,25,2,11:28:01 AM,25,2,11:31:06 AM,25,2,11:31:50 AM,25,2,11:43:29 AM,13,24.0,,-1.3250048,36.869718799999994,-1.270766,36.810988,Rider_Id_848
-Order_No_22693,User_Id_1469,Bike,3,Business,21,5,1:21:31 PM,21,5,1:22:07 PM,21,5,1:28:05 PM,21,5,1:38:54 PM,6,27.1,,-1.2571472,36.795063299999995,-1.269974,36.77537279999999,Rider_Id_561
-Order_No_16269,User_Id_375,Bike,3,Business,7,5,11:24:32 AM,7,5,11:47:38 AM,7,5,12:18:17 PM,7,5,12:22:44 PM,5,25.3,,-1.277285,36.813266,-1.2525704,36.7955754,Rider_Id_726
-Order_No_2200,User_Id_2271,Bike,3,Business,8,1,5:00:30 PM,8,1,5:01:47 PM,8,1,5:16:36 PM,8,1,5:32:20 PM,11,28.8,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_8
-Order_No_22928,User_Id_2582,Bike,3,Business,11,1,10:33:34 AM,11,1,10:34:18 AM,11,1,10:45:41 AM,11,1,10:53:02 AM,3,26.4,,-1.2551895,36.7822034,-1.2644742,36.808345700000004,Rider_Id_690
-Order_No_6176,User_Id_3517,Bike,3,Business,21,2,2:11:43 PM,21,2,2:12:02 PM,21,2,2:22:12 PM,21,2,3:07:30 PM,16,25.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137
-Order_No_3735,User_Id_2828,Bike,3,Business,26,5,3:41:11 PM,26,5,3:47:56 PM,26,5,3:54:04 PM,26,5,4:01:48 PM,3,,,-1.2685707,36.806496100000004,-1.2615888000000002,36.7928732,Rider_Id_578
-Order_No_13633,User_Id_3091,Bike,3,Business,6,4,1:55:18 PM,6,4,2:20:37 PM,6,4,2:22:33 PM,6,4,2:40:39 PM,9,,,-1.2551895,36.7822034,-1.3012008,36.764868,Rider_Id_379
-Order_No_22814,User_Id_136,Bike,3,Business,23,2,12:25:02 PM,23,2,12:31:56 PM,23,2,12:41:40 PM,23,2,12:45:48 PM,14,,,-1.3254522,36.721658399999995,-1.2950408000000002,36.809817200000005,Rider_Id_519
-Order_No_13608,User_Id_1527,Bike,3,Business,24,5,12:31:09 PM,24,5,12:31:24 PM,24,5,12:31:50 PM,24,5,1:18:13 PM,10,21.2,,-1.3004062,36.829741,-1.2555317,36.8562313,Rider_Id_70
-Order_No_24370,User_Id_3417,Bike,1,Personal,4,1,4:23:43 PM,4,1,4:28:12 PM,4,1,4:32:57 PM,4,1,5:33:28 PM,9,21.2,,-1.2833106,36.82402379999999,-1.3332749,36.870814700000004,Rider_Id_360
-Order_No_15540,User_Id_2150,Bike,3,Business,8,6,1:03:05 PM,8,6,1:03:15 PM,8,6,1:13:20 PM,8,6,1:23:02 PM,11,22.4,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_736
-Order_No_27906,User_Id_49,Bike,3,Business,7,5,12:29:55 PM,7,5,1:29:13 PM,7,5,1:43:20 PM,7,5,2:05:54 PM,4,27.0,,-1.2790196000000003,36.8160867,-1.2977386999999998,36.803144700000004,Rider_Id_725
-Order_No_23209,User_Id_2828,Bike,3,Business,20,1,2:26:05 PM,20,1,2:26:20 PM,20,1,2:29:37 PM,20,1,2:33:45 PM,5,21.4,,-1.2696183,36.8021778,-1.2772389,36.7855864,Rider_Id_434
-Order_No_4920,User_Id_1368,Bike,3,Business,18,4,11:51:20 AM,18,4,11:57:55 AM,18,4,12:31:44 PM,18,4,12:35:06 PM,5,25.7,,-1.2571472,36.795063299999995,-1.289925,36.820209000000006,Rider_Id_328
-Order_No_4492,User_Id_3147,Bike,3,Business,17,1,8:58:35 AM,17,1,8:58:51 AM,17,1,8:58:53 AM,17,1,9:27:49 AM,14,19.1,,-1.3226301999999999,36.8719901,-1.3472066,36.7692638,Rider_Id_345
-Order_No_7458,User_Id_3621,Bike,3,Business,29,1,8:15:25 AM,29,1,8:15:35 AM,29,1,8:24:28 AM,29,1,8:28:24 AM,18,,,-1.2836583000000001,36.7886014,-1.3123348999999997,36.9152001,Rider_Id_778
-Order_No_15063,User_Id_868,Bike,3,Business,1,5,10:23:08 AM,1,5,12:20:52 PM,1,5,12:27:00 PM,1,5,12:40:32 PM,8,26.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_691
-Order_No_11031,User_Id_3040,Bike,1,Personal,20,4,2:21:19 PM,20,4,2:21:35 PM,20,4,2:33:30 PM,20,4,2:43:43 PM,9,27.9,,-1.2248308,36.801257299999996,-1.2647535,36.8156064,Rider_Id_770
-Order_No_17028,User_Id_3184,Bike,3,Business,12,2,9:04:19 AM,12,2,9:05:26 AM,12,2,9:42:51 AM,12,2,9:49:56 AM,11,16.1,,-1.3067663,36.8438792,-1.3457877,36.8811894,Rider_Id_302
-Order_No_19643,User_Id_2803,Bike,3,Business,8,5,8:42:04 AM,8,5,8:42:23 AM,8,5,8:47:24 AM,8,5,8:54:39 AM,17,20.6,,-1.269767,36.7351068,-1.2765736,36.851364600000004,Rider_Id_880
-Order_No_20037,User_Id_868,Bike,3,Business,2,3,10:00:30 AM,2,3,10:01:08 AM,2,3,10:36:58 AM,2,3,10:47:42 AM,3,24.2,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_88
-Order_No_21791,User_Id_346,Bike,3,Business,31,7,10:35:58 AM,31,7,10:36:59 AM,31,7,10:54:21 AM,31,7,11:00:42 AM,5,27.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_119
-Order_No_21048,User_Id_2828,Bike,1,Business,27,3,2:08:50 PM,27,3,2:33:31 PM,27,3,2:51:04 PM,27,3,3:16:02 PM,5,,,-1.2479361999999998,36.772759,-1.2615888000000002,36.7928732,Rider_Id_498
-Order_No_24059,User_Id_2330,Bike,3,Business,28,2,1:47:16 PM,28,2,1:51:24 PM,28,2,2:14:37 PM,28,2,2:56:06 PM,15,23.3,,-1.3015197,36.7658456,-1.2336843000000002,36.800725899999996,Rider_Id_652
-Order_No_3025,User_Id_1326,Bike,1,Personal,27,3,10:17:21 AM,27,3,10:17:31 AM,27,3,10:22:13 AM,27,3,10:47:37 AM,7,,,-1.2821211000000001,36.8222021,-1.2551895,36.7822034,Rider_Id_585
-Order_No_23602,User_Id_439,Bike,3,Business,5,2,11:52:49 AM,5,2,12:07:49 PM,5,2,12:18:46 PM,5,2,12:29:31 PM,8,23.5,,-1.2910168,36.836938399999994,-1.3250048,36.869718799999994,Rider_Id_72
-Order_No_26845,User_Id_393,Bike,3,Business,14,1,3:25:21 PM,14,1,3:39:24 PM,14,1,3:39:40 PM,14,1,4:36:12 PM,4,23.2,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_515
-Order_No_22915,User_Id_85,Bike,2,Business,29,2,12:27:48 PM,29,2,12:28:37 PM,29,2,12:35:57 PM,29,2,12:37:28 PM,9,21.3,,-1.2672290000000002,36.806399,-1.3018758,36.75908629999999,Rider_Id_101
-Order_No_21093,User_Id_111,Bike,3,Business,6,1,3:06:45 PM,6,1,3:07:44 PM,6,1,3:13:39 PM,6,1,3:17:48 PM,4,,,-1.2551895,36.7822034,-1.2675613000000001,36.8133001,Rider_Id_598
-Order_No_18500,User_Id_1034,Bike,1,Personal,25,2,12:12:49 PM,25,2,12:17:04 PM,25,2,12:36:37 PM,25,2,12:54:37 PM,4,23.6,,-1.2715686000000002,36.8023526,-1.2946764,36.8184646,Rider_Id_338
-Order_No_19296,User_Id_1544,Bike,3,Business,6,3,10:57:29 AM,6,3,10:59:34 AM,6,3,11:07:22 AM,6,3,11:10:13 AM,7,,,-1.2885029,36.825604999999996,-1.2529072,36.8525066,Rider_Id_953
-Order_No_864,User_Id_202,Bike,1,Personal,15,6,10:35:44 AM,15,6,10:36:02 AM,15,6,10:48:27 AM,15,6,10:50:34 AM,12,,,-1.3178524,36.7139804,-1.3222489,36.786702500000004,Rider_Id_288
-Order_No_3465,User_Id_3292,Bike,1,Personal,27,4,9:00:04 AM,27,4,9:01:17 AM,27,4,9:16:10 AM,27,4,9:23:41 AM,8,18.9,3.1,-1.3044313,36.818504,-1.2538731,36.7850223,Rider_Id_167
-Order_No_21886,User_Id_2066,Bike,3,Business,27,3,10:50:09 AM,27,3,10:51:12 AM,27,3,11:06:57 AM,27,3,11:16:39 AM,15,26.3,,-1.3210188999999999,36.841055100000005,-1.3287648,36.744573200000005,Rider_Id_519
-Order_No_12759,User_Id_91,Bike,3,Personal,5,2,9:06:32 AM,5,2,9:07:51 AM,5,2,9:35:28 AM,5,2,10:13:18 AM,9,20.8,2.1,-1.2775276000000002,36.8140021,-1.2990731000000002,36.848805799999994,Rider_Id_436
-Order_No_19421,User_Id_3426,Bike,3,Business,12,2,4:10:50 PM,12,2,4:13:44 PM,12,2,4:24:53 PM,12,2,4:28:02 PM,21,,,-1.2232123,36.8932547,-1.3012008,36.764868,Rider_Id_58
-Order_No_10729,User_Id_1527,Bike,3,Business,9,2,8:15:09 AM,9,2,8:15:30 AM,9,2,8:24:55 AM,9,2,8:37:54 AM,4,20.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206
-Order_No_7994,User_Id_867,Bike,3,Business,18,1,11:21:03 AM,18,1,11:21:38 AM,18,1,11:22:04 AM,18,1,11:37:49 AM,10,26.7,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_472
-Order_No_20065,User_Id_970,Bike,3,Business,13,4,10:11:44 AM,13,4,10:12:17 AM,13,4,10:16:38 AM,13,4,10:17:50 AM,3,18.3,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_362
-Order_No_3526,User_Id_3560,Bike,3,Business,5,1,1:43:46 PM,5,1,2:08:11 PM,5,1,2:20:30 PM,5,1,2:39:27 PM,5,26.0,,-1.2591018999999999,36.800577000000004,-1.279395,36.825364,Rider_Id_858
-Order_No_11586,User_Id_972,Bike,3,Business,22,1,2:53:17 PM,22,1,3:09:59 PM,22,1,3:21:20 PM,22,1,3:22:58 PM,6,26.2,,-1.2862033000000002,36.8152966,-1.2528485,36.7953722,Rider_Id_343
-Order_No_24506,User_Id_3665,Bike,3,Personal,4,5,8:07:43 AM,4,5,8:09:25 AM,4,5,8:47:29 AM,4,5,8:51:45 AM,7,19.4,51.9,-1.253087,36.793333000000004,-1.2758338,36.7858201,Rider_Id_159
-Order_No_8720,User_Id_2674,Bike,3,Personal,22,2,8:00:45 PM,22,2,8:00:52 PM,22,2,8:03:28 PM,22,2,8:08:47 PM,6,21.7,,-1.2963097,36.7688221,-1.2797013000000002,36.7299371,Rider_Id_202
-Order_No_27287,User_Id_3080,Bike,3,Business,14,5,12:49:55 PM,14,5,12:50:06 PM,14,5,12:50:33 PM,14,5,12:56:37 PM,3,21.8,,-1.2551895,36.7822034,-1.2649861,36.7989408,Rider_Id_690
-Order_No_10367,User_Id_1341,Bike,3,Business,7,1,12:55:07 PM,7,1,1:23:34 PM,7,1,1:42:24 PM,7,1,1:53:07 PM,7,20.3,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_751
-Order_No_11110,User_Id_1473,Bike,3,Business,14,1,8:36:07 AM,14,1,10:19:51 AM,14,1,10:30:51 AM,14,1,10:40:34 AM,16,23.3,,-1.3044458,36.8233667,-1.3700383,36.9190174,Rider_Id_31
-Order_No_25999,User_Id_2523,Bike,3,Business,5,3,12:07:07 PM,5,3,12:07:51 PM,5,3,12:18:54 PM,5,3,12:20:51 PM,15,22.4,,-1.2551895,36.7822034,-1.3265997,36.8721129,Rider_Id_108
-Order_No_19487,User_Id_2102,Bike,3,Business,22,5,4:28:30 PM,22,5,4:32:51 PM,22,5,4:45:35 PM,22,5,5:25:29 PM,13,28.6,,-1.3323107,36.8692616,-1.387054,36.919474,Rider_Id_127
-Order_No_1505,User_Id_416,Bike,3,Business,5,5,2:49:35 PM,5,5,2:53:25 PM,5,5,3:48:57 PM,5,5,3:55:40 PM,5,27.4,,-1.2900209999999999,36.828206,-1.2917868000000001,36.7872675,Rider_Id_155
-Order_No_8181,User_Id_302,Bike,3,Business,27,6,3:57:23 PM,27,6,4:07:12 PM,27,6,4:14:11 PM,27,6,4:21:22 PM,2,,,-1.2649605,36.798177700000004,-1.2661981000000002,36.815471200000005,Rider_Id_949
-Order_No_17915,User_Id_3401,Bike,1,Personal,27,4,12:33:36 PM,27,4,12:34:03 PM,27,4,12:36:40 PM,27,4,12:46:35 PM,2,22.9,,-1.2685423999999998,36.8156049,-1.2697141,36.798177700000004,Rider_Id_280
-Order_No_27599,User_Id_1961,Bike,3,Business,22,3,10:01:49 AM,22,3,10:10:44 AM,22,3,10:21:26 AM,22,3,11:07:50 AM,17,23.2,,-1.2600926000000001,36.8088685,-1.3188358999999998,36.703852600000005,Rider_Id_427
-Order_No_523,User_Id_2596,Bike,3,Business,15,5,1:43:58 PM,15,5,1:44:52 PM,15,5,1:57:37 PM,15,5,2:06:49 PM,18,28.2,,-1.2963097,36.7688221,-1.3343128999999998,36.885417499999996,Rider_Id_831
-Order_No_5101,User_Id_60,Bike,3,Business,12,1,3:24:04 PM,12,1,3:40:54 PM,12,1,4:07:53 PM,12,1,4:10:24 PM,12,26.9,,-1.307143,36.825009,-1.2296618000000001,36.84382720000001,Rider_Id_633
-Order_No_12402,User_Id_2939,Bike,2,Personal,25,5,4:29:38 PM,25,5,4:32:42 PM,25,5,4:58:07 PM,25,5,5:06:01 PM,8,21.2,,-1.2701869,36.7745681,-1.2764786,36.81947879999999,Rider_Id_144
-Order_No_23347,User_Id_3669,Bike,3,Business,21,3,4:35:28 PM,21,3,4:36:52 PM,21,3,4:46:56 PM,21,3,4:52:06 PM,10,,,-1.2615888000000002,36.7928732,-1.3149766999999999,36.8447159,Rider_Id_920
-Order_No_15607,User_Id_1521,Bike,2,Personal,27,4,5:40:40 PM,27,4,5:50:52 PM,27,4,6:10:20 PM,27,4,6:24:31 PM,5,23.1,,-1.2850325,36.822932,-1.3007714,36.8001722,Rider_Id_329
-Order_No_24798,User_Id_102,Bike,3,Personal,11,4,1:35:21 PM,11,4,2:51:37 PM,11,4,3:36:45 PM,11,4,4:07:29 PM,4,27.2,,-1.2676134,36.833926899999994,-1.2866976,36.8315965,Rider_Id_790
-Order_No_5223,User_Id_1719,Bike,3,Business,6,4,1:31:44 PM,6,4,1:35:35 PM,6,4,2:02:14 PM,6,4,2:17:09 PM,15,21.4,,-1.2825379,36.843502,-1.23966,36.75258,Rider_Id_829
-Order_No_22108,User_Id_1953,Bike,3,Personal,4,4,3:24:05 PM,4,4,3:57:43 PM,4,4,4:10:49 PM,4,4,4:18:39 PM,12,24.7,,-1.2810888999999999,36.8247135,-1.3050206,36.8997049,Rider_Id_865
-Order_No_5918,User_Id_478,Bike,3,Business,2,4,10:25:38 AM,2,4,11:17:42 AM,2,4,11:30:13 AM,2,4,11:42:06 AM,15,22.5,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_101
-Order_No_263,User_Id_510,Bike,3,Business,7,5,4:23:02 PM,7,5,4:27:33 PM,7,5,4:28:40 PM,7,5,4:31:33 PM,14,,,-1.3562374,36.904295399999995,-1.2860183,36.8975338,Rider_Id_162
-Order_No_21847,User_Id_2212,Bike,3,Business,9,4,12:59:23 PM,9,4,1:08:34 PM,9,4,1:21:43 PM,9,4,1:26:02 PM,11,,,-1.2571472,36.795063299999995,-1.3151162,36.856998100000006,Rider_Id_96
-Order_No_19112,User_Id_2358,Bike,4,Business,27,5,11:47:04 AM,27,5,11:47:13 AM,27,5,12:11:34 PM,27,5,12:17:54 PM,19,22.6,,-1.3256903999999998,36.8747943,-1.1903523,36.9064927,Rider_Id_844
-Order_No_5221,User_Id_3339,Bike,3,Business,27,3,11:42:59 AM,27,3,11:43:37 AM,27,3,11:51:19 AM,27,3,11:56:39 AM,11,26.0,,-1.3139386,36.812180600000005,-1.2551895,36.7822034,Rider_Id_206
-Order_No_2108,User_Id_1148,Bike,3,Business,9,4,12:44:51 PM,9,4,12:53:00 PM,9,4,1:05:35 PM,9,4,1:21:19 PM,3,,,-1.3104867,36.8495593,-1.311818,36.8638544,Rider_Id_591
-Order_No_23856,User_Id_1500,Bike,3,Business,31,4,3:29:45 PM,31,4,3:36:02 PM,31,4,3:38:57 PM,31,4,3:51:12 PM,8,28.7,,-1.3004062,36.829741,-1.2636051000000001,36.7850995,Rider_Id_576
-Order_No_11478,User_Id_2195,Bike,3,Business,18,6,9:42:42 AM,18,6,9:43:05 AM,18,6,9:49:34 AM,18,6,10:14:40 AM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309
-Order_No_27280,User_Id_3354,Bike,3,Business,30,2,12:24:41 PM,30,2,12:39:59 PM,30,2,1:03:31 PM,30,2,1:32:14 PM,7,,,-1.2571472,36.795063299999995,-1.3018239999999999,36.783976,Rider_Id_205
-Order_No_11469,User_Id_432,Bike,3,Business,27,1,2:43:42 PM,27,1,2:46:10 PM,27,1,2:52:44 PM,27,1,2:57:21 PM,9,23.0,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_96
-Order_No_8540,User_Id_2066,Bike,3,Business,4,1,2:39:45 PM,4,1,2:40:02 PM,4,1,2:56:41 PM,4,1,3:01:09 PM,11,22.8,,-1.316961,36.820337,-1.2529484,36.8651347,Rider_Id_224
-Order_No_18560,User_Id_3404,Bike,3,Business,25,5,12:54:06 PM,25,5,12:55:49 PM,25,5,1:18:45 PM,25,5,1:24:47 PM,7,26.4,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_88
-Order_No_17741,User_Id_3084,Bike,3,Business,5,2,3:50:22 PM,5,2,4:36:38 PM,5,2,4:55:14 PM,5,2,5:02:18 PM,17,,,-1.2153260000000001,36.891993799999995,-1.2767256999999999,36.787393,Rider_Id_8
-Order_No_18139,User_Id_1882,Bike,1,Business,7,2,6:30:30 PM,7,2,6:30:59 PM,7,2,6:38:59 PM,7,2,6:44:45 PM,1,23.1,,-1.2591018999999999,36.800577000000004,-1.2536041999999998,36.806489500000005,Rider_Id_116
-Order_No_12471,User_Id_646,Bike,3,Personal,19,3,12:09:36 PM,19,3,12:15:29 PM,19,3,12:24:31 PM,19,3,12:31:06 PM,2,22.2,,-1.2951264,36.798382000000004,-1.2809121,36.7852524,Rider_Id_720
-Order_No_12661,User_Id_928,Bike,3,Business,9,4,2:36:27 PM,9,4,2:38:17 PM,9,4,2:53:56 PM,9,4,3:06:17 PM,7,,,-1.2571472,36.795063299999995,-1.2922364,36.78955379999999,Rider_Id_92
-Order_No_19047,User_Id_2066,Bike,3,Business,9,4,2:17:13 PM,9,4,2:17:32 PM,9,4,2:22:06 PM,9,4,2:24:07 PM,5,26.0,,-1.316961,36.820337,-1.2996681,36.806841299999995,Rider_Id_865
-Order_No_9167,User_Id_302,Bike,3,Business,5,5,7:02:37 PM,5,5,7:07:42 PM,5,5,7:22:09 PM,5,5,7:23:18 PM,3,24.6,,-1.2649605,36.798177700000004,-1.2513861000000002,36.8174281,Rider_Id_607
-Order_No_19845,User_Id_2270,Bike,2,Personal,29,6,5:08:01 PM,29,6,5:08:24 PM,29,6,5:20:58 PM,29,6,5:25:42 PM,26,23.8,,-1.3211348,36.820145200000006,-1.1923911,36.944733299999996,Rider_Id_83
-Order_No_7471,User_Id_2423,Bike,3,Business,6,3,2:42:46 PM,6,3,2:44:30 PM,6,3,2:50:52 PM,6,3,3:55:05 PM,6,27.5,,-1.2571472,36.795063299999995,-1.2967701999999999,36.8040381,Rider_Id_178
-Order_No_18242,User_Id_1075,Bike,3,Business,23,4,9:17:13 AM,23,4,9:17:44 AM,23,4,9:32:30 AM,23,4,9:47:56 AM,5,,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_491
-Order_No_4296,User_Id_12,Bike,3,Business,9,1,8:37:33 AM,9,1,8:39:45 AM,9,1,9:16:21 AM,9,1,9:24:04 AM,9,15.3,,-1.2400209999999998,36.849990000000005,-1.2864963,36.8252086,Rider_Id_808
-Order_No_12812,User_Id_1594,Bike,3,Business,10,5,3:22:43 PM,10,5,3:25:01 PM,10,5,3:46:25 PM,10,5,3:47:41 PM,15,,,-1.225322,36.80855,-1.2952639,36.756260600000005,Rider_Id_689
-Order_No_23119,User_Id_2242,Bike,3,Business,3,1,11:30:25 AM,3,1,11:31:06 AM,3,1,11:39:48 AM,3,1,11:48:16 AM,10,20.5,,-1.3260432,36.834863299999995,-1.2574087999999999,36.799712,Rider_Id_726
-Order_No_9894,User_Id_3599,Bike,3,Business,5,3,9:36:31 AM,5,3,9:36:50 AM,5,3,10:05:55 AM,5,3,10:35:13 AM,14,22.5,,-1.3302996,36.8707935,-1.317767,36.898252899999996,Rider_Id_48
-Order_No_20176,User_Id_647,Bike,3,Business,31,5,2:20:03 PM,31,5,2:22:31 PM,31,5,2:28:54 PM,31,5,2:40:25 PM,9,24.5,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_490
-Order_No_2696,User_Id_2876,Bike,1,Personal,2,1,11:43:16 AM,2,1,11:43:25 AM,2,1,11:48:22 AM,2,1,11:55:45 AM,6,19.0,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_148
-Order_No_15459,User_Id_3647,Bike,3,Business,2,3,12:37:40 PM,2,3,12:39:02 PM,2,3,12:44:28 PM,2,3,12:48:59 PM,7,23.8,,-1.2726389999999999,36.794723,-1.2612277,36.8318549,Rider_Id_337
-Order_No_12034,User_Id_2844,Bike,3,Personal,16,1,2:21:11 PM,16,1,2:41:20 PM,16,1,2:43:00 PM,16,1,2:59:19 PM,6,22.5,,-1.2833620000000001,36.8260149,-1.312469,36.815177,Rider_Id_672
-Order_No_27038,User_Id_2216,Bike,3,Business,1,1,1:15:27 PM,1,1,1:15:42 PM,1,1,1:21:58 PM,1,1,1:39:56 PM,8,25.4,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_731
-Order_No_6960,User_Id_1920,Bike,3,Business,28,3,7:31:19 AM,28,3,7:32:32 AM,28,3,7:47:28 AM,28,3,7:52:13 AM,15,20.6,,-1.2551895,36.7822034,-1.3221141,36.8636835,Rider_Id_205
-Order_No_23606,User_Id_20,Bike,3,Business,20,2,3:41:12 PM,20,2,3:56:48 PM,20,2,4:32:19 PM,20,2,4:39:43 PM,27,24.0,,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_222
-Order_No_22510,User_Id_2516,Bike,1,Personal,29,3,12:23:18 PM,29,3,12:24:23 PM,29,3,12:35:03 PM,29,3,12:40:01 PM,6,18.4,,-1.2860203000000001,36.7832538,-1.2934656999999998,36.8212737,Rider_Id_249
-Order_No_19800,User_Id_1502,Bike,3,Business,12,6,12:22:23 PM,12,6,12:56:37 PM,12,6,1:14:52 PM,12,6,1:27:01 PM,14,,,-1.207546,36.7913976,-1.2232123,36.8932547,Rider_Id_870
-Order_No_24409,User_Id_3338,Bike,3,Business,11,2,1:01:05 PM,11,2,1:02:58 PM,11,2,1:12:47 PM,11,2,1:19:50 PM,5,19.6,,-1.2736695,36.8028996,-1.2551895,36.7822034,Rider_Id_815
-Order_No_20726,User_Id_1282,Bike,1,Personal,23,7,2:52:31 PM,23,7,2:52:47 PM,23,7,3:03:58 PM,23,7,3:08:48 PM,22,26.4,,-1.2905113000000001,36.759113799999994,-1.2711571000000002,36.901728799999994,Rider_Id_465
-Order_No_12656,User_Id_893,Bike,3,Business,22,1,1:09:17 PM,22,1,1:33:28 PM,22,1,1:53:25 PM,22,1,1:57:34 PM,5,26.2,,-1.2663194,36.809901,-1.2872652,36.826761299999994,Rider_Id_612
-Order_No_8246,User_Id_635,Bike,3,Business,13,3,1:44:20 PM,13,3,1:48:17 PM,13,3,1:55:22 PM,13,3,2:40:58 PM,7,29.4,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_177
-Order_No_23996,User_Id_1363,Bike,3,Business,13,6,11:56:48 AM,13,6,11:57:02 AM,13,6,12:08:07 PM,13,6,12:14:29 PM,4,26.0,,-1.3004062,36.829741,-1.2907473999999999,36.8453439,Rider_Id_291
-Order_No_27325,User_Id_3558,Bike,2,Personal,10,2,9:17:24 AM,10,2,9:21:35 AM,10,2,9:30:49 AM,10,2,9:36:29 AM,8,16.5,,-1.2763916000000002,36.8246826,-1.2905046,36.7828277,Rider_Id_726
-Order_No_13758,User_Id_3689,Bike,3,Business,8,6,2:50:05 PM,8,6,2:50:27 PM,8,6,2:52:28 PM,8,6,2:58:14 PM,5,24.8,,-1.2527958000000001,36.8003131,-1.2793686000000002,36.82592329999999,Rider_Id_820
-Order_No_13346,User_Id_978,Bike,1,Personal,30,3,11:33:38 AM,30,3,12:03:32 PM,30,3,12:33:27 PM,30,3,12:42:12 PM,5,22.3,,-1.3084067,36.83450139999999,-1.2865677,36.825334600000005,Rider_Id_808
-Order_No_15308,User_Id_1544,Bike,3,Business,25,6,11:53:31 AM,25,6,12:05:19 PM,25,6,12:40:19 PM,25,6,12:42:49 PM,8,,,-1.2794301000000001,36.816247,-1.3037415,36.8564954,Rider_Id_921
-Order_No_16540,User_Id_3503,Bike,2,Personal,11,2,12:32:21 PM,11,2,12:34:06 PM,11,2,12:47:43 PM,11,2,12:51:48 PM,4,24.4,,-1.2828204,36.78426210000001,-1.2861322,36.793986600000004,Rider_Id_851
-Order_No_27944,User_Id_2212,Bike,3,Business,7,4,12:24:34 PM,7,4,12:26:42 PM,7,4,12:28:27 PM,7,4,12:43:46 PM,7,24.4,,-1.2571472,36.795063299999995,-1.2991249,36.8012652,Rider_Id_19
-Order_No_388,User_Id_2008,Bike,3,Business,14,3,3:11:31 PM,14,3,3:11:57 PM,14,3,3:23:49 PM,14,3,3:44:41 PM,16,25.6,,-1.2508229,36.7895263,-1.33924,36.760736200000004,Rider_Id_764
-Order_No_6536,User_Id_478,Bike,3,Business,22,3,11:19:02 AM,22,3,12:12:21 PM,22,3,12:25:18 PM,22,3,12:30:35 PM,6,23.3,,-1.2551895,36.7822034,-1.2857094999999998,36.8188266,Rider_Id_155
-Order_No_12694,User_Id_393,Bike,3,Business,18,1,9:18:03 AM,18,1,9:19:02 AM,18,1,9:31:51 AM,18,1,10:07:43 AM,4,22.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_448
-Order_No_3894,User_Id_2372,Bike,2,Personal,11,5,12:24:41 PM,11,5,12:24:46 PM,11,5,12:38:32 PM,11,5,12:51:21 PM,16,20.7,,-1.3319466999999998,36.776699799999996,-1.2765737,36.8175399,Rider_Id_62
-Order_No_20545,User_Id_2330,Bike,3,Business,1,5,4:22:40 PM,1,5,4:23:37 PM,1,5,4:31:01 PM,1,5,4:35:28 PM,9,26.6,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_246
-Order_No_11730,User_Id_3689,Bike,3,Business,17,1,2:18:58 PM,17,1,4:12:52 PM,17,1,4:14:28 PM,17,1,4:51:39 PM,6,25.4,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_272
-Order_No_14126,User_Id_3521,Bike,3,Business,15,1,10:42:48 AM,15,1,10:43:05 AM,15,1,10:48:59 AM,15,1,10:53:56 AM,1,,,-1.2723298,36.8037146,-1.2696908000000002,36.81519,Rider_Id_710
-Order_No_10545,User_Id_2290,Bike,3,Business,1,4,2:40:26 PM,1,4,2:40:41 PM,1,4,2:43:21 PM,1,4,2:50:31 PM,3,,,-1.2615888000000002,36.7928732,-1.2601,36.796528,Rider_Id_260
-Order_No_26099,User_Id_2956,Bike,3,Business,11,4,3:27:18 PM,11,4,3:27:44 PM,11,4,3:40:23 PM,11,4,3:45:24 PM,1,27.2,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_658
-Order_No_18488,User_Id_1084,Bike,2,Personal,9,1,5:58:01 PM,9,1,6:00:05 PM,9,1,6:03:48 PM,9,1,6:11:03 PM,3,20.3,,-1.2840353999999998,36.786742700000005,-1.3007345,36.7741017,Rider_Id_86
-Order_No_3683,User_Id_1898,Bike,3,Personal,4,4,1:44:18 PM,4,4,1:47:36 PM,4,4,2:01:14 PM,4,4,2:02:18 PM,8,25.0,,-1.3261286,36.8469625,-1.29759,36.815769,Rider_Id_402
-Order_No_21855,User_Id_2200,Bike,3,Business,29,5,11:17:55 AM,29,5,11:23:03 AM,29,5,11:27:01 AM,29,5,11:37:50 AM,5,26.6,,-1.273488,36.804158,-1.290894,36.822971,Rider_Id_907
-Order_No_8542,User_Id_3104,Bike,1,Personal,20,3,3:28:29 PM,20,3,3:28:54 PM,20,3,3:51:39 PM,20,3,3:55:49 PM,27,23.2,,-1.3421985,36.9040545,-1.2524958000000002,36.7261025,Rider_Id_543
-Order_No_17918,User_Id_1402,Bike,3,Business,4,1,11:05:14 AM,4,1,11:05:44 AM,4,1,11:11:31 AM,4,1,11:20:14 AM,2,26.8,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_206
-Order_No_23819,User_Id_2587,Bike,3,Business,12,1,12:34:29 PM,12,1,12:34:53 PM,12,1,12:50:39 PM,12,1,1:06:57 PM,5,24.0,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_88
-Order_No_2117,User_Id_2363,Bike,1,Personal,9,6,5:19:23 PM,9,6,5:20:02 PM,9,6,5:21:32 PM,9,6,5:32:48 PM,12,19.1,,-1.2649605,36.798177700000004,-1.20079,36.77198,Rider_Id_116
-Order_No_19097,User_Id_1402,Bike,3,Business,15,1,12:15:51 PM,15,1,12:17:27 PM,15,1,12:23:30 PM,15,1,12:35:15 PM,1,,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_99
-Order_No_14604,User_Id_1754,Bike,1,Business,3,5,1:34:02 PM,3,5,2:57:01 PM,3,5,2:57:06 PM,3,5,3:00:24 PM,13,25.0,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_900
-Order_No_19662,User_Id_1500,Bike,3,Business,9,2,8:48:10 AM,9,2,8:49:00 AM,9,2,8:53:12 AM,9,2,9:05:05 AM,8,20.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_155
-Order_No_1448,User_Id_567,Bike,3,Business,14,2,9:31:25 AM,14,2,9:32:01 AM,14,2,9:39:32 AM,14,2,9:45:59 AM,8,,,-1.2766906,36.8202151,-1.2963097,36.7688221,Rider_Id_462
-Order_No_13975,User_Id_2271,Bike,3,Business,30,2,11:43:20 AM,30,2,11:50:19 AM,30,2,12:11:02 PM,30,2,12:17:11 PM,14,,,-1.293515,36.897607,-1.272828,36.816608,Rider_Id_855
-Order_No_11358,User_Id_552,Bike,3,Business,6,3,11:34:05 AM,6,3,12:02:17 PM,6,3,12:34:51 PM,6,3,12:40:50 PM,21,20.0,,-1.3562374,36.904295399999995,-1.2553615,36.789537599999996,Rider_Id_322
-Order_No_19260,User_Id_190,Bike,3,Business,2,4,2:21:54 PM,2,4,2:22:38 PM,2,4,2:31:21 PM,2,4,2:39:56 PM,6,,,-1.3275885,36.862155200000004,-1.3270347,36.866736200000005,Rider_Id_205
-Order_No_20153,User_Id_3283,Bike,3,Business,30,2,3:08:52 PM,30,2,3:16:41 PM,30,2,3:23:58 PM,30,2,3:33:35 PM,6,,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_843
-Order_No_7423,User_Id_265,Bike,3,Business,12,2,4:04:41 PM,12,2,4:07:39 PM,12,2,4:15:47 PM,12,2,4:29:34 PM,4,,,-1.3047132,36.808955299999994,-1.300921,36.828195,Rider_Id_99
-Order_No_13147,User_Id_3599,Bike,3,Business,28,2,11:40:55 AM,28,2,11:40:55 AM,28,2,11:41:28 AM,28,2,12:13:55 PM,14,21.5,,-1.3302996,36.8707935,-1.2748221000000002,36.7817865,Rider_Id_772
-Order_No_12230,User_Id_393,Bike,3,Business,7,4,12:19:51 PM,7,4,12:20:03 PM,7,4,12:20:06 PM,7,4,12:39:29 PM,4,27.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39
-Order_No_21673,User_Id_726,Bike,3,Personal,20,3,1:34:19 PM,20,3,1:35:06 PM,20,3,1:37:36 PM,20,3,1:47:47 PM,4,28.5,,-1.2836657,36.7666744,-1.2757858,36.788684100000005,Rider_Id_217
-Order_No_23095,User_Id_1828,Bike,1,Personal,26,4,11:32:18 AM,26,4,11:32:24 AM,26,4,11:33:38 AM,26,4,11:42:49 AM,3,19.1,,-1.2538731,36.7850223,-1.263419,36.8060348,Rider_Id_379
-Order_No_4026,User_Id_3426,Bike,3,Business,26,5,10:20:59 AM,26,5,10:46:57 AM,26,5,10:58:19 AM,26,5,11:19:24 AM,11,,,-1.3014461000000002,36.7661381,-1.295324,36.839628999999995,Rider_Id_726
-Order_No_9718,User_Id_3624,Bike,1,Personal,16,6,11:32:20 AM,16,6,11:32:43 AM,16,6,11:42:50 AM,16,6,12:00:11 PM,17,19.4,,-1.286943,36.800976,-1.2091558999999998,36.7753704,Rider_Id_148
-Order_No_10930,User_Id_635,Bike,3,Business,21,1,2:58:48 PM,21,1,2:59:24 PM,21,1,3:09:39 PM,21,1,3:25:01 PM,8,27.6,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_674
-Order_No_28026,User_Id_3291,Bike,3,Business,28,5,1:31:11 PM,28,5,1:31:46 PM,28,5,1:39:28 PM,28,5,1:40:33 PM,4,25.4,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_26
-Order_No_1707,User_Id_1991,Bike,3,Personal,27,7,4:56:18 PM,27,7,4:56:43 PM,27,7,5:37:49 PM,27,7,5:44:58 PM,14,,,-1.32189,36.8589727,-1.3047583999999999,36.773910799999996,Rider_Id_26
-Order_No_22051,User_Id_1280,Bike,3,Business,4,1,3:55:27 PM,4,1,3:55:39 PM,4,1,3:59:21 PM,4,1,4:04:19 PM,4,,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_523
-Order_No_6491,User_Id_1500,Bike,3,Business,14,3,2:14:13 PM,14,3,2:14:52 PM,14,3,2:29:03 PM,14,3,2:30:10 PM,10,25.6,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_257
-Order_No_26191,User_Id_3291,Bike,3,Business,3,1,9:14:01 AM,3,1,9:14:47 AM,3,1,9:16:51 AM,3,1,9:26:48 AM,11,18.4,5.0,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_161
-Order_No_25762,User_Id_2154,Bike,3,Business,5,5,5:46:25 PM,5,5,5:50:08 PM,5,5,6:00:58 PM,5,5,6:04:52 PM,9,26.3,,-1.2492395,36.7986032,-1.2765736,36.851364600000004,Rider_Id_96
-Order_No_6087,User_Id_733,Bike,3,Business,13,3,2:16:43 PM,13,3,2:51:27 PM,13,3,3:00:25 PM,13,3,3:09:48 PM,7,27.0,,-1.2551895,36.7822034,-1.2795549,36.7627011,Rider_Id_862
-Order_No_19822,User_Id_291,Bike,1,Personal,29,5,11:18:28 AM,29,5,11:38:08 AM,29,5,11:52:55 AM,29,5,11:55:25 AM,6,19.5,,-1.2556829999999999,36.813771,-1.2920972,36.8102988,Rider_Id_616
-Order_No_8667,User_Id_1500,Bike,3,Business,22,3,10:49:05 AM,22,3,10:53:18 AM,22,3,10:59:21 AM,22,3,11:06:21 AM,7,23.1,,-1.2574219,36.7927073,-1.2770708,36.8231093,Rider_Id_139
-Order_No_23674,User_Id_733,Bike,3,Business,18,2,3:37:12 PM,18,2,3:37:32 PM,18,2,3:44:05 PM,18,2,3:47:09 PM,18,25.7,,-1.2584143,36.8048002,-1.3251247,36.899592600000005,Rider_Id_456
-Order_No_2691,User_Id_2419,Bike,1,Personal,24,1,8:59:35 AM,24,1,8:59:53 AM,24,1,9:00:08 AM,24,1,9:19:30 AM,3,15.5,,-1.2551895,36.7822034,-1.2663351,36.8099425,Rider_Id_881
-Order_No_21006,User_Id_776,Bike,1,Personal,10,2,10:50:27 AM,10,2,10:50:38 AM,10,2,11:02:44 AM,10,2,11:03:48 AM,13,20.1,,-1.278967,36.8236745,-1.3145553,36.749384899999995,Rider_Id_629
-Order_No_10304,User_Id_517,Bike,3,Business,19,2,7:59:09 AM,19,2,7:59:20 AM,19,2,7:59:26 AM,19,2,8:45:08 AM,14,20.9,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_39
-Order_No_18320,User_Id_3420,Bike,3,Business,6,2,11:24:00 AM,6,2,11:46:56 AM,6,2,11:51:06 AM,6,2,12:02:10 PM,6,25.2,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_774
-Order_No_2900,User_Id_2982,Bike,3,Business,30,4,10:25:47 AM,30,4,11:12:03 AM,30,4,11:32:00 AM,30,4,12:00:01 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_416
-Order_No_3181,User_Id_1471,Bike,3,Business,7,3,11:08:13 AM,7,3,11:09:19 AM,7,3,11:16:17 AM,7,3,11:17:24 AM,5,25.1,,-1.2551895,36.7822034,-1.2677588,36.8169359,Rider_Id_467
-Order_No_10356,User_Id_246,Bike,3,Business,2,2,12:37:38 PM,2,2,12:38:31 PM,2,2,12:41:53 PM,2,2,12:47:54 PM,2,20.8,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_848
-Order_No_9758,User_Id_2708,Bike,2,Personal,9,4,5:50:11 PM,9,4,5:50:41 PM,9,4,5:57:25 PM,9,4,6:00:25 PM,3,23.2,,-1.3018758,36.75908629999999,-1.2901638000000002,36.7787225,Rider_Id_247
-Order_No_18366,User_Id_3355,Bike,3,Business,16,7,1:19:16 PM,16,7,1:20:29 PM,16,7,1:33:10 PM,16,7,1:39:56 PM,7,24.2,,-1.2584143,36.8048002,-1.3052491000000002,36.8223899,Rider_Id_725
-Order_No_12576,User_Id_1990,Bike,3,Business,5,2,11:26:00 AM,5,2,11:26:39 AM,5,2,11:29:02 AM,5,2,12:05:30 PM,9,25.3,,-1.286341,36.8172393,-1.275442,36.766020700000006,Rider_Id_564
-Order_No_10437,User_Id_2828,Bike,1,Business,20,1,2:55:01 PM,20,1,2:58:42 PM,20,1,3:06:08 PM,20,1,3:10:51 PM,8,21.5,,-1.2696183,36.8021778,-1.31522,36.837006,Rider_Id_249
-Order_No_16689,User_Id_3080,Bike,3,Business,20,1,11:51:27 AM,20,1,11:51:35 AM,20,1,11:57:26 AM,20,1,12:11:16 PM,12,20.3,,-1.2551895,36.7822034,-1.3322618,36.8045412,Rider_Id_213
-Order_No_19180,User_Id_2520,Bike,2,Personal,27,1,8:48:26 AM,27,1,8:49:07 AM,27,1,8:59:15 AM,27,1,9:05:18 AM,13,16.6,,-1.277783,36.7816464,-1.3083777,36.85900770000001,Rider_Id_249
-Order_No_558,User_Id_956,Bike,3,Personal,30,3,4:09:12 PM,30,3,4:10:49 PM,30,3,4:36:58 PM,30,3,4:41:28 PM,17,27.5,,-1.3136451999999998,36.7974014,-1.2183760000000001,36.8821822,Rider_Id_328
-Order_No_4530,User_Id_517,Bike,3,Business,28,5,1:49:22 PM,28,5,1:49:30 PM,28,5,1:54:02 PM,28,5,1:58:37 PM,11,25.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523
-Order_No_8858,User_Id_873,Bike,1,Personal,1,1,8:45:31 AM,1,1,8:47:16 AM,1,1,8:55:10 AM,1,1,8:59:42 AM,9,18.6,,-1.3308746999999999,36.8567485,-1.2878148999999999,36.8146245,Rider_Id_263
-Order_No_4086,User_Id_3517,Bike,3,Business,29,5,10:28:02 AM,29,5,10:35:07 AM,29,5,10:58:40 AM,29,5,11:20:10 AM,24,26.8,,-1.300921,36.828195,-1.2573831,36.9830159,Rider_Id_411
-Order_No_8514,User_Id_2330,Bike,3,Business,27,1,10:21:25 AM,27,1,10:22:10 AM,27,1,10:24:13 AM,27,1,10:25:35 AM,9,18.1,0.9,-1.3015197,36.7658456,-1.2645318,36.7981775,Rider_Id_344
-Order_No_16276,User_Id_2982,Bike,3,Business,29,6,11:13:06 AM,29,6,11:14:36 AM,29,6,11:58:27 AM,29,6,12:20:29 PM,18,24.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_404
-Order_No_20456,User_Id_3559,Bike,1,Personal,1,5,12:03:51 PM,1,5,12:04:25 PM,1,5,12:26:52 PM,1,5,12:37:24 PM,9,22.7,,-1.2788235000000001,36.7876985,-1.3090663,36.813686499999996,Rider_Id_755
-Order_No_1578,User_Id_909,Bike,3,Business,25,4,3:17:44 PM,25,4,3:26:29 PM,25,4,3:45:19 PM,25,4,3:55:17 PM,16,20.6,,-1.3052491000000002,36.8223899,-1.225322,36.80855,Rider_Id_532
-Order_No_8532,User_Id_3291,Bike,3,Business,23,2,12:37:25 PM,23,2,12:58:10 PM,23,2,1:11:23 PM,23,2,1:36:53 PM,9,,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_168
-Order_No_7923,User_Id_53,Bike,3,Business,14,4,11:45:47 AM,14,4,2:17:15 PM,14,4,2:17:38 PM,14,4,2:18:52 PM,5,28.1,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_518
-Order_No_10648,User_Id_1209,Bike,3,Business,10,5,10:24:28 AM,10,5,10:25:11 AM,10,5,10:25:21 AM,10,5,10:45:03 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_79
-Order_No_17094,User_Id_1384,Bike,3,Business,6,4,10:28:41 AM,6,4,10:29:21 AM,6,4,10:29:22 AM,6,4,10:41:58 AM,25,19.9,,-1.2517448,36.7924971,-1.1594754999999999,36.9509792,Rider_Id_762
-Order_No_5302,User_Id_3647,Bike,3,Business,12,5,9:34:07 AM,12,5,9:35:01 AM,12,5,9:37:45 AM,12,5,9:42:21 AM,8,21.6,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_205
-Order_No_22595,User_Id_393,Bike,3,Business,10,5,8:44:42 AM,10,5,8:45:14 AM,10,5,8:47:55 AM,10,5,9:04:20 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211
-Order_No_24757,User_Id_2982,Bike,3,Business,13,4,10:21:17 AM,13,4,10:22:14 AM,13,4,10:32:34 AM,13,4,10:49:37 AM,18,23.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_656
-Order_No_11366,User_Id_1482,Bike,3,Business,21,2,9:54:01 AM,21,2,9:54:12 AM,21,2,10:28:28 AM,21,2,10:30:31 AM,15,,,-1.2649605,36.798177700000004,-1.3385790000000002,36.89630379999999,Rider_Id_96
-Order_No_23383,User_Id_393,Bike,3,Business,30,2,12:16:13 PM,30,2,12:20:27 PM,30,2,12:33:10 PM,30,2,12:40:11 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_709
-Order_No_15233,User_Id_1594,Bike,3,Business,14,7,3:56:24 PM,14,7,4:15:26 PM,14,7,4:43:33 PM,14,7,4:45:03 PM,10,27.6,,-1.2285834999999998,36.882646799999996,-1.1898857,36.9103054,Rider_Id_679
-Order_No_20343,User_Id_1841,Bike,3,Business,7,2,9:29:14 AM,7,2,9:29:23 AM,7,2,9:45:39 AM,7,2,9:51:28 AM,8,16.7,,-1.2571472,36.795063299999995,-1.3007233999999999,36.805301799999995,Rider_Id_882
-Order_No_26410,User_Id_2583,Bike,3,Personal,24,3,4:16:01 PM,24,3,4:17:15 PM,24,3,4:27:32 PM,24,3,4:32:59 PM,17,,,-1.2607568999999998,36.7747575,-1.2091558999999998,36.7753704,Rider_Id_245
-Order_No_16281,User_Id_3621,Bike,3,Business,15,7,2:01:16 PM,15,7,2:01:40 PM,15,7,2:14:12 PM,15,7,2:17:02 PM,8,19.6,,-1.2836583000000001,36.7886014,-1.3068030000000002,36.831527,Rider_Id_722
-Order_No_26361,User_Id_2874,Bike,2,Personal,9,6,5:19:57 PM,9,6,5:20:20 PM,9,6,5:41:51 PM,9,6,5:44:07 PM,23,19.2,,-1.22606,36.80039,-1.3340505,36.711903799999995,Rider_Id_116
-Order_No_12801,User_Id_393,Bike,3,Business,19,3,11:39:24 AM,19,3,11:39:42 AM,19,3,11:39:50 AM,19,3,11:54:08 AM,14,22.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515
-Order_No_6453,User_Id_2937,Bike,2,Personal,6,4,7:43:59 AM,6,4,7:59:57 AM,6,4,8:24:05 AM,6,4,8:27:13 AM,10,17.9,,-1.2269178,36.839239299999996,-1.2881035,36.8142095,Rider_Id_453
-Order_No_18965,User_Id_1162,Bike,3,Business,8,2,3:52:13 PM,8,2,3:54:03 PM,8,2,4:14:46 PM,8,2,4:26:32 PM,16,25.4,,-1.3122610000000001,36.794658,-1.3111777,36.90666829999999,Rider_Id_847
-Order_No_18737,User_Id_1502,Bike,3,Business,16,3,1:18:52 PM,16,3,1:22:18 PM,16,3,1:43:40 PM,16,3,1:58:47 PM,15,26.8,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_783
-Order_No_4310,User_Id_3091,Bike,3,Business,16,4,3:31:19 PM,16,4,3:32:02 PM,16,4,4:02:59 PM,16,4,4:19:09 PM,4,,,-1.290894,36.822971,-1.2965841999999999,36.8234722,Rider_Id_106
-Order_No_6770,User_Id_3318,Bike,3,Business,24,3,9:33:31 AM,24,3,9:48:29 AM,24,3,9:56:09 AM,24,3,10:00:14 AM,21,,,-1.4226524999999999,36.9530825,-1.3004062,36.829741,Rider_Id_498
-Order_No_20634,User_Id_1642,Bike,3,Business,12,2,11:55:40 AM,12,2,12:30:57 PM,12,2,12:35:34 PM,12,2,1:03:24 PM,5,20.9,,-1.288269,36.818275299999996,-1.2728069,36.7962362,Rider_Id_217
-Order_No_13667,User_Id_1959,Bike,3,Business,3,5,1:19:14 PM,3,5,1:19:53 PM,3,5,1:31:54 PM,3,5,1:36:34 PM,9,,,-1.2945178999999998,36.7685349,-1.2551895,36.7822034,Rider_Id_197
-Order_No_25094,User_Id_265,Bike,3,Business,21,3,4:49:29 PM,21,3,5:05:54 PM,21,3,5:12:36 PM,21,3,5:19:30 PM,5,,,-1.300921,36.828195,-1.3042113000000002,36.8683022,Rider_Id_669
-Order_No_13871,User_Id_2989,Bike,3,Business,13,5,8:38:45 AM,13,5,8:38:55 AM,13,5,8:40:58 AM,13,5,9:18:23 AM,19,15.5,,-1.264788,36.7883897,-1.3463041999999998,36.768164,Rider_Id_103
-Order_No_15912,User_Id_12,Bike,3,Business,6,4,12:36:27 PM,6,4,12:49:02 PM,6,4,1:12:34 PM,6,4,1:17:51 PM,9,23.9,,-1.2400209999999998,36.849990000000005,-1.2864963,36.8252086,Rider_Id_764
-Order_No_25215,User_Id_302,Bike,3,Business,28,6,6:48:55 PM,28,6,6:49:16 PM,28,6,6:54:52 PM,28,6,7:11:41 PM,6,23.2,,-1.2649605,36.798177700000004,-1.2909633999999999,36.7813995,Rider_Id_427
-Order_No_2976,User_Id_1798,Bike,3,Business,30,2,11:13:21 AM,30,2,11:13:36 AM,30,2,11:25:36 AM,30,2,11:29:52 AM,3,22.4,,-1.2615888000000002,36.7928732,-1.2630856000000001,36.808280700000005,Rider_Id_50
-Order_No_24264,User_Id_814,Bike,3,Business,11,5,3:53:13 PM,11,5,3:53:45 PM,11,5,4:09:14 PM,11,5,4:11:56 PM,12,26.6,,-1.3014461000000002,36.7661381,-1.25282,36.68764,Rider_Id_210
-Order_No_20817,User_Id_3154,Bike,1,Business,12,3,4:33:35 PM,12,3,4:33:49 PM,12,3,4:41:23 PM,12,3,4:55:28 PM,16,24.1,,-1.3326173000000001,36.71462329999999,-1.3233496,36.8139159,Rider_Id_620
-Order_No_7527,User_Id_3300,Bike,3,Business,11,2,1:04:46 PM,11,2,1:07:28 PM,11,2,1:11:02 PM,11,2,1:26:04 PM,9,19.6,,-1.259478,36.801781299999995,-1.303596,36.778378000000004,Rider_Id_484
-Order_No_7264,User_Id_2258,Bike,3,Business,17,3,10:33:57 AM,17,3,10:34:18 AM,17,3,10:34:30 AM,17,3,10:41:35 AM,3,25.6,,-1.2646084,36.8028645,-1.2635089,36.78873370000001,Rider_Id_446
-Order_No_10177,User_Id_1271,Bike,3,Business,10,3,9:47:04 AM,10,3,9:48:54 AM,10,3,10:09:52 AM,10,3,10:32:41 AM,8,27.9,,-1.3244885,36.897791999999995,-1.3263105,36.8622904,Rider_Id_907
-Order_No_16406,User_Id_646,Bike,3,Personal,15,1,1:58:50 PM,15,1,1:59:12 PM,15,1,2:02:00 PM,15,1,2:06:51 PM,6,,,-1.2951264,36.798382000000004,-1.2665327,36.8108799,Rider_Id_195
-Order_No_11835,User_Id_111,Bike,3,Business,15,3,3:01:37 PM,15,3,3:01:53 PM,15,3,3:37:28 PM,15,3,3:41:18 PM,4,26.1,,-1.2551895,36.7822034,-1.2728947,36.807942600000004,Rider_Id_422
-Order_No_13139,User_Id_393,Bike,3,Business,2,2,8:43:32 AM,2,2,8:46:03 AM,2,2,8:54:06 AM,2,2,8:59:29 AM,4,20.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_397
-Order_No_7761,User_Id_685,Bike,3,Business,10,1,2:05:15 PM,10,1,2:08:33 PM,10,1,2:17:04 PM,10,1,2:21:16 PM,5,18.2,,-1.265432,36.795034,-1.2876365,36.816096,Rider_Id_778
-Order_No_21559,User_Id_1245,Bike,3,Business,2,3,1:44:50 PM,2,3,1:45:06 PM,2,3,1:55:55 PM,2,3,2:20:55 PM,31,25.9,,-1.300921,36.828195,-1.3844264,36.676747,Rider_Id_718
-Order_No_27250,User_Id_2828,Bike,3,Business,1,5,9:46:29 AM,1,5,10:17:33 AM,1,5,10:35:32 AM,1,5,11:04:00 AM,13,28.7,,-1.2710687,36.8142839,-1.2778747,36.883182899999994,Rider_Id_716
-Order_No_13418,User_Id_1245,Bike,3,Business,10,5,1:25:24 PM,10,5,1:30:52 PM,10,5,1:39:48 PM,10,5,2:14:49 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_943
-Order_No_20363,User_Id_2174,Bike,3,Business,14,2,12:32:17 PM,14,2,12:34:19 PM,14,2,12:34:28 PM,14,2,1:31:27 PM,16,,,-1.2482781,36.884091999999995,-1.2016076999999998,36.930397,Rider_Id_86
-Order_No_9992,User_Id_3647,Bike,3,Business,1,1,3:47:51 PM,1,1,3:49:35 PM,1,1,4:11:09 PM,1,1,4:17:59 PM,8,,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_223
-Order_No_11784,User_Id_1526,Bike,3,Personal,12,2,3:54:34 PM,12,2,3:55:01 PM,12,2,4:26:39 PM,12,2,4:54:28 PM,30,26.6,,-1.1471704,36.96360429999999,-1.2886166000000001,36.78368029999999,Rider_Id_882
-Order_No_4624,User_Id_411,Bike,3,Business,19,3,3:44:36 PM,19,3,3:45:54 PM,19,3,4:11:26 PM,19,3,4:15:46 PM,11,24.5,,-1.3008601,36.8542593,-1.2768326,36.8073804,Rider_Id_779
-Order_No_22760,User_Id_2200,Bike,3,Business,6,1,9:27:48 AM,6,1,9:29:14 AM,6,1,9:32:42 AM,6,1,9:43:30 AM,5,,,-1.273488,36.804158,-1.290894,36.822971,Rider_Id_119
-Order_No_17622,User_Id_2587,Bike,3,Business,12,1,12:04:12 PM,12,1,12:22:43 PM,12,1,12:29:58 PM,12,1,12:46:40 PM,13,24.0,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_744
-Order_No_9565,User_Id_111,Bike,3,Business,16,2,12:15:49 PM,16,2,12:16:46 PM,16,2,12:21:45 PM,16,2,12:23:06 PM,13,,,-1.2551895,36.7822034,-1.3233955,36.856294399999996,Rider_Id_570
-Order_No_5162,User_Id_543,Bike,3,Business,19,6,10:39:32 AM,19,6,10:44:21 AM,19,6,10:55:34 AM,19,6,11:00:54 AM,5,21.5,,-1.306654,36.841896999999996,-1.3094562,36.8167806,Rider_Id_892
-Order_No_24657,User_Id_3560,Bike,3,Business,22,5,11:01:44 AM,22,5,11:05:05 AM,22,5,11:23:22 AM,22,5,11:34:40 AM,1,28.7,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_119
-Order_No_385,User_Id_3586,Bike,3,Business,17,3,12:21:22 PM,17,3,12:21:35 PM,17,3,12:38:33 PM,17,3,12:41:52 PM,10,25.6,,-1.275442,36.766020700000006,-1.2810888999999999,36.8247135,Rider_Id_829
-Order_No_7312,User_Id_2423,Bike,3,Business,13,3,1:10:31 PM,13,3,1:12:52 PM,13,3,1:13:02 PM,13,3,1:36:09 PM,8,27.0,,-1.2551895,36.7822034,-1.300722,36.816865,Rider_Id_406
-Order_No_6779,User_Id_1816,Bike,1,Business,16,1,2:42:11 PM,16,1,2:42:43 PM,16,1,2:52:13 PM,16,1,2:53:51 PM,5,22.4,,-1.296684,36.770171999999995,-1.3028391000000001,36.7951112,Rider_Id_140
-Order_No_292,User_Id_1070,Bike,1,Personal,30,4,8:58:28 AM,30,4,8:59:08 AM,30,4,9:08:37 AM,30,4,9:14:05 AM,14,16.3,,-1.25537,36.7756,-1.3193656,36.7957179,Rider_Id_334
-Order_No_569,User_Id_1207,Bike,3,Business,12,3,11:35:31 AM,12,3,11:44:12 AM,12,3,12:07:27 PM,12,3,12:11:43 PM,5,19.3,,-1.2854453000000001,36.818836299999994,-1.2628638,36.8070399,Rider_Id_538
-Order_No_131,User_Id_3525,Bike,3,Business,5,3,2:59:52 PM,5,3,3:37:59 PM,5,3,3:49:59 PM,5,3,3:54:19 PM,17,24.9,,-1.3700383,36.9190174,-1.295324,36.839628999999995,Rider_Id_532
-Order_No_22105,User_Id_391,Bike,3,Business,29,2,2:15:21 PM,29,2,2:19:07 PM,29,2,2:57:50 PM,29,2,3:26:15 PM,5,26.8,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_696
-Order_No_12577,User_Id_1976,Bike,3,Business,22,5,8:35:36 AM,22,5,8:52:10 AM,22,5,9:19:16 AM,22,5,9:21:04 AM,8,,,-1.2809356,36.82322620000001,-1.257028,36.7810435,Rider_Id_127
-Order_No_5562,User_Id_1280,Bike,3,Business,12,2,2:41:47 PM,12,2,2:42:44 PM,12,2,2:57:14 PM,12,2,2:58:37 PM,8,,,-1.2551895,36.7822034,-1.2897215,36.7945959,Rider_Id_131
-Order_No_8403,User_Id_1381,Bike,2,Personal,4,5,1:26:08 PM,4,5,1:26:36 PM,4,5,1:45:09 PM,4,5,1:48:57 PM,10,24.4,,-1.22606,36.80039,-1.2668611,36.795638700000005,Rider_Id_103
-Order_No_13922,User_Id_3080,Bike,3,Business,13,3,9:57:36 AM,13,3,9:57:57 AM,13,3,10:46:14 AM,13,3,10:47:17 AM,12,,,-1.3282097,36.847382200000006,-1.2571472,36.795063299999995,Rider_Id_349
-Order_No_27835,User_Id_2587,Bike,3,Business,2,3,11:47:05 AM,2,3,12:32:28 PM,2,3,12:32:33 PM,2,3,12:51:22 PM,14,24.2,,-1.2860183,36.8975338,-1.28878,36.8168312,Rider_Id_886
-Order_No_4306,User_Id_2306,Bike,3,Business,1,5,2:18:11 PM,1,5,2:19:06 PM,1,5,2:19:31 PM,1,5,2:54:34 PM,16,32.1,,-1.3271108999999999,36.8093583,-1.2271998,36.808490500000005,Rider_Id_89
-Order_No_26343,User_Id_1363,Bike,3,Business,21,1,10:17:09 AM,21,1,10:18:43 AM,21,1,10:33:18 AM,21,1,10:37:44 AM,7,25.3,,-1.3004062,36.829741,-1.2655306,36.828148,Rider_Id_758
-Order_No_27850,User_Id_2013,Bike,3,Personal,18,1,8:50:43 AM,18,1,8:51:55 AM,18,1,9:11:53 AM,18,1,9:14:20 AM,12,15.2,0.1,-1.2840461,36.7934021,-1.238874,36.807889,Rider_Id_808
-Order_No_6834,User_Id_1402,Bike,3,Business,15,6,11:26:30 AM,15,6,11:40:44 AM,15,6,11:58:26 AM,15,6,12:03:05 PM,2,22.1,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_879
-Order_No_28082,User_Id_1578,Bike,3,Business,30,3,10:47:56 AM,30,3,10:48:43 AM,30,3,11:05:01 AM,30,3,11:38:23 AM,8,22.7,,-1.3147073999999999,36.811721999999996,-1.2765736,36.851364600000004,Rider_Id_808
-Order_No_3420,User_Id_3126,Bike,1,Personal,16,1,6:27:19 PM,16,1,6:28:37 PM,16,1,6:40:32 PM,16,1,6:51:09 PM,3,,,-1.2597777000000001,36.7927743,-1.2659148999999998,36.805432200000006,Rider_Id_116
-Order_No_21523,User_Id_516,Bike,3,Business,30,2,10:52:48 AM,30,2,10:54:03 AM,30,2,11:08:12 AM,30,2,11:11:59 AM,7,,,-1.2551895,36.7822034,-1.2540684,36.823951799999996,Rider_Id_547
-Order_No_26330,User_Id_3283,Bike,3,Business,21,4,2:01:10 PM,21,4,2:01:20 PM,21,4,2:03:24 PM,21,4,2:22:01 PM,5,26.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_383
-Order_No_23616,User_Id_2395,Bike,1,Personal,13,7,10:35:07 AM,13,7,10:35:12 AM,13,7,10:36:46 AM,13,7,10:44:26 AM,3,23.8,,-1.2763494,36.7671783,-1.2812903,36.780851899999995,Rider_Id_449
-Order_No_384,User_Id_3503,Bike,2,Personal,3,3,2:07:06 PM,3,3,2:27:33 PM,3,3,2:33:15 PM,3,3,3:02:19 PM,14,26.6,,-1.3299841000000001,36.8466903,-1.2828204,36.78426210000001,Rider_Id_427
-Order_No_3986,User_Id_2587,Bike,3,Business,8,6,1:12:53 PM,8,6,1:13:10 PM,8,6,1:21:01 PM,8,6,1:34:58 PM,1,24.7,,-1.28878,36.8168312,-1.2803414,36.8265456,Rider_Id_74
-Order_No_20128,User_Id_1271,Bike,3,Business,25,1,11:24:41 AM,25,1,11:25:38 AM,25,1,11:32:06 AM,25,1,11:43:19 AM,9,25.4,,-1.3244885,36.897791999999995,-1.3223621,36.8459855,Rider_Id_675
-Order_No_4203,User_Id_1209,Bike,3,Business,24,3,3:30:35 PM,24,3,3:34:11 PM,24,3,3:42:22 PM,24,3,3:47:26 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_956
-Order_No_2996,User_Id_393,Bike,3,Business,26,3,10:22:08 AM,26,3,10:22:40 AM,26,3,10:40:53 AM,26,3,10:55:30 AM,4,24.0,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_731
-Order_No_6915,User_Id_2066,Bike,3,Business,26,5,12:47:35 PM,26,5,12:47:54 PM,26,5,12:48:21 PM,26,5,1:04:32 PM,21,,,-1.3210188999999999,36.841055100000005,-1.207546,36.7913976,Rider_Id_120
-Order_No_21106,User_Id_2154,Bike,3,Business,9,6,9:08:09 AM,9,6,9:08:41 AM,9,6,9:13:16 AM,9,6,9:19:47 AM,8,21.0,,-1.2413316,36.878552500000005,-1.2765736,36.851364600000004,Rider_Id_932
-Order_No_19422,User_Id_635,Bike,3,Business,12,5,10:30:14 AM,12,5,10:30:52 AM,12,5,10:31:58 AM,12,5,11:47:14 AM,10,26.5,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_595
-Order_No_26249,User_Id_1693,Bike,3,Business,15,5,10:33:15 AM,15,5,10:35:15 AM,15,5,10:36:00 AM,15,5,10:42:53 AM,7,25.7,,-1.2993714,36.819697399999995,-1.2574087999999999,36.799712,Rider_Id_178
-Order_No_24908,User_Id_1304,Bike,3,Business,4,2,1:53:33 PM,4,2,2:32:58 PM,4,2,3:07:44 PM,4,2,3:09:53 PM,13,24.0,10.9,-1.3226301999999999,36.8719901,-1.29387,36.7907813,Rider_Id_936
-Order_No_13253,User_Id_2344,Bike,3,Business,14,3,12:09:41 PM,14,3,12:21:59 PM,14,3,12:30:39 PM,14,3,12:32:57 PM,7,24.5,,-1.2551895,36.7822034,-1.2875845,36.8135104,Rider_Id_88
-Order_No_17067,User_Id_635,Bike,3,Business,19,1,12:02:24 PM,19,1,12:04:01 PM,19,1,12:05:23 PM,19,1,12:17:52 PM,4,24.6,,-1.3016417,36.8271679,-1.3134523999999999,36.8064495,Rider_Id_669
-Order_No_16,User_Id_346,Bike,3,Business,5,2,12:07:03 PM,5,2,12:13:37 PM,5,2,12:13:49 PM,5,2,12:31:54 PM,3,23.8,,-1.28878,36.8168312,-1.2676593,36.8103939,Rider_Id_70
-Order_No_7440,User_Id_60,Bike,3,Business,19,2,1:26:43 PM,19,2,1:27:16 PM,19,2,1:27:27 PM,19,2,1:37:36 PM,12,30.1,,-1.307143,36.825009,-1.2296618000000001,36.84382720000001,Rider_Id_31
-Order_No_17353,User_Id_2978,Bike,3,Business,3,2,11:36:24 AM,3,2,11:39:03 AM,3,2,12:08:57 PM,3,2,12:11:09 PM,7,18.6,,-1.278612,36.8195876,-1.2551895,36.7822034,Rider_Id_726
-Order_No_5131,User_Id_868,Bike,3,Business,20,4,2:48:08 PM,20,4,4:01:54 PM,20,4,4:14:58 PM,20,4,4:23:17 PM,6,25.2,,-1.265715,36.823815,-1.307143,36.825009,Rider_Id_52
-Order_No_13931,User_Id_3222,Bike,3,Business,24,4,10:07:23 AM,24,4,10:08:13 AM,24,4,10:08:51 AM,24,4,10:15:32 AM,16,19.2,,-1.2551895,36.7822034,-1.3312488,36.8801409,Rider_Id_268
-Order_No_11111,User_Id_2801,Bike,3,Business,8,1,11:25:32 AM,8,1,11:26:09 AM,8,1,11:30:22 AM,8,1,11:48:36 AM,9,24.4,,-1.2690701000000002,36.8314977,-1.2595421999999998,36.7871178,Rider_Id_179
-Order_No_32,User_Id_3382,Bike,1,Personal,16,3,3:32:30 PM,16,3,3:32:52 PM,16,3,3:49:11 PM,16,3,3:58:30 PM,14,25.3,,-1.2261913,36.836811700000005,-1.322859,36.846287700000005,Rider_Id_410
-Order_No_10876,User_Id_3613,Bike,3,Business,23,2,3:49:44 PM,23,2,3:57:00 PM,23,2,4:16:24 PM,23,2,4:27:09 PM,6,19.0,,-1.2733622,36.7704045,-1.2676041,36.8062903,Rider_Id_952
-Order_No_24077,User_Id_3391,Bike,1,Personal,19,5,10:23:37 AM,19,5,10:24:21 AM,19,5,10:36:42 AM,19,5,10:41:21 AM,3,25.1,,-1.2998789,36.816896299999996,-1.304214,36.8276533,Rider_Id_698
-Order_No_17997,User_Id_2716,Bike,3,Business,17,4,8:46:07 AM,17,4,8:46:23 AM,17,4,8:54:57 AM,17,4,8:59:55 AM,9,,,-1.2865066,36.7327098,-1.2917868000000001,36.7872675,Rider_Id_205
-Order_No_7919,User_Id_635,Bike,3,Business,12,4,8:13:01 AM,12,4,8:55:42 AM,12,4,9:05:26 AM,12,4,9:14:13 AM,3,16.1,,-1.2509176000000002,36.8246646,-1.2599563,36.799343900000004,Rider_Id_427
-Order_No_22436,User_Id_988,Bike,3,Personal,13,1,3:20:05 PM,13,1,3:20:15 PM,13,1,3:29:43 PM,13,1,3:41:39 PM,11,19.6,,-1.3196808999999998,36.8410572,-1.2844665000000002,36.7866113,Rider_Id_449
-Order_No_4898,User_Id_2289,Bike,3,Business,27,3,2:13:29 PM,27,3,2:14:40 PM,27,3,2:15:05 PM,27,3,2:21:58 PM,9,29.3,,-1.2551895,36.7822034,-1.2857117,36.7968296,Rider_Id_523
-Order_No_24293,User_Id_117,Bike,1,Personal,30,1,9:19:46 AM,30,1,9:20:45 AM,30,1,9:31:19 AM,30,1,9:40:08 AM,7,16.3,1.9,-1.2732868999999998,36.832159000000004,-1.2601,36.796528,Rider_Id_174
-Order_No_8158,User_Id_111,Bike,3,Business,9,4,3:44:50 PM,9,4,4:02:41 PM,9,4,4:05:15 PM,9,4,4:10:44 PM,3,,,-1.2551895,36.7822034,-1.2568411,36.8012533,Rider_Id_538
-Order_No_14597,User_Id_3140,Bike,1,Personal,27,4,11:29:48 AM,27,4,11:34:24 AM,27,4,11:58:05 AM,27,4,12:01:22 PM,9,22.8,,-1.2850643999999998,36.8343657,-1.3032409,36.8815432,Rider_Id_817
-Order_No_20935,User_Id_75,Bike,2,Personal,12,3,3:13:37 PM,12,3,3:14:00 PM,12,3,3:41:14 PM,12,3,3:48:21 PM,19,26.5,,-1.2367887,36.8628024,-1.3014462,36.7607695,Rider_Id_384
-Order_No_13627,User_Id_269,Bike,3,Business,6,4,11:54:14 AM,6,4,12:02:11 PM,6,4,12:29:06 PM,6,4,12:37:52 PM,15,24.3,,-1.3472066,36.7692638,-1.3226301999999999,36.8719901,Rider_Id_114
-Order_No_16579,User_Id_2579,Bike,3,Business,21,3,9:47:50 AM,21,3,9:49:19 AM,21,3,10:00:21 AM,21,3,10:07:18 AM,6,16.9,1.1,-1.2592947,36.7698085,-1.287428,36.7800821,Rider_Id_649
-Order_No_24655,User_Id_393,Bike,3,Business,14,4,12:31:26 PM,14,4,1:05:34 PM,14,4,1:18:28 PM,14,4,1:42:34 PM,11,27.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_365
-Order_No_374,User_Id_1500,Bike,3,Business,16,4,11:19:54 AM,16,4,11:20:42 AM,16,4,11:29:56 AM,16,4,11:31:55 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161
-Order_No_21709,User_Id_393,Bike,3,Business,7,2,10:28:56 AM,7,2,10:29:26 AM,7,2,10:29:32 AM,7,2,11:31:52 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39
-Order_No_15412,User_Id_723,Bike,1,Personal,31,2,12:26:54 PM,31,2,12:27:40 PM,31,2,12:38:29 PM,31,2,12:46:05 PM,3,18.3,,-1.3034735,36.7986213,-1.2985749,36.8088,Rider_Id_88
-Order_No_16180,User_Id_3326,Bike,3,Business,6,4,12:07:05 PM,6,4,12:08:28 PM,6,4,12:14:38 PM,6,4,12:49:15 PM,8,24.0,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_517
-Order_No_13798,User_Id_3211,Bike,3,Personal,3,2,9:14:08 AM,3,2,9:15:46 AM,3,2,9:36:34 AM,3,2,9:41:14 AM,9,15.1,2.0,-1.2783772,36.8201617,-1.2804664,36.7701111,Rider_Id_844
-Order_No_24243,User_Id_3283,Bike,3,Business,27,4,12:45:42 PM,27,4,12:46:03 PM,27,4,1:13:55 PM,27,4,1:30:10 PM,13,22.9,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_86
-Order_No_3313,User_Id_1715,Bike,3,Business,29,2,2:49:45 PM,29,2,2:52:20 PM,29,2,3:15:21 PM,29,2,3:17:11 PM,3,23.6,,-1.253966,36.7987692,-1.2654447,36.797867100000005,Rider_Id_749
-Order_No_23420,User_Id_2866,Bike,3,Business,4,1,1:07:39 PM,4,1,1:08:27 PM,4,1,1:18:44 PM,4,1,1:25:41 PM,4,25.4,,-1.2696183,36.8021778,-1.2551895,36.7822034,Rider_Id_885
-Order_No_18854,User_Id_3147,Bike,3,Business,12,2,1:35:40 PM,12,2,1:35:50 PM,12,2,1:41:35 PM,12,2,1:47:01 PM,12,26.9,,-1.3472066,36.7692638,-1.2880128999999998,36.8308097,Rider_Id_584
-Order_No_15123,User_Id_517,Bike,3,Business,29,3,9:53:40 AM,29,3,9:53:54 AM,29,3,9:54:01 AM,29,3,10:09:50 AM,14,18.1,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_2414,User_Id_2289,Bike,3,Business,11,5,5:11:06 PM,11,5,5:12:29 PM,11,5,5:14:18 PM,11,5,5:23:59 PM,7,24.5,,-1.2551895,36.7822034,-1.2961143,36.815843,Rider_Id_510
-Order_No_17533,User_Id_3354,Bike,3,Business,13,1,12:49:09 PM,13,1,12:49:26 PM,13,1,12:55:57 PM,13,1,1:07:32 PM,4,,,-1.2571472,36.795063299999995,-1.2747549999999999,36.7906585,Rider_Id_56
-Order_No_23520,User_Id_302,Bike,3,Business,12,5,4:16:50 PM,12,5,4:18:25 PM,12,5,4:18:46 PM,12,5,4:30:14 PM,3,27.2,,-1.2649605,36.798177700000004,-1.2682322,36.793592700000005,Rider_Id_574
-Order_No_25622,User_Id_1329,Bike,3,Business,4,2,11:22:48 AM,4,2,11:22:57 AM,4,2,11:28:50 AM,4,2,11:34:22 AM,2,18.6,,-1.273056,36.811298,-1.2667532,36.8024002,Rider_Id_16
-Order_No_5451,User_Id_1646,Bike,3,Business,20,6,2:25:45 PM,20,6,2:27:04 PM,20,6,2:37:59 PM,20,6,2:45:04 PM,7,,,-1.2517448,36.7924971,-1.2547586000000002,36.762835700000004,Rider_Id_576
-Order_No_22702,User_Id_393,Bike,3,Business,9,4,3:41:04 PM,9,4,3:53:02 PM,9,4,3:53:04 PM,9,4,4:54:13 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_39
-Order_No_12958,User_Id_2364,Bike,3,Personal,15,1,3:00:24 PM,15,1,3:00:48 PM,15,1,3:16:58 PM,15,1,3:23:15 PM,12,,,-1.3121154,36.7109299,-1.273786,36.7806375,Rider_Id_686
-Order_No_18726,User_Id_3686,Bike,3,Business,14,3,12:11:28 PM,14,3,12:12:24 PM,14,3,12:27:55 PM,14,3,12:33:29 PM,6,24.5,,-1.2551895,36.7822034,-1.269974,36.77537279999999,Rider_Id_228
-Order_No_1249,User_Id_1162,Bike,2,Business,28,5,8:49:19 AM,28,5,8:50:35 AM,28,5,9:07:33 AM,28,5,9:14:44 AM,4,20.8,,-1.3122610000000001,36.794658,-1.2883346000000002,36.8148489,Rider_Id_208
-Order_No_22087,User_Id_2701,Bike,3,Business,23,5,12:51:24 PM,23,5,12:52:43 PM,23,5,1:06:49 PM,23,5,1:09:17 PM,4,24.0,,-1.2765736,36.851364600000004,-1.2857548,36.819062,Rider_Id_134
-Order_No_9156,User_Id_393,Bike,3,Business,7,4,10:43:39 AM,7,4,10:44:02 AM,7,4,10:44:11 AM,7,4,11:30:07 AM,14,24.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_239
-Order_No_11913,User_Id_755,Bike,1,Personal,8,7,4:01:18 PM,8,7,4:01:49 PM,8,7,4:16:05 PM,8,7,4:25:46 PM,13,21.4,,-1.282357,36.782501,-1.2289456,36.765112200000004,Rider_Id_465
-Order_No_26120,User_Id_20,Bike,3,Business,4,2,11:14:14 AM,4,2,11:25:56 AM,4,2,11:56:50 AM,4,2,12:06:57 PM,6,19.6,,-1.2628638,36.8070399,-1.300722,36.816865,Rider_Id_62
-Order_No_9046,User_Id_3434,Bike,3,Business,2,2,12:51:07 PM,2,2,1:30:34 PM,2,2,1:34:23 PM,2,2,1:45:31 PM,1,29.5,,-1.311342,36.913268,-1.3123348999999997,36.9152001,Rider_Id_855
-Order_No_17160,User_Id_868,Bike,3,Business,2,4,9:40:18 AM,2,4,9:40:39 AM,2,4,10:00:31 AM,2,4,10:02:29 AM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_205
-Order_No_26858,User_Id_3558,Bike,2,Personal,20,5,5:34:10 PM,20,5,5:39:43 PM,20,5,5:50:55 PM,20,5,5:57:40 PM,6,20.0,,-1.2763916000000002,36.8246826,-1.295408,36.802892,Rider_Id_427
-Order_No_15080,User_Id_1882,Bike,3,Business,28,4,7:46:22 PM,28,4,7:48:37 PM,28,4,8:07:22 PM,28,4,8:40:01 PM,6,22.8,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_269
-Order_No_1106,User_Id_265,Bike,3,Business,23,6,11:41:54 AM,23,6,11:42:56 AM,23,6,11:56:10 AM,23,6,12:00:00 PM,7,26.1,,-1.2857117,36.7968296,-1.300921,36.828195,Rider_Id_332
-Order_No_25735,User_Id_522,Bike,3,Personal,11,1,3:20:54 PM,11,1,3:24:14 PM,11,1,3:28:02 PM,11,1,3:40:10 PM,12,23.1,,-1.2643343,36.7447423,-1.278612,36.8195876,Rider_Id_268
-Order_No_26623,User_Id_3689,Bike,3,Business,31,4,12:10:06 PM,31,4,12:10:20 PM,31,4,12:18:56 PM,31,4,12:21:10 PM,9,26.5,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_857
-Order_No_6681,User_Id_3674,Bike,3,Business,12,2,10:23:32 AM,12,2,10:23:43 AM,12,2,10:29:47 AM,12,2,10:35:41 AM,13,28.0,,-1.2551895,36.7822034,-1.3184737,36.862645,Rider_Id_520
-Order_No_26398,User_Id_1363,Bike,3,Business,4,2,9:45:55 AM,4,2,10:32:35 AM,4,2,10:34:49 AM,4,2,10:36:10 AM,6,20.5,,-1.3004062,36.829741,-1.2617028000000001,36.7983849,Rider_Id_591
-Order_No_13855,User_Id_2457,Bike,3,Business,23,4,2:43:29 PM,23,4,2:44:00 PM,23,4,2:44:09 PM,23,4,2:49:24 PM,6,24.4,,-1.2551895,36.7822034,-1.2407027,36.765004299999994,Rider_Id_576
-Order_No_5039,User_Id_2722,Bike,2,Personal,7,5,4:02:46 PM,7,5,4:05:59 PM,7,5,4:14:46 PM,7,5,4:27:22 PM,11,24.9,,-1.2321733,36.7974377,-1.2901669,36.7685878,Rider_Id_410
-Order_No_7906,User_Id_1054,Bike,3,Personal,17,3,3:17:13 PM,17,3,3:31:40 PM,17,3,3:41:04 PM,17,3,3:57:08 PM,11,28.2,,-1.2640243999999998,36.804768200000005,-1.2222487,36.80814229999999,Rider_Id_726
-Order_No_23207,User_Id_1687,Bike,3,Business,7,4,10:12:46 AM,7,4,10:23:16 AM,7,4,11:16:29 AM,7,4,11:32:30 AM,2,16.8,,-1.2816617000000001,36.81634443,-1.2874648000000002,36.8271985,Rider_Id_159
-Order_No_22640,User_Id_393,Bike,3,Business,10,5,2:42:57 PM,10,5,2:44:28 PM,10,5,2:44:33 PM,10,5,3:12:53 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10
-Order_No_10782,User_Id_322,Bike,3,Business,28,3,3:56:01 PM,28,3,3:56:46 PM,28,3,4:07:21 PM,28,3,4:21:55 PM,22,,,-1.174484,36.914608,-1.2452664,36.802854100000005,Rider_Id_627
-Order_No_17422,User_Id_3183,Bike,1,Personal,29,3,3:26:38 PM,29,3,4:42:04 PM,29,3,4:50:32 PM,29,3,4:55:16 PM,16,19.2,,-1.2994854999999998,36.8164026,-1.364627,36.7410927,Rider_Id_216
-Order_No_16663,User_Id_3323,Bike,2,Personal,29,3,7:27:03 AM,29,3,7:54:20 AM,29,3,7:58:50 AM,29,3,8:15:13 AM,6,15.6,,-1.2885364,36.7995016,-1.269534,36.80697,Rider_Id_722
-Order_No_6005,User_Id_1281,Bike,3,Business,7,1,3:26:50 PM,7,1,3:28:57 PM,7,1,3:57:18 PM,7,1,4:06:55 PM,8,20.6,,-1.2707348,36.765176700000005,-1.269534,36.80697,Rider_Id_729
-Order_No_3008,User_Id_3525,Bike,3,Business,31,5,12:49:02 PM,31,5,1:08:34 PM,31,5,1:15:49 PM,31,5,1:20:13 PM,15,21.8,,-1.3700383,36.9190174,-1.2981102,36.8293513,Rider_Id_582
-Order_No_15743,User_Id_3108,Bike,3,Business,13,3,1:04:41 PM,13,3,1:12:48 PM,13,3,1:13:09 PM,13,3,1:37:50 PM,9,27.3,,-1.2551895,36.7822034,-1.3069127,36.835137200000005,Rider_Id_406
-Order_No_17638,User_Id_3108,Bike,3,Business,16,2,2:55:37 PM,16,2,2:57:40 PM,16,2,3:14:35 PM,16,2,3:18:48 PM,9,27.4,,-1.2551895,36.7822034,-1.3043649,36.7816132,Rider_Id_726
-Order_No_13026,User_Id_2696,Bike,3,Personal,13,4,1:07:31 PM,13,4,1:40:20 PM,13,4,2:03:24 PM,13,4,2:14:50 PM,11,26.4,,-1.2540878,36.7044367,-1.296797,36.776452500000005,Rider_Id_536
-Order_No_17717,User_Id_3613,Bike,3,Business,12,1,11:26:16 AM,12,1,11:31:33 AM,12,1,12:07:23 PM,12,1,12:09:14 PM,4,23.9,,-1.2733622,36.7704045,-1.2941848999999999,36.7895754,Rider_Id_829
-Order_No_24372,User_Id_1271,Bike,3,Business,18,2,3:54:38 PM,18,2,4:11:43 PM,18,2,4:19:24 PM,18,2,4:23:06 PM,19,25.6,,-1.3107918,36.761794,-1.3226301999999999,36.8719901,Rider_Id_631
-Order_No_11588,User_Id_393,Bike,3,Business,22,3,11:34:05 AM,22,3,11:34:48 AM,22,3,12:16:48 PM,22,3,12:37:50 PM,10,23.2,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_56
-Order_No_15848,User_Id_1075,Bike,3,Business,24,3,2:16:40 PM,24,3,2:17:06 PM,24,3,2:25:03 PM,24,3,2:29:44 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_205
-Order_No_21640,User_Id_173,Bike,3,Business,16,5,6:03:25 PM,16,5,6:08:19 PM,16,5,6:16:58 PM,16,5,6:20:23 PM,2,26.0,,-1.2993231,36.7753372,-1.294598,36.781639,Rider_Id_102
-Order_No_12389,User_Id_867,Bike,3,Business,8,1,11:15:44 AM,8,1,11:15:56 AM,8,1,11:16:50 AM,8,1,11:46:50 AM,9,27.9,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_876
-Order_No_4969,User_Id_1719,Bike,3,Business,14,4,11:07:55 AM,14,4,11:11:08 AM,14,4,11:34:03 AM,14,4,11:36:35 AM,16,19.2,,-1.296797,36.776452500000005,-1.2330694,36.745451,Rider_Id_379
-Order_No_13494,User_Id_3718,Bike,3,Business,19,3,11:48:29 AM,19,3,11:54:56 AM,19,3,12:01:01 PM,19,3,12:05:51 PM,5,22.0,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_206
-Order_No_26704,User_Id_1226,Bike,2,Personal,12,5,8:49:51 AM,12,5,8:51:14 AM,12,5,9:06:04 AM,12,5,9:18:34 AM,5,18.8,,-1.2981625,36.813014200000005,-1.2753757,36.8026592,Rider_Id_85
-Order_No_20391,User_Id_2582,Bike,3,Business,14,1,11:59:05 AM,14,1,11:59:18 AM,14,1,12:16:29 PM,14,1,12:21:44 PM,22,23.6,,-1.2551895,36.7822034,-1.333148,36.922453000000004,Rider_Id_205
-Order_No_6519,User_Id_517,Bike,3,Business,23,6,10:55:07 AM,23,6,10:55:19 AM,23,6,11:00:28 AM,23,6,11:14:16 AM,11,26.3,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_436
-Order_No_11641,User_Id_136,Bike,3,Business,17,4,12:25:27 PM,17,4,12:25:48 PM,17,4,12:28:02 PM,17,4,12:30:58 PM,10,25.2,,-1.2531983,36.8069669,-1.3009676000000001,36.7657189,Rider_Id_205
-Order_No_21538,User_Id_1263,Bike,2,Personal,19,2,3:59:35 PM,19,2,3:59:54 PM,19,2,4:09:23 PM,19,2,4:24:05 PM,14,18.5,,-1.269534,36.80697,-1.2962888000000001,36.8892359,Rider_Id_116
-Order_No_11817,User_Id_3647,Bike,3,Business,17,5,9:31:24 AM,17,5,9:32:10 AM,17,5,9:43:13 AM,17,5,9:46:44 AM,4,14.5,,-1.2726389999999999,36.794723,-1.2676758999999997,36.801804,Rider_Id_882
-Order_No_24931,User_Id_391,Bike,3,Business,3,5,10:50:14 AM,3,5,10:52:09 AM,3,5,11:04:19 AM,3,5,12:15:08 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_709
-Order_No_8885,User_Id_3264,Bike,3,Business,18,2,12:18:17 PM,18,2,12:33:44 PM,18,2,12:34:04 PM,18,2,12:41:14 PM,9,,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_518
-Order_No_20649,User_Id_3647,Bike,3,Business,9,4,3:37:08 PM,9,4,3:37:16 PM,9,4,3:44:44 PM,9,4,3:52:59 PM,16,25.7,,-1.2726389999999999,36.794723,-1.339046,36.762596,Rider_Id_400
-Order_No_7120,User_Id_3639,Bike,1,Personal,18,6,10:54:32 AM,18,6,10:55:40 AM,18,6,11:40:35 AM,18,6,12:02:38 PM,15,18.9,,-1.3581835,36.7553227,-1.2765737,36.8175399,Rider_Id_939
-Order_No_2469,User_Id_1502,Bike,3,Business,3,6,3:51:49 PM,3,6,3:56:32 PM,3,6,4:14:10 PM,3,6,4:20:13 PM,4,22.1,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_420
-Order_No_16321,User_Id_1642,Bike,3,Business,27,3,1:59:08 PM,27,3,1:59:31 PM,27,3,2:14:35 PM,27,3,2:23:24 PM,5,29.3,,-1.2726506000000002,36.7960153,-1.2789469,36.7796691,Rider_Id_855
-Order_No_20350,User_Id_3331,Bike,3,Business,5,3,10:27:01 AM,5,3,10:35:14 AM,5,3,10:40:26 AM,5,3,11:05:51 AM,14,22.5,,-1.2551895,36.7822034,-1.2294985,36.8775495,Rider_Id_598
-Order_No_4275,User_Id_3283,Bike,3,Business,21,4,2:30:11 PM,21,4,2:30:46 PM,21,4,2:44:34 PM,21,4,3:08:55 PM,7,26.1,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_919
-Order_No_13564,User_Id_1500,Bike,3,Business,26,2,2:42:12 PM,26,2,2:42:25 PM,26,2,2:42:32 PM,26,2,3:06:46 PM,10,30.8,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_239
-Order_No_21266,User_Id_1600,Bike,3,Business,19,2,9:37:15 AM,19,2,9:37:29 AM,19,2,9:38:01 AM,19,2,9:44:21 AM,11,,,-1.3169591,36.7000029,-1.2961607,36.778829200000004,Rider_Id_647
-Order_No_7,User_Id_60,Bike,3,Business,27,4,9:35:31 AM,27,4,9:36:02 AM,27,4,9:47:43 AM,27,4,9:53:17 AM,8,18.6,3.0,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_882
-Order_No_5993,User_Id_496,Bike,3,Business,20,4,4:49:24 PM,20,4,4:55:36 PM,20,4,5:03:57 PM,20,4,5:05:18 PM,4,22.9,,-1.2551895,36.7822034,-1.2734413,36.81293529999999,Rider_Id_190
-Order_No_13613,User_Id_3507,Bike,3,Personal,23,1,10:04:12 AM,23,1,10:04:30 AM,23,1,10:22:39 AM,23,1,10:28:37 AM,15,14.4,,-1.2944628,36.7522739,-1.3266433000000002,36.832018,Rider_Id_465
-Order_No_7607,User_Id_2118,Bike,1,Personal,4,4,7:44:09 PM,4,4,7:44:32 PM,4,4,7:58:25 PM,4,4,8:05:19 PM,5,,,-1.2952098,36.7825532,-1.265432,36.795034,Rider_Id_578
-Order_No_3816,User_Id_286,Bike,3,Business,30,5,3:52:13 PM,30,5,3:52:58 PM,30,5,4:03:54 PM,30,5,4:09:13 PM,24,24.7,,-1.2551895,36.7822034,-1.364732,36.658943,Rider_Id_205
-Order_No_11440,User_Id_3291,Bike,3,Business,9,5,11:48:10 AM,9,5,11:56:31 AM,9,5,12:13:30 PM,9,5,12:27:06 PM,8,25.7,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_719
-Order_No_7660,User_Id_2534,Bike,3,Personal,18,4,2:37:35 PM,18,4,2:38:28 PM,18,4,3:07:57 PM,18,4,3:17:06 PM,11,,,-1.3447657,36.900965299999996,-1.3127877000000001,36.848198700000005,Rider_Id_883
-Order_No_8373,User_Id_2676,Bike,3,Business,27,4,9:30:45 AM,27,4,9:31:32 AM,27,4,9:57:53 AM,27,4,10:17:18 AM,8,18.8,3.1,-1.2862659,36.8264616,-1.2963097,36.7688221,Rider_Id_726
-Order_No_27190,User_Id_136,Bike,3,Business,13,1,4:23:18 PM,13,1,4:23:39 PM,13,1,4:30:18 PM,13,1,4:34:35 PM,16,19.1,,-1.2914795000000001,36.824897299999996,-1.3326173000000001,36.71462329999999,Rider_Id_213
-Order_No_13548,User_Id_552,Bike,3,Business,27,2,4:49:29 PM,27,2,4:50:13 PM,27,2,4:57:22 PM,27,2,5:27:59 PM,20,22.2,,-1.2527958000000001,36.8003131,-1.3632162,36.9108253,Rider_Id_946
-Order_No_14482,User_Id_3264,Bike,3,Business,17,1,12:23:14 PM,17,1,12:45:21 PM,17,1,1:03:39 PM,17,1,1:13:32 PM,9,23.8,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_395
-Order_No_16890,User_Id_948,Bike,3,Business,29,3,1:27:15 PM,29,3,1:32:33 PM,29,3,1:50:56 PM,29,3,2:17:10 PM,11,21.0,,-1.3151858,36.854623,-1.2594473999999998,36.8089122,Rider_Id_883
-Order_No_18276,User_Id_393,Bike,3,Business,31,4,10:13:10 AM,31,4,10:13:17 AM,31,4,10:21:05 AM,31,4,10:26:04 AM,14,20.5,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_523
-Order_No_4402,User_Id_157,Bike,3,Business,14,6,5:04:23 PM,14,6,5:05:19 PM,14,6,5:13:14 PM,14,6,5:25:07 PM,2,19.1,,-1.2320941,36.8152017,-1.2248308,36.801257299999996,Rider_Id_427
-Order_No_12309,User_Id_1439,Bike,3,Business,3,1,12:48:04 PM,3,1,12:49:58 PM,3,1,1:03:52 PM,3,1,1:19:31 PM,6,21.3,,-1.2962486999999998,36.7772971,-1.2749270000000001,36.8028302,Rider_Id_669
-Order_No_17072,User_Id_859,Bike,2,Personal,29,5,2:20:26 PM,29,5,2:20:47 PM,29,5,2:22:53 PM,29,5,2:35:57 PM,5,21.1,,-1.2857671,36.7875614,-1.3159617000000001,36.8117934,Rider_Id_898
-Order_No_22393,User_Id_2750,Bike,3,Business,4,4,11:05:13 AM,4,4,11:05:24 AM,4,4,11:11:18 AM,4,4,11:15:38 AM,6,,,-1.275442,36.766020700000006,-1.2659148999999998,36.805432200000006,Rider_Id_663
-Order_No_9495,User_Id_136,Bike,3,Business,2,4,11:04:39 AM,2,4,11:05:04 AM,2,4,11:37:04 AM,2,4,11:38:18 AM,16,,,-1.3254522,36.721658399999995,-1.2551895,36.7822034,Rider_Id_783
-Order_No_24505,User_Id_3295,Bike,3,Business,11,2,11:24:52 AM,11,2,11:25:44 AM,11,2,11:28:58 AM,11,2,11:40:55 AM,12,24.4,,-1.2571472,36.795063299999995,-1.2409346,36.874305299999996,Rider_Id_279
-Order_No_22094,User_Id_1205,Bike,3,Business,12,2,12:42:38 PM,12,2,12:43:28 PM,12,2,12:50:33 PM,12,2,12:55:45 PM,7,28.3,,-1.2495931,36.80650870000001,-1.2958383,36.8073162,Rider_Id_953
-Order_No_20593,User_Id_3686,Bike,3,Business,26,3,3:11:11 PM,26,3,3:11:23 PM,26,3,3:11:56 PM,26,3,3:16:08 PM,8,27.3,,-1.2551895,36.7822034,-1.2924639,36.77957979999999,Rider_Id_38
-Order_No_14040,User_Id_3147,Bike,3,Business,10,4,2:47:51 PM,10,4,2:48:41 PM,10,4,3:00:17 PM,10,4,3:03:36 PM,14,25.1,,-1.3226301999999999,36.8719901,-1.3472066,36.7692638,Rider_Id_48
-Order_No_23068,User_Id_1133,Bike,3,Business,28,2,10:02:35 AM,28,2,10:03:41 AM,28,2,10:14:02 AM,28,2,10:22:18 AM,4,18.5,,-1.2696823999999998,36.8026164,-1.2571472,36.795063299999995,Rider_Id_147
-Order_No_22115,User_Id_3080,Bike,3,Business,13,2,10:22:13 AM,13,2,10:22:34 AM,13,2,11:04:45 AM,13,2,11:17:31 AM,19,22.2,,-1.2860183,36.8975338,-1.2551895,36.7822034,Rider_Id_554
-Order_No_21962,User_Id_393,Bike,3,Business,28,2,11:58:49 AM,28,2,11:59:16 AM,28,2,12:02:11 PM,28,2,12:14:57 PM,4,21.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_211
-Order_No_15739,User_Id_677,Bike,3,Business,7,2,12:50:56 PM,7,2,12:52:49 PM,7,2,1:07:23 PM,7,2,1:36:44 PM,9,,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_843
-Order_No_12301,User_Id_3291,Bike,3,Business,3,5,2:20:31 PM,3,5,2:23:15 PM,3,5,2:34:18 PM,3,5,2:40:54 PM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_745
-Order_No_8193,User_Id_3091,Bike,3,Business,11,2,10:26:36 AM,11,2,10:27:38 AM,11,2,10:41:57 AM,11,2,11:08:06 AM,6,19.4,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_139
-Order_No_24032,User_Id_570,Bike,3,Business,18,2,11:09:40 AM,18,2,11:10:05 AM,18,2,11:16:03 AM,18,2,11:29:10 AM,6,,,-1.2551895,36.7822034,-1.284382,36.774604499999995,Rider_Id_720
-Order_No_20693,User_Id_72,Bike,1,Personal,3,1,12:31:38 PM,3,1,12:32:02 PM,3,1,12:49:00 PM,3,1,12:55:41 PM,11,20.6,,-1.3033794,36.7979256,-1.3227037,36.847087200000004,Rider_Id_238
-Order_No_22461,User_Id_1787,Bike,3,Business,19,5,10:39:52 AM,19,5,10:40:35 AM,19,5,10:47:36 AM,19,5,10:50:14 AM,3,,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_691
-Order_No_22055,User_Id_3222,Bike,3,Business,5,2,9:39:41 AM,5,2,9:39:50 AM,5,2,9:40:36 AM,5,2,9:46:56 AM,3,17.8,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_649
-Order_No_377,User_Id_633,Bike,3,Business,1,5,12:40:22 PM,1,5,12:47:34 PM,1,5,1:06:11 PM,1,5,1:07:52 PM,5,28.4,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_48
-Order_No_18957,User_Id_2330,Bike,3,Business,13,4,11:21:52 AM,13,4,12:03:38 PM,13,4,12:10:20 PM,13,4,12:17:33 PM,5,22.1,,-1.2642903,36.80021370000001,-1.2846026000000001,36.8219425,Rider_Id_247
-Order_No_6523,User_Id_265,Bike,3,Business,13,2,2:26:14 PM,13,2,2:42:17 PM,13,2,2:55:14 PM,13,2,3:00:59 PM,7,26.4,,-1.2599563,36.799343900000004,-1.300921,36.828195,Rider_Id_744
-Order_No_27239,User_Id_190,Bike,3,Business,8,1,1:34:44 PM,8,1,1:35:55 PM,8,1,2:16:57 PM,8,1,2:25:10 PM,6,29.6,,-1.3278522,36.8711134,-1.3270347,36.866736200000005,Rider_Id_763
-Order_No_26151,User_Id_3686,Bike,3,Business,7,7,11:39:02 AM,7,7,11:41:03 AM,7,7,11:56:24 AM,7,7,11:59:30 AM,13,25.4,,-1.2551895,36.7822034,-1.2025741,36.786551700000004,Rider_Id_453
-Order_No_13343,User_Id_1292,Bike,3,Business,17,4,12:52:52 PM,17,4,12:53:16 PM,17,4,1:00:14 PM,17,4,1:03:07 PM,3,25.4,,-1.2894456,36.778094700000004,-1.2897215,36.7945959,Rider_Id_246
-Order_No_13049,User_Id_2008,Bike,3,Business,19,2,4:08:09 PM,19,2,4:11:32 PM,19,2,4:33:41 PM,19,2,4:41:47 PM,3,28.1,,-1.2508229,36.7895263,-1.2714767,36.8138291,Rider_Id_411
-Order_No_23677,User_Id_3186,Bike,3,Business,22,4,11:25:29 AM,22,4,11:30:35 AM,22,4,11:39:50 AM,22,4,11:41:16 AM,5,22.4,,-1.2980228999999999,36.78865,-1.2797782,36.7627862,Rider_Id_558
-Order_No_15117,User_Id_3686,Bike,3,Business,22,5,3:47:55 PM,22,5,3:49:23 PM,22,5,4:06:19 PM,22,5,4:07:39 PM,11,27.1,,-1.2551895,36.7822034,-1.3233496,36.8139159,Rider_Id_928
-Order_No_7196,User_Id_1787,Bike,3,Business,12,2,1:16:03 PM,12,2,1:17:37 PM,12,2,1:29:23 PM,12,2,1:42:29 PM,11,,,-1.2789476999999998,36.8122359,-1.2283403,36.8822756,Rider_Id_267
-Order_No_16918,User_Id_136,Bike,3,Business,22,5,3:31:39 PM,22,5,3:32:02 PM,22,5,3:43:47 PM,22,5,3:45:50 PM,14,27.2,,-1.3254522,36.721658399999995,-1.2902869,36.8085396,Rider_Id_622
-Order_No_16643,User_Id_1268,Bike,1,Personal,23,4,2:50:27 PM,23,4,2:50:50 PM,23,4,3:07:28 PM,23,4,3:10:17 PM,9,20.6,,-1.293554,36.763113,-1.2983805,36.8198439,Rider_Id_465
-Order_No_17675,User_Id_2904,Bike,3,Personal,18,3,11:27:35 AM,18,3,11:27:49 AM,18,3,11:41:32 AM,18,3,11:43:50 AM,5,20.0,,-1.2970896,36.7771786,-1.2746436,36.803682,Rider_Id_752
-Order_No_4110,User_Id_1500,Bike,3,Business,29,4,8:54:11 AM,29,4,8:54:45 AM,29,4,9:05:32 AM,29,4,9:17:58 AM,8,18.9,4.0,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_911
-Order_No_8000,User_Id_1245,Bike,3,Business,8,5,4:36:18 PM,8,5,4:57:18 PM,8,5,5:04:17 PM,8,5,5:16:09 PM,19,28.2,,-1.2602337,36.79905479999999,-1.3447331,36.724076399999994,Rider_Id_427
-Order_No_14403,User_Id_3264,Bike,3,Business,22,2,2:01:24 PM,22,2,2:02:06 PM,22,2,2:39:17 PM,22,2,2:41:05 PM,6,21.4,,-1.2626906,36.7827022,-1.2707742,36.7578609,Rider_Id_770
-Order_No_25143,User_Id_391,Bike,3,Business,2,4,1:42:53 PM,2,4,1:44:54 PM,2,4,2:31:55 PM,2,4,2:35:48 PM,5,,,-1.3091799,36.831327200000004,-1.2784063,36.8222933,Rider_Id_815
-Order_No_19575,User_Id_1855,Bike,1,Personal,26,3,9:09:46 AM,26,3,9:10:26 AM,26,3,9:34:44 AM,26,3,9:58:54 AM,11,19.2,,-1.3099804,36.8983789,-1.2855798999999999,36.817620899999994,Rider_Id_612
-Order_No_23790,User_Id_3147,Bike,3,Business,8,4,9:28:25 AM,8,4,9:28:32 AM,8,4,9:44:18 AM,8,4,9:47:17 AM,17,20.5,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_533
-Order_No_1682,User_Id_1850,Bike,3,Business,8,5,1:13:57 PM,8,5,2:37:59 PM,8,5,2:49:03 PM,8,5,3:08:00 PM,9,30.2,,-1.2551895,36.7822034,-1.3074264,36.822612299999996,Rider_Id_547
-Order_No_27075,User_Id_391,Bike,3,Business,29,1,1:49:05 PM,29,1,2:03:53 PM,29,1,2:09:56 PM,29,1,2:46:35 PM,20,24.5,,-1.319613,36.711055,-1.2784063,36.8222933,Rider_Id_70
-Order_No_25528,User_Id_3360,Bike,2,Personal,27,4,4:44:57 PM,27,4,4:45:20 PM,27,4,4:49:00 PM,27,4,5:23:59 PM,11,22.9,,-1.3278522,36.8711134,-1.3266874,36.7986312,Rider_Id_102
-Order_No_17435,User_Id_3214,Bike,3,Business,15,2,2:37:02 PM,15,2,2:37:46 PM,15,2,2:50:46 PM,15,2,2:54:13 PM,12,24.6,,-1.2315181,36.814054299999995,-1.289795,36.7957132,Rider_Id_899
-Order_No_84,User_Id_186,Bike,3,Business,5,5,2:29:13 PM,5,5,2:49:57 PM,5,5,3:04:00 PM,5,5,3:07:11 PM,23,27.6,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_48
-Order_No_5664,User_Id_1484,Bike,3,Business,31,3,1:49:58 PM,31,3,1:51:16 PM,31,3,2:16:38 PM,31,3,2:18:23 PM,15,26.8,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_583
-Order_No_27948,User_Id_83,Bike,3,Personal,3,3,4:47:52 PM,3,3,4:48:39 PM,3,3,5:05:08 PM,3,3,5:16:20 PM,10,23.6,,-1.2849377,36.824196,-1.2676943,36.781365799999996,Rider_Id_906
-Order_No_12937,User_Id_393,Bike,3,Business,20,3,8:42:33 AM,20,3,8:42:48 AM,20,3,8:43:08 AM,20,3,8:47:11 AM,5,21.0,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_111
-Order_No_526,User_Id_2414,Bike,2,Personal,4,2,6:06:26 PM,4,2,6:07:12 PM,4,2,6:20:55 PM,4,2,6:33:24 PM,4,21.3,,-1.2962832,36.784968299999996,-1.308217,36.77413,Rider_Id_482
-Order_No_1773,User_Id_2216,Bike,3,Business,2,5,8:48:42 AM,2,5,8:49:48 AM,2,5,9:21:18 AM,2,5,9:28:51 AM,3,18.2,17.0,-1.2551895,36.7822034,-1.2654348,36.8022464,Rider_Id_395
-Order_No_939,User_Id_3115,Bike,3,Business,12,1,9:41:34 AM,12,1,10:25:24 AM,12,1,10:26:45 AM,12,1,10:39:13 AM,8,24.0,,-1.2593294,36.7183756,-1.2963097,36.7688221,Rider_Id_105
-Order_No_14046,User_Id_3647,Bike,3,Business,30,4,12:39:27 PM,30,4,12:40:04 PM,30,4,1:04:21 PM,30,4,1:08:47 PM,4,,,-1.2726389999999999,36.794723,-1.2762743,36.815278899999996,Rider_Id_937
-Order_No_13342,User_Id_136,Bike,3,Business,28,4,9:46:37 AM,28,4,9:47:08 AM,28,4,9:57:24 AM,28,4,10:01:05 AM,22,22.5,,-1.3254522,36.721658399999995,-1.2261501,36.7600434,Rider_Id_647
-Order_No_28125,User_Id_136,Bike,3,Business,20,4,1:45:04 PM,20,4,2:00:22 PM,20,4,2:17:45 PM,20,4,2:19:27 PM,16,25.4,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_470
-Order_No_18470,User_Id_2823,Bike,3,Business,7,1,5:48:42 PM,7,1,5:53:47 PM,7,1,6:13:25 PM,7,1,6:29:04 PM,20,20.2,,-1.2141875,36.791039000000005,-1.3018758,36.75908629999999,Rider_Id_56
-Order_No_22771,User_Id_2766,Bike,1,Personal,9,3,10:52:59 AM,9,3,10:58:24 AM,9,3,11:57:04 AM,9,3,12:13:30 PM,8,21.2,,-1.3214137,36.8569475,-1.2851847,36.8314086,Rider_Id_698
-Order_No_17133,User_Id_478,Bike,3,Business,5,2,9:07:58 AM,5,2,9:08:11 AM,5,2,9:08:15 AM,5,2,9:15:22 AM,15,21.2,2.0,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_523
-Order_No_18877,User_Id_2956,Bike,3,Business,20,3,11:01:33 AM,20,3,11:02:09 AM,20,3,11:44:35 AM,20,3,12:00:18 PM,13,26.3,,-1.207546,36.7913976,-1.2527958000000001,36.8003131,Rider_Id_391
-Order_No_16447,User_Id_20,Bike,3,Business,5,5,11:53:19 AM,5,5,11:54:08 AM,5,5,12:02:52 PM,5,5,12:25:07 PM,14,24.6,,-1.2629666000000002,36.8068369,-1.3278522,36.8711134,Rider_Id_119
-Order_No_19040,User_Id_1954,Bike,3,Business,18,5,8:32:27 AM,18,5,8:47:44 AM,18,5,8:58:31 AM,18,5,9:04:49 AM,9,17.8,,-1.289498,36.818878000000005,-1.329568,36.8520825,Rider_Id_531
-Order_No_23042,User_Id_2202,Bike,3,Business,17,4,4:14:24 PM,17,4,4:17:42 PM,17,4,4:41:38 PM,17,4,4:48:03 PM,8,21.9,,-1.2626906,36.7827022,-1.290894,36.822971,Rider_Id_742
-Order_No_7694,User_Id_2800,Bike,2,Personal,6,1,9:31:39 AM,6,1,9:32:31 AM,6,1,9:46:47 AM,6,1,9:51:32 AM,6,14.8,,-1.272704,36.793127899999995,-1.2942764,36.7969749,Rider_Id_791
-Order_No_2972,User_Id_346,Bike,3,Business,12,3,3:19:34 PM,12,3,3:42:21 PM,12,3,3:52:25 PM,12,3,4:09:11 PM,1,20.8,,-1.28878,36.8168312,-1.2795827,36.819726200000005,Rider_Id_272
-Order_No_11205,User_Id_2365,Bike,3,Personal,31,4,4:18:51 PM,31,4,4:19:59 PM,31,4,4:57:47 PM,31,4,5:13:13 PM,10,27.4,,-1.3056005,36.7567122,-1.3154433,36.806385999999996,Rider_Id_303
-Order_No_19312,User_Id_844,Bike,1,Personal,3,2,12:40:54 PM,3,2,2:44:19 PM,3,2,2:45:46 PM,3,2,2:56:50 PM,19,20.0,,-1.2343087,36.84749,-1.2513528999999999,36.752171999999995,Rider_Id_101
-Order_No_9219,User_Id_1642,Bike,3,Business,21,5,10:15:55 AM,21,5,10:16:21 AM,21,5,10:23:49 AM,21,5,10:30:16 AM,3,20.0,,-1.2728069,36.7962362,-1.2655332000000001,36.8094648,Rider_Id_343
-Order_No_19648,User_Id_1105,Bike,3,Business,7,3,2:31:26 PM,7,3,2:45:56 PM,7,3,3:03:06 PM,7,3,3:09:40 PM,16,28.1,,-1.3322618,36.8045412,-1.3196979,36.7009845,Rider_Id_914
-Order_No_17358,User_Id_1687,Bike,3,Business,12,5,9:13:36 AM,12,5,9:14:11 AM,12,5,9:15:09 AM,12,5,9:24:20 AM,2,18.4,,-1.2649605,36.798177700000004,-1.2676176000000001,36.7956644,Rider_Id_647
-Order_No_25759,User_Id_346,Bike,3,Business,2,2,3:52:55 PM,2,2,3:58:49 PM,2,2,4:10:12 PM,2,2,4:42:35 PM,9,28.4,,-1.28878,36.8168312,-1.2393878,36.8690478,Rider_Id_910
-Order_No_26749,User_Id_3698,Bike,3,Business,17,3,12:09:33 PM,17,3,12:10:12 PM,17,3,12:14:56 PM,17,3,12:38:22 PM,17,,,-1.2285995,36.901237200000004,-1.3244885,36.897791999999995,Rider_Id_387
-Order_No_21360,User_Id_830,Bike,3,Business,29,1,3:40:11 PM,29,1,3:42:21 PM,29,1,3:42:26 PM,29,1,4:00:12 PM,6,24.5,,-1.2551895,36.7822034,-1.2793686000000002,36.82592329999999,Rider_Id_713
-Order_No_24027,User_Id_3689,Bike,1,Business,19,4,2:03:20 PM,19,4,2:03:51 PM,19,4,2:11:07 PM,19,4,2:20:18 PM,2,20.4,,-1.2571472,36.795063299999995,-1.2527958000000001,36.8003131,Rider_Id_100
-Order_No_15655,User_Id_3647,Bike,3,Business,3,1,1:03:53 PM,3,1,1:04:27 PM,3,1,1:35:28 PM,3,1,1:38:37 PM,5,23.2,,-1.2726389999999999,36.794723,-1.2655477,36.8001682,Rider_Id_79
-Order_No_12839,User_Id_3201,Bike,3,Business,15,5,3:14:39 PM,15,5,3:20:57 PM,15,5,3:26:02 PM,15,5,3:39:36 PM,6,28.0,,-1.28878,36.8168312,-1.2584143,36.8048002,Rider_Id_686
-Order_No_693,User_Id_2829,Bike,3,Business,12,1,2:08:21 PM,12,1,2:10:31 PM,12,1,2:31:03 PM,12,1,3:02:00 PM,21,26.9,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_333
-Order_No_2685,User_Id_265,Bike,3,Business,1,5,3:13:41 PM,1,5,3:14:15 PM,1,5,3:23:03 PM,1,5,3:40:18 PM,17,28.3,,-1.300921,36.828195,-1.2266483999999998,36.8772086,Rider_Id_882
-Order_No_4989,User_Id_3091,Bike,3,Business,7,5,10:48:22 AM,7,5,10:48:33 AM,7,5,11:11:56 AM,7,5,11:32:49 AM,25,24.9,,-1.207546,36.7913976,-1.3025826,36.7670807,Rider_Id_560
-Order_No_27421,User_Id_2271,Bike,3,Business,17,3,11:17:20 AM,17,3,11:17:50 AM,17,3,11:31:50 AM,17,3,11:46:40 AM,20,,,-1.2628473,36.781805,-1.293515,36.897607,Rider_Id_4
-Order_No_26378,User_Id_2940,Bike,3,Personal,6,3,3:20:20 PM,6,3,3:23:24 PM,6,3,4:04:04 PM,6,3,4:09:10 PM,8,,,-1.2993556,36.8146776,-1.326825,36.864345,Rider_Id_459
-Order_No_21054,User_Id_3571,Bike,3,Business,17,1,12:02:29 PM,17,1,12:24:40 PM,17,1,12:43:43 PM,17,1,12:53:12 PM,17,23.5,,-1.2271998,36.808490500000005,-1.3163815,36.8542784,Rider_Id_205
-Order_No_16343,User_Id_1147,Bike,2,Personal,31,5,1:14:09 PM,31,5,1:15:02 PM,31,5,1:24:28 PM,31,5,1:33:25 PM,9,19.9,,-1.2781868,36.8236786,-1.3024388,36.859699,Rider_Id_207
-Order_No_23935,User_Id_2528,Bike,3,Business,4,4,8:33:40 AM,4,4,8:33:59 AM,4,4,8:46:15 AM,4,4,9:27:09 AM,3,22.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_953
-Order_No_23428,User_Id_393,Bike,3,Business,17,4,4:05:08 PM,17,4,4:05:51 PM,17,4,4:17:35 PM,17,4,4:24:39 PM,10,27.8,,-1.3231001,36.8283559,-1.2574219,36.7927073,Rider_Id_518
-Order_No_21734,User_Id_3269,Bike,1,Personal,5,4,6:04:58 PM,5,4,6:27:16 PM,5,4,6:45:23 PM,5,4,7:01:57 PM,13,20.6,,-1.2409807,36.8144219,-1.3039695,36.783826700000006,Rider_Id_808
-Order_No_18645,User_Id_1522,Bike,3,Business,12,3,10:46:29 AM,12,3,10:50:50 AM,12,3,10:51:28 AM,12,3,11:06:34 AM,1,19.4,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_427
-Order_No_4719,User_Id_1075,Bike,3,Business,29,5,2:51:59 PM,29,5,2:54:32 PM,29,5,3:04:12 PM,29,5,3:06:29 PM,8,29.0,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_326
-Order_No_26512,User_Id_1341,Bike,3,Business,29,3,11:00:14 AM,29,3,11:00:47 AM,29,3,11:08:58 AM,29,3,11:15:43 AM,13,,,-1.259478,36.801781299999995,-1.331619,36.847976,Rider_Id_96
-Order_No_19840,User_Id_393,Bike,3,Business,12,2,3:25:34 PM,12,2,3:26:07 PM,12,2,3:36:19 PM,12,2,4:01:30 PM,14,26.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_497
-Order_No_16351,User_Id_1280,Bike,3,Business,5,1,8:36:59 AM,5,1,8:38:07 AM,5,1,9:03:20 AM,5,1,9:10:28 AM,13,18.8,,-1.3221141,36.8636835,-1.2654348,36.8022464,Rider_Id_956
-Order_No_17702,User_Id_3091,Bike,3,Business,25,4,12:27:12 PM,25,4,1:24:53 PM,25,4,1:27:17 PM,25,4,2:07:33 PM,9,,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_855
-Order_No_18345,User_Id_59,Bike,3,Business,6,4,2:59:36 PM,6,4,3:01:04 PM,6,4,3:09:28 PM,6,4,3:13:53 PM,6,21.3,,-1.2649605,36.798177700000004,-1.290894,36.822971,Rider_Id_20
-Order_No_7101,User_Id_3496,Bike,3,Business,30,2,4:04:10 PM,30,2,4:14:10 PM,30,2,4:25:03 PM,30,2,4:39:47 PM,17,,,-1.300921,36.828195,-1.3297531,36.7063699,Rider_Id_299
-Order_No_15957,User_Id_1783,Bike,3,Personal,30,3,9:17:27 AM,30,3,9:29:42 AM,30,3,9:50:32 AM,30,3,9:57:15 AM,7,17.3,,-1.3175119,36.8058513,-1.2824911,36.8218042,Rider_Id_844
-Order_No_16403,User_Id_3521,Bike,3,Business,8,2,3:29:59 PM,8,2,3:33:50 PM,8,2,3:49:23 PM,8,2,3:53:29 PM,3,25.3,,-1.2696908000000002,36.81519,-1.2665065,36.7970081,Rider_Id_268
-Order_No_12095,User_Id_393,Bike,3,Business,8,4,3:27:30 PM,8,4,3:27:49 PM,8,4,3:35:16 PM,8,4,3:43:34 PM,10,27.4,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_878
-Order_No_7590,User_Id_3339,Bike,3,Business,16,3,2:38:45 PM,16,3,2:40:17 PM,16,3,3:04:39 PM,16,3,3:15:07 PM,22,25.3,,-1.3173868999999998,36.8112044,-1.3932312,36.9379199,Rider_Id_581
-Order_No_3071,User_Id_867,Bike,3,Business,24,3,11:30:53 AM,24,3,11:34:22 AM,24,3,11:51:54 AM,24,3,11:59:25 AM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_448
-Order_No_21005,User_Id_893,Bike,3,Business,26,2,10:04:03 AM,26,2,10:04:25 AM,26,2,10:09:36 AM,26,2,10:12:58 AM,4,21.9,,-1.2594473999999998,36.8089122,-1.2560455,36.780086700000005,Rider_Id_472
-Order_No_27136,User_Id_3115,Bike,3,Business,6,1,6:47:52 PM,6,1,6:48:11 PM,6,1,6:55:38 PM,6,1,7:27:14 PM,2,,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_155
-Order_No_8052,User_Id_3686,Bike,3,Business,17,3,1:18:47 PM,17,3,1:19:07 PM,17,3,1:19:35 PM,17,3,1:26:48 PM,10,,,-1.2551895,36.7822034,-1.2940111,36.7966518,Rider_Id_854
-Order_No_21193,User_Id_2256,Bike,1,Personal,1,1,6:47:36 PM,1,1,6:47:47 PM,1,1,7:21:40 PM,1,1,7:24:33 PM,22,23.4,,-1.3255854999999999,36.86476270000001,-1.2323938,36.7254545,Rider_Id_532
-Order_No_14253,User_Id_3768,Bike,1,Personal,11,5,9:57:12 AM,11,5,9:57:42 AM,11,5,10:12:31 AM,11,5,10:18:26 AM,27,18.0,5.0,-1.2877907,36.830065000000005,-1.3611971,36.6538278,Rider_Id_159
-Order_No_21773,User_Id_1792,Bike,3,Business,22,4,9:08:38 AM,22,4,9:11:51 AM,22,4,9:20:04 AM,22,4,9:26:43 AM,5,19.5,2.9,-1.2684998,36.7998268,-1.2809121,36.7852524,Rider_Id_147
-Order_No_10513,User_Id_1879,Bike,1,Personal,15,2,1:39:00 PM,15,2,1:41:34 PM,15,2,2:23:14 PM,15,2,2:34:17 PM,13,24.7,,-1.235348,36.760016,-1.2994595,36.7885482,Rider_Id_543
-Order_No_904,User_Id_1500,Bike,3,Business,12,1,12:31:58 PM,12,1,12:42:45 PM,12,1,12:51:01 PM,12,1,1:13:32 PM,8,23.9,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_518
-Order_No_27896,User_Id_868,Bike,3,Business,7,4,11:06:35 AM,7,4,11:07:50 AM,7,4,11:43:21 AM,7,4,11:54:23 AM,4,27.8,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_857
-Order_No_14824,User_Id_965,Bike,1,Personal,17,5,9:47:55 AM,17,5,9:48:05 AM,17,5,9:58:20 AM,17,5,10:08:03 AM,22,14.1,,-1.2857094999999998,36.8188266,-1.1682728999999998,36.953616700000005,Rider_Id_648
-Order_No_15137,User_Id_2801,Bike,3,Business,6,4,11:10:59 AM,6,4,11:13:48 AM,6,4,11:17:52 AM,6,4,11:31:15 AM,10,24.1,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_848
-Order_No_12629,User_Id_1271,Bike,3,Business,7,5,1:37:29 PM,7,5,2:04:36 PM,7,5,2:18:54 PM,7,5,2:21:08 PM,8,24.8,,-1.3244885,36.897791999999995,-1.3221141,36.8636835,Rider_Id_874
-Order_No_8559,User_Id_393,Bike,3,Business,1,4,12:22:48 PM,1,4,12:22:55 PM,1,4,12:26:59 PM,1,4,12:39:20 PM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_761
-Order_No_25816,User_Id_35,Bike,2,Personal,2,6,3:52:09 PM,2,6,3:52:44 PM,2,6,4:00:13 PM,2,6,4:07:49 PM,17,23.8,,-1.2824577,36.7967953,-1.3703367,36.746665500000006,Rider_Id_322
-Order_No_24058,User_Id_633,Bike,3,Business,8,2,1:40:45 PM,8,2,1:41:40 PM,8,2,1:41:47 PM,8,2,2:11:50 PM,4,25.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_619
-Order_No_16943,User_Id_2159,Bike,3,Business,9,5,9:36:14 AM,9,5,9:43:14 AM,9,5,9:55:20 AM,9,5,10:15:00 AM,7,20.1,,-1.2839186000000002,36.8222431,-1.304735,36.784605400000004,Rider_Id_512
-Order_No_5060,User_Id_526,Bike,2,Personal,9,1,1:46:59 PM,9,1,1:48:35 PM,9,1,1:54:56 PM,9,1,2:11:09 PM,4,21.2,,-1.260232,36.807334000000004,-1.2545163000000001,36.8243717,Rider_Id_147
-Order_No_9841,User_Id_2330,Bike,3,Business,18,1,4:06:56 PM,18,1,4:07:21 PM,18,1,4:10:41 PM,18,1,4:14:02 PM,9,30.6,,-1.2642903,36.80021370000001,-1.3015197,36.7658456,Rider_Id_952
-Order_No_14410,User_Id_248,Bike,3,Business,23,2,3:34:54 PM,23,2,3:36:11 PM,23,2,4:01:16 PM,23,2,4:08:00 PM,9,19.1,,-1.2557778999999998,36.7879648,-1.304735,36.784605400000004,Rider_Id_46
-Order_No_24314,User_Id_2127,Bike,3,Personal,11,1,2:43:35 PM,11,1,3:07:55 PM,11,1,3:54:05 PM,11,1,4:02:58 PM,5,28.0,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_26
-Order_No_12407,User_Id_2335,Bike,3,Business,6,3,1:41:56 PM,6,3,1:42:28 PM,6,3,2:10:18 PM,6,3,2:14:34 PM,22,,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_295
-Order_No_24373,User_Id_1059,Bike,1,Personal,17,3,2:54:02 PM,17,3,2:54:11 PM,17,3,3:02:05 PM,17,3,3:05:09 PM,11,28.5,,-1.3303649,36.726018700000004,-1.296567,36.7813901,Rider_Id_832
-Order_No_20158,User_Id_3647,Bike,3,Business,12,1,3:51:09 PM,12,1,3:54:45 PM,12,1,4:04:57 PM,12,1,4:12:14 PM,21,26.7,,-1.2726389999999999,36.794723,-1.1917520000000001,36.9152737,Rider_Id_620
-Order_No_15980,User_Id_265,Bike,3,Business,5,3,1:52:30 PM,5,3,2:18:29 PM,5,3,2:18:31 PM,5,3,2:37:05 PM,4,24.8,,-1.2825816,36.8092346,-1.300921,36.828195,Rider_Id_813
-Order_No_21897,User_Id_830,Bike,3,Business,25,2,1:12:41 PM,25,2,1:12:56 PM,25,2,1:41:30 PM,25,2,1:44:36 PM,16,27.3,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_19
-Order_No_6958,User_Id_2982,Bike,3,Business,31,3,3:52:32 PM,31,3,3:52:37 PM,31,3,3:54:28 PM,31,3,4:01:38 PM,18,27.2,,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_573
-Order_No_15070,User_Id_3398,Bike,3,Business,17,1,10:14:05 AM,17,1,10:14:09 AM,17,1,10:21:02 AM,17,1,10:55:05 AM,30,25.1,,-1.3169591,36.7000029,-1.2413316,36.878552500000005,Rider_Id_478
-Order_No_6466,User_Id_3560,Bike,3,Business,28,5,3:59:21 PM,28,5,4:02:12 PM,28,5,4:13:29 PM,28,5,4:20:09 PM,5,25.4,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_576
-Order_No_18367,User_Id_1205,Bike,3,Business,21,4,10:04:55 AM,21,4,10:05:45 AM,21,4,10:12:24 AM,21,4,10:57:53 AM,4,27.8,,-1.2800617,36.7883831,-1.2495931,36.80650870000001,Rider_Id_228
-Order_No_17049,User_Id_3194,Bike,3,Business,27,3,10:19:50 AM,27,3,10:20:37 AM,27,3,10:32:20 AM,27,3,10:35:39 AM,7,25.9,,-1.2811695,36.8200313,-1.2551895,36.7822034,Rider_Id_365
-Order_No_17065,User_Id_3249,Bike,2,Personal,9,4,8:13:36 PM,9,4,8:14:02 PM,9,4,8:34:23 PM,9,4,8:35:46 PM,20,18.6,,-1.3168075000000001,36.8626441,-1.2593591999999998,36.7775322,Rider_Id_874
-Order_No_27744,User_Id_3283,Bike,3,Business,1,5,11:58:37 AM,1,5,12:18:01 PM,1,5,12:34:25 PM,1,5,12:42:10 PM,5,26.5,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_290
-Order_No_1561,User_Id_2700,Bike,3,Business,17,5,3:38:16 PM,17,5,3:42:16 PM,17,5,3:52:41 PM,17,5,3:59:20 PM,10,20.3,,-1.2343087,36.84749,-1.2845834999999999,36.8079121,Rider_Id_96
-Order_No_7465,User_Id_3470,Bike,3,Business,22,4,12:28:30 PM,22,4,1:08:48 PM,22,4,1:28:37 PM,22,4,1:31:28 PM,7,22.7,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_714
-Order_No_12952,User_Id_370,Bike,1,Personal,21,1,6:48:00 PM,21,1,6:55:04 PM,21,1,7:10:35 PM,21,1,7:17:35 PM,7,,,-1.2682178999999998,36.8009289,-1.2867716999999999,36.773523700000005,Rider_Id_134
-Order_No_12337,User_Id_3647,Bike,3,Business,7,5,3:54:27 PM,7,5,4:05:03 PM,7,5,4:15:26 PM,7,5,4:19:17 PM,6,26.6,,-1.2726389999999999,36.794723,-1.3004846,36.790159100000004,Rider_Id_920
-Order_No_19426,User_Id_868,Bike,3,Business,25,6,11:35:54 AM,25,6,11:46:40 AM,25,6,12:02:14 PM,25,6,12:08:17 PM,9,,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_676
-Order_No_19149,User_Id_1927,Bike,3,Business,1,1,2:44:35 PM,1,1,2:44:48 PM,1,1,2:59:19 PM,1,1,3:24:41 PM,15,25.6,,-1.2642719999999998,36.7978791,-1.3472066,36.7692638,Rider_Id_857
-Order_No_10119,User_Id_2713,Bike,2,Personal,2,3,8:57:55 AM,2,3,9:03:00 AM,2,3,9:19:50 AM,2,3,9:26:31 AM,7,18.1,1.0,-1.2669636,36.8353831,-1.29912,36.81806160000001,Rider_Id_906
-Order_No_22351,User_Id_3186,Bike,3,Business,1,4,2:26:21 PM,1,4,2:27:29 PM,1,4,2:41:05 PM,1,4,2:42:48 PM,7,,,-1.2980228999999999,36.78865,-1.2574087999999999,36.799712,Rider_Id_722
-Order_No_19393,User_Id_1809,Bike,3,Business,4,3,10:21:50 AM,4,3,10:34:14 AM,4,3,10:44:33 AM,4,3,10:56:04 AM,17,17.2,,-1.3463041999999998,36.768164,-1.2646084,36.8028645,Rider_Id_914
-Order_No_28021,User_Id_393,Bike,3,Business,26,5,2:57:22 PM,26,5,2:58:08 PM,26,5,2:58:22 PM,26,5,3:13:40 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_300,User_Id_2956,Bike,3,Business,17,3,4:20:53 PM,17,3,4:33:41 PM,17,3,4:55:09 PM,17,3,5:07:32 PM,6,,,-1.2584143,36.8048002,-1.2854151000000003,36.8233848,Rider_Id_435
-Order_No_8839,User_Id_346,Bike,3,Business,13,4,11:11:42 AM,13,4,11:12:05 AM,13,4,11:18:57 AM,13,4,11:21:05 AM,1,22.1,,-1.28878,36.8168312,-1.2857548,36.819062,Rider_Id_831
-Order_No_7865,User_Id_2008,Bike,3,Business,4,3,8:36:55 AM,4,3,8:37:12 AM,4,3,8:56:00 AM,4,3,9:00:55 AM,11,15.2,,-1.2508229,36.7895263,-1.2884996000000002,36.74222520000001,Rider_Id_116
-Order_No_906,User_Id_2330,Bike,3,Business,6,1,6:13:06 PM,6,1,6:13:37 PM,6,1,6:15:55 PM,6,1,6:24:47 PM,8,,,-1.3015197,36.7658456,-1.3263903000000001,36.717988500000004,Rider_Id_161
-Order_No_2087,User_Id_3326,Bike,3,Business,10,1,2:28:39 PM,10,1,2:29:23 PM,10,1,3:12:08 PM,10,1,3:13:56 PM,8,,,-1.2551895,36.7822034,-1.304344,36.812274,Rider_Id_742
-Order_No_5321,User_Id_2330,Bike,3,Business,4,2,5:09:46 PM,4,2,5:12:53 PM,4,2,5:20:42 PM,4,2,5:23:04 PM,25,21.0,,-1.2124582,36.802894,-1.1930572,36.934890100000004,Rider_Id_116
-Order_No_5010,User_Id_2801,Bike,3,Business,23,2,12:48:39 PM,23,2,12:49:34 PM,23,2,12:52:46 PM,23,2,12:56:50 PM,2,,,-1.2674273999999999,36.787082700000006,-1.25681,36.7730826,Rider_Id_695
-Order_No_17328,User_Id_2767,Bike,3,Business,14,5,10:06:00 AM,14,5,10:06:11 AM,14,5,10:16:18 AM,14,5,10:20:23 AM,11,18.1,,-1.257773,36.795486,-1.2248308,36.801257299999996,Rider_Id_131
-Order_No_13696,User_Id_1237,Bike,1,Personal,2,2,3:40:37 PM,2,2,3:43:41 PM,2,2,3:54:07 PM,2,2,4:35:56 PM,3,23.4,,-1.2599563,36.799343900000004,-1.2741728,36.79691079999999,Rider_Id_802
-Order_No_23965,User_Id_552,Bike,3,Business,25,4,9:56:36 AM,25,4,9:57:02 AM,25,4,9:57:51 AM,25,4,10:06:25 AM,22,16.3,1.9,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_171
-Order_No_8761,User_Id_633,Bike,3,Business,6,4,12:09:38 PM,6,4,12:11:02 PM,6,4,12:25:36 PM,6,4,12:29:11 PM,4,24.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_532
-Order_No_1666,User_Id_2913,Bike,3,Personal,5,5,5:37:46 PM,5,5,5:49:39 PM,5,5,5:55:28 PM,5,5,5:56:50 PM,6,24.6,,-1.300663,36.790907000000004,-1.2791714,36.825599700000005,Rider_Id_614
-Order_No_19074,User_Id_3291,Bike,3,Business,29,5,11:50:28 AM,29,5,11:51:28 AM,29,5,12:17:12 PM,29,5,12:24:16 PM,4,26.7,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_52
-Order_No_8142,User_Id_3599,Bike,3,Business,12,3,10:44:20 AM,12,3,11:58:32 AM,12,3,12:08:13 PM,12,3,12:18:44 PM,11,19.5,,-1.3302996,36.8707935,-1.3136451999999998,36.7974014,Rider_Id_675
-Order_No_14892,User_Id_3560,Bike,3,Business,19,6,10:52:43 AM,19,6,10:53:23 AM,19,6,11:21:32 AM,19,6,11:31:11 AM,8,25.3,,-1.3014461000000002,36.7661381,-1.279395,36.825364,Rider_Id_362
-Order_No_6726,User_Id_2521,Bike,3,Business,30,6,8:47:49 AM,30,6,8:48:00 AM,30,6,8:54:24 AM,30,6,9:01:59 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_15
-Order_No_17570,User_Id_2412,Bike,3,Business,27,4,12:35:27 PM,27,4,12:35:48 PM,27,4,12:40:30 PM,27,4,12:52:38 PM,14,22.9,,-1.2297202,36.881950700000004,-1.2551895,36.7822034,Rider_Id_754
-Order_No_20331,User_Id_3408,Bike,2,Personal,17,5,11:01:31 AM,17,5,11:02:08 AM,17,5,11:04:05 AM,17,5,11:08:00 AM,6,17.7,,-1.2854453000000001,36.818836299999994,-1.2941023999999999,36.79578,Rider_Id_812
-Order_No_1068,User_Id_81,Bike,3,Business,17,1,2:26:31 PM,17,1,2:26:43 PM,17,1,2:32:52 PM,17,1,2:35:51 PM,4,24.5,,-1.2641496,36.791413899999995,-1.2555811000000001,36.802267,Rider_Id_259
-Order_No_7007,User_Id_1882,Bike,1,Business,4,2,7:27:01 AM,4,2,7:30:42 AM,4,2,8:12:18 AM,4,2,8:19:50 AM,12,16.3,,-1.2956882,36.7861845,-1.3169591,36.7000029,Rider_Id_328
-Order_No_13047,User_Id_1097,Bike,1,Personal,9,3,4:19:50 PM,9,3,4:20:40 PM,9,3,4:29:37 PM,9,3,4:35:13 PM,11,21.7,,-1.264788,36.7883897,-1.3099145,36.802277399999994,Rider_Id_103
-Order_No_23969,User_Id_305,Bike,1,Personal,8,1,7:35:39 AM,8,1,7:39:54 AM,8,1,7:51:41 AM,8,1,7:55:26 AM,9,19.6,,-1.2591018999999999,36.800577000000004,-1.2994194,36.7647917,Rider_Id_875
-Order_No_751,User_Id_3283,Bike,3,Business,9,6,3:15:30 PM,9,6,3:22:44 PM,9,6,3:37:14 PM,9,6,3:38:51 PM,6,27.1,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_332
-Order_No_6101,User_Id_2828,Bike,3,Business,22,1,6:00:31 PM,22,1,6:02:57 PM,22,1,6:11:44 PM,22,1,6:18:04 PM,11,22.1,,-1.2615888000000002,36.7928732,-1.3159197,36.8516724,Rider_Id_102
-Order_No_11754,User_Id_2030,Bike,3,Business,29,1,12:49:23 PM,29,1,12:49:57 PM,29,1,12:54:22 PM,29,1,12:59:17 PM,3,,,-1.2969741000000001,36.7856612,-1.295908,36.791433000000005,Rider_Id_726
-Order_No_27568,User_Id_1544,Bike,3,Business,23,4,12:33:03 PM,23,4,12:50:33 PM,23,4,1:05:51 PM,23,4,1:09:35 PM,4,23.4,,-1.2869955,36.8202749,-1.273754,36.800557899999994,Rider_Id_148
-Order_No_6413,User_Id_3752,Bike,3,Business,29,1,10:11:35 AM,29,1,10:12:22 AM,29,1,10:17:22 AM,29,1,10:26:06 AM,5,,,-1.311356,36.8492094,-1.2965799999999998,36.823699,Rider_Id_106
-Order_No_14498,User_Id_609,Bike,2,Personal,6,3,10:25:50 AM,6,3,10:25:59 AM,6,3,10:46:41 AM,6,3,10:50:02 AM,15,19.8,,-1.3302996,36.8707935,-1.3677142,36.9136314,Rider_Id_543
-Order_No_27556,User_Id_756,Bike,2,Personal,2,2,2:41:44 PM,2,2,2:45:22 PM,2,2,3:12:18 PM,2,2,3:18:16 PM,8,26.3,,-1.276572,36.810984000000005,-1.284382,36.774604499999995,Rider_Id_532
-Order_No_8711,User_Id_635,Bike,3,Business,20,1,2:00:41 PM,20,1,2:01:10 PM,20,1,2:10:11 PM,20,1,3:25:13 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_863
-Order_No_4615,User_Id_3688,Bike,3,Business,28,1,2:50:30 PM,28,1,2:51:03 PM,28,1,2:57:28 PM,28,1,3:01:11 PM,6,26.4,,-1.2551895,36.7822034,-1.2737591000000001,36.814252200000006,Rider_Id_381
-Order_No_5057,User_Id_2869,Bike,3,Business,21,2,10:40:54 AM,21,2,10:44:48 AM,21,2,11:19:59 AM,21,2,11:37:38 AM,9,22.5,,-1.28878,36.8168312,-1.3025826,36.7670807,Rider_Id_155
-Order_No_735,User_Id_1325,Bike,2,Personal,18,4,12:59:03 PM,18,4,12:59:21 PM,18,4,1:13:17 PM,18,4,1:24:42 PM,15,25.5,,-1.1855066,36.825464600000004,-1.2153120000000002,36.9049037,Rider_Id_208
-Order_No_7534,User_Id_2195,Bike,3,Business,6,6,4:23:15 PM,6,6,4:23:55 PM,6,6,4:37:47 PM,6,6,4:50:24 PM,16,27.2,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_460
-Order_No_18725,User_Id_3275,Bike,1,Personal,16,6,12:59:40 PM,16,6,1:00:20 PM,16,6,1:02:36 PM,16,6,1:30:05 PM,12,19.2,,-1.22556,36.80813,-1.1855066,36.825464600000004,Rider_Id_870
-Order_No_17737,User_Id_1199,Bike,1,Personal,18,4,1:21:04 PM,18,4,1:39:51 PM,18,4,1:57:20 PM,18,4,2:04:44 PM,7,26.7,,-1.2924639,36.77957979999999,-1.2820581,36.8284909,Rider_Id_477
-Order_No_8273,User_Id_1995,Bike,3,Business,1,4,11:07:28 AM,1,4,11:07:36 AM,1,4,11:26:53 AM,1,4,11:28:45 AM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_620
-Order_No_6656,User_Id_3300,Bike,3,Business,22,3,3:31:50 PM,22,3,3:33:33 PM,22,3,3:49:15 PM,22,3,3:54:51 PM,9,24.1,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_930
-Order_No_9686,User_Id_3621,Bike,3,Business,8,3,11:14:41 AM,8,3,11:15:29 AM,8,3,11:31:05 AM,8,3,11:34:06 AM,6,,,-1.2836583000000001,36.7886014,-1.2951243000000001,36.828134399999996,Rider_Id_291
-Order_No_3228,User_Id_3291,Bike,3,Business,11,6,8:53:59 AM,11,6,8:54:16 AM,11,6,8:56:39 AM,11,6,9:04:47 AM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_650
-Order_No_15296,User_Id_868,Bike,3,Business,8,2,12:37:24 PM,8,2,12:38:18 PM,8,2,12:41:38 PM,8,2,12:48:07 PM,8,23.3,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_878
-Order_No_18126,User_Id_1703,Bike,3,Personal,4,3,9:59:42 AM,4,3,12:16:30 PM,4,3,12:26:56 PM,4,3,12:34:22 PM,3,17.2,,-1.2806883999999998,36.8204868,-1.2896398999999998,36.812449799999996,Rider_Id_79
-Order_No_9764,User_Id_2086,Bike,1,Personal,23,6,11:07:11 AM,23,6,11:07:53 AM,23,6,11:17:37 AM,23,6,11:27:01 AM,5,,,-1.2994194,36.7647917,-1.3012780000000002,36.7934817,Rider_Id_208
-Order_No_16076,User_Id_2567,Bike,1,Personal,29,3,3:50:05 PM,29,3,3:50:53 PM,29,3,4:06:24 PM,29,3,4:09:00 PM,9,18.9,,-1.3182646,36.821312799999994,-1.260039,36.8159152,Rider_Id_224
-Order_No_11543,User_Id_3517,Bike,3,Business,11,5,9:12:37 AM,11,5,9:13:34 AM,11,5,9:22:45 AM,11,5,9:47:47 AM,15,20.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_136
-Order_No_8143,User_Id_246,Bike,3,Business,5,1,2:38:52 PM,5,1,2:39:28 PM,5,1,2:42:20 PM,5,1,3:12:19 PM,1,26.2,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_846
-Order_No_7373,User_Id_346,Bike,3,Business,29,2,1:17:40 PM,29,2,1:19:13 PM,29,2,1:21:40 PM,29,2,1:38:27 PM,2,27.2,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_828
-Order_No_12767,User_Id_1822,Bike,3,Personal,29,2,10:33:19 AM,29,2,10:34:10 AM,29,2,10:56:29 AM,29,2,11:13:58 AM,17,21.6,,-1.1994789,36.81673320000001,-1.178056,36.9312342,Rider_Id_100
-Order_No_18133,User_Id_2423,Bike,3,Business,13,3,1:56:06 PM,13,3,4:07:21 PM,13,3,4:26:27 PM,13,3,4:28:45 PM,6,27.3,,-1.2551895,36.7822034,-1.2863371000000001,36.8200261,Rider_Id_260
-Order_No_14635,User_Id_3420,Bike,3,Business,4,4,2:03:27 PM,4,4,2:33:07 PM,4,4,2:34:09 PM,4,4,2:41:30 PM,6,24.6,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_914
-Order_No_17644,User_Id_1329,Bike,3,Business,1,1,2:44:26 PM,1,1,2:45:47 PM,1,1,2:53:09 PM,1,1,2:54:56 PM,12,,,-1.273056,36.811298,-1.2231711,36.86801129999999,Rider_Id_166
-Order_No_12231,User_Id_3149,Bike,3,Business,14,2,11:13:56 AM,14,2,11:14:35 AM,14,2,11:29:43 AM,14,2,11:43:14 AM,5,21.5,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_213
-Order_No_21458,User_Id_867,Bike,3,Business,14,5,9:31:54 AM,14,5,9:45:46 AM,14,5,9:51:38 AM,14,5,10:11:27 AM,9,17.3,,-1.2574219,36.7927073,-1.2991441000000001,36.7528804,Rider_Id_868
-Order_No_12713,User_Id_592,Bike,1,Personal,18,3,2:10:34 PM,18,3,2:10:57 PM,18,3,2:17:43 PM,18,3,2:25:10 PM,14,20.6,,-1.2884354,36.760676600000004,-1.3239488000000001,36.831683500000004,Rider_Id_726
-Order_No_25581,User_Id_393,Bike,3,Business,19,2,9:45:50 AM,19,2,9:47:24 AM,19,2,10:04:23 AM,19,2,10:14:01 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_194
-Order_No_17207,User_Id_1329,Bike,3,Business,16,6,3:36:08 PM,16,6,3:37:22 PM,16,6,3:47:20 PM,16,6,3:52:51 PM,7,,,-1.273056,36.811298,-1.3112893,36.8201664,Rider_Id_461
-Order_No_18981,User_Id_1995,Bike,3,Business,17,3,6:07:09 PM,17,3,6:08:33 PM,17,3,6:18:52 PM,17,3,6:21:46 PM,7,,,-1.2551895,36.7822034,-1.2813012,36.832396200000005,Rider_Id_96
-Order_No_20710,User_Id_3372,Bike,3,Business,30,6,3:47:10 PM,30,6,3:51:47 PM,30,6,4:10:19 PM,30,6,4:13:56 PM,13,23.9,,-1.304033,36.7848695,-1.232004,36.7902146,Rider_Id_449
-Order_No_12866,User_Id_346,Bike,3,Business,30,2,9:11:54 AM,30,2,9:12:44 AM,30,2,9:17:41 AM,30,2,9:28:02 AM,2,,,-1.28878,36.8168312,-1.291768,36.8297208,Rider_Id_204
-Order_No_4929,User_Id_2454,Bike,3,Business,18,4,3:38:56 PM,18,4,3:39:08 PM,18,4,4:05:17 PM,18,4,4:18:41 PM,12,,,-1.2341294,36.8065148,-1.2941413,36.7958937,Rider_Id_815
-Order_No_21350,User_Id_1961,Bike,3,Business,8,3,2:28:00 PM,8,3,2:29:02 PM,8,3,2:39:04 PM,8,3,3:06:26 PM,19,,,-1.307143,36.825009,-1.3181159,36.710155900000004,Rider_Id_591
-Order_No_3570,User_Id_733,Bike,3,Business,1,5,5:18:23 PM,1,5,5:19:40 PM,1,5,5:36:09 PM,1,5,5:42:51 PM,13,26.4,,-1.2551895,36.7822034,-1.3257473,36.859206799999995,Rider_Id_953
-Order_No_9838,User_Id_178,Bike,2,Personal,11,4,6:21:19 PM,11,4,6:23:44 PM,11,4,6:35:32 PM,11,4,6:41:10 PM,12,24.0,,-1.3157969999999999,36.8436497,-1.304842,36.90576,Rider_Id_734
-Order_No_8984,User_Id_2330,Bike,3,Business,5,2,12:49:47 PM,5,2,12:52:38 PM,5,2,1:02:34 PM,5,2,1:20:21 PM,10,23.7,,-1.2687407,36.7373043,-1.2642903,36.80021370000001,Rider_Id_269
-Order_No_24501,User_Id_517,Bike,3,Business,3,1,8:47:50 AM,3,1,8:49:59 AM,3,1,8:57:10 AM,3,1,8:59:27 AM,10,18.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_523
-Order_No_23023,User_Id_53,Bike,3,Business,27,6,10:53:35 AM,27,6,10:55:48 AM,27,6,11:06:25 AM,27,6,11:14:40 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_843
-Order_No_7086,User_Id_198,Bike,3,Business,20,1,12:04:45 PM,20,1,12:07:56 PM,20,1,12:22:30 PM,20,1,12:29:17 PM,10,20.3,,-1.3396256000000002,36.8863135,-1.3382056999999998,36.8944104,Rider_Id_248
-Order_No_20537,User_Id_1607,Bike,2,Personal,22,2,9:19:28 AM,22,2,9:19:35 AM,22,2,9:53:06 AM,22,2,10:00:15 AM,16,17.7,,-1.293973,36.80169,-1.2998783,36.9070838,Rider_Id_265
-Order_No_9641,User_Id_53,Bike,3,Business,30,4,2:14:19 PM,30,4,2:15:31 PM,30,4,2:19:15 PM,30,4,2:26:34 PM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_330
-Order_No_27662,User_Id_1500,Bike,3,Business,3,3,11:53:44 AM,3,3,11:56:42 AM,3,3,12:20:19 PM,3,3,12:23:57 PM,11,27.3,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_155
-Order_No_26429,User_Id_1500,Bike,3,Business,2,2,10:25:44 AM,2,2,10:57:43 AM,2,2,11:11:34 AM,2,2,11:23:55 AM,8,21.0,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_87
-Order_No_9585,User_Id_2154,Bike,3,Business,7,4,1:52:02 PM,7,4,1:52:34 PM,7,4,2:04:35 PM,7,4,2:15:50 PM,5,31.2,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_118
-Order_No_17472,User_Id_1502,Bike,3,Business,20,3,11:03:57 AM,20,3,11:04:18 AM,20,3,11:28:39 AM,20,3,11:42:27 AM,12,26.2,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_686
-Order_No_11665,User_Id_53,Bike,3,Business,10,5,10:20:09 AM,10,5,10:25:19 AM,10,5,10:39:42 AM,10,5,10:43:09 AM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_790
-Order_No_26606,User_Id_517,Bike,3,Business,28,5,8:11:45 AM,28,5,8:12:11 AM,28,5,8:12:17 AM,28,5,8:33:58 AM,4,19.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515
-Order_No_24467,User_Id_2900,Bike,1,Business,1,1,2:03:09 PM,1,1,2:03:30 PM,1,1,2:04:49 PM,1,1,2:17:50 PM,9,25.8,,-1.2975066000000002,36.76894229999999,-1.2606476999999998,36.722469399999994,Rider_Id_519
-Order_No_12146,User_Id_510,Bike,3,Business,25,1,11:14:41 AM,25,1,11:27:28 AM,25,1,11:37:57 AM,25,1,11:39:03 AM,6,25.6,,-1.2859912,36.8756811,-1.2860183,36.8975338,Rider_Id_779
-Order_No_17331,User_Id_2066,Bike,3,Business,1,3,2:00:12 PM,1,3,2:04:08 PM,1,3,2:26:08 PM,1,3,2:29:10 PM,8,,,-1.279284,36.820813,-1.3270716,36.8406371,Rider_Id_624
-Order_No_12150,User_Id_2150,Bike,3,Business,13,6,11:57:31 AM,13,6,11:58:17 AM,13,6,12:10:14 PM,13,6,12:17:15 PM,13,26.1,,-1.3033561000000002,36.8163631,-1.3444459999999998,36.756597,Rider_Id_166
-Order_No_17977,User_Id_3647,Bike,3,Business,22,5,4:46:44 PM,22,5,4:48:17 PM,22,5,4:54:15 PM,22,5,5:09:26 PM,15,25.5,,-1.2726389999999999,36.794723,-1.2156006000000001,36.8916865,Rider_Id_85
-Order_No_3464,User_Id_661,Bike,3,Business,11,4,1:36:11 PM,11,4,1:42:57 PM,11,4,1:43:44 PM,11,4,1:57:07 PM,17,26.5,,-1.2482465,36.8889062,-1.2963097,36.7688221,Rider_Id_293
-Order_No_2877,User_Id_3599,Bike,3,Business,3,5,1:58:33 PM,3,5,2:06:25 PM,3,5,2:48:29 PM,3,5,2:52:49 PM,14,,,-1.3302996,36.8707935,-1.2538731,36.7850223,Rider_Id_675
-Order_No_2751,User_Id_2223,Bike,2,Personal,6,5,10:02:59 AM,6,5,10:03:14 AM,6,5,10:07:27 AM,6,5,10:13:04 AM,4,15.1,,-1.3022056,36.787139,-1.2945073999999999,36.8083195,Rider_Id_116
-Order_No_4757,User_Id_1146,Bike,3,Business,3,3,2:31:49 PM,3,3,2:31:59 PM,3,3,2:32:42 PM,3,3,3:16:37 PM,5,28.0,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_668
-Order_No_14783,User_Id_111,Bike,3,Business,9,4,12:44:16 PM,9,4,12:47:26 PM,9,4,12:49:00 PM,9,4,12:53:47 PM,19,,,-1.2551895,36.7822034,-1.3343055,36.73296320000001,Rider_Id_952
-Order_No_3593,User_Id_835,Bike,3,Business,3,3,1:31:50 PM,3,3,1:32:18 PM,3,3,1:42:00 PM,3,3,1:43:49 PM,12,28.2,,-1.2917868000000001,36.7872675,-1.2418799999999999,36.765229999999995,Rider_Id_99
-Order_No_27645,User_Id_3560,Bike,3,Business,7,3,3:30:14 PM,7,3,3:48:37 PM,7,3,4:01:32 PM,7,3,4:09:43 PM,18,28.4,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_237
-Order_No_26715,User_Id_510,Bike,3,Business,24,7,11:41:32 AM,24,7,11:50:55 AM,24,7,12:02:43 PM,24,7,12:11:42 PM,11,,,-1.2297202,36.874550799999994,-1.2860183,36.8975338,Rider_Id_845
-Order_No_15082,User_Id_2423,Bike,3,Business,30,2,5:18:59 PM,30,2,5:27:23 PM,30,2,5:51:47 PM,30,2,5:58:24 PM,4,,,-1.2551895,36.7822034,-1.2643794,36.816234,Rider_Id_177
-Order_No_11027,User_Id_3091,Bike,3,Business,3,1,1:20:34 PM,3,1,1:24:44 PM,3,1,1:44:06 PM,3,1,2:17:17 PM,6,23.1,,-1.290894,36.822971,-1.30435,36.860009999999996,Rider_Id_921
-Order_No_14632,User_Id_3491,Bike,3,Business,16,2,5:27:39 PM,16,2,5:28:02 PM,16,2,5:37:21 PM,16,2,5:41:11 PM,10,24.9,,-1.2883158000000001,36.8199114,-1.2297202,36.881950700000004,Rider_Id_89
-Order_No_10183,User_Id_3647,Bike,3,Business,16,2,11:18:23 AM,16,2,11:19:33 AM,16,2,11:24:38 AM,16,2,11:28:23 AM,6,23.5,,-1.2726389999999999,36.794723,-1.2876643,36.8248768,Rider_Id_208
-Order_No_5505,User_Id_3091,Bike,3,Business,17,3,3:10:22 PM,17,3,3:11:38 PM,17,3,3:51:23 PM,17,3,3:52:44 PM,9,,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_92
-Order_No_1977,User_Id_2509,Bike,3,Business,26,2,9:16:58 AM,26,2,9:22:31 AM,26,2,9:36:01 AM,26,2,9:52:42 AM,4,,,-1.2571472,36.795063299999995,-1.2904753,36.8144721,Rider_Id_953
-Order_No_25292,User_Id_1981,Bike,2,Personal,15,1,11:53:43 AM,15,1,12:10:11 PM,15,1,12:35:36 PM,15,1,12:41:12 PM,14,,,-1.3239363000000002,36.729842299999994,-1.2744447,36.786133899999996,Rider_Id_379
-Order_No_25991,User_Id_2257,Bike,3,Personal,16,4,2:38:13 PM,16,4,2:39:55 PM,16,4,3:18:14 PM,16,4,3:22:34 PM,6,19.1,,-1.2624924,36.7961688,-1.2755629,36.813152200000005,Rider_Id_857
-Order_No_13202,User_Id_635,Bike,3,Business,3,3,11:11:51 AM,3,3,11:12:49 AM,3,3,11:25:25 AM,3,3,11:34:53 AM,8,27.4,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_504
-Order_No_24919,User_Id_2112,Bike,3,Business,21,3,1:19:19 PM,21,3,1:21:05 PM,21,3,1:39:05 PM,21,3,1:44:05 PM,17,23.0,3.0,-1.2156006000000001,36.8916865,-1.3008656,36.845465999999995,Rider_Id_88
-Order_No_26549,User_Id_393,Bike,3,Business,21,4,11:16:10 AM,21,4,11:16:18 AM,21,4,11:16:33 AM,21,4,11:36:27 AM,14,25.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_842
-Order_No_13916,User_Id_2224,Bike,3,Business,18,1,10:23:20 AM,18,1,10:23:46 AM,18,1,10:42:50 AM,18,1,10:43:55 AM,15,27.9,,-1.2132866000000002,36.867827399999996,-1.2530752,36.7979863,Rider_Id_327
-Order_No_1210,User_Id_435,Bike,3,Business,14,5,11:27:53 AM,14,5,11:35:27 AM,14,5,11:49:13 AM,14,5,11:58:22 AM,7,22.0,,-1.300921,36.828195,-1.3271684,36.8173021,Rider_Id_76
-Order_No_26820,User_Id_540,Bike,3,Business,23,1,9:43:03 AM,23,1,9:43:32 AM,23,1,9:53:40 AM,23,1,9:57:18 AM,4,14.4,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_519
-Order_No_10348,User_Id_1210,Bike,2,Personal,12,5,2:58:33 PM,12,5,2:58:46 PM,12,5,2:58:52 PM,12,5,3:09:07 PM,6,27.1,,-1.2658334999999998,36.804286700000006,-1.2850454,36.7938777,Rider_Id_393
-Order_No_13154,User_Id_1494,Bike,1,Personal,12,4,12:29:13 PM,12,4,12:29:44 PM,12,4,12:31:51 PM,12,4,12:54:50 PM,10,18.8,,-1.3047891,36.7739622,-1.331101,36.7566023,Rider_Id_101
-Order_No_9625,User_Id_2112,Bike,3,Business,18,1,10:01:21 AM,18,1,10:04:57 AM,18,1,10:12:57 AM,18,1,10:29:04 AM,12,21.3,,-1.28878,36.8168312,-1.2156006000000001,36.8916865,Rider_Id_26
-Order_No_1015,User_Id_1304,Bike,3,Business,5,5,5:06:57 PM,5,5,5:07:20 PM,5,5,5:13:11 PM,5,5,5:21:14 PM,3,26.2,,-1.2551895,36.7822034,-1.2693017,36.8022778,Rider_Id_690
-Order_No_24815,User_Id_1209,Bike,3,Business,3,5,10:06:27 AM,3,5,10:07:26 AM,3,5,10:12:23 AM,3,5,10:24:24 AM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_709
-Order_No_14653,User_Id_141,Bike,3,Personal,12,2,4:03:09 PM,12,2,4:05:19 PM,12,2,4:35:13 PM,12,2,5:10:14 PM,13,26.6,,-1.2871938,36.8195031,-1.2658003,36.8983864,Rider_Id_332
-Order_No_2890,User_Id_84,Bike,3,Business,30,3,4:34:44 PM,30,3,4:35:06 PM,30,3,4:36:19 PM,30,3,4:47:19 PM,20,27.3,,-1.2571472,36.795063299999995,-1.3632162,36.9108253,Rider_Id_517
-Order_No_12275,User_Id_3068,Bike,3,Personal,10,5,10:25:51 AM,10,5,10:27:40 AM,10,5,10:40:54 AM,10,5,10:46:38 AM,22,19.1,,-1.2513528999999999,36.752171999999995,-1.3203334999999998,36.6831543,Rider_Id_215
-Order_No_14491,User_Id_435,Bike,3,Business,14,5,3:25:03 PM,14,5,3:25:28 PM,14,5,3:25:32 PM,14,5,4:07:25 PM,6,23.1,,-1.300921,36.828195,-1.3264614,36.8472485,Rider_Id_669
-Order_No_12132,User_Id_2828,Bike,3,Business,17,5,11:26:26 AM,17,5,11:26:50 AM,17,5,11:36:49 AM,17,5,11:52:13 AM,23,17.7,,-1.2615888000000002,36.7928732,-1.364732,36.658943,Rider_Id_222
-Order_No_8131,User_Id_2523,Bike,3,Business,11,5,11:09:34 AM,11,5,11:10:31 AM,11,5,11:14:53 AM,11,5,11:21:36 AM,15,21.1,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_105
-Order_No_9864,User_Id_3251,Bike,3,Business,5,1,10:52:04 AM,5,1,10:53:48 AM,5,1,11:09:29 AM,5,1,11:12:22 AM,4,24.6,,-1.2833534,36.8165865,-1.3059713,36.8200566,Rider_Id_920
-Order_No_12473,User_Id_3201,Bike,3,Business,28,4,3:02:07 PM,28,4,3:12:14 PM,28,4,3:39:06 PM,28,4,3:53:57 PM,6,27.4,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_448
-Order_No_3363,User_Id_3194,Bike,3,Business,6,3,1:03:36 PM,6,3,1:04:26 PM,6,3,1:35:38 PM,6,3,1:45:03 PM,7,,,-1.2738397,36.8186697,-1.2551895,36.7822034,Rider_Id_119
-Order_No_23653,User_Id_1966,Bike,3,Business,12,3,4:13:29 PM,12,3,4:32:45 PM,12,3,4:34:14 PM,12,3,4:48:04 PM,21,20.4,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_56
-Order_No_1588,User_Id_1689,Bike,1,Personal,28,2,1:16:07 PM,28,2,1:17:47 PM,28,2,1:26:33 PM,28,2,1:35:31 PM,7,19.3,,-1.2890253999999999,36.7990753,-1.2616841,36.801999,Rider_Id_88
-Order_No_21075,User_Id_81,Bike,3,Business,2,3,3:24:26 PM,2,3,3:25:55 PM,2,3,3:38:54 PM,2,3,3:46:22 PM,9,25.8,,-1.2641496,36.791413899999995,-1.2768129,36.8403079,Rider_Id_85
-Order_No_16892,User_Id_1167,Bike,1,Personal,28,5,11:22:21 AM,28,5,11:22:35 AM,28,5,11:37:59 AM,28,5,11:51:24 AM,8,25.0,,-1.2969741000000001,36.7856612,-1.2609700000000001,36.787286,Rider_Id_751
-Order_No_16752,User_Id_635,Bike,3,Business,8,4,3:55:09 PM,8,4,3:57:48 PM,8,4,4:05:26 PM,8,4,4:18:55 PM,2,27.2,,-1.2637530000000001,36.807790000000004,-1.2602337,36.79905479999999,Rider_Id_519
-Order_No_8326,User_Id_3545,Bike,1,Personal,9,1,10:02:58 AM,9,1,10:03:24 AM,9,1,10:43:39 AM,9,1,10:48:48 AM,12,20.1,,-1.2912668,36.7656703,-1.3502295,36.7242597,Rider_Id_598
-Order_No_6688,User_Id_2982,Bike,3,Business,29,3,9:31:08 AM,29,3,9:31:31 AM,29,3,9:31:55 AM,29,3,10:06:55 AM,18,17.8,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_41
-Order_No_5045,User_Id_3391,Bike,1,Personal,6,6,11:13:02 AM,6,6,11:19:18 AM,6,6,11:47:32 AM,6,6,12:00:36 PM,7,24.1,,-1.2998789,36.816896299999996,-1.3314446000000002,36.8264081,Rider_Id_328
-Order_No_20971,User_Id_635,Bike,3,Business,1,1,3:28:44 PM,1,1,4:42:34 PM,1,1,4:49:09 PM,1,1,5:05:37 PM,8,27.5,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_691
-Order_No_2954,User_Id_136,Bike,3,Business,3,1,3:22:40 PM,3,1,3:25:24 PM,3,1,3:37:05 PM,3,1,3:44:56 PM,4,23.1,,-1.2965045,36.7913036,-1.2950408000000002,36.809817200000005,Rider_Id_177
-Order_No_4439,User_Id_442,Bike,3,Personal,13,3,2:27:54 PM,13,3,2:28:17 PM,13,3,2:36:03 PM,13,3,2:38:11 PM,16,27.0,,-1.2609745,36.7886626,-1.207546,36.7913976,Rider_Id_937
-Order_No_2700,User_Id_635,Bike,3,Business,27,3,12:21:06 PM,27,3,12:36:53 PM,27,3,12:47:15 PM,27,3,12:59:03 PM,16,25.8,,-1.2602337,36.79905479999999,-1.2100345,36.8493221,Rider_Id_222
-Order_No_5981,User_Id_3707,Bike,3,Personal,10,3,10:47:22 AM,10,3,10:47:39 AM,10,3,10:48:14 AM,10,3,11:00:39 AM,18,27.9,,-1.1708105,36.8336102,-1.2897399,36.8270975,Rider_Id_564
-Order_No_16327,User_Id_635,Bike,3,Business,8,1,9:09:41 AM,8,1,9:10:10 AM,8,1,9:14:45 AM,8,1,9:45:30 AM,19,22.1,,-1.300921,36.828195,-1.1942396999999998,36.8554738,Rider_Id_329
-Order_No_1184,User_Id_2239,Bike,2,Personal,19,4,4:02:19 PM,19,4,4:03:02 PM,19,4,4:16:26 PM,19,4,4:30:58 PM,6,20.6,,-1.2985749,36.8088,-1.2697141,36.798177700000004,Rider_Id_84
-Order_No_23141,User_Id_633,Bike,3,Business,10,4,4:08:09 PM,10,4,4:08:37 PM,10,4,4:08:43 PM,10,4,5:12:01 PM,4,23.7,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_39
-Order_No_26157,User_Id_1882,Bike,1,Business,20,7,12:31:07 PM,20,7,2:18:06 PM,20,7,2:40:20 PM,20,7,2:42:24 PM,14,20.4,,-1.2847654,36.820105,-1.2156006000000001,36.8916865,Rider_Id_334
-Order_No_17586,User_Id_1658,Bike,2,Personal,28,5,2:05:07 PM,28,5,3:02:05 PM,28,5,3:02:39 PM,28,5,3:23:18 PM,9,25.5,,-1.2561103999999998,36.7497052,-1.2769868,36.78598579999999,Rider_Id_268
-Order_No_16277,User_Id_393,Bike,3,Business,18,5,1:01:19 PM,18,5,1:01:38 PM,18,5,1:09:15 PM,18,5,1:16:02 PM,11,20.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523
-Order_No_13812,User_Id_3686,Bike,3,Business,14,3,3:06:23 PM,14,3,4:02:35 PM,14,3,4:17:40 PM,14,3,4:21:02 PM,7,25.7,,-1.2551895,36.7822034,-1.2830276000000003,36.8297516,Rider_Id_554
-Order_No_13563,User_Id_186,Bike,3,Business,2,3,3:51:00 PM,2,3,3:51:30 PM,2,3,4:00:26 PM,2,3,4:16:37 PM,5,26.2,,-1.2890399,36.8164942,-1.2571472,36.795063299999995,Rider_Id_698
-Order_No_27882,User_Id_1500,Bike,3,Business,29,3,3:52:42 PM,29,3,3:57:02 PM,29,3,3:57:13 PM,29,3,4:26:24 PM,9,20.9,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_349
-Order_No_25379,User_Id_3635,Bike,1,Business,5,5,5:08:59 PM,5,5,5:09:07 PM,5,5,5:14:14 PM,5,5,5:25:51 PM,3,24.7,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_449
-Order_No_20951,User_Id_812,Bike,1,Personal,28,6,11:11:20 AM,28,6,11:11:52 AM,28,6,11:26:58 AM,28,6,11:37:33 AM,18,24.1,,-1.2791223999999999,36.8306878,-1.2121451,36.9044352,Rider_Id_103
-Order_No_3244,User_Id_640,Bike,3,Business,17,1,10:15:31 AM,17,1,10:16:02 AM,17,1,10:20:15 AM,17,1,10:28:22 AM,10,18.3,,-1.304033,36.7848695,-1.2625087,36.8263725,Rider_Id_409
-Order_No_17084,User_Id_3496,Bike,3,Business,2,3,11:27:28 AM,2,3,11:27:33 AM,2,3,11:27:39 AM,2,3,12:02:08 PM,15,24.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_120
-Order_No_25168,User_Id_1544,Bike,3,Business,11,2,9:07:30 AM,11,2,9:08:55 AM,11,2,9:29:57 AM,11,2,9:35:42 AM,7,16.7,,-1.2924639,36.77957979999999,-1.2538731,36.7850223,Rider_Id_133
-Order_No_25935,User_Id_2987,Bike,3,Business,28,2,2:52:09 PM,28,2,2:52:29 PM,28,2,3:03:14 PM,28,2,3:04:47 PM,11,19.4,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_4
-Order_No_8981,User_Id_1749,Bike,3,Personal,5,2,4:26:59 PM,5,2,4:29:06 PM,5,2,4:30:48 PM,5,2,4:42:23 PM,5,,,-1.288167,36.8181484,-1.2621576,36.8103473,Rider_Id_674
-Order_No_25568,User_Id_391,Bike,3,Business,16,6,11:04:27 AM,16,6,11:06:03 AM,16,6,12:02:48 PM,16,6,12:15:43 PM,5,25.6,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_448
-Order_No_503,User_Id_53,Bike,3,Business,12,6,8:55:03 AM,12,6,8:55:50 AM,12,6,8:55:55 AM,12,6,9:30:23 AM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_619
-Order_No_3767,User_Id_2423,Bike,3,Business,25,5,3:49:08 PM,25,5,4:35:51 PM,25,5,4:45:27 PM,25,5,4:49:30 PM,4,21.2,,-1.2553615,36.789537599999996,-1.2707979,36.8065786,Rider_Id_742
-Order_No_7019,User_Id_3686,Bike,3,Business,3,2,1:19:06 PM,3,2,1:19:43 PM,3,2,1:21:03 PM,3,2,1:33:26 PM,4,20.2,,-1.2551895,36.7822034,-1.2690931,36.8065779,Rider_Id_449
-Order_No_22949,User_Id_635,Bike,3,Business,24,3,3:51:52 PM,24,3,4:10:44 PM,24,3,4:14:24 PM,24,3,4:21:41 PM,7,18.4,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_878
-Order_No_6090,User_Id_1205,Bike,3,Business,21,2,2:49:54 PM,21,2,3:00:07 PM,21,2,3:00:52 PM,21,2,3:17:49 PM,4,25.1,,-1.2495931,36.80650870000001,-1.2663889,36.832210499999995,Rider_Id_58
-Order_No_7738,User_Id_2627,Bike,3,Business,24,4,2:16:49 PM,24,4,2:17:00 PM,24,4,2:21:35 PM,24,4,2:24:28 PM,2,28.4,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_50
-Order_No_23626,User_Id_1287,Bike,3,Business,28,4,6:23:18 PM,28,4,6:23:42 PM,28,4,6:28:39 PM,28,4,6:30:18 PM,3,27.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_452
-Order_No_11155,User_Id_1502,Bike,3,Business,19,2,1:45:05 PM,19,2,1:45:53 PM,19,2,2:05:28 PM,19,2,2:17:25 PM,11,29.8,,-1.2859912,36.8756811,-1.2232123,36.8932547,Rider_Id_339
-Order_No_10672,User_Id_2330,Bike,3,Business,30,2,2:56:06 PM,30,2,3:18:14 PM,30,2,3:46:57 PM,30,2,3:53:41 PM,2,,,-1.2642903,36.80021370000001,-1.2612417,36.813863299999994,Rider_Id_843
-Order_No_5272,User_Id_3064,Bike,3,Business,8,5,2:43:09 PM,8,5,3:07:10 PM,8,5,3:19:06 PM,8,5,3:21:35 PM,12,30.2,,-1.2571472,36.795063299999995,-1.2404912000000001,36.88477879999999,Rider_Id_305
-Order_No_10147,User_Id_2521,Bike,3,Business,25,5,9:44:44 AM,25,5,9:45:00 AM,25,5,9:45:15 AM,25,5,10:07:35 AM,11,20.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_623
-Order_No_17445,User_Id_478,Bike,3,Business,8,5,12:43:51 PM,8,5,1:10:43 PM,8,5,1:19:09 PM,8,5,1:29:15 PM,3,24.2,,-1.2551895,36.7822034,-1.2628638,36.8070399,Rider_Id_210
-Order_No_10443,User_Id_3647,Bike,3,Business,7,2,3:50:51 PM,7,2,3:51:39 PM,7,2,4:06:22 PM,7,2,4:10:48 PM,6,,,-1.2726389999999999,36.794723,-1.2976836999999999,36.76632070000001,Rider_Id_155
-Order_No_9050,User_Id_733,Bike,3,Business,7,2,5:02:53 PM,7,2,5:04:05 PM,7,2,5:06:09 PM,7,2,5:23:04 PM,8,,,-1.2571472,36.795063299999995,-1.3031971000000002,36.786314600000004,Rider_Id_383
-Order_No_21073,User_Id_1787,Bike,3,Business,30,2,5:10:24 PM,30,2,5:10:45 PM,30,2,5:17:57 PM,30,2,5:33:02 PM,9,23.8,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_397
-Order_No_5579,User_Id_1687,Bike,3,Business,11,5,1:38:50 PM,11,5,1:40:15 PM,11,5,1:58:22 PM,11,5,2:18:11 PM,4,26.4,,-1.2899256000000001,36.815551,-1.2633048999999998,36.803125099999995,Rider_Id_237
-Order_No_26318,User_Id_18,Bike,1,Personal,28,4,3:15:14 PM,28,4,3:16:02 PM,28,4,3:25:07 PM,28,4,3:32:56 PM,7,,,-1.2960302,36.771131700000005,-1.2895261,36.8164407,Rider_Id_268
-Order_No_3883,User_Id_265,Bike,3,Business,16,5,2:28:18 PM,16,5,3:48:25 PM,16,5,3:52:26 PM,16,5,4:11:14 PM,7,,,-1.2668734,36.8054026,-1.300921,36.828195,Rider_Id_539
-Order_No_21730,User_Id_2121,Bike,2,Personal,15,1,7:35:22 AM,15,1,7:36:05 AM,15,1,7:40:57 AM,15,1,7:52:31 AM,4,,,-1.2874,36.833966700000005,-1.2925919,36.81705770000001,Rider_Id_79
-Order_No_18183,User_Id_2008,Bike,3,Business,8,5,9:44:33 AM,8,5,9:45:30 AM,8,5,9:55:51 AM,8,5,9:59:52 AM,9,20.7,,-1.2508229,36.7895263,-1.2380935,36.80729470000001,Rider_Id_681
-Order_No_21841,User_Id_2491,Bike,1,Personal,17,3,10:47:56 AM,17,3,10:51:12 AM,17,3,10:53:03 AM,17,3,11:04:25 AM,4,25.3,,-1.3084722,36.8224665,-1.2878148999999999,36.8146245,Rider_Id_338
-Order_No_15273,User_Id_535,Bike,3,Business,30,4,3:11:33 PM,30,4,3:12:01 PM,30,4,3:15:16 PM,30,4,4:08:25 PM,17,,,-1.2615888000000002,36.7928732,-1.2491258,36.689425299999996,Rider_Id_576
-Order_No_12967,User_Id_640,Bike,3,Business,18,4,6:08:16 PM,18,4,6:09:02 PM,18,4,6:14:24 PM,18,4,6:31:06 PM,8,,,-1.2969741000000001,36.7856612,-1.2709035,36.8168523,Rider_Id_815
-Order_No_16418,User_Id_2869,Bike,3,Business,23,6,11:06:59 AM,23,6,11:07:25 AM,23,6,11:40:04 AM,23,6,11:41:47 AM,3,26.5,,-1.3025826,36.7670807,-1.2765492,36.7669809,Rider_Id_112
-Order_No_18803,User_Id_211,Bike,1,Personal,27,4,12:06:02 PM,27,4,12:06:42 PM,27,4,12:21:37 PM,27,4,12:22:40 PM,4,22.7,,-1.299059,36.78756,-1.2918688,36.7603541,Rider_Id_328
-Order_No_24740,User_Id_1754,Bike,3,Business,2,5,11:24:58 AM,2,5,11:25:08 AM,2,5,11:35:04 AM,2,5,11:37:01 AM,21,23.4,,-1.225322,36.80855,-1.2860183,36.8975338,Rider_Id_866
-Order_No_2963,User_Id_633,Bike,3,Business,16,3,8:53:09 AM,16,3,8:53:20 AM,16,3,8:53:36 AM,16,3,9:17:07 AM,11,19.6,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_571
-Order_No_1143,User_Id_1307,Bike,3,Business,20,7,1:44:31 PM,20,7,1:48:00 PM,20,7,2:23:03 PM,20,7,2:27:51 PM,5,20.3,,-1.2844665000000002,36.7866113,-1.2948802,36.766374,Rider_Id_956
-Order_No_23277,User_Id_393,Bike,3,Business,6,2,7:49:55 AM,6,2,8:24:59 AM,6,2,8:25:12 AM,6,2,9:23:10 AM,4,19.9,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_684
-Order_No_7442,User_Id_1205,Bike,3,Business,1,3,1:07:29 PM,1,3,1:08:01 PM,1,3,1:31:15 PM,1,3,1:57:38 PM,3,24.1,,-1.2810807,36.8144229,-1.2974472,36.8156997,Rider_Id_19
-Order_No_20061,User_Id_3326,Bike,3,Business,27,3,3:03:25 PM,27,3,3:04:13 PM,27,3,3:09:36 PM,27,3,3:10:38 PM,4,,,-1.2551895,36.7822034,-1.2670213,36.7994482,Rider_Id_45
-Order_No_13360,User_Id_569,Bike,3,Personal,16,1,9:27:50 AM,16,1,9:27:58 AM,16,1,9:53:05 AM,16,1,9:54:49 AM,14,14.7,,-1.308819,36.8421201,-1.2981752,36.7578336,Rider_Id_951
-Order_No_13959,User_Id_136,Bike,3,Business,15,2,11:35:26 AM,15,2,11:35:31 AM,15,2,12:00:59 PM,15,2,12:04:35 PM,18,24.1,,-1.3254522,36.721658399999995,-1.2760462,36.807469700000006,Rider_Id_244
-Order_No_18228,User_Id_1578,Bike,3,Business,2,2,11:18:40 AM,2,2,11:55:44 AM,2,2,12:08:03 PM,2,2,12:27:00 PM,6,21.0,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_532
-Order_No_19251,User_Id_1850,Bike,3,Business,29,1,3:16:21 PM,29,1,3:41:04 PM,29,1,3:53:22 PM,29,1,4:07:12 PM,6,24.2,,-1.2551895,36.7822034,-1.2854988999999999,36.8218183,Rider_Id_56
-Order_No_12609,User_Id_2546,Bike,1,Personal,18,6,10:37:04 AM,18,6,10:37:20 AM,18,6,10:43:18 AM,18,6,10:47:31 AM,12,19.0,,-1.2840605,36.8191699,-1.3343128999999998,36.863711200000004,Rider_Id_414
-Order_No_1312,User_Id_136,Bike,3,Business,1,1,10:04:01 AM,1,1,10:09:39 AM,1,1,10:17:52 AM,1,1,10:21:56 AM,5,21.5,,-1.2654716000000001,36.7983635,-1.2633232,36.830506799999995,Rider_Id_910
-Order_No_12903,User_Id_1075,Bike,3,Business,17,4,11:24:13 AM,17,4,11:24:37 AM,17,4,11:34:19 AM,17,4,11:40:10 AM,1,25.3,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_733
-Order_No_13012,User_Id_1502,Bike,3,Business,7,5,12:43:47 PM,7,5,12:44:05 PM,7,5,12:51:59 PM,7,5,1:00:52 PM,23,25.2,,-1.2232123,36.8932547,-1.3226301999999999,36.8719901,Rider_Id_12
-Order_No_654,User_Id_2718,Bike,3,Business,11,1,12:29:49 PM,11,1,1:06:47 PM,11,1,1:08:46 PM,11,1,1:17:41 PM,8,18.0,,-1.2601,36.796528,-1.2859858,36.8224519,Rider_Id_393
-Order_No_5453,User_Id_3064,Bike,3,Business,1,5,4:01:46 PM,1,5,4:05:05 PM,1,5,4:20:10 PM,1,5,4:26:04 PM,10,28.5,,-1.2571472,36.795063299999995,-1.3239488000000001,36.831683500000004,Rider_Id_225
-Order_No_5289,User_Id_2330,Bike,3,Business,29,5,4:41:15 PM,29,5,4:45:14 PM,29,5,5:01:14 PM,29,5,5:21:19 PM,7,28.2,,-1.2890715,36.8157588,-1.2873232,36.765857200000006,Rider_Id_907
-Order_No_3009,User_Id_867,Bike,3,Business,21,4,12:55:52 PM,21,4,1:13:33 PM,21,4,1:32:25 PM,21,4,1:47:12 PM,15,26.3,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_328
-Order_No_3889,User_Id_3621,Bike,3,Business,24,7,1:33:47 PM,24,7,2:00:07 PM,24,7,2:28:35 PM,24,7,2:31:53 PM,8,22.8,,-1.2836583000000001,36.7886014,-1.276245,36.836329299999996,Rider_Id_74
-Order_No_27854,User_Id_3547,Bike,3,Business,1,1,12:47:27 PM,1,1,12:47:42 PM,1,1,12:57:53 PM,1,1,1:30:16 PM,16,,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_435
-Order_No_9710,User_Id_3283,Bike,3,Business,21,3,12:18:15 PM,21,3,12:18:45 PM,21,3,12:27:17 PM,21,3,12:55:22 PM,6,21.9,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_237
-Order_No_21768,User_Id_3445,Bike,1,Personal,3,5,4:33:10 PM,3,5,4:33:35 PM,3,5,5:10:02 PM,3,5,5:12:57 PM,4,22.6,,-1.2615983,36.8083096,-1.2516793,36.786481,Rider_Id_452
-Order_No_4257,User_Id_2183,Bike,3,Business,13,5,10:36:57 AM,13,5,10:37:22 AM,13,5,10:45:14 AM,13,5,10:50:27 AM,8,,,-1.2551895,36.7822034,-1.2939912,36.8161329,Rider_Id_726
-Order_No_26106,User_Id_498,Bike,1,Personal,15,2,9:01:56 AM,15,2,9:03:46 AM,15,2,9:23:50 AM,15,2,9:25:19 AM,5,18.1,2.0,-1.3046965,36.8016456,-1.3178671999999998,36.812166299999994,Rider_Id_782
-Order_No_16497,User_Id_2066,Bike,3,Business,28,7,1:35:59 PM,28,7,1:36:10 PM,28,7,1:56:21 PM,28,7,2:00:12 PM,21,,,-1.3210188999999999,36.841055100000005,-1.207546,36.7913976,Rider_Id_68
-Order_No_6204,User_Id_2579,Bike,3,Business,31,4,2:40:16 PM,31,4,2:41:02 PM,31,4,2:55:05 PM,31,4,3:08:12 PM,3,28.6,,-1.2944423999999999,36.77979379999999,-1.2804664,36.7701111,Rider_Id_246
-Order_No_4717,User_Id_2200,Bike,3,Business,14,2,3:00:37 PM,14,2,3:04:03 PM,14,2,3:19:08 PM,14,2,3:29:42 PM,4,23.9,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_213
-Order_No_4708,User_Id_683,Bike,1,Personal,17,4,12:47:44 PM,17,4,12:47:58 PM,17,4,12:50:09 PM,17,4,1:46:03 PM,12,23.7,,-1.275931,36.8516227,-1.2105983999999999,36.8109774,Rider_Id_789
-Order_No_71,User_Id_1191,Bike,1,Personal,29,5,11:23:54 AM,29,5,11:24:27 AM,29,5,11:28:46 AM,29,5,11:32:21 AM,11,19.9,,-1.3352545,36.8659639,-1.2962832,36.784968299999996,Rider_Id_192
-Order_No_27982,User_Id_1990,Bike,3,Business,27,3,12:32:46 PM,27,3,12:36:20 PM,27,3,12:49:25 PM,27,3,1:07:42 PM,10,26.1,,-1.2767064,36.8227148,-1.275442,36.766020700000006,Rider_Id_564
-Order_No_25389,User_Id_2150,Bike,3,Business,19,2,12:44:49 PM,19,2,12:45:42 PM,19,2,12:55:20 PM,19,2,1:03:52 PM,13,27.4,,-1.3033561000000002,36.8163631,-1.3444459999999998,36.756597,Rider_Id_338
-Order_No_13927,User_Id_527,Bike,2,Personal,26,6,10:10:28 AM,26,6,10:10:35 AM,26,6,10:28:58 AM,26,6,10:31:21 AM,13,18.5,,-1.3103985,36.9114173,-1.3295139,36.8985677,Rider_Id_618
-Order_No_13275,User_Id_1281,Bike,3,Business,30,4,5:01:49 PM,30,4,5:03:17 PM,30,4,5:05:32 PM,30,4,5:16:02 PM,7,20.3,,-1.268501,36.801065,-1.2707742,36.7578609,Rider_Id_574
-Order_No_13666,User_Id_1687,Bike,3,Business,13,4,10:50:57 AM,13,4,10:51:23 AM,13,4,10:56:00 AM,13,4,11:29:47 AM,12,21.9,,-1.2626574,36.8014034,-1.3288106000000002,36.8697344,Rider_Id_734
-Order_No_21042,User_Id_103,Bike,3,Business,27,6,12:35:44 PM,27,6,12:56:27 PM,27,6,1:42:12 PM,27,6,1:47:15 PM,13,,,-1.2907037000000001,36.9164122,-1.286553,36.8273374,Rider_Id_895
-Order_No_9662,User_Id_1363,Bike,3,Business,11,2,11:47:27 AM,11,2,11:48:56 AM,11,2,12:06:33 PM,11,2,12:09:23 PM,2,24.3,,-1.3004062,36.829741,-1.2940216999999998,36.8213142,Rider_Id_882
-Order_No_12205,User_Id_3392,Bike,1,Personal,19,5,1:11:39 PM,19,5,1:57:53 PM,19,5,2:19:39 PM,19,5,2:30:13 PM,6,27.2,,-1.2834669,36.8297516,-1.2942764,36.7969749,Rider_Id_559
-Order_No_10436,User_Id_635,Bike,3,Business,14,5,12:52:36 PM,14,5,12:53:08 PM,14,5,12:59:31 PM,14,5,1:12:05 PM,4,24.2,,-1.2599563,36.799343900000004,-1.2822753,36.7916886,Rider_Id_620
-Order_No_15560,User_Id_99,Bike,3,Business,10,5,9:22:10 AM,10,5,9:22:40 AM,10,5,9:39:18 AM,10,5,10:08:14 AM,6,,,-1.3020513999999999,36.77482979999999,-1.2934656999999998,36.8212737,Rider_Id_106
-Order_No_11316,User_Id_2586,Bike,1,Personal,28,1,12:33:46 PM,28,1,12:41:38 PM,28,1,12:53:21 PM,28,1,12:54:57 PM,6,22.6,,-1.2819626,36.7540971,-1.2879437,36.7923297,Rider_Id_46
-Order_No_2198,User_Id_2263,Bike,1,Personal,7,2,3:45:02 PM,7,2,4:19:53 PM,7,2,4:32:47 PM,7,2,4:38:53 PM,6,23.2,,-1.3302996,36.8707935,-1.318049,36.8556554,Rider_Id_848
-Order_No_3874,User_Id_1474,Bike,1,Personal,18,1,6:28:12 PM,18,1,6:54:35 PM,18,1,7:01:14 PM,18,1,8:01:55 PM,13,,,-1.2912419,36.877528399999996,-1.2786893999999998,36.8032321,Rider_Id_890
-Order_No_2434,User_Id_867,Bike,3,Business,2,2,9:59:25 AM,2,2,10:01:02 AM,2,2,10:01:22 AM,2,2,10:43:04 AM,10,26.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_15
-Order_No_14267,User_Id_3560,Bike,3,Business,23,6,3:55:03 PM,23,6,4:02:30 PM,23,6,4:10:00 PM,23,6,4:11:34 PM,8,,,-1.279395,36.825364,-1.3270716,36.8406371,Rider_Id_202
-Order_No_22452,User_Id_820,Bike,1,Personal,11,5,12:33:28 PM,11,5,12:36:05 PM,11,5,12:55:23 PM,11,5,1:03:10 PM,5,20.5,,-1.2890399,36.8164942,-1.2630828,36.805053799999996,Rider_Id_742
-Order_No_19979,User_Id_136,Bike,3,Business,22,1,12:17:38 PM,22,1,12:18:04 PM,22,1,12:21:31 PM,22,1,12:30:39 PM,20,25.0,,-1.2536041999999998,36.806489500000005,-1.3094447,36.9107706,Rider_Id_45
-Order_No_16364,User_Id_2587,Bike,3,Business,21,1,11:58:39 AM,21,1,11:59:29 AM,21,1,12:07:05 PM,21,1,12:17:56 PM,5,25.3,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_289
-Order_No_6831,User_Id_2112,Bike,3,Business,2,2,1:25:12 PM,2,2,1:28:07 PM,2,2,1:30:55 PM,2,2,1:40:40 PM,13,26.3,,-1.2872899,36.820434000000006,-1.2156006000000001,36.8916865,Rider_Id_648
-Order_No_20799,User_Id_3432,Bike,3,Business,4,2,10:44:03 AM,4,2,10:44:21 AM,4,2,10:55:23 AM,4,2,11:01:42 AM,8,18.7,,-1.260812,36.807545000000005,-1.3016279,36.7777746,Rider_Id_216
-Order_No_22187,User_Id_302,Bike,3,Business,24,7,1:12:29 PM,24,7,1:13:01 PM,24,7,1:27:31 PM,24,7,1:42:48 PM,8,,,-1.2976313999999998,36.777256,-1.2649605,36.798177700000004,Rider_Id_101
-Order_No_15256,User_Id_968,Bike,2,Personal,22,5,6:36:05 PM,22,5,6:36:17 PM,22,5,6:40:26 PM,22,5,6:46:44 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_103
-Order_No_10834,User_Id_294,Bike,1,Personal,5,4,11:57:48 AM,5,4,11:58:37 AM,5,4,12:24:26 PM,5,4,12:27:34 PM,14,,,-1.2996372999999999,36.769372100000005,-1.2522271,36.77722670000001,Rider_Id_74
-Order_No_26880,User_Id_1075,Bike,3,Business,15,1,11:45:12 AM,15,1,11:46:52 AM,15,1,11:47:07 AM,15,1,11:58:16 AM,5,,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_338
-Order_No_3066,User_Id_1705,Bike,3,Business,7,1,2:30:14 PM,7,1,2:30:21 PM,7,1,2:32:12 PM,7,1,2:40:23 PM,15,26.6,,-1.2551895,36.7822034,-1.2022306,36.7893305,Rider_Id_72
-Order_No_11944,User_Id_733,Bike,3,Business,5,2,11:47:23 AM,5,2,11:48:25 AM,5,2,11:55:34 AM,5,2,12:01:05 PM,15,23.7,,-1.2551895,36.7822034,-1.3265961000000002,36.8718676,Rider_Id_167
-Order_No_7778,User_Id_357,Bike,3,Business,15,1,12:17:17 PM,15,1,12:19:15 PM,15,1,12:32:59 PM,15,1,1:03:38 PM,7,,,-1.304127,36.805743,-1.2945178999999998,36.7685349,Rider_Id_708
-Order_No_6151,User_Id_2100,Bike,3,Business,1,4,1:40:22 PM,1,4,2:04:41 PM,1,4,2:19:01 PM,1,4,2:23:05 PM,9,,,-1.2860183,36.8975338,-1.2765736,36.851364600000004,Rider_Id_230
-Order_No_7566,User_Id_3599,Bike,3,Business,28,3,1:08:32 PM,28,3,1:11:38 PM,28,3,1:27:54 PM,28,3,2:05:12 PM,17,,,-1.3302996,36.8707935,-1.2949438999999998,36.7604214,Rider_Id_551
-Order_No_15999,User_Id_733,Bike,3,Business,17,3,3:44:36 PM,17,3,3:51:25 PM,17,3,3:51:55 PM,17,3,4:39:05 PM,8,,,-1.2878913,36.8089253,-1.2551895,36.7822034,Rider_Id_613
-Order_No_10248,User_Id_2412,Bike,3,Business,11,1,10:01:23 AM,11,1,10:03:28 AM,11,1,10:19:13 AM,11,1,10:22:43 AM,10,14.5,,-1.2873639,36.8256076,-1.2297202,36.874550799999994,Rider_Id_752
-Order_No_24520,User_Id_2700,Bike,1,Business,2,6,9:34:07 AM,2,6,11:58:24 AM,2,6,12:22:13 PM,2,6,12:42:33 PM,22,23.8,,-1.3895183,36.816418299999995,-1.3052671999999998,36.753853899999996,Rider_Id_654
-Order_No_4699,User_Id_1,Bike,3,Business,5,5,3:42:46 PM,5,5,3:44:55 PM,5,5,3:54:03 PM,5,5,3:58:52 PM,3,26.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_208
-Order_No_25560,User_Id_3291,Bike,3,Business,9,6,4:05:44 PM,9,6,4:06:49 PM,9,6,4:18:26 PM,9,6,4:22:16 PM,8,27.0,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_605
-Order_No_27820,User_Id_3635,Bike,1,Business,28,5,3:04:20 PM,28,5,3:27:27 PM,28,5,3:44:20 PM,28,5,3:53:40 PM,9,25.5,,-1.2615888000000002,36.7928732,-1.3131503999999998,36.8159265,Rider_Id_38
-Order_No_20641,User_Id_2132,Bike,3,Business,7,4,9:05:56 AM,7,4,9:06:18 AM,7,4,9:12:39 AM,7,4,9:48:01 AM,12,19.3,,-1.2384056,36.8718698,-1.295748,36.834246,Rider_Id_75
-Order_No_22514,User_Id_3149,Bike,3,Business,14,4,2:36:03 PM,14,4,2:37:28 PM,14,4,2:38:27 PM,14,4,3:09:16 PM,6,27.4,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_901
-Order_No_27189,User_Id_1882,Bike,3,Business,23,5,7:14:00 PM,23,5,7:21:50 PM,23,5,7:41:47 PM,23,5,7:48:31 PM,5,20.4,,-1.2958783,36.7928125,-1.2877809999999998,36.817865999999995,Rider_Id_554
-Order_No_43,User_Id_3115,Bike,3,Business,29,2,10:10:33 AM,29,2,10:12:02 AM,29,2,10:25:21 AM,29,2,10:48:06 AM,12,21.6,,-1.3108966999999998,36.8340544,-1.296797,36.776452500000005,Rider_Id_543
-Order_No_19382,User_Id_391,Bike,3,Business,26,2,12:11:46 PM,26,2,12:11:57 PM,26,2,12:15:54 PM,26,2,12:24:55 PM,5,27.5,,-1.2602337,36.79905479999999,-1.2867454,36.8259017,Rider_Id_456
-Order_No_15893,User_Id_1081,Bike,3,Business,5,2,3:11:21 PM,5,2,3:11:38 PM,5,2,3:16:00 PM,5,2,3:25:31 PM,23,27.4,,-1.2283403,36.8822756,-1.3472066,36.7692638,Rider_Id_594
-Order_No_6551,User_Id_2509,Bike,3,Business,25,2,4:19:37 PM,25,2,4:20:01 PM,25,2,4:25:25 PM,25,2,4:31:43 PM,8,27.2,,-1.305399,36.822184899999996,-1.2571472,36.795063299999995,Rider_Id_612
-Order_No_3248,User_Id_1482,Bike,3,Business,27,3,1:51:52 PM,27,3,1:52:00 PM,27,3,1:52:14 PM,27,3,2:07:26 PM,19,29.3,,-1.1612628999999999,36.8268239,-1.2649605,36.798177700000004,Rider_Id_576
-Order_No_13279,User_Id_3201,Bike,3,Business,8,5,11:54:30 AM,8,5,11:55:03 AM,8,5,12:01:33 PM,8,5,12:12:01 PM,1,24.1,,-1.2561334,36.7990458,-1.2584143,36.8048002,Rider_Id_540
-Order_No_10044,User_Id_2151,Bike,1,Personal,23,3,11:04:18 AM,23,3,11:09:47 AM,23,3,11:19:54 AM,23,3,11:32:37 AM,6,21.8,,-1.2733223,36.795100500000004,-1.2550097,36.8045852,Rider_Id_406
-Order_No_301,User_Id_2304,Bike,1,Personal,14,5,1:54:25 PM,14,5,1:54:55 PM,14,5,2:17:40 PM,14,5,2:22:30 PM,12,27.3,,-1.2963097,36.7688221,-1.3549418,36.7280528,Rider_Id_312
-Order_No_26370,User_Id_1288,Bike,1,Personal,14,4,2:43:26 PM,14,4,3:01:14 PM,14,4,3:30:07 PM,14,4,3:40:40 PM,4,21.2,,-1.2861764999999998,36.8209086,-1.305399,36.822184899999996,Rider_Id_268
-Order_No_2815,User_Id_432,Bike,3,Business,24,5,1:08:31 PM,24,5,1:14:04 PM,24,5,1:25:19 PM,24,5,1:35:54 PM,22,23.1,,-1.3562374,36.904295399999995,-1.3012008,36.764868,Rider_Id_205
-Order_No_19869,User_Id_3295,Bike,3,Business,12,3,2:59:25 PM,12,3,3:02:45 PM,12,3,3:03:58 PM,12,3,3:21:32 PM,33,21.2,,-1.2571472,36.795063299999995,-1.4097093,36.953208200000006,Rider_Id_875
-Order_No_24136,User_Id_110,Bike,3,Personal,1,1,12:23:00 PM,1,1,12:26:30 PM,1,1,12:52:32 PM,1,1,1:06:30 PM,9,,,-1.3257471,36.8775667,-1.3076261999999998,36.8325382,Rider_Id_883
-Order_No_21051,User_Id_2504,Bike,3,Business,11,4,12:39:21 PM,11,4,12:39:39 PM,11,4,12:49:41 PM,11,4,12:51:08 PM,9,25.8,,-1.2963097,36.7688221,-1.2601197,36.7985271,Rider_Id_699
-Order_No_24818,User_Id_3647,Bike,3,Business,24,3,3:31:21 PM,24,3,3:32:08 PM,24,3,3:39:39 PM,24,3,3:45:22 PM,8,,,-1.2726389999999999,36.794723,-1.2575370000000001,36.843066,Rider_Id_263
-Order_No_8894,User_Id_2330,Bike,3,Business,22,5,10:11:58 AM,22,5,10:20:04 AM,22,5,10:40:29 AM,22,5,10:45:23 AM,8,,,-1.2873232,36.765857200000006,-1.293082,36.8278578,Rider_Id_828
-Order_No_22878,User_Id_3355,Bike,3,Business,10,5,1:20:04 PM,10,5,1:20:22 PM,10,5,1:39:18 PM,10,5,1:54:28 PM,17,21.9,,-1.2154586,36.8830064,-1.3052491000000002,36.8223899,Rider_Id_415
-Order_No_20856,User_Id_1792,Bike,3,Business,28,3,11:41:31 AM,28,3,11:44:54 AM,28,3,11:53:08 AM,28,3,11:56:50 AM,12,,,-1.2809121,36.7852524,-1.2513528999999999,36.752171999999995,Rider_Id_917
-Order_No_13438,User_Id_1363,Bike,3,Business,7,5,2:50:29 PM,7,5,3:08:15 PM,7,5,3:16:05 PM,7,5,3:19:27 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_907
-Order_No_22725,User_Id_2521,Bike,3,Business,30,6,10:31:18 AM,30,6,10:31:40 AM,30,6,10:32:17 AM,30,6,10:40:29 AM,11,26.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_111
-Order_No_7105,User_Id_111,Bike,3,Business,19,2,12:42:45 PM,19,2,12:43:09 PM,19,2,12:43:17 PM,19,2,12:48:19 PM,13,26.5,,-1.2551895,36.7822034,-1.3233955,36.856294399999996,Rider_Id_518
-Order_No_18527,User_Id_1246,Bike,3,Business,7,4,3:47:23 PM,7,4,3:47:43 PM,7,4,3:52:06 PM,7,4,3:57:35 PM,2,20.2,,-1.2579303999999998,36.8019667,-1.2658334999999998,36.804286700000006,Rider_Id_425
-Order_No_8556,User_Id_1133,Bike,3,Business,31,2,8:50:20 AM,31,2,9:16:00 AM,31,2,9:39:16 AM,31,2,9:50:50 AM,20,17.0,0.9,-1.3508657,36.9019028,-1.2571472,36.795063299999995,Rider_Id_877
-Order_No_27605,User_Id_771,Bike,1,Personal,17,1,3:43:31 PM,17,1,3:43:49 PM,17,1,3:57:00 PM,17,1,4:07:04 PM,24,28.3,,-1.3178402,36.8737336,-1.3207446000000003,36.6969684,Rider_Id_676
-Order_No_2730,User_Id_3309,Bike,3,Personal,6,5,2:17:03 PM,6,5,2:18:53 PM,6,5,2:31:39 PM,6,5,2:48:13 PM,9,22.7,,-1.255149,36.805748,-1.2995135,36.793363299999996,Rider_Id_103
-Order_No_13165,User_Id_3557,Bike,3,Business,6,3,10:58:28 AM,6,3,10:58:44 AM,6,3,11:19:04 AM,6,3,11:33:42 AM,7,22.5,,-1.289882,36.8105888,-1.2546732,36.808679600000005,Rider_Id_899
-Order_No_11872,User_Id_635,Bike,3,Business,6,4,10:21:11 AM,6,4,10:21:37 AM,6,4,10:21:45 AM,6,4,11:32:09 AM,8,19.6,,-1.300921,36.828195,-1.2674338,36.7941791,Rider_Id_855
-Order_No_25691,User_Id_3688,Bike,3,Business,3,3,3:28:43 PM,3,3,3:30:12 PM,3,3,4:11:27 PM,3,3,4:13:10 PM,10,28.2,,-1.2285834999999998,36.882646799999996,-1.2736466,36.8164042,Rider_Id_76
-Order_No_1871,User_Id_186,Bike,3,Business,21,3,3:49:05 PM,21,3,3:50:11 PM,21,3,3:50:25 PM,21,3,4:17:16 PM,4,23.1,2.9,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_848
-Order_No_18482,User_Id_3149,Bike,3,Business,7,4,2:30:55 PM,7,4,2:35:34 PM,7,4,2:46:06 PM,7,4,3:23:13 PM,5,27.9,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_576
-Order_No_22735,User_Id_533,Bike,3,Business,6,1,2:39:48 PM,6,1,2:40:30 PM,6,1,2:55:13 PM,6,1,2:58:35 PM,20,,,-1.2594538999999998,36.8067841,-1.3700383,36.9190174,Rider_Id_484
-Order_No_4668,User_Id_3149,Bike,3,Business,3,4,12:24:41 PM,3,4,12:28:31 PM,3,4,12:35:45 PM,3,4,12:48:20 PM,5,23.5,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_901
-Order_No_1040,User_Id_3135,Bike,3,Personal,12,5,11:44:14 AM,12,5,11:47:30 AM,12,5,11:47:56 AM,12,5,12:16:34 PM,12,24.3,,-1.2756364,36.853548499999995,-1.277585,36.77145,Rider_Id_179
-Order_No_10837,User_Id_1032,Bike,1,Personal,17,2,4:43:25 PM,17,2,4:43:49 PM,17,2,4:55:46 PM,17,2,5:03:15 PM,7,24.2,,-1.2914795000000001,36.824897299999996,-1.2969741000000001,36.7856612,Rider_Id_281
-Order_No_22077,User_Id_1512,Bike,1,Personal,5,5,5:34:49 PM,5,5,5:36:34 PM,5,5,6:15:41 PM,5,5,6:18:14 PM,13,24.6,,-1.2880228999999999,36.7892191,-1.2330694,36.745451,Rider_Id_383
-Order_No_19714,User_Id_3222,Bike,3,Business,1,4,12:36:32 PM,1,4,12:47:30 PM,1,4,12:47:36 PM,1,4,12:58:18 PM,9,,,-1.2551895,36.7822034,-1.2959604999999998,36.7873854,Rider_Id_268
-Order_No_3663,User_Id_3565,Bike,3,Business,14,1,11:37:59 AM,14,1,11:56:25 AM,14,1,12:00:52 PM,14,1,12:12:18 PM,7,22.8,,-1.2844665000000002,36.7866113,-1.2839026000000002,36.8215225,Rider_Id_555
-Order_No_7204,User_Id_733,Bike,3,Business,8,2,2:10:22 PM,8,2,2:11:41 PM,8,2,2:19:30 PM,8,2,2:42:03 PM,22,25.2,,-1.2878913,36.8089253,-1.403476,36.947887,Rider_Id_703
-Order_No_10284,User_Id_1877,Bike,3,Personal,23,4,1:24:45 PM,23,4,1:26:29 PM,23,4,1:38:59 PM,23,4,1:56:39 PM,32,20.8,,-1.2156006000000001,36.8916865,-1.371951,36.726864899999995,Rider_Id_882
-Order_No_4232,User_Id_393,Bike,3,Business,10,5,11:23:49 AM,10,5,11:24:14 AM,10,5,11:29:00 AM,10,5,11:39:43 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_687
-Order_No_11328,User_Id_1791,Bike,3,Personal,2,7,10:12:52 AM,2,7,10:13:37 AM,2,7,10:39:45 AM,2,7,11:10:59 AM,16,24.4,,-1.3496012,36.6698157,-1.2947685,36.778583399999995,Rider_Id_533
-Order_No_12429,User_Id_136,Bike,3,Business,11,2,1:51:40 PM,11,2,1:57:51 PM,11,2,2:26:30 PM,11,2,2:37:21 PM,6,21.2,,-1.3360945,36.7445491,-1.3254522,36.721658399999995,Rider_Id_155
-Order_No_6615,User_Id_2423,Bike,3,Business,29,5,2:20:06 PM,29,5,4:08:31 PM,29,5,4:18:28 PM,29,5,4:20:47 PM,8,21.4,,-1.2551895,36.7822034,-1.300722,36.816865,Rider_Id_726
-Order_No_16429,User_Id_2989,Bike,3,Business,7,1,8:15:27 AM,7,1,8:16:48 AM,7,1,8:41:00 AM,7,1,8:47:28 AM,20,21.6,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_486
-Order_No_10850,User_Id_1499,Bike,2,Personal,14,6,10:00:04 AM,14,6,10:00:14 AM,14,6,10:15:05 AM,14,6,10:20:55 AM,5,15.2,,-1.2873232,36.765857200000006,-1.2835228,36.7437619,Rider_Id_465
-Order_No_2486,User_Id_3201,Bike,3,Business,29,3,11:51:16 AM,29,3,11:51:42 AM,29,3,12:00:38 PM,29,3,12:20:13 PM,1,,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_424
-Order_No_16201,User_Id_867,Bike,3,Business,6,3,10:36:17 AM,6,3,10:38:32 AM,6,3,10:39:00 AM,6,3,11:02:40 AM,10,22.5,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_842
-Order_No_10235,User_Id_2593,Bike,2,Business,4,4,2:19:52 PM,4,4,2:20:24 PM,4,4,2:37:17 PM,4,4,2:41:05 PM,7,24.9,,-1.2615888000000002,36.7928732,-1.3021707,36.816844399999994,Rider_Id_844
-Order_No_12375,User_Id_3599,Bike,3,Business,13,4,2:33:53 PM,13,4,2:49:52 PM,13,4,3:36:00 PM,13,4,3:39:26 PM,28,23.3,,-1.3302996,36.8707935,-1.4287625,36.9875447,Rider_Id_772
-Order_No_7164,User_Id_1920,Bike,3,Business,19,1,9:13:21 AM,19,1,9:13:26 AM,19,1,9:16:12 AM,19,1,9:36:36 AM,17,19.2,,-1.2551895,36.7822034,-1.3323949,36.8915219,Rider_Id_932
-Order_No_4936,User_Id_1591,Bike,2,Personal,21,2,11:04:29 AM,21,2,11:05:26 AM,21,2,11:29:12 AM,21,2,11:35:21 AM,5,19.8,,-1.2421591,36.8153854,-1.2655306,36.828148,Rider_Id_400
-Order_No_20420,User_Id_2360,Bike,3,Business,13,3,10:23:20 AM,13,3,10:24:32 AM,13,3,11:08:34 AM,13,3,11:18:51 AM,11,,,-1.2835228,36.7437619,-1.2551895,36.7822034,Rider_Id_751
-Order_No_1218,User_Id_3621,Bike,3,Business,12,2,12:57:35 PM,12,2,1:16:23 PM,12,2,1:32:43 PM,12,2,1:35:12 PM,5,,,-1.2836583000000001,36.7886014,-1.2810888999999999,36.8247135,Rider_Id_709
-Order_No_16863,User_Id_2195,Bike,3,Business,3,3,3:00:11 PM,3,3,3:00:28 PM,3,3,3:26:06 PM,3,3,3:30:21 PM,6,28.3,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_843
-Order_No_26133,User_Id_111,Bike,3,Business,18,4,11:27:28 AM,18,4,11:28:49 AM,18,4,11:36:06 AM,18,4,11:41:06 AM,9,,,-1.2551895,36.7822034,-1.2819989999999999,36.826887,Rider_Id_576
-Order_No_2648,User_Id_2066,Bike,3,Business,14,5,4:52:32 PM,14,5,4:52:51 PM,14,5,4:54:36 PM,14,5,4:55:50 PM,9,21.4,,-1.3210188999999999,36.841055100000005,-1.291216,36.8746657,Rider_Id_270
-Order_No_13335,User_Id_1329,Bike,3,Business,7,4,4:33:51 PM,7,4,4:34:37 PM,7,4,4:40:56 PM,7,4,4:45:48 PM,6,29.3,,-1.273056,36.811298,-1.2832105,36.7777664,Rider_Id_378
-Order_No_20446,User_Id_84,Bike,3,Business,14,4,3:03:01 PM,14,4,4:24:10 PM,14,4,4:34:53 PM,14,4,4:38:06 PM,14,26.6,,-1.2551895,36.7822034,-1.3263105,36.8622904,Rider_Id_276
-Order_No_27426,User_Id_3517,Bike,3,Business,12,3,10:21:08 AM,12,3,10:22:09 AM,12,3,10:22:58 AM,12,3,11:00:32 AM,16,19.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_95
-Order_No_14001,User_Id_718,Bike,3,Business,18,1,2:07:32 PM,18,1,2:10:25 PM,18,1,2:21:18 PM,18,1,2:28:41 PM,6,31.0,,-1.2551895,36.7822034,-1.2881035,36.8142095,Rider_Id_206
-Order_No_23824,User_Id_1500,Bike,3,Business,3,3,9:00:03 AM,3,3,9:01:35 AM,3,3,9:01:38 AM,3,3,9:50:41 AM,8,21.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_70
-Order_No_18153,User_Id_2521,Bike,3,Business,13,6,8:18:49 AM,13,6,8:35:34 AM,13,6,9:00:55 AM,13,6,9:04:48 AM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208
-Order_No_15918,User_Id_2626,Bike,3,Business,27,6,10:52:56 AM,27,6,10:53:12 AM,27,6,11:09:33 AM,27,6,11:18:58 AM,3,,,-1.3043649,36.7816132,-1.2954956,36.760385799999995,Rider_Id_948
-Order_No_6070,User_Id_1075,Bike,3,Business,24,1,3:11:04 PM,24,1,3:11:19 PM,24,1,3:17:15 PM,24,1,3:24:56 PM,3,24.6,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_79
-Order_No_5878,User_Id_635,Bike,3,Business,10,1,2:29:00 PM,10,1,2:29:24 PM,10,1,2:33:43 PM,10,1,2:36:11 PM,2,18.8,,-1.2633732,36.813168299999994,-1.2599563,36.799343900000004,Rider_Id_283
-Order_No_1085,User_Id_3720,Bike,2,Personal,3,2,9:25:45 AM,3,2,9:26:35 AM,3,2,9:50:18 AM,3,2,10:03:05 AM,11,15.1,1.9,-1.3001235,36.89229829999999,-1.3272264999999999,36.8231497,Rider_Id_618
-Order_No_26461,User_Id_1256,Bike,1,Personal,24,2,11:04:12 AM,24,2,11:06:05 AM,24,2,11:25:33 AM,24,2,11:29:35 AM,8,17.5,,-1.2652108,36.8056075,-1.3025666999999999,36.7803495,Rider_Id_144
-Order_No_22691,User_Id_1976,Bike,3,Business,18,1,4:25:42 PM,18,1,4:27:06 PM,18,1,4:37:09 PM,18,1,4:39:59 PM,9,28.7,,-1.257028,36.7810435,-1.2655306,36.828148,Rider_Id_933
-Order_No_15275,User_Id_346,Bike,3,Business,11,5,10:17:09 AM,11,5,10:19:20 AM,11,5,10:59:39 AM,11,5,11:04:03 AM,18,21.2,,-1.3065978999999999,36.7186349,-1.28878,36.8168312,Rider_Id_725
-Order_No_19933,User_Id_1245,Bike,3,Business,20,1,8:27:43 AM,20,1,8:43:08 AM,20,1,9:26:34 AM,20,1,9:42:18 AM,14,14.2,,-1.3882008,36.7699445,-1.3198615,36.7110323,Rider_Id_939
-Order_No_17771,User_Id_3351,Bike,2,Personal,20,5,1:10:51 PM,20,5,1:11:23 PM,20,5,1:17:06 PM,20,5,1:24:06 PM,8,18.7,,-1.2864309999999999,36.818411,-1.293554,36.763113,Rider_Id_456
-Order_No_24123,User_Id_2503,Bike,2,Business,18,4,12:55:55 PM,18,4,12:56:52 PM,18,4,12:58:00 PM,18,4,1:15:41 PM,8,25.4,,-1.2925236,36.7795556,-1.2551895,36.7822034,Rider_Id_79
-Order_No_9774,User_Id_360,Bike,3,Personal,31,4,9:18:49 AM,31,4,9:19:02 AM,31,4,9:31:42 AM,31,4,9:55:07 AM,29,17.3,,-1.2959053,36.7898158,-1.142527,36.91799,Rider_Id_79
-Order_No_6568,User_Id_2532,Bike,3,Business,11,2,10:43:06 AM,11,2,11:08:10 AM,11,2,11:15:02 AM,11,2,12:01:17 PM,6,19.4,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_206
-Order_No_19809,User_Id_2156,Bike,2,Personal,9,3,10:20:20 AM,9,3,10:20:36 AM,9,3,10:59:26 AM,9,3,11:03:56 AM,11,21.2,,-1.305947,36.849334999999996,-1.2620419,36.788804,Rider_Id_618
-Order_No_23925,User_Id_3560,Bike,3,Business,5,5,9:45:51 AM,5,5,10:19:20 AM,5,5,10:22:26 AM,5,5,10:36:29 AM,2,24.0,,-1.2793686000000002,36.82592329999999,-1.2848173999999999,36.826163,Rider_Id_648
-Order_No_5178,User_Id_2454,Bike,3,Business,22,2,2:27:21 PM,22,2,2:29:46 PM,22,2,2:49:44 PM,22,2,2:53:40 PM,15,25.4,,-1.2219928999999998,36.8349924,-1.2993231,36.7753372,Rider_Id_660
-Order_No_991,User_Id_633,Bike,3,Business,26,2,4:00:05 PM,26,2,4:16:10 PM,26,2,4:16:18 PM,26,2,4:35:56 PM,4,28.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_111
-Order_No_19348,User_Id_186,Bike,3,Business,30,4,10:45:00 AM,30,4,10:46:06 AM,30,4,10:57:54 AM,30,4,11:06:23 AM,7,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_764
-Order_No_18530,User_Id_72,Bike,1,Personal,10,2,9:55:08 AM,10,2,9:56:32 AM,10,2,10:02:17 AM,10,2,10:06:17 AM,5,16.1,,-1.3227037,36.847087200000004,-1.3038645,36.8684596,Rider_Id_791
-Order_No_24543,User_Id_916,Bike,3,Business,10,5,12:49:38 PM,10,5,12:50:54 PM,10,5,12:55:56 PM,10,5,1:18:56 PM,5,19.2,,-1.3321944,36.889232899999996,-1.325517,36.907821999999996,Rider_Id_102
-Order_No_14034,User_Id_3060,Bike,3,Business,6,3,9:20:48 AM,6,3,9:24:00 AM,6,3,9:33:02 AM,6,3,9:34:11 AM,4,16.7,,-1.269671,36.821544,-1.2513591,36.7947674,Rider_Id_175
-Order_No_17090,User_Id_3631,Bike,3,Personal,6,1,1:15:05 PM,6,1,1:15:47 PM,6,1,1:27:17 PM,6,1,1:31:30 PM,5,,,-1.2963097,36.7688221,-1.2799006000000002,36.7916973,Rider_Id_678
-Order_No_4288,User_Id_3688,Bike,3,Business,11,2,2:50:38 PM,11,2,2:52:57 PM,11,2,2:55:59 PM,11,2,3:02:22 PM,2,21.3,,-1.2551895,36.7822034,-1.2567464,36.7915273,Rider_Id_161
-Order_No_12472,User_Id_187,Bike,3,Business,3,2,3:04:57 PM,3,2,3:08:24 PM,3,2,3:21:03 PM,3,2,3:27:04 PM,8,20.4,,-1.3014461000000002,36.7661381,-1.3169591,36.7000029,Rider_Id_140
-Order_No_24708,User_Id_824,Bike,1,Business,19,5,1:16:23 PM,19,5,1:16:41 PM,19,5,1:36:21 PM,19,5,1:44:44 PM,21,27.2,,-1.207546,36.7913976,-1.1234436,36.6770756,Rider_Id_337
-Order_No_26036,User_Id_1329,Bike,3,Business,13,4,1:08:28 PM,13,4,1:09:39 PM,13,4,1:16:21 PM,13,4,1:17:36 PM,9,22.2,,-1.273056,36.811298,-1.2521132,36.755977200000004,Rider_Id_279
-Order_No_18442,User_Id_393,Bike,3,Business,26,5,3:50:38 PM,26,5,3:55:31 PM,26,5,4:12:05 PM,26,5,4:15:27 PM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_161
-Order_No_15638,User_Id_1527,Bike,3,Business,29,3,4:23:25 PM,29,3,4:26:32 PM,29,3,4:36:21 PM,29,3,4:41:08 PM,12,21.0,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_168
-Order_No_3120,User_Id_3091,Bike,1,Business,6,1,2:45:52 PM,6,1,2:46:44 PM,6,1,3:14:52 PM,6,1,3:22:34 PM,8,24.4,,-1.3014461000000002,36.7661381,-1.2977705,36.81341389999999,Rider_Id_327
-Order_No_15521,User_Id_3291,Bike,3,Business,27,3,4:31:18 PM,27,3,4:31:56 PM,27,3,4:33:47 PM,27,3,4:40:01 PM,4,27.7,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_698
-Order_No_18742,User_Id_1245,Bike,3,Business,28,3,1:43:38 PM,28,3,1:43:57 PM,28,3,1:51:39 PM,28,3,2:13:52 PM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_74
-Order_No_19805,User_Id_136,Bike,3,Business,14,4,11:53:01 AM,14,4,11:53:46 AM,14,4,12:04:49 PM,14,4,12:13:57 PM,15,23.5,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_378
-Order_No_17618,User_Id_1461,Bike,2,Personal,17,7,11:36:57 AM,17,7,12:11:30 PM,17,7,12:20:01 PM,17,7,12:25:43 PM,2,15.7,,-1.2720768999999998,36.8180604,-1.2660631000000002,36.8343554,Rider_Id_727
-Order_No_22261,User_Id_867,Bike,3,Business,6,4,8:59:06 AM,6,4,8:59:31 AM,6,4,8:59:35 AM,6,4,10:11:56 AM,12,16.1,4.0,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_855
-Order_No_5474,User_Id_627,Bike,3,Business,29,1,9:07:00 AM,29,1,9:09:08 AM,29,1,9:51:34 AM,29,1,9:53:13 AM,10,18.8,,-1.2551895,36.7822034,-1.2299678,36.81530789999999,Rider_Id_948
-Order_No_24693,User_Id_824,Bike,3,Business,13,2,1:30:03 PM,13,2,1:43:59 PM,13,2,1:48:03 PM,13,2,2:08:46 PM,17,26.1,,-1.207546,36.7913976,-1.2896398999999998,36.812449799999996,Rider_Id_49
-Order_No_25348,User_Id_3147,Bike,3,Business,6,1,8:35:54 AM,6,1,8:36:33 AM,6,1,8:46:25 AM,6,1,9:04:15 AM,17,,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_96
-Order_No_6868,User_Id_3470,Bike,3,Business,29,4,11:18:40 AM,29,4,11:43:25 AM,29,4,11:56:11 AM,29,4,12:07:54 PM,5,21.4,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_539
-Order_No_6333,User_Id_2174,Bike,3,Business,27,3,10:05:22 AM,27,3,10:05:53 AM,27,3,10:05:59 AM,27,3,11:06:26 AM,14,26.2,,-1.2482781,36.884091999999995,-1.263736,36.780353000000005,Rider_Id_86
-Order_No_4931,User_Id_635,Bike,3,Business,11,4,11:09:23 AM,11,4,11:12:31 AM,11,4,11:14:21 AM,11,4,11:58:03 AM,7,25.7,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_436
-Order_No_3678,User_Id_3201,Bike,3,Business,20,3,3:19:59 PM,20,3,3:20:12 PM,20,3,3:20:21 PM,20,3,3:21:23 PM,9,29.4,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_613
-Order_No_13808,User_Id_1874,Bike,3,Business,31,3,11:52:36 AM,31,3,12:04:18 PM,31,3,12:44:07 PM,31,3,12:45:13 PM,7,24.7,,-1.2782498,36.8251128,-1.2571472,36.795063299999995,Rider_Id_775
-Order_No_24664,User_Id_3647,Bike,3,Business,27,1,2:59:41 PM,27,1,3:01:37 PM,27,1,3:20:29 PM,27,1,3:26:10 PM,9,22.9,,-1.2726389999999999,36.794723,-1.2703375,36.8247799,Rider_Id_835
-Order_No_504,User_Id_677,Bike,3,Business,16,2,2:30:46 PM,16,2,2:31:04 PM,16,2,3:25:38 PM,16,2,3:31:11 PM,3,,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_167
-Order_No_26405,User_Id_1932,Bike,1,Business,21,1,1:12:06 PM,21,1,1:13:09 PM,21,1,1:24:38 PM,21,1,1:27:02 PM,2,19.8,,-1.2867716999999999,36.773523700000005,-1.2922857,36.781327399999995,Rider_Id_334
-Order_No_4450,User_Id_868,Bike,3,Business,12,1,3:55:30 PM,12,1,3:57:05 PM,12,1,3:59:52 PM,12,1,4:10:43 PM,10,27.1,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_836
-Order_No_17007,User_Id_970,Bike,3,Business,6,1,11:25:05 AM,6,1,11:25:31 AM,6,1,11:26:42 AM,6,1,11:31:09 AM,9,,,-1.2551895,36.7822034,-1.306431,36.812042,Rider_Id_168
-Order_No_8153,User_Id_173,Bike,3,Business,17,6,5:31:41 PM,17,6,6:02:22 PM,17,6,6:12:41 PM,17,6,6:14:55 PM,3,19.8,,-1.2993231,36.7753372,-1.3089298,36.7761994,Rider_Id_952
-Order_No_1701,User_Id_970,Bike,3,Business,5,4,11:45:36 AM,5,4,12:09:17 PM,5,4,12:18:07 PM,5,4,12:22:51 PM,6,,,-1.2551895,36.7822034,-1.2834774,36.8218085,Rider_Id_726
-Order_No_19446,User_Id_1552,Bike,1,Personal,29,6,1:54:25 PM,29,6,1:56:54 PM,29,6,2:04:42 PM,29,6,2:11:00 PM,6,27.8,,-1.2836832,36.8306719,-1.2668093,36.7966542,Rider_Id_622
-Order_No_394,User_Id_3172,Bike,3,Personal,24,4,11:23:03 AM,24,4,11:23:42 AM,24,4,11:31:56 AM,24,4,11:38:05 AM,2,21.2,,-1.279285,36.826057399999996,-1.2842286,36.8150609,Rider_Id_360
-Order_No_1646,User_Id_1329,Bike,3,Business,27,1,2:37:41 PM,27,1,2:39:05 PM,27,1,2:51:02 PM,27,1,3:06:17 PM,11,23.1,,-1.273056,36.811298,-1.2217641000000001,36.788444,Rider_Id_411
-Order_No_1134,User_Id_2931,Bike,3,Business,21,4,9:33:59 AM,21,4,9:34:09 AM,21,4,9:45:06 AM,21,4,9:47:10 AM,14,21.8,,-1.2452664,36.802854100000005,-1.3139038,36.8580987,Rider_Id_770
-Order_No_20230,User_Id_391,Bike,3,Business,22,3,11:06:01 AM,22,3,11:09:28 AM,22,3,11:10:27 AM,22,3,11:40:43 AM,5,22.9,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_158
-Order_No_8942,User_Id_1237,Bike,3,Personal,23,3,10:41:59 AM,23,3,10:42:18 AM,23,3,10:48:39 AM,23,3,10:52:39 AM,5,21.9,,-1.2741728,36.79691079999999,-1.279767,36.7695313,Rider_Id_543
-Order_No_24883,User_Id_265,Bike,3,Business,25,4,11:45:00 AM,25,4,11:45:57 AM,25,4,11:47:24 AM,25,4,12:06:52 PM,4,20.1,,-1.300921,36.828195,-1.308231,36.857306,Rider_Id_848
-Order_No_17182,User_Id_1482,Bike,3,Business,18,2,3:26:36 PM,18,2,3:28:00 PM,18,2,3:28:20 PM,18,2,4:26:30 PM,16,25.7,,-1.2534301,36.800098299999995,-1.3385790000000002,36.89630379999999,Rider_Id_31
-Order_No_1847,User_Id_2216,Bike,3,Business,25,2,11:42:04 AM,25,2,11:42:17 AM,25,2,11:50:32 AM,25,2,11:52:44 AM,4,24.0,,-1.2551895,36.7822034,-1.2754242,36.813927899999996,Rider_Id_215
-Order_No_9792,User_Id_561,Bike,1,Personal,6,4,8:28:08 PM,6,4,8:28:32 PM,6,4,8:29:29 PM,6,4,9:17:49 PM,7,17.8,,-1.2546941999999999,36.7832536,-1.238146,36.764936600000006,Rider_Id_874
-Order_No_12594,User_Id_478,Bike,3,Business,18,1,9:41:03 AM,18,1,9:41:36 AM,18,1,9:52:33 AM,18,1,10:02:02 AM,2,21.3,,-1.2650458999999998,36.807989299999996,-1.2571472,36.795063299999995,Rider_Id_383
-Order_No_27720,User_Id_1271,Bike,3,Business,11,5,4:24:10 PM,11,5,4:24:18 PM,11,5,4:54:18 PM,11,5,4:56:19 PM,11,24.1,,-1.3107918,36.761794,-1.2864733,36.8239937,Rider_Id_451
-Order_No_9452,User_Id_2627,Bike,3,Business,9,3,2:28:24 PM,9,3,2:28:40 PM,9,3,2:39:54 PM,9,3,2:44:01 PM,2,25.2,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_801
-Order_No_19253,User_Id_1329,Bike,3,Business,11,1,12:16:20 PM,11,1,12:17:26 PM,11,1,12:17:34 PM,11,1,12:21:12 PM,3,24.0,,-1.273056,36.811298,-1.2567464,36.7915273,Rider_Id_315
-Order_No_2366,User_Id_186,Bike,3,Business,17,4,3:20:50 PM,17,4,3:21:09 PM,17,4,3:27:35 PM,17,4,3:30:50 PM,22,27.9,,-1.3700383,36.9190174,-1.2615888000000002,36.7928732,Rider_Id_296
-Order_No_15206,User_Id_3680,Bike,2,Personal,21,1,1:22:03 PM,21,1,1:34:52 PM,21,1,1:51:23 PM,21,1,1:54:41 PM,5,21.6,,-1.2759536,36.8077214,-1.2844712999999999,36.8106871,Rider_Id_742
-Order_No_1605,User_Id_530,Bike,2,Personal,23,4,4:44:58 PM,23,4,4:45:11 PM,23,4,4:53:44 PM,23,4,5:06:58 PM,20,20.2,,-1.271996,36.826296,-1.3610501,36.7520889,Rider_Id_449
-Order_No_25625,User_Id_3571,Bike,3,Business,21,5,1:33:51 PM,21,5,1:34:36 PM,21,5,1:48:48 PM,21,5,2:08:24 PM,2,27.1,,-1.2337534,36.80858859,-1.238874,36.807889,Rider_Id_882
-Order_No_8498,User_Id_591,Bike,3,Business,2,3,12:18:11 PM,2,3,12:20:08 PM,2,3,12:25:15 PM,2,3,12:45:31 PM,10,22.0,,-1.2864374,36.8202657,-1.22606,36.80039,Rider_Id_406
-Order_No_23758,User_Id_2457,Bike,3,Business,26,5,11:53:36 AM,26,5,1:03:38 PM,26,5,1:18:25 PM,26,5,1:56:02 PM,8,,,-1.2551895,36.7822034,-1.3016151000000002,36.811735399999996,Rider_Id_70
-Order_No_26427,User_Id_3203,Bike,3,Business,4,1,3:06:18 PM,4,1,3:08:16 PM,4,1,3:26:15 PM,4,1,3:34:41 PM,13,22.5,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_852
-Order_No_20155,User_Id_722,Bike,3,Business,30,2,10:50:43 AM,30,2,10:52:51 AM,30,2,10:59:57 AM,30,2,11:04:57 AM,16,,,-1.25896,36.8016691,-1.3222489,36.786702500000004,Rider_Id_289
-Order_No_6866,User_Id_346,Bike,3,Business,11,2,1:55:50 PM,11,2,1:58:48 PM,11,2,2:05:45 PM,11,2,2:07:50 PM,1,21.4,,-1.28878,36.8168312,-1.2795827,36.819726200000005,Rider_Id_265
-Order_No_18900,User_Id_3080,Bike,3,Business,13,4,3:43:02 PM,13,4,3:45:02 PM,13,4,3:53:15 PM,13,4,3:58:02 PM,4,23.3,,-1.2551895,36.7822034,-1.2689426000000001,36.8060017,Rider_Id_269
-Order_No_5464,User_Id_1920,Bike,3,Business,11,4,3:28:19 PM,11,4,3:29:26 PM,11,4,3:51:49 PM,11,4,3:54:14 PM,8,27.2,,-1.3014461000000002,36.7661381,-1.2981352,36.8213822,Rider_Id_327
-Order_No_15762,User_Id_186,Bike,3,Business,19,2,2:35:46 PM,19,2,2:36:32 PM,19,2,2:44:52 PM,19,2,3:03:45 PM,5,29.9,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_267
-Order_No_18395,User_Id_867,Bike,3,Business,17,4,9:56:27 AM,17,4,9:56:30 AM,17,4,9:56:33 AM,17,4,10:12:20 AM,15,,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_914
-Order_No_9536,User_Id_2069,Bike,3,Business,12,1,10:21:46 AM,12,1,10:25:03 AM,12,1,10:44:13 AM,12,1,10:54:53 AM,9,23.6,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_649
-Order_No_6980,User_Id_1304,Bike,3,Business,19,3,2:55:16 PM,19,3,2:55:34 PM,19,3,3:28:16 PM,19,3,3:31:27 PM,16,24.3,,-1.2859912,36.8756811,-1.3190102,36.923069299999995,Rider_Id_114
-Order_No_17225,User_Id_3414,Bike,1,Personal,12,6,12:54:09 PM,12,6,12:58:57 PM,12,6,1:06:44 PM,12,6,1:17:55 PM,7,22.9,,-1.299539,36.827744,-1.2794985,36.8006196,Rider_Id_207
-Order_No_14212,User_Id_1882,Bike,3,Business,5,6,4:52:08 PM,5,6,4:52:21 PM,5,6,5:06:14 PM,5,6,5:28:34 PM,7,22.0,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_427
-Order_No_26037,User_Id_439,Bike,3,Business,13,3,3:28:10 PM,13,3,3:36:13 PM,13,3,3:49:07 PM,13,3,4:00:46 PM,10,27.4,,-1.2887801,36.8297743,-1.3323107,36.8692616,Rider_Id_863
-Order_No_5423,User_Id_2901,Bike,3,Business,7,5,1:53:00 PM,7,5,1:54:07 PM,7,5,2:10:53 PM,7,5,2:22:52 PM,14,,,-1.2976788000000001,36.802791600000006,-1.3411886999999998,36.8950653,Rider_Id_622
-Order_No_10968,User_Id_948,Bike,3,Business,28,2,8:38:21 AM,28,2,8:39:04 AM,28,2,8:44:12 AM,28,2,8:48:13 AM,2,18.5,,-1.2700436000000002,36.820997600000005,-1.2594473999999998,36.8089122,Rider_Id_409
-Order_No_4162,User_Id_1402,Bike,3,Business,9,2,1:44:52 PM,9,2,1:46:47 PM,9,2,2:04:38 PM,9,2,2:27:46 PM,21,30.7,,-1.323615,36.700475,-1.281754,36.8324229,Rider_Id_853
-Order_No_27620,User_Id_53,Bike,3,Business,8,2,9:21:53 AM,8,2,10:22:11 AM,8,2,10:38:32 AM,8,2,10:48:36 AM,5,23.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_725
-Order_No_19077,User_Id_1578,Bike,3,Business,30,2,1:24:13 PM,30,2,1:26:16 PM,30,2,2:00:30 PM,30,2,2:05:12 PM,17,27.6,,-1.28878,36.8168312,-1.3632162,36.9108253,Rider_Id_532
-Order_No_15641,User_Id_186,Bike,3,Business,12,5,9:37:02 AM,12,5,9:37:09 AM,12,5,10:13:33 AM,12,5,10:16:22 AM,23,21.7,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_863
-Order_No_19126,User_Id_2436,Bike,3,Business,26,2,3:11:34 PM,26,2,3:12:04 PM,26,2,3:19:59 PM,26,2,3:22:25 PM,10,30.9,,-1.2551895,36.7822034,-1.2300008,36.80436470000001,Rider_Id_553
-Order_No_20989,User_Id_1340,Bike,3,Business,25,5,10:03:48 AM,25,5,10:05:54 AM,25,5,10:27:37 AM,25,5,10:30:09 AM,6,20.6,,-1.2707178999999997,36.7979713,-1.2969293,36.8027733,Rider_Id_521
-Order_No_5913,User_Id_238,Bike,3,Business,5,2,12:47:41 PM,5,2,12:47:49 PM,5,2,12:47:53 PM,5,2,1:06:39 PM,15,25.5,,-1.3077869,36.844320700000004,-1.362953,36.904214,Rider_Id_239
-Order_No_12247,User_Id_3354,Bike,3,Business,22,1,11:25:01 AM,22,1,11:26:39 AM,22,1,11:33:14 AM,22,1,11:41:17 AM,17,24.9,,-1.3700383,36.9190174,-1.3173868999999998,36.8112044,Rider_Id_101
-Order_No_3355,User_Id_2200,Bike,3,Business,25,1,11:13:31 AM,25,1,11:16:12 AM,25,1,11:26:10 AM,25,1,11:50:00 AM,1,27.0,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_953
-Order_No_20986,User_Id_1484,Bike,3,Business,17,4,11:19:59 AM,17,4,11:20:31 AM,17,4,11:23:34 AM,17,4,11:27:40 AM,3,25.2,,-1.2551895,36.7822034,-1.25896,36.8016691,Rider_Id_829
-Order_No_24033,User_Id_393,Bike,3,Business,15,1,9:53:50 AM,15,1,9:53:59 AM,15,1,9:54:07 AM,15,1,10:21:13 AM,14,20.5,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349
-Order_No_27895,User_Id_868,Bike,3,Business,19,2,3:31:23 PM,19,2,3:31:39 PM,19,2,3:33:04 PM,19,2,3:37:58 PM,5,30.1,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_343
-Order_No_3337,User_Id_972,Bike,3,Business,1,4,10:49:59 AM,1,4,10:54:26 AM,1,4,11:08:19 AM,1,4,11:09:33 AM,5,,,-1.2862033000000002,36.8152966,-1.3016653,36.8323628,Rider_Id_936
-Order_No_3227,User_Id_246,Bike,3,Business,8,2,2:23:56 PM,8,2,2:29:27 PM,8,2,2:39:50 PM,8,2,2:48:02 PM,10,25.4,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_427
-Order_No_10449,User_Id_2718,Bike,3,Business,1,4,3:49:58 PM,1,4,3:52:01 PM,1,4,4:26:28 PM,1,4,4:32:53 PM,2,,,-1.2668815,36.8017244,-1.2657456,36.8087424,Rider_Id_726
-Order_No_10250,User_Id_1966,Bike,3,Business,11,1,12:51:48 PM,11,1,12:52:30 PM,11,1,1:06:25 PM,11,1,1:39:58 PM,21,29.0,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_238
-Order_No_5528,User_Id_3203,Bike,3,Business,14,5,9:55:27 AM,14,5,9:55:40 AM,14,5,9:56:51 AM,14,5,10:02:25 AM,16,18.1,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_933
-Order_No_26694,User_Id_2801,Bike,3,Business,12,3,8:42:49 AM,12,3,8:43:45 AM,12,3,8:57:06 AM,12,3,9:04:35 AM,10,,,-1.2674273999999999,36.787082700000006,-1.307726,36.839117,Rider_Id_582
-Order_No_22220,User_Id_3175,Bike,3,Business,1,4,12:45:47 PM,1,4,12:46:44 PM,1,4,12:50:51 PM,1,4,12:55:15 PM,16,,,-1.2681003999999998,36.7952081,-1.207546,36.7913976,Rider_Id_167
-Order_No_16690,User_Id_136,Bike,3,Business,15,5,11:34:34 AM,15,5,11:34:50 AM,15,5,11:41:56 AM,15,5,12:07:06 PM,15,25.7,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_703
-Order_No_16655,User_Id_2030,Bike,3,Business,8,2,10:25:54 AM,8,2,10:46:30 AM,8,2,10:59:23 AM,8,2,11:16:48 AM,12,23.3,,-1.1612808000000001,36.9563278,-1.2156006000000001,36.8916865,Rider_Id_819
-Order_No_3283,User_Id_3694,Bike,3,Personal,8,1,10:46:25 AM,8,1,10:47:18 AM,8,1,10:56:59 AM,8,1,11:00:57 AM,4,24.5,,-1.2776963,36.7961705,-1.2678232,36.7876405,Rider_Id_795
-Order_No_20376,User_Id_1850,Bike,3,Business,2,2,1:32:05 PM,2,2,1:33:13 PM,2,2,1:53:52 PM,2,2,2:16:08 PM,4,29.5,,-1.2571472,36.795063299999995,-1.2889396,36.81554860000001,Rider_Id_119
-Order_No_13999,User_Id_1165,Bike,1,Personal,10,5,12:03:22 PM,10,5,12:03:43 PM,10,5,12:12:32 PM,10,5,12:20:18 PM,6,19.0,,-1.264917,36.8150651,-1.2846575,36.7883974,Rider_Id_414
-Order_No_14419,User_Id_265,Bike,3,Business,6,1,3:22:56 PM,6,1,3:24:05 PM,6,1,3:30:11 PM,6,1,4:01:50 PM,5,,,-1.3117438999999997,36.8108257,-1.300921,36.828195,Rider_Id_910
-Order_No_18652,User_Id_3291,Bike,3,Business,4,6,10:28:02 AM,4,6,10:29:24 AM,4,6,10:36:35 AM,4,6,10:46:16 AM,12,,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_75
-Order_No_11900,User_Id_1899,Bike,3,Business,18,6,10:58:16 AM,18,6,10:58:48 AM,18,6,11:11:23 AM,18,6,11:20:47 AM,7,19.0,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_415
-Order_No_4185,User_Id_478,Bike,3,Business,26,5,1:53:05 PM,26,5,1:53:16 PM,26,5,1:55:09 PM,26,5,1:59:00 PM,23,,,-1.2551895,36.7822034,-1.34364,36.8925345,Rider_Id_168
-Order_No_6795,User_Id_1724,Bike,3,Business,2,1,10:05:26 AM,2,1,10:06:02 AM,2,1,10:14:04 AM,2,1,10:24:23 AM,8,16.8,,-1.2958383,36.8073162,-1.2551895,36.7822034,Rider_Id_103
-Order_No_3480,User_Id_3486,Bike,3,Business,21,5,12:49:44 PM,21,5,1:21:22 PM,21,5,1:21:29 PM,21,5,1:23:32 PM,7,22.8,,-1.2551895,36.7822034,-1.2849035,36.8103615,Rider_Id_725
-Order_No_18589,User_Id_391,Bike,3,Business,17,5,12:06:45 PM,17,5,12:12:05 PM,17,5,1:10:34 PM,17,5,1:12:12 PM,10,,,-1.2867454,36.8259017,-1.2293754,36.862432399999996,Rider_Id_362
-Order_No_26242,User_Id_2982,Bike,3,Business,10,5,2:35:49 PM,10,5,2:38:11 PM,10,5,2:38:37 PM,10,5,2:54:38 PM,18,,,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_61
-Order_No_21437,User_Id_1245,Bike,3,Business,23,2,3:06:33 PM,23,2,3:17:00 PM,23,2,3:33:56 PM,23,2,3:58:48 PM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_506
-Order_No_16845,User_Id_1961,Bike,3,Business,27,6,10:43:53 AM,27,6,10:44:11 AM,27,6,11:10:35 AM,27,6,11:15:04 AM,21,,,-1.3181159,36.710155900000004,-1.331619,36.847976,Rider_Id_299
-Order_No_9989,User_Id_1425,Bike,3,Business,22,5,1:49:35 PM,22,5,1:50:12 PM,22,5,2:10:32 PM,22,5,2:12:06 PM,15,31.2,,-1.338283,36.8871775,-1.29387,36.7907813,Rider_Id_883
-Order_No_9506,User_Id_2207,Bike,3,Business,21,4,10:19:36 AM,21,4,10:22:34 AM,21,4,10:37:07 AM,21,4,10:42:28 AM,5,27.8,,-1.2939186000000003,36.8069693,-1.2696908000000002,36.81519,Rider_Id_61
-Order_No_3193,User_Id_1234,Bike,2,Personal,28,1,11:09:12 AM,28,1,11:25:58 AM,28,1,11:36:17 AM,28,1,11:54:20 AM,6,22.7,,-1.2738167,36.8157821,-1.3041128999999998,36.810493900000004,Rider_Id_752
-Order_No_23529,User_Id_1271,Bike,3,Business,1,5,1:00:44 PM,1,5,1:01:09 PM,1,5,1:01:26 PM,1,5,1:11:16 PM,9,28.6,,-1.2649605,36.798177700000004,-1.3014461000000002,36.7661381,Rider_Id_698
-Order_No_12270,User_Id_2330,Bike,3,Business,9,5,12:36:29 PM,9,5,12:36:31 PM,9,5,12:40:31 PM,9,5,12:42:00 PM,23,25.6,,-1.3376825,36.7575489,-1.2284323,36.8757058,Rider_Id_62
-Order_No_11109,User_Id_552,Bike,3,Business,20,3,4:29:17 PM,20,3,4:29:41 PM,20,3,4:53:55 PM,20,3,5:14:07 PM,17,27.7,,-1.291768,36.8297208,-1.335477,36.930829700000004,Rider_Id_785
-Order_No_24518,User_Id_1261,Bike,1,Personal,3,2,2:39:36 PM,3,2,2:46:08 PM,3,2,2:55:32 PM,3,2,3:01:12 PM,9,20.3,,-1.266362,36.796939,-1.2966785,36.7896469,Rider_Id_770
-Order_No_2676,User_Id_2412,Bike,3,Business,11,2,11:54:28 AM,11,2,11:54:44 AM,11,2,11:55:26 AM,11,2,12:01:36 PM,10,19.4,,-1.2765736,36.851364600000004,-1.2285834999999998,36.882646799999996,Rider_Id_845
-Order_No_3324,User_Id_3283,Bike,3,Business,12,3,10:45:53 AM,12,3,10:51:04 AM,12,3,11:14:50 AM,12,3,11:24:05 AM,19,19.3,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_883
-Order_No_6616,User_Id_391,Bike,3,Business,24,3,11:33:14 AM,24,3,11:50:36 AM,24,3,11:51:30 AM,24,3,12:25:49 PM,4,,,-1.2839729,36.8115146,-1.300921,36.828195,Rider_Id_86
-Order_No_5070,User_Id_1209,Bike,3,Business,4,6,9:12:12 AM,4,6,9:14:13 AM,4,6,9:46:55 AM,4,6,9:48:03 AM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_132
-Order_No_24767,User_Id_2582,Bike,3,Business,11,1,10:37:16 AM,11,1,10:37:38 AM,11,1,10:56:46 AM,11,1,11:00:42 AM,22,26.8,,-1.2551895,36.7822034,-1.333148,36.922453000000004,Rider_Id_238
-Order_No_21848,User_Id_3517,Bike,3,Business,24,5,4:00:06 PM,24,5,4:05:50 PM,24,5,4:14:49 PM,24,5,4:43:15 PM,19,20.2,,-1.3207708,36.8685919,-1.3882008,36.7699445,Rider_Id_647
-Order_No_18994,User_Id_3283,Bike,3,Business,17,4,4:36:20 PM,17,4,5:00:53 PM,17,4,5:19:32 PM,17,4,5:27:20 PM,13,26.0,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_52
-Order_No_7166,User_Id_1809,Bike,3,Business,19,5,12:41:00 PM,19,5,12:41:30 PM,19,5,12:48:41 PM,19,5,12:53:42 PM,2,24.9,,-1.2646084,36.8028645,-1.2591018999999999,36.800577000000004,Rider_Id_453
-Order_No_889,User_Id_2984,Bike,1,Personal,13,3,8:49:21 AM,13,3,8:55:16 AM,13,3,9:26:34 AM,13,3,9:35:23 AM,6,16.1,,-1.2623805000000001,36.7805404,-1.2772403,36.8196193,Rider_Id_752
-Order_No_19642,User_Id_3560,Bike,3,Business,1,3,10:04:27 AM,1,3,10:04:38 AM,1,3,10:14:22 AM,1,3,10:55:40 AM,5,19.9,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_414
-Order_No_24191,User_Id_2125,Bike,2,Personal,3,4,9:44:23 AM,3,4,9:45:01 AM,3,4,9:48:16 AM,3,4,10:15:13 AM,15,17.5,16.0,-1.3053306999999998,36.7768064,-1.2315379,36.8156795,Rider_Id_493
-Order_No_19213,User_Id_3044,Bike,3,Business,12,2,12:26:48 PM,12,2,12:28:06 PM,12,2,12:38:33 PM,12,2,12:39:36 PM,4,24.4,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_523
-Order_No_17480,User_Id_496,Bike,3,Business,30,2,12:08:10 PM,30,2,12:12:40 PM,30,2,12:27:43 PM,30,2,12:30:17 PM,8,,,-1.2551895,36.7822034,-1.2940713,36.7890758,Rider_Id_937
-Order_No_6344,User_Id_3647,Bike,3,Business,10,5,9:51:30 AM,10,5,9:52:30 AM,10,5,10:05:49 AM,10,5,10:10:43 AM,11,,,-1.2726389999999999,36.794723,-1.3127877000000001,36.848198700000005,Rider_Id_770
-Order_No_13181,User_Id_31,Bike,3,Business,4,2,4:07:37 PM,4,2,4:08:16 PM,4,2,4:28:07 PM,4,2,5:15:43 PM,3,21.2,,-1.317151,36.865734,-1.3189252,36.861726899999994,Rider_Id_890
-Order_No_4525,User_Id_2801,Bike,3,Business,13,3,9:22:27 AM,13,3,9:22:37 AM,13,3,9:24:00 AM,13,3,9:27:25 AM,9,20.0,,-1.2674273999999999,36.787082700000006,-1.2945073999999999,36.8083195,Rider_Id_576
-Order_No_10190,User_Id_3262,Bike,3,Personal,29,1,12:48:37 PM,29,1,12:48:51 PM,29,1,12:49:32 PM,29,1,1:12:39 PM,34,,,-1.2836832,36.8306719,-1.0921598999999997,37.005025,Rider_Id_456
-Order_No_25994,User_Id_2982,Bike,3,Business,28,4,12:49:52 PM,28,4,12:50:16 PM,28,4,1:00:19 PM,28,4,1:32:46 PM,18,27.6,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_691
-Order_No_25038,User_Id_2588,Bike,3,Personal,16,3,12:26:47 PM,16,3,12:32:29 PM,16,3,1:09:17 PM,16,3,1:11:12 PM,7,24.0,,-1.2530951000000001,36.814167499999996,-1.2983124,36.8104456,Rider_Id_749
-Order_No_6703,User_Id_393,Bike,3,Business,25,2,12:18:52 PM,25,2,12:19:04 PM,25,2,12:30:58 PM,25,2,12:45:37 PM,6,23.9,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_39
-Order_No_11506,User_Id_1882,Bike,3,Business,28,1,12:37:44 PM,28,1,12:37:58 PM,28,1,12:41:55 PM,28,1,12:49:49 PM,9,23.6,,-1.2958783,36.7928125,-1.253784,36.79907,Rider_Id_383
-Order_No_12858,User_Id_633,Bike,3,Business,28,4,12:49:32 PM,28,4,12:51:05 PM,28,4,1:04:30 PM,28,4,1:13:55 PM,4,27.9,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_813
-Order_No_6952,User_Id_3149,Bike,3,Business,1,5,12:36:50 PM,1,5,12:46:51 PM,1,5,12:53:38 PM,1,5,12:54:47 PM,7,26.3,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_576
-Order_No_18728,User_Id_393,Bike,3,Business,30,2,11:47:53 AM,30,2,11:55:33 AM,30,2,12:07:17 PM,30,2,12:44:40 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_245
-Order_No_7262,User_Id_567,Bike,3,Business,14,4,1:52:30 PM,14,4,1:53:00 PM,14,4,1:57:46 PM,14,4,2:06:20 PM,9,27.8,,-1.309056,36.827720899999996,-1.2963097,36.7688221,Rider_Id_206
-Order_No_10789,User_Id_1565,Bike,1,Personal,13,1,4:19:11 PM,13,1,4:19:24 PM,13,1,4:21:07 PM,13,1,4:37:50 PM,10,19.2,,-1.2915968999999998,36.823763899999996,-1.2982544,36.7500835,Rider_Id_942
-Order_No_22209,User_Id_931,Bike,1,Personal,19,5,2:19:16 PM,19,5,2:20:20 PM,19,5,2:31:26 PM,19,5,2:35:33 PM,5,27.5,,-1.2644443,36.7734547,-1.2975066000000002,36.76894229999999,Rider_Id_854
-Order_No_17563,User_Id_1354,Bike,1,Personal,28,5,11:22:25 AM,28,5,11:27:04 AM,28,5,11:38:22 AM,28,5,11:45:10 AM,9,25.1,,-1.2349281,36.809878000000005,-1.2551895,36.7822034,Rider_Id_96
-Order_No_23229,User_Id_3560,Bike,3,Business,20,3,3:31:53 PM,20,3,3:33:16 PM,20,3,3:39:58 PM,20,3,3:45:07 PM,2,28.2,,-1.279395,36.825364,-1.2950632,36.8196747,Rider_Id_206
-Order_No_8899,User_Id_1076,Bike,1,Personal,3,2,1:07:12 PM,3,2,1:12:16 PM,3,2,1:33:09 PM,3,2,1:36:32 PM,9,20.0,,-1.3200277,36.8206816,-1.2693101999999998,36.798177700000004,Rider_Id_312
-Order_No_17989,User_Id_3688,Bike,3,Business,13,3,3:57:15 PM,13,3,3:57:43 PM,13,3,4:31:09 PM,13,3,4:32:14 PM,14,27.4,,-1.2551895,36.7822034,-1.3263105,36.8622904,Rider_Id_579
-Order_No_17524,User_Id_571,Bike,3,Business,4,4,4:14:42 PM,4,4,4:14:49 PM,4,4,4:15:00 PM,4,4,4:23:31 PM,15,27.3,,-1.3236332,36.701152,-1.2793503000000002,36.8039715,Rider_Id_831
-Order_No_21449,User_Id_1874,Bike,3,Business,9,1,3:08:15 PM,9,1,3:10:37 PM,9,1,3:36:56 PM,9,1,3:40:15 PM,7,21.2,,-1.2907672,36.8252167,-1.2571191000000002,36.8019652,Rider_Id_858
-Order_No_9955,User_Id_1244,Bike,3,Business,30,6,11:18:46 AM,30,6,11:20:34 AM,30,6,11:23:34 AM,30,6,11:41:39 AM,11,21.9,,-1.318062,36.849326,-1.303596,36.778378000000004,Rider_Id_654
-Order_No_27821,User_Id_3517,Bike,3,Business,11,4,2:57:14 PM,11,4,2:58:54 PM,11,4,3:05:19 PM,11,4,3:19:50 PM,15,27.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_728
-Order_No_7692,User_Id_2718,Bike,3,Business,25,5,10:41:27 AM,25,5,10:42:08 AM,25,5,10:46:37 AM,25,5,11:02:07 AM,3,21.0,,-1.2595466999999998,36.8113044,-1.2601,36.796528,Rider_Id_727
-Order_No_10476,User_Id_3625,Bike,1,Personal,27,3,11:28:46 AM,27,3,11:30:58 AM,27,3,11:55:05 AM,27,3,12:04:53 PM,16,,,-1.2725144,36.82721779999999,-1.3568040000000001,36.746196000000005,Rider_Id_159
-Order_No_7958,User_Id_3291,Bike,3,Business,20,1,2:16:50 PM,20,1,2:17:28 PM,20,1,2:17:48 PM,20,1,2:29:11 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_598
-Order_No_5865,User_Id_1245,Bike,3,Business,6,4,8:45:30 AM,6,4,9:06:45 AM,6,4,9:17:17 AM,6,4,9:32:38 AM,19,18.6,2.9,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_90
-Order_No_12600,User_Id_1551,Bike,1,Personal,10,2,1:37:29 PM,10,2,1:39:19 PM,10,2,1:51:57 PM,10,2,1:59:20 PM,5,21.3,,-1.307726,36.839117,-1.2911796,36.821506799999995,Rider_Id_629
-Order_No_245,User_Id_720,Bike,3,Personal,25,1,1:57:22 PM,25,1,1:58:17 PM,25,1,2:12:39 PM,25,1,2:43:21 PM,13,29.9,,-1.285355,36.795287,-1.2373976999999998,36.7598693,Rider_Id_678
-Order_No_19894,User_Id_478,Bike,3,Business,8,5,11:21:47 AM,8,5,11:22:35 AM,8,5,11:34:24 AM,8,5,11:40:25 AM,4,28.3,,-1.2551895,36.7822034,-1.2657464,36.780114399999995,Rider_Id_269
-Order_No_7517,User_Id_2150,Bike,3,Business,17,3,12:14:53 PM,17,3,12:20:31 PM,17,3,12:22:33 PM,17,3,12:37:28 PM,6,,,-1.3181159,36.710155900000004,-1.3444459999999998,36.756597,Rider_Id_533
-Order_No_18403,User_Id_3557,Bike,3,Business,11,4,12:14:55 PM,11,4,12:15:02 PM,11,4,12:20:43 PM,11,4,12:30:54 PM,6,23.7,,-1.2894818000000001,36.788053399999995,-1.2546732,36.808679600000005,Rider_Id_948
-Order_No_593,User_Id_1502,Bike,3,Business,12,6,1:09:18 PM,12,6,1:09:30 PM,12,6,1:27:24 PM,12,6,1:35:06 PM,15,24.9,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_199
-Order_No_8969,User_Id_2762,Bike,1,Personal,28,5,2:59:07 PM,28,5,3:28:58 PM,28,5,3:52:37 PM,28,5,4:12:10 PM,22,25.7,,-1.2842286,36.8150609,-1.2454778999999998,36.9490919,Rider_Id_96
-Order_No_6565,User_Id_1500,Bike,3,Business,22,3,1:47:11 PM,22,3,1:52:58 PM,22,3,1:59:45 PM,22,3,2:07:17 PM,8,24.5,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_603
-Order_No_10940,User_Id_700,Bike,3,Business,6,4,10:33:03 AM,6,4,10:33:13 AM,6,4,10:44:53 AM,6,4,10:57:29 AM,5,22.2,,-1.2943360000000002,36.795332,-1.283527,36.82365120000001,Rider_Id_365
-Order_No_11177,User_Id_3521,Bike,3,Business,1,1,1:26:37 PM,1,1,2:04:38 PM,1,1,2:18:33 PM,1,1,2:32:33 PM,9,,,-1.233018,36.8004477,-1.2696908000000002,36.81519,Rider_Id_484
-Order_No_13874,User_Id_3222,Bike,3,Business,14,2,3:51:23 PM,14,2,3:51:45 PM,14,2,3:51:54 PM,14,2,3:57:19 PM,5,,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_576
-Order_No_21873,User_Id_2112,Bike,3,Business,22,4,1:42:51 PM,22,4,3:35:02 PM,22,4,3:40:28 PM,22,4,3:46:16 PM,4,26.6,,-1.2297202,36.881950700000004,-1.2156006000000001,36.8916865,Rider_Id_130
-Order_No_20246,User_Id_1471,Bike,3,Business,20,3,11:58:26 AM,20,3,11:59:11 AM,20,3,12:00:01 PM,20,3,12:06:16 PM,9,27.3,,-1.2551895,36.7822034,-1.296797,36.776452500000005,Rider_Id_780
-Order_No_21351,User_Id_467,Bike,3,Business,20,1,3:30:25 PM,20,1,3:31:21 PM,20,1,3:37:13 PM,20,1,3:43:24 PM,7,,,-1.304735,36.784605400000004,-1.2736094999999998,36.8011282,Rider_Id_247
-Order_No_23556,User_Id_3201,Bike,3,Business,7,3,1:32:22 PM,7,3,1:35:19 PM,7,3,1:51:34 PM,7,3,2:36:18 PM,5,28.3,,-1.2857548,36.819062,-1.2584143,36.8048002,Rider_Id_812
-Order_No_20335,User_Id_1500,Bike,3,Business,30,2,10:50:22 AM,30,2,10:50:56 AM,30,2,11:00:41 AM,30,2,11:36:09 AM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_436
-Order_No_17554,User_Id_3647,Bike,3,Business,25,1,8:56:11 AM,25,1,8:56:50 AM,25,1,9:15:39 AM,25,1,9:18:12 AM,13,15.0,,-1.2726389999999999,36.794723,-1.3120931,36.7078029,Rider_Id_449
-Order_No_12186,User_Id_2763,Bike,3,Business,4,2,9:57:05 AM,4,2,9:57:41 AM,4,2,10:01:40 AM,4,2,10:05:38 AM,3,16.7,98.9,-1.2551895,36.7822034,-1.2644742,36.808345700000004,Rider_Id_211
-Order_No_3155,User_Id_2528,Bike,3,Business,26,2,2:32:33 PM,26,2,2:32:47 PM,26,2,2:35:46 PM,26,2,2:44:27 PM,3,30.8,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_411
-Order_No_19609,User_Id_1792,Bike,3,Business,21,4,3:03:22 PM,21,4,3:04:43 PM,21,4,3:14:22 PM,21,4,3:18:44 PM,7,22.4,,-1.2805537,36.7920739,-1.2629944,36.7998816,Rider_Id_898
-Order_No_6484,User_Id_635,Bike,3,Business,14,5,10:24:00 AM,14,5,10:24:25 AM,14,5,10:24:56 AM,14,5,11:29:42 AM,10,21.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_864
-Order_No_20477,User_Id_3647,Bike,3,Business,29,3,4:33:28 PM,29,3,4:35:03 PM,29,3,4:45:46 PM,29,3,4:53:28 PM,10,19.3,,-1.2726389999999999,36.794723,-1.3114291999999999,36.7360592,Rider_Id_427
-Order_No_19980,User_Id_3689,Bike,3,Business,6,2,9:17:44 AM,6,2,9:20:59 AM,6,2,9:24:37 AM,6,2,9:29:19 AM,10,19.9,,-1.2527958000000001,36.8003131,-1.2934638,36.7668028,Rider_Id_279
-Order_No_25688,User_Id_3647,Bike,3,Business,16,2,1:52:10 PM,16,2,1:52:24 PM,16,2,2:25:28 PM,16,2,2:28:27 PM,11,,,-1.2726389999999999,36.794723,-1.3142909999999999,36.8478646,Rider_Id_448
-Order_No_20184,User_Id_3320,Bike,3,Business,6,2,10:34:37 AM,6,2,10:35:01 AM,6,2,11:03:02 AM,6,2,11:22:01 AM,14,24.9,,-1.328259,36.860975,-1.271823,36.789215000000006,Rider_Id_338
-Order_No_24102,User_Id_2789,Bike,3,Business,8,2,12:03:22 PM,8,2,12:09:24 PM,8,2,12:54:07 PM,8,2,1:08:30 PM,14,23.4,,-1.2287282,36.8115304,-1.304735,36.784605400000004,Rider_Id_258
-Order_No_550,User_Id_2383,Bike,3,Business,27,3,10:35:30 AM,27,3,10:37:37 AM,27,3,10:41:31 AM,27,3,10:43:38 AM,5,26.1,,-1.2608993,36.799246000000004,-1.2795796,36.819062,Rider_Id_253
-Order_No_5760,User_Id_393,Bike,3,Business,13,3,1:02:26 PM,13,3,1:02:55 PM,13,3,1:16:10 PM,13,3,1:24:18 PM,11,24.7,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_858
-Order_No_3394,User_Id_2627,Bike,3,Business,5,5,12:19:33 PM,5,5,12:20:33 PM,5,5,12:43:39 PM,5,5,12:50:56 PM,17,24.7,,-1.2859912,36.8756811,-1.2571472,36.795063299999995,Rider_Id_99
-Order_No_25668,User_Id_3291,Bike,3,Business,4,1,11:52:52 AM,4,1,11:55:29 AM,4,1,11:57:39 AM,4,1,12:05:04 PM,8,25.4,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_604
-Order_No_7336,User_Id_3149,Bike,3,Business,20,1,10:35:52 AM,20,1,10:36:37 AM,20,1,10:48:08 AM,20,1,10:55:59 AM,12,,,-1.3167113000000001,36.8301563,-1.2769540000000001,36.765926,Rider_Id_223
-Order_No_13829,User_Id_2805,Bike,3,Business,14,1,1:41:58 PM,14,1,1:42:51 PM,14,1,1:55:09 PM,14,1,2:04:01 PM,13,25.6,,-1.2854406,36.789409,-1.3169591,36.7000029,Rider_Id_801
-Order_No_2275,User_Id_3560,Bike,3,Business,7,4,10:06:53 AM,7,4,10:07:56 AM,7,4,10:12:33 AM,7,4,10:18:14 AM,14,,,-1.279395,36.825364,-1.3247309999999999,36.907109000000005,Rider_Id_378
-Order_No_14763,User_Id_3710,Bike,1,Business,5,4,3:49:32 PM,5,4,4:00:23 PM,5,4,4:23:06 PM,5,4,4:29:19 PM,4,22.4,,-1.2551895,36.7822034,-1.2740396,36.7993518,Rider_Id_467
-Order_No_20546,User_Id_324,Bike,3,Personal,15,2,10:30:39 AM,15,2,10:30:59 AM,15,2,10:40:21 AM,15,2,10:41:29 AM,15,24.5,,-1.2653676,36.79110120000001,-1.332942,36.714065000000005,Rider_Id_85
-Order_No_22776,User_Id_3214,Bike,3,Business,23,5,3:10:33 PM,23,5,3:15:35 PM,23,5,3:55:26 PM,23,5,4:02:14 PM,21,26.5,,-1.289795,36.7957132,-1.3190102,36.923069299999995,Rider_Id_956
-Order_No_17277,User_Id_3621,Bike,3,Business,6,1,10:19:53 AM,6,1,10:20:14 AM,6,1,10:39:36 AM,6,1,10:50:19 AM,6,,,-1.2836583000000001,36.7886014,-1.2931121,36.8224572,Rider_Id_119
-Order_No_22847,User_Id_2521,Bike,3,Business,16,5,1:52:17 PM,16,5,1:55:27 PM,16,5,1:55:40 PM,16,5,2:01:26 PM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_157
-Order_No_10425,User_Id_1402,Bike,3,Business,29,5,12:30:16 PM,29,5,12:53:08 PM,29,5,1:15:04 PM,29,5,1:38:53 PM,6,29.1,,-1.2527958000000001,36.8003131,-1.2813012,36.832396200000005,Rider_Id_763
-Order_No_14169,User_Id_60,Bike,3,Business,19,2,2:14:40 PM,19,2,2:15:31 PM,19,2,2:22:31 PM,19,2,2:36:00 PM,6,29.5,,-1.228911,36.881876,-1.2296618000000001,36.84382720000001,Rider_Id_75
-Order_No_10689,User_Id_3091,Bike,3,Business,18,5,11:48:58 AM,18,5,11:49:37 AM,18,5,11:58:28 AM,18,5,12:00:35 PM,17,20.0,,-1.2413316,36.878552500000005,-1.3012008,36.764868,Rider_Id_581
-Order_No_9019,User_Id_2196,Bike,2,Personal,6,5,3:34:11 PM,6,5,3:47:32 PM,6,5,3:57:14 PM,6,5,4:06:59 PM,5,22.6,,-1.2668071,36.8068157,-1.2913246,36.8019522,Rider_Id_905
-Order_No_7158,User_Id_3517,Bike,3,Business,7,3,11:37:50 AM,7,3,11:38:11 AM,7,3,11:53:30 AM,7,3,11:55:08 AM,16,25.2,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_343
-Order_No_5420,User_Id_1365,Bike,1,Personal,22,2,2:37:37 PM,22,2,2:41:23 PM,22,2,3:01:33 PM,22,2,3:14:02 PM,4,21.3,,-1.292943,36.808292,-1.3005044,36.7871991,Rider_Id_726
-Order_No_10334,User_Id_3282,Bike,2,Personal,18,6,12:21:34 PM,18,6,12:21:41 PM,18,6,12:24:21 PM,18,6,12:31:00 PM,8,18.8,,-1.290059,36.7703475,-1.2861764999999998,36.8209086,Rider_Id_45
-Order_No_6669,User_Id_2169,Bike,2,Personal,11,5,4:32:18 PM,11,5,4:38:13 PM,11,5,4:57:42 PM,11,5,5:00:05 PM,9,20.3,,-1.311208,36.778743,-1.2613754,36.795771,Rider_Id_742
-Order_No_10964,User_Id_2066,Bike,3,Business,13,1,10:55:21 AM,13,1,10:56:24 AM,13,1,11:08:19 AM,13,1,11:11:50 AM,12,,,-1.2502493000000001,36.7923169,-1.3210188999999999,36.841055100000005,Rider_Id_205
-Order_No_8665,User_Id_3420,Bike,3,Business,23,2,12:04:43 PM,23,2,12:05:20 PM,23,2,12:06:03 PM,23,2,12:14:10 PM,6,,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_206
-Order_No_2066,User_Id_2670,Bike,1,Personal,8,1,9:34:10 AM,8,1,9:34:58 AM,8,1,9:35:51 AM,8,1,9:40:16 AM,4,19.6,,-1.305399,36.822184899999996,-1.3240024,36.811902200000006,Rider_Id_88
-Order_No_7671,User_Id_2828,Bike,3,Business,14,4,1:57:08 PM,14,4,2:08:31 PM,14,4,2:09:33 PM,14,4,2:32:34 PM,10,27.6,,-1.2615888000000002,36.7928732,-1.2343087,36.84749,Rider_Id_576
-Order_No_11828,User_Id_3557,Bike,3,Business,19,2,10:26:03 AM,19,2,10:26:49 AM,19,2,10:30:35 AM,19,2,10:35:55 AM,6,26.7,,-1.2596241000000001,36.762407,-1.2546732,36.808679600000005,Rider_Id_518
-Order_No_4816,User_Id_1244,Bike,3,Business,14,4,12:37:49 PM,14,4,1:11:10 PM,14,4,1:31:02 PM,14,4,1:39:48 PM,7,27.9,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_210
-Order_No_5196,User_Id_2801,Bike,3,Business,13,2,2:42:11 PM,13,2,2:55:52 PM,13,2,3:10:24 PM,13,2,3:11:35 PM,10,26.2,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_916
-Order_No_14313,User_Id_1078,Bike,3,Business,21,5,1:14:30 PM,21,5,1:30:43 PM,21,5,1:30:46 PM,21,5,1:47:32 PM,9,24.5,,-1.2793911,36.83043789999999,-1.2482465,36.8889062,Rider_Id_813
-Order_No_36,User_Id_1329,Bike,3,Business,13,6,12:20:55 PM,13,6,12:23:29 PM,13,6,12:28:37 PM,13,6,12:31:11 PM,7,26.1,,-1.273056,36.811298,-1.2756120000000002,36.76379779999999,Rider_Id_660
-Order_No_22492,User_Id_1500,Bike,3,Business,10,1,12:46:38 PM,10,1,12:47:29 PM,10,1,12:48:05 PM,10,1,1:20:55 PM,13,18.5,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_702
-Order_No_23269,User_Id_1585,Bike,1,Business,14,5,7:06:36 PM,14,5,7:06:48 PM,14,5,7:06:52 PM,14,5,7:13:28 PM,3,22.0,,-1.2963097,36.7688221,-1.2852908,36.7557439,Rider_Id_813
-Order_No_12069,User_Id_706,Bike,3,Business,6,3,11:04:09 AM,6,3,11:14:13 AM,6,3,11:28:27 AM,6,3,11:45:33 AM,12,22.4,,-1.3559093,36.910045200000006,-1.3077869,36.844320700000004,Rider_Id_206
-Order_No_19946,User_Id_2587,Bike,3,Business,25,5,2:36:14 PM,25,5,2:41:02 PM,25,5,2:46:33 PM,25,5,2:52:53 PM,5,29.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_268
-Order_No_6809,User_Id_2159,Bike,3,Business,2,1,10:30:20 AM,2,1,10:31:37 AM,2,1,10:37:48 AM,2,1,11:01:13 AM,7,19.2,,-1.2697141,36.798177700000004,-1.304735,36.784605400000004,Rider_Id_467
-Order_No_21895,User_Id_1075,Bike,3,Business,6,1,4:24:40 PM,6,1,4:25:14 PM,6,1,4:30:17 PM,6,1,4:44:01 PM,20,,,-1.3562374,36.904295399999995,-1.2527958000000001,36.8003131,Rider_Id_709
-Order_No_19387,User_Id_941,Bike,1,Personal,8,1,12:43:48 PM,8,1,12:53:02 PM,8,1,12:57:58 PM,8,1,1:12:43 PM,8,24.4,,-1.2807298,36.8299116,-1.3288576,36.8465936,Rider_Id_662
-Order_No_8411,User_Id_1721,Bike,3,Business,16,4,2:08:27 PM,16,4,2:09:22 PM,16,4,2:30:37 PM,16,4,2:40:05 PM,3,,,-1.2551895,36.7822034,-1.2638132,36.784977500000004,Rider_Id_155
-Order_No_2987,User_Id_3012,Bike,1,Personal,1,1,2:13:20 PM,1,1,2:13:30 PM,1,1,2:20:13 PM,1,1,2:40:28 PM,3,25.8,,-1.2571472,36.795063299999995,-1.2561416,36.7882386,Rider_Id_647
-Order_No_17956,User_Id_1500,Bike,3,Business,22,5,3:43:12 PM,22,5,3:45:29 PM,22,5,3:45:35 PM,22,5,4:26:41 PM,9,27.1,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_472
-Order_No_23418,User_Id_2763,Bike,3,Business,9,4,3:35:11 PM,9,4,3:52:23 PM,9,4,3:53:34 PM,9,4,3:59:59 PM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_177
-Order_No_2622,User_Id_3096,Bike,3,Personal,22,5,7:23:26 PM,22,5,7:23:41 PM,22,5,7:35:37 PM,22,5,7:39:59 PM,11,21.5,,-1.2285834999999998,36.882646799999996,-1.1971022,36.926895200000004,Rider_Id_895
-Order_No_747,User_Id_635,Bike,3,Business,13,4,3:34:27 PM,13,4,4:21:52 PM,13,4,4:24:35 PM,13,4,4:27:24 PM,8,26.3,,-1.2599563,36.799343900000004,-1.2633397,36.7454238,Rider_Id_216
-Order_No_8178,User_Id_136,Bike,3,Business,13,4,2:23:31 PM,13,4,2:25:26 PM,13,4,2:30:33 PM,13,4,2:40:45 PM,16,22.2,,-1.2908945,36.82465139999999,-1.3254522,36.721658399999995,Rider_Id_320
-Order_No_24575,User_Id_2801,Bike,3,Business,19,2,1:25:22 PM,19,2,1:26:41 PM,19,2,1:44:10 PM,19,2,1:51:41 PM,6,29.8,,-1.2595421999999998,36.7871178,-1.2896398999999998,36.812449799999996,Rider_Id_660
-Order_No_20699,User_Id_3517,Bike,3,Business,15,2,9:41:43 AM,15,2,9:42:01 AM,15,2,9:56:30 AM,15,2,10:51:26 AM,15,24.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_148
-Order_No_2764,User_Id_201,Bike,3,Business,2,5,11:35:37 AM,2,5,11:35:54 AM,2,5,11:40:18 AM,2,5,11:45:17 AM,20,23.5,,-1.2551895,36.7822034,-1.2742942,36.9194976,Rider_Id_38
-Order_No_1596,User_Id_633,Bike,3,Business,21,1,2:11:03 PM,21,1,2:56:32 PM,21,1,2:58:10 PM,21,1,3:05:17 PM,12,27.6,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_436
-Order_No_945,User_Id_3282,Bike,3,Personal,26,2,2:38:44 PM,26,2,2:39:14 PM,26,2,2:44:16 PM,26,2,2:54:38 PM,7,29.5,,-1.2925919,36.81705770000001,-1.290059,36.7703475,Rider_Id_906
-Order_No_11604,User_Id_3398,Bike,3,Business,31,4,1:23:46 PM,31,4,1:23:57 PM,31,4,1:38:55 PM,31,4,1:42:19 PM,16,22.8,,-1.3108107,36.8244221,-1.3169591,36.7000029,Rider_Id_219
-Order_No_22951,User_Id_633,Bike,3,Business,21,3,11:50:51 AM,21,3,11:52:01 AM,21,3,11:56:01 AM,21,3,12:02:52 PM,4,21.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_619
-Order_No_3824,User_Id_3647,Bike,3,Business,11,2,1:03:58 PM,11,2,1:06:32 PM,11,2,1:13:05 PM,11,2,1:19:51 PM,12,19.2,,-1.2726389999999999,36.794723,-1.3178263000000001,36.7141818,Rider_Id_155
-Order_No_22018,User_Id_2523,Bike,3,Business,12,2,4:19:20 PM,12,2,4:21:59 PM,12,2,4:27:59 PM,12,2,4:29:53 PM,9,,,-1.2551895,36.7822034,-1.2955333999999998,36.7742857,Rider_Id_343
-Order_No_6811,User_Id_3252,Bike,1,Personal,12,2,9:35:12 AM,12,2,9:38:56 AM,12,2,9:52:29 AM,12,2,10:00:18 AM,11,16.1,,-1.30435,36.860009999999996,-1.2639198,36.8069299,Rider_Id_498
-Order_No_10841,User_Id_3378,Bike,3,Personal,28,4,11:19:07 AM,28,4,11:19:21 AM,28,4,11:20:42 AM,28,4,11:32:17 AM,9,28.0,,-1.2551895,36.7822034,-1.2948751,36.7938907,Rider_Id_876
-Order_No_16545,User_Id_1471,Bike,3,Business,21,3,12:49:17 PM,21,3,12:49:47 PM,21,3,12:55:02 PM,21,3,1:33:27 PM,14,23.1,2.9,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_520
-Order_No_5097,User_Id_2154,Bike,3,Business,12,2,4:15:01 PM,12,2,4:23:00 PM,12,2,4:44:36 PM,12,2,4:47:46 PM,9,28.4,,-1.2860183,36.8975338,-1.2765736,36.851364600000004,Rider_Id_377
-Order_No_641,User_Id_970,Bike,3,Business,10,1,3:25:32 PM,10,1,5:30:04 PM,10,1,5:41:02 PM,10,1,5:49:19 PM,5,18.9,,-1.2551895,36.7822034,-1.2808188,36.810013700000006,Rider_Id_247
-Order_No_27187,User_Id_2767,Bike,3,Business,16,4,11:30:44 AM,16,4,11:31:50 AM,16,4,11:39:04 AM,16,4,11:43:18 AM,5,14.7,,-1.2612893,36.8147402,-1.25373,36.787929999999996,Rider_Id_857
-Order_No_9172,User_Id_1746,Bike,3,Business,6,1,5:33:10 PM,6,1,5:33:26 PM,6,1,5:50:29 PM,6,1,5:57:23 PM,4,22.3,,-1.3092046,36.7458722,-1.296797,36.776452500000005,Rider_Id_541
-Order_No_23880,User_Id_2344,Bike,3,Business,30,3,12:38:56 PM,30,3,12:46:22 PM,30,3,12:59:41 PM,30,3,1:04:52 PM,7,25.1,,-1.2551895,36.7822034,-1.286937,36.824674200000004,Rider_Id_276
-Order_No_26256,User_Id_186,Bike,3,Business,11,1,10:25:59 AM,11,1,10:26:35 AM,11,1,10:30:11 AM,11,1,10:40:42 AM,20,26.6,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_786
-Order_No_27956,User_Id_1280,Bike,3,Business,7,4,7:38:13 AM,7,4,7:39:01 AM,7,4,7:51:35 AM,7,4,7:55:47 AM,18,,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_523
-Order_No_25018,User_Id_2987,Bike,3,Business,24,4,3:33:06 PM,24,4,3:34:03 PM,24,4,4:22:25 PM,24,4,4:49:11 PM,10,21.4,,-1.332175,36.8819641,-1.3280091,36.8524146,Rider_Id_618
-Order_No_27241,User_Id_3326,Bike,3,Business,7,3,12:16:56 PM,7,3,2:40:16 PM,7,3,2:58:34 PM,7,3,3:04:15 PM,8,28.2,,-1.2551895,36.7822034,-1.304344,36.812274,Rider_Id_230
-Order_No_16762,User_Id_3091,Bike,3,Business,22,3,2:45:37 PM,22,3,2:46:19 PM,22,3,2:55:47 PM,22,3,3:09:13 PM,9,25.8,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_726
-Order_No_26006,User_Id_1934,Bike,2,Personal,8,1,4:51:22 PM,8,1,4:57:27 PM,8,1,5:58:14 PM,8,1,6:13:43 PM,7,18.4,,-1.2862252,36.8234218,-1.304033,36.7848695,Rider_Id_328
-Order_No_15000,User_Id_1551,Bike,1,Personal,23,3,8:12:30 AM,23,3,8:43:53 AM,23,3,9:20:18 AM,23,3,9:54:16 AM,4,16.8,15.9,-1.3100636,36.8616816,-1.307726,36.839117,Rider_Id_87
-Order_No_10401,User_Id_1642,Bike,3,Business,5,5,2:38:03 PM,5,5,2:39:15 PM,5,5,2:54:07 PM,5,5,3:05:33 PM,4,27.6,,-1.2628638,36.8070399,-1.2726506000000002,36.7960153,Rider_Id_166
-Order_No_22093,User_Id_1231,Bike,3,Business,10,4,4:36:47 PM,10,4,4:40:05 PM,10,4,4:51:53 PM,10,4,4:53:13 PM,2,22.0,,-1.2821421999999998,36.8161565,-1.290894,36.822971,Rider_Id_100
-Order_No_18212,User_Id_2546,Bike,1,Personal,8,1,3:29:37 PM,8,1,3:31:04 PM,8,1,3:42:50 PM,8,1,3:48:02 PM,18,24.8,,-1.3021558000000002,36.810686499999996,-1.3664581,36.9177807,Rider_Id_449
-Order_No_17629,User_Id_811,Bike,3,Personal,3,4,10:54:11 AM,3,4,10:55:56 AM,3,4,11:05:27 AM,3,4,11:20:18 AM,6,22.2,,-1.3024794,36.8228284,-1.2963097,36.7688221,Rider_Id_101
-Order_No_6843,User_Id_59,Bike,3,Business,28,4,1:50:25 PM,28,4,2:01:36 PM,28,4,2:15:38 PM,28,4,3:22:53 PM,4,27.4,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_436
-Order_No_4510,User_Id_2330,Bike,3,Business,11,2,4:20:58 PM,11,2,4:34:26 PM,11,2,4:34:32 PM,11,2,4:44:57 PM,3,21.0,,-1.3015197,36.7658456,-1.2868601,36.7738025,Rider_Id_74
-Order_No_2369,User_Id_809,Bike,1,Personal,18,1,9:24:36 AM,18,1,9:45:24 AM,18,1,10:05:34 AM,18,1,10:14:48 AM,6,14.8,0.1,-1.2661263999999999,36.8030359,-1.2980815,36.8085686,Rider_Id_808
-Order_No_3361,User_Id_2383,Bike,3,Business,5,5,11:29:58 AM,5,5,11:30:19 AM,5,5,12:02:57 PM,5,5,12:04:23 PM,2,24.4,,-1.2608993,36.799246000000004,-1.2724084999999998,36.8062525,Rider_Id_290
-Order_No_1926,User_Id_2190,Bike,3,Business,13,3,10:40:17 AM,13,3,10:41:10 AM,13,3,10:54:41 AM,13,3,11:06:50 AM,13,,,-1.3019530000000001,36.788458,-1.3430735,36.7105064,Rider_Id_332
-Order_No_8727,User_Id_391,Bike,3,Business,26,4,11:14:30 AM,26,4,11:29:45 AM,26,4,11:56:24 AM,26,4,12:04:33 PM,5,19.0,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_844
-Order_No_5696,User_Id_265,Bike,3,Business,10,5,11:51:14 AM,10,5,11:51:30 AM,10,5,11:51:43 AM,10,5,12:10:36 PM,3,19.1,,-1.300921,36.828195,-1.2983805,36.8198439,Rider_Id_179
-Order_No_6563,User_Id_2674,Bike,3,Personal,27,6,6:09:46 PM,27,6,6:12:15 PM,27,6,6:46:30 PM,27,6,7:33:16 PM,8,,,-1.2968742,36.793780299999995,-1.2797013000000002,36.7299371,Rider_Id_161
-Order_No_942,User_Id_706,Bike,3,Business,28,1,10:15:36 AM,28,1,10:41:11 AM,28,1,10:48:12 AM,28,1,11:05:48 AM,12,23.6,,-1.3559093,36.910045200000006,-1.3077869,36.844320700000004,Rider_Id_585
-Order_No_19927,User_Id_510,Bike,3,Business,7,1,9:34:49 AM,7,1,9:34:58 AM,7,1,10:01:25 AM,7,1,10:32:10 AM,13,24.6,,-1.2283403,36.8822756,-1.2860183,36.8975338,Rider_Id_351
-Order_No_1740,User_Id_3370,Bike,3,Business,31,5,12:39:15 PM,31,5,12:48:49 PM,31,5,1:02:44 PM,31,5,1:20:07 PM,7,21.9,,-1.2818812,36.82681420000001,-1.2551895,36.7822034,Rider_Id_20
-Order_No_23228,User_Id_1075,Bike,3,Business,12,5,12:06:09 PM,12,5,12:08:22 PM,12,5,12:31:19 PM,12,5,12:45:02 PM,15,26.4,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_8
-Order_No_4202,User_Id_3521,Bike,3,Business,30,2,2:10:54 PM,30,2,3:27:42 PM,30,2,3:46:16 PM,30,2,3:58:33 PM,9,27.4,,-1.2248308,36.801257299999996,-1.2768326,36.8073804,Rider_Id_726
-Order_No_20404,User_Id_3727,Bike,3,Business,7,4,3:45:12 PM,7,4,3:45:56 PM,7,4,3:54:29 PM,7,4,3:58:42 PM,8,31.1,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_698
-Order_No_6260,User_Id_1976,Bike,3,Business,21,4,10:45:12 AM,21,4,10:45:32 AM,21,4,11:03:17 AM,21,4,11:06:40 AM,8,25.2,,-1.257028,36.7810435,-1.2882073,36.82376729999999,Rider_Id_411
-Order_No_25858,User_Id_517,Bike,3,Business,13,2,7:48:31 AM,13,2,9:31:15 AM,13,2,9:43:23 AM,13,2,10:01:13 AM,4,19.7,9.1,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_80
-Order_No_2385,User_Id_2066,Bike,3,Business,31,2,2:41:20 PM,31,2,2:42:12 PM,31,2,2:42:25 PM,31,2,2:51:18 PM,11,22.3,,-1.316961,36.820337,-1.264384,36.767412799999995,Rider_Id_777
-Order_No_8240,User_Id_3232,Bike,3,Business,18,4,9:20:21 AM,18,4,9:34:08 AM,18,4,9:40:03 AM,18,4,9:41:11 AM,6,20.7,,-1.2551895,36.7822034,-1.2881035,36.8142095,Rider_Id_467
-Order_No_23187,User_Id_3565,Bike,3,Business,10,6,9:15:52 AM,10,6,9:22:33 AM,10,6,9:44:01 AM,10,6,10:21:07 AM,41,21.0,,-1.399029,36.757255,-1.1707617000000001,36.9675086,Rider_Id_647
-Order_No_6605,User_Id_2532,Bike,3,Business,10,5,3:59:24 PM,10,5,4:03:15 PM,10,5,4:19:34 PM,10,5,4:39:00 PM,3,,,-1.2571472,36.795063299999995,-1.2753108999999998,36.8130185,Rider_Id_547
-Order_No_26556,User_Id_1976,Bike,3,Business,25,5,8:43:12 AM,25,5,8:44:26 AM,25,5,8:54:13 AM,25,5,8:56:12 AM,6,20.8,,-1.257028,36.7810435,-1.2614967,36.7420697,Rider_Id_72
-Order_No_27489,User_Id_2066,Bike,3,Business,30,4,1:23:19 PM,30,4,1:26:14 PM,30,4,2:16:23 PM,30,4,2:19:00 PM,6,20.7,,-1.316961,36.820337,-1.2896398999999998,36.812449799999996,Rider_Id_447
-Order_No_10881,User_Id_2066,Bike,3,Business,21,4,12:20:51 PM,21,4,12:21:50 PM,21,4,12:39:38 PM,21,4,12:48:34 PM,12,28.1,,-1.3210188999999999,36.841055100000005,-1.314193,36.916132,Rider_Id_448
-Order_No_18573,User_Id_2701,Bike,3,Business,31,4,9:29:05 AM,31,4,9:29:56 AM,31,4,9:48:18 AM,31,4,9:50:45 AM,1,17.4,,-1.279395,36.825364,-1.2821421999999998,36.8161565,Rider_Id_159
-Order_No_5044,User_Id_2150,Bike,3,Business,11,4,9:10:57 AM,11,4,9:11:21 AM,11,4,9:24:28 AM,11,4,9:44:29 AM,11,22.1,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_507
-Order_No_1355,User_Id_49,Bike,3,Business,21,5,4:22:58 PM,21,5,4:46:44 PM,21,5,4:53:43 PM,21,5,4:55:09 PM,2,22.9,,-1.2790196000000003,36.8160867,-1.2906502,36.814118,Rider_Id_300
-Order_No_23545,User_Id_2532,Bike,3,Business,6,4,10:29:29 AM,6,4,10:46:18 AM,6,4,11:08:15 AM,6,4,11:18:31 AM,14,24.2,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_48
-Order_No_19498,User_Id_1346,Bike,3,Business,18,1,3:52:22 PM,18,1,3:52:55 PM,18,1,4:10:07 PM,18,1,4:17:31 PM,2,29.7,,-1.3035493999999999,36.7914336,-1.2917868000000001,36.7872675,Rider_Id_76
-Order_No_9199,User_Id_2112,Bike,3,Business,31,4,3:22:43 PM,31,4,3:22:51 PM,31,4,3:23:11 PM,31,4,3:39:35 PM,13,28.6,,-1.2156006000000001,36.8916865,-1.207546,36.7913976,Rider_Id_248
-Order_No_20109,User_Id_1797,Bike,2,Personal,13,1,5:04:09 PM,13,1,5:05:29 PM,13,1,5:08:34 PM,13,1,5:27:23 PM,5,19.4,,-1.2827576,36.783024100000006,-1.2971378999999998,36.8019794,Rider_Id_228
-Order_No_577,User_Id_393,Bike,3,Business,6,2,7:49:13 AM,6,2,8:04:32 AM,6,2,8:29:47 AM,6,2,8:50:21 AM,14,19.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_932
-Order_No_22355,User_Id_633,Bike,3,Business,4,1,12:06:11 PM,4,1,12:06:34 PM,4,1,12:09:02 PM,4,1,12:19:56 PM,4,25.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_829
-Order_No_1113,User_Id_3568,Bike,1,Personal,17,3,10:48:28 AM,17,3,10:49:54 AM,17,3,10:52:54 AM,17,3,10:57:22 AM,7,25.3,,-1.2675332000000001,36.808561,-1.2948997,36.7875428,Rider_Id_16
-Order_No_19786,User_Id_3222,Bike,3,Business,8,5,11:11:25 AM,8,5,11:12:24 AM,8,5,11:23:40 AM,8,5,11:26:53 AM,6,28.3,,-1.2551895,36.7822034,-1.2803414,36.8265456,Rider_Id_278
-Order_No_15507,User_Id_2330,Bike,3,Business,29,1,12:43:34 PM,29,1,12:43:53 PM,29,1,12:44:11 PM,29,1,1:01:36 PM,13,,,-1.2890715,36.8157588,-1.2124582,36.802894,Rider_Id_812
-Order_No_24459,User_Id_2363,Bike,1,Personal,6,5,6:06:08 PM,6,5,6:06:16 PM,6,5,6:11:34 PM,6,5,6:21:27 PM,12,,,-1.2649605,36.798177700000004,-1.20079,36.77198,Rider_Id_103
-Order_No_11353,User_Id_1551,Bike,1,Personal,19,4,8:51:05 AM,19,4,8:51:48 AM,19,4,8:52:34 AM,19,4,9:08:02 AM,11,14.7,,-1.2863371000000001,36.8200261,-1.3349663,36.858008500000004,Rider_Id_89
-Order_No_2557,User_Id_2132,Bike,3,Business,29,1,10:05:59 AM,29,1,10:06:13 AM,29,1,10:53:41 AM,29,1,11:09:19 AM,27,,,-1.2384056,36.8718698,-1.3858142,36.7641498,Rider_Id_86
-Order_No_21717,User_Id_3291,Bike,3,Business,19,6,8:53:50 AM,19,6,8:54:13 AM,19,6,9:03:41 AM,19,6,9:15:15 AM,5,21.4,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_512
-Order_No_16311,User_Id_970,Bike,3,Business,3,5,9:41:11 AM,3,5,9:43:19 AM,3,5,10:08:35 AM,3,5,10:12:11 AM,4,,,-1.2551895,36.7822034,-1.2525704,36.7955754,Rider_Id_394
-Order_No_24056,User_Id_419,Bike,3,Business,13,3,10:08:23 AM,13,3,10:08:45 AM,13,3,10:14:03 AM,13,3,10:32:30 AM,14,20.2,,-1.2579303999999998,36.8019667,-1.3053865,36.891733200000004,Rider_Id_116
-Order_No_478,User_Id_3686,Bike,3,Business,13,3,4:25:47 PM,13,3,4:26:07 PM,13,3,4:29:04 PM,13,3,4:39:38 PM,9,27.6,,-1.2551895,36.7822034,-1.2945128000000001,36.8396881,Rider_Id_177
-Order_No_10324,User_Id_3473,Bike,1,Personal,28,5,12:16:22 PM,28,5,12:25:04 PM,28,5,12:29:27 PM,28,5,12:47:51 PM,8,25.3,,-1.3233955,36.856294399999996,-1.2950408000000002,36.809817200000005,Rider_Id_100
-Order_No_9920,User_Id_1927,Bike,3,Business,28,3,1:48:30 PM,28,3,1:48:58 PM,28,3,2:21:10 PM,28,3,2:34:59 PM,20,,,-1.3463041999999998,36.768164,-1.2413316,36.878552500000005,Rider_Id_870
-Order_No_21176,User_Id_1500,Bike,3,Business,16,2,10:09:41 AM,16,2,10:10:25 AM,16,2,10:13:00 AM,16,2,10:15:39 AM,8,,,-1.2638185,36.7930057,-1.3017117,36.8149763,Rider_Id_855
-Order_No_4538,User_Id_3647,Bike,3,Business,13,4,1:15:20 PM,13,4,1:16:19 PM,13,4,1:35:03 PM,13,4,1:42:38 PM,2,23.2,,-1.2726389999999999,36.794723,-1.2746436,36.803682,Rider_Id_168
-Order_No_2586,User_Id_3149,Bike,3,Business,21,2,11:36:31 AM,21,2,11:38:31 AM,21,2,11:39:07 AM,21,2,11:51:11 AM,9,22.3,,-1.2770708,36.8231093,-1.3167113000000001,36.8301563,Rider_Id_436
-Order_No_23238,User_Id_893,Bike,3,Business,16,2,1:02:36 PM,16,2,1:04:07 PM,16,2,1:11:29 PM,16,2,1:13:03 PM,8,,,-1.2663194,36.809901,-1.2985153,36.7876043,Rider_Id_394
-Order_No_14520,User_Id_644,Bike,1,Personal,27,4,3:37:09 PM,27,4,3:37:34 PM,27,4,3:41:26 PM,27,4,3:47:19 PM,15,25.3,,-1.2672588,36.8000252,-1.2016352,36.8200903,Rider_Id_383
-Order_No_4223,User_Id_3203,Bike,3,Business,17,6,4:22:34 PM,17,6,4:27:25 PM,17,6,4:44:41 PM,17,6,4:55:43 PM,12,19.8,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_510
-Order_No_2566,User_Id_3557,Bike,3,Business,15,1,1:35:49 PM,15,1,1:36:05 PM,15,1,1:41:28 PM,15,1,1:45:35 PM,4,,,-1.2546732,36.808679600000005,-1.27778,36.803925,Rider_Id_467
-Order_No_11581,User_Id_3698,Bike,3,Business,7,5,1:01:32 PM,7,5,1:02:32 PM,7,5,1:17:55 PM,7,5,1:45:00 PM,11,,,-1.2669247,36.908649700000005,-1.3244885,36.897791999999995,Rider_Id_75
-Order_No_12541,User_Id_867,Bike,3,Business,27,6,10:11:40 AM,27,6,10:12:21 AM,27,6,10:26:58 AM,27,6,10:34:41 AM,14,,,-1.2991441000000001,36.7528804,-1.3167113000000001,36.8301563,Rider_Id_161
-Order_No_25451,User_Id_3017,Bike,3,Business,15,5,11:02:08 AM,15,5,11:08:27 AM,15,5,11:15:02 AM,15,5,11:16:27 AM,7,20.1,,-1.299313,36.78314879999999,-1.3077027,36.81635429999999,Rider_Id_680
-Order_No_22944,User_Id_1024,Bike,1,Personal,26,3,3:06:21 PM,26,3,3:07:27 PM,26,3,3:17:23 PM,26,3,3:21:04 PM,5,27.0,,-1.2977386999999998,36.803144700000004,-1.2892762,36.764129,Rider_Id_722
-Order_No_14054,User_Id_1075,Bike,3,Business,16,3,1:45:08 PM,16,3,1:45:32 PM,16,3,1:49:59 PM,16,3,1:52:38 PM,3,26.9,,-1.2527958000000001,36.8003131,-1.2615888000000002,36.7928732,Rider_Id_547
-Order_No_5931,User_Id_1500,Bike,3,Business,27,3,3:32:47 PM,27,3,3:33:11 PM,27,3,3:43:22 PM,27,3,3:48:27 PM,11,29.5,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_127
-Order_No_12962,User_Id_533,Bike,3,Business,30,2,12:42:29 PM,30,2,12:43:32 PM,30,2,12:43:37 PM,30,2,12:58:45 PM,6,22.5,,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_731
-Order_No_10460,User_Id_3426,Bike,3,Business,6,3,3:59:50 PM,6,3,4:18:38 PM,6,3,4:32:11 PM,6,3,4:34:08 PM,21,25.8,,-1.2232123,36.8932547,-1.3012008,36.764868,Rider_Id_199
-Order_No_16456,User_Id_645,Bike,1,Personal,10,1,4:19:11 PM,10,1,4:20:07 PM,10,1,4:32:12 PM,10,1,4:44:38 PM,11,18.8,,-1.2655306,36.828148,-1.3269776000000002,36.8055554,Rider_Id_427
-Order_No_646,User_Id_238,Bike,3,Business,25,4,3:16:38 PM,25,4,3:16:52 PM,25,4,3:17:34 PM,25,4,3:26:31 PM,5,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_349
-Order_No_3493,User_Id_393,Bike,3,Business,27,1,2:07:35 PM,27,1,2:07:35 PM,27,1,2:07:44 PM,27,1,2:13:58 PM,4,23.2,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_863
-Order_No_21719,User_Id_2412,Bike,3,Business,16,4,4:58:36 PM,16,4,5:11:38 PM,16,4,5:56:21 PM,16,4,6:03:59 PM,12,19.4,,-1.2248308,36.801257299999996,-1.2297202,36.881950700000004,Rider_Id_102
-Order_No_24288,User_Id_3560,Bike,3,Business,21,1,11:13:18 AM,21,1,11:13:40 AM,21,1,11:42:32 AM,21,1,11:45:52 AM,13,25.4,,-1.2232123,36.8932547,-1.279395,36.825364,Rider_Id_456
-Order_No_21663,User_Id_3079,Bike,1,Personal,3,4,8:10:59 AM,3,4,8:20:30 AM,3,4,8:38:00 AM,3,4,8:42:52 AM,5,17.5,16.0,-1.273056,36.811298,-1.2969293,36.8027733,Rider_Id_241
-Order_No_3590,User_Id_811,Bike,3,Personal,7,5,12:22:09 PM,7,5,12:24:30 PM,7,5,12:45:20 PM,7,5,1:02:14 PM,3,25.6,,-1.2962832,36.784968299999996,-1.2963097,36.7688221,Rider_Id_343
-Order_No_6380,User_Id_2436,Bike,3,Business,4,1,2:16:32 PM,4,1,2:16:53 PM,4,1,2:35:38 PM,4,1,2:39:07 PM,5,,,-1.2551895,36.7822034,-1.2696908000000002,36.81519,Rider_Id_147
-Order_No_17389,User_Id_2582,Bike,3,Business,19,2,12:24:53 PM,19,2,12:26:25 PM,19,2,12:36:53 PM,19,2,12:46:50 PM,11,26.6,,-1.2571472,36.795063299999995,-1.3322618,36.8045412,Rider_Id_411
-Order_No_21205,User_Id_1500,Bike,3,Business,8,5,5:26:50 PM,8,5,5:26:58 PM,8,5,5:27:04 PM,8,5,5:29:33 PM,9,25.2,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_523
-Order_No_11484,User_Id_1500,Bike,3,Business,17,3,11:58:33 AM,17,3,11:59:32 AM,17,3,11:59:45 AM,17,3,12:19:57 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206
-Order_No_5974,User_Id_163,Bike,1,Personal,20,3,9:19:22 AM,20,3,9:20:26 AM,20,3,9:44:01 AM,20,3,9:55:31 AM,21,16.6,0.4,-1.2107923999999999,36.803070899999994,-1.296797,36.776452500000005,Rider_Id_31
-Order_No_21708,User_Id_393,Bike,3,Business,27,2,8:09:35 AM,27,2,9:16:07 AM,27,2,9:32:56 AM,27,2,9:56:24 AM,10,18.5,2.1,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_708
-Order_No_13273,User_Id_867,Bike,3,Business,11,4,9:54:37 AM,11,4,9:55:59 AM,11,4,10:01:15 AM,11,4,10:09:11 AM,9,22.0,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_438
-Order_No_23276,User_Id_3718,Bike,3,Business,7,3,4:01:56 PM,7,3,4:04:52 PM,7,3,4:12:11 PM,7,3,4:24:29 PM,23,28.3,,-1.2650027,36.8126242,-1.2919200000000002,36.95459,Rider_Id_884
-Order_No_25328,User_Id_1724,Bike,3,Business,15,1,1:15:54 PM,15,1,1:16:37 PM,15,1,1:25:49 PM,15,1,1:32:12 PM,11,,,-1.2941648000000001,36.7977315,-1.32189,36.8589727,Rider_Id_263
-Order_No_23133,User_Id_190,Bike,3,Business,3,1,9:06:18 AM,3,1,9:07:29 AM,3,1,9:35:31 AM,3,1,9:43:22 AM,6,18.4,,-1.3278522,36.8711134,-1.3270347,36.866736200000005,Rider_Id_611
-Order_No_10884,User_Id_1914,Bike,2,Personal,2,1,2:51:45 PM,2,1,3:19:11 PM,2,1,3:31:58 PM,2,1,3:38:33 PM,6,19.9,,-1.2798711,36.8207118,-1.2969741000000001,36.7856612,Rider_Id_531
-Order_No_7685,User_Id_1899,Bike,3,Business,11,6,9:22:01 AM,11,6,9:22:21 AM,11,6,9:29:28 AM,11,6,10:35:02 AM,13,21.2,,-1.2600926000000001,36.8088685,-1.331619,36.847976,Rider_Id_882
-Order_No_14589,User_Id_3283,Bike,3,Business,26,2,10:19:51 AM,26,2,10:20:20 AM,26,2,10:36:20 AM,26,2,10:55:44 AM,6,27.5,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_268
-Order_No_22363,User_Id_393,Bike,3,Business,7,4,2:37:55 PM,7,4,2:39:40 PM,7,4,2:51:07 PM,7,4,3:05:36 PM,4,27.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523
-Order_No_26254,User_Id_627,Bike,3,Business,16,4,3:02:58 PM,16,4,3:03:34 PM,16,4,3:10:53 PM,16,4,3:20:10 PM,19,,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_147
-Order_No_22116,User_Id_515,Bike,3,Business,2,4,3:18:16 PM,2,4,3:18:52 PM,2,4,3:27:47 PM,2,4,3:33:33 PM,7,26.0,,-1.2901131000000001,36.7923153,-1.2574087999999999,36.799712,Rider_Id_100
-Order_No_13168,User_Id_2956,Bike,3,Business,3,1,3:32:15 PM,3,1,4:16:55 PM,3,1,4:31:33 PM,3,1,4:39:34 PM,14,22.3,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_279
-Order_No_150,User_Id_3462,Bike,1,Personal,11,3,9:36:17 AM,11,3,9:37:12 AM,11,3,9:48:43 AM,11,3,9:49:54 AM,6,15.1,1.0,-1.2556829999999999,36.813771,-1.2842863000000002,36.7951109,Rider_Id_427
-Order_No_1623,User_Id_3239,Bike,3,Personal,10,5,9:45:36 AM,10,5,9:46:51 AM,10,5,10:04:39 AM,10,5,10:07:15 AM,11,13.3,,-1.2836832,36.8306719,-1.23054,36.878262,Rider_Id_648
-Order_No_24188,User_Id_111,Bike,3,Business,28,1,9:51:02 AM,28,1,9:52:52 AM,28,1,10:02:09 AM,28,1,10:05:22 AM,8,19.3,,-1.2551895,36.7822034,-1.2944292,36.779356899999996,Rider_Id_674
-Order_No_139,User_Id_3651,Bike,3,Business,7,5,3:38:51 PM,7,5,3:54:11 PM,7,5,3:57:51 PM,7,5,4:11:04 PM,8,,,-1.2571472,36.795063299999995,-1.3138211000000002,36.8257935,Rider_Id_253
-Order_No_14992,User_Id_2523,Bike,3,Business,5,2,11:49:47 AM,5,2,11:51:28 AM,5,2,12:17:31 PM,5,2,12:32:54 PM,14,23.8,,-1.2551895,36.7822034,-1.2402455,36.8791753,Rider_Id_25
-Order_No_18253,User_Id_1659,Bike,1,Personal,6,4,10:06:33 AM,6,4,10:06:49 AM,6,4,10:17:00 AM,6,4,10:40:48 AM,11,22.2,,-1.258348,36.844901799999995,-1.3066143,36.8161046,Rider_Id_447
-Order_No_5051,User_Id_3264,Bike,3,Business,4,2,2:15:37 PM,4,2,2:18:14 PM,4,2,2:29:28 PM,4,2,2:31:42 PM,3,22.5,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_921
-Order_No_24854,User_Id_1998,Bike,3,Personal,16,3,12:21:22 PM,16,3,12:21:42 PM,16,3,12:22:44 PM,16,3,12:48:38 PM,8,22.8,,-1.2557778999999998,36.7879648,-1.2945178999999998,36.7685349,Rider_Id_451
-Order_No_13051,User_Id_1796,Bike,3,Business,6,3,10:21:22 AM,6,3,10:24:31 AM,6,3,10:35:16 AM,6,3,10:42:36 AM,7,22.6,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_114
-Order_No_20769,User_Id_391,Bike,3,Business,15,3,3:23:51 PM,15,3,3:25:28 PM,15,3,3:31:54 PM,15,3,4:46:32 PM,5,25.2,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_921
-Order_No_15050,User_Id_2330,Bike,3,Business,8,1,9:54:48 AM,8,1,9:54:57 AM,8,1,9:56:08 AM,8,1,10:11:47 AM,17,21.8,,-1.2642903,36.80021370000001,-1.3272343,36.7110502,Rider_Id_96
-Order_No_3524,User_Id_2330,Bike,3,Business,16,3,10:50:43 AM,16,3,10:51:53 AM,16,3,11:03:52 AM,16,3,11:19:19 AM,8,22.7,,-1.2642903,36.80021370000001,-1.3040812,36.7924217,Rider_Id_764
-Order_No_14849,User_Id_188,Bike,1,Personal,20,5,4:59:19 PM,20,5,5:01:49 PM,20,5,5:09:45 PM,20,5,5:31:33 PM,8,19.9,,-1.2824911,36.8218042,-1.3078623999999999,36.7697658,Rider_Id_812
-Order_No_5855,User_Id_323,Bike,3,Business,8,4,1:28:28 PM,8,4,1:45:40 PM,8,4,2:20:21 PM,8,4,2:21:43 PM,17,27.2,,-1.2869733,36.822090100000004,-1.3632162,36.9108253,Rider_Id_74
-Order_No_27055,User_Id_2423,Bike,3,Business,31,2,12:07:48 PM,31,2,12:08:09 PM,31,2,12:20:53 PM,31,2,12:50:25 PM,6,18.6,,-1.2571472,36.795063299999995,-1.300722,36.816865,Rider_Id_844
-Order_No_5751,User_Id_1746,Bike,3,Business,12,5,4:27:44 PM,12,5,4:45:00 PM,12,5,5:23:55 PM,12,5,5:48:10 PM,19,26.4,,-1.2701893999999998,36.90246,-1.2975066000000002,36.76894229999999,Rider_Id_507
-Order_No_4421,User_Id_1502,Bike,3,Business,25,4,9:34:58 AM,25,4,9:47:29 AM,25,4,9:48:22 AM,25,4,10:08:42 AM,9,16.2,2.1,-1.2566586000000002,36.792968200000004,-1.2343087,36.84749,Rider_Id_704
-Order_No_7443,User_Id_2226,Bike,1,Personal,14,4,10:55:09 AM,14,4,11:05:53 AM,14,4,11:19:28 AM,14,4,11:22:25 AM,5,18.9,,-1.298043,36.771853,-1.3104733999999998,36.7363698,Rider_Id_635
-Order_No_16646,User_Id_868,Bike,3,Business,2,4,9:04:36 AM,2,4,9:04:58 AM,2,4,9:08:53 AM,2,4,9:21:02 AM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_178
-Order_No_3668,User_Id_3140,Bike,1,Personal,27,4,10:10:07 AM,27,4,10:10:55 AM,27,4,10:25:14 AM,27,4,10:28:54 AM,10,19.0,3.0,-1.2850643999999998,36.8343657,-1.2832325,36.76767279999999,Rider_Id_208
-Order_No_3857,User_Id_1932,Bike,3,Business,6,5,12:05:28 PM,6,5,12:09:32 PM,6,5,12:53:17 PM,6,5,12:54:55 PM,5,20.4,,-1.2985986999999999,36.7809119,-1.3075701000000002,36.7504306,Rider_Id_140
-Order_No_6557,User_Id_1864,Bike,2,Personal,19,5,10:52:53 AM,19,5,10:53:04 AM,19,5,11:00:36 AM,19,5,11:11:56 AM,16,24.9,,-1.2626765,36.795598999999996,-1.3189978999999998,36.7104076,Rider_Id_731
-Order_No_13657,User_Id_3404,Bike,3,Business,29,4,12:31:31 PM,29,4,12:35:26 PM,29,4,12:46:28 PM,29,4,1:18:04 PM,5,21.6,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_744
-Order_No_14662,User_Id_1124,Bike,3,Business,15,3,12:35:07 PM,15,3,12:35:56 PM,15,3,12:38:53 PM,15,3,12:57:42 PM,11,18.1,,-1.269018,36.796803000000004,-1.2812601000000001,36.73402720000001,Rider_Id_446
-Order_No_17744,User_Id_2330,Bike,3,Business,15,2,1:33:10 PM,15,2,1:34:32 PM,15,2,1:37:34 PM,15,2,2:05:42 PM,12,24.4,,-1.2284323,36.8757058,-1.2642903,36.80021370000001,Rider_Id_75
-Order_No_11674,User_Id_706,Bike,3,Business,30,5,2:33:06 PM,30,5,2:33:36 PM,30,5,2:39:05 PM,30,5,3:12:46 PM,3,24.8,,-1.3079928,36.8389933,-1.3077869,36.844320700000004,Rider_Id_661
-Order_No_15153,User_Id_2200,Bike,3,Business,20,2,4:40:27 PM,20,2,4:54:15 PM,20,2,5:02:12 PM,20,2,5:16:42 PM,2,23.6,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_926
-Order_No_18096,User_Id_3647,Bike,3,Business,30,4,4:01:19 PM,30,4,4:01:26 PM,30,4,4:03:08 PM,30,4,4:21:15 PM,18,21.0,,-1.2726389999999999,36.794723,-1.1771233,36.7608016,Rider_Id_720
-Order_No_12908,User_Id_439,Bike,3,Business,27,2,3:12:22 PM,27,2,3:13:04 PM,27,2,3:19:34 PM,27,2,3:22:19 PM,5,25.7,,-1.3323107,36.8692616,-1.3168075000000001,36.8626441,Rider_Id_812
-Order_No_19406,User_Id_496,Bike,3,Business,20,1,12:35:05 PM,20,1,12:35:18 PM,20,1,12:36:13 PM,20,1,12:46:27 PM,15,,,-1.2285834999999998,36.882646799999996,-1.2551895,36.7822034,Rider_Id_839
-Order_No_6416,User_Id_2956,Bike,3,Business,28,4,12:14:23 PM,28,4,12:14:42 PM,28,4,12:15:31 PM,28,4,12:31:52 PM,9,27.8,,-1.3004062,36.829741,-1.2584143,36.8048002,Rider_Id_178
-Order_No_6524,User_Id_2700,Bike,1,Business,10,2,5:17:56 PM,10,2,5:18:09 PM,10,2,5:25:39 PM,10,2,5:33:44 PM,6,20.4,,-1.2665973999999998,36.7921181,-1.293973,36.80169,Rider_Id_951
-Order_No_25400,User_Id_1874,Bike,3,Business,12,2,3:22:41 PM,12,2,3:23:09 PM,12,2,3:23:20 PM,12,2,3:43:02 PM,12,27.0,,-1.2571472,36.795063299999995,-1.3035721000000002,36.7361169,Rider_Id_279
-Order_No_12959,User_Id_1244,Bike,3,Business,20,3,10:13:18 AM,20,3,10:13:30 AM,20,3,10:35:49 AM,20,3,10:57:10 AM,18,27.3,,-1.293515,36.897607,-1.303596,36.778378000000004,Rider_Id_377
-Order_No_11004,User_Id_627,Bike,3,Business,24,5,12:20:32 PM,24,5,12:23:47 PM,24,5,12:29:40 PM,24,5,12:33:52 PM,7,21.4,,-1.2551895,36.7822034,-1.2848211999999999,36.822415899999996,Rider_Id_921
-Order_No_22696,User_Id_868,Bike,3,Business,5,2,12:49:14 PM,5,2,12:51:58 PM,5,2,1:07:11 PM,5,2,1:22:21 PM,9,21.6,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_852
-Order_No_2999,User_Id_265,Bike,3,Business,27,2,12:39:21 PM,27,2,12:40:35 PM,27,2,12:43:29 PM,27,2,12:56:14 PM,3,23.7,,-1.306654,36.841896999999996,-1.300921,36.828195,Rider_Id_519
-Order_No_7200,User_Id_1754,Bike,3,Business,16,4,3:21:50 PM,16,4,3:41:52 PM,16,4,3:57:08 PM,16,4,4:06:56 PM,19,18.9,,-1.2860183,36.8975338,-1.2551895,36.7822034,Rider_Id_589
-Order_No_11393,User_Id_3196,Bike,2,Personal,24,2,11:07:46 AM,24,2,11:10:25 AM,24,2,11:35:24 AM,24,2,11:37:54 AM,8,17.2,,-1.3185531000000001,36.794371500000004,-1.2835586,36.8157644,Rider_Id_88
diff --git a/utils/data/train_data.csv b/utils/data/train_data.csv
deleted file mode 100644
index 1c7c586..0000000
--- a/utils/data/train_data.csv
+++ /dev/null
@@ -1,18021 +0,0 @@
-Order No,User Id,Vehicle Type,Platform Type,Personal or Business,Placement - Day of Month,Placement - Weekday (Mo = 1),Placement - Time,Confirmation - Day of Month,Confirmation - Weekday (Mo = 1),Confirmation - Time,Arrival at Pickup - Day of Month,Arrival at Pickup - Weekday (Mo = 1),Arrival at Pickup - Time,Pickup - Day of Month,Pickup - Weekday (Mo = 1),Pickup - Time,Arrival at Destination - Day of Month,Arrival at Destination - Weekday (Mo = 1),Arrival at Destination - Time,Distance (KM),Temperature,Precipitation in millimeters,Pickup Lat,Pickup Long,Destination Lat,Destination Long,Rider Id,Time from Pickup to Arrival
-Order_No_2874,User_Id_3647,Bike,3,Business,7,3,12:53:49 PM,7,3,12:54:44 PM,7,3,1:04:52 PM,7,3,1:10:29 PM,7,3,1:21:45 PM,3,25.1,,-1.2726389999999999,36.794723,-1.2674482,36.8076994,Rider_Id_646,676
-Order_No_26860,User_Id_2763,Bike,3,Business,27,3,2:23:20 PM,27,3,2:23:38 PM,27,3,2:24:56 PM,27,3,2:38:01 PM,27,3,3:16:17 PM,14,29.0,,-1.2551895,36.7822034,-1.3015998,36.8764939,Rider_Id_579,2296
-Order_No_10343,User_Id_2884,Bike,3,Personal,3,1,12:57:09 PM,3,1,12:57:40 PM,3,1,1:09:24 PM,3,1,1:18:18 PM,3,1,1:26:09 PM,3,21.7,,-1.3021968000000002,36.7874256,-1.3039411,36.7695316,Rider_Id_146,471
-Order_No_9039,User_Id_1033,Bike,3,Personal,26,2,1:18:16 PM,26,2,1:18:53 PM,26,2,1:18:56 PM,26,2,1:28:14 PM,26,2,1:50:29 PM,15,26.0,,-1.2227615,36.8989174,-1.2631940000000002,36.797624,Rider_Id_345,1335
-Order_No_16609,User_Id_1209,Bike,3,Business,15,1,2:04:21 PM,15,1,2:04:45 PM,15,1,2:12:57 PM,15,1,2:23:09 PM,15,1,2:30:35 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206,446
-Order_No_9516,User_Id_2154,Bike,3,Business,6,2,1:24:20 PM,6,2,2:03:56 PM,6,2,2:26:26 PM,6,2,2:29:55 PM,6,2,2:44:19 PM,6,27.3,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_878,864
-Order_No_15830,User_Id_2858,Bike,3,Business,7,5,12:59:16 PM,7,5,12:59:29 PM,7,5,1:13:00 PM,7,5,1:14:19 PM,7,5,1:59:09 PM,18,24.9,,-1.3164106000000002,36.6952197,-1.2534301,36.800098299999995,Rider_Id_167,2690
-Order_No_22495,User_Id_235,Bike,3,Personal,29,2,7:24:42 AM,29,2,7:25:54 AM,29,2,7:59:25 AM,29,2,8:06:42 AM,29,2,9:00:00 AM,16,18.2,,-1.2643343,36.7447423,-1.3173868999999998,36.8112044,Rider_Id_936,3198
-Order_No_11690,User_Id_3450,Bike,3,Business,5,3,3:18:01 PM,5,3,3:24:17 PM,5,3,3:27:55 PM,5,3,3:43:07 PM,5,3,4:22:19 PM,7,25.2,,-1.2551895,36.7822034,-1.2575370000000001,36.843066,Rider_Id_614,2352
-Order_No_17948,User_Id_59,Bike,3,Business,14,4,1:03:46 PM,14,4,1:05:18 PM,14,4,1:12:47 PM,14,4,1:24:30 PM,14,4,2:04:31 PM,16,24.8,,-1.290894,36.822971,-1.3124537,36.925365,Rider_Id_548,2401
-Order_No_26298,User_Id_2523,Bike,3,Business,26,1,12:09:50 PM,26,1,12:28:00 PM,26,1,12:41:15 PM,26,1,12:42:49 PM,26,1,12:57:50 PM,9,23.2,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_425,901
-Order_No_19198,User_Id_53,Bike,3,Business,1,4,11:46:18 AM,1,4,11:46:26 AM,1,4,11:46:34 AM,1,4,12:22:13 PM,1,4,12:52:38 PM,19,,,-1.3177546999999998,36.8303703,-1.3192788999999998,36.711299100000005,Rider_Id_619,1825
-Order_No_23887,User_Id_2987,Bike,1,Business,12,4,3:18:07 PM,12,4,3:21:32 PM,12,4,4:01:13 PM,12,4,4:09:43 PM,12,4,4:20:55 PM,4,21.6,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_427,672
-Order_No_27830,User_Id_640,Bike,3,Business,14,2,10:57:19 AM,14,2,10:58:46 AM,14,2,11:03:34 AM,14,2,11:10:08 AM,14,2,11:22:14 AM,5,21.3,,-1.304033,36.7848695,-1.2884997,36.8183895,Rider_Id_249,726
-Order_No_18990,User_Id_750,Bike,1,Personal,10,5,5:28:31 PM,10,5,5:29:21 PM,10,5,5:57:26 PM,10,5,6:03:20 PM,10,5,6:16:20 PM,5,21.1,,-1.2876659,36.8179934,-1.3012895,36.8035556,Rider_Id_213,780
-Order_No_15192,User_Id_2423,Bike,3,Business,11,1,12:54:17 PM,11,1,12:56:31 PM,11,1,1:04:32 PM,11,1,1:12:40 PM,11,1,1:31:55 PM,7,26.4,,-1.2551895,36.7822034,-1.285213,36.817885,Rider_Id_576,1155
-Order_No_24160,User_Id_733,Bike,3,Business,31,4,1:21:23 PM,31,4,1:43:55 PM,31,4,1:58:27 PM,31,4,2:19:13 PM,31,4,2:58:13 PM,5,28.9,,-1.2571472,36.795063299999995,-1.2884243,36.820724799999994,Rider_Id_332,2340
-Order_No_15310,User_Id_1527,Bike,3,Business,10,3,9:29:34 AM,10,3,9:30:11 AM,10,3,9:32:45 AM,10,3,9:40:54 AM,10,3,10:00:00 AM,9,21.9,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_394,1146
-Order_No_7478,User_Id_1796,Bike,3,Business,17,3,2:12:02 PM,17,3,2:12:18 PM,17,3,2:22:09 PM,17,3,2:37:59 PM,17,3,3:17:59 PM,17,,,-1.2296618000000001,36.84382720000001,-1.331619,36.847976,Rider_Id_8,2400
-Order_No_21939,User_Id_1280,Bike,3,Business,13,4,9:07:55 AM,13,4,9:08:49 AM,13,4,9:23:45 AM,13,4,9:32:26 AM,13,4,9:38:24 AM,3,18.5,,-1.2551895,36.7822034,-1.2647688,36.775086,Rider_Id_310,358
-Order_No_18968,User_Id_12,Bike,3,Business,23,4,10:09:05 AM,23,4,10:17:04 AM,23,4,10:32:49 AM,23,4,10:34:08 AM,23,4,11:05:13 AM,25,23.2,,-1.2400209999999998,36.849990000000005,-1.3724413,36.9284922,Rider_Id_8,1865
-Order_No_5520,User_Id_2236,Bike,1,Personal,23,3,2:27:11 PM,23,3,2:50:12 PM,23,3,3:03:05 PM,23,3,3:06:44 PM,23,3,3:29:11 PM,8,22.2,,-1.3245486000000002,36.796141799999994,-1.283714,36.8212782,Rider_Id_79,1347
-Order_No_5434,User_Id_1502,Bike,3,Business,29,1,9:01:26 AM,29,1,9:01:46 AM,29,1,9:15:48 AM,29,1,9:17:50 AM,29,1,9:41:25 AM,14,19.1,,-1.2566586000000002,36.792968200000004,-1.2232123,36.8932547,Rider_Id_854,1415
-Order_No_18953,User_Id_1720,Bike,3,Personal,2,1,3:16:26 PM,2,1,3:22:20 PM,2,1,3:53:33 PM,2,1,3:57:17 PM,2,1,4:42:39 PM,15,19.9,,-1.2866269,36.82463679999999,-1.3203209999999999,36.7195522,Rider_Id_905,2722
-Order_No_12990,User_Id_1578,Bike,3,Business,28,3,3:32:32 PM,28,3,5:49:25 PM,28,3,6:00:34 PM,28,3,6:12:10 PM,28,3,6:39:55 PM,10,23.0,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_511,1665
-Order_No_6472,User_Id_2828,Bike,3,Business,6,4,2:11:25 PM,6,4,2:11:36 PM,6,4,2:22:38 PM,6,4,2:48:35 PM,6,4,4:34:26 PM,24,,,-1.3623272,36.941084499999995,-1.2560822,36.820991799999994,Rider_Id_222,6351
-Order_No_21582,User_Id_3651,Bike,3,Business,18,2,8:59:15 AM,18,2,9:01:49 AM,18,2,9:10:43 AM,18,2,9:18:09 AM,18,2,9:36:54 AM,6,16.5,,-1.2876324,36.8186574,-1.2571472,36.795063299999995,Rider_Id_857,1125
-Order_No_11477,User_Id_3404,Bike,3,Business,3,6,2:41:08 PM,3,6,2:42:18 PM,3,6,2:49:36 PM,3,6,2:52:06 PM,3,6,3:25:33 PM,14,22.3,,-1.279395,36.825364,-1.3287857,36.9042514,Rider_Id_237,2007
-Order_No_25330,User_Id_1342,Bike,3,Business,17,3,12:35:36 PM,17,3,12:39:51 PM,17,3,12:53:44 PM,17,3,1:01:19 PM,17,3,1:22:44 PM,10,25.6,,-1.2631940000000002,36.797624,-1.3025826,36.7670807,Rider_Id_937,1285
-Order_No_25504,User_Id_2828,Bike,3,Business,27,3,3:50:22 PM,27,3,3:50:42 PM,27,3,4:08:50 PM,27,3,4:11:37 PM,27,3,5:21:56 PM,22,29.7,,-1.2615888000000002,36.7928732,-1.3981712,36.7673241,Rider_Id_147,4219
-Order_No_17802,User_Id_496,Bike,3,Business,19,1,3:01:14 PM,19,1,3:11:19 PM,19,1,3:17:15 PM,19,1,3:22:48 PM,19,1,3:37:59 PM,6,25.0,,-1.2551895,36.7822034,-1.280753,36.8191175,Rider_Id_456,911
-Order_No_25915,User_Id_440,Bike,1,Personal,5,5,6:42:19 PM,5,5,6:44:35 PM,5,5,6:59:26 PM,5,5,7:01:35 PM,5,5,7:23:43 PM,12,25.0,,-1.3222489,36.786702500000004,-1.2724885,36.785699900000004,Rider_Id_328,1328
-Order_No_21746,User_Id_2854,Bike,3,Business,4,2,8:25:00 AM,4,2,8:27:12 AM,4,2,8:27:36 AM,4,2,8:44:06 AM,4,2,8:51:01 AM,1,16.1,82.9,-1.2963097,36.7688221,-1.2948781,36.7844523,Rider_Id_875,415
-Order_No_696,User_Id_3222,Bike,3,Business,20,3,10:36:48 AM,20,3,10:37:08 AM,20,3,10:38:11 AM,20,3,10:42:31 AM,20,3,11:15:06 AM,5,27.1,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_579,1955
-Order_No_9004,User_Id_867,Bike,3,Business,18,1,3:52:46 PM,18,1,3:53:44 PM,18,1,4:10:24 PM,18,1,4:29:29 PM,18,1,4:41:49 PM,11,30.9,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_327,740
-Order_No_205,User_Id_1280,Bike,3,Business,3,5,3:33:53 PM,3,5,3:36:11 PM,3,5,3:46:37 PM,3,5,3:48:18 PM,3,5,4:19:10 PM,11,,,-1.2551895,36.7822034,-1.2639757,36.860282299999994,Rider_Id_553,1852
-Order_No_2633,User_Id_2509,Bike,3,Business,25,1,3:10:02 PM,25,1,3:10:45 PM,25,1,3:39:08 PM,25,1,3:45:16 PM,25,1,4:05:24 PM,6,28.1,,-1.2571472,36.795063299999995,-1.2989287,36.81630820000001,Rider_Id_25,1208
-Order_No_12946,User_Id_2828,Bike,3,Business,29,4,12:11:41 PM,29,4,12:13:05 PM,29,4,12:16:43 PM,29,4,12:20:20 PM,29,4,12:27:46 PM,3,21.7,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_574,446
-Order_No_1882,User_Id_2154,Bike,3,Business,28,3,1:17:58 PM,28,3,1:18:29 PM,28,3,1:27:43 PM,28,3,1:34:37 PM,28,3,2:09:27 PM,12,,,-1.303031,36.758577,-1.2765736,36.851364600000004,Rider_Id_675,2090
-Order_No_1390,User_Id_478,Bike,3,Business,25,5,2:00:53 PM,25,5,2:04:56 PM,25,5,2:13:52 PM,25,5,2:15:04 PM,25,5,2:26:15 PM,5,23.5,,-1.2899658999999999,36.769412,-1.2973682,36.785987799999994,Rider_Id_334,671
-Order_No_17661,User_Id_3091,Bike,3,Business,3,3,10:30:14 AM,3,3,10:31:04 AM,3,3,10:38:36 AM,3,3,11:06:56 AM,3,3,11:17:35 AM,7,27.2,,-1.290894,36.822971,-1.3117395,36.8592009,Rider_Id_61,639
-Order_No_16795,User_Id_627,Bike,3,Business,12,5,12:04:07 PM,12,5,12:04:19 PM,12,5,12:04:27 PM,12,5,12:12:27 PM,12,5,12:27:07 PM,3,26.6,,-1.2551895,36.7822034,-1.2584505,36.807181,Rider_Id_349,880
-Order_No_1285,User_Id_111,Bike,3,Business,6,4,1:35:31 PM,6,4,1:35:59 PM,6,4,1:39:33 PM,6,4,1:47:47 PM,6,4,2:18:23 PM,9,21.5,,-1.2551895,36.7822034,-1.3023756000000002,36.7949276,Rider_Id_676,1836
-Order_No_10875,User_Id_3653,Bike,2,Personal,17,5,11:25:49 AM,17,5,11:26:23 AM,17,5,11:41:51 AM,17,5,11:47:17 AM,17,5,12:07:16 PM,6,17.6,,-1.2724556,36.7931938,-1.2934656999999998,36.8212737,Rider_Id_552,1199
-Order_No_20601,User_Id_2701,Bike,3,Business,17,4,9:25:12 AM,17,4,9:37:48 AM,17,4,10:18:04 AM,17,4,10:24:28 AM,17,4,10:54:11 AM,12,,,-1.225322,36.80855,-1.2765736,36.851364600000004,Rider_Id_870,1783
-Order_No_7782,User_Id_2653,Bike,1,Personal,26,3,12:04:31 PM,26,3,12:04:48 PM,26,3,12:16:20 PM,26,3,12:28:29 PM,26,3,12:51:59 PM,5,24.1,,-1.2867716999999999,36.773523700000005,-1.2985749,36.8088,Rider_Id_565,1410
-Order_No_1576,User_Id_2297,Bike,2,Personal,16,6,3:00:40 PM,16,6,3:10:03 PM,16,6,3:19:09 PM,16,6,3:25:01 PM,16,6,3:41:56 PM,8,22.9,,-1.290724,36.81947,-1.2473038,36.788470700000005,Rider_Id_844,1015
-Order_No_16022,User_Id_2423,Bike,3,Business,12,2,9:16:23 AM,12,2,9:16:33 AM,12,2,9:17:36 AM,12,2,9:26:53 AM,12,2,9:38:38 AM,4,17.9,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_958,705
-Order_No_1293,User_Id_599,Bike,1,Personal,18,3,3:58:59 PM,18,3,4:00:16 PM,18,3,4:08:13 PM,18,3,4:31:27 PM,18,3,5:02:29 PM,4,20.5,,-1.3005593,36.835929799999995,-1.3200277,36.8206816,Rider_Id_939,1862
-Order_No_5728,User_Id_2066,Bike,3,Business,30,4,4:17:34 PM,30,4,4:17:41 PM,30,4,4:29:43 PM,30,4,4:31:15 PM,30,4,4:56:47 PM,9,20.9,,-1.316961,36.820337,-1.2940428999999998,36.756330299999995,Rider_Id_449,1532
-Order_No_9950,User_Id_3050,Bike,3,Business,27,3,3:05:17 PM,27,3,3:07:33 PM,27,3,3:16:33 PM,27,3,3:24:08 PM,27,3,3:51:08 PM,10,29.3,,-1.2945073999999999,36.8083195,-1.3104733999999998,36.7363698,Rider_Id_801,1620
-Order_No_24559,User_Id_1335,Bike,3,Personal,10,1,11:14:56 AM,10,1,11:15:40 AM,10,1,11:24:44 AM,10,1,11:27:08 AM,10,1,11:46:02 AM,5,,,-1.2611987,36.80609129999999,-1.2549476000000002,36.8269576,Rider_Id_622,1134
-Order_No_21489,User_Id_2112,Bike,3,Business,8,2,2:35:15 PM,8,2,2:49:50 PM,8,2,3:11:15 PM,8,2,3:12:26 PM,8,2,4:06:41 PM,12,25.3,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_687,3255
-Order_No_15315,User_Id_2112,Bike,3,Business,10,1,3:40:13 PM,10,1,3:40:30 PM,10,1,3:47:35 PM,10,1,4:15:47 PM,10,1,5:17:29 PM,23,18.1,,-1.3472066,36.7692638,-1.2156006000000001,36.8916865,Rider_Id_123,3702
-Order_No_24898,User_Id_186,Bike,3,Business,3,4,10:56:17 AM,3,4,11:48:16 AM,3,4,12:12:41 PM,3,4,12:18:19 PM,3,4,1:18:18 PM,24,22.1,,-1.3700383,36.9190174,-1.2579303999999998,36.8019667,Rider_Id_761,3599
-Order_No_7304,User_Id_52,Bike,3,Personal,20,5,2:02:01 PM,20,5,2:02:41 PM,20,5,2:27:09 PM,20,5,2:31:35 PM,20,5,3:10:32 PM,9,20.5,,-1.2741347,36.834216299999994,-1.2963097,36.7688221,Rider_Id_398,2337
-Order_No_20910,User_Id_3209,Bike,3,Personal,29,5,10:01:03 AM,29,5,10:02:32 AM,29,5,10:07:21 AM,29,5,10:18:36 AM,29,5,10:28:13 AM,1,21.0,,-1.2932155,36.7877116,-1.2977197,36.7861445,Rider_Id_119,577
-Order_No_11245,User_Id_3283,Bike,3,Business,25,1,4:21:39 PM,25,1,4:21:58 PM,25,1,4:28:21 PM,25,1,4:42:42 PM,25,1,5:03:19 PM,5,28.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_118,1237
-Order_No_2258,User_Id_393,Bike,3,Business,26,2,9:59:02 AM,26,2,10:00:06 AM,26,2,10:09:49 AM,26,2,10:26:22 AM,26,2,11:00:33 AM,14,21.9,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_126,2051
-Order_No_20090,User_Id_1329,Bike,3,Business,6,3,11:32:57 AM,6,3,11:33:30 AM,6,3,11:37:28 AM,6,3,11:39:00 AM,6,3,11:53:48 AM,10,,,-1.273056,36.811298,-1.2698167999999999,36.7421898,Rider_Id_216,888
-Order_No_1381,User_Id_1647,Bike,3,Business,27,6,3:47:26 PM,27,6,3:47:39 PM,27,6,3:55:08 PM,27,6,4:03:11 PM,27,6,4:25:08 PM,4,,,-1.2190317,36.8825092,-1.2238103,36.8772588,Rider_Id_291,1317
-Order_No_17089,User_Id_2183,Bike,3,Business,28,2,8:58:31 AM,28,2,8:58:57 AM,28,2,8:59:38 AM,28,2,9:12:02 AM,28,2,9:29:37 AM,8,15.9,51.1,-1.2551895,36.7822034,-1.2939912,36.8161329,Rider_Id_726,1055
-Order_No_28162,User_Id_598,Bike,2,Business,19,3,1:36:13 PM,19,3,1:36:46 PM,19,3,1:52:42 PM,19,3,2:47:28 PM,19,3,2:57:44 PM,2,25.8,,-1.2536797,36.7995085,-1.2655332000000001,36.8094648,Rider_Id_101,616
-Order_No_47,User_Id_53,Bike,3,Business,7,2,9:20:38 AM,7,2,9:20:56 AM,7,2,9:30:19 AM,7,2,10:12:34 AM,7,2,10:29:53 AM,5,16.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_208,1039
-Order_No_9413,User_Id_3689,Bike,3,Business,29,6,12:08:56 PM,29,6,12:09:15 PM,29,6,12:15:45 PM,29,6,12:23:29 PM,29,6,12:35:01 PM,1,24.0,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_477,692
-Order_No_23832,User_Id_1205,Bike,3,Business,12,1,9:52:58 AM,12,1,10:12:49 AM,12,1,10:19:19 AM,12,1,10:23:32 AM,12,1,10:41:28 AM,8,19.2,0.5,-1.257309,36.806008399999996,-1.2902738,36.776594200000005,Rider_Id_829,1076
-Order_No_20729,User_Id_2521,Bike,3,Business,26,6,11:42:01 AM,26,6,11:43:28 AM,26,6,11:59:00 AM,26,6,12:01:13 PM,26,6,12:32:28 PM,14,25.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1875
-Order_No_25290,User_Id_2008,Bike,3,Business,2,2,2:10:51 PM,2,2,2:12:55 PM,2,2,2:19:35 PM,2,2,2:23:24 PM,2,2,2:51:22 PM,7,29.6,,-1.2508229,36.7895263,-1.2902539,36.8211322,Rider_Id_858,1678
-Order_No_5322,User_Id_1927,Bike,3,Business,23,4,2:18:04 PM,23,4,2:18:19 PM,23,4,2:27:44 PM,23,4,2:31:13 PM,23,4,3:08:47 PM,25,20.6,,-1.2232123,36.8932547,-1.3472066,36.7692638,Rider_Id_754,2254
-Order_No_22471,User_Id_704,Bike,3,Business,26,2,12:23:57 PM,26,2,12:55:02 PM,26,2,1:02:26 PM,26,2,1:27:41 PM,26,2,2:01:43 PM,20,,,-1.3404187,36.7614685,-1.2860183,36.8975338,Rider_Id_636,2042
-Order_No_26924,User_Id_948,Bike,3,Business,10,1,10:51:53 AM,10,1,10:52:21 AM,10,1,10:54:57 AM,10,1,10:59:20 AM,10,1,11:23:52 AM,7,18.6,,-1.2936611,36.8260953,-1.2594473999999998,36.8089122,Rider_Id_247,1472
-Order_No_16820,User_Id_1368,Bike,3,Business,30,2,10:56:42 AM,30,2,11:03:23 AM,30,2,11:24:28 AM,30,2,11:38:04 AM,30,2,12:15:06 PM,13,,,-1.2571472,36.795063299999995,-1.302453,36.8668919,Rider_Id_155,2222
-Order_No_9138,User_Id_3283,Bike,3,Business,16,4,10:35:54 AM,16,4,10:36:30 AM,16,4,11:00:30 AM,16,4,11:14:38 AM,16,4,11:26:02 AM,6,,,-1.2663631000000002,36.8163329,-1.2628473,36.781805,Rider_Id_712,684
-Order_No_16746,User_Id_633,Bike,3,Business,22,2,12:09:01 PM,22,2,12:40:38 PM,22,2,12:41:18 PM,22,2,12:57:20 PM,22,2,1:22:28 PM,9,24.2,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_445,1508
-Order_No_3661,User_Id_1899,Bike,3,Business,26,4,9:21:27 AM,26,4,9:21:52 AM,26,4,9:36:21 AM,26,4,9:39:41 AM,26,4,9:48:16 AM,6,15.4,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_726,515
-Order_No_16437,User_Id_393,Bike,3,Business,8,1,2:09:41 PM,8,1,2:09:46 PM,8,1,2:09:52 PM,8,1,2:24:20 PM,8,1,2:51:10 PM,14,29.8,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,1610
-Order_No_13282,User_Id_867,Bike,3,Business,15,4,9:10:02 AM,15,4,9:18:21 AM,15,4,9:33:35 AM,15,4,9:48:47 AM,15,4,10:19:07 AM,10,19.8,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_532,1820
-Order_No_27006,User_Id_401,Bike,1,Personal,12,5,10:59:30 AM,12,5,11:05:37 AM,12,5,11:27:37 AM,12,5,11:30:35 AM,12,5,11:53:52 AM,7,24.4,,-1.2844858000000001,36.79558839999999,-1.2628638,36.8070399,Rider_Id_532,1397
-Order_No_5894,User_Id_3013,Bike,3,Personal,18,6,10:05:30 AM,18,6,10:05:39 AM,18,6,10:41:17 AM,18,6,11:05:10 AM,18,6,12:03:11 PM,26,19.2,,-1.2850643999999998,36.8343657,-1.3510653000000001,36.6665115,Rider_Id_208,3481
-Order_No_878,User_Id_3291,Bike,3,Business,19,2,12:30:47 PM,19,2,12:31:36 PM,19,2,12:36:18 PM,19,2,12:50:05 PM,19,2,12:57:04 PM,5,27.6,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_147,419
-Order_No_27655,User_Id_3688,Bike,3,Business,4,2,3:13:56 PM,4,2,3:15:46 PM,4,2,3:15:58 PM,4,2,3:27:20 PM,4,2,3:43:07 PM,5,20.2,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_350,947
-Order_No_9858,User_Id_1515,Bike,1,Personal,15,2,9:00:45 AM,15,2,9:01:33 AM,15,2,9:18:56 AM,15,2,9:23:04 AM,15,2,9:50:44 AM,12,17.9,1.9,-1.2934527,36.7755139,-1.3255286,36.7835132,Rider_Id_202,1660
-Order_No_11116,User_Id_2828,Bike,3,Business,16,6,6:04:16 PM,16,6,6:04:28 PM,16,6,6:12:45 PM,16,6,6:27:25 PM,16,6,6:50:23 PM,17,23.8,,-1.2615888000000002,36.7928732,-1.2333156,36.896378399999996,Rider_Id_103,1378
-Order_No_2436,User_Id_2801,Bike,3,Business,22,5,2:51:08 PM,22,5,2:51:58 PM,22,5,3:05:28 PM,22,5,3:15:34 PM,22,5,3:43:24 PM,12,27.1,,-1.3108386,36.7555239,-1.2674273999999999,36.787082700000006,Rider_Id_708,1670
-Order_No_3815,User_Id_3547,Bike,3,Business,8,2,11:27:06 AM,8,2,11:28:08 AM,8,2,11:40:15 AM,8,2,11:41:46 AM,8,2,12:22:29 PM,17,23.2,,-1.2856319,36.767299799999996,-1.3194518,36.869396,Rider_Id_958,2443
-Order_No_12545,User_Id_661,Bike,3,Business,22,2,3:21:16 PM,22,2,3:21:49 PM,22,2,3:33:16 PM,22,2,3:48:44 PM,22,2,4:03:26 PM,4,21.5,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_329,882
-Order_No_5867,User_Id_3434,Bike,3,Business,10,1,3:51:27 PM,10,1,4:06:47 PM,10,1,4:29:42 PM,10,1,4:45:21 PM,10,1,5:59:26 PM,29,18.9,,-1.1612808000000001,36.9563278,-1.2963097,36.7688221,Rider_Id_520,4445
-Order_No_2588,User_Id_3291,Bike,3,Business,1,5,2:52:07 PM,1,5,2:53:37 PM,1,5,2:59:39 PM,1,5,3:10:17 PM,1,5,3:24:41 PM,5,31.9,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_663,864
-Order_No_1473,User_Id_3647,Bike,3,Business,4,2,5:18:51 PM,4,2,5:19:21 PM,4,2,5:28:03 PM,4,2,5:34:17 PM,4,2,5:38:25 PM,2,22.2,,-1.2726389999999999,36.794723,-1.2741728,36.79691079999999,Rider_Id_486,248
-Order_No_24766,User_Id_346,Bike,3,Business,17,4,9:50:58 AM,17,4,9:51:54 AM,17,4,10:03:03 AM,17,4,10:05:36 AM,17,4,10:21:15 AM,5,,,-1.28878,36.8168312,-1.2584143,36.8048002,Rider_Id_41,939
-Order_No_11918,User_Id_795,Bike,3,Personal,17,2,2:18:02 PM,17,2,2:21:11 PM,17,2,2:24:23 PM,17,2,2:27:21 PM,17,2,3:37:17 PM,11,24.3,,-1.3104733999999998,36.7363698,-1.2551895,36.7822034,Rider_Id_735,4196
-Order_No_25194,User_Id_1687,Bike,3,Business,4,4,8:42:19 AM,4,4,8:42:41 AM,4,4,8:59:05 AM,4,4,9:02:29 AM,4,4,9:12:58 AM,4,18.5,,-1.2890333,36.8234839,-1.2896933000000002,36.792903499999994,Rider_Id_88,629
-Order_No_4641,User_Id_2579,Bike,3,Business,9,3,10:02:44 AM,9,3,10:12:05 AM,9,3,10:19:31 AM,9,3,10:28:14 AM,9,3,10:45:08 AM,10,17.8,0.6,-1.2536855,36.8245276,-1.287428,36.7800821,Rider_Id_207,1014
-Order_No_5761,User_Id_2006,Bike,3,Business,1,4,9:25:10 AM,1,4,9:25:28 AM,1,4,9:49:50 AM,1,4,9:58:10 AM,1,4,11:11:25 AM,16,,,-1.2469508,36.824448499999995,-1.3231725,36.864017700000005,Rider_Id_89,4395
-Order_No_4245,User_Id_1500,Bike,3,Business,9,2,8:42:20 AM,9,2,8:42:50 AM,9,2,9:01:15 AM,9,2,9:34:32 AM,9,2,9:41:44 AM,4,21.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_43,432
-Order_No_26947,User_Id_1072,Bike,1,Personal,25,1,4:17:12 PM,25,1,4:20:52 PM,25,1,4:26:10 PM,25,1,4:28:32 PM,25,1,4:45:39 PM,8,,,-1.261849,36.808254,-1.2337192,36.814119299999994,Rider_Id_427,1027
-Order_No_3388,User_Id_2154,Bike,3,Business,24,1,1:56:38 PM,24,1,1:56:43 PM,24,1,2:03:52 PM,24,1,2:15:29 PM,24,1,2:35:03 PM,6,24.9,,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_26,1174
-Order_No_15034,User_Id_2756,Bike,3,Business,17,3,3:17:02 PM,17,3,3:26:29 PM,17,3,3:35:32 PM,17,3,3:36:47 PM,17,3,4:05:24 PM,6,,,-1.2814609,36.826593200000005,-1.2879691,36.7803495,Rider_Id_790,1717
-Order_No_22667,User_Id_1271,Bike,3,Business,11,2,10:04:14 AM,11,2,10:04:33 AM,11,2,10:34:16 AM,11,2,10:41:23 AM,11,2,12:00:30 PM,27,19.5,,-1.3244885,36.897791999999995,-1.3204931,36.7043102,Rider_Id_205,4747
-Order_No_3641,User_Id_517,Bike,3,Business,3,1,12:13:08 PM,3,1,12:17:01 PM,3,1,12:31:25 PM,3,1,12:38:42 PM,3,1,1:18:37 PM,14,22.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_878,2395
-Order_No_3458,User_Id_540,Bike,3,Business,7,5,2:03:41 PM,7,5,2:04:47 PM,7,5,2:15:24 PM,7,5,2:29:17 PM,7,5,2:47:25 PM,6,26.8,,-1.2571472,36.795063299999995,-1.2967701999999999,36.8040381,Rider_Id_147,1088
-Order_No_7464,User_Id_1075,Bike,3,Business,12,2,2:06:14 PM,12,2,2:07:11 PM,12,2,2:15:35 PM,12,2,2:32:47 PM,12,2,2:38:20 PM,2,27.1,,-1.2561334,36.7990458,-1.2527958000000001,36.8003131,Rider_Id_279,333
-Order_No_7420,User_Id_1603,Bike,1,Personal,15,5,10:18:35 AM,15,5,10:18:57 AM,15,5,10:34:17 AM,15,5,10:39:53 AM,15,5,11:00:40 AM,6,19.9,,-1.2830603,36.8298283,-1.3220191000000001,36.840016600000006,Rider_Id_444,1247
-Order_No_8254,User_Id_1953,Bike,3,Personal,14,1,4:08:31 PM,14,1,4:16:46 PM,14,1,4:46:23 PM,14,1,4:51:18 PM,14,1,5:28:21 PM,11,23.4,,-1.2802576,36.8246917,-1.2833564,36.741974600000006,Rider_Id_101,2223
-Order_No_13091,User_Id_3689,Bike,3,Business,10,1,10:07:53 AM,10,1,10:08:02 AM,10,1,10:15:07 AM,10,1,10:34:17 AM,10,1,10:40:29 AM,2,23.7,,-1.2571472,36.795063299999995,-1.2527958000000001,36.8003131,Rider_Id_720,372
-Order_No_16421,User_Id_3283,Bike,3,Business,1,4,2:03:05 PM,1,4,2:24:28 PM,1,4,2:32:12 PM,1,4,2:48:20 PM,1,4,3:06:39 PM,13,,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_406,1099
-Order_No_25809,User_Id_265,Bike,3,Business,27,4,2:35:42 PM,27,4,2:35:53 PM,27,4,2:53:44 PM,27,4,3:03:41 PM,27,4,3:26:18 PM,7,23.8,,-1.2609397,36.8010827,-1.300921,36.828195,Rider_Id_275,1357
-Order_No_1619,User_Id_2457,Bike,3,Business,7,3,11:25:48 AM,7,3,11:29:42 AM,7,3,11:35:51 AM,7,3,11:39:10 AM,7,3,12:25:32 PM,8,25.1,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_206,2782
-Order_No_24875,User_Id_3023,Bike,2,Personal,23,1,11:35:23 AM,23,1,11:35:28 AM,23,1,11:37:55 AM,23,1,11:46:19 AM,23,1,12:03:46 PM,7,18.4,,-1.3010773000000002,36.7752554,-1.2799394,36.8192374,Rider_Id_88,1047
-Order_No_9714,User_Id_2804,Bike,3,Business,8,4,1:36:02 PM,8,4,1:36:35 PM,8,4,1:36:50 PM,8,4,1:49:51 PM,8,4,2:01:20 PM,2,27.3,,-1.2642903,36.80021370000001,-1.2571472,36.795063299999995,Rider_Id_268,689
-Order_No_23409,User_Id_1502,Bike,3,Business,13,3,1:24:58 PM,13,3,1:25:15 PM,13,3,1:49:07 PM,13,3,1:54:32 PM,13,3,2:05:43 PM,5,27.1,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_958,671
-Order_No_12222,User_Id_2360,Bike,3,Business,7,5,1:12:13 PM,7,5,1:49:22 PM,7,5,2:00:59 PM,7,5,2:02:40 PM,7,5,2:27:22 PM,5,26.7,,-1.2551895,36.7822034,-1.2700552,36.817067,Rider_Id_857,1482
-Order_No_6300,User_Id_660,Bike,3,Personal,1,3,11:16:15 AM,1,3,11:16:23 AM,1,3,11:28:41 AM,1,3,11:45:45 AM,1,3,12:03:07 PM,7,20.0,,-1.284282,36.822545500000004,-1.323971,36.851224200000004,Rider_Id_414,1042
-Order_No_4138,User_Id_3091,Bike,3,Business,10,1,12:04:16 PM,10,1,12:13:03 PM,10,1,12:26:16 PM,10,1,12:31:02 PM,10,1,12:56:37 PM,5,18.4,,-1.273488,36.804158,-1.290894,36.822971,Rider_Id_868,1535
-Order_No_13727,User_Id_186,Bike,3,Business,6,3,12:09:27 PM,6,3,12:09:42 PM,6,3,12:29:33 PM,6,3,12:32:55 PM,6,3,1:12:20 PM,17,,,-1.2571472,36.795063299999995,-1.3169591,36.7000029,Rider_Id_4,2365
-Order_No_7642,User_Id_2996,Bike,2,Personal,26,2,3:11:05 PM,26,2,3:16:06 PM,26,2,3:54:22 PM,26,2,3:55:30 PM,26,2,4:19:49 PM,19,,,-1.2294985,36.8775495,-1.1500224,36.9557348,Rider_Id_549,1459
-Order_No_18055,User_Id_346,Bike,3,Business,30,3,1:16:20 PM,30,3,1:17:44 PM,30,3,1:34:47 PM,30,3,1:45:07 PM,30,3,2:08:27 PM,5,29.0,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_404,1400
-Order_No_15624,User_Id_2200,Bike,3,Business,29,1,3:04:12 PM,29,1,3:07:10 PM,29,1,4:23:45 PM,29,1,4:28:13 PM,29,1,4:45:39 PM,7,24.6,,-1.290894,36.822971,-1.2615888000000002,36.7928732,Rider_Id_736,1046
-Order_No_26113,User_Id_1500,Bike,3,Business,5,2,12:14:01 PM,5,2,12:15:05 PM,5,2,12:24:33 PM,5,2,12:52:37 PM,5,2,12:54:04 PM,8,25.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_41,87
-Order_No_25618,User_Id_2330,Bike,3,Business,8,3,10:30:25 AM,8,3,10:31:28 AM,8,3,10:36:40 AM,8,3,10:39:08 AM,8,3,10:59:54 AM,17,,,-1.3015197,36.7658456,-1.2326041,36.8009029,Rider_Id_205,1246
-Order_No_26794,User_Id_2470,Bike,1,Personal,8,1,10:20:44 AM,8,1,10:24:37 AM,8,1,10:41:48 AM,8,1,10:46:42 AM,8,1,11:12:03 AM,8,24.7,,-1.317337,36.82698,-1.3323107,36.8692616,Rider_Id_648,1521
-Order_No_25023,User_Id_1245,Bike,3,Business,26,5,8:13:25 AM,26,5,8:13:46 AM,26,5,8:34:38 AM,26,5,8:59:24 AM,26,5,9:34:58 AM,22,,,-1.300921,36.828195,-1.3077309,36.685621399999995,Rider_Id_206,2134
-Order_No_17124,User_Id_85,Bike,2,Business,29,5,8:54:39 AM,29,5,8:54:47 AM,29,5,9:03:02 AM,29,5,9:08:38 AM,29,5,9:21:52 AM,5,,,-1.2903421,36.7885282,-1.3014461000000002,36.7661381,Rider_Id_334,794
-Order_No_12975,User_Id_1507,Bike,2,Personal,12,2,2:09:41 PM,12,2,2:22:21 PM,12,2,2:56:22 PM,12,2,3:12:27 PM,12,2,3:22:16 PM,5,23.0,,-1.281068,36.817578000000005,-1.2659148999999998,36.805432200000006,Rider_Id_616,589
-Order_No_12920,User_Id_2801,Bike,3,Business,12,3,9:28:12 AM,12,3,9:28:28 AM,12,3,9:37:37 AM,12,3,9:49:33 AM,12,3,10:01:18 AM,9,,,-1.2958024,36.7622504,-1.2674273999999999,36.787082700000006,Rider_Id_161,705
-Order_No_704,User_Id_1500,Bike,3,Business,12,5,12:40:44 PM,12,5,12:41:09 PM,12,5,12:41:13 PM,12,5,1:09:58 PM,12,5,1:11:09 PM,8,26.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_137,71
-Order_No_16521,User_Id_2749,Bike,1,Personal,17,3,12:35:16 PM,17,3,12:36:07 PM,17,3,12:45:33 PM,17,3,12:47:32 PM,17,3,12:59:38 PM,6,25.4,,-1.288897,36.771189,-1.2700476,36.8049204,Rider_Id_299,726
-Order_No_8833,User_Id_567,Bike,3,Business,21,5,11:01:09 AM,21,5,11:01:21 AM,21,5,11:10:16 AM,21,5,11:14:12 AM,21,5,11:22:08 AM,2,20.2,,-1.2982678,36.757883899999996,-1.2963097,36.7688221,Rider_Id_79,476
-Order_No_10059,User_Id_3686,Bike,3,Business,8,4,3:38:39 PM,8,4,3:38:48 PM,8,4,3:49:46 PM,8,4,3:55:18 PM,8,4,5:03:46 PM,18,27.2,,-1.2551895,36.7822034,-1.2902161,36.893618100000005,Rider_Id_911,4108
-Order_No_25079,User_Id_517,Bike,3,Business,4,2,1:45:39 PM,4,2,1:45:47 PM,4,2,1:46:16 PM,4,2,1:51:56 PM,4,2,2:28:16 PM,14,23.7,11.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2180
-Order_No_9027,User_Id_3465,Bike,3,Business,15,3,9:36:13 AM,15,3,9:36:24 AM,15,3,9:36:38 AM,15,3,9:46:15 AM,15,3,10:00:08 AM,4,,,-1.2551895,36.7822034,-1.2728058,36.8049903,Rider_Id_291,833
-Order_No_24674,User_Id_3655,Bike,1,Personal,7,4,3:42:46 PM,7,4,3:51:08 PM,7,4,4:09:49 PM,7,4,4:12:55 PM,7,4,4:45:52 PM,8,19.9,,-1.2941791999999999,36.763363700000006,-1.2943371000000001,36.8267,Rider_Id_212,1977
-Order_No_15289,User_Id_733,Bike,3,Business,30,2,11:58:21 AM,30,2,11:58:32 AM,30,2,12:02:37 PM,30,2,12:04:21 PM,30,2,12:40:06 PM,15,22.2,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_878,2145
-Order_No_23834,User_Id_3621,Bike,3,Business,19,2,8:35:26 AM,19,2,8:35:42 AM,19,2,8:38:54 AM,19,2,8:44:23 AM,19,2,8:49:51 AM,4,21.3,,-1.2836583000000001,36.7886014,-1.2913246,36.8019522,Rider_Id_247,328
-Order_No_9799,User_Id_3201,Bike,3,Business,30,4,4:55:04 PM,30,4,4:57:12 PM,30,4,5:05:57 PM,30,4,5:16:01 PM,30,4,5:53:08 PM,14,,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_7,2227
-Order_No_13684,User_Id_3711,Bike,3,Business,14,5,3:24:07 PM,14,5,3:39:48 PM,14,5,3:44:07 PM,14,5,4:03:27 PM,14,5,4:28:46 PM,9,23.9,,-1.2676026000000002,36.810362899999994,-1.3014461000000002,36.7661381,Rider_Id_454,1519
-Order_No_22475,User_Id_1245,Bike,3,Business,17,6,11:50:16 AM,17,6,11:50:56 AM,17,6,12:14:55 PM,17,6,12:32:21 PM,17,6,1:37:19 PM,17,23.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_244,3898
-Order_No_12153,User_Id_496,Bike,3,Business,25,4,3:41:59 PM,25,4,3:42:12 PM,25,4,3:50:04 PM,25,4,3:57:03 PM,25,4,4:51:12 PM,23,,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_72,3249
-Order_No_2801,User_Id_1245,Bike,3,Business,25,4,12:00:26 PM,25,4,12:00:40 PM,25,4,12:03:53 PM,25,4,12:24:21 PM,25,4,1:05:30 PM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_570,2469
-Order_No_120,User_Id_2271,Bike,3,Business,14,5,4:59:41 PM,14,5,5:20:37 PM,14,5,5:32:50 PM,14,5,5:47:33 PM,14,5,6:21:17 PM,14,22.9,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_932,2024
-Order_No_22164,User_Id_136,Bike,3,Business,7,3,2:51:10 PM,7,3,2:52:46 PM,7,3,2:52:48 PM,7,3,3:11:31 PM,7,3,3:41:49 PM,16,28.3,,-1.3254522,36.721658399999995,-1.2531983,36.8069669,Rider_Id_813,1818
-Order_No_4384,User_Id_2763,Bike,3,Business,16,5,2:53:07 PM,16,5,5:24:41 PM,16,5,5:26:14 PM,16,5,5:30:39 PM,16,5,5:43:20 PM,8,26.4,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_774,761
-Order_No_18277,User_Id_3641,Bike,3,Business,13,4,10:43:35 AM,13,4,12:57:27 PM,13,4,1:05:28 PM,13,4,1:42:58 PM,13,4,1:58:55 PM,7,22.5,,-1.2551895,36.7822034,-1.2905079,36.8222229,Rider_Id_320,957
-Order_No_6987,User_Id_633,Bike,3,Business,15,5,11:38:22 AM,15,5,11:41:23 AM,15,5,11:45:57 AM,15,5,11:54:25 AM,15,5,12:14:43 PM,9,26.1,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_764,1218
-Order_No_18171,User_Id_1467,Bike,3,Personal,4,4,10:50:53 AM,4,4,10:51:27 AM,4,4,11:04:46 AM,4,4,11:14:06 AM,4,4,11:45:20 AM,10,,,-1.2984591,36.7855438,-1.274381,36.7272265,Rider_Id_622,1874
-Order_No_20049,User_Id_3718,Bike,3,Business,5,5,2:39:54 PM,5,5,2:45:30 PM,5,5,2:56:29 PM,5,5,3:15:07 PM,5,5,3:26:57 PM,12,26.7,,-1.2650027,36.8126242,-1.2103372,36.835961,Rider_Id_86,710
-Order_No_19420,User_Id_393,Bike,3,Business,17,3,10:11:14 AM,17,3,10:11:34 AM,17,3,10:11:43 AM,17,3,10:36:17 AM,17,3,10:36:19 AM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_39,2
-Order_No_19054,User_Id_393,Bike,3,Business,20,3,8:52:40 AM,20,3,8:52:50 AM,20,3,8:52:55 AM,20,3,9:44:23 AM,20,3,9:44:25 AM,14,20.9,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_239,2
-Order_No_5316,User_Id_1329,Bike,3,Business,13,1,3:20:18 PM,13,1,3:23:28 PM,13,1,3:37:25 PM,13,1,3:40:25 PM,13,1,3:56:42 PM,7,,,-1.273056,36.811298,-1.2670841,36.768055100000005,Rider_Id_576,977
-Order_No_25485,User_Id_2899,Bike,3,Personal,6,6,7:27:23 PM,6,6,7:27:42 PM,6,6,7:36:25 PM,6,6,7:39:11 PM,6,6,7:49:37 PM,5,23.6,,-1.2978607,36.7783638,-1.272704,36.793127899999995,Rider_Id_855,626
-Order_No_27984,User_Id_2982,Bike,3,Business,8,5,4:42:20 PM,8,5,4:45:02 PM,8,5,4:45:24 PM,8,5,5:15:14 PM,8,5,5:24:02 PM,12,27.9,,-1.300921,36.828195,-1.2658003,36.8983864,Rider_Id_70,528
-Order_No_7829,User_Id_733,Bike,3,Business,18,1,12:45:35 PM,18,1,12:46:36 PM,18,1,12:55:27 PM,18,1,1:06:42 PM,18,1,1:24:48 PM,3,26.9,,-1.2551895,36.7822034,-1.2692689,36.8055169,Rider_Id_279,1086
-Order_No_24859,User_Id_3300,Bike,3,Business,16,4,3:14:16 PM,16,4,3:16:15 PM,16,4,3:16:31 PM,16,4,4:42:20 PM,16,4,4:42:31 PM,8,,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_864,11
-Order_No_2631,User_Id_3355,Bike,3,Business,19,1,2:51:48 PM,19,1,2:58:29 PM,19,1,3:04:14 PM,19,1,3:28:04 PM,19,1,3:46:48 PM,8,25.4,,-1.3332749,36.870814700000004,-1.3052491000000002,36.8223899,Rider_Id_258,1124
-Order_No_22063,User_Id_254,Bike,2,Personal,13,6,10:27:04 AM,13,6,10:27:21 AM,13,6,10:36:56 AM,13,6,10:52:11 AM,13,6,11:14:51 AM,12,25.2,,-1.3080353999999998,36.853147299999996,-1.2524976,36.8062935,Rider_Id_662,1360
-Order_No_26882,User_Id_928,Bike,3,Business,15,2,9:50:56 AM,15,2,9:52:01 AM,15,2,10:00:11 AM,15,2,10:29:06 AM,15,2,11:25:06 AM,9,20.4,,-1.2985749,36.8088,-1.2571472,36.795063299999995,Rider_Id_829,3360
-Order_No_17035,User_Id_3560,Bike,3,Business,11,5,11:01:50 AM,11,5,11:03:50 AM,11,5,11:13:04 AM,11,5,11:34:12 AM,11,5,11:38:45 AM,2,21.2,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_124,273
-Order_No_14215,User_Id_2987,Bike,3,Business,31,3,2:57:49 PM,31,3,2:59:48 PM,31,3,3:22:14 PM,31,3,3:25:06 PM,31,3,3:41:21 PM,12,27.1,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_131,975
-Order_No_13834,User_Id_1244,Bike,3,Business,21,1,11:28:30 AM,21,1,11:28:42 AM,21,1,11:28:48 AM,21,1,11:47:12 AM,21,1,12:00:52 PM,8,25.6,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_842,820
-Order_No_25804,User_Id_928,Bike,3,Business,27,3,1:02:43 PM,27,3,1:05:25 PM,27,3,1:17:56 PM,27,3,1:38:09 PM,27,3,2:12:21 PM,8,,,-1.2551895,36.7822034,-1.2995132,36.8043032,Rider_Id_327,2052
-Order_No_4224,User_Id_1482,Bike,3,Business,27,3,12:54:11 PM,27,3,12:54:34 PM,27,3,1:00:55 PM,27,3,1:04:03 PM,27,3,1:36:22 PM,16,26.1,,-1.2649605,36.798177700000004,-1.177145,36.8295169,Rider_Id_576,1939
-Order_No_11159,User_Id_1304,Bike,3,Business,11,2,12:35:52 PM,11,2,12:36:02 PM,11,2,12:36:56 PM,11,2,12:46:22 PM,11,2,1:27:22 PM,7,24.4,,-1.2859912,36.8756811,-1.2416156,36.8736721,Rider_Id_771,2460
-Order_No_9581,User_Id_2701,Bike,3,Business,19,1,10:26:47 AM,19,1,10:28:54 AM,19,1,10:37:25 AM,19,1,10:39:33 AM,19,1,10:59:21 AM,7,24.9,,-1.2765736,36.851364600000004,-1.2597528999999998,36.806629,Rider_Id_817,1188
-Order_No_20926,User_Id_633,Bike,3,Business,27,3,10:29:46 AM,27,3,10:31:16 AM,27,3,10:31:21 AM,27,3,10:42:57 AM,27,3,10:43:02 AM,6,26.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_515,5
-Order_No_15544,User_Id_1245,Bike,3,Business,8,2,12:41:54 PM,8,2,12:42:02 PM,8,2,12:51:40 PM,8,2,1:22:30 PM,8,2,2:01:37 PM,17,23.5,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_580,2347
-Order_No_713,User_Id_635,Bike,3,Business,10,1,11:42:20 AM,10,1,12:47:32 PM,10,1,12:52:30 PM,10,1,1:34:48 PM,10,1,1:52:45 PM,6,18.6,,-1.300921,36.828195,-1.3229345,36.8566974,Rider_Id_661,1077
-Order_No_18449,User_Id_788,Bike,3,Business,16,6,2:33:12 PM,16,6,2:33:35 PM,16,6,2:36:51 PM,16,6,2:42:50 PM,16,6,3:17:10 PM,7,,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_448,2060
-Order_No_2911,User_Id_3584,Bike,2,Personal,14,5,11:59:20 AM,14,5,11:59:56 AM,14,5,12:13:33 PM,14,5,12:22:26 PM,14,5,12:46:14 PM,7,24.5,,-1.3172395000000001,36.839269,-1.299542,36.7911201,Rider_Id_810,1428
-Order_No_15907,User_Id_1297,Bike,2,Personal,20,6,9:54:48 AM,20,6,9:55:04 AM,20,6,10:01:34 AM,20,6,10:07:51 AM,20,6,10:29:39 AM,13,19.8,,-1.3446058,36.760517799999995,-1.2872974,36.810295399999994,Rider_Id_22,1308
-Order_No_2116,User_Id_3222,Bike,3,Business,8,5,9:52:52 AM,8,5,9:53:31 AM,8,5,9:55:39 AM,8,5,10:00:56 AM,8,5,10:35:26 AM,12,22.9,,-1.2551895,36.7822034,-1.3184778999999998,36.8373109,Rider_Id_523,2070
-Order_No_3552,User_Id_3283,Bike,3,Business,8,1,9:42:35 AM,8,1,9:43:09 AM,8,1,9:49:59 AM,8,1,10:01:25 AM,8,1,10:09:10 AM,6,22.1,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_812,465
-Order_No_13856,User_Id_1927,Bike,3,Business,6,1,3:44:46 PM,6,1,3:45:06 PM,6,1,3:48:24 PM,6,1,4:06:27 PM,6,1,4:32:56 PM,19,,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_484,1589
-Order_No_3144,User_Id_2008,Bike,3,Business,1,1,7:35:35 AM,1,1,7:49:20 AM,1,1,8:08:24 AM,1,1,8:20:49 AM,1,1,8:40:39 AM,8,18.2,,-1.2508229,36.7895263,-1.29075,36.814503,Rider_Id_722,1190
-Order_No_13050,User_Id_2436,Bike,3,Business,18,4,2:12:24 PM,18,4,2:12:49 PM,18,4,2:15:21 PM,18,4,2:19:54 PM,18,4,2:30:49 PM,4,26.8,,-1.2551895,36.7822034,-1.2728947,36.807942600000004,Rider_Id_848,655
-Order_No_6396,User_Id_2102,Bike,3,Business,9,2,4:39:30 PM,9,2,4:40:29 PM,9,2,5:02:54 PM,9,2,5:14:21 PM,9,2,5:43:10 PM,14,27.7,,-1.3250048,36.869718799999994,-1.2613768,36.801941799999994,Rider_Id_613,1729
-Order_No_11719,User_Id_393,Bike,3,Business,26,5,12:13:27 PM,26,5,12:13:58 PM,26,5,12:14:05 PM,26,5,12:45:29 PM,26,5,1:23:39 PM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_515,2290
-Order_No_24156,User_Id_2258,Bike,3,Business,23,5,9:14:04 AM,23,5,10:54:08 AM,23,5,11:00:32 AM,23,5,11:02:56 AM,23,5,11:57:07 AM,7,24.1,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_477,3251
-Order_No_6432,User_Id_3686,Bike,3,Business,3,3,10:07:07 AM,3,3,10:07:15 AM,3,3,10:08:41 AM,3,3,10:14:27 AM,3,3,10:35:19 AM,10,21.3,,-1.2551895,36.7822034,-1.303148,36.792768,Rider_Id_709,1252
-Order_No_5527,User_Id_3404,Bike,3,Business,5,3,2:22:06 PM,5,3,2:28:30 PM,5,3,2:51:19 PM,5,3,2:56:50 PM,5,3,3:46:51 PM,9,25.0,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_436,3001
-Order_No_16330,User_Id_788,Bike,3,Business,7,3,1:19:15 PM,7,3,1:19:40 PM,7,3,1:46:20 PM,7,3,2:17:32 PM,7,3,2:29:43 PM,7,28.1,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_257,731
-Order_No_6004,User_Id_3087,Bike,2,Personal,19,3,2:54:28 PM,19,3,3:09:57 PM,19,3,3:38:11 PM,19,3,3:44:31 PM,19,3,4:18:00 PM,17,25.4,,-1.1855066,36.825464600000004,-1.2619538000000001,36.80876370000001,Rider_Id_882,2009
-Order_No_25199,User_Id_2184,Bike,1,Personal,30,1,2:44:03 PM,30,1,3:04:45 PM,30,1,3:29:56 PM,30,1,3:32:45 PM,30,1,3:55:33 PM,6,20.7,,-1.2764786,36.81947879999999,-1.2444408000000002,36.7934817,Rider_Id_882,1368
-Order_No_11242,User_Id_1389,Bike,3,Business,18,5,2:08:21 PM,18,5,2:09:47 PM,18,5,2:26:42 PM,18,5,2:44:38 PM,18,5,3:09:16 PM,15,26.8,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_902,1478
-Order_No_4155,User_Id_1280,Bike,3,Business,15,4,9:07:29 AM,15,4,9:07:39 AM,15,4,9:18:21 AM,15,4,9:26:03 AM,15,4,9:39:21 AM,4,19.6,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_290,798
-Order_No_26239,User_Id_3283,Bike,3,Business,4,4,10:59:51 AM,4,4,11:03:26 AM,4,4,11:19:39 AM,4,4,11:28:09 AM,4,4,11:37:11 AM,5,25.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_770,542
-Order_No_17597,User_Id_3689,Bike,3,Business,6,1,11:25:58 AM,6,1,11:26:46 AM,6,1,11:35:20 AM,6,1,11:40:40 AM,6,1,11:58:45 AM,8,20.3,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_174,1085
-Order_No_444,User_Id_893,Bike,3,Business,26,2,9:39:54 AM,26,2,9:40:31 AM,26,2,9:45:50 AM,26,2,9:51:51 AM,26,2,9:58:34 AM,2,21.8,,-1.2664491000000002,36.8102685,-1.2561022,36.8220661,Rider_Id_383,403
-Order_No_23772,User_Id_20,Bike,3,Business,26,2,11:27:35 AM,26,2,11:30:55 AM,26,2,11:48:54 AM,26,2,12:00:58 PM,26,2,12:52:07 PM,18,27.4,,-1.3160119,36.896730100000006,-1.2629666000000002,36.8068369,Rider_Id_436,3069
-Order_No_3222,User_Id_496,Bike,3,Business,30,4,2:46:18 PM,30,4,2:46:38 PM,30,4,2:48:19 PM,30,4,3:10:08 PM,30,4,3:10:12 PM,2,20.8,,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_881,4
-Order_No_26342,User_Id_3295,Bike,3,Business,5,4,4:30:45 PM,5,4,4:34:05 PM,5,4,4:34:24 PM,5,4,5:00:46 PM,5,4,5:01:04 PM,2,20.6,,-1.2579303999999998,36.8019667,-1.255392,36.792539,Rider_Id_51,18
-Order_No_11802,User_Id_635,Bike,3,Business,25,6,9:25:47 AM,25,6,9:26:33 AM,25,6,9:55:23 AM,25,6,10:15:46 AM,25,6,10:37:26 AM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_448,1300
-Order_No_10086,User_Id_186,Bike,3,Business,7,1,2:59:47 PM,7,1,3:01:12 PM,7,1,3:12:04 PM,7,1,3:17:59 PM,7,1,3:31:03 PM,7,26.7,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_404,784
-Order_No_20296,User_Id_2174,Bike,3,Business,30,3,10:27:54 AM,30,3,10:27:58 AM,30,3,10:32:57 AM,30,3,11:52:06 AM,30,3,12:38:00 PM,33,25.2,,-1.2482781,36.884091999999995,-1.324878,36.7070285,Rider_Id_82,2754
-Order_No_11545,User_Id_2229,Bike,3,Business,14,5,1:26:01 PM,14,5,1:26:29 PM,14,5,1:29:54 PM,14,5,1:37:09 PM,14,5,2:08:28 PM,20,22.8,,-1.3247683000000001,36.85803,-1.2355327,36.8822975,Rider_Id_364,1879
-Order_No_9066,User_Id_1173,Bike,1,Personal,3,3,3:25:30 PM,3,3,3:30:14 PM,3,3,3:37:26 PM,3,3,3:38:49 PM,3,3,3:59:54 PM,8,26.8,,-1.2931082,36.7822758,-1.261395,36.8263891,Rider_Id_754,1265
-Order_No_16224,User_Id_2789,Bike,3,Business,9,2,10:09:01 AM,9,2,10:10:29 AM,9,2,10:25:19 AM,9,2,10:30:49 AM,9,2,10:50:51 AM,8,20.9,,-1.304735,36.784605400000004,-1.2592428,36.7894541,Rider_Id_953,1202
-Order_No_20338,User_Id_391,Bike,3,Business,7,5,4:38:51 PM,7,5,4:43:48 PM,7,5,4:53:58 PM,7,5,5:19:18 PM,7,5,5:47:42 PM,4,21.4,,-1.2867454,36.8259017,-1.300921,36.828195,Rider_Id_916,1704
-Order_No_10123,User_Id_3647,Bike,3,Business,9,4,9:12:14 AM,9,4,9:12:46 AM,9,4,9:28:31 AM,9,4,9:33:30 AM,9,4,9:48:37 AM,6,,,-1.2726389999999999,36.794723,-1.290789,36.8024859,Rider_Id_438,907
-Order_No_6803,User_Id_824,Bike,3,Business,28,4,3:33:11 PM,28,4,3:33:25 PM,28,4,3:36:49 PM,28,4,4:07:40 PM,28,4,4:35:57 PM,18,29.9,,-1.2914308,36.801886700000004,-1.207546,36.7913976,Rider_Id_801,1697
-Order_No_8347,User_Id_2677,Bike,3,Business,21,1,2:11:15 PM,21,1,2:11:30 PM,21,1,2:14:58 PM,21,1,2:46:26 PM,21,1,2:48:11 PM,14,27.7,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_436,105
-Order_No_23097,User_Id_111,Bike,3,Business,16,4,10:04:39 AM,16,4,10:05:17 AM,16,4,10:16:27 AM,16,4,10:24:59 AM,16,4,11:17:54 AM,13,,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_831,3175
-Order_No_19887,User_Id_1148,Bike,3,Business,2,2,2:36:00 PM,2,2,3:00:15 PM,2,2,3:04:31 PM,2,2,3:25:59 PM,2,2,3:44:29 PM,8,26.4,,-1.3100586,36.850032899999995,-1.298598,36.7953396,Rider_Id_848,1110
-Order_No_16207,User_Id_3621,Bike,3,Business,13,3,3:05:32 PM,13,3,3:08:21 PM,13,3,3:32:39 PM,13,3,3:36:21 PM,13,3,4:04:29 PM,6,27.4,,-1.2836583000000001,36.7886014,-1.2915265,36.829847799999996,Rider_Id_513,1688
-Order_No_1461,User_Id_704,Bike,1,Business,29,7,1:13:50 PM,29,7,1:14:01 PM,29,7,1:25:48 PM,29,7,1:48:58 PM,29,7,2:39:35 PM,17,23.5,,-1.2649605,36.798177700000004,-1.2860183,36.8975338,Rider_Id_648,3037
-Order_No_15135,User_Id_1250,Bike,1,Personal,28,2,9:26:24 AM,28,2,9:26:59 AM,28,2,9:32:28 AM,28,2,9:45:29 AM,28,2,10:00:21 AM,7,16.0,51.0,-1.2675333999999998,36.7658246,-1.303382,36.790658,Rider_Id_105,892
-Order_No_3274,User_Id_2982,Bike,3,Business,14,1,11:15:17 AM,14,1,11:31:47 AM,14,1,12:10:48 PM,14,1,12:33:34 PM,14,1,1:42:31 PM,24,23.7,,-1.177202,36.8351183,-1.325469,36.862403,Rider_Id_75,4137
-Order_No_9195,User_Id_3557,Bike,3,Business,7,5,12:14:42 PM,7,5,12:24:21 PM,7,5,12:33:48 PM,7,5,12:58:04 PM,7,5,12:58:43 PM,3,24.9,,-1.269534,36.80697,-1.2546732,36.808679600000005,Rider_Id_503,39
-Order_No_4265,User_Id_3239,Bike,3,Personal,30,3,10:21:32 AM,30,3,10:31:11 AM,30,3,10:39:54 AM,30,3,10:48:23 AM,30,3,11:27:36 AM,9,22.3,,-1.2839322,36.8292426,-1.2950763,36.7645525,Rider_Id_742,2353
-Order_No_8703,User_Id_3283,Bike,3,Business,20,4,2:29:08 PM,20,4,2:29:17 PM,20,4,2:29:35 PM,20,4,2:44:32 PM,20,4,2:51:57 PM,4,28.1,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_874,445
-Order_No_23878,User_Id_1205,Bike,3,Business,13,3,8:18:05 AM,13,3,8:18:41 AM,13,3,8:37:47 AM,13,3,8:39:46 AM,13,3,9:00:16 AM,9,16.2,,-1.2707742,36.7578609,-1.2475136,36.7703179,Rider_Id_752,1230
-Order_No_7555,User_Id_909,Bike,3,Business,29,1,4:06:13 PM,29,1,4:08:09 PM,29,1,4:20:02 PM,29,1,4:24:29 PM,29,1,5:02:06 PM,7,,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_733,2257
-Order_No_16653,User_Id_2956,Bike,3,Business,4,3,10:33:53 AM,4,3,10:34:18 AM,4,3,10:42:11 AM,4,3,11:00:39 AM,4,3,11:39:50 AM,17,17.1,,-1.3165542,36.700067,-1.2584143,36.8048002,Rider_Id_636,2351
-Order_No_8877,User_Id_1340,Bike,3,Business,31,4,11:50:33 AM,31,4,11:50:46 AM,31,4,11:52:56 AM,31,4,12:06:55 PM,31,4,12:06:58 PM,2,26.4,,-1.2667532,36.8024002,-1.2707178999999997,36.7979713,Rider_Id_19,3
-Order_No_3397,User_Id_2523,Bike,3,Business,13,5,2:55:46 PM,13,5,2:56:08 PM,13,5,3:10:42 PM,13,5,3:14:23 PM,13,5,4:11:16 PM,19,19.0,,-1.2551895,36.7822034,-1.3327653999999998,36.8853117,Rider_Id_465,3413
-Order_No_19776,User_Id_3686,Bike,3,Business,14,1,2:33:46 PM,14,1,2:34:16 PM,14,1,2:43:18 PM,14,1,2:45:33 PM,14,1,3:10:56 PM,7,25.4,,-1.2559559999999999,36.789867,-1.2993181,36.8098551,Rider_Id_782,1523
-Order_No_20700,User_Id_1209,Bike,3,Business,10,4,4:22:18 PM,10,4,4:43:38 PM,10,4,4:49:25 PM,10,4,4:58:10 PM,10,4,5:11:18 PM,5,22.3,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_623,788
-Order_No_7005,User_Id_2358,Bike,4,Business,11,3,8:39:18 AM,11,3,8:40:01 AM,11,3,9:13:02 AM,11,3,9:27:36 AM,11,3,10:21:28 AM,34,15.4,1.1,-1.3256903999999998,36.8747943,-1.3256159,36.8617394,Rider_Id_121,3232
-Order_No_5756,User_Id_830,Bike,3,Business,14,4,9:53:09 AM,14,4,9:53:34 AM,14,4,10:01:35 AM,14,4,10:08:11 AM,14,4,10:28:24 AM,7,18.8,,-1.2904016,36.82263029999999,-1.2551895,36.7822034,Rider_Id_571,1213
-Order_No_13219,User_Id_2423,Bike,3,Business,7,4,10:10:18 AM,7,4,10:13:37 AM,7,4,10:40:46 AM,7,4,10:57:28 AM,7,4,11:49:37 AM,6,27.8,,-1.2571472,36.795063299999995,-1.2900209999999999,36.828206,Rider_Id_520,3129
-Order_No_17495,User_Id_2982,Bike,3,Business,8,5,10:17:51 AM,8,5,10:18:18 AM,8,5,10:25:16 AM,8,5,10:43:06 AM,8,5,11:30:13 AM,18,28.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_519,2827
-Order_No_9412,User_Id_393,Bike,3,Business,13,1,4:06:22 PM,13,1,4:06:43 PM,13,1,4:15:33 PM,13,1,4:29:54 PM,13,1,4:42:29 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,755
-Order_No_26403,User_Id_2723,Bike,1,Personal,3,3,1:21:09 PM,3,3,1:25:37 PM,3,3,1:25:48 PM,3,3,2:16:54 PM,3,3,2:33:03 PM,4,26.7,,-1.2784495,36.7981729,-1.2543898999999998,36.8091271,Rider_Id_51,969
-Order_No_15774,User_Id_2956,Bike,3,Business,11,2,1:56:13 PM,11,2,1:56:21 PM,11,2,2:25:37 PM,11,2,2:30:43 PM,11,2,2:46:02 PM,2,22.4,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_547,919
-Order_No_24869,User_Id_635,Bike,3,Business,27,1,3:50:52 PM,27,1,3:51:52 PM,27,1,3:55:16 PM,27,1,4:33:26 PM,27,1,4:33:31 PM,8,,,-1.2602337,36.79905479999999,-1.2544269,36.750729299999996,Rider_Id_491,5
-Order_No_12981,User_Id_419,Bike,3,Business,9,4,5:35:42 PM,9,4,5:36:54 PM,9,4,5:45:10 PM,9,4,5:49:47 PM,9,4,5:55:44 PM,3,,,-1.2571472,36.795063299999995,-1.2551895,36.7822034,Rider_Id_205,357
-Order_No_17377,User_Id_3725,Bike,2,Personal,7,5,5:26:20 PM,7,5,5:30:18 PM,7,5,5:40:51 PM,7,5,5:46:12 PM,7,5,6:02:45 PM,7,22.2,,-1.2619527,36.806748999999996,-1.2637102,36.7610487,Rider_Id_449,993
-Order_No_2360,User_Id_3264,Bike,3,Business,31,3,4:36:43 PM,31,3,4:38:37 PM,31,3,4:53:18 PM,31,3,5:00:28 PM,31,3,5:24:34 PM,8,24.5,,-1.2551895,36.7822034,-1.2962618000000001,36.792933399999995,Rider_Id_208,1446
-Order_No_8721,User_Id_435,Bike,3,Business,7,5,11:19:46 AM,7,5,11:25:23 AM,7,5,11:44:33 AM,7,5,11:55:00 AM,7,5,12:19:27 PM,6,,,-1.2997385000000001,36.801952899999996,-1.300921,36.828195,Rider_Id_133,1467
-Order_No_5093,User_Id_2956,Bike,3,Business,5,5,12:11:32 PM,5,5,12:16:25 PM,5,5,12:42:13 PM,5,5,1:02:14 PM,5,5,1:30:30 PM,6,24.7,,-1.28878,36.8168312,-1.2584143,36.8048002,Rider_Id_695,1696
-Order_No_15291,User_Id_1482,Bike,3,Business,1,1,11:07:01 AM,1,1,11:07:22 AM,1,1,11:14:03 AM,1,1,11:17:37 AM,1,1,12:00:47 PM,18,,,-1.2649605,36.798177700000004,-1.3196979,36.7009845,Rider_Id_863,2590
-Order_No_22866,User_Id_2627,Bike,3,Business,18,1,1:55:24 PM,18,1,1:56:21 PM,18,1,2:07:16 PM,18,1,2:10:35 PM,18,1,2:21:38 PM,2,29.5,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_318,663
-Order_No_3990,User_Id_1874,Bike,3,Business,28,3,1:04:04 PM,28,3,1:04:37 PM,28,3,1:16:27 PM,28,3,1:18:25 PM,28,3,1:41:34 PM,9,,,-1.2993093999999998,36.777206899999996,-1.2571472,36.795063299999995,Rider_Id_353,1389
-Order_No_18510,User_Id_2143,Bike,3,Business,20,2,1:50:52 PM,20,2,1:58:53 PM,20,2,2:13:34 PM,20,2,2:17:13 PM,20,2,2:27:42 PM,3,23.8,,-1.2985252,36.78427070000001,-1.3039706,36.7688174,Rider_Id_952,629
-Order_No_26716,User_Id_2469,Bike,3,Business,21,2,11:18:26 AM,21,2,11:19:43 AM,21,2,11:57:51 AM,21,2,12:00:10 PM,21,2,12:38:19 PM,15,22.5,,-1.303924,36.8758792,-1.369541,36.907455,Rider_Id_697,2289
-Order_No_25912,User_Id_2982,Bike,3,Business,10,5,1:31:49 PM,10,5,1:34:36 PM,10,5,1:35:15 PM,10,5,2:21:06 PM,10,5,3:05:51 PM,14,,,-1.300921,36.828195,-1.221085,36.8587279,Rider_Id_661,2685
-Order_No_13423,User_Id_1724,Bike,3,Business,24,5,9:09:51 AM,24,5,9:10:09 AM,24,5,9:24:26 AM,24,5,9:34:20 AM,24,5,9:43:21 AM,7,15.6,,-1.293393,36.790237,-1.2551895,36.7822034,Rider_Id_56,541
-Order_No_18424,User_Id_3686,Bike,3,Business,5,2,3:02:04 PM,5,2,3:02:28 PM,5,2,3:05:50 PM,5,2,3:26:59 PM,5,2,3:44:43 PM,8,,,-1.2551895,36.7822034,-1.2947278999999998,36.780382,Rider_Id_885,1064
-Order_No_21027,User_Id_2834,Bike,3,Business,27,1,2:41:27 PM,27,1,2:41:55 PM,27,1,3:14:13 PM,27,1,3:30:59 PM,27,1,3:31:32 PM,2,22.9,,-1.290894,36.822971,-1.2819053999999999,36.8237089,Rider_Id_855,33
-Order_No_9902,User_Id_3688,Bike,3,Business,29,5,11:12:46 AM,29,5,11:14:05 AM,29,5,11:19:45 AM,29,5,11:22:18 AM,29,5,12:05:28 PM,21,26.9,,-1.2551895,36.7822034,-1.2123576,36.642656200000005,Rider_Id_763,2590
-Order_No_8853,User_Id_3647,Bike,3,Business,6,4,11:46:22 AM,6,4,11:48:21 AM,6,4,11:58:06 AM,6,4,12:00:01 PM,6,4,12:16:13 PM,6,23.9,,-1.2726389999999999,36.794723,-1.3023756000000002,36.7949276,Rider_Id_375,972
-Order_No_14816,User_Id_3222,Bike,3,Business,8,1,12:08:55 PM,8,1,12:09:24 PM,8,1,12:09:48 PM,8,1,12:24:58 PM,8,1,12:27:41 PM,4,24.4,,-1.2551895,36.7822034,-1.2697141,36.798177700000004,Rider_Id_179,163
-Order_No_27389,User_Id_2330,Bike,3,Business,17,5,1:25:10 PM,17,5,1:26:30 PM,17,5,1:43:49 PM,17,5,1:50:32 PM,17,5,2:13:23 PM,14,,,-1.2925641,36.897644899999996,-1.3798085,36.9290528,Rider_Id_856,1371
-Order_No_8444,User_Id_3647,Bike,3,Business,2,5,3:17:41 PM,2,5,3:20:53 PM,2,5,3:28:01 PM,2,5,3:31:43 PM,2,5,3:45:31 PM,4,25.3,,-1.2726389999999999,36.794723,-1.272484,36.815382,Rider_Id_726,828
-Order_No_2321,User_Id_2523,Bike,3,Business,10,1,11:17:16 AM,10,1,11:17:50 AM,10,1,11:17:59 AM,10,1,11:26:13 AM,10,1,11:40:24 AM,1,18.3,,-1.2551895,36.7822034,-1.2549259,36.7919494,Rider_Id_168,851
-Order_No_5936,User_Id_1148,Bike,3,Business,28,2,10:03:03 AM,28,2,10:19:56 AM,28,2,10:26:49 AM,28,2,10:43:56 AM,28,2,10:52:52 AM,2,17.8,,-1.3100586,36.850032899999995,-1.3088186000000002,36.8352514,Rider_Id_65,536
-Order_No_12444,User_Id_3686,Bike,3,Business,13,3,11:19:29 AM,13,3,11:19:46 AM,13,3,11:26:00 AM,13,3,11:28:01 AM,13,3,1:04:57 PM,5,25.0,,-1.2551895,36.7822034,-1.245477,36.785037,Rider_Id_452,5816
-Order_No_7885,User_Id_633,Bike,3,Business,1,5,10:22:22 AM,1,5,10:23:22 AM,1,5,10:32:09 AM,1,5,10:45:49 AM,1,5,11:43:20 AM,12,28.4,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_156,3451
-Order_No_3672,User_Id_3718,Bike,3,Business,5,1,2:30:47 PM,5,1,2:31:53 PM,5,1,2:42:24 PM,5,1,2:43:31 PM,5,1,2:52:27 PM,2,26.1,,-1.2650027,36.8126242,-1.2670614,36.8077774,Rider_Id_38,536
-Order_No_16778,User_Id_2532,Bike,3,Business,9,4,8:55:19 AM,9,4,10:17:37 AM,9,4,10:23:09 AM,9,4,10:32:24 AM,9,4,10:56:26 AM,6,,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_953,1442
-Order_No_16661,User_Id_1340,Bike,3,Business,20,3,10:10:44 AM,20,3,10:14:32 AM,20,3,10:25:17 AM,20,3,10:37:54 AM,20,3,10:51:44 AM,5,27.4,,-1.2707178999999997,36.7979713,-1.288036,36.827211,Rider_Id_119,830
-Order_No_23338,User_Id_1960,Bike,3,Personal,12,2,10:04:11 AM,12,2,10:26:25 AM,12,2,10:41:32 AM,12,2,10:44:52 AM,12,2,11:03:24 AM,7,20.8,,-1.3023756000000002,36.7949276,-1.2574087999999999,36.799712,Rider_Id_366,1112
-Order_No_18917,User_Id_3773,Bike,3,Personal,28,5,2:53:54 PM,28,5,2:54:18 PM,28,5,2:58:53 PM,28,5,3:01:03 PM,28,5,3:16:47 PM,6,25.4,,-1.2591018999999999,36.800577000000004,-1.270701,36.837875700000005,Rider_Id_280,944
-Order_No_10510,User_Id_1555,Bike,3,Business,10,2,8:56:59 AM,10,2,8:59:43 AM,10,2,9:13:24 AM,10,2,10:35:46 AM,10,2,10:35:59 AM,10,20.5,,-1.3052491000000002,36.8223899,-1.3404187,36.7614685,Rider_Id_927,13
-Order_No_7342,User_Id_31,Bike,3,Business,19,4,12:24:38 PM,19,4,12:24:50 PM,19,4,12:25:22 PM,19,4,12:37:22 PM,19,4,1:04:46 PM,13,20.1,,-1.3121969,36.7238784,-1.2615888000000002,36.7928732,Rider_Id_326,1644
-Order_No_17992,User_Id_3426,Bike,3,Business,31,1,2:08:25 PM,31,1,2:08:43 PM,31,1,2:18:56 PM,31,1,2:24:29 PM,31,1,2:42:57 PM,9,24.1,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_854,1108
-Order_No_26800,User_Id_3356,Bike,3,Business,28,4,10:17:22 AM,28,4,10:42:17 AM,28,4,10:58:49 AM,28,4,11:01:22 AM,28,4,11:35:20 AM,14,23.9,,-1.2036664,36.819262200000004,-1.2867843,36.8134769,Rider_Id_781,2038
-Order_No_11992,User_Id_519,Bike,3,Personal,12,3,8:36:08 AM,12,3,8:37:58 AM,12,3,9:01:09 AM,12,3,9:05:57 AM,12,3,9:28:14 AM,6,16.5,,-1.2662524,36.824479499999995,-1.2745551000000002,36.7979797,Rider_Id_698,1337
-Order_No_28073,User_Id_1927,Bike,3,Business,8,1,10:46:15 AM,8,1,10:46:36 AM,8,1,11:06:42 AM,8,1,11:22:28 AM,8,1,11:52:48 AM,18,24.6,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_100,1820
-Order_No_13751,User_Id_2330,Bike,3,Business,3,5,1:18:45 PM,3,5,1:29:23 PM,3,5,1:37:52 PM,3,5,1:40:08 PM,3,5,1:55:04 PM,8,,,-1.3192788999999998,36.711299100000005,-1.3015197,36.7658456,Rider_Id_326,896
-Order_No_9744,User_Id_248,Bike,3,Business,27,5,9:25:02 AM,27,5,9:25:14 AM,27,5,9:25:27 AM,27,5,9:49:27 AM,27,5,10:06:37 AM,7,16.4,,-1.3173868999999998,36.8112044,-1.304735,36.784605400000004,Rider_Id_105,1030
-Order_No_27258,User_Id_186,Bike,3,Business,22,5,12:37:56 PM,22,5,12:39:33 PM,22,5,12:50:47 PM,22,5,1:04:17 PM,22,5,1:57:14 PM,4,28.4,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_521,3177
-Order_No_140,User_Id_2523,Bike,3,Business,2,2,9:05:17 AM,2,2,9:07:35 AM,2,2,9:17:51 AM,2,2,9:26:41 AM,2,2,9:41:31 AM,9,20.6,,-1.2551895,36.7822034,-1.2786703000000001,36.826044,Rider_Id_383,890
-Order_No_4455,User_Id_2873,Bike,1,Personal,26,6,8:18:41 PM,26,6,8:18:57 PM,26,6,8:23:11 PM,26,6,8:49:33 PM,26,6,9:08:57 PM,9,18.4,,-1.2808006,36.8327169,-1.2872411,36.774577799999996,Rider_Id_654,1164
-Order_No_5066,User_Id_2330,Bike,3,Business,22,5,12:30:56 PM,22,5,12:32:48 PM,22,5,12:40:38 PM,22,5,12:59:53 PM,22,5,12:59:56 PM,4,28.7,,-1.2873232,36.765857200000006,-1.2921463,36.8029626,Rider_Id_613,3
-Order_No_20982,User_Id_2869,Bike,3,Business,11,6,7:01:21 PM,11,6,7:02:01 PM,11,6,7:20:02 PM,11,6,7:22:43 PM,11,6,8:03:03 PM,10,,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_521,2420
-Order_No_10820,User_Id_1281,Bike,3,Business,25,2,10:49:53 AM,25,2,10:52:27 AM,25,2,11:04:30 AM,25,2,11:19:26 AM,25,2,12:20:53 PM,9,23.9,,-1.2869041,36.8240057,-1.2706815,36.7646286,Rider_Id_532,3687
-Order_No_7633,User_Id_1410,Bike,3,Business,11,2,1:52:51 PM,11,2,1:54:39 PM,11,2,2:03:32 PM,11,2,2:27:14 PM,11,2,2:55:15 PM,20,21.4,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_156,1681
-Order_No_12202,User_Id_393,Bike,3,Business,13,3,11:27:52 AM,13,3,11:28:02 AM,13,3,11:28:15 AM,13,3,12:16:34 PM,13,3,12:16:36 PM,4,24.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,2
-Order_No_26502,User_Id_1051,Bike,3,Business,28,1,12:21:09 PM,28,1,12:21:43 PM,28,1,12:21:47 PM,28,1,12:26:40 PM,28,1,12:57:22 PM,14,23.8,,-1.2963097,36.7688221,-1.2500308999999998,36.6690476,Rider_Id_24,1842
-Order_No_23284,User_Id_3355,Bike,3,Business,24,3,11:33:04 AM,24,3,11:34:56 AM,24,3,11:50:13 AM,24,3,11:52:58 AM,24,3,12:14:39 PM,10,20.1,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_352,1301
-Order_No_18723,User_Id_2828,Bike,1,Business,3,5,3:45:42 PM,3,5,3:46:47 PM,3,5,4:09:41 PM,3,5,4:17:16 PM,3,5,4:59:31 PM,16,25.3,,-1.2615888000000002,36.7928732,-1.2515508999999998,36.9076672,Rider_Id_520,2535
-Order_No_13289,User_Id_3647,Bike,3,Business,14,5,10:30:23 AM,14,5,10:33:30 AM,14,5,11:09:01 AM,14,5,11:17:21 AM,14,5,11:41:51 AM,5,21.8,,-1.2726389999999999,36.794723,-1.2797536,36.8223726,Rider_Id_739,1470
-Order_No_20813,User_Id_2436,Bike,3,Business,28,3,3:28:42 PM,28,3,3:44:47 PM,28,3,3:46:42 PM,28,3,3:48:30 PM,28,3,4:10:11 PM,7,,,-1.2551895,36.7822034,-1.2854453000000001,36.818836299999994,Rider_Id_878,1301
-Order_No_6330,User_Id_2066,Bike,3,Business,28,3,5:07:09 PM,28,3,6:14:54 PM,28,3,6:37:25 PM,28,3,6:40:56 PM,28,3,6:58:00 PM,9,23.3,,-1.3210188999999999,36.841055100000005,-1.267452,36.806015,Rider_Id_607,1024
-Order_No_12630,User_Id_3002,Bike,1,Personal,16,4,9:45:16 AM,16,4,9:46:12 AM,16,4,10:02:46 AM,16,4,10:04:08 AM,16,4,10:25:41 AM,9,15.7,,-1.2528485,36.7953722,-1.2248308,36.801257299999996,Rider_Id_726,1293
-Order_No_22322,User_Id_3496,Bike,3,Business,7,5,3:02:23 PM,7,5,3:29:30 PM,7,5,3:45:20 PM,7,5,3:53:35 PM,7,5,4:27:00 PM,16,26.8,,-1.300921,36.828195,-1.2947214,36.7179558,Rider_Id_445,2005
-Order_No_23374,User_Id_3264,Bike,3,Business,24,3,9:05:09 AM,24,3,9:07:20 AM,24,3,9:26:51 AM,24,3,9:29:04 AM,24,3,9:45:38 AM,7,,,-1.2551895,36.7822034,-1.2889913999999998,36.820944299999994,Rider_Id_953,994
-Order_No_7921,User_Id_2066,Bike,3,Business,8,6,10:36:44 AM,8,6,10:56:15 AM,8,6,11:29:29 AM,8,6,11:36:14 AM,8,6,12:04:52 PM,17,18.2,,-1.3210188999999999,36.841055100000005,-1.2826971999999999,36.970281299999996,Rider_Id_48,1718
-Order_No_9798,User_Id_49,Bike,3,Business,11,1,11:29:39 AM,11,1,11:34:11 AM,11,1,11:40:06 AM,11,1,11:48:23 AM,11,1,11:55:27 AM,3,23.9,,-1.278957,36.8290484,-1.2651643,36.807221399999996,Rider_Id_289,424
-Order_No_22700,User_Id_20,Bike,3,Business,27,3,1:05:38 PM,27,3,1:06:30 PM,27,3,1:13:49 PM,27,3,1:19:42 PM,27,3,1:39:47 PM,5,26.0,,-1.2629666000000002,36.8068369,-1.2764950000000002,36.8205966,Rider_Id_785,1205
-Order_No_14676,User_Id_1209,Bike,3,Business,17,3,1:07:14 PM,17,3,1:07:41 PM,17,3,1:26:42 PM,17,3,1:32:22 PM,17,3,1:51:53 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_177,1171
-Order_No_20316,User_Id_2976,Bike,3,Business,28,4,10:20:12 AM,28,4,10:20:57 AM,28,4,10:35:48 AM,28,4,10:39:07 AM,28,4,11:01:26 AM,6,28.1,,-1.2992482,36.786561799999994,-1.2696819,36.8028694,Rider_Id_801,1339
-Order_No_4894,User_Id_2956,Bike,3,Business,29,1,10:13:49 AM,29,1,10:14:41 AM,29,1,10:28:34 AM,29,1,10:41:38 AM,29,1,10:59:16 AM,6,,,-1.2615888000000002,36.7928732,-1.2897368,36.8226783,Rider_Id_119,1058
-Order_No_17700,User_Id_1148,Bike,3,Business,25,6,10:05:01 AM,25,6,10:05:20 AM,25,6,10:29:07 AM,25,6,11:01:13 AM,25,6,12:00:58 PM,17,,,-1.3099295,36.8436807,-1.3671327,36.75642379999999,Rider_Id_2,3585
-Order_No_9582,User_Id_3647,Bike,3,Business,28,2,10:32:16 AM,28,2,10:34:27 AM,28,2,10:52:50 AM,28,2,11:01:04 AM,28,2,11:15:40 AM,5,21.3,,-1.2726389999999999,36.794723,-1.2854173999999998,36.7951152,Rider_Id_617,876
-Order_No_16232,User_Id_2982,Bike,3,Business,13,4,8:47:46 AM,13,4,8:48:23 AM,13,4,8:53:21 AM,13,4,9:58:22 AM,13,4,10:28:27 AM,14,18.5,,-1.300921,36.828195,-1.3195936000000001,36.916169700000005,Rider_Id_459,1805
-Order_No_6815,User_Id_219,Bike,3,Personal,6,1,11:08:51 AM,6,1,11:17:01 AM,6,1,11:31:35 AM,6,1,11:34:30 AM,6,1,11:47:40 AM,6,20.3,,-1.2668256000000002,36.80631279999999,-1.305023,36.80294,Rider_Id_754,790
-Order_No_12992,User_Id_733,Bike,3,Business,18,2,12:58:38 PM,18,2,12:59:22 PM,18,2,1:20:06 PM,18,2,1:30:29 PM,18,2,2:01:19 PM,25,22.7,,-1.2604833,36.7352777,-1.3194518,36.869396,Rider_Id_756,1850
-Order_No_1489,User_Id_2924,Bike,2,Personal,4,3,9:36:33 AM,4,3,9:37:03 AM,4,3,9:54:26 AM,4,3,10:14:17 AM,4,3,10:49:32 AM,18,14.9,,-1.3233955,36.856294399999996,-1.2664227,36.7453671,Rider_Id_833,2115
-Order_No_17994,User_Id_357,Bike,3,Business,19,2,10:35:53 AM,19,2,10:36:49 AM,19,2,10:40:33 AM,19,2,10:55:08 AM,19,2,11:12:08 AM,6,26.8,,-1.2700027,36.7669015,-1.2945178999999998,36.7685349,Rider_Id_268,1020
-Order_No_19485,User_Id_1761,Bike,3,Business,5,2,10:42:42 AM,5,2,11:04:08 AM,5,2,11:13:21 AM,5,2,11:20:25 AM,5,2,11:53:24 AM,17,23.8,,-1.3339943,36.7263409,-1.2830989,36.8165739,Rider_Id_952,1979
-Order_No_2915,User_Id_2729,Bike,2,Personal,13,5,1:41:56 PM,13,5,1:49:31 PM,13,5,2:08:27 PM,13,5,2:20:04 PM,13,5,2:36:48 PM,3,19.4,,-1.2857094999999998,36.8188266,-1.298529,36.816482,Rider_Id_752,1004
-Order_No_21196,User_Id_3599,Bike,3,Business,18,4,12:16:25 PM,18,4,12:54:46 PM,18,4,1:16:23 PM,18,4,1:48:27 PM,18,4,2:10:49 PM,7,,,-1.3302996,36.8707935,-1.3053489999999999,36.829708000000004,Rider_Id_690,1342
-Order_No_27506,User_Id_966,Bike,3,Business,1,1,1:20:43 PM,1,1,1:21:10 PM,1,1,1:31:24 PM,1,1,1:33:38 PM,1,1,2:06:37 PM,18,,,-1.3339943,36.7263409,-1.305087,36.862673,Rider_Id_548,1979
-Order_No_21401,User_Id_1478,Bike,3,Personal,18,5,1:47:56 PM,18,5,2:09:35 PM,18,5,3:06:33 PM,18,5,3:10:10 PM,18,5,3:52:05 PM,6,24.4,,-1.2894847,36.7556088,-1.2545665000000001,36.75978189999999,Rider_Id_618,2515
-Order_No_9164,User_Id_3194,Bike,3,Business,22,5,1:05:46 PM,22,5,1:06:39 PM,22,5,1:10:53 PM,22,5,1:19:18 PM,22,5,1:31:04 PM,7,,,-1.2551895,36.7822034,-1.2737396,36.836564200000005,Rider_Id_156,706
-Order_No_7686,User_Id_661,Bike,3,Business,6,4,1:49:57 PM,6,4,1:50:26 PM,6,4,1:56:01 PM,6,4,2:12:56 PM,6,4,2:32:33 PM,6,23.2,,-1.2744161,36.8032725,-1.2963097,36.7688221,Rider_Id_655,1177
-Order_No_777,User_Id_730,Bike,3,Personal,30,3,2:29:53 PM,30,3,2:30:03 PM,30,3,2:30:40 PM,30,3,2:35:17 PM,30,3,2:50:57 PM,2,24.9,,-1.2963097,36.7688221,-1.3043821000000002,36.7840586,Rider_Id_618,940
-Order_No_13159,User_Id_136,Bike,3,Business,15,5,12:45:31 PM,15,5,12:45:57 PM,15,5,12:46:18 PM,15,5,12:52:25 PM,15,5,1:31:04 PM,14,26.0,,-1.3254522,36.721658399999995,-1.2963367000000001,36.8094235,Rider_Id_458,2319
-Order_No_6901,User_Id_2956,Bike,3,Business,26,2,9:57:24 AM,26,2,9:57:59 AM,26,2,10:15:05 AM,26,2,10:16:25 AM,26,2,10:33:09 AM,7,21.8,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_318,1004
-Order_No_13533,User_Id_180,Bike,2,Personal,11,2,6:27:50 PM,11,2,6:29:04 PM,11,2,6:35:39 PM,11,2,6:55:52 PM,11,2,7:05:36 PM,4,23.4,,-1.2668611,36.795638700000005,-1.2813497999999999,36.7930729,Rider_Id_379,584
-Order_No_12029,User_Id_136,Bike,3,Business,25,1,2:49:20 PM,25,1,2:49:46 PM,25,1,3:01:39 PM,25,1,3:09:41 PM,25,1,3:22:16 PM,5,28.1,,-1.2965045,36.7913036,-1.2963367000000001,36.8094235,Rider_Id_783,755
-Order_No_6346,User_Id_2828,Bike,3,Business,14,3,4:31:57 PM,14,3,4:36:00 PM,14,3,4:50:37 PM,14,3,5:01:48 PM,14,3,5:23:10 PM,7,23.4,,-1.308819,36.8421201,-1.3280558,36.8816781,Rider_Id_708,1282
-Order_No_1727,User_Id_2335,Bike,3,Business,23,3,11:54:23 AM,23,3,11:54:39 AM,23,3,12:07:15 PM,23,3,12:08:57 PM,23,3,12:53:27 PM,22,,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_932,2670
-Order_No_21293,User_Id_3404,Bike,3,Business,21,3,1:42:49 PM,21,3,2:12:23 PM,21,3,2:38:36 PM,21,3,2:42:21 PM,21,3,3:06:47 PM,13,23.2,3.0,-1.2232123,36.8932547,-1.279395,36.825364,Rider_Id_622,1466
-Order_No_5744,User_Id_2521,Bike,3,Business,3,6,8:24:20 AM,3,6,9:27:48 AM,3,6,9:39:22 AM,3,6,9:59:25 AM,3,6,10:09:13 AM,4,19.7,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_331,588
-Order_No_925,User_Id_1372,Bike,3,Business,18,4,5:10:48 PM,18,4,5:11:56 PM,18,4,5:32:10 PM,18,4,5:39:27 PM,18,4,6:04:52 PM,12,,,-1.3193233,36.840990999999995,-1.3472066,36.7692638,Rider_Id_154,1525
-Order_No_3712,User_Id_1280,Bike,3,Business,14,5,10:52:26 AM,14,5,10:52:36 AM,14,5,10:54:33 AM,14,5,10:59:11 AM,14,5,11:05:05 AM,4,21.8,,-1.2551895,36.7822034,-1.2585521000000002,36.7945765,Rider_Id_727,354
-Order_No_22587,User_Id_2195,Bike,3,Business,3,3,2:23:27 PM,3,3,2:23:45 PM,3,3,2:41:06 PM,3,3,2:42:58 PM,3,3,2:57:52 PM,10,28.2,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_341,894
-Order_No_11329,User_Id_2763,Bike,3,Business,31,5,10:16:23 AM,31,5,10:17:54 AM,31,5,10:40:16 AM,31,5,10:56:35 AM,31,5,11:21:53 AM,12,21.8,,-1.3014461000000002,36.7661381,-1.2991616000000001,36.842450899999996,Rider_Id_855,1518
-Order_No_25061,User_Id_3711,Bike,3,Business,24,4,5:51:57 PM,24,4,5:52:31 PM,24,4,6:01:53 PM,24,4,6:09:32 PM,24,4,6:43:41 PM,3,26.1,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_246,2049
-Order_No_1500,User_Id_3326,Bike,3,Business,8,1,2:38:25 PM,8,1,2:40:14 PM,8,1,2:47:57 PM,8,1,2:57:27 PM,8,1,3:14:57 PM,8,29.8,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_99,1050
-Order_No_3585,User_Id_3686,Bike,3,Business,29,1,11:27:26 AM,29,1,11:28:14 AM,29,1,11:43:38 AM,29,1,11:45:56 AM,29,1,12:03:32 PM,9,,,-1.2551895,36.7822034,-1.3016653,36.8323628,Rider_Id_208,1056
-Order_No_2761,User_Id_2523,Bike,3,Business,4,2,10:54:13 AM,4,2,10:54:30 AM,4,2,10:55:11 AM,4,2,10:58:19 AM,4,2,11:11:25 AM,3,20.4,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_829,786
-Order_No_10584,User_Id_1389,Bike,3,Business,25,4,1:31:05 PM,25,4,1:41:11 PM,25,4,1:55:26 PM,25,4,1:59:56 PM,25,4,3:14:24 PM,31,21.0,,-1.3165542,36.700067,-1.207546,36.7913976,Rider_Id_936,4468
-Order_No_3246,User_Id_3201,Bike,3,Business,6,3,12:50:27 PM,6,3,12:50:47 PM,6,3,1:40:02 PM,6,3,1:44:18 PM,6,3,2:10:13 PM,3,27.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_540,1555
-Order_No_10525,User_Id_3291,Bike,3,Business,14,4,1:27:56 PM,14,4,1:29:24 PM,14,4,2:05:06 PM,14,4,2:11:07 PM,14,4,2:23:34 PM,5,28.0,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_228,747
-Order_No_17147,User_Id_2556,Bike,1,Personal,6,5,3:20:45 PM,6,5,3:20:58 PM,6,5,3:26:38 PM,6,5,3:34:39 PM,6,5,4:05:10 PM,16,22.3,,-1.2933541000000002,36.7958784,-1.2151769,36.8846524,Rider_Id_103,1831
-Order_No_20880,User_Id_1725,Bike,3,Business,8,5,6:03:44 PM,8,5,6:04:54 PM,8,5,6:18:23 PM,8,5,6:23:33 PM,8,5,6:30:23 PM,2,20.0,,-1.2652108,36.8056075,-1.2644742,36.808345700000004,Rider_Id_100,410
-Order_No_16186,User_Id_3647,Bike,3,Business,1,1,12:23:28 PM,1,1,12:24:34 PM,1,1,12:45:53 PM,1,1,12:47:50 PM,1,1,1:12:49 PM,5,,,-1.2726389999999999,36.794723,-1.2567464,36.7915273,Rider_Id_168,1499
-Order_No_13685,User_Id_928,Bike,3,Business,30,2,1:30:02 PM,30,2,1:35:32 PM,30,2,1:35:37 PM,30,2,2:25:15 PM,30,2,2:48:41 PM,7,,,-1.2551895,36.7822034,-1.2857241000000001,36.820348100000004,Rider_Id_61,1406
-Order_No_16010,User_Id_633,Bike,3,Business,15,4,2:40:36 PM,15,4,2:49:44 PM,15,4,3:12:53 PM,15,4,3:25:06 PM,15,4,3:45:33 PM,9,27.7,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_554,1227
-Order_No_8494,User_Id_3291,Bike,3,Business,22,6,9:09:26 AM,22,6,9:10:23 AM,22,6,9:19:25 AM,22,6,9:58:57 AM,22,6,10:14:11 AM,12,17.6,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_85,914
-Order_No_10491,User_Id_3621,Bike,3,Business,21,4,7:54:11 AM,21,4,7:54:44 AM,21,4,8:26:42 AM,21,4,8:33:03 AM,21,4,8:53:47 AM,11,21.6,,-1.2836583000000001,36.7886014,-1.322999,36.783263299999994,Rider_Id_829,1244
-Order_No_15047,User_Id_1705,Bike,3,Business,28,1,12:57:56 PM,28,1,12:58:04 PM,28,1,1:01:50 PM,28,1,1:11:40 PM,28,1,2:09:24 PM,28,23.5,,-1.2551895,36.7822034,-1.1429062,36.9300994,Rider_Id_623,3464
-Order_No_18653,User_Id_1687,Bike,3,Business,5,1,10:27:45 AM,5,1,10:28:56 AM,5,1,10:40:08 AM,5,1,10:45:36 AM,5,1,10:58:40 AM,5,24.4,,-1.2626574,36.8014034,-1.2655381,36.8354648,Rider_Id_148,784
-Order_No_19175,User_Id_3507,Bike,3,Personal,28,5,11:00:42 AM,28,5,11:01:08 AM,28,5,11:12:17 AM,28,5,11:18:05 AM,28,5,11:35:26 AM,5,25.1,,-1.2944628,36.7522739,-1.2941023999999999,36.79578,Rider_Id_412,1041
-Order_No_13940,User_Id_1976,Bike,3,Business,18,3,9:31:13 AM,18,3,9:32:10 AM,18,3,9:48:03 AM,18,3,9:51:53 AM,18,3,10:30:09 AM,10,14.8,,-1.257028,36.7810435,-1.2812601000000001,36.73402720000001,Rider_Id_234,2296
-Order_No_3941,User_Id_3647,Bike,3,Business,17,4,10:10:30 AM,17,4,10:13:40 AM,17,4,10:21:32 AM,17,4,10:24:07 AM,17,4,10:45:28 AM,3,,,-1.2726389999999999,36.794723,-1.2727979999999999,36.8124195,Rider_Id_512,1281
-Order_No_23352,User_Id_2666,Bike,3,Personal,28,4,9:55:51 AM,28,4,9:56:32 AM,28,4,10:25:06 AM,28,4,10:31:27 AM,28,4,11:19:01 AM,16,,,-1.2859912,36.8756811,-1.2615888000000002,36.7928732,Rider_Id_906,2854
-Order_No_1149,User_Id_868,Bike,3,Business,29,1,10:16:31 AM,29,1,10:16:51 AM,29,1,10:17:58 AM,29,1,10:29:41 AM,29,1,10:50:15 AM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_155,1234
-Order_No_4671,User_Id_2207,Bike,3,Business,3,1,9:42:21 AM,3,1,9:43:24 AM,3,1,10:00:31 AM,3,1,10:24:27 AM,3,1,10:49:09 AM,8,18.2,,-1.2597649,36.775631,-1.2768326,36.8073804,Rider_Id_446,1482
-Order_No_9496,User_Id_3752,Bike,3,Business,3,5,2:36:18 PM,3,5,2:38:16 PM,3,5,2:46:02 PM,3,5,2:54:28 PM,3,5,3:05:10 PM,1,,,-1.2696908000000002,36.81519,-1.2753108999999998,36.8130185,Rider_Id_819,642
-Order_No_26829,User_Id_1500,Bike,3,Business,9,2,8:47:42 AM,9,2,8:49:11 AM,9,2,9:05:17 AM,9,2,9:22:36 AM,9,2,9:33:48 AM,6,20.8,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_438,672
-Order_No_21230,User_Id_1245,Bike,3,Business,4,1,11:17:12 AM,4,1,11:17:45 AM,4,1,11:18:08 AM,4,1,12:15:05 PM,4,1,12:19:38 PM,12,25.6,,-1.300921,36.828195,-1.3425200000000002,36.739000000000004,Rider_Id_472,273
-Order_No_26042,User_Id_2387,Bike,1,Personal,16,2,9:29:55 AM,16,2,9:30:08 AM,16,2,9:43:10 AM,16,2,9:46:04 AM,16,2,9:55:06 AM,5,,,-1.2535486,36.7933541,-1.2633493999999998,36.7959696,Rider_Id_726,542
-Order_No_17348,User_Id_1502,Bike,3,Business,20,3,11:25:31 AM,20,3,11:26:02 AM,20,3,11:37:35 AM,20,3,11:42:47 AM,20,3,11:53:47 AM,5,26.2,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_75,660
-Order_No_17183,User_Id_1856,Bike,3,Business,16,5,2:22:59 PM,16,5,3:13:20 PM,16,5,3:34:43 PM,16,5,3:38:32 PM,16,5,4:13:34 PM,4,,,-1.273009,36.802807,-1.2684319,36.81658529999999,Rider_Id_554,2102
-Order_No_5662,User_Id_635,Bike,3,Business,22,3,9:45:28 AM,22,3,10:01:32 AM,22,3,10:30:22 AM,22,3,10:39:50 AM,22,3,11:50:35 AM,28,23.3,,-1.2847613000000002,36.967084,-1.2602337,36.79905479999999,Rider_Id_730,4245
-Order_No_5416,User_Id_2801,Bike,3,Business,15,1,12:02:51 PM,15,1,12:03:38 PM,15,1,12:28:48 PM,15,1,12:31:02 PM,15,1,12:56:51 PM,9,,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_363,1549
-Order_No_10414,User_Id_2412,Bike,3,Business,5,6,5:33:21 PM,5,6,5:33:33 PM,5,6,5:33:37 PM,5,6,5:53:57 PM,5,6,6:05:38 PM,3,21.8,,-1.2156006000000001,36.8916865,-1.2285834999999998,36.882646799999996,Rider_Id_338,701
-Order_No_27943,User_Id_496,Bike,3,Business,19,1,2:54:48 PM,19,1,2:56:43 PM,19,1,2:59:10 PM,19,1,3:05:02 PM,19,1,3:26:32 PM,11,25.1,,-1.2967108,36.787113500000004,-1.2551895,36.7822034,Rider_Id_597,1290
-Order_No_9753,User_Id_3781,Bike,3,Business,25,4,12:27:13 PM,25,4,1:07:43 PM,25,4,1:35:22 PM,25,4,2:16:29 PM,25,4,2:51:01 PM,10,20.8,,-1.2128018,36.83263710000001,-1.26071,36.8206332,Rider_Id_237,2072
-Order_No_3294,User_Id_2387,Bike,1,Personal,25,1,10:16:56 AM,25,1,10:17:38 AM,25,1,10:29:00 AM,25,1,10:33:54 AM,25,1,11:01:37 AM,11,,,-1.2535486,36.7933541,-1.2969741000000001,36.7856612,Rider_Id_116,1663
-Order_No_16143,User_Id_867,Bike,3,Business,26,2,10:04:10 AM,26,2,10:31:27 AM,26,2,10:47:47 AM,26,2,10:56:05 AM,26,2,11:28:36 AM,11,27.7,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_119,1951
-Order_No_109,User_Id_1482,Bike,3,Business,7,5,3:49:36 PM,7,5,4:18:38 PM,7,5,4:57:03 PM,7,5,5:24:30 PM,7,5,5:35:37 PM,4,22.9,,-1.3231725,36.864017700000005,-1.3385790000000002,36.89630379999999,Rider_Id_48,667
-Order_No_17550,User_Id_867,Bike,3,Business,6,1,10:09:58 AM,6,1,10:11:15 AM,6,1,10:29:44 AM,6,1,10:52:25 AM,6,1,11:58:30 AM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_216,3965
-Order_No_11525,User_Id_2330,Bike,3,Business,12,5,3:12:17 PM,12,5,3:13:32 PM,12,5,3:27:11 PM,12,5,3:28:35 PM,12,5,4:04:26 PM,16,,,-1.233018,36.8004477,-1.3015197,36.7658456,Rider_Id_7,2151
-Order_No_24169,User_Id_2200,Bike,3,Business,5,1,2:27:08 PM,5,1,2:41:08 PM,5,1,2:55:20 PM,5,1,3:00:50 PM,5,1,3:02:27 PM,2,26.2,,-1.2897368,36.8226783,-1.290894,36.822971,Rider_Id_31,97
-Order_No_10686,User_Id_2568,Bike,1,Personal,30,3,2:28:03 PM,30,3,2:29:23 PM,30,3,2:46:34 PM,30,3,2:54:22 PM,30,3,3:56:28 PM,30,25.2,,-1.2861183999999999,36.8159704,-1.1140714,37.0056922,Rider_Id_101,3726
-Order_No_12082,User_Id_1304,Bike,3,Business,28,4,2:45:45 PM,28,4,2:47:08 PM,28,4,2:57:27 PM,28,4,3:02:16 PM,28,4,3:23:30 PM,8,,,-1.2551895,36.7822034,-1.2883346000000002,36.8148489,Rider_Id_427,1274
-Order_No_25531,User_Id_2423,Bike,3,Business,12,3,2:38:23 PM,12,3,2:40:44 PM,12,3,2:40:51 PM,12,3,3:03:13 PM,12,3,4:05:11 PM,7,21.1,,-1.2571472,36.795063299999995,-1.3038156,36.812273299999994,Rider_Id_338,3718
-Order_No_10070,User_Id_3506,Bike,3,Personal,1,3,11:48:03 AM,1,3,11:49:29 AM,1,3,11:58:45 AM,1,3,12:02:51 PM,1,3,12:26:42 PM,14,,,-1.296268,36.785084000000005,-1.2563531000000001,36.7896742,Rider_Id_141,1431
-Order_No_26423,User_Id_3560,Bike,3,Business,28,2,5:14:38 PM,28,2,5:15:10 PM,28,2,5:20:29 PM,28,2,5:24:24 PM,28,2,5:42:57 PM,3,18.2,,-1.3052491000000002,36.8223899,-1.279395,36.825364,Rider_Id_384,1113
-Order_No_25274,User_Id_3291,Bike,3,Business,11,2,4:09:49 PM,11,2,4:40:13 PM,11,2,4:56:08 PM,11,2,4:59:35 PM,11,2,5:37:14 PM,12,20.6,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_27,2259
-Order_No_7375,User_Id_3017,Bike,3,Business,19,6,4:28:42 PM,19,6,4:28:53 PM,19,6,4:39:34 PM,19,6,4:44:45 PM,19,6,4:52:15 PM,3,21.7,,-1.299313,36.78314879999999,-1.2930931,36.7559267,Rider_Id_103,450
-Order_No_1916,User_Id_20,Bike,3,Business,30,4,11:52:19 AM,30,4,11:52:27 AM,30,4,11:53:06 AM,30,4,12:25:37 PM,30,4,12:29:14 PM,7,20.3,,-1.2628638,36.8070399,-1.3034166999999999,36.8197498,Rider_Id_620,217
-Order_No_20947,User_Id_3698,Bike,3,Business,15,1,2:40:03 PM,15,1,2:40:32 PM,15,1,2:52:17 PM,15,1,3:09:35 PM,15,1,4:05:48 PM,22,,,-1.3244885,36.897791999999995,-1.261403,36.769224200000004,Rider_Id_856,3373
-Order_No_22585,User_Id_1448,Bike,3,Business,8,3,3:00:29 PM,8,3,3:00:48 PM,8,3,3:10:23 PM,8,3,3:11:42 PM,8,3,3:36:57 PM,12,25.8,,-1.2615888000000002,36.7928732,-1.3215393999999998,36.836621,Rider_Id_726,1515
-Order_No_21702,User_Id_2801,Bike,3,Business,10,1,12:55:28 PM,10,1,12:56:12 PM,10,1,1:01:46 PM,10,1,1:11:11 PM,10,1,1:35:46 PM,8,18.4,,-1.300722,36.816865,-1.2674273999999999,36.787082700000006,Rider_Id_709,1475
-Order_No_25512,User_Id_816,Bike,3,Business,3,5,11:39:53 AM,3,5,11:40:27 AM,3,5,11:53:32 AM,3,5,11:59:56 AM,3,5,12:14:05 PM,4,21.4,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_857,849
-Order_No_6248,User_Id_3420,Bike,3,Business,11,1,11:14:54 AM,11,1,11:25:38 AM,11,1,11:42:58 AM,11,1,11:45:57 AM,11,1,12:01:09 PM,7,18.1,,-1.265981,36.793612100000004,-1.3046007,36.821174299999996,Rider_Id_726,912
-Order_No_24048,User_Id_2901,Bike,3,Business,16,2,9:15:56 AM,16,2,9:16:31 AM,16,2,10:03:42 AM,16,2,10:06:47 AM,16,2,10:18:07 AM,3,,,-1.2946918,36.801154100000005,-1.2839993,36.791851,Rider_Id_468,680
-Order_No_25707,User_Id_1075,Bike,3,Business,5,5,9:33:01 AM,5,5,9:33:25 AM,5,5,9:40:07 AM,5,5,9:56:52 AM,5,5,10:05:27 AM,2,20.0,,-1.2527958000000001,36.8003131,-1.2610837,36.8021315,Rider_Id_394,515
-Order_No_4357,User_Id_2658,Bike,3,Business,10,3,4:29:07 PM,10,3,4:31:26 PM,10,3,5:06:49 PM,10,3,5:13:34 PM,10,3,6:07:46 PM,8,27.3,,-1.2813012,36.832396200000005,-1.2963097,36.7688221,Rider_Id_660,3252
-Order_No_21823,User_Id_1991,Bike,3,Personal,5,2,3:09:50 PM,5,2,3:17:51 PM,5,2,3:31:08 PM,5,2,3:36:12 PM,5,2,3:53:07 PM,5,,,-1.2963097,36.7688221,-1.290641,36.812940999999995,Rider_Id_763,1015
-Order_No_3833,User_Id_1872,Bike,1,Personal,4,6,10:57:21 AM,4,6,10:57:36 AM,4,6,11:07:57 AM,4,6,11:34:59 AM,4,6,11:50:26 AM,5,20.0,,-1.2540707,36.8186208,-1.2865677,36.825334600000005,Rider_Id_754,927
-Order_No_1093,User_Id_1500,Bike,3,Business,21,5,11:51:28 AM,21,5,12:44:14 PM,21,5,12:45:14 PM,21,5,12:51:35 PM,21,5,1:32:07 PM,7,20.0,,-1.3004062,36.829741,-1.2696795,36.8640088,Rider_Id_532,2432
-Order_No_12581,User_Id_393,Bike,3,Business,4,1,8:29:47 AM,4,1,8:35:33 AM,4,1,8:44:13 AM,4,1,9:04:36 AM,4,1,9:24:01 AM,11,21.7,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_154,1165
-Order_No_4362,User_Id_2174,Bike,3,Business,13,6,11:46:40 AM,13,6,11:47:05 AM,13,6,12:01:04 PM,13,6,12:12:28 PM,13,6,1:28:57 PM,27,26.1,,-1.2482781,36.884091999999995,-1.324878,36.7070285,Rider_Id_387,4589
-Order_No_4897,User_Id_3647,Bike,3,Business,29,2,4:24:01 PM,29,2,4:25:21 PM,29,2,4:45:44 PM,29,2,4:50:02 PM,29,2,5:29:37 PM,8,,,-1.2726389999999999,36.794723,-1.2965924,36.8179314,Rider_Id_755,2375
-Order_No_3529,User_Id_598,Bike,2,Business,24,1,5:56:16 PM,24,1,5:57:25 PM,24,1,6:16:17 PM,24,1,6:20:38 PM,24,1,6:50:09 PM,12,24.3,,-1.2329501,36.808385200000004,-1.299299,36.807308,Rider_Id_612,1771
-Order_No_3810,User_Id_3434,Bike,3,Business,4,1,3:17:04 PM,4,1,3:18:02 PM,4,1,3:33:25 PM,4,1,3:40:24 PM,4,1,4:16:05 PM,10,22.8,,-1.2963097,36.7688221,-1.304969,36.846623,Rider_Id_200,2141
-Order_No_28174,User_Id_1725,Bike,3,Business,25,3,12:25:29 PM,25,3,12:25:55 PM,25,3,12:36:25 PM,25,3,12:46:15 PM,25,3,1:03:40 PM,4,,,-1.2652108,36.8056075,-1.2566078,36.7832089,Rider_Id_88,1045
-Order_No_19017,User_Id_1976,Bike,1,Business,30,6,2:34:16 PM,30,6,3:19:57 PM,30,6,3:36:12 PM,30,6,3:39:18 PM,30,6,3:56:05 PM,2,23.9,,-1.2560639999999998,36.773179999999996,-1.257028,36.7810435,Rider_Id_844,1007
-Order_No_7976,User_Id_2200,Bike,3,Business,20,2,3:58:25 PM,20,2,4:52:20 PM,20,2,5:07:26 PM,20,2,5:27:55 PM,20,2,5:38:45 PM,2,24.0,,-1.2885315,36.8230419,-1.290894,36.822971,Rider_Id_583,650
-Order_No_2056,User_Id_2734,Bike,1,Personal,17,1,3:54:02 PM,17,1,3:54:44 PM,17,1,4:08:59 PM,17,1,4:11:13 PM,17,1,4:24:03 PM,7,28.3,,-1.2713865,36.8058221,-1.3012013999999998,36.8043289,Rider_Id_857,770
-Order_No_21332,User_Id_1103,Bike,3,Business,29,1,2:45:32 PM,29,1,2:47:39 PM,29,1,3:04:46 PM,29,1,3:08:56 PM,29,1,3:28:21 PM,7,24.6,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_878,1165
-Order_No_5330,User_Id_3517,Bike,3,Business,7,4,2:34:48 PM,7,4,2:34:54 PM,7,4,2:35:09 PM,7,4,2:43:38 PM,7,4,3:27:15 PM,16,27.7,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_164,2617
-Order_No_24163,User_Id_136,Bike,3,Business,25,4,1:28:02 PM,25,4,1:29:30 PM,25,4,1:47:19 PM,25,4,1:48:48 PM,25,4,2:26:00 PM,15,20.8,,-1.3326173000000001,36.71462329999999,-1.2560874,36.7931031,Rider_Id_750,2232
-Order_No_13112,User_Id_2829,Bike,3,Business,2,5,4:41:17 PM,2,5,5:00:41 PM,2,5,5:01:55 PM,2,5,5:03:50 PM,2,5,5:31:17 PM,6,24.0,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_761,1647
-Order_No_6401,User_Id_53,Bike,3,Business,28,4,11:17:21 AM,28,4,11:18:16 AM,28,4,11:41:07 AM,28,4,11:58:26 AM,28,4,12:18:16 PM,5,23.6,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_489,1190
-Order_No_1397,User_Id_2216,Bike,3,Business,25,4,11:55:09 AM,25,4,11:55:26 AM,25,4,11:58:27 AM,25,4,12:03:53 PM,25,4,12:20:23 PM,5,19.9,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_464,990
-Order_No_24415,User_Id_3491,Bike,3,Business,6,4,4:43:55 PM,6,4,4:44:39 PM,6,4,5:05:45 PM,6,4,5:09:13 PM,6,4,5:43:11 PM,13,22.3,,-1.2248308,36.801257299999996,-1.2297202,36.881950700000004,Rider_Id_882,2038
-Order_No_21348,User_Id_1976,Bike,3,Business,26,2,3:29:53 PM,26,2,3:30:08 PM,26,2,3:46:59 PM,26,2,3:52:08 PM,26,2,4:19:29 PM,6,29.7,,-1.257028,36.7810435,-1.2534301,36.800098299999995,Rider_Id_167,1641
-Order_No_8430,User_Id_1496,Bike,1,Personal,12,5,8:51:38 PM,12,5,8:52:27 PM,12,5,9:13:24 PM,12,5,9:34:47 PM,12,5,9:54:53 PM,14,21.9,,-1.207546,36.7913976,-1.2544802,36.811182200000005,Rider_Id_103,1206
-Order_No_7595,User_Id_3668,Bike,1,Personal,20,5,9:57:57 AM,20,5,9:58:22 AM,20,5,10:06:27 AM,20,5,10:10:41 AM,20,5,10:41:21 AM,8,14.4,,-1.3031971000000002,36.786314600000004,-1.2559559999999999,36.772901,Rider_Id_142,1840
-Order_No_25056,User_Id_111,Bike,3,Business,14,4,3:49:30 PM,14,4,4:10:19 PM,14,4,4:18:55 PM,14,4,4:21:30 PM,14,4,4:41:01 PM,5,28.2,,-1.2551895,36.7822034,-1.26071,36.8206332,Rider_Id_19,1171
-Order_No_9211,User_Id_3064,Bike,3,Business,8,4,3:57:00 PM,8,4,4:06:44 PM,8,4,4:14:22 PM,8,4,4:15:45 PM,8,4,4:35:30 PM,9,27.2,,-1.2571472,36.795063299999995,-1.2942232,36.790023600000005,Rider_Id_596,1185
-Order_No_27594,User_Id_3329,Bike,1,Business,20,5,5:59:09 PM,20,5,7:00:35 PM,20,5,7:13:33 PM,20,5,7:15:10 PM,20,5,7:30:53 PM,5,19.8,,-1.2859576000000001,36.8197604,-1.2672588,36.8000252,Rider_Id_672,943
-Order_No_16211,User_Id_2679,Bike,3,Business,18,1,2:31:58 PM,18,1,2:37:12 PM,18,1,2:59:57 PM,18,1,3:01:13 PM,18,1,3:12:08 PM,4,17.2,,-1.2680643,36.7763499,-1.2857671,36.7875614,Rider_Id_441,655
-Order_No_11949,User_Id_2523,Bike,3,Business,4,1,2:54:52 PM,4,1,3:15:33 PM,4,1,3:37:14 PM,4,1,3:39:56 PM,4,1,4:03:40 PM,9,28.6,,-1.2551895,36.7822034,-1.3074264,36.822612299999996,Rider_Id_328,1424
-Order_No_7522,User_Id_269,Bike,3,Business,2,7,2:55:09 PM,2,7,3:27:14 PM,2,7,3:33:55 PM,2,7,3:38:11 PM,2,7,3:51:14 PM,9,25.0,,-1.3226301999999999,36.8719901,-1.3700383,36.9190174,Rider_Id_932,783
-Order_No_6547,User_Id_3635,Bike,1,Business,9,2,10:51:34 AM,9,2,11:05:28 AM,9,2,11:07:38 AM,9,2,11:19:00 AM,9,2,12:11:04 PM,11,22.2,,-1.2615888000000002,36.7928732,-1.2538003999999998,36.7303906,Rider_Id_956,3124
-Order_No_25980,User_Id_893,Bike,3,Business,20,3,12:42:03 PM,20,3,12:44:29 PM,20,3,12:45:08 PM,20,3,12:49:31 PM,20,3,12:58:19 PM,4,26.2,,-1.2810453000000002,36.811803499999996,-1.2663194,36.809901,Rider_Id_669,528
-Order_No_3437,User_Id_3621,Bike,3,Business,31,3,2:18:22 PM,31,3,2:18:46 PM,31,3,2:29:12 PM,31,3,2:30:36 PM,31,3,2:49:26 PM,5,26.9,,-1.2836583000000001,36.7886014,-1.2810888999999999,36.8247135,Rider_Id_634,1130
-Order_No_2435,User_Id_3222,Bike,3,Business,21,1,2:31:19 PM,21,1,2:31:44 PM,21,1,2:36:17 PM,21,1,2:58:12 PM,21,1,2:58:23 PM,8,27.6,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_576,11
-Order_No_1709,User_Id_393,Bike,3,Business,5,2,10:27:27 AM,5,2,10:28:02 AM,5,2,10:28:30 AM,5,2,10:35:24 AM,5,2,11:03:55 AM,14,23.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1711
-Order_No_16530,User_Id_136,Bike,3,Business,6,1,9:07:31 AM,6,1,9:39:03 AM,6,1,10:05:42 AM,6,1,11:04:54 AM,6,1,11:48:25 AM,16,,,-1.2908945,36.82465139999999,-1.3254522,36.721658399999995,Rider_Id_106,2611
-Order_No_13783,User_Id_2330,Bike,1,Business,6,6,4:30:11 PM,6,6,4:39:34 PM,6,6,4:53:47 PM,6,6,5:12:18 PM,6,6,5:23:31 PM,12,25.3,,-1.2920004999999999,36.90400220000001,-1.2284323,36.8757058,Rider_Id_397,673
-Order_No_26350,User_Id_3560,Bike,3,Business,22,3,2:05:34 PM,22,3,2:27:10 PM,22,3,3:03:10 PM,22,3,3:52:01 PM,22,3,4:13:39 PM,5,24.2,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_139,1298
-Order_No_6594,User_Id_1576,Bike,3,Business,29,2,4:17:25 PM,29,2,4:29:00 PM,29,2,4:43:11 PM,29,2,4:57:05 PM,29,2,5:30:07 PM,7,22.5,,-1.2740924999999999,36.8340107,-1.3100608999999999,36.820536499999996,Rider_Id_949,1982
-Order_No_1768,User_Id_2112,Bike,3,Business,23,5,4:48:54 PM,23,5,5:17:56 PM,23,5,5:32:41 PM,23,5,5:34:38 PM,23,5,6:31:57 PM,14,24.6,,-1.225322,36.80855,-1.2156006000000001,36.8916865,Rider_Id_660,3439
-Order_No_8922,User_Id_816,Bike,3,Business,6,3,2:42:14 PM,6,3,3:15:34 PM,6,3,3:34:22 PM,6,3,3:42:50 PM,6,3,4:05:21 PM,8,,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_276,1351
-Order_No_4772,User_Id_867,Bike,3,Business,25,1,11:11:02 AM,25,1,11:15:28 AM,25,1,11:28:14 AM,25,1,11:49:08 AM,25,1,12:19:58 PM,10,27.3,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_506,1850
-Order_No_25546,User_Id_1502,Bike,3,Business,19,2,12:02:12 PM,19,2,12:04:18 PM,19,2,12:17:16 PM,19,2,12:29:33 PM,19,2,12:42:31 PM,9,26.5,,-1.264627,36.842259000000006,-1.2562809,36.792382,Rider_Id_247,778
-Order_No_6033,User_Id_1724,Bike,3,Business,24,5,10:34:15 AM,24,5,10:35:12 AM,24,5,10:45:55 AM,24,5,10:49:20 AM,24,5,11:01:50 AM,6,21.2,,-1.2551895,36.7822034,-1.2805309,36.7944074,Rider_Id_205,750
-Order_No_16938,User_Id_1433,Bike,1,Personal,21,6,10:15:12 AM,21,6,10:15:22 AM,21,6,10:24:47 AM,21,6,11:13:18 AM,21,6,11:54:30 AM,21,16.3,,-1.2653589,36.8090021,-1.2995252,36.6871615,Rider_Id_427,2472
-Order_No_19918,User_Id_323,Bike,3,Business,21,4,12:44:40 PM,21,4,12:48:35 PM,21,4,12:59:34 PM,21,4,1:13:27 PM,21,4,1:35:28 PM,7,25.1,,-1.2813973,36.828920399999994,-1.2551895,36.7822034,Rider_Id_452,1321
-Order_No_25608,User_Id_1309,Bike,2,Personal,5,4,9:04:24 AM,5,4,9:06:30 AM,5,4,9:16:30 AM,5,4,9:22:01 AM,5,4,9:44:01 AM,8,,,-1.296797,36.776452500000005,-1.2621576,36.8103473,Rider_Id_449,1320
-Order_No_22024,User_Id_3647,Bike,3,Business,2,4,12:11:06 PM,2,4,12:12:01 PM,2,4,12:22:22 PM,2,4,12:28:50 PM,2,4,12:41:10 PM,5,,,-1.2726389999999999,36.794723,-1.2655477,36.8001682,Rider_Id_279,740
-Order_No_16658,User_Id_3149,Bike,3,Business,6,3,11:37:20 AM,6,3,11:43:24 AM,6,3,12:02:04 PM,6,3,12:10:14 PM,6,3,12:36:17 PM,9,,,-1.2770708,36.8231093,-1.3167113000000001,36.8301563,Rider_Id_763,1563
-Order_No_16048,User_Id_2830,Bike,1,Personal,8,3,9:21:51 AM,8,3,9:22:11 AM,8,3,9:38:48 AM,8,3,9:43:06 AM,8,3,10:11:46 AM,12,17.5,,-1.2549476000000002,36.8269576,-1.2718777,36.7503475,Rider_Id_519,1720
-Order_No_8905,User_Id_2976,Bike,3,Business,24,4,9:38:34 AM,24,4,9:40:15 AM,24,4,9:53:20 AM,24,4,10:01:51 AM,24,4,10:13:14 AM,6,16.8,,-1.2696819,36.8028694,-1.2478668,36.8069224,Rider_Id_116,683
-Order_No_8603,User_Id_718,Bike,3,Business,17,5,2:31:16 PM,17,5,2:31:41 PM,17,5,2:31:50 PM,17,5,2:36:47 PM,17,5,3:03:00 PM,12,,,-1.2551895,36.7822034,-1.3037415,36.8564954,Rider_Id_79,1573
-Order_No_2153,User_Id_1346,Bike,3,Business,5,5,12:56:29 PM,5,5,12:56:44 PM,5,5,1:02:36 PM,5,5,1:05:16 PM,5,5,1:24:31 PM,6,24.5,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_138,1155
-Order_No_27407,User_Id_3264,Bike,3,Business,11,4,11:10:33 AM,11,4,11:10:56 AM,11,4,11:14:33 AM,11,4,11:21:02 AM,11,4,11:21:35 AM,3,25.8,,-1.2551895,36.7822034,-1.2642665000000002,36.778828600000004,Rider_Id_698,33
-Order_No_6597,User_Id_3496,Bike,3,Business,30,2,12:46:21 PM,30,2,1:03:48 PM,30,2,1:09:16 PM,30,2,1:14:34 PM,30,2,1:43:47 PM,16,,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_263,1753
-Order_No_3499,User_Id_221,Bike,1,Personal,14,5,3:15:21 PM,14,5,3:22:29 PM,14,5,3:31:46 PM,14,5,3:39:17 PM,14,5,4:32:34 PM,7,27.2,,-1.2853338,36.819447499999995,-1.2887613000000002,36.77305379999999,Rider_Id_365,3197
-Order_No_20171,User_Id_3032,Bike,3,Business,8,4,2:51:31 PM,8,4,3:46:37 PM,8,4,3:59:04 PM,8,4,4:02:53 PM,8,4,4:17:38 PM,5,27.1,,-1.2571472,36.795063299999995,-1.2850639,36.8179454,Rider_Id_79,885
-Order_No_198,User_Id_3263,Bike,2,Personal,19,5,4:49:56 PM,19,5,4:51:01 PM,19,5,5:05:04 PM,19,5,6:01:01 PM,19,5,6:13:46 PM,6,24.2,,-1.3390024,36.8746657,-1.3221141,36.8636835,Rider_Id_179,765
-Order_No_22058,User_Id_2866,Bike,3,Business,2,4,2:01:09 PM,2,4,2:01:32 PM,2,4,2:02:47 PM,2,4,2:35:02 PM,2,4,3:18:47 PM,26,26.0,,-1.2551895,36.7822034,-1.3916458999999999,36.93058920000001,Rider_Id_105,2625
-Order_No_7498,User_Id_1329,Bike,3,Business,20,2,10:53:44 AM,20,2,12:52:31 PM,20,2,1:15:09 PM,20,2,1:16:22 PM,20,2,1:35:09 PM,7,19.9,,-1.273056,36.811298,-1.3006167,36.792557,Rider_Id_268,1127
-Order_No_14827,User_Id_970,Bike,3,Business,5,2,1:36:11 PM,5,2,1:36:33 PM,5,2,1:38:29 PM,5,2,1:42:15 PM,5,2,2:10:40 PM,16,,,-1.2551895,36.7822034,-1.1874676000000002,36.7424548,Rider_Id_341,1705
-Order_No_10751,User_Id_2066,Bike,3,Business,30,1,11:05:40 AM,30,1,12:31:50 PM,30,1,12:40:57 PM,30,1,12:42:20 PM,30,1,1:12:39 PM,7,22.6,,-1.316961,36.820337,-1.3263415,36.772282700000005,Rider_Id_229,1819
-Order_No_8506,User_Id_1304,Bike,3,Business,6,2,2:30:00 PM,6,2,3:09:40 PM,6,2,3:11:31 PM,6,2,3:24:43 PM,6,2,4:10:45 PM,13,27.5,,-1.3332749,36.870814700000004,-1.2684319,36.81658529999999,Rider_Id_315,2762
-Order_No_14732,User_Id_1500,Bike,3,Business,25,4,3:26:28 PM,25,4,4:24:23 PM,25,4,4:48:16 PM,25,4,4:50:09 PM,25,4,5:23:42 PM,9,,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_219,2013
-Order_No_3033,User_Id_3599,Bike,3,Business,15,3,11:38:13 AM,15,3,11:38:30 AM,15,3,11:58:01 AM,15,3,12:11:22 PM,15,3,12:39:57 PM,14,,,-1.3302996,36.8707935,-1.2935549,36.7703935,Rider_Id_675,1715
-Order_No_3054,User_Id_1469,Bike,3,Business,2,4,2:46:41 PM,2,4,2:47:15 PM,2,4,3:34:12 PM,2,4,3:53:09 PM,2,4,4:19:36 PM,6,,,-1.2571472,36.795063299999995,-1.2896398999999998,36.812449799999996,Rider_Id_422,1587
-Order_No_2722,User_Id_3647,Bike,3,Business,17,1,1:46:02 PM,17,1,1:46:55 PM,17,1,1:57:01 PM,17,1,2:01:41 PM,17,1,2:46:30 PM,26,24.5,,-1.2726389999999999,36.794723,-1.1586139,36.9561064,Rider_Id_279,2689
-Order_No_17920,User_Id_421,Bike,3,Business,18,2,1:47:36 PM,18,2,1:47:53 PM,18,2,2:01:52 PM,18,2,2:31:34 PM,18,2,3:14:32 PM,28,25.4,,-1.2975066000000002,36.76894229999999,-1.1612808000000001,36.9563278,Rider_Id_607,2578
-Order_No_26677,User_Id_2330,Bike,3,Business,11,2,4:18:30 PM,11,2,4:22:25 PM,11,2,4:29:05 PM,11,2,4:33:00 PM,11,2,5:05:16 PM,15,20.8,,-1.3015197,36.7658456,-1.2336843000000002,36.800725899999996,Rider_Id_622,1936
-Order_No_11886,User_Id_516,Bike,3,Business,2,4,12:57:37 PM,2,4,12:58:29 PM,2,4,1:03:19 PM,2,4,1:05:25 PM,2,4,2:07:05 PM,20,,,-1.2551895,36.7822034,-1.3195936000000001,36.916169700000005,Rider_Id_647,3700
-Order_No_9176,User_Id_706,Bike,3,Business,24,3,8:06:30 AM,24,3,8:07:58 AM,24,3,8:57:41 AM,24,3,9:05:23 AM,24,3,9:23:04 AM,7,17.1,1.0,-1.3077869,36.844320700000004,-1.3177546999999998,36.8303703,Rider_Id_532,1061
-Order_No_1074,User_Id_1244,Bike,3,Business,6,3,12:17:03 PM,6,3,12:19:17 PM,6,3,12:26:38 PM,6,3,12:32:07 PM,6,3,12:49:12 PM,8,22.7,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_85,1025
-Order_No_10339,User_Id_3647,Bike,3,Business,20,1,10:14:32 AM,20,1,10:15:26 AM,20,1,10:34:14 AM,20,1,10:35:42 AM,20,1,11:08:10 AM,14,,,-1.3175925,36.8742349,-1.2726389999999999,36.794723,Rider_Id_364,1948
-Order_No_23907,User_Id_1502,Bike,3,Business,19,1,9:33:17 AM,19,1,9:33:33 AM,19,1,9:34:53 AM,19,1,9:38:08 AM,19,1,10:13:15 AM,13,19.4,,-1.2232123,36.8932547,-1.2752846000000002,36.802702200000006,Rider_Id_447,2107
-Order_No_19750,User_Id_2035,Bike,2,Personal,27,1,1:15:45 PM,27,1,1:16:28 PM,27,1,1:31:08 PM,27,1,1:32:49 PM,27,1,1:45:56 PM,4,,,-1.2929576999999999,36.7955434,-1.2790289,36.8039715,Rider_Id_749,787
-Order_No_21371,User_Id_3283,Bike,3,Business,22,4,12:15:04 PM,22,4,12:17:14 PM,22,4,12:27:00 PM,22,4,12:47:08 PM,22,4,1:11:55 PM,7,22.8,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_31,1487
-Order_No_25591,User_Id_2030,Bike,3,Business,26,1,9:32:30 AM,26,1,9:47:14 AM,26,1,10:11:02 AM,26,1,10:12:26 AM,26,1,10:20:26 AM,2,17.9,4.9,-1.2702120000000001,36.82108770000001,-1.2753108999999998,36.8130185,Rider_Id_259,480
-Order_No_16001,User_Id_3599,Bike,3,Business,12,5,2:09:14 PM,12,5,2:11:19 PM,12,5,2:30:31 PM,12,5,2:41:58 PM,12,5,3:26:55 PM,14,,,-1.3302996,36.8707935,-1.2538731,36.7850223,Rider_Id_856,2697
-Order_No_21433,User_Id_3434,Bike,3,Business,25,2,12:39:03 PM,25,2,12:39:32 PM,25,2,12:53:17 PM,25,2,12:59:30 PM,25,2,1:50:29 PM,22,23.9,,-1.2729121,36.8325402,-1.1612808000000001,36.9563278,Rider_Id_96,3059
-Order_No_27826,User_Id_1244,Bike,3,Business,11,1,10:58:46 AM,11,1,10:58:55 AM,11,1,11:40:56 AM,11,1,12:12:10 PM,11,1,12:34:32 PM,22,26.4,,-1.3343128999999998,36.885417499999996,-1.303596,36.778378000000004,Rider_Id_242,1342
-Order_No_27561,User_Id_3222,Bike,3,Business,3,3,9:42:32 AM,3,3,9:42:42 AM,3,3,9:51:35 AM,3,3,9:57:25 AM,3,3,10:32:55 AM,8,21.5,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_177,2130
-Order_No_16433,User_Id_1062,Bike,3,Business,17,5,7:43:02 PM,17,5,7:44:37 PM,17,5,8:04:24 PM,17,5,8:10:22 PM,17,5,8:50:14 PM,14,,,-1.238874,36.807889,-1.2577288999999998,36.74107720000001,Rider_Id_517,2392
-Order_No_18343,User_Id_346,Bike,3,Business,11,2,12:39:05 PM,11,2,12:42:54 PM,11,2,1:16:18 PM,11,2,1:20:17 PM,11,2,1:28:45 PM,1,19.6,,-1.2795827,36.819726200000005,-1.28878,36.8168312,Rider_Id_790,508
-Order_No_509,User_Id_2789,Bike,3,Business,8,1,11:52:39 AM,8,1,11:52:55 AM,8,1,12:05:11 PM,8,1,12:10:38 PM,8,1,12:39:10 PM,7,27.9,,-1.304735,36.784605400000004,-1.265432,36.795034,Rider_Id_166,1712
-Order_No_5717,User_Id_1584,Bike,3,Business,7,4,2:37:40 PM,7,4,2:41:06 PM,7,4,2:53:15 PM,7,4,3:04:09 PM,7,4,3:35:54 PM,6,31.2,,-1.2857524,36.819680600000005,-1.2969741000000001,36.7856612,Rider_Id_698,1905
-Order_No_18344,User_Id_393,Bike,3,Business,7,5,8:47:43 AM,7,5,8:51:36 AM,7,5,9:20:53 AM,7,5,9:43:56 AM,7,5,9:56:55 AM,8,,,-1.3228003000000002,36.8306435,-1.2770708,36.8231093,Rider_Id_921,779
-Order_No_24338,User_Id_3326,Bike,3,Business,20,1,1:27:54 PM,20,1,1:28:07 PM,20,1,1:33:17 PM,20,1,1:54:37 PM,20,1,1:54:41 PM,5,,,-1.2551895,36.7822034,-1.2738933000000001,36.8016678,Rider_Id_931,4
-Order_No_25672,User_Id_2801,Bike,3,Business,9,2,3:14:39 PM,9,2,3:15:17 PM,9,2,3:40:18 PM,9,2,4:09:06 PM,9,2,5:03:33 PM,21,22.4,,-1.3104506,36.8687235,-1.3298292999999999,36.7128909,Rider_Id_890,3267
-Order_No_15903,User_Id_1664,Bike,3,Personal,13,4,2:33:11 PM,13,4,3:01:15 PM,13,4,3:52:32 PM,13,4,4:02:05 PM,13,4,4:24:34 PM,5,22.4,,-1.324658,36.834047999999996,-1.286937,36.824674200000004,Rider_Id_48,1349
-Order_No_7574,User_Id_978,Bike,1,Personal,2,6,9:07:38 AM,2,6,9:08:56 AM,2,6,9:20:14 AM,2,6,9:31:13 AM,2,6,9:59:33 AM,5,20.1,,-1.3084067,36.83450139999999,-1.2805312,36.832668700000006,Rider_Id_844,1700
-Order_No_11916,User_Id_2066,Bike,3,Business,13,1,2:49:49 PM,13,1,2:50:32 PM,13,1,2:55:07 PM,13,1,2:57:23 PM,13,1,3:30:28 PM,9,,,-1.3210188999999999,36.841055100000005,-1.2837549,36.840138200000005,Rider_Id_233,1985
-Order_No_1577,User_Id_935,Bike,1,Personal,1,3,2:56:46 PM,1,3,2:58:20 PM,1,3,3:19:02 PM,1,3,4:00:17 PM,1,3,4:32:58 PM,17,23.9,,-1.2960265,36.8780276,-1.3005488,36.7722248,Rider_Id_886,1961
-Order_No_17658,User_Id_1075,Bike,3,Business,3,1,2:14:57 PM,3,1,2:25:59 PM,3,1,2:37:41 PM,3,1,3:04:38 PM,3,1,3:04:39 PM,8,23.2,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_709,1
-Order_No_7981,User_Id_1578,Bike,3,Business,20,4,3:54:03 PM,20,4,4:08:25 PM,20,4,4:26:57 PM,20,4,4:40:00 PM,20,4,4:56:03 PM,2,22.8,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_150,963
-Order_No_25910,User_Id_2801,Bike,3,Business,4,1,8:06:03 AM,4,1,8:06:59 AM,4,1,8:22:50 AM,4,1,8:24:35 AM,4,1,8:47:54 AM,4,22.1,,-1.2674273999999999,36.787082700000006,-1.2738521999999999,36.799281799999996,Rider_Id_290,1399
-Order_No_2547,User_Id_1344,Bike,1,Personal,31,5,5:04:59 PM,31,5,5:05:51 PM,31,5,5:10:27 PM,31,5,5:13:33 PM,31,5,5:34:48 PM,9,19.7,,-1.2567464,36.7915273,-1.2963097,36.7688221,Rider_Id_131,1275
-Order_No_11420,User_Id_1105,Bike,3,Business,19,2,10:46:43 AM,19,2,10:47:01 AM,19,2,11:11:36 AM,19,2,11:14:04 AM,19,2,11:56:11 AM,10,26.4,,-1.3193222,36.8084901,-1.2594753,36.806273,Rider_Id_468,2527
-Order_No_27957,User_Id_3091,Bike,3,Business,9,4,3:04:55 PM,9,4,3:12:34 PM,9,4,3:28:20 PM,9,4,4:16:08 PM,9,4,4:16:14 PM,18,,,-1.290894,36.822971,-1.3190102,36.923069299999995,Rider_Id_921,6
-Order_No_26002,User_Id_892,Bike,2,Personal,26,3,4:06:45 PM,26,3,4:10:56 PM,26,3,4:23:07 PM,26,3,4:28:29 PM,26,3,4:33:34 PM,2,27.2,,-1.306735,36.8256226,-1.312475,36.8225778,Rider_Id_85,305
-Order_No_2456,User_Id_1271,Bike,3,Business,12,5,9:12:47 AM,12,5,9:12:56 AM,12,5,9:48:30 AM,12,5,10:52:31 AM,12,5,11:37:01 AM,13,24.6,,-1.2772469,36.8370656,-1.3107918,36.761794,Rider_Id_208,2670
-Order_No_22177,User_Id_3686,Bike,3,Business,30,2,2:37:55 PM,30,2,3:27:21 PM,30,2,3:31:37 PM,30,2,3:37:54 PM,30,2,4:15:13 PM,17,,,-1.2551895,36.7822034,-1.3310643,36.8884157,Rider_Id_438,2239
-Order_No_3646,User_Id_3291,Bike,3,Business,30,4,10:25:36 AM,30,4,10:25:57 AM,30,4,10:41:35 AM,30,4,11:00:53 AM,30,4,11:33:04 AM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_310,1931
-Order_No_14265,User_Id_3450,Bike,3,Business,8,5,11:01:16 AM,8,5,11:07:48 AM,8,5,11:28:06 AM,8,5,11:33:02 AM,8,5,11:50:36 AM,7,24.3,,-1.2551895,36.7822034,-1.292044,36.821938,Rider_Id_489,1054
-Order_No_27815,User_Id_136,Bike,3,Business,10,1,12:55:17 PM,10,1,12:56:11 PM,10,1,12:57:59 PM,10,1,1:08:24 PM,10,1,1:16:44 PM,1,18.7,,-1.2654716000000001,36.7983635,-1.2531983,36.8069669,Rider_Id_547,500
-Order_No_8237,User_Id_2412,Bike,3,Business,8,4,4:56:14 PM,8,4,4:56:26 PM,8,4,5:03:57 PM,8,4,5:18:08 PM,8,4,6:12:55 PM,18,25.6,,-1.3014461000000002,36.7661381,-1.2297202,36.881950700000004,Rider_Id_755,3287
-Order_No_12684,User_Id_3547,Bike,3,Business,29,1,11:28:07 AM,29,1,11:28:27 AM,29,1,11:34:42 AM,29,1,11:45:25 AM,29,1,12:24:31 PM,17,,,-1.2761408,36.7710839,-1.3189252,36.861726899999994,Rider_Id_269,2346
-Order_No_13097,User_Id_2801,Bike,3,Business,4,3,3:34:35 PM,4,3,3:36:49 PM,4,3,3:55:31 PM,4,3,4:13:04 PM,4,3,4:31:33 PM,6,18.3,,-1.2765492,36.7669809,-1.2885417,36.810052299999995,Rider_Id_101,1109
-Order_No_2499,User_Id_1768,Bike,1,Personal,3,3,9:04:52 AM,3,3,9:06:09 AM,3,3,9:31:52 AM,3,3,9:38:11 AM,3,3,9:57:04 AM,10,18.7,,-1.2750257,36.8157165,-1.301809,36.75643,Rider_Id_754,1133
-Order_No_5719,User_Id_2480,Bike,3,Personal,17,4,1:15:02 PM,17,4,1:15:25 PM,17,4,1:25:13 PM,17,4,1:30:49 PM,17,4,1:47:50 PM,9,22.4,,-1.3024794,36.8228284,-1.3330427,36.8635157,Rider_Id_676,1021
-Order_No_25627,User_Id_302,Bike,3,Business,13,3,12:34:51 PM,13,3,12:39:37 PM,13,3,12:51:17 PM,13,3,12:58:24 PM,13,3,1:16:56 PM,8,,,-1.2649605,36.798177700000004,-1.3121752,36.8083282,Rider_Id_119,1112
-Order_No_24271,User_Id_635,Bike,3,Business,19,6,10:14:46 AM,19,6,10:15:20 AM,19,6,10:27:38 AM,19,6,10:33:39 AM,19,6,11:06:01 AM,8,25.4,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_899,1942
-Order_No_18757,User_Id_186,Bike,3,Business,13,4,2:47:02 PM,13,4,2:53:09 PM,13,4,3:32:03 PM,13,4,3:42:55 PM,13,4,4:48:14 PM,23,23.0,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_271,3919
-Order_No_20871,User_Id_1776,Bike,3,Personal,6,4,1:43:00 PM,6,4,1:44:30 PM,6,4,2:05:26 PM,6,4,2:11:50 PM,6,4,2:35:43 PM,9,21.2,,-1.2535486,36.7933541,-1.2622285,36.8534706,Rider_Id_131,1433
-Order_No_14078,User_Id_909,Bike,3,Business,6,3,3:34:43 PM,6,3,3:34:57 PM,6,3,3:49:20 PM,6,3,3:51:14 PM,6,3,5:05:35 PM,7,27.6,,-1.225322,36.80855,-1.2527958000000001,36.8003131,Rider_Id_428,4461
-Order_No_16592,User_Id_928,Bike,3,Business,30,2,3:22:50 PM,30,2,5:28:27 PM,30,2,5:29:11 PM,30,2,5:33:26 PM,30,2,6:08:10 PM,7,,,-1.2551895,36.7822034,-1.2936428999999998,36.8277508,Rider_Id_910,2084
-Order_No_11095,User_Id_2112,Bike,3,Business,24,3,12:25:08 PM,24,3,12:25:23 PM,24,3,12:46:31 PM,24,3,12:48:13 PM,24,3,1:10:38 PM,16,19.8,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_587,1345
-Order_No_2533,User_Id_704,Bike,1,Business,2,2,3:43:20 PM,2,2,3:44:16 PM,2,2,3:59:18 PM,2,2,4:17:27 PM,2,2,5:22:25 PM,17,26.1,,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_937,3898
-Order_No_8478,User_Id_393,Bike,3,Business,29,5,8:38:07 AM,29,5,8:38:20 AM,29,5,8:38:36 AM,29,5,8:52:34 AM,29,5,9:32:35 AM,14,21.0,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,2401
-Order_No_21030,User_Id_136,Bike,3,Business,18,2,8:55:23 AM,18,2,8:55:37 AM,18,2,9:02:23 AM,18,2,9:12:26 AM,18,2,9:37:45 AM,20,19.6,,-1.2943371000000001,36.8267,-1.3319268999999998,36.7142325,Rider_Id_404,1519
-Order_No_25753,User_Id_1342,Bike,3,Business,4,2,5:50:28 PM,4,2,5:51:16 PM,4,2,5:58:17 PM,4,2,6:06:26 PM,4,2,6:36:49 PM,10,20.9,,-1.2527958000000001,36.8003131,-1.3025826,36.7670807,Rider_Id_105,1823
-Order_No_18749,User_Id_590,Bike,3,Business,25,1,11:45:22 AM,25,1,11:45:55 AM,25,1,11:48:01 AM,25,1,11:50:01 AM,25,1,11:54:11 AM,2,27.1,,-1.2509028999999998,36.7883695,-1.2538731,36.7850223,Rider_Id_756,250
-Order_No_1151,User_Id_1280,Bike,3,Business,11,1,3:17:18 PM,11,1,3:20:01 PM,11,1,3:26:05 PM,11,1,3:31:18 PM,11,1,3:31:25 PM,3,29.0,,-1.2551895,36.7822034,-1.2638132,36.784977500000004,Rider_Id_430,7
-Order_No_14548,User_Id_893,Bike,3,Business,24,2,1:50:07 PM,24,2,1:50:53 PM,24,2,1:51:49 PM,24,2,2:37:04 PM,24,2,2:58:22 PM,5,18.5,,-1.2876365,36.816096,-1.2663194,36.809901,Rider_Id_929,1278
-Order_No_6229,User_Id_3510,Bike,1,Business,14,1,10:51:33 AM,14,1,10:51:45 AM,14,1,11:08:03 AM,14,1,11:18:09 AM,14,1,11:45:25 AM,11,23.1,,-1.2603992,36.787859399999995,-1.22589,36.7822548,Rider_Id_865,1636
-Order_No_8269,User_Id_2227,Bike,3,Personal,21,4,10:37:10 AM,21,4,10:37:36 AM,21,4,10:48:53 AM,21,4,10:49:59 AM,21,4,11:14:51 AM,7,25.1,,-1.2541275,36.8263055,-1.2958383,36.8073162,Rider_Id_660,1492
-Order_No_8651,User_Id_1544,Bike,3,Business,9,4,12:00:03 PM,9,4,12:01:04 PM,9,4,12:10:06 PM,9,4,12:51:49 PM,9,4,1:19:12 PM,18,,,-1.1924678,36.7880546,-1.2977968999999998,36.8104085,Rider_Id_812,1643
-Order_No_14305,User_Id_590,Bike,3,Business,8,1,9:37:38 AM,8,1,9:39:51 AM,8,1,9:51:56 AM,8,1,9:58:15 AM,8,1,10:08:40 AM,3,19.9,,-1.257773,36.795486,-1.2620835,36.809074,Rider_Id_770,625
-Order_No_13883,User_Id_3149,Bike,3,Business,28,3,2:21:22 PM,28,3,2:21:49 PM,28,3,2:21:55 PM,28,3,2:46:51 PM,28,3,2:46:55 PM,5,,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_619,4
-Order_No_17249,User_Id_3372,Bike,3,Business,25,1,4:13:04 PM,25,1,4:13:24 PM,25,1,4:19:03 PM,25,1,4:23:03 PM,25,1,4:38:15 PM,4,,,-1.2969741000000001,36.7856612,-1.2872751,36.7889153,Rider_Id_474,912
-Order_No_4271,User_Id_1850,Bike,3,Business,13,1,4:27:37 PM,13,1,4:27:59 PM,13,1,4:29:16 PM,13,1,4:34:34 PM,13,1,4:44:37 PM,2,,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_411,603
-Order_No_19458,User_Id_893,Bike,3,Business,28,2,1:20:48 PM,28,2,1:21:02 PM,28,2,1:21:19 PM,28,2,2:05:59 PM,28,2,2:26:20 PM,9,23.4,,-1.247957,36.879598,-1.2208123,36.856526099999996,Rider_Id_669,1221
-Order_No_19026,User_Id_1209,Bike,3,Business,5,5,2:09:10 PM,5,5,2:11:47 PM,5,5,2:21:26 PM,5,5,2:39:18 PM,5,5,3:10:02 PM,4,27.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_61,1844
-Order_No_2579,User_Id_393,Bike,3,Business,17,5,10:14:38 AM,17,5,10:14:51 AM,17,5,10:14:58 AM,17,5,10:56:28 AM,17,5,10:57:18 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_39,50
-Order_No_26013,User_Id_1389,Bike,3,Business,10,5,4:58:49 PM,10,5,5:00:47 PM,10,5,5:11:56 PM,10,5,5:22:55 PM,10,5,5:52:28 PM,14,,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_96,1773
-Order_No_24002,User_Id_1500,Bike,3,Business,26,2,4:14:11 PM,26,2,4:24:14 PM,26,2,4:24:26 PM,26,2,4:35:02 PM,26,2,4:57:13 PM,10,28.4,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_881,1331
-Order_No_9497,User_Id_893,Bike,3,Business,30,2,2:10:10 PM,30,2,3:02:57 PM,30,2,3:24:05 PM,30,2,3:38:46 PM,30,2,3:53:49 PM,4,,,-1.261247,36.826993,-1.2594473999999998,36.8089122,Rider_Id_96,903
-Order_No_3705,User_Id_2701,Bike,3,Business,2,2,3:41:41 PM,2,2,3:51:19 PM,2,2,4:26:35 PM,2,2,4:40:05 PM,2,2,5:29:01 PM,4,23.4,,-1.281754,36.8324229,-1.2765736,36.851364600000004,Rider_Id_612,2936
-Order_No_27274,User_Id_3326,Bike,3,Business,8,2,10:00:31 AM,8,2,10:01:18 AM,8,2,10:14:19 AM,8,2,10:30:53 AM,8,2,11:31:16 AM,22,19.2,,-1.2551895,36.7822034,-1.341804,36.920576000000004,Rider_Id_357,3623
-Order_No_27322,User_Id_2355,Bike,3,Personal,8,2,11:06:09 AM,8,2,11:07:00 AM,8,2,11:14:33 AM,8,2,11:15:49 AM,8,2,11:55:08 AM,10,23.5,,-1.2608932,36.8143218,-1.3083777,36.85900770000001,Rider_Id_715,2359
-Order_No_6105,User_Id_1482,Bike,3,Business,18,5,11:50:17 AM,18,5,11:52:27 AM,18,5,12:09:14 PM,18,5,12:43:21 PM,18,5,12:56:37 PM,8,20.3,,-1.3214137,36.8569475,-1.3385790000000002,36.89630379999999,Rider_Id_622,796
-Order_No_12749,User_Id_872,Bike,3,Business,4,1,10:50:06 AM,4,1,10:50:30 AM,4,1,11:13:38 AM,4,1,11:14:48 AM,4,1,11:58:21 AM,22,25.6,,-1.2798368999999998,36.7752995,-1.1671865,36.826073799999996,Rider_Id_269,2613
-Order_No_14629,User_Id_393,Bike,3,Business,18,1,8:53:44 AM,18,1,8:54:47 AM,18,1,8:56:23 AM,18,1,9:14:51 AM,18,1,9:51:14 AM,14,22.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,2183
-Order_No_14470,User_Id_2325,Bike,2,Personal,7,6,8:03:58 AM,7,6,8:47:41 AM,7,6,8:57:23 AM,7,6,9:10:55 AM,7,6,9:21:29 AM,4,14.1,,-1.2611916,36.8444489,-1.2857902,36.8316231,Rider_Id_88,634
-Order_No_20763,User_Id_2330,Bike,3,Business,10,1,2:36:18 PM,10,1,2:37:57 PM,10,1,2:52:49 PM,10,1,2:59:39 PM,10,1,3:26:12 PM,5,18.2,,-1.3040812,36.7924217,-1.2890715,36.8157588,Rider_Id_109,1593
-Order_No_11537,User_Id_952,Bike,1,Personal,2,7,12:49:17 PM,2,7,12:49:47 PM,2,7,1:09:22 PM,2,7,1:14:10 PM,2,7,1:25:56 PM,4,18.7,,-1.2735166,36.801255600000005,-1.2813497999999999,36.7930729,Rider_Id_194,706
-Order_No_539,User_Id_635,Bike,3,Business,18,5,12:48:00 PM,18,5,12:48:36 PM,18,5,1:11:53 PM,18,5,1:19:56 PM,18,5,1:34:15 PM,2,20.1,,-1.2599563,36.799343900000004,-1.2649247,36.8085655,Rider_Id_611,859
-Order_No_1895,User_Id_3094,Bike,2,Personal,19,5,4:21:01 PM,19,5,4:22:39 PM,19,5,4:33:48 PM,19,5,4:45:27 PM,19,5,5:09:41 PM,2,24.3,,-1.2763087,36.8170342,-1.2597528999999998,36.806629,Rider_Id_467,1454
-Order_No_15571,User_Id_2330,Bike,3,Business,7,4,12:24:42 PM,7,4,12:24:53 PM,7,4,12:31:33 PM,7,4,12:37:37 PM,7,4,12:57:49 PM,9,24.1,,-1.2642903,36.80021370000001,-1.2326041,36.8009029,Rider_Id_953,1212
-Order_No_91,User_Id_478,Bike,3,Business,9,2,9:18:12 AM,9,2,9:18:52 AM,9,2,9:19:59 AM,9,2,9:31:44 AM,9,2,10:30:13 AM,8,20.6,,-1.2551895,36.7822034,-1.3045333000000001,36.82280720000001,Rider_Id_177,3509
-Order_No_11627,User_Id_3222,Bike,3,Business,28,1,1:01:38 PM,28,1,1:03:57 PM,28,1,1:18:55 PM,28,1,1:22:54 PM,28,1,1:35:04 PM,4,23.5,,-1.2551895,36.7822034,-1.2656922,36.813694399999996,Rider_Id_434,730
-Order_No_19706,User_Id_136,Bike,3,Business,14,2,10:48:32 AM,14,2,10:48:53 AM,14,2,10:58:15 AM,14,2,11:14:42 AM,14,2,11:49:20 AM,20,,,-1.2943371000000001,36.8267,-1.3254522,36.721658399999995,Rider_Id_622,2078
-Order_No_16575,User_Id_412,Bike,3,Business,13,4,2:48:09 PM,13,4,2:53:03 PM,13,4,3:04:11 PM,13,4,3:21:18 PM,13,4,3:24:13 PM,2,23.3,,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_310,175
-Order_No_6962,User_Id_1205,Bike,3,Business,12,2,2:02:17 PM,12,2,2:03:19 PM,12,2,2:12:54 PM,12,2,2:14:07 PM,12,2,2:29:43 PM,3,27.0,,-1.2495931,36.80650870000001,-1.2561964,36.7885001,Rider_Id_953,936
-Order_No_22798,User_Id_2448,Bike,2,Personal,9,3,3:19:39 PM,9,3,3:25:31 PM,9,3,3:25:48 PM,9,3,3:45:22 PM,9,3,4:13:19 PM,7,23.3,,-1.2871299999999999,36.818133,-1.2962603000000001,36.7766164,Rider_Id_410,1677
-Order_No_27468,User_Id_391,Bike,3,Business,20,1,9:13:05 AM,20,1,9:14:19 AM,20,1,9:46:48 AM,20,1,9:53:11 AM,20,1,10:20:00 AM,15,19.3,,-1.3248898,36.886686700000006,-1.2867454,36.8259017,Rider_Id_411,1609
-Order_No_24397,User_Id_2021,Bike,3,Business,2,2,12:22:27 PM,2,2,12:22:48 PM,2,2,12:27:31 PM,2,2,12:29:30 PM,2,2,12:44:53 PM,8,26.3,,-1.29475,36.83421,-1.3263285,36.8926624,Rider_Id_343,923
-Order_No_25793,User_Id_3283,Bike,3,Business,3,4,1:11:48 PM,3,4,1:12:06 PM,3,4,1:17:40 PM,3,4,1:33:31 PM,3,4,1:58:41 PM,9,25.2,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_543,1510
-Order_No_17297,User_Id_3291,Bike,3,Business,23,2,12:43:41 PM,23,2,12:45:22 PM,23,2,12:56:21 PM,23,2,1:05:20 PM,23,2,1:11:02 PM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_205,342
-Order_No_7830,User_Id_2348,Bike,3,Personal,10,5,1:01:37 PM,10,5,1:02:45 PM,10,5,1:12:27 PM,10,5,1:34:40 PM,10,5,1:58:40 PM,10,22.0,,-1.2611578,36.814530700000006,-1.312469,36.815177,Rider_Id_427,1440
-Order_No_14654,User_Id_3517,Bike,3,Business,13,3,9:13:32 AM,13,3,9:13:45 AM,13,3,9:13:58 AM,13,3,10:12:56 AM,13,3,10:16:31 AM,16,17.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_70,215
-Order_No_19837,User_Id_1849,Bike,3,Business,30,2,2:19:02 PM,30,2,2:41:13 PM,30,2,2:41:40 PM,30,2,2:44:36 PM,30,2,2:58:57 PM,7,27.4,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_726,861
-Order_No_21563,User_Id_868,Bike,3,Business,27,6,12:26:44 PM,27,6,12:28:44 PM,27,6,12:37:46 PM,27,6,12:54:46 PM,27,6,12:54:49 PM,6,,,-1.3033561000000002,36.8163631,-1.265715,36.823815,Rider_Id_19,3
-Order_No_22307,User_Id_1205,Bike,3,Business,27,5,4:49:25 PM,27,5,4:50:01 PM,27,5,4:59:41 PM,27,5,5:02:00 PM,27,5,5:18:06 PM,8,17.8,,-1.2810807,36.8144229,-1.2618539,36.777240500000005,Rider_Id_857,966
-Order_No_25200,User_Id_238,Bike,3,Business,4,2,1:49:21 PM,4,2,1:50:56 PM,4,2,1:59:51 PM,4,2,2:01:21 PM,4,2,2:16:44 PM,7,22.7,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_211,923
-Order_No_4326,User_Id_393,Bike,3,Business,17,5,9:43:20 AM,17,5,9:43:51 AM,17,5,9:47:04 AM,17,5,10:05:53 AM,17,5,10:14:48 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,535
-Order_No_25584,User_Id_1502,Bike,3,Business,18,2,4:12:44 PM,18,2,4:16:19 PM,18,2,4:16:21 PM,18,2,4:51:04 PM,18,2,5:23:23 PM,13,23.6,,-1.2584143,36.8048002,-1.2232123,36.8932547,Rider_Id_874,1939
-Order_No_9820,User_Id_1789,Bike,3,Business,19,3,11:33:46 AM,19,3,11:34:01 AM,19,3,11:43:38 AM,19,3,11:57:33 AM,19,3,12:33:34 PM,20,22.2,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_803,2161
-Order_No_11105,User_Id_136,Bike,3,Business,14,2,11:00:31 AM,14,2,11:08:27 AM,14,2,12:02:29 PM,14,2,12:14:15 PM,14,2,1:04:48 PM,17,,,-1.3165103999999999,36.707772600000006,-1.2654716000000001,36.7983635,Rider_Id_315,3033
-Order_No_7075,User_Id_1402,Bike,3,Business,28,2,8:45:59 AM,28,2,8:51:31 AM,28,2,9:08:00 AM,28,2,9:16:36 AM,28,2,9:32:23 AM,6,18.6,,-1.2571472,36.795063299999995,-1.2813012,36.832396200000005,Rider_Id_89,947
-Order_No_14144,User_Id_2208,Bike,2,Personal,20,5,9:36:45 AM,20,5,9:37:12 AM,20,5,9:47:38 AM,20,5,9:52:50 AM,20,5,10:02:13 AM,3,14.5,,-1.2738167,36.8157821,-1.2874292,36.823314,Rider_Id_456,563
-Order_No_27752,User_Id_867,Bike,3,Business,27,2,10:30:25 AM,27,2,10:31:37 AM,27,2,10:31:50 AM,27,2,10:37:31 AM,27,2,10:56:47 AM,9,23.8,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_649,1156
-Order_No_19585,User_Id_3283,Bike,3,Business,14,4,2:34:13 PM,14,4,2:37:32 PM,14,4,2:49:36 PM,14,4,2:57:05 PM,14,4,3:19:40 PM,14,27.6,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_8,1355
-Order_No_17986,User_Id_3291,Bike,3,Business,19,2,1:26:12 PM,19,2,1:27:04 PM,19,2,1:27:32 PM,19,2,1:36:03 PM,19,2,1:44:18 PM,5,29.8,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_75,495
-Order_No_27960,User_Id_3674,Bike,3,Business,18,5,4:15:02 PM,18,5,4:19:50 PM,18,5,4:21:49 PM,18,5,4:23:22 PM,18,5,4:51:20 PM,11,27.2,,-1.2551895,36.7822034,-1.2343087,36.84749,Rider_Id_279,1678
-Order_No_18306,User_Id_1591,Bike,2,Personal,27,4,2:35:50 PM,27,4,2:36:16 PM,27,4,2:38:21 PM,27,4,2:41:40 PM,27,4,2:51:55 PM,5,25.0,,-1.262359,36.822946,-1.2771989,36.808975200000006,Rider_Id_427,615
-Order_No_10787,User_Id_1976,Bike,3,Business,30,5,11:46:57 AM,30,5,11:48:07 AM,30,5,12:01:16 PM,30,5,12:08:08 PM,30,5,12:45:50 PM,14,23.6,,-1.257028,36.7810435,-1.3222627,36.823409600000005,Rider_Id_138,2262
-Order_No_8447,User_Id_635,Bike,3,Business,21,2,2:40:08 PM,21,2,2:41:15 PM,21,2,2:51:16 PM,21,2,3:55:01 PM,21,2,4:43:34 PM,16,25.2,,-1.3081376,36.83297720000001,-1.26365,36.719989,Rider_Id_591,2913
-Order_No_16602,User_Id_3283,Bike,3,Business,7,5,3:08:22 PM,7,5,3:10:20 PM,7,5,3:23:30 PM,7,5,3:33:54 PM,7,5,3:44:10 PM,4,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_427,616
-Order_No_2387,User_Id_3521,Bike,3,Business,8,1,11:10:34 AM,8,1,11:10:57 AM,8,1,11:42:28 AM,8,1,12:04:17 PM,8,1,12:17:49 PM,7,28.0,,-1.2524627000000002,36.7772593,-1.2696908000000002,36.81519,Rider_Id_709,812
-Order_No_5437,User_Id_3772,Bike,1,Personal,13,3,11:45:02 AM,13,3,11:45:51 AM,13,3,11:47:04 AM,13,3,11:54:14 AM,13,3,12:11:17 PM,7,19.9,,-1.3054347,36.824202899999996,-1.274716,36.789582,Rider_Id_116,1023
-Order_No_4551,User_Id_3283,Bike,3,Business,7,1,9:02:43 AM,7,1,9:03:07 AM,7,1,9:07:54 AM,7,1,9:17:18 AM,7,1,9:36:01 AM,7,21.3,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_512,1123
-Order_No_2147,User_Id_1148,Bike,3,Business,18,4,11:24:45 AM,18,4,11:33:21 AM,18,4,11:44:49 AM,18,4,12:11:17 PM,18,4,12:24:08 PM,3,25.7,,-1.3100586,36.850032899999995,-1.311818,36.8638544,Rider_Id_69,771
-Order_No_20156,User_Id_868,Bike,3,Business,26,5,8:45:35 AM,26,5,8:45:54 AM,26,5,8:48:33 AM,26,5,9:11:53 AM,26,5,9:16:38 AM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_607,285
-Order_No_13580,User_Id_2982,Bike,3,Business,3,4,11:36:44 AM,3,4,11:37:18 AM,3,4,11:46:32 AM,3,4,11:59:12 AM,3,4,12:39:34 PM,18,23.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_31,2422
-Order_No_2546,User_Id_1329,Bike,3,Business,9,4,1:51:15 PM,9,4,1:53:29 PM,9,4,1:59:01 PM,9,4,2:01:49 PM,9,4,2:48:01 PM,7,,,-1.273056,36.811298,-1.2912496,36.772774299999995,Rider_Id_959,2772
-Order_No_17621,User_Id_2412,Bike,3,Business,1,6,4:21:56 PM,1,6,4:22:52 PM,1,6,4:47:49 PM,1,6,4:57:40 PM,1,6,5:21:13 PM,12,19.5,,-1.2527958000000001,36.8003131,-1.2297202,36.881950700000004,Rider_Id_427,1413
-Order_No_26525,User_Id_58,Bike,3,Business,7,1,11:09:04 AM,7,1,11:10:36 AM,7,1,11:25:55 AM,7,1,11:54:18 AM,7,1,12:12:53 PM,6,21.3,,-1.2951620000000001,36.8115914,-1.2765736,36.851364600000004,Rider_Id_100,1115
-Order_No_11542,User_Id_1995,Bike,3,Business,17,3,4:50:39 PM,17,3,5:09:59 PM,17,3,5:10:18 PM,17,3,5:13:58 PM,17,3,5:46:13 PM,10,,,-1.2551895,36.7822034,-1.2765736,36.851364600000004,Rider_Id_613,1935
-Order_No_10393,User_Id_3291,Bike,3,Business,1,1,2:36:47 PM,1,1,2:39:21 PM,1,1,2:55:15 PM,1,1,3:18:15 PM,1,1,3:43:20 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_686,1505
-Order_No_21423,User_Id_2750,Bike,3,Business,13,2,10:12:22 AM,13,2,10:14:47 AM,13,2,10:21:00 AM,13,2,10:22:47 AM,13,2,10:39:55 AM,7,19.8,8.9,-1.275442,36.766020700000006,-1.2892350000000001,36.8135805,Rider_Id_778,1028
-Order_No_26809,User_Id_2866,Bike,3,Business,28,4,11:36:36 AM,28,4,11:42:20 AM,28,4,11:42:26 AM,28,4,11:49:51 AM,28,4,12:22:48 PM,11,28.1,,-1.2551895,36.7822034,-1.3197323,36.8120386,Rider_Id_518,1977
-Order_No_12731,User_Id_635,Bike,3,Business,5,6,9:29:43 AM,5,6,9:31:05 AM,5,6,9:41:08 AM,5,6,9:54:04 AM,5,6,10:19:09 AM,9,19.0,,-1.2599563,36.799343900000004,-1.312124,36.8154885,Rider_Id_753,1505
-Order_No_3027,User_Id_1865,Bike,1,Personal,20,1,4:02:39 PM,20,1,4:02:55 PM,20,1,4:09:32 PM,20,1,4:14:08 PM,20,1,5:04:30 PM,23,21.3,,-1.214428,36.883258000000005,-1.3197748,36.82807929999999,Rider_Id_898,3022
-Order_No_9512,User_Id_3316,Bike,3,Personal,21,2,1:44:33 PM,21,2,1:45:48 PM,21,2,2:10:04 PM,21,2,2:13:08 PM,21,2,2:56:34 PM,9,25.2,,-1.2795216,36.77067520000001,-1.2856826000000001,36.8255265,Rider_Id_344,2606
-Order_No_3336,User_Id_3127,Bike,3,Business,21,3,10:39:50 AM,21,3,10:39:59 AM,21,3,11:20:20 AM,21,3,11:29:23 AM,21,3,12:14:10 PM,14,22.1,,-1.2388499,36.872726799999995,-1.2918246,36.801584999999996,Rider_Id_199,2687
-Order_No_19904,User_Id_3291,Bike,3,Business,29,2,11:07:20 AM,29,2,11:07:51 AM,29,2,11:08:36 AM,29,2,11:17:19 AM,29,2,11:25:46 AM,4,23.5,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_906,507
-Order_No_11302,User_Id_246,Bike,3,Business,27,5,1:39:44 PM,27,5,1:40:08 PM,27,5,1:45:35 PM,27,5,1:50:03 PM,27,5,2:10:48 PM,17,24.0,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_844,1245
-Order_No_27739,User_Id_2523,Bike,3,Business,11,2,9:46:54 AM,11,2,9:50:38 AM,11,2,9:59:20 AM,11,2,10:04:19 AM,11,2,10:24:40 AM,7,16.7,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_247,1221
-Order_No_19338,User_Id_136,Bike,3,Business,21,4,9:53:53 AM,21,4,10:21:21 AM,21,4,10:38:34 AM,21,4,10:44:02 AM,21,4,11:22:50 AM,11,25.1,,-1.3254522,36.721658399999995,-1.2913552,36.7875945,Rider_Id_831,2328
-Order_No_21751,User_Id_2945,Bike,2,Personal,11,5,8:45:59 PM,11,5,8:49:57 PM,11,5,8:50:05 PM,11,5,9:06:04 PM,11,5,9:13:59 PM,2,18.1,2.9,-1.2892766000000002,36.8122848,-1.2824577,36.7967953,Rider_Id_886,475
-Order_No_16969,User_Id_20,Bike,3,Business,7,2,9:51:48 AM,7,2,9:52:16 AM,7,2,10:04:28 AM,7,2,10:07:22 AM,7,2,10:40:11 AM,7,,,-1.2763494,36.7671783,-1.2629666000000002,36.8068369,Rider_Id_502,1969
-Order_No_18688,User_Id_3283,Bike,3,Business,6,4,9:38:13 AM,6,4,9:42:28 AM,6,4,9:50:07 AM,6,4,9:57:41 AM,6,4,10:09:55 AM,5,18.8,3.1,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_646,734
-Order_No_18350,User_Id_2992,Bike,1,Personal,10,5,6:27:34 PM,10,5,6:28:52 PM,10,5,6:43:51 PM,10,5,6:47:38 PM,10,5,7:06:42 PM,5,21.1,,-1.2883158000000001,36.8199114,-1.307774,36.840754100000005,Rider_Id_213,1144
-Order_No_6146,User_Id_635,Bike,3,Business,3,3,1:30:09 PM,3,3,1:36:03 PM,3,3,2:05:05 PM,3,3,2:18:35 PM,3,3,3:11:03 PM,7,28.1,,-1.3081376,36.83297720000001,-1.2648164,36.8057234,Rider_Id_508,3148
-Order_No_5435,User_Id_256,Bike,3,Personal,13,5,12:44:28 PM,13,5,12:48:11 PM,13,5,1:04:52 PM,13,5,1:35:24 PM,13,5,1:53:36 PM,5,19.4,,-1.280572,36.826162,-1.3207146,36.8305771,Rider_Id_88,1092
-Order_No_869,User_Id_3647,Bike,3,Business,7,4,12:04:20 PM,7,4,12:05:23 PM,7,4,12:13:19 PM,7,4,12:19:33 PM,7,4,12:51:41 PM,15,27.7,,-1.2726389999999999,36.794723,-1.2129686000000002,36.886176500000005,Rider_Id_937,1928
-Order_No_9701,User_Id_111,Bike,3,Business,9,4,3:43:58 PM,9,4,4:02:38 PM,9,4,4:20:15 PM,9,4,4:26:36 PM,9,4,4:35:53 PM,4,,,-1.2551895,36.7822034,-1.2707177,36.8009828,Rider_Id_576,557
-Order_No_11826,User_Id_2351,Bike,3,Personal,9,6,3:53:47 PM,9,6,3:54:02 PM,9,6,4:01:11 PM,9,6,4:04:28 PM,9,6,4:25:19 PM,11,27.3,,-1.3066143,36.8161046,-1.2830518999999998,36.8808254,Rider_Id_691,1251
-Order_No_1874,User_Id_3291,Bike,3,Business,15,3,10:00:25 AM,15,3,10:04:07 AM,15,3,10:19:20 AM,15,3,10:27:05 AM,15,3,11:21:05 AM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_427,3240
-Order_No_7331,User_Id_3149,Bike,3,Business,13,4,2:34:25 PM,13,4,3:49:21 PM,13,4,4:01:30 PM,13,4,4:22:44 PM,13,4,4:42:08 PM,5,22.3,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_362,1164
-Order_No_938,User_Id_301,Bike,3,Personal,26,2,10:56:14 AM,26,2,11:28:48 AM,26,2,11:46:35 AM,26,2,11:54:36 AM,26,2,12:14:52 PM,8,,,-1.3711733,36.7319722,-1.31754,36.70131,Rider_Id_549,1216
-Order_No_19491,User_Id_2521,Bike,3,Business,25,1,3:09:19 PM,25,1,3:10:59 PM,25,1,3:38:55 PM,25,1,4:48:42 PM,25,1,5:00:47 PM,4,27.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_328,725
-Order_No_24031,User_Id_3688,Bike,3,Business,11,1,2:28:18 PM,11,1,2:28:56 PM,11,1,2:40:48 PM,11,1,2:50:33 PM,11,1,3:40:43 PM,15,28.0,,-1.3194518,36.869396,-1.2551895,36.7822034,Rider_Id_510,3010
-Order_No_12387,User_Id_265,Bike,3,Business,3,5,10:09:46 AM,3,5,10:18:34 AM,3,5,11:05:00 AM,3,5,11:18:53 AM,3,5,12:06:39 PM,4,,,-1.300921,36.828195,-1.2788182,36.820668299999994,Rider_Id_155,2866
-Order_No_19447,User_Id_20,Bike,3,Business,24,3,11:06:00 AM,24,3,11:15:22 AM,24,3,11:43:47 AM,24,3,11:56:08 AM,24,3,12:32:19 PM,5,19.8,,-1.2878081000000001,36.8200516,-1.2654348,36.8022464,Rider_Id_660,2171
-Order_No_24572,User_Id_2174,Bike,3,Business,21,5,10:14:11 AM,21,5,10:14:36 AM,21,5,10:14:49 AM,21,5,11:13:18 AM,21,5,12:23:42 PM,28,22.9,,-1.2482781,36.884091999999995,-1.391222,36.760676000000004,Rider_Id_946,4224
-Order_No_14259,User_Id_2069,Bike,3,Business,4,2,11:15:09 AM,4,2,11:15:30 AM,4,2,11:29:51 AM,4,2,11:59:13 AM,4,2,12:14:27 PM,16,19.5,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_598,914
-Order_No_23734,User_Id_246,Bike,3,Business,14,5,10:15:55 AM,14,5,10:16:13 AM,14,5,10:27:49 AM,14,5,11:06:53 AM,14,5,11:47:35 AM,8,24.5,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_698,2442
-Order_No_8875,User_Id_2200,Bike,3,Business,11,7,10:46:37 AM,11,7,10:54:55 AM,11,7,10:55:03 AM,11,7,11:02:58 AM,11,7,11:13:29 AM,2,25.2,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_206,631
-Order_No_23665,User_Id_1926,Bike,3,Business,29,4,3:55:40 PM,29,4,4:34:04 PM,29,4,4:46:17 PM,29,4,4:49:25 PM,29,4,5:35:09 PM,6,20.9,,-1.2878209999999999,36.827056,-1.2571472,36.795063299999995,Rider_Id_436,2744
-Order_No_17337,User_Id_1244,Bike,3,Business,4,1,1:59:48 PM,4,1,2:22:52 PM,4,1,2:25:41 PM,4,1,2:27:18 PM,4,1,2:59:36 PM,8,28.6,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_605,1938
-Order_No_23037,User_Id_928,Bike,3,Business,23,3,11:16:37 AM,23,3,11:17:04 AM,23,3,11:17:11 AM,23,3,11:24:53 AM,23,3,11:55:31 AM,17,,,-1.2551895,36.7822034,-1.2264168,36.8767125,Rider_Id_518,1838
-Order_No_12106,User_Id_300,Bike,1,Personal,27,4,6:04:35 PM,27,4,6:04:48 PM,27,4,6:06:13 PM,27,4,6:23:11 PM,27,4,6:56:23 PM,12,23.1,,-1.2857763999999998,36.8236622,-1.2237818999999999,36.8768042,Rider_Id_338,1992
-Order_No_14168,User_Id_2295,Bike,1,Personal,31,2,1:07:17 PM,31,2,1:08:40 PM,31,2,1:28:27 PM,31,2,1:32:21 PM,31,2,2:00:07 PM,7,22.1,,-1.2857117,36.7968296,-1.2835586,36.8125641,Rider_Id_722,1666
-Order_No_10734,User_Id_1724,Bike,3,Business,1,4,11:35:53 AM,1,4,11:43:44 AM,1,4,12:07:24 PM,1,4,12:14:01 PM,1,4,12:43:28 PM,8,,,-1.2793172,36.827656700000006,-1.2551895,36.7822034,Rider_Id_268,1767
-Order_No_11834,User_Id_1205,Bike,3,Business,20,4,10:45:06 AM,20,4,10:45:17 AM,20,4,10:58:01 AM,20,4,11:26:13 AM,20,4,11:44:43 AM,8,24.6,,-1.2706815,36.7646286,-1.257309,36.806008399999996,Rider_Id_770,1110
-Order_No_12995,User_Id_635,Bike,3,Business,8,1,1:54:52 PM,8,1,1:55:05 PM,8,1,1:59:37 PM,8,1,2:10:14 PM,8,1,2:26:46 PM,8,24.8,,-1.2599563,36.799343900000004,-1.22589,36.7822548,Rider_Id_178,992
-Order_No_27486,User_Id_3560,Bike,3,Business,20,4,1:13:14 PM,20,4,1:24:37 PM,20,4,1:47:52 PM,20,4,2:07:59 PM,20,4,2:38:52 PM,5,25.3,,-1.3001909,36.816362700000006,-1.279395,36.825364,Rider_Id_725,1853
-Order_No_7272,User_Id_1502,Bike,3,Business,7,4,2:58:36 PM,7,4,4:37:43 PM,7,4,4:56:59 PM,7,4,5:10:45 PM,7,4,5:56:24 PM,14,29.4,,-1.207546,36.7913976,-1.2232123,36.8932547,Rider_Id_830,2739
-Order_No_1512,User_Id_1721,Bike,3,Business,30,2,12:10:05 PM,30,2,12:13:22 PM,30,2,12:25:34 PM,30,2,12:36:21 PM,30,2,1:20:54 PM,14,,,-1.2551895,36.7822034,-1.242902,36.870334,Rider_Id_598,2673
-Order_No_26553,User_Id_346,Bike,3,Business,7,2,3:41:57 PM,7,2,3:42:49 PM,7,2,3:51:31 PM,7,2,4:05:21 PM,7,2,4:17:17 PM,6,,,-1.2615888000000002,36.7928732,-1.28878,36.8168312,Rider_Id_205,716
-Order_No_21085,User_Id_2154,Bike,3,Business,13,4,1:03:33 PM,13,4,1:04:15 PM,13,4,1:11:53 PM,13,4,1:23:30 PM,13,4,1:43:49 PM,13,22.2,,-1.2156006000000001,36.8916865,-1.2765736,36.851364600000004,Rider_Id_60,1219
-Order_No_22998,User_Id_598,Bike,1,Business,23,6,10:26:36 AM,23,6,10:26:58 AM,23,6,11:08:10 AM,23,6,11:20:42 AM,23,6,11:47:57 AM,19,,,-1.2138373,36.799857200000005,-1.2590946,36.7607115,Rider_Id_101,1635
-Order_No_3278,User_Id_2464,Bike,3,Business,31,5,3:44:26 PM,31,5,3:58:02 PM,31,5,4:23:42 PM,31,5,4:30:53 PM,31,5,5:04:57 PM,4,24.9,,-1.2658424,36.8101804,-1.279395,36.825364,Rider_Id_79,2044
-Order_No_17566,User_Id_1346,Bike,3,Business,3,1,11:16:09 AM,3,1,11:17:19 AM,3,1,11:17:33 AM,3,1,11:56:44 AM,3,1,11:56:47 AM,20,21.3,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_855,3
-Order_No_22924,User_Id_1753,Bike,2,Business,12,5,2:52:53 PM,12,5,2:53:04 PM,12,5,2:53:45 PM,12,5,3:03:50 PM,12,5,3:18:14 PM,5,27.1,,-1.2551895,36.7822034,-1.2696908000000002,36.81519,Rider_Id_875,864
-Order_No_27409,User_Id_3201,Bike,3,Business,22,3,9:24:07 AM,22,3,9:24:40 AM,22,3,9:32:59 AM,22,3,9:42:07 AM,22,3,10:10:28 AM,9,19.0,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_161,1701
-Order_No_11803,User_Id_1882,Bike,3,Business,13,6,9:39:06 AM,13,6,9:39:24 AM,13,6,9:39:39 AM,13,6,10:02:06 AM,13,6,10:37:27 AM,7,20.2,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_613,2121
-Order_No_26813,User_Id_3565,Bike,3,Business,8,5,8:33:12 AM,8,5,8:43:38 AM,8,5,8:59:12 AM,8,5,9:14:49 AM,8,5,9:28:53 AM,8,15.1,1.1,-1.2844665000000002,36.7866113,-1.3090663,36.813686499999996,Rider_Id_379,844
-Order_No_6073,User_Id_2132,Bike,3,Business,12,5,11:39:52 AM,12,5,11:45:16 AM,12,5,11:55:31 AM,12,5,12:13:19 PM,12,5,12:26:03 PM,8,26.6,,-1.2384056,36.8718698,-1.275931,36.8516227,Rider_Id_865,764
-Order_No_6390,User_Id_1469,Bike,3,Business,6,4,2:53:38 PM,6,4,2:53:44 PM,6,4,3:01:00 PM,6,4,3:08:48 PM,6,4,3:08:49 PM,3,,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_647,1
-Order_No_18339,User_Id_1142,Bike,3,Personal,8,2,3:39:39 PM,8,2,3:42:32 PM,8,2,4:11:34 PM,8,2,4:16:38 PM,8,2,4:58:49 PM,8,25.2,,-1.2775276000000002,36.8140021,-1.3238573,36.8492744,Rider_Id_48,2531
-Order_No_29,User_Id_2207,Bike,3,Business,6,3,2:14:59 PM,6,3,2:15:59 PM,6,3,2:39:49 PM,6,3,2:54:12 PM,6,3,3:31:46 PM,17,,,-1.3234404,36.708763299999994,-1.2696908000000002,36.81519,Rider_Id_604,2254
-Order_No_2120,User_Id_1340,Bike,3,Business,1,3,3:29:39 PM,1,3,3:30:42 PM,1,3,3:40:18 PM,1,3,3:47:08 PM,1,3,4:08:43 PM,6,23.8,,-1.2945798000000002,36.7737755,-1.2707178999999997,36.7979713,Rider_Id_140,1295
-Order_No_9175,User_Id_1719,Bike,3,Business,20,3,11:09:23 AM,20,3,11:09:53 AM,20,3,11:13:52 AM,20,3,11:19:02 AM,20,3,11:26:12 AM,3,27.1,,-1.2975066000000002,36.76894229999999,-1.2962832,36.784968299999996,Rider_Id_816,430
-Order_No_5151,User_Id_1500,Bike,3,Business,10,1,9:41:28 AM,10,1,9:42:18 AM,10,1,9:48:39 AM,10,1,9:50:49 AM,10,1,10:10:34 AM,9,16.7,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161,1185
-Order_No_868,User_Id_1169,Bike,1,Personal,2,2,2:37:56 PM,2,2,2:39:16 PM,2,2,2:43:28 PM,2,2,2:52:17 PM,2,2,3:12:42 PM,8,26.1,,-1.2918246,36.801584999999996,-1.3104733999999998,36.7363698,Rider_Id_597,1225
-Order_No_22618,User_Id_3647,Bike,3,Business,23,4,12:04:46 PM,23,4,12:12:15 PM,23,4,12:20:27 PM,23,4,12:27:58 PM,23,4,12:36:49 PM,4,23.2,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_484,531
-Order_No_729,User_Id_3032,Bike,3,Business,11,5,3:27:25 PM,11,5,3:27:31 PM,11,5,3:37:46 PM,11,5,3:38:55 PM,11,5,4:08:05 PM,16,26.6,,-1.2571472,36.795063299999995,-1.2117467,36.900294099999996,Rider_Id_932,1750
-Order_No_19031,User_Id_2521,Bike,3,Business,5,6,11:25:16 AM,5,6,11:25:21 AM,5,6,11:25:32 AM,5,6,11:28:05 AM,5,6,11:57:10 AM,14,22.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1745
-Order_No_8368,User_Id_1304,Bike,3,Business,11,5,4:41:33 PM,11,5,4:46:17 PM,11,5,4:55:55 PM,11,5,4:57:56 PM,11,5,5:38:18 PM,14,24.2,,-1.2859912,36.8756811,-1.2933017,36.8008304,Rider_Id_26,2422
-Order_No_7310,User_Id_2066,Bike,3,Business,3,1,10:50:34 AM,3,1,10:51:11 AM,3,1,10:56:29 AM,3,1,11:04:18 AM,3,1,11:21:01 AM,2,20.7,,-1.316961,36.820337,-1.3251583999999998,36.8199384,Rider_Id_328,1003
-Order_No_11060,User_Id_649,Bike,3,Business,27,4,3:10:45 PM,27,4,3:11:01 PM,27,4,3:12:42 PM,27,4,3:26:35 PM,27,4,4:25:44 PM,22,24.9,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_64,3549
-Order_No_21809,User_Id_163,Bike,1,Personal,3,1,1:39:51 PM,3,1,1:40:19 PM,3,1,1:49:04 PM,3,1,1:55:34 PM,3,1,2:19:41 PM,12,23.0,,-1.2901786,36.824294,-1.2320608999999998,36.752092,Rider_Id_882,1447
-Order_No_8758,User_Id_2676,Bike,3,Business,23,1,12:54:17 PM,23,1,12:55:17 PM,23,1,12:56:31 PM,23,1,1:03:22 PM,23,1,1:12:36 PM,4,18.5,,-1.2963097,36.7688221,-1.2764692,36.758011100000004,Rider_Id_100,554
-Order_No_733,User_Id_1764,Bike,1,Personal,5,2,11:35:46 AM,5,2,11:40:58 AM,5,2,11:55:14 AM,5,2,12:02:20 PM,5,2,12:08:18 PM,2,21.7,,-1.279199,36.822492600000004,-1.2789476999999998,36.8122359,Rider_Id_925,358
-Order_No_6363,User_Id_374,Bike,2,Personal,8,5,8:15:08 PM,8,5,8:25:10 PM,8,5,8:37:52 PM,8,5,8:39:39 PM,8,5,8:50:11 PM,5,18.2,,-1.2831463,36.76360220000001,-1.2900726999999999,36.7890059,Rider_Id_334,632
-Order_No_3832,User_Id_3222,Bike,3,Business,8,1,3:12:51 PM,8,1,3:17:58 PM,8,1,4:00:43 PM,8,1,4:01:46 PM,8,1,4:16:49 PM,8,24.8,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_857,903
-Order_No_11945,User_Id_3239,Bike,3,Personal,6,4,11:01:25 AM,6,4,11:02:45 AM,6,4,11:40:28 AM,6,4,11:51:31 AM,6,4,12:07:07 PM,9,21.8,,-1.2655332000000001,36.8094648,-1.2969741000000001,36.7856612,Rider_Id_574,936
-Order_No_1242,User_Id_393,Bike,3,Business,8,5,12:09:54 PM,8,5,12:10:35 PM,8,5,12:19:29 PM,8,5,12:29:29 PM,8,5,12:29:32 PM,4,24.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_903,3
-Order_No_6690,User_Id_1299,Bike,3,Business,2,3,11:53:53 AM,2,3,11:54:05 AM,2,3,11:58:55 AM,2,3,12:05:34 PM,2,3,12:25:46 PM,7,21.9,,-1.282543,36.824618,-1.25537,36.7756,Rider_Id_742,1212
-Order_No_12587,User_Id_295,Bike,3,Business,31,3,2:37:12 PM,31,3,4:08:52 PM,31,3,4:15:58 PM,31,3,4:18:00 PM,31,3,4:40:17 PM,6,26.8,,-1.2967108,36.787113500000004,-1.286925,36.821915999999995,Rider_Id_583,1337
-Order_No_8488,User_Id_2154,Bike,3,Business,26,6,12:56:54 PM,26,6,12:59:45 PM,26,6,1:07:43 PM,26,6,1:17:06 PM,26,6,1:31:18 PM,6,25.9,,-1.2765736,36.851364600000004,-1.2878913,36.8089253,Rider_Id_510,852
-Order_No_7485,User_Id_265,Bike,3,Business,21,1,11:39:33 AM,21,1,11:54:49 AM,21,1,12:30:59 PM,21,1,12:45:35 PM,21,1,1:04:22 PM,10,25.7,,-1.341891,36.761269,-1.300921,36.828195,Rider_Id_576,1127
-Order_No_7168,User_Id_3291,Bike,3,Business,8,5,12:52:49 PM,8,5,1:00:58 PM,8,5,1:04:30 PM,8,5,1:22:12 PM,8,5,1:46:35 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_312,1463
-Order_No_23344,User_Id_419,Bike,3,Business,14,2,9:57:52 AM,14,2,9:58:18 AM,14,2,10:02:57 AM,14,2,10:27:09 AM,14,2,10:51:18 AM,14,,,-1.2571472,36.795063299999995,-1.3189252,36.861726899999994,Rider_Id_520,1449
-Order_No_8129,User_Id_3689,Bike,3,Business,15,4,12:09:10 PM,15,4,12:29:08 PM,15,4,12:55:29 PM,15,4,1:00:29 PM,15,4,1:08:47 PM,3,24.2,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_125,498
-Order_No_12128,User_Id_531,Bike,1,Personal,16,1,1:01:17 PM,16,1,1:01:37 PM,16,1,1:03:11 PM,16,1,1:47:49 PM,16,1,2:02:33 PM,6,22.6,,-1.2864733,36.8239937,-1.2615983,36.8083096,Rider_Id_726,884
-Order_No_16449,User_Id_1780,Bike,1,Personal,5,3,12:50:23 PM,5,3,12:50:46 PM,5,3,12:57:52 PM,5,3,1:06:25 PM,5,3,2:23:05 PM,15,22.1,,-1.206696,36.847709,-1.2956951,36.812924200000005,Rider_Id_882,4600
-Order_No_20414,User_Id_3149,Bike,3,Business,8,5,3:33:13 PM,8,5,3:34:36 PM,8,5,3:34:43 PM,8,5,3:50:22 PM,8,5,3:50:26 PM,1,30.3,,-1.3276043,36.836921999999994,-1.3167113000000001,36.8301563,Rider_Id_515,4
-Order_No_1565,User_Id_2801,Bike,3,Business,30,5,10:40:09 AM,30,5,10:42:23 AM,30,5,10:52:35 AM,30,5,10:57:14 AM,30,5,11:20:00 AM,11,23.6,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_623,1366
-Order_No_22635,User_Id_3647,Bike,3,Business,17,4,2:02:03 PM,17,4,2:02:38 PM,17,4,2:05:00 PM,17,4,2:38:44 PM,17,4,3:25:26 PM,28,27.9,,-1.2726389999999999,36.794723,-1.138784,36.970811,Rider_Id_741,2802
-Order_No_23456,User_Id_2330,Bike,3,Business,28,1,11:14:12 AM,28,1,11:14:22 AM,28,1,11:16:47 AM,28,1,11:19:24 AM,28,1,11:34:07 AM,6,23.6,,-1.2915246999999999,36.805575700000006,-1.3015197,36.7658456,Rider_Id_191,883
-Order_No_18794,User_Id_391,Bike,3,Business,20,3,11:38:58 AM,20,3,11:39:12 AM,20,3,11:43:44 AM,20,3,11:53:41 AM,20,3,12:04:14 PM,5,27.1,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_336,633
-Order_No_12332,User_Id_979,Bike,3,Personal,8,1,1:59:36 PM,8,1,2:00:35 PM,8,1,2:02:39 PM,8,1,2:32:15 PM,8,1,2:57:23 PM,14,29.4,,-1.2635089,36.78873370000001,-1.3204931,36.7043102,Rider_Id_291,1508
-Order_No_9139,User_Id_1178,Bike,3,Business,28,5,11:41:44 AM,28,5,11:42:22 AM,28,5,11:57:22 AM,28,5,11:59:10 AM,28,5,12:25:06 PM,13,25.1,,-1.2802850000000001,36.780954,-1.3169591,36.7000029,Rider_Id_731,1556
-Order_No_1968,User_Id_3669,Bike,3,Business,16,5,4:50:53 PM,16,5,4:51:32 PM,16,5,5:01:18 PM,16,5,5:07:37 PM,16,5,5:27:03 PM,17,26.1,,-1.2615888000000002,36.7928732,-1.2491258,36.689425299999996,Rider_Id_640,1166
-Order_No_7794,User_Id_2894,Bike,3,Business,19,5,4:38:25 PM,19,5,4:38:55 PM,19,5,4:53:38 PM,19,5,5:02:58 PM,19,5,5:20:37 PM,6,24.6,,-1.2974913000000001,36.805429600000004,-1.2621576,36.8103473,Rider_Id_755,1059
-Order_No_14740,User_Id_3560,Bike,3,Business,4,1,4:38:03 PM,4,1,4:38:10 PM,4,1,4:38:51 PM,4,1,4:42:26 PM,4,1,4:59:37 PM,12,27.8,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_195,1031
-Order_No_15258,User_Id_3525,Bike,3,Business,7,5,11:27:19 AM,7,5,11:27:26 AM,7,5,11:31:04 AM,7,5,11:50:00 AM,7,5,12:30:57 PM,21,24.9,,-1.2551895,36.7822034,-1.3700383,36.9190174,Rider_Id_828,2457
-Order_No_771,User_Id_3647,Bike,3,Business,10,5,1:51:58 PM,10,5,1:52:24 PM,10,5,1:54:46 PM,10,5,2:02:56 PM,10,5,2:32:00 PM,6,,,-1.2985749,36.8088,-1.2726389999999999,36.794723,Rider_Id_778,1744
-Order_No_6647,User_Id_1500,Bike,3,Business,19,2,11:33:15 AM,19,2,11:34:09 AM,19,2,11:34:52 AM,19,2,12:06:03 PM,19,2,12:20:37 PM,8,26.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_26,874
-Order_No_22476,User_Id_393,Bike,3,Business,16,3,2:25:14 PM,16,3,2:25:27 PM,16,3,2:25:41 PM,16,3,3:15:47 PM,16,3,3:28:43 PM,4,27.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_80,776
-Order_No_16293,User_Id_2801,Bike,3,Business,8,5,12:44:20 PM,8,5,12:45:02 PM,8,5,12:55:14 PM,8,5,1:01:19 PM,8,5,1:39:01 PM,11,,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_942,2262
-Order_No_16140,User_Id_3517,Bike,3,Business,10,3,10:48:05 AM,10,3,10:48:20 AM,10,3,10:55:36 AM,10,3,11:08:37 AM,10,3,11:40:23 AM,19,28.0,,-1.3211823999999999,36.868387299999995,-1.3882008,36.7699445,Rider_Id_690,1906
-Order_No_15380,User_Id_3201,Bike,3,Business,11,4,3:36:08 PM,11,4,3:36:27 PM,11,4,3:42:36 PM,11,4,3:48:53 PM,11,4,3:55:06 PM,1,26.9,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_880,373
-Order_No_26515,User_Id_3484,Bike,1,Personal,15,6,11:31:36 AM,15,6,11:31:48 AM,15,6,11:56:21 AM,15,6,12:00:04 PM,15,6,12:45:05 PM,14,,,-1.2927600000000001,36.783799,-1.2367491000000002,36.8700944,Rider_Id_612,2701
-Order_No_20731,User_Id_3355,Bike,3,Business,9,2,11:18:50 AM,9,2,11:26:30 AM,9,2,11:32:33 AM,9,2,11:37:24 AM,9,2,12:06:44 PM,6,21.8,,-1.3052491000000002,36.8223899,-1.276245,36.836329299999996,Rider_Id_208,1760
-Order_No_25670,User_Id_2616,Bike,3,Personal,24,1,2:36:09 PM,24,1,2:36:19 PM,24,1,2:47:43 PM,24,1,2:54:30 PM,24,1,3:17:20 PM,10,,,-1.2294985,36.8775495,-1.2785465,36.817907399999996,Rider_Id_858,1370
-Order_No_13069,User_Id_3560,Bike,3,Business,20,1,2:30:10 PM,20,1,2:30:56 PM,20,1,2:45:15 PM,20,1,2:50:42 PM,20,1,2:58:17 PM,2,,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_622,455
-Order_No_3069,User_Id_3201,Bike,3,Business,5,2,1:06:03 PM,5,2,1:06:38 PM,5,2,1:08:55 PM,5,2,1:33:33 PM,5,2,1:33:40 PM,2,,,-1.2584143,36.8048002,-1.2561334,36.7990458,Rider_Id_875,7
-Order_No_7925,User_Id_113,Bike,1,Business,10,5,10:53:35 AM,10,5,10:53:39 AM,10,5,11:00:25 AM,10,5,11:03:12 AM,10,5,11:23:32 AM,8,19.1,,-1.2844653,36.8206676,-1.2963097,36.7688221,Rider_Id_229,1220
-Order_No_369,User_Id_633,Bike,3,Business,14,4,2:45:03 PM,14,4,3:21:37 PM,14,4,3:44:33 PM,14,4,3:56:29 PM,14,4,4:11:13 PM,6,27.8,,-1.3083266000000002,36.8494756,-1.3004062,36.829741,Rider_Id_448,884
-Order_No_28012,User_Id_1244,Bike,3,Business,6,6,3:03:27 PM,6,6,3:03:35 PM,6,6,3:03:53 PM,6,6,3:05:55 PM,6,6,3:33:26 PM,9,28.0,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_613,1651
-Order_No_1364,User_Id_2330,Bike,3,Business,12,1,6:12:20 PM,12,1,6:12:34 PM,12,1,6:13:57 PM,12,1,6:33:16 PM,12,1,7:01:33 PM,9,24.0,,-1.2642903,36.80021370000001,-1.3015197,36.7658456,Rider_Id_45,1697
-Order_No_12776,User_Id_3283,Bike,3,Business,25,1,1:36:59 PM,25,1,1:37:22 PM,25,1,1:38:08 PM,25,1,1:50:15 PM,25,1,2:09:18 PM,4,28.0,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_574,1143
-Order_No_15013,User_Id_2180,Bike,3,Personal,18,1,8:54:18 AM,18,1,8:54:53 AM,18,1,9:30:55 AM,18,1,10:00:07 AM,18,1,11:02:36 AM,22,21.3,,-1.207546,36.7913976,-1.2963097,36.7688221,Rider_Id_190,3749
-Order_No_20992,User_Id_393,Bike,3,Business,30,2,3:54:04 PM,30,2,4:04:08 PM,30,2,4:04:21 PM,30,2,4:53:32 PM,30,2,4:53:36 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_10,4
-Order_No_20737,User_Id_2627,Bike,3,Business,17,3,2:36:24 PM,17,3,2:41:06 PM,17,3,2:41:22 PM,17,3,3:00:24 PM,17,3,3:37:26 PM,5,,,-1.2571472,36.795063299999995,-1.2857548,36.819062,Rider_Id_576,2222
-Order_No_16993,User_Id_2041,Bike,1,Business,6,3,1:23:50 PM,6,3,1:36:13 PM,6,3,1:38:24 PM,6,3,1:40:50 PM,6,3,1:40:55 PM,3,21.6,,-1.290894,36.822971,-1.2677588,36.8169359,Rider_Id_714,5
-Order_No_24565,User_Id_2327,Bike,1,Personal,5,4,1:58:25 PM,5,4,2:07:20 PM,5,4,2:22:48 PM,5,4,2:29:14 PM,5,4,2:52:03 PM,8,22.2,,-1.3106677,36.82331370000001,-1.296797,36.776452500000005,Rider_Id_88,1369
-Order_No_26988,User_Id_393,Bike,3,Business,26,2,9:01:57 AM,26,2,9:02:32 AM,26,2,9:02:47 AM,26,2,10:05:17 AM,26,2,10:05:51 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_239,34
-Order_No_8504,User_Id_393,Bike,3,Business,28,4,10:12:56 AM,28,4,10:13:12 AM,28,4,10:13:37 AM,28,4,10:21:59 AM,28,4,10:41:41 AM,11,19.6,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_809,1182
-Order_No_3522,User_Id_2216,Bike,3,Business,17,4,2:38:37 PM,17,4,2:39:52 PM,17,4,2:51:15 PM,17,4,2:54:01 PM,17,4,3:30:27 PM,10,28.1,,-1.2551895,36.7822034,-1.3108783000000002,36.8392726,Rider_Id_26,2186
-Order_No_16161,User_Id_2412,Bike,3,Business,5,5,12:34:58 PM,5,5,12:35:21 PM,5,5,12:41:43 PM,5,5,12:48:36 PM,5,5,1:18:11 PM,14,24.0,,-1.2297202,36.881950700000004,-1.2615888000000002,36.7928732,Rider_Id_74,1775
-Order_No_4181,User_Id_798,Bike,3,Business,17,1,12:52:45 PM,17,1,12:53:06 PM,17,1,12:56:25 PM,17,1,1:03:18 PM,17,1,1:42:45 PM,15,24.8,,-1.304033,36.7848695,-1.2898851,36.87915039999999,Rider_Id_101,2367
-Order_No_20981,User_Id_868,Bike,3,Business,18,4,2:23:56 PM,18,4,2:26:21 PM,18,4,2:32:04 PM,18,4,2:44:14 PM,18,4,2:49:03 PM,3,,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_695,289
-Order_No_16749,User_Id_2521,Bike,3,Business,16,6,8:57:19 AM,16,6,8:57:26 AM,16,6,8:57:30 AM,16,6,9:14:51 AM,16,6,9:14:52 AM,6,20.0,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_62,1
-Order_No_26974,User_Id_3064,Bike,3,Business,23,5,10:37:03 AM,23,5,10:38:01 AM,23,5,10:42:18 AM,23,5,10:43:38 AM,23,5,11:06:07 AM,20,24.2,,-1.2571472,36.795063299999995,-1.2110432,36.9114173,Rider_Id_387,1349
-Order_No_20490,User_Id_1066,Bike,1,Personal,13,3,2:56:00 PM,13,3,2:57:33 PM,13,3,3:14:45 PM,13,3,4:13:32 PM,13,3,4:35:44 PM,9,23.5,,-1.3038459999999998,36.779483,-1.2579303999999998,36.8019667,Rider_Id_474,1332
-Order_No_23210,User_Id_1469,Bike,3,Business,2,4,2:49:42 PM,2,4,2:53:14 PM,2,4,3:17:31 PM,2,4,3:30:59 PM,2,4,3:51:06 PM,7,,,-1.2571472,36.795063299999995,-1.3038156,36.812273299999994,Rider_Id_607,1207
-Order_No_21851,User_Id_1637,Bike,3,Business,21,1,12:36:00 PM,21,1,12:39:47 PM,21,1,12:52:53 PM,21,1,12:56:48 PM,21,1,1:22:35 PM,15,25.6,,-1.207546,36.7913976,-1.2615888000000002,36.7928732,Rider_Id_611,1547
-Order_No_23217,User_Id_2989,Bike,3,Business,12,4,12:57:39 PM,12,4,12:59:55 PM,12,4,1:47:46 PM,12,4,2:06:36 PM,12,4,3:15:07 PM,21,21.8,,-1.2248308,36.801257299999996,-1.3404187,36.7614685,Rider_Id_519,4111
-Order_No_13431,User_Id_1724,Bike,3,Business,5,5,6:20:14 PM,5,5,6:40:32 PM,5,5,6:40:39 PM,5,5,7:06:14 PM,5,5,7:23:59 PM,15,24.6,,-1.2551895,36.7822034,-1.32189,36.8589727,Rider_Id_886,1065
-Order_No_23285,User_Id_867,Bike,3,Business,11,1,2:10:11 PM,11,1,2:11:46 PM,11,1,2:19:04 PM,11,1,2:25:21 PM,11,1,2:56:14 PM,15,28.7,,-1.2991441000000001,36.7528804,-1.3167113000000001,36.8301563,Rider_Id_8,1853
-Order_No_4616,User_Id_273,Bike,2,Personal,14,2,5:15:30 PM,14,2,5:16:52 PM,14,2,5:31:28 PM,14,2,5:44:45 PM,14,2,6:00:02 PM,6,23.3,,-1.2751745,36.765767600000004,-1.2649605,36.798177700000004,Rider_Id_536,917
-Order_No_21310,User_Id_633,Bike,3,Business,26,1,2:00:32 PM,26,1,2:07:08 PM,26,1,2:23:17 PM,26,1,2:34:49 PM,26,1,2:48:27 PM,4,22.2,0.8,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_654,818
-Order_No_16260,User_Id_2989,Bike,3,Business,29,6,3:05:04 PM,29,6,3:05:27 PM,29,6,3:15:13 PM,29,6,3:19:13 PM,29,6,3:51:03 PM,15,26.5,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_208,1910
-Order_No_27331,User_Id_1578,Bike,3,Business,5,3,8:30:20 AM,5,3,9:42:20 AM,5,3,9:57:02 AM,5,3,10:22:57 AM,5,3,10:47:14 AM,9,17.9,,-1.28878,36.8168312,-1.2413316,36.878552500000005,Rider_Id_477,1457
-Order_No_20070,User_Id_2523,Bike,3,Business,3,2,2:30:42 PM,3,2,2:31:16 PM,3,2,2:39:09 PM,3,2,2:45:00 PM,3,2,3:06:54 PM,5,20.1,,-1.2551895,36.7822034,-1.2650523,36.786679799999995,Rider_Id_851,1314
-Order_No_585,User_Id_1958,Bike,3,Business,11,2,11:36:17 AM,11,2,11:45:39 AM,11,2,12:08:16 PM,11,2,12:12:54 PM,11,2,1:03:17 PM,23,19.4,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_583,3023
-Order_No_947,User_Id_198,Bike,3,Business,21,2,2:10:36 PM,21,2,2:12:41 PM,21,2,2:47:50 PM,21,2,2:49:02 PM,21,2,3:24:25 PM,26,25.2,,-1.3396256000000002,36.8863135,-1.3234404,36.708763299999994,Rider_Id_48,2123
-Order_No_26246,User_Id_391,Bike,3,Business,20,1,2:54:20 PM,20,1,3:20:16 PM,20,1,3:21:14 PM,20,1,3:43:33 PM,20,1,4:56:35 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_740,4382
-Order_No_4501,User_Id_2112,Bike,3,Business,18,6,3:40:45 PM,18,6,3:41:06 PM,18,6,4:01:38 PM,18,6,4:11:03 PM,18,6,4:22:01 PM,4,20.6,,-1.2297202,36.881950700000004,-1.2156006000000001,36.8916865,Rider_Id_384,658
-Order_No_5299,User_Id_2226,Bike,3,Personal,15,1,10:09:25 AM,15,1,10:09:44 AM,15,1,10:22:29 AM,15,1,10:27:49 AM,15,1,10:53:36 AM,9,,,-1.3104733999999998,36.7363698,-1.2985749,36.8088,Rider_Id_337,1547
-Order_No_5300,User_Id_635,Bike,3,Business,13,3,2:14:29 PM,13,3,2:16:56 PM,13,3,2:27:34 PM,13,3,2:35:36 PM,13,3,3:03:51 PM,8,27.1,,-1.2944363,36.7983424,-1.2602337,36.79905479999999,Rider_Id_513,1695
-Order_No_14254,User_Id_868,Bike,3,Business,12,6,1:20:11 PM,12,6,1:34:44 PM,12,6,1:54:51 PM,12,6,1:58:20 PM,12,6,2:20:10 PM,9,25.5,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_742,1310
-Order_No_3191,User_Id_2869,Bike,3,Business,6,3,11:21:03 AM,6,3,11:21:47 AM,6,3,11:49:47 AM,6,3,12:16:54 PM,6,3,12:52:54 PM,9,,,-1.28878,36.8168312,-1.3025826,36.7670807,Rider_Id_118,2160
-Order_No_20583,User_Id_1471,Bike,3,Business,7,4,3:12:42 PM,7,4,3:13:08 PM,7,4,3:13:13 PM,7,4,3:26:13 PM,7,4,3:26:20 PM,5,31.1,,-1.2551895,36.7822034,-1.2592947,36.7698085,Rider_Id_523,7
-Order_No_25900,User_Id_72,Bike,1,Personal,15,1,10:49:05 AM,15,1,10:49:30 AM,15,1,11:01:24 AM,15,1,11:05:53 AM,15,1,11:23:48 AM,8,,,-1.2901786,36.824294,-1.3227037,36.847087200000004,Rider_Id_208,1075
-Order_No_16600,User_Id_3080,Bike,3,Business,21,4,11:11:26 AM,21,4,11:12:54 AM,21,4,11:20:54 AM,21,4,11:25:33 AM,21,4,12:29:00 PM,8,21.9,,-1.2551895,36.7822034,-1.2879618,36.82008629999999,Rider_Id_444,3807
-Order_No_117,User_Id_186,Bike,3,Business,25,4,4:26:54 PM,25,4,4:27:24 PM,25,4,4:33:14 PM,25,4,4:36:56 PM,25,4,4:53:50 PM,4,,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_935,1014
-Order_No_20512,User_Id_2041,Bike,1,Business,8,5,1:08:18 PM,8,5,1:21:07 PM,8,5,2:08:01 PM,8,5,2:27:57 PM,8,5,2:57:00 PM,11,21.7,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_379,1743
-Order_No_17208,User_Id_1471,Bike,3,Business,13,3,11:03:37 AM,13,3,11:07:20 AM,13,3,11:10:46 AM,13,3,11:15:02 AM,13,3,11:37:32 AM,9,24.8,,-1.2551895,36.7822034,-1.301796,36.8012024,Rider_Id_933,1350
-Order_No_25708,User_Id_53,Bike,3,Business,10,5,11:53:35 AM,10,5,11:55:11 AM,10,5,12:10:22 PM,10,5,12:35:21 PM,10,5,12:54:50 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_448,1169
-Order_No_7616,User_Id_540,Bike,3,Business,31,5,10:31:08 AM,31,5,10:40:41 AM,31,5,10:50:19 AM,31,5,11:00:31 AM,31,5,11:13:11 AM,5,16.9,,-1.2571472,36.795063299999995,-1.2623805000000001,36.7805404,Rider_Id_222,760
-Order_No_18256,User_Id_1926,Bike,3,Business,4,1,6:25:35 PM,4,1,6:27:29 PM,4,1,6:51:51 PM,4,1,6:59:52 PM,4,1,7:28:03 PM,10,21.1,,-1.3332749,36.870814700000004,-1.2810586000000002,36.883829399999996,Rider_Id_890,1691
-Order_No_7235,User_Id_2330,Bike,3,Business,19,2,11:56:08 AM,19,2,11:57:05 AM,19,2,12:13:37 PM,19,2,12:21:12 PM,19,2,12:43:21 PM,11,27.5,,-1.2890715,36.8157588,-1.2325706,36.808392299999994,Rider_Id_764,1329
-Order_No_14686,User_Id_372,Bike,1,Business,6,3,10:59:21 AM,6,3,11:49:23 AM,6,3,12:07:29 PM,6,3,12:11:53 PM,6,3,12:45:16 PM,8,19.8,,-1.2765736,36.851364600000004,-1.324462,36.867212,Rider_Id_616,2003
-Order_No_12855,User_Id_1484,Bike,3,Business,1,4,11:25:26 AM,1,4,1:43:50 PM,1,4,2:13:18 PM,1,4,2:47:05 PM,1,4,2:58:16 PM,9,,,-1.2551895,36.7822034,-1.2959613,36.7684114,Rider_Id_337,671
-Order_No_3477,User_Id_2801,Bike,3,Business,16,2,12:24:51 PM,16,2,12:25:49 PM,16,2,12:41:54 PM,16,2,12:50:03 PM,16,2,1:23:45 PM,15,,,-1.3104506,36.8687235,-1.2674273999999999,36.787082700000006,Rider_Id_448,2022
-Order_No_11676,User_Id_788,Bike,3,Business,9,5,1:20:38 PM,9,5,1:22:22 PM,9,5,1:33:14 PM,9,5,1:52:01 PM,9,5,2:10:18 PM,10,26.2,,-1.225322,36.80855,-1.2653185,36.8358841,Rider_Id_101,1097
-Order_No_7066,User_Id_346,Bike,3,Business,1,5,9:06:32 AM,1,5,9:11:15 AM,1,5,9:30:47 AM,1,5,9:39:41 AM,1,5,10:16:22 AM,3,,,-1.28878,36.8168312,-1.2896398999999998,36.812449799999996,Rider_Id_72,2201
-Order_No_14512,User_Id_1500,Bike,3,Business,21,4,3:19:23 PM,21,4,3:19:56 PM,21,4,3:27:17 PM,21,4,3:33:17 PM,21,4,4:02:46 PM,8,26.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_686,1769
-Order_No_23982,User_Id_2605,Bike,3,Business,10,4,4:22:02 PM,10,4,4:22:12 PM,10,4,4:22:37 PM,10,4,4:35:30 PM,10,4,5:15:29 PM,22,23.4,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_200,2399
-Order_No_22650,User_Id_2931,Bike,3,Business,30,2,10:01:06 AM,30,2,10:01:19 AM,30,2,10:32:01 AM,30,2,10:43:21 AM,30,2,11:25:03 AM,23,,,-1.3442152,36.9199984,-1.2452664,36.802854100000005,Rider_Id_585,2502
-Order_No_26926,User_Id_2271,Bike,3,Business,30,2,1:59:25 PM,30,2,2:03:03 PM,30,2,2:43:21 PM,30,2,2:48:47 PM,30,2,3:34:00 PM,17,,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_883,2713
-Order_No_14669,User_Id_1484,Bike,3,Business,17,4,12:40:14 PM,17,4,12:40:45 PM,17,4,12:46:31 PM,17,4,12:50:33 PM,17,4,12:55:13 PM,4,25.4,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_881,280
-Order_No_21186,User_Id_3470,Bike,3,Business,29,4,11:49:20 AM,29,4,12:20:43 PM,29,4,12:49:22 PM,29,4,1:21:52 PM,29,4,1:35:42 PM,2,21.3,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_362,830
-Order_No_12018,User_Id_3547,Bike,3,Business,17,3,1:45:21 PM,17,3,1:45:36 PM,17,3,1:45:45 PM,17,3,2:14:15 PM,17,3,2:46:25 PM,21,,,-1.3285517,36.9018,-1.2761408,36.7710839,Rider_Id_856,1930
-Order_No_5228,User_Id_3491,Bike,3,Business,26,2,11:50:19 AM,26,2,11:51:25 AM,26,2,11:57:22 AM,26,2,12:39:08 PM,26,2,1:11:02 PM,11,27.5,,-1.290894,36.822971,-1.2283403,36.8822756,Rider_Id_329,1914
-Order_No_11818,User_Id_3565,Bike,3,Business,25,5,7:37:39 AM,25,5,7:38:01 AM,25,5,7:52:30 AM,25,5,8:15:27 AM,25,5,8:47:33 AM,20,17.1,,-1.2844665000000002,36.7866113,-1.355309,36.6541393,Rider_Id_116,1926
-Order_No_9735,User_Id_1578,Bike,3,Business,10,4,1:10:36 PM,10,4,1:15:18 PM,10,4,1:41:05 PM,10,4,1:59:02 PM,10,4,3:05:17 PM,10,21.8,0.9,-1.2765736,36.851364600000004,-1.2559559999999999,36.789867,Rider_Id_248,3975
-Order_No_14145,User_Id_186,Bike,3,Business,5,4,11:58:20 AM,5,4,12:13:38 PM,5,4,12:22:33 PM,5,4,12:41:55 PM,5,4,1:32:39 PM,24,,,-1.3700383,36.9190174,-1.2579303999999998,36.8019667,Rider_Id_631,3044
-Order_No_6313,User_Id_788,Bike,3,Business,28,1,11:57:01 AM,28,1,11:58:14 AM,28,1,12:16:42 PM,28,1,12:33:19 PM,28,1,1:02:46 PM,6,23.8,,-1.225322,36.80855,-1.2608591999999998,36.831197700000004,Rider_Id_547,1767
-Order_No_23540,User_Id_391,Bike,3,Business,1,4,10:28:27 AM,1,4,11:07:45 AM,1,4,11:19:37 AM,1,4,11:26:54 AM,1,4,12:01:18 PM,18,,,-1.3882008,36.7699445,-1.2867454,36.8259017,Rider_Id_88,2064
-Order_No_16047,User_Id_3051,Bike,3,Personal,27,5,9:40:04 AM,27,5,9:42:10 AM,27,5,9:49:52 AM,27,5,9:54:48 AM,27,5,10:09:31 AM,7,16.0,,-1.2702333000000001,36.80065,-1.2965824,36.815302200000005,Rider_Id_726,883
-Order_No_28042,User_Id_2195,Bike,3,Business,7,2,10:38:55 AM,7,2,11:44:20 AM,7,2,12:07:08 PM,7,2,12:20:40 PM,7,2,12:48:49 PM,17,,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_675,1689
-Order_No_8590,User_Id_3647,Bike,3,Business,6,4,12:15:14 PM,6,4,12:15:47 PM,6,4,12:29:32 PM,6,4,12:35:14 PM,6,4,12:50:17 PM,3,19.7,,-1.2726389999999999,36.794723,-1.2667386,36.7937745,Rider_Id_155,903
-Order_No_7832,User_Id_346,Bike,3,Business,3,1,12:41:35 PM,3,1,12:43:46 PM,3,1,1:21:22 PM,3,1,2:13:53 PM,3,1,2:13:58 PM,4,23.2,,-1.2571472,36.795063299999995,-1.28878,36.8168312,Rider_Id_76,5
-Order_No_19838,User_Id_2066,Bike,3,Business,1,3,2:46:01 PM,1,3,2:46:19 PM,1,3,2:54:22 PM,1,3,2:56:43 PM,1,3,3:19:19 PM,11,,,-1.3210188999999999,36.841055100000005,-1.2664669,36.7921612,Rider_Id_233,1356
-Order_No_9521,User_Id_3698,Bike,3,Business,11,2,3:23:47 PM,11,2,3:58:17 PM,11,2,4:14:38 PM,11,2,4:25:52 PM,11,2,5:00:52 PM,19,21.2,,-1.3244885,36.897791999999995,-1.2567393,36.814323200000004,Rider_Id_75,2100
-Order_No_21695,User_Id_2216,Bike,3,Business,22,5,1:04:10 PM,22,5,1:04:43 PM,22,5,1:27:57 PM,22,5,1:47:50 PM,22,5,2:50:33 PM,10,27.1,,-1.2551895,36.7822034,-1.3108783000000002,36.8392726,Rider_Id_48,3763
-Order_No_97,User_Id_733,Bike,3,Business,8,4,10:11:49 AM,8,4,10:11:58 AM,8,4,10:16:31 AM,8,4,10:32:31 AM,8,4,10:58:29 AM,4,26.1,,-1.2551895,36.7822034,-1.2662728,36.8151265,Rider_Id_620,1558
-Order_No_11785,User_Id_1959,Bike,3,Business,25,4,11:16:07 AM,25,4,11:16:16 AM,25,4,11:27:33 AM,25,4,11:44:07 AM,25,4,12:10:06 PM,19,,,-1.2560455,36.780086700000005,-1.3207349,36.8819157,Rider_Id_438,1559
-Order_No_7182,User_Id_517,Bike,3,Business,3,4,8:08:44 AM,3,4,8:19:33 AM,3,4,8:42:54 AM,3,4,8:52:17 AM,3,4,9:17:37 AM,11,20.6,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_436,1520
-Order_No_18522,User_Id_287,Bike,1,Personal,1,3,4:35:18 PM,1,3,4:36:35 PM,1,3,4:48:06 PM,1,3,5:37:26 PM,1,3,6:02:06 PM,5,23.3,,-1.2765298999999999,36.822030600000005,-1.3004846,36.790159100000004,Rider_Id_414,1480
-Order_No_26837,User_Id_2546,Bike,1,Personal,1,1,11:53:46 AM,1,1,12:00:54 PM,1,1,12:42:55 PM,1,1,12:45:17 PM,1,1,12:57:05 PM,4,24.1,,-1.2970553,36.801092100000005,-1.2902552,36.8179349,Rider_Id_88,708
-Order_No_19541,User_Id_393,Bike,3,Business,19,2,11:07:00 AM,19,2,11:07:10 AM,19,2,11:07:14 AM,19,2,11:16:20 AM,19,2,11:41:20 AM,14,26.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_111,1500
-Order_No_18370,User_Id_2347,Bike,3,Personal,2,1,12:50:03 PM,2,1,12:51:33 PM,2,1,1:08:40 PM,2,1,1:14:14 PM,2,1,1:33:27 PM,4,19.0,,-1.2813973,36.828920399999994,-1.292278,36.797717999999996,Rider_Id_338,1153
-Order_No_18293,User_Id_393,Bike,3,Business,8,2,9:54:45 AM,8,2,10:04:52 AM,8,2,10:12:25 AM,8,2,10:28:16 AM,8,2,11:01:32 AM,13,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_912,1996
-Order_No_2195,User_Id_2982,Bike,3,Business,13,4,2:44:34 PM,13,4,2:46:44 PM,13,4,3:17:26 PM,13,4,3:26:14 PM,13,4,4:00:52 PM,14,22.9,,-1.300921,36.828195,-1.2163391000000001,36.8404088,Rider_Id_340,2078
-Order_No_14000,User_Id_3649,Bike,1,Personal,15,1,3:10:01 PM,15,1,3:11:43 PM,15,1,3:33:04 PM,15,1,3:39:58 PM,15,1,4:39:28 PM,11,,,-1.2782790000000002,36.827314,-1.2959724,36.7569331,Rider_Id_328,3570
-Order_No_28132,User_Id_938,Bike,1,Personal,9,3,9:20:16 AM,9,3,9:20:54 AM,9,3,9:33:52 AM,9,3,9:45:49 AM,9,3,10:23:13 AM,15,17.6,0.8,-1.3048328000000002,36.9129202,-1.2857929,36.82408289999999,Rider_Id_543,2244
-Order_No_24614,User_Id_3318,Bike,3,Business,29,1,10:52:20 AM,29,1,10:52:45 AM,29,1,10:53:30 AM,29,1,11:12:20 AM,29,1,11:27:40 AM,5,,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_206,920
-Order_No_4506,User_Id_2931,Bike,3,Business,26,2,12:07:36 PM,26,2,12:58:56 PM,26,2,1:15:28 PM,26,2,1:30:26 PM,26,2,2:04:08 PM,20,26.2,,-1.3433181,36.9138018,-1.2727979999999999,36.8124195,Rider_Id_222,2022
-Order_No_8979,User_Id_3147,Bike,3,Business,24,5,11:58:25 AM,24,5,2:16:04 PM,24,5,2:39:59 PM,24,5,2:52:00 PM,24,5,3:27:20 PM,25,23.2,,-1.3922788000000001,36.9370094,-1.3472066,36.7692638,Rider_Id_344,2120
-Order_No_8830,User_Id_302,Bike,3,Business,13,2,8:22:07 PM,13,2,8:22:39 PM,13,2,8:32:59 PM,13,2,8:40:41 PM,13,2,8:59:14 PM,7,20.4,,-1.2649605,36.798177700000004,-1.2895725,36.775179,Rider_Id_413,1113
-Order_No_7416,User_Id_2789,Bike,3,Business,25,4,8:24:00 AM,25,4,8:29:06 AM,25,4,8:44:38 AM,25,4,8:53:47 AM,25,4,8:56:40 AM,2,,,-1.3035165000000002,36.7910264,-1.304735,36.784605400000004,Rider_Id_678,173
-Order_No_15782,User_Id_709,Bike,2,Personal,25,2,8:06:37 AM,25,2,8:06:47 AM,25,2,8:22:11 AM,25,2,8:33:22 AM,25,2,8:56:48 AM,10,18.0,,-1.2982544,36.7500835,-1.2626765,36.795598999999996,Rider_Id_640,1406
-Order_No_6818,User_Id_3689,Bike,3,Business,26,5,2:20:08 PM,26,5,2:20:14 PM,26,5,2:28:55 PM,26,5,2:32:31 PM,26,5,2:46:10 PM,2,,,-1.2615888000000002,36.7928732,-1.2527958000000001,36.8003131,Rider_Id_228,819
-Order_No_6044,User_Id_1581,Bike,3,Personal,19,5,8:55:42 AM,19,5,8:56:05 AM,19,5,8:57:24 AM,19,5,9:05:43 AM,19,5,9:20:02 AM,2,20.0,,-1.2696908000000002,36.81519,-1.2753902,36.808223,Rider_Id_531,859
-Order_No_16237,User_Id_1500,Bike,3,Business,12,2,3:57:41 PM,12,2,3:59:24 PM,12,2,4:16:06 PM,12,2,4:29:41 PM,12,2,4:54:23 PM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_590,1482
-Order_No_3732,User_Id_236,Bike,3,Business,13,3,9:38:30 AM,13,3,9:40:22 AM,13,3,10:01:31 AM,13,3,10:04:25 AM,13,3,10:33:24 AM,8,16.2,,-1.289498,36.818878000000005,-1.3235665,36.849126899999995,Rider_Id_208,1739
-Order_No_16368,User_Id_1014,Bike,3,Business,30,4,9:53:43 AM,30,4,9:54:29 AM,30,4,10:07:50 AM,30,4,10:17:50 AM,30,4,10:35:16 AM,12,16.0,,-1.3234104,36.7989459,-1.2733546,36.7696069,Rider_Id_249,1046
-Order_No_19639,User_Id_2801,Bike,3,Business,28,3,12:55:51 PM,28,3,1:00:18 PM,28,3,1:07:40 PM,28,3,1:28:01 PM,28,3,2:21:33 PM,9,,,-1.2595421999999998,36.7871178,-1.2948468000000002,36.8235652,Rider_Id_137,3212
-Order_No_7337,User_Id_2658,Bike,3,Business,11,4,5:23:46 PM,11,4,5:25:12 PM,11,4,6:18:06 PM,11,4,6:41:41 PM,11,4,6:42:44 PM,8,26.3,,-1.2813012,36.832396200000005,-1.2963097,36.7688221,Rider_Id_119,63
-Order_No_1715,User_Id_1787,Bike,3,Business,5,5,11:36:54 AM,5,5,11:45:24 AM,5,5,12:20:10 PM,5,5,12:43:43 PM,5,5,1:02:42 PM,9,24.5,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_589,1139
-Order_No_13529,User_Id_3621,Bike,3,Business,20,2,11:01:17 AM,20,2,11:03:45 AM,20,2,11:11:59 AM,20,2,11:17:09 AM,20,2,11:44:59 AM,11,19.8,,-1.2836583000000001,36.7886014,-1.3472066,36.7692638,Rider_Id_322,1670
-Order_No_12681,User_Id_409,Bike,3,Personal,15,5,12:28:34 PM,15,5,12:30:28 PM,15,5,12:59:52 PM,15,5,1:09:56 PM,15,5,1:31:48 PM,6,26.6,,-1.312469,36.815177,-1.304735,36.784605400000004,Rider_Id_328,1312
-Order_No_4748,User_Id_391,Bike,3,Business,10,5,9:00:34 AM,10,5,9:01:08 AM,10,5,9:16:22 AM,10,5,10:02:19 AM,10,5,10:11:19 AM,21,,,-1.2847613000000002,36.967084,-1.2867454,36.8259017,Rider_Id_819,540
-Order_No_3643,User_Id_1642,Bike,3,Business,26,1,3:02:30 PM,26,1,3:42:24 PM,26,1,3:50:10 PM,26,1,4:03:12 PM,26,1,4:20:15 PM,6,22.3,0.7,-1.2726506000000002,36.7960153,-1.2889024,36.8187402,Rider_Id_88,1023
-Order_No_25101,User_Id_3686,Bike,3,Business,11,1,10:23:18 AM,11,1,10:23:39 AM,11,1,10:23:56 AM,11,1,10:29:43 AM,11,1,10:35:33 AM,2,21.1,,-1.2551895,36.7822034,-1.2567464,36.7915273,Rider_Id_26,350
-Order_No_13331,User_Id_2070,Bike,3,Business,8,2,10:25:26 AM,8,2,10:25:46 AM,8,2,10:33:06 AM,8,2,10:41:16 AM,8,2,11:06:35 AM,17,23.6,,-1.298529,36.816482,-1.3361484,36.91611279999999,Rider_Id_31,1519
-Order_No_25732,User_Id_3510,Bike,1,Business,20,3,3:37:15 PM,20,3,3:38:28 PM,20,3,3:38:58 PM,20,3,3:56:46 PM,20,3,4:20:36 PM,14,23.4,,-1.2472211000000002,36.7826109,-1.3104733999999998,36.7363698,Rider_Id_379,1430
-Order_No_25729,User_Id_2587,Bike,3,Business,13,2,12:37:49 PM,13,2,12:59:38 PM,13,2,1:09:34 PM,13,2,1:18:52 PM,13,2,2:17:55 PM,4,22.5,,-1.28878,36.8168312,-1.2857246999999998,36.789429299999995,Rider_Id_447,3543
-Order_No_5892,User_Id_3326,Bike,3,Business,12,2,4:34:50 PM,12,2,4:43:58 PM,12,2,4:51:14 PM,12,2,5:06:56 PM,12,2,5:58:04 PM,8,21.1,,-1.2579303999999998,36.8019667,-1.3090727,36.831526200000006,Rider_Id_820,3068
-Order_No_5094,User_Id_3326,Bike,3,Business,8,5,1:01:58 PM,8,5,1:12:36 PM,8,5,1:29:20 PM,8,5,1:35:07 PM,8,5,2:09:26 PM,19,27.4,,-1.2551895,36.7822034,-1.3244885,36.897791999999995,Rider_Id_281,2059
-Order_No_1756,User_Id_111,Bike,3,Business,18,5,12:41:01 PM,18,5,12:42:31 PM,18,5,12:52:11 PM,18,5,12:55:29 PM,18,5,1:17:37 PM,8,20.0,,-1.2551895,36.7822034,-1.2925507999999999,36.8109381,Rider_Id_764,1328
-Order_No_24808,User_Id_2102,Bike,3,Business,28,1,10:42:24 AM,28,1,10:42:34 AM,28,1,10:43:56 AM,28,1,11:01:56 AM,28,1,11:33:15 AM,15,22.9,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_543,1879
-Order_No_4371,User_Id_1148,Bike,3,Business,5,6,8:41:58 AM,5,6,9:09:44 AM,5,6,9:27:22 AM,5,6,9:35:32 AM,5,6,9:58:04 AM,9,18.9,,-1.3100586,36.850032899999995,-1.2703427999999999,36.8336071,Rider_Id_742,1352
-Order_No_13755,User_Id_478,Bike,3,Business,13,2,10:50:28 AM,13,2,11:11:09 AM,13,2,11:47:29 AM,13,2,11:52:29 AM,13,2,12:11:03 PM,9,22.5,,-1.2913246,36.8019522,-1.2571472,36.795063299999995,Rider_Id_578,1114
-Order_No_19073,User_Id_393,Bike,3,Business,5,5,11:53:13 AM,5,5,11:53:45 AM,5,5,11:59:53 AM,5,5,12:02:27 PM,5,5,12:09:44 PM,5,24.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_506,437
-Order_No_3124,User_Id_2801,Bike,3,Business,21,3,1:30:33 PM,21,3,1:30:46 PM,21,3,2:03:50 PM,21,3,2:06:21 PM,21,3,2:43:34 PM,15,23.1,3.0,-1.2595421999999998,36.7871178,-1.3219125,36.714515000000006,Rider_Id_817,2233
-Order_No_4912,User_Id_2112,Bike,3,Business,22,5,9:54:43 AM,22,5,9:55:44 AM,22,5,10:02:33 AM,22,5,10:08:13 AM,22,5,10:24:01 AM,9,22.1,,-1.2156006000000001,36.8916865,-1.178056,36.9312342,Rider_Id_484,948
-Order_No_2756,User_Id_1985,Bike,1,Personal,1,1,4:43:29 PM,1,1,4:58:44 PM,1,1,5:14:43 PM,1,1,5:15:53 PM,1,1,5:46:06 PM,14,23.6,,-1.2746171,36.881536600000004,-1.2759432,36.7892706,Rider_Id_933,1813
-Order_No_1609,User_Id_3214,Bike,3,Business,7,4,2:48:46 PM,7,4,2:50:13 PM,7,4,3:30:02 PM,7,4,3:46:51 PM,7,4,4:15:23 PM,12,31.2,,-1.2315181,36.814054299999995,-1.289795,36.7957132,Rider_Id_52,1712
-Order_No_17662,User_Id_1057,Bike,2,Personal,30,1,1:42:46 PM,30,1,1:44:00 PM,30,1,1:52:05 PM,30,1,1:57:58 PM,30,1,2:17:44 PM,7,20.5,,-1.2879236,36.769815200000004,-1.2661263999999999,36.8030359,Rider_Id_754,1186
-Order_No_13647,User_Id_2335,Bike,3,Business,12,5,2:13:53 PM,12,5,2:14:13 PM,12,5,2:42:11 PM,12,5,2:44:18 PM,12,5,3:19:29 PM,22,,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_955,2111
-Order_No_21579,User_Id_1527,Bike,3,Business,28,2,11:44:25 AM,28,2,11:44:50 AM,28,2,11:44:57 AM,28,2,11:53:37 AM,28,2,12:10:12 PM,9,21.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_576,995
-Order_No_17633,User_Id_1738,Bike,3,Personal,27,1,2:28:16 PM,27,1,2:29:12 PM,27,1,2:50:05 PM,27,1,2:58:09 PM,27,1,3:36:06 PM,14,23.1,,-1.3122610000000001,36.794658,-1.2315181,36.814054299999995,Rider_Id_548,2277
-Order_No_28207,User_Id_2423,Bike,3,Business,22,5,12:36:12 PM,22,5,12:37:43 PM,22,5,12:55:53 PM,22,5,1:01:44 PM,22,5,1:15:53 PM,8,,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_205,849
-Order_No_3433,User_Id_3318,Bike,3,Business,10,3,4:52:32 PM,10,3,4:53:34 PM,10,3,5:25:25 PM,10,3,5:30:47 PM,10,3,5:42:35 PM,10,27.2,,-1.338283,36.8871775,-1.3004062,36.829741,Rider_Id_126,708
-Order_No_20750,User_Id_1809,Bike,3,Business,30,3,12:07:07 PM,30,3,12:07:17 PM,30,3,12:27:45 PM,30,3,12:38:23 PM,30,3,12:45:58 PM,2,22.6,,-1.2584143,36.8048002,-1.2646084,36.8028645,Rider_Id_216,455
-Order_No_10445,User_Id_3701,Bike,3,Business,12,3,12:04:54 PM,12,3,12:05:05 PM,12,3,12:24:12 PM,12,3,12:40:32 PM,12,3,1:00:11 PM,7,23.2,,-1.2611430000000001,36.826916,-1.2684239,36.79524620000001,Rider_Id_674,1179
-Order_No_24391,User_Id_1721,Bike,3,Business,30,2,9:15:49 AM,30,2,9:16:25 AM,30,2,9:25:41 AM,30,2,9:44:21 AM,30,2,9:52:03 AM,2,,,-1.2571472,36.795063299999995,-1.2495931,36.80650870000001,Rider_Id_607,462
-Order_No_6562,User_Id_1293,Bike,3,Personal,20,1,11:09:39 AM,20,1,11:35:25 AM,20,1,11:59:35 AM,20,1,12:07:39 PM,20,1,12:22:01 PM,6,20.3,,-1.2348866000000003,36.8029959,-1.2543163999999998,36.831860999999996,Rider_Id_882,862
-Order_No_6735,User_Id_627,Bike,3,Business,22,5,10:29:07 AM,22,5,10:30:42 AM,22,5,10:36:49 AM,22,5,10:47:42 AM,22,5,10:47:44 AM,3,28.3,,-1.2551895,36.7822034,-1.2672748999999999,36.7851294,Rider_Id_598,2
-Order_No_22224,User_Id_2271,Bike,3,Business,15,5,2:16:55 PM,15,5,2:18:53 PM,15,5,2:35:24 PM,15,5,2:42:17 PM,15,5,3:09:10 PM,14,27.8,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_439,1613
-Order_No_3119,User_Id_3754,Bike,3,Business,3,4,3:09:39 PM,3,4,3:10:09 PM,3,4,3:10:42 PM,3,4,3:20:52 PM,3,4,3:46:01 PM,12,26.1,,-1.2571472,36.795063299999995,-1.3271684,36.8173021,Rider_Id_539,1509
-Order_No_4297,User_Id_1506,Bike,1,Personal,25,2,6:35:21 PM,25,2,6:35:27 PM,25,2,6:53:16 PM,25,2,6:59:04 PM,25,2,8:13:26 PM,16,24.2,,-1.2016352,36.8200903,-1.2611642,36.7949932,Rider_Id_453,4462
-Order_No_12486,User_Id_2829,Bike,3,Business,31,5,1:32:54 PM,31,5,1:38:25 PM,31,5,1:44:35 PM,31,5,1:51:58 PM,31,5,2:06:43 PM,6,24.6,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_709,885
-Order_No_18820,User_Id_1882,Bike,3,Business,6,7,10:06:18 AM,6,7,10:32:44 AM,6,7,11:00:34 AM,6,7,11:18:26 AM,6,7,11:18:30 AM,4,23.0,,-1.277018,36.820337,-1.2588774999999999,36.8079744,Rider_Id_246,4
-Order_No_26790,User_Id_1085,Bike,3,Business,12,3,2:55:55 PM,12,3,2:58:53 PM,12,3,3:11:26 PM,12,3,3:22:30 PM,12,3,3:57:50 PM,7,21.0,,-1.2628638,36.8070399,-1.232864,36.808470799999995,Rider_Id_36,2120
-Order_No_18144,User_Id_3521,Bike,3,Business,22,5,3:46:24 PM,22,5,3:55:29 PM,22,5,3:55:51 PM,22,5,5:08:22 PM,22,5,5:08:32 PM,2,,,-1.2652808999999998,36.808065899999995,-1.2768326,36.8073804,Rider_Id_51,10
-Order_No_2582,User_Id_1687,Bike,3,Business,28,3,9:46:21 AM,28,3,9:46:42 AM,28,3,9:52:23 AM,28,3,10:07:02 AM,28,3,10:20:03 AM,2,20.5,,-1.2962832,36.784968299999996,-1.2944171999999998,36.80025620000001,Rider_Id_612,781
-Order_No_12039,User_Id_867,Bike,3,Business,9,3,8:42:46 AM,9,3,9:51:41 AM,9,3,9:59:24 AM,9,3,10:26:22 AM,9,3,10:53:00 AM,10,20.3,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_496,1598
-Order_No_15498,User_Id_1283,Bike,3,Personal,26,6,5:15:27 PM,26,6,5:16:07 PM,26,6,5:33:49 PM,26,6,5:41:10 PM,26,6,6:04:55 PM,8,21.9,,-1.2864309999999999,36.818411,-1.2948886999999998,36.7683938,Rider_Id_755,1425
-Order_No_28040,User_Id_111,Bike,3,Business,27,5,4:12:11 PM,27,5,4:13:52 PM,27,5,4:48:26 PM,27,5,4:55:40 PM,27,5,5:14:38 PM,8,17.6,,-1.2551895,36.7822034,-1.2930488000000002,36.828087700000005,Rider_Id_836,1138
-Order_No_12406,User_Id_186,Bike,3,Business,7,1,10:48:11 AM,7,1,10:56:31 AM,7,1,11:52:41 AM,7,1,12:14:27 PM,7,1,1:30:23 PM,24,21.6,,-1.3700383,36.9190174,-1.2579303999999998,36.8019667,Rider_Id_753,4556
-Order_No_9976,User_Id_3683,Bike,3,Business,28,1,12:00:54 PM,28,1,12:01:19 PM,28,1,12:09:01 PM,28,1,12:12:26 PM,28,1,12:32:48 PM,7,23.8,,-1.2897368,36.8226783,-1.2765492,36.7669809,Rider_Id_601,1222
-Order_No_22066,User_Id_2330,Bike,3,Business,8,3,12:23:04 PM,8,3,12:24:32 PM,8,3,12:53:12 PM,8,3,12:58:40 PM,8,3,1:26:21 PM,13,,,-1.2291477,36.8440295,-1.1926151000000003,36.9347904,Rider_Id_7,1661
-Order_No_16985,User_Id_53,Bike,3,Business,27,3,9:27:17 AM,27,3,9:28:40 AM,27,3,9:34:19 AM,27,3,10:10:25 AM,27,3,10:16:38 AM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_436,373
-Order_No_27677,User_Id_1856,Bike,3,Business,16,3,12:20:05 PM,16,3,12:25:25 PM,16,3,1:05:08 PM,16,3,1:10:28 PM,16,3,1:35:31 PM,6,23.8,,-1.273009,36.802807,-1.2930705,36.827943299999994,Rider_Id_100,1503
-Order_No_19885,User_Id_1839,Bike,3,Personal,11,1,10:50:55 AM,11,1,10:51:08 AM,11,1,10:51:42 AM,11,1,10:56:06 AM,11,1,11:12:15 AM,11,26.6,,-1.2963097,36.7688221,-1.3314048,36.8229367,Rider_Id_345,969
-Order_No_21494,User_Id_3291,Bike,3,Business,13,3,10:59:20 AM,13,3,10:59:32 AM,13,3,11:20:21 AM,13,3,11:40:55 AM,13,3,12:17:06 PM,16,24.8,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_276,2171
-Order_No_3647,User_Id_2174,Bike,3,Business,18,5,2:08:07 PM,18,5,2:08:09 PM,18,5,2:08:58 PM,18,5,2:33:49 PM,18,5,3:54:24 PM,24,24.4,,-1.2482781,36.884091999999995,-1.28372,36.7085454,Rider_Id_33,4835
-Order_No_14100,User_Id_1500,Bike,3,Business,14,5,2:56:53 PM,14,5,3:04:14 PM,14,5,3:44:14 PM,14,5,3:45:52 PM,14,5,4:07:45 PM,9,22.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_422,1313
-Order_No_27326,User_Id_20,Bike,3,Business,29,1,12:53:47 PM,29,1,12:54:03 PM,29,1,1:11:55 PM,29,1,1:26:30 PM,29,1,1:45:07 PM,13,,,-1.2616841,36.801999,-1.3343128999999998,36.863711200000004,Rider_Id_72,1117
-Order_No_9634,User_Id_950,Bike,2,Personal,11,2,9:28:58 AM,11,2,9:35:37 AM,11,2,9:44:57 AM,11,2,10:08:49 AM,11,2,10:21:50 AM,8,18.5,1.9,-1.2639238000000002,36.7889646,-1.2481219,36.7530824,Rider_Id_482,781
-Order_No_9431,User_Id_412,Bike,3,Business,2,2,2:41:12 PM,2,2,2:42:14 PM,2,2,2:47:40 PM,2,2,3:21:59 PM,2,2,3:49:53 PM,4,29.6,,-1.2628473,36.781805,-1.266837,36.799248999999996,Rider_Id_168,1674
-Order_No_5127,User_Id_1280,Bike,3,Business,6,3,12:19:52 PM,6,3,12:20:57 PM,6,3,12:43:20 PM,6,3,12:45:36 PM,6,3,1:31:46 PM,9,,,-1.2551895,36.7822034,-1.287079,36.7811277,Rider_Id_817,2770
-Order_No_7521,User_Id_640,Bike,3,Business,23,4,3:47:28 PM,23,4,3:48:00 PM,23,4,3:49:27 PM,23,4,4:14:08 PM,23,4,4:28:39 PM,7,24.6,,-1.304033,36.7848695,-1.2824911,36.8218042,Rider_Id_119,871
-Order_No_18166,User_Id_393,Bike,3,Business,28,4,8:29:56 AM,28,4,8:30:15 AM,28,4,8:45:17 AM,28,4,9:14:22 AM,28,4,9:14:24 AM,8,19.5,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_910,2
-Order_No_23635,User_Id_265,Bike,3,Business,13,4,11:03:55 AM,13,4,11:21:06 AM,13,4,11:25:11 AM,13,4,11:37:52 AM,13,4,11:52:33 AM,5,22.0,,-1.2835586,36.8125641,-1.300921,36.828195,Rider_Id_669,881
-Order_No_23254,User_Id_393,Bike,3,Business,11,4,9:34:42 AM,11,4,9:34:59 AM,11,4,9:35:53 AM,11,4,10:00:09 AM,11,4,10:20:17 AM,10,18.6,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1208
-Order_No_11140,User_Id_1075,Bike,3,Business,16,3,6:23:28 PM,16,3,6:23:41 PM,16,3,6:27:28 PM,16,3,6:36:29 PM,16,3,6:44:01 PM,3,23.5,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_72,452
-Order_No_16611,User_Id_3201,Bike,3,Business,7,4,11:05:41 AM,7,4,11:06:22 AM,7,4,11:10:21 AM,7,4,11:37:11 AM,7,4,11:39:45 AM,1,27.4,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_205,154
-Order_No_1345,User_Id_1721,Bike,3,Business,25,5,1:59:24 PM,25,5,2:00:03 PM,25,5,2:10:04 PM,25,5,2:15:59 PM,25,5,2:28:46 PM,5,29.1,,-1.3147073999999999,36.811721999999996,-1.2954581,36.810997,Rider_Id_15,767
-Order_No_2710,User_Id_2200,Bike,3,Business,28,4,4:38:07 PM,28,4,4:52:18 PM,28,4,4:59:14 PM,28,4,5:06:15 PM,28,4,5:45:00 PM,21,25.6,,-1.3165542,36.700067,-1.290894,36.822971,Rider_Id_622,2325
-Order_No_18103,User_Id_1583,Bike,3,Business,3,3,2:23:58 PM,3,3,2:24:26 PM,3,3,2:59:39 PM,3,3,3:29:47 PM,3,3,3:57:55 PM,21,28.3,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_863,1688
-Order_No_28210,User_Id_718,Bike,3,Business,30,4,11:14:44 AM,30,4,11:17:36 AM,30,4,11:39:56 AM,30,4,11:41:26 AM,30,4,12:07:59 PM,13,,,-1.2551895,36.7822034,-1.3305930000000001,36.848696999999994,Rider_Id_176,1593
-Order_No_26991,User_Id_3560,Bike,3,Business,17,3,10:24:09 AM,17,3,10:24:59 AM,17,3,10:48:20 AM,17,3,10:52:10 AM,17,3,11:11:37 AM,5,25.5,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_477,1167
-Order_No_18372,User_Id_1038,Bike,1,Business,21,4,11:32:24 AM,21,4,11:43:03 AM,21,4,12:02:11 PM,21,4,12:09:21 PM,21,4,12:42:48 PM,9,21.8,,-1.3035983999999998,36.8035843,-1.2563754999999999,36.8215762,Rider_Id_549,2007
-Order_No_1453,User_Id_517,Bike,3,Business,3,4,12:41:36 PM,3,4,12:42:28 PM,3,4,12:44:33 PM,3,4,1:25:11 PM,3,4,1:25:15 PM,4,23.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,4
-Order_No_77,User_Id_2271,Bike,3,Business,4,1,4:24:54 PM,4,1,4:25:48 PM,4,1,4:32:06 PM,4,1,4:42:21 PM,4,1,5:55:09 PM,14,26.2,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_404,4368
-Order_No_13860,User_Id_2987,Bike,3,Business,6,5,4:30:43 PM,6,5,4:50:37 PM,6,5,4:55:59 PM,6,5,5:03:46 PM,6,5,5:16:53 PM,4,,,-1.2702120000000001,36.82108770000001,-1.263987,36.798891,Rider_Id_103,787
-Order_No_27595,User_Id_784,Bike,1,Personal,4,1,11:50:40 AM,4,1,11:51:03 AM,4,1,11:51:22 AM,4,1,12:03:40 PM,4,1,12:34:48 PM,19,21.2,,-1.2770071000000003,36.832167,-1.3565067,36.9062082,Rider_Id_397,1868
-Order_No_8534,User_Id_2104,Bike,3,Business,2,1,4:15:44 PM,2,1,4:45:06 PM,2,1,4:48:29 PM,2,1,4:54:33 PM,2,1,5:18:52 PM,7,19.5,,-1.2527958000000001,36.8003131,-1.3057159999999999,36.809882,Rider_Id_192,1459
-Order_No_4964,User_Id_2331,Bike,3,Business,12,3,10:34:48 AM,12,3,10:57:22 AM,12,3,11:07:27 AM,12,3,11:10:53 AM,12,3,12:30:50 PM,46,19.6,,-1.3398006000000002,36.717935,-1.1838303,36.9543295,Rider_Id_815,4797
-Order_No_16288,User_Id_393,Bike,3,Business,21,4,10:07:01 AM,21,4,10:07:13 AM,21,4,10:07:20 AM,21,4,10:25:19 AM,21,4,10:33:02 AM,4,18.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_111,463
-Order_No_4102,User_Id_1336,Bike,3,Business,4,5,4:42:52 PM,4,5,5:08:26 PM,4,5,5:21:54 PM,4,5,5:26:48 PM,4,5,5:38:48 PM,2,21.8,,-1.290894,36.822971,-1.2885029,36.825604999999996,Rider_Id_583,720
-Order_No_24114,User_Id_3560,Bike,3,Business,6,1,9:44:20 AM,6,1,9:45:48 AM,6,1,10:14:43 AM,6,1,10:22:35 AM,6,1,10:45:22 AM,13,14.8,,-1.2156006000000001,36.8916865,-1.279395,36.825364,Rider_Id_19,1367
-Order_No_24921,User_Id_393,Bike,3,Business,18,5,2:29:19 PM,18,5,2:29:23 PM,18,5,2:33:54 PM,18,5,2:44:01 PM,18,5,3:32:56 PM,14,27.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,2935
-Order_No_26168,User_Id_111,Bike,3,Business,1,4,11:47:06 AM,1,4,11:56:20 AM,1,4,12:08:05 PM,1,4,12:10:38 PM,1,4,12:35:30 PM,7,,,-1.2551895,36.7822034,-1.2672002,36.8245542,Rider_Id_660,1492
-Order_No_10002,User_Id_2876,Bike,1,Personal,3,2,11:35:04 AM,3,2,11:35:17 AM,3,2,11:38:38 AM,3,2,11:44:19 AM,3,2,11:56:51 AM,6,18.4,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_103,752
-Order_No_27795,User_Id_246,Bike,3,Business,4,2,2:40:35 PM,4,2,5:08:43 PM,4,2,5:10:02 PM,4,2,5:20:33 PM,4,2,5:56:45 PM,6,22.2,,-1.281754,36.8324229,-1.2584143,36.8048002,Rider_Id_878,2172
-Order_No_12338,User_Id_3333,Bike,1,Personal,30,1,11:12:16 AM,30,1,11:12:38 AM,30,1,11:22:11 AM,30,1,11:29:08 AM,30,1,11:49:10 AM,9,22.6,,-1.2951188999999999,36.7699791,-1.2536041999999998,36.806489500000005,Rider_Id_350,1202
-Order_No_14703,User_Id_393,Bike,3,Business,31,3,9:48:23 AM,31,3,10:24:36 AM,31,3,10:33:31 AM,31,3,10:48:34 AM,31,3,11:05:33 AM,10,24.5,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1019
-Order_No_25856,User_Id_2100,Bike,3,Business,30,4,1:52:20 PM,30,4,1:55:57 PM,30,4,2:15:47 PM,30,4,2:17:32 PM,30,4,2:59:56 PM,23,21.0,,-1.2765736,36.851364600000004,-1.3169591,36.7000029,Rider_Id_400,2544
-Order_No_1303,User_Id_2064,Bike,2,Personal,31,5,8:31:56 AM,31,5,8:32:16 AM,31,5,8:34:33 AM,31,5,8:58:59 AM,31,5,9:10:22 AM,3,14.8,,-1.2649605,36.798177700000004,-1.2676593,36.8103939,Rider_Id_446,683
-Order_No_1503,User_Id_3755,Bike,1,Personal,18,4,9:43:51 AM,18,4,9:44:11 AM,18,4,9:46:41 AM,18,4,9:49:50 AM,18,4,10:06:10 AM,6,20.6,,-1.2956756,36.771938299999995,-1.2617028000000001,36.7983849,Rider_Id_680,980
-Order_No_4684,User_Id_2154,Bike,3,Business,23,3,11:30:27 AM,23,3,11:31:20 AM,23,3,11:34:16 AM,23,3,11:38:51 AM,23,3,12:06:27 PM,6,,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_758,1656
-Order_No_3490,User_Id_20,Bike,3,Business,27,1,12:40:12 PM,27,1,12:40:30 PM,27,1,1:07:07 PM,27,1,1:41:48 PM,27,1,1:59:33 PM,5,,,-1.2880771,36.8269099,-1.2628638,36.8070399,Rider_Id_213,1065
-Order_No_19046,User_Id_635,Bike,3,Business,22,5,12:41:10 PM,22,5,12:43:39 PM,22,5,12:55:51 PM,22,5,1:00:01 PM,22,5,1:18:55 PM,7,,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_25,1134
-Order_No_4117,User_Id_3222,Bike,3,Business,28,4,2:37:02 PM,28,4,2:37:42 PM,28,4,2:48:42 PM,28,4,2:50:40 PM,28,4,3:14:01 PM,6,27.2,,-1.2551895,36.7822034,-1.2833713,36.82332279999999,Rider_Id_953,1401
-Order_No_7932,User_Id_3557,Bike,3,Business,12,2,1:23:09 PM,12,2,1:23:35 PM,12,2,1:29:45 PM,12,2,1:34:35 PM,12,2,1:43:37 PM,1,,,-1.2583017,36.8023526,-1.2546732,36.808679600000005,Rider_Id_660,542
-Order_No_5927,User_Id_1341,Bike,3,Business,20,1,1:30:08 PM,20,1,1:33:11 PM,20,1,1:49:53 PM,20,1,1:58:46 PM,20,1,2:26:58 PM,12,,,-1.293515,36.897607,-1.331619,36.847976,Rider_Id_158,1692
-Order_No_8109,User_Id_510,Bike,3,Business,19,1,2:30:02 PM,19,1,2:30:39 PM,19,1,2:30:45 PM,19,1,3:09:03 PM,19,1,3:49:37 PM,13,25.4,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_402,2434
-Order_No_28261,User_Id_3689,Bike,3,Business,28,3,11:13:49 AM,28,3,11:14:13 AM,28,3,11:14:16 AM,28,3,11:47:41 AM,28,3,12:11:11 PM,13,,,-1.2363091999999998,36.882361700000004,-1.2527958000000001,36.8003131,Rider_Id_450,1410
-Order_No_19102,User_Id_1527,Bike,3,Business,27,1,10:31:36 AM,27,1,10:33:02 AM,27,1,10:39:45 AM,27,1,10:44:36 AM,27,1,11:04:51 AM,9,20.7,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_96,1215
-Order_No_22892,User_Id_1796,Bike,3,Business,13,6,10:13:34 AM,13,6,10:13:41 AM,13,6,10:20:04 AM,13,6,10:33:27 AM,13,6,10:46:17 AM,7,26.0,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_155,770
-Order_No_13697,User_Id_3560,Bike,3,Business,22,2,9:44:36 AM,22,2,10:15:53 AM,22,2,10:20:14 AM,22,2,10:24:59 AM,22,2,10:35:42 AM,4,17.5,,-1.2793686000000002,36.82592329999999,-1.303105,36.819134999999996,Rider_Id_932,643
-Order_No_1665,User_Id_2982,Bike,3,Business,23,3,12:08:25 PM,23,3,12:09:07 PM,23,3,12:18:46 PM,23,3,12:49:03 PM,23,3,1:24:18 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_336,2115
-Order_No_8035,User_Id_1774,Bike,1,Personal,28,5,1:44:03 PM,28,5,1:45:05 PM,28,5,2:26:03 PM,28,5,2:32:27 PM,28,5,3:30:31 PM,26,25.6,,-1.260232,36.807334000000004,-1.4027553,36.9514974,Rider_Id_446,3484
-Order_No_16178,User_Id_3032,Bike,3,Business,10,1,11:11:07 AM,10,1,11:11:16 AM,10,1,11:14:58 AM,10,1,11:23:31 AM,10,1,11:46:40 AM,8,,,-1.2571472,36.795063299999995,-1.2946358999999998,36.8352718,Rider_Id_882,1389
-Order_No_17238,User_Id_1482,Bike,3,Business,6,2,2:29:48 PM,6,2,2:32:04 PM,6,2,2:32:51 PM,6,2,2:42:24 PM,6,2,3:08:05 PM,13,27.5,,-1.3385790000000002,36.89630379999999,-1.2819448,36.8243884,Rider_Id_648,1541
-Order_No_3138,User_Id_970,Bike,3,Business,24,2,7:11:44 AM,24,2,7:13:12 AM,24,2,7:43:42 AM,24,2,7:49:49 AM,24,2,8:21:34 AM,9,14.5,,-1.2551895,36.7822034,-1.2944841999999999,36.8011625,Rider_Id_726,1905
-Order_No_13439,User_Id_214,Bike,1,Personal,26,3,9:08:25 AM,26,3,9:15:26 AM,26,3,9:34:09 AM,26,3,9:41:50 AM,26,3,9:55:39 AM,4,19.0,,-1.2615584,36.814950599999996,-1.2633493999999998,36.7959696,Rider_Id_259,829
-Order_No_18983,User_Id_20,Bike,3,Business,5,2,3:49:40 PM,5,2,3:49:54 PM,5,2,4:07:28 PM,5,2,4:16:08 PM,5,2,4:49:32 PM,14,22.6,,-1.2745321000000003,36.765088,-1.3129872999999999,36.8420022,Rider_Id_852,2004
-Order_No_11889,User_Id_3647,Bike,3,Business,13,3,12:50:14 PM,13,3,12:50:35 PM,13,3,12:59:49 PM,13,3,1:07:08 PM,13,3,1:19:37 PM,3,,,-1.2726389999999999,36.794723,-1.2655477,36.8001682,Rider_Id_521,749
-Order_No_13665,User_Id_640,Bike,3,Business,25,1,9:19:47 AM,25,1,9:21:32 AM,25,1,9:38:11 AM,25,1,10:10:52 AM,25,1,10:22:48 AM,3,20.9,,-1.2969741000000001,36.7856612,-1.2878914,36.7728222,Rider_Id_678,716
-Order_No_7931,User_Id_2041,Bike,1,Business,14,5,11:30:37 AM,14,5,11:32:28 AM,14,5,12:02:16 PM,14,5,12:09:47 PM,14,5,12:30:46 PM,6,24.2,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_726,1259
-Order_No_8775,User_Id_928,Bike,3,Business,30,3,9:08:57 AM,30,3,9:14:43 AM,30,3,9:29:11 AM,30,3,9:30:41 AM,30,3,9:55:49 AM,10,19.5,,-1.2551895,36.7822034,-1.2999305,36.843617,Rider_Id_576,1508
-Order_No_22499,User_Id_3298,Bike,1,Personal,4,2,4:31:32 PM,4,2,4:41:31 PM,4,2,4:53:03 PM,4,2,5:18:38 PM,4,2,5:46:54 PM,6,21.0,,-1.2865016000000002,36.8186143,-1.3087478000000001,36.8042656,Rider_Id_268,1696
-Order_No_2615,User_Id_1075,Bike,3,Business,30,3,11:54:49 AM,30,3,11:55:05 AM,30,3,12:02:29 PM,30,3,12:08:53 PM,30,3,12:16:05 PM,1,25.2,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_428,432
-Order_No_20715,User_Id_1073,Bike,3,Business,4,4,5:17:52 PM,4,4,5:18:54 PM,4,4,5:26:37 PM,4,4,5:31:51 PM,4,4,6:04:57 PM,10,,,-1.2563576,36.7881585,-1.2983805,36.8198439,Rider_Id_74,1986
-Order_No_4592,User_Id_1880,Bike,3,Business,25,5,3:19:32 PM,25,5,3:20:13 PM,25,5,3:22:01 PM,25,5,3:38:16 PM,25,5,3:56:01 PM,6,23.6,,-1.2579303999999998,36.8019667,-1.2809498,36.8252309,Rider_Id_379,1065
-Order_No_26886,User_Id_242,Bike,3,Business,3,4,12:23:39 PM,3,4,12:24:03 PM,3,4,12:56:46 PM,3,4,12:58:29 PM,3,4,12:58:35 PM,3,21.8,,-1.2559559999999999,36.789867,-1.25537,36.7756,Rider_Id_408,6
-Order_No_22651,User_Id_3674,Bike,3,Business,1,4,2:26:28 PM,1,4,2:31:11 PM,1,4,2:31:17 PM,1,4,2:40:54 PM,1,4,3:09:36 PM,7,,,-1.2551895,36.7822034,-1.2537505,36.8326484,Rider_Id_580,1722
-Order_No_6111,User_Id_2776,Bike,3,Personal,1,5,9:39:28 AM,1,5,9:39:38 AM,1,5,9:54:30 AM,1,5,10:12:09 AM,1,5,10:45:17 AM,15,17.5,,-1.2143841999999998,36.8122348,-1.2748221000000002,36.7817865,Rider_Id_633,1988
-Order_No_12847,User_Id_1802,Bike,2,Personal,15,5,10:53:39 AM,15,5,10:55:47 AM,15,5,11:06:49 AM,15,5,11:22:56 AM,15,5,11:41:34 AM,6,20.0,,-1.272704,36.793127899999995,-1.2949855,36.7830349,Rider_Id_56,1118
-Order_No_8420,User_Id_3469,Bike,3,Personal,21,1,11:05:52 AM,21,1,11:09:27 AM,21,1,11:42:28 AM,21,1,11:54:45 AM,21,1,12:34:24 PM,13,19.8,,-1.2583393,36.7464632,-1.2900075,36.8032564,Rider_Id_334,2379
-Order_No_22069,User_Id_3647,Bike,3,Business,22,5,2:43:01 PM,22,5,2:44:57 PM,22,5,2:52:10 PM,22,5,3:00:51 PM,22,5,3:12:20 PM,5,31.0,,-1.2726389999999999,36.794723,-1.2800978,36.776917100000006,Rider_Id_576,689
-Order_No_7894,User_Id_3422,Bike,3,Business,31,4,10:48:17 AM,31,4,10:49:23 AM,31,4,10:58:37 AM,31,4,11:02:52 AM,31,4,11:21:08 AM,13,26.3,,-1.3155299999999999,36.863732,-1.3731701,36.9262851,Rider_Id_418,1096
-Order_No_9889,User_Id_2763,Bike,3,Business,28,1,1:20:39 PM,28,1,1:21:15 PM,28,1,1:33:40 PM,28,1,1:42:23 PM,28,1,2:02:43 PM,13,26.2,,-1.2551895,36.7822034,-1.3218459999999999,36.82826,Rider_Id_178,1220
-Order_No_13562,User_Id_873,Bike,3,Personal,3,1,12:18:20 PM,3,1,12:19:31 PM,3,1,12:30:02 PM,3,1,12:32:23 PM,3,1,12:53:11 PM,4,20.4,,-1.2890374,36.8165815,-1.3075266,36.8292369,Rider_Id_857,1248
-Order_No_14874,User_Id_1715,Bike,3,Business,31,2,9:27:58 AM,31,2,9:28:15 AM,31,2,9:36:11 AM,31,2,9:39:26 AM,31,2,9:52:13 AM,3,16.9,0.9,-1.2608993,36.799246000000004,-1.2770765,36.80805529999999,Rider_Id_882,767
-Order_No_26832,User_Id_661,Bike,3,Business,12,4,12:35:25 PM,12,4,12:36:12 PM,12,4,1:02:48 PM,12,4,1:16:53 PM,12,4,1:26:18 PM,4,18.9,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_140,565
-Order_No_17082,User_Id_1004,Bike,3,Business,30,2,2:20:24 PM,30,2,2:22:49 PM,30,2,2:33:09 PM,30,2,2:37:53 PM,30,2,3:12:42 PM,9,,,-1.3250048,36.869718799999994,-1.333148,36.922453000000004,Rider_Id_378,2089
-Order_No_20390,User_Id_2330,Bike,3,Business,15,1,4:44:00 PM,15,1,4:47:02 PM,15,1,4:58:10 PM,15,1,5:10:04 PM,15,1,5:26:30 PM,8,,,-1.3263903000000001,36.717988500000004,-1.3015197,36.7658456,Rider_Id_910,986
-Order_No_4611,User_Id_3496,Bike,3,Business,21,5,2:33:20 PM,21,5,3:23:22 PM,21,5,3:30:51 PM,21,5,3:37:34 PM,21,5,3:57:55 PM,7,24.8,,-1.300921,36.828195,-1.2941585,36.7947375,Rider_Id_829,1221
-Order_No_16353,User_Id_564,Bike,3,Business,9,3,12:52:31 PM,9,3,1:03:27 PM,9,3,1:08:46 PM,9,3,1:11:53 PM,9,3,1:29:23 PM,6,25.3,,-1.2960774,36.819004799999995,-1.2688224,36.794406200000005,Rider_Id_848,1050
-Order_No_18842,User_Id_2154,Bike,3,Business,8,6,11:02:24 AM,8,6,11:09:57 AM,8,6,11:27:44 AM,8,6,11:43:42 AM,8,6,12:11:16 PM,6,19.8,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_243,1654
-Order_No_22125,User_Id_909,Bike,3,Business,22,3,2:49:08 PM,22,3,3:13:31 PM,22,3,3:30:08 PM,22,3,3:37:33 PM,22,3,3:58:28 PM,7,24.4,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_7,1255
-Order_No_27224,User_Id_53,Bike,3,Business,19,2,8:59:06 AM,19,2,9:00:00 AM,19,2,9:09:23 AM,19,2,9:13:14 AM,19,2,9:26:10 AM,4,21.0,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_378,776
-Order_No_4387,User_Id_517,Bike,3,Business,27,4,10:21:21 AM,27,4,10:21:26 AM,27,4,10:24:35 AM,27,4,10:33:13 AM,27,4,10:59:49 AM,14,23.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1596
-Order_No_22391,User_Id_517,Bike,3,Business,22,4,7:28:48 AM,22,4,9:16:24 AM,22,4,9:25:19 AM,22,4,9:54:13 AM,22,4,9:54:15 AM,4,19.6,3.0,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_828,2
-Order_No_20920,User_Id_1642,Bike,3,Business,28,3,11:46:35 AM,28,3,12:53:13 PM,28,3,1:10:13 PM,28,3,1:16:14 PM,28,3,1:35:46 PM,6,,,-1.2883829,36.8177296,-1.2726506000000002,36.7960153,Rider_Id_698,1172
-Order_No_7328,User_Id_111,Bike,3,Business,2,4,3:27:50 PM,2,4,3:28:46 PM,2,4,3:38:27 PM,2,4,3:43:56 PM,2,4,4:23:36 PM,10,26.2,,-1.2551895,36.7822034,-1.3156451999999998,36.815802000000005,Rider_Id_882,2380
-Order_No_24449,User_Id_432,Bike,3,Business,16,2,4:52:48 PM,16,2,4:53:13 PM,16,2,4:59:07 PM,16,2,5:14:54 PM,16,2,6:01:30 PM,9,,,-1.3012008,36.764868,-1.2584143,36.8048002,Rider_Id_507,2796
-Order_No_16656,User_Id_3647,Bike,3,Business,17,1,8:49:40 AM,17,1,8:50:03 AM,17,1,8:58:23 AM,17,1,9:04:16 AM,17,1,9:21:08 AM,8,19.0,,-1.3011233999999998,36.830237,-1.2726389999999999,36.794723,Rider_Id_674,1012
-Order_No_6660,User_Id_3496,Bike,3,Business,6,3,3:57:51 PM,6,3,3:58:02 PM,6,3,3:58:45 PM,6,3,4:00:15 PM,6,3,4:00:22 PM,2,22.0,,-1.2903643,36.8319433,-1.300921,36.828195,Rider_Id_927,7
-Order_No_9657,User_Id_1108,Bike,3,Personal,22,3,3:58:53 PM,22,3,4:00:32 PM,22,3,4:13:18 PM,22,3,4:17:40 PM,22,3,4:53:28 PM,11,24.1,,-1.3321701000000001,36.8776153,-1.2857521,36.82774570000001,Rider_Id_276,2148
-Order_No_18559,User_Id_1473,Bike,3,Business,10,3,10:40:35 AM,10,3,11:21:38 AM,10,3,11:32:53 AM,10,3,11:36:17 AM,10,3,12:00:23 PM,12,27.8,,-1.3700383,36.9190174,-1.3231187,36.9033708,Rider_Id_233,1446
-Order_No_24823,User_Id_533,Bike,3,Business,24,1,9:56:58 AM,24,1,9:57:08 AM,24,1,9:59:44 AM,24,1,10:06:45 AM,24,1,10:14:58 AM,6,15.6,,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_38,493
-Order_No_8406,User_Id_1578,Bike,3,Business,18,2,9:48:39 AM,18,2,9:48:56 AM,18,2,10:24:44 AM,18,2,10:59:42 AM,18,2,11:26:46 AM,16,22.6,,-1.2143386999999999,36.7914548,-1.28878,36.8168312,Rider_Id_101,1624
-Order_No_6171,User_Id_1390,Bike,1,Personal,21,4,8:31:50 AM,21,4,9:04:14 AM,21,4,9:17:51 AM,21,4,9:22:52 AM,21,4,10:01:47 AM,9,17.1,,-1.2994194,36.7647917,-1.2889576,36.81833579999999,Rider_Id_79,2335
-Order_No_8776,User_Id_286,Bike,3,Business,30,5,4:06:44 PM,30,5,4:09:28 PM,30,5,4:24:15 PM,30,5,4:31:27 PM,30,5,4:51:35 PM,17,24.7,,-1.2551895,36.7822034,-1.214301,36.895182,Rider_Id_516,1208
-Order_No_24201,User_Id_2956,Bike,3,Business,25,4,3:42:23 PM,25,4,3:46:19 PM,25,4,4:01:08 PM,25,4,4:05:03 PM,25,4,4:17:17 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_709,734
-Order_No_26372,User_Id_2828,Bike,3,Business,2,6,9:37:19 AM,2,6,10:18:42 AM,2,6,10:49:20 AM,2,6,10:50:53 AM,2,6,11:21:19 AM,19,,,-1.2778747,36.883182899999994,-1.3846585,36.9375131,Rider_Id_59,1826
-Order_No_10009,User_Id_393,Bike,3,Business,4,4,9:47:41 AM,4,4,9:49:12 AM,4,4,9:57:54 AM,4,4,10:15:11 AM,4,4,10:29:09 AM,4,21.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_245,838
-Order_No_22937,User_Id_3517,Bike,3,Business,19,2,12:30:10 PM,19,2,12:30:53 PM,19,2,12:42:16 PM,19,2,12:59:01 PM,19,2,1:21:39 PM,14,27.6,,-1.300921,36.828195,-1.3647738,36.744296500000004,Rider_Id_858,1358
-Order_No_21528,User_Id_322,Bike,3,Business,14,5,2:45:03 PM,14,5,2:45:10 PM,14,5,3:18:33 PM,14,5,3:28:05 PM,14,5,4:07:26 PM,15,27.1,,-1.2452664,36.802854100000005,-1.3231725,36.864017700000005,Rider_Id_844,2361
-Order_No_26392,User_Id_3686,Bike,3,Business,5,2,12:07:07 PM,5,2,12:11:07 PM,5,2,12:32:46 PM,5,2,12:33:48 PM,5,2,1:11:49 PM,7,25.3,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_362,2281
-Order_No_555,User_Id_412,Bike,3,Business,28,4,8:43:35 AM,28,4,8:44:09 AM,28,4,9:17:20 AM,28,4,9:40:02 AM,28,4,10:10:38 AM,10,19.6,,-1.303596,36.778378000000004,-1.266837,36.799248999999996,Rider_Id_306,1836
-Order_No_18014,User_Id_1500,Bike,3,Business,25,1,8:13:39 AM,25,1,8:13:56 AM,25,1,8:46:56 AM,25,1,8:50:44 AM,25,1,9:15:37 AM,10,21.2,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_831,1493
-Order_No_4676,User_Id_3295,Bike,3,Business,13,4,1:49:09 PM,13,4,1:49:22 PM,13,4,1:54:54 PM,13,4,2:00:37 PM,13,4,2:13:18 PM,3,23.3,,-1.2571472,36.795063299999995,-1.2549259,36.7919494,Rider_Id_381,761
-Order_No_16594,User_Id_633,Bike,3,Business,15,5,1:59:24 PM,15,5,1:59:43 PM,15,5,1:59:47 PM,15,5,2:11:09 PM,15,5,2:11:11 PM,4,17.9,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_10,2
-Order_No_5345,User_Id_393,Bike,3,Business,22,5,8:24:03 AM,22,5,8:29:02 AM,22,5,8:35:44 AM,22,5,8:56:21 AM,22,5,9:20:34 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_778,1453
-Order_No_464,User_Id_53,Bike,3,Business,8,5,3:18:09 PM,8,5,3:18:55 PM,8,5,3:50:29 PM,8,5,3:56:46 PM,8,5,4:26:13 PM,8,30.6,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_653,1767
-Order_No_7695,User_Id_2804,Bike,3,Business,21,4,11:53:14 AM,21,4,11:54:01 AM,21,4,12:14:05 PM,21,4,12:19:11 PM,21,4,12:28:21 PM,4,25.3,,-1.2690017,36.79852460000001,-1.2551895,36.7822034,Rider_Id_167,550
-Order_No_7602,User_Id_2540,Bike,1,Personal,12,4,10:42:44 AM,12,4,10:54:52 AM,12,4,11:01:30 AM,12,4,11:10:41 AM,12,4,11:43:01 AM,16,18.9,,-1.2021921999999998,36.8904146,-1.285611,36.9047023,Rider_Id_806,1940
-Order_No_23005,User_Id_635,Bike,3,Business,13,4,8:56:54 AM,13,4,9:00:42 AM,13,4,10:33:38 AM,13,4,11:01:24 AM,13,4,11:20:38 AM,10,22.1,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_787,1154
-Order_No_27396,User_Id_2803,Bike,3,Business,24,4,3:19:31 PM,24,4,3:20:16 PM,24,4,3:29:26 PM,24,4,3:33:27 PM,24,4,3:45:09 PM,3,28.1,,-1.2765736,36.851364600000004,-1.2703427999999999,36.8336071,Rider_Id_928,702
-Order_No_3625,User_Id_265,Bike,3,Business,28,3,10:25:15 AM,28,3,10:56:27 AM,28,3,11:09:51 AM,28,3,11:14:44 AM,28,3,11:39:05 AM,6,,,-1.300921,36.828195,-1.2937829,36.8043644,Rider_Id_114,1461
-Order_No_19033,User_Id_868,Bike,3,Business,23,4,2:41:56 PM,23,4,2:42:11 PM,23,4,2:42:29 PM,23,4,2:57:09 PM,23,4,3:10:10 PM,5,24.6,,-1.259478,36.801781299999995,-1.265715,36.823815,Rider_Id_178,781
-Order_No_7839,User_Id_1461,Bike,2,Personal,28,2,9:13:16 AM,28,2,9:13:57 AM,28,2,9:23:09 AM,28,2,9:29:08 AM,28,2,9:53:12 AM,6,16.0,51.1,-1.2720768999999998,36.8180604,-1.2635051000000002,36.7829162,Rider_Id_937,1444
-Order_No_14501,User_Id_2112,Bike,3,Business,27,6,10:31:43 AM,27,6,10:31:57 AM,27,6,11:02:23 AM,27,6,11:10:03 AM,27,6,11:55:14 AM,14,,,-1.2927792,36.9053492,-1.2156006000000001,36.8916865,Rider_Id_248,2711
-Order_No_14775,User_Id_577,Bike,1,Personal,18,4,12:33:12 PM,18,4,12:33:46 PM,18,4,12:40:40 PM,18,4,12:56:31 PM,18,4,1:49:19 PM,12,25.5,,-1.3270347,36.866736200000005,-1.2662728,36.8151265,Rider_Id_789,3168
-Order_No_23218,User_Id_2763,Bike,3,Business,30,4,12:06:32 PM,30,4,12:07:19 PM,30,4,12:07:39 PM,30,4,12:27:45 PM,30,4,1:16:34 PM,14,20.5,,-1.2551895,36.7822034,-1.3015998,36.8764939,Rider_Id_446,2929
-Order_No_27765,User_Id_2956,Bike,3,Business,23,2,2:53:49 PM,23,2,2:57:17 PM,23,2,3:03:18 PM,23,2,3:18:20 PM,23,2,3:55:03 PM,9,19.0,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_565,2203
-Order_No_12762,User_Id_3411,Bike,1,Personal,20,3,1:14:18 PM,20,3,1:23:04 PM,20,3,1:24:39 PM,20,3,1:48:04 PM,20,3,2:08:07 PM,8,23.2,,-1.2855798999999999,36.817620899999994,-1.2919323,36.7659602,Rider_Id_661,1203
-Order_No_21385,User_Id_649,Bike,3,Business,4,1,9:41:12 AM,4,1,9:41:40 AM,4,1,9:50:54 AM,4,1,10:01:32 AM,4,1,10:25:42 AM,8,17.8,,-1.2615888000000002,36.7928732,-1.3034396000000001,36.832999799999996,Rider_Id_56,1450
-Order_No_21285,User_Id_3295,Bike,3,Business,31,4,8:33:08 AM,31,4,8:34:15 AM,31,4,8:45:26 AM,31,4,8:49:30 AM,31,4,8:57:01 AM,2,17.3,,-1.2579303999999998,36.8019667,-1.255392,36.792539,Rider_Id_808,451
-Order_No_18414,User_Id_136,Bike,3,Business,31,4,4:27:09 PM,31,4,4:28:23 PM,31,4,4:45:35 PM,31,4,4:47:10 PM,31,4,4:58:33 PM,2,27.7,,-1.2531983,36.8069669,-1.2733047,36.8109744,Rider_Id_428,683
-Order_No_752,User_Id_1642,Bike,3,Business,24,4,12:54:08 PM,24,4,1:03:35 PM,24,4,1:14:58 PM,24,4,1:22:36 PM,24,4,1:51:04 PM,6,21.4,,-1.2728069,36.7962362,-1.288269,36.818275299999996,Rider_Id_116,1708
-Order_No_5821,User_Id_393,Bike,3,Business,10,1,1:17:47 PM,10,1,1:19:23 PM,10,1,1:45:52 PM,10,1,1:47:48 PM,10,1,1:56:20 PM,4,18.5,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_567,512
-Order_No_10247,User_Id_3150,Bike,3,Business,25,5,2:55:06 PM,25,5,2:59:04 PM,25,5,3:13:28 PM,25,5,3:19:46 PM,25,5,3:43:24 PM,6,23.6,,-1.2508229,36.7895263,-1.2848488999999998,36.825009,Rider_Id_116,1418
-Order_No_17510,User_Id_2330,Bike,3,Business,26,2,6:33:37 PM,26,2,7:13:57 PM,26,2,7:24:11 PM,26,2,7:33:54 PM,26,2,7:53:36 PM,7,22.7,,-1.2325706,36.808392299999994,-1.2649605,36.798177700000004,Rider_Id_72,1182
-Order_No_20525,User_Id_517,Bike,3,Business,2,3,11:49:50 AM,2,3,11:49:57 AM,2,3,11:50:05 AM,2,3,11:54:51 AM,2,3,12:04:55 PM,4,24.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,604
-Order_No_21018,User_Id_1080,Bike,1,Personal,28,4,3:56:37 PM,28,4,3:56:41 PM,28,4,4:09:49 PM,28,4,4:15:24 PM,28,4,4:50:09 PM,15,,,-1.2735236,36.7992196,-1.2129741000000003,36.788811200000005,Rider_Id_895,2085
-Order_No_24159,User_Id_1027,Bike,3,Personal,4,2,3:42:33 PM,4,2,3:52:51 PM,4,2,3:58:14 PM,4,2,4:06:45 PM,4,2,4:06:46 PM,4,22.4,,-1.2728424999999999,36.791039000000005,-1.2734594,36.8170558,Rider_Id_539,1
-Order_No_9694,User_Id_302,Bike,3,Business,10,7,12:30:05 PM,10,7,12:31:19 PM,10,7,12:42:04 PM,10,7,12:49:43 PM,10,7,1:05:21 PM,10,,,-1.2649605,36.798177700000004,-1.3007726000000002,36.774364399999996,Rider_Id_303,938
-Order_No_16808,User_Id_136,Bike,3,Business,16,5,3:25:40 PM,16,5,4:26:14 PM,16,5,4:49:45 PM,16,5,4:52:12 PM,16,5,5:45:27 PM,22,26.2,,-1.3326173000000001,36.71462329999999,-1.2372853000000001,36.7611857,Rider_Id_40,3195
-Order_No_17513,User_Id_324,Bike,3,Personal,6,3,10:28:37 AM,6,3,10:48:16 AM,6,3,11:14:31 AM,6,3,11:43:44 AM,6,3,12:32:08 PM,16,19.9,,-1.332942,36.714065000000005,-1.2653676,36.79110120000001,Rider_Id_618,2904
-Order_No_27629,User_Id_598,Bike,1,Business,21,1,5:35:02 PM,21,1,5:36:17 PM,21,1,5:51:53 PM,21,1,5:55:28 PM,21,1,6:41:54 PM,4,,,-1.2536797,36.7995085,-1.2653906000000001,36.8134406,Rider_Id_668,2786
-Order_No_7137,User_Id_2154,Bike,3,Business,28,3,3:20:50 PM,28,3,3:26:00 PM,28,3,3:55:00 PM,28,3,4:00:39 PM,28,3,4:30:40 PM,16,,,-1.207546,36.7913976,-1.2765736,36.851364600000004,Rider_Id_504,1801
-Order_No_23419,User_Id_868,Bike,3,Business,15,3,11:55:55 AM,15,3,11:56:16 AM,15,3,12:25:38 PM,15,3,12:42:39 PM,15,3,12:49:53 PM,6,,,-1.3033561000000002,36.8163631,-1.265715,36.823815,Rider_Id_61,434
-Order_No_4552,User_Id_3283,Bike,3,Business,25,5,1:42:56 PM,25,5,1:45:05 PM,25,5,1:48:20 PM,25,5,2:04:50 PM,25,5,2:52:19 PM,6,23.2,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_116,2849
-Order_No_20716,User_Id_2549,Bike,2,Personal,14,1,2:11:37 PM,14,1,2:12:01 PM,14,1,2:12:25 PM,14,1,2:29:17 PM,14,1,2:47:24 PM,6,25.7,,-1.2864298,36.8224345,-1.2941023999999999,36.79578,Rider_Id_747,1087
-Order_No_19365,User_Id_2000,Bike,3,Business,4,1,10:27:18 AM,4,1,10:27:37 AM,4,1,10:53:18 AM,4,1,10:57:05 AM,4,1,11:31:54 AM,15,26.7,,-1.2551895,36.7822034,-1.3226301999999999,36.8719901,Rider_Id_846,2089
-Order_No_20557,User_Id_1205,Bike,1,Business,8,2,11:29:37 AM,8,2,11:49:46 AM,8,2,12:05:13 PM,8,2,12:15:38 PM,8,2,12:37:19 PM,3,21.3,,-1.281767,36.8220791,-1.2958383,36.8073162,Rider_Id_241,1301
-Order_No_8637,User_Id_3613,Bike,3,Business,25,1,12:53:56 PM,25,1,12:55:09 PM,25,1,12:55:50 PM,25,1,1:07:17 PM,25,1,1:22:15 PM,4,27.0,,-1.2733622,36.7704045,-1.287332,36.744498,Rider_Id_460,898
-Order_No_15397,User_Id_1724,Bike,3,Business,4,2,8:36:08 AM,4,2,8:38:07 AM,4,2,9:23:59 AM,4,2,9:28:58 AM,4,2,9:55:52 AM,9,16.7,98.9,-1.2907853999999999,36.7774108,-1.2638898,36.8223638,Rider_Id_774,1614
-Order_No_499,User_Id_733,Bike,3,Business,24,5,11:55:47 AM,24,5,1:05:02 PM,24,5,1:18:49 PM,24,5,1:36:26 PM,24,5,1:48:25 PM,7,22.9,,-1.2551895,36.7822034,-1.2857241000000001,36.820348100000004,Rider_Id_948,719
-Order_No_20996,User_Id_928,Bike,3,Business,30,3,11:24:51 AM,30,3,11:26:52 AM,30,3,11:29:48 AM,30,3,11:32:25 AM,30,3,11:57:23 AM,7,25.2,,-1.2551895,36.7822034,-1.2878913,36.8089253,Rider_Id_362,1498
-Order_No_2228,User_Id_633,Bike,3,Business,13,3,1:54:10 PM,13,3,1:54:58 PM,13,3,2:06:37 PM,13,3,2:19:01 PM,13,3,2:30:17 PM,4,29.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_233,676
-Order_No_18363,User_Id_1246,Bike,3,Business,8,5,3:03:01 PM,8,5,3:04:33 PM,8,5,3:14:46 PM,8,5,3:24:18 PM,8,5,3:38:05 PM,8,27.1,,-1.2571472,36.795063299999995,-1.3007534,36.7964576,Rider_Id_62,827
-Order_No_11770,User_Id_265,Bike,3,Business,24,3,9:49:54 AM,24,3,9:50:23 AM,24,3,9:51:58 AM,24,3,10:05:25 AM,24,3,10:36:39 AM,10,16.8,0.9,-1.300921,36.828195,-1.2990817,36.7654866,Rider_Id_519,1874
-Order_No_11120,User_Id_1796,Bike,3,Business,24,3,1:21:08 PM,24,3,1:27:27 PM,24,3,1:38:37 PM,24,3,1:49:53 PM,24,3,2:04:28 PM,11,,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_675,875
-Order_No_23723,User_Id_2330,Bike,3,Business,30,3,3:37:20 PM,30,3,3:38:08 PM,30,3,3:52:53 PM,30,3,3:57:14 PM,30,3,4:42:03 PM,9,29.0,,-1.2680363000000001,36.799046600000004,-1.3015197,36.7658456,Rider_Id_311,2689
-Order_No_10335,User_Id_3222,Bike,3,Business,8,5,2:31:20 PM,8,5,2:32:02 PM,8,5,2:32:31 PM,8,5,2:40:48 PM,8,5,3:13:14 PM,9,30.3,,-1.2551895,36.7822034,-1.3049238,36.7841898,Rider_Id_518,1946
-Order_No_27159,User_Id_3647,Bike,3,Business,7,3,1:24:44 PM,7,3,1:27:44 PM,7,3,1:37:41 PM,7,3,1:41:19 PM,7,3,1:57:26 PM,3,28.3,,-1.2726389999999999,36.794723,-1.265971,36.766806,Rider_Id_646,967
-Order_No_21439,User_Id_2767,Bike,3,Business,14,3,11:30:37 AM,14,3,11:40:39 AM,14,3,11:58:48 AM,14,3,12:03:53 PM,14,3,12:55:37 PM,9,24.4,,-1.257773,36.795486,-1.2332276000000002,36.800892299999994,Rider_Id_66,3104
-Order_No_17825,User_Id_635,Bike,3,Business,15,5,9:26:28 AM,15,5,9:27:05 AM,15,5,9:36:00 AM,15,5,9:54:02 AM,15,5,10:05:36 AM,7,19.2,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_61,694
-Order_No_6271,User_Id_393,Bike,3,Business,7,2,11:27:24 AM,7,2,11:27:47 AM,7,2,11:29:57 AM,7,2,11:56:08 AM,7,2,12:08:31 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,743
-Order_No_22350,User_Id_246,Bike,3,Business,27,2,1:33:39 PM,27,2,1:33:48 PM,27,2,1:36:25 PM,27,2,1:41:13 PM,27,2,1:59:30 PM,10,25.8,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_685,1097
-Order_No_24399,User_Id_1075,Bike,3,Business,11,1,11:22:53 AM,11,1,11:23:35 AM,11,1,11:30:16 AM,11,1,11:40:20 AM,11,1,11:48:24 AM,1,24.0,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_439,484
-Order_No_3151,User_Id_3283,Bike,3,Business,11,2,1:27:53 PM,11,2,1:29:05 PM,11,2,1:48:33 PM,11,2,1:59:01 PM,11,2,2:14:36 PM,5,22.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_38,935
-Order_No_3610,User_Id_391,Bike,3,Business,10,5,3:03:05 PM,10,5,3:13:23 PM,10,5,3:50:10 PM,10,5,4:09:54 PM,10,5,5:41:20 PM,3,,,-1.2784063,36.8222933,-1.270766,36.810988,Rider_Id_76,5486
-Order_No_17039,User_Id_2823,Bike,3,Business,16,3,10:13:07 AM,16,3,10:13:46 AM,16,3,10:21:31 AM,16,3,10:28:39 AM,16,3,10:59:34 AM,11,19.8,,-1.2601708999999999,36.8002238,-1.3018758,36.75908629999999,Rider_Id_353,1855
-Order_No_23476,User_Id_1705,Bike,3,Business,18,1,3:19:01 PM,18,1,3:19:10 PM,18,1,3:26:56 PM,18,1,3:30:27 PM,18,1,4:23:07 PM,28,30.6,,-1.2551895,36.7822034,-1.1429062,36.9300994,Rider_Id_452,3160
-Order_No_23048,User_Id_718,Bike,3,Business,2,2,11:41:09 AM,2,2,11:41:48 AM,2,2,11:50:51 AM,2,2,11:52:46 AM,2,2,12:08:22 PM,4,21.2,,-1.2551895,36.7822034,-1.265981,36.793612100000004,Rider_Id_937,936
-Order_No_11592,User_Id_1243,Bike,1,Business,1,1,11:31:16 AM,1,1,11:32:08 AM,1,1,11:37:37 AM,1,1,11:41:31 AM,1,1,12:12:09 PM,10,24.2,,-1.2615888000000002,36.7928732,-1.304524,36.775371,Rider_Id_228,1838
-Order_No_13217,User_Id_3177,Bike,3,Business,15,4,1:10:02 PM,15,4,1:14:59 PM,15,4,1:28:57 PM,15,4,1:35:02 PM,15,4,2:41:05 PM,20,27.7,,-1.3019530000000001,36.788458,-1.2617477,36.6701414,Rider_Id_558,3963
-Order_No_6455,User_Id_635,Bike,3,Business,7,2,1:54:10 PM,7,2,1:55:08 PM,7,2,1:55:11 PM,7,2,2:28:12 PM,7,2,2:29:29 PM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_41,77
-Order_No_13246,User_Id_1393,Bike,1,Business,24,7,1:57:51 PM,24,7,2:00:15 PM,24,7,2:44:28 PM,24,7,2:46:11 PM,24,7,3:22:53 PM,13,22.9,,-1.2601708999999999,36.8002238,-1.2156006000000001,36.8916865,Rider_Id_45,2202
-Order_No_27156,User_Id_2987,Bike,3,Business,12,1,12:30:25 PM,12,1,12:32:42 PM,12,1,12:43:07 PM,12,1,12:46:33 PM,12,1,1:14:11 PM,10,23.8,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_399,1658
-Order_No_19066,User_Id_2710,Bike,3,Personal,5,2,3:09:12 PM,5,2,3:09:24 PM,5,2,3:21:58 PM,5,2,3:23:35 PM,5,2,3:54:01 PM,11,,,-1.2535486,36.7933541,-1.2980228999999999,36.78865,Rider_Id_523,1826
-Order_No_5384,User_Id_3404,Bike,3,Business,16,6,3:25:22 PM,16,6,3:25:52 PM,16,6,3:39:58 PM,16,6,4:02:11 PM,16,6,4:30:29 PM,6,28.5,,-1.279395,36.825364,-1.3185736000000001,36.8258933,Rider_Id_119,1698
-Order_No_744,User_Id_1861,Bike,1,Personal,16,4,11:34:15 AM,16,4,11:37:00 AM,16,4,12:14:30 PM,16,4,12:23:50 PM,16,4,1:01:58 PM,11,14.7,,-1.2311903,36.8102105,-1.2802618000000001,36.8191684,Rider_Id_213,2288
-Order_No_25365,User_Id_136,Bike,3,Business,4,4,9:55:45 AM,4,4,9:56:27 AM,4,4,10:26:49 AM,4,4,10:45:52 AM,4,4,11:33:57 AM,15,,,-1.3326173000000001,36.71462329999999,-1.3233496,36.8139159,Rider_Id_565,2885
-Order_No_1522,User_Id_517,Bike,3,Business,6,4,8:44:55 AM,6,4,9:43:01 AM,6,4,10:12:33 AM,6,4,10:15:29 AM,6,4,10:54:28 AM,14,18.9,3.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2339
-Order_No_16850,User_Id_1628,Bike,3,Personal,7,5,1:05:14 PM,7,5,1:06:46 PM,7,5,1:22:58 PM,7,5,1:36:30 PM,7,5,2:13:53 PM,14,,,-1.3376959,36.7605707,-1.2963097,36.7688221,Rider_Id_868,2243
-Order_No_6728,User_Id_1346,Bike,3,Business,15,1,10:26:22 AM,15,1,10:26:41 AM,15,1,10:27:28 AM,15,1,10:34:25 AM,15,1,10:46:38 AM,6,,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_720,733
-Order_No_2990,User_Id_3078,Bike,1,Personal,20,5,4:48:54 PM,20,5,4:50:13 PM,20,5,5:00:53 PM,20,5,5:13:29 PM,20,5,5:29:43 PM,4,20.2,,-1.2555969999999999,36.830024,-1.2860785,36.8107713,Rider_Id_427,974
-Order_No_23135,User_Id_3112,Bike,1,Personal,28,5,7:12:00 PM,28,5,7:12:51 PM,28,5,7:42:16 PM,28,5,7:50:17 PM,28,5,8:32:45 PM,12,19.3,,-1.296567,36.8748023,-1.3120584,36.8052037,Rider_Id_532,2548
-Order_No_9693,User_Id_3131,Bike,3,Personal,14,1,11:20:07 AM,14,1,11:20:16 AM,14,1,11:52:23 AM,14,1,12:07:18 PM,14,1,12:38:07 PM,10,23.3,,-1.3261306000000002,36.850596,-1.263419,36.8060348,Rider_Id_926,1849
-Order_No_24108,User_Id_2200,Bike,3,Business,26,1,10:44:35 AM,26,1,11:38:15 AM,26,1,11:53:43 AM,26,1,12:00:43 PM,26,1,12:06:00 PM,1,22.9,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_88,317
-Order_No_6225,User_Id_3295,Bike,3,Business,8,5,1:32:20 PM,8,5,2:39:37 PM,8,5,2:47:44 PM,8,5,3:55:29 PM,8,5,4:30:38 PM,21,30.6,,-1.2571472,36.795063299999995,-1.3987597999999999,36.767703600000004,Rider_Id_404,2109
-Order_No_4060,User_Id_1997,Bike,3,Personal,13,4,3:45:05 PM,13,4,3:47:50 PM,13,4,4:05:36 PM,13,4,4:44:11 PM,13,4,5:19:52 PM,19,22.4,,-1.2882850000000001,36.815912,-1.3398006000000002,36.717935,Rider_Id_921,2141
-Order_No_11070,User_Id_3647,Bike,3,Business,27,2,3:33:48 PM,27,2,3:34:37 PM,27,2,3:46:06 PM,27,2,3:53:13 PM,27,2,4:20:13 PM,17,25.4,,-1.2726389999999999,36.794723,-1.207546,36.7913976,Rider_Id_222,1620
-Order_No_28232,User_Id_3247,Bike,3,Business,10,1,10:52:47 AM,10,1,10:53:53 AM,10,1,11:05:08 AM,10,1,11:08:25 AM,10,1,11:38:21 AM,18,23.5,,-1.2571472,36.795063299999995,-1.2578249,36.68032720000001,Rider_Id_353,1796
-Order_No_18739,User_Id_2956,Bike,3,Business,27,1,10:56:47 AM,27,1,10:57:21 AM,27,1,10:59:29 AM,27,1,11:04:45 AM,27,1,11:58:11 AM,6,23.9,,-1.2584143,36.8048002,-1.2882803,36.7815881,Rider_Id_222,3206
-Order_No_25869,User_Id_2174,Bike,3,Business,4,5,12:16:18 PM,4,5,12:16:22 PM,4,5,12:18:29 PM,4,5,12:54:44 PM,4,5,1:28:09 PM,17,17.1,,-1.2482781,36.884091999999995,-1.1689085,36.82128970000001,Rider_Id_946,2005
-Order_No_1873,User_Id_3647,Bike,3,Business,5,5,10:36:26 AM,5,5,10:37:13 AM,5,5,10:56:22 AM,5,5,11:02:07 AM,5,5,11:42:14 AM,8,24.4,,-1.2726389999999999,36.794723,-1.3041018999999998,36.8105732,Rider_Id_843,2407
-Order_No_11747,User_Id_868,Bike,3,Business,10,5,12:21:14 PM,10,5,12:22:46 PM,10,5,12:53:33 PM,10,5,1:27:14 PM,10,5,1:30:12 PM,9,,,-1.2297799999999999,36.836215,-1.265715,36.823815,Rider_Id_570,178
-Order_No_17600,User_Id_633,Bike,3,Business,5,2,3:43:20 PM,5,2,3:44:36 PM,5,2,3:50:45 PM,5,2,3:55:36 PM,5,2,4:13:46 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_154,1090
-Order_No_176,User_Id_2469,Bike,3,Business,11,5,1:38:50 PM,11,5,1:40:32 PM,11,5,1:56:05 PM,11,5,2:08:41 PM,11,5,2:24:36 PM,10,26.4,,-1.303924,36.8758792,-1.2389424999999998,36.8775718,Rider_Id_124,955
-Order_No_22891,User_Id_2349,Bike,3,Business,17,1,10:33:08 AM,17,1,10:45:57 AM,17,1,10:52:48 AM,17,1,10:56:30 AM,17,1,11:29:35 AM,5,23.5,,-1.2784495,36.7981729,-1.2927422,36.81833579999999,Rider_Id_48,1985
-Order_No_22602,User_Id_1102,Bike,3,Business,1,1,9:45:37 AM,1,1,9:49:48 AM,1,1,10:57:22 AM,1,1,11:14:30 AM,1,1,11:14:37 AM,10,,,-1.338283,36.8871775,-1.3004062,36.829741,Rider_Id_507,7
-Order_No_688,User_Id_2207,Bike,3,Business,18,4,2:22:46 PM,18,4,2:23:48 PM,18,4,2:24:37 PM,18,4,2:38:53 PM,18,4,2:46:07 PM,2,27.0,,-1.2704656,36.7948766,-1.2768326,36.8073804,Rider_Id_881,434
-Order_No_15452,User_Id_1874,Bike,3,Business,8,5,2:00:58 PM,8,5,2:02:13 PM,8,5,2:51:39 PM,8,5,3:07:35 PM,8,5,4:27:31 PM,12,27.2,,-1.2571472,36.795063299999995,-1.2369033,36.8699546,Rider_Id_755,4796
-Order_No_986,User_Id_1271,Bike,3,Business,25,5,11:36:21 AM,25,5,11:37:18 AM,25,5,11:45:13 AM,25,5,11:51:01 AM,25,5,12:36:07 PM,19,26.2,,-1.3244885,36.897791999999995,-1.2649605,36.798177700000004,Rider_Id_418,2706
-Order_No_28047,User_Id_2987,Bike,3,Business,30,3,12:41:35 PM,30,3,12:43:36 PM,30,3,12:51:52 PM,30,3,12:56:09 PM,30,3,1:10:28 PM,11,22.6,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_408,859
-Order_No_4279,User_Id_136,Bike,3,Business,22,5,2:25:15 PM,22,5,2:26:53 PM,22,5,2:38:55 PM,22,5,2:46:09 PM,22,5,3:11:21 PM,13,27.5,,-1.3254522,36.721658399999995,-1.290641,36.812940999999995,Rider_Id_959,1512
-Order_No_7514,User_Id_2423,Bike,3,Business,22,5,1:51:28 PM,22,5,1:51:59 PM,22,5,2:03:42 PM,22,5,2:07:36 PM,22,5,2:23:44 PM,5,,,-1.2551895,36.7822034,-1.2658315,36.809049,Rider_Id_140,968
-Order_No_3093,User_Id_2804,Bike,3,Business,17,3,4:23:10 PM,17,3,4:23:17 PM,17,3,4:23:27 PM,17,3,4:26:41 PM,17,3,5:04:16 PM,15,28.3,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_789,2255
-Order_No_26915,User_Id_420,Bike,3,Business,12,3,3:23:58 PM,12,3,3:36:09 PM,12,3,3:48:11 PM,12,3,3:57:30 PM,12,3,4:16:45 PM,10,26.7,,-1.3080097,36.7494062,-1.2611642,36.7949932,Rider_Id_312,1155
-Order_No_8337,User_Id_1925,Bike,2,Personal,8,2,3:11:48 PM,8,2,3:12:10 PM,8,2,3:12:26 PM,8,2,3:35:17 PM,8,2,3:42:45 PM,7,23.3,,-1.2859912,36.8756811,-1.2860183,36.8975338,Rider_Id_408,448
-Order_No_23879,User_Id_1271,Bike,3,Business,6,4,3:58:47 PM,6,4,4:14:12 PM,6,4,4:37:36 PM,6,4,4:43:50 PM,6,4,5:30:22 PM,26,22.4,,-1.3244885,36.897791999999995,-1.3949989,36.7654761,Rider_Id_519,2792
-Order_No_18521,User_Id_761,Bike,3,Business,12,5,11:06:25 AM,12,5,11:06:35 AM,12,5,11:29:20 AM,12,5,11:31:17 AM,12,5,11:45:25 AM,7,26.6,,-1.2693101999999998,36.798177700000004,-1.298762,36.763645600000004,Rider_Id_649,848
-Order_No_6957,User_Id_393,Bike,3,Business,24,3,10:13:39 AM,24,3,11:48:39 AM,24,3,11:50:29 AM,24,3,12:21:51 PM,24,3,12:40:10 PM,10,19.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1099
-Order_No_26126,User_Id_2203,Bike,2,Personal,26,4,4:21:11 PM,26,4,4:22:10 PM,26,4,4:30:05 PM,26,4,4:34:40 PM,26,4,4:53:51 PM,8,19.4,,-1.3129578000000002,36.8463197,-1.2925919,36.81705770000001,Rider_Id_427,1151
-Order_No_11843,User_Id_2200,Bike,3,Business,19,2,2:19:19 PM,19,2,2:47:00 PM,19,2,2:50:28 PM,19,2,3:02:34 PM,19,2,4:00:32 PM,11,29.1,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_484,3478
-Order_No_21731,User_Id_2763,Bike,3,Business,2,5,12:46:01 PM,2,5,12:46:51 PM,2,5,12:49:19 PM,2,5,12:53:54 PM,2,5,1:19:47 PM,11,23.3,,-1.2551895,36.7822034,-1.3250103000000002,36.8388817,Rider_Id_498,1553
-Order_No_8698,User_Id_1976,Bike,3,Business,23,5,2:30:21 PM,23,5,2:59:52 PM,23,5,3:05:24 PM,23,5,3:09:53 PM,23,5,3:34:00 PM,16,26.3,,-1.257028,36.7810435,-1.2230709,36.8374011,Rider_Id_38,1447
-Order_No_10704,User_Id_627,Bike,3,Business,19,5,1:00:22 PM,19,5,1:01:47 PM,19,5,1:42:44 PM,19,5,1:57:36 PM,19,5,2:30:36 PM,6,27.4,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_751,1980
-Order_No_2142,User_Id_3426,Bike,3,Business,1,4,10:28:15 AM,1,4,10:28:29 AM,1,4,10:36:41 AM,1,4,10:39:12 AM,1,4,11:30:11 AM,20,,,-1.2283403,36.8822756,-1.3014461000000002,36.7661381,Rider_Id_685,3059
-Order_No_10635,User_Id_3541,Bike,2,Personal,7,5,3:26:16 PM,7,5,3:27:42 PM,7,5,3:42:03 PM,7,5,3:50:20 PM,7,5,4:17:25 PM,9,25.0,,-1.2750415000000002,36.7965813,-1.2804227,36.8250375,Rider_Id_89,1625
-Order_No_326,User_Id_1946,Bike,1,Personal,13,1,9:11:35 AM,13,1,9:12:11 AM,13,1,9:31:44 AM,13,1,9:41:11 AM,13,1,9:54:25 AM,5,15.4,,-1.2913382,36.8018508,-1.2765064,36.8215724,Rider_Id_234,794
-Order_No_27133,User_Id_3283,Bike,3,Business,27,3,1:12:14 PM,27,3,1:12:45 PM,27,3,1:14:58 PM,27,3,1:32:55 PM,27,3,1:40:22 PM,5,29.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_393,447
-Order_No_6810,User_Id_3127,Bike,3,Business,17,1,12:18:59 PM,17,1,12:23:16 PM,17,1,12:54:31 PM,17,1,12:57:05 PM,17,1,1:23:07 PM,5,23.8,,-1.2918246,36.801584999999996,-1.2987440000000001,36.817823700000005,Rider_Id_436,1562
-Order_No_26316,User_Id_1211,Bike,2,Personal,15,1,12:57:27 PM,15,1,12:57:34 PM,15,1,1:11:49 PM,15,1,1:21:50 PM,15,1,1:33:27 PM,8,,,-1.2647688,36.775086,-1.2855798999999999,36.817620899999994,Rider_Id_462,697
-Order_No_21304,User_Id_1724,Bike,3,Business,28,4,4:56:46 PM,28,4,4:58:19 PM,28,4,5:07:37 PM,28,4,5:10:02 PM,28,4,5:31:51 PM,8,,,-1.2915306999999998,36.790586499999996,-1.2551895,36.7822034,Rider_Id_449,1309
-Order_No_1821,User_Id_2279,Bike,3,Business,20,3,3:57:24 PM,20,3,4:02:15 PM,20,3,4:35:15 PM,20,3,4:39:25 PM,20,3,5:10:52 PM,5,28.0,,-1.2897368,36.8226783,-1.3057362,36.81788,Rider_Id_763,1887
-Order_No_2203,User_Id_2523,Bike,3,Business,26,4,3:22:44 PM,26,4,3:23:48 PM,26,4,3:32:58 PM,26,4,3:46:41 PM,26,4,3:48:06 PM,3,22.1,,-1.2551895,36.7822034,-1.2570047,36.7836756,Rider_Id_116,85
-Order_No_24388,User_Id_496,Bike,3,Business,9,2,11:26:25 AM,9,2,11:51:18 AM,9,2,12:00:25 PM,9,2,12:02:46 PM,9,2,1:02:51 PM,7,22.0,,-1.2551895,36.7822034,-1.2889024,36.8187402,Rider_Id_268,3605
-Order_No_23676,User_Id_1500,Bike,3,Business,15,6,11:57:14 AM,15,6,11:58:03 AM,15,6,12:07:12 PM,15,6,12:10:49 PM,15,6,12:21:19 PM,6,19.1,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_341,630
-Order_No_8438,User_Id_3355,Bike,3,Business,8,2,4:20:24 PM,8,2,4:29:40 PM,8,2,4:50:35 PM,8,2,4:54:14 PM,8,2,5:11:40 PM,8,23.4,,-1.3052491000000002,36.8223899,-1.2615888000000002,36.7928732,Rider_Id_607,1046
-Order_No_19705,User_Id_1363,Bike,3,Business,3,4,2:29:31 PM,3,4,2:46:56 PM,3,4,3:06:31 PM,3,4,3:10:37 PM,3,4,3:25:32 PM,7,26.1,,-1.3004062,36.829741,-1.2650042,36.790392700000005,Rider_Id_607,895
-Order_No_22403,User_Id_2069,Bike,3,Business,20,2,3:33:30 PM,20,2,3:53:47 PM,20,2,3:59:29 PM,20,2,4:05:31 PM,20,2,4:28:39 PM,16,23.8,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_620,1388
-Order_No_25066,User_Id_3674,Bike,3,Business,19,3,10:22:06 AM,19,3,10:22:33 AM,19,3,10:59:47 AM,19,3,11:03:45 AM,19,3,11:17:58 AM,4,22.4,,-1.2551895,36.7822034,-1.2707177,36.8009828,Rider_Id_167,853
-Order_No_18897,User_Id_3194,Bike,3,Business,9,2,9:41:03 AM,9,2,9:41:21 AM,9,2,9:52:19 AM,9,2,10:07:04 AM,9,2,10:34:48 AM,16,20.7,,-1.2551895,36.7822034,-1.1874676000000002,36.7424548,Rider_Id_438,1664
-Order_No_27522,User_Id_30,Bike,1,Personal,31,5,2:30:25 PM,31,5,2:30:52 PM,31,5,2:45:02 PM,31,5,2:46:47 PM,31,5,2:53:32 PM,2,20.0,,-1.2675613000000001,36.8133001,-1.252973,36.8165107,Rider_Id_754,405
-Order_No_14415,User_Id_2069,Bike,3,Business,26,2,4:07:06 PM,26,2,4:08:13 PM,26,2,4:22:54 PM,26,2,4:33:45 PM,26,2,5:08:09 PM,16,28.3,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_678,2064
-Order_No_27013,User_Id_674,Bike,1,Personal,19,5,2:59:08 PM,19,5,2:59:17 PM,19,5,3:06:24 PM,19,5,3:07:58 PM,19,5,3:18:08 PM,2,27.2,,-1.2676163999999999,36.8075243,-1.264917,36.8150651,Rider_Id_407,610
-Order_No_26965,User_Id_1500,Bike,3,Business,15,5,3:26:18 PM,15,5,3:30:30 PM,15,5,3:50:48 PM,15,5,3:52:28 PM,15,5,4:12:22 PM,9,17.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_863,1194
-Order_No_10743,User_Id_2066,Bike,3,Business,8,5,10:16:43 AM,8,5,10:19:46 AM,8,5,10:31:31 AM,8,5,10:38:55 AM,8,5,10:54:12 AM,4,,,-1.316961,36.820337,-1.3083582,36.837903600000004,Rider_Id_905,917
-Order_No_21024,User_Id_3201,Bike,3,Business,23,4,1:38:22 PM,23,4,1:39:16 PM,23,4,1:58:08 PM,23,4,2:03:51 PM,23,4,2:23:49 PM,4,24.8,,-1.2584143,36.8048002,-1.2736094999999998,36.8011282,Rider_Id_783,1198
-Order_No_4337,User_Id_1245,Bike,3,Business,21,4,8:22:43 AM,21,4,8:23:58 AM,21,4,8:24:52 AM,21,4,9:18:03 AM,21,4,9:45:42 AM,17,21.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_10,1659
-Order_No_27566,User_Id_1927,Bike,3,Business,11,2,4:19:47 PM,11,2,4:22:54 PM,11,2,4:23:40 PM,11,2,4:46:16 PM,11,2,5:19:24 PM,19,20.6,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_293,1988
-Order_No_16156,User_Id_2271,Bike,3,Business,29,5,10:07:23 AM,29,5,10:08:48 AM,29,5,10:23:40 AM,29,5,10:31:08 AM,29,5,11:05:04 AM,21,21.0,,-1.3444459999999998,36.756597,-1.293515,36.897607,Rider_Id_533,2036
-Order_No_10985,User_Id_2837,Bike,3,Personal,4,5,2:17:51 PM,4,5,2:18:06 PM,4,5,2:53:49 PM,4,5,2:55:09 PM,4,5,4:15:01 PM,18,,,-1.3457877,36.8811894,-1.2534301,36.800098299999995,Rider_Id_48,4792
-Order_No_12199,User_Id_393,Bike,3,Business,23,4,10:32:31 AM,23,4,10:33:23 AM,23,4,10:48:26 AM,23,4,11:16:36 AM,23,4,11:35:43 AM,11,23.5,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_96,1147
-Order_No_15527,User_Id_1559,Bike,2,Personal,27,4,2:06:37 PM,27,4,2:06:59 PM,27,4,2:19:58 PM,27,4,2:32:44 PM,27,4,3:15:57 PM,13,25.0,,-1.3082357,36.8161482,-1.2287936000000002,36.826978999999994,Rider_Id_532,2593
-Order_No_5251,User_Id_797,Bike,3,Business,23,4,4:08:48 PM,23,4,4:09:10 PM,23,4,4:29:22 PM,23,4,4:36:38 PM,23,4,4:48:02 PM,4,,,-1.2566688999999998,36.779695200000006,-1.2551895,36.7822034,Rider_Id_269,684
-Order_No_6542,User_Id_3557,Bike,3,Business,9,5,9:10:30 AM,9,5,9:11:34 AM,9,5,9:39:14 AM,9,5,9:48:40 AM,9,5,9:58:20 AM,3,20.4,,-1.2519787,36.780649100000005,-1.2622145,36.8087391,Rider_Id_101,580
-Order_No_8573,User_Id_1373,Bike,2,Personal,7,6,12:33:07 PM,7,6,12:33:21 PM,7,6,12:49:13 PM,7,6,12:52:27 PM,7,6,1:21:19 PM,10,18.8,,-1.2906604,36.8318242,-1.2884354,36.760676600000004,Rider_Id_33,1732
-Order_No_3624,User_Id_2112,Bike,3,Business,17,1,2:16:05 PM,17,1,2:20:44 PM,17,1,2:25:46 PM,17,1,2:32:09 PM,17,1,3:17:16 PM,21,24.7,,-1.2156006000000001,36.8916865,-1.2795549,36.7627011,Rider_Id_387,2707
-Order_No_9356,User_Id_60,Bike,3,Business,19,6,10:39:09 AM,19,6,10:41:23 AM,19,6,10:56:49 AM,19,6,11:04:38 AM,19,6,11:36:12 AM,8,25.4,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_857,1894
-Order_No_24814,User_Id_811,Bike,2,Personal,17,2,2:17:13 PM,17,2,2:23:25 PM,17,2,2:37:24 PM,17,2,2:39:01 PM,17,2,3:19:57 PM,8,24.5,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_848,2456
-Order_No_5234,User_Id_733,Bike,3,Business,30,2,11:10:44 AM,30,2,11:15:49 AM,30,2,12:13:18 PM,30,2,12:22:57 PM,30,2,1:19:35 PM,9,,,-1.2551895,36.7822034,-1.3002553999999997,36.8256566,Rider_Id_394,3398
-Order_No_11208,User_Id_2066,Bike,3,Business,9,4,9:53:17 AM,9,4,9:53:25 AM,9,4,9:58:02 AM,9,4,9:59:08 AM,9,4,10:18:37 AM,10,17.0,,-1.316961,36.820337,-1.3239725,36.8827383,Rider_Id_754,1169
-Order_No_14532,User_Id_2436,Bike,3,Business,27,4,1:20:50 PM,27,4,1:21:18 PM,27,4,1:24:17 PM,27,4,2:38:27 PM,27,4,2:38:40 PM,20,25.0,,-1.2551895,36.7822034,-1.3023387,36.9066847,Rider_Id_49,13
-Order_No_7598,User_Id_900,Bike,2,Personal,2,2,10:24:30 AM,2,2,10:31:20 AM,2,2,10:43:00 AM,2,2,10:46:59 AM,2,2,12:16:38 PM,20,21.2,,-1.3508036,36.7093385,-1.2590031000000002,36.8078168,Rider_Id_805,5379
-Order_No_1894,User_Id_635,Bike,3,Business,8,5,10:44:04 AM,8,5,10:44:42 AM,8,5,10:57:38 AM,8,5,11:12:22 AM,8,5,11:39:16 AM,7,28.2,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_590,1614
-Order_No_22763,User_Id_3222,Bike,3,Business,26,5,2:41:59 PM,26,5,2:46:51 PM,26,5,2:57:52 PM,26,5,2:59:20 PM,26,5,3:26:53 PM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_260,1653
-Order_No_6116,User_Id_59,Bike,3,Business,13,2,12:13:49 PM,13,2,12:14:03 PM,13,2,12:17:44 PM,13,2,12:24:04 PM,13,2,12:54:49 PM,14,22.4,,-1.2232123,36.8932547,-1.290894,36.822971,Rider_Id_447,1845
-Order_No_17434,User_Id_733,Bike,3,Business,21,5,10:47:38 AM,21,5,10:48:09 AM,21,5,10:48:15 AM,21,5,10:54:15 AM,21,5,11:19:11 AM,16,22.8,,-1.2551895,36.7822034,-1.324282,36.86625,Rider_Id_41,1496
-Order_No_9762,User_Id_1568,Bike,3,Business,15,2,10:58:37 AM,15,2,10:58:54 AM,15,2,10:59:05 AM,15,2,11:15:08 AM,15,2,11:33:31 AM,9,22.9,,-1.2962603000000001,36.7766164,-1.2579303999999998,36.8019667,Rider_Id_623,1103
-Order_No_20334,User_Id_2869,Bike,3,Business,18,4,10:36:14 AM,18,4,10:38:31 AM,18,4,10:48:19 AM,18,4,11:04:45 AM,18,4,11:39:01 AM,10,,,-1.3472066,36.7692638,-1.3025826,36.7670807,Rider_Id_533,2056
-Order_No_9405,User_Id_393,Bike,3,Business,26,5,9:41:03 AM,26,5,9:41:25 AM,26,5,9:41:30 AM,26,5,10:17:44 AM,26,5,10:17:46 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_10,2
-Order_No_18461,User_Id_2066,Bike,3,Business,15,1,2:13:52 PM,15,1,2:15:11 PM,15,1,2:47:53 PM,15,1,3:13:55 PM,15,1,3:28:50 PM,6,,,-1.2978549,36.856480700000006,-1.3210188999999999,36.841055100000005,Rider_Id_168,895
-Order_No_25154,User_Id_3108,Bike,3,Business,30,3,12:26:38 PM,30,3,12:30:07 PM,30,3,12:34:20 PM,30,3,12:36:01 PM,30,3,1:27:18 PM,16,25.1,,-1.2551895,36.7822034,-1.2893678,36.8842169,Rider_Id_206,3077
-Order_No_11591,User_Id_3217,Bike,3,Business,18,1,5:34:00 PM,18,1,5:41:01 PM,18,1,6:32:29 PM,18,1,7:03:04 PM,18,1,7:41:26 PM,10,,,-1.2601708999999999,36.8002238,-1.214328,36.7911903,Rider_Id_549,2302
-Order_No_11350,User_Id_1237,Bike,1,Personal,14,5,9:30:34 AM,14,5,9:32:03 AM,14,5,9:42:24 AM,14,5,9:49:47 AM,14,5,10:02:57 AM,2,18.4,,-1.2741728,36.79691079999999,-1.2611987,36.80609129999999,Rider_Id_228,790
-Order_No_11964,User_Id_635,Bike,3,Business,10,5,9:45:44 AM,10,5,9:46:37 AM,10,5,10:03:58 AM,10,5,10:06:57 AM,10,5,10:22:05 AM,7,13.7,,-1.2599563,36.799343900000004,-1.269656,36.767011,Rider_Id_574,908
-Order_No_15187,User_Id_1927,Bike,3,Business,8,2,3:37:39 PM,8,2,3:39:53 PM,8,2,3:55:07 PM,8,2,4:03:42 PM,8,2,5:10:06 PM,15,25.3,,-1.2642719999999998,36.7978791,-1.3467388999999999,36.7690312,Rider_Id_421,3984
-Order_No_24699,User_Id_53,Bike,3,Business,14,5,12:43:23 PM,14,5,12:44:36 PM,14,5,1:07:32 PM,14,5,1:20:14 PM,14,5,1:35:50 PM,5,21.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_921,936
-Order_No_12637,User_Id_1271,Bike,3,Business,11,1,1:00:53 PM,11,1,1:01:03 PM,11,1,1:01:19 PM,11,1,1:29:25 PM,11,1,1:50:58 PM,14,23.9,,-1.310613,36.8101948,-1.3244885,36.897791999999995,Rider_Id_925,1293
-Order_No_24259,User_Id_3291,Bike,3,Business,26,6,12:36:52 PM,26,6,12:37:55 PM,26,6,12:46:17 PM,26,6,1:03:23 PM,26,6,1:34:40 PM,9,25.9,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_501,1877
-Order_No_26711,User_Id_3434,Bike,3,Business,13,5,2:35:21 PM,13,5,2:35:37 PM,13,5,2:45:12 PM,13,5,2:52:50 PM,13,5,3:26:54 PM,17,19.0,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_951,2044
-Order_No_12445,User_Id_1363,Bike,3,Business,16,2,12:23:42 PM,16,2,12:25:10 PM,16,2,12:27:09 PM,16,2,12:33:55 PM,16,2,1:03:01 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_686,1746
-Order_No_27194,User_Id_1792,Bike,3,Business,23,3,12:51:54 PM,23,3,12:55:03 PM,23,3,1:08:43 PM,23,3,1:11:48 PM,23,3,1:40:54 PM,12,,,-1.2809121,36.7852524,-1.3104506,36.8687235,Rider_Id_124,1746
-Order_No_13158,User_Id_1245,Bike,3,Business,6,6,11:30:56 AM,6,6,11:31:13 AM,6,6,12:20:49 PM,6,6,12:31:57 PM,6,6,1:08:43 PM,15,26.8,,-1.300921,36.828195,-1.2121659,36.811243700000006,Rider_Id_448,2206
-Order_No_1683,User_Id_3098,Bike,1,Business,4,5,9:12:38 AM,4,5,9:39:16 AM,4,5,9:54:15 AM,4,5,10:00:55 AM,4,5,10:26:12 AM,8,19.4,51.9,-1.2777926000000002,36.8215788,-1.2963097,36.7688221,Rider_Id_676,1517
-Order_No_22293,User_Id_1518,Bike,3,Personal,16,6,12:44:35 PM,16,6,12:45:10 PM,16,6,12:54:50 PM,16,6,1:02:26 PM,16,6,1:14:33 PM,5,25.8,,-1.3104748,36.8129945,-1.2877831,36.8146624,Rider_Id_206,727
-Order_No_17311,User_Id_3291,Bike,3,Business,2,4,2:44:06 PM,2,4,2:44:59 PM,2,4,3:00:49 PM,2,4,3:02:13 PM,2,4,3:09:32 PM,2,26.0,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_882,439
-Order_No_216,User_Id_1363,Bike,3,Business,30,6,9:16:57 AM,30,6,9:17:20 AM,30,6,9:17:28 AM,30,6,9:19:41 AM,30,6,9:35:22 AM,4,,,-1.3004062,36.829741,-1.2819608,36.8109939,Rider_Id_111,941
-Order_No_21536,User_Id_1500,Bike,3,Business,15,3,10:57:01 AM,15,3,10:57:24 AM,15,3,10:57:49 AM,15,3,11:29:27 AM,15,3,11:45:42 AM,13,,,-1.2638185,36.7930057,-1.2434951,36.6760986,Rider_Id_291,975
-Order_No_23960,User_Id_1927,Bike,3,Business,20,3,10:24:11 AM,20,3,10:24:19 AM,20,3,10:24:23 AM,20,3,10:28:49 AM,20,3,10:55:56 AM,14,20.7,,-1.3472066,36.7692638,-1.2594538999999998,36.8067841,Rider_Id_26,1627
-Order_No_12835,User_Id_1638,Bike,1,Personal,10,3,5:59:36 PM,10,3,6:00:30 PM,10,3,6:19:55 PM,10,3,6:22:42 PM,10,3,6:54:18 PM,11,22.7,,-1.3247535,36.888057200000006,-1.2773974,36.9678677,Rider_Id_532,1896
-Order_No_722,User_Id_111,Bike,3,Business,4,5,3:23:19 PM,4,5,3:40:19 PM,4,5,3:44:39 PM,4,5,3:47:15 PM,4,5,4:32:15 PM,18,,,-1.2551895,36.7822034,-1.3277669,36.8975169,Rider_Id_829,2700
-Order_No_12489,User_Id_2159,Bike,3,Business,23,2,3:20:11 PM,23,2,3:22:46 PM,23,2,3:27:50 PM,23,2,3:48:02 PM,23,2,4:14:22 PM,7,19.0,,-1.304735,36.784605400000004,-1.2736241000000001,36.80692979999999,Rider_Id_903,1580
-Order_No_27686,User_Id_2064,Bike,2,Personal,12,2,9:48:47 AM,12,2,9:53:06 AM,12,2,10:24:14 AM,12,2,10:27:40 AM,12,2,11:19:17 AM,8,15.9,,-1.2959613,36.7684114,-1.2664882,36.803337400000004,Rider_Id_441,3097
-Order_No_24609,User_Id_3621,Bike,3,Business,11,2,12:29:28 PM,11,2,12:33:08 PM,11,2,12:42:17 PM,11,2,12:44:26 PM,11,2,12:56:03 PM,5,19.3,,-1.2836583000000001,36.7886014,-1.3050332,36.8100437,Rider_Id_826,697
-Order_No_19783,User_Id_3147,Bike,3,Business,14,5,1:35:18 PM,14,5,1:49:02 PM,14,5,2:13:31 PM,14,5,2:17:03 PM,14,5,2:41:04 PM,11,23.8,,-1.3472066,36.7692638,-1.2951620000000001,36.8115914,Rider_Id_184,1441
-Order_No_4882,User_Id_1471,Bike,3,Business,10,5,12:48:07 PM,10,5,12:52:23 PM,10,5,12:53:37 PM,10,5,1:23:50 PM,10,5,1:50:46 PM,15,,,-1.2285834999999998,36.882646799999996,-1.3077163,36.8043786,Rider_Id_102,1616
-Order_No_8705,User_Id_1920,Bike,3,Business,6,4,2:07:22 PM,6,4,2:15:08 PM,6,4,2:15:17 PM,6,4,2:27:00 PM,6,4,3:01:59 PM,7,,,-1.2551895,36.7822034,-1.2575690000000002,36.842164000000004,Rider_Id_382,2099
-Order_No_10862,User_Id_2789,Bike,3,Business,19,2,5:02:01 PM,19,2,5:02:46 PM,19,2,5:18:43 PM,19,2,5:25:18 PM,19,2,5:50:59 PM,6,28.4,,-1.304735,36.784605400000004,-1.2896398999999998,36.812449799999996,Rider_Id_678,1541
-Order_No_23922,User_Id_2457,Bike,3,Business,17,3,3:51:42 PM,17,3,4:10:21 PM,17,3,4:27:34 PM,17,3,4:31:29 PM,17,3,4:45:35 PM,5,28.3,,-1.2551895,36.7822034,-1.279309,36.8196569,Rider_Id_208,846
-Order_No_9884,User_Id_3560,Bike,3,Business,22,3,1:01:52 PM,22,3,1:04:29 PM,22,3,1:30:37 PM,22,3,1:37:45 PM,22,3,2:13:32 PM,12,24.5,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_99,2147
-Order_No_25760,User_Id_1500,Bike,3,Business,10,1,10:05:42 AM,10,1,10:08:37 AM,10,1,10:15:49 AM,10,1,10:21:20 AM,10,1,10:41:20 AM,11,18.3,2.1,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_496,1200
-Order_No_3349,User_Id_391,Bike,3,Business,30,4,1:30:24 PM,30,4,1:31:48 PM,30,4,1:35:06 PM,30,4,2:44:31 PM,30,4,2:47:12 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_436,161
-Order_No_707,User_Id_1075,Bike,3,Business,23,7,2:04:42 PM,23,7,2:07:03 PM,23,7,2:17:59 PM,23,7,2:50:20 PM,23,7,3:07:13 PM,9,25.0,,-1.289654,36.7838999,-1.2527958000000001,36.8003131,Rider_Id_206,1013
-Order_No_12568,User_Id_2412,Bike,3,Business,26,5,3:26:03 PM,26,5,3:26:47 PM,26,5,3:28:28 PM,26,5,3:47:24 PM,26,5,4:25:52 PM,18,,,-1.3014461000000002,36.7661381,-1.2283403,36.8822756,Rider_Id_855,2308
-Order_No_15842,User_Id_2987,Bike,3,Business,21,3,4:19:15 PM,21,3,4:24:29 PM,21,3,4:33:56 PM,21,3,4:35:44 PM,21,3,5:00:08 PM,4,,,-1.2702120000000001,36.82108770000001,-1.2534301,36.800098299999995,Rider_Id_727,1464
-Order_No_18670,User_Id_393,Bike,3,Business,24,3,11:47:37 AM,24,3,11:55:13 AM,24,3,12:21:47 PM,24,3,12:37:57 PM,24,3,1:00:16 PM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_921,1339
-Order_No_2958,User_Id_2523,Bike,3,Business,13,1,9:14:30 AM,13,1,9:15:13 AM,13,1,9:20:41 AM,13,1,9:24:41 AM,13,1,10:04:24 AM,10,,,-1.2551895,36.7822034,-1.297131,36.7604242,Rider_Id_50,2383
-Order_No_24552,User_Id_3194,Bike,3,Business,25,1,3:31:24 PM,25,1,3:31:37 PM,25,1,3:35:16 PM,25,1,3:39:39 PM,25,1,4:09:40 PM,7,30.3,,-1.2551895,36.7822034,-1.2885503999999999,36.8066908,Rider_Id_784,1801
-Order_No_27078,User_Id_3356,Bike,3,Business,9,4,10:54:32 AM,9,4,10:55:14 AM,9,4,11:32:25 AM,9,4,11:38:18 AM,9,4,12:19:16 PM,16,,,-1.2036664,36.819262200000004,-1.2980085,36.8044778,Rider_Id_7,2458
-Order_No_8085,User_Id_3404,Bike,3,Business,23,5,1:51:33 PM,23,5,1:53:31 PM,23,5,2:35:48 PM,23,5,2:53:37 PM,23,5,3:04:56 PM,1,26.2,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_268,679
-Order_No_13896,User_Id_2066,Bike,3,Business,14,2,11:40:27 AM,14,2,11:41:26 AM,14,2,12:05:55 PM,14,2,12:07:04 PM,14,2,12:30:07 PM,5,21.7,,-1.316961,36.820337,-1.3004846,36.790159100000004,Rider_Id_234,1383
-Order_No_353,User_Id_2694,Bike,1,Personal,3,4,12:13:43 PM,3,4,12:28:29 PM,3,4,12:35:09 PM,3,4,1:14:58 PM,3,4,1:38:08 PM,6,21.8,,-1.3154433,36.806385999999996,-1.2801649,36.8315686,Rider_Id_929,1390
-Order_No_23801,User_Id_2447,Bike,1,Personal,4,3,4:50:24 PM,4,3,4:56:38 PM,4,3,5:18:29 PM,4,3,5:33:35 PM,4,3,6:07:35 PM,15,,,-1.3413926999999999,36.86289,-1.2559559999999999,36.772901,Rider_Id_478,2040
-Order_No_14901,User_Id_248,Bike,3,Business,10,3,9:01:39 AM,10,3,9:02:39 AM,10,3,9:11:49 AM,10,3,9:20:43 AM,10,3,9:34:36 AM,4,21.8,,-1.2994771999999999,36.804825,-1.304735,36.784605400000004,Rider_Id_99,833
-Order_No_14980,User_Id_3283,Bike,3,Business,17,3,3:28:12 PM,17,3,3:29:16 PM,17,3,3:47:45 PM,17,3,3:54:55 PM,17,3,4:09:17 PM,6,,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_161,862
-Order_No_8013,User_Id_2423,Bike,3,Business,8,5,12:04:33 PM,8,5,12:09:46 PM,8,5,1:00:16 PM,8,5,1:04:54 PM,8,5,1:28:38 PM,9,28.6,,-1.2551895,36.7822034,-1.294869,36.815327,Rider_Id_785,1424
-Order_No_18933,User_Id_867,Bike,3,Business,7,3,2:06:40 PM,7,3,2:44:49 PM,7,3,2:55:20 PM,7,3,3:00:54 PM,7,3,3:25:24 PM,9,28.1,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_956,1470
-Order_No_19886,User_Id_53,Bike,3,Business,12,5,3:46:44 PM,12,5,3:53:17 PM,12,5,4:03:01 PM,12,5,4:13:40 PM,12,5,4:38:05 PM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_99,1465
-Order_No_21408,User_Id_3064,Bike,3,Business,22,1,11:59:03 AM,22,1,11:59:49 AM,22,1,12:15:06 PM,22,1,12:16:41 PM,22,1,12:40:40 PM,10,24.8,,-1.3011151,36.8391222,-1.2571472,36.795063299999995,Rider_Id_38,1439
-Order_No_22342,User_Id_2956,Bike,3,Business,13,3,1:30:28 PM,13,3,1:33:28 PM,13,3,1:35:55 PM,13,3,1:47:13 PM,13,3,1:47:17 PM,2,27.3,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_651,4
-Order_No_9613,User_Id_3283,Bike,3,Business,23,6,10:28:01 AM,23,6,10:28:15 AM,23,6,10:28:33 AM,23,6,11:04:17 AM,23,6,11:14:39 AM,5,26.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_427,622
-Order_No_19063,User_Id_867,Bike,3,Business,21,1,10:26:40 AM,21,1,10:27:56 AM,21,1,10:42:36 AM,21,1,10:53:32 AM,21,1,11:20:38 AM,10,25.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_576,1626
-Order_No_17052,User_Id_1724,Bike,3,Business,26,5,10:49:11 AM,26,5,10:49:22 AM,26,5,10:54:33 AM,26,5,10:59:23 AM,26,5,11:29:30 AM,11,,,-1.2551895,36.7822034,-1.2991616000000001,36.842450899999996,Rider_Id_177,1807
-Order_No_521,User_Id_3283,Bike,3,Business,16,6,12:39:14 PM,16,6,12:39:41 PM,16,6,12:45:10 PM,16,6,12:55:44 PM,16,6,1:05:37 PM,6,25.8,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_461,593
-Order_No_8159,User_Id_2517,Bike,1,Personal,31,4,3:44:53 PM,31,4,3:46:44 PM,31,4,4:06:26 PM,31,4,4:13:06 PM,31,4,4:41:25 PM,8,22.7,,-1.2624061000000002,36.7953252,-1.2930073,36.778818799999996,Rider_Id_844,1699
-Order_No_21993,User_Id_2956,Bike,3,Business,24,3,9:21:25 AM,24,3,9:28:26 AM,24,3,9:41:04 AM,24,3,10:23:21 AM,24,3,10:44:22 AM,6,,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_263,1261
-Order_No_27817,User_Id_739,Bike,1,Personal,20,1,3:44:50 PM,20,1,3:46:00 PM,20,1,3:56:40 PM,20,1,4:10:41 PM,20,1,5:08:11 PM,11,21.3,,-1.2655698999999998,36.8162505,-1.329312,36.787642,Rider_Id_40,3450
-Order_No_8451,User_Id_3621,Bike,3,Business,22,1,10:04:03 AM,22,1,10:04:54 AM,22,1,10:22:05 AM,22,1,10:30:45 AM,22,1,10:55:47 AM,16,,,-1.2836583000000001,36.7886014,-1.2083221000000002,36.8392443,Rider_Id_161,1502
-Order_No_13452,User_Id_3686,Bike,3,Business,30,4,3:30:32 PM,30,4,3:30:46 PM,30,4,3:31:22 PM,30,4,3:38:47 PM,30,4,4:09:09 PM,9,,,-1.2551895,36.7822034,-1.2948751,36.7938907,Rider_Id_709,1822
-Order_No_2189,User_Id_3771,Bike,3,Personal,16,2,8:32:23 AM,16,2,8:32:40 AM,16,2,8:47:50 AM,16,2,8:52:55 AM,16,2,9:20:48 AM,7,,,-1.2696908000000002,36.81519,-1.298437,36.781971,Rider_Id_99,1673
-Order_No_15086,User_Id_3494,Bike,3,Personal,17,5,12:15:34 PM,17,5,12:16:12 PM,17,5,12:27:52 PM,17,5,12:40:04 PM,17,5,12:57:34 PM,7,17.6,,-1.2848173999999999,36.826163,-1.2963097,36.7688221,Rider_Id_754,1050
-Order_No_14286,User_Id_867,Bike,3,Business,8,2,10:29:16 AM,8,2,10:30:02 AM,8,2,10:32:03 AM,8,2,10:56:19 AM,8,2,11:14:26 AM,9,21.6,,-1.2569006,36.7925141,-1.306378,36.7519845,Rider_Id_345,1087
-Order_No_16492,User_Id_1787,Bike,3,Business,13,4,11:58:04 AM,13,4,11:59:18 AM,13,4,12:08:30 PM,13,4,12:11:05 PM,13,4,12:31:19 PM,5,22.3,,-1.2283403,36.8822756,-1.2467896,36.869873299999995,Rider_Id_60,1214
-Order_No_2284,User_Id_2987,Bike,3,Business,3,1,11:57:29 AM,3,1,11:57:52 AM,3,1,12:24:47 PM,3,1,12:28:58 PM,3,1,12:53:37 PM,13,20.7,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_4,1479
-Order_No_27994,User_Id_246,Bike,3,Business,8,2,5:00:13 PM,8,2,5:00:22 PM,8,2,5:01:01 PM,8,2,5:09:40 PM,8,2,5:25:19 PM,3,23.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_45,939
-Order_No_1088,User_Id_1675,Bike,1,Personal,25,6,1:17:12 PM,25,6,1:17:22 PM,25,6,1:23:09 PM,25,6,1:24:23 PM,25,6,1:51:54 PM,18,,,-1.282716,36.821009000000004,-1.2121451,36.9044352,Rider_Id_449,1651
-Order_No_17266,User_Id_1927,Bike,3,Business,7,2,2:27:08 PM,7,2,2:27:56 PM,7,2,2:28:56 PM,7,2,2:39:22 PM,7,2,3:56:11 PM,23,,,-1.2285834999999998,36.882646799999996,-1.3472066,36.7692638,Rider_Id_519,4609
-Order_No_5589,User_Id_3201,Bike,3,Business,18,1,10:29:21 AM,18,1,10:29:44 AM,18,1,10:40:41 AM,18,1,10:45:59 AM,18,1,10:52:43 AM,1,,,-1.2649605,36.798177700000004,-1.2632534,36.799342700000004,Rider_Id_100,404
-Order_No_22011,User_Id_33,Bike,3,Personal,9,4,1:30:35 PM,9,4,1:39:09 PM,9,4,2:07:41 PM,9,4,2:16:27 PM,9,4,2:42:34 PM,6,,,-1.2814609,36.826593200000005,-1.2544051,36.7982934,Rider_Id_341,1567
-Order_No_27045,User_Id_404,Bike,1,Personal,21,4,4:06:03 PM,21,4,4:31:29 PM,21,4,4:39:11 PM,21,4,4:59:17 PM,21,4,5:11:07 PM,5,20.1,,-1.331669,36.879234999999994,-1.3124015,36.9225064,Rider_Id_890,710
-Order_No_2568,User_Id_2423,Bike,3,Business,14,4,3:31:43 PM,14,4,3:34:03 PM,14,4,3:39:52 PM,14,4,3:45:49 PM,14,4,3:53:44 PM,3,27.8,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_446,475
-Order_No_8505,User_Id_1926,Bike,3,Business,13,3,10:50:11 AM,13,3,10:50:43 AM,13,3,10:58:51 AM,13,3,11:02:24 AM,13,3,11:35:51 AM,16,,,-1.3263105,36.8622904,-1.2571472,36.795063299999995,Rider_Id_145,2007
-Order_No_11848,User_Id_2194,Bike,1,Personal,6,4,11:44:21 AM,6,4,11:47:00 AM,6,4,12:20:03 PM,6,4,12:31:55 PM,6,4,12:51:48 PM,7,22.1,,-1.3211235000000001,36.7765164,-1.2994217,36.8009779,Rider_Id_447,1193
-Order_No_5320,User_Id_633,Bike,3,Business,10,4,1:14:52 PM,10,4,1:15:44 PM,10,4,1:22:26 PM,10,4,1:32:15 PM,10,4,1:36:55 PM,4,25.4,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_878,280
-Order_No_10426,User_Id_3647,Bike,3,Business,4,2,10:53:53 AM,4,2,10:55:00 AM,4,2,11:07:46 AM,4,2,11:09:40 AM,4,2,11:46:30 AM,14,18.9,,-1.2726389999999999,36.794723,-1.2880046,36.868857299999995,Rider_Id_330,2210
-Order_No_19513,User_Id_868,Bike,3,Business,28,1,11:53:30 AM,28,1,12:28:09 PM,28,1,12:39:28 PM,28,1,12:50:53 PM,28,1,1:03:21 PM,5,22.5,,-1.2600926000000001,36.8088685,-1.272828,36.816608,Rider_Id_852,748
-Order_No_12187,User_Id_3331,Bike,3,Business,14,4,2:39:03 PM,14,4,2:40:15 PM,14,4,2:51:50 PM,14,4,2:56:57 PM,14,4,3:13:15 PM,4,28.1,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_290,978
-Order_No_8110,User_Id_53,Bike,3,Business,14,5,3:32:45 PM,14,5,3:40:52 PM,14,5,3:47:19 PM,14,5,3:49:20 PM,14,5,4:06:41 PM,8,22.8,,-1.2831137,36.8197134,-1.3167113000000001,36.8301563,Rider_Id_89,1041
-Order_No_28147,User_Id_718,Bike,3,Business,4,2,10:26:12 AM,4,2,11:26:05 AM,4,2,11:39:10 AM,4,2,11:42:21 AM,4,2,11:57:00 AM,4,19.5,,-1.2551895,36.7822034,-1.2718638999999998,36.8060408,Rider_Id_112,879
-Order_No_5468,User_Id_3222,Bike,3,Business,4,4,3:03:29 PM,4,4,3:03:46 PM,4,4,3:06:23 PM,4,4,3:09:12 PM,4,4,3:12:35 PM,3,27.5,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_690,203
-Order_No_2386,User_Id_3647,Bike,3,Business,30,3,11:12:50 AM,30,3,11:13:29 AM,30,3,11:28:04 AM,30,3,11:32:34 AM,30,3,11:56:53 AM,7,25.2,,-1.2726389999999999,36.794723,-1.2972659,36.770864700000004,Rider_Id_281,1459
-Order_No_14123,User_Id_3573,Bike,1,Personal,3,5,3:12:11 PM,3,5,3:12:35 PM,3,5,3:32:41 PM,3,5,3:51:04 PM,3,5,4:06:12 PM,4,25.2,,-1.273854,36.8003368,-1.2631940000000002,36.797624,Rider_Id_795,908
-Order_No_17818,User_Id_567,Bike,3,Business,23,2,2:15:03 PM,23,2,2:19:55 PM,23,2,2:30:34 PM,23,2,3:04:37 PM,23,2,3:20:28 PM,5,18.8,,-1.273056,36.811298,-1.295908,36.791433000000005,Rider_Id_878,951
-Order_No_9579,User_Id_1271,Bike,3,Business,6,4,3:01:08 PM,6,4,3:46:32 PM,6,4,4:08:48 PM,6,4,4:18:35 PM,6,4,4:28:24 PM,5,23.6,,-1.3244885,36.897791999999995,-1.3123977,36.902807,Rider_Id_890,589
-Order_No_26646,User_Id_2216,Bike,3,Business,21,3,2:28:36 PM,21,3,2:36:43 PM,21,3,2:43:30 PM,21,3,2:50:06 PM,21,3,3:35:19 PM,8,23.3,2.9,-1.2551895,36.7822034,-1.2965924,36.8179314,Rider_Id_948,2713
-Order_No_11259,User_Id_3283,Bike,3,Business,28,2,12:45:42 PM,28,2,12:46:14 PM,28,2,1:21:48 PM,28,2,1:38:48 PM,28,2,1:54:32 PM,5,23.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_732,944
-Order_No_18752,User_Id_2523,Bike,3,Business,4,5,12:45:47 PM,4,5,12:46:15 PM,4,5,1:07:43 PM,4,5,1:10:33 PM,4,5,2:14:14 PM,26,17.4,,-1.2516167,36.8678844,-1.3117864,36.6973045,Rider_Id_457,3821
-Order_No_3200,User_Id_988,Bike,2,Personal,3,7,12:20:01 PM,3,7,12:20:33 PM,3,7,12:41:58 PM,3,7,1:13:42 PM,3,7,1:29:21 PM,8,21.9,,-1.2297202,36.874550799999994,-1.2113559999999999,36.905189799999995,Rider_Id_408,939
-Order_No_9815,User_Id_2523,Bike,3,Business,4,1,11:29:43 AM,4,1,11:30:02 AM,4,1,11:32:40 AM,4,1,11:36:36 AM,4,1,12:43:45 PM,17,26.7,,-1.2551895,36.7822034,-1.2216605,36.90517929999999,Rider_Id_409,4029
-Order_No_6685,User_Id_868,Bike,3,Business,16,2,11:23:23 AM,16,2,12:09:05 PM,16,2,12:27:52 PM,16,2,12:43:48 PM,16,2,12:55:49 PM,9,,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_291,721
-Order_No_9224,User_Id_3606,Bike,3,Business,5,2,4:23:59 PM,5,2,4:28:30 PM,5,2,4:34:33 PM,5,2,4:36:51 PM,5,2,4:55:34 PM,6,,,-1.2628638,36.8070399,-1.2623805000000001,36.7805404,Rider_Id_575,1123
-Order_No_1800,User_Id_2828,Bike,3,Business,7,6,2:15:30 PM,7,6,2:15:48 PM,7,6,2:16:08 PM,7,6,2:46:42 PM,7,6,2:47:37 PM,3,21.5,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_51,55
-Order_No_20581,User_Id_3599,Bike,3,Business,26,5,9:08:33 AM,26,5,9:17:07 AM,26,5,9:43:18 AM,26,5,10:02:01 AM,26,5,11:11:18 AM,20,,,-1.3302996,36.8707935,-1.2163391000000001,36.8404088,Rider_Id_519,4157
-Order_No_17695,User_Id_435,Bike,3,Business,24,5,3:08:26 PM,24,5,3:09:12 PM,24,5,3:09:19 PM,24,5,3:20:17 PM,24,5,3:40:36 PM,9,23.0,,-1.3081376,36.83297720000001,-1.257681,36.797778,Rider_Id_515,1219
-Order_No_14739,User_Id_415,Bike,3,Personal,26,3,2:25:33 PM,26,3,2:25:56 PM,26,3,2:56:26 PM,26,3,3:00:53 PM,26,3,3:45:53 PM,15,27.0,,-1.2773427,36.8266035,-1.1708105,36.8336102,Rider_Id_31,2700
-Order_No_25877,User_Id_1102,Bike,3,Business,29,5,12:37:47 PM,29,5,12:38:28 PM,29,5,12:49:49 PM,29,5,1:29:41 PM,29,5,1:59:09 PM,10,26.9,,-1.3333374,36.866736200000005,-1.3047132,36.808955299999994,Rider_Id_855,1768
-Order_No_16187,User_Id_3264,Bike,3,Business,1,1,8:49:56 AM,1,1,8:50:23 AM,1,1,9:02:00 AM,1,1,9:04:20 AM,1,1,9:26:58 AM,9,18.5,,-1.2551895,36.7822034,-1.3088824,36.8311983,Rider_Id_38,1358
-Order_No_21122,User_Id_3599,Bike,3,Business,8,6,10:06:47 AM,8,6,10:06:47 AM,8,6,11:14:02 AM,8,6,11:15:51 AM,8,6,11:47:49 AM,7,20.2,,-1.3302996,36.8707935,-1.3205832,36.8401395,Rider_Id_459,1918
-Order_No_1176,User_Id_733,Bike,3,Business,31,4,11:35:03 AM,31,4,11:35:34 AM,31,4,11:52:06 AM,31,4,12:00:13 PM,31,4,12:50:30 PM,7,26.4,,-1.2854453000000001,36.818836299999994,-1.2551895,36.7822034,Rider_Id_489,3017
-Order_No_20594,User_Id_2978,Bike,3,Business,27,1,11:30:16 AM,27,1,11:30:22 AM,27,1,11:40:36 AM,27,1,11:48:18 AM,27,1,12:29:14 PM,24,23.9,,-1.3076278000000001,36.9158578,-1.2551895,36.7822034,Rider_Id_874,2456
-Order_No_16751,User_Id_2016,Bike,1,Personal,24,1,1:46:42 PM,24,1,1:46:49 PM,24,1,2:05:59 PM,24,1,2:28:10 PM,24,1,3:03:32 PM,18,,,-1.317549,36.898101399999994,-1.2871136,36.785507,Rider_Id_253,2122
-Order_No_14017,User_Id_309,Bike,3,Business,4,2,11:04:00 AM,4,2,11:06:16 AM,4,2,11:25:45 AM,4,2,11:26:53 AM,4,2,11:51:52 AM,6,20.5,,-1.2156006000000001,36.8916865,-1.2413316,36.878552500000005,Rider_Id_92,1499
-Order_No_15625,User_Id_3326,Bike,3,Business,8,1,10:06:38 AM,8,1,10:06:51 AM,8,1,10:09:48 AM,8,1,10:15:38 AM,8,1,10:20:47 AM,3,19.8,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_932,309
-Order_No_9186,User_Id_2154,Bike,3,Business,13,4,3:13:04 PM,13,4,3:13:36 PM,13,4,3:28:55 PM,13,4,3:33:31 PM,13,4,3:57:06 PM,10,22.4,,-1.2860183,36.8975338,-1.2765736,36.851364600000004,Rider_Id_817,1415
-Order_No_6968,User_Id_269,Bike,1,Business,4,4,2:30:54 PM,4,4,4:21:58 PM,4,4,4:41:34 PM,4,4,4:44:53 PM,4,4,5:12:38 PM,10,,,-1.3332749,36.870814700000004,-1.2810586000000002,36.883829399999996,Rider_Id_890,1665
-Order_No_20105,User_Id_1368,Bike,3,Business,23,5,10:11:57 AM,23,5,10:24:06 AM,23,5,10:26:38 AM,23,5,10:32:59 AM,23,5,10:48:29 AM,7,24.1,,-1.2571472,36.795063299999995,-1.2957370000000001,36.800789,Rider_Id_878,930
-Order_No_25874,User_Id_3669,Bike,3,Business,7,2,12:26:31 PM,7,2,12:27:28 PM,7,2,12:58:38 PM,7,2,1:09:38 PM,7,2,1:28:58 PM,4,,,-1.2615888000000002,36.7928732,-1.2560822,36.820991799999994,Rider_Id_646,1160
-Order_No_20912,User_Id_2102,Bike,3,Business,16,4,8:27:07 AM,16,4,8:28:24 AM,16,4,8:35:40 AM,16,4,8:53:38 AM,16,4,9:13:36 AM,13,,,-1.2613768,36.801941799999994,-1.3323107,36.8692616,Rider_Id_383,1198
-Order_No_26345,User_Id_2783,Bike,2,Personal,11,3,11:35:49 AM,11,3,11:36:32 AM,11,3,12:03:17 PM,11,3,12:11:49 PM,11,3,12:16:40 PM,2,18.8,,-1.2959749,36.7770396,-1.2952108999999998,36.775706400000004,Rider_Id_103,291
-Order_No_11965,User_Id_251,Bike,1,Personal,23,1,10:27:42 AM,23,1,10:28:02 AM,23,1,10:36:22 AM,23,1,10:44:14 AM,23,1,10:58:01 AM,4,18.6,,-1.3099145,36.802277399999994,-1.2871136,36.785507,Rider_Id_844,827
-Order_No_27672,User_Id_909,Bike,3,Business,30,5,10:25:11 AM,30,5,10:26:34 AM,30,5,10:41:10 AM,30,5,10:42:24 AM,30,5,11:13:21 AM,13,23.8,,-1.225322,36.80855,-1.1668055,36.830444799999995,Rider_Id_928,1857
-Order_No_28109,User_Id_53,Bike,3,Business,14,1,10:06:42 AM,14,1,10:08:10 AM,14,1,10:53:10 AM,14,1,11:05:36 AM,14,1,11:25:04 AM,5,23.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_725,1168
-Order_No_185,User_Id_3519,Bike,1,Personal,13,6,6:14:00 PM,13,6,6:15:13 PM,13,6,6:27:17 PM,13,6,6:30:32 PM,13,6,6:47:25 PM,5,26.6,,-1.2817426,36.7846148,-1.289498,36.818878000000005,Rider_Id_956,1013
-Order_No_13409,User_Id_3283,Bike,3,Business,21,1,1:31:33 PM,21,1,1:32:08 PM,21,1,1:36:40 PM,21,1,1:42:31 PM,21,1,1:51:03 PM,4,27.8,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_279,512
-Order_No_28112,User_Id_2828,Bike,3,Business,1,4,10:02:47 AM,1,4,10:03:16 AM,1,4,10:10:19 AM,1,4,10:14:24 AM,1,4,10:41:24 AM,13,,,-1.2615888000000002,36.7928732,-1.3332749,36.870814700000004,Rider_Id_116,1620
-Order_No_20327,User_Id_1376,Bike,2,Personal,12,4,5:42:54 PM,12,4,5:43:10 PM,12,4,6:06:23 PM,12,4,6:17:49 PM,12,4,6:41:57 PM,13,20.5,,-1.2798711,36.8207118,-1.26566,36.72291,Rider_Id_806,1448
-Order_No_12217,User_Id_958,Bike,3,Business,2,4,4:37:22 PM,2,4,4:38:46 PM,2,4,4:52:08 PM,2,4,5:08:35 PM,2,4,5:48:23 PM,18,,,-1.3323107,36.8692616,-1.2323371,36.864816999999995,Rider_Id_411,2388
-Order_No_21783,User_Id_432,Bike,3,Business,13,1,2:33:14 PM,13,1,2:34:11 PM,13,1,2:53:31 PM,13,1,2:56:14 PM,13,1,3:30:26 PM,9,,,-1.290894,36.822971,-1.3012008,36.764868,Rider_Id_849,2052
-Order_No_9034,User_Id_265,Bike,3,Business,7,2,1:44:32 PM,7,2,1:45:25 PM,7,2,2:08:34 PM,7,2,2:37:13 PM,7,2,2:52:02 PM,10,24.7,,-1.341891,36.761269,-1.300921,36.828195,Rider_Id_101,889
-Order_No_21713,User_Id_1329,Bike,3,Business,15,1,11:31:05 AM,15,1,12:40:46 PM,15,1,12:49:14 PM,15,1,12:50:45 PM,15,1,1:06:59 PM,6,,,-1.273056,36.811298,-1.297324,36.787863200000004,Rider_Id_147,974
-Order_No_9194,User_Id_893,Bike,3,Business,27,3,12:01:57 PM,27,3,12:03:19 PM,27,3,12:13:20 PM,27,3,12:14:51 PM,27,3,12:41:41 PM,8,25.9,,-1.2663194,36.809901,-1.2455906,36.817496899999995,Rider_Id_660,1610
-Order_No_24480,User_Id_2154,Bike,3,Business,4,4,10:23:50 AM,4,4,10:24:10 AM,4,4,10:32:14 AM,4,4,10:58:51 AM,4,4,11:43:41 AM,15,25.4,,-1.3472066,36.7692638,-1.2765736,36.851364600000004,Rider_Id_693,2690
-Order_No_23584,User_Id_1102,Bike,3,Business,11,4,8:14:02 AM,11,4,8:14:14 AM,11,4,8:45:52 AM,11,4,8:49:34 AM,11,4,9:19:42 AM,15,21.9,,-1.3090241000000002,36.7357231,-1.3004062,36.829741,Rider_Id_855,1808
-Order_No_6203,User_Id_3283,Bike,3,Business,7,1,9:39:52 AM,7,1,9:40:24 AM,7,1,9:47:47 AM,7,1,9:58:39 AM,7,1,10:19:30 AM,6,21.3,,-1.265715,36.823815,-1.2628473,36.781805,Rider_Id_289,1251
-Order_No_22420,User_Id_3017,Bike,3,Business,25,5,6:23:18 PM,25,5,6:24:57 PM,25,5,6:33:43 PM,25,5,6:39:35 PM,25,5,6:46:51 PM,4,21.1,,-1.299313,36.78314879999999,-1.3023678,36.7976564,Rider_Id_886,436
-Order_No_22427,User_Id_469,Bike,3,Personal,18,5,12:37:47 PM,18,5,2:23:07 PM,18,5,3:02:07 PM,18,5,3:03:53 PM,18,5,3:34:35 PM,6,24.6,,-1.2888627000000001,36.8416584,-1.3090663,36.813686499999996,Rider_Id_192,1842
-Order_No_24000,User_Id_478,Bike,3,Business,30,2,8:12:10 AM,30,2,8:12:35 AM,30,2,8:26:24 AM,30,2,8:33:44 AM,30,2,9:06:28 AM,10,,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_247,1964
-Order_No_1228,User_Id_3647,Bike,3,Business,13,2,10:31:19 AM,13,2,10:32:18 AM,13,2,10:41:58 AM,13,2,10:46:58 AM,13,2,11:22:33 AM,25,22.5,,-1.2726389999999999,36.794723,-1.1567082,36.9618034,Rider_Id_648,2135
-Order_No_18070,User_Id_478,Bike,3,Business,15,2,10:34:56 AM,15,2,10:36:39 AM,15,2,10:41:38 AM,15,2,10:50:08 AM,15,2,11:07:27 AM,6,24.4,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_903,1039
-Order_No_24320,User_Id_811,Bike,3,Personal,9,2,3:45:02 PM,9,2,3:45:59 PM,9,2,3:59:15 PM,9,2,4:05:00 PM,9,2,4:14:24 PM,6,30.7,,-1.311706,36.802841,-1.2963097,36.7688221,Rider_Id_61,564
-Order_No_5789,User_Id_1719,Bike,3,Business,11,5,1:20:45 PM,11,5,1:20:55 PM,11,5,1:22:42 PM,11,5,1:24:21 PM,11,5,1:39:34 PM,3,21.0,,-1.2963097,36.7688221,-1.2931728,36.7888371,Rider_Id_205,913
-Order_No_6858,User_Id_928,Bike,3,Business,14,2,10:41:48 AM,14,2,10:52:17 AM,14,2,10:59:50 AM,14,2,11:08:33 AM,14,2,11:22:55 AM,7,,,-1.2551895,36.7822034,-1.2938211000000002,36.8270021,Rider_Id_953,862
-Order_No_7673,User_Id_136,Bike,3,Business,28,4,1:12:55 PM,28,4,1:13:15 PM,28,4,1:13:28 PM,28,4,1:21:36 PM,28,4,1:53:20 PM,21,,,-1.3326173000000001,36.71462329999999,-1.2587285,36.834776399999996,Rider_Id_886,1904
-Order_No_24514,User_Id_2982,Bike,3,Business,21,1,12:26:46 PM,21,1,12:27:25 PM,21,1,12:28:57 PM,21,1,1:28:24 PM,21,1,2:04:51 PM,18,25.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_607,2187
-Order_No_10866,User_Id_2956,Bike,3,Business,13,3,2:26:00 PM,13,3,2:26:55 PM,13,3,2:32:02 PM,13,3,2:34:29 PM,13,3,2:47:48 PM,1,29.0,,-1.2584143,36.8048002,-1.2594538999999998,36.8067841,Rider_Id_318,799
-Order_No_21761,User_Id_2349,Bike,3,Business,4,5,10:57:31 AM,4,5,10:58:02 AM,4,5,11:26:46 AM,4,5,11:29:39 AM,4,5,11:47:13 AM,4,17.2,,-1.2784495,36.7981729,-1.2613768,36.801941799999994,Rider_Id_932,1054
-Order_No_11063,User_Id_2587,Bike,3,Business,18,1,8:54:22 AM,18,1,8:55:38 AM,18,1,9:06:51 AM,18,1,9:16:38 AM,18,1,9:43:06 AM,8,21.9,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_468,1588
-Order_No_14952,User_Id_706,Bike,3,Business,7,3,11:21:04 AM,7,3,11:23:29 AM,7,3,11:41:27 AM,7,3,11:58:52 AM,7,3,12:13:20 PM,5,24.9,,-1.32671,36.8600138,-1.3077869,36.844320700000004,Rider_Id_559,868
-Order_No_2409,User_Id_3621,Bike,3,Business,9,4,8:59:34 AM,9,4,9:04:16 AM,9,4,9:23:30 AM,9,4,9:26:52 AM,9,4,9:50:33 AM,6,,,-1.2836583000000001,36.7886014,-1.286268,36.827218200000004,Rider_Id_291,1421
-Order_No_21973,User_Id_3149,Bike,3,Business,28,2,3:04:23 PM,28,2,3:04:49 PM,28,2,3:19:28 PM,28,2,3:34:58 PM,28,2,3:45:44 PM,6,19.6,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_87,646
-Order_No_10223,User_Id_239,Bike,1,Personal,17,1,3:53:55 PM,17,1,3:54:10 PM,17,1,4:07:04 PM,17,1,4:23:20 PM,17,1,5:09:43 PM,10,28.3,,-1.32571,36.805533000000004,-1.2677753,36.7998955,Rider_Id_612,2783
-Order_No_7094,User_Id_1417,Bike,3,Business,28,4,1:28:47 PM,28,4,1:29:53 PM,28,4,1:32:21 PM,28,4,1:36:41 PM,28,4,3:01:56 PM,6,27.3,,-1.2741728,36.79691079999999,-1.2940511000000001,36.821930200000004,Rider_Id_88,5115
-Order_No_20273,User_Id_382,Bike,1,Personal,19,2,3:28:51 PM,19,2,3:31:25 PM,19,2,3:46:45 PM,19,2,3:59:28 PM,19,2,4:26:13 PM,8,18.5,,-1.2967218,36.808978700000004,-1.2655306,36.828148,Rider_Id_340,1605
-Order_No_13971,User_Id_3194,Bike,3,Business,28,4,5:49:38 PM,28,4,5:50:28 PM,28,4,6:12:09 PM,28,4,6:18:45 PM,28,4,6:51:53 PM,13,28.2,,-1.3012008,36.764868,-1.2597436000000002,36.8282526,Rider_Id_925,1988
-Order_No_17914,User_Id_1195,Bike,3,Business,26,2,4:15:02 PM,26,2,4:16:15 PM,26,2,4:41:13 PM,26,2,4:50:31 PM,26,2,5:07:49 PM,7,29.6,,-1.3228003000000002,36.8306435,-1.3012841999999998,36.8561033,Rider_Id_448,1038
-Order_No_13594,User_Id_2469,Bike,3,Business,26,1,4:17:21 PM,26,1,4:21:15 PM,26,1,4:58:14 PM,26,1,5:07:14 PM,26,1,5:29:36 PM,7,20.2,,-1.2907843,36.8298908,-1.303924,36.8758792,Rider_Id_916,1342
-Order_No_8669,User_Id_1553,Bike,1,Personal,6,5,10:54:17 AM,6,5,11:23:38 AM,6,5,11:36:33 AM,6,5,11:41:08 AM,6,5,11:59:39 AM,8,20.5,,-1.2343087,36.84749,-1.2813544,36.8203521,Rider_Id_726,1111
-Order_No_17769,User_Id_3283,Bike,3,Business,30,2,10:16:03 AM,30,2,10:17:41 AM,30,2,10:33:38 AM,30,2,10:40:56 AM,30,2,11:04:33 AM,13,,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_686,1417
-Order_No_2902,User_Id_2066,Bike,3,Business,2,5,1:14:07 PM,2,5,1:15:03 PM,2,5,1:29:55 PM,2,5,2:07:09 PM,2,5,2:07:13 PM,10,24.9,,-1.316961,36.820337,-1.256522,36.786413200000005,Rider_Id_854,4
-Order_No_17529,User_Id_2412,Bike,3,Business,1,3,9:50:06 AM,1,3,9:50:14 AM,1,3,9:50:43 AM,1,3,10:10:47 AM,1,3,10:41:42 AM,12,,,-1.2297202,36.881950700000004,-1.296567,36.819852000000004,Rider_Id_272,1855
-Order_No_7390,User_Id_302,Bike,3,Business,6,3,1:06:25 PM,6,3,1:07:54 PM,6,3,1:27:41 PM,6,3,1:51:07 PM,6,3,2:16:18 PM,9,,,-1.3039695,36.783826700000006,-1.2649605,36.798177700000004,Rider_Id_118,1511
-Order_No_3331,User_Id_635,Bike,3,Business,21,2,10:42:58 AM,21,2,10:43:28 AM,21,2,10:45:41 AM,21,2,10:55:56 AM,21,2,11:11:40 AM,3,22.6,,-1.257069,36.81467,-1.2602337,36.79905479999999,Rider_Id_445,944
-Order_No_2174,User_Id_3711,Bike,3,Business,23,3,6:43:55 PM,23,3,6:44:37 PM,23,3,6:52:44 PM,23,3,7:18:58 PM,23,3,7:19:10 PM,3,24.3,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_246,12
-Order_No_23941,User_Id_1469,Bike,3,Business,30,2,8:38:50 AM,30,2,8:40:03 AM,30,2,9:34:26 AM,30,2,9:38:14 AM,30,2,9:51:47 AM,4,17.4,,-1.2551895,36.7822034,-1.2617131000000001,36.8051999,Rider_Id_219,813
-Order_No_19237,User_Id_3420,Bike,3,Business,28,4,3:03:22 PM,28,4,3:04:49 PM,28,4,3:30:00 PM,28,4,5:00:46 PM,28,4,5:03:59 PM,7,25.7,,-1.265981,36.793612100000004,-1.3046007,36.821174299999996,Rider_Id_291,193
-Order_No_10397,User_Id_3698,Bike,3,Business,10,5,4:06:08 PM,10,5,4:17:58 PM,10,5,4:26:28 PM,10,5,4:31:05 PM,10,5,4:43:41 PM,7,,,-1.3244885,36.897791999999995,-1.3190102,36.923069299999995,Rider_Id_377,756
-Order_No_9311,User_Id_2330,Bike,3,Business,31,5,5:15:56 PM,31,5,5:18:12 PM,31,5,5:22:48 PM,31,5,5:31:09 PM,31,5,5:49:43 PM,12,23.9,,-1.2642903,36.80021370000001,-1.2284323,36.8757058,Rider_Id_247,1114
-Order_No_6751,User_Id_2532,Bike,3,Business,16,4,10:31:43 AM,16,4,10:32:42 AM,16,4,10:39:02 AM,16,4,10:45:29 AM,16,4,11:01:41 AM,6,,,-1.2551895,36.7822034,-1.2905033000000001,36.8096462,Rider_Id_290,972
-Order_No_20006,User_Id_1268,Bike,1,Personal,25,5,5:18:28 PM,25,5,5:19:04 PM,25,5,5:29:39 PM,25,5,5:33:38 PM,25,5,6:07:37 PM,9,21.1,,-1.2745187,36.7657388,-1.2988168,36.8198572,Rider_Id_334,2039
-Order_No_805,User_Id_948,Bike,3,Business,11,2,3:06:52 PM,11,2,3:09:57 PM,11,2,3:22:31 PM,11,2,3:29:39 PM,11,2,3:38:35 PM,2,21.2,,-1.2696908000000002,36.81519,-1.2594473999999998,36.8089122,Rider_Id_575,536
-Order_No_21551,User_Id_1388,Bike,3,Personal,9,2,2:25:41 PM,9,2,2:26:05 PM,9,2,2:30:05 PM,9,2,3:16:55 PM,9,2,3:28:40 PM,4,30.8,,-1.2611987,36.80609129999999,-1.2895261,36.8164407,Rider_Id_803,705
-Order_No_4781,User_Id_1250,Bike,1,Personal,14,6,1:21:49 PM,14,6,1:22:25 PM,14,6,1:46:30 PM,14,6,2:10:49 PM,14,6,2:36:11 PM,9,19.1,,-1.3014461000000002,36.7661381,-1.2598589,36.7470168,Rider_Id_648,1522
-Order_No_18733,User_Id_517,Bike,3,Business,22,4,12:50:31 PM,22,4,12:55:40 PM,22,4,12:55:50 PM,22,4,1:05:34 PM,22,4,1:36:07 PM,10,22.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_619,1833
-Order_No_3320,User_Id_1467,Bike,3,Personal,25,3,1:53:43 PM,25,3,1:54:26 PM,25,3,2:10:02 PM,25,3,2:12:39 PM,25,3,2:29:10 PM,6,17.5,,-1.2984591,36.7855438,-1.2688224,36.794406200000005,Rider_Id_415,991
-Order_No_20751,User_Id_393,Bike,3,Business,25,1,2:30:34 PM,25,1,2:30:53 PM,25,1,2:40:08 PM,25,1,2:49:39 PM,25,1,2:57:23 PM,4,30.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_25,464
-Order_No_7579,User_Id_1500,Bike,3,Business,4,2,8:25:22 AM,4,2,8:25:55 AM,4,2,8:31:12 AM,4,2,8:52:32 AM,4,2,9:09:25 AM,8,16.3,99.1,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_211,1013
-Order_No_5104,User_Id_2546,Bike,1,Personal,28,2,9:09:08 AM,28,2,9:09:27 AM,28,2,9:28:54 AM,28,2,9:31:35 AM,28,2,10:02:00 AM,17,15.8,50.9,-1.304127,36.805743,-1.304842,36.90576,Rider_Id_680,1825
-Order_No_1896,User_Id_733,Bike,3,Business,30,2,2:18:46 PM,30,2,2:22:03 PM,30,2,2:31:55 PM,30,2,2:42:25 PM,30,2,2:54:46 PM,5,,,-1.2551895,36.7822034,-1.2629461000000002,36.76531370000001,Rider_Id_937,741
-Order_No_9542,User_Id_2132,Bike,3,Business,20,3,9:15:34 AM,20,3,9:16:01 AM,20,3,9:16:56 AM,20,3,10:04:57 AM,20,3,11:11:42 AM,37,20.9,,-1.2384056,36.8718698,-1.0546336,36.773910799999996,Rider_Id_86,4005
-Order_No_19638,User_Id_426,Bike,3,Business,5,1,8:13:13 AM,5,1,8:15:37 AM,5,1,9:19:05 AM,5,1,9:26:07 AM,5,1,9:48:51 AM,6,18.7,,-1.3323107,36.8692616,-1.3097713,36.849494,Rider_Id_849,1364
-Order_No_25347,User_Id_2677,Bike,3,Business,16,2,4:30:49 PM,16,2,4:32:02 PM,16,2,4:39:00 PM,16,2,4:47:42 PM,16,2,5:14:44 PM,9,,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_193,1622
-Order_No_902,User_Id_3291,Bike,3,Business,15,3,2:19:04 PM,15,3,2:40:40 PM,15,3,2:54:45 PM,15,3,3:14:11 PM,15,3,3:34:27 PM,9,21.7,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_179,1216
-Order_No_16869,User_Id_2662,Bike,1,Personal,2,4,8:47:07 AM,2,4,8:47:41 AM,2,4,9:01:27 AM,2,4,9:04:41 AM,2,4,9:17:46 AM,7,13.2,,-1.2809077,36.825558,-1.3039411,36.7695316,Rider_Id_754,785
-Order_No_11411,User_Id_393,Bike,3,Business,12,5,11:23:34 AM,12,5,11:24:13 AM,12,5,11:24:20 AM,12,5,11:35:03 AM,12,5,11:35:07 AM,4,26.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,4
-Order_No_18198,User_Id_3355,Bike,3,Business,16,3,3:42:49 PM,16,3,3:43:21 PM,16,3,3:49:26 PM,16,3,4:00:16 PM,16,3,4:15:23 PM,7,26.9,,-1.2584143,36.8048002,-1.3052491000000002,36.8223899,Rider_Id_926,907
-Order_No_1834,User_Id_1402,Bike,3,Business,3,1,2:45:43 PM,3,1,3:05:04 PM,3,1,3:15:36 PM,3,1,3:27:52 PM,3,1,3:52:00 PM,6,23.4,,-1.2527958000000001,36.797624400000004,-1.2813012,36.832396200000005,Rider_Id_491,1448
-Order_No_16313,User_Id_517,Bike,3,Business,3,4,10:38:07 AM,3,4,10:38:31 AM,3,4,10:41:28 AM,3,4,10:44:52 AM,3,4,10:58:33 AM,5,23.6,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_523,821
-Order_No_7070,User_Id_391,Bike,3,Business,30,6,11:43:21 AM,30,6,11:44:10 AM,30,6,12:06:23 PM,30,6,12:39:53 PM,30,6,1:42:21 PM,5,26.7,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_507,3748
-Order_No_5934,User_Id_1750,Bike,1,Personal,30,6,10:12:42 AM,30,6,10:13:18 AM,30,6,10:26:22 AM,30,6,10:34:47 AM,30,6,10:49:14 AM,12,22.2,,-1.2840605,36.8191699,-1.2675351000000001,36.7456136,Rider_Id_208,867
-Order_No_23681,User_Id_3370,Bike,3,Business,12,1,4:33:21 PM,12,1,6:57:10 PM,12,1,6:57:17 PM,12,1,7:02:43 PM,12,1,7:21:33 PM,7,23.6,,-1.2615888000000002,36.7928732,-1.2897508,36.8256156,Rider_Id_406,1130
-Order_No_13862,User_Id_1271,Bike,3,Business,17,2,1:21:10 PM,17,2,1:21:17 PM,17,2,1:29:53 PM,17,2,2:08:01 PM,17,2,2:27:13 PM,14,24.3,,-1.3092700000000002,36.81326,-1.3244885,36.897791999999995,Rider_Id_844,1152
-Order_No_12030,User_Id_193,Bike,3,Personal,21,5,3:25:19 PM,21,5,3:25:47 PM,21,5,3:36:16 PM,21,5,3:41:29 PM,21,5,4:23:58 PM,21,24.8,,-1.3632162,36.9108253,-1.2615584,36.814950599999996,Rider_Id_205,2549
-Order_No_4658,User_Id_1342,Bike,3,Business,21,4,1:31:16 PM,21,4,1:33:52 PM,21,4,1:34:02 PM,21,4,1:55:23 PM,21,4,2:40:25 PM,10,22.7,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_354,2702
-Order_No_16365,User_Id_2828,Bike,3,Business,26,5,11:23:06 AM,26,5,11:24:15 AM,26,5,11:39:01 AM,26,5,11:49:02 AM,26,5,12:10:45 PM,7,,,-1.2325706,36.808392299999994,-1.267181,36.8076681,Rider_Id_101,1303
-Order_No_12526,User_Id_1417,Bike,3,Business,18,1,3:50:12 PM,18,1,3:55:24 PM,18,1,4:08:35 PM,18,1,4:14:21 PM,18,1,4:36:49 PM,7,29.6,,-1.2932569,36.8318009,-1.2741728,36.79691079999999,Rider_Id_506,1348
-Order_No_15780,User_Id_2190,Bike,3,Business,26,3,12:07:22 PM,26,3,12:07:51 PM,26,3,12:21:46 PM,26,3,12:24:56 PM,26,3,12:37:40 PM,5,23.8,,-1.3019530000000001,36.788458,-1.2869041,36.8240057,Rider_Id_726,764
-Order_No_22226,User_Id_2150,Bike,3,Business,17,5,1:23:55 PM,17,5,1:29:27 PM,17,5,1:38:54 PM,17,5,1:44:20 PM,17,5,2:03:56 PM,9,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_328,1176
-Order_No_27633,User_Id_1822,Bike,1,Personal,28,5,9:10:16 AM,28,5,10:00:18 AM,28,5,10:20:45 AM,28,5,10:22:22 AM,28,5,10:32:53 AM,8,20.8,,-1.2036664,36.819262200000004,-1.2365703000000001,36.805436799999995,Rider_Id_216,631
-Order_No_2583,User_Id_1850,Bike,3,Business,13,2,4:01:27 PM,13,2,4:17:20 PM,13,2,4:27:31 PM,13,2,4:28:54 PM,13,2,5:06:26 PM,9,26.3,,-1.290894,36.822971,-1.2859912,36.8756811,Rider_Id_622,2252
-Order_No_23245,User_Id_439,Bike,3,Business,23,5,2:25:53 PM,23,5,2:35:17 PM,23,5,2:40:56 PM,23,5,2:57:40 PM,23,5,3:49:18 PM,23,26.5,,-1.3323107,36.8692616,-1.2633397,36.7454238,Rider_Id_810,3098
-Order_No_23197,User_Id_1502,Bike,3,Business,20,3,2:44:12 PM,20,3,2:45:03 PM,20,3,2:57:19 PM,20,3,2:58:35 PM,20,3,3:34:01 PM,17,28.2,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_108,2126
-Order_No_12076,User_Id_1363,Bike,3,Business,29,5,10:19:54 AM,29,5,10:34:24 AM,29,5,11:19:23 AM,29,5,11:23:32 AM,29,5,11:53:10 AM,9,26.7,,-1.3004062,36.829741,-1.2544108999999999,36.8460525,Rider_Id_291,1778
-Order_No_25,User_Id_3647,Bike,3,Business,22,1,12:53:12 PM,22,1,12:54:50 PM,22,1,1:05:03 PM,22,1,1:15:15 PM,22,1,1:33:41 PM,5,24.7,,-1.2726389999999999,36.794723,-1.2941557,36.7792012,Rider_Id_328,1106
-Order_No_25142,User_Id_3557,Bike,3,Business,11,4,12:08:31 PM,11,4,12:09:33 PM,11,4,12:15:49 PM,11,4,12:31:21 PM,11,4,12:36:29 PM,3,25.7,,-1.2538608999999998,36.7932126,-1.2546732,36.808679600000005,Rider_Id_484,308
-Order_No_12779,User_Id_2523,Bike,3,Business,10,3,9:49:26 AM,10,3,9:50:15 AM,10,3,9:52:05 AM,10,3,9:56:41 AM,10,3,10:07:50 AM,4,22.0,,-1.2551895,36.7822034,-1.2693662000000001,36.8085295,Rider_Id_576,669
-Order_No_21256,User_Id_3326,Bike,3,Business,8,1,2:34:38 PM,8,1,2:35:29 PM,8,1,2:40:17 PM,8,1,2:44:12 PM,8,1,2:55:40 PM,3,29.4,,-1.2571472,36.795063299999995,-1.2681518,36.809816399999995,Rider_Id_937,688
-Order_No_6654,User_Id_1205,Bike,3,Business,26,2,12:28:58 PM,26,2,12:29:27 PM,26,2,12:32:13 PM,26,2,12:38:21 PM,26,2,12:53:00 PM,3,27.5,,-1.2495931,36.80650870000001,-1.2714767,36.8138291,Rider_Id_484,879
-Order_No_6447,User_Id_1850,Bike,3,Business,27,1,2:17:18 PM,27,1,2:17:36 PM,27,1,2:23:56 PM,27,1,2:27:46 PM,27,1,3:23:29 PM,22,23.2,,-1.2551895,36.7822034,-1.3668652000000001,36.730459,Rider_Id_931,3343
-Order_No_20836,User_Id_1500,Bike,3,Business,17,4,12:42:21 PM,17,4,12:42:44 PM,17,4,12:42:58 PM,17,4,1:14:54 PM,17,4,1:31:29 PM,8,25.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_518,995
-Order_No_9519,User_Id_2526,Bike,3,Personal,22,5,9:11:20 AM,22,5,9:11:34 AM,22,5,9:11:50 AM,22,5,10:07:43 AM,22,5,10:15:49 AM,4,,,-1.2999943999999999,36.758728999999995,-1.3104733999999998,36.7363698,Rider_Id_523,486
-Order_No_3939,User_Id_1573,Bike,1,Personal,5,5,11:11:41 AM,5,5,11:12:40 AM,5,5,11:21:01 AM,5,5,11:22:26 AM,5,5,11:35:08 AM,4,23.8,,-1.273056,36.811298,-1.2724885,36.785699900000004,Rider_Id_16,762
-Order_No_13087,User_Id_2523,Bike,3,Business,30,2,1:32:06 PM,30,2,1:37:56 PM,30,2,2:26:50 PM,30,2,2:29:04 PM,30,2,2:52:17 PM,7,,,-1.2551895,36.7822034,-1.2989287,36.81630820000001,Rider_Id_570,1393
-Order_No_13486,User_Id_2801,Bike,3,Business,26,2,12:34:21 PM,26,2,12:34:45 PM,26,2,1:33:03 PM,26,2,1:39:38 PM,26,2,2:45:29 PM,14,31.0,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_94,3951
-Order_No_23455,User_Id_1329,Bike,3,Business,30,2,5:20:22 PM,30,2,5:34:04 PM,30,2,5:47:36 PM,30,2,5:50:20 PM,30,2,6:29:46 PM,12,,,-1.273056,36.811298,-1.2672045,36.717206899999994,Rider_Id_937,2366
-Order_No_24633,User_Id_2289,Bike,3,Business,27,2,10:08:30 AM,27,2,10:08:54 AM,27,2,10:26:01 AM,27,2,10:29:26 AM,27,2,11:24:36 AM,21,18.6,1.9,-1.2551895,36.7822034,-1.364627,36.7410927,Rider_Id_799,3310
-Order_No_20079,User_Id_2112,Bike,3,Business,1,1,11:13:34 AM,1,1,11:14:38 AM,1,1,11:34:47 AM,1,1,11:51:23 AM,1,1,12:19:03 PM,16,,,-1.2857246999999998,36.789429299999995,-1.2156006000000001,36.8916865,Rider_Id_468,1660
-Order_No_6193,User_Id_393,Bike,3,Business,19,3,8:04:51 AM,19,3,10:48:03 AM,19,3,10:48:12 AM,19,3,11:34:05 AM,19,3,11:34:07 AM,4,22.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_623,2
-Order_No_16817,User_Id_3493,Bike,1,Personal,7,4,1:08:15 PM,7,4,1:15:50 PM,7,4,1:23:54 PM,7,4,1:38:23 PM,7,4,2:24:11 PM,9,19.9,,-1.3339461000000001,36.821912899999994,-1.2994217,36.8009779,Rider_Id_890,2748
-Order_No_26960,User_Id_1578,Bike,3,Business,20,2,12:39:29 PM,20,2,12:41:38 PM,20,2,12:59:50 PM,20,2,1:02:19 PM,20,2,1:17:12 PM,4,19.6,,-1.28878,36.8168312,-1.303711,36.81549329999999,Rider_Id_456,893
-Order_No_2015,User_Id_1329,Bike,3,Business,19,2,9:08:41 AM,19,2,9:37:41 AM,19,2,9:51:31 AM,19,2,9:52:48 AM,19,2,10:16:37 AM,6,,,-1.273056,36.811298,-1.2926563999999998,36.78731810000001,Rider_Id_660,1429
-Order_No_25956,User_Id_3194,Bike,3,Business,23,5,1:42:06 PM,23,5,1:42:29 PM,23,5,1:42:49 PM,23,5,1:46:54 PM,23,5,2:24:55 PM,3,26.3,,-1.2551895,36.7822034,-1.2652916,36.8076704,Rider_Id_322,2281
-Order_No_24216,User_Id_3064,Bike,3,Business,8,5,2:51:49 PM,8,5,2:52:08 PM,8,5,2:53:39 PM,8,5,3:02:38 PM,8,5,3:35:13 PM,20,27.1,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_770,1955
-Order_No_21818,User_Id_2412,Bike,3,Business,28,1,10:38:48 AM,28,1,10:41:50 AM,28,1,11:23:05 AM,28,1,11:26:41 AM,28,1,11:46:33 AM,10,23.7,,-1.2285834999999998,36.882646799999996,-1.2880128999999998,36.8308097,Rider_Id_906,1192
-Order_No_7610,User_Id_2989,Bike,3,Business,30,1,9:30:01 AM,30,1,9:30:20 AM,30,1,9:36:59 AM,30,1,10:19:54 AM,30,1,10:33:29 AM,15,16.3,1.9,-1.3332749,36.870814700000004,-1.3472066,36.7692638,Rider_Id_805,815
-Order_No_10380,User_Id_928,Bike,3,Business,28,4,11:35:13 AM,28,4,11:36:41 AM,28,4,11:43:19 AM,28,4,11:44:27 AM,28,4,12:13:15 PM,7,28.1,,-1.2551895,36.7822034,-1.2961143,36.815843,Rider_Id_831,1728
-Order_No_26032,User_Id_239,Bike,1,Personal,26,3,5:04:29 PM,26,3,5:05:21 PM,26,3,5:13:59 PM,26,3,5:25:16 PM,26,3,5:58:08 PM,11,19.5,,-1.32571,36.805533000000004,-1.273808,36.8402896,Rider_Id_85,1972
-Order_No_19958,User_Id_1246,Bike,3,Business,10,1,9:42:30 AM,10,1,9:54:09 AM,10,1,10:09:46 AM,10,1,10:15:17 AM,10,1,10:27:39 AM,2,18.6,1.9,-1.2527958000000001,36.8003131,-1.2682478000000001,36.8072852,Rider_Id_112,742
-Order_No_16823,User_Id_3686,Bike,3,Business,31,4,9:43:39 AM,31,4,9:44:26 AM,31,4,9:46:11 AM,31,4,9:53:04 AM,31,4,10:19:04 AM,9,20.6,,-1.2551895,36.7822034,-1.2875983,36.790156599999996,Rider_Id_518,1560
-Order_No_12448,User_Id_393,Bike,3,Business,23,3,11:38:42 AM,23,3,11:39:58 AM,23,3,11:58:25 AM,23,3,12:06:03 PM,23,3,12:44:57 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_876,2334
-Order_No_14838,User_Id_1110,Bike,1,Personal,30,1,11:04:10 AM,30,1,11:04:24 AM,30,1,11:14:32 AM,30,1,11:20:52 AM,30,1,11:40:32 AM,9,22.8,,-1.274165,36.814577,-1.2833608,36.757746600000004,Rider_Id_754,1180
-Order_No_18501,User_Id_3149,Bike,3,Business,8,1,9:57:54 AM,8,1,9:58:15 AM,8,1,10:00:55 AM,8,1,10:14:47 AM,8,1,10:25:42 AM,5,22.0,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_728,655
-Order_No_9875,User_Id_1469,Bike,3,Business,25,5,9:03:53 AM,25,5,9:04:12 AM,25,5,9:05:34 AM,25,5,9:13:09 AM,25,5,9:35:29 AM,8,20.7,,-1.2551895,36.7822034,-1.2812624,36.7714609,Rider_Id_412,1340
-Order_No_4560,User_Id_1244,Bike,3,Business,5,3,9:04:37 AM,5,3,9:04:49 AM,5,3,9:45:10 AM,5,3,9:54:26 AM,5,3,10:48:30 AM,18,18.4,,-1.293515,36.897607,-1.303596,36.778378000000004,Rider_Id_753,3244
-Order_No_18137,User_Id_2200,Bike,3,Business,23,5,5:16:01 PM,23,5,5:20:43 PM,23,5,5:25:19 PM,23,5,5:34:06 PM,23,5,6:36:30 PM,7,24.4,,-1.2668734,36.8054026,-1.3124088,36.8252715,Rider_Id_775,3744
-Order_No_312,User_Id_1341,Bike,3,Business,9,4,4:26:33 PM,9,4,4:53:00 PM,9,4,4:56:40 PM,9,4,5:04:30 PM,9,4,5:32:27 PM,18,,,-1.228911,36.881876,-1.331619,36.847976,Rider_Id_291,1677
-Order_No_13315,User_Id_2216,Bike,3,Business,23,3,9:54:59 AM,23,3,9:56:24 AM,23,3,10:06:05 AM,23,3,10:08:52 AM,23,3,10:37:59 AM,16,,,-1.2551895,36.7822034,-1.3194518,36.869396,Rider_Id_576,1747
-Order_No_23601,User_Id_1716,Bike,1,Personal,25,5,5:27:51 PM,25,5,5:28:43 PM,25,5,5:36:11 PM,25,5,5:48:23 PM,25,5,6:16:34 PM,9,21.1,,-1.3276043,36.836921999999994,-1.273009,36.802807,Rider_Id_345,1691
-Order_No_450,User_Id_3686,Bike,3,Business,1,5,12:18:30 PM,1,5,12:18:43 PM,1,5,12:23:42 PM,1,5,12:27:25 PM,1,5,12:37:08 PM,5,26.6,,-1.2551895,36.7822034,-1.2760462,36.807469700000006,Rider_Id_622,583
-Order_No_5285,User_Id_1544,Bike,3,Business,17,5,12:57:56 PM,17,5,1:45:40 PM,17,5,2:06:04 PM,17,5,2:26:25 PM,17,5,2:55:57 PM,15,,,-1.2813012,36.832396200000005,-1.2000932,36.7754458,Rider_Id_76,1772
-Order_No_10856,User_Id_928,Bike,3,Business,1,1,12:47:31 PM,1,1,12:48:02 PM,1,1,12:55:16 PM,1,1,1:10:31 PM,1,1,1:37:19 PM,8,24.2,,-1.2945041000000002,36.8151527,-1.2551895,36.7822034,Rider_Id_847,1608
-Order_No_6664,User_Id_2069,Bike,3,Business,27,1,11:49:29 AM,27,1,11:52:57 AM,27,1,11:56:55 AM,27,1,12:09:20 PM,27,1,12:29:51 PM,14,20.7,,-1.3033561000000002,36.8163631,-1.228911,36.881876,Rider_Id_119,1231
-Order_No_27090,User_Id_2042,Bike,3,Business,6,4,12:12:28 PM,6,4,12:12:34 PM,6,4,12:13:20 PM,6,4,12:27:47 PM,6,4,1:01:40 PM,12,22.1,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_900,2033
-Order_No_22996,User_Id_1081,Bike,3,Business,6,3,10:19:42 AM,6,3,10:19:54 AM,6,3,10:28:30 AM,6,3,10:32:07 AM,6,3,11:07:37 AM,13,22.3,,-1.2551895,36.7822034,-1.3218459999999999,36.82826,Rider_Id_598,2130
-Order_No_3545,User_Id_1271,Bike,3,Business,23,2,12:11:31 PM,23,2,12:13:20 PM,23,2,12:29:08 PM,23,2,12:36:23 PM,23,2,12:53:59 PM,8,18.2,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_532,1056
-Order_No_4925,User_Id_1085,Bike,3,Business,22,6,11:22:41 AM,22,6,11:23:08 AM,22,6,11:29:18 AM,22,6,11:48:58 AM,22,6,12:15:34 PM,17,23.1,,-1.2628638,36.8070399,-1.316539,36.706856200000004,Rider_Id_489,1596
-Order_No_15384,User_Id_3496,Bike,3,Business,30,2,11:20:01 AM,30,2,11:32:56 AM,30,2,11:46:08 AM,30,2,12:16:33 PM,30,2,12:38:42 PM,5,,,-1.300921,36.828195,-1.323971,36.851224200000004,Rider_Id_613,1329
-Order_No_26268,User_Id_2701,Bike,3,Business,11,2,1:57:34 PM,11,2,2:00:18 PM,11,2,2:11:56 PM,11,2,2:17:38 PM,11,2,2:55:33 PM,6,21.1,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_868,2275
-Order_No_26040,User_Id_2383,Bike,3,Business,16,4,10:55:21 AM,16,4,10:56:44 AM,16,4,11:25:43 AM,16,4,11:30:13 AM,16,4,11:42:42 AM,4,,,-1.2608993,36.799246000000004,-1.2732784,36.8029946,Rider_Id_435,749
-Order_No_24170,User_Id_1329,Bike,3,Business,6,4,10:10:42 AM,6,4,10:11:37 AM,6,4,10:23:08 AM,6,4,10:30:55 AM,6,4,11:08:01 AM,18,19.0,3.0,-1.273056,36.811298,-1.3189978999999998,36.7104076,Rider_Id_222,2226
-Order_No_7389,User_Id_1721,Bike,3,Business,22,5,2:58:42 PM,22,5,3:01:15 PM,22,5,3:09:02 PM,22,5,3:11:23 PM,22,5,3:40:15 PM,13,27.2,,-1.2551895,36.7822034,-1.3083777,36.85900770000001,Rider_Id_154,1732
-Order_No_21774,User_Id_2801,Bike,3,Business,5,5,12:22:27 PM,5,5,12:22:52 PM,5,5,12:52:54 PM,5,5,12:57:31 PM,5,5,2:09:02 PM,16,24.3,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_668,4291
-Order_No_18193,User_Id_1075,Bike,3,Business,28,1,2:59:22 PM,28,1,2:59:33 PM,28,1,3:02:55 PM,28,1,3:13:50 PM,28,1,3:47:10 PM,1,26.4,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_647,2000
-Order_No_1797,User_Id_2423,Bike,3,Business,7,1,3:22:51 PM,7,1,4:03:04 PM,7,1,4:32:59 PM,7,1,4:34:39 PM,7,1,5:40:56 PM,8,19.9,,-1.2559559999999999,36.789867,-1.2985031999999999,36.7841906,Rider_Id_937,3977
-Order_No_19694,User_Id_2601,Bike,3,Personal,29,3,5:08:04 PM,29,3,5:08:49 PM,29,3,5:13:38 PM,29,3,5:22:35 PM,29,3,5:46:43 PM,6,21.2,,-1.2963097,36.7688221,-1.2896715,36.7254189,Rider_Id_176,1448
-Order_No_11720,User_Id_187,Bike,3,Business,10,6,3:46:03 PM,10,6,3:49:41 PM,10,6,4:07:07 PM,10,6,4:11:51 PM,10,6,4:37:52 PM,8,24.8,,-1.3014461000000002,36.7661381,-1.3169591,36.7000029,Rider_Id_949,1561
-Order_No_15796,User_Id_3283,Bike,3,Business,27,1,2:21:32 PM,27,1,2:21:57 PM,27,1,2:37:38 PM,27,1,2:47:00 PM,27,1,3:03:18 PM,7,23.0,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_441,978
-Order_No_8936,User_Id_1402,Bike,3,Business,18,4,9:39:33 AM,18,4,9:40:46 AM,18,4,10:01:38 AM,18,4,10:05:33 AM,18,4,10:21:11 AM,6,,,-1.2813012,36.832396200000005,-1.2649605,36.798177700000004,Rider_Id_394,938
-Order_No_16804,User_Id_909,Bike,3,Business,12,5,2:22:10 PM,12,5,2:30:01 PM,12,5,2:45:59 PM,12,5,2:53:05 PM,12,5,3:39:01 PM,24,,,-1.225322,36.80855,-1.3251247,36.899592600000005,Rider_Id_14,2756
-Order_No_26594,User_Id_2317,Bike,1,Personal,23,3,2:22:58 PM,23,3,2:23:18 PM,23,3,2:31:04 PM,23,3,2:35:05 PM,23,3,3:08:45 PM,28,22.4,,-1.3077027,36.81635429999999,-1.167619,36.969287,Rider_Id_409,2020
-Order_No_21621,User_Id_1473,Bike,3,Business,24,3,3:57:22 PM,24,3,3:57:53 PM,24,3,4:09:36 PM,24,3,4:11:22 PM,24,3,4:35:50 PM,16,,,-1.3700383,36.9190174,-1.3044458,36.8233667,Rider_Id_99,1468
-Order_No_23656,User_Id_2956,Bike,3,Business,18,7,11:35:30 AM,18,7,12:33:29 PM,18,7,12:41:07 PM,18,7,12:44:08 PM,18,7,1:05:01 PM,9,24.1,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_532,1253
-Order_No_8164,User_Id_2030,Bike,3,Business,21,5,11:32:47 AM,21,5,11:33:23 AM,21,5,11:37:54 AM,21,5,11:42:47 AM,21,5,12:07:24 PM,3,20.2,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_402,1477
-Order_No_12232,User_Id_1103,Bike,3,Business,4,1,3:07:19 PM,4,1,3:13:25 PM,4,1,3:28:50 PM,4,1,3:40:26 PM,4,1,4:29:08 PM,7,28.5,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_751,2922
-Order_No_17125,User_Id_3647,Bike,3,Business,2,2,12:29:42 PM,2,2,12:57:58 PM,2,2,1:17:53 PM,2,2,1:27:24 PM,2,2,1:58:47 PM,7,26.3,,-1.2726389999999999,36.794723,-1.282772,36.82599929999999,Rider_Id_96,1883
-Order_No_10924,User_Id_2523,Bike,3,Business,11,5,11:10:17 AM,11,5,11:11:07 AM,11,5,11:14:41 AM,11,5,11:21:04 AM,11,5,11:41:26 AM,8,21.1,,-1.2551895,36.7822034,-1.2914531,36.7810162,Rider_Id_74,1222
-Order_No_20661,User_Id_2908,Bike,3,Personal,14,4,11:38:02 AM,14,4,11:40:40 AM,14,4,11:47:14 AM,14,4,11:53:41 AM,14,4,12:25:20 PM,7,23.9,,-1.2929747,36.76463579999999,-1.3031315,36.8175661,Rider_Id_38,1899
-Order_No_27767,User_Id_1245,Bike,3,Business,7,2,11:10:55 AM,7,2,11:11:17 AM,7,2,11:16:43 AM,7,2,11:41:19 AM,7,2,12:23:56 PM,16,,,-1.300921,36.828195,-1.3760172,36.7412505,Rider_Id_581,2557
-Order_No_14297,User_Id_647,Bike,3,Business,3,3,12:04:57 PM,3,3,12:05:47 PM,3,3,12:10:40 PM,3,3,12:15:30 PM,3,3,12:40:49 PM,12,27.6,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_545,1519
-Order_No_10098,User_Id_497,Bike,1,Personal,4,3,10:37:42 AM,4,3,10:39:24 AM,4,3,10:46:47 AM,4,3,10:51:07 AM,4,3,11:05:12 AM,6,17.2,,-1.266294,36.8057971,-1.2942892,36.7761329,Rider_Id_379,845
-Order_No_1593,User_Id_2718,Bike,3,Business,1,5,3:45:52 PM,1,5,4:05:43 PM,1,5,4:16:57 PM,1,5,4:19:32 PM,1,5,4:31:06 PM,5,28.5,,-1.2668815,36.8017244,-1.2962832,36.784968299999996,Rider_Id_205,694
-Order_No_16780,User_Id_2521,Bike,3,Business,1,1,1:30:20 PM,1,1,1:31:26 PM,1,1,1:41:03 PM,1,1,1:52:31 PM,1,1,1:59:21 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_507,410
-Order_No_27277,User_Id_1927,Bike,3,Business,8,6,11:09:54 AM,8,6,11:21:38 AM,8,6,11:30:42 AM,8,6,11:31:55 AM,8,6,11:56:06 AM,10,18.2,,-1.3052491000000002,36.8223899,-1.3472066,36.7692638,Rider_Id_477,1451
-Order_No_17655,User_Id_2066,Bike,3,Business,7,1,3:51:42 PM,7,1,3:52:09 PM,7,1,4:10:04 PM,7,1,4:14:54 PM,7,1,4:54:24 PM,16,26.7,,-1.3210188999999999,36.841055100000005,-1.3916458999999999,36.93058920000001,Rider_Id_457,2370
-Order_No_3393,User_Id_3647,Bike,3,Business,16,2,10:44:54 AM,16,2,10:45:17 AM,16,2,10:52:43 AM,16,2,10:59:21 AM,16,2,11:10:46 AM,4,,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_520,685
-Order_No_5571,User_Id_393,Bike,3,Business,18,1,9:49:43 AM,18,1,9:50:09 AM,18,1,9:59:30 AM,18,1,10:16:42 AM,18,1,10:48:18 AM,11,21.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_177,1896
-Order_No_25095,User_Id_269,Bike,3,Business,30,3,3:37:02 PM,30,3,4:04:47 PM,30,3,4:19:45 PM,30,3,4:30:35 PM,30,3,4:54:51 PM,12,25.3,,-1.3332749,36.870814700000004,-1.3886344,36.9372718,Rider_Id_474,1456
-Order_No_12561,User_Id_265,Bike,3,Business,3,5,9:35:33 AM,3,5,9:36:34 AM,3,5,10:21:49 AM,3,5,10:43:06 AM,3,5,11:07:53 AM,13,,,-1.235348,36.760016,-1.300921,36.828195,Rider_Id_564,1487
-Order_No_9809,User_Id_1255,Bike,1,Personal,18,4,12:49:54 PM,18,4,12:52:47 PM,18,4,1:08:00 PM,18,4,1:12:04 PM,18,4,1:32:20 PM,9,25.6,,-1.2833895,36.8212908,-1.290059,36.7703475,Rider_Id_477,1216
-Order_No_7803,User_Id_1754,Bike,3,Business,12,7,10:55:13 AM,12,7,10:55:22 AM,12,7,11:32:06 AM,12,7,11:42:33 AM,12,7,12:22:31 PM,29,19.6,,-1.3169591,36.7000029,-1.2860183,36.8975338,Rider_Id_478,2398
-Order_No_1001,User_Id_2066,Bike,3,Business,1,1,3:25:36 PM,1,1,3:26:27 PM,1,1,3:32:49 PM,1,1,3:41:01 PM,1,1,4:11:31 PM,12,25.7,,-1.316961,36.820337,-1.3610501,36.7520889,Rider_Id_402,1830
-Order_No_5361,User_Id_2733,Bike,3,Business,19,1,9:36:07 AM,19,1,10:37:50 AM,19,1,10:41:57 AM,19,1,11:00:09 AM,19,1,11:11:23 AM,2,24.8,,-1.3014497,36.758247499999996,-1.2963097,36.7688221,Rider_Id_509,674
-Order_No_8852,User_Id_633,Bike,3,Business,12,1,8:10:37 AM,12,1,8:12:09 AM,12,1,8:44:05 AM,12,1,8:54:26 AM,12,1,9:04:38 AM,4,19.6,0.4,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_684,612
-Order_No_20123,User_Id_1271,Bike,3,Business,29,2,4:04:15 PM,29,2,4:35:23 PM,29,2,4:56:19 PM,29,2,5:02:27 PM,29,2,5:13:06 PM,9,,,-1.3244885,36.897791999999995,-1.3323107,36.8692616,Rider_Id_79,639
-Order_No_606,User_Id_2523,Bike,3,Business,4,1,11:02:34 AM,4,1,11:02:44 AM,4,1,11:10:33 AM,4,1,11:13:00 AM,4,1,11:50:45 AM,20,25.5,,-1.2551895,36.7822034,-1.1541755,36.7454964,Rider_Id_131,2265
-Order_No_15115,User_Id_1482,Bike,3,Business,4,2,12:46:16 PM,4,2,12:47:59 PM,4,2,1:18:16 PM,4,2,1:23:50 PM,4,2,1:43:06 PM,8,19.6,,-1.3385790000000002,36.89630379999999,-1.3319425,36.894470899999995,Rider_Id_113,1156
-Order_No_5825,User_Id_2066,Bike,3,Business,10,1,12:59:56 PM,10,1,1:00:55 PM,10,1,1:05:11 PM,10,1,1:10:44 PM,10,1,1:32:50 PM,9,,,-1.316961,36.820337,-1.2655306,36.828148,Rider_Id_742,1326
-Order_No_20739,User_Id_246,Bike,3,Business,11,6,10:31:37 AM,11,6,10:32:01 AM,11,6,10:37:30 AM,11,6,10:39:42 AM,11,6,10:53:21 AM,5,21.2,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_674,819
-Order_No_22597,User_Id_1645,Bike,1,Personal,11,4,9:50:01 AM,11,4,9:54:38 AM,11,4,10:05:56 AM,11,4,10:14:32 AM,11,4,10:39:50 AM,8,18.3,,-1.2727268999999999,36.8298864,-1.3118872,36.8039137,Rider_Id_844,1518
-Order_No_14293,User_Id_2701,Bike,3,Business,23,4,11:42:38 AM,23,4,11:44:11 AM,23,4,11:50:31 AM,23,4,11:53:47 AM,23,4,1:05:45 PM,17,23.2,,-1.2765736,36.851364600000004,-1.3705766000000001,36.9120742,Rider_Id_404,4318
-Order_No_8297,User_Id_391,Bike,3,Business,3,3,11:38:01 AM,3,3,11:38:53 AM,3,3,12:03:37 PM,3,3,12:09:38 PM,3,3,1:03:53 PM,2,27.3,,-1.2784063,36.8222933,-1.2870774,36.8259832,Rider_Id_204,3255
-Order_No_7964,User_Id_2030,Bike,3,Business,14,5,11:28:20 AM,14,5,12:30:37 PM,14,5,12:43:23 PM,14,5,12:49:26 PM,14,5,1:24:19 PM,6,24.6,,-1.3396256000000002,36.8863135,-1.3333725,36.871761299999996,Rider_Id_516,2093
-Order_No_12414,User_Id_3283,Bike,3,Business,16,3,2:36:29 PM,16,3,2:37:10 PM,16,3,3:19:35 PM,16,3,3:29:38 PM,16,3,3:38:59 PM,6,27.1,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_52,561
-Order_No_25968,User_Id_633,Bike,3,Business,27,3,4:01:43 PM,27,3,4:02:40 PM,27,3,4:10:13 PM,27,3,4:16:26 PM,27,3,4:24:29 PM,4,29.2,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_85,483
-Order_No_14365,User_Id_3641,Bike,3,Business,26,2,2:22:29 PM,26,2,2:45:33 PM,26,2,2:49:55 PM,26,2,2:52:46 PM,26,2,3:18:05 PM,8,,,-1.2551895,36.7822034,-1.2998903,36.8179213,Rider_Id_726,1519
-Order_No_1171,User_Id_2532,Bike,3,Business,15,2,10:41:07 AM,15,2,10:41:13 AM,15,2,10:41:24 AM,15,2,11:03:40 AM,15,2,11:26:36 AM,12,24.5,,-1.2571472,36.795063299999995,-1.329568,36.8520825,Rider_Id_449,1376
-Order_No_10082,User_Id_1245,Bike,3,Business,9,4,8:59:26 AM,9,4,8:59:36 AM,9,4,9:41:46 AM,9,4,10:06:37 AM,9,4,11:17:35 AM,16,17.0,,-1.3878504999999999,36.7692885,-1.300921,36.828195,Rider_Id_939,4258
-Order_No_7384,User_Id_2789,Bike,3,Business,21,5,9:29:30 AM,21,5,9:30:20 AM,21,5,9:38:38 AM,21,5,9:47:20 AM,21,5,10:14:11 AM,10,19.6,,-1.304735,36.784605400000004,-1.3297531,36.7063699,Rider_Id_829,1611
-Order_No_24927,User_Id_624,Bike,3,Business,26,1,11:14:07 AM,26,1,11:31:27 AM,26,1,11:41:49 AM,26,1,11:55:51 AM,26,1,12:20:05 PM,12,23.1,,-1.307143,36.825009,-1.2297799999999999,36.836215,Rider_Id_622,1454
-Order_No_24316,User_Id_346,Bike,3,Business,15,1,3:48:48 PM,15,1,3:50:12 PM,15,1,3:59:02 PM,15,1,4:11:29 PM,15,1,4:31:17 PM,11,,,-1.2413316,36.878552500000005,-1.28878,36.8168312,Rider_Id_709,1188
-Order_No_26352,User_Id_2132,Bike,3,Business,11,4,12:52:15 PM,11,4,1:03:35 PM,11,4,1:36:03 PM,11,4,1:40:01 PM,11,4,2:51:34 PM,14,26.8,,-1.2384056,36.8718698,-1.2981102,36.8293513,Rider_Id_279,4293
-Order_No_1425,User_Id_2090,Bike,3,Personal,14,2,8:53:56 AM,14,2,8:54:42 AM,14,2,9:17:43 AM,14,2,9:34:05 AM,14,2,9:40:48 AM,2,15.5,,-1.2671296,36.81257720000001,-1.2597034,36.8003065,Rider_Id_592,403
-Order_No_20785,User_Id_1245,Bike,3,Business,30,2,9:08:27 AM,30,2,9:08:36 AM,30,2,9:16:22 AM,30,2,9:38:38 AM,30,2,10:04:12 AM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_595,1534
-Order_No_25842,User_Id_3647,Bike,3,Business,4,1,2:26:12 PM,4,1,2:26:48 PM,4,1,2:37:48 PM,4,1,2:44:57 PM,4,1,3:25:22 PM,28,28.5,,-1.2726389999999999,36.794723,-1.1846355,36.948572799999994,Rider_Id_147,2425
-Order_No_23720,User_Id_391,Bike,3,Business,23,2,1:00:31 PM,23,2,1:00:50 PM,23,2,1:18:47 PM,23,2,1:47:23 PM,23,2,1:47:27 PM,4,,,-1.2839729,36.8115146,-1.2867454,36.8259017,Rider_Id_613,4
-Order_No_5616,User_Id_136,Bike,3,Business,17,3,10:08:47 AM,17,3,10:09:03 AM,17,3,10:14:23 AM,17,3,10:22:15 AM,17,3,11:02:39 AM,18,,,-1.3165103999999999,36.707772600000006,-1.2531983,36.8069669,Rider_Id_99,2424
-Order_No_24905,User_Id_575,Bike,1,Personal,5,4,9:42:57 AM,5,4,9:43:28 AM,5,4,9:48:36 AM,5,4,10:14:00 AM,5,4,10:31:47 AM,9,,,-1.2684962,36.8079433,-1.2998486999999999,36.7625853,Rider_Id_116,1067
-Order_No_12174,User_Id_696,Bike,2,Personal,28,2,10:03:33 AM,28,2,10:04:51 AM,28,2,10:15:13 AM,28,2,10:29:31 AM,28,2,10:53:47 AM,9,15.9,50.9,-1.2858511000000001,36.7946184,-1.3217370000000002,36.81797879999999,Rider_Id_726,1456
-Order_No_19573,User_Id_517,Bike,3,Business,4,3,1:29:19 PM,4,3,1:29:45 PM,4,3,1:36:22 PM,4,3,1:59:03 PM,4,3,1:59:18 PM,8,18.3,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_656,15
-Order_No_4528,User_Id_391,Bike,3,Business,30,2,11:34:04 AM,30,2,11:36:40 AM,30,2,12:10:21 PM,30,2,12:38:23 PM,30,2,2:04:47 PM,18,,,-1.2867454,36.8259017,-1.3878504999999999,36.7692885,Rider_Id_106,5184
-Order_No_20744,User_Id_3355,Bike,3,Business,24,2,11:20:25 AM,24,2,11:21:12 AM,24,2,11:38:06 AM,24,2,11:42:01 AM,24,2,11:57:46 AM,6,17.4,,-1.2765736,36.851364600000004,-1.3052491000000002,36.8223899,Rider_Id_174,945
-Order_No_7488,User_Id_393,Bike,3,Business,20,2,10:30:23 AM,20,2,11:27:30 AM,20,2,11:42:36 AM,20,2,11:54:45 AM,20,2,12:29:46 PM,10,20.0,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_281,2101
-Order_No_6994,User_Id_1687,Bike,3,Business,20,2,9:47:52 AM,20,2,10:21:30 AM,20,2,11:03:55 AM,20,2,11:05:35 AM,20,2,11:33:37 AM,4,19.6,,-1.2626574,36.8014034,-1.2895261,36.8164407,Rider_Id_585,1682
-Order_No_20591,User_Id_3291,Bike,3,Business,4,6,11:11:01 AM,4,6,11:11:20 AM,4,6,11:21:15 AM,4,6,11:27:12 AM,4,6,11:40:44 AM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_547,812
-Order_No_3078,User_Id_1500,Bike,3,Business,12,5,8:22:30 AM,12,5,8:24:31 AM,12,5,8:48:07 AM,12,5,8:51:38 AM,12,5,9:09:42 AM,9,21.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_576,1084
-Order_No_7672,User_Id_1342,Bike,3,Business,25,3,1:03:20 PM,25,3,1:03:41 PM,25,3,1:08:00 PM,25,3,1:14:28 PM,25,3,1:55:38 PM,9,,,-1.3025826,36.7670807,-1.2620912,36.8013562,Rider_Id_140,2470
-Order_No_559,User_Id_816,Bike,3,Business,18,1,3:19:16 PM,18,1,3:19:52 PM,18,1,3:22:58 PM,18,1,3:45:43 PM,18,1,4:05:28 PM,6,30.8,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_845,1185
-Order_No_11489,User_Id_1850,Bike,3,Business,17,3,3:29:18 PM,17,3,3:29:28 PM,17,3,3:29:39 PM,17,3,3:40:59 PM,17,3,3:41:05 PM,3,,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_291,6
-Order_No_441,User_Id_496,Bike,3,Business,3,3,3:01:18 PM,3,3,3:01:50 PM,3,3,3:03:43 PM,3,3,3:10:13 PM,3,3,3:57:00 PM,23,28.4,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_709,2807
-Order_No_11016,User_Id_1687,Bike,3,Business,9,4,2:24:24 PM,9,4,2:26:17 PM,9,4,2:43:25 PM,9,4,2:49:37 PM,9,4,3:04:26 PM,3,25.7,,-1.2890333,36.8234839,-1.2675979,36.815565299999996,Rider_Id_882,889
-Order_No_12365,User_Id_3521,Bike,3,Business,9,2,2:24:35 PM,9,2,2:25:16 PM,9,2,2:38:05 PM,9,2,2:45:04 PM,9,2,3:42:37 PM,4,30.4,,-1.2696908000000002,36.81519,-1.2643693999999999,36.785413500000004,Rider_Id_523,3453
-Order_No_16177,User_Id_1108,Bike,3,Personal,26,2,3:35:55 PM,26,2,3:37:15 PM,26,2,3:56:45 PM,26,2,4:05:15 PM,26,2,4:17:27 PM,7,29.5,,-1.2857521,36.82774570000001,-1.3210541,36.8169608,Rider_Id_336,732
-Order_No_15239,User_Id_419,Bike,3,Business,11,1,10:51:11 AM,11,1,11:00:09 AM,11,1,11:11:41 AM,11,1,11:15:20 AM,11,1,11:33:27 AM,9,24.1,,-1.2571472,36.795063299999995,-1.2326041,36.8009029,Rider_Id_576,1087
-Order_No_6671,User_Id_1062,Bike,3,Business,12,1,5:16:40 PM,12,1,5:26:33 PM,12,1,5:46:34 PM,12,1,5:50:22 PM,12,1,6:35:33 PM,11,23.6,,-1.238874,36.807889,-1.2967701999999999,36.8040381,Rider_Id_108,2711
-Order_No_12379,User_Id_2622,Bike,2,Personal,2,3,10:38:28 AM,2,3,10:45:48 AM,2,3,11:04:07 AM,2,3,11:09:49 AM,2,3,11:35:32 AM,6,21.8,,-1.292206,36.800284000000005,-1.300921,36.828195,Rider_Id_479,1543
-Order_No_11764,User_Id_3647,Bike,3,Business,9,5,4:34:55 PM,9,5,4:58:51 PM,9,5,5:20:25 PM,9,5,5:28:39 PM,9,5,6:07:46 PM,11,25.2,,-1.2726389999999999,36.794723,-1.3094179,36.71859179999999,Rider_Id_304,2347
-Order_No_28186,User_Id_136,Bike,3,Business,20,4,2:21:57 PM,20,4,2:22:16 PM,20,4,2:26:09 PM,20,4,2:30:01 PM,20,4,2:52:31 PM,9,28.2,,-1.2582973999999998,36.8002238,-1.3025826,36.7670807,Rider_Id_535,1350
-Order_No_24880,User_Id_393,Bike,3,Business,14,5,10:12:39 AM,14,5,10:12:49 AM,14,5,10:14:18 AM,14,5,10:27:57 AM,14,5,10:55:23 AM,11,17.3,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_35,1646
-Order_No_17862,User_Id_776,Bike,1,Personal,12,6,11:13:07 AM,12,6,11:19:45 AM,12,6,11:33:35 AM,12,6,12:21:57 PM,12,6,12:43:51 PM,7,23.1,,-1.2665773999999999,36.8069908,-1.3175119,36.8058513,Rider_Id_103,1314
-Order_No_4547,User_Id_1209,Bike,3,Business,25,5,1:53:01 PM,25,5,1:53:21 PM,25,5,2:07:36 PM,25,5,2:16:52 PM,25,5,2:43:01 PM,9,29.1,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_238,1569
-Order_No_22580,User_Id_3572,Bike,1,Personal,3,3,10:19:59 AM,3,3,10:20:41 AM,3,3,10:26:06 AM,3,3,10:38:21 AM,3,3,10:45:02 AM,2,23.9,,-1.3129945,36.831824499999996,-1.3088824,36.8311983,Rider_Id_848,401
-Order_No_18261,User_Id_265,Bike,3,Business,14,4,3:49:59 PM,14,4,3:53:52 PM,14,4,4:09:48 PM,14,4,4:11:01 PM,14,4,4:58:28 PM,12,28.1,,-1.300921,36.828195,-1.2332988999999999,36.8130698,Rider_Id_248,2847
-Order_No_18976,User_Id_2112,Bike,3,Business,28,4,2:08:24 PM,28,4,2:09:01 PM,28,4,2:15:59 PM,28,4,2:28:08 PM,28,4,2:56:14 PM,14,30.0,,-1.2156006000000001,36.8916865,-1.2591018999999999,36.800577000000004,Rider_Id_655,1686
-Order_No_16533,User_Id_3222,Bike,3,Business,21,2,3:17:48 PM,21,2,3:19:49 PM,21,2,3:34:37 PM,21,2,3:40:28 PM,21,2,3:51:26 PM,4,25.1,,-1.2551895,36.7822034,-1.2695287,36.798177700000004,Rider_Id_930,658
-Order_No_5400,User_Id_3288,Bike,3,Business,6,2,12:50:07 PM,6,2,3:32:08 PM,6,2,3:32:25 PM,6,2,3:37:55 PM,6,2,4:04:16 PM,4,27.3,,-1.3080696000000003,36.8237191,-1.2845766,36.8245168,Rider_Id_445,1581
-Order_No_13339,User_Id_265,Bike,3,Business,4,1,1:24:06 PM,4,1,1:24:36 PM,4,1,1:46:40 PM,4,1,2:25:54 PM,4,1,2:46:15 PM,13,22.6,,-1.300921,36.828195,-1.3745889999999998,36.772046,Rider_Id_844,1221
-Order_No_3475,User_Id_2828,Bike,1,Business,20,5,6:23:36 PM,20,5,6:32:21 PM,20,5,6:45:09 PM,20,5,6:51:11 PM,20,5,7:18:07 PM,9,20.1,,-1.305399,36.822184899999996,-1.328751,36.8818019,Rider_Id_549,1616
-Order_No_17936,User_Id_3718,Bike,3,Business,18,4,9:20:24 AM,18,4,9:26:20 AM,18,4,9:54:55 AM,18,4,10:02:36 AM,18,4,10:28:04 AM,5,20.6,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_147,1528
-Order_No_5100,User_Id_111,Bike,3,Business,18,4,2:59:11 PM,18,4,2:59:22 PM,18,4,3:01:50 PM,18,4,3:06:59 PM,18,4,4:11:17 PM,8,26.8,,-1.2551895,36.7822034,-1.298094,36.803149,Rider_Id_805,3858
-Order_No_25664,User_Id_2801,Bike,3,Business,19,5,11:41:27 AM,19,5,11:42:14 AM,19,5,11:46:30 AM,19,5,11:48:26 AM,19,5,12:13:41 PM,10,25.1,,-1.2595421999999998,36.7871178,-1.2983805,36.8198439,Rider_Id_22,1515
-Order_No_12277,User_Id_3283,Bike,3,Business,2,2,11:38:06 AM,2,2,11:54:01 AM,2,2,11:56:35 AM,2,2,12:05:26 PM,2,2,12:15:33 PM,5,26.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_103,607
-Order_No_20244,User_Id_1133,Bike,3,Business,1,1,11:08:21 AM,1,1,11:08:44 AM,1,1,11:25:16 AM,1,1,11:29:24 AM,1,1,12:21:59 PM,16,,,-1.3263105,36.8622904,-1.2551895,36.7822034,Rider_Id_168,3155
-Order_No_3489,User_Id_2044,Bike,3,Personal,14,6,5:16:47 PM,14,6,5:17:08 PM,14,6,5:35:33 PM,14,6,5:52:51 PM,14,6,6:08:31 PM,7,18.8,,-1.2566628999999998,36.772639700000006,-1.2517392,36.82496079999999,Rider_Id_956,940
-Order_No_6800,User_Id_1075,Bike,3,Business,23,4,10:07:02 AM,23,4,10:18:21 AM,23,4,10:37:35 AM,23,4,10:43:35 AM,23,4,11:08:45 AM,5,23.2,,-1.2527958000000001,36.8003131,-1.2821421999999998,36.8161565,Rider_Id_139,1510
-Order_No_142,User_Id_3398,Bike,3,Business,19,3,12:48:31 PM,19,3,12:48:39 PM,19,3,12:48:43 PM,19,3,1:05:46 PM,19,3,1:28:23 PM,22,,,-1.3169591,36.7000029,-1.4076398,36.6741513,Rider_Id_813,1357
-Order_No_4918,User_Id_393,Bike,3,Business,5,2,8:50:41 AM,5,2,8:50:57 AM,5,2,8:51:36 AM,5,2,9:38:31 AM,5,2,9:38:33 AM,14,21.2,2.0,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_619,2
-Order_No_25345,User_Id_649,Bike,3,Business,24,5,9:48:03 AM,24,5,9:49:16 AM,24,5,9:50:20 AM,24,5,10:10:04 AM,24,5,10:46:04 AM,7,18.0,,-1.2551895,36.7822034,-1.2854089,36.8284874,Rider_Id_438,2160
-Order_No_13441,User_Id_1754,Bike,3,Business,9,7,11:59:51 AM,9,7,12:07:14 PM,9,7,12:21:17 PM,9,7,12:22:35 PM,9,7,12:45:52 PM,12,23.6,,-1.2860183,36.8975338,-1.3052491000000002,36.8223899,Rider_Id_532,1397
-Order_No_2708,User_Id_2330,Bike,3,Business,15,5,2:54:56 PM,15,5,2:58:26 PM,15,5,3:20:48 PM,15,5,3:37:40 PM,15,5,4:13:05 PM,15,27.9,,-1.2284323,36.8757058,-1.2124582,36.802894,Rider_Id_238,2125
-Order_No_4415,User_Id_3291,Bike,3,Business,1,5,10:28:34 AM,1,5,10:30:00 AM,1,5,10:35:11 AM,1,5,10:44:22 AM,1,5,11:14:54 AM,6,28.7,,-1.2600926000000001,36.8088685,-1.2783848999999998,36.8163445,Rider_Id_434,1832
-Order_No_7961,User_Id_2801,Bike,3,Business,10,5,11:35:43 AM,10,5,11:36:12 AM,10,5,11:55:30 AM,10,5,11:59:05 AM,10,5,12:21:04 PM,10,,,-1.2674273999999999,36.787082700000006,-1.2653185,36.8358841,Rider_Id_930,1319
-Order_No_27039,User_Id_2216,Bike,3,Business,8,1,12:29:11 PM,8,1,12:29:30 PM,8,1,12:42:26 PM,8,1,12:53:59 PM,8,1,1:07:49 PM,2,24.3,,-1.2551895,36.7822034,-1.2635051000000002,36.7829162,Rider_Id_566,830
-Order_No_26299,User_Id_1171,Bike,2,Personal,22,2,10:51:04 AM,22,2,10:51:08 AM,22,2,10:52:14 AM,22,2,11:01:50 AM,22,2,11:41:20 AM,34,19.2,,-1.2571264,36.8118347,-1.4168588,36.953003499999994,Rider_Id_633,2370
-Order_No_16084,User_Id_2069,Bike,3,Business,25,4,9:42:25 AM,25,4,9:46:23 AM,25,4,10:02:28 AM,25,4,10:22:07 AM,25,4,10:57:44 AM,9,16.5,1.9,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_646,2137
-Order_No_4896,User_Id_830,Bike,3,Business,21,5,4:12:26 PM,21,5,4:12:42 PM,21,5,4:32:24 PM,21,5,4:36:51 PM,21,5,5:54:19 PM,20,22.8,,-1.2551895,36.7822034,-1.3523738,36.7266216,Rider_Id_38,4648
-Order_No_13024,User_Id_3291,Bike,3,Business,29,6,12:07:08 PM,29,6,12:07:33 PM,29,6,12:14:53 PM,29,6,12:27:30 PM,29,6,12:46:15 PM,12,23.8,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_698,1125
-Order_No_6477,User_Id_269,Bike,3,Business,7,2,1:42:04 PM,7,2,1:42:34 PM,7,2,1:47:52 PM,7,2,2:05:11 PM,7,2,2:23:00 PM,12,24.7,,-1.2672290000000002,36.806399,-1.3332749,36.870814700000004,Rider_Id_726,1069
-Order_No_6091,User_Id_3404,Bike,3,Business,8,5,10:03:22 AM,8,5,10:38:56 AM,8,5,11:31:02 AM,8,5,12:31:22 PM,8,5,12:38:58 PM,11,24.0,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_436,456
-Order_No_1627,User_Id_1280,Bike,3,Business,10,1,11:23:38 AM,10,1,11:24:00 AM,10,1,11:29:54 AM,10,1,11:34:26 AM,10,1,11:41:10 AM,3,23.4,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_446,404
-Order_No_23919,User_Id_3291,Bike,3,Business,12,2,1:11:43 PM,12,2,1:12:18 PM,12,2,1:12:35 PM,12,2,1:19:56 PM,12,2,1:29:46 PM,5,24.4,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_188,590
-Order_No_20032,User_Id_3283,Bike,3,Business,18,6,12:38:40 PM,18,6,12:39:42 PM,18,6,12:47:24 PM,18,6,1:07:13 PM,18,6,1:20:58 PM,5,21.0,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_191,825
-Order_No_25639,User_Id_2436,Bike,3,Business,11,1,2:02:20 PM,11,1,2:04:17 PM,11,1,2:14:05 PM,11,1,2:24:41 PM,11,1,2:48:25 PM,13,28.9,,-1.2571472,36.795063299999995,-1.2343236000000002,36.8861,Rider_Id_25,1424
-Order_No_16390,User_Id_865,Bike,3,Business,16,4,9:56:06 AM,16,4,10:12:29 AM,16,4,10:45:08 AM,16,4,10:53:18 AM,16,4,11:25:38 AM,22,,,-1.174484,36.914608,-1.2456412000000001,36.7955298,Rider_Id_484,1940
-Order_No_17589,User_Id_3404,Bike,3,Business,27,4,3:59:35 PM,27,4,3:59:58 PM,27,4,4:14:17 PM,27,4,4:23:17 PM,27,4,4:50:48 PM,7,23.8,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_579,1651
-Order_No_22972,User_Id_868,Bike,3,Business,21,1,2:10:29 PM,21,1,2:20:03 PM,21,1,2:21:42 PM,21,1,2:40:29 PM,21,1,2:58:20 PM,8,27.4,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_86,1071
-Order_No_21733,User_Id_948,Bike,3,Business,4,4,12:46:56 PM,4,4,12:47:26 PM,4,4,1:47:15 PM,4,4,1:50:38 PM,4,4,2:04:40 PM,3,27.2,,-1.2547101,36.809327200000006,-1.2696908000000002,36.81519,Rider_Id_547,842
-Order_No_18222,User_Id_816,Bike,3,Business,17,1,1:46:04 PM,17,1,2:07:27 PM,17,1,2:23:43 PM,17,1,2:46:36 PM,17,1,3:16:45 PM,6,24.7,,-1.2534301,36.800098299999995,-1.290894,36.822971,Rider_Id_300,1809
-Order_No_21407,User_Id_909,Bike,3,Business,3,5,1:30:23 PM,3,5,1:37:35 PM,3,5,1:51:38 PM,3,5,1:59:40 PM,3,5,2:26:43 PM,14,,,-1.225322,36.80855,-1.2413316,36.878552500000005,Rider_Id_72,1623
-Order_No_21249,User_Id_640,Bike,3,Business,22,4,10:59:18 AM,22,4,10:59:56 AM,22,4,11:04:54 AM,22,4,11:08:29 AM,22,4,11:17:37 AM,3,22.8,,-1.304033,36.7848695,-1.3004846,36.790159100000004,Rider_Id_558,548
-Order_No_6449,User_Id_1642,Bike,3,Business,9,6,10:23:01 AM,9,6,10:23:45 AM,9,6,10:29:37 AM,9,6,10:40:28 AM,9,6,10:52:54 AM,4,24.8,,-1.2642903,36.80021370000001,-1.2726506000000002,36.7960153,Rider_Id_547,746
-Order_No_13643,User_Id_2956,Bike,3,Business,16,4,12:20:49 PM,16,4,12:21:14 PM,16,4,12:24:45 PM,16,4,12:29:42 PM,16,4,1:49:00 PM,16,,,-1.2584143,36.8048002,-1.216682,36.908577799999996,Rider_Id_250,4758
-Order_No_13499,User_Id_1170,Bike,2,Personal,13,1,8:10:46 AM,13,1,8:11:00 AM,13,1,8:13:29 AM,13,1,8:34:57 AM,13,1,8:54:29 AM,7,15.0,,-1.2559559999999999,36.772901,-1.2732412,36.811767700000004,Rider_Id_720,1172
-Order_No_1079,User_Id_265,Bike,3,Business,7,2,2:28:53 PM,7,2,2:29:06 PM,7,2,2:31:26 PM,7,2,2:53:25 PM,7,2,3:08:49 PM,7,,,-1.2673786,36.8081593,-1.300921,36.828195,Rider_Id_291,924
-Order_No_10759,User_Id_733,Bike,3,Business,4,2,1:02:36 PM,4,2,1:12:30 PM,4,2,1:17:32 PM,4,2,1:27:35 PM,4,2,1:54:42 PM,20,19.6,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_74,1627
-Order_No_22356,User_Id_2030,Bike,3,Business,25,4,2:26:54 PM,25,4,2:33:52 PM,25,4,2:45:10 PM,25,4,3:01:01 PM,25,4,3:27:50 PM,3,20.9,,-1.257309,36.806008399999996,-1.2753108999999998,36.8130185,Rider_Id_112,1609
-Order_No_10468,User_Id_323,Bike,3,Business,13,3,2:00:15 PM,13,3,2:01:10 PM,13,3,2:02:22 PM,13,3,2:28:57 PM,13,3,2:35:23 PM,4,29.3,,-1.2611987,36.80609129999999,-1.2551895,36.7822034,Rider_Id_925,386
-Order_No_15182,User_Id_3496,Bike,3,Business,26,6,11:50:14 AM,26,6,11:50:43 AM,26,6,11:59:13 AM,26,6,12:10:26 PM,26,6,12:39:38 PM,19,26.2,,-1.300921,36.828195,-1.2016806999999998,36.915339700000004,Rider_Id_882,1752
-Order_No_22258,User_Id_1796,Bike,3,Business,29,5,10:52:15 AM,29,5,10:52:52 AM,29,5,10:56:26 AM,29,5,11:01:07 AM,29,5,11:26:02 AM,15,26.6,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_854,1495
-Order_No_16225,User_Id_3283,Bike,3,Business,15,2,10:09:14 AM,15,2,10:09:27 AM,15,2,10:13:34 AM,15,2,10:17:15 AM,15,2,10:29:29 AM,6,20.5,,-1.265715,36.823815,-1.2628473,36.781805,Rider_Id_674,734
-Order_No_27265,User_Id_2084,Bike,1,Personal,1,1,1:42:12 PM,1,1,1:45:23 PM,1,1,1:51:47 PM,1,1,2:00:21 PM,1,1,2:19:25 PM,3,25.8,,-1.265715,36.808525,-1.2793148,36.8148394,Rider_Id_263,1144
-Order_No_8073,User_Id_246,Bike,3,Business,14,4,4:28:32 PM,14,4,4:29:32 PM,14,4,4:43:46 PM,14,4,4:45:37 PM,14,4,4:56:37 PM,2,21.1,,-1.2584143,36.8048002,-1.263499,36.801454799999995,Rider_Id_898,660
-Order_No_218,User_Id_808,Bike,2,Business,25,5,4:11:00 PM,25,5,4:11:41 PM,25,5,4:39:31 PM,25,5,4:49:59 PM,25,5,5:21:19 PM,7,21.4,,-1.310518,36.837305,-1.2651643,36.807221399999996,Rider_Id_858,1880
-Order_No_2470,User_Id_3291,Bike,3,Business,22,4,10:52:36 AM,22,4,10:53:19 AM,22,4,10:57:12 AM,22,4,10:58:26 AM,22,4,11:04:59 AM,2,22.7,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_727,393
-Order_No_11976,User_Id_2691,Bike,3,Business,30,2,10:27:26 AM,30,2,10:29:41 AM,30,2,10:47:38 AM,30,2,11:03:18 AM,30,2,11:24:34 AM,8,,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_57,1276
-Order_No_7677,User_Id_2920,Bike,3,Personal,23,3,9:50:37 AM,23,3,9:51:23 AM,23,3,9:55:51 AM,23,3,10:04:16 AM,23,3,10:48:57 AM,36,,,-1.2969741000000001,36.7856612,-1.1137700000000001,37.002043,Rider_Id_40,2681
-Order_No_541,User_Id_3441,Bike,3,Business,3,5,12:02:28 PM,3,5,12:02:37 PM,3,5,12:35:53 PM,3,5,12:40:11 PM,3,5,1:14:25 PM,3,21.6,,-1.2998459999999998,36.7940155,-1.3008254,36.80498970000001,Rider_Id_322,2054
-Order_No_8466,User_Id_2344,Bike,3,Business,14,4,12:49:54 PM,14,4,2:00:02 PM,14,4,2:45:18 PM,14,4,2:49:19 PM,14,4,3:31:08 PM,9,27.9,,-1.2551895,36.7822034,-1.2453158000000002,36.810082799999996,Rider_Id_547,2509
-Order_No_5830,User_Id_2008,Bike,3,Business,24,5,3:23:48 PM,24,5,3:25:36 PM,24,5,3:58:10 PM,24,5,4:11:36 PM,24,5,4:39:35 PM,7,23.4,,-1.2508229,36.7895263,-1.2798853000000001,36.8327028,Rider_Id_384,1679
-Order_No_1802,User_Id_478,Bike,3,Business,20,4,12:14:40 PM,20,4,12:14:49 PM,20,4,12:47:51 PM,20,4,12:53:09 PM,20,4,1:24:22 PM,12,22.3,,-1.3013451,36.8747364,-1.2571472,36.795063299999995,Rider_Id_205,1873
-Order_No_12533,User_Id_1889,Bike,1,Personal,13,4,9:41:33 AM,13,4,9:41:53 AM,13,4,10:00:03 AM,13,4,10:10:15 AM,13,4,10:32:50 AM,10,18.6,,-1.2946768999999998,36.7501951,-1.2590031000000002,36.8078168,Rider_Id_336,1355
-Order_No_17921,User_Id_633,Bike,3,Business,22,5,2:10:49 PM,22,5,2:11:34 PM,22,5,2:23:33 PM,22,5,2:33:11 PM,22,5,2:54:44 PM,9,30.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_682,1293
-Order_No_27281,User_Id_53,Bike,3,Business,15,5,10:33:50 AM,15,5,10:37:12 AM,15,5,10:40:47 AM,15,5,11:05:13 AM,15,5,11:51:32 AM,5,26.1,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_763,2779
-Order_No_7731,User_Id_2389,Bike,1,Personal,17,3,8:39:51 AM,17,3,8:40:32 AM,17,3,8:51:40 AM,17,3,8:56:11 AM,17,3,9:03:44 AM,7,21.0,,-1.2813649,36.7764385,-1.2566888999999999,36.7930784,Rider_Id_88,453
-Order_No_26533,User_Id_1544,Bike,3,Business,4,5,9:18:13 AM,4,5,11:17:15 AM,4,5,11:27:24 AM,4,5,11:53:13 AM,4,5,12:04:50 PM,5,22.3,,-1.2858323,36.829994799999994,-1.3037058000000001,36.79574470000001,Rider_Id_100,697
-Order_No_12425,User_Id_1245,Bike,3,Business,27,3,12:06:16 PM,27,3,12:12:54 PM,27,3,12:20:21 PM,27,3,12:36:20 PM,27,3,1:06:56 PM,13,26.0,,-1.3882008,36.7699445,-1.3421303999999998,36.718364,Rider_Id_661,1836
-Order_No_7707,User_Id_1281,Bike,3,Business,23,1,12:06:25 PM,23,1,12:06:52 PM,23,1,12:13:22 PM,23,1,1:17:41 PM,23,1,1:50:15 PM,10,18.4,,-1.269534,36.80697,-1.275442,36.766020700000006,Rider_Id_616,1954
-Order_No_502,User_Id_891,Bike,1,Personal,15,6,10:46:02 AM,15,6,10:46:13 AM,15,6,10:56:23 AM,15,6,11:10:41 AM,15,6,11:30:20 AM,11,,,-1.2674273999999999,36.787082700000006,-1.3197098999999999,36.831231200000005,Rider_Id_56,1179
-Order_No_23059,User_Id_1471,Bike,3,Business,16,3,1:07:51 PM,16,3,1:08:21 PM,16,3,1:16:00 PM,16,3,1:19:35 PM,16,3,1:43:52 PM,9,22.9,,-1.2551895,36.7822034,-1.2972916,36.7880497,Rider_Id_88,1457
-Order_No_14045,User_Id_1217,Bike,3,Personal,28,2,10:38:36 AM,28,2,10:39:55 AM,28,2,10:49:22 AM,28,2,10:50:26 AM,28,2,11:16:50 AM,16,21.3,,-1.2175583,36.884343,-1.269534,36.80697,Rider_Id_152,1584
-Order_No_20333,User_Id_2587,Bike,3,Business,15,5,3:40:09 PM,15,5,3:44:34 PM,15,5,3:54:43 PM,15,5,4:06:20 PM,15,5,4:24:53 PM,4,27.9,,-1.28878,36.8168312,-1.2636813999999998,36.802996500000006,Rider_Id_268,1113
-Order_No_21125,User_Id_1500,Bike,3,Business,3,3,10:11:35 AM,3,3,10:11:49 AM,3,3,10:57:43 AM,3,3,11:03:51 AM,3,3,11:38:39 AM,11,27.4,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_448,2088
-Order_No_22824,User_Id_3718,Bike,3,Business,16,2,3:14:35 PM,16,2,3:15:39 PM,16,2,3:16:16 PM,16,2,3:32:02 PM,16,2,3:46:21 PM,5,27.5,,-1.2650027,36.8126242,-1.2635744,36.781677,Rider_Id_51,859
-Order_No_7173,User_Id_1188,Bike,3,Personal,21,1,1:43:05 PM,21,1,1:49:53 PM,21,1,2:07:23 PM,21,1,2:15:41 PM,21,1,2:39:11 PM,13,21.4,,-1.1884386,36.935050700000005,-1.2494637,36.8705482,Rider_Id_444,1410
-Order_No_13964,User_Id_496,Bike,3,Business,10,3,2:16:53 PM,10,3,2:18:28 PM,10,3,2:46:13 PM,10,3,2:50:41 PM,10,3,3:05:30 PM,6,30.5,,-1.2551895,36.7822034,-1.28359,36.823091999999995,Rider_Id_89,889
-Order_No_18632,User_Id_1874,Bike,3,Business,30,5,9:43:26 AM,30,5,9:44:01 AM,30,5,10:13:08 AM,30,5,10:28:08 AM,30,5,11:31:58 AM,9,18.8,,-1.2938875,36.7774083,-1.2571472,36.795063299999995,Rider_Id_956,3830
-Order_No_27802,User_Id_3073,Bike,3,Business,7,2,9:59:07 AM,7,2,9:59:28 AM,7,2,10:04:08 AM,7,2,10:35:45 AM,7,2,10:43:51 AM,8,23.0,,-1.298529,36.816482,-1.2551895,36.7822034,Rider_Id_74,486
-Order_No_21262,User_Id_246,Bike,3,Business,30,5,5:15:16 PM,30,5,5:15:31 PM,30,5,5:24:01 PM,30,5,5:26:11 PM,30,5,5:38:44 PM,2,21.9,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_879,753
-Order_No_25492,User_Id_176,Bike,1,Personal,19,4,2:50:49 PM,19,4,2:53:57 PM,19,4,3:02:30 PM,19,4,3:15:49 PM,19,4,3:29:14 PM,4,20.7,,-1.298529,36.816482,-1.2864298,36.8224345,Rider_Id_752,805
-Order_No_23191,User_Id_2847,Bike,2,Personal,19,5,4:35:32 PM,19,5,4:36:46 PM,19,5,4:45:39 PM,19,5,4:59:43 PM,19,5,5:19:13 PM,6,24.2,,-1.2675613000000001,36.8133001,-1.2822773,36.795312100000004,Rider_Id_607,1170
-Order_No_3343,User_Id_3565,Bike,3,Business,8,4,3:15:46 PM,8,4,3:16:27 PM,8,4,3:37:53 PM,8,4,3:43:49 PM,8,4,4:13:57 PM,17,27.1,,-1.2844665000000002,36.7866113,-1.1985673000000001,36.836368799999995,Rider_Id_518,1808
-Order_No_17690,User_Id_1043,Bike,2,Personal,13,1,11:10:40 AM,13,1,11:16:22 AM,13,1,11:26:36 AM,13,1,11:51:44 AM,13,1,12:32:32 PM,15,17.3,,-1.2143386999999999,36.7914548,-1.2876782,36.8285015,Rider_Id_144,2448
-Order_No_10501,User_Id_1245,Bike,3,Business,20,2,8:14:15 AM,20,2,8:14:19 AM,20,2,8:17:50 AM,20,2,8:28:46 AM,20,2,9:02:33 AM,17,18.9,,-1.2599563,36.799343900000004,-1.3198615,36.7110323,Rider_Id_401,2027
-Order_No_4502,User_Id_893,Bike,3,Business,17,5,3:40:48 PM,17,5,3:41:55 PM,17,5,3:42:53 PM,17,5,3:52:09 PM,17,5,4:06:21 PM,4,,,-1.2663194,36.809901,-1.2575406999999998,36.7868197,Rider_Id_547,852
-Order_No_16145,User_Id_3300,Bike,3,Business,13,1,10:47:06 AM,13,1,10:47:57 AM,13,1,11:20:05 AM,13,1,11:39:19 AM,13,1,12:04:17 PM,16,17.0,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_124,1498
-Order_No_7704,User_Id_111,Bike,3,Business,18,2,9:54:02 AM,18,2,9:54:37 AM,18,2,9:58:11 AM,18,2,9:59:13 AM,18,2,10:16:35 AM,9,16.4,,-1.2551895,36.7822034,-1.2785228000000002,36.8274086,Rider_Id_85,1042
-Order_No_7080,User_Id_393,Bike,3,Business,25,1,1:06:11 PM,25,1,1:06:54 PM,25,1,1:18:44 PM,25,1,1:21:09 PM,25,1,1:41:27 PM,6,26.9,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_111,1218
-Order_No_16894,User_Id_1469,Bike,3,Business,19,1,3:04:56 PM,19,1,3:06:22 PM,19,1,3:36:52 PM,19,1,3:39:21 PM,19,1,4:04:43 PM,16,25.2,,-1.2551895,36.7822034,-1.325469,36.862403,Rider_Id_147,1522
-Order_No_23225,User_Id_2066,Bike,3,Business,8,1,5:28:32 PM,8,1,5:33:13 PM,8,1,5:48:01 PM,8,1,6:03:34 PM,8,1,6:43:32 PM,6,18.3,,-1.316961,36.820337,-1.2986563,36.7833661,Rider_Id_932,2398
-Order_No_802,User_Id_2587,Bike,3,Business,23,6,11:56:23 AM,23,6,11:58:56 AM,23,6,12:13:14 PM,23,6,12:19:46 PM,23,6,1:05:12 PM,5,26.4,,-1.2527958000000001,36.8003131,-1.28878,36.8168312,Rider_Id_498,2726
-Order_No_1825,User_Id_1500,Bike,3,Business,8,1,12:15:04 PM,8,1,12:20:35 PM,8,1,12:37:42 PM,8,1,12:42:15 PM,8,1,12:54:13 PM,6,28.0,,-1.2759989,36.820884,-1.2638185,36.7930057,Rider_Id_448,718
-Order_No_11079,User_Id_1927,Bike,3,Business,8,2,2:36:36 PM,8,2,2:44:55 PM,8,2,2:53:35 PM,8,2,2:55:00 PM,8,2,3:38:51 PM,17,25.1,,-1.3467388999999999,36.7690312,-1.2551895,36.7822034,Rider_Id_470,2631
-Order_No_5294,User_Id_2216,Bike,3,Business,4,5,10:42:38 AM,4,5,10:42:52 AM,4,5,10:43:24 AM,4,5,11:25:03 AM,4,5,12:08:30 PM,23,17.1,,-1.2867627000000001,36.883446,-1.1612808000000001,36.9563278,Rider_Id_31,2607
-Order_No_843,User_Id_3547,Bike,3,Business,20,3,3:59:19 PM,20,3,4:00:44 PM,20,3,4:11:00 PM,20,3,4:43:53 PM,20,3,5:20:49 PM,18,27.4,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_483,2216
-Order_No_7038,User_Id_1304,Bike,3,Business,15,4,10:41:32 AM,15,4,10:42:34 AM,15,4,11:08:53 AM,15,4,11:22:37 AM,15,4,11:54:37 AM,9,24.6,,-1.2551895,36.7822034,-1.2874243,36.7504414,Rider_Id_470,1920
-Order_No_23964,User_Id_3674,Bike,3,Business,11,5,10:52:55 AM,11,5,10:53:05 AM,11,5,10:53:54 AM,11,5,11:03:27 AM,11,5,12:17:44 PM,22,20.8,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_331,4457
-Order_No_27838,User_Id_3647,Bike,3,Business,12,3,2:44:41 PM,12,3,2:47:27 PM,12,3,3:07:42 PM,12,3,3:12:37 PM,12,3,3:41:06 PM,5,20.8,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_158,1709
-Order_No_17345,User_Id_53,Bike,3,Business,16,3,3:50:44 PM,16,3,3:51:31 PM,16,3,3:51:41 PM,16,3,4:09:55 PM,16,3,4:30:25 PM,8,27.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_515,1230
-Order_No_9698,User_Id_867,Bike,3,Business,31,4,1:09:08 PM,31,4,1:09:20 PM,31,4,1:09:28 PM,31,4,1:11:09 PM,31,4,1:37:59 PM,15,26.4,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_518,1610
-Order_No_7532,User_Id_1536,Bike,1,Personal,21,5,3:02:08 PM,21,5,3:02:22 PM,21,5,3:09:38 PM,21,5,3:48:59 PM,21,5,4:23:17 PM,20,27.3,,-1.3028448000000001,36.7625787,-1.3205334999999998,36.8789718,Rider_Id_449,2058
-Order_No_27273,User_Id_1244,Bike,3,Business,13,6,9:23:43 AM,13,6,9:24:11 AM,13,6,9:33:28 AM,13,6,9:54:04 AM,13,6,10:20:17 AM,16,19.8,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_8,1573
-Order_No_2659,User_Id_1458,Bike,1,Personal,28,4,10:35:17 AM,28,4,10:36:19 AM,28,4,10:49:26 AM,28,4,10:53:33 AM,28,4,11:43:31 AM,10,,,-1.263499,36.801454799999995,-1.3197323,36.8120386,Rider_Id_279,2998
-Order_No_491,User_Id_1961,Bike,3,Business,20,1,3:15:16 PM,20,1,3:16:22 PM,20,1,3:27:34 PM,20,1,3:32:20 PM,20,1,4:14:14 PM,17,,,-1.266837,36.799248999999996,-1.3188358999999998,36.703852600000005,Rider_Id_720,2514
-Order_No_9923,User_Id_265,Bike,3,Business,14,3,4:11:49 PM,14,3,4:12:13 PM,14,3,4:23:02 PM,14,3,4:38:02 PM,14,3,5:33:30 PM,19,23.4,,-1.300921,36.828195,-1.3430735,36.7105064,Rider_Id_532,3328
-Order_No_20522,User_Id_1787,Bike,3,Business,4,1,5:34:04 PM,4,1,5:34:39 PM,4,1,5:38:05 PM,4,1,5:51:55 PM,4,1,6:14:26 PM,12,27.8,,-1.2603403,36.7998848,-1.2285834999999998,36.882646799999996,Rider_Id_178,1351
-Order_No_10316,User_Id_1840,Bike,1,Personal,27,1,4:17:17 PM,27,1,4:17:22 PM,27,1,4:17:55 PM,27,1,5:19:11 PM,27,1,5:58:42 PM,9,17.3,,-1.300673,36.815381200000004,-1.2559559999999999,36.772901,Rider_Id_213,2371
-Order_No_15524,User_Id_3049,Bike,1,Personal,27,5,3:44:47 PM,27,5,3:58:36 PM,27,5,4:02:28 PM,27,5,4:13:51 PM,27,5,4:28:41 PM,10,24.2,,-1.2845902,36.825472399999995,-1.2297202,36.881950700000004,Rider_Id_174,890
-Order_No_26721,User_Id_2310,Bike,1,Business,3,5,12:33:04 PM,3,5,12:33:16 PM,3,5,12:49:57 PM,3,5,12:56:48 PM,3,5,1:26:07 PM,6,21.5,,-1.2927792,36.9053492,-1.2859912,36.8756811,Rider_Id_933,1759
-Order_No_16063,User_Id_2069,Bike,3,Business,29,5,10:53:57 AM,29,5,12:10:39 PM,29,5,12:23:10 PM,29,5,12:37:53 PM,29,5,1:17:09 PM,6,26.8,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_519,2356
-Order_No_10278,User_Id_420,Bike,3,Business,21,4,4:30:19 PM,21,4,4:30:39 PM,21,4,4:32:07 PM,21,4,4:42:49 PM,21,4,5:07:23 PM,9,,,-1.2517448,36.7924971,-1.2915306999999998,36.790586499999996,Rider_Id_81,1474
-Order_No_6824,User_Id_2448,Bike,2,Personal,25,5,2:36:22 PM,25,5,2:55:12 PM,25,5,3:22:12 PM,25,5,3:28:03 PM,25,5,3:56:57 PM,8,23.6,,-1.3251583999999998,36.8199384,-1.265715,36.808525,Rider_Id_888,1734
-Order_No_178,User_Id_1205,Bike,3,Business,12,5,12:28:29 PM,12,5,12:33:22 PM,12,5,12:42:57 PM,12,5,12:53:31 PM,12,5,1:45:27 PM,9,24.6,,-1.257309,36.806008399999996,-1.2950736999999999,36.7648455,Rider_Id_578,3116
-Order_No_15028,User_Id_2719,Bike,1,Business,5,3,2:20:15 PM,5,3,2:23:32 PM,5,3,2:37:49 PM,5,3,2:52:57 PM,5,3,3:07:18 PM,7,23.7,,-1.3323107,36.8692616,-1.3244885,36.897791999999995,Rider_Id_330,861
-Order_No_10794,User_Id_2560,Bike,1,Personal,13,1,12:00:23 PM,13,1,12:01:26 PM,13,1,12:08:32 PM,13,1,12:12:19 PM,13,1,12:26:44 PM,4,17.1,,-1.2658334999999998,36.804286700000006,-1.280753,36.8191175,Rider_Id_531,865
-Order_No_9901,User_Id_1249,Bike,3,Business,15,5,12:24:50 PM,15,5,12:26:30 PM,15,5,12:43:23 PM,15,5,12:50:41 PM,15,5,1:24:51 PM,13,26.3,,-1.2679513999999998,36.8095213,-1.3189252,36.861726899999994,Rider_Id_856,2050
-Order_No_21367,User_Id_2649,Bike,3,Business,3,1,2:54:24 PM,3,1,2:56:03 PM,3,1,3:11:36 PM,3,1,3:14:11 PM,3,1,3:25:15 PM,2,23.3,,-1.2752846000000002,36.802702200000006,-1.2607674,36.808564399999995,Rider_Id_539,664
-Order_No_21142,User_Id_1271,Bike,3,Business,22,3,10:54:21 AM,22,3,11:06:30 AM,22,3,11:13:30 AM,22,3,11:31:51 AM,22,3,11:47:24 AM,13,23.2,,-1.3244885,36.897791999999995,-1.3092700000000002,36.81326,Rider_Id_345,933
-Order_No_10313,User_Id_2102,Bike,3,Business,15,1,1:54:47 PM,15,1,1:57:28 PM,15,1,2:11:24 PM,15,1,2:16:56 PM,15,1,2:51:32 PM,14,,,-1.3250048,36.869718799999994,-1.2613768,36.801941799999994,Rider_Id_19,2076
-Order_No_8893,User_Id_2066,Bike,3,Business,23,5,12:45:05 PM,23,5,1:05:06 PM,23,5,1:26:01 PM,23,5,1:28:38 PM,23,5,1:54:34 PM,13,24.2,,-1.3270716,36.8406371,-1.338133,36.75417,Rider_Id_242,1556
-Order_No_7765,User_Id_1004,Bike,3,Business,11,2,2:24:54 PM,11,2,2:29:24 PM,11,2,2:41:00 PM,11,2,2:48:58 PM,11,2,3:11:30 PM,9,21.5,,-1.3250048,36.869718799999994,-1.333148,36.922453000000004,Rider_Id_495,1352
-Order_No_14781,User_Id_1293,Bike,3,Personal,28,2,12:47:58 PM,28,2,12:48:15 PM,28,2,1:18:04 PM,28,2,1:30:24 PM,28,2,2:08:52 PM,18,17.7,,-1.3152336000000002,36.711640100000004,-1.2543163999999998,36.831860999999996,Rider_Id_393,2308
-Order_No_22544,User_Id_635,Bike,3,Business,14,5,9:12:56 AM,14,5,9:13:09 AM,14,5,9:13:20 AM,14,5,9:53:56 AM,14,5,10:10:26 AM,10,17.1,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_2,990
-Order_No_18492,User_Id_2112,Bike,3,Business,25,1,4:41:12 PM,25,1,4:43:11 PM,25,1,5:05:59 PM,25,1,5:08:24 PM,25,1,6:18:09 PM,12,27.7,,-1.2886923000000001,36.8241519,-1.2156006000000001,36.8916865,Rider_Id_679,4185
-Order_No_8239,User_Id_2689,Bike,1,Business,12,5,10:54:32 AM,12,5,10:54:40 AM,12,5,11:16:31 AM,12,5,12:13:14 PM,12,5,1:11:29 PM,19,24.6,,-1.3053865,36.891733200000004,-1.2894847,36.7556088,Rider_Id_248,3495
-Order_No_6635,User_Id_2030,Bike,3,Business,25,1,11:49:55 AM,25,1,11:52:09 AM,25,1,11:56:32 AM,25,1,12:01:29 PM,25,1,12:13:58 PM,2,,,-1.304735,36.784605400000004,-1.295908,36.791433000000005,Rider_Id_252,749
-Order_No_17742,User_Id_2271,Bike,3,Business,15,6,10:48:19 AM,15,6,10:48:43 AM,15,6,10:49:42 AM,15,6,11:06:19 AM,15,6,11:47:28 AM,17,,,-1.2600926000000001,36.8088685,-1.293515,36.897607,Rider_Id_574,2469
-Order_No_3942,User_Id_1245,Bike,3,Business,21,4,12:39:06 PM,21,4,12:39:34 PM,21,4,12:52:35 PM,21,4,2:07:50 PM,21,4,3:04:11 PM,17,31.0,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_728,3381
-Order_No_2323,User_Id_1453,Bike,1,Business,30,1,11:39:47 AM,30,1,11:39:59 AM,30,1,11:48:45 AM,30,1,12:00:19 PM,30,1,12:19:56 PM,7,22.4,,-1.2628638,36.8070399,-1.2956882,36.7861845,Rider_Id_320,1177
-Order_No_21004,User_Id_1999,Bike,1,Personal,13,4,3:13:11 PM,13,4,3:14:39 PM,13,4,3:27:06 PM,13,4,3:33:47 PM,13,4,3:41:00 PM,3,26.4,,-1.2649605,36.798177700000004,-1.2771989,36.808975200000006,Rider_Id_100,433
-Order_No_26948,User_Id_346,Bike,3,Business,12,3,12:23:39 PM,12,3,12:29:29 PM,12,3,12:42:44 PM,12,3,1:20:17 PM,12,3,1:40:20 PM,5,19.4,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_106,1203
-Order_No_14522,User_Id_2804,Bike,3,Business,30,2,2:53:01 PM,30,2,3:01:20 PM,30,2,3:10:32 PM,30,2,3:11:39 PM,30,2,3:55:28 PM,8,,,-1.2551895,36.7822034,-1.2941848999999999,36.7895754,Rider_Id_770,2629
-Order_No_25565,User_Id_3565,Bike,3,Business,26,6,9:56:50 PM,26,6,9:57:59 PM,26,6,10:06:15 PM,26,6,10:13:16 PM,26,6,10:35:58 PM,6,18.5,,-1.2879041000000002,36.8217926,-1.2844665000000002,36.7866113,Rider_Id_186,1362
-Order_No_22722,User_Id_1880,Bike,3,Business,30,2,12:24:37 PM,30,2,2:12:59 PM,30,2,2:36:05 PM,30,2,2:46:09 PM,30,2,3:22:28 PM,1,,,-1.2571472,36.795063299999995,-1.2568411,36.8012533,Rider_Id_653,2179
-Order_No_4315,User_Id_1280,Bike,3,Business,30,2,10:48:53 AM,30,2,10:58:20 AM,30,2,11:03:10 AM,30,2,11:18:20 AM,30,2,12:13:32 PM,8,,,-1.2551895,36.7822034,-1.2993556,36.8146776,Rider_Id_291,3312
-Order_No_21429,User_Id_2829,Bike,3,Business,20,3,1:13:39 PM,20,3,1:15:17 PM,20,3,1:21:42 PM,20,3,1:23:26 PM,20,3,2:24:37 PM,24,20.0,,-1.3700383,36.9190174,-1.2579303999999998,36.8019667,Rider_Id_676,3671
-Order_No_26182,User_Id_1927,Bike,3,Business,14,1,3:42:01 PM,14,1,3:45:43 PM,14,1,4:03:04 PM,14,1,4:10:55 PM,14,1,4:38:46 PM,19,25.6,,-1.2859912,36.8756811,-1.3404187,36.7614685,Rider_Id_720,1671
-Order_No_6187,User_Id_517,Bike,3,Business,8,5,2:08:16 PM,8,5,2:08:34 PM,8,5,2:09:21 PM,8,5,2:24:30 PM,8,5,2:24:41 PM,5,27.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_15,11
-Order_No_22903,User_Id_2030,Bike,3,Business,17,3,10:43:25 AM,17,3,10:43:55 AM,17,3,10:55:46 AM,17,3,10:57:30 AM,17,3,11:14:00 AM,3,,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_548,990
-Order_No_20848,User_Id_393,Bike,3,Business,6,4,10:35:30 AM,6,4,10:35:47 AM,6,4,10:38:54 AM,6,4,10:44:09 AM,6,4,11:00:10 AM,5,19.5,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_155,961
-Order_No_21316,User_Id_3525,Bike,3,Business,21,4,1:05:45 PM,21,4,1:44:56 PM,21,4,1:51:37 PM,21,4,1:58:36 PM,21,4,2:21:29 PM,13,26.5,,-1.3700383,36.9190174,-1.3239488000000001,36.831683500000004,Rider_Id_519,1373
-Order_No_8502,User_Id_400,Bike,3,Business,29,4,2:41:34 PM,29,4,2:42:23 PM,29,4,2:46:07 PM,29,4,4:01:58 PM,29,4,4:34:45 PM,12,24.8,,-1.3051399,36.833555700000005,-1.275442,36.766020700000006,Rider_Id_519,1967
-Order_No_22019,User_Id_1642,Bike,3,Business,20,1,1:33:27 PM,20,1,1:48:25 PM,20,1,1:48:32 PM,20,1,2:01:38 PM,20,1,2:15:27 PM,5,,,-1.2808159,36.8160545,-1.2728069,36.7962362,Rider_Id_79,829
-Order_No_9180,User_Id_1500,Bike,3,Business,14,5,9:19:29 AM,14,5,9:19:51 AM,14,5,9:28:37 AM,14,5,9:42:33 AM,14,5,10:04:22 AM,8,17.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_106,1309
-Order_No_2494,User_Id_2330,Bike,3,Business,9,4,10:45:15 AM,9,4,10:45:38 AM,9,4,10:48:25 AM,9,4,11:12:05 AM,9,4,12:08:39 PM,1,,,-1.2642903,36.80021370000001,-1.2655213,36.8003795,Rider_Id_76,3394
-Order_No_11311,User_Id_3283,Bike,3,Business,2,6,2:46:00 PM,2,6,2:48:13 PM,2,6,3:00:15 PM,2,6,3:12:46 PM,2,6,3:25:08 PM,5,29.0,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_691,742
-Order_No_23795,User_Id_2587,Bike,3,Business,10,6,11:53:58 AM,10,6,11:56:34 AM,10,6,12:14:17 PM,10,6,1:05:42 PM,10,6,1:05:46 PM,16,23.8,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_928,4
-Order_No_11172,User_Id_1500,Bike,3,Business,1,1,8:43:58 AM,1,1,8:45:32 AM,1,1,8:57:26 AM,1,1,9:02:01 AM,1,1,9:23:51 AM,8,21.2,,-1.2879247999999999,36.754638799999995,-1.2638185,36.7930057,Rider_Id_306,1310
-Order_No_2303,User_Id_393,Bike,3,Business,17,5,3:39:18 PM,17,5,3:43:22 PM,17,5,4:11:56 PM,17,5,4:30:24 PM,17,5,4:55:33 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_85,1509
-Order_No_16481,User_Id_1075,Bike,3,Business,15,5,3:58:57 PM,15,5,4:15:01 PM,15,5,4:33:34 PM,15,5,4:44:04 PM,15,5,5:09:43 PM,8,27.5,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_956,1539
-Order_No_8268,User_Id_3300,Bike,3,Business,31,5,8:58:05 AM,31,5,8:58:19 AM,31,5,9:32:58 AM,31,5,9:51:38 AM,31,5,10:21:53 AM,17,,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_351,1815
-Order_No_9264,User_Id_2509,Bike,3,Business,21,5,10:55:34 AM,21,5,12:22:22 PM,21,5,12:27:44 PM,21,5,12:30:06 PM,21,5,12:53:05 PM,5,20.1,,-1.2571472,36.795063299999995,-1.2864673999999998,36.820262,Rider_Id_726,1379
-Order_No_6278,User_Id_242,Bike,3,Business,23,3,10:04:18 AM,23,3,10:15:03 AM,23,3,10:27:02 AM,23,3,10:36:39 AM,23,3,11:02:54 AM,10,21.9,,-1.2553615,36.789537599999996,-1.3173868999999998,36.8112044,Rider_Id_808,1575
-Order_No_17536,User_Id_1792,Bike,3,Business,23,1,3:36:01 PM,23,1,3:38:10 PM,23,1,3:53:35 PM,23,1,3:56:13 PM,23,1,4:13:31 PM,5,,,-1.2805537,36.7920739,-1.2689015,36.7986106,Rider_Id_844,1038
-Order_No_9791,User_Id_3599,Bike,3,Business,1,1,12:22:51 PM,1,1,12:23:48 PM,1,1,12:59:14 PM,1,1,1:27:16 PM,1,1,2:05:22 PM,13,24.1,,-1.3302996,36.8707935,-1.2709086,36.7929516,Rider_Id_54,2286
-Order_No_16738,User_Id_3186,Bike,3,Business,15,5,2:07:50 PM,15,5,2:52:01 PM,15,5,3:02:52 PM,15,5,3:04:40 PM,15,5,3:38:49 PM,5,27.8,,-1.2980228999999999,36.78865,-1.2874648000000002,36.8271985,Rider_Id_548,2049
-Order_No_25490,User_Id_2674,Bike,3,Personal,16,7,11:53:01 AM,16,7,11:53:28 AM,16,7,12:01:44 PM,16,7,12:12:28 PM,16,7,12:24:00 PM,8,23.4,,-1.2968742,36.793780299999995,-1.2797013000000002,36.7299371,Rider_Id_337,692
-Order_No_18178,User_Id_3525,Bike,3,Business,7,4,7:59:46 AM,7,4,8:49:55 AM,7,4,9:32:49 AM,7,4,9:33:59 AM,7,4,9:50:17 AM,13,,,-1.3700383,36.9190174,-1.3239488000000001,36.831683500000004,Rider_Id_863,978
-Order_No_13683,User_Id_2100,Bike,3,Business,1,4,1:56:53 PM,1,4,2:26:22 PM,1,4,2:45:50 PM,1,4,2:47:13 PM,1,4,3:05:22 PM,8,,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_817,1089
-Order_No_4063,User_Id_868,Bike,3,Business,30,4,10:48:59 AM,30,4,11:06:15 AM,30,4,11:52:16 AM,30,4,12:50:37 PM,30,4,12:50:45 PM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_787,8
-Order_No_17497,User_Id_2866,Bike,3,Business,14,1,12:51:07 PM,14,1,12:51:22 PM,14,1,12:54:51 PM,14,1,1:07:45 PM,14,1,1:40:46 PM,22,23.4,,-1.2551895,36.7822034,-1.3692201000000002,36.9252718,Rider_Id_45,1981
-Order_No_827,User_Id_3326,Bike,3,Business,16,2,4:09:03 PM,16,2,4:12:15 PM,16,2,4:26:34 PM,16,2,4:44:16 PM,16,2,5:39:58 PM,8,,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_422,3342
-Order_No_3457,User_Id_2523,Bike,3,Business,13,2,9:42:02 AM,13,2,9:49:13 AM,13,2,10:03:03 AM,13,2,10:06:06 AM,13,2,11:05:48 AM,10,19.9,9.0,-1.2551895,36.7822034,-1.3072605,36.84744570000001,Rider_Id_829,3582
-Order_No_17697,User_Id_2069,Bike,3,Business,4,6,1:20:00 PM,4,6,1:33:30 PM,4,6,1:44:06 PM,4,6,1:55:20 PM,4,6,2:11:12 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_670,952
-Order_No_12130,User_Id_627,Bike,3,Business,19,2,12:28:42 PM,19,2,12:29:26 PM,19,2,12:30:55 PM,19,2,12:42:07 PM,19,2,1:10:58 PM,17,27.3,,-1.2551895,36.7822034,-1.3339943,36.7263409,Rider_Id_630,1731
-Order_No_17760,User_Id_2801,Bike,3,Business,14,5,3:01:22 PM,14,5,3:03:25 PM,14,5,3:12:53 PM,14,5,3:26:44 PM,14,5,3:42:43 PM,6,23.1,,-1.3109801,36.8810758,-1.307726,36.839117,Rider_Id_907,959
-Order_No_18943,User_Id_3222,Bike,3,Business,3,1,3:35:24 PM,3,1,3:38:52 PM,3,1,3:55:28 PM,3,1,4:01:43 PM,3,1,4:33:05 PM,10,23.0,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_161,1882
-Order_No_23554,User_Id_1669,Bike,2,Personal,25,2,9:35:49 AM,25,2,9:35:56 AM,25,2,9:42:44 AM,25,2,9:57:59 AM,25,2,10:11:03 AM,3,17.9,,-1.2572951,36.79886,-1.2753902,36.808223,Rider_Id_208,784
-Order_No_9530,User_Id_419,Bike,3,Business,16,3,1:55:43 PM,16,3,1:56:25 PM,16,3,2:00:43 PM,16,3,2:06:00 PM,16,3,3:18:23 PM,6,26.9,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_446,4343
-Order_No_3332,User_Id_2178,Bike,1,Personal,14,5,9:24:41 AM,14,5,9:27:05 AM,14,5,9:45:57 AM,14,5,9:48:23 AM,14,5,10:02:54 AM,5,18.1,,-1.2793503000000002,36.8039715,-1.2998551999999999,36.818707700000004,Rider_Id_726,871
-Order_No_10264,User_Id_2869,Bike,3,Business,4,1,8:44:22 AM,4,1,8:44:52 AM,4,1,8:50:01 AM,4,1,8:52:30 AM,4,1,9:17:36 AM,9,22.0,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_330,1506
-Order_No_25030,User_Id_1850,Bike,3,Business,10,1,2:15:31 PM,10,1,2:16:03 PM,10,1,2:23:23 PM,10,1,2:41:40 PM,10,1,2:58:52 PM,8,18.2,,-1.2891116000000002,36.810438,-1.2551895,36.7822034,Rider_Id_907,1032
-Order_No_13472,User_Id_2330,Bike,3,Business,8,3,1:10:58 PM,8,3,1:11:56 PM,8,3,1:19:36 PM,8,3,2:05:28 PM,8,3,2:05:40 PM,3,,,-1.3015197,36.7658456,-1.294597,36.7742999,Rider_Id_119,12
-Order_No_1981,User_Id_1991,Bike,3,Personal,9,2,1:42:47 PM,9,2,1:43:00 PM,9,2,1:48:35 PM,9,2,1:55:49 PM,9,2,2:30:33 PM,15,30.4,,-1.2963097,36.7688221,-1.32189,36.8589727,Rider_Id_168,2084
-Order_No_18321,User_Id_1850,Bike,3,Business,3,3,10:03:24 AM,3,3,10:04:36 AM,3,3,10:05:10 AM,3,3,10:21:41 AM,3,3,10:43:02 AM,6,21.5,,-1.3173868999999998,36.8112044,-1.285382,36.8162878,Rider_Id_258,1281
-Order_No_4759,User_Id_3109,Bike,2,Personal,18,3,12:16:22 PM,18,3,12:17:34 PM,18,3,12:26:15 PM,18,3,12:48:27 PM,18,3,1:15:20 PM,9,19.7,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_140,1613
-Order_No_6236,User_Id_3621,Bike,3,Business,28,4,2:44:36 PM,28,4,2:44:49 PM,28,4,3:01:38 PM,28,4,3:04:21 PM,28,4,3:38:13 PM,20,27.6,,-1.2836583000000001,36.7886014,-1.3103985,36.9114173,Rider_Id_709,2032
-Order_No_8064,User_Id_136,Bike,3,Business,21,4,5:14:56 PM,21,4,5:15:08 PM,21,4,5:29:36 PM,21,4,5:45:20 PM,21,4,6:05:59 PM,21,,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_336,1239
-Order_No_17929,User_Id_633,Bike,3,Business,10,4,9:22:31 AM,10,4,9:45:01 AM,10,4,9:53:38 AM,10,4,10:05:35 AM,10,4,10:19:23 AM,8,19.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_515,828
-Order_No_20855,User_Id_1271,Bike,3,Business,18,5,12:29:30 PM,18,5,12:29:49 PM,18,5,12:33:25 PM,18,5,12:55:24 PM,18,5,1:29:19 PM,26,20.3,,-1.3244885,36.897791999999995,-1.3949989,36.7654761,Rider_Id_510,2035
-Order_No_19753,User_Id_3427,Bike,3,Personal,29,2,7:21:17 AM,29,2,8:24:28 AM,29,2,8:44:37 AM,29,2,8:52:58 AM,29,2,9:12:10 AM,7,16.7,,-1.2720268999999997,36.8174107,-1.2755226,36.7811904,Rider_Id_101,1152
-Order_No_18883,User_Id_2296,Bike,1,Personal,28,5,10:52:15 AM,28,5,10:53:07 AM,28,5,10:59:26 AM,28,5,11:04:35 AM,28,5,11:18:00 AM,7,25.0,,-1.2655306,36.828148,-1.2918246,36.801584999999996,Rider_Id_216,805
-Order_No_15815,User_Id_2559,Bike,3,Personal,20,4,5:34:08 PM,20,4,5:35:04 PM,20,4,5:56:49 PM,20,4,6:03:17 PM,20,4,7:13:21 PM,18,22.8,,-1.3008819,36.7612515,-1.2125712,36.796415100000004,Rider_Id_862,4204
-Order_No_26271,User_Id_2381,Bike,3,Personal,5,2,10:15:47 AM,5,2,10:17:40 AM,5,2,10:24:21 AM,5,2,10:53:40 AM,5,2,11:13:14 AM,8,21.3,,-1.2950041,36.814358299999995,-1.278067,36.783487,Rider_Id_322,1174
-Order_No_1686,User_Id_3491,Bike,3,Business,1,3,3:46:10 PM,1,3,3:46:24 PM,1,3,3:50:44 PM,1,3,3:52:20 PM,1,3,3:52:32 PM,3,,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_521,12
-Order_No_13361,User_Id_3043,Bike,3,Business,9,3,12:36:15 PM,9,3,1:10:42 PM,9,3,1:12:45 PM,9,3,1:21:21 PM,9,3,2:39:27 PM,7,21.0,,-1.2893753000000001,36.824286900000004,-1.2884831,36.773488,Rider_Id_813,4686
-Order_No_28119,User_Id_2100,Bike,3,Business,20,4,11:18:52 AM,20,4,12:51:06 PM,20,4,1:02:29 PM,20,4,1:17:12 PM,20,4,1:33:03 PM,5,24.5,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_532,951
-Order_No_10721,User_Id_265,Bike,3,Business,8,5,3:52:00 PM,8,5,4:06:56 PM,8,5,4:18:31 PM,8,5,4:26:45 PM,8,5,4:40:28 PM,4,30.5,,-1.300921,36.828195,-1.3117438999999997,36.8108257,Rider_Id_164,823
-Order_No_15007,User_Id_2436,Bike,3,Business,6,3,11:48:22 AM,6,3,11:50:32 AM,6,3,12:15:07 PM,6,3,12:47:03 PM,6,3,1:11:32 PM,18,20.2,,-1.2553615,36.789537599999996,-1.2198413000000001,36.9054958,Rider_Id_116,1469
-Order_No_1975,User_Id_2030,Bike,3,Business,16,5,2:29:04 PM,16,5,3:18:52 PM,16,5,3:27:32 PM,16,5,3:57:20 PM,16,5,4:12:25 PM,4,,,-1.2508229,36.7895263,-1.2753108999999998,36.8130185,Rider_Id_434,905
-Order_No_17614,User_Id_2528,Bike,3,Business,18,1,4:19:41 PM,18,1,4:21:19 PM,18,1,4:28:54 PM,18,1,4:36:16 PM,18,1,4:49:38 PM,3,28.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_763,802
-Order_No_2620,User_Id_496,Bike,3,Business,7,2,10:10:54 AM,7,2,10:12:13 AM,7,2,10:25:43 AM,7,2,10:30:05 AM,7,2,10:48:10 AM,8,,,-1.2571472,36.795063299999995,-1.2980228999999999,36.78865,Rider_Id_768,1085
-Order_No_17477,User_Id_1995,Bike,3,Business,22,4,9:13:27 AM,22,4,11:28:42 AM,22,4,11:50:39 AM,22,4,11:52:11 AM,22,4,12:42:21 PM,7,22.6,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_928,3010
-Order_No_23175,User_Id_2200,Bike,3,Business,29,5,12:28:29 PM,29,5,12:34:15 PM,29,5,12:50:07 PM,29,5,1:12:01 PM,29,5,1:43:10 PM,6,26.8,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_686,1869
-Order_No_19536,User_Id_1209,Bike,3,Business,30,2,8:12:05 AM,30,2,8:12:40 AM,30,2,8:23:47 AM,30,2,8:29:18 AM,30,2,9:10:59 AM,9,,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_99,2501
-Order_No_24135,User_Id_3149,Bike,3,Business,15,2,12:57:00 PM,15,2,12:57:20 PM,15,2,1:01:05 PM,15,2,1:05:32 PM,15,2,1:18:34 PM,7,24.3,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_685,782
-Order_No_17686,User_Id_3718,Bike,3,Business,13,4,12:41:05 PM,13,4,12:42:15 PM,13,4,12:56:11 PM,13,4,12:58:48 PM,13,4,1:22:19 PM,5,22.2,,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_254,1411
-Order_No_15855,User_Id_1075,Bike,3,Business,18,1,12:38:47 PM,18,1,12:55:33 PM,18,1,1:11:51 PM,18,1,1:18:14 PM,18,1,1:48:02 PM,16,27.8,,-1.339046,36.762596,-1.2527958000000001,36.8003131,Rider_Id_468,1788
-Order_No_2489,User_Id_2212,Bike,3,Business,5,1,2:54:44 PM,5,1,2:59:44 PM,5,1,3:19:26 PM,5,1,3:21:49 PM,5,1,3:47:22 PM,7,26.2,,-1.2571472,36.795063299999995,-1.2941585,36.7947375,Rider_Id_112,1533
-Order_No_22755,User_Id_1850,Bike,3,Business,9,4,8:51:16 AM,9,4,9:01:18 AM,9,4,9:18:52 AM,9,4,9:22:12 AM,9,4,9:57:58 AM,8,17.3,,-1.2551895,36.7822034,-1.2995101,36.8185828,Rider_Id_882,2146
-Order_No_1952,User_Id_3633,Bike,3,Personal,16,7,3:19:22 PM,16,7,3:20:29 PM,16,7,3:41:21 PM,16,7,3:47:55 PM,16,7,4:08:56 PM,7,24.3,,-1.2963097,36.7688221,-1.2998789,36.816896299999996,Rider_Id_725,1261
-Order_No_19676,User_Id_111,Bike,3,Business,6,5,12:20:21 PM,6,5,12:20:31 PM,6,5,12:24:26 PM,6,5,12:31:58 PM,6,5,1:41:12 PM,22,20.1,,-1.2551895,36.7822034,-1.333148,36.922453000000004,Rider_Id_192,4154
-Order_No_19133,User_Id_3283,Bike,3,Business,21,4,12:45:39 PM,21,4,12:45:59 PM,21,4,12:48:18 PM,21,4,12:53:11 PM,21,4,1:14:29 PM,7,25.2,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_958,1278
-Order_No_23519,User_Id_3491,Bike,3,Business,13,3,9:45:14 AM,13,3,9:52:13 AM,13,3,10:12:01 AM,13,3,10:18:28 AM,13,3,11:29:12 AM,11,20.4,,-1.2803666999999999,36.8247756,-1.2283403,36.8822756,Rider_Id_311,4244
-Order_No_5426,User_Id_1222,Bike,3,Personal,16,2,4:29:32 PM,16,2,4:39:02 PM,16,2,4:51:07 PM,16,2,4:57:19 PM,16,2,5:21:34 PM,7,25.1,,-1.2707178999999997,36.7979713,-1.3027767,36.7730036,Rider_Id_823,1455
-Order_No_7406,User_Id_3295,Bike,3,Business,10,4,9:37:44 AM,10,4,9:38:18 AM,10,4,9:38:58 AM,10,4,9:46:58 AM,10,4,9:47:42 AM,2,19.2,,-1.2571472,36.795063299999995,-1.2739424,36.815535700000005,Rider_Id_536,44
-Order_No_26844,User_Id_186,Bike,3,Business,10,3,11:46:37 AM,10,3,11:47:28 AM,10,3,12:23:24 PM,10,3,1:06:39 PM,10,3,1:08:19 PM,20,27.9,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_652,100
-Order_No_21941,User_Id_3283,Bike,3,Business,18,5,10:14:39 AM,18,5,10:14:53 AM,18,5,10:38:19 AM,18,5,10:48:15 AM,18,5,11:25:02 AM,19,21.5,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_466,2207
-Order_No_10122,User_Id_2551,Bike,1,Personal,13,1,10:43:45 AM,13,1,10:44:41 AM,13,1,10:56:49 AM,13,1,11:04:22 AM,13,1,11:27:12 AM,10,17.0,,-1.2789537,36.828639200000005,-1.2833608,36.757746600000004,Rider_Id_249,1370
-Order_No_19316,User_Id_1075,Bike,3,Business,28,2,10:28:58 AM,28,2,10:30:25 AM,28,2,10:36:19 AM,28,2,11:03:55 AM,28,2,11:24:46 AM,5,21.2,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_843,1251
-Order_No_25059,User_Id_868,Bike,3,Business,5,3,4:43:50 PM,5,3,5:11:05 PM,5,3,5:11:10 PM,5,3,5:21:58 PM,5,3,5:21:59 PM,3,22.7,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_813,1
-Order_No_1192,User_Id_53,Bike,3,Business,26,5,3:22:25 PM,26,5,3:23:08 PM,26,5,3:23:13 PM,26,5,3:30:52 PM,26,5,3:43:43 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_320,771
-Order_No_21405,User_Id_394,Bike,3,Personal,26,5,11:33:11 AM,26,5,11:36:47 AM,26,5,12:01:41 PM,26,5,12:08:30 PM,26,5,12:33:06 PM,6,,,-1.2741662,36.7969144,-1.2908945,36.82465139999999,Rider_Id_953,1476
-Order_No_17401,User_Id_2657,Bike,3,Personal,28,2,11:05:53 AM,28,2,11:07:08 AM,28,2,11:11:58 AM,28,2,11:18:03 AM,28,2,11:42:38 AM,11,21.3,,-1.2752846000000002,36.802702200000006,-1.311818,36.8638544,Rider_Id_778,1475
-Order_No_22553,User_Id_2008,Bike,3,Business,17,3,4:42:39 PM,17,3,4:53:06 PM,17,3,4:55:19 PM,17,3,5:07:51 PM,17,3,5:27:08 PM,5,24.8,,-1.2866011999999998,36.8195691,-1.2508229,36.7895263,Rider_Id_208,1157
-Order_No_1084,User_Id_2150,Bike,3,Business,6,1,12:57:25 PM,6,1,12:57:44 PM,6,1,1:01:24 PM,6,1,1:19:11 PM,6,1,1:39:06 PM,11,20.7,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_297,1195
-Order_No_13795,User_Id_1850,Bike,3,Business,13,1,4:26:08 PM,13,1,4:27:07 PM,13,1,4:39:37 PM,13,1,4:48:16 PM,13,1,5:05:36 PM,7,,,-1.290894,36.822971,-1.2566586000000002,36.792968200000004,Rider_Id_291,1040
-Order_No_17054,User_Id_1336,Bike,3,Business,15,1,12:16:46 PM,15,1,12:19:56 PM,15,1,12:26:01 PM,15,1,12:31:31 PM,15,1,12:48:05 PM,6,,,-1.2891921999999998,36.825189,-1.2756303,36.8074406,Rider_Id_726,994
-Order_No_17980,User_Id_867,Bike,3,Business,13,4,10:22:58 AM,13,4,11:15:30 AM,13,4,11:26:23 AM,13,4,11:32:17 AM,13,4,11:59:56 AM,14,22.0,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_914,1659
-Order_No_13587,User_Id_375,Bike,3,Business,30,2,2:14:06 PM,30,2,2:19:55 PM,30,2,2:27:57 PM,30,2,2:43:26 PM,30,2,3:04:11 PM,9,,,-1.2551895,36.7822034,-1.2975098,36.8397842,Rider_Id_613,1245
-Order_No_1749,User_Id_1882,Bike,3,Business,6,3,6:30:45 PM,6,3,6:31:10 PM,6,3,6:44:12 PM,6,3,6:45:50 PM,6,3,7:12:09 PM,14,,,-1.2268871000000001,36.8073946,-1.2956882,36.7861845,Rider_Id_607,1579
-Order_No_5684,User_Id_868,Bike,3,Business,12,1,3:54:03 PM,12,1,5:18:36 PM,12,1,5:35:45 PM,12,1,5:48:21 PM,12,1,6:01:08 PM,9,23.7,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_453,767
-Order_No_23591,User_Id_1271,Bike,3,Business,14,3,1:39:30 PM,14,3,1:45:46 PM,14,3,1:59:20 PM,14,3,3:00:54 PM,14,3,3:46:03 PM,11,25.5,,-1.3107918,36.761794,-1.2569651000000002,36.784949299999994,Rider_Id_660,2709
-Order_No_1383,User_Id_299,Bike,3,Business,31,5,9:27:48 AM,31,5,9:28:05 AM,31,5,9:28:32 AM,31,5,9:39:28 AM,31,5,10:21:52 AM,7,,,-1.2628638,36.8070399,-1.2884231000000002,36.779456599999996,Rider_Id_72,2544
-Order_No_27020,User_Id_1500,Bike,3,Business,22,3,10:58:16 AM,22,3,10:59:01 AM,22,3,11:00:20 AM,22,3,11:26:11 AM,22,3,11:29:05 AM,8,23.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,174
-Order_No_6071,User_Id_2412,Bike,3,Business,20,4,1:04:37 PM,20,4,2:11:39 PM,20,4,2:15:14 PM,20,4,2:22:32 PM,20,4,2:30:10 PM,3,25.3,,-1.223452,36.8659278,-1.2285834999999998,36.882646799999996,Rider_Id_517,458
-Order_No_26261,User_Id_3560,Bike,3,Business,15,4,2:27:54 PM,15,4,2:29:28 PM,15,4,2:35:44 PM,15,4,2:46:11 PM,15,4,3:07:33 PM,11,27.4,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_237,1282
-Order_No_4669,User_Id_1687,Bike,1,Business,18,4,8:50:14 AM,18,4,8:52:12 AM,18,4,9:03:27 AM,18,4,9:10:47 AM,18,4,9:32:33 AM,9,20.8,,-1.2626574,36.8014034,-1.31861,36.8113967,Rider_Id_754,1306
-Order_No_6846,User_Id_1082,Bike,2,Personal,16,4,10:11:20 AM,16,4,10:12:11 AM,16,4,10:44:09 AM,16,4,10:45:39 AM,16,4,11:11:45 AM,7,14.8,,-1.291768,36.8297208,-1.2963097,36.7688221,Rider_Id_402,1566
-Order_No_24989,User_Id_1148,Bike,3,Business,3,3,2:57:15 PM,3,3,2:57:36 PM,3,3,3:12:51 PM,3,3,3:19:26 PM,3,3,3:56:38 PM,12,28.0,,-1.3100586,36.850032899999995,-1.25537,36.7756,Rider_Id_519,2232
-Order_No_24227,User_Id_738,Bike,3,Business,19,5,8:13:01 AM,19,5,8:15:08 AM,19,5,8:55:49 AM,19,5,8:58:37 AM,19,5,9:21:13 AM,9,20.3,,-1.2881479999999998,36.7850276,-1.2673093999999998,36.7241334,Rider_Id_328,1356
-Order_No_11632,User_Id_1985,Bike,1,Personal,15,2,11:22:32 AM,15,2,11:23:22 AM,15,2,12:06:10 PM,15,2,12:08:54 PM,15,2,1:09:47 PM,17,22.7,,-1.265568,36.882778,-1.3374229,36.9091381,Rider_Id_661,3653
-Order_No_14385,User_Id_554,Bike,1,Personal,23,3,10:12:10 AM,23,3,10:12:57 AM,23,3,10:21:20 AM,23,3,10:34:00 AM,23,3,10:49:22 AM,8,21.8,,-1.2942764,36.7969749,-1.269059,36.8108027,Rider_Id_726,922
-Order_No_16632,User_Id_2154,Bike,3,Business,29,4,2:23:26 PM,29,4,3:43:22 PM,29,4,3:52:44 PM,29,4,4:08:20 PM,29,4,4:38:59 PM,8,24.9,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_434,1839
-Order_No_14173,User_Id_570,Bike,3,Business,18,2,12:28:47 PM,18,2,12:58:00 PM,18,2,1:04:31 PM,18,2,1:15:44 PM,18,2,1:25:42 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_658,598
-Order_No_27482,User_Id_3470,Bike,3,Business,9,5,12:26:23 PM,9,5,1:53:12 PM,9,5,2:02:38 PM,9,5,2:07:27 PM,9,5,2:21:13 PM,5,26.3,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_265,826
-Order_No_12970,User_Id_2582,Bike,3,Business,5,2,1:50:37 PM,5,2,1:51:25 PM,5,2,2:07:32 PM,5,2,2:10:11 PM,5,2,2:19:23 PM,2,27.5,,-1.2551895,36.7822034,-1.2580991000000001,36.794151899999996,Rider_Id_205,552
-Order_No_5543,User_Id_1131,Bike,3,Business,26,5,6:17:20 PM,26,5,6:19:23 PM,26,5,6:30:20 PM,26,5,6:32:31 PM,26,5,6:44:29 PM,8,,,-1.225322,36.80855,-1.207546,36.7913976,Rider_Id_7,718
-Order_No_14358,User_Id_540,Bike,3,Business,1,4,3:49:42 PM,1,4,3:52:39 PM,1,4,4:04:49 PM,1,4,4:06:11 PM,1,4,4:18:21 PM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_79,730
-Order_No_19668,User_Id_1205,Bike,3,Business,21,5,1:35:14 PM,21,5,1:39:11 PM,21,5,1:59:49 PM,21,5,2:10:55 PM,21,5,2:40:23 PM,14,24.7,,-1.2645258,36.76833870000001,-1.2230806,36.814893700000006,Rider_Id_655,1768
-Order_No_9915,User_Id_1471,Bike,3,Business,30,2,11:12:52 AM,30,2,11:23:56 AM,30,2,11:37:20 AM,30,2,11:41:04 AM,30,2,12:38:49 PM,17,,,-1.2551895,36.7822034,-1.3323949,36.8915219,Rider_Id_96,3465
-Order_No_17291,User_Id_3331,Bike,3,Business,25,5,12:50:54 PM,25,5,12:51:40 PM,25,5,12:52:53 PM,25,5,12:58:56 PM,25,5,1:13:26 PM,4,26.2,,-1.2551895,36.7822034,-1.273526,36.80664,Rider_Id_15,870
-Order_No_8271,User_Id_1500,Bike,3,Business,27,6,8:24:20 AM,27,6,8:25:33 AM,27,6,9:11:13 AM,27,6,9:12:29 AM,27,6,9:42:01 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_309,1772
-Order_No_14443,User_Id_3218,Bike,3,Business,7,1,9:47:26 AM,7,1,9:47:57 AM,7,1,9:49:23 AM,7,1,10:03:40 AM,7,1,10:16:25 AM,5,18.5,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_408,765
-Order_No_6186,User_Id_533,Bike,3,Business,15,4,2:49:07 PM,15,4,2:54:12 PM,15,4,3:01:44 PM,15,4,3:17:02 PM,15,4,3:29:14 PM,7,27.6,,-1.2571472,36.795063299999995,-1.2765492,36.7669809,Rider_Id_131,732
-Order_No_22481,User_Id_2803,Bike,3,Business,30,2,11:04:47 AM,30,2,11:05:19 AM,30,2,11:37:38 AM,30,2,11:53:02 AM,30,2,12:25:04 PM,17,22.1,,-1.269767,36.7351068,-1.2765736,36.851364600000004,Rider_Id_336,1922
-Order_No_16432,User_Id_2178,Bike,1,Personal,26,4,6:59:07 PM,26,4,7:03:16 PM,26,4,7:16:10 PM,26,4,7:20:59 PM,26,4,7:44:43 PM,11,19.5,,-1.3013991000000003,36.8948904,-1.3077027,36.81635429999999,Rider_Id_890,1424
-Order_No_3111,User_Id_259,Bike,3,Personal,17,4,10:42:35 AM,17,4,12:23:20 PM,17,4,12:34:09 PM,17,4,12:48:03 PM,17,4,1:05:28 PM,6,22.3,,-1.312469,36.815177,-1.304033,36.7848695,Rider_Id_782,1045
-Order_No_28156,User_Id_3647,Bike,3,Business,11,2,4:15:11 PM,11,2,4:32:51 PM,11,2,4:50:46 PM,11,2,4:56:15 PM,11,2,5:33:46 PM,10,21.2,,-1.2726389999999999,36.794723,-1.2461233,36.767478499999996,Rider_Id_404,2251
-Order_No_2020,User_Id_2701,Bike,3,Business,5,2,11:05:32 AM,5,2,11:07:29 AM,5,2,11:36:22 AM,5,2,11:37:22 AM,5,2,11:59:58 AM,11,23.4,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_530,1356
-Order_No_7407,User_Id_627,Bike,3,Business,4,4,4:09:51 PM,4,4,4:10:59 PM,4,4,4:14:03 PM,4,4,4:29:19 PM,4,4,4:33:50 PM,8,27.2,,-1.2551895,36.7822034,-1.2908427,36.7829424,Rider_Id_411,271
-Order_No_8180,User_Id_3521,Bike,3,Business,26,2,1:14:53 PM,26,2,1:15:51 PM,26,2,1:22:34 PM,26,2,1:27:17 PM,26,2,1:32:24 PM,2,,,-1.2621261000000001,36.8079755,-1.2768326,36.8073804,Rider_Id_379,307
-Order_No_13348,User_Id_633,Bike,3,Business,11,5,10:06:14 AM,11,5,10:13:50 AM,11,5,10:18:30 AM,11,5,10:24:35 AM,11,5,10:41:48 AM,9,21.1,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_878,1033
-Order_No_28066,User_Id_432,Bike,3,Business,27,3,3:20:09 PM,27,3,3:21:11 PM,27,3,3:33:49 PM,27,3,3:35:49 PM,27,3,4:14:17 PM,9,29.6,,-1.3165542,36.700067,-1.3012008,36.764868,Rider_Id_226,2308
-Order_No_16301,User_Id_1280,Bike,3,Business,4,5,10:42:05 AM,4,5,10:43:05 AM,4,5,10:48:02 AM,4,5,11:15:21 AM,4,5,11:31:21 AM,8,22.4,,-1.2559559999999999,36.789867,-1.293297,36.760739,Rider_Id_446,960
-Order_No_8634,User_Id_867,Bike,3,Business,10,3,1:11:01 PM,10,3,1:11:45 PM,10,3,1:16:11 PM,10,3,1:41:57 PM,10,3,2:29:32 PM,10,30.8,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_394,2855
-Order_No_7834,User_Id_1424,Bike,3,Business,31,3,4:20:06 PM,31,3,4:21:20 PM,31,3,4:54:49 PM,31,3,4:58:29 PM,31,3,5:26:43 PM,13,24.6,,-1.329424,36.862726,-1.3015700000000001,36.7871,Rider_Id_731,1694
-Order_No_17711,User_Id_3295,Bike,3,Business,15,5,4:04:00 PM,15,5,4:04:41 PM,15,5,4:07:48 PM,15,5,4:18:28 PM,15,5,4:30:27 PM,3,27.8,,-1.2571472,36.795063299999995,-1.2679513999999998,36.8095213,Rider_Id_107,719
-Order_No_9751,User_Id_972,Bike,3,Business,22,3,2:17:05 PM,22,3,2:18:26 PM,22,3,2:26:38 PM,22,3,2:46:41 PM,22,3,3:43:21 PM,6,25.6,,-1.2862033000000002,36.8152966,-1.2855921000000001,36.7788349,Rider_Id_446,3400
-Order_No_22863,User_Id_496,Bike,3,Business,30,2,2:32:20 PM,30,2,3:17:31 PM,30,2,4:14:35 PM,30,2,5:03:31 PM,30,2,5:03:33 PM,7,,,-1.2904753,36.8144721,-1.2551895,36.7822034,Rider_Id_613,2
-Order_No_4012,User_Id_439,Bike,3,Business,14,5,2:27:50 PM,14,5,2:28:10 PM,14,5,2:53:20 PM,14,5,3:00:10 PM,14,5,3:45:32 PM,13,23.1,,-1.2613768,36.801941799999994,-1.3328208000000001,36.8692292,Rider_Id_567,2722
-Order_No_14618,User_Id_533,Bike,3,Business,29,2,5:00:30 PM,29,2,5:00:44 PM,29,2,5:00:52 PM,29,2,5:06:13 PM,29,2,5:21:00 PM,4,,,-1.3014461000000002,36.7661381,-1.2765492,36.7669809,Rider_Id_523,887
-Order_No_7597,User_Id_2016,Bike,1,Personal,8,1,11:32:11 AM,8,1,11:38:36 AM,8,1,12:02:45 PM,8,1,12:04:02 PM,8,1,12:46:55 PM,9,24.7,,-1.2871136,36.785507,-1.2883482,36.852724,Rider_Id_932,2573
-Order_No_4322,User_Id_14,Bike,2,Personal,30,3,12:33:13 PM,30,3,12:56:40 PM,30,3,1:08:08 PM,30,3,1:16:48 PM,30,3,1:54:55 PM,5,22.4,,-1.3070122,36.8287218,-1.2916556000000001,36.8262516,Rider_Id_100,2287
-Order_No_19475,User_Id_1271,Bike,3,Business,26,4,11:09:43 AM,26,4,11:10:24 AM,26,4,11:18:04 AM,26,4,12:22:58 PM,26,4,12:52:58 PM,18,18.8,,-1.2600931999999998,36.8041689,-1.3244885,36.897791999999995,Rider_Id_103,1800
-Order_No_21255,User_Id_3525,Bike,3,Business,3,3,10:35:50 AM,3,3,11:01:19 AM,3,3,11:14:58 AM,3,3,11:17:57 AM,3,3,11:42:33 AM,12,27.6,,-1.3700383,36.9190174,-1.3194518,36.869396,Rider_Id_233,1476
-Order_No_1007,User_Id_733,Bike,3,Business,6,1,8:37:17 AM,6,1,8:37:23 AM,6,1,8:48:37 AM,6,1,8:57:36 AM,6,1,9:31:46 AM,17,14.8,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_400,2050
-Order_No_1703,User_Id_1543,Bike,1,Personal,25,1,2:39:53 PM,25,1,2:41:48 PM,25,1,3:12:47 PM,25,1,3:16:57 PM,25,1,3:37:19 PM,3,,,-1.26559,36.8027,-1.2678871999999999,36.7944356,Rider_Id_379,1222
-Order_No_2750,User_Id_478,Bike,3,Business,8,5,1:14:46 PM,8,5,1:18:47 PM,8,5,1:36:26 PM,8,5,1:41:42 PM,8,5,2:12:17 PM,5,27.1,,-1.2551895,36.7822034,-1.2760915000000002,36.8081029,Rider_Id_932,1835
-Order_No_7033,User_Id_2102,Bike,3,Business,20,1,4:36:57 PM,20,1,4:37:49 PM,20,1,4:59:14 PM,20,1,5:00:36 PM,20,1,5:28:12 PM,15,,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_7,1656
-Order_No_19366,User_Id_627,Bike,3,Business,8,1,2:15:36 PM,8,1,2:16:29 PM,8,1,2:19:44 PM,8,1,2:33:05 PM,8,1,2:48:33 PM,2,29.5,,-1.2571472,36.795063299999995,-1.2517841,36.801338799999996,Rider_Id_507,928
-Order_No_11614,User_Id_2763,Bike,3,Business,1,1,11:59:33 AM,1,1,12:00:26 PM,1,1,12:01:25 PM,1,1,12:09:35 PM,1,1,12:17:55 PM,3,,,-1.2551895,36.7822034,-1.260792,36.796768,Rider_Id_177,500
-Order_No_12623,User_Id_1302,Bike,2,Personal,28,1,9:07:29 AM,28,1,9:09:14 AM,28,1,9:36:43 AM,28,1,9:49:26 AM,28,1,10:04:11 AM,7,17.9,,-1.2702333000000001,36.80065,-1.2970034,36.833321399999996,Rider_Id_808,885
-Order_No_12620,User_Id_1495,Bike,1,Personal,16,2,10:42:41 AM,16,2,10:43:23 AM,16,2,10:44:18 AM,16,2,11:05:22 AM,16,2,11:15:12 AM,8,23.7,,-1.2528485,36.7953722,-1.2698167999999999,36.7421898,Rider_Id_875,590
-Order_No_10993,User_Id_2066,Bike,3,Business,23,2,9:57:10 AM,23,2,10:00:46 AM,23,2,10:16:13 AM,23,2,10:30:52 AM,23,2,11:05:33 AM,6,,,-1.3210188999999999,36.841055100000005,-1.2854193,36.829616200000004,Rider_Id_155,2081
-Order_No_3985,User_Id_3175,Bike,3,Business,17,2,8:26:06 AM,17,2,8:29:45 AM,17,2,8:29:50 AM,17,2,8:34:33 AM,17,2,8:56:51 AM,8,14.7,,-1.2681003999999998,36.7952081,-1.297701,36.806896,Rider_Id_345,1338
-Order_No_9416,User_Id_3497,Bike,1,Personal,20,4,10:51:36 AM,20,4,10:52:10 AM,20,4,11:00:05 AM,20,4,11:02:27 AM,20,4,11:20:42 AM,9,24.2,,-1.2556829999999999,36.813771,-1.22589,36.7822548,Rider_Id_857,1095
-Order_No_24199,User_Id_2253,Bike,1,Personal,3,5,4:17:56 PM,3,5,4:23:28 PM,3,5,4:56:03 PM,3,5,5:20:37 PM,3,5,5:56:54 PM,10,22.9,,-1.2619756000000002,36.800139,-1.321916,36.825801,Rider_Id_400,2177
-Order_No_3125,User_Id_1422,Bike,3,Business,9,3,2:15:45 PM,9,3,2:21:59 PM,9,3,2:22:26 PM,9,3,2:33:20 PM,9,3,3:37:32 PM,8,25.3,,-1.3181843,36.839482700000005,-1.286553,36.8273374,Rider_Id_48,3852
-Order_No_13599,User_Id_2628,Bike,1,Personal,8,5,12:31:14 PM,8,5,12:33:20 PM,8,5,12:52:59 PM,8,5,1:18:45 PM,8,5,2:07:19 PM,18,,,-1.2224143,36.8896164,-1.2966323,36.7875842,Rider_Id_441,2914
-Order_No_14884,User_Id_1195,Bike,3,Business,25,1,3:59:05 PM,25,1,3:59:31 PM,25,1,4:40:10 PM,25,1,5:09:28 PM,25,1,5:40:52 PM,20,28.3,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_855,1884
-Order_No_3153,User_Id_2279,Bike,3,Business,29,4,2:39:54 PM,29,4,2:52:25 PM,29,4,3:03:41 PM,29,4,3:18:26 PM,29,4,4:02:26 PM,16,25.0,,-1.2897368,36.8226783,-1.3700383,36.9190174,Rider_Id_267,2640
-Order_No_6909,User_Id_1724,Bike,3,Business,6,4,1:10:57 PM,6,4,1:20:57 PM,6,4,1:57:32 PM,6,4,2:08:36 PM,6,4,2:17:29 PM,3,,,-1.259382,36.807204999999996,-1.2551895,36.7822034,Rider_Id_379,533
-Order_No_16687,User_Id_3115,Bike,3,Business,16,4,8:23:55 AM,16,4,8:24:24 AM,16,4,8:39:14 AM,16,4,8:41:28 AM,16,4,8:50:14 AM,2,,,-1.2963097,36.7688221,-1.2969741000000001,36.7856612,Rider_Id_468,526
-Order_No_12163,User_Id_2955,Bike,1,Personal,17,5,1:04:10 PM,17,5,1:04:28 PM,17,5,1:11:13 PM,17,5,1:23:28 PM,17,5,1:59:23 PM,10,17.8,,-1.2943371000000001,36.8267,-1.2332988999999999,36.8130698,Rider_Id_387,2155
-Order_No_1468,User_Id_1955,Bike,3,Business,23,2,1:07:14 PM,23,2,1:07:30 PM,23,2,1:30:30 PM,23,2,1:36:19 PM,23,2,2:12:32 PM,19,19.2,,-1.347383,36.879001200000005,-1.2571472,36.795063299999995,Rider_Id_532,2173
-Order_No_9944,User_Id_1280,Bike,3,Business,24,2,9:53:36 AM,24,2,9:54:27 AM,24,2,10:00:00 AM,24,2,10:05:46 AM,24,2,10:13:59 AM,3,14.4,,-1.2551895,36.7822034,-1.2653589,36.8090021,Rider_Id_726,493
-Order_No_11233,User_Id_1304,Bike,3,Business,12,2,9:58:19 AM,12,2,9:59:20 AM,12,2,10:16:31 AM,12,2,10:22:14 AM,12,2,11:12:55 AM,13,16.0,,-1.2551895,36.7822034,-1.3318059,36.8359596,Rider_Id_844,3041
-Order_No_5237,User_Id_2391,Bike,1,Personal,19,5,9:10:29 PM,19,5,9:28:54 PM,19,5,9:46:11 PM,19,5,9:54:29 PM,19,5,9:57:26 PM,1,20.1,,-1.2955961,36.7703081,-1.2993093999999998,36.777206899999996,Rider_Id_299,177
-Order_No_13372,User_Id_868,Bike,3,Business,31,2,1:09:03 PM,31,2,1:09:20 PM,31,2,1:14:54 PM,31,2,1:36:37 PM,31,2,2:02:35 PM,7,22.2,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_519,1558
-Order_No_24218,User_Id_909,Bike,3,Business,5,5,1:26:52 PM,5,5,1:28:30 PM,5,5,2:17:48 PM,5,5,2:27:33 PM,5,5,3:20:22 PM,14,26.5,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_812,3169
-Order_No_25501,User_Id_1136,Bike,2,Personal,25,2,7:45:19 AM,25,2,7:45:34 AM,25,2,7:58:00 AM,25,2,8:01:30 AM,25,2,8:34:46 AM,13,18.0,,-1.1992343,36.788489399999996,-1.267452,36.806015,Rider_Id_881,1996
-Order_No_5592,User_Id_2941,Bike,1,Personal,9,2,9:12:24 AM,9,2,9:12:27 AM,9,2,9:18:08 AM,9,2,9:32:43 AM,9,2,9:56:23 AM,13,17.7,4.0,-1.2977386999999998,36.803144700000004,-1.2288358999999998,36.8492095,Rider_Id_829,1420
-Order_No_7609,User_Id_718,Bike,3,Business,26,1,3:33:05 PM,26,1,3:33:42 PM,26,1,4:45:36 PM,26,1,4:46:46 PM,26,1,6:03:33 PM,23,20.1,,-1.2551895,36.7822034,-1.3447079,36.91105,Rider_Id_401,4607
-Order_No_26391,User_Id_3602,Bike,3,Business,11,2,9:23:52 AM,11,2,9:24:16 AM,11,2,9:32:14 AM,11,2,9:36:26 AM,11,2,10:34:08 AM,20,16.8,,-1.2963097,36.7688221,-1.3329455,36.8885006,Rider_Id_226,3462
-Order_No_22534,User_Id_2982,Bike,3,Business,14,3,9:13:33 AM,14,3,9:22:48 AM,14,3,9:34:43 AM,14,3,10:09:50 AM,14,3,10:44:50 AM,18,18.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_715,2100
-Order_No_16766,User_Id_2310,Bike,3,Business,15,2,2:25:17 PM,15,2,3:34:40 PM,15,2,3:46:12 PM,15,2,3:52:17 PM,15,2,4:19:35 PM,9,24.4,,-1.290894,36.822971,-1.2859912,36.8756811,Rider_Id_742,1638
-Order_No_23981,User_Id_53,Bike,3,Business,26,5,12:54:35 PM,26,5,12:54:58 PM,26,5,1:15:16 PM,26,5,1:44:43 PM,26,5,2:11:25 PM,11,,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_855,1602
-Order_No_25082,User_Id_635,Bike,3,Business,2,4,12:28:44 PM,2,4,1:13:15 PM,2,4,1:37:47 PM,2,4,1:39:48 PM,2,4,2:07:17 PM,7,,,-1.3081376,36.83297720000001,-1.2705336,36.7943509,Rider_Id_106,1649
-Order_No_23058,User_Id_2271,Bike,3,Business,18,1,1:08:28 PM,18,1,1:09:04 PM,18,1,1:23:53 PM,18,1,1:40:58 PM,18,1,2:09:43 PM,11,30.9,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_75,1725
-Order_No_23647,User_Id_437,Bike,3,Business,13,3,2:31:40 PM,13,3,2:39:54 PM,13,3,2:45:47 PM,13,3,2:52:56 PM,13,3,3:00:59 PM,1,27.3,,-1.2551895,36.7822034,-1.2625271999999998,36.7941625,Rider_Id_107,483
-Order_No_9782,User_Id_393,Bike,3,Business,25,2,11:47:34 AM,25,2,11:59:47 AM,25,2,12:05:01 PM,25,2,12:23:35 PM,25,2,12:54:00 PM,10,23.7,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_623,1825
-Order_No_2992,User_Id_1346,Bike,3,Business,23,5,2:28:52 PM,23,5,2:36:36 PM,23,5,2:54:19 PM,23,5,2:59:23 PM,23,5,3:16:27 PM,6,26.2,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_322,1024
-Order_No_17793,User_Id_3635,Bike,1,Business,24,1,5:31:01 PM,24,1,5:32:17 PM,24,1,5:48:32 PM,24,1,5:51:32 PM,24,1,6:09:39 PM,8,24.6,,-1.2615888000000002,36.7928732,-1.2729121,36.8325402,Rider_Id_537,1087
-Order_No_9032,User_Id_419,Bike,3,Business,31,3,9:53:35 AM,31,3,10:01:24 AM,31,3,10:07:03 AM,31,3,10:12:18 AM,31,3,10:29:24 AM,6,19.2,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_726,1026
-Order_No_18655,User_Id_1451,Bike,1,Personal,23,1,11:11:25 AM,23,1,11:11:35 AM,23,1,11:18:20 AM,23,1,11:22:37 AM,23,1,11:42:46 AM,9,18.3,,-1.3039695,36.783826700000006,-1.3192808,36.7121888,Rider_Id_519,1209
-Order_No_17983,User_Id_1195,Bike,3,Business,7,4,8:42:13 AM,7,4,8:42:58 AM,7,4,9:02:06 AM,7,4,9:18:33 AM,7,4,9:56:56 AM,15,,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_678,2303
-Order_No_9193,User_Id_2162,Bike,3,Business,2,4,10:07:35 AM,2,4,11:17:07 AM,2,4,11:19:42 AM,2,4,11:36:34 AM,2,4,12:04:31 PM,12,22.3,,-1.2615888000000002,36.7928732,-1.3215393999999998,36.836621,Rider_Id_574,1677
-Order_No_570,User_Id_391,Bike,3,Business,28,1,2:19:10 PM,28,1,2:19:35 PM,28,1,2:33:19 PM,28,1,2:48:32 PM,28,1,3:03:39 PM,5,26.2,,-1.2784063,36.8222933,-1.300921,36.828195,Rider_Id_336,907
-Order_No_12988,User_Id_2216,Bike,3,Business,29,5,11:58:55 AM,29,5,12:07:27 PM,29,5,12:27:01 PM,29,5,12:56:44 PM,29,5,1:05:17 PM,1,19.7,,-1.2551895,36.7822034,-1.2606254,36.783056,Rider_Id_101,513
-Order_No_3023,User_Id_2866,Bike,3,Business,7,1,2:59:50 PM,7,1,3:00:20 PM,7,1,3:00:57 PM,7,1,3:11:28 PM,7,1,3:49:36 PM,22,27.0,,-1.2551895,36.7822034,-1.3692201000000002,36.9252718,Rider_Id_647,2288
-Order_No_8805,User_Id_238,Bike,3,Business,16,3,3:01:48 PM,16,3,3:01:58 PM,16,3,3:56:33 PM,16,3,4:08:24 PM,16,3,4:18:03 PM,3,27.2,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_56,579
-Order_No_5599,User_Id_517,Bike,3,Business,4,2,2:40:00 PM,4,2,3:23:55 PM,4,2,3:23:58 PM,4,2,3:40:33 PM,4,2,4:07:25 PM,14,24.0,10.9,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_10,1612
-Order_No_19132,User_Id_867,Bike,3,Business,4,6,10:53:14 AM,4,6,10:54:03 AM,4,6,11:39:30 AM,4,6,11:49:54 AM,4,6,12:17:27 PM,12,,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_800,1653
-Order_No_975,User_Id_84,Bike,3,Business,21,3,3:40:04 PM,21,3,3:43:15 PM,21,3,3:53:14 PM,21,3,3:56:02 PM,21,3,4:50:12 PM,15,23.3,3.1,-1.2551895,36.7822034,-1.3280121999999999,36.7861446,Rider_Id_456,3250
-Order_No_395,User_Id_3711,Bike,3,Business,19,3,6:57:10 PM,19,3,7:24:09 PM,19,3,7:24:13 PM,19,3,7:42:09 PM,19,3,8:03:08 PM,10,19.7,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_517,1259
-Order_No_8470,User_Id_893,Bike,3,Business,28,3,3:46:33 PM,28,3,5:19:10 PM,28,3,5:29:54 PM,28,3,5:33:50 PM,28,3,5:48:00 PM,5,23.0,,-1.2747111,36.80839829999999,-1.258348,36.844901799999995,Rider_Id_685,850
-Order_No_22017,User_Id_3718,Bike,3,Business,18,2,10:00:17 AM,18,2,10:00:34 AM,18,2,10:05:10 AM,18,2,10:12:08 AM,18,2,10:29:15 AM,5,16.6,,-1.2646305,36.8120134,-1.2906618,36.8131222,Rider_Id_857,1027
-Order_No_21305,User_Id_649,Bike,3,Business,12,1,3:20:15 PM,12,1,3:20:49 PM,12,1,3:36:10 PM,12,1,3:41:12 PM,12,1,4:16:06 PM,14,26.7,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_112,2094
-Order_No_19467,User_Id_2457,Bike,3,Business,12,2,1:29:18 PM,12,2,1:30:03 PM,12,2,1:32:03 PM,12,2,1:33:46 PM,12,2,1:48:39 PM,8,27.1,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_434,893
-Order_No_26233,User_Id_3203,Bike,3,Business,17,1,3:00:28 PM,17,1,3:00:38 PM,17,1,3:01:06 PM,17,1,3:14:26 PM,17,1,3:24:51 PM,10,24.6,,-1.2727268999999999,36.8298864,-1.228911,36.881876,Rider_Id_408,625
-Order_No_7890,User_Id_1402,Bike,3,Business,17,1,10:33:28 AM,17,1,10:36:18 AM,17,1,11:00:32 AM,17,1,11:07:08 AM,17,1,11:23:27 AM,4,23.6,,-1.2765736,36.851364600000004,-1.281754,36.8324229,Rider_Id_511,979
-Order_No_7174,User_Id_3647,Bike,3,Business,13,2,10:37:06 AM,13,2,10:40:01 AM,13,2,11:28:30 AM,13,2,11:31:59 AM,13,2,1:10:48 PM,10,22.5,,-1.2726389999999999,36.794723,-1.3108783000000002,36.8392726,Rider_Id_112,5929
-Order_No_22441,User_Id_265,Bike,3,Business,19,3,10:30:13 AM,19,3,10:37:10 AM,19,3,10:51:33 AM,19,3,10:59:45 AM,19,3,11:19:30 AM,9,22.4,,-1.2580565000000001,36.808482500000004,-1.300921,36.828195,Rider_Id_72,1185
-Order_No_21995,User_Id_3329,Bike,3,Business,24,5,11:47:47 AM,24,5,11:51:30 AM,24,5,12:20:55 PM,24,5,12:24:54 PM,24,5,12:53:22 PM,6,21.0,,-1.2859576000000001,36.8197604,-1.3123271,36.814738299999995,Rider_Id_85,1708
-Order_No_15765,User_Id_3295,Bike,3,Business,6,4,9:18:53 AM,6,4,9:19:42 AM,6,4,9:20:25 AM,6,4,9:35:19 AM,6,4,10:36:49 AM,20,18.9,3.1,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_926,3690
-Order_No_8037,User_Id_3755,Bike,1,Personal,16,3,11:24:49 AM,16,3,11:34:47 AM,16,3,11:40:06 AM,16,3,11:45:15 AM,16,3,12:12:48 PM,7,24.0,,-1.2965004999999998,36.7791835,-1.28249,36.81909829999999,Rider_Id_756,1653
-Order_No_19797,User_Id_3517,Bike,3,Business,12,2,9:02:08 AM,12,2,9:03:14 AM,12,2,9:52:00 AM,12,2,10:25:44 AM,12,2,10:52:04 AM,15,20.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_763,1580
-Order_No_20530,User_Id_2763,Bike,3,Business,22,5,3:07:40 PM,22,5,3:08:51 PM,22,5,3:36:04 PM,22,5,3:51:39 PM,22,5,4:21:22 PM,13,27.5,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_300,1783
-Order_No_14607,User_Id_186,Bike,3,Business,17,3,4:04:38 PM,17,3,4:13:00 PM,17,3,4:18:54 PM,17,3,5:00:41 PM,17,3,5:48:25 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_177,2864
-Order_No_18282,User_Id_3647,Bike,3,Business,12,3,9:39:39 AM,12,3,9:40:31 AM,12,3,9:49:20 AM,12,3,9:51:21 AM,12,3,10:02:06 AM,6,,,-1.264316,36.816067600000004,-1.2726389999999999,36.794723,Rider_Id_207,645
-Order_No_8188,User_Id_575,Bike,1,Personal,8,5,9:39:34 AM,8,5,9:46:11 AM,8,5,10:01:49 AM,8,5,10:06:08 AM,8,5,10:35:15 AM,6,15.1,1.0,-1.2857690000000002,36.760073,-1.2976558999999999,36.791414700000004,Rider_Id_742,1747
-Order_No_17792,User_Id_168,Bike,1,Personal,13,1,12:17:41 PM,13,1,12:27:54 PM,13,1,12:44:35 PM,13,1,12:50:38 PM,13,1,1:13:27 PM,8,17.2,,-1.2977044,36.864510100000004,-1.2977768,36.8110285,Rider_Id_213,1369
-Order_No_1437,User_Id_635,Bike,3,Business,14,4,2:12:48 PM,14,4,2:18:43 PM,14,4,2:40:13 PM,14,4,2:46:30 PM,14,4,3:02:45 PM,5,27.8,,-1.2602337,36.79905479999999,-1.2793503000000002,36.8039715,Rider_Id_452,975
-Order_No_5449,User_Id_2828,Bike,2,Business,3,2,3:54:16 PM,3,2,3:55:03 PM,3,2,3:57:55 PM,3,2,4:00:41 PM,3,2,4:10:32 PM,3,20.0,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_103,591
-Order_No_6754,User_Id_1920,Bike,3,Business,4,2,11:08:00 AM,4,2,11:40:12 AM,4,2,11:49:43 AM,4,2,11:52:19 AM,4,2,12:03:18 PM,4,19.5,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_547,659
-Order_No_27262,User_Id_391,Bike,3,Business,7,5,12:34:22 PM,7,5,1:06:40 PM,7,5,1:20:34 PM,7,5,1:36:37 PM,7,5,2:04:14 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_158,1657
-Order_No_24030,User_Id_2457,Bike,3,Business,7,3,3:33:08 PM,7,3,3:54:23 PM,7,3,3:54:54 PM,7,3,3:58:02 PM,7,3,4:20:26 PM,8,28.2,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_315,1344
-Order_No_4751,User_Id_53,Bike,3,Business,9,4,10:42:05 AM,9,4,10:42:30 AM,9,4,10:54:05 AM,9,4,10:57:44 AM,9,4,11:28:34 AM,8,,,-1.2770708,36.8231093,-1.2769540000000001,36.765926,Rider_Id_843,1850
-Order_No_18392,User_Id_2412,Bike,3,Business,4,4,10:17:52 AM,4,4,10:26:35 AM,4,4,10:26:51 AM,4,4,11:13:24 AM,4,4,11:17:35 AM,5,25.5,,-1.2077728,36.870531899999996,-1.2283403,36.8822756,Rider_Id_633,251
-Order_No_24982,User_Id_248,Bike,3,Business,13,3,4:02:22 PM,13,3,4:03:05 PM,13,3,4:33:31 PM,13,3,4:45:45 PM,13,3,5:20:56 PM,8,27.5,,-1.304735,36.784605400000004,-1.254191,36.809913200000004,Rider_Id_855,2111
-Order_No_25467,User_Id_2677,Bike,3,Business,2,5,9:59:58 AM,2,5,10:00:05 AM,2,5,10:10:57 AM,2,5,10:16:25 AM,2,5,11:09:03 AM,26,17.8,17.0,-1.306378,36.7519845,-1.3440557,36.909965299999996,Rider_Id_536,3158
-Order_No_26640,User_Id_2407,Bike,2,Personal,10,3,3:15:17 PM,10,3,3:15:49 PM,10,3,3:36:59 PM,10,3,3:41:58 PM,10,3,3:55:01 PM,3,24.8,,-1.2922888000000001,36.7755707,-1.2901382,36.784678,Rider_Id_612,783
-Order_No_16671,User_Id_1500,Bike,3,Business,4,4,12:55:09 PM,4,4,12:55:51 PM,4,4,1:02:51 PM,4,4,1:27:48 PM,4,4,1:51:28 PM,8,25.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_690,1420
-Order_No_11277,User_Id_928,Bike,3,Business,18,1,1:38:59 PM,18,1,1:40:00 PM,18,1,1:40:40 PM,18,1,1:53:15 PM,18,1,2:13:43 PM,9,29.4,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_708,1228
-Order_No_26528,User_Id_1047,Bike,3,Personal,4,1,11:01:53 AM,4,1,11:04:03 AM,4,1,11:13:59 AM,4,1,11:21:14 AM,4,1,11:50:01 AM,9,26.5,,-1.3077869,36.844320700000004,-1.26071,36.8206332,Rider_Id_416,1727
-Order_No_20825,User_Id_2523,Bike,3,Business,30,4,2:51:43 PM,30,4,2:53:19 PM,30,4,2:56:16 PM,30,4,3:02:51 PM,30,4,3:39:15 PM,9,20.9,,-1.2551895,36.7822034,-1.2977565,36.8025534,Rider_Id_574,2184
-Order_No_26331,User_Id_2021,Bike,3,Business,25,1,10:19:35 AM,25,1,10:19:43 AM,25,1,10:25:33 AM,25,1,10:30:42 AM,25,1,10:48:36 AM,18,,,-1.2945552,36.839749700000006,-1.3510811000000003,36.894806,Rider_Id_238,1074
-Order_No_9951,User_Id_2801,Bike,3,Business,25,1,8:37:33 AM,25,1,8:37:48 AM,25,1,8:42:21 AM,25,1,10:10:36 AM,25,1,10:54:51 AM,15,,,-1.3104506,36.8687235,-1.2674273999999999,36.787082700000006,Rider_Id_26,2655
-Order_No_17605,User_Id_1961,Bike,3,Business,27,1,12:16:50 PM,27,1,12:18:07 PM,27,1,12:36:26 PM,27,1,12:40:38 PM,27,1,12:57:27 PM,9,21.0,,-1.303596,36.778378000000004,-1.3188358999999998,36.703852600000005,Rider_Id_709,1009
-Order_No_23937,User_Id_3767,Bike,1,Personal,12,2,2:17:55 PM,12,2,2:18:49 PM,12,2,2:33:24 PM,12,2,2:35:33 PM,12,2,2:56:53 PM,11,23.1,,-1.308819,36.8421201,-1.2649783,36.807143599999996,Rider_Id_726,1280
-Order_No_5139,User_Id_2987,Bike,3,Business,28,2,1:10:23 PM,28,2,1:13:07 PM,28,2,1:45:38 PM,28,2,1:58:47 PM,28,2,2:25:36 PM,9,19.5,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_810,1609
-Order_No_19518,User_Id_1363,Bike,3,Business,26,2,2:09:49 PM,26,2,2:10:33 PM,26,2,2:17:06 PM,26,2,2:21:29 PM,26,2,2:52:43 PM,10,31.2,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_863,1874
-Order_No_18170,User_Id_203,Bike,3,Business,30,3,9:57:29 AM,30,3,9:59:00 AM,30,3,10:26:25 AM,30,3,10:35:54 AM,30,3,10:57:30 AM,3,25.1,,-1.2851882,36.7557956,-1.2963097,36.7688221,Rider_Id_774,1296
-Order_No_7371,User_Id_2523,Bike,3,Business,21,5,2:58:08 PM,21,5,3:10:41 PM,21,5,3:39:58 PM,21,5,3:47:43 PM,21,5,4:11:25 PM,8,24.7,,-1.2551895,36.7822034,-1.3038408000000001,36.818713,Rider_Id_383,1422
-Order_No_26045,User_Id_1280,Bike,3,Business,10,5,3:24:47 PM,10,5,3:31:12 PM,10,5,3:50:21 PM,10,5,3:51:39 PM,10,5,4:08:31 PM,5,,,-1.2551895,36.7822034,-1.2701385,36.8235208,Rider_Id_687,1012
-Order_No_17344,User_Id_978,Bike,1,Personal,15,6,9:18:50 AM,15,6,9:20:03 AM,15,6,9:28:53 AM,15,6,9:37:05 AM,15,6,9:37:15 AM,5,17.4,,-1.3084067,36.83450139999999,-1.3066678999999999,36.86485220000001,Rider_Id_179,10
-Order_No_27711,User_Id_2284,Bike,1,Personal,2,6,11:41:26 AM,2,6,11:54:00 AM,2,6,12:22:41 PM,2,6,12:33:13 PM,2,6,12:52:05 PM,10,24.0,,-1.2147538999999998,36.7993624,-1.2517392,36.82496079999999,Rider_Id_870,1132
-Order_No_9622,User_Id_2134,Bike,3,Personal,10,2,6:49:56 PM,10,2,6:51:07 PM,10,2,7:07:07 PM,10,2,7:14:26 PM,10,2,7:36:47 PM,11,20.8,,-1.2503693,36.69705101,-1.2951355,36.768931,Rider_Id_474,1341
-Order_No_20008,User_Id_1271,Bike,3,Business,23,1,3:53:56 PM,23,1,3:54:32 PM,23,1,3:54:36 PM,23,1,4:11:04 PM,23,1,4:25:00 PM,15,,,-1.3244885,36.897791999999995,-1.2911643,36.8772933,Rider_Id_345,836
-Order_No_14963,User_Id_2200,Bike,3,Business,1,4,10:09:23 AM,1,4,10:09:31 AM,1,4,10:12:59 AM,1,4,10:15:00 AM,1,4,10:49:33 AM,4,,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_171,2073
-Order_No_10913,User_Id_1934,Bike,2,Personal,28,5,10:24:07 AM,28,5,10:24:53 AM,28,5,10:30:27 AM,28,5,10:33:54 AM,28,5,10:48:24 AM,5,25.1,,-1.2871136,36.785507,-1.2726506000000002,36.7960153,Rider_Id_232,870
-Order_No_22935,User_Id_265,Bike,3,Business,28,5,9:04:59 AM,28,5,9:05:15 AM,28,5,9:15:02 AM,28,5,9:23:09 AM,28,5,10:08:57 AM,21,20.8,,-1.300921,36.828195,-1.3233875,36.68783079999999,Rider_Id_761,2748
-Order_No_22622,User_Id_53,Bike,3,Business,6,3,9:18:19 AM,6,3,9:21:37 AM,6,3,9:29:57 AM,6,3,9:56:48 AM,6,3,10:07:09 AM,7,16.6,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_953,621
-Order_No_25863,User_Id_1209,Bike,3,Business,24,3,2:11:44 PM,24,3,2:12:01 PM,24,3,2:13:50 PM,24,3,2:17:18 PM,24,3,2:36:00 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_690,1122
-Order_No_27636,User_Id_3727,Bike,3,Business,22,3,12:59:21 PM,22,3,1:01:30 PM,22,3,1:16:56 PM,22,3,1:20:15 PM,22,3,1:31:07 PM,2,23.0,,-1.2963097,36.7688221,-1.3044204,36.788282200000005,Rider_Id_269,652
-Order_No_22388,User_Id_2829,Bike,3,Business,7,2,9:29:41 AM,7,2,9:33:06 AM,7,2,9:45:07 AM,7,2,9:49:54 AM,7,2,10:00:30 AM,6,,,-1.2571472,36.795063299999995,-1.2933788999999998,36.808753499999995,Rider_Id_119,636
-Order_No_7356,User_Id_2207,Bike,3,Business,14,4,10:58:53 AM,14,4,11:00:58 AM,14,4,11:27:46 AM,14,4,11:35:36 AM,14,4,11:58:58 AM,5,23.7,,-1.2885944,36.8175619,-1.2696908000000002,36.81519,Rider_Id_660,1402
-Order_No_16730,User_Id_1271,Bike,3,Business,20,2,5:15:46 PM,20,2,5:17:24 PM,20,2,5:57:37 PM,20,2,6:15:55 PM,20,2,7:06:33 PM,17,23.6,,-1.3244885,36.897791999999995,-1.212785,36.8904703,Rider_Id_520,3038
-Order_No_26890,User_Id_635,Bike,3,Business,9,3,9:30:06 AM,9,3,10:52:30 AM,9,3,10:54:06 AM,9,3,11:03:23 AM,9,3,11:10:43 AM,4,21.1,,-1.2788381000000002,36.8140116,-1.2602337,36.79905479999999,Rider_Id_100,440
-Order_No_9952,User_Id_1205,Bike,3,Business,3,1,4:01:29 PM,3,1,4:05:58 PM,3,1,4:21:17 PM,3,1,4:27:19 PM,3,1,4:42:05 PM,6,23.1,,-1.2707623,36.7581285,-1.2657370000000001,36.7889295,Rider_Id_269,886
-Order_No_16844,User_Id_3149,Bike,3,Business,30,2,11:40:56 AM,30,2,11:43:19 AM,30,2,11:43:30 AM,30,2,12:16:18 PM,30,2,12:16:28 PM,12,,,-1.3167113000000001,36.8301563,-1.2769540000000001,36.765926,Rider_Id_515,10
-Order_No_4054,User_Id_2344,Bike,3,Business,14,4,12:46:25 PM,14,4,1:10:49 PM,14,4,1:24:46 PM,14,4,1:30:30 PM,14,4,2:24:20 PM,8,23.6,,-1.2551895,36.7822034,-1.2475788,36.82154379999999,Rider_Id_210,3230
-Order_No_9725,User_Id_3682,Bike,1,Personal,27,4,12:53:48 PM,27,4,1:00:43 PM,27,4,1:11:47 PM,27,4,1:24:50 PM,27,4,1:42:53 PM,8,22.8,,-1.2401893,36.7961714,-1.2029332,36.786827,Rider_Id_882,1083
-Order_No_22010,User_Id_1471,Bike,3,Business,29,2,12:25:29 PM,29,2,12:25:44 PM,29,2,12:39:38 PM,29,2,1:05:59 PM,29,2,1:22:25 PM,11,21.5,,-1.2815348999999998,36.8293307,-1.2927792,36.9053492,Rider_Id_812,986
-Order_No_18484,User_Id_1146,Bike,3,Business,22,3,3:56:23 PM,22,3,4:00:01 PM,22,3,4:06:27 PM,22,3,4:20:11 PM,22,3,4:31:24 PM,5,24.3,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_211,673
-Order_No_25162,User_Id_2249,Bike,3,Business,28,3,10:12:43 AM,28,3,10:12:55 AM,28,3,10:19:57 AM,28,3,10:22:18 AM,28,3,10:58:00 AM,15,20.4,,-1.2375831,36.79785858,-1.3030661000000001,36.7512784,Rider_Id_575,2142
-Order_No_9927,User_Id_517,Bike,3,Business,31,1,12:04:54 PM,31,1,12:54:59 PM,31,1,12:55:03 PM,31,1,1:03:23 PM,31,1,1:03:25 PM,4,21.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,2
-Order_No_7768,User_Id_517,Bike,3,Business,13,2,8:28:28 AM,13,2,9:27:36 AM,13,2,9:51:48 AM,13,2,10:01:42 AM,13,2,11:00:15 AM,10,19.7,9.1,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_593,3513
-Order_No_15177,User_Id_2287,Bike,1,Personal,4,3,1:39:26 PM,4,3,1:40:26 PM,4,3,2:01:54 PM,4,3,2:03:19 PM,4,3,2:15:31 PM,10,18.3,,-1.296797,36.776452500000005,-1.315756,36.813058000000005,Rider_Id_74,732
-Order_No_7588,User_Id_1179,Bike,3,Business,21,1,12:51:47 PM,21,1,12:51:56 PM,21,1,1:24:18 PM,21,1,1:27:14 PM,21,1,1:55:32 PM,21,25.6,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_456,1698
-Order_No_14478,User_Id_3498,Bike,2,Personal,14,5,3:55:33 PM,14,5,3:58:34 PM,14,5,4:08:29 PM,14,5,4:10:22 PM,14,5,4:25:04 PM,4,26.9,,-1.2735236,36.7992196,-1.2896398999999998,36.812449799999996,Rider_Id_312,882
-Order_No_14155,User_Id_1502,Bike,3,Business,7,7,11:19:27 AM,7,7,11:19:46 AM,7,7,11:46:09 AM,7,7,11:48:10 AM,7,7,12:06:35 PM,5,25.3,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_435,1105
-Order_No_24646,User_Id_2112,Bike,3,Business,26,2,5:22:40 PM,26,2,5:23:02 PM,26,2,5:30:31 PM,26,2,5:55:00 PM,26,2,6:05:31 PM,5,28.5,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_8,631
-Order_No_14808,User_Id_2290,Bike,3,Business,19,3,2:34:48 PM,19,3,2:34:57 PM,19,3,2:43:39 PM,19,3,2:56:00 PM,19,3,3:34:03 PM,18,25.7,,-1.2615888000000002,36.7928732,-1.338283,36.8871775,Rider_Id_38,2283
-Order_No_8901,User_Id_2326,Bike,1,Personal,7,2,2:08:58 PM,7,2,2:10:01 PM,7,2,2:20:59 PM,7,2,2:28:02 PM,7,2,2:49:31 PM,4,24.6,,-1.2746076000000002,36.8325519,-1.2750393,36.8151527,Rider_Id_174,1289
-Order_No_11704,User_Id_408,Bike,1,Personal,16,2,10:00:57 AM,16,2,10:05:49 AM,16,2,10:21:23 AM,16,2,11:01:13 AM,16,2,11:11:15 AM,4,23.6,,-1.2791714,36.825599700000005,-1.2736094999999998,36.8011282,Rider_Id_208,602
-Order_No_19564,User_Id_635,Bike,3,Business,11,1,2:08:49 PM,11,1,2:12:44 PM,11,1,2:18:43 PM,11,1,2:22:12 PM,11,1,2:30:23 PM,4,28.7,,-1.2599563,36.799343900000004,-1.2731645,36.7900343,Rider_Id_52,491
-Order_No_16099,User_Id_1329,Bike,3,Business,26,5,11:50:18 AM,26,5,11:51:24 AM,26,5,11:54:09 AM,26,5,11:58:18 AM,26,5,12:08:12 PM,4,,,-1.273056,36.811298,-1.2624633,36.786519,Rider_Id_875,594
-Order_No_15806,User_Id_3674,Bike,3,Business,22,3,2:14:29 PM,22,3,2:16:07 PM,22,3,2:31:21 PM,22,3,2:46:13 PM,22,3,3:27:24 PM,10,25.6,,-1.3034175,36.794467700000006,-1.2551895,36.7822034,Rider_Id_384,2471
-Order_No_17782,User_Id_868,Bike,3,Business,6,1,3:02:45 PM,6,1,3:04:10 PM,6,1,3:32:00 PM,6,1,3:37:09 PM,6,1,4:02:31 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_624,1522
-Order_No_27578,User_Id_3291,Bike,3,Business,12,2,11:23:29 AM,12,2,11:24:15 AM,12,2,12:05:28 PM,12,2,12:19:36 PM,12,2,12:41:17 PM,12,28.1,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_75,1301
-Order_No_27295,User_Id_1193,Bike,1,Personal,16,3,11:03:48 AM,16,3,11:05:04 AM,16,3,11:15:20 AM,16,3,11:20:44 AM,16,3,11:39:08 AM,10,24.0,,-1.3020912,36.808145,-1.2579303999999998,36.8019667,Rider_Id_742,1104
-Order_No_15922,User_Id_2200,Bike,3,Business,25,1,1:51:31 PM,25,1,1:52:38 PM,25,1,2:04:22 PM,25,1,2:23:54 PM,25,1,3:34:27 PM,8,30.1,,-1.290894,36.822971,-1.2612974,36.7993448,Rider_Id_907,4233
-Order_No_7188,User_Id_928,Bike,3,Business,4,1,1:37:55 PM,4,1,1:38:33 PM,4,1,1:53:08 PM,4,1,2:01:38 PM,4,1,2:36:57 PM,10,,,-1.3263105,36.8622904,-1.290894,36.822971,Rider_Id_926,2119
-Order_No_5144,User_Id_1389,Bike,3,Business,19,3,6:02:08 PM,19,3,6:02:17 PM,19,3,6:17:14 PM,19,3,6:26:21 PM,19,3,6:54:15 PM,13,22.5,,-1.2534301,36.800098299999995,-1.207546,36.7913976,Rider_Id_191,1674
-Order_No_23897,User_Id_248,Bike,3,Business,12,2,3:44:27 PM,12,2,3:45:13 PM,12,2,4:09:22 PM,12,2,4:15:26 PM,12,2,4:37:19 PM,11,27.1,,-1.2645805,36.752543,-1.304735,36.784605400000004,Rider_Id_240,1313
-Order_No_20738,User_Id_3686,Bike,3,Business,17,2,10:39:38 AM,17,2,10:39:57 AM,17,2,10:44:19 AM,17,2,11:03:06 AM,17,2,11:16:32 AM,4,19.2,,-1.2551895,36.7822034,-1.2728058,36.8049903,Rider_Id_74,806
-Order_No_25972,User_Id_1789,Bike,3,Business,20,1,12:25:54 PM,20,1,12:26:19 PM,20,1,12:41:12 PM,20,1,12:42:42 PM,20,1,1:10:28 PM,13,,,-1.2917868000000001,36.7872675,-1.332454,36.8835186,Rider_Id_158,1666
-Order_No_25381,User_Id_517,Bike,3,Business,3,4,8:15:04 AM,3,4,8:15:26 AM,3,4,8:17:58 AM,3,4,8:25:34 AM,3,4,8:50:49 AM,14,20.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1515
-Order_No_18065,User_Id_1642,Bike,3,Business,1,1,11:26:15 AM,1,1,11:29:29 AM,1,1,12:01:06 PM,1,1,12:10:25 PM,1,1,12:39:14 PM,13,,,-1.3316276000000002,36.8727316,-1.2726506000000002,36.7960153,Rider_Id_126,1729
-Order_No_16853,User_Id_1077,Bike,1,Personal,6,4,8:48:40 AM,6,4,8:52:08 AM,6,4,9:10:49 AM,6,4,9:18:50 AM,6,4,9:41:53 AM,9,17.5,,-1.2219928999999998,36.8349924,-1.279032,36.8258482,Rider_Id_882,1383
-Order_No_2772,User_Id_3599,Bike,3,Business,24,5,11:00:02 AM,24,5,11:02:17 AM,24,5,11:02:48 AM,24,5,11:21:09 AM,24,5,12:38:16 PM,21,21.3,,-1.3302996,36.8707935,-1.2228458,36.885251200000006,Rider_Id_519,4627
-Order_No_334,User_Id_375,Bike,3,Business,29,1,2:15:37 PM,29,1,2:16:00 PM,29,1,2:17:03 PM,29,1,2:23:31 PM,29,1,2:46:31 PM,10,,,-1.2551895,36.7822034,-1.2518813999999998,36.859660999999996,Rider_Id_168,1380
-Order_No_5738,User_Id_265,Bike,3,Business,8,5,11:51:40 AM,8,5,11:52:54 AM,8,5,11:58:45 AM,8,5,12:01:05 PM,8,5,12:40:22 PM,8,24.0,,-1.300921,36.828195,-1.3319466999999998,36.776699799999996,Rider_Id_147,2357
-Order_No_23128,User_Id_1926,Bike,3,Business,15,5,1:42:24 PM,15,5,1:43:14 PM,15,5,2:02:44 PM,15,5,3:01:30 PM,15,5,3:11:06 PM,11,27.9,,-1.3263105,36.8622904,-1.2884651000000003,36.8249609,Rider_Id_686,576
-Order_No_2333,User_Id_2987,Bike,3,Business,5,1,3:18:48 PM,5,1,3:34:54 PM,5,1,3:41:33 PM,5,1,3:47:21 PM,5,1,4:02:11 PM,10,26.0,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_427,890
-Order_No_27647,User_Id_2383,Bike,3,Business,3,3,8:34:15 AM,3,3,8:35:09 AM,3,3,8:42:25 AM,3,3,8:45:43 AM,3,3,8:51:42 AM,2,18.6,,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_125,359
-Order_No_27002,User_Id_3517,Bike,3,Business,31,3,8:36:22 AM,31,3,8:36:33 AM,31,3,8:36:52 AM,31,3,9:30:16 AM,31,3,9:34:21 AM,15,18.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137,245
-Order_No_10560,User_Id_238,Bike,3,Business,15,5,2:03:38 PM,15,5,2:06:58 PM,15,5,2:08:38 PM,15,5,2:39:00 PM,15,5,2:42:32 PM,4,28.1,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_239,212
-Order_No_2508,User_Id_1413,Bike,1,Business,18,4,10:38:06 AM,18,4,10:41:32 AM,18,4,10:58:34 AM,18,4,11:02:05 AM,18,4,11:18:30 AM,4,25.3,,-1.2830057,36.81754779999999,-1.2740396,36.7993518,Rider_Id_477,985
-Order_No_27840,User_Id_3718,Bike,3,Business,28,3,10:43:18 AM,28,3,10:54:05 AM,28,3,11:20:20 AM,28,3,11:29:30 AM,28,3,11:56:26 AM,5,,,-1.2972834999999998,36.809687,-1.2650027,36.8126242,Rider_Id_362,1616
-Order_No_6805,User_Id_432,Bike,3,Business,4,4,3:39:40 PM,4,4,3:40:18 PM,4,4,3:51:55 PM,4,4,3:54:08 PM,4,4,4:36:11 PM,20,27.1,,-1.3012008,36.764868,-1.2232123,36.8932547,Rider_Id_272,2523
-Order_No_3677,User_Id_1561,Bike,3,Business,15,2,12:40:49 PM,15,2,12:41:10 PM,15,2,1:01:34 PM,15,2,1:49:50 PM,15,2,2:48:40 PM,35,24.4,,-1.236909,36.653621,-1.328259,36.860975,Rider_Id_854,3530
-Order_No_22002,User_Id_2727,Bike,3,Personal,14,4,11:28:59 AM,14,4,11:34:34 AM,14,4,11:50:46 AM,14,4,11:53:31 AM,14,4,12:14:06 PM,7,23.8,,-1.2925556,36.7903149,-1.2632246999999999,36.777536,Rider_Id_428,1235
-Order_No_7886,User_Id_3641,Bike,3,Business,2,5,2:24:06 PM,2,5,2:37:21 PM,2,5,2:44:41 PM,2,5,2:49:08 PM,2,5,3:11:54 PM,5,25.1,,-1.2551895,36.7822034,-1.2515637,36.8119508,Rider_Id_340,1366
-Order_No_16722,User_Id_867,Bike,3,Business,11,1,10:07:51 AM,11,1,10:08:18 AM,11,1,10:09:38 AM,11,1,10:42:15 AM,11,1,10:47:27 AM,10,26.8,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_403,312
-Order_No_4917,User_Id_633,Bike,3,Business,26,2,12:37:10 PM,26,2,12:37:40 PM,26,2,12:42:28 PM,26,2,12:58:56 PM,26,2,1:07:15 PM,4,26.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_895,499
-Order_No_25709,User_Id_2154,Bike,3,Business,30,6,1:15:02 PM,30,6,3:06:20 PM,30,6,3:06:27 PM,30,6,3:25:54 PM,30,6,3:26:14 PM,4,24.0,,-1.2765736,36.851364600000004,-1.2813973,36.828920399999994,Rider_Id_886,20
-Order_No_13207,User_Id_3034,Bike,3,Business,17,3,12:08:54 PM,17,3,12:57:52 PM,17,3,12:58:46 PM,17,3,1:08:09 PM,17,3,1:47:12 PM,5,25.6,,-1.2671137,36.8102851,-1.2981625,36.813014200000005,Rider_Id_578,2343
-Order_No_8976,User_Id_420,Bike,3,Business,6,4,3:50:01 PM,6,4,4:03:12 PM,6,4,4:07:58 PM,6,4,4:16:31 PM,6,4,4:57:07 PM,8,,,-1.281614,36.8275424,-1.2609537,36.7910164,Rider_Id_280,2436
-Order_No_25125,User_Id_3244,Bike,3,Personal,16,4,10:38:00 AM,16,4,10:38:19 AM,16,4,10:53:12 AM,16,4,10:57:45 AM,16,4,11:22:47 AM,9,,,-1.3103512,36.814996,-1.2549476000000002,36.8269576,Rider_Id_591,1502
-Order_No_18157,User_Id_391,Bike,3,Business,3,1,1:43:19 PM,3,1,1:44:24 PM,3,1,2:01:25 PM,3,1,2:03:12 PM,3,1,2:25:18 PM,5,23.0,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_158,1326
-Order_No_16070,User_Id_559,Bike,3,Personal,10,2,11:43:11 AM,10,2,11:46:44 AM,10,2,11:58:49 AM,10,2,12:04:20 PM,10,2,12:27:50 PM,8,20.2,,-1.2668071,36.8068157,-1.3117438999999997,36.8108257,Rider_Id_937,1410
-Order_No_12861,User_Id_2200,Bike,3,Business,6,2,12:35:00 PM,6,2,1:19:01 PM,6,2,1:35:03 PM,6,2,1:44:59 PM,6,2,2:04:25 PM,7,27.3,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_733,1166
-Order_No_10039,User_Id_1247,Bike,3,Business,26,3,9:20:24 AM,26,3,9:20:59 AM,26,3,9:23:55 AM,26,3,9:38:41 AM,26,3,10:01:43 AM,6,18.8,,-1.3034396000000001,36.832999799999996,-1.300284,36.804223,Rider_Id_848,1382
-Order_No_3786,User_Id_1558,Bike,3,Personal,21,4,4:33:17 PM,21,4,4:47:27 PM,21,4,4:53:01 PM,21,4,5:10:53 PM,21,4,5:49:44 PM,6,19.9,,-1.2696087999999999,36.8257411,-1.278067,36.783487,Rider_Id_638,2331
-Order_No_5419,User_Id_2330,Bike,3,Business,2,2,5:18:05 PM,2,2,5:18:23 PM,2,2,5:26:06 PM,2,2,5:50:25 PM,2,2,5:53:22 PM,9,28.6,,-1.2642903,36.80021370000001,-1.3015197,36.7658456,Rider_Id_318,177
-Order_No_798,User_Id_2150,Bike,3,Business,10,5,3:53:31 PM,10,5,4:00:30 PM,10,5,4:21:00 PM,10,5,4:35:41 PM,10,5,5:08:07 PM,9,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_292,1946
-Order_No_2667,User_Id_467,Bike,3,Business,28,2,12:28:12 PM,28,2,12:29:42 PM,28,2,12:40:56 PM,28,2,12:44:42 PM,28,2,1:10:09 PM,6,21.1,,-1.2867843,36.8134769,-1.304735,36.784605400000004,Rider_Id_831,1527
-Order_No_26112,User_Id_2154,Bike,3,Business,16,4,9:05:24 AM,16,4,9:10:24 AM,16,4,9:19:08 AM,16,4,9:20:19 AM,16,4,9:35:06 AM,8,,,-1.2571472,36.795063299999995,-1.2765736,36.851364600000004,Rider_Id_576,887
-Order_No_13223,User_Id_1082,Bike,2,Personal,28,5,9:58:56 AM,28,5,10:00:08 AM,28,5,10:17:41 AM,28,5,10:26:44 AM,28,5,10:38:51 AM,3,20.8,,-1.2963097,36.7688221,-1.2924173,36.779781400000005,Rider_Id_565,727
-Order_No_10740,User_Id_2521,Bike,3,Business,25,5,12:15:39 PM,25,5,12:16:38 PM,25,5,12:16:44 PM,25,5,12:26:55 PM,25,5,12:44:46 PM,4,26.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_518,1071
-Order_No_8025,User_Id_27,Bike,1,Personal,19,4,9:46:42 AM,19,4,9:48:29 AM,19,4,10:01:06 AM,19,4,10:08:15 AM,19,4,10:29:01 AM,7,14.7,,-1.2569714,36.7799637,-1.2558953000000002,36.7339105,Rider_Id_752,1246
-Order_No_14807,User_Id_1500,Bike,3,Business,14,5,2:09:34 PM,14,5,2:16:59 PM,14,5,2:24:56 PM,14,5,2:26:39 PM,14,5,3:19:48 PM,11,23.7,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_914,3189
-Order_No_6796,User_Id_2330,Bike,3,Business,25,6,6:12:01 PM,25,6,6:12:34 PM,25,6,6:28:01 PM,25,6,6:37:55 PM,25,6,7:11:39 PM,6,,,-1.2915246999999999,36.805575700000006,-1.3015197,36.7658456,Rider_Id_826,2024
-Order_No_15122,User_Id_1715,Bike,3,Business,3,4,11:33:13 AM,3,4,11:33:59 AM,3,4,11:42:34 AM,3,4,12:11:13 PM,3,4,12:38:58 PM,10,21.9,,-1.3209819999999999,36.828491,-1.253966,36.7987692,Rider_Id_720,1665
-Order_No_26793,User_Id_533,Bike,3,Business,8,5,3:10:58 PM,8,5,3:15:10 PM,8,5,3:33:45 PM,8,5,4:03:39 PM,8,5,4:26:13 PM,7,30.2,,-1.2571472,36.795063299999995,-1.2765492,36.7669809,Rider_Id_327,1354
-Order_No_799,User_Id_3547,Bike,3,Business,3,3,12:37:08 PM,3,3,12:37:45 PM,3,3,1:08:04 PM,3,3,1:15:45 PM,3,3,2:06:10 PM,17,27.2,,-1.2761408,36.7710839,-1.3194518,36.869396,Rider_Id_269,3025
-Order_No_25954,User_Id_136,Bike,3,Business,27,4,1:16:34 PM,27,4,1:16:51 PM,27,4,1:28:14 PM,27,4,1:52:39 PM,27,4,2:21:38 PM,17,23.7,,-1.2531983,36.8069669,-1.3165103999999999,36.707772600000006,Rider_Id_427,1739
-Order_No_4260,User_Id_2528,Bike,3,Business,9,2,9:37:50 AM,9,2,10:16:34 AM,9,2,10:24:35 AM,9,2,10:32:43 AM,9,2,11:00:26 AM,7,26.8,,-1.2551895,36.7822034,-1.2932780000000001,36.827789,Rider_Id_709,1663
-Order_No_23206,User_Id_391,Bike,3,Business,14,2,11:56:37 AM,14,2,12:13:15 PM,14,2,12:25:47 PM,14,2,12:38:17 PM,14,2,1:01:26 PM,7,,,-1.2784063,36.8222933,-1.3082842,36.843419399999995,Rider_Id_204,1389
-Order_No_4963,User_Id_3078,Bike,1,Personal,10,3,10:12:28 AM,10,3,10:13:01 AM,10,3,10:15:34 AM,10,3,10:34:40 AM,10,3,10:54:15 AM,11,22.1,,-1.2871164,36.7652395,-1.2555969999999999,36.830024,Rider_Id_731,1175
-Order_No_10723,User_Id_3283,Bike,3,Business,16,1,1:52:02 PM,16,1,1:53:47 PM,16,1,2:23:46 PM,16,1,2:36:35 PM,16,1,2:53:03 PM,5,22.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_951,988
-Order_No_23022,User_Id_3295,Bike,3,Business,23,4,12:00:44 PM,23,4,12:01:35 PM,23,4,12:11:42 PM,23,4,12:33:45 PM,23,4,1:04:25 PM,12,23.2,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_131,1840
-Order_No_14118,User_Id_432,Bike,3,Business,14,5,5:48:35 PM,14,5,5:49:09 PM,14,5,6:01:32 PM,14,5,6:18:29 PM,14,5,6:43:18 PM,14,21.3,,-1.225322,36.80855,-1.3012008,36.764868,Rider_Id_178,1489
-Order_No_22148,User_Id_3326,Bike,3,Business,7,3,4:18:46 PM,7,3,4:20:20 PM,7,3,4:50:08 PM,7,3,4:55:16 PM,7,3,6:08:29 PM,13,25.0,,-1.2551895,36.7822034,-1.3133713,36.8541174,Rider_Id_287,4393
-Order_No_4342,User_Id_1181,Bike,3,Personal,19,2,2:17:57 PM,19,2,2:19:01 PM,19,2,2:46:13 PM,19,2,2:51:23 PM,19,2,3:27:13 PM,9,29.2,,-1.2597436000000002,36.8282526,-1.2258409,36.7786803,Rider_Id_63,2150
-Order_No_24411,User_Id_469,Bike,1,Personal,22,2,12:28:50 PM,22,2,12:29:35 PM,22,2,12:53:23 PM,22,2,1:08:30 PM,22,2,1:38:26 PM,13,19.2,,-1.3264635,36.888388899999995,-1.3090663,36.813686499999996,Rider_Id_895,1796
-Order_No_4516,User_Id_136,Bike,3,Business,20,5,1:05:25 PM,20,5,1:05:30 PM,20,5,1:15:08 PM,20,5,1:35:01 PM,20,5,2:02:59 PM,12,20.6,,-1.2738338,36.782403,-1.3326173000000001,36.71462329999999,Rider_Id_844,1678
-Order_No_23920,User_Id_238,Bike,3,Business,18,1,12:18:42 PM,18,1,12:19:26 PM,18,1,12:28:59 PM,18,1,12:32:17 PM,18,1,12:51:42 PM,8,26.9,,-1.3077869,36.844320700000004,-1.2908798000000001,36.8033158,Rider_Id_708,1165
-Order_No_4672,User_Id_2801,Bike,3,Business,7,5,3:40:20 PM,7,5,3:46:37 PM,7,5,4:23:08 PM,7,5,4:26:18 PM,7,5,4:51:37 PM,9,24.7,,-1.2595421999999998,36.7871178,-1.2976940000000001,36.749788,Rider_Id_517,1519
-Order_No_552,User_Id_3355,Bike,3,Business,15,2,9:45:16 AM,15,2,9:45:51 AM,15,2,10:10:56 AM,15,2,10:32:54 AM,15,2,10:50:50 AM,10,24.5,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_246,1076
-Order_No_25231,User_Id_635,Bike,3,Business,14,5,2:44:25 PM,14,5,3:44:05 PM,14,5,4:08:52 PM,14,5,4:17:44 PM,14,5,4:50:49 PM,16,23.0,,-1.319613,36.711055,-1.2599563,36.799343900000004,Rider_Id_310,1985
-Order_No_23239,User_Id_1724,Bike,3,Business,2,1,3:15:01 PM,2,1,3:38:04 PM,2,1,3:45:12 PM,2,1,3:49:35 PM,2,1,4:11:04 PM,8,20.0,,-1.288966,36.791814,-1.2551895,36.7822034,Rider_Id_86,1289
-Order_No_10358,User_Id_1142,Bike,1,Personal,6,1,9:47:12 AM,6,1,10:11:37 AM,6,1,10:21:34 AM,6,1,10:30:00 AM,6,1,11:04:52 AM,15,14.9,,-1.2561398000000001,36.8194374,-1.291264,36.888704100000005,Rider_Id_427,2092
-Order_No_26670,User_Id_3332,Bike,3,Personal,1,3,1:21:49 PM,1,3,1:21:58 PM,1,3,1:42:47 PM,1,3,1:49:59 PM,1,3,2:28:56 PM,10,24.1,,-1.2801075000000002,36.8100471,-1.23894,36.76171,Rider_Id_234,2337
-Order_No_14236,User_Id_3565,Bike,3,Business,9,6,8:31:33 AM,9,6,8:31:46 AM,9,6,8:40:51 AM,9,6,8:42:23 AM,9,6,9:29:30 AM,26,20.2,,-1.2844665000000002,36.7866113,-1.4010166000000002,36.9457687,Rider_Id_326,2827
-Order_No_8162,User_Id_1162,Bike,3,Business,6,5,9:34:23 AM,6,5,9:41:11 AM,6,5,9:56:51 AM,6,5,10:02:50 AM,6,5,10:37:38 AM,5,14.9,,-1.3122610000000001,36.794658,-1.281577,36.79382929999999,Rider_Id_465,2088
-Order_No_11457,User_Id_3214,Bike,3,Business,2,5,10:17:21 AM,2,5,10:47:58 AM,2,5,11:18:27 AM,2,5,11:21:44 AM,2,5,11:50:01 AM,8,23.7,,-1.289795,36.7957132,-1.2604408,36.8206328,Rider_Id_612,1697
-Order_No_12355,User_Id_868,Bike,3,Business,6,4,2:05:11 PM,6,4,2:29:03 PM,6,4,2:33:43 PM,6,4,2:39:13 PM,6,4,2:53:41 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_685,868
-Order_No_7525,User_Id_510,Bike,3,Business,22,3,4:17:40 PM,22,3,4:17:58 PM,22,3,4:21:28 PM,22,3,4:32:51 PM,22,3,5:09:27 PM,17,23.8,,-1.2571472,36.795063299999995,-1.2860183,36.8975338,Rider_Id_96,2196
-Order_No_24053,User_Id_265,Bike,3,Business,12,5,12:55:46 PM,12,5,12:56:46 PM,12,5,1:02:53 PM,12,5,1:16:44 PM,12,5,2:01:41 PM,13,26.4,,-1.300921,36.828195,-1.2327841999999998,36.808763899999995,Rider_Id_699,2697
-Order_No_25740,User_Id_2982,Bike,3,Business,6,3,9:43:59 AM,6,3,9:48:52 AM,6,3,10:06:15 AM,6,3,10:30:47 AM,6,3,11:00:40 AM,15,,,-1.2675613000000001,36.8133001,-1.177202,36.8351183,Rider_Id_205,1793
-Order_No_18,User_Id_1205,Bike,3,Business,11,4,11:16:52 AM,11,4,11:17:07 AM,11,4,11:22:04 AM,11,4,11:29:04 AM,11,4,11:49:43 AM,8,25.4,,-1.2495931,36.80650870000001,-1.2942896000000002,36.8020525,Rider_Id_205,1239
-Order_No_5943,User_Id_1405,Bike,3,Business,29,3,1:44:58 PM,29,3,1:46:33 PM,29,3,2:16:51 PM,29,3,3:42:55 PM,29,3,3:42:57 PM,21,20.9,,-1.2535486,36.7933541,-1.3478563000000001,36.8935824,Rider_Id_652,2
-Order_No_2881,User_Id_3599,Bike,3,Business,31,3,9:19:09 AM,31,3,9:34:34 AM,31,3,10:07:47 AM,31,3,10:49:30 AM,31,3,11:11:47 AM,15,24.8,,-1.3302996,36.8707935,-1.2520942,36.7915863,Rider_Id_179,1337
-Order_No_4483,User_Id_2847,Bike,3,Personal,6,5,2:36:55 PM,6,5,2:40:21 PM,6,5,2:54:07 PM,6,5,3:06:02 PM,6,5,3:36:56 PM,10,22.3,,-1.3261878999999999,36.86017929999999,-1.2822773,36.795312100000004,Rider_Id_147,1854
-Order_No_4486,User_Id_3647,Bike,3,Business,8,1,12:57:33 PM,8,1,12:58:44 PM,8,1,1:11:19 PM,8,1,1:14:51 PM,8,1,1:49:19 PM,14,27.8,,-1.2726389999999999,36.794723,-1.3038645,36.8684596,Rider_Id_131,2068
-Order_No_12079,User_Id_3444,Bike,1,Personal,20,4,10:39:17 AM,20,4,10:41:29 AM,20,4,10:53:34 AM,20,4,10:57:06 AM,20,4,11:49:35 AM,7,24.4,,-1.263419,36.8060348,-1.2951297,36.8335521,Rider_Id_565,3149
-Order_No_8330,User_Id_248,Bike,3,Business,6,1,10:39:06 AM,6,1,10:39:27 AM,6,1,10:59:41 AM,6,1,11:02:25 AM,6,1,11:13:19 AM,1,20.4,,-1.3025666999999999,36.7803495,-1.304735,36.784605400000004,Rider_Id_956,654
-Order_No_11035,User_Id_1693,Bike,3,Business,24,1,3:57:02 PM,24,1,3:57:32 PM,24,1,4:15:19 PM,24,1,4:21:51 PM,24,1,4:32:16 PM,4,,,-1.269534,36.80697,-1.2574087999999999,36.799712,Rider_Id_434,625
-Order_No_724,User_Id_2330,Bike,3,Business,11,1,2:53:42 PM,11,1,2:55:06 PM,11,1,3:03:35 PM,11,1,3:08:56 PM,11,1,3:26:07 PM,8,28.1,,-1.3015197,36.7658456,-1.3270197,36.7037303,Rider_Id_178,1031
-Order_No_6929,User_Id_1195,Bike,3,Business,27,3,11:36:07 AM,27,3,11:37:00 AM,27,3,11:42:47 AM,27,3,12:23:55 PM,27,3,12:40:29 PM,14,26.0,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_771,994
-Order_No_11068,User_Id_3404,Bike,3,Business,21,5,9:24:16 AM,21,5,9:26:03 AM,21,5,9:38:03 AM,21,5,9:44:40 AM,21,5,9:54:03 AM,2,19.5,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_404,563
-Order_No_6094,User_Id_1790,Bike,3,Business,28,4,1:07:54 PM,28,4,1:08:23 PM,28,4,1:13:47 PM,28,4,1:17:20 PM,28,4,1:50:29 PM,8,,,-1.2966785,36.7896469,-1.2551895,36.7822034,Rider_Id_216,1989
-Order_No_15998,User_Id_1932,Bike,3,Business,16,3,11:55:01 AM,16,3,11:58:23 AM,16,3,12:18:12 PM,16,3,12:23:53 PM,16,3,12:39:35 PM,5,23.8,,-1.2922857,36.781327399999995,-1.3075701000000002,36.7504306,Rider_Id_782,942
-Order_No_5203,User_Id_635,Bike,3,Business,28,4,9:26:11 AM,28,4,9:26:44 AM,28,4,9:34:58 AM,28,4,10:23:50 AM,28,4,11:41:30 AM,20,19.4,,-1.3081376,36.83297720000001,-1.2339926,36.7176351,Rider_Id_448,4660
-Order_No_7067,User_Id_649,Bike,1,Business,22,2,11:24:40 AM,22,2,11:48:56 AM,22,2,11:55:57 AM,22,2,12:31:38 PM,22,2,1:13:57 PM,9,19.2,,-1.3034396000000001,36.832999799999996,-1.2626906,36.7827022,Rider_Id_892,2539
-Order_No_14968,User_Id_540,Bike,3,Business,27,1,3:58:44 PM,27,1,3:59:18 PM,27,1,4:02:20 PM,27,1,5:16:34 PM,27,1,5:39:46 PM,9,,,-1.2945073999999999,36.8083195,-1.2551895,36.7822034,Rider_Id_19,1392
-Order_No_7999,User_Id_3175,Bike,3,Business,1,4,8:56:51 AM,1,4,8:57:16 AM,1,4,8:59:53 AM,1,4,9:02:48 AM,1,4,9:20:18 AM,4,,,-1.2681003999999998,36.7952081,-1.263419,36.8060348,Rider_Id_720,1050
-Order_No_24146,User_Id_2436,Bike,3,Business,30,5,1:36:03 PM,30,5,1:40:10 PM,30,5,1:58:11 PM,30,5,3:13:37 PM,30,5,3:31:48 PM,10,24.7,,-1.2551895,36.7822034,-1.2295205,36.812042600000005,Rider_Id_928,1091
-Order_No_16649,User_Id_892,Bike,2,Personal,14,5,12:15:07 PM,14,5,12:18:00 PM,14,5,12:33:56 PM,14,5,12:39:46 PM,14,5,12:46:21 PM,2,24.5,,-1.306735,36.8256226,-1.312475,36.8225778,Rider_Id_844,395
-Order_No_22360,User_Id_1245,Bike,3,Business,31,5,9:50:18 AM,31,5,9:59:10 AM,31,5,10:14:31 AM,31,5,10:33:45 AM,31,5,11:08:40 AM,23,21.9,,-1.300921,36.828195,-1.3434861999999999,36.6895464,Rider_Id_712,2095
-Order_No_7253,User_Id_2271,Bike,3,Business,29,1,2:47:15 PM,29,1,2:49:08 PM,29,1,3:00:28 PM,29,1,3:01:34 PM,29,1,3:31:35 PM,14,,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_318,1801
-Order_No_27852,User_Id_3222,Bike,3,Business,7,5,11:42:38 AM,7,5,12:17:40 PM,7,5,12:24:47 PM,7,5,12:26:14 PM,7,5,12:33:49 PM,4,25.2,,-1.2551895,36.7822034,-1.2715686000000002,36.8023526,Rider_Id_390,455
-Order_No_26203,User_Id_2069,Bike,3,Business,20,3,12:32:02 PM,20,3,12:32:12 PM,20,3,12:32:22 PM,20,3,1:19:40 PM,20,3,1:19:41 PM,17,26.4,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_406,1
-Order_No_9997,User_Id_2066,Bike,3,Business,28,4,10:08:20 AM,28,4,10:30:56 AM,28,4,10:33:44 AM,28,4,10:42:18 AM,28,4,11:10:58 AM,7,28.0,,-1.3210188999999999,36.841055100000005,-1.2865958,36.8244585,Rider_Id_101,1720
-Order_No_28165,User_Id_391,Bike,3,Business,11,2,2:30:11 PM,11,2,2:35:33 PM,11,2,2:35:35 PM,11,2,2:56:52 PM,11,2,2:58:41 PM,4,21.2,,-1.2867454,36.8259017,-1.300921,36.828195,Rider_Id_41,109
-Order_No_4161,User_Id_2112,Bike,3,Business,8,2,2:51:35 PM,8,2,2:57:49 PM,8,2,3:15:15 PM,8,2,3:18:33 PM,8,2,3:56:11 PM,12,25.2,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_150,2258
-Order_No_14527,User_Id_517,Bike,3,Business,5,4,9:20:11 AM,5,4,9:21:10 AM,5,4,9:22:04 AM,5,4,10:12:00 AM,5,4,10:13:56 AM,8,,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_284,116
-Order_No_2661,User_Id_2436,Bike,3,Business,27,2,3:50:18 PM,27,2,3:57:57 PM,27,2,4:13:52 PM,27,2,4:16:14 PM,27,2,4:47:12 PM,10,25.8,,-1.2551895,36.7822034,-1.2363747999999999,36.817750200000006,Rider_Id_453,1858
-Order_No_4118,User_Id_3647,Bike,3,Business,24,4,12:03:50 PM,24,4,12:04:51 PM,24,4,12:17:50 PM,24,4,12:22:32 PM,24,4,12:41:31 PM,6,24.6,,-1.2726389999999999,36.794723,-1.3004846,36.790159100000004,Rider_Id_269,1139
-Order_No_9703,User_Id_706,Bike,3,Business,9,3,11:37:10 AM,9,3,11:37:29 AM,9,3,11:48:30 AM,9,3,11:54:08 AM,9,3,12:50:55 PM,15,25.2,,-1.2991441000000001,36.7528804,-1.3077869,36.844320700000004,Rider_Id_436,3407
-Order_No_10134,User_Id_393,Bike,3,Business,8,1,8:46:54 AM,8,1,8:47:33 AM,8,1,9:01:21 AM,8,1,9:32:52 AM,8,1,9:53:08 AM,8,22.1,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_910,1216
-Order_No_23641,User_Id_2425,Bike,1,Personal,21,5,1:01:08 PM,21,5,1:01:27 PM,21,5,1:08:36 PM,21,5,1:11:33 PM,21,5,2:11:25 PM,15,19.8,,-1.3195185,36.8180902,-1.2475028000000001,36.877469700000006,Rider_Id_612,3592
-Order_No_24783,User_Id_1443,Bike,1,Personal,12,4,2:45:27 PM,12,4,2:54:42 PM,12,4,3:11:54 PM,12,4,3:24:02 PM,12,4,3:32:46 PM,6,21.5,,-1.3154433,36.806385999999996,-1.2962832,36.784968299999996,Rider_Id_445,524
-Order_No_26206,User_Id_2509,Bike,3,Business,21,3,12:52:46 PM,21,3,1:12:39 PM,21,3,1:22:30 PM,21,3,1:38:15 PM,21,3,2:53:53 PM,22,23.0,3.0,-1.3287857,36.9042514,-1.2571472,36.795063299999995,Rider_Id_900,4538
-Order_No_4145,User_Id_1336,Bike,3,Business,25,4,2:42:24 PM,25,4,2:44:50 PM,25,4,3:02:46 PM,25,4,3:58:46 PM,25,4,4:02:33 PM,9,,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_507,227
-Order_No_26273,User_Id_1304,Bike,3,Business,17,4,2:50:47 PM,17,4,2:51:32 PM,17,4,3:02:44 PM,17,4,3:09:02 PM,17,4,3:17:45 PM,3,23.9,,-1.2626906,36.7827022,-1.2655477,36.8001682,Rider_Id_742,523
-Order_No_8823,User_Id_1624,Bike,2,Personal,24,4,6:39:26 PM,24,4,6:39:56 PM,24,4,6:49:02 PM,24,4,6:50:58 PM,24,4,7:13:09 PM,7,21.2,,-1.2629944,36.7998816,-1.288621,36.778846,Rider_Id_103,1331
-Order_No_17982,User_Id_2335,Bike,3,Business,13,1,11:46:07 AM,13,1,11:46:30 AM,13,1,11:48:53 AM,13,1,12:00:04 PM,13,1,12:33:43 PM,22,,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_161,2019
-Order_No_4127,User_Id_2330,Bike,3,Business,20,4,10:50:25 AM,20,4,10:51:52 AM,20,4,10:59:56 AM,20,4,11:06:55 AM,20,4,11:24:44 AM,10,22.3,,-1.2642903,36.80021370000001,-1.2687407,36.7373043,Rider_Id_50,1069
-Order_No_18987,User_Id_393,Bike,3,Business,6,1,10:04:05 AM,6,1,10:04:24 AM,6,1,10:20:59 AM,6,1,10:35:03 AM,6,1,10:52:24 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_910,1041
-Order_No_5026,User_Id_1796,Bike,3,Business,8,5,12:50:08 PM,8,5,12:50:22 PM,8,5,12:57:23 PM,8,5,1:08:11 PM,8,5,1:23:53 PM,7,24.1,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_718,942
-Order_No_26695,User_Id_2987,Bike,3,Business,14,3,4:28:05 PM,14,3,4:31:50 PM,14,3,4:32:03 PM,14,3,4:43:01 PM,14,3,4:55:51 PM,3,23.3,,-1.260232,36.807334000000004,-1.2702120000000001,36.82108770000001,Rider_Id_40,770
-Order_No_21280,User_Id_357,Bike,3,Business,17,3,11:05:23 AM,17,3,11:07:00 AM,17,3,11:19:44 AM,17,3,12:03:26 PM,17,3,12:43:23 PM,16,,,-1.2326041,36.8009029,-1.2945178999999998,36.7685349,Rider_Id_870,2397
-Order_No_12655,User_Id_517,Bike,3,Business,27,6,12:27:47 PM,27,6,12:28:49 PM,27,6,12:28:53 PM,27,6,1:11:55 PM,27,6,1:12:10 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_39,15
-Order_No_501,User_Id_357,Bike,3,Business,26,5,3:28:38 PM,26,5,3:38:27 PM,26,5,3:48:41 PM,26,5,3:53:37 PM,26,5,4:08:32 PM,3,,,-1.2945178999999998,36.7685349,-1.2955333999999998,36.7742857,Rider_Id_770,895
-Order_No_17904,User_Id_1329,Bike,3,Business,28,2,2:51:08 PM,28,2,2:51:52 PM,28,2,3:08:45 PM,28,2,3:10:29 PM,28,2,3:26:38 PM,3,23.3,,-1.273056,36.811298,-1.2572151000000003,36.7993483,Rider_Id_434,969
-Order_No_7398,User_Id_733,Bike,3,Business,7,4,12:20:52 PM,7,4,12:22:12 PM,7,4,12:31:29 PM,7,4,12:35:20 PM,7,4,1:27:45 PM,21,27.7,,-1.2551895,36.7822034,-1.3407764,36.9100521,Rider_Id_660,3145
-Order_No_27591,User_Id_811,Bike,3,Personal,3,3,10:41:20 AM,3,3,10:41:39 AM,3,3,10:58:09 AM,3,3,11:02:54 AM,3,3,11:26:47 AM,11,23.9,,-1.3295831,36.7388987,-1.2963097,36.7688221,Rider_Id_844,1433
-Order_No_19142,User_Id_635,Bike,3,Business,21,1,1:36:44 PM,21,1,1:42:13 PM,21,1,1:50:34 PM,21,1,2:17:30 PM,21,1,2:32:53 PM,7,27.6,,-1.3081376,36.83297720000001,-1.2599563,36.799343900000004,Rider_Id_809,923
-Order_No_19932,User_Id_936,Bike,2,Personal,4,5,12:47:52 PM,4,5,12:48:12 PM,4,5,1:32:18 PM,4,5,1:59:56 PM,4,5,2:43:31 PM,16,24.2,,-1.2833695,36.855152399999994,-1.2702053,36.7467916,Rider_Id_581,2615
-Order_No_1246,User_Id_970,Bike,3,Business,18,2,11:53:22 AM,18,2,11:53:51 AM,18,2,11:55:35 AM,18,2,12:16:41 PM,18,2,12:43:57 PM,5,,,-1.2551895,36.7822034,-1.2715611,36.795354100000004,Rider_Id_651,1636
-Order_No_3976,User_Id_1874,Bike,3,Business,20,2,11:35:31 AM,20,2,11:42:22 AM,20,2,11:48:47 AM,20,2,11:56:05 AM,20,2,12:31:35 PM,8,19.6,,-1.2571472,36.795063299999995,-1.2964591,36.7776175,Rider_Id_268,2130
-Order_No_2042,User_Id_1329,Bike,3,Business,12,2,9:40:05 AM,12,2,9:43:36 AM,12,2,9:55:10 AM,12,2,9:57:09 AM,12,2,10:15:49 AM,3,18.2,,-1.273056,36.811298,-1.25896,36.8016691,Rider_Id_332,1120
-Order_No_14234,User_Id_2801,Bike,3,Business,26,2,11:33:51 AM,26,2,11:34:05 AM,26,2,11:45:32 AM,26,2,11:47:58 AM,26,2,12:04:48 PM,4,27.4,,-1.2595421999999998,36.7871178,-1.2738521999999999,36.799281799999996,Rider_Id_290,1010
-Order_No_27971,User_Id_3689,Bike,3,Business,20,4,4:10:07 PM,20,4,4:28:45 PM,20,4,4:37:56 PM,20,4,4:47:35 PM,20,4,5:11:11 PM,10,22.7,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_845,1416
-Order_No_14778,User_Id_517,Bike,3,Business,30,3,12:32:35 PM,30,3,12:33:38 PM,30,3,12:34:14 PM,30,3,12:50:18 PM,30,3,1:02:18 PM,4,25.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,720
-Order_No_20245,User_Id_1271,Bike,3,Business,9,2,3:37:06 PM,9,2,3:37:57 PM,9,2,4:05:04 PM,9,2,4:18:22 PM,9,2,4:43:24 PM,16,30.4,,-1.3244885,36.897791999999995,-1.2896431000000002,36.8086201,Rider_Id_770,1502
-Order_No_9238,User_Id_2299,Bike,1,Personal,5,4,1:05:55 PM,5,4,1:18:40 PM,5,4,2:09:55 PM,5,4,2:16:03 PM,5,4,2:43:54 PM,8,22.2,,-1.2248308,36.801257299999996,-1.2689785,36.8082713,Rider_Id_131,1671
-Order_No_16427,User_Id_42,Bike,3,Business,8,4,1:51:36 PM,8,4,1:52:05 PM,8,4,1:58:09 PM,8,4,2:09:24 PM,8,4,2:16:24 PM,5,27.1,,-1.2564152,36.779280299999996,-1.2705336,36.7943509,Rider_Id_56,420
-Order_No_12562,User_Id_2956,Bike,3,Business,23,5,1:37:57 PM,23,5,1:38:39 PM,23,5,1:48:56 PM,23,5,2:02:49 PM,23,5,2:06:45 PM,1,26.4,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_547,236
-Order_No_10955,User_Id_2521,Bike,3,Business,26,6,10:15:51 AM,26,6,10:16:00 AM,26,6,10:16:08 AM,26,6,10:22:22 AM,26,6,10:29:13 AM,4,20.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,411
-Order_No_15761,User_Id_269,Bike,3,Business,12,1,10:11:24 AM,12,1,10:13:21 AM,12,1,10:23:46 AM,12,1,10:47:18 AM,12,1,11:26:34 AM,16,23.7,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_761,2356
-Order_No_10662,User_Id_909,Bike,3,Business,25,4,4:26:36 PM,25,4,4:29:31 PM,25,4,4:52:16 PM,25,4,5:12:06 PM,25,4,5:28:16 PM,7,,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_193,970
-Order_No_14983,User_Id_2828,Bike,3,Business,18,1,11:28:05 AM,18,1,11:29:52 AM,18,1,11:44:11 AM,18,1,12:31:18 PM,18,1,12:48:30 PM,7,,,-1.2889576,36.81833579999999,-1.2615888000000002,36.7928732,Rider_Id_744,1032
-Order_No_26759,User_Id_3752,Bike,3,Business,3,5,2:32:45 PM,3,5,2:53:47 PM,3,5,3:08:54 PM,3,5,3:15:14 PM,3,5,3:29:54 PM,10,,,-1.2290378999999998,36.782594399999994,-1.2753108999999998,36.8130185,Rider_Id_607,880
-Order_No_5764,User_Id_2066,Bike,3,Business,15,3,11:59:29 AM,15,3,12:00:02 PM,15,3,12:03:12 PM,15,3,12:13:42 PM,15,3,12:31:40 PM,9,18.0,,-1.316961,36.820337,-1.2979513,36.7504699,Rider_Id_213,1078
-Order_No_27641,User_Id_2154,Bike,3,Business,10,1,10:57:58 AM,10,1,10:59:09 AM,10,1,11:03:27 AM,10,1,11:10:31 AM,10,1,11:32:31 AM,13,18.5,,-1.2156006000000001,36.8916865,-1.2765736,36.851364600000004,Rider_Id_60,1320
-Order_No_28046,User_Id_1271,Bike,3,Business,23,3,3:39:02 PM,23,3,3:39:16 PM,23,3,4:00:17 PM,23,3,4:14:04 PM,23,3,5:22:12 PM,23,27.5,,-1.3107918,36.761794,-1.2927792,36.9053492,Rider_Id_831,4088
-Order_No_11688,User_Id_1584,Bike,3,Business,26,5,9:18:14 AM,26,5,9:48:38 AM,26,5,10:31:42 AM,26,5,10:51:05 AM,26,5,11:06:33 AM,7,,,-1.2771155,36.826679999999996,-1.2969741000000001,36.7856612,Rider_Id_88,928
-Order_No_18843,User_Id_246,Bike,3,Business,13,4,11:13:07 AM,13,4,11:15:20 AM,13,4,11:18:42 AM,13,4,11:29:53 AM,13,4,11:36:51 AM,3,23.3,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_100,418
-Order_No_10161,User_Id_393,Bike,3,Business,1,4,8:00:57 AM,1,4,8:01:38 AM,1,4,8:01:52 AM,1,4,9:18:05 AM,1,4,9:18:37 AM,4,,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_157,32
-Order_No_20196,User_Id_1340,Bike,3,Business,1,5,10:30:16 AM,1,5,10:30:57 AM,1,5,10:42:47 AM,1,5,10:45:33 AM,1,5,10:51:58 AM,2,26.4,,-1.2707178999999997,36.7979713,-1.2667532,36.8024002,Rider_Id_547,385
-Order_No_17588,User_Id_412,Bike,3,Business,13,5,1:14:52 PM,13,5,1:14:59 PM,13,5,1:20:08 PM,13,5,1:27:45 PM,13,5,1:40:18 PM,3,,,-1.272828,36.816608,-1.266837,36.799248999999996,Rider_Id_806,753
-Order_No_8510,User_Id_1527,Bike,3,Business,2,4,2:46:04 PM,2,4,2:46:35 PM,2,4,2:50:59 PM,2,4,2:52:05 PM,2,4,3:52:17 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_411,3612
-Order_No_9336,User_Id_1402,Bike,3,Business,15,5,9:25:34 AM,15,5,9:26:05 AM,15,5,9:37:56 AM,15,5,9:43:06 AM,15,5,10:05:27 AM,9,19.2,,-1.2813012,36.832396200000005,-1.2571472,36.795063299999995,Rider_Id_855,1341
-Order_No_16387,User_Id_2982,Bike,3,Business,6,3,1:43:20 PM,6,3,1:50:30 PM,6,3,1:50:43 PM,6,3,2:15:41 PM,6,3,2:48:49 PM,18,27.6,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_445,1988
-Order_No_10945,User_Id_248,Bike,3,Business,8,1,2:40:22 PM,8,1,2:40:54 PM,8,1,2:53:04 PM,8,1,3:03:15 PM,8,1,3:27:15 PM,9,25.0,,-1.304735,36.784605400000004,-1.2674273999999999,36.787082700000006,Rider_Id_565,1440
-Order_No_1539,User_Id_496,Bike,3,Business,27,5,3:56:55 PM,27,5,3:59:36 PM,27,5,4:34:59 PM,27,5,4:39:27 PM,27,5,4:56:27 PM,8,17.7,,-1.2551895,36.7822034,-1.2994217,36.8009779,Rider_Id_616,1020
-Order_No_25922,User_Id_2763,Bike,3,Business,21,2,4:16:35 PM,21,2,4:17:26 PM,21,2,4:18:15 PM,21,2,4:40:58 PM,21,2,4:41:07 PM,4,24.1,,-1.2551895,36.7822034,-1.260792,36.796768,Rider_Id_651,9
-Order_No_5620,User_Id_2042,Bike,3,Business,26,1,3:28:51 PM,26,1,3:43:05 PM,26,1,4:04:33 PM,26,1,4:15:55 PM,26,1,4:39:16 PM,13,22.3,0.8,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_539,1401
-Order_No_18574,User_Id_3201,Bike,3,Business,12,4,11:37:10 AM,12,4,11:37:34 AM,12,4,11:38:22 AM,12,4,12:24:56 PM,12,4,12:25:10 PM,1,19.1,,-1.2536041999999998,36.806489500000005,-1.2649605,36.798177700000004,Rider_Id_51,14
-Order_No_230,User_Id_1809,Bike,3,Business,15,2,2:52:21 PM,15,2,2:52:56 PM,15,2,2:57:35 PM,15,2,3:11:37 PM,15,2,3:17:58 PM,2,24.8,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_634,381
-Order_No_14846,User_Id_3291,Bike,3,Business,9,3,2:43:01 PM,9,3,2:44:15 PM,9,3,2:46:15 PM,9,3,2:55:38 PM,9,3,2:56:57 PM,2,25.4,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_105,79
-Order_No_10458,User_Id_1781,Bike,3,Business,3,5,10:03:35 AM,3,5,10:03:46 AM,3,5,10:04:27 AM,3,5,10:13:35 AM,3,5,10:45:05 AM,11,17.6,,-1.2571472,36.795063299999995,-1.2271875,36.8522471,Rider_Id_105,1890
-Order_No_9001,User_Id_651,Bike,1,Personal,9,2,10:17:00 AM,9,2,10:29:16 AM,9,2,10:43:49 AM,9,2,10:48:03 AM,9,2,11:01:55 AM,6,22.1,,-1.2794169,36.818084899999995,-1.2538731,36.7850223,Rider_Id_281,832
-Order_No_10741,User_Id_1705,Bike,3,Business,15,4,12:16:15 PM,15,4,12:16:32 PM,15,4,12:19:55 PM,15,4,12:21:42 PM,15,4,1:15:46 PM,17,24.2,,-1.2551895,36.7822034,-1.3393558,36.898241799999994,Rider_Id_283,3244
-Order_No_26337,User_Id_2139,Bike,3,Personal,20,3,6:46:21 PM,20,3,6:48:57 PM,20,3,7:13:37 PM,20,3,7:22:03 PM,20,3,8:00:22 PM,9,27.9,,-1.2336611000000002,36.806181,-1.1926934999999999,36.7574155,Rider_Id_318,2299
-Order_No_5090,User_Id_778,Bike,3,Business,6,3,2:25:02 PM,6,3,2:25:31 PM,6,3,2:34:14 PM,6,3,2:36:59 PM,6,3,2:53:31 PM,7,,,-1.2535486,36.7933541,-1.2717174,36.775388299999996,Rider_Id_523,992
-Order_No_18857,User_Id_3557,Bike,3,Business,12,2,1:50:32 PM,12,2,1:50:47 PM,12,2,2:22:02 PM,12,2,2:50:31 PM,12,2,2:50:34 PM,5,,,-1.2895261,36.8164407,-1.2546732,36.808679600000005,Rider_Id_314,3
-Order_No_13258,User_Id_94,Bike,1,Personal,28,2,10:54:43 AM,28,2,10:55:02 AM,28,2,11:06:00 AM,28,2,11:54:26 AM,28,2,12:04:10 PM,3,17.4,,-1.2719423,36.8205148,-1.265007,36.8149619,Rider_Id_414,584
-Order_No_3156,User_Id_3487,Bike,3,Business,6,4,1:45:12 PM,6,4,1:47:49 PM,6,4,1:58:39 PM,6,4,2:09:02 PM,6,4,2:40:35 PM,5,21.1,,-1.2527958000000001,36.797624400000004,-1.279395,36.825364,Rider_Id_788,1893
-Order_No_1972,User_Id_3300,Bike,3,Business,9,2,9:24:01 AM,9,2,9:24:15 AM,9,2,9:30:01 AM,9,2,9:46:03 AM,9,2,10:12:11 AM,17,20.9,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_484,1568
-Order_No_22520,User_Id_3560,Bike,3,Business,27,3,3:32:44 PM,27,3,3:46:51 PM,27,3,3:53:13 PM,27,3,3:58:20 PM,27,3,5:44:05 PM,16,,,-1.3562374,36.904295399999995,-1.279395,36.825364,Rider_Id_890,6345
-Order_No_4212,User_Id_867,Bike,3,Business,15,2,9:20:36 AM,15,2,9:21:59 AM,15,2,9:34:05 AM,15,2,10:19:09 AM,15,2,10:53:01 AM,10,20.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_725,2032
-Order_No_20223,User_Id_265,Bike,3,Business,2,5,3:41:41 PM,2,5,3:42:51 PM,2,5,3:53:52 PM,2,5,3:57:33 PM,2,5,4:41:34 PM,18,25.3,,-1.300921,36.828195,-1.3117864,36.6973045,Rider_Id_890,2641
-Order_No_24232,User_Id_2691,Bike,3,Business,21,3,1:16:53 PM,21,3,3:16:03 PM,21,3,3:25:34 PM,21,3,3:38:14 PM,21,3,3:46:32 PM,5,23.2,3.0,-1.295034,36.782050500000004,-1.2984649,36.8171889,Rider_Id_669,498
-Order_No_5250,User_Id_393,Bike,3,Business,18,4,2:17:21 PM,18,4,2:34:07 PM,18,4,2:48:29 PM,18,4,3:00:48 PM,18,4,3:18:15 PM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_461,1047
-Order_No_9825,User_Id_2647,Bike,1,Personal,14,4,5:54:33 PM,14,4,6:15:43 PM,14,4,6:27:10 PM,14,4,6:30:00 PM,14,4,6:56:38 PM,5,21.4,,-1.2838296999999999,36.810923200000005,-1.2536855,36.8245276,Rider_Id_79,1598
-Order_No_13997,User_Id_2488,Bike,1,Personal,10,5,5:38:13 PM,10,5,5:40:18 PM,10,5,6:05:36 PM,10,5,6:12:55 PM,10,5,6:31:46 PM,8,21.1,,-1.2747671,36.8139951,-1.2343087,36.84749,Rider_Id_229,1131
-Order_No_27053,User_Id_111,Bike,3,Business,17,3,10:21:35 AM,17,3,10:22:06 AM,17,3,10:22:51 AM,17,3,10:31:36 AM,17,3,11:00:58 AM,6,,,-1.2551895,36.7822034,-1.2793803999999998,36.792418299999994,Rider_Id_708,1762
-Order_No_6046,User_Id_1004,Bike,3,Business,19,3,12:36:06 PM,19,3,12:45:47 PM,19,3,12:52:11 PM,19,3,1:16:01 PM,19,3,1:39:00 PM,10,22.4,,-1.3681253,36.915529799999995,-1.3323107,36.8692616,Rider_Id_419,1379
-Order_No_21102,User_Id_190,Bike,3,Business,29,1,12:15:15 PM,29,1,12:18:57 PM,29,1,12:41:20 PM,29,1,1:25:17 PM,29,1,1:40:24 PM,6,,,-1.3237,36.876194,-1.3270347,36.866736200000005,Rider_Id_956,907
-Order_No_13749,User_Id_3080,Bike,3,Business,30,5,4:01:20 PM,30,5,4:17:05 PM,30,5,4:32:08 PM,30,5,4:41:05 PM,30,5,5:04:41 PM,12,21.8,,-1.3207146,36.8305771,-1.2551895,36.7822034,Rider_Id_647,1416
-Order_No_13611,User_Id_1874,Bike,3,Business,9,2,2:53:51 PM,9,2,2:54:08 PM,9,2,3:11:24 PM,9,2,3:12:55 PM,9,2,4:01:47 PM,20,30.6,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_216,2932
-Order_No_2848,User_Id_3655,Bike,1,Personal,4,4,12:03:40 PM,4,4,12:16:35 PM,4,4,12:30:14 PM,4,4,12:34:05 PM,4,4,12:46:05 PM,4,,,-1.288709,36.802659000000006,-1.2943371000000001,36.8267,Rider_Id_844,720
-Order_No_5682,User_Id_2987,Bike,3,Business,19,5,11:58:24 AM,19,5,11:59:10 AM,19,5,12:20:20 PM,19,5,12:22:08 PM,19,5,1:13:45 PM,10,25.1,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_268,3097
-Order_No_17362,User_Id_1369,Bike,2,Personal,10,2,11:43:43 AM,10,2,11:44:17 AM,10,2,11:58:52 AM,10,2,12:00:31 PM,10,2,12:16:21 PM,7,20.3,,-1.3011736999999999,36.743672,-1.2998129999999999,36.787139200000006,Rider_Id_629,950
-Order_No_21671,User_Id_1500,Bike,3,Business,12,6,11:10:17 AM,12,6,11:33:02 AM,12,6,11:43:22 AM,12,6,12:14:20 PM,12,6,12:31:46 PM,8,22.9,,-1.3004062,36.829741,-1.2569006,36.7925141,Rider_Id_56,1046
-Order_No_1049,User_Id_2132,Bike,3,Business,1,4,12:03:49 PM,1,4,12:04:24 PM,1,4,12:15:52 PM,1,4,12:20:00 PM,1,4,12:53:56 PM,9,,,-1.2384056,36.8718698,-1.281914,36.83449,Rider_Id_775,2036
-Order_No_24796,User_Id_2344,Bike,3,Business,1,5,3:54:54 PM,1,5,5:42:41 PM,1,5,6:11:24 PM,1,5,6:15:20 PM,1,5,6:47:04 PM,13,30.2,,-1.2825422,36.73247,-1.3131471000000001,36.817656799999995,Rider_Id_238,1904
-Order_No_10642,User_Id_1932,Bike,3,Business,23,1,2:47:08 PM,23,1,2:47:12 PM,23,1,3:04:23 PM,23,1,3:18:34 PM,23,1,3:38:00 PM,10,,,-1.3254542,36.7968763,-1.2985986999999999,36.7809119,Rider_Id_844,1166
-Order_No_15699,User_Id_895,Bike,1,Personal,10,2,10:42:36 AM,10,2,10:42:51 AM,10,2,10:55:21 AM,10,2,11:05:06 AM,10,2,11:33:29 AM,17,20.1,,-1.2917729,36.809799700000006,-1.2022306,36.7893305,Rider_Id_103,1703
-Order_No_21066,User_Id_3560,Bike,3,Business,28,1,1:16:16 PM,28,1,1:17:20 PM,28,1,1:39:46 PM,28,1,1:41:20 PM,28,1,2:03:29 PM,8,26.1,,-1.279395,36.825364,-1.2914687,36.7692172,Rider_Id_862,1329
-Order_No_7352,User_Id_187,Bike,3,Business,5,5,3:52:37 PM,5,5,3:52:45 PM,5,5,3:56:21 PM,5,5,4:03:47 PM,5,5,4:59:36 PM,27,26.7,,-1.3169591,36.7000029,-1.2413316,36.878552500000005,Rider_Id_520,3349
-Order_No_4019,User_Id_2335,Bike,3,Business,19,2,9:53:42 AM,19,2,9:53:59 AM,19,2,10:04:42 AM,19,2,10:09:44 AM,19,2,10:54:56 AM,22,21.2,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_177,2712
-Order_No_10760,User_Id_2310,Bike,1,Business,24,1,10:18:12 AM,24,1,10:18:45 AM,24,1,10:47:41 AM,24,1,11:08:43 AM,24,1,11:08:46 AM,6,22.3,,-1.2711571000000002,36.901728799999994,-1.2859912,36.8756811,Rider_Id_771,3
-Order_No_20429,User_Id_2987,Bike,3,Business,3,1,10:10:06 AM,3,1,10:15:05 AM,3,1,10:48:58 AM,3,1,10:52:45 AM,3,1,11:16:22 AM,3,22.1,,-1.2702120000000001,36.82108770000001,-1.2870774,36.8259832,Rider_Id_531,1417
-Order_No_10500,User_Id_3222,Bike,3,Business,5,1,2:38:41 PM,5,1,2:38:50 PM,5,1,2:42:06 PM,5,1,2:49:17 PM,5,1,3:05:35 PM,7,25.8,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_795,978
-Order_No_12579,User_Id_186,Bike,3,Business,22,5,8:58:00 AM,22,5,9:00:05 AM,22,5,9:44:21 AM,22,5,9:47:03 AM,22,5,10:30:17 AM,23,22.0,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_863,2594
-Order_No_25799,User_Id_3147,Bike,3,Business,19,3,11:04:35 AM,19,3,11:04:53 AM,19,3,11:24:35 AM,19,3,12:05:36 PM,19,3,12:29:25 PM,17,22.1,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_216,1429
-Order_No_19034,User_Id_2543,Bike,2,Personal,7,5,5:00:54 PM,7,5,5:06:17 PM,7,5,5:47:35 PM,7,5,5:50:36 PM,7,5,6:07:27 PM,5,22.4,,-1.2915418,36.7979019,-1.3021964,36.768168100000004,Rider_Id_86,1011
-Order_No_14290,User_Id_317,Bike,1,Personal,6,6,3:36:04 PM,6,6,3:36:43 PM,6,6,3:50:32 PM,6,6,3:51:32 PM,6,6,4:03:52 PM,4,27.8,,-1.2849416,36.772907700000005,-1.2858786,36.7888062,Rider_Id_674,740
-Order_No_2291,User_Id_1245,Bike,3,Business,15,3,2:05:06 PM,15,3,2:06:02 PM,15,3,2:18:24 PM,15,3,2:46:43 PM,15,3,3:14:04 PM,17,26.0,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_211,1641
-Order_No_17298,User_Id_1500,Bike,3,Business,12,2,9:44:56 AM,12,2,9:45:42 AM,12,2,9:45:46 AM,12,2,10:01:59 AM,12,2,10:19:36 AM,8,17.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_576,1057
-Order_No_22859,User_Id_393,Bike,3,Business,29,1,12:45:26 PM,29,1,12:45:34 PM,29,1,12:46:41 PM,29,1,1:02:21 PM,29,1,1:58:32 PM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,3371
-Order_No_28218,User_Id_2000,Bike,3,Business,11,4,11:32:08 AM,11,4,11:32:20 AM,11,4,11:32:38 AM,11,4,11:42:23 AM,11,4,11:53:37 AM,4,25.6,,-1.2551895,36.7822034,-1.266294,36.8057971,Rider_Id_576,674
-Order_No_18524,User_Id_3283,Bike,3,Business,25,5,10:15:33 AM,25,5,10:16:09 AM,25,5,10:22:31 AM,25,5,10:29:34 AM,25,5,10:37:45 AM,5,20.5,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_4,491
-Order_No_24533,User_Id_2149,Bike,1,Personal,29,6,11:39:06 AM,29,6,11:39:31 AM,29,6,11:41:12 AM,29,6,11:45:45 AM,29,6,12:11:08 PM,5,24.8,,-1.2675332000000001,36.808561,-1.2887816,36.8088885,Rider_Id_795,1523
-Order_No_4403,User_Id_1245,Bike,3,Business,22,6,9:11:48 AM,22,6,9:11:56 AM,22,6,9:23:41 AM,22,6,9:56:28 AM,22,6,10:19:39 AM,19,19.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_424,1391
-Order_No_25795,User_Id_2715,Bike,3,Personal,3,5,2:16:43 PM,3,5,2:19:15 PM,3,5,2:34:29 PM,3,5,2:43:08 PM,3,5,3:08:52 PM,9,25.0,,-1.2951188999999999,36.7699791,-1.312406,36.827447,Rider_Id_548,1544
-Order_No_7662,User_Id_1329,Bike,3,Business,14,5,9:26:04 AM,14,5,9:27:16 AM,14,5,9:27:24 AM,14,5,9:42:19 AM,14,5,10:12:02 AM,5,17.0,,-1.273056,36.811298,-1.3056277,36.817316600000005,Rider_Id_89,1783
-Order_No_3497,User_Id_2582,Bike,3,Business,29,5,11:48:01 AM,29,5,1:05:43 PM,29,5,1:18:32 PM,29,5,1:24:55 PM,29,5,2:23:14 PM,8,19.5,,-1.2551895,36.7822034,-1.2901228,36.8234237,Rider_Id_268,3499
-Order_No_14623,User_Id_3283,Bike,3,Business,18,5,11:25:15 AM,18,5,11:25:23 AM,18,5,11:27:32 AM,18,5,11:46:17 AM,18,5,11:56:29 AM,5,19.9,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_383,612
-Order_No_13018,User_Id_640,Bike,3,Business,12,5,4:35:45 PM,12,5,4:36:24 PM,12,5,4:50:12 PM,12,5,4:55:18 PM,12,5,5:18:01 PM,6,25.9,,-1.304033,36.7848695,-1.294766,36.825895700000004,Rider_Id_208,1363
-Order_No_3315,User_Id_962,Bike,3,Personal,17,5,3:27:57 PM,17,5,3:34:59 PM,17,5,4:02:14 PM,17,5,4:04:17 PM,17,5,4:42:25 PM,9,20.4,,-1.345199,36.7664115,-1.2943818,36.7858765,Rider_Id_312,2288
-Order_No_19441,User_Id_2330,Bike,3,Business,18,1,1:40:02 PM,18,1,1:41:18 PM,18,1,1:49:20 PM,18,1,1:50:29 PM,18,1,2:09:01 PM,7,30.6,,-1.2680363000000001,36.799046600000004,-1.2873232,36.765857200000006,Rider_Id_778,1112
-Order_No_19416,User_Id_1578,Bike,3,Business,27,2,10:36:53 AM,27,2,10:59:46 AM,27,2,11:16:43 AM,27,2,11:23:50 AM,27,2,11:31:33 AM,2,24.0,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_456,463
-Order_No_7940,User_Id_2801,Bike,3,Business,14,2,12:57:47 PM,14,2,1:13:08 PM,14,2,1:39:45 PM,14,2,2:07:24 PM,14,2,2:41:01 PM,29,24.2,,-1.1538233,36.950874,-1.2595421999999998,36.7871178,Rider_Id_522,2017
-Order_No_3938,User_Id_2069,Bike,3,Business,13,3,3:23:46 PM,13,3,3:25:13 PM,13,3,3:41:56 PM,13,3,3:50:10 PM,13,3,4:16:54 PM,12,29.1,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_825,1604
-Order_No_4334,User_Id_909,Bike,3,Business,21,1,1:03:13 PM,21,1,1:03:39 PM,21,1,1:39:12 PM,21,1,1:54:39 PM,21,1,2:23:47 PM,13,27.6,,-1.2283403,36.8822756,-1.225322,36.80855,Rider_Id_845,1748
-Order_No_21584,User_Id_12,Bike,3,Business,9,3,11:02:34 AM,9,3,11:19:30 AM,9,3,11:54:08 AM,9,3,11:55:10 AM,9,3,12:28:35 PM,9,25.1,,-1.2400209999999998,36.849990000000005,-1.2864963,36.8252086,Rider_Id_48,2005
-Order_No_6232,User_Id_2523,Bike,3,Business,17,5,1:31:52 PM,17,5,1:34:06 PM,17,5,1:40:39 PM,17,5,1:57:01 PM,17,5,2:08:21 PM,4,,,-1.2551895,36.7822034,-1.2754343000000001,36.8127158,Rider_Id_147,680
-Order_No_2743,User_Id_1160,Bike,1,Personal,15,1,12:47:19 PM,15,1,12:47:36 PM,15,1,12:48:48 PM,15,1,12:58:39 PM,15,1,1:28:55 PM,13,,,-1.2297202,36.881950700000004,-1.1748796000000001,36.8321847,Rider_Id_704,1816
-Order_No_10932,User_Id_437,Bike,3,Business,29,4,3:28:37 PM,29,4,3:46:01 PM,29,4,3:51:34 PM,29,4,3:55:51 PM,29,4,4:04:51 PM,3,24.7,,-1.2551895,36.7822034,-1.268027,36.8037005,Rider_Id_228,540
-Order_No_22515,User_Id_1637,Bike,3,Business,10,1,1:13:13 PM,10,1,1:15:08 PM,10,1,1:15:18 PM,10,1,1:30:22 PM,10,1,2:47:54 PM,15,23.3,,-1.2615888000000002,36.7928732,-1.2232123,36.8932547,Rider_Id_725,4652
-Order_No_6591,User_Id_867,Bike,3,Business,18,2,3:04:01 PM,18,2,3:10:56 PM,18,2,3:34:00 PM,18,2,3:54:46 PM,18,2,4:17:54 PM,10,25.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_62,1388
-Order_No_17546,User_Id_20,Bike,3,Business,11,4,2:57:16 PM,11,4,3:00:41 PM,11,4,3:17:40 PM,11,4,3:23:00 PM,11,4,4:08:56 PM,6,26.8,,-1.2629666000000002,36.8068369,-1.2937193,36.8236214,Rider_Id_576,2756
-Order_No_2591,User_Id_38,Bike,1,Personal,22,3,1:42:01 PM,22,3,1:42:30 PM,22,3,1:42:36 PM,22,3,1:48:35 PM,22,3,2:08:15 PM,14,25.7,,-1.2962832,36.784968299999996,-1.3207708,36.8685919,Rider_Id_726,1180
-Order_No_5458,User_Id_517,Bike,3,Business,31,1,12:05:45 PM,31,1,12:06:00 PM,31,1,12:09:12 PM,31,1,12:10:47 PM,31,1,12:38:08 PM,14,21.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1641
-Order_No_1861,User_Id_60,Bike,3,Business,1,4,10:22:14 AM,1,4,10:25:19 AM,1,4,10:47:08 AM,1,4,10:53:21 AM,1,4,11:43:09 AM,9,,,-1.2783848999999998,36.8163445,-1.2296618000000001,36.84382720000001,Rider_Id_6,2988
-Order_No_15166,User_Id_393,Bike,3,Business,3,5,9:39:22 AM,3,5,9:39:57 AM,3,5,9:48:05 AM,3,5,9:52:54 AM,3,5,10:37:53 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_591,2699
-Order_No_23619,User_Id_3355,Bike,3,Business,27,2,10:05:50 AM,27,2,10:51:54 AM,27,2,11:06:09 AM,27,2,11:23:38 AM,27,2,11:43:30 AM,4,23.9,,-1.291768,36.8297208,-1.3052491000000002,36.8223899,Rider_Id_510,1192
-Order_No_9473,User_Id_2854,Bike,3,Business,30,3,1:01:40 PM,30,3,1:38:30 PM,30,3,1:54:30 PM,30,3,2:15:20 PM,30,3,2:45:02 PM,15,24.9,,-1.2287313999999998,36.8367354,-1.2963097,36.7688221,Rider_Id_752,1782
-Order_No_25500,User_Id_265,Bike,3,Business,22,5,10:30:47 AM,22,5,10:30:55 AM,22,5,10:35:57 AM,22,5,10:41:10 AM,22,5,11:30:42 AM,11,,,-1.300921,36.828195,-1.3459625,36.9073925,Rider_Id_127,2972
-Order_No_16962,User_Id_2063,Bike,3,Business,8,2,11:43:41 AM,8,2,11:48:03 AM,8,2,11:59:53 AM,8,2,12:02:02 PM,8,2,12:36:32 PM,9,21.2,,-1.2559559999999999,36.789867,-1.3018758,36.75908629999999,Rider_Id_782,2070
-Order_No_19986,User_Id_893,Bike,3,Business,7,2,2:08:20 PM,7,2,2:10:47 PM,7,2,2:30:46 PM,7,2,2:32:46 PM,7,2,3:20:04 PM,5,24.8,,-1.2663194,36.809901,-1.2854193,36.829616200000004,Rider_Id_519,2838
-Order_No_24162,User_Id_1761,Bike,3,Business,18,5,12:06:50 PM,18,5,12:10:01 PM,18,5,12:36:43 PM,18,5,12:42:48 PM,18,5,1:47:50 PM,14,21.4,,-1.3688433,36.7402114,-1.3028223,36.7879534,Rider_Id_543,3902
-Order_No_1814,User_Id_2801,Bike,3,Business,14,3,10:08:27 AM,14,3,10:08:38 AM,14,3,10:08:51 AM,14,3,10:12:23 AM,14,3,10:39:51 AM,14,18.5,,-1.3288576,36.8465936,-1.2595421999999998,36.7871178,Rider_Id_387,1648
-Order_No_10888,User_Id_420,Bike,3,Business,26,2,7:12:22 PM,26,2,7:27:14 PM,26,2,7:37:01 PM,26,2,7:51:42 PM,26,2,8:00:29 PM,6,22.5,,-1.2517448,36.7924971,-1.2643683000000001,36.7777614,Rider_Id_361,527
-Order_No_24452,User_Id_393,Bike,3,Business,8,5,11:13:09 AM,8,5,11:13:59 AM,8,5,11:24:48 AM,8,5,11:39:09 AM,8,5,11:48:27 AM,4,28.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_701,558
-Order_No_2421,User_Id_2330,Bike,3,Business,31,4,11:39:57 AM,31,4,11:41:09 AM,31,4,11:58:06 AM,31,4,12:03:14 PM,31,4,12:19:11 PM,8,26.3,,-1.2324923,36.836377500000005,-1.2524916000000001,36.8656196,Rider_Id_377,957
-Order_No_15949,User_Id_225,Bike,3,Personal,13,3,12:03:46 PM,13,3,12:06:28 PM,13,3,1:38:30 PM,13,3,1:41:48 PM,13,3,2:00:31 PM,13,27.4,,-1.285851,36.816496,-1.320722,36.8803076,Rider_Id_48,1123
-Order_No_709,User_Id_1836,Bike,1,Personal,22,5,11:52:52 AM,22,5,12:57:51 PM,22,5,12:59:49 PM,22,5,1:16:21 PM,22,5,1:40:02 PM,10,21.8,,-1.2217966999999998,36.810990000000004,-1.2717851,36.810536299999995,Rider_Id_101,1421
-Order_No_4593,User_Id_1075,Bike,3,Business,9,4,11:39:21 AM,9,4,11:40:16 AM,9,4,12:01:48 PM,9,4,1:04:37 PM,9,4,1:28:47 PM,10,,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_61,1450
-Order_No_8233,User_Id_3599,Bike,3,Business,11,6,8:39:15 AM,11,6,8:39:41 AM,11,6,9:26:14 AM,11,6,9:36:28 AM,11,6,10:24:34 AM,8,,,-1.3302996,36.8707935,-1.3074264,36.822612299999996,Rider_Id_459,2886
-Order_No_5757,User_Id_265,Bike,3,Business,29,5,8:40:16 AM,29,5,8:40:59 AM,29,5,8:53:20 AM,29,5,9:05:24 AM,29,5,9:28:27 AM,13,20.8,,-1.300921,36.828195,-1.3195936000000001,36.916169700000005,Rider_Id_206,1383
-Order_No_18956,User_Id_136,Bike,3,Business,17,4,4:42:36 PM,17,4,4:51:27 PM,17,4,5:02:41 PM,17,4,5:05:44 PM,17,4,5:57:35 PM,16,26.0,,-1.3254522,36.721658399999995,-1.2531983,36.8069669,Rider_Id_831,3111
-Order_No_12793,User_Id_111,Bike,3,Business,19,2,12:43:18 PM,19,2,12:43:54 PM,19,2,12:43:59 PM,19,2,12:52:24 PM,19,2,1:45:03 PM,13,26.5,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_576,3159
-Order_No_12051,User_Id_517,Bike,3,Business,8,6,10:15:01 AM,8,6,10:18:48 AM,8,6,10:20:19 AM,8,6,10:31:03 AM,8,6,10:50:01 AM,11,17.0,3.9,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_211,1138
-Order_No_9472,User_Id_3194,Bike,3,Business,19,1,12:26:24 PM,19,1,12:29:55 PM,19,1,12:43:10 PM,19,1,12:46:15 PM,19,1,12:57:30 PM,3,24.7,,-1.2551895,36.7822034,-1.257309,36.806008399999996,Rider_Id_38,675
-Order_No_6925,User_Id_2174,Bike,3,Business,12,3,10:29:56 AM,12,3,10:34:39 AM,12,3,10:37:51 AM,12,3,10:56:54 AM,12,3,11:40:33 AM,27,19.5,,-1.2489964,36.8771594,-1.3339943,36.7263409,Rider_Id_854,2619
-Order_No_9071,User_Id_3076,Bike,1,Personal,21,1,12:06:18 PM,21,1,12:08:36 PM,21,1,12:18:53 PM,21,1,12:34:44 PM,21,1,1:07:21 PM,8,20.0,,-1.3034072,36.821803499999994,-1.2615584,36.814950599999996,Rider_Id_286,1957
-Order_No_4373,User_Id_2691,Bike,3,Business,4,1,11:29:06 AM,4,1,11:29:28 AM,4,1,11:39:23 AM,4,1,11:43:10 AM,4,1,12:28:36 PM,21,25.5,,-1.2551895,36.7822034,-1.3705766000000001,36.9120742,Rider_Id_498,2726
-Order_No_9786,User_Id_2330,Bike,3,Business,7,2,10:35:43 AM,7,2,10:36:04 AM,7,2,10:47:38 AM,7,2,10:54:42 AM,7,2,11:13:21 AM,9,,,-1.2645318,36.7981775,-1.2687407,36.7373043,Rider_Id_484,1119
-Order_No_20638,User_Id_1959,Bike,1,Business,17,4,10:28:30 AM,17,4,10:43:58 AM,17,4,11:20:45 AM,17,4,11:30:57 AM,17,4,11:55:00 AM,13,22.6,,-1.2141875,36.791039000000005,-1.2559559999999999,36.789867,Rider_Id_875,1443
-Order_No_12508,User_Id_970,Bike,3,Business,18,1,8:34:56 AM,18,1,8:35:56 AM,18,1,8:43:18 AM,18,1,8:46:41 AM,18,1,9:08:19 AM,6,22.1,,-1.2551895,36.7822034,-1.2657935,36.7953389,Rider_Id_778,1298
-Order_No_2908,User_Id_2530,Bike,1,Personal,14,7,3:21:50 PM,14,7,3:22:23 PM,14,7,3:31:05 PM,14,7,3:36:10 PM,14,7,3:44:56 PM,6,28.6,,-1.2049707,36.785895700000005,-1.238874,36.807889,Rider_Id_453,526
-Order_No_23650,User_Id_1882,Bike,3,Business,6,3,3:01:13 PM,6,3,3:01:39 PM,6,3,3:15:32 PM,6,3,3:37:18 PM,6,3,3:52:59 PM,8,,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_655,941
-Order_No_18292,User_Id_1075,Bike,3,Business,28,1,5:28:08 PM,28,1,5:29:53 PM,28,1,5:46:11 PM,28,1,5:54:50 PM,28,1,6:30:07 PM,15,25.4,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_779,2117
-Order_No_5456,User_Id_393,Bike,3,Business,16,3,11:29:21 AM,16,3,11:42:41 AM,16,3,11:43:08 AM,16,3,11:53:26 AM,16,3,12:20:09 PM,11,22.6,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_623,1603
-Order_No_9541,User_Id_1071,Bike,1,Personal,19,4,9:11:21 AM,19,4,9:12:33 AM,19,4,9:38:27 AM,19,4,9:59:32 AM,19,4,10:26:02 AM,11,14.5,,-1.3301743000000001,36.8487987,-1.2689015,36.7986106,Rider_Id_208,1590
-Order_No_25226,User_Id_1075,Bike,3,Business,29,1,3:16:29 PM,29,1,3:16:56 PM,29,1,3:19:34 PM,29,1,3:21:59 PM,29,1,3:48:54 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_204,1615
-Order_No_9262,User_Id_1075,Bike,3,Business,16,2,4:27:24 PM,16,2,4:29:19 PM,16,2,4:40:57 PM,16,2,4:45:06 PM,16,2,5:08:37 PM,8,,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_166,1411
-Order_No_11797,User_Id_1996,Bike,3,Business,11,4,10:30:37 AM,11,4,10:30:51 AM,11,4,10:52:20 AM,11,4,11:09:05 AM,11,4,11:26:05 AM,6,25.4,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_507,1020
-Order_No_3011,User_Id_1245,Bike,3,Business,25,4,2:27:01 PM,25,4,2:31:29 PM,25,4,2:40:50 PM,25,4,2:47:54 PM,25,4,3:18:29 PM,14,21.0,,-1.3878504999999999,36.7692885,-1.3198615,36.7110323,Rider_Id_456,1835
-Order_No_9244,User_Id_3647,Bike,3,Business,30,4,2:00:23 PM,30,4,2:01:39 PM,30,4,2:20:57 PM,30,4,2:25:14 PM,30,4,2:45:27 PM,6,,,-1.2726389999999999,36.794723,-1.2972916,36.7880497,Rider_Id_868,1213
-Order_No_26817,User_Id_3283,Bike,3,Business,19,6,3:19:24 PM,19,6,3:19:56 PM,19,6,3:25:34 PM,19,6,3:33:43 PM,19,6,3:43:11 PM,5,24.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_51,568
-Order_No_22840,User_Id_909,Bike,3,Business,16,4,12:50:22 PM,16,4,1:06:39 PM,16,4,1:14:25 PM,16,4,1:24:22 PM,16,4,2:04:07 PM,27,,,-1.3632162,36.9108253,-1.225322,36.80855,Rider_Id_877,2385
-Order_No_3820,User_Id_184,Bike,1,Personal,4,1,11:45:29 AM,4,1,11:48:43 AM,4,1,12:07:19 PM,4,1,12:12:50 PM,4,1,12:35:34 PM,8,21.2,,-1.2821577,36.8220574,-1.3237598999999998,36.8052613,Rider_Id_906,1364
-Order_No_17939,User_Id_1687,Bike,3,Business,10,5,1:38:32 PM,10,5,1:39:11 PM,10,5,1:53:16 PM,10,5,1:55:52 PM,10,5,2:13:35 PM,3,,,-1.2626574,36.8014034,-1.2755629,36.813152200000005,Rider_Id_76,1063
-Order_No_20162,User_Id_2346,Bike,1,Personal,26,6,5:11:36 PM,26,6,5:12:32 PM,26,6,5:45:24 PM,26,6,5:50:08 PM,26,6,6:03:34 PM,6,22.2,,-1.3054537,36.8284768,-1.3121275,36.8519202,Rider_Id_304,806
-Order_No_9654,User_Id_2457,Bike,3,Business,13,1,12:51:21 PM,13,1,12:51:47 PM,13,1,12:52:00 PM,13,1,12:56:00 PM,13,1,1:11:25 PM,6,,,-1.2551895,36.7822034,-1.2425149,36.789013700000005,Rider_Id_576,925
-Order_No_11794,User_Id_3291,Bike,3,Business,2,2,11:21:32 AM,2,2,11:26:17 AM,2,2,11:44:13 AM,2,2,11:48:15 AM,2,2,12:18:24 PM,8,26.0,,-1.2963367000000001,36.8094235,-1.2600926000000001,36.8088685,Rider_Id_740,1809
-Order_No_20062,User_Id_2079,Bike,3,Personal,30,3,10:44:05 AM,30,3,10:44:53 AM,30,3,11:22:19 AM,30,3,11:36:46 AM,30,3,12:37:29 PM,21,24.8,,-1.333148,36.922453000000004,-1.2681003999999998,36.7952081,Rider_Id_404,3643
-Order_No_4800,User_Id_2330,Bike,3,Business,27,3,4:20:36 PM,27,3,4:26:36 PM,27,3,4:39:32 PM,27,3,4:43:19 PM,27,3,5:06:48 PM,9,27.7,,-1.2124582,36.802894,-1.2637056999999998,36.805170000000004,Rider_Id_547,1409
-Order_No_16682,User_Id_2053,Bike,1,Personal,8,2,3:28:03 PM,8,2,3:38:05 PM,8,2,4:08:14 PM,8,2,4:16:56 PM,8,2,4:45:13 PM,9,23.2,,-1.2934216,36.7965683,-1.2559559999999999,36.789867,Rider_Id_937,1697
-Order_No_3372,User_Id_1191,Bike,1,Personal,3,1,4:30:13 PM,3,1,4:30:32 PM,3,1,4:52:42 PM,3,1,4:55:11 PM,3,1,5:12:17 PM,7,22.0,,-1.32589,36.831679,-1.3282758,36.8590281,Rider_Id_87,1026
-Order_No_23412,User_Id_1280,Bike,3,Business,26,5,1:20:31 PM,26,5,1:25:23 PM,26,5,1:26:52 PM,26,5,1:30:13 PM,26,5,1:48:29 PM,3,,,-1.2551895,36.7822034,-1.2654348,36.8022464,Rider_Id_578,1096
-Order_No_6839,User_Id_3017,Bike,1,Business,9,3,4:51:28 PM,9,3,4:51:50 PM,9,3,5:00:41 PM,9,3,5:02:14 PM,9,3,5:30:42 PM,7,21.9,,-1.2991542999999999,36.7827912,-1.2753108999999998,36.8130185,Rider_Id_761,1708
-Order_No_1400,User_Id_3599,Bike,3,Business,19,5,12:35:35 PM,19,5,12:51:22 PM,19,5,12:52:37 PM,19,5,1:26:20 PM,19,5,1:33:44 PM,10,24.7,,-1.3302996,36.8707935,-1.3132708999999998,36.9127044,Rider_Id_179,444
-Order_No_20970,User_Id_635,Bike,3,Business,12,3,2:06:19 PM,12,3,2:08:35 PM,12,3,2:25:47 PM,12,3,2:30:41 PM,12,3,3:14:51 PM,10,20.9,,-1.2599563,36.799343900000004,-1.2105068,36.7859421,Rider_Id_739,2650
-Order_No_1086,User_Id_3669,Bike,3,Business,29,2,10:57:18 AM,29,2,10:57:42 AM,29,2,11:03:17 AM,29,2,11:09:58 AM,29,2,11:22:15 AM,3,23.5,,-1.2641662,36.8136393,-1.2615888000000002,36.7928732,Rider_Id_79,737
-Order_No_26662,User_Id_733,Bike,3,Business,5,2,10:45:57 AM,5,2,10:46:13 AM,5,2,10:46:39 AM,5,2,10:54:21 AM,5,2,11:18:20 AM,10,23.7,,-1.2551895,36.7822034,-1.3031971000000002,36.786314600000004,Rider_Id_518,1439
-Order_No_6658,User_Id_366,Bike,2,Personal,1,3,1:49:54 PM,1,3,1:50:02 PM,1,3,1:50:12 PM,1,3,2:03:54 PM,1,3,2:15:18 PM,5,24.1,,-1.2857671,36.7875614,-1.268027,36.8037005,Rider_Id_260,684
-Order_No_28205,User_Id_3222,Bike,3,Business,20,3,9:36:39 AM,20,3,9:36:52 AM,20,3,9:54:21 AM,20,3,9:57:24 AM,20,3,10:52:12 AM,30,20.7,,-1.2551895,36.7822034,-1.1429062,36.9300994,Rider_Id_953,3288
-Order_No_20472,User_Id_3291,Bike,3,Business,4,6,12:14:31 PM,4,6,12:31:13 PM,4,6,12:54:03 PM,4,6,1:09:51 PM,4,6,1:38:13 PM,17,,,-1.3444459999999998,36.756597,-1.2600926000000001,36.8088685,Rider_Id_538,1702
-Order_No_3681,User_Id_2789,Bike,3,Business,26,2,1:44:19 PM,26,2,1:49:58 PM,26,2,2:05:26 PM,26,2,2:09:32 PM,26,2,2:51:43 PM,17,29.8,,-1.3634667,36.7389097,-1.304735,36.784605400000004,Rider_Id_227,2531
-Order_No_16127,User_Id_2200,Bike,3,Business,28,4,1:20:30 PM,28,4,2:41:49 PM,28,4,3:07:28 PM,28,4,3:22:14 PM,28,4,4:02:49 PM,17,29.9,,-1.3700383,36.9190174,-1.290894,36.822971,Rider_Id_19,2435
-Order_No_26504,User_Id_2541,Bike,1,Personal,9,1,5:31:40 PM,9,1,5:33:18 PM,9,1,6:09:53 PM,9,1,6:23:39 PM,9,1,6:49:55 PM,8,20.4,,-1.29237,36.8241297,-1.2955041999999999,36.77146870000001,Rider_Id_858,1576
-Order_No_1430,User_Id_1205,Bike,3,Business,12,2,1:47:07 PM,12,2,1:48:05 PM,12,2,1:53:25 PM,12,2,1:55:16 PM,12,2,2:14:45 PM,8,,,-1.2495931,36.80650870000001,-1.2839651,36.789170399999996,Rider_Id_318,1169
-Order_No_16476,User_Id_406,Bike,1,Personal,28,1,11:52:07 AM,28,1,11:53:35 AM,28,1,12:03:28 PM,28,1,12:08:28 PM,28,1,12:22:06 PM,6,22.6,,-1.2890399,36.8164942,-1.2553615,36.789537599999996,Rider_Id_852,818
-Order_No_20172,User_Id_435,Bike,3,Business,24,5,10:17:12 AM,24,5,10:17:26 AM,24,5,10:22:51 AM,24,5,10:31:38 AM,24,5,11:09:37 AM,16,18.1,,-1.300921,36.828195,-1.2094552,36.807328600000005,Rider_Id_158,2279
-Order_No_5340,User_Id_71,Bike,3,Business,28,1,1:00:05 PM,28,1,1:01:17 PM,28,1,1:12:50 PM,28,1,1:21:26 PM,28,1,1:26:47 PM,1,23.9,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_21,321
-Order_No_3740,User_Id_1081,Bike,3,Business,15,5,2:44:25 PM,15,5,2:46:58 PM,15,5,3:25:40 PM,15,5,3:27:37 PM,15,5,4:08:41 PM,12,28.2,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_194,2464
-Order_No_1156,User_Id_3203,Bike,3,Business,8,5,3:16:28 PM,8,5,3:52:21 PM,8,5,4:06:32 PM,8,5,4:48:57 PM,8,5,5:13:58 PM,17,19.8,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_116,1501
-Order_No_24004,User_Id_1552,Bike,1,Personal,28,5,11:14:10 AM,28,5,11:14:42 AM,28,5,11:40:42 AM,28,5,11:46:26 AM,28,5,12:06:23 PM,3,25.3,,-1.2755028999999998,36.813658000000004,-1.2610283000000002,36.793628600000005,Rider_Id_844,1197
-Order_No_817,User_Id_2335,Bike,3,Business,19,1,11:51:29 AM,19,1,11:51:45 AM,19,1,12:08:44 PM,19,1,12:25:48 PM,19,1,1:13:29 PM,22,24.5,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_254,2861
-Order_No_23721,User_Id_1500,Bike,3,Business,16,2,11:05:28 AM,16,2,12:08:26 PM,16,2,12:18:47 PM,16,2,12:57:40 PM,16,2,1:17:21 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_843,1181
-Order_No_4003,User_Id_1304,Bike,3,Business,30,2,12:07:45 PM,30,2,12:08:07 PM,30,2,12:23:52 PM,30,2,12:26:12 PM,30,2,1:36:13 PM,13,22.1,,-1.3332749,36.870814700000004,-1.2697141,36.798177700000004,Rider_Id_544,4201
-Order_No_8255,User_Id_1471,Bike,3,Business,15,5,12:37:02 PM,15,5,12:37:24 PM,15,5,12:42:59 PM,15,5,12:47:32 PM,15,5,12:58:08 PM,3,26.6,,-1.2551895,36.7822034,-1.2649783,36.807143599999996,Rider_Id_720,636
-Order_No_11099,User_Id_2964,Bike,3,Business,30,2,9:56:43 AM,30,2,9:57:19 AM,30,2,10:07:24 AM,30,2,10:11:33 AM,30,2,10:31:46 AM,9,,,-1.2551895,36.7822034,-1.2977565,36.8025534,Rider_Id_96,1213
-Order_No_22774,User_Id_2523,Bike,3,Business,8,1,12:28:29 PM,8,1,12:30:11 PM,8,1,1:02:22 PM,8,1,1:07:20 PM,8,1,1:31:56 PM,5,28.0,,-1.2551895,36.7822034,-1.2657199,36.7914391,Rider_Id_161,1476
-Order_No_1918,User_Id_3689,Bike,3,Business,16,5,7:36:19 PM,16,5,7:48:43 PM,16,5,8:05:40 PM,16,5,8:14:27 PM,16,5,8:44:36 PM,9,20.1,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_554,1809
-Order_No_10364,User_Id_2436,Bike,3,Business,7,4,11:45:02 AM,7,4,11:45:14 AM,7,4,11:49:19 AM,7,4,11:53:15 AM,7,4,12:02:52 PM,4,27.4,,-1.2551895,36.7822034,-1.2715686000000002,36.8023526,Rider_Id_523,577
-Order_No_3168,User_Id_393,Bike,3,Business,14,2,10:58:45 AM,14,2,11:03:41 AM,14,2,11:13:24 AM,14,2,11:23:53 AM,14,2,11:46:31 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_778,1358
-Order_No_26709,User_Id_2539,Bike,3,Business,16,3,3:28:06 PM,16,3,3:28:44 PM,16,3,3:36:33 PM,16,3,3:39:52 PM,16,3,3:49:35 PM,8,27.0,,-1.2930037,36.812647399999996,-1.2647126,36.8049247,Rider_Id_576,583
-Order_No_27803,User_Id_3295,Bike,3,Business,22,3,3:47:42 PM,22,3,3:48:00 PM,22,3,3:54:32 PM,22,3,4:10:07 PM,22,3,4:29:27 PM,13,25.9,,-1.2571472,36.795063299999995,-1.3226301999999999,36.8719901,Rider_Id_789,1160
-Order_No_10455,User_Id_276,Bike,2,Personal,18,1,1:20:55 PM,18,1,1:39:13 PM,18,1,1:45:31 PM,18,1,1:50:46 PM,18,1,2:30:07 PM,5,17.1,,-1.2854453000000001,36.818836299999994,-1.2509176000000002,36.8246646,Rider_Id_444,2361
-Order_No_5647,User_Id_2523,Bike,3,Business,20,3,2:43:59 PM,20,3,2:44:16 PM,20,3,2:47:23 PM,20,3,2:52:08 PM,20,3,3:18:00 PM,8,29.2,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_709,1552
-Order_No_10822,User_Id_265,Bike,3,Business,21,4,2:20:36 PM,21,4,2:21:29 PM,21,4,2:38:56 PM,21,4,3:55:10 PM,21,4,4:24:44 PM,12,26.5,,-1.2414003,36.8094718,-1.300921,36.828195,Rider_Id_952,1774
-Order_No_7134,User_Id_1245,Bike,3,Business,30,5,8:40:38 AM,30,5,8:41:21 AM,30,5,8:41:38 AM,30,5,9:29:13 AM,30,5,9:37:56 AM,17,18.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_472,523
-Order_No_25404,User_Id_2344,Bike,3,Business,19,3,12:24:52 PM,19,3,3:05:08 PM,19,3,3:11:15 PM,19,3,3:16:40 PM,19,3,3:29:05 PM,8,25.8,,-1.2551895,36.7822034,-1.2857684999999999,36.7866843,Rider_Id_857,745
-Order_No_203,User_Id_2982,Bike,3,Business,13,6,10:07:30 AM,13,6,10:08:12 AM,13,6,10:22:31 AM,13,6,10:25:17 AM,13,6,10:51:38 AM,19,19.9,,-1.2691246,36.7992584,-1.177202,36.8351183,Rider_Id_394,1581
-Order_No_19176,User_Id_53,Bike,3,Business,2,4,1:18:17 PM,2,4,1:35:10 PM,2,4,1:58:04 PM,2,4,2:02:56 PM,2,4,2:19:58 PM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_92,1022
-Order_No_23552,User_Id_1932,Bike,3,Business,28,4,1:14:05 PM,28,4,1:20:56 PM,28,4,1:55:07 PM,28,4,2:03:57 PM,28,4,2:28:00 PM,7,,,-1.2865096,36.8169364,-1.2922857,36.781327399999995,Rider_Id_268,1443
-Order_No_4845,User_Id_1329,Bike,3,Business,4,5,2:14:13 PM,4,5,2:17:55 PM,4,5,2:17:57 PM,4,5,3:02:19 PM,4,5,3:18:37 PM,6,,,-1.273056,36.811298,-1.2886399,36.792454799999994,Rider_Id_813,978
-Order_No_6439,User_Id_187,Bike,3,Business,31,2,11:32:42 AM,31,2,11:33:02 AM,31,2,11:48:32 AM,31,2,11:56:00 AM,31,2,12:34:42 PM,17,18.4,,-1.3165542,36.700067,-1.2597034,36.8003065,Rider_Id_350,2322
-Order_No_15935,User_Id_1075,Bike,3,Business,6,6,3:46:04 PM,6,6,3:46:22 PM,6,6,4:04:37 PM,6,6,4:08:17 PM,6,6,4:18:49 PM,1,27.9,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_318,632
-Order_No_1265,User_Id_1850,Bike,3,Business,20,1,3:39:56 PM,20,1,3:48:00 PM,20,1,3:52:57 PM,20,1,3:54:25 PM,20,1,4:19:50 PM,7,21.5,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_79,1525
-Order_No_24200,User_Id_3404,Bike,3,Business,5,3,12:54:55 PM,5,3,1:21:19 PM,5,3,1:28:28 PM,5,3,1:41:30 PM,5,3,2:16:41 PM,13,25.2,,-1.2232123,36.8932547,-1.279395,36.825364,Rider_Id_648,2111
-Order_No_15262,User_Id_2532,Bike,3,Business,27,2,3:30:12 PM,27,2,3:32:57 PM,27,2,3:33:37 PM,27,2,3:42:07 PM,27,2,3:57:23 PM,6,25.7,,-1.2551895,36.7822034,-1.2868598999999998,36.821542,Rider_Id_268,916
-Order_No_11252,User_Id_1687,Bike,3,Business,3,2,1:36:34 PM,3,2,1:46:10 PM,3,2,1:58:02 PM,3,2,2:01:35 PM,3,2,2:14:05 PM,4,20.2,,-1.2817542,36.816866999999995,-1.2634530000000002,36.7955612,Rider_Id_266,750
-Order_No_16932,User_Id_493,Bike,3,Personal,9,3,5:07:23 PM,9,3,5:26:02 PM,9,3,5:38:54 PM,9,3,5:42:15 PM,9,3,5:59:51 PM,5,21.8,,-1.2591113,36.8267853,-1.2628937,36.798536999999996,Rider_Id_103,1056
-Order_No_10645,User_Id_1673,Bike,2,Personal,14,5,3:19:42 PM,14,5,3:20:37 PM,14,5,3:34:21 PM,14,5,3:48:56 PM,14,5,4:08:02 PM,7,27.0,,-1.261395,36.8263891,-1.2859608999999999,36.7852628,Rider_Id_427,1146
-Order_No_14570,User_Id_1205,Bike,3,Business,5,1,10:28:41 AM,5,1,10:29:36 AM,5,1,10:36:11 AM,5,1,10:41:10 AM,5,1,10:55:29 AM,4,24.7,,-1.257309,36.806008399999996,-1.2640571000000003,36.7883547,Rider_Id_370,859
-Order_No_23943,User_Id_3647,Bike,3,Business,14,2,3:25:47 PM,14,2,3:26:08 PM,14,2,3:38:59 PM,14,2,3:45:30 PM,14,2,3:55:31 PM,4,,,-1.2762743,36.815278899999996,-1.2726389999999999,36.794723,Rider_Id_770,601
-Order_No_20224,User_Id_1623,Bike,1,Personal,17,2,3:26:19 PM,17,2,3:41:42 PM,17,2,4:16:53 PM,17,2,4:22:18 PM,17,2,4:52:44 PM,11,24.3,,-1.3327653999999998,36.8853117,-1.3159617000000001,36.8117934,Rider_Id_465,1826
-Order_No_13464,User_Id_970,Bike,3,Business,1,1,1:18:41 PM,1,1,1:19:40 PM,1,1,1:25:23 PM,1,1,1:32:31 PM,1,1,1:49:35 PM,10,,,-1.2551895,36.7822034,-1.2518813999999998,36.859660999999996,Rider_Id_634,1024
-Order_No_16877,User_Id_3355,Bike,3,Business,23,5,1:29:34 PM,23,5,1:37:20 PM,23,5,2:03:21 PM,23,5,2:04:47 PM,23,5,2:11:21 PM,3,26.2,,-1.3052491000000002,36.8223899,-1.3207146,36.8305771,Rider_Id_848,394
-Order_No_7747,User_Id_617,Bike,3,Personal,27,3,10:58:33 AM,27,3,10:59:31 AM,27,3,11:05:51 AM,27,3,11:20:41 AM,27,3,12:04:44 PM,12,,,-1.3222627,36.823409600000005,-1.2519209999999998,36.828379,Rider_Id_865,2643
-Order_No_14080,User_Id_2412,Bike,3,Business,28,2,10:32:03 AM,28,2,10:33:02 AM,28,2,11:01:54 AM,28,2,11:04:54 AM,28,2,11:58:25 AM,14,21.2,,-1.2283403,36.8822756,-1.290894,36.822971,Rider_Id_788,3211
-Order_No_10861,User_Id_3686,Bike,3,Business,28,1,12:30:07 PM,28,1,12:30:44 PM,28,1,12:40:35 PM,28,1,12:47:32 PM,28,1,1:15:15 PM,8,23.8,,-1.2551895,36.7822034,-1.254405,36.747597999999996,Rider_Id_486,1663
-Order_No_11979,User_Id_1051,Bike,3,Business,23,3,1:38:45 PM,23,3,1:38:49 PM,23,3,1:39:24 PM,23,3,1:42:38 PM,23,3,2:10:06 PM,14,27.4,,-1.2963097,36.7688221,-1.2500308999999998,36.6690476,Rider_Id_786,1648
-Order_No_2293,User_Id_2946,Bike,2,Personal,23,3,3:24:21 PM,23,3,3:32:16 PM,23,3,4:11:28 PM,23,3,4:14:02 PM,23,3,4:30:27 PM,7,22.3,,-1.2785534,36.823050099999996,-1.2749662,36.7658781,Rider_Id_116,985
-Order_No_12004,User_Id_2801,Bike,3,Business,17,5,9:37:50 AM,17,5,9:38:17 AM,17,5,10:02:24 AM,17,5,10:16:19 AM,17,5,10:30:07 AM,7,,,-1.285355,36.795287,-1.2674273999999999,36.787082700000006,Rider_Id_607,828
-Order_No_22532,User_Id_3214,Bike,3,Business,30,2,9:21:23 AM,30,2,9:22:12 AM,30,2,9:22:35 AM,30,2,9:34:29 AM,30,2,9:51:32 AM,8,,,-1.289795,36.7957132,-1.2566586000000002,36.792968200000004,Rider_Id_613,1023
-Order_No_11929,User_Id_552,Bike,3,Business,31,5,11:20:02 AM,31,5,11:21:43 AM,31,5,11:38:07 AM,31,5,11:53:58 AM,31,5,12:55:10 PM,18,21.8,,-1.279395,36.825364,-1.3562374,36.904295399999995,Rider_Id_582,3672
-Order_No_5804,User_Id_1363,Bike,3,Business,3,3,12:25:52 PM,3,3,12:29:45 PM,3,3,12:46:58 PM,3,3,12:52:39 PM,3,3,1:07:25 PM,3,24.0,,-1.3004062,36.829741,-1.2830186000000001,36.8232619,Rider_Id_21,886
-Order_No_5087,User_Id_265,Bike,3,Business,12,2,2:49:17 PM,12,2,2:49:37 PM,12,2,2:55:49 PM,12,2,3:01:12 PM,12,2,3:29:10 PM,19,27.2,,-1.300921,36.828195,-1.3886344,36.9372718,Rider_Id_283,1678
-Order_No_21468,User_Id_53,Bike,3,Business,7,4,11:32:33 AM,7,4,11:33:01 AM,7,4,11:38:33 AM,7,4,11:55:40 AM,7,4,12:09:25 PM,5,24.2,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_462,825
-Order_No_407,User_Id_265,Bike,3,Business,19,1,11:15:03 AM,19,1,11:27:43 AM,19,1,12:00:43 PM,19,1,12:18:51 PM,19,1,12:39:40 PM,12,24.8,,-1.2525335,36.875113899999995,-1.300921,36.828195,Rider_Id_633,1249
-Order_No_11733,User_Id_3686,Bike,3,Business,2,2,9:42:07 AM,2,2,9:42:49 AM,2,2,9:55:45 AM,2,2,10:00:01 AM,2,2,10:25:36 AM,10,20.5,,-1.2551895,36.7822034,-1.3018312,36.781749700000006,Rider_Id_167,1535
-Order_No_11180,User_Id_1209,Bike,3,Business,7,2,4:56:34 PM,7,2,4:56:42 PM,7,2,4:56:47 PM,7,2,5:05:25 PM,7,2,5:11:41 PM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_411,376
-Order_No_15152,User_Id_3728,Bike,2,Personal,2,3,2:51:39 PM,2,3,3:14:30 PM,2,3,3:32:05 PM,2,3,3:35:56 PM,2,3,3:50:45 PM,3,23.3,,-1.2814037,36.815031299999994,-1.2890399,36.8164942,Rider_Id_219,889
-Order_No_20893,User_Id_3291,Bike,3,Business,23,5,3:21:48 PM,23,5,3:25:06 PM,23,5,3:28:09 PM,23,5,3:40:31 PM,23,5,3:58:42 PM,11,26.4,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_913,1091
-Order_No_19234,User_Id_3326,Bike,3,Business,21,4,3:02:54 PM,21,4,3:04:13 PM,21,4,3:04:17 PM,21,4,3:08:54 PM,21,4,3:20:55 PM,4,26.5,,-1.2551895,36.7822034,-1.2738521999999999,36.799281799999996,Rider_Id_523,721
-Order_No_19452,User_Id_2801,Bike,3,Business,25,1,1:53:36 PM,25,1,1:54:48 PM,25,1,2:13:35 PM,25,1,2:17:07 PM,25,1,2:17:13 PM,3,30.0,,-1.2595421999999998,36.7871178,-1.2566688999999998,36.779695200000006,Rider_Id_56,6
-Order_No_8261,User_Id_61,Bike,1,Personal,26,3,12:21:38 PM,26,3,12:22:43 PM,26,3,12:38:06 PM,26,3,12:58:31 PM,26,3,1:24:55 PM,13,24.0,,-1.3357318999999999,36.8621006,-1.2560455,36.780086700000005,Rider_Id_54,1584
-Order_No_15147,User_Id_2985,Bike,2,Personal,14,5,10:50:19 AM,14,5,10:50:27 AM,14,5,11:10:16 AM,14,5,11:11:39 AM,14,5,11:22:34 AM,3,24.4,,-1.2222688999999998,36.854933700000004,-1.22966,36.84401,Rider_Id_933,655
-Order_No_3536,User_Id_1273,Bike,2,Personal,22,7,2:03:51 PM,22,7,2:04:02 PM,22,7,2:11:30 PM,22,7,2:19:14 PM,22,7,2:22:23 PM,2,19.2,,-1.2786893999999998,36.8032321,-1.2687973999999997,36.7960339,Rider_Id_858,189
-Order_No_3326,User_Id_868,Bike,3,Business,11,6,11:49:19 AM,11,6,11:50:07 AM,11,6,11:57:36 AM,11,6,12:07:02 PM,11,6,12:20:33 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_775,811
-Order_No_11342,User_Id_778,Bike,3,Business,7,4,8:28:56 AM,7,4,8:33:39 AM,7,4,9:05:32 AM,7,4,9:10:07 AM,7,4,9:36:39 AM,7,,,-1.2535486,36.7933541,-1.2782978,36.827323799999995,Rider_Id_101,1592
-Order_No_10702,User_Id_1046,Bike,1,Personal,11,4,12:33:14 PM,11,4,12:39:29 PM,11,4,12:45:19 PM,11,4,12:47:43 PM,11,4,1:10:07 PM,6,24.0,,-1.2858016,36.8226814,-1.2571651000000001,36.803220700000004,Rider_Id_208,1344
-Order_No_25996,User_Id_1059,Bike,1,Personal,12,2,9:41:46 AM,12,2,9:49:26 AM,12,2,10:15:18 AM,12,2,10:21:25 AM,12,2,10:45:01 AM,11,16.0,,-1.296567,36.7813901,-1.3303649,36.726018700000004,Rider_Id_808,1416
-Order_No_14559,User_Id_2982,Bike,3,Business,27,4,10:50:51 AM,27,4,10:51:28 AM,27,4,11:03:11 AM,27,4,11:07:00 AM,27,4,11:51:13 AM,18,22.8,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_715,2653
-Order_No_1730,User_Id_1304,Bike,3,Business,4,2,1:55:13 PM,4,2,3:17:10 PM,4,2,3:59:29 PM,4,2,4:01:46 PM,4,2,4:57:35 PM,15,24.0,10.9,-1.3226301999999999,36.8719901,-1.2612111000000001,36.783151600000004,Rider_Id_817,3349
-Order_No_10206,User_Id_2956,Bike,3,Business,11,4,2:58:52 PM,11,4,2:59:24 PM,11,4,3:12:52 PM,11,4,3:26:12 PM,11,4,4:06:36 PM,17,26.9,,-1.323615,36.700475,-1.2584143,36.8048002,Rider_Id_691,2424
-Order_No_4141,User_Id_2190,Bike,3,Business,25,5,2:00:39 PM,25,5,2:01:01 PM,25,5,2:14:06 PM,25,5,2:23:43 PM,25,5,2:48:19 PM,6,29.0,,-1.3019530000000001,36.788458,-1.2904406000000002,36.7598219,Rider_Id_332,1476
-Order_No_25146,User_Id_1927,Bike,3,Business,30,5,12:54:17 PM,30,5,12:54:40 PM,30,5,1:06:25 PM,30,5,1:15:32 PM,30,5,1:49:21 PM,15,23.6,,-1.2571472,36.795063299999995,-1.3463041999999998,36.768164,Rider_Id_205,2029
-Order_No_21931,User_Id_2154,Bike,3,Business,11,5,11:07:03 AM,11,5,11:08:12 AM,11,5,11:22:04 AM,11,5,11:31:14 AM,11,5,11:57:23 AM,5,21.0,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_150,1569
-Order_No_27193,User_Id_1473,Bike,3,Business,28,4,11:47:40 AM,28,4,12:57:18 PM,28,4,1:08:52 PM,28,4,1:16:22 PM,28,4,1:31:47 PM,13,,,-1.3700383,36.9190174,-1.3206958,36.8769553,Rider_Id_6,925
-Order_No_5096,User_Id_217,Bike,3,Personal,6,3,9:30:10 AM,6,3,9:32:37 AM,6,3,10:01:26 AM,6,3,10:08:30 AM,6,3,10:31:42 AM,6,16.0,14.0,-1.2950041,36.814358299999995,-1.2825581,36.7775416,Rider_Id_105,1392
-Order_No_4451,User_Id_525,Bike,1,Personal,27,4,11:11:08 AM,27,4,11:13:46 AM,27,4,11:28:25 AM,27,4,11:35:12 AM,27,4,11:52:25 AM,12,22.7,,-1.3321614,36.782028100000005,-1.3202459999999998,36.836938399999994,Rider_Id_865,1033
-Order_No_14616,User_Id_22,Bike,2,Personal,14,1,10:06:20 AM,14,1,10:06:32 AM,14,1,10:14:46 AM,14,1,10:20:07 AM,14,1,10:38:02 AM,9,18.5,0.3,-1.3112325,36.8146321,-1.2789368,36.7864201,Rider_Id_216,1075
-Order_No_15360,User_Id_646,Bike,3,Personal,2,2,2:28:52 PM,2,2,2:30:24 PM,2,2,2:39:53 PM,2,2,3:17:19 PM,2,2,3:37:53 PM,16,26.3,,-1.2469508,36.824448499999995,-1.3231725,36.864017700000005,Rider_Id_472,1234
-Order_No_26170,User_Id_848,Bike,3,Personal,22,2,2:08:11 PM,22,2,2:09:38 PM,22,2,2:30:07 PM,22,2,2:39:26 PM,22,2,3:15:37 PM,17,21.5,,-1.3122473999999997,36.8728544,-1.2527374,36.7776152,Rider_Id_742,2171
-Order_No_12739,User_Id_496,Bike,3,Business,3,3,3:02:00 PM,3,3,3:12:23 PM,3,3,3:39:14 PM,3,3,3:47:46 PM,3,3,4:17:38 PM,7,28.0,,-1.2551895,36.7822034,-1.2802803,36.8271747,Rider_Id_855,1792
-Order_No_10388,User_Id_972,Bike,3,Business,13,5,9:57:17 AM,13,5,9:59:27 AM,13,5,10:32:25 AM,13,5,10:38:29 AM,13,5,10:57:32 AM,6,,,-1.2862033000000002,36.8152966,-1.3025666999999999,36.7803495,Rider_Id_752,1143
-Order_No_25482,User_Id_3222,Bike,3,Business,28,2,9:03:26 AM,28,2,9:06:04 AM,28,2,9:18:25 AM,28,2,9:19:29 AM,28,2,9:46:15 AM,9,18.8,,-1.2551895,36.7822034,-1.3074264,36.822612299999996,Rider_Id_706,1606
-Order_No_24175,User_Id_3214,Bike,3,Business,9,5,10:51:04 AM,9,5,10:55:43 AM,9,5,11:33:40 AM,9,5,11:37:27 AM,9,5,12:39:58 PM,7,25.6,,-1.289795,36.7957132,-1.2633273999999999,36.8073226,Rider_Id_322,3751
-Order_No_26727,User_Id_2182,Bike,1,Personal,31,2,12:06:32 PM,31,2,12:21:49 PM,31,2,12:41:18 PM,31,2,12:50:29 PM,31,2,1:37:14 PM,26,18.3,,-1.4003836,36.7585447,-1.2355548,36.84741939999999,Rider_Id_95,2805
-Order_No_2742,User_Id_286,Bike,3,Business,30,5,3:07:54 PM,30,5,3:17:13 PM,30,5,3:43:11 PM,30,5,3:48:50 PM,30,5,4:46:18 PM,8,24.4,,-1.2551895,36.7822034,-1.2899197,36.8191269,Rider_Id_436,3448
-Order_No_1260,User_Id_800,Bike,1,Personal,27,4,1:31:53 PM,27,4,1:32:06 PM,27,4,1:37:50 PM,27,4,1:46:27 PM,27,4,2:09:22 PM,11,25.2,,-1.2602044,36.7871719,-1.3091686000000002,36.742967799999995,Rider_Id_38,1375
-Order_No_6189,User_Id_2884,Bike,2,Personal,24,4,9:50:51 AM,24,4,10:28:56 AM,24,4,10:51:42 AM,24,4,11:09:16 AM,24,4,11:45:02 AM,20,21.2,,-1.2137756000000002,36.7996657,-1.2764692,36.758011100000004,Rider_Id_441,2146
-Order_No_9845,User_Id_3686,Bike,3,Business,11,1,2:29:00 PM,11,1,2:29:38 PM,11,1,2:36:24 PM,11,1,2:42:55 PM,11,1,2:42:57 PM,5,27.8,,-1.2551895,36.7822034,-1.2596666,36.7627206,Rider_Id_647,2
-Order_No_2497,User_Id_1687,Bike,3,Business,31,4,2:46:21 PM,31,4,2:47:38 PM,31,4,3:06:18 PM,31,4,3:07:40 PM,31,4,3:21:16 PM,5,28.6,,-1.2729802,36.793734,-1.3025299,36.7901963,Rider_Id_205,816
-Order_No_2576,User_Id_3669,Bike,3,Business,5,3,3:40:50 PM,5,3,3:42:19 PM,5,3,4:09:45 PM,5,3,4:10:53 PM,5,3,4:55:06 PM,15,23.8,,-1.2615888000000002,36.7928732,-1.3282758,36.8590281,Rider_Id_399,2653
-Order_No_8023,User_Id_1246,Bike,3,Business,26,5,9:28:10 AM,26,5,9:36:12 AM,26,5,9:48:19 AM,26,5,9:50:50 AM,26,5,10:16:05 AM,16,,,-1.3700383,36.9190174,-1.312469,36.815177,Rider_Id_412,1515
-Order_No_21243,User_Id_1064,Bike,1,Personal,4,2,10:16:35 AM,4,2,10:16:55 AM,4,2,10:24:53 AM,4,2,10:38:54 AM,4,2,11:05:00 AM,11,18.7,,-1.2643693999999999,36.785413500000004,-1.2365703000000001,36.805436799999995,Rider_Id_754,1566
-Order_No_10302,User_Id_228,Bike,3,Business,9,2,2:59:16 PM,9,2,3:00:27 PM,9,2,3:14:43 PM,9,2,3:19:17 PM,9,2,4:03:19 PM,5,30.5,,-1.2842665,36.7967626,-1.3024794,36.8228284,Rider_Id_787,2642
-Order_No_9882,User_Id_1402,Bike,3,Business,13,4,12:19:48 PM,13,4,12:23:06 PM,13,4,12:23:12 PM,13,4,12:35:08 PM,13,4,1:24:50 PM,16,22.1,,-1.288924,36.831527,-1.3297531,36.7063699,Rider_Id_338,2982
-Order_No_22072,User_Id_2514,Bike,3,Business,19,1,11:42:18 AM,19,1,11:44:31 AM,19,1,12:10:02 PM,19,1,12:23:17 PM,19,1,12:32:18 PM,2,24.9,,-1.2764851000000002,36.8007126,-1.266294,36.8057971,Rider_Id_932,541
-Order_No_7852,User_Id_2216,Bike,3,Business,7,4,12:35:58 PM,7,4,12:36:09 PM,7,4,12:36:47 PM,7,4,12:44:48 PM,7,4,1:13:35 PM,15,27.7,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_523,1727
-Order_No_25119,User_Id_2464,Bike,3,Business,14,5,4:10:20 PM,14,5,4:14:49 PM,14,5,4:41:53 PM,14,5,4:43:16 PM,14,5,5:19:26 PM,5,21.5,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_673,2170
-Order_No_26853,User_Id_1316,Bike,3,Business,5,5,4:22:47 PM,5,5,4:24:29 PM,5,5,4:40:59 PM,5,5,4:42:36 PM,5,5,5:11:38 PM,13,26.3,,-1.2551895,36.7822034,-1.2257539,36.845929,Rider_Id_937,1742
-Order_No_20620,User_Id_3434,Bike,3,Business,20,1,3:25:27 PM,20,1,3:27:09 PM,20,1,3:58:46 PM,20,1,4:00:56 PM,20,1,4:24:26 PM,10,21.5,,-1.304969,36.846623,-1.2963097,36.7688221,Rider_Id_249,1410
-Order_No_21862,User_Id_3431,Bike,1,Personal,26,4,10:24:50 AM,26,4,10:25:11 AM,26,4,10:39:02 AM,26,4,10:55:03 AM,26,4,11:10:51 AM,8,18.8,,-1.2822850000000001,36.822655,-1.2572139,36.791487599999996,Rider_Id_726,948
-Order_No_18988,User_Id_3689,Bike,3,Business,14,5,5:38:52 PM,14,5,5:39:36 PM,14,5,5:40:32 PM,14,5,5:53:36 PM,14,5,6:32:56 PM,3,23.0,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_107,2360
-Order_No_24779,User_Id_1073,Bike,3,Business,5,4,11:38:40 AM,5,4,11:38:48 AM,5,4,11:41:50 AM,5,4,11:51:21 AM,5,4,12:01:10 PM,5,,,-1.2596969999999998,36.800664000000005,-1.2559294,36.788465,Rider_Id_726,589
-Order_No_17501,User_Id_1017,Bike,3,Business,12,3,11:40:35 AM,12,3,11:41:06 AM,12,3,11:50:15 AM,12,3,12:10:25 PM,12,3,12:34:50 PM,4,23.2,,-1.2663351,36.8099425,-1.2551895,36.7822034,Rider_Id_116,1465
-Order_No_18263,User_Id_510,Bike,3,Business,17,1,4:10:01 PM,17,1,4:28:22 PM,17,1,5:13:17 PM,17,1,5:23:15 PM,17,1,6:26:09 PM,12,22.8,,-1.3226301999999999,36.8719901,-1.2860183,36.8975338,Rider_Id_436,3774
-Order_No_17059,User_Id_2439,Bike,1,Business,29,2,2:32:46 PM,29,2,2:45:03 PM,29,2,3:17:37 PM,29,2,3:55:02 PM,29,2,4:09:49 PM,7,24.0,,-1.290894,36.822971,-1.2615888000000002,36.7928732,Rider_Id_379,887
-Order_No_5610,User_Id_1469,Bike,3,Business,15,4,3:38:56 PM,15,4,3:40:44 PM,15,4,3:43:12 PM,15,4,3:50:35 PM,15,4,4:37:06 PM,18,27.7,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_647,2791
-Order_No_7260,User_Id_540,Bike,3,Business,14,4,10:49:55 AM,14,4,10:50:36 AM,14,4,11:07:33 AM,14,4,11:12:12 AM,14,4,11:12:15 AM,3,23.7,,-1.2551895,36.7822034,-1.2673725,36.8077775,Rider_Id_647,3
-Order_No_3362,User_Id_673,Bike,2,Personal,30,4,2:43:40 PM,30,4,2:47:09 PM,30,4,3:06:13 PM,30,4,3:12:56 PM,30,4,3:27:08 PM,4,20.8,,-1.2854453000000001,36.818836299999994,-1.2684666999999998,36.8002891,Rider_Id_447,852
-Order_No_6123,User_Id_2804,Bike,3,Business,22,5,12:19:55 PM,22,5,12:20:25 PM,22,5,12:36:16 PM,22,5,12:48:58 PM,22,5,12:49:06 PM,3,21.7,,-1.254191,36.809913200000004,-1.2579303999999998,36.8019667,Rider_Id_427,8
-Order_No_8892,User_Id_2583,Bike,3,Personal,26,5,2:44:09 PM,26,5,2:46:44 PM,26,5,3:12:47 PM,26,5,3:15:45 PM,26,5,3:30:02 PM,6,,,-1.2607568999999998,36.7747575,-1.2744463,36.8132178,Rider_Id_709,857
-Order_No_2103,User_Id_2587,Bike,3,Business,27,3,3:21:10 PM,27,3,3:41:33 PM,27,3,4:38:59 PM,27,3,4:55:52 PM,27,3,5:18:53 PM,3,27.5,,-1.2994771999999999,36.804825,-1.28878,36.8168312,Rider_Id_668,1381
-Order_No_6450,User_Id_1862,Bike,1,Personal,28,1,1:08:23 PM,28,1,1:09:17 PM,28,1,1:17:29 PM,28,1,1:34:51 PM,28,1,1:55:58 PM,8,24.4,,-1.2738167,36.8157821,-1.277256,36.7572427,Rider_Id_852,1267
-Order_No_7658,User_Id_1410,Bike,3,Business,26,3,3:27:51 PM,26,3,3:28:46 PM,26,3,3:39:27 PM,26,3,5:11:08 PM,26,3,6:02:31 PM,12,19.5,,-1.300921,36.828195,-1.2308211000000002,36.867904100000004,Rider_Id_932,3083
-Order_No_19356,User_Id_1372,Bike,3,Business,9,3,8:16:29 AM,9,3,8:26:24 AM,9,3,8:36:27 AM,9,3,8:42:12 AM,9,3,9:00:41 AM,5,20.1,,-1.2885417,36.810052299999995,-1.287428,36.7800821,Rider_Id_62,1109
-Order_No_20569,User_Id_3201,Bike,3,Business,30,2,2:33:23 PM,30,2,3:00:40 PM,30,2,3:07:51 PM,30,2,3:17:34 PM,30,2,3:26:21 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_484,527
-Order_No_20773,User_Id_478,Bike,1,Business,2,1,8:40:28 AM,2,1,8:40:44 AM,2,1,8:44:47 AM,2,1,8:49:11 AM,2,1,9:16:18 AM,15,16.6,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_379,1627
-Order_No_1832,User_Id_111,Bike,3,Business,3,4,11:43:14 AM,3,4,11:43:24 AM,3,4,11:48:03 AM,3,4,11:54:53 AM,3,4,12:09:00 PM,7,23.3,,-1.2551895,36.7822034,-1.2732603999999998,36.823046000000005,Rider_Id_477,847
-Order_No_22340,User_Id_1500,Bike,3,Business,6,4,11:01:54 AM,6,4,11:06:57 AM,6,4,11:17:30 AM,6,4,11:28:23 AM,6,4,11:46:59 AM,8,23.9,,-1.29508,36.796863,-1.2574219,36.7927073,Rider_Id_523,1116
-Order_No_17170,User_Id_718,Bike,3,Business,8,5,4:08:02 PM,8,5,4:14:16 PM,8,5,4:33:42 PM,8,5,4:36:56 PM,8,5,5:10:56 PM,22,28.0,,-1.2551895,36.7822034,-1.1851027,36.9392531,Rider_Id_885,2040
-Order_No_11736,User_Id_3687,Bike,1,Personal,15,3,11:45:38 AM,15,3,11:57:52 AM,15,3,12:15:16 PM,15,3,12:26:48 PM,15,3,12:51:53 PM,10,18.1,,-1.289498,36.818878000000005,-1.3074581,36.7434,Rider_Id_726,1505
-Order_No_656,User_Id_3222,Bike,3,Business,22,3,3:36:02 PM,22,3,3:42:23 PM,22,3,3:42:56 PM,22,3,4:11:18 PM,22,3,4:11:24 PM,7,24.3,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_843,6
-Order_No_11088,User_Id_60,Bike,3,Business,21,3,12:39:44 PM,21,3,1:27:01 PM,21,3,1:43:49 PM,21,3,1:51:15 PM,21,3,2:33:59 PM,15,23.3,2.9,-1.303596,36.778378000000004,-1.2296618000000001,36.84382720000001,Rider_Id_596,2564
-Order_No_22605,User_Id_1500,Bike,3,Business,11,6,9:38:18 AM,11,6,9:38:32 AM,11,6,9:38:34 AM,11,6,10:00:48 AM,11,6,10:02:30 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_472,102
-Order_No_20712,User_Id_2679,Bike,3,Business,7,2,3:56:54 PM,7,2,3:57:45 PM,7,2,4:13:32 PM,7,2,4:21:00 PM,7,2,4:38:56 PM,4,24.7,,-1.2902031999999999,36.787392600000004,-1.2680643,36.7763499,Rider_Id_85,1076
-Order_No_25283,User_Id_3291,Bike,3,Business,28,4,10:13:09 AM,28,4,10:13:19 AM,28,4,10:13:26 AM,28,4,10:32:37 AM,28,4,10:58:36 AM,12,27.8,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_406,1559
-Order_No_17078,User_Id_893,Bike,3,Business,22,3,11:36:22 AM,22,3,11:37:04 AM,22,3,12:06:29 PM,22,3,12:12:55 PM,22,3,12:22:47 PM,4,23.0,,-1.2663194,36.809901,-1.2607621999999998,36.8227608,Rider_Id_484,592
-Order_No_2989,User_Id_909,Bike,3,Business,15,1,2:42:52 PM,15,1,2:43:18 PM,15,1,3:06:17 PM,15,1,3:09:29 PM,15,1,3:47:18 PM,16,,,-1.225322,36.80855,-1.2949438999999998,36.7604214,Rider_Id_547,2269
-Order_No_26369,User_Id_1502,Bike,3,Business,16,7,11:22:14 AM,16,7,11:22:51 AM,16,7,11:31:07 AM,16,7,12:10:43 PM,16,7,12:58:39 PM,12,23.3,,-1.28878,36.8168312,-1.2232123,36.8932547,Rider_Id_26,2876
-Order_No_21911,User_Id_3517,Bike,3,Business,11,5,3:16:20 PM,11,5,3:16:40 PM,11,5,3:16:53 PM,11,5,3:31:35 PM,11,5,4:20:21 PM,15,26.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_581,2926
-Order_No_27718,User_Id_3689,Bike,3,Business,24,7,11:01:15 AM,24,7,11:34:25 AM,24,7,11:44:15 AM,24,7,11:55:08 AM,24,7,12:15:30 PM,10,,,-1.28878,36.8168312,-1.225322,36.80855,Rider_Id_26,1222
-Order_No_797,User_Id_2523,Bike,3,Business,26,5,2:22:15 PM,26,5,2:23:33 PM,26,5,2:39:10 PM,26,5,2:42:10 PM,26,5,3:11:02 PM,13,,,-1.2551895,36.7822034,-1.3024388,36.859699,Rider_Id_675,1732
-Order_No_2565,User_Id_1342,Bike,3,Business,17,2,4:31:17 PM,17,2,4:31:35 PM,17,2,4:35:43 PM,17,2,4:44:24 PM,17,2,5:33:21 PM,9,24.0,,-1.3014461000000002,36.7661381,-1.2571472,36.795063299999995,Rider_Id_735,2937
-Order_No_23910,User_Id_1121,Bike,2,Personal,16,6,12:27:17 PM,16,6,12:27:34 PM,16,6,12:45:24 PM,16,6,12:59:45 PM,16,6,2:20:19 PM,28,19.2,,-1.3103228999999998,36.8966208,-1.1656053999999998,36.992563200000006,Rider_Id_543,4834
-Order_No_12024,User_Id_246,Bike,3,Business,12,6,12:42:33 PM,12,6,12:44:18 PM,12,6,12:54:32 PM,12,6,12:56:25 PM,12,6,1:17:47 PM,5,,,-1.2584143,36.8048002,-1.2857548,36.819062,Rider_Id_575,1282
-Order_No_17022,User_Id_647,Bike,3,Business,13,4,12:07:46 PM,13,4,12:08:05 PM,13,4,12:15:48 PM,13,4,12:33:16 PM,13,4,12:34:14 PM,8,22.0,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_70,58
-Order_No_25944,User_Id_136,Bike,3,Business,14,4,10:26:06 AM,14,4,12:30:49 PM,14,4,12:37:36 PM,14,4,12:39:43 PM,14,4,1:11:44 PM,14,19.0,,-1.3326173000000001,36.71462329999999,-1.3012173,36.8093987,Rider_Id_635,1921
-Order_No_14060,User_Id_3002,Bike,1,Personal,11,3,11:28:12 AM,11,3,11:28:36 AM,11,3,11:28:43 AM,11,3,11:42:09 AM,11,3,12:00:29 PM,9,19.0,,-1.2528485,36.7953722,-1.2846195,36.7958316,Rider_Id_546,1100
-Order_No_21979,User_Id_1443,Bike,1,Personal,5,6,3:23:07 PM,5,6,3:39:29 PM,5,6,4:05:57 PM,5,6,4:43:40 PM,5,6,5:11:50 PM,9,22.7,,-1.2750798,36.8451936,-1.3154433,36.806385999999996,Rider_Id_742,1690
-Order_No_1769,User_Id_420,Bike,3,Business,6,4,12:52:44 PM,6,4,2:22:31 PM,6,4,3:06:54 PM,6,4,3:14:36 PM,6,4,3:29:20 PM,6,23.5,,-1.2785723,36.8153585,-1.2657464,36.780114399999995,Rider_Id_561,884
-Order_No_144,User_Id_2079,Bike,3,Personal,17,1,4:18:28 PM,17,1,4:18:56 PM,17,1,4:24:59 PM,17,1,4:27:26 PM,17,1,4:35:47 PM,3,24.6,,-1.2681003999999998,36.7952081,-1.2733047,36.8109744,Rider_Id_50,501
-Order_No_13081,User_Id_3365,Bike,2,Personal,17,3,2:37:47 PM,17,3,2:37:59 PM,17,3,2:39:15 PM,17,3,2:54:04 PM,17,3,3:16:15 PM,6,28.4,,-1.2795796,36.819062,-1.307774,36.840754100000005,Rider_Id_365,1331
-Order_No_26046,User_Id_2067,Bike,1,Personal,18,1,11:28:13 AM,18,1,11:33:12 AM,18,1,11:45:27 AM,18,1,11:54:01 AM,18,1,12:14:08 PM,10,,,-1.2579223999999998,36.7920879,-1.304033,36.7848695,Rider_Id_937,1207
-Order_No_1987,User_Id_2330,Bike,3,Business,15,3,10:10:57 AM,15,3,10:14:23 AM,15,3,10:19:32 AM,15,3,10:25:57 AM,15,3,10:34:42 AM,3,,,-1.2921463,36.8029626,-1.2890715,36.8157588,Rider_Id_119,525
-Order_No_14260,User_Id_478,Bike,3,Business,9,2,12:47:25 PM,9,2,12:48:14 PM,9,2,12:49:27 PM,9,2,12:58:32 PM,9,2,1:26:34 PM,15,22.1,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_651,1682
-Order_No_14880,User_Id_1245,Bike,3,Business,14,5,9:51:09 AM,14,5,9:51:22 AM,14,5,9:51:46 AM,14,5,10:22:07 AM,14,5,10:52:57 AM,17,16.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_515,1850
-Order_No_27386,User_Id_67,Bike,3,Personal,18,1,11:18:01 AM,18,1,11:18:58 AM,18,1,11:44:08 AM,18,1,11:47:58 AM,18,1,12:35:52 PM,11,27.1,,-1.2583017,36.8023526,-1.3108386,36.7555239,Rider_Id_448,2874
-Order_No_1332,User_Id_393,Bike,3,Business,25,4,4:06:14 PM,25,4,4:06:49 PM,25,4,4:07:09 PM,25,4,4:21:51 PM,25,4,4:33:24 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_168,693
-Order_No_25034,User_Id_1976,Bike,3,Business,20,3,3:30:58 PM,20,3,3:31:10 PM,20,3,3:38:10 PM,20,3,3:46:46 PM,20,3,4:06:44 PM,9,29.3,,-1.257028,36.7810435,-1.2321733,36.7974377,Rider_Id_828,1198
-Order_No_15304,User_Id_1865,Bike,1,Personal,28,5,9:06:44 AM,28,5,9:09:07 AM,28,5,9:30:14 AM,28,5,10:02:18 AM,28,5,10:27:32 AM,13,20.7,,-1.2765688999999998,36.8406151,-1.2166078000000002,36.882501,Rider_Id_895,1514
-Order_No_21580,User_Id_2112,Bike,3,Business,11,1,6:21:22 PM,11,1,6:25:48 PM,11,1,6:28:46 PM,11,1,6:34:40 PM,11,1,6:51:53 PM,11,27.4,,-1.2156006000000001,36.8916865,-1.2786703000000001,36.826044,Rider_Id_416,1033
-Order_No_16464,User_Id_3661,Bike,3,Business,15,5,9:40:02 AM,15,5,9:40:47 AM,15,5,10:19:30 AM,15,5,10:22:20 AM,15,5,10:35:38 AM,6,19.4,,-1.2132866000000002,36.867827399999996,-1.2413316,36.878552500000005,Rider_Id_812,798
-Order_No_26492,User_Id_2330,Bike,3,Business,28,4,3:17:54 PM,28,4,3:19:15 PM,28,4,3:56:51 PM,28,4,3:59:54 PM,28,4,4:15:59 PM,8,29.9,,-1.3328934,36.7112141,-1.3015197,36.7658456,Rider_Id_226,965
-Order_No_18410,User_Id_635,Bike,3,Business,27,6,9:39:26 AM,27,6,10:16:07 AM,27,6,10:31:17 AM,27,6,10:48:05 AM,27,6,11:14:56 AM,8,,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_131,1611
-Order_No_7970,User_Id_496,Bike,3,Business,3,4,11:53:24 AM,3,4,11:55:30 AM,3,4,12:04:06 PM,3,4,12:07:11 PM,3,4,12:46:06 PM,9,21.8,,-1.2559559999999999,36.789867,-1.251162,36.8277973,Rider_Id_147,2335
-Order_No_3330,User_Id_1850,Bike,3,Business,25,4,12:58:36 PM,25,4,1:24:10 PM,25,4,1:24:17 PM,25,4,1:30:21 PM,25,4,1:36:28 PM,2,,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_709,367
-Order_No_23655,User_Id_3222,Bike,3,Business,6,2,10:15:57 AM,6,2,10:23:30 AM,6,2,10:31:12 AM,6,2,10:37:03 AM,6,2,11:09:38 AM,11,25.0,,-1.2551895,36.7822034,-1.3233496,36.8139159,Rider_Id_726,1955
-Order_No_3462,User_Id_2564,Bike,3,Business,9,4,2:49:08 PM,9,4,2:54:12 PM,9,4,2:59:54 PM,9,4,3:09:15 PM,9,4,3:51:24 PM,9,,,-1.2795195,36.82346939999999,-1.2271998,36.808490500000005,Rider_Id_416,2529
-Order_No_25327,User_Id_3636,Bike,1,Personal,7,7,9:14:45 AM,7,7,9:15:24 AM,7,7,9:24:40 AM,7,7,9:27:17 AM,7,7,10:02:41 AM,18,20.0,,-1.2991247,36.7642354,-1.2240292,36.8156884,Rider_Id_846,2124
-Order_No_19294,User_Id_2677,Bike,3,Business,28,2,9:24:32 AM,28,2,9:24:45 AM,28,2,9:27:28 AM,28,2,9:44:19 AM,28,2,10:10:15 AM,10,18.8,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_591,1556
-Order_No_6910,User_Id_2388,Bike,2,Personal,16,4,11:39:42 AM,16,4,11:40:03 AM,16,4,11:51:19 AM,16,4,11:58:10 AM,16,4,12:54:17 PM,12,14.5,,-1.2885364,36.7995016,-1.3192808,36.7121888,Rider_Id_234,3367
-Order_No_21183,User_Id_2283,Bike,2,Personal,7,1,10:01:08 AM,7,1,10:01:55 AM,7,1,10:02:39 AM,7,1,10:08:24 AM,7,1,10:34:03 AM,11,18.6,,-1.2696341,36.791664000000004,-1.239774,36.848471,Rider_Id_65,1539
-Order_No_9299,User_Id_2330,Bike,3,Business,7,2,12:26:51 PM,7,2,12:33:50 PM,7,2,12:49:21 PM,7,2,12:53:39 PM,7,2,1:27:58 PM,4,23.1,,-1.2614329,36.8094365,-1.2751225,36.822839200000004,Rider_Id_519,2059
-Order_No_13712,User_Id_111,Bike,3,Business,7,3,10:48:29 AM,7,3,10:49:21 AM,7,3,10:53:11 AM,7,3,10:56:11 AM,7,3,11:42:11 AM,11,25.3,,-1.2551895,36.7822034,-1.2432427,36.8642382,Rider_Id_279,2760
-Order_No_5733,User_Id_1724,Bike,3,Business,10,4,12:13:07 PM,10,4,12:13:14 PM,10,4,12:20:04 PM,10,4,12:29:01 PM,10,4,12:44:37 PM,7,24.5,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_658,936
-Order_No_26481,User_Id_374,Bike,2,Personal,16,3,9:54:50 AM,16,3,10:05:12 AM,16,3,10:09:27 AM,16,3,10:12:09 AM,16,3,10:25:49 AM,6,19.8,,-1.2580978999999999,36.7959844,-1.2900726999999999,36.7890059,Rider_Id_663,820
-Order_No_7920,User_Id_1245,Bike,3,Business,5,2,2:46:40 PM,5,2,2:47:30 PM,5,2,3:39:05 PM,5,2,4:19:33 PM,5,2,4:54:38 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_654,2105
-Order_No_20646,User_Id_3091,Bike,3,Business,9,2,4:50:01 PM,9,2,4:50:33 PM,9,2,4:57:42 PM,9,2,5:08:29 PM,9,2,5:33:57 PM,6,27.9,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_394,1528
-Order_No_1890,User_Id_1468,Bike,1,Personal,22,6,5:38:06 PM,22,6,5:38:49 PM,22,6,5:52:13 PM,22,6,6:14:03 PM,22,6,6:42:13 PM,16,23.2,,-1.2675613000000001,36.8133001,-1.3370737,36.74708,Rider_Id_427,1690
-Order_No_1639,User_Id_3283,Bike,3,Business,20,1,1:33:29 PM,20,1,1:34:36 PM,20,1,1:42:30 PM,20,1,1:58:45 PM,20,1,2:12:14 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_183,809
-Order_No_6922,User_Id_1687,Bike,3,Business,22,5,2:00:31 PM,22,5,2:00:51 PM,22,5,2:30:01 PM,22,5,2:39:42 PM,22,5,2:50:51 PM,4,31.0,,-1.2626574,36.8014034,-1.2966453000000002,36.8191342,Rider_Id_76,669
-Order_No_952,User_Id_1905,Bike,3,Personal,22,3,12:07:16 AM,22,3,12:08:35 AM,22,3,12:12:11 AM,22,3,12:49:52 AM,22,3,12:49:57 AM,2,18.6,,-1.3039706,36.7688174,-1.301809,36.75643,Rider_Id_398,5
-Order_No_16386,User_Id_1244,Bike,3,Business,5,6,12:38:27 PM,5,6,12:39:07 PM,5,6,12:42:50 PM,5,6,12:54:23 PM,5,6,1:11:59 PM,9,22.3,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_72,1056
-Order_No_11902,User_Id_240,Bike,2,Personal,5,2,10:41:10 AM,5,2,10:41:33 AM,5,2,10:47:47 AM,5,2,10:57:08 AM,5,2,11:22:15 AM,11,21.4,,-1.2311903,36.8102105,-1.269534,36.80697,Rider_Id_633,1507
-Order_No_5947,User_Id_265,Bike,3,Business,11,4,1:02:58 PM,11,4,1:03:52 PM,11,4,1:27:50 PM,11,4,1:35:27 PM,11,4,1:46:17 PM,4,26.9,,-1.300921,36.828195,-1.2951620000000001,36.8115914,Rider_Id_855,650
-Order_No_9702,User_Id_3291,Bike,3,Business,9,4,12:25:35 PM,9,4,12:29:08 PM,9,4,12:44:09 PM,9,4,12:54:00 PM,9,4,1:21:43 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_328,1663
-Order_No_10068,User_Id_393,Bike,3,Business,13,4,1:34:50 PM,13,4,1:35:10 PM,13,4,1:42:22 PM,13,4,1:56:14 PM,13,4,2:06:33 PM,4,23.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_891,619
-Order_No_14587,User_Id_1831,Bike,3,Business,11,1,8:43:42 AM,11,1,8:44:52 AM,11,1,9:26:00 AM,11,1,9:30:40 AM,11,1,10:38:59 AM,15,17.9,,-1.3562374,36.904295399999995,-1.282543,36.824618,Rider_Id_863,4099
-Order_No_24983,User_Id_60,Bike,3,Business,28,4,1:48:44 PM,28,4,1:49:06 PM,28,4,1:52:21 PM,28,4,2:02:33 PM,28,4,2:31:50 PM,15,27.2,,-1.303596,36.778378000000004,-1.2296618000000001,36.84382720000001,Rider_Id_484,1757
-Order_No_10755,User_Id_1827,Bike,3,Business,29,2,10:40:21 AM,29,2,10:41:15 AM,29,2,10:46:24 AM,29,2,11:06:30 AM,29,2,11:39:44 AM,17,21.2,,-1.2600926000000001,36.8088685,-1.293515,36.897607,Rider_Id_888,1994
-Order_No_3050,User_Id_467,Bike,3,Business,8,3,3:58:25 PM,8,3,4:00:57 PM,8,3,4:21:30 PM,8,3,4:25:44 PM,8,3,4:49:13 PM,12,,,-1.304735,36.784605400000004,-1.313266,36.6935289,Rider_Id_161,1409
-Order_No_3716,User_Id_1575,Bike,3,Personal,24,5,12:04:51 PM,24,5,12:05:46 PM,24,5,12:13:06 PM,24,5,12:18:08 PM,24,5,12:26:36 PM,3,21.4,,-1.2452735000000001,36.7911588,-1.2670196000000002,36.807700200000006,Rider_Id_674,508
-Order_No_17582,User_Id_2261,Bike,1,Personal,25,1,12:00:28 PM,25,1,12:47:59 PM,25,1,1:03:12 PM,25,1,1:15:04 PM,25,1,1:25:42 PM,4,,,-1.2878148999999999,36.8146245,-1.273009,36.802807,Rider_Id_265,638
-Order_No_18683,User_Id_3517,Bike,3,Business,5,2,9:41:25 AM,5,2,9:41:29 AM,5,2,9:41:41 AM,5,2,9:45:21 AM,5,2,10:38:58 AM,21,18.0,,-1.3882008,36.7699445,-1.2878914,36.7728222,Rider_Id_191,3217
-Order_No_21037,User_Id_3115,Bike,3,Business,14,3,11:33:31 AM,14,3,11:52:10 AM,14,3,12:04:52 PM,14,3,12:18:34 PM,14,3,12:42:42 PM,8,24.4,,-1.2916155,36.84079129999999,-1.2963097,36.7688221,Rider_Id_622,1448
-Order_No_21337,User_Id_3409,Bike,2,Personal,20,6,1:19:24 PM,20,6,1:20:42 PM,20,6,1:32:38 PM,20,6,1:39:53 PM,20,6,1:51:58 PM,9,28.5,,-1.3149256999999999,36.8441635,-1.2657148,36.815146399999996,Rider_Id_622,725
-Order_No_21771,User_Id_3439,Bike,3,Business,10,1,1:13:19 PM,10,1,1:13:22 PM,10,1,1:45:21 PM,10,1,1:50:28 PM,10,1,2:02:58 PM,5,18.3,,-1.2393157,36.766887700000005,-1.2566688999999998,36.779695200000006,Rider_Id_689,750
-Order_No_16728,User_Id_816,Bike,3,Business,13,1,10:07:46 AM,13,1,10:08:42 AM,13,1,10:18:44 AM,13,1,11:04:51 AM,13,1,11:59:58 AM,10,17.4,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_101,3307
-Order_No_18440,User_Id_1244,Bike,3,Business,19,3,11:25:23 AM,19,3,11:25:50 AM,19,3,11:36:30 AM,19,3,11:47:33 AM,19,3,12:03:32 PM,6,,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_116,959
-Order_No_27562,User_Id_3030,Bike,3,Business,4,3,12:00:14 PM,4,3,12:00:35 PM,4,3,12:04:55 PM,4,3,12:14:12 PM,4,3,12:30:10 PM,5,17.0,,-1.3004846,36.790159100000004,-1.2866011999999998,36.8195691,Rider_Id_726,958
-Order_No_2152,User_Id_3367,Bike,3,Personal,1,1,11:54:15 AM,1,1,11:54:34 AM,1,1,12:20:29 PM,1,1,12:23:42 PM,1,1,1:05:59 PM,18,24.0,,-1.1983542,36.804424499999996,-1.3043649,36.7816132,Rider_Id_726,2537
-Order_No_22672,User_Id_1245,Bike,3,Business,15,5,11:29:20 AM,15,5,11:29:33 AM,15,5,11:37:18 AM,15,5,11:56:53 AM,15,5,12:43:32 PM,22,26.0,,-1.300921,36.828195,-1.3312261,36.6882186,Rider_Id_127,2799
-Order_No_24385,User_Id_2691,Bike,2,Business,3,3,1:26:33 PM,3,3,1:28:35 PM,3,3,1:54:40 PM,3,3,2:00:39 PM,3,3,2:20:25 PM,8,26.9,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_770,1186
-Order_No_13635,User_Id_2956,Bike,3,Business,25,4,11:56:51 AM,25,4,11:58:09 AM,25,4,12:02:46 PM,25,4,12:06:28 PM,25,4,12:20:56 PM,9,20.1,,-1.2584143,36.8048002,-1.2692149,36.74492,Rider_Id_726,868
-Order_No_3217,User_Id_2187,Bike,3,Business,15,3,9:44:15 AM,15,3,9:45:18 AM,15,3,9:51:30 AM,15,3,10:03:10 AM,15,3,10:17:57 AM,3,,,-1.28878,36.8168312,-1.2909536,36.8407162,Rider_Id_204,887
-Order_No_10700,User_Id_2828,Bike,3,Business,13,3,12:42:58 PM,13,3,12:43:17 PM,13,3,12:49:02 PM,13,3,1:03:18 PM,13,3,1:47:48 PM,22,20.0,,-1.2649605,36.798177700000004,-1.4039004,36.7543649,Rider_Id_620,2670
-Order_No_5054,User_Id_552,Bike,3,Business,15,1,2:07:11 PM,15,1,2:07:23 PM,15,1,2:19:30 PM,15,1,2:36:44 PM,15,1,3:24:45 PM,14,,,-1.2860183,36.8975338,-1.335477,36.930829700000004,Rider_Id_155,2881
-Order_No_21836,User_Id_186,Bike,3,Business,6,1,11:25:32 AM,6,1,11:25:40 AM,6,1,11:30:52 AM,6,1,11:49:20 AM,6,1,12:16:30 PM,17,20.3,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_857,1630
-Order_No_6609,User_Id_733,Bike,3,Business,12,5,5:49:52 PM,12,5,5:58:40 PM,12,5,5:59:06 PM,12,5,6:05:59 PM,12,5,6:25:45 PM,8,26.4,,-1.2551895,36.7822034,-1.2977386999999998,36.803144700000004,Rider_Id_674,1186
-Order_No_21187,User_Id_2032,Bike,3,Personal,19,2,10:51:20 AM,19,2,10:53:09 AM,19,2,11:10:43 AM,19,2,11:19:39 AM,19,2,11:45:06 AM,11,27.6,,-1.275931,36.8516227,-1.2234518,36.8811525,Rider_Id_377,1527
-Order_No_290,User_Id_2796,Bike,1,Personal,21,4,12:53:37 PM,21,4,1:04:59 PM,21,4,1:32:08 PM,21,4,1:47:14 PM,21,4,2:29:45 PM,10,22.6,,-1.3052671999999998,36.753853899999996,-1.268027,36.8037005,Rider_Id_101,2551
-Order_No_25588,User_Id_375,Bike,3,Business,12,2,6:23:06 PM,12,2,6:23:14 PM,12,2,6:24:07 PM,12,2,6:33:18 PM,12,2,6:38:55 PM,2,26.5,,-1.2551895,36.7822034,-1.2566078,36.7832089,Rider_Id_958,337
-Order_No_4,User_Id_2377,Bike,3,Personal,12,2,2:58:52 PM,12,2,2:59:22 PM,12,2,3:18:55 PM,12,2,3:52:51 PM,12,2,4:20:59 PM,5,,,-1.3016067,36.8404485,-1.2855653999999999,36.8244835,Rider_Id_181,1688
-Order_No_23703,User_Id_788,Bike,3,Business,6,4,3:06:38 PM,6,4,3:58:51 PM,6,4,4:03:53 PM,6,4,4:11:01 PM,6,4,4:36:04 PM,7,,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_857,1503
-Order_No_25784,User_Id_1964,Bike,3,Personal,25,2,3:36:02 PM,25,2,3:36:11 PM,25,2,3:44:28 PM,25,2,3:48:43 PM,25,2,4:10:49 PM,14,27.3,,-1.3332749,36.870814700000004,-1.2670196000000002,36.807700200000006,Rider_Id_543,1326
-Order_No_15727,User_Id_3599,Bike,3,Business,29,4,10:54:05 AM,29,4,11:11:25 AM,29,4,11:52:57 AM,29,4,12:33:19 PM,29,4,12:39:14 PM,15,21.5,,-1.3302996,36.8707935,-1.3132708999999998,36.9127044,Rider_Id_794,355
-Order_No_17971,User_Id_246,Bike,3,Business,24,1,11:59:16 AM,24,1,11:59:24 AM,24,1,12:28:20 PM,24,1,1:07:18 PM,24,1,1:09:00 PM,2,22.4,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_647,102
-Order_No_7567,User_Id_246,Bike,3,Business,27,5,6:24:40 PM,27,5,6:37:03 PM,27,5,6:45:53 PM,27,5,6:50:21 PM,27,5,7:10:59 PM,2,17.6,,-1.2584143,36.8048002,-1.2671296,36.81257720000001,Rider_Id_519,1238
-Order_No_16217,User_Id_3222,Bike,3,Business,3,1,3:49:45 PM,3,1,3:50:49 PM,3,1,3:58:46 PM,3,1,4:05:19 PM,3,1,4:15:58 PM,4,23.0,,-1.2551895,36.7822034,-1.2754343000000001,36.8127158,Rider_Id_827,639
-Order_No_22408,User_Id_96,Bike,3,Personal,14,4,11:37:00 AM,14,4,11:39:13 AM,14,4,12:08:43 PM,14,4,12:10:41 PM,14,4,12:41:19 PM,13,19.0,,-1.3073569999999999,36.834251,-1.2672291,36.7743861,Rider_Id_845,1838
-Order_No_15433,User_Id_2015,Bike,1,Personal,24,1,11:03:50 AM,24,1,11:04:49 AM,24,1,11:33:08 AM,24,1,11:51:21 AM,24,1,12:10:09 PM,9,22.7,,-1.3472066,36.7692638,-1.2956882,36.7861845,Rider_Id_65,1128
-Order_No_25489,User_Id_1363,Bike,3,Business,10,5,3:09:37 PM,10,5,3:18:12 PM,10,5,3:20:11 PM,10,5,3:28:23 PM,10,5,4:14:47 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_68,2784
-Order_No_2654,User_Id_1148,Bike,3,Business,7,3,12:48:52 PM,7,3,12:52:22 PM,7,3,1:13:38 PM,7,3,1:42:19 PM,7,3,2:25:54 PM,10,28.4,,-1.3100586,36.850032899999995,-1.3003465,36.7879609,Rider_Id_660,2615
-Order_No_26852,User_Id_242,Bike,3,Business,29,5,9:44:46 AM,29,5,9:49:25 AM,29,5,10:07:57 AM,29,5,10:26:12 AM,29,5,10:43:25 AM,4,,,-1.2551895,36.7822034,-1.2697141,36.798177700000004,Rider_Id_159,1033
-Order_No_4062,User_Id_3557,Bike,3,Business,4,4,3:19:02 PM,4,4,3:24:34 PM,4,4,3:36:10 PM,4,4,3:40:50 PM,4,4,4:02:44 PM,7,24.9,,-1.2925919,36.81705770000001,-1.2546732,36.808679600000005,Rider_Id_74,1314
-Order_No_19335,User_Id_1578,Bike,3,Business,5,4,1:03:37 PM,5,4,1:10:11 PM,5,4,1:15:11 PM,5,4,1:28:38 PM,5,4,2:50:34 PM,10,,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_851,4916
-Order_No_5575,User_Id_2956,Bike,3,Business,4,1,1:43:03 PM,4,1,1:43:18 PM,4,1,2:10:05 PM,4,1,2:14:06 PM,4,1,2:52:37 PM,3,28.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_727,2311
-Order_No_2858,User_Id_1502,Bike,3,Business,20,3,9:04:16 AM,20,3,9:06:14 AM,20,3,9:24:46 AM,20,3,9:37:12 AM,20,3,10:00:05 AM,14,,,-1.2571472,36.795063299999995,-1.2232123,36.8932547,Rider_Id_275,1373
-Order_No_25805,User_Id_1787,Bike,3,Business,8,6,9:14:28 AM,8,6,9:16:13 AM,8,6,10:06:55 AM,8,6,10:11:50 AM,8,6,10:34:57 AM,10,16.0,14.9,-1.2765736,36.851364600000004,-1.2283403,36.8822756,Rider_Id_781,1387
-Order_No_26930,User_Id_2102,Bike,3,Business,27,2,9:38:01 AM,27,2,9:39:01 AM,27,2,10:01:38 AM,27,2,10:09:16 AM,27,2,10:51:42 AM,15,18.5,2.1,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_114,2546
-Order_No_19399,User_Id_3426,Bike,3,Business,31,3,8:49:38 AM,31,3,8:50:37 AM,31,3,9:04:46 AM,31,3,9:14:58 AM,31,3,9:47:42 AM,11,19.3,,-1.3472066,36.7692638,-1.3025826,36.7670807,Rider_Id_559,1964
-Order_No_21386,User_Id_2803,Bike,3,Business,18,4,9:51:23 AM,18,4,9:51:41 AM,18,4,10:23:32 AM,18,4,10:28:14 AM,18,4,10:52:55 AM,3,,,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_686,1481
-Order_No_15987,User_Id_2216,Bike,3,Business,16,1,1:03:13 PM,16,1,1:03:26 PM,16,1,1:07:36 PM,16,1,1:20:18 PM,16,1,1:44:02 PM,10,19.2,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_951,1424
-Order_No_6312,User_Id_2677,Bike,3,Business,18,4,3:24:58 PM,18,4,3:25:30 PM,18,4,3:41:40 PM,18,4,3:57:30 PM,18,4,4:43:59 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_438,2789
-Order_No_10168,User_Id_1250,Bike,1,Personal,10,4,2:54:55 PM,10,4,2:59:17 PM,10,4,3:19:08 PM,10,4,3:23:39 PM,10,4,3:52:31 PM,7,21.6,1.1,-1.2891773000000002,36.84557879999999,-1.2620741000000002,36.815814700000004,Rider_Id_727,1732
-Order_No_4239,User_Id_3201,Bike,3,Business,24,3,1:32:15 PM,24,3,1:32:27 PM,24,3,1:53:28 PM,24,3,2:06:44 PM,24,3,2:14:34 PM,6,18.5,,-1.2584143,36.8048002,-1.2623805000000001,36.7805404,Rider_Id_287,470
-Order_No_27397,User_Id_3599,Bike,3,Business,23,6,11:15:18 AM,23,6,12:01:17 PM,23,6,12:24:03 PM,23,6,12:36:03 PM,23,6,1:05:45 PM,13,26.2,,-1.3302996,36.8707935,-1.2713370000000002,36.786712,Rider_Id_675,1782
-Order_No_17772,User_Id_3283,Bike,3,Business,5,3,9:10:05 AM,5,3,9:11:01 AM,5,3,9:22:13 AM,5,3,9:30:58 AM,5,3,9:49:30 AM,7,17.8,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_829,1112
-Order_No_5806,User_Id_252,Bike,3,Personal,27,1,3:59:42 PM,27,1,3:59:52 PM,27,1,4:09:12 PM,27,1,4:23:43 PM,27,1,4:47:38 PM,5,23.2,,-1.2615888000000002,36.7928732,-1.2598848999999999,36.8219979,Rider_Id_868,1435
-Order_No_13019,User_Id_1006,Bike,1,Personal,17,5,8:36:42 AM,17,5,8:37:00 AM,17,5,8:45:08 AM,17,5,8:55:39 AM,17,5,9:31:21 AM,18,14.5,,-1.2729217,36.762482899999995,-1.2363091999999998,36.882361700000004,Rider_Id_379,2142
-Order_No_17701,User_Id_412,Bike,3,Business,18,2,10:37:49 AM,18,2,10:39:06 AM,18,2,10:52:14 AM,18,2,11:13:33 AM,18,2,11:25:13 AM,4,,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_547,700
-Order_No_26422,User_Id_186,Bike,3,Business,10,1,1:57:08 PM,10,1,2:55:13 PM,10,1,2:59:04 PM,10,1,3:16:01 PM,10,1,4:37:05 PM,23,19.0,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_498,4864
-Order_No_16982,User_Id_1307,Bike,3,Business,19,6,11:45:51 AM,19,6,11:46:07 AM,19,6,11:54:31 AM,19,6,12:01:14 PM,19,6,12:17:53 PM,5,21.8,,-1.2844665000000002,36.7866113,-1.2948802,36.766374,Rider_Id_898,999
-Order_No_23908,User_Id_627,Bike,3,Business,13,4,12:06:58 PM,13,4,12:07:20 PM,13,4,12:10:53 PM,13,4,12:23:05 PM,13,4,12:28:12 PM,3,23.4,,-1.2571472,36.795063299999995,-1.2569383,36.7880021,Rider_Id_647,307
-Order_No_16367,User_Id_20,Bike,3,Business,28,1,3:18:22 PM,28,1,3:20:17 PM,28,1,3:40:09 PM,28,1,3:46:15 PM,28,1,4:31:42 PM,12,24.7,,-1.2629666000000002,36.8068369,-1.313616,36.862965700000004,Rider_Id_480,2727
-Order_No_24685,User_Id_187,Bike,3,Business,25,7,12:55:17 PM,25,7,12:55:34 PM,25,7,1:10:09 PM,25,7,1:25:42 PM,25,7,2:13:36 PM,18,23.1,,-1.2584143,36.8048002,-1.3169591,36.7000029,Rider_Id_680,2874
-Order_No_14815,User_Id_3291,Bike,3,Business,29,6,10:06:12 AM,29,6,10:06:23 AM,29,6,10:09:44 AM,29,6,10:24:27 AM,29,6,10:41:05 AM,5,19.2,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_576,998
-Order_No_2250,User_Id_868,Bike,3,Business,5,6,1:37:40 PM,5,6,1:37:48 PM,5,6,1:41:05 PM,5,6,1:46:43 PM,5,6,2:16:29 PM,7,22.0,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_512,1786
-Order_No_9567,User_Id_393,Bike,3,Business,6,4,9:47:27 AM,6,4,9:47:44 AM,6,4,10:01:01 AM,6,4,10:10:53 AM,6,4,10:36:27 AM,11,16.3,3.9,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_2,1534
-Order_No_13516,User_Id_1120,Bike,3,Personal,29,2,12:57:13 PM,29,2,12:59:16 PM,29,2,1:14:51 PM,29,2,1:22:54 PM,29,2,1:27:39 PM,2,21.6,,-1.2594753,36.806273,-1.2638788,36.799846200000005,Rider_Id_103,285
-Order_No_14154,User_Id_1329,Bike,3,Business,30,5,10:35:38 AM,30,5,10:36:24 AM,30,5,10:41:09 AM,30,5,10:47:44 AM,30,5,11:33:40 AM,17,23.5,,-1.273056,36.811298,-1.3242283000000001,36.701819,Rider_Id_4,2756
-Order_No_19466,User_Id_1911,Bike,2,Personal,20,4,11:10:51 AM,20,4,11:12:32 AM,20,4,11:24:41 AM,20,4,11:42:52 AM,20,4,12:08:16 PM,12,24.2,,-1.2662257,36.798181299999996,-1.2049707,36.785895700000005,Rider_Id_536,1524
-Order_No_16860,User_Id_718,Bike,3,Business,6,2,2:50:28 PM,6,2,4:00:38 PM,6,2,4:10:48 PM,6,2,5:09:19 PM,6,2,5:26:41 PM,10,,,-1.2551895,36.7822034,-1.321627,36.835193,Rider_Id_208,1042
-Order_No_19994,User_Id_136,Bike,3,Business,20,3,11:56:44 AM,20,3,12:14:30 PM,20,3,1:06:26 PM,20,3,1:11:13 PM,20,3,1:38:31 PM,13,27.3,,-1.3254522,36.721658399999995,-1.25537,36.7756,Rider_Id_240,1638
-Order_No_10719,User_Id_302,Bike,3,Business,3,7,4:08:21 PM,3,7,4:10:54 PM,3,7,4:23:33 PM,3,7,4:32:02 PM,3,7,5:09:22 PM,10,27.4,,-1.2649605,36.798177700000004,-1.3116591999999998,36.80466989999999,Rider_Id_311,2240
-Order_No_26522,User_Id_2869,Bike,3,Business,3,3,9:11:46 AM,3,3,9:15:39 AM,3,3,9:22:20 AM,3,3,9:31:01 AM,3,3,10:25:41 AM,19,21.2,,-1.2859912,36.8756811,-1.3025826,36.7670807,Rider_Id_50,3280
-Order_No_4805,User_Id_2150,Bike,3,Business,10,3,10:51:20 AM,10,3,10:51:52 AM,10,3,10:52:28 AM,10,3,11:24:05 AM,10,3,11:31:55 AM,12,27.9,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_459,470
-Order_No_23233,User_Id_2195,Bike,1,Business,25,2,10:07:08 AM,25,2,10:07:27 AM,25,2,10:17:37 AM,25,2,10:47:03 AM,25,2,11:12:47 AM,10,23.7,,-1.265715,36.823815,-1.3313643,36.857742200000004,Rider_Id_96,1544
-Order_No_12102,User_Id_265,Bike,3,Business,30,2,12:44:32 PM,30,2,12:48:14 PM,30,2,12:53:24 PM,30,2,1:32:45 PM,30,2,1:56:02 PM,16,,,-1.300921,36.828195,-1.3710624,36.7483481,Rider_Id_923,1397
-Order_No_21083,User_Id_496,Bike,3,Business,17,4,3:45:26 PM,17,4,3:47:48 PM,17,4,3:58:54 PM,17,4,4:03:13 PM,17,4,4:29:31 PM,9,24.0,,-1.2626906,36.7827022,-1.2980228999999999,36.78865,Rider_Id_279,1578
-Order_No_20603,User_Id_393,Bike,3,Business,18,1,9:17:46 AM,18,1,9:18:26 AM,18,1,9:26:52 AM,18,1,10:11:48 AM,18,1,10:11:54 AM,11,22.1,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_263,6
-Order_No_1746,User_Id_2200,Bike,3,Business,26,1,11:27:52 AM,26,1,12:03:01 PM,26,1,12:26:29 PM,26,1,12:55:26 PM,26,1,1:29:21 PM,3,22.8,,-1.290894,36.822971,-1.3012618999999999,36.821369700000005,Rider_Id_614,2035
-Order_No_27643,User_Id_391,Bike,3,Business,27,6,10:54:25 AM,27,6,11:07:13 AM,27,6,11:14:30 AM,27,6,11:17:58 AM,27,6,11:33:04 AM,5,,,-1.2784063,36.8222933,-1.3081376,36.83297720000001,Rider_Id_341,906
-Order_No_4172,User_Id_302,Bike,3,Business,7,3,4:36:51 PM,7,3,4:37:25 PM,7,3,4:42:47 PM,7,3,4:54:29 PM,7,3,5:05:50 PM,3,25.1,,-1.2649605,36.798177700000004,-1.2647103000000002,36.8002593,Rider_Id_935,681
-Order_No_17534,User_Id_1342,Bike,3,Business,16,2,10:54:23 AM,16,2,10:55:24 AM,16,2,11:46:04 AM,16,2,12:57:48 PM,16,2,1:51:05 PM,9,23.6,,-1.28878,36.8168312,-1.3025826,36.7670807,Rider_Id_303,3197
-Order_No_12645,User_Id_393,Bike,3,Business,1,5,9:40:23 AM,1,5,10:17:41 AM,1,5,10:25:54 AM,1,5,10:36:30 AM,1,5,10:50:56 AM,4,26.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,866
-Order_No_3957,User_Id_3669,Bike,3,Business,11,1,5:21:27 PM,11,1,5:22:36 PM,11,1,5:28:39 PM,11,1,5:52:07 PM,11,1,6:16:44 PM,5,,,-1.2615888000000002,36.7928732,-1.2628008999999998,36.8262921,Rider_Id_205,1477
-Order_No_6776,User_Id_2559,Bike,3,Personal,31,4,5:16:02 PM,31,4,5:18:23 PM,31,4,5:46:58 PM,31,4,5:51:50 PM,31,4,6:37:26 PM,15,27.4,,-1.3008819,36.7612515,-1.2192191000000001,36.8055632,Rider_Id_269,2736
-Order_No_22000,User_Id_1642,Bike,3,Business,16,2,3:52:13 PM,16,2,3:56:58 PM,16,2,4:53:40 PM,16,2,5:08:48 PM,16,2,5:46:34 PM,6,24.9,,-1.2951620000000001,36.8115914,-1.2728069,36.7962362,Rider_Id_726,2266
-Order_No_10843,User_Id_1920,Bike,3,Business,29,1,4:13:42 PM,29,1,4:14:06 PM,29,1,5:04:19 PM,29,1,5:53:34 PM,29,1,6:22:19 PM,4,23.3,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_866,1725
-Order_No_6199,User_Id_53,Bike,3,Business,26,6,10:10:46 AM,26,6,10:11:53 AM,26,6,10:11:58 AM,26,6,10:37:27 AM,26,6,10:38:52 AM,8,26.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_239,85
-Order_No_22862,User_Id_1473,Bike,3,Business,27,3,4:20:41 PM,27,3,4:21:35 PM,27,3,4:53:02 PM,27,3,5:28:29 PM,27,3,6:28:41 PM,15,,,-1.2156006000000001,36.8916865,-1.3153122,36.8965598,Rider_Id_208,3612
-Order_No_10274,User_Id_2430,Bike,1,Personal,8,1,10:09:33 AM,8,1,10:16:28 AM,8,1,10:34:41 AM,8,1,11:04:48 AM,8,1,11:21:22 AM,9,24.3,,-1.2913146999999998,36.8232705,-1.317151,36.865734,Rider_Id_328,994
-Order_No_3591,User_Id_1850,Bike,3,Business,27,1,2:35:08 PM,27,1,2:35:56 PM,27,1,2:46:45 PM,27,1,2:58:11 PM,27,1,3:17:28 PM,6,23.1,,-1.2571472,36.795063299999995,-1.2890847,36.8048724,Rider_Id_131,1157
-Order_No_23069,User_Id_2454,Bike,3,Business,8,1,3:24:23 PM,8,1,3:28:23 PM,8,1,3:37:05 PM,8,1,3:48:56 PM,8,1,4:03:35 PM,6,29.4,,-1.3251496,36.7982151,-1.2944148000000002,36.803355100000005,Rider_Id_375,879
-Order_No_12699,User_Id_2330,Bike,3,Business,1,5,6:29:13 PM,1,5,6:29:36 PM,1,5,6:47:50 PM,1,5,6:50:39 PM,1,5,7:11:28 PM,9,26.4,,-1.2649605,36.798177700000004,-1.2326041,36.8009029,Rider_Id_547,1249
-Order_No_9722,User_Id_2523,Bike,3,Business,27,1,11:24:21 AM,27,1,11:29:16 AM,27,1,11:36:15 AM,27,1,11:38:18 AM,27,1,12:05:53 PM,19,20.9,,-1.2551895,36.7822034,-1.1708105,36.8336102,Rider_Id_564,1655
-Order_No_3387,User_Id_633,Bike,3,Business,18,1,12:06:22 PM,18,1,12:07:02 PM,18,1,12:09:03 PM,18,1,12:33:07 PM,18,1,12:49:56 PM,9,27.9,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_154,1009
-Order_No_15330,User_Id_302,Bike,3,Business,21,1,2:00:40 PM,21,1,2:01:05 PM,21,1,2:11:37 PM,21,1,2:28:51 PM,21,1,2:57:12 PM,4,27.8,,-1.2649605,36.798177700000004,-1.2546636999999998,36.8248917,Rider_Id_428,1701
-Order_No_21402,User_Id_1205,Bike,3,Business,9,2,8:48:43 AM,9,2,8:50:37 AM,9,2,9:11:53 AM,9,2,9:24:00 AM,9,2,9:49:05 AM,8,17.6,4.0,-1.2706815,36.7646286,-1.257309,36.806008399999996,Rider_Id_368,1505
-Order_No_18275,User_Id_3326,Bike,3,Business,22,5,1:06:32 PM,22,5,1:08:44 PM,22,5,1:38:58 PM,22,5,1:42:49 PM,22,5,1:50:26 PM,2,31.2,,-1.2571472,36.795063299999995,-1.2697141,36.798177700000004,Rider_Id_405,457
-Order_No_19929,User_Id_3517,Bike,3,Business,26,5,8:23:54 AM,26,5,8:24:14 AM,26,5,8:37:05 AM,26,5,9:34:25 AM,26,5,10:06:30 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_211,1925
-Order_No_12382,User_Id_921,Bike,3,Personal,1,5,2:35:01 PM,1,5,2:38:30 PM,1,5,2:46:22 PM,1,5,2:49:09 PM,1,5,3:23:58 PM,7,31.8,,-1.3144103999999999,36.8462411,-1.2903067,36.8151026,Rider_Id_283,2089
-Order_No_24185,User_Id_1578,Bike,3,Business,11,2,2:34:55 PM,11,2,2:44:19 PM,11,2,3:21:31 PM,11,2,3:36:57 PM,11,2,3:37:12 PM,2,22.3,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_692,15
-Order_No_17680,User_Id_1927,Bike,3,Business,24,4,8:46:21 AM,24,4,8:46:39 AM,24,4,9:03:52 AM,24,4,9:09:15 AM,24,4,9:46:51 AM,18,19.2,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_817,2256
-Order_No_11119,User_Id_2691,Bike,3,Business,14,3,12:33:49 PM,14,3,12:34:25 PM,14,3,12:44:08 PM,14,3,12:52:32 PM,14,3,1:05:01 PM,8,24.7,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_85,749
-Order_No_21757,User_Id_778,Bike,3,Business,21,5,8:11:02 AM,21,5,8:16:37 AM,21,5,8:38:34 AM,21,5,8:48:17 AM,21,5,9:07:01 AM,6,19.3,,-1.2771155,36.826679999999996,-1.2535486,36.7933541,Rider_Id_26,1124
-Order_No_525,User_Id_346,Bike,3,Business,23,2,1:04:51 PM,23,2,1:05:11 PM,23,2,1:22:51 PM,23,2,1:33:48 PM,23,2,2:21:35 PM,15,,,-1.3562374,36.904295399999995,-1.28878,36.8168312,Rider_Id_276,2867
-Order_No_19971,User_Id_2106,Bike,1,Personal,2,6,1:06:44 PM,2,6,1:07:35 PM,2,6,1:35:47 PM,2,6,1:50:40 PM,2,6,2:36:18 PM,21,24.1,,-1.3285987,36.903742799999996,-1.1985673000000001,36.836368799999995,Rider_Id_765,2738
-Order_No_22784,User_Id_2113,Bike,3,Personal,24,3,3:10:13 PM,24,3,3:13:12 PM,24,3,3:22:21 PM,24,3,3:37:02 PM,24,3,3:55:45 PM,8,,,-1.274165,36.814577,-1.275442,36.766020700000006,Rider_Id_345,1123
-Order_No_21453,User_Id_517,Bike,3,Business,30,4,3:07:43 PM,30,4,3:09:13 PM,30,4,3:44:11 PM,30,4,4:11:12 PM,30,4,5:06:01 PM,11,,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_459,3289
-Order_No_19900,User_Id_635,Bike,3,Business,29,1,12:46:54 PM,29,1,12:47:50 PM,29,1,1:10:30 PM,29,1,2:26:09 PM,29,1,2:50:21 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_85,1452
-Order_No_22041,User_Id_2974,Bike,3,Business,27,2,9:26:26 AM,27,2,9:40:28 AM,27,2,9:45:26 AM,27,2,9:55:41 AM,27,2,10:20:46 AM,11,18.6,2.1,-1.287428,36.7800821,-1.3244268,36.839032200000005,Rider_Id_379,1505
-Order_No_15529,User_Id_2200,Bike,3,Business,2,5,10:10:42 AM,2,5,10:46:03 AM,2,5,10:46:24 AM,2,5,12:02:51 PM,2,5,12:23:29 PM,6,23.5,,-1.260232,36.807334000000004,-1.290894,36.822971,Rider_Id_785,1238
-Order_No_27920,User_Id_84,Bike,3,Business,19,1,2:01:07 PM,19,1,2:01:46 PM,19,1,2:06:08 PM,19,1,2:10:51 PM,19,1,2:24:37 PM,3,25.3,,-1.2551895,36.7822034,-1.2524976,36.8062935,Rider_Id_509,826
-Order_No_9282,User_Id_2701,Bike,3,Business,16,6,11:43:45 AM,16,6,11:50:23 AM,16,6,12:28:52 PM,16,6,12:31:08 PM,16,6,12:44:20 PM,3,19.1,,-1.279395,36.825364,-1.2651643,36.807221399999996,Rider_Id_208,792
-Order_No_10333,User_Id_2100,Bike,3,Business,27,4,12:47:57 PM,27,4,12:48:29 PM,27,4,1:03:59 PM,27,4,1:12:41 PM,27,4,1:32:15 PM,6,23.0,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_933,1174
-Order_No_12832,User_Id_2436,Bike,3,Business,18,4,2:14:11 PM,18,4,2:15:01 PM,18,4,2:15:06 PM,18,4,2:27:05 PM,18,4,2:44:10 PM,14,26.6,,-1.2551895,36.7822034,-1.242902,36.870334,Rider_Id_871,1025
-Order_No_311,User_Id_2200,Bike,3,Business,30,2,10:12:12 AM,30,2,11:03:52 AM,30,2,11:34:10 AM,30,2,11:36:38 AM,30,2,11:45:42 AM,2,22.5,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_895,544
-Order_No_20312,User_Id_1271,Bike,3,Business,28,4,5:01:34 PM,28,4,5:35:25 PM,28,4,5:42:18 PM,28,4,5:45:46 PM,28,4,5:56:50 PM,8,27.9,,-1.3244885,36.897791999999995,-1.3263105,36.8622904,Rider_Id_675,664
-Order_No_27932,User_Id_10,Bike,2,Personal,21,4,10:35:59 AM,21,4,10:42:56 AM,21,4,11:01:39 AM,21,4,11:20:32 AM,21,4,11:35:58 AM,7,21.5,,-1.2663778,36.8076024,-1.2930931,36.7559267,Rider_Id_427,926
-Order_No_1875,User_Id_246,Bike,3,Business,22,5,2:50:33 PM,22,5,2:50:40 PM,22,5,2:51:13 PM,22,5,2:59:38 PM,22,5,3:04:36 PM,2,,,-1.2584143,36.8048002,-1.2620912,36.8013562,Rider_Id_379,298
-Order_No_18221,User_Id_1856,Bike,3,Business,13,2,1:07:35 PM,13,2,1:12:03 PM,13,2,2:04:17 PM,13,2,2:15:11 PM,13,2,2:55:25 PM,2,26.3,,-1.273009,36.802807,-1.263987,36.798891,Rider_Id_875,2414
-Order_No_20897,User_Id_3203,Bike,3,Business,26,2,3:18:05 PM,26,2,3:18:38 PM,26,2,3:36:24 PM,26,2,3:47:17 PM,26,2,4:02:33 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_427,916
-Order_No_19866,User_Id_3722,Bike,1,Personal,28,2,2:12:53 PM,28,2,2:14:55 PM,28,2,2:42:22 PM,28,2,2:50:07 PM,28,2,4:00:27 PM,5,19.4,,-1.2872899,36.820434000000006,-1.2898353999999999,36.787270299999996,Rider_Id_447,4220
-Order_No_11303,User_Id_2767,Bike,3,Business,6,2,1:00:40 PM,6,2,1:01:31 PM,6,2,1:03:09 PM,6,2,1:06:56 PM,6,2,1:19:32 PM,9,24.8,,-1.257773,36.795486,-1.2295075,36.801697100000005,Rider_Id_107,756
-Order_No_4088,User_Id_1245,Bike,3,Business,22,2,1:43:06 PM,22,2,1:59:06 PM,22,2,2:43:18 PM,22,2,2:52:45 PM,22,2,3:29:09 PM,14,25.4,,-1.3882008,36.7699445,-1.3198615,36.7110323,Rider_Id_379,2184
-Order_No_11687,User_Id_136,Bike,3,Business,18,2,3:57:35 PM,18,2,3:59:19 PM,18,2,4:00:18 PM,18,2,4:04:48 PM,18,2,4:28:28 PM,11,25.7,,-1.3326173000000001,36.71462329999999,-1.2909633999999999,36.7813995,Rider_Id_48,1420
-Order_No_25673,User_Id_1398,Bike,3,Business,6,3,8:25:36 AM,6,3,8:26:32 AM,6,3,8:32:01 AM,6,3,8:51:23 AM,6,3,9:15:32 AM,10,,,-1.2615584,36.814950599999996,-1.2945178999999998,36.7685349,Rider_Id_698,1449
-Order_No_18498,User_Id_540,Bike,3,Business,19,2,1:47:07 PM,19,2,1:47:56 PM,19,2,2:00:40 PM,19,2,2:02:07 PM,19,2,2:33:54 PM,8,29.5,,-1.2551895,36.7822034,-1.3028118,36.8174764,Rider_Id_948,1907
-Order_No_25340,User_Id_1205,Bike,3,Business,17,3,11:54:40 AM,17,3,12:07:25 PM,17,3,12:15:17 PM,17,3,12:17:43 PM,17,3,12:37:51 PM,7,25.6,,-1.257309,36.806008399999996,-1.2963544,36.8200071,Rider_Id_22,1208
-Order_No_16103,User_Id_1085,Bike,3,Business,3,7,10:19:06 AM,3,7,11:41:22 AM,3,7,11:50:32 AM,3,7,11:52:59 AM,3,7,12:13:37 PM,7,28.3,,-1.2536275,36.798793200000006,-1.232864,36.808470799999995,Rider_Id_467,1238
-Order_No_21267,User_Id_2701,Bike,3,Business,24,5,11:31:27 AM,24,5,11:37:53 AM,24,5,11:53:42 AM,24,5,11:54:45 AM,24,5,12:11:01 PM,4,21.3,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_338,976
-Order_No_19709,User_Id_3262,Bike,3,Personal,23,2,12:02:33 PM,23,2,12:02:59 PM,23,2,12:12:55 PM,23,2,12:14:20 PM,23,2,12:59:03 PM,35,,,-1.255611,36.815386,-1.0921598999999997,37.005025,Rider_Id_710,2683
-Order_No_2725,User_Id_2314,Bike,2,Personal,20,5,12:10:18 PM,20,5,12:10:34 PM,20,5,12:26:43 PM,20,5,12:31:23 PM,20,5,12:54:02 PM,12,18.3,,-1.2895159,36.7884326,-1.2482518,36.7550829,Rider_Id_281,1359
-Order_No_11231,User_Id_265,Bike,3,Business,23,4,11:58:57 AM,23,4,11:59:37 AM,23,4,12:15:41 PM,23,4,12:25:45 PM,23,4,12:55:08 PM,9,18.4,,-1.300921,36.828195,-1.2557724,36.829521899999996,Rider_Id_929,1763
-Order_No_25689,User_Id_1367,Bike,3,Business,29,1,1:41:00 PM,29,1,1:45:36 PM,29,1,2:02:55 PM,29,1,2:59:55 PM,29,1,3:32:33 PM,23,24.2,,-1.3285517,36.9018,-1.275442,36.766020700000006,Rider_Id_164,1958
-Order_No_21521,User_Id_2801,Bike,3,Business,25,1,9:02:06 AM,25,1,9:02:44 AM,25,1,9:08:44 AM,25,1,9:41:29 AM,25,1,9:52:18 AM,7,20.9,,-1.2595421999999998,36.7871178,-1.2898747,36.76612479999999,Rider_Id_258,649
-Order_No_11048,User_Id_393,Bike,3,Business,30,5,9:38:34 AM,30,5,9:55:23 AM,30,5,9:55:44 AM,30,5,10:13:09 AM,30,5,10:42:02 AM,14,18.9,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_619,1733
-Order_No_4673,User_Id_496,Bike,3,Business,28,2,3:37:25 PM,28,2,3:38:19 PM,28,2,3:44:09 PM,28,2,3:51:19 PM,28,2,3:59:56 PM,2,23.7,,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_407,517
-Order_No_12933,User_Id_2112,Bike,3,Business,30,2,4:19:11 PM,30,2,4:19:21 PM,30,2,4:19:40 PM,30,2,4:32:23 PM,30,2,4:58:28 PM,15,23.6,,-1.305399,36.822184899999996,-1.2156006000000001,36.8916865,Rider_Id_450,1565
-Order_No_16886,User_Id_647,Bike,3,Business,15,1,4:08:40 PM,15,1,4:13:57 PM,15,1,4:28:59 PM,15,1,4:42:22 PM,15,1,5:04:07 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_678,1305
-Order_No_6719,User_Id_3674,Bike,3,Business,6,2,2:31:57 PM,6,2,2:32:13 PM,6,2,2:33:25 PM,6,2,2:39:18 PM,6,2,3:08:13 PM,22,27.7,,-1.2551895,36.7822034,-1.1851027,36.9392531,Rider_Id_551,1735
-Order_No_7846,User_Id_393,Bike,3,Business,25,1,8:51:46 AM,25,1,8:53:11 AM,25,1,8:53:20 AM,25,1,9:17:59 AM,25,1,9:41:25 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_842,1406
-Order_No_14272,User_Id_2532,Bike,3,Business,19,5,9:21:46 AM,19,5,9:28:07 AM,19,5,9:50:23 AM,19,5,9:53:30 AM,19,5,10:14:00 AM,7,20.4,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_79,1230
-Order_No_18167,User_Id_393,Bike,3,Business,19,6,11:33:25 AM,19,6,11:33:37 AM,19,6,11:36:40 AM,19,6,11:44:36 AM,19,6,11:52:42 AM,4,25.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,486
-Order_No_9531,User_Id_3283,Bike,3,Business,22,5,12:12:35 PM,22,5,12:15:10 PM,22,5,12:24:17 PM,22,5,12:30:03 PM,22,5,1:11:27 PM,7,28.3,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_306,2484
-Order_No_23912,User_Id_2701,Bike,3,Business,29,1,9:37:38 AM,29,1,9:39:02 AM,29,1,9:39:05 AM,29,1,10:17:38 AM,29,1,10:55:21 AM,4,19.0,,-1.281754,36.8324229,-1.2765736,36.851364600000004,Rider_Id_456,2263
-Order_No_5186,User_Id_1363,Bike,3,Business,4,2,9:08:04 AM,4,2,9:34:42 AM,4,2,9:42:06 AM,4,2,9:43:08 AM,4,2,10:03:19 AM,3,16.5,98.9,-1.3004062,36.829741,-1.2929499,36.8180835,Rider_Id_158,1211
-Order_No_3205,User_Id_868,Bike,3,Business,21,5,3:48:53 PM,21,5,3:59:50 PM,21,5,4:05:08 PM,21,5,4:16:11 PM,21,5,4:40:09 PM,8,24.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_279,1438
-Order_No_1150,User_Id_53,Bike,3,Business,7,1,11:49:55 AM,7,1,12:02:10 PM,7,1,12:31:10 PM,7,1,12:33:09 PM,7,1,12:58:29 PM,6,25.0,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_436,1520
-Order_No_11463,User_Id_706,Bike,3,Business,2,3,10:22:05 AM,2,3,10:26:21 AM,2,3,10:26:51 AM,2,3,10:34:01 AM,2,3,10:47:49 AM,6,24.0,,-1.3077869,36.844320700000004,-1.3228003000000002,36.8306435,Rider_Id_848,828
-Order_No_23148,User_Id_1246,Bike,3,Business,10,4,11:37:12 AM,10,4,11:37:20 AM,10,4,11:48:01 AM,10,4,11:52:16 AM,10,4,12:03:17 PM,2,24.5,,-1.2534301,36.800098299999995,-1.2682478000000001,36.8072852,Rider_Id_547,661
-Order_No_16453,User_Id_2337,Bike,1,Personal,14,5,1:50:32 PM,14,5,1:52:16 PM,14,5,2:02:13 PM,14,5,2:04:19 PM,14,5,2:32:46 PM,10,27.0,,-1.2321733,36.7974377,-1.2962535,36.8151842,Rider_Id_726,1707
-Order_No_20680,User_Id_1856,Bike,3,Business,23,4,3:58:48 PM,23,4,3:59:10 PM,23,4,4:04:43 PM,23,4,4:09:26 PM,23,4,4:18:31 PM,4,20.6,,-1.273009,36.802807,-1.263987,36.798891,Rider_Id_620,545
-Order_No_21192,User_Id_1329,Bike,3,Business,11,4,10:38:08 AM,11,4,10:39:07 AM,11,4,10:44:28 AM,11,4,10:50:06 AM,11,4,11:15:35 AM,8,25.7,,-1.273056,36.811298,-1.3222949,36.81312379999999,Rider_Id_435,1529
-Order_No_9192,User_Id_2112,Bike,3,Business,4,2,11:40:45 AM,4,2,11:42:18 AM,4,2,12:03:02 PM,4,2,12:17:41 PM,4,2,12:38:26 PM,14,19.0,,-1.2956951,36.812924200000005,-1.2156006000000001,36.8916865,Rider_Id_248,1245
-Order_No_22692,User_Id_987,Bike,2,Personal,6,1,6:08:55 PM,6,1,6:10:08 PM,6,1,6:31:11 PM,6,1,6:41:51 PM,6,1,7:06:01 PM,8,22.5,,-1.2866544,36.82179779999999,-1.3193552,36.8012769,Rider_Id_956,1450
-Order_No_22611,User_Id_540,Bike,3,Business,17,1,2:56:33 PM,17,1,2:57:48 PM,17,1,3:09:00 PM,17,1,3:14:29 PM,17,1,3:25:09 PM,4,24.6,,-1.2551895,36.7822034,-1.2665065,36.7970081,Rider_Id_290,640
-Order_No_4541,User_Id_1340,Bike,3,Business,28,3,10:25:46 AM,28,3,10:26:36 AM,28,3,10:33:20 AM,28,3,10:45:59 AM,28,3,11:08:05 AM,9,,,-1.2707178999999997,36.7979713,-1.2602386,36.7503491,Rider_Id_131,1326
-Order_No_253,User_Id_1568,Bike,3,Business,4,1,4:17:02 PM,4,1,4:19:55 PM,4,1,4:42:31 PM,4,1,5:00:52 PM,4,1,5:51:40 PM,13,26.2,,-1.2897368,36.8226783,-1.2421591,36.8939721,Rider_Id_26,3048
-Order_No_26889,User_Id_2982,Bike,3,Business,21,4,12:33:01 PM,21,4,12:34:01 PM,21,4,12:46:16 PM,21,4,12:57:49 PM,21,4,1:24:36 PM,15,25.2,,-1.2855173,36.8474224,-1.177202,36.8351183,Rider_Id_576,1607
-Order_No_23009,User_Id_2801,Bike,3,Business,3,1,11:02:04 AM,3,1,11:02:29 AM,3,1,11:16:15 AM,3,1,11:30:35 AM,3,1,11:37:33 AM,3,22.1,,-1.2529635000000001,36.788540399999995,-1.2595421999999998,36.7871178,Rider_Id_717,418
-Order_No_17325,User_Id_635,Bike,3,Business,31,5,9:45:34 AM,31,5,9:45:52 AM,31,5,9:49:17 AM,31,5,10:06:20 AM,31,5,10:27:03 AM,16,,,-1.2602337,36.79905479999999,-1.2782208000000002,36.706262200000005,Rider_Id_90,1243
-Order_No_7449,User_Id_3651,Bike,3,Business,7,3,3:50:09 PM,7,3,4:17:26 PM,7,3,4:50:25 PM,7,3,4:59:24 PM,7,3,5:19:59 PM,5,25.0,,-1.2904118999999998,36.7898312,-1.2895383999999999,36.827670700000006,Rider_Id_612,1235
-Order_No_26128,User_Id_3115,Bike,1,Business,28,1,2:11:30 PM,28,1,2:35:11 PM,28,1,2:53:51 PM,28,1,2:58:38 PM,28,1,3:38:20 PM,23,24.5,,-1.3160976999999998,36.913164200000004,-1.2963097,36.7688221,Rider_Id_543,2382
-Order_No_17259,User_Id_2523,Bike,3,Business,7,5,3:47:41 PM,7,5,3:54:01 PM,7,5,4:14:42 PM,7,5,4:17:47 PM,7,5,4:32:33 PM,5,,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_147,886
-Order_No_14910,User_Id_1578,Bike,3,Business,8,4,1:09:07 PM,8,4,3:02:13 PM,8,4,3:25:49 PM,8,4,3:37:01 PM,8,4,4:00:41 PM,6,27.4,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_219,1420
-Order_No_22407,User_Id_1176,Bike,3,Business,6,4,2:44:12 PM,6,4,2:45:38 PM,6,4,3:24:02 PM,6,4,3:27:16 PM,6,4,3:52:26 PM,22,,,-1.2812014999999999,36.82612460000001,-1.1682728999999998,36.953616700000005,Rider_Id_622,1510
-Order_No_7841,User_Id_1976,Bike,3,Business,25,1,12:12:20 PM,25,1,12:12:32 PM,25,1,12:12:55 PM,25,1,12:18:39 PM,25,1,12:33:12 PM,7,25.7,,-1.257028,36.7810435,-1.2615048,36.8116724,Rider_Id_523,873
-Order_No_18323,User_Id_3543,Bike,1,Personal,5,7,12:17:52 PM,5,7,12:18:26 PM,5,7,12:28:20 PM,5,7,12:37:19 PM,5,7,12:50:47 PM,6,19.1,,-1.2927663,36.787560799999994,-1.2696568000000001,36.7586514,Rider_Id_101,808
-Order_No_9325,User_Id_2125,Bike,2,Personal,28,5,4:42:13 PM,28,5,4:43:37 PM,28,5,5:08:32 PM,28,5,5:24:27 PM,28,5,6:03:00 PM,8,,,-1.3066189,36.780550500000004,-1.2835073000000001,36.8275282,Rider_Id_328,2313
-Order_No_16043,User_Id_3517,Bike,3,Business,17,5,2:11:25 PM,17,5,2:12:48 PM,17,5,2:23:42 PM,17,5,2:47:01 PM,17,5,3:44:05 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_332,3424
-Order_No_350,User_Id_2801,Bike,3,Business,14,3,9:05:33 AM,14,3,9:06:23 AM,14,3,9:12:11 AM,14,3,9:22:27 AM,14,3,9:32:00 AM,4,18.4,,-1.2595421999999998,36.7871178,-1.2664428,36.809453000000005,Rider_Id_125,573
-Order_No_26968,User_Id_1184,Bike,3,Personal,22,2,12:52:21 PM,22,2,12:54:40 PM,22,2,1:14:59 PM,22,2,1:26:59 PM,22,2,1:51:13 PM,6,19.5,,-1.2860198,36.8176562,-1.3043649,36.7816132,Rider_Id_844,1454
-Order_No_3004,User_Id_3322,Bike,3,Personal,26,2,1:18:58 PM,26,2,1:21:16 PM,26,2,1:24:42 PM,26,2,1:43:54 PM,26,2,1:53:03 PM,5,29.9,,-1.2883158000000001,36.8199114,-1.2991116000000003,36.7960658,Rider_Id_156,549
-Order_No_13114,User_Id_2931,Bike,3,Business,5,2,12:01:10 PM,5,2,12:01:43 PM,5,2,12:12:41 PM,5,2,12:15:44 PM,5,2,1:02:49 PM,22,25.4,,-1.2452664,36.802854100000005,-1.3433181,36.9138018,Rider_Id_318,2825
-Order_No_1529,User_Id_3222,Bike,3,Business,4,4,11:52:15 AM,4,4,11:52:50 AM,4,4,11:58:54 AM,4,4,12:10:28 PM,4,4,1:13:34 PM,16,25.4,,-1.2551895,36.7822034,-1.3189252,36.861726899999994,Rider_Id_932,3786
-Order_No_20193,User_Id_524,Bike,1,Personal,27,4,2:19:17 PM,27,4,2:19:26 PM,27,4,2:39:26 PM,27,4,2:43:59 PM,27,4,3:08:03 PM,11,25.2,,-1.2575406999999998,36.7868197,-1.312469,36.815177,Rider_Id_72,1444
-Order_No_17288,User_Id_3404,Bike,3,Business,17,1,9:23:03 AM,17,1,9:24:47 AM,17,1,9:32:08 AM,17,1,9:42:40 AM,17,1,9:51:08 AM,4,19.2,,-1.279395,36.825364,-1.2790653,36.7907262,Rider_Id_194,508
-Order_No_20287,User_Id_3557,Bike,3,Business,14,4,3:43:52 PM,14,4,3:45:49 PM,14,4,3:54:16 PM,14,4,4:05:26 PM,14,4,4:29:10 PM,10,27.8,,-1.2546732,36.808679600000005,-1.2892762,36.764129,Rider_Id_854,1424
-Order_No_27558,User_Id_1482,Bike,3,Business,26,1,12:32:23 PM,26,1,12:46:31 PM,26,1,12:56:30 PM,26,1,12:58:51 PM,26,1,1:06:18 PM,10,23.2,,-1.3385790000000002,36.89630379999999,-1.3341604,36.86697410000001,Rider_Id_105,447
-Order_No_18805,User_Id_1335,Bike,3,Personal,14,4,4:50:10 PM,14,4,4:54:04 PM,14,4,4:54:32 PM,14,4,5:00:52 PM,14,4,5:11:37 PM,4,26.2,,-1.2611987,36.80609129999999,-1.2604408,36.8206328,Rider_Id_409,645
-Order_No_18286,User_Id_3203,Bike,3,Business,14,2,10:36:44 AM,14,2,10:37:02 AM,14,2,10:57:59 AM,14,2,11:16:29 AM,14,2,12:20:50 PM,17,21.6,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_519,3861
-Order_No_26973,User_Id_2245,Bike,3,Personal,18,5,2:30:08 PM,18,5,2:31:05 PM,18,5,3:29:50 PM,18,5,3:37:14 PM,18,5,4:38:22 PM,23,24.6,,-1.2866269,36.82463679999999,-1.3510653000000001,36.6665115,Rider_Id_543,3668
-Order_No_4573,User_Id_295,Bike,3,Business,18,2,11:36:28 AM,18,2,1:48:02 PM,18,2,1:48:08 PM,18,2,2:06:36 PM,18,2,3:29:30 PM,8,25.5,,-1.2967108,36.787113500000004,-1.2622851000000002,36.7893989,Rider_Id_942,4974
-Order_No_6359,User_Id_928,Bike,3,Business,30,3,10:04:53 AM,30,3,10:05:34 AM,30,3,10:14:06 AM,30,3,10:18:28 AM,30,3,10:26:45 AM,4,19.6,,-1.2551895,36.7822034,-1.2585521000000002,36.7945765,Rider_Id_822,497
-Order_No_1877,User_Id_3647,Bike,3,Business,19,1,4:14:19 PM,19,1,4:17:18 PM,19,1,4:26:38 PM,19,1,4:31:07 PM,19,1,4:49:06 PM,4,25.0,,-1.2726389999999999,36.794723,-1.2669451,36.8033576,Rider_Id_536,1079
-Order_No_23564,User_Id_393,Bike,3,Business,22,5,3:11:09 PM,22,5,3:36:45 PM,22,5,3:56:19 PM,22,5,3:59:08 PM,22,5,4:15:04 PM,4,27.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_778,956
-Order_No_2138,User_Id_868,Bike,3,Business,8,5,2:09:54 PM,8,5,2:10:47 PM,8,5,2:16:38 PM,8,5,2:26:21 PM,8,5,2:33:24 PM,5,27.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_845,423
-Order_No_18925,User_Id_3355,Bike,3,Business,30,2,4:46:38 PM,30,2,4:47:38 PM,30,2,4:51:13 PM,30,2,4:56:02 PM,30,2,6:13:15 PM,7,23.7,,-1.2584143,36.8048002,-1.3052491000000002,36.8223899,Rider_Id_532,4633
-Order_No_7056,User_Id_633,Bike,3,Business,14,4,3:19:39 PM,14,4,3:22:32 PM,14,4,3:28:13 PM,14,4,3:49:12 PM,14,4,3:59:22 PM,5,27.6,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_728,610
-Order_No_2376,User_Id_1927,Bike,3,Business,23,5,3:15:24 PM,23,5,3:29:25 PM,23,5,3:52:43 PM,23,5,4:02:54 PM,23,5,5:16:22 PM,15,26.6,,-1.2561334,36.7990458,-1.3463041999999998,36.768164,Rider_Id_410,4408
-Order_No_22057,User_Id_2069,Bike,3,Business,8,6,1:22:17 PM,8,6,1:23:17 PM,8,6,1:23:22 PM,8,6,1:38:06 PM,8,6,1:53:05 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_698,899
-Order_No_22591,User_Id_190,Bike,3,Business,28,2,12:53:15 PM,28,2,12:53:57 PM,28,2,1:13:16 PM,28,2,1:33:11 PM,28,2,1:33:14 PM,4,23.7,,-1.329568,36.8520825,-1.3270347,36.866736200000005,Rider_Id_161,3
-Order_No_11187,User_Id_2318,Bike,2,Personal,22,5,12:38:23 PM,22,5,12:44:42 PM,22,5,12:51:06 PM,22,5,1:03:10 PM,22,5,1:29:17 PM,14,21.5,,-1.3339943,36.7263409,-1.3190714,36.8016277,Rider_Id_95,1567
-Order_No_23526,User_Id_3291,Bike,3,Business,29,6,12:04:28 PM,29,6,12:04:49 PM,29,6,12:19:36 PM,29,6,12:27:54 PM,29,6,12:40:13 PM,4,23.8,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_456,739
-Order_No_17816,User_Id_1402,Bike,3,Business,20,1,9:36:53 AM,20,1,9:37:22 AM,20,1,9:39:09 AM,20,1,9:44:22 AM,20,1,9:51:57 AM,1,18.9,,-1.279395,36.825364,-1.2813012,36.832396200000005,Rider_Id_456,455
-Order_No_14610,User_Id_2532,Bike,3,Business,7,5,11:07:10 AM,7,5,11:37:50 AM,7,5,11:42:43 AM,7,5,11:43:49 AM,7,5,12:02:17 PM,7,,,-1.2551895,36.7822034,-1.284959,36.821733,Rider_Id_910,1108
-Order_No_12613,User_Id_142,Bike,3,Business,22,3,8:57:18 AM,22,3,8:58:06 AM,22,3,9:00:33 AM,22,3,9:23:45 AM,22,3,9:43:20 AM,8,18.8,,-1.2601197,36.7985271,-1.2963097,36.7688221,Rider_Id_131,1175
-Order_No_25468,User_Id_2229,Bike,3,Business,21,2,2:33:49 PM,21,2,2:34:10 PM,21,2,2:39:49 PM,21,2,2:48:21 PM,21,2,3:13:42 PM,10,25.2,,-1.3190408000000002,36.7114416,-1.2974014999999999,36.7772406,Rider_Id_524,1521
-Order_No_6110,User_Id_3222,Bike,3,Business,18,5,11:17:12 AM,18,5,11:17:38 AM,18,5,11:21:38 AM,18,5,11:26:56 AM,18,5,11:47:09 AM,9,20.2,,-1.2551895,36.7822034,-1.2977565,36.8025534,Rider_Id_343,1213
-Order_No_26179,User_Id_1329,Bike,3,Business,6,4,3:20:10 PM,6,4,3:23:40 PM,6,4,3:33:17 PM,6,4,3:35:12 PM,6,4,3:59:17 PM,6,21.3,,-1.273056,36.811298,-1.2882120000000001,36.778334,Rider_Id_472,1445
-Order_No_3012,User_Id_718,Bike,3,Business,31,5,4:22:15 PM,31,5,4:25:07 PM,31,5,4:25:41 PM,31,5,4:29:36 PM,31,5,4:45:29 PM,5,24.9,,-1.2551895,36.7822034,-1.2730278,36.79187879999999,Rider_Id_709,953
-Order_No_6539,User_Id_2627,Bike,3,Business,4,1,11:12:14 AM,4,1,11:12:54 AM,4,1,11:20:59 AM,4,1,11:27:40 AM,4,1,11:50:32 AM,9,26.8,,-1.3014461000000002,36.7661381,-1.2571472,36.795063299999995,Rider_Id_576,1372
-Order_No_17923,User_Id_3621,Bike,3,Business,13,6,2:26:37 PM,13,6,2:27:07 PM,13,6,2:27:18 PM,13,6,2:35:42 PM,13,6,3:36:31 PM,18,27.7,,-1.2836583000000001,36.7886014,-1.2720281999999998,36.910749100000004,Rider_Id_314,3649
-Order_No_23027,User_Id_2344,Bike,3,Business,1,4,1:45:10 PM,1,4,1:46:45 PM,1,4,2:01:18 PM,1,4,2:03:25 PM,1,4,2:19:19 PM,8,,,-1.2551895,36.7822034,-1.2958383,36.8073162,Rider_Id_578,954
-Order_No_9945,User_Id_1075,Bike,3,Business,27,3,11:47:19 AM,27,3,11:48:04 AM,27,3,11:57:38 AM,27,3,12:00:27 PM,27,3,12:32:55 PM,11,26.2,,-1.30435,36.860009999999996,-1.2527958000000001,36.8003131,Rider_Id_448,1948
-Order_No_10838,User_Id_3699,Bike,1,Personal,18,3,2:15:10 PM,18,3,2:16:12 PM,18,3,2:33:01 PM,18,3,2:38:15 PM,18,3,3:12:52 PM,12,20.5,,-1.2921233,36.806385999999996,-1.2963418,36.8855114,Rider_Id_427,2077
-Order_No_26014,User_Id_1683,Bike,3,Business,17,1,1:10:03 PM,17,1,1:34:15 PM,17,1,1:43:12 PM,17,1,1:50:18 PM,17,1,1:50:28 PM,6,24.7,,-1.2535486,36.7933541,-1.2765334,36.80150510000001,Rider_Id_501,10
-Order_No_22274,User_Id_3688,Bike,3,Business,13,3,4:46:44 PM,13,3,4:48:19 PM,13,3,5:00:02 PM,13,3,5:03:37 PM,13,3,5:53:40 PM,23,25.8,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_622,3003
-Order_No_3597,User_Id_787,Bike,1,Personal,29,5,2:27:17 PM,29,5,2:37:08 PM,29,5,2:50:38 PM,29,5,2:59:02 PM,29,5,3:15:32 PM,4,21.0,,-1.2940713,36.7890758,-1.2914687,36.7692172,Rider_Id_726,990
-Order_No_1180,User_Id_849,Bike,1,Personal,9,2,12:17:56 PM,9,2,12:20:20 PM,9,2,1:05:01 PM,9,2,1:25:05 PM,9,2,2:03:28 PM,7,22.0,,-1.2869663999999998,36.794529600000004,-1.2652108,36.8056075,Rider_Id_932,2303
-Order_No_5681,User_Id_1280,Bike,3,Business,30,2,10:12:41 AM,30,2,10:13:24 AM,30,2,10:23:11 AM,30,2,10:26:37 AM,30,2,10:37:25 AM,4,,,-1.2551895,36.7822034,-1.2715686000000002,36.8023526,Rider_Id_607,648
-Order_No_25962,User_Id_640,Bike,3,Business,10,1,10:54:11 AM,10,1,10:55:36 AM,10,1,11:18:09 AM,10,1,11:29:38 AM,10,1,12:23:21 PM,10,18.7,,-1.304033,36.7848695,-1.2625087,36.8263725,Rider_Id_938,3223
-Order_No_16587,User_Id_1684,Bike,1,Personal,14,5,11:31:53 AM,14,5,11:32:04 AM,14,5,11:34:42 AM,14,5,12:18:01 PM,14,5,12:49:22 PM,6,24.5,,-1.2740396,36.7993518,-1.2933788999999998,36.808753499999995,Rider_Id_832,1881
-Order_No_5692,User_Id_793,Bike,3,Personal,4,1,12:13:00 PM,4,1,12:14:30 PM,4,1,12:31:19 PM,4,1,12:36:12 PM,4,1,12:56:00 PM,6,25.4,,-1.2651573999999999,36.7474347,-1.256522,36.786413200000005,Rider_Id_411,1188
-Order_No_14347,User_Id_2030,Bike,3,Business,18,5,12:06:09 PM,18,5,2:08:58 PM,18,5,2:23:26 PM,18,5,2:26:17 PM,18,5,2:41:56 PM,12,27.0,,-1.1612808000000001,36.9563278,-1.2156006000000001,36.8916865,Rider_Id_845,939
-Order_No_15963,User_Id_1500,Bike,3,Business,30,2,8:11:25 AM,30,2,8:12:18 AM,30,2,8:24:05 AM,30,2,8:26:17 AM,30,2,8:44:50 AM,5,,,-1.2638185,36.7930057,-1.282937,36.781698399999996,Rider_Id_247,1113
-Order_No_19660,User_Id_526,Bike,2,Personal,27,1,10:05:45 AM,27,1,10:06:02 AM,27,1,10:12:56 AM,27,1,10:17:55 AM,27,1,10:35:57 AM,4,16.8,,-1.268572,36.802839,-1.2650502000000001,36.79563710000001,Rider_Id_754,1082
-Order_No_8859,User_Id_1787,Bike,3,Business,6,3,10:55:07 AM,6,3,10:55:22 AM,6,3,10:56:02 AM,6,3,11:32:05 AM,6,3,12:01:38 PM,12,,,-1.2285834999999998,36.882646799999996,-1.260039,36.8159152,Rider_Id_8,1773
-Order_No_9116,User_Id_391,Bike,3,Business,30,2,11:17:56 AM,30,2,11:20:05 AM,30,2,11:25:49 AM,30,2,11:53:56 AM,30,2,12:21:47 PM,14,22.2,,-1.300921,36.828195,-1.2226694,36.86786,Rider_Id_450,1671
-Order_No_9118,User_Id_1329,Bike,3,Business,18,2,7:13:29 PM,18,2,7:13:55 PM,18,2,7:21:04 PM,18,2,7:24:44 PM,18,2,7:31:16 PM,4,23.8,,-1.273056,36.811298,-1.2615888000000002,36.7928732,Rider_Id_427,392
-Order_No_17603,User_Id_142,Bike,3,Business,27,1,9:18:22 AM,27,1,9:18:42 AM,27,1,9:46:23 AM,27,1,9:48:02 AM,27,1,10:11:30 AM,6,17.9,1.0,-1.2857723,36.808805799999995,-1.2963097,36.7688221,Rider_Id_341,1408
-Order_No_21363,User_Id_2894,Bike,3,Business,30,5,1:45:14 PM,30,5,2:02:40 PM,30,5,2:34:49 PM,30,5,2:38:53 PM,30,5,3:28:47 PM,6,24.7,,-1.2906618,36.8131222,-1.2638898,36.8223638,Rider_Id_725,2994
-Order_No_7914,User_Id_136,Bike,3,Business,11,2,9:49:37 AM,11,2,10:04:52 AM,11,2,10:17:35 AM,11,2,10:20:34 AM,11,2,10:54:30 AM,16,16.4,42.9,-1.3254522,36.721658399999995,-1.2536041999999998,36.806489500000005,Rider_Id_647,2036
-Order_No_4714,User_Id_3283,Bike,3,Business,30,6,12:03:23 PM,30,6,12:04:07 PM,30,6,12:15:57 PM,30,6,12:57:39 PM,30,6,1:20:02 PM,10,26.4,,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_349,1343
-Order_No_21023,User_Id_877,Bike,2,Personal,19,5,11:34:38 AM,19,5,11:36:51 AM,19,5,12:09:49 PM,19,5,12:15:25 PM,19,5,12:44:43 PM,12,24.8,,-1.249986,36.786390600000004,-1.2949652,36.7687874,Rider_Id_565,1758
-Order_No_8696,User_Id_3686,Bike,3,Business,10,3,3:49:45 PM,10,3,3:51:10 PM,10,3,3:56:24 PM,10,3,4:10:17 PM,10,3,4:42:27 PM,12,30.7,,-1.2551895,36.7822034,-1.2987332,36.8463349,Rider_Id_177,1930
-Order_No_14271,User_Id_3647,Bike,3,Business,27,2,11:51:24 AM,27,2,11:52:48 AM,27,2,12:15:09 PM,27,2,12:20:32 PM,27,2,12:34:17 PM,4,23.6,,-1.2726389999999999,36.794723,-1.2627937,36.815695700000006,Rider_Id_467,825
-Order_No_11247,User_Id_1482,Bike,3,Business,26,3,9:31:02 AM,26,3,9:31:27 AM,26,3,9:58:32 AM,26,3,10:11:26 AM,26,3,10:50:18 AM,17,18.8,,-1.3415373,36.7295377,-1.2822773,36.795312100000004,Rider_Id_565,2332
-Order_No_13448,User_Id_1974,Bike,1,Personal,5,5,11:17:46 AM,5,5,11:19:18 AM,5,5,11:31:30 AM,5,5,11:40:08 AM,5,5,12:00:42 PM,6,23.6,,-1.2942764,36.7969749,-1.2715686000000002,36.8023526,Rider_Id_722,1234
-Order_No_18030,User_Id_393,Bike,3,Business,17,3,8:54:33 AM,17,3,8:55:13 AM,17,3,9:06:08 AM,17,3,9:09:08 AM,17,3,9:45:38 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_148,2190
-Order_No_20342,User_Id_3291,Bike,3,Business,23,2,1:23:54 PM,23,2,1:24:07 PM,23,2,1:29:08 PM,23,2,1:42:32 PM,23,2,2:22:43 PM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_695,2411
-Order_No_23273,User_Id_2553,Bike,1,Personal,3,2,2:57:17 PM,3,2,2:59:18 PM,3,2,3:12:14 PM,3,2,3:15:08 PM,3,2,3:26:04 PM,6,20.3,,-1.2765492,36.7669809,-1.2551895,36.7822034,Rider_Id_103,656
-Order_No_7663,User_Id_1502,Bike,3,Business,11,2,1:17:51 PM,11,2,2:07:41 PM,11,2,2:10:37 PM,11,2,2:35:48 PM,11,2,3:20:33 PM,13,21.5,,-1.215909,36.886055999999996,-1.279395,36.825364,Rider_Id_519,2685
-Order_No_10484,User_Id_2956,Bike,3,Business,3,1,3:03:08 PM,3,1,3:03:32 PM,3,1,3:04:55 PM,3,1,3:14:06 PM,3,1,3:19:21 PM,3,23.2,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_205,315
-Order_No_6630,User_Id_1280,Bike,3,Business,17,4,8:13:40 AM,17,4,8:14:04 AM,17,4,8:36:40 AM,17,4,8:42:49 AM,17,4,9:03:12 AM,4,,,-1.2551895,36.7822034,-1.2650877,36.814764399999994,Rider_Id_362,1223
-Order_No_16875,User_Id_2330,Bike,3,Business,23,4,1:25:50 PM,23,4,1:32:25 PM,23,4,1:44:00 PM,23,4,1:53:33 PM,23,4,2:01:54 PM,4,24.6,,-1.2680363000000001,36.799046600000004,-1.2890715,36.8157588,Rider_Id_448,501
-Order_No_7777,User_Id_1162,Bike,3,Business,29,2,2:59:14 PM,29,2,2:59:33 PM,29,2,3:05:36 PM,29,2,3:07:33 PM,29,2,3:35:46 PM,14,26.9,,-1.280909,36.739554999999996,-1.2733047,36.8109744,Rider_Id_205,1693
-Order_No_25754,User_Id_496,Bike,3,Business,18,4,2:04:11 PM,18,4,2:04:59 PM,18,4,2:12:57 PM,18,4,2:15:54 PM,18,4,2:40:57 PM,4,26.9,,-1.2551895,36.7822034,-1.2645023000000002,36.8033789,Rider_Id_876,1503
-Order_No_28226,User_Id_3491,Bike,3,Business,21,5,10:07:58 AM,21,5,10:08:19 AM,21,5,10:49:34 AM,21,5,11:02:41 AM,21,5,11:27:47 AM,14,20.1,,-1.2297202,36.881950700000004,-1.2551895,36.7822034,Rider_Id_925,1506
-Order_No_26550,User_Id_633,Bike,3,Business,20,2,4:59:56 PM,20,2,5:00:08 PM,20,2,5:00:34 PM,20,2,5:02:32 PM,20,2,5:16:36 PM,5,23.8,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_39,844
-Order_No_8517,User_Id_633,Bike,3,Business,21,4,8:35:37 AM,21,4,8:36:17 AM,21,4,8:42:38 AM,21,4,8:52:09 AM,21,4,9:02:13 AM,4,22.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,604
-Order_No_2224,User_Id_1678,Bike,2,Personal,18,1,3:40:21 PM,18,1,3:51:22 PM,18,1,4:06:30 PM,18,1,4:18:38 PM,18,1,4:29:45 PM,6,17.4,,-1.2939132,36.8081606,-1.2812997,36.798667200000004,Rider_Id_425,667
-Order_No_22295,User_Id_1075,Bike,3,Business,21,5,2:14:32 PM,21,5,2:17:54 PM,21,5,2:28:55 PM,21,5,2:31:00 PM,21,5,2:43:30 PM,3,24.4,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_191,750
-Order_No_23262,User_Id_1500,Bike,3,Business,20,3,12:54:11 PM,20,3,12:55:53 PM,20,3,1:11:58 PM,20,3,1:25:20 PM,20,3,1:44:34 PM,10,26.4,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_858,1154
-Order_No_18437,User_Id_3686,Bike,3,Business,26,2,3:21:08 PM,26,2,3:21:57 PM,26,2,3:26:23 PM,26,2,3:33:51 PM,26,2,3:45:30 PM,4,31.2,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_652,699
-Order_No_10504,User_Id_1334,Bike,2,Personal,1,1,7:08:02 AM,1,1,7:21:36 AM,1,1,8:07:52 AM,1,1,8:10:42 AM,1,1,8:31:39 AM,6,18.2,,-1.3086326000000001,36.8080886,-1.304033,36.7848695,Rider_Id_208,1257
-Order_No_11134,User_Id_2306,Bike,3,Business,12,2,9:34:00 AM,12,2,9:34:52 AM,12,2,9:49:44 AM,12,2,9:57:18 AM,12,2,10:46:31 AM,19,18.0,,-1.2271998,36.808490500000005,-1.3241108,36.8692908,Rider_Id_31,2953
-Order_No_24081,User_Id_3194,Bike,3,Business,24,4,12:19:57 PM,24,4,12:20:19 PM,24,4,12:25:42 PM,24,4,12:39:47 PM,24,4,1:02:03 PM,6,24.6,,-1.2551895,36.7822034,-1.280753,36.8191175,Rider_Id_336,1336
-Order_No_9359,User_Id_3599,Bike,3,Business,30,2,11:00:32 AM,30,2,11:27:37 AM,30,2,11:36:45 AM,30,2,11:56:37 AM,30,2,12:43:03 PM,8,22.3,,-1.3302996,36.8707935,-1.2953659,36.82494620000001,Rider_Id_268,2786
-Order_No_1835,User_Id_2065,Bike,3,Personal,27,5,9:36:53 AM,27,5,9:37:02 AM,27,5,9:41:21 AM,27,5,9:58:18 AM,27,5,10:24:29 AM,15,16.1,,-1.2883346000000002,36.8148489,-1.1980111,36.834036,Rider_Id_844,1571
-Order_No_9024,User_Id_1280,Bike,3,Business,9,3,5:08:41 PM,9,3,5:09:37 PM,9,3,5:46:21 PM,9,3,5:48:49 PM,9,3,6:26:00 PM,15,21.9,,-1.2927792,36.9053492,-1.2989879999999998,36.81075,Rider_Id_581,2231
-Order_No_6624,User_Id_2030,Bike,3,Business,17,1,8:26:11 AM,17,1,8:26:51 AM,17,1,9:14:08 AM,17,1,9:23:30 AM,17,1,9:41:07 AM,3,18.8,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_267,1057
-Order_No_15993,User_Id_3689,Bike,3,Business,20,4,4:20:23 PM,20,4,4:20:56 PM,20,4,4:48:26 PM,20,4,4:50:10 PM,20,4,5:05:38 PM,3,22.9,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_228,928
-Order_No_17778,User_Id_2216,Bike,3,Business,23,2,10:41:17 AM,23,2,11:11:38 AM,23,2,11:15:16 AM,23,2,11:27:56 AM,23,2,12:21:21 PM,18,18.2,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_880,3205
-Order_No_5078,User_Id_867,Bike,3,Business,15,5,11:13:03 AM,15,5,11:13:54 AM,15,5,11:14:38 AM,15,5,11:46:25 AM,15,5,12:16:46 PM,10,26.0,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_154,1821
-Order_No_5695,User_Id_3621,Bike,3,Business,29,5,10:04:18 AM,29,5,10:05:30 AM,29,5,10:17:03 AM,29,5,10:18:17 AM,29,5,10:32:18 AM,3,20.7,,-1.2836583000000001,36.7886014,-1.3025666999999999,36.7803495,Rider_Id_468,841
-Order_No_3351,User_Id_1148,Bike,3,Business,17,5,2:14:14 PM,17,5,2:15:02 PM,17,5,2:48:59 PM,17,5,2:52:45 PM,17,5,3:31:05 PM,15,,,-1.3104867,36.8495593,-1.3671327,36.75642379999999,Rider_Id_321,2300
-Order_No_20455,User_Id_1205,Bike,3,Business,13,3,2:10:12 PM,13,3,2:14:52 PM,13,3,2:46:32 PM,13,3,2:52:59 PM,13,3,3:23:31 PM,10,27.0,,-1.2586964,36.775008500000006,-1.2402231000000001,36.7652808,Rider_Id_936,1832
-Order_No_10369,User_Id_2946,Bike,2,Personal,14,2,10:29:04 AM,14,2,10:29:24 AM,14,2,10:40:03 AM,14,2,10:45:39 AM,14,2,11:02:33 AM,7,21.4,,-1.2749662,36.7658781,-1.289521,36.819072399999996,Rider_Id_648,1014
-Order_No_20648,User_Id_20,Bike,3,Business,9,4,1:13:22 PM,9,4,1:14:21 PM,9,4,1:37:09 PM,9,4,1:38:50 PM,9,4,1:56:40 PM,8,,,-1.2629666000000002,36.8068369,-1.2763494,36.7671783,Rider_Id_227,1070
-Order_No_5491,User_Id_393,Bike,3,Business,21,5,7:52:51 AM,21,5,8:38:17 AM,21,5,8:38:20 AM,21,5,8:47:37 AM,21,5,9:17:27 AM,11,19.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_10,1790
-Order_No_1353,User_Id_393,Bike,3,Business,18,2,9:35:02 AM,18,2,10:54:08 AM,18,2,10:54:14 AM,18,2,11:20:22 AM,18,2,11:37:21 AM,11,,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,1019
-Order_No_4807,User_Id_1482,Bike,3,Business,1,4,10:24:43 AM,1,4,10:25:03 AM,1,4,10:26:21 AM,1,4,10:36:34 AM,1,4,11:18:34 AM,20,,,-1.3385790000000002,36.89630379999999,-1.2482465,36.8889062,Rider_Id_268,2520
-Order_No_22860,User_Id_2150,Bike,3,Business,19,2,10:42:00 AM,19,2,10:42:25 AM,19,2,10:46:54 AM,19,2,11:01:28 AM,19,2,11:20:03 AM,14,27.5,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_327,1115
-Order_No_17239,User_Id_2523,Bike,3,Business,13,3,5:16:56 PM,13,3,5:17:30 PM,13,3,5:23:07 PM,13,3,5:29:49 PM,13,3,5:41:01 PM,8,26.0,,-1.2551895,36.7822034,-1.2832259,36.795837299999995,Rider_Id_205,672
-Order_No_7240,User_Id_393,Bike,3,Business,29,1,12:50:59 PM,29,1,12:51:23 PM,29,1,12:53:45 PM,29,1,1:10:10 PM,29,1,1:24:24 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,854
-Order_No_17110,User_Id_2271,Bike,3,Business,18,2,12:26:22 PM,18,2,12:26:45 PM,18,2,12:27:08 PM,18,2,12:44:06 PM,18,2,1:14:35 PM,14,,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_937,1829
-Order_No_25469,User_Id_1610,Bike,1,Personal,19,2,10:49:52 AM,19,2,10:50:01 AM,19,2,10:56:31 AM,19,2,11:25:03 AM,19,2,12:00:00 PM,15,,,-1.2962832,36.784968299999996,-1.22589,36.7822548,Rider_Id_148,2097
-Order_No_2461,User_Id_1682,Bike,1,Personal,9,2,11:52:11 AM,9,2,11:54:52 AM,9,2,12:14:33 PM,9,2,12:38:39 PM,9,2,12:49:40 PM,5,21.8,,-1.288167,36.8181484,-1.2942764,36.7969749,Rider_Id_565,661
-Order_No_25357,User_Id_2900,Bike,3,Business,13,1,12:20:20 PM,13,1,12:20:47 PM,13,1,12:22:27 PM,13,1,12:26:52 PM,13,1,1:02:16 PM,20,17.2,,-1.2963097,36.7688221,-1.2198413000000001,36.9054958,Rider_Id_754,2124
-Order_No_3739,User_Id_1422,Bike,3,Business,1,1,3:52:50 PM,1,1,3:57:43 PM,1,1,4:13:09 PM,1,1,4:46:11 PM,1,1,4:58:13 PM,3,23.6,,-1.3345871999999999,36.861314799999995,-1.3261286,36.8469625,Rider_Id_532,722
-Order_No_1406,User_Id_2272,Bike,1,Personal,19,5,3:37:05 PM,19,5,3:38:01 PM,19,5,3:44:59 PM,19,5,3:56:07 PM,19,5,4:20:04 PM,9,27.6,,-1.281196,36.8179321,-1.2702719,36.7688649,Rider_Id_208,1437
-Order_No_24688,User_Id_2154,Bike,3,Business,23,3,12:47:25 PM,23,3,12:48:49 PM,23,3,1:01:22 PM,23,3,1:11:00 PM,23,3,1:27:07 PM,5,,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_489,967
-Order_No_15941,User_Id_1363,Bike,3,Business,7,5,2:29:48 PM,7,5,2:35:04 PM,7,5,2:55:07 PM,7,5,2:56:34 PM,7,5,3:32:42 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_109,2168
-Order_No_6585,User_Id_2200,Bike,3,Business,30,2,12:45:15 PM,30,2,12:51:59 PM,30,2,1:05:27 PM,30,2,1:36:39 PM,30,2,1:48:31 PM,6,27.6,,-1.2571472,36.795063299999995,-1.290894,36.822971,Rider_Id_687,712
-Order_No_26570,User_Id_948,Bike,3,Business,15,3,1:07:34 PM,15,3,1:07:54 PM,15,3,1:12:04 PM,15,3,1:26:22 PM,15,3,1:26:29 PM,1,,,-1.2684666999999998,36.8002891,-1.2594473999999998,36.8089122,Rider_Id_424,7
-Order_No_8289,User_Id_1926,Bike,3,Business,21,4,11:09:14 AM,21,4,11:09:37 AM,21,4,11:25:48 AM,21,4,11:34:54 AM,21,4,12:10:41 PM,16,28.2,,-1.3263105,36.8622904,-1.2843046,36.774613200000005,Rider_Id_516,2147
-Order_No_1169,User_Id_393,Bike,3,Business,15,5,8:40:41 AM,15,5,8:40:54 AM,15,5,8:40:59 AM,15,5,8:52:08 AM,15,5,9:17:32 AM,14,20.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_111,1524
-Order_No_8402,User_Id_2801,Bike,3,Business,4,2,12:37:14 PM,4,2,1:18:28 PM,4,2,1:18:32 PM,4,2,1:53:36 PM,4,2,2:11:29 PM,10,24.0,11.0,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_813,1073
-Order_No_1261,User_Id_2587,Bike,3,Business,10,1,8:29:28 AM,10,1,8:31:02 AM,10,1,8:51:45 AM,10,1,9:06:42 AM,10,1,9:49:02 AM,11,18.3,,-1.2283403,36.8822756,-1.28878,36.8168312,Rider_Id_467,2540
-Order_No_27732,User_Id_633,Bike,3,Business,24,3,9:03:25 AM,24,3,9:18:23 AM,24,3,9:38:34 AM,24,3,9:46:59 AM,24,3,9:57:23 AM,6,16.9,0.9,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_208,624
-Order_No_16259,User_Id_2523,Bike,3,Business,18,4,2:34:23 PM,18,4,2:38:00 PM,18,4,2:51:40 PM,18,4,3:20:13 PM,18,4,3:25:58 PM,2,,,-1.2551895,36.7822034,-1.2580991000000001,36.794151899999996,Rider_Id_436,345
-Order_No_23178,User_Id_635,Bike,3,Business,7,2,3:29:49 PM,7,2,3:33:26 PM,7,2,3:53:04 PM,7,2,4:00:58 PM,7,2,4:15:01 PM,5,,,-1.2857763999999998,36.8236622,-1.2602337,36.79905479999999,Rider_Id_92,843
-Order_No_8695,User_Id_412,Bike,3,Business,4,1,3:02:42 PM,4,1,3:03:52 PM,4,1,3:03:56 PM,4,1,3:34:02 PM,4,1,3:36:09 PM,6,28.5,,-1.2858945,36.8161197,-1.2628473,36.781805,Rider_Id_41,127
-Order_No_27701,User_Id_1544,Bike,3,Business,10,1,12:57:23 PM,10,1,1:00:05 PM,10,1,1:09:11 PM,10,1,1:16:05 PM,10,1,1:45:26 PM,7,18.5,,-1.2813012,36.832396200000005,-1.2860203000000001,36.7832538,Rider_Id_868,1761
-Order_No_10997,User_Id_3505,Bike,2,Personal,15,3,2:24:16 PM,15,3,2:24:25 PM,15,3,2:29:31 PM,15,3,2:44:03 PM,15,3,3:02:57 PM,7,21.5,,-1.3004846,36.790159100000004,-1.2682478000000001,36.8072852,Rider_Id_213,1134
-Order_No_851,User_Id_289,Bike,2,Personal,28,5,10:17:49 AM,28,5,10:18:53 AM,28,5,10:32:04 AM,28,5,10:45:10 AM,28,5,10:51:28 AM,5,25.1,,-1.2726925,36.7951829,-1.2551895,36.7822034,Rider_Id_446,378
-Order_No_21469,User_Id_700,Bike,3,Business,25,2,10:10:34 AM,25,2,10:11:36 AM,25,2,10:32:31 AM,25,2,10:37:46 AM,25,2,11:02:12 AM,11,23.7,,-1.3205163999999998,36.8098882,-1.2551856000000001,36.7822492,Rider_Id_329,1466
-Order_No_24440,User_Id_91,Bike,3,Personal,26,2,12:23:18 PM,26,2,12:23:45 PM,26,2,1:01:41 PM,26,2,1:20:08 PM,26,2,1:42:45 PM,9,26.2,,-1.2990731000000002,36.848805799999994,-1.2775276000000002,36.8140021,Rider_Id_569,1357
-Order_No_7462,User_Id_467,Bike,3,Business,14,2,8:11:30 AM,14,2,8:12:36 AM,14,2,8:24:24 AM,14,2,8:28:41 AM,14,2,9:14:25 AM,12,,,-1.304735,36.784605400000004,-1.313266,36.6935289,Rider_Id_177,2744
-Order_No_12182,User_Id_3621,Bike,3,Business,8,5,3:33:53 PM,8,5,3:34:35 PM,8,5,3:41:14 PM,8,5,3:45:55 PM,8,5,4:01:06 PM,6,21.5,,-1.2836583000000001,36.7886014,-1.3014461000000002,36.7661381,Rider_Id_322,911
-Order_No_481,User_Id_3201,Bike,3,Business,14,4,11:32:31 AM,14,4,11:44:44 AM,14,4,11:49:53 AM,14,4,12:04:52 PM,14,4,12:04:57 PM,1,24.9,,-1.2584143,36.8048002,-1.2594538999999998,36.8067841,Rider_Id_681,5
-Order_No_27563,User_Id_1195,Bike,3,Business,20,3,11:50:07 AM,20,3,11:50:54 AM,20,3,11:59:56 AM,20,3,12:01:43 PM,20,3,12:16:14 PM,5,27.3,,-1.2983042,36.8261141,-1.3228003000000002,36.8306435,Rider_Id_206,871
-Order_No_22752,User_Id_3521,Bike,3,Business,8,1,8:57:13 AM,8,1,8:58:04 AM,8,1,9:10:26 AM,8,1,9:22:23 AM,8,1,9:36:43 AM,7,19.9,,-1.2956517,36.8036292,-1.2696908000000002,36.81519,Rider_Id_105,860
-Order_No_24666,User_Id_1809,Bike,3,Business,5,3,4:06:29 PM,5,3,4:07:19 PM,5,3,4:08:11 PM,5,3,4:15:10 PM,5,3,4:39:20 PM,5,24.9,,-1.2571472,36.795063299999995,-1.2886366000000002,36.8245436,Rider_Id_379,1450
-Order_No_20851,User_Id_1667,Bike,1,Business,30,4,11:23:03 AM,30,4,11:23:27 AM,30,4,11:40:27 AM,30,4,11:47:31 AM,30,4,12:11:41 PM,9,20.2,,-1.2808075,36.8317585,-1.2894256000000002,36.7620285,Rider_Id_74,1450
-Order_No_25651,User_Id_838,Bike,3,Business,16,4,12:30:12 PM,16,4,12:32:04 PM,16,4,12:32:08 PM,16,4,12:43:28 PM,16,4,1:16:14 PM,13,,,-1.1648303,36.951368,-1.2241625,36.8851115,Rider_Id_68,1966
-Order_No_1488,User_Id_635,Bike,3,Business,30,3,9:54:02 AM,30,3,9:54:28 AM,30,3,10:04:14 AM,30,3,10:07:06 AM,30,3,10:27:53 AM,11,19.8,,-1.2397191,36.8733845,-1.2602337,36.79905479999999,Rider_Id_906,1247
-Order_No_6058,User_Id_3647,Bike,3,Business,19,1,3:58:33 PM,19,1,3:58:40 PM,19,1,3:59:17 PM,19,1,4:04:17 PM,19,1,4:04:21 PM,1,25.2,,-1.2726389999999999,36.794723,-1.2741728,36.79691079999999,Rider_Id_647,4
-Order_No_12862,User_Id_2403,Bike,2,Personal,9,6,12:53:13 PM,9,6,12:53:19 PM,9,6,1:15:35 PM,9,6,1:23:05 PM,9,6,2:15:23 PM,14,17.4,,-1.3046156,36.765857200000006,-1.2696795,36.8640088,Rider_Id_6,3138
-Order_No_25557,User_Id_2548,Bike,2,Business,10,3,10:46:17 AM,10,3,10:46:42 AM,10,3,10:46:54 AM,10,3,11:02:24 AM,10,3,11:07:02 AM,4,22.3,,-1.2944266000000002,36.787804,-1.2957616000000003,36.7669835,Rider_Id_865,278
-Order_No_16569,User_Id_972,Bike,3,Business,3,1,12:28:16 PM,3,1,12:29:11 PM,3,1,12:35:04 PM,3,1,1:07:41 PM,3,1,2:34:37 PM,15,20.7,,-1.2862033000000002,36.8152966,-1.3080998,36.6999638,Rider_Id_272,5216
-Order_No_15736,User_Id_3147,Bike,3,Business,25,1,1:27:29 PM,25,1,1:27:39 PM,25,1,1:33:24 PM,25,1,1:48:04 PM,25,1,2:21:27 PM,17,27.9,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_854,2003
-Order_No_5232,User_Id_3647,Bike,3,Business,5,5,2:32:23 PM,5,5,2:32:32 PM,5,5,2:51:01 PM,5,5,2:55:27 PM,5,5,3:33:40 PM,16,27.7,,-1.2726389999999999,36.794723,-1.296266,36.8882943,Rider_Id_843,2293
-Order_No_16852,User_Id_635,Bike,3,Business,19,2,9:31:29 AM,19,2,9:42:50 AM,19,2,9:46:24 AM,19,2,9:52:50 AM,19,2,10:27:31 AM,7,15.6,1.0,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_932,2081
-Order_No_16864,User_Id_2330,Bike,3,Business,30,2,3:00:26 PM,30,2,3:18:15 PM,30,2,3:24:45 PM,30,2,3:30:51 PM,30,2,3:48:24 PM,10,,,-1.2687407,36.7373043,-1.2671943,36.8062533,Rider_Id_269,1053
-Order_No_26062,User_Id_1642,Bike,3,Business,9,2,9:59:42 AM,9,2,10:00:07 AM,9,2,10:11:28 AM,9,2,10:28:36 AM,9,2,10:39:57 AM,7,20.6,,-1.2966535,36.8123136,-1.2726506000000002,36.7960153,Rider_Id_119,681
-Order_No_4144,User_Id_978,Bike,1,Personal,17,2,1:05:05 PM,17,2,1:06:48 PM,17,2,1:27:32 PM,17,2,1:46:56 PM,17,2,2:06:45 PM,5,24.6,,-1.3084067,36.83450139999999,-1.2767856000000002,36.8285595,Rider_Id_752,1189
-Order_No_22617,User_Id_2377,Bike,3,Business,31,4,2:49:00 PM,31,4,2:50:02 PM,31,4,2:57:04 PM,31,4,3:02:54 PM,31,4,3:45:48 PM,16,28.7,,-1.2963097,36.7688221,-1.2954158000000002,36.8934011,Rider_Id_276,2574
-Order_No_26197,User_Id_1245,Bike,3,Business,26,2,12:44:49 PM,26,2,12:45:02 PM,26,2,12:45:05 PM,26,2,1:27:13 PM,26,2,2:07:22 PM,19,26.3,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_450,2409
-Order_No_9603,User_Id_2587,Bike,3,Business,27,5,3:43:46 PM,27,5,3:43:52 PM,27,5,3:59:25 PM,27,5,4:05:55 PM,27,5,4:15:09 PM,2,23.9,,-1.2967839,36.8128576,-1.2885528999999998,36.816014100000004,Rider_Id_229,554
-Order_No_26887,User_Id_948,Bike,3,Business,3,5,12:53:49 PM,3,5,12:54:05 PM,3,5,1:09:03 PM,3,5,1:14:59 PM,3,5,2:33:11 PM,17,,,-1.207546,36.7913976,-1.2696908000000002,36.81519,Rider_Id_190,4692
-Order_No_4521,User_Id_2015,Bike,3,Personal,12,3,2:26:26 PM,12,3,2:27:34 PM,12,3,2:38:28 PM,12,3,2:43:40 PM,12,3,3:08:47 PM,10,26.3,,-1.2918688,36.7603541,-1.267522,36.827995,Rider_Id_465,1507
-Order_No_1320,User_Id_2982,Bike,3,Business,2,3,12:26:02 PM,2,3,12:26:19 PM,2,3,12:39:44 PM,2,3,1:05:58 PM,2,3,1:41:14 PM,18,24.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_457,2116
-Order_No_3853,User_Id_1527,Bike,3,Business,17,5,3:45:20 PM,17,5,3:46:32 PM,17,5,4:20:44 PM,17,5,4:25:38 PM,17,5,4:50:49 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_291,1511
-Order_No_21612,User_Id_1186,Bike,3,Personal,20,3,4:09:06 PM,20,3,4:20:21 PM,20,3,4:29:04 PM,20,3,4:39:49 PM,20,3,5:28:42 PM,11,21.5,,-1.300921,36.828195,-1.274807,36.771931,Rider_Id_312,2933
-Order_No_17528,User_Id_2200,Bike,3,Business,12,5,2:47:45 PM,12,5,3:33:24 PM,12,5,3:35:19 PM,12,5,3:39:30 PM,12,5,3:55:26 PM,10,27.0,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_622,956
-Order_No_6748,User_Id_1471,Bike,3,Business,17,1,12:43:38 PM,17,1,12:50:36 PM,17,1,1:05:22 PM,17,1,1:36:58 PM,17,1,1:37:32 PM,9,24.5,,-1.2551895,36.7822034,-1.2972916,36.7880497,Rider_Id_847,34
-Order_No_20673,User_Id_78,Bike,1,Personal,9,1,7:13:09 PM,9,1,7:13:25 PM,9,1,7:13:46 PM,9,1,7:20:06 PM,9,1,7:32:00 PM,6,20.6,,-1.2765492,36.7669809,-1.2705336,36.7943509,Rider_Id_334,714
-Order_No_25821,User_Id_2599,Bike,1,Business,26,4,4:18:35 PM,26,4,4:20:34 PM,26,4,4:39:34 PM,26,4,4:48:34 PM,26,4,5:08:55 PM,7,19.5,,-1.2867454,36.8259017,-1.2560455,36.780086700000005,Rider_Id_250,1221
-Order_No_6663,User_Id_2801,Bike,3,Business,13,4,9:15:31 AM,13,4,9:15:49 AM,13,4,9:20:12 AM,13,4,9:24:39 AM,13,4,9:41:43 AM,7,18.7,,-1.2674273999999999,36.787082700000006,-1.2827545,36.79361289999999,Rider_Id_161,1024
-Order_No_9564,User_Id_436,Bike,1,Personal,4,3,10:48:50 AM,4,3,10:52:49 AM,4,3,11:09:25 AM,4,3,11:16:28 AM,4,3,11:49:14 AM,10,17.2,,-1.2950041,36.814358299999995,-1.329424,36.862726,Rider_Id_329,1966
-Order_No_8552,User_Id_2271,Bike,3,Business,12,4,3:55:58 PM,12,4,4:02:24 PM,12,4,4:11:37 PM,12,4,4:19:52 PM,12,4,4:58:11 PM,14,21.9,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_806,2299
-Order_No_23201,User_Id_2039,Bike,2,Personal,27,3,10:40:21 AM,27,3,10:41:38 AM,27,3,10:58:06 AM,27,3,11:00:14 AM,27,3,11:10:57 AM,4,,,-1.2951179,36.7684083,-1.2755226,36.7811904,Rider_Id_749,643
-Order_No_4336,User_Id_393,Bike,3,Business,6,1,11:01:02 AM,6,1,11:01:44 AM,6,1,11:04:48 AM,6,1,11:12:21 AM,6,1,11:41:39 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_25,1758
-Order_No_25765,User_Id_3091,Bike,3,Business,17,3,3:39:17 PM,17,3,3:40:29 PM,17,3,4:03:29 PM,17,3,4:07:54 PM,17,3,4:28:46 PM,8,28.6,,-1.3025826,36.7670807,-1.2615888000000002,36.7928732,Rider_Id_352,1252
-Order_No_8137,User_Id_3521,Bike,3,Business,27,3,12:17:26 PM,27,3,12:18:01 PM,27,3,12:24:07 PM,27,3,12:36:30 PM,27,3,12:46:42 PM,2,26.0,,-1.2701582,36.807443299999996,-1.2696908000000002,36.81519,Rider_Id_96,612
-Order_No_11195,User_Id_2586,Bike,1,Personal,27,5,3:50:32 PM,27,5,3:51:26 PM,27,5,4:29:56 PM,27,5,4:36:39 PM,27,5,4:47:46 PM,5,17.8,,-1.2809423,36.754003600000004,-1.2979147,36.785781400000005,Rider_Id_465,667
-Order_No_16454,User_Id_627,Bike,3,Business,10,5,4:30:13 PM,10,5,4:36:03 PM,10,5,4:55:49 PM,10,5,4:59:36 PM,10,5,5:33:24 PM,9,,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_422,2028
-Order_No_23694,User_Id_3517,Bike,3,Business,8,1,10:52:06 AM,8,1,11:23:30 AM,8,1,11:43:20 AM,8,1,11:49:04 AM,8,1,12:13:38 PM,16,28.0,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_506,1474
-Order_No_6017,User_Id_393,Bike,3,Business,11,5,1:24:54 PM,11,5,1:25:51 PM,11,5,1:34:08 PM,11,5,1:39:17 PM,11,5,2:11:49 PM,14,26.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_583,1952
-Order_No_3606,User_Id_2069,Bike,3,Business,24,5,10:18:48 AM,24,5,10:19:57 AM,24,5,10:30:48 AM,24,5,10:37:22 AM,24,5,11:03:32 AM,14,21.6,,-1.3033561000000002,36.8163631,-1.228911,36.881876,Rider_Id_591,1570
-Order_No_23204,User_Id_1209,Bike,3,Business,23,2,4:26:09 PM,23,2,4:34:09 PM,23,2,4:40:18 PM,23,2,4:48:11 PM,23,2,4:56:25 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_96,494
-Order_No_26908,User_Id_71,Bike,3,Business,11,1,9:10:16 AM,11,1,9:11:24 AM,11,1,9:48:39 AM,11,1,9:56:10 AM,11,1,10:24:30 AM,5,14.5,,-1.2821108,36.8250555,-1.262073,36.7986087,Rider_Id_89,1700
-Order_No_25626,User_Id_3647,Bike,3,Business,15,2,2:16:20 PM,15,2,2:16:28 PM,15,2,2:32:48 PM,15,2,2:37:19 PM,15,2,3:21:34 PM,18,24.5,,-1.2726389999999999,36.794723,-1.3073803,36.6825842,Rider_Id_269,2655
-Order_No_1244,User_Id_3647,Bike,3,Business,27,3,4:51:21 PM,27,3,4:52:09 PM,27,3,5:04:09 PM,27,3,5:08:34 PM,27,3,5:28:53 PM,7,27.6,,-1.2726389999999999,36.794723,-1.3039771,36.8187071,Rider_Id_669,1219
-Order_No_3492,User_Id_1280,Bike,3,Business,17,4,12:43:37 PM,17,4,12:45:05 PM,17,4,12:59:41 PM,17,4,1:00:57 PM,17,4,1:12:11 PM,5,25.4,,-1.2551895,36.7822034,-1.2736466,36.8164042,Rider_Id_547,674
-Order_No_21152,User_Id_516,Bike,3,Business,9,4,2:59:30 PM,9,4,3:09:28 PM,9,4,3:28:46 PM,9,4,3:31:51 PM,9,4,3:51:03 PM,9,,,-1.2950408000000002,36.809817200000005,-1.2551895,36.7822034,Rider_Id_538,1152
-Order_No_10208,User_Id_391,Bike,3,Business,25,1,3:32:14 PM,25,1,3:36:57 PM,25,1,4:33:15 PM,25,1,5:06:07 PM,25,1,5:30:31 PM,5,28.3,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_99,1464
-Order_No_21957,User_Id_647,Bike,3,Business,21,2,12:41:39 PM,21,2,12:42:37 PM,21,2,12:51:55 PM,21,2,1:13:04 PM,21,2,1:33:39 PM,2,22.3,,-1.3004062,36.829741,-1.2915199,36.8289158,Rider_Id_2,1235
-Order_No_18217,User_Id_2330,Bike,1,Business,15,1,4:41:58 PM,15,1,4:43:27 PM,15,1,4:52:02 PM,15,1,4:59:10 PM,15,1,5:28:45 PM,9,,,-1.2671943,36.8062533,-1.30176,36.765658,Rider_Id_45,1775
-Order_No_22777,User_Id_1927,Bike,3,Business,2,2,12:57:55 PM,2,2,12:58:10 PM,2,2,1:08:25 PM,2,2,1:11:02 PM,2,2,1:51:42 PM,20,26.2,,-1.3472066,36.7692638,-1.2927792,36.9053492,Rider_Id_763,2440
-Order_No_22161,User_Id_3295,Bike,3,Business,10,1,3:21:47 PM,10,1,3:22:18 PM,10,1,3:25:05 PM,10,1,3:37:06 PM,10,1,4:09:39 PM,14,18.3,,-1.2571472,36.795063299999995,-1.3312396,36.857532299999995,Rider_Id_177,1953
-Order_No_20464,User_Id_2000,Bike,3,Business,12,2,1:43:41 PM,12,2,1:44:22 PM,12,2,1:45:34 PM,12,2,1:52:47 PM,12,2,1:53:15 PM,3,,,-1.2551895,36.7822034,-1.2595421999999998,36.7871178,Rider_Id_634,28
-Order_No_27621,User_Id_2956,Bike,3,Business,2,1,12:11:07 PM,2,1,12:17:58 PM,2,1,12:24:00 PM,2,1,12:26:08 PM,2,1,12:39:29 PM,5,19.3,,-1.2584143,36.8048002,-1.2885315,36.8230419,Rider_Id_726,801
-Order_No_15559,User_Id_3107,Bike,3,Business,10,5,9:53:26 AM,10,5,10:21:21 AM,10,5,10:43:22 AM,10,5,10:48:53 AM,10,5,11:12:14 AM,9,19.3,,-1.2930128,36.8356869,-1.256958,36.8144929,Rider_Id_88,1401
-Order_No_10028,User_Id_3149,Bike,3,Business,16,3,10:42:36 AM,16,3,10:42:43 AM,16,3,10:51:49 AM,16,3,11:19:55 AM,16,3,11:33:53 AM,6,22.8,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_111,838
-Order_No_16541,User_Id_2271,Bike,3,Business,3,5,2:47:56 PM,3,5,2:48:33 PM,3,5,2:55:03 PM,3,5,3:09:28 PM,3,5,3:36:35 PM,14,25.4,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_174,1627
-Order_No_11814,User_Id_2008,Bike,3,Business,21,2,12:10:46 PM,21,2,12:11:41 PM,21,2,12:18:28 PM,21,2,12:23:09 PM,21,2,1:24:03 PM,6,22.5,,-1.2508229,36.7895263,-1.2791714,36.825599700000005,Rider_Id_831,3654
-Order_No_18152,User_Id_1341,Bike,3,Business,15,3,9:51:50 AM,15,3,9:52:42 AM,15,3,10:05:53 AM,15,3,10:45:44 AM,15,3,10:58:11 AM,10,17.9,,-1.3393547,36.8910035,-1.3313643,36.857742200000004,Rider_Id_116,747
-Order_No_3626,User_Id_2866,Bike,3,Business,5,1,4:17:17 PM,5,1,4:29:30 PM,5,1,4:29:36 PM,5,1,4:43:06 PM,5,1,4:52:57 PM,3,24.2,,-1.2551895,36.7822034,-1.255081,36.802335,Rider_Id_107,591
-Order_No_25238,User_Id_3426,Bike,3,Business,20,3,11:31:10 AM,20,3,11:32:56 AM,20,3,11:38:02 AM,20,3,11:51:57 AM,20,3,12:09:28 PM,9,26.4,,-1.3165542,36.700067,-1.3012008,36.764868,Rider_Id_654,1051
-Order_No_22071,User_Id_2216,Bike,3,Business,22,2,2:47:38 PM,22,2,2:51:22 PM,22,2,3:09:00 PM,22,2,3:15:37 PM,22,2,3:47:32 PM,9,21.3,,-1.2626906,36.7827022,-1.3038912,36.7961524,Rider_Id_301,1915
-Order_No_26237,User_Id_972,Bike,3,Business,15,2,11:26:56 AM,15,2,11:36:35 AM,15,2,11:48:19 AM,15,2,11:59:12 AM,15,2,12:08:18 PM,3,23.0,,-1.2862033000000002,36.8152966,-1.3057159999999999,36.809882,Rider_Id_100,546
-Order_No_26477,User_Id_1771,Bike,1,Personal,13,3,4:35:11 PM,13,3,5:13:39 PM,13,3,5:32:34 PM,13,3,5:36:59 PM,13,3,5:50:21 PM,4,22.0,,-1.2418468999999999,36.8880358,-1.2297202,36.874550799999994,Rider_Id_851,802
-Order_No_27151,User_Id_733,Bike,3,Business,26,1,3:32:37 PM,26,1,3:33:58 PM,26,1,3:46:50 PM,26,1,3:49:44 PM,26,1,4:45:20 PM,15,22.1,0.7,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_778,3336
-Order_No_28101,User_Id_1329,Bike,3,Business,6,4,2:33:08 PM,6,4,2:33:29 PM,6,4,2:39:39 PM,6,4,2:43:17 PM,6,4,2:59:15 PM,5,21.1,,-1.273056,36.811298,-1.2677061,36.7776554,Rider_Id_435,958
-Order_No_16335,User_Id_1646,Bike,3,Business,13,4,3:41:49 PM,13,4,3:42:35 PM,13,4,4:06:37 PM,13,4,4:16:11 PM,13,4,4:31:27 PM,9,23.1,,-1.2517448,36.7924971,-1.2706961,36.7643345,Rider_Id_55,916
-Order_No_19106,User_Id_3264,Bike,3,Business,31,2,9:52:53 AM,31,2,9:53:01 AM,31,2,10:07:50 AM,31,2,10:12:01 AM,31,2,11:01:55 AM,12,17.1,1.1,-1.2551895,36.7822034,-1.2121659,36.811243700000006,Rider_Id_452,2994
-Order_No_19562,User_Id_1281,Bike,3,Business,25,5,12:22:04 PM,25,5,12:23:37 PM,25,5,12:39:54 PM,25,5,1:03:51 PM,25,5,1:25:14 PM,9,21.0,,-1.2817083999999999,36.8143646,-1.275442,36.766020700000006,Rider_Id_186,1283
-Order_No_3849,User_Id_1687,Bike,3,Business,2,4,8:36:05 AM,2,4,8:36:35 AM,2,4,9:31:16 AM,2,4,9:32:23 AM,2,4,10:01:57 AM,11,,,-1.2357874,36.870708,-1.2884799,36.82179179999999,Rider_Id_815,1774
-Order_No_12692,User_Id_2931,Bike,3,Business,23,5,1:04:53 PM,23,5,1:06:04 PM,23,5,1:20:13 PM,23,5,1:22:27 PM,23,5,2:10:58 PM,8,24.2,,-1.2452664,36.802854100000005,-1.2939816000000002,36.8118469,Rider_Id_829,2911
-Order_No_6996,User_Id_2956,Bike,3,Business,12,2,1:31:19 PM,12,2,1:31:54 PM,12,2,1:32:47 PM,12,2,1:47:35 PM,12,2,1:54:02 PM,1,,,-1.2584143,36.8048002,-1.2664491000000002,36.8102685,Rider_Id_547,387
-Order_No_27216,User_Id_549,Bike,2,Personal,22,3,12:44:53 PM,22,3,12:53:03 PM,22,3,1:24:15 PM,22,3,1:27:50 PM,22,3,2:11:55 PM,26,22.8,,-1.1923911,36.944733299999996,-1.3108674,36.795817,Rider_Id_384,2645
-Order_No_11607,User_Id_834,Bike,2,Personal,28,4,9:50:12 AM,28,4,9:51:16 AM,28,4,10:02:13 AM,28,4,10:07:05 AM,28,4,10:36:19 AM,7,,,-1.2935318,36.77845670000001,-1.2714267,36.81105529999999,Rider_Id_474,1754
-Order_No_15179,User_Id_2763,Bike,3,Business,29,2,9:41:36 AM,29,2,9:42:01 AM,29,2,9:44:34 AM,29,2,9:53:10 AM,29,2,10:25:05 AM,13,18.3,,-1.2551895,36.7822034,-1.307504,36.859812700000006,Rider_Id_576,1915
-Order_No_1942,User_Id_2330,Bike,3,Business,11,5,1:02:56 PM,11,5,1:04:03 PM,11,5,1:21:32 PM,11,5,1:28:46 PM,11,5,1:55:33 PM,12,20.9,,-1.2642903,36.80021370000001,-1.2524916000000001,36.8656196,Rider_Id_416,1607
-Order_No_4726,User_Id_2514,Bike,3,Business,13,2,11:11:27 AM,13,2,11:50:58 AM,13,2,12:14:38 PM,13,2,12:28:45 PM,13,2,1:50:26 PM,4,22.3,,-1.2830057,36.81754779999999,-1.266294,36.8057971,Rider_Id_612,4901
-Order_No_6174,User_Id_2524,Bike,1,Personal,4,3,9:31:39 AM,4,3,9:31:53 AM,4,3,9:38:59 AM,4,3,9:45:15 AM,4,3,10:08:15 AM,13,15.0,,-1.2500303999999998,36.874167299999996,-1.2792094999999999,36.7948725,Rider_Id_347,1380
-Order_No_2859,User_Id_960,Bike,1,Personal,31,4,5:04:07 PM,31,4,5:59:52 PM,31,4,6:00:13 PM,31,4,6:29:04 PM,31,4,7:12:05 PM,6,22.4,,-1.2747111,36.80839829999999,-1.3088824,36.8311983,Rider_Id_100,2581
-Order_No_24631,User_Id_3075,Bike,1,Personal,28,5,1:50:58 PM,28,5,1:52:10 PM,28,5,2:05:38 PM,28,5,2:14:08 PM,28,5,2:33:36 PM,6,25.6,,-1.3103512,36.814996,-1.2990731000000002,36.848805799999994,Rider_Id_932,1168
-Order_No_17392,User_Id_136,Bike,3,Business,6,3,3:01:04 PM,6,3,3:02:37 PM,6,3,3:18:59 PM,6,3,3:25:12 PM,6,3,4:18:01 PM,30,27.6,,-1.3094447,36.9107706,-1.3254522,36.721658399999995,Rider_Id_404,3169
-Order_No_14953,User_Id_778,Bike,3,Business,13,4,12:12:14 PM,13,4,12:16:09 PM,13,4,12:56:03 PM,13,4,1:08:23 PM,13,4,2:13:05 PM,5,21.8,,-1.2535486,36.7933541,-1.2564152,36.779280299999996,Rider_Id_741,3882
-Order_No_5357,User_Id_345,Bike,3,Business,6,3,10:41:23 AM,6,3,10:52:13 AM,6,3,11:13:38 AM,6,3,11:23:54 AM,6,3,12:07:47 PM,24,19.8,,-1.3632162,36.9108253,-1.2579303999999998,36.8019667,Rider_Id_543,2633
-Order_No_14847,User_Id_3642,Bike,2,Personal,5,2,4:14:05 PM,5,2,4:16:00 PM,5,2,4:21:11 PM,5,2,4:44:02 PM,5,2,4:58:04 PM,4,21.6,,-1.2922364,36.78955379999999,-1.2933048,36.8211515,Rider_Id_620,842
-Order_No_18927,User_Id_3496,Bike,3,Business,30,2,2:23:30 PM,30,2,2:24:37 PM,30,2,2:30:29 PM,30,2,2:52:17 PM,30,2,3:13:07 PM,12,,,-1.300921,36.828195,-1.2799576000000001,36.7630239,Rider_Id_856,1250
-Order_No_6165,User_Id_3517,Bike,3,Business,22,5,9:10:35 AM,22,5,9:11:26 AM,22,5,9:11:39 AM,22,5,10:27:01 AM,22,5,10:52:27 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_145,1526
-Order_No_11093,User_Id_1724,Bike,3,Business,7,5,9:34:55 AM,7,5,9:36:40 AM,7,5,9:56:09 AM,7,5,9:59:06 AM,7,5,10:31:55 AM,7,18.1,,-1.2551895,36.7822034,-1.2885503999999999,36.8066908,Rider_Id_19,1969
-Order_No_15783,User_Id_1882,Bike,3,Business,15,5,8:34:43 AM,15,5,9:12:18 AM,15,5,9:15:49 AM,15,5,9:45:25 AM,15,5,10:04:12 AM,7,20.3,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_326,1127
-Order_No_27301,User_Id_187,Bike,3,Business,11,1,2:09:19 PM,11,1,2:10:03 PM,11,1,2:25:36 PM,11,1,2:33:08 PM,11,1,2:59:05 PM,10,28.2,,-1.303031,36.758577,-1.3169591,36.7000029,Rider_Id_46,1557
-Order_No_22075,User_Id_2956,Bike,3,Business,21,2,11:42:11 AM,21,2,11:42:53 AM,21,2,11:48:06 AM,21,2,11:56:58 AM,21,2,12:06:53 PM,5,22.3,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_622,595
-Order_No_23000,User_Id_2982,Bike,3,Business,4,1,9:16:04 AM,4,1,9:16:20 AM,4,1,9:18:23 AM,4,1,9:45:21 AM,4,1,10:28:43 AM,18,21.7,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_332,2602
-Order_No_25989,User_Id_3283,Bike,3,Business,28,2,10:54:10 AM,28,2,10:54:42 AM,28,2,11:02:31 AM,28,2,11:09:13 AM,28,2,11:27:37 AM,6,21.2,,-1.2663631000000002,36.8163329,-1.2628473,36.781805,Rider_Id_289,1104
-Order_No_6627,User_Id_1402,Bike,3,Business,18,4,8:15:07 AM,18,4,8:15:28 AM,18,4,8:15:58 AM,18,4,8:48:57 AM,18,4,8:55:09 AM,2,,,-1.28878,36.8168312,-1.2813012,36.832396200000005,Rider_Id_61,372
-Order_No_781,User_Id_2978,Bike,3,Business,2,1,12:40:00 PM,2,1,12:40:24 PM,2,1,12:41:33 PM,2,1,12:49:03 PM,2,1,12:55:26 PM,4,19.2,,-1.2551895,36.7822034,-1.2652108,36.8056075,Rider_Id_101,383
-Order_No_17891,User_Id_3621,Bike,3,Business,17,1,10:20:40 AM,17,1,10:21:01 AM,17,1,11:07:30 AM,17,1,11:08:40 AM,17,1,11:25:19 AM,5,25.0,,-1.2836583000000001,36.7886014,-1.3041128999999998,36.810493900000004,Rider_Id_844,999
-Order_No_16717,User_Id_3115,Bike,3,Business,25,4,10:53:05 AM,25,4,10:53:24 AM,25,4,11:02:37 AM,25,4,11:08:27 AM,25,4,11:51:04 AM,21,20.2,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_247,2557
-Order_No_12111,User_Id_392,Bike,1,Business,9,3,5:26:27 PM,9,3,6:14:32 PM,9,3,6:19:10 PM,9,3,6:43:14 PM,9,3,7:24:15 PM,14,21.5,,-1.2297202,36.874550799999994,-1.2559559999999999,36.789867,Rider_Id_633,2461
-Order_No_25859,User_Id_1500,Bike,3,Business,29,3,10:02:08 AM,29,3,10:03:26 AM,29,3,10:03:38 AM,29,3,10:56:40 AM,29,3,10:57:35 AM,10,,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_855,55
-Order_No_1846,User_Id_527,Bike,2,Personal,23,3,2:06:00 PM,23,3,2:06:13 PM,23,3,2:15:31 PM,23,3,2:35:27 PM,23,3,3:12:41 PM,13,22.3,,-1.2861764999999998,36.8209086,-1.3295139,36.8985677,Rider_Id_360,2234
-Order_No_4130,User_Id_547,Bike,1,Personal,6,5,11:28:54 AM,6,5,11:30:36 AM,6,5,11:49:46 AM,6,5,12:07:25 PM,6,5,12:29:02 PM,6,20.5,,-1.3082165000000001,36.8320031,-1.2780652,36.8263376,Rider_Id_844,1297
-Order_No_20031,User_Id_393,Bike,3,Business,31,3,7:42:28 AM,31,3,8:10:29 AM,31,3,8:12:35 AM,31,3,9:13:48 AM,31,3,9:13:51 AM,4,19.3,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_731,3
-Order_No_19123,User_Id_2956,Bike,3,Business,8,3,8:14:34 AM,8,3,8:15:28 AM,8,3,9:15:38 AM,8,3,9:20:13 AM,8,3,9:45:23 AM,6,,,-1.2584143,36.8048002,-1.2907726000000002,36.7860794,Rider_Id_207,1510
-Order_No_14257,User_Id_1502,Bike,3,Business,4,1,3:47:08 PM,4,1,3:55:11 PM,4,1,3:55:18 PM,4,1,3:58:53 PM,4,1,4:56:27 PM,23,,,-1.3472066,36.7692638,-1.2232123,36.8932547,Rider_Id_649,3454
-Order_No_7624,User_Id_2523,Bike,3,Business,30,4,12:19:49 PM,30,4,12:20:11 PM,30,4,12:31:24 PM,30,4,12:47:19 PM,30,4,1:13:05 PM,13,,,-1.2551895,36.7822034,-1.3083777,36.85900770000001,Rider_Id_448,1546
-Order_No_19217,User_Id_1724,Bike,3,Business,10,3,3:12:43 PM,10,3,3:13:02 PM,10,3,3:22:35 PM,10,3,3:24:18 PM,10,3,3:33:59 PM,7,30.6,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_291,581
-Order_No_6865,User_Id_2415,Bike,3,Personal,13,3,9:16:14 AM,13,3,9:18:47 AM,13,3,9:31:09 AM,13,3,10:01:34 AM,13,3,10:24:33 AM,4,16.1,,-1.2832653,36.8248575,-1.3057159999999999,36.809882,Rider_Id_46,1379
-Order_No_8884,User_Id_3201,Bike,3,Business,13,3,3:41:27 PM,13,3,3:42:08 PM,13,3,3:42:45 PM,13,3,3:53:54 PM,13,3,3:55:55 PM,2,27.1,,-1.2615888000000002,36.7928732,-1.2584143,36.8048002,Rider_Id_56,121
-Order_No_9288,User_Id_1316,Bike,3,Business,11,1,3:52:35 PM,11,1,3:55:31 PM,11,1,4:06:03 PM,11,1,4:08:04 PM,11,1,5:00:03 PM,23,28.9,,-1.2551895,36.7822034,-1.3448483,36.9004081,Rider_Id_520,3119
-Order_No_3112,User_Id_3032,Bike,3,Business,24,4,2:28:54 PM,24,4,2:29:51 PM,24,4,2:39:38 PM,24,4,3:00:06 PM,24,4,3:18:00 PM,5,28.3,,-1.2429898000000001,36.7851062,-1.2571472,36.795063299999995,Rider_Id_329,1074
-Order_No_6440,User_Id_2565,Bike,3,Personal,25,5,11:22:14 AM,25,5,11:22:29 AM,25,5,11:27:50 AM,25,5,11:51:24 AM,25,5,12:16:11 PM,11,26.6,,-1.3135847,36.850545600000004,-1.257309,36.806008399999996,Rider_Id_647,1487
-Order_No_24667,User_Id_1724,Bike,3,Business,26,5,10:17:08 AM,26,5,10:17:33 AM,26,5,10:23:23 AM,26,5,10:35:24 AM,26,5,11:03:48 AM,11,,,-1.32189,36.8589727,-1.2941648000000001,36.7977315,Rider_Id_709,1704
-Order_No_19870,User_Id_600,Bike,1,Personal,7,7,12:28:36 PM,7,7,12:49:13 PM,7,7,1:06:28 PM,7,7,1:16:13 PM,7,7,1:33:00 PM,3,25.4,,-1.2915306999999998,36.790586499999996,-1.2852583999999998,36.8043641,Rider_Id_789,1007
-Order_No_20314,User_Id_60,Bike,3,Business,7,2,9:48:45 AM,7,2,9:49:02 AM,7,2,9:57:31 AM,7,2,10:07:02 AM,7,2,10:17:37 AM,7,,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_8,635
-Order_No_19177,User_Id_2412,Bike,3,Business,17,5,1:50:07 PM,17,5,1:50:42 PM,17,5,1:59:23 PM,17,5,2:11:13 PM,17,5,2:19:49 PM,3,,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_126,516
-Order_No_13468,User_Id_2869,Bike,3,Business,5,5,10:24:56 AM,5,5,10:25:12 AM,5,5,10:39:01 AM,5,5,10:43:57 AM,5,5,11:04:41 AM,10,24.5,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_630,1244
-Order_No_8011,User_Id_323,Bike,3,Business,9,4,11:16:30 AM,9,4,11:16:38 AM,9,4,11:17:41 AM,9,4,11:22:04 AM,9,4,12:07:01 PM,23,23.9,,-1.3632162,36.9108253,-1.2611987,36.80609129999999,Rider_Id_584,2697
-Order_No_4108,User_Id_3647,Bike,3,Business,20,1,3:53:42 PM,20,1,3:54:50 PM,20,1,4:14:18 PM,20,1,4:16:46 PM,20,1,5:16:19 PM,4,21.4,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_415,3573
-Order_No_9122,User_Id_71,Bike,3,Business,2,4,8:24:32 AM,2,4,8:24:41 AM,2,4,8:36:15 AM,2,4,8:44:20 AM,2,4,9:10:04 AM,11,13.2,,-1.225322,36.80855,-1.2891921999999998,36.825189,Rider_Id_409,1544
-Order_No_8247,User_Id_3689,Bike,3,Business,27,4,11:37:29 AM,27,4,12:19:44 PM,27,4,12:25:49 PM,27,4,12:29:24 PM,27,4,12:43:27 PM,2,22.8,,-1.2527958000000001,36.8003131,-1.2620912,36.8013562,Rider_Id_96,843
-Order_No_5371,User_Id_640,Bike,3,Business,6,1,9:14:17 AM,6,1,9:14:48 AM,6,1,9:28:12 AM,6,1,9:30:10 AM,6,1,10:06:19 AM,2,14.9,,-1.2946581,36.779292100000006,-1.304033,36.7848695,Rider_Id_722,2169
-Order_No_17748,User_Id_1245,Bike,3,Business,18,4,10:58:52 AM,18,4,10:59:24 AM,18,4,11:01:11 AM,18,4,11:48:10 AM,18,4,12:23:54 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_661,2144
-Order_No_20402,User_Id_286,Bike,3,Business,4,1,9:03:33 AM,4,1,9:03:47 AM,4,1,9:04:38 AM,4,1,9:27:28 AM,4,1,9:57:48 AM,10,21.8,,-1.2597436000000002,36.8282526,-1.2551895,36.7822034,Rider_Id_627,1820
-Order_No_23140,User_Id_1508,Bike,2,Personal,6,1,8:56:20 AM,6,1,8:56:30 AM,6,1,9:01:38 AM,6,1,9:04:35 AM,6,1,9:24:32 AM,11,15.2,,-1.2874215,36.7767292,-1.257984,36.745016,Rider_Id_636,1197
-Order_No_2873,User_Id_2871,Bike,1,Personal,10,5,8:58:55 AM,10,5,8:59:13 AM,10,5,9:03:25 AM,10,5,9:08:13 AM,10,5,9:22:50 AM,6,13.6,,-1.2623805000000001,36.7805404,-1.2772389,36.7855864,Rider_Id_45,877
-Order_No_24405,User_Id_2074,Bike,2,Personal,27,5,9:21:19 AM,27,5,9:29:18 AM,27,5,9:41:03 AM,27,5,9:47:34 AM,27,5,10:13:24 AM,5,16.0,,-1.2741728,36.79691079999999,-1.2949473999999999,36.8270407,Rider_Id_519,1550
-Order_No_6051,User_Id_3450,Bike,3,Business,7,4,3:33:26 PM,7,4,3:38:12 PM,7,4,3:48:37 PM,7,4,3:59:34 PM,7,4,4:18:06 PM,10,27.9,,-1.2551895,36.7822034,-1.304023,36.788389,Rider_Id_416,1112
-Order_No_123,User_Id_2283,Bike,2,Personal,5,3,1:21:34 PM,5,3,1:22:28 PM,5,3,1:30:30 PM,5,3,1:32:29 PM,5,3,2:20:42 PM,6,23.7,,-1.2696341,36.791664000000004,-1.2958383,36.8073162,Rider_Id_167,2893
-Order_No_19699,User_Id_1500,Bike,3,Business,3,4,3:57:51 PM,3,4,3:58:05 PM,3,4,3:58:12 PM,3,4,4:00:32 PM,3,4,4:29:22 PM,22,26.2,,-1.2574219,36.7927073,-1.1930572,36.934890100000004,Rider_Id_523,1730
-Order_No_10499,User_Id_2789,Bike,3,Business,4,4,10:33:55 AM,4,4,10:34:29 AM,4,4,10:57:36 AM,4,4,11:47:34 AM,4,4,12:06:26 PM,6,25.7,,-1.2902869,36.8085396,-1.304735,36.784605400000004,Rider_Id_819,1132
-Order_No_18546,User_Id_3599,Bike,3,Business,28,4,10:01:47 AM,28,4,10:02:08 AM,28,4,10:25:11 AM,28,4,10:41:55 AM,28,4,11:08:23 AM,11,23.8,,-1.3302996,36.8707935,-1.3329031999999998,36.7888802,Rider_Id_675,1588
-Order_No_5962,User_Id_718,Bike,3,Business,3,5,3:07:09 PM,3,5,3:08:10 PM,3,5,3:14:32 PM,3,5,3:22:58 PM,3,5,4:16:59 PM,8,,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_161,3241
-Order_No_3216,User_Id_868,Bike,3,Business,18,4,12:05:01 PM,18,4,12:08:19 PM,18,4,12:08:54 PM,18,4,12:20:37 PM,18,4,1:02:48 PM,17,,,-1.3181159,36.710155900000004,-1.265715,36.823815,Rider_Id_548,2531
-Order_No_20136,User_Id_1209,Bike,3,Business,16,2,3:25:29 PM,16,2,3:25:40 PM,16,2,3:25:47 PM,16,2,3:49:59 PM,16,2,3:50:01 PM,20,,,-1.3077309,36.685621399999995,-1.3004062,36.829741,Rider_Id_10,2
-Order_No_2703,User_Id_1976,Bike,3,Business,30,2,11:26:36 AM,30,2,11:28:56 AM,30,2,11:52:43 AM,30,2,12:01:07 PM,30,2,12:44:01 PM,9,22.2,,-1.2812601000000001,36.73402720000001,-1.257028,36.7810435,Rider_Id_240,2574
-Order_No_6661,User_Id_3426,Bike,3,Business,9,3,12:22:40 PM,9,3,12:25:36 PM,9,3,12:35:43 PM,9,3,12:37:29 PM,9,3,12:59:15 PM,9,25.1,,-1.3165542,36.700067,-1.3012008,36.764868,Rider_Id_544,1306
-Order_No_23488,User_Id_3719,Bike,2,Personal,18,5,4:21:34 PM,18,5,4:24:31 PM,18,5,4:27:00 PM,18,5,4:48:57 PM,18,5,5:00:07 PM,7,22.3,,-1.3024794,36.8228284,-1.3318059,36.8359596,Rider_Id_832,670
-Order_No_24181,User_Id_3647,Bike,3,Business,22,4,5:36:29 PM,22,4,5:38:26 PM,22,4,5:50:30 PM,22,4,5:54:14 PM,22,4,6:12:27 PM,5,24.1,,-1.2726389999999999,36.794723,-1.2654594,36.8216832,Rider_Id_531,1093
-Order_No_23475,User_Id_914,Bike,3,Business,6,3,12:41:15 PM,6,3,12:41:39 PM,6,3,12:56:09 PM,6,3,1:07:42 PM,6,3,1:34:12 PM,11,,,-1.2670213,36.7994482,-1.3053578000000001,36.860791299999995,Rider_Id_622,1590
-Order_No_6244,User_Id_868,Bike,3,Business,23,6,2:21:22 PM,23,6,2:22:38 PM,23,6,2:35:32 PM,23,6,2:45:20 PM,23,6,2:57:16 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_268,716
-Order_No_7502,User_Id_1772,Bike,3,Personal,2,2,1:04:53 PM,2,2,1:05:17 PM,2,2,1:05:25 PM,2,2,1:17:38 PM,2,2,1:34:21 PM,5,20.9,,-1.2916556000000001,36.8262516,-1.3175119,36.8058513,Rider_Id_726,1003
-Order_No_13344,User_Id_1259,Bike,2,Business,8,1,9:57:26 AM,8,1,9:57:38 AM,8,1,10:19:07 AM,8,1,10:29:31 AM,8,1,11:24:19 AM,25,19.8,,-1.2359565000000001,36.76227279999999,-1.3478563000000001,36.8935824,Rider_Id_865,3288
-Order_No_11698,User_Id_59,Bike,3,Business,23,2,4:26:27 PM,23,2,4:39:07 PM,23,2,4:49:03 PM,23,2,4:59:29 PM,23,2,5:31:36 PM,6,,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_855,1927
-Order_No_23525,User_Id_1075,Bike,3,Business,22,5,12:10:50 PM,22,5,12:11:23 PM,22,5,12:20:10 PM,22,5,12:22:19 PM,22,5,1:23:15 PM,9,,,-1.2527958000000001,36.8003131,-1.3047101,36.7832899,Rider_Id_275,3656
-Order_No_17441,User_Id_2437,Bike,2,Personal,25,2,2:44:13 PM,25,2,2:50:56 PM,25,2,3:03:33 PM,25,2,3:10:03 PM,25,2,3:40:29 PM,8,26.9,,-1.3015998,36.8764939,-1.3255466999999999,36.8804607,Rider_Id_754,1826
-Order_No_8320,User_Id_3496,Bike,3,Business,23,2,10:54:53 AM,23,2,10:55:12 AM,23,2,11:07:07 AM,23,2,12:19:33 PM,23,2,12:19:41 PM,18,17.8,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_210,8
-Order_No_25110,User_Id_1792,Bike,3,Business,16,4,4:16:58 PM,16,4,4:17:12 PM,16,4,4:23:10 PM,16,4,4:26:58 PM,16,4,5:04:08 PM,14,19.0,,-1.2805537,36.7920739,-1.2311903,36.8102105,Rider_Id_531,2230
-Order_No_23688,User_Id_1577,Bike,1,Personal,4,2,8:11:42 AM,4,2,8:12:29 AM,4,2,8:19:52 AM,4,2,8:24:28 AM,4,2,8:40:44 AM,7,16.4,,-1.2816233999999997,36.8239846,-1.3006208000000001,36.77274439999999,Rider_Id_726,976
-Order_No_1681,User_Id_1245,Bike,3,Business,23,3,10:14:25 AM,23,3,10:15:01 AM,23,3,10:25:18 AM,23,3,10:46:13 AM,23,3,11:22:32 AM,17,,,-1.300921,36.828195,-1.3204931,36.7043102,Rider_Id_487,2179
-Order_No_27589,User_Id_1642,Bike,3,Business,11,2,10:21:11 AM,11,2,10:56:56 AM,11,2,11:07:54 AM,11,2,11:10:53 AM,11,2,11:24:44 AM,8,19.2,,-1.2726506000000002,36.7960153,-1.2970034,36.833321399999996,Rider_Id_404,831
-Order_No_7992,User_Id_1407,Bike,1,Personal,2,1,9:23:12 AM,2,1,9:23:57 AM,2,1,9:51:36 AM,2,1,9:55:03 AM,2,1,10:22:07 AM,12,16.5,,-1.3081599,36.7787704,-1.324009,36.845882700000004,Rider_Id_74,1624
-Order_No_3547,User_Id_627,Bike,3,Business,8,5,12:59:41 PM,8,5,1:00:24 PM,8,5,1:06:15 PM,8,5,1:32:47 PM,8,5,1:46:37 PM,6,21.5,,-1.2579303999999998,36.8019667,-1.3001813,36.811122,Rider_Id_116,830
-Order_No_6502,User_Id_970,Bike,3,Business,17,1,4:23:29 PM,17,1,4:25:03 PM,17,1,4:30:12 PM,17,1,4:35:40 PM,17,1,4:47:30 PM,4,22.6,,-1.2551895,36.7822034,-1.2693662000000001,36.8085295,Rider_Id_685,710
-Order_No_3056,User_Id_3560,Bike,3,Business,2,6,1:18:55 PM,2,6,1:26:50 PM,2,6,1:49:58 PM,2,6,2:03:48 PM,2,6,2:18:17 PM,11,31.1,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_812,869
-Order_No_24654,User_Id_733,Bike,3,Business,14,5,1:15:26 PM,14,5,1:15:45 PM,14,5,1:15:46 PM,14,5,1:23:04 PM,14,5,1:53:29 PM,14,21.8,,-1.2551895,36.7822034,-1.3265961000000002,36.8718676,Rider_Id_168,1825
-Order_No_5602,User_Id_2987,Bike,1,Business,13,5,12:35:12 PM,13,5,12:37:53 PM,13,5,12:47:04 PM,13,5,12:50:55 PM,13,5,1:03:06 PM,4,,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_726,731
-Order_No_23773,User_Id_633,Bike,3,Business,27,2,1:53:02 PM,27,2,1:53:16 PM,27,2,2:04:58 PM,27,2,2:09:01 PM,27,2,2:32:49 PM,4,25.4,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_942,1428
-Order_No_3047,User_Id_3621,Bike,3,Business,8,1,5:50:01 PM,8,1,5:50:18 PM,8,1,6:05:33 PM,8,1,6:06:54 PM,8,1,6:29:48 PM,5,29.2,,-1.2836583000000001,36.7886014,-1.2811503,36.8235884,Rider_Id_344,1374
-Order_No_11722,User_Id_246,Bike,3,Business,18,2,3:00:01 PM,18,2,3:00:09 PM,18,2,3:01:00 PM,18,2,3:20:40 PM,18,2,3:20:44 PM,3,27.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_520,4
-Order_No_23578,User_Id_533,Bike,3,Business,26,1,4:59:07 PM,26,1,4:59:22 PM,26,1,5:04:03 PM,26,1,5:07:41 PM,26,1,5:20:05 PM,6,20.2,,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_290,744
-Order_No_7591,User_Id_2733,Bike,3,Business,13,4,9:52:22 AM,13,4,9:52:54 AM,13,4,9:58:00 AM,13,4,10:24:31 AM,13,4,10:24:34 AM,2,18.5,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_831,3
-Order_No_12054,User_Id_867,Bike,3,Business,23,4,11:53:44 AM,23,4,11:54:44 AM,23,4,12:01:28 PM,23,4,12:02:53 PM,23,4,1:05:25 PM,10,23.4,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_582,3752
-Order_No_22743,User_Id_3404,Bike,3,Business,15,5,2:44:13 PM,15,5,2:44:36 PM,15,5,2:44:43 PM,15,5,2:58:48 PM,15,5,3:12:59 PM,5,17.7,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_330,851
-Order_No_27182,User_Id_393,Bike,3,Business,14,1,10:28:18 AM,14,1,10:28:27 AM,14,1,10:36:19 AM,14,1,10:51:47 AM,14,1,10:59:04 AM,4,23.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_659,437
-Order_No_25349,User_Id_3149,Bike,3,Business,7,2,11:35:05 AM,7,2,11:35:16 AM,7,2,11:38:07 AM,7,2,11:45:11 AM,7,2,12:20:32 PM,11,,,-1.3167113000000001,36.8301563,-1.2520537,36.830067799999995,Rider_Id_155,2121
-Order_No_25636,User_Id_393,Bike,3,Business,20,3,2:24:50 PM,20,3,2:25:02 PM,20,3,2:25:36 PM,20,3,2:41:40 PM,20,3,2:54:00 PM,4,29.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_15,740
-Order_No_4359,User_Id_1746,Bike,3,Business,5,5,8:54:46 AM,5,5,9:08:17 AM,5,5,9:23:30 AM,5,5,9:26:31 AM,5,5,10:17:41 AM,11,17.7,,-1.2712358,36.8345749,-1.3039706,36.7688174,Rider_Id_107,3070
-Order_No_16697,User_Id_2828,Bike,3,Business,1,5,9:21:07 AM,1,5,10:17:21 AM,1,5,10:35:43 AM,1,5,11:04:18 AM,1,5,12:00:53 PM,14,28.8,,-1.2710687,36.8142839,-1.2137605,36.8788137,Rider_Id_716,3395
-Order_No_18402,User_Id_2763,Bike,3,Business,13,4,1:16:38 PM,13,4,1:17:21 PM,13,4,1:23:44 PM,13,4,1:27:29 PM,13,4,1:50:45 PM,7,22.2,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_709,1396
-Order_No_11816,User_Id_590,Bike,3,Business,22,5,10:42:08 AM,22,5,10:47:59 AM,22,5,11:11:00 AM,22,5,11:16:27 AM,22,5,12:09:43 PM,24,28.3,,-1.3343055,36.73296320000001,-1.2296129,36.7641955,Rider_Id_958,3196
-Order_No_11861,User_Id_3108,Bike,3,Business,9,4,9:43:20 AM,9,4,9:46:14 AM,9,4,10:32:45 AM,9,4,10:44:42 AM,9,4,11:24:58 AM,10,23.6,,-1.2551895,36.7822034,-1.314309,36.8392657,Rider_Id_770,2416
-Order_No_12859,User_Id_1329,Bike,3,Business,11,1,2:25:53 PM,11,1,2:27:28 PM,11,1,2:39:12 PM,11,1,2:41:11 PM,11,1,3:27:59 PM,13,28.1,,-1.273056,36.811298,-1.2778747,36.883182899999994,Rider_Id_611,2808
-Order_No_21339,User_Id_3686,Bike,3,Business,10,1,10:06:42 AM,10,1,10:10:15 AM,10,1,10:22:04 AM,10,1,10:24:43 AM,10,1,10:42:49 AM,8,16.5,,-1.2551895,36.7822034,-1.2913205,36.822356899999996,Rider_Id_434,1086
-Order_No_11656,User_Id_664,Bike,1,Personal,12,5,8:04:48 AM,12,5,9:03:43 AM,12,5,9:11:03 AM,12,5,9:16:00 AM,12,5,9:30:46 AM,4,18.6,,-1.2517392,36.82496079999999,-1.2632571000000001,36.793890399999995,Rider_Id_882,886
-Order_No_26081,User_Id_432,Bike,3,Business,14,5,2:29:11 PM,14,5,2:30:52 PM,14,5,2:32:04 PM,14,5,3:17:28 PM,14,5,3:17:29 PM,9,23.1,,-1.279395,36.825364,-1.3012008,36.764868,Rider_Id_36,1
-Order_No_27738,User_Id_1500,Bike,3,Business,16,2,8:46:38 AM,16,2,8:46:56 AM,16,2,8:47:28 AM,16,2,9:42:02 AM,16,2,10:01:21 AM,10,,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_855,1159
-Order_No_22941,User_Id_3560,Bike,3,Business,24,1,8:51:01 AM,24,1,8:51:21 AM,24,1,8:56:17 AM,24,1,9:11:53 AM,24,1,9:26:25 AM,1,19.7,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_933,872
-Order_No_22908,User_Id_1244,Bike,3,Business,19,6,10:46:14 AM,19,6,10:46:32 AM,19,6,11:10:18 AM,19,6,11:17:02 AM,19,6,11:58:48 AM,16,25.0,,-1.3346773,36.8842734,-1.303596,36.778378000000004,Rider_Id_777,2506
-Order_No_9502,User_Id_2804,Bike,3,Business,30,2,3:17:02 PM,30,2,3:23:24 PM,30,2,3:26:37 PM,30,2,3:58:31 PM,30,2,4:00:50 PM,3,,,-1.2571472,36.795063299999995,-1.2657469,36.802822799999994,Rider_Id_206,139
-Order_No_26247,User_Id_1850,Bike,3,Business,2,4,4:17:18 PM,2,4,4:17:34 PM,2,4,4:27:45 PM,2,4,4:31:04 PM,2,4,4:31:08 PM,3,,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_291,4
-Order_No_25371,User_Id_3225,Bike,1,Personal,2,2,2:57:44 PM,2,2,3:01:32 PM,2,2,3:06:59 PM,2,2,3:09:22 PM,2,2,3:18:07 PM,3,26.1,,-1.2844516000000001,36.8245932,-1.2940511000000001,36.821930200000004,Rider_Id_343,525
-Order_No_5152,User_Id_53,Bike,3,Business,16,3,1:09:59 PM,16,3,1:15:51 PM,16,3,1:27:29 PM,16,3,1:34:09 PM,16,3,1:59:11 PM,11,27.2,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_576,1502
-Order_No_4078,User_Id_3647,Bike,3,Business,12,5,4:35:55 PM,12,5,4:38:06 PM,12,5,4:47:32 PM,12,5,4:52:03 PM,12,5,5:14:15 PM,5,26.4,,-1.2726389999999999,36.794723,-1.2857524,36.819680600000005,Rider_Id_506,1332
-Order_No_23945,User_Id_2801,Bike,3,Business,12,3,1:01:08 PM,12,3,1:03:20 PM,12,3,1:15:33 PM,12,3,1:16:40 PM,12,3,2:12:13 PM,16,19.5,,-1.2674273999999999,36.787082700000006,-1.3109801,36.8810758,Rider_Id_521,3333
-Order_No_11193,User_Id_253,Bike,1,Personal,29,2,7:11:01 AM,29,2,7:11:22 AM,29,2,7:22:25 AM,29,2,7:25:19 AM,29,2,7:42:45 AM,10,11.2,,-1.3043649,36.7816132,-1.263483,36.8005343,Rider_Id_103,1046
-Order_No_6002,User_Id_2200,Bike,3,Business,15,6,2:01:14 PM,15,6,2:33:24 PM,15,6,2:42:41 PM,15,6,3:04:52 PM,15,6,3:20:22 PM,6,23.6,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_266,930
-Order_No_26426,User_Id_1933,Bike,1,Personal,10,1,6:13:04 PM,10,1,6:14:06 PM,10,1,6:19:25 PM,10,1,6:24:06 PM,10,1,6:34:30 PM,5,19.2,,-1.2656592,36.8014259,-1.2543898999999998,36.8091271,Rider_Id_86,624
-Order_No_24769,User_Id_393,Bike,3,Business,30,2,9:28:34 AM,30,2,9:29:08 AM,30,2,9:33:50 AM,30,2,9:43:59 AM,30,2,10:13:15 AM,14,17.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_555,1756
-Order_No_23628,User_Id_661,Bike,3,Business,27,4,2:28:12 PM,27,4,2:28:31 PM,27,4,2:47:55 PM,27,4,2:54:24 PM,27,4,3:16:46 PM,11,25.2,,-1.3102768999999999,36.850293,-1.2963097,36.7688221,Rider_Id_932,1342
-Order_No_13699,User_Id_718,Bike,3,Business,23,5,12:33:58 PM,23,5,12:36:20 PM,23,5,12:45:23 PM,23,5,1:09:23 PM,23,5,1:33:20 PM,6,24.0,,-1.2551895,36.7822034,-1.2839568000000001,36.823310600000006,Rider_Id_124,1437
-Order_No_22710,User_Id_187,Bike,3,Business,12,2,2:24:12 PM,12,2,2:24:34 PM,12,2,2:30:19 PM,12,2,3:29:56 PM,12,2,4:37:36 PM,28,27.2,,-1.2285834999999998,36.882646799999996,-1.3169591,36.7000029,Rider_Id_75,4060
-Order_No_9658,User_Id_718,Bike,3,Business,28,4,3:33:30 PM,28,4,3:35:10 PM,28,4,3:44:49 PM,28,4,3:46:06 PM,28,4,3:59:31 PM,4,30.0,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_830,805
-Order_No_25442,User_Id_517,Bike,3,Business,30,4,3:41:08 PM,30,4,3:41:23 PM,30,4,3:41:38 PM,30,4,3:59:52 PM,30,4,3:59:55 PM,5,,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_349,3
-Order_No_11475,User_Id_3727,Bike,3,Business,11,2,2:44:39 PM,11,2,3:19:45 PM,11,2,3:20:00 PM,11,2,3:25:38 PM,11,2,3:43:47 PM,6,22.5,,-1.2963097,36.7688221,-1.2904993,36.80438770000001,Rider_Id_848,1089
-Order_No_25120,User_Id_3517,Bike,3,Business,22,5,3:35:41 PM,22,5,3:37:41 PM,22,5,3:39:00 PM,22,5,4:15:27 PM,22,5,4:54:52 PM,20,31.0,,-1.300921,36.828195,-1.3813339,36.737727299999996,Rider_Id_829,2365
-Order_No_15344,User_Id_2167,Bike,3,Personal,16,2,8:48:27 AM,16,2,8:49:30 AM,16,2,9:14:25 AM,16,2,9:16:01 AM,16,2,9:38:08 AM,10,,,-1.3187647,36.8004283,-1.2625087,36.8263725,Rider_Id_933,1327
-Order_No_20066,User_Id_3689,Bike,3,Business,21,3,3:44:51 PM,21,3,3:50:03 PM,21,3,3:50:20 PM,21,3,4:15:48 PM,21,3,4:15:50 PM,2,23.4,3.0,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_295,2
-Order_No_15373,User_Id_982,Bike,3,Business,15,5,12:08:50 PM,15,5,12:09:00 PM,15,5,12:10:40 PM,15,5,12:29:52 PM,15,5,1:10:54 PM,22,25.8,,-1.3079421,36.8390989,-1.2517871,36.6795716,Rider_Id_595,2462
-Order_No_11642,User_Id_3434,Bike,3,Business,18,2,9:03:38 AM,18,2,9:04:03 AM,18,2,9:52:54 AM,18,2,10:03:18 AM,18,2,10:34:16 AM,17,19.8,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_649,1858
-Order_No_23472,User_Id_391,Bike,3,Business,29,3,2:41:01 PM,29,3,2:47:25 PM,29,3,3:03:00 PM,29,3,3:21:11 PM,29,3,3:46:57 PM,5,20.9,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_183,1546
-Order_No_14217,User_Id_3591,Bike,3,Personal,15,6,11:44:32 AM,15,6,11:52:57 AM,15,6,12:28:41 PM,15,6,12:35:56 PM,15,6,12:58:29 PM,10,19.1,,-1.3165524,36.7080859,-1.3653686999999999,36.7337644,Rider_Id_226,1353
-Order_No_19164,User_Id_3553,Bike,1,Personal,25,6,2:35:05 PM,25,6,2:35:22 PM,25,6,2:43:12 PM,25,6,2:58:21 PM,25,6,3:11:13 PM,6,,,-1.2893883999999998,36.828207,-1.2724556,36.7931938,Rider_Id_45,772
-Order_No_2737,User_Id_3291,Bike,3,Business,26,5,10:51:10 AM,26,5,10:52:51 AM,26,5,11:02:06 AM,26,5,11:07:56 AM,26,5,11:17:27 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_287,571
-Order_No_15519,User_Id_198,Bike,3,Business,21,3,3:16:27 PM,21,3,3:16:49 PM,21,3,3:32:50 PM,21,3,3:35:28 PM,21,3,4:05:12 PM,12,23.1,3.0,-1.3396256000000002,36.8863135,-1.3799485,36.9349923,Rider_Id_343,1784
-Order_No_24507,User_Id_2567,Bike,1,Personal,29,3,11:24:15 AM,29,3,11:25:12 AM,29,3,11:37:08 AM,29,3,11:39:01 AM,29,3,11:57:14 AM,7,18.7,,-1.3182646,36.821312799999994,-1.2977386999999998,36.803144700000004,Rider_Id_249,1093
-Order_No_15391,User_Id_3635,Bike,1,Business,8,6,4:33:44 PM,8,6,4:41:39 PM,8,6,4:55:30 PM,8,6,5:03:45 PM,8,6,5:12:43 PM,3,23.2,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_427,538
-Order_No_6820,User_Id_868,Bike,3,Business,15,5,3:00:24 PM,15,5,3:01:13 PM,15,5,3:08:25 PM,15,5,3:13:05 PM,15,5,3:22:57 PM,4,17.7,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_205,592
-Order_No_24627,User_Id_1500,Bike,3,Business,13,1,9:57:07 AM,13,1,9:57:25 AM,13,1,10:26:29 AM,13,1,10:34:23 AM,13,1,10:44:36 AM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_448,613
-Order_No_15526,User_Id_816,Bike,3,Business,23,5,11:32:49 AM,23,5,12:48:47 PM,23,5,1:00:06 PM,23,5,1:14:12 PM,23,5,1:45:44 PM,6,24.3,,-1.2534301,36.800098299999995,-1.290894,36.822971,Rider_Id_477,1892
-Order_No_12513,User_Id_3355,Bike,3,Business,10,4,2:44:04 PM,10,4,3:03:10 PM,10,4,3:17:08 PM,10,4,3:25:34 PM,10,4,3:49:17 PM,10,21.8,1.1,-1.3008998999999999,36.893280299999994,-1.3052491000000002,36.8223899,Rider_Id_216,1423
-Order_No_18767,User_Id_2583,Bike,3,Personal,20,1,3:41:50 PM,20,1,3:43:25 PM,20,1,3:49:46 PM,20,1,3:53:10 PM,20,1,4:06:09 PM,6,,,-1.26249,36.799715,-1.2607568999999998,36.7747575,Rider_Id_959,779
-Order_No_24020,User_Id_2423,Bike,3,Business,14,5,2:29:21 PM,14,5,2:30:37 PM,14,5,2:31:04 PM,14,5,2:34:47 PM,14,5,3:04:43 PM,7,23.0,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_868,1796
-Order_No_14823,User_Id_3091,Bike,3,Business,26,3,1:34:50 PM,26,3,1:36:10 PM,26,3,1:44:32 PM,26,3,2:00:03 PM,26,3,2:18:13 PM,6,27.0,,-1.3012008,36.764868,-1.2939132,36.8081606,Rider_Id_565,1090
-Order_No_9290,User_Id_402,Bike,3,Business,22,3,1:33:36 PM,22,3,1:42:36 PM,22,3,1:42:54 PM,22,3,2:00:56 PM,22,3,2:01:03 PM,1,24.5,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_704,7
-Order_No_26155,User_Id_1342,Bike,3,Business,6,2,11:33:06 AM,6,2,11:34:34 AM,6,2,12:09:28 PM,6,2,12:22:22 PM,6,2,12:47:37 PM,10,24.9,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_412,1515
-Order_No_20231,User_Id_2801,Bike,3,Business,15,1,2:10:53 PM,15,1,2:47:41 PM,15,1,3:10:06 PM,15,1,3:38:10 PM,15,1,3:56:34 PM,13,,,-1.3546228,36.9013783,-1.307726,36.839117,Rider_Id_126,1104
-Order_No_6109,User_Id_1579,Bike,3,Personal,27,1,2:01:43 PM,27,1,2:02:28 PM,27,1,2:12:29 PM,27,1,2:19:02 PM,27,1,2:41:24 PM,12,,,-1.2902552999999999,36.7949332,-1.3221935,36.8464972,Rider_Id_749,1342
-Order_No_1144,User_Id_198,Bike,3,Business,21,4,11:58:06 AM,21,4,11:58:35 AM,21,4,12:27:14 PM,21,4,1:06:24 PM,21,4,1:48:39 PM,24,25.4,,-1.2261753,36.8077307,-1.3396256000000002,36.8863135,Rider_Id_91,2535
-Order_No_19607,User_Id_53,Bike,3,Business,15,5,3:07:09 PM,15,5,3:14:20 PM,15,5,3:15:05 PM,15,5,4:23:50 PM,15,5,4:45:38 PM,5,17.9,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_291,1308
-Order_No_13401,User_Id_246,Bike,3,Business,22,2,2:11:53 PM,22,2,2:12:04 PM,22,2,2:23:17 PM,22,2,2:34:17 PM,22,2,3:05:54 PM,17,25.4,,-1.3169591,36.7000029,-1.2584143,36.8048002,Rider_Id_125,1897
-Order_No_250,User_Id_136,Bike,3,Business,1,4,10:00:28 AM,1,4,10:02:16 AM,1,4,10:11:33 AM,1,4,10:13:09 AM,1,4,10:37:01 AM,11,,,-1.3326173000000001,36.71462329999999,-1.2909633999999999,36.7813995,Rider_Id_288,1432
-Order_No_8218,User_Id_2801,Bike,3,Business,20,1,12:00:53 PM,20,1,12:01:32 PM,20,1,12:06:08 PM,20,1,12:13:57 PM,20,1,12:34:10 PM,11,,,-1.2674273999999999,36.787082700000006,-1.3108386,36.7555239,Rider_Id_438,1213
-Order_No_9543,User_Id_1047,Bike,3,Personal,18,2,2:18:14 PM,18,2,2:19:54 PM,18,2,3:19:14 PM,18,2,3:20:50 PM,18,2,4:49:22 PM,12,25.8,,-1.2608591999999998,36.831197700000004,-1.3077869,36.844320700000004,Rider_Id_522,5312
-Order_No_16472,User_Id_391,Bike,3,Business,28,4,8:18:15 AM,28,4,10:40:59 AM,28,4,10:41:10 AM,28,4,11:29:42 AM,28,4,11:29:55 AM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_927,13
-Order_No_19298,User_Id_2533,Bike,1,Personal,17,3,10:53:30 AM,17,3,10:53:52 AM,17,3,11:15:05 AM,17,3,11:18:40 AM,17,3,11:41:31 AM,9,25.3,,-1.2727624,36.7674062,-1.3047026000000002,36.8172033,Rider_Id_116,1371
-Order_No_10528,User_Id_1341,Bike,3,Business,5,2,11:49:54 AM,5,2,11:50:51 AM,5,2,11:56:55 AM,5,2,12:09:07 PM,5,2,12:38:51 PM,13,21.4,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_379,1784
-Order_No_25583,User_Id_2956,Bike,3,Business,22,5,3:25:29 PM,22,5,3:27:56 PM,22,5,3:31:24 PM,22,5,3:38:02 PM,22,5,3:45:50 PM,3,31.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_607,468
-Order_No_17332,User_Id_909,Bike,3,Business,21,1,1:02:26 PM,21,1,1:03:22 PM,21,1,1:07:06 PM,21,1,1:28:08 PM,21,1,1:51:15 PM,7,25.3,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_803,1387
-Order_No_15601,User_Id_1475,Bike,3,Business,12,5,1:07:42 PM,12,5,1:08:26 PM,12,5,1:17:39 PM,12,5,1:32:49 PM,12,5,2:05:04 PM,12,27.1,,-1.3379798999999999,36.883545299999994,-1.3233496,36.8139159,Rider_Id_948,1935
-Order_No_17734,User_Id_868,Bike,3,Business,28,2,8:52:50 AM,28,2,9:41:51 AM,28,2,10:13:59 AM,28,2,10:29:35 AM,28,2,10:53:47 AM,10,18.5,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_864,1452
-Order_No_2986,User_Id_3560,Bike,3,Business,9,3,2:06:01 PM,9,3,2:30:22 PM,9,3,2:52:29 PM,9,3,2:56:30 PM,9,3,3:45:22 PM,12,25.5,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_235,2932
-Order_No_25002,User_Id_1688,Bike,3,Business,6,3,2:32:30 PM,6,3,2:32:59 PM,6,3,2:38:06 PM,6,3,3:18:32 PM,6,3,4:10:11 PM,19,27.5,,-1.300921,36.828195,-1.223961,36.716941299999995,Rider_Id_26,3099
-Order_No_628,User_Id_59,Bike,3,Business,28,4,1:51:10 PM,28,4,1:52:54 PM,28,4,2:09:27 PM,28,4,2:19:21 PM,28,4,2:42:08 PM,9,27.3,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_709,1367
-Order_No_10757,User_Id_3456,Bike,3,Personal,22,6,8:42:55 AM,22,6,8:43:17 AM,22,6,9:14:59 AM,22,6,9:26:38 AM,22,6,9:29:11 AM,3,17.7,,-1.2640571000000003,36.7883547,-1.262492,36.80388370000001,Rider_Id_731,153
-Order_No_21661,User_Id_867,Bike,3,Business,14,4,9:02:32 AM,14,4,9:06:27 AM,14,4,9:08:28 AM,14,4,9:37:21 AM,14,4,10:00:59 AM,10,17.9,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_101,1418
-Order_No_14320,User_Id_2528,Bike,3,Business,4,1,8:30:40 AM,4,1,8:31:19 AM,4,1,8:46:14 AM,4,1,9:00:22 AM,4,1,9:10:49 AM,3,21.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_267,627
-Order_No_18532,User_Id_2716,Bike,3,Business,9,2,3:20:32 PM,9,2,3:21:06 PM,9,2,3:30:37 PM,9,2,3:40:26 PM,9,2,5:14:22 PM,5,30.4,,-1.2917868000000001,36.7872675,-1.3038156,36.812273299999994,Rider_Id_548,5636
-Order_No_304,User_Id_3419,Bike,3,Personal,14,5,12:59:40 PM,14,5,12:59:52 PM,14,5,1:13:40 PM,14,5,1:36:26 PM,14,5,2:02:00 PM,17,23.7,,-1.215892,36.913345,-1.1768393999999998,36.840138200000005,Rider_Id_124,1534
-Order_No_969,User_Id_2978,Bike,3,Business,21,5,2:08:34 PM,21,5,2:09:10 PM,21,5,2:10:13 PM,21,5,2:14:04 PM,21,5,3:38:37 PM,7,27.1,,-1.2793642,36.8295717,-1.2551895,36.7822034,Rider_Id_447,5073
-Order_No_716,User_Id_20,Bike,3,Business,12,5,10:17:21 AM,12,5,10:18:25 AM,12,5,10:49:37 AM,12,5,10:52:08 AM,12,5,10:52:16 AM,5,26.7,,-1.278353,36.824045,-1.2628638,36.8070399,Rider_Id_843,8
-Order_No_23331,User_Id_323,Bike,1,Business,7,5,9:26:33 AM,7,5,9:26:47 AM,7,5,9:37:31 AM,7,5,10:07:06 AM,7,5,10:28:44 AM,18,18.2,,-1.2854453000000001,36.818836299999994,-1.3632162,36.9108253,Rider_Id_74,1298
-Order_No_25497,User_Id_3560,Bike,3,Business,2,2,1:27:20 PM,2,2,1:32:22 PM,2,2,1:52:36 PM,2,2,2:07:28 PM,2,2,2:38:30 PM,11,29.5,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_953,1862
-Order_No_19615,User_Id_393,Bike,3,Business,19,6,9:11:17 AM,19,6,9:12:29 AM,19,6,10:02:12 AM,19,6,10:24:41 AM,19,6,10:24:48 AM,4,21.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_725,7
-Order_No_1422,User_Id_393,Bike,3,Business,16,4,2:07:22 PM,16,4,2:07:34 PM,16,4,2:07:38 PM,16,4,2:26:20 PM,16,4,2:27:02 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,42
-Order_No_3711,User_Id_868,Bike,3,Business,22,2,2:41:50 PM,22,2,2:44:46 PM,22,2,2:45:47 PM,22,2,3:21:21 PM,22,2,3:36:25 PM,10,25.5,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_75,904
-Order_No_24865,User_Id_868,Bike,3,Business,15,6,4:40:06 PM,15,6,4:40:28 PM,15,6,4:48:12 PM,15,6,5:02:31 PM,15,6,5:17:06 PM,7,,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_484,875
-Order_No_23921,User_Id_3621,Bike,3,Business,26,5,3:22:57 PM,26,5,3:23:22 PM,26,5,3:30:24 PM,26,5,3:32:19 PM,26,5,3:49:32 PM,4,,,-1.2836583000000001,36.7886014,-1.2825816,36.8092346,Rider_Id_923,1033
-Order_No_23898,User_Id_2402,Bike,3,Business,19,2,3:56:59 PM,19,2,3:58:08 PM,19,2,4:16:10 PM,19,2,4:22:19 PM,19,2,4:39:57 PM,7,29.5,,-1.2963097,36.7688221,-1.2905407,36.822185600000005,Rider_Id_855,1058
-Order_No_11101,User_Id_1873,Bike,3,Business,11,2,5:39:17 PM,11,2,5:42:30 PM,11,2,5:53:42 PM,11,2,5:57:06 PM,11,2,6:19:47 PM,9,21.0,,-1.2571472,36.795063299999995,-1.3138646999999999,36.811896000000004,Rider_Id_208,1361
-Order_No_11583,User_Id_1809,Bike,3,Business,17,4,10:01:52 AM,17,4,10:02:09 AM,17,4,10:28:47 AM,17,4,10:30:54 AM,17,4,11:31:12 AM,17,,,-1.3472066,36.7692638,-1.2571472,36.795063299999995,Rider_Id_197,3618
-Order_No_14407,User_Id_627,Bike,3,Business,26,4,12:05:32 PM,26,4,12:09:26 PM,26,4,12:52:15 PM,26,4,12:55:27 PM,26,4,1:09:18 PM,7,19.1,,-1.2844516000000001,36.8245932,-1.2551895,36.7822034,Rider_Id_764,831
-Order_No_4806,User_Id_478,Bike,3,Business,12,3,1:45:23 PM,12,3,1:47:36 PM,12,3,1:48:31 PM,12,3,1:54:13 PM,12,3,1:59:22 PM,2,20.8,,-1.2551895,36.7822034,-1.2571341,36.7904501,Rider_Id_709,309
-Order_No_14457,User_Id_1033,Bike,3,Personal,23,6,11:44:14 AM,23,6,11:45:03 AM,23,6,12:03:52 PM,23,6,12:20:00 PM,23,6,12:33:29 PM,2,,,-1.260232,36.807334000000004,-1.2606419,36.8055244,Rider_Id_407,809
-Order_No_6965,User_Id_2030,Bike,3,Business,15,3,1:03:10 PM,15,3,1:03:16 PM,15,3,1:11:49 PM,15,3,1:33:35 PM,15,3,1:52:24 PM,2,21.5,,-1.304735,36.784605400000004,-1.295908,36.791433000000005,Rider_Id_213,1129
-Order_No_22411,User_Id_2195,Bike,1,Business,29,3,9:36:00 AM,29,3,9:40:48 AM,29,3,9:44:52 AM,29,3,9:52:00 AM,29,3,9:58:27 AM,4,15.2,,-1.318062,36.849326,-1.3313643,36.857742200000004,Rider_Id_882,387
-Order_No_4246,User_Id_3283,Bike,3,Business,6,3,11:57:05 AM,6,3,11:57:20 AM,6,3,11:58:28 AM,6,3,12:06:38 PM,6,3,12:20:42 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_156,844
-Order_No_1755,User_Id_207,Bike,3,Personal,30,3,2:54:29 PM,30,3,2:55:08 PM,30,3,3:10:33 PM,30,3,3:14:47 PM,30,3,4:13:30 PM,37,25.2,,-1.2928928,36.79455849,-1.0924846000000001,37.010811700000005,Rider_Id_543,3523
-Order_No_3860,User_Id_393,Bike,3,Business,23,4,10:00:55 AM,23,4,10:01:13 AM,23,4,10:02:48 AM,23,4,10:17:01 AM,23,4,10:27:16 AM,5,,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_709,615
-Order_No_16858,User_Id_3517,Bike,3,Business,7,4,1:37:18 PM,7,4,1:38:47 PM,7,4,1:44:57 PM,7,4,1:48:23 PM,7,4,2:11:30 PM,16,27.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_136,1387
-Order_No_12138,User_Id_3689,Bike,3,Business,31,3,9:58:38 AM,31,3,9:58:48 AM,31,3,10:04:25 AM,31,3,10:20:07 AM,31,3,10:28:00 AM,3,19.2,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_216,473
-Order_No_27531,User_Id_788,Bike,3,Business,5,2,3:30:10 PM,5,2,3:30:38 PM,5,2,3:43:44 PM,5,2,3:47:57 PM,5,2,4:08:25 PM,7,27.6,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_426,1228
-Order_No_23770,User_Id_2956,Bike,3,Business,28,6,1:15:35 PM,28,6,1:15:39 PM,28,6,1:24:20 PM,28,6,1:26:59 PM,28,6,1:48:52 PM,9,23.9,,-1.3052491000000002,36.8223899,-1.2584143,36.8048002,Rider_Id_229,1313
-Order_No_19565,User_Id_3560,Bike,3,Business,31,1,1:37:21 PM,31,1,1:37:52 PM,31,1,1:44:10 PM,31,1,2:19:15 PM,31,1,2:28:36 PM,1,23.9,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_477,561
-Order_No_22298,User_Id_3222,Bike,3,Business,31,4,1:49:46 PM,31,4,1:49:53 PM,31,4,2:01:11 PM,31,4,2:07:34 PM,31,4,2:20:12 PM,1,29.0,,-1.2551895,36.7822034,-1.2552271000000002,36.7853553,Rider_Id_361,758
-Order_No_9130,User_Id_3718,Bike,3,Business,7,5,2:52:33 PM,7,5,2:53:07 PM,7,5,3:15:48 PM,7,5,3:16:55 PM,7,5,3:26:51 PM,2,26.8,,-1.2650027,36.8126242,-1.2649605,36.798177700000004,Rider_Id_685,596
-Order_No_19815,User_Id_2523,Bike,3,Business,10,5,9:43:23 AM,10,5,9:43:32 AM,10,5,9:45:42 AM,10,5,9:51:50 AM,10,5,10:00:07 AM,8,,,-1.2551895,36.7822034,-1.2829828,36.797206200000005,Rider_Id_438,497
-Order_No_20590,User_Id_1378,Bike,2,Personal,3,5,11:16:11 AM,3,5,11:16:31 AM,3,5,11:21:09 AM,3,5,11:27:39 AM,3,5,11:38:58 AM,3,21.3,,-1.2628638,36.8070399,-1.2611642,36.7949932,Rider_Id_882,679
-Order_No_4928,User_Id_3214,Bike,3,Business,30,2,11:53:36 AM,30,2,11:54:45 AM,30,2,12:16:13 PM,30,2,12:26:14 PM,30,2,12:33:09 PM,2,22.4,,-1.289795,36.7957132,-1.295432,36.7870052,Rider_Id_29,415
-Order_No_4149,User_Id_2862,Bike,1,Personal,14,7,1:28:06 PM,14,7,1:43:36 PM,14,7,2:03:23 PM,14,7,2:10:54 PM,14,7,2:30:06 PM,9,28.8,,-1.2868838,36.8284259,-1.331602,36.785391499999996,Rider_Id_956,1152
-Order_No_5076,User_Id_3651,Bike,3,Business,3,2,12:30:21 PM,3,2,12:31:17 PM,3,2,12:39:46 PM,3,2,12:43:30 PM,3,2,12:52:27 PM,5,18.5,,-1.2579303999999998,36.8019667,-1.2793803999999998,36.792418299999994,Rider_Id_795,537
-Order_No_10914,User_Id_2801,Bike,3,Business,14,2,4:00:02 PM,14,2,4:00:33 PM,14,2,4:05:04 PM,14,2,4:10:34 PM,14,2,4:32:03 PM,7,,,-1.2674273999999999,36.787082700000006,-1.285355,36.795287,Rider_Id_161,1289
-Order_No_19192,User_Id_1107,Bike,2,Personal,16,4,2:31:12 PM,16,4,2:31:27 PM,16,4,2:39:15 PM,16,4,2:44:37 PM,16,4,3:28:23 PM,11,18.9,,-1.2728058,36.8049903,-1.3339461000000001,36.821912899999994,Rider_Id_409,2626
-Order_No_1057,User_Id_1384,Bike,3,Business,2,4,7:47:29 AM,2,4,7:48:37 AM,2,4,8:24:27 AM,2,4,8:27:41 AM,2,4,9:04:54 AM,18,,,-1.2517448,36.7924971,-1.3198543,36.8848254,Rider_Id_92,2233
-Order_No_3202,User_Id_868,Bike,3,Business,1,4,9:03:21 AM,1,4,9:04:57 AM,1,4,9:24:13 AM,1,4,9:28:42 AM,1,4,9:51:54 AM,9,,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_31,1392
-Order_No_13022,User_Id_1484,Bike,3,Business,28,1,2:30:11 PM,28,1,2:30:39 PM,28,1,2:32:40 PM,28,1,2:42:14 PM,28,1,2:42:18 PM,4,26.1,,-1.2551895,36.7822034,-1.269568,36.80543529999999,Rider_Id_647,4
-Order_No_10673,User_Id_3120,Bike,1,Personal,11,1,1:34:42 PM,11,1,1:37:54 PM,11,1,2:17:03 PM,11,1,2:25:50 PM,11,1,2:55:25 PM,7,23.4,,-1.3255193,36.825871899999996,-1.2885417,36.810052299999995,Rider_Id_46,1775
-Order_No_563,User_Id_2509,Bike,3,Business,4,5,12:17:03 PM,4,5,12:17:19 PM,4,5,12:17:21 PM,4,5,12:26:54 PM,4,5,1:43:32 PM,20,17.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_932,4598
-Order_No_10925,User_Id_516,Bike,3,Business,28,1,3:23:23 PM,28,1,3:25:20 PM,28,1,3:37:52 PM,28,1,3:39:17 PM,28,1,3:49:18 PM,3,26.3,,-1.2551895,36.7822034,-1.2611987,36.80609129999999,Rider_Id_535,601
-Order_No_14886,User_Id_1458,Bike,1,Personal,9,4,1:30:57 PM,9,4,1:31:47 PM,9,4,1:32:43 PM,9,4,1:39:40 PM,9,4,2:05:23 PM,13,25.7,,-1.263499,36.801454799999995,-1.2540878,36.7044367,Rider_Id_379,1543
-Order_No_14318,User_Id_1085,Bike,3,Business,29,1,2:33:38 PM,29,1,2:36:56 PM,29,1,2:49:23 PM,29,1,2:52:50 PM,29,1,3:16:47 PM,6,24.6,,-1.2628638,36.8070399,-1.2877501,36.814659899999995,Rider_Id_684,1437
-Order_No_27380,User_Id_1346,Bike,3,Business,6,3,4:03:36 PM,6,3,4:04:32 PM,6,3,4:08:09 PM,6,3,4:15:36 PM,6,3,4:25:38 PM,2,,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_564,602
-Order_No_10579,User_Id_3521,Bike,3,Business,29,5,1:32:06 PM,29,5,1:34:04 PM,29,5,2:00:23 PM,29,5,2:06:04 PM,29,5,2:12:53 PM,1,29.1,,-1.2723298,36.8037146,-1.2696908000000002,36.81519,Rider_Id_547,409
-Order_No_21634,User_Id_1250,Bike,1,Personal,14,1,2:58:12 PM,14,1,3:00:02 PM,14,1,3:09:19 PM,14,1,3:29:21 PM,14,1,4:01:02 PM,8,25.8,,-1.3034396000000001,36.832999799999996,-1.2620741000000002,36.815814700000004,Rider_Id_101,1901
-Order_No_9026,User_Id_2960,Bike,1,Personal,28,4,11:50:38 AM,28,4,11:50:42 AM,28,4,11:50:50 AM,28,4,11:59:27 AM,28,4,12:10:46 PM,3,,,-1.2763916000000002,36.8246826,-1.2876659,36.8179934,Rider_Id_698,679
-Order_No_14562,User_Id_2066,Bike,3,Business,18,4,11:31:10 AM,18,4,11:31:51 AM,18,4,11:40:40 AM,18,4,11:44:36 AM,18,4,12:09:39 PM,9,,,-1.3210188999999999,36.841055100000005,-1.3131274,36.8905653,Rider_Id_863,1503
-Order_No_4264,User_Id_1500,Bike,3,Business,28,2,9:45:48 AM,28,2,9:46:05 AM,28,2,9:50:58 AM,28,2,10:03:41 AM,28,2,10:45:18 AM,8,18.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_191,2497
-Order_No_5410,User_Id_3560,Bike,3,Business,7,4,2:52:43 PM,7,4,2:56:23 PM,7,4,3:07:18 PM,7,4,3:14:40 PM,7,4,3:46:13 PM,13,31.1,,-1.2156006000000001,36.8916865,-1.279395,36.825364,Rider_Id_836,1893
-Order_No_5810,User_Id_622,Bike,1,Business,8,5,12:36:39 PM,8,5,1:19:53 PM,8,5,1:37:13 PM,8,5,1:50:55 PM,8,5,2:03:03 PM,6,21.3,,-1.29237,36.8241297,-1.2951362,36.7876989,Rider_Id_549,728
-Order_No_25196,User_Id_1500,Bike,3,Business,14,3,9:10:25 AM,14,3,9:45:18 AM,14,3,10:25:45 AM,14,3,10:52:00 AM,14,3,11:14:56 AM,8,24.5,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_436,1376
-Order_No_4304,User_Id_2532,Bike,3,Business,31,3,11:13:40 AM,31,3,11:14:38 AM,31,3,11:24:06 AM,31,3,11:58:52 AM,31,3,12:34:03 PM,12,24.9,,-1.2571472,36.795063299999995,-1.329568,36.8520825,Rider_Id_56,2111
-Order_No_25524,User_Id_2781,Bike,3,Personal,23,3,8:30:54 PM,23,3,8:32:08 PM,23,3,8:47:32 PM,23,3,8:50:05 PM,23,3,9:20:25 PM,16,21.3,,-1.296599,36.7629672,-1.2235201,36.80206079999999,Rider_Id_711,1820
-Order_No_20241,User_Id_3389,Bike,1,Personal,15,1,2:21:49 PM,15,1,2:22:06 PM,15,1,2:42:02 PM,15,1,2:54:40 PM,15,1,3:26:14 PM,19,,,-1.2661263999999999,36.8030359,-1.3653686999999999,36.7337644,Rider_Id_882,1894
-Order_No_3662,User_Id_3560,Bike,3,Business,5,2,1:36:32 PM,5,2,2:03:01 PM,5,2,2:03:36 PM,5,2,2:10:32 PM,5,2,2:33:45 PM,9,27.7,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_365,1393
-Order_No_9003,User_Id_646,Bike,3,Personal,9,2,6:48:36 PM,9,2,7:18:07 PM,9,2,7:27:15 PM,9,2,7:31:30 PM,9,2,7:55:13 PM,9,19.0,,-1.2800815,36.775352399999996,-1.2521881000000001,36.7537237,Rider_Id_45,1423
-Order_No_5179,User_Id_206,Bike,2,Personal,15,1,3:29:44 PM,15,1,3:29:56 PM,15,1,3:43:17 PM,15,1,3:49:16 PM,15,1,4:19:37 PM,20,,,-1.2596241000000001,36.762407,-1.1864337,36.8400838,Rider_Id_116,1821
-Order_No_3776,User_Id_251,Bike,1,Personal,28,1,10:23:41 AM,28,1,10:25:40 AM,28,1,11:08:33 AM,28,1,11:16:16 AM,28,1,11:33:26 AM,7,22.7,,-1.3099145,36.802277399999994,-1.2945178999999998,36.7685349,Rider_Id_808,1030
-Order_No_4642,User_Id_49,Bike,3,Business,28,3,9:21:59 AM,28,3,9:27:40 AM,28,3,9:58:44 AM,28,3,10:16:47 AM,28,3,10:26:33 AM,5,20.5,,-1.3294489999999999,36.890451,-1.3508657,36.9019028,Rider_Id_113,586
-Order_No_11348,User_Id_2423,Bike,3,Business,27,3,3:12:29 PM,27,3,3:13:14 PM,27,3,3:16:52 PM,27,3,3:36:40 PM,27,3,3:54:57 PM,8,29.5,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_85,1097
-Order_No_11033,User_Id_3621,Bike,3,Business,21,7,11:24:33 AM,21,7,11:24:52 AM,21,7,11:49:39 AM,21,7,11:55:19 AM,21,7,12:19:08 PM,6,25.2,,-1.2836583000000001,36.7886014,-1.2622145,36.8087391,Rider_Id_789,1429
-Order_No_12968,User_Id_868,Bike,3,Business,20,4,3:30:04 PM,20,4,3:58:45 PM,20,4,4:24:39 PM,20,4,4:36:08 PM,20,4,4:51:03 PM,10,22.6,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_86,895
-Order_No_1829,User_Id_1545,Bike,1,Personal,16,4,10:12:18 AM,16,4,10:12:50 AM,16,4,10:28:12 AM,16,4,10:40:28 AM,16,4,10:49:36 AM,5,14.5,,-1.2535486,36.7933541,-1.2633493999999998,36.7959696,Rider_Id_520,548
-Order_No_23905,User_Id_53,Bike,3,Business,10,1,2:16:29 PM,10,1,2:36:20 PM,10,1,3:13:48 PM,10,1,3:24:06 PM,10,1,4:02:54 PM,8,18.2,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_55,2328
-Order_No_21903,User_Id_3621,Bike,3,Business,15,4,3:39:16 PM,15,4,3:41:17 PM,15,4,4:03:02 PM,15,4,4:06:03 PM,15,4,4:25:05 PM,6,27.5,,-1.2836583000000001,36.7886014,-1.25896,36.8016691,Rider_Id_228,1142
-Order_No_26196,User_Id_2332,Bike,1,Personal,25,3,2:08:28 PM,25,3,2:08:39 PM,25,3,2:28:47 PM,25,3,2:31:45 PM,25,3,2:56:36 PM,20,17.9,,-1.215678,36.873872,-1.293393,36.790237,Rider_Id_265,1491
-Order_No_25306,User_Id_3064,Bike,3,Business,9,1,11:01:24 AM,9,1,11:01:34 AM,9,1,11:20:53 AM,9,1,11:31:40 AM,9,1,11:44:29 AM,5,20.3,,-1.2571191000000002,36.8019652,-1.2549476000000002,36.8269576,Rider_Id_379,769
-Order_No_14192,User_Id_635,Bike,3,Business,7,1,9:51:57 AM,7,1,10:07:16 AM,7,1,11:02:01 AM,7,1,11:09:04 AM,7,1,11:44:54 AM,21,24.6,,-1.3882008,36.7699445,-1.2599563,36.799343900000004,Rider_Id_878,2150
-Order_No_19791,User_Id_1148,Bike,3,Business,13,3,12:11:16 PM,13,3,12:13:26 PM,13,3,12:26:14 PM,13,3,12:30:54 PM,13,3,1:01:13 PM,9,,,-1.3100586,36.850032899999995,-1.2762965,36.8211047,Rider_Id_61,1819
-Order_No_6011,User_Id_3727,Bike,3,Business,27,2,11:26:50 AM,27,2,11:27:03 AM,27,2,11:31:25 AM,27,2,11:34:28 AM,27,2,12:02:48 PM,8,24.0,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_649,1700
-Order_No_22497,User_Id_517,Bike,3,Business,29,4,10:55:10 AM,29,4,10:55:12 AM,29,4,10:55:22 AM,29,4,11:19:36 AM,29,4,11:19:38 AM,5,21.7,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_619,2
-Order_No_14813,User_Id_2987,Bike,3,Business,8,4,10:49:52 AM,8,4,11:11:29 AM,8,4,11:21:02 AM,8,4,11:22:20 AM,8,4,11:32:56 AM,3,26.4,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_517,636
-Order_No_5338,User_Id_1146,Bike,3,Business,14,5,2:08:34 PM,14,5,2:09:01 PM,14,5,2:11:47 PM,14,5,2:21:29 PM,14,5,2:37:13 PM,7,23.2,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_211,944
-Order_No_3790,User_Id_517,Bike,3,Business,2,3,9:31:34 AM,2,3,9:31:43 AM,2,3,9:33:26 AM,2,3,9:44:31 AM,2,3,10:12:28 AM,14,20.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_188,1677
-Order_No_356,User_Id_517,Bike,3,Business,6,3,10:10:14 AM,6,3,10:10:28 AM,6,3,10:10:38 AM,6,3,10:19:30 AM,6,3,10:50:34 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_809,1864
-Order_No_19262,User_Id_3655,Bike,1,Personal,6,6,3:35:59 PM,6,6,3:39:37 PM,6,6,3:54:24 PM,6,6,4:08:00 PM,6,6,4:48:09 PM,12,27.8,,-1.2332988999999999,36.8130698,-1.2832116000000002,36.788102,Rider_Id_662,2409
-Order_No_15821,User_Id_329,Bike,3,Personal,13,5,2:27:01 PM,13,5,2:28:50 PM,13,5,3:04:41 PM,13,5,3:12:06 PM,13,5,4:03:35 PM,17,19.1,,-1.2866269,36.82463679999999,-1.3610501,36.7520889,Rider_Id_844,3089
-Order_No_19187,User_Id_3669,Bike,3,Business,10,5,1:47:51 PM,10,5,2:18:25 PM,10,5,2:29:37 PM,10,5,2:49:03 PM,10,5,3:08:54 PM,4,,,-1.2615888000000002,36.7928732,-1.2651643,36.807221399999996,Rider_Id_675,1191
-Order_No_17707,User_Id_186,Bike,3,Business,30,4,8:28:00 AM,30,4,8:32:34 AM,30,4,8:42:51 AM,30,4,9:09:55 AM,30,4,10:13:53 AM,6,16.2,,-1.2902869,36.8085396,-1.2571472,36.795063299999995,Rider_Id_566,3838
-Order_No_6573,User_Id_3147,Bike,3,Business,21,5,11:10:30 AM,21,5,11:10:35 AM,21,5,11:10:50 AM,21,5,11:49:24 AM,21,5,12:22:02 PM,31,19.8,,-1.3472066,36.7692638,-1.1923911,36.944733299999996,Rider_Id_345,1958
-Order_No_25579,User_Id_2330,Bike,3,Business,10,1,1:14:48 PM,10,1,1:16:57 PM,10,1,1:30:50 PM,10,1,1:37:48 PM,10,1,1:58:23 PM,9,18.5,,-1.3015197,36.7658456,-1.2645318,36.7981775,Rider_Id_778,1235
-Order_No_9763,User_Id_718,Bike,3,Business,30,4,2:35:00 PM,30,4,2:37:46 PM,30,4,3:16:49 PM,30,4,3:22:10 PM,30,4,4:14:58 PM,7,,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_106,3168
-Order_No_12346,User_Id_1075,Bike,3,Business,5,6,11:43:55 AM,5,6,11:44:03 AM,5,6,11:44:11 AM,5,6,11:53:27 AM,5,6,12:32:37 PM,12,22.5,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_557,2350
-Order_No_19135,User_Id_3291,Bike,3,Business,30,3,9:05:12 AM,30,3,9:05:36 AM,30,3,9:20:42 AM,30,3,9:23:51 AM,30,3,9:31:43 AM,2,19.5,,-1.2600926000000001,36.8088685,-1.2686413,36.8126976,Rider_Id_72,472
-Order_No_3325,User_Id_2790,Bike,1,Personal,14,2,12:09:09 PM,14,2,12:10:35 PM,14,2,12:20:31 PM,14,2,12:25:45 PM,14,2,12:42:57 PM,4,21.6,,-1.2891921999999998,36.825189,-1.298529,36.816482,Rider_Id_629,1032
-Order_No_3598,User_Id_2457,Bike,3,Business,7,3,1:45:36 PM,7,3,1:46:05 PM,7,3,1:49:36 PM,7,3,1:50:42 PM,7,3,2:36:50 PM,14,28.2,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_555,2768
-Order_No_10582,User_Id_2801,Bike,3,Business,22,3,3:21:40 PM,22,3,3:39:06 PM,22,3,3:43:05 PM,22,3,3:45:58 PM,22,3,4:05:25 PM,8,24.5,,-1.2674273999999999,36.787082700000006,-1.2944584,36.7614555,Rider_Id_709,1167
-Order_No_24073,User_Id_186,Bike,3,Business,7,4,11:01:59 AM,7,4,11:02:23 AM,7,4,11:07:05 AM,7,4,11:15:53 AM,7,4,11:28:42 AM,5,24.2,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_19,769
-Order_No_21365,User_Id_1849,Bike,3,Business,5,5,2:00:25 PM,5,5,2:01:45 PM,5,5,2:05:50 PM,5,5,2:14:05 PM,5,5,2:33:04 PM,6,26.7,,-1.2571472,36.795063299999995,-1.290894,36.822971,Rider_Id_231,1139
-Order_No_1308,User_Id_2801,Bike,3,Business,16,4,12:51:01 PM,16,4,12:52:10 PM,16,4,12:53:38 PM,16,4,1:05:49 PM,16,4,1:17:41 PM,4,,,-1.273488,36.804158,-1.2674273999999999,36.787082700000006,Rider_Id_622,712
-Order_No_5937,User_Id_1500,Bike,3,Business,16,2,8:45:08 AM,16,2,8:49:11 AM,16,2,8:57:31 AM,16,2,9:20:08 AM,16,2,9:38:08 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_843,1080
-Order_No_2611,User_Id_553,Bike,3,Business,7,5,3:20:04 PM,7,5,3:41:45 PM,7,5,3:49:33 PM,7,5,3:58:17 PM,7,5,4:20:14 PM,6,26.8,,-1.2765492,36.7669809,-1.2551895,36.7822034,Rider_Id_332,1317
-Order_No_161,User_Id_970,Bike,3,Business,12,3,2:15:11 PM,12,3,2:17:40 PM,12,3,2:30:21 PM,12,3,2:46:31 PM,12,3,2:57:35 PM,1,21.0,,-1.2571472,36.795063299999995,-1.2629594,36.8077626,Rider_Id_147,664
-Order_No_13116,User_Id_1146,Bike,3,Business,8,1,10:33:22 AM,8,1,10:33:35 AM,8,1,10:35:09 AM,8,1,11:13:04 AM,8,1,11:30:22 AM,7,28.1,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_728,1038
-Order_No_17272,User_Id_2174,Bike,3,Business,7,2,1:24:35 PM,7,2,1:24:40 PM,7,2,1:31:22 PM,7,2,2:04:18 PM,7,2,2:46:13 PM,19,24.6,,-1.2482781,36.884091999999995,-1.2941448999999998,36.7592641,Rider_Id_12,2515
-Order_No_9185,User_Id_1976,Bike,3,Business,16,3,3:40:52 PM,16,3,3:41:10 PM,16,3,3:51:06 PM,16,3,4:01:29 PM,16,3,4:11:44 PM,6,26.9,,-1.257028,36.7810435,-1.2690931,36.8065779,Rider_Id_52,615
-Order_No_7973,User_Id_3730,Bike,2,Personal,15,1,12:22:21 PM,15,1,12:22:42 PM,15,1,12:49:50 PM,15,1,12:57:53 PM,15,1,1:16:54 PM,8,,,-1.3929709,36.7504644,-1.3440693000000001,36.7518442,Rider_Id_328,1141
-Order_No_10459,User_Id_1724,Bike,3,Business,22,2,3:08:21 PM,22,2,3:09:15 PM,22,2,3:22:55 PM,22,2,3:31:03 PM,22,2,4:03:11 PM,16,21.3,,-1.2626906,36.7827022,-1.312745,36.875715,Rider_Id_620,1928
-Order_No_13592,User_Id_1205,Bike,3,Business,18,2,3:28:38 PM,18,2,3:29:13 PM,18,2,3:40:12 PM,18,2,4:19:40 PM,18,2,4:24:51 PM,16,25.4,,-1.2810807,36.8144229,-1.3395431999999998,36.72301829999999,Rider_Id_329,311
-Order_No_18122,User_Id_2804,Bike,3,Business,18,5,2:48:00 PM,18,5,2:50:30 PM,18,5,3:01:46 PM,18,5,3:05:35 PM,18,5,3:24:04 PM,11,27.0,,-1.3229203999999999,36.8190265,-1.2551895,36.7822034,Rider_Id_111,1109
-Order_No_8325,User_Id_2763,Bike,3,Business,4,1,2:55:19 PM,4,1,2:58:31 PM,4,1,3:09:00 PM,4,1,3:14:25 PM,4,1,3:49:08 PM,5,,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_205,2083
-Order_No_18041,User_Id_391,Bike,3,Business,22,6,1:26:17 PM,22,6,1:46:09 PM,22,6,1:55:13 PM,22,6,2:02:22 PM,22,6,2:28:59 PM,13,26.0,,-1.2867454,36.8259017,-1.2054954,36.8985954,Rider_Id_237,1597
-Order_No_14299,User_Id_3149,Bike,3,Business,7,2,9:52:50 AM,7,2,9:54:30 AM,7,2,10:08:01 AM,7,2,10:22:50 AM,7,2,10:49:50 AM,11,,,-1.2520537,36.830067799999995,-1.3167113000000001,36.8301563,Rider_Id_864,1620
-Order_No_26767,User_Id_246,Bike,3,Business,9,6,3:12:40 PM,9,6,3:13:10 PM,9,6,3:22:22 PM,9,6,3:25:22 PM,9,6,3:32:52 PM,2,19.0,,-1.2601708999999999,36.8002238,-1.2584143,36.8048002,Rider_Id_37,450
-Order_No_7357,User_Id_3635,Bike,1,Business,11,2,9:56:48 AM,11,2,9:56:56 AM,11,2,10:26:07 AM,11,2,10:36:45 AM,11,2,11:00:33 AM,8,24.1,,-1.3052491000000002,36.8223899,-1.2615888000000002,36.7928732,Rider_Id_848,1428
-Order_No_8084,User_Id_2867,Bike,1,Personal,7,2,12:55:38 PM,7,2,12:55:58 PM,7,2,1:11:26 PM,7,2,1:32:43 PM,7,2,1:51:47 PM,8,25.0,,-1.2156006000000001,36.8916865,-1.1905588,36.9284611,Rider_Id_895,1144
-Order_No_7505,User_Id_1500,Bike,3,Business,5,5,9:22:38 AM,5,5,9:22:51 AM,5,5,9:25:21 AM,5,5,9:45:21 AM,5,5,9:59:26 AM,8,19.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,845
-Order_No_15230,User_Id_635,Bike,3,Business,11,2,10:04:19 AM,11,2,10:04:45 AM,11,2,10:09:20 AM,11,2,10:31:35 AM,11,2,10:47:38 AM,10,16.8,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_399,963
-Order_No_9376,User_Id_1927,Bike,3,Business,13,3,10:13:49 AM,13,3,10:14:10 AM,13,3,10:35:28 AM,13,3,10:44:08 AM,13,3,11:18:38 AM,15,,,-1.2571472,36.795063299999995,-1.3472066,36.7692638,Rider_Id_712,2070
-Order_No_3235,User_Id_292,Bike,1,Personal,24,1,7:34:16 AM,24,1,7:35:48 AM,24,1,7:59:15 AM,24,1,8:02:37 AM,24,1,8:22:41 AM,7,15.7,,-1.3173868999999998,36.8112044,-1.2917868000000001,36.7872675,Rider_Id_722,1204
-Order_No_25596,User_Id_868,Bike,3,Business,13,1,10:45:15 AM,13,1,11:20:52 AM,13,1,11:32:58 AM,13,1,11:41:56 AM,13,1,11:55:03 AM,5,17.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_268,787
-Order_No_12673,User_Id_2216,Bike,3,Business,29,2,10:15:05 AM,29,2,10:19:03 AM,29,2,10:23:30 AM,29,2,10:36:26 AM,29,2,10:45:54 AM,4,21.6,,-1.2553615,36.789537599999996,-1.269534,36.80697,Rider_Id_103,568
-Order_No_19679,User_Id_3339,Bike,3,Business,14,1,12:12:57 PM,14,1,12:13:46 PM,14,1,12:21:51 PM,14,1,12:23:26 PM,14,1,12:40:11 PM,5,23.1,,-1.2559559999999999,36.789867,-1.2738187,36.7879151,Rider_Id_875,1005
-Order_No_19600,User_Id_393,Bike,3,Business,14,5,9:59:48 AM,14,5,10:00:04 AM,14,5,10:00:11 AM,14,5,10:10:25 AM,14,5,10:38:54 AM,14,17.3,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_168,1709
-Order_No_21920,User_Id_3017,Bike,3,Business,10,2,5:19:01 PM,10,2,5:48:54 PM,10,2,5:55:22 PM,10,2,6:03:44 PM,10,2,6:24:51 PM,9,20.5,,-1.299313,36.78314879999999,-1.3202198,36.802606899999994,Rider_Id_465,1267
-Order_No_5411,User_Id_3647,Bike,3,Business,16,2,2:54:25 PM,16,2,2:55:37 PM,16,2,3:05:24 PM,16,2,3:11:49 PM,16,2,3:31:14 PM,8,,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_924,1165
-Order_No_25695,User_Id_2701,Bike,3,Business,22,2,12:12:49 PM,22,2,12:32:32 PM,22,2,1:07:06 PM,22,2,1:11:19 PM,22,2,1:32:31 PM,10,24.2,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_870,1272
-Order_No_10706,User_Id_2200,Bike,3,Business,26,5,1:13:36 PM,26,5,1:22:47 PM,26,5,2:06:20 PM,26,5,2:08:22 PM,26,5,3:08:09 PM,11,,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_6,3587
-Order_No_2906,User_Id_2396,Bike,1,Personal,2,2,12:59:40 PM,2,2,1:00:54 PM,2,2,1:22:27 PM,2,2,1:35:48 PM,2,2,1:54:05 PM,3,26.2,,-1.2896398999999998,36.812449799999996,-1.2849951000000002,36.824174600000006,Rider_Id_328,1097
-Order_No_17624,User_Id_393,Bike,3,Business,21,4,3:32:03 PM,21,4,3:32:13 PM,21,4,3:32:17 PM,21,4,4:16:44 PM,21,4,4:16:47 PM,11,26.2,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_10,3
-Order_No_14325,User_Id_510,Bike,3,Business,25,5,3:29:54 PM,25,5,3:47:20 PM,25,5,3:59:38 PM,25,5,4:03:53 PM,25,5,4:45:48 PM,18,23.3,,-1.2860183,36.8975338,-1.2553615,36.789537599999996,Rider_Id_589,2515
-Order_No_12840,User_Id_570,Bike,3,Business,6,4,1:45:26 PM,6,4,1:46:49 PM,6,4,2:09:49 PM,6,4,2:29:40 PM,6,4,2:49:57 PM,7,21.3,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_20,1217
-Order_No_8177,User_Id_3723,Bike,1,Personal,12,5,2:50:24 PM,12,5,2:50:50 PM,12,5,3:06:00 PM,12,5,3:07:46 PM,12,5,3:26:17 PM,8,27.2,,-1.300722,36.816865,-1.2560455,36.780086700000005,Rider_Id_751,1111
-Order_No_10956,User_Id_2501,Bike,1,Personal,24,5,1:27:15 PM,24,5,1:27:34 PM,24,5,1:35:20 PM,24,5,1:53:26 PM,24,5,2:11:22 PM,6,23.2,,-1.2951620000000001,36.8115914,-1.3188148000000002,36.809247799999994,Rider_Id_402,1076
-Order_No_9415,User_Id_136,Bike,3,Business,10,5,9:53:37 AM,10,5,9:54:39 AM,10,5,10:00:00 AM,10,5,10:02:59 AM,10,5,10:20:53 AM,10,,,-1.3254522,36.721658399999995,-1.2835448,36.7708792,Rider_Id_72,1074
-Order_No_2727,User_Id_2102,Bike,3,Business,30,2,11:37:41 AM,30,2,11:38:06 AM,30,2,11:50:58 AM,30,2,11:57:16 AM,30,2,12:39:38 PM,15,22.2,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_329,2542
-Order_No_6619,User_Id_190,Bike,3,Business,29,3,1:55:08 PM,29,3,2:13:15 PM,29,3,3:05:37 PM,29,3,3:19:00 PM,29,3,4:22:14 PM,32,21.0,,-1.1621358,36.9528801,-1.3270347,36.866736200000005,Rider_Id_139,3794
-Order_No_24922,User_Id_2621,Bike,2,Personal,3,1,8:52:53 AM,3,1,8:53:17 AM,3,1,9:26:24 AM,3,1,9:34:58 AM,3,1,10:02:42 AM,11,14.8,,-1.3138389,36.840756799999994,-1.2985031999999999,36.7821448,Rider_Id_654,1664
-Order_No_16803,User_Id_2066,Bike,3,Business,2,4,1:14:21 PM,2,4,1:15:09 PM,2,4,1:42:02 PM,2,4,1:43:26 PM,2,4,2:11:36 PM,17,,,-1.3210188999999999,36.841055100000005,-1.3339943,36.7263409,Rider_Id_410,1690
-Order_No_775,User_Id_1179,Bike,3,Business,6,4,4:37:06 PM,6,4,4:49:24 PM,6,4,4:57:19 PM,6,4,5:03:33 PM,6,4,5:53:05 PM,21,22.6,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_130,2972
-Order_No_3577,User_Id_2982,Bike,3,Business,27,6,8:54:59 AM,27,6,8:55:19 AM,27,6,9:20:35 AM,27,6,9:50:47 AM,27,6,10:28:56 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_796,2289
-Order_No_5040,User_Id_3674,Bike,3,Business,5,2,11:52:11 AM,5,2,11:52:25 AM,5,2,11:57:38 AM,5,2,12:07:06 PM,5,2,12:26:44 PM,5,25.7,,-1.2551895,36.7822034,-1.2753902,36.808223,Rider_Id_831,1178
-Order_No_15236,User_Id_3247,Bike,3,Business,14,2,11:14:24 AM,14,2,11:19:17 AM,14,2,11:27:47 AM,14,2,11:38:29 AM,14,2,11:50:53 AM,6,,,-1.2889913999999998,36.820944299999994,-1.2571472,36.795063299999995,Rider_Id_19,744
-Order_No_7917,User_Id_868,Bike,3,Business,23,4,3:01:44 PM,23,4,3:08:07 PM,23,4,3:36:06 PM,23,4,3:45:45 PM,23,4,4:05:36 PM,9,24.8,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_710,1191
-Order_No_2424,User_Id_1500,Bike,3,Business,11,1,2:12:29 PM,11,1,2:14:10 PM,11,1,2:14:56 PM,11,1,2:20:41 PM,11,1,2:44:19 PM,8,28.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_177,1418
-Order_No_11201,User_Id_265,Bike,3,Business,29,1,1:13:58 PM,29,1,1:26:05 PM,29,1,1:50:53 PM,29,1,2:02:38 PM,29,1,2:22:39 PM,7,,,-1.300921,36.828195,-1.2817426,36.7846148,Rider_Id_787,1201
-Order_No_23485,User_Id_1245,Bike,3,Business,3,1,1:53:17 PM,3,1,2:49:19 PM,3,1,4:01:03 PM,3,1,4:43:27 PM,3,1,5:23:33 PM,16,22.6,,-1.300921,36.828195,-1.3558865,36.715668900000004,Rider_Id_581,2406
-Order_No_16705,User_Id_1995,Bike,3,Business,25,4,11:56:31 AM,25,4,11:58:03 AM,25,4,11:59:17 AM,25,4,12:10:09 PM,25,4,12:35:11 PM,6,,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_709,1502
-Order_No_6181,User_Id_1756,Bike,2,Personal,28,5,1:11:52 PM,28,5,1:13:53 PM,28,5,1:21:17 PM,28,5,1:27:34 PM,28,5,1:45:01 PM,4,24.9,,-1.3021964,36.768168100000004,-1.2923412,36.795197200000004,Rider_Id_932,1047
-Order_No_18283,User_Id_3245,Bike,3,Business,13,2,4:07:18 PM,13,2,4:20:49 PM,13,2,4:30:37 PM,13,2,4:48:21 PM,13,2,5:36:44 PM,8,23.5,,-1.257309,36.806008399999996,-1.2224213,36.816525299999995,Rider_Id_101,2903
-Order_No_25250,User_Id_393,Bike,3,Business,21,4,12:02:58 PM,21,4,12:03:06 PM,21,4,12:03:10 PM,21,4,12:40:58 PM,21,4,12:41:00 PM,14,25.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_619,2
-Order_No_24331,User_Id_2412,Bike,3,Business,10,1,1:48:31 PM,10,1,3:47:09 PM,10,1,3:51:25 PM,10,1,4:06:14 PM,10,1,4:18:09 PM,11,18.7,,-1.2803414,36.8265456,-1.2285834999999998,36.882646799999996,Rider_Id_812,715
-Order_No_21578,User_Id_1075,Bike,3,Business,11,1,2:13:01 PM,11,1,2:14:13 PM,11,1,2:21:33 PM,11,1,2:22:40 PM,11,1,2:33:26 PM,3,28.7,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_913,646
-Order_No_8987,User_Id_1245,Bike,3,Business,8,3,8:46:48 AM,8,3,8:47:23 AM,8,3,8:56:34 AM,8,3,9:05:17 AM,8,3,9:23:05 AM,6,17.5,,-1.300921,36.828195,-1.3026036,36.79348770000001,Rider_Id_281,1068
-Order_No_9191,User_Id_265,Bike,3,Business,15,5,3:20:02 PM,15,5,3:30:06 PM,15,5,4:11:32 PM,15,5,4:27:40 PM,15,5,5:13:37 PM,24,18.0,,-1.2903933,36.6988935,-1.300921,36.828195,Rider_Id_831,2757
-Order_No_14648,User_Id_2457,Bike,3,Business,26,5,12:35:33 PM,26,5,12:53:48 PM,26,5,1:00:18 PM,26,5,1:06:40 PM,26,5,1:26:24 PM,8,,,-1.2551895,36.7822034,-1.2904049,36.8055154,Rider_Id_726,1184
-Order_No_14899,User_Id_633,Bike,3,Business,6,3,2:05:33 PM,6,3,2:06:44 PM,6,3,2:19:26 PM,6,3,2:28:09 PM,6,3,2:40:15 PM,5,27.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_785,726
-Order_No_9226,User_Id_1502,Bike,3,Business,6,1,9:58:12 AM,6,1,9:58:40 AM,6,1,10:12:39 AM,6,1,10:23:02 AM,6,1,10:52:41 AM,12,,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_206,1779
-Order_No_2115,User_Id_1304,Bike,3,Business,29,1,3:33:21 PM,29,1,4:14:07 PM,29,1,4:14:44 PM,29,1,5:18:14 PM,29,1,5:35:32 PM,9,23.3,,-1.2824535,36.8265153,-1.3332749,36.870814700000004,Rider_Id_669,1038
-Order_No_17514,User_Id_2066,Bike,3,Business,27,5,1:37:07 PM,27,5,1:37:55 PM,27,5,1:43:47 PM,27,5,1:45:50 PM,27,5,2:01:43 PM,6,23.9,,-1.316961,36.820337,-1.2966323,36.7875842,Rider_Id_250,953
-Order_No_8754,User_Id_1584,Bike,3,Business,10,5,2:18:04 PM,10,5,2:19:29 PM,10,5,3:04:02 PM,10,5,3:06:58 PM,10,5,3:26:58 PM,5,,,-1.2969741000000001,36.7856612,-1.2896398999999998,36.812449799999996,Rider_Id_304,1200
-Order_No_5030,User_Id_1289,Bike,2,Personal,24,4,11:37:53 AM,24,4,11:38:13 AM,24,4,11:46:46 AM,24,4,12:19:39 PM,24,4,12:32:24 PM,2,21.0,,-1.281196,36.8179321,-1.2833534,36.8165865,Rider_Id_888,765
-Order_No_25277,User_Id_393,Bike,3,Business,23,2,1:10:23 PM,23,2,1:10:39 PM,23,2,1:26:25 PM,23,2,1:51:17 PM,23,2,2:27:37 PM,14,19.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_518,2180
-Order_No_16829,User_Id_391,Bike,3,Business,12,3,10:19:41 AM,12,3,10:35:39 AM,12,3,10:46:51 AM,12,3,11:00:41 AM,12,3,11:23:29 AM,5,19.5,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_365,1368
-Order_No_15191,User_Id_3291,Bike,3,Business,24,4,12:52:35 PM,24,4,12:52:47 PM,24,4,12:55:03 PM,24,4,1:00:01 PM,24,4,1:00:03 PM,2,24.4,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_40,2
-Order_No_6493,User_Id_1363,Bike,3,Business,16,4,2:49:39 PM,16,4,2:50:20 PM,16,4,2:52:44 PM,16,4,3:03:20 PM,16,4,3:34:49 PM,10,19.1,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_213,1889
-Order_No_28158,User_Id_84,Bike,3,Business,3,3,11:33:52 AM,3,3,11:34:00 AM,3,3,11:39:08 AM,3,3,11:42:36 AM,3,3,12:27:26 PM,22,27.3,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_865,2690
-Order_No_19166,User_Id_3291,Bike,3,Business,1,1,10:01:14 AM,1,1,10:01:42 AM,1,1,10:01:52 AM,1,1,10:10:35 AM,1,1,10:25:18 AM,4,21.3,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_397,883
-Order_No_13575,User_Id_2360,Bike,3,Business,7,4,10:43:59 AM,7,4,10:44:13 AM,7,4,10:47:27 AM,7,4,11:29:28 AM,7,4,11:40:32 AM,3,24.1,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_953,664
-Order_No_8898,User_Id_2041,Bike,1,Business,5,2,3:43:17 PM,5,2,3:43:53 PM,5,2,3:45:01 PM,5,2,3:49:14 PM,5,2,3:49:19 PM,3,22.9,,-1.290894,36.822971,-1.2677588,36.8169359,Rider_Id_586,5
-Order_No_15771,User_Id_3599,Bike,3,Business,22,3,11:32:35 AM,22,3,11:42:15 AM,22,3,12:04:39 PM,22,3,12:21:25 PM,22,3,12:49:01 PM,12,22.9,,-1.3302996,36.8707935,-1.3251825000000002,36.78975870000001,Rider_Id_211,1656
-Order_No_19633,User_Id_3689,Bike,3,Business,8,4,6:00:34 PM,8,4,6:01:05 PM,8,4,6:09:22 PM,8,4,6:11:15 PM,8,4,6:23:14 PM,1,25.8,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_578,719
-Order_No_23074,User_Id_733,Bike,3,Business,29,1,4:07:41 PM,29,1,4:08:01 PM,29,1,4:11:25 PM,29,1,4:23:19 PM,29,1,4:51:25 PM,10,24.5,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_208,1686
-Order_No_23460,User_Id_3647,Bike,3,Business,7,5,2:27:40 PM,7,5,2:27:57 PM,7,5,2:35:59 PM,7,5,2:38:08 PM,7,5,2:52:56 PM,4,26.7,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_467,888
-Order_No_21091,User_Id_136,Bike,3,Business,23,6,11:58:57 AM,23,6,11:59:37 AM,23,6,12:15:37 PM,23,6,12:20:00 PM,23,6,1:09:39 PM,16,,,-1.3254522,36.721658399999995,-1.2768129,36.817446399999994,Rider_Id_831,2979
-Order_No_23413,User_Id_2066,Bike,3,Business,13,1,2:41:32 PM,13,1,2:42:30 PM,13,1,3:09:56 PM,13,1,3:21:41 PM,13,1,3:55:27 PM,12,,,-1.2502493000000001,36.7923169,-1.3210188999999999,36.841055100000005,Rider_Id_300,2026
-Order_No_12395,User_Id_1245,Bike,3,Business,2,2,9:23:05 AM,2,2,9:25:14 AM,2,2,9:26:13 AM,2,2,10:11:45 AM,2,2,10:44:44 AM,4,15.1,,-1.300921,36.828195,-1.3173868999999998,36.8112044,Rider_Id_328,1979
-Order_No_5143,User_Id_1280,Bike,3,Business,11,4,7:23:59 AM,11,4,7:25:11 AM,11,4,7:59:09 AM,11,4,8:02:57 AM,11,4,8:35:23 AM,8,18.7,,-1.2551895,36.7822034,-1.2974510000000001,36.803117,Rider_Id_761,1946
-Order_No_5116,User_Id_3661,Bike,3,Business,23,1,12:20:58 PM,23,1,12:21:12 PM,23,1,12:33:03 PM,23,1,12:34:07 PM,23,1,12:57:33 PM,16,18.7,,-1.2207911,36.86817620000001,-1.2538731,36.7850223,Rider_Id_144,1406
-Order_No_6283,User_Id_3149,Bike,3,Business,26,5,12:55:26 PM,26,5,12:56:57 PM,26,5,1:06:13 PM,26,5,1:23:41 PM,26,5,1:40:34 PM,7,,,-1.3167113000000001,36.8301563,-1.3174473999999998,36.858596500000004,Rider_Id_7,1013
-Order_No_6398,User_Id_2066,Bike,3,Business,27,5,2:12:05 PM,27,5,2:12:24 PM,27,5,2:23:13 PM,27,5,2:27:38 PM,27,5,2:39:57 PM,4,24.0,,-1.316961,36.820337,-1.318943,36.7909742,Rider_Id_857,739
-Order_No_19742,User_Id_3291,Bike,3,Business,16,2,10:12:38 AM,16,2,10:15:54 AM,16,2,10:18:51 AM,16,2,10:53:20 AM,16,2,11:04:21 AM,5,,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_291,661
-Order_No_13477,User_Id_3149,Bike,3,Business,26,5,3:20:28 PM,26,5,3:21:18 PM,26,5,3:22:14 PM,26,5,3:46:25 PM,26,5,3:55:50 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_211,565
-Order_No_2148,User_Id_2613,Bike,1,Personal,5,5,9:03:56 AM,5,5,9:12:37 AM,5,5,9:20:55 AM,5,5,9:27:54 AM,5,5,9:43:25 AM,9,18.0,,-1.306245,36.819297799999994,-1.2655306,36.828148,Rider_Id_726,931
-Order_No_20531,User_Id_3080,Bike,3,Business,30,5,10:37:54 AM,30,5,10:45:05 AM,30,5,10:59:04 AM,30,5,11:02:43 AM,30,5,11:22:31 AM,9,23.5,,-1.2551895,36.7822034,-1.2948834999999999,36.7830474,Rider_Id_259,1188
-Order_No_21744,User_Id_999,Bike,1,Personal,7,4,1:24:05 PM,7,4,1:29:10 PM,7,4,2:13:12 PM,7,4,2:23:16 PM,7,4,2:43:17 PM,5,20.3,,-1.2853965,36.8183118,-1.2852142,36.79402929999999,Rider_Id_844,1201
-Order_No_16926,User_Id_570,Bike,3,Business,17,4,10:16:16 AM,17,4,10:16:48 AM,17,4,10:31:18 AM,17,4,10:33:36 AM,17,4,10:47:01 AM,6,25.5,,-1.2551895,36.7822034,-1.284382,36.774604499999995,Rider_Id_50,805
-Order_No_21426,User_Id_2330,Bike,3,Business,11,4,3:13:29 PM,11,4,3:17:59 PM,11,4,3:45:12 PM,11,4,3:46:56 PM,11,4,4:51:13 PM,17,26.9,,-1.2854537,36.9051024,-1.2536275,36.798793200000006,Rider_Id_697,3857
-Order_No_8483,User_Id_393,Bike,3,Business,26,2,12:26:11 PM,26,2,12:26:33 PM,26,2,12:29:20 PM,26,2,12:35:13 PM,26,2,1:20:12 PM,11,27.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_168,2699
-Order_No_2835,User_Id_2523,Bike,3,Business,1,5,12:21:30 PM,1,5,12:21:39 PM,1,5,12:24:16 PM,1,5,12:30:18 PM,1,5,1:40:47 PM,20,26.6,,-1.2551895,36.7822034,-1.3176378,36.900693100000005,Rider_Id_793,4229
-Order_No_2167,User_Id_2523,Bike,3,Business,20,2,4:41:27 PM,20,2,5:04:17 PM,20,2,5:05:04 PM,20,2,5:30:24 PM,20,2,6:31:54 PM,9,23.8,,-1.2551895,36.7822034,-1.2941585,36.7947375,Rider_Id_554,3690
-Order_No_11261,User_Id_2627,Bike,3,Business,10,1,3:06:03 PM,10,1,3:06:55 PM,10,1,3:17:06 PM,10,1,3:23:39 PM,10,1,3:29:00 PM,2,18.1,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_547,321
-Order_No_20210,User_Id_909,Bike,3,Business,27,5,11:09:08 AM,27,5,11:09:52 AM,27,5,11:26:57 AM,27,5,11:37:25 AM,27,5,11:54:14 AM,3,22.5,,-1.225322,36.80855,-1.2324936000000002,36.8224146,Rider_Id_281,1009
-Order_No_9385,User_Id_1927,Bike,3,Business,6,3,2:36:57 PM,6,3,2:42:57 PM,6,3,3:03:21 PM,6,3,3:12:17 PM,6,3,3:43:39 PM,12,,,-1.3472066,36.7692638,-1.2880128999999998,36.8308097,Rider_Id_278,1882
-Order_No_12399,User_Id_1502,Bike,3,Business,20,3,2:07:10 PM,20,3,2:10:33 PM,20,3,2:26:37 PM,20,3,2:32:23 PM,20,3,2:56:12 PM,12,28.4,,-1.279395,36.825364,-1.2232123,36.8932547,Rider_Id_206,1429
-Order_No_22714,User_Id_136,Bike,3,Business,11,2,10:30:55 AM,11,2,10:32:08 AM,11,2,10:48:41 AM,11,2,11:06:15 AM,11,2,11:20:48 AM,9,19.5,,-1.2313147,36.80168629999999,-1.2210156,36.855674,Rider_Id_484,873
-Order_No_26167,User_Id_1927,Bike,3,Business,6,6,11:41:18 AM,6,6,11:41:25 AM,6,6,11:41:55 AM,6,6,12:03:42 PM,6,6,12:24:15 PM,12,24.1,,-1.290894,36.822971,-1.3463041999999998,36.768164,Rider_Id_74,1233
-Order_No_3548,User_Id_1882,Bike,1,Business,24,2,10:09:02 AM,24,2,10:09:10 AM,24,2,10:24:52 AM,24,2,10:48:27 AM,24,2,11:12:11 AM,8,17.3,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_144,1424
-Order_No_17267,User_Id_2828,Bike,3,Business,28,4,3:38:42 PM,28,4,3:39:03 PM,28,4,3:40:59 PM,28,4,3:47:15 PM,28,4,3:49:50 PM,3,27.5,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_598,155
-Order_No_27030,User_Id_2436,Bike,3,Business,18,4,12:28:16 PM,18,4,12:29:03 PM,18,4,12:38:23 PM,18,4,12:48:48 PM,18,4,1:13:46 PM,5,,,-1.2696908000000002,36.81519,-1.2551895,36.7822034,Rider_Id_498,1498
-Order_No_19849,User_Id_633,Bike,3,Business,26,2,2:02:33 PM,26,2,2:02:48 PM,26,2,2:03:06 PM,26,2,2:53:06 PM,26,2,2:53:29 PM,9,31.2,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_669,23
-Order_No_23953,User_Id_3685,Bike,3,Personal,16,3,1:43:48 PM,16,3,2:02:05 PM,16,3,2:22:47 PM,16,3,2:24:40 PM,16,3,2:44:09 PM,8,25.6,,-1.296599,36.7629672,-1.2934656999999998,36.8212737,Rider_Id_812,1169
-Order_No_18307,User_Id_2587,Bike,3,Business,12,1,12:56:34 PM,12,1,1:11:40 PM,12,1,1:22:43 PM,12,1,1:25:00 PM,12,1,1:52:48 PM,16,24.0,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_879,1668
-Order_No_24509,User_Id_2627,Bike,3,Business,13,4,10:21:04 AM,13,4,10:21:25 AM,13,4,10:26:26 AM,13,4,10:33:02 AM,13,4,10:38:36 AM,2,21.9,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_147,334
-Order_No_6927,User_Id_3521,Bike,3,Business,11,2,1:50:58 PM,11,2,1:58:37 PM,11,2,2:05:06 PM,11,2,2:14:16 PM,11,2,2:22:05 PM,2,25.5,,-1.2701582,36.807443299999996,-1.2696908000000002,36.81519,Rider_Id_100,469
-Order_No_27713,User_Id_3686,Bike,3,Business,14,3,9:16:55 AM,14,3,9:46:15 AM,14,3,9:52:21 AM,14,3,10:01:08 AM,14,3,10:15:52 AM,8,18.3,,-1.2551895,36.7822034,-1.2956951,36.812924200000005,Rider_Id_726,884
-Order_No_22959,User_Id_1527,Bike,3,Business,15,3,2:54:22 PM,15,3,2:57:22 PM,15,3,3:10:40 PM,15,3,3:15:27 PM,15,3,3:34:29 PM,5,26.0,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_148,1142
-Order_No_7693,User_Id_136,Bike,3,Business,18,5,5:27:02 PM,18,5,5:29:18 PM,18,5,5:46:50 PM,18,5,5:55:39 PM,18,5,6:27:54 PM,9,22.4,,-1.2962832,36.784968299999996,-1.260232,36.807334000000004,Rider_Id_742,1935
-Order_No_8622,User_Id_1146,Bike,3,Business,24,5,10:18:05 AM,24,5,10:18:41 AM,24,5,10:19:22 AM,24,5,10:33:02 AM,24,5,10:44:05 AM,5,21.6,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_211,663
-Order_No_6338,User_Id_1959,Bike,1,Business,3,3,6:37:15 PM,3,3,6:37:48 PM,3,3,6:43:47 PM,3,3,7:12:04 PM,3,3,7:23:12 PM,14,23.8,,-1.2559559999999999,36.789867,-1.1882752,36.772118,Rider_Id_56,668
-Order_No_9681,User_Id_3283,Bike,3,Business,25,1,11:05:06 AM,25,1,11:06:32 AM,25,1,11:09:36 AM,25,1,11:28:31 AM,25,1,11:36:59 AM,5,27.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_383,508
-Order_No_10878,User_Id_1990,Bike,3,Business,4,4,9:58:41 AM,4,4,9:59:37 AM,4,4,10:08:15 AM,4,4,10:24:04 AM,4,4,11:05:19 AM,10,21.6,,-1.286341,36.8172393,-1.275442,36.766020700000006,Rider_Id_119,2475
-Order_No_21130,User_Id_909,Bike,3,Business,8,1,2:52:15 PM,8,1,2:53:06 PM,8,1,3:12:31 PM,8,1,3:20:51 PM,8,1,3:41:44 PM,8,29.4,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_547,1253
-Order_No_17417,User_Id_2358,Bike,4,Business,4,4,12:43:13 PM,4,4,1:08:52 PM,4,4,1:23:30 PM,4,4,1:25:21 PM,4,4,2:07:51 PM,36,,,-1.3256903999999998,36.8747943,-1.2540898999999999,36.70427279999999,Rider_Id_31,2550
-Order_No_26277,User_Id_136,Bike,3,Business,4,2,10:32:36 AM,4,2,10:38:31 AM,4,2,11:04:43 AM,4,2,11:22:32 AM,4,2,11:33:35 AM,6,20.7,,-1.3376931,36.751266799999996,-1.3254522,36.721658399999995,Rider_Id_204,663
-Order_No_8253,User_Id_1544,Bike,3,Business,30,4,2:33:52 PM,30,4,2:41:25 PM,30,4,3:20:20 PM,30,4,3:22:59 PM,30,4,4:07:55 PM,7,,,-1.2813012,36.832396200000005,-1.3197072,36.8160354,Rider_Id_255,2696
-Order_No_24737,User_Id_635,Bike,3,Business,4,2,11:54:22 AM,4,2,11:55:08 AM,4,2,11:56:53 AM,4,2,12:07:18 PM,4,2,12:18:40 PM,6,18.7,,-1.2975039,36.8110959,-1.2599563,36.799343900000004,Rider_Id_216,682
-Order_No_18986,User_Id_3439,Bike,3,Business,14,5,2:33:36 PM,14,5,2:33:36 PM,14,5,2:33:55 PM,14,5,2:45:19 PM,14,5,2:56:45 PM,6,22.8,,-1.2649605,36.798177700000004,-1.2393157,36.766887700000005,Rider_Id_709,686
-Order_No_21575,User_Id_494,Bike,1,Personal,25,5,4:26:48 PM,25,5,4:36:26 PM,25,5,4:58:47 PM,25,5,5:09:08 PM,25,5,5:52:35 PM,3,21.4,,-1.2824911,36.8218042,-1.303664,36.818777000000004,Rider_Id_192,2607
-Order_No_16898,User_Id_3689,Bike,3,Business,25,3,3:05:24 PM,25,3,3:09:07 PM,25,3,3:13:45 PM,25,3,3:30:34 PM,25,3,3:55:59 PM,6,17.6,,-1.2911905000000001,36.822654299999996,-1.2527958000000001,36.8003131,Rider_Id_208,1525
-Order_No_5363,User_Id_1594,Bike,3,Business,10,5,3:12:54 PM,10,5,5:42:53 PM,10,5,5:46:56 PM,10,5,5:53:14 PM,10,5,6:09:02 PM,7,,,-1.2620912,36.8013562,-1.225322,36.80855,Rider_Id_178,948
-Order_No_3378,User_Id_2335,Bike,3,Business,3,1,11:37:08 AM,3,1,1:02:36 PM,3,1,1:09:20 PM,3,1,1:17:15 PM,3,1,1:34:59 PM,8,22.4,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_845,1064
-Order_No_5238,User_Id_3686,Bike,3,Business,10,4,10:19:03 AM,10,4,10:19:12 AM,10,4,10:20:56 AM,10,4,10:28:08 AM,10,4,11:13:24 AM,18,18.4,,-1.2559559999999999,36.789867,-1.2198413000000001,36.9054958,Rider_Id_247,2716
-Order_No_22747,User_Id_1500,Bike,3,Business,4,4,11:34:54 AM,4,4,11:36:39 AM,4,4,11:47:58 AM,4,4,11:50:00 AM,4,4,12:18:30 PM,10,25.3,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_933,1710
-Order_No_10567,User_Id_1075,Bike,3,Business,21,4,1:52:45 PM,21,4,1:57:43 PM,21,4,2:23:03 PM,21,4,2:45:01 PM,21,4,2:53:24 PM,6,26.3,,-1.2885029,36.825604999999996,-1.2527958000000001,36.8003131,Rider_Id_330,503
-Order_No_18714,User_Id_1721,Bike,3,Business,26,2,4:38:12 PM,26,2,4:39:37 PM,26,2,4:39:46 PM,26,2,4:44:01 PM,26,2,5:02:34 PM,8,28.5,,-1.2551895,36.7822034,-1.289379,36.792949,Rider_Id_523,1113
-Order_No_6461,User_Id_3582,Bike,3,Business,21,4,1:51:56 PM,21,4,1:52:28 PM,21,4,1:55:28 PM,21,4,2:09:47 PM,21,4,3:32:01 PM,37,26.5,,-1.2384056,36.8718698,-1.0546336,36.773910799999996,Rider_Id_75,4934
-Order_No_27916,User_Id_2777,Bike,3,Business,6,4,2:39:54 PM,6,4,3:24:21 PM,6,4,3:25:12 PM,6,4,3:29:56 PM,6,4,4:24:41 PM,7,,,-1.2820628,36.825323600000004,-1.2551895,36.7822034,Rider_Id_351,3285
-Order_No_6498,User_Id_970,Bike,3,Business,12,2,10:14:15 AM,12,2,10:18:18 AM,12,2,10:26:43 AM,12,2,10:31:16 AM,12,2,11:12:51 AM,8,17.9,,-1.2551895,36.7822034,-1.2935508,36.794894899999996,Rider_Id_650,2495
-Order_No_2674,User_Id_136,Bike,3,Business,27,4,12:29:17 PM,27,4,12:29:57 PM,27,4,12:36:44 PM,27,4,12:51:08 PM,27,4,1:14:53 PM,11,23.1,,-1.2965045,36.7913036,-1.3165103999999999,36.707772600000006,Rider_Id_45,1425
-Order_No_2001,User_Id_3348,Bike,3,Business,5,1,1:37:11 PM,5,1,1:38:57 PM,5,1,1:49:39 PM,5,1,1:53:56 PM,5,1,2:59:38 PM,12,25.9,,-1.2196878,36.851447,-1.2695287,36.798177700000004,Rider_Id_268,3942
-Order_No_27919,User_Id_2956,Bike,3,Business,12,1,3:58:22 PM,12,1,4:11:46 PM,12,1,4:12:43 PM,12,1,4:26:15 PM,12,1,5:00:26 PM,9,26.7,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_699,2051
-Order_No_6228,User_Id_1405,Bike,3,Business,7,5,11:01:19 AM,7,5,11:30:09 AM,7,5,12:22:36 PM,7,5,1:12:10 PM,7,5,2:11:08 PM,13,25.3,,-1.306219,36.869912400000004,-1.3478563000000001,36.8935824,Rider_Id_48,3538
-Order_No_25024,User_Id_2866,Bike,3,Business,11,5,12:32:31 PM,11,5,12:32:44 PM,11,5,12:40:22 PM,11,5,12:55:50 PM,11,5,1:25:19 PM,22,21.1,,-1.2571472,36.795063299999995,-1.3448483,36.9004081,Rider_Id_845,1769
-Order_No_13421,User_Id_1154,Bike,3,Business,2,3,10:06:35 AM,2,3,10:06:49 AM,2,3,10:11:09 AM,2,3,10:19:50 AM,2,3,10:37:03 AM,8,20.1,,-1.300722,36.7882234,-1.2551895,36.7822034,Rider_Id_829,1033
-Order_No_551,User_Id_1075,Bike,3,Business,9,4,11:33:24 AM,9,4,11:33:45 AM,9,4,11:39:05 AM,9,4,12:08:11 PM,9,4,12:34:24 PM,15,,,-1.2232123,36.8932547,-1.2527958000000001,36.8003131,Rider_Id_484,1573
-Order_No_27609,User_Id_1482,Bike,3,Business,18,1,2:06:46 PM,18,1,2:23:02 PM,18,1,2:23:56 PM,18,1,2:35:30 PM,18,1,3:43:11 PM,17,30.8,,-1.2941752,36.7705608,-1.3385790000000002,36.89630379999999,Rider_Id_800,4061
-Order_No_2816,User_Id_3718,Bike,3,Business,6,4,11:39:58 AM,6,4,12:07:08 PM,6,4,12:12:08 PM,6,4,12:33:01 PM,6,4,1:12:18 PM,6,24.2,,-1.3002801000000002,36.8140096,-1.2650027,36.8126242,Rider_Id_19,2357
-Order_No_13466,User_Id_1882,Bike,3,Business,4,6,10:43:41 AM,4,6,10:47:46 AM,4,6,11:09:01 AM,4,6,11:19:18 AM,4,6,11:44:36 AM,14,,,-1.2156006000000001,36.8916865,-1.2591018999999999,36.800577000000004,Rider_Id_930,1518
-Order_No_1365,User_Id_3264,Bike,3,Business,21,1,2:33:47 PM,21,1,2:35:10 PM,21,1,2:40:25 PM,21,1,2:45:44 PM,21,1,3:18:07 PM,16,27.6,,-1.2551895,36.7822034,-1.2778747,36.883182899999994,Rider_Id_670,1943
-Order_No_22007,User_Id_3291,Bike,3,Business,16,2,11:18:42 AM,16,2,12:09:58 PM,16,2,12:21:37 PM,16,2,12:51:24 PM,16,2,1:09:31 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_119,1087
-Order_No_2346,User_Id_2330,Bike,3,Business,2,6,12:24:33 PM,2,6,12:24:45 PM,2,6,12:32:21 PM,2,6,12:33:33 PM,2,6,12:41:38 PM,2,27.7,,-1.2873232,36.765857200000006,-1.3015197,36.7658456,Rider_Id_368,485
-Order_No_20311,User_Id_391,Bike,3,Business,8,5,12:33:44 PM,8,5,12:37:23 PM,8,5,1:03:41 PM,8,5,1:34:04 PM,8,5,2:37:14 PM,5,27.0,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_686,3790
-Order_No_15287,User_Id_3201,Bike,3,Business,19,3,1:25:38 PM,19,3,1:25:48 PM,19,3,1:35:32 PM,19,3,1:45:26 PM,19,3,1:52:52 PM,2,24.5,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_875,446
-Order_No_10475,User_Id_1753,Bike,3,Business,17,5,10:52:30 AM,17,5,10:53:35 AM,17,5,11:02:49 AM,17,5,11:15:29 AM,17,5,11:20:00 AM,2,,,-1.2551895,36.7822034,-1.2567464,36.7915273,Rider_Id_259,271
-Order_No_28123,User_Id_265,Bike,3,Business,7,3,3:11:19 PM,7,3,3:12:30 PM,7,3,3:43:41 PM,7,3,3:53:56 PM,7,3,4:14:03 PM,12,28.2,,-1.2427291,36.7821206,-1.300921,36.828195,Rider_Id_257,1207
-Order_No_14905,User_Id_393,Bike,3,Business,15,5,8:50:54 AM,15,5,8:51:08 AM,15,5,8:51:12 AM,15,5,9:06:47 AM,15,5,9:15:57 AM,4,19.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_809,550
-Order_No_20418,User_Id_391,Bike,3,Business,22,5,10:35:02 AM,22,5,10:39:03 AM,22,5,11:14:41 AM,22,5,11:23:05 AM,22,5,11:58:14 AM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_448,2109
-Order_No_19272,User_Id_3291,Bike,3,Business,1,1,9:15:05 AM,1,1,9:18:55 AM,1,1,9:48:19 AM,1,1,9:53:36 AM,1,1,10:13:29 AM,11,21.3,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_855,1193
-Order_No_17736,User_Id_2989,Bike,3,Business,18,4,2:22:40 PM,18,4,2:22:47 PM,18,4,2:23:46 PM,18,4,2:46:58 PM,18,4,3:12:55 PM,12,26.7,,-1.290894,36.822971,-1.3472066,36.7692638,Rider_Id_531,1557
-Order_No_3186,User_Id_53,Bike,3,Business,27,3,11:33:37 AM,27,3,11:35:08 AM,27,3,11:46:49 AM,27,3,11:50:10 AM,27,3,12:17:45 PM,5,26.3,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_436,1655
-Order_No_18912,User_Id_393,Bike,3,Business,14,2,1:37:50 PM,14,2,1:38:04 PM,14,2,1:38:10 PM,14,2,1:58:55 PM,14,2,1:58:58 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,3
-Order_No_19154,User_Id_866,Bike,1,Personal,5,2,11:35:22 AM,5,2,11:49:30 AM,5,2,12:27:29 PM,5,2,12:32:06 PM,5,2,12:58:14 PM,8,21.5,,-1.296797,36.776452500000005,-1.2886685,36.8297138,Rider_Id_100,1568
-Order_No_435,User_Id_20,Bike,3,Business,4,2,2:01:15 PM,4,2,2:02:47 PM,4,2,2:28:01 PM,4,2,2:50:29 PM,4,2,3:16:52 PM,13,22.6,,-1.3278522,36.8711134,-1.2629666000000002,36.8068369,Rider_Id_25,1583
-Order_No_26353,User_Id_1346,Bike,3,Business,28,4,4:06:15 PM,28,4,4:06:52 PM,28,4,4:15:53 PM,28,4,4:20:41 PM,28,4,4:37:49 PM,2,29.9,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_649,1028
-Order_No_20872,User_Id_3647,Bike,3,Business,21,5,4:02:18 PM,21,5,4:03:20 PM,21,5,4:15:41 PM,21,5,4:19:18 PM,21,5,4:50:04 PM,10,27.2,,-1.2726389999999999,36.794723,-1.234958,36.8104235,Rider_Id_882,1846
-Order_No_22372,User_Id_3450,Bike,3,Business,15,5,9:52:21 AM,15,5,9:58:21 AM,15,5,10:13:37 AM,15,5,10:17:59 AM,15,5,10:56:50 AM,8,20.3,,-1.2551895,36.7822034,-1.2802913999999999,36.8296544,Rider_Id_311,2331
-Order_No_12080,User_Id_3147,Bike,3,Business,2,2,11:06:03 AM,2,2,11:06:23 AM,2,2,11:06:43 AM,2,2,11:28:06 AM,2,2,11:30:25 AM,3,20.8,,-1.3467388999999999,36.7690312,-1.346755,36.756349,Rider_Id_179,139
-Order_No_20587,User_Id_1500,Bike,3,Business,5,1,8:33:34 AM,5,1,12:34:54 PM,5,1,12:35:14 PM,5,1,12:42:36 PM,5,1,1:05:36 PM,8,24.7,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_208,1380
-Order_No_14135,User_Id_2733,Bike,3,Business,29,3,4:12:30 PM,29,3,4:15:27 PM,29,3,4:18:17 PM,29,3,4:32:31 PM,29,3,4:37:27 PM,2,21.0,,-1.3039695,36.783826700000006,-1.2963097,36.7688221,Rider_Id_310,296
-Order_No_6362,User_Id_661,Bike,3,Business,3,6,12:01:39 PM,3,6,12:02:08 PM,3,6,12:05:19 PM,3,6,12:10:48 PM,3,6,12:25:06 PM,3,23.7,,-1.2963097,36.7688221,-1.2976558999999999,36.791414700000004,Rider_Id_202,858
-Order_No_17610,User_Id_59,Bike,3,Business,7,5,3:18:50 PM,7,5,3:19:02 PM,7,5,3:28:05 PM,7,5,3:29:06 PM,7,5,3:44:32 PM,7,25.0,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_88,926
-Order_No_12899,User_Id_3109,Bike,2,Personal,4,3,3:29:21 PM,4,3,3:48:21 PM,4,3,4:12:33 PM,4,3,4:27:59 PM,4,3,4:55:08 PM,11,18.2,,-1.3107918,36.761794,-1.2551895,36.7822034,Rider_Id_465,1629
-Order_No_23406,User_Id_2066,Bike,3,Business,2,5,1:16:36 PM,2,5,1:32:11 PM,2,5,1:56:59 PM,2,5,1:59:11 PM,2,5,2:20:55 PM,11,25.0,,-1.316961,36.820337,-1.2463211,36.789487,Rider_Id_237,1304
-Order_No_16798,User_Id_1341,Bike,3,Business,23,6,10:52:37 AM,23,6,10:54:13 AM,23,6,11:04:52 AM,23,6,11:10:44 AM,23,6,11:34:36 AM,11,,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_329,1432
-Order_No_19881,User_Id_1961,Bike,3,Business,8,6,12:25:20 PM,8,6,12:38:42 PM,8,6,12:59:39 PM,8,6,1:24:22 PM,8,6,1:48:56 PM,9,19.8,,-1.303596,36.778378000000004,-1.3188358999999998,36.703852600000005,Rider_Id_427,1474
-Order_No_376,User_Id_3547,Bike,3,Business,8,1,11:59:50 AM,8,1,12:00:24 PM,8,1,12:12:36 PM,8,1,12:16:47 PM,8,1,12:57:29 PM,22,27.8,,-1.2761408,36.7710839,-1.3160976999999998,36.913164200000004,Rider_Id_217,2442
-Order_No_19464,User_Id_391,Bike,3,Business,7,2,12:01:35 PM,7,2,12:02:30 PM,7,2,12:37:59 PM,7,2,12:41:18 PM,7,2,1:04:48 PM,10,,,-1.2867454,36.8259017,-1.2224188,36.8868824,Rider_Id_959,1410
-Order_No_18520,User_Id_1368,Bike,3,Business,9,4,3:14:44 PM,9,4,3:16:31 PM,9,4,3:58:29 PM,9,4,4:06:41 PM,9,4,4:28:49 PM,8,25.8,,-1.2907672,36.8252167,-1.2571472,36.795063299999995,Rider_Id_213,1328
-Order_No_16347,User_Id_426,Bike,3,Business,5,2,11:01:50 AM,5,2,11:19:51 AM,5,2,11:28:50 AM,5,2,11:45:03 AM,5,2,11:56:53 AM,4,21.4,,-1.3323107,36.8692616,-1.3321944,36.889232899999996,Rider_Id_812,710
-Order_No_18617,User_Id_1976,Bike,3,Business,11,4,4:26:33 PM,11,4,4:27:12 PM,11,4,4:34:15 PM,11,4,4:36:54 PM,11,4,4:55:24 PM,7,24.1,,-1.257028,36.7810435,-1.2979633999999998,36.7719936,Rider_Id_38,1110
-Order_No_717,User_Id_3401,Bike,1,Personal,27,1,1:06:20 PM,27,1,1:07:05 PM,27,1,1:17:01 PM,27,1,1:30:46 PM,27,1,1:38:12 PM,2,23.9,,-1.2621576,36.8103473,-1.2697141,36.798177700000004,Rider_Id_886,446
-Order_No_338,User_Id_3718,Bike,3,Business,2,5,10:19:53 AM,2,5,10:24:52 AM,2,5,11:14:27 AM,2,5,11:31:33 AM,2,5,11:44:28 AM,5,23.5,,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_726,775
-Order_No_1671,User_Id_265,Bike,3,Business,3,1,2:48:14 PM,3,1,3:01:40 PM,3,1,3:01:48 PM,3,1,3:16:50 PM,3,1,3:26:50 PM,6,23.4,,-1.3022899,36.869332,-1.3016417,36.8271679,Rider_Id_406,600
-Order_No_27753,User_Id_2717,Bike,3,Business,14,2,11:19:34 AM,14,2,11:20:29 AM,14,2,11:32:30 AM,14,2,11:46:58 AM,14,2,11:59:49 AM,7,21.3,,-1.2839568000000001,36.823310600000006,-1.2551895,36.7822034,Rider_Id_857,771
-Order_No_28181,User_Id_517,Bike,3,Business,3,1,9:04:58 AM,3,1,9:05:37 AM,3,1,9:34:58 AM,3,1,9:48:08 AM,3,1,10:20:00 AM,14,18.1,4.9,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_448,1912
-Order_No_16013,User_Id_1453,Bike,3,Business,14,4,11:54:08 AM,14,4,12:48:55 PM,14,4,1:01:10 PM,14,4,1:02:43 PM,14,4,1:22:52 PM,9,19.2,,-1.3169591,36.7000029,-1.3014461000000002,36.7661381,Rider_Id_636,1209
-Order_No_2251,User_Id_1205,Bike,3,Business,15,2,9:10:51 AM,15,2,9:15:32 AM,15,2,9:22:03 AM,15,2,9:30:53 AM,15,2,9:56:50 AM,7,20.4,,-1.257309,36.806008399999996,-1.2958383,36.8073162,Rider_Id_101,1557
-Order_No_1721,User_Id_3239,Bike,3,Personal,14,2,9:18:33 AM,14,2,9:18:54 AM,14,2,9:37:35 AM,14,2,9:39:42 AM,14,2,10:19:41 AM,20,15.6,,-1.2836832,36.8306719,-1.3745998,36.931403499999995,Rider_Id_583,2399
-Order_No_21049,User_Id_3149,Bike,3,Business,9,6,9:47:51 AM,9,6,9:48:54 AM,9,6,9:58:10 AM,9,6,10:12:48 AM,9,6,10:37:59 AM,5,20.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_311,1511
-Order_No_16692,User_Id_1724,Bike,3,Business,24,4,12:00:43 PM,24,4,12:00:55 PM,24,4,12:04:14 PM,24,4,12:13:42 PM,24,4,12:36:50 PM,9,21.4,,-1.2553615,36.789537599999996,-1.2970278999999998,36.761356,Rider_Id_228,1388
-Order_No_4512,User_Id_1363,Bike,3,Business,31,4,2:43:14 PM,31,4,2:45:42 PM,31,4,2:55:37 PM,31,4,2:56:41 PM,31,4,3:02:35 PM,4,28.6,,-1.3004062,36.829741,-1.312469,36.815177,Rider_Id_88,354
-Order_No_8327,User_Id_2289,Bike,3,Business,21,3,12:51:49 PM,21,3,12:59:44 PM,21,3,1:24:14 PM,21,3,1:30:10 PM,21,3,2:22:52 PM,7,22.0,,-1.2551895,36.7822034,-1.2864673999999998,36.820262,Rider_Id_436,3162
-Order_No_10927,User_Id_393,Bike,3,Business,6,3,9:15:16 AM,6,3,9:15:24 AM,6,3,9:15:30 AM,6,3,9:36:39 AM,6,3,10:07:27 AM,14,16.6,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_523,1848
-Order_No_5201,User_Id_1329,Bike,3,Business,2,2,11:26:01 AM,2,2,11:28:22 AM,2,2,12:15:17 PM,2,2,12:16:47 PM,2,2,12:30:52 PM,3,26.3,,-1.273056,36.811298,-1.2605635,36.7985638,Rider_Id_167,845
-Order_No_5886,User_Id_136,Bike,3,Business,20,2,12:38:43 PM,20,2,12:41:32 PM,20,2,12:49:23 PM,20,2,12:53:11 PM,20,2,12:59:45 PM,1,20.0,,-1.2534301,36.800098299999995,-1.2652979,36.800039899999994,Rider_Id_823,394
-Order_No_24287,User_Id_1162,Bike,3,Business,12,2,9:46:40 AM,12,2,9:48:31 AM,12,2,10:19:09 AM,12,2,10:34:41 AM,12,2,10:48:59 AM,7,28.3,,-1.3122610000000001,36.794658,-1.296599,36.7629672,Rider_Id_267,858
-Order_No_1881,User_Id_3291,Bike,3,Business,25,1,12:10:11 PM,25,1,12:10:53 PM,25,1,12:20:18 PM,25,1,12:39:10 PM,25,1,12:49:29 PM,4,27.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_377,619
-Order_No_11701,User_Id_868,Bike,3,Business,25,5,8:46:00 AM,25,5,8:46:38 AM,25,5,9:16:53 AM,25,5,9:52:26 AM,25,5,10:22:24 AM,14,20.8,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_332,1798
-Order_No_2184,User_Id_2188,Bike,2,Personal,25,3,2:44:11 PM,25,3,2:44:44 PM,25,3,3:05:52 PM,25,3,3:21:12 PM,25,3,3:43:56 PM,8,17.5,,-1.2970278999999998,36.761356,-1.269534,36.80697,Rider_Id_754,1364
-Order_No_22416,User_Id_1890,Bike,1,Personal,21,5,1:08:52 PM,21,5,1:09:01 PM,21,5,1:33:11 PM,21,5,1:44:12 PM,21,5,2:11:27 PM,15,27.2,,-1.2658334999999998,36.804286700000006,-1.2540928,36.69782,Rider_Id_328,1635
-Order_No_13368,User_Id_2200,Bike,3,Business,5,6,12:10:45 PM,5,6,12:11:30 PM,5,6,12:19:46 PM,5,6,12:21:47 PM,5,6,12:52:16 PM,3,22.4,,-1.3052491000000002,36.8223899,-1.290894,36.822971,Rider_Id_26,1829
-Order_No_10950,User_Id_3177,Bike,3,Business,8,4,12:15:10 PM,8,4,12:16:58 PM,8,4,12:18:30 PM,8,4,12:49:46 PM,8,4,1:14:34 PM,18,26.1,,-1.3253106000000001,36.8903296,-1.3019530000000001,36.788458,Rider_Id_886,1488
-Order_No_5758,User_Id_1724,Bike,3,Business,12,5,11:09:27 AM,12,5,11:12:58 AM,12,5,11:35:01 AM,12,5,11:36:58 AM,12,5,12:17:09 PM,8,24.7,,-1.2551895,36.7822034,-1.2972794,36.801481700000004,Rider_Id_147,2411
-Order_No_5921,User_Id_3291,Bike,3,Business,16,4,3:11:42 PM,16,4,3:12:17 PM,16,4,3:25:17 PM,16,4,3:33:58 PM,16,4,3:46:04 PM,6,,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_910,726
-Order_No_114,User_Id_265,Bike,3,Business,1,6,12:35:29 PM,1,6,12:35:38 PM,1,6,12:39:10 PM,1,6,12:57:53 PM,1,6,2:02:57 PM,18,18.7,,-1.300921,36.828195,-1.3623272,36.941084499999995,Rider_Id_890,3904
-Order_No_12280,User_Id_2258,Bike,3,Business,9,5,2:24:22 PM,9,5,2:25:44 PM,9,5,2:39:37 PM,9,5,2:47:54 PM,9,5,3:55:30 PM,29,26.2,,-1.2571472,36.795063299999995,-1.1517585,36.9560679,Rider_Id_620,4056
-Order_No_15542,User_Id_2216,Bike,3,Business,11,5,11:48:08 AM,11,5,11:50:24 AM,11,5,12:07:34 PM,11,5,12:37:25 PM,11,5,12:59:31 PM,8,20.9,,-1.2551895,36.7822034,-1.2965924,36.8179314,Rider_Id_101,1326
-Order_No_15370,User_Id_958,Bike,3,Business,3,1,1:09:16 PM,3,1,1:10:23 PM,3,1,1:12:38 PM,3,1,1:43:09 PM,3,1,2:04:10 PM,15,23.2,,-1.3263105,36.8622904,-1.2826971999999999,36.970281299999996,Rider_Id_25,1261
-Order_No_12538,User_Id_592,Bike,1,Personal,17,1,1:20:25 PM,17,1,1:20:38 PM,17,1,1:22:27 PM,17,1,1:58:41 PM,17,1,2:16:03 PM,10,28.2,,-1.2728947,36.807942600000004,-1.2949438999999998,36.7604214,Rider_Id_895,1042
-Order_No_17506,User_Id_3647,Bike,3,Business,21,1,11:57:50 AM,21,1,11:58:58 AM,21,1,12:04:42 PM,21,1,12:08:35 PM,21,1,12:16:28 PM,3,25.4,,-1.2726389999999999,36.794723,-1.2717174,36.775388299999996,Rider_Id_315,473
-Order_No_24837,User_Id_2069,Bike,3,Business,4,6,11:30:51 AM,4,6,11:36:22 AM,4,6,12:04:37 PM,4,6,12:20:09 PM,4,6,12:33:04 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_775,775
-Order_No_23099,User_Id_1075,Bike,3,Business,18,4,11:32:42 AM,18,4,11:37:30 AM,18,4,11:59:07 AM,18,4,12:04:10 PM,18,4,12:25:44 PM,5,,,-1.2527958000000001,36.8003131,-1.2885315,36.8230419,Rider_Id_705,1294
-Order_No_2554,User_Id_562,Bike,3,Business,20,1,1:56:23 PM,20,1,1:56:36 PM,20,1,2:03:07 PM,20,1,2:22:35 PM,20,1,2:49:10 PM,13,,,-1.3227181000000001,36.8528097,-1.333148,36.922453000000004,Rider_Id_709,1595
-Order_No_19327,User_Id_1329,Bike,3,Business,29,4,6:32:44 PM,29,4,6:32:48 PM,29,4,6:32:54 PM,29,4,6:34:09 PM,29,4,6:43:09 PM,6,20.8,,-1.273056,36.811298,-1.2623805000000001,36.7805404,Rider_Id_345,540
-Order_No_19975,User_Id_33,Bike,3,Personal,16,2,7:31:23 AM,16,2,9:06:38 AM,16,2,9:07:47 AM,16,2,9:13:11 AM,16,2,10:14:39 AM,5,,,-1.2544051,36.7982934,-1.2814609,36.826593200000005,Rider_Id_303,3688
-Order_No_5815,User_Id_1469,Bike,3,Business,10,3,4:23:37 PM,10,3,4:25:26 PM,10,3,4:25:31 PM,10,3,5:04:57 PM,10,3,5:11:20 PM,8,27.5,,-1.2551895,36.7822034,-1.3004062,36.829741,Rider_Id_291,383
-Order_No_9015,User_Id_496,Bike,3,Business,14,2,10:57:37 AM,14,2,10:58:36 AM,14,2,11:05:37 AM,14,2,11:24:46 AM,14,2,12:20:11 PM,9,,,-1.2551895,36.7822034,-1.2980228999999999,36.78865,Rider_Id_617,3325
-Order_No_21491,User_Id_3647,Bike,3,Business,31,3,3:34:35 PM,31,3,3:35:19 PM,31,3,3:38:34 PM,31,3,3:43:06 PM,31,3,4:36:23 PM,6,26.8,,-1.2726389999999999,36.794723,-1.2939186000000003,36.8069693,Rider_Id_878,3197
-Order_No_344,User_Id_577,Bike,3,Personal,14,5,8:43:22 AM,14,5,8:43:44 AM,14,5,8:55:46 AM,14,5,9:19:12 AM,14,5,9:32:41 AM,1,18.1,,-1.2614346,36.8283124,-1.2626954,36.8273738,Rider_Id_882,809
-Order_No_27358,User_Id_1993,Bike,2,Personal,23,4,9:18:16 AM,23,4,9:19:39 AM,23,4,9:35:43 AM,23,4,9:39:30 AM,23,4,9:54:31 AM,5,15.7,,-1.2900726999999999,36.7890059,-1.2871938,36.8195031,Rider_Id_425,901
-Order_No_23327,User_Id_2330,Bike,3,Business,29,3,12:36:32 PM,29,3,12:36:47 PM,29,3,12:39:17 PM,29,3,12:43:54 PM,29,3,1:05:53 PM,19,,,-1.2642903,36.80021370000001,-1.1926151000000003,36.9347904,Rider_Id_96,1319
-Order_No_22500,User_Id_391,Bike,3,Business,11,2,9:03:06 AM,11,2,9:04:11 AM,11,2,9:09:33 AM,11,2,9:41:17 AM,11,2,10:06:29 AM,5,16.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_448,1512
-Order_No_20143,User_Id_1075,Bike,3,Business,9,4,1:24:42 PM,9,4,1:39:17 PM,9,4,1:52:44 PM,9,4,1:58:36 PM,9,4,2:21:19 PM,6,,,-1.2813012,36.832396200000005,-1.2527958000000001,36.8003131,Rider_Id_92,1363
-Order_No_27892,User_Id_3420,Bike,3,Business,7,5,2:44:14 PM,7,5,2:53:16 PM,7,5,3:04:41 PM,7,5,3:20:56 PM,7,5,4:00:36 PM,7,25.0,,-1.265981,36.793612100000004,-1.3046007,36.821174299999996,Rider_Id_726,2380
-Order_No_25418,User_Id_246,Bike,3,Business,29,1,11:55:45 AM,29,1,12:02:54 PM,29,1,12:05:20 PM,29,1,12:12:07 PM,29,1,12:22:32 PM,7,,,-1.2584143,36.8048002,-1.2697662,36.7595857,Rider_Id_107,625
-Order_No_13989,User_Id_1245,Bike,3,Business,12,6,10:35:56 AM,12,6,10:36:31 AM,12,6,10:43:14 AM,12,6,10:58:37 AM,12,6,11:35:00 AM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_110,2183
-Order_No_11616,User_Id_2718,Bike,3,Business,25,2,12:45:00 PM,25,2,12:45:26 PM,25,2,12:54:10 PM,25,2,1:10:26 PM,25,2,2:11:07 PM,23,23.8,,-1.2668815,36.8017244,-1.3592118,36.6717545,Rider_Id_520,3641
-Order_No_23179,User_Id_2008,Bike,3,Business,22,1,1:30:48 PM,22,1,1:32:25 PM,22,1,1:42:21 PM,22,1,1:51:32 PM,22,1,2:15:04 PM,6,26.4,,-1.2508229,36.7895263,-1.2714569,36.8278662,Rider_Id_279,1412
-Order_No_26847,User_Id_3599,Bike,3,Business,6,1,2:22:18 PM,6,1,3:01:24 PM,6,1,3:07:13 PM,6,1,3:21:48 PM,6,1,4:01:55 PM,12,,,-1.3302996,36.8707935,-1.2922992,36.798303499999996,Rider_Id_675,2407
-Order_No_13208,User_Id_1209,Bike,3,Business,10,3,12:31:24 PM,10,3,12:31:38 PM,10,3,12:42:28 PM,10,3,12:45:11 PM,10,3,1:05:02 PM,9,28.0,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_394,1191
-Order_No_14692,User_Id_1329,Bike,3,Business,17,4,11:11:06 AM,17,4,11:17:31 AM,17,4,11:18:40 AM,17,4,11:25:56 AM,17,4,11:49:07 AM,6,25.5,,-1.273056,36.811298,-1.2338121000000002,36.7968279,Rider_Id_215,1391
-Order_No_22166,User_Id_393,Bike,3,Business,25,2,8:34:25 AM,25,2,9:05:43 AM,25,2,9:20:57 AM,25,2,9:31:02 AM,25,2,9:55:38 AM,10,17.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1476
-Order_No_19372,User_Id_2330,Bike,3,Business,2,2,2:32:43 PM,2,2,2:33:37 PM,2,2,2:56:37 PM,2,2,2:58:18 PM,2,2,3:38:51 PM,7,29.5,,-1.2671943,36.8062533,-1.2873232,36.765857200000006,Rider_Id_167,2433
-Order_No_6023,User_Id_867,Bike,3,Business,11,5,3:55:15 PM,11,5,3:56:01 PM,11,5,3:56:18 PM,11,5,4:10:30 PM,11,5,4:35:07 PM,10,26.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_914,1477
-Order_No_4867,User_Id_391,Bike,3,Business,3,5,1:44:20 PM,3,5,2:09:00 PM,3,5,2:21:00 PM,3,5,2:43:15 PM,3,5,3:04:28 PM,5,,,-1.300921,36.828195,-1.2839729,36.8115146,Rider_Id_591,1273
-Order_No_16478,User_Id_1336,Bike,3,Business,24,3,10:43:08 AM,24,3,10:59:58 AM,24,3,11:11:03 AM,24,3,11:17:20 AM,24,3,11:35:44 AM,2,20.1,,-1.2891921999999998,36.825189,-1.2866011999999998,36.8195691,Rider_Id_857,1104
-Order_No_27938,User_Id_3403,Bike,3,Business,6,3,1:18:38 PM,6,3,1:56:28 PM,6,3,2:50:47 PM,6,3,2:52:51 PM,6,3,3:19:35 PM,8,21.8,,-1.2553615,36.789537599999996,-1.3038353,36.8107609,Rider_Id_742,1604
-Order_No_12466,User_Id_2330,Bike,3,Business,1,1,12:44:23 PM,1,1,12:46:55 PM,1,1,12:53:38 PM,1,1,12:54:58 PM,1,1,1:12:23 PM,8,,,-1.2890715,36.8157588,-1.3015197,36.7658456,Rider_Id_855,1045
-Order_No_18924,User_Id_2712,Bike,1,Business,17,3,1:53:16 PM,17,3,1:53:24 PM,17,3,1:53:42 PM,17,3,2:08:45 PM,17,3,2:39:18 PM,21,28.6,,-1.2252722,36.8756717,-1.2963097,36.7688221,Rider_Id_704,1833
-Order_No_18466,User_Id_1280,Bike,3,Business,18,4,2:51:18 PM,18,4,2:56:28 PM,18,4,3:05:11 PM,18,4,3:09:28 PM,18,4,3:54:38 PM,5,,,-1.2551895,36.7822034,-1.2812416000000002,36.8192411,Rider_Id_16,2710
-Order_No_17636,User_Id_3247,Bike,3,Business,27,2,11:53:06 AM,27,2,12:14:56 PM,27,2,12:37:02 PM,27,2,12:43:22 PM,27,2,1:51:22 PM,9,23.9,,-1.2571472,36.795063299999995,-1.2343087,36.84749,Rider_Id_928,4080
-Order_No_11067,User_Id_138,Bike,3,Business,27,3,1:19:00 PM,27,3,1:19:37 PM,27,3,1:33:00 PM,27,3,1:34:04 PM,27,3,2:55:26 PM,20,29.3,,-1.3562374,36.904295399999995,-1.2615888000000002,36.7928732,Rider_Id_606,4882
-Order_No_26166,User_Id_1500,Bike,3,Business,18,4,3:10:12 PM,18,4,3:10:30 PM,18,4,3:11:18 PM,18,4,3:39:24 PM,18,4,4:11:52 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_668,1948
-Order_No_15561,User_Id_868,Bike,3,Business,21,4,12:01:11 PM,21,4,12:01:54 PM,21,4,12:27:49 PM,21,4,12:40:19 PM,21,4,1:12:20 PM,14,25.3,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_377,1921
-Order_No_4217,User_Id_1500,Bike,3,Business,24,3,11:48:33 AM,24,3,12:47:56 PM,24,3,12:53:55 PM,24,3,1:33:55 PM,24,3,1:49:04 PM,8,18.3,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_260,909
-Order_No_27606,User_Id_3091,Bike,3,Business,28,5,10:45:49 AM,28,5,10:46:02 AM,28,5,10:50:47 AM,28,5,10:55:47 AM,28,5,11:17:22 AM,10,23.7,,-1.2527958000000001,36.8003131,-1.3012008,36.764868,Rider_Id_764,1295
-Order_No_16718,User_Id_3689,Bike,3,Business,28,3,3:59:59 PM,28,3,4:05:49 PM,28,3,4:17:12 PM,28,3,4:23:44 PM,28,3,4:57:28 PM,13,,,-1.207546,36.7913976,-1.2527958000000001,36.8003131,Rider_Id_626,2024
-Order_No_7486,User_Id_2701,Bike,3,Business,29,4,11:16:16 AM,29,4,11:33:02 AM,29,4,11:41:04 AM,29,4,11:44:27 AM,29,4,12:04:45 PM,5,21.5,,-1.2765736,36.851364600000004,-1.3083777,36.85900770000001,Rider_Id_69,1218
-Order_No_22819,User_Id_1848,Bike,2,Personal,4,1,4:49:59 PM,4,1,5:04:09 PM,4,1,5:08:34 PM,4,1,5:20:29 PM,4,1,5:30:56 PM,5,20.9,,-1.2805757,36.8223948,-1.2724556,36.7931938,Rider_Id_539,627
-Order_No_7142,User_Id_3017,Bike,3,Business,4,5,2:04:10 PM,4,5,2:21:10 PM,4,5,2:25:06 PM,4,5,2:29:33 PM,4,5,2:40:28 PM,6,24.3,,-1.2915271000000002,36.782702,-1.2658315,36.809049,Rider_Id_865,655
-Order_No_10215,User_Id_3727,Bike,2,Business,12,4,11:55:36 AM,12,4,11:57:49 AM,12,4,12:24:05 PM,12,4,12:25:29 PM,12,4,1:12:09 PM,7,18.8,,-1.2963097,36.7688221,-1.2875013000000002,36.8171787,Rider_Id_752,2800
-Order_No_15128,User_Id_1724,Bike,3,Business,9,2,10:25:10 AM,9,2,10:25:19 AM,9,2,10:26:56 AM,9,2,10:35:29 AM,9,2,11:15:57 AM,15,27.1,,-1.2551895,36.7822034,-1.3205111,36.7801016,Rider_Id_831,2428
-Order_No_18040,User_Id_3031,Bike,3,Personal,30,3,9:48:50 AM,30,3,9:48:59 AM,30,3,10:07:39 AM,30,3,10:14:15 AM,30,3,11:15:25 AM,19,17.4,,-1.2614838,36.7530984,-1.2803022,36.8830972,Rider_Id_761,3670
-Order_No_18110,User_Id_1919,Bike,3,Business,15,2,2:12:14 PM,15,2,2:13:37 PM,15,2,2:27:48 PM,15,2,2:30:33 PM,15,2,3:09:08 PM,7,24.5,,-1.2575690000000002,36.842164000000004,-1.2731645,36.7900343,Rider_Id_685,2315
-Order_No_14438,User_Id_2804,Bike,3,Business,17,1,12:10:09 PM,17,1,12:11:55 PM,17,1,12:37:29 PM,17,1,12:56:10 PM,17,1,12:56:14 PM,1,23.9,,-1.2642903,36.80021370000001,-1.2571472,36.795063299999995,Rider_Id_49,4
-Order_No_12564,User_Id_1209,Bike,3,Business,17,3,9:59:03 AM,17,3,10:08:28 AM,17,3,10:20:53 AM,17,3,10:32:08 AM,17,3,10:45:40 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_438,812
-Order_No_25466,User_Id_517,Bike,3,Business,29,3,3:40:39 PM,29,3,3:42:54 PM,29,3,3:43:22 PM,29,3,4:17:13 PM,29,3,4:17:15 PM,4,21.3,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_10,2
-Order_No_19250,User_Id_1461,Bike,2,Personal,8,3,6:58:37 PM,8,3,6:58:55 PM,8,3,7:22:02 PM,8,3,7:30:42 PM,8,3,8:04:43 PM,5,24.1,,-1.2720768999999998,36.8180604,-1.2765334,36.80150510000001,Rider_Id_519,2041
-Order_No_10074,User_Id_2828,Bike,3,Business,13,5,1:29:56 PM,13,5,1:30:35 PM,13,5,1:32:15 PM,13,5,1:39:51 PM,13,5,1:51:36 PM,3,19.2,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_791,705
-Order_No_5989,User_Id_2931,Bike,3,Business,12,5,11:55:06 AM,12,5,11:55:12 AM,12,5,11:55:21 AM,12,5,11:57:24 AM,12,5,12:57:25 PM,18,26.5,,-1.2452664,36.802854100000005,-1.294215,36.8977958,Rider_Id_796,3601
-Order_No_23343,User_Id_3283,Bike,3,Business,17,4,4:35:07 PM,17,4,4:35:18 PM,17,4,4:38:28 PM,17,4,4:45:19 PM,17,4,5:10:33 PM,7,25.9,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_246,1514
-Order_No_10345,User_Id_3283,Bike,3,Business,21,4,2:02:14 PM,21,4,2:03:19 PM,21,4,2:12:14 PM,21,4,2:15:58 PM,21,4,2:37:13 PM,6,30.9,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_498,1275
-Order_No_13095,User_Id_3255,Bike,1,Personal,28,5,10:36:09 AM,28,5,10:41:12 AM,28,5,10:57:26 AM,28,5,11:08:23 AM,28,5,11:26:11 AM,6,25.0,,-1.2662973999999998,36.836278,-1.273854,36.8003368,Rider_Id_795,1068
-Order_No_12569,User_Id_788,Bike,3,Business,14,4,3:39:16 PM,14,4,3:41:49 PM,14,4,3:46:32 PM,14,4,4:01:53 PM,14,4,4:22:39 PM,13,27.9,,-1.2283403,36.8822756,-1.225322,36.80855,Rider_Id_383,1246
-Order_No_19282,User_Id_1484,Bike,3,Business,6,4,2:57:03 PM,6,4,3:00:03 PM,6,4,3:04:35 PM,6,4,3:11:15 PM,6,4,3:27:51 PM,4,,,-1.2551895,36.7822034,-1.2764851000000002,36.8007126,Rider_Id_518,996
-Order_No_5035,User_Id_393,Bike,3,Business,21,4,12:18:13 PM,21,4,12:18:23 PM,21,4,12:18:57 PM,21,4,1:27:20 PM,21,4,1:27:22 PM,14,28.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,2
-Order_No_22827,User_Id_1225,Bike,2,Personal,19,2,8:48:35 AM,19,2,9:01:11 AM,19,2,9:03:31 AM,19,2,9:14:09 AM,19,2,9:37:17 AM,9,15.2,1.1,-1.2551895,36.7822034,-1.2951297,36.8335521,Rider_Id_726,1388
-Order_No_14187,User_Id_3641,Bike,3,Business,13,3,12:07:43 PM,13,3,12:07:50 PM,13,3,12:08:37 PM,13,3,12:12:14 PM,13,3,12:20:01 PM,3,,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_709,467
-Order_No_18997,User_Id_2112,Bike,3,Business,24,1,10:33:49 AM,24,1,10:34:08 AM,24,1,10:53:51 AM,24,1,11:07:53 AM,24,1,11:18:07 AM,4,22.4,,-1.2297202,36.881950700000004,-1.2156006000000001,36.8916865,Rider_Id_320,614
-Order_No_24665,User_Id_928,Bike,3,Business,17,5,1:28:37 PM,17,5,1:31:45 PM,17,5,1:46:05 PM,17,5,2:04:10 PM,17,5,2:24:15 PM,6,,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_855,1205
-Order_No_4855,User_Id_1245,Bike,3,Business,8,1,3:23:08 PM,8,1,3:23:30 PM,8,1,3:42:47 PM,8,1,3:57:46 PM,8,1,4:29:49 PM,17,29.7,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519,1923
-Order_No_4574,User_Id_391,Bike,3,Business,29,3,11:09:00 AM,29,3,11:10:42 AM,29,3,11:18:44 AM,29,3,11:27:06 AM,29,3,11:52:41 AM,5,,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_139,1535
-Order_No_19960,User_Id_3647,Bike,3,Business,7,3,3:59:16 PM,7,3,4:00:59 PM,7,3,4:14:34 PM,7,3,4:18:50 PM,7,3,4:31:14 PM,7,28.3,,-1.2726389999999999,36.794723,-1.3014461000000002,36.7661381,Rider_Id_279,744
-Order_No_1579,User_Id_3560,Bike,3,Business,6,3,2:32:06 PM,6,3,2:33:02 PM,6,3,2:37:54 PM,6,3,2:47:34 PM,6,3,2:49:01 PM,1,,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_410,87
-Order_No_17552,User_Id_635,Bike,3,Business,27,6,9:01:29 AM,27,6,9:06:43 AM,27,6,9:06:47 AM,27,6,9:39:29 AM,27,6,9:53:40 AM,8,,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_61,851
-Order_No_27418,User_Id_2279,Bike,3,Business,18,1,8:24:40 AM,18,1,8:31:26 AM,18,1,8:45:55 AM,18,1,8:55:25 AM,18,1,9:23:01 AM,6,22.2,,-1.2897368,36.8226783,-1.2969741000000001,36.7856612,Rider_Id_451,1656
-Order_No_6697,User_Id_2828,Bike,2,Business,30,6,12:49:56 PM,30,6,12:51:03 PM,30,6,1:08:41 PM,30,6,1:14:35 PM,30,6,1:26:32 PM,3,21.9,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_795,717
-Order_No_19425,User_Id_635,Bike,3,Business,2,3,3:01:25 PM,2,3,3:03:04 PM,2,3,3:07:32 PM,2,3,3:40:55 PM,2,3,4:37:26 PM,7,26.1,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_557,3391
-Order_No_7256,User_Id_2587,Bike,3,Business,23,3,2:39:42 PM,23,3,2:43:43 PM,23,3,3:19:59 PM,23,3,3:23:32 PM,23,3,4:37:11 PM,7,27.4,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_268,4419
-Order_No_2798,User_Id_393,Bike,3,Business,15,1,1:20:28 PM,15,1,1:20:46 PM,15,1,1:21:29 PM,15,1,1:31:30 PM,15,1,1:38:54 PM,4,,,-1.3167113000000001,36.8301563,-1.3002553999999997,36.8256566,Rider_Id_349,444
-Order_No_26463,User_Id_540,Bike,3,Business,3,5,9:37:50 AM,3,5,9:40:53 AM,3,5,9:47:27 AM,3,5,9:51:48 AM,3,5,10:16:41 AM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_599,1493
-Order_No_21370,User_Id_393,Bike,3,Business,2,4,8:37:23 AM,2,4,8:37:32 AM,2,4,8:38:37 AM,2,4,8:58:12 AM,2,4,9:12:10 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_438,838
-Order_No_10830,User_Id_2956,Bike,3,Business,2,2,1:06:50 PM,2,2,1:09:13 PM,2,2,1:31:23 PM,2,2,1:47:18 PM,2,2,1:54:55 PM,2,29.2,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_318,457
-Order_No_25544,User_Id_1469,Bike,3,Business,14,4,9:44:10 AM,14,4,9:44:22 AM,14,4,9:49:01 AM,14,4,9:50:14 AM,14,4,9:59:46 AM,3,17.8,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_932,572
-Order_No_25278,User_Id_391,Bike,3,Business,7,5,12:13:32 PM,7,5,1:04:55 PM,7,5,1:20:43 PM,7,5,1:38:07 PM,7,5,2:03:51 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_158,1544
-Order_No_17342,User_Id_868,Bike,3,Business,15,3,12:55:07 PM,15,3,12:56:28 PM,15,3,1:02:40 PM,15,3,1:12:03 PM,15,3,1:40:11 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_675,1688
-Order_No_17045,User_Id_1245,Bike,3,Business,8,6,11:43:45 AM,8,6,11:44:34 AM,8,6,11:58:27 AM,8,6,12:13:42 PM,8,6,12:47:15 PM,9,19.8,,-1.300921,36.828195,-1.2885712999999999,36.764981799999994,Rider_Id_739,2013
-Order_No_25982,User_Id_359,Bike,2,Personal,7,4,3:17:43 PM,7,4,3:18:32 PM,7,4,3:24:00 PM,7,4,3:31:06 PM,7,4,3:46:19 PM,4,20.1,,-1.2924173,36.779781400000005,-1.3043728,36.8044157,Rider_Id_101,913
-Order_No_28031,User_Id_3717,Bike,2,Personal,10,1,3:23:01 PM,10,1,3:24:50 PM,10,1,3:45:28 PM,10,1,3:48:52 PM,10,1,4:04:13 PM,7,,,-1.2899638999999998,36.803717,-1.3173868999999998,36.8112044,Rider_Id_148,921
-Order_No_8029,User_Id_3686,Bike,3,Business,31,3,12:49:36 PM,31,3,12:50:53 PM,31,3,1:05:59 PM,31,3,1:30:20 PM,31,3,1:42:21 PM,8,24.6,,-1.2551895,36.7822034,-1.2945073999999999,36.8083195,Rider_Id_744,721
-Order_No_1789,User_Id_1874,Bike,3,Business,21,4,2:31:28 PM,21,4,2:34:24 PM,21,4,2:48:25 PM,21,4,2:53:46 PM,21,4,3:29:02 PM,5,26.5,,-1.2571472,36.795063299999995,-1.2765676000000001,36.8191974,Rider_Id_411,2116
-Order_No_7483,User_Id_2271,Bike,3,Business,24,4,1:51:09 PM,24,4,2:44:07 PM,24,4,2:59:18 PM,24,4,3:47:51 PM,24,4,4:26:41 PM,14,22.5,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_116,2330
-Order_No_18233,User_Id_635,Bike,3,Business,14,2,11:57:42 AM,14,2,11:59:35 AM,14,2,11:59:40 AM,14,2,12:23:57 PM,14,2,12:29:08 PM,7,,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_472,311
-Order_No_21710,User_Id_190,Bike,3,Business,14,5,2:48:20 PM,14,5,2:48:59 PM,14,5,2:54:44 PM,14,5,3:01:05 PM,14,5,3:21:19 PM,11,23.2,,-1.2919479,36.7996653,-1.3270347,36.866736200000005,Rider_Id_538,1214
-Order_No_15682,User_Id_347,Bike,1,Personal,30,1,1:36:13 PM,30,1,1:36:17 PM,30,1,1:36:57 PM,30,1,1:59:32 PM,30,1,2:46:31 PM,14,20.6,,-1.3223068,36.7929881,-1.2763120000000001,36.882742,Rider_Id_229,2819
-Order_No_26989,User_Id_1402,Bike,3,Business,31,4,12:09:17 PM,31,4,12:10:45 PM,31,4,12:28:47 PM,31,4,12:33:21 PM,31,4,12:41:55 PM,1,26.5,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_696,514
-Order_No_6349,User_Id_3039,Bike,2,Personal,9,3,5:23:39 PM,9,3,5:27:06 PM,9,3,5:34:48 PM,9,3,5:46:26 PM,9,3,5:55:47 PM,3,21.9,,-1.2892762,36.764129,-1.2962603000000001,36.7766164,Rider_Id_355,561
-Order_No_8844,User_Id_2100,Bike,3,Business,29,3,4:44:32 PM,29,3,4:45:20 PM,29,3,4:52:58 PM,29,3,5:08:08 PM,29,3,5:21:09 PM,6,19.0,,-1.2765736,36.851364600000004,-1.28593,36.8151111,Rider_Id_629,781
-Order_No_2978,User_Id_570,Bike,3,Business,18,2,11:11:05 AM,18,2,11:12:05 AM,18,2,11:23:09 AM,18,2,11:25:45 AM,18,2,12:18:06 PM,6,,,-1.2551895,36.7822034,-1.2866011999999998,36.8195691,Rider_Id_150,3141
-Order_No_3940,User_Id_3711,Bike,3,Business,20,4,10:32:43 AM,20,4,10:45:25 AM,20,4,10:55:08 AM,20,4,11:02:57 AM,20,4,11:19:37 AM,9,22.1,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_320,1000
-Order_No_2133,User_Id_2330,Bike,3,Business,7,5,3:26:18 PM,7,5,3:28:53 PM,7,5,3:33:12 PM,7,5,3:40:31 PM,7,5,3:56:50 PM,9,27.0,,-1.3015197,36.7658456,-1.2642903,36.80021370000001,Rider_Id_920,979
-Order_No_6758,User_Id_2956,Bike,3,Business,15,4,11:01:58 AM,15,4,11:02:13 AM,15,4,11:15:37 AM,15,4,11:24:50 AM,15,4,11:33:08 AM,2,24.6,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_407,498
-Order_No_4323,User_Id_419,Bike,3,Business,7,3,10:18:52 AM,7,3,10:22:00 AM,7,3,10:41:00 AM,7,3,10:48:10 AM,7,3,11:03:16 AM,6,25.1,,-1.2571472,36.795063299999995,-1.289882,36.8105888,Rider_Id_550,906
-Order_No_15247,User_Id_2264,Bike,1,Personal,19,3,3:42:50 PM,19,3,3:43:06 PM,19,3,3:53:38 PM,19,3,3:58:44 PM,19,3,4:13:35 PM,6,25.6,,-1.2943401,36.7965612,-1.2697141,36.798177700000004,Rider_Id_754,891
-Order_No_18082,User_Id_2279,Bike,3,Business,16,3,1:26:18 PM,16,3,3:44:32 PM,16,3,3:47:51 PM,16,3,4:10:55 PM,16,3,4:48:34 PM,7,26.8,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_268,2259
-Order_No_5290,User_Id_3689,Bike,3,Business,6,1,3:55:12 PM,6,1,3:56:21 PM,6,1,4:05:34 PM,6,1,4:56:18 PM,6,1,5:48:53 PM,10,22.2,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_636,3155
-Order_No_5318,User_Id_3291,Bike,3,Business,22,2,12:50:42 PM,22,2,12:51:00 PM,22,2,1:37:08 PM,22,2,2:29:59 PM,22,2,2:59:50 PM,12,25.4,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_82,1791
-Order_No_6481,User_Id_758,Bike,3,Business,9,2,10:00:30 AM,9,2,10:01:35 AM,9,2,10:40:18 AM,9,2,10:45:18 AM,9,2,11:27:03 AM,5,27.0,,-1.2878081000000001,36.8200516,-1.2917868000000001,36.7872675,Rider_Id_204,2505
-Order_No_26865,User_Id_2866,Bike,3,Business,9,3,12:04:24 PM,9,3,12:05:35 PM,9,3,12:19:09 PM,9,3,12:27:22 PM,9,3,12:38:10 PM,5,21.1,,-1.31861,36.8113967,-1.3047026000000002,36.8172033,Rider_Id_100,648
-Order_No_19172,User_Id_186,Bike,3,Business,25,4,11:23:29 AM,25,4,11:23:45 AM,25,4,11:36:11 AM,25,4,11:39:27 AM,25,4,12:08:57 PM,17,,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_622,1770
-Order_No_2693,User_Id_1927,Bike,3,Business,18,1,10:39:55 AM,18,1,10:40:21 AM,18,1,10:48:58 AM,18,1,10:52:05 AM,18,1,11:15:04 AM,10,28.2,,-1.3165542,36.700067,-1.3472066,36.7692638,Rider_Id_326,1379
-Order_No_544,User_Id_667,Bike,1,Personal,25,6,1:25:51 PM,25,6,1:25:58 PM,25,6,1:29:17 PM,25,6,1:35:18 PM,25,6,1:48:34 PM,6,,,-1.2866544,36.82179779999999,-1.296452,36.775749,Rider_Id_857,796
-Order_No_8491,User_Id_1488,Bike,1,Personal,6,1,8:10:29 AM,6,1,8:10:41 AM,6,1,8:20:17 AM,6,1,8:26:25 AM,6,1,8:47:05 AM,12,14.8,,-1.3128657,36.7024749,-1.290499,36.784363,Rider_Id_636,1240
-Order_No_3253,User_Id_2956,Bike,3,Business,4,1,3:06:23 PM,4,1,3:11:19 PM,4,1,3:26:52 PM,4,1,3:33:05 PM,4,1,3:54:38 PM,10,,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_953,1293
-Order_No_20134,User_Id_393,Bike,3,Business,4,5,2:39:48 PM,4,5,2:42:57 PM,4,5,2:51:04 PM,4,5,2:57:19 PM,4,5,3:19:47 PM,7,24.4,,-1.3177546999999998,36.8303703,-1.3083266000000002,36.8494756,Rider_Id_948,1348
-Order_No_841,User_Id_302,Bike,3,Business,22,7,1:57:26 PM,22,7,2:00:34 PM,22,7,2:06:19 PM,22,7,2:11:10 PM,22,7,2:24:08 PM,6,19.2,,-1.2649605,36.798177700000004,-1.2855153,36.7834515,Rider_Id_194,778
-Order_No_22343,User_Id_439,Bike,3,Business,8,4,1:44:12 PM,8,4,1:45:28 PM,8,4,1:51:38 PM,8,4,2:01:17 PM,8,4,2:34:42 PM,14,27.1,,-1.2696823999999998,36.8026164,-1.3323107,36.8692616,Rider_Id_449,2005
-Order_No_7269,User_Id_3599,Bike,3,Business,30,6,8:09:14 AM,30,6,8:09:28 AM,30,6,8:58:48 AM,30,6,9:12:27 AM,30,6,9:37:45 AM,18,,,-1.3302996,36.8707935,-1.3609149,36.7288735,Rider_Id_459,1518
-Order_No_24560,User_Id_3560,Bike,3,Business,23,3,3:01:53 PM,23,3,3:56:48 PM,23,3,3:56:57 PM,23,3,4:22:39 PM,23,3,5:05:36 PM,12,27.2,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_828,2577
-Order_No_11824,User_Id_3560,Bike,3,Business,7,2,11:30:42 AM,7,2,11:35:41 AM,7,2,11:40:14 AM,7,2,11:56:06 AM,7,2,12:27:40 PM,21,23.3,,-1.279395,36.825364,-1.3916458999999999,36.93058920000001,Rider_Id_124,1894
-Order_No_23125,User_Id_3681,Bike,1,Personal,6,5,3:45:52 PM,6,5,3:46:02 PM,6,5,3:53:40 PM,6,5,4:08:47 PM,6,5,5:15:56 PM,19,22.4,,-1.2945798000000002,36.7737755,-1.2145231,36.8944761,Rider_Id_192,4029
-Order_No_21464,User_Id_868,Bike,3,Business,24,4,11:58:45 AM,24,4,1:26:23 PM,24,4,1:45:04 PM,24,4,1:56:06 PM,24,4,2:24:13 PM,9,22.8,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_808,1687
-Order_No_17995,User_Id_20,Bike,3,Business,6,1,2:21:41 PM,6,1,2:35:35 PM,6,1,2:40:14 PM,6,1,2:41:54 PM,6,1,2:58:39 PM,5,,,-1.2629666000000002,36.8068369,-1.2764950000000002,36.8205966,Rider_Id_281,1005
-Order_No_3840,User_Id_2978,Bike,3,Business,10,2,10:00:09 AM,10,2,10:00:26 AM,10,2,10:06:27 AM,10,2,10:10:20 AM,10,2,10:26:36 AM,9,16.4,,-1.2551895,36.7822034,-1.2942764,36.7969749,Rider_Id_103,976
-Order_No_990,User_Id_2066,Bike,3,Business,27,5,2:44:52 PM,27,5,2:44:57 PM,27,5,2:52:20 PM,27,5,4:10:03 PM,27,5,4:21:05 PM,7,24.2,,-1.316961,36.820337,-1.299313,36.78314879999999,Rider_Id_857,662
-Order_No_9509,User_Id_627,Bike,3,Business,12,3,10:06:42 AM,12,3,10:08:05 AM,12,3,10:19:11 AM,12,3,10:22:11 AM,12,3,10:39:05 AM,8,,,-1.2551895,36.7822034,-1.2821378,36.7740428,Rider_Id_407,1014
-Order_No_19273,User_Id_2330,Bike,3,Business,12,5,7:14:02 PM,12,5,7:31:36 PM,12,5,7:41:02 PM,12,5,7:42:06 PM,12,5,8:02:51 PM,8,23.1,,-1.2324923,36.836377500000005,-1.233018,36.8004477,Rider_Id_484,1245
-Order_No_9667,User_Id_3689,Bike,3,Business,26,1,3:52:21 PM,26,1,3:58:35 PM,26,1,4:01:41 PM,26,1,4:07:01 PM,26,1,4:25:54 PM,3,22.2,0.8,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_216,1133
-Order_No_5218,User_Id_2794,Bike,3,Personal,3,4,3:54:23 PM,3,4,3:55:01 PM,3,4,3:58:16 PM,3,4,4:06:26 PM,3,4,4:14:13 PM,4,25.9,,-1.2733047,36.8109744,-1.2598848999999999,36.8219979,Rider_Id_247,467
-Order_No_2498,User_Id_3669,Bike,3,Business,15,5,3:00:15 PM,15,5,3:16:22 PM,15,5,3:35:34 PM,15,5,3:47:41 PM,15,5,4:24:33 PM,9,28.1,,-1.2560455,36.780086700000005,-1.3107918,36.761794,Rider_Id_411,2212
-Order_No_5861,User_Id_2801,Bike,3,Business,10,5,8:30:55 AM,10,5,8:33:01 AM,10,5,8:33:14 AM,10,5,8:42:04 AM,10,5,8:57:27 AM,8,,,-1.289081,36.8300409,-1.2674273999999999,36.787082700000006,Rider_Id_531,923
-Order_No_1498,User_Id_1451,Bike,1,Personal,20,1,5:59:42 PM,20,1,5:59:56 PM,20,1,6:06:30 PM,20,1,6:11:27 PM,20,1,6:35:28 PM,9,20.3,,-1.2931317,36.7778105,-1.3182646,36.821312799999994,Rider_Id_105,1441
-Order_No_3057,User_Id_393,Bike,3,Business,28,4,12:36:24 PM,28,4,12:36:37 PM,28,4,12:37:05 PM,28,4,1:22:51 PM,28,4,1:22:53 PM,4,23.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10,2
-Order_No_4034,User_Id_2454,Bike,3,Business,19,3,12:05:55 PM,19,3,12:06:36 PM,19,3,12:07:34 PM,19,3,12:38:20 PM,19,3,12:43:24 PM,4,,,-1.2804664,36.7701111,-1.2993231,36.7753372,Rider_Id_179,304
-Order_No_6019,User_Id_3403,Bike,3,Business,6,2,3:28:18 PM,6,2,3:39:16 PM,6,2,4:03:21 PM,6,2,4:06:07 PM,6,2,4:39:40 PM,16,27.7,,-1.2551895,36.7822034,-1.3326173000000001,36.71462329999999,Rider_Id_123,2013
-Order_No_12543,User_Id_1725,Bike,1,Business,9,4,4:06:38 PM,9,4,4:07:54 PM,9,4,4:32:14 PM,9,4,4:36:44 PM,9,4,5:23:52 PM,8,23.0,,-1.2914795000000001,36.824897299999996,-1.26578,36.781,Rider_Id_519,2828
-Order_No_23657,User_Id_2154,Bike,3,Business,14,4,1:04:42 PM,14,4,1:38:22 PM,14,4,1:53:34 PM,14,4,2:16:31 PM,14,4,2:24:36 PM,6,27.7,,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_377,485
-Order_No_26193,User_Id_3651,Bike,3,Business,16,5,2:21:12 PM,16,5,3:02:19 PM,16,5,3:08:42 PM,16,5,3:10:55 PM,16,5,4:29:34 PM,7,,,-1.2571472,36.795063299999995,-1.2482518,36.7550829,Rider_Id_470,4719
-Order_No_20115,User_Id_248,Bike,3,Business,18,3,2:28:42 PM,18,3,2:29:19 PM,18,3,2:39:17 PM,18,3,2:45:41 PM,18,3,3:14:36 PM,8,20.9,,-1.304735,36.784605400000004,-1.265715,36.808525,Rider_Id_844,1735
-Order_No_22875,User_Id_1594,Bike,3,Business,20,3,2:10:55 PM,20,3,2:11:19 PM,20,3,2:11:37 PM,20,3,2:24:31 PM,20,3,2:44:29 PM,5,29.2,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_614,1198
-Order_No_25679,User_Id_2412,Bike,3,Business,27,4,8:53:24 AM,27,4,9:04:40 AM,27,4,9:23:16 AM,27,4,9:37:48 AM,27,4,9:50:03 AM,5,18.7,3.1,-1.2156006000000001,36.8916865,-1.2297202,36.881950700000004,Rider_Id_704,735
-Order_No_11822,User_Id_3450,Bike,3,Business,11,1,11:04:34 AM,11,1,11:19:59 AM,11,1,11:26:48 AM,11,1,11:31:33 AM,11,1,12:10:17 PM,17,26.4,,-1.2551895,36.7822034,-1.2859912,36.8756811,Rider_Id_268,2324
-Order_No_13588,User_Id_3278,Bike,2,Personal,17,3,4:11:44 PM,17,3,4:12:39 PM,17,3,4:22:41 PM,17,3,4:36:54 PM,17,3,4:56:24 PM,11,24.8,,-1.3117438999999997,36.8108257,-1.3141805,36.7397786,Rider_Id_207,1170
-Order_No_15595,User_Id_3621,Bike,3,Business,6,1,1:51:18 PM,6,1,1:52:26 PM,6,1,2:01:00 PM,6,1,2:06:57 PM,6,1,2:33:09 PM,13,,,-1.2836583000000001,36.7886014,-1.3352545,36.8659639,Rider_Id_427,1572
-Order_No_16329,User_Id_3647,Bike,3,Business,10,1,4:24:30 PM,10,1,4:24:36 PM,10,1,4:25:20 PM,10,1,4:34:00 PM,10,1,5:40:46 PM,27,19.1,,-1.2726389999999999,36.794723,-1.4108268,36.9441185,Rider_Id_680,4006
-Order_No_1854,User_Id_1075,Bike,3,Business,5,3,3:59:40 PM,5,3,4:00:02 PM,5,3,4:05:37 PM,5,3,4:14:52 PM,5,3,4:22:34 PM,1,24.8,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_881,462
-Order_No_27134,User_Id_391,Bike,3,Business,26,2,9:14:36 AM,26,2,9:16:07 AM,26,2,9:39:22 AM,26,2,10:00:07 AM,26,2,10:23:10 AM,5,,,-1.2755752,36.8535746,-1.300921,36.828195,Rider_Id_408,1383
-Order_No_4281,User_Id_3283,Bike,3,Business,24,1,2:34:48 PM,24,1,2:50:00 PM,24,1,2:53:07 PM,24,1,3:07:24 PM,24,1,3:41:41 PM,14,24.5,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_147,2057
-Order_No_9042,User_Id_136,Bike,3,Business,20,4,2:22:45 PM,20,4,2:23:59 PM,20,4,2:51:17 PM,20,4,3:04:30 PM,20,4,4:21:34 PM,16,28.1,,-1.3326173000000001,36.71462329999999,-1.3233496,36.8139159,Rider_Id_565,4624
-Order_No_26545,User_Id_795,Bike,3,Personal,12,2,11:07:39 AM,12,2,11:08:17 AM,12,2,11:25:37 AM,12,2,11:30:21 AM,12,2,11:58:58 AM,11,20.8,,-1.3104733999999998,36.7363698,-1.2551895,36.7822034,Rider_Id_140,1717
-Order_No_1094,User_Id_2079,Bike,3,Personal,25,5,9:12:06 AM,25,5,9:12:55 AM,25,5,9:15:55 AM,25,5,9:18:00 AM,25,5,10:03:21 AM,22,20.7,,-1.2681003999999998,36.7952081,-1.333148,36.922453000000004,Rider_Id_620,2721
-Order_No_22549,User_Id_3080,Bike,3,Business,13,3,12:51:32 PM,13,3,12:53:07 PM,13,3,1:02:28 PM,13,3,1:04:34 PM,13,3,1:36:01 PM,7,24.8,,-1.2551895,36.7822034,-1.2884243,36.820724799999994,Rider_Id_199,1887
-Order_No_26854,User_Id_2714,Bike,1,Personal,17,4,5:54:54 PM,17,4,6:46:24 PM,17,4,7:25:53 PM,17,4,7:27:01 PM,17,4,7:55:34 PM,17,21.8,,-1.3518598,36.7324661,-1.2813497999999999,36.7930729,Rider_Id_919,1713
-Order_No_1615,User_Id_2063,Bike,3,Business,15,2,10:01:57 AM,15,2,10:28:51 AM,15,2,10:34:56 AM,15,2,10:39:35 AM,15,2,11:40:53 AM,8,22.9,,-1.2626906,36.7827022,-1.2904016,36.82263029999999,Rider_Id_786,3678
-Order_No_834,User_Id_788,Bike,3,Business,16,3,9:00:30 AM,16,3,9:01:37 AM,16,3,9:22:30 AM,16,3,9:36:39 AM,16,3,10:22:40 AM,11,19.8,,-1.225322,36.80855,-1.291768,36.8297208,Rider_Id_101,2761
-Order_No_25768,User_Id_1502,Bike,3,Business,23,2,12:00:36 PM,23,2,12:00:56 PM,23,2,12:13:00 PM,23,2,12:41:49 PM,23,2,1:19:32 PM,24,,,-1.3562374,36.904295399999995,-1.2232123,36.8932547,Rider_Id_19,2263
-Order_No_12421,User_Id_3586,Bike,3,Business,22,5,2:32:52 PM,22,5,2:36:56 PM,22,5,2:38:55 PM,22,5,2:42:08 PM,22,5,3:53:53 PM,9,31.0,,-1.2854453000000001,36.818836299999994,-1.2675333999999998,36.7658246,Rider_Id_521,4305
-Order_No_22598,User_Id_2364,Bike,3,Personal,8,1,2:13:43 PM,8,1,2:14:10 PM,8,1,2:22:29 PM,8,1,2:37:04 PM,8,1,3:01:17 PM,12,29.4,,-1.273786,36.7806375,-1.3121154,36.7109299,Rider_Id_652,1453
-Order_No_14670,User_Id_2330,Bike,3,Business,5,2,3:44:22 PM,5,2,3:51:18 PM,5,2,4:22:24 PM,5,2,4:40:26 PM,5,2,5:46:38 PM,22,25.0,,-1.3206433000000002,36.9117533,-1.2642903,36.80021370000001,Rider_Id_6,3972
-Order_No_18796,User_Id_1724,Bike,3,Business,10,1,8:16:43 AM,10,1,8:17:14 AM,10,1,9:13:06 AM,10,1,9:15:15 AM,10,1,9:36:59 AM,5,16.4,,-1.2638132,36.784977500000004,-1.2551895,36.7822034,Rider_Id_475,1304
-Order_No_3659,User_Id_1500,Bike,3,Business,21,4,1:12:34 PM,21,4,1:14:02 PM,21,4,1:20:15 PM,21,4,1:55:20 PM,21,4,2:12:06 PM,11,26.3,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_828,1006
-Order_No_10726,User_Id_3657,Bike,2,Personal,4,5,12:36:39 PM,4,5,12:38:56 PM,4,5,12:50:22 PM,4,5,12:56:51 PM,4,5,1:28:18 PM,9,22.7,,-1.22606,36.80039,-1.2683619,36.806599299999995,Rider_Id_729,1887
-Order_No_16704,User_Id_1480,Bike,3,Personal,6,4,10:43:20 AM,6,4,10:44:45 AM,6,4,11:10:56 AM,6,4,11:13:48 AM,6,4,11:29:23 AM,7,19.8,,-1.2911796,36.821506799999995,-1.2615888000000002,36.7928732,Rider_Id_158,935
-Order_No_7318,User_Id_1458,Bike,1,Personal,27,7,10:45:45 AM,27,7,11:06:50 AM,27,7,11:30:10 AM,27,7,11:37:52 AM,27,7,11:59:21 AM,14,20.3,,-1.274381,36.7272265,-1.3075974,36.8100855,Rider_Id_103,1289
-Order_No_21131,User_Id_3339,Bike,3,Business,5,5,10:15:21 AM,5,5,10:17:38 AM,5,5,10:28:57 AM,5,5,10:40:27 AM,5,5,10:49:11 AM,9,24.0,,-1.2551895,36.7822034,-1.3014461000000002,36.7661381,Rider_Id_754,524
-Order_No_16884,User_Id_2865,Bike,1,Personal,9,4,9:40:19 AM,9,4,9:40:46 AM,9,4,9:50:56 AM,9,4,9:55:10 AM,9,4,10:16:19 AM,8,17.3,,-1.324249,36.858201,-1.3183563,36.8140249,Rider_Id_726,1269
-Order_No_14463,User_Id_2900,Bike,3,Business,19,2,10:27:52 AM,19,2,10:28:18 AM,19,2,10:36:22 AM,19,2,10:38:49 AM,19,2,11:24:20 AM,31,26.7,,-1.2963097,36.7688221,-1.1399609,36.9472164,Rider_Id_299,2731
-Order_No_21972,User_Id_60,Bike,3,Business,4,1,1:50:21 PM,4,1,1:51:32 PM,4,1,1:57:03 PM,4,1,2:37:20 PM,4,1,2:59:21 PM,14,28.5,,-1.2628473,36.781805,-1.2296618000000001,36.84382720000001,Rider_Id_882,1321
-Order_No_22191,User_Id_3533,Bike,3,Business,16,5,1:29:16 PM,16,5,1:39:40 PM,16,5,1:51:41 PM,16,5,1:59:32 PM,16,5,2:28:38 PM,9,,,-1.2615888000000002,36.7928732,-1.29515,36.844874,Rider_Id_660,1746
-Order_No_25147,User_Id_3599,Bike,3,Business,13,4,1:33:20 PM,13,4,1:44:18 PM,13,4,1:54:35 PM,13,4,2:36:23 PM,13,4,3:49:46 PM,8,26.3,,-1.3302996,36.8707935,-1.3079595,36.833501500000004,Rider_Id_312,4403
-Order_No_5995,User_Id_718,Bike,3,Business,16,4,12:39:21 PM,16,4,12:47:39 PM,16,4,12:54:10 PM,16,4,12:58:56 PM,16,4,1:23:25 PM,8,14.7,,-1.2551895,36.7822034,-1.2964347999999999,36.8182757,Rider_Id_848,1469
-Order_No_25924,User_Id_3621,Bike,3,Business,25,1,9:41:22 AM,25,1,9:42:43 AM,25,1,9:47:15 AM,25,1,9:56:36 AM,25,1,10:10:21 AM,7,21.3,,-1.2836583000000001,36.7886014,-1.2640571000000003,36.7883547,Rider_Id_156,825
-Order_No_11055,User_Id_2803,Bike,3,Business,13,3,1:14:14 PM,13,3,1:14:40 PM,13,3,1:19:13 PM,13,3,1:20:35 PM,13,3,1:55:30 PM,16,24.6,,-1.2765736,36.851364600000004,-1.269767,36.7351068,Rider_Id_411,2095
-Order_No_8985,User_Id_2457,Bike,3,Business,7,3,11:30:55 AM,7,3,11:31:34 AM,7,3,11:38:31 AM,7,3,11:44:11 AM,7,3,12:42:00 PM,3,25.3,,-1.2551895,36.7822034,-1.2544973,36.8061844,Rider_Id_646,3469
-Order_No_12136,User_Id_295,Bike,3,Business,14,3,11:59:26 AM,14,3,1:21:11 PM,14,3,1:45:38 PM,14,3,1:48:18 PM,14,3,2:05:18 PM,6,25.9,,-1.2967108,36.787113500000004,-1.286925,36.821915999999995,Rider_Id_284,1020
-Order_No_16399,User_Id_2330,Bike,3,Business,15,5,12:15:06 PM,15,5,12:15:58 PM,15,5,12:19:36 PM,15,5,12:22:45 PM,15,5,12:32:49 PM,3,26.2,,-1.3015197,36.7658456,-1.2873232,36.765857200000006,Rider_Id_575,604
-Order_No_27642,User_Id_1663,Bike,1,Personal,23,3,11:54:34 AM,23,3,11:57:11 AM,23,3,12:23:50 PM,23,3,12:28:01 PM,23,3,1:09:26 PM,8,21.6,,-1.2798711,36.8207118,-1.3205669,36.836216799999995,Rider_Id_87,2485
-Order_No_11013,User_Id_136,Bike,3,Business,23,4,10:17:35 AM,23,4,10:22:57 AM,23,4,10:29:40 AM,23,4,10:33:29 AM,23,4,10:59:07 AM,14,23.3,,-1.3254522,36.721658399999995,-1.2950408000000002,36.809817200000005,Rider_Id_855,1538
-Order_No_12119,User_Id_1363,Bike,3,Business,3,5,10:30:48 AM,3,5,10:31:12 AM,3,5,10:31:58 AM,3,5,10:48:07 AM,3,5,10:54:32 AM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_686,385
-Order_No_16405,User_Id_1329,Bike,3,Business,9,6,11:29:57 AM,9,6,11:30:27 AM,9,6,12:01:01 PM,9,6,12:03:26 PM,9,6,12:31:33 PM,14,24.9,,-1.273056,36.811298,-1.3446058,36.760517799999995,Rider_Id_660,1687
-Order_No_5275,User_Id_1075,Bike,3,Business,29,5,12:10:36 PM,29,5,12:12:33 PM,29,5,12:51:58 PM,29,5,12:57:59 PM,29,5,1:05:55 PM,1,26.9,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_96,476
-Order_No_15776,User_Id_265,Bike,3,Business,5,3,9:38:17 AM,5,3,9:40:57 AM,5,3,9:49:20 AM,5,3,9:56:08 AM,5,3,10:14:43 AM,6,17.8,,-1.2739008,36.813977200000004,-1.300921,36.828195,Rider_Id_596,1115
-Order_No_9250,User_Id_2412,Bike,3,Business,28,5,12:01:24 PM,28,5,12:02:11 PM,28,5,12:10:58 PM,28,5,12:13:37 PM,28,5,12:13:50 PM,3,23.9,,-1.2156006000000001,36.8916865,-1.2285834999999998,36.882646799999996,Rider_Id_803,13
-Order_No_4469,User_Id_1244,Bike,3,Business,7,1,12:42:08 PM,7,1,12:42:23 PM,7,1,12:47:22 PM,7,1,12:56:07 PM,7,1,1:10:36 PM,8,25.0,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_655,869
-Order_No_18787,User_Id_955,Bike,2,Personal,4,3,2:36:16 PM,4,3,2:39:07 PM,4,3,2:51:55 PM,4,3,3:00:36 PM,4,3,3:12:31 PM,8,18.4,,-1.2579171999999998,36.79383370000001,-1.2846195,36.7958316,Rider_Id_103,715
-Order_No_5730,User_Id_627,Bike,3,Business,17,3,7:47:39 AM,17,3,7:49:06 AM,17,3,8:09:41 AM,17,3,8:21:02 AM,17,3,8:40:53 AM,7,,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_155,1191
-Order_No_22686,User_Id_998,Bike,2,Personal,25,3,9:14:00 AM,25,3,9:14:16 AM,25,3,9:16:53 AM,25,3,9:25:16 AM,25,3,9:45:44 AM,5,13.6,,-1.3043821000000002,36.7840586,-1.3228978,36.778502200000005,Rider_Id_726,1228
-Order_No_3097,User_Id_1724,Bike,3,Business,4,3,8:33:13 AM,4,3,8:34:09 AM,4,3,8:49:40 AM,4,3,9:00:18 AM,4,3,9:24:54 AM,14,15.2,,-1.2551895,36.7822034,-1.2482465,36.8889062,Rider_Id_101,1476
-Order_No_13637,User_Id_2412,Bike,3,Business,28,5,2:35:56 PM,28,5,2:38:26 PM,28,5,3:06:10 PM,28,5,3:23:43 PM,28,5,3:42:56 PM,9,25.6,,-1.2297202,36.881950700000004,-1.2236966,36.9290782,Rider_Id_408,1153
-Order_No_27461,User_Id_635,Bike,3,Business,10,1,10:41:06 AM,10,1,10:47:00 AM,10,1,11:14:51 AM,10,1,11:39:13 AM,10,1,11:39:21 AM,5,18.7,,-1.2867454,36.8259017,-1.2602337,36.79905479999999,Rider_Id_766,8
-Order_No_15286,User_Id_1930,Bike,2,Personal,17,1,12:15:29 PM,17,1,12:15:43 PM,17,1,12:19:14 PM,17,1,12:22:10 PM,17,1,12:44:43 PM,10,25.0,,-1.2873232,36.765857200000006,-1.2655306,36.828148,Rider_Id_88,1353
-Order_No_24425,User_Id_60,Bike,3,Business,7,2,9:45:31 AM,7,2,9:47:05 AM,7,2,10:10:09 AM,7,2,10:22:31 AM,7,2,10:47:03 AM,10,,,-1.2600926000000001,36.8088685,-1.2297799999999999,36.836215,Rider_Id_147,1472
-Order_No_11959,User_Id_576,Bike,1,Personal,8,3,11:53:53 AM,8,3,11:54:31 AM,8,3,12:01:27 PM,8,3,12:11:00 PM,8,3,12:31:14 PM,5,22.1,,-1.30217,36.770468,-1.2760258999999998,36.769888,Rider_Id_179,1214
-Order_No_8743,User_Id_3283,Bike,3,Business,26,2,12:48:59 PM,26,2,12:49:47 PM,26,2,12:51:57 PM,26,2,12:56:34 PM,26,2,1:22:20 PM,9,27.6,,-1.2999309,36.830799799999994,-1.2628473,36.781805,Rider_Id_519,1546
-Order_No_19204,User_Id_635,Bike,3,Business,8,3,10:18:03 AM,8,3,10:18:39 AM,8,3,10:18:53 AM,8,3,11:54:07 AM,8,3,12:09:42 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_206,935
-Order_No_18975,User_Id_3688,Bike,3,Business,19,4,4:03:12 PM,19,4,4:03:30 PM,19,4,4:17:41 PM,19,4,4:21:01 PM,19,4,4:28:07 PM,3,20.7,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_903,426
-Order_No_16067,User_Id_3621,Bike,3,Business,2,6,2:37:07 PM,2,6,2:43:33 PM,2,6,3:04:35 PM,2,6,3:08:21 PM,2,6,3:44:41 PM,15,30.9,,-1.2836583000000001,36.7886014,-1.2285834999999998,36.882646799999996,Rider_Id_25,2180
-Order_No_14496,User_Id_3673,Bike,1,Personal,12,5,10:38:12 AM,12,5,10:38:18 AM,12,5,10:44:50 AM,12,5,10:49:57 AM,12,5,11:03:12 AM,8,24.5,,-1.32571,36.805533000000004,-1.3196808999999998,36.8410572,Rider_Id_785,795
-Order_No_14991,User_Id_136,Bike,3,Business,6,1,10:54:54 AM,6,1,10:55:51 AM,6,1,11:04:05 AM,6,1,11:08:25 AM,6,1,11:23:03 AM,4,,,-1.2965045,36.7913036,-1.3009676000000001,36.7657189,Rider_Id_952,878
-Order_No_20732,User_Id_1500,Bike,3,Business,21,5,3:13:53 PM,21,5,3:17:22 PM,21,5,3:29:24 PM,21,5,4:26:10 PM,21,5,4:42:52 PM,8,27.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_925,1002
-Order_No_8737,User_Id_346,Bike,3,Business,19,5,2:43:20 PM,19,5,2:44:18 PM,19,5,2:56:53 PM,19,5,3:06:26 PM,19,5,3:23:25 PM,6,,,-1.28878,36.8168312,-1.2527958000000001,36.8003131,Rider_Id_705,1019
-Order_No_13263,User_Id_928,Bike,3,Business,13,1,1:23:30 PM,13,1,1:23:48 PM,13,1,1:28:27 PM,13,1,2:17:27 PM,13,1,2:44:43 PM,8,,,-1.2878913,36.8089253,-1.2551895,36.7822034,Rider_Id_451,1636
-Order_No_19000,User_Id_53,Bike,3,Business,8,5,11:48:39 AM,8,5,11:50:41 AM,8,5,12:17:31 PM,8,5,12:28:22 PM,8,5,12:46:51 PM,5,28.4,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_88,1109
-Order_No_26775,User_Id_867,Bike,3,Business,21,1,11:41:43 AM,21,1,12:00:01 PM,21,1,12:11:33 PM,21,1,12:29:14 PM,21,1,12:53:14 PM,10,25.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_518,1440
-Order_No_21113,User_Id_111,Bike,3,Business,7,3,12:19:34 PM,7,3,12:19:45 PM,7,3,12:21:10 PM,7,3,12:32:40 PM,7,3,12:43:01 PM,4,25.0,,-1.2551895,36.7822034,-1.2663525,36.812833700000006,Rider_Id_932,621
-Order_No_15705,User_Id_2956,Bike,3,Business,17,2,9:55:42 AM,17,2,9:57:44 AM,17,2,10:20:18 AM,17,2,10:56:31 AM,17,2,11:13:59 AM,7,19.0,,-1.2885528999999998,36.816014100000004,-1.2584143,36.8048002,Rider_Id_714,1048
-Order_No_15865,User_Id_1500,Bike,3,Business,15,5,10:10:05 AM,15,5,10:10:13 AM,15,5,10:10:44 AM,15,5,10:16:20 AM,15,5,10:41:43 AM,13,19.3,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_448,1523
-Order_No_11579,User_Id_3647,Bike,3,Business,12,3,4:50:13 PM,12,3,4:51:51 PM,12,3,5:02:23 PM,12,3,5:05:43 PM,12,3,5:44:51 PM,18,20.4,,-1.2726389999999999,36.794723,-1.344748,36.745184,Rider_Id_158,2348
-Order_No_9054,User_Id_2330,Bike,3,Business,20,3,3:48:09 PM,20,3,3:48:31 PM,20,3,4:05:25 PM,20,3,4:15:35 PM,20,3,4:34:47 PM,10,23.1,,-1.2524916000000001,36.8656196,-1.2614329,36.8094365,Rider_Id_851,1152
-Order_No_19359,User_Id_3391,Bike,1,Personal,27,4,12:05:43 PM,27,4,12:06:40 PM,27,4,12:25:08 PM,27,4,12:50:15 PM,27,4,1:19:43 PM,5,23.0,,-1.2998789,36.816896299999996,-1.283016,36.801959499999995,Rider_Id_87,1768
-Order_No_8563,User_Id_394,Bike,3,Personal,29,1,8:50:38 AM,29,1,8:51:06 AM,29,1,9:38:36 AM,29,1,9:44:22 AM,29,1,11:01:18 AM,19,,,-1.3175925,36.8742349,-1.2383091999999998,36.7573096,Rider_Id_792,4616
-Order_No_21522,User_Id_1271,Bike,3,Business,5,2,9:33:32 AM,5,2,10:47:46 AM,5,2,11:02:20 AM,5,2,11:14:58 AM,5,2,11:27:18 AM,6,21.4,,-1.3332749,36.870814700000004,-1.3244885,36.897791999999995,Rider_Id_890,740
-Order_No_26127,User_Id_835,Bike,3,Business,14,2,3:55:54 PM,14,2,3:59:09 PM,14,2,4:11:13 PM,14,2,4:41:50 PM,14,2,5:02:00 PM,15,,,-1.3002926000000001,36.8668523,-1.3700383,36.9190174,Rider_Id_411,1210
-Order_No_27230,User_Id_302,Bike,3,Business,10,7,3:09:15 PM,10,7,3:11:04 PM,10,7,3:33:38 PM,10,7,3:36:20 PM,10,7,4:04:41 PM,8,29.1,,-1.2649605,36.798177700000004,-1.3064585,36.8119065,Rider_Id_459,1701
-Order_No_9354,User_Id_1370,Bike,3,Personal,18,1,12:50:01 PM,18,1,12:51:03 PM,18,1,1:02:40 PM,18,1,1:10:00 PM,18,1,1:36:02 PM,11,27.8,,-1.261692,36.80161,-1.32671,36.8600138,Rider_Id_219,1562
-Order_No_11936,User_Id_3496,Bike,3,Business,3,3,3:37:41 PM,3,3,3:37:52 PM,3,3,3:41:58 PM,3,3,4:01:50 PM,3,3,4:49:48 PM,12,26.7,,-1.300921,36.828195,-1.2638504,36.918938,Rider_Id_848,2878
-Order_No_937,User_Id_3295,Bike,3,Business,28,2,3:04:23 PM,28,2,3:06:00 PM,28,2,3:41:49 PM,28,2,3:43:46 PM,28,2,4:06:22 PM,10,23.4,,-1.2571472,36.795063299999995,-1.2998093000000002,36.859522600000005,Rider_Id_260,1356
-Order_No_132,User_Id_517,Bike,3,Business,27,4,10:22:21 AM,27,4,10:22:57 AM,27,4,10:23:02 AM,27,4,10:34:17 AM,27,4,10:56:17 AM,14,23.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,1320
-Order_No_11700,User_Id_393,Bike,3,Business,13,4,2:01:11 PM,13,4,2:01:43 PM,13,4,2:12:22 PM,13,4,2:27:14 PM,13,4,2:39:14 PM,4,23.0,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_538,720
-Order_No_22122,User_Id_867,Bike,3,Business,2,3,12:37:15 PM,2,3,12:37:18 PM,2,3,12:38:03 PM,2,3,1:05:43 PM,2,3,1:31:04 PM,14,22.0,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_294,1521
-Order_No_10971,User_Id_248,Bike,3,Business,20,1,1:31:11 PM,20,1,1:31:38 PM,20,1,1:45:00 PM,20,1,1:48:49 PM,20,1,2:19:13 PM,8,,,-1.304735,36.784605400000004,-1.2635089,36.78873370000001,Rider_Id_177,1824
-Order_No_2876,User_Id_2792,Bike,3,Personal,16,3,2:15:17 PM,16,3,2:38:38 PM,16,3,2:45:29 PM,16,3,2:51:35 PM,16,3,3:32:06 PM,6,25.7,,-1.3019136999999998,36.7925105,-1.286937,36.824674200000004,Rider_Id_352,2431
-Order_No_20028,User_Id_2521,Bike,3,Business,1,1,2:02:30 PM,1,1,2:03:05 PM,1,1,2:15:12 PM,1,1,2:21:14 PM,1,1,2:27:41 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_809,387
-Order_No_25992,User_Id_3644,Bike,1,Personal,13,5,11:42:02 AM,13,5,11:42:26 AM,13,5,11:43:29 AM,13,5,11:59:11 AM,13,5,12:24:33 PM,5,,,-1.3205506999999999,36.8426247,-1.317411,36.818222,Rider_Id_546,1522
-Order_No_4926,User_Id_2330,Bike,3,Business,29,3,1:51:25 PM,29,3,1:53:12 PM,29,3,1:57:05 PM,29,3,2:24:15 PM,29,3,2:59:04 PM,14,20.9,,-1.285908,36.8980126,-1.2671943,36.8062533,Rider_Id_272,2089
-Order_No_16265,User_Id_2987,Bike,3,Business,29,1,11:35:44 AM,29,1,11:41:44 AM,29,1,11:42:17 AM,29,1,11:53:27 AM,29,1,12:18:18 PM,4,,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_51,1491
-Order_No_11541,User_Id_1104,Bike,2,Business,17,1,9:00:33 AM,17,1,9:00:52 AM,17,1,9:07:51 AM,17,1,9:09:40 AM,17,1,9:17:57 AM,4,18.1,,-1.2639238000000002,36.7889646,-1.2551895,36.7822034,Rider_Id_731,497
-Order_No_21955,User_Id_375,Bike,3,Business,29,3,4:10:34 PM,29,3,4:14:33 PM,29,3,4:34:19 PM,29,3,4:45:41 PM,29,3,5:29:39 PM,8,18.9,,-1.2551895,36.7822034,-1.2939132,36.8081606,Rider_Id_498,2638
-Order_No_15376,User_Id_1133,Bike,3,Business,28,4,2:17:21 PM,28,4,2:18:26 PM,28,4,2:31:56 PM,28,4,2:37:20 PM,28,4,3:09:22 PM,13,27.4,,-1.2944292,36.779356899999996,-1.3263105,36.8622904,Rider_Id_599,1922
-Order_No_18648,User_Id_3487,Bike,3,Business,12,3,4:54:36 PM,12,3,4:56:01 PM,12,3,5:04:44 PM,12,3,5:08:51 PM,12,3,5:15:21 PM,1,20.5,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_36,390
-Order_No_25319,User_Id_2421,Bike,2,Personal,15,1,10:04:24 AM,15,1,10:04:52 AM,15,1,10:15:24 AM,15,1,10:21:06 AM,15,1,10:54:45 AM,20,,,-1.2724096,36.8135361,-1.3447079,36.91105,Rider_Id_409,2019
-Order_No_4779,User_Id_2701,Bike,3,Business,5,3,10:33:45 AM,5,3,10:38:25 AM,5,3,10:52:47 AM,5,3,10:54:42 AM,5,3,11:31:01 AM,4,22.4,,-1.2765736,36.851364600000004,-1.2857548,36.819062,Rider_Id_660,2179
-Order_No_21735,User_Id_3599,Bike,3,Business,28,4,2:26:17 PM,28,4,2:26:41 PM,28,4,2:55:21 PM,28,4,3:19:17 PM,28,4,3:47:15 PM,7,27.5,,-1.3302996,36.8707935,-1.3094481000000002,36.8652486,Rider_Id_218,1678
-Order_No_10542,User_Id_1535,Bike,1,Business,6,3,4:29:13 PM,6,3,4:31:29 PM,6,3,4:42:44 PM,6,3,4:52:07 PM,6,3,5:30:26 PM,10,19.8,,-1.2897368,36.8226783,-1.2994188,36.8903586,Rider_Id_360,2299
-Order_No_24262,User_Id_1368,Bike,3,Business,11,2,10:32:58 AM,11,2,10:34:11 AM,11,2,10:41:57 AM,11,2,10:50:29 AM,11,2,11:13:06 AM,7,19.6,,-1.2571472,36.795063299999995,-1.2898353999999999,36.787270299999996,Rider_Id_345,1357
-Order_No_23112,User_Id_3404,Bike,3,Business,11,2,1:00:50 PM,11,2,1:34:42 PM,11,2,1:37:28 PM,11,2,1:56:43 PM,11,2,2:03:09 PM,1,22.5,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_845,386
-Order_No_10030,User_Id_1899,Bike,3,Business,23,3,9:28:59 AM,23,3,10:10:36 AM,23,3,10:57:47 AM,23,3,11:03:27 AM,23,3,11:20:26 AM,15,21.6,,-1.3444459999999998,36.756597,-1.331619,36.847976,Rider_Id_846,1019
-Order_No_18731,User_Id_315,Bike,2,Personal,6,6,10:15:52 AM,6,6,10:19:05 AM,6,6,10:29:56 AM,6,6,10:32:49 AM,6,6,10:58:33 AM,10,24.3,,-1.3172339,36.820191799999996,-1.278068,36.8739571,Rider_Id_74,1544
-Order_No_12418,User_Id_2412,Bike,3,Business,8,3,2:04:29 PM,8,3,2:05:26 PM,8,3,2:13:34 PM,8,3,2:22:32 PM,8,3,3:28:48 PM,23,,,-1.339046,36.762596,-1.2283403,36.8822756,Rider_Id_533,3976
-Order_No_21297,User_Id_3658,Bike,3,Personal,10,2,9:15:28 AM,10,2,9:22:43 AM,10,2,9:45:45 AM,10,2,9:52:30 AM,10,2,10:36:35 AM,5,16.2,,-1.2886923000000001,36.8241519,-1.2624229999999999,36.801647100000004,Rider_Id_726,2645
-Order_No_9487,User_Id_393,Bike,3,Business,17,3,2:54:32 PM,17,3,2:54:56 PM,17,3,2:55:02 PM,17,3,3:55:25 PM,17,3,3:55:29 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,4
-Order_No_20990,User_Id_435,Bike,3,Business,22,3,11:07:15 AM,22,3,11:08:12 AM,22,3,11:20:40 AM,22,3,11:28:16 AM,22,3,12:23:14 PM,12,23.3,,-1.2637139,36.76565479999999,-1.300921,36.828195,Rider_Id_269,3298
-Order_No_16412,User_Id_2212,Bike,3,Business,5,5,3:47:33 PM,5,5,3:48:02 PM,5,5,4:02:48 PM,5,5,4:54:29 PM,5,5,4:54:59 PM,14,26.2,,-1.2571472,36.795063299999995,-1.3291155000000001,36.8861,Rider_Id_307,30
-Order_No_16816,User_Id_3718,Bike,3,Business,5,5,3:49:04 PM,5,5,3:56:11 PM,5,5,4:05:08 PM,5,5,4:12:59 PM,5,5,4:27:58 PM,8,26.4,,-1.2566078,36.7832089,-1.2879697,36.8020506,Rider_Id_427,899
-Order_No_6462,User_Id_3340,Bike,3,Business,10,5,2:02:22 PM,10,5,2:03:02 PM,10,5,2:19:23 PM,10,5,2:44:41 PM,10,5,3:36:13 PM,21,21.7,,-1.2097181000000001,36.794259100000005,-1.2751928,36.7429386,Rider_Id_208,3092
-Order_No_8219,User_Id_2523,Bike,3,Business,31,4,12:43:19 PM,31,4,12:43:41 PM,31,4,12:50:00 PM,31,4,1:03:36 PM,31,4,1:03:39 PM,4,26.3,,-1.2551895,36.7822034,-1.2689015,36.7986106,Rider_Id_916,3
-Order_No_8837,User_Id_3230,Bike,2,Personal,3,4,9:06:33 AM,3,4,9:23:46 AM,3,4,9:34:08 AM,3,4,10:00:14 AM,3,4,10:20:17 AM,5,17.2,16.1,-1.2655306,36.828148,-1.2914795000000001,36.824897299999996,Rider_Id_89,1203
-Order_No_2629,User_Id_248,Bike,3,Business,12,4,4:54:35 PM,12,4,4:55:06 PM,12,4,4:59:58 PM,12,4,5:29:26 PM,12,4,5:56:10 PM,10,20.6,,-1.304735,36.784605400000004,-1.254191,36.809913200000004,Rider_Id_919,1604
-Order_No_15303,User_Id_1395,Bike,1,Personal,17,7,12:56:20 PM,17,7,1:23:30 PM,17,7,1:29:55 PM,17,7,1:31:49 PM,17,7,1:46:53 PM,10,15.7,,-1.3015907,36.7684633,-1.3149256999999999,36.8441635,Rider_Id_185,904
-Order_No_16064,User_Id_2412,Bike,3,Business,26,4,11:03:18 AM,26,4,11:03:30 AM,26,4,11:17:05 AM,26,4,11:28:57 AM,26,4,12:17:09 PM,14,18.9,,-1.2297202,36.881950700000004,-1.2571472,36.795063299999995,Rider_Id_614,2892
-Order_No_2111,User_Id_3552,Bike,1,Personal,8,1,12:46:25 PM,8,1,12:55:52 PM,8,1,1:04:48 PM,8,1,1:22:07 PM,8,1,1:32:03 PM,4,24.4,,-1.3099295,36.8436807,-1.3046007,36.821174299999996,Rider_Id_932,596
-Order_No_13551,User_Id_393,Bike,3,Business,7,5,12:04:17 PM,7,5,12:04:48 PM,7,5,12:15:58 PM,7,5,12:31:27 PM,7,5,12:58:40 PM,11,,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_591,1633
-Order_No_21685,User_Id_1687,Bike,3,Business,7,3,11:45:17 AM,7,3,11:45:45 AM,7,3,11:51:28 AM,7,3,12:16:58 PM,7,3,12:21:47 PM,2,24.9,,-1.2626574,36.8014034,-1.262718,36.797771000000004,Rider_Id_744,289
-Order_No_23404,User_Id_948,Bike,3,Business,28,2,10:12:16 AM,28,2,10:12:54 AM,28,2,10:30:11 AM,28,2,10:35:14 AM,28,2,10:45:35 AM,3,21.4,,-1.2594473999999998,36.8089122,-1.2662846,36.797013299999996,Rider_Id_829,621
-Order_No_18031,User_Id_3122,Bike,1,Personal,26,2,10:29:54 AM,26,2,10:30:05 AM,26,2,10:40:45 AM,26,2,11:06:59 AM,26,2,12:07:03 PM,15,,,-1.2750992,36.868900200000006,-1.2670847,36.7858231,Rider_Id_584,3604
-Order_No_20043,User_Id_3517,Bike,3,Business,26,6,10:26:10 AM,26,6,10:26:38 AM,26,6,10:28:02 AM,26,6,10:55:44 AM,26,6,11:41:50 AM,15,25.8,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_736,2766
-Order_No_13143,User_Id_2330,Bike,3,Business,24,4,6:02:40 PM,24,4,6:02:50 PM,24,4,6:15:09 PM,24,4,6:21:02 PM,24,4,6:54:40 PM,15,25.9,,-1.3143328,36.8137434,-1.3270197,36.7037303,Rider_Id_519,2018
-Order_No_17439,User_Id_20,Bike,3,Business,28,4,9:06:48 AM,28,4,9:08:29 AM,28,4,9:46:25 AM,28,4,9:48:19 AM,28,4,10:26:20 AM,13,19.5,,-1.3277438999999998,36.8646958,-1.2629666000000002,36.8068369,Rider_Id_763,2281
-Order_No_15618,User_Id_53,Bike,3,Business,8,5,1:50:37 PM,8,5,2:40:07 PM,8,5,3:05:32 PM,8,5,3:17:25 PM,8,5,3:59:54 PM,4,30.4,,-1.2770708,36.8231093,-1.3024505,36.8243189,Rider_Id_349,2549
-Order_No_26506,User_Id_2457,Bike,3,Business,9,4,2:53:57 PM,9,4,2:55:20 PM,9,4,3:18:37 PM,9,4,3:21:31 PM,9,4,3:38:44 PM,5,,,-1.2551895,36.7822034,-1.2740461,36.798519799999994,Rider_Id_547,1033
-Order_No_25885,User_Id_1578,Bike,3,Business,18,2,8:23:22 AM,18,2,8:58:05 AM,18,2,9:22:58 AM,18,2,9:33:11 AM,18,2,9:50:26 AM,2,16.6,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_932,1035
-Order_No_13602,User_Id_1544,Bike,3,Business,24,5,4:24:03 PM,24,5,5:20:22 PM,24,5,5:53:42 PM,24,5,6:09:46 PM,24,5,6:36:52 PM,13,20.1,,-1.2794301000000001,36.816247,-1.3123977,36.902807,Rider_Id_411,1626
-Order_No_6646,User_Id_2956,Bike,3,Business,11,2,2:30:26 PM,11,2,2:45:45 PM,11,2,2:54:06 PM,11,2,3:00:07 PM,11,2,3:17:09 PM,7,21.5,,-1.2911796,36.821506799999995,-1.2584143,36.8048002,Rider_Id_906,1022
-Order_No_26982,User_Id_1329,Bike,3,Business,8,5,7:42:50 PM,8,5,7:45:15 PM,8,5,7:53:21 PM,8,5,7:59:09 PM,8,5,8:16:09 PM,3,21.8,,-1.273056,36.811298,-1.2766169,36.791816,Rider_Id_427,1020
-Order_No_726,User_Id_1081,Bike,3,Business,6,3,12:22:44 PM,6,3,12:23:12 PM,6,3,1:02:53 PM,6,3,1:10:13 PM,6,3,1:51:17 PM,21,22.5,,-1.2860183,36.8975338,-1.3025826,36.7670807,Rider_Id_377,2464
-Order_No_5021,User_Id_280,Bike,1,Personal,2,6,3:39:42 PM,2,6,3:48:19 PM,2,6,3:53:39 PM,2,6,4:09:22 PM,2,6,4:23:23 PM,8,23.8,,-1.2696183,36.8021778,-1.2850645,36.84268370000001,Rider_Id_852,841
-Order_No_18937,User_Id_2330,Bike,3,Business,26,5,5:18:58 PM,26,5,5:20:08 PM,26,5,5:27:02 PM,26,5,5:30:55 PM,26,5,5:53:27 PM,6,,,-1.2645318,36.7981775,-1.2915246999999999,36.805575700000006,Rider_Id_168,1352
-Order_No_8619,User_Id_1162,Bike,2,Business,9,2,9:04:12 AM,9,2,9:07:22 AM,9,2,9:21:34 AM,9,2,9:50:58 AM,9,2,10:16:21 AM,8,17.5,3.9,-1.3122610000000001,36.794658,-1.2661263999999999,36.8030359,Rider_Id_329,1523
-Order_No_9463,User_Id_248,Bike,3,Business,23,4,10:05:04 AM,23,4,10:05:49 AM,23,4,10:17:51 AM,23,4,10:27:05 AM,23,4,10:33:52 AM,1,,,-1.304735,36.784605400000004,-1.2993231,36.7753372,Rider_Id_607,407
-Order_No_13295,User_Id_3635,Bike,1,Business,4,4,2:44:02 PM,4,4,2:44:38 PM,4,4,2:58:55 PM,4,4,3:00:02 PM,4,4,3:09:27 PM,3,25.0,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_72,565
-Order_No_7793,User_Id_2330,Bike,3,Business,31,5,1:02:20 PM,31,5,1:13:29 PM,31,5,1:59:55 PM,31,5,2:01:22 PM,31,5,2:54:28 PM,23,24.6,,-1.3798085,36.9290528,-1.2642903,36.80021370000001,Rider_Id_233,3186
-Order_No_26250,User_Id_2828,Bike,3,Business,22,3,4:28:15 PM,22,3,4:31:53 PM,22,3,5:24:46 PM,22,3,5:26:59 PM,22,3,5:49:13 PM,7,23.3,,-1.2918239,36.8235665,-1.2615888000000002,36.7928732,Rider_Id_674,1334
-Order_No_21062,User_Id_2383,Bike,3,Business,18,2,3:15:41 PM,18,2,3:16:38 PM,18,2,3:17:05 PM,18,2,3:24:32 PM,18,2,3:26:45 PM,2,25.4,,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_31,133
-Order_No_18232,User_Id_2627,Bike,3,Business,14,2,2:53:58 PM,14,2,2:54:30 PM,14,2,3:00:02 PM,14,2,3:01:35 PM,14,2,3:12:40 PM,2,,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_138,665
-Order_No_12068,User_Id_2119,Bike,3,Personal,9,3,1:03:45 PM,9,3,1:05:49 PM,9,3,1:24:30 PM,9,3,1:42:14 PM,9,3,2:05:55 PM,3,23.5,,-1.280572,36.826162,-1.2706041000000001,36.830314200000004,Rider_Id_101,1421
-Order_No_27297,User_Id_637,Bike,2,Business,4,2,1:01:52 PM,4,2,1:05:06 PM,4,2,1:27:50 PM,4,2,1:30:57 PM,4,2,2:25:27 PM,12,19.0,,-1.2651218000000002,36.8228765,-1.2577288999999998,36.74107720000001,Rider_Id_754,3270
-Order_No_25522,User_Id_3517,Bike,3,Business,2,2,10:23:19 AM,2,2,10:23:30 AM,2,2,10:23:32 AM,2,2,11:03:16 AM,2,2,11:27:18 AM,15,26.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_595,1442
-Order_No_20821,User_Id_393,Bike,3,Business,24,3,4:20:43 PM,24,3,4:20:56 PM,24,3,4:22:15 PM,24,3,4:40:50 PM,24,3,4:52:19 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,689
-Order_No_3106,User_Id_2439,Bike,3,Business,13,6,10:33:49 AM,13,6,11:55:20 AM,13,6,12:05:51 PM,13,6,12:08:47 PM,13,6,12:29:16 PM,8,25.9,,-1.3014461000000002,36.7661381,-1.2886923000000001,36.8241519,Rider_Id_843,1229
-Order_No_1907,User_Id_2017,Bike,3,Business,9,1,7:22:58 AM,9,1,7:27:22 AM,9,1,7:56:38 AM,9,1,8:01:13 AM,9,1,8:11:41 AM,2,15.1,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_735,628
-Order_No_12725,User_Id_3688,Bike,3,Business,12,5,5:07:01 PM,12,5,5:08:26 PM,12,5,5:09:30 PM,12,5,5:31:14 PM,12,5,5:45:13 PM,8,26.4,,-1.2551895,36.7822034,-1.2856826000000001,36.8255265,Rider_Id_291,839
-Order_No_17042,User_Id_835,Bike,3,Business,13,3,11:00:28 AM,13,3,11:01:31 AM,13,3,11:14:38 AM,13,3,11:25:03 AM,13,3,11:41:34 AM,7,20.1,,-1.2998129999999999,36.787139200000006,-1.304174,36.821956400000005,Rider_Id_726,991
-Order_No_13765,User_Id_1724,Bike,3,Business,18,4,12:06:25 PM,18,4,12:07:27 PM,18,4,12:11:48 PM,18,4,12:15:01 PM,18,4,12:46:41 PM,14,,,-1.2551895,36.7822034,-1.312745,36.875715,Rider_Id_690,1900
-Order_No_20127,User_Id_893,Bike,3,Business,4,1,1:48:56 PM,4,1,1:50:29 PM,4,1,1:56:37 PM,4,1,1:58:39 PM,4,1,2:17:27 PM,6,,,-1.2663194,36.809901,-1.2807336,36.7791105,Rider_Id_535,1128
-Order_No_26675,User_Id_867,Bike,3,Business,7,3,10:10:22 AM,7,3,10:33:40 AM,7,3,11:00:06 AM,7,3,11:04:28 AM,7,3,11:53:46 AM,10,24.9,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_948,2958
-Order_No_19832,User_Id_2330,Bike,3,Business,21,4,10:31:37 AM,21,4,10:32:14 AM,21,4,10:40:41 AM,21,4,10:46:49 AM,21,4,11:00:42 AM,5,25.2,,-1.2642903,36.80021370000001,-1.2545163000000001,36.8243717,Rider_Id_547,833
-Order_No_27616,User_Id_867,Bike,3,Business,28,4,9:01:52 AM,28,4,9:02:29 AM,28,4,9:17:54 AM,28,4,9:21:01 AM,28,4,10:04:41 AM,10,22.4,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_448,2620
-Order_No_22547,User_Id_1927,Bike,3,Business,28,5,10:09:36 AM,28,5,10:09:40 AM,28,5,10:10:22 AM,28,5,10:11:47 AM,28,5,11:20:26 AM,20,20.7,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_179,4119
-Order_No_15726,User_Id_393,Bike,3,Business,29,1,2:32:28 PM,29,1,2:39:22 PM,29,1,2:39:26 PM,29,1,3:15:26 PM,29,1,3:40:32 PM,14,24.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_128,1506
-Order_No_15221,User_Id_412,Bike,3,Business,15,1,10:38:41 AM,15,1,10:38:57 AM,15,1,10:46:38 AM,15,1,10:55:27 AM,15,1,10:55:33 AM,4,,,-1.266837,36.799248999999996,-1.272828,36.816608,Rider_Id_424,6
-Order_No_17079,User_Id_2763,Bike,3,Business,15,4,11:09:00 AM,15,4,11:09:14 AM,15,4,11:17:19 AM,15,4,11:20:09 AM,15,4,11:43:29 AM,4,24.4,,-1.2551895,36.7822034,-1.2717851,36.810536299999995,Rider_Id_167,1400
-Order_No_10618,User_Id_1304,Bike,3,Business,7,5,12:55:12 PM,7,5,1:11:33 PM,7,5,1:37:49 PM,7,5,1:42:21 PM,7,5,2:30:01 PM,9,26.7,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_205,2860
-Order_No_23432,User_Id_510,Bike,3,Business,22,3,4:04:55 PM,22,3,4:06:29 PM,22,3,4:33:46 PM,22,3,4:52:34 PM,22,3,5:32:40 PM,18,23.9,,-1.2860183,36.8975338,-1.2668093,36.7966542,Rider_Id_697,2406
-Order_No_15875,User_Id_2154,Bike,3,Business,2,3,1:57:37 PM,2,3,1:58:17 PM,2,3,2:05:04 PM,2,3,2:26:18 PM,2,3,3:05:23 PM,5,26.0,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_933,2345
-Order_No_8642,User_Id_20,Bike,3,Business,1,3,10:58:13 AM,1,3,10:59:23 AM,1,3,11:05:37 AM,1,3,11:14:16 AM,1,3,11:28:14 AM,2,20.1,,-1.2628638,36.8070399,-1.263419,36.8060348,Rider_Id_208,838
-Order_No_5811,User_Id_1500,Bike,3,Business,28,4,1:04:16 PM,28,4,1:05:28 PM,28,4,1:12:10 PM,28,4,1:50:44 PM,28,4,1:52:53 PM,8,30.1,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,129
-Order_No_23612,User_Id_3115,Bike,3,Business,30,3,2:31:20 PM,30,3,2:31:26 PM,30,3,2:31:52 PM,30,3,2:42:18 PM,30,3,3:21:23 PM,11,28.8,,-1.2963097,36.7688221,-1.3108783000000002,36.8392726,Rider_Id_6,2345
-Order_No_17322,User_Id_2175,Bike,3,Business,15,5,9:30:12 AM,15,5,9:31:09 AM,15,5,9:31:19 AM,15,5,9:45:27 AM,15,5,10:07:19 AM,8,19.1,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_828,1312
-Order_No_19700,User_Id_419,Bike,3,Business,5,1,2:31:05 PM,5,1,3:30:17 PM,5,1,3:34:06 PM,5,1,3:49:43 PM,5,1,4:15:03 PM,6,26.1,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_19,1520
-Order_No_23165,User_Id_1721,Bike,3,Business,11,4,3:14:45 PM,11,4,3:16:19 PM,11,4,3:36:59 PM,11,4,3:42:43 PM,11,4,4:21:36 PM,9,27.2,,-1.2571472,36.795063299999995,-1.2295205,36.812042600000005,Rider_Id_933,2333
-Order_No_19635,User_Id_3222,Bike,3,Business,25,5,1:51:02 PM,25,5,1:51:33 PM,25,5,1:52:10 PM,25,5,1:58:23 PM,25,5,3:02:26 PM,22,29.1,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_579,3843
-Order_No_12003,User_Id_3422,Bike,3,Business,27,6,10:01:51 AM,27,6,11:19:16 AM,27,6,11:28:59 AM,27,6,11:36:30 AM,27,6,12:18:04 PM,15,,,-1.2776783,36.813190000000006,-1.3258393999999998,36.7118422,Rider_Id_937,2494
-Order_No_7947,User_Id_2763,Bike,3,Business,15,3,1:42:30 PM,15,3,1:43:21 PM,15,3,1:49:13 PM,15,3,1:51:26 PM,15,3,2:05:32 PM,5,21.5,,-1.2551895,36.7822034,-1.2659232,36.8235607,Rider_Id_415,846
-Order_No_14036,User_Id_190,Bike,3,Business,6,2,10:42:42 AM,6,2,11:11:33 AM,6,2,11:19:19 AM,6,2,11:28:05 AM,6,2,11:38:52 AM,3,24.9,,-1.3270347,36.866736200000005,-1.3385185,36.8725654,Rider_Id_559,647
-Order_No_23244,User_Id_2344,Bike,3,Business,29,2,4:08:38 PM,29,2,4:17:40 PM,29,2,4:32:28 PM,29,2,4:41:00 PM,29,2,5:05:55 PM,6,22.5,,-1.2553615,36.789537599999996,-1.280753,36.8191175,Rider_Id_805,1495
-Order_No_12268,User_Id_814,Bike,3,Business,31,2,1:36:01 PM,31,2,1:37:38 PM,31,2,1:51:27 PM,31,2,1:57:07 PM,31,2,2:44:10 PM,21,22.1,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_329,2823
-Order_No_16534,User_Id_2523,Bike,3,Business,25,1,10:48:09 AM,25,1,10:48:35 AM,25,1,10:53:27 AM,25,1,10:59:03 AM,25,1,11:17:45 AM,7,,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_908,1122
-Order_No_17427,User_Id_861,Bike,3,Personal,28,1,12:38:28 PM,28,1,12:39:06 PM,28,1,12:44:53 PM,28,1,12:58:08 PM,28,1,1:33:33 PM,20,22.9,,-1.2661263999999999,36.8030359,-1.335477,36.930829700000004,Rider_Id_103,2125
-Order_No_24219,User_Id_1392,Bike,3,Business,27,1,11:23:43 AM,27,1,11:29:10 AM,27,1,11:52:05 AM,27,1,12:02:38 PM,27,1,12:15:31 PM,3,20.7,,-1.2551895,36.7822034,-1.2683511,36.8006573,Rider_Id_617,773
-Order_No_26983,User_Id_1422,Bike,3,Business,13,4,3:27:51 PM,13,4,3:28:40 PM,13,4,3:42:09 PM,13,4,3:46:56 PM,13,4,3:56:31 PM,4,22.2,,-1.3261286,36.8469625,-1.3221059,36.8606598,Rider_Id_205,575
-Order_No_16218,User_Id_2174,Bike,3,Business,21,4,12:06:51 PM,21,4,12:07:00 PM,21,4,12:07:04 PM,21,4,12:57:56 PM,21,4,1:45:54 PM,29,28.2,,-1.2482781,36.884091999999995,-1.3927559999999999,36.7495015,Rider_Id_86,2878
-Order_No_20025,User_Id_3521,Bike,3,Business,9,2,9:07:40 AM,9,2,9:08:20 AM,9,2,9:15:48 AM,9,2,9:22:31 AM,9,2,9:33:02 AM,2,20.9,,-1.2696908000000002,36.81519,-1.2701354,36.807216700000005,Rider_Id_276,631
-Order_No_9529,User_Id_1244,Bike,3,Business,21,1,10:33:46 AM,21,1,10:34:00 AM,21,1,10:38:41 AM,21,1,10:52:26 AM,21,1,11:08:55 AM,9,25.7,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_607,989
-Order_No_23571,User_Id_1484,Bike,3,Business,30,3,2:40:04 PM,30,3,2:41:56 PM,30,3,2:46:56 PM,30,3,2:48:58 PM,30,3,3:02:46 PM,4,29.0,,-1.2551895,36.7822034,-1.2738521999999999,36.799281799999996,Rider_Id_576,828
-Order_No_13284,User_Id_2612,Bike,2,Personal,8,1,10:39:23 AM,8,1,11:03:59 AM,8,1,11:11:39 AM,8,1,11:15:16 AM,8,1,11:20:53 AM,2,24.6,,-1.2995738000000001,36.8199719,-1.286937,36.824674200000004,Rider_Id_662,337
-Order_No_1534,User_Id_1073,Bike,3,Business,4,5,1:58:34 PM,4,5,1:58:51 PM,4,5,2:04:44 PM,4,5,2:10:19 PM,4,5,2:51:20 PM,9,,,-1.2563576,36.7881585,-1.3017433,36.814958600000004,Rider_Id_382,2461
-Order_No_11625,User_Id_3689,Bike,3,Business,26,5,12:14:47 PM,26,5,12:15:02 PM,26,5,12:22:38 PM,26,5,12:35:41 PM,26,5,12:42:15 PM,1,,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_566,394
-Order_No_14003,User_Id_1471,Bike,3,Business,9,5,4:39:05 PM,9,5,4:47:42 PM,9,5,4:56:18 PM,9,5,5:10:26 PM,9,5,5:39:56 PM,9,24.9,,-1.2551895,36.7822034,-1.2917868000000001,36.7872675,Rider_Id_536,1770
-Order_No_13824,User_Id_2884,Bike,2,Personal,1,5,9:08:18 AM,1,5,9:09:06 AM,1,5,9:40:42 AM,1,5,9:45:47 AM,1,5,11:22:59 AM,12,17.9,,-1.2764692,36.758011100000004,-1.3084771000000002,36.68971629999999,Rider_Id_651,5832
-Order_No_17882,User_Id_778,Bike,3,Business,15,1,7:34:31 AM,15,1,7:36:16 AM,15,1,7:45:08 AM,15,1,7:47:35 AM,15,1,8:11:07 AM,10,20.7,,-1.2535486,36.7933541,-1.275931,36.8516227,Rider_Id_107,1412
-Order_No_2445,User_Id_3128,Bike,1,Personal,21,5,10:39:39 AM,21,5,10:39:52 AM,21,5,11:08:55 AM,21,5,11:17:05 AM,21,5,11:39:39 AM,5,20.1,,-1.2653957,36.8384937,-1.2753108999999998,36.8130185,Rider_Id_427,1354
-Order_No_22872,User_Id_1205,Bike,3,Business,26,5,11:53:33 AM,26,5,12:10:19 PM,26,5,12:16:34 PM,26,5,12:19:35 PM,26,5,12:29:44 PM,3,,,-1.257309,36.806008399999996,-1.2700436000000002,36.820997600000005,Rider_Id_383,609
-Order_No_27883,User_Id_1737,Bike,1,Personal,13,1,9:55:18 AM,13,1,9:56:18 AM,13,1,10:03:53 AM,13,1,10:05:23 AM,13,1,10:25:37 AM,9,15.4,,-1.2665973999999998,36.7921181,-1.2950408000000002,36.809817200000005,Rider_Id_770,1214
-Order_No_15708,User_Id_1146,Bike,3,Business,5,2,1:15:48 PM,5,2,1:17:04 PM,5,2,1:17:10 PM,5,2,2:00:30 PM,5,2,2:20:15 PM,6,27.3,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_809,1185
-Order_No_2177,User_Id_2828,Bike,3,Business,13,4,4:00:35 PM,13,4,4:09:53 PM,13,4,4:27:15 PM,13,4,4:37:40 PM,13,4,4:46:15 PM,3,22.1,,-1.2685707,36.806496100000004,-1.2615888000000002,36.7928732,Rider_Id_829,515
-Order_No_10441,User_Id_3300,Bike,3,Business,6,1,5:06:51 PM,6,1,5:07:14 PM,6,1,5:10:15 PM,6,1,5:23:45 PM,6,1,5:46:13 PM,9,,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_72,1348
-Order_No_24139,User_Id_3291,Bike,3,Business,14,2,12:39:34 PM,14,2,12:41:31 PM,14,2,12:51:31 PM,14,2,1:06:06 PM,14,2,1:22:44 PM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_361,998
-Order_No_1099,User_Id_496,Bike,3,Business,17,3,4:32:35 PM,17,3,5:34:37 PM,17,3,5:49:31 PM,17,3,5:54:56 PM,17,3,6:08:20 PM,6,24.8,,-1.2551895,36.7822034,-1.2785434,36.821532700000006,Rider_Id_208,804
-Order_No_3175,User_Id_2804,Bike,3,Business,11,1,2:33:40 PM,11,1,2:36:46 PM,11,1,2:42:41 PM,11,1,2:49:29 PM,11,1,3:00:54 PM,4,28.8,,-1.2551895,36.7822034,-1.2656922,36.813694399999996,Rider_Id_655,685
-Order_No_16375,User_Id_1250,Bike,1,Personal,11,4,3:13:18 PM,11,4,3:36:04 PM,11,4,3:47:40 PM,11,4,3:51:21 PM,11,4,4:14:01 PM,10,26.6,,-1.2619924,36.82266729999999,-1.2675333999999998,36.7658246,Rider_Id_406,1360
-Order_No_10221,User_Id_2346,Bike,1,Personal,30,6,3:32:58 PM,30,6,3:38:40 PM,30,6,3:54:03 PM,30,6,3:58:40 PM,30,6,4:23:25 PM,8,24.2,,-1.3054537,36.8284768,-1.2813742000000001,36.855892700000005,Rider_Id_212,1485
-Order_No_23503,User_Id_1368,Bike,3,Business,28,2,3:24:04 PM,28,2,3:24:47 PM,28,2,4:28:39 PM,28,2,4:31:22 PM,28,2,4:41:15 PM,4,23.4,,-1.2571472,36.795063299999995,-1.2674273999999999,36.787082700000006,Rider_Id_491,593
-Order_No_20933,User_Id_2789,Bike,3,Business,27,2,8:42:13 AM,27,2,8:49:33 AM,27,2,8:54:56 AM,27,2,9:06:01 AM,27,2,9:22:00 AM,7,18.7,2.0,-1.2862116000000001,36.81147,-1.304735,36.784605400000004,Rider_Id_726,959
-Order_No_12698,User_Id_3674,Bike,3,Business,26,2,10:02:42 AM,26,2,10:02:50 AM,26,2,10:03:14 AM,26,2,10:08:10 AM,26,2,10:13:25 AM,2,21.8,,-1.2551895,36.7822034,-1.2657756000000002,36.7926481,Rider_Id_291,315
-Order_No_26110,User_Id_627,Bike,3,Business,26,5,3:10:34 PM,26,5,3:10:45 PM,26,5,3:11:04 PM,26,5,3:17:30 PM,26,5,3:26:22 PM,6,,,-1.2551895,36.7822034,-1.2729291999999999,36.7679079,Rider_Id_438,532
-Order_No_3586,User_Id_1557,Bike,3,Business,29,2,11:12:39 AM,29,2,11:34:57 AM,29,2,11:40:37 AM,29,2,11:54:25 AM,29,2,12:16:33 PM,8,21.6,,-1.3147073999999999,36.811721999999996,-1.268027,36.8037005,Rider_Id_101,1328
-Order_No_24052,User_Id_577,Bike,1,Personal,13,1,2:33:37 PM,13,1,2:33:56 PM,13,1,2:42:29 PM,13,1,2:44:48 PM,13,1,3:07:37 PM,12,19.6,,-1.3270347,36.866736200000005,-1.2662728,36.8151265,Rider_Id_650,1369
-Order_No_17827,User_Id_1244,Bike,3,Business,28,4,11:07:31 AM,28,4,11:08:57 AM,28,4,11:23:54 AM,28,4,11:34:50 AM,28,4,11:55:25 AM,11,27.9,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_675,1235
-Order_No_28136,User_Id_3506,Bike,1,Personal,1,1,7:17:31 AM,1,1,7:24:19 AM,1,1,7:55:55 AM,1,1,7:59:05 AM,1,1,8:18:54 AM,6,18.2,,-1.2563531000000001,36.7896742,-1.2655306,36.828148,Rider_Id_50,1189
-Order_No_3694,User_Id_3214,Bike,3,Business,27,3,10:08:53 AM,27,3,10:09:19 AM,27,3,10:13:59 AM,27,3,10:19:18 AM,27,3,10:32:09 AM,3,21.5,,-1.304735,36.784605400000004,-1.289795,36.7957132,Rider_Id_708,771
-Order_No_9972,User_Id_1410,Bike,3,Business,1,4,8:31:25 AM,1,4,8:34:44 AM,1,4,8:49:25 AM,1,4,9:07:22 AM,1,4,9:30:36 AM,6,,,-1.300921,36.828195,-1.2941023999999999,36.79578,Rider_Id_206,1394
-Order_No_7626,User_Id_867,Bike,3,Business,19,1,11:08:55 AM,19,1,12:39:39 PM,19,1,12:52:43 PM,19,1,1:08:17 PM,19,1,1:33:41 PM,10,24.9,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_952,1524
-Order_No_10447,User_Id_1207,Bike,3,Business,6,3,11:53:06 AM,6,3,11:56:54 AM,6,3,12:06:55 PM,6,3,12:21:09 PM,6,3,12:45:06 PM,7,22.7,,-1.2629666000000002,36.8068369,-1.2745321000000003,36.765088,Rider_Id_887,1437
-Order_No_1685,User_Id_2554,Bike,3,Business,19,3,2:26:27 PM,19,3,2:47:10 PM,19,3,2:53:29 PM,19,3,3:11:15 PM,19,3,3:11:20 PM,6,24.4,,-1.2861764999999998,36.8209086,-1.2867836000000001,36.78325220000001,Rider_Id_404,5
-Order_No_24093,User_Id_3434,Bike,3,Business,23,2,11:15:37 AM,23,2,11:16:26 AM,23,2,11:49:20 AM,23,2,12:02:00 PM,23,2,12:36:36 PM,20,17.8,,-1.3244885,36.897791999999995,-1.2963097,36.7688221,Rider_Id_498,2076
-Order_No_21803,User_Id_393,Bike,3,Business,4,6,10:44:59 AM,4,6,10:48:22 AM,4,6,11:06:07 AM,4,6,11:07:49 AM,4,6,11:39:12 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_206,1883
-Order_No_22524,User_Id_1280,Bike,3,Business,8,5,10:59:04 AM,8,5,10:59:53 AM,8,5,10:59:59 AM,8,5,11:10:47 AM,8,5,12:43:39 PM,20,28.3,,-1.2551895,36.7822034,-1.1914276000000001,36.8954304,Rider_Id_373,5572
-Order_No_18545,User_Id_1245,Bike,3,Business,21,2,2:01:56 PM,21,2,2:02:10 PM,21,2,2:02:26 PM,21,2,2:23:51 PM,21,2,2:55:36 PM,17,25.0,,-1.300921,36.828195,-1.3049392,36.7148492,Rider_Id_595,1905
-Order_No_17308,User_Id_553,Bike,3,Business,23,2,2:44:21 PM,23,2,3:07:57 PM,23,2,3:34:42 PM,23,2,3:48:45 PM,23,2,4:27:55 PM,23,19.2,,-1.3632162,36.9108253,-1.2551895,36.7822034,Rider_Id_222,2350
-Order_No_2078,User_Id_2373,Bike,1,Personal,16,4,3:46:49 PM,16,4,3:47:10 PM,16,4,4:04:36 PM,16,4,4:31:24 PM,16,4,5:45:51 PM,15,19.1,,-1.2868838,36.8284259,-1.3326173000000001,36.71462329999999,Rider_Id_711,4467
-Order_No_1474,User_Id_3149,Bike,3,Business,23,3,1:23:34 PM,23,3,1:26:25 PM,23,3,1:26:29 PM,23,3,2:18:56 PM,23,3,2:18:58 PM,5,27.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_39,2
-Order_No_16017,User_Id_3253,Bike,1,Personal,6,5,10:02:20 AM,6,5,10:02:28 AM,6,5,10:13:20 AM,6,5,10:27:09 AM,6,5,11:03:36 AM,14,14.8,,-1.2238486000000002,36.8566907,-1.2615888000000002,36.7928732,Rider_Id_224,2187
-Order_No_7121,User_Id_2701,Bike,3,Business,23,3,10:26:05 AM,23,3,10:26:57 AM,23,3,10:27:01 AM,23,3,10:41:58 AM,23,3,10:53:42 AM,6,,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_137,704
-Order_No_20927,User_Id_2069,Bike,3,Business,11,1,12:28:40 PM,11,1,12:28:56 PM,11,1,12:53:06 PM,11,1,1:05:39 PM,11,1,1:18:42 PM,9,26.6,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_655,783
-Order_No_6931,User_Id_1820,Bike,3,Personal,3,5,11:52:04 AM,3,5,11:52:31 AM,3,5,11:56:21 AM,3,5,12:10:22 PM,3,5,12:33:34 PM,6,21.2,,-1.302088,36.831162,-1.2753108999999998,36.8130185,Rider_Id_89,1392
-Order_No_22630,User_Id_3365,Bike,2,Personal,2,4,3:27:11 PM,2,4,3:27:32 PM,2,4,3:28:03 PM,2,4,3:41:10 PM,2,4,4:10:58 PM,5,26.3,,-1.2860198,36.8176562,-1.2812997,36.798667200000004,Rider_Id_89,1788
-Order_No_1808,User_Id_3547,Bike,3,Business,31,4,9:47:01 AM,31,4,9:47:11 AM,31,4,10:37:52 AM,31,4,10:39:59 AM,31,4,11:21:25 AM,18,26.4,,-1.2856319,36.767299799999996,-1.3194518,36.869396,Rider_Id_362,2486
-Order_No_9484,User_Id_517,Bike,3,Business,4,5,2:58:33 PM,4,5,2:59:11 PM,4,5,3:03:11 PM,4,5,3:12:44 PM,4,5,3:48:41 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,2157
-Order_No_12764,User_Id_3147,Bike,3,Business,29,1,2:22:49 PM,29,1,2:23:12 PM,29,1,2:24:55 PM,29,1,2:32:41 PM,29,1,2:58:56 PM,17,,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_695,1575
-Order_No_20749,User_Id_136,Bike,3,Business,15,3,2:32:19 PM,15,3,2:33:46 PM,15,3,2:52:36 PM,15,3,3:16:06 PM,15,3,4:06:18 PM,17,25.9,,-1.2629838,36.8086926,-1.3254522,36.721658399999995,Rider_Id_325,3012
-Order_No_10921,User_Id_459,Bike,1,Personal,6,5,12:10:16 PM,6,5,12:16:57 PM,6,5,12:48:46 PM,6,5,12:57:43 PM,6,5,1:42:44 PM,10,20.4,,-1.3189407,36.8381862,-1.2655306,36.828148,Rider_Id_465,2701
-Order_No_12394,User_Id_370,Bike,1,Personal,2,4,5:11:05 PM,2,4,5:11:15 PM,2,4,5:14:47 PM,2,4,5:22:34 PM,2,4,5:47:41 PM,8,23.7,,-1.3028391000000001,36.7951112,-1.2649605,36.798177700000004,Rider_Id_322,1507
-Order_No_6262,User_Id_2314,Bike,2,Personal,11,2,7:19:16 PM,11,2,7:19:45 PM,11,2,7:43:20 PM,11,2,7:51:00 PM,11,2,8:19:35 PM,11,18.4,,-1.2839842,36.7921296,-1.2482518,36.7550829,Rider_Id_379,1715
-Order_No_25193,User_Id_3689,Bike,3,Business,23,4,10:34:56 AM,23,4,10:35:38 AM,23,4,11:29:18 AM,23,4,11:35:51 AM,23,4,12:06:53 PM,14,23.3,,-1.225322,36.80855,-1.1720874,36.8219039,Rider_Id_362,1862
-Order_No_13383,User_Id_970,Bike,3,Business,11,1,11:31:52 AM,11,1,11:32:09 AM,11,1,11:32:24 AM,11,1,11:37:22 AM,11,1,11:43:07 AM,3,26.4,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_793,345
-Order_No_1305,User_Id_517,Bike,3,Business,27,6,8:55:03 AM,27,6,8:55:15 AM,27,6,8:55:19 AM,27,6,9:38:10 AM,27,6,9:38:13 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_619,3
-Order_No_19488,User_Id_2271,Bike,3,Business,25,1,11:36:54 AM,25,1,11:43:27 AM,25,1,11:51:46 AM,25,1,12:06:53 PM,25,1,12:28:41 PM,11,25.7,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_283,1308
-Order_No_2560,User_Id_1724,Bike,3,Business,28,4,5:07:32 PM,28,4,5:08:12 PM,28,4,5:08:17 PM,28,4,5:30:24 PM,28,4,5:56:12 PM,11,27.9,,-1.32189,36.8589727,-1.2941648000000001,36.7977315,Rider_Id_647,1548
-Order_No_20571,User_Id_2150,Bike,3,Business,14,2,11:37:30 AM,14,2,11:44:30 AM,14,2,12:18:44 PM,14,2,12:24:04 PM,14,2,12:41:39 PM,6,,,-1.3181159,36.710155900000004,-1.3444459999999998,36.756597,Rider_Id_3,1055
-Order_No_5055,User_Id_3091,Bike,3,Business,14,2,4:57:49 PM,14,2,4:58:09 PM,14,2,5:01:01 PM,14,2,5:10:51 PM,14,2,5:45:55 PM,10,23.6,,-1.3014461000000002,36.7661381,-1.25896,36.8016691,Rider_Id_882,2104
-Order_No_22738,User_Id_1591,Bike,2,Personal,6,4,2:16:57 PM,6,4,2:44:14 PM,6,4,3:12:50 PM,6,4,3:31:48 PM,6,4,3:48:46 PM,9,23.3,,-1.2616056,36.879064899999996,-1.2655306,36.828148,Rider_Id_179,1018
-Order_No_7206,User_Id_1363,Bike,3,Business,14,4,9:06:53 AM,14,4,9:32:30 AM,14,4,9:39:20 AM,14,4,9:41:41 AM,14,4,10:10:51 AM,4,15.3,,-1.3004062,36.829741,-1.2819785,36.81015120000001,Rider_Id_285,1750
-Order_No_22624,User_Id_3647,Bike,3,Business,15,3,3:05:04 PM,15,3,3:05:37 PM,15,3,3:14:04 PM,15,3,3:21:38 PM,15,3,3:51:09 PM,6,25.8,,-1.2726389999999999,36.794723,-1.2977386999999998,36.803144700000004,Rider_Id_155,1771
-Order_No_24458,User_Id_2804,Bike,3,Business,17,3,1:04:44 PM,17,3,1:08:40 PM,17,3,1:15:47 PM,17,3,1:21:12 PM,17,3,1:34:49 PM,7,25.3,,-1.2551895,36.7822034,-1.279767,36.7695313,Rider_Id_208,817
-Order_No_9377,User_Id_2523,Bike,3,Business,8,1,12:26:41 PM,8,1,12:27:03 PM,8,1,12:31:10 PM,8,1,12:41:07 PM,8,1,1:06:09 PM,8,27.8,,-1.2551895,36.7822034,-1.2913246,36.8019522,Rider_Id_958,1502
-Order_No_13435,User_Id_3669,Bike,3,Business,7,4,1:31:20 PM,7,4,1:31:36 PM,7,4,1:38:18 PM,7,4,1:43:29 PM,7,4,1:56:41 PM,3,27.7,,-1.2615888000000002,36.7928732,-1.2641662,36.8136393,Rider_Id_708,792
-Order_No_13459,User_Id_1929,Bike,3,Personal,15,7,11:23:20 AM,15,7,11:32:54 AM,15,7,11:38:50 AM,15,7,11:59:20 AM,15,7,12:36:54 PM,17,18.1,,-1.2022049,36.8000124,-1.2879178999999998,36.7903517,Rider_Id_101,2254
-Order_No_18303,User_Id_1966,Bike,3,Business,12,3,10:31:50 AM,12,3,10:46:01 AM,12,3,10:58:17 AM,12,3,11:44:02 AM,12,3,12:19:48 PM,11,19.5,,-1.3700383,36.9190174,-1.3239211000000002,36.8707656,Rider_Id_436,2146
-Order_No_17919,User_Id_478,Bike,3,Business,8,5,1:06:14 PM,8,5,1:11:22 PM,8,5,1:21:15 PM,8,5,1:22:30 PM,8,5,1:33:20 PM,1,24.3,,-1.2551895,36.7822034,-1.2538731,36.7850223,Rider_Id_467,650
-Order_No_26958,User_Id_246,Bike,3,Business,24,4,10:03:59 AM,24,4,10:04:07 AM,24,4,10:33:21 AM,24,4,10:47:19 AM,24,4,11:44:25 AM,17,24.4,,-1.3472066,36.7692638,-1.2584143,36.8048002,Rider_Id_267,3426
-Order_No_18979,User_Id_1995,Bike,3,Business,2,4,5:27:55 PM,2,4,5:29:07 PM,2,4,5:40:24 PM,2,4,5:58:51 PM,2,4,6:27:13 PM,7,,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_81,1702
-Order_No_27213,User_Id_1384,Bike,3,Business,5,5,10:07:09 AM,5,5,10:08:10 AM,5,5,10:14:32 AM,5,5,10:22:31 AM,5,5,11:11:26 AM,17,19.6,,-1.2517448,36.7924971,-1.16478,36.8223899,Rider_Id_205,2935
-Order_No_26978,User_Id_972,Bike,3,Business,22,4,12:21:49 PM,22,4,12:24:51 PM,22,4,1:00:46 PM,22,4,1:06:09 PM,22,4,1:13:47 PM,2,22.5,,-1.2862033000000002,36.8152966,-1.2859714,36.8056536,Rider_Id_237,458
-Order_No_28034,User_Id_718,Bike,3,Business,29,3,8:57:27 AM,29,3,8:57:40 AM,29,3,9:01:41 AM,29,3,9:04:58 AM,29,3,9:37:01 AM,5,17.8,,-1.2551895,36.7822034,-1.2684816,36.808562200000004,Rider_Id_155,1923
-Order_No_25450,User_Id_3291,Bike,3,Business,1,5,10:34:19 AM,1,5,10:34:45 AM,1,5,10:50:24 AM,1,5,10:55:17 AM,1,5,11:07:01 AM,4,26.7,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_276,704
-Order_No_13882,User_Id_136,Bike,3,Business,2,5,11:10:59 AM,2,5,11:11:31 AM,2,5,11:31:28 AM,2,5,11:35:40 AM,2,5,12:01:02 PM,11,23.3,,-1.2962832,36.784968299999996,-1.3254522,36.721658399999995,Rider_Id_232,1522
-Order_No_15349,User_Id_635,Bike,3,Business,21,4,4:43:47 PM,21,4,4:44:45 PM,21,4,4:47:40 PM,21,4,4:58:58 PM,21,4,5:13:24 PM,6,28.6,,-1.2602337,36.79905479999999,-1.2556108,36.8400444,Rider_Id_709,866
-Order_No_24936,User_Id_2682,Bike,1,Personal,18,2,12:34:59 PM,18,2,12:35:17 PM,18,2,12:44:47 PM,18,2,12:50:29 PM,18,2,1:16:49 PM,5,22.7,,-1.2753108999999998,36.8130185,-1.2985749,36.8088,Rider_Id_844,1580
-Order_No_25295,User_Id_2030,Bike,3,Business,28,4,1:29:56 PM,28,4,1:46:15 PM,28,4,2:01:41 PM,28,4,2:10:56 PM,28,4,2:25:37 PM,2,,,-1.2972654,36.8330766,-1.2965799999999998,36.823699,Rider_Id_752,881
-Order_No_16055,User_Id_2197,Bike,3,Personal,3,4,8:23:38 AM,3,4,8:52:31 AM,3,4,9:12:45 AM,3,4,9:14:22 AM,3,4,9:43:38 AM,7,20.5,,-1.32589,36.831679,-1.2795225000000001,36.8323501,Rider_Id_399,1756
-Order_No_4031,User_Id_539,Bike,2,Personal,19,5,4:05:24 PM,19,5,4:12:32 PM,19,5,4:16:21 PM,19,5,4:30:16 PM,19,5,4:42:31 PM,5,27.5,,-1.2983805,36.8198439,-1.3072409999999999,36.830236799999994,Rider_Id_268,735
-Order_No_7629,User_Id_111,Bike,3,Business,9,2,2:36:40 PM,9,2,2:45:21 PM,9,2,2:52:35 PM,9,2,2:53:48 PM,9,2,3:20:24 PM,9,30.7,,-1.2551895,36.7822034,-1.3123782,36.8181528,Rider_Id_505,1596
-Order_No_738,User_Id_847,Bike,3,Business,18,4,12:15:34 PM,18,4,12:15:55 PM,18,4,12:39:26 PM,18,4,12:47:48 PM,18,4,1:21:44 PM,13,,,-1.3290948999999999,36.8767589,-1.287353,36.826992,Rider_Id_341,2036
-Order_No_24714,User_Id_1471,Bike,3,Business,13,3,1:23:59 PM,13,3,1:24:33 PM,13,3,1:42:02 PM,13,3,1:57:16 PM,13,3,2:20:33 PM,10,27.0,,-1.2551895,36.7822034,-1.3126476,36.831455,Rider_Id_206,1397
-Order_No_16134,User_Id_3565,Bike,3,Business,18,7,1:30:39 PM,18,7,2:31:24 PM,18,7,2:40:40 PM,18,7,2:50:36 PM,18,7,3:19:38 PM,9,27.5,,-1.2286304,36.8422473,-1.2227615,36.8989174,Rider_Id_473,1742
-Order_No_17873,User_Id_136,Bike,3,Business,4,2,11:05:22 AM,4,2,11:06:00 AM,4,2,11:14:04 AM,4,2,11:21:47 AM,4,2,11:47:35 AM,4,20.3,,-1.2965045,36.7913036,-1.2950408000000002,36.809817200000005,Rider_Id_161,1548
-Order_No_25823,User_Id_1868,Bike,3,Business,3,1,11:19:27 AM,3,1,11:20:28 AM,3,1,11:27:28 AM,3,1,11:31:02 AM,3,1,11:52:48 AM,17,21.5,,-1.2752846000000002,36.802702200000006,-1.207546,36.7913976,Rider_Id_96,1306
-Order_No_819,User_Id_2330,Bike,3,Business,26,5,5:15:38 PM,26,5,5:16:49 PM,26,5,5:24:03 PM,26,5,5:27:44 PM,26,5,5:36:21 PM,5,,,-1.3040812,36.7924217,-1.293082,36.8278578,Rider_Id_906,517
-Order_No_12741,User_Id_412,Bike,3,Business,28,6,11:57:30 AM,28,6,11:59:13 AM,28,6,11:59:54 AM,28,6,12:12:08 PM,28,6,12:20:32 PM,4,24.2,,-1.2628473,36.781805,-1.266837,36.799248999999996,Rider_Id_101,504
-Order_No_3825,User_Id_2956,Bike,3,Business,9,5,3:20:15 PM,9,5,4:01:11 PM,9,5,4:45:06 PM,9,5,4:46:34 PM,9,5,5:04:39 PM,7,24.8,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_453,1085
-Order_No_20621,User_Id_2509,Bike,3,Business,28,4,1:01:48 PM,28,4,1:03:46 PM,28,4,1:29:52 PM,28,4,1:43:53 PM,28,4,2:06:20 PM,6,27.4,,-1.2571472,36.795063299999995,-1.2933788999999998,36.808753499999995,Rider_Id_177,1347
-Order_No_5928,User_Id_3283,Bike,3,Business,14,2,9:35:43 AM,14,2,9:36:33 AM,14,2,9:58:21 AM,14,2,10:24:08 AM,14,2,10:53:07 AM,19,,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_195,1739
-Order_No_19864,User_Id_1363,Bike,3,Business,11,2,12:35:25 PM,11,2,12:37:48 PM,11,2,12:42:06 PM,11,2,12:47:18 PM,11,2,12:58:54 PM,3,19.6,,-1.3004062,36.829741,-1.2936428999999998,36.8277508,Rider_Id_868,696
-Order_No_15591,User_Id_868,Bike,3,Business,19,2,2:41:25 PM,19,2,2:42:56 PM,19,2,2:54:46 PM,19,2,3:01:24 PM,19,2,3:16:04 PM,10,29.5,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_8,880
-Order_No_1994,User_Id_3283,Bike,3,Business,17,3,3:00:14 PM,17,3,3:00:29 PM,17,3,3:04:25 PM,17,3,3:17:49 PM,17,3,3:25:46 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_484,477
-Order_No_20346,User_Id_1721,Bike,3,Business,1,1,12:07:12 PM,1,1,12:07:56 PM,1,1,12:20:30 PM,1,1,12:30:25 PM,1,1,12:47:29 PM,7,,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_682,1024
-Order_No_27173,User_Id_909,Bike,3,Business,14,2,2:48:26 PM,14,2,2:48:26 PM,14,2,2:50:37 PM,14,2,3:01:35 PM,14,2,3:36:41 PM,7,,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_510,2106
-Order_No_7760,User_Id_1920,Bike,3,Business,21,3,2:02:41 PM,21,3,2:27:18 PM,21,3,2:36:49 PM,21,3,2:41:28 PM,21,3,2:56:13 PM,5,23.4,3.0,-1.3169591,36.7000029,-1.3339943,36.7263409,Rider_Id_317,885
-Order_No_24145,User_Id_3388,Bike,1,Personal,27,1,2:38:15 PM,27,1,2:39:05 PM,27,1,2:42:25 PM,27,1,2:49:59 PM,27,1,3:00:05 PM,5,,,-1.2583017,36.8023526,-1.2791714,36.825599700000005,Rider_Id_213,606
-Order_No_1986,User_Id_435,Bike,3,Business,3,1,11:09:30 AM,3,1,11:27:59 AM,3,1,12:10:32 PM,3,1,12:37:32 PM,3,1,1:27:29 PM,27,21.7,,-1.1621358,36.9528801,-1.300921,36.828195,Rider_Id_815,2997
-Order_No_10056,User_Id_53,Bike,3,Business,29,5,2:39:21 PM,29,5,2:41:00 PM,29,5,2:41:28 PM,29,5,3:00:54 PM,29,5,3:21:23 PM,5,28.9,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_901,1229
-Order_No_16434,User_Id_2423,Bike,3,Business,13,3,1:02:16 PM,13,3,1:03:30 PM,13,3,1:12:24 PM,13,3,1:20:04 PM,13,3,1:36:17 PM,4,24.7,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_913,973
-Order_No_25938,User_Id_635,Bike,3,Business,14,6,10:37:27 AM,14,6,10:38:23 AM,14,6,10:47:26 AM,14,6,10:57:31 AM,14,6,11:12:08 AM,8,17.7,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_427,877
-Order_No_15322,User_Id_1724,Bike,3,Business,11,2,3:41:03 PM,11,2,3:49:21 PM,11,2,4:27:00 PM,11,2,4:30:27 PM,11,2,5:03:18 PM,15,21.5,,-1.2551895,36.7822034,-1.3259468,36.8650665,Rider_Id_567,1971
-Order_No_19064,User_Id_275,Bike,2,Personal,22,3,11:05:19 AM,22,3,11:05:56 AM,22,3,11:12:25 AM,22,3,11:18:05 AM,22,3,11:25:36 AM,5,22.8,,-1.3066143,36.8161046,-1.2913246,36.8019522,Rider_Id_249,451
-Order_No_28044,User_Id_53,Bike,3,Business,2,4,11:14:56 AM,2,4,11:16:04 AM,2,4,11:23:50 AM,2,4,11:39:15 AM,2,4,12:16:00 PM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_341,2205
-Order_No_23569,User_Id_3494,Bike,3,Personal,18,3,4:35:33 PM,18,3,4:37:14 PM,18,3,4:57:37 PM,18,3,5:00:40 PM,18,3,5:22:12 PM,7,20.7,,-1.2963097,36.7688221,-1.2842286,36.8150609,Rider_Id_951,1292
-Order_No_26376,User_Id_2150,Bike,3,Business,4,2,12:07:56 PM,4,2,12:08:38 PM,4,2,12:27:20 PM,4,2,12:37:44 PM,4,2,1:02:25 PM,14,20.7,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_438,1481
-Order_No_5675,User_Id_1179,Bike,3,Business,25,5,11:20:32 AM,25,5,11:23:52 AM,25,5,11:35:18 AM,25,5,11:51:37 AM,25,5,12:29:07 PM,21,26.2,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_457,2250
-Order_No_14018,User_Id_53,Bike,3,Business,18,5,9:38:39 AM,18,5,9:40:16 AM,18,5,9:54:53 AM,18,5,10:05:00 AM,18,5,10:22:41 AM,5,20.3,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_725,1061
-Order_No_14841,User_Id_1351,Bike,1,Personal,19,5,1:17:00 PM,19,5,1:17:56 PM,19,5,1:19:27 PM,19,5,1:32:35 PM,19,5,2:04:04 PM,16,27.5,,-1.2854453000000001,36.818836299999994,-1.323682,36.708787,Rider_Id_456,1889
-Order_No_3496,User_Id_114,Bike,1,Personal,27,4,2:11:55 PM,27,4,2:12:13 PM,27,4,2:22:04 PM,27,4,2:36:17 PM,27,4,2:55:23 PM,9,24.9,,-1.2846899999999999,36.826112,-1.311943,36.833113399999995,Rider_Id_31,1146
-Order_No_7263,User_Id_1257,Bike,3,Business,13,4,1:47:23 PM,13,4,1:48:38 PM,13,4,2:12:38 PM,13,4,2:14:58 PM,13,4,2:57:14 PM,12,22.5,,-1.264384,36.767412799999995,-1.3178524,36.7139804,Rider_Id_811,2536
-Order_No_21114,User_Id_3521,Bike,3,Business,6,1,2:59:29 PM,6,1,3:00:19 PM,6,1,3:23:44 PM,6,1,3:28:31 PM,6,1,3:37:49 PM,1,,,-1.2665327,36.8108799,-1.2696908000000002,36.81519,Rider_Id_132,558
-Order_No_12327,User_Id_242,Bike,3,Business,29,5,9:50:16 AM,29,5,11:17:47 AM,29,5,11:23:01 AM,29,5,11:25:56 AM,29,5,11:50:24 AM,8,19.5,,-1.2551895,36.7822034,-1.3024794,36.8228284,Rider_Id_808,1468
-Order_No_15573,User_Id_2587,Bike,3,Business,5,3,3:42:33 PM,5,3,3:42:55 PM,5,3,3:53:47 PM,5,3,4:02:48 PM,5,3,4:25:50 PM,13,24.0,,-1.28878,36.8168312,-1.225322,36.80855,Rider_Id_449,1382
-Order_No_27458,User_Id_1744,Bike,2,Personal,19,5,2:16:01 PM,19,5,2:16:18 PM,19,5,2:16:44 PM,19,5,2:32:06 PM,19,5,3:09:45 PM,16,27.6,,-1.3178263000000001,36.7141818,-1.2726573,36.8267008,Rider_Id_456,2259
-Order_No_51,User_Id_412,Bike,3,Business,31,4,2:29:05 PM,31,4,3:44:20 PM,31,4,3:47:59 PM,31,4,4:10:58 PM,31,4,4:32:22 PM,12,28.9,,-1.228911,36.881876,-1.266837,36.799248999999996,Rider_Id_75,1284
-Order_No_23788,User_Id_3291,Bike,3,Business,11,2,1:33:04 PM,11,2,2:14:11 PM,11,2,2:22:16 PM,11,2,3:26:55 PM,11,2,3:42:46 PM,11,22.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_382,951
-Order_No_17280,User_Id_1424,Bike,3,Business,3,1,10:52:29 AM,3,1,10:53:40 AM,3,1,11:04:03 AM,3,1,11:13:03 AM,3,1,11:46:42 AM,11,22.1,,-1.3015700000000001,36.7871,-1.257984,36.745016,Rider_Id_647,2019
-Order_No_8845,User_Id_3599,Bike,3,Business,4,2,12:26:46 PM,4,2,12:27:52 PM,4,2,12:36:33 PM,4,2,12:58:22 PM,4,2,1:30:28 PM,17,19.3,,-1.3302996,36.8707935,-1.2912918,36.758140999999995,Rider_Id_130,1926
-Order_No_4274,User_Id_3380,Bike,3,Personal,9,2,11:55:43 AM,9,2,11:56:44 AM,9,2,12:08:37 PM,9,2,12:26:35 PM,9,2,12:43:54 PM,8,26.8,,-1.2846475,36.8275903,-1.2963097,36.7688221,Rider_Id_855,1039
-Order_No_5311,User_Id_635,Bike,3,Business,6,6,9:50:41 AM,6,6,9:53:02 AM,6,6,10:01:42 AM,6,6,10:31:49 AM,6,6,10:53:02 AM,10,19.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_760,1273
-Order_No_3263,User_Id_3547,Bike,3,Business,24,3,12:09:43 PM,24,3,12:10:05 PM,24,3,12:26:24 PM,24,3,12:42:42 PM,24,3,1:19:24 PM,16,,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_377,2202
-Order_No_24281,User_Id_2582,Bike,3,Business,10,1,4:34:54 PM,10,1,4:55:47 PM,10,1,5:12:15 PM,10,1,5:15:52 PM,10,1,5:35:29 PM,9,18.7,,-1.2551895,36.7822034,-1.2985642,36.7707474,Rider_Id_112,1177
-Order_No_23063,User_Id_2277,Bike,3,Business,8,4,9:08:18 AM,8,4,9:09:06 AM,8,4,9:16:19 AM,8,4,9:26:23 AM,8,4,9:43:37 AM,7,20.4,,-1.3036370000000002,36.816116799999996,-1.2934656999999998,36.764473100000004,Rider_Id_726,1034
-Order_No_6372,User_Id_28,Bike,3,Business,3,4,10:12:42 AM,3,4,10:23:04 AM,3,4,10:35:10 AM,3,4,10:40:59 AM,3,4,10:56:47 AM,5,22.1,,-1.2745107,36.797450299999994,-1.2998459999999998,36.7940155,Rider_Id_580,948
-Order_No_9934,User_Id_3291,Bike,3,Business,25,4,9:41:03 AM,25,4,9:41:12 AM,25,4,9:48:26 AM,25,4,9:57:35 AM,25,4,10:07:11 AM,5,,,-1.2566688999999998,36.779695200000006,-1.2600926000000001,36.8088685,Rider_Id_438,576
-Order_No_25317,User_Id_53,Bike,3,Business,3,1,2:15:52 PM,3,1,2:28:34 PM,3,1,2:33:23 PM,3,1,2:40:35 PM,3,1,3:02:28 PM,5,23.1,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_158,1313
-Order_No_17488,User_Id_1408,Bike,1,Personal,24,1,12:57:58 PM,24,1,12:58:14 PM,24,1,1:12:00 PM,24,1,1:15:18 PM,24,1,1:38:14 PM,8,22.3,,-1.293886,36.7596951,-1.277247,36.8207518,Rider_Id_328,1376
-Order_No_1772,User_Id_393,Bike,3,Business,9,4,10:20:11 AM,9,4,10:20:25 AM,9,4,10:20:30 AM,9,4,10:37:03 AM,9,4,11:10:44 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,2021
-Order_No_25597,User_Id_2387,Bike,1,Personal,19,5,8:57:15 AM,19,5,8:57:50 AM,19,5,9:02:30 AM,19,5,9:03:35 AM,19,5,9:12:22 AM,5,20.4,,-1.2535486,36.7933541,-1.2633493999999998,36.7959696,Rider_Id_453,527
-Order_No_14412,User_Id_3149,Bike,3,Business,28,5,3:20:11 PM,28,5,3:23:13 PM,28,5,3:46:53 PM,28,5,4:19:53 PM,28,5,4:20:15 PM,5,25.5,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_829,22
-Order_No_6323,User_Id_2271,Bike,3,Business,11,5,3:56:56 PM,11,5,3:57:09 PM,11,5,3:57:14 PM,11,5,4:11:56 PM,11,5,4:27:57 PM,11,22.4,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_397,961
-Order_No_2770,User_Id_2691,Bike,3,Business,6,2,12:56:06 PM,6,2,12:59:12 PM,6,2,1:05:11 PM,6,2,1:12:19 PM,6,2,1:51:58 PM,7,25.0,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_427,2379
-Order_No_16303,User_Id_374,Bike,2,Personal,18,3,8:14:12 AM,18,3,8:15:20 AM,18,3,8:19:18 AM,18,3,8:20:31 AM,18,3,8:30:57 AM,6,14.4,,-1.2580978999999999,36.7959844,-1.2900726999999999,36.7890059,Rider_Id_379,626
-Order_No_27665,User_Id_3432,Bike,3,Business,28,1,12:20:34 PM,28,1,12:21:23 PM,28,1,12:23:30 PM,28,1,12:33:06 PM,28,1,1:08:57 PM,12,23.7,,-1.2672588,36.8000252,-1.3299358000000001,36.8524971,Rider_Id_275,2151
-Order_No_18494,User_Id_3621,Bike,3,Business,14,1,1:45:42 PM,14,1,1:46:00 PM,14,1,1:55:52 PM,14,1,1:58:06 PM,14,1,2:20:02 PM,8,25.8,,-1.2836583000000001,36.7886014,-1.2546732,36.7923244,Rider_Id_382,1316
-Order_No_7274,User_Id_393,Bike,3,Business,7,4,3:58:10 PM,7,4,3:59:24 PM,7,4,4:12:24 PM,7,4,4:23:06 PM,7,4,4:48:38 PM,11,30.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_178,1532
-Order_No_2480,User_Id_3647,Bike,3,Business,17,4,11:20:03 AM,17,4,11:21:24 AM,17,4,11:32:42 AM,17,4,11:39:41 AM,17,4,11:57:09 AM,5,25.3,,-1.2726389999999999,36.794723,-1.2854173999999998,36.7951152,Rider_Id_518,1048
-Order_No_10275,User_Id_393,Bike,3,Business,26,2,1:47:40 PM,26,2,1:47:51 PM,26,2,1:49:35 PM,26,2,1:58:38 PM,26,2,2:17:10 PM,11,29.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_26,1112
-Order_No_24778,User_Id_3283,Bike,3,Business,20,3,9:37:03 AM,20,3,9:37:38 AM,20,3,9:41:16 AM,20,3,9:43:51 AM,20,3,9:56:08 AM,4,20.8,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_216,737
-Order_No_15417,User_Id_1764,Bike,1,Personal,7,1,10:38:32 AM,7,1,10:39:52 AM,7,1,10:53:54 AM,7,1,10:56:59 AM,7,1,11:28:45 AM,8,21.3,,-1.279199,36.822492600000004,-1.2962603000000001,36.7766164,Rider_Id_771,1906
-Order_No_2423,User_Id_2523,Bike,3,Business,5,3,3:31:47 PM,5,3,3:33:03 PM,5,3,3:44:06 PM,5,3,3:48:24 PM,5,3,4:34:08 PM,19,25.1,,-1.2551895,36.7822034,-1.3285517,36.9018,Rider_Id_646,2744
-Order_No_18316,User_Id_3686,Bike,3,Business,25,2,9:42:53 AM,25,2,9:43:01 AM,25,2,9:43:49 AM,25,2,10:03:01 AM,25,2,10:43:33 AM,22,18.0,,-1.2551895,36.7822034,-1.3342256000000001,36.9106741,Rider_Id_38,2432
-Order_No_25236,User_Id_1402,Bike,3,Business,29,5,12:26:48 PM,29,5,12:28:06 PM,29,5,12:41:52 PM,29,5,12:43:28 PM,29,5,1:39:13 PM,16,26.8,,-1.3374393999999998,36.7339115,-1.2813012,36.832396200000005,Rider_Id_691,3345
-Order_No_23810,User_Id_517,Bike,3,Business,12,4,9:47:28 AM,12,4,9:47:52 AM,12,4,9:48:26 AM,12,4,9:56:01 AM,12,4,10:32:25 AM,14,16.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_18,2184
-Order_No_27332,User_Id_2216,Bike,3,Business,8,4,12:26:47 PM,8,4,1:02:17 PM,8,4,1:13:19 PM,8,4,1:34:33 PM,8,4,2:09:42 PM,20,27.4,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_932,2109
-Order_No_22809,User_Id_2726,Bike,3,Personal,14,1,3:10:53 PM,14,1,3:11:04 PM,14,1,3:18:39 PM,14,1,3:28:09 PM,14,1,4:20:57 PM,12,25.9,,-1.2812624,36.7714609,-1.3271684,36.8173021,Rider_Id_46,3168
-Order_No_8293,User_Id_2271,Bike,3,Business,19,3,1:57:45 PM,19,3,1:57:57 PM,19,3,1:58:32 PM,19,3,2:02:45 PM,19,3,2:38:45 PM,16,24.5,,-1.293515,36.897607,-1.2752846000000002,36.802702200000006,Rider_Id_925,2160
-Order_No_7042,User_Id_3355,Bike,3,Business,15,2,3:11:20 PM,15,2,3:11:43 PM,15,2,3:21:51 PM,15,2,3:23:45 PM,15,2,4:06:48 PM,10,24.4,,-1.3052491000000002,36.8223899,-1.325086,36.887894,Rider_Id_436,2583
-Order_No_23597,User_Id_3291,Bike,3,Business,26,2,9:36:22 AM,26,2,9:36:55 AM,26,2,9:57:08 AM,26,2,10:14:42 AM,26,2,10:30:24 AM,4,22.0,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_309,942
-Order_No_19718,User_Id_248,Bike,3,Business,27,2,7:21:08 AM,27,2,7:21:19 AM,27,2,7:46:37 AM,27,2,7:48:26 AM,27,2,8:12:49 AM,10,18.9,2.1,-1.304735,36.784605400000004,-1.254191,36.809913200000004,Rider_Id_612,1463
-Order_No_16496,User_Id_1787,Bike,3,Business,21,3,12:32:01 PM,21,3,12:36:53 PM,21,3,1:36:53 PM,21,3,2:06:46 PM,21,3,2:23:20 PM,11,23.1,3.0,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_812,994
-Order_No_12848,User_Id_3295,Bike,3,Business,14,4,9:04:15 AM,14,4,9:04:41 AM,14,4,9:06:19 AM,14,4,9:32:27 AM,14,4,9:52:45 AM,11,15.4,,-1.2579303999999998,36.8019667,-1.3025043,36.753532899999996,Rider_Id_379,1218
-Order_No_2589,User_Id_1329,Bike,3,Business,9,6,1:57:24 PM,9,6,1:58:09 PM,9,6,2:02:53 PM,9,6,2:04:12 PM,9,6,2:35:43 PM,6,27.2,,-1.273056,36.811298,-1.2870051000000002,36.771624700000004,Rider_Id_26,1891
-Order_No_24436,User_Id_2112,Bike,3,Business,19,3,2:52:23 PM,19,3,2:59:23 PM,19,3,3:05:00 PM,19,3,3:15:07 PM,19,3,3:26:05 PM,4,24.4,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_250,658
-Order_No_20067,User_Id_2811,Bike,1,Personal,25,2,10:55:05 AM,25,2,11:01:33 AM,25,2,11:16:47 AM,25,2,11:28:23 AM,25,2,11:40:34 AM,4,23.8,,-1.2848376,36.802762200000004,-1.284879,36.824597100000005,Rider_Id_79,731
-Order_No_25523,User_Id_246,Bike,3,Business,11,1,12:31:53 PM,11,1,12:32:13 PM,11,1,1:04:40 PM,11,1,1:10:57 PM,11,1,1:16:27 PM,2,18.2,,-1.2601708999999999,36.8002238,-1.2584143,36.8048002,Rider_Id_100,330
-Order_No_6025,User_Id_2509,Bike,3,Business,31,3,2:58:50 PM,31,3,2:59:14 PM,31,3,3:16:08 PM,31,3,3:18:08 PM,31,3,3:56:57 PM,20,27.0,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_956,2329
-Order_No_7340,User_Id_2668,Bike,3,Personal,29,2,3:24:22 PM,29,2,3:25:56 PM,29,2,3:35:10 PM,29,2,3:37:19 PM,29,2,4:03:56 PM,15,26.8,,-1.2878216,36.799597999999996,-1.331707,36.877913,Rider_Id_79,1597
-Order_No_16442,User_Id_2397,Bike,3,Personal,14,5,11:31:37 AM,14,5,11:32:10 AM,14,5,11:40:17 AM,14,5,11:43:10 AM,14,5,11:59:27 AM,4,24.4,,-1.2788182,36.820668299999994,-1.2659573,36.8092527,Rider_Id_434,977
-Order_No_4937,User_Id_265,Bike,3,Business,25,4,8:30:24 AM,25,4,8:51:55 AM,25,4,8:52:19 AM,25,4,9:44:45 AM,25,4,10:03:55 AM,8,16.6,1.9,-1.254904,36.809583,-1.300921,36.828195,Rider_Id_69,1150
-Order_No_26031,User_Id_1882,Bike,3,Business,7,5,9:55:47 AM,7,5,9:56:11 AM,7,5,10:09:29 AM,7,5,10:12:02 AM,7,5,10:45:53 AM,22,18.4,,-1.2156006000000001,36.8916865,-1.3014461000000002,36.7661381,Rider_Id_846,2031
-Order_No_4858,User_Id_3222,Bike,3,Business,5,5,3:27:42 PM,5,5,3:28:49 PM,5,5,3:35:42 PM,5,5,3:38:16 PM,5,5,3:53:02 PM,4,27.6,,-1.2551895,36.7822034,-1.2727979999999999,36.8124195,Rider_Id_682,886
-Order_No_14771,User_Id_567,Bike,3,Business,10,5,4:24:27 PM,10,5,4:32:15 PM,10,5,4:49:14 PM,10,5,4:54:08 PM,10,5,5:11:30 PM,6,,,-1.3038156,36.812273299999994,-1.2963097,36.7688221,Rider_Id_161,1042
-Order_No_5150,User_Id_286,Bike,3,Business,14,2,4:55:04 PM,14,2,4:55:37 PM,14,2,5:06:03 PM,14,2,5:13:00 PM,14,2,5:34:29 PM,8,,,-1.2855404,36.794430600000005,-1.2551895,36.7822034,Rider_Id_76,1289
-Order_No_1103,User_Id_2987,Bike,3,Business,4,3,1:20:43 PM,4,3,1:21:22 PM,4,3,1:27:38 PM,4,3,1:31:42 PM,4,3,2:19:27 PM,3,17.0,,-1.2702120000000001,36.82108770000001,-1.2635486999999999,36.8177196,Rider_Id_379,2865
-Order_No_12633,User_Id_2333,Bike,2,Personal,12,3,9:00:31 AM,12,3,9:01:13 AM,12,3,9:07:25 AM,12,3,9:12:01 AM,12,3,9:20:31 AM,2,16.3,,-1.2903421,36.7885282,-1.28467,36.794438,Rider_Id_322,510
-Order_No_24675,User_Id_2044,Bike,1,Personal,15,2,9:28:02 AM,15,2,9:38:27 AM,15,2,10:10:39 AM,15,2,10:33:14 AM,15,2,10:58:28 AM,7,22.8,,-1.2517392,36.82496079999999,-1.297512,36.821511,Rider_Id_676,1514
-Order_No_23362,User_Id_517,Bike,3,Business,7,1,9:56:52 AM,7,1,9:56:58 AM,7,1,9:57:03 AM,7,1,10:33:19 AM,7,1,10:33:24 AM,11,24.9,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,5
-Order_No_2505,User_Id_3264,Bike,3,Business,24,3,9:03:32 AM,24,3,9:04:53 AM,24,3,9:28:42 AM,24,3,9:29:46 AM,24,3,10:27:36 AM,12,,,-1.2551895,36.7822034,-1.3174857,36.801993700000004,Rider_Id_793,3470
-Order_No_17198,User_Id_562,Bike,3,Business,17,5,8:24:04 AM,17,5,8:25:06 AM,17,5,9:12:45 AM,17,5,9:19:21 AM,17,5,10:10:52 AM,17,,,-1.3227181000000001,36.8528097,-1.2185253,36.817412700000006,Rider_Id_233,3091
-Order_No_12519,User_Id_3222,Bike,3,Business,13,3,12:11:40 PM,13,3,12:12:49 PM,13,3,12:21:01 PM,13,3,12:22:12 PM,13,3,12:42:21 PM,6,,,-1.2551895,36.7822034,-1.2869955,36.8202749,Rider_Id_764,1209
-Order_No_9072,User_Id_53,Bike,3,Business,15,1,3:02:10 PM,15,1,3:05:25 PM,15,1,3:05:44 PM,15,1,3:14:08 PM,15,1,3:34:58 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_843,1250
-Order_No_3259,User_Id_1329,Bike,3,Business,26,5,2:46:42 PM,26,5,2:49:54 PM,26,5,3:30:34 PM,26,5,3:32:45 PM,26,5,3:37:11 PM,1,,,-1.273056,36.811298,-1.2753108999999998,36.8130185,Rider_Id_607,266
-Order_No_10254,User_Id_605,Bike,2,Personal,6,4,1:11:35 PM,6,4,1:12:11 PM,6,4,1:21:49 PM,6,4,1:29:02 PM,6,4,2:13:27 PM,17,22.2,,-1.3012780000000002,36.7934817,-1.3332754,36.880569,Rider_Id_865,2665
-Order_No_1725,User_Id_20,Bike,3,Business,7,5,12:49:38 PM,7,5,12:52:12 PM,7,5,1:21:10 PM,7,5,1:23:03 PM,7,5,1:38:41 PM,8,25.5,,-1.2629666000000002,36.8068369,-1.2930128,36.8356869,Rider_Id_726,938
-Order_No_18578,User_Id_1074,Bike,2,Personal,22,2,11:51:36 AM,22,2,11:53:11 AM,22,2,12:14:12 PM,22,2,12:31:04 PM,22,2,1:02:03 PM,10,19.3,,-1.2762345,36.8543591,-1.2857684999999999,36.7866843,Rider_Id_845,1859
-Order_No_13201,User_Id_868,Bike,3,Business,13,3,10:28:22 AM,13,3,10:29:39 AM,13,3,10:37:33 AM,13,3,12:04:22 PM,13,3,12:19:14 PM,6,24.7,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_258,892
-Order_No_19384,User_Id_627,Bike,3,Business,20,3,12:50:39 PM,20,3,12:51:45 PM,20,3,1:01:41 PM,20,3,1:04:56 PM,20,3,1:29:59 PM,12,27.2,,-1.2551895,36.7822034,-1.3056581,36.8445565,Rider_Id_576,1503
-Order_No_4420,User_Id_1899,Bike,3,Business,16,1,12:51:16 PM,16,1,12:51:28 PM,16,1,1:04:47 PM,16,1,1:10:41 PM,16,1,1:38:58 PM,11,19.1,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_88,1697
-Order_No_22049,User_Id_237,Bike,3,Business,20,1,3:40:36 PM,20,1,3:41:18 PM,20,1,3:56:00 PM,20,1,4:46:42 PM,20,1,5:22:55 PM,10,,,-1.312942,36.8152651,-1.2551895,36.7822034,Rider_Id_80,2173
-Order_No_17811,User_Id_1205,Bike,3,Business,22,5,1:26:13 PM,22,5,1:27:13 PM,22,5,1:54:35 PM,22,5,2:08:01 PM,22,5,2:33:50 PM,9,31.0,,-1.2973188,36.7895013,-1.2495931,36.80650870000001,Rider_Id_263,1549
-Order_No_6231,User_Id_60,Bike,3,Business,15,3,11:37:34 AM,15,3,11:37:56 AM,15,3,11:52:13 AM,15,3,12:00:28 PM,15,3,12:21:01 PM,11,,,-1.266837,36.799248999999996,-1.2296618000000001,36.84382720000001,Rider_Id_369,1233
-Order_No_12921,User_Id_1578,Bike,3,Business,19,3,12:13:57 PM,19,3,12:18:59 PM,19,3,12:32:32 PM,19,3,12:34:21 PM,19,3,1:25:16 PM,15,22.3,,-1.28878,36.8168312,-1.216682,36.908577799999996,Rider_Id_899,3055
-Order_No_21236,User_Id_1471,Bike,3,Business,21,2,11:05:25 AM,21,2,11:06:16 AM,21,2,11:20:57 AM,21,2,11:23:06 AM,21,2,11:45:45 AM,9,22.3,,-1.2989465,36.79403789999999,-1.2551895,36.7822034,Rider_Id_148,1359
-Order_No_27580,User_Id_3291,Bike,3,Business,18,1,11:46:12 AM,18,1,11:46:30 AM,18,1,11:49:23 AM,18,1,11:56:29 AM,18,1,12:39:46 PM,17,27.9,,-1.3444459999999998,36.756597,-1.2600926000000001,36.8088685,Rider_Id_649,2597
-Order_No_16027,User_Id_633,Bike,3,Business,15,5,11:29:15 AM,15,5,11:29:24 AM,15,5,11:29:40 AM,15,5,11:38:08 AM,15,5,11:38:16 AM,5,26.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_10,8
-Order_No_14507,User_Id_3214,Bike,3,Business,17,5,11:08:56 AM,17,5,11:09:37 AM,17,5,11:19:29 AM,17,5,11:29:38 AM,17,5,11:46:45 AM,5,,,-1.2951243000000001,36.828134399999996,-1.289795,36.7957132,Rider_Id_790,1027
-Order_No_4902,User_Id_2150,Bike,3,Business,14,1,12:15:11 PM,14,1,12:31:01 PM,14,1,12:36:54 PM,14,1,12:50:26 PM,14,1,1:32:14 PM,11,23.6,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_837,2508
-Order_No_24479,User_Id_2869,Bike,3,Business,5,2,8:57:04 AM,5,2,8:57:31 AM,5,2,9:32:19 AM,5,2,9:42:49 AM,5,2,10:01:05 AM,10,21.0,2.0,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_880,1096
-Order_No_7949,User_Id_2271,Bike,3,Business,18,1,12:42:00 PM,18,1,12:44:36 PM,18,1,12:48:41 PM,18,1,1:01:43 PM,18,1,1:37:15 PM,19,26.8,,-1.303596,36.778378000000004,-1.293515,36.897607,Rider_Id_327,2132
-Order_No_26953,User_Id_286,Bike,3,Business,5,1,9:44:01 AM,5,1,9:51:30 AM,5,1,9:58:27 AM,5,1,10:06:14 AM,5,1,10:23:35 AM,10,18.4,,-1.2672002,36.8245542,-1.2551895,36.7822034,Rider_Id_237,1041
-Order_No_7283,User_Id_1299,Bike,3,Business,13,1,2:03:49 PM,13,1,2:04:42 PM,13,1,2:11:28 PM,13,1,2:17:39 PM,13,1,2:42:13 PM,7,19.7,,-1.282543,36.824618,-1.2823843000000001,36.7734371,Rider_Id_85,1474
-Order_No_7081,User_Id_816,Bike,3,Business,15,5,2:10:06 PM,15,5,2:11:01 PM,15,5,2:31:26 PM,15,5,2:37:07 PM,15,5,3:04:18 PM,14,27.9,,-1.2283403,36.8822756,-1.290894,36.822971,Rider_Id_8,1631
-Order_No_20093,User_Id_3108,Bike,3,Business,12,2,10:50:19 AM,12,2,10:50:39 AM,12,2,11:02:24 AM,12,2,11:11:45 AM,12,2,11:40:33 AM,17,24.4,,-1.2551895,36.7822034,-1.3393558,36.898241799999994,Rider_Id_932,1728
-Order_No_22155,User_Id_2587,Bike,3,Business,18,1,11:18:44 AM,18,1,11:19:16 AM,18,1,11:19:36 AM,18,1,11:30:42 AM,18,1,11:47:47 AM,2,27.1,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_931,1025
-Order_No_12705,User_Id_1500,Bike,3,Business,3,5,3:04:45 PM,3,5,3:06:56 PM,3,5,3:07:00 PM,3,5,3:30:07 PM,3,5,3:30:12 PM,13,,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_515,5
-Order_No_3178,User_Id_867,Bike,3,Business,27,1,10:39:41 AM,27,1,10:39:54 AM,27,1,10:40:00 AM,27,1,11:08:28 AM,27,1,11:13:36 AM,10,20.8,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_70,308
-Order_No_9853,User_Id_2763,Bike,3,Business,28,4,2:26:53 PM,28,4,2:27:06 PM,28,4,2:34:25 PM,28,4,2:53:05 PM,28,4,3:07:15 PM,15,30.0,,-1.2551895,36.7822034,-1.3255854999999999,36.86476270000001,Rider_Id_137,850
-Order_No_12404,User_Id_1459,Bike,3,Business,23,5,2:14:03 PM,23,5,2:30:06 PM,23,5,2:51:29 PM,23,5,3:14:03 PM,23,5,3:43:22 PM,15,26.4,,-1.264945,36.784678,-1.3307985,36.8036471,Rider_Id_101,1759
-Order_No_19302,User_Id_3283,Bike,3,Business,28,5,10:24:20 AM,28,5,10:24:34 AM,28,5,10:27:11 AM,28,5,10:36:53 AM,28,5,10:43:51 AM,5,23.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_453,418
-Order_No_1997,User_Id_3689,Bike,2,Business,14,2,8:46:40 AM,14,2,8:47:24 AM,14,2,8:56:51 AM,14,2,9:01:05 AM,14,2,9:16:29 AM,3,15.4,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_159,924
-Order_No_10227,User_Id_3080,Bike,3,Business,13,5,12:19:10 PM,13,5,12:19:20 PM,13,5,12:26:51 PM,13,5,1:02:24 PM,13,5,1:28:58 PM,17,,,-1.2551895,36.7822034,-1.3434,36.711224,Rider_Id_116,1594
-Order_No_4330,User_Id_868,Bike,3,Business,18,4,9:47:22 AM,18,4,9:47:51 AM,18,4,10:02:54 AM,18,4,10:07:37 AM,18,4,10:36:58 AM,9,,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_7,1761
-Order_No_25498,User_Id_948,Bike,3,Business,28,2,11:13:14 AM,28,2,11:14:48 AM,28,2,12:08:53 PM,28,2,12:12:15 PM,28,2,1:03:29 PM,11,21.1,,-1.3024388,36.859699,-1.2594473999999998,36.8089122,Rider_Id_25,3074
-Order_No_21322,User_Id_1927,Bike,3,Business,1,5,2:23:06 PM,1,5,2:24:48 PM,1,5,2:47:24 PM,1,5,2:55:04 PM,1,5,3:27:46 PM,17,28.7,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_267,1962
-Order_No_4166,User_Id_496,Bike,3,Business,20,4,4:58:48 PM,20,4,5:51:22 PM,20,4,6:06:56 PM,20,4,6:13:19 PM,20,4,6:34:01 PM,7,22.8,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_845,1242
-Order_No_25520,User_Id_286,Bike,3,Business,1,4,10:14:36 AM,1,4,10:20:24 AM,1,4,10:39:17 AM,1,4,10:48:35 AM,1,4,11:13:28 AM,9,,,-1.2551895,36.7822034,-1.2945552,36.839749700000006,Rider_Id_469,1493
-Order_No_16239,User_Id_788,Bike,3,Business,3,4,1:02:37 PM,3,4,1:03:02 PM,3,4,1:16:11 PM,3,4,1:22:34 PM,3,4,1:45:11 PM,10,23.3,,-1.225322,36.80855,-1.2885315,36.8230419,Rider_Id_31,1357
-Order_No_5531,User_Id_1469,Bike,3,Business,8,1,3:56:14 PM,8,1,3:56:40 PM,8,1,4:00:18 PM,8,1,4:09:23 PM,8,1,4:39:11 PM,4,29.8,,-1.2551895,36.7822034,-1.2627937,36.815695700000006,Rider_Id_166,1788
-Order_No_12234,User_Id_3404,Bike,3,Business,22,6,4:16:47 PM,22,6,4:57:40 PM,22,6,5:09:30 PM,22,6,5:13:23 PM,22,6,5:34:58 PM,11,23.8,,-1.279395,36.825364,-1.3206958,36.8769553,Rider_Id_576,1295
-Order_No_2,User_Id_3517,Bike,3,Business,18,4,10:09:10 AM,18,4,10:09:50 AM,18,4,10:12:28 AM,18,4,10:51:52 AM,18,4,11:23:55 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_148,1923
-Order_No_7129,User_Id_2030,Bike,3,Business,8,1,2:35:31 PM,8,1,3:27:22 PM,8,1,3:45:48 PM,8,1,3:57:03 PM,8,1,4:01:33 PM,8,29.5,,-1.2279031,36.787544700000005,-1.2324469999999998,36.802205,Rider_Id_411,270
-Order_No_9592,User_Id_3727,Bike,3,Business,28,1,3:35:07 PM,28,1,3:35:30 PM,28,1,3:44:10 PM,28,1,3:46:49 PM,28,1,5:00:19 PM,8,26.1,,-1.2963097,36.7688221,-1.3004062,36.829741,Rider_Id_571,4410
-Order_No_17396,User_Id_635,Bike,3,Business,6,3,3:28:17 PM,6,3,3:32:19 PM,6,3,3:40:40 PM,6,3,3:42:46 PM,6,3,3:57:22 PM,3,21.9,,-1.2599563,36.799343900000004,-1.257069,36.81467,Rider_Id_446,876
-Order_No_24474,User_Id_3727,Bike,3,Business,8,5,9:53:56 AM,8,5,9:55:24 AM,8,5,10:05:33 AM,8,5,10:10:15 AM,8,5,10:30:58 AM,6,22.8,,-1.2638598,36.7808674,-1.293393,36.790237,Rider_Id_245,1243
-Order_No_11251,User_Id_2701,Bike,3,Business,4,2,11:00:43 AM,4,2,11:18:00 AM,4,2,11:45:01 AM,4,2,11:49:28 AM,4,2,12:06:06 PM,6,20.4,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_158,998
-Order_No_3491,User_Id_2663,Bike,1,Personal,15,6,10:26:19 AM,15,6,10:26:44 AM,15,6,10:36:03 AM,15,6,10:42:22 AM,15,6,10:49:48 AM,4,,,-1.2971378999999998,36.8019794,-1.2817331,36.818384,Rider_Id_622,446
-Order_No_13227,User_Id_3291,Bike,3,Business,4,4,9:42:10 AM,4,4,9:54:17 AM,4,4,10:08:45 AM,4,4,10:20:22 AM,4,4,10:44:41 AM,12,18.6,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_847,1459
-Order_No_11597,User_Id_1245,Bike,3,Business,5,5,1:37:50 PM,5,5,1:38:19 PM,5,5,1:52:44 PM,5,5,2:27:25 PM,5,5,2:54:57 PM,14,27.6,,-1.300921,36.828195,-1.3374393999999998,36.7339115,Rider_Id_863,1652
-Order_No_19730,User_Id_3517,Bike,3,Business,11,1,1:13:52 PM,11,1,1:14:25 PM,11,1,1:39:02 PM,11,1,1:50:38 PM,11,1,2:22:20 PM,13,28.0,,-1.3338218000000002,36.8135552,-1.3882008,36.7699445,Rider_Id_328,1902
-Order_No_5455,User_Id_2150,Bike,3,Business,7,4,3:58:35 PM,7,4,3:58:56 PM,7,4,4:08:28 PM,7,4,4:14:11 PM,7,4,4:33:42 PM,14,31.1,,-1.3313643,36.857742200000004,-1.3444459999999998,36.756597,Rider_Id_675,1171
-Order_No_26101,User_Id_1531,Bike,1,Personal,16,2,6:26:16 PM,16,2,6:27:11 PM,16,2,6:42:51 PM,16,2,6:48:25 PM,16,2,7:36:37 PM,14,25.2,,-1.2547264999999999,36.7540908,-1.3106473,36.730895399999994,Rider_Id_116,2892
-Order_No_17872,User_Id_635,Bike,3,Business,1,5,11:24:47 AM,1,5,11:40:39 AM,1,5,11:40:59 AM,1,5,12:10:05 PM,1,5,12:28:32 PM,7,26.6,,-1.3012141000000002,36.826223999999996,-1.2599563,36.799343900000004,Rider_Id_669,1107
-Order_No_12949,User_Id_1329,Bike,3,Business,13,6,12:26:20 PM,13,6,12:28:15 PM,13,6,12:39:04 PM,13,6,12:40:43 PM,13,6,1:25:43 PM,11,26.1,,-1.273056,36.811298,-1.3189908000000001,36.777074,Rider_Id_641,2700
-Order_No_16765,User_Id_3138,Bike,1,Personal,2,2,1:39:07 PM,2,2,1:39:52 PM,2,2,1:48:08 PM,2,2,1:52:19 PM,2,2,2:05:05 PM,4,26.2,,-1.2924639,36.77957979999999,-1.2804664,36.7701111,Rider_Id_38,766
-Order_No_11806,User_Id_830,Bike,3,Business,2,5,3:45:43 PM,2,5,3:51:10 PM,2,5,4:01:06 PM,2,5,4:09:04 PM,2,5,4:43:34 PM,14,25.2,,-1.2972659,36.8267178,-1.3681253,36.915529799999995,Rider_Id_779,2070
-Order_No_14644,User_Id_1207,Bike,3,Business,2,6,1:49:15 PM,2,6,2:13:36 PM,2,6,2:14:03 PM,2,6,2:38:05 PM,2,6,3:10:36 PM,14,31.0,,-1.2629666000000002,36.8068369,-1.207546,36.7913976,Rider_Id_657,1951
-Order_No_12099,User_Id_1207,Bike,3,Business,26,5,10:44:05 AM,26,5,10:44:29 AM,26,5,10:53:04 AM,26,5,11:09:13 AM,26,5,12:10:32 PM,12,,,-1.2629666000000002,36.8068369,-1.2351541000000001,36.8748505,Rider_Id_934,3679
-Order_No_3657,User_Id_2801,Bike,3,Business,25,1,3:17:01 PM,25,1,3:17:46 PM,25,1,3:39:10 PM,25,1,4:29:18 PM,25,1,4:53:49 PM,9,27.9,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_267,1471
-Order_No_19049,User_Id_1500,Bike,3,Business,27,3,12:01:17 PM,27,3,12:01:30 PM,27,3,12:01:50 PM,27,3,12:12:08 PM,27,3,12:35:42 PM,10,26.3,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_698,1414
-Order_No_28087,User_Id_1326,Bike,1,Personal,28,1,8:35:50 AM,28,1,8:36:18 AM,28,1,8:49:41 AM,28,1,8:52:20 AM,28,1,9:06:39 AM,7,17.9,,-1.2899256000000001,36.815551,-1.2553615,36.789537599999996,Rider_Id_808,859
-Order_No_18278,User_Id_2582,Bike,3,Business,11,1,3:05:49 PM,11,1,3:07:51 PM,11,1,3:24:32 PM,11,1,3:44:08 PM,11,1,3:44:14 PM,8,28.7,,-1.2854406,36.789409,-1.2551895,36.7822034,Rider_Id_314,6
-Order_No_26451,User_Id_552,Bike,3,Business,16,2,1:51:54 PM,16,2,1:52:45 PM,16,2,2:21:21 PM,16,2,2:37:12 PM,16,2,3:23:08 PM,14,,,-1.2860183,36.8975338,-1.3562374,36.904295399999995,Rider_Id_126,2756
-Order_No_16832,User_Id_3599,Bike,3,Business,4,6,9:23:04 AM,4,6,9:25:00 AM,4,6,9:49:40 AM,4,6,10:05:57 AM,4,6,10:35:30 AM,11,,,-1.3302996,36.8707935,-1.290789,36.8024859,Rider_Id_675,1773
-Order_No_9649,User_Id_3647,Bike,3,Business,5,5,4:11:20 PM,5,5,4:22:25 PM,5,5,4:27:47 PM,5,5,4:29:49 PM,5,5,4:46:29 PM,5,26.4,,-1.2726389999999999,36.794723,-1.2807268,36.786665899999996,Rider_Id_795,1000
-Order_No_16508,User_Id_2933,Bike,2,Personal,21,6,2:46:38 PM,21,6,2:46:49 PM,21,6,3:21:10 PM,21,6,3:25:29 PM,21,6,4:29:06 PM,7,18.5,,-1.2721028,36.795348499999996,-1.2518056999999998,36.7997135,Rider_Id_194,3817
-Order_No_13980,User_Id_788,Bike,3,Business,26,5,11:22:12 AM,26,5,1:46:18 PM,26,5,1:59:22 PM,26,5,2:04:04 PM,26,5,2:43:03 PM,14,,,-1.28878,36.8168312,-1.225322,36.80855,Rider_Id_148,2339
-Order_No_5404,User_Id_640,Bike,3,Business,11,4,4:17:31 PM,11,4,4:18:07 PM,11,4,4:25:52 PM,11,4,4:29:02 PM,11,4,4:41:32 PM,3,26.7,,-1.304033,36.7848695,-1.2945106000000002,36.7728398,Rider_Id_299,750
-Order_No_7404,User_Id_393,Bike,3,Business,21,2,12:01:25 PM,21,2,12:03:47 PM,21,2,12:19:29 PM,21,2,12:29:35 PM,21,2,1:04:52 PM,11,22.5,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_448,2117
-Order_No_14476,User_Id_2732,Bike,3,Personal,19,5,8:27:23 AM,19,5,8:49:25 AM,19,5,9:10:13 AM,19,5,9:20:54 AM,19,5,9:39:07 AM,3,20.1,,-1.2857248,36.8228262,-1.2957196000000002,36.8046677,Rider_Id_829,1093
-Order_No_19707,User_Id_2216,Bike,3,Business,23,1,11:24:32 AM,23,1,11:24:40 AM,23,1,11:34:41 AM,23,1,11:38:50 AM,23,1,12:24:54 PM,18,18.3,,-1.2551895,36.7822034,-1.3105809,36.889729200000005,Rider_Id_937,2764
-Order_No_25014,User_Id_71,Bike,3,Business,12,1,9:56:25 AM,12,1,10:00:07 AM,12,1,10:08:53 AM,12,1,10:13:13 AM,12,1,10:25:21 AM,9,19.6,0.5,-1.2891921999999998,36.825189,-1.2551895,36.7822034,Rider_Id_88,728
-Order_No_922,User_Id_3289,Bike,3,Personal,24,4,10:57:16 AM,24,4,10:58:03 AM,24,4,11:10:33 AM,24,4,11:14:20 AM,24,4,11:25:00 AM,3,24.7,,-1.3002726999999998,36.7668028,-1.2974406,36.7765953,Rider_Id_559,640
-Order_No_6522,User_Id_2946,Bike,2,Personal,8,5,1:17:49 PM,8,5,1:23:59 PM,8,5,1:58:57 PM,8,5,2:09:52 PM,8,5,3:16:57 PM,8,21.7,,-1.2983462,36.8139996,-1.2749662,36.7658781,Rider_Id_431,4025
-Order_No_17021,User_Id_3624,Bike,1,Personal,4,2,6:53:18 AM,4,2,6:53:30 AM,4,2,7:05:42 AM,4,2,7:31:24 AM,4,2,7:58:41 AM,14,14.3,,-1.2091558999999998,36.7753704,-1.26559,36.8027,Rider_Id_720,1637
-Order_No_2193,User_Id_111,Bike,3,Business,30,5,1:25:22 PM,30,5,1:26:23 PM,30,5,2:00:24 PM,30,5,2:08:11 PM,30,5,3:01:28 PM,13,24.8,,-1.2551895,36.7822034,-1.3083777,36.85900770000001,Rider_Id_279,3197
-Order_No_21756,User_Id_3565,Bike,3,Business,9,6,9:47:37 AM,9,6,9:48:02 AM,9,6,9:55:57 AM,9,6,10:08:46 AM,9,6,10:35:27 AM,19,19.9,,-1.2844665000000002,36.7866113,-1.3146822,36.8900627,Rider_Id_862,1601
-Order_No_23889,User_Id_2216,Bike,3,Business,2,2,10:02:35 AM,2,2,10:02:41 AM,2,2,10:03:01 AM,2,2,10:27:30 AM,2,2,11:03:48 AM,21,20.6,,-1.2551895,36.7822034,-1.3705766000000001,36.9120742,Rider_Id_576,2178
-Order_No_17908,User_Id_1117,Bike,1,Personal,28,6,12:34:46 PM,28,6,12:35:04 PM,28,6,12:50:55 PM,28,6,1:35:17 PM,28,6,1:51:50 PM,10,24.9,,-1.307726,36.839117,-1.3472066,36.7692638,Rider_Id_754,993
-Order_No_3129,User_Id_3718,Bike,3,Business,13,2,10:29:12 AM,13,2,10:48:09 AM,13,2,11:03:15 AM,13,2,11:09:15 AM,13,2,12:10:09 PM,5,22.2,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_589,3654
-Order_No_14614,User_Id_633,Bike,3,Business,25,5,11:42:21 AM,25,5,11:42:54 AM,25,5,11:46:13 AM,25,5,11:52:48 AM,25,5,12:14:57 PM,9,26.3,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_523,1329
-Order_No_27874,User_Id_2523,Bike,3,Business,17,5,9:49:48 AM,17,5,9:50:00 AM,17,5,9:51:43 AM,17,5,10:11:03 AM,17,5,10:24:31 AM,4,,,-1.2551895,36.7822034,-1.2651643,36.807221399999996,Rider_Id_373,808
-Order_No_15084,User_Id_1724,Bike,3,Business,25,2,1:24:06 PM,25,2,1:24:39 PM,25,2,1:29:31 PM,25,2,1:35:43 PM,25,2,1:46:00 PM,2,27.3,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_62,617
-Order_No_12044,User_Id_2457,Bike,3,Business,30,2,11:53:16 AM,30,2,11:55:43 AM,30,2,11:56:34 AM,30,2,12:02:27 PM,30,2,12:19:10 PM,6,22.1,,-1.2551895,36.7822034,-1.2830989,36.8165739,Rider_Id_215,1003
-Order_No_10450,User_Id_3300,Bike,3,Business,22,3,1:16:10 PM,22,3,1:22:38 PM,22,3,1:27:17 PM,22,3,1:31:19 PM,22,3,1:45:35 PM,7,23.3,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_952,856
-Order_No_22739,User_Id_1754,Bike,3,Business,8,6,12:48:31 PM,8,6,12:49:37 PM,8,6,12:51:46 PM,8,6,12:55:53 PM,8,6,1:23:16 PM,15,18.1,,-1.2156006000000001,36.8916865,-1.2860183,36.8975338,Rider_Id_819,1643
-Order_No_22906,User_Id_1391,Bike,1,Personal,25,1,5:04:01 PM,25,1,5:05:07 PM,25,1,5:12:52 PM,25,1,5:18:05 PM,25,1,5:28:20 PM,5,,,-1.250142,36.788154999999996,-1.2764851000000002,36.8007126,Rider_Id_351,615
-Order_No_2788,User_Id_2987,Bike,3,Business,23,1,1:52:34 PM,23,1,2:20:25 PM,23,1,2:51:26 PM,23,1,3:01:49 PM,23,1,3:35:14 PM,2,,,-1.2702120000000001,36.82108770000001,-1.2800881000000002,36.8147037,Rider_Id_174,2005
-Order_No_4199,User_Id_2987,Bike,3,Business,16,2,5:29:50 PM,16,2,5:30:22 PM,16,2,5:40:22 PM,16,2,5:47:06 PM,16,2,6:10:03 PM,12,25.3,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_427,1377
-Order_No_24387,User_Id_2532,Bike,3,Business,10,5,11:57:01 AM,10,5,11:58:48 AM,10,5,12:01:15 PM,10,5,12:07:31 PM,10,5,12:41:25 PM,6,,,-1.2824911,36.8218042,-1.2571472,36.795063299999995,Rider_Id_709,2034
-Order_No_23497,User_Id_2828,Bike,3,Business,12,5,9:30:41 AM,12,5,9:30:52 AM,12,5,9:38:15 AM,12,5,9:47:00 AM,12,5,10:13:25 AM,13,18.4,,-1.2615888000000002,36.7928732,-1.330929,36.835151,Rider_Id_932,1585
-Order_No_9610,User_Id_635,Bike,3,Business,29,4,2:56:17 PM,29,4,2:59:45 PM,29,4,3:10:28 PM,29,4,3:28:32 PM,29,4,3:55:34 PM,7,25.0,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_646,1622
-Order_No_23081,User_Id_1389,Bike,3,Business,6,4,5:11:13 PM,6,4,5:17:33 PM,6,4,5:18:25 PM,6,4,5:35:16 PM,6,4,6:02:48 PM,14,22.6,,-1.2232123,36.8932547,-1.207546,36.7913976,Rider_Id_79,1652
-Order_No_6206,User_Id_248,Bike,3,Business,12,3,9:02:47 AM,12,3,9:03:27 AM,12,3,9:23:01 AM,12,3,9:27:22 AM,12,3,9:52:35 AM,6,,,-1.304735,36.784605400000004,-1.3071226,36.8075525,Rider_Id_831,1513
-Order_No_16202,User_Id_914,Bike,3,Business,14,4,1:30:46 PM,14,4,1:32:30 PM,14,4,1:59:37 PM,14,4,2:10:06 PM,14,4,2:29:36 PM,11,27.5,,-1.2670213,36.7994482,-1.2434453,36.8640044,Rider_Id_52,1170
-Order_No_364,User_Id_3647,Bike,3,Business,12,2,3:34:00 PM,12,2,3:41:35 PM,12,2,4:15:40 PM,12,2,4:20:55 PM,12,2,4:36:38 PM,7,27.0,,-1.2726389999999999,36.794723,-1.2651573999999999,36.7474347,Rider_Id_783,943
-Order_No_28234,User_Id_893,Bike,3,Business,3,1,12:34:37 PM,3,1,12:36:10 PM,3,1,12:49:10 PM,3,1,12:53:14 PM,3,1,1:17:23 PM,13,21.7,,-1.2663194,36.809901,-1.247957,36.879598,Rider_Id_574,1449
-Order_No_3771,User_Id_3748,Bike,1,Personal,5,7,6:35:42 AM,5,7,7:04:02 AM,5,7,8:00:09 AM,5,7,8:15:56 AM,5,7,8:49:43 AM,20,15.6,,-1.1917753,36.9211989,-1.2772632,36.792225099999996,Rider_Id_836,2027
-Order_No_23930,User_Id_1271,Bike,3,Business,18,1,4:16:36 PM,18,1,4:17:03 PM,18,1,4:25:37 PM,18,1,4:34:37 PM,18,1,5:17:27 PM,21,29.3,,-1.3108386,36.7555239,-1.2184661,36.8260859,Rider_Id_785,2570
-Order_No_3285,User_Id_2207,Bike,3,Business,12,2,2:04:12 PM,12,2,2:06:02 PM,12,2,2:43:29 PM,12,2,2:53:31 PM,12,2,3:17:13 PM,5,,,-1.2897399,36.8270975,-1.2696908000000002,36.81519,Rider_Id_448,1422
-Order_No_26811,User_Id_1811,Bike,2,Personal,10,2,9:57:29 AM,10,2,9:59:34 AM,10,2,10:06:38 AM,10,2,10:16:33 AM,10,2,10:59:00 AM,8,16.3,,-1.294869,36.815327,-1.2804664,36.7701111,Rider_Id_431,2547
-Order_No_16978,User_Id_2066,Bike,3,Business,6,4,10:33:16 AM,6,4,10:35:26 AM,6,4,10:58:39 AM,6,4,11:01:44 AM,6,4,11:32:05 AM,8,22.1,,-1.316961,36.820337,-1.3084775,36.861188299999995,Rider_Id_820,1821
-Order_No_11923,User_Id_2523,Bike,3,Business,25,1,11:41:50 AM,25,1,11:42:30 AM,25,1,11:55:53 AM,25,1,12:03:30 PM,25,1,12:09:07 PM,3,27.0,,-1.2551895,36.7822034,-1.273488,36.804158,Rider_Id_607,337
-Order_No_6431,User_Id_346,Bike,3,Business,29,2,1:13:04 PM,29,2,1:14:19 PM,29,2,1:20:58 PM,29,2,1:25:57 PM,29,2,1:44:16 PM,7,21.6,,-1.2885528999999998,36.816014100000004,-1.2579303999999998,36.8019667,Rider_Id_726,1099
-Order_No_11911,User_Id_393,Bike,3,Business,11,5,12:55:11 PM,11,5,12:55:20 PM,11,5,12:57:39 PM,11,5,1:00:51 PM,11,5,1:32:15 PM,14,20.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1884
-Order_No_12628,User_Id_2492,Bike,3,Personal,5,2,1:26:58 PM,5,2,1:28:00 PM,5,2,1:44:41 PM,5,2,1:54:14 PM,5,2,2:08:32 PM,3,,,-1.2778447,36.830721399999995,-1.2869955,36.8202749,Rider_Id_812,858
-Order_No_25927,User_Id_18,Bike,1,Personal,22,6,2:28:16 PM,22,6,2:28:32 PM,22,6,2:39:06 PM,22,6,2:41:59 PM,22,6,2:51:54 PM,1,,,-1.2869313,36.766848700000004,-1.296684,36.770171999999995,Rider_Id_465,595
-Order_No_8809,User_Id_3283,Bike,3,Business,3,1,11:18:04 AM,3,1,11:18:46 AM,3,1,11:49:40 AM,3,1,11:59:05 AM,3,1,12:18:00 PM,13,21.5,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_567,1135
-Order_No_5394,User_Id_1209,Bike,3,Business,13,6,9:06:14 AM,13,6,9:06:32 AM,13,6,9:12:06 AM,13,6,9:20:59 AM,13,6,9:53:54 AM,9,20.2,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_155,1975
-Order_No_27758,User_Id_1335,Bike,3,Personal,12,3,1:42:18 PM,12,3,1:42:43 PM,12,3,1:43:14 PM,12,3,1:50:08 PM,12,3,2:05:56 PM,5,26.3,,-1.2611987,36.80609129999999,-1.2613573,36.83402,Rider_Id_726,948
-Order_No_15044,User_Id_3517,Bike,3,Business,23,5,8:57:48 AM,23,5,8:57:58 AM,23,5,8:58:05 AM,23,5,10:20:34 AM,23,5,10:26:17 AM,15,20.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137,343
-Order_No_15518,User_Id_53,Bike,3,Business,8,3,1:07:46 PM,8,3,1:08:59 PM,8,3,1:11:47 PM,8,3,1:22:35 PM,8,3,1:47:09 PM,14,,,-1.2770708,36.8231093,-1.219817,36.903273,Rider_Id_276,1474
-Order_No_27886,User_Id_1410,Bike,3,Business,17,5,4:40:14 PM,17,5,4:40:28 PM,17,5,4:47:39 PM,17,5,5:11:11 PM,17,5,6:01:03 PM,20,,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_349,2992
-Order_No_22300,User_Id_1807,Bike,3,Business,29,3,10:17:37 AM,29,3,10:17:52 AM,29,3,10:32:44 AM,29,3,10:35:32 AM,29,3,10:50:13 AM,5,18.5,,-1.2682478000000001,36.8072852,-1.2640571000000003,36.7883547,Rider_Id_400,881
-Order_No_19721,User_Id_1075,Bike,3,Business,7,2,10:58:39 AM,7,2,10:59:07 AM,7,2,11:09:44 AM,7,2,11:27:00 AM,7,2,11:46:27 AM,5,,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_341,1167
-Order_No_18431,User_Id_2330,Bike,3,Business,1,5,10:13:49 AM,1,5,10:14:26 AM,1,5,10:24:59 AM,1,5,10:26:40 AM,1,5,10:47:08 AM,9,,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_368,1228
-Order_No_13057,User_Id_1402,Bike,3,Business,14,4,12:08:45 PM,14,4,12:09:08 PM,14,4,12:15:51 PM,14,4,12:19:49 PM,14,4,12:37:50 PM,11,25.1,,-1.2283403,36.8822756,-1.2813012,36.832396200000005,Rider_Id_456,1081
-Order_No_27608,User_Id_3283,Bike,3,Business,18,1,9:20:37 AM,18,1,9:23:01 AM,18,1,9:38:44 AM,18,1,10:00:42 AM,18,1,10:22:24 AM,13,21.3,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_459,1302
-Order_No_13052,User_Id_250,Bike,2,Personal,16,4,11:19:46 AM,16,4,11:20:52 AM,16,4,11:33:06 AM,16,4,11:34:35 AM,16,4,11:45:03 AM,2,14.7,,-1.2642903,36.80021370000001,-1.269534,36.80697,Rider_Id_650,628
-Order_No_20130,User_Id_510,Bike,3,Business,25,4,2:47:39 PM,25,4,3:18:51 PM,25,4,3:36:31 PM,25,4,3:37:39 PM,25,4,3:54:22 PM,10,20.6,,-1.2765736,36.851364600000004,-1.2860183,36.8975338,Rider_Id_402,1003
-Order_No_14514,User_Id_136,Bike,3,Business,8,4,1:23:52 PM,8,4,1:41:03 PM,8,4,1:41:25 PM,8,4,1:51:58 PM,8,4,2:25:23 PM,15,27.3,,-1.3254522,36.721658399999995,-1.2781133,36.81621679999999,Rider_Id_633,2005
-Order_No_21381,User_Id_1995,Bike,3,Business,15,4,12:05:14 PM,15,4,12:56:32 PM,15,4,1:11:39 PM,15,4,1:14:39 PM,15,4,1:35:04 PM,7,24.3,,-1.2551895,36.7822034,-1.2918290000000001,36.829358,Rider_Id_928,1225
-Order_No_14444,User_Id_2069,Bike,3,Business,20,3,3:12:35 PM,20,3,3:13:10 PM,20,3,3:26:32 PM,20,3,3:40:56 PM,20,3,4:02:32 PM,9,29.6,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309,1296
-Order_No_26630,User_Id_2956,Bike,3,Business,12,1,10:09:15 AM,12,1,10:09:32 AM,12,1,10:21:17 AM,12,1,10:28:07 AM,12,1,10:47:07 AM,2,19.5,0.4,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_112,1140
-Order_No_20220,User_Id_186,Bike,3,Business,17,3,12:30:59 PM,17,3,12:31:32 PM,17,3,12:35:01 PM,17,3,12:48:02 PM,17,3,1:17:07 PM,5,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_843,1745
-Order_No_3885,User_Id_186,Bike,3,Business,26,5,3:27:13 PM,26,5,3:34:55 PM,26,5,4:32:13 PM,26,5,4:40:24 PM,26,5,5:01:36 PM,4,,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_228,1272
-Order_No_26327,User_Id_718,Bike,3,Business,9,5,4:07:24 PM,9,5,4:17:03 PM,9,5,4:34:04 PM,9,5,4:39:59 PM,9,5,5:25:29 PM,15,25.0,,-1.2551895,36.7822034,-1.3221141,36.8636835,Rider_Id_849,2730
-Order_No_1759,User_Id_496,Bike,3,Business,23,4,11:30:38 AM,23,4,11:31:10 AM,23,4,11:32:40 AM,23,4,12:10:34 PM,23,4,12:52:12 PM,23,18.5,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_223,2498
-Order_No_14625,User_Id_867,Bike,3,Business,12,5,9:33:15 AM,12,5,9:33:59 AM,12,5,9:56:37 AM,12,5,10:29:38 AM,12,5,10:29:43 AM,11,21.7,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_99,5
-Order_No_13990,User_Id_1205,Bike,3,Business,24,5,2:18:56 PM,24,5,2:37:28 PM,24,5,2:48:20 PM,24,5,2:57:22 PM,24,5,3:28:18 PM,8,22.9,,-1.2495931,36.80650870000001,-1.2904696000000002,36.7963745,Rider_Id_427,1856
-Order_No_12501,User_Id_3517,Bike,3,Business,4,6,10:11:06 AM,4,6,10:11:16 AM,4,6,10:20:24 AM,4,6,10:21:41 AM,4,6,11:10:39 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_614,2938
-Order_No_7621,User_Id_1410,Bike,3,Business,7,5,10:41:58 AM,7,5,10:50:12 AM,7,5,11:02:56 AM,7,5,11:22:37 AM,7,5,12:00:59 PM,5,25.1,,-1.300921,36.828195,-1.2797664,36.826966999999996,Rider_Id_725,2302
-Order_No_12811,User_Id_1329,Bike,3,Business,14,2,10:32:03 AM,14,2,10:33:01 AM,14,2,10:55:14 AM,14,2,10:58:37 AM,14,2,11:11:57 AM,4,,,-1.273056,36.811298,-1.290894,36.8031109,Rider_Id_576,800
-Order_No_617,User_Id_1859,Bike,1,Business,15,3,11:20:12 AM,15,3,11:21:02 AM,15,3,11:30:39 AM,15,3,11:33:39 AM,15,3,12:08:01 PM,8,18.1,,-1.2993666000000001,36.8232195,-1.2825803999999998,36.87750870000001,Rider_Id_402,2062
-Order_No_6302,User_Id_1280,Bike,3,Business,15,2,10:26:29 AM,15,2,10:26:36 AM,15,2,10:33:27 AM,15,2,10:35:44 AM,15,2,10:45:41 AM,3,24.5,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_322,597
-Order_No_8393,User_Id_3683,Bike,3,Business,8,1,2:06:57 PM,8,1,2:07:51 PM,8,1,2:14:53 PM,8,1,2:22:45 PM,8,1,3:12:32 PM,7,24.7,,-1.2897368,36.8226783,-1.2765492,36.7669809,Rider_Id_857,2987
-Order_No_21787,User_Id_1845,Bike,3,Business,8,5,3:24:21 PM,8,5,3:27:55 PM,8,5,4:12:25 PM,8,5,4:20:04 PM,8,5,5:13:37 PM,9,21.5,,-1.2998129999999999,36.787139200000006,-1.2567075,36.7739831,Rider_Id_431,3213
-Order_No_24301,User_Id_1926,Bike,3,Business,25,1,1:36:44 PM,25,1,1:38:02 PM,25,1,1:53:23 PM,25,1,2:12:02 PM,25,1,2:19:42 PM,4,30.0,,-1.2897368,36.8226783,-1.263706,36.812036,Rider_Id_119,460
-Order_No_12605,User_Id_136,Bike,3,Business,19,2,4:19:48 PM,19,2,4:20:34 PM,19,2,4:36:44 PM,19,2,4:39:50 PM,19,2,4:57:48 PM,7,28.3,,-1.3009676000000001,36.7657189,-1.2848376,36.802762200000004,Rider_Id_545,1078
-Order_No_21413,User_Id_3091,Bike,3,Business,26,1,12:08:36 PM,26,1,12:08:46 PM,26,1,12:14:18 PM,26,1,12:30:48 PM,26,1,12:57:21 PM,10,23.2,,-1.2527958000000001,36.8003131,-1.3012008,36.764868,Rider_Id_322,1593
-Order_No_19239,User_Id_2407,Bike,2,Personal,31,4,12:14:45 PM,31,4,12:15:40 PM,31,4,12:17:21 PM,31,4,12:24:03 PM,31,4,12:28:00 PM,2,22.0,,-1.2711441,36.8030777,-1.2657199,36.7914391,Rider_Id_379,237
-Order_No_4939,User_Id_633,Bike,3,Business,19,2,12:01:29 PM,19,2,12:02:10 PM,19,2,12:08:44 PM,19,2,12:27:45 PM,19,2,12:35:57 PM,5,27.7,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_111,492
-Order_No_12720,User_Id_3391,Bike,1,Personal,5,5,6:27:31 PM,5,5,6:44:38 PM,5,5,6:59:10 PM,5,5,7:01:02 PM,5,5,7:13:47 PM,5,24.9,,-1.2998789,36.816896299999996,-1.283016,36.801959499999995,Rider_Id_365,765
-Order_No_2746,User_Id_3669,Bike,3,Business,16,3,12:57:58 PM,16,3,12:58:06 PM,16,3,1:02:35 PM,16,3,1:12:16 PM,16,3,1:40:12 PM,19,23.0,,-1.2615888000000002,36.7928732,-1.2708104,36.910537,Rider_Id_598,1676
-Order_No_8070,User_Id_1361,Bike,1,Personal,14,4,1:44:38 PM,14,4,2:34:25 PM,14,4,2:37:30 PM,14,4,2:45:47 PM,14,4,3:40:12 PM,6,21.6,,-1.2861764999999998,36.8209086,-1.2948784,36.776632799999994,Rider_Id_531,3265
-Order_No_12955,User_Id_2150,Bike,3,Business,30,4,10:28:28 AM,30,4,10:28:51 AM,30,4,10:29:05 AM,30,4,10:38:39 AM,30,4,11:00:49 AM,14,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_855,1330
-Order_No_5741,User_Id_3291,Bike,3,Business,14,5,1:44:25 PM,14,5,1:45:20 PM,14,5,1:59:36 PM,14,5,2:09:41 PM,14,5,2:33:36 PM,12,22.9,,-1.318062,36.849326,-1.2600926000000001,36.8088685,Rider_Id_270,1435
-Order_No_16109,User_Id_3557,Bike,3,Business,28,3,2:53:07 PM,28,3,2:54:09 PM,28,3,3:02:27 PM,28,3,3:20:20 PM,28,3,3:31:58 PM,3,,,-1.269534,36.80697,-1.2546732,36.808679600000005,Rider_Id_911,698
-Order_No_18386,User_Id_475,Bike,3,Business,5,1,2:54:17 PM,5,1,2:54:34 PM,5,1,2:58:01 PM,5,1,3:33:01 PM,5,1,4:09:07 PM,19,26.0,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_45,2166
-Order_No_1209,User_Id_3698,Bike,3,Business,6,4,1:45:02 PM,6,4,1:45:24 PM,6,4,1:45:48 PM,6,4,2:20:51 PM,6,4,2:53:43 PM,18,21.2,,-1.2649605,36.798177700000004,-1.3244885,36.897791999999995,Rider_Id_832,1972
-Order_No_4542,User_Id_3291,Bike,3,Business,14,4,1:28:05 PM,14,4,1:29:40 PM,14,4,2:05:13 PM,14,4,2:11:16 PM,14,4,2:24:06 PM,5,27.8,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_228,770
-Order_No_11619,User_Id_2330,Bike,3,Business,20,3,4:50:21 PM,20,3,4:55:24 PM,20,3,5:15:23 PM,20,3,5:22:52 PM,20,3,5:57:01 PM,12,27.9,,-1.2854537,36.9051024,-1.2284323,36.8757058,Rider_Id_126,2049
-Order_No_20834,User_Id_79,Bike,1,Personal,5,5,4:14:58 PM,5,5,5:56:55 PM,5,5,6:37:59 PM,5,5,6:45:24 PM,5,5,7:22:11 PM,15,24.9,,-1.3372002,36.7538932,-1.2684319,36.81658529999999,Rider_Id_268,2207
-Order_No_16935,User_Id_830,Bike,3,Business,24,4,3:23:41 PM,24,4,4:33:43 PM,24,4,4:48:32 PM,24,4,4:51:54 PM,24,4,5:33:41 PM,8,26.0,,-1.2551895,36.7822034,-1.2904016,36.82263029999999,Rider_Id_332,2507
-Order_No_3537,User_Id_2216,Bike,3,Business,4,2,9:19:06 AM,4,2,10:33:26 AM,4,2,10:50:34 AM,4,2,10:56:03 AM,4,2,11:15:38 AM,8,19.6,,-1.2551895,36.7822034,-1.2980815,36.8085686,Rider_Id_607,1175
-Order_No_20060,User_Id_2956,Bike,3,Business,11,2,5:44:05 PM,11,2,5:44:44 PM,11,2,5:56:02 PM,11,2,5:59:40 PM,11,2,6:12:24 PM,3,21.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_547,764
-Order_No_8586,User_Id_1939,Bike,2,Personal,3,5,9:57:37 AM,3,5,9:59:13 AM,3,5,10:11:27 AM,3,5,10:21:35 AM,3,5,10:32:49 AM,4,17.2,,-1.2851847,36.8314086,-1.3019862,36.827752399999994,Rider_Id_754,674
-Order_No_201,User_Id_246,Bike,3,Business,15,3,12:36:50 PM,15,3,12:37:02 PM,15,3,12:46:51 PM,15,3,1:01:17 PM,15,3,1:43:24 PM,7,18.0,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_334,2527
-Order_No_8036,User_Id_3674,Bike,3,Business,21,4,9:49:09 AM,21,4,9:49:24 AM,21,4,9:50:48 AM,21,4,9:59:05 AM,21,4,10:06:33 AM,4,21.6,,-1.2551895,36.7822034,-1.2630856000000001,36.808280700000005,Rider_Id_205,448
-Order_No_21692,User_Id_393,Bike,3,Business,23,2,8:56:56 AM,23,2,8:57:10 AM,23,2,8:57:24 AM,23,2,10:28:19 AM,23,2,10:28:21 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_10,2
-Order_No_26881,User_Id_393,Bike,3,Business,22,5,8:18:19 AM,22,5,8:45:45 AM,22,5,8:46:27 AM,22,5,9:01:38 AM,22,5,9:16:56 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_809,918
-Order_No_11544,User_Id_868,Bike,3,Business,23,2,1:05:12 PM,23,2,1:07:41 PM,23,2,1:08:08 PM,23,2,1:38:26 PM,23,2,1:41:56 PM,3,,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_531,210
-Order_No_27955,User_Id_635,Bike,3,Business,14,4,1:36:24 PM,14,4,1:37:30 PM,14,4,1:55:12 PM,14,4,2:18:50 PM,14,4,2:38:40 PM,7,28.1,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_715,1190
-Order_No_21581,User_Id_3688,Bike,3,Business,16,4,4:04:07 PM,16,4,4:04:22 PM,16,4,4:10:56 PM,16,4,4:16:08 PM,16,4,4:28:13 PM,4,19.1,,-1.2551895,36.7822034,-1.2728058,36.8049903,Rider_Id_882,725
-Order_No_8886,User_Id_711,Bike,3,Business,25,1,1:20:30 PM,25,1,1:21:22 PM,25,1,1:22:48 PM,25,1,2:28:02 PM,25,1,2:46:46 PM,10,27.9,,-1.3247683000000001,36.85803,-1.335477,36.930829700000004,Rider_Id_26,1124
-Order_No_13558,User_Id_2701,Bike,3,Business,22,4,9:24:41 AM,22,4,9:25:11 AM,22,4,9:56:09 AM,22,4,10:19:14 AM,22,4,11:00:40 AM,11,19.4,3.0,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_925,2486
-Order_No_17922,User_Id_1260,Bike,3,Business,16,2,11:57:46 AM,16,2,12:08:41 PM,16,2,12:09:52 PM,16,2,12:48:21 PM,16,2,12:58:35 PM,12,,,-1.2551895,36.7822034,-1.3322618,36.8045412,Rider_Id_436,614
-Order_No_17738,User_Id_3647,Bike,3,Business,3,3,11:23:49 AM,3,3,11:24:30 AM,3,3,11:32:31 AM,3,3,11:40:10 AM,3,3,11:56:50 AM,6,27.6,,-1.2726389999999999,36.794723,-1.290789,36.8024859,Rider_Id_520,1000
-Order_No_11917,User_Id_393,Bike,3,Business,15,5,9:52:55 AM,15,5,9:53:05 AM,15,5,9:55:41 AM,15,5,10:08:16 AM,15,5,10:40:51 AM,14,19.3,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,1955
-Order_No_24190,User_Id_2069,Bike,3,Business,26,5,12:49:07 PM,26,5,12:49:18 PM,26,5,12:49:29 PM,26,5,1:04:19 PM,26,5,1:29:31 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_155,1512
-Order_No_25435,User_Id_2701,Bike,3,Business,23,4,1:26:20 PM,23,4,1:26:52 PM,23,4,1:34:34 PM,23,4,1:47:00 PM,23,4,1:58:43 PM,1,21.0,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_89,703
-Order_No_10583,User_Id_2174,Bike,3,Business,5,2,2:18:53 PM,5,2,2:19:30 PM,5,2,2:29:50 PM,5,2,2:58:19 PM,5,2,3:30:51 PM,19,22.6,,-1.2482781,36.884091999999995,-1.3205111,36.7801016,Rider_Id_408,1952
-Order_No_1953,User_Id_1410,Bike,3,Business,1,5,12:56:50 PM,1,5,1:23:25 PM,1,5,1:51:44 PM,1,5,2:00:21 PM,1,5,2:33:13 PM,21,31.7,,-1.2847613000000002,36.967084,-1.300921,36.828195,Rider_Id_933,1972
-Order_No_26856,User_Id_265,Bike,3,Business,19,2,3:04:27 PM,19,2,3:05:01 PM,19,2,3:21:18 PM,19,2,3:27:46 PM,19,2,4:25:28 PM,16,29.4,,-1.300921,36.828195,-1.2051858999999998,36.836315,Rider_Id_863,3462
-Order_No_2639,User_Id_3339,Bike,3,Business,14,4,3:23:20 PM,14,4,3:24:18 PM,14,4,3:47:17 PM,14,4,3:50:22 PM,14,4,4:10:58 PM,2,27.5,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_660,1236
-Order_No_6208,User_Id_2008,Bike,3,Business,10,3,3:04:18 PM,10,3,3:04:39 PM,10,3,3:18:36 PM,10,3,3:31:26 PM,10,3,4:12:48 PM,16,30.6,,-1.2508229,36.7895263,-1.3287376,36.7140875,Rider_Id_131,2482
-Order_No_11278,User_Id_136,Bike,3,Business,19,2,12:30:13 PM,19,2,12:30:46 PM,19,2,1:15:51 PM,19,2,1:19:28 PM,19,2,1:37:27 PM,5,27.5,,-1.2654716000000001,36.7983635,-1.2549476000000002,36.8269576,Rider_Id_547,1079
-Order_No_10535,User_Id_545,Bike,3,Business,11,2,9:08:04 AM,11,2,9:08:27 AM,11,2,9:27:09 AM,11,2,9:30:01 AM,11,2,10:09:55 AM,20,16.4,,-1.2963097,36.7688221,-1.3197021,36.9092821,Rider_Id_177,2394
-Order_No_16133,User_Id_554,Bike,1,Personal,11,5,11:04:10 AM,11,5,11:10:25 AM,11,5,11:24:40 AM,11,5,11:27:09 AM,11,5,11:54:39 AM,5,20.8,,-1.2942764,36.7969749,-1.3127303999999997,36.8189817,Rider_Id_196,1650
-Order_No_6061,User_Id_393,Bike,3,Business,5,2,2:19:41 PM,5,2,2:23:10 PM,5,2,2:34:08 PM,5,2,2:55:39 PM,5,2,3:26:45 PM,11,27.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_901,1866
-Order_No_10810,User_Id_3211,Bike,3,Personal,22,3,9:21:11 AM,22,3,9:21:35 AM,22,3,9:28:48 AM,22,3,9:36:52 AM,22,3,10:04:05 AM,14,15.3,,-1.2783772,36.8201617,-1.2098918,36.791852899999995,Rider_Id_414,1633
-Order_No_20443,User_Id_990,Bike,1,Business,6,5,10:51:07 AM,6,5,10:51:32 AM,6,5,10:57:42 AM,6,5,12:02:46 PM,6,5,12:46:25 PM,19,20.2,,-1.272675,36.8129051,-1.3628038,36.714921000000004,Rider_Id_379,2619
-Order_No_13333,User_Id_3295,Bike,3,Business,22,4,11:36:07 AM,22,4,11:54:41 AM,22,4,12:19:46 PM,22,4,12:30:13 PM,22,4,12:35:59 PM,4,22.6,,-1.2571472,36.795063299999995,-1.286583,36.8159584,Rider_Id_660,346
-Order_No_18049,User_Id_1546,Bike,3,Business,3,3,8:55:46 AM,3,3,8:58:23 AM,3,3,9:09:36 AM,3,3,9:16:51 AM,3,3,9:56:48 AM,10,21.2,,-1.2649471,36.7453303,-1.2684319,36.81658529999999,Rider_Id_269,2397
-Order_No_23061,User_Id_659,Bike,3,Personal,18,4,1:22:04 PM,18,4,1:23:08 PM,18,4,1:23:52 PM,18,4,1:32:48 PM,18,4,1:33:43 PM,2,,,-1.2852583999999998,36.8043641,-1.2945362,36.7928847,Rider_Id_132,55
-Order_No_27921,User_Id_475,Bike,3,Business,30,3,12:14:24 PM,30,3,12:17:07 PM,30,3,12:34:25 PM,30,3,12:39:45 PM,30,3,1:17:50 PM,28,22.7,,-1.3632162,36.9108253,-1.3339943,36.7263409,Rider_Id_95,2285
-Order_No_25216,User_Id_393,Bike,3,Business,31,3,12:11:47 PM,31,3,12:29:39 PM,31,3,12:56:14 PM,31,3,1:06:56 PM,31,3,1:48:58 PM,14,24.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_761,2522
-Order_No_16489,User_Id_2212,Bike,3,Business,7,4,10:58:53 AM,7,4,10:59:34 AM,7,4,11:10:25 AM,7,4,11:15:39 AM,7,4,11:26:10 AM,5,24.5,,-1.2571472,36.795063299999995,-1.2357748,36.7900524,Rider_Id_717,631
-Order_No_24687,User_Id_510,Bike,3,Business,31,5,4:33:49 PM,31,5,4:45:49 PM,31,5,4:52:09 PM,31,5,5:00:46 PM,31,5,5:21:35 PM,10,23.8,,-1.2765736,36.851364600000004,-1.2860183,36.8975338,Rider_Id_291,1249
-Order_No_6935,User_Id_3501,Bike,1,Personal,13,3,1:21:24 PM,13,3,1:34:27 PM,13,3,1:38:07 PM,13,3,1:48:34 PM,13,3,2:14:58 PM,6,23.4,,-1.2658334999999998,36.804286700000006,-1.2944292,36.779356899999996,Rider_Id_726,1584
-Order_No_26574,User_Id_136,Bike,3,Business,11,6,11:49:36 AM,11,6,11:49:53 AM,11,6,11:54:28 AM,11,6,12:01:11 PM,11,6,12:10:17 PM,1,,,-1.2654716000000001,36.7983635,-1.2531983,36.8069669,Rider_Id_172,546
-Order_No_15224,User_Id_3291,Bike,3,Business,7,5,12:03:05 PM,7,5,12:04:03 PM,7,5,12:12:53 PM,7,5,12:17:31 PM,7,5,12:40:00 PM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_27,1349
-Order_No_11320,User_Id_2271,Bike,3,Business,2,4,11:12:29 AM,2,4,11:13:24 AM,2,4,11:25:20 AM,2,4,11:34:14 AM,2,4,12:11:42 PM,14,,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_930,2248
-Order_No_5895,User_Id_3326,Bike,3,Business,31,3,12:51:31 PM,31,3,2:47:18 PM,31,3,3:03:31 PM,31,3,3:05:00 PM,31,3,3:19:23 PM,8,27.2,,-1.2551895,36.7822034,-1.2812624,36.7714609,Rider_Id_866,863
-Order_No_15885,User_Id_391,Bike,3,Business,9,4,2:35:50 PM,9,4,2:40:37 PM,9,4,2:52:58 PM,9,4,3:08:39 PM,9,4,3:27:22 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_291,1123
-Order_No_2313,User_Id_1075,Bike,3,Business,28,1,3:03:13 PM,28,1,3:04:01 PM,28,1,3:19:53 PM,28,1,3:23:04 PM,28,1,3:29:34 PM,3,26.3,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_190,390
-Order_No_9849,User_Id_3689,Bike,3,Business,31,5,4:32:48 PM,31,5,4:43:11 PM,31,5,5:02:40 PM,31,5,5:04:16 PM,31,5,5:26:44 PM,7,23.7,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_139,1348
-Order_No_17323,User_Id_3560,Bike,3,Business,14,3,1:29:33 PM,14,3,1:30:09 PM,14,3,1:33:43 PM,14,3,1:55:31 PM,14,3,2:14:23 PM,1,25.5,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_88,1132
-Order_No_6836,User_Id_1568,Bike,3,Business,28,1,2:20:33 PM,28,1,2:28:41 PM,28,1,2:56:33 PM,28,1,3:10:21 PM,28,1,4:01:23 PM,6,26.4,,-1.2897368,36.8226783,-1.3271374,36.8495534,Rider_Id_669,3062
-Order_No_22419,User_Id_3091,Bike,3,Business,5,3,11:07:20 AM,5,3,12:23:46 PM,5,3,12:26:23 PM,5,3,12:34:50 PM,5,3,12:54:21 PM,8,22.3,,-1.3025826,36.7670807,-1.2899256000000001,36.815551,Rider_Id_124,1171
-Order_No_15827,User_Id_1637,Bike,3,Business,5,1,3:48:20 PM,5,1,3:48:27 PM,5,1,3:51:12 PM,5,1,4:12:27 PM,5,1,4:36:18 PM,14,25.9,,-1.2615888000000002,36.7928732,-1.2283403,36.8822756,Rider_Id_551,1431
-Order_No_1545,User_Id_419,Bike,3,Business,13,4,10:11:09 AM,13,4,10:11:46 AM,13,4,10:21:17 AM,13,4,10:30:04 AM,13,4,11:07:46 AM,20,18.3,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_766,2262
-Order_No_19271,User_Id_3688,Bike,3,Business,18,4,3:46:45 PM,18,4,3:50:21 PM,18,4,4:00:31 PM,18,4,4:04:11 PM,18,4,5:07:36 PM,7,,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_576,3805
-Order_No_9776,User_Id_2383,Bike,3,Business,5,2,3:57:33 PM,5,2,3:58:48 PM,5,2,4:10:49 PM,5,2,4:13:44 PM,5,2,4:31:11 PM,5,,,-1.2608993,36.799246000000004,-1.2816233999999997,36.8239846,Rider_Id_452,1047
-Order_No_13805,User_Id_393,Bike,3,Business,13,4,8:56:12 AM,13,4,10:00:20 AM,13,4,10:00:31 AM,13,4,10:42:42 AM,13,4,10:42:45 AM,4,21.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,3
-Order_No_11648,User_Id_1954,Bike,3,Business,6,3,3:09:21 PM,6,3,3:11:20 PM,6,3,3:23:58 PM,6,3,3:34:42 PM,6,3,3:54:30 PM,5,21.6,,-1.289498,36.818878000000005,-1.2944081,36.7897046,Rider_Id_752,1188
-Order_No_18353,User_Id_53,Bike,3,Business,15,3,2:15:31 PM,15,3,2:16:06 PM,15,3,2:27:05 PM,15,3,2:35:32 PM,15,3,3:08:56 PM,5,25.8,,-1.2770708,36.8231093,-1.2945073999999999,36.8083195,Rider_Id_204,2004
-Order_No_1718,User_Id_1882,Bike,3,Business,14,1,12:55:22 PM,14,1,12:57:30 PM,14,1,1:01:15 PM,14,1,1:11:27 PM,14,1,1:32:00 PM,5,23.7,,-1.2588774999999999,36.8079744,-1.2877809999999998,36.817865999999995,Rider_Id_903,1233
-Order_No_27544,User_Id_2030,Bike,3,Business,16,2,11:33:12 AM,16,2,12:45:20 PM,16,2,1:04:06 PM,16,2,1:21:18 PM,16,2,1:42:18 PM,15,,,-1.1648303,36.951368,-1.2285834999999998,36.882646799999996,Rider_Id_96,1260
-Order_No_12028,User_Id_3660,Bike,1,Personal,24,2,12:20:07 PM,24,2,12:22:02 PM,24,2,12:36:34 PM,24,2,12:40:18 PM,24,2,1:00:16 PM,5,17.3,,-1.2833620000000001,36.8260149,-1.2655477,36.8001682,Rider_Id_88,1198
-Order_No_10404,User_Id_3355,Bike,3,Business,14,2,1:25:09 PM,14,2,1:26:39 PM,14,2,1:34:28 PM,14,2,1:38:08 PM,14,2,1:54:28 PM,3,24.0,,-1.3052491000000002,36.8223899,-1.291768,36.8297208,Rider_Id_857,980
-Order_No_24963,User_Id_2271,Bike,3,Business,14,1,11:20:43 AM,14,1,11:20:53 AM,14,1,11:20:58 AM,14,1,11:35:45 AM,14,1,11:50:00 AM,11,22.8,,-1.307143,36.825009,-1.293515,36.897607,Rider_Id_589,855
-Order_No_13394,User_Id_3727,Bike,3,Business,16,2,11:43:40 AM,16,2,12:08:37 PM,16,2,12:12:28 PM,16,2,12:15:34 PM,16,2,12:30:19 PM,3,,,-1.2963097,36.7688221,-1.293393,36.790237,Rider_Id_154,885
-Order_No_18562,User_Id_2956,Bike,3,Business,25,5,10:30:41 AM,25,5,10:43:02 AM,25,5,10:53:23 AM,25,5,12:08:07 PM,25,5,12:50:32 PM,19,21.2,,-1.3632162,36.9108253,-1.290894,36.822971,Rider_Id_100,2545
-Order_No_21056,User_Id_635,Bike,3,Business,30,5,2:41:46 PM,30,5,2:42:03 PM,30,5,2:45:59 PM,30,5,3:11:44 PM,30,5,3:24:19 PM,8,24.4,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_551,755
-Order_No_8484,User_Id_2801,Bike,3,Business,9,5,3:04:49 PM,9,5,3:21:58 PM,9,5,3:23:50 PM,9,5,3:33:38 PM,9,5,3:56:22 PM,8,26.3,,-1.2595421999999998,36.7871178,-1.2873708000000001,36.814908,Rider_Id_848,1364
-Order_No_9757,User_Id_1244,Bike,3,Business,8,2,9:35:23 AM,8,2,9:36:33 AM,8,2,9:37:36 AM,8,2,9:51:14 AM,8,2,10:33:33 AM,9,19.1,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_107,2539
-Order_No_13775,User_Id_391,Bike,3,Business,15,1,2:17:24 PM,15,1,2:23:18 PM,15,1,2:23:44 PM,15,1,2:55:53 PM,15,1,3:17:56 PM,5,,,-1.2784063,36.8222933,-1.3081376,36.83297720000001,Rider_Id_613,1323
-Order_No_10237,User_Id_552,Bike,3,Business,19,2,4:29:00 PM,19,2,4:31:39 PM,19,2,4:54:19 PM,19,2,5:00:21 PM,19,2,5:44:03 PM,17,19.8,,-1.2873639,36.8256076,-1.3632162,36.9108253,Rider_Id_340,2622
-Order_No_148,User_Id_20,Bike,3,Business,18,4,3:08:41 PM,18,4,3:42:03 PM,18,4,4:53:49 PM,18,4,5:05:30 PM,18,4,6:00:47 PM,27,,,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_539,3317
-Order_No_24355,User_Id_2271,Bike,3,Business,8,5,3:18:43 PM,8,5,3:27:23 PM,8,5,3:44:17 PM,8,5,3:49:48 PM,8,5,4:15:48 PM,11,27.1,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_581,1560
-Order_No_759,User_Id_2346,Bike,1,Personal,25,1,2:47:36 PM,25,1,2:59:01 PM,25,1,3:04:04 PM,25,1,3:06:32 PM,25,1,4:13:13 PM,8,,,-1.3054537,36.8284768,-1.2813742000000001,36.855892700000005,Rider_Id_890,4001
-Order_No_968,User_Id_3602,Bike,3,Business,13,3,2:40:44 PM,13,3,2:41:19 PM,13,3,3:00:44 PM,13,3,3:36:37 PM,13,3,3:46:40 PM,2,29.4,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_205,603
-Order_No_10882,User_Id_2369,Bike,3,Personal,13,3,9:14:06 AM,13,3,9:14:17 AM,13,3,9:28:29 AM,13,3,9:29:47 AM,13,3,9:44:07 AM,6,16.1,,-1.2661263999999999,36.8030359,-1.2857671,36.7875614,Rider_Id_635,860
-Order_No_8774,User_Id_1482,Bike,3,Business,20,1,3:30:15 PM,20,1,3:31:19 PM,20,1,3:39:45 PM,20,1,3:44:46 PM,20,1,3:55:20 PM,3,,,-1.2736466,36.8164042,-1.2649605,36.798177700000004,Rider_Id_123,634
-Order_No_10655,User_Id_2457,Bike,3,Business,26,5,11:52:03 AM,26,5,1:00:26 PM,26,5,1:02:53 PM,26,5,1:07:12 PM,26,5,1:32:20 PM,6,,,-1.2551895,36.7822034,-1.2884146,36.810766799999996,Rider_Id_380,1508
-Order_No_7367,User_Id_2577,Bike,2,Personal,5,3,3:08:05 PM,5,3,3:08:19 PM,5,3,3:39:51 PM,5,3,4:04:34 PM,5,3,4:37:37 PM,11,23.8,,-1.3008998999999999,36.893280299999994,-1.3122178999999998,36.823696000000005,Rider_Id_447,1983
-Order_No_25573,User_Id_251,Bike,1,Personal,4,1,10:02:00 AM,4,1,10:02:09 AM,4,1,10:16:20 AM,4,1,10:27:42 AM,4,1,11:12:22 AM,7,18.0,,-1.3099145,36.802277399999994,-1.2695376,36.803230799999994,Rider_Id_752,2680
-Order_No_5112,User_Id_3598,Bike,1,Personal,9,2,12:18:14 PM,9,2,12:33:05 PM,9,2,12:40:11 PM,9,2,12:41:55 PM,9,2,1:40:49 PM,8,21.8,,-1.2675333999999998,36.7658246,-1.2877501,36.814659899999995,Rider_Id_131,3534
-Order_No_21447,User_Id_883,Bike,1,Personal,11,5,2:07:18 PM,11,5,2:23:40 PM,11,5,2:38:55 PM,11,5,2:43:29 PM,11,5,3:14:00 PM,15,22.8,,-1.2384876,36.877810700000005,-1.3025286,36.7964474,Rider_Id_393,1831
-Order_No_9,User_Id_868,Bike,3,Business,17,1,1:12:36 PM,17,1,1:13:00 PM,17,1,1:13:26 PM,17,1,1:58:13 PM,17,1,2:11:13 PM,10,24.8,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_260,780
-Order_No_17152,User_Id_265,Bike,3,Business,15,4,10:35:18 AM,15,4,10:48:03 AM,15,4,11:03:14 AM,15,4,11:34:30 AM,15,4,11:53:46 AM,5,24.4,,-1.300921,36.828195,-1.2824367,36.8353831,Rider_Id_607,1156
-Order_No_15556,User_Id_3517,Bike,3,Business,10,1,9:00:47 AM,10,1,9:01:12 AM,10,1,9:01:22 AM,10,1,9:45:52 AM,10,1,9:53:58 AM,16,16.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_70,486
-Order_No_15097,User_Id_3091,Bike,3,Business,26,1,8:29:27 AM,26,1,8:33:22 AM,26,1,9:37:19 AM,26,1,9:41:41 AM,26,1,10:56:21 AM,8,17.8,4.9,-1.3012008,36.764868,-1.2615888000000002,36.7928732,Rider_Id_322,4480
-Order_No_8866,User_Id_2212,Bike,3,Business,9,4,9:09:07 AM,9,4,9:09:19 AM,9,4,9:18:43 AM,9,4,9:28:07 AM,9,4,9:56:31 AM,14,,,-1.2571472,36.795063299999995,-1.3291155000000001,36.8861,Rider_Id_96,1704
-Order_No_3951,User_Id_2701,Bike,3,Business,21,2,3:06:08 PM,21,2,3:06:37 PM,21,2,3:07:07 PM,21,2,3:40:10 PM,21,2,4:05:26 PM,15,24.9,,-1.3472066,36.7692638,-1.2765736,36.851364600000004,Rider_Id_815,1516
-Order_No_14671,User_Id_53,Bike,3,Business,11,4,10:30:52 AM,11,4,10:45:04 AM,11,4,11:05:47 AM,11,4,11:11:37 AM,11,4,11:34:45 AM,6,23.9,,-1.2574219,36.7927073,-1.2770708,36.8231093,Rider_Id_208,1388
-Order_No_18828,User_Id_635,Bike,3,Business,26,2,1:11:09 PM,26,2,1:13:02 PM,26,2,1:36:01 PM,26,2,2:29:00 PM,26,2,2:50:27 PM,6,29.8,,-1.3042118,36.830121500000004,-1.2602337,36.79905479999999,Rider_Id_649,1287
-Order_No_25687,User_Id_2855,Bike,3,Business,14,4,9:51:05 AM,14,4,9:51:59 AM,14,4,9:58:42 AM,14,4,10:04:41 AM,14,4,10:16:34 AM,4,17.9,,-1.2945178999999998,36.7685349,-1.2871136,36.785507,Rider_Id_462,713
-Order_No_3172,User_Id_393,Bike,3,Business,20,3,11:35:53 AM,20,3,11:36:55 AM,20,3,11:37:01 AM,20,3,12:03:19 PM,20,3,12:13:10 PM,4,26.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_111,591
-Order_No_27923,User_Id_2987,Bike,3,Business,21,3,4:05:12 PM,21,3,4:06:37 PM,21,3,4:31:59 PM,21,3,4:33:05 PM,21,3,4:53:53 PM,11,,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_208,1248
-Order_No_13235,User_Id_3621,Bike,3,Business,10,3,9:21:35 AM,10,3,9:22:07 AM,10,3,9:26:03 AM,10,3,9:33:00 AM,10,3,9:59:43 AM,9,21.6,,-1.2836583000000001,36.7886014,-1.31864,36.8417533,Rider_Id_379,1603
-Order_No_13717,User_Id_1245,Bike,3,Business,23,2,12:22:07 PM,23,2,12:22:34 PM,23,2,12:23:44 PM,23,2,12:30:47 PM,23,2,1:03:45 PM,19,17.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_210,1978
-Order_No_1219,User_Id_1050,Bike,2,Personal,4,3,8:28:02 AM,4,3,8:29:04 AM,4,3,8:57:01 AM,4,3,9:39:45 AM,4,3,9:57:23 AM,8,14.9,,-1.294765,36.8472408,-1.3215473,36.8435326,Rider_Id_956,1058
-Order_No_13955,User_Id_3517,Bike,3,Business,7,4,10:51:58 AM,7,4,11:09:55 AM,7,4,11:16:45 AM,7,4,11:26:49 AM,7,4,11:49:44 AM,14,24.2,,-1.3198615,36.7110323,-1.3882008,36.7699445,Rider_Id_805,1375
-Order_No_24098,User_Id_867,Bike,3,Business,15,4,10:58:51 AM,15,4,10:59:14 AM,15,4,11:16:13 AM,15,4,11:49:34 AM,15,4,12:15:19 PM,15,24.5,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_588,1545
-Order_No_15022,User_Id_3264,Bike,3,Business,15,4,12:49:56 PM,15,4,1:14:33 PM,15,4,1:15:30 PM,15,4,1:41:35 PM,15,4,1:59:34 PM,8,27.6,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_167,1079
-Order_No_12723,User_Id_1363,Bike,3,Business,3,5,3:10:05 PM,3,5,3:12:53 PM,3,5,3:27:19 PM,3,5,3:28:25 PM,3,5,4:16:59 PM,7,,,-1.3004062,36.829741,-1.3272104,36.85284179999999,Rider_Id_292,2914
-Order_No_14656,User_Id_2866,Bike,3,Business,18,4,2:25:59 PM,18,4,2:40:03 PM,18,4,2:47:49 PM,18,4,3:00:48 PM,18,4,3:22:47 PM,9,27.0,,-1.2551895,36.7822034,-1.301796,36.8012024,Rider_Id_726,1319
-Order_No_11071,User_Id_3222,Bike,3,Business,9,2,3:43:03 PM,9,2,3:43:27 PM,9,2,3:43:39 PM,9,2,3:48:09 PM,9,2,4:07:20 PM,8,30.8,,-1.2551895,36.7822034,-1.2944702,36.80097070000001,Rider_Id_576,1151
-Order_No_3758,User_Id_3194,Bike,3,Business,4,2,4:50:32 PM,4,2,4:51:15 PM,4,2,4:53:46 PM,4,2,4:59:27 PM,4,2,5:05:04 PM,3,22.6,,-1.2551895,36.7822034,-1.2597367,36.7848482,Rider_Id_337,337
-Order_No_12520,User_Id_2989,Bike,3,Business,31,1,1:18:21 PM,31,1,1:18:53 PM,31,1,1:31:27 PM,31,1,1:38:26 PM,31,1,1:59:37 PM,12,23.7,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_26,1271
-Order_No_16171,User_Id_246,Bike,3,Business,29,4,3:20:20 PM,29,4,3:57:39 PM,29,4,4:05:56 PM,29,4,4:10:00 PM,29,4,4:32:13 PM,4,24.6,,-1.2584143,36.8048002,-1.2848173999999999,36.826163,Rider_Id_436,1333
-Order_No_23309,User_Id_2580,Bike,1,Personal,22,3,9:40:24 AM,22,3,9:42:10 AM,22,3,9:54:22 AM,22,3,10:01:06 AM,22,3,10:20:31 AM,10,15.1,,-1.3271684,36.8173021,-1.3025666999999999,36.7803495,Rider_Id_343,1165
-Order_No_2763,User_Id_1211,Bike,2,Personal,18,4,12:36:47 PM,18,4,12:37:55 PM,18,4,12:51:22 PM,18,4,12:54:03 PM,18,4,1:36:47 PM,8,25.3,,-1.2868870000000001,36.762097999999995,-1.279051,36.817508000000004,Rider_Id_72,2564
-Order_No_27138,User_Id_517,Bike,3,Business,5,6,10:34:08 AM,5,6,10:34:25 AM,5,6,10:37:49 AM,5,6,10:39:42 AM,5,6,10:47:10 AM,4,22.5,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_543,448
-Order_No_18412,User_Id_1856,Bike,3,Business,5,1,10:37:43 AM,5,1,10:39:09 AM,5,1,11:21:58 AM,5,1,11:27:27 AM,5,1,11:51:28 AM,6,24.3,,-1.273009,36.802807,-1.3001813,36.811122,Rider_Id_206,1441
-Order_No_25453,User_Id_201,Bike,3,Business,24,3,3:16:47 PM,24,3,3:22:51 PM,24,3,3:33:49 PM,24,3,3:39:03 PM,24,3,4:20:25 PM,6,18.2,,-1.2551895,36.7822034,-1.2857248,36.8228262,Rider_Id_916,2482
-Order_No_9921,User_Id_2987,Bike,3,Business,6,3,4:59:16 PM,6,3,5:03:07 PM,6,3,5:11:47 PM,6,3,5:14:04 PM,6,3,5:24:09 PM,4,20.1,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_103,605
-Order_No_1059,User_Id_2346,Bike,1,Personal,13,3,1:28:21 PM,13,3,1:34:06 PM,13,3,1:38:12 PM,13,3,1:39:58 PM,13,3,2:06:46 PM,7,23.4,,-1.3054537,36.8284768,-1.2756364,36.853548499999995,Rider_Id_890,1608
-Order_No_6298,User_Id_640,Bike,3,Business,12,1,10:36:17 AM,12,1,10:37:06 AM,12,1,10:53:26 AM,12,1,11:00:39 AM,12,1,11:42:14 AM,14,24.0,,-1.3246455,36.8754415,-1.304033,36.7848695,Rider_Id_31,2495
-Order_No_9367,User_Id_1687,Bike,3,Business,15,2,1:07:31 PM,15,2,1:07:38 PM,15,2,1:12:24 PM,15,2,1:28:12 PM,15,2,2:03:50 PM,21,22.8,,-1.3098453,36.8392995,-1.1853633,36.9350604,Rider_Id_188,2138
-Order_No_20336,User_Id_1346,Bike,3,Business,10,4,10:41:04 AM,10,4,10:41:41 AM,10,4,10:54:10 AM,10,4,10:57:43 AM,10,4,11:14:55 AM,7,,,-1.2998129999999999,36.787139200000006,-1.2695287,36.798177700000004,Rider_Id_345,1032
-Order_No_10783,User_Id_3380,Bike,3,Personal,19,4,12:35:20 PM,19,4,12:38:33 PM,19,4,12:44:04 PM,19,4,12:47:28 PM,19,4,1:08:50 PM,7,20.2,,-1.296797,36.776452500000005,-1.2824911,36.8218042,Rider_Id_634,1282
-Order_No_24706,User_Id_2561,Bike,3,Business,29,4,12:17:05 PM,29,4,12:17:41 PM,29,4,12:24:07 PM,29,4,12:29:40 PM,29,4,12:39:17 PM,4,21.7,,-1.2564152,36.779280299999996,-1.2551895,36.7822034,Rider_Id_920,577
-Order_No_13369,User_Id_3508,Bike,2,Personal,22,6,12:05:26 PM,22,6,12:05:54 PM,22,6,12:30:13 PM,22,6,12:47:25 PM,22,6,1:13:53 PM,8,21.2,,-1.2755887,36.8742523,-1.2677646,36.8298466,Rider_Id_408,1588
-Order_No_14174,User_Id_3426,Bike,3,Business,2,6,12:16:54 PM,2,6,1:09:24 PM,2,6,1:11:53 PM,2,6,1:20:25 PM,2,6,1:49:54 PM,9,27.5,,-1.279395,36.825364,-1.3012008,36.764868,Rider_Id_819,1769
-Order_No_15566,User_Id_1500,Bike,3,Business,27,1,12:49:04 PM,27,1,12:50:07 PM,27,1,12:58:42 PM,27,1,1:03:08 PM,27,1,1:29:14 PM,10,20.6,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_829,1566
-Order_No_11211,User_Id_3621,Bike,3,Business,6,1,12:58:42 PM,6,1,12:59:07 PM,6,1,1:06:20 PM,6,1,1:07:53 PM,6,1,1:33:47 PM,11,,,-1.2836583000000001,36.7886014,-1.2647206,36.7259313,Rider_Id_858,1554
-Order_No_5523,User_Id_3047,Bike,3,Personal,2,2,1:50:12 PM,2,2,2:02:44 PM,2,2,2:15:39 PM,2,2,2:18:20 PM,2,2,2:37:48 PM,7,26.3,,-1.2850643999999998,36.8343657,-1.3230166,36.8391623,Rider_Id_328,1168
-Order_No_25455,User_Id_1502,Bike,3,Business,20,3,11:04:24 AM,20,3,11:05:48 AM,20,3,11:11:06 AM,20,3,11:32:11 AM,20,3,12:51:06 PM,12,26.2,,-1.290894,36.822971,-1.2232123,36.8932547,Rider_Id_268,4735
-Order_No_10394,User_Id_2195,Bike,3,Business,13,1,2:50:03 PM,13,1,2:53:40 PM,13,1,3:32:18 PM,13,1,3:42:48 PM,13,1,4:03:57 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_787,1269
-Order_No_4493,User_Id_3283,Bike,3,Business,4,2,12:10:26 PM,4,2,12:58:07 PM,4,2,1:11:20 PM,4,2,1:33:59 PM,4,2,1:55:08 PM,10,22.5,,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_591,1269
-Order_No_9098,User_Id_3283,Bike,3,Business,2,2,10:45:42 AM,2,2,10:53:14 AM,2,2,11:24:48 AM,2,2,11:31:32 AM,2,2,11:38:17 AM,4,26.4,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_103,405
-Order_No_17027,User_Id_1033,Bike,1,Personal,18,4,10:56:24 AM,18,4,10:57:17 AM,18,4,11:01:31 AM,18,4,11:02:47 AM,18,4,11:25:41 AM,9,25.3,,-1.2327933,36.803990899999995,-1.2654983,36.79686220000001,Rider_Id_453,1374
-Order_No_19608,User_Id_2495,Bike,1,Personal,25,3,5:26:25 PM,25,3,5:27:02 PM,25,3,5:33:04 PM,25,3,5:47:36 PM,25,3,6:00:12 PM,4,17.7,,-1.266294,36.8057971,-1.2655306,36.828148,Rider_Id_648,756
-Order_No_16514,User_Id_823,Bike,3,Personal,26,2,2:25:00 PM,26,2,2:25:16 PM,26,2,2:32:45 PM,26,2,2:37:12 PM,26,2,3:34:46 PM,19,31.2,,-1.2485958000000001,36.78882279999999,-1.3195469,36.69768679999999,Rider_Id_147,3454
-Order_No_753,User_Id_1245,Bike,3,Business,14,4,9:05:53 AM,14,4,9:10:05 AM,14,4,9:23:15 AM,14,4,9:33:44 AM,14,4,10:27:34 AM,18,19.1,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_847,3230
-Order_No_4484,User_Id_1753,Bike,2,Business,13,6,8:55:09 AM,13,6,8:56:49 AM,13,6,9:14:33 AM,13,6,9:16:42 AM,13,6,9:36:48 AM,10,,,-1.2551895,36.7822034,-1.3046156,36.765857200000006,Rider_Id_178,1206
-Order_No_21554,User_Id_2359,Bike,1,Personal,5,5,1:40:20 PM,5,5,1:48:42 PM,5,5,2:09:07 PM,5,5,2:25:08 PM,5,5,2:41:30 PM,6,26.6,,-1.2853965,36.8183118,-1.2812776000000001,36.7810331,Rider_Id_328,982
-Order_No_7727,User_Id_1620,Bike,3,Personal,3,3,12:32:27 PM,3,3,12:34:09 PM,3,3,1:01:37 PM,3,3,1:12:54 PM,3,3,1:36:07 PM,11,23.9,,-1.261849,36.808254,-1.227132,36.8374277,Rider_Id_328,1393
-Order_No_23085,User_Id_1561,Bike,3,Business,25,1,1:29:27 PM,25,1,2:01:14 PM,25,1,2:26:12 PM,25,1,2:59:18 PM,25,1,3:11:28 PM,7,27.9,,-1.3124921999999999,36.880497399999996,-1.328259,36.860975,Rider_Id_448,730
-Order_No_7259,User_Id_391,Bike,3,Business,20,3,1:38:36 PM,20,3,1:40:18 PM,20,3,2:08:38 PM,20,3,2:29:06 PM,20,3,2:51:15 PM,5,29.4,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_223,1329
-Order_No_11292,User_Id_2195,Bike,1,Business,15,6,9:29:21 AM,15,6,9:29:36 AM,15,6,9:31:27 AM,15,6,9:51:10 AM,15,6,10:25:08 AM,8,17.5,,-1.2783848999999998,36.8163445,-1.3313643,36.857742200000004,Rider_Id_338,2038
-Order_No_3169,User_Id_1502,Bike,3,Business,12,1,1:39:44 PM,12,1,1:58:06 PM,12,1,2:08:44 PM,12,1,2:13:56 PM,12,1,3:36:27 PM,11,26.8,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_490,4951
-Order_No_2490,User_Id_2803,Bike,3,Business,13,3,1:45:17 PM,13,3,2:09:52 PM,13,3,2:30:05 PM,13,3,2:36:17 PM,13,3,3:01:20 PM,5,27.2,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_660,1503
-Order_No_4180,User_Id_1030,Bike,1,Personal,5,2,3:21:32 PM,5,2,3:25:26 PM,5,2,4:03:05 PM,5,2,4:33:00 PM,5,2,4:47:42 PM,6,21.7,,-1.2553615,36.789537599999996,-1.2886923000000001,36.8241519,Rider_Id_820,882
-Order_No_1142,User_Id_2330,Bike,3,Business,27,1,1:18:09 PM,27,1,1:34:07 PM,27,1,1:45:02 PM,27,1,1:50:01 PM,27,1,2:12:10 PM,22,23.3,,-1.1926151000000003,36.9347904,-1.2645318,36.7981775,Rider_Id_8,1329
-Order_No_17420,User_Id_136,Bike,3,Business,5,1,5:24:54 PM,5,1,5:40:42 PM,5,1,6:04:21 PM,5,1,6:06:25 PM,5,1,7:10:11 PM,18,24.4,,-1.3326173000000001,36.71462329999999,-1.256958,36.8144929,Rider_Id_761,3826
-Order_No_18457,User_Id_2931,Bike,3,Business,17,1,12:16:03 PM,17,1,12:19:53 PM,17,1,12:29:19 PM,17,1,12:34:30 PM,17,1,1:33:27 PM,22,23.7,,-1.2452664,36.802854100000005,-1.3433181,36.9138018,Rider_Id_107,3537
-Order_No_8706,User_Id_2174,Bike,3,Business,8,2,10:37:32 AM,8,2,10:37:36 AM,8,2,10:38:10 AM,8,2,11:05:16 AM,8,2,11:34:10 AM,15,23.5,,-1.2482781,36.884091999999995,-1.263736,36.780353000000005,Rider_Id_82,1734
-Order_No_20952,User_Id_2627,Bike,3,Business,22,5,8:12:08 AM,22,5,8:13:47 AM,22,5,8:19:46 AM,22,5,8:35:24 AM,22,5,8:41:38 AM,2,22.2,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_520,374
-Order_No_837,User_Id_391,Bike,3,Business,27,6,10:13:50 AM,27,6,11:54:47 AM,27,6,12:01:27 PM,27,6,12:05:53 PM,27,6,12:16:27 PM,5,,,-1.2777923999999998,36.8231577,-1.3016417,36.8271679,Rider_Id_237,634
-Order_No_21311,User_Id_2174,Bike,3,Business,2,2,4:01:33 PM,2,2,4:02:28 PM,2,2,4:02:50 PM,2,2,4:05:28 PM,2,2,4:25:35 PM,15,29.3,,-1.214071,36.7985052,-1.2482781,36.884091999999995,Rider_Id_86,1207
-Order_No_21480,User_Id_835,Bike,3,Business,15,5,12:13:08 PM,15,5,12:13:18 PM,15,5,12:19:12 PM,15,5,12:42:20 PM,15,5,1:00:39 PM,20,26.1,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_678,1099
-Order_No_7810,User_Id_59,Bike,3,Business,26,2,5:18:15 PM,26,2,5:19:18 PM,26,2,5:40:34 PM,26,2,5:44:31 PM,26,2,6:11:37 PM,6,29.5,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_194,1626
-Order_No_23902,User_Id_3186,Bike,3,Business,28,2,2:49:27 PM,28,2,2:51:24 PM,28,2,3:00:57 PM,28,2,3:04:40 PM,28,2,3:40:04 PM,6,23.3,,-1.2980228999999999,36.78865,-1.2822909,36.8193456,Rider_Id_826,2124
-Order_No_25764,User_Id_2828,Bike,2,Business,16,1,3:53:22 PM,16,1,3:53:33 PM,16,1,3:56:52 PM,16,1,4:24:46 PM,16,1,5:10:48 PM,23,22.6,,-1.2615888000000002,36.7928732,-1.3663376,36.658212400000004,Rider_Id_105,2762
-Order_No_11572,User_Id_1473,Bike,3,Business,21,3,3:28:27 PM,21,3,3:28:41 PM,21,3,4:04:07 PM,21,3,5:04:12 PM,21,3,5:27:59 PM,29,,,-1.2099761999999998,36.781053,-1.3700383,36.9190174,Rider_Id_940,1427
-Order_No_17833,User_Id_685,Bike,3,Business,8,3,10:08:06 AM,8,3,10:09:17 AM,8,3,10:21:57 AM,8,3,10:29:41 AM,8,3,10:58:58 AM,5,,,-1.2726506000000002,36.7960153,-1.2887076000000002,36.825465799999996,Rider_Id_155,1757
-Order_No_3157,User_Id_3647,Bike,3,Business,23,3,10:43:46 AM,23,3,10:44:09 AM,23,3,10:54:11 AM,23,3,10:58:44 AM,23,3,11:31:19 AM,20,,,-1.2726389999999999,36.794723,-1.2162985,36.906510499999996,Rider_Id_627,1955
-Order_No_1131,User_Id_2761,Bike,3,Personal,21,5,10:54:37 AM,21,5,10:55:49 AM,21,5,11:10:49 AM,21,5,11:12:36 AM,21,5,11:39:57 AM,11,23.0,,-1.3038645,36.8684596,-1.2950041,36.814358299999995,Rider_Id_675,1641
-Order_No_21951,User_Id_155,Bike,3,Personal,14,1,12:43:33 PM,14,1,12:46:58 PM,14,1,12:57:38 PM,14,1,1:01:22 PM,14,1,1:21:29 PM,4,23.4,,-1.2713370000000002,36.786712,-1.290701,36.782992,Rider_Id_382,1207
-Order_No_27682,User_Id_186,Bike,3,Business,17,1,1:10:09 PM,17,1,1:43:15 PM,17,1,1:54:15 PM,17,1,1:58:47 PM,17,1,2:24:21 PM,17,24.7,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_807,1534
-Order_No_26373,User_Id_1688,Bike,3,Business,30,2,1:15:29 PM,30,2,1:17:43 PM,30,2,1:18:50 PM,30,2,1:37:22 PM,30,2,2:22:53 PM,16,,,-1.300921,36.828195,-1.3990905,36.7682846,Rider_Id_155,2731
-Order_No_18636,User_Id_1087,Bike,3,Business,20,4,1:04:18 PM,20,4,1:06:11 PM,20,4,1:29:43 PM,20,4,1:45:10 PM,20,4,2:51:53 PM,15,25.3,,-1.2713865,36.8058221,-1.3277669,36.8975169,Rider_Id_404,4003
-Order_No_810,User_Id_3201,Bike,3,Business,18,4,3:44:26 PM,18,4,3:45:12 PM,18,4,4:09:59 PM,18,4,4:15:58 PM,18,4,4:19:59 PM,2,,,-1.2584143,36.8048002,-1.2591018999999999,36.800577000000004,Rider_Id_394,241
-Order_No_4627,User_Id_3698,Bike,3,Business,7,5,1:47:42 PM,7,5,2:15:40 PM,7,5,2:16:31 PM,7,5,2:24:12 PM,7,5,3:16:17 PM,22,,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_75,3125
-Order_No_5659,User_Id_1874,Bike,3,Business,8,5,3:45:00 PM,8,5,4:08:23 PM,8,5,4:17:42 PM,8,5,4:23:58 PM,8,5,5:18:38 PM,16,30.3,,-1.2571472,36.795063299999995,-1.3214765,36.7125341,Rider_Id_519,3280
-Order_No_23015,User_Id_1497,Bike,3,Personal,13,3,2:44:28 PM,13,3,4:05:20 PM,13,3,4:17:22 PM,13,3,4:20:06 PM,13,3,4:41:53 PM,5,23.7,,-1.2998129999999999,36.787139200000006,-1.2861764999999998,36.8209086,Rider_Id_752,1307
-Order_No_27536,User_Id_1262,Bike,1,Personal,5,5,4:40:49 PM,5,5,4:52:06 PM,5,5,5:28:22 PM,5,5,5:32:44 PM,5,5,6:13:35 PM,11,24.6,,-1.2870003,36.8312206,-1.2778523000000002,36.90945429999999,Rider_Id_933,2451
-Order_No_14222,User_Id_540,Bike,3,Business,18,4,4:06:00 PM,18,4,4:07:08 PM,18,4,4:18:20 PM,18,4,4:20:09 PM,18,4,4:35:02 PM,4,27.0,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_208,893
-Order_No_9593,User_Id_2587,Bike,3,Business,7,1,8:19:18 AM,7,1,8:20:37 AM,7,1,8:28:41 AM,7,1,8:31:40 AM,7,1,9:10:14 AM,5,21.2,,-1.28878,36.8168312,-1.2591018999999999,36.800577000000004,Rider_Id_404,2314
-Order_No_21936,User_Id_3604,Bike,3,Personal,20,3,5:18:32 PM,20,3,5:19:31 PM,20,3,5:19:34 PM,20,3,5:20:48 PM,20,3,5:56:13 PM,18,27.9,,-1.2963097,36.7688221,-1.2153260000000001,36.891993799999995,Rider_Id_786,2125
-Order_No_6294,User_Id_419,Bike,3,Business,25,1,2:14:59 PM,25,1,2:15:23 PM,25,1,2:15:47 PM,25,1,2:31:14 PM,25,1,4:25:24 PM,20,27.9,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_647,6850
-Order_No_23654,User_Id_136,Bike,3,Business,11,2,1:24:06 PM,11,2,1:26:49 PM,11,2,1:37:43 PM,11,2,1:41:25 PM,11,2,2:01:55 PM,14,21.5,,-1.3254522,36.721658399999995,-1.2950408000000002,36.809817200000005,Rider_Id_821,1230
-Order_No_13045,User_Id_3149,Bike,3,Business,16,4,4:07:23 PM,16,4,4:08:42 PM,16,4,4:15:39 PM,16,4,4:37:25 PM,16,4,4:44:11 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_328,406
-Order_No_4299,User_Id_3369,Bike,3,Business,3,1,2:37:51 PM,3,1,2:41:16 PM,3,1,3:01:25 PM,3,1,3:04:46 PM,3,1,3:30:56 PM,11,22.9,,-1.2973415000000001,36.7707031,-1.2970761999999998,36.8453541,Rider_Id_820,1570
-Order_No_25545,User_Id_3326,Bike,3,Business,7,4,2:04:25 PM,7,4,2:04:43 PM,7,4,2:07:53 PM,7,4,2:15:55 PM,7,4,2:47:59 PM,15,31.0,,-1.2551895,36.7822034,-1.3288106000000002,36.8697344,Rider_Id_291,1924
-Order_No_17917,User_Id_1329,Bike,3,Business,27,2,4:32:13 PM,27,2,4:32:46 PM,27,2,4:59:25 PM,27,2,5:01:04 PM,27,2,5:19:35 PM,6,22.2,,-1.273056,36.811298,-1.2940853,36.786666499999995,Rider_Id_674,1111
-Order_No_24361,User_Id_1245,Bike,3,Business,9,2,12:09:17 PM,9,2,12:09:23 PM,9,2,12:09:41 PM,9,2,12:19:13 PM,9,2,12:42:49 PM,18,22.2,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_179,1416
-Order_No_16128,User_Id_2112,Bike,3,Business,7,4,12:38:17 PM,7,4,12:39:29 PM,7,4,12:53:49 PM,7,4,1:35:50 PM,7,4,1:54:55 PM,15,31.0,,-1.2615888000000002,36.7928732,-1.2156006000000001,36.8916865,Rider_Id_501,1145
-Order_No_8900,User_Id_2829,Bike,3,Business,8,2,9:41:56 AM,8,2,9:42:04 AM,8,2,9:48:09 AM,8,2,9:53:03 AM,8,2,10:57:12 AM,21,19.1,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_379,3849
-Order_No_21448,User_Id_283,Bike,3,Business,5,4,12:12:38 PM,5,4,12:13:26 PM,5,4,12:13:32 PM,5,4,12:31:17 PM,5,4,12:43:34 PM,6,,,-1.2804227,36.8250375,-1.2731645,36.7902246,Rider_Id_345,737
-Order_No_20810,User_Id_393,Bike,3,Business,27,3,8:24:50 AM,27,3,8:25:08 AM,27,3,8:25:13 AM,27,3,8:38:20 AM,27,3,8:56:48 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_901,1108
-Order_No_20465,User_Id_1934,Bike,2,Personal,15,3,6:38:56 PM,15,3,6:39:21 PM,15,3,6:51:22 PM,15,3,7:03:03 PM,15,3,7:22:34 PM,4,21.1,,-1.2559294,36.788465,-1.2664882,36.803337400000004,Rider_Id_857,1171
-Order_No_16945,User_Id_3295,Bike,3,Business,13,4,2:17:49 PM,13,4,2:18:15 PM,13,4,2:25:11 PM,13,4,2:28:33 PM,13,4,2:47:23 PM,7,23.2,,-1.2571472,36.795063299999995,-1.300663,36.790907000000004,Rider_Id_259,1130
-Order_No_8588,User_Id_2860,Bike,1,Personal,31,4,9:07:44 AM,31,4,9:08:57 AM,31,4,9:11:31 AM,31,4,9:18:57 AM,31,4,9:35:08 AM,7,17.4,,-1.2605635,36.7985638,-1.287428,36.7800821,Rider_Id_379,971
-Order_No_16626,User_Id_1148,Bike,3,Business,11,6,10:32:33 AM,11,6,10:32:51 AM,11,6,10:42:45 AM,11,6,11:06:04 AM,11,6,11:09:15 AM,2,20.8,,-1.3100586,36.850032899999995,-1.3088186000000002,36.8352514,Rider_Id_179,191
-Order_No_2299,User_Id_3621,Bike,3,Business,7,5,10:46:06 AM,7,5,10:46:45 AM,7,5,10:54:17 AM,7,5,10:58:22 AM,7,5,11:18:45 AM,6,,,-1.2836583000000001,36.7886014,-1.3001926000000001,36.7659299,Rider_Id_709,1223
-Order_No_26114,User_Id_2423,Bike,3,Business,4,1,11:58:49 AM,4,1,12:01:13 PM,4,1,12:21:50 PM,4,1,12:26:16 PM,4,1,12:49:19 PM,7,26.5,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_88,1383
-Order_No_10897,User_Id_3650,Bike,3,Business,14,2,9:29:02 AM,14,2,9:29:28 AM,14,2,9:40:22 AM,14,2,9:45:01 AM,14,2,9:53:20 AM,4,15.6,,-1.2848376,36.802762200000004,-1.3033059999999999,36.7899884,Rider_Id_249,499
-Order_No_27166,User_Id_778,Bike,3,Business,20,3,9:24:51 AM,20,3,9:26:51 AM,20,3,9:43:11 AM,20,3,9:49:46 AM,20,3,10:20:38 AM,10,21.1,,-1.275931,36.8516227,-1.2535486,36.7933541,Rider_Id_76,1852
-Order_No_1258,User_Id_3283,Bike,3,Business,22,5,9:04:35 AM,22,5,9:05:06 AM,22,5,9:11:47 AM,22,5,9:32:48 AM,22,5,10:15:01 AM,13,,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_101,2533
-Order_No_17111,User_Id_1461,Bike,2,Personal,31,2,5:16:04 PM,31,2,5:17:34 PM,31,2,5:22:40 PM,31,2,5:29:10 PM,31,2,5:40:28 PM,5,19.9,,-1.2720768999999998,36.8180604,-1.2710749,36.801451899999996,Rider_Id_207,678
-Order_No_15146,User_Id_3669,Bike,3,Business,10,5,1:49:22 PM,10,5,2:10:32 PM,10,5,2:19:46 PM,10,5,2:47:38 PM,10,5,3:44:39 PM,17,,,-1.2615888000000002,36.7928732,-1.2491258,36.689425299999996,Rider_Id_256,3421
-Order_No_27118,User_Id_1753,Bike,2,Business,20,4,1:21:42 PM,20,4,1:22:00 PM,20,4,1:32:33 PM,20,4,2:43:18 PM,20,4,3:05:09 PM,4,28.2,,-1.2710687,36.8142839,-1.2551895,36.7822034,Rider_Id_467,1311
-Order_No_3709,User_Id_2330,Bike,3,Business,3,3,5:13:47 PM,3,3,5:14:10 PM,3,3,5:26:34 PM,3,3,5:27:48 PM,3,3,5:49:38 PM,4,23.7,,-1.2465253,36.8162559,-1.2267055,36.8013889,Rider_Id_391,1310
-Order_No_24608,User_Id_3669,Bike,3,Business,10,5,5:16:51 PM,10,5,5:23:34 PM,10,5,5:24:37 PM,10,5,6:40:10 PM,10,5,7:38:30 PM,25,,,-1.2615888000000002,36.7928732,-1.3899636,36.748237700000004,Rider_Id_864,3500
-Order_No_25811,User_Id_940,Bike,1,Personal,4,1,1:04:24 PM,4,1,1:09:23 PM,4,1,1:13:46 PM,4,1,1:19:41 PM,4,1,1:55:54 PM,5,21.0,,-1.2861764999999998,36.8209086,-1.2766169,36.791816,Rider_Id_845,2173
-Order_No_24728,User_Id_1075,Bike,3,Business,7,4,1:01:22 PM,7,4,1:02:11 PM,7,4,1:12:47 PM,7,4,1:19:56 PM,7,4,1:28:09 PM,2,27.5,,-1.2679657,36.8081617,-1.2527958000000001,36.8003131,Rider_Id_658,493
-Order_No_27904,User_Id_281,Bike,2,Personal,22,5,4:27:09 PM,22,5,5:03:17 PM,22,5,5:16:38 PM,22,5,5:25:51 PM,22,5,5:53:08 PM,8,,,-1.3014461000000002,36.7661381,-1.2666441000000002,36.7420196,Rider_Id_449,1637
-Order_No_27520,User_Id_2956,Bike,3,Business,27,3,3:46:45 PM,27,3,3:47:15 PM,27,3,4:11:26 PM,27,3,4:17:08 PM,27,3,4:27:06 PM,3,29.3,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_428,598
-Order_No_7719,User_Id_3175,Bike,3,Business,17,3,9:52:17 AM,17,3,9:52:53 AM,17,3,10:05:06 AM,17,3,10:07:16 AM,17,3,11:14:29 AM,16,21.1,,-1.2681003999999998,36.7952081,-1.207546,36.7913976,Rider_Id_932,4033
-Order_No_3530,User_Id_2457,Bike,3,Business,11,2,12:22:05 PM,13,4,1:27:30 PM,13,4,1:32:38 PM,13,4,1:53:47 PM,13,4,2:24:41 PM,8,22.5,,-1.2915484,36.8297516,-1.2551895,36.7822034,Rider_Id_247,1854
-Order_No_14707,User_Id_1148,Bike,3,Business,22,4,9:59:55 AM,22,4,11:00:41 AM,22,4,11:24:54 AM,22,4,11:45:09 AM,22,4,12:07:40 PM,9,22.4,,-1.3100586,36.850032899999995,-1.2675238000000002,36.8127148,Rider_Id_268,1351
-Order_No_9794,User_Id_6,Bike,3,Personal,21,1,10:05:02 AM,21,1,10:06:12 AM,21,1,10:18:52 AM,21,1,10:40:50 AM,21,1,11:07:03 AM,9,25.3,,-1.307059,36.8489195,-1.2720652,36.8217122,Rider_Id_627,1573
-Order_No_21998,User_Id_3701,Bike,3,Business,16,2,10:09:26 AM,16,2,10:09:55 AM,16,2,10:18:34 AM,16,2,10:23:20 AM,16,2,10:27:58 AM,2,,,-1.2612573999999999,36.7879622,-1.2635744,36.781677,Rider_Id_161,278
-Order_No_20056,User_Id_1083,Bike,1,Personal,26,6,3:36:31 PM,26,6,3:47:27 PM,26,6,3:59:24 PM,26,6,4:02:51 PM,26,6,4:15:43 PM,5,24.0,,-1.3154004,36.8123328,-1.2862033000000002,36.8152966,Rider_Id_742,772
-Order_No_3781,User_Id_3557,Bike,3,Business,16,5,5:06:34 PM,16,5,5:23:00 PM,16,5,5:36:08 PM,16,5,5:45:05 PM,16,5,6:01:26 PM,6,26.2,,-1.2820581,36.8284909,-1.2546732,36.808679600000005,Rider_Id_615,981
-Order_No_26943,User_Id_2523,Bike,3,Business,6,1,9:54:59 AM,6,1,9:57:01 AM,6,1,10:02:05 AM,6,1,10:14:29 AM,6,1,10:27:09 AM,4,,,-1.2551895,36.7822034,-1.2696908000000002,36.81519,Rider_Id_881,760
-Order_No_15688,User_Id_1761,Bike,3,Business,27,4,2:54:20 PM,27,4,2:55:05 PM,27,4,3:07:01 PM,27,4,3:12:07 PM,27,4,4:07:20 PM,28,24.9,,-1.3339943,36.7263409,-1.2134646,36.8755266,Rider_Id_123,3313
-Order_No_24532,User_Id_3660,Bike,1,Personal,11,2,3:36:19 PM,11,2,3:40:09 PM,11,2,3:58:25 PM,11,2,4:03:03 PM,11,2,4:16:44 PM,4,25.6,,-1.2833335,36.8260084,-1.292163,36.8000995,Rider_Id_338,821
-Order_No_19504,User_Id_302,Bike,3,Business,1,2,7:38:08 PM,1,2,7:39:46 PM,1,2,7:51:45 PM,1,2,8:03:04 PM,1,2,8:03:06 PM,3,18.5,,-1.2649605,36.798177700000004,-1.2611642,36.7949932,Rider_Id_517,2
-Order_No_2955,User_Id_2154,Bike,3,Business,27,6,2:28:56 PM,27,6,2:31:46 PM,27,6,2:41:45 PM,27,6,3:01:25 PM,27,6,3:23:23 PM,8,,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_709,1318
-Order_No_7052,User_Id_1459,Bike,3,Business,4,2,3:46:25 PM,4,2,3:47:11 PM,4,2,3:52:06 PM,4,2,3:53:32 PM,4,2,4:28:56 PM,17,20.6,,-1.264945,36.784678,-1.3429554,36.7262559,Rider_Id_848,2124
-Order_No_6928,User_Id_393,Bike,3,Business,6,4,2:30:51 PM,6,4,2:32:28 PM,6,4,3:02:54 PM,6,4,3:05:08 PM,6,4,3:42:42 PM,4,21.3,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_271,2254
-Order_No_21649,User_Id_1544,Bike,3,Business,3,1,3:15:48 PM,3,1,3:16:36 PM,3,1,3:43:34 PM,3,1,3:48:29 PM,3,1,4:30:31 PM,4,23.1,,-1.2861764999999998,36.8209086,-1.293973,36.80169,Rider_Id_168,2522
-Order_No_18317,User_Id_633,Bike,3,Business,8,2,4:25:22 PM,8,2,4:32:09 PM,8,2,4:42:41 PM,8,2,4:43:53 PM,8,2,4:54:43 PM,4,23.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_523,650
-Order_No_27661,User_Id_795,Bike,3,Personal,21,1,11:07:00 AM,21,1,11:07:48 AM,21,1,11:15:21 AM,21,1,11:20:04 AM,21,1,11:53:17 AM,4,19.9,,-1.3104733999999998,36.7363698,-1.2966729,36.7490784,Rider_Id_844,1993
-Order_No_25894,User_Id_868,Bike,3,Business,20,2,9:05:49 AM,20,2,9:07:50 AM,20,2,9:16:48 AM,20,2,9:24:36 AM,20,2,9:36:30 AM,5,18.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_453,714
-Order_No_9271,User_Id_302,Bike,3,Business,6,6,10:29:30 AM,6,6,10:30:51 AM,6,6,10:38:25 AM,6,6,10:48:47 AM,6,6,12:08:56 PM,12,26.8,,-1.2649605,36.798177700000004,-1.3024388,36.859699,Rider_Id_161,4809
-Order_No_25275,User_Id_517,Bike,3,Business,27,4,1:18:58 PM,27,4,1:19:04 PM,27,4,1:22:03 PM,27,4,1:30:50 PM,27,4,1:44:32 PM,11,22.9,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,822
-Order_No_18896,User_Id_3517,Bike,3,Business,16,4,2:29:24 PM,16,4,2:29:37 PM,16,4,2:29:39 PM,16,4,3:12:39 PM,16,4,3:39:34 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_450,1615
-Order_No_13265,User_Id_2066,Bike,3,Business,16,2,3:54:43 PM,16,2,3:55:33 PM,16,2,4:06:21 PM,16,2,4:37:30 PM,16,2,5:16:40 PM,5,25.1,,-1.316961,36.820337,-1.3007714,36.8001722,Rider_Id_726,2350
-Order_No_10781,User_Id_3465,Bike,3,Business,2,4,8:14:10 AM,2,4,8:14:55 AM,2,4,8:24:53 AM,2,4,8:30:30 AM,2,4,8:36:15 AM,3,,,-1.2551895,36.7822034,-1.268501,36.801065,Rider_Id_72,345
-Order_No_2222,User_Id_3201,Bike,3,Business,19,5,2:44:29 PM,19,5,2:44:55 PM,19,5,2:49:08 PM,19,5,2:57:45 PM,19,5,3:07:18 PM,3,27.5,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_881,573
-Order_No_25373,User_Id_571,Bike,3,Business,18,5,4:06:08 PM,18,5,4:06:12 PM,18,5,4:07:56 PM,18,5,4:30:13 PM,18,5,5:26:19 PM,18,27.1,,-1.3236332,36.701152,-1.265174,36.80940220000001,Rider_Id_831,3366
-Order_No_22043,User_Id_53,Bike,3,Business,14,2,3:52:57 PM,14,2,3:59:33 PM,14,2,4:10:10 PM,14,2,4:11:18 PM,14,2,4:36:59 PM,8,,,-1.2770708,36.8231093,-1.2769540000000001,36.765926,Rider_Id_843,1541
-Order_No_10412,User_Id_552,Bike,3,Business,26,2,9:55:02 AM,26,2,10:07:26 AM,26,2,10:17:42 AM,26,2,10:28:11 AM,26,2,11:22:47 AM,18,,,-1.290894,36.822971,-1.335477,36.930829700000004,Rider_Id_905,3276
-Order_No_26420,User_Id_2509,Bike,3,Business,6,4,3:23:50 PM,6,4,3:25:25 PM,6,4,3:28:26 PM,6,4,3:35:39 PM,6,4,3:45:10 PM,4,,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_932,571
-Order_No_2977,User_Id_111,Bike,3,Business,5,1,3:09:28 PM,5,1,3:15:00 PM,5,1,3:26:35 PM,5,1,3:27:37 PM,5,1,3:56:19 PM,9,26.2,,-1.2551895,36.7822034,-1.2790138,36.8269909,Rider_Id_11,1722
-Order_No_13655,User_Id_2216,Bike,3,Business,11,5,12:05:11 PM,11,5,12:55:45 PM,11,5,1:11:32 PM,11,5,1:15:46 PM,11,5,1:40:19 PM,6,20.9,,-1.2551895,36.7822034,-1.2844013,36.8227553,Rider_Id_831,1473
-Order_No_28074,User_Id_3686,Bike,3,Business,20,5,9:18:50 AM,20,5,9:19:06 AM,20,5,9:23:30 AM,20,5,9:31:43 AM,20,5,9:48:24 AM,8,14.6,,-1.2551895,36.7822034,-1.2893032,36.7739356,Rider_Id_147,1001
-Order_No_27312,User_Id_3291,Bike,3,Business,27,1,1:28:16 PM,27,1,1:28:46 PM,27,1,1:34:04 PM,27,1,1:54:24 PM,27,1,2:47:19 PM,8,23.2,,-1.3040553,36.8092658,-1.2600926000000001,36.8088685,Rider_Id_72,3175
-Order_No_21916,User_Id_970,Bike,3,Business,12,1,3:06:36 PM,12,1,3:19:51 PM,12,1,3:37:31 PM,12,1,3:38:53 PM,12,1,4:02:18 PM,11,27.0,,-1.2551895,36.7822034,-1.2217966999999998,36.810990000000004,Rider_Id_744,1405
-Order_No_1009,User_Id_1271,Bike,3,Business,14,1,3:06:52 PM,14,1,3:08:47 PM,14,1,3:17:48 PM,14,1,3:27:49 PM,14,1,4:11:06 PM,18,25.7,,-1.3244885,36.897791999999995,-1.2209733,36.904574600000004,Rider_Id_575,2597
-Order_No_13021,User_Id_3201,Bike,3,Business,19,2,1:22:07 PM,19,2,1:31:00 PM,19,2,1:37:40 PM,19,2,1:46:13 PM,19,2,2:18:59 PM,17,29.4,,-1.3463041999999998,36.768164,-1.2584143,36.8048002,Rider_Id_338,1966
-Order_No_365,User_Id_2330,Bike,3,Business,26,5,4:22:18 PM,26,5,4:36:48 PM,26,5,4:45:41 PM,26,5,4:47:08 PM,26,5,4:59:36 PM,5,,,-1.293082,36.8278578,-1.3040812,36.7924217,Rider_Id_843,748
-Order_No_8132,User_Id_2982,Bike,3,Business,19,2,4:11:54 PM,19,2,4:12:07 PM,19,2,4:24:27 PM,19,2,4:25:48 PM,19,2,5:10:55 PM,13,30.0,,-1.307164,36.8347807,-1.2210156,36.855674,Rider_Id_448,2707
-Order_No_19795,User_Id_393,Bike,3,Business,2,4,1:35:51 PM,2,4,1:36:04 PM,2,4,2:08:06 PM,2,4,2:49:02 PM,2,4,2:49:14 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,12
-Order_No_21019,User_Id_1974,Bike,3,Personal,15,5,11:28:18 AM,15,5,11:28:38 AM,15,5,11:35:10 AM,15,5,11:51:22 AM,15,5,12:12:15 PM,7,26.3,,-1.3019530000000001,36.788458,-1.2715686000000002,36.8023526,Rider_Id_856,1253
-Order_No_12982,User_Id_540,Bike,3,Business,27,1,2:29:43 PM,27,1,2:30:45 PM,27,1,2:42:10 PM,27,1,2:51:24 PM,27,1,3:12:42 PM,6,23.0,,-1.2584143,36.8048002,-1.3010058999999998,36.8216816,Rider_Id_709,1278
-Order_No_26103,User_Id_1964,Bike,3,Personal,11,3,1:18:50 PM,11,3,1:21:08 PM,11,3,1:27:17 PM,11,3,1:30:00 PM,11,3,1:52:28 PM,10,19.1,,-1.3332749,36.870814700000004,-1.2877501,36.814659899999995,Rider_Id_45,1348
-Order_No_25886,User_Id_1502,Bike,3,Business,22,5,3:58:55 PM,22,5,3:59:06 PM,22,5,4:08:32 PM,22,5,4:10:22 PM,22,5,4:34:23 PM,13,27.1,,-1.2860183,36.8975338,-1.2232123,36.8932547,Rider_Id_456,1441
-Order_No_815,User_Id_20,Bike,3,Business,13,2,3:21:20 PM,13,2,3:21:42 PM,13,2,3:25:56 PM,13,2,3:27:03 PM,13,2,3:28:22 PM,1,26.4,,-1.2629666000000002,36.8068369,-1.2648363,36.802375399999995,Rider_Id_501,79
-Order_No_8639,User_Id_1642,Bike,3,Business,14,5,12:39:51 PM,14,5,1:05:28 PM,14,5,1:15:22 PM,14,5,1:25:22 PM,14,5,1:35:29 PM,5,21.3,,-1.2846195,36.7958316,-1.2728069,36.7962362,Rider_Id_607,607
-Order_No_8010,User_Id_393,Bike,3,Business,15,2,12:56:04 PM,15,2,12:56:25 PM,15,2,12:56:27 PM,15,2,1:06:57 PM,15,2,1:41:25 PM,11,24.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_914,2068
-Order_No_2466,User_Id_830,Bike,3,Business,25,4,5:27:58 PM,25,4,5:28:12 PM,25,4,5:29:29 PM,25,4,5:36:36 PM,25,4,5:57:29 PM,9,,,-1.2551895,36.7822034,-1.2938100000000001,36.7685631,Rider_Id_45,1253
-Order_No_27252,User_Id_3222,Bike,3,Business,8,5,2:23:05 PM,8,5,2:24:35 PM,8,5,2:40:12 PM,8,5,2:50:29 PM,8,5,3:38:11 PM,8,30.5,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_76,2862
-Order_No_22430,User_Id_3291,Bike,3,Business,19,6,9:16:44 AM,19,6,9:17:32 AM,19,6,9:24:54 AM,19,6,9:33:17 AM,19,6,9:47:27 AM,5,21.6,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_89,850
-Order_No_2091,User_Id_2457,Bike,3,Business,10,2,8:38:25 AM,10,2,8:59:23 AM,10,2,9:28:54 AM,10,2,9:49:51 AM,10,2,10:17:37 AM,7,16.1,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_101,1666
-Order_No_3992,User_Id_1224,Bike,1,Personal,6,1,12:14:55 PM,6,1,12:22:26 PM,6,1,12:32:56 PM,6,1,12:35:04 PM,6,1,12:58:23 PM,10,20.7,,-1.2841895,36.793093799999994,-1.320062,36.7999133,Rider_Id_449,1399
-Order_No_13313,User_Id_2008,Bike,3,Business,22,5,2:51:19 PM,22,5,2:52:41 PM,22,5,3:13:15 PM,22,5,3:30:24 PM,22,5,4:11:40 PM,17,27.5,,-1.2508229,36.7895263,-1.2482377,36.6673782,Rider_Id_52,2476
-Order_No_254,User_Id_1713,Bike,2,Personal,23,3,12:58:39 PM,23,3,1:00:40 PM,23,3,1:28:30 PM,23,3,1:31:41 PM,23,3,1:59:09 PM,7,21.7,,-1.3025643,36.8276716,-1.2741662,36.7969144,Rider_Id_116,1648
-Order_No_8231,User_Id_631,Bike,1,Personal,23,3,9:25:19 AM,23,3,9:36:38 AM,23,3,9:56:06 AM,23,3,10:06:26 AM,23,3,10:32:19 AM,14,16.8,16.1,-1.2750282,36.85322360000001,-1.2124618999999999,36.8739332,Rider_Id_103,1553
-Order_No_1487,User_Id_1162,Bike,3,Business,7,2,9:06:39 AM,7,2,9:08:35 AM,7,2,9:30:34 AM,7,2,9:39:11 AM,7,2,9:58:52 AM,8,16.8,,-1.3122610000000001,36.794658,-1.2733047,36.8109744,Rider_Id_301,1181
-Order_No_8958,User_Id_2190,Bike,3,Business,15,3,4:05:13 PM,15,3,4:05:30 PM,15,3,4:12:46 PM,15,3,4:48:09 PM,15,3,5:42:49 PM,16,21.0,,-1.3019530000000001,36.788458,-1.206696,36.847709,Rider_Id_848,3280
-Order_No_2051,User_Id_3450,Bike,3,Business,6,3,4:04:10 PM,6,3,4:04:20 PM,6,3,4:07:53 PM,6,3,4:26:02 PM,6,3,4:35:39 PM,9,,,-1.2551895,36.7822034,-1.2942,36.7777614,Rider_Id_953,577
-Order_No_22766,User_Id_3291,Bike,3,Business,11,6,9:19:33 AM,11,6,9:19:52 AM,11,6,9:29:39 AM,11,6,9:37:35 AM,11,6,9:54:30 AM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_309,1015
-Order_No_3463,User_Id_393,Bike,3,Business,13,2,11:51:05 AM,13,2,12:01:45 PM,13,2,12:41:52 PM,13,2,12:48:36 PM,13,2,1:43:07 PM,10,22.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_518,3271
-Order_No_17516,User_Id_2066,Bike,3,Business,27,5,8:48:04 AM,27,5,8:48:30 AM,27,5,8:55:08 AM,27,5,9:09:23 AM,27,5,9:47:51 AM,16,16.0,,-1.316961,36.820337,-1.218673,36.8888713,Rider_Id_951,2308
-Order_No_19289,User_Id_868,Bike,3,Business,12,2,12:28:03 PM,12,2,12:29:15 PM,12,2,12:36:32 PM,12,2,12:45:20 PM,12,2,12:45:21 PM,5,24.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_250,1
-Order_No_20359,User_Id_1927,Bike,3,Business,22,4,3:10:28 PM,22,4,3:23:22 PM,22,4,3:36:41 PM,22,4,3:51:39 PM,22,4,4:16:30 PM,16,26.5,,-1.3323107,36.8692616,-1.3463041999999998,36.768164,Rider_Id_559,1491
-Order_No_15582,User_Id_3201,Bike,3,Business,19,1,3:13:38 PM,19,1,3:13:48 PM,19,1,3:20:08 PM,19,1,3:35:13 PM,19,1,3:35:15 PM,1,25.1,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_647,2
-Order_No_17393,User_Id_2174,Bike,3,Business,10,1,10:40:52 AM,10,1,10:40:56 AM,10,1,10:44:01 AM,10,1,11:52:18 AM,10,1,12:24:30 PM,16,,,-1.2482781,36.884091999999995,-1.2037968000000001,36.7802765,Rider_Id_82,1932
-Order_No_12441,User_Id_2083,Bike,3,Personal,25,1,11:55:20 AM,25,1,11:55:30 AM,25,1,12:07:47 PM,25,1,12:19:41 PM,25,1,12:42:05 PM,11,25.5,,-1.2981239,36.8367977,-1.2615888000000002,36.7928732,Rider_Id_858,1344
-Order_No_1087,User_Id_3222,Bike,3,Business,6,4,9:32:21 AM,6,4,9:47:47 AM,6,4,10:15:17 AM,6,4,10:29:26 AM,6,4,11:26:09 AM,7,18.7,2.9,-1.2551895,36.7822034,-1.2883438999999999,36.8092304,Rider_Id_56,3403
-Order_No_7720,User_Id_1473,Bike,3,Business,11,4,9:52:57 AM,11,4,9:53:23 AM,11,4,10:14:21 AM,11,4,10:55:45 AM,11,4,11:11:00 AM,23,25.4,,-1.3700383,36.9190174,-1.2551895,36.7822034,Rider_Id_436,915
-Order_No_17418,User_Id_269,Bike,3,Business,24,1,12:05:30 PM,24,1,12:06:19 PM,24,1,12:16:03 PM,24,1,12:45:09 PM,24,1,1:17:04 PM,14,22.7,,-1.2615888000000002,36.7928732,-1.3226301999999999,36.8719901,Rider_Id_858,1915
-Order_No_1337,User_Id_273,Bike,2,Personal,14,4,3:27:09 PM,14,4,3:35:48 PM,14,4,3:42:50 PM,14,4,4:52:10 PM,14,4,5:21:26 PM,8,21.2,,-1.2803499,36.827340299999996,-1.2751745,36.765767600000004,Rider_Id_526,1756
-Order_No_16750,User_Id_2227,Bike,3,Personal,19,2,10:27:58 AM,19,2,10:46:53 AM,19,2,11:15:26 AM,19,2,11:17:23 AM,19,2,12:02:41 PM,10,,,-1.3039695,36.783826700000006,-1.2541275,36.8263055,Rider_Id_354,2718
-Order_No_9221,User_Id_788,Bike,3,Business,25,1,1:11:59 PM,25,1,1:15:58 PM,25,1,2:04:11 PM,25,1,2:19:46 PM,25,1,2:55:13 PM,10,28.1,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_863,2127
-Order_No_8536,User_Id_435,Bike,3,Business,21,2,2:37:04 PM,21,2,2:38:04 PM,21,2,2:42:20 PM,21,2,2:52:27 PM,21,2,3:22:16 PM,8,25.0,,-1.2844011999999998,36.777325299999994,-1.300921,36.828195,Rider_Id_16,1789
-Order_No_6076,User_Id_3534,Bike,1,Personal,31,4,3:35:45 PM,31,4,3:46:00 PM,31,4,4:27:49 PM,31,4,4:29:27 PM,31,4,4:44:26 PM,8,23.1,,-1.242265,36.88087279999999,-1.2021921999999998,36.8904146,Rider_Id_742,899
-Order_No_21589,User_Id_1346,Bike,3,Business,6,3,3:36:01 PM,6,3,3:37:37 PM,6,3,3:59:29 PM,6,3,4:01:36 PM,6,3,4:37:02 PM,6,27.7,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_540,2126
-Order_No_15810,User_Id_2718,Bike,3,Business,9,3,10:28:26 AM,9,3,10:28:53 AM,9,3,10:35:03 AM,9,3,10:42:07 AM,9,3,11:15:10 AM,17,21.0,,-1.2601,36.796528,-1.3360448999999999,36.884483700000004,Rider_Id_541,1983
-Order_No_1236,User_Id_1244,Bike,3,Business,11,1,1:50:32 PM,11,1,2:01:52 PM,11,1,2:10:11 PM,11,1,2:31:57 PM,11,1,2:52:33 PM,11,27.8,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_178,1236
-Order_No_9528,User_Id_1471,Bike,3,Business,15,5,12:31:56 PM,15,5,12:34:25 PM,15,5,12:58:30 PM,15,5,1:13:44 PM,15,5,1:49:11 PM,15,26.5,,-1.3194518,36.869396,-1.2551895,36.7822034,Rider_Id_697,2127
-Order_No_28011,User_Id_1500,Bike,3,Business,29,1,11:19:04 AM,29,1,11:23:32 AM,29,1,11:32:20 AM,29,1,12:05:56 PM,29,1,12:25:12 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_573,1156
-Order_No_8439,User_Id_475,Bike,3,Business,29,1,4:26:25 PM,29,1,4:28:06 PM,29,1,4:32:58 PM,29,1,4:37:21 PM,29,1,5:08:28 PM,8,23.4,,-1.2551895,36.7822034,-1.2925919,36.81705770000001,Rider_Id_660,1867
-Order_No_15462,User_Id_2987,Bike,3,Business,31,2,12:35:33 PM,31,2,12:39:18 PM,31,2,12:51:50 PM,31,2,12:54:01 PM,31,2,1:18:41 PM,11,18.7,,-1.2702120000000001,36.82108770000001,-1.3280091,36.8524146,Rider_Id_322,1480
-Order_No_2813,User_Id_1362,Bike,1,Personal,6,5,8:19:49 PM,6,5,8:20:16 PM,6,5,8:32:40 PM,6,5,8:42:35 PM,6,5,9:09:11 PM,16,18.4,,-1.2856498,36.794565999999996,-1.2143841999999998,36.8122348,Rider_Id_105,1596
-Order_No_21378,User_Id_281,Bike,2,Personal,13,1,12:32:36 PM,13,1,12:33:08 PM,13,1,12:44:25 PM,13,1,12:47:03 PM,13,1,1:08:03 PM,8,17.0,,-1.2996372999999999,36.769372100000005,-1.2795796,36.819062,Rider_Id_229,1260
-Order_No_11990,User_Id_2271,Bike,3,Business,15,6,4:47:16 PM,15,6,4:49:51 PM,15,6,4:57:30 PM,15,6,5:20:14 PM,15,6,5:55:40 PM,11,20.8,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_75,2126
-Order_No_1518,User_Id_3355,Bike,3,Business,17,1,2:04:19 PM,17,1,2:37:06 PM,17,1,2:44:30 PM,17,1,2:50:04 PM,17,1,3:10:26 PM,6,24.8,,-1.3052491000000002,36.8223899,-1.2980272,36.7832507,Rider_Id_518,1222
-Order_No_10960,User_Id_3557,Bike,3,Business,13,3,9:20:05 AM,13,3,9:20:15 AM,13,3,9:34:14 AM,13,3,9:38:42 AM,13,3,10:00:10 AM,10,20.4,,-1.2546732,36.808679600000005,-1.3014468000000001,36.7720335,Rider_Id_205,1288
-Order_No_3279,User_Id_3404,Bike,3,Business,13,2,2:36:30 PM,13,2,3:03:29 PM,13,2,3:11:16 PM,13,2,3:15:08 PM,13,2,4:20:49 PM,7,26.4,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_660,3941
-Order_No_12170,User_Id_1291,Bike,3,Personal,7,2,3:26:54 PM,7,2,3:27:28 PM,7,2,3:47:27 PM,7,2,3:50:29 PM,7,2,4:31:50 PM,9,24.6,,-1.2580991000000001,36.794151899999996,-1.2963097,36.7688221,Rider_Id_726,2481
-Order_No_20075,User_Id_2285,Bike,3,Personal,13,1,11:08:53 AM,13,1,11:09:10 AM,13,1,11:16:18 AM,13,1,11:17:36 AM,13,1,12:19:19 PM,16,,,-1.351453,36.899315,-1.2950041,36.814358299999995,Rider_Id_504,3703
-Order_No_17180,User_Id_3517,Bike,3,Business,11,2,9:30:55 AM,11,2,9:53:22 AM,11,2,10:13:32 AM,11,2,10:33:51 AM,11,2,11:05:47 AM,15,19.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_849,1916
-Order_No_9443,User_Id_3288,Bike,3,Business,21,1,12:39:00 PM,21,1,12:46:34 PM,21,1,1:01:31 PM,21,1,1:06:10 PM,21,1,1:19:58 PM,4,25.7,,-1.3080696000000003,36.8237191,-1.2845766,36.8245168,Rider_Id_758,828
-Order_No_7058,User_Id_1341,Bike,3,Business,17,3,9:12:27 AM,17,3,9:13:30 AM,17,3,9:54:33 AM,17,3,10:10:08 AM,17,3,10:29:30 AM,13,,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_855,1162
-Order_No_22733,User_Id_2271,Bike,3,Business,21,4,2:20:31 PM,21,4,3:14:17 PM,21,4,3:20:54 PM,21,4,3:34:13 PM,21,4,3:56:54 PM,11,26.5,,-1.307143,36.825009,-1.293515,36.897607,Rider_Id_332,1361
-Order_No_3589,User_Id_3560,Bike,3,Business,20,3,3:24:20 PM,20,3,3:30:40 PM,20,3,3:32:18 PM,20,3,3:49:28 PM,20,3,4:28:46 PM,13,28.2,,-1.2156006000000001,36.8916865,-1.279395,36.825364,Rider_Id_539,2358
-Order_No_18869,User_Id_2987,Bike,3,Business,10,4,9:32:48 AM,10,4,10:00:00 AM,10,4,10:13:37 AM,10,4,10:21:57 AM,10,4,10:49:21 AM,7,18.2,,-1.2702120000000001,36.82108770000001,-1.3064585,36.8119065,Rider_Id_101,1644
-Order_No_20000,User_Id_3647,Bike,3,Business,11,1,3:07:51 PM,11,1,3:08:59 PM,11,1,3:10:01 PM,11,1,3:26:46 PM,11,1,3:38:02 PM,3,28.1,,-1.2726389999999999,36.794723,-1.2674482,36.8076994,Rider_Id_26,676
-Order_No_16610,User_Id_3149,Bike,3,Business,17,1,3:23:18 PM,17,1,3:44:17 PM,17,1,4:19:15 PM,17,1,4:32:52 PM,17,1,4:48:07 PM,6,22.6,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_581,915
-Order_No_15809,User_Id_635,Bike,3,Business,13,3,12:04:40 PM,13,3,12:27:20 PM,13,3,12:34:29 PM,13,3,1:00:56 PM,13,3,1:37:51 PM,7,,,-1.3081376,36.83297720000001,-1.2599563,36.799343900000004,Rider_Id_332,2215
-Order_No_314,User_Id_867,Bike,3,Business,1,1,4:26:32 PM,1,1,4:29:51 PM,1,1,4:47:01 PM,1,1,4:48:57 PM,1,1,5:20:45 PM,11,27.6,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_682,1908
-Order_No_25124,User_Id_718,Bike,3,Business,6,3,3:36:47 PM,6,3,4:15:31 PM,6,3,4:51:32 PM,6,3,4:54:16 PM,6,3,5:30:03 PM,12,19.8,,-1.2553615,36.789537599999996,-1.3083777,36.85900770000001,Rider_Id_808,2147
-Order_No_23714,User_Id_1271,Bike,3,Business,4,4,9:22:22 AM,4,4,9:23:04 AM,4,4,9:33:06 AM,4,4,9:39:10 AM,4,4,10:53:46 AM,24,21.9,,-1.3244885,36.897791999999995,-1.3108386,36.7555239,Rider_Id_404,4476
-Order_No_5843,User_Id_3218,Bike,3,Business,14,4,10:32:37 AM,14,4,11:24:02 AM,14,4,11:31:01 AM,14,4,11:37:13 AM,14,4,11:54:20 AM,4,19.2,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_444,1027
-Order_No_2347,User_Id_1578,Bike,3,Business,6,3,12:42:34 PM,6,3,12:43:17 PM,6,3,12:54:26 PM,6,3,12:59:43 PM,6,3,1:40:45 PM,12,22.5,,-1.2865597,36.8237743,-1.2859912,36.8756811,Rider_Id_557,2462
-Order_No_7767,User_Id_2825,Bike,2,Personal,1,1,12:29:47 PM,1,1,12:45:22 PM,1,1,1:13:34 PM,1,1,1:17:57 PM,1,1,2:11:33 PM,26,23.9,,-1.2156006000000001,36.8916865,-1.3855778,36.9269619,Rider_Id_408,3216
-Order_No_6268,User_Id_1271,Bike,3,Business,2,4,3:04:00 PM,2,4,3:04:20 PM,2,4,3:44:52 PM,2,4,3:53:19 PM,2,4,4:23:46 PM,20,26.2,,-1.3244885,36.897791999999995,-1.2847520000000001,36.7748784,Rider_Id_886,1827
-Order_No_9995,User_Id_867,Bike,3,Business,28,2,1:27:02 PM,28,2,1:27:33 PM,28,2,1:29:15 PM,28,2,2:09:26 PM,28,2,2:36:45 PM,10,23.6,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_686,1639
-Order_No_19050,User_Id_393,Bike,3,Business,21,4,9:36:56 AM,21,4,9:37:08 AM,21,4,9:38:45 AM,21,4,10:23:08 AM,21,4,10:35:57 AM,14,19.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515,769
-Order_No_4790,User_Id_3291,Bike,3,Business,14,1,12:59:10 PM,14,1,1:08:54 PM,14,1,1:21:16 PM,14,1,1:30:19 PM,14,1,2:14:34 PM,16,23.7,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_404,2655
-Order_No_14878,User_Id_2216,Bike,3,Business,18,5,11:50:13 AM,18,5,11:50:23 AM,18,5,11:53:36 AM,18,5,12:07:03 PM,18,5,12:28:48 PM,8,20.1,,-1.2551895,36.7822034,-1.303658,36.817707500000004,Rider_Id_958,1305
-Order_No_27186,User_Id_2801,Bike,3,Business,31,3,9:38:17 AM,31,3,9:38:34 AM,31,3,9:58:45 AM,31,3,10:00:48 AM,31,3,10:29:59 AM,10,19.0,,-1.2595421999999998,36.7871178,-1.2986123,36.8076281,Rider_Id_259,1751
-Order_No_28202,User_Id_3283,Bike,3,Business,29,5,12:42:38 PM,29,5,12:50:58 PM,29,5,1:19:57 PM,29,5,1:24:19 PM,29,5,1:40:47 PM,5,26.7,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_435,988
-Order_No_5978,User_Id_2982,Bike,3,Business,14,2,12:37:35 PM,14,2,12:55:02 PM,14,2,1:08:47 PM,14,2,1:17:38 PM,14,2,1:48:42 PM,18,,,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_419,1864
-Order_No_11970,User_Id_706,Bike,3,Business,4,5,2:00:45 PM,4,5,2:01:02 PM,4,5,2:09:59 PM,4,5,2:17:03 PM,4,5,2:27:14 PM,4,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_237,611
-Order_No_11555,User_Id_393,Bike,3,Business,11,1,3:50:49 PM,11,1,3:53:34 PM,11,1,3:59:42 PM,11,1,4:15:54 PM,11,1,4:42:51 PM,11,28.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_191,1617
-Order_No_13370,User_Id_2279,Bike,3,Business,17,4,1:31:37 PM,17,4,2:43:14 PM,17,4,3:27:30 PM,17,4,3:28:37 PM,17,4,3:51:55 PM,5,28.1,,-1.2897368,36.8226783,-1.2962832,36.784968299999996,Rider_Id_268,1398
-Order_No_24874,User_Id_2813,Bike,3,Personal,1,3,10:40:25 AM,1,3,10:42:11 AM,1,3,10:48:19 AM,1,3,11:48:52 AM,1,3,11:56:58 AM,5,19.8,,-1.2776072,36.8227604,-1.2762383999999998,36.7929522,Rider_Id_726,486
-Order_No_22507,User_Id_3404,Bike,3,Business,4,1,3:02:30 PM,4,1,3:02:48 PM,4,1,3:17:29 PM,4,1,3:19:55 PM,4,1,3:47:20 PM,2,28.5,,-1.2882183999999999,36.826346,-1.279395,36.825364,Rider_Id_311,1645
-Order_No_22456,User_Id_868,Bike,3,Business,10,1,11:07:37 AM,10,1,12:04:49 PM,10,1,12:13:23 PM,10,1,12:25:09 PM,10,1,12:36:01 PM,4,23.6,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_646,652
-Order_No_3108,User_Id_635,Bike,3,Business,7,1,11:28:02 AM,7,1,11:43:33 AM,7,1,12:16:43 PM,7,1,12:52:34 PM,7,1,1:09:34 PM,8,24.6,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_72,1020
-Order_No_14447,User_Id_3317,Bike,2,Personal,25,2,1:42:51 PM,25,2,1:51:18 PM,25,2,2:30:59 PM,25,2,2:35:09 PM,25,2,3:13:18 PM,17,26.9,,-1.1859289,36.895155200000005,-1.2517392,36.82496079999999,Rider_Id_581,2289
-Order_No_17866,User_Id_2330,Bike,3,Business,29,3,5:59:31 PM,29,3,5:59:54 PM,29,3,6:09:33 PM,29,3,6:13:17 PM,29,3,6:13:19 PM,5,21.3,,-1.3040812,36.7924217,-1.2890715,36.8157588,Rider_Id_800,2
-Order_No_10962,User_Id_733,Bike,3,Business,13,3,2:24:11 PM,13,3,2:39:06 PM,13,3,2:47:35 PM,13,3,2:55:40 PM,13,3,3:30:19 PM,13,27.4,,-1.2551895,36.7822034,-1.2998093000000002,36.859522600000005,Rider_Id_958,2079
-Order_No_5666,User_Id_2956,Bike,3,Business,21,3,1:53:48 PM,21,3,2:09:16 PM,21,3,2:36:38 PM,21,3,2:39:28 PM,21,3,3:19:05 PM,8,23.4,3.1,-1.2584143,36.8048002,-1.296485,36.7931959,Rider_Id_685,2377
-Order_No_2542,User_Id_1329,Bike,3,Business,28,5,9:52:16 AM,28,5,9:53:35 AM,28,5,10:02:24 AM,28,5,10:03:53 AM,28,5,10:33:42 AM,17,19.2,,-1.273056,36.811298,-1.3103985,36.9114173,Rider_Id_648,1789
-Order_No_4027,User_Id_393,Bike,3,Business,24,4,8:42:47 AM,24,4,9:24:44 AM,24,4,9:41:22 AM,24,4,10:19:02 AM,24,4,10:19:05 AM,11,19.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,3
-Order_No_20505,User_Id_2982,Bike,3,Business,10,5,9:21:14 AM,10,5,10:16:34 AM,10,5,10:26:03 AM,10,5,10:41:54 AM,10,5,11:42:26 AM,18,19.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_471,3632
-Order_No_23576,User_Id_1364,Bike,1,Personal,9,4,9:40:12 AM,9,4,9:41:09 AM,9,4,10:05:43 AM,9,4,10:21:22 AM,9,4,10:44:56 AM,5,17.2,,-1.2915968999999998,36.823763899999996,-1.2745107,36.797450299999994,Rider_Id_532,1414
-Order_No_21504,User_Id_3283,Bike,3,Business,30,2,2:58:07 PM,30,2,3:34:15 PM,30,2,3:57:55 PM,30,2,4:12:36 PM,30,2,4:43:20 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_519,1844
-Order_No_17051,User_Id_797,Bike,3,Business,25,5,1:50:18 PM,25,5,1:51:05 PM,25,5,1:57:10 PM,25,5,2:07:02 PM,25,5,2:26:12 PM,13,29.4,,-1.2551895,36.7822034,-1.2336076,36.8336102,Rider_Id_328,1150
-Order_No_10883,User_Id_635,Bike,3,Business,27,6,11:03:15 AM,27,6,11:28:42 AM,27,6,11:41:47 AM,27,6,12:22:56 PM,27,6,12:36:12 PM,12,,,-1.2602337,36.79905479999999,-1.2208123,36.856526099999996,Rider_Id_61,796
-Order_No_20108,User_Id_1244,Bike,3,Business,13,6,11:33:37 AM,13,6,11:34:53 AM,13,6,11:41:04 AM,13,6,12:28:23 PM,13,6,12:50:03 PM,8,26.1,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_713,1300
-Order_No_890,User_Id_733,Bike,3,Business,26,5,3:47:10 PM,26,5,3:47:25 PM,26,5,3:51:22 PM,26,5,3:53:14 PM,26,5,4:42:05 PM,10,,,-1.2551895,36.7822034,-1.2903067,36.83756339999999,Rider_Id_422,2931
-Order_No_15139,User_Id_1798,Bike,3,Business,29,4,1:32:13 PM,29,4,1:33:02 PM,29,4,1:40:38 PM,29,4,1:46:03 PM,29,4,2:04:55 PM,6,25.0,,-1.2615888000000002,36.7928732,-1.2860152,36.826003,Rider_Id_330,1132
-Order_No_15465,User_Id_267,Bike,3,Personal,11,2,11:33:01 AM,11,2,11:33:11 AM,11,2,11:33:24 AM,11,2,11:43:43 AM,11,2,12:18:51 PM,3,24.3,,-1.2833106,36.82402379999999,-1.2985749,36.8088,Rider_Id_338,2108
-Order_No_13135,User_Id_942,Bike,2,Personal,11,3,11:00:11 AM,11,3,11:00:46 AM,11,3,11:52:13 AM,11,3,12:14:45 PM,11,3,12:36:13 PM,11,19.2,,-1.2620197,36.7800096,-1.3147095,36.847233700000004,Rider_Id_101,1288
-Order_No_24046,User_Id_516,Bike,3,Business,11,1,1:45:11 PM,11,1,1:48:22 PM,11,1,2:02:46 PM,11,1,2:05:14 PM,11,1,2:27:30 PM,8,27.8,,-1.2551895,36.7822034,-1.2821378,36.7740428,Rider_Id_332,1336
-Order_No_2747,User_Id_3434,Bike,3,Business,20,1,3:52:19 PM,20,1,3:53:13 PM,20,1,3:57:15 PM,20,1,4:09:26 PM,20,1,4:44:39 PM,19,21.2,,-1.2963097,36.7688221,-1.3261258999999999,36.904086,Rider_Id_100,2113
-Order_No_17496,User_Id_1568,Bike,3,Business,19,1,1:13:48 PM,19,1,1:31:40 PM,19,1,1:33:51 PM,19,1,1:44:41 PM,19,1,2:07:49 PM,5,25.0,,-1.2897368,36.8226783,-1.2666695,36.8328283,Rider_Id_477,1388
-Order_No_9215,User_Id_3291,Bike,3,Business,18,2,12:38:19 PM,18,2,12:42:51 PM,18,2,12:49:20 PM,18,2,1:04:09 PM,18,2,1:24:36 PM,9,,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_932,1227
-Order_No_12784,User_Id_1616,Bike,1,Personal,31,5,4:06:15 PM,31,5,4:06:29 PM,31,5,4:12:46 PM,31,5,4:16:27 PM,31,5,4:53:07 PM,6,19.8,,-1.2798072,36.8290023,-1.311706,36.802841,Rider_Id_447,2200
-Order_No_10202,User_Id_3557,Bike,3,Business,26,1,10:39:07 AM,26,1,12:01:26 PM,26,1,12:26:36 PM,26,1,12:41:37 PM,26,1,1:57:07 PM,5,22.8,,-1.2830057,36.81754779999999,-1.2546732,36.808679600000005,Rider_Id_260,4530
-Order_No_6558,User_Id_1500,Bike,3,Business,26,2,10:50:32 AM,26,2,10:51:16 AM,26,2,11:07:19 AM,26,2,11:12:33 AM,26,2,11:25:24 AM,6,27.7,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_448,771
-Order_No_22541,User_Id_2956,Bike,3,Business,11,4,4:29:06 PM,11,4,4:29:27 PM,11,4,4:36:41 PM,11,4,4:41:30 PM,11,4,4:49:31 PM,2,26.7,,-1.2584143,36.8048002,-1.2671296,36.81257720000001,Rider_Id_547,481
-Order_No_9630,User_Id_2587,Bike,3,Business,21,1,3:35:10 PM,21,1,3:36:01 PM,21,1,3:36:28 PM,21,1,3:54:13 PM,21,1,4:25:56 PM,7,27.6,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_86,1903
-Order_No_17851,User_Id_1329,Bike,3,Business,22,5,1:02:59 PM,22,5,1:03:11 PM,22,5,1:10:33 PM,22,5,1:14:14 PM,22,5,1:26:19 PM,5,,,-1.273056,36.811298,-1.2910549999999998,36.786744,Rider_Id_655,725
-Order_No_6611,User_Id_3489,Bike,1,Personal,6,6,9:43:40 AM,6,6,9:43:47 AM,6,6,9:52:30 AM,6,6,9:56:18 AM,6,6,10:34:45 AM,17,19.0,,-1.3039695,36.783826700000006,-1.364732,36.658943,Rider_Id_847,2307
-Order_No_6147,User_Id_2658,Bike,3,Business,10,5,4:00:20 PM,10,5,4:09:55 PM,10,5,5:26:05 PM,10,5,5:35:29 PM,10,5,6:01:47 PM,7,,,-1.2924639,36.77957979999999,-1.2813012,36.832396200000005,Rider_Id_843,1578
-Order_No_22097,User_Id_928,Bike,3,Business,27,1,9:04:22 AM,27,1,9:05:12 AM,27,1,9:16:25 AM,27,1,9:24:15 AM,27,1,9:38:32 AM,10,18.1,1.0,-1.3050110000000001,36.80385379999999,-1.2551895,36.7822034,Rider_Id_438,857
-Order_No_15833,User_Id_2279,Bike,3,Business,18,1,1:28:53 PM,18,1,1:33:34 PM,18,1,2:16:51 PM,18,1,2:25:52 PM,18,1,2:40:42 PM,1,30.9,,-1.2897368,36.8226783,-1.2824535,36.8265153,Rider_Id_686,890
-Order_No_20665,User_Id_783,Bike,2,Personal,16,2,1:41:28 PM,16,2,1:51:38 PM,16,2,2:05:47 PM,16,2,2:07:51 PM,16,2,2:28:20 PM,13,27.2,,-1.2963367000000001,36.81168710000001,-1.2380935,36.80729470000001,Rider_Id_726,1229
-Order_No_14925,User_Id_1336,Bike,3,Business,22,2,12:35:34 PM,22,2,12:48:06 PM,22,2,12:54:47 PM,22,2,1:04:56 PM,22,2,1:53:34 PM,8,24.1,,-1.2885029,36.825604999999996,-1.3038645,36.8684596,Rider_Id_819,2918
-Order_No_24295,User_Id_3599,Bike,3,Business,7,5,12:01:48 PM,7,5,12:02:50 PM,7,5,12:12:47 PM,7,5,12:22:00 PM,7,5,12:42:57 PM,5,,,-1.3302996,36.8707935,-1.3308323,36.8840398,Rider_Id_675,1257
-Order_No_15748,User_Id_393,Bike,3,Business,13,3,12:39:29 PM,13,3,12:39:41 PM,13,3,12:39:46 PM,13,3,12:50:23 PM,13,3,1:18:42 PM,14,24.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_901,1699
-Order_No_25860,User_Id_1281,Bike,3,Business,31,5,8:55:47 AM,31,5,8:55:59 AM,31,5,9:11:04 AM,31,5,9:13:29 AM,31,5,9:53:54 AM,9,14.7,,-1.2707742,36.7578609,-1.2854453000000001,36.818836299999994,Rider_Id_88,2425
-Order_No_18756,User_Id_3669,Bike,3,Business,30,2,11:15:55 AM,30,2,11:20:56 AM,30,2,11:42:31 AM,30,2,12:00:02 PM,30,2,12:23:11 PM,15,,,-1.2615888000000002,36.7928732,-1.2156006000000001,36.8916865,Rider_Id_8,1389
-Order_No_17338,User_Id_1582,Bike,1,Business,5,5,2:42:27 PM,5,5,2:45:42 PM,5,5,2:59:57 PM,5,5,3:01:56 PM,5,5,3:21:05 PM,5,26.5,,-1.2735999,36.8218131,-1.2508229,36.7895263,Rider_Id_407,1149
-Order_No_14226,User_Id_1578,Bike,3,Business,19,3,10:17:00 AM,19,3,10:17:08 AM,19,3,10:24:26 AM,19,3,10:35:42 AM,19,3,11:17:50 AM,4,22.4,,-1.28878,36.8168312,-1.303711,36.81549329999999,Rider_Id_79,2528
-Order_No_27836,User_Id_1316,Bike,1,Business,14,4,2:51:48 PM,14,4,2:52:37 PM,14,4,2:59:02 PM,14,4,3:07:59 PM,14,4,3:16:14 PM,3,21.2,,-1.2551895,36.7822034,-1.2635283000000002,36.7946012,Rider_Id_908,495
-Order_No_12037,User_Id_3291,Bike,3,Business,8,2,4:57:20 PM,8,2,5:01:14 PM,8,2,5:23:51 PM,8,2,5:30:47 PM,8,2,6:02:10 PM,10,23.5,,-1.2600926000000001,36.8088685,-1.2297799999999999,36.836215,Rider_Id_862,1883
-Order_No_5567,User_Id_682,Bike,2,Personal,26,4,10:38:56 AM,26,4,10:39:46 AM,26,4,10:46:53 AM,26,4,10:56:04 AM,26,4,11:06:24 AM,5,18.8,,-1.2947685,36.778583399999995,-1.2721028,36.795348499999996,Rider_Id_140,620
-Order_No_25872,User_Id_318,Bike,2,Personal,29,2,3:38:22 PM,29,2,3:41:42 PM,29,2,3:51:18 PM,29,2,3:56:42 PM,29,2,4:36:52 PM,25,23.8,,-1.2137756000000002,36.7996657,-1.3117411,36.7106558,Rider_Id_116,2410
-Order_No_2448,User_Id_265,Bike,3,Business,14,4,12:37:55 PM,14,4,12:38:30 PM,14,4,12:40:08 PM,14,4,12:54:25 PM,14,4,1:13:42 PM,8,25.0,,-1.2948834999999999,36.7830474,-1.300921,36.828195,Rider_Id_519,1157
-Order_No_19675,User_Id_2982,Bike,3,Business,1,1,11:06:29 AM,1,1,11:07:15 AM,1,1,11:10:05 AM,1,1,11:38:46 AM,1,1,12:13:35 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_297,2089
-Order_No_11932,User_Id_557,Bike,3,Business,29,1,9:44:14 AM,29,1,9:44:24 AM,29,1,9:59:15 AM,29,1,10:07:33 AM,29,1,10:34:34 AM,14,,,-1.238874,36.807889,-1.2969741000000001,36.7856612,Rider_Id_96,1621
-Order_No_8982,User_Id_1710,Bike,3,Personal,15,2,10:43:11 AM,15,2,10:43:24 AM,15,2,10:44:10 AM,15,2,10:52:00 AM,15,2,11:07:52 AM,5,24.1,,-1.2963097,36.7688221,-1.2950041,36.814358299999995,Rider_Id_958,952
-Order_No_27939,User_Id_633,Bike,3,Business,19,6,9:37:45 AM,19,6,9:38:03 AM,19,6,9:50:38 AM,19,6,9:59:09 AM,19,6,9:59:11 AM,4,21.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_523,2
-Order_No_10435,User_Id_136,Bike,3,Business,18,4,2:14:19 PM,18,4,2:14:30 PM,18,4,2:35:25 PM,18,4,2:40:44 PM,18,4,3:16:44 PM,15,,,-1.3254522,36.721658399999995,-1.2870705,36.82560170000001,Rider_Id_435,2160
-Order_No_249,User_Id_3352,Bike,3,Personal,17,3,2:08:25 PM,17,3,2:08:36 PM,17,3,2:17:43 PM,17,3,2:22:53 PM,17,3,2:53:56 PM,13,,,-1.3189908000000001,36.777074,-1.2592947,36.7698085,Rider_Id_166,1863
-Order_No_15183,User_Id_1237,Bike,1,Personal,28,1,10:59:38 AM,28,1,11:00:07 AM,28,1,11:03:48 AM,28,1,11:11:29 AM,28,1,11:31:10 AM,3,22.7,,-1.2786952,36.8244479,-1.2668611,36.795638700000005,Rider_Id_852,1181
-Order_No_3903,User_Id_2216,Bike,3,Business,20,1,12:11:26 PM,20,1,2:23:17 PM,20,1,2:23:38 PM,20,1,2:37:24 PM,20,1,3:35:48 PM,5,21.5,,-1.2551895,36.7822034,-1.2620741000000002,36.815814700000004,Rider_Id_881,3504
-Order_No_17761,User_Id_393,Bike,3,Business,18,2,10:45:22 AM,18,2,12:01:37 PM,18,2,12:01:54 PM,18,2,12:24:33 PM,18,2,12:45:23 PM,11,,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_10,1250
-Order_No_21463,User_Id_2763,Bike,3,Business,6,3,9:20:30 AM,6,3,9:22:16 AM,6,3,9:30:29 AM,6,3,9:33:57 AM,6,3,9:58:08 AM,8,,,-1.2551895,36.7822034,-1.298094,36.803149,Rider_Id_885,1451
-Order_No_9726,User_Id_1469,Bike,3,Business,1,5,11:43:40 AM,1,5,11:44:37 AM,1,5,11:57:31 AM,1,5,12:17:25 PM,1,5,12:36:13 PM,4,28.4,,-1.2698515,36.783161799999995,-1.2749245,36.8069557,Rider_Id_576,1128
-Order_No_19221,User_Id_3091,Bike,3,Business,13,4,2:32:22 PM,13,4,2:32:48 PM,13,4,2:34:19 PM,13,4,2:40:40 PM,13,4,3:43:50 PM,17,23.0,,-1.3165542,36.700067,-1.290894,36.822971,Rider_Id_582,3790
-Order_No_18607,User_Id_1809,Bike,3,Business,27,3,11:30:22 AM,27,3,11:30:37 AM,27,3,11:36:18 AM,27,3,11:40:56 AM,27,3,11:48:15 AM,1,,,-1.2646084,36.8028645,-1.2620912,36.8013562,Rider_Id_345,439
-Order_No_15088,User_Id_2237,Bike,3,Business,17,6,11:01:46 AM,17,6,11:05:23 AM,17,6,11:32:35 AM,17,6,11:41:28 AM,17,6,12:05:41 PM,15,24.0,,-1.266571,36.801406,-1.2036664,36.819262200000004,Rider_Id_19,1453
-Order_No_2341,User_Id_3238,Bike,3,Business,4,2,11:51:36 AM,4,2,12:22:24 PM,4,2,1:05:04 PM,4,2,1:11:05 PM,4,2,2:01:23 PM,24,19.4,,-1.3976138,36.782965999999995,-1.2883874,36.7756411,Rider_Id_205,3018
-Order_No_3510,User_Id_1067,Bike,1,Personal,4,1,4:25:36 PM,4,1,4:59:23 PM,4,1,5:12:38 PM,4,1,5:21:33 PM,4,1,5:31:33 PM,4,20.8,,-1.2621828000000002,36.7949276,-1.273056,36.811298,Rider_Id_434,600
-Order_No_6397,User_Id_2136,Bike,1,Personal,4,3,2:42:28 PM,4,3,4:40:04 PM,4,3,4:45:59 PM,4,3,4:55:44 PM,4,3,5:11:13 PM,6,,,-1.2672588,36.8000252,-1.2939816000000002,36.8118469,Rider_Id_427,929
-Order_No_9368,User_Id_3091,Bike,3,Business,26,3,4:12:31 PM,26,3,4:12:50 PM,26,3,4:17:10 PM,26,3,4:25:46 PM,26,3,4:55:25 PM,8,27.2,,-1.3012008,36.764868,-1.2615888000000002,36.7928732,Rider_Id_62,1779
-Order_No_11849,User_Id_1865,Bike,1,Personal,14,5,9:14:04 AM,14,5,9:14:25 AM,14,5,9:27:15 AM,14,5,9:41:14 AM,14,5,10:06:16 AM,13,18.4,,-1.275931,36.8516227,-1.2166078000000002,36.882501,Rider_Id_467,1502
-Order_No_4470,User_Id_248,Bike,3,Business,1,5,8:11:31 AM,1,5,8:12:16 AM,1,5,8:12:48 AM,1,5,8:36:00 AM,1,5,8:58:18 AM,8,23.3,,-1.2635089,36.78873370000001,-1.304735,36.784605400000004,Rider_Id_523,1338
-Order_No_22769,User_Id_247,Bike,3,Personal,27,4,2:35:20 PM,27,4,2:36:26 PM,27,4,2:48:39 PM,27,4,3:03:15 PM,27,4,3:06:50 PM,4,25.2,,-1.2850643999999998,36.8343657,-1.2900260000000001,36.8165275,Rider_Id_531,215
-Order_No_21852,User_Id_393,Bike,3,Business,17,5,11:11:26 AM,17,5,11:15:17 AM,17,5,11:29:17 AM,17,5,11:33:35 AM,17,5,12:16:11 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_328,2556
-Order_No_22491,User_Id_2987,Bike,3,Business,14,2,12:57:39 PM,14,2,1:13:26 PM,14,2,1:39:05 PM,14,2,1:49:25 PM,14,2,2:45:42 PM,25,24.0,,-1.3692158,36.76965870000001,-1.3450191999999999,36.9211859,Rider_Id_676,3377
-Order_No_3741,User_Id_2112,Bike,3,Business,24,4,6:24:51 PM,24,4,6:25:25 PM,24,4,6:25:36 PM,24,4,6:40:59 PM,24,4,6:50:51 PM,5,25.8,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_86,592
-Order_No_10901,User_Id_265,Bike,3,Business,11,1,2:06:33 PM,11,1,2:16:26 PM,11,1,2:23:35 PM,11,1,2:37:44 PM,11,1,2:52:03 PM,6,28.1,,-1.300921,36.828195,-1.2662229,36.83603970000001,Rider_Id_206,859
-Order_No_20573,User_Id_1561,Bike,3,Business,7,3,11:28:38 AM,7,3,11:30:05 AM,7,3,11:38:42 AM,7,3,11:42:53 AM,7,3,12:49:37 PM,15,25.2,,-1.2570088,36.801667,-1.328259,36.860975,Rider_Id_660,4004
-Order_No_17491,User_Id_2676,Bike,3,Business,4,1,11:13:02 AM,4,1,11:18:21 AM,4,1,11:36:36 AM,4,1,11:41:23 AM,4,1,12:21:29 PM,15,26.5,,-1.2261913,36.836811700000005,-1.2963097,36.7688221,Rider_Id_52,2406
-Order_No_26092,User_Id_1603,Bike,1,Personal,11,6,10:07:25 AM,11,6,10:09:07 AM,11,6,10:26:15 AM,11,6,10:32:21 AM,11,6,10:51:10 AM,6,20.8,,-1.2830603,36.8298283,-1.3217456,36.847143100000004,Rider_Id_754,1129
-Order_No_23882,User_Id_867,Bike,3,Business,21,3,7:46:26 AM,21,3,8:10:23 AM,21,3,8:32:44 AM,21,3,8:34:27 AM,21,3,8:53:57 AM,9,17.0,1.1,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_762,1170
-Order_No_916,User_Id_3398,Bike,3,Business,5,3,9:46:45 AM,5,3,9:53:30 AM,5,3,10:09:21 AM,5,3,10:15:33 AM,5,3,10:51:24 AM,17,18.2,,-1.2534301,36.800098299999995,-1.3169591,36.7000029,Rider_Id_878,2151
-Order_No_24124,User_Id_2154,Bike,3,Business,31,3,2:16:19 PM,31,3,2:23:21 PM,31,3,2:46:00 PM,31,3,3:01:04 PM,31,3,3:24:54 PM,5,27.2,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_906,1430
-Order_No_9755,User_Id_3557,Bike,3,Business,21,2,3:06:07 PM,21,2,3:07:09 PM,21,2,3:19:14 PM,21,2,3:32:17 PM,21,2,4:20:07 PM,8,24.9,,-1.3012173,36.8093987,-1.2546732,36.808679600000005,Rider_Id_411,2870
-Order_No_22788,User_Id_2801,Bike,3,Business,4,4,12:34:45 PM,4,4,12:37:25 PM,4,4,12:44:02 PM,4,4,12:54:43 PM,4,4,1:29:25 PM,11,25.7,,-1.2674273999999999,36.787082700000006,-1.307726,36.839117,Rider_Id_126,2082
-Order_No_24912,User_Id_2866,Bike,3,Business,1,4,3:21:24 PM,1,4,3:26:57 PM,1,4,3:37:00 PM,1,4,3:46:49 PM,1,4,4:20:27 PM,8,,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_498,2018
-Order_No_27201,User_Id_868,Bike,3,Business,13,6,12:03:39 PM,13,6,12:04:57 PM,13,6,12:21:30 PM,13,6,12:25:54 PM,13,6,12:48:35 PM,4,26.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_507,1361
-Order_No_5612,User_Id_2829,Bike,3,Business,27,1,10:43:01 AM,27,1,10:44:14 AM,27,1,11:04:52 AM,27,1,11:29:30 AM,27,1,11:55:24 AM,6,20.8,,-1.2571472,36.795063299999995,-1.2933788999999998,36.808753499999995,Rider_Id_115,1554
-Order_No_12097,User_Id_2900,Bike,3,Business,18,5,1:21:47 PM,18,5,1:22:06 PM,18,5,1:22:40 PM,18,5,1:34:32 PM,18,5,2:02:49 PM,16,27.0,,-1.3329777,36.866203000000006,-1.2963097,36.7688221,Rider_Id_449,1697
-Order_No_8545,User_Id_2801,Bike,3,Business,3,1,3:16:53 PM,3,1,3:19:01 PM,3,1,3:26:49 PM,3,1,3:31:08 PM,3,1,3:46:30 PM,5,23.1,,-1.2674273999999999,36.787082700000006,-1.2753108999999998,36.8130185,Rider_Id_952,922
-Order_No_21374,User_Id_60,Bike,3,Business,16,4,11:17:20 AM,16,4,11:17:28 AM,16,4,11:17:50 AM,16,4,11:40:43 AM,16,4,11:46:05 AM,8,14.7,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_406,322
-Order_No_19839,User_Id_246,Bike,3,Business,29,1,5:20:13 PM,29,1,5:40:19 PM,29,1,5:44:53 PM,29,1,5:55:13 PM,29,1,6:25:25 PM,17,23.4,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_867,1812
-Order_No_6230,User_Id_3355,Bike,3,Business,25,4,4:57:50 PM,25,4,5:09:31 PM,25,4,5:31:34 PM,25,4,5:39:05 PM,25,4,6:01:55 PM,8,,,-1.3226301999999999,36.8719901,-1.305399,36.822184899999996,Rider_Id_184,1370
-Order_No_8660,User_Id_1304,Bike,3,Business,5,1,11:45:03 AM,5,1,11:45:28 AM,5,1,11:46:24 AM,5,1,11:55:52 AM,5,1,12:09:08 PM,6,24.3,,-1.3332749,36.870814700000004,-1.2999305,36.843617,Rider_Id_848,796
-Order_No_24930,User_Id_1880,Bike,3,Business,26,4,2:41:19 PM,26,4,2:41:46 PM,26,4,3:16:32 PM,26,4,3:26:24 PM,26,4,4:29:18 PM,2,21.9,,-1.2571472,36.795063299999995,-1.2672588,36.8000252,Rider_Id_519,3774
-Order_No_2054,User_Id_1075,Bike,3,Business,31,4,3:34:00 PM,31,4,3:34:35 PM,31,4,3:47:27 PM,31,4,4:09:25 PM,31,4,4:32:20 PM,13,28.7,,-1.207546,36.7913976,-1.2527958000000001,36.8003131,Rider_Id_52,1375
-Order_No_9685,User_Id_1484,Bike,3,Business,19,1,9:48:29 AM,19,1,9:48:56 AM,19,1,9:53:09 AM,19,1,9:57:50 AM,19,1,10:18:50 AM,8,19.1,,-1.2551895,36.7822034,-1.304344,36.812274,Rider_Id_708,1260
-Order_No_12593,User_Id_733,Bike,3,Business,6,4,3:13:12 PM,6,4,3:14:43 PM,6,4,3:16:29 PM,6,4,3:21:06 PM,6,4,3:48:20 PM,7,21.2,,-1.2551895,36.7822034,-1.2821928999999999,36.8281227,Rider_Id_709,1634
-Order_No_8850,User_Id_2069,Bike,3,Business,10,6,12:21:09 PM,10,6,12:21:22 PM,10,6,12:33:19 PM,10,6,12:46:25 PM,10,6,1:29:20 PM,13,23.8,,-1.293515,36.897607,-1.228911,36.881876,Rider_Id_554,2575
-Order_No_14611,User_Id_71,Bike,3,Business,30,3,10:43:44 AM,30,3,10:44:12 AM,30,3,10:57:41 AM,30,3,11:02:52 AM,30,3,11:16:36 AM,2,22.5,,-1.2821108,36.8250555,-1.2819053999999999,36.8237089,Rider_Id_480,824
-Order_No_14459,User_Id_136,Bike,3,Business,18,4,11:35:42 AM,18,4,11:35:48 AM,18,4,11:48:47 AM,18,4,12:21:45 PM,18,4,1:00:21 PM,28,25.6,,-1.3094447,36.9107706,-1.3326173000000001,36.71462329999999,Rider_Id_207,2316
-Order_No_26972,User_Id_1349,Bike,1,Personal,26,3,12:28:20 PM,26,3,12:28:43 PM,26,3,12:45:44 PM,26,3,1:26:24 PM,26,3,1:31:56 PM,25,24.2,,-1.1891303,36.896427,-1.286622,36.756370000000004,Rider_Id_387,332
-Order_No_27866,User_Id_633,Bike,3,Business,14,3,2:49:21 PM,14,3,3:00:06 PM,14,3,3:02:40 PM,14,3,3:43:24 PM,14,3,3:43:27 PM,4,25.7,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_239,3
-Order_No_10764,User_Id_1527,Bike,3,Business,9,2,2:35:30 PM,9,2,2:35:46 PM,9,2,2:40:00 PM,9,2,2:54:58 PM,9,2,3:20:17 PM,9,30.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_796,1519
-Order_No_13617,User_Id_2727,Bike,3,Personal,23,3,10:31:38 AM,23,3,10:31:50 AM,23,3,10:51:06 AM,23,3,11:10:25 AM,23,3,11:35:14 AM,15,,,-1.2375778999999998,36.8649718,-1.2925556,36.7903149,Rider_Id_124,1489
-Order_No_5835,User_Id_3560,Bike,3,Business,11,6,2:28:01 PM,11,6,2:34:27 PM,11,6,3:14:29 PM,11,6,3:24:01 PM,11,6,3:37:07 PM,2,,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_668,786
-Order_No_18182,User_Id_1329,Bike,3,Business,24,5,4:21:12 PM,24,5,4:22:18 PM,24,5,4:37:13 PM,24,5,4:38:33 PM,24,5,5:53:48 PM,6,20.1,,-1.273056,36.811298,-1.2974317,36.8028609,Rider_Id_203,4515
-Order_No_4324,User_Id_2994,Bike,1,Personal,24,5,6:06:37 PM,24,5,6:07:47 PM,24,5,6:22:46 PM,24,5,6:28:42 PM,24,5,6:55:48 PM,9,,,-1.268027,36.8037005,-1.2610895,36.725102299999996,Rider_Id_131,1626
-Order_No_17667,User_Id_1502,Bike,3,Business,15,5,2:13:18 PM,15,5,2:18:45 PM,15,5,2:21:01 PM,15,5,3:01:23 PM,15,5,3:20:22 PM,12,27.9,,-1.279395,36.825364,-1.2232123,36.8932547,Rider_Id_718,1139
-Order_No_934,User_Id_1347,Bike,1,Personal,27,4,11:15:43 AM,27,4,11:19:24 AM,27,4,11:20:02 AM,27,4,11:55:58 AM,27,4,12:07:09 PM,6,22.9,,-1.3085095,36.8342635,-1.326825,36.864345,Rider_Id_179,671
-Order_No_27335,User_Id_517,Bike,3,Business,31,5,11:59:58 AM,31,5,12:03:01 PM,31,5,12:03:08 PM,31,5,12:33:14 PM,31,5,12:38:18 PM,4,21.9,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,304
-Order_No_27191,User_Id_2801,Bike,3,Business,25,1,10:17:13 AM,25,1,10:17:24 AM,25,1,10:25:05 AM,25,1,10:31:47 AM,25,1,10:47:38 AM,7,15.2,,-1.2809846999999999,36.8274169,-1.2595421999999998,36.7871178,Rider_Id_88,951
-Order_No_21610,User_Id_854,Bike,1,Personal,20,4,9:16:24 AM,20,4,9:17:09 AM,20,4,9:17:19 AM,20,4,9:42:32 AM,20,4,10:05:02 AM,14,17.8,,-1.2209733,36.904574600000004,-1.2651643,36.807221399999996,Rider_Id_886,1350
-Order_No_17116,User_Id_1127,Bike,1,Personal,12,5,10:37:24 AM,12,5,10:38:04 AM,12,5,10:48:37 AM,12,5,10:50:28 AM,12,5,11:17:40 AM,14,24.3,,-1.2917868000000001,36.7872675,-1.2874367,36.880444700000005,Rider_Id_328,1632
-Order_No_14619,User_Id_2454,Bike,3,Business,6,4,4:32:46 PM,6,4,4:34:32 PM,6,4,4:49:08 PM,6,4,4:54:26 PM,6,4,5:00:58 PM,2,22.7,,-1.2948525,36.7724424,-1.2993231,36.7753372,Rider_Id_674,392
-Order_No_11582,User_Id_1195,Bike,3,Business,19,2,8:13:10 AM,19,2,8:13:42 AM,19,2,8:16:00 AM,19,2,8:34:33 AM,19,2,8:53:35 AM,4,20.9,,-1.3228003000000002,36.8306435,-1.2983042,36.8261141,Rider_Id_901,1142
-Order_No_20293,User_Id_3651,Bike,3,Business,13,2,12:49:49 PM,13,2,1:11:30 PM,13,2,1:13:51 PM,13,2,1:33:42 PM,13,2,2:28:51 PM,6,26.4,,-1.2571472,36.795063299999995,-1.2883346000000002,36.8148489,Rider_Id_881,3309
-Order_No_8747,User_Id_246,Bike,3,Business,15,2,12:16:04 PM,15,2,12:20:00 PM,15,2,12:30:26 PM,15,2,12:38:24 PM,15,2,12:53:37 PM,7,23.0,,-1.2864963,36.8252086,-1.2642719999999998,36.7978791,Rider_Id_100,913
-Order_No_20188,User_Id_811,Bike,3,Personal,10,2,11:58:42 AM,10,2,11:59:09 AM,10,2,12:17:34 PM,10,2,12:19:29 PM,10,2,12:38:17 PM,8,20.3,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_312,1128
-Order_No_13425,User_Id_788,Bike,3,Business,17,4,11:38:34 AM,17,4,11:38:42 AM,17,4,11:39:41 AM,17,4,11:54:08 AM,17,4,12:23:52 PM,16,25.5,,-1.2859912,36.8756811,-1.225322,36.80855,Rider_Id_952,1784
-Order_No_14865,User_Id_438,Bike,3,Personal,7,4,4:58:12 PM,7,4,4:58:25 PM,7,4,5:22:28 PM,7,4,5:24:06 PM,7,4,5:38:57 PM,5,29.2,,-1.2563576,36.7881585,-1.2695287,36.798177700000004,Rider_Id_291,891
-Order_No_8012,User_Id_3565,Bike,3,Business,10,7,12:31:45 PM,10,7,12:33:55 PM,10,7,12:56:47 PM,10,7,1:01:06 PM,10,7,1:52:28 PM,29,,,-1.2844665000000002,36.7866113,-1.1428716,36.962346999999994,Rider_Id_267,3082
-Order_No_26010,User_Id_3689,Bike,3,Business,8,5,5:59:53 PM,8,5,6:00:08 PM,8,5,6:03:11 PM,8,5,6:13:50 PM,8,5,6:31:35 PM,7,25.4,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_383,1065
-Order_No_11829,User_Id_3517,Bike,3,Business,21,4,11:48:30 AM,21,4,12:07:58 PM,21,4,12:10:51 PM,21,4,12:31:31 PM,21,4,1:23:21 PM,16,25.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_362,3110
-Order_No_19844,User_Id_1899,Bike,3,Business,30,4,12:05:06 PM,30,4,12:06:51 PM,30,4,12:06:55 PM,30,4,12:21:02 PM,30,4,12:33:54 PM,9,,,-1.3033561000000002,36.8163631,-1.331619,36.847976,Rider_Id_531,772
-Order_No_19068,User_Id_3217,Bike,3,Business,19,6,12:13:42 PM,19,6,12:13:48 PM,19,6,12:16:35 PM,19,6,12:20:10 PM,19,6,1:01:33 PM,5,21.8,,-1.225322,36.80855,-1.214328,36.7911903,Rider_Id_103,2483
-Order_No_12179,User_Id_635,Bike,3,Business,29,3,11:44:14 AM,29,3,11:44:37 AM,29,3,11:48:20 AM,29,3,11:59:43 AM,29,3,12:53:02 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_158,3199
-Order_No_15668,User_Id_2987,Bike,3,Business,5,1,12:59:54 PM,5,1,1:02:55 PM,5,1,1:34:09 PM,5,1,1:37:06 PM,5,1,1:51:33 PM,3,26.1,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_410,867
-Order_No_16082,User_Id_2409,Bike,3,Personal,8,3,3:41:11 PM,8,3,3:42:56 PM,8,3,4:06:27 PM,8,3,4:12:01 PM,8,3,4:34:34 PM,9,25.6,,-1.2990731000000002,36.848805799999994,-1.2775276000000002,36.8140021,Rider_Id_857,1353
-Order_No_9683,User_Id_1687,Bike,3,Business,16,3,11:54:21 AM,16,3,11:55:10 AM,16,3,12:01:29 PM,16,3,12:06:31 PM,16,3,12:27:55 PM,7,22.7,,-1.2626574,36.8014034,-1.3014732,36.7781201,Rider_Id_576,1284
-Order_No_19670,User_Id_108,Bike,1,Personal,23,3,4:43:18 PM,23,3,4:49:44 PM,23,3,4:58:59 PM,23,3,5:09:21 PM,23,3,5:33:49 PM,11,20.5,,-1.2762223,36.8009675,-1.3210621999999999,36.802019,Rider_Id_633,1468
-Order_No_21144,User_Id_2956,Bike,3,Business,11,1,11:50:47 AM,11,1,11:51:10 AM,11,1,11:54:45 AM,11,1,12:02:57 PM,11,1,12:15:17 PM,4,26.6,,-1.2584143,36.8048002,-1.2866011999999998,36.8195691,Rider_Id_953,740
-Order_No_19942,User_Id_2521,Bike,3,Business,15,5,1:58:59 PM,15,5,2:44:55 PM,15,5,2:44:59 PM,15,5,3:43:28 PM,15,5,3:43:31 PM,8,28.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_39,3
-Order_No_13890,User_Id_1966,Bike,3,Business,19,2,10:15:41 AM,19,2,10:15:58 AM,19,2,10:26:17 AM,19,2,10:37:03 AM,19,2,11:09:36 AM,21,26.4,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_242,1953
-Order_No_2030,User_Id_1527,Bike,3,Business,5,5,9:09:48 AM,5,5,9:09:57 AM,5,5,9:18:47 AM,5,5,9:27:41 AM,5,5,9:41:40 AM,4,19.6,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_709,839
-Order_No_14817,User_Id_1316,Bike,3,Business,7,4,9:58:36 AM,7,4,9:59:31 AM,7,4,10:16:54 AM,7,4,10:21:32 AM,7,4,10:50:38 AM,11,,,-1.3173868999999998,36.8112044,-1.2551895,36.7822034,Rider_Id_863,1746
-Order_No_14895,User_Id_2982,Bike,3,Business,9,6,10:58:30 AM,9,6,10:58:51 AM,9,6,11:00:32 AM,9,6,11:34:02 AM,9,6,12:30:02 PM,18,24.8,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_258,3360
-Order_No_17190,User_Id_3283,Bike,3,Business,4,5,9:18:49 AM,4,5,9:19:12 AM,4,5,9:20:55 AM,4,5,9:34:41 AM,4,5,9:43:39 AM,5,17.2,0.5,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_427,538
-Order_No_6134,User_Id_3647,Bike,3,Business,27,1,11:44:47 AM,27,1,11:46:30 AM,27,1,12:03:37 PM,27,1,12:11:57 PM,27,1,12:38:06 PM,4,21.0,,-1.2726389999999999,36.794723,-1.2627620000000002,36.772194,Rider_Id_770,1569
-Order_No_6734,User_Id_60,Bike,3,Business,15,2,12:17:13 PM,15,2,12:17:25 PM,15,2,12:18:27 PM,15,2,12:56:29 PM,15,2,12:56:49 PM,10,24.4,,-1.2600926000000001,36.8088685,-1.2296618000000001,36.84382720000001,Rider_Id_517,20
-Order_No_21440,User_Id_2335,Bike,3,Business,25,2,3:59:17 PM,25,2,3:59:28 PM,25,2,4:08:18 PM,25,2,4:13:24 PM,25,2,4:58:50 PM,22,27.0,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_228,2726
-Order_No_17547,User_Id_1578,Bike,3,Business,8,4,4:20:15 PM,8,4,4:31:34 PM,8,4,4:42:24 PM,8,4,4:57:30 PM,8,4,5:12:40 PM,2,25.5,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_879,910
-Order_No_2731,User_Id_1271,Bike,3,Business,1,1,1:32:32 PM,1,1,1:51:35 PM,1,1,3:07:49 PM,1,1,3:14:09 PM,1,1,4:11:37 PM,16,,,-1.2927792,36.9053492,-1.2649605,36.798177700000004,Rider_Id_404,3448
-Order_No_13107,User_Id_3064,Bike,3,Business,2,5,11:02:13 AM,2,5,11:03:39 AM,2,5,11:50:46 AM,2,5,11:59:53 AM,2,5,12:28:59 PM,7,23.7,,-1.2571472,36.795063299999995,-1.298437,36.781971,Rider_Id_733,1746
-Order_No_6124,User_Id_2330,Bike,3,Business,8,1,5:21:13 PM,8,1,5:22:04 PM,8,1,5:37:50 PM,8,1,5:41:48 PM,8,1,6:20:17 PM,7,29.2,,-1.233018,36.8004477,-1.2645318,36.7981775,Rider_Id_613,2309
-Order_No_23274,User_Id_2150,Bike,3,Business,14,4,3:00:35 PM,14,4,3:02:54 PM,14,4,3:17:06 PM,14,4,3:23:41 PM,14,4,3:41:26 PM,12,27.4,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_856,1065
-Order_No_24803,User_Id_635,Bike,3,Business,24,4,12:34:50 PM,24,4,12:35:20 PM,24,4,12:37:23 PM,24,4,1:15:39 PM,24,4,1:17:41 PM,8,24.3,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_70,122
-Order_No_1886,User_Id_3326,Bike,3,Business,27,3,2:03:31 PM,27,3,2:03:44 PM,27,3,2:14:31 PM,27,3,2:31:57 PM,27,3,3:38:54 PM,22,29.5,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_177,4017
-Order_No_27778,User_Id_3205,Bike,1,Business,1,6,2:40:50 PM,1,6,2:41:23 PM,1,6,3:06:20 PM,1,6,3:12:02 PM,1,6,3:37:14 PM,11,19.6,,-1.3087323,36.857041100000004,-1.2747671,36.8139951,Rider_Id_622,1512
-Order_No_9283,User_Id_1726,Bike,1,Personal,9,2,9:05:03 AM,9,2,9:38:18 AM,9,2,10:01:08 AM,9,2,10:05:16 AM,9,2,10:25:15 AM,7,17.6,3.9,-1.3191114,36.844874,-1.2863371000000001,36.8200261,Rider_Id_662,1199
-Order_No_5219,User_Id_1482,Bike,3,Business,1,5,2:03:35 PM,1,5,2:04:45 PM,1,5,2:20:28 PM,1,5,3:07:36 PM,1,5,3:39:28 PM,16,28.7,,-1.2534301,36.800098299999995,-1.3385790000000002,36.89630379999999,Rider_Id_741,1912
-Order_No_27298,User_Id_1209,Bike,3,Business,2,4,8:52:03 AM,2,4,8:53:02 AM,2,4,9:01:45 AM,2,4,9:06:06 AM,2,4,9:26:11 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_96,1205
-Order_No_24751,User_Id_1471,Bike,3,Business,19,3,11:19:16 AM,19,3,11:30:00 AM,19,3,12:20:05 PM,19,3,12:24:34 PM,19,3,12:35:55 PM,6,22.2,,-1.2551895,36.7822034,-1.2415412000000001,36.764160700000005,Rider_Id_453,681
-Order_No_9814,User_Id_3650,Bike,3,Business,12,2,11:27:07 AM,12,2,11:34:38 AM,12,2,11:44:44 AM,12,2,11:48:21 AM,12,2,12:05:34 PM,7,28.1,,-1.2848376,36.802762200000004,-1.3088186000000002,36.8352514,Rider_Id_779,1033
-Order_No_20481,User_Id_2677,Bike,3,Business,14,2,11:12:57 AM,14,2,11:14:14 AM,14,2,11:19:38 AM,14,2,11:35:42 AM,14,2,12:07:13 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_298,1891
-Order_No_28209,User_Id_2066,Bike,3,Business,2,4,3:49:10 PM,2,4,3:49:28 PM,2,4,3:57:39 PM,2,4,4:01:04 PM,2,4,4:13:41 PM,5,26.0,,-1.316961,36.820337,-1.3043821000000002,36.7840586,Rider_Id_857,757
-Order_No_28092,User_Id_635,Bike,3,Business,5,3,11:04:49 AM,5,3,11:11:00 AM,5,3,11:17:03 AM,5,3,11:19:02 AM,5,3,12:31:40 PM,7,22.2,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_436,4358
-Order_No_12957,User_Id_393,Bike,3,Business,8,1,12:28:01 PM,8,1,12:28:36 PM,8,1,12:39:17 PM,8,1,12:45:43 PM,8,1,1:13:38 PM,14,28.1,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_728,1675
-Order_No_1897,User_Id_2069,Bike,3,Business,13,1,11:56:39 AM,13,1,11:59:32 AM,13,1,12:04:52 PM,13,1,12:21:40 PM,13,1,12:42:32 PM,12,,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_675,1252
-Order_No_16919,User_Id_3326,Bike,3,Business,8,3,3:47:15 PM,8,3,3:47:35 PM,8,3,3:50:03 PM,8,3,4:06:45 PM,8,3,4:27:20 PM,7,25.7,,-1.2571472,36.795063299999995,-1.304344,36.812274,Rider_Id_882,1235
-Order_No_1943,User_Id_3372,Bike,3,Business,18,3,10:46:56 AM,18,3,10:47:56 AM,18,3,10:54:18 AM,18,3,11:07:28 AM,18,3,11:56:14 AM,11,19.9,,-1.3169591,36.7000029,-1.2969741000000001,36.7856612,Rider_Id_844,2926
-Order_No_9091,User_Id_2330,Bike,3,Business,2,4,12:28:33 PM,2,4,12:29:01 PM,2,4,12:29:40 PM,2,4,12:40:32 PM,2,4,12:40:35 PM,3,,,-1.3015197,36.7658456,-1.294597,36.7742999,Rider_Id_99,3
-Order_No_3426,User_Id_868,Bike,3,Business,16,1,1:31:00 PM,16,1,1:49:38 PM,16,1,2:00:08 PM,16,1,2:22:11 PM,16,1,2:47:44 PM,8,22.3,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_234,1533
-Order_No_26421,User_Id_928,Bike,3,Business,19,2,3:15:48 PM,19,2,3:16:58 PM,19,2,3:32:08 PM,19,2,3:35:42 PM,19,2,3:47:23 PM,5,29.4,,-1.2551895,36.7822034,-1.2657199,36.7914391,Rider_Id_456,701
-Order_No_17854,User_Id_867,Bike,3,Business,27,3,10:49:23 AM,27,3,10:49:47 AM,27,3,10:49:59 AM,27,3,11:19:27 AM,27,3,11:37:01 AM,10,26.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_15,1054
-Order_No_6445,User_Id_1246,Bike,3,Business,8,3,11:46:26 AM,8,3,11:50:12 AM,8,3,11:50:46 AM,8,3,12:26:32 PM,8,3,12:26:42 PM,2,22.1,,-1.263987,36.798891,-1.2682478000000001,36.8072852,Rider_Id_51,10
-Order_No_22082,User_Id_2008,Bike,3,Business,29,1,1:02:18 PM,29,1,1:02:43 PM,29,1,1:07:33 PM,29,1,1:52:40 PM,29,1,3:03:15 PM,12,24.4,,-1.3084775,36.861188299999995,-1.2508229,36.7895263,Rider_Id_123,4235
-Order_No_20285,User_Id_867,Bike,3,Business,20,3,2:55:49 PM,20,3,2:57:11 PM,20,3,3:15:04 PM,20,3,3:17:56 PM,20,3,3:40:34 PM,10,29.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_728,1358
-Order_No_11939,User_Id_443,Bike,1,Personal,22,6,1:19:30 PM,22,6,1:20:24 PM,22,6,1:30:26 PM,22,6,1:33:37 PM,22,6,1:43:42 PM,5,,,-1.2857763999999998,36.8236622,-1.2747549999999999,36.7906585,Rider_Id_85,605
-Order_No_7721,User_Id_3602,Bike,3,Business,21,4,8:36:22 AM,21,4,8:41:15 AM,21,4,9:36:39 AM,21,4,9:40:18 AM,21,4,10:11:54 AM,10,19.0,,-1.3023756000000002,36.7949276,-1.3224102,36.83072070000001,Rider_Id_452,1896
-Order_No_10423,User_Id_2523,Bike,3,Business,20,2,12:51:20 PM,20,2,2:21:03 PM,20,2,2:23:27 PM,20,2,2:37:34 PM,20,2,2:52:31 PM,7,24.2,,-1.2551895,36.7822034,-1.2989287,36.81630820000001,Rider_Id_848,897
-Order_No_2643,User_Id_2066,Bike,3,Business,21,6,3:29:57 PM,21,6,3:30:38 PM,21,6,3:30:45 PM,21,6,3:39:39 PM,21,6,4:03:13 PM,8,18.4,,-1.316961,36.820337,-1.2661406000000002,36.80852120000001,Rider_Id_929,1414
-Order_No_9165,User_Id_3247,Bike,3,Business,17,3,10:10:16 AM,17,3,10:13:39 AM,17,3,10:20:50 AM,17,3,10:24:56 AM,17,3,10:50:27 AM,10,21.1,,-1.2571472,36.795063299999995,-1.31861,36.8113967,Rider_Id_207,1531
-Order_No_17774,User_Id_868,Bike,3,Business,2,2,11:14:15 AM,2,2,11:14:57 AM,2,2,11:30:06 AM,2,2,11:38:13 AM,2,2,11:53:49 AM,5,20.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_532,936
-Order_No_23559,User_Id_1342,Bike,3,Business,12,3,8:32:45 AM,12,3,8:32:59 AM,12,3,8:33:36 AM,12,3,9:20:24 AM,12,3,9:47:20 AM,12,16.6,,-1.295324,36.839628999999995,-1.3025826,36.7670807,Rider_Id_179,1616
-Order_No_22711,User_Id_1500,Bike,3,Business,26,2,11:01:19 AM,26,2,11:01:42 AM,26,2,11:04:51 AM,26,2,11:11:07 AM,26,2,11:31:24 AM,9,26.0,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_154,1217
-Order_No_7533,User_Id_679,Bike,1,Personal,24,2,3:58:14 PM,24,2,4:00:39 PM,24,2,4:01:10 PM,24,2,4:15:22 PM,24,2,4:40:44 PM,8,18.6,,-1.3083777,36.85900770000001,-1.278612,36.8195876,Rider_Id_546,1522
-Order_No_21653,User_Id_1500,Bike,3,Business,15,1,3:01:08 PM,15,1,3:01:46 PM,15,1,3:08:14 PM,15,1,3:11:02 PM,15,1,3:40:21 PM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_796,1759
-Order_No_23735,User_Id_635,Bike,3,Business,7,1,11:49:08 AM,7,1,11:50:29 AM,7,1,11:52:41 AM,7,1,12:08:13 PM,7,1,12:32:51 PM,11,24.9,,-1.2599563,36.799343900000004,-1.3229345,36.8566974,Rider_Id_541,1478
-Order_No_5870,User_Id_1882,Bike,3,Business,8,1,6:25:23 PM,8,1,6:26:37 PM,8,1,6:28:33 PM,8,1,6:37:27 PM,8,1,7:18:43 PM,7,29.2,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_300,2476
-Order_No_27733,User_Id_478,Bike,3,Business,24,5,8:49:17 AM,24,5,8:50:02 AM,24,5,9:07:45 AM,24,5,9:50:00 AM,24,5,10:29:05 AM,20,18.2,,-1.1671421,36.8314458,-1.2551895,36.7822034,Rider_Id_740,2345
-Order_No_27378,User_Id_1029,Bike,2,Personal,12,2,12:05:27 PM,12,2,12:06:53 PM,12,2,12:22:45 PM,12,2,1:26:21 PM,12,2,2:21:50 PM,8,21.1,,-1.2765492,36.7669809,-1.2507752,36.755662799999996,Rider_Id_140,3329
-Order_No_9520,User_Id_438,Bike,3,Personal,8,6,2:27:32 PM,8,6,2:27:54 PM,8,6,2:31:06 PM,8,6,2:43:37 PM,8,6,2:46:52 PM,3,,,-1.2603403,36.7998848,-1.2489719,36.785026200000004,Rider_Id_839,195
-Order_No_3303,User_Id_2063,Bike,3,Business,6,2,10:20:43 AM,6,2,10:20:48 AM,6,2,10:24:05 AM,6,2,10:31:18 AM,6,2,11:03:24 AM,18,19.8,,-1.2551895,36.7822034,-1.3289016000000002,36.8765964,Rider_Id_932,1926
-Order_No_24682,User_Id_3028,Bike,1,Personal,17,2,12:24:33 PM,17,2,12:24:54 PM,17,2,12:41:23 PM,17,2,12:44:41 PM,17,2,12:52:38 PM,3,19.2,,-1.290239,36.7620412,-1.3014461000000002,36.7661381,Rider_Id_844,477
-Order_No_7822,User_Id_3647,Bike,3,Business,8,5,9:21:16 AM,8,5,9:22:07 AM,8,5,9:33:56 AM,8,5,9:40:45 AM,8,5,9:58:18 AM,8,20.7,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_427,1053
-Order_No_17300,User_Id_2258,Bike,3,Business,11,5,1:00:08 PM,11,5,1:00:13 PM,11,5,1:19:19 PM,11,5,1:23:54 PM,11,5,2:06:26 PM,20,20.9,,-1.2646084,36.8028645,-1.3700383,36.9190174,Rider_Id_734,2552
-Order_No_18779,User_Id_393,Bike,3,Business,17,4,9:01:22 AM,17,4,9:01:57 AM,17,4,9:02:14 AM,17,4,9:16:32 AM,17,4,9:46:17 AM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_188,1785
-Order_No_17671,User_Id_577,Bike,1,Personal,2,4,9:50:18 AM,2,4,9:50:29 AM,2,4,9:56:31 AM,2,4,10:01:57 AM,2,4,10:27:16 AM,11,13.2,,-1.2662728,36.8151265,-1.3270347,36.866736200000005,Rider_Id_932,1519
-Order_No_16416,User_Id_2958,Bike,3,Personal,9,3,12:20:17 PM,9,3,12:21:30 PM,9,3,12:30:41 PM,9,3,12:42:16 PM,9,3,12:56:52 PM,3,25.2,,-1.2913246,36.8019522,-1.3123652,36.796426600000004,Rider_Id_559,876
-Order_No_22740,User_Id_618,Bike,1,Personal,15,1,9:26:56 AM,15,1,9:29:02 AM,15,1,9:41:18 AM,15,1,9:44:18 AM,15,1,9:57:38 AM,5,,,-1.2767423,36.82007670000001,-1.3008433000000001,36.8036256,Rider_Id_88,800
-Order_No_26643,User_Id_2956,Bike,3,Business,12,5,2:24:03 PM,12,5,2:49:46 PM,12,5,2:56:54 PM,12,5,3:07:44 PM,12,5,3:30:40 PM,7,27.0,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_726,1376
-Order_No_22615,User_Id_2641,Bike,3,Business,28,4,11:51:08 AM,28,4,11:51:23 AM,28,4,12:00:09 PM,28,4,12:21:33 PM,28,4,12:42:41 PM,6,23.8,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_279,1268
-Order_No_25611,User_Id_868,Bike,3,Business,28,4,1:58:58 PM,28,4,1:59:12 PM,28,4,2:27:21 PM,28,4,2:30:39 PM,28,4,3:04:59 PM,6,27.3,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_787,2060
-Order_No_19404,User_Id_2956,Bike,3,Business,6,6,9:15:33 AM,6,6,9:15:41 AM,6,6,9:16:24 AM,6,6,9:52:21 AM,6,6,10:06:50 AM,17,19.5,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_291,869
-Order_No_6637,User_Id_3674,Bike,3,Business,30,2,1:35:52 PM,30,2,1:38:47 PM,30,2,1:55:25 PM,30,2,2:02:34 PM,30,2,2:46:43 PM,15,,,-1.2551895,36.7822034,-1.3329777,36.866203000000006,Rider_Id_418,2649
-Order_No_11212,User_Id_2869,Bike,3,Business,3,1,11:37:50 AM,3,1,11:39:17 AM,3,1,11:53:59 AM,3,1,12:02:57 PM,3,1,12:18:07 PM,3,21.4,,-1.3014461000000002,36.7661381,-1.2763494,36.7671783,Rider_Id_496,910
-Order_No_15464,User_Id_2271,Bike,3,Business,17,1,11:21:12 AM,17,1,11:21:20 AM,17,1,11:21:28 AM,17,1,11:41:44 AM,17,1,11:52:19 AM,14,25.1,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_408,635
-Order_No_22842,User_Id_1865,Bike,1,Personal,10,1,4:00:54 PM,10,1,4:02:32 PM,10,1,4:47:27 PM,10,1,4:52:10 PM,10,1,5:22:56 PM,15,19.1,,-1.2166078000000002,36.882501,-1.2951243000000001,36.828134399999996,Rider_Id_406,1846
-Order_No_1879,User_Id_246,Bike,3,Business,26,5,4:12:45 PM,26,5,4:21:28 PM,26,5,4:35:36 PM,26,5,4:44:26 PM,26,5,5:43:01 PM,6,,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_580,3515
-Order_No_12019,User_Id_3370,Bike,3,Business,6,4,12:25:43 PM,6,4,12:25:43 PM,6,4,12:35:31 PM,6,4,12:50:49 PM,6,4,1:40:31 PM,7,19.8,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_20,2982
-Order_No_431,User_Id_3517,Bike,3,Business,14,4,1:11:41 PM,14,4,1:16:21 PM,14,4,1:39:56 PM,14,4,2:25:01 PM,14,4,2:48:37 PM,16,27.8,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_164,1416
-Order_No_11560,User_Id_1237,Bike,1,Personal,5,2,3:43:26 PM,5,2,3:49:18 PM,5,2,4:11:08 PM,5,2,4:23:28 PM,5,2,4:48:03 PM,6,22.7,,-1.2898178999999999,36.827084899999996,-1.2733223,36.795100500000004,Rider_Id_742,1475
-Order_No_23740,User_Id_1687,Bike,3,Business,10,4,10:54:49 AM,10,4,10:55:28 AM,10,4,11:00:43 AM,10,4,11:11:09 AM,10,4,11:32:39 AM,3,24.7,,-1.2626574,36.8014034,-1.2701978999999999,36.8138177,Rider_Id_447,1290
-Order_No_26307,User_Id_1402,Bike,3,Business,6,4,9:28:01 AM,6,4,9:30:19 AM,6,4,10:01:04 AM,6,4,10:02:35 AM,6,4,10:20:43 AM,5,16.0,3.9,-1.3140622,36.816246299999996,-1.2813012,36.832396200000005,Rider_Id_582,1088
-Order_No_1458,User_Id_265,Bike,3,Business,15,5,1:46:04 PM,15,5,2:00:18 PM,15,5,2:32:46 PM,15,5,2:38:16 PM,15,5,3:21:52 PM,7,28.0,,-1.2642978,36.819261,-1.300921,36.828195,Rider_Id_611,2616
-Order_No_7383,User_Id_2956,Bike,3,Business,14,2,9:53:20 AM,14,2,9:54:24 AM,14,2,10:06:58 AM,14,2,10:14:47 AM,14,2,10:42:28 AM,7,,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_383,1661
-Order_No_27742,User_Id_552,Bike,3,Business,29,4,11:52:02 AM,29,4,12:26:25 PM,29,4,12:41:59 PM,29,4,12:46:22 PM,29,4,1:03:56 PM,9,21.4,,-1.3226301999999999,36.8719901,-1.3632162,36.9108253,Rider_Id_43,1054
-Order_No_11365,User_Id_2829,Bike,3,Business,9,3,1:13:37 PM,9,3,1:14:28 PM,9,3,1:23:38 PM,9,3,1:25:00 PM,9,3,2:04:42 PM,15,25.3,,-1.2571472,36.795063299999995,-1.2190317,36.8825092,Rider_Id_512,2382
-Order_No_20804,User_Id_2523,Bike,3,Business,19,2,10:15:16 AM,19,2,10:18:01 AM,19,2,10:24:43 AM,19,2,10:29:56 AM,19,2,11:16:28 AM,10,20.9,,-1.2551895,36.7822034,-1.3073569,36.82021629999999,Rider_Id_147,2792
-Order_No_4728,User_Id_393,Bike,3,Business,8,1,1:08:24 PM,8,1,1:12:34 PM,8,1,1:24:53 PM,8,1,1:34:02 PM,8,1,1:57:57 PM,8,29.7,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_306,1435
-Order_No_25329,User_Id_1500,Bike,3,Business,20,3,1:53:46 PM,20,3,1:53:59 PM,20,3,1:54:02 PM,20,3,2:12:36 PM,20,3,2:12:39 PM,8,28.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_10,3
-Order_No_5949,User_Id_1878,Bike,1,Business,4,4,7:41:10 PM,4,4,7:44:12 PM,4,4,8:33:00 PM,4,4,8:38:07 PM,4,4,9:45:59 PM,18,,,-1.2551895,36.7822034,-1.3327068000000002,36.77972379999999,Rider_Id_578,4072
-Order_No_20914,User_Id_391,Bike,3,Business,3,1,10:53:58 AM,3,1,10:54:04 AM,3,1,10:58:45 AM,3,1,11:44:47 AM,3,1,12:10:38 PM,5,20.5,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_402,1551
-Order_No_8547,User_Id_1315,Bike,1,Personal,18,4,12:05:16 PM,18,4,12:12:54 PM,18,4,12:33:33 PM,18,4,12:36:36 PM,18,4,1:10:15 PM,18,25.6,,-1.3138208,36.91437,-1.2802618000000001,36.8191684,Rider_Id_213,2019
-Order_No_22666,User_Id_2066,Bike,3,Business,13,1,11:05:26 AM,13,1,11:07:29 AM,13,1,11:43:50 AM,13,1,11:49:14 AM,13,1,12:11:51 PM,8,,,-1.3210188999999999,36.841055100000005,-1.2853821,36.8333546,Rider_Id_56,1357
-Order_No_25641,User_Id_3565,Bike,3,Business,3,7,1:36:02 PM,3,7,1:36:08 PM,3,7,1:44:17 PM,3,7,1:48:25 PM,3,7,2:37:21 PM,29,30.2,,-1.2844665000000002,36.7866113,-1.1428716,36.962346999999994,Rider_Id_206,2936
-Order_No_1483,User_Id_187,Bike,3,Business,16,3,2:37:41 PM,16,3,2:37:50 PM,16,3,2:38:57 PM,16,3,2:50:21 PM,16,3,3:21:38 PM,17,26.8,,-1.3169591,36.7000029,-1.2551895,36.7822034,Rider_Id_404,1877
-Order_No_7578,User_Id_563,Bike,1,Personal,25,2,4:13:24 PM,25,2,4:16:43 PM,25,2,4:31:55 PM,25,2,4:43:24 PM,25,2,4:59:46 PM,9,24.2,,-1.2595707,36.7634701,-1.2747336999999999,36.8320368,Rider_Id_812,982
-Order_No_13488,User_Id_1500,Bike,3,Business,19,5,2:07:52 PM,19,5,3:06:35 PM,19,5,3:18:37 PM,19,5,3:47:08 PM,19,5,4:12:23 PM,8,27.2,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_751,1515
-Order_No_14916,User_Id_1614,Bike,1,Personal,23,4,3:33:56 PM,23,4,3:35:31 PM,23,4,3:48:01 PM,23,4,3:54:49 PM,23,4,4:13:34 PM,4,21.0,,-1.2791223999999999,36.8306878,-1.2670219999999999,36.807941,Rider_Id_213,1125
-Order_No_4256,User_Id_516,Bike,3,Business,14,1,10:36:39 AM,14,1,10:37:00 AM,14,1,10:46:01 AM,14,1,10:47:25 AM,14,1,11:41:38 AM,20,23.4,,-1.2551895,36.7822034,-1.3195936000000001,36.916169700000005,Rider_Id_620,3253
-Order_No_4010,User_Id_53,Bike,3,Business,13,4,8:49:39 AM,13,4,9:08:14 AM,13,4,9:21:38 AM,13,4,9:37:06 AM,13,4,9:57:58 AM,5,18.4,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_858,1252
-Order_No_5944,User_Id_1818,Bike,3,Business,13,1,9:16:40 AM,13,1,9:17:16 AM,13,1,9:26:12 AM,13,1,9:40:50 AM,13,1,10:00:59 AM,10,15.2,,-1.2551895,36.7822034,-1.304524,36.775371,Rider_Id_452,1209
-Order_No_23638,User_Id_1323,Bike,1,Business,8,5,5:48:24 PM,8,5,5:48:52 PM,8,5,5:54:54 PM,8,5,6:15:22 PM,8,5,6:58:02 PM,20,19.8,,-1.2579303999999998,36.8019667,-1.3700383,36.9190174,Rider_Id_116,2560
-Order_No_11362,User_Id_2200,Bike,3,Business,30,4,10:56:47 AM,30,4,10:57:17 AM,30,4,11:15:35 AM,30,4,11:20:26 AM,30,4,12:08:52 PM,16,,,-1.2156006000000001,36.8916865,-1.290894,36.822971,Rider_Id_788,2906
-Order_No_631,User_Id_2102,Bike,3,Business,5,1,2:45:37 PM,5,1,2:46:34 PM,5,1,3:04:49 PM,5,1,3:12:21 PM,5,1,3:45:48 PM,15,26.0,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_485,2007
-Order_No_14592,User_Id_2665,Bike,3,Business,13,2,3:22:18 PM,13,2,3:33:30 PM,13,2,3:51:41 PM,13,2,4:24:53 PM,13,2,4:24:57 PM,15,26.0,,-1.3241728000000001,36.7210093,-1.2603992,36.787859399999995,Rider_Id_288,4
-Order_No_19256,User_Id_2067,Bike,1,Personal,4,2,1:50:40 PM,4,2,1:51:16 PM,4,2,2:19:44 PM,4,2,2:29:43 PM,4,2,2:39:53 PM,6,20.3,,-1.2981102,36.8293513,-1.304033,36.7848695,Rider_Id_88,610
-Order_No_3921,User_Id_1329,Bike,3,Business,15,5,9:39:24 AM,15,5,9:39:55 AM,15,5,9:44:58 AM,15,5,9:48:00 AM,15,5,10:14:30 AM,12,19.0,,-1.273056,36.811298,-1.2672045,36.717206899999994,Rider_Id_953,1590
-Order_No_19982,User_Id_3222,Bike,3,Business,29,4,10:14:48 AM,29,4,10:15:45 AM,29,4,10:23:04 AM,29,4,10:24:47 AM,29,4,10:33:22 AM,4,18.7,4.0,-1.2551895,36.7822034,-1.2698539,36.8013674,Rider_Id_822,515
-Order_No_4633,User_Id_635,Bike,3,Business,4,2,11:45:52 AM,4,2,11:46:35 AM,4,2,11:56:29 AM,4,2,12:46:27 PM,4,2,1:13:42 PM,14,18.6,,-1.3016417,36.8271679,-1.2333129999999999,36.764508,Rider_Id_882,1635
-Order_No_8757,User_Id_1271,Bike,3,Business,14,1,3:16:23 PM,14,1,3:51:15 PM,14,1,4:10:04 PM,14,1,4:21:24 PM,14,1,4:54:20 PM,19,25.8,,-1.3244885,36.897791999999995,-1.2649605,36.798177700000004,Rider_Id_516,1976
-Order_No_17458,User_Id_3283,Bike,3,Business,20,5,10:46:43 AM,20,5,10:46:57 AM,20,5,10:49:53 AM,20,5,11:05:06 AM,20,5,11:17:52 AM,6,18.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_951,766
-Order_No_24789,User_Id_948,Bike,3,Business,16,4,12:40:11 PM,16,4,12:41:08 PM,16,4,1:21:05 PM,16,4,1:24:12 PM,16,4,1:32:28 PM,2,,,-1.2594473999999998,36.8089122,-1.2696908000000002,36.81519,Rider_Id_435,496
-Order_No_4895,User_Id_2439,Bike,3,Business,27,2,2:01:23 PM,27,2,2:01:39 PM,27,2,2:03:47 PM,27,2,2:43:52 PM,27,2,3:03:16 PM,4,25.4,,-1.2551895,36.7822034,-1.2689924,36.798157,Rider_Id_812,1164
-Order_No_7813,User_Id_3331,Bike,3,Business,15,2,9:50:58 AM,15,2,9:51:12 AM,15,2,9:51:29 AM,15,2,9:56:47 AM,15,2,10:12:03 AM,5,20.5,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_518,916
-Order_No_9783,User_Id_1410,Bike,3,Business,2,3,11:29:47 AM,2,3,11:50:08 AM,2,3,11:54:58 AM,2,3,1:33:12 PM,2,3,1:33:20 PM,21,23.6,,-1.2847613000000002,36.967084,-1.300921,36.828195,Rider_Id_606,8
-Order_No_11388,User_Id_2944,Bike,3,Personal,6,4,2:22:00 PM,6,4,2:25:37 PM,6,4,2:44:16 PM,6,4,2:57:50 PM,6,4,3:24:11 PM,10,21.1,,-1.2772989,36.8526035,-1.2642333000000001,36.779548299999995,Rider_Id_815,1581
-Order_No_2866,User_Id_3547,Bike,3,Business,14,5,9:57:31 AM,14,5,9:57:40 AM,14,5,10:37:33 AM,14,5,11:08:13 AM,14,5,11:57:58 AM,16,21.9,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_25,2985
-Order_No_21705,User_Id_3560,Bike,3,Business,7,3,12:44:55 PM,7,3,1:40:57 PM,7,3,1:53:45 PM,7,3,2:00:14 PM,7,3,2:32:01 PM,8,28.4,,-1.3014461000000002,36.7661381,-1.279395,36.825364,Rider_Id_260,1907
-Order_No_2060,User_Id_136,Bike,3,Business,21,1,2:29:05 PM,21,1,2:40:26 PM,21,1,3:08:41 PM,21,1,3:12:24 PM,21,1,4:12:55 PM,20,27.5,,-1.3254522,36.721658399999995,-1.2768704,36.8251777,Rider_Id_489,3631
-Order_No_2223,User_Id_1162,Bike,3,Business,18,6,1:35:14 PM,18,6,1:36:38 PM,18,6,1:57:19 PM,18,6,2:17:29 PM,18,6,2:47:25 PM,10,20.1,,-1.2560221999999999,36.7886679,-1.3122610000000001,36.794658,Rider_Id_161,1796
-Order_No_26707,User_Id_1244,Bike,3,Business,1,5,3:06:14 PM,1,5,3:13:59 PM,1,5,3:24:15 PM,1,5,3:36:07 PM,1,5,4:13:23 PM,11,32.1,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_332,2236
-Order_No_14130,User_Id_635,Bike,3,Business,3,5,11:23:30 AM,3,5,11:30:28 AM,3,5,12:12:06 PM,3,5,12:59:44 PM,3,5,1:28:41 PM,9,,,-1.300921,36.828195,-1.2708331,36.790419299999996,Rider_Id_292,1737
-Order_No_11347,User_Id_635,Bike,3,Business,12,5,12:52:54 PM,12,5,12:55:46 PM,12,5,1:10:53 PM,12,5,1:16:23 PM,12,5,1:23:00 PM,2,26.7,,-1.2679513999999998,36.8095213,-1.2599563,36.799343900000004,Rider_Id_394,397
-Order_No_15995,User_Id_2330,Bike,3,Business,14,4,5:00:54 PM,14,4,5:02:43 PM,14,4,5:13:54 PM,14,4,5:24:39 PM,14,4,5:37:48 PM,4,25.8,,-1.3040812,36.7924217,-1.3015197,36.7658456,Rider_Id_119,789
-Order_No_26946,User_Id_2423,Bike,3,Business,27,5,12:27:50 PM,27,5,12:27:55 PM,27,5,12:35:31 PM,27,5,12:41:06 PM,27,5,1:08:29 PM,6,22.3,,-1.2571472,36.795063299999995,-1.300722,36.816865,Rider_Id_933,1643
-Order_No_14563,User_Id_1792,Bike,3,Business,25,2,9:23:55 AM,25,2,9:28:20 AM,25,2,9:40:04 AM,25,2,9:44:47 AM,25,2,9:59:46 AM,6,18.1,,-1.2850851,36.82176,-1.2809121,36.7852524,Rider_Id_857,899
-Order_No_10176,User_Id_3517,Bike,3,Business,21,2,10:49:35 AM,21,2,10:49:46 AM,21,2,10:59:10 AM,21,2,11:20:22 AM,21,2,11:51:50 AM,16,22.6,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_211,1888
-Order_No_26469,User_Id_3521,Bike,3,Business,27,3,4:16:39 PM,27,3,4:17:25 PM,27,3,4:32:09 PM,27,3,4:35:05 PM,27,3,4:43:38 PM,2,27.5,,-1.2679971,36.808893700000006,-1.2696908000000002,36.81519,Rider_Id_879,513
-Order_No_12225,User_Id_1954,Bike,3,Business,31,4,9:38:34 AM,31,4,9:39:04 AM,31,4,10:14:50 AM,31,4,11:09:52 AM,31,4,11:20:20 AM,4,21.6,,-1.289498,36.818878000000005,-1.269534,36.80697,Rider_Id_216,628
-Order_No_15889,User_Id_1500,Bike,3,Business,8,5,3:45:55 PM,8,5,4:06:20 PM,8,5,4:32:01 PM,8,5,4:47:19 PM,8,5,5:20:17 PM,11,28.1,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_701,1978
-Order_No_1283,User_Id_867,Bike,3,Business,30,4,3:01:19 PM,30,4,3:02:06 PM,30,4,3:22:01 PM,30,4,3:23:25 PM,30,4,3:48:20 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_2,1495
-Order_No_6348,User_Id_970,Bike,3,Business,14,1,3:43:22 PM,14,1,3:43:30 PM,14,1,3:43:43 PM,14,1,3:57:04 PM,14,1,4:02:23 PM,3,25.6,,-1.2559559999999999,36.789867,-1.2629594,36.8077626,Rider_Id_900,319
-Order_No_11538,User_Id_517,Bike,3,Business,13,2,8:26:45 AM,13,2,8:28:17 AM,13,2,8:29:09 AM,13,2,8:35:47 AM,13,2,9:05:15 AM,14,19.8,9.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_10,1768
-Order_No_10390,User_Id_633,Bike,3,Business,11,5,10:04:20 AM,11,5,10:09:19 AM,11,5,10:22:42 AM,11,5,10:37:43 AM,11,5,10:59:16 AM,11,21.1,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_580,1293
-Order_No_15598,User_Id_2829,Bike,3,Business,1,3,9:49:02 AM,1,3,9:49:11 AM,1,3,9:59:47 AM,1,3,10:03:47 AM,1,3,10:52:35 AM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_520,2928
-Order_No_13639,User_Id_2828,Bike,3,Business,1,4,9:31:38 AM,1,4,9:32:02 AM,1,4,9:36:19 AM,1,4,9:59:00 AM,1,4,10:43:44 AM,13,,,-1.2615888000000002,36.7928732,-1.3332749,36.870814700000004,Rider_Id_574,2684
-Order_No_3656,User_Id_1422,Bike,1,Business,31,2,2:25:49 PM,31,2,2:27:06 PM,31,2,2:39:55 PM,31,2,2:47:00 PM,31,2,3:21:29 PM,5,22.1,,-1.3094746000000002,36.8326881,-1.2897793,36.8185788,Rider_Id_857,2069
-Order_No_17218,User_Id_3032,Bike,3,Business,12,5,4:38:50 PM,12,5,4:41:40 PM,12,5,4:49:16 PM,12,5,5:11:50 PM,12,5,5:37:03 PM,2,26.7,,-1.2571472,36.795063299999995,-1.2728058,36.8049903,Rider_Id_523,1513
-Order_No_8927,User_Id_3686,Bike,3,Business,15,5,3:23:52 PM,15,5,3:24:18 PM,15,5,3:28:20 PM,15,5,3:29:35 PM,15,5,4:00:20 PM,5,17.9,,-1.2551895,36.7822034,-1.2655953000000002,36.791546399999994,Rider_Id_690,1845
-Order_No_5573,User_Id_3222,Bike,3,Business,5,3,3:07:34 PM,5,3,3:11:29 PM,5,3,3:41:43 PM,5,3,3:43:43 PM,5,3,4:33:29 PM,5,25.0,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_547,2986
-Order_No_22811,User_Id_2216,Bike,3,Business,30,3,6:37:31 PM,30,3,6:37:37 PM,30,3,6:40:56 PM,30,3,6:50:42 PM,30,3,7:19:53 PM,18,,,-1.2553615,36.789537599999996,-1.3491868,36.71116729999999,Rider_Id_116,1751
-Order_No_329,User_Id_2586,Bike,1,Personal,3,5,2:26:53 PM,3,5,2:28:58 PM,3,5,2:42:41 PM,3,5,2:49:05 PM,3,5,3:49:09 PM,10,25.3,,-1.2809423,36.754003600000004,-1.3246122999999999,36.790226700000005,Rider_Id_726,3604
-Order_No_14511,User_Id_2168,Bike,3,Personal,23,7,1:34:35 PM,23,7,1:34:45 PM,23,7,2:02:14 PM,23,7,2:23:05 PM,23,7,3:09:37 PM,28,26.5,,-1.314437,36.712353,-1.3023387,36.9066847,Rider_Id_312,2792
-Order_No_4531,User_Id_868,Bike,3,Business,26,5,3:11:32 PM,26,5,3:12:18 PM,26,5,3:28:24 PM,26,5,3:35:41 PM,26,5,4:42:16 PM,14,,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_411,3995
-Order_No_25789,User_Id_1075,Bike,3,Business,8,1,4:43:46 PM,8,1,4:47:06 PM,8,1,5:10:56 PM,8,1,5:18:22 PM,8,1,6:04:30 PM,15,29.2,,-1.2859912,36.8756811,-1.2527958000000001,36.8003131,Rider_Id_76,2768
-Order_No_9460,User_Id_3599,Bike,3,Business,3,1,1:46:15 PM,3,1,1:46:15 PM,3,1,2:07:12 PM,3,1,2:53:47 PM,3,1,3:54:52 PM,18,23.2,,-1.3302996,36.8707935,-1.2707623,36.7581285,Rider_Id_772,3665
-Order_No_24844,User_Id_3283,Bike,3,Business,6,4,1:10:17 PM,6,4,1:10:46 PM,6,4,1:11:01 PM,6,4,1:26:16 PM,6,4,1:38:58 PM,6,19.5,,-1.2663631000000002,36.8163329,-1.2628473,36.781805,Rider_Id_855,762
-Order_No_28266,User_Id_738,Bike,3,Business,28,5,8:39:13 AM,28,5,8:42:37 AM,28,5,8:47:02 AM,28,5,8:53:21 AM,28,5,9:30:58 AM,6,20.8,,-1.2881479999999998,36.7850276,-1.2892190000000001,36.73749771,Rider_Id_612,2257
-Order_No_21216,User_Id_2869,Bike,3,Business,12,3,2:59:58 PM,12,3,3:02:26 PM,12,3,3:10:46 PM,12,3,3:13:21 PM,12,3,4:18:08 PM,8,20.8,,-1.3025826,36.7670807,-1.2886385,36.8240195,Rider_Id_942,3887
-Order_No_2984,User_Id_1075,Bike,3,Business,4,1,12:58:16 PM,4,1,12:59:26 PM,4,1,1:20:27 PM,4,1,1:24:29 PM,4,1,1:32:58 PM,2,25.5,,-1.2527958000000001,36.8003131,-1.2658334999999998,36.804286700000006,Rider_Id_379,509
-Order_No_21983,User_Id_1207,Bike,3,Business,5,1,11:10:07 AM,5,1,11:10:25 AM,5,1,11:18:40 AM,5,1,11:35:06 AM,5,1,11:49:23 AM,13,24.7,,-1.207546,36.7913976,-1.2351541000000001,36.8748505,Rider_Id_101,857
-Order_No_22942,User_Id_393,Bike,3,Business,11,2,9:59:23 AM,11,2,9:59:53 AM,11,2,10:01:58 AM,11,2,10:08:46 AM,11,2,10:45:02 AM,13,16.6,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_36,2176
-Order_No_10127,User_Id_3283,Bike,3,Business,7,2,10:07:16 AM,7,2,10:22:49 AM,7,2,10:32:12 AM,7,2,10:39:14 AM,7,2,10:56:03 AM,14,,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_8,1009
-Order_No_23599,User_Id_2701,Bike,3,Business,13,3,2:44:06 PM,13,3,3:42:46 PM,13,3,3:43:36 PM,13,3,4:14:50 PM,13,3,4:59:16 PM,16,23.5,,-1.3562374,36.904295399999995,-1.279395,36.825364,Rider_Id_223,2666
-Order_No_7441,User_Id_449,Bike,2,Personal,9,6,11:02:14 AM,9,6,11:02:34 AM,9,6,11:19:22 AM,9,6,11:22:28 AM,9,6,11:58:19 AM,18,17.4,,-1.2775066000000002,36.789653200000004,-1.3052036,36.6872046,Rider_Id_543,2151
-Order_No_543,User_Id_496,Bike,3,Business,5,5,4:34:47 PM,5,5,4:35:08 PM,5,5,4:36:39 PM,5,5,4:57:28 PM,5,5,5:20:24 PM,12,24.7,,-1.2646084,36.8028645,-1.2044082,36.77751370000001,Rider_Id_882,1376
-Order_No_6982,User_Id_1271,Bike,3,Business,28,3,12:53:40 PM,28,3,1:51:08 PM,28,3,2:12:04 PM,28,3,2:25:43 PM,28,3,2:43:31 PM,12,,,-1.3244885,36.897791999999995,-1.302527,36.854657200000005,Rider_Id_69,1068
-Order_No_27698,User_Id_265,Bike,3,Business,19,2,12:26:36 PM,19,2,12:27:41 PM,19,2,12:34:44 PM,19,2,12:48:57 PM,19,2,1:16:14 PM,10,27.5,,-1.300921,36.828195,-1.3000978,36.7658352,Rider_Id_712,1637
-Order_No_4389,User_Id_3689,Bike,3,Business,8,4,3:35:15 PM,8,4,3:36:13 PM,8,4,3:53:42 PM,8,4,4:06:18 PM,8,4,5:10:39 PM,2,27.3,,-1.2571472,36.795063299999995,-1.2527958000000001,36.8003131,Rider_Id_434,3861
-Order_No_13819,User_Id_1882,Bike,3,Business,4,4,10:49:26 AM,4,4,10:51:19 AM,4,4,10:59:06 AM,4,4,11:04:36 AM,4,4,11:35:24 AM,15,25.5,,-1.2160906999999999,36.885194899999995,-1.253784,36.79907,Rider_Id_686,1848
-Order_No_4965,User_Id_393,Bike,3,Business,15,5,10:47:59 AM,15,5,10:48:24 AM,15,5,10:54:46 AM,15,5,11:11:15 AM,15,5,11:20:30 AM,4,25.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_233,555
-Order_No_8641,User_Id_2733,Bike,3,Business,5,3,9:28:14 AM,5,3,9:30:58 AM,5,3,9:46:53 AM,5,3,10:17:29 AM,5,3,10:31:13 AM,2,18.0,,-1.3047482,36.784967200000004,-1.2963097,36.7688221,Rider_Id_404,824
-Order_No_19637,User_Id_3432,Bike,3,Business,12,6,11:26:21 AM,12,6,11:26:52 AM,12,6,11:45:42 AM,12,6,11:53:56 AM,12,6,12:37:51 PM,13,,,-1.260812,36.807545000000005,-1.308862,36.880646,Rider_Id_498,2635
-Order_No_2571,User_Id_1874,Bike,3,Business,5,1,11:15:52 AM,5,1,11:16:35 AM,5,1,11:23:32 AM,5,1,12:59:55 PM,5,1,1:00:18 PM,18,24.3,,-1.2571472,36.795063299999995,-1.3258631,36.9038477,Rider_Id_284,23
-Order_No_10898,User_Id_3625,Bike,1,Personal,16,4,2:50:29 PM,16,4,2:51:14 PM,16,4,3:11:07 PM,16,4,3:18:14 PM,16,4,3:48:49 PM,16,18.9,,-1.2725144,36.82721779999999,-1.3568040000000001,36.746196000000005,Rider_Id_414,1835
-Order_No_27859,User_Id_142,Bike,3,Business,30,3,1:17:29 PM,30,3,1:18:11 PM,30,3,1:28:47 PM,30,3,1:34:12 PM,30,3,1:57:14 PM,8,29.2,,-1.2570328000000002,36.7869744,-1.2963097,36.7688221,Rider_Id_328,1382
-Order_No_5581,User_Id_391,Bike,3,Business,1,4,11:58:45 AM,1,4,12:04:04 PM,1,4,12:08:10 PM,1,4,12:16:05 PM,1,4,12:35:31 PM,5,,,-1.2777923999999998,36.8231577,-1.3016417,36.8271679,Rider_Id_88,1166
-Order_No_14148,User_Id_53,Bike,3,Business,25,1,2:48:30 PM,25,1,2:49:06 PM,25,1,3:11:27 PM,25,1,3:54:25 PM,25,1,3:56:41 PM,8,28.0,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_262,136
-Order_No_46,User_Id_1932,Bike,3,Business,9,1,1:06:06 PM,9,1,1:06:45 PM,9,1,1:18:00 PM,9,1,1:19:59 PM,9,1,1:35:29 PM,7,20.5,,-1.2985986999999999,36.7809119,-1.2736498,36.8061911,Rider_Id_312,930
-Order_No_1848,User_Id_358,Bike,3,Business,24,4,2:45:18 PM,24,4,2:51:26 PM,24,4,3:28:10 PM,24,4,3:40:47 PM,24,4,4:12:24 PM,6,28.3,,-1.2795796,36.819062,-1.2580991000000001,36.794151899999996,Rider_Id_755,1897
-Order_No_27929,User_Id_904,Bike,3,Business,29,4,9:29:37 AM,29,4,9:30:24 AM,29,4,9:38:16 AM,29,4,9:42:56 AM,29,4,10:07:08 AM,7,18.7,4.0,-1.2925845,36.811226899999994,-1.2665558000000001,36.8056479,Rider_Id_338,1452
-Order_No_15261,User_Id_2112,Bike,3,Business,29,3,6:05:15 PM,29,3,6:50:33 PM,29,3,7:03:08 PM,29,3,7:07:27 PM,29,3,7:17:43 PM,4,19.3,,-1.2297202,36.881950700000004,-1.2156006000000001,36.8916865,Rider_Id_384,616
-Order_No_16460,User_Id_3137,Bike,2,Personal,4,2,9:41:37 AM,4,2,9:41:50 AM,4,2,9:48:05 AM,4,2,9:51:23 AM,4,2,10:00:27 AM,7,16.6,,-1.2923116000000001,36.7916244,-1.302088,36.831162,Rider_Id_249,544
-Order_No_11561,User_Id_3222,Bike,3,Business,26,1,10:53:52 AM,26,1,10:55:31 AM,26,1,11:03:51 AM,26,1,11:06:00 AM,26,1,11:37:19 AM,20,23.0,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_190,1879
-Order_No_1317,User_Id_1527,Bike,3,Business,2,4,1:15:09 PM,2,4,1:20:44 PM,2,4,1:39:44 PM,2,4,1:46:56 PM,2,4,2:27:40 PM,3,,,-1.3004062,36.829741,-1.2977627,36.815038799999996,Rider_Id_211,2444
-Order_No_4517,User_Id_3689,Bike,3,Business,20,3,1:52:11 PM,20,3,1:54:03 PM,20,3,2:19:07 PM,20,3,2:22:53 PM,20,3,2:42:52 PM,10,23.3,,-1.3014461000000002,36.7661381,-1.2601708999999999,36.8002238,Rider_Id_4,1199
-Order_No_2163,User_Id_136,Bike,3,Business,13,5,11:07:04 AM,13,5,11:07:14 AM,13,5,11:46:54 AM,13,5,12:47:17 PM,13,5,1:27:46 PM,19,,,-1.2407216,36.788855299999994,-1.3326173000000001,36.71462329999999,Rider_Id_101,2429
-Order_No_10062,User_Id_346,Bike,3,Business,18,1,9:18:09 AM,18,1,9:18:32 AM,18,1,9:20:58 AM,18,1,9:33:47 AM,18,1,9:37:08 AM,1,21.2,,-1.28878,36.8168312,-1.2857548,36.819062,Rider_Id_378,201
-Order_No_26265,User_Id_928,Bike,3,Business,23,1,1:15:38 PM,23,1,1:15:55 PM,23,1,1:23:43 PM,23,1,1:35:02 PM,23,1,2:14:04 PM,12,,,-1.2551895,36.7822034,-1.312734,36.855414,Rider_Id_519,2342
-Order_No_24851,User_Id_1500,Bike,3,Business,7,1,1:00:24 PM,7,1,1:29:40 PM,7,1,1:46:05 PM,7,1,1:56:25 PM,7,1,2:31:18 PM,12,20.5,,-1.2569006,36.7925141,-1.3177546999999998,36.8303703,Rider_Id_223,2093
-Order_No_15331,User_Id_2587,Bike,3,Business,16,2,3:30:20 PM,16,2,3:31:14 PM,16,2,4:31:53 PM,16,2,4:35:38 PM,16,2,4:51:50 PM,4,,,-1.28878,36.8168312,-1.2922992,36.798303499999996,Rider_Id_123,972
-Order_No_24463,User_Id_2782,Bike,1,Personal,28,4,8:42:38 AM,28,4,8:43:49 AM,28,4,9:02:10 AM,28,4,9:05:11 AM,28,4,9:34:05 AM,11,,,-1.2967701999999999,36.8040381,-1.3404187,36.7614685,Rider_Id_465,1734
-Order_No_17650,User_Id_3647,Bike,3,Business,6,4,9:13:03 AM,6,4,9:15:12 AM,6,4,9:25:17 AM,6,4,9:29:30 AM,6,4,9:43:22 AM,7,18.6,2.9,-1.2726389999999999,36.794723,-1.2533103,36.8057423,Rider_Id_720,832
-Order_No_7199,User_Id_1205,Bike,3,Business,20,3,12:30:35 PM,20,3,12:31:14 PM,20,3,12:38:16 PM,20,3,12:45:18 PM,20,3,1:03:17 PM,7,27.3,,-1.2973188,36.7895013,-1.2495931,36.80650870000001,Rider_Id_829,1079
-Order_No_10715,User_Id_3679,Bike,2,Personal,8,6,2:38:53 PM,8,6,2:39:00 PM,8,6,3:01:37 PM,8,6,3:14:11 PM,8,6,3:43:48 PM,17,24.8,,-1.2870003,36.8312206,-1.3662811000000001,36.75421420000001,Rider_Id_414,1777
-Order_No_2652,User_Id_3718,Bike,3,Business,16,5,2:35:53 PM,16,5,2:36:58 PM,16,5,2:37:29 PM,16,5,2:53:12 PM,16,5,2:57:09 PM,1,,,-1.2650027,36.8126242,-1.2768326,36.8073804,Rider_Id_848,237
-Order_No_7511,User_Id_633,Bike,3,Business,2,6,11:09:48 AM,2,6,11:09:54 AM,2,6,11:09:58 AM,2,6,11:32:35 AM,2,6,11:32:38 AM,11,27.5,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_239,3
-Order_No_16219,User_Id_2521,Bike,3,Business,15,5,1:50:18 PM,15,5,1:50:26 PM,15,5,1:50:33 PM,15,5,2:04:30 PM,15,5,2:04:52 PM,5,27.9,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_10,22
-Order_No_19342,User_Id_1764,Bike,1,Personal,1,1,9:45:49 AM,1,1,9:47:13 AM,1,1,10:12:39 AM,1,1,10:18:56 AM,1,1,10:40:08 AM,8,18.4,,-1.3253972,36.801463899999995,-1.2962832,36.784968299999996,Rider_Id_38,1272
-Order_No_24554,User_Id_1500,Bike,3,Business,24,4,10:29:51 AM,24,4,10:32:49 AM,24,4,10:33:24 AM,24,4,10:52:09 AM,24,4,10:52:17 AM,7,24.7,,-1.2574219,36.7927073,-1.2916738,36.790105700000005,Rider_Id_10,8
-Order_No_24850,User_Id_1677,Bike,3,Personal,20,1,9:36:24 AM,20,1,9:37:48 AM,20,1,10:18:08 AM,20,1,10:27:17 AM,20,1,10:58:08 AM,14,19.2,,-1.23966,36.75258,-1.2963097,36.7688221,Rider_Id_876,1851
-Order_No_13497,User_Id_1308,Bike,1,Business,10,5,2:46:33 PM,10,5,2:46:40 PM,10,5,2:59:40 PM,10,5,3:04:29 PM,10,5,3:30:21 PM,8,21.6,,-1.2631940000000002,36.797624,-1.2944381,36.773762,Rider_Id_400,1552
-Order_No_12455,User_Id_1229,Bike,2,Personal,2,3,9:12:50 PM,2,3,9:26:28 PM,2,3,9:31:16 PM,2,3,9:39:31 PM,2,3,9:45:43 PM,3,16.9,15.9,-1.3016513,36.7649321,-1.2947685,36.778583399999995,Rider_Id_312,372
-Order_No_7892,User_Id_3372,Bike,3,Business,6,5,9:13:04 AM,6,5,9:13:33 AM,6,5,9:30:46 AM,6,5,9:35:29 AM,6,5,9:41:32 AM,2,15.0,,-1.2901638000000002,36.7787225,-1.304033,36.7848695,Rider_Id_116,363
-Order_No_24980,User_Id_1214,Bike,3,Personal,20,3,1:19:05 PM,20,3,1:20:10 PM,20,3,1:35:34 PM,20,3,1:39:15 PM,20,3,2:38:40 PM,16,23.2,,-1.2866269,36.82463679999999,-1.3276514,36.7121761,Rider_Id_95,3565
-Order_No_6412,User_Id_3312,Bike,3,Business,8,2,11:20:59 AM,8,2,11:22:14 AM,8,2,11:30:22 AM,8,2,11:35:45 AM,8,2,12:05:54 PM,11,23.3,,-1.2583017,36.8023526,-1.3108386,36.7555239,Rider_Id_878,1809
-Order_No_2646,User_Id_1051,Bike,1,Business,7,2,1:57:43 PM,7,2,1:58:34 PM,7,2,1:58:55 PM,7,2,2:02:54 PM,7,2,2:55:08 PM,15,24.9,,-1.2963097,36.7688221,-1.2500308999999998,36.6690476,Rider_Id_939,3134
-Order_No_13542,User_Id_3727,Bike,3,Business,3,2,12:11:07 PM,3,2,12:12:39 PM,3,2,12:17:12 PM,3,2,12:21:39 PM,3,2,12:45:14 PM,8,18.4,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_74,1415
-Order_No_13306,User_Id_2413,Bike,3,Business,5,6,10:22:17 AM,5,6,10:23:20 AM,5,6,10:36:02 AM,5,6,10:41:36 AM,5,6,11:02:36 AM,9,22.5,,-1.2882803,36.7815881,-1.3033748,36.8430714,Rider_Id_940,1260
-Order_No_23157,User_Id_2458,Bike,3,Business,15,3,3:13:14 PM,15,3,3:13:51 PM,15,3,3:30:53 PM,15,3,3:34:17 PM,15,3,4:15:46 PM,5,21.6,,-1.2791312,36.81611289999999,-1.2857671,36.7875614,Rider_Id_229,2489
-Order_No_5422,User_Id_3514,Bike,2,Personal,14,7,1:07:02 PM,14,7,1:07:16 PM,14,7,1:18:41 PM,14,7,1:29:40 PM,14,7,2:18:32 PM,16,25.1,,-1.3197995,36.847672100000004,-1.2664227,36.7453671,Rider_Id_532,2932
-Order_No_12608,User_Id_3404,Bike,3,Business,23,3,4:31:35 PM,23,3,4:32:39 PM,23,3,4:39:09 PM,23,3,4:45:50 PM,23,3,5:01:47 PM,5,24.3,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_490,957
-Order_No_25559,User_Id_739,Bike,1,Personal,17,3,2:49:56 PM,17,3,2:50:22 PM,17,3,3:05:53 PM,17,3,3:14:27 PM,17,3,4:16:56 PM,25,28.3,,-1.207546,36.7913976,-1.3291184,36.7892374,Rider_Id_299,3749
-Order_No_16756,User_Id_3669,Bike,3,Business,7,2,2:15:51 PM,7,2,2:16:21 PM,7,2,2:37:00 PM,7,2,2:48:09 PM,7,2,3:44:37 PM,24,,,-1.2615888000000002,36.7928732,-1.34364,36.8925345,Rider_Id_427,3388
-Order_No_5332,User_Id_1500,Bike,3,Business,19,1,8:49:00 AM,19,1,9:02:48 AM,19,1,9:54:24 AM,19,1,9:55:26 AM,19,1,10:22:26 AM,8,19.2,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_532,1620
-Order_No_12923,User_Id_909,Bike,3,Business,18,1,4:07:28 PM,18,1,4:10:59 PM,18,1,4:27:20 PM,18,1,4:30:00 PM,18,1,4:58:19 PM,10,30.6,,-1.2765736,36.851364600000004,-1.225322,36.80855,Rider_Id_148,1699
-Order_No_10502,User_Id_590,Bike,3,Business,25,4,1:19:13 PM,25,4,1:26:05 PM,25,4,1:57:44 PM,25,4,2:09:10 PM,25,4,2:30:34 PM,4,21.0,,-1.2743555,36.799393,-1.257773,36.795486,Rider_Id_112,1284
-Order_No_27349,User_Id_1502,Bike,3,Business,31,4,2:42:29 PM,31,4,2:57:39 PM,31,4,3:11:29 PM,31,4,3:14:23 PM,31,4,3:23:52 PM,5,22.9,,-1.2156006000000001,36.8916865,-1.2294985,36.8775495,Rider_Id_742,569
-Order_No_4943,User_Id_186,Bike,3,Business,18,5,4:18:30 PM,18,5,4:20:48 PM,18,5,4:40:17 PM,18,5,4:44:15 PM,18,5,4:54:25 PM,4,25.7,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_263,610
-Order_No_7504,User_Id_527,Bike,2,Personal,30,4,1:28:33 PM,30,4,1:27:51 PM,30,4,1:41:55 PM,30,4,1:49:44 PM,30,4,2:14:00 PM,12,20.6,,-1.2861764999999998,36.8209086,-1.3319425,36.894470899999995,Rider_Id_45,1456
-Order_No_10384,User_Id_3517,Bike,3,Business,8,3,10:43:12 AM,8,3,10:44:23 AM,8,3,10:50:12 AM,8,3,11:27:02 AM,8,3,11:53:18 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_583,1576
-Order_No_920,User_Id_214,Bike,1,Personal,6,3,8:31:37 AM,6,3,8:31:54 AM,6,3,8:56:42 AM,6,3,9:01:22 AM,6,3,9:17:55 AM,7,15.8,13.9,-1.2616213,36.8221128,-1.2838866000000002,36.7802282,Rider_Id_103,993
-Order_No_27143,User_Id_33,Bike,3,Personal,7,4,1:28:00 PM,7,4,1:29:13 PM,7,4,1:30:56 PM,7,4,1:42:11 PM,7,4,1:52:09 PM,2,27.8,,-1.2814609,36.826593200000005,-1.2770826000000002,36.817408799999995,Rider_Id_845,598
-Order_No_26325,User_Id_485,Bike,3,Personal,7,1,12:38:45 PM,7,1,12:56:31 PM,7,1,1:30:20 PM,7,1,1:35:37 PM,7,1,2:23:21 PM,14,26.8,,-1.3463428999999998,36.9069888,-1.289498,36.818878000000005,Rider_Id_420,2864
-Order_No_12011,User_Id_391,Bike,3,Business,14,4,9:33:53 AM,14,4,9:35:03 AM,14,4,9:47:37 AM,14,4,9:58:40 AM,14,4,10:19:25 AM,16,19.0,,-1.2784063,36.8222933,-1.2026875,36.912910700000005,Rider_Id_61,1245
-Order_No_11925,User_Id_391,Bike,3,Business,23,2,1:36:56 PM,23,2,1:43:31 PM,23,2,1:47:57 PM,23,2,2:10:16 PM,23,2,2:23:08 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_456,772
-Order_No_16026,User_Id_3080,Bike,3,Business,13,3,12:48:55 PM,13,3,12:50:27 PM,13,3,12:59:04 PM,13,3,1:06:09 PM,13,3,1:33:31 PM,17,24.6,,-1.2551895,36.7822034,-1.3421303999999998,36.718364,Rider_Id_845,1642
-Order_No_8827,User_Id_1329,Bike,3,Business,20,2,2:32:49 PM,20,2,2:37:41 PM,20,2,2:56:10 PM,20,2,2:58:32 PM,20,2,3:15:38 PM,5,23.8,,-1.273056,36.811298,-1.2950408000000002,36.809817200000005,Rider_Id_247,1026
-Order_No_19094,User_Id_393,Bike,3,Business,24,5,10:44:40 AM,24,5,10:45:34 AM,24,5,11:12:44 AM,24,5,11:28:51 AM,24,5,12:14:46 PM,11,21.3,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_564,2755
-Order_No_8587,User_Id_84,Bike,3,Business,14,4,10:36:55 AM,14,4,11:43:28 AM,14,4,12:15:22 PM,14,4,12:17:45 PM,14,4,1:03:39 PM,22,19.1,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_895,2754
-Order_No_23928,User_Id_2521,Bike,3,Business,30,6,11:34:29 AM,30,6,11:34:39 AM,30,6,11:34:44 AM,30,6,12:22:39 PM,30,6,12:22:42 PM,14,26.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_10,3
-Order_No_8190,User_Id_1691,Bike,2,Personal,11,6,11:02:55 AM,11,6,11:03:13 AM,11,6,11:19:43 AM,11,6,11:23:19 AM,11,6,11:59:30 AM,22,21.2,,-1.2978795,36.7508321,-1.2124618999999999,36.8739332,Rider_Id_45,2171
-Order_No_5145,User_Id_3569,Bike,3,Personal,12,3,9:20:17 AM,12,3,9:25:47 AM,12,3,9:57:51 AM,12,3,9:59:27 AM,12,3,10:34:27 AM,17,,,-1.2915317,36.8322227,-1.3858142,36.7641498,Rider_Id_487,2100
-Order_No_20566,User_Id_2230,Bike,1,Personal,30,4,10:22:50 AM,30,4,10:27:07 AM,30,4,10:29:07 AM,30,4,10:59:27 AM,30,4,11:14:15 AM,10,20.5,,-1.2974472,36.8156997,-1.2450649,36.7869245,Rider_Id_179,888
-Order_No_24001,User_Id_570,Bike,3,Business,4,1,12:46:18 PM,4,1,12:46:39 PM,4,1,12:52:39 PM,4,1,12:56:02 PM,4,1,1:13:43 PM,3,25.7,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_227,1061
-Order_No_22570,User_Id_3011,Bike,3,Personal,31,2,3:52:32 PM,31,2,3:54:08 PM,31,2,4:16:48 PM,31,2,4:31:07 PM,31,2,4:46:53 PM,4,22.1,,-1.2966453000000002,36.8191342,-1.2931437,36.7874139,Rider_Id_452,946
-Order_No_17970,User_Id_2207,Bike,3,Business,29,4,3:11:54 PM,29,4,3:16:49 PM,29,4,3:23:39 PM,29,4,3:30:43 PM,29,4,3:47:32 PM,3,24.7,,-1.265981,36.793612100000004,-1.2768326,36.8073804,Rider_Id_829,1009
-Order_No_24576,User_Id_517,Bike,3,Business,4,2,10:05:11 AM,4,2,10:08:03 AM,4,2,10:16:04 AM,4,2,10:18:03 AM,4,2,10:56:11 AM,14,15.8,83.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2288
-Order_No_27532,User_Id_517,Bike,3,Business,5,3,9:32:15 AM,5,3,9:38:52 AM,5,3,9:55:30 AM,5,3,10:49:05 AM,5,3,11:20:00 AM,14,22.5,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_188,1855
-Order_No_7553,User_Id_704,Bike,3,Business,14,4,11:43:23 AM,14,4,11:43:31 AM,14,4,11:44:13 AM,14,4,11:45:45 AM,14,4,11:55:54 AM,18,18.8,,-1.2860183,36.8975338,-1.2551895,36.7822034,Rider_Id_408,609
-Order_No_13359,User_Id_2216,Bike,3,Business,25,1,12:46:44 PM,25,1,12:47:49 PM,25,1,1:25:17 PM,25,1,1:28:09 PM,25,1,2:03:15 PM,17,25.6,,-1.2551895,36.7822034,-1.3398006000000002,36.717935,Rider_Id_399,2106
-Order_No_24644,User_Id_871,Bike,2,Personal,7,2,3:43:08 PM,7,2,3:43:31 PM,7,2,3:54:13 PM,7,2,3:57:47 PM,7,2,4:17:20 PM,9,25.0,,-1.2865507999999999,36.8238701,-1.2315181,36.814054299999995,Rider_Id_414,1173
-Order_No_316,User_Id_393,Bike,3,Business,31,3,8:41:42 AM,31,3,8:47:58 AM,31,3,8:48:26 AM,31,3,9:03:16 AM,31,3,9:46:33 AM,14,19.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_97,2597
-Order_No_19101,User_Id_1402,Bike,3,Business,3,3,11:23:23 AM,3,3,11:29:45 AM,3,3,11:37:33 AM,3,3,11:42:55 AM,3,3,12:06:27 PM,5,27.4,,-1.2584143,36.8048002,-1.2813012,36.832396200000005,Rider_Id_709,1412
-Order_No_6760,User_Id_1712,Bike,1,Personal,6,4,8:48:02 AM,6,4,8:48:32 AM,6,4,9:08:31 AM,6,4,9:14:17 AM,6,4,9:34:42 AM,13,17.9,,-1.3206958,36.8769553,-1.305568,36.820064,Rider_Id_820,1225
-Order_No_19346,User_Id_3639,Bike,1,Personal,24,5,9:02:48 AM,24,5,9:04:22 AM,24,5,9:13:32 AM,24,5,9:19:48 AM,24,5,9:37:56 AM,11,15.8,,-1.2765737,36.8175399,-1.3214253,36.845932700000006,Rider_Id_754,1088
-Order_No_14647,User_Id_1500,Bike,3,Business,5,2,2:57:51 PM,5,2,2:59:01 PM,5,2,3:26:16 PM,5,2,3:53:37 PM,5,2,4:28:59 PM,8,27.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_311,2122
-Order_No_4979,User_Id_391,Bike,3,Business,31,5,2:22:46 PM,31,5,2:30:07 PM,31,5,3:19:00 PM,31,5,4:14:44 PM,31,5,4:37:22 PM,5,24.5,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_673,1358
-Order_No_266,User_Id_1502,Bike,3,Business,31,1,10:34:15 AM,31,1,10:34:30 AM,31,1,10:53:07 AM,31,1,11:11:23 AM,31,1,11:11:40 AM,5,21.2,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_812,17
-Order_No_20916,User_Id_3647,Bike,3,Business,5,2,2:26:41 PM,5,2,2:27:09 PM,5,2,2:55:29 PM,5,2,3:01:02 PM,5,2,4:22:28 PM,16,27.3,,-1.2726389999999999,36.794723,-1.3500286,36.7314905,Rider_Id_279,4886
-Order_No_2156,User_Id_3623,Bike,1,Personal,23,1,11:44:21 AM,23,1,11:44:55 AM,23,1,11:53:07 AM,23,1,11:54:55 AM,23,1,12:14:20 PM,4,18.7,,-1.265007,36.8149619,-1.2840013000000001,36.823108000000005,Rider_Id_329,1165
-Order_No_23713,User_Id_1482,Bike,3,Business,13,4,12:52:10 PM,13,4,12:52:40 PM,13,4,12:53:29 PM,13,4,1:02:49 PM,13,4,1:42:15 PM,22,22.2,,-1.3385790000000002,36.89630379999999,-1.3686413,36.7463562,Rider_Id_31,2366
-Order_No_5898,User_Id_2946,Bike,2,Personal,4,2,12:07:58 PM,4,2,12:09:46 PM,4,2,12:25:18 PM,4,2,12:29:57 PM,4,2,12:50:48 PM,7,18.6,,-1.2749662,36.7658781,-1.289521,36.819072399999996,Rider_Id_88,1251
-Order_No_9700,User_Id_3352,Bike,3,Personal,23,1,8:15:35 AM,23,1,8:16:14 AM,23,1,8:47:47 AM,23,1,8:51:58 AM,23,1,9:29:47 AM,10,14.4,,-1.2547780000000002,36.790006,-1.3127303999999997,36.8189817,Rider_Id_726,2269
-Order_No_2829,User_Id_1364,Bike,1,Personal,20,1,9:42:22 AM,20,1,9:42:33 AM,20,1,9:47:10 AM,20,1,9:57:35 AM,20,1,10:22:59 AM,13,14.2,,-1.2745107,36.797450299999994,-1.3282758,36.8590281,Rider_Id_805,1524
-Order_No_23779,User_Id_475,Bike,3,Business,10,4,2:01:49 PM,10,4,2:02:10 PM,10,4,2:04:05 PM,10,4,2:10:43 PM,10,4,2:31:25 PM,12,25.1,,-1.2551895,36.7822034,-1.2416156,36.8736721,Rider_Id_958,1242
-Order_No_6367,User_Id_2423,Bike,3,Business,14,4,1:30:10 PM,14,4,2:25:02 PM,14,4,2:30:17 PM,14,4,2:37:55 PM,14,4,3:11:40 PM,6,28.2,,-1.2551895,36.7822034,-1.285382,36.8162878,Rider_Id_863,2025
-Order_No_15467,User_Id_1271,Bike,3,Business,12,2,5:16:17 PM,12,2,5:17:32 PM,12,2,5:25:33 PM,12,2,5:30:42 PM,12,2,6:19:13 PM,23,28.4,,-1.3107918,36.761794,-1.2927792,36.9053492,Rider_Id_343,2911
-Order_No_2097,User_Id_2804,Bike,3,Business,18,4,8:28:04 AM,18,4,8:28:24 AM,18,4,9:06:34 AM,18,4,9:21:35 AM,18,4,9:46:59 AM,12,,,-1.3270716,36.8406371,-1.2571472,36.795063299999995,Rider_Id_68,1524
-Order_No_25580,User_Id_2872,Bike,3,Business,16,2,2:55:40 PM,16,2,2:56:28 PM,16,2,3:08:50 PM,16,2,3:14:31 PM,16,2,3:28:14 PM,8,,,-1.2601197,36.7985271,-1.2963097,36.7688221,Rider_Id_911,823
-Order_No_18871,User_Id_2044,Bike,1,Personal,13,7,2:48:36 PM,13,7,2:50:08 PM,13,7,3:10:25 PM,13,7,3:18:53 PM,13,7,3:32:22 PM,6,25.9,,-1.2559556,36.772909399999996,-1.2517392,36.82496079999999,Rider_Id_103,809
-Order_No_12662,User_Id_136,Bike,3,Business,23,5,11:46:21 AM,23,5,11:46:38 AM,23,5,12:07:02 PM,23,5,12:16:04 PM,23,5,12:32:26 PM,4,24.1,,-1.2965045,36.7913036,-1.3009676000000001,36.7657189,Rider_Id_322,982
-Order_No_20116,User_Id_145,Bike,1,Personal,6,3,1:08:37 PM,6,3,1:37:43 PM,6,3,1:53:44 PM,6,3,2:02:17 PM,6,3,2:30:11 PM,9,21.8,,-1.2871938,36.8195031,-1.3271931000000001,36.84524570000001,Rider_Id_360,1674
-Order_No_172,User_Id_2866,Bike,3,Business,5,3,4:17:22 PM,5,3,4:48:09 PM,5,3,4:55:33 PM,5,3,4:58:58 PM,5,3,6:02:20 PM,11,22.6,,-1.2551895,36.7822034,-1.3197323,36.8120386,Rider_Id_267,3802
-Order_No_8307,User_Id_781,Bike,3,Business,28,5,10:48:46 AM,28,5,10:49:08 AM,28,5,10:54:52 AM,28,5,11:04:35 AM,28,5,11:29:10 AM,8,24.9,,-1.2696908000000002,36.81519,-1.322064,36.817157,Rider_Id_726,1475
-Order_No_19306,User_Id_1156,Bike,2,Personal,8,1,10:38:41 AM,8,1,10:41:21 AM,8,1,10:51:47 AM,8,1,11:09:15 AM,8,1,11:30:44 AM,10,24.5,,-1.324282,36.86625,-1.2861874,36.8128448,Rider_Id_871,1289
-Order_No_6997,User_Id_635,Bike,3,Business,13,1,10:35:11 AM,13,1,10:35:30 AM,13,1,10:38:24 AM,13,1,10:43:21 AM,13,1,11:09:09 AM,11,,,-1.2602337,36.79905479999999,-1.2105068,36.7859421,Rider_Id_953,1548
-Order_No_1526,User_Id_3727,Bike,3,Business,4,5,8:50:38 AM,4,5,8:53:24 AM,4,5,9:22:59 AM,4,5,9:28:29 AM,4,5,9:50:46 AM,7,19.4,52.1,-1.2866011999999998,36.8195691,-1.2963097,36.7688221,Rider_Id_449,1337
-Order_No_8742,User_Id_3404,Bike,3,Business,12,2,11:20:15 AM,12,2,11:21:55 AM,12,2,11:29:14 AM,12,2,11:52:18 AM,12,2,12:08:36 PM,5,28.0,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_953,978
-Order_No_27626,User_Id_1500,Bike,3,Business,28,5,3:58:26 PM,28,5,3:58:31 PM,28,5,4:00:18 PM,28,5,4:04:55 PM,28,5,4:18:30 PM,13,25.6,,-1.2574219,36.7927073,-1.3228003000000002,36.8306435,Rider_Id_619,815
-Order_No_24358,User_Id_2066,Bike,3,Business,2,5,5:01:30 PM,2,5,5:04:54 PM,2,5,5:14:11 PM,2,5,5:16:00 PM,2,5,5:31:33 PM,4,24.0,,-1.316961,36.820337,-1.2974510000000001,36.8210544,Rider_Id_932,933
-Order_No_24265,User_Id_627,Bike,3,Business,22,4,3:30:43 PM,22,4,3:31:06 PM,22,4,3:32:43 PM,22,4,3:38:18 PM,22,4,3:38:23 PM,3,26.5,,-1.2551895,36.7822034,-1.2567075,36.7739831,Rider_Id_876,5
-Order_No_599,User_Id_3127,Bike,3,Business,4,2,9:45:22 AM,4,2,10:12:15 AM,4,2,10:48:13 AM,4,2,10:52:04 AM,4,2,11:34:55 AM,11,19.3,,-1.2814031000000001,36.82016470000001,-1.2388499,36.872726799999995,Rider_Id_202,2571
-Order_No_10260,User_Id_412,Bike,3,Business,2,2,9:58:03 AM,2,2,9:58:30 AM,2,2,10:45:04 AM,2,2,11:15:53 AM,2,2,11:23:05 AM,4,26.4,,-1.2628473,36.781805,-1.266837,36.799248999999996,Rider_Id_436,432
-Order_No_5125,User_Id_3560,Bike,3,Business,15,1,3:58:43 PM,15,1,3:58:59 PM,15,1,4:03:01 PM,15,1,4:06:05 PM,15,1,4:21:48 PM,7,,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_330,943
-Order_No_8551,User_Id_302,Bike,3,Business,9,6,5:01:17 PM,9,6,5:02:14 PM,9,6,5:16:21 PM,9,6,5:18:53 PM,9,6,6:07:40 PM,12,28.0,,-1.2649605,36.798177700000004,-1.2987697,36.746851,Rider_Id_132,2927
-Order_No_28235,User_Id_393,Bike,3,Business,31,3,2:07:55 PM,31,3,2:12:53 PM,31,3,2:13:01 PM,31,3,2:25:02 PM,31,3,2:57:28 PM,14,27.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_239,1946
-Order_No_13649,User_Id_346,Bike,3,Business,13,4,9:10:41 AM,13,4,9:13:13 AM,13,4,9:25:52 AM,13,4,9:38:09 AM,13,4,9:45:30 AM,1,18.4,,-1.28878,36.8168312,-1.2795827,36.819726200000005,Rider_Id_698,441
-Order_No_14558,User_Id_955,Bike,2,Personal,27,5,1:42:49 PM,27,5,1:43:13 PM,27,5,1:59:37 PM,27,5,2:03:55 PM,27,5,2:45:32 PM,19,24.0,,-1.2579171999999998,36.79383370000001,-1.3270790000000001,36.881469,Rider_Id_951,2497
-Order_No_4692,User_Id_3098,Bike,3,Business,29,1,9:24:05 AM,29,1,9:25:09 AM,29,1,9:36:22 AM,29,1,9:40:04 AM,29,1,9:58:00 AM,7,,,-1.2689186000000001,36.80016810000001,-1.2963097,36.7688221,Rider_Id_306,1076
-Order_No_1925,User_Id_3599,Bike,3,Business,10,5,9:50:38 AM,10,5,10:02:33 AM,10,5,10:27:14 AM,10,5,10:38:08 AM,10,5,10:48:22 AM,9,,,-1.3302996,36.8707935,-1.3280558,36.8816781,Rider_Id_675,614
-Order_No_27912,User_Id_3241,Bike,3,Business,10,1,3:09:25 PM,10,1,3:10:26 PM,10,1,3:16:39 PM,10,1,3:22:35 PM,10,1,3:43:40 PM,8,18.5,,-1.2551895,36.7822034,-1.288924,36.831527,Rider_Id_291,1265
-Order_No_19093,User_Id_1890,Bike,1,Personal,29,2,8:03:19 AM,29,2,8:12:36 AM,29,2,8:24:34 AM,29,2,8:38:19 AM,29,2,9:10:38 AM,21,16.5,,-1.3855778,36.9269619,-1.2763120000000001,36.882742,Rider_Id_932,1939
-Order_No_1140,User_Id_3064,Bike,3,Business,2,2,11:42:26 AM,2,2,11:42:44 AM,2,2,11:43:08 AM,2,2,11:48:42 AM,2,2,12:29:02 PM,13,20.9,,-1.2571472,36.795063299999995,-1.303924,36.8758792,Rider_Id_6,2420
-Order_No_1962,User_Id_1421,Bike,1,Personal,6,4,12:22:27 PM,6,4,12:22:52 PM,6,4,12:42:05 PM,6,4,12:50:28 PM,6,4,1:25:36 PM,12,21.8,,-1.3207146,36.8305771,-1.2609088,36.7948069,Rider_Id_340,2108
-Order_No_21739,User_Id_393,Bike,3,Business,29,5,8:35:50 AM,29,5,8:37:11 AM,29,5,8:51:54 AM,29,5,9:01:33 AM,29,5,9:45:13 AM,8,20.9,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_507,2620
-Order_No_15185,User_Id_2550,Bike,3,Personal,7,2,11:59:51 AM,7,2,12:16:30 PM,7,2,12:46:29 PM,7,2,1:04:24 PM,7,2,1:44:26 PM,7,,,-1.2941,36.765793,-1.2904753,36.8144721,Rider_Id_267,2402
-Order_No_13055,User_Id_1244,Bike,3,Business,24,1,10:30:11 AM,24,1,10:30:21 AM,24,1,10:33:17 AM,24,1,10:45:16 AM,24,1,11:03:53 AM,8,22.7,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_829,1117
-Order_No_3240,User_Id_2931,Bike,3,Business,21,2,9:14:57 AM,21,2,9:15:53 AM,21,2,9:22:30 AM,21,2,9:24:17 AM,21,2,10:11:54 AM,15,,,-1.2452664,36.802854100000005,-1.3231725,36.864017700000005,Rider_Id_547,2857
-Order_No_9526,User_Id_1051,Bike,3,Business,9,1,12:46:27 PM,9,1,12:47:15 PM,9,1,12:56:10 PM,9,1,1:00:14 PM,9,1,1:39:33 PM,21,20.4,,-1.2963097,36.7688221,-1.3106598999999999,36.9179186,Rider_Id_749,2359
-Order_No_26818,User_Id_2803,Bike,1,Business,24,1,2:08:35 PM,24,1,2:09:14 PM,24,1,2:30:33 PM,24,1,2:48:58 PM,24,1,3:00:26 PM,3,,,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_851,688
-Order_No_7605,User_Id_2174,Bike,3,Business,8,3,11:08:38 AM,8,3,11:08:44 AM,8,3,11:08:51 AM,8,3,11:37:52 AM,8,3,11:48:59 AM,5,22.1,,-1.2482781,36.884091999999995,-1.2297202,36.881950700000004,Rider_Id_946,667
-Order_No_15964,User_Id_2866,Bike,3,Business,30,5,3:07:19 PM,30,5,3:08:45 PM,30,5,3:23:32 PM,30,5,3:38:26 PM,30,5,4:58:27 PM,11,24.7,,-1.2571472,36.795063299999995,-1.3322618,36.8045412,Rider_Id_523,4801
-Order_No_24231,User_Id_1205,Bike,3,Business,27,3,1:59:15 PM,27,3,2:21:05 PM,27,3,2:21:16 PM,27,3,2:31:09 PM,27,3,2:46:03 PM,4,,,-1.2810807,36.8144229,-1.2714767,36.8138291,Rider_Id_268,894
-Order_No_18480,User_Id_2819,Bike,1,Personal,25,2,9:40:45 AM,25,2,9:41:09 AM,25,2,9:54:38 AM,25,2,9:57:35 AM,25,2,10:22:26 AM,14,18.0,,-1.2842018,36.832440999999996,-1.3385790000000002,36.89630379999999,Rider_Id_449,1491
-Order_No_27415,User_Id_517,Bike,3,Business,4,5,8:24:25 AM,4,5,8:27:10 AM,4,5,8:31:31 AM,4,5,8:33:11 AM,4,5,8:49:51 AM,11,17.6,0.5,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,1000
-Order_No_5776,User_Id_2423,Bike,3,Business,27,3,3:09:40 PM,27,3,3:12:48 PM,27,3,3:13:12 PM,27,3,3:35:34 PM,27,3,3:53:12 PM,6,29.3,,-1.2551895,36.7822034,-1.2884308,36.821751,Rider_Id_576,1058
-Order_No_7349,User_Id_1993,Bike,2,Personal,5,5,9:21:43 AM,5,5,9:21:50 AM,5,5,9:22:46 AM,5,5,9:33:34 AM,5,5,9:48:38 AM,5,17.6,,-1.2900726999999999,36.7890059,-1.2871938,36.8195031,Rider_Id_722,904
-Order_No_8621,User_Id_867,Bike,3,Business,14,5,11:32:35 AM,14,5,11:33:06 AM,14,5,11:36:38 AM,14,5,11:38:52 AM,14,5,12:02:17 PM,10,22.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_211,1405
-Order_No_26754,User_Id_1369,Bike,2,Personal,14,1,10:43:21 AM,14,1,10:43:35 AM,14,1,10:52:58 AM,14,1,11:06:52 AM,14,1,11:31:03 AM,8,23.0,,-1.2915306999999998,36.790586499999996,-1.3145553,36.749384899999995,Rider_Id_223,1451
-Order_No_11539,User_Id_391,Bike,3,Business,23,2,10:13:12 AM,23,2,10:13:45 AM,23,2,10:20:32 AM,23,2,10:23:22 AM,23,2,11:00:48 AM,5,,,-1.2784063,36.8222933,-1.3081376,36.83297720000001,Rider_Id_456,2246
-Order_No_15589,User_Id_3647,Bike,3,Business,22,3,11:54:04 AM,22,3,11:55:17 AM,22,3,12:05:16 PM,22,3,12:08:00 PM,22,3,12:25:22 PM,5,22.9,,-1.265432,36.795034,-1.2828362,36.818923600000005,Rider_Id_504,1042
-Order_No_5960,User_Id_1363,Bike,3,Business,18,4,11:12:28 AM,18,4,11:14:24 AM,18,4,11:26:27 AM,18,4,12:00:10 PM,18,4,1:01:46 PM,8,,,-1.2545163000000001,36.8243717,-1.3004062,36.829741,Rider_Id_438,3696
-Order_No_13736,User_Id_972,Bike,3,Business,25,4,9:31:02 AM,25,4,9:34:31 AM,25,4,9:36:52 AM,25,4,9:39:36 AM,25,4,9:50:54 AM,5,16.4,1.9,-1.2862033000000002,36.8152966,-1.3019136999999998,36.7925105,Rider_Id_726,678
-Order_No_19524,User_Id_1484,Bike,3,Business,14,4,2:20:07 PM,14,4,2:22:14 PM,14,4,2:36:37 PM,14,4,2:39:57 PM,14,4,2:52:41 PM,10,27.8,,-1.2551895,36.7822034,-1.2579051,36.726363799999994,Rider_Id_709,764
-Order_No_26334,User_Id_265,Bike,3,Business,15,5,3:08:58 PM,15,5,3:30:45 PM,15,5,3:40:10 PM,15,5,3:45:24 PM,15,5,4:03:05 PM,4,18.0,,-1.2880771,36.8269099,-1.300921,36.828195,Rider_Id_709,1061
-Order_No_25554,User_Id_84,Bike,3,Business,15,5,3:23:36 PM,15,5,3:25:36 PM,15,5,3:53:05 PM,15,5,3:58:14 PM,15,5,4:36:31 PM,19,28.2,,-1.2551895,36.7822034,-1.2860183,36.8975338,Rider_Id_547,2297
-Order_No_1663,User_Id_1245,Bike,3,Business,26,2,12:31:59 PM,26,2,12:32:11 PM,26,2,12:34:53 PM,26,2,2:04:09 PM,26,2,3:04:23 PM,17,31.2,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_459,3614
-Order_No_17405,User_Id_1316,Bike,1,Business,7,2,9:38:43 AM,7,2,9:40:11 AM,7,2,9:47:46 AM,7,2,9:52:12 AM,7,2,10:19:44 AM,14,16.6,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_726,1652
-Order_No_17768,User_Id_3040,Bike,1,Personal,9,7,11:11:06 AM,9,7,11:11:26 AM,9,7,11:56:05 AM,9,7,12:00:06 PM,9,7,12:47:18 PM,19,23.7,,-1.2248308,36.801257299999996,-1.3319466999999998,36.776699799999996,Rider_Id_934,2832
-Order_No_18654,User_Id_2998,Bike,1,Personal,13,5,10:54:22 AM,13,5,11:37:35 AM,13,5,11:45:31 AM,13,5,11:47:43 AM,13,5,12:03:25 PM,6,,,-1.2617341999999998,36.79272329999999,-1.2858323,36.829994799999994,Rider_Id_786,942
-Order_No_9591,User_Id_1874,Bike,3,Business,14,4,3:05:46 PM,14,4,3:24:16 PM,14,4,3:51:14 PM,14,4,3:54:57 PM,14,4,4:58:23 PM,12,28.1,,-1.2571472,36.795063299999995,-1.238805,36.884373,Rider_Id_824,3806
-Order_No_1281,User_Id_733,Bike,3,Business,14,4,1:01:54 PM,14,4,1:14:19 PM,14,4,1:26:41 PM,14,4,1:32:54 PM,14,4,2:05:28 PM,20,27.8,,-1.2551895,36.7822034,-1.3185307,36.9001089,Rider_Id_205,1954
-Order_No_7057,User_Id_3115,Bike,3,Business,5,5,9:36:19 AM,5,5,9:37:38 AM,5,5,9:52:05 AM,5,5,10:02:19 AM,5,5,10:15:54 AM,6,17.7,,-1.2559559999999999,36.772901,-1.2963097,36.7688221,Rider_Id_336,815
-Order_No_11891,User_Id_1724,Bike,3,Business,11,4,11:32:52 AM,11,4,11:33:12 AM,11,4,11:34:11 AM,11,4,11:44:18 AM,11,4,12:07:14 PM,8,25.6,,-1.2551895,36.7822034,-1.2941648000000001,36.7977315,Rider_Id_698,1376
-Order_No_18748,User_Id_3291,Bike,3,Business,4,1,10:45:47 AM,4,1,10:46:23 AM,4,1,10:55:54 AM,4,1,10:58:11 AM,4,1,11:29:29 AM,8,25.4,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_71,1878
-Order_No_11026,User_Id_1329,Bike,3,Business,20,4,5:22:15 PM,20,4,5:22:52 PM,20,4,5:24:00 PM,20,4,5:26:40 PM,20,4,5:43:04 PM,5,22.8,,-1.273056,36.811298,-1.2984973999999998,36.8142189,Rider_Id_205,984
-Order_No_2892,User_Id_2825,Bike,2,Personal,15,1,3:00:33 PM,15,1,3:01:19 PM,15,1,3:09:37 PM,15,1,3:16:52 PM,15,1,3:50:53 PM,13,,,-1.2156006000000001,36.8916865,-1.2599377,36.8117119,Rider_Id_704,2041
-Order_No_23011,User_Id_346,Bike,3,Business,28,4,3:43:39 PM,28,4,3:44:58 PM,28,4,4:12:47 PM,28,4,4:59:07 PM,28,4,5:32:39 PM,14,25.8,,-1.2860183,36.8975338,-1.28878,36.8168312,Rider_Id_709,2012
-Order_No_8930,User_Id_1880,Bike,3,Business,12,5,3:18:30 PM,12,5,3:21:22 PM,12,5,3:23:00 PM,12,5,3:30:19 PM,12,5,3:56:16 PM,12,26.9,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_698,1557
-Order_No_18816,User_Id_2801,Bike,3,Business,19,1,12:11:05 PM,19,1,12:11:20 PM,19,1,12:19:43 PM,19,1,12:28:57 PM,19,1,1:07:58 PM,15,24.9,,-1.2595421999999998,36.7871178,-1.3104506,36.8687235,Rider_Id_620,2341
-Order_No_658,User_Id_188,Bike,1,Personal,17,5,2:30:43 PM,17,5,2:31:37 PM,17,5,2:46:40 PM,17,5,2:50:54 PM,17,5,3:09:21 PM,9,20.0,,-1.3078623999999999,36.7697658,-1.3028531,36.818680900000004,Rider_Id_312,1107
-Order_No_25460,User_Id_1146,Bike,3,Business,29,1,1:13:20 PM,29,1,1:14:36 PM,29,1,1:22:13 PM,29,1,1:41:33 PM,29,1,1:53:43 PM,7,,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_206,730
-Order_No_1747,User_Id_136,Bike,3,Business,15,3,3:29:28 PM,15,3,3:29:59 PM,15,3,3:35:22 PM,15,3,3:40:31 PM,15,3,3:57:59 PM,7,21.5,,-1.2649605,36.798177700000004,-1.2962832,36.784968299999996,Rider_Id_415,1048
-Order_No_11577,User_Id_1827,Bike,3,Business,5,6,11:55:34 AM,5,6,11:55:42 AM,5,6,11:55:59 AM,5,6,12:16:05 PM,5,6,12:30:34 PM,11,22.2,,-1.318062,36.849326,-1.293515,36.897607,Rider_Id_408,869
-Order_No_22712,User_Id_2198,Bike,1,Personal,20,4,12:59:30 PM,20,4,1:00:12 PM,20,4,1:20:12 PM,20,4,1:21:45 PM,20,4,2:55:20 PM,10,24.6,,-1.26249,36.799715,-1.2916198,36.864443,Rider_Id_38,5615
-Order_No_6335,User_Id_2523,Bike,3,Business,14,4,3:13:44 PM,14,4,3:19:30 PM,14,4,3:55:37 PM,14,4,3:59:11 PM,14,4,4:11:31 PM,6,27.9,,-1.2551895,36.7822034,-1.2357748,36.7900524,Rider_Id_337,740
-Order_No_12789,User_Id_3164,Bike,2,Personal,2,3,11:37:11 AM,2,3,11:37:34 AM,2,3,11:52:44 AM,2,3,12:02:13 PM,2,3,12:14:12 PM,5,21.8,,-1.2991542999999999,36.7827912,-1.2735236,36.7992196,Rider_Id_761,719
-Order_No_27478,User_Id_909,Bike,3,Business,25,4,5:01:15 PM,25,4,5:02:06 PM,25,4,5:11:54 PM,25,4,5:16:15 PM,25,4,5:54:00 PM,7,,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_205,2265
-Order_No_21604,User_Id_3300,Bike,3,Business,17,3,12:49:50 PM,17,3,12:51:14 PM,17,3,1:27:33 PM,17,3,1:31:35 PM,17,3,2:37:22 PM,11,,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_106,3947
-Order_No_12686,User_Id_2505,Bike,1,Personal,2,2,8:54:13 AM,2,2,8:54:27 AM,2,2,9:13:12 AM,2,2,9:16:53 AM,2,2,10:13:04 AM,24,15.2,,-1.312469,36.815177,-1.269018,36.67827,Rider_Id_532,3371
-Order_No_17473,User_Id_2847,Bike,3,Personal,9,4,2:33:18 PM,9,4,2:36:33 PM,9,4,2:45:12 PM,9,4,2:48:05 PM,9,4,3:32:37 PM,14,25.9,,-1.3385790000000002,36.89630379999999,-1.288269,36.818275299999996,Rider_Id_213,2672
-Order_No_7437,User_Id_265,Bike,3,Business,26,5,1:16:44 PM,26,5,1:56:37 PM,26,5,2:00:36 PM,26,5,2:44:35 PM,26,5,3:24:30 PM,9,,,-1.300921,36.828195,-1.3034899,36.7733256,Rider_Id_519,2395
-Order_No_17753,User_Id_627,Bike,3,Business,25,4,3:32:27 PM,25,4,3:32:50 PM,25,4,3:33:24 PM,25,4,4:42:56 PM,25,4,4:44:24 PM,18,,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_613,88
-Order_No_20370,User_Id_2587,Bike,3,Business,15,1,3:08:57 PM,15,1,3:23:41 PM,15,1,3:23:45 PM,15,1,3:46:02 PM,15,1,4:09:55 PM,5,,,-1.3165719,36.842084299999996,-1.28878,36.8168312,Rider_Id_613,1433
-Order_No_1868,User_Id_1502,Bike,3,Business,22,3,3:07:22 PM,22,3,3:50:21 PM,22,3,4:09:50 PM,22,3,4:27:25 PM,22,3,5:03:16 PM,12,24.3,,-1.279395,36.825364,-1.2232123,36.8932547,Rider_Id_410,2151
-Order_No_7775,User_Id_2330,Bike,3,Business,18,1,12:11:36 PM,18,1,12:12:11 PM,18,1,12:24:40 PM,18,1,12:31:08 PM,18,1,12:31:11 PM,5,27.8,,-1.2355732,36.878606299999994,-1.2524916000000001,36.8656196,Rider_Id_521,3
-Order_No_11635,User_Id_1246,Bike,3,Business,31,5,8:44:42 AM,31,5,8:44:58 AM,31,5,8:46:27 AM,31,5,8:59:46 AM,31,5,9:10:49 AM,2,14.8,,-1.2571472,36.795063299999995,-1.2550226999999998,36.8023076,Rider_Id_536,663
-Order_No_28240,User_Id_2523,Bike,3,Business,1,5,12:20:03 PM,1,5,12:20:38 PM,1,5,12:28:12 PM,1,5,12:35:54 PM,1,5,12:49:51 PM,8,26.7,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_4,837
-Order_No_24120,User_Id_3686,Bike,3,Business,17,2,10:40:00 AM,17,2,10:40:34 AM,17,2,10:46:04 AM,17,2,10:55:10 AM,17,2,11:06:58 AM,6,18.9,,-1.2551895,36.7822034,-1.2707742,36.7578609,Rider_Id_116,708
-Order_No_2092,User_Id_3487,Bike,3,Business,14,5,2:28:01 PM,14,5,2:29:30 PM,14,5,2:55:28 PM,14,5,3:16:25 PM,14,5,3:40:48 PM,1,22.8,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_436,1463
-Order_No_1110,User_Id_1724,Bike,3,Business,14,2,10:48:07 AM,14,2,10:50:44 AM,14,2,11:02:53 AM,14,2,11:08:25 AM,14,2,11:21:43 AM,6,,,-1.2551895,36.7822034,-1.2814273,36.8132815,Rider_Id_959,798
-Order_No_8372,User_Id_3735,Bike,1,Personal,21,5,2:18:17 PM,21,5,2:19:40 PM,21,5,2:50:10 PM,21,5,3:03:34 PM,21,5,3:22:32 PM,7,27.1,,-1.2886771000000001,36.8735531,-1.3296282,36.8580804,Rider_Id_532,1138
-Order_No_25964,User_Id_893,Bike,3,Business,15,3,2:21:21 PM,15,3,2:23:01 PM,15,3,2:24:07 PM,15,3,2:57:20 PM,15,3,3:12:18 PM,6,21.6,,-1.2938211000000002,36.8270021,-1.2663194,36.809901,Rider_Id_882,898
-Order_No_7748,User_Id_3669,Bike,3,Business,10,5,3:11:16 PM,10,5,3:12:55 PM,10,5,3:39:59 PM,10,5,3:47:12 PM,10,5,4:28:51 PM,15,,,-1.2615888000000002,36.7928732,-1.2396938,36.8938893,Rider_Id_660,2499
-Order_No_20460,User_Id_1927,Bike,3,Business,11,6,12:59:44 PM,11,6,2:18:50 PM,11,6,2:34:17 PM,11,6,2:38:06 PM,11,6,2:58:49 PM,8,,,-1.3472066,36.7692638,-1.3165542,36.700067,Rider_Id_572,1243
-Order_No_20698,User_Id_2227,Bike,3,Personal,14,4,12:07:20 PM,14,4,12:19:53 PM,14,4,12:53:32 PM,14,4,12:59:16 PM,14,4,1:17:06 PM,10,19.1,,-1.2541275,36.8263055,-1.2470959,36.8729823,Rider_Id_820,1070
-Order_No_15313,User_Id_3283,Bike,3,Business,21,1,12:45:31 PM,21,1,12:46:14 PM,21,1,12:48:58 PM,21,1,12:53:44 PM,21,1,1:05:03 PM,4,25.5,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_247,679
-Order_No_4044,User_Id_3042,Bike,3,Business,31,2,10:01:38 AM,31,2,10:02:18 AM,31,2,10:16:00 AM,31,2,10:17:25 AM,31,2,10:36:15 AM,4,17.1,1.1,-1.2903421,36.7885282,-1.2935056999999999,36.810180200000005,Rider_Id_933,1130
-Order_No_14203,User_Id_1642,Bike,3,Business,14,4,10:45:01 AM,14,4,11:05:34 AM,14,4,11:16:07 AM,14,4,11:18:56 AM,14,4,11:36:45 AM,9,23.6,,-1.2726506000000002,36.7960153,-1.3082781,36.831651,Rider_Id_205,1069
-Order_No_17831,User_Id_3647,Bike,3,Business,7,4,5:00:45 PM,7,4,5:01:28 PM,7,4,5:11:47 PM,7,4,5:25:57 PM,7,4,5:25:59 PM,4,25.7,,-1.2726389999999999,36.794723,-1.2765492,36.7669809,Rider_Id_295,2
-Order_No_26617,User_Id_3599,Bike,3,Business,10,5,9:46:02 AM,10,5,9:47:26 AM,10,5,10:23:14 AM,10,5,10:33:58 AM,10,5,10:49:58 AM,7,,,-1.3302996,36.8707935,-1.3270347,36.866736200000005,Rider_Id_787,960
-Order_No_19286,User_Id_2959,Bike,2,Personal,6,1,9:33:34 AM,6,1,9:35:00 AM,6,1,9:47:33 AM,6,1,9:54:50 AM,6,1,10:07:23 AM,4,14.9,,-1.2634530000000002,36.7955612,-1.2866011999999998,36.8195691,Rider_Id_857,753
-Order_No_26257,User_Id_2523,Bike,3,Business,22,4,2:28:26 PM,22,4,2:32:33 PM,22,4,3:00:48 PM,22,4,3:03:26 PM,22,4,3:23:06 PM,8,26.2,,-1.2551895,36.7822034,-1.2904792999999999,36.771609600000005,Rider_Id_941,1180
-Order_No_17428,User_Id_1271,Bike,3,Business,12,5,5:38:21 PM,12,5,5:39:07 PM,12,5,5:59:11 PM,12,5,6:01:30 PM,12,5,7:02:58 PM,15,25.8,,-1.3244885,36.897791999999995,-1.281196,36.8179321,Rider_Id_519,3688
-Order_No_24867,User_Id_467,Bike,3,Business,9,4,9:06:24 AM,9,4,9:07:52 AM,9,4,9:17:32 AM,9,4,9:21:17 AM,9,4,9:46:59 AM,12,,,-1.304735,36.784605400000004,-1.311818,36.8638544,Rider_Id_576,1542
-Order_No_16355,User_Id_2183,Bike,3,Business,28,2,10:34:47 AM,28,2,10:35:51 AM,28,2,10:45:30 AM,28,2,10:51:33 AM,28,2,11:31:09 AM,12,17.8,,-1.2551895,36.7822034,-1.3135294,36.8531378,Rider_Id_50,2376
-Order_No_18671,User_Id_3647,Bike,3,Business,9,4,11:18:18 AM,9,4,11:18:54 AM,9,4,11:33:35 AM,9,4,11:37:24 AM,9,4,12:01:06 PM,12,,,-1.2726389999999999,36.794723,-1.2219928999999998,36.8349924,Rider_Id_770,1422
-Order_No_20963,User_Id_2066,Bike,3,Business,17,6,1:31:05 PM,17,6,1:37:50 PM,17,6,1:46:53 PM,17,6,1:57:35 PM,17,6,2:30:54 PM,18,24.0,,-1.3270716,36.8406371,-1.2156006000000001,36.8916865,Rider_Id_882,1999
-Order_No_8290,User_Id_867,Bike,3,Business,29,5,2:55:26 PM,29,5,2:56:19 PM,29,5,3:05:31 PM,29,5,3:12:35 PM,29,5,3:32:28 PM,9,28.9,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_484,1193
-Order_No_26063,User_Id_579,Bike,3,Business,23,2,2:37:57 PM,23,2,2:54:20 PM,23,2,3:33:21 PM,23,2,3:37:57 PM,23,2,4:33:54 PM,20,19.2,,-1.3204931,36.7043102,-1.2774729999999999,36.83119779999999,Rider_Id_844,3357
-Order_No_16040,User_Id_3399,Bike,3,Personal,17,5,9:13:14 PM,17,5,9:32:54 PM,17,5,9:49:42 PM,17,5,9:58:06 PM,17,5,10:10:33 PM,9,16.7,,-1.2513528999999999,36.752171999999995,-1.2948886999999998,36.7683938,Rider_Id_874,747
-Order_No_9252,User_Id_3573,Bike,1,Personal,18,3,12:01:03 PM,18,3,12:04:08 PM,18,3,12:36:02 PM,18,3,12:45:06 PM,18,3,1:15:43 PM,5,20.1,,-1.2631940000000002,36.797624,-1.2836832,36.8306719,Rider_Id_844,1837
-Order_No_11236,User_Id_633,Bike,3,Business,4,2,9:23:16 AM,4,2,9:38:33 AM,4,2,9:55:06 AM,4,2,9:58:57 AM,4,2,10:07:24 AM,4,15.8,83.0,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_932,507
-Order_No_11028,User_Id_435,Bike,3,Business,14,5,2:25:31 PM,14,5,2:26:31 PM,14,5,2:48:45 PM,14,5,2:58:05 PM,14,5,3:46:12 PM,16,23.2,,-1.300921,36.828195,-1.2094552,36.807328600000005,Rider_Id_149,2887
-Order_No_18567,User_Id_1397,Bike,3,Business,15,5,3:21:51 PM,15,5,3:39:28 PM,15,5,4:03:32 PM,15,5,4:06:02 PM,15,5,5:09:57 PM,6,27.9,,-1.2628638,36.8070399,-1.2913246,36.8019522,Rider_Id_697,3835
-Order_No_24586,User_Id_2980,Bike,1,Personal,2,2,9:54:54 AM,2,2,9:56:14 AM,2,2,10:08:27 AM,2,2,10:10:52 AM,2,2,10:18:57 AM,7,15.1,,-1.2669099,36.79774329999999,-1.2854789,36.8240689,Rider_Id_731,485
-Order_No_27902,User_Id_1304,Bike,3,Business,12,5,2:36:23 PM,12,5,2:39:50 PM,12,5,2:42:50 PM,12,5,3:10:11 PM,12,5,3:27:22 PM,8,,,-1.2551895,36.7822034,-1.3038408000000001,36.818713,Rider_Id_291,1031
-Order_No_25794,User_Id_3222,Bike,3,Business,22,1,3:55:00 PM,22,1,3:57:37 PM,22,1,4:04:24 PM,22,1,4:07:15 PM,22,1,5:14:16 PM,7,26.3,,-1.2551895,36.7822034,-1.2647473,36.842301899999995,Rider_Id_412,4021
-Order_No_25847,User_Id_3698,Bike,3,Business,10,1,1:11:29 PM,10,1,1:12:20 PM,10,1,1:30:30 PM,10,1,1:37:21 PM,10,1,1:55:30 PM,14,18.3,,-1.3244885,36.897791999999995,-1.3073569,36.82021629999999,Rider_Id_883,1089
-Order_No_23382,User_Id_1001,Bike,1,Personal,10,3,2:25:19 PM,10,3,2:25:33 PM,10,3,2:37:30 PM,10,3,2:50:48 PM,10,3,3:49:02 PM,19,24.7,,-1.2801648,36.824169700000006,-1.3386942,36.710521299999996,Rider_Id_844,3494
-Order_No_26828,User_Id_2330,Bike,3,Business,29,3,3:14:11 PM,29,3,3:18:54 PM,29,3,3:33:46 PM,29,3,3:45:04 PM,29,3,4:16:46 PM,11,21.1,,-1.2915246999999999,36.805575700000006,-1.2687407,36.7373043,Rider_Id_429,1902
-Order_No_26360,User_Id_2869,Bike,3,Business,23,4,5:29:51 PM,23,4,5:34:25 PM,23,4,6:17:04 PM,23,4,6:20:57 PM,23,4,6:57:07 PM,9,,,-1.291768,36.8297208,-1.3025826,36.7670807,Rider_Id_158,2170
-Order_No_18086,User_Id_928,Bike,3,Business,14,3,4:07:40 PM,14,3,4:07:59 PM,14,3,4:18:35 PM,14,3,4:28:01 PM,14,3,4:47:29 PM,2,25.7,,-1.2551895,36.7822034,-1.2622120000000001,36.7886506,Rider_Id_738,1168
-Order_No_28233,User_Id_1224,Bike,1,Personal,22,5,12:48:44 PM,22,5,12:49:40 PM,22,5,12:52:47 PM,22,5,1:04:16 PM,22,5,1:20:13 PM,3,21.9,,-1.304033,36.7848695,-1.2923533,36.7883364,Rider_Id_474,957
-Order_No_3582,User_Id_627,Bike,3,Business,1,1,2:01:15 PM,1,1,2:02:15 PM,1,1,2:08:36 PM,1,1,2:35:30 PM,1,1,3:03:40 PM,13,,,-1.2527958000000001,36.8003131,-1.2998684999999999,36.8643907,Rider_Id_383,1690
-Order_No_4347,User_Id_2789,Bike,3,Business,9,4,12:40:14 PM,9,4,12:43:25 PM,9,4,12:50:46 PM,9,4,12:57:33 PM,9,4,1:19:05 PM,10,,,-1.304735,36.784605400000004,-1.3239248,36.7091136,Rider_Id_599,1292
-Order_No_15173,User_Id_81,Bike,3,Business,10,1,4:06:38 PM,10,1,4:07:28 PM,10,1,4:08:12 PM,10,1,4:13:16 PM,10,1,4:13:49 PM,2,18.7,,-1.2596969999999998,36.800664000000005,-1.2641496,36.791413899999995,Rider_Id_651,33
-Order_No_4339,User_Id_733,Bike,3,Business,14,4,1:03:37 PM,14,4,3:31:23 PM,14,4,3:47:41 PM,14,4,3:49:12 PM,14,4,4:13:54 PM,9,28.1,,-1.2551895,36.7822034,-1.304078,36.83014,Rider_Id_571,1482
-Order_No_520,User_Id_1341,Bike,3,Business,10,5,1:58:26 PM,10,5,1:59:07 PM,10,5,2:35:38 PM,10,5,2:38:04 PM,10,5,3:08:00 PM,13,,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_907,1796
-Order_No_13853,User_Id_1719,Bike,3,Business,29,5,2:27:22 PM,29,5,2:32:52 PM,29,5,2:54:53 PM,29,5,3:12:23 PM,29,5,3:32:18 PM,7,28.8,,-1.2897368,36.8226783,-1.296797,36.776452500000005,Rider_Id_907,1195
-Order_No_17235,User_Id_2423,Bike,3,Business,30,2,10:57:28 AM,30,2,10:59:03 AM,30,2,11:02:56 AM,30,2,11:18:34 AM,30,2,11:23:40 AM,3,,,-1.2551895,36.7822034,-1.256522,36.786413200000005,Rider_Id_291,306
-Order_No_10823,User_Id_2200,Bike,3,Business,24,1,11:26:43 AM,24,1,1:47:21 PM,24,1,2:02:53 PM,24,1,2:12:55 PM,24,1,2:43:22 PM,11,24.6,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_194,1827
-Order_No_8083,User_Id_1207,Bike,3,Business,22,5,4:01:55 PM,22,5,4:28:43 PM,22,5,4:50:22 PM,22,5,5:18:54 PM,22,5,5:51:07 PM,17,25.5,,-1.3169591,36.7000029,-1.2652279,36.8020159,Rider_Id_576,1933
-Order_No_13904,User_Id_1048,Bike,2,Personal,6,6,2:49:22 PM,6,6,3:00:36 PM,6,6,3:12:21 PM,6,6,3:13:30 PM,6,6,3:30:39 PM,7,27.8,,-1.2879618,36.82008629999999,-1.2980357,36.784830299999996,Rider_Id_933,1029
-Order_No_13837,User_Id_3147,Bike,3,Business,14,5,12:17:08 PM,14,5,12:17:19 PM,14,5,12:30:05 PM,14,5,12:36:59 PM,14,5,1:07:11 PM,17,24.5,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_882,1812
-Order_No_16788,User_Id_3355,Bike,3,Business,5,3,3:33:39 PM,5,3,3:54:01 PM,5,3,4:01:14 PM,5,3,4:02:57 PM,5,3,4:07:28 PM,2,25.2,,-1.3052491000000002,36.8223899,-1.2983805,36.8198439,Rider_Id_551,271
-Order_No_14346,User_Id_1964,Bike,3,Personal,14,1,12:46:53 PM,14,1,12:48:17 PM,14,1,1:05:15 PM,14,1,1:10:11 PM,14,1,1:56:19 PM,10,23.7,,-1.32671,36.8600138,-1.2904753,36.8144721,Rider_Id_743,2768
-Order_No_9482,User_Id_677,Bike,3,Business,10,3,1:06:54 PM,10,3,1:07:29 PM,10,3,1:12:54 PM,10,3,1:16:14 PM,10,3,1:47:01 PM,20,27.9,,-1.2283403,36.8822756,-1.3014461000000002,36.7661381,Rider_Id_299,1847
-Order_No_6072,User_Id_3578,Bike,3,Business,25,4,2:33:48 PM,25,4,2:34:02 PM,25,4,2:44:41 PM,25,4,3:28:45 PM,25,4,4:07:35 PM,15,,,-1.2615888000000002,36.7928732,-1.2232123,36.8932547,Rider_Id_690,2330
-Order_No_5032,User_Id_3698,Bike,3,Business,26,5,11:10:40 AM,26,5,11:10:40 AM,26,5,11:15:39 AM,26,5,11:21:57 AM,26,5,12:17:37 PM,15,,,-1.3244885,36.897791999999995,-1.2285834999999998,36.882646799999996,Rider_Id_863,3340
-Order_No_20968,User_Id_3321,Bike,3,Personal,30,4,4:30:15 PM,30,4,4:34:10 PM,30,4,4:46:07 PM,30,4,5:50:05 PM,30,4,6:06:58 PM,5,,,-1.2861764999999998,36.8209086,-1.2723552,36.7881306,Rider_Id_796,1013
-Order_No_16905,User_Id_700,Bike,3,Business,30,5,10:07:41 AM,30,5,10:09:28 AM,30,5,11:20:36 AM,30,5,11:28:36 AM,30,5,12:11:55 PM,7,23.5,,-1.2764659999999999,36.828239,-1.3205163999999998,36.8098882,Rider_Id_79,2599
-Order_No_3468,User_Id_3491,Bike,3,Business,31,3,10:58:25 AM,31,3,11:21:59 AM,31,3,11:45:46 AM,31,3,11:52:10 AM,31,3,12:16:40 PM,10,24.7,,-1.275931,36.8516227,-1.2297202,36.881950700000004,Rider_Id_237,1470
-Order_No_27754,User_Id_2934,Bike,1,Personal,5,4,3:58:12 PM,5,4,4:04:14 PM,5,4,4:15:31 PM,5,4,4:17:39 PM,5,4,4:44:08 PM,10,22.0,,-1.2560221999999999,36.7886679,-1.299313,36.78314879999999,Rider_Id_726,1589
-Order_No_22004,User_Id_3030,Bike,3,Business,23,2,3:37:57 PM,23,2,3:39:40 PM,23,2,4:09:06 PM,23,2,4:14:40 PM,23,2,4:51:41 PM,10,18.9,,-1.2866011999999998,36.8195691,-1.2598589,36.7470168,Rider_Id_208,2221
-Order_No_8426,User_Id_1075,Bike,3,Business,16,7,2:08:07 PM,16,7,2:08:26 PM,16,7,2:12:16 PM,16,7,2:17:25 PM,16,7,2:23:11 PM,1,24.0,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_103,346
-Order_No_5401,User_Id_1719,Bike,3,Business,7,5,8:28:29 AM,7,5,8:30:57 AM,7,5,8:45:28 AM,7,5,8:53:57 AM,7,5,9:27:17 AM,12,,,-1.353975,36.7263802,-1.2963097,36.7688221,Rider_Id_643,2000
-Order_No_25767,User_Id_302,Bike,3,Business,16,2,7:37:23 PM,16,2,7:47:16 PM,16,2,7:51:15 PM,16,2,7:57:35 PM,16,2,8:04:54 PM,2,21.5,,-1.2649605,36.798177700000004,-1.2514074,36.817666700000004,Rider_Id_103,439
-Order_No_2378,User_Id_3201,Bike,3,Business,12,2,12:25:31 PM,12,2,12:26:48 PM,12,2,12:27:20 PM,12,2,12:29:22 PM,12,2,1:23:30 PM,21,28.4,,-1.3562374,36.904295399999995,-1.2584143,36.8048002,Rider_Id_385,3248
-Order_No_13510,User_Id_246,Bike,3,Business,21,1,2:02:29 PM,21,1,3:20:05 PM,21,1,3:37:06 PM,21,1,4:09:31 PM,21,1,4:43:12 PM,10,21.6,,-1.214328,36.7911903,-1.2584143,36.8048002,Rider_Id_654,2021
-Order_No_6792,User_Id_1699,Bike,3,Business,12,5,9:50:08 AM,12,5,9:51:55 AM,12,5,10:09:17 AM,12,5,10:11:55 AM,12,5,10:27:29 AM,8,18.6,,-1.3108674,36.795817,-1.269534,36.80697,Rider_Id_299,934
-Order_No_27747,User_Id_3283,Bike,3,Business,15,5,10:21:44 AM,15,5,10:22:12 AM,15,5,10:30:30 AM,15,5,11:02:50 AM,15,5,11:22:45 AM,7,25.9,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_210,1195
-Order_No_12611,User_Id_1978,Bike,2,Personal,4,6,12:35:58 PM,4,6,12:36:14 PM,4,6,12:47:25 PM,4,6,12:58:03 PM,4,6,1:04:40 PM,2,20.0,,-1.2881186000000002,36.7958595,-1.2874313999999998,36.7764912,Rider_Id_400,397
-Order_No_220,User_Id_649,Bike,3,Business,26,5,3:05:01 PM,26,5,3:07:11 PM,26,5,3:20:54 PM,26,5,3:41:51 PM,26,5,4:01:46 PM,7,,,-1.2901786,36.824294,-1.2551895,36.7822034,Rider_Id_19,1195
-Order_No_18007,User_Id_633,Bike,3,Business,8,5,11:10:24 AM,8,5,11:11:29 AM,8,5,11:44:34 AM,8,5,11:48:22 AM,8,5,12:02:08 PM,4,28.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_349,826
-Order_No_17837,User_Id_2154,Bike,3,Business,2,5,3:22:10 PM,2,5,3:59:45 PM,2,5,4:00:05 PM,2,5,4:08:25 PM,2,5,4:44:08 PM,13,25.3,,-1.2765736,36.851364600000004,-1.3472066,36.7692638,Rider_Id_699,2143
-Order_No_23424,User_Id_2423,Bike,3,Business,18,4,3:05:40 PM,18,4,3:07:31 PM,18,4,3:11:40 PM,18,4,3:28:49 PM,18,4,3:52:13 PM,8,,,-1.2551895,36.7822034,-1.3012173,36.8093987,Rider_Id_161,1404
-Order_No_19138,User_Id_111,Bike,3,Business,26,2,2:22:18 PM,26,2,2:22:37 PM,26,2,2:23:39 PM,26,2,2:27:32 PM,26,2,3:33:25 PM,12,29.9,,-1.2551895,36.7822034,-1.2987332,36.8463349,Rider_Id_266,3953
-Order_No_1636,User_Id_1721,Bike,3,Business,24,1,9:33:07 AM,24,1,9:33:33 AM,24,1,9:46:50 AM,24,1,9:55:10 AM,24,1,10:10:33 AM,8,15.8,,-1.2551895,36.7822034,-1.287428,36.7800821,Rider_Id_881,923
-Order_No_8102,User_Id_1384,Bike,3,Business,10,5,2:38:32 PM,10,5,2:40:21 PM,10,5,3:37:39 PM,10,5,3:40:24 PM,10,5,4:41:03 PM,19,,,-1.2517448,36.7924971,-1.1927453000000001,36.8985941,Rider_Id_580,3639
-Order_No_3088,User_Id_2523,Bike,3,Business,15,5,11:37:56 AM,15,5,11:41:25 AM,15,5,12:03:29 PM,15,5,12:06:50 PM,15,5,12:54:14 PM,8,26.6,,-1.2803414,36.8265456,-1.3130629999999999,36.8726225,Rider_Id_686,2844
-Order_No_7854,User_Id_3339,Bike,3,Business,21,4,10:53:59 AM,21,4,10:54:41 AM,21,4,11:06:29 AM,21,4,11:08:04 AM,21,4,11:38:49 AM,9,25.3,,-1.2551895,36.7822034,-1.2962618000000001,36.792933399999995,Rider_Id_436,1845
-Order_No_22742,User_Id_3149,Bike,3,Business,25,4,3:03:35 PM,25,4,3:04:10 PM,25,4,3:05:34 PM,25,4,3:43:07 PM,25,4,3:51:11 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_206,484
-Order_No_16266,User_Id_1882,Bike,3,Business,5,2,4:08:22 PM,5,2,4:12:51 PM,5,2,4:31:47 PM,5,2,4:39:38 PM,5,2,5:12:43 PM,13,,,-1.2534301,36.800098299999995,-1.2156006000000001,36.8916865,Rider_Id_300,1985
-Order_No_11136,User_Id_3201,Bike,3,Business,30,2,1:56:19 PM,30,2,1:56:28 PM,30,2,1:59:53 PM,30,2,2:03:08 PM,30,2,2:09:36 PM,2,27.2,,-1.2584143,36.8048002,-1.2591018999999999,36.800577000000004,Rider_Id_587,388
-Order_No_5376,User_Id_2066,Bike,3,Business,8,5,1:53:50 PM,8,5,1:54:54 PM,8,5,2:05:39 PM,8,5,2:10:24 PM,8,5,2:44:20 PM,18,27.0,,-1.3210188999999999,36.841055100000005,-1.3987597999999999,36.767703600000004,Rider_Id_267,2036
-Order_No_19868,User_Id_738,Bike,3,Business,4,4,2:09:49 PM,4,4,2:13:36 PM,4,4,2:42:43 PM,4,4,2:48:56 PM,4,4,3:10:29 PM,11,24.6,,-1.2881479999999998,36.7850276,-1.3261985,36.7926168,Rider_Id_38,1293
-Order_No_21596,User_Id_2207,Bike,3,Business,23,5,11:28:10 AM,23,5,11:30:22 AM,23,5,11:34:30 AM,23,5,11:44:05 AM,23,5,11:51:58 AM,2,24.0,,-1.2684666999999998,36.8002891,-1.2768326,36.8073804,Rider_Id_720,473
-Order_No_3907,User_Id_1245,Bike,3,Business,10,4,10:54:55 AM,10,4,10:55:23 AM,10,4,11:11:37 AM,10,4,11:30:59 AM,10,4,12:04:18 PM,19,24.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_95,1999
-Order_No_15817,User_Id_567,Bike,3,Business,29,3,1:35:51 PM,29,3,1:36:52 PM,29,3,1:54:22 PM,29,3,2:02:30 PM,29,3,2:46:42 PM,7,21.2,,-1.2963097,36.7688221,-1.295723,36.822691999999996,Rider_Id_76,2652
-Order_No_15605,User_Id_2330,Bike,3,Business,6,1,1:11:55 PM,6,1,1:12:20 PM,6,1,1:14:37 PM,6,1,1:19:59 PM,6,1,1:41:24 PM,9,,,-1.2642903,36.80021370000001,-1.2326041,36.8009029,Rider_Id_96,1285
-Order_No_17911,User_Id_3669,Bike,3,Business,2,4,3:32:53 PM,2,4,3:42:47 PM,2,4,3:59:58 PM,2,4,4:27:10 PM,2,4,5:45:08 PM,11,,,-1.2615888000000002,36.7928732,-1.3251583999999998,36.8199384,Rider_Id_292,4678
-Order_No_19692,User_Id_909,Bike,3,Business,24,3,3:42:35 PM,24,3,3:43:00 PM,24,3,4:04:54 PM,24,3,4:14:28 PM,24,3,4:41:07 PM,14,,,-1.225322,36.80855,-1.2413316,36.878552500000005,Rider_Id_607,1599
-Order_No_18849,User_Id_3283,Bike,3,Business,22,2,1:20:09 PM,22,2,1:28:07 PM,22,2,1:43:02 PM,22,2,1:56:53 PM,22,2,2:16:10 PM,13,25.4,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_691,1157
-Order_No_13321,User_Id_1205,Bike,3,Business,8,1,11:45:07 AM,8,1,11:45:57 AM,8,1,12:06:54 PM,8,1,12:13:32 PM,8,1,12:52:44 PM,7,28.0,,-1.2495931,36.80650870000001,-1.2974510000000001,36.803117,Rider_Id_435,2352
-Order_No_24868,User_Id_2423,Bike,3,Business,12,2,1:31:55 PM,12,2,1:51:56 PM,12,2,1:55:11 PM,12,2,2:16:08 PM,12,2,2:52:07 PM,7,,,-1.2861764999999998,36.8209086,-1.2551895,36.7822034,Rider_Id_247,2159
-Order_No_16547,User_Id_3291,Bike,3,Business,23,4,9:04:40 AM,23,4,9:05:03 AM,23,4,9:12:33 AM,23,4,9:24:24 AM,23,4,9:37:44 AM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_624,800
-Order_No_15947,User_Id_1329,Bike,3,Business,30,6,3:55:13 PM,30,6,3:55:35 PM,30,6,4:08:30 PM,30,6,4:59:44 PM,30,6,4:59:47 PM,6,28.1,,-1.273056,36.811298,-1.2886434,36.770986799999996,Rider_Id_613,3
-Order_No_15489,User_Id_2066,Bike,3,Business,7,2,4:34:20 PM,7,2,4:35:00 PM,7,2,5:01:57 PM,7,2,5:03:58 PM,7,2,5:36:25 PM,12,,,-1.3210188999999999,36.841055100000005,-1.304842,36.90576,Rider_Id_233,1947
-Order_No_14025,User_Id_198,Bike,3,Business,21,4,2:14:00 PM,21,4,3:08:44 PM,21,4,3:38:32 PM,21,4,3:40:58 PM,21,4,4:20:15 PM,21,22.7,,-1.3396256000000002,36.8863135,-1.3412198,36.7355439,Rider_Id_285,2357
-Order_No_8077,User_Id_1724,Bike,3,Business,2,3,9:57:45 AM,2,3,9:57:54 AM,2,3,9:58:35 AM,2,3,10:07:24 AM,2,3,10:07:26 AM,2,19.8,,-1.2551895,36.7822034,-1.2571472,36.795063299999995,Rider_Id_647,2
-Order_No_26804,User_Id_1205,Bike,3,Business,16,2,3:14:14 PM,16,2,3:15:53 PM,16,2,3:24:03 PM,16,2,3:46:32 PM,16,2,4:02:47 PM,8,,,-1.2707623,36.7581285,-1.2495931,36.80650870000001,Rider_Id_363,975
-Order_No_6031,User_Id_1798,Bike,3,Business,16,5,2:49:22 PM,16,5,3:05:35 PM,16,5,3:22:10 PM,16,5,3:24:32 PM,16,5,3:52:33 PM,8,,,-1.2615888000000002,36.7928732,-1.2882311000000002,36.7943274,Rider_Id_131,1681
-Order_No_21377,User_Id_1527,Bike,3,Business,15,3,11:56:02 AM,15,3,11:56:48 AM,15,3,12:17:58 PM,15,3,12:24:13 PM,15,3,12:35:24 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_610,671
-Order_No_22334,User_Id_1719,Bike,3,Business,9,3,10:04:32 AM,9,3,10:04:57 AM,9,3,10:10:33 AM,9,3,10:20:52 AM,9,3,11:12:39 AM,16,20.2,,-1.2975066000000002,36.76894229999999,-1.2330694,36.745451,Rider_Id_246,3107
-Order_No_11725,User_Id_1578,Bike,3,Business,8,1,10:25:57 AM,8,1,10:28:15 AM,8,1,10:34:56 AM,8,1,10:39:55 AM,8,1,11:00:27 AM,9,24.6,,-1.28878,36.8168312,-1.2413316,36.878552500000005,Rider_Id_88,1232
-Order_No_12516,User_Id_53,Bike,3,Business,17,3,12:24:55 PM,17,3,12:28:09 PM,17,3,12:31:59 PM,17,3,12:40:54 PM,17,3,12:55:03 PM,7,,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_291,849
-Order_No_5746,User_Id_510,Bike,3,Business,30,4,10:44:18 AM,30,4,10:56:28 AM,30,4,11:32:47 AM,30,4,11:38:23 AM,30,4,11:57:27 AM,10,,,-1.2765736,36.851364600000004,-1.2860183,36.8975338,Rider_Id_404,1144
-Order_No_24978,User_Id_2587,Bike,3,Business,13,3,11:23:35 AM,13,3,11:36:45 AM,13,3,11:59:24 AM,13,3,12:06:24 PM,13,3,12:38:53 PM,11,24.8,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_311,1949
-Order_No_14777,User_Id_2801,Bike,3,Business,27,5,12:28:07 PM,27,5,12:28:49 PM,27,5,12:45:29 PM,27,5,12:50:40 PM,27,5,1:25:59 PM,10,22.5,,-1.3011583,36.841318900000005,-1.2595421999999998,36.7871178,Rider_Id_167,2119
-Order_No_6814,User_Id_552,Bike,3,Business,24,5,2:15:52 PM,24,5,2:16:53 PM,24,5,2:32:56 PM,24,5,3:14:42 PM,24,5,3:40:18 PM,13,23.1,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_621,1536
-Order_No_11952,User_Id_843,Bike,3,Personal,13,4,10:02:45 AM,13,4,10:03:03 AM,13,4,10:04:42 AM,13,4,10:10:44 AM,13,4,10:21:30 AM,8,18.0,1.9,-1.300722,36.7882234,-1.2652279,36.8020159,Rider_Id_216,646
-Order_No_11946,User_Id_1271,Bike,3,Business,21,4,1:29:57 PM,21,4,1:30:34 PM,21,4,1:44:53 PM,21,4,1:53:02 PM,21,4,2:26:15 PM,17,30.9,,-1.2285995,36.901237200000004,-1.3244885,36.897791999999995,Rider_Id_691,1993
-Order_No_25644,User_Id_560,Bike,1,Personal,1,1,10:10:55 AM,1,1,10:11:57 AM,1,1,10:23:03 AM,1,1,10:30:50 AM,1,1,11:08:39 AM,8,18.6,,-1.2611578,36.814530700000006,-1.2242368000000001,36.814868100000005,Rider_Id_409,2269
-Order_No_1654,User_Id_393,Bike,3,Business,29,5,1:55:52 PM,29,5,1:56:14 PM,29,5,2:10:20 PM,29,5,2:21:22 PM,29,5,2:49:24 PM,8,29.1,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_910,1682
-Order_No_17861,User_Id_3115,Bike,3,Business,16,2,2:19:39 PM,16,2,2:21:19 PM,16,2,2:41:49 PM,16,2,3:00:48 PM,16,2,3:29:55 PM,9,27.6,,-1.2739208,36.8310153,-1.2963097,36.7688221,Rider_Id_518,1747
-Order_No_13156,User_Id_3291,Bike,3,Business,5,2,10:14:48 AM,5,2,10:16:20 AM,5,2,10:33:33 AM,5,2,10:50:42 AM,5,2,11:01:21 AM,4,25.4,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_52,639
-Order_No_14235,User_Id_2330,Bike,3,Business,28,4,12:49:23 PM,28,4,1:07:55 PM,28,4,1:31:11 PM,28,4,1:32:42 PM,28,4,1:57:38 PM,4,29.8,,-1.2642903,36.80021370000001,-1.2890715,36.8157588,Rider_Id_511,1496
-Order_No_3970,User_Id_2726,Bike,3,Personal,9,3,12:25:01 PM,9,3,12:25:59 PM,9,3,12:35:36 PM,9,3,12:42:15 PM,9,3,1:32:06 PM,21,25.2,,-1.2812624,36.7714609,-1.306416,36.916820200000004,Rider_Id_800,2991
-Order_No_27093,User_Id_173,Bike,3,Business,19,5,11:13:18 AM,19,5,11:14:08 AM,19,5,11:20:22 AM,19,5,11:55:32 AM,19,5,12:17:45 PM,11,25.0,,-1.3207146,36.8305771,-1.2993231,36.7753372,Rider_Id_1,1333
-Order_No_335,User_Id_1417,Bike,3,Business,2,4,4:11:26 PM,2,4,4:11:46 PM,2,4,4:25:23 PM,2,4,4:41:22 PM,2,4,4:56:18 PM,5,,,-1.2985749,36.8088,-1.2741728,36.79691079999999,Rider_Id_96,896
-Order_No_22813,User_Id_246,Bike,3,Business,20,4,1:00:12 PM,20,4,1:00:32 PM,20,4,1:02:35 PM,20,4,1:07:40 PM,20,4,1:30:03 PM,17,22.0,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_598,1343
-Order_No_28203,User_Id_1865,Bike,1,Personal,7,7,2:02:17 PM,7,7,2:25:51 PM,7,7,2:43:46 PM,7,7,2:47:58 PM,7,7,3:10:40 PM,16,28.0,,-1.2166078000000002,36.882501,-1.2790653,36.7907262,Rider_Id_397,1362
-Order_No_23222,User_Id_426,Bike,3,Business,25,2,1:41:13 PM,25,2,2:20:24 PM,25,2,2:35:56 PM,25,2,2:42:34 PM,25,2,3:06:40 PM,10,26.9,,-1.2824367,36.8353831,-1.3323107,36.8692616,Rider_Id_38,1446
-Order_No_14854,User_Id_3017,Bike,3,Business,7,5,6:24:50 PM,7,5,6:26:19 PM,7,5,6:33:32 PM,7,5,6:39:30 PM,7,5,6:58:09 PM,5,22.3,,-1.2993231,36.7753372,-1.269974,36.77537279999999,Rider_Id_86,1119
-Order_No_14517,User_Id_867,Bike,3,Business,30,5,11:19:02 AM,30,5,11:48:02 AM,30,5,11:52:10 AM,30,5,12:04:07 PM,30,5,12:34:18 PM,10,23.7,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_523,1811
-Order_No_15216,User_Id_896,Bike,3,Personal,4,1,12:24:52 PM,4,1,12:27:37 PM,4,1,12:46:23 PM,4,1,12:58:42 PM,4,1,1:31:56 PM,20,25.5,,-1.3247683000000001,36.85803,-1.2163547,36.8862404,Rider_Id_856,1994
-Order_No_17286,User_Id_136,Bike,3,Business,14,4,5:24:57 PM,14,4,5:38:59 PM,14,4,5:48:13 PM,14,4,5:52:48 PM,14,4,6:33:59 PM,17,21.3,,-1.3326173000000001,36.71462329999999,-1.3233496,36.8139159,Rider_Id_101,2471
-Order_No_6906,User_Id_867,Bike,3,Business,10,1,9:53:29 AM,10,1,9:55:27 AM,10,1,10:05:55 AM,10,1,10:09:05 AM,10,1,11:00:44 AM,13,16.7,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_864,3099
-Order_No_6351,User_Id_1075,Bike,3,Business,31,5,12:38:48 PM,31,5,12:46:59 PM,31,5,12:49:08 PM,31,5,1:01:00 PM,31,5,1:14:58 PM,2,21.9,,-1.2527958000000001,36.8003131,-1.2690931,36.8065779,Rider_Id_72,838
-Order_No_26564,User_Id_3064,Bike,3,Business,4,2,9:40:16 AM,4,2,9:43:04 AM,4,2,9:53:38 AM,4,2,10:08:55 AM,4,2,11:11:56 AM,20,15.8,82.9,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_630,3781
-Order_No_5130,User_Id_3355,Bike,3,Business,4,4,2:54:19 PM,4,4,2:54:45 PM,4,4,2:55:28 PM,4,4,3:06:21 PM,4,4,4:17:35 PM,17,24.8,,-1.3052491000000002,36.8223899,-1.3448483,36.9004081,Rider_Id_81,4274
-Order_No_11646,User_Id_517,Bike,3,Business,3,1,3:40:04 PM,3,1,3:42:30 PM,3,1,3:43:28 PM,3,1,3:51:12 PM,3,1,4:16:27 PM,11,23.3,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_158,1515
-Order_No_19939,User_Id_2616,Bike,3,Personal,5,6,3:42:45 PM,5,6,3:49:23 PM,5,6,4:09:07 PM,5,6,4:31:58 PM,5,6,5:08:37 PM,14,23.9,,-1.1891303,36.896427,-1.27871,36.875113899999995,Rider_Id_304,2199
-Order_No_2388,User_Id_1594,Bike,3,Business,26,5,1:04:10 PM,26,5,1:04:21 PM,26,5,1:21:08 PM,26,5,1:57:56 PM,26,5,2:34:03 PM,14,,,-1.207546,36.7913976,-1.2156006000000001,36.8916865,Rider_Id_411,2167
-Order_No_9322,User_Id_970,Bike,3,Business,7,4,3:34:03 PM,7,4,3:34:18 PM,7,4,3:41:51 PM,7,4,3:47:43 PM,7,4,4:33:10 PM,19,31.0,,-1.2571472,36.795063299999995,-1.3325582,36.740669700000005,Rider_Id_401,2727
-Order_No_25446,User_Id_2956,Bike,3,Business,14,1,12:43:56 PM,14,1,1:03:53 PM,14,1,1:03:59 PM,14,1,1:45:10 PM,14,1,2:28:45 PM,11,25.7,,-1.2860183,36.8975338,-1.290894,36.822971,Rider_Id_408,2615
-Order_No_7144,User_Id_1796,Bike,3,Business,14,4,3:10:06 PM,14,4,3:13:16 PM,14,4,3:22:31 PM,14,4,3:31:23 PM,14,4,3:56:20 PM,11,27.6,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_276,1497
-Order_No_20668,User_Id_2216,Bike,3,Business,28,5,4:41:00 PM,28,5,4:41:29 PM,28,5,5:08:29 PM,28,5,5:13:24 PM,28,5,6:05:39 PM,15,24.5,,-1.2867627000000001,36.883446,-1.2198413000000001,36.9054958,Rider_Id_56,3135
-Order_No_24174,User_Id_2310,Bike,1,Business,22,2,3:56:53 PM,22,2,4:00:24 PM,22,2,4:00:28 PM,22,2,4:15:14 PM,22,2,4:33:31 PM,11,21.1,,-1.2859912,36.8756811,-1.3332749,36.870814700000004,Rider_Id_406,1097
-Order_No_9144,User_Id_111,Bike,3,Business,16,1,1:37:03 PM,16,1,1:37:20 PM,16,1,1:38:55 PM,16,1,1:41:20 PM,16,1,2:02:03 PM,6,22.2,,-1.2551895,36.7822034,-1.2765641,36.793782,Rider_Id_103,1243
-Order_No_9070,User_Id_3521,Bike,3,Business,9,2,1:10:00 PM,9,2,1:10:20 PM,9,2,1:17:36 PM,9,2,1:19:36 PM,9,2,1:34:13 PM,2,26.8,,-1.2701354,36.807216700000005,-1.2696908000000002,36.81519,Rider_Id_88,877
-Order_No_11846,User_Id_2523,Bike,3,Business,5,3,12:06:35 PM,5,3,12:08:06 PM,5,3,12:16:15 PM,5,3,12:18:22 PM,5,3,12:32:25 PM,5,22.4,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_566,843
-Order_No_5794,User_Id_2801,Bike,3,Business,13,4,9:00:44 AM,13,4,9:01:39 AM,13,4,9:07:40 AM,13,4,9:13:37 AM,13,4,9:33:21 AM,11,18.7,,-1.2674273999999999,36.787082700000006,-1.307726,36.839117,Rider_Id_205,1184
-Order_No_12816,User_Id_1329,Bike,3,Business,18,4,11:02:16 AM,18,4,11:02:40 AM,18,4,11:08:11 AM,18,4,11:09:36 AM,18,4,11:27:57 AM,7,,,-1.273056,36.811298,-1.2902438999999999,36.7683384,Rider_Id_96,1101
-Order_No_20393,User_Id_323,Bike,3,Business,14,3,3:54:56 PM,14,3,3:59:49 PM,14,3,4:22:45 PM,14,3,4:33:03 PM,14,3,4:53:25 PM,14,23.4,,-1.3072409999999999,36.830236799999994,-1.3632162,36.9108253,Rider_Id_219,1222
-Order_No_4424,User_Id_1256,Bike,1,Personal,6,1,2:01:20 PM,6,1,2:02:56 PM,6,1,2:15:57 PM,6,1,2:25:40 PM,6,1,2:58:43 PM,9,24.6,,-1.2684962,36.8079433,-1.2287313999999998,36.8367354,Rider_Id_519,1983
-Order_No_11682,User_Id_3635,Bike,1,Business,20,1,4:14:05 PM,20,1,4:17:47 PM,20,1,4:18:19 PM,20,1,5:04:07 PM,20,1,5:48:25 PM,7,20.5,,-1.2615888000000002,36.7928732,-1.2772389,36.7855864,Rider_Id_179,2658
-Order_No_24186,User_Id_517,Bike,3,Business,27,6,11:05:07 AM,27,6,11:07:28 AM,27,6,11:07:37 AM,27,6,11:17:40 AM,27,6,11:24:02 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_161,382
-Order_No_6807,User_Id_383,Bike,3,Business,21,5,1:16:24 PM,21,5,1:23:23 PM,21,5,1:29:41 PM,21,5,1:37:02 PM,21,5,2:45:15 PM,17,24.4,,-1.374163,36.913444,-1.2882411,36.827495500000005,Rider_Id_685,4093
-Order_No_12821,User_Id_3689,Bike,1,Business,16,1,9:44:36 AM,16,1,9:44:46 AM,16,1,9:51:07 AM,16,1,9:54:01 AM,16,1,10:05:07 AM,9,14.7,,-1.260232,36.807334000000004,-1.2248308,36.801257299999996,Rider_Id_103,666
-Order_No_25731,User_Id_3450,Bike,3,Business,3,4,2:41:22 PM,3,4,2:42:07 PM,3,4,4:04:33 PM,3,4,4:07:43 PM,3,4,4:32:55 PM,6,25.0,,-1.2825816,36.8092346,-1.2559559999999999,36.789867,Rider_Id_916,1512
-Order_No_460,User_Id_2978,Bike,3,Business,27,1,3:28:07 PM,27,1,3:28:21 PM,27,1,3:34:42 PM,27,1,3:40:21 PM,27,1,4:00:45 PM,6,,,-1.2677819,36.8006922,-1.2977768,36.8110285,Rider_Id_88,1224
-Order_No_20957,User_Id_604,Bike,3,Business,20,3,8:33:19 AM,20,3,8:33:43 AM,20,3,8:45:12 AM,20,3,8:46:19 AM,20,3,9:11:45 AM,8,,,-1.2551895,36.7822034,-1.2995101,36.8185828,Rider_Id_188,1526
-Order_No_19511,User_Id_2523,Bike,3,Business,9,5,2:24:50 PM,9,5,2:33:06 PM,9,5,2:42:09 PM,9,5,2:54:45 PM,9,5,3:55:52 PM,8,26.3,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_726,3667
-Order_No_10174,User_Id_3242,Bike,1,Personal,16,2,1:46:54 PM,16,2,1:47:14 PM,16,2,2:02:32 PM,16,2,2:39:55 PM,16,2,3:08:27 PM,14,27.3,,-1.3255316000000001,36.711542,-1.2939816000000002,36.8118469,Rider_Id_847,1712
-Order_No_4978,User_Id_1271,Bike,3,Business,5,2,3:22:15 PM,5,2,3:23:08 PM,5,2,3:41:01 PM,5,2,3:44:24 PM,5,2,4:20:48 PM,10,27.5,,-1.3108386,36.7555239,-1.2649605,36.798177700000004,Rider_Id_198,2184
-Order_No_15318,User_Id_2804,Bike,3,Business,2,4,10:54:42 AM,2,4,10:55:30 AM,2,4,11:05:45 AM,2,4,11:12:40 AM,2,4,11:37:43 AM,7,,,-1.2571472,36.795063299999995,-1.2981352,36.8213822,Rider_Id_138,1503
-Order_No_5298,User_Id_3111,Bike,3,Personal,21,5,11:43:32 AM,21,5,11:43:55 AM,21,5,12:18:26 PM,21,5,12:26:59 PM,21,5,1:00:36 PM,15,22.6,,-1.2363091999999998,36.882361700000004,-1.2966535,36.8123136,Rider_Id_86,2017
-Order_No_2682,User_Id_3291,Bike,3,Business,13,3,2:36:21 PM,13,3,2:38:34 PM,13,3,2:59:06 PM,13,3,3:07:58 PM,13,3,3:30:21 PM,5,23.3,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_726,1343
-Order_No_10279,User_Id_2523,Bike,3,Business,13,1,9:19:47 AM,13,1,9:28:29 AM,13,1,9:29:52 AM,13,1,9:38:21 AM,13,1,10:08:33 AM,6,,,-1.2551895,36.7822034,-1.2904753,36.8144721,Rider_Id_692,1812
-Order_No_2808,User_Id_238,Bike,3,Business,26,5,10:35:35 AM,26,5,10:35:58 AM,26,5,10:59:38 AM,26,5,11:00:42 AM,26,5,11:13:24 AM,3,,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_613,762
-Order_No_6674,User_Id_186,Bike,3,Business,22,5,1:04:31 PM,22,5,1:09:02 PM,22,5,1:20:35 PM,22,5,1:26:55 PM,22,5,1:43:31 PM,6,28.7,,-1.2571472,36.795063299999995,-1.2913246,36.8019522,Rider_Id_448,996
-Order_No_25251,User_Id_2190,Bike,3,Business,12,4,12:00:09 PM,12,4,12:00:59 PM,12,4,12:12:09 PM,12,4,12:25:58 PM,12,4,12:36:48 PM,5,19.1,,-1.3019530000000001,36.788458,-1.277261,36.790856,Rider_Id_312,650
-Order_No_6063,User_Id_334,Bike,3,Personal,5,6,2:58:08 PM,5,6,5:01:08 PM,5,6,5:10:43 PM,5,6,5:13:58 PM,5,6,5:45:53 PM,17,23.0,,-1.2963097,36.7688221,-1.3253826000000002,36.8822315,Rider_Id_522,1915
-Order_No_27272,User_Id_3291,Bike,3,Business,18,1,10:48:16 AM,18,1,10:57:02 AM,18,1,11:00:28 AM,18,1,11:16:53 AM,18,1,11:46:45 AM,8,27.9,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_210,1792
-Order_No_7998,User_Id_1153,Bike,2,Personal,7,2,7:09:01 PM,7,2,7:10:21 PM,7,2,7:19:00 PM,7,2,7:27:25 PM,7,2,7:57:44 PM,11,22.8,,-1.2620159,36.7956487,-1.3159017,36.783604,Rider_Id_329,1819
-Order_No_18076,User_Id_2770,Bike,1,Personal,18,1,2:30:33 PM,18,1,2:30:58 PM,18,1,2:34:55 PM,18,1,2:37:34 PM,18,1,3:25:02 PM,9,17.1,,-1.312469,36.815177,-1.2555718,36.8462247,Rider_Id_676,2848
-Order_No_27087,User_Id_3080,Bike,3,Business,12,3,1:19:57 PM,12,3,1:20:17 PM,12,3,1:33:53 PM,12,3,1:50:23 PM,12,3,1:50:31 PM,3,26.3,,-1.2643693999999999,36.785413500000004,-1.2551895,36.7822034,Rider_Id_566,8
-Order_No_6037,User_Id_136,Bike,3,Business,1,3,5:38:15 PM,1,3,5:38:42 PM,1,3,5:53:57 PM,1,3,5:57:32 PM,1,3,6:36:10 PM,16,23.5,,-1.3326173000000001,36.71462329999999,-1.2649605,36.798177700000004,Rider_Id_334,2318
-Order_No_8781,User_Id_1471,Bike,3,Business,13,3,11:14:51 AM,13,3,11:15:14 AM,13,3,11:18:00 AM,13,3,11:27:34 AM,13,3,11:27:47 AM,3,24.7,,-1.2551895,36.7822034,-1.2705336,36.7943509,Rider_Id_720,13
-Order_No_19644,User_Id_3283,Bike,3,Business,14,4,4:20:26 PM,14,4,4:22:33 PM,14,4,4:32:48 PM,14,4,4:41:22 PM,14,4,4:55:56 PM,6,26.2,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_427,874
-Order_No_15517,User_Id_2596,Bike,3,Business,18,5,12:50:08 PM,18,5,12:50:20 PM,18,5,12:50:36 PM,18,5,1:11:10 PM,18,5,2:12:02 PM,18,20.3,,-1.3263040000000001,36.8665245,-1.2482465,36.8889062,Rider_Id_191,3652
-Order_No_22506,User_Id_1562,Bike,2,Personal,8,7,1:44:06 PM,8,7,1:54:42 PM,8,7,2:38:39 PM,8,7,2:44:11 PM,8,7,2:54:09 PM,2,21.7,,-1.288966,36.791814,-1.2834504999999998,36.7856036,Rider_Id_465,598
-Order_No_7061,User_Id_1792,Bike,3,Business,22,2,2:29:13 PM,22,2,2:29:21 PM,22,2,2:44:40 PM,22,2,2:47:21 PM,22,2,3:23:37 PM,19,25.5,,-1.2809121,36.7852524,-1.3094447,36.9107706,Rider_Id_449,2176
-Order_No_25057,User_Id_2987,Bike,3,Business,29,4,9:48:50 AM,29,4,9:49:28 AM,29,4,10:03:20 AM,29,4,10:06:02 AM,29,4,10:18:50 AM,4,18.5,3.9,-1.2702120000000001,36.82108770000001,-1.2534301,36.800098299999995,Rider_Id_612,768
-Order_No_26769,User_Id_3614,Bike,2,Personal,8,1,12:32:33 PM,8,1,12:33:04 PM,8,1,12:45:19 PM,8,1,12:56:45 PM,8,1,1:12:09 PM,11,24.3,,-1.2546636999999998,36.8248917,-1.2100548,36.7809686,Rider_Id_427,924
-Order_No_12291,User_Id_2096,Bike,1,Personal,5,6,8:59:35 AM,5,6,8:59:52 AM,5,6,9:08:04 AM,5,6,9:15:27 AM,5,6,10:11:14 AM,24,19.1,,-1.2821108,36.8250555,-1.3587746,36.65174520000001,Rider_Id_338,3347
-Order_No_2715,User_Id_2216,Bike,3,Business,5,2,12:36:05 PM,5,2,12:36:53 PM,5,2,1:31:39 PM,5,2,1:51:27 PM,5,2,2:44:57 PM,25,,,-1.2867627000000001,36.883446,-1.1429062,36.9300994,Rider_Id_697,3210
-Order_No_13039,User_Id_3752,Bike,3,Business,3,5,2:19:55 PM,3,5,2:22:25 PM,3,5,2:29:47 PM,3,5,3:12:56 PM,3,5,3:24:03 PM,5,,,-1.3216968999999998,36.8313968,-1.2965799999999998,36.823699,Rider_Id_125,667
-Order_No_3851,User_Id_1304,Bike,3,Business,31,3,1:37:41 PM,31,3,1:38:36 PM,31,3,1:53:55 PM,31,3,1:55:44 PM,31,3,2:50:03 PM,17,27.1,,-1.3332749,36.870814700000004,-1.2805699,36.7730714,Rider_Id_30,3259
-Order_No_9126,User_Id_3686,Bike,3,Business,20,3,1:10:43 PM,20,3,1:11:51 PM,20,3,1:23:34 PM,20,3,1:25:40 PM,20,3,1:44:54 PM,10,27.0,,-1.2551895,36.7822034,-1.303148,36.792768,Rider_Id_770,1154
-Order_No_16505,User_Id_2166,Bike,2,Personal,11,3,11:11:20 AM,11,3,11:15:05 AM,11,3,11:36:16 AM,11,3,11:42:28 AM,11,3,11:57:22 AM,8,19.0,,-1.2804989,36.83034620000001,-1.2343087,36.84749,Rider_Id_808,894
-Order_No_15124,User_Id_3599,Bike,3,Business,2,4,11:50:25 AM,2,4,11:55:58 AM,2,4,12:44:03 PM,2,4,12:47:56 PM,2,4,1:27:28 PM,12,,,-1.3302996,36.8707935,-1.3209614,36.78088270000001,Rider_Id_864,2372
-Order_No_4789,User_Id_2522,Bike,3,Personal,18,2,7:27:08 AM,18,2,9:01:31 AM,18,2,9:21:05 AM,18,2,9:29:43 AM,18,2,10:08:59 AM,9,16.3,,-1.258348,36.844901799999995,-1.307774,36.840754100000005,Rider_Id_427,2356
-Order_No_12478,User_Id_391,Bike,3,Business,7,5,8:50:41 AM,7,5,8:51:02 AM,7,5,9:54:25 AM,7,5,10:50:32 AM,7,5,10:50:35 AM,20,,,-1.319613,36.711055,-1.2867454,36.8259017,Rider_Id_855,3
-Order_No_25542,User_Id_1882,Bike,3,Business,19,2,7:02:51 PM,19,2,7:03:18 PM,19,2,7:09:47 PM,19,2,7:36:12 PM,19,2,7:50:07 PM,8,23.6,,-1.2958783,36.7928125,-1.2591018999999999,36.800577000000004,Rider_Id_336,835
-Order_No_897,User_Id_3295,Bike,3,Business,28,4,10:20:07 AM,28,4,10:20:24 AM,28,4,10:20:42 AM,28,4,10:31:06 AM,28,4,10:41:01 AM,3,22.5,,-1.2571472,36.795063299999995,-1.2753108999999998,36.8130185,Rider_Id_623,595
-Order_No_2082,User_Id_552,Bike,3,Business,10,1,11:22:03 AM,10,1,11:27:11 AM,10,1,11:46:03 AM,10,1,12:01:46 PM,10,1,1:09:18 PM,18,18.3,,-1.290894,36.822971,-1.3562374,36.904295399999995,Rider_Id_271,4052
-Order_No_26146,User_Id_1329,Bike,3,Business,30,2,2:52:53 PM,30,2,4:07:53 PM,30,2,4:23:35 PM,30,2,4:26:50 PM,30,2,4:39:34 PM,3,,,-1.273056,36.811298,-1.2546732,36.808679600000005,Rider_Id_953,764
-Order_No_24935,User_Id_1335,Bike,3,Personal,14,4,4:44:19 PM,14,4,4:44:28 PM,14,4,4:59:22 PM,14,4,5:02:19 PM,14,4,5:36:34 PM,5,21.0,,-1.2611987,36.80609129999999,-1.2536855,36.8245276,Rider_Id_804,2055
-Order_No_2688,User_Id_2946,Bike,2,Personal,8,3,12:36:20 PM,8,3,12:38:17 PM,8,3,12:46:16 PM,8,3,1:14:29 PM,8,3,1:38:28 PM,7,22.2,,-1.2749662,36.7658781,-1.289521,36.819072399999996,Rider_Id_179,1439
-Order_No_27207,User_Id_3127,Bike,3,Business,1,1,2:50:12 PM,1,1,2:51:41 PM,1,1,3:02:55 PM,1,1,3:05:11 PM,1,1,3:24:49 PM,12,25.4,,-1.2918246,36.801584999999996,-1.3368903,36.767101000000004,Rider_Id_865,1178
-Order_No_27454,User_Id_1471,Bike,3,Business,30,2,11:10:48 AM,30,2,11:15:58 AM,30,2,12:13:32 PM,30,2,12:30:46 PM,30,2,1:32:16 PM,9,,,-1.2551895,36.7822034,-1.3081376,36.83297720000001,Rider_Id_394,3690
-Order_No_9748,User_Id_3201,Bike,3,Business,19,3,3:44:29 PM,19,3,3:48:59 PM,19,3,4:01:38 PM,19,3,4:08:14 PM,19,3,4:49:15 PM,15,25.5,,-1.3463041999999998,36.768164,-1.2649605,36.798177700000004,Rider_Id_328,2461
-Order_No_12433,User_Id_873,Bike,1,Personal,26,3,12:13:21 PM,26,3,12:13:36 PM,26,3,12:27:11 PM,26,3,12:38:03 PM,26,3,1:04:24 PM,8,24.0,,-1.3308746999999999,36.8567485,-1.2872899,36.820434000000006,Rider_Id_698,1581
-Order_No_13950,User_Id_3091,Bike,3,Business,30,5,1:09:59 PM,30,5,1:10:04 PM,30,5,1:10:08 PM,30,5,1:22:30 PM,30,5,1:34:43 PM,4,23.7,,-1.3012008,36.764868,-1.2980272,36.783883700000004,Rider_Id_786,733
-Order_No_1592,User_Id_3560,Bike,3,Business,27,3,4:28:28 PM,27,3,4:32:01 PM,27,3,4:39:00 PM,27,3,4:47:44 PM,27,3,5:29:17 PM,5,27.7,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_351,2493
-Order_No_27403,User_Id_540,Bike,3,Business,23,5,12:13:08 PM,23,5,12:27:17 PM,23,5,12:35:11 PM,23,5,12:44:36 PM,23,5,12:57:43 PM,6,23.9,,-1.2571472,36.795063299999995,-1.2967701999999999,36.8040381,Rider_Id_178,787
-Order_No_3077,User_Id_1899,Bike,3,Business,10,1,2:53:30 PM,10,1,2:57:32 PM,10,1,3:24:19 PM,10,1,3:35:06 PM,10,1,4:04:56 PM,13,,,-1.293515,36.897607,-1.331619,36.847976,Rider_Id_548,1790
-Order_No_5309,User_Id_3560,Bike,3,Business,12,6,11:21:19 AM,12,6,11:29:16 AM,12,6,11:35:26 AM,12,6,11:56:45 AM,12,6,12:06:19 PM,2,23.2,,-1.28871,36.825125299999996,-1.279395,36.825364,Rider_Id_742,574
-Order_No_15140,User_Id_2200,Bike,3,Business,15,3,3:53:49 PM,15,3,3:55:09 PM,15,3,4:11:33 PM,15,3,4:21:36 PM,15,3,5:01:45 PM,11,21.6,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_234,2409
-Order_No_2677,User_Id_1755,Bike,3,Personal,12,4,6:02:03 PM,12,4,6:03:34 PM,12,4,6:11:59 PM,12,4,6:26:56 PM,12,4,6:40:53 PM,6,20.7,,-1.2610959,36.828102200000004,-1.2963483999999998,36.807258399999995,Rider_Id_427,837
-Order_No_21707,User_Id_1329,Bike,3,Business,21,5,10:16:40 AM,21,5,10:16:52 AM,21,5,10:19:46 AM,21,5,10:21:59 AM,21,5,10:35:29 AM,5,19.3,,-1.273056,36.811298,-1.2905055,36.778964,Rider_Id_720,810
-Order_No_11651,User_Id_391,Bike,3,Business,1,4,3:46:27 PM,1,4,3:50:37 PM,1,4,4:03:36 PM,1,4,4:23:06 PM,1,4,5:12:15 PM,18,,,-1.2857573999999998,36.8295667,-1.3878504999999999,36.7692885,Rider_Id_942,2949
-Order_No_2171,User_Id_1205,Bike,3,Business,24,5,1:08:07 PM,24,5,1:11:34 PM,24,5,1:23:52 PM,24,5,1:30:25 PM,24,5,1:48:26 PM,4,20.9,,-1.2810807,36.8144229,-1.2680829,36.798204,Rider_Id_88,1081
-Order_No_20687,User_Id_1260,Bike,3,Business,7,2,4:10:58 PM,7,2,4:11:40 PM,7,2,4:16:19 PM,7,2,4:35:25 PM,7,2,4:48:51 PM,6,23.0,,-1.2890399,36.8164942,-1.2551895,36.7822034,Rider_Id_107,806
-Order_No_6138,User_Id_1280,Bike,3,Business,18,1,10:28:34 AM,18,1,10:29:02 AM,18,1,10:38:02 AM,18,1,10:42:46 AM,18,1,10:54:25 AM,5,27.8,,-1.2551895,36.7822034,-1.265981,36.793612100000004,Rider_Id_147,699
-Order_No_12311,User_Id_2358,Bike,4,Business,3,1,5:28:46 PM,3,1,5:29:06 PM,3,1,5:29:47 PM,3,1,5:42:04 PM,3,1,5:48:00 PM,2,22.0,,-1.302222,36.75873,-1.296797,36.776452500000005,Rider_Id_449,356
-Order_No_26371,User_Id_2587,Bike,3,Business,3,4,2:48:44 PM,3,4,2:49:06 PM,3,4,2:58:47 PM,3,4,3:11:27 PM,3,4,3:34:34 PM,13,26.0,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_101,1387
-Order_No_6787,User_Id_3201,Bike,3,Business,28,4,9:14:35 AM,28,4,9:14:50 AM,28,4,9:22:00 AM,28,4,9:32:40 AM,28,4,9:47:42 AM,9,19.5,,-1.2967701999999999,36.8040381,-1.2584143,36.8048002,Rider_Id_933,902
-Order_No_25820,User_Id_2358,Bike,4,Business,8,5,1:15:27 PM,8,5,1:17:23 PM,8,5,1:54:50 PM,8,5,2:02:18 PM,8,5,2:24:05 PM,4,21.5,,-1.2884227,36.772450899999995,-1.2790653,36.7907262,Rider_Id_148,1307
-Order_No_22910,User_Id_1280,Bike,3,Business,11,4,7:43:42 AM,11,4,7:43:55 AM,11,4,8:13:21 AM,11,4,8:46:16 AM,11,4,9:24:47 AM,18,18.7,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_478,2311
-Order_No_14709,User_Id_1346,Bike,3,Business,13,1,11:27:25 AM,13,1,11:28:01 AM,13,1,11:37:35 AM,13,1,11:50:21 AM,13,1,12:07:14 PM,6,,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_435,1013
-Order_No_6352,User_Id_136,Bike,3,Business,18,1,12:20:12 PM,18,1,12:21:12 PM,18,1,12:30:44 PM,18,1,12:36:58 PM,18,1,12:53:40 PM,11,27.9,,-1.3254522,36.721658399999995,-1.2887613000000002,36.77305379999999,Rider_Id_72,1002
-Order_No_15279,User_Id_1428,Bike,3,Personal,18,1,12:30:54 PM,18,1,1:15:30 PM,18,1,1:34:52 PM,18,1,1:40:03 PM,18,1,1:55:15 PM,8,17.2,,-1.300106,36.7584546,-1.3178524,36.7139804,Rider_Id_312,912
-Order_No_14927,User_Id_577,Bike,3,Personal,18,2,2:00:12 PM,18,2,2:02:31 PM,18,2,2:13:21 PM,18,2,2:23:08 PM,18,2,2:48:54 PM,12,25.7,,-1.3270347,36.866736200000005,-1.2662728,36.8151265,Rider_Id_647,1546
-Order_No_18425,User_Id_3426,Bike,3,Business,4,1,2:48:16 PM,4,1,2:49:16 PM,4,1,2:52:44 PM,4,1,3:08:04 PM,4,1,4:32:19 PM,9,28.5,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_275,5055
-Order_No_27212,User_Id_1346,Bike,3,Business,23,4,3:54:46 PM,23,4,3:54:59 PM,23,4,4:00:51 PM,23,4,4:07:02 PM,23,4,4:21:53 PM,5,20.9,,-1.2998129999999999,36.787139200000006,-1.2764851000000002,36.8007126,Rider_Id_249,891
-Order_No_1502,User_Id_3221,Bike,3,Business,15,5,12:50:36 PM,15,5,12:52:07 PM,15,5,12:59:40 PM,15,5,1:16:01 PM,15,5,1:27:30 PM,4,26.4,,-1.2811172,36.8156831,-1.2738521999999999,36.799281799999996,Rider_Id_906,689
-Order_No_6301,User_Id_233,Bike,1,Personal,5,4,9:27:11 AM,5,4,9:33:24 AM,5,4,9:35:27 AM,5,4,9:52:22 AM,5,4,10:06:06 AM,7,,,-1.2897399,36.8270975,-1.2561913999999998,36.794654200000004,Rider_Id_208,824
-Order_No_5996,User_Id_2987,Bike,3,Business,16,1,12:15:18 PM,16,1,12:49:54 PM,16,1,12:56:12 PM,16,1,1:02:11 PM,16,1,1:18:15 PM,10,19.1,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_427,964
-Order_No_21990,User_Id_1271,Bike,3,Business,8,1,1:40:08 PM,8,1,2:57:56 PM,8,1,3:20:59 PM,8,1,3:48:18 PM,8,1,4:11:53 PM,8,25.1,,-1.3244885,36.897791999999995,-1.2917003999999999,36.8802381,Rider_Id_890,1415
-Order_No_14595,User_Id_1925,Bike,2,Personal,19,2,3:22:15 PM,19,2,3:48:53 PM,19,2,4:04:16 PM,19,2,4:20:21 PM,19,2,4:49:40 PM,12,18.4,,-1.2860183,36.8975338,-1.325808,36.861664000000005,Rider_Id_543,1759
-Order_No_12115,User_Id_2195,Bike,1,Business,28,5,11:43:57 AM,28,5,11:44:46 AM,28,5,11:49:57 AM,28,5,12:00:18 PM,28,5,12:20:05 PM,13,24.9,,-1.303596,36.778378000000004,-1.3313643,36.857742200000004,Rider_Id_100,1187
-Order_No_11574,User_Id_3547,Bike,3,Business,9,4,11:07:50 AM,9,4,11:11:55 AM,9,4,11:32:14 AM,9,4,12:03:31 PM,9,4,12:03:37 PM,11,,,-1.2901543,36.8316523,-1.2856319,36.767299799999996,Rider_Id_921,6
-Order_No_4033,User_Id_2216,Bike,3,Business,21,4,3:32:20 PM,21,4,3:33:54 PM,21,4,3:36:52 PM,21,4,4:10:27 PM,21,4,4:24:29 PM,3,26.1,,-1.2551895,36.7822034,-1.2568411,36.8012533,Rider_Id_822,842
-Order_No_8946,User_Id_1724,Bike,3,Business,26,4,4:46:26 PM,26,4,4:47:38 PM,26,4,4:49:58 PM,26,4,5:21:20 PM,26,4,5:38:10 PM,6,19.6,,-1.2859677,36.8215312,-1.2849763,36.7841767,Rider_Id_340,1010
-Order_No_3381,User_Id_1329,Bike,3,Business,11,4,10:37:33 AM,11,4,10:38:05 AM,11,4,10:55:31 AM,11,4,10:58:29 AM,11,4,11:25:49 AM,19,25.8,,-1.273056,36.811298,-1.1987504999999998,36.9322761,Rider_Id_404,1640
-Order_No_16131,User_Id_3217,Bike,3,Business,13,3,3:43:49 PM,13,3,3:44:19 PM,13,3,3:46:30 PM,13,3,3:57:51 PM,13,3,4:45:09 PM,21,23.5,,-1.214328,36.7911903,-1.2216291000000001,36.9180415,Rider_Id_101,2838
-Order_No_12196,User_Id_970,Bike,3,Business,28,2,12:20:33 PM,28,2,12:21:46 PM,28,2,12:29:51 PM,28,2,12:34:39 PM,28,2,12:58:16 PM,8,21.1,,-1.2571472,36.795063299999995,-1.2651573999999999,36.7474347,Rider_Id_162,1417
-Order_No_13865,User_Id_2030,Bike,3,Business,3,4,9:54:06 AM,3,4,10:05:55 AM,3,4,10:22:09 AM,3,4,10:24:31 AM,3,4,10:31:38 AM,2,20.2,,-1.328259,36.860975,-1.3333725,36.871761299999996,Rider_Id_559,427
-Order_No_15772,User_Id_1724,Bike,3,Business,27,1,3:29:12 PM,27,1,3:33:42 PM,27,1,3:37:14 PM,27,1,3:55:52 PM,27,1,4:24:55 PM,8,23.1,,-1.2920721000000002,36.831188,-1.2551895,36.7822034,Rider_Id_89,1743
-Order_No_17625,User_Id_3487,Bike,3,Business,26,5,3:39:06 PM,26,5,3:47:05 PM,26,5,3:56:30 PM,26,5,4:17:31 PM,26,5,4:48:22 PM,11,,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_281,1851
-Order_No_434,User_Id_1245,Bike,3,Business,7,1,4:33:43 PM,7,1,4:52:09 PM,7,1,5:18:42 PM,7,1,5:22:05 PM,7,1,5:54:47 PM,11,24.4,,-1.3198615,36.7110323,-1.3841736,36.7418921,Rider_Id_862,1962
-Order_No_1245,User_Id_867,Bike,3,Business,11,4,8:07:22 AM,11,4,8:10:57 AM,11,4,8:39:38 AM,11,4,9:11:12 AM,11,4,9:37:43 AM,10,22.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_206,1591
-Order_No_7046,User_Id_1484,Bike,3,Business,4,1,3:22:27 PM,4,1,3:22:59 PM,4,1,3:24:11 PM,4,1,3:37:24 PM,4,1,3:57:29 PM,3,28.6,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_828,1205
-Order_No_16213,User_Id_1207,Bike,3,Business,14,4,12:19:31 PM,14,4,12:38:47 PM,14,4,1:35:39 PM,14,4,1:38:29 PM,14,4,2:29:03 PM,12,27.9,,-1.2629666000000002,36.8068369,-1.2351541000000001,36.8748505,Rider_Id_268,3034
-Order_No_6694,User_Id_2828,Bike,3,Business,23,6,2:16:26 PM,23,6,2:16:40 PM,23,6,2:23:46 PM,23,6,3:22:43 PM,23,6,3:41:06 PM,25,28.1,,-1.2615888000000002,36.7928732,-1.3593378,36.9433184,Rider_Id_863,1103
-Order_No_17081,User_Id_1724,Bike,3,Business,8,4,9:44:07 AM,8,4,9:45:12 AM,8,4,9:58:10 AM,8,4,10:04:30 AM,8,4,10:32:21 AM,9,20.4,,-1.2551895,36.7822034,-1.2980272,36.783883700000004,Rider_Id_279,1671
-Order_No_8450,User_Id_3710,Bike,3,Business,27,2,9:51:48 AM,27,2,11:26:22 AM,27,2,11:29:43 AM,27,2,11:40:12 AM,27,2,12:25:49 PM,7,24.0,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_268,2737
-Order_No_8749,User_Id_3456,Bike,3,Personal,9,4,2:10:29 PM,9,4,2:10:45 PM,9,4,2:13:15 PM,9,4,2:24:47 PM,9,4,2:57:50 PM,8,25.9,,-1.282357,36.782501,-1.311547,36.818749,Rider_Id_722,1983
-Order_No_27425,User_Id_649,Bike,3,Business,4,4,8:19:24 AM,4,4,8:19:40 AM,4,4,8:52:51 AM,4,4,9:01:06 AM,4,4,9:47:32 AM,25,18.3,,-1.2551895,36.7822034,-1.3798624,36.945458,Rider_Id_478,2786
-Order_No_12665,User_Id_893,Bike,3,Business,14,2,1:22:52 PM,14,2,1:24:26 PM,14,2,1:30:12 PM,14,2,1:37:06 PM,14,2,1:55:46 PM,8,,,-1.2663194,36.809901,-1.2985153,36.7876043,Rider_Id_337,1120
-Order_No_18297,User_Id_1500,Bike,3,Business,7,2,11:18:21 AM,7,2,11:22:57 AM,7,2,11:37:41 AM,7,2,11:39:14 AM,7,2,11:56:56 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161,1062
-Order_No_23947,User_Id_2775,Bike,3,Business,5,5,1:53:36 PM,5,5,1:55:10 PM,5,5,2:05:39 PM,5,5,2:14:23 PM,5,5,2:23:06 PM,3,26.8,,-1.2675332000000001,36.808561,-1.26071,36.8206332,Rider_Id_754,523
-Order_No_4884,User_Id_1410,Bike,3,Business,9,2,8:19:10 AM,9,2,8:19:20 AM,9,2,8:20:12 AM,9,2,9:47:18 AM,9,2,9:53:36 AM,19,17.6,4.0,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_70,378
-Order_No_2062,User_Id_2423,Bike,3,Business,8,5,12:07:03 PM,8,5,12:09:11 PM,8,5,12:19:05 PM,8,5,12:23:32 PM,8,5,12:38:09 PM,8,28.2,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_336,877
-Order_No_13177,User_Id_3613,Bike,3,Business,4,1,11:42:05 AM,4,1,11:44:18 AM,4,1,11:55:28 AM,4,1,12:02:53 PM,4,1,12:29:41 PM,9,26.4,,-1.2733622,36.7704045,-1.3044313,36.818504,Rider_Id_496,1608
-Order_No_9233,User_Id_3201,Bike,3,Business,18,4,10:02:53 AM,18,4,10:16:03 AM,18,4,10:16:46 AM,18,4,10:21:07 AM,18,4,10:33:55 AM,4,20.6,,-1.2805757,36.8223948,-1.2642719999999998,36.7978791,Rider_Id_583,768
-Order_No_25425,User_Id_1578,Bike,3,Business,5,1,11:23:58 AM,5,1,11:25:36 AM,5,1,11:36:04 AM,5,1,11:59:57 AM,5,1,12:03:49 PM,1,24.4,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_519,232
-Order_No_9052,User_Id_2931,Bike,3,Business,16,3,9:12:43 AM,16,3,9:12:53 AM,16,3,9:47:41 AM,16,3,10:18:19 AM,16,3,10:56:17 AM,23,19.5,,-1.3433181,36.9138018,-1.2452664,36.802854100000005,Rider_Id_404,2278
-Order_No_16535,User_Id_2200,Bike,3,Business,21,5,10:54:00 AM,21,5,12:48:40 PM,21,5,12:57:29 PM,21,5,1:15:07 PM,21,5,1:23:50 PM,2,22.7,,-1.290894,36.822971,-1.2848173999999999,36.826163,Rider_Id_489,523
-Order_No_5269,User_Id_3194,Bike,3,Business,19,1,3:43:59 PM,19,1,3:45:14 PM,19,1,3:53:48 PM,19,1,3:59:33 PM,19,1,4:16:04 PM,8,25.0,,-1.2551895,36.7822034,-1.2597436000000002,36.8282526,Rider_Id_649,991
-Order_No_22612,User_Id_136,Bike,3,Business,20,2,3:29:14 PM,20,2,3:34:08 PM,20,2,4:01:21 PM,20,2,4:36:58 PM,20,2,5:15:01 PM,19,23.7,,-1.336582,36.871736999999996,-1.3254522,36.721658399999995,Rider_Id_559,2283
-Order_No_9197,User_Id_1245,Bike,3,Business,21,2,9:38:29 AM,21,2,9:38:50 AM,21,2,9:39:07 AM,21,2,10:14:43 AM,21,2,10:39:55 AM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_120,1512
-Order_No_24298,User_Id_1346,Bike,3,Business,13,5,12:23:56 PM,13,5,12:24:19 PM,13,5,12:39:40 PM,13,5,12:44:00 PM,13,5,1:24:13 PM,20,,,-1.2998129999999999,36.787139200000006,-1.3700383,36.9190174,Rider_Id_844,2413
-Order_No_27889,User_Id_3641,Bike,3,Business,11,1,2:22:01 PM,11,1,2:24:10 PM,11,1,2:28:14 PM,11,1,2:31:48 PM,11,1,2:35:23 PM,4,28.7,,-1.2551895,36.7822034,-1.254373,36.7760066,Rider_Id_242,215
-Order_No_25246,User_Id_2066,Bike,3,Business,19,2,12:42:25 PM,19,2,12:43:21 PM,19,2,12:58:27 PM,19,2,1:03:37 PM,19,2,1:20:54 PM,11,26.6,,-1.3210188999999999,36.841055100000005,-1.3202327,36.786831400000004,Rider_Id_914,1037
-Order_No_692,User_Id_1527,Bike,3,Business,9,4,10:45:31 AM,9,4,10:45:52 AM,9,4,10:47:07 AM,9,4,11:07:16 AM,9,4,11:11:28 AM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_472,252
-Order_No_16522,User_Id_2828,Bike,3,Business,12,2,11:54:37 AM,12,2,11:55:29 AM,12,2,12:06:45 PM,12,2,12:08:47 PM,12,2,12:16:31 PM,2,21.2,,-1.2615888000000002,36.7928732,-1.2560455,36.780086700000005,Rider_Id_726,464
-Order_No_11361,User_Id_2956,Bike,3,Business,2,2,1:13:03 PM,2,2,1:17:48 PM,2,2,2:04:40 PM,2,2,2:06:44 PM,2,2,2:33:01 PM,10,29.6,,-1.2584143,36.8048002,-1.3129578000000002,36.8463197,Rider_Id_660,1577
-Order_No_4990,User_Id_794,Bike,2,Personal,15,1,7:52:54 AM,15,1,8:01:03 AM,15,1,8:17:43 AM,15,1,8:20:37 AM,15,1,8:33:11 AM,6,,,-1.2627203000000002,36.7929743,-1.2902247,36.7922037,Rider_Id_726,754
-Order_No_14540,User_Id_1253,Bike,2,Personal,22,5,12:18:20 PM,22,5,12:19:43 PM,22,5,12:42:20 PM,22,5,12:48:56 PM,22,5,1:00:32 PM,5,21.6,,-1.2861764999999998,36.8209086,-1.2785621999999999,36.8541857,Rider_Id_906,696
-Order_No_27209,User_Id_2066,Bike,3,Business,24,3,11:17:16 AM,24,3,11:19:02 AM,24,3,12:19:10 PM,24,3,12:21:06 PM,24,3,12:42:11 PM,12,,,-1.3270716,36.8406371,-1.338558,36.9215488,Rider_Id_48,1265
-Order_No_1664,User_Id_2154,Bike,3,Business,16,3,8:34:34 AM,16,3,8:34:46 AM,16,3,8:39:07 AM,16,3,8:42:24 AM,16,3,8:58:41 AM,7,20.0,,-1.2885528999999998,36.816014100000004,-1.2626906,36.7827022,Rider_Id_79,977
-Order_No_3224,User_Id_1453,Bike,3,Business,18,1,11:22:28 AM,18,1,11:25:52 AM,18,1,11:37:52 AM,18,1,12:08:03 PM,18,1,12:24:11 PM,8,,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_663,968
-Order_No_14027,User_Id_3643,Bike,1,Personal,4,6,1:12:48 PM,4,6,1:13:26 PM,4,6,1:20:35 PM,4,6,1:25:35 PM,4,6,2:02:23 PM,13,19.6,,-1.2872344,36.7773949,-1.3333892,36.6923956,Rider_Id_65,2208
-Order_No_22182,User_Id_246,Bike,3,Business,13,4,10:52:46 AM,13,4,10:53:08 AM,13,4,10:54:54 AM,13,4,11:01:34 AM,13,4,11:02:53 AM,1,21.8,,-1.2584143,36.8048002,-1.2597528999999998,36.806629,Rider_Id_56,79
-Order_No_12160,User_Id_2152,Bike,2,Personal,5,6,1:18:30 PM,5,6,1:29:03 PM,5,6,1:39:28 PM,5,6,1:44:33 PM,5,6,2:06:07 PM,9,23.6,,-1.2718504,36.8246277,-1.3202198,36.802606899999994,Rider_Id_479,1294
-Order_No_19584,User_Id_2066,Bike,3,Business,25,6,2:00:32 PM,25,6,2:01:11 PM,25,6,2:21:15 PM,25,6,2:27:53 PM,25,6,2:41:23 PM,6,,,-1.3270716,36.8406371,-1.3206958,36.8769553,Rider_Id_2,810
-Order_No_26393,User_Id_1471,Bike,3,Business,11,2,12:42:37 PM,11,2,12:43:06 PM,11,2,12:47:57 PM,11,2,12:55:38 PM,11,2,1:48:01 PM,26,24.4,,-1.2551895,36.7822034,-1.1605816,36.8977958,Rider_Id_629,3143
-Order_No_17999,User_Id_517,Bike,3,Business,29,2,9:00:21 AM,29,2,9:00:55 AM,29,2,9:01:01 AM,29,2,9:46:49 AM,29,2,9:46:52 AM,4,18.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,3
-Order_No_9316,User_Id_393,Bike,3,Business,4,2,2:33:01 PM,4,2,2:33:59 PM,4,2,2:38:43 PM,4,2,2:43:36 PM,4,2,2:59:54 PM,4,22.5,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_211,978
-Order_No_24821,User_Id_2659,Bike,3,Business,3,1,11:52:08 AM,3,1,11:58:50 AM,3,1,12:10:57 PM,3,1,12:17:03 PM,3,1,12:40:17 PM,7,22.4,,-1.2917868000000001,36.7872675,-1.269534,36.80697,Rider_Id_822,1394
-Order_No_10342,User_Id_3647,Bike,3,Business,10,1,12:21:20 PM,10,1,12:23:37 PM,10,1,12:46:45 PM,10,1,12:52:18 PM,10,1,1:37:10 PM,14,18.5,,-1.2726389999999999,36.794723,-1.2285834999999998,36.882646799999996,Rider_Id_766,2692
-Order_No_21924,User_Id_1368,Bike,3,Business,22,1,11:12:32 AM,22,1,11:13:17 AM,22,1,11:14:00 AM,22,1,11:30:43 AM,22,1,11:45:24 AM,5,25.0,,-1.2571472,36.795063299999995,-1.2578831,36.7755412,Rider_Id_446,881
-Order_No_7497,User_Id_2330,Bike,3,Business,12,5,2:43:04 PM,12,5,2:44:47 PM,12,5,2:49:21 PM,12,5,2:54:44 PM,12,5,3:11:42 PM,7,,,-1.2890715,36.8157588,-1.2645318,36.7981775,Rider_Id_79,1018
-Order_No_6648,User_Id_1304,Bike,3,Business,28,1,1:55:06 PM,28,1,2:08:02 PM,28,1,2:10:11 PM,28,1,2:16:45 PM,28,1,2:30:04 PM,10,24.7,,-1.2909536,36.8407162,-1.2553615,36.789537599999996,Rider_Id_379,799
-Order_No_15724,User_Id_909,Bike,3,Business,11,1,2:27:56 PM,11,1,2:31:27 PM,11,1,2:39:37 PM,11,1,2:42:39 PM,11,1,3:06:01 PM,14,28.2,,-1.2156006000000001,36.8916865,-1.225322,36.80855,Rider_Id_199,1402
-Order_No_20904,User_Id_914,Bike,3,Business,4,1,11:53:47 AM,4,1,11:54:21 AM,4,1,12:36:46 PM,4,1,12:40:13 PM,4,1,1:06:13 PM,11,25.8,,-1.3053578000000001,36.860791299999995,-1.2670213,36.7994482,Rider_Id_206,1560
-Order_No_7026,User_Id_2200,Bike,3,Business,20,3,12:07:13 PM,20,3,12:07:33 PM,20,3,12:14:55 PM,20,3,12:33:12 PM,20,3,12:47:51 PM,7,27.0,,-1.290894,36.822971,-1.2612974,36.7993448,Rider_Id_785,879
-Order_No_20474,User_Id_1544,Bike,3,Business,10,5,3:41:44 PM,10,5,3:48:10 PM,10,5,4:10:23 PM,10,5,4:55:57 PM,10,5,5:19:08 PM,3,,,-1.2873232,36.765857200000006,-1.291692,36.7528145,Rider_Id_567,1391
-Order_No_4255,User_Id_570,Bike,3,Business,14,5,2:13:44 PM,14,5,2:14:36 PM,14,5,2:20:55 PM,14,5,2:27:30 PM,14,5,2:41:03 PM,6,22.8,,-1.2551895,36.7822034,-1.2825816,36.8092346,Rider_Id_50,813
-Order_No_9505,User_Id_325,Bike,3,Business,19,2,9:36:06 AM,19,2,9:36:18 AM,19,2,9:36:33 AM,19,2,9:45:54 AM,19,2,9:54:59 AM,3,21.0,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_952,545
-Order_No_20112,User_Id_3080,Bike,3,Business,3,1,12:19:16 PM,3,1,1:19:44 PM,3,1,1:30:22 PM,3,1,1:46:27 PM,3,1,2:36:01 PM,21,23.2,,-1.2825223,36.901456599999996,-1.2551895,36.7822034,Rider_Id_62,2974
-Order_No_5246,User_Id_2946,Bike,2,Personal,15,3,9:38:01 AM,15,3,9:39:08 AM,15,3,9:58:19 AM,15,3,10:02:08 AM,15,3,10:17:28 AM,8,16.3,,-1.2749662,36.7658781,-1.2983462,36.8139996,Rider_Id_726,920
-Order_No_25245,User_Id_1004,Bike,3,Business,14,2,9:10:07 AM,14,2,9:11:22 AM,14,2,9:32:06 AM,14,2,9:34:04 AM,14,2,9:57:48 AM,10,,,-1.3250048,36.869718799999994,-1.2983805,36.8198439,Rider_Id_910,1424
-Order_No_11780,User_Id_3295,Bike,3,Business,22,3,11:08:15 AM,22,3,11:09:07 AM,22,3,11:27:57 AM,22,3,11:33:59 AM,22,3,11:56:05 AM,5,22.9,,-1.2571472,36.795063299999995,-1.2841726,36.8179589,Rider_Id_213,1326
-Order_No_28184,User_Id_2982,Bike,3,Business,7,4,12:11:43 PM,7,4,12:12:07 PM,7,4,12:18:09 PM,7,4,12:40:58 PM,7,4,1:14:55 PM,18,24.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_114,2037
-Order_No_8410,User_Id_1406,Bike,1,Personal,7,4,11:04:39 AM,7,4,11:41:41 AM,7,4,11:44:37 AM,7,4,11:53:58 AM,7,4,12:09:25 PM,5,17.2,,-1.2737013000000001,36.806912200000006,-1.2883346000000002,36.8148489,Rider_Id_116,927
-Order_No_17327,User_Id_1652,Bike,1,Personal,23,4,2:26:21 PM,23,4,2:29:31 PM,23,4,2:39:50 PM,23,4,2:45:41 PM,23,4,2:58:11 PM,5,21.0,,-1.286937,36.824674200000004,-1.3113795,36.8367695,Rider_Id_213,750
-Order_No_1076,User_Id_1484,Bike,3,Business,5,2,4:25:12 PM,5,2,4:28:04 PM,5,2,4:44:17 PM,5,2,4:55:19 PM,5,2,5:33:22 PM,11,21.7,,-1.2579303999999998,36.8019667,-1.3083777,36.85900770000001,Rider_Id_101,2283
-Order_No_14166,User_Id_1595,Bike,2,Personal,5,6,4:21:14 PM,5,6,4:21:51 PM,5,6,4:30:33 PM,5,6,4:43:33 PM,5,6,5:57:41 PM,16,23.0,,-1.267452,36.806015,-1.2536725,36.680491499999995,Rider_Id_633,4448
-Order_No_12491,User_Id_2174,Bike,3,Business,12,5,12:15:12 PM,12,5,12:18:47 PM,12,5,12:19:19 PM,12,5,12:33:56 PM,12,5,12:56:23 PM,16,24.6,,-1.2482781,36.884091999999995,-1.2108864,36.78070220000001,Rider_Id_307,1347
-Order_No_1960,User_Id_1246,Bike,3,Business,31,2,9:24:09 AM,31,2,9:24:15 AM,31,2,9:34:13 AM,31,2,9:56:38 AM,31,2,10:35:18 AM,19,16.9,0.9,-1.2689785,36.8082713,-1.3700383,36.9190174,Rider_Id_520,2320
-Order_No_16856,User_Id_2523,Bike,3,Business,10,1,9:44:51 AM,10,1,9:45:05 AM,10,1,9:45:13 AM,10,1,9:52:30 AM,10,1,10:27:50 AM,14,18.1,,-1.2551895,36.7822034,-1.302453,36.8668919,Rider_Id_105,2120
-Order_No_7268,User_Id_867,Bike,3,Business,15,5,9:11:42 AM,15,5,9:31:54 AM,15,5,9:32:13 AM,15,5,10:02:15 AM,15,5,10:05:49 AM,11,19.0,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_472,214
-Order_No_13689,User_Id_1245,Bike,3,Business,19,2,1:21:55 PM,19,2,1:22:46 PM,19,2,2:08:36 PM,19,2,2:18:19 PM,19,2,2:48:17 PM,14,18.5,,-1.300921,36.828195,-1.33355,36.729409999999994,Rider_Id_297,1798
-Order_No_20724,User_Id_2030,Bike,3,Business,10,1,12:46:01 PM,10,1,1:26:19 PM,10,1,1:29:51 PM,10,1,1:37:53 PM,10,1,1:58:02 PM,2,19.0,,-1.2839651,36.789170399999996,-1.295908,36.791433000000005,Rider_Id_194,1209
-Order_No_824,User_Id_3669,Bike,3,Business,25,1,10:00:55 AM,25,1,10:01:02 AM,25,1,10:01:05 AM,25,1,10:05:45 AM,25,1,10:35:52 AM,20,,,-1.2615888000000002,36.7928732,-1.362319,36.743983,Rider_Id_933,1807
-Order_No_19724,User_Id_3149,Bike,3,Business,26,6,9:13:06 AM,26,6,9:13:15 AM,26,6,9:13:20 AM,26,6,10:09:53 AM,26,6,10:18:13 AM,5,20.2,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_15,500
-Order_No_9656,User_Id_265,Bike,3,Business,7,5,10:45:11 AM,7,5,10:46:27 AM,7,5,10:58:19 AM,7,5,11:04:52 AM,7,5,11:13:33 AM,3,25.1,,-1.3207146,36.8305771,-1.300921,36.828195,Rider_Id_685,521
-Order_No_16944,User_Id_2289,Bike,3,Business,2,3,2:56:09 PM,2,3,2:56:44 PM,2,3,3:08:33 PM,2,3,3:12:11 PM,2,3,3:21:50 PM,5,23.5,,-1.2559559999999999,36.789867,-1.2764851000000002,36.8007126,Rider_Id_676,579
-Order_No_7351,User_Id_2801,Bike,3,Business,15,5,11:57:26 AM,15,5,11:58:11 AM,15,5,12:20:28 PM,15,5,12:27:21 PM,15,5,1:29:45 PM,9,26.1,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_341,3744
-Order_No_3564,User_Id_2423,Bike,3,Business,30,2,10:42:01 AM,30,2,10:48:26 AM,30,2,11:00:24 AM,30,2,11:10:57 AM,30,2,11:28:53 AM,7,,,-1.2551895,36.7822034,-1.2824744,36.804724,Rider_Id_245,1076
-Order_No_15731,User_Id_1368,Bike,3,Business,8,2,12:38:39 PM,8,2,12:39:59 PM,8,2,12:49:56 PM,8,2,12:55:34 PM,8,2,1:04:07 PM,4,23.4,,-1.2571472,36.795063299999995,-1.2519787,36.780649100000005,Rider_Id_178,513
-Order_No_19922,User_Id_357,Bike,3,Business,8,3,1:12:38 PM,8,3,1:13:10 PM,8,3,1:27:51 PM,8,3,1:32:41 PM,8,3,1:42:16 PM,2,,,-1.295319,36.760178,-1.2945178999999998,36.7685349,Rider_Id_269,575
-Order_No_17390,User_Id_2059,Bike,1,Personal,11,4,11:23:35 AM,11,4,11:24:59 AM,11,4,11:25:49 AM,11,4,11:47:52 AM,11,4,12:00:09 PM,9,23.8,,-1.2741728,36.79691079999999,-1.2217641000000001,36.788444,Rider_Id_179,737
-Order_No_25385,User_Id_1850,Bike,3,Business,18,1,12:17:57 PM,18,1,12:18:25 PM,18,1,12:20:51 PM,18,1,12:29:31 PM,18,1,12:40:38 PM,3,26.9,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_446,667
-Order_No_14805,User_Id_439,Bike,3,Business,22,2,2:13:08 PM,22,2,2:13:46 PM,22,2,2:39:50 PM,22,2,2:48:31 PM,22,2,3:24:49 PM,17,21.2,,-1.3323107,36.8692616,-1.2636912,36.788437,Rider_Id_661,2178
-Order_No_24242,User_Id_2330,Bike,3,Business,27,3,3:15:34 PM,27,3,3:15:51 PM,27,3,3:17:38 PM,27,3,3:28:58 PM,27,3,3:41:17 PM,4,28.9,,-1.2915246999999999,36.805575700000006,-1.2873232,36.765857200000006,Rider_Id_89,739
-Order_No_6126,User_Id_2174,Bike,3,Business,22,3,12:16:17 PM,22,3,12:16:17 PM,22,3,12:20:30 PM,22,3,1:21:26 PM,22,3,1:21:29 PM,16,23.3,,-1.2482781,36.884091999999995,-1.2016076999999998,36.930397,Rider_Id_86,3
-Order_No_24971,User_Id_3295,Bike,3,Business,13,2,8:35:21 AM,13,2,8:36:20 AM,13,2,8:44:28 AM,13,2,8:56:28 AM,13,2,9:24:10 AM,11,19.9,9.1,-1.2571472,36.795063299999995,-1.3229345,36.8566974,Rider_Id_708,1662
-Order_No_12647,User_Id_1573,Bike,3,Personal,7,4,4:38:00 PM,7,4,4:39:03 PM,7,4,4:39:20 PM,7,4,4:48:30 PM,7,4,5:11:00 PM,10,29.6,,-1.273056,36.811298,-1.2524627000000002,36.7772593,Rider_Id_839,1350
-Order_No_19863,User_Id_1102,Bike,3,Business,26,5,2:52:26 PM,26,5,2:52:49 PM,26,5,3:02:35 PM,26,5,3:06:06 PM,26,5,3:13:11 PM,2,,,-1.3004062,36.829741,-1.2945128000000001,36.8396881,Rider_Id_506,425
-Order_No_6587,User_Id_3630,Bike,2,Personal,1,5,4:33:28 PM,1,5,4:34:14 PM,1,5,5:13:14 PM,1,5,5:21:58 PM,1,5,5:54:04 PM,19,22.6,,-1.2793961,36.883899799999995,-1.214328,36.7911903,Rider_Id_100,1926
-Order_No_24441,User_Id_868,Bike,3,Business,16,4,5:02:20 PM,16,4,5:07:38 PM,16,4,5:35:39 PM,16,4,5:46:39 PM,16,4,6:06:39 PM,8,19.2,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_82,1200
-Order_No_13806,User_Id_3532,Bike,2,Personal,12,5,1:07:56 PM,12,5,1:08:52 PM,12,5,1:13:16 PM,12,5,1:23:39 PM,12,5,2:02:16 PM,5,24.4,,-1.2795195,36.8230726,-1.2885364,36.7995016,Rider_Id_698,2317
-Order_No_11661,User_Id_1568,Bike,3,Business,31,4,5:01:10 PM,31,4,5:05:34 PM,31,4,5:24:55 PM,31,4,5:40:49 PM,31,4,6:06:52 PM,13,22.5,,-1.2897368,36.8226783,-1.2441325,36.8962053,Rider_Id_116,1563
-Order_No_736,User_Id_2112,Bike,3,Business,14,5,12:05:44 PM,14,5,12:08:59 PM,14,5,12:13:08 PM,14,5,12:48:47 PM,14,5,1:07:05 PM,14,24.5,,-1.2927792,36.9053492,-1.2156006000000001,36.8916865,Rider_Id_408,1098
-Order_No_22719,User_Id_1831,Bike,3,Business,6,1,3:01:05 PM,6,1,3:10:11 PM,6,1,3:22:51 PM,6,1,3:24:48 PM,6,1,4:20:17 PM,21,,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_19,3329
-Order_No_9384,User_Id_2464,Bike,3,Business,31,5,5:09:00 PM,31,5,5:13:40 PM,31,5,5:13:43 PM,31,5,5:15:13 PM,31,5,5:28:21 PM,7,23.5,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_79,788
-Order_No_24639,User_Id_2412,Bike,3,Business,16,5,5:16:24 PM,16,5,5:36:08 PM,16,5,5:38:27 PM,16,5,5:46:49 PM,16,5,6:35:45 PM,11,26.3,,-1.2297202,36.881950700000004,-1.2836183,36.8297208,Rider_Id_19,2936
-Order_No_25157,User_Id_1075,Bike,3,Business,4,5,3:45:03 PM,4,5,3:45:11 PM,4,5,3:57:09 PM,4,5,3:59:45 PM,4,5,4:08:04 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_361,499
-Order_No_23194,User_Id_3300,Bike,3,Business,2,4,9:50:23 AM,2,4,9:51:17 AM,2,4,10:03:07 AM,2,4,10:07:38 AM,2,4,10:34:50 AM,16,,,-1.2296618000000001,36.84382720000001,-1.303596,36.778378000000004,Rider_Id_8,1632
-Order_No_17268,User_Id_3099,Bike,1,Personal,28,5,11:05:14 AM,28,5,11:09:39 AM,28,5,11:24:34 AM,28,5,11:32:59 AM,28,5,11:47:59 AM,5,25.1,,-1.3205669,36.836216799999995,-1.2932015,36.828491799999995,Rider_Id_532,900
-Order_No_4914,User_Id_2066,Bike,3,Business,17,6,1:28:39 PM,17,6,1:29:31 PM,17,6,2:14:31 PM,17,6,2:16:51 PM,17,6,3:06:46 PM,14,24.1,,-1.3270716,36.8406371,-1.3855778,36.9269619,Rider_Id_559,2995
-Order_No_13084,User_Id_562,Bike,3,Business,14,5,12:22:48 PM,14,5,12:24:26 PM,14,5,12:36:11 PM,14,5,12:42:10 PM,14,5,1:08:10 PM,8,21.9,,-1.3227181000000001,36.8528097,-1.2814273,36.8132815,Rider_Id_243,1560
-Order_No_452,User_Id_2212,Bike,3,Business,21,2,12:03:37 PM,21,2,12:04:42 PM,21,2,12:13:12 PM,21,2,12:16:23 PM,21,2,12:54:53 PM,13,22.7,,-1.2571472,36.795063299999995,-1.302453,36.8668919,Rider_Id_190,2310
-Order_No_17945,User_Id_190,Bike,3,Business,19,2,3:44:41 PM,19,2,3:44:53 PM,19,2,3:44:55 PM,19,2,4:02:55 PM,19,2,4:02:57 PM,10,29.8,,-1.2740837,36.8212592,-1.3270347,36.866736200000005,Rider_Id_813,2
-Order_No_15073,User_Id_3647,Bike,3,Business,6,3,11:34:34 AM,6,3,11:35:10 AM,6,3,11:48:33 AM,6,3,11:53:54 AM,6,3,12:17:27 PM,8,,,-1.2726389999999999,36.794723,-1.302559,36.819555,Rider_Id_518,1413
-Order_No_13518,User_Id_2587,Bike,3,Business,13,4,9:13:18 AM,13,4,9:24:29 AM,13,4,9:53:02 AM,13,4,10:05:31 AM,13,4,10:23:22 AM,5,18.1,1.9,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_899,1071
-Order_No_20850,User_Id_1245,Bike,3,Business,30,2,3:38:31 PM,30,2,4:08:35 PM,30,2,4:18:04 PM,30,2,4:37:54 PM,30,2,5:11:08 PM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_661,1994
-Order_No_15057,User_Id_85,Bike,2,Business,11,3,8:51:16 AM,11,3,8:51:34 AM,11,3,8:57:41 AM,11,3,9:04:10 AM,11,3,9:31:36 AM,9,15.4,0.9,-1.3014461000000002,36.7661381,-1.2571191000000002,36.8019652,Rider_Id_735,1646
-Order_No_8537,User_Id_3295,Bike,3,Business,28,1,9:09:22 AM,28,1,9:09:29 AM,28,1,9:13:02 AM,28,1,9:15:10 AM,28,1,9:25:05 AM,8,17.9,,-1.2983698000000001,36.807233200000006,-1.2559559999999999,36.772901,Rider_Id_207,595
-Order_No_7506,User_Id_3147,Bike,3,Business,31,4,2:36:13 PM,31,4,2:47:20 PM,31,4,3:19:12 PM,31,4,3:24:32 PM,31,4,4:16:12 PM,17,22.9,,-1.3404187,36.7614685,-1.2546732,36.808679600000005,Rider_Id_898,3100
-Order_No_15202,User_Id_1363,Bike,3,Business,19,4,8:50:07 AM,19,4,8:50:37 AM,19,4,8:53:39 AM,19,4,8:54:54 AM,19,4,9:06:36 AM,4,14.5,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_951,702
-Order_No_23261,User_Id_37,Bike,3,Business,13,3,12:13:40 PM,13,3,12:23:55 PM,13,3,12:35:59 PM,13,3,12:41:44 PM,13,3,1:09:39 PM,11,24.9,,-1.3263105,36.8622904,-1.2933788999999998,36.808753499999995,Rider_Id_114,1675
-Order_No_8041,User_Id_393,Bike,3,Business,14,5,12:14:09 PM,14,5,12:37:26 PM,14,5,12:37:58 PM,14,5,1:12:03 PM,14,5,1:35:47 PM,11,21.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_619,1424
-Order_No_175,User_Id_3355,Bike,3,Business,20,2,11:49:31 AM,20,2,11:49:48 AM,20,2,12:05:39 PM,20,2,12:09:39 PM,20,2,12:39:40 PM,12,19.6,,-1.2860183,36.8975338,-1.3052491000000002,36.8223899,Rider_Id_477,1801
-Order_No_27762,User_Id_2828,Bike,1,Business,16,4,10:52:52 AM,16,4,10:54:32 AM,16,4,11:02:41 AM,16,4,11:33:16 AM,16,4,11:58:14 AM,9,14.7,,-1.3044267,36.8264567,-1.2615888000000002,36.7928732,Rider_Id_848,1498
-Order_No_25848,User_Id_788,Bike,3,Business,8,2,11:56:56 AM,8,2,11:58:03 AM,8,2,12:08:48 PM,8,2,12:17:52 PM,8,2,12:25:50 PM,9,23.5,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_501,478
-Order_No_21817,User_Id_338,Bike,1,Personal,6,5,1:51:32 PM,6,5,1:51:54 PM,6,5,1:58:01 PM,6,5,2:00:42 PM,6,5,2:11:54 PM,7,22.7,,-1.282986,36.767414,-1.2538731,36.7850223,Rider_Id_103,672
-Order_No_16028,User_Id_2514,Bike,3,Business,26,5,9:11:06 AM,26,5,9:12:04 AM,26,5,9:17:15 AM,26,5,9:24:08 AM,26,5,9:28:43 AM,1,,,-1.266294,36.8057971,-1.2684666999999998,36.8002891,Rider_Id_875,275
-Order_No_831,User_Id_3686,Bike,3,Business,16,1,10:49:49 AM,16,1,10:50:00 AM,16,1,10:51:34 AM,16,1,10:55:48 AM,16,1,11:12:00 AM,14,19.1,,-1.2551895,36.7822034,-1.242902,36.870334,Rider_Id_726,972
-Order_No_14774,User_Id_3222,Bike,3,Business,21,4,1:59:54 PM,21,4,2:00:30 PM,21,4,2:08:48 PM,21,4,2:10:26 PM,21,4,2:44:38 PM,8,26.3,,-1.2551895,36.7822034,-1.3012618999999999,36.821369700000005,Rider_Id_761,2052
-Order_No_24365,User_Id_3214,Bike,3,Business,21,2,3:41:27 PM,21,2,3:42:11 PM,21,2,3:55:51 PM,21,2,3:57:31 PM,21,2,4:30:46 PM,4,25.1,,-1.289795,36.7957132,-1.2890399,36.8164942,Rider_Id_148,1995
-Order_No_25086,User_Id_2112,Bike,3,Business,21,1,2:57:33 PM,21,1,2:57:39 PM,21,1,2:57:45 PM,21,1,3:09:46 PM,21,1,3:30:55 PM,14,21.7,,-1.22556,36.80813,-1.2156006000000001,36.8916865,Rider_Id_771,1269
-Order_No_26774,User_Id_552,Bike,3,Business,12,4,1:41:59 PM,12,4,1:42:11 PM,12,4,1:47:26 PM,12,4,2:21:40 PM,12,4,2:44:42 PM,18,21.7,,-1.2799394,36.8192374,-1.3562374,36.904295399999995,Rider_Id_906,1382
-Order_No_1388,User_Id_540,Bike,3,Business,29,3,3:13:26 PM,29,3,3:15:17 PM,29,3,3:27:11 PM,29,3,3:37:53 PM,29,3,4:02:36 PM,5,19.2,,-1.2571472,36.795063299999995,-1.2890893,36.8268633,Rider_Id_19,1483
-Order_No_26893,User_Id_1280,Bike,3,Business,3,4,11:16:15 AM,3,4,11:16:29 AM,3,4,11:47:18 AM,3,4,12:04:20 PM,3,4,12:47:36 PM,19,22.0,,-1.2927792,36.9053492,-1.2551856000000001,36.7822492,Rider_Id_900,2596
-Order_No_4284,User_Id_830,Bike,3,Business,26,5,11:58:58 AM,26,5,2:21:49 PM,26,5,2:43:07 PM,26,5,2:46:03 PM,26,5,3:08:49 PM,8,,,-1.2551895,36.7822034,-1.2904016,36.82263029999999,Rider_Id_253,1366
-Order_No_22861,User_Id_3599,Bike,3,Business,29,5,9:52:13 AM,29,5,10:09:49 AM,29,5,10:29:13 AM,29,5,11:00:32 AM,29,5,12:07:18 PM,23,26.7,,-1.3302996,36.8707935,-1.2017309999999999,36.841800799999994,Rider_Id_519,4006
-Order_No_16744,User_Id_1584,Bike,3,Business,27,1,11:37:53 AM,27,1,11:38:12 AM,27,1,11:39:23 AM,27,1,11:47:05 AM,27,1,12:06:09 PM,4,23.9,,-1.304033,36.7848695,-1.2885417,36.810052299999995,Rider_Id_890,1144
-Order_No_10511,User_Id_199,Bike,1,Personal,21,1,2:49:56 PM,21,1,2:54:14 PM,21,1,3:00:27 PM,21,1,3:11:53 PM,21,1,3:32:47 PM,6,21.5,,-1.2873639,36.8256076,-1.26325,36.788000000000004,Rider_Id_777,1254
-Order_No_17668,User_Id_1207,Bike,3,Business,11,1,12:10:05 PM,11,1,12:10:13 PM,11,1,12:27:00 PM,11,1,12:33:45 PM,11,1,1:04:54 PM,20,24.1,,-1.2156718999999998,36.7990906,-1.2745321000000003,36.765088,Rider_Id_932,1869
-Order_No_20004,User_Id_2008,Bike,3,Business,8,3,11:25:34 AM,8,3,11:27:14 AM,8,3,11:41:49 AM,8,3,11:46:25 AM,8,3,12:08:55 PM,10,,,-1.2921818,36.810193100000006,-1.2508229,36.7895263,Rider_Id_831,1350
-Order_No_7161,User_Id_2763,Bike,3,Business,11,4,1:24:53 PM,11,4,1:41:25 PM,11,4,1:54:35 PM,11,4,2:36:36 PM,11,4,3:53:48 PM,11,26.8,,-1.2991616000000001,36.842450899999996,-1.2551895,36.7822034,Rider_Id_349,4632
-Order_No_25599,User_Id_1340,Bike,3,Business,28,4,2:42:40 PM,28,4,2:43:04 PM,28,4,2:45:48 PM,28,4,2:58:48 PM,28,4,3:47:46 PM,19,27.2,,-1.2707178999999997,36.7979713,-1.166938,36.824351,Rider_Id_755,2938
-Order_No_13478,User_Id_1340,Bike,3,Business,13,3,2:11:09 PM,13,3,2:12:23 PM,13,3,2:25:33 PM,13,3,2:27:20 PM,13,3,2:32:44 PM,2,29.2,,-1.2593809999999999,36.8024189,-1.2707178999999997,36.7979713,Rider_Id_40,324
-Order_No_5802,User_Id_248,Bike,3,Business,17,3,9:17:47 AM,17,3,9:19:19 AM,17,3,9:47:06 AM,17,3,9:53:17 AM,17,3,11:01:52 AM,12,,,-1.304735,36.784605400000004,-1.313266,36.6935289,Rider_Id_327,4115
-Order_No_18626,User_Id_237,Bike,3,Business,21,4,10:04:56 AM,21,4,10:07:37 AM,21,4,10:21:17 AM,21,4,10:28:01 AM,21,4,10:45:45 AM,5,21.6,,-1.2551895,36.7822034,-1.2764098999999998,36.8208451,Rider_Id_682,1064
-Order_No_10944,User_Id_3647,Bike,3,Business,19,3,9:37:15 AM,19,3,9:38:35 AM,19,3,9:45:13 AM,19,3,9:49:06 AM,19,3,10:21:49 AM,13,19.6,,-1.2726389999999999,36.794723,-1.3216456,36.7145156,Rider_Id_574,1963
-Order_No_2810,User_Id_1439,Bike,3,Business,17,5,9:39:43 AM,17,5,9:40:25 AM,17,5,9:52:45 AM,17,5,9:58:37 AM,17,5,10:25:04 AM,6,,,-1.2962486999999998,36.7772971,-1.2749270000000001,36.8028302,Rider_Id_197,1587
-Order_No_26165,User_Id_3091,Bike,3,Business,5,4,3:46:04 PM,5,4,4:00:28 PM,5,4,4:18:21 PM,5,4,4:24:21 PM,5,4,4:45:19 PM,10,22.0,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_379,1258
-Order_No_26870,User_Id_914,Bike,3,Business,22,5,11:39:22 AM,22,5,11:43:30 AM,22,5,12:06:13 PM,22,5,12:11:18 PM,22,5,12:11:56 PM,6,,,-1.2670213,36.7994482,-1.2989287,36.81630820000001,Rider_Id_19,38
-Order_No_211,User_Id_2033,Bike,2,Personal,4,6,10:12:40 AM,4,6,10:13:16 AM,4,6,10:26:08 AM,4,6,10:40:06 AM,4,6,10:58:44 AM,8,19.9,,-1.2519209999999998,36.828379,-1.2513591,36.7947674,Rider_Id_427,1118
-Order_No_13309,User_Id_695,Bike,1,Personal,27,5,2:41:48 PM,27,5,2:42:36 PM,27,5,2:51:44 PM,27,5,2:57:17 PM,27,5,3:35:37 PM,13,24.1,,-1.2029267,36.7877173,-1.2770826000000002,36.817408799999995,Rider_Id_144,2300
-Order_No_1724,User_Id_3647,Bike,3,Business,20,5,10:15:08 AM,20,5,10:27:29 AM,20,5,10:32:42 AM,20,5,10:34:22 AM,20,5,10:58:31 AM,6,18.3,,-1.2726389999999999,36.794723,-1.2872899,36.820434000000006,Rider_Id_616,1449
-Order_No_4794,User_Id_3168,Bike,3,Personal,6,3,12:43:34 PM,6,3,1:34:36 PM,6,3,1:51:13 PM,6,3,1:53:15 PM,6,3,2:07:47 PM,3,22.0,,-1.2597431000000001,36.8023063,-1.2765641,36.793782,Rider_Id_956,872
-Order_No_5190,User_Id_2828,Bike,3,Business,6,3,7:59:13 AM,6,3,8:00:13 AM,6,3,8:31:39 AM,6,3,8:49:43 AM,6,3,9:26:59 AM,12,,,-1.3263105,36.8622904,-1.3193822,36.7949906,Rider_Id_61,2236
-Order_No_6678,User_Id_706,Bike,3,Business,25,5,1:34:03 PM,25,5,1:37:24 PM,25,5,1:58:51 PM,25,5,2:00:45 PM,25,5,2:14:25 PM,5,29.0,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_206,820
-Order_No_22373,User_Id_3222,Bike,3,Business,19,1,12:03:29 PM,19,1,12:04:33 PM,19,1,12:18:39 PM,19,1,12:20:32 PM,19,1,1:24:56 PM,14,24.7,,-1.2551895,36.7822034,-1.2516167,36.8678844,Rider_Id_147,3864
-Order_No_11765,User_Id_3332,Bike,3,Personal,9,2,12:15:01 PM,9,2,12:16:35 PM,9,2,12:39:06 PM,9,2,12:41:59 PM,9,2,1:31:24 PM,12,27.0,,-1.2463396,36.7547983,-1.282843,36.821474,Rider_Id_177,2965
-Order_No_27991,User_Id_1484,Bike,3,Business,22,2,3:11:49 PM,22,2,3:13:28 PM,22,2,3:25:45 PM,22,2,3:36:53 PM,22,2,4:00:45 PM,13,21.4,,-1.2579303999999998,36.8019667,-1.2343236000000002,36.8861,Rider_Id_580,1432
-Order_No_408,User_Id_788,Bike,3,Business,5,2,5:06:55 PM,5,2,5:15:57 PM,5,2,5:24:57 PM,5,2,5:31:41 PM,5,2,6:04:15 PM,10,,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_953,1954
-Order_No_12542,User_Id_633,Bike,3,Business,14,4,3:52:12 PM,14,4,3:53:39 PM,14,4,3:59:26 PM,14,4,4:25:39 PM,14,4,4:26:48 PM,4,27.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_411,69
-Order_No_26073,User_Id_3668,Bike,1,Personal,2,4,2:56:12 PM,2,4,2:57:27 PM,2,4,3:10:29 PM,2,4,3:12:27 PM,2,4,3:30:03 PM,6,26.4,,-1.2798711,36.8207118,-1.312469,36.815177,Rider_Id_857,1056
-Order_No_22636,User_Id_1803,Bike,1,Personal,30,3,7:19:18 AM,30,3,7:21:31 AM,30,3,7:35:58 AM,30,3,7:40:45 AM,30,3,8:10:59 AM,5,17.3,,-1.2673271000000002,36.8064451,-1.2875183000000001,36.802234399999996,Rider_Id_103,1814
-Order_No_19853,User_Id_2360,Bike,3,Business,24,1,2:30:15 PM,24,1,2:33:40 PM,24,1,3:11:14 PM,24,1,3:14:20 PM,24,1,3:30:17 PM,8,24.5,,-1.2703375,36.8247799,-1.2551895,36.7822034,Rider_Id_362,957
-Order_No_26536,User_Id_60,Bike,3,Business,7,2,11:45:08 AM,7,2,11:45:41 AM,7,2,11:47:40 AM,7,2,11:55:41 AM,7,2,12:21:54 PM,15,23.2,,-1.303596,36.778378000000004,-1.2296618000000001,36.84382720000001,Rider_Id_517,1573
-Order_No_7135,User_Id_2982,Bike,3,Business,3,3,12:21:44 PM,3,3,12:22:23 PM,3,3,12:22:42 PM,3,3,1:23:54 PM,3,3,1:52:48 PM,18,23.9,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_120,1734
-Order_No_5414,User_Id_1146,Bike,3,Business,18,4,4:03:17 PM,18,4,4:06:48 PM,18,4,4:10:30 PM,18,4,4:28:25 PM,18,4,5:39:58 PM,7,,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_506,4293
-Order_No_28022,User_Id_3115,Bike,3,Business,8,3,12:53:28 PM,8,3,12:59:01 PM,8,3,1:09:10 PM,8,3,1:13:50 PM,8,3,2:16:28 PM,7,,,-1.2963097,36.7688221,-1.2897978,36.8267388,Rider_Id_448,3758
-Order_No_20375,User_Id_2712,Bike,1,Business,25,2,11:47:42 AM,25,2,11:49:08 AM,25,2,11:55:59 AM,25,2,12:06:45 PM,25,2,12:33:49 PM,15,23.7,,-1.2944292,36.779356899999996,-1.2252722,36.8756717,Rider_Id_851,1624
-Order_No_21607,User_Id_1705,Bike,3,Business,11,1,9:15:28 AM,11,1,9:18:10 AM,11,1,9:22:07 AM,11,1,9:28:26 AM,11,1,9:41:41 AM,8,17.8,,-1.2551895,36.7822034,-1.2714995,36.837573299999995,Rider_Id_576,795
-Order_No_21123,User_Id_3599,Bike,3,Business,2,2,11:57:08 AM,2,2,11:58:16 AM,2,2,12:50:18 PM,2,2,12:59:38 PM,2,2,2:00:07 PM,17,26.1,,-1.3302996,36.8707935,-1.2619185,36.759662,Rider_Id_787,3629
-Order_No_23064,User_Id_2781,Bike,3,Personal,11,6,12:54:33 PM,11,6,12:55:59 PM,11,6,1:38:00 PM,11,6,1:39:06 PM,11,6,2:30:14 PM,14,,,-1.296599,36.7629672,-1.3393293,36.6642273,Rider_Id_523,3068
-Order_No_22688,User_Id_868,Bike,3,Business,27,6,11:55:16 AM,27,6,11:57:59 AM,27,6,12:03:44 PM,27,6,12:14:15 PM,27,6,12:28:26 PM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_161,851
-Order_No_25615,User_Id_633,Bike,3,Business,26,2,11:35:01 AM,26,2,11:35:30 AM,26,2,11:40:57 AM,26,2,11:44:23 AM,26,2,12:07:51 PM,11,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_494,1408
-Order_No_20253,User_Id_111,Bike,3,Business,24,4,12:49:29 PM,24,4,12:52:47 PM,24,4,1:08:39 PM,24,4,1:11:40 PM,24,4,1:45:30 PM,9,24.7,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_755,2030
-Order_No_20463,User_Id_3560,Bike,3,Business,14,4,1:15:50 PM,14,4,1:18:37 PM,14,4,1:21:01 PM,14,4,1:25:44 PM,14,4,1:45:45 PM,5,24.8,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_660,1201
-Order_No_7827,User_Id_3291,Bike,3,Business,6,2,11:23:01 AM,6,2,11:24:03 AM,6,2,11:29:25 AM,6,2,11:54:30 AM,6,2,12:07:43 PM,5,24.8,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_519,793
-Order_No_13797,User_Id_1927,Bike,3,Business,12,2,11:29:04 AM,12,2,11:30:06 AM,12,2,12:09:06 PM,12,2,12:19:54 PM,12,2,12:41:21 PM,9,24.4,,-1.3014461000000002,36.7661381,-1.3472066,36.7692638,Rider_Id_800,1287
-Order_No_15588,User_Id_3490,Bike,2,Personal,3,5,12:12:12 PM,3,5,12:13:11 PM,3,5,12:26:39 PM,3,5,12:30:22 PM,3,5,12:42:15 PM,4,21.5,,-1.269441,36.798544,-1.2738167,36.8157821,Rider_Id_882,713
-Order_No_25868,User_Id_3667,Bike,3,Personal,24,2,12:20:23 PM,24,2,12:20:36 PM,24,2,12:24:57 PM,24,2,12:30:38 PM,24,2,12:59:31 PM,14,17.3,,-1.2684239,36.79524620000001,-1.3248448,36.856469700000005,Rider_Id_229,1733
-Order_No_21889,User_Id_1620,Bike,3,Personal,21,5,9:00:58 AM,21,5,9:02:11 AM,21,5,9:08:35 AM,21,5,9:25:49 AM,21,5,9:41:58 AM,11,19.6,,-1.261849,36.808254,-1.227132,36.8374277,Rider_Id_649,969
-Order_No_3965,User_Id_186,Bike,3,Business,1,5,3:46:38 PM,1,5,3:48:24 PM,1,5,3:48:55 PM,1,5,4:03:28 PM,1,5,4:13:14 PM,4,28.6,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_81,586
-Order_No_3484,User_Id_3291,Bike,3,Business,29,5,9:20:49 AM,29,5,9:21:20 AM,29,5,9:52:45 AM,29,5,10:17:35 AM,29,5,11:01:21 AM,16,20.9,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_377,2626
-Order_No_20757,User_Id_3565,Bike,3,Business,18,7,2:04:30 PM,18,7,2:06:43 PM,18,7,2:13:08 PM,18,7,2:59:05 PM,18,7,3:28:32 PM,16,27.3,,-1.3275382,36.8468107,-1.2861046,36.7407181,Rider_Id_675,1767
-Order_No_11018,User_Id_3491,Bike,3,Business,24,1,5:16:56 PM,24,1,5:17:06 PM,24,1,5:25:00 PM,24,1,5:29:32 PM,24,1,6:02:35 PM,11,24.4,,-1.263987,36.798891,-1.2297202,36.881950700000004,Rider_Id_72,1983
-Order_No_11678,User_Id_3222,Bike,3,Business,16,2,3:04:41 PM,16,2,3:06:50 PM,16,2,3:15:54 PM,16,2,3:22:06 PM,16,2,3:56:09 PM,8,27.5,,-1.2551895,36.7822034,-1.2868736,36.780588200000004,Rider_Id_648,2043
-Order_No_18597,User_Id_1882,Bike,3,Business,20,3,8:51:26 AM,20,3,8:51:58 AM,20,3,9:04:40 AM,20,3,9:32:55 AM,20,3,9:45:15 AM,6,21.0,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_125,740
-Order_No_23379,User_Id_1850,Bike,3,Business,18,1,1:24:57 PM,18,1,1:26:09 PM,18,1,2:00:17 PM,18,1,2:17:37 PM,18,1,3:08:49 PM,8,29.5,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_942,3072
-Order_No_12570,User_Id_3283,Bike,3,Business,25,5,1:20:46 PM,25,5,1:20:59 PM,25,5,1:25:55 PM,25,5,1:42:37 PM,25,5,1:54:24 PM,7,29.2,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_906,707
-Order_No_19891,User_Id_2267,Bike,2,Personal,19,7,2:46:10 PM,19,7,2:49:21 PM,19,7,3:05:40 PM,19,7,3:11:03 PM,19,7,3:17:51 PM,2,21.0,,-1.2965499,36.7850592,-1.2938713,36.7917214,Rider_Id_148,408
-Order_No_28236,User_Id_3557,Bike,3,Business,22,3,11:51:05 AM,22,3,11:51:29 AM,22,3,11:54:26 AM,22,3,11:58:08 AM,22,3,12:13:54 PM,3,23.2,,-1.2546732,36.808679600000005,-1.2753108999999998,36.8130185,Rider_Id_930,946
-Order_No_6859,User_Id_785,Bike,1,Personal,11,5,8:26:53 AM,11,5,8:31:27 AM,11,5,8:39:41 AM,11,5,8:49:50 AM,11,5,9:04:29 AM,3,18.1,5.0,-1.2910747,36.831784899999995,-1.3012173,36.8093987,Rider_Id_79,879
-Order_No_9728,User_Id_3599,Bike,3,Business,27,6,12:08:32 PM,27,6,12:42:23 PM,27,6,1:25:14 PM,27,6,1:42:12 PM,27,6,2:19:36 PM,14,,,-1.3302996,36.8707935,-1.2603403,36.7998848,Rider_Id_787,2244
-Order_No_16491,User_Id_635,Bike,3,Business,5,2,8:24:57 AM,5,2,8:41:30 AM,5,2,9:20:40 AM,5,2,9:34:21 AM,5,2,10:14:16 AM,10,17.5,3.9,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_100,2395
-Order_No_2079,User_Id_3186,Bike,3,Business,7,2,11:24:04 AM,7,2,11:25:21 AM,7,2,11:35:38 AM,7,2,11:42:03 AM,7,2,11:52:47 AM,4,,,-1.2980228999999999,36.78865,-1.2950041,36.814358299999995,Rider_Id_865,644
-Order_No_19115,User_Id_2154,Bike,3,Business,2,3,11:30:33 AM,2,3,11:42:55 AM,2,3,12:03:04 PM,2,3,12:04:36 PM,2,3,12:40:42 PM,5,24.1,,-1.2765736,36.851364600000004,-1.2885315,36.8230419,Rider_Id_134,2166
-Order_No_27954,User_Id_322,Bike,3,Business,5,1,5:29:41 PM,5,1,5:30:19 PM,5,1,5:45:13 PM,5,1,5:48:43 PM,5,1,5:59:45 PM,3,24.6,,-1.2452664,36.802854100000005,-1.260624,36.807159000000006,Rider_Id_287,662
-Order_No_27947,User_Id_2982,Bike,3,Business,15,4,1:08:10 PM,15,4,1:09:17 PM,15,4,1:18:52 PM,15,4,2:12:10 PM,15,4,2:16:41 PM,18,27.6,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_70,271
-Order_No_23041,User_Id_3354,Bike,3,Business,19,1,2:47:51 PM,19,1,2:50:45 PM,19,1,2:58:38 PM,19,1,3:01:07 PM,19,1,3:21:16 PM,10,25.0,,-1.3700383,36.9190174,-1.3179243999999999,36.9031396,Rider_Id_504,1209
-Order_No_27936,User_Id_2587,Bike,3,Business,28,5,10:36:57 AM,28,5,10:37:16 AM,28,5,10:53:51 AM,28,5,11:02:37 AM,28,5,11:09:38 AM,1,23.7,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_456,421
-Order_No_21701,User_Id_1920,Bike,3,Business,14,3,3:36:34 PM,14,3,4:10:17 PM,14,3,4:26:51 PM,14,3,4:57:02 PM,14,3,5:41:48 PM,18,23.3,,-1.2551895,36.7822034,-1.2688353,36.9013859,Rider_Id_403,2686
-Order_No_23624,User_Id_288,Bike,1,Personal,30,1,9:19:03 AM,30,1,12:36:17 PM,30,1,12:44:57 PM,30,1,12:51:31 PM,30,1,1:08:23 PM,7,22.4,,-1.2876365,36.816096,-1.296797,36.776452500000005,Rider_Id_19,1012
-Order_No_19647,User_Id_1304,Bike,3,Business,15,4,4:03:45 PM,15,4,4:04:42 PM,15,4,4:21:41 PM,15,4,4:22:53 PM,15,4,4:48:44 PM,3,27.5,,-1.2551895,36.7822034,-1.2562366000000003,36.8038565,Rider_Id_866,1551
-Order_No_23759,User_Id_393,Bike,3,Business,16,4,9:34:43 AM,16,4,9:34:59 AM,16,4,9:35:12 AM,16,4,10:10:34 AM,16,4,10:33:18 AM,14,15.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_659,1364
-Order_No_12189,User_Id_1209,Bike,3,Business,23,2,10:43:14 AM,23,2,10:43:33 AM,23,2,10:43:59 AM,23,2,10:53:20 AM,23,2,11:14:20 AM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_855,1260
-Order_No_28227,User_Id_71,Bike,3,Business,12,1,3:20:07 PM,12,1,3:39:13 PM,12,1,3:39:16 PM,12,1,4:08:53 PM,12,1,4:31:53 PM,11,27.0,,-1.2283403,36.8822756,-1.2891921999999998,36.825189,Rider_Id_403,1380
-Order_No_27050,User_Id_2174,Bike,3,Business,29,6,11:12:34 AM,29,6,11:12:38 AM,29,6,11:16:37 AM,29,6,12:04:37 PM,29,6,1:07:32 PM,33,25.1,,-1.2482781,36.884091999999995,-1.324878,36.7070285,Rider_Id_946,3775
-Order_No_24206,User_Id_1386,Bike,2,Personal,9,4,1:17:34 PM,9,4,1:17:55 PM,9,4,1:27:33 PM,9,4,1:33:01 PM,9,4,1:42:16 PM,4,25.9,,-1.3040899,36.7842136,-1.2998136999999998,36.7606908,Rider_Id_779,555
-Order_No_1564,User_Id_633,Bike,3,Business,20,3,1:20:50 PM,20,3,1:20:58 PM,20,3,1:21:01 PM,20,3,1:40:20 PM,20,3,1:40:22 PM,9,28.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_39,2
-Order_No_20790,User_Id_540,Bike,3,Business,28,4,11:53:51 AM,28,4,12:02:42 PM,28,4,12:17:03 PM,28,4,12:18:30 PM,28,4,12:36:44 PM,7,28.1,,-1.2886385,36.8240195,-1.2551895,36.7822034,Rider_Id_425,1094
-Order_No_8690,User_Id_3647,Bike,3,Business,20,3,5:00:48 PM,20,3,5:02:02 PM,20,3,5:07:06 PM,20,3,5:09:58 PM,20,3,5:26:21 PM,6,28.0,,-1.2726389999999999,36.794723,-1.2876643,36.8248768,Rider_Id_263,983
-Order_No_24544,User_Id_1051,Bike,3,Business,19,1,9:45:43 AM,19,1,10:34:59 AM,19,1,10:38:29 AM,19,1,11:06:36 AM,19,1,11:45:43 AM,8,24.6,,-1.2891921999999998,36.825189,-1.2963097,36.7688221,Rider_Id_462,2347
-Order_No_25913,User_Id_2200,Bike,3,Business,30,6,1:41:32 PM,30,6,1:42:56 PM,30,6,1:52:08 PM,30,6,1:55:40 PM,30,6,2:16:27 PM,6,28.4,,-1.2649605,36.798177700000004,-1.290894,36.822971,Rider_Id_660,1247
-Order_No_17156,User_Id_2066,Bike,3,Business,21,4,3:49:49 PM,21,4,4:12:22 PM,21,4,4:20:41 PM,21,4,4:28:31 PM,21,4,5:05:17 PM,10,26.3,,-1.3210188999999999,36.841055100000005,-1.2655953000000002,36.791546399999994,Rider_Id_436,2206
-Order_No_11669,User_Id_3387,Bike,2,Personal,20,4,10:08:50 AM,20,4,10:09:18 AM,20,4,10:17:08 AM,20,4,10:20:59 AM,20,4,10:32:20 AM,7,17.7,,-1.2624366000000002,36.7632187,-1.2628638,36.8070399,Rider_Id_38,681
-Order_No_24438,User_Id_478,Bike,3,Business,2,4,3:33:54 PM,2,4,3:34:28 PM,2,4,3:44:33 PM,2,4,3:56:58 PM,2,4,4:07:26 PM,3,,,-1.2617028000000001,36.7983849,-1.2551895,36.7822034,Rider_Id_660,628
-Order_No_13374,User_Id_3291,Bike,3,Business,18,2,12:52:51 PM,18,2,12:56:12 PM,18,2,1:36:51 PM,18,2,1:40:29 PM,18,2,2:47:33 PM,12,25.4,,-1.318062,36.849326,-1.2600926000000001,36.8088685,Rider_Id_75,4024
-Order_No_14640,User_Id_1642,Bike,3,Business,29,1,10:40:54 AM,29,1,10:41:12 AM,29,1,10:41:53 AM,29,1,10:56:30 AM,29,1,11:16:57 AM,5,,,-1.2726506000000002,36.7960153,-1.2887076000000002,36.825465799999996,Rider_Id_785,1227
-Order_No_19067,User_Id_2159,Bike,3,Business,29,1,11:15:24 AM,29,1,11:15:40 AM,29,1,11:24:04 AM,29,1,11:43:00 AM,29,1,12:06:05 PM,10,,,-1.2588381000000002,36.791414700000004,-1.304735,36.784605400000004,Rider_Id_105,1385
-Order_No_9090,User_Id_2412,Bike,3,Business,5,5,8:08:49 AM,5,5,8:09:02 AM,5,5,8:39:02 AM,5,5,9:01:42 AM,5,5,9:07:19 AM,3,20.0,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_291,337
-Order_No_9339,User_Id_3214,Bike,3,Business,7,5,1:07:29 PM,7,5,1:08:58 PM,7,5,1:21:34 PM,7,5,1:25:46 PM,7,5,2:06:11 PM,21,25.0,,-1.289795,36.7957132,-1.3450476,36.9176745,Rider_Id_263,2425
-Order_No_1549,User_Id_3192,Bike,2,Personal,11,4,3:19:15 PM,11,4,3:28:40 PM,11,4,3:33:15 PM,11,4,3:48:12 PM,11,4,4:19:33 PM,6,26.5,,-1.2658315,36.809049,-1.2965824,36.815302200000005,Rider_Id_86,1881
-Order_No_24636,User_Id_3064,Bike,3,Business,17,4,3:28:20 PM,17,4,3:29:46 PM,17,4,3:36:17 PM,17,4,3:41:44 PM,17,4,4:06:04 PM,9,27.9,,-1.2571472,36.795063299999995,-1.292188,36.764855,Rider_Id_361,1460
-Order_No_25172,User_Id_1209,Bike,3,Business,10,4,10:02:52 AM,10,4,10:25:28 AM,10,4,10:25:38 AM,10,4,10:28:51 AM,10,4,10:47:21 AM,9,18.6,,-1.2569006,36.7925141,-1.3004062,36.829741,Rider_Id_173,1110
-Order_No_25092,User_Id_517,Bike,3,Business,2,3,12:58:28 PM,2,3,12:59:11 PM,2,3,1:07:19 PM,2,3,1:19:51 PM,2,3,1:37:25 PM,11,24.0,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_284,1054
-Order_No_25939,User_Id_3203,Bike,3,Business,9,4,10:33:35 AM,9,4,10:36:49 AM,9,4,11:00:46 AM,9,4,11:05:02 AM,9,4,11:17:30 AM,6,23.8,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_427,748
-Order_No_26311,User_Id_1209,Bike,3,Business,23,2,2:13:37 PM,23,2,2:14:02 PM,23,2,2:14:43 PM,23,2,2:38:02 PM,23,2,2:41:57 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_70,235
-Order_No_6845,User_Id_2956,Bike,3,Business,8,3,4:05:09 PM,8,3,4:07:06 PM,8,3,4:36:16 PM,8,3,4:45:33 PM,8,3,5:06:45 PM,9,24.1,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_147,1272
-Order_No_27708,User_Id_3647,Bike,3,Business,6,2,3:40:33 PM,6,2,4:02:59 PM,6,2,4:23:28 PM,6,2,4:28:10 PM,6,2,4:49:03 PM,7,27.6,,-1.2726389999999999,36.794723,-1.2981625,36.813014200000005,Rider_Id_854,1253
-Order_No_25771,User_Id_546,Bike,2,Personal,20,1,10:43:49 AM,20,1,10:47:00 AM,20,1,10:54:10 AM,20,1,10:57:02 AM,20,1,11:09:29 AM,5,20.2,,-1.2547104,36.7530824,-1.2377049999999998,36.7663283,Rider_Id_932,747
-Order_No_2949,User_Id_478,Bike,3,Business,30,2,2:56:11 PM,30,2,4:11:44 PM,30,2,4:22:25 PM,30,2,4:25:20 PM,30,2,5:47:05 PM,8,,,-1.2551895,36.7822034,-1.2857117,36.7968296,Rider_Id_548,4905
-Order_No_14114,User_Id_909,Bike,3,Business,5,5,1:05:35 PM,5,5,1:06:06 PM,5,5,1:12:13 PM,5,5,1:23:52 PM,5,5,1:55:30 PM,10,24.7,,-1.225322,36.80855,-1.2653185,36.8358841,Rider_Id_108,1898
-Order_No_2581,User_Id_1768,Bike,3,Personal,8,2,11:58:31 AM,8,2,11:59:23 AM,8,2,12:20:31 PM,8,2,12:22:24 PM,8,2,1:03:25 PM,19,23.3,,-1.2750257,36.8157165,-1.3229034,36.712712100000005,Rider_Id_263,2461
-Order_No_15468,User_Id_1578,Bike,3,Business,5,5,9:15:38 AM,5,5,9:16:21 AM,5,5,9:18:04 AM,5,5,9:55:50 AM,5,5,10:08:02 AM,6,18.0,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_74,732
-Order_No_23915,User_Id_2412,Bike,3,Business,5,2,4:28:56 PM,5,2,4:34:30 PM,5,2,4:50:14 PM,5,2,5:59:34 PM,5,2,6:01:14 PM,11,,,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_119,100
-Order_No_1982,User_Id_1687,Bike,3,Business,25,5,3:39:07 PM,25,5,3:52:23 PM,25,5,4:33:38 PM,25,5,4:45:28 PM,25,5,5:14:10 PM,4,26.4,,-1.28164,36.816866999999995,-1.2676176000000001,36.7956644,Rider_Id_277,1722
-Order_No_24531,User_Id_1264,Bike,1,Personal,16,2,9:04:12 AM,16,2,9:04:19 AM,16,2,9:07:15 AM,16,2,9:12:42 AM,16,2,9:28:14 AM,4,,,-1.2707178999999997,36.7979713,-1.2807567,36.8076073,Rider_Id_22,932
-Order_No_26992,User_Id_1329,Bike,3,Business,24,5,2:10:49 PM,24,5,2:12:09 PM,24,5,2:23:41 PM,24,5,2:28:38 PM,24,5,2:39:08 PM,3,23.0,,-1.273056,36.811298,-1.2655953000000002,36.791546399999994,Rider_Id_152,630
-Order_No_6026,User_Id_1414,Bike,1,Personal,15,6,12:29:35 PM,15,6,12:31:27 PM,15,6,12:46:34 PM,15,6,12:55:38 PM,15,6,1:05:22 PM,6,,,-1.3159197,36.8516724,-1.2987976,36.829477000000004,Rider_Id_179,584
-Order_No_20779,User_Id_91,Bike,3,Personal,5,5,12:26:50 PM,5,5,12:27:35 PM,5,5,12:28:42 PM,5,5,12:34:48 PM,5,5,12:56:13 PM,8,24.5,,-1.2877831,36.8146624,-1.2990731000000002,36.848805799999994,Rider_Id_404,1285
-Order_No_4066,User_Id_2212,Bike,3,Business,8,3,11:14:33 AM,8,3,11:15:14 AM,8,3,11:23:01 AM,8,3,11:27:57 AM,8,3,11:58:52 AM,11,,,-1.2571472,36.795063299999995,-1.3151162,36.856998100000006,Rider_Id_43,1855
-Order_No_13385,User_Id_1927,Bike,3,Business,22,6,12:23:12 PM,22,6,12:41:26 PM,22,6,12:51:57 PM,22,6,12:55:11 PM,22,6,1:56:55 PM,26,23.1,,-1.3472066,36.7692638,-1.207546,36.7913976,Rider_Id_200,3704
-Order_No_24847,User_Id_868,Bike,3,Business,17,5,9:59:30 AM,17,5,10:00:06 AM,17,5,10:04:24 AM,17,5,10:20:38 AM,17,5,10:36:04 AM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_675,926
-Order_No_25743,User_Id_867,Bike,3,Business,7,5,12:14:01 PM,7,5,12:19:33 PM,7,5,12:27:38 PM,7,5,12:59:38 PM,7,5,2:00:49 PM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_821,3671
-Order_No_28144,User_Id_1246,Bike,3,Business,10,4,12:12:09 PM,10,4,12:13:15 PM,10,4,12:13:29 PM,10,4,1:34:25 PM,10,4,1:54:17 PM,16,25.2,,-1.2943371000000001,36.8267,-1.3700383,36.9190174,Rider_Id_813,1192
-Order_No_6755,User_Id_778,Bike,3,Business,17,3,12:58:33 PM,17,3,1:00:51 PM,17,3,1:23:05 PM,17,3,1:30:13 PM,17,3,1:57:21 PM,10,,,-1.275931,36.8516227,-1.2535486,36.7933541,Rider_Id_99,1628
-Order_No_8322,User_Id_2587,Bike,3,Business,3,5,2:54:26 PM,3,5,2:59:53 PM,3,5,3:19:37 PM,3,5,3:37:26 PM,3,5,3:42:55 PM,5,,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_790,329
-Order_No_10769,User_Id_635,Bike,3,Business,18,5,9:19:59 AM,18,5,9:20:11 AM,18,5,9:24:25 AM,18,5,10:39:13 AM,18,5,10:56:37 AM,8,20.2,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_111,1044
-Order_No_12129,User_Id_186,Bike,3,Business,18,1,4:14:02 PM,18,1,4:14:36 PM,18,1,4:23:54 PM,18,1,4:26:53 PM,18,1,4:39:05 PM,5,30.6,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_622,732
-Order_No_16783,User_Id_3201,Bike,3,Business,14,1,3:17:36 PM,14,1,3:17:48 PM,14,1,3:22:27 PM,14,1,3:36:12 PM,14,1,3:47:55 PM,9,25.8,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_456,703
-Order_No_11313,User_Id_3097,Bike,3,Personal,16,6,9:16:50 AM,16,6,10:54:44 AM,16,6,11:22:32 AM,16,6,11:34:31 AM,16,6,12:49:14 PM,28,25.9,,-1.2866269,36.82463679999999,-1.4242042,36.717817,Rider_Id_59,4483
-Order_No_14723,User_Id_393,Bike,3,Business,19,2,2:55:36 PM,19,2,2:55:48 PM,19,2,2:56:39 PM,19,2,3:31:34 PM,19,2,3:31:36 PM,4,30.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_555,2
-Order_No_26956,User_Id_3291,Bike,3,Business,10,1,11:44:46 AM,10,1,11:45:52 AM,10,1,11:46:38 AM,10,1,12:07:35 PM,10,1,12:15:01 PM,5,18.5,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_258,446
-Order_No_12422,User_Id_2368,Bike,3,Personal,26,4,4:34:35 PM,26,4,4:36:23 PM,26,4,4:43:36 PM,26,4,4:57:58 PM,26,4,5:11:14 PM,7,19.8,,-1.3175119,36.8058513,-1.2764851000000002,36.8007126,Rider_Id_88,796
-Order_No_7169,User_Id_3560,Bike,3,Business,4,1,3:45:12 PM,4,1,3:53:52 PM,4,1,4:04:05 PM,4,1,4:14:52 PM,4,1,4:34:53 PM,11,,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_942,1201
-Order_No_4036,User_Id_3194,Bike,3,Business,1,5,12:43:16 PM,1,5,12:51:51 PM,1,5,1:19:41 PM,1,5,1:40:04 PM,1,5,2:06:53 PM,5,28.7,,-1.2571472,36.795063299999995,-1.2802083999999998,36.8196685,Rider_Id_489,1609
-Order_No_27140,User_Id_2752,Bike,2,Personal,22,3,4:09:18 PM,22,3,4:12:28 PM,22,3,4:28:05 PM,22,3,4:33:34 PM,22,3,4:58:39 PM,9,23.0,,-1.2876324,36.8186574,-1.2323371,36.864816999999995,Rider_Id_148,1505
-Order_No_23255,User_Id_3331,Bike,3,Business,14,3,9:22:56 AM,14,3,9:30:50 AM,14,3,9:43:35 AM,14,3,9:44:47 AM,14,3,10:07:03 AM,5,18.3,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_228,1336
-Order_No_16184,User_Id_868,Bike,3,Business,18,4,3:22:27 PM,18,4,3:40:11 PM,18,4,3:53:00 PM,18,4,4:02:35 PM,18,4,4:34:10 PM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_523,1895
-Order_No_21727,User_Id_3523,Bike,3,Personal,2,2,7:12:10 AM,2,2,7:12:29 AM,2,2,7:35:24 AM,2,2,7:38:46 AM,2,2,8:28:32 AM,18,15.2,,-1.2903149999999999,36.75737720000001,-1.2235201,36.80206079999999,Rider_Id_432,2986
-Order_No_18874,User_Id_3479,Bike,1,Personal,11,5,12:02:31 PM,11,5,12:07:29 PM,11,5,12:22:44 PM,11,5,12:28:11 PM,11,5,1:02:23 PM,9,20.7,,-1.2962603000000001,36.7766164,-1.2696011999999999,36.820865600000005,Rider_Id_101,2052
-Order_No_6241,User_Id_3557,Bike,3,Business,5,2,9:40:08 AM,5,2,9:56:54 AM,5,2,10:07:01 AM,5,2,10:09:01 AM,5,2,10:26:08 AM,6,18.1,,-1.2546732,36.808679600000005,-1.288167,36.8181484,Rider_Id_276,1027
-Order_No_12452,User_Id_3560,Bike,3,Business,3,1,3:28:50 PM,3,1,4:38:21 PM,3,1,4:39:26 PM,3,1,4:52:43 PM,3,1,5:20:52 PM,11,17.5,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_812,1689
-Order_No_7872,User_Id_3686,Bike,3,Business,6,3,3:33:03 PM,6,3,3:36:14 PM,6,3,3:47:21 PM,6,3,3:52:02 PM,6,3,4:14:28 PM,8,27.6,,-1.2551895,36.7822034,-1.3004062,36.829741,Rider_Id_958,1346
-Order_No_7564,User_Id_896,Bike,3,Personal,4,1,2:30:41 PM,4,1,3:06:53 PM,4,1,3:30:48 PM,4,1,3:32:25 PM,4,1,3:50:55 PM,6,,,-1.3247683000000001,36.85803,-1.3119323999999999,36.8382423,Rider_Id_856,1110
-Order_No_27027,User_Id_2651,Bike,3,Business,11,6,9:28:26 AM,11,6,9:29:31 AM,11,6,9:41:08 AM,11,6,9:52:24 AM,11,6,10:13:24 AM,8,,,-1.2872136,36.8181901,-1.3014497,36.758247499999996,Rider_Id_341,1260
-Order_No_17299,User_Id_3283,Bike,3,Business,2,6,10:29:50 AM,2,6,10:43:00 AM,2,6,10:52:07 AM,2,6,11:04:38 AM,2,6,11:26:07 AM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_197,1289
-Order_No_4225,User_Id_2987,Bike,3,Business,28,2,4:37:06 PM,28,2,4:37:42 PM,28,2,4:47:00 PM,28,2,4:49:43 PM,28,2,5:05:53 PM,4,18.2,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_4,970
-Order_No_27255,User_Id_3404,Bike,3,Business,26,2,3:49:47 PM,26,2,3:52:33 PM,26,2,4:06:06 PM,26,2,4:08:15 PM,26,2,4:19:31 PM,7,30.8,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_156,676
-Order_No_588,User_Id_1578,Bike,3,Business,9,1,3:23:58 PM,9,1,3:33:19 PM,9,1,3:34:51 PM,9,1,3:41:29 PM,9,1,3:58:51 PM,11,21.0,,-1.2765736,36.851364600000004,-1.2551895,36.7822034,Rider_Id_101,1042
-Order_No_17112,User_Id_1075,Bike,3,Business,8,1,12:30:40 PM,8,1,12:38:02 PM,8,1,1:18:11 PM,8,1,1:26:24 PM,8,1,1:50:54 PM,8,27.7,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_843,1470
-Order_No_2239,User_Id_2701,Bike,3,Business,11,2,1:59:56 PM,11,2,2:03:09 PM,11,2,2:18:09 PM,11,2,2:21:05 PM,11,2,3:24:50 PM,13,21.2,,-1.2765736,36.851364600000004,-1.2852139,36.751683,Rider_Id_4,3825
-Order_No_3421,User_Id_343,Bike,3,Personal,3,4,9:28:04 AM,3,4,9:28:16 AM,3,4,9:40:33 AM,3,4,9:43:47 AM,3,4,10:03:27 AM,13,20.5,,-1.1855066,36.825464600000004,-1.267224,36.8442033,Rider_Id_932,1180
-Order_No_21669,User_Id_867,Bike,3,Business,8,5,9:44:49 AM,8,5,9:47:10 AM,8,5,9:47:16 AM,8,5,10:28:06 AM,8,5,10:52:14 AM,10,20.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_828,1448
-Order_No_16393,User_Id_540,Bike,3,Business,17,1,12:36:35 PM,17,1,12:38:11 PM,17,1,12:46:44 PM,17,1,12:51:49 PM,17,1,1:05:42 PM,3,23.7,,-1.2551895,36.7822034,-1.2636813999999998,36.802996500000006,Rider_Id_353,833
-Order_No_21436,User_Id_1469,Bike,3,Business,5,2,3:25:15 PM,5,2,3:26:50 PM,5,2,3:47:59 PM,5,2,4:03:02 PM,5,2,4:14:03 PM,4,27.7,,-1.2551895,36.7822034,-1.2738521999999999,36.799281799999996,Rider_Id_72,661
-Order_No_13715,User_Id_3621,Bike,3,Business,22,6,5:32:49 PM,22,6,5:33:17 PM,22,6,5:33:22 PM,22,6,5:51:24 PM,22,6,6:28:27 PM,5,23.9,,-1.2836583000000001,36.7886014,-1.3046156,36.765857200000006,Rider_Id_725,2223
-Order_No_14965,User_Id_3295,Bike,3,Business,22,3,8:59:17 AM,22,3,9:01:07 AM,22,3,9:04:05 AM,22,3,9:16:06 AM,22,3,9:52:15 AM,22,19.0,,-1.2571472,36.795063299999995,-1.354539,36.9056204,Rider_Id_576,2169
-Order_No_15657,User_Id_1873,Bike,3,Business,9,3,3:02:05 PM,9,3,3:02:15 PM,9,3,3:02:30 PM,9,3,3:14:23 PM,9,3,4:04:33 PM,23,25.2,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_456,3010
-Order_No_27951,User_Id_517,Bike,3,Business,3,1,12:39:15 PM,3,1,12:39:58 PM,3,1,12:54:41 PM,3,1,1:03:08 PM,3,1,1:12:40 PM,4,21.4,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_2,572
-Order_No_21535,User_Id_3201,Bike,3,Business,10,1,1:34:16 PM,10,1,1:37:51 PM,10,1,1:40:12 PM,10,1,1:46:48 PM,10,1,3:06:03 PM,5,18.5,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_690,4755
-Order_No_15244,User_Id_793,Bike,3,Personal,14,1,10:07:43 AM,14,1,11:07:36 AM,14,1,11:33:45 AM,14,1,11:44:55 AM,14,1,12:41:07 PM,29,23.3,,-1.3813211,36.7725266,-1.2643343,36.7447423,Rider_Id_798,3372
-Order_No_24131,User_Id_393,Bike,3,Business,11,1,2:15:46 PM,11,1,2:17:20 PM,11,1,2:17:26 PM,11,1,2:55:22 PM,11,1,2:55:24 PM,11,28.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,2
-Order_No_13776,User_Id_1402,Bike,3,Business,25,5,10:47:59 AM,25,5,10:53:23 AM,25,5,11:04:03 AM,25,5,11:10:08 AM,25,5,11:29:27 AM,1,26.6,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_104,1159
-Order_No_19353,User_Id_2279,Bike,3,Business,15,1,2:12:50 PM,15,1,2:14:11 PM,15,1,2:41:13 PM,15,1,2:58:02 PM,15,1,3:44:51 PM,8,,,-1.298529,36.816482,-1.2571472,36.795063299999995,Rider_Id_352,2809
-Order_No_16974,User_Id_1961,Bike,3,Business,7,5,1:45:42 PM,7,5,2:14:01 PM,7,5,3:44:38 PM,7,5,4:26:12 PM,7,5,5:02:47 PM,25,,,-1.3393547,36.8910035,-1.3188358999999998,36.703852600000005,Rider_Id_787,2195
-Order_No_1693,User_Id_391,Bike,3,Business,18,4,3:27:05 PM,18,4,3:28:50 PM,18,4,3:42:05 PM,18,4,4:07:18 PM,18,4,4:49:39 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_843,2541
-Order_No_15199,User_Id_393,Bike,3,Business,20,1,9:59:51 AM,20,1,10:02:14 AM,20,1,10:07:59 AM,20,1,10:24:28 AM,20,1,11:04:50 AM,7,19.2,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_143,2422
-Order_No_535,User_Id_3647,Bike,3,Business,8,3,12:28:46 PM,8,3,12:29:48 PM,8,3,12:49:37 PM,8,3,12:50:50 PM,8,3,12:59:44 PM,5,,,-1.2748221000000002,36.7817865,-1.2726389999999999,36.794723,Rider_Id_854,534
-Order_No_16770,User_Id_1561,Bike,3,Business,12,5,12:38:46 PM,12,5,12:39:13 PM,12,5,12:49:54 PM,12,5,12:58:33 PM,12,5,1:49:56 PM,16,24.7,,-1.2869217,36.7732203,-1.328259,36.860975,Rider_Id_299,3083
-Order_No_25613,User_Id_2987,Bike,3,Business,18,2,12:37:22 PM,18,2,12:37:33 PM,18,2,12:37:38 PM,18,2,12:56:52 PM,18,2,1:08:53 PM,7,22.5,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_886,721
-Order_No_870,User_Id_3391,Bike,1,Personal,28,2,11:17:43 AM,28,2,11:18:13 AM,28,2,11:28:08 AM,28,2,11:33:48 AM,28,2,11:56:34 AM,10,17.8,,-1.3223238000000002,36.8320056,-1.3263415,36.772282700000005,Rider_Id_726,1366
-Order_No_2991,User_Id_868,Bike,3,Business,26,5,8:47:10 AM,26,5,8:48:27 AM,26,5,9:37:19 AM,26,5,9:52:16 AM,26,5,10:35:24 AM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_204,2588
-Order_No_1485,User_Id_706,Bike,3,Business,18,2,3:30:54 PM,18,2,3:39:13 PM,18,2,4:36:46 PM,18,2,4:38:04 PM,18,2,4:40:36 PM,6,23.5,,-1.3077869,36.844320700000004,-1.3228003000000002,36.8306435,Rider_Id_728,152
-Order_No_17886,User_Id_2768,Bike,3,Business,11,6,8:19:39 AM,11,6,8:19:51 AM,11,6,8:43:38 AM,11,6,9:42:17 AM,11,6,9:42:19 AM,26,,,-1.2857763999999998,36.8236622,-1.1344906000000001,36.9123061,Rider_Id_68,2
-Order_No_18887,User_Id_3560,Bike,3,Business,11,1,11:54:32 AM,11,1,2:15:25 PM,11,1,2:19:28 PM,11,1,2:22:38 PM,11,1,3:03:49 PM,10,27.9,,-1.2859912,36.8756811,-1.279395,36.825364,Rider_Id_803,2471
-Order_No_17361,User_Id_635,Bike,3,Business,28,5,2:53:03 PM,28,5,2:56:01 PM,28,5,3:02:19 PM,28,5,3:03:55 PM,28,5,3:12:37 PM,7,25.3,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_937,522
-Order_No_23145,User_Id_540,Bike,3,Business,3,3,2:12:46 PM,3,3,2:12:56 PM,3,3,2:16:54 PM,3,3,2:22:40 PM,3,3,2:40:30 PM,8,28.3,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_250,1070
-Order_No_25062,User_Id_20,Bike,3,Business,7,5,9:59:48 AM,7,5,10:00:30 AM,7,5,10:07:11 AM,7,5,10:10:57 AM,7,5,10:30:09 AM,9,20.4,1.0,-1.2648363,36.802375399999995,-1.3125593,36.8430941,Rider_Id_878,1152
-Order_No_25667,User_Id_2523,Bike,3,Business,3,5,9:04:17 AM,3,5,9:04:54 AM,3,5,9:18:29 AM,3,5,9:19:53 AM,3,5,9:49:42 AM,9,,,-1.2551895,36.7822034,-1.3025666999999999,36.7803495,Rider_Id_876,1789
-Order_No_2600,User_Id_1146,Bike,3,Business,7,4,2:33:50 PM,7,4,2:50:25 PM,7,4,3:08:26 PM,7,4,3:19:20 PM,7,4,3:49:58 PM,7,27.9,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_26,1838
-Order_No_5793,User_Id_2789,Bike,3,Business,4,1,3:50:18 PM,4,1,3:50:40 PM,4,1,3:59:15 PM,4,1,4:00:23 PM,4,1,4:25:04 PM,4,28.6,,-1.304735,36.784605400000004,-1.2804664,36.7701111,Rider_Id_468,1481
-Order_No_17387,User_Id_516,Bike,3,Business,30,4,1:11:54 PM,30,4,1:16:57 PM,30,4,1:24:13 PM,30,4,1:30:59 PM,30,4,1:51:08 PM,8,,,-1.2896398999999998,36.812449799999996,-1.2551895,36.7822034,Rider_Id_372,1209
-Order_No_11778,User_Id_3599,Bike,3,Business,2,4,8:53:57 AM,2,4,9:18:41 AM,2,4,9:39:51 AM,2,4,9:49:22 AM,2,4,10:10:53 AM,7,,,-1.3302996,36.8707935,-1.3031652,36.8466816,Rider_Id_675,1291
-Order_No_3038,User_Id_2330,Bike,3,Business,26,2,9:31:35 AM,26,2,9:32:00 AM,26,2,9:37:45 AM,26,2,9:41:54 AM,26,2,9:47:09 AM,3,,,-1.3015197,36.7658456,-1.2873232,36.765857200000006,Rider_Id_327,315
-Order_No_16708,User_Id_2801,Bike,3,Business,7,1,3:24:45 PM,7,1,3:25:21 PM,7,1,3:46:37 PM,7,1,3:48:45 PM,7,1,4:40:36 PM,13,20.4,,-1.2595421999999998,36.7871178,-1.3288576,36.8465936,Rider_Id_510,3111
-Order_No_12777,User_Id_346,Bike,3,Business,16,4,1:11:37 PM,16,4,1:12:41 PM,16,4,1:46:09 PM,16,4,1:48:54 PM,16,4,2:40:47 PM,3,,,-1.2909536,36.8407162,-1.28878,36.8168312,Rider_Id_790,3113
-Order_No_23169,User_Id_60,Bike,3,Business,16,6,11:22:06 AM,16,6,11:23:09 AM,16,6,11:43:59 AM,16,6,11:59:18 AM,16,6,12:07:47 PM,8,25.7,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_459,509
-Order_No_26755,User_Id_1244,Bike,3,Business,6,2,11:40:44 AM,6,2,12:46:24 PM,6,2,1:37:47 PM,6,2,1:51:59 PM,6,2,2:11:26 PM,11,27.3,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_179,1167
-Order_No_18158,User_Id_3560,Bike,3,Business,7,5,4:17:17 PM,7,5,5:16:36 PM,7,5,5:21:42 PM,7,5,5:35:08 PM,7,5,5:35:11 PM,1,23.0,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_928,3
-Order_No_1387,User_Id_3283,Bike,3,Business,22,5,9:34:22 AM,22,5,9:34:48 AM,22,5,9:34:53 AM,22,5,10:35:10 AM,22,5,10:59:20 AM,14,28.4,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_260,1450
-Order_No_26232,User_Id_3404,Bike,3,Business,21,3,11:39:04 AM,21,3,11:39:27 AM,21,3,11:39:35 AM,21,3,11:57:22 AM,21,3,12:14:06 PM,11,22.2,,-1.2283403,36.8822756,-1.279395,36.825364,Rider_Id_659,1004
-Order_No_3171,User_Id_3432,Bike,3,Business,9,5,11:09:15 AM,9,5,11:09:58 AM,9,5,11:16:48 AM,9,5,11:56:56 AM,9,5,12:55:55 PM,3,25.7,,-1.3139898,36.867698600000004,-1.3270347,36.866736200000005,Rider_Id_56,3539
-Order_No_23992,User_Id_3108,Bike,3,Business,8,3,12:11:23 PM,8,3,12:18:26 PM,8,3,12:30:38 PM,8,3,12:37:50 PM,8,3,1:43:01 PM,14,22.4,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_498,3911
-Order_No_22250,User_Id_1075,Bike,3,Business,22,5,8:29:17 AM,22,5,8:29:53 AM,22,5,8:35:35 AM,22,5,8:37:47 AM,22,5,8:37:56 AM,4,,,-1.2657464,36.780114399999995,-1.2527958000000001,36.8003131,Rider_Id_108,9
-Order_No_5286,User_Id_330,Bike,1,Personal,15,6,11:15:40 AM,15,6,11:16:08 AM,15,6,11:31:00 AM,15,6,11:35:51 AM,15,6,11:53:15 AM,7,,,-1.3244691000000002,36.804587299999994,-1.2914795000000001,36.824897299999996,Rider_Id_281,1044
-Order_No_23630,User_Id_2982,Bike,3,Business,7,2,9:10:32 AM,7,2,9:10:44 AM,7,2,9:18:06 AM,7,2,10:04:05 AM,7,2,11:08:54 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_519,3889
-Order_No_19015,User_Id_2271,Bike,3,Business,5,6,2:05:59 PM,5,6,2:06:06 PM,5,6,2:10:11 PM,5,6,2:21:44 PM,5,6,3:02:13 PM,18,22.0,,-1.2560183999999999,36.7934342,-1.293515,36.897607,Rider_Id_195,2429
-Order_No_19835,User_Id_1329,Bike,3,Business,3,3,11:14:57 AM,3,3,11:15:58 AM,3,3,11:22:22 AM,3,3,11:25:02 AM,3,3,11:38:00 AM,3,27.3,,-1.273056,36.811298,-1.2607855,36.8163104,Rider_Id_99,778
-Order_No_16352,User_Id_2236,Bike,1,Personal,13,3,1:56:45 PM,13,3,2:02:31 PM,13,3,2:12:36 PM,13,3,2:14:26 PM,13,3,2:32:23 PM,6,23.6,,-1.2901261999999998,36.7951047,-1.3263415,36.772282700000005,Rider_Id_312,1077
-Order_No_21011,User_Id_3121,Bike,3,Personal,30,4,2:54:13 PM,30,4,2:55:12 PM,30,4,3:04:10 PM,30,4,3:07:39 PM,30,4,3:12:09 PM,2,,,-1.2685707,36.806496100000004,-1.2753108999999998,36.8130185,Rider_Id_96,270
-Order_No_22336,User_Id_1961,Bike,3,Business,20,1,8:59:42 AM,20,1,9:18:21 AM,20,1,9:24:05 AM,20,1,9:30:45 AM,20,1,9:48:15 AM,9,19.1,,-1.303596,36.778378000000004,-1.3188358999999998,36.703852600000005,Rider_Id_628,1050
-Order_No_1620,User_Id_2784,Bike,3,Business,8,2,9:05:20 AM,8,2,9:06:05 AM,8,2,9:13:40 AM,8,2,9:17:46 AM,8,2,10:08:59 AM,17,19.1,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_436,3073
-Order_No_17004,User_Id_186,Bike,3,Business,20,1,3:00:30 PM,20,1,3:02:45 PM,20,1,3:16:42 PM,20,1,3:17:50 PM,20,1,3:32:36 PM,6,,,-1.2571472,36.795063299999995,-1.2985749,36.8088,Rider_Id_253,886
-Order_No_5494,User_Id_2037,Bike,1,Personal,18,4,2:24:34 PM,18,4,2:31:14 PM,18,4,2:41:17 PM,18,4,2:59:49 PM,18,4,3:20:30 PM,3,26.6,,-1.2864309999999999,36.818411,-1.2903277,36.80556,Rider_Id_477,1241
-Order_No_7764,User_Id_2521,Bike,3,Business,30,6,8:48:51 AM,30,6,8:49:04 AM,30,6,8:49:34 AM,30,6,9:01:21 AM,30,6,9:18:55 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_349,1054
-Order_No_11006,User_Id_1007,Bike,3,Personal,23,4,1:49:03 PM,23,4,1:49:25 PM,23,4,2:03:50 PM,23,4,2:13:05 PM,23,4,2:42:14 PM,11,24.5,,-1.2943445,36.7515778,-1.2643794,36.816234,Rider_Id_217,1749
-Order_No_21127,User_Id_186,Bike,3,Business,6,3,3:26:05 PM,6,3,3:29:44 PM,6,3,3:45:31 PM,6,3,3:51:19 PM,6,3,4:06:21 PM,9,22.0,,-1.2975039,36.8110959,-1.2579303999999998,36.8019667,Rider_Id_742,902
-Order_No_21271,User_Id_633,Bike,3,Business,1,5,1:27:40 PM,1,5,1:28:53 PM,1,5,1:38:15 PM,1,5,1:39:42 PM,1,5,1:48:07 PM,4,31.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_728,505
-Order_No_21046,User_Id_1329,Bike,3,Business,7,5,6:10:11 PM,7,5,6:14:30 PM,7,5,6:15:59 PM,7,5,6:22:23 PM,7,5,6:46:38 PM,6,21.7,,-1.273056,36.811298,-1.2832105,36.7777664,Rider_Id_75,1455
-Order_No_20743,User_Id_3447,Bike,1,Personal,25,5,12:27:40 PM,25,5,12:35:49 PM,25,5,1:14:57 PM,25,5,1:23:47 PM,25,5,2:10:52 PM,13,21.2,,-1.3221448999999998,36.8565652,-1.2562673999999998,36.8191447,Rider_Id_116,2825
-Order_No_908,User_Id_3147,Bike,3,Business,12,5,12:55:58 PM,12,5,1:06:38 PM,12,5,1:48:12 PM,12,5,1:49:35 PM,12,5,2:38:49 PM,16,,,-1.3472066,36.7692638,-1.2546732,36.808679600000005,Rider_Id_76,2954
-Order_No_8482,User_Id_868,Bike,3,Business,13,4,10:46:01 AM,13,4,10:55:09 AM,13,4,10:56:13 AM,13,4,11:52:57 AM,13,4,12:23:56 PM,10,23.3,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_900,1859
-Order_No_20294,User_Id_1304,Bike,3,Business,18,4,11:47:57 AM,18,4,11:48:36 AM,18,4,11:50:48 AM,18,4,11:55:27 AM,18,4,12:30:16 PM,13,25.5,,-1.3226301999999999,36.8719901,-1.269534,36.80697,Rider_Id_532,2089
-Order_No_6394,User_Id_3055,Bike,1,Personal,26,3,4:34:33 PM,26,3,4:35:33 PM,26,3,5:06:49 PM,26,3,5:08:37 PM,26,3,5:43:44 PM,9,19.5,,-1.2248308,36.801257299999996,-1.2169587,36.8420167,Rider_Id_607,2107
-Order_No_26658,User_Id_868,Bike,3,Business,20,3,10:01:42 AM,20,3,10:02:04 AM,20,3,10:51:37 AM,20,3,10:56:04 AM,20,3,11:30:38 AM,14,26.3,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_377,2074
-Order_No_4811,User_Id_2801,Bike,3,Business,25,1,10:53:47 AM,25,1,10:54:03 AM,25,1,10:54:47 AM,25,1,11:15:03 AM,25,1,11:43:50 AM,8,,,-1.2595421999999998,36.7871178,-1.2925919,36.81705770000001,Rider_Id_103,1727
-Order_No_23906,User_Id_113,Bike,3,Business,13,1,3:07:29 PM,13,1,3:08:18 PM,13,1,3:16:32 PM,13,1,3:25:42 PM,13,1,3:49:11 PM,7,19.6,,-1.2963097,36.7688221,-1.2663558000000001,36.805564600000004,Rider_Id_229,1409
-Order_No_24485,User_Id_3108,Bike,3,Business,15,1,2:58:36 PM,15,1,3:05:29 PM,15,1,3:12:46 PM,15,1,3:15:24 PM,15,1,3:37:44 PM,6,,,-1.2551895,36.7822034,-1.2817351000000001,36.8186929,Rider_Id_692,1340
-Order_No_4135,User_Id_393,Bike,3,Business,8,1,12:41:19 PM,8,1,12:42:06 PM,8,1,12:49:16 PM,8,1,1:04:40 PM,8,1,1:15:48 PM,4,27.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_687,668
-Order_No_21822,User_Id_2523,Bike,3,Business,15,3,9:54:51 AM,15,3,9:55:13 AM,15,3,9:55:30 AM,15,3,10:03:05 AM,15,3,10:42:31 AM,20,,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_709,2366
-Order_No_21949,User_Id_414,Bike,1,Personal,1,3,3:43:11 PM,1,3,3:43:34 PM,1,3,3:50:39 PM,1,3,4:06:13 PM,1,3,4:25:23 PM,4,24.1,,-1.2861764999999998,36.8209086,-1.3001373999999999,36.820929799999995,Rider_Id_857,1150
-Order_No_9234,User_Id_2804,Bike,3,Business,12,2,10:22:36 AM,12,2,10:23:12 AM,12,2,10:30:04 AM,12,2,10:40:37 AM,12,2,10:44:54 AM,4,24.3,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_770,257
-Order_No_27692,User_Id_1500,Bike,3,Business,15,3,4:23:30 PM,15,3,4:23:52 PM,15,3,4:28:43 PM,15,3,4:31:51 PM,15,3,5:11:53 PM,9,25.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_690,2402
-Order_No_1375,User_Id_867,Bike,3,Business,18,4,11:05:11 AM,18,4,11:05:41 AM,18,4,11:05:56 AM,18,4,11:56:05 AM,18,4,12:28:06 PM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_855,1921
-Order_No_4177,User_Id_1500,Bike,3,Business,24,3,10:43:31 AM,24,3,10:44:28 AM,24,3,10:45:21 AM,24,3,11:40:27 AM,24,3,11:40:31 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_507,4
-Order_No_23270,User_Id_1469,Bike,3,Business,13,5,4:48:29 PM,13,5,4:49:12 PM,13,5,5:04:31 PM,13,5,5:05:50 PM,13,5,5:22:14 PM,3,19.4,,-1.2551895,36.7822034,-1.2681821000000002,36.8011109,Rider_Id_937,984
-Order_No_15026,User_Id_1874,Bike,3,Business,15,4,3:06:09 PM,15,4,3:06:59 PM,15,4,3:20:35 PM,15,4,3:26:44 PM,15,4,3:59:20 PM,12,27.7,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_935,1956
-Order_No_4776,User_Id_928,Bike,3,Business,27,3,9:06:30 AM,27,3,9:07:33 AM,27,3,9:07:46 AM,27,3,9:40:34 AM,27,3,9:51:20 AM,6,,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_446,646
-Order_No_25081,User_Id_625,Bike,3,Business,15,6,9:56:09 AM,15,6,10:51:32 AM,15,6,11:06:03 AM,15,6,11:08:58 AM,15,6,11:36:27 AM,4,21.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_332,1649
-Order_No_21010,User_Id_2982,Bike,3,Business,8,4,2:04:52 PM,8,4,2:05:00 PM,8,4,2:05:37 PM,8,4,2:14:27 PM,8,4,3:22:19 PM,22,27.5,,-1.1956524,36.948806899999994,-1.177202,36.8351183,Rider_Id_519,4072
-Order_No_7889,User_Id_3203,Bike,3,Business,9,3,11:19:11 AM,9,3,12:52:45 PM,9,3,12:52:51 PM,9,3,1:31:00 PM,9,3,1:40:45 PM,9,21.1,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_397,585
-Order_No_24768,User_Id_3295,Bike,3,Business,6,4,11:59:47 AM,6,4,12:00:14 PM,6,4,12:25:35 PM,6,4,12:28:58 PM,6,4,1:11:52 PM,8,19.5,,-1.2571472,36.795063299999995,-1.2814233,36.8251177,Rider_Id_739,2574
-Order_No_16925,User_Id_2523,Bike,3,Business,2,3,3:19:05 PM,2,3,3:19:23 PM,2,3,3:27:22 PM,2,3,3:29:50 PM,2,3,3:45:43 PM,16,25.8,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_647,953
-Order_No_21203,User_Id_1500,Bike,3,Business,28,4,8:27:22 AM,28,4,8:28:45 AM,28,4,8:42:28 AM,28,4,9:25:10 AM,28,4,9:33:57 AM,8,19.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,527
-Order_No_28060,User_Id_478,Bike,3,Business,12,3,1:07:52 PM,12,3,1:12:40 PM,12,3,1:32:19 PM,12,3,1:38:36 PM,12,3,2:25:27 PM,8,20.9,,-1.3045333000000001,36.82280720000001,-1.2603403,36.7998848,Rider_Id_155,2811
-Order_No_15608,User_Id_393,Bike,3,Business,22,5,12:51:14 PM,22,5,12:51:28 PM,22,5,12:51:33 PM,22,5,1:01:05 PM,22,5,1:10:49 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_111,584
-Order_No_7414,User_Id_970,Bike,3,Business,16,4,8:08:16 AM,16,4,8:09:09 AM,16,4,8:35:02 AM,16,4,8:41:14 AM,16,4,8:57:54 AM,6,,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_155,1000
-Order_No_22724,User_Id_3283,Bike,3,Business,22,5,10:06:55 AM,22,5,10:07:33 AM,22,5,10:12:05 AM,22,5,10:26:19 AM,22,5,10:41:12 AM,5,16.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_116,893
-Order_No_18177,User_Id_3599,Bike,3,Business,14,5,9:43:27 AM,14,5,11:18:52 AM,14,5,11:42:29 AM,14,5,11:44:30 AM,14,5,12:18:04 PM,8,21.3,,-1.2914687,36.7692172,-1.25789,36.79268,Rider_Id_470,2014
-Order_No_2880,User_Id_3448,Bike,1,Personal,19,5,3:10:16 PM,19,5,3:17:57 PM,19,5,3:44:17 PM,19,5,3:50:14 PM,19,5,4:30:19 PM,19,27.2,,-1.28621,36.825633,-1.374639,36.9253261,Rider_Id_612,2405
-Order_No_6821,User_Id_3221,Bike,3,Business,21,4,2:36:30 PM,21,4,2:37:09 PM,21,4,2:51:10 PM,21,4,2:52:20 PM,21,4,3:12:33 PM,5,30.8,,-1.2737206,36.8072379,-1.2611818000000001,36.8271669,Rider_Id_434,1213
-Order_No_11892,User_Id_2677,Bike,3,Business,22,3,11:58:39 AM,22,3,11:59:26 AM,22,3,11:59:30 AM,22,3,12:33:03 PM,22,3,12:36:21 PM,10,22.9,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_137,198
-Order_No_18215,User_Id_3635,Bike,1,Business,7,5,3:56:31 PM,7,5,3:56:39 PM,7,5,3:56:58 PM,7,5,4:01:48 PM,7,5,4:26:21 PM,7,24.7,,-1.2615888000000002,36.7928732,-1.295156,36.809430600000006,Rider_Id_848,1473
-Order_No_14999,User_Id_393,Bike,3,Business,11,1,10:18:09 AM,11,1,10:18:18 AM,11,1,10:18:23 AM,11,1,10:54:39 AM,11,1,10:55:02 AM,11,26.6,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_39,23
-Order_No_20005,User_Id_1205,Bike,3,Business,31,4,10:26:54 AM,31,4,10:27:22 AM,31,4,10:50:27 AM,31,4,11:02:18 AM,31,4,11:45:57 AM,9,26.6,,-1.2495931,36.80650870000001,-1.2159986999999999,36.8226959,Rider_Id_953,2619
-Order_No_674,User_Id_1502,Bike,3,Business,15,4,11:58:53 AM,15,4,11:59:06 AM,15,4,12:21:54 PM,15,4,12:24:04 PM,15,4,1:15:33 PM,17,24.2,,-1.2232123,36.8932547,-1.2551895,36.7822034,Rider_Id_812,3089
-Order_No_27756,User_Id_516,Bike,3,Business,30,5,11:08:03 AM,30,5,11:08:30 AM,30,5,11:20:48 AM,30,5,11:33:21 AM,30,5,11:56:18 AM,6,23.5,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_19,1377
-Order_No_8069,User_Id_1177,Bike,1,Personal,19,2,12:56:38 PM,19,2,12:56:54 PM,19,2,1:19:46 PM,19,2,1:26:56 PM,19,2,2:07:21 PM,8,,,-1.2915306999999998,36.790586499999996,-1.3017794,36.8240888,Rider_Id_202,2425
-Order_No_3508,User_Id_1144,Bike,3,Personal,7,1,2:12:39 PM,7,1,2:13:03 PM,7,1,2:33:43 PM,7,1,2:51:45 PM,7,1,3:19:48 PM,18,20.7,,-1.2860049,36.815504499999996,-1.3656046999999998,36.7414061,Rider_Id_406,1683
-Order_No_12889,User_Id_3434,Bike,3,Business,17,5,10:34:28 AM,17,5,10:35:18 AM,17,5,11:11:26 AM,17,5,11:19:19 AM,17,5,12:22:24 PM,11,,,-1.3053135,36.8605848,-1.2417321000000001,36.8886277,Rider_Id_668,3785
-Order_No_14141,User_Id_430,Bike,3,Business,4,6,11:07:25 AM,4,6,11:07:57 AM,4,6,12:06:14 PM,4,6,12:12:01 PM,4,6,1:01:13 PM,18,19.9,,-1.3321944,36.889232899999996,-1.2611667,36.8056908,Rider_Id_700,2952
-Order_No_22653,User_Id_3222,Bike,3,Business,13,2,12:20:43 PM,13,2,12:33:33 PM,13,2,1:06:27 PM,13,2,1:09:08 PM,13,2,1:27:13 PM,10,22.2,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_578,1085
-Order_No_6098,User_Id_2330,Bike,3,Business,10,3,4:58:52 PM,10,3,5:00:22 PM,10,3,5:15:11 PM,10,3,5:19:44 PM,10,3,5:37:17 PM,7,27.3,,-1.2536275,36.798793200000006,-1.2921463,36.8029626,Rider_Id_251,1053
-Order_No_14093,User_Id_517,Bike,3,Business,14,6,10:35:58 AM,14,6,10:37:08 AM,14,6,10:43:40 AM,14,6,10:53:24 AM,14,6,10:58:57 AM,7,17.8,,-1.3177546999999998,36.8303703,-1.3012841999999998,36.8561033,Rider_Id_687,333
-Order_No_15203,User_Id_3291,Bike,3,Business,30,2,2:38:05 PM,30,2,2:43:01 PM,30,2,2:50:23 PM,30,2,2:58:52 PM,30,2,3:41:10 PM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_315,2538
-Order_No_15632,User_Id_2472,Bike,3,Business,16,3,2:52:49 PM,16,3,3:09:39 PM,16,3,3:17:57 PM,16,3,3:21:04 PM,16,3,3:46:37 PM,8,25.7,,-1.3081007,36.839634999999994,-1.2633493999999998,36.7959696,Rider_Id_949,1533
-Order_No_6277,User_Id_1500,Bike,3,Business,12,2,4:38:14 PM,12,2,4:39:24 PM,12,2,4:40:41 PM,12,2,4:54:28 PM,12,2,5:19:57 PM,8,28.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_8,1529
-Order_No_21630,User_Id_972,Bike,3,Business,4,4,9:47:10 AM,4,4,9:48:51 AM,4,4,10:21:59 AM,4,4,10:24:43 AM,4,4,10:40:03 AM,7,18.3,,-1.2862033000000002,36.8152966,-1.31861,36.8113967,Rider_Id_726,920
-Order_No_23188,User_Id_2154,Bike,3,Business,11,2,3:23:01 PM,11,2,4:11:10 PM,11,2,4:20:48 PM,11,2,5:10:32 PM,11,2,5:40:24 PM,8,21.3,,-1.2571472,36.795063299999995,-1.2765736,36.851364600000004,Rider_Id_399,1792
-Order_No_9599,User_Id_2691,Bike,3,Business,26,1,10:23:46 AM,26,1,10:40:04 AM,26,1,10:42:19 AM,26,1,11:05:19 AM,26,1,11:32:20 AM,8,22.9,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_536,1621
-Order_No_15887,User_Id_1075,Bike,3,Business,28,4,11:56:39 AM,28,4,11:57:01 AM,28,4,12:05:38 PM,28,4,12:40:30 PM,28,4,12:40:32 PM,1,23.9,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_167,2
-Order_No_16138,User_Id_948,Bike,3,Business,9,4,2:07:34 PM,9,4,2:24:09 PM,9,4,2:28:19 PM,9,4,2:51:01 PM,9,4,4:06:08 PM,10,,,-1.3200694,36.793130600000005,-1.2696908000000002,36.81519,Rider_Id_557,4507
-Order_No_12139,User_Id_2718,Bike,3,Business,1,1,2:29:08 PM,1,1,2:31:15 PM,1,1,2:43:26 PM,1,1,2:45:48 PM,1,1,3:04:43 PM,11,,,-1.2668815,36.8017244,-1.2467428999999999,36.7479091,Rider_Id_955,1135
-Order_No_4687,User_Id_868,Bike,3,Business,18,2,10:50:51 AM,18,2,10:50:57 AM,18,2,10:51:45 AM,18,2,11:02:59 AM,18,2,11:20:24 AM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_45,1045
-Order_No_24834,User_Id_1959,Bike,3,Business,25,4,6:11:05 PM,25,4,6:12:05 PM,25,4,6:33:31 PM,25,4,6:36:25 PM,25,4,6:59:53 PM,10,,,-1.2551895,36.7822034,-1.2664227,36.7453671,Rider_Id_263,1408
-Order_No_7330,User_Id_3496,Bike,3,Business,5,2,11:49:29 AM,5,2,11:50:22 AM,5,2,11:59:41 AM,5,2,12:06:54 PM,5,2,12:15:15 PM,5,21.6,,-1.300921,36.828195,-1.332243,36.8068914,Rider_Id_852,501
-Order_No_9883,User_Id_3432,Bike,3,Business,28,2,9:29:12 AM,28,2,9:30:21 AM,28,2,9:53:43 AM,28,2,10:23:34 AM,28,2,10:42:23 AM,10,18.9,,-1.3139898,36.867698600000004,-1.283962,36.8142095,Rider_Id_68,1129
-Order_No_8019,User_Id_2353,Bike,1,Personal,6,5,12:52:44 PM,6,5,12:56:52 PM,6,5,12:57:11 PM,6,5,1:09:51 PM,6,5,1:24:20 PM,5,20.3,,-1.28633,36.82125,-1.2903643,36.8319433,Rider_Id_79,869
-Order_No_24502,User_Id_1500,Bike,3,Business,29,5,9:26:28 AM,29,5,9:27:58 AM,29,5,10:11:37 AM,29,5,10:45:28 AM,29,5,11:07:40 AM,8,26.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_763,1332
-Order_No_15204,User_Id_3264,Bike,3,Business,15,4,12:51:57 PM,15,4,12:52:48 PM,15,4,12:58:19 PM,15,4,1:01:48 PM,15,4,1:09:10 PM,3,24.4,,-1.2551895,36.7822034,-1.2642333000000001,36.779548299999995,Rider_Id_290,442
-Order_No_25029,User_Id_393,Bike,3,Business,29,1,11:28:53 AM,29,1,11:29:12 AM,29,1,11:31:18 AM,29,1,11:41:58 AM,29,1,11:51:23 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,565
-Order_No_11742,User_Id_2216,Bike,3,Business,27,3,11:07:11 AM,27,3,11:07:32 AM,27,3,11:09:59 AM,27,3,11:13:10 AM,27,3,11:27:55 AM,7,26.3,,-1.2551895,36.7822034,-1.2817426,36.7846148,Rider_Id_267,885
-Order_No_3229,User_Id_1777,Bike,3,Personal,24,4,2:29:49 PM,24,4,2:30:46 PM,24,4,2:44:45 PM,24,4,2:46:45 PM,24,4,3:05:21 PM,8,22.5,,-1.2493273999999999,36.7529258,-1.288406,36.760079,Rider_Id_334,1116
-Order_No_1368,User_Id_1102,Bike,3,Business,7,5,3:16:51 PM,7,5,3:37:18 PM,7,5,4:07:49 PM,7,5,4:08:56 PM,7,5,4:22:32 PM,8,,,-1.3253021999999999,36.8803995,-1.3004062,36.829741,Rider_Id_139,816
-Order_No_16997,User_Id_3007,Bike,3,Personal,1,5,6:20:10 PM,1,5,6:22:55 PM,1,5,6:25:33 PM,1,5,7:11:30 PM,1,5,7:21:12 PM,5,26.5,,-1.277551,36.8273641,-1.2913382,36.8018508,Rider_Id_237,582
-Order_No_2635,User_Id_1341,Bike,3,Business,18,1,10:15:29 AM,18,1,10:17:34 AM,18,1,10:26:40 AM,18,1,10:53:38 AM,18,1,11:16:58 AM,7,28.1,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_311,1400
-Order_No_23705,User_Id_3194,Bike,3,Business,15,5,11:57:46 AM,15,5,11:58:11 AM,15,5,12:13:41 PM,15,5,12:15:52 PM,15,5,12:37:28 PM,8,26.2,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_523,1296
-Order_No_26505,User_Id_3589,Bike,1,Personal,10,2,7:44:36 AM,10,2,7:44:47 AM,10,2,7:49:25 AM,10,2,8:01:29 AM,10,2,8:17:02 AM,9,16.4,,-1.2571191000000002,36.8019652,-1.2248308,36.801257299999996,Rider_Id_116,933
-Order_No_15454,User_Id_797,Bike,3,Business,18,3,7:53:59 AM,18,3,7:54:13 AM,18,3,7:58:02 AM,18,3,8:03:53 AM,18,3,8:37:21 AM,15,14.5,,-1.2551895,36.7822034,-1.2686837,36.8891847,Rider_Id_116,2008
-Order_No_18046,User_Id_510,Bike,3,Business,1,1,2:19:45 PM,1,1,2:20:17 PM,1,1,2:27:29 PM,1,1,2:39:22 PM,1,1,3:25:40 PM,21,25.6,,-1.225322,36.80855,-1.2860183,36.8975338,Rider_Id_882,2778
-Order_No_19913,User_Id_2579,Bike,3,Business,7,3,12:04:39 PM,7,3,12:08:51 PM,7,3,12:26:02 PM,7,3,12:31:26 PM,7,3,12:47:52 PM,7,25.2,,-1.287428,36.7800821,-1.2950041,36.814358299999995,Rider_Id_509,986
-Order_No_12239,User_Id_3201,Bike,3,Business,20,2,10:57:38 AM,20,2,10:59:04 AM,20,2,11:06:10 AM,20,2,11:17:21 AM,20,2,11:33:09 AM,3,19.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_926,948
-Order_No_23748,User_Id_1207,Bike,3,Business,26,2,6:22:09 PM,26,2,6:22:36 PM,26,2,6:25:50 PM,26,2,6:29:37 PM,26,2,6:49:25 PM,12,28.4,,-1.2629666000000002,36.8068369,-1.2351541000000001,36.8748505,Rider_Id_178,1188
-Order_No_8768,User_Id_2801,Bike,3,Business,8,5,4:15:20 PM,8,5,4:18:49 PM,8,5,4:19:42 PM,8,5,4:36:24 PM,8,5,5:14:54 PM,13,25.4,,-1.2595421999999998,36.7871178,-1.298905,36.8538783,Rider_Id_828,2310
-Order_No_5463,User_Id_3291,Bike,3,Business,28,2,9:29:45 AM,28,2,9:30:43 AM,28,2,9:31:25 AM,28,2,10:04:40 AM,28,2,10:04:47 AM,4,18.9,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_99,7
-Order_No_26814,User_Id_635,Bike,3,Business,6,3,12:44:05 PM,6,3,12:44:29 PM,6,3,12:51:50 PM,6,3,1:42:19 PM,6,3,1:54:25 PM,6,27.6,,-1.3081376,36.83297720000001,-1.3229345,36.8566974,Rider_Id_95,726
-Order_No_19297,User_Id_1673,Bike,2,Personal,25,3,12:19:51 PM,25,3,12:20:28 PM,25,3,12:33:01 PM,25,3,12:40:01 PM,25,3,1:05:32 PM,8,,,-1.261395,36.8263891,-1.288512,36.7731539,Rider_Id_313,1531
-Order_No_3352,User_Id_3526,Bike,3,Business,31,5,2:51:50 PM,31,5,2:57:57 PM,31,5,3:25:56 PM,31,5,3:31:33 PM,31,5,4:08:09 PM,13,24.9,,-1.300722,36.7882234,-1.3094235,36.87,Rider_Id_624,2196
-Order_No_19722,User_Id_3235,Bike,1,Personal,28,5,11:59:27 AM,28,5,12:03:17 PM,28,5,12:10:11 PM,28,5,12:31:34 PM,28,5,12:51:22 PM,6,25.3,,-1.2637296,36.8102898,-1.310011,36.820824,Rider_Id_698,1188
-Order_No_15499,User_Id_3517,Bike,3,Business,23,3,11:53:48 AM,23,3,12:20:28 PM,23,3,12:39:39 PM,23,3,12:51:49 PM,23,3,1:17:54 PM,16,,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_293,1565
-Order_No_2420,User_Id_3460,Bike,3,Personal,18,4,3:27:55 PM,18,4,3:28:06 PM,18,4,3:29:20 PM,18,4,3:37:26 PM,18,4,4:13:40 PM,16,,,-1.298598,36.7953396,-1.2145575000000002,36.8898511,Rider_Id_801,2174
-Order_No_24992,User_Id_3283,Bike,3,Business,14,4,12:18:55 PM,14,4,12:28:06 PM,14,4,12:39:04 PM,14,4,12:45:17 PM,14,4,1:03:05 PM,7,23.7,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_290,1068
-Order_No_20191,User_Id_357,Bike,3,Business,23,5,9:44:19 AM,23,5,11:07:45 AM,23,5,11:32:50 AM,23,5,11:35:02 AM,23,5,12:00:03 PM,6,24.0,,-1.2945178999999998,36.7685349,-1.2848376,36.802762200000004,Rider_Id_596,1501
-Order_No_8554,User_Id_2982,Bike,3,Business,30,3,3:23:35 PM,30,3,3:24:13 PM,30,3,3:53:21 PM,30,3,4:01:15 PM,30,3,5:02:15 PM,18,29.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_519,3660
-Order_No_416,User_Id_3689,Bike,3,Business,26,6,12:22:50 PM,26,6,12:25:00 PM,26,6,12:46:17 PM,26,6,12:52:49 PM,26,6,1:05:59 PM,5,22.5,,-1.279395,36.825364,-1.2601708999999999,36.8002238,Rider_Id_752,790
-Order_No_13015,User_Id_2200,Bike,3,Business,27,3,3:41:04 PM,27,3,3:47:21 PM,27,3,3:55:11 PM,27,3,4:13:22 PM,27,3,4:13:28 PM,1,29.7,,-1.290894,36.822971,-1.2824911,36.8218042,Rider_Id_386,6
-Order_No_26917,User_Id_1363,Bike,3,Business,7,5,9:51:14 AM,7,5,10:04:59 AM,7,5,10:19:53 AM,7,5,10:45:10 AM,7,5,10:45:15 AM,3,,,-1.3004062,36.829741,-1.2854089,36.8284874,Rider_Id_582,5
-Order_No_3982,User_Id_1329,Bike,3,Business,24,4,12:31:14 PM,24,4,12:39:23 PM,24,4,12:49:28 PM,24,4,12:55:27 PM,24,4,1:42:49 PM,6,21.0,,-1.273056,36.811298,-1.2930317,36.781091100000005,Rider_Id_742,2842
-Order_No_4435,User_Id_909,Bike,3,Business,2,3,3:20:46 PM,2,3,3:23:41 PM,2,3,4:01:20 PM,2,3,4:11:34 PM,2,3,4:22:59 PM,7,25.9,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_870,685
-Order_No_2233,User_Id_48,Bike,1,Personal,15,1,10:25:29 AM,15,1,10:25:59 AM,15,1,10:46:44 AM,15,1,10:52:18 AM,15,1,11:26:16 AM,14,,,-1.3163867,36.8558249,-1.246772,36.883293,Rider_Id_751,2038
-Order_No_20178,User_Id_2987,Bike,3,Business,27,4,12:36:25 PM,27,4,12:37:03 PM,27,4,12:48:11 PM,27,4,12:49:47 PM,27,4,1:06:14 PM,3,23.0,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_517,987
-Order_No_17912,User_Id_2789,Bike,3,Business,21,5,10:44:12 AM,21,5,11:01:27 AM,21,5,11:09:19 AM,21,5,11:11:34 AM,21,5,11:32:38 AM,10,22.8,,-1.304735,36.784605400000004,-1.2642903,36.80021370000001,Rider_Id_594,1264
-Order_No_17450,User_Id_393,Bike,3,Business,18,5,1:59:46 PM,18,5,1:59:53 PM,18,5,1:59:56 PM,18,5,2:36:45 PM,18,5,2:36:46 PM,11,27.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_619,1
-Order_No_3780,User_Id_3222,Bike,3,Business,8,1,12:24:15 PM,8,1,12:24:20 PM,8,1,12:24:32 PM,8,1,12:34:22 PM,8,1,1:12:42 PM,22,24.7,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_120,2300
-Order_No_13212,User_Id_3686,Bike,3,Business,22,2,2:18:07 PM,22,2,2:18:41 PM,22,2,2:18:53 PM,22,2,2:51:44 PM,22,2,3:04:15 PM,3,21.4,,-1.2626906,36.7827022,-1.2607765,36.8080021,Rider_Id_51,751
-Order_No_1361,User_Id_633,Bike,3,Business,8,5,3:26:53 PM,8,5,3:41:36 PM,8,5,3:49:10 PM,8,5,3:52:39 PM,8,5,4:10:17 PM,9,30.5,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_245,1058
-Order_No_14119,User_Id_3647,Bike,3,Business,15,3,4:07:49 PM,15,3,4:09:00 PM,15,3,4:18:06 PM,15,3,4:22:53 PM,15,3,4:43:25 PM,9,26.0,,-1.2726389999999999,36.794723,-1.2703427999999999,36.8336071,Rider_Id_708,1232
-Order_No_26456,User_Id_2030,Bike,3,Business,8,3,2:37:18 PM,8,3,2:37:54 PM,8,3,2:41:52 PM,8,3,2:44:26 PM,8,3,2:58:04 PM,3,25.7,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_754,818
-Order_No_2168,User_Id_269,Bike,3,Business,30,3,3:29:20 PM,30,3,4:11:57 PM,30,3,4:24:29 PM,30,3,4:27:26 PM,30,3,5:15:09 PM,10,25.2,,-1.3332749,36.870814700000004,-1.2810586000000002,36.883829399999996,Rider_Id_742,2863
-Order_No_9576,User_Id_3695,Bike,1,Personal,18,2,10:34:34 AM,18,2,10:36:17 AM,18,2,10:49:18 AM,18,2,11:18:33 AM,18,2,11:37:57 AM,3,22.6,,-1.2782978,36.827323799999995,-1.2894917,36.8056192,Rider_Id_857,1164
-Order_No_14487,User_Id_3647,Bike,3,Business,11,1,2:08:54 PM,11,1,2:09:22 PM,11,1,2:19:00 PM,11,1,2:25:23 PM,11,1,3:46:54 PM,33,28.2,,-1.2726389999999999,36.794723,-1.1105305,36.6461267,Rider_Id_770,4891
-Order_No_15978,User_Id_3226,Bike,3,Personal,16,2,10:43:49 AM,16,2,10:50:42 AM,16,2,11:00:11 AM,16,2,11:04:48 AM,16,2,11:14:36 AM,7,23.3,,-1.2669026,36.844527299999996,-1.2567464,36.7915273,Rider_Id_22,588
-Order_No_9105,User_Id_514,Bike,3,Personal,24,1,2:28:47 PM,24,1,2:30:43 PM,24,1,3:18:32 PM,24,1,3:27:08 PM,24,1,3:42:53 PM,4,24.8,,-1.2861764999999998,36.8209086,-1.2768326,36.8073804,Rider_Id_436,945
-Order_No_7214,User_Id_2330,Bike,3,Business,24,3,2:41:07 PM,24,3,2:41:34 PM,24,3,2:41:41 PM,24,3,3:11:37 PM,24,3,3:11:42 PM,7,,,-1.293082,36.8278578,-1.2536275,36.798793200000006,Rider_Id_613,5
-Order_No_2401,User_Id_478,Bike,3,Business,8,5,11:16:38 AM,8,5,11:18:06 AM,8,5,11:25:50 AM,8,5,11:33:00 AM,8,5,12:40:05 PM,15,28.2,,-1.2551895,36.7822034,-1.3278522,36.8711134,Rider_Id_147,4025
-Order_No_19516,User_Id_909,Bike,3,Business,11,4,1:52:27 PM,11,4,2:50:52 PM,11,4,3:04:28 PM,11,4,3:13:05 PM,11,4,3:40:51 PM,7,27.0,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_710,1666
-Order_No_5240,User_Id_1578,Bike,3,Business,7,3,12:52:35 PM,7,3,12:52:48 PM,7,3,12:56:53 PM,7,3,1:08:44 PM,7,3,1:08:48 PM,2,24.9,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_57,4
-Order_No_13034,User_Id_1724,Bike,3,Business,5,1,3:18:30 PM,5,1,3:19:07 PM,5,1,3:38:24 PM,5,1,3:43:56 PM,5,1,4:00:00 PM,8,25.8,,-1.2985559,36.783791,-1.2551895,36.7822034,Rider_Id_937,964
-Order_No_8242,User_Id_3661,Bike,3,Business,4,1,12:05:26 PM,4,1,1:02:47 PM,4,1,1:12:46 PM,4,1,1:21:29 PM,4,1,2:06:10 PM,15,26.8,,-1.2134646,36.8755266,-1.3090727,36.831526200000006,Rider_Id_510,2681
-Order_No_21430,User_Id_2869,Bike,3,Business,8,6,5:36:10 PM,8,6,5:39:23 PM,8,6,5:46:12 PM,8,6,5:58:07 PM,8,6,6:25:34 PM,16,21.6,,-1.2326041,36.8009029,-1.3025826,36.7670807,Rider_Id_161,1647
-Order_No_22345,User_Id_2214,Bike,2,Personal,28,1,10:38:22 AM,28,1,10:46:24 AM,28,1,11:16:33 AM,28,1,11:23:12 AM,28,1,11:46:02 AM,8,22.9,,-1.2750975,36.8289032,-1.3165209999999998,36.869310999999996,Rider_Id_888,1370
-Order_No_5766,User_Id_3686,Bike,3,Business,22,5,2:24:10 PM,22,5,2:32:25 PM,22,5,2:40:30 PM,22,5,2:49:33 PM,22,5,3:07:18 PM,9,31.0,,-1.2551895,36.7822034,-1.2955333999999998,36.7742857,Rider_Id_247,1065
-Order_No_20419,User_Id_2975,Bike,2,Personal,1,1,2:12:56 PM,1,1,2:13:09 PM,1,1,2:17:40 PM,1,1,2:23:13 PM,1,1,2:44:07 PM,12,25.7,,-1.2639639999999999,36.7854632,-1.3072409999999999,36.830236799999994,Rider_Id_905,1254
-Order_No_18773,User_Id_2987,Bike,3,Business,18,3,11:27:05 AM,18,3,11:28:20 AM,18,3,11:32:28 AM,18,3,11:37:21 AM,18,3,11:47:05 AM,4,19.9,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_100,584
-Order_No_15500,User_Id_2391,Bike,3,Personal,14,4,9:07:49 PM,14,4,9:08:27 PM,14,4,9:18:28 PM,14,4,9:23:38 PM,14,4,9:27:58 PM,2,,,-1.277783,36.7816464,-1.2900726999999999,36.7890059,Rider_Id_334,260
-Order_No_915,User_Id_2457,Bike,3,Business,26,5,3:00:33 PM,26,5,3:04:27 PM,26,5,3:21:29 PM,26,5,3:27:45 PM,26,5,3:44:21 PM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_467,996
-Order_No_28103,User_Id_1845,Bike,3,Business,8,5,11:08:57 AM,8,5,11:11:32 AM,8,5,11:42:37 AM,8,5,11:57:46 AM,8,5,12:49:21 PM,7,,,-1.2567075,36.7739831,-1.2998129999999999,36.787139200000006,Rider_Id_844,3095
-Order_No_1141,User_Id_646,Bike,3,Personal,3,3,10:31:07 AM,3,3,10:31:16 AM,3,3,10:42:58 AM,3,3,10:49:23 AM,3,3,11:35:29 AM,27,23.9,,-1.2469508,36.824448499999995,-1.1846355,36.948572799999994,Rider_Id_31,2766
-Order_No_3140,User_Id_3283,Bike,3,Business,7,2,12:48:04 PM,7,2,12:48:18 PM,7,2,12:53:18 PM,7,2,1:04:52 PM,7,2,1:13:42 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_178,530
-Order_No_10159,User_Id_635,Bike,3,Business,10,1,4:38:56 PM,10,1,4:43:20 PM,10,1,4:45:32 PM,10,1,4:47:20 PM,10,1,4:57:42 PM,5,19.2,,-1.2599563,36.799343900000004,-1.24309,36.77747,Rider_Id_770,622
-Order_No_6428,User_Id_1687,Bike,3,Business,30,3,9:53:45 AM,30,3,9:54:02 AM,30,3,10:08:49 AM,30,3,10:12:36 AM,30,3,10:28:36 AM,8,17.2,,-1.28164,36.816866999999995,-1.3129578000000002,36.8463197,Rider_Id_808,960
-Order_No_13166,User_Id_2801,Bike,3,Business,23,5,11:15:54 AM,23,5,11:21:07 AM,23,5,11:25:35 AM,23,5,11:41:51 AM,23,5,11:56:20 AM,6,24.0,,-1.2684319,36.81658529999999,-1.2595421999999998,36.7871178,Rider_Id_547,869
-Order_No_26194,User_Id_1081,Bike,3,Business,14,4,3:51:54 PM,14,4,4:36:34 PM,14,4,4:48:16 PM,14,4,4:56:51 PM,14,4,5:28:34 PM,14,26.2,,-1.2551895,36.7822034,-1.3213918,36.7915399,Rider_Id_336,1903
-Order_No_25461,User_Id_2821,Bike,2,Personal,30,1,9:36:31 AM,30,1,10:17:01 AM,30,1,10:28:40 AM,30,1,10:30:29 AM,30,1,11:07:08 AM,19,16.5,2.0,-1.3284258999999998,36.726068,-1.3083777,36.85900770000001,Rider_Id_791,2199
-Order_No_9888,User_Id_3291,Bike,3,Business,31,5,9:52:02 AM,31,5,9:52:32 AM,31,5,9:55:26 AM,31,5,10:01:26 AM,31,5,10:22:02 AM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_176,1236
-Order_No_5146,User_Id_3194,Bike,3,Business,9,2,11:48:41 AM,9,2,11:50:41 AM,9,2,12:00:14 PM,9,2,12:02:54 PM,9,2,12:25:04 PM,9,22.2,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_268,1330
-Order_No_20383,User_Id_3718,Bike,3,Business,9,2,2:48:11 PM,9,2,2:48:22 PM,9,2,2:55:57 PM,9,2,3:02:57 PM,9,2,3:10:29 PM,1,22.7,,-1.2650027,36.8126242,-1.2675613000000001,36.8133001,Rider_Id_620,452
-Order_No_13136,User_Id_1497,Bike,3,Personal,12,4,12:43:10 PM,12,4,12:44:05 PM,12,4,12:57:04 PM,12,4,12:58:50 PM,12,4,1:17:07 PM,3,19.0,,-1.2998129999999999,36.787139200000006,-1.2913246,36.8019522,Rider_Id_898,1097
-Order_No_25180,User_Id_3326,Bike,3,Business,23,4,12:35:45 PM,23,4,12:36:10 PM,23,4,12:46:27 PM,23,4,12:52:40 PM,23,4,2:02:21 PM,7,23.2,,-1.2551895,36.7822034,-1.2989287,36.81630820000001,Rider_Id_155,4181
-Order_No_23278,User_Id_540,Bike,3,Business,23,5,12:08:13 PM,23,5,12:42:12 PM,23,5,12:55:07 PM,23,5,1:05:59 PM,23,5,1:18:39 PM,5,24.1,,-1.2571472,36.795063299999995,-1.2896171,36.8241291,Rider_Id_88,760
-Order_No_6485,User_Id_2523,Bike,3,Business,4,5,12:39:53 PM,4,5,12:40:20 PM,4,5,12:54:16 PM,4,5,1:08:09 PM,4,5,1:17:53 PM,7,17.5,,-1.2516167,36.8678844,-1.2867627000000001,36.883446,Rider_Id_320,584
-Order_No_17780,User_Id_3727,Bike,3,Business,6,2,1:39:22 PM,6,2,1:53:01 PM,6,2,1:57:26 PM,6,2,1:59:32 PM,6,2,2:11:50 PM,3,27.4,,-1.2963097,36.7688221,-1.2884128000000001,36.7906666,Rider_Id_958,738
-Order_No_25020,User_Id_868,Bike,3,Business,28,2,11:07:09 AM,28,2,11:43:43 AM,28,2,12:09:11 PM,28,2,12:23:01 PM,28,2,1:29:24 PM,14,17.5,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_810,3983
-Order_No_24476,User_Id_633,Bike,3,Business,21,3,11:03:38 AM,21,3,11:24:11 AM,21,3,11:33:58 AM,21,3,11:52:25 AM,21,3,12:17:19 PM,8,21.9,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_260,1494
-Order_No_3670,User_Id_3560,Bike,3,Business,26,5,8:46:41 AM,26,5,9:37:31 AM,26,5,9:41:40 AM,26,5,9:59:31 AM,26,5,9:59:36 AM,6,,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_878,5
-Order_No_19803,User_Id_788,Bike,3,Business,20,3,12:16:36 PM,20,3,12:18:04 PM,20,3,12:33:48 PM,20,3,1:01:13 PM,20,3,1:38:27 PM,10,26.5,,-1.279395,36.825364,-1.225322,36.80855,Rider_Id_436,2234
-Order_No_1739,User_Id_3574,Bike,1,Personal,5,3,1:36:25 PM,5,3,1:37:14 PM,5,3,1:50:05 PM,5,3,1:57:10 PM,5,3,2:11:03 PM,6,23.6,,-1.2876575000000001,36.8282355,-1.257309,36.806008399999996,Rider_Id_726,833
-Order_No_7021,User_Id_393,Bike,3,Business,27,3,10:27:23 AM,27,3,10:28:07 AM,27,3,10:28:15 AM,27,3,10:39:36 AM,27,3,10:47:36 AM,4,26.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_901,480
-Order_No_11955,User_Id_1243,Bike,1,Business,7,2,4:19:24 PM,7,2,4:21:21 PM,7,2,4:32:05 PM,7,2,4:40:20 PM,7,2,5:02:10 PM,9,23.0,,-1.3034396000000001,36.832999799999996,-1.2551895,36.7822034,Rider_Id_754,1310
-Order_No_14518,User_Id_3669,Bike,3,Business,5,2,9:20:15 AM,5,2,9:20:25 AM,5,2,9:21:25 AM,5,2,9:39:03 AM,5,2,9:56:55 AM,17,20.9,2.1,-1.2615888000000002,36.7928732,-1.2077728,36.870531899999996,Rider_Id_517,1072
-Order_No_27214,User_Id_1271,Bike,3,Business,4,4,12:22:33 PM,4,4,12:27:28 PM,4,4,12:38:02 PM,4,4,12:55:33 PM,4,4,1:35:30 PM,13,,,-1.3244885,36.897791999999995,-1.2777296999999999,36.845552399999995,Rider_Id_101,2397
-Order_No_9863,User_Id_402,Bike,3,Business,21,2,6:21:37 PM,21,2,6:24:17 PM,21,2,6:39:00 PM,21,2,6:40:03 PM,21,2,7:12:32 PM,6,24.1,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_139,1949
-Order_No_24042,User_Id_3149,Bike,3,Business,7,5,1:25:04 PM,7,5,1:25:44 PM,7,5,1:25:53 PM,7,5,1:47:45 PM,7,5,1:55:48 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_2,483
-Order_No_3399,User_Id_393,Bike,3,Business,2,6,10:25:20 AM,2,6,10:26:10 AM,2,6,10:36:36 AM,2,6,10:45:08 AM,2,6,11:03:31 AM,11,27.7,,-1.3167113000000001,36.8301563,-1.2636051000000001,36.7850995,Rider_Id_783,1103
-Order_No_13923,User_Id_1874,Bike,3,Business,12,3,12:05:58 PM,12,3,12:06:47 PM,12,3,12:13:41 PM,12,3,12:24:48 PM,12,3,12:40:14 PM,7,23.0,,-1.2871938,36.8195031,-1.2571472,36.795063299999995,Rider_Id_338,926
-Order_No_23405,User_Id_1915,Bike,2,Personal,20,4,11:26:07 AM,20,4,11:26:38 AM,20,4,11:37:36 AM,20,4,11:44:51 AM,20,4,12:01:04 PM,9,24.2,,-1.2797886,36.8169389,-1.2295205,36.812042600000005,Rider_Id_88,973
-Order_No_10115,User_Id_393,Bike,3,Business,7,2,2:15:11 PM,7,2,2:16:41 PM,7,2,2:26:20 PM,7,2,2:44:16 PM,7,2,2:58:26 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_206,850
-Order_No_22042,User_Id_3689,Bike,3,Business,5,1,12:41:08 PM,5,1,12:42:21 PM,5,1,12:49:22 PM,5,1,1:05:19 PM,5,1,1:05:42 PM,6,24.4,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_660,23
-Order_No_27865,User_Id_2112,Bike,3,Business,27,6,10:16:59 AM,27,6,10:31:42 AM,27,6,11:09:13 AM,27,6,11:18:17 AM,27,6,12:31:10 PM,23,,,-1.3472066,36.7692638,-1.2156006000000001,36.8916865,Rider_Id_206,4373
-Order_No_16035,User_Id_3283,Bike,3,Business,16,2,8:49:57 AM,16,2,8:50:18 AM,16,2,8:53:27 AM,16,2,9:09:05 AM,16,2,9:26:09 AM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_306,1024
-Order_No_17381,User_Id_393,Bike,3,Business,21,3,7:38:05 AM,21,3,8:54:00 AM,21,3,9:14:05 AM,21,3,9:41:33 AM,21,3,10:10:53 AM,14,16.8,0.9,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,1760
-Order_No_960,User_Id_1787,Bike,3,Business,7,2,4:01:42 PM,7,2,4:07:22 PM,7,2,4:25:34 PM,7,2,4:31:29 PM,7,2,5:11:54 PM,24,,,-1.2283403,36.8822756,-1.3393061000000002,36.7674236,Rider_Id_299,2425
-Order_No_13006,User_Id_3557,Bike,3,Business,11,1,12:03:50 PM,11,1,12:04:59 PM,11,1,12:23:08 PM,11,1,12:24:09 PM,11,1,12:33:20 PM,4,26.6,,-1.2597431000000001,36.8023063,-1.2622145,36.8087391,Rider_Id_682,551
-Order_No_13211,User_Id_2521,Bike,3,Business,10,5,1:45:30 PM,10,5,1:45:41 PM,10,5,1:46:00 PM,10,5,2:04:08 PM,10,5,2:11:46 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,458
-Order_No_8684,User_Id_198,Bike,3,Business,4,1,8:14:31 AM,4,1,8:31:49 AM,4,1,9:05:29 AM,4,1,9:07:28 AM,4,1,9:38:48 AM,13,18.2,,-1.3396256000000002,36.8863135,-1.3008471000000001,36.8099068,Rider_Id_618,1880
-Order_No_15009,User_Id_2008,Bike,3,Business,5,5,8:13:04 AM,5,5,8:26:09 AM,5,5,8:47:58 AM,5,5,9:37:57 AM,5,5,9:53:25 AM,6,17.6,,-1.2858606000000001,36.8249342,-1.2508229,36.7895263,Rider_Id_329,928
-Order_No_21697,User_Id_1340,Bike,3,Business,1,1,8:35:43 AM,1,1,8:37:19 AM,1,1,8:44:01 AM,1,1,8:49:56 AM,1,1,9:01:57 AM,4,21.3,,-1.2707178999999997,36.7979713,-1.2734594,36.8170558,Rider_Id_99,721
-Order_No_2833,User_Id_972,Bike,3,Business,23,3,12:35:30 PM,23,3,12:43:18 PM,23,3,12:52:15 PM,23,3,12:59:24 PM,23,3,1:40:46 PM,16,22.0,,-1.2862033000000002,36.8152966,-1.332624,36.913187,Rider_Id_888,2482
-Order_No_994,User_Id_1927,Bike,3,Business,1,1,2:45:19 PM,1,1,2:52:03 PM,1,1,3:02:33 PM,1,1,3:15:43 PM,1,1,4:27:52 PM,12,25.4,,-1.3463041999999998,36.768164,-1.2866011999999998,36.8195691,Rider_Id_268,4329
-Order_No_4745,User_Id_3214,Bike,3,Business,26,2,11:47:03 AM,26,2,11:47:45 AM,26,2,11:58:35 AM,26,2,12:03:54 PM,26,2,12:57:04 PM,21,26.3,,-1.289795,36.7957132,-1.3190102,36.923069299999995,Rider_Id_837,3190
-Order_No_1343,User_Id_2866,Bike,3,Business,26,3,2:29:07 PM,26,3,2:29:16 PM,26,3,2:31:48 PM,26,3,2:33:54 PM,26,3,2:52:15 PM,3,27.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_881,1101
-Order_No_171,User_Id_3295,Bike,3,Business,29,1,7:59:13 AM,29,1,8:51:30 AM,29,1,9:12:35 AM,29,1,9:19:23 AM,29,1,9:36:31 AM,7,18.9,,-1.2814233,36.8251177,-1.2571472,36.795063299999995,Rider_Id_795,1028
-Order_No_8890,User_Id_265,Bike,3,Business,18,5,4:06:37 PM,18,5,4:34:28 PM,18,5,4:56:02 PM,18,5,4:58:08 PM,18,5,5:14:27 PM,4,25.6,,-1.2897399,36.8270975,-1.300921,36.828195,Rider_Id_899,979
-Order_No_21232,User_Id_3558,Bike,2,Personal,13,6,1:04:42 PM,13,6,1:06:44 PM,13,6,1:25:10 PM,13,6,1:52:53 PM,13,6,2:04:34 PM,4,28.2,,-1.2856374,36.82289,-1.2912851,36.79725139999999,Rider_Id_662,701
-Order_No_25367,User_Id_173,Bike,3,Business,10,6,3:19:40 PM,10,6,3:21:46 PM,10,6,3:22:09 PM,10,6,3:31:00 PM,10,6,3:48:54 PM,7,24.8,,-1.2993231,36.7753372,-1.2611642,36.7949932,Rider_Id_282,1074
-Order_No_9942,User_Id_393,Bike,3,Business,25,4,4:40:41 PM,25,4,4:41:11 PM,25,4,4:44:28 PM,25,4,5:11:22 PM,25,4,5:52:01 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,2439
-Order_No_14681,User_Id_2423,Bike,3,Business,11,1,10:51:01 AM,11,1,1:02:28 PM,11,1,1:08:38 PM,11,1,1:15:20 PM,11,1,1:33:39 PM,7,24.0,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_267,1099
-Order_No_19734,User_Id_452,Bike,1,Personal,2,3,3:37:07 PM,2,3,4:39:12 PM,2,3,4:41:37 PM,2,3,5:12:36 PM,2,3,5:44:21 PM,7,18.1,,-1.3251583999999998,36.8199384,-1.2861764999999998,36.8209086,Rider_Id_236,1905
-Order_No_156,User_Id_2587,Bike,3,Business,8,3,3:37:23 PM,8,3,3:39:30 PM,8,3,3:44:57 PM,8,3,3:49:09 PM,8,3,4:07:22 PM,7,,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_155,1093
-Order_No_19521,User_Id_1578,Bike,3,Business,20,2,4:10:11 PM,20,2,5:03:54 PM,20,2,5:13:32 PM,20,2,5:16:23 PM,20,2,5:44:32 PM,16,23.7,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_903,1689
-Order_No_19612,User_Id_268,Bike,3,Business,28,4,8:52:28 AM,28,4,8:52:49 AM,28,4,9:13:34 AM,28,4,9:14:47 AM,28,4,9:30:35 AM,13,22.7,,-1.3211235000000001,36.7765164,-1.3263105,36.8622904,Rider_Id_328,948
-Order_No_3060,User_Id_371,Bike,1,Personal,24,1,12:45:13 PM,24,1,12:45:46 PM,24,1,12:52:56 PM,24,1,12:59:59 PM,24,1,1:15:09 PM,7,22.5,,-1.2671001,36.7977185,-1.237177,36.756116999999996,Rider_Id_882,910
-Order_No_26938,User_Id_2358,Bike,4,Business,15,6,10:30:17 AM,15,6,10:31:59 AM,15,6,10:44:12 AM,15,6,10:48:53 AM,15,6,11:30:23 AM,18,,,-1.3256903999999998,36.8747943,-1.2223457,36.8904633,Rider_Id_89,2490
-Order_No_6695,User_Id_928,Bike,3,Business,17,3,12:02:46 PM,17,3,12:52:38 PM,17,3,1:02:43 PM,17,3,1:07:32 PM,17,3,2:06:16 PM,6,25.2,,-1.2551895,36.7822034,-1.2855666000000001,36.819492600000004,Rider_Id_338,3524
-Order_No_9414,User_Id_1081,Bike,3,Business,13,3,10:56:40 AM,13,3,10:57:05 AM,13,3,10:57:15 AM,13,3,11:06:20 AM,13,3,11:22:04 AM,8,24.8,,-1.2551895,36.7822034,-1.287428,36.7800821,Rider_Id_518,944
-Order_No_23952,User_Id_3139,Bike,2,Personal,5,2,2:42:16 PM,5,2,2:42:38 PM,5,2,2:52:25 PM,5,2,2:56:17 PM,5,2,3:16:23 PM,4,22.6,,-1.2854453000000001,36.818836299999994,-1.2965924,36.8179314,Rider_Id_531,1206
-Order_No_10954,User_Id_1081,Bike,3,Business,13,3,2:19:20 PM,13,3,2:24:40 PM,13,3,2:25:01 PM,13,3,2:32:33 PM,13,3,2:53:56 PM,10,27.0,,-1.2551895,36.7822034,-1.3004846,36.790159100000004,Rider_Id_523,1283
-Order_No_1183,User_Id_2801,Bike,3,Business,6,2,8:55:31 AM,6,2,8:56:01 AM,6,2,9:23:49 AM,6,2,9:42:54 AM,6,2,10:53:49 AM,14,19.9,,-1.3271374,36.8495534,-1.2595421999999998,36.7871178,Rider_Id_520,4255
-Order_No_10652,User_Id_3214,Bike,3,Business,23,4,2:43:51 PM,23,4,2:44:54 PM,23,4,2:45:03 PM,23,4,3:04:30 PM,23,4,3:04:35 PM,2,24.6,,-1.3030203000000002,36.790269099999996,-1.289795,36.7957132,Rider_Id_445,5
-Order_No_4438,User_Id_1500,Bike,3,Business,11,5,3:37:27 PM,11,5,4:03:51 PM,11,5,4:28:51 PM,11,5,4:33:37 PM,11,5,4:49:57 PM,6,24.3,,-1.2770708,36.8231093,-1.2574219,36.7927073,Rider_Id_436,980
-Order_No_15541,User_Id_250,Bike,2,Personal,20,5,12:30:32 PM,20,5,12:32:16 PM,20,5,12:47:39 PM,20,5,12:53:05 PM,20,5,1:08:58 PM,4,18.7,,-1.2874733999999999,36.762071500000005,-1.2874313999999998,36.7764912,Rider_Id_521,953
-Order_No_13305,User_Id_2247,Bike,2,Personal,29,2,4:23:53 PM,29,2,4:24:43 PM,29,2,4:40:19 PM,29,2,4:44:07 PM,29,2,5:24:29 PM,21,22.2,,-1.2846,36.7873609,-1.3664581,36.9177807,Rider_Id_919,2422
-Order_No_19597,User_Id_2956,Bike,3,Business,25,1,1:53:58 PM,25,1,1:54:59 PM,25,1,2:06:34 PM,25,1,2:07:48 PM,25,1,2:14:58 PM,2,30.2,,-1.2584143,36.8048002,-1.2620912,36.8013562,Rider_Id_318,430
-Order_No_92,User_Id_1724,Bike,3,Business,25,1,11:21:40 AM,25,1,11:21:46 AM,25,1,11:22:06 AM,25,1,11:34:45 AM,25,1,12:23:52 PM,23,25.7,,-1.2551895,36.7822034,-1.3724413,36.9284922,Rider_Id_430,2947
-Order_No_19549,User_Id_3602,Bike,3,Business,1,3,8:42:25 AM,1,3,8:44:31 AM,1,3,9:13:15 AM,1,3,9:18:40 AM,1,3,10:00:35 AM,8,,,-1.2963097,36.7688221,-1.2672045,36.717206899999994,Rider_Id_935,2515
-Order_No_1292,User_Id_2069,Bike,3,Business,7,3,11:08:27 AM,7,3,11:09:29 AM,7,3,11:14:37 AM,7,3,11:23:52 AM,7,3,11:35:10 AM,9,24.9,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_320,678
-Order_No_23535,User_Id_2523,Bike,3,Business,6,4,2:03:37 PM,6,4,2:04:20 PM,6,4,2:19:30 PM,6,4,2:24:03 PM,6,4,2:29:57 PM,3,21.1,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_570,354
-Order_No_22821,User_Id_1469,Bike,3,Business,29,2,11:24:47 AM,29,2,11:26:51 AM,29,2,11:32:37 AM,29,2,11:42:33 AM,29,2,12:00:58 PM,7,23.7,,-1.2551895,36.7822034,-1.2963492,36.8093377,Rider_Id_89,1105
-Order_No_9886,User_Id_306,Bike,1,Personal,10,5,8:30:38 AM,10,5,8:31:16 AM,10,5,8:48:17 AM,10,5,8:53:11 AM,10,5,9:34:00 AM,16,13.3,,-1.3159197,36.8516724,-1.2830073,36.7662294,Rider_Id_234,2449
-Order_No_23575,User_Id_1402,Bike,3,Business,6,6,1:17:25 PM,6,6,1:18:59 PM,6,6,1:20:31 PM,6,6,1:45:27 PM,6,6,1:45:32 PM,1,28.2,,-1.279395,36.825364,-1.2813012,36.832396200000005,Rider_Id_102,5
-Order_No_16283,User_Id_3462,Bike,1,Personal,16,4,12:14:58 PM,16,4,12:15:19 PM,16,4,12:31:27 PM,16,4,12:34:15 PM,16,4,12:48:44 PM,5,14.7,,-1.2556829999999999,36.813771,-1.2721028,36.795348499999996,Rider_Id_857,869
-Order_No_13754,User_Id_393,Bike,3,Business,28,1,12:25:39 PM,28,1,12:26:49 PM,28,1,12:32:09 PM,28,1,12:36:55 PM,28,1,1:02:00 PM,14,22.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_543,1505
-Order_No_26741,User_Id_2330,Bike,3,Business,26,5,1:26:51 PM,26,5,1:27:08 PM,26,5,1:45:07 PM,26,5,1:53:48 PM,26,5,2:04:39 PM,8,,,-1.1926151000000003,36.9347904,-1.2284323,36.8757058,Rider_Id_710,651
-Order_No_25577,User_Id_928,Bike,3,Business,11,1,12:43:42 PM,11,1,12:44:12 PM,11,1,12:56:54 PM,11,1,12:59:16 PM,11,1,1:07:31 PM,2,26.6,,-1.2551895,36.7822034,-1.2592428,36.7894541,Rider_Id_932,495
-Order_No_25136,User_Id_1927,Bike,3,Business,26,5,2:27:11 PM,26,5,2:31:48 PM,26,5,2:38:55 PM,26,5,2:49:25 PM,26,5,3:11:20 PM,1,,,-1.3463041999999998,36.768164,-1.3387568,36.753431799999994,Rider_Id_532,1315
-Order_No_10634,User_Id_2828,Bike,3,Business,5,3,2:07:19 PM,5,3,3:07:34 PM,5,3,3:07:42 PM,5,3,3:29:44 PM,5,3,4:06:33 PM,8,24.8,,-1.2615888000000002,36.7928732,-1.3034072,36.821803499999994,Rider_Id_382,2209
-Order_No_512,User_Id_1023,Bike,3,Personal,11,6,3:30:25 PM,11,6,3:30:41 PM,11,6,3:38:20 PM,11,6,3:41:58 PM,11,6,3:50:08 PM,3,21.6,,-1.3039695,36.783826700000006,-1.2873232,36.765857200000006,Rider_Id_45,490
-Order_No_7076,User_Id_173,Bike,3,Business,21,5,9:07:53 PM,21,5,9:08:16 PM,21,5,9:10:33 PM,21,5,9:28:34 PM,21,5,9:43:15 PM,6,18.0,,-1.2993231,36.7753372,-1.2637515,36.78856629999999,Rider_Id_187,881
-Order_No_23257,User_Id_1329,Bike,3,Business,24,5,5:51:52 PM,24,5,5:52:04 PM,24,5,5:52:49 PM,24,5,5:57:22 PM,24,5,6:25:09 PM,13,20.0,,-1.273056,36.811298,-1.2153260000000001,36.891993799999995,Rider_Id_119,1667
-Order_No_10990,User_Id_1075,Bike,3,Business,1,5,9:07:27 AM,1,5,9:16:05 AM,1,5,9:32:08 AM,1,5,9:43:55 AM,1,5,10:36:29 AM,18,,,-1.3169591,36.7000029,-1.2527958000000001,36.8003131,Rider_Id_831,3154
-Order_No_27167,User_Id_2271,Bike,3,Business,10,1,3:15:10 PM,10,1,3:15:53 PM,10,1,3:33:46 PM,10,1,3:38:49 PM,10,1,4:13:55 PM,17,18.2,,-1.2600926000000001,36.8088685,-1.293515,36.897607,Rider_Id_690,2106
-Order_No_8508,User_Id_590,Bike,3,Business,22,4,12:19:16 PM,22,4,12:21:19 PM,22,4,12:27:42 PM,22,4,12:32:05 PM,22,4,12:33:58 PM,2,22.7,,-1.257773,36.795486,-1.2644742,36.808345700000004,Rider_Id_574,113
-Order_No_2071,User_Id_2330,Bike,3,Business,12,4,3:01:29 PM,12,4,3:03:22 PM,12,4,3:55:32 PM,12,4,4:15:05 PM,12,4,4:24:48 PM,4,21.8,,-1.2243841,36.8024757,-1.2465253,36.8162559,Rider_Id_951,583
-Order_No_25044,User_Id_2900,Bike,3,Business,19,2,9:11:30 AM,19,2,9:13:27 AM,19,2,9:33:17 AM,19,2,9:39:20 AM,19,2,10:11:16 AM,16,,,-1.2963097,36.7688221,-1.3189252,36.861726899999994,Rider_Id_545,1916
-Order_No_12184,User_Id_1075,Bike,3,Business,22,5,1:46:52 PM,22,5,1:47:52 PM,22,5,1:56:24 PM,22,5,2:05:58 PM,22,5,2:52:21 PM,10,31.1,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_952,2783
-Order_No_13517,User_Id_3283,Bike,3,Business,26,5,4:04:09 PM,26,5,4:04:24 PM,26,5,4:25:51 PM,26,5,4:34:47 PM,26,5,5:01:46 PM,13,,,-1.318062,36.849326,-1.2628473,36.781805,Rider_Id_168,1619
-Order_No_7292,User_Id_2644,Bike,2,Personal,17,2,11:56:23 AM,17,2,11:58:40 AM,17,2,12:08:46 PM,17,2,12:23:55 PM,17,2,12:35:22 PM,4,19.0,,-1.2885417,36.810052299999995,-1.2906604,36.8318242,Rider_Id_629,687
-Order_No_5049,User_Id_3291,Bike,3,Business,4,5,12:43:50 PM,4,5,1:27:45 PM,4,5,1:44:07 PM,4,5,2:15:04 PM,4,5,2:42:41 PM,18,,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_439,1657
-Order_No_27968,User_Id_3147,Bike,3,Business,5,2,10:36:31 AM,5,2,10:36:44 AM,5,2,10:43:34 AM,5,2,10:57:02 AM,5,2,11:41:00 AM,21,23.8,,-1.2326041,36.8009029,-1.3472066,36.7692638,Rider_Id_72,2638
-Order_No_14800,User_Id_2145,Bike,3,Personal,12,3,11:07:10 AM,12,3,11:23:13 AM,12,3,11:54:33 AM,12,3,12:04:27 PM,12,3,12:23:00 PM,6,19.6,,-1.2752846000000002,36.802702200000006,-1.2926563999999998,36.78731810000001,Rider_Id_547,1113
-Order_No_14237,User_Id_302,Bike,3,Business,21,3,3:11:23 PM,21,3,3:12:15 PM,21,3,3:14:11 PM,21,3,3:23:54 PM,21,3,3:38:38 PM,7,23.2,3.1,-1.2649605,36.798177700000004,-1.2852953999999999,36.789636200000004,Rider_Id_446,884
-Order_No_4729,User_Id_3271,Bike,1,Personal,8,5,12:58:07 PM,8,5,1:10:28 PM,8,5,1:26:27 PM,8,5,1:35:45 PM,8,5,1:51:48 PM,5,21.7,,-1.2924222,36.789286499999996,-1.2670058000000002,36.8041388,Rider_Id_268,963
-Order_No_3213,User_Id_635,Bike,3,Business,18,4,3:45:38 PM,18,4,3:46:55 PM,18,4,4:03:50 PM,18,4,4:11:06 PM,18,4,4:34:39 PM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_924,1413
-Order_No_8717,User_Id_3564,Bike,1,Personal,16,7,6:48:10 PM,16,7,7:30:23 PM,16,7,7:44:54 PM,16,7,8:00:33 PM,16,7,8:23:56 PM,13,19.5,,-1.3472066,36.7692638,-1.3292721,36.6656534,Rider_Id_654,1403
-Order_No_25015,User_Id_2049,Bike,1,Personal,3,4,3:16:29 PM,3,4,3:36:34 PM,3,4,3:44:44 PM,3,4,3:52:39 PM,3,4,4:16:40 PM,8,24.9,,-1.2932803,36.774089000000004,-1.2853860000000001,36.8304605,Rider_Id_100,1441
-Order_No_16324,User_Id_1422,Bike,3,Business,28,5,3:57:31 PM,28,5,3:57:52 PM,28,5,4:17:35 PM,28,5,4:35:07 PM,28,5,5:19:32 PM,10,,,-1.307774,36.840754100000005,-1.3053865,36.891733200000004,Rider_Id_731,2665
-Order_No_3134,User_Id_3300,Bike,3,Business,8,3,10:43:01 AM,8,3,10:43:16 AM,8,3,11:20:20 AM,8,3,11:41:29 AM,8,3,12:02:14 PM,9,22.4,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_100,1245
-Order_No_9177,User_Id_1329,Bike,3,Business,15,5,9:34:51 AM,15,5,9:35:13 AM,15,5,9:44:02 AM,15,5,9:45:47 AM,15,5,10:04:49 AM,10,19.1,,-1.273056,36.811298,-1.3270347,36.866736200000005,Rider_Id_338,1142
-Order_No_11198,User_Id_1564,Bike,3,Personal,9,6,10:15:29 AM,9,6,10:16:28 AM,9,6,10:34:27 AM,9,6,10:38:10 AM,9,6,10:54:26 AM,6,24.6,,-1.2646374,36.723567100000004,-1.2577288999999998,36.74107720000001,Rider_Id_829,976
-Order_No_10376,User_Id_2676,Bike,3,Business,1,3,3:24:56 PM,1,3,3:26:19 PM,1,3,3:40:03 PM,1,3,4:14:07 PM,1,3,4:20:49 PM,2,23.7,,-1.3047482,36.784967200000004,-1.2963097,36.7688221,Rider_Id_882,402
-Order_No_8524,User_Id_1280,Bike,3,Business,28,4,3:42:21 PM,28,4,3:44:10 PM,28,4,3:57:30 PM,28,4,3:59:56 PM,28,4,4:13:00 PM,5,29.9,,-1.2551895,36.7822034,-1.265981,36.793612100000004,Rider_Id_329,784
-Order_No_4603,User_Id_1107,Bike,2,Personal,13,5,3:02:44 PM,13,5,3:03:01 PM,13,5,3:10:20 PM,13,5,3:13:32 PM,13,5,3:43:21 PM,12,19.1,,-1.2728058,36.8049903,-1.3232436,36.77993,Rider_Id_629,1789
-Order_No_25285,User_Id_3283,Bike,3,Business,27,6,11:09:41 AM,27,6,11:10:02 AM,27,6,11:13:37 AM,27,6,11:46:31 AM,27,6,11:58:09 AM,6,,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_19,698
-Order_No_4285,User_Id_1384,Bike,3,Business,13,1,2:32:57 PM,13,1,2:34:31 PM,13,1,2:46:03 PM,13,1,2:52:59 PM,13,1,3:18:07 PM,20,,,-1.2517448,36.7924971,-1.2060739999999999,36.924713,Rider_Id_484,1508
-Order_No_3196,User_Id_1792,Bike,3,Business,28,3,1:55:56 PM,28,3,2:01:33 PM,28,3,2:21:19 PM,28,3,2:24:01 PM,28,3,2:45:15 PM,4,,,-1.2809121,36.7852524,-1.2892529,36.7740575,Rider_Id_477,1274
-Order_No_14467,User_Id_3647,Bike,3,Business,9,3,3:51:20 PM,9,3,4:00:03 PM,9,3,4:09:46 PM,9,3,4:11:54 PM,9,3,4:52:01 PM,7,23.3,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_782,2407
-Order_No_195,User_Id_1471,Bike,3,Business,17,3,3:22:13 PM,17,3,3:22:41 PM,17,3,3:26:36 PM,17,3,3:43:25 PM,17,3,4:09:40 PM,8,,,-1.2551895,36.7822034,-1.3037386999999998,36.8175661,Rider_Id_709,1575
-Order_No_23549,User_Id_53,Bike,3,Business,13,3,2:19:10 PM,13,3,2:19:45 PM,13,3,2:29:48 PM,13,3,2:56:39 PM,13,3,3:13:55 PM,7,29.1,,-1.3012841999999998,36.8561033,-1.2750282,36.85322360000001,Rider_Id_218,1036
-Order_No_24297,User_Id_2789,Bike,3,Business,29,5,4:53:15 PM,29,5,4:53:55 PM,29,5,5:05:12 PM,29,5,5:07:48 PM,29,5,5:33:32 PM,8,27.8,,-1.2690931,36.8065779,-1.304735,36.784605400000004,Rider_Id_177,1544
-Order_No_19022,User_Id_540,Bike,3,Business,22,3,1:53:27 PM,22,3,2:03:15 PM,22,3,2:13:13 PM,22,3,2:16:09 PM,22,3,2:29:33 PM,7,24.2,,-1.2551895,36.7822034,-1.2897978,36.8267388,Rider_Id_491,804
-Order_No_6833,User_Id_3689,Bike,3,Business,5,1,5:55:04 PM,5,1,5:55:24 PM,5,1,6:02:00 PM,5,1,6:15:03 PM,5,1,6:42:43 PM,10,24.2,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_105,1660
-Order_No_3068,User_Id_1927,Bike,3,Business,23,5,3:45:55 PM,23,5,3:58:05 PM,23,5,4:14:09 PM,23,5,4:37:03 PM,23,5,5:07:20 PM,17,24.7,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_101,1817
-Order_No_23030,User_Id_2196,Bike,3,Personal,4,1,12:46:22 PM,4,1,12:47:26 PM,4,1,1:34:26 PM,4,1,1:53:36 PM,4,1,2:08:37 PM,9,22.6,,-1.2553615,36.789537599999996,-1.2945178999999998,36.7685349,Rider_Id_248,901
-Order_No_3934,User_Id_3247,Bike,3,Business,8,1,2:15:16 PM,8,1,2:15:37 PM,8,1,2:17:02 PM,8,1,2:28:36 PM,8,1,2:44:23 PM,3,29.6,,-1.273488,36.804158,-1.2571472,36.795063299999995,Rider_Id_108,947
-Order_No_7233,User_Id_2718,Bike,3,Business,6,4,3:09:41 PM,6,4,3:11:48 PM,6,4,3:30:48 PM,6,4,3:41:01 PM,6,4,3:49:46 PM,2,,,-1.2741118,36.802009399999996,-1.2657456,36.8087424,Rider_Id_454,525
-Order_No_6926,User_Id_2989,Bike,3,Business,11,5,1:22:21 PM,11,5,1:30:13 PM,11,5,1:55:48 PM,11,5,2:06:28 PM,11,5,2:32:50 PM,19,26.3,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_114,1582
-Order_No_8697,User_Id_1363,Bike,3,Business,28,3,2:50:02 PM,28,3,2:56:23 PM,28,3,2:56:28 PM,28,3,3:11:35 PM,28,3,3:11:37 PM,5,,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_515,2
-Order_No_17212,User_Id_744,Bike,2,Personal,6,3,10:29:28 AM,6,3,10:29:54 AM,6,3,10:39:24 AM,6,3,10:40:25 AM,6,3,11:52:59 AM,7,19.9,,-1.2916556000000001,36.8262516,-1.3159197,36.8516724,Rider_Id_89,4354
-Order_No_2362,User_Id_2190,Bike,3,Business,13,3,11:08:46 AM,13,3,11:09:00 AM,13,3,11:28:04 AM,13,3,11:43:02 AM,13,3,12:00:30 PM,5,,,-1.3109027,36.768945200000005,-1.3019530000000001,36.788458,Rider_Id_678,1048
-Order_No_15770,User_Id_3627,Bike,1,Personal,30,1,2:22:12 PM,30,1,2:24:09 PM,30,1,3:54:50 PM,30,1,4:08:04 PM,30,1,4:38:59 PM,6,20.5,,-1.2395181,36.765513,-1.2655332000000001,36.8094648,Rider_Id_519,1855
-Order_No_18349,User_Id_3669,Bike,3,Business,11,1,4:48:58 PM,11,1,4:49:53 PM,11,1,4:58:04 PM,11,1,5:01:40 PM,11,1,5:23:00 PM,9,,,-1.2615888000000002,36.7928732,-1.3124088,36.8252715,Rider_Id_280,1280
-Order_No_18913,User_Id_478,Bike,3,Business,5,1,1:24:45 PM,5,1,1:25:15 PM,5,1,1:37:27 PM,5,1,2:20:46 PM,5,1,2:20:47 PM,10,26.0,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_19,1
-Order_No_6381,User_Id_1647,Bike,3,Business,29,1,4:53:29 PM,29,1,5:12:07 PM,29,1,5:25:55 PM,29,1,5:33:00 PM,29,1,6:02:12 PM,16,,,-1.2190317,36.8825092,-1.3277669,36.8975169,Rider_Id_815,1752
-Order_No_2179,User_Id_2869,Bike,3,Business,7,2,2:04:19 PM,7,2,2:20:53 PM,7,2,2:24:22 PM,7,2,3:01:27 PM,7,2,3:53:23 PM,10,,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_263,3116
-Order_No_15172,User_Id_2200,Bike,3,Business,24,1,1:08:49 PM,24,1,1:12:01 PM,24,1,1:20:09 PM,24,1,1:25:09 PM,24,1,1:42:22 PM,6,22.9,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_576,1033
-Order_No_25723,User_Id_868,Bike,3,Business,20,3,11:39:46 AM,20,3,11:40:13 AM,20,3,11:44:07 AM,20,3,11:48:51 AM,20,3,12:03:43 PM,7,26.5,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_786,892
-Order_No_5926,User_Id_1402,Bike,3,Business,30,2,2:56:24 PM,30,2,3:17:28 PM,30,2,3:28:11 PM,30,2,3:46:17 PM,30,2,4:32:01 PM,13,,,-1.339046,36.762596,-1.2813012,36.832396200000005,Rider_Id_155,2744
-Order_No_12092,User_Id_1719,Bike,3,Business,8,2,10:03:41 AM,8,2,10:03:51 AM,8,2,10:18:48 AM,8,2,10:27:29 AM,8,2,11:03:21 AM,15,18.8,,-1.2330694,36.745451,-1.296797,36.776452500000005,Rider_Id_442,2152
-Order_No_17486,User_Id_2801,Bike,3,Business,11,1,1:20:49 PM,11,1,1:21:51 PM,11,1,1:22:09 PM,11,1,2:16:09 PM,11,1,2:33:15 PM,7,29.0,,-1.2595421999999998,36.7871178,-1.2809846999999999,36.8274169,Rider_Id_926,1026
-Order_No_2226,User_Id_3669,Bike,3,Business,11,2,4:46:15 PM,11,2,5:08:01 PM,11,2,5:22:51 PM,11,2,5:30:45 PM,11,2,6:04:45 PM,13,20.6,,-1.2615888000000002,36.7928732,-1.2095044,36.783206400000005,Rider_Id_675,2040
-Order_No_480,User_Id_3283,Bike,3,Business,2,2,10:38:22 AM,2,2,10:38:37 AM,2,2,10:43:46 AM,2,2,10:53:40 AM,2,2,11:03:04 AM,5,21.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_882,564
-Order_No_13133,User_Id_1544,Bike,3,Business,24,3,3:28:27 PM,24,3,3:59:07 PM,24,3,4:22:10 PM,24,3,4:37:21 PM,24,3,5:01:29 PM,6,,,-1.2792868999999998,36.8226482,-1.3169998,36.829752500000005,Rider_Id_448,1448
-Order_No_19630,User_Id_3283,Bike,3,Business,4,2,1:50:15 PM,4,2,1:56:57 PM,4,2,2:20:21 PM,4,2,2:59:02 PM,4,2,3:51:54 PM,13,22.7,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_864,3172
-Order_No_3145,User_Id_3647,Bike,3,Business,11,4,4:28:27 PM,11,4,4:29:29 PM,11,4,5:11:31 PM,11,4,5:20:37 PM,11,4,6:01:07 PM,19,24.2,,-1.2726389999999999,36.794723,-1.1940334,36.918732399999996,Rider_Id_606,2430
-Order_No_6129,User_Id_970,Bike,3,Business,26,2,8:21:36 AM,26,2,8:28:17 AM,26,2,8:52:10 AM,26,2,8:56:11 AM,26,2,9:15:12 AM,6,,,-1.2551895,36.7822034,-1.2904753,36.8144721,Rider_Id_953,1141
-Order_No_11523,User_Id_1102,Bike,3,Business,29,5,4:35:53 PM,29,5,4:36:29 PM,29,5,4:45:42 PM,29,5,4:47:19 PM,29,5,5:10:32 PM,4,28.2,,-1.3004062,36.829741,-1.2980085,36.8044778,Rider_Id_787,1393
-Order_No_11776,User_Id_3355,Bike,3,Business,20,1,4:49:14 PM,20,1,4:50:05 PM,20,1,4:59:02 PM,20,1,5:02:14 PM,20,1,5:22:35 PM,8,20.5,,-1.305399,36.822184899999996,-1.2551895,36.7822034,Rider_Id_519,1221
-Order_No_15870,User_Id_2956,Bike,3,Business,6,4,3:33:04 PM,6,4,3:33:55 PM,6,4,3:39:13 PM,6,4,3:56:49 PM,6,4,4:14:47 PM,9,21.4,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_855,1078
-Order_No_22123,User_Id_1032,Bike,1,Personal,24,2,3:50:11 PM,24,2,4:10:26 PM,24,2,4:34:38 PM,24,2,4:35:55 PM,24,2,4:58:40 PM,12,18.9,,-1.2287313999999998,36.8367354,-1.250142,36.788154999999996,Rider_Id_465,1365
-Order_No_3485,User_Id_1882,Bike,3,Business,3,7,10:16:27 AM,3,7,11:37:43 AM,3,7,12:27:12 PM,3,7,12:28:33 PM,3,7,1:00:51 PM,16,28.3,,-1.3169591,36.7000029,-1.2877809999999998,36.817865999999995,Rider_Id_303,1938
-Order_No_10887,User_Id_2209,Bike,3,Business,16,3,4:28:49 PM,16,3,4:40:48 PM,16,3,5:07:46 PM,16,3,5:10:35 PM,16,3,5:52:02 PM,10,19.9,,-1.2626906,36.7827022,-1.3090203999999999,36.84091420000001,Rider_Id_432,2487
-Order_No_3476,User_Id_393,Bike,3,Business,13,1,9:27:16 AM,13,1,9:27:28 AM,13,1,9:27:35 AM,13,1,10:17:35 AM,13,1,10:17:37 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_515,2
-Order_No_10097,User_Id_2803,Bike,3,Business,8,5,2:33:37 PM,8,5,2:37:09 PM,8,5,2:57:15 PM,8,5,3:00:11 PM,8,5,3:11:08 PM,3,27.3,,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_72,657
-Order_No_871,User_Id_186,Bike,3,Business,14,2,11:35:41 AM,14,2,12:00:21 PM,14,2,12:08:30 PM,14,2,12:14:53 PM,14,2,12:58:47 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_690,2634
-Order_No_4982,User_Id_136,Bike,3,Business,9,4,4:25:25 PM,9,4,4:43:58 PM,9,4,4:54:13 PM,9,4,4:55:15 PM,9,4,5:17:30 PM,9,,,-1.3165103999999999,36.707772600000006,-1.3009676000000001,36.7657189,Rider_Id_829,1335
-Order_No_5973,User_Id_2701,Bike,3,Business,7,5,10:19:36 AM,7,5,10:20:46 AM,7,5,10:47:19 AM,7,5,10:50:41 AM,7,5,11:04:28 AM,5,,,-1.2765736,36.851364600000004,-1.3083777,36.85900770000001,Rider_Id_448,827
-Order_No_18243,User_Id_1996,Bike,3,Business,10,3,2:20:42 PM,10,3,2:22:20 PM,10,3,2:36:15 PM,10,3,2:59:32 PM,10,3,3:24:37 PM,6,30.7,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_613,1505
-Order_No_18761,User_Id_3201,Bike,3,Business,8,5,11:33:57 AM,8,5,11:35:30 AM,8,5,11:47:45 AM,8,5,11:50:45 AM,8,5,12:10:17 PM,10,28.3,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_52,1172
-Order_No_6600,User_Id_2200,Bike,3,Business,27,1,12:46:24 PM,27,1,12:47:48 PM,27,1,1:08:30 PM,27,1,1:29:19 PM,27,1,2:12:06 PM,7,20.9,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_673,2567
-Order_No_24845,User_Id_2894,Bike,3,Business,30,5,4:51:56 PM,30,5,4:56:52 PM,30,5,5:07:17 PM,30,5,5:12:22 PM,30,5,5:22:29 PM,3,22.2,,-1.2906618,36.8131222,-1.2961951999999999,36.7876396,Rider_Id_79,607
-Order_No_23763,User_Id_3689,Bike,3,Business,8,5,2:37:17 PM,8,5,2:38:52 PM,8,5,2:57:18 PM,8,5,3:08:34 PM,8,5,4:04:47 PM,12,21.4,,-1.2297202,36.874550799999994,-1.2601708999999999,36.8002238,Rider_Id_844,3373
-Order_No_19515,User_Id_1500,Bike,3,Business,13,6,11:18:32 AM,13,6,11:31:50 AM,13,6,11:59:17 AM,13,6,12:03:38 PM,13,6,12:16:21 PM,6,25.8,,-1.2759989,36.820884,-1.2638185,36.7930057,Rider_Id_660,763
-Order_No_13835,User_Id_3688,Bike,3,Business,14,5,1:44:05 PM,14,5,1:44:17 PM,14,5,2:07:50 PM,14,5,2:15:00 PM,14,5,3:02:48 PM,21,22.9,,-1.2153260000000001,36.891993799999995,-1.3194518,36.869396,Rider_Id_510,2868
-Order_No_26708,User_Id_1785,Bike,1,Business,6,1,8:50:15 AM,6,1,8:50:30 AM,6,1,9:01:36 AM,6,1,9:03:46 AM,6,1,9:24:28 AM,10,15.0,,-1.2892762,36.764129,-1.3205294,36.819726200000005,Rider_Id_648,1242
-Order_No_17587,User_Id_2330,Bike,3,Business,31,4,5:38:00 PM,31,4,5:50:40 PM,31,4,6:08:00 PM,31,4,6:16:37 PM,31,4,6:41:23 PM,9,27.5,,-1.3999741,36.7519812,-1.3443583000000001,36.7571528,Rider_Id_328,1486
-Order_No_5133,User_Id_123,Bike,3,Business,5,6,12:16:33 PM,5,6,12:17:31 PM,5,6,12:37:14 PM,5,6,12:43:41 PM,5,6,1:03:21 PM,11,22.5,,-1.2482465,36.8889062,-1.2954275,36.8996685,Rider_Id_124,1180
-Order_No_10295,User_Id_391,Bike,3,Business,23,4,3:33:26 PM,23,4,3:38:00 PM,23,4,4:03:45 PM,23,4,5:17:32 PM,23,4,5:30:09 PM,4,,,-1.300921,36.828195,-1.2897368,36.8226783,Rider_Id_223,757
-Order_No_26096,User_Id_1849,Bike,3,Business,14,3,4:40:39 PM,14,3,4:45:34 PM,14,3,4:46:11 PM,14,3,5:04:56 PM,14,3,5:40:21 PM,8,23.4,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_669,2125
-Order_No_19036,User_Id_2532,Bike,3,Business,9,4,10:51:01 AM,9,4,10:52:24 AM,9,4,11:23:08 AM,9,4,11:37:25 AM,9,4,11:59:01 AM,13,,,-1.3352545,36.8659639,-1.2942,36.7777614,Rider_Id_25,1296
-Order_No_19205,User_Id_2987,Bike,3,Business,25,4,12:03:04 PM,25,4,12:26:19 PM,25,4,12:52:31 PM,25,4,12:54:35 PM,25,4,1:11:02 PM,3,19.8,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_646,987
-Order_No_6832,User_Id_2132,Bike,3,Business,23,5,10:39:28 AM,23,5,11:05:53 AM,23,5,11:40:55 AM,23,5,11:54:26 AM,23,5,12:54:52 PM,9,24.1,,-1.2384056,36.8718698,-1.2879314,36.8418388,Rider_Id_660,3626
-Order_No_17845,User_Id_2532,Bike,3,Business,21,4,1:44:26 PM,21,4,1:44:59 PM,21,4,1:46:06 PM,21,4,2:23:06 PM,21,4,2:47:12 PM,7,31.2,,-1.2571472,36.795063299999995,-1.2821378,36.7740428,Rider_Id_779,1446
-Order_No_17885,User_Id_3475,Bike,1,Personal,13,7,7:59:18 AM,13,7,8:09:22 AM,13,7,8:21:40 AM,13,7,8:38:41 AM,13,7,8:56:50 AM,12,19.9,,-1.3230485,36.839476,-1.26578,36.781,Rider_Id_805,1089
-Order_No_13077,User_Id_3372,Bike,3,Business,31,4,6:41:03 PM,31,4,6:41:45 PM,31,4,6:56:18 PM,31,4,7:12:29 PM,31,4,7:23:33 PM,5,22.1,,-1.2860372,36.7549573,-1.2872751,36.7889153,Rider_Id_449,664
-Order_No_9106,User_Id_1527,Bike,3,Business,30,2,10:13:09 AM,30,2,10:14:02 AM,30,2,10:26:04 AM,30,2,10:38:50 AM,30,2,10:47:11 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_663,501
-Order_No_25887,User_Id_1075,Bike,3,Business,5,3,5:02:33 PM,5,3,5:33:55 PM,5,3,5:44:37 PM,5,3,5:47:26 PM,5,3,6:11:38 PM,10,22.5,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_685,1452
-Order_No_8154,User_Id_3157,Bike,3,Personal,7,1,10:05:58 AM,7,1,10:06:42 AM,7,1,10:18:33 AM,7,1,10:28:44 AM,7,1,10:53:33 AM,4,18.2,,-1.300722,36.816865,-1.2794007,36.8202274,Rider_Id_886,1489
-Order_No_27834,User_Id_393,Bike,3,Business,2,5,2:11:15 PM,2,5,3:54:52 PM,2,5,3:55:26 PM,2,5,4:03:11 PM,2,5,4:31:12 PM,10,25.1,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1681
-Order_No_26604,User_Id_1402,Bike,3,Business,16,2,2:35:23 PM,16,2,2:37:11 PM,16,2,2:46:41 PM,16,2,2:54:38 PM,16,2,3:04:22 PM,1,,,-1.2793686000000002,36.82592329999999,-1.2813012,36.832396200000005,Rider_Id_404,584
-Order_No_8728,User_Id_1271,Bike,3,Business,7,1,4:22:44 PM,7,1,4:32:23 PM,7,1,4:46:32 PM,7,1,4:49:15 PM,7,1,5:40:22 PM,23,24.4,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_654,3067
-Order_No_19907,User_Id_1687,Bike,3,Business,24,4,3:24:54 PM,24,4,3:25:35 PM,24,4,3:39:05 PM,24,4,3:50:56 PM,24,4,4:04:59 PM,3,28.1,,-1.2626574,36.8014034,-1.2729802,36.793734,Rider_Id_19,843
-Order_No_20266,User_Id_142,Bike,3,Business,18,2,2:56:35 PM,18,2,3:19:02 PM,18,2,3:26:41 PM,18,2,3:36:22 PM,18,2,3:55:16 PM,3,25.8,,-1.2963097,36.7688221,-1.3026886,36.752225,Rider_Id_829,1134
-Order_No_24489,User_Id_743,Bike,1,Personal,31,2,11:31:30 AM,31,2,11:31:34 AM,31,2,11:39:19 AM,31,2,11:58:20 AM,31,2,12:19:42 PM,13,18.6,,-1.2668264999999999,36.7680484,-1.2624935,36.8434339,Rider_Id_229,1282
-Order_No_22092,User_Id_3426,Bike,3,Business,1,4,12:25:25 PM,1,4,12:29:38 PM,1,4,1:26:42 PM,1,4,1:31:52 PM,1,4,2:18:49 PM,3,,,-1.3014461000000002,36.7661381,-1.2974961,36.781282399999995,Rider_Id_86,2817
-Order_No_24647,User_Id_136,Bike,3,Business,11,1,9:15:52 AM,11,1,9:17:37 AM,11,1,9:41:51 AM,11,1,9:59:19 AM,11,1,10:14:36 AM,12,17.8,,-1.2738338,36.782403,-1.3254522,36.721658399999995,Rider_Id_205,917
-Order_No_18419,User_Id_1500,Bike,3,Business,28,5,8:15:35 AM,28,5,8:23:18 AM,28,5,8:42:39 AM,28,5,8:54:14 AM,28,5,9:09:40 AM,8,19.3,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_436,926
-Order_No_553,User_Id_867,Bike,3,Business,21,3,10:47:09 AM,21,3,11:00:51 AM,21,3,11:04:45 AM,21,3,11:12:22 AM,21,3,11:31:08 AM,9,21.9,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_878,1126
-Order_No_26397,User_Id_1363,Bike,3,Business,26,5,12:00:37 PM,26,5,12:15:23 PM,26,5,12:21:57 PM,26,5,12:23:53 PM,26,5,12:44:15 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_867,1222
-Order_No_4000,User_Id_1073,Bike,3,Business,24,2,2:38:19 PM,24,2,2:38:34 PM,24,2,2:46:20 PM,24,2,3:02:18 PM,24,2,3:22:31 PM,11,18.6,,-1.2559294,36.788465,-1.2941508999999998,36.7947974,Rider_Id_616,1213
-Order_No_17271,User_Id_3149,Bike,3,Business,29,3,10:02:46 AM,29,3,10:03:04 AM,29,3,10:03:11 AM,29,3,10:43:46 AM,29,3,11:04:03 AM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_399,1217
-Order_No_15538,User_Id_3698,Bike,3,Business,28,2,11:54:28 AM,28,2,11:55:35 AM,28,2,12:35:22 PM,28,2,12:42:36 PM,28,2,1:40:00 PM,26,21.5,,-1.3107918,36.761794,-1.3190102,36.923069299999995,Rider_Id_584,3444
-Order_No_4702,User_Id_3433,Bike,1,Personal,15,1,5:03:25 PM,15,1,5:03:32 PM,15,1,5:04:36 PM,15,1,5:10:37 PM,15,1,5:17:11 PM,2,,,-1.2571472,36.795063299999995,-1.254191,36.809913200000004,Rider_Id_720,394
-Order_No_10996,User_Id_1698,Bike,3,Personal,12,2,3:27:08 PM,12,2,3:40:35 PM,12,2,3:58:36 PM,12,2,4:00:39 PM,12,2,4:26:04 PM,7,23.1,,-1.2622227,36.795888,-1.2989879999999998,36.81075,Rider_Id_898,1525
-Order_No_12333,User_Id_3062,Bike,3,Personal,21,5,7:49:59 PM,21,5,7:54:53 PM,21,5,8:18:30 PM,21,5,8:25:48 PM,21,5,8:40:52 PM,4,18.1,,-1.2913246,36.8019522,-1.277783,36.7816464,Rider_Id_247,904
-Order_No_21704,User_Id_1623,Bike,1,Personal,30,6,4:02:07 PM,30,6,4:12:49 PM,30,6,5:00:17 PM,30,6,5:05:14 PM,30,6,5:36:01 PM,12,21.9,,-1.3205334999999998,36.8789718,-1.3784696,36.9430328,Rider_Id_212,1847
-Order_No_22196,User_Id_1245,Bike,3,Business,31,5,3:59:35 PM,31,5,4:04:14 PM,31,5,4:12:09 PM,31,5,5:15:22 PM,31,5,6:03:34 PM,18,23.6,,-1.300921,36.828195,-1.3157163,36.706653,Rider_Id_936,2892
-Order_No_16438,User_Id_2513,Bike,3,Personal,23,3,2:04:58 PM,23,3,2:05:24 PM,23,3,2:19:09 PM,23,3,2:30:18 PM,23,3,2:43:42 PM,6,27.3,,-1.2905048000000001,36.8037419,-1.3102566999999998,36.837762899999994,Rider_Id_548,804
-Order_No_18271,User_Id_2497,Bike,3,Business,26,5,10:29:53 AM,26,5,10:55:51 AM,26,5,11:23:25 AM,26,5,11:26:26 AM,26,5,11:48:32 AM,9,,,-1.3235518999999998,36.7211193,-1.2956756,36.771938299999995,Rider_Id_829,1326
-Order_No_14809,User_Id_633,Bike,3,Business,24,6,11:21:17 AM,24,6,11:23:14 AM,24,6,11:42:13 AM,24,6,11:46:14 AM,24,6,11:58:07 AM,4,24.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_668,713
-Order_No_7922,User_Id_2200,Bike,3,Business,13,3,2:02:21 PM,13,3,2:06:21 PM,13,3,2:21:38 PM,13,3,2:30:26 PM,13,3,2:55:31 PM,6,29.2,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_223,1505
-Order_No_16060,User_Id_3322,Bike,1,Personal,20,3,1:55:36 PM,20,3,1:56:07 PM,20,3,2:06:30 PM,20,3,2:10:38 PM,20,3,2:31:18 PM,9,23.0,,-1.2535486,36.7933541,-1.2966822,36.787816299999996,Rider_Id_116,1240
-Order_No_8571,User_Id_2956,Bike,3,Business,15,4,9:08:27 AM,15,4,9:12:04 AM,15,4,9:18:34 AM,15,4,9:22:24 AM,15,4,10:07:39 AM,10,19.5,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_103,2715
-Order_No_11009,User_Id_2889,Bike,1,Personal,7,1,1:29:49 PM,7,1,1:30:52 PM,7,1,1:40:03 PM,7,1,1:47:49 PM,7,1,2:01:14 PM,4,20.7,,-1.2720542,36.7910241,-1.263419,36.8060348,Rider_Id_100,805
-Order_No_9096,User_Id_393,Bike,3,Business,8,5,10:00:23 AM,8,5,10:00:34 AM,8,5,10:04:34 AM,8,5,10:19:56 AM,8,5,10:49:22 AM,14,20.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1766
-Order_No_7851,User_Id_1004,Bike,3,Business,23,4,2:43:07 PM,23,4,2:43:38 PM,23,4,3:03:58 PM,23,4,3:09:36 PM,23,4,3:54:38 PM,11,24.8,,-1.3250048,36.869718799999994,-1.2815969999999999,36.826482,Rider_Id_883,2702
-Order_No_23401,User_Id_15,Bike,1,Personal,6,3,3:14:15 PM,6,3,3:14:38 PM,6,3,3:25:24 PM,6,3,3:34:26 PM,6,3,4:17:56 PM,17,21.9,,-1.2940553,36.818442499999996,-1.2022306,36.7893305,Rider_Id_852,2610
-Order_No_17998,User_Id_2330,Bike,3,Business,10,1,9:47:14 AM,10,1,9:48:37 AM,10,1,10:00:32 AM,10,1,10:29:50 AM,10,1,11:00:25 AM,16,16.6,,-1.2921463,36.8029626,-1.2925641,36.897644899999996,Rider_Id_92,1835
-Order_No_13827,User_Id_705,Bike,3,Personal,4,1,8:51:35 AM,4,1,8:51:59 AM,4,1,9:25:57 AM,4,1,9:31:07 AM,4,1,9:59:42 AM,9,22.2,,-1.3175119,36.8058513,-1.2706815,36.7646286,Rider_Id_548,1715
-Order_No_16836,User_Id_3149,Bike,3,Business,26,2,11:07:18 AM,26,2,11:09:00 AM,26,2,11:18:58 AM,26,2,11:40:18 AM,26,2,11:52:45 AM,12,26.3,,-1.2638185,36.7930057,-1.3167113000000001,36.8301563,Rider_Id_778,747
-Order_No_16487,User_Id_2135,Bike,1,Personal,29,3,12:45:50 PM,29,3,12:46:28 PM,29,3,12:53:48 PM,29,3,12:58:39 PM,29,3,1:08:52 PM,5,18.4,,-1.3119323999999999,36.8382423,-1.2960687,36.8167909,Rider_Id_539,613
-Order_No_14663,User_Id_1882,Bike,3,Business,10,1,12:41:31 PM,10,1,12:43:09 PM,10,1,12:51:10 PM,10,1,1:19:25 PM,10,1,2:35:12 PM,36,18.6,,-1.2877809999999998,36.817865999999995,-1.2200123999999999,36.6065939,Rider_Id_462,4547
-Order_No_15659,User_Id_1642,Bike,3,Business,3,3,11:22:02 AM,3,3,11:22:44 AM,3,3,11:57:14 AM,3,3,12:05:20 PM,3,3,12:35:23 PM,5,27.4,,-1.288269,36.818275299999996,-1.2728069,36.7962362,Rider_Id_760,1803
-Order_No_19930,User_Id_1162,Bike,3,Business,20,3,9:29:00 AM,20,3,9:29:54 AM,20,3,9:55:13 AM,20,3,10:03:31 AM,20,3,10:27:44 AM,9,16.6,0.4,-1.3122610000000001,36.794658,-1.2663132,36.7955271,Rider_Id_465,1453
-Order_No_25914,User_Id_1209,Bike,3,Business,18,2,9:38:10 AM,18,2,9:46:37 AM,18,2,9:59:35 AM,18,2,10:09:55 AM,18,2,10:32:28 AM,9,19.6,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_496,1353
-Order_No_26649,User_Id_393,Bike,3,Business,6,3,11:48:06 AM,6,3,11:48:19 AM,6,3,11:48:24 AM,6,3,12:00:29 PM,6,3,12:44:55 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_15,2666
-Order_No_24097,User_Id_1502,Bike,3,Business,4,2,5:16:40 PM,4,2,5:16:48 PM,4,2,5:24:44 PM,4,2,5:29:04 PM,4,2,5:37:33 PM,4,22.3,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_685,509
-Order_No_11907,User_Id_635,Bike,3,Business,2,4,3:21:56 PM,2,4,3:23:15 PM,2,4,3:23:20 PM,2,4,4:22:25 PM,2,4,4:44:09 PM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_926,1304
-Order_No_10454,User_Id_3201,Bike,3,Business,5,2,2:53:05 PM,5,2,2:53:36 PM,5,2,3:04:29 PM,5,2,3:10:38 PM,5,2,3:34:27 PM,10,,,-1.303806,36.783041,-1.2584143,36.8048002,Rider_Id_425,1429
-Order_No_7989,User_Id_517,Bike,3,Business,29,4,11:24:42 AM,29,4,11:47:48 AM,29,4,11:55:32 AM,29,4,12:02:03 PM,29,4,1:08:10 PM,14,21.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,3967
-Order_No_20831,User_Id_640,Bike,3,Business,14,4,9:35:57 AM,14,4,9:36:29 AM,14,4,9:50:05 AM,14,4,9:52:55 AM,14,4,10:19:06 AM,6,18.9,,-1.2969741000000001,36.7856612,-1.2833106,36.82402379999999,Rider_Id_118,1571
-Order_No_19698,User_Id_246,Bike,3,Business,22,2,1:59:12 PM,22,2,2:00:42 PM,22,2,2:05:24 PM,22,2,2:14:57 PM,22,2,3:06:03 PM,5,25.8,,-1.2584143,36.8048002,-1.2929499,36.8180835,Rider_Id_903,3066
-Order_No_23794,User_Id_552,Bike,3,Business,28,5,11:11:58 AM,28,5,11:14:10 AM,28,5,11:19:48 AM,28,5,11:21:51 AM,28,5,11:54:21 AM,17,23.6,,-1.279395,36.825364,-1.3632162,36.9108253,Rider_Id_456,1950
-Order_No_12768,User_Id_2330,Bike,3,Business,8,1,1:42:18 PM,8,1,1:47:14 PM,8,1,2:01:11 PM,8,1,2:03:48 PM,8,1,2:23:02 PM,9,29.7,,-1.2124582,36.802894,-1.2645318,36.7981775,Rider_Id_72,1154
-Order_No_1634,User_Id_2763,Bike,3,Business,22,5,2:49:19 PM,22,5,2:50:15 PM,22,5,2:51:36 PM,22,5,2:54:09 PM,22,5,3:04:38 PM,4,30.9,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_263,629
-Order_No_22455,User_Id_2200,Bike,3,Business,11,2,12:56:20 PM,11,2,1:12:20 PM,11,2,1:29:08 PM,11,2,1:36:12 PM,11,2,1:48:37 PM,2,22.2,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_511,745
-Order_No_17836,User_Id_3222,Bike,3,Business,6,3,3:06:20 PM,6,3,3:09:13 PM,6,3,3:23:05 PM,6,3,3:26:46 PM,6,3,4:03:43 PM,15,27.4,,-1.2551895,36.7822034,-1.317151,36.865734,Rider_Id_761,2217
-Order_No_7411,User_Id_3686,Bike,3,Business,30,4,11:12:09 AM,30,4,11:12:34 AM,30,4,11:13:17 AM,30,4,11:22:00 AM,30,4,1:19:15 PM,12,,,-1.2551895,36.7822034,-1.3210621999999999,36.802019,Rider_Id_187,7035
-Order_No_9817,User_Id_2113,Bike,2,Personal,25,5,2:07:54 PM,25,5,2:11:06 PM,25,5,2:22:06 PM,25,5,2:37:15 PM,25,5,3:05:08 PM,8,23.3,,-1.2738167,36.8157821,-1.275442,36.766020700000006,Rider_Id_755,1673
-Order_No_7436,User_Id_2747,Bike,1,Personal,2,1,3:06:05 PM,2,1,3:18:22 PM,2,1,3:32:43 PM,2,1,3:37:51 PM,2,1,3:57:33 PM,7,19.8,,-1.2738397,36.8186697,-1.2980228999999999,36.78865,Rider_Id_726,1182
-Order_No_18594,User_Id_3599,Bike,3,Business,22,4,11:31:15 AM,22,4,11:49:11 AM,22,4,12:10:51 PM,22,4,12:43:30 PM,22,4,1:17:35 PM,21,22.5,,-1.3302996,36.8707935,-1.3156678,36.713569,Rider_Id_240,2045
-Order_No_19329,User_Id_2658,Bike,3,Business,8,5,12:41:46 PM,8,5,12:45:33 PM,8,5,12:50:00 PM,8,5,1:01:29 PM,8,5,2:11:09 PM,18,28.6,,-1.2885029,36.825604999999996,-1.3871626,36.7705016,Rider_Id_779,4180
-Order_No_19235,User_Id_1299,Bike,3,Business,28,1,9:43:50 AM,28,1,9:47:08 AM,28,1,10:02:17 AM,28,1,10:10:50 AM,28,1,10:25:06 AM,7,18.2,,-1.282543,36.824618,-1.2575406999999998,36.7868197,Rider_Id_88,856
-Order_No_9653,User_Id_2423,Bike,3,Business,22,5,12:07:33 PM,22,5,12:07:53 PM,22,5,12:13:50 PM,22,5,12:21:40 PM,22,5,12:43:53 PM,8,,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_411,1333
-Order_No_12679,User_Id_3295,Bike,3,Business,12,2,2:58:50 PM,12,2,2:59:35 PM,12,2,3:40:55 PM,12,2,3:45:40 PM,12,2,3:53:30 PM,2,,,-1.2571472,36.795063299999995,-1.2495931,36.80650870000001,Rider_Id_107,470
-Order_No_3871,User_Id_1363,Bike,3,Business,17,5,12:52:55 PM,17,5,12:55:41 PM,17,5,12:55:54 PM,17,5,1:06:43 PM,17,5,1:23:50 PM,6,,,-1.3004062,36.829741,-1.269534,36.80697,Rider_Id_855,1027
-Order_No_10036,User_Id_20,Bike,3,Business,22,5,1:07:01 PM,22,5,1:07:38 PM,22,5,1:13:38 PM,22,5,1:16:24 PM,22,5,1:40:39 PM,3,,,-1.2629666000000002,36.8068369,-1.2749270000000001,36.8028302,Rider_Id_354,1455
-Order_No_12696,User_Id_517,Bike,3,Business,7,1,12:22:45 PM,7,1,1:03:51 PM,7,1,1:28:38 PM,7,1,1:30:55 PM,7,1,1:30:58 PM,4,24.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,3
-Order_No_6195,User_Id_136,Bike,3,Business,7,2,11:28:15 AM,7,2,11:44:41 AM,7,2,12:01:16 PM,7,2,12:05:30 PM,7,2,12:47:31 PM,18,,,-1.3254522,36.721658399999995,-1.2518056999999998,36.7997135,Rider_Id_831,2521
-Order_No_9255,User_Id_2209,Bike,3,Business,4,1,9:50:46 AM,4,1,10:36:06 AM,4,1,10:39:18 AM,4,1,10:46:25 AM,4,1,11:02:22 AM,7,21.0,,-1.2624632,36.8405321,-1.3034166999999999,36.8197498,Rider_Id_852,957
-Order_No_21534,User_Id_1307,Bike,3,Business,3,7,2:38:40 PM,3,7,2:39:47 PM,3,7,3:08:58 PM,3,7,3:14:59 PM,3,7,4:03:05 PM,19,22.8,,-1.2844665000000002,36.7866113,-1.2671459999999999,36.9065104,Rider_Id_919,2886
-Order_No_16878,User_Id_1217,Bike,3,Personal,27,3,12:45:12 PM,27,3,12:45:25 PM,27,3,12:47:35 PM,27,3,12:56:28 PM,27,3,1:37:32 PM,14,26.0,,-1.2175583,36.884343,-1.2799676000000002,36.8221967,Rider_Id_829,2464
-Order_No_16970,User_Id_2200,Bike,3,Business,19,5,1:20:52 PM,19,5,1:21:17 PM,19,5,1:26:08 PM,19,5,1:40:56 PM,19,5,2:19:19 PM,12,27.2,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_383,2303
-Order_No_16214,User_Id_1107,Bike,2,Personal,3,2,1:35:15 PM,3,2,1:35:48 PM,3,2,1:44:58 PM,3,2,1:46:20 PM,3,2,1:57:57 PM,3,20.4,,-1.2728058,36.8049903,-1.260964,36.820423999999996,Rider_Id_770,697
-Order_No_26208,User_Id_2987,Bike,3,Business,18,1,12:10:39 PM,18,1,12:30:24 PM,18,1,12:30:28 PM,18,1,1:09:57 PM,18,1,1:16:47 PM,3,,,-1.2702120000000001,36.82108770000001,-1.263987,36.798891,Rider_Id_813,410
-Order_No_2415,User_Id_3291,Bike,3,Business,8,6,1:44:54 PM,8,6,2:34:54 PM,8,6,2:52:12 PM,8,6,3:08:35 PM,8,6,3:18:56 PM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_427,621
-Order_No_2528,User_Id_3326,Bike,3,Business,12,5,4:19:34 PM,12,5,4:19:47 PM,12,5,4:20:57 PM,12,5,4:25:27 PM,12,5,4:43:26 PM,9,,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_576,1079
-Order_No_27833,User_Id_439,Bike,3,Business,5,3,10:51:32 AM,5,3,10:52:18 AM,5,3,11:10:02 AM,5,3,11:12:20 AM,5,3,11:48:39 AM,13,22.6,,-1.2613768,36.801941799999994,-1.3323107,36.8692616,Rider_Id_90,2179
-Order_No_4752,User_Id_3450,Bike,3,Business,14,5,8:40:18 AM,14,5,10:37:53 AM,14,5,10:51:40 AM,14,5,11:05:22 AM,14,5,12:13:59 PM,24,21.4,,-1.2551895,36.7822034,-1.4011846000000001,36.761232899999996,Rider_Id_937,4117
-Order_No_26955,User_Id_1039,Bike,1,Personal,7,7,3:56:52 PM,7,7,3:57:03 PM,7,7,4:23:10 PM,7,7,4:49:16 PM,7,7,5:18:10 PM,14,23.2,,-1.2343087,36.84749,-1.3220724,36.8933648,Rider_Id_208,1734
-Order_No_1613,User_Id_1984,Bike,2,Business,19,7,10:43:09 AM,19,7,10:46:21 AM,19,7,11:15:56 AM,19,7,11:18:08 AM,19,7,11:29:25 AM,3,17.8,,-1.3025826,36.7670807,-1.2764692,36.758011100000004,Rider_Id_400,677
-Order_No_26004,User_Id_1809,Bike,3,Business,17,1,12:52:50 PM,17,1,12:53:09 PM,17,1,12:57:36 PM,17,1,1:05:19 PM,17,1,1:05:21 PM,2,23.8,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_647,2
-Order_No_1710,User_Id_1849,Bike,3,Business,9,4,10:28:47 AM,9,4,10:29:53 AM,9,4,10:30:29 AM,9,4,11:23:53 AM,9,4,11:37:03 AM,6,,,-1.2571472,36.795063299999995,-1.290894,36.822971,Rider_Id_341,790
-Order_No_17823,User_Id_1809,Bike,3,Business,11,4,3:37:48 PM,11,4,3:38:41 PM,11,4,3:45:37 PM,11,4,3:54:19 PM,11,4,4:03:23 PM,1,27.0,,-1.2649605,36.798177700000004,-1.2571472,36.795063299999995,Rider_Id_699,544
-Order_No_3557,User_Id_60,Bike,3,Business,16,3,11:35:14 AM,16,3,11:37:48 AM,16,3,11:45:57 AM,16,3,11:54:03 AM,16,3,12:14:44 PM,10,23.0,,-1.2600926000000001,36.8088685,-1.2296618000000001,36.84382720000001,Rider_Id_674,1241
-Order_No_22078,User_Id_111,Bike,3,Business,8,1,8:28:40 AM,8,1,8:28:46 AM,8,1,8:29:45 AM,8,1,8:40:18 AM,8,1,9:07:56 AM,20,21.7,,-1.2551895,36.7822034,-1.3129275,36.9235657,Rider_Id_291,1658
-Order_No_15603,User_Id_1850,Bike,3,Business,8,5,11:12:30 AM,8,5,11:13:48 AM,8,5,11:23:53 AM,8,5,11:30:19 AM,8,5,11:44:58 AM,7,28.3,,-1.2571472,36.795063299999995,-1.3074264,36.822612299999996,Rider_Id_70,879
-Order_No_4163,User_Id_3404,Bike,3,Business,23,5,1:00:35 PM,23,5,1:02:53 PM,23,5,1:22:01 PM,23,5,1:38:22 PM,23,5,2:03:48 PM,9,26.4,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_660,1526
-Order_No_21170,User_Id_3291,Bike,3,Business,27,1,10:10:12 AM,27,1,10:12:02 AM,27,1,10:23:08 AM,27,1,10:29:21 AM,27,1,10:50:46 AM,11,17.9,1.0,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_826,1285
-Order_No_68,User_Id_2987,Bike,3,Business,6,2,1:46:35 PM,6,2,2:02:32 PM,6,2,2:21:48 PM,6,2,2:24:12 PM,6,2,3:36:01 PM,13,27.3,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_920,4309
-Order_No_5533,User_Id_3149,Bike,3,Business,15,1,1:33:47 PM,15,1,1:34:04 PM,15,1,1:34:18 PM,15,1,2:05:34 PM,15,1,2:09:06 PM,6,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_41,212
-Order_No_4954,User_Id_3688,Bike,3,Business,20,3,11:52:20 AM,20,3,11:52:46 AM,20,3,11:58:15 AM,20,3,12:03:49 PM,20,3,12:11:56 PM,3,20.3,,-1.2551895,36.7822034,-1.2560593999999998,36.7798023,Rider_Id_101,487
-Order_No_26282,User_Id_500,Bike,1,Personal,18,4,6:28:29 PM,18,4,6:48:21 PM,18,4,7:16:16 PM,18,4,7:23:26 PM,18,4,7:41:26 PM,7,23.4,,-1.3124203,36.786086299999994,-1.270186,36.802279299999995,Rider_Id_612,1080
-Order_No_11163,User_Id_635,Bike,3,Business,26,5,9:33:51 AM,26,5,9:44:32 AM,26,5,10:12:18 AM,26,5,10:16:36 AM,26,5,10:58:54 AM,18,,,-1.2777923999999998,36.8231577,-1.3190102,36.923069299999995,Rider_Id_99,2538
-Order_No_28172,User_Id_391,Bike,3,Business,25,6,8:46:04 AM,25,6,9:02:21 AM,25,6,9:52:50 AM,25,6,9:58:12 AM,25,6,10:54:19 AM,16,,,-1.3198615,36.7110323,-1.2867454,36.8259017,Rider_Id_538,3367
-Order_No_12369,User_Id_2330,Bike,3,Business,23,4,3:44:26 PM,23,4,3:44:47 PM,23,4,3:50:15 PM,23,4,3:54:11 PM,23,4,4:51:00 PM,21,24.7,,-1.3015197,36.7658456,-1.2925641,36.897644899999996,Rider_Id_161,3409
-Order_No_3110,User_Id_3599,Bike,3,Business,20,1,2:31:16 PM,20,1,2:31:32 PM,20,1,3:09:29 PM,20,1,3:21:55 PM,20,1,4:21:39 PM,14,,,-1.3302996,36.8707935,-1.2825951999999998,36.7871705,Rider_Id_675,3584
-Order_No_5807,User_Id_3211,Bike,3,Personal,3,2,10:05:44 AM,3,2,10:07:11 AM,3,2,10:27:37 AM,3,2,10:31:20 AM,3,2,11:02:41 AM,11,15.4,1.9,-1.2846475,36.8275903,-1.2245407,36.7583778,Rider_Id_208,1881
-Order_No_3669,User_Id_246,Bike,3,Business,13,4,10:51:35 AM,13,4,12:17:10 PM,13,4,12:27:29 PM,13,4,12:31:15 PM,13,4,12:59:09 PM,10,22.0,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_646,1674
-Order_No_951,User_Id_122,Bike,3,Business,28,5,12:21:02 PM,28,5,12:21:27 PM,28,5,12:43:34 PM,28,5,1:25:06 PM,28,5,1:42:54 PM,6,23.6,,-1.2641496,36.791413899999995,-1.2722993,36.8219844,Rider_Id_477,1068
-Order_No_27729,User_Id_237,Bike,3,Business,13,6,10:12:00 AM,13,6,10:15:54 AM,13,6,10:44:11 AM,13,6,10:52:53 AM,13,6,11:49:44 AM,37,25.8,,-1.1962799,36.9521972,-1.3516579,36.729836299999995,Rider_Id_484,3411
-Order_No_1436,User_Id_3647,Bike,3,Business,23,3,10:41:26 AM,23,3,10:41:38 AM,23,3,10:50:21 AM,23,3,10:59:17 AM,23,3,11:11:02 AM,3,,,-1.2726389999999999,36.794723,-1.266294,36.8057971,Rider_Id_611,705
-Order_No_13525,User_Id_3647,Bike,3,Business,22,3,12:36:05 PM,22,3,12:36:49 PM,22,3,1:03:18 PM,22,3,1:13:50 PM,22,3,1:13:56 PM,6,23.3,,-1.2985749,36.8088,-1.2726389999999999,36.794723,Rider_Id_138,6
-Order_No_10272,User_Id_1205,Bike,3,Business,17,4,11:17:21 AM,17,4,11:23:12 AM,17,4,11:37:50 AM,17,4,11:41:38 AM,17,4,11:54:43 AM,3,22.3,,-1.2814178,36.8154096,-1.300073,36.808242299999996,Rider_Id_544,785
-Order_No_25479,User_Id_2804,Bike,3,Business,29,1,2:28:13 PM,29,1,2:28:59 PM,29,1,2:57:23 PM,29,1,3:03:26 PM,29,1,3:33:32 PM,3,,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_607,1806
-Order_No_2975,User_Id_2980,Bike,1,Personal,11,3,10:00:40 AM,11,3,10:04:55 AM,11,3,10:12:18 AM,11,3,10:17:02 AM,11,3,10:44:46 AM,7,15.1,1.1,-1.2669099,36.79774329999999,-1.2969741000000001,36.7856612,Rider_Id_427,1664
-Order_No_1809,User_Id_510,Bike,3,Business,30,5,9:29:58 AM,30,5,9:35:17 AM,30,5,9:59:30 AM,30,5,10:04:15 AM,30,5,10:23:49 AM,6,19.0,,-1.290683,36.871041,-1.2860183,36.8975338,Rider_Id_114,1174
-Order_No_386,User_Id_1368,Bike,3,Business,8,2,12:11:56 PM,8,2,12:12:59 PM,8,2,12:28:35 PM,8,2,12:35:26 PM,8,2,12:48:00 PM,7,23.5,,-1.2571472,36.795063299999995,-1.29387,36.7907813,Rider_Id_19,754
-Order_No_14372,User_Id_2200,Bike,3,Business,22,1,3:38:18 PM,22,1,3:49:07 PM,22,1,4:04:42 PM,22,1,4:18:38 PM,22,1,4:27:45 PM,2,26.3,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_101,547
-Order_No_27659,User_Id_1304,Bike,3,Business,24,1,11:36:32 AM,24,1,11:37:44 AM,24,1,11:43:50 AM,24,1,11:48:24 AM,24,1,12:08:08 PM,7,22.7,,-1.2551895,36.7822034,-1.2963492,36.8093377,Rider_Id_415,1184
-Order_No_13534,User_Id_1679,Bike,1,Personal,31,5,3:36:11 PM,31,5,3:37:10 PM,31,5,4:20:51 PM,31,5,4:23:13 PM,31,5,4:50:44 PM,13,20.2,,-1.2629666000000002,36.8068369,-1.3291184,36.7892374,Rider_Id_857,1651
-Order_No_7117,User_Id_2200,Bike,3,Business,2,6,12:05:19 PM,2,6,1:43:31 PM,2,6,2:14:42 PM,2,6,2:29:27 PM,2,6,3:08:46 PM,12,31.2,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_434,2359
-Order_No_9652,User_Id_1710,Bike,3,Personal,22,3,1:04:44 PM,22,3,1:14:54 PM,22,3,1:36:25 PM,22,3,1:40:02 PM,22,3,1:54:02 PM,6,24.5,,-1.2877501,36.814659899999995,-1.2963097,36.7688221,Rider_Id_539,840
-Order_No_4392,User_Id_2733,Bike,3,Business,24,5,12:33:22 PM,24,5,12:34:53 PM,24,5,12:50:12 PM,24,5,12:54:46 PM,24,5,2:41:36 PM,8,21.5,,-1.2963097,36.7688221,-1.2813012,36.832396200000005,Rider_Id_800,6410
-Order_No_6433,User_Id_246,Bike,3,Business,30,5,10:52:03 AM,30,5,11:11:02 AM,30,5,11:16:06 AM,30,5,12:15:07 PM,30,5,12:20:49 PM,2,23.8,,-1.2615888000000002,36.7928732,-1.2584143,36.8048002,Rider_Id_28,342
-Order_No_4784,User_Id_1964,Bike,3,Personal,11,5,11:21:02 AM,11,5,11:22:10 AM,11,5,11:36:36 AM,11,5,11:39:38 AM,11,5,11:51:58 AM,7,21.1,,-1.317772,36.824446,-1.32671,36.8600138,Rider_Id_130,740
-Order_No_9301,User_Id_758,Bike,3,Business,5,2,4:19:37 PM,5,2,4:23:22 PM,5,2,4:40:08 PM,5,2,4:42:43 PM,5,2,5:00:34 PM,2,,,-1.2917868000000001,36.7872675,-1.2978908000000002,36.796321500000005,Rider_Id_953,1071
-Order_No_19080,User_Id_1280,Bike,3,Business,25,4,2:16:43 PM,25,4,2:17:11 PM,25,4,2:17:58 PM,25,4,2:23:51 PM,25,4,2:47:19 PM,16,,,-1.2551895,36.7822034,-1.3297531,36.7063699,Rider_Id_695,1408
-Order_No_9188,User_Id_3686,Bike,3,Business,13,2,9:35:35 AM,13,2,9:37:32 AM,13,2,9:45:53 AM,13,2,9:49:11 AM,13,2,10:08:37 AM,15,19.9,8.9,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_231,1166
-Order_No_11706,User_Id_1424,Bike,3,Business,28,5,2:48:56 PM,28,5,2:50:31 PM,28,5,2:58:21 PM,28,5,3:05:25 PM,28,5,3:29:21 PM,14,25.6,,-1.3015700000000001,36.7871,-1.329424,36.862726,Rider_Id_100,1436
-Order_No_23467,User_Id_3184,Bike,3,Business,8,4,11:49:40 AM,8,4,11:50:14 AM,8,4,12:03:38 PM,8,4,12:04:51 PM,8,4,12:53:38 PM,17,26.5,,-1.3396256000000002,36.8863135,-1.2662453000000002,36.8219775,Rider_Id_712,2927
-Order_No_15650,User_Id_3147,Bike,3,Business,14,3,12:49:22 PM,14,3,12:50:24 PM,14,3,12:52:13 PM,14,3,12:53:53 PM,14,3,1:26:06 PM,10,24.8,,-1.3472066,36.7692638,-1.316315,36.8268861,Rider_Id_878,1933
-Order_No_6930,User_Id_1927,Bike,3,Business,25,6,3:34:55 PM,25,6,3:47:08 PM,25,6,4:18:05 PM,25,6,4:33:58 PM,25,6,5:05:36 PM,19,,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_384,1898
-Order_No_11272,User_Id_421,Bike,3,Business,19,3,12:18:41 PM,19,3,12:28:00 PM,19,3,12:41:19 PM,19,3,12:48:46 PM,19,3,1:17:19 PM,8,22.0,,-1.2975066000000002,36.76894229999999,-1.3004062,36.829741,Rider_Id_708,1713
-Order_No_26213,User_Id_3500,Bike,3,Business,9,3,3:01:15 PM,9,3,3:01:36 PM,9,3,3:07:56 PM,9,3,3:17:34 PM,9,3,3:40:40 PM,7,25.1,,-1.2906104999999999,36.805327899999995,-1.2621828000000002,36.7949276,Rider_Id_622,1386
-Order_No_17957,User_Id_2125,Bike,2,Personal,29,2,4:57:23 PM,29,2,4:58:07 PM,29,2,5:03:37 PM,29,2,5:25:02 PM,29,2,6:01:59 PM,17,22.5,,-1.3055016000000002,36.7844071,-1.2237053,36.817471999999995,Rider_Id_116,2217
-Order_No_9205,User_Id_2899,Bike,3,Personal,14,5,11:12:23 AM,14,5,11:13:13 AM,14,5,11:37:06 AM,14,5,11:42:17 AM,14,5,12:00:34 PM,4,21.9,,-1.2862436000000002,36.791017,-1.298762,36.763645600000004,Rider_Id_655,1097
-Order_No_18682,User_Id_3283,Bike,3,Business,26,2,10:44:41 AM,26,2,10:44:58 AM,26,2,10:46:42 AM,26,2,10:52:36 AM,26,2,11:02:09 AM,5,27.7,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_607,573
-Order_No_14879,User_Id_432,Bike,3,Business,21,2,4:05:37 PM,21,2,4:07:16 PM,21,2,4:18:55 PM,21,2,4:29:23 PM,21,2,4:57:38 PM,12,25.0,,-1.3012008,36.764868,-1.2765736,36.851364600000004,Rider_Id_124,1695
-Order_No_13444,User_Id_1736,Bike,3,Personal,5,2,10:57:59 AM,5,2,10:58:58 AM,5,2,11:12:19 AM,5,2,11:24:43 AM,5,2,11:43:45 AM,14,25.3,,-1.296485,36.7931959,-1.3189252,36.861726899999994,Rider_Id_222,1142
-Order_No_15427,User_Id_2523,Bike,3,Business,2,2,12:12:56 PM,2,2,12:13:18 PM,2,2,12:21:51 PM,2,2,12:25:25 PM,2,2,12:58:27 PM,4,26.3,,-1.2551895,36.7822034,-1.2651643,36.807221399999996,Rider_Id_690,1982
-Order_No_3835,User_Id_2987,Bike,3,Business,2,5,11:44:01 AM,2,5,11:45:45 AM,2,5,11:58:41 AM,2,5,12:07:56 PM,2,5,12:43:06 PM,13,23.7,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_100,2110
-Order_No_18755,User_Id_3504,Bike,2,Personal,14,4,9:56:20 AM,14,4,10:09:00 AM,14,4,10:14:48 AM,14,4,10:17:09 AM,14,4,10:29:58 AM,5,15.3,,-1.2911636,36.7857333,-1.3136451999999998,36.7974014,Rider_Id_379,769
-Order_No_20677,User_Id_393,Bike,3,Business,13,2,11:45:11 AM,13,2,12:00:03 PM,13,2,12:16:41 PM,13,2,12:32:07 PM,13,2,1:03:54 PM,14,22.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_626,1907
-Order_No_16771,User_Id_3203,Bike,3,Business,23,1,11:05:29 AM,23,1,11:25:15 AM,23,1,11:42:26 AM,23,1,11:52:36 AM,23,1,12:07:53 PM,6,18.4,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_144,917
-Order_No_6682,User_Id_3377,Bike,2,Personal,17,3,11:53:57 AM,17,3,11:55:14 AM,17,3,11:58:48 AM,17,3,12:06:42 PM,17,3,12:49:00 PM,25,25.6,,-1.3039610000000001,36.780440500000005,-1.1860322,36.9379956,Rider_Id_406,2538
-Order_No_22695,User_Id_2069,Bike,3,Business,19,6,4:40:08 PM,19,6,4:40:38 PM,19,6,4:52:32 PM,19,6,5:01:10 PM,19,6,5:25:13 PM,9,26.2,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_819,1443
-Order_No_25122,User_Id_661,Bike,3,Business,11,1,10:51:25 AM,11,1,11:07:07 AM,11,1,11:22:09 AM,11,1,11:36:20 AM,11,1,12:16:55 PM,4,18.3,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_140,2435
-Order_No_3445,User_Id_296,Bike,3,Business,23,3,8:22:37 AM,23,3,8:22:52 AM,23,3,8:27:27 AM,23,3,8:39:29 AM,23,3,9:10:59 AM,17,,,-1.305459,36.891870700000005,-1.2571472,36.795063299999995,Rider_Id_620,1890
-Order_No_23633,User_Id_635,Bike,3,Business,10,1,1:30:15 PM,10,1,1:48:18 PM,10,1,2:29:17 PM,10,1,2:49:56 PM,10,1,3:40:27 PM,28,18.3,,-1.2847613000000002,36.967084,-1.2602337,36.79905479999999,Rider_Id_581,3031
-Order_No_25897,User_Id_3533,Bike,3,Business,29,2,11:18:53 AM,29,2,11:19:29 AM,29,2,11:26:18 AM,29,2,11:42:23 AM,29,2,12:20:07 PM,9,23.7,,-1.2615888000000002,36.7928732,-1.292188,36.764855,Rider_Id_862,2264
-Order_No_14986,User_Id_867,Bike,3,Business,10,1,3:27:07 PM,10,1,3:30:23 PM,10,1,3:38:41 PM,10,1,3:46:50 PM,10,1,4:13:26 PM,9,18.5,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_491,1596
-Order_No_10276,User_Id_755,Bike,1,Personal,9,6,2:47:01 PM,9,6,2:48:16 PM,9,6,3:15:36 PM,9,6,3:17:59 PM,9,6,3:52:07 PM,16,19.3,,-1.282357,36.782501,-1.2300082,36.8867427,Rider_Id_940,2048
-Order_No_12876,User_Id_1502,Bike,3,Business,23,2,12:24:00 PM,23,2,12:25:22 PM,23,2,1:11:25 PM,23,2,1:35:12 PM,23,2,2:01:42 PM,15,,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_263,1590
-Order_No_25719,User_Id_393,Bike,3,Business,11,1,8:36:47 AM,11,1,8:37:03 AM,11,1,8:39:30 AM,11,1,8:55:12 AM,11,1,9:23:28 AM,11,18.2,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_523,1696
-Order_No_7172,User_Id_393,Bike,3,Business,14,5,10:00:25 AM,14,5,10:00:50 AM,14,5,10:03:59 AM,14,5,10:39:43 AM,14,5,10:39:46 AM,14,21.8,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_863,3
-Order_No_15854,User_Id_1402,Bike,3,Business,8,1,11:06:00 AM,8,1,11:07:41 AM,8,1,11:29:43 AM,8,1,11:43:26 AM,8,1,1:06:21 PM,12,27.7,,-1.2813012,36.832396200000005,-1.3472066,36.7692638,Rider_Id_521,4975
-Order_No_14024,User_Id_1473,Bike,3,Business,29,1,1:06:16 PM,29,1,1:07:31 PM,29,1,1:40:27 PM,29,1,1:41:41 PM,29,1,2:22:20 PM,17,,,-1.2845683,36.8128703,-1.3700383,36.9190174,Rider_Id_106,2439
-Order_No_8548,User_Id_1241,Bike,2,Personal,6,1,2:11:34 PM,6,1,2:12:13 PM,6,1,2:22:42 PM,6,1,2:34:31 PM,6,1,3:05:46 PM,8,24.6,,-1.3012173,36.8093987,-1.261842,36.784859999999995,Rider_Id_449,1875
-Order_No_22440,User_Id_607,Bike,2,Personal,9,1,4:05:15 PM,9,1,4:05:40 PM,9,1,4:14:04 PM,9,1,4:18:48 PM,9,1,4:29:32 PM,3,21.3,,-1.2981353999999998,36.775998799999996,-1.2858511000000001,36.7946184,Rider_Id_312,644
-Order_No_1418,User_Id_393,Bike,3,Business,31,4,8:39:09 AM,31,4,8:39:27 AM,31,4,8:39:37 AM,31,4,8:54:37 AM,31,4,9:03:42 AM,4,20.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_842,545
-Order_No_19688,User_Id_1280,Bike,3,Business,5,3,11:13:18 AM,5,3,11:14:24 AM,5,3,11:21:54 AM,5,3,11:26:00 AM,5,3,11:32:20 AM,3,22.0,,-1.2551895,36.7822034,-1.2654348,36.8022464,Rider_Id_116,380
-Order_No_33,User_Id_633,Bike,3,Business,15,5,8:17:49 AM,15,5,8:18:09 AM,15,5,8:18:11 AM,15,5,8:20:43 AM,15,5,8:52:18 AM,8,19.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_10,1895
-Order_No_23506,User_Id_186,Bike,3,Business,5,5,11:03:02 AM,5,5,11:08:03 AM,5,5,11:23:17 AM,5,5,11:29:14 AM,5,5,11:44:58 AM,6,24.6,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_576,944
-Order_No_25131,User_Id_895,Bike,1,Personal,4,3,4:33:35 PM,4,3,4:40:15 PM,4,3,4:52:39 PM,4,3,5:30:30 PM,4,3,5:47:37 PM,4,,,-1.2917729,36.809799700000006,-1.3044313,36.818504,Rider_Id_192,1027
-Order_No_8087,User_Id_633,Bike,3,Business,22,5,1:24:23 PM,22,5,1:24:58 PM,22,5,1:25:01 PM,22,5,1:52:21 PM,22,5,1:52:25 PM,9,30.9,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_515,4
-Order_No_9767,User_Id_2956,Bike,3,Business,9,2,5:12:45 PM,9,2,5:13:26 PM,9,2,5:18:44 PM,9,2,5:38:13 PM,9,2,6:13:53 PM,6,27.7,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_119,2140
-Order_No_5500,User_Id_1856,Bike,3,Business,8,4,12:35:40 PM,8,4,12:40:38 PM,8,4,12:55:59 PM,8,4,1:00:47 PM,8,4,1:18:12 PM,6,26.1,,-1.3001813,36.811122,-1.273009,36.802807,Rider_Id_615,1045
-Order_No_27521,User_Id_1329,Bike,3,Business,7,5,10:02:04 AM,7,5,10:02:48 AM,7,5,10:08:47 AM,7,5,10:13:30 AM,7,5,10:26:52 AM,5,,,-1.273056,36.811298,-1.2945799999999998,36.794917,Rider_Id_178,802
-Order_No_15707,User_Id_2069,Bike,3,Business,28,4,12:09:22 PM,28,4,12:25:00 PM,28,4,12:54:51 PM,28,4,12:58:40 PM,28,4,1:46:26 PM,12,23.7,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_306,2866
-Order_No_7613,User_Id_2174,Bike,3,Business,24,5,10:35:28 AM,24,5,10:35:28 AM,24,5,10:35:52 AM,24,5,11:36:59 AM,24,5,12:10:43 PM,6,21.4,,-1.2482781,36.884091999999995,-1.2643042,36.8424251,Rider_Id_86,2024
-Order_No_21780,User_Id_1880,Bike,3,Business,14,5,9:36:18 AM,14,5,10:30:55 AM,14,5,10:37:15 AM,14,5,11:03:45 AM,14,5,11:37:50 AM,6,21.6,,-1.2571472,36.795063299999995,-1.2917253,36.7800652,Rider_Id_383,2045
-Order_No_14539,User_Id_868,Bike,3,Business,10,5,2:42:08 PM,10,5,2:42:49 PM,10,5,3:02:02 PM,10,5,3:23:48 PM,10,5,3:46:10 PM,9,21.9,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_882,1342
-Order_No_26659,User_Id_393,Bike,3,Business,21,4,10:54:45 AM,21,4,10:59:31 AM,21,4,11:02:10 AM,21,4,11:30:31 AM,21,4,11:30:33 AM,4,25.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,2
-Order_No_26864,User_Id_3222,Bike,3,Business,4,2,2:42:02 PM,4,2,2:45:21 PM,4,2,2:57:21 PM,4,2,3:04:19 PM,4,2,3:25:54 PM,8,22.7,,-1.2551895,36.7822034,-1.2922992,36.798303499999996,Rider_Id_161,1295
-Order_No_3203,User_Id_1402,Bike,3,Business,25,6,3:32:48 PM,25,6,3:34:46 PM,25,6,3:46:08 PM,25,6,4:26:39 PM,25,6,4:29:50 PM,1,,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_106,191
-Order_No_17841,User_Id_1245,Bike,3,Business,19,3,1:33:30 PM,19,3,1:42:20 PM,19,3,1:54:52 PM,19,3,2:49:25 PM,19,3,3:16:55 PM,17,24.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_82,1650
-Order_No_23708,User_Id_640,Bike,3,Business,14,5,9:46:16 AM,14,5,9:46:47 AM,14,5,10:10:00 AM,14,5,10:17:14 AM,14,5,10:30:03 AM,4,17.3,,-1.304033,36.7848695,-1.2985749,36.8088,Rider_Id_510,769
-Order_No_6742,User_Id_3557,Bike,3,Business,23,3,10:53:02 AM,23,3,10:54:33 AM,23,3,11:03:32 AM,23,3,11:29:55 AM,23,3,11:43:13 AM,9,,,-1.2929747,36.76463579999999,-1.2546732,36.808679600000005,Rider_Id_247,798
-Order_No_420,User_Id_2132,Bike,3,Business,19,1,8:57:01 AM,19,1,9:25:39 AM,19,1,9:59:43 AM,19,1,10:24:36 AM,19,1,10:48:40 AM,9,19.4,,-1.2384056,36.8718698,-1.2881663,36.831401299999996,Rider_Id_669,1444
-Order_No_20827,User_Id_3201,Bike,3,Business,7,4,8:38:55 AM,7,4,8:40:53 AM,7,4,9:39:28 AM,7,4,9:44:08 AM,7,4,10:43:00 AM,17,,,-1.3463041999999998,36.768164,-1.2584143,36.8048002,Rider_Id_831,3532
-Order_No_15422,User_Id_1816,Bike,1,Business,28,2,11:17:47 AM,28,2,11:19:55 AM,28,2,11:51:51 AM,28,2,12:03:11 PM,28,2,12:48:25 PM,15,17.5,,-1.3149620000000002,36.865528000000005,-1.296684,36.770171999999995,Rider_Id_228,2714
-Order_No_18533,User_Id_1469,Bike,3,Business,19,4,1:34:56 PM,19,4,1:35:11 PM,19,4,1:45:31 PM,19,4,1:48:46 PM,19,4,2:29:35 PM,7,20.4,,-1.2551895,36.7822034,-1.282451,36.8097806,Rider_Id_465,2449
-Order_No_22957,User_Id_2271,Bike,3,Business,9,2,2:05:37 PM,9,2,2:05:48 PM,9,2,2:09:25 PM,9,2,2:32:39 PM,9,2,3:10:32 PM,12,30.8,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_306,2273
-Order_No_8971,User_Id_3222,Bike,3,Business,8,5,2:19:21 PM,8,5,2:20:01 PM,8,5,2:49:25 PM,8,5,2:53:39 PM,8,5,3:31:55 PM,4,27.1,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_913,2296
-Order_No_999,User_Id_3291,Bike,3,Business,13,2,11:33:34 AM,13,2,11:36:58 AM,13,2,11:41:18 AM,13,2,11:43:18 AM,13,2,11:50:45 AM,2,22.1,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_663,447
-Order_No_21824,User_Id_635,Bike,3,Business,7,2,12:06:38 PM,7,2,12:12:17 PM,7,2,12:32:14 PM,7,2,12:54:02 PM,7,2,1:25:13 PM,8,,,-1.2602337,36.79905479999999,-1.22589,36.7822548,Rider_Id_309,1871
-Order_No_6541,User_Id_3517,Bike,3,Business,14,2,10:13:29 AM,14,2,10:14:35 AM,14,2,10:41:47 AM,14,2,11:13:33 AM,14,2,12:11:57 PM,6,,,-1.300921,36.828195,-1.2850275,36.8301566,Rider_Id_106,3504
-Order_No_17177,User_Id_393,Bike,3,Business,30,5,1:55:34 PM,30,5,2:09:24 PM,30,5,2:19:19 PM,30,5,2:22:22 PM,30,5,2:52:32 PM,14,24.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_663,1810
-Order_No_10042,User_Id_2610,Bike,3,Business,10,4,3:48:15 PM,10,4,3:51:41 PM,10,4,4:02:34 PM,10,4,4:07:54 PM,10,4,4:22:24 PM,6,21.8,1.1,-1.3038377,36.787937,-1.3075444999999999,36.823668700000006,Rider_Id_749,870
-Order_No_25412,User_Id_393,Bike,3,Business,20,4,7:50:19 AM,20,4,7:50:55 AM,20,4,8:45:48 AM,20,4,8:50:17 AM,20,4,9:00:56 AM,4,18.0,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_731,639
-Order_No_8215,User_Id_3557,Bike,3,Business,5,1,1:21:24 PM,5,1,1:24:23 PM,5,1,1:57:13 PM,5,1,2:29:57 PM,5,1,2:56:38 PM,9,26.2,,-1.2981352,36.8213822,-1.2546732,36.808679600000005,Rider_Id_112,1601
-Order_No_4792,User_Id_2876,Bike,1,Personal,2,2,11:41:21 AM,2,2,11:43:08 AM,2,2,11:48:04 AM,2,2,11:56:00 AM,2,2,12:10:09 PM,6,21.2,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_12,849
-Order_No_11864,User_Id_186,Bike,3,Business,18,1,4:15:34 PM,18,1,4:17:32 PM,18,1,4:20:50 PM,18,1,4:40:56 PM,18,1,4:53:00 PM,4,28.5,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_26,724
-Order_No_20922,User_Id_3300,Bike,3,Business,7,5,8:42:39 AM,7,5,8:44:06 AM,7,5,8:51:04 AM,7,5,9:02:45 AM,7,5,9:38:40 AM,8,,,-1.265715,36.823815,-1.303596,36.778378000000004,Rider_Id_27,2155
-Order_No_299,User_Id_293,Bike,1,Personal,27,5,1:11:15 PM,27,5,1:11:53 PM,27,5,1:19:53 PM,27,5,1:47:53 PM,27,5,2:32:14 PM,15,23.8,,-1.2653589,36.8090021,-1.3472066,36.7692638,Rider_Id_620,2661
-Order_No_83,User_Id_3456,Bike,3,Personal,20,1,2:58:51 PM,20,1,2:59:01 PM,20,1,3:04:10 PM,20,1,3:05:29 PM,20,1,3:24:45 PM,7,21.3,,-1.2802850000000001,36.780954,-1.2538731,36.7850223,Rider_Id_467,1156
-Order_No_24226,User_Id_3599,Bike,3,Business,26,2,1:03:30 PM,26,2,1:04:07 PM,26,2,1:18:04 PM,26,2,1:41:17 PM,26,2,2:20:35 PM,13,31.1,,-1.3302996,36.8707935,-1.2744447,36.786133899999996,Rider_Id_675,2358
-Order_No_19858,User_Id_1945,Bike,2,Personal,2,6,1:18:39 PM,2,6,1:19:14 PM,2,6,1:29:22 PM,2,6,1:34:35 PM,2,6,1:52:52 PM,10,23.9,,-1.2568739,36.8179321,-1.289469,36.765738,Rider_Id_116,1097
-Order_No_24937,User_Id_1146,Bike,3,Business,8,5,11:15:06 AM,8,5,11:15:51 AM,8,5,11:17:49 AM,8,5,11:55:53 AM,8,5,12:14:06 PM,5,28.4,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_686,1093
-Order_No_19463,User_Id_3647,Bike,3,Business,17,4,9:54:18 AM,17,4,9:55:25 AM,17,4,10:15:24 AM,17,4,10:30:16 AM,17,4,10:43:52 AM,4,,,-1.2726389999999999,36.794723,-1.2686614,36.8031594,Rider_Id_49,816
-Order_No_6125,User_Id_393,Bike,3,Business,20,4,12:13:21 PM,20,4,12:21:28 PM,20,4,12:21:32 PM,20,4,1:02:31 PM,20,4,1:43:39 PM,11,22.0,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_239,2468
-Order_No_11662,User_Id_2793,Bike,1,Personal,20,4,2:30:26 PM,20,4,2:30:39 PM,20,4,2:37:50 PM,20,4,2:41:09 PM,20,4,2:57:50 PM,6,28.1,,-1.2967108,36.787113500000004,-1.2866011999999998,36.8195691,Rider_Id_338,1001
-Order_No_11414,User_Id_1329,Bike,3,Business,20,2,10:56:46 AM,20,2,10:57:59 AM,20,2,11:10:03 AM,20,2,11:14:59 AM,20,2,11:49:25 AM,12,19.6,,-1.273056,36.811298,-1.2019821000000002,36.8054972,Rider_Id_937,2066
-Order_No_24695,User_Id_1500,Bike,3,Business,22,5,3:37:12 PM,22,5,3:40:15 PM,22,5,3:51:22 PM,22,5,4:27:07 PM,22,5,4:44:49 PM,8,31.2,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_448,1062
-Order_No_23029,User_Id_909,Bike,3,Business,25,5,10:29:14 AM,25,5,10:29:35 AM,25,5,10:30:00 AM,25,5,10:36:37 AM,25,5,10:54:00 AM,9,26.4,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_829,1043
-Order_No_21120,User_Id_1271,Bike,3,Business,8,1,10:14:58 AM,8,1,10:15:06 AM,8,1,10:34:38 AM,8,1,10:41:29 AM,8,1,11:14:27 AM,18,24.4,,-1.2600931999999998,36.8041689,-1.3244885,36.897791999999995,Rider_Id_937,1978
-Order_No_22367,User_Id_1250,Bike,1,Personal,18,5,10:11:02 AM,18,5,10:20:41 AM,18,5,10:32:42 AM,18,5,11:14:43 AM,18,5,11:33:00 AM,3,21.4,,-1.2662716,36.815023700000005,-1.2620741000000002,36.815814700000004,Rider_Id_909,1097
-Order_No_20652,User_Id_3151,Bike,1,Personal,29,2,10:11:59 AM,29,2,10:13:26 AM,29,2,10:33:45 AM,29,2,10:44:29 AM,29,2,11:05:43 AM,7,21.6,,-1.3271684,36.8173021,-1.312469,36.815177,Rider_Id_101,1274
-Order_No_7009,User_Id_627,Bike,3,Business,20,3,2:18:45 PM,20,3,2:18:58 PM,20,3,2:19:57 PM,20,3,2:29:07 PM,20,3,2:49:30 PM,4,29.6,,-1.2551895,36.7822034,-1.2695287,36.798177700000004,Rider_Id_177,1223
-Order_No_27529,User_Id_3237,Bike,2,Personal,3,2,6:56:59 PM,3,2,7:00:32 PM,3,2,7:09:48 PM,3,2,7:12:44 PM,3,2,7:28:03 PM,5,18.8,,-1.2786893999999998,36.8032321,-1.2902539,36.8211322,Rider_Id_951,919
-Order_No_26548,User_Id_2523,Bike,3,Business,3,5,2:39:01 PM,3,5,2:39:36 PM,3,5,2:50:30 PM,3,5,2:56:27 PM,3,5,3:21:18 PM,11,25.0,,-1.2551895,36.7822034,-1.3173868999999998,36.8112044,Rider_Id_857,1491
-Order_No_13789,User_Id_460,Bike,3,Personal,30,4,1:59:33 PM,30,4,2:00:12 PM,30,4,2:26:17 PM,30,4,2:32:11 PM,30,4,2:48:55 PM,5,,,-1.261647,36.798846000000005,-1.2524976,36.8062935,Rider_Id_221,1004
-Order_No_26807,User_Id_1568,Bike,3,Business,28,1,2:23:24 PM,28,1,2:30:01 PM,28,1,2:37:02 PM,28,1,2:53:04 PM,28,1,3:14:09 PM,8,26.2,,-1.2897368,36.8226783,-1.263989,36.765174,Rider_Id_88,1265
-Order_No_17679,User_Id_1417,Bike,3,Business,13,3,11:00:28 AM,13,3,11:01:17 AM,13,3,11:10:54 AM,13,3,11:13:41 AM,13,3,11:38:41 AM,14,24.6,,-1.2741728,36.79691079999999,-1.3323107,36.8692616,Rider_Id_880,1500
-Order_No_27569,User_Id_2987,Bike,3,Business,7,3,4:27:51 PM,7,3,4:32:06 PM,7,3,4:32:57 PM,7,3,5:01:32 PM,7,3,5:52:03 PM,13,25.3,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_718,3031
-Order_No_1215,User_Id_2130,Bike,3,Personal,4,2,8:58:52 AM,4,2,9:00:06 AM,4,2,9:42:30 AM,4,2,9:52:59 AM,4,2,10:34:48 AM,12,16.7,98.9,-1.2310607,36.803577600000004,-1.2533491,36.861591100000005,Rider_Id_564,2509
-Order_No_1524,User_Id_2330,Bike,3,Business,22,3,6:16:21 PM,22,3,6:16:37 PM,22,3,6:17:19 PM,22,3,6:18:34 PM,22,3,6:37:57 PM,2,24.1,,-1.294597,36.7742999,-1.3015197,36.7658456,Rider_Id_362,1163
-Order_No_1165,User_Id_3201,Bike,3,Business,6,2,2:02:03 PM,6,2,2:03:32 PM,6,2,2:03:40 PM,6,2,2:10:59 PM,6,2,2:14:10 PM,2,27.6,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_579,191
-Order_No_16414,User_Id_1934,Bike,2,Personal,11,2,3:09:31 PM,11,2,3:10:23 PM,11,2,3:14:49 PM,11,2,3:25:15 PM,11,2,3:47:47 PM,10,25.3,,-1.304033,36.7848695,-1.2563576,36.7881585,Rider_Id_4,1352
-Order_No_8348,User_Id_3647,Bike,3,Business,29,3,12:27:19 PM,29,3,12:28:05 PM,29,3,12:29:16 PM,29,3,12:40:49 PM,29,3,1:07:13 PM,6,,,-1.2985749,36.8088,-1.2726389999999999,36.794723,Rider_Id_146,1584
-Order_No_5795,User_Id_391,Bike,3,Business,23,2,12:24:27 PM,23,2,12:26:32 PM,23,2,12:39:23 PM,23,2,1:08:43 PM,23,2,2:13:25 PM,13,,,-1.2867454,36.8259017,-1.2121839,36.892427399999995,Rider_Id_99,3882
-Order_No_26231,User_Id_633,Bike,3,Business,22,4,11:23:29 AM,22,4,11:46:49 AM,22,4,11:58:15 AM,22,4,12:00:14 PM,22,4,12:11:59 PM,4,22.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_581,705
-Order_No_2132,User_Id_2330,Bike,3,Business,11,2,12:47:51 PM,11,2,12:52:21 PM,11,2,1:13:07 PM,11,2,1:32:17 PM,11,2,1:57:42 PM,14,21.4,,-1.285908,36.8980126,-1.2921463,36.8029626,Rider_Id_126,1525
-Order_No_19369,User_Id_393,Bike,3,Business,3,5,1:57:16 PM,3,5,2:00:28 PM,3,5,2:02:28 PM,3,5,2:17:30 PM,3,5,2:28:17 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_328,647
-Order_No_15453,User_Id_1764,Bike,1,Personal,27,3,11:35:04 AM,27,3,11:37:36 AM,27,3,11:59:21 AM,27,3,12:03:19 PM,27,3,12:28:10 PM,10,,,-1.3307951,36.79775479999999,-1.2833534,36.8165865,Rider_Id_338,1491
-Order_No_12701,User_Id_867,Bike,3,Business,8,1,2:16:57 PM,8,1,2:18:50 PM,8,1,2:27:55 PM,8,1,2:32:21 PM,8,1,2:58:51 PM,9,29.4,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_166,1590
-Order_No_27761,User_Id_3188,Bike,1,Personal,2,4,11:59:59 AM,2,4,12:00:25 PM,2,4,12:03:50 PM,2,4,12:16:44 PM,2,4,12:33:23 PM,7,22.5,,-1.2985153,36.7876043,-1.2729652,36.8064396,Rider_Id_726,999
-Order_No_23167,User_Id_3606,Bike,3,Business,5,2,3:48:04 PM,5,2,3:48:49 PM,5,2,3:57:49 PM,5,2,4:02:20 PM,5,2,4:22:20 PM,2,,,-1.2628638,36.8070399,-1.2733047,36.8109744,Rider_Id_318,1200
-Order_No_17026,User_Id_2701,Bike,3,Business,16,3,10:30:16 AM,16,3,10:31:09 AM,16,3,10:57:14 AM,16,3,10:59:32 AM,16,3,11:17:32 AM,6,22.6,,-1.2765736,36.851364600000004,-1.2573382,36.877362,Rider_Id_530,1080
-Order_No_12660,User_Id_1038,Bike,1,Business,10,2,8:28:36 AM,10,2,8:29:02 AM,10,2,8:41:44 AM,10,2,8:42:52 AM,10,2,9:24:02 AM,16,16.5,,-1.3099145,36.802277399999994,-1.3123977,36.902807,Rider_Id_45,2470
-Order_No_3906,User_Id_3701,Bike,3,Business,13,1,11:26:13 AM,13,1,11:26:25 AM,13,1,11:31:10 AM,13,1,11:32:36 AM,13,1,11:54:31 AM,5,,,-1.2612573999999999,36.7879622,-1.2519787,36.780649100000005,Rider_Id_521,1315
-Order_No_13242,User_Id_1248,Bike,3,Personal,3,5,1:58:31 PM,3,5,1:59:12 PM,3,5,2:11:05 PM,3,5,2:14:13 PM,3,5,2:35:36 PM,9,25.1,,-1.288086,36.821115999999996,-1.3061513999999999,36.8612054,Rider_Id_754,1283
-Order_No_22741,User_Id_1026,Bike,2,Personal,4,4,5:01:28 PM,4,4,5:01:45 PM,4,4,5:05:00 PM,4,4,5:16:04 PM,4,4,5:32:13 PM,5,,,-1.312942,36.8152651,-1.2916556000000001,36.8262516,Rider_Id_942,969
-Order_No_24988,User_Id_2154,Bike,3,Business,28,3,11:58:53 AM,28,3,12:00:23 PM,28,3,12:22:14 PM,28,3,12:27:54 PM,28,3,12:50:29 PM,8,,,-1.2584143,36.8048002,-1.2765736,36.851364600000004,Rider_Id_674,1355
-Order_No_4544,User_Id_2523,Bike,3,Business,29,2,2:38:36 PM,29,2,2:38:56 PM,29,2,2:39:48 PM,29,2,2:48:20 PM,29,2,3:05:40 PM,8,27.0,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_698,1040
-Order_No_1278,User_Id_2330,Bike,3,Business,15,2,3:02:49 PM,15,2,3:04:48 PM,15,2,3:05:35 PM,15,2,3:08:54 PM,15,2,3:34:06 PM,5,24.8,,-1.2890715,36.8157588,-1.2637056999999998,36.805170000000004,Rider_Id_247,1512
-Order_No_23466,User_Id_635,Bike,3,Business,14,3,10:38:25 AM,14,3,10:40:15 AM,14,3,10:49:09 AM,14,3,11:20:10 AM,14,3,11:32:53 AM,5,24.5,,-1.300921,36.828195,-1.3218128,36.86190381,Rider_Id_858,763
-Order_No_12566,User_Id_1363,Bike,3,Business,1,1,11:28:38 AM,1,1,11:41:58 AM,1,1,11:56:22 AM,1,1,11:58:56 AM,1,1,12:18:04 PM,9,,,-1.3004062,36.829741,-1.2551895,36.7822034,Rider_Id_763,1148
-Order_No_2822,User_Id_346,Bike,3,Business,19,2,1:42:26 PM,19,2,2:03:43 PM,19,2,2:37:40 PM,19,2,2:55:18 PM,19,2,3:31:56 PM,14,30.1,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_377,2198
-Order_No_5561,User_Id_475,Bike,3,Business,21,4,12:20:20 PM,21,4,12:26:35 PM,21,4,12:26:39 PM,21,4,12:37:03 PM,21,4,1:09:24 PM,8,25.0,,-1.2551895,36.7822034,-1.3024794,36.8228284,Rider_Id_86,1941
-Order_No_22034,User_Id_1370,Bike,3,Personal,27,1,2:05:33 PM,27,1,2:06:15 PM,27,1,3:02:30 PM,27,1,3:05:09 PM,27,1,3:29:50 PM,8,22.9,,-1.261692,36.80161,-1.2804664,36.7701111,Rider_Id_139,1481
-Order_No_13656,User_Id_393,Bike,3,Business,25,4,8:17:57 AM,25,4,8:18:35 AM,25,4,8:18:45 AM,25,4,9:29:21 AM,25,4,10:09:35 AM,14,16.5,2.0,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_608,2414
-Order_No_8925,User_Id_1075,Bike,3,Business,4,2,9:45:31 AM,4,2,11:25:32 AM,4,2,11:44:08 AM,4,2,11:51:19 AM,4,2,12:11:06 PM,6,20.6,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_953,1187
-Order_No_17451,User_Id_432,Bike,3,Business,2,4,12:16:02 PM,2,4,12:21:04 PM,2,4,12:32:49 PM,2,4,12:35:49 PM,2,4,12:56:47 PM,9,,,-1.3012008,36.764868,-1.2813012,36.832396200000005,Rider_Id_250,1258
-Order_No_28185,User_Id_3647,Bike,3,Business,10,3,3:54:42 PM,10,3,3:54:52 PM,10,3,4:06:02 PM,10,3,4:08:48 PM,10,3,4:29:51 PM,8,30.6,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_92,1263
-Order_No_23318,User_Id_60,Bike,3,Business,6,4,4:35:40 PM,6,4,4:35:58 PM,6,4,4:41:00 PM,6,4,4:47:55 PM,6,4,5:14:51 PM,8,21.9,,-1.2663631000000002,36.8163329,-1.2296618000000001,36.84382720000001,Rider_Id_76,1616
-Order_No_18703,User_Id_3217,Bike,3,Business,14,4,10:20:53 AM,14,4,10:46:31 AM,14,4,10:46:52 AM,14,4,11:10:39 AM,14,4,11:31:04 AM,12,19.2,,-1.2644742,36.808345700000004,-1.214328,36.7911903,Rider_Id_354,1225
-Order_No_23018,User_Id_302,Bike,3,Business,1,4,6:54:54 PM,1,4,6:56:22 PM,1,4,7:42:21 PM,1,4,7:46:31 PM,1,4,7:55:45 PM,4,19.3,,-1.2649605,36.798177700000004,-1.262896,36.8231727,Rider_Id_4,554
-Order_No_22344,User_Id_3282,Bike,2,Personal,30,1,12:11:15 PM,30,1,12:11:33 PM,30,1,12:31:42 PM,30,1,12:36:34 PM,30,1,12:54:13 PM,8,22.8,,-1.2864673999999998,36.820262,-1.290059,36.7703475,Rider_Id_414,1059
-Order_No_14004,User_Id_635,Bike,3,Business,26,6,11:08:12 AM,26,6,11:11:26 AM,26,6,11:19:41 AM,26,6,11:45:00 AM,26,6,11:58:51 AM,8,26.0,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_842,831
-Order_No_10699,User_Id_816,Bike,3,Business,25,3,9:50:20 AM,25,3,9:51:15 AM,25,3,10:04:18 AM,25,3,10:09:20 AM,25,3,10:52:42 AM,7,13.7,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_338,2602
-Order_No_1347,User_Id_111,Bike,3,Business,14,1,9:50:17 AM,14,1,9:52:19 AM,14,1,10:07:58 AM,14,1,10:09:56 AM,14,1,10:43:03 AM,12,18.7,0.2,-1.2559559999999999,36.789867,-1.3093293,36.8499354,Rider_Id_770,1987
-Order_No_22634,User_Id_1195,Bike,3,Business,27,2,11:44:31 AM,27,2,11:44:57 AM,27,2,11:57:43 AM,27,2,12:06:05 PM,27,2,12:30:22 PM,15,23.8,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_240,1457
-Order_No_16618,User_Id_1962,Bike,1,Personal,28,2,12:23:28 PM,28,2,12:23:56 PM,28,2,12:29:17 PM,28,2,12:34:21 PM,28,2,1:05:35 PM,15,17.4,,-1.2287313999999998,36.8367354,-1.2959768999999999,36.7853374,Rider_Id_88,1874
-Order_No_15658,User_Id_1209,Bike,3,Business,6,1,10:57:36 AM,6,1,10:58:11 AM,6,1,11:32:11 AM,6,1,11:36:26 AM,6,1,11:50:30 AM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_790,844
-Order_No_12476,User_Id_3683,Bike,3,Business,19,5,11:04:26 AM,19,5,11:05:02 AM,19,5,11:16:58 AM,19,5,11:19:07 AM,19,5,12:00:50 PM,7,24.7,,-1.2897368,36.8226783,-1.2765492,36.7669809,Rider_Id_829,2503
-Order_No_17647,User_Id_53,Bike,3,Business,22,3,3:10:09 PM,22,3,3:59:41 PM,22,3,4:23:45 PM,22,3,4:48:27 PM,22,3,5:10:00 PM,5,24.0,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_422,1293
-Order_No_18266,User_Id_1865,Bike,1,Personal,31,5,5:42:15 PM,31,5,5:48:24 PM,31,5,6:01:58 PM,31,5,6:08:03 PM,31,5,6:33:25 PM,14,20.0,,-1.2166078000000002,36.882501,-1.2782978,36.827323799999995,Rider_Id_629,1522
-Order_No_5964,User_Id_704,Bike,3,Business,8,5,2:26:20 PM,8,5,3:25:01 PM,8,5,3:50:33 PM,8,5,3:57:44 PM,8,5,4:25:39 PM,13,21.6,,-1.2860183,36.8975338,-1.3332749,36.870814700000004,Rider_Id_900,1675
-Order_No_565,User_Id_1250,Bike,1,Personal,21,1,2:51:39 PM,21,1,2:54:45 PM,21,1,3:17:32 PM,21,1,3:22:10 PM,21,1,3:45:33 PM,9,21.4,,-1.2743743,36.7657321,-1.2620741000000002,36.815814700000004,Rider_Id_320,1403
-Order_No_13083,User_Id_2978,Bike,3,Business,24,5,8:48:05 AM,24,5,8:48:11 AM,24,5,9:08:27 AM,24,5,9:12:26 AM,24,5,9:43:51 AM,22,15.8,,-1.2551895,36.7822034,-1.3076278000000001,36.9158578,Rider_Id_865,1885
-Order_No_6471,User_Id_2532,Bike,3,Business,5,3,10:40:47 AM,5,3,10:55:13 AM,5,3,11:03:37 AM,5,3,11:06:29 AM,5,3,11:23:23 AM,8,22.3,,-1.2551895,36.7822034,-1.2950408000000002,36.809817200000005,Rider_Id_550,1014
-Order_No_27843,User_Id_3108,Bike,3,Business,7,3,9:31:17 AM,7,3,9:35:19 AM,7,3,9:45:58 AM,7,3,9:50:53 AM,7,3,10:16:36 AM,13,,,-1.2551895,36.7822034,-1.2917771,36.857582,Rider_Id_932,1543
-Order_No_9380,User_Id_1953,Bike,3,Personal,15,2,9:30:08 AM,15,2,9:47:31 AM,15,2,10:09:12 AM,15,2,10:23:25 AM,15,2,10:52:33 AM,9,18.2,1.9,-1.3332749,36.870814700000004,-1.2802576,36.8246917,Rider_Id_100,1748
-Order_No_22321,User_Id_53,Bike,3,Business,27,6,12:07:19 PM,27,6,12:08:10 PM,27,6,12:27:07 PM,27,6,12:42:59 PM,27,6,12:58:45 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_790,946
-Order_No_21427,User_Id_2982,Bike,3,Business,12,5,12:19:00 PM,12,5,12:19:09 PM,12,5,12:41:53 PM,12,5,1:00:59 PM,12,5,1:51:37 PM,22,24.4,,-1.300921,36.828195,-1.1917753,36.9211989,Rider_Id_581,3038
-Order_No_12550,User_Id_3231,Bike,2,Personal,8,3,2:14:25 PM,8,3,2:16:06 PM,8,3,2:30:59 PM,8,3,2:40:37 PM,8,3,3:06:55 PM,6,26.0,,-1.2858016,36.8226814,-1.2817856,36.788100899999996,Rider_Id_622,1578
-Order_No_16157,User_Id_2279,Bike,3,Business,15,5,2:02:41 PM,15,5,2:06:36 PM,15,5,2:56:25 PM,15,5,2:57:47 PM,15,5,3:03:00 PM,4,28.1,,-1.2897368,36.8226783,-1.2627937,36.815695700000006,Rider_Id_614,313
-Order_No_5791,User_Id_3201,Bike,3,Business,20,2,3:44:50 PM,20,2,3:45:00 PM,20,2,3:50:37 PM,20,2,3:57:07 PM,20,2,4:12:18 PM,5,24.2,,-1.2584143,36.8048002,-1.2639639999999999,36.7854632,Rider_Id_79,911
-Order_No_6715,User_Id_1959,Bike,3,Business,11,1,12:34:52 PM,11,1,12:35:20 PM,11,1,12:40:34 PM,11,1,12:42:54 PM,11,1,1:11:47 PM,15,26.8,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_411,1733
-Order_No_17380,User_Id_186,Bike,3,Business,17,3,8:51:03 AM,17,3,8:52:13 AM,17,3,9:09:40 AM,17,3,9:15:38 AM,17,3,9:28:57 AM,6,21.1,,-1.2890399,36.8164942,-1.2571472,36.795063299999995,Rider_Id_85,799
-Order_No_27907,User_Id_2423,Bike,3,Business,14,4,1:27:17 PM,14,4,1:33:03 PM,14,4,1:33:35 PM,14,4,1:43:22 PM,14,4,1:44:00 PM,2,28.0,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_446,38
-Order_No_20392,User_Id_2834,Bike,3,Business,8,3,3:21:25 PM,8,3,3:28:18 PM,8,3,3:54:27 PM,8,3,3:58:26 PM,8,3,5:00:36 PM,12,,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_422,3730
-Order_No_14755,User_Id_1245,Bike,3,Business,19,3,12:43:23 PM,19,3,12:43:34 PM,19,3,12:49:00 PM,19,3,12:55:47 PM,19,3,1:53:54 PM,22,22.1,,-1.300921,36.828195,-1.300793,36.6871435,Rider_Id_674,3487
-Order_No_5499,User_Id_635,Bike,3,Business,4,1,1:50:23 PM,4,1,2:55:10 PM,4,1,3:03:20 PM,4,1,3:15:05 PM,4,1,3:38:11 PM,7,,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_332,1386
-Order_No_13644,User_Id_3326,Bike,3,Business,4,2,10:49:27 AM,4,2,10:50:39 AM,4,2,10:57:58 AM,4,2,11:14:34 AM,4,2,11:28:35 AM,7,20.5,,-1.2571472,36.795063299999995,-1.2932015,36.828491799999995,Rider_Id_921,841
-Order_No_27235,User_Id_134,Bike,3,Business,28,4,3:10:47 PM,28,4,3:11:23 PM,28,4,3:20:47 PM,28,4,3:25:07 PM,28,4,3:37:35 PM,5,30.1,,-1.2609745,36.7886626,-1.2743958,36.8215915,Rider_Id_785,748
-Order_No_1569,User_Id_551,Bike,2,Personal,3,4,9:16:56 AM,3,4,9:27:31 AM,3,4,9:48:37 AM,3,4,9:55:10 AM,3,4,10:13:02 AM,7,17.4,16.0,-1.3191045,36.794662100000004,-1.2977386999999998,36.803144700000004,Rider_Id_543,1072
-Order_No_6252,User_Id_391,Bike,3,Business,22,3,1:21:13 PM,22,3,1:47:37 PM,22,3,2:17:53 PM,22,3,2:27:59 PM,22,3,2:43:20 PM,5,24.2,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_921,921
-Order_No_5876,User_Id_2523,Bike,3,Business,14,4,3:12:56 PM,14,4,3:40:50 PM,14,4,3:53:55 PM,14,4,3:59:39 PM,14,4,4:42:28 PM,23,27.8,,-1.2551895,36.7822034,-1.343439,36.912065000000005,Rider_Id_862,2569
-Order_No_16872,User_Id_2660,Bike,2,Personal,13,4,6:18:14 PM,13,4,6:19:21 PM,13,4,6:45:30 PM,13,4,6:47:11 PM,13,4,7:07:19 PM,8,24.6,,-1.2596351000000001,36.8063205,-1.3175119,36.8058513,Rider_Id_328,1208
-Order_No_16597,User_Id_3149,Bike,3,Business,16,4,11:52:08 AM,16,4,11:52:46 AM,16,4,11:55:11 AM,16,4,12:18:00 PM,16,4,12:26:39 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_953,519
-Order_No_5387,User_Id_582,Bike,1,Personal,6,6,12:29:43 PM,6,6,12:29:59 PM,6,6,12:38:21 PM,6,6,12:49:10 PM,6,6,1:18:05 PM,9,24.2,,-1.2709035,36.8168523,-1.299259,36.763364,Rider_Id_263,1735
-Order_No_26296,User_Id_2200,Bike,3,Business,27,1,12:43:51 PM,27,1,12:51:02 PM,27,1,1:02:39 PM,27,1,1:09:39 PM,27,1,1:37:53 PM,9,24.0,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_414,1694
-Order_No_12255,User_Id_3149,Bike,3,Business,31,5,3:45:00 PM,31,5,3:58:01 PM,31,5,4:12:33 PM,31,5,4:23:49 PM,31,5,4:41:36 PM,6,24.7,,-1.3083266000000002,36.8494756,-1.3228003000000002,36.8306435,Rider_Id_178,1067
-Order_No_59,User_Id_633,Bike,3,Business,14,4,12:20:59 PM,14,4,12:33:53 PM,14,4,12:52:33 PM,14,4,12:56:20 PM,14,4,1:11:01 PM,5,23.9,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_339,881
-Order_No_23251,User_Id_265,Bike,3,Business,24,3,8:32:49 AM,24,3,8:33:19 AM,24,3,8:52:28 AM,24,3,9:08:07 AM,24,3,9:25:57 AM,7,,,-1.2960265,36.8780276,-1.300921,36.828195,Rider_Id_263,1070
-Order_No_4045,User_Id_635,Bike,3,Business,31,5,8:43:22 AM,31,5,8:43:54 AM,31,5,8:55:07 AM,31,5,9:10:40 AM,31,5,9:32:09 AM,7,,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_582,1289
-Order_No_701,User_Id_3381,Bike,3,Business,31,3,10:13:14 AM,31,3,11:26:10 AM,31,3,11:26:15 AM,31,3,11:35:25 AM,31,3,11:53:45 AM,5,24.6,,-1.3172551000000001,36.8208701,-1.279662,36.814902000000004,Rider_Id_456,1100
-Order_No_19122,User_Id_2801,Bike,3,Business,8,1,12:30:30 PM,8,1,12:31:25 PM,8,1,12:47:21 PM,8,1,12:57:46 PM,8,1,1:38:05 PM,16,24.4,,-1.3301235,36.7070434,-1.2595421999999998,36.7871178,Rider_Id_565,2419
-Order_No_19502,User_Id_1113,Bike,1,Personal,13,6,2:41:07 PM,13,6,2:42:04 PM,13,6,2:43:15 PM,13,6,3:05:02 PM,13,6,3:22:41 PM,13,27.9,,-1.263987,36.798891,-1.2156006000000001,36.8916865,Rider_Id_100,1059
-Order_No_11474,User_Id_970,Bike,3,Business,11,1,11:16:52 AM,11,1,11:26:28 AM,11,1,11:38:34 AM,11,1,11:43:38 AM,11,1,11:59:44 AM,6,24.1,,-1.2551895,36.7822034,-1.2904753,36.8144721,Rider_Id_336,966
-Order_No_11563,User_Id_3291,Bike,3,Business,17,3,11:05:25 AM,17,3,11:06:54 AM,17,3,11:07:51 AM,17,3,11:29:48 AM,17,3,11:44:53 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_245,905
-Order_No_14785,User_Id_393,Bike,3,Business,26,2,8:03:14 AM,26,2,8:03:57 AM,26,2,8:04:02 AM,26,2,8:11:26 AM,26,2,8:28:06 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_809,1000
-Order_No_8267,User_Id_391,Bike,3,Business,8,1,12:57:15 PM,8,1,12:57:43 PM,8,1,1:04:25 PM,8,1,1:31:51 PM,8,1,2:07:05 PM,16,24.4,,-1.300921,36.828195,-1.3540391999999999,36.9078543,Rider_Id_890,2114
-Order_No_4875,User_Id_3647,Bike,3,Business,29,2,2:59:21 PM,29,2,3:07:12 PM,29,2,3:18:51 PM,29,2,3:23:37 PM,29,2,3:40:00 PM,6,27.0,,-1.2726389999999999,36.794723,-1.2979383,36.7930618,Rider_Id_829,983
-Order_No_5047,User_Id_496,Bike,3,Business,7,2,3:03:02 PM,7,2,3:03:25 PM,7,2,3:03:33 PM,7,2,3:10:27 PM,7,2,3:26:21 PM,3,,,-1.2551895,36.7822034,-1.273488,36.804158,Rider_Id_856,954
-Order_No_10643,User_Id_635,Bike,3,Business,24,4,3:56:11 PM,24,4,3:57:18 PM,24,4,4:14:18 PM,24,4,4:30:09 PM,24,4,4:55:31 PM,8,28.5,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_399,1522
-Order_No_16296,User_Id_391,Bike,3,Business,25,4,10:45:34 AM,25,4,11:36:13 AM,25,4,11:54:43 AM,25,4,12:07:39 PM,25,4,12:24:44 PM,5,20.0,,-1.3016417,36.8271679,-1.2867454,36.8259017,Rider_Id_477,1025
-Order_No_20943,User_Id_1075,Bike,3,Business,28,4,11:56:34 AM,28,4,11:57:22 AM,28,4,12:14:25 PM,28,4,12:36:43 PM,28,4,12:36:50 PM,3,28.2,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_658,7
-Order_No_24482,User_Id_868,Bike,3,Business,9,3,11:33:51 AM,9,3,11:35:33 AM,9,3,11:53:09 AM,9,3,12:11:40 PM,9,3,12:20:49 PM,6,25.3,,-1.265715,36.823815,-1.2628473,36.781805,Rider_Id_878,549
-Order_No_5239,User_Id_2457,Bike,3,Business,18,2,10:35:54 AM,18,2,10:51:47 AM,18,2,10:53:48 AM,18,2,11:00:01 AM,18,2,11:26:15 AM,7,,,-1.2551895,36.7822034,-1.2891116000000002,36.810438,Rider_Id_518,1574
-Order_No_3067,User_Id_3599,Bike,3,Business,13,4,2:47:25 PM,13,4,2:47:47 PM,13,4,2:53:15 PM,13,4,3:35:48 PM,13,4,4:00:18 PM,14,22.9,,-1.3302996,36.8707935,-1.2485879,36.8018025,Rider_Id_891,1470
-Order_No_2105,User_Id_296,Bike,1,Personal,9,1,12:08:48 PM,9,1,12:09:26 PM,9,1,12:13:49 PM,9,1,12:19:41 PM,9,1,12:47:40 PM,7,20.1,,-1.2772632,36.792225099999996,-1.2962731,36.811186299999996,Rider_Id_334,1679
-Order_No_5407,User_Id_706,Bike,3,Business,25,5,3:22:21 PM,25,5,3:22:45 PM,25,5,3:31:24 PM,25,5,3:34:59 PM,25,5,4:12:08 PM,15,29.4,,-1.3077869,36.844320700000004,-1.3559093,36.910045200000006,Rider_Id_436,2229
-Order_No_4991,User_Id_718,Bike,3,Business,10,5,10:56:20 AM,10,5,11:07:34 AM,10,5,11:17:21 AM,10,5,11:24:18 AM,10,5,12:04:15 PM,8,,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_591,2397
-Order_No_1456,User_Id_3283,Bike,3,Business,22,5,4:12:35 PM,22,5,4:13:03 PM,22,5,4:18:33 PM,22,5,4:25:10 PM,22,5,4:44:04 PM,7,27.5,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_210,1134
-Order_No_27924,User_Id_3521,Bike,3,Business,11,4,12:36:25 PM,11,4,12:37:51 PM,11,4,12:43:33 PM,11,4,12:55:50 PM,11,4,1:06:58 PM,5,25.8,,-1.3179056,36.8393819,-1.2874648000000002,36.8271985,Rider_Id_438,668
-Order_No_22371,User_Id_3557,Bike,3,Business,4,2,4:06:20 PM,4,2,4:17:35 PM,4,2,4:52:31 PM,4,2,5:18:12 PM,4,2,5:18:14 PM,9,22.3,,-1.2876782,36.7605992,-1.2506993,36.808966600000005,Rider_Id_652,2
-Order_No_25936,User_Id_1725,Bike,3,Business,24,4,5:09:16 PM,24,4,5:24:50 PM,24,4,6:10:56 PM,24,4,6:19:30 PM,24,4,6:49:32 PM,16,21.3,,-1.1867518999999997,36.8369389,-1.2680709,36.7929823,Rider_Id_260,1802
-Order_No_14083,User_Id_2763,Bike,3,Business,22,1,2:47:57 PM,22,1,3:06:35 PM,22,1,3:13:11 PM,22,1,3:17:00 PM,22,1,3:34:38 PM,6,26.1,,-1.2849882,36.823155,-1.2571472,36.795063299999995,Rider_Id_662,1058
-Order_No_18660,User_Id_3560,Bike,3,Business,7,1,11:29:28 AM,7,1,11:30:40 AM,7,1,11:30:42 AM,7,1,11:49:18 AM,7,1,11:58:30 AM,1,24.9,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_268,552
-Order_No_8915,User_Id_3194,Bike,3,Business,21,4,10:41:47 AM,21,4,10:42:25 AM,21,4,10:51:07 AM,21,4,10:56:42 AM,21,4,11:39:23 AM,16,27.8,,-1.2551895,36.7822034,-1.3356493,36.892766200000004,Rider_Id_784,2561
-Order_No_8496,User_Id_393,Bike,3,Business,6,3,10:32:24 AM,6,3,10:32:41 AM,6,3,10:32:52 AM,6,3,11:18:19 AM,6,3,11:18:34 AM,4,22.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,15
-Order_No_14693,User_Id_788,Bike,3,Business,5,2,12:09:16 PM,5,2,12:09:36 PM,5,2,12:19:04 PM,5,2,12:21:53 PM,5,2,1:01:30 PM,15,23.8,,-1.2232123,36.8932547,-1.225322,36.80855,Rider_Id_691,2377
-Order_No_24940,User_Id_2691,Bike,3,Business,27,2,10:15:02 AM,27,2,10:23:19 AM,27,2,10:38:31 AM,27,2,10:46:41 AM,27,2,11:21:31 AM,8,23.7,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_164,2090
-Order_No_13073,User_Id_2030,Bike,3,Business,18,2,2:18:44 PM,18,2,2:19:06 PM,18,2,2:19:16 PM,18,2,2:21:40 PM,18,2,2:45:29 PM,3,25.4,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_220,1429
-Order_No_21214,User_Id_3494,Bike,1,Personal,7,4,10:27:17 AM,7,4,11:02:06 AM,7,4,11:02:41 AM,7,4,11:10:22 AM,7,4,11:25:33 AM,7,17.0,,-1.2733223,36.795100500000004,-1.2935303,36.7988142,Rider_Id_216,911
-Order_No_15495,User_Id_3091,Bike,3,Business,22,4,3:54:21 PM,22,4,3:58:07 PM,22,4,4:03:18 PM,22,4,4:12:54 PM,22,4,4:39:26 PM,9,26.6,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_685,1592
-Order_No_20435,User_Id_3517,Bike,3,Business,7,4,9:20:30 AM,7,4,10:07:32 AM,7,4,10:26:10 AM,7,4,10:40:11 AM,7,4,11:05:16 AM,16,27.7,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_885,1505
-Order_No_697,User_Id_704,Bike,3,Business,31,5,12:43:50 PM,31,5,12:54:20 PM,31,5,1:12:17 PM,31,5,1:21:39 PM,31,5,1:41:40 PM,9,21.7,,-1.2413316,36.878552500000005,-1.2860183,36.8975338,Rider_Id_199,1201
-Order_No_20756,User_Id_136,Bike,3,Business,26,4,3:08:13 PM,26,4,3:08:42 PM,26,4,3:47:51 PM,26,4,3:58:48 PM,26,4,4:23:16 PM,8,22.0,,-1.3014461000000002,36.7661381,-1.3326173000000001,36.71462329999999,Rider_Id_229,1468
-Order_No_23697,User_Id_3647,Bike,3,Business,6,4,4:13:34 PM,6,4,4:21:22 PM,6,4,4:22:09 PM,6,4,5:06:38 PM,6,4,5:06:42 PM,9,21.6,,-1.2726389999999999,36.794723,-1.235348,36.760016,Rider_Id_855,4
-Order_No_22458,User_Id_1146,Bike,3,Business,6,3,9:55:14 AM,6,3,9:56:52 AM,6,3,10:08:44 AM,6,3,10:29:02 AM,6,3,10:42:06 AM,5,16.6,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_581,784
-Order_No_9879,User_Id_1882,Bike,3,Business,27,6,9:35:57 AM,27,6,9:36:18 AM,27,6,10:28:37 AM,27,6,10:45:46 AM,27,6,11:06:51 AM,14,,,-1.2156006000000001,36.8916865,-1.2591018999999999,36.800577000000004,Rider_Id_19,1265
-Order_No_6334,User_Id_635,Bike,3,Business,7,4,9:57:14 AM,7,4,9:57:37 AM,7,4,10:20:55 AM,7,4,10:39:44 AM,7,4,11:13:45 AM,7,27.7,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_459,2041
-Order_No_18334,User_Id_1271,Bike,3,Business,6,5,1:00:51 PM,6,5,1:01:22 PM,6,5,1:14:22 PM,6,5,1:23:35 PM,6,5,1:58:23 PM,16,20.2,,-1.3205669,36.836216799999995,-1.2598589,36.7470168,Rider_Id_791,2088
-Order_No_21539,User_Id_1500,Bike,3,Business,4,5,11:09:52 AM,4,5,11:17:45 AM,4,5,11:29:05 AM,4,5,11:56:46 AM,4,5,12:10:52 PM,8,17.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_555,846
-Order_No_6478,User_Id_3270,Bike,1,Personal,10,1,11:03:19 AM,10,1,11:19:29 AM,10,1,12:12:09 PM,10,1,12:17:53 PM,10,1,1:34:25 PM,15,,,-1.2350906000000001,36.705332899999995,-1.2738387,36.8179279,Rider_Id_580,4592
-Order_No_15074,User_Id_1959,Bike,3,Business,25,1,1:50:30 PM,25,1,2:00:12 PM,25,1,2:17:55 PM,25,1,2:19:10 PM,25,1,2:41:17 PM,20,30.1,,-1.2095151000000002,36.916994,-1.2551895,36.7822034,Rider_Id_206,1327
-Order_No_4940,User_Id_302,Bike,3,Business,16,2,7:36:31 PM,16,2,7:37:10 PM,16,2,7:43:33 PM,16,2,7:48:51 PM,16,2,8:00:23 PM,4,21.7,,-1.2649605,36.798177700000004,-1.2821569,36.7984426,Rider_Id_299,692
-Order_No_9448,User_Id_2200,Bike,3,Business,4,1,4:08:48 PM,4,1,4:20:03 PM,4,1,4:32:44 PM,4,1,4:41:04 PM,4,1,5:01:09 PM,7,27.4,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_953,1205
-Order_No_14766,User_Id_970,Bike,3,Business,17,1,12:41:35 PM,17,1,12:50:36 PM,17,1,1:16:11 PM,17,1,1:17:37 PM,17,1,1:55:45 PM,9,23.9,,-1.2551895,36.7822034,-1.3041128999999998,36.810493900000004,Rider_Id_847,2288
-Order_No_18698,User_Id_868,Bike,3,Business,15,2,1:04:42 PM,15,2,1:05:12 PM,15,2,1:10:39 PM,15,2,1:15:31 PM,15,2,1:24:31 PM,4,24.2,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_279,540
-Order_No_13469,User_Id_3435,Bike,3,Business,4,4,2:39:46 PM,4,4,2:49:32 PM,4,4,3:05:52 PM,4,4,3:09:46 PM,4,4,3:29:56 PM,7,27.2,,-1.3008826,36.8378114,-1.2696341,36.8042045,Rider_Id_907,1210
-Order_No_1541,User_Id_3525,Bike,3,Business,8,3,1:18:01 PM,8,3,1:36:48 PM,8,3,1:57:45 PM,8,3,2:35:20 PM,8,3,3:24:30 PM,18,26.0,,-1.2099573000000001,36.885975200000004,-1.2571472,36.795063299999995,Rider_Id_384,2950
-Order_No_17260,User_Id_393,Bike,3,Business,8,2,11:35:25 AM,8,2,11:35:35 AM,8,2,11:35:44 AM,8,2,12:36:23 PM,8,2,12:36:26 PM,11,23.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_10,3
-Order_No_27437,User_Id_2767,Bike,3,Business,10,6,8:17:39 AM,10,6,8:37:32 AM,10,6,9:03:06 AM,10,6,9:56:24 AM,10,6,10:24:16 AM,10,20.9,,-1.3079928,36.8389933,-1.257773,36.795486,Rider_Id_646,1672
-Order_No_3103,User_Id_3291,Bike,3,Business,30,4,12:06:48 PM,30,4,12:07:36 PM,30,4,12:17:35 PM,30,4,12:25:41 PM,30,4,12:41:12 PM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_245,931
-Order_No_18829,User_Id_2132,Bike,3,Business,18,1,1:21:46 PM,18,1,1:22:17 PM,18,1,1:40:09 PM,18,1,1:57:52 PM,18,1,2:29:06 PM,14,29.6,,-1.2384056,36.8718698,-1.3376052,36.901917,Rider_Id_691,1874
-Order_No_19625,User_Id_510,Bike,3,Business,31,5,11:28:41 AM,31,5,11:29:49 AM,31,5,11:48:16 AM,31,5,11:53:00 AM,31,5,12:25:43 PM,18,21.5,,-1.2860183,36.8975338,-1.2629594,36.8077626,Rider_Id_377,1963
-Order_No_19595,User_Id_393,Bike,3,Business,15,3,8:39:42 AM,15,3,8:40:13 AM,15,3,8:44:53 AM,15,3,9:03:22 AM,15,3,9:35:59 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_263,1957
-Order_No_8150,User_Id_2066,Bike,3,Business,24,3,12:38:19 PM,24,3,12:39:39 PM,24,3,12:55:44 PM,24,3,12:58:08 PM,24,3,1:28:22 PM,13,,,-1.3270716,36.8406371,-1.3050802,36.7540383,Rider_Id_663,1814
-Order_No_9601,User_Id_2190,Bike,3,Business,8,1,2:36:11 PM,8,1,2:37:52 PM,8,1,3:12:42 PM,8,1,3:22:00 PM,8,1,3:40:20 PM,8,29.8,,-1.3019530000000001,36.788458,-1.2649605,36.798177700000004,Rider_Id_686,1100
-Order_No_25001,User_Id_136,Bike,3,Business,29,2,9:10:24 AM,29,2,9:10:46 AM,29,2,9:10:53 AM,29,2,9:16:13 AM,29,2,9:18:17 AM,1,18.3,,-1.2654716000000001,36.7983635,-1.2531983,36.8069669,Rider_Id_793,124
-Order_No_12500,User_Id_970,Bike,3,Business,2,2,3:20:21 PM,2,2,3:20:39 PM,2,2,3:29:02 PM,2,2,3:32:34 PM,2,2,3:54:59 PM,8,26.0,,-1.2551895,36.7822034,-1.2925919,36.81705770000001,Rider_Id_43,1345
-Order_No_26751,User_Id_3108,Bike,3,Business,16,2,2:48:31 PM,16,2,2:50:00 PM,16,2,2:57:35 PM,16,2,3:12:49 PM,16,2,3:37:26 PM,16,27.4,,-1.2551895,36.7822034,-1.3167553,36.869485,Rider_Id_116,1477
-Order_No_15563,User_Id_776,Bike,1,Personal,1,5,3:17:00 PM,1,5,3:29:10 PM,1,5,3:29:26 PM,1,5,3:42:31 PM,1,5,3:58:14 PM,6,24.2,,-1.278967,36.8236745,-1.2796952,36.7829895,Rider_Id_805,943
-Order_No_12469,User_Id_186,Bike,3,Business,18,4,10:41:23 AM,18,4,10:42:01 AM,18,4,10:44:03 AM,18,4,10:52:06 AM,18,4,11:45:35 AM,24,25.7,,-1.2571472,36.795063299999995,-1.3611971,36.6538278,Rider_Id_215,3209
-Order_No_4930,User_Id_1850,Bike,3,Business,2,4,4:15:41 PM,2,4,4:18:23 PM,2,4,4:29:57 PM,2,4,4:35:52 PM,2,4,5:18:52 PM,8,,,-1.2551895,36.7822034,-1.3027475,36.8178196,Rider_Id_815,2580
-Order_No_8252,User_Id_1397,Bike,3,Business,12,2,3:43:28 PM,12,2,3:55:01 PM,12,2,4:16:16 PM,12,2,4:18:24 PM,12,2,5:00:57 PM,13,26.9,,-1.2616841,36.801999,-1.2630542,36.708175399999995,Rider_Id_862,2553
-Order_No_12316,User_Id_2784,Bike,3,Business,11,1,2:00:37 PM,11,1,2:09:34 PM,11,1,2:34:56 PM,11,1,2:58:26 PM,11,1,3:25:33 PM,8,28.7,,-1.277551,36.8273641,-1.2963097,36.7688221,Rider_Id_448,1627
-Order_No_12710,User_Id_47,Bike,1,Personal,17,3,1:26:01 PM,17,3,1:27:25 PM,17,3,1:42:26 PM,17,3,1:51:34 PM,17,3,2:31:19 PM,9,28.6,,-1.2758945,36.7648661,-1.2546418,36.7994074,Rider_Id_299,2385
-Order_No_11490,User_Id_1329,Bike,3,Business,27,2,4:14:08 PM,27,2,4:15:48 PM,27,2,4:21:06 PM,27,2,4:30:41 PM,27,2,4:40:23 PM,6,25.5,,-1.273056,36.811298,-1.2668264999999999,36.7680484,Rider_Id_439,582
-Order_No_5544,User_Id_150,Bike,3,Business,4,3,11:56:45 AM,4,3,11:57:42 AM,4,3,12:02:21 PM,4,3,12:15:39 PM,4,3,12:47:49 PM,8,17.1,,-1.3039695,36.783826700000006,-1.2661263999999999,36.8030359,Rider_Id_917,1930
-Order_No_21079,User_Id_868,Bike,3,Business,24,3,10:22:46 AM,24,3,10:23:00 AM,24,3,10:29:19 AM,24,3,10:46:41 AM,24,3,10:46:46 AM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547,5
-Order_No_5398,User_Id_2779,Bike,1,Personal,11,3,5:21:19 PM,11,3,6:26:04 PM,11,3,6:30:04 PM,11,3,6:38:11 PM,11,3,7:15:03 PM,10,19.8,,-1.3198867,36.798424,-1.276245,36.836329299999996,Rider_Id_103,2212
-Order_No_16857,User_Id_2866,Bike,3,Business,20,2,9:55:05 AM,20,2,9:55:35 AM,20,2,10:04:04 AM,20,2,10:13:27 AM,20,2,10:50:09 AM,21,19.0,,-1.2551895,36.7822034,-1.332624,36.913187,Rider_Id_651,2202
-Order_No_2468,User_Id_2330,Bike,3,Business,12,2,2:04:44 PM,12,2,2:05:27 PM,12,2,2:12:01 PM,12,2,2:21:05 PM,12,2,2:44:30 PM,11,27.1,,-1.2524916000000001,36.8656196,-1.2326041,36.8009029,Rider_Id_377,1405
-Order_No_11051,User_Id_2701,Bike,3,Business,29,1,2:14:33 PM,29,1,2:16:07 PM,29,1,2:16:12 PM,29,1,3:00:19 PM,29,1,3:14:22 PM,8,24.2,,-1.2584143,36.8048002,-1.2765736,36.851364600000004,Rider_Id_260,843
-Order_No_20205,User_Id_437,Bike,3,Business,10,4,4:10:10 PM,10,4,4:10:28 PM,10,4,4:13:15 PM,10,4,4:24:37 PM,10,4,4:24:47 PM,2,25.4,,-1.2551895,36.7822034,-1.2571472,36.795063299999995,Rider_Id_536,10
-Order_No_21222,User_Id_1527,Bike,3,Business,8,3,9:36:18 AM,8,3,9:36:34 AM,8,3,9:36:48 AM,8,3,9:55:45 AM,8,3,10:14:24 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_258,1119
-Order_No_17809,User_Id_1271,Bike,3,Business,2,1,12:50:40 PM,2,1,1:02:02 PM,2,1,1:24:58 PM,2,1,1:39:23 PM,2,1,2:11:53 PM,19,20.2,,-1.3244885,36.897791999999995,-1.254191,36.809913200000004,Rider_Id_103,1950
-Order_No_15874,User_Id_3283,Bike,3,Business,30,3,9:21:48 AM,30,3,9:22:09 AM,30,3,9:38:14 AM,30,3,10:12:07 AM,30,3,10:32:33 AM,14,19.6,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_691,1226
-Order_No_2334,User_Id_2329,Bike,2,Personal,16,2,9:44:45 AM,16,2,9:45:24 AM,16,2,10:26:35 AM,16,2,10:33:10 AM,16,2,10:57:55 AM,11,23.4,,-1.2647213,36.729226700000005,-1.2922461,36.7873602,Rider_Id_832,1485
-Order_No_23101,User_Id_3689,Bike,1,Business,16,1,9:57:59 AM,16,1,10:46:40 AM,16,1,10:56:54 AM,16,1,11:05:03 AM,16,1,11:28:59 AM,20,19.2,,-1.1853633,36.9350604,-1.260232,36.807334000000004,Rider_Id_644,1436
-Order_No_26476,User_Id_3599,Bike,3,Business,29,5,9:55:42 AM,29,5,9:58:41 AM,29,5,10:24:05 AM,29,5,11:05:29 AM,29,5,11:16:54 AM,5,26.8,,-1.3302996,36.8707935,-1.3121275,36.8519202,Rider_Id_675,685
-Order_No_21287,User_Id_1280,Bike,3,Business,14,5,2:51:07 PM,14,5,3:14:09 PM,14,5,3:21:35 PM,14,5,3:30:06 PM,14,5,3:56:09 PM,15,23.8,,-1.2551895,36.7822034,-1.3323107,36.8692616,Rider_Id_300,1563
-Order_No_20543,User_Id_1687,Bike,3,Business,24,3,2:32:35 PM,24,3,2:33:12 PM,24,3,2:40:32 PM,24,3,2:42:05 PM,24,3,3:01:10 PM,4,,,-1.2627937,36.815695700000006,-1.2732803,36.794037700000004,Rider_Id_16,1145
-Order_No_10,User_Id_491,Bike,1,Personal,24,5,11:39:26 AM,24,5,11:40:55 AM,24,5,11:48:22 AM,24,5,11:57:03 AM,24,5,12:09:56 PM,4,20.8,,-1.29387,36.7907813,-1.2808482,36.8003686,Rider_Id_865,773
-Order_No_3543,User_Id_3661,Bike,3,Business,7,4,10:17:52 AM,7,4,10:20:22 AM,7,4,10:39:30 AM,7,4,10:41:54 AM,7,4,11:28:45 AM,19,24.5,,-1.2134646,36.8755266,-1.3208162,36.8451209,Rider_Id_691,2811
-Order_No_3256,User_Id_126,Bike,3,Personal,3,1,9:40:20 AM,3,1,9:41:21 AM,3,1,9:53:15 AM,3,1,9:55:33 AM,3,1,10:21:30 AM,7,18.1,5.1,-1.2731477,36.795080299999995,-1.295723,36.822691999999996,Rider_Id_778,1557
-Order_No_5651,User_Id_2987,Bike,3,Business,17,2,12:47:10 PM,17,2,12:48:20 PM,17,2,12:52:47 PM,17,2,1:06:16 PM,17,2,1:26:58 PM,11,18.9,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_100,1242
-Order_No_12621,User_Id_496,Bike,3,Business,5,5,1:22:10 PM,5,5,1:22:30 PM,5,5,1:27:37 PM,5,5,1:33:05 PM,5,5,1:40:41 PM,2,27.6,,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_690,456
-Order_No_1573,User_Id_1245,Bike,3,Business,24,3,12:26:47 PM,24,3,12:28:21 PM,24,3,12:45:03 PM,24,3,1:05:19 PM,24,3,1:43:25 PM,19,19.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_857,2286
-Order_No_15347,User_Id_1148,Bike,3,Business,29,2,2:14:41 PM,29,2,2:15:16 PM,29,2,2:47:55 PM,29,2,2:49:05 PM,29,2,3:28:21 PM,12,26.8,,-1.3100586,36.850032899999995,-1.2559559999999999,36.772901,Rider_Id_206,2356
-Order_No_19361,User_Id_1205,Bike,3,Business,31,4,9:50:22 AM,31,4,9:50:34 AM,31,4,10:14:50 AM,31,4,10:21:30 AM,31,4,10:30:51 AM,5,20.3,,-1.2495931,36.80650870000001,-1.2475136,36.7703179,Rider_Id_607,561
-Order_No_22748,User_Id_970,Bike,3,Business,6,3,12:44:12 PM,6,3,12:44:32 PM,6,3,12:47:32 PM,6,3,12:49:53 PM,6,3,1:04:05 PM,2,22.5,,-1.2551895,36.7822034,-1.2546732,36.7923244,Rider_Id_91,852
-Order_No_24530,User_Id_3095,Bike,3,Personal,30,3,12:35:20 PM,30,3,12:36:32 PM,30,3,12:46:59 PM,30,3,12:57:28 PM,30,3,2:17:20 PM,14,24.9,,-1.3015700000000001,36.7871,-1.2633397,36.7454238,Rider_Id_101,4792
-Order_No_5641,User_Id_635,Bike,3,Business,30,4,12:42:57 PM,30,4,12:43:27 PM,30,4,1:33:46 PM,30,4,1:38:24 PM,30,4,2:01:21 PM,5,,,-1.2549476000000002,36.8269576,-1.2602337,36.79905479999999,Rider_Id_27,1377
-Order_No_14969,User_Id_1594,Bike,3,Business,11,4,2:33:34 PM,11,4,3:11:22 PM,11,4,3:26:05 PM,11,4,3:29:15 PM,11,4,4:00:43 PM,16,26.9,,-1.2156006000000001,36.8916865,-1.3285517,36.9018,Rider_Id_75,1888
-Order_No_4901,User_Id_776,Bike,1,Personal,31,4,11:33:21 AM,31,4,11:34:44 AM,31,4,11:37:47 AM,31,4,11:47:38 AM,31,4,11:47:50 AM,1,21.8,,-1.2665773999999999,36.8069908,-1.273333,36.8122251,Rider_Id_51,12
-Order_No_19350,User_Id_2200,Bike,3,Business,1,4,3:01:49 PM,1,4,3:09:08 PM,1,4,3:20:56 PM,1,4,3:22:03 PM,1,4,3:52:19 PM,9,,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_31,1816
-Order_No_2891,User_Id_269,Bike,3,Business,23,2,3:15:46 PM,23,2,3:16:38 PM,23,2,3:40:27 PM,23,2,3:46:48 PM,23,2,4:29:54 PM,16,19.1,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_675,2586
-Order_No_7480,User_Id_85,Bike,2,Business,19,5,1:45:20 PM,19,5,1:47:25 PM,19,5,1:49:31 PM,19,5,1:56:49 PM,19,5,2:38:20 PM,9,27.5,,-1.2672290000000002,36.806399,-1.3014461000000002,36.7661381,Rider_Id_279,2491
-Order_No_10091,User_Id_3291,Bike,3,Business,2,6,10:09:09 AM,2,6,12:22:23 PM,2,6,12:35:44 PM,2,6,12:47:40 PM,2,6,1:38:26 PM,17,27.3,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_691,3046
-Order_No_25571,User_Id_1927,Bike,3,Business,8,5,2:21:23 PM,8,5,2:21:55 PM,8,5,2:30:10 PM,8,5,2:36:29 PM,8,5,3:18:40 PM,25,27.2,,-1.2232123,36.8932547,-1.3472066,36.7692638,Rider_Id_803,2531
-Order_No_3692,User_Id_2433,Bike,2,Personal,29,3,1:00:59 PM,29,3,1:11:23 PM,29,3,1:16:55 PM,29,3,1:22:07 PM,29,3,1:52:21 PM,10,18.5,,-1.2295205,36.812042600000005,-1.2728069,36.7962362,Rider_Id_320,1814
-Order_No_26102,User_Id_2066,Bike,3,Business,7,2,11:16:01 AM,7,2,11:16:11 AM,7,2,11:28:03 AM,7,2,11:29:42 AM,7,2,11:51:36 AM,8,23.3,,-1.316961,36.820337,-1.2681181000000001,36.8022805,Rider_Id_229,1314
-Order_No_9117,User_Id_2869,Bike,3,Business,4,2,2:54:51 PM,4,2,3:04:04 PM,4,2,3:19:10 PM,4,2,3:32:16 PM,4,2,3:54:24 PM,9,22.4,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_269,1328
-Order_No_524,User_Id_154,Bike,2,Business,22,5,12:49:15 PM,22,5,12:49:28 PM,22,5,1:04:20 PM,22,5,1:27:35 PM,22,5,1:46:01 PM,11,21.8,,-1.2551895,36.7822034,-1.3107918,36.761794,Rider_Id_116,1106
-Order_No_19893,User_Id_3669,Bike,3,Business,16,3,9:05:02 AM,16,3,9:05:09 AM,16,3,9:38:47 AM,16,3,10:06:59 AM,16,3,10:31:12 AM,19,19.8,,-1.2504436,36.889729200000005,-1.2615888000000002,36.7928732,Rider_Id_456,1453
-Order_No_26735,User_Id_2461,Bike,3,Business,9,2,2:09:21 PM,9,2,2:11:47 PM,9,2,2:31:01 PM,9,2,2:33:27 PM,9,2,2:48:01 PM,9,22.5,,-1.2373976999999998,36.7598693,-1.2790653,36.7907262,Rider_Id_754,874
-Order_No_24464,User_Id_230,Bike,1,Personal,19,2,5:03:10 PM,19,2,5:03:35 PM,19,2,5:10:27 PM,19,2,5:17:36 PM,19,2,5:26:28 PM,2,19.8,,-1.2952784,36.7793271,-1.2880228999999999,36.7892191,Rider_Id_312,532
-Order_No_594,User_Id_3647,Bike,3,Business,19,1,5:13:46 PM,19,1,5:14:02 PM,19,1,5:22:29 PM,19,1,5:28:56 PM,19,1,5:38:12 PM,3,23.8,,-1.2726389999999999,36.794723,-1.2727979999999999,36.8124195,Rider_Id_79,556
-Order_No_7020,User_Id_1865,Bike,1,Personal,5,5,10:21:01 AM,5,5,10:21:21 AM,5,5,10:34:40 AM,5,5,11:19:54 AM,5,5,11:23:59 AM,25,23.8,,-1.2166078000000002,36.882501,-1.2709857,36.743748499999995,Rider_Id_704,245
-Order_No_21148,User_Id_868,Bike,3,Business,28,4,1:04:01 PM,28,4,1:05:51 PM,28,4,1:15:29 PM,28,4,1:23:40 PM,28,4,1:42:27 PM,8,28.2,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_341,1127
-Order_No_17130,User_Id_3560,Bike,3,Business,20,3,12:14:14 PM,20,3,12:15:54 PM,20,3,12:24:14 PM,20,3,12:26:15 PM,20,3,12:46:30 PM,7,26.5,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_41,1215
-Order_No_17414,User_Id_2047,Bike,1,Personal,11,4,12:39:46 PM,11,4,1:27:13 PM,11,4,1:45:28 PM,11,4,1:49:39 PM,11,4,2:04:13 PM,7,26.5,,-1.3107959999999999,36.862829999999995,-1.3044313,36.818504,Rider_Id_532,874
-Order_No_18045,User_Id_2195,Bike,3,Business,23,6,10:35:55 AM,23,6,10:36:52 AM,23,6,10:51:49 AM,23,6,10:59:45 AM,23,6,11:45:03 AM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_306,2718
-Order_No_5472,User_Id_954,Bike,1,Personal,4,4,10:36:18 AM,4,4,10:36:49 AM,4,4,10:43:19 AM,4,4,10:57:07 AM,4,4,11:39:23 AM,18,,,-1.2763087,36.8170342,-1.3123394,36.907962,Rider_Id_101,2536
-Order_No_8335,User_Id_1271,Bike,3,Business,25,3,3:08:07 PM,25,3,4:06:12 PM,25,3,4:18:02 PM,25,3,4:25:45 PM,25,3,4:54:00 PM,14,17.6,,-1.3244885,36.897791999999995,-1.2932780000000001,36.827789,Rider_Id_629,1695
-Order_No_2838,User_Id_598,Bike,1,Business,7,2,1:24:53 PM,7,2,1:25:29 PM,7,2,1:45:13 PM,7,2,1:59:05 PM,7,2,2:57:42 PM,16,24.9,,-1.2329501,36.808385200000004,-1.3226392,36.8405839,Rider_Id_882,3517
-Order_No_3573,User_Id_3326,Bike,3,Business,17,5,3:40:52 PM,17,5,3:41:56 PM,17,5,3:50:56 PM,17,5,3:54:59 PM,17,5,4:27:07 PM,3,,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_147,1928
-Order_No_24324,User_Id_496,Bike,3,Business,7,4,9:17:20 AM,7,4,9:19:27 AM,7,4,9:48:40 AM,7,4,9:49:50 AM,7,4,10:23:58 AM,3,14.2,0.9,-1.2553615,36.789537599999996,-1.2595466999999998,36.8113044,Rider_Id_131,2048
-Order_No_4586,User_Id_3003,Bike,1,Personal,20,4,10:18:26 AM,20,4,10:27:06 AM,20,4,10:44:27 AM,20,4,10:57:00 AM,20,4,11:29:08 AM,6,24.5,,-1.322999,36.783263299999994,-1.296797,36.776452500000005,Rider_Id_612,1928
-Order_No_20200,User_Id_2627,Bike,3,Business,11,2,2:57:45 PM,11,2,2:59:11 PM,11,2,3:08:43 PM,11,2,4:37:02 PM,11,2,5:16:43 PM,20,21.1,,-1.2571472,36.795063299999995,-1.3632162,36.9108253,Rider_Id_828,2381
-Order_No_4406,User_Id_2259,Bike,1,Personal,21,5,6:03:42 PM,21,5,6:04:35 PM,21,5,6:25:42 PM,21,5,6:48:27 PM,21,5,6:58:07 PM,4,25.4,,-1.2946581,36.779292100000006,-1.300037,36.805514,Rider_Id_465,580
-Order_No_13946,User_Id_2965,Bike,1,Personal,15,1,1:33:21 PM,15,1,1:36:26 PM,15,1,2:04:34 PM,15,1,2:07:36 PM,15,1,3:03:25 PM,8,,,-1.2744440000000001,36.8196443,-1.2629461000000002,36.76531370000001,Rider_Id_646,3349
-Order_No_5305,User_Id_28,Bike,3,Business,6,4,2:50:12 PM,6,4,2:51:33 PM,6,4,3:14:44 PM,6,4,3:17:33 PM,6,4,3:59:35 PM,20,,,-1.300722,36.7882234,-1.3705766000000001,36.9120742,Rider_Id_387,2522
-Order_No_16767,User_Id_2304,Bike,1,Personal,22,3,5:35:19 PM,22,3,5:35:27 PM,22,3,5:40:37 PM,22,3,5:55:48 PM,22,3,6:36:14 PM,21,23.0,,-1.2918163999999999,36.8773248,-1.3549418,36.7280528,Rider_Id_890,2426
-Order_No_5050,User_Id_1146,Bike,3,Business,13,3,9:16:15 AM,13,3,9:16:33 AM,13,3,9:16:37 AM,13,3,9:37:05 AM,13,3,9:51:28 AM,7,17.1,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_111,863
-Order_No_6368,User_Id_265,Bike,3,Business,20,5,3:24:06 PM,20,5,4:11:54 PM,20,5,4:12:08 PM,20,5,4:32:25 PM,20,5,4:57:38 PM,7,20.1,,-1.300921,36.828195,-1.2908072,36.7860517,Rider_Id_890,1513
-Order_No_27080,User_Id_1430,Bike,1,Personal,26,3,5:59:24 PM,26,3,6:05:39 PM,26,3,6:29:17 PM,26,3,6:32:11 PM,26,3,7:14:38 PM,8,19.4,,-1.3194988,36.796668100000005,-1.3159197,36.8516724,Rider_Id_328,2547
-Order_No_3454,User_Id_1683,Bike,3,Business,27,6,4:26:01 PM,27,6,4:26:51 PM,27,6,4:34:34 PM,27,6,4:48:26 PM,27,6,5:23:43 PM,14,,,-1.2765334,36.80150510000001,-1.2133243,36.795854299999995,Rider_Id_148,2117
-Order_No_3759,User_Id_1304,Bike,3,Business,5,3,11:54:40 AM,5,3,11:55:27 AM,5,3,12:00:36 PM,5,3,12:08:25 PM,5,3,12:27:33 PM,10,22.0,,-1.2551895,36.7822034,-1.3126476,36.831455,Rider_Id_116,1148
-Order_No_13140,User_Id_1927,Bike,3,Business,14,5,12:32:11 PM,14,5,12:39:09 PM,14,5,12:50:08 PM,14,5,12:59:08 PM,14,5,1:18:28 PM,8,21.3,,-1.3467388999999999,36.7690312,-1.3165542,36.700067,Rider_Id_147,1160
-Order_No_4170,User_Id_3218,Bike,3,Business,7,4,4:30:41 PM,7,4,4:45:49 PM,7,4,4:45:53 PM,7,4,4:47:49 PM,7,4,5:06:21 PM,9,17.7,,-1.2927792,36.9053492,-1.2765736,36.851364600000004,Rider_Id_589,1112
-Order_No_3707,User_Id_867,Bike,3,Business,26,3,10:52:15 AM,26,3,10:52:55 AM,26,3,11:06:15 AM,26,3,11:08:42 AM,26,3,11:40:42 AM,14,24.0,,-1.3077869,36.844320700000004,-1.306378,36.7519845,Rider_Id_496,1920
-Order_No_26049,User_Id_393,Bike,3,Business,1,5,9:28:06 AM,1,5,9:36:33 AM,1,5,9:36:38 AM,1,5,10:35:53 AM,1,5,10:36:14 AM,14,26.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_10,21
-Order_No_11729,User_Id_478,Bike,3,Business,13,4,3:23:22 PM,13,4,3:23:37 PM,13,4,3:26:58 PM,13,4,3:41:37 PM,13,4,4:09:14 PM,11,22.9,,-1.2551895,36.7822034,-1.3188898999999998,36.8394916,Rider_Id_690,1657
-Order_No_12403,User_Id_1738,Bike,3,Personal,31,5,12:07:33 PM,31,5,12:08:47 PM,31,5,12:16:47 PM,31,5,12:18:25 PM,31,5,12:42:27 PM,7,17.2,,-1.2963097,36.7688221,-1.2855169,36.8157719,Rider_Id_749,1442
-Order_No_21182,User_Id_3567,Bike,3,Business,28,2,10:29:52 AM,28,2,10:54:59 AM,28,2,11:19:27 AM,28,2,11:21:13 AM,28,2,12:16:48 PM,17,21.5,,-1.3339943,36.7263409,-1.2863663,36.8243643,Rider_Id_831,3335
-Order_No_27363,User_Id_722,Bike,3,Business,29,1,2:59:49 PM,29,1,3:01:10 PM,29,1,3:20:30 PM,29,1,3:49:26 PM,29,1,4:15:40 PM,15,,,-1.32943,36.894242999999996,-1.3041128999999998,36.810493900000004,Rider_Id_856,1574
-Order_No_19202,User_Id_1502,Bike,3,Business,6,1,12:54:14 PM,6,1,12:56:31 PM,6,1,1:29:10 PM,6,1,2:09:09 PM,6,1,2:14:36 PM,12,,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_193,327
-Order_No_22287,User_Id_0,Bike,1,Personal,3,7,11:19:39 AM,3,7,11:23:13 AM,3,7,11:44:21 AM,3,7,11:58:18 AM,3,7,12:49:16 PM,17,22.1,,-1.3093096000000002,36.9060479,-1.2699958,36.795639,Rider_Id_890,3058
-Order_No_22234,User_Id_1271,Bike,3,Business,26,1,3:43:45 PM,26,1,4:17:04 PM,26,1,4:26:58 PM,26,1,4:43:02 PM,26,1,4:53:33 PM,8,19.9,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_675,631
-Order_No_18999,User_Id_1527,Bike,3,Business,2,4,12:22:08 PM,2,4,12:27:13 PM,2,4,12:48:35 PM,2,4,12:50:14 PM,2,4,12:57:36 PM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_910,442
-Order_No_4498,User_Id_3496,Bike,3,Business,13,5,2:56:42 PM,13,5,3:01:26 PM,13,5,3:10:40 PM,13,5,4:21:49 PM,13,5,4:38:28 PM,13,19.4,,-1.332175,36.8819641,-1.300921,36.828195,Rider_Id_192,999
-Order_No_13895,User_Id_53,Bike,3,Business,16,2,12:49:57 PM,16,2,12:51:03 PM,16,2,1:01:51 PM,16,2,1:35:54 PM,16,2,1:53:29 PM,7,,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_855,1055
-Order_No_10537,User_Id_1073,Bike,3,Business,22,5,4:26:59 PM,22,5,4:31:09 PM,22,5,4:45:19 PM,22,5,4:57:42 PM,22,5,5:12:13 PM,6,28.7,,-1.2561964,36.7885001,-1.2566688999999998,36.779695200000006,Rider_Id_177,871
-Order_No_24088,User_Id_2080,Bike,3,Personal,15,1,2:28:00 PM,15,1,2:31:06 PM,15,1,2:44:29 PM,15,1,2:49:45 PM,15,1,3:02:24 PM,7,,,-1.2948998,36.789164,-1.2671137,36.8102851,Rider_Id_291,759
-Order_No_12951,User_Id_633,Bike,3,Business,14,3,8:24:07 AM,14,3,8:24:38 AM,14,3,8:35:32 AM,14,3,8:40:53 AM,14,3,8:53:41 AM,4,18.4,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_620,768
-Order_No_21682,User_Id_3486,Bike,3,Business,18,4,8:55:58 AM,18,4,9:13:52 AM,18,4,9:20:44 AM,18,4,9:24:05 AM,18,4,10:10:20 AM,7,20.8,,-1.2551895,36.7822034,-1.2915968999999998,36.823763899999996,Rider_Id_208,2775
-Order_No_23780,User_Id_2435,Bike,2,Personal,28,5,1:30:27 PM,28,5,1:31:24 PM,28,5,1:55:32 PM,28,5,2:10:26 PM,28,5,2:29:24 PM,7,25.5,,-1.2962893,36.7708484,-1.2646846999999999,36.7906823,Rider_Id_597,1138
-Order_No_5796,User_Id_496,Bike,3,Business,18,3,4:12:08 PM,18,3,4:12:17 PM,18,3,4:14:00 PM,18,3,4:16:28 PM,18,3,4:32:33 PM,8,20.5,,-1.2551895,36.7822034,-1.287428,36.7800821,Rider_Id_951,965
-Order_No_20141,User_Id_665,Bike,1,Business,28,5,9:03:37 AM,28,5,9:03:51 AM,28,5,9:14:52 AM,28,5,9:29:51 AM,28,5,10:16:49 AM,17,20.5,,-1.2843046,36.774613200000005,-1.2859912,36.8756811,Rider_Id_11,2818
-Order_No_8789,User_Id_1060,Bike,1,Personal,13,5,9:22:49 AM,13,5,9:23:01 AM,13,5,9:34:09 AM,13,5,9:41:52 AM,13,5,10:20:24 AM,17,15.7,,-1.2631940000000002,36.797624,-1.317767,36.898252899999996,Rider_Id_116,2312
-Order_No_8244,User_Id_928,Bike,3,Business,24,3,4:05:56 PM,24,3,4:06:12 PM,24,3,4:08:57 PM,24,3,4:14:46 PM,24,3,4:59:00 PM,12,18.6,,-1.2551895,36.7822034,-1.2484601999999998,36.855067,Rider_Id_184,2654
-Order_No_22929,User_Id_2651,Bike,3,Business,20,1,12:55:10 PM,20,1,12:56:34 PM,20,1,1:04:17 PM,20,1,1:25:32 PM,20,1,2:10:05 PM,12,,,-1.3247683000000001,36.85803,-1.2652611,36.8062739,Rider_Id_430,2673
-Order_No_21207,User_Id_3091,Bike,3,Business,13,2,3:00:37 PM,13,2,3:03:04 PM,13,2,3:27:11 PM,13,2,3:41:30 PM,13,2,4:17:52 PM,10,26.3,,-1.3012008,36.764868,-1.25896,36.8016691,Rider_Id_328,2182
-Order_No_11092,User_Id_136,Bike,3,Business,14,4,1:09:54 PM,14,4,1:17:48 PM,14,4,1:28:58 PM,14,4,1:30:11 PM,14,4,2:21:21 PM,13,25.2,,-1.3254522,36.721658399999995,-1.25537,36.7756,Rider_Id_831,3070
-Order_No_26728,User_Id_228,Bike,3,Business,22,5,9:06:47 AM,22,5,9:07:13 AM,22,5,9:11:39 AM,22,5,9:14:05 AM,22,5,9:54:45 AM,5,22.2,,-1.2842665,36.7967626,-1.273488,36.804158,Rider_Id_933,2440
-Order_No_6261,User_Id_635,Bike,3,Business,5,5,2:25:13 PM,5,5,2:25:59 PM,5,5,2:28:31 PM,5,5,2:51:01 PM,5,5,3:09:17 PM,8,27.8,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_343,1096
-Order_No_207,User_Id_2587,Bike,3,Business,5,5,3:48:16 PM,5,5,3:56:14 PM,5,5,4:08:37 PM,5,5,4:10:55 PM,5,5,4:40:01 PM,9,27.5,,-1.28878,36.8168312,-1.3097517,36.8645223,Rider_Id_48,1746
-Order_No_15257,User_Id_3218,Bike,3,Business,13,3,9:54:49 AM,13,3,9:55:51 AM,13,3,10:05:05 AM,13,3,10:06:32 AM,13,3,10:32:52 AM,10,16.2,,-1.2765736,36.851364600000004,-1.2551895,36.7822034,Rider_Id_444,1580
-Order_No_12904,User_Id_391,Bike,3,Business,17,5,4:15:02 PM,17,5,4:23:14 PM,17,5,4:52:11 PM,17,5,5:46:27 PM,17,5,6:30:59 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_868,2672
-Order_No_24684,User_Id_3743,Bike,3,Business,26,2,3:43:42 PM,26,2,3:44:17 PM,26,2,3:52:01 PM,26,2,4:08:51 PM,26,2,4:28:34 PM,4,29.9,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_595,1183
-Order_No_10464,User_Id_346,Bike,3,Business,4,5,5:58:35 PM,4,5,6:03:29 PM,4,5,6:07:47 PM,4,5,6:10:29 PM,4,5,6:19:02 PM,2,22.3,,-1.2885528999999998,36.816014100000004,-1.2951243000000001,36.828134399999996,Rider_Id_742,513
-Order_No_4448,User_Id_3599,Bike,3,Business,14,4,1:23:37 PM,14,4,1:56:29 PM,14,4,2:26:37 PM,14,4,3:09:07 PM,14,4,3:54:57 PM,13,28.2,,-1.3302996,36.8707935,-1.2913552,36.7875945,Rider_Id_152,2750
-Order_No_6705,User_Id_2282,Bike,3,Personal,20,3,2:46:16 PM,20,3,2:46:39 PM,20,3,2:51:37 PM,20,3,2:55:56 PM,20,3,3:37:49 PM,8,28.2,,-1.2735393999999998,36.833775200000005,-1.3043649,36.7816132,Rider_Id_698,2513
-Order_No_16786,User_Id_2805,Bike,3,Business,29,2,3:20:21 PM,29,2,3:20:55 PM,29,2,3:25:23 PM,29,2,3:36:01 PM,29,2,4:26:15 PM,17,26.8,,-1.2534301,36.800098299999995,-1.3169591,36.7000029,Rider_Id_902,3014
-Order_No_4020,User_Id_1271,Bike,3,Business,15,5,10:14:14 AM,15,5,10:14:30 AM,15,5,10:33:48 AM,15,5,11:00:19 AM,15,5,11:23:55 AM,24,26.1,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_675,1416
-Order_No_19936,User_Id_1500,Bike,3,Business,13,1,2:13:24 PM,13,1,2:13:56 PM,13,1,2:14:16 PM,13,1,3:03:07 PM,13,1,3:04:37 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_472,90
-Order_No_14660,User_Id_1796,Bike,3,Business,14,4,3:18:22 PM,14,4,3:21:19 PM,14,4,3:21:29 PM,14,4,3:50:49 PM,14,4,4:20:14 PM,13,27.4,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_669,1765
-Order_No_27417,User_Id_635,Bike,3,Business,10,1,10:28:23 AM,10,1,10:29:06 AM,10,1,10:33:36 AM,10,1,11:42:05 AM,10,1,12:01:24 PM,8,23.6,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_849,1159
-Order_No_10189,User_Id_3647,Bike,3,Business,9,5,12:25:13 PM,9,5,12:27:17 PM,9,5,12:36:43 PM,9,5,12:43:06 PM,9,5,1:35:27 PM,3,25.5,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_518,3141
-Order_No_14190,User_Id_3339,Bike,3,Business,27,4,10:09:02 AM,27,4,10:09:43 AM,27,4,10:11:00 AM,27,4,10:14:09 AM,27,4,10:46:36 AM,9,18.9,3.0,-1.2551895,36.7822034,-1.3084468999999999,36.8263054,Rider_Id_62,1947
-Order_No_21834,User_Id_1452,Bike,2,Personal,2,1,1:24:02 PM,2,1,1:25:46 PM,2,1,1:46:16 PM,2,1,1:51:39 PM,2,1,2:08:24 PM,8,20.2,,-1.2884128000000001,36.7906666,-1.260232,36.807334000000004,Rider_Id_74,1005
-Order_No_25362,User_Id_186,Bike,3,Business,5,5,11:08:57 AM,5,5,11:10:37 AM,5,5,11:36:28 AM,5,5,11:41:08 AM,5,5,12:20:55 PM,4,23.8,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_215,2387
-Order_No_21642,User_Id_2212,Bike,3,Business,10,5,8:45:36 AM,10,5,8:46:59 AM,10,5,8:54:01 AM,10,5,9:03:45 AM,10,5,9:32:46 AM,14,,,-1.2571472,36.795063299999995,-1.3291155000000001,36.8861,Rider_Id_843,1741
-Order_No_20104,User_Id_2722,Bike,2,Personal,31,5,3:59:05 PM,31,5,4:05:39 PM,31,5,4:27:36 PM,31,5,4:45:01 PM,31,5,5:13:04 PM,10,19.9,,-1.2915306999999998,36.790586499999996,-1.232004,36.7902146,Rider_Id_882,1683
-Order_No_5683,User_Id_3432,Bike,3,Business,11,2,8:45:56 AM,11,2,8:47:14 AM,11,2,9:19:52 AM,11,2,9:27:32 AM,11,2,9:42:17 AM,7,16.4,43.0,-1.2672588,36.8000252,-1.2941023999999999,36.79578,Rider_Id_676,885
-Order_No_19185,User_Id_2330,Bike,3,Business,28,4,3:17:04 PM,28,4,3:17:35 PM,28,4,3:43:19 PM,28,4,3:49:56 PM,28,4,4:27:40 PM,8,30.1,,-1.293082,36.8278578,-1.3015197,36.7658456,Rider_Id_61,2264
-Order_No_13102,User_Id_2784,Bike,3,Business,15,5,7:48:08 PM,15,5,7:49:09 PM,15,5,7:55:43 PM,15,5,8:13:42 PM,15,5,8:13:44 PM,4,22.0,,-1.2963097,36.7688221,-1.287332,36.744498,Rider_Id_26,2
-Order_No_11977,User_Id_3080,Bike,3,Business,2,1,10:03:29 AM,2,1,10:05:33 AM,2,1,10:14:37 AM,2,1,10:26:15 AM,2,1,10:35:40 AM,6,16.6,,-1.2551895,36.7822034,-1.2833106,36.82402379999999,Rider_Id_714,565
-Order_No_14928,User_Id_909,Bike,3,Business,5,2,2:06:25 PM,5,2,2:08:38 PM,5,2,2:40:22 PM,5,2,2:44:49 PM,5,2,3:14:06 PM,9,,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_154,1757
-Order_No_9499,User_Id_186,Bike,3,Business,26,5,11:06:38 AM,26,5,11:06:45 AM,26,5,11:09:18 AM,26,5,11:11:41 AM,26,5,11:42:55 AM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_62,1874
-Order_No_9575,User_Id_2547,Bike,3,Business,22,2,10:19:55 AM,22,2,10:21:32 AM,22,2,10:31:37 AM,22,2,10:48:18 AM,22,2,11:09:56 AM,11,19.4,,-1.3028118,36.8174764,-1.3321944,36.889232899999996,Rider_Id_216,1298
-Order_No_4280,User_Id_3686,Bike,3,Business,6,3,11:56:14 AM,6,3,11:56:43 AM,6,3,11:57:48 AM,6,3,12:00:20 PM,6,3,12:37:51 PM,8,22.5,,-1.2551895,36.7822034,-1.2925919,36.81705770000001,Rider_Id_771,2251
-Order_No_16153,User_Id_893,Bike,3,Business,2,2,9:23:36 AM,2,2,9:24:35 AM,2,2,9:27:55 AM,2,2,9:29:43 AM,2,2,9:44:07 AM,7,20.6,,-1.2663194,36.809901,-1.2847316,36.7658086,Rider_Id_205,864
-Order_No_1396,User_Id_868,Bike,3,Business,4,2,11:04:42 AM,4,2,11:05:50 AM,4,2,11:12:33 AM,4,2,11:22:26 AM,4,2,11:27:02 AM,3,18.9,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_676,276
-Order_No_6592,User_Id_3046,Bike,2,Personal,28,5,1:50:12 PM,28,5,1:53:37 PM,28,5,1:56:53 PM,28,5,1:59:52 PM,28,5,2:17:17 PM,4,25.4,,-1.2795796,36.819062,-1.2658334999999998,36.804286700000006,Rider_Id_21,1045
-Order_No_12601,User_Id_496,Bike,3,Business,28,5,4:26:47 PM,28,5,4:28:29 PM,28,5,4:29:12 PM,28,5,4:46:19 PM,28,5,5:15:11 PM,8,,,-1.2571472,36.795063299999995,-1.2696795,36.8640088,Rider_Id_31,1732
-Order_No_6057,User_Id_3420,Bike,3,Business,27,3,5:21:15 PM,27,3,5:21:52 PM,27,3,5:48:15 PM,27,3,6:10:46 PM,27,3,6:10:49 PM,8,27.4,,-1.3046007,36.821174299999996,-1.326774,36.78780689999999,Rider_Id_613,3
-Order_No_22386,User_Id_1535,Bike,1,Business,19,4,10:20:49 AM,19,4,10:22:23 AM,19,4,10:33:17 AM,19,4,10:55:03 AM,19,4,11:19:43 AM,9,20.2,,-1.2945178999999998,36.7685349,-1.2571472,36.795063299999995,Rider_Id_5,1480
-Order_No_23439,User_Id_1329,Bike,3,Business,5,2,11:30:05 AM,5,2,11:33:18 AM,5,2,11:41:00 AM,5,2,11:42:40 AM,5,2,12:04:29 PM,6,21.3,,-1.273056,36.811298,-1.2951297,36.8335521,Rider_Id_808,1309
-Order_No_2889,User_Id_3518,Bike,3,Business,20,1,9:36:27 AM,20,1,9:37:00 AM,20,1,9:42:54 AM,20,1,10:14:33 AM,20,1,10:32:17 AM,7,14.1,,-1.2551895,36.7822034,-1.2821883,36.826961100000005,Rider_Id_449,1064
-Order_No_8863,User_Id_3647,Bike,3,Business,28,3,11:58:23 AM,28,3,11:59:06 AM,28,3,12:03:18 PM,28,3,12:11:08 PM,28,3,12:23:19 PM,5,,,-1.2962618000000001,36.792933399999995,-1.2726389999999999,36.794723,Rider_Id_914,731
-Order_No_12257,User_Id_2042,Bike,3,Business,29,1,10:09:38 AM,29,1,10:09:58 AM,29,1,10:10:04 AM,29,1,10:11:51 AM,29,1,10:48:20 AM,17,19.2,,-1.2860183,36.8975338,-1.2857671,36.7875614,Rider_Id_466,2189
-Order_No_23296,User_Id_1754,Bike,3,Business,23,2,12:03:16 PM,23,2,12:03:21 PM,23,2,12:03:40 PM,23,2,12:22:55 PM,23,2,12:36:49 PM,12,17.9,,-1.3052491000000002,36.8223899,-1.2860183,36.8975338,Rider_Id_589,834
-Order_No_17113,User_Id_393,Bike,3,Business,8,3,1:58:46 PM,8,3,1:59:08 PM,8,3,2:13:52 PM,8,3,2:32:36 PM,8,3,2:43:55 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_843,679
-Order_No_13079,User_Id_649,Bike,3,Business,26,2,11:55:39 AM,26,2,11:56:27 AM,26,2,12:03:25 PM,26,2,12:10:25 PM,26,2,12:30:02 PM,10,26.4,,-1.2551895,36.7822034,-1.2529072,36.8525066,Rider_Id_831,1177
-Order_No_4220,User_Id_3091,Bike,3,Business,29,3,11:33:06 AM,29,3,11:34:13 AM,29,3,11:39:22 AM,29,3,11:42:56 AM,29,3,11:48:49 AM,2,,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_276,353
-Order_No_22855,User_Id_718,Bike,3,Business,3,5,4:05:50 PM,3,5,4:08:06 PM,3,5,4:16:33 PM,3,5,4:19:58 PM,3,5,4:37:22 PM,4,,,-1.2551895,36.7822034,-1.2707177,36.8009828,Rider_Id_933,1044
-Order_No_14556,User_Id_733,Bike,3,Business,8,1,1:45:34 PM,8,1,1:45:41 PM,8,1,1:51:52 PM,8,1,2:05:49 PM,8,1,2:42:20 PM,23,29.5,,-1.2551895,36.7822034,-1.3452448000000001,36.910166600000004,Rider_Id_786,2191
-Order_No_1470,User_Id_49,Bike,3,Business,21,5,10:54:19 AM,21,5,12:00:07 PM,21,5,12:00:18 PM,21,5,12:08:48 PM,21,5,1:13:59 PM,5,22.7,,-1.2866544,36.82179779999999,-1.2959006000000002,36.8353606,Rider_Id_698,3911
-Order_No_19388,User_Id_246,Bike,3,Business,30,1,5:38:59 PM,30,1,5:39:43 PM,30,1,6:22:23 PM,30,1,6:30:36 PM,30,1,6:47:01 PM,4,16.2,,-1.2332902000000001,36.7971244,-1.2584143,36.8048002,Rider_Id_140,985
-Order_No_10811,User_Id_496,Bike,3,Business,7,2,11:05:39 AM,7,2,11:07:43 AM,7,2,11:35:45 AM,7,2,11:42:29 AM,7,2,12:19:36 PM,20,,,-1.1740894,36.8249762,-1.2551895,36.7822034,Rider_Id_279,2227
-Order_No_24434,User_Id_1882,Bike,3,Business,22,4,7:56:14 AM,22,4,8:06:58 AM,22,4,8:41:58 AM,22,4,8:56:41 AM,22,4,9:35:56 AM,13,19.5,3.1,-1.2958783,36.7928125,-1.3175825,36.7004796,Rider_Id_762,2355
-Order_No_3535,User_Id_928,Bike,3,Business,10,5,5:11:46 PM,10,5,5:13:55 PM,10,5,5:26:01 PM,10,5,5:30:47 PM,10,5,5:45:34 PM,2,,,-1.2551895,36.7822034,-1.2652708000000001,36.7971508,Rider_Id_689,887
-Order_No_12991,User_Id_3331,Bike,3,Business,27,3,11:19:27 AM,27,3,11:19:54 AM,27,3,11:20:07 AM,27,3,11:23:40 AM,27,3,12:06:48 PM,9,26.1,,-1.2551895,36.7822034,-1.2922364,36.78955379999999,Rider_Id_801,2588
-Order_No_7073,User_Id_309,Bike,3,Personal,20,5,9:55:38 AM,20,5,9:56:10 AM,20,5,10:14:40 AM,20,5,10:20:30 AM,20,5,10:41:39 AM,6,14.7,,-1.29387,36.7907813,-1.2707583,36.8237131,Rider_Id_844,1269
-Order_No_10826,User_Id_2284,Bike,3,Personal,29,2,12:00:47 PM,29,2,12:09:43 PM,29,2,12:41:09 PM,29,2,12:45:21 PM,29,2,12:57:10 PM,6,21.3,,-1.2517392,36.82496079999999,-1.2823851000000002,36.8186472,Rider_Id_320,709
-Order_No_25449,User_Id_2828,Bike,3,Business,27,3,3:55:52 PM,27,3,4:10:40 PM,27,3,4:12:08 PM,27,3,4:33:57 PM,27,3,5:13:05 PM,20,27.7,,-1.2615888000000002,36.7928732,-1.3681253,36.915529799999995,Rider_Id_926,2348
-Order_No_16490,User_Id_3175,Bike,3,Business,8,5,2:40:18 PM,8,5,2:44:21 PM,8,5,2:52:56 PM,8,5,2:55:50 PM,8,5,3:48:05 PM,15,21.3,,-1.2612417,36.7958868,-1.3169591,36.7000029,Rider_Id_474,3135
-Order_No_19479,User_Id_1724,Bike,3,Business,6,2,10:17:19 AM,6,2,10:20:53 AM,6,2,10:32:36 AM,6,2,10:36:54 AM,6,2,10:56:03 AM,9,24.9,,-1.2551895,36.7822034,-1.2941648000000001,36.7977315,Rider_Id_726,1149
-Order_No_20961,User_Id_3147,Bike,3,Business,28,1,2:51:23 PM,28,1,3:57:19 PM,28,1,4:02:57 PM,28,1,4:19:23 PM,28,1,4:39:56 PM,10,26.4,,-1.3169591,36.7000029,-1.3467388999999999,36.7690312,Rider_Id_770,1233
-Order_No_750,User_Id_2677,Bike,3,Business,25,4,2:36:58 PM,25,4,2:54:08 PM,25,4,3:07:11 PM,25,4,3:10:13 PM,25,4,3:39:16 PM,15,,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_674,1743
-Order_No_21509,User_Id_2789,Bike,3,Business,28,1,4:10:45 PM,28,1,4:11:07 PM,28,1,4:17:02 PM,28,1,4:22:45 PM,28,1,4:37:54 PM,5,26.4,,-1.304735,36.784605400000004,-1.300037,36.805514,Rider_Id_354,909
-Order_No_12400,User_Id_1267,Bike,1,Personal,6,1,11:33:35 AM,6,1,11:33:44 AM,6,1,11:42:20 AM,6,1,12:00:47 PM,6,1,12:22:22 PM,9,20.3,,-1.2874215,36.7767292,-1.308366,36.823613,Rider_Id_329,1295
-Order_No_11322,User_Id_393,Bike,3,Business,4,1,8:28:29 AM,4,1,8:30:00 AM,4,1,8:46:07 AM,4,1,9:08:10 AM,4,1,9:22:14 AM,4,21.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_378,844
-Order_No_21999,User_Id_1075,Bike,3,Business,14,4,1:23:22 PM,14,4,3:37:56 PM,14,4,4:07:17 PM,14,4,4:10:59 PM,14,4,4:45:13 PM,6,28.0,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_590,2054
-Order_No_21210,User_Id_635,Bike,3,Business,3,4,3:22:16 PM,3,4,3:23:22 PM,3,4,3:37:53 PM,3,4,3:59:20 PM,3,4,4:19:59 PM,8,26.0,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_576,1239
-Order_No_15616,User_Id_2330,Bike,3,Business,7,4,5:13:08 PM,7,4,5:13:20 PM,7,4,5:23:54 PM,7,4,5:45:52 PM,7,4,5:45:57 PM,10,25.6,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_246,5
-Order_No_20905,User_Id_2740,Bike,3,Personal,14,4,3:06:10 PM,14,4,3:36:35 PM,14,4,3:59:34 PM,14,4,4:12:38 PM,14,4,4:50:24 PM,11,21.5,,-1.2638822,36.789604499999996,-1.3127303999999997,36.8189817,Rider_Id_791,2266
-Order_No_9865,User_Id_1329,Bike,3,Business,25,4,3:16:12 PM,25,4,3:17:49 PM,25,4,3:23:26 PM,25,4,3:34:46 PM,25,4,3:43:46 PM,6,,,-1.273056,36.811298,-1.2467205,36.772443200000005,Rider_Id_119,540
-Order_No_21594,User_Id_2066,Bike,3,Business,29,3,1:26:36 PM,29,3,1:27:01 PM,29,3,1:33:27 PM,29,3,1:58:32 PM,29,3,2:21:41 PM,10,18.9,,-1.316961,36.820337,-1.2551895,36.7822034,Rider_Id_229,1389
-Order_No_9646,User_Id_1500,Bike,3,Business,2,2,11:52:15 AM,2,2,11:55:12 AM,2,2,12:14:04 PM,2,2,12:36:12 PM,2,2,12:57:12 PM,8,26.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_507,1260
-Order_No_8843,User_Id_496,Bike,3,Business,19,3,3:35:04 PM,19,3,3:36:59 PM,19,3,3:43:04 PM,19,3,3:44:10 PM,19,3,4:56:25 PM,23,24.3,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_11,4335
-Order_No_11429,User_Id_2106,Bike,1,Personal,22,5,1:09:37 PM,22,5,1:11:04 PM,22,5,1:20:36 PM,22,5,1:40:54 PM,22,5,2:03:52 PM,6,,,-1.2769628,36.815049,-1.3143523000000001,36.803827500000004,Rider_Id_444,1378
-Order_No_10357,User_Id_867,Bike,3,Business,29,5,9:16:49 AM,29,5,9:18:01 AM,29,5,9:29:27 AM,29,5,9:42:27 AM,29,5,10:06:53 AM,10,20.9,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_728,1466
-Order_No_5375,User_Id_2733,Bike,3,Business,19,2,12:32:49 PM,19,2,12:42:46 PM,19,2,12:57:44 PM,19,2,1:08:09 PM,19,2,1:50:29 PM,8,27.3,,-1.2813012,36.832396200000005,-1.2963097,36.7688221,Rider_Id_831,2540
-Order_No_3632,User_Id_2976,Bike,3,Business,29,1,8:07:16 AM,29,1,9:11:45 AM,29,1,9:24:54 AM,29,1,9:30:21 AM,29,1,9:46:53 AM,4,,,-1.2606251,36.7833256,-1.2562796999999999,36.8075858,Rider_Id_427,992
-Order_No_22661,User_Id_2445,Bike,2,Personal,11,2,9:15:59 AM,11,2,9:18:17 AM,11,2,9:44:19 AM,11,2,9:58:10 AM,11,2,10:03:38 AM,5,18.5,2.0,-1.2564677,36.7730828,-1.2664227,36.7453671,Rider_Id_179,328
-Order_No_20348,User_Id_3711,Bike,3,Business,21,5,4:16:16 PM,21,5,4:54:40 PM,21,5,5:05:31 PM,21,5,5:17:04 PM,21,5,5:29:34 PM,5,23.0,,-1.2903421,36.7885282,-1.3014461000000002,36.7661381,Rider_Id_489,750
-Order_No_11956,User_Id_3091,Bike,3,Business,14,4,6:43:04 AM,14,4,6:48:17 AM,14,4,8:14:26 AM,14,4,8:17:26 AM,14,4,8:59:49 AM,9,17.7,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_926,2543
-Order_No_7175,User_Id_410,Bike,1,Personal,17,1,4:44:38 PM,17,1,4:51:07 PM,17,1,4:58:34 PM,17,1,5:13:18 PM,17,1,6:18:53 PM,18,25.6,,-1.1917753,36.9211989,-1.2361443,36.810851799999995,Rider_Id_447,3935
-Order_No_2124,User_Id_3525,Bike,3,Business,28,4,3:56:56 PM,28,4,3:58:36 PM,28,4,4:07:23 PM,28,4,4:10:38 PM,28,4,4:25:37 PM,7,27.2,,-1.2571472,36.795063299999995,-1.263989,36.765174,Rider_Id_411,899
-Order_No_14976,User_Id_892,Bike,2,Personal,25,2,10:35:41 AM,25,2,10:35:58 AM,25,2,10:50:47 AM,25,2,10:56:37 AM,25,2,11:42:37 AM,15,23.9,,-1.2243841,36.8024757,-1.312475,36.8225778,Rider_Id_882,2760
-Order_No_10471,User_Id_3718,Bike,3,Business,30,2,10:26:55 AM,30,2,10:31:35 AM,30,2,10:40:50 AM,30,2,10:48:40 AM,30,2,11:20:29 AM,4,22.2,,-1.2650027,36.8126242,-1.2921233,36.806385999999996,Rider_Id_519,1909
-Order_No_10262,User_Id_3285,Bike,2,Personal,18,1,3:25:38 PM,18,1,3:28:24 PM,18,1,3:37:35 PM,18,1,3:41:39 PM,18,1,4:08:58 PM,9,17.4,,-1.2635425,36.8021963,-1.2940768999999999,36.749157399999994,Rider_Id_726,1639
-Order_No_19396,User_Id_3621,Bike,3,Business,6,4,6:40:34 PM,6,4,6:41:31 PM,6,4,7:02:16 PM,6,4,7:07:15 PM,6,4,7:40:38 PM,9,22.8,,-1.2836583000000001,36.7886014,-1.3212496999999999,36.82452021,Rider_Id_607,2003
-Order_No_15719,User_Id_2330,Bike,3,Business,17,5,2:46:43 PM,17,5,2:47:09 PM,17,5,2:52:42 PM,17,5,2:59:51 PM,17,5,3:14:07 PM,3,,,-1.3015197,36.7658456,-1.294597,36.7742999,Rider_Id_161,856
-Order_No_13919,User_Id_1882,Bike,3,Business,12,3,10:05:32 AM,12,3,10:05:43 AM,12,3,10:10:04 AM,12,3,10:16:50 AM,12,3,10:27:56 AM,5,21.3,,-1.2956882,36.7861845,-1.3014461000000002,36.7661381,Rider_Id_881,666
-Order_No_26629,User_Id_516,Bike,3,Business,23,5,1:47:25 PM,23,5,1:50:13 PM,23,5,2:10:13 PM,23,5,2:13:00 PM,23,5,2:30:49 PM,10,26.5,,-1.2551895,36.7822034,-1.2982347,36.760636100000006,Rider_Id_933,1069
-Order_No_19477,User_Id_248,Bike,3,Business,30,2,11:36:04 AM,30,2,11:48:53 AM,30,2,12:30:39 PM,30,2,1:10:18 PM,30,2,1:21:46 PM,5,,,-1.304735,36.784605400000004,-1.280511,36.7889896,Rider_Id_132,688
-Order_No_1101,User_Id_1473,Bike,3,Business,20,1,2:46:46 PM,20,1,2:47:11 PM,20,1,2:52:47 PM,20,1,2:58:17 PM,20,1,3:42:31 PM,19,,,-1.2682478000000001,36.8072852,-1.3700383,36.9190174,Rider_Id_183,2654
-Order_No_27911,User_Id_393,Bike,3,Business,13,3,3:50:47 PM,13,3,3:51:00 PM,13,3,3:51:04 PM,13,3,4:15:47 PM,13,3,4:38:44 PM,11,27.3,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_842,1377
-Order_No_12690,User_Id_2154,Bike,3,Business,15,4,12:00:59 PM,15,4,12:10:24 PM,15,4,12:13:55 PM,15,4,12:16:10 PM,15,4,12:37:57 PM,11,24.2,,-1.2765736,36.851364600000004,-1.2551895,36.7822034,Rider_Id_72,1307
-Order_No_21752,User_Id_1888,Bike,1,Personal,2,1,7:09:02 PM,2,1,7:09:38 PM,2,1,7:22:24 PM,2,1,7:31:40 PM,2,1,8:12:38 PM,14,19.3,,-1.2629944,36.7998816,-1.3246122999999999,36.790226700000005,Rider_Id_159,2458
-Order_No_19806,User_Id_3686,Bike,3,Business,6,4,4:22:51 PM,6,4,4:23:36 PM,6,4,4:31:39 PM,6,4,4:34:41 PM,6,4,4:46:37 PM,4,22.4,,-1.2551895,36.7822034,-1.2695287,36.798177700000004,Rider_Id_575,716
-Order_No_14639,User_Id_635,Bike,3,Business,23,4,10:53:27 AM,23,4,10:58:19 AM,23,4,11:08:59 AM,23,4,11:18:57 AM,23,4,11:30:50 AM,4,18.5,,-1.2599563,36.799343900000004,-1.2833335,36.8260084,Rider_Id_179,713
-Order_No_3762,User_Id_2827,Bike,3,Business,3,4,12:44:35 PM,3,4,12:53:23 PM,3,4,1:07:26 PM,3,4,1:12:32 PM,3,4,1:24:10 PM,5,23.3,,-1.2850643999999998,36.8343657,-1.2692689,36.8055169,Rider_Id_404,698
-Order_No_16174,User_Id_1551,Bike,1,Personal,21,5,12:07:10 PM,21,5,12:07:37 PM,21,5,12:10:08 PM,21,5,12:47:03 PM,21,5,12:57:03 PM,8,20.2,,-1.3099295,36.8436807,-1.3349663,36.858008500000004,Rider_Id_179,600
-Order_No_7800,User_Id_391,Bike,3,Business,2,2,10:21:06 AM,2,2,10:27:54 AM,2,2,10:43:54 AM,2,2,11:24:35 AM,2,2,11:47:06 AM,5,26.4,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_668,1351
-Order_No_446,User_Id_393,Bike,3,Business,9,4,9:40:38 AM,9,4,9:40:54 AM,9,4,9:44:04 AM,9,4,10:08:08 AM,9,4,10:23:01 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_343,893
-Order_No_18959,User_Id_517,Bike,3,Business,8,6,12:14:44 PM,8,6,12:16:09 PM,8,6,12:48:07 PM,8,6,12:54:41 PM,8,6,1:02:27 PM,4,20.0,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_448,466
-Order_No_11613,User_Id_2582,Bike,3,Business,16,4,4:21:46 PM,16,4,4:22:01 PM,16,4,4:24:21 PM,16,4,4:28:08 PM,16,4,5:00:02 PM,18,,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_931,1914
-Order_No_26789,User_Id_393,Bike,3,Business,15,1,1:49:01 PM,15,1,1:50:11 PM,15,1,1:53:35 PM,15,1,2:02:31 PM,15,1,2:10:47 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_942,496
-Order_No_10622,User_Id_3291,Bike,3,Business,31,1,1:55:05 PM,31,1,1:55:35 PM,31,1,2:01:32 PM,31,1,2:13:04 PM,31,1,2:23:40 PM,5,23.7,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_320,636
-Order_No_18398,User_Id_3282,Bike,3,Personal,6,1,2:20:21 PM,6,1,2:21:06 PM,6,1,2:28:51 PM,6,1,2:32:21 PM,6,1,2:44:20 PM,8,24.7,,-1.2639002000000001,36.7916805,-1.290059,36.7703475,Rider_Id_754,719
-Order_No_21577,User_Id_3295,Bike,3,Business,21,3,3:57:48 PM,21,3,4:02:07 PM,21,3,4:03:09 PM,21,3,4:13:20 PM,21,3,4:32:46 PM,5,23.3,3.1,-1.2571472,36.795063299999995,-1.2645258,36.76833870000001,Rider_Id_227,1166
-Order_No_11149,User_Id_393,Bike,3,Business,6,1,10:03:13 AM,6,1,10:03:34 AM,6,1,10:07:56 AM,6,1,10:36:45 AM,6,1,11:00:02 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_690,1397
-Order_No_2521,User_Id_733,Bike,3,Business,26,1,11:00:55 AM,26,1,11:01:11 AM,26,1,11:11:56 AM,26,1,11:13:13 AM,26,1,11:26:48 AM,7,23.2,,-1.2551895,36.7822034,-1.2795549,36.7627011,Rider_Id_274,815
-Order_No_15451,User_Id_2982,Bike,3,Business,5,3,12:10:57 PM,5,3,12:11:04 PM,5,3,12:11:13 PM,5,3,12:12:24 PM,5,3,12:59:20 PM,18,22.5,,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_340,2816
-Order_No_11240,User_Id_2911,Bike,1,Personal,17,4,1:40:39 PM,17,4,1:41:07 PM,17,4,1:54:22 PM,17,4,1:59:31 PM,17,4,2:05:48 PM,3,23.7,,-1.3066143,36.8161046,-1.3004062,36.829741,Rider_Id_742,377
-Order_No_16849,User_Id_28,Bike,3,Business,12,5,2:04:52 PM,12,5,2:08:36 PM,12,5,2:30:44 PM,12,5,2:45:46 PM,12,5,3:21:13 PM,13,27.3,,-1.3022899,36.869332,-1.2917868000000001,36.7872675,Rider_Id_882,2127
-Order_No_12753,User_Id_909,Bike,3,Business,18,1,2:20:27 PM,18,1,2:23:42 PM,18,1,2:44:47 PM,18,1,2:48:22 PM,18,1,3:09:47 PM,8,31.0,,-1.225322,36.80855,-1.2733047,36.8109744,Rider_Id_953,1285
-Order_No_17224,User_Id_1983,Bike,3,Personal,2,6,9:47:26 AM,2,6,9:47:46 AM,2,6,9:50:13 AM,2,6,9:58:58 AM,2,6,10:08:34 AM,4,20.1,,-1.2827946000000001,36.768944899999994,-1.2952108999999998,36.775706400000004,Rider_Id_735,576
-Order_No_27220,User_Id_1384,Bike,3,Business,26,5,8:52:48 AM,26,5,8:52:58 AM,26,5,9:04:09 AM,26,5,9:09:59 AM,26,5,9:38:51 AM,18,,,-1.2517448,36.7924971,-1.3296677,36.884587599999996,Rider_Id_96,1732
-Order_No_12208,User_Id_3444,Bike,1,Personal,10,2,12:37:42 PM,10,2,12:37:58 PM,10,2,12:59:39 PM,10,2,1:06:37 PM,10,2,1:55:07 PM,9,20.5,,-1.263419,36.8060348,-1.2906539,36.7523571,Rider_Id_116,2910
-Order_No_15300,User_Id_533,Bike,3,Business,21,1,2:27:55 PM,21,1,2:28:25 PM,21,1,2:43:51 PM,21,1,2:57:34 PM,21,1,3:10:35 PM,4,27.7,,-1.3014461000000002,36.7661381,-1.2765492,36.7669809,Rider_Id_79,781
-Order_No_9229,User_Id_2066,Bike,3,Business,21,4,3:29:12 PM,21,4,3:35:50 PM,21,4,3:44:21 PM,21,4,3:53:20 PM,21,4,4:10:52 PM,8,22.4,,-1.316961,36.820337,-1.3270347,36.866736200000005,Rider_Id_444,1052
-Order_No_8272,User_Id_1191,Bike,1,Personal,3,1,1:12:16 PM,3,1,1:14:05 PM,3,1,1:22:33 PM,3,1,1:29:28 PM,3,1,1:52:41 PM,5,20.4,,-1.2974510000000001,36.8210544,-1.2922258,36.779687100000004,Rider_Id_932,1393
-Order_No_9146,User_Id_3213,Bike,1,Personal,18,4,3:11:09 PM,18,4,3:11:17 PM,18,4,3:19:48 PM,18,4,3:22:34 PM,18,4,3:52:39 PM,20,26.7,,-1.2270655000000001,36.8809785,-1.3007534,36.7964576,Rider_Id_281,1805
-Order_No_6919,User_Id_1425,Bike,3,Business,26,5,4:51:55 PM,26,5,4:53:40 PM,26,5,5:35:30 PM,26,5,5:41:20 PM,26,5,6:04:04 PM,10,,,-1.338283,36.8871775,-1.3004062,36.829741,Rider_Id_68,1364
-Order_No_13410,User_Id_830,Bike,3,Business,1,4,12:45:09 PM,1,4,1:54:41 PM,1,4,1:59:40 PM,1,4,2:24:50 PM,1,4,2:24:53 PM,8,,,-1.2551895,36.7822034,-1.2904016,36.82263029999999,Rider_Id_726,3
-Order_No_27084,User_Id_3091,Bike,3,Business,27,2,10:46:34 AM,27,2,10:47:29 AM,27,2,11:02:37 AM,27,2,11:10:28 AM,27,2,11:44:12 AM,14,23.7,,-1.2634438000000001,36.848769700000005,-1.3012008,36.764868,Rider_Id_775,2024
-Order_No_7338,User_Id_2112,Bike,3,Business,22,4,3:31:36 PM,22,4,3:32:03 PM,22,4,3:33:31 PM,22,4,3:55:59 PM,22,4,4:18:35 PM,13,26.6,,-1.2527958000000001,36.8003131,-1.2156006000000001,36.8916865,Rider_Id_708,1356
-Order_No_20885,User_Id_1052,Bike,3,Personal,16,3,9:09:49 AM,16,3,9:15:14 AM,16,3,9:32:06 AM,16,3,9:36:36 AM,16,3,9:52:51 AM,7,20.1,,-1.290894,36.822971,-1.2626906,36.7827022,Rider_Id_290,975
-Order_No_15840,User_Id_510,Bike,3,Business,22,1,11:35:52 AM,22,1,11:36:33 AM,22,1,11:54:22 AM,22,1,11:56:11 AM,22,1,12:33:02 PM,22,24.9,,-1.2860183,36.8975338,-1.225322,36.80855,Rider_Id_96,2211
-Order_No_17507,User_Id_1643,Bike,1,Personal,12,5,6:13:01 PM,12,5,6:13:09 PM,12,5,6:21:11 PM,12,5,6:24:38 PM,12,5,6:56:05 PM,15,25.7,,-1.2658334999999998,36.804286700000006,-1.207546,36.7913976,Rider_Id_881,1887
-Order_No_9887,User_Id_1402,Bike,3,Business,16,2,8:35:03 AM,16,2,8:48:43 AM,16,2,9:03:35 AM,16,2,9:06:44 AM,16,2,9:22:11 AM,8,,,-1.2891921999999998,36.825189,-1.2551895,36.7822034,Rider_Id_726,927
-Order_No_81,User_Id_3339,Bike,3,Business,2,2,3:18:18 PM,2,2,3:18:30 PM,2,2,3:27:28 PM,2,2,3:29:09 PM,2,2,3:29:11 PM,2,29.2,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_405,2
-Order_No_14856,User_Id_3115,Bike,3,Business,1,3,11:54:01 AM,1,3,11:54:13 AM,1,3,11:58:32 AM,1,3,12:09:07 PM,1,3,12:39:08 PM,10,19.9,,-1.2615584,36.814950599999996,-1.2963097,36.7688221,Rider_Id_329,1801
-Order_No_22165,User_Id_517,Bike,3,Business,29,4,10:09:56 AM,29,4,10:10:08 AM,29,4,10:13:31 AM,29,4,10:19:45 AM,29,4,11:01:26 AM,14,18.7,4.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2501
-Order_No_3085,User_Id_435,Bike,3,Business,14,5,12:57:04 PM,14,5,12:57:23 PM,14,5,1:01:28 PM,14,5,1:09:21 PM,14,5,1:17:19 PM,3,22.1,,-1.300921,36.828195,-1.3176092,36.8142275,Rider_Id_178,478
-Order_No_3130,User_Id_3326,Bike,3,Business,6,3,4:21:34 PM,6,3,4:26:52 PM,6,3,4:39:42 PM,6,3,4:44:32 PM,6,3,5:40:34 PM,9,,,-1.2551895,36.7822034,-1.2987732,36.7932128,Rider_Id_269,3362
-Order_No_5268,User_Id_2271,Bike,3,Business,27,3,12:41:56 PM,27,3,1:53:51 PM,27,3,1:58:35 PM,27,3,2:02:50 PM,27,3,2:35:12 PM,14,29.3,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_306,1942
-Order_No_3686,User_Id_2143,Bike,3,Business,18,4,10:12:51 AM,18,4,10:13:24 AM,18,4,10:21:25 AM,18,4,10:27:46 AM,18,4,11:15:37 AM,9,20.4,,-1.296797,36.776452500000005,-1.2575406999999998,36.7868197,Rider_Id_854,2871
-Order_No_7547,User_Id_3291,Bike,3,Business,2,2,1:25:28 PM,2,2,1:31:02 PM,2,2,1:31:08 PM,2,2,1:44:01 PM,2,2,2:24:37 PM,12,29.3,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_518,2436
-Order_No_20437,User_Id_1245,Bike,3,Business,9,6,10:58:31 AM,9,6,11:31:57 AM,9,6,11:38:05 AM,9,6,11:47:53 AM,9,6,12:04:31 PM,7,17.1,,-1.2599563,36.799343900000004,-1.300921,36.828195,Rider_Id_844,998
-Order_No_17289,User_Id_1471,Bike,3,Business,30,2,11:14:42 AM,30,2,11:16:27 AM,30,2,11:41:48 AM,30,2,11:44:55 AM,30,2,12:45:13 PM,8,,,-1.2551895,36.7822034,-1.3037386999999998,36.8175661,Rider_Id_533,3618
-Order_No_5578,User_Id_2216,Bike,3,Business,7,5,10:38:07 AM,7,5,10:39:04 AM,7,5,10:39:52 AM,7,5,10:47:13 AM,7,5,10:55:37 AM,4,,,-1.2551895,36.7822034,-1.2736094999999998,36.8011282,Rider_Id_161,504
-Order_No_4534,User_Id_1205,Bike,3,Business,11,2,2:51:51 PM,11,2,2:55:16 PM,11,2,3:23:00 PM,11,2,3:28:41 PM,11,2,3:52:32 PM,7,22.5,,-1.257309,36.806008399999996,-1.2958383,36.8073162,Rider_Id_268,1431
-Order_No_12383,User_Id_2438,Bike,2,Personal,5,4,9:38:53 AM,5,4,9:39:31 AM,5,4,9:45:40 AM,5,4,9:55:01 AM,5,4,10:01:53 AM,3,,,-1.2753108999999998,36.8130185,-1.2650042,36.790392700000005,Rider_Id_100,412
-Order_No_20353,User_Id_391,Bike,3,Business,22,3,8:36:54 AM,22,3,8:37:45 AM,22,3,9:10:06 AM,22,3,9:51:17 AM,22,3,10:17:27 AM,19,18.8,,-1.300921,36.828195,-1.3334681000000002,36.7116093,Rider_Id_654,1570
-Order_No_6460,User_Id_868,Bike,3,Business,25,4,11:03:43 AM,25,4,11:03:53 AM,25,4,11:08:08 AM,25,4,11:14:58 AM,25,4,11:27:48 AM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_709,770
-Order_No_20521,User_Id_646,Bike,3,Personal,21,5,2:56:20 PM,21,5,3:15:01 PM,21,5,3:45:55 PM,21,5,3:51:17 PM,21,5,4:01:01 PM,3,24.6,,-1.2733047,36.8109744,-1.284289,36.822292,Rider_Id_456,584
-Order_No_3567,User_Id_496,Bike,3,Business,24,5,11:36:52 AM,24,5,11:37:37 AM,24,5,11:50:45 AM,24,5,11:54:42 AM,24,5,12:05:08 PM,4,21.6,,-1.2551895,36.7822034,-1.2662728,36.8151265,Rider_Id_337,626
-Order_No_6029,User_Id_970,Bike,3,Business,16,2,2:21:58 PM,16,2,2:22:11 PM,16,2,2:22:20 PM,16,2,2:28:22 PM,16,2,2:38:39 PM,5,,,-1.2551895,36.7822034,-1.2728069,36.7962362,Rider_Id_291,617
-Order_No_538,User_Id_2280,Bike,2,Personal,24,1,2:23:36 PM,24,1,2:24:57 PM,24,1,2:33:13 PM,24,1,2:37:54 PM,24,1,2:50:25 PM,3,,,-1.274089,36.7997508,-1.2675613000000001,36.8133001,Rider_Id_882,751
-Order_No_24246,User_Id_2987,Bike,3,Business,2,1,1:08:39 PM,2,1,1:10:39 PM,2,1,1:25:08 PM,2,1,1:37:44 PM,2,1,1:58:31 PM,10,20.0,,-1.3043821000000002,36.7840586,-1.2649605,36.798177700000004,Rider_Id_192,1247
-Order_No_14868,User_Id_2216,Bike,3,Business,5,5,1:05:48 PM,5,5,1:06:49 PM,5,5,1:25:26 PM,5,5,2:06:36 PM,5,5,2:20:48 PM,8,26.8,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_247,852
-Order_No_9690,User_Id_3194,Bike,3,Business,15,4,12:00:11 PM,15,4,12:00:22 PM,15,4,12:35:46 PM,15,4,12:48:09 PM,15,4,1:11:33 PM,17,24.3,,-1.2859912,36.8756811,-1.2551895,36.7822034,Rider_Id_589,1404
-Order_No_21347,User_Id_223,Bike,1,Personal,8,1,1:37:10 PM,8,1,1:37:46 PM,8,1,1:50:50 PM,8,1,1:54:34 PM,8,1,2:16:12 PM,12,24.9,,-1.2807298,36.8299116,-1.2155671000000001,36.8898931,Rider_Id_219,1298
-Order_No_10821,User_Id_1370,Bike,1,Personal,7,4,4:30:37 PM,7,4,4:36:21 PM,7,4,5:07:48 PM,7,4,5:19:12 PM,7,4,5:37:34 PM,4,17.5,,-1.276974,36.8226145,-1.261692,36.80161,Rider_Id_808,1102
-Order_No_25346,User_Id_136,Bike,3,Business,4,2,11:48:28 AM,4,2,11:48:52 AM,4,2,11:54:09 AM,4,2,12:04:36 PM,4,2,12:26:10 PM,4,20.7,,-1.2965045,36.7913036,-1.3009676000000001,36.7657189,Rider_Id_155,1294
-Order_No_19058,User_Id_1500,Bike,3,Business,9,3,11:35:26 AM,9,3,11:58:36 AM,9,3,12:00:12 PM,9,3,12:16:38 PM,9,3,1:36:52 PM,8,21.2,,-1.3004062,36.829741,-1.2569006,36.7925141,Rider_Id_543,4814
-Order_No_24132,User_Id_1329,Bike,3,Business,1,4,12:48:21 PM,1,4,12:49:19 PM,1,4,12:58:28 PM,1,4,12:59:51 PM,1,4,1:13:46 PM,4,,,-1.273056,36.811298,-1.2548181,36.7888636,Rider_Id_434,835
-Order_No_13550,User_Id_867,Bike,3,Business,16,3,1:28:59 PM,16,3,1:37:06 PM,16,3,1:48:45 PM,16,3,1:55:19 PM,16,3,2:20:10 PM,10,25.7,,-1.306378,36.7519845,-1.2996533000000001,36.829262899999996,Rider_Id_543,1491
-Order_No_16934,User_Id_136,Bike,3,Business,18,1,2:42:50 PM,18,1,3:13:26 PM,18,1,3:28:04 PM,18,1,3:51:27 PM,18,1,4:25:52 PM,16,17.3,,-1.3326173000000001,36.71462329999999,-1.2649605,36.798177700000004,Rider_Id_543,2065
-Order_No_19091,User_Id_475,Bike,3,Business,8,5,12:30:18 PM,8,5,12:32:02 PM,8,5,12:41:49 PM,8,5,12:46:05 PM,8,5,1:17:46 PM,8,24.2,,-1.2551895,36.7822034,-1.2925919,36.81705770000001,Rider_Id_338,1901
-Order_No_12172,User_Id_202,Bike,1,Personal,2,4,5:52:32 PM,2,4,5:53:01 PM,2,4,6:16:00 PM,2,4,6:28:55 PM,2,4,6:43:30 PM,7,23.5,,-1.287428,36.7800821,-1.2795796,36.819062,Rider_Id_636,875
-Order_No_19208,User_Id_635,Bike,3,Business,13,1,9:39:15 AM,13,1,9:43:06 AM,13,1,9:48:44 AM,13,1,10:18:12 AM,13,1,10:45:48 AM,8,,,-1.300921,36.828195,-1.2910549999999998,36.786744,Rider_Id_591,1656
-Order_No_13986,User_Id_2412,Bike,3,Business,14,4,12:51:56 PM,14,4,1:52:43 PM,14,4,2:42:42 PM,14,4,2:47:56 PM,14,4,3:35:37 PM,11,27.8,,-1.2285834999999998,36.882646799999996,-1.2803414,36.8265456,Rider_Id_686,2861
-Order_No_26243,User_Id_830,Bike,3,Business,12,2,5:58:50 PM,12,2,6:00:43 PM,12,2,6:01:13 PM,12,2,6:10:22 PM,12,2,6:17:43 PM,2,28.3,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_881,441
-Order_No_26637,User_Id_570,Bike,3,Business,2,5,1:51:11 PM,2,5,1:51:52 PM,2,5,2:02:04 PM,2,5,2:03:35 PM,2,5,2:57:21 PM,17,25.3,,-1.2551895,36.7822034,-1.2855846000000002,36.88261129999999,Rider_Id_284,3226
-Order_No_191,User_Id_2869,Bike,3,Business,26,5,9:48:38 AM,26,5,9:49:22 AM,26,5,10:14:26 AM,26,5,10:18:45 AM,26,5,10:52:40 AM,3,,,-1.3014461000000002,36.7661381,-1.2763494,36.7671783,Rider_Id_263,2035
-Order_No_26189,User_Id_1687,Bike,3,Business,21,1,2:09:21 PM,21,1,2:10:21 PM,21,1,2:17:15 PM,21,1,2:33:33 PM,21,1,2:52:10 PM,16,21.6,,-1.2886381000000002,36.81718309,-1.2185721,36.9000783,Rider_Id_633,1117
-Order_No_4620,User_Id_391,Bike,3,Business,12,3,11:42:30 AM,12,3,11:47:25 AM,12,3,11:58:11 AM,12,3,12:30:12 PM,12,3,12:32:21 PM,5,19.5,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_921,129
-Order_No_8173,User_Id_3517,Bike,3,Business,11,1,11:59:54 AM,11,1,12:00:15 PM,11,1,12:08:06 PM,11,1,1:02:48 PM,11,1,1:34:54 PM,16,18.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_340,1926
-Order_No_11200,User_Id_3116,Bike,1,Personal,4,1,3:41:15 PM,4,1,3:41:36 PM,4,1,3:57:31 PM,4,1,4:05:01 PM,4,1,4:21:31 PM,10,22.8,,-1.270766,36.810988,-1.2353665,36.8128044,Rider_Id_116,990
-Order_No_19124,User_Id_2308,Bike,3,Business,3,3,12:33:49 PM,3,3,12:49:39 PM,3,3,1:44:33 PM,3,3,2:15:28 PM,3,3,2:48:44 PM,12,26.6,,-1.2556139,36.9759716,-1.3160976999999998,36.913164200000004,Rider_Id_836,1996
-Order_No_26446,User_Id_635,Bike,3,Business,2,2,9:39:26 AM,2,2,9:40:35 AM,2,2,9:50:51 AM,2,2,10:03:58 AM,2,2,10:33:24 AM,7,20.7,,-1.3081376,36.83297720000001,-1.2643647,36.8127028,Rider_Id_843,1766
-Order_No_13375,User_Id_2866,Bike,3,Business,18,2,3:09:25 PM,18,2,3:28:21 PM,18,2,3:46:02 PM,18,2,3:53:03 PM,18,2,4:08:18 PM,6,25.8,,-1.2551895,36.7822034,-1.265715,36.823815,Rider_Id_547,915
-Order_No_14538,User_Id_1980,Bike,2,Personal,10,1,1:11:55 PM,10,1,1:13:56 PM,10,1,1:27:30 PM,10,1,1:36:50 PM,10,1,1:50:40 PM,4,,,-1.2918239,36.8235665,-1.3057159999999999,36.809882,Rider_Id_672,830
-Order_No_12974,User_Id_909,Bike,3,Business,14,4,1:52:49 PM,14,4,1:53:08 PM,14,4,1:59:23 PM,14,4,2:01:42 PM,14,4,2:25:47 PM,7,27.6,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_295,1445
-Order_No_9148,User_Id_3547,Bike,3,Business,7,2,10:39:52 AM,7,2,10:40:26 AM,7,2,11:11:59 AM,7,2,11:48:44 AM,7,2,12:43:33 PM,11,,,-1.278956,36.8375411,-1.2761408,36.7710839,Rider_Id_790,3289
-Order_No_12512,User_Id_706,Bike,3,Business,19,2,2:15:13 PM,19,2,2:16:12 PM,19,2,2:36:43 PM,19,2,2:39:43 PM,19,2,3:14:51 PM,14,18.5,,-1.3077869,36.844320700000004,-1.3561916999999999,36.910099100000004,Rider_Id_949,2108
-Order_No_6365,User_Id_633,Bike,3,Business,4,1,3:38:49 PM,4,1,3:39:03 PM,4,1,3:39:25 PM,4,1,3:40:27 PM,4,1,3:40:29 PM,6,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_239,2
-Order_No_6514,User_Id_3560,Bike,3,Business,15,5,11:03:13 AM,15,5,11:09:39 AM,15,5,11:13:58 AM,15,5,11:20:27 AM,15,5,11:55:13 AM,4,25.8,,-1.279395,36.825364,-1.2658424,36.8101804,Rider_Id_698,2086
-Order_No_1109,User_Id_455,Bike,3,Business,5,2,3:15:45 PM,5,2,3:16:14 PM,5,2,3:19:47 PM,5,2,3:38:41 PM,5,2,3:56:45 PM,7,27.3,,-1.2956882,36.7861845,-1.2588774999999999,36.8079744,Rider_Id_576,1084
-Order_No_27881,User_Id_769,Bike,1,Personal,15,2,4:38:35 PM,15,2,4:40:18 PM,15,2,5:03:12 PM,15,2,5:10:53 PM,15,2,5:24:31 PM,7,23.5,,-1.2988291,36.7795686,-1.3104076999999998,36.804355,Rider_Id_865,818
-Order_No_25213,User_Id_2304,Bike,3,Personal,26,2,9:57:35 AM,26,2,10:21:54 AM,26,2,10:51:35 AM,26,2,10:52:41 AM,26,2,11:33:29 AM,20,,,-1.2035228,36.7772505,-1.2963097,36.7688221,Rider_Id_101,2448
-Order_No_14661,User_Id_2030,Bike,3,Business,15,1,11:34:50 AM,15,1,11:45:17 AM,15,1,12:03:49 PM,15,1,12:24:43 PM,15,1,12:36:41 PM,6,,,-1.3396256000000002,36.8863135,-1.3333725,36.871761299999996,Rider_Id_101,718
-Order_No_26172,User_Id_2112,Bike,3,Business,12,3,10:43:25 AM,12,3,10:57:36 AM,12,3,10:59:57 AM,12,3,11:12:47 AM,12,3,11:34:20 AM,7,19.4,,-1.2156006000000001,36.8916865,-1.2475028000000001,36.877469700000006,Rider_Id_75,1293
-Order_No_5075,User_Id_3647,Bike,3,Business,10,4,4:15:06 PM,10,4,4:16:02 PM,10,4,4:24:23 PM,10,4,4:25:30 PM,10,4,4:44:57 PM,3,25.0,,-1.2726389999999999,36.794723,-1.2729843,36.8105717,Rider_Id_569,1167
-Order_No_7769,User_Id_1874,Bike,3,Business,23,5,5:47:31 PM,23,5,5:48:48 PM,23,5,6:01:45 PM,23,5,6:45:49 PM,23,5,6:48:51 PM,7,24.4,,-1.2571472,36.795063299999995,-1.2942232,36.790023600000005,Rider_Id_649,182
-Order_No_19594,User_Id_463,Bike,3,Personal,8,3,11:28:55 AM,8,3,11:31:11 AM,8,3,11:33:25 AM,8,3,11:41:41 AM,8,3,12:05:17 PM,12,,,-1.2842665,36.7967626,-1.2220632,36.8427636,Rider_Id_564,1416
-Order_No_9323,User_Id_3194,Bike,3,Business,30,3,10:36:13 AM,30,3,10:37:17 AM,30,3,10:42:36 AM,30,3,10:51:24 AM,30,3,11:08:15 AM,8,25.1,,-1.2551895,36.7822034,-1.2597436000000002,36.8282526,Rider_Id_691,1011
-Order_No_2673,User_Id_3234,Bike,3,Business,8,5,9:49:41 AM,8,5,9:50:11 AM,8,5,10:22:06 AM,8,5,10:58:12 AM,8,5,11:36:25 AM,18,,,-1.3038543,36.83810510000001,-1.3343138,36.70867929999999,Rider_Id_618,2293
-Order_No_17934,User_Id_3222,Bike,3,Business,18,5,2:30:31 PM,18,5,2:31:08 PM,18,5,2:39:01 PM,18,5,2:42:16 PM,18,5,2:50:55 PM,5,26.9,,-1.2551895,36.7822034,-1.2592947,36.7698085,Rider_Id_566,519
-Order_No_7643,User_Id_1679,Bike,1,Personal,18,4,6:23:45 PM,18,4,6:24:39 PM,18,4,6:28:34 PM,18,4,6:45:38 PM,18,4,7:05:18 PM,5,23.5,,-1.2629666000000002,36.8068369,-1.2839568000000001,36.823310600000006,Rider_Id_956,1180
-Order_No_21444,User_Id_391,Bike,3,Business,21,5,10:16:36 AM,21,5,10:28:28 AM,21,5,10:35:58 AM,21,5,10:45:06 AM,21,5,11:57:33 AM,21,22.7,,-1.319613,36.711055,-1.2867454,36.8259017,Rider_Id_831,4347
-Order_No_5053,User_Id_635,Bike,3,Business,25,1,1:40:32 PM,25,1,1:40:50 PM,25,1,1:45:30 PM,25,1,2:13:32 PM,25,1,2:32:20 PM,7,28.2,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_550,1128
-Order_No_23558,User_Id_2174,Bike,3,Business,29,3,10:42:12 AM,29,3,10:42:12 AM,29,3,10:43:33 AM,29,3,11:45:25 AM,29,3,11:53:27 AM,29,,,-1.2482781,36.884091999999995,-1.3927559999999999,36.7495015,Rider_Id_86,482
-Order_No_17875,User_Id_3647,Bike,3,Business,29,3,9:26:46 AM,29,3,9:28:07 AM,29,3,9:37:41 AM,29,3,9:43:01 AM,29,3,9:53:50 AM,5,18.0,,-1.2726389999999999,36.794723,-1.2938802,36.793938700000005,Rider_Id_655,649
-Order_No_9442,User_Id_1245,Bike,3,Business,27,3,8:04:24 AM,27,3,8:08:20 AM,27,3,8:54:09 AM,27,3,9:31:42 AM,27,3,10:01:51 AM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_581,1809
-Order_No_27848,User_Id_868,Bike,3,Business,15,3,9:39:37 AM,15,3,10:05:19 AM,15,3,10:12:47 AM,15,3,10:23:03 AM,15,3,10:59:16 AM,17,,,-1.3181159,36.710155900000004,-1.265715,36.823815,Rider_Id_326,2173
-Order_No_26861,User_Id_2587,Bike,3,Business,21,5,2:12:33 PM,21,5,2:12:45 PM,21,5,2:28:47 PM,21,5,2:46:15 PM,21,5,3:01:53 PM,6,27.3,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_336,938
-Order_No_28106,User_Id_3080,Bike,3,Business,26,5,3:14:21 PM,26,5,3:15:26 PM,26,5,3:41:04 PM,26,5,3:45:09 PM,26,5,4:25:59 PM,8,,,-1.2551895,36.7822034,-1.285928,36.788121999999994,Rider_Id_660,2450
-Order_No_564,User_Id_3017,Bike,3,Business,13,5,7:05:02 PM,13,5,7:05:35 PM,13,5,7:14:42 PM,13,5,7:16:35 PM,13,5,7:29:52 PM,3,19.2,,-1.299313,36.78314879999999,-1.2894256000000002,36.7620285,Rider_Id_648,797
-Order_No_6014,User_Id_1075,Bike,3,Business,18,1,12:29:09 PM,18,1,12:30:19 PM,18,1,1:08:26 PM,18,1,1:17:34 PM,18,1,1:57:28 PM,18,28.2,,-1.2852569,36.8976121,-1.2527958000000001,36.8003131,Rider_Id_697,2394
-Order_No_27611,User_Id_1882,Bike,3,Business,11,7,8:41:37 AM,11,7,8:42:07 AM,11,7,9:24:13 AM,11,7,9:45:29 AM,11,7,10:34:48 AM,12,20.7,,-1.2958783,36.7928125,-1.3169591,36.7000029,Rider_Id_593,2959
-Order_No_24925,User_Id_3355,Bike,3,Business,9,1,3:49:46 PM,9,1,4:09:52 PM,9,1,4:15:55 PM,9,1,4:21:06 PM,9,1,4:49:03 PM,12,21.0,,-1.3052491000000002,36.8223899,-1.2927792,36.9053492,Rider_Id_116,1677
-Order_No_21688,User_Id_2481,Bike,2,Personal,2,7,5:53:27 PM,2,7,5:53:35 PM,2,7,6:23:07 PM,2,7,6:43:03 PM,2,7,7:06:02 PM,13,,,-1.3205294,36.819726200000005,-1.3705953000000002,36.74066989999999,Rider_Id_179,1379
-Order_No_352,User_Id_3599,Bike,3,Business,23,4,11:16:19 AM,23,4,11:17:28 AM,23,4,11:59:34 AM,23,4,12:11:38 PM,23,4,1:49:44 PM,8,23.2,,-1.3302996,36.8707935,-1.2953659,36.82494620000001,Rider_Id_787,5886
-Order_No_18625,User_Id_1227,Bike,1,Personal,4,2,10:49:23 AM,4,2,10:50:57 AM,4,2,11:17:45 AM,4,2,11:29:16 AM,4,2,11:53:43 AM,8,18.8,,-1.269534,36.80697,-1.2929747,36.76463579999999,Rider_Id_117,1467
-Order_No_17253,User_Id_2982,Bike,3,Business,8,3,11:58:20 AM,8,3,11:58:35 AM,8,3,11:58:52 AM,8,3,12:47:58 PM,8,3,1:34:48 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_581,2810
-Order_No_17498,User_Id_2200,Bike,3,Business,27,3,2:27:45 PM,27,3,4:17:02 PM,27,3,4:27:00 PM,27,3,4:35:05 PM,27,3,5:09:53 PM,9,27.5,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_43,2088
-Order_No_9749,User_Id_2627,Bike,3,Business,23,5,10:10:24 AM,23,5,10:22:19 AM,23,5,10:39:21 AM,23,5,10:50:55 AM,23,5,11:39:26 AM,17,24.1,,-1.3472066,36.7692638,-1.2571472,36.795063299999995,Rider_Id_254,2911
-Order_No_19373,User_Id_3540,Bike,2,Personal,25,3,8:16:29 AM,25,3,8:16:39 AM,25,3,8:27:28 AM,25,3,8:32:42 AM,25,3,9:16:08 AM,17,13.7,,-1.265324,36.782244,-1.3006051,36.680343900000004,Rider_Id_379,2606
-Order_No_15745,User_Id_3291,Bike,3,Business,21,2,9:59:40 AM,21,2,10:00:05 AM,21,2,10:38:06 AM,21,2,11:47:35 AM,21,2,11:59:21 AM,22,22.7,,-1.335477,36.930829700000004,-1.2600926000000001,36.8088685,Rider_Id_404,706
-Order_No_17927,User_Id_2763,Bike,3,Business,25,4,2:39:30 PM,25,4,2:39:52 PM,25,4,2:48:41 PM,25,4,2:54:51 PM,25,4,3:16:30 PM,7,20.9,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_337,1299
-Order_No_28095,User_Id_633,Bike,3,Business,16,3,2:29:26 PM,16,3,2:30:29 PM,16,3,2:46:02 PM,16,3,3:01:39 PM,16,3,3:09:49 PM,4,26.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_188,490
-Order_No_22895,User_Id_2801,Bike,3,Business,30,4,2:36:28 PM,30,4,2:37:02 PM,30,4,2:48:53 PM,30,4,2:50:59 PM,30,4,3:10:08 PM,10,,,-1.2674273999999999,36.787082700000006,-1.301005,36.8306195,Rider_Id_205,1149
-Order_No_1537,User_Id_2394,Bike,3,Personal,13,3,6:41:59 PM,13,3,6:43:14 PM,13,3,6:54:10 PM,13,3,7:01:00 PM,13,3,7:34:20 PM,17,26.0,,-1.2927792,36.9053492,-1.2722185,36.792252500000004,Rider_Id_622,2000
-Order_No_14338,User_Id_1271,Bike,3,Business,13,2,10:13:30 AM,13,2,10:40:53 AM,13,2,10:50:50 AM,13,2,10:57:11 AM,13,2,11:17:27 AM,10,22.3,,-1.2943371000000001,36.8267,-1.3107918,36.761794,Rider_Id_276,1216
-Order_No_11437,User_Id_2956,Bike,3,Business,2,2,4:41:13 PM,2,2,4:42:25 PM,2,2,4:51:51 PM,2,2,4:53:04 PM,2,2,5:08:48 PM,3,28.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_913,944
-Order_No_17559,User_Id_2866,Bike,3,Business,24,1,12:15:32 PM,24,1,12:34:20 PM,24,1,12:36:38 PM,24,1,12:45:29 PM,24,1,1:07:22 PM,15,22.7,,-1.2551895,36.7822034,-1.2178921999999999,36.8881236,Rider_Id_848,1313
-Order_No_12197,User_Id_2703,Bike,3,Personal,28,1,9:32:42 AM,28,1,9:33:22 AM,28,1,9:40:53 AM,28,1,9:42:56 AM,28,1,9:58:50 AM,3,19.3,,-1.2963097,36.7688221,-1.2991116000000003,36.7960658,Rider_Id_548,954
-Order_No_22513,User_Id_867,Bike,3,Business,31,5,12:29:16 PM,31,5,12:36:10 PM,31,5,1:09:37 PM,31,5,1:14:13 PM,31,5,1:48:03 PM,14,21.5,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_676,2030
-Order_No_12141,User_Id_393,Bike,3,Business,10,1,9:45:23 AM,10,1,9:45:39 AM,10,1,9:48:17 AM,10,1,10:01:58 AM,10,1,10:26:19 AM,11,16.4,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_36,1461
-Order_No_27157,User_Id_1500,Bike,3,Business,9,4,12:57:51 PM,9,4,12:58:45 PM,9,4,12:59:22 PM,9,4,1:12:40 PM,9,4,1:31:36 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_168,1136
-Order_No_223,User_Id_540,Bike,3,Business,2,5,12:00:52 PM,2,5,12:11:40 PM,2,5,12:34:03 PM,2,5,12:38:20 PM,2,5,12:58:29 PM,9,23.5,,-1.3036803,36.8181344,-1.2551895,36.7822034,Rider_Id_199,1209
-Order_No_14975,User_Id_867,Bike,3,Business,9,5,11:48:09 AM,9,5,11:48:16 AM,9,5,11:48:22 AM,9,5,11:57:45 AM,9,5,12:29:47 PM,14,25.9,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_97,1922
-Order_No_26490,User_Id_2701,Bike,3,Business,9,5,12:12:00 PM,9,5,12:16:24 PM,9,5,12:21:38 PM,9,5,12:24:51 PM,9,5,12:49:42 PM,8,25.5,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_660,1491
-Order_No_3135,User_Id_635,Bike,3,Business,13,3,3:46:22 PM,13,3,3:54:20 PM,13,3,4:00:24 PM,13,3,4:30:08 PM,13,3,4:55:46 PM,8,23.6,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_406,1538
-Order_No_3611,User_Id_3631,Bike,3,Personal,9,2,10:42:00 AM,9,2,10:43:37 AM,9,2,10:50:23 AM,9,2,10:55:31 AM,9,2,11:02:14 AM,2,22.0,,-1.2963097,36.7688221,-1.3043649,36.7816132,Rider_Id_750,403
-Order_No_10626,User_Id_901,Bike,1,Personal,18,3,9:30:59 AM,18,3,11:21:20 AM,18,3,11:36:30 AM,18,3,11:39:25 AM,18,3,12:16:33 PM,17,20.0,,-1.3688433,36.7402114,-1.2962832,36.784968299999996,Rider_Id_74,2228
-Order_No_8292,User_Id_246,Bike,3,Business,25,2,4:48:05 PM,25,2,4:48:13 PM,25,2,4:48:37 PM,25,2,4:55:51 PM,25,2,5:04:54 PM,3,24.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_881,543
-Order_No_4994,User_Id_3470,Bike,3,Business,8,3,5:40:32 PM,8,3,5:42:44 PM,8,3,5:42:48 PM,8,3,5:52:13 PM,8,3,7:03:46 PM,11,,,-1.28878,36.8168312,-1.2916953000000002,36.8855273,Rider_Id_61,4293
-Order_No_25325,User_Id_3688,Bike,3,Business,23,5,12:27:48 PM,23,5,12:28:41 PM,23,5,12:35:10 PM,23,5,12:39:44 PM,23,5,12:58:09 PM,6,24.1,,-1.2551895,36.7822034,-1.2655306,36.828148,Rider_Id_228,1105
-Order_No_24086,User_Id_2330,Bike,3,Business,25,4,5:18:41 PM,25,4,5:18:52 PM,25,4,6:07:12 PM,25,4,6:11:54 PM,25,4,7:02:58 PM,16,,,-1.2915246999999999,36.805575700000006,-1.2925641,36.897644899999996,Rider_Id_678,3064
-Order_No_21763,User_Id_893,Bike,3,Business,4,1,12:07:21 PM,4,1,12:08:17 PM,4,1,12:08:58 PM,4,1,12:38:25 PM,4,1,12:54:18 PM,5,25.7,,-1.2825953,36.8152516,-1.2663194,36.809901,Rider_Id_267,953
-Order_No_20280,User_Id_3201,Bike,3,Business,7,5,8:32:44 AM,7,5,8:33:36 AM,7,5,8:39:41 AM,7,5,8:52:23 AM,7,5,9:49:25 AM,11,,,-1.2584143,36.8048002,-1.3053578000000001,36.860791299999995,Rider_Id_72,3422
-Order_No_18352,User_Id_2680,Bike,3,Business,1,4,10:07:01 AM,1,4,10:08:47 AM,1,4,10:34:02 AM,1,4,10:40:42 AM,1,4,11:13:41 AM,8,,,-1.2857521,36.82774570000001,-1.3039706,36.7688174,Rider_Id_554,1979
-Order_No_22851,User_Id_2330,Bike,3,Business,11,4,12:38:21 PM,11,4,12:40:13 PM,11,4,12:54:42 PM,11,4,12:57:04 PM,11,4,1:15:12 PM,9,25.8,,-1.2124582,36.802894,-1.2642903,36.80021370000001,Rider_Id_695,1088
-Order_No_27174,User_Id_59,Bike,3,Business,7,4,2:54:43 PM,7,4,2:55:02 PM,7,4,3:04:00 PM,7,4,3:20:48 PM,7,4,4:06:32 PM,6,31.1,,-1.290894,36.822971,-1.2726925,36.7951829,Rider_Id_831,2744
-Order_No_18678,User_Id_246,Bike,3,Business,24,3,12:40:11 PM,24,3,12:41:13 PM,24,3,12:47:43 PM,24,3,12:50:48 PM,24,3,1:45:56 PM,6,19.7,,-1.2584143,36.8048002,-1.2876659,36.80467879999999,Rider_Id_660,3308
-Order_No_7022,User_Id_186,Bike,3,Business,8,1,4:07:29 PM,8,1,4:09:09 PM,8,1,4:23:40 PM,8,1,4:38:48 PM,8,1,5:31:18 PM,4,18.5,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_215,3150
-Order_No_7861,User_Id_469,Bike,3,Personal,15,6,12:42:34 PM,15,6,12:43:42 PM,15,6,12:55:41 PM,15,6,1:08:54 PM,15,6,1:31:19 PM,11,21.9,,-1.3162018999999998,36.81332379999999,-1.3253826000000002,36.8822315,Rider_Id_620,1345
-Order_No_13144,User_Id_346,Bike,3,Business,17,5,3:43:33 PM,17,5,3:44:15 PM,17,5,4:12:51 PM,17,5,4:17:40 PM,17,5,4:46:41 PM,12,,,-1.28878,36.8168312,-1.3472066,36.7692638,Rider_Id_815,1741
-Order_No_14593,User_Id_3647,Bike,3,Business,3,3,4:57:23 PM,3,3,4:58:32 PM,3,3,5:29:48 PM,3,3,5:36:58 PM,3,3,6:27:58 PM,3,27.6,,-1.2726389999999999,36.794723,-1.2727979999999999,36.8124195,Rider_Id_690,3060
-Order_No_20387,User_Id_718,Bike,3,Business,25,3,9:37:45 AM,25,3,9:38:00 AM,25,3,9:44:22 AM,25,3,9:49:50 AM,25,3,11:57:16 AM,22,13.7,,-1.2551895,36.7822034,-1.3309482,36.9229943,Rider_Id_519,7646
-Order_No_12657,User_Id_3283,Bike,3,Business,31,3,12:50:21 PM,31,3,1:05:49 PM,31,3,1:32:40 PM,31,3,1:48:12 PM,31,3,2:23:57 PM,18,26.9,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_715,2145
-Order_No_7754,User_Id_325,Bike,3,Business,29,2,9:55:11 AM,29,2,9:55:26 AM,29,2,10:01:14 AM,29,2,10:08:23 AM,29,2,10:16:22 AM,3,16.4,,-1.2632355,36.7796905,-1.2553615,36.789537599999996,Rider_Id_103,479
-Order_No_11331,User_Id_2956,Bike,3,Business,2,2,5:41:04 PM,2,2,5:41:42 PM,2,2,5:46:36 PM,2,2,5:58:26 PM,2,2,6:24:52 PM,14,28.4,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_7,1586
-Order_No_21560,User_Id_1719,Bike,3,Business,9,3,9:18:58 AM,9,3,9:19:12 AM,9,3,9:47:24 AM,9,3,9:50:06 AM,9,3,10:00:10 AM,3,20.4,,-1.2859206,36.78882720000001,-1.296797,36.776452500000005,Rider_Id_315,604
-Order_No_13831,User_Id_2200,Bike,3,Business,26,1,3:56:53 PM,26,1,4:21:39 PM,26,1,4:31:48 PM,26,1,4:36:46 PM,26,1,4:45:33 PM,2,19.8,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_942,527
-Order_No_14216,User_Id_3565,Bike,3,Business,30,5,4:07:01 PM,30,5,4:07:23 PM,30,5,4:12:22 PM,30,5,4:24:11 PM,30,5,4:47:34 PM,9,24.5,,-1.2844665000000002,36.7866113,-1.3207146,36.8305771,Rider_Id_609,1403
-Order_No_11996,User_Id_2523,Bike,3,Business,22,5,3:37:01 PM,22,5,3:38:23 PM,22,5,3:49:21 PM,22,5,3:58:18 PM,22,5,5:00:29 PM,8,27.5,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_831,3731
-Order_No_16431,User_Id_1882,Bike,1,Business,6,6,9:29:42 AM,6,6,9:30:50 AM,6,6,10:19:30 AM,6,6,10:27:17 AM,6,6,10:48:31 AM,8,19.1,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_257,1274
-Order_No_21103,User_Id_2982,Bike,3,Business,2,3,3:00:09 PM,2,3,3:00:31 PM,2,3,3:19:16 PM,2,3,3:46:12 PM,2,3,4:16:09 PM,18,26.0,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_457,1797
-Order_No_19764,User_Id_2523,Bike,3,Business,16,1,10:47:26 AM,16,1,11:26:50 AM,16,1,11:35:40 AM,16,1,11:39:15 AM,16,1,12:08:11 PM,8,19.5,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_140,1736
-Order_No_23521,User_Id_1148,Bike,3,Business,28,4,10:39:26 AM,28,4,11:05:32 AM,28,4,11:19:41 AM,28,4,11:21:25 AM,28,4,11:54:12 AM,10,28.0,,-1.3100586,36.850032899999995,-1.2649605,36.798177700000004,Rider_Id_61,1967
-Order_No_21260,User_Id_2956,Bike,3,Business,15,5,8:18:49 AM,15,5,8:19:19 AM,15,5,8:24:26 AM,15,5,8:26:28 AM,15,5,8:37:01 AM,7,19.1,,-1.2584143,36.8048002,-1.2801883,36.8234728,Rider_Id_682,633
-Order_No_5310,User_Id_3311,Bike,3,Personal,10,5,12:16:17 PM,10,5,12:16:34 PM,10,5,12:21:32 PM,10,5,12:26:00 PM,10,5,12:43:55 PM,6,,,-1.2944292,36.779356899999996,-1.2702347999999999,36.812096000000004,Rider_Id_259,1075
-Order_No_26017,User_Id_1389,Bike,3,Business,25,4,1:21:58 PM,25,4,1:24:03 PM,25,4,1:32:09 PM,25,4,1:38:11 PM,25,4,2:00:28 PM,14,20.9,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_32,1337
-Order_No_7632,User_Id_3355,Bike,3,Business,22,2,2:55:47 PM,22,2,2:56:18 PM,22,2,3:23:21 PM,22,2,3:50:04 PM,22,2,4:22:38 PM,13,21.3,,-1.2297202,36.874550799999994,-1.3052491000000002,36.8223899,Rider_Id_771,1954
-Order_No_15857,User_Id_393,Bike,3,Business,13,4,4:01:04 PM,13,4,4:01:14 PM,13,4,4:02:13 PM,13,4,4:18:43 PM,13,4,4:18:45 PM,4,23.0,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_863,2
-Order_No_13138,User_Id_633,Bike,3,Business,28,4,11:33:09 AM,28,4,11:33:24 AM,28,4,11:43:24 AM,28,4,11:44:49 AM,28,4,11:54:56 AM,4,28.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_114,607
-Order_No_27819,User_Id_1245,Bike,3,Business,9,4,2:20:00 PM,9,4,2:22:24 PM,9,4,2:25:01 PM,9,4,2:47:19 PM,9,4,3:20:43 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_728,2004
-Order_No_23932,User_Id_1687,Bike,3,Business,7,5,11:44:12 AM,7,5,11:50:50 AM,7,5,12:00:00 PM,7,5,12:02:21 PM,7,5,12:36:40 PM,3,25.4,,-1.2626574,36.8014034,-1.2755629,36.813152200000005,Rider_Id_857,2059
-Order_No_877,User_Id_310,Bike,3,Personal,24,1,11:19:27 AM,24,1,11:21:15 AM,24,1,11:32:34 AM,24,1,11:42:56 AM,24,1,12:21:39 PM,16,22.6,,-1.1855066,36.825464600000004,-1.2824535,36.8265153,Rider_Id_281,2323
-Order_No_18583,User_Id_420,Bike,3,Business,19,2,4:04:32 PM,19,2,4:04:53 PM,19,2,4:51:17 PM,19,2,4:56:21 PM,19,2,6:21:29 PM,25,28.0,,-1.3175758999999998,36.7059218,-1.208956,36.8162198,Rider_Id_805,5108
-Order_No_3273,User_Id_2521,Bike,3,Business,27,4,1:46:29 PM,27,4,1:46:55 PM,27,4,1:47:02 PM,27,4,2:39:22 PM,27,4,2:40:15 PM,5,25.0,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_10,53
-Order_No_24045,User_Id_1244,Bike,3,Business,25,1,11:41:41 AM,25,1,11:41:55 AM,25,1,11:45:18 AM,25,1,11:53:12 AM,25,1,12:33:18 PM,7,27.0,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_383,2406
-Order_No_14933,User_Id_59,Bike,3,Business,13,2,11:34:05 AM,13,2,11:45:21 AM,13,2,11:48:10 AM,13,2,12:04:13 PM,13,2,12:41:59 PM,7,22.5,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_360,2266
-Order_No_16924,User_Id_2054,Bike,1,Personal,19,4,11:06:41 AM,19,4,11:10:06 AM,19,4,12:07:30 PM,19,4,12:08:37 PM,19,4,12:28:13 PM,11,20.2,,-1.3507360000000002,36.8960004,-1.312734,36.855414,Rider_Id_726,1176
-Order_No_14493,User_Id_1754,Bike,3,Business,6,2,9:58:04 AM,6,2,9:58:26 AM,6,2,10:21:37 AM,6,2,10:25:16 AM,6,2,10:55:36 AM,12,19.9,,-1.2860183,36.8975338,-1.3052491000000002,36.8223899,Rider_Id_237,1820
-Order_No_25509,User_Id_71,Bike,3,Business,4,3,12:38:48 PM,4,3,12:39:44 PM,4,3,12:53:22 PM,4,3,12:56:26 PM,4,3,1:59:34 PM,9,17.2,,-1.2891921999999998,36.825189,-1.2646084,36.8028645,Rider_Id_498,3788
-Order_No_4584,User_Id_1372,Bike,3,Business,19,2,10:54:08 AM,19,2,10:55:06 AM,19,2,11:03:35 AM,19,2,11:08:04 AM,19,2,11:46:47 AM,29,26.4,,-1.3193233,36.840990999999995,-1.1729239,36.9666617,Rider_Id_404,2323
-Order_No_9494,User_Id_1500,Bike,3,Business,24,3,9:47:41 AM,24,3,9:47:52 AM,24,3,9:54:56 AM,24,3,9:59:17 AM,24,3,10:19:36 AM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_411,1219
-Order_No_2160,User_Id_71,Bike,3,Business,9,6,10:14:14 AM,9,6,10:16:26 AM,9,6,10:20:04 AM,9,6,10:24:47 AM,9,6,10:29:45 AM,2,15.2,,-1.279395,36.825364,-1.2821108,36.8250555,Rider_Id_379,298
-Order_No_24811,User_Id_391,Bike,3,Business,15,1,3:44:28 PM,15,1,3:50:40 PM,15,1,3:55:54 PM,15,1,4:20:59 PM,15,1,4:21:04 PM,5,,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_99,5
-Order_No_19315,User_Id_2582,Bike,3,Business,15,5,10:17:49 AM,15,5,10:34:22 AM,15,5,10:38:16 AM,15,5,10:45:59 AM,15,5,11:03:10 AM,9,26.2,,-1.3273709999999999,36.840590999999996,-1.2703801000000001,36.7985261,Rider_Id_411,1031
-Order_No_5068,User_Id_3147,Bike,3,Business,8,1,9:46:22 AM,8,1,9:46:38 AM,8,1,9:50:22 AM,8,1,9:59:23 AM,8,1,10:34:32 AM,23,19.6,,-1.3467388999999999,36.7690312,-1.3002993,36.9153417,Rider_Id_420,2109
-Order_No_16799,User_Id_2147,Bike,3,Personal,20,1,1:26:59 PM,20,1,1:28:11 PM,20,1,2:02:46 PM,20,1,2:09:47 PM,20,1,3:17:23 PM,21,,,-1.2575690000000002,36.842164000000004,-1.3334681000000002,36.7116093,Rider_Id_155,4056
-Order_No_12251,User_Id_1976,Bike,3,Business,15,6,9:43:01 AM,15,6,9:43:16 AM,15,6,9:55:41 AM,15,6,10:03:17 AM,15,6,10:23:28 AM,14,17.7,,-1.326471,36.8487849,-1.257028,36.7810435,Rider_Id_882,1211
-Order_No_7227,User_Id_2069,Bike,3,Business,23,4,4:30:31 PM,23,4,4:32:02 PM,23,4,4:38:11 PM,23,4,4:56:31 PM,23,4,5:43:37 PM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_20,2826
-Order_No_5397,User_Id_3291,Bike,3,Business,2,2,12:01:11 PM,2,2,12:07:20 PM,2,2,12:25:47 PM,2,2,12:34:40 PM,2,2,12:52:34 PM,5,26.3,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_907,1074
-Order_No_623,User_Id_1402,Bike,3,Business,21,4,2:25:32 PM,21,4,2:26:55 PM,21,4,2:46:50 PM,21,4,3:06:09 PM,21,4,3:15:14 PM,1,31.2,,-1.2793686000000002,36.82592329999999,-1.2813012,36.832396200000005,Rider_Id_61,545
-Order_No_8968,User_Id_2561,Bike,3,Business,15,1,9:25:43 AM,15,1,9:27:03 AM,15,1,9:33:10 AM,15,1,9:50:19 AM,15,1,10:06:59 AM,6,20.7,,-1.2551895,36.7822034,-1.2888261,36.8145446,Rider_Id_161,1000
-Order_No_457,User_Id_478,Bike,3,Business,15,3,12:04:38 PM,15,3,12:05:00 PM,15,3,12:05:07 PM,15,3,12:15:54 PM,15,3,12:39:26 PM,8,,,-1.2551895,36.7822034,-1.3045333000000001,36.82280720000001,Rider_Id_576,1412
-Order_No_10255,User_Id_3091,Bike,3,Business,3,3,12:27:08 PM,3,3,12:27:35 PM,3,3,12:37:11 PM,3,3,12:41:23 PM,3,3,1:06:19 PM,8,24.1,,-1.3012008,36.764868,-1.2551895,36.7822034,Rider_Id_535,1496
-Order_No_19601,User_Id_3147,Bike,3,Business,21,3,10:12:07 AM,21,3,10:12:23 AM,21,3,10:20:18 AM,21,3,10:28:55 AM,21,3,10:57:52 AM,12,16.8,1.1,-1.2831203000000002,36.8316065,-1.3472066,36.7692638,Rider_Id_622,1737
-Order_No_20843,User_Id_136,Bike,3,Business,21,2,1:10:45 PM,21,2,1:21:09 PM,21,2,1:25:40 PM,21,2,1:28:47 PM,21,2,1:55:02 PM,14,22.5,,-1.3254522,36.721658399999995,-1.2904993,36.80438770000001,Rider_Id_207,1575
-Order_No_13714,User_Id_868,Bike,3,Business,8,1,11:47:10 AM,8,1,11:50:27 AM,8,1,11:52:08 AM,8,1,12:10:43 PM,8,1,12:10:51 PM,3,28.1,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_564,8
-Order_No_8723,User_Id_1271,Bike,3,Business,10,1,3:01:35 PM,10,1,3:19:41 PM,10,1,3:57:39 PM,10,1,4:17:08 PM,10,1,4:59:36 PM,27,18.9,,-1.3244885,36.897791999999995,-1.3204931,36.7043102,Rider_Id_12,2548
-Order_No_27813,User_Id_552,Bike,3,Business,10,5,4:41:39 PM,10,5,4:50:44 PM,10,5,5:12:38 PM,10,5,5:18:05 PM,10,5,6:10:47 PM,18,,,-1.28878,36.8168312,-1.3562374,36.904295399999995,Rider_Id_416,3162
-Order_No_8579,User_Id_3291,Bike,3,Business,7,3,2:25:02 PM,7,3,2:25:16 PM,7,3,2:30:22 PM,7,3,2:37:30 PM,7,3,2:47:45 PM,4,28.3,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_932,615
-Order_No_12178,User_Id_3080,Bike,3,Business,5,3,10:36:27 AM,5,3,10:36:48 AM,5,3,11:00:01 AM,5,3,11:31:15 AM,5,3,11:51:38 AM,22,22.2,,-1.3195527,36.891105200000005,-1.2551895,36.7822034,Rider_Id_543,1223
-Order_No_18812,User_Id_1281,Bike,3,Business,29,5,12:28:03 PM,29,5,12:31:01 PM,29,5,12:52:57 PM,29,5,12:55:45 PM,29,5,1:19:41 PM,12,19.7,,-1.275442,36.766020700000006,-1.2767064,36.8227148,Rider_Id_808,1436
-Order_No_22387,User_Id_3689,Bike,3,Business,10,2,3:35:33 PM,10,2,4:21:22 PM,10,2,4:35:07 PM,10,2,4:55:02 PM,10,2,5:01:08 PM,5,20.8,,-1.2527958000000001,36.8003131,-1.2857548,36.819062,Rider_Id_606,366
-Order_No_8538,User_Id_1548,Bike,1,Personal,9,1,5:28:38 PM,9,1,5:30:07 PM,9,1,5:46:41 PM,9,1,5:55:11 PM,9,1,6:10:33 PM,4,20.3,,-1.2950408000000002,36.809817200000005,-1.3136451999999998,36.7974014,Rider_Id_312,922
-Order_No_26431,User_Id_1243,Bike,3,Business,13,3,11:50:00 AM,13,3,11:50:40 AM,13,3,11:52:19 AM,13,3,12:01:06 PM,13,3,12:30:35 PM,9,,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_761,1769
-Order_No_23621,User_Id_634,Bike,1,Personal,30,1,5:12:42 PM,30,1,5:12:57 PM,30,1,5:19:33 PM,30,1,5:32:08 PM,30,1,5:32:36 PM,3,16.0,,-1.2861764999999998,36.8209086,-1.2651643,36.807221399999996,Rider_Id_857,28
-Order_No_2870,User_Id_3222,Bike,3,Business,8,1,12:05:53 PM,8,1,12:06:22 PM,8,1,12:13:44 PM,8,1,12:16:12 PM,8,1,12:26:06 PM,5,24.5,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_178,594
-Order_No_4364,User_Id_3355,Bike,3,Business,14,1,10:57:59 AM,14,1,10:58:31 AM,14,1,11:35:00 AM,14,1,11:49:40 AM,14,1,12:16:55 PM,13,23.6,,-1.32943,36.894242999999996,-1.3052491000000002,36.8223899,Rider_Id_837,1635
-Order_No_14437,User_Id_186,Bike,3,Business,8,4,10:13:48 AM,8,4,10:17:20 AM,8,4,10:29:16 AM,8,4,10:39:04 AM,8,4,11:19:19 AM,23,26.3,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_532,2415
-Order_No_5102,User_Id_1363,Bike,3,Business,6,1,1:29:52 PM,6,1,1:31:44 PM,6,1,1:39:17 PM,6,1,1:50:49 PM,6,1,2:06:56 PM,4,24.5,,-1.3004062,36.829741,-1.2904993,36.80438770000001,Rider_Id_471,967
-Order_No_22563,User_Id_2069,Bike,3,Business,24,4,10:47:25 AM,24,4,10:47:38 AM,24,4,10:48:22 AM,24,4,11:01:01 AM,24,4,11:11:19 AM,9,24.5,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_86,618
-Order_No_18351,User_Id_3647,Bike,3,Business,6,4,4:16:36 PM,6,4,4:17:31 PM,6,4,4:26:53 PM,6,4,4:45:37 PM,6,4,4:58:54 PM,5,22.6,,-1.2726389999999999,36.794723,-1.2891063999999999,36.7743692,Rider_Id_857,797
-Order_No_12292,User_Id_635,Bike,3,Business,11,4,10:20:24 AM,11,4,10:20:32 AM,11,4,10:21:39 AM,11,4,10:28:20 AM,11,4,10:45:16 AM,8,22.0,,-1.2602337,36.79905479999999,-1.292188,36.764855,Rider_Id_831,1016
-Order_No_12294,User_Id_438,Bike,3,Personal,13,4,5:02:16 PM,13,4,5:02:34 PM,13,4,5:08:44 PM,13,4,5:13:10 PM,13,4,5:30:25 PM,6,22.7,,-1.2564309999999999,36.779887,-1.2563576,36.7881585,Rider_Id_709,1035
-Order_No_1231,User_Id_1329,Bike,3,Business,19,2,2:02:19 PM,19,2,2:02:53 PM,19,2,2:29:22 PM,19,2,2:35:08 PM,19,2,3:12:06 PM,4,30.2,,-1.273056,36.811298,-1.284835,36.8152125,Rider_Id_795,2218
-Order_No_18663,User_Id_1075,Bike,3,Business,18,5,2:58:49 PM,18,5,3:00:28 PM,18,5,3:02:06 PM,18,5,3:24:52 PM,18,5,3:43:24 PM,7,26.9,,-1.2527958000000001,36.8003131,-1.234958,36.8104235,Rider_Id_86,1112
-Order_No_11878,User_Id_2330,Bike,3,Business,28,1,5:36:43 PM,28,1,5:41:01 PM,28,1,6:13:04 PM,28,1,6:17:35 PM,28,1,6:35:16 PM,4,25.3,,-1.293082,36.8278578,-1.2915246999999999,36.805575700000006,Rider_Id_206,1061
-Order_No_21296,User_Id_265,Bike,3,Business,6,3,3:39:41 PM,6,3,3:40:11 PM,6,3,3:40:15 PM,6,3,4:40:20 PM,6,3,5:08:54 PM,15,26.1,,-1.300921,36.828195,-1.2156006000000001,36.8916865,Rider_Id_942,1714
-Order_No_4618,User_Id_970,Bike,3,Business,17,3,2:41:03 PM,17,3,2:41:09 PM,17,3,2:41:15 PM,17,3,2:46:09 PM,17,3,3:06:55 PM,4,,,-1.2551895,36.7822034,-1.2693662000000001,36.8085295,Rider_Id_576,1246
-Order_No_6421,User_Id_1787,Bike,3,Business,28,4,10:56:54 AM,28,4,11:08:16 AM,28,4,11:40:50 AM,28,4,11:42:56 AM,28,4,12:01:17 PM,10,28.0,,-1.2285834999999998,36.882646799999996,-1.2684319,36.81658529999999,Rider_Id_711,1101
-Order_No_14230,User_Id_517,Bike,3,Business,3,1,12:40:09 PM,3,1,12:43:06 PM,3,1,12:43:26 PM,3,1,1:15:02 PM,3,1,1:15:05 PM,11,21.7,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_855,3
-Order_No_22997,User_Id_1389,Bike,3,Business,15,1,1:35:14 PM,15,1,2:18:15 PM,15,1,2:28:03 PM,15,1,2:53:26 PM,15,1,3:31:00 PM,18,,,-1.2859912,36.8756811,-1.207546,36.7913976,Rider_Id_482,2254
-Order_No_13326,User_Id_868,Bike,3,Business,20,2,9:43:23 AM,20,2,9:46:30 AM,20,2,9:57:45 AM,20,2,10:08:41 AM,20,2,10:21:59 AM,5,19.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_178,798
-Order_No_27841,User_Id_2803,Bike,3,Business,4,2,12:33:40 PM,4,2,1:23:17 PM,4,2,1:29:31 PM,4,2,1:34:19 PM,4,2,1:48:01 PM,3,24.0,11.1,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_521,822
-Order_No_15756,User_Id_2347,Bike,1,Personal,25,5,2:00:20 PM,25,5,2:02:33 PM,25,5,2:25:01 PM,25,5,2:34:28 PM,25,5,3:15:45 PM,6,23.2,,-1.292278,36.797717999999996,-1.303723,36.771715500000006,Rider_Id_805,2477
-Order_No_7183,User_Id_1439,Bike,3,Business,9,4,11:11:34 AM,9,4,11:12:29 AM,9,4,11:19:59 AM,9,4,11:23:31 AM,9,4,11:44:17 AM,6,,,-1.2962486999999998,36.7772971,-1.2749270000000001,36.8028302,Rider_Id_548,1246
-Order_No_101,User_Id_3674,Bike,3,Business,29,1,2:44:18 PM,29,1,2:47:28 PM,29,1,2:49:33 PM,29,1,2:55:46 PM,29,1,3:07:59 PM,4,24.2,,-1.2551895,36.7822034,-1.2651643,36.807221399999996,Rider_Id_287,733
-Order_No_9446,User_Id_1687,Bike,3,Business,3,3,8:57:51 AM,3,3,8:58:09 AM,3,3,9:04:42 AM,3,3,9:13:58 AM,3,3,9:29:23 AM,6,18.3,,-1.2626574,36.8014034,-1.3025299,36.7901963,Rider_Id_337,925
-Order_No_1135,User_Id_1205,Bike,3,Business,27,3,11:17:31 AM,27,3,11:17:41 AM,27,3,11:20:29 AM,27,3,11:24:22 AM,27,3,11:48:11 AM,8,25.9,,-1.2495931,36.80650870000001,-1.2973188,36.7895013,Rider_Id_484,1429
-Order_No_16685,User_Id_283,Bike,3,Business,20,4,6:00:54 PM,20,4,6:57:09 PM,20,4,6:58:18 PM,20,4,7:08:26 PM,20,4,7:41:44 PM,2,22.6,,-1.2804227,36.8250375,-1.285442,36.831270700000005,Rider_Id_404,1998
-Order_No_16791,User_Id_3560,Bike,3,Business,13,4,2:22:54 PM,13,4,2:23:57 PM,13,4,2:44:33 PM,13,4,2:57:18 PM,13,4,3:17:47 PM,5,23.3,,-1.2527958000000001,36.797624400000004,-1.279395,36.825364,Rider_Id_868,1229
-Order_No_18125,User_Id_111,Bike,3,Business,10,5,11:07:05 AM,10,5,11:10:18 AM,10,5,11:10:25 AM,10,5,11:21:25 AM,10,5,11:55:58 AM,12,,,-1.2551895,36.7822034,-1.318049,36.8556554,Rider_Id_843,2073
-Order_No_21651,User_Id_3727,Bike,3,Business,11,2,2:10:47 PM,11,2,2:11:56 PM,11,2,2:12:52 PM,11,2,2:23:37 PM,11,2,2:39:25 PM,8,22.2,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_375,948
-Order_No_20462,User_Id_186,Bike,3,Business,7,4,10:57:56 AM,7,4,10:58:37 AM,7,4,11:10:16 AM,7,4,11:24:10 AM,7,4,12:02:37 PM,20,27.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_831,2307
-Order_No_20315,User_Id_112,Bike,2,Personal,10,4,6:10:05 PM,10,4,6:13:54 PM,10,4,6:40:37 PM,10,4,6:44:38 PM,10,4,7:36:36 PM,13,22.3,,-1.3238698999999998,36.8488766,-1.2827576,36.783024100000006,Rider_Id_661,3118
-Order_No_10809,User_Id_246,Bike,3,Business,11,1,6:11:02 PM,11,1,6:11:25 PM,11,1,6:16:23 PM,11,1,6:20:10 PM,11,1,6:37:09 PM,7,,,-1.2584143,36.8048002,-1.2765492,36.7669809,Rider_Id_147,1019
-Order_No_6148,User_Id_1162,Bike,3,Business,17,5,9:20:52 AM,17,5,9:25:36 AM,17,5,9:35:25 AM,17,5,9:39:21 AM,17,5,10:09:20 AM,8,,,-1.3122610000000001,36.794658,-1.2776982,36.83126,Rider_Id_344,1799
-Order_No_11573,User_Id_3295,Bike,3,Business,8,5,10:30:58 AM,8,5,10:31:46 AM,8,5,10:38:36 AM,8,5,10:43:31 AM,8,5,11:20:30 AM,16,28.6,,-1.2571472,36.795063299999995,-1.27915,36.89929,Rider_Id_520,2219
-Order_No_16024,User_Id_3635,Bike,1,Business,8,1,2:22:13 PM,8,1,2:22:23 PM,8,1,2:25:31 PM,8,1,2:35:43 PM,8,1,2:46:46 PM,3,25.0,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_409,663
-Order_No_3100,User_Id_393,Bike,3,Business,18,1,9:06:45 AM,18,1,9:07:16 AM,18,1,9:07:20 AM,18,1,9:16:07 AM,18,1,9:51:59 AM,14,21.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_111,2152
-Order_No_12814,User_Id_1964,Bike,3,Personal,5,2,10:57:05 AM,5,2,10:57:43 AM,5,2,11:01:30 AM,5,2,11:06:13 AM,5,2,11:21:34 AM,9,25.3,,-1.32671,36.8600138,-1.2965799999999998,36.823699,Rider_Id_425,921
-Order_No_8501,User_Id_2956,Bike,3,Business,15,4,1:25:56 PM,15,4,1:26:16 PM,15,4,1:48:58 PM,15,4,1:55:51 PM,15,4,2:06:18 PM,3,27.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_287,627
-Order_No_2851,User_Id_986,Bike,3,Personal,8,3,3:14:57 PM,8,3,3:15:53 PM,8,3,3:32:32 PM,8,3,3:37:16 PM,8,3,4:14:16 PM,21,25.8,,-1.2639472999999999,36.782965999999995,-1.3264415,36.8793525,Rider_Id_449,2220
-Order_No_13225,User_Id_811,Bike,3,Personal,14,1,3:09:17 PM,14,1,3:09:45 PM,14,1,3:18:16 PM,14,1,3:20:16 PM,14,1,3:33:52 PM,4,25.9,,-1.2963097,36.7688221,-1.2944457,36.7923516,Rider_Id_862,816
-Order_No_15075,User_Id_2333,Bike,2,Personal,18,2,4:04:34 PM,18,2,4:06:21 PM,18,2,4:16:17 PM,18,2,4:19:22 PM,18,2,4:25:38 PM,2,27.2,,-1.2903421,36.7885282,-1.28467,36.794438,Rider_Id_409,376
-Order_No_15496,User_Id_3493,Bike,1,Personal,24,1,9:32:42 AM,24,1,9:33:08 AM,24,1,9:51:47 AM,24,1,9:55:30 AM,24,1,10:24:52 AM,16,15.4,,-1.3278463,36.819099,-1.2623805000000001,36.7805404,Rider_Id_328,1762
-Order_No_12582,User_Id_1724,Bike,3,Business,4,2,2:06:59 PM,4,2,2:11:59 PM,4,2,2:21:26 PM,4,2,2:24:38 PM,4,2,2:43:30 PM,6,24.0,11.0,-1.2551895,36.7822034,-1.2655306,36.828148,Rider_Id_646,1132
-Order_No_18196,User_Id_1363,Bike,3,Business,30,2,4:02:52 PM,30,2,4:11:06 PM,30,2,4:17:09 PM,30,2,4:18:17 PM,30,2,4:29:10 PM,6,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_328,653
-Order_No_15353,User_Id_1724,Bike,3,Business,6,4,12:00:02 PM,6,4,12:00:35 PM,6,4,12:15:34 PM,6,4,12:19:58 PM,6,4,12:37:17 PM,4,19.7,,-1.2551895,36.7822034,-1.266294,36.8057971,Rider_Id_161,1039
-Order_No_1771,User_Id_844,Bike,1,Personal,7,4,10:50:52 AM,7,4,11:06:49 AM,7,4,11:17:14 AM,7,4,11:24:49 AM,7,4,12:03:41 PM,12,16.8,,-1.2343087,36.84749,-1.2551895,36.7822034,Rider_Id_852,2332
-Order_No_13312,User_Id_1493,Bike,1,Personal,17,2,6:05:44 PM,17,2,6:06:54 PM,17,2,6:15:27 PM,17,2,6:18:27 PM,17,2,6:25:47 PM,4,23.9,,-1.260526,36.7873569,-1.2639198,36.8069299,Rider_Id_510,440
-Order_No_4967,User_Id_1246,Bike,3,Business,28,4,2:22:20 PM,28,4,2:22:40 PM,28,4,2:24:24 PM,28,4,2:39:56 PM,28,4,2:45:52 PM,2,27.2,,-1.2571472,36.795063299999995,-1.273488,36.804158,Rider_Id_770,356
-Order_No_14749,User_Id_71,Bike,3,Business,18,2,8:10:27 AM,18,2,8:29:26 AM,18,2,8:54:49 AM,18,2,9:08:57 AM,18,2,9:37:26 AM,11,19.7,,-1.225322,36.80855,-1.281754,36.8324229,Rider_Id_101,1709
-Order_No_12181,User_Id_2763,Bike,3,Business,12,3,12:28:30 PM,12,3,12:33:07 PM,12,3,12:38:56 PM,12,3,12:43:15 PM,12,3,12:55:29 PM,4,19.4,,-1.2551895,36.7822034,-1.2696908000000002,36.81519,Rider_Id_290,734
-Order_No_15493,User_Id_868,Bike,3,Business,15,6,10:54:22 AM,15,6,10:54:33 AM,15,6,10:54:38 AM,15,6,11:02:56 AM,15,6,11:12:08 AM,7,19.4,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_564,552
-Order_No_16435,User_Id_733,Bike,3,Business,26,3,12:19:34 PM,26,3,12:19:40 PM,26,3,12:20:08 PM,26,3,12:51:15 PM,26,3,1:25:43 PM,15,24.0,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_449,2068
-Order_No_9057,User_Id_503,Bike,3,Business,7,4,10:51:04 AM,7,4,10:57:15 AM,7,4,11:15:21 AM,7,4,11:30:39 AM,7,4,12:07:47 PM,15,17.1,,-1.2859912,36.8756811,-1.2940713,36.7890758,Rider_Id_100,2228
-Order_No_1582,User_Id_867,Bike,3,Business,25,1,2:31:08 PM,25,1,2:31:17 PM,25,1,2:33:39 PM,25,1,2:35:14 PM,25,1,3:16:40 PM,20,30.2,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_349,2486
-Order_No_24412,User_Id_1020,Bike,1,Personal,15,1,3:24:25 PM,15,1,3:36:27 PM,15,1,3:46:47 PM,15,1,3:48:56 PM,15,1,4:06:29 PM,7,,,-1.2764786,36.81947879999999,-1.2559559999999999,36.772901,Rider_Id_208,1053
-Order_No_7554,User_Id_1502,Bike,3,Business,20,2,12:10:20 PM,20,2,12:10:54 PM,20,2,12:26:52 PM,20,2,12:34:52 PM,20,2,1:04:58 PM,15,19.7,,-1.225322,36.80855,-1.2232123,36.8932547,Rider_Id_937,1806
-Order_No_10277,User_Id_1075,Bike,3,Business,29,4,2:13:45 PM,29,4,2:47:40 PM,29,4,3:00:53 PM,29,4,3:09:17 PM,29,4,3:44:07 PM,9,24.8,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_79,2090
-Order_No_8228,User_Id_1798,Bike,3,Business,17,1,1:20:55 PM,17,1,1:21:14 PM,17,1,1:26:38 PM,17,1,1:36:19 PM,17,1,1:46:35 PM,2,28.3,,-1.2615888000000002,36.7928732,-1.2642903,36.80021370000001,Rider_Id_620,616
-Order_No_14245,User_Id_1796,Bike,3,Business,15,5,10:03:05 AM,15,5,10:03:21 AM,15,5,10:06:15 AM,15,5,10:20:27 AM,15,5,10:50:46 AM,11,19.4,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_63,1819
-Order_No_24866,User_Id_1445,Bike,3,Personal,25,1,12:26:55 PM,25,1,12:28:15 PM,25,1,12:31:23 PM,25,1,12:32:33 PM,25,1,12:50:23 PM,7,,,-1.2661246000000002,36.7961775,-1.2883071000000001,36.755052299999996,Rider_Id_379,1070
-Order_No_19168,User_Id_3695,Bike,1,Personal,28,5,6:08:48 PM,28,5,6:11:06 PM,28,5,6:19:49 PM,28,5,6:28:17 PM,28,5,6:36:44 PM,3,,,-1.2857315,36.7953424,-1.2894917,36.8056192,Rider_Id_279,507
-Order_No_28198,User_Id_972,Bike,3,Business,15,1,4:24:37 PM,15,1,4:26:15 PM,15,1,4:31:53 PM,15,1,4:41:38 PM,15,1,5:14:00 PM,12,,,-1.2862033000000002,36.8152966,-1.265568,36.882778,Rider_Id_800,1942
-Order_No_19914,User_Id_3291,Bike,3,Business,27,6,9:04:03 AM,27,6,9:05:09 AM,27,6,9:21:05 AM,27,6,9:27:26 AM,27,6,9:35:48 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_783,502
-Order_No_8751,User_Id_1953,Bike,3,Personal,5,6,2:19:24 PM,5,6,2:21:09 PM,5,6,2:35:22 PM,5,6,2:45:17 PM,5,6,3:28:52 PM,8,23.8,,-1.2874940000000001,36.819503000000005,-1.2982423,36.8719867,Rider_Id_742,2615
-Order_No_26859,User_Id_2523,Bike,3,Business,12,1,10:59:06 AM,12,1,11:05:36 AM,12,1,11:11:46 AM,12,1,11:19:33 AM,12,1,11:45:37 AM,8,23.8,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_519,1564
-Order_No_20511,User_Id_740,Bike,1,Personal,6,4,11:54:53 AM,6,4,11:56:43 AM,6,4,12:19:06 PM,6,4,12:30:24 PM,6,4,12:55:14 PM,11,21.9,,-1.2830421,36.8819483,-1.2893883999999998,36.828207,Rider_Id_124,1490
-Order_No_19618,User_Id_635,Bike,3,Business,29,6,11:50:03 AM,29,6,11:52:02 AM,29,6,12:02:03 PM,29,6,12:49:03 PM,29,6,1:08:03 PM,8,24.1,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_26,1140
-Order_No_25734,User_Id_3621,Bike,3,Business,20,6,10:00:11 AM,20,6,10:00:29 AM,20,6,10:14:33 AM,20,6,10:47:34 AM,20,6,10:47:38 AM,12,,,-1.2836583000000001,36.7886014,-1.2423902,36.8554947,Rider_Id_613,4
-Order_No_7381,User_Id_1075,Bike,3,Business,17,4,1:35:36 PM,17,4,1:36:33 PM,17,4,1:51:16 PM,17,4,1:58:52 PM,17,4,2:13:31 PM,8,28.2,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_250,879
-Order_No_19739,User_Id_393,Bike,3,Business,27,1,11:39:06 AM,27,1,11:41:15 AM,27,1,11:49:23 AM,27,1,12:15:20 PM,27,1,12:18:53 PM,11,20.9,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_2,213
-Order_No_23213,User_Id_432,Bike,3,Business,8,1,2:35:37 PM,8,1,2:36:42 PM,8,1,3:06:17 PM,8,1,3:11:39 PM,8,1,3:49:49 PM,21,29.7,,-1.2232123,36.8932547,-1.3012008,36.764868,Rider_Id_8,2290
-Order_No_18808,User_Id_111,Bike,3,Business,30,2,2:52:50 PM,30,2,2:59:26 PM,30,2,3:10:28 PM,30,2,3:11:45 PM,30,2,3:30:49 PM,4,,,-1.2551895,36.7822034,-1.2585521000000002,36.7945765,Rider_Id_131,1144
-Order_No_3438,User_Id_478,Bike,3,Business,30,2,12:00:44 PM,30,2,12:07:18 PM,30,2,12:10:33 PM,30,2,12:44:11 PM,30,2,12:44:25 PM,4,,,-1.2551895,36.7822034,-1.2715686000000002,36.8023526,Rider_Id_535,14
-Order_No_2256,User_Id_186,Bike,3,Business,18,2,12:04:22 PM,18,2,12:04:34 PM,18,2,12:04:56 PM,18,2,12:11:15 PM,18,2,12:58:20 PM,26,,,-1.3700383,36.9190174,-1.2232123,36.8932547,Rider_Id_876,2825
-Order_No_5839,User_Id_1209,Bike,3,Business,5,5,3:36:24 PM,5,5,3:37:58 PM,5,5,4:00:32 PM,5,5,4:02:05 PM,5,5,4:19:28 PM,5,27.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_783,1043
-Order_No_10646,User_Id_2200,Bike,3,Business,9,6,3:25:53 PM,9,6,3:26:00 PM,9,6,3:39:41 PM,9,6,3:45:54 PM,9,6,4:21:09 PM,12,29.8,,-1.290894,36.822971,-1.2927792,36.9053492,Rider_Id_278,2115
-Order_No_7559,User_Id_3261,Bike,2,Personal,29,2,12:53:59 PM,29,2,12:56:27 PM,29,2,1:29:45 PM,29,2,1:31:46 PM,29,2,2:14:29 PM,16,21.2,,-1.2067441,36.8090189,-1.1742369,36.7071302,Rider_Id_749,2563
-Order_No_21328,User_Id_1584,Bike,3,Business,8,1,11:46:41 AM,8,1,11:52:11 AM,8,1,12:10:11 PM,8,1,12:23:59 PM,8,1,12:38:47 PM,7,27.7,,-1.2842021000000001,36.8281144,-1.2969741000000001,36.7856612,Rider_Id_291,888
-Order_No_15241,User_Id_496,Bike,3,Business,17,3,9:32:38 AM,17,3,9:32:50 AM,17,3,9:34:42 AM,17,3,9:40:28 AM,17,3,9:46:46 AM,2,,,-1.2551895,36.7822034,-1.2566078,36.7832089,Rider_Id_535,378
-Order_No_25661,User_Id_1882,Bike,1,Business,13,1,5:30:02 PM,13,1,5:30:35 PM,13,1,5:39:35 PM,13,1,5:43:20 PM,13,1,6:03:10 PM,8,19.1,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_208,1190
-Order_No_2093,User_Id_1245,Bike,3,Business,19,3,1:23:03 PM,19,3,1:23:39 PM,19,3,1:38:27 PM,19,3,1:57:56 PM,19,3,2:41:51 PM,20,24.4,,-1.300921,36.828195,-1.3494207,36.70254129999999,Rider_Id_958,2635
-Order_No_10137,User_Id_3291,Bike,3,Business,5,2,8:21:56 AM,5,2,8:22:08 AM,5,2,9:03:08 AM,5,2,9:21:49 AM,5,2,10:09:16 AM,12,18.2,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_332,2847
-Order_No_2886,User_Id_3621,Bike,3,Business,26,6,11:16:07 AM,26,6,11:17:05 AM,26,6,11:22:29 AM,26,6,11:27:40 AM,26,6,11:43:28 AM,5,26.2,,-1.2836583000000001,36.7886014,-1.2884243,36.820724799999994,Rider_Id_317,948
-Order_No_584,User_Id_12,Bike,3,Business,12,4,1:17:00 PM,12,4,1:20:16 PM,12,4,1:46:26 PM,12,4,1:56:43 PM,12,4,2:21:39 PM,11,21.8,,-1.2400209999999998,36.849990000000005,-1.2655477,36.8001682,Rider_Id_937,1496
-Order_No_4186,User_Id_391,Bike,3,Business,9,4,11:58:05 AM,9,4,12:32:39 PM,9,4,12:44:24 PM,9,4,1:07:56 PM,9,4,1:37:35 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_92,1779
-Order_No_20135,User_Id_1584,Bike,3,Business,30,5,9:17:31 AM,30,5,10:05:45 AM,30,5,10:08:16 AM,30,5,10:45:44 AM,30,5,11:07:08 AM,8,23.5,,-1.304033,36.7848695,-1.2654094999999999,36.7973495,Rider_Id_19,1284
-Order_No_812,User_Id_1500,Bike,3,Business,30,2,1:00:19 PM,30,2,1:00:33 PM,30,2,1:00:38 PM,30,2,1:03:38 PM,30,2,1:28:59 PM,12,22.4,,-1.2574219,36.7927073,-1.3177546999999998,36.8303703,Rider_Id_10,1521
-Order_No_19048,User_Id_3752,Bike,3,Business,12,3,2:49:00 PM,12,3,2:51:53 PM,12,3,2:59:47 PM,12,3,3:02:05 PM,12,3,3:29:43 PM,7,20.9,,-1.2495931,36.80650870000001,-1.2962832,36.784968299999996,Rider_Id_155,1658
-Order_No_2960,User_Id_2767,Bike,3,Business,4,2,4:21:50 PM,4,2,4:24:57 PM,4,2,4:33:46 PM,4,2,5:01:49 PM,4,2,5:16:20 PM,5,22.4,,-1.257773,36.795486,-1.273854,36.8003368,Rider_Id_646,871
-Order_No_2645,User_Id_2150,Bike,3,Business,25,4,1:58:43 PM,25,4,1:59:02 PM,25,4,2:01:30 PM,25,4,2:10:59 PM,25,4,2:27:17 PM,11,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_438,978
-Order_No_206,User_Id_732,Bike,1,Personal,25,5,12:21:18 PM,25,5,12:28:55 PM,25,5,12:41:47 PM,25,5,1:00:55 PM,25,5,1:27:11 PM,7,21.0,,-1.2961951999999999,36.7876396,-1.2636912,36.788437,Rider_Id_888,1576
-Order_No_23199,User_Id_3149,Bike,3,Business,7,4,9:50:47 AM,7,4,9:51:20 AM,7,4,9:56:35 AM,7,4,10:34:15 AM,7,4,10:47:03 AM,5,27.8,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_849,768
-Order_No_26559,User_Id_868,Bike,3,Business,4,3,10:05:45 AM,4,3,10:07:57 AM,4,3,10:31:11 AM,4,3,10:39:59 AM,4,3,11:04:32 AM,8,16.8,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_951,1473
-Order_No_8999,User_Id_1605,Bike,1,Business,22,3,2:07:25 PM,22,3,2:07:42 PM,22,3,2:24:40 PM,22,3,2:33:35 PM,22,3,3:08:56 PM,17,25.5,,-1.2853266,36.822418799999994,-1.3310215,36.7067229,Rider_Id_45,2121
-Order_No_5747,User_Id_1502,Bike,3,Business,13,1,4:03:34 PM,13,1,4:07:07 PM,13,1,4:31:01 PM,13,1,5:01:46 PM,13,1,5:36:04 PM,13,,,-1.2232123,36.8932547,-1.279395,36.825364,Rider_Id_787,2058
-Order_No_24003,User_Id_393,Bike,3,Business,23,4,3:11:43 PM,23,4,3:12:23 PM,23,4,3:12:26 PM,23,4,3:14:49 PM,23,4,3:15:45 PM,5,24.5,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_39,56
-Order_No_18548,User_Id_457,Bike,3,Personal,14,5,2:16:53 PM,14,5,2:19:04 PM,14,5,2:23:28 PM,14,5,2:29:08 PM,14,5,2:42:34 PM,6,23.8,,-1.2985749,36.8088,-1.2630521000000001,36.806801899999996,Rider_Id_607,806
-Order_No_1138,User_Id_2411,Bike,3,Business,28,1,10:58:14 AM,28,1,11:01:45 AM,28,1,11:11:10 AM,28,1,11:20:42 AM,28,1,11:32:41 AM,5,23.5,,-1.2615888000000002,36.7928732,-1.28101,36.8115102,Rider_Id_596,719
-Order_No_18033,User_Id_1246,Bike,3,Business,14,2,9:59:36 AM,14,2,9:59:50 AM,14,2,10:08:46 AM,14,2,10:13:52 AM,14,2,10:34:07 AM,8,15.7,,-1.2571472,36.795063299999995,-1.2817557,36.763556099999995,Rider_Id_452,1215
-Order_No_20585,User_Id_2982,Bike,3,Business,24,6,9:44:36 AM,24,6,9:46:50 AM,24,6,10:15:08 AM,24,6,10:24:10 AM,24,6,11:16:04 AM,30,18.4,,-1.300921,36.828195,-1.1302992,36.700878100000004,Rider_Id_715,3114
-Order_No_18259,User_Id_136,Bike,3,Business,27,3,2:46:59 PM,27,3,2:47:23 PM,27,3,3:02:20 PM,27,3,3:06:48 PM,27,3,3:25:07 PM,8,,,-1.3014461000000002,36.7661381,-1.3326173000000001,36.71462329999999,Rider_Id_735,1099
-Order_No_825,User_Id_635,Bike,3,Business,12,2,10:52:47 AM,12,2,11:10:24 AM,12,2,11:22:30 AM,12,2,11:26:46 AM,12,2,11:43:30 AM,7,24.7,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_550,1004
-Order_No_8938,User_Id_1336,Bike,3,Business,3,5,10:51:23 AM,3,5,10:51:56 AM,3,5,11:11:25 AM,3,5,11:23:00 AM,3,5,11:38:02 AM,3,21.5,,-1.2765736,36.851364600000004,-1.2891921999999998,36.825189,Rider_Id_174,902
-Order_No_5412,User_Id_2011,Bike,1,Personal,2,4,11:44:03 AM,2,4,11:45:08 AM,2,4,11:57:48 AM,2,4,12:03:13 PM,2,4,12:36:11 PM,10,22.4,,-1.3199853000000001,36.843423,-1.2753108999999998,36.8130185,Rider_Id_85,1978
-Order_No_14354,User_Id_3283,Bike,3,Business,6,6,10:36:44 AM,6,6,10:36:57 AM,6,6,10:37:44 AM,6,6,10:43:59 AM,6,6,11:05:40 AM,7,26.6,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_166,1301
-Order_No_7546,User_Id_186,Bike,3,Business,22,4,4:28:35 PM,22,4,4:31:18 PM,22,4,4:32:00 PM,22,4,4:42:56 PM,22,4,5:00:33 PM,4,24.3,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_447,1057
-Order_No_20869,User_Id_192,Bike,1,Personal,21,1,11:19:37 AM,21,1,11:30:12 AM,21,1,11:52:37 AM,21,1,11:58:07 AM,21,1,12:07:34 PM,5,20.0,,-1.2846899999999999,36.8317048,-1.312406,36.827447,Rider_Id_888,567
-Order_No_10735,User_Id_1724,Bike,3,Business,23,1,2:16:30 PM,23,1,2:16:43 PM,23,1,2:22:07 PM,23,1,2:25:09 PM,23,1,2:51:49 PM,15,,,-1.2551895,36.7822034,-1.3272857,36.8748204,Rider_Id_616,1600
-Order_No_3384,User_Id_2093,Bike,3,Personal,17,4,12:40:23 PM,17,4,12:55:51 PM,17,4,1:29:34 PM,17,4,1:36:29 PM,17,4,2:41:23 PM,33,27.9,,-1.1954821000000002,36.9064954,-1.287085,36.7232836,Rider_Id_691,3894
-Order_No_27371,User_Id_186,Bike,3,Business,13,3,10:16:00 AM,13,3,10:16:32 AM,13,3,10:44:43 AM,13,3,10:55:49 AM,13,3,11:05:52 AM,7,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_564,603
-Order_No_8959,User_Id_1874,Bike,3,Business,5,2,9:45:12 AM,5,2,9:51:15 AM,5,2,10:08:37 AM,5,2,10:16:04 AM,5,2,10:39:16 AM,5,21.1,2.1,-1.286553,36.8273374,-1.2571472,36.795063299999995,Rider_Id_885,1392
-Order_No_11989,User_Id_346,Bike,3,Business,21,4,12:30:55 PM,21,4,12:35:14 PM,21,4,12:48:32 PM,21,4,12:54:23 PM,21,4,1:13:23 PM,5,25.3,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_831,1140
-Order_No_15797,User_Id_355,Bike,1,Personal,3,3,11:38:42 AM,3,3,11:39:25 AM,3,3,11:51:27 AM,3,3,12:03:41 PM,3,3,12:08:43 PM,4,24.2,,-1.2615616000000003,36.809303299999996,-1.2741728,36.79691079999999,Rider_Id_754,302
-Order_No_4798,User_Id_346,Bike,3,Business,12,2,12:36:35 PM,12,2,12:38:11 PM,12,2,12:41:51 PM,12,2,12:59:16 PM,12,2,1:17:06 PM,5,24.4,,-1.2620912,36.8013562,-1.28878,36.8168312,Rider_Id_379,1070
-Order_No_22884,User_Id_661,Bike,3,Business,6,5,12:20:00 PM,6,5,1:26:21 PM,6,5,1:38:31 PM,6,5,1:54:00 PM,6,5,2:06:43 PM,4,22.4,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_140,763
-Order_No_13892,User_Id_1578,Bike,3,Business,28,1,11:49:07 AM,28,1,11:49:14 AM,28,1,11:51:38 AM,28,1,12:11:10 PM,28,1,12:44:15 PM,18,23.7,,-1.3472066,36.7692638,-1.2859912,36.8756811,Rider_Id_272,1985
-Order_No_22190,User_Id_2801,Bike,3,Business,26,2,1:33:53 PM,26,2,1:35:02 PM,26,2,1:48:02 PM,26,2,1:53:38 PM,26,2,2:10:31 PM,7,29.6,,-1.2674273999999999,36.787082700000006,-1.2827545,36.79361289999999,Rider_Id_720,1013
-Order_No_9697,User_Id_1882,Bike,1,Business,1,6,9:15:08 AM,1,6,9:15:48 AM,1,6,9:27:58 AM,1,6,9:32:58 AM,1,6,10:07:01 AM,17,15.0,,-1.2268871000000001,36.8073946,-1.2156006000000001,36.8916865,Rider_Id_453,2043
-Order_No_10779,User_Id_1081,Bike,3,Business,5,2,4:45:37 PM,5,2,4:48:44 PM,5,2,5:07:38 PM,5,2,5:16:02 PM,5,2,6:12:00 PM,21,25.2,,-1.2860183,36.8975338,-1.3012008,36.764868,Rider_Id_411,3358
-Order_No_17412,User_Id_496,Bike,3,Business,12,3,4:15:42 PM,12,3,4:16:04 PM,12,3,4:23:22 PM,12,3,4:25:46 PM,12,3,4:53:51 PM,12,26.3,,-1.2551895,36.7822034,-1.3269776000000002,36.8055554,Rider_Id_283,1685
-Order_No_11005,User_Id_972,Bike,3,Business,26,6,3:26:16 PM,26,6,3:26:50 PM,26,6,3:40:23 PM,26,6,3:44:07 PM,26,6,4:13:40 PM,9,28.2,,-1.2862033000000002,36.8152966,-1.290239,36.7620412,Rider_Id_21,1773
-Order_No_6081,User_Id_2285,Bike,3,Personal,22,3,9:17:36 AM,22,3,9:38:01 AM,22,3,9:53:23 AM,22,3,10:06:43 AM,22,3,10:42:47 AM,15,18.7,,-1.3365372,36.728126399999994,-1.2950041,36.814358299999995,Rider_Id_815,2164
-Order_No_10941,User_Id_2330,Bike,3,Business,21,2,11:39:40 AM,21,2,11:40:06 AM,21,2,11:45:59 AM,21,2,12:02:27 PM,21,2,12:16:35 PM,6,22.6,,-1.2645318,36.7981775,-1.2464559,36.816985700000004,Rider_Id_119,848
-Order_No_8990,User_Id_2701,Bike,3,Business,27,2,9:51:19 AM,27,2,10:00:32 AM,27,2,10:10:38 AM,27,2,10:12:28 AM,27,2,11:18:25 AM,9,18.9,2.1,-1.2765736,36.851364600000004,-1.2475028000000001,36.877469700000006,Rider_Id_434,3957
-Order_No_10989,User_Id_733,Bike,3,Business,20,3,4:34:41 PM,20,3,4:35:38 PM,20,3,4:45:55 PM,20,3,4:59:27 PM,20,3,5:04:44 PM,5,27.7,,-1.2551895,36.7822034,-1.2629461000000002,36.76531370000001,Rider_Id_875,317
-Order_No_12134,User_Id_393,Bike,3,Business,29,1,8:12:20 AM,29,1,8:48:29 AM,29,1,8:48:33 AM,29,1,9:12:23 AM,29,1,9:35:29 AM,10,18.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_39,1386
-Order_No_18476,User_Id_2763,Bike,3,Business,22,3,7:57:59 AM,22,3,7:58:46 AM,22,3,8:28:43 AM,22,3,8:33:21 AM,22,3,9:05:05 AM,5,19.0,,-1.2551895,36.7822034,-1.2696908000000002,36.81519,Rider_Id_228,1904
-Order_No_2692,User_Id_1850,Bike,3,Business,15,1,10:19:55 AM,15,1,10:20:35 AM,15,1,10:24:54 AM,15,1,10:25:58 AM,15,1,10:43:12 AM,3,20.9,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_913,1034
-Order_No_22555,User_Id_3283,Bike,3,Business,31,4,3:47:15 PM,31,4,3:55:45 PM,31,4,4:17:14 PM,31,4,4:24:11 PM,31,4,4:35:52 PM,5,28.7,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_489,701
-Order_No_1378,User_Id_1578,Bike,3,Business,10,1,10:25:51 AM,10,1,10:41:00 AM,10,1,11:00:17 AM,10,1,11:04:02 AM,10,1,11:18:55 AM,6,23.7,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_876,893
-Order_No_8427,User_Id_964,Bike,1,Personal,2,1,11:00:39 AM,2,1,11:03:00 AM,2,1,11:45:11 AM,2,1,11:50:18 AM,2,1,12:25:11 PM,10,19.3,,-1.26559,36.8027,-1.3205506999999999,36.8426247,Rider_Id_951,2093
-Order_No_10372,User_Id_1500,Bike,3,Business,8,5,10:20:44 AM,8,5,10:27:44 AM,8,5,10:30:33 AM,8,5,10:33:54 AM,8,5,10:54:49 AM,9,28.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_332,1255
-Order_No_26059,User_Id_3517,Bike,3,Business,30,2,12:59:15 PM,30,2,1:03:13 PM,30,2,1:11:17 PM,30,2,2:23:27 PM,30,2,2:31:38 PM,1,,,-1.2546732,36.808679600000005,-1.2599563,36.799343900000004,Rider_Id_855,491
-Order_No_23214,User_Id_1207,Bike,3,Business,5,6,10:33:41 AM,5,6,10:34:02 AM,5,6,10:44:20 AM,5,6,11:14:32 AM,5,6,11:24:15 AM,8,22.3,,-1.2628638,36.8070399,-1.2745321000000003,36.765088,Rider_Id_427,583
-Order_No_11445,User_Id_344,Bike,1,Personal,5,5,11:54:35 AM,5,5,11:57:26 AM,5,5,12:05:38 PM,5,5,12:07:08 PM,5,5,12:22:14 PM,5,23.6,,-1.2848376,36.802762200000004,-1.2901669,36.7685878,Rider_Id_383,906
-Order_No_1190,User_Id_1578,Bike,3,Business,5,1,10:41:03 AM,5,1,10:41:43 AM,5,1,11:14:46 AM,5,1,11:16:08 AM,5,1,11:40:13 AM,6,24.7,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_477,1445
-Order_No_3742,User_Id_1280,Bike,3,Business,11,1,9:30:12 AM,11,1,9:30:37 AM,11,1,10:13:47 AM,11,1,10:18:20 AM,11,1,10:20:12 AM,3,21.0,,-1.2551895,36.7822034,-1.2638132,36.784977500000004,Rider_Id_932,112
-Order_No_13691,User_Id_1954,Bike,3,Business,14,4,2:24:40 PM,14,4,2:33:53 PM,14,4,2:54:11 PM,14,4,3:06:35 PM,14,4,3:25:28 PM,4,21.5,,-1.289498,36.818878000000005,-1.2857117,36.7968296,Rider_Id_531,1133
-Order_No_6130,User_Id_3186,Bike,3,Business,12,3,2:12:44 PM,12,3,2:19:18 PM,12,3,2:40:01 PM,12,3,2:45:24 PM,12,3,3:17:50 PM,5,21.2,,-1.2981352,36.8213822,-1.2854406,36.789409,Rider_Id_766,1946
-Order_No_63,User_Id_3621,Bike,3,Business,30,6,9:15:37 AM,30,6,9:16:22 AM,30,6,9:28:46 AM,30,6,9:33:10 AM,30,6,10:07:23 AM,9,,,-1.2836583000000001,36.7886014,-1.3222535,36.8246176,Rider_Id_508,2053
-Order_No_3348,User_Id_3560,Bike,3,Business,29,2,4:51:58 PM,29,2,4:55:50 PM,29,2,4:56:20 PM,29,2,5:16:45 PM,29,2,5:25:53 PM,2,,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_237,548
-Order_No_26496,User_Id_393,Bike,3,Business,8,5,10:42:55 AM,8,5,10:43:41 AM,8,5,10:47:05 AM,8,5,10:51:57 AM,8,5,11:16:52 AM,11,28.6,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_411,1495
-Order_No_23219,User_Id_391,Bike,3,Business,12,3,11:53:37 AM,12,3,12:03:04 PM,12,3,12:11:49 PM,12,3,12:33:45 PM,12,3,1:34:05 PM,16,19.2,,-1.319613,36.711055,-1.2784063,36.8222933,Rider_Id_739,3620
-Order_No_22870,User_Id_1245,Bike,3,Business,29,1,3:35:59 PM,29,1,3:37:46 PM,29,1,4:02:05 PM,29,1,4:23:49 PM,29,1,4:40:52 PM,13,,,-1.300921,36.828195,-1.335145,36.739322200000004,Rider_Id_341,1023
-Order_No_7051,User_Id_60,Bike,3,Business,4,6,10:29:21 AM,4,6,10:29:30 AM,4,6,10:35:31 AM,4,6,10:44:53 AM,4,6,10:55:34 AM,8,19.8,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_857,641
-Order_No_27074,User_Id_1017,Bike,3,Business,14,3,3:45:01 PM,14,3,4:08:04 PM,14,3,4:20:07 PM,14,3,4:35:50 PM,14,3,5:00:01 PM,11,23.7,,-1.322064,36.817157,-1.2551895,36.7822034,Rider_Id_178,1451
-Order_No_10541,User_Id_3496,Bike,3,Business,17,4,3:05:17 PM,17,4,3:32:27 PM,17,4,3:38:10 PM,17,4,3:44:13 PM,17,4,4:14:23 PM,5,24.1,,-1.2850694,36.8236625,-1.3016417,36.8271679,Rider_Id_949,1810
-Order_No_9907,User_Id_2057,Bike,2,Personal,23,4,11:42:14 AM,23,4,11:43:17 AM,23,4,12:01:39 PM,23,4,12:57:47 PM,23,4,1:05:44 PM,4,18.6,,-1.2603178999999998,36.8305747,-1.2630521000000001,36.806801899999996,Rider_Id_857,477
-Order_No_10469,User_Id_2956,Bike,3,Business,6,1,2:03:16 PM,6,1,2:09:31 PM,6,1,2:29:12 PM,6,1,2:33:24 PM,6,1,2:51:46 PM,8,,,-1.2878913,36.8089253,-1.2584143,36.8048002,Rider_Id_132,1102
-Order_No_19771,User_Id_1389,Bike,3,Business,26,5,4:40:30 PM,26,5,4:41:13 PM,26,5,4:54:46 PM,26,5,5:28:26 PM,26,5,6:00:55 PM,15,,,-1.28878,36.8168312,-1.207546,36.7913976,Rider_Id_341,1949
-Order_No_24285,User_Id_18,Bike,3,Personal,25,5,3:21:07 PM,25,5,3:21:53 PM,25,5,3:26:45 PM,25,5,3:37:53 PM,25,5,4:12:00 PM,11,23.3,,-1.2960302,36.771131700000005,-1.355845,36.7088261,Rider_Id_478,2047
-Order_No_10711,User_Id_2066,Bike,3,Business,10,5,10:26:41 AM,10,5,10:26:47 AM,10,5,10:33:37 AM,10,5,10:43:26 AM,10,5,10:54:59 AM,5,19.3,,-1.316961,36.820337,-1.2918862,36.807062200000004,Rider_Id_754,693
-Order_No_24966,User_Id_3491,Bike,3,Business,14,2,1:50:09 PM,14,2,1:50:31 PM,14,2,2:14:15 PM,14,2,2:26:53 PM,14,2,2:38:50 PM,9,23.9,,-1.2810586000000002,36.883829399999996,-1.2297202,36.881950700000004,Rider_Id_812,717
-Order_No_10828,User_Id_661,Bike,3,Business,25,3,2:50:54 PM,25,3,2:51:19 PM,25,3,3:00:20 PM,25,3,3:30:18 PM,25,3,3:47:43 PM,4,17.7,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_140,1045
-Order_No_12939,User_Id_1482,Bike,3,Business,3,1,9:20:56 AM,3,1,9:21:56 AM,3,1,10:05:38 AM,3,1,10:17:28 AM,3,1,10:52:52 AM,8,18.2,,-1.3122473999999997,36.8728544,-1.3385790000000002,36.89630379999999,Rider_Id_114,2124
-Order_No_26835,User_Id_3510,Bike,1,Business,4,3,10:26:33 AM,4,3,10:29:05 AM,4,3,10:34:25 AM,4,3,10:41:04 AM,4,3,10:51:22 AM,3,17.1,,-1.2646579,36.825321100000004,-1.265715,36.808525,Rider_Id_531,618
-Order_No_25220,User_Id_733,Bike,3,Business,14,1,10:03:36 AM,14,1,10:03:44 AM,14,1,10:04:23 AM,14,1,10:08:14 AM,14,1,10:51:27 AM,21,20.2,,-1.2551895,36.7822034,-1.3407764,36.9100521,Rider_Id_353,2593
-Order_No_19527,User_Id_3355,Bike,3,Business,9,3,8:23:48 AM,9,3,10:14:50 AM,9,3,10:21:13 AM,9,3,10:35:31 AM,9,3,10:46:58 AM,4,21.3,,-1.3052491000000002,36.8223899,-1.2866011999999998,36.8195691,Rider_Id_100,687
-Order_No_19614,User_Id_1073,Bike,3,Business,6,3,11:42:47 AM,6,3,11:43:52 AM,6,3,11:53:38 AM,6,3,11:59:57 AM,6,3,12:15:55 PM,10,22.4,,-1.2561964,36.7885001,-1.2766659,36.8432432,Rider_Id_103,958
-Order_No_8040,User_Id_357,Bike,3,Business,13,2,11:37:45 AM,13,2,11:42:47 AM,13,2,11:57:30 AM,13,2,12:01:19 PM,13,2,12:34:24 PM,4,22.3,,-1.2945178999999998,36.7685349,-1.298419,36.7908721,Rider_Id_533,1985
-Order_No_14960,User_Id_3192,Bike,2,Personal,30,1,10:33:47 AM,30,1,10:36:32 AM,30,1,10:52:00 AM,30,1,11:02:48 AM,30,1,11:23:10 AM,6,22.8,,-1.3041617,36.7627771,-1.2913246,36.8019522,Rider_Id_634,1222
-Order_No_8355,User_Id_3291,Bike,3,Business,30,2,9:38:15 AM,30,2,9:39:21 AM,30,2,9:56:43 AM,30,2,10:34:15 AM,30,2,10:50:47 AM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_106,992
-Order_No_14038,User_Id_2956,Bike,3,Business,17,5,12:18:55 PM,17,5,12:19:18 PM,17,5,12:28:40 PM,17,5,12:46:49 PM,17,5,1:18:22 PM,17,,,-1.3165542,36.700067,-1.2584143,36.8048002,Rider_Id_205,1893
-Order_No_19410,User_Id_2009,Bike,2,Personal,15,1,11:20:42 AM,15,1,11:21:00 AM,15,1,11:21:46 AM,15,1,11:36:13 AM,15,1,11:54:18 AM,8,,,-1.3072409999999999,36.830236799999994,-1.2956882,36.7861845,Rider_Id_449,1085
-Order_No_18129,User_Id_868,Bike,3,Business,21,5,4:49:02 PM,21,5,5:14:59 PM,21,5,5:41:16 PM,21,5,5:57:19 PM,21,5,6:14:16 PM,10,23.0,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_147,1017
-Order_No_25477,User_Id_1502,Bike,3,Business,22,3,9:41:15 AM,22,3,9:41:53 AM,22,3,10:29:35 AM,22,3,10:35:51 AM,22,3,11:29:00 AM,12,23.3,,-1.279395,36.825364,-1.2232123,36.8932547,Rider_Id_155,3189
-Order_No_27083,User_Id_53,Bike,3,Business,9,4,9:40:05 AM,9,4,9:42:53 AM,9,4,9:46:39 AM,9,4,10:07:03 AM,9,4,10:32:45 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_709,1542
-Order_No_22200,User_Id_517,Bike,3,Business,28,5,8:14:11 AM,28,5,8:14:26 AM,28,5,8:17:52 AM,28,5,8:21:32 AM,28,5,8:48:08 AM,14,19.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1596
-Order_No_20833,User_Id_153,Bike,1,Personal,25,2,11:59:27 AM,25,2,12:04:06 PM,25,2,12:13:22 PM,25,2,12:24:53 PM,25,2,12:39:53 PM,9,23.9,,-1.2862252,36.8234218,-1.2315379,36.8156795,Rider_Id_522,900
-Order_No_19287,User_Id_2112,Bike,3,Business,27,2,10:47:00 AM,27,2,11:23:30 AM,27,2,11:37:00 AM,27,2,11:48:03 AM,27,2,12:29:20 PM,12,23.9,,-1.279395,36.825364,-1.2156006000000001,36.8916865,Rider_Id_404,2477
-Order_No_7716,User_Id_1280,Bike,3,Business,18,4,1:45:46 PM,18,4,1:46:15 PM,18,4,1:53:39 PM,18,4,1:55:20 PM,18,4,2:31:48 PM,16,26.9,,-1.2551895,36.7822034,-1.3291454,36.7123591,Rider_Id_532,2188
-Order_No_22799,User_Id_2651,Bike,3,Business,7,2,4:22:39 PM,7,2,4:23:36 PM,7,2,5:15:42 PM,7,2,5:34:26 PM,7,2,6:52:53 PM,16,,,-1.3247683000000001,36.85803,-1.3014497,36.758247499999996,Rider_Id_106,4707
-Order_No_15272,User_Id_3659,Bike,1,Personal,8,1,10:39:56 AM,8,1,10:41:46 AM,8,1,10:45:42 AM,8,1,10:49:24 AM,8,1,11:24:51 AM,5,24.3,,-1.2582368,36.7986818,-1.2790289,36.8039715,Rider_Id_881,2127
-Order_No_3540,User_Id_3291,Bike,3,Business,10,5,10:23:47 AM,10,5,10:25:34 AM,10,5,11:04:17 AM,10,5,11:15:51 AM,10,5,11:49:29 AM,18,19.1,,-1.342789,36.896724400000004,-1.2600926000000001,36.8088685,Rider_Id_414,2018
-Order_No_21132,User_Id_346,Bike,3,Business,8,5,11:46:17 AM,8,5,12:01:46 PM,8,5,12:03:01 PM,8,5,12:40:11 PM,8,5,1:04:50 PM,14,28.6,,-1.2156006000000001,36.8916865,-1.28878,36.8168312,Rider_Id_836,1479
-Order_No_11960,User_Id_2330,Bike,3,Business,20,1,10:18:41 AM,20,1,10:19:14 AM,20,1,10:21:39 AM,20,1,10:26:02 AM,20,1,10:45:37 AM,7,19.3,,-1.2642903,36.80021370000001,-1.2557778999999998,36.7879648,Rider_Id_205,1175
-Order_No_921,User_Id_1410,Bike,3,Business,2,4,2:06:07 PM,2,4,2:07:57 PM,2,4,2:37:08 PM,2,4,3:04:11 PM,2,4,3:59:27 PM,20,,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_458,3316
-Order_No_5557,User_Id_3434,Bike,3,Business,2,5,2:42:14 PM,2,5,2:48:14 PM,2,5,2:49:45 PM,2,5,3:09:00 PM,2,5,3:26:44 PM,4,25.2,,-1.3253826000000002,36.8822315,-1.3123562,36.9157558,Rider_Id_925,1064
-Order_No_23086,User_Id_1642,Bike,3,Business,13,3,2:39:32 PM,13,3,2:40:43 PM,13,3,3:12:03 PM,13,3,3:16:44 PM,13,3,3:54:21 PM,4,27.0,,-1.2728069,36.7962362,-1.2765492,36.7669809,Rider_Id_574,2257
-Order_No_13535,User_Id_3149,Bike,3,Business,20,3,2:48:11 PM,20,3,2:48:20 PM,20,3,2:57:05 PM,20,3,3:11:44 PM,20,3,3:28:07 PM,5,29.2,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_349,983
-Order_No_2214,User_Id_3091,Bike,3,Business,13,1,10:34:46 AM,13,1,10:36:57 AM,13,1,10:47:25 AM,13,1,11:09:13 AM,13,1,11:15:50 AM,2,,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_289,397
-Order_No_10728,User_Id_3689,Bike,3,Business,29,6,12:54:09 PM,29,6,12:54:19 PM,29,6,1:02:38 PM,29,6,1:10:30 PM,29,6,1:21:28 PM,2,25.2,,-1.2527958000000001,36.8003131,-1.2661263999999999,36.8030359,Rider_Id_881,658
-Order_No_10431,User_Id_2691,Bike,3,Business,20,3,10:29:05 AM,20,3,10:50:24 AM,20,3,11:10:53 AM,20,3,11:15:14 AM,20,3,12:02:09 PM,6,26.2,,-1.2551895,36.7822034,-1.2690701000000002,36.8314977,Rider_Id_674,2815
-Order_No_6444,User_Id_186,Bike,3,Business,30,4,4:16:25 PM,30,4,4:18:29 PM,30,4,4:29:18 PM,30,4,4:39:48 PM,30,4,5:21:11 PM,4,,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_42,2483
-Order_No_5478,User_Id_1642,Bike,3,Business,5,1,11:54:16 AM,5,1,12:06:00 PM,5,1,12:14:46 PM,5,1,12:46:27 PM,5,1,1:02:00 PM,5,24.4,,-1.2768982,36.8262879,-1.2728069,36.7962362,Rider_Id_268,933
-Order_No_27490,User_Id_2209,Bike,3,Business,27,2,1:01:46 PM,27,2,1:06:49 PM,27,2,1:18:41 PM,27,2,1:21:01 PM,27,2,1:48:08 PM,18,23.6,,-1.2551895,36.7822034,-1.2701893999999998,36.90246,Rider_Id_933,1627
-Order_No_6740,User_Id_1500,Bike,3,Business,19,2,10:07:57 AM,19,2,10:08:09 AM,19,2,10:08:15 AM,19,2,10:33:44 AM,19,2,10:35:48 AM,8,27.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,124
-Order_No_1542,User_Id_868,Bike,3,Business,19,2,1:33:18 PM,19,2,1:34:10 PM,19,2,1:44:03 PM,19,2,1:53:52 PM,19,2,1:53:57 PM,3,29.9,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_124,5
-Order_No_19082,User_Id_391,Bike,3,Business,24,5,3:51:21 PM,24,5,4:17:24 PM,24,5,4:23:14 PM,24,5,5:14:23 PM,24,5,5:47:18 PM,5,20.2,,-1.3064419,36.8373494,-1.2867454,36.8259017,Rider_Id_567,1975
-Order_No_18627,User_Id_53,Bike,3,Business,21,4,3:36:58 PM,21,4,3:41:59 PM,21,4,3:52:11 PM,21,4,4:17:39 PM,21,4,4:34:40 PM,5,26.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_448,1021
-Order_No_21007,User_Id_2521,Bike,3,Business,9,6,11:07:40 AM,9,6,11:07:53 AM,9,6,11:07:57 AM,9,6,11:56:14 AM,9,6,11:56:17 AM,4,27.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,3
-Order_No_20395,User_Id_768,Bike,1,Personal,28,5,7:25:15 AM,28,5,7:26:40 AM,28,5,7:49:01 AM,28,5,7:51:29 AM,28,5,8:16:35 AM,18,20.7,,-1.2849763,36.7841767,-1.2000518,36.7805946,Rider_Id_726,1506
-Order_No_8130,User_Id_391,Bike,3,Business,15,1,4:11:08 PM,15,1,4:27:07 PM,15,1,4:31:06 PM,15,1,4:47:32 PM,15,1,5:16:31 PM,5,,,-1.2599563,36.799343900000004,-1.2784063,36.8222933,Rider_Id_88,1739
-Order_No_19896,User_Id_704,Bike,3,Business,17,5,8:51:20 AM,17,5,8:56:16 AM,17,5,9:40:58 AM,17,5,9:49:30 AM,17,5,10:04:27 AM,8,,,-1.2860183,36.8975338,-1.2910116000000003,36.8573231,Rider_Id_547,897
-Order_No_1385,User_Id_2864,Bike,2,Personal,23,1,1:54:01 PM,23,1,1:56:11 PM,23,1,2:04:33 PM,23,1,2:18:26 PM,23,1,2:40:17 PM,6,,,-1.2892959,36.837867100000004,-1.3047132,36.808955299999994,Rider_Id_844,1311
-Order_No_16081,User_Id_517,Bike,3,Business,16,6,8:05:48 AM,16,6,8:06:07 AM,16,6,8:26:07 AM,16,6,8:32:04 AM,16,6,8:36:52 AM,4,20.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_127,288
-Order_No_5586,User_Id_868,Bike,3,Business,23,4,9:53:22 AM,23,4,9:56:43 AM,23,4,10:08:54 AM,23,4,10:22:41 AM,23,4,10:40:39 AM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_484,1078
-Order_No_2039,User_Id_671,Bike,1,Personal,6,4,1:42:22 PM,6,4,1:42:29 PM,6,4,1:52:38 PM,6,4,1:57:34 PM,6,4,2:37:04 PM,24,23.2,,-1.2343087,36.84749,-1.370436,36.7536015,Rider_Id_449,2370
-Order_No_23732,User_Id_53,Bike,3,Business,20,1,2:57:08 PM,20,1,2:57:20 PM,20,1,2:57:27 PM,20,1,3:19:02 PM,20,1,3:20:37 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_41,95
-Order_No_11122,User_Id_59,Bike,3,Business,14,3,3:58:51 PM,14,3,4:39:47 PM,14,3,4:54:30 PM,14,3,5:54:55 PM,14,3,6:19:03 PM,9,23.7,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_926,1448
-Order_No_21406,User_Id_3331,Bike,3,Business,14,4,11:07:59 AM,14,4,11:11:00 AM,14,4,11:40:55 AM,14,4,11:48:43 AM,14,4,12:20:36 PM,7,23.5,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_19,1913
-Order_No_7802,User_Id_2582,Bike,3,Business,18,4,1:16:09 PM,18,4,1:16:22 PM,18,4,1:16:29 PM,18,4,1:35:05 PM,18,4,2:05:36 PM,27,,,-1.2551895,36.7822034,-1.167619,36.969287,Rider_Id_291,1831
-Order_No_18131,User_Id_3201,Bike,3,Business,9,4,2:10:43 PM,9,4,2:12:35 PM,9,4,2:15:09 PM,9,4,2:32:35 PM,9,4,2:42:27 PM,2,,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_361,592
-Order_No_16801,User_Id_3201,Bike,3,Business,13,2,3:11:52 PM,13,2,3:12:00 PM,13,2,3:12:20 PM,13,2,3:14:09 PM,13,2,3:20:27 PM,3,26.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_720,378
-Order_No_2262,User_Id_1882,Bike,3,Business,20,7,8:53:34 AM,20,7,8:53:59 AM,20,7,9:16:18 AM,20,7,9:31:53 AM,20,7,10:03:18 AM,15,21.8,,-1.2273427,36.799468299999994,-1.2160906999999999,36.885194899999995,Rider_Id_512,1885
-Order_No_27420,User_Id_2150,Bike,3,Business,18,4,8:48:53 AM,18,4,8:49:07 AM,18,4,9:00:59 AM,18,4,9:14:18 AM,18,4,9:45:25 AM,11,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_435,1867
-Order_No_19950,User_Id_1271,Bike,3,Business,21,3,11:50:23 AM,21,3,12:19:27 PM,21,3,12:26:52 PM,21,3,12:45:59 PM,21,3,1:09:13 PM,11,21.8,,-1.3244885,36.897791999999995,-1.2715944,36.9114173,Rider_Id_675,1394
-Order_No_9869,User_Id_346,Bike,3,Business,20,1,9:01:05 AM,20,1,9:03:40 AM,20,1,9:26:19 AM,20,1,9:34:10 AM,20,1,9:36:44 AM,1,19.3,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_75,154
-Order_No_164,User_Id_3688,Bike,3,Business,19,3,12:18:19 PM,19,3,12:19:24 PM,19,3,12:27:50 PM,19,3,12:31:10 PM,19,3,12:45:53 PM,4,22.1,,-1.2551895,36.7822034,-1.2622868999999999,36.8172251,Rider_Id_50,883
-Order_No_10505,User_Id_391,Bike,3,Business,9,4,8:19:35 AM,9,4,8:34:08 AM,9,4,9:11:07 AM,9,4,9:28:54 AM,9,4,10:13:41 AM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_106,2687
-Order_No_28193,User_Id_1209,Bike,3,Business,18,5,8:39:51 AM,18,5,8:56:48 AM,18,5,9:10:12 AM,18,5,9:15:13 AM,18,5,9:24:59 AM,4,20.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_580,586
-Order_No_6022,User_Id_136,Bike,3,Business,21,2,12:05:58 PM,21,2,12:07:02 PM,21,2,12:16:40 PM,21,2,12:25:20 PM,21,2,12:50:11 PM,17,22.3,,-1.2629838,36.8086926,-1.3254522,36.721658399999995,Rider_Id_404,1491
-Order_No_11887,User_Id_3295,Bike,3,Business,10,5,12:09:28 PM,10,5,12:14:42 PM,10,5,12:26:43 PM,10,5,12:28:13 PM,10,5,12:45:22 PM,8,,,-1.2571472,36.795063299999995,-1.2966323,36.7875842,Rider_Id_953,1029
-Order_No_17996,User_Id_2064,Bike,2,Personal,24,4,10:44:23 AM,24,4,10:44:34 AM,24,4,10:53:52 AM,24,4,10:56:48 AM,24,4,11:04:50 AM,5,21.2,,-1.263894,36.77163,-1.2743552,36.811436,Rider_Id_116,482
-Order_No_16678,User_Id_3326,Bike,3,Business,5,5,10:44:11 AM,5,5,10:45:56 AM,5,5,10:56:12 AM,5,5,11:00:48 AM,5,5,11:43:06 AM,7,24.4,,-1.2551895,36.7822034,-1.2989287,36.81630820000001,Rider_Id_570,2538
-Order_No_5086,User_Id_1329,Bike,3,Business,22,2,12:18:25 PM,22,2,12:18:55 PM,22,2,12:19:33 PM,22,2,12:34:59 PM,22,2,12:48:48 PM,6,19.4,,-1.273056,36.811298,-1.2946581,36.779292100000006,Rider_Id_51,829
-Order_No_14029,User_Id_3246,Bike,3,Business,20,2,2:40:06 PM,20,2,2:40:16 PM,20,2,2:47:50 PM,20,2,2:59:43 PM,20,2,3:53:20 PM,21,23.9,,-1.3165542,36.700067,-1.2925919,36.81705770000001,Rider_Id_64,3217
-Order_No_8206,User_Id_3203,Bike,3,Business,5,5,3:09:28 PM,5,5,3:11:20 PM,5,5,3:13:41 PM,5,5,3:43:03 PM,5,5,3:56:27 PM,9,26.4,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_408,804
-Order_No_20305,User_Id_1500,Bike,3,Business,18,2,11:33:37 AM,18,2,11:36:57 AM,18,2,11:46:29 AM,18,2,12:11:43 PM,18,2,12:31:44 PM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_518,1201
-Order_No_8599,User_Id_2207,Bike,3,Business,22,5,12:24:23 PM,22,5,12:26:03 PM,22,5,12:43:06 PM,22,5,12:53:33 PM,22,5,1:05:46 PM,2,28.6,,-1.2682478000000001,36.8072852,-1.2696908000000002,36.81519,Rider_Id_448,733
-Order_No_18218,User_Id_1075,Bike,3,Business,21,4,12:00:47 PM,21,4,12:01:36 PM,21,4,12:13:49 PM,21,4,12:19:30 PM,21,4,12:39:54 PM,6,25.1,,-1.2527958000000001,36.8003131,-1.2629461000000002,36.76531370000001,Rider_Id_138,1224
-Order_No_10474,User_Id_246,Bike,3,Business,3,1,8:40:41 AM,3,1,8:40:52 AM,3,1,8:42:01 AM,3,1,8:47:40 AM,3,1,8:47:51 AM,3,15.2,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_875,11
-Order_No_6884,User_Id_41,Bike,2,Personal,11,1,1:56:08 PM,11,1,2:22:21 PM,11,1,2:38:16 PM,11,1,2:50:29 PM,11,1,3:07:34 PM,5,23.4,,-1.3000399999999999,36.835390000000004,-1.2856827,36.8211379,Rider_Id_844,1025
-Order_No_17540,User_Id_3434,Bike,3,Business,25,5,2:00:53 PM,25,5,2:01:21 PM,25,5,2:11:20 PM,25,5,2:18:01 PM,25,5,2:48:41 PM,20,29.1,,-1.2963097,36.7688221,-1.3189343999999998,36.9087593,Rider_Id_674,1840
-Order_No_3714,User_Id_635,Bike,3,Business,5,4,8:26:53 AM,5,4,8:27:22 AM,5,4,8:32:26 AM,5,4,8:40:13 AM,5,4,8:56:46 AM,6,,,-1.2599563,36.799343900000004,-1.2987976,36.829477000000004,Rider_Id_726,993
-Order_No_1891,User_Id_3326,Bike,3,Business,8,4,9:10:49 AM,8,4,9:11:08 AM,8,4,9:17:37 AM,8,4,9:25:51 AM,8,4,9:27:07 AM,3,20.5,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_405,76
-Order_No_3944,User_Id_2485,Bike,1,Personal,11,4,4:55:55 PM,11,4,4:58:58 PM,11,4,5:14:32 PM,11,4,5:27:23 PM,11,4,5:37:07 PM,3,23.8,,-1.2808667,36.7883545,-1.2938245000000002,36.7913345,Rider_Id_612,584
-Order_No_5982,User_Id_2956,Bike,3,Business,11,4,2:57:22 PM,11,4,2:57:35 PM,11,4,2:59:00 PM,11,4,3:28:31 PM,11,4,3:28:33 PM,2,27.0,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_547,2
-Order_No_26520,User_Id_972,Bike,3,Business,7,3,8:27:11 AM,7,3,8:28:05 AM,7,3,8:37:12 AM,7,3,8:39:40 AM,7,3,9:01:23 AM,10,,,-1.286282,36.8217484,-1.2844303,36.8781656,Rider_Id_235,1303
-Order_No_1616,User_Id_1329,Bike,3,Business,9,3,1:28:45 PM,9,3,1:29:07 PM,9,3,1:34:26 PM,9,3,1:36:16 PM,9,3,1:52:27 PM,7,25.3,,-1.273056,36.811298,-1.225021,36.801815999999995,Rider_Id_655,971
-Order_No_11467,User_Id_2271,Bike,3,Business,13,3,12:04:17 PM,13,3,12:08:12 PM,13,3,12:17:23 PM,13,3,12:43:16 PM,13,3,1:09:37 PM,12,24.9,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_267,1581
-Order_No_17781,User_Id_420,Bike,3,Business,19,3,3:15:32 PM,19,3,3:28:58 PM,19,3,3:38:26 PM,19,3,3:40:37 PM,19,3,4:18:10 PM,10,24.2,,-1.2517448,36.7924971,-1.3043821000000002,36.7840586,Rider_Id_65,2253
-Order_No_3201,User_Id_2701,Bike,3,Business,23,2,3:53:42 PM,23,2,4:01:17 PM,23,2,4:29:15 PM,23,2,4:33:22 PM,23,2,5:13:37 PM,9,19.3,,-1.2765736,36.851364600000004,-1.2475028000000001,36.877469700000006,Rider_Id_237,2415
-Order_No_21151,User_Id_2987,Bike,3,Business,6,3,12:08:48 PM,6,3,12:21:58 PM,6,3,12:37:00 PM,6,3,12:38:55 PM,6,3,12:53:41 PM,10,19.8,,-1.2702120000000001,36.82108770000001,-1.2297202,36.874550799999994,Rider_Id_103,886
-Order_No_20329,User_Id_1205,Bike,3,Business,20,4,12:17:36 PM,20,4,12:17:50 PM,20,4,12:26:30 PM,20,4,12:52:23 PM,20,4,1:37:09 PM,19,24.3,,-1.257309,36.806008399999996,-1.364303,36.74292,Rider_Id_72,2686
-Order_No_7524,User_Id_1882,Bike,3,Business,30,3,11:42:23 AM,30,3,11:45:09 AM,30,3,11:56:13 AM,30,3,12:21:07 PM,30,3,12:41:41 PM,7,25.0,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_246,1234
-Order_No_22628,User_Id_3283,Bike,3,Business,29,1,9:19:22 AM,29,1,9:20:01 AM,29,1,9:27:58 AM,29,1,9:29:20 AM,29,1,9:44:57 AM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_770,937
-Order_No_9857,User_Id_2112,Bike,3,Business,11,1,9:27:01 AM,11,1,9:27:22 AM,11,1,9:29:42 AM,11,1,9:38:19 AM,11,1,10:05:00 AM,14,18.2,,-1.2156006000000001,36.8916865,-1.2676026000000002,36.810362899999994,Rider_Id_31,1601
-Order_No_14677,User_Id_635,Bike,3,Business,19,2,3:34:37 PM,19,2,3:51:41 PM,19,2,4:15:52 PM,19,2,4:20:33 PM,19,2,4:57:54 PM,15,30.0,,-1.3198615,36.7110323,-1.2602337,36.79905479999999,Rider_Id_269,2241
-Order_No_11056,User_Id_3491,Bike,3,Business,15,5,10:07:01 AM,15,5,10:12:33 AM,15,5,10:32:16 AM,15,5,10:40:51 AM,15,5,11:06:48 AM,12,26.5,,-1.2283403,36.8822756,-1.2591018999999999,36.800577000000004,Rider_Id_831,1557
-Order_No_24061,User_Id_3017,Bike,3,Business,9,6,7:20:27 PM,9,6,7:21:44 PM,9,6,7:29:27 PM,9,6,7:37:48 PM,9,6,7:50:06 PM,5,17.1,,-1.299313,36.78314879999999,-1.3095596999999999,36.797295,Rider_Id_116,738
-Order_No_28028,User_Id_2956,Bike,3,Business,15,3,11:09:04 AM,15,3,11:09:43 AM,15,3,11:24:47 AM,15,3,11:28:12 AM,15,3,12:09:08 PM,7,,,-1.2584143,36.8048002,-1.3008433000000001,36.8036256,Rider_Id_660,2456
-Order_No_25228,User_Id_868,Bike,3,Business,5,2,12:10:29 PM,5,2,12:11:04 PM,5,2,12:18:33 PM,5,2,12:27:42 PM,5,2,12:45:49 PM,8,21.3,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_116,1087
-Order_No_23168,User_Id_3193,Bike,1,Personal,5,3,4:25:18 PM,5,3,4:27:13 PM,5,3,4:41:28 PM,5,3,5:01:55 PM,5,3,5:34:03 PM,7,22.0,,-1.3139279,36.8208897,-1.2668611,36.795638700000005,Rider_Id_447,1928
-Order_No_13982,User_Id_2709,Bike,3,Personal,29,5,2:32:59 PM,29,5,2:35:40 PM,29,5,2:51:30 PM,29,5,2:54:24 PM,29,5,3:10:50 PM,6,28.8,,-1.2571472,36.795063299999995,-1.2945041000000002,36.8151527,Rider_Id_245,986
-Order_No_2605,User_Id_3275,Bike,1,Personal,9,4,11:04:45 AM,9,4,11:34:56 AM,9,4,12:00:47 PM,9,4,12:07:51 PM,9,4,12:25:13 PM,11,23.7,,-1.1855066,36.825464600000004,-1.2143386999999999,36.7914548,Rider_Id_670,1042
-Order_No_4385,User_Id_1271,Bike,3,Business,24,1,12:06:08 PM,24,1,1:00:10 PM,24,1,1:43:34 PM,24,1,1:56:31 PM,24,1,2:35:44 PM,23,24.9,,-1.3244885,36.897791999999995,-1.2784722,36.7556652,Rider_Id_925,2353
-Order_No_17981,User_Id_914,Bike,3,Business,25,1,12:19:45 PM,25,1,12:20:29 PM,25,1,12:31:15 PM,25,1,12:34:57 PM,25,1,1:07:53 PM,7,27.1,,-1.2670213,36.7994482,-1.2979313000000001,36.809542,Rider_Id_709,1976
-Order_No_28201,User_Id_799,Bike,1,Personal,19,2,10:41:54 AM,19,2,10:52:18 AM,19,2,10:58:03 AM,19,2,11:10:13 AM,19,2,12:00:12 PM,6,,,-1.304033,36.7848695,-1.2795261,36.826018499999996,Rider_Id_431,2999
-Order_No_27372,User_Id_633,Bike,3,Business,17,4,12:03:14 PM,17,4,12:04:41 PM,17,4,12:12:36 PM,17,4,12:18:27 PM,17,4,12:26:43 PM,4,25.6,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_588,496
-Order_No_13206,User_Id_1473,Bike,3,Business,7,3,9:05:15 AM,7,3,9:08:42 AM,7,3,9:11:16 AM,7,3,9:21:32 AM,7,3,9:52:56 AM,15,,,-1.207546,36.7913976,-1.2571472,36.795063299999995,Rider_Id_31,1884
-Order_No_1778,User_Id_1484,Bike,3,Business,17,4,12:46:44 PM,17,4,12:46:52 PM,17,4,12:51:51 PM,17,4,1:09:33 PM,17,4,1:18:07 PM,2,25.6,,-1.2571472,36.795063299999995,-1.2567464,36.7915273,Rider_Id_167,514
-Order_No_11564,User_Id_2330,Bike,3,Business,9,4,2:21:58 PM,9,4,2:22:51 PM,9,4,2:34:06 PM,9,4,2:39:49 PM,9,4,3:04:09 PM,13,,,-1.2921463,36.8029626,-1.3192788999999998,36.711299100000005,Rider_Id_675,1460
-Order_No_26795,User_Id_2956,Bike,3,Business,20,2,12:54:27 PM,20,2,1:34:04 PM,20,2,1:44:53 PM,20,2,1:48:06 PM,20,2,2:26:41 PM,6,24.0,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_674,2315
-Order_No_18686,User_Id_2821,Bike,2,Personal,13,4,8:05:17 AM,13,4,8:11:26 AM,13,4,8:36:57 AM,13,4,8:39:42 AM,13,4,9:15:14 AM,14,18.4,,-1.3299021999999998,36.725612700000006,-1.2981625,36.813014200000005,Rider_Id_288,2132
-Order_No_17964,User_Id_3300,Bike,3,Business,8,3,10:44:45 AM,8,3,10:45:28 AM,8,3,10:58:00 AM,8,3,11:17:00 AM,8,3,11:32:56 AM,8,22.1,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_179,956
-Order_No_12435,User_Id_3283,Bike,3,Business,14,1,9:52:09 AM,14,1,9:52:29 AM,14,1,10:01:20 AM,14,1,10:15:41 AM,14,1,10:24:42 AM,4,20.2,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_427,541
-Order_No_4553,User_Id_248,Bike,3,Business,27,3,2:43:52 PM,27,3,2:44:40 PM,27,3,2:58:26 PM,27,3,3:15:30 PM,27,3,3:40:48 PM,3,29.3,,-1.2929747,36.76463579999999,-1.304735,36.784605400000004,Rider_Id_682,1518
-Order_No_4209,User_Id_302,Bike,3,Business,13,7,2:03:28 PM,13,7,2:36:52 PM,13,7,2:43:07 PM,13,7,2:53:39 PM,13,7,3:39:42 PM,10,24.6,,-1.3039695,36.783826700000006,-1.2649605,36.798177700000004,Rider_Id_541,2763
-Order_No_27336,User_Id_1882,Bike,3,Business,24,7,3:26:16 PM,24,7,3:27:02 PM,24,7,3:39:02 PM,24,7,3:53:21 PM,24,7,3:53:23 PM,7,,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_119,2
-Order_No_11746,User_Id_391,Bike,3,Business,14,2,1:30:53 PM,14,2,1:31:24 PM,14,2,1:37:41 PM,14,2,1:49:56 PM,14,2,2:06:06 PM,7,,,-1.2867454,36.8259017,-1.2901261999999998,36.7951047,Rider_Id_291,970
-Order_No_8122,User_Id_635,Bike,3,Business,21,5,10:29:56 AM,21,5,10:30:36 AM,21,5,10:32:51 AM,21,5,10:39:59 AM,21,5,10:50:17 AM,2,20.0,,-1.2599563,36.799343900000004,-1.2618264,36.8141179,Rider_Id_147,618
-Order_No_18584,User_Id_393,Bike,3,Business,16,2,9:09:37 AM,16,2,9:18:28 AM,16,2,9:33:45 AM,16,2,9:49:28 AM,16,2,10:23:01 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_709,2013
-Order_No_1447,User_Id_3291,Bike,3,Business,28,2,8:57:38 AM,28,2,9:03:28 AM,28,2,9:10:44 AM,28,2,9:16:12 AM,28,2,9:27:16 AM,4,18.5,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_715,664
-Order_No_3818,User_Id_928,Bike,3,Business,17,4,3:45:58 PM,17,4,3:47:50 PM,17,4,3:48:40 PM,17,4,3:59:20 PM,17,4,4:49:18 PM,15,27.9,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_215,2998
-Order_No_9895,User_Id_1500,Bike,3,Business,24,3,12:36:52 PM,24,3,12:42:58 PM,24,3,12:43:54 PM,24,3,12:47:23 PM,24,3,1:24:04 PM,15,20.0,,-1.2574219,36.7927073,-1.3301235,36.7070434,Rider_Id_731,2201
-Order_No_7402,User_Id_1882,Bike,3,Business,5,5,7:44:24 PM,5,5,7:45:44 PM,5,5,7:58:34 PM,5,5,8:07:04 PM,5,5,8:45:10 PM,14,22.7,,-1.2156006000000001,36.8916865,-1.2268871000000001,36.8073946,Rider_Id_649,2286
-Order_No_8769,User_Id_3222,Bike,3,Business,25,1,11:59:18 AM,25,1,11:59:31 AM,25,1,11:59:43 AM,25,1,12:04:11 PM,25,1,12:11:52 PM,3,25.7,,-1.2551895,36.7822034,-1.2622145,36.8087391,Rider_Id_576,461
-Order_No_227,User_Id_1829,Bike,3,Personal,24,1,12:49:03 PM,24,1,12:50:24 PM,24,1,12:58:10 PM,24,1,1:01:15 PM,24,1,1:48:56 PM,7,22.5,,-1.3056847,36.8101341,-1.2949652,36.7687874,Rider_Id_764,2861
-Order_No_20717,User_Id_3426,Bike,3,Business,14,3,3:54:08 PM,14,3,4:05:19 PM,14,3,4:15:56 PM,14,3,4:23:45 PM,14,3,4:45:38 PM,8,25.7,,-1.3014461000000002,36.7661381,-1.2597528999999998,36.806629,Rider_Id_268,1313
-Order_No_5427,User_Id_635,Bike,3,Business,28,1,10:56:19 AM,28,1,10:56:48 AM,28,1,11:07:54 AM,28,1,11:14:10 AM,28,1,11:23:23 AM,3,23.9,,-1.2764098999999998,36.8208451,-1.2602337,36.79905479999999,Rider_Id_379,553
-Order_No_13298,User_Id_1209,Bike,3,Business,8,1,2:54:36 PM,8,1,3:00:39 PM,8,1,3:13:27 PM,8,1,3:16:44 PM,8,1,3:30:10 PM,4,29.6,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_855,806
-Order_No_24500,User_Id_2633,Bike,1,Personal,19,6,2:03:38 PM,19,6,2:04:08 PM,19,6,2:14:39 PM,19,6,2:19:10 PM,19,6,2:41:43 PM,14,24.2,,-1.2916556000000001,36.8852415,-1.2944292,36.779356899999996,Rider_Id_633,1353
-Order_No_15133,User_Id_1849,Bike,3,Business,20,4,4:13:30 PM,20,4,4:16:32 PM,20,4,4:26:51 PM,20,4,4:30:28 PM,20,4,5:07:34 PM,5,25.1,,-1.290894,36.822971,-1.2900089,36.7885633,Rider_Id_576,2226
-Order_No_3995,User_Id_2956,Bike,3,Business,9,5,2:13:25 PM,9,5,2:16:03 PM,9,5,2:26:23 PM,9,5,2:30:21 PM,9,5,3:11:22 PM,13,26.0,,-1.2584143,36.8048002,-1.2859912,36.8756811,Rider_Id_646,2461
-Order_No_458,User_Id_1402,Bike,3,Business,18,3,11:21:29 AM,18,3,11:30:03 AM,18,3,11:58:25 AM,18,3,12:11:14 PM,18,3,12:41:51 PM,11,19.9,,-1.225322,36.80855,-1.2891921999999998,36.825189,Rider_Id_726,1837
-Order_No_22560,User_Id_265,Bike,3,Business,18,2,9:21:41 AM,18,2,9:44:08 AM,18,2,10:10:34 AM,18,2,10:22:11 AM,18,2,10:45:58 AM,7,19.5,,-1.268589,36.800623,-1.300921,36.828195,Rider_Id_819,1427
-Order_No_24624,User_Id_2008,Bike,3,Business,10,4,5:00:12 PM,10,4,5:03:16 PM,10,4,5:08:56 PM,10,4,5:29:17 PM,10,4,6:03:38 PM,16,23.5,,-1.2508229,36.7895263,-1.2434951,36.6760986,Rider_Id_247,2061
-Order_No_4230,User_Id_1346,Bike,3,Business,21,1,10:50:07 AM,21,1,10:50:20 AM,21,1,10:50:52 AM,21,1,10:55:34 AM,21,1,11:23:49 AM,6,25.6,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_676,1695
-Order_No_18127,User_Id_2997,Bike,1,Personal,2,4,9:24:17 AM,2,4,9:24:33 AM,2,4,9:37:32 AM,2,4,9:43:57 AM,2,4,9:59:23 AM,7,13.4,,-1.2849815,36.7767751,-1.2551895,36.7822034,Rider_Id_754,926
-Order_No_9475,User_Id_1206,Bike,3,Personal,26,4,4:28:27 PM,26,4,4:29:40 PM,26,4,4:36:07 PM,26,4,4:38:54 PM,26,4,4:44:03 PM,2,19.7,,-1.2915246999999999,36.805575700000006,-1.2933834,36.7944832,Rider_Id_216,309
-Order_No_10285,User_Id_435,Bike,3,Business,30,4,11:35:59 AM,30,4,11:36:27 AM,30,4,11:44:32 AM,30,4,11:47:36 AM,30,4,12:20:03 PM,18,,,-1.319613,36.711055,-1.300921,36.828195,Rider_Id_571,1947
-Order_No_11025,User_Id_2330,Bike,3,Business,30,3,2:27:40 PM,30,3,2:27:47 PM,30,3,2:28:20 PM,30,3,2:56:57 PM,30,3,3:04:54 PM,9,29.1,,-1.2649605,36.798177700000004,-1.2326041,36.8009029,Rider_Id_137,477
-Order_No_27225,User_Id_868,Bike,3,Business,17,3,1:56:56 PM,17,3,1:57:29 PM,17,3,2:03:49 PM,17,3,2:08:48 PM,17,3,2:23:00 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547,852
-Order_No_24746,User_Id_3701,Bike,3,Business,13,4,11:28:34 AM,13,4,11:30:03 AM,13,4,11:48:17 AM,13,4,12:16:34 PM,13,4,1:07:48 PM,12,22.0,,-1.3104733999999998,36.7363698,-1.2612573999999999,36.7879622,Rider_Id_441,3074
-Order_No_11703,User_Id_3698,Bike,3,Business,7,2,10:59:55 AM,7,2,11:00:55 AM,7,2,11:13:00 AM,7,2,11:16:07 AM,7,2,11:43:20 AM,18,,,-1.2649605,36.798177700000004,-1.3244885,36.897791999999995,Rider_Id_911,1633
-Order_No_229,User_Id_1907,Bike,2,Personal,26,3,2:04:52 PM,26,3,2:05:46 PM,26,3,2:12:50 PM,26,3,2:16:07 PM,26,3,2:37:53 PM,8,27.1,,-1.2861183999999999,36.8159704,-1.30217,36.770468,Rider_Id_698,1306
-Order_No_6214,User_Id_3560,Bike,3,Business,28,5,11:35:39 AM,28,5,11:35:56 AM,28,5,11:36:03 AM,28,5,11:42:13 AM,28,5,11:54:35 AM,5,23.8,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_31,742
-Order_No_9331,User_Id_2804,Bike,3,Business,21,1,12:55:58 PM,21,1,12:56:21 PM,21,1,12:57:02 PM,21,1,1:04:16 PM,21,1,1:09:43 PM,4,25.6,,-1.2551895,36.7822034,-1.2669736,36.8040985,Rider_Id_379,327
-Order_No_27483,User_Id_496,Bike,3,Business,2,4,11:50:19 AM,2,4,11:50:47 AM,2,4,11:51:53 AM,2,4,11:58:50 AM,2,4,12:35:27 PM,23,,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_695,2197
-Order_No_5134,User_Id_393,Bike,3,Business,22,2,2:14:30 PM,22,2,2:51:08 PM,22,2,2:59:29 PM,22,2,3:41:47 PM,22,2,3:42:13 PM,14,21.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_656,26
-Order_No_19848,User_Id_1280,Bike,3,Business,31,5,4:28:54 PM,31,5,4:36:36 PM,31,5,4:54:59 PM,31,5,4:56:55 PM,31,5,5:28:10 PM,8,23.9,,-1.2551895,36.7822034,-1.2993556,36.8146776,Rider_Id_233,1875
-Order_No_6403,User_Id_3710,Bike,3,Business,31,2,1:19:58 PM,31,2,1:20:29 PM,31,2,1:31:30 PM,31,2,1:36:34 PM,31,2,2:10:22 PM,10,22.3,,-1.3088274,36.839771500000005,-1.2551895,36.7822034,Rider_Id_414,2028
-Order_No_20811,User_Id_1500,Bike,3,Business,26,2,9:28:22 AM,26,2,12:15:25 PM,26,2,12:17:24 PM,26,2,1:21:51 PM,26,2,1:29:35 PM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_494,464
-Order_No_20692,User_Id_3291,Bike,3,Business,17,4,4:19:54 PM,17,4,4:21:05 PM,17,4,4:21:47 PM,17,4,4:30:22 PM,17,4,4:30:26 PM,5,28.2,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_395,4
-Order_No_22894,User_Id_928,Bike,3,Business,30,3,9:45:47 AM,30,3,9:46:04 AM,30,3,10:14:20 AM,30,3,10:39:26 AM,30,3,11:21:57 AM,18,25.2,,-1.3319466999999998,36.776699799999996,-1.2551895,36.7822034,Rider_Id_328,2551
-Order_No_14845,User_Id_2587,Bike,3,Business,23,6,11:51:54 AM,23,6,11:56:37 AM,23,6,11:58:31 AM,23,6,12:06:47 PM,23,6,12:23:44 PM,5,26.5,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_576,1017
-Order_No_11421,User_Id_3283,Bike,3,Business,27,4,2:56:45 PM,27,4,2:57:14 PM,27,4,3:02:41 PM,27,4,3:08:59 PM,27,4,3:21:11 PM,4,23.7,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_167,732
-Order_No_22731,User_Id_393,Bike,3,Business,26,2,11:54:31 AM,26,2,11:54:51 AM,26,2,11:54:57 AM,26,2,12:06:24 PM,26,2,12:13:30 PM,4,26.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_15,426
-Order_No_3070,User_Id_1899,Bike,3,Business,4,6,10:20:14 AM,4,6,10:20:40 AM,4,6,10:28:41 AM,4,6,10:30:36 AM,4,6,10:48:09 AM,11,15.6,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_414,1053
-Order_No_12804,User_Id_3201,Bike,3,Business,14,3,3:41:14 PM,14,3,3:44:25 PM,14,3,3:45:14 PM,14,3,3:55:54 PM,14,3,4:08:59 PM,4,25.7,,-1.2642719999999998,36.7978791,-1.2731645,36.7900343,Rider_Id_651,785
-Order_No_22442,User_Id_817,Bike,3,Personal,8,3,12:12:55 PM,8,3,12:13:10 PM,8,3,12:24:37 PM,8,3,12:29:19 PM,8,3,12:46:16 PM,6,22.0,,-1.2636813999999998,36.802996500000006,-1.2894633,36.764223,Rider_Id_932,1017
-Order_No_5056,User_Id_2801,Bike,3,Business,14,2,10:00:14 AM,14,2,10:00:19 AM,14,2,10:08:03 AM,14,2,10:12:46 AM,14,2,11:25:06 AM,10,15.4,,-1.2595421999999998,36.7871178,-1.304119,36.819649,Rider_Id_229,4340
-Order_No_5385,User_Id_3686,Bike,3,Business,8,5,9:14:33 AM,8,5,9:15:05 AM,8,5,9:15:12 AM,8,5,9:20:48 AM,8,5,9:44:08 AM,8,20.6,,-1.2551895,36.7822034,-1.2950408000000002,36.809817200000005,Rider_Id_926,1400
-Order_No_27097,User_Id_1882,Bike,3,Business,8,2,12:42:09 PM,8,2,12:44:58 PM,8,2,1:08:18 PM,8,2,1:17:22 PM,8,2,1:30:00 PM,8,23.5,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_205,758
-Order_No_23430,User_Id_2983,Bike,1,Personal,23,6,10:32:44 AM,23,6,10:43:19 AM,23,6,11:05:33 AM,23,6,11:17:15 AM,23,6,11:46:21 AM,13,,,-1.2789537,36.828639200000005,-1.2835228,36.7437619,Rider_Id_752,1746
-Order_No_15512,User_Id_1578,Bike,3,Business,4,4,11:06:13 AM,4,4,11:08:58 AM,4,4,11:41:05 AM,4,4,11:49:47 AM,4,4,12:17:59 PM,10,,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_532,1692
-Order_No_255,User_Id_2509,Bike,3,Business,13,3,1:15:14 PM,13,3,1:16:41 PM,13,3,1:26:00 PM,13,3,1:43:01 PM,13,3,2:02:07 PM,5,29.4,,-1.2571472,36.795063299999995,-1.286553,36.8273374,Rider_Id_341,1146
-Order_No_1966,User_Id_706,Bike,3,Business,13,2,2:32:25 PM,13,2,2:37:07 PM,13,2,2:47:34 PM,13,2,2:50:37 PM,13,2,3:02:25 PM,6,26.4,,-1.3077869,36.844320700000004,-1.3239488000000001,36.831683500000004,Rider_Id_900,708
-Order_No_18072,User_Id_2533,Bike,1,Personal,17,3,2:47:25 PM,17,3,2:51:36 PM,17,3,3:03:13 PM,17,3,3:04:47 PM,17,3,3:34:43 PM,6,28.4,,-1.2727624,36.7674062,-1.289718,36.8193,Rider_Id_952,1796
-Order_No_23730,User_Id_1244,Bike,3,Business,14,5,3:12:56 PM,14,5,3:14:13 PM,14,5,3:28:54 PM,14,5,3:36:45 PM,14,5,4:14:05 PM,16,23.7,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_708,2240
-Order_No_14376,User_Id_1687,Bike,3,Business,22,5,10:05:18 AM,22,5,10:06:13 AM,22,5,10:26:12 AM,22,5,10:36:00 AM,22,5,11:15:35 AM,9,,,-1.2626574,36.8014034,-1.3251583999999998,36.8199384,Rider_Id_205,2375
-Order_No_16603,User_Id_51,Bike,2,Personal,18,1,12:49:19 PM,18,1,12:50:45 PM,18,1,1:11:59 PM,18,1,1:16:23 PM,18,1,2:10:18 PM,22,,,-1.2580991000000001,36.794151899999996,-1.1441246999999999,36.7197047,Rider_Id_802,3235
-Order_No_12885,User_Id_237,Bike,3,Business,25,4,8:30:48 AM,25,4,8:31:52 AM,25,4,8:35:15 AM,25,4,8:42:37 AM,25,4,9:07:46 AM,8,16.4,2.1,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_125,1509
-Order_No_23212,User_Id_721,Bike,2,Personal,25,1,11:34:23 AM,25,1,11:34:41 AM,25,1,11:45:01 AM,25,1,11:48:18 AM,25,1,12:14:23 PM,15,,,-1.2952108999999998,36.775706400000004,-1.2324936000000002,36.8224146,Rider_Id_116,1565
-Order_No_19569,User_Id_1882,Bike,3,Business,23,5,7:08:50 PM,23,5,7:09:13 PM,23,5,8:47:02 PM,23,5,8:52:37 PM,23,5,9:32:01 PM,13,20.1,,-1.2958783,36.7928125,-1.3175825,36.7004796,Rider_Id_829,2364
-Order_No_12664,User_Id_2523,Bike,3,Business,19,2,9:34:55 AM,19,2,9:35:40 AM,19,2,9:41:40 AM,19,2,9:48:38 AM,19,2,9:57:55 AM,4,,,-1.2551895,36.7822034,-1.2722283,36.805013,Rider_Id_720,557
-Order_No_1936,User_Id_2801,Bike,3,Business,24,4,2:52:28 PM,24,4,2:52:48 PM,24,4,2:59:16 PM,24,4,3:02:24 PM,24,4,4:09:04 PM,15,28.2,,-1.2595421999999998,36.7871178,-1.3104506,36.8687235,Rider_Id_579,4000
-Order_No_19689,User_Id_642,Bike,2,Personal,3,1,2:14:31 PM,3,1,2:17:48 PM,3,1,2:36:15 PM,3,1,2:40:42 PM,3,1,2:48:27 PM,3,23.2,,-1.2854453000000001,36.818836299999994,-1.2790558,36.8151527,Rider_Id_208,465
-Order_No_5585,User_Id_1103,Bike,3,Business,18,1,8:59:09 AM,18,1,9:23:47 AM,18,1,10:08:25 AM,18,1,10:12:20 AM,18,1,10:31:22 AM,8,21.2,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_88,1142
-Order_No_9150,User_Id_3560,Bike,3,Business,7,4,1:02:44 PM,7,4,1:09:23 PM,7,4,1:23:00 PM,7,4,1:32:28 PM,7,4,1:59:36 PM,8,30.9,,-1.3014461000000002,36.7661381,-1.279395,36.825364,Rider_Id_763,1628
-Order_No_19241,User_Id_3064,Bike,3,Business,26,4,2:31:56 PM,26,4,3:47:08 PM,26,4,3:55:52 PM,26,4,4:00:12 PM,26,4,4:24:15 PM,5,22.2,,-1.2571472,36.795063299999995,-1.2549476000000002,36.8269576,Rider_Id_131,1443
-Order_No_11083,User_Id_2154,Bike,3,Business,15,5,4:44:11 PM,15,5,4:44:54 PM,15,5,4:54:15 PM,15,5,5:01:27 PM,15,5,5:20:43 PM,8,27.6,,-1.2571472,36.795063299999995,-1.2765736,36.851364600000004,Rider_Id_207,1156
-Order_No_22088,User_Id_181,Bike,2,Personal,12,5,7:52:40 AM,12,5,7:53:21 AM,12,5,8:05:01 AM,12,5,8:06:15 AM,12,5,8:25:53 AM,9,18.8,,-1.2564152,36.779280299999996,-1.2392628,36.7661643,Rider_Id_453,1178
-Order_No_16092,User_Id_2957,Bike,3,Personal,16,2,11:09:25 AM,16,2,12:08:02 PM,16,2,12:18:02 PM,16,2,12:21:09 PM,16,2,12:54:27 PM,6,,,-1.3014462,36.7607695,-1.2772632,36.792225099999996,Rider_Id_166,1998
-Order_No_15398,User_Id_2154,Bike,3,Business,13,1,10:50:56 AM,13,1,10:52:00 AM,13,1,10:52:54 AM,13,1,11:24:06 AM,13,1,11:30:51 AM,6,17.0,,-1.3052491000000002,36.8223899,-1.2765736,36.851364600000004,Rider_Id_179,405
-Order_No_22454,User_Id_1329,Bike,3,Business,10,3,7:02:52 PM,10,3,7:06:05 PM,10,3,7:15:17 PM,10,3,7:21:01 PM,10,3,7:35:43 PM,5,27.5,,-1.273056,36.811298,-1.2913246,36.8019522,Rider_Id_96,882
-Order_No_25428,User_Id_3186,Bike,3,Business,13,4,9:31:48 AM,13,4,9:32:44 AM,13,4,9:44:04 AM,13,4,9:48:22 AM,13,4,10:06:31 AM,5,18.3,,-1.2854406,36.789409,-1.2981352,36.8213822,Rider_Id_176,1089
-Order_No_17169,User_Id_2383,Bike,3,Business,3,3,9:10:55 AM,3,3,9:15:31 AM,3,3,9:17:01 AM,3,3,9:20:33 AM,3,3,9:30:58 AM,2,21.6,,-1.2608993,36.799246000000004,-1.2682478000000001,36.8072852,Rider_Id_205,625
-Order_No_24786,User_Id_3355,Bike,3,Business,4,4,8:18:28 AM,4,4,8:21:21 AM,4,4,8:42:51 AM,4,4,8:45:46 AM,4,4,9:05:57 AM,10,18.5,,-1.3052491000000002,36.8223899,-1.325086,36.887894,Rider_Id_208,1211
-Order_No_12001,User_Id_1469,Bike,3,Business,5,2,3:30:53 PM,5,2,3:31:19 PM,5,2,3:49:44 PM,5,2,4:02:44 PM,5,2,4:39:54 PM,2,27.6,,-1.2571472,36.795063299999995,-1.2567464,36.7915273,Rider_Id_547,2230
-Order_No_25502,User_Id_1927,Bike,3,Business,13,2,6:20:56 PM,13,2,6:21:04 PM,13,2,6:21:07 PM,13,2,6:33:03 PM,13,2,6:48:23 PM,10,23.4,,-1.3169591,36.7000029,-1.3472066,36.7692638,Rider_Id_874,920
-Order_No_18225,User_Id_3326,Bike,3,Business,21,4,10:34:05 AM,21,4,10:34:15 AM,21,4,10:36:35 AM,21,4,10:43:33 AM,21,4,10:57:09 AM,9,25.2,,-1.2938875,36.7774083,-1.2551895,36.7822034,Rider_Id_854,816
-Order_No_5350,User_Id_2523,Bike,3,Business,28,3,1:04:01 PM,28,3,1:10:58 PM,28,3,1:15:11 PM,28,3,1:16:31 PM,28,3,1:45:25 PM,7,,,-1.2551895,36.7822034,-1.2875845,36.8135104,Rider_Id_661,1734
-Order_No_25603,User_Id_2489,Bike,2,Personal,3,5,2:08:07 PM,3,5,2:10:01 PM,3,5,2:25:31 PM,3,5,2:29:34 PM,3,5,2:48:29 PM,8,25.0,,-1.2801014,36.821042299999995,-1.3272104,36.85284179999999,Rider_Id_812,1135
-Order_No_5522,User_Id_3201,Bike,3,Business,23,5,9:48:03 AM,23,5,9:49:42 AM,23,5,9:57:16 AM,23,5,10:03:27 AM,23,5,10:18:19 AM,6,20.1,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_878,892
-Order_No_11813,User_Id_3499,Bike,1,Personal,7,6,11:38:17 AM,7,6,12:54:37 PM,7,6,1:11:39 PM,7,6,1:17:03 PM,7,6,1:36:56 PM,8,19.2,,-1.3178263000000001,36.7141818,-1.2885712999999999,36.764981799999994,Rider_Id_636,1193
-Order_No_20676,User_Id_2081,Bike,1,Personal,27,5,2:00:54 PM,27,5,2:18:24 PM,27,5,2:33:18 PM,27,5,2:36:18 PM,27,5,3:13:56 PM,15,23.9,,-1.3149338,36.7135703,-1.2802871999999998,36.828631099999996,Rider_Id_726,2258
-Order_No_5346,User_Id_758,Bike,3,Business,30,4,2:37:29 PM,30,4,2:37:47 PM,30,4,2:46:22 PM,30,4,2:51:32 PM,30,4,3:05:58 PM,8,,,-1.2917868000000001,36.7872675,-1.2566688999999998,36.779695200000006,Rider_Id_783,866
-Order_No_15408,User_Id_3390,Bike,3,Personal,29,5,9:45:01 AM,29,5,9:45:47 AM,29,5,10:05:54 AM,29,5,10:18:24 AM,29,5,10:42:49 AM,5,20.9,,-1.2866011999999998,36.8195691,-1.2609721,36.8480813,Rider_Id_436,1465
-Order_No_12757,User_Id_467,Bike,3,Business,14,2,12:16:53 PM,14,2,12:17:21 PM,14,2,12:22:49 PM,14,2,12:38:00 PM,14,2,12:39:53 PM,4,,,-1.304735,36.784605400000004,-1.2913246,36.8019522,Rider_Id_678,113
-Order_No_5193,User_Id_323,Bike,1,Business,17,5,3:09:25 PM,17,5,3:09:33 PM,17,5,3:16:19 PM,17,5,3:34:39 PM,17,5,4:58:36 PM,24,20.4,,-1.3632162,36.9108253,-1.2559559999999999,36.789867,Rider_Id_406,5037
-Order_No_13960,User_Id_3647,Bike,3,Business,26,2,3:05:07 PM,26,2,3:07:42 PM,26,2,3:26:30 PM,26,2,3:36:37 PM,26,2,3:56:24 PM,6,31.0,,-1.2726389999999999,36.794723,-1.2944363,36.7983424,Rider_Id_76,1187
-Order_No_27283,User_Id_517,Bike,3,Business,10,1,9:30:57 AM,10,1,9:47:17 AM,10,1,10:14:12 AM,10,1,10:24:21 AM,10,1,10:54:45 AM,14,18.7,2.1,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_523,1824
-Order_No_28216,User_Id_1440,Bike,2,Personal,20,4,1:05:03 PM,20,4,1:05:43 PM,20,4,1:41:49 PM,20,4,1:44:44 PM,20,4,2:08:54 PM,10,27.9,,-1.3217370000000002,36.81797879999999,-1.3005797,36.7729756,Rider_Id_79,1450
-Order_No_4737,User_Id_3291,Bike,3,Business,30,1,10:19:55 AM,30,1,10:23:14 AM,30,1,11:01:45 AM,30,1,11:07:08 AM,30,1,11:24:50 AM,11,22.6,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_906,1062
-Order_No_15466,User_Id_3446,Bike,1,Personal,4,4,8:18:05 AM,4,4,8:18:41 AM,4,4,8:37:26 AM,4,4,8:41:36 AM,4,4,9:46:22 AM,23,18.5,,-1.3186521999999998,36.769988399999995,-1.2166078000000002,36.882501,Rider_Id_847,3886
-Order_No_23263,User_Id_3521,Bike,3,Business,8,1,2:16:48 PM,8,1,2:17:52 PM,8,1,2:34:33 PM,8,1,2:48:55 PM,8,1,3:01:00 PM,7,29.5,,-1.2874648000000002,36.8271985,-1.32589,36.831679,Rider_Id_790,725
-Order_No_27452,User_Id_3283,Bike,3,Business,17,4,10:54:47 AM,17,4,10:55:07 AM,17,4,10:56:12 AM,17,4,11:01:46 AM,17,4,11:26:33 AM,18,25.2,,-1.3444459999999998,36.756597,-1.2628473,36.781805,Rider_Id_741,1487
-Order_No_6039,User_Id_540,Bike,3,Business,20,4,10:05:57 AM,20,4,10:08:40 AM,20,4,10:15:29 AM,20,4,10:19:39 AM,20,4,10:51:33 AM,8,18.7,0.2,-1.2551895,36.7822034,-1.2964347999999999,36.8182757,Rider_Id_404,1914
-Order_No_4349,User_Id_2330,Bike,3,Business,31,5,1:35:38 PM,31,5,3:08:26 PM,31,5,3:10:41 PM,31,5,3:31:58 PM,31,5,3:50:21 PM,9,24.9,,-1.2124582,36.802894,-1.2645318,36.7981775,Rider_Id_7,1103
-Order_No_24617,User_Id_1280,Bike,3,Business,27,3,5:03:46 PM,27,3,5:03:56 PM,27,3,5:04:31 PM,27,3,5:19:19 PM,27,3,5:19:22 PM,3,27.6,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_576,3
-Order_No_2266,User_Id_478,Bike,3,Business,8,5,7:41:24 AM,8,5,7:41:43 AM,8,5,7:44:55 AM,8,5,8:14:43 AM,8,5,8:33:37 AM,3,22.8,,-1.2551895,36.7822034,-1.2642903,36.80021370000001,Rider_Id_720,1134
-Order_No_3161,User_Id_2521,Bike,3,Business,29,6,9:57:57 AM,29,6,9:58:06 AM,29,6,9:58:09 AM,29,6,10:02:38 AM,29,6,10:05:04 AM,4,19.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10,146
-Order_No_15438,User_Id_2587,Bike,3,Business,9,6,9:19:49 AM,9,6,9:20:12 AM,9,6,9:24:04 AM,9,6,9:43:52 AM,9,6,10:50:57 AM,12,20.0,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_771,4025
-Order_No_8471,User_Id_1856,Bike,3,Business,10,5,12:04:33 PM,10,5,12:04:55 PM,10,5,12:11:50 PM,10,5,12:27:15 PM,10,5,12:29:48 PM,7,,,-1.273009,36.802807,-1.2653185,36.8358841,Rider_Id_778,153
-Order_No_17829,User_Id_2625,Bike,1,Personal,22,5,1:22:45 PM,22,5,1:24:56 PM,22,5,1:42:55 PM,22,5,1:48:01 PM,22,5,2:02:58 PM,5,,,-1.2739208,36.8310153,-1.2655306,36.828148,Rider_Id_579,897
-Order_No_332,User_Id_2083,Bike,3,Personal,27,3,2:46:03 PM,27,3,2:46:16 PM,27,3,2:46:33 PM,27,3,2:55:59 PM,27,3,3:33:13 PM,9,29.1,,-1.2615888000000002,36.7928732,-1.2981239,36.8367977,Rider_Id_279,2234
-Order_No_3284,User_Id_3426,Bike,3,Business,5,3,5:48:46 PM,5,3,5:49:03 PM,5,3,5:54:14 PM,5,3,6:02:11 PM,5,3,6:12:08 PM,3,22.4,,-1.2765492,36.7669809,-1.3014461000000002,36.7661381,Rider_Id_847,597
-Order_No_4480,User_Id_733,Bike,3,Business,3,1,11:45:11 AM,3,1,11:45:47 AM,3,1,11:52:33 AM,3,1,11:55:50 AM,3,1,12:05:18 PM,4,21.4,,-1.2551895,36.7822034,-1.2638801000000002,36.7715842,Rider_Id_167,568
-Order_No_17355,User_Id_633,Bike,3,Business,7,4,8:19:03 AM,7,4,8:19:32 AM,7,4,8:23:37 AM,7,4,8:35:03 AM,7,4,8:41:57 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_329,414
-Order_No_7296,User_Id_1874,Bike,3,Business,14,4,3:22:47 PM,14,4,5:05:12 PM,14,4,5:13:41 PM,14,4,5:14:42 PM,14,4,5:52:35 PM,7,26.3,,-1.2571472,36.795063299999995,-1.304592,36.822007,Rider_Id_410,2273
-Order_No_20502,User_Id_1402,Bike,3,Business,15,1,2:28:12 PM,15,1,2:41:30 PM,15,1,2:53:37 PM,15,1,3:04:38 PM,15,1,3:53:35 PM,17,,,-1.3263903000000001,36.717988500000004,-1.2813012,36.832396200000005,Rider_Id_448,2937
-Order_No_900,User_Id_391,Bike,3,Business,12,2,3:58:37 PM,12,2,4:02:00 PM,12,2,4:14:14 PM,12,2,4:20:05 PM,12,2,4:39:18 PM,2,,,-1.2867454,36.8259017,-1.279658,36.817699,Rider_Id_448,1153
-Order_No_12900,User_Id_590,Bike,3,Business,3,1,3:54:01 PM,3,1,3:54:17 PM,3,1,4:15:04 PM,3,1,4:17:52 PM,3,1,4:21:03 PM,2,23.4,,-1.257773,36.795486,-1.2529635000000001,36.788540399999995,Rider_Id_216,191
-Order_No_23682,User_Id_3493,Bike,1,Personal,8,1,8:28:54 AM,8,1,8:29:25 AM,8,1,8:42:31 AM,8,1,8:48:24 AM,8,1,9:19:02 AM,16,19.8,,-1.3278463,36.819099,-1.2623805000000001,36.7805404,Rider_Id_328,1838
-Order_No_14747,User_Id_2439,Bike,1,Business,5,2,4:20:51 PM,5,2,4:26:07 PM,5,2,4:29:24 PM,5,2,4:36:36 PM,5,2,5:28:20 PM,7,21.3,,-1.2805198,36.827761200000005,-1.2615888000000002,36.7928732,Rider_Id_925,3104
-Order_No_24730,User_Id_2691,Bike,3,Business,15,3,8:40:51 AM,15,3,8:41:12 AM,15,3,8:47:38 AM,15,3,8:52:39 AM,15,3,9:07:24 AM,8,16.6,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_449,885
-Order_No_15383,User_Id_1500,Bike,3,Business,15,3,9:58:28 AM,15,3,10:03:53 AM,15,3,10:11:01 AM,15,3,10:13:09 AM,15,3,10:32:30 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_161,1161
-Order_No_5716,User_Id_893,Bike,3,Business,9,4,1:57:55 PM,9,4,1:58:21 PM,9,4,2:06:37 PM,9,4,2:11:36 PM,9,4,2:26:16 PM,5,,,-1.2732603999999998,36.823046000000005,-1.2663194,36.809901,Rider_Id_709,880
-Order_No_9811,User_Id_795,Bike,3,Personal,15,2,11:46:48 AM,15,2,11:47:45 AM,15,2,12:02:09 PM,15,2,12:03:21 PM,15,2,12:30:17 PM,12,24.5,,-1.3322618,36.8045412,-1.3104733999999998,36.7363698,Rider_Id_15,1616
-Order_No_23341,User_Id_2377,Bike,3,Personal,9,6,10:05:02 AM,9,6,10:05:57 AM,9,6,10:13:35 AM,9,6,10:25:06 AM,9,6,10:39:12 AM,4,20.9,,-1.3083443000000001,36.833289,-1.3173868999999998,36.8112044,Rider_Id_956,846
-Order_No_22158,User_Id_1402,Bike,3,Business,27,4,3:03:19 PM,27,4,3:04:41 PM,27,4,3:13:38 PM,27,4,3:21:26 PM,27,4,3:34:17 PM,2,23.9,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_793,771
-Order_No_20302,User_Id_1687,Bike,3,Business,22,5,3:44:01 PM,22,5,3:44:26 PM,22,5,4:01:53 PM,22,5,4:03:43 PM,22,5,4:19:23 PM,5,27.2,,-1.2626574,36.8014034,-1.2901228,36.8234237,Rider_Id_88,940
-Order_No_17727,User_Id_393,Bike,3,Business,29,5,8:39:39 AM,29,5,8:39:52 AM,29,5,8:46:18 AM,29,5,8:56:43 AM,29,5,9:21:23 AM,11,20.8,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_933,1480
-Order_No_2687,User_Id_53,Bike,3,Business,4,2,11:52:43 AM,4,2,11:57:03 AM,4,2,12:04:06 PM,4,2,12:09:31 PM,4,2,12:25:17 PM,5,20.6,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_894,946
-Order_No_26513,User_Id_2625,Bike,1,Personal,27,5,2:49:30 PM,27,5,2:49:42 PM,27,5,3:06:26 PM,27,5,3:13:45 PM,27,5,3:55:33 PM,14,24.1,,-1.2248308,36.801257299999996,-1.2962832,36.784968299999996,Rider_Id_882,2508
-Order_No_15401,User_Id_3536,Bike,1,Personal,11,4,9:38:42 AM,11,4,9:39:35 AM,11,4,9:53:40 AM,11,4,10:19:40 AM,11,4,10:44:12 AM,13,18.4,,-1.2782261000000001,36.822500899999994,-1.2035228,36.7772505,Rider_Id_726,1472
-Order_No_534,User_Id_203,Bike,3,Business,8,5,11:53:28 AM,8,5,11:53:41 AM,8,5,11:54:20 AM,8,5,11:58:08 AM,8,5,12:21:52 PM,10,24.0,,-1.3167113000000001,36.8301563,-1.2963097,36.7688221,Rider_Id_523,1424
-Order_No_22854,User_Id_1244,Bike,3,Business,9,6,12:47:23 PM,9,6,12:47:38 PM,9,6,1:09:20 PM,9,6,1:16:49 PM,9,6,1:39:47 PM,8,24.9,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_427,1378
-Order_No_5476,User_Id_3291,Bike,3,Business,13,4,2:49:09 PM,13,4,2:53:49 PM,13,4,3:11:19 PM,13,4,3:18:43 PM,13,4,3:33:12 PM,4,22.2,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_727,869
-Order_No_6944,User_Id_2163,Bike,3,Personal,7,5,2:32:23 PM,7,5,2:34:54 PM,7,5,3:10:37 PM,7,5,3:12:40 PM,7,5,3:49:35 PM,8,,,-1.2429898000000001,36.7851062,-1.2778241000000001,36.8139525,Rider_Id_204,2215
-Order_No_15813,User_Id_3080,Bike,3,Business,21,2,9:16:56 AM,21,2,9:17:39 AM,21,2,9:25:44 AM,21,2,9:27:21 AM,21,2,9:42:05 AM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_520,884
-Order_No_1336,User_Id_3560,Bike,3,Business,15,3,9:12:29 AM,15,3,9:12:56 AM,15,3,9:20:56 AM,15,3,9:29:22 AM,15,3,9:32:29 AM,1,,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_812,187
-Order_No_1053,User_Id_2931,Bike,3,Business,11,5,9:58:50 AM,11,5,9:59:05 AM,11,5,9:59:19 AM,11,5,10:00:24 AM,11,5,10:46:05 AM,22,20.8,,-1.2452664,36.802854100000005,-1.3433181,36.9138018,Rider_Id_512,2741
-Order_No_25750,User_Id_53,Bike,3,Business,10,1,3:30:01 PM,10,1,3:45:07 PM,10,1,3:57:40 PM,10,1,4:20:41 PM,10,1,4:43:16 PM,8,18.4,,-1.2770708,36.8231093,-1.2769540000000001,36.765926,Rider_Id_910,1355
-Order_No_25159,User_Id_3674,Bike,3,Business,13,2,2:39:58 PM,13,2,2:40:36 PM,13,2,2:41:07 PM,13,2,2:46:15 PM,13,2,3:08:49 PM,12,26.3,,-1.2551895,36.7822034,-1.3097713,36.849494,Rider_Id_120,1354
-Order_No_24574,User_Id_1332,Bike,3,Personal,3,7,2:01:32 PM,3,7,2:02:30 PM,3,7,2:16:16 PM,3,7,2:23:04 PM,3,7,2:46:46 PM,15,30.0,,-1.254904,36.809583,-1.2462208000000001,36.6881422,Rider_Id_275,1422
-Order_No_9932,User_Id_3582,Bike,3,Business,2,6,1:08:34 PM,2,6,1:25:40 PM,2,6,1:52:11 PM,2,6,1:54:36 PM,2,6,2:33:21 PM,17,29.0,,-1.2384056,36.8718698,-1.2765492,36.7669809,Rider_Id_34,2325
-Order_No_18115,User_Id_3262,Bike,3,Personal,9,4,11:22:03 AM,9,4,11:22:46 AM,9,4,11:32:59 AM,9,4,11:47:53 AM,9,4,1:00:16 PM,32,,,-1.2640106000000002,36.834660799999995,-1.0921598999999997,37.005025,Rider_Id_627,4343
-Order_No_15415,User_Id_649,Bike,3,Business,2,5,4:46:07 PM,2,5,4:46:55 PM,2,5,5:00:22 PM,2,5,5:02:48 PM,2,5,5:16:26 PM,3,24.2,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_330,818
-Order_No_5430,User_Id_2789,Bike,3,Business,18,1,11:31:41 AM,18,1,11:33:10 AM,18,1,11:43:34 AM,18,1,11:44:44 AM,18,1,12:17:32 PM,7,27.1,,-1.304735,36.784605400000004,-1.2875013000000002,36.8171787,Rider_Id_548,1968
-Order_No_25077,User_Id_2468,Bike,3,Business,26,1,11:38:58 AM,26,1,11:41:55 AM,26,1,11:53:15 AM,26,1,12:02:28 PM,26,1,12:13:29 PM,7,22.9,,-1.2857671,36.7875614,-1.260317,36.7989145,Rider_Id_101,661
-Order_No_2804,User_Id_517,Bike,3,Business,31,5,11:18:05 AM,31,5,11:18:43 AM,31,5,11:20:57 AM,31,5,11:34:00 AM,31,5,11:45:43 AM,4,21.7,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,703
-Order_No_24637,User_Id_2587,Bike,3,Business,29,1,1:15:03 PM,29,1,1:16:04 PM,29,1,1:20:11 PM,29,1,1:25:46 PM,29,1,2:07:53 PM,5,,,-1.28878,36.8168312,-1.2614176000000001,36.8213992,Rider_Id_101,2527
-Order_No_16614,User_Id_2946,Bike,2,Personal,27,1,12:47:29 PM,27,1,12:49:45 PM,27,1,1:04:52 PM,27,1,1:10:30 PM,27,1,1:28:14 PM,8,24.1,,-1.289521,36.819072399999996,-1.2749662,36.7658781,Rider_Id_74,1064
-Order_No_22734,User_Id_536,Bike,3,Personal,6,1,10:10:22 AM,6,1,10:10:46 AM,6,1,10:11:08 AM,6,1,10:54:32 AM,6,1,11:23:44 AM,9,20.5,,-1.23851,36.88478,-1.2176348,36.8495951,Rider_Id_539,1752
-Order_No_8585,User_Id_909,Bike,3,Business,18,4,3:59:47 PM,18,4,4:15:18 PM,18,4,5:05:22 PM,18,4,5:08:40 PM,18,4,6:02:23 PM,12,,,-1.225322,36.80855,-1.2765736,36.851364600000004,Rider_Id_56,3223
-Order_No_16615,User_Id_3404,Bike,3,Business,21,4,10:47:13 AM,21,4,10:58:22 AM,21,4,11:06:26 AM,21,4,11:18:27 AM,21,4,11:41:38 AM,8,25.3,,-1.3014461000000002,36.7661381,-1.279395,36.825364,Rider_Id_88,1391
-Order_No_7956,User_Id_2691,Bike,2,Business,1,1,10:13:53 AM,1,1,10:35:13 AM,1,1,10:47:59 AM,1,1,10:54:35 AM,1,1,11:21:13 AM,17,23.9,,-1.362319,36.743983,-1.2857671,36.7875614,Rider_Id_449,1598
-Order_No_9136,User_Id_1642,Bike,3,Business,29,4,12:56:21 PM,29,4,1:10:17 PM,29,4,1:35:09 PM,29,4,1:38:37 PM,29,4,2:04:27 PM,6,24.7,,-1.2728069,36.7962362,-1.2516391000000002,36.8114886,Rider_Id_404,1550
-Order_No_21956,User_Id_3326,Bike,3,Business,28,4,2:24:47 PM,28,4,2:26:57 PM,28,4,3:02:16 PM,28,4,3:23:59 PM,28,4,3:54:19 PM,7,30.2,,-1.2571472,36.795063299999995,-1.304344,36.812274,Rider_Id_399,1820
-Order_No_14754,User_Id_627,Bike,3,Business,21,3,3:51:34 PM,21,3,3:52:22 PM,21,3,4:01:19 PM,21,3,4:46:55 PM,21,3,5:22:37 PM,18,,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_222,2142
-Order_No_2113,User_Id_3283,Bike,3,Business,14,5,10:06:47 AM,14,5,10:07:22 AM,14,5,10:13:06 AM,14,5,10:36:36 AM,14,5,11:01:24 AM,13,21.8,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_690,1488
-Order_No_18332,User_Id_2436,Bike,3,Business,2,2,10:56:37 AM,2,2,10:58:44 AM,2,2,11:10:38 AM,2,2,11:16:11 AM,2,2,11:42:56 AM,12,26.0,,-1.2571472,36.795063299999995,-1.2647206,36.7259313,Rider_Id_470,1605
-Order_No_7071,User_Id_1724,Bike,3,Business,7,1,12:29:49 PM,7,1,12:31:00 PM,7,1,12:40:29 PM,7,1,12:49:07 PM,7,1,1:10:56 PM,7,24.7,,-1.2551895,36.7822034,-1.2875845,36.8135104,Rider_Id_268,1309
-Order_No_3029,User_Id_3194,Bike,3,Business,5,5,9:48:55 AM,5,5,9:49:12 AM,5,5,10:07:29 AM,5,5,10:22:55 AM,5,5,10:44:05 AM,7,19.6,,-1.2811695,36.8200313,-1.2551895,36.7822034,Rider_Id_448,1270
-Order_No_12464,User_Id_909,Bike,3,Business,15,5,2:03:17 PM,15,5,2:04:19 PM,15,5,2:18:15 PM,15,5,2:47:21 PM,15,5,3:25:37 PM,14,17.7,,-1.2156006000000001,36.8916865,-1.225322,36.80855,Rider_Id_238,2296
-Order_No_27572,User_Id_3470,Bike,3,Business,21,2,1:12:06 PM,21,2,2:42:19 PM,21,2,3:04:45 PM,21,2,3:06:06 PM,21,2,3:26:22 PM,7,24.9,,-1.2551895,36.7822034,-1.2929499,36.8180835,Rider_Id_369,1216
-Order_No_11363,User_Id_1797,Bike,3,Personal,4,4,8:51:49 AM,4,4,8:53:15 AM,4,4,9:13:16 AM,4,4,9:25:08 AM,4,4,10:02:22 AM,3,21.6,,-1.2933788999999998,36.808753499999995,-1.2962966999999999,36.8020231,Rider_Id_61,2234
-Order_No_25960,User_Id_816,Bike,3,Business,22,2,3:51:36 PM,22,2,4:02:09 PM,22,2,4:16:41 PM,22,2,4:22:14 PM,22,2,5:19:33 PM,7,25.6,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_332,3439
-Order_No_20754,User_Id_2344,Bike,3,Business,31,4,11:46:15 AM,31,4,11:46:44 AM,31,4,12:01:59 PM,31,4,12:03:25 PM,31,4,12:17:21 PM,8,26.3,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_862,836
-Order_No_6778,User_Id_1075,Bike,3,Business,22,6,1:59:35 PM,22,6,2:00:05 PM,22,6,2:11:00 PM,22,6,2:33:41 PM,22,6,2:58:47 PM,15,26.0,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_685,1506
-Order_No_26669,User_Id_733,Bike,3,Business,17,2,1:37:35 PM,17,2,1:37:51 PM,17,2,1:43:54 PM,17,2,1:48:13 PM,17,2,2:27:33 PM,16,24.4,,-1.2551895,36.7822034,-1.3169591,36.7000029,Rider_Id_519,2360
-Order_No_11554,User_Id_3291,Bike,3,Business,10,4,10:55:14 AM,10,4,10:55:39 AM,10,4,10:58:24 AM,10,4,11:13:43 AM,10,4,11:29:00 AM,11,24.8,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_24,917
-Order_No_4476,User_Id_53,Bike,3,Business,15,1,1:28:02 PM,15,1,1:32:03 PM,15,1,2:43:28 PM,15,1,2:58:11 PM,15,1,3:31:31 PM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_435,2000
-Order_No_16781,User_Id_346,Bike,3,Business,4,3,4:02:32 PM,4,3,4:04:42 PM,4,3,4:12:03 PM,4,3,4:25:54 PM,4,3,4:33:42 PM,1,18.2,,-1.279395,36.825364,-1.2885528999999998,36.816014100000004,Rider_Id_531,468
-Order_No_15314,User_Id_1329,Bike,3,Business,19,3,11:05:16 AM,19,3,11:06:50 AM,19,3,11:07:49 AM,19,3,11:16:20 AM,19,3,11:29:42 AM,3,22.0,,-1.273056,36.811298,-1.253966,36.7987692,Rider_Id_107,802
-Order_No_5887,User_Id_59,Bike,3,Business,6,3,12:34:44 PM,6,3,12:56:30 PM,6,3,1:37:58 PM,6,3,2:20:17 PM,6,3,2:20:32 PM,5,21.7,,-1.290894,36.822971,-1.2599388999999999,36.8074963,Rider_Id_606,15
-Order_No_26079,User_Id_3599,Bike,3,Business,27,1,12:47:16 PM,27,1,12:48:58 PM,27,1,1:10:56 PM,27,1,1:59:51 PM,27,1,2:20:04 PM,13,23.0,,-1.3302996,36.8707935,-1.3189908000000001,36.777074,Rider_Id_218,1213
-Order_No_9403,User_Id_3091,Bike,3,Business,3,3,8:51:25 AM,3,3,8:52:45 AM,3,3,9:01:38 AM,3,3,9:17:05 AM,3,3,9:35:31 AM,7,21.6,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_61,1106
-Order_No_27158,User_Id_3434,Bike,3,Business,28,2,12:07:28 PM,28,2,12:08:24 PM,28,2,12:58:49 PM,28,2,1:09:19 PM,28,2,1:45:18 PM,14,17.6,,-1.2321358,36.872784700000004,-1.1612808000000001,36.9563278,Rider_Id_384,2159
-Order_No_27987,User_Id_391,Bike,3,Business,20,3,10:08:19 AM,20,3,10:12:11 AM,20,3,10:54:22 AM,20,3,11:13:06 AM,20,3,11:23:25 AM,5,27.0,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_785,619
-Order_No_6056,User_Id_3689,Bike,3,Business,9,5,1:44:20 PM,9,5,5:10:12 PM,9,5,5:36:26 PM,9,5,5:40:14 PM,9,5,5:53:41 PM,2,25.0,,-1.2615888000000002,36.7928732,-1.2527958000000001,36.8003131,Rider_Id_432,807
-Order_No_9818,User_Id_1500,Bike,3,Business,28,3,9:20:36 AM,28,3,10:10:54 AM,28,3,10:24:11 AM,28,3,10:51:03 AM,28,3,11:13:50 AM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_660,1367
-Order_No_1256,User_Id_1502,Bike,3,Business,16,2,11:57:51 AM,16,2,12:08:31 PM,16,2,12:16:59 PM,16,2,12:21:32 PM,16,2,12:42:35 PM,14,,,-1.2571472,36.795063299999995,-1.2232123,36.8932547,Rider_Id_96,1263
-Order_No_26586,User_Id_1721,Bike,3,Business,2,4,10:52:57 AM,2,4,10:53:16 AM,2,4,11:00:39 AM,2,4,11:06:26 AM,2,4,11:29:44 AM,12,,,-1.2551895,36.7822034,-1.3239488000000001,36.831683500000004,Rider_Id_411,1398
-Order_No_22793,User_Id_2509,Bike,3,Business,8,4,3:08:26 PM,8,4,3:12:11 PM,8,4,3:23:53 PM,8,4,3:26:28 PM,8,4,3:39:14 PM,4,27.3,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_338,766
-Order_No_2929,User_Id_516,Bike,3,Business,16,4,12:02:35 PM,16,4,12:02:45 PM,16,4,12:04:42 PM,16,4,12:20:22 PM,16,4,1:03:51 PM,19,,,-1.2551895,36.7822034,-1.2824533999999999,36.9118791,Rider_Id_411,2609
-Order_No_596,User_Id_2763,Bike,3,Business,2,2,11:09:34 AM,2,2,11:12:30 AM,2,2,11:13:47 AM,2,2,11:26:49 AM,2,2,11:38:34 AM,5,26.2,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_377,705
-Order_No_18908,User_Id_1215,Bike,2,Personal,30,1,6:58:25 PM,30,1,6:59:15 PM,30,1,7:21:57 PM,30,1,7:31:19 PM,30,1,7:59:43 PM,6,16.2,,-1.3094838,36.8206907,-1.2969741000000001,36.7856612,Rider_Id_939,1704
-Order_No_19402,User_Id_3264,Bike,3,Business,3,5,2:23:43 PM,3,5,2:26:06 PM,3,5,2:38:08 PM,3,5,2:41:09 PM,3,5,3:03:21 PM,3,,,-1.2551895,36.7822034,-1.2594538999999998,36.8067841,Rider_Id_322,1332
-Order_No_17406,User_Id_2523,Bike,3,Business,14,5,9:39:54 AM,14,5,11:48:50 AM,14,5,11:56:57 AM,14,5,12:00:07 PM,14,5,12:11:33 PM,2,21.7,,-1.2551895,36.7822034,-1.2580991000000001,36.794151899999996,Rider_Id_566,686
-Order_No_13301,User_Id_3017,Bike,3,Business,9,7,4:04:08 PM,9,7,4:31:40 PM,9,7,5:11:40 PM,9,7,5:13:55 PM,9,7,5:40:39 PM,6,24.5,,-1.2993231,36.7753372,-1.3179303,36.8108344,Rider_Id_328,1604
-Order_No_26294,User_Id_2469,Bike,3,Business,5,5,3:12:23 PM,5,5,3:25:37 PM,5,5,3:44:07 PM,5,5,3:45:52 PM,5,5,4:45:33 PM,12,27.7,,-1.303924,36.8758792,-1.2734248,36.8202136,Rider_Id_233,3581
-Order_No_19850,User_Id_2277,Bike,3,Business,20,5,2:03:59 PM,20,5,2:04:29 PM,20,5,2:16:20 PM,20,5,2:40:34 PM,20,5,2:57:53 PM,9,20.6,,-1.2585521000000002,36.7945765,-1.3030546,36.8101808,Rider_Id_379,1039
-Order_No_865,User_Id_1245,Bike,3,Business,15,1,5:02:09 PM,15,1,5:02:52 PM,15,1,5:08:13 PM,15,1,5:15:15 PM,15,1,5:53:33 PM,16,,,-1.2602337,36.79905479999999,-1.3198615,36.7110323,Rider_Id_177,2298
-Order_No_12259,User_Id_3331,Bike,3,Business,1,5,5:28:34 PM,1,5,5:29:52 PM,1,5,5:32:52 PM,1,5,5:38:10 PM,1,5,6:05:17 PM,8,26.6,,-1.2551895,36.7822034,-1.2821378,36.7740428,Rider_Id_523,1627
-Order_No_21553,User_Id_2457,Bike,3,Business,26,5,3:02:19 PM,26,5,4:43:31 PM,26,5,4:58:56 PM,26,5,5:12:37 PM,26,5,5:59:07 PM,8,,,-1.2551895,36.7822034,-1.3025131,36.817387,Rider_Id_656,2790
-Order_No_7665,User_Id_1443,Bike,1,Personal,9,2,10:21:14 AM,9,2,10:21:45 AM,9,2,10:39:29 AM,9,2,10:50:10 AM,9,2,11:01:36 AM,6,22.0,,-1.3056005,36.7567122,-1.2946818000000002,36.7576745,Rider_Id_46,686
-Order_No_7640,User_Id_2506,Bike,3,Personal,13,3,9:35:22 AM,13,3,9:36:37 AM,13,3,9:41:54 AM,13,3,10:14:11 AM,13,3,10:36:11 AM,9,20.0,,-1.289498,36.818878000000005,-1.3263105,36.8622904,Rider_Id_768,1320
-Order_No_24994,User_Id_1787,Bike,3,Business,13,4,1:29:55 PM,13,4,1:30:11 PM,13,4,1:30:55 PM,13,4,1:37:14 PM,13,4,1:56:08 PM,5,23.2,,-1.2283403,36.8822756,-1.2467896,36.869873299999995,Rider_Id_416,1134
-Order_No_7675,User_Id_320,Bike,2,Personal,29,2,11:29:19 AM,29,2,11:29:36 AM,29,2,11:40:57 AM,29,2,11:49:51 AM,29,2,12:34:43 PM,20,21.6,,-1.2049707,36.785895700000005,-1.2892762,36.764129,Rider_Id_116,2692
-Order_No_24795,User_Id_2532,Bike,3,Business,5,5,3:55:25 PM,5,5,3:57:17 PM,5,5,4:15:31 PM,5,5,4:32:32 PM,5,5,4:56:18 PM,6,24.7,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_906,1426
-Order_No_2662,User_Id_868,Bike,3,Business,13,4,3:51:27 PM,13,4,4:00:53 PM,13,4,4:00:57 PM,13,4,4:12:34 PM,13,4,4:12:41 PM,3,22.9,,-1.2783848999999998,36.8163445,-1.2663631000000002,36.8163329,Rider_Id_89,7
-Order_No_14811,User_Id_2330,Bike,3,Business,22,3,1:47:12 PM,22,3,2:54:21 PM,22,3,2:59:54 PM,22,3,3:04:33 PM,22,3,3:25:27 PM,9,24.5,,-1.2642903,36.80021370000001,-1.2328823,36.8436797,Rider_Id_740,1254
-Order_No_16426,User_Id_1892,Bike,2,Personal,1,1,3:51:01 PM,1,1,3:54:20 PM,1,1,4:07:49 PM,1,1,4:11:01 PM,1,1,4:32:08 PM,9,25.7,,-1.2652835,36.836025899999996,-1.22589,36.7822548,Rider_Id_622,1267
-Order_No_18676,User_Id_3550,Bike,1,Personal,14,4,11:32:07 AM,14,4,11:41:22 AM,14,4,11:44:08 AM,14,4,12:17:29 PM,14,4,1:00:00 PM,15,18.9,,-1.2194351,36.8343591,-1.297721,36.7887912,Rider_Id_340,2551
-Order_No_10323,User_Id_2174,Bike,3,Business,9,2,11:39:47 AM,9,2,11:41:33 AM,9,2,11:47:34 AM,9,2,12:49:53 PM,9,2,1:31:42 PM,16,26.8,,-1.2482781,36.884091999999995,-1.2979242,36.7902416,Rider_Id_216,2509
-Order_No_23859,User_Id_1143,Bike,1,Personal,17,3,8:28:27 AM,17,3,8:29:03 AM,17,3,8:30:06 AM,17,3,8:41:39 AM,17,3,9:10:52 AM,13,21.0,,-1.2551895,36.7822034,-1.29928,36.85631,Rider_Id_726,1753
-Order_No_18287,User_Id_2069,Bike,3,Business,25,6,1:11:05 PM,25,6,1:11:17 PM,25,6,1:18:51 PM,25,6,1:36:34 PM,25,6,1:55:49 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309,1155
-Order_No_2249,User_Id_3647,Bike,3,Business,21,1,3:08:33 PM,21,1,3:08:45 PM,21,1,3:10:43 PM,21,1,3:26:41 PM,21,1,3:29:06 PM,3,27.7,,-1.2726389999999999,36.794723,-1.2727979999999999,36.8124195,Rider_Id_818,145
-Order_No_24649,User_Id_2150,Bike,3,Business,12,3,10:20:36 AM,12,3,10:21:19 AM,12,3,11:13:37 AM,12,3,11:25:29 AM,12,3,11:56:56 AM,14,19.3,,-1.2783848999999998,36.8163445,-1.3444459999999998,36.756597,Rider_Id_341,1887
-Order_No_22337,User_Id_1075,Bike,3,Business,4,4,3:10:14 PM,4,4,3:10:28 PM,4,4,3:28:27 PM,4,4,3:32:22 PM,4,4,4:03:28 PM,1,27.4,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_205,1866
-Order_No_26227,User_Id_2412,Bike,3,Business,13,3,2:53:24 PM,13,3,3:41:09 PM,13,3,4:12:41 PM,13,3,4:46:51 PM,13,3,5:07:04 PM,14,26.2,,-1.2615888000000002,36.7928732,-1.2285834999999998,36.882646799999996,Rider_Id_411,1213
-Order_No_11894,User_Id_2304,Bike,1,Personal,8,3,2:00:31 PM,8,3,2:01:23 PM,8,3,2:18:07 PM,8,3,2:20:29 PM,8,3,3:18:29 PM,12,26.0,,-1.2963097,36.7688221,-1.3549418,36.7280528,Rider_Id_903,3480
-Order_No_25247,User_Id_302,Bike,3,Business,6,4,11:59:26 AM,6,4,12:26:40 PM,6,4,12:39:30 PM,6,4,12:41:01 PM,6,4,1:02:54 PM,4,24.1,,-1.2649605,36.798177700000004,-1.2720139,36.8241971,Rider_Id_382,1313
-Order_No_25133,User_Id_2308,Bike,3,Business,18,4,10:25:25 AM,18,4,10:28:41 AM,18,4,10:29:03 AM,18,4,10:58:01 AM,18,4,11:19:48 AM,10,25.3,,-1.295324,36.839628999999995,-1.2963097,36.7688221,Rider_Id_179,1307
-Order_No_20702,User_Id_1245,Bike,3,Business,15,2,2:58:08 PM,15,2,3:20:07 PM,15,2,3:29:31 PM,15,2,3:41:52 PM,15,2,4:26:05 PM,18,24.4,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_14,2653
-Order_No_16337,User_Id_1926,Bike,3,Business,15,1,12:54:12 PM,15,1,12:56:16 PM,15,1,1:04:28 PM,15,1,1:31:53 PM,15,1,1:53:24 PM,16,,,-1.3263105,36.8622904,-1.2551895,36.7822034,Rider_Id_484,1291
-Order_No_18633,User_Id_1502,Bike,3,Business,7,7,11:34:52 AM,7,7,11:59:18 AM,7,7,12:05:37 PM,7,7,12:32:58 PM,7,7,12:49:50 PM,12,25.7,,-1.290894,36.822971,-1.2232123,36.8932547,Rider_Id_438,1012
-Order_No_22678,User_Id_3486,Bike,3,Business,19,3,3:34:12 PM,19,3,3:34:45 PM,19,3,3:37:44 PM,19,3,3:46:01 PM,19,3,3:57:08 PM,4,24.4,,-1.2551895,36.7822034,-1.2739743000000001,36.8139238,Rider_Id_107,667
-Order_No_23075,User_Id_1500,Bike,3,Business,22,5,8:52:52 AM,22,5,8:53:12 AM,22,5,8:55:39 AM,22,5,9:22:06 AM,22,5,9:41:48 AM,8,22.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_349,1182
-Order_No_378,User_Id_393,Bike,3,Business,26,2,12:47:59 PM,26,2,12:48:24 PM,26,2,12:48:28 PM,26,2,1:10:23 PM,26,2,1:10:27 PM,4,26.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10,4
-Order_No_23869,User_Id_1148,Bike,3,Business,5,2,2:24:18 PM,5,2,2:49:42 PM,5,2,3:06:29 PM,5,2,3:30:20 PM,5,2,3:49:05 PM,9,,,-1.3100586,36.850032899999995,-1.2658334999999998,36.804286700000006,Rider_Id_61,1125
-Order_No_25185,User_Id_635,Bike,3,Business,13,6,10:56:30 AM,13,6,10:57:47 AM,13,6,10:58:06 AM,13,6,12:37:40 PM,13,6,1:03:25 PM,19,25.3,,-1.300921,36.828195,-1.319613,36.711055,Rider_Id_179,1545
-Order_No_26281,User_Id_42,Bike,3,Business,10,1,2:23:21 PM,10,1,2:29:47 PM,10,1,2:38:16 PM,10,1,2:50:21 PM,10,1,2:58:35 PM,2,,,-1.25537,36.7756,-1.2560455,36.780086700000005,Rider_Id_742,494
-Order_No_9189,User_Id_635,Bike,3,Business,25,1,2:51:51 PM,25,1,2:52:38 PM,25,1,3:08:09 PM,25,1,3:25:25 PM,25,1,3:42:52 PM,9,29.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_126,1047
-Order_No_27010,User_Id_627,Bike,3,Business,16,2,2:49:58 PM,16,2,2:50:10 PM,16,2,2:52:52 PM,16,2,3:05:21 PM,16,2,3:26:24 PM,11,,,-1.2571472,36.795063299999995,-1.3108386,36.7555239,Rider_Id_708,1263
-Order_No_10114,User_Id_1500,Bike,3,Business,3,3,9:44:53 AM,3,3,9:45:30 AM,3,3,10:08:00 AM,3,3,10:19:05 AM,3,3,10:38:27 AM,6,21.4,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_436,1162
-Order_No_26514,User_Id_3203,Bike,3,Business,10,2,12:51:12 PM,10,2,12:51:36 PM,10,2,12:56:10 PM,10,2,1:14:05 PM,10,2,1:35:50 PM,16,20.1,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_312,1305
-Order_No_2895,User_Id_1927,Bike,3,Business,7,5,2:55:19 PM,7,5,3:02:42 PM,7,5,3:08:02 PM,7,5,3:19:14 PM,7,5,3:47:25 PM,18,26.9,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_622,1691
-Order_No_15443,User_Id_3647,Bike,3,Business,15,3,3:16:30 PM,15,3,3:16:45 PM,15,3,3:26:58 PM,15,3,3:33:41 PM,15,3,4:04:53 PM,10,25.8,,-1.2726389999999999,36.794723,-1.3251583999999998,36.8199384,Rider_Id_576,1872
-Order_No_25945,User_Id_2523,Bike,3,Business,6,3,2:52:27 PM,6,3,2:56:56 PM,6,3,3:47:41 PM,6,3,3:55:09 PM,6,3,4:39:47 PM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_106,2678
-Order_No_13395,User_Id_1927,Bike,3,Business,14,2,2:52:58 PM,14,2,2:53:34 PM,14,2,2:59:44 PM,14,2,3:06:48 PM,14,2,3:34:24 PM,15,,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_953,1656
-Order_No_17558,User_Id_2069,Bike,3,Business,10,1,3:12:34 PM,10,1,3:15:40 PM,10,1,3:26:53 PM,10,1,3:45:09 PM,10,1,4:11:31 PM,15,18.1,,-1.318062,36.849326,-1.228911,36.881876,Rider_Id_416,1582
-Order_No_12649,User_Id_3686,Bike,3,Business,8,4,4:02:52 PM,8,4,4:03:33 PM,8,4,4:12:17 PM,8,4,4:17:33 PM,8,4,4:24:39 PM,3,27.5,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_620,426
-Order_No_8453,User_Id_2871,Bike,1,Personal,20,5,11:24:44 AM,20,5,11:26:01 AM,20,5,11:49:14 AM,20,5,12:00:52 PM,20,5,12:15:34 PM,8,18.5,,-1.288922,36.814826000000004,-1.2623805000000001,36.7805404,Rider_Id_159,882
-Order_No_21303,User_Id_3621,Bike,3,Business,16,6,12:45:12 PM,16,6,12:45:35 PM,16,6,1:36:21 PM,16,6,1:38:44 PM,16,6,2:21:26 PM,7,28.7,,-1.2836583000000001,36.7886014,-1.3064585,36.8119065,Rider_Id_885,2562
-Order_No_882,User_Id_1329,Bike,3,Business,12,3,3:27:44 PM,12,3,3:30:32 PM,12,3,3:38:38 PM,12,3,3:40:28 PM,12,3,4:19:02 PM,18,20.8,,-1.273056,36.811298,-1.332624,36.913187,Rider_Id_538,2314
-Order_No_4479,User_Id_2330,Bike,3,Business,13,3,10:32:25 AM,13,3,10:33:26 AM,13,3,10:42:11 AM,13,3,10:52:25 AM,13,3,11:13:24 AM,10,,,-1.2649605,36.798177700000004,-1.3015197,36.7658456,Rider_Id_318,1259
-Order_No_708,User_Id_1114,Bike,3,Business,18,1,2:25:14 PM,18,1,2:57:46 PM,18,1,3:12:47 PM,18,1,3:18:00 PM,18,1,3:31:48 PM,5,29.7,,-1.32671,36.8600138,-1.3077869,36.844320700000004,Rider_Id_691,828
-Order_No_9821,User_Id_970,Bike,3,Business,27,3,2:10:11 PM,27,3,2:10:22 PM,27,3,2:12:36 PM,27,3,2:18:26 PM,27,3,2:28:30 PM,3,29.7,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_708,604
-Order_No_4472,User_Id_2956,Bike,3,Business,4,2,3:27:11 PM,4,2,3:27:51 PM,4,2,3:33:39 PM,4,2,3:36:28 PM,4,2,3:45:26 PM,3,23.9,11.0,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_446,538
-Order_No_379,User_Id_1995,Bike,3,Business,25,4,3:26:25 PM,25,4,3:27:38 PM,25,4,4:02:03 PM,25,4,4:25:32 PM,25,4,4:36:33 PM,7,,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_815,661
-Order_No_14013,User_Id_2073,Bike,3,Business,17,1,1:50:50 PM,17,1,2:06:51 PM,17,1,2:26:12 PM,17,1,2:53:32 PM,17,1,3:20:38 PM,21,28.4,,-1.3882008,36.7699445,-1.2849829,36.8542111,Rider_Id_539,1626
-Order_No_19499,User_Id_1502,Bike,3,Business,14,5,9:26:58 AM,14,5,10:32:07 AM,14,5,10:57:26 AM,14,5,11:22:22 AM,14,5,11:52:01 AM,12,21.6,,-1.290894,36.822971,-1.2232123,36.8932547,Rider_Id_281,1779
-Order_No_14500,User_Id_635,Bike,3,Business,8,5,10:50:08 AM,8,5,11:00:34 AM,8,5,11:27:31 AM,8,5,11:34:36 AM,8,5,12:11:01 PM,7,28.6,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_309,2185
-Order_No_17379,User_Id_1329,Bike,3,Business,18,1,3:22:27 PM,18,1,3:22:58 PM,18,1,3:24:04 PM,18,1,3:30:03 PM,18,1,3:30:53 PM,2,31.0,,-1.273056,36.811298,-1.2673838,36.8069481,Rider_Id_19,50
-Order_No_15306,User_Id_1075,Bike,3,Business,13,3,12:46:37 PM,13,3,12:47:20 PM,13,3,1:10:09 PM,13,3,1:11:52 PM,13,3,1:23:53 PM,2,,,-1.2679657,36.8081617,-1.2527958000000001,36.8003131,Rider_Id_448,721
-Order_No_13509,User_Id_1402,Bike,3,Business,19,2,11:03:56 AM,19,2,11:04:30 AM,19,2,11:27:07 AM,19,2,11:42:42 AM,19,2,11:45:10 AM,1,26.5,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_462,148
-Order_No_21189,User_Id_2741,Bike,1,Personal,3,5,10:45:54 AM,3,5,10:46:32 AM,3,5,11:04:56 AM,3,5,11:08:45 AM,3,5,11:31:22 AM,11,21.6,,-1.322999,36.783263299999994,-1.2884066,36.7866984,Rider_Id_207,1357
-Order_No_10979,User_Id_635,Bike,3,Business,18,1,9:53:21 AM,18,1,10:30:47 AM,18,1,10:37:36 AM,18,1,11:04:07 AM,18,1,11:17:25 AM,7,27.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_463,798
-Order_No_1133,User_Id_1719,Bike,3,Business,29,3,12:00:36 PM,29,3,12:00:40 PM,29,3,12:07:15 PM,29,3,12:09:45 PM,29,3,12:37:27 PM,17,18.6,,-1.3039411,36.7695316,-1.2330694,36.745451,Rider_Id_425,1662
-Order_No_21986,User_Id_868,Bike,3,Business,9,3,3:01:00 PM,9,3,3:02:20 PM,9,3,3:07:19 PM,9,3,3:09:32 PM,9,3,3:22:21 PM,7,25.5,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_72,769
-Order_No_11529,User_Id_7,Bike,1,Personal,28,4,4:27:54 PM,28,4,4:28:32 PM,28,4,4:38:00 PM,28,4,4:44:19 PM,28,4,4:59:04 PM,4,,,-1.2551895,36.7822034,-1.2738167,36.8157821,Rider_Id_434,885
-Order_No_21095,User_Id_248,Bike,3,Business,5,4,2:23:02 PM,5,4,2:24:54 PM,5,4,2:54:53 PM,5,4,3:12:30 PM,5,4,4:03:16 PM,9,22.4,,-1.2560455,36.780086700000005,-1.304735,36.784605400000004,Rider_Id_844,3046
-Order_No_2297,User_Id_1500,Bike,3,Business,24,4,3:47:37 PM,24,4,3:48:35 PM,24,4,3:51:34 PM,24,4,4:01:48 PM,24,4,4:26:51 PM,11,28.5,,-1.3228003000000002,36.8306435,-1.2574219,36.7927073,Rider_Id_523,1503
-Order_No_10332,User_Id_517,Bike,3,Business,3,1,2:52:15 PM,3,1,2:58:29 PM,3,1,2:58:33 PM,3,1,3:05:47 PM,3,1,3:33:58 PM,14,23.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_515,1691
-Order_No_13545,User_Id_3,Bike,1,Personal,29,5,2:16:04 PM,29,5,2:21:39 PM,29,5,2:46:27 PM,29,5,3:16:56 PM,29,5,4:14:04 PM,8,21.0,,-1.2949771,36.8649898,-1.3123977,36.902807,Rider_Id_648,3428
-Order_No_12481,User_Id_3647,Bike,3,Business,5,2,1:38:16 PM,5,2,1:38:37 PM,5,2,1:42:04 PM,5,2,2:28:14 PM,5,2,2:28:17 PM,17,27.3,,-1.2726389999999999,36.794723,-1.3382067,36.677339,Rider_Id_295,3
-Order_No_2658,User_Id_867,Bike,3,Business,17,4,1:25:33 PM,17,4,1:26:15 PM,17,4,1:33:12 PM,17,4,1:53:49 PM,17,4,2:18:09 PM,10,28.2,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_576,1460
-Order_No_349,User_Id_1955,Bike,3,Business,19,5,4:58:33 PM,19,5,4:58:43 PM,19,5,4:59:13 PM,19,5,5:10:34 PM,19,5,5:47:26 PM,12,24.2,,-1.3332749,36.870814700000004,-1.2927792,36.9053492,Rider_Id_81,2212
-Order_No_19039,User_Id_393,Bike,3,Business,2,4,11:07:03 AM,2,4,11:07:48 AM,2,4,11:29:23 AM,2,4,11:37:13 AM,2,4,12:10:03 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_815,1970
-Order_No_10650,User_Id_3599,Bike,3,Business,30,4,11:06:16 AM,30,4,11:07:24 AM,30,4,11:24:28 AM,30,4,11:36:24 AM,30,4,12:13:04 PM,13,,,-1.3302996,36.8707935,-1.2735236,36.7992196,Rider_Id_516,2200
-Order_No_18744,User_Id_71,Bike,3,Business,22,4,2:22:16 PM,22,4,3:36:11 PM,22,4,3:54:33 PM,22,4,3:59:46 PM,22,4,4:48:28 PM,9,26.4,,-1.2891921999999998,36.825189,-1.2551895,36.7822034,Rider_Id_554,2922
-Order_No_26069,User_Id_2587,Bike,3,Business,24,3,11:16:41 AM,24,3,11:19:33 AM,24,3,11:28:11 AM,24,3,11:38:43 AM,24,3,12:09:29 PM,6,19.9,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_268,1846
-Order_No_20503,User_Id_2200,Bike,3,Business,31,3,9:01:12 AM,31,3,9:01:34 AM,31,3,9:02:50 AM,31,3,9:47:41 AM,31,3,9:58:00 AM,9,19.0,,-1.3025826,36.7670807,-1.290894,36.822971,Rider_Id_330,619
-Order_No_22578,User_Id_3496,Bike,3,Business,14,4,3:56:41 PM,14,4,4:12:00 PM,14,4,4:21:00 PM,14,4,4:38:44 PM,14,4,5:03:11 PM,13,25.8,,-1.332175,36.8819641,-1.300921,36.828195,Rider_Id_856,1467
-Order_No_13254,User_Id_20,Bike,3,Business,11,3,10:44:01 AM,11,3,10:45:03 AM,11,3,10:51:54 AM,11,3,11:00:43 AM,11,3,11:34:18 AM,6,18.9,,-1.2628638,36.8070399,-1.300722,36.816865,Rider_Id_726,2015
-Order_No_12615,User_Id_3647,Bike,3,Business,11,4,12:22:04 PM,11,4,12:23:08 PM,11,4,12:52:04 PM,11,4,12:58:10 PM,11,4,1:30:09 PM,17,25.5,,-1.2726389999999999,36.794723,-1.2870469999999998,36.890427,Rider_Id_205,1919
-Order_No_6873,User_Id_1787,Bike,3,Business,22,5,3:52:07 PM,22,5,3:55:18 PM,22,5,4:03:38 PM,22,5,4:36:39 PM,22,5,5:04:37 PM,11,28.5,,-1.28878,36.8168312,-1.2283403,36.8822756,Rider_Id_267,1678
-Order_No_25902,User_Id_633,Bike,3,Business,19,2,9:09:08 AM,19,2,9:09:58 AM,19,2,9:21:29 AM,19,2,9:30:11 AM,19,2,9:45:42 AM,4,21.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_953,931
-Order_No_6744,User_Id_1007,Bike,3,Personal,15,1,5:06:12 PM,15,1,5:06:43 PM,15,1,5:18:57 PM,15,1,5:27:05 PM,15,1,5:37:02 PM,3,,,-1.2943445,36.7515778,-1.2935549,36.7703935,Rider_Id_907,597
-Order_No_1205,User_Id_867,Bike,3,Business,12,2,2:09:36 PM,12,2,2:10:13 PM,12,2,2:24:55 PM,12,2,2:29:16 PM,12,2,2:49:32 PM,10,27.2,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_24,1216
-Order_No_18599,User_Id_3560,Bike,3,Business,29,5,5:37:22 PM,29,5,5:38:40 PM,29,5,6:08:58 PM,29,5,6:56:43 PM,29,5,6:56:58 PM,5,28.2,,-1.279395,36.825364,-1.3072409999999999,36.830236799999994,Rider_Id_613,15
-Order_No_7749,User_Id_1920,Bike,3,Business,5,3,1:15:16 PM,5,3,2:22:10 PM,5,3,2:32:10 PM,5,3,2:48:15 PM,5,3,3:14:37 PM,9,24.8,,-1.2551895,36.7822034,-1.2981352,36.8213822,Rider_Id_596,1582
-Order_No_14876,User_Id_2330,Bike,3,Business,10,5,4:23:08 PM,10,5,4:26:47 PM,10,5,4:41:41 PM,10,5,5:01:24 PM,10,5,5:21:24 PM,6,,,-1.2645318,36.7981775,-1.2464559,36.816985700000004,Rider_Id_126,1200
-Order_No_28025,User_Id_1154,Bike,3,Business,25,1,12:35:29 PM,25,1,12:35:53 PM,25,1,12:36:19 PM,25,1,12:50:46 PM,25,1,1:27:12 PM,20,27.0,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_942,2186
-Order_No_7523,User_Id_2066,Bike,3,Business,6,3,3:23:27 PM,6,3,3:25:04 PM,6,3,4:07:54 PM,6,3,4:13:50 PM,6,3,4:24:59 PM,5,,,-1.3270716,36.8406371,-1.3231725,36.864017700000005,Rider_Id_247,669
-Order_No_14961,User_Id_1469,Bike,3,Business,12,5,4:05:05 PM,12,5,4:05:22 PM,12,5,4:07:54 PM,12,5,4:23:09 PM,12,5,4:48:08 PM,14,,,-1.2551895,36.7822034,-1.3263105,36.8622904,Rider_Id_695,1499
-Order_No_15083,User_Id_1850,Bike,3,Business,28,1,11:41:41 AM,28,1,11:51:12 AM,28,1,12:09:04 PM,28,1,12:11:49 PM,28,1,12:32:18 PM,6,23.7,,-1.2551895,36.7822034,-1.2785434,36.821532700000006,Rider_Id_862,1229
-Order_No_1413,User_Id_2718,Bike,3,Business,18,4,2:16:45 PM,18,4,2:19:08 PM,18,4,2:53:08 PM,18,4,3:02:22 PM,18,4,3:22:05 PM,4,26.6,,-1.2904753,36.8144721,-1.2668815,36.8017244,Rider_Id_247,1183
-Order_No_25562,User_Id_867,Bike,3,Business,9,4,9:50:47 AM,9,4,9:51:21 AM,9,4,10:00:35 AM,9,4,10:05:56 AM,9,4,10:48:57 AM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_205,2581
-Order_No_13203,User_Id_1724,Bike,3,Business,6,4,5:11:33 PM,6,4,5:21:13 PM,6,4,5:30:51 PM,6,4,5:39:12 PM,6,4,6:19:23 PM,14,22.4,,-1.2551895,36.7822034,-1.3271684,36.8173021,Rider_Id_38,2411
-Order_No_17206,User_Id_1271,Bike,3,Business,4,4,10:25:45 AM,4,4,10:26:16 AM,4,4,10:40:40 AM,4,4,11:02:08 AM,4,4,11:30:54 AM,19,25.5,,-1.3263105,36.8622904,-1.3107918,36.761794,Rider_Id_130,1726
-Order_No_13086,User_Id_33,Bike,3,Personal,9,6,1:10:57 PM,9,6,1:11:25 PM,9,6,1:26:45 PM,9,6,1:28:28 PM,9,6,1:45:21 PM,6,24.6,,-1.2814609,36.826593200000005,-1.3159617000000001,36.8117934,Rider_Id_89,1013
-Order_No_7125,User_Id_53,Bike,3,Business,2,2,2:50:30 PM,2,2,2:56:15 PM,2,2,3:05:15 PM,2,2,3:13:01 PM,2,2,3:31:24 PM,5,29.4,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_507,1103
-Order_No_5701,User_Id_3647,Bike,3,Business,15,2,11:31:51 AM,15,2,11:32:06 AM,15,2,11:42:19 AM,15,2,11:53:35 AM,15,2,12:09:47 PM,8,24.4,,-1.2726389999999999,36.794723,-1.2575370000000001,36.843066,Rider_Id_801,972
-Order_No_22790,User_Id_1271,Bike,3,Business,17,2,1:07:06 PM,17,2,1:07:36 PM,17,2,1:24:42 PM,17,2,1:25:49 PM,17,2,1:56:24 PM,28,19.2,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_650,1835
-Order_No_28118,User_Id_761,Bike,3,Business,16,4,4:08:35 PM,16,4,4:08:41 PM,16,4,4:19:55 PM,16,4,4:59:36 PM,16,4,5:32:06 PM,17,19.3,,-1.3361686000000002,36.7520734,-1.2693101999999998,36.798177700000004,Rider_Id_414,1950
-Order_No_7847,User_Id_2271,Bike,3,Business,2,3,11:47:54 AM,2,3,11:47:59 AM,2,3,11:48:02 AM,2,3,12:09:44 PM,2,3,12:30:22 PM,14,24.1,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_886,1238
-Order_No_4847,User_Id_1244,Bike,3,Business,29,5,12:48:15 PM,29,5,12:52:57 PM,29,5,1:14:18 PM,29,5,1:48:32 PM,29,5,2:19:06 PM,9,29.0,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_655,1834
-Order_No_17642,User_Id_53,Bike,3,Business,18,4,1:30:33 PM,18,4,1:32:18 PM,18,4,1:42:20 PM,18,4,2:20:24 PM,18,4,2:33:16 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_910,772
-Order_No_4812,User_Id_3701,Bike,3,Business,13,2,2:18:56 PM,13,2,2:20:45 PM,13,2,2:31:00 PM,13,2,2:34:25 PM,13,2,2:48:53 PM,4,26.1,,-1.2684239,36.79524620000001,-1.2842753999999998,36.786354700000004,Rider_Id_279,868
-Order_No_8490,User_Id_3647,Bike,3,Business,28,1,5:09:27 PM,28,1,5:13:36 PM,28,1,5:27:55 PM,28,1,5:31:38 PM,28,1,5:58:00 PM,11,25.2,,-1.2726389999999999,36.794723,-1.318411,36.8059305,Rider_Id_343,1582
-Order_No_4973,User_Id_2069,Bike,3,Business,26,2,2:09:16 PM,26,2,2:09:45 PM,26,2,2:10:41 PM,26,2,2:27:40 PM,26,2,2:51:16 PM,14,31.1,,-1.3033561000000002,36.8163631,-1.228911,36.881876,Rider_Id_614,1416
-Order_No_16025,User_Id_2476,Bike,3,Business,23,6,12:59:50 PM,23,6,1:00:00 PM,23,6,1:19:10 PM,23,6,1:36:44 PM,23,6,2:05:01 PM,12,28.3,,-1.2795594,36.847264700000004,-1.264836,36.805372999999996,Rider_Id_206,1697
-Order_No_12228,User_Id_3674,Bike,3,Business,30,2,1:38:47 PM,30,2,1:40:58 PM,30,2,1:50:14 PM,30,2,1:54:58 PM,30,2,2:30:04 PM,14,,,-1.2551895,36.7822034,-1.2493718999999999,36.8780276,Rider_Id_96,2106
-Order_No_9353,User_Id_2021,Bike,3,Business,19,2,3:39:45 PM,19,2,3:40:44 PM,19,2,3:51:05 PM,19,2,3:57:41 PM,19,2,4:46:12 PM,13,29.3,,-1.29475,36.83421,-1.3287857,36.9042514,Rider_Id_181,2911
-Order_No_18304,User_Id_3149,Bike,3,Business,24,3,2:44:45 PM,24,3,2:48:16 PM,24,3,2:55:06 PM,24,3,3:43:51 PM,24,3,3:59:02 PM,6,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_211,911
-Order_No_6704,User_Id_3602,Bike,3,Business,19,2,3:20:58 PM,19,2,3:36:17 PM,19,2,4:09:48 PM,19,2,4:11:40 PM,19,2,4:54:19 PM,20,29.9,,-1.3929354,36.7640148,-1.2895725,36.775179,Rider_Id_468,2559
-Order_No_26688,User_Id_3247,Bike,3,Business,6,2,2:44:30 PM,6,2,2:48:02 PM,6,2,2:52:32 PM,6,2,2:58:55 PM,6,2,3:05:42 PM,2,27.6,,-1.2571472,36.795063299999995,-1.2690931,36.8065779,Rider_Id_541,407
-Order_No_8800,User_Id_1133,Bike,3,Business,20,3,10:54:01 AM,20,3,10:54:44 AM,20,3,11:01:25 AM,20,3,11:05:38 AM,20,3,11:26:34 AM,9,26.2,,-1.2975066000000002,36.76894229999999,-1.2571472,36.795063299999995,Rider_Id_761,1256
-Order_No_14609,User_Id_1341,Bike,3,Business,20,1,1:11:35 PM,20,1,1:12:43 PM,20,1,1:27:05 PM,20,1,1:31:51 PM,20,1,1:54:33 PM,13,,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_709,1362
-Order_No_9904,User_Id_598,Bike,2,Business,5,5,2:53:09 PM,5,5,2:54:58 PM,5,5,3:04:31 PM,5,5,3:06:09 PM,5,5,3:31:07 PM,8,26.8,,-1.2311903,36.8102105,-1.257309,36.806008399999996,Rider_Id_754,1498
-Order_No_11552,User_Id_633,Bike,3,Business,25,5,4:03:51 PM,25,5,4:16:13 PM,25,5,4:16:22 PM,25,5,4:29:26 PM,25,5,4:49:50 PM,8,29.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_555,1224
-Order_No_5798,User_Id_2066,Bike,3,Business,13,1,11:45:29 AM,13,1,11:45:29 AM,13,1,11:47:17 AM,13,1,11:58:00 AM,13,1,12:15:39 PM,7,,,-1.3210188999999999,36.841055100000005,-1.282451,36.8097806,Rider_Id_205,1059
-Order_No_11844,User_Id_2499,Bike,2,Personal,19,5,2:22:46 PM,19,5,2:22:54 PM,19,5,2:28:50 PM,19,5,2:40:37 PM,19,5,2:59:14 PM,9,27.5,,-1.2946768999999998,36.7501951,-1.312469,36.815177,Rider_Id_622,1117
-Order_No_16002,User_Id_1243,Bike,1,Business,28,5,1:16:53 PM,28,5,1:18:08 PM,28,5,1:28:49 PM,28,5,1:44:06 PM,28,5,2:39:42 PM,10,25.7,,-1.304524,36.775371,-1.2551895,36.7822034,Rider_Id_179,3336
-Order_No_625,User_Id_1701,Bike,1,Personal,5,2,2:55:30 PM,5,2,3:08:32 PM,5,2,3:39:52 PM,5,2,3:45:40 PM,5,2,4:36:11 PM,11,22.7,,-1.3218455,36.721300899999996,-1.3007998,36.7894954,Rider_Id_735,3031
-Order_No_25807,User_Id_1441,Bike,2,Personal,16,2,10:31:59 AM,16,2,10:32:16 AM,16,2,10:32:59 AM,16,2,10:48:49 AM,16,2,11:13:58 AM,10,23.6,,-1.3142268999999998,36.839245,-1.2849763,36.7841767,Rider_Id_188,1509
-Order_No_20326,User_Id_552,Bike,3,Business,18,4,12:05:26 PM,18,4,12:06:29 PM,18,4,12:06:35 PM,18,4,12:08:01 PM,18,4,12:55:58 PM,7,,,-1.335477,36.930829700000004,-1.3562374,36.904295399999995,Rider_Id_346,2877
-Order_No_26221,User_Id_439,Bike,3,Business,22,3,2:26:38 PM,22,3,2:27:34 PM,22,3,2:44:42 PM,22,3,2:51:43 PM,22,3,3:56:19 PM,24,24.1,,-1.2558953000000002,36.7339105,-1.3323107,36.8692616,Rider_Id_868,3876
-Order_No_3470,User_Id_880,Bike,1,Personal,15,3,8:59:08 AM,15,3,9:00:49 AM,15,3,9:19:23 AM,15,3,9:28:20 AM,15,3,9:46:46 AM,4,16.5,,-1.3012173,36.8093987,-1.3066966999999998,36.785934000000005,Rider_Id_208,1106
-Order_No_19701,User_Id_3326,Bike,3,Business,19,2,10:01:13 AM,19,2,10:04:37 AM,19,2,10:13:57 AM,19,2,10:17:54 AM,19,2,10:39:34 AM,9,20.9,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_531,1300
-Order_No_22856,User_Id_2360,Bike,1,Business,20,4,11:55:00 AM,20,4,11:55:40 AM,20,4,11:59:37 AM,20,4,12:10:56 PM,20,4,12:17:42 PM,4,24.3,,-1.2708857,36.8135766,-1.2551895,36.7822034,Rider_Id_620,406
-Order_No_1071,User_Id_238,Bike,3,Business,22,5,11:29:33 AM,22,5,11:29:58 AM,22,5,11:36:53 AM,22,5,11:39:23 AM,22,5,12:04:45 PM,15,,,-1.3077869,36.844320700000004,-1.362953,36.904214,Rider_Id_114,1522
-Order_No_6631,User_Id_1568,Bike,3,Business,25,5,10:14:57 AM,25,5,10:15:21 AM,25,5,10:25:16 AM,25,5,10:31:20 AM,25,5,11:05:11 AM,17,17.1,,-1.2897368,36.8226783,-1.1708105,36.8336102,Rider_Id_812,2031
-Order_No_13746,User_Id_1500,Bike,3,Business,26,5,10:06:31 AM,26,5,10:07:49 AM,26,5,10:12:09 AM,26,5,10:13:49 AM,26,5,10:34:14 AM,8,,,-1.2638185,36.7930057,-1.3018147,36.751816,Rider_Id_119,1225
-Order_No_26080,User_Id_59,Bike,3,Business,14,2,2:09:11 PM,14,2,2:17:48 PM,14,2,2:43:47 PM,14,2,2:52:35 PM,14,2,3:19:28 PM,18,24.0,,-1.3216456,36.7145156,-1.290894,36.822971,Rider_Id_549,1613
-Order_No_3164,User_Id_3123,Bike,3,Personal,19,3,4:22:03 PM,19,3,4:36:20 PM,19,3,4:55:24 PM,19,3,5:35:16 PM,19,3,6:25:41 PM,15,22.3,,-1.2879431000000001,36.82700689999999,-1.2090481000000002,36.7471131,Rider_Id_576,3025
-Order_No_1266,User_Id_2330,Bike,3,Business,7,4,3:23:29 PM,7,4,3:24:44 PM,7,4,3:42:29 PM,7,4,3:44:09 PM,7,4,4:07:22 PM,9,27.9,,-1.2324923,36.836377500000005,-1.293082,36.8278578,Rider_Id_206,1393
-Order_No_12078,User_Id_3128,Bike,1,Personal,22,2,10:44:25 AM,22,2,10:56:56 AM,22,2,11:14:36 AM,22,2,11:20:57 AM,22,2,12:33:06 PM,12,19.5,,-1.2047753,36.7915359,-1.2753108999999998,36.8130185,Rider_Id_147,4329
-Order_No_6823,User_Id_3086,Bike,3,Business,11,6,2:52:47 PM,11,6,2:54:34 PM,11,6,3:09:46 PM,11,6,3:30:25 PM,11,6,3:31:16 PM,6,,,-1.2889913999999998,36.820944299999994,-1.2512895,36.844617299999996,Rider_Id_921,51
-Order_No_1048,User_Id_1850,Bike,3,Business,25,1,3:13:51 PM,25,1,3:14:21 PM,25,1,3:22:57 PM,25,1,3:44:21 PM,25,1,3:46:57 PM,4,28.0,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_778,156
-Order_No_24856,User_Id_1687,Bike,3,Business,21,3,11:56:27 AM,21,3,12:06:24 PM,21,3,12:15:31 PM,21,3,12:21:49 PM,21,3,12:27:46 PM,1,21.9,,-1.2962832,36.784968299999996,-1.2961951999999999,36.7876396,Rider_Id_31,357
-Order_No_16256,User_Id_53,Bike,3,Business,6,3,4:09:51 PM,6,3,4:20:33 PM,6,3,4:29:00 PM,6,3,4:30:55 PM,6,3,4:44:13 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_728,798
-Order_No_25898,User_Id_2216,Bike,3,Business,19,2,11:12:18 AM,19,2,11:12:44 AM,19,2,11:16:24 AM,19,2,11:18:13 AM,19,2,11:36:58 AM,7,26.7,,-1.2551895,36.7822034,-1.2887816,36.8088885,Rider_Id_854,1125
-Order_No_11451,User_Id_1690,Bike,3,Business,13,3,2:30:45 PM,13,3,3:04:13 PM,13,3,3:07:20 PM,13,3,3:14:27 PM,13,3,3:32:06 PM,4,23.6,,-1.304033,36.7848695,-1.2977768,36.8110285,Rider_Id_312,1059
-Order_No_2861,User_Id_302,Bike,3,Business,2,2,1:38:12 PM,2,2,1:38:40 PM,2,2,1:42:57 PM,2,2,1:52:30 PM,2,2,1:59:20 PM,7,26.3,,-1.2649605,36.798177700000004,-1.2563792999999999,36.7850151,Rider_Id_882,410
-Order_No_21431,User_Id_2150,Bike,3,Business,6,1,9:52:55 AM,6,1,10:28:50 AM,6,1,10:37:02 AM,6,1,10:41:37 AM,6,1,11:08:53 AM,9,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_660,1636
-Order_No_4320,User_Id_567,Bike,3,Business,20,4,4:43:41 PM,20,4,4:57:27 PM,20,4,5:01:25 PM,20,4,5:10:26 PM,20,4,5:25:03 PM,6,22.8,,-1.2982678,36.757883899999996,-1.2745551000000002,36.7979797,Rider_Id_574,877
-Order_No_16402,User_Id_3234,Bike,1,Business,26,4,10:25:32 AM,26,4,11:02:12 AM,26,4,11:14:04 AM,26,4,11:30:58 AM,26,4,12:38:07 PM,14,18.7,,-1.3152796999999998,36.861145799999996,-1.2874711,36.764620799999996,Rider_Id_519,4029
-Order_No_10806,User_Id_2585,Bike,2,Personal,12,4,7:38:36 PM,12,4,7:39:52 PM,12,4,7:51:29 PM,12,4,8:04:37 PM,12,4,8:40:25 PM,12,18.3,,-1.2745187,36.787643599999996,-1.2593591999999998,36.7775322,Rider_Id_101,2148
-Order_No_8583,User_Id_2423,Bike,3,Business,14,4,1:28:15 PM,14,4,3:35:33 PM,14,4,3:37:50 PM,14,4,3:52:05 PM,14,4,4:06:11 PM,6,27.8,,-1.2551895,36.7822034,-1.2854988999999999,36.8218183,Rider_Id_513,846
-Order_No_22637,User_Id_2801,Bike,3,Business,23,3,8:49:50 AM,23,3,8:49:59 AM,23,3,8:59:07 AM,23,3,9:06:24 AM,23,3,9:29:46 AM,8,16.8,15.9,-1.2595421999999998,36.7871178,-1.2896398999999998,36.812449799999996,Rider_Id_103,1402
-Order_No_23977,User_Id_3586,Bike,3,Business,3,1,10:32:14 AM,3,1,10:46:10 AM,3,1,10:56:35 AM,3,1,11:05:32 AM,3,1,11:43:19 AM,9,22.3,,-1.2869041,36.8240057,-1.275442,36.766020700000006,Rider_Id_235,2267
-Order_No_20774,User_Id_904,Bike,3,Business,25,4,3:03:34 PM,25,4,3:06:48 PM,25,4,3:24:18 PM,25,4,3:42:57 PM,25,4,3:56:56 PM,5,21.0,,-1.2596969999999998,36.802203000000006,-1.2663722,36.820742200000005,Rider_Id_544,839
-Order_No_14903,User_Id_1271,Bike,3,Business,13,3,10:09:17 AM,13,3,10:09:25 AM,13,3,10:09:31 AM,13,3,10:43:26 AM,13,3,10:56:29 AM,15,24.8,,-1.3244885,36.897791999999995,-1.2224188,36.8868824,Rider_Id_874,783
-Order_No_22290,User_Id_393,Bike,3,Business,8,3,1:43:24 PM,8,3,1:43:37 PM,8,3,1:52:58 PM,8,3,2:22:45 PM,8,3,2:49:51 PM,15,,,-1.306378,36.7519845,-1.3167113000000001,36.8301563,Rider_Id_831,1626
-Order_No_20874,User_Id_2000,Bike,3,Business,7,3,12:12:30 PM,7,3,12:12:59 PM,7,3,12:20:47 PM,7,3,12:38:48 PM,7,3,1:05:39 PM,3,25.3,,-1.2551895,36.7822034,-1.2595466999999998,36.8113044,Rider_Id_620,1611
-Order_No_24906,User_Id_1544,Bike,3,Business,11,2,12:36:13 PM,11,2,12:38:51 PM,11,2,1:46:28 PM,11,2,2:11:28 PM,11,2,3:15:01 PM,14,21.5,,-1.2851603,36.824966700000004,-1.3390024,36.8746657,Rider_Id_673,3813
-Order_No_26258,User_Id_1899,Bike,3,Business,25,1,2:07:03 PM,25,1,2:07:44 PM,25,1,2:07:49 PM,25,1,2:18:47 PM,25,1,2:34:21 PM,11,,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_406,934
-Order_No_21483,User_Id_3689,Bike,3,Business,28,5,2:30:16 PM,28,5,2:30:32 PM,28,5,2:43:45 PM,28,5,2:48:49 PM,28,5,3:15:18 PM,7,25.7,,-1.2527958000000001,36.8003131,-1.2973654,36.8105527,Rider_Id_948,1589
-Order_No_11184,User_Id_1182,Bike,2,Personal,16,1,10:48:00 AM,16,1,10:50:08 AM,16,1,11:04:33 AM,16,1,11:16:39 AM,16,1,11:33:39 AM,9,19.1,,-1.2686620000000002,36.8016018,-1.2367559,36.8082168,Rider_Id_279,1020
-Order_No_24534,User_Id_3647,Bike,3,Business,28,3,1:51:45 PM,28,3,1:52:35 PM,28,3,2:07:55 PM,28,3,2:17:23 PM,28,3,3:12:26 PM,21,,,-1.2726389999999999,36.794723,-1.1848195,36.915471999999994,Rider_Id_295,3303
-Order_No_26857,User_Id_662,Bike,1,Personal,9,4,10:50:43 AM,9,4,10:51:30 AM,9,4,11:03:07 AM,9,4,11:07:03 AM,9,4,11:51:52 AM,15,23.8,,-1.2951723999999998,36.7758148,-1.3368903,36.767101000000004,Rider_Id_265,2689
-Order_No_1548,User_Id_3379,Bike,3,Personal,3,5,12:19:42 PM,3,5,12:20:12 PM,3,5,12:28:04 PM,3,5,12:55:17 PM,3,5,1:24:02 PM,10,21.4,,-1.2985031999999999,36.7821448,-1.2754091,36.8529305,Rider_Id_722,1725
-Order_No_53,User_Id_1926,Bike,3,Business,6,4,10:10:37 AM,6,4,10:10:58 AM,6,4,10:58:21 AM,6,4,11:04:17 AM,6,4,11:36:59 AM,11,19.6,,-1.34364,36.8925345,-1.3065588,36.9076533,Rider_Id_856,1962
-Order_No_21185,User_Id_393,Bike,3,Business,30,2,8:43:35 AM,30,2,8:48:23 AM,30,2,9:02:47 AM,30,2,9:29:26 AM,30,2,9:29:32 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_921,6
-Order_No_19937,User_Id_540,Bike,3,Business,18,5,2:16:19 PM,18,5,2:52:20 PM,18,5,3:02:23 PM,18,5,3:04:40 PM,18,5,3:31:46 PM,8,27.1,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_250,1626
-Order_No_24938,User_Id_3091,Bike,3,Business,17,3,9:03:51 AM,17,3,9:05:10 AM,17,3,9:17:13 AM,17,3,9:53:37 AM,17,3,10:00:06 AM,2,,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_508,389
-Order_No_21592,User_Id_2658,Bike,3,Business,29,3,8:46:01 AM,29,3,8:55:03 AM,29,3,9:20:33 AM,29,3,9:31:18 AM,29,3,9:57:57 AM,7,17.9,,-1.2924639,36.77957979999999,-1.2813012,36.832396200000005,Rider_Id_582,1599
-Order_No_11862,User_Id_700,Bike,3,Business,8,2,11:31:58 AM,8,2,11:32:30 AM,8,2,11:58:11 AM,8,2,12:02:03 PM,8,2,12:17:10 PM,8,21.2,,-1.2948633999999999,36.7651632,-1.2844520000000001,36.8300885,Rider_Id_100,907
-Order_No_16488,User_Id_246,Bike,3,Business,14,3,3:22:09 PM,14,3,3:23:05 PM,14,3,3:29:50 PM,14,3,3:36:11 PM,14,3,4:05:54 PM,9,25.6,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_671,1783
-Order_No_7971,User_Id_1111,Bike,1,Personal,1,1,12:35:35 PM,1,1,12:44:28 PM,1,1,12:57:13 PM,1,1,1:10:51 PM,1,1,1:24:20 PM,5,24.2,,-1.2821883,36.826961100000005,-1.2911030000000001,36.851413,Rider_Id_21,809
-Order_No_25047,User_Id_3633,Bike,3,Personal,9,3,7:19:44 AM,9,3,8:12:28 AM,9,3,8:47:35 AM,9,3,8:50:27 AM,9,3,9:13:21 AM,7,17.7,0.6,-1.2963097,36.7688221,-1.3050868,36.8191906,Rider_Id_571,1374
-Order_No_26731,User_Id_393,Bike,3,Business,8,4,1:01:51 PM,8,4,1:02:29 PM,8,4,1:11:11 PM,8,4,1:26:28 PM,8,4,2:00:28 PM,14,26.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_581,2040
-Order_No_10481,User_Id_3295,Bike,3,Business,23,5,10:45:09 AM,23,5,11:59:11 AM,23,5,12:16:28 PM,23,5,12:23:04 PM,23,5,12:56:13 PM,8,24.3,,-1.2909536,36.8407162,-1.2571472,36.795063299999995,Rider_Id_769,1989
-Order_No_15714,User_Id_1075,Bike,3,Business,9,2,5:25:55 PM,9,2,5:26:23 PM,9,2,5:54:17 PM,9,2,6:32:45 PM,9,2,6:32:59 PM,1,27.8,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_106,14
-Order_No_26918,User_Id_3599,Bike,3,Business,8,6,9:03:24 AM,8,6,9:03:24 AM,8,6,10:10:25 AM,8,6,10:13:57 AM,8,6,10:53:02 AM,12,16.6,4.1,-1.3302996,36.8707935,-1.2903421,36.7885282,Rider_Id_772,2345
-Order_No_23532,User_Id_788,Bike,3,Business,22,5,1:35:11 PM,22,5,1:39:09 PM,22,5,2:12:28 PM,22,5,2:13:55 PM,22,5,2:52:48 PM,9,27.2,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_154,2333
-Order_No_15883,User_Id_248,Bike,3,Business,14,4,12:45:14 PM,14,4,12:47:37 PM,14,4,1:12:29 PM,14,4,1:32:06 PM,14,4,2:00:38 PM,6,27.6,,-1.2971877,36.8270159,-1.304735,36.784605400000004,Rider_Id_863,1712
-Order_No_9058,User_Id_2294,Bike,3,Business,22,3,7:55:26 AM,22,3,7:56:51 AM,22,3,8:38:43 AM,22,3,8:46:14 AM,22,3,9:20:22 AM,12,18.8,,-1.2764051,36.879138399999995,-1.2861405000000001,36.807887799999996,Rider_Id_557,2048
-Order_No_22749,User_Id_3080,Bike,3,Business,13,3,12:49:29 PM,13,3,12:49:37 PM,13,3,12:53:51 PM,13,3,12:58:54 PM,13,3,1:05:11 PM,5,24.8,,-1.2551895,36.7822034,-1.2662639999999998,36.762418,Rider_Id_379,377
-Order_No_26475,User_Id_2330,Bike,3,Business,20,1,4:25:58 PM,20,1,4:26:08 PM,20,1,4:30:20 PM,20,1,4:33:23 PM,20,1,4:58:54 PM,13,20.6,,-1.2915246999999999,36.805575700000006,-1.3270197,36.7037303,Rider_Id_932,1531
-Order_No_19919,User_Id_533,Bike,3,Business,3,3,12:54:16 PM,3,3,12:55:12 PM,3,3,1:06:54 PM,3,3,1:10:02 PM,3,3,1:22:36 PM,6,27.5,,-1.2765492,36.7669809,-1.2551895,36.7822034,Rider_Id_576,754
-Order_No_10200,User_Id_2956,Bike,3,Business,11,1,9:07:06 AM,11,1,9:07:29 AM,11,1,9:32:04 AM,11,1,9:35:22 AM,11,1,9:54:41 AM,9,21.0,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_576,1159
-Order_No_27670,User_Id_1787,Bike,3,Business,15,1,12:25:21 PM,15,1,12:36:04 PM,15,1,12:36:14 PM,15,1,1:09:29 PM,15,1,1:38:37 PM,9,,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_397,1748
-Order_No_16465,User_Id_3521,Bike,3,Business,2,2,4:09:09 PM,2,2,4:13:38 PM,2,2,4:40:57 PM,2,2,4:43:20 PM,2,2,4:53:14 PM,2,28.7,,-1.2701582,36.807443299999996,-1.2696908000000002,36.81519,Rider_Id_690,594
-Order_No_6603,User_Id_1927,Bike,3,Business,5,2,5:02:43 PM,5,2,5:07:25 PM,5,2,5:53:05 PM,5,2,6:00:25 PM,5,2,6:29:01 PM,10,,,-1.3169591,36.7000029,-1.3472066,36.7692638,Rider_Id_789,1716
-Order_No_8352,User_Id_2703,Bike,3,Personal,20,4,12:03:52 PM,20,4,1:39:33 PM,20,4,1:47:20 PM,20,4,1:50:56 PM,20,4,2:08:03 PM,7,25.1,,-1.2799996999999999,36.8237091,-1.296797,36.776452500000005,Rider_Id_576,1027
-Order_No_12842,User_Id_635,Bike,3,Business,13,3,2:38:52 PM,13,3,2:40:05 PM,13,3,2:50:09 PM,13,3,3:06:26 PM,13,3,3:50:41 PM,24,27.0,,-1.2602337,36.79905479999999,-1.1613850000000001,36.947309999999995,Rider_Id_571,2655
-Order_No_6590,User_Id_2330,Bike,3,Business,4,2,5:30:23 PM,4,2,6:20:24 PM,4,2,6:25:32 PM,4,2,6:31:13 PM,4,2,6:53:13 PM,9,22.6,,-1.2645318,36.7981775,-1.2124582,36.802894,Rider_Id_564,1320
-Order_No_1610,User_Id_28,Bike,3,Business,20,5,9:45:33 AM,20,5,9:46:19 AM,20,5,9:59:03 AM,20,5,10:05:36 AM,20,5,10:17:09 AM,7,14.6,,-1.2998459999999998,36.7940155,-1.2783772,36.8201617,Rider_Id_752,693
-Order_No_3893,User_Id_1244,Bike,3,Business,3,6,9:15:40 AM,3,6,9:18:30 AM,3,6,9:35:22 AM,3,6,9:50:19 AM,3,6,10:05:38 AM,8,20.0,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_427,919
-Order_No_16761,User_Id_2324,Bike,1,Personal,14,1,1:29:34 PM,14,1,1:32:55 PM,14,1,1:59:41 PM,14,1,2:05:45 PM,14,1,2:18:30 PM,3,25.7,,-1.2934675,36.7659545,-1.2860203000000001,36.7832538,Rider_Id_279,765
-Order_No_12176,User_Id_136,Bike,3,Business,19,2,2:55:22 PM,19,2,2:55:33 PM,19,2,3:01:18 PM,19,2,3:12:28 PM,19,2,3:46:49 PM,3,18.3,,-1.2649605,36.798177700000004,-1.273854,36.8003368,Rider_Id_116,2061
-Order_No_18220,User_Id_2383,Bike,3,Business,10,5,3:38:25 PM,10,5,3:42:48 PM,10,5,3:53:25 PM,10,5,3:56:39 PM,10,5,4:21:58 PM,7,,,-1.2608993,36.799246000000004,-1.2892435,36.8221852,Rider_Id_341,1519
-Order_No_15223,User_Id_867,Bike,3,Business,13,3,7:36:53 AM,13,3,7:37:45 AM,13,3,7:56:52 AM,13,3,8:01:38 AM,13,3,8:31:09 AM,10,17.2,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_206,1771
-Order_No_5452,User_Id_2701,Bike,3,Business,12,3,10:38:04 AM,12,3,10:44:56 AM,12,3,10:57:58 AM,12,3,11:01:15 AM,12,3,11:31:12 AM,5,19.3,,-1.2765736,36.851364600000004,-1.3083777,36.85900770000001,Rider_Id_399,1797
-Order_No_111,User_Id_1809,Bike,3,Business,6,2,11:49:04 AM,6,2,11:49:11 AM,6,2,11:56:35 AM,6,2,12:19:57 PM,6,2,12:29:46 PM,2,25.1,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_720,589
-Order_No_21723,User_Id_346,Bike,3,Business,21,4,12:17:22 PM,21,4,12:23:25 PM,21,4,12:23:44 PM,21,4,12:35:48 PM,21,4,12:50:57 PM,5,25.1,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_31,909
-Order_No_820,User_Id_391,Bike,3,Business,3,1,1:22:51 PM,3,1,2:10:28 PM,3,1,2:18:08 PM,3,1,2:49:15 PM,3,1,2:57:21 PM,7,23.4,,-1.2522036,36.8565636,-1.2784063,36.8222933,Rider_Id_472,486
-Order_No_21657,User_Id_1845,Bike,3,Business,6,4,8:10:25 AM,6,4,8:14:01 AM,6,4,9:30:48 AM,6,4,9:36:22 AM,6,4,9:52:47 AM,1,19.0,2.9,-1.2917868000000001,36.7872675,-1.3004846,36.790159100000004,Rider_Id_451,985
-Order_No_27016,User_Id_1471,Bike,3,Business,15,5,12:30:38 PM,15,5,12:30:58 PM,15,5,12:40:28 PM,15,5,12:44:51 PM,15,5,1:08:53 PM,8,26.5,,-1.2551895,36.7822034,-1.3021707,36.816844399999994,Rider_Id_411,1442
-Order_No_22516,User_Id_540,Bike,3,Business,16,3,10:42:14 AM,16,3,10:42:23 AM,16,3,10:44:12 AM,16,3,10:56:43 AM,16,3,11:01:07 AM,3,23.0,,-1.2551895,36.7822034,-1.2636813999999998,36.802996500000006,Rider_Id_74,264
-Order_No_10592,User_Id_3669,Bike,3,Business,10,4,10:05:57 AM,10,4,10:44:48 AM,10,4,11:09:01 AM,10,4,11:15:35 AM,10,4,11:38:33 AM,6,24.5,,-1.2615888000000002,36.7928732,-1.2897368,36.8226783,Rider_Id_489,1378
-Order_No_19196,User_Id_1514,Bike,3,Personal,6,3,10:32:09 AM,6,3,10:32:39 AM,6,3,10:34:06 AM,6,3,10:44:45 AM,6,3,10:53:30 AM,6,,,-1.3044313,36.818504,-1.3189407,36.8381862,Rider_Id_829,525
-Order_No_5192,User_Id_2587,Bike,3,Business,26,5,1:23:52 PM,26,5,1:24:01 PM,26,5,1:24:47 PM,26,5,1:49:35 PM,26,5,2:15:18 PM,8,,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_581,1543
-Order_No_12248,User_Id_1368,Bike,3,Business,22,2,4:51:05 PM,22,2,4:51:18 PM,22,2,4:52:39 PM,22,2,4:58:05 PM,22,2,5:09:58 PM,2,25.1,,-1.2664491000000002,36.8102685,-1.2571472,36.795063299999995,Rider_Id_486,713
-Order_No_1917,User_Id_470,Bike,1,Personal,24,5,2:00:49 PM,24,5,2:01:58 PM,24,5,2:46:10 PM,24,5,3:13:11 PM,24,5,3:39:29 PM,4,23.1,,-1.2845766,36.8245168,-1.2583017,36.8023526,Rider_Id_384,1578
-Order_No_1073,User_Id_3621,Bike,3,Business,4,4,12:05:07 PM,4,4,12:05:39 PM,4,4,12:17:25 PM,4,4,12:21:35 PM,4,4,12:52:23 PM,22,25.6,,-1.2836583000000001,36.7886014,-1.1926735000000002,36.931181200000005,Rider_Id_299,1848
-Order_No_2472,User_Id_1209,Bike,3,Business,14,2,8:25:31 AM,14,2,8:26:09 AM,14,2,8:40:21 AM,14,2,10:02:24 AM,14,2,10:11:41 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,557
-Order_No_13642,User_Id_2523,Bike,3,Business,6,4,9:40:54 AM,6,4,9:45:02 AM,6,4,10:35:58 AM,6,4,10:48:18 AM,6,4,11:56:59 AM,16,24.2,,-1.2551895,36.7822034,-1.3194518,36.869396,Rider_Id_101,4121
-Order_No_2255,User_Id_419,Bike,3,Business,3,3,1:54:12 PM,3,3,1:56:02 PM,3,3,1:56:25 PM,3,3,2:31:08 PM,3,3,2:44:52 PM,7,26.7,,-1.2886923000000001,36.8241519,-1.2571472,36.795063299999995,Rider_Id_6,824
-Order_No_10684,User_Id_914,Bike,3,Business,31,5,2:56:14 PM,31,5,2:58:40 PM,31,5,3:39:53 PM,31,5,3:58:45 PM,31,5,4:42:46 PM,11,24.8,,-1.2670213,36.7994482,-1.3270716,36.8406371,Rider_Id_155,2641
-Order_No_8912,User_Id_2330,Bike,3,Business,12,2,12:40:36 PM,12,2,12:41:12 PM,12,2,1:15:48 PM,12,2,1:17:44 PM,12,2,1:33:20 PM,8,28.0,,-1.3015197,36.7658456,-1.2671943,36.8062533,Rider_Id_327,936
-Order_No_25417,User_Id_1329,Bike,3,Business,17,1,9:00:24 AM,17,1,10:27:29 AM,17,1,11:12:55 AM,17,1,11:15:56 AM,17,1,11:44:15 AM,18,23.8,,-1.3653171,36.7530868,-1.273056,36.811298,Rider_Id_205,1699
-Order_No_4464,User_Id_3291,Bike,3,Business,14,4,11:02:02 AM,14,4,11:09:40 AM,14,4,11:16:19 AM,14,4,11:18:47 AM,14,4,11:38:12 AM,11,23.8,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_958,1165
-Order_No_23719,User_Id_3291,Bike,3,Business,15,3,9:25:29 AM,15,3,9:26:09 AM,15,3,9:31:59 AM,15,3,9:50:41 AM,15,3,10:14:43 AM,11,16.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_88,1442
-Order_No_8627,User_Id_412,Bike,3,Business,31,4,9:17:12 AM,31,4,9:17:23 AM,31,4,9:24:26 AM,31,4,9:26:13 AM,31,4,9:33:52 AM,2,20.6,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_535,459
-Order_No_15386,User_Id_706,Bike,3,Business,14,5,2:07:27 PM,14,5,2:08:00 PM,14,5,2:08:09 PM,14,5,2:16:39 PM,14,5,2:25:01 PM,5,23.8,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_31,502
-Order_No_27851,User_Id_1642,Bike,3,Business,22,1,12:01:57 PM,22,1,12:28:35 PM,22,1,12:48:12 PM,22,1,12:49:15 PM,22,1,1:41:06 PM,5,25.0,,-1.2728069,36.7962362,-1.2887076000000002,36.825465799999996,Rider_Id_857,3111
-Order_No_19816,User_Id_1113,Bike,1,Personal,24,5,6:19:10 PM,24,5,6:19:17 PM,24,5,6:27:20 PM,24,5,6:30:52 PM,24,5,6:56:36 PM,13,,,-1.2156006000000001,36.8916865,-1.2669291999999999,36.811891100000004,Rider_Id_384,1544
-Order_No_19759,User_Id_1402,Bike,3,Business,12,3,1:36:17 PM,12,3,1:38:18 PM,12,3,2:04:03 PM,12,3,2:15:16 PM,12,3,2:38:24 PM,6,21.1,,-1.288924,36.831527,-1.2584143,36.8048002,Rider_Id_36,1388
-Order_No_6777,User_Id_248,Bike,3,Business,13,3,11:29:53 AM,13,3,11:30:44 AM,13,3,11:39:01 AM,13,3,11:44:04 AM,13,3,12:14:38 PM,9,,,-1.304735,36.784605400000004,-1.2753108999999998,36.8130185,Rider_Id_548,1834
-Order_No_7208,User_Id_928,Bike,3,Business,29,5,1:15:23 PM,29,5,1:15:58 PM,29,5,1:21:45 PM,29,5,1:25:12 PM,29,5,1:52:16 PM,9,26.9,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_291,1624
-Order_No_1876,User_Id_1768,Bike,3,Personal,20,4,3:32:21 PM,20,4,4:40:39 PM,20,4,4:50:57 PM,20,4,4:55:28 PM,20,4,5:30:25 PM,6,22.9,,-1.2750257,36.8157165,-1.2983805,36.8198439,Rider_Id_82,2097
-Order_No_5485,User_Id_393,Bike,3,Business,20,4,2:05:48 PM,20,4,2:06:11 PM,20,4,2:06:16 PM,20,4,3:08:12 PM,20,4,3:08:17 PM,4,25.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,5
-Order_No_15792,User_Id_909,Bike,3,Business,17,3,4:08:47 PM,17,3,4:18:18 PM,17,3,4:26:43 PM,17,3,4:36:18 PM,17,3,5:06:41 PM,12,,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_634,1823
-Order_No_14513,User_Id_2200,Bike,3,Business,5,5,1:13:49 PM,5,5,1:14:42 PM,5,5,2:03:59 PM,5,5,2:21:20 PM,5,5,2:29:42 PM,2,26.8,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_736,502
-Order_No_22745,User_Id_633,Bike,3,Business,23,3,9:32:54 AM,23,3,9:33:21 AM,23,3,9:33:27 AM,23,3,9:47:16 AM,23,3,9:56:59 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_188,583
-Order_No_4526,User_Id_136,Bike,3,Business,22,5,3:52:35 PM,22,5,3:55:19 PM,22,5,3:57:57 PM,22,5,4:28:44 PM,22,5,4:35:18 PM,3,30.8,,-1.2551895,36.7822034,-1.2654716000000001,36.7983635,Rider_Id_607,394
-Order_No_21138,User_Id_186,Bike,3,Business,13,3,10:12:01 AM,13,3,10:12:19 AM,13,3,10:15:32 AM,13,3,10:18:20 AM,13,3,11:33:05 AM,23,20.3,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_231,4485
-Order_No_22556,User_Id_797,Bike,3,Business,15,3,2:02:30 PM,15,3,3:21:33 PM,15,3,3:21:50 PM,15,3,3:31:24 PM,15,3,3:54:12 PM,8,21.5,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_89,1368
-Order_No_879,User_Id_2330,Bike,3,Business,21,4,2:27:05 PM,21,4,2:42:58 PM,21,4,3:24:51 PM,21,4,3:32:23 PM,21,4,3:50:55 PM,5,26.5,,-1.2649605,36.798177700000004,-1.293082,36.8278578,Rider_Id_88,1112
-Order_No_23176,User_Id_1568,Bike,3,Business,4,1,3:46:53 PM,4,1,3:49:54 PM,4,1,4:03:03 PM,4,1,4:36:22 PM,4,1,5:11:54 PM,6,26.1,,-1.2897368,36.8226783,-1.3271374,36.8495534,Rider_Id_774,2132
-Order_No_14204,User_Id_3194,Bike,3,Business,30,3,10:34:01 AM,30,3,10:34:13 AM,30,3,10:41:22 AM,30,3,10:43:32 AM,30,3,11:04:39 AM,5,24.8,,-1.2551895,36.7822034,-1.273754,36.800557899999994,Rider_Id_428,1267
-Order_No_9168,User_Id_1232,Bike,1,Personal,19,2,1:13:43 PM,19,2,1:28:30 PM,19,2,1:42:13 PM,19,2,1:48:43 PM,19,2,2:18:15 PM,15,18.5,,-1.2631312,36.7138908,-1.3101721000000002,36.8049755,Rider_Id_735,1772
-Order_No_8674,User_Id_1209,Bike,3,Business,4,6,11:49:26 AM,4,6,11:49:44 AM,4,6,12:11:51 PM,4,6,12:21:14 PM,4,6,1:00:16 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_831,2342
-Order_No_5479,User_Id_3050,Bike,3,Business,1,5,12:11:54 PM,1,5,12:12:18 PM,1,5,12:13:05 PM,1,5,12:27:39 PM,1,5,1:01:31 PM,7,28.8,,-1.2945073999999999,36.8083195,-1.2760462,36.807469700000006,Rider_Id_354,2032
-Order_No_18813,User_Id_2174,Bike,3,Business,22,3,9:35:43 AM,22,3,9:35:44 AM,22,3,9:42:48 AM,22,3,10:44:56 AM,22,3,10:55:17 AM,20,23.2,,-1.2482781,36.884091999999995,-1.1707617000000001,36.9675086,Rider_Id_86,621
-Order_No_19993,User_Id_190,Bike,3,Business,11,2,2:41:56 PM,11,2,3:02:53 PM,11,2,3:09:49 PM,11,2,3:13:06 PM,11,2,3:21:49 PM,4,22.4,,-1.3155299999999999,36.863732,-1.3270347,36.866736200000005,Rider_Id_900,523
-Order_No_9931,User_Id_633,Bike,3,Business,21,4,3:51:53 PM,21,4,3:56:25 PM,21,4,4:08:41 PM,21,4,4:16:53 PM,21,4,4:34:34 PM,9,26.2,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_206,1061
-Order_No_17637,User_Id_2989,Bike,3,Business,7,3,10:20:38 AM,7,3,10:21:37 AM,7,3,10:35:23 AM,7,3,10:45:41 AM,7,3,11:14:50 AM,9,25.1,,-1.303031,36.758577,-1.3472066,36.7692638,Rider_Id_548,1749
-Order_No_9666,User_Id_111,Bike,3,Business,17,3,12:19:12 PM,17,3,12:27:05 PM,17,3,12:28:34 PM,17,3,12:34:22 PM,17,3,12:52:05 PM,8,25.2,,-1.2551895,36.7822034,-1.2521881000000001,36.7537237,Rider_Id_208,1063
-Order_No_4731,User_Id_3194,Bike,3,Business,23,4,3:35:38 PM,23,4,3:36:05 PM,23,4,3:42:44 PM,23,4,3:50:05 PM,23,4,4:01:01 PM,6,20.8,,-1.2551895,36.7822034,-1.2811695,36.8200313,Rider_Id_726,656
-Order_No_17398,User_Id_1148,Bike,3,Business,21,4,12:36:47 PM,21,4,12:54:26 PM,21,4,1:29:53 PM,21,4,1:49:07 PM,21,4,2:54:38 PM,11,30.9,,-1.3100586,36.850032899999995,-1.2549895,36.826582200000004,Rider_Id_181,3931
-Order_No_25473,User_Id_1244,Bike,3,Business,12,2,9:28:35 AM,12,2,9:28:59 AM,12,2,9:41:45 AM,12,2,9:53:36 AM,12,2,10:17:09 AM,9,18.0,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_427,1413
-Order_No_7741,User_Id_186,Bike,3,Business,9,4,4:27:28 PM,9,4,4:48:02 PM,9,4,4:57:54 PM,9,4,5:04:11 PM,9,4,5:18:23 PM,6,,,-1.2571472,36.795063299999995,-1.2904016,36.82263029999999,Rider_Id_341,852
-Order_No_20159,User_Id_1591,Bike,2,Personal,19,5,3:39:10 PM,19,5,3:40:32 PM,19,5,3:46:48 PM,19,5,3:48:48 PM,19,5,4:10:20 PM,10,27.2,,-1.2967108,36.787113500000004,-1.2655306,36.828148,Rider_Id_282,1292
-Order_No_10031,User_Id_1329,Bike,3,Business,30,2,2:54:32 PM,30,2,3:22:52 PM,30,2,3:26:53 PM,30,2,3:31:35 PM,30,2,4:12:57 PM,4,,,-1.273056,36.811298,-1.2646568999999999,36.780944399999996,Rider_Id_119,2482
-Order_No_6132,User_Id_868,Bike,3,Business,15,3,11:58:40 AM,15,3,12:32:35 PM,15,3,1:04:02 PM,15,3,1:19:22 PM,15,3,2:28:57 PM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_292,4175
-Order_No_12250,User_Id_3664,Bike,1,Personal,19,6,4:10:27 PM,19,6,4:20:03 PM,19,6,4:24:57 PM,19,6,4:32:22 PM,19,6,4:44:03 PM,3,21.6,,-1.3040486999999998,36.8211935,-1.307774,36.840754100000005,Rider_Id_196,701
-Order_No_18780,User_Id_1195,Bike,3,Business,22,5,10:00:26 AM,22,5,10:01:08 AM,22,5,10:19:31 AM,22,5,10:37:45 AM,22,5,11:59:23 AM,38,,,-1.3228003000000002,36.8306435,-1.1360575000000002,36.6308896,Rider_Id_519,4898
-Order_No_23258,User_Id_2509,Bike,3,Business,29,5,11:36:37 AM,29,5,11:37:26 AM,29,5,11:39:59 AM,29,5,11:45:35 AM,29,5,12:19:30 PM,14,26.7,,-1.2571472,36.795063299999995,-1.206696,36.847709,Rider_Id_770,2035
-Order_No_24891,User_Id_478,Bike,1,Business,6,1,3:35:43 PM,6,1,3:40:54 PM,6,1,3:52:27 PM,6,1,3:58:55 PM,6,1,4:08:23 PM,2,24.7,,-1.2621261000000001,36.8079755,-1.2631940000000002,36.797624,Rider_Id_520,568
-Order_No_8943,User_Id_3635,Bike,1,Business,18,4,5:46:54 PM,18,4,5:47:34 PM,18,4,5:54:37 PM,18,4,6:03:45 PM,18,4,6:36:48 PM,14,23.6,,-1.2615888000000002,36.7928732,-1.188126,36.7714175,Rider_Id_105,1983
-Order_No_14588,User_Id_412,Bike,3,Business,30,2,2:43:38 PM,30,2,2:58:29 PM,30,2,3:01:58 PM,30,2,3:04:27 PM,30,2,3:43:47 PM,12,,,-1.228911,36.881876,-1.266837,36.799248999999996,Rider_Id_488,2360
-Order_No_17436,User_Id_970,Bike,3,Business,15,3,9:48:07 AM,15,3,9:49:54 AM,15,3,10:01:57 AM,15,3,10:11:28 AM,15,3,10:44:10 AM,8,,,-1.2571472,36.795063299999995,-1.312469,36.815177,Rider_Id_344,1962
-Order_No_4318,User_Id_2701,Bike,3,Business,17,4,5:13:42 PM,17,4,5:14:00 PM,17,4,5:18:44 PM,17,4,5:20:45 PM,17,4,5:58:51 PM,14,22.2,,-1.279395,36.825364,-1.2600745,36.722676799999995,Rider_Id_207,2286
-Order_No_24069,User_Id_1527,Bike,3,Business,13,1,1:31:55 PM,13,1,1:34:37 PM,13,1,1:46:06 PM,13,1,1:52:31 PM,13,1,2:03:30 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_435,659
-Order_No_1269,User_Id_788,Bike,3,Business,14,3,1:31:21 PM,14,3,1:36:56 PM,14,3,2:00:49 PM,14,3,2:21:28 PM,14,3,3:00:52 PM,11,25.9,,-1.2571472,36.795063299999995,-1.225322,36.80855,Rider_Id_266,2364
-Order_No_16793,User_Id_2216,Bike,3,Business,9,3,2:43:34 PM,9,3,2:44:31 PM,9,3,3:10:31 PM,9,3,3:27:05 PM,9,3,4:06:43 PM,12,25.5,,-1.2551895,36.7822034,-1.3202198,36.802606899999994,Rider_Id_45,2378
-Order_No_26888,User_Id_3686,Bike,3,Business,14,4,11:15:10 AM,14,4,11:44:12 AM,14,4,11:59:26 AM,14,4,12:02:59 PM,14,4,12:23:39 PM,8,23.6,,-1.2551895,36.7822034,-1.299911,36.818856,Rider_Id_339,1240
-Order_No_7618,User_Id_3689,Bike,3,Business,10,1,2:20:41 PM,10,1,2:21:20 PM,10,1,2:22:03 PM,10,1,2:28:28 PM,10,1,2:33:34 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_648,306
-Order_No_1546,User_Id_1500,Bike,3,Business,4,5,3:10:08 PM,4,5,4:02:56 PM,4,5,4:04:38 PM,4,5,4:06:55 PM,4,5,4:27:51 PM,10,,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_555,1256
-Order_No_19200,User_Id_1469,Bike,3,Business,4,1,10:32:45 AM,4,1,10:34:07 AM,4,1,10:45:54 AM,4,1,10:48:05 AM,4,1,11:08:23 AM,9,26.5,,-1.2551895,36.7822034,-1.2974988,36.7956306,Rider_Id_263,1218
-Order_No_6954,User_Id_567,Bike,3,Business,5,2,2:54:51 PM,5,2,2:55:49 PM,5,2,3:28:54 PM,5,2,3:30:57 PM,5,2,3:54:29 PM,10,22.5,,-1.2583483999999998,36.8129524,-1.2963097,36.7688221,Rider_Id_474,1412
-Order_No_21500,User_Id_1051,Bike,3,Business,29,1,10:54:18 AM,29,1,12:23:21 PM,29,1,12:49:19 PM,29,1,12:59:48 PM,29,1,1:25:43 PM,19,,,-1.2482465,36.8889062,-1.1538233,36.950874,Rider_Id_237,1555
-Order_No_11867,User_Id_570,Bike,3,Business,30,4,3:13:02 PM,30,4,3:20:29 PM,30,4,3:36:56 PM,30,4,3:38:25 PM,30,4,4:16:49 PM,7,,,-1.2551895,36.7822034,-1.291768,36.8297208,Rider_Id_868,2304
-Order_No_9439,User_Id_2801,Bike,3,Business,7,5,1:21:32 PM,7,5,1:30:11 PM,7,5,1:51:57 PM,7,5,2:01:37 PM,7,5,2:21:04 PM,8,26.9,,-1.2595421999999998,36.7871178,-1.2912139999999999,36.78577,Rider_Id_178,1167
-Order_No_13002,User_Id_2169,Bike,2,Personal,23,4,2:52:12 PM,23,4,2:52:46 PM,23,4,3:10:31 PM,23,4,3:19:23 PM,23,4,3:41:07 PM,9,20.6,,-1.311208,36.778743,-1.2669099,36.79774329999999,Rider_Id_749,1304
-Order_No_27402,User_Id_1404,Bike,2,Personal,25,5,5:42:30 PM,25,5,5:44:02 PM,25,5,6:07:34 PM,25,5,6:37:07 PM,25,5,7:03:04 PM,18,21.0,,-1.3271247,36.895160499999996,-1.2918309,36.7963716,Rider_Id_765,1557
-Order_No_1844,User_Id_976,Bike,2,Personal,19,4,8:40:52 AM,19,4,8:44:55 AM,19,4,9:03:39 AM,19,4,9:57:26 AM,19,4,10:26:43 AM,8,14.5,,-1.312745,36.875715,-1.3284461,36.8204084,Rider_Id_415,1757
-Order_No_2887,User_Id_3686,Bike,3,Business,18,4,9:57:03 AM,18,4,9:57:26 AM,18,4,9:57:40 AM,18,4,10:03:04 AM,18,4,10:21:16 AM,8,,,-1.2551895,36.7822034,-1.29752,36.793808299999995,Rider_Id_576,1092
-Order_No_10713,User_Id_391,Bike,3,Business,18,1,11:05:45 AM,18,1,11:06:15 AM,18,1,11:16:27 AM,18,1,11:26:54 AM,18,1,11:38:39 AM,5,27.9,,-1.2602337,36.79905479999999,-1.2867454,36.8259017,Rider_Id_855,705
-Order_No_7156,User_Id_2935,Bike,3,Business,20,5,11:03:14 AM,20,5,11:03:45 AM,20,5,11:15:08 AM,20,5,11:26:35 AM,20,5,11:56:06 AM,9,18.7,,-1.2961922,36.791994,-1.2528485,36.7953722,Rider_Id_752,1771
-Order_No_23844,User_Id_3686,Bike,3,Business,21,4,9:43:54 AM,21,4,9:46:53 AM,21,4,9:56:46 AM,21,4,10:07:14 AM,21,4,10:20:09 AM,7,19.0,,-1.2551895,36.7822034,-1.2653185,36.8358841,Rider_Id_31,775
-Order_No_19747,User_Id_230,Bike,1,Personal,19,2,5:50:02 PM,19,2,5:50:39 PM,19,2,6:30:09 PM,19,2,6:36:18 PM,19,2,6:40:54 PM,2,19.6,,-1.2880228999999999,36.7892191,-1.2952784,36.7793271,Rider_Id_260,276
-Order_No_19379,User_Id_867,Bike,3,Business,1,1,11:39:34 AM,1,1,11:40:18 AM,1,1,11:40:22 AM,1,1,12:25:51 PM,1,1,12:30:35 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_137,284
-Order_No_12237,User_Id_3355,Bike,3,Business,6,4,12:41:34 PM,6,4,1:46:10 PM,6,4,1:54:38 PM,6,4,2:27:11 PM,6,4,2:52:23 PM,6,,,-1.2743818999999998,36.8130914,-1.3052491000000002,36.8223899,Rider_Id_878,1512
-Order_No_10771,User_Id_571,Bike,3,Business,24,4,3:31:26 PM,24,4,3:47:41 PM,24,4,4:21:36 PM,24,4,4:32:35 PM,24,4,5:38:06 PM,19,25.9,,-1.3236332,36.701152,-1.2627937,36.815695700000006,Rider_Id_660,3931
-Order_No_11343,User_Id_2987,Bike,3,Business,5,2,5:43:55 PM,5,2,5:54:37 PM,5,2,6:05:14 PM,5,2,6:20:36 PM,5,2,6:29:04 PM,4,21.4,,-1.3014461000000002,36.7661381,-1.3043821000000002,36.7840586,Rider_Id_116,508
-Order_No_21997,User_Id_1642,Bike,3,Business,19,2,2:24:57 PM,19,2,2:25:13 PM,19,2,2:33:55 PM,19,2,2:36:22 PM,19,2,2:58:17 PM,5,29.8,,-1.2887076000000002,36.825465799999996,-1.2726506000000002,36.7960153,Rider_Id_330,1315
-Order_No_5443,User_Id_1502,Bike,3,Business,22,6,3:04:23 PM,22,6,3:04:30 PM,22,6,3:04:45 PM,22,6,3:10:46 PM,22,6,3:37:05 PM,13,26.0,,-1.2527958000000001,36.8003131,-1.2232123,36.8932547,Rider_Id_685,1579
-Order_No_2067,User_Id_2587,Bike,3,Business,15,5,3:33:08 PM,15,5,3:36:24 PM,15,5,3:47:09 PM,15,5,3:55:17 PM,15,5,5:10:52 PM,5,28.1,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_634,4535
-Order_No_3936,User_Id_1966,Bike,3,Business,12,2,2:29:42 PM,12,2,2:31:32 PM,12,2,2:46:13 PM,12,2,3:00:22 PM,12,2,3:37:38 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_99,2236
-Order_No_23596,User_Id_186,Bike,3,Business,13,2,1:48:12 PM,13,2,1:56:16 PM,13,2,1:57:50 PM,13,2,2:01:36 PM,13,2,2:41:39 PM,18,26.0,,-1.3700383,36.9190174,-1.2902869,36.8085396,Rider_Id_516,2403
-Order_No_9742,User_Id_3701,Bike,3,Business,27,3,12:10:42 PM,27,3,12:10:54 PM,27,3,12:20:35 PM,27,3,12:22:36 PM,27,3,12:45:56 PM,17,25.8,,-1.2612573999999999,36.7879622,-1.2077541999999999,36.871098700000005,Rider_Id_932,1400
-Order_No_15436,User_Id_517,Bike,3,Business,28,5,10:05:38 AM,28,5,10:05:50 AM,28,5,10:05:55 AM,28,5,10:12:28 AM,28,5,10:28:55 AM,11,19.5,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_619,987
-Order_No_8746,User_Id_2328,Bike,2,Personal,7,2,9:05:43 AM,7,2,9:05:55 AM,7,2,9:32:35 AM,7,2,9:37:20 AM,7,2,10:04:31 AM,13,16.5,,-1.2423801,36.8478805,-1.22589,36.7822548,Rider_Id_124,1631
-Order_No_24694,User_Id_867,Bike,3,Business,3,1,1:45:29 PM,3,1,1:45:46 PM,3,1,1:47:37 PM,3,1,2:07:41 PM,3,1,2:32:25 PM,10,23.3,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_591,1484
-Order_No_27098,User_Id_1705,Bike,3,Business,8,4,3:31:34 PM,8,4,3:31:41 PM,8,4,3:31:45 PM,8,4,3:44:35 PM,8,4,4:26:19 PM,28,27.3,,-1.2551895,36.7822034,-1.1427273,36.9371213,Rider_Id_871,2504
-Order_No_14127,User_Id_393,Bike,3,Business,10,4,3:38:54 PM,10,4,3:39:43 PM,10,4,3:55:28 PM,10,4,4:07:58 PM,10,4,4:35:46 PM,10,25.2,,-1.3231001,36.8283559,-1.2574219,36.7927073,Rider_Id_518,1668
-Order_No_19225,User_Id_3647,Bike,3,Business,6,1,12:48:35 PM,6,1,1:00:45 PM,6,1,1:05:04 PM,6,1,1:12:08 PM,6,1,2:42:23 PM,18,,,-1.3073803,36.6825842,-1.2726389999999999,36.794723,Rider_Id_831,5415
-Order_No_899,User_Id_3689,Bike,3,Business,27,5,3:19:43 PM,27,5,3:20:05 PM,27,5,3:20:15 PM,27,5,3:30:58 PM,27,5,3:44:20 PM,3,24.2,,-1.260232,36.807334000000004,-1.2551895,36.7822034,Rider_Id_74,802
-Order_No_8835,User_Id_778,Bike,3,Business,19,2,11:40:23 AM,19,2,11:41:02 AM,19,2,11:48:31 AM,19,2,12:01:18 PM,19,2,12:11:43 PM,3,26.6,,-1.2535486,36.7933541,-1.2601197,36.7985271,Rider_Id_461,625
-Order_No_24540,User_Id_2344,Bike,3,Business,20,3,10:33:38 AM,20,3,10:37:18 AM,20,3,10:41:40 AM,20,3,10:45:28 AM,20,3,11:06:24 AM,8,26.6,,-1.2551895,36.7822034,-1.291282,36.798229,Rider_Id_489,1256
-Order_No_17805,User_Id_439,Bike,3,Business,15,4,10:00:06 AM,15,4,10:00:21 AM,15,4,10:04:11 AM,15,4,10:19:04 AM,15,4,11:05:44 AM,15,19.6,,-1.3323107,36.8692616,-1.2968083000000001,36.787097499999994,Rider_Id_387,2800
-Order_No_21826,User_Id_98,Bike,3,Personal,14,4,9:23:24 AM,14,4,9:26:14 AM,14,4,9:45:16 AM,14,4,9:48:02 AM,14,4,10:02:15 AM,8,15.5,,-1.2560221999999999,36.7886679,-1.22606,36.80039,Rider_Id_726,853
-Order_No_20298,User_Id_393,Bike,3,Business,6,2,10:57:34 AM,6,2,10:57:41 AM,6,2,10:58:57 AM,6,2,11:05:25 AM,6,2,11:34:59 AM,10,24.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_10,1774
-Order_No_2962,User_Id_3109,Bike,2,Personal,6,3,1:30:46 PM,6,3,1:32:25 PM,6,3,1:39:23 PM,6,3,1:40:44 PM,6,3,2:07:14 PM,5,21.6,,-1.2553615,36.789537599999996,-1.2726358999999998,36.7896003,Rider_Id_322,1590
-Order_No_21976,User_Id_814,Bike,3,Business,26,5,4:20:10 PM,26,5,4:23:49 PM,26,5,4:30:34 PM,26,5,4:44:46 PM,26,5,4:59:17 PM,4,,,-1.2564152,36.779280299999996,-1.2551895,36.7822034,Rider_Id_326,871
-Order_No_22644,User_Id_2306,Bike,3,Business,9,4,1:54:07 PM,9,4,1:56:16 PM,9,4,2:52:26 PM,9,4,2:57:38 PM,9,4,3:29:12 PM,14,,,-1.2271998,36.808490500000005,-1.2962832,36.784968299999996,Rider_Id_161,1894
-Order_No_27567,User_Id_517,Bike,3,Business,30,3,11:10:58 AM,30,3,11:18:15 AM,30,3,11:26:37 AM,30,3,11:57:10 AM,30,3,12:08:36 PM,4,25.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,686
-Order_No_13146,User_Id_733,Bike,3,Business,18,4,12:47:57 PM,18,4,12:48:10 PM,18,4,12:51:12 PM,18,4,1:02:07 PM,18,4,1:20:15 PM,10,25.5,,-1.2551895,36.7822034,-1.3031971000000002,36.786314600000004,Rider_Id_932,1088
-Order_No_1164,User_Id_3689,Bike,3,Business,2,5,2:22:21 PM,2,5,2:23:37 PM,2,5,2:30:31 PM,2,5,2:40:35 PM,2,5,2:53:29 PM,1,25.1,,-1.2527958000000001,36.8003131,-1.2677278,36.8001549,Rider_Id_112,774
-Order_No_21585,User_Id_391,Bike,3,Business,7,2,10:37:18 AM,7,2,10:46:30 AM,7,2,10:50:50 AM,7,2,11:23:41 AM,7,2,11:49:47 AM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_298,1566
-Order_No_7424,User_Id_393,Bike,3,Business,1,1,2:59:57 PM,1,1,3:00:13 PM,1,1,3:06:02 PM,1,1,3:11:45 PM,1,1,3:45:18 PM,14,25.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,2013
-Order_No_7531,User_Id_1075,Bike,3,Business,27,3,3:30:24 PM,27,3,3:31:28 PM,27,3,3:41:29 PM,27,3,3:45:50 PM,27,3,4:10:23 PM,7,29.4,,-1.2878913,36.8089253,-1.2527958000000001,36.8003131,Rider_Id_50,1473
-Order_No_14339,User_Id_533,Bike,3,Business,8,5,4:57:50 PM,8,5,4:58:09 PM,8,5,5:07:07 PM,8,5,5:17:04 PM,8,5,5:35:26 PM,6,28.1,,-1.2765492,36.7669809,-1.2571472,36.795063299999995,Rider_Id_205,1102
-Order_No_25616,User_Id_2069,Bike,3,Business,1,5,11:40:34 AM,1,5,11:44:52 AM,1,5,11:53:45 AM,1,5,12:04:20 PM,1,5,12:19:30 PM,9,28.6,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_329,910
-Order_No_16899,User_Id_2763,Bike,3,Business,26,5,11:56:48 AM,26,5,11:57:01 AM,26,5,11:57:31 AM,26,5,12:18:26 PM,26,5,12:35:55 PM,12,,,-1.2551895,36.7822034,-1.2335612,36.84518920000001,Rider_Id_291,1049
-Order_No_12925,User_Id_2919,Bike,1,Personal,11,1,7:59:27 AM,11,1,7:59:53 AM,11,1,8:13:01 AM,11,1,8:21:12 AM,11,1,8:39:08 AM,17,14.5,,-1.3047132,36.808955299999994,-1.3441051000000002,36.8971273,Rider_Id_71,1076
-Order_No_10354,User_Id_1402,Bike,3,Business,4,2,12:27:50 PM,4,2,1:08:14 PM,4,2,1:22:25 PM,4,2,1:37:37 PM,4,2,1:53:59 PM,11,22.5,,-1.2285834999999998,36.882646799999996,-1.2813012,36.832396200000005,Rider_Id_291,982
-Order_No_16871,User_Id_718,Bike,3,Business,27,2,12:08:22 PM,27,2,12:17:42 PM,27,2,12:28:13 PM,27,2,12:30:01 PM,27,2,1:00:29 PM,11,23.9,,-1.2551895,36.7822034,-1.3250103000000002,36.8388817,Rider_Id_905,1828
-Order_No_15463,User_Id_3264,Bike,3,Business,22,2,12:16:16 PM,22,2,12:16:51 PM,22,2,12:23:22 PM,22,2,12:25:39 PM,22,2,12:41:21 PM,8,23.9,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_259,942
-Order_No_10986,User_Id_2789,Bike,3,Business,4,1,3:44:37 PM,4,1,3:45:37 PM,4,1,4:04:28 PM,4,1,4:08:34 PM,4,1,4:23:59 PM,5,,,-1.304735,36.784605400000004,-1.2877501,36.814659899999995,Rider_Id_590,925
-Order_No_12556,User_Id_2330,Bike,3,Business,11,1,2:53:06 PM,11,1,3:03:18 PM,11,1,3:23:33 PM,11,1,3:25:57 PM,11,1,3:44:50 PM,7,28.2,,-1.2687407,36.7373043,-1.3015197,36.7658456,Rider_Id_778,1133
-Order_No_21357,User_Id_1100,Bike,2,Personal,12,2,7:25:22 AM,12,2,7:25:46 AM,12,2,7:35:58 AM,12,2,7:46:47 AM,12,2,8:06:55 AM,7,16.0,,-1.2764851000000002,36.8007126,-1.3025826,36.7670807,Rider_Id_334,1208
-Order_No_2830,User_Id_2601,Bike,3,Personal,2,2,3:40:09 PM,2,2,5:06:26 PM,2,2,5:50:47 PM,2,2,6:02:31 PM,2,2,6:17:53 PM,6,23.4,,-1.2611370000000002,36.728513799999995,-1.2812549,36.7236622,Rider_Id_565,922
-Order_No_27513,User_Id_186,Bike,3,Business,6,4,3:59:46 PM,6,4,4:00:48 PM,6,4,4:01:26 PM,6,4,4:19:45 PM,6,4,5:00:26 PM,20,21.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_720,2441
-Order_No_24958,User_Id_1765,Bike,1,Personal,20,1,4:21:30 PM,20,1,4:23:18 PM,20,1,4:24:47 PM,20,1,4:48:28 PM,20,1,5:03:32 PM,4,20.3,,-1.2802618000000001,36.8191684,-1.302922,36.816383,Rider_Id_698,904
-Order_No_10386,User_Id_2120,Bike,1,Personal,9,3,6:28:41 PM,9,3,6:29:34 PM,9,3,6:39:36 PM,9,3,6:47:01 PM,9,3,7:11:09 PM,8,21.7,,-1.262207,36.790153000000004,-1.22589,36.7822548,Rider_Id_103,1448
-Order_No_20069,User_Id_393,Bike,3,Business,4,2,11:26:25 AM,4,2,11:26:47 AM,4,2,11:26:55 AM,4,2,11:29:26 AM,4,2,12:09:56 PM,11,20.4,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_39,2430
-Order_No_5668,User_Id_393,Bike,3,Business,3,3,9:35:02 AM,3,3,9:35:17 AM,3,3,9:37:07 AM,3,3,9:56:18 AM,3,3,10:26:50 AM,10,18.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_623,1832
-Order_No_2913,User_Id_3560,Bike,3,Business,28,4,3:14:13 PM,28,4,3:14:33 PM,28,4,3:31:15 PM,28,4,4:57:41 PM,28,4,5:40:59 PM,5,27.8,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_223,2598
-Order_No_13300,User_Id_2904,Bike,3,Personal,2,6,12:38:07 PM,2,6,12:38:43 PM,2,6,12:51:27 PM,2,6,1:08:48 PM,2,6,1:22:15 PM,7,23.9,,-1.2590031000000002,36.8078168,-1.2970896,36.7771786,Rider_Id_100,807
-Order_No_1194,User_Id_868,Bike,3,Business,30,4,9:21:41 AM,30,4,9:22:26 AM,30,4,9:31:15 AM,30,4,9:37:41 AM,30,4,9:54:16 AM,8,19.4,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_548,995
-Order_No_14630,User_Id_2468,Bike,3,Business,17,3,9:10:49 AM,17,3,9:11:06 AM,17,3,9:17:50 AM,17,3,9:28:43 AM,17,3,9:45:08 AM,7,,,-1.2884231000000002,36.779456599999996,-1.3127303999999997,36.8189817,Rider_Id_161,985
-Order_No_27286,User_Id_1329,Bike,3,Business,6,3,10:56:28 AM,6,3,10:57:01 AM,6,3,11:03:21 AM,6,3,11:04:51 AM,6,3,11:16:01 AM,3,,,-1.273056,36.811298,-1.2641579,36.8216792,Rider_Id_660,670
-Order_No_15382,User_Id_2454,Bike,3,Business,23,2,10:18:49 AM,23,2,10:20:57 AM,23,2,10:34:17 AM,23,2,10:44:45 AM,23,2,11:08:01 AM,10,17.8,,-1.3270423999999998,36.7040693,-1.2993231,36.7753372,Rider_Id_565,1396
-Order_No_6936,User_Id_1977,Bike,2,Personal,14,2,2:16:55 PM,14,2,2:17:26 PM,14,2,2:23:14 PM,14,2,2:28:59 PM,14,2,2:45:44 PM,4,24.2,,-1.2795796,36.819062,-1.2790653,36.7907262,Rider_Id_74,1005
-Order_No_8144,User_Id_1976,Bike,3,Business,25,1,4:33:16 PM,25,1,4:33:45 PM,25,1,4:40:17 PM,25,1,4:42:10 PM,25,1,5:01:54 PM,9,28.4,,-1.257028,36.7810435,-1.2581438999999999,36.7236396,Rider_Id_291,1184
-Order_No_14228,User_Id_2112,Bike,3,Business,7,4,1:31:44 PM,7,4,1:31:55 PM,7,4,1:46:43 PM,7,4,1:59:21 PM,7,4,1:59:24 PM,5,31.0,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_633,3
-Order_No_23440,User_Id_633,Bike,3,Business,28,1,3:20:16 PM,28,1,3:20:22 PM,28,1,3:20:35 PM,28,1,4:11:20 PM,28,1,4:11:23 PM,4,26.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_914,3
-Order_No_21415,User_Id_2539,Bike,3,Business,9,5,12:03:54 PM,9,5,12:04:49 PM,9,5,12:12:33 PM,9,5,12:17:02 PM,9,5,12:48:12 PM,5,25.6,,-1.2969741000000001,36.7856612,-1.2930037,36.812647399999996,Rider_Id_282,1870
-Order_No_13261,User_Id_3403,Bike,3,Business,28,4,1:07:26 PM,28,4,1:12:06 PM,28,4,1:18:50 PM,28,4,1:35:19 PM,28,4,1:44:18 PM,6,27.4,,-1.2551895,36.7822034,-1.2827261,36.82315429999999,Rider_Id_576,539
-Order_No_11961,User_Id_1389,Bike,3,Business,20,2,2:14:28 PM,20,2,2:17:42 PM,20,2,2:37:12 PM,20,2,2:47:39 PM,20,2,2:48:15 PM,8,23.8,,-1.207546,36.7913976,-1.2326041,36.8009029,Rider_Id_870,36
-Order_No_10156,User_Id_868,Bike,3,Business,6,1,9:43:42 AM,6,1,9:44:43 AM,6,1,9:45:03 AM,6,1,10:05:29 AM,6,1,10:21:00 AM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_828,931
-Order_No_20575,User_Id_136,Bike,3,Business,20,3,4:02:09 PM,20,3,4:08:47 PM,20,3,4:26:06 PM,20,3,4:29:44 PM,20,3,5:08:17 PM,16,28.6,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_605,2313
-Order_No_22414,User_Id_2330,Bike,3,Business,30,4,5:40:30 PM,30,4,5:42:55 PM,30,4,5:54:06 PM,30,4,6:08:17 PM,30,4,6:38:53 PM,12,,,-1.2642903,36.80021370000001,-1.2284323,36.8757058,Rider_Id_567,1836
-Order_No_7178,User_Id_2535,Bike,2,Personal,5,3,1:36:48 PM,5,3,1:37:32 PM,5,3,1:59:29 PM,5,3,2:02:10 PM,5,3,2:36:34 PM,5,23.9,,-1.2942209999999998,36.7719395,-1.3095596999999999,36.797295,Rider_Id_88,2064
-Order_No_24475,User_Id_627,Bike,3,Business,16,2,11:40:23 AM,16,2,11:40:38 AM,16,2,11:54:57 AM,16,2,12:11:41 PM,16,2,12:11:43 PM,2,23.6,,-1.2551895,36.7822034,-1.2580991000000001,36.794151899999996,Rider_Id_56,2
-Order_No_23410,User_Id_2178,Bike,1,Personal,13,4,11:52:59 AM,13,4,11:57:39 AM,13,4,12:24:22 PM,13,4,12:34:54 PM,13,4,12:43:25 PM,4,23.4,,-1.2998551999999999,36.818707700000004,-1.2815172,36.8336326,Rider_Id_179,511
-Order_No_26921,User_Id_265,Bike,3,Business,26,1,10:53:22 AM,26,1,10:53:30 AM,26,1,11:13:01 AM,26,1,11:49:22 AM,26,1,12:08:36 PM,19,23.2,,-1.207546,36.7913976,-1.300921,36.828195,Rider_Id_874,1154
-Order_No_24278,User_Id_2330,Bike,3,Business,5,4,2:59:21 PM,5,4,3:01:18 PM,5,4,3:08:11 PM,5,4,3:12:24 PM,5,4,3:22:19 PM,2,22.3,,-1.2614329,36.8094365,-1.2682178999999998,36.8009289,Rider_Id_100,595
-Order_No_22642,User_Id_682,Bike,2,Personal,3,5,3:45:58 PM,3,5,4:26:39 PM,3,5,4:33:32 PM,3,5,4:38:35 PM,3,5,5:12:24 PM,10,22.6,,-1.3142268999999998,36.839245,-1.2947685,36.778583399999995,Rider_Id_471,2029
-Order_No_25926,User_Id_909,Bike,3,Business,21,3,11:11:26 AM,21,3,11:19:28 AM,21,3,12:05:27 PM,21,3,12:08:34 PM,21,3,12:31:05 PM,10,22.0,,-1.225322,36.80855,-1.1785179,36.7557799,Rider_Id_829,1351
-Order_No_6520,User_Id_1243,Bike,1,Business,8,1,3:54:26 PM,8,1,3:55:15 PM,8,1,4:00:42 PM,8,1,4:09:09 PM,8,1,4:31:01 PM,10,24.9,,-1.304524,36.775371,-1.2551895,36.7822034,Rider_Id_754,1312
-Order_No_5884,User_Id_3454,Bike,1,Personal,6,3,4:55:25 PM,6,3,5:05:59 PM,6,3,5:20:35 PM,6,3,5:28:03 PM,6,3,5:58:39 PM,11,20.0,,-1.2879431000000001,36.82700689999999,-1.2624366000000002,36.7632187,Rider_Id_742,1836
-Order_No_23339,User_Id_510,Bike,3,Business,26,6,10:52:07 AM,26,6,10:52:37 AM,26,6,10:52:42 AM,26,6,11:04:37 AM,26,6,11:24:28 AM,11,22.4,,-1.2860183,36.8975338,-1.325808,36.861664000000005,Rider_Id_408,1191
-Order_No_11921,User_Id_2987,Bike,3,Business,29,2,3:01:07 PM,29,2,3:04:32 PM,29,2,3:16:44 PM,29,2,3:27:57 PM,29,2,3:39:28 PM,4,23.8,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_852,691
-Order_No_24082,User_Id_265,Bike,3,Business,3,3,11:42:12 AM,3,3,11:46:25 AM,3,3,11:46:52 AM,3,3,11:51:45 AM,3,3,12:08:12 PM,4,27.5,,-1.300921,36.828195,-1.3205163999999998,36.8098882,Rider_Id_661,987
-Order_No_27200,User_Id_373,Bike,2,Personal,14,4,12:53:22 PM,14,4,12:53:28 PM,14,4,1:09:40 PM,14,4,1:19:35 PM,14,4,1:56:03 PM,22,19.1,,-1.2523598999999999,36.8017648,-1.3376943000000001,36.9139331,Rider_Id_752,2188
-Order_No_6724,User_Id_2008,Bike,3,Business,27,3,9:55:54 AM,27,3,9:56:24 AM,27,3,10:07:30 AM,27,3,10:09:39 AM,27,3,11:06:16 AM,4,,,-1.2508229,36.7895263,-1.26278,36.826409999999996,Rider_Id_452,3397
-Order_No_17334,User_Id_3647,Bike,3,Business,30,2,2:54:56 PM,30,2,3:03:14 PM,30,2,3:25:11 PM,30,2,3:33:39 PM,30,2,4:17:55 PM,17,,,-1.2726389999999999,36.794723,-1.2242728999999999,36.8880921,Rider_Id_831,2656
-Order_No_22677,User_Id_3647,Bike,3,Business,11,5,8:59:13 AM,11,5,8:59:54 AM,11,5,9:06:29 AM,11,5,9:16:30 AM,11,5,9:37:26 AM,5,21.1,,-1.2962618000000001,36.792933399999995,-1.2726389999999999,36.794723,Rider_Id_862,1256
-Order_No_11842,User_Id_2330,Bike,1,Business,4,6,10:25:04 AM,4,6,10:25:55 AM,4,6,10:34:22 AM,4,6,10:47:35 AM,4,6,11:13:10 AM,7,19.6,,-1.2602688,36.7992556,-1.2915246999999999,36.805575700000006,Rider_Id_882,1535
-Order_No_25152,User_Id_3711,Bike,3,Business,23,3,2:55:21 PM,23,3,2:56:01 PM,23,3,3:01:16 PM,23,3,3:05:23 PM,23,3,3:37:16 PM,3,27.5,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_210,1913
-Order_No_18384,User_Id_1363,Bike,3,Business,23,4,2:45:51 PM,23,4,2:50:20 PM,23,4,2:54:50 PM,23,4,2:55:56 PM,23,4,3:10:01 PM,3,24.5,,-1.3004062,36.829741,-1.2883163999999998,36.8146724,Rider_Id_158,845
-Order_No_22160,User_Id_3491,Bike,3,Business,2,5,12:50:14 PM,2,5,1:03:56 PM,2,5,1:05:08 PM,2,5,1:09:17 PM,2,5,1:44:53 PM,19,23.6,,-1.2297202,36.881950700000004,-1.15135,36.9566534,Rider_Id_704,2136
-Order_No_22175,User_Id_1724,Bike,3,Business,7,2,11:39:59 AM,7,2,11:40:22 AM,7,2,11:40:29 AM,7,2,11:44:57 AM,7,2,11:56:02 AM,4,,,-1.2551895,36.7822034,-1.2690013,36.8092872,Rider_Id_576,665
-Order_No_12805,User_Id_1209,Bike,3,Business,25,4,3:43:23 PM,25,4,3:45:44 PM,25,4,3:45:55 PM,25,4,4:09:37 PM,25,4,4:29:29 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_926,1192
-Order_No_4562,User_Id_2451,Bike,1,Personal,23,6,12:52:59 PM,23,6,12:54:37 PM,23,6,1:04:31 PM,23,6,1:18:39 PM,23,6,1:38:52 PM,4,,,-1.2795261,36.826018499999996,-1.3034072,36.821803499999994,Rider_Id_531,1213
-Order_No_6607,User_Id_1927,Bike,3,Business,8,6,1:22:06 PM,8,6,1:35:35 PM,8,6,1:51:14 PM,8,6,1:54:33 PM,8,6,2:35:16 PM,12,23.3,2.1,-1.290894,36.822971,-1.3463041999999998,36.768164,Rider_Id_569,2443
-Order_No_6003,User_Id_2271,Bike,3,Business,18,6,10:39:42 AM,18,6,10:40:04 AM,18,6,10:59:16 AM,18,6,11:15:11 AM,18,6,11:35:17 AM,12,21.3,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_68,1206
-Order_No_3864,User_Id_1635,Bike,1,Personal,20,4,10:04:39 AM,20,4,10:04:50 AM,20,4,10:10:23 AM,20,4,10:18:30 AM,20,4,10:43:34 AM,8,17.9,,-1.2652808999999998,36.808065899999995,-1.3207146,36.8305771,Rider_Id_726,1504
-Order_No_13411,User_Id_2521,Bike,3,Business,11,6,10:09:33 AM,11,6,10:24:52 AM,11,6,10:41:24 AM,11,6,12:09:55 PM,11,6,12:22:25 PM,5,20.9,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_223,750
-Order_No_8339,User_Id_3525,Bike,3,Business,7,2,12:30:16 PM,7,2,1:15:52 PM,7,2,1:26:48 PM,7,2,1:31:42 PM,7,2,2:06:45 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_233,2103
-Order_No_12927,User_Id_2100,Bike,3,Business,23,2,12:04:07 PM,23,2,12:11:51 PM,23,2,12:23:39 PM,23,2,12:37:30 PM,23,2,1:02:45 PM,6,18.1,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_662,1515
-Order_No_26313,User_Id_496,Bike,3,Business,17,3,9:24:13 AM,17,3,9:24:32 AM,17,3,9:26:08 AM,17,3,9:49:21 AM,17,3,9:56:18 AM,2,,,-1.2551895,36.7822034,-1.2517846000000001,36.7938086,Rider_Id_177,417
-Order_No_13704,User_Id_2066,Bike,3,Business,1,3,2:16:05 PM,1,3,2:17:30 PM,1,3,2:38:08 PM,1,3,2:44:23 PM,1,3,3:02:49 PM,5,,,-1.3210188999999999,36.841055100000005,-1.2983805,36.8198439,Rider_Id_106,1106
-Order_No_3687,User_Id_1432,Bike,1,Personal,15,3,9:26:58 AM,15,3,9:27:09 AM,15,3,10:06:05 AM,15,3,10:09:42 AM,15,3,10:41:05 AM,11,16.4,,-1.2912419,36.877528399999996,-1.2950408000000002,36.809817200000005,Rider_Id_84,1883
-Order_No_239,User_Id_2509,Bike,3,Business,5,2,3:43:21 PM,5,2,3:43:41 PM,5,2,3:45:36 PM,5,2,3:54:10 PM,5,2,4:22:55 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_24,1725
-Order_No_17131,User_Id_393,Bike,3,Business,27,2,10:13:14 AM,27,2,10:47:17 AM,27,2,10:54:59 AM,27,2,11:23:01 AM,27,2,11:53:49 AM,14,23.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_878,1848
-Order_No_27694,User_Id_248,Bike,3,Business,27,1,12:03:49 PM,27,1,12:04:57 PM,27,1,12:10:09 PM,27,1,12:38:23 PM,27,1,1:06:19 PM,7,20.7,,-1.2729652,36.8064396,-1.304735,36.784605400000004,Rider_Id_183,1676
-Order_No_26021,User_Id_3080,Bike,3,Business,21,2,11:05:06 AM,21,2,11:06:43 AM,21,2,11:10:52 AM,21,2,11:25:44 AM,21,2,11:47:33 AM,8,22.3,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_138,1309
-Order_No_1118,User_Id_2017,Bike,3,Business,22,5,4:02:53 PM,22,5,4:04:53 PM,22,5,4:22:56 PM,22,5,4:44:05 PM,22,5,5:22:23 PM,8,,,-1.2963097,36.7688221,-1.281614,36.8275424,Rider_Id_444,2298
-Order_No_27784,User_Id_540,Bike,3,Business,20,4,4:01:15 PM,20,4,4:02:36 PM,20,4,4:50:51 PM,20,4,5:03:37 PM,20,4,5:51:43 PM,7,22.5,,-1.2571472,36.795063299999995,-1.2987440000000001,36.817823700000005,Rider_Id_52,2886
-Order_No_26289,User_Id_972,Bike,3,Business,24,5,9:17:19 AM,24,5,9:17:37 AM,24,5,9:25:09 AM,24,5,9:30:54 AM,24,5,9:44:28 AM,4,15.7,,-1.2862033000000002,36.8152966,-1.2973948999999998,36.816038,Rider_Id_531,814
-Order_No_20271,User_Id_3494,Bike,3,Personal,2,4,9:22:39 AM,2,4,9:22:58 AM,2,4,9:48:34 AM,2,4,9:49:39 AM,2,4,10:07:08 AM,8,13.4,,-1.2787309,36.819494399999996,-1.3039411,36.7695316,Rider_Id_857,1049
-Order_No_7489,User_Id_1882,Bike,3,Business,23,6,10:13:20 AM,23,6,10:18:07 AM,23,6,10:31:16 AM,23,6,10:47:33 AM,23,6,11:03:09 AM,7,,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_427,936
-Order_No_23944,User_Id_2701,Bike,3,Business,24,5,10:44:02 AM,24,5,10:56:53 AM,24,5,11:34:26 AM,24,5,11:53:23 AM,24,5,12:23:01 PM,18,21.5,,-1.3562374,36.904295399999995,-1.2765736,36.851364600000004,Rider_Id_524,1778
-Order_No_9545,User_Id_1721,Bike,3,Business,14,4,8:20:36 AM,14,4,8:21:53 AM,14,4,8:36:19 AM,14,4,8:47:38 AM,14,4,10:05:55 AM,13,17.7,,-1.2551895,36.7822034,-1.3053578000000001,36.860791299999995,Rider_Id_131,4697
-Order_No_10102,User_Id_2965,Bike,3,Personal,18,4,8:15:29 AM,18,4,8:16:39 AM,18,4,8:31:12 AM,18,4,8:34:27 AM,18,4,8:48:26 AM,7,,,-1.2629461000000002,36.76531370000001,-1.2760462,36.807469700000006,Rider_Id_290,839
-Order_No_20663,User_Id_3521,Bike,3,Business,22,5,11:47:36 AM,22,5,11:50:59 AM,22,5,12:11:34 PM,22,5,12:19:01 PM,22,5,12:28:33 PM,3,28.3,,-1.2533659,36.8142394,-1.2696908000000002,36.81519,Rider_Id_52,572
-Order_No_2811,User_Id_3355,Bike,3,Business,3,5,12:57:20 PM,3,5,12:57:24 PM,3,5,12:57:41 PM,3,5,1:02:15 PM,3,5,1:22:37 PM,6,21.5,,-1.3052491000000002,36.8223899,-1.2721466000000001,36.8155359,Rider_Id_293,1222
-Order_No_23700,User_Id_3283,Bike,3,Business,6,3,2:43:43 PM,6,3,2:44:56 PM,6,3,3:15:57 PM,6,3,3:31:29 PM,6,3,3:47:02 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_434,933
-Order_No_6492,User_Id_2801,Bike,3,Business,24,1,10:55:39 AM,24,1,10:55:54 AM,24,1,11:02:55 AM,24,1,11:05:20 AM,24,1,11:28:43 AM,7,22.5,,-1.2595421999999998,36.7871178,-1.281577,36.79382929999999,Rider_Id_38,1403
-Order_No_18940,User_Id_346,Bike,3,Business,7,4,2:38:32 PM,7,4,2:39:56 PM,7,4,2:48:00 PM,7,4,3:03:32 PM,7,4,3:35:11 PM,4,31.3,,-1.2646973999999997,36.80517879999999,-1.28878,36.8168312,Rider_Id_795,1899
-Order_No_17454,User_Id_53,Bike,3,Business,9,2,2:23:21 PM,9,2,2:24:44 PM,9,2,2:29:57 PM,9,2,2:47:36 PM,9,2,3:04:50 PM,7,30.8,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_394,1034
-Order_No_25774,User_Id_2330,Bike,3,Business,11,4,9:55:59 AM,11,4,10:00:51 AM,11,4,10:14:46 AM,11,4,10:21:06 AM,11,4,10:39:01 AM,6,22.1,,-1.2642903,36.80021370000001,-1.2915246999999999,36.805575700000006,Rider_Id_576,1075
-Order_No_14386,User_Id_1551,Bike,1,Personal,10,1,10:46:56 AM,10,1,10:47:05 AM,10,1,10:53:35 AM,10,1,11:18:44 AM,10,1,11:56:56 AM,14,,,-1.2863371000000001,36.8200261,-1.3221853000000001,36.7111258,Rider_Id_124,2292
-Order_No_21805,User_Id_20,Bike,3,Business,10,1,9:27:57 AM,10,1,9:28:49 AM,10,1,9:35:27 AM,10,1,9:37:11 AM,10,1,9:52:46 AM,5,16.4,,-1.2566688999999998,36.779695200000006,-1.2629666000000002,36.8068369,Rider_Id_708,935
-Order_No_20003,User_Id_770,Bike,1,Personal,11,3,12:17:53 PM,11,3,12:19:28 PM,11,3,12:34:36 PM,11,3,12:37:24 PM,11,3,1:11:57 PM,9,19.1,,-1.2846475,36.8275903,-1.3258649,36.8000584,Rider_Id_808,2073
-Order_No_6159,User_Id_1208,Bike,1,Personal,24,1,9:44:43 AM,24,1,9:45:11 AM,24,1,10:12:13 AM,24,1,10:34:16 AM,24,1,11:12:32 AM,15,22.3,,-1.2100103000000002,36.797424,-1.3003196000000001,36.8171416,Rider_Id_865,2296
-Order_No_2173,User_Id_3404,Bike,3,Business,26,5,4:57:41 PM,26,5,4:57:49 PM,26,5,4:59:35 PM,26,5,5:09:57 PM,26,5,5:30:27 PM,8,,,-1.3014461000000002,36.7661381,-1.2793686000000002,36.82592329999999,Rider_Id_743,1230
-Order_No_23130,User_Id_3355,Bike,3,Business,21,6,11:49:47 AM,21,6,11:53:16 AM,21,6,12:02:41 PM,21,6,1:03:00 PM,21,6,1:30:22 PM,10,16.3,,-1.3404187,36.7614685,-1.3052491000000002,36.8223899,Rider_Id_735,1642
-Order_No_18505,User_Id_496,Bike,3,Business,26,3,11:57:01 AM,26,3,11:57:19 AM,26,3,11:59:46 AM,26,3,12:04:19 PM,26,3,1:09:42 PM,23,24.0,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_937,3923
-Order_No_23851,User_Id_3211,Bike,3,Personal,20,1,3:32:39 PM,20,1,3:33:07 PM,20,1,3:44:24 PM,20,1,3:49:20 PM,20,1,4:16:13 PM,5,21.4,,-1.2846475,36.8275903,-1.2994217,36.8009779,Rider_Id_548,1613
-Order_No_23620,User_Id_2404,Bike,2,Personal,7,2,1:53:42 PM,7,2,1:54:48 PM,7,2,2:06:27 PM,7,2,2:19:04 PM,7,2,2:41:41 PM,8,25.0,,-1.3233955,36.856294399999996,-1.3057159999999999,36.809882,Rider_Id_402,1357
-Order_No_4278,User_Id_1280,Bike,3,Business,6,4,8:59:25 AM,6,4,9:00:34 AM,6,4,9:03:55 AM,6,4,9:10:01 AM,6,4,10:07:32 AM,15,16.3,3.9,-1.2551895,36.7822034,-1.3288106000000002,36.8697344,Rider_Id_155,3451
-Order_No_20381,User_Id_3017,Bike,3,Business,14,6,3:04:18 PM,14,6,3:05:14 PM,14,6,3:09:57 PM,14,6,3:20:47 PM,14,6,3:54:43 PM,16,18.9,,-1.299313,36.78314879999999,-1.3263285,36.8926624,Rider_Id_45,2036
-Order_No_18646,User_Id_2812,Bike,3,Personal,20,3,9:59:57 AM,20,3,10:32:07 AM,20,3,10:47:33 AM,20,3,10:52:27 AM,20,3,11:09:22 AM,4,20.1,,-1.264917,36.8150651,-1.2897399,36.8270975,Rider_Id_338,1015
-Order_No_9756,User_Id_473,Bike,2,Personal,31,4,10:10:27 AM,31,4,10:11:11 AM,31,4,10:19:48 AM,31,4,10:22:58 AM,31,4,11:11:45 AM,13,17.3,,-1.2930705,36.827943299999994,-1.3541409,36.7555557,Rider_Id_159,2927
-Order_No_8103,User_Id_1363,Bike,3,Business,30,6,9:18:48 AM,30,6,9:18:59 AM,30,6,9:33:04 AM,30,6,9:34:29 AM,30,6,9:44:41 AM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_728,612
-Order_No_12751,User_Id_1340,Bike,3,Business,14,4,9:25:48 AM,14,4,9:26:51 AM,14,4,9:32:39 AM,14,4,9:34:11 AM,14,4,9:56:47 AM,3,18.9,,-1.2707178999999997,36.7979713,-1.264917,36.8150651,Rider_Id_953,1356
-Order_No_18325,User_Id_393,Bike,3,Business,9,3,1:53:42 PM,9,3,1:56:12 PM,9,3,1:57:47 PM,9,3,2:37:43 PM,9,3,2:55:17 PM,5,25.5,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_926,1054
-Order_No_18019,User_Id_1790,Bike,3,Business,26,1,1:38:59 PM,26,1,1:39:42 PM,26,1,1:44:29 PM,26,1,1:49:03 PM,26,1,2:05:16 PM,6,22.0,0.7,-1.2852583999999998,36.8043641,-1.2637056999999998,36.805170000000004,Rider_Id_167,973
-Order_No_5568,User_Id_2808,Bike,3,Business,8,3,10:02:38 AM,8,3,10:09:37 AM,8,3,10:22:41 AM,8,3,10:36:16 AM,8,3,11:01:07 AM,5,22.0,,-1.263499,36.801454799999995,-1.278612,36.8195876,Rider_Id_882,1491
-Order_No_17139,User_Id_3302,Bike,1,Personal,26,2,2:29:59 PM,26,2,2:30:21 PM,26,2,2:40:14 PM,26,2,2:47:25 PM,26,2,3:27:09 PM,18,,,-1.31661,36.820315,-1.2251345,36.8835216,Rider_Id_549,2384
-Order_No_27306,User_Id_3283,Bike,3,Business,29,2,10:54:19 AM,29,2,10:54:51 AM,29,2,11:11:11 AM,29,2,11:19:41 AM,29,2,11:41:02 AM,13,23.5,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_691,1281
-Order_No_23301,User_Id_3283,Bike,3,Business,28,4,12:57:08 PM,28,4,12:58:44 PM,28,4,1:44:58 PM,28,4,2:14:34 PM,28,4,2:33:36 PM,5,29.9,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_52,1142
-Order_No_12826,User_Id_420,Bike,3,Business,30,2,12:35:59 PM,30,2,12:54:09 PM,30,2,1:27:24 PM,30,2,1:33:44 PM,30,2,2:19:54 PM,22,27.4,,-1.2384923,36.9007845,-1.2720806,36.7751959,Rider_Id_900,2770
-Order_No_2875,User_Id_316,Bike,1,Personal,21,1,12:02:27 PM,21,1,12:02:59 PM,21,1,12:12:12 PM,21,1,12:18:15 PM,21,1,12:47:51 PM,20,20.0,,-1.3339943,36.7263409,-1.3231725,36.864017700000005,Rider_Id_893,1776
-Order_No_12612,User_Id_540,Bike,3,Business,30,2,1:30:12 PM,30,2,1:33:10 PM,30,2,1:41:29 PM,30,2,1:48:49 PM,30,2,2:12:24 PM,10,,,-1.2551895,36.7822034,-1.2976368,36.751982700000006,Rider_Id_937,1415
-Order_No_7224,User_Id_2956,Bike,3,Business,26,2,2:04:14 PM,26,2,2:04:49 PM,26,2,2:10:53 PM,26,2,2:31:55 PM,26,2,2:52:12 PM,9,30.8,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_354,1217
-Order_No_19156,User_Id_3213,Bike,1,Personal,10,2,9:30:12 AM,10,2,9:30:42 AM,10,2,9:43:07 AM,10,2,10:07:24 AM,10,2,10:17:58 AM,3,16.1,,-1.2854173999999998,36.7951152,-1.3007534,36.7964576,Rider_Id_465,634
-Order_No_10983,User_Id_1684,Bike,1,Personal,16,2,12:12:00 PM,16,2,12:12:07 PM,16,2,12:34:21 PM,16,2,12:39:36 PM,16,2,1:07:14 PM,14,23.6,,-1.2087364999999999,36.785105200000004,-1.2731645,36.7900343,Rider_Id_103,1658
-Order_No_11472,User_Id_816,Bike,3,Business,15,4,9:41:03 AM,15,4,10:29:26 AM,15,4,10:53:01 AM,15,4,10:58:14 AM,15,4,11:19:28 AM,10,24.5,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_878,1274
-Order_No_24888,User_Id_1363,Bike,3,Business,15,5,11:25:45 AM,15,5,11:26:12 AM,15,5,11:36:03 AM,15,5,11:41:04 AM,15,5,12:00:16 PM,3,25.7,,-1.3004062,36.829741,-1.2929499,36.8180835,Rider_Id_76,1152
-Order_No_26862,User_Id_1075,Bike,3,Business,6,3,12:46:40 PM,6,3,12:47:10 PM,6,3,12:48:03 PM,6,3,1:04:13 PM,6,3,1:38:46 PM,5,,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_267,2073
-Order_No_21994,User_Id_3674,Bike,3,Business,28,2,2:44:17 PM,28,2,2:45:44 PM,28,2,2:46:43 PM,28,2,2:50:03 PM,28,2,3:42:05 PM,9,23.5,,-1.2551895,36.7822034,-1.3073464,36.797542799999995,Rider_Id_576,3122
-Order_No_27031,User_Id_1527,Bike,3,Business,17,5,8:51:21 AM,17,5,8:52:34 AM,17,5,8:56:34 AM,17,5,9:12:01 AM,17,5,9:27:44 AM,7,,,-1.3004062,36.829741,-1.2909735,36.77825870000001,Rider_Id_538,943
-Order_No_19908,User_Id_1961,Bike,3,Business,27,1,12:26:07 PM,27,1,12:26:42 PM,27,1,12:58:37 PM,27,1,1:09:10 PM,27,1,1:51:51 PM,19,20.6,,-1.331619,36.847976,-1.3188358999999998,36.703852600000005,Rider_Id_25,2561
-Order_No_2590,User_Id_2733,Bike,3,Business,29,5,7:13:08 PM,29,5,7:17:41 PM,29,5,7:22:21 PM,29,5,8:02:58 PM,29,5,8:32:56 PM,6,,,-1.2963097,36.7688221,-1.2825422,36.73247,Rider_Id_71,1798
-Order_No_22213,User_Id_237,Bike,3,Business,7,5,3:55:48 PM,7,5,5:24:04 PM,7,5,5:28:58 PM,7,5,5:31:36 PM,7,5,5:43:17 PM,5,23.2,,-1.2551895,36.7822034,-1.2812416000000002,36.8192411,Rider_Id_523,701
-Order_No_18580,User_Id_3355,Bike,3,Business,28,5,2:46:39 PM,28,5,2:48:00 PM,28,5,2:50:49 PM,28,5,3:07:18 PM,28,5,3:31:31 PM,8,25.7,,-1.3052491000000002,36.8223899,-1.2615888000000002,36.7928732,Rider_Id_848,1453
-Order_No_6870,User_Id_1087,Bike,3,Business,3,3,11:26:14 AM,3,3,11:27:42 AM,3,3,11:37:57 AM,3,3,11:40:00 AM,3,3,12:21:23 PM,16,24.2,,-1.2679971,36.808893700000006,-1.3277669,36.8975169,Rider_Id_38,2483
-Order_No_7111,User_Id_1926,Bike,3,Business,30,6,12:33:31 PM,30,6,12:33:46 PM,30,6,12:40:29 PM,30,6,12:53:47 PM,30,6,1:04:07 PM,4,22.0,,-1.3042209999999999,36.884308000000004,-1.3050321000000003,36.88432460000001,Rider_Id_397,620
-Order_No_3280,User_Id_393,Bike,3,Business,11,5,10:23:59 AM,11,5,10:24:20 AM,11,5,10:24:24 AM,11,5,11:13:09 AM,11,5,11:13:13 AM,14,21.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_239,4
-Order_No_16961,User_Id_3283,Bike,3,Business,11,2,1:00:01 PM,11,2,1:19:43 PM,11,2,1:23:30 PM,11,2,1:29:13 PM,11,2,2:33:15 PM,13,19.4,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_456,3842
-Order_No_5553,User_Id_1994,Bike,3,Business,13,1,9:13:55 AM,13,1,9:15:05 AM,13,1,9:36:51 AM,13,1,9:44:00 AM,13,1,10:39:25 AM,20,,,-1.2861764999999998,36.8209086,-1.2811816,36.9591729,Rider_Id_341,3325
-Order_No_25682,User_Id_3291,Bike,3,Business,30,4,10:24:22 AM,30,4,10:25:51 AM,30,4,10:26:05 AM,30,4,10:46:43 AM,30,4,10:46:52 AM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_497,9
-Order_No_3048,User_Id_2280,Bike,2,Personal,4,3,3:13:56 PM,4,3,3:15:34 PM,4,3,3:29:49 PM,4,3,3:35:52 PM,4,3,3:40:52 PM,2,18.5,,-1.2744872,36.7926777,-1.2786893999999998,36.8032321,Rider_Id_103,300
-Order_No_1030,User_Id_3404,Bike,3,Business,18,2,8:45:11 AM,18,2,8:47:24 AM,18,2,9:01:24 AM,18,2,9:13:49 AM,18,2,9:41:12 AM,10,19.8,,-1.279395,36.825364,-1.2404912000000001,36.88477879999999,Rider_Id_26,1643
-Order_No_22537,User_Id_446,Bike,3,Personal,6,3,3:52:00 PM,6,3,4:00:24 PM,6,3,4:15:06 PM,6,3,4:19:49 PM,6,3,4:58:05 PM,7,21.9,,-1.289498,36.818878000000005,-1.2562249,36.786866100000005,Rider_Id_208,2296
-Order_No_19827,User_Id_2439,Bike,3,Business,22,4,10:07:02 AM,22,4,10:07:42 AM,22,4,10:14:05 AM,22,4,10:24:43 AM,22,4,10:46:24 AM,14,19.8,3.0,-1.2615888000000002,36.7928732,-1.2297202,36.881950700000004,Rider_Id_31,1301
-Order_No_5329,User_Id_151,Bike,3,Business,22,1,9:07:35 AM,22,1,9:10:16 AM,22,1,9:21:15 AM,22,1,9:23:51 AM,22,1,9:40:58 AM,7,20.1,,-1.2751745,36.765767600000004,-1.2620725,36.802651700000006,Rider_Id_449,1027
-Order_No_21126,User_Id_909,Bike,3,Business,30,5,1:28:40 PM,30,5,2:07:51 PM,30,5,2:18:49 PM,30,5,2:19:49 PM,30,5,3:04:49 PM,8,24.8,,-1.225322,36.80855,-1.2596969999999998,36.800802000000004,Rider_Id_447,2700
-Order_No_24817,User_Id_868,Bike,3,Business,28,2,12:29:56 PM,28,2,12:31:38 PM,28,2,12:38:48 PM,28,2,12:48:09 PM,28,2,1:01:15 PM,9,21.2,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_7,786
-Order_No_26698,User_Id_1962,Bike,1,Personal,9,1,1:32:31 PM,9,1,1:48:16 PM,9,1,2:00:20 PM,9,1,2:05:04 PM,9,1,2:35:05 PM,14,20.9,,-1.3208162,36.8451209,-1.2569503,36.78347279999999,Rider_Id_45,1801
-Order_No_23811,User_Id_302,Bike,3,Business,13,4,7:01:03 PM,13,4,7:05:54 PM,13,4,7:16:25 PM,13,4,7:29:42 PM,13,4,7:43:32 PM,7,24.3,,-1.2649605,36.798177700000004,-1.2971378999999998,36.8019794,Rider_Id_427,830
-Order_No_19451,User_Id_3599,Bike,3,Business,26,1,10:57:00 AM,26,1,11:17:35 AM,26,1,12:17:13 PM,26,1,12:22:11 PM,26,1,1:21:34 PM,15,23.0,,-1.3302996,36.8707935,-1.2912772,36.7734495,Rider_Id_660,3563
-Order_No_17467,User_Id_3621,Bike,3,Business,6,4,8:53:33 AM,6,4,8:53:54 AM,6,4,9:10:20 AM,6,4,9:14:15 AM,6,4,9:22:19 AM,4,18.7,2.9,-1.2836583000000001,36.7886014,-1.30217,36.770468,Rider_Id_88,484
-Order_No_19605,User_Id_1473,Bike,3,Business,9,1,7:45:26 AM,9,1,8:09:14 AM,9,1,8:50:33 AM,9,1,8:56:02 AM,9,1,9:25:03 AM,17,15.4,,-1.27871,36.819573,-1.3700383,36.9190174,Rider_Id_654,1741
-Order_No_21257,User_Id_627,Bike,3,Business,27,3,3:02:24 PM,27,3,3:02:52 PM,27,3,3:03:15 PM,27,3,3:08:32 PM,27,3,3:28:39 PM,11,29.1,,-1.2551895,36.7822034,-1.3108386,36.7555239,Rider_Id_523,1207
-Order_No_3090,User_Id_1017,Bike,3,Business,30,4,11:35:49 AM,30,4,11:36:18 AM,30,4,11:36:31 AM,30,4,11:45:00 AM,30,4,12:00:11 PM,3,,,-1.2551895,36.7822034,-1.2649605,36.798177700000004,Rider_Id_576,911
-Order_No_725,User_Id_146,Bike,3,Personal,28,5,12:28:31 PM,28,5,12:34:00 PM,28,5,12:49:13 PM,28,5,12:55:05 PM,28,5,1:30:59 PM,16,25.3,,-1.326545,36.667483000000004,-1.2985153,36.7876043,Rider_Id_731,2154
-Order_No_10213,User_Id_3635,Bike,1,Business,29,6,4:35:52 PM,29,6,4:38:26 PM,29,6,4:54:52 PM,29,6,4:59:06 PM,29,6,5:16:55 PM,6,23.8,,-1.2615888000000002,36.7928732,-1.282937,36.781698399999996,Rider_Id_916,1069
-Order_No_10143,User_Id_2330,Bike,3,Business,14,1,3:11:23 PM,14,1,3:12:06 PM,14,1,3:15:31 PM,14,1,3:23:04 PM,14,1,3:42:51 PM,7,25.9,,-1.2921463,36.8029626,-1.2649605,36.798177700000004,Rider_Id_237,1187
-Order_No_17965,User_Id_2587,Bike,3,Business,5,5,4:50:48 PM,5,5,4:51:08 PM,5,5,5:01:21 PM,5,5,5:26:29 PM,5,5,5:59:46 PM,22,26.2,,-1.28878,36.8168312,-1.3165542,36.700067,Rider_Id_456,1997
-Order_No_3165,User_Id_3080,Bike,3,Business,27,2,9:39:58 AM,27,2,9:50:48 AM,27,2,10:01:49 AM,27,2,10:12:17 AM,27,2,10:40:56 AM,15,18.7,2.1,-1.2551895,36.7822034,-1.3231725,36.864017700000005,Rider_Id_81,1719
-Order_No_15298,User_Id_3686,Bike,3,Business,9,3,10:11:22 AM,9,3,10:11:27 AM,9,3,10:11:32 AM,9,3,10:17:20 AM,9,3,10:51:09 AM,19,17.6,0.8,-1.2559559999999999,36.789867,-1.334474,36.88389,Rider_Id_771,2029
-Order_No_10928,User_Id_1112,Bike,2,Personal,3,2,11:01:44 AM,3,2,11:05:57 AM,3,2,11:16:12 AM,3,2,11:27:11 AM,3,2,11:37:52 AM,3,18.3,,-1.2615584,36.814950599999996,-1.2621576,36.8103473,Rider_Id_467,641
-Order_No_20366,User_Id_2627,Bike,3,Business,5,2,11:48:37 AM,5,2,11:49:36 AM,5,2,12:15:28 PM,5,2,12:17:23 PM,5,2,12:42:44 PM,8,23.5,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_804,1521
-Order_No_14019,User_Id_3291,Bike,3,Business,15,6,12:55:12 PM,15,6,12:55:50 PM,15,6,12:58:45 PM,15,6,1:30:01 PM,15,6,1:38:33 PM,4,21.9,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_404,512
-Order_No_8301,User_Id_87,Bike,2,Personal,22,2,10:40:58 AM,22,2,10:42:24 AM,22,2,10:56:00 AM,22,2,10:59:40 AM,22,2,11:26:22 AM,7,19.2,,-1.2735236,36.7992196,-1.3004311000000002,36.7524273,Rider_Id_432,1602
-Order_No_9616,User_Id_3283,Bike,3,Business,1,1,11:50:24 AM,1,1,12:13:59 PM,1,1,12:33:34 PM,1,1,12:52:42 PM,1,1,1:02:18 PM,6,,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_61,576
-Order_No_21711,User_Id_265,Bike,3,Business,10,1,10:45:07 AM,10,1,10:45:57 AM,10,1,11:23:48 AM,10,1,11:44:40 AM,10,1,11:59:20 AM,5,23.3,,-1.3273709999999999,36.840590999999996,-1.300921,36.828195,Rider_Id_48,880
-Order_No_4050,User_Id_3067,Bike,1,Personal,10,1,8:08:31 PM,10,1,8:08:53 PM,10,1,8:33:12 PM,10,1,8:43:36 PM,10,1,9:07:04 PM,10,18.8,2.1,-1.2615584,36.814950599999996,-1.3251583999999998,36.8199384,Rider_Id_517,1408
-Order_No_8117,User_Id_1730,Bike,1,Personal,9,4,11:59:22 AM,9,4,12:01:02 PM,9,4,12:12:30 PM,9,4,12:20:08 PM,9,4,12:32:25 PM,5,23.7,,-1.2634530000000002,36.7955612,-1.2795197,36.8168287,Rider_Id_857,737
-Order_No_5424,User_Id_1218,Bike,2,Personal,27,5,9:27:36 AM,27,5,9:50:43 AM,27,5,10:13:00 AM,27,5,10:14:36 AM,27,5,10:51:58 AM,19,16.2,,-1.3605299,36.7579536,-1.2630828,36.805053799999996,Rider_Id_194,2242
-Order_No_6038,User_Id_1719,Bike,3,Business,29,3,9:10:24 AM,29,3,9:14:28 AM,29,3,9:51:42 AM,29,3,9:58:28 AM,29,3,10:32:17 AM,12,17.9,,-1.353975,36.7263802,-1.2963097,36.7688221,Rider_Id_572,2029
-Order_No_20166,User_Id_2521,Bike,3,Business,9,6,10:49:41 AM,9,6,10:50:05 AM,9,6,10:56:15 AM,9,6,11:08:03 AM,9,6,11:36:27 AM,8,27.1,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_349,1704
-Order_No_21844,User_Id_3291,Bike,3,Business,17,1,10:12:32 AM,17,1,10:13:09 AM,17,1,10:20:29 AM,17,1,10:28:25 AM,17,1,10:38:47 AM,4,18.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_882,622
-Order_No_23079,User_Id_2896,Bike,3,Business,21,3,8:53:13 AM,21,3,8:53:52 AM,21,3,9:09:34 AM,21,3,9:21:01 AM,21,3,10:05:19 AM,12,17.0,1.1,-1.3338005,36.863026,-1.2944363,36.7983424,Rider_Id_114,2658
-Order_No_17499,User_Id_3426,Bike,3,Business,28,7,2:50:15 PM,28,7,3:57:52 PM,28,7,4:21:39 PM,28,7,4:26:37 PM,28,7,5:02:45 PM,16,,,-1.225322,36.80855,-1.3014461000000002,36.7661381,Rider_Id_588,2168
-Order_No_27537,User_Id_2956,Bike,3,Business,14,4,11:27:45 AM,14,4,11:28:38 AM,14,4,11:45:31 AM,14,4,11:49:59 AM,14,4,12:39:07 PM,5,25.2,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_106,2948
-Order_No_5013,User_Id_2069,Bike,3,Business,8,3,11:22:39 AM,8,3,11:22:48 AM,8,3,11:31:14 AM,8,3,11:40:43 AM,8,3,11:54:27 AM,11,22.2,,-1.2783848999999998,36.8163445,-1.228911,36.881876,Rider_Id_857,824
-Order_No_1029,User_Id_2904,Bike,3,Personal,1,1,3:53:34 PM,1,1,3:54:48 PM,1,1,3:56:12 PM,1,1,4:14:25 PM,1,1,4:34:07 PM,5,25.7,,-1.2970896,36.7771786,-1.3030678,36.7554371,Rider_Id_519,1182
-Order_No_17824,User_Id_3091,Bike,1,Business,31,2,4:15:10 PM,31,2,4:16:39 PM,31,2,4:22:56 PM,31,2,4:43:35 PM,31,2,4:54:14 PM,5,20.1,,-1.3014461000000002,36.7661381,-1.2858511000000001,36.7946184,Rider_Id_207,639
-Order_No_15982,User_Id_2596,Bike,3,Business,11,5,11:10:20 AM,11,5,11:11:20 AM,11,5,11:21:36 AM,11,5,11:37:32 AM,11,5,12:08:16 PM,12,21.0,,-1.3053135,36.8605848,-1.2963097,36.7688221,Rider_Id_281,1844
-Order_No_7434,User_Id_970,Bike,3,Business,28,4,9:43:28 AM,28,4,9:43:47 AM,28,4,9:44:02 AM,28,4,9:49:11 AM,28,4,9:56:41 AM,6,22.4,,-1.2551895,36.7822034,-1.266533,36.765838200000005,Rider_Id_801,450
-Order_No_16154,User_Id_2828,Bike,3,Business,6,3,7:58:34 AM,6,3,7:58:58 AM,6,3,8:15:56 AM,6,3,8:24:35 AM,6,3,9:22:53 AM,21,,,-1.3263105,36.8622904,-1.3981712,36.7673241,Rider_Id_332,3498
-Order_No_23820,User_Id_2154,Bike,3,Business,21,5,10:43:58 AM,21,5,10:45:52 AM,21,5,11:04:01 AM,21,5,11:09:24 AM,21,5,11:41:06 AM,8,22.8,,-1.2765736,36.851364600000004,-1.2613451999999998,36.805508,Rider_Id_150,1902
-Order_No_21568,User_Id_914,Bike,3,Business,24,5,1:53:54 PM,24,5,1:54:07 PM,24,5,2:02:39 PM,24,5,2:12:08 PM,24,5,2:40:03 PM,11,22.9,,-1.2670213,36.7994482,-1.3270716,36.8406371,Rider_Id_119,1675
-Order_No_26253,User_Id_2310,Bike,1,Business,31,2,4:30:35 PM,31,2,4:31:43 PM,31,2,4:43:49 PM,31,2,4:50:24 PM,31,2,5:09:36 PM,8,20.0,,-1.2765736,36.851364600000004,-1.2859912,36.8756811,Rider_Id_263,1152
-Order_No_1178,User_Id_393,Bike,3,Business,9,2,12:19:43 PM,9,2,12:19:54 PM,9,2,12:25:14 PM,9,2,12:26:24 PM,9,2,12:57:08 PM,5,27.0,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_151,1844
-Order_No_14725,User_Id_3496,Bike,3,Business,8,5,2:09:55 PM,8,5,2:33:55 PM,8,5,3:25:16 PM,8,5,3:44:29 PM,8,5,4:02:07 PM,6,21.5,,-1.3053408000000002,36.8771044,-1.300921,36.828195,Rider_Id_192,1058
-Order_No_11982,User_Id_2154,Bike,3,Business,2,2,10:48:42 AM,2,2,10:50:42 AM,2,2,10:58:50 AM,2,2,11:00:35 AM,2,2,11:29:59 AM,10,26.2,,-1.2551895,36.7822034,-1.2765736,36.851364600000004,Rider_Id_690,1764
-Order_No_25746,User_Id_3091,Bike,3,Business,4,2,1:26:49 PM,4,2,1:28:08 PM,4,2,1:29:05 PM,4,2,1:35:13 PM,4,2,2:05:17 PM,9,22.7,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_709,1804
-Order_No_13033,User_Id_1809,Bike,3,Business,4,4,11:40:36 AM,4,4,11:48:24 AM,4,4,12:05:09 PM,4,4,1:05:33 PM,4,4,1:14:48 PM,8,25.5,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_436,555
-Order_No_7432,User_Id_478,Bike,3,Business,8,5,12:29:26 PM,8,5,12:32:25 PM,8,5,12:41:44 PM,8,5,1:01:20 PM,8,5,1:21:35 PM,8,24.4,,-1.2551895,36.7822034,-1.3045333000000001,36.82280720000001,Rider_Id_79,1215
-Order_No_15610,User_Id_2462,Bike,3,Personal,17,2,11:59:36 AM,17,2,12:00:31 PM,17,2,12:08:18 PM,17,2,12:13:00 PM,17,2,12:32:07 PM,8,19.2,,-1.2778747,36.883182899999994,-1.3015998,36.8764939,Rider_Id_618,1147
-Order_No_7400,User_Id_1075,Bike,3,Business,6,3,1:50:36 PM,6,3,1:50:58 PM,6,3,2:07:19 PM,6,3,2:13:51 PM,6,3,2:20:39 PM,1,27.3,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_205,408
-Order_No_9605,User_Id_42,Bike,3,Business,9,5,2:27:43 PM,9,5,2:38:24 PM,9,5,2:59:37 PM,9,5,3:53:25 PM,9,5,4:12:05 PM,5,26.4,,-1.2564152,36.779280299999996,-1.2705336,36.7943509,Rider_Id_774,1120
-Order_No_15428,User_Id_508,Bike,2,Personal,14,2,1:52:45 PM,14,2,1:54:31 PM,14,2,2:10:35 PM,14,2,2:13:24 PM,14,2,2:47:38 PM,10,24.2,,-1.2782498,36.8251128,-1.2702844,36.880320899999994,Rider_Id_857,2054
-Order_No_14189,User_Id_2271,Bike,3,Business,15,1,1:40:16 PM,15,1,1:51:54 PM,15,1,1:53:52 PM,15,1,2:15:12 PM,15,1,2:32:56 PM,11,,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_75,1064
-Order_No_27276,User_Id_2521,Bike,3,Business,25,5,1:03:36 PM,25,5,1:06:01 PM,25,5,1:19:21 PM,25,5,1:24:47 PM,25,5,1:36:53 PM,5,26.2,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_436,726
-Order_No_21147,User_Id_1082,Bike,1,Personal,5,3,4:28:47 PM,5,3,4:29:28 PM,5,3,4:36:37 PM,5,3,4:39:30 PM,5,3,5:39:40 PM,17,22.3,,-1.304033,36.7848695,-1.2606138,36.6963627,Rider_Id_215,3610
-Order_No_18206,User_Id_2993,Bike,2,Personal,15,6,3:24:20 PM,15,6,3:24:36 PM,15,6,3:34:03 PM,15,6,3:47:42 PM,15,6,4:04:26 PM,8,25.7,,-1.2594656000000002,36.8019631,-1.2946558000000001,36.760775,Rider_Id_148,1004
-Order_No_10905,User_Id_633,Bike,3,Business,13,3,3:43:03 PM,13,3,3:43:31 PM,13,3,3:50:03 PM,13,3,3:53:16 PM,13,3,4:03:18 PM,4,29.1,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206,602
-Order_No_6586,User_Id_391,Bike,3,Business,11,1,2:04:38 PM,11,1,2:57:43 PM,11,1,3:13:24 PM,11,1,3:24:06 PM,11,1,3:37:30 PM,5,28.1,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_336,804
-Order_No_18365,User_Id_2330,Bike,3,Business,15,3,10:39:06 AM,15,3,10:40:26 AM,15,3,11:02:17 AM,15,3,11:03:21 AM,15,3,11:26:47 AM,11,,,-1.2890715,36.8157588,-1.2987263999999998,36.8831224,Rider_Id_204,1406
-Order_No_895,User_Id_2271,Bike,3,Business,31,4,5:04:38 PM,31,4,5:05:37 PM,31,4,5:42:23 PM,31,4,5:57:06 PM,31,4,6:32:19 PM,15,27.3,,-1.2296618000000001,36.84382720000001,-1.293515,36.897607,Rider_Id_775,2113
-Order_No_12540,User_Id_1880,Bike,3,Business,16,4,11:56:27 AM,16,4,12:22:31 PM,16,4,12:31:53 PM,16,4,12:39:35 PM,16,4,12:48:58 PM,2,,,-1.2571472,36.795063299999995,-1.2706941,36.8132766,Rider_Id_675,563
-Order_No_1359,User_Id_2423,Bike,3,Business,4,1,9:28:10 AM,4,1,9:35:35 AM,4,1,9:35:39 AM,4,1,9:41:44 AM,4,1,10:06:27 AM,7,21.9,,-1.2551895,36.7822034,-1.2814031000000001,36.82016470000001,Rider_Id_576,1483
-Order_No_10034,User_Id_3381,Bike,3,Business,14,3,9:45:27 AM,14,3,10:10:18 AM,14,3,10:46:00 AM,14,3,10:48:03 AM,14,3,11:01:22 AM,4,24.6,,-1.279662,36.814902000000004,-1.2731645,36.7900343,Rider_Id_477,799
-Order_No_7968,User_Id_1544,Bike,3,Business,3,1,4:17:02 PM,3,1,4:57:26 PM,3,1,5:24:58 PM,3,1,5:35:59 PM,3,1,5:46:44 PM,2,17.6,,-1.288565,36.841325700000006,-1.2885029,36.825604999999996,Rider_Id_158,645
-Order_No_18722,User_Id_2069,Bike,3,Business,1,1,1:24:51 PM,1,1,1:27:10 PM,1,1,1:40:14 PM,1,1,1:50:16 PM,1,1,2:11:57 PM,12,,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_691,1301
-Order_No_26583,User_Id_1882,Bike,3,Business,6,3,7:05:19 PM,6,3,7:06:51 PM,6,3,7:19:31 PM,6,3,7:22:58 PM,6,3,7:40:03 PM,7,,,-1.2603403,36.7998848,-1.2268871000000001,36.8073946,Rider_Id_119,1025
-Order_No_13229,User_Id_2904,Bike,3,Personal,11,1,12:13:32 PM,11,1,12:18:59 PM,11,1,12:35:57 PM,11,1,12:37:23 PM,11,1,1:39:08 PM,13,17.9,,-1.3565822,36.7437878,-1.2970896,36.7771786,Rider_Id_890,3705
-Order_No_21470,User_Id_3247,Bike,3,Business,13,4,10:53:31 AM,13,4,10:54:32 AM,13,4,11:08:54 AM,13,4,11:15:44 AM,13,4,11:27:12 AM,5,22.1,,-1.2571472,36.795063299999995,-1.2425149,36.789013700000005,Rider_Id_937,688
-Order_No_10064,User_Id_868,Bike,3,Business,27,6,12:38:50 PM,27,6,12:40:00 PM,27,6,12:44:26 PM,27,6,12:51:37 PM,27,6,1:04:26 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_178,769
-Order_No_12194,User_Id_1724,Bike,3,Business,2,4,12:22:20 PM,2,4,12:26:15 PM,2,4,12:38:07 PM,2,4,12:44:00 PM,2,4,1:06:04 PM,8,,,-1.2551895,36.7822034,-1.3015998,36.816646500000004,Rider_Id_96,1324
-Order_No_3350,User_Id_3599,Bike,3,Business,27,6,9:35:45 AM,27,6,11:09:00 AM,27,6,12:00:25 PM,27,6,12:23:05 PM,27,6,1:00:18 PM,14,,,-1.3302996,36.8707935,-1.3374736,36.9207076,Rider_Id_461,2233
-Order_No_9454,User_Id_2483,Bike,1,Business,5,4,3:34:17 PM,5,4,3:34:43 PM,5,4,3:46:27 PM,5,4,3:49:49 PM,5,4,4:23:11 PM,14,22.3,,-1.2559559999999999,36.789867,-1.3060302,36.7665044,Rider_Id_45,2002
-Order_No_24638,User_Id_585,Bike,1,Personal,6,3,7:06:22 AM,6,3,7:32:53 AM,6,3,7:44:38 AM,6,3,7:52:49 AM,6,3,8:13:08 AM,10,15.8,14.0,-1.2340872999999999,36.7996694,-1.2597649,36.775631,Rider_Id_103,1219
-Order_No_4316,User_Id_111,Bike,3,Business,2,3,11:50:27 AM,2,3,11:50:42 AM,2,3,11:50:53 AM,2,3,11:57:03 AM,2,3,12:32:12 PM,18,24.2,,-1.2551895,36.7822034,-1.319048,36.8943118,Rider_Id_518,2109
-Order_No_10067,User_Id_2212,Bike,3,Business,9,4,8:35:33 AM,9,4,8:36:21 AM,9,4,8:44:42 AM,9,4,8:50:02 AM,9,4,8:56:19 AM,3,,,-1.2571472,36.795063299999995,-1.2693017,36.8022778,Rider_Id_96,377
-Order_No_22395,User_Id_867,Bike,3,Business,28,3,2:47:41 PM,28,3,2:50:49 PM,28,3,2:52:06 PM,28,3,3:10:52 PM,28,3,3:36:39 PM,10,,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_708,1547
-Order_No_24189,User_Id_1244,Bike,3,Business,14,4,1:40:27 PM,14,4,1:41:03 PM,14,4,2:38:32 PM,14,4,2:53:25 PM,14,4,3:15:03 PM,11,28.1,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_856,1298
-Order_No_13040,User_Id_3283,Bike,3,Business,4,4,3:15:26 PM,4,4,3:18:00 PM,4,4,3:24:57 PM,4,4,3:45:46 PM,4,4,4:04:26 PM,7,27.5,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_484,1120
-Order_No_10273,User_Id_2858,Bike,3,Business,30,2,3:59:46 PM,30,2,4:00:17 PM,30,2,4:15:31 PM,30,2,4:18:51 PM,30,2,5:01:08 PM,18,27.3,,-1.3164106000000002,36.6952197,-1.2605635,36.7985638,Rider_Id_200,2537
-Order_No_12322,User_Id_2928,Bike,3,Personal,17,1,3:04:52 PM,17,1,3:07:01 PM,17,1,3:19:19 PM,17,1,3:36:59 PM,17,1,3:47:43 PM,3,24.7,,-1.2728947,36.807942600000004,-1.2561811999999999,36.809713,Rider_Id_375,644
-Order_No_21937,User_Id_868,Bike,3,Business,1,1,9:04:31 AM,1,1,9:07:19 AM,1,1,9:17:54 AM,1,1,9:33:09 AM,1,1,9:39:43 AM,3,21.3,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_119,394
-Order_No_2656,User_Id_3217,Bike,3,Business,26,4,1:46:54 PM,26,4,1:48:38 PM,26,4,2:24:47 PM,26,4,2:27:32 PM,26,4,2:44:52 PM,4,21.8,,-1.225322,36.80855,-1.214328,36.7911903,Rider_Id_519,1040
-Order_No_11416,User_Id_3194,Bike,3,Business,4,2,2:53:11 PM,4,2,3:41:55 PM,4,2,3:47:54 PM,4,2,4:07:06 PM,4,2,5:00:29 PM,4,20.3,,-1.2571472,36.795063299999995,-1.28164,36.816866999999995,Rider_Id_651,3203
-Order_No_2368,User_Id_2521,Bike,3,Business,29,6,10:26:31 AM,29,6,10:26:38 AM,29,6,10:26:46 AM,29,6,10:28:10 AM,29,6,10:34:51 AM,4,19.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,401
-Order_No_23328,User_Id_3283,Bike,3,Business,22,3,8:52:50 AM,22,3,8:53:04 AM,22,3,9:06:30 AM,22,3,9:16:10 AM,22,3,9:27:30 AM,6,15.3,,-1.265715,36.823815,-1.2628473,36.781805,Rider_Id_379,680
-Order_No_18911,User_Id_3149,Bike,3,Business,13,4,2:52:55 PM,13,4,2:53:44 PM,13,4,2:57:18 PM,13,4,3:08:36 PM,13,4,3:15:12 PM,5,23.1,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_863,396
-Order_No_5336,User_Id_1245,Bike,3,Business,18,4,12:19:22 PM,18,4,12:20:08 PM,18,4,12:27:43 PM,18,4,12:52:27 PM,18,4,1:17:47 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_299,1520
-Order_No_16644,User_Id_1280,Bike,1,Business,21,1,10:15:52 AM,21,1,10:16:34 AM,21,1,10:28:59 AM,21,1,11:20:11 AM,21,1,11:22:55 AM,18,20.0,,-1.2626906,36.7827022,-1.2927978999999998,36.89817,Rider_Id_656,164
-Order_No_1763,User_Id_1046,Bike,1,Personal,15,6,3:48:08 PM,15,6,3:49:00 PM,15,6,4:20:16 PM,15,6,4:23:06 PM,15,6,4:52:26 PM,6,26.1,,-1.3054537,36.8284768,-1.2790501,36.82618660000001,Rider_Id_328,1760
-Order_No_5690,User_Id_1500,Bike,3,Business,7,4,11:46:36 AM,7,4,11:46:48 AM,7,4,11:48:10 AM,7,4,11:56:48 AM,7,4,12:14:48 PM,8,27.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,1080
-Order_No_5377,User_Id_20,Bike,3,Business,25,4,1:21:52 PM,25,4,1:23:37 PM,25,4,1:44:15 PM,25,4,1:50:42 PM,25,4,2:18:33 PM,5,21.0,,-1.2877451000000002,36.8157852,-1.2628638,36.8070399,Rider_Id_662,1671
-Order_No_20786,User_Id_142,Bike,3,Business,28,2,2:04:22 PM,28,2,2:04:22 PM,28,2,2:08:00 PM,28,2,2:45:53 PM,28,2,4:35:31 PM,11,23.3,,-1.2963097,36.7688221,-1.2568865,36.7945489,Rider_Id_71,6578
-Order_No_9044,User_Id_393,Bike,3,Business,26,2,8:33:36 AM,26,2,8:37:51 AM,26,2,8:48:12 AM,26,2,9:08:18 AM,26,2,9:37:56 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_329,1778
-Order_No_16113,User_Id_1087,Bike,3,Business,21,5,1:08:15 PM,21,5,1:15:14 PM,21,5,1:31:59 PM,21,5,1:33:22 PM,21,5,2:09:25 PM,11,24.6,,-1.2679971,36.808893700000006,-1.2482465,36.8889062,Rider_Id_857,2163
-Order_No_13183,User_Id_914,Bike,3,Business,19,1,10:56:07 AM,19,1,10:57:35 AM,19,1,11:07:53 AM,19,1,11:15:22 AM,19,1,11:34:30 AM,7,24.8,,-1.2670213,36.7994482,-1.2970034,36.833321399999996,Rider_Id_786,1148
-Order_No_20023,User_Id_2789,Bike,3,Business,2,2,10:28:20 AM,2,2,10:28:44 AM,2,2,10:34:37 AM,2,2,10:36:07 AM,2,2,10:46:08 AM,3,26.0,,-1.304735,36.784605400000004,-1.2945178999999998,36.7685349,Rider_Id_259,601
-Order_No_3933,User_Id_2091,Bike,1,Personal,14,4,11:18:00 AM,14,4,11:19:49 AM,14,4,11:47:19 AM,14,4,11:59:38 AM,14,4,12:39:21 PM,14,18.8,,-1.2733223,36.795100500000004,-1.3371926,36.7250933,Rider_Id_449,2383
-Order_No_8897,User_Id_478,Bike,3,Business,8,5,12:46:41 PM,8,5,1:37:44 PM,8,5,1:46:11 PM,8,5,1:49:26 PM,8,5,2:11:13 PM,8,27.2,,-1.2551895,36.7822034,-1.2990813,36.8034213,Rider_Id_727,1307
-Order_No_19429,User_Id_1927,Bike,3,Business,17,3,2:51:21 PM,17,3,2:52:37 PM,17,3,3:18:23 PM,17,3,3:24:36 PM,17,3,3:54:32 PM,12,,,-1.3472066,36.7692638,-1.2855798999999999,36.817620899999994,Rider_Id_855,1796
-Order_No_5019,User_Id_3291,Bike,3,Business,2,6,11:50:27 AM,2,6,11:51:22 AM,2,6,11:56:13 AM,2,6,12:39:13 PM,2,6,12:39:16 PM,4,27.5,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_472,3
-Order_No_23050,User_Id_1075,Bike,3,Business,8,1,12:01:09 PM,8,1,12:02:45 PM,8,1,12:29:40 PM,8,1,12:31:20 PM,8,1,1:17:06 PM,10,28.0,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_316,2746
-Order_No_4231,User_Id_3410,Bike,3,Business,1,1,12:24:08 PM,1,1,12:24:28 PM,1,1,12:34:09 PM,1,1,1:15:12 PM,1,1,1:15:29 PM,21,24.1,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_41,17
-Order_No_15732,User_Id_2241,Bike,1,Personal,14,6,11:29:38 AM,14,6,11:29:44 AM,14,6,11:37:12 AM,14,6,11:43:46 AM,14,6,12:24:16 PM,16,17.7,,-1.2815528999999999,36.8308217,-1.2555129999999999,36.752421999999996,Rider_Id_629,2430
-Order_No_20936,User_Id_1880,Bike,3,Business,9,2,2:54:59 PM,9,2,2:55:24 PM,9,2,2:56:41 PM,9,2,3:06:52 PM,9,2,3:33:07 PM,12,30.8,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_131,1575
-Order_No_20339,User_Id_2700,Bike,3,Business,10,1,10:38:40 AM,10,1,10:56:17 AM,10,1,11:07:21 AM,10,1,11:30:15 AM,10,1,11:49:41 AM,7,23.7,,-1.25537,36.7756,-1.2845834999999999,36.8079121,Rider_Id_669,1166
-Order_No_4354,User_Id_959,Bike,2,Personal,18,4,1:00:14 PM,18,4,1:01:22 PM,18,4,1:20:58 PM,18,4,1:31:48 PM,18,4,1:59:29 PM,6,25.7,,-1.281892,36.7834972,-1.2544802,36.811182200000005,Rider_Id_565,1661
-Order_No_9645,User_Id_1578,Bike,3,Business,24,5,9:34:32 AM,24,5,9:36:19 AM,24,5,9:52:42 AM,24,5,9:54:12 AM,24,5,10:15:17 AM,6,15.6,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_249,1265
-Order_No_12838,User_Id_186,Bike,3,Business,8,5,9:00:05 AM,8,5,9:01:33 AM,8,5,9:16:49 AM,8,5,9:30:37 AM,8,5,9:44:32 AM,7,22.8,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_259,835
-Order_No_25474,User_Id_624,Bike,3,Business,27,2,1:21:05 PM,27,2,1:21:29 PM,27,2,1:29:52 PM,27,2,1:37:16 PM,27,2,2:02:13 PM,15,25.6,,-1.303596,36.778378000000004,-1.2297799999999999,36.836215,Rider_Id_178,1497
-Order_No_9674,User_Id_248,Bike,3,Business,30,2,9:04:31 AM,30,2,9:05:15 AM,30,2,9:21:43 AM,30,2,9:28:06 AM,30,2,9:44:11 AM,8,,,-1.304735,36.784605400000004,-1.3112499,36.8278836,Rider_Id_177,965
-Order_No_23464,User_Id_3647,Bike,3,Business,31,5,4:38:14 PM,31,5,4:47:48 PM,31,5,5:07:25 PM,31,5,5:13:03 PM,31,5,5:25:01 PM,6,23.9,,-1.2726389999999999,36.794723,-1.3023756000000002,36.7949276,Rider_Id_404,718
-Order_No_442,User_Id_510,Bike,3,Business,3,3,12:23:57 PM,3,3,12:24:14 PM,3,3,12:26:19 PM,3,3,12:42:10 PM,3,3,12:49:51 PM,13,24.2,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_40,461
-Order_No_18693,User_Id_604,Bike,3,Business,26,2,8:56:47 AM,26,2,8:57:27 AM,26,2,8:58:02 AM,26,2,9:02:59 AM,26,2,9:25:02 AM,9,,,-1.2551895,36.7822034,-1.2959783,36.7860021,Rider_Id_258,1323
-Order_No_27773,User_Id_2718,Bike,3,Business,15,2,12:00:54 PM,15,2,12:01:09 PM,15,2,12:08:05 PM,15,2,12:15:38 PM,15,2,12:48:26 PM,20,24.4,,-1.2668815,36.8017244,-1.3415466999999999,36.6696294,Rider_Id_31,1968
-Order_No_14243,User_Id_2763,Bike,3,Business,16,3,2:46:19 PM,16,3,2:49:30 PM,16,3,2:50:06 PM,16,3,3:01:04 PM,16,3,3:17:33 PM,10,27.2,,-1.2571472,36.795063299999995,-1.3221935,36.8464972,Rider_Id_31,989
-Order_No_1570,User_Id_517,Bike,3,Business,29,3,10:10:45 AM,29,3,10:12:06 AM,29,3,10:32:48 AM,29,3,10:39:04 AM,29,3,11:10:58 AM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_829,1914
-Order_No_12675,User_Id_2849,Bike,1,Personal,19,6,7:47:23 AM,19,6,8:03:50 AM,19,6,8:32:12 AM,19,6,8:35:18 AM,19,6,9:14:34 AM,20,16.5,,-1.1972198,36.814220500000005,-1.2836933000000001,36.7606941,Rider_Id_4,2356
-Order_No_22616,User_Id_331,Bike,1,Personal,19,5,3:27:12 PM,19,5,3:38:05 PM,19,5,3:51:24 PM,19,5,4:00:35 PM,19,5,4:27:56 PM,14,27.6,,-1.1992502,36.8294932,-1.255392,36.792539,Rider_Id_247,1641
-Order_No_12340,User_Id_3291,Bike,3,Business,12,2,1:41:09 PM,12,2,1:41:36 PM,12,2,1:50:43 PM,12,2,2:09:49 PM,12,2,2:37:05 PM,12,27.1,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_513,1636
-Order_No_11751,User_Id_3372,Bike,3,Business,7,4,1:05:12 PM,7,4,1:07:14 PM,7,4,1:10:47 PM,7,4,1:21:13 PM,7,4,1:46:43 PM,10,17.2,,-1.304033,36.7848695,-1.3169591,36.7000029,Rider_Id_379,1530
-Order_No_22521,User_Id_2545,Bike,2,Personal,2,4,9:08:39 AM,2,4,9:08:47 AM,2,4,9:10:54 AM,2,4,9:39:40 AM,2,4,10:53:07 AM,18,13.3,,-1.2800277,36.78772070000001,-1.2049707,36.785895700000005,Rider_Id_680,4407
-Order_No_25010,User_Id_540,Bike,3,Business,3,1,2:56:08 PM,3,1,3:46:16 PM,3,1,3:53:31 PM,3,1,3:57:38 PM,3,1,4:34:23 PM,8,23.6,,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_523,2205
-Order_No_15753,User_Id_517,Bike,3,Business,12,6,12:13:08 PM,12,6,12:13:14 PM,12,6,12:13:17 PM,12,6,12:20:58 PM,12,6,12:28:23 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,445
-Order_No_1988,User_Id_592,Bike,3,Personal,20,1,2:01:05 PM,20,1,2:01:23 PM,20,1,2:13:02 PM,20,1,2:48:40 PM,20,1,3:19:56 PM,15,,,-1.312745,36.875715,-1.2637791,36.79200720000001,Rider_Id_68,1876
-Order_No_17400,User_Id_3688,Bike,3,Business,6,4,3:59:24 PM,6,4,4:00:52 PM,6,4,4:14:26 PM,6,4,4:15:39 PM,6,4,4:43:35 PM,10,21.4,,-1.2551895,36.7822034,-1.3071891000000002,36.8462027,Rider_Id_490,1676
-Order_No_18141,User_Id_633,Bike,3,Business,22,3,3:50:05 PM,22,3,3:55:25 PM,22,3,3:59:28 PM,22,3,4:05:03 PM,22,3,4:09:02 PM,2,25.9,,-1.3080091999999999,36.8284543,-1.3004062,36.829741,Rider_Id_445,239
-Order_No_4797,User_Id_2987,Bike,3,Business,6,1,12:54:12 PM,6,1,12:55:28 PM,6,1,1:02:47 PM,6,1,1:08:19 PM,6,1,1:23:46 PM,10,20.3,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_174,927
-Order_No_4764,User_Id_2212,Bike,3,Business,2,4,2:11:21 PM,2,4,2:11:43 PM,2,4,2:22:26 PM,2,4,2:33:44 PM,2,4,2:55:33 PM,4,26.0,,-1.2646084,36.8028645,-1.2559559999999999,36.772901,Rider_Id_520,1309
-Order_No_4405,User_Id_1342,Bike,3,Business,23,3,1:26:07 PM,23,3,1:36:07 PM,23,3,1:53:35 PM,23,3,2:04:29 PM,23,3,2:22:39 PM,4,22.0,,-1.3025826,36.7670807,-1.2962832,36.784968299999996,Rider_Id_890,1090
-Order_No_25027,User_Id_186,Bike,3,Business,24,4,8:55:21 AM,24,4,8:55:54 AM,24,4,9:55:25 AM,24,4,9:56:56 AM,24,4,10:46:14 AM,23,19.6,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_498,2958
-Order_No_11948,User_Id_3214,Bike,3,Business,10,5,3:11:04 PM,10,5,3:13:38 PM,10,5,3:25:26 PM,10,5,3:27:39 PM,10,5,3:36:57 PM,1,,,-1.2899733,36.7908611,-1.289795,36.7957132,Rider_Id_427,558
-Order_No_13719,User_Id_1329,Bike,3,Business,6,3,11:36:16 AM,6,3,11:37:11 AM,6,3,11:37:51 AM,6,3,11:50:06 AM,6,3,12:07:44 PM,5,,,-1.273056,36.811298,-1.2857671,36.7875614,Rider_Id_373,1058
-Order_No_20703,User_Id_2763,Bike,3,Business,13,3,9:35:46 AM,13,3,9:37:10 AM,13,3,9:48:03 AM,13,3,9:50:07 AM,13,3,10:02:41 AM,5,20.0,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_318,754
-Order_No_20675,User_Id_2663,Bike,1,Personal,29,6,4:54:02 PM,29,6,4:54:46 PM,29,6,5:04:14 PM,29,6,5:14:31 PM,29,6,5:26:10 PM,3,23.6,,-1.2884066,36.7866984,-1.2931317,36.7778105,Rider_Id_282,699
-Order_No_10564,User_Id_1927,Bike,3,Business,6,4,12:13:34 PM,6,4,12:15:25 PM,6,4,12:35:37 PM,6,4,12:50:55 PM,6,4,1:27:27 PM,12,19.8,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_36,2192
-Order_No_18984,User_Id_1815,Bike,3,Business,4,4,10:22:56 AM,4,4,10:24:37 AM,4,4,10:33:35 AM,4,4,10:35:06 AM,4,4,11:14:11 AM,9,,,-1.2661263999999999,36.8030359,-1.3197323,36.8120386,Rider_Id_751,2345
-Order_No_25496,User_Id_1249,Bike,3,Business,10,3,10:00:30 AM,10,3,10:00:36 AM,10,3,10:01:05 AM,10,3,10:07:52 AM,10,3,10:51:17 AM,19,21.8,,-1.2551895,36.7822034,-1.2860183,36.8975338,Rider_Id_831,2605
-Order_No_15548,User_Id_3688,Bike,3,Business,30,3,10:27:10 AM,30,3,10:27:17 AM,30,3,10:30:15 AM,30,3,10:37:23 AM,30,3,10:37:28 AM,3,24.8,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_191,5
-Order_No_2019,User_Id_53,Bike,3,Business,2,6,9:58:51 AM,2,6,10:03:23 AM,2,6,10:14:48 AM,2,6,10:22:29 AM,2,6,10:40:11 AM,5,21.2,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_26,1062
-Order_No_13245,User_Id_3557,Bike,3,Business,6,4,3:49:43 PM,6,4,4:05:22 PM,6,4,4:29:37 PM,6,4,4:44:06 PM,6,4,5:40:09 PM,14,22.7,,-1.2825803999999998,36.87750870000001,-1.2546732,36.808679600000005,Rider_Id_250,3363
-Order_No_27721,User_Id_3647,Bike,3,Business,7,5,9:50:59 AM,7,5,9:51:30 AM,7,5,10:20:53 AM,7,5,10:22:03 AM,7,5,10:45:34 AM,5,,,-1.2726389999999999,36.794723,-1.2854173999999998,36.7951152,Rider_Id_172,1411
-Order_No_7334,User_Id_2658,Bike,3,Business,7,5,5:28:06 PM,7,5,5:30:25 PM,7,5,5:42:07 PM,7,5,7:04:18 PM,7,5,8:17:58 PM,12,23.1,,-1.2694178,36.812692799999994,-1.28877,36.7403,Rider_Id_303,4420
-Order_No_15917,User_Id_3201,Bike,3,Business,20,3,10:17:58 AM,20,3,10:23:13 AM,20,3,11:06:16 AM,20,3,11:17:12 AM,20,3,11:57:43 AM,17,26.4,,-1.3463041999999998,36.768164,-1.2584143,36.8048002,Rider_Id_267,2431
-Order_No_20486,User_Id_154,Bike,2,Business,12,5,9:09:33 AM,12,5,9:09:43 AM,12,5,9:10:20 AM,12,5,9:25:55 AM,12,5,10:34:33 AM,16,18.8,,-1.2960265,36.8780276,-1.2551895,36.7822034,Rider_Id_43,4118
-Order_No_8838,User_Id_1242,Bike,3,Business,12,5,12:58:23 PM,12,5,1:48:00 PM,12,5,1:58:45 PM,12,5,2:07:50 PM,12,5,2:17:07 PM,4,,,-1.217907,36.8709199,-1.2167531,36.882588500000004,Rider_Id_484,557
-Order_No_26806,User_Id_288,Bike,1,Personal,20,3,10:45:10 AM,20,3,10:47:39 AM,20,3,11:01:11 AM,20,3,11:11:13 AM,20,3,12:21:47 PM,7,20.4,,-1.2876365,36.816096,-1.296797,36.776452500000005,Rider_Id_431,4234
-Order_No_8605,User_Id_1895,Bike,1,Personal,27,3,5:53:07 PM,27,3,5:54:31 PM,27,3,6:17:57 PM,27,3,7:19:40 PM,27,3,7:53:36 PM,15,,,-1.2887938,36.828583200000004,-1.2419155000000002,36.732125700000005,Rider_Id_581,2036
-Order_No_18558,User_Id_961,Bike,1,Personal,21,1,1:19:13 PM,21,1,1:44:53 PM,21,1,2:36:58 PM,21,1,2:39:02 PM,21,1,2:56:17 PM,5,21.5,,-1.2769486,36.8261591,-1.2918495,36.82844,Rider_Id_874,1035
-Order_No_25190,User_Id_3291,Bike,3,Business,27,4,12:38:52 PM,27,4,12:54:05 PM,27,4,1:17:55 PM,27,4,1:51:01 PM,27,4,2:33:24 PM,18,23.8,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_416,2543
-Order_No_6321,User_Id_435,Bike,3,Business,12,3,2:06:27 PM,12,3,2:08:33 PM,12,3,2:17:07 PM,12,3,2:40:37 PM,12,3,3:18:21 PM,16,21.0,,-1.300921,36.828195,-1.2094552,36.807328600000005,Rider_Id_211,2264
-Order_No_20508,User_Id_2330,Bike,3,Business,26,2,9:30:01 AM,26,2,9:30:25 AM,26,2,9:51:38 AM,26,2,9:59:53 AM,26,2,10:20:48 AM,14,,,-1.2854537,36.9051024,-1.3800951000000001,36.9297274,Rider_Id_466,1255
-Order_No_10072,User_Id_22,Bike,2,Personal,8,3,9:08:51 AM,8,3,9:10:22 AM,8,3,9:32:02 AM,8,3,9:39:52 AM,8,3,10:09:36 AM,7,17.4,,-1.3112325,36.8146321,-1.2764886999999998,36.8180775,Rider_Id_700,1784
-Order_No_11444,User_Id_478,Bike,3,Business,7,3,3:35:55 PM,7,3,3:39:14 PM,7,3,4:21:59 PM,7,3,4:25:35 PM,7,3,4:42:21 PM,11,28.2,,-1.2701354,36.807216700000005,-1.331619,36.847976,Rider_Id_343,1006
-Order_No_18265,User_Id_1099,Bike,3,Business,26,4,12:55:54 PM,26,4,12:56:01 PM,26,4,1:05:27 PM,26,4,1:06:48 PM,26,4,1:21:19 PM,2,18.7,,-1.2699491000000003,36.8135968,-1.2661949,36.820530700000006,Rider_Id_754,871
-Order_No_6050,User_Id_3091,Bike,3,Business,18,5,8:31:18 AM,18,5,8:31:27 AM,18,5,8:31:47 AM,18,5,8:39:57 AM,18,5,9:01:26 AM,9,20.3,,-1.3012008,36.764868,-1.2676026000000002,36.810362899999994,Rider_Id_105,1289
-Order_No_6009,User_Id_3291,Bike,3,Business,26,2,11:14:24 AM,26,2,11:14:52 AM,26,2,11:18:23 AM,26,2,11:23:19 AM,26,2,11:37:58 AM,5,26.3,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_269,879
-Order_No_24349,User_Id_3647,Bike,3,Business,7,2,1:07:47 PM,7,2,1:08:14 PM,7,2,1:16:00 PM,7,2,1:23:30 PM,7,2,1:39:03 PM,5,23.3,,-1.2795796,36.819062,-1.2726389999999999,36.794723,Rider_Id_414,933
-Order_No_19266,User_Id_53,Bike,3,Business,1,1,12:01:25 PM,1,1,12:03:08 PM,1,1,12:28:36 PM,1,1,12:29:57 PM,1,1,1:16:20 PM,6,,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_910,2783
-Order_No_18950,User_Id_2069,Bike,3,Business,27,4,2:31:07 PM,27,4,2:31:32 PM,27,4,2:40:07 PM,27,4,2:45:56 PM,27,4,2:57:41 PM,6,23.5,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_845,705
-Order_No_15666,User_Id_1209,Bike,3,Business,11,2,1:46:57 PM,11,2,3:05:37 PM,11,2,3:21:50 PM,11,2,3:35:04 PM,11,2,3:46:46 PM,4,22.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_914,702
-Order_No_3993,User_Id_391,Bike,3,Business,4,2,2:01:56 PM,4,2,2:07:05 PM,4,2,2:25:00 PM,4,2,2:42:57 PM,4,2,3:10:54 PM,5,22.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_643,1677
-Order_No_4374,User_Id_3674,Bike,3,Business,7,4,9:39:29 AM,7,4,9:39:43 AM,7,4,10:10:58 AM,7,4,10:17:25 AM,7,4,11:22:24 AM,10,14.4,1.0,-1.2551895,36.7822034,-1.2951386,36.797008899999994,Rider_Id_720,3899
-Order_No_14956,User_Id_2159,Bike,3,Business,20,4,1:40:12 PM,20,4,1:40:27 PM,20,4,1:46:14 PM,20,4,3:21:22 PM,20,4,3:44:25 PM,11,28.1,,-1.304735,36.784605400000004,-1.3112651000000002,36.8495898,Rider_Id_882,1383
-Order_No_7123,User_Id_500,Bike,1,Personal,13,6,3:57:35 PM,13,6,3:59:28 PM,13,6,4:07:34 PM,13,6,4:13:51 PM,13,6,4:35:12 PM,12,27.9,,-1.3024388,36.859699,-1.2634530000000002,36.7955612,Rider_Id_875,1281
-Order_No_64,User_Id_1746,Bike,3,Business,2,4,5:10:41 PM,2,4,5:11:31 PM,2,4,5:44:34 PM,2,4,5:52:46 PM,2,4,6:50:07 PM,20,23.8,,-1.3065588,36.9076533,-1.296797,36.776452500000005,Rider_Id_549,3441
-Order_No_6847,User_Id_2989,Bike,3,Business,29,1,1:51:25 PM,29,1,2:12:58 PM,29,1,2:53:43 PM,29,1,2:57:08 PM,29,1,3:36:19 PM,20,24.4,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_46,2351
-Order_No_4379,User_Id_2858,Bike,3,Business,23,5,9:06:46 AM,23,5,9:10:09 AM,23,5,9:28:00 AM,23,5,9:30:32 AM,23,5,10:03:25 AM,12,20.3,,-1.3166008,36.7021489,-1.2675333999999998,36.7658246,Rider_Id_871,1973
-Order_No_5327,User_Id_867,Bike,3,Business,15,6,10:04:43 AM,15,6,10:06:24 AM,15,6,10:31:42 AM,15,6,10:35:31 AM,15,6,10:57:08 AM,15,19.2,,-1.2991441000000001,36.7528804,-1.3167113000000001,36.8301563,Rider_Id_344,1297
-Order_No_7225,User_Id_3647,Bike,3,Business,4,1,1:24:00 PM,4,1,1:26:13 PM,4,1,1:36:39 PM,4,1,1:42:32 PM,4,1,1:58:23 PM,8,,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_607,951
-Order_No_13124,User_Id_1726,Bike,1,Personal,23,3,11:55:08 AM,23,3,11:55:33 AM,23,3,11:57:58 AM,23,3,12:05:47 PM,23,3,12:19:32 PM,5,22.0,,-1.3007534,36.7964576,-1.2787848999999998,36.8194705,Rider_Id_698,825
-Order_No_3266,User_Id_1249,Bike,3,Business,15,5,4:32:37 PM,15,5,4:45:31 PM,15,5,5:10:05 PM,15,5,5:13:01 PM,15,5,5:52:52 PM,18,27.8,,-1.3189252,36.861726899999994,-1.3516579,36.729836299999995,Rider_Id_856,2391
-Order_No_8232,User_Id_2579,Bike,3,Business,22,4,10:51:14 AM,22,4,10:52:05 AM,22,4,11:18:18 AM,22,4,11:27:27 AM,22,4,11:40:02 AM,7,22.4,,-1.287428,36.7800821,-1.2592947,36.7698085,Rider_Id_345,755
-Order_No_13236,User_Id_3291,Bike,3,Business,30,5,2:46:59 PM,30,5,2:47:59 PM,30,5,2:55:26 PM,30,5,2:59:19 PM,30,5,3:05:40 PM,2,24.8,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_913,381
-Order_No_6,User_Id_3550,Bike,1,Personal,3,5,2:37:48 PM,3,5,3:29:21 PM,3,5,3:43:04 PM,3,5,4:03:58 PM,3,5,4:12:25 PM,4,25.0,,-1.3003913999999999,36.7340157,-1.2998201999999999,36.764399700000006,Rider_Id_636,507
-Order_No_27249,User_Id_393,Bike,3,Business,3,5,3:38:13 PM,3,5,3:40:29 PM,3,5,3:40:32 PM,3,5,4:28:06 PM,3,5,4:28:13 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_619,7
-Order_No_19325,User_Id_1927,Bike,3,Business,24,7,9:53:19 AM,24,7,10:07:40 AM,24,7,10:28:18 AM,24,7,10:50:49 AM,24,7,11:08:48 AM,12,,,-1.290894,36.822971,-1.3463041999999998,36.768164,Rider_Id_26,1079
-Order_No_27147,User_Id_2113,Bike,2,Personal,8,5,3:21:34 PM,8,5,3:35:20 PM,8,5,3:35:56 PM,8,5,3:57:03 PM,8,5,4:23:54 PM,8,21.3,,-1.2738167,36.8157821,-1.275442,36.766020700000006,Rider_Id_379,1611
-Order_No_2947,User_Id_3175,Bike,3,Business,9,5,11:07:51 AM,9,5,11:08:24 AM,9,5,11:19:52 AM,9,5,11:29:42 AM,9,5,11:37:10 AM,4,25.5,,-1.2559559999999999,36.772901,-1.2681003999999998,36.7952081,Rider_Id_518,448
-Order_No_14147,User_Id_1280,Bike,3,Business,3,1,2:37:50 PM,3,1,2:49:19 PM,3,1,3:01:49 PM,3,1,3:06:57 PM,3,1,3:22:17 PM,4,23.2,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_228,920
-Order_No_13948,User_Id_3006,Bike,2,Personal,15,3,8:31:45 AM,15,3,8:32:15 AM,15,3,8:41:38 AM,15,3,8:44:54 AM,15,3,9:08:18 AM,10,16.2,,-1.2854406,36.789409,-1.2463211,36.789487,Rider_Id_343,1404
-Order_No_9909,User_Id_2008,Bike,3,Business,19,3,2:29:25 PM,19,3,2:46:12 PM,19,3,2:52:44 PM,19,3,2:58:45 PM,19,3,4:44:58 PM,11,24.4,,-1.3008589,36.8447612,-1.2508229,36.7895263,Rider_Id_75,6373
-Order_No_18373,User_Id_1271,Bike,3,Business,30,2,12:17:43 PM,30,2,12:17:48 PM,30,2,12:18:19 PM,30,2,12:30:04 PM,30,2,12:56:06 PM,12,22.4,,-1.3107918,36.761794,-1.254191,36.809913200000004,Rider_Id_136,1562
-Order_No_8099,User_Id_3182,Bike,2,Personal,4,5,10:47:01 PM,4,5,10:59:22 PM,4,5,11:10:00 PM,4,5,11:13:41 PM,4,5,11:19:56 PM,3,17.8,,-1.3023008999999999,36.762143200000004,-1.2901669,36.7685878,Rider_Id_312,375
-Order_No_26868,User_Id_1927,Bike,3,Business,18,1,12:53:31 PM,18,1,12:54:12 PM,18,1,1:21:47 PM,18,1,1:26:36 PM,18,1,2:29:12 PM,20,27.9,,-1.2860183,36.8975338,-1.3472066,36.7692638,Rider_Id_341,3756
-Order_No_9589,User_Id_2412,Bike,3,Business,17,3,3:52:27 PM,17,3,3:54:49 PM,17,3,4:17:32 PM,17,3,4:26:19 PM,17,3,4:36:11 PM,3,,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_669,592
-Order_No_12548,User_Id_1410,Bike,3,Business,27,3,12:20:24 PM,27,3,12:20:39 PM,27,3,12:27:52 PM,27,3,1:09:42 PM,27,3,1:33:56 PM,18,26.2,,-1.3387453,36.91185170000001,-1.2847613000000002,36.967084,Rider_Id_242,1454
-Order_No_7995,User_Id_302,Bike,3,Business,15,5,6:27:24 PM,15,5,6:28:29 PM,15,5,6:41:12 PM,15,5,6:43:11 PM,15,5,7:01:39 PM,8,27.6,,-1.2649605,36.798177700000004,-1.2930663999999998,36.78495,Rider_Id_541,1108
-Order_No_11628,User_Id_1927,Bike,3,Business,18,1,11:58:47 AM,18,1,11:59:20 AM,18,1,12:01:45 PM,18,1,12:37:18 PM,18,1,1:03:37 PM,12,26.8,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_458,1579
-Order_No_22952,User_Id_3149,Bike,3,Business,5,2,10:25:10 AM,5,2,10:25:20 AM,5,2,10:28:36 AM,5,2,11:24:50 AM,5,2,11:32:33 AM,6,25.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_448,463
-Order_No_13044,User_Id_1329,Bike,3,Business,13,2,4:01:50 PM,13,2,4:02:44 PM,13,2,4:13:35 PM,13,2,4:15:07 PM,13,2,4:22:14 PM,1,26.4,,-1.273056,36.811298,-1.2697893,36.8124106,Rider_Id_56,427
-Order_No_14417,User_Id_391,Bike,3,Business,6,3,10:32:42 AM,6,3,10:56:11 AM,6,3,11:07:23 AM,6,3,11:18:17 AM,6,3,11:57:24 AM,23,,,-1.2784063,36.8222933,-1.2847613000000002,36.967084,Rider_Id_769,2347
-Order_No_11637,User_Id_1955,Bike,3,Business,17,3,3:52:58 PM,17,3,3:53:51 PM,17,3,3:58:17 PM,17,3,4:10:52 PM,17,3,4:46:32 PM,15,28.4,,-1.3332749,36.870814700000004,-1.2955333999999998,36.7742857,Rider_Id_69,2140
-Order_No_16510,User_Id_3530,Bike,1,Personal,5,4,10:31:31 AM,5,4,12:07:27 PM,5,4,12:11:15 PM,5,4,12:36:19 PM,5,4,1:22:58 PM,28,,,-1.2143841999999998,36.8122348,-1.3169591,36.7000029,Rider_Id_103,2799
-Order_No_13838,User_Id_633,Bike,3,Business,12,1,10:00:38 AM,12,1,10:01:04 AM,12,1,10:22:36 AM,12,1,10:27:43 AM,12,1,10:51:12 AM,4,19.5,0.3,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_95,1409
-Order_No_8390,User_Id_1567,Bike,2,Personal,10,3,10:42:46 AM,10,3,10:43:06 AM,10,3,10:49:01 AM,10,3,10:52:54 AM,10,3,11:30:58 AM,15,22.3,,-1.2705739,36.8023118,-1.2326778999999999,36.6871223,Rider_Id_74,2284
-Order_No_14460,User_Id_2200,Bike,3,Business,29,6,10:12:39 AM,29,6,10:13:53 AM,29,6,10:22:13 AM,29,6,10:37:28 AM,29,6,10:55:24 AM,6,25.2,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_817,1076
-Order_No_24336,User_Id_1927,Bike,3,Business,13,1,1:06:38 PM,13,1,1:07:36 PM,13,1,1:25:53 PM,13,1,2:00:20 PM,13,1,2:24:35 PM,12,,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_728,1455
-Order_No_913,User_Id_868,Bike,3,Business,23,1,4:54:57 PM,23,1,4:56:54 PM,23,1,5:22:51 PM,23,1,5:51:12 PM,23,1,6:00:28 PM,5,19.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_174,556
-Order_No_26000,User_Id_1281,Bike,3,Business,16,3,3:45:38 PM,16,3,3:47:24 PM,16,3,4:03:36 PM,16,3,4:22:12 PM,16,3,4:45:27 PM,6,27.1,,-1.2675333999999998,36.7658246,-1.2985294,36.782493,Rider_Id_246,1395
-Order_No_17143,User_Id_982,Bike,3,Business,1,1,1:13:15 PM,1,1,1:29:41 PM,1,1,2:03:03 PM,1,1,2:20:33 PM,1,1,3:23:52 PM,49,,,-1.2517871,36.6795716,-1.4136642,36.957045,Rider_Id_756,3799
-Order_No_7911,User_Id_393,Bike,3,Business,27,1,8:38:42 AM,27,1,8:39:14 AM,27,1,8:39:17 AM,27,1,9:02:49 AM,27,1,9:12:27 AM,4,17.9,1.1,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_206,578
-Order_No_5122,User_Id_1061,Bike,1,Business,11,5,12:28:56 PM,11,5,12:30:24 PM,11,5,12:45:04 PM,11,5,12:49:15 PM,11,5,1:05:28 PM,9,20.9,,-1.3001366,36.7678144,-1.2559559999999999,36.789867,Rider_Id_865,973
-Order_No_22775,User_Id_1271,Bike,3,Business,21,4,12:36:59 PM,21,4,12:37:19 PM,21,4,12:37:32 PM,21,4,1:14:53 PM,21,4,1:23:14 PM,7,27.8,,-1.3263105,36.8622904,-1.3244885,36.897791999999995,Rider_Id_746,501
-Order_No_23062,User_Id_1796,Bike,3,Business,18,4,9:45:36 AM,18,4,9:52:02 AM,18,4,10:04:47 AM,18,4,10:17:20 AM,18,4,10:40:01 AM,11,,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_710,1361
-Order_No_20203,User_Id_1988,Bike,1,Personal,17,1,10:46:27 AM,17,1,10:46:33 AM,17,1,10:52:21 AM,17,1,11:03:13 AM,17,1,11:31:56 AM,12,25.1,,-1.2839763000000002,36.760310600000004,-1.2725144,36.82721779999999,Rider_Id_45,1723
-Order_No_5656,User_Id_3582,Bike,3,Business,13,3,4:20:02 PM,13,3,5:14:22 PM,13,3,5:31:29 PM,13,3,5:40:48 PM,13,3,6:12:52 PM,10,26.1,,-1.2384056,36.8718698,-1.2906091,36.8405869,Rider_Id_411,1924
-Order_No_13762,User_Id_1249,Bike,3,Business,15,4,5:21:02 PM,15,4,6:00:30 PM,15,4,6:34:52 PM,15,4,6:44:55 PM,15,4,7:22:47 PM,4,25.1,,-1.3168075000000001,36.8626441,-1.3189252,36.861726899999994,Rider_Id_304,2272
-Order_No_14727,User_Id_3358,Bike,2,Personal,17,4,2:57:43 PM,17,4,3:02:52 PM,17,4,3:12:28 PM,17,4,3:25:24 PM,17,4,3:36:50 PM,7,23.8,,-1.2869217,36.7732203,-1.2682322,36.793592700000005,Rider_Id_101,686
-Order_No_419,User_Id_53,Bike,3,Business,18,4,12:50:45 PM,18,4,12:51:52 PM,18,4,12:51:58 PM,18,4,1:09:55 PM,18,4,1:15:11 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_472,316
-Order_No_9346,User_Id_1065,Bike,2,Personal,13,5,3:14:12 PM,13,5,3:15:54 PM,13,5,3:42:55 PM,13,5,3:46:01 PM,13,5,4:21:45 PM,6,19.1,,-1.2745107,36.797450299999994,-1.2981625,36.813014200000005,Rider_Id_939,2144
-Order_No_18362,User_Id_868,Bike,3,Business,29,2,2:39:16 PM,29,2,2:40:12 PM,29,2,2:48:02 PM,29,2,3:01:32 PM,29,2,3:12:22 PM,5,27.1,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_611,650
-Order_No_27054,User_Id_2330,Bike,3,Business,30,2,12:17:10 PM,30,2,12:32:27 PM,30,2,12:33:59 PM,30,2,12:42:36 PM,30,2,1:10:54 PM,8,,,-1.3015197,36.7658456,-1.2890715,36.8157588,Rider_Id_99,1698
-Order_No_24469,User_Id_53,Bike,3,Business,25,1,2:15:58 PM,25,1,2:17:16 PM,25,1,2:31:00 PM,25,1,2:35:24 PM,25,1,2:51:09 PM,5,29.9,,-1.2770708,36.8231093,-1.29508,36.796863,Rider_Id_855,945
-Order_No_27388,User_Id_496,Bike,3,Business,22,1,4:39:04 PM,22,1,4:39:56 PM,22,1,4:41:33 PM,22,1,4:57:43 PM,22,1,5:17:44 PM,8,22.4,,-1.2551895,36.7822034,-1.2941557,36.7792012,Rider_Id_536,1201
-Order_No_27353,User_Id_944,Bike,1,Personal,9,4,4:26:41 PM,9,4,4:29:13 PM,9,4,5:16:12 PM,9,4,5:35:42 PM,9,4,5:48:00 PM,6,23.0,,-1.2327522,36.801882299999995,-1.2555969999999999,36.830024,Rider_Id_213,738
-Order_No_11986,User_Id_2216,Bike,3,Business,15,3,9:17:49 AM,15,3,9:18:28 AM,15,3,9:27:05 AM,15,3,9:31:29 AM,15,3,10:40:31 AM,9,16.4,,-1.2551895,36.7822034,-1.2946558000000001,36.7908316,Rider_Id_933,4142
-Order_No_23122,User_Id_627,Bike,3,Business,12,3,10:04:45 AM,12,3,10:05:31 AM,12,3,10:11:58 AM,12,3,10:15:12 AM,12,3,10:39:14 AM,11,,,-1.2551895,36.7822034,-1.3039596000000002,36.845414700000006,Rider_Id_161,1442
-Order_No_21208,User_Id_1792,Bike,3,Business,9,3,10:32:58 AM,9,3,10:33:46 AM,9,3,10:52:10 AM,9,3,10:58:18 AM,9,3,11:48:12 AM,19,25.3,,-1.3094447,36.9107706,-1.2809121,36.7852524,Rider_Id_237,2994
-Order_No_2340,User_Id_393,Bike,3,Business,28,4,8:42:23 AM,28,4,8:42:40 AM,28,4,8:45:04 AM,28,4,8:49:20 AM,28,4,9:14:21 AM,4,19.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,1501
-Order_No_22096,User_Id_3264,Bike,3,Business,7,2,11:44:00 AM,7,2,11:44:44 AM,7,2,11:48:06 AM,7,2,11:50:26 AM,7,2,12:28:01 PM,12,23.0,,-1.2551895,36.7822034,-1.2121659,36.811243700000006,Rider_Id_116,2255
-Order_No_2181,User_Id_1208,Bike,1,Personal,29,5,3:06:42 PM,29,5,3:18:09 PM,29,5,4:05:45 PM,29,5,4:07:51 PM,29,5,4:41:25 PM,22,21.0,,-1.3003196000000001,36.8171416,-1.2100103000000002,36.797424,Rider_Id_265,2014
-Order_No_18706,User_Id_3186,Bike,3,Business,8,3,2:58:18 PM,8,3,2:59:05 PM,8,3,3:11:14 PM,8,3,3:12:42 PM,8,3,3:43:02 PM,8,,,-1.2980228999999999,36.78865,-1.2635089,36.78873370000001,Rider_Id_277,1820
-Order_No_22947,User_Id_3283,Bike,3,Business,27,3,1:49:25 PM,27,3,1:49:51 PM,27,3,2:01:15 PM,27,3,2:08:19 PM,27,3,2:28:47 PM,6,29.6,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_660,1228
-Order_No_15423,User_Id_3698,Bike,3,Business,21,2,3:59:20 PM,21,2,4:00:32 PM,21,2,4:20:41 PM,21,2,5:16:18 PM,21,2,5:16:20 PM,22,23.9,,-1.3244885,36.897791999999995,-1.3014461000000002,36.7661381,Rider_Id_856,2
-Order_No_5911,User_Id_2154,Bike,3,Business,2,5,12:40:20 PM,2,5,12:42:09 PM,2,5,1:11:39 PM,2,5,1:34:00 PM,2,5,1:51:31 PM,9,25.2,,-1.2860183,36.8975338,-1.2765736,36.851364600000004,Rider_Id_900,1051
-Order_No_26449,User_Id_1961,Bike,3,Business,18,6,10:45:49 AM,18,6,10:46:52 AM,18,6,11:01:09 AM,18,6,11:05:12 AM,18,6,11:42:08 AM,18,21.1,,-1.331619,36.847976,-1.3188358999999998,36.703852600000005,Rider_Id_205,2216
-Order_No_2616,User_Id_3283,Bike,3,Business,14,4,9:49:12 AM,14,4,9:49:24 AM,14,4,9:54:27 AM,14,4,10:30:48 AM,14,4,10:43:49 AM,5,18.9,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_107,781
-Order_No_19226,User_Id_3647,Bike,3,Business,10,5,3:53:00 PM,10,5,3:54:14 PM,10,5,4:10:34 PM,10,5,4:16:13 PM,10,5,5:01:15 PM,19,,,-1.2726389999999999,36.794723,-1.3443916999999999,36.6700333,Rider_Id_279,2702
-Order_No_27787,User_Id_3147,Bike,3,Business,31,3,1:08:44 PM,31,3,1:09:21 PM,31,3,1:13:59 PM,31,3,1:30:33 PM,31,3,1:50:49 PM,10,24.8,,-1.3169591,36.7000029,-1.3472066,36.7692638,Rider_Id_24,1216
-Order_No_10754,User_Id_375,Bike,3,Business,9,2,8:03:07 AM,9,2,8:03:25 AM,9,2,8:31:39 AM,9,2,8:34:13 AM,9,2,8:47:51 AM,5,20.6,,-1.2551895,36.7822034,-1.2658315,36.809049,Rider_Id_166,818
-Order_No_13415,User_Id_1809,Bike,3,Business,6,3,3:25:35 PM,6,3,3:30:09 PM,6,3,4:06:21 PM,6,3,4:14:15 PM,6,3,4:39:18 PM,6,22.0,,-1.2646084,36.8028645,-1.291768,36.8297208,Rider_Id_742,1503
-Order_No_26377,User_Id_2523,Bike,3,Business,9,1,12:03:18 PM,9,1,12:03:39 PM,9,1,12:06:49 PM,9,1,12:17:50 PM,9,1,12:56:31 PM,18,20.3,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_520,2321
-Order_No_21863,User_Id_302,Bike,3,Business,15,6,12:11:29 PM,15,6,12:11:53 PM,15,6,12:15:00 PM,15,6,12:30:49 PM,15,6,12:30:57 PM,1,,,-1.2649605,36.798177700000004,-1.2676026000000002,36.810362899999994,Rider_Id_882,8
-Order_No_6107,User_Id_2228,Bike,1,Personal,28,4,1:22:14 PM,28,4,1:22:20 PM,28,4,1:25:04 PM,28,4,1:34:19 PM,28,4,1:46:33 PM,5,,,-1.2601197,36.7985271,-1.2479361999999998,36.772759,Rider_Id_925,734
-Order_No_260,User_Id_1880,Bike,3,Business,28,1,3:06:03 PM,28,1,3:10:35 PM,28,1,3:14:13 PM,28,1,3:23:23 PM,28,1,4:06:23 PM,12,26.3,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_26,2580
-Order_No_14125,User_Id_2200,Bike,3,Business,18,4,2:10:42 PM,18,4,2:55:51 PM,18,4,2:56:03 PM,18,4,3:23:06 PM,18,4,4:07:00 PM,11,26.9,,-1.3472066,36.7692638,-1.290894,36.822971,Rider_Id_669,2634
-Order_No_25712,User_Id_1882,Bike,3,Business,5,5,11:40:11 AM,5,5,11:41:54 AM,5,5,11:44:43 AM,5,5,11:49:09 AM,5,5,12:13:12 PM,4,24.3,,-1.2958783,36.7928125,-1.3011335,36.7658946,Rider_Id_460,1443
-Order_No_21022,User_Id_2330,Bike,3,Business,29,2,2:24:24 PM,29,2,3:58:33 PM,29,2,4:29:51 PM,29,2,4:33:39 PM,29,2,5:32:56 PM,13,,,-1.2124582,36.802894,-1.293082,36.8278578,Rider_Id_332,3557
-Order_No_14280,User_Id_533,Bike,3,Business,29,2,3:26:46 PM,29,2,4:24:55 PM,29,2,4:39:11 PM,29,2,4:40:33 PM,29,2,4:53:33 PM,3,,,-1.2765492,36.7669809,-1.3014461000000002,36.7661381,Rider_Id_523,780
-Order_No_7011,User_Id_3613,Bike,3,Business,18,4,2:12:27 PM,18,4,2:13:37 PM,18,4,2:34:59 PM,18,4,2:47:33 PM,18,4,3:08:27 PM,7,27.0,,-1.2730868,36.7706594,-1.298529,36.816482,Rider_Id_213,1254
-Order_No_3953,User_Id_635,Bike,3,Business,27,1,11:24:48 AM,27,1,11:26:44 AM,27,1,11:27:51 AM,27,1,11:32:29 AM,27,1,11:47:17 AM,7,20.9,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_576,888
-Order_No_16361,User_Id_3738,Bike,3,Business,3,5,3:22:27 PM,3,5,3:22:37 PM,3,5,3:35:40 PM,3,5,3:38:44 PM,3,5,4:01:23 PM,9,25.4,,-1.3014461000000002,36.7661381,-1.2597034,36.8003065,Rider_Id_898,1359
-Order_No_17479,User_Id_795,Bike,3,Personal,20,4,2:56:44 PM,20,4,2:57:47 PM,20,4,3:09:34 PM,20,4,3:16:04 PM,20,4,3:38:22 PM,7,28.0,,-1.3104733999999998,36.7363698,-1.2874290000000002,36.786357,Rider_Id_65,1338
-Order_No_5308,User_Id_3565,Bike,3,Business,11,5,8:23:49 AM,11,5,8:24:13 AM,11,5,8:56:10 AM,11,5,9:05:04 AM,11,5,9:48:59 AM,20,17.9,4.9,-1.2844665000000002,36.7866113,-1.355309,36.6541393,Rider_Id_45,2635
-Order_No_12271,User_Id_3283,Bike,3,Business,9,1,9:05:30 AM,9,1,9:34:33 AM,9,1,9:38:27 AM,9,1,9:59:17 AM,9,1,10:30:41 AM,13,15.5,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_951,1884
-Order_No_15617,User_Id_635,Bike,3,Business,30,4,1:02:43 PM,30,4,1:03:19 PM,30,4,1:12:08 PM,30,4,1:27:46 PM,30,4,1:57:55 PM,18,,,-1.3204931,36.7043102,-1.300921,36.828195,Rider_Id_344,1809
-Order_No_8279,User_Id_669,Bike,1,Personal,3,3,10:54:04 AM,3,3,11:19:11 AM,3,3,11:25:39 AM,3,3,11:39:19 AM,3,3,11:51:03 AM,6,23.8,,-1.2824911,36.8218042,-1.2971892,36.781417600000005,Rider_Id_88,704
-Order_No_22975,User_Id_1363,Bike,3,Business,29,3,9:52:08 AM,29,3,9:52:25 AM,29,3,9:52:40 AM,29,3,10:19:05 AM,29,3,10:19:09 AM,10,18.0,,-1.3004062,36.829741,-1.2729291999999999,36.7679079,Rider_Id_515,4
-Order_No_22223,User_Id_633,Bike,3,Business,16,6,10:55:38 AM,16,6,10:56:50 AM,16,6,11:03:43 AM,16,6,11:05:31 AM,16,6,11:14:16 AM,4,25.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_778,525
-Order_No_18852,User_Id_1231,Bike,3,Business,14,2,1:52:16 PM,14,2,1:52:52 PM,14,2,1:58:28 PM,14,2,2:01:05 PM,14,2,2:23:16 PM,8,24.0,,-1.2821421999999998,36.8161565,-1.3014461000000002,36.7661381,Rider_Id_726,1331
-Order_No_19804,User_Id_1684,Bike,1,Personal,17,1,2:18:25 PM,17,1,2:18:46 PM,17,1,2:33:54 PM,17,1,3:01:05 PM,17,1,3:30:49 PM,17,28.5,,-1.2095044,36.783206400000005,-1.2933788999999998,36.808753499999995,Rider_Id_882,1784
-Order_No_634,User_Id_167,Bike,2,Personal,24,4,3:45:00 PM,24,4,3:45:25 PM,24,4,3:55:59 PM,24,4,3:59:24 PM,24,4,4:19:50 PM,13,22.7,,-1.3339943,36.7263409,-1.3157973,36.815148,Rider_Id_312,1226
-Order_No_6508,User_Id_2907,Bike,2,Personal,7,5,10:29:45 AM,7,5,10:33:30 AM,7,5,11:10:40 AM,7,5,11:15:32 AM,7,5,11:31:33 AM,8,25.6,,-1.2672426,36.7884223,-1.2934278000000001,36.7930583,Rider_Id_820,961
-Order_No_11896,User_Id_2658,Bike,3,Business,11,1,9:37:05 AM,11,1,9:37:33 AM,11,1,9:45:05 AM,11,1,10:05:59 AM,11,1,10:30:19 AM,8,18.0,,-1.2885029,36.825604999999996,-1.2975066000000002,36.76894229999999,Rider_Id_25,1460
-Order_No_7305,User_Id_3663,Bike,3,Personal,10,4,10:07:38 AM,10,4,10:07:47 AM,10,4,10:09:51 AM,10,4,10:38:35 AM,10,4,11:20:19 AM,18,,,-1.279285,36.826057399999996,-1.1691114,36.7616509,Rider_Id_813,2504
-Order_No_22238,User_Id_3091,Bike,3,Business,24,3,3:34:47 PM,24,3,3:52:42 PM,24,3,4:06:35 PM,24,3,4:12:22 PM,24,3,4:31:19 PM,4,,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_531,1137
-Order_No_28122,User_Id_778,Bike,3,Business,3,3,1:50:22 PM,3,3,1:50:49 PM,3,3,1:54:45 PM,3,3,2:14:59 PM,3,3,2:15:03 PM,4,28.4,,-1.2535486,36.7933541,-1.2656922,36.813694399999996,Rider_Id_953,4
-Order_No_17651,User_Id_393,Bike,3,Business,28,4,10:08:48 AM,28,4,10:09:51 AM,28,4,10:23:07 AM,28,4,10:50:17 AM,28,4,11:31:57 AM,11,28.2,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_154,2500
-Order_No_4057,User_Id_393,Bike,3,Business,20,1,8:31:18 AM,20,1,8:32:40 AM,20,1,8:58:52 AM,20,1,9:07:07 AM,20,1,9:42:55 AM,14,14.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,2148
-Order_No_10465,User_Id_795,Bike,3,Personal,5,2,10:57:47 AM,5,2,10:59:50 AM,5,2,11:31:28 AM,5,2,11:36:40 AM,5,2,11:52:56 AM,6,21.3,,-1.34338,36.711834,-1.3104733999999998,36.7363698,Rider_Id_116,976
-Order_No_7978,User_Id_265,Bike,3,Business,10,3,4:21:58 PM,10,3,4:26:46 PM,10,3,4:51:44 PM,10,3,4:59:51 PM,10,3,5:37:27 PM,7,27.3,,-1.300921,36.828195,-1.2721466000000001,36.8155359,Rider_Id_76,2256
-Order_No_16606,User_Id_3669,Bike,3,Business,20,3,5:01:08 PM,20,3,5:01:39 PM,20,3,5:26:20 PM,20,3,5:46:02 PM,20,3,6:22:15 PM,7,27.7,,-1.2615888000000002,36.7928732,-1.2772389,36.7855864,Rider_Id_510,2173
-Order_No_6274,User_Id_635,Bike,3,Business,7,2,11:02:00 AM,7,2,11:02:25 AM,7,2,11:16:46 AM,7,2,11:46:07 AM,7,2,12:10:26 PM,8,23.3,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_519,1459
-Order_No_6430,User_Id_3426,Bike,3,Business,14,1,3:52:21 PM,14,1,3:53:23 PM,14,1,4:02:16 PM,14,1,4:08:15 PM,14,1,4:34:05 PM,9,25.9,,-1.2676026000000002,36.810362899999994,-1.3012008,36.764868,Rider_Id_205,1550
-Order_No_13404,User_Id_2607,Bike,2,Personal,11,6,11:22:41 AM,11,6,11:23:06 AM,11,6,11:23:36 AM,11,6,11:31:04 AM,11,6,11:39:02 AM,3,21.1,,-1.2860203000000001,36.7832538,-1.2917966,36.7730221,Rider_Id_88,478
-Order_No_25144,User_Id_1502,Bike,3,Business,15,1,4:41:46 PM,15,1,4:42:41 PM,15,1,4:49:48 PM,15,1,5:06:07 PM,15,1,5:15:50 PM,5,,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_75,583
-Order_No_11268,User_Id_2523,Bike,3,Business,11,1,12:39:57 PM,11,1,12:40:58 PM,11,1,1:01:58 PM,11,1,1:10:04 PM,11,1,1:53:59 PM,7,26.8,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_131,2635
-Order_No_21105,User_Id_2454,Bike,3,Business,14,3,12:39:30 PM,14,3,2:20:22 PM,14,3,2:25:07 PM,14,3,4:04:19 PM,14,3,4:39:11 PM,21,25.6,,-1.3510811000000003,36.894806,-1.2993231,36.7753372,Rider_Id_510,2092
-Order_No_9897,User_Id_2111,Bike,2,Personal,30,3,8:52:37 AM,30,3,8:55:26 AM,30,3,8:55:36 AM,30,3,9:40:42 AM,30,3,10:06:51 AM,18,17.6,,-1.1943194,36.8270384,-1.2807232,36.7677141,Rider_Id_633,1569
-Order_No_1729,User_Id_3560,Bike,3,Business,24,1,7:51:10 AM,24,1,7:51:33 AM,24,1,8:20:01 AM,24,1,8:31:55 AM,24,1,8:57:22 AM,10,19.5,,-1.2859912,36.8756811,-1.279395,36.825364,Rider_Id_26,1527
-Order_No_14285,User_Id_2183,Bike,3,Business,5,1,10:43:54 AM,5,1,10:44:31 AM,5,1,11:25:19 AM,5,1,11:48:46 AM,5,1,12:15:02 PM,7,24.3,,-1.2551895,36.7822034,-1.2549476000000002,36.8269576,Rider_Id_916,1576
-Order_No_23929,User_Id_1329,Bike,3,Business,8,5,10:53:49 AM,8,5,10:54:43 AM,8,5,11:08:26 AM,8,5,11:15:56 AM,8,5,11:31:12 AM,5,28.2,,-1.273056,36.811298,-1.3010058999999998,36.8216816,Rider_Id_119,916
-Order_No_22659,User_Id_2989,Bike,3,Business,20,6,2:00:36 PM,20,6,2:11:15 PM,20,6,2:26:07 PM,20,6,2:53:20 PM,20,6,2:55:18 PM,11,28.3,,-1.3472066,36.7692638,-1.301796,36.8012024,Rider_Id_612,118
-Order_No_1660,User_Id_2587,Bike,3,Business,19,6,1:43:32 PM,19,6,1:44:51 PM,19,6,1:53:12 PM,19,6,2:03:11 PM,19,6,2:19:02 PM,5,27.8,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_899,951
-Order_No_9960,User_Id_393,Bike,3,Business,10,4,1:33:12 PM,10,4,1:33:29 PM,10,4,1:33:32 PM,10,4,1:43:19 PM,10,4,2:07:28 PM,11,25.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_188,1449
-Order_No_17594,User_Id_2271,Bike,3,Business,30,2,11:46:21 AM,30,2,11:54:38 AM,30,2,12:14:13 PM,30,2,12:23:27 PM,30,2,12:56:23 PM,12,,,-1.293515,36.897607,-1.228911,36.881876,Rider_Id_883,1976
-Order_No_25135,User_Id_2532,Bike,3,Business,9,2,3:54:45 PM,9,2,4:17:27 PM,9,2,4:18:11 PM,9,2,4:43:20 PM,9,2,4:58:22 PM,6,27.6,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_119,902
-Order_No_13925,User_Id_1935,Bike,1,Personal,24,2,12:03:03 PM,24,2,12:03:55 PM,24,2,12:14:05 PM,24,2,12:21:18 PM,24,2,12:42:26 PM,10,17.2,,-1.2576857,36.779919899999996,-1.2892697,36.81545139999999,Rider_Id_379,1268
-Order_No_25265,User_Id_1856,Bike,3,Business,2,5,4:14:56 PM,2,5,4:15:22 PM,2,5,4:23:50 PM,2,5,4:26:23 PM,2,5,4:54:01 PM,8,25.0,,-1.273009,36.802807,-1.2592335,36.8449224,Rider_Id_620,1658
-Order_No_13503,User_Id_2829,Bike,3,Business,13,1,2:26:20 PM,13,1,2:27:00 PM,13,1,2:46:02 PM,13,1,2:56:46 PM,13,1,3:42:41 PM,21,,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_205,2755
-Order_No_2606,User_Id_1811,Bike,2,Personal,2,2,2:31:45 PM,2,2,2:32:11 PM,2,2,2:35:03 PM,2,2,2:48:49 PM,2,2,3:04:12 PM,8,26.3,,-1.2854453000000001,36.818836299999994,-1.2804664,36.7701111,Rider_Id_906,923
-Order_No_28035,User_Id_419,Bike,3,Business,7,4,12:37:23 PM,7,4,12:43:55 PM,7,4,12:54:20 PM,7,4,1:19:35 PM,7,4,1:42:26 PM,7,24.3,,-1.2571472,36.795063299999995,-1.3012173,36.8093987,Rider_Id_576,1371
-Order_No_25561,User_Id_1150,Bike,1,Personal,22,6,11:20:54 AM,22,6,11:22:27 AM,22,6,11:45:51 AM,22,6,11:53:09 AM,22,6,12:19:43 PM,8,21.3,,-1.2569523,36.8254961,-1.2934697,36.7799724,Rider_Id_89,1594
-Order_No_8104,User_Id_2523,Bike,3,Business,25,1,11:39:43 AM,25,1,11:39:58 AM,25,1,11:47:32 AM,25,1,11:48:42 AM,25,1,12:08:28 PM,7,27.3,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_933,1186
-Order_No_1553,User_Id_3147,Bike,3,Business,18,5,11:48:59 AM,18,5,11:59:40 AM,18,5,12:22:55 PM,18,5,12:30:21 PM,18,5,1:15:42 PM,18,21.2,,-1.3467388999999999,36.7690312,-1.2626906,36.7827022,Rider_Id_290,2721
-Order_No_7378,User_Id_2523,Bike,3,Business,13,1,2:46:26 PM,13,1,2:47:01 PM,13,1,2:47:09 PM,13,1,3:33:48 PM,13,1,3:54:54 PM,12,19.5,,-1.2551895,36.7822034,-1.2416156,36.8736721,Rider_Id_101,1266
-Order_No_22850,User_Id_393,Bike,3,Business,28,4,2:49:23 PM,28,4,2:49:59 PM,28,4,3:05:12 PM,28,4,3:07:38 PM,28,4,3:29:46 PM,4,30.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_362,1328
-Order_No_5852,User_Id_954,Bike,1,Personal,26,6,3:54:39 PM,26,6,3:54:54 PM,26,6,4:11:35 PM,26,6,4:15:10 PM,26,6,5:19:00 PM,23,24.1,,-1.2389424999999998,36.8775718,-1.3296591,36.7572949,Rider_Id_479,3830
-Order_No_10179,User_Id_654,Bike,2,Personal,25,2,6:50:58 PM,25,2,6:51:08 PM,25,2,7:14:02 PM,25,2,7:27:53 PM,25,2,8:15:31 PM,21,24.1,,-1.1967675,36.8064464,-1.2949438999999998,36.7604214,Rider_Id_956,2858
-Order_No_27644,User_Id_496,Bike,3,Business,30,2,5:05:12 PM,30,2,7:23:48 PM,30,2,7:26:25 PM,30,2,7:38:06 PM,30,2,7:57:24 PM,10,,,-1.2551895,36.7822034,-1.2518813999999998,36.859660999999996,Rider_Id_96,1158
-Order_No_13720,User_Id_3426,Bike,3,Business,7,1,9:39:44 AM,7,1,9:40:17 AM,7,1,9:44:32 AM,7,1,9:52:13 AM,7,1,10:06:03 AM,9,21.5,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_655,830
-Order_No_19116,User_Id_2801,Bike,3,Business,23,2,8:49:30 AM,23,2,8:51:26 AM,23,2,9:03:15 AM,23,2,9:07:10 AM,23,2,9:30:54 AM,8,,,-1.2674273999999999,36.787082700000006,-1.2809846999999999,36.8274169,Rider_Id_953,1424
-Order_No_13687,User_Id_751,Bike,2,Personal,4,3,8:28:43 AM,4,3,8:29:43 AM,4,3,8:41:22 AM,4,3,8:49:48 AM,4,3,9:10:38 AM,9,15.2,,-1.2727979999999999,36.8124195,-1.3014461000000002,36.7661381,Rider_Id_379,1250
-Order_No_28043,User_Id_2521,Bike,3,Business,5,5,1:53:05 PM,5,5,1:54:05 PM,5,5,2:01:40 PM,5,5,2:19:21 PM,5,5,2:32:17 PM,6,27.7,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_855,776
-Order_No_20772,User_Id_1792,Bike,3,Business,12,2,11:38:12 AM,12,2,11:38:22 AM,12,2,11:51:21 AM,12,2,11:55:09 AM,12,2,12:19:28 PM,11,20.9,,-1.288159,36.792527500000006,-1.2519209999999998,36.828379,Rider_Id_635,1459
-Order_No_14152,User_Id_868,Bike,3,Business,26,5,2:04:07 PM,26,5,2:04:15 PM,26,5,2:04:32 PM,26,5,2:32:29 PM,26,5,3:09:37 PM,10,,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_155,2228
-Order_No_9842,User_Id_635,Bike,3,Business,23,5,10:42:45 AM,23,5,11:32:38 AM,23,5,11:54:48 AM,23,5,12:16:41 PM,23,5,12:37:11 PM,6,24.1,,-1.3016417,36.8271679,-1.3229345,36.8566974,Rider_Id_208,1230
-Order_No_21082,User_Id_1927,Bike,3,Business,18,1,9:45:41 AM,18,1,9:45:59 AM,18,1,10:00:59 AM,18,1,10:06:12 AM,18,1,10:51:42 AM,14,21.2,,-1.3012008,36.764868,-1.3472066,36.7692638,Rider_Id_517,2730
-Order_No_27009,User_Id_3404,Bike,3,Business,26,1,10:56:26 AM,26,1,11:50:08 AM,26,1,12:06:45 PM,26,1,12:18:36 PM,26,1,12:53:49 PM,15,23.2,,-1.279395,36.825364,-1.3251247,36.899592600000005,Rider_Id_928,2113
-Order_No_19117,User_Id_3693,Bike,1,Personal,2,2,1:00:15 PM,2,2,1:01:00 PM,2,2,1:11:35 PM,2,2,1:34:44 PM,2,2,1:51:52 PM,6,26.1,,-1.2854453000000001,36.818836299999994,-1.2971709999999999,36.783278,Rider_Id_343,1028
-Order_No_21811,User_Id_2179,Bike,3,Business,7,4,10:25:56 AM,7,4,10:26:20 AM,7,4,10:42:53 AM,7,4,10:52:17 AM,7,4,10:58:47 AM,2,24.5,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_547,390
-Order_No_17373,User_Id_3007,Bike,3,Personal,15,1,3:10:05 PM,15,1,3:11:37 PM,15,1,3:11:56 PM,15,1,3:41:20 PM,15,1,3:53:51 PM,5,,,-1.277551,36.8273641,-1.2935303,36.7988142,Rider_Id_338,751
-Order_No_20539,User_Id_1329,Bike,3,Business,18,2,11:18:26 AM,18,2,11:34:38 AM,18,2,11:49:05 AM,18,2,11:58:59 AM,18,2,12:14:56 PM,4,,,-1.273056,36.811298,-1.2867132,36.8118433,Rider_Id_101,957
-Order_No_9574,User_Id_3201,Bike,3,Business,7,4,3:13:59 PM,7,4,3:14:11 PM,7,4,3:19:56 PM,7,4,3:33:19 PM,7,4,3:53:54 PM,14,27.7,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_52,1235
-Order_No_27978,User_Id_311,Bike,3,Business,13,1,9:18:37 AM,13,1,9:20:01 AM,13,1,9:33:50 AM,13,1,9:35:42 AM,13,1,9:53:08 AM,4,15.3,,-1.2998129999999999,36.787139200000006,-1.2890399,36.8164942,Rider_Id_592,1046
-Order_No_27949,User_Id_2030,Bike,3,Business,30,2,2:51:41 PM,30,2,3:07:37 PM,30,2,3:21:59 PM,30,2,3:23:12 PM,30,2,3:44:21 PM,4,,,-1.335316,36.8845063,-1.3333725,36.871761299999996,Rider_Id_76,1269
-Order_No_16346,User_Id_1687,Bike,3,Business,17,4,11:39:39 AM,17,4,11:39:51 AM,17,4,11:46:03 AM,17,4,11:50:46 AM,17,4,12:02:33 PM,4,25.2,,-1.2626574,36.8014034,-1.28164,36.816866999999995,Rider_Id_365,707
-Order_No_13868,User_Id_2289,Bike,3,Business,4,1,12:41:21 PM,4,1,12:42:36 PM,4,1,12:46:40 PM,4,1,12:47:47 PM,4,1,1:03:41 PM,9,25.8,,-1.2551895,36.7822034,-1.301809,36.75643,Rider_Id_269,954
-Order_No_26497,User_Id_2021,Bike,3,Business,13,4,3:53:07 PM,13,4,4:04:30 PM,13,4,4:08:42 PM,13,4,4:15:40 PM,13,4,4:47:03 PM,25,23.0,,-1.2945128000000001,36.8396881,-1.4238401000000003,36.9730061,Rider_Id_675,1883
-Order_No_15925,User_Id_1754,Bike,3,Business,2,4,1:52:17 PM,2,4,1:56:15 PM,2,4,2:03:53 PM,2,4,2:16:23 PM,2,4,2:44:54 PM,12,,,-1.279395,36.825364,-1.2860183,36.8975338,Rider_Id_291,1711
-Order_No_15362,User_Id_2523,Bike,3,Business,16,4,12:55:14 PM,16,4,12:55:29 PM,16,4,12:55:51 PM,16,4,1:00:47 PM,16,4,1:28:49 PM,12,,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_663,1682
-Order_No_26205,User_Id_2801,Bike,3,Business,21,4,3:29:47 PM,21,4,3:30:39 PM,21,4,3:51:00 PM,21,4,4:05:14 PM,21,4,4:32:59 PM,9,26.3,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_953,1665
-Order_No_25370,User_Id_3064,Bike,3,Business,14,4,3:16:03 PM,14,4,3:18:41 PM,14,4,3:22:37 PM,14,4,3:28:58 PM,14,4,3:47:31 PM,6,27.4,,-1.2571472,36.795063299999995,-1.2985749,36.8088,Rider_Id_937,1113
-Order_No_2655,User_Id_880,Bike,3,Personal,30,4,11:35:05 AM,30,4,11:35:15 AM,30,4,11:42:50 AM,30,4,11:48:20 AM,30,4,11:58:50 AM,4,20.1,,-1.2995135,36.793363299999996,-1.2892529,36.7740575,Rider_Id_312,630
-Order_No_26448,User_Id_3019,Bike,2,Personal,3,3,12:09:21 PM,3,3,12:09:53 PM,3,3,12:14:51 PM,3,3,12:20:06 PM,3,3,12:47:24 PM,12,23.9,,-1.265715,36.808525,-1.3255681000000001,36.8738816,Rider_Id_882,1638
-Order_No_17645,User_Id_868,Bike,3,Business,13,6,1:48:59 PM,13,6,1:49:14 PM,13,6,1:49:56 PM,13,6,2:08:30 PM,13,6,2:18:41 PM,4,27.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_72,611
-Order_No_27791,User_Id_1271,Bike,3,Business,11,5,9:45:47 AM,11,5,9:45:53 AM,11,5,9:49:37 AM,11,5,9:52:23 AM,11,5,10:40:45 AM,22,20.9,,-1.3244885,36.897791999999995,-1.2831463,36.76360220000001,Rider_Id_457,2902
-Order_No_16789,User_Id_706,Bike,3,Business,23,2,1:26:28 PM,23,2,1:32:57 PM,23,2,1:38:07 PM,23,2,1:39:18 PM,23,2,1:48:07 PM,4,18.8,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_69,529
-Order_No_5265,User_Id_248,Bike,3,Business,26,4,11:57:21 AM,26,4,11:57:27 AM,26,4,12:15:31 PM,26,4,12:22:30 PM,26,4,12:36:20 PM,3,19.1,,-1.304735,36.784605400000004,-1.2929747,36.76463579999999,Rider_Id_229,830
-Order_No_1716,User_Id_3108,Bike,3,Business,29,5,2:09:24 PM,29,5,2:17:19 PM,29,5,2:35:02 PM,29,5,2:37:07 PM,29,5,4:00:52 PM,14,21.1,,-1.2551895,36.7822034,-1.3164474,36.857586100000006,Rider_Id_88,5025
-Order_No_20776,User_Id_1263,Bike,2,Personal,9,6,10:39:38 AM,9,6,10:40:37 AM,9,6,10:52:22 AM,9,6,10:56:38 AM,9,6,11:26:45 AM,16,17.2,,-1.2697858999999998,36.7946203,-1.2962888000000001,36.8892359,Rider_Id_598,1807
-Order_No_572,User_Id_1787,Bike,3,Business,6,5,8:31:16 AM,6,5,8:31:24 AM,6,5,9:01:06 AM,6,5,9:03:41 AM,6,5,10:04:19 AM,16,14.8,,-1.2297202,36.874550799999994,-1.2592947,36.7698085,Rider_Id_644,3638
-Order_No_4433,User_Id_2152,Bike,2,Personal,10,4,12:57:52 PM,10,4,12:59:40 PM,10,4,1:00:30 PM,10,4,1:14:02 PM,10,4,1:38:36 PM,7,,,-1.2718504,36.8246277,-1.2684893999999998,36.780451,Rider_Id_777,1474
-Order_No_10187,User_Id_788,Bike,3,Business,23,5,12:17:57 PM,23,5,12:29:10 PM,23,5,12:55:10 PM,23,5,12:57:39 PM,23,5,1:17:00 PM,7,24.0,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_343,1161
-Order_No_23021,User_Id_867,Bike,3,Business,28,2,2:18:03 PM,28,2,2:18:11 PM,28,2,2:18:22 PM,28,2,2:20:52 PM,28,2,3:04:41 PM,14,19.5,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_748,2629
-Order_No_27064,User_Id_393,Bike,3,Business,6,2,7:53:47 AM,6,2,9:21:37 AM,6,2,9:26:17 AM,6,2,9:54:57 AM,6,2,10:13:16 AM,10,19.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_62,1099
-Order_No_11681,User_Id_1271,Bike,3,Business,28,3,3:18:30 PM,28,3,3:26:36 PM,28,3,3:38:00 PM,28,3,4:11:59 PM,28,3,4:20:03 PM,9,,,-1.3244885,36.897791999999995,-1.3206958,36.8769553,Rider_Id_532,484
-Order_No_23799,User_Id_412,Bike,3,Business,7,2,3:40:27 PM,7,2,3:40:52 PM,7,2,3:46:15 PM,7,2,4:00:20 PM,7,2,4:15:43 PM,3,,,-1.272828,36.816608,-1.266837,36.799248999999996,Rider_Id_309,923
-Order_No_23392,User_Id_1159,Bike,1,Personal,20,1,11:08:09 AM,20,1,11:08:55 AM,20,1,11:29:35 AM,20,1,11:42:48 AM,20,1,12:10:03 PM,13,20.3,,-1.3034318,36.7520402,-1.3068448000000001,36.8348641,Rider_Id_402,1635
-Order_No_12083,User_Id_1850,Bike,3,Business,25,1,3:16:31 PM,25,1,3:17:15 PM,25,1,3:40:47 PM,25,1,3:42:20 PM,25,1,4:41:39 PM,9,27.9,,-1.2551895,36.7822034,-1.3074264,36.822612299999996,Rider_Id_817,3559
-Order_No_21225,User_Id_1724,Bike,3,Business,6,1,2:10:00 PM,6,1,2:10:24 PM,6,1,2:22:06 PM,6,1,2:24:47 PM,6,1,2:42:55 PM,8,24.6,,-1.2905046,36.7828277,-1.2551895,36.7822034,Rider_Id_722,1088
-Order_No_7252,User_Id_1363,Bike,3,Business,29,1,9:28:09 AM,29,1,9:28:22 AM,29,1,9:28:50 AM,29,1,9:38:13 AM,29,1,10:10:37 AM,13,,,-1.3004062,36.829741,-1.238805,36.884373,Rider_Id_206,1944
-Order_No_18735,User_Id_1346,Bike,3,Business,7,5,1:33:41 PM,7,5,1:34:39 PM,7,5,1:41:34 PM,7,5,1:44:48 PM,7,5,2:06:28 PM,6,,,-1.3047889,36.821443200000004,-1.2917868000000001,36.7872675,Rider_Id_826,1300
-Order_No_16774,User_Id_1584,Bike,3,Business,13,3,1:51:19 PM,13,3,1:57:33 PM,13,3,2:30:01 PM,13,3,2:32:36 PM,13,3,2:52:33 PM,7,29.1,,-1.2771155,36.826679999999996,-1.2969741000000001,36.7856612,Rider_Id_763,1197
-Order_No_5700,User_Id_2207,Bike,3,Business,22,1,8:40:23 AM,22,1,8:41:05 AM,22,1,8:56:35 AM,22,1,9:03:03 AM,22,1,9:26:49 AM,9,19.7,,-1.2325706,36.808392299999994,-1.2768326,36.8073804,Rider_Id_453,1426
-Order_No_3310,User_Id_867,Bike,3,Business,14,5,9:21:04 AM,14,5,9:25:01 AM,14,5,9:42:01 AM,14,5,10:18:46 AM,14,5,10:56:49 AM,10,17.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_896,2283
-Order_No_19538,User_Id_2828,Bike,1,Business,28,5,1:38:07 PM,28,5,1:39:06 PM,28,5,1:41:04 PM,28,5,1:56:19 PM,28,5,2:34:51 PM,14,25.5,,-1.2615888000000002,36.7928732,-1.326774,36.78780689999999,Rider_Id_651,2312
-Order_No_19669,User_Id_59,Bike,3,Business,16,2,4:28:24 PM,16,2,4:29:46 PM,16,2,4:50:10 PM,16,2,4:55:19 PM,16,2,5:24:53 PM,5,,,-1.290894,36.822971,-1.2944841999999999,36.8011625,Rider_Id_583,1774
-Order_No_15669,User_Id_393,Bike,3,Business,1,4,8:24:14 AM,1,4,8:26:12 AM,1,4,8:51:42 AM,1,4,9:00:33 AM,1,4,9:53:13 AM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,3160
-Order_No_23342,User_Id_3689,Bike,3,Business,31,1,1:28:30 PM,31,1,1:32:52 PM,31,1,2:00:00 PM,31,1,2:17:10 PM,31,1,2:59:11 PM,16,24.1,,-1.3472066,36.7692638,-1.2527958000000001,36.8003131,Rider_Id_428,2521
-Order_No_6374,User_Id_3295,Bike,3,Business,7,3,12:34:12 PM,7,3,12:34:28 PM,7,3,12:35:41 PM,7,3,12:45:27 PM,7,3,1:14:43 PM,12,25.2,,-1.2571472,36.795063299999995,-1.3270347,36.866736200000005,Rider_Id_470,1756
-Order_No_23565,User_Id_265,Bike,3,Business,15,1,9:58:43 AM,15,1,10:01:48 AM,15,1,10:16:14 AM,15,1,10:27:02 AM,15,1,10:45:14 AM,7,,,-1.2609397,36.8010827,-1.300921,36.828195,Rider_Id_85,1092
-Order_No_26187,User_Id_20,Bike,3,Business,12,2,9:46:56 AM,12,2,10:08:51 AM,12,2,10:24:46 AM,12,2,10:28:54 AM,12,2,10:44:11 AM,6,16.3,,-1.2628638,36.8070399,-1.300722,36.816865,Rider_Id_216,917
-Order_No_13498,User_Id_3689,Bike,3,Business,21,3,1:26:38 PM,21,3,3:02:58 PM,21,3,3:23:23 PM,21,3,3:26:48 PM,21,3,3:33:57 PM,3,23.0,2.9,-1.2527958000000001,36.8003131,-1.2615888000000002,36.7928732,Rider_Id_557,429
-Order_No_16282,User_Id_2828,Bike,3,Business,16,6,6:03:33 PM,16,6,6:04:23 PM,16,6,6:18:46 PM,16,6,6:25:43 PM,16,6,6:56:57 PM,11,23.8,,-1.3207349,36.8819157,-1.28872,36.90297,Rider_Id_675,1874
-Order_No_25270,User_Id_393,Bike,3,Business,13,2,1:34:54 PM,13,2,2:11:08 PM,13,2,2:11:23 PM,13,2,3:06:32 PM,13,2,3:33:59 PM,14,26.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_555,1647
-Order_No_2941,User_Id_1148,Bike,3,Business,28,3,1:15:12 PM,28,3,1:15:40 PM,28,3,1:16:42 PM,28,3,1:42:49 PM,28,3,2:27:37 PM,15,,,-1.3100586,36.850032899999995,-1.2409761000000001,36.776006200000005,Rider_Id_519,2688
-Order_No_11168,User_Id_1343,Bike,1,Personal,25,3,5:23:01 PM,25,3,5:23:37 PM,25,3,5:31:34 PM,25,3,5:57:45 PM,25,3,6:18:32 PM,9,17.6,,-1.2699441999999999,36.8206553,-1.2917966,36.7730221,Rider_Id_427,1247
-Order_No_18529,User_Id_2412,Bike,3,Business,25,1,3:17:05 PM,25,1,3:18:01 PM,25,1,3:40:16 PM,25,1,3:45:13 PM,25,1,4:06:54 PM,11,27.9,,-1.2803414,36.8265456,-1.2285834999999998,36.882646799999996,Rider_Id_341,1301
-Order_No_11915,User_Id_3222,Bike,3,Business,8,5,2:23:43 PM,8,5,2:24:14 PM,8,5,2:32:04 PM,8,5,2:36:15 PM,8,5,2:47:42 PM,3,30.5,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_658,687
-Order_No_8913,User_Id_868,Bike,3,Business,11,2,3:20:08 PM,11,2,3:27:56 PM,11,2,3:41:22 PM,11,2,4:05:38 PM,11,2,4:05:51 PM,4,21.5,,-1.259478,36.801781299999995,-1.2663631000000002,36.8163329,Rider_Id_245,13
-Order_No_22575,User_Id_393,Bike,3,Business,26,2,3:54:22 PM,26,2,3:54:31 PM,26,2,3:54:42 PM,26,2,4:48:56 PM,26,2,4:48:58 PM,14,28.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,2
-Order_No_306,User_Id_478,Bike,3,Business,30,2,10:44:11 AM,30,2,10:50:22 AM,30,2,11:05:56 AM,30,2,11:11:29 AM,30,2,11:30:55 AM,5,,,-1.2551895,36.7822034,-1.2658315,36.809049,Rider_Id_40,1166
-Order_No_26498,User_Id_2154,Bike,3,Business,8,6,9:57:54 AM,8,6,9:58:08 AM,8,6,10:01:42 AM,8,6,10:22:30 AM,8,6,10:40:27 AM,8,16.8,4.0,-1.2527958000000001,36.797624400000004,-1.2765736,36.851364600000004,Rider_Id_72,1077
-Order_No_3634,User_Id_985,Bike,1,Personal,11,5,1:18:21 PM,11,5,1:18:38 PM,11,5,1:51:09 PM,11,5,2:02:55 PM,11,5,3:12:43 PM,14,22.5,,-1.226218,36.849712,-1.3108368000000001,36.8226308,Rider_Id_100,4188
-Order_No_17608,User_Id_2956,Bike,3,Business,6,6,3:35:18 PM,6,6,3:35:47 PM,6,6,3:48:34 PM,6,6,4:11:29 PM,6,6,4:11:34 PM,6,28.1,,-1.28878,36.8168312,-1.2584143,36.8048002,Rider_Id_119,5
-Order_No_612,User_Id_2987,Bike,3,Business,7,2,11:42:15 AM,7,2,11:42:31 AM,7,2,11:46:26 AM,7,2,11:48:27 AM,7,2,12:06:36 PM,7,23.3,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_754,1089
-Order_No_7147,User_Id_3661,Bike,3,Business,21,4,1:49:50 PM,21,4,2:35:23 PM,21,4,2:41:50 PM,21,4,2:47:04 PM,21,4,3:14:45 PM,15,22.7,,-1.2207911,36.86817620000001,-1.3090727,36.831526200000006,Rider_Id_851,1661
-Order_No_23208,User_Id_3635,Bike,3,Business,13,2,5:22:28 PM,13,2,5:24:25 PM,13,2,5:41:11 PM,13,2,5:49:02 PM,13,2,6:06:39 PM,7,23.4,,-1.2615888000000002,36.7928732,-1.2865726,36.764218400000004,Rider_Id_550,1057
-Order_No_7515,User_Id_2216,Bike,3,Business,10,1,2:31:34 PM,10,1,2:31:52 PM,10,1,2:32:31 PM,10,1,2:43:34 PM,10,1,3:12:54 PM,8,18.5,,-1.2551895,36.7822034,-1.3040024,36.808296999999996,Rider_Id_168,1760
-Order_No_2089,User_Id_1724,Bike,3,Business,10,4,12:15:18 PM,10,4,12:16:20 PM,10,4,12:18:39 PM,10,4,12:29:44 PM,10,4,12:56:00 PM,8,24.7,,-1.2551895,36.7822034,-1.291282,36.798229,Rider_Id_720,1576
-Order_No_27192,User_Id_1410,Bike,3,Business,24,5,4:13:28 PM,24,5,4:16:21 PM,24,5,4:19:24 PM,24,5,4:34:33 PM,24,5,5:23:42 PM,20,19.8,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_218,2949
-Order_No_9371,User_Id_2241,Bike,1,Personal,6,4,10:32:55 AM,6,4,10:33:51 AM,6,4,10:42:08 AM,6,4,10:57:14 AM,6,4,11:17:54 AM,9,22.2,,-1.2782261000000001,36.822500899999994,-1.2555129999999999,36.752421999999996,Rider_Id_726,1240
-Order_No_23158,User_Id_136,Bike,3,Business,10,3,1:19:26 PM,10,3,1:20:01 PM,10,3,1:37:39 PM,10,3,1:39:42 PM,10,3,2:22:25 PM,16,30.6,,-1.3254522,36.721658399999995,-1.2561502,36.80770029999999,Rider_Id_545,2563
-Order_No_7627,User_Id_393,Bike,3,Business,11,4,11:40:07 AM,11,4,11:40:53 AM,11,4,11:41:07 AM,11,4,12:25:52 PM,11,4,12:43:59 PM,10,23.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1087
-Order_No_14948,User_Id_238,Bike,3,Business,31,3,9:35:54 AM,31,3,9:36:05 AM,31,3,9:49:12 AM,31,3,9:53:04 AM,31,3,10:07:19 AM,3,19.2,,-1.3077869,36.844320700000004,-1.3080091999999999,36.8284543,Rider_Id_206,855
-Order_No_4971,User_Id_1584,Bike,3,Business,6,2,12:48:49 PM,6,2,12:49:01 PM,6,2,12:54:35 PM,6,2,1:01:39 PM,6,2,1:27:22 PM,7,25.2,,-1.2969741000000001,36.7856612,-1.2715686000000002,36.8023526,Rider_Id_829,1543
-Order_No_11713,User_Id_3417,Bike,1,Personal,4,1,10:12:10 AM,4,1,10:15:42 AM,4,1,10:20:57 AM,4,1,11:40:27 AM,4,1,12:03:29 PM,9,20.8,,-1.2817945,36.825805100000004,-1.2809666000000002,36.8765961,Rider_Id_618,1382
-Order_No_23332,User_Id_2982,Bike,3,Business,27,4,12:44:19 PM,27,4,12:44:52 PM,27,4,12:45:13 PM,27,4,12:53:24 PM,27,4,1:33:57 PM,18,23.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_31,2433
-Order_No_17813,User_Id_136,Bike,3,Business,12,2,3:26:09 PM,12,2,3:27:48 PM,12,2,3:41:36 PM,12,2,3:53:10 PM,12,2,4:28:16 PM,18,,,-1.2908945,36.82465139999999,-1.3254522,36.721658399999995,Rider_Id_365,2106
-Order_No_17164,User_Id_53,Bike,3,Business,9,2,9:32:30 AM,9,2,9:35:52 AM,9,2,9:44:47 AM,9,2,9:49:28 AM,9,2,10:08:04 AM,5,20.6,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_709,1116
-Order_No_17075,User_Id_393,Bike,3,Business,26,2,1:43:17 PM,26,2,1:43:27 PM,26,2,1:43:31 PM,26,2,2:30:22 PM,26,2,2:30:24 PM,14,31.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515,2
-Order_No_1791,User_Id_190,Bike,3,Business,9,2,11:09:18 AM,9,2,11:09:55 AM,9,2,11:37:05 AM,9,2,12:42:11 PM,9,2,12:42:14 PM,6,26.8,,-1.3278522,36.8711134,-1.3270347,36.866736200000005,Rider_Id_459,3
-Order_No_18089,User_Id_1363,Bike,3,Business,20,3,2:08:38 PM,20,3,2:10:53 PM,20,3,2:25:13 PM,20,3,2:26:19 PM,20,3,3:00:03 PM,7,28.4,,-1.3004062,36.829741,-1.2862436000000002,36.791017,Rider_Id_448,2024
-Order_No_5671,User_Id_2330,Bike,3,Business,19,2,10:14:25 AM,19,2,10:14:41 AM,19,2,10:17:21 AM,19,2,10:25:33 AM,19,2,10:38:09 AM,6,21.1,,-1.2915246999999999,36.805575700000006,-1.3015197,36.7658456,Rider_Id_327,756
-Order_No_11370,User_Id_1146,Bike,3,Business,1,1,4:48:19 PM,1,1,4:49:57 PM,1,1,4:58:25 PM,1,1,5:06:55 PM,1,1,5:21:14 PM,5,27.5,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_506,859
-Order_No_24691,User_Id_908,Bike,3,Business,15,1,2:50:25 PM,15,1,4:05:44 PM,15,1,4:25:43 PM,15,1,4:27:03 PM,15,1,4:47:58 PM,6,,,-1.2731962,36.8072499,-1.3014515,36.8120092,Rider_Id_352,1255
-Order_No_13902,User_Id_3326,Bike,3,Business,16,2,10:15:38 AM,16,2,10:16:39 AM,16,2,10:23:31 AM,16,2,10:30:53 AM,16,2,10:51:59 AM,8,,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_85,1266
-Order_No_2316,User_Id_3201,Bike,3,Business,9,3,3:48:13 PM,9,3,3:48:24 PM,9,3,3:48:47 PM,9,3,4:14:02 PM,9,3,4:14:03 PM,2,25.2,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_501,1
-Order_No_25658,User_Id_187,Bike,3,Business,12,2,2:37:20 PM,12,2,2:37:59 PM,12,2,2:38:51 PM,12,2,3:01:25 PM,12,2,4:31:27 PM,21,27.1,,-1.3108783000000002,36.8392726,-1.3169591,36.7000029,Rider_Id_876,5402
-Order_No_28140,User_Id_2677,Bike,3,Business,8,2,2:48:16 PM,8,2,2:50:43 PM,8,2,3:00:04 PM,8,2,3:03:15 PM,8,2,3:39:48 PM,14,25.2,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_237,2193
-Order_No_28048,User_Id_3625,Bike,1,Personal,29,5,10:26:55 AM,29,5,10:31:22 AM,29,5,10:57:39 AM,29,5,11:07:11 AM,29,5,11:31:22 AM,10,19.7,,-1.2725144,36.82721779999999,-1.2994194,36.7647917,Rider_Id_874,1451
-Order_No_16810,User_Id_3283,Bike,3,Business,23,6,9:53:14 AM,23,6,9:53:26 AM,23,6,10:03:22 AM,23,6,10:13:10 AM,23,6,10:30:46 AM,7,19.1,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_112,1056
-Order_No_15880,User_Id_659,Bike,2,Personal,9,6,10:38:49 AM,9,6,10:41:22 AM,9,6,11:04:00 AM,9,6,11:13:19 AM,9,6,11:40:55 AM,6,17.3,,-1.2852583999999998,36.8043641,-1.2670582000000001,36.7680056,Rider_Id_752,1656
-Order_No_23390,User_Id_3291,Bike,3,Business,13,4,10:23:52 AM,13,4,10:24:22 AM,13,4,10:26:58 AM,13,4,10:34:49 AM,13,4,10:42:58 AM,5,22.0,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_564,489
-Order_No_13247,User_Id_3434,Bike,3,Business,8,5,8:35:16 AM,8,5,8:36:26 AM,8,5,8:57:46 AM,8,5,9:06:35 AM,8,5,9:19:33 AM,5,20.6,,-1.3253826000000002,36.8822315,-1.3189343999999998,36.9087593,Rider_Id_329,778
-Order_No_2309,User_Id_20,Bike,3,Business,6,4,3:40:25 PM,6,4,3:44:43 PM,6,4,4:04:40 PM,6,4,4:11:30 PM,6,4,4:34:21 PM,12,,,-1.3125593,36.8430941,-1.2629666000000002,36.8068369,Rider_Id_205,1371
-Order_No_959,User_Id_3557,Bike,3,Business,18,4,1:03:19 PM,18,4,1:04:56 PM,18,4,1:05:35 PM,18,4,1:14:21 PM,18,4,1:26:26 PM,4,25.3,,-1.2857524,36.819680600000005,-1.2546732,36.808679600000005,Rider_Id_875,725
-Order_No_20325,User_Id_2827,Bike,3,Business,24,1,10:08:17 AM,24,1,10:10:14 AM,24,1,10:17:47 AM,24,1,10:21:10 AM,24,1,10:37:25 AM,5,19.6,,-1.2850643999999998,36.8343657,-1.2544663999999999,36.8224273,Rider_Id_576,975
-Order_No_21729,User_Id_1966,Bike,3,Business,28,4,3:30:14 PM,28,4,3:31:34 PM,28,4,3:47:27 PM,28,4,4:17:39 PM,28,4,5:00:52 PM,21,30.1,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_769,2593
-Order_No_18342,User_Id_1329,Bike,3,Business,29,4,5:23:00 PM,29,4,5:43:04 PM,29,4,5:43:06 PM,29,4,6:04:42 PM,29,4,6:14:41 PM,6,20.9,,-1.273056,36.811298,-1.2623805000000001,36.7805404,Rider_Id_345,599
-Order_No_24126,User_Id_1961,Bike,3,Business,10,1,1:17:29 PM,10,1,1:17:55 PM,10,1,1:18:01 PM,10,1,2:20:49 PM,10,1,2:20:56 PM,19,18.5,,-1.2663631000000002,36.8163329,-1.3188358999999998,36.703852600000005,Rider_Id_864,7
-Order_No_9040,User_Id_2159,Bike,3,Business,14,2,4:51:23 PM,14,2,4:52:18 PM,14,2,5:04:33 PM,14,2,5:41:33 PM,14,2,6:07:30 PM,9,23.2,,-1.2654878999999999,36.7984789,-1.304735,36.784605400000004,Rider_Id_213,1557
-Order_No_8344,User_Id_2402,Bike,3,Business,19,2,2:28:35 PM,19,2,2:32:09 PM,19,2,2:59:38 PM,19,2,3:02:41 PM,19,2,3:41:24 PM,7,29.4,,-1.2902539,36.8211322,-1.2975066000000002,36.76894229999999,Rider_Id_341,2323
-Order_No_9313,User_Id_3391,Bike,1,Personal,3,1,11:58:09 AM,3,1,11:58:25 AM,3,1,12:06:07 PM,3,1,12:08:14 PM,3,1,12:31:27 PM,6,20.6,,-1.3009926,36.8208532,-1.3229541,36.8312466,Rider_Id_249,1393
-Order_No_23330,User_Id_393,Bike,3,Business,22,5,7:46:35 AM,22,5,7:47:29 AM,22,5,7:59:05 AM,22,5,8:07:05 AM,22,5,8:17:42 AM,4,22.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_910,637
-Order_No_24612,User_Id_1690,Bike,3,Business,27,3,3:17:09 PM,27,3,3:26:44 PM,27,3,3:42:58 PM,27,3,3:57:19 PM,27,3,4:21:14 PM,6,,,-1.2938211000000002,36.8270021,-1.304033,36.7848695,Rider_Id_431,1435
-Order_No_3270,User_Id_3404,Bike,3,Business,22,6,11:58:52 AM,22,6,11:59:10 AM,22,6,12:01:36 PM,22,6,12:06:29 PM,22,6,12:21:44 PM,5,21.2,,-1.2591018999999999,36.800577000000004,-1.2793686000000002,36.82592329999999,Rider_Id_933,915
-Order_No_9713,User_Id_2701,Bike,3,Business,7,5,10:47:01 AM,7,5,11:24:17 AM,7,5,11:34:53 AM,7,5,11:42:41 AM,7,5,12:53:53 PM,12,,,-1.302605,36.766219,-1.2765736,36.851364600000004,Rider_Id_827,4272
-Order_No_4024,User_Id_2914,Bike,2,Personal,23,6,12:11:53 PM,23,6,12:12:42 PM,23,6,12:35:25 PM,23,6,12:47:11 PM,23,6,1:08:31 PM,5,,,-1.3019593,36.7521092,-1.2637698999999998,36.7500558,Rider_Id_752,1280
-Order_No_27029,User_Id_1578,Bike,3,Business,25,5,3:02:54 PM,25,5,3:04:02 PM,25,5,3:30:20 PM,25,5,3:35:31 PM,25,5,4:35:58 PM,15,29.3,,-1.28878,36.8168312,-1.216682,36.908577799999996,Rider_Id_553,3627
-Order_No_12,User_Id_1934,Bike,2,Personal,16,3,10:57:05 AM,16,3,11:27:53 AM,16,3,11:42:45 AM,16,3,11:49:23 AM,16,3,12:18:01 PM,6,24.0,,-1.2875013000000002,36.8171787,-1.2502923000000001,36.788756799999994,Rider_Id_949,1718
-Order_No_1695,User_Id_1502,Bike,3,Business,14,5,3:31:16 PM,14,5,4:20:11 PM,14,5,4:33:29 PM,14,5,4:35:15 PM,14,5,5:44:03 PM,3,22.9,,-1.2232123,36.8932547,-1.2198413000000001,36.9054958,Rider_Id_854,4128
-Order_No_25188,User_Id_1502,Bike,3,Business,20,3,3:35:19 PM,20,3,3:35:30 PM,20,3,3:43:09 PM,20,3,4:44:58 PM,20,3,4:57:34 PM,23,27.6,,-1.3472066,36.7692638,-1.2232123,36.8932547,Rider_Id_436,756
-Order_No_25415,User_Id_2255,Bike,1,Personal,24,4,4:26:10 PM,24,4,4:26:32 PM,24,4,4:43:11 PM,24,4,5:09:30 PM,24,4,5:49:48 PM,15,21.2,,-1.289498,36.818878000000005,-1.3169591,36.7000029,Rider_Id_360,2418
-Order_No_13224,User_Id_2330,Bike,3,Business,27,6,10:59:28 AM,27,6,11:02:48 AM,27,6,11:14:29 AM,27,6,11:26:49 AM,27,6,11:44:56 AM,9,,,-1.2645318,36.7981775,-1.2326041,36.8009029,Rider_Id_96,1087
-Order_No_26118,User_Id_111,Bike,3,Business,17,3,11:09:00 AM,17,3,11:09:58 AM,17,3,11:21:44 AM,17,3,11:43:29 AM,17,3,12:27:37 PM,8,25.4,,-1.2551895,36.7822034,-1.2868736,36.780588200000004,Rider_Id_857,2648
-Order_No_27373,User_Id_3517,Bike,3,Business,8,2,1:08:12 PM,8,2,1:08:26 PM,8,2,1:08:32 PM,8,2,2:15:22 PM,8,2,2:43:07 PM,15,25.3,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_871,1665
-Order_No_13793,User_Id_1882,Bike,3,Business,4,1,8:08:52 AM,4,1,8:09:08 AM,4,1,8:14:53 AM,4,1,8:45:07 AM,4,1,9:02:30 AM,6,22.0,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_715,1043
-Order_No_25529,User_Id_3291,Bike,3,Business,4,4,3:03:57 PM,4,4,3:04:53 PM,4,4,3:15:35 PM,4,4,3:36:07 PM,4,4,4:00:07 PM,9,27.4,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_166,1440
-Order_No_166,User_Id_1244,Bike,3,Business,26,6,11:06:22 AM,26,6,11:08:36 AM,26,6,11:36:55 AM,26,6,11:55:43 AM,26,6,12:45:49 PM,16,25.9,,-1.3346773,36.8842734,-1.303596,36.778378000000004,Rider_Id_520,3006
-Order_No_3910,User_Id_633,Bike,3,Business,3,6,11:12:14 AM,3,6,11:15:36 AM,3,6,11:39:25 AM,3,6,11:52:10 AM,3,6,12:06:02 PM,4,23.5,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_56,832
-Order_No_9872,User_Id_3557,Bike,3,Business,24,3,4:13:16 PM,24,3,4:13:40 PM,24,3,4:20:05 PM,24,3,4:37:27 PM,24,3,4:37:58 PM,3,18.4,,-1.2659913999999999,36.828365500000004,-1.2546732,36.808679600000005,Rider_Id_579,31
-Order_No_14626,User_Id_2992,Bike,1,Personal,25,3,4:45:45 PM,25,3,4:46:26 PM,25,3,4:56:35 PM,25,3,5:04:33 PM,25,3,5:21:51 PM,6,17.6,,-1.2911796,36.821506799999995,-1.296268,36.785084000000005,Rider_Id_808,1038
-Order_No_9696,User_Id_969,Bike,1,Personal,31,4,1:28:07 PM,31,4,1:47:14 PM,31,4,2:09:41 PM,31,4,2:21:20 PM,31,4,2:40:10 PM,5,22.7,,-1.2850851,36.82176,-1.3004846,36.790159100000004,Rider_Id_844,1130
-Order_No_20439,User_Id_20,Bike,3,Business,5,3,2:53:26 PM,5,3,2:59:49 PM,5,3,3:33:16 PM,5,3,3:45:18 PM,5,3,3:57:45 PM,5,23.9,,-1.2629666000000002,36.8068369,-1.2881332,36.821433899999995,Rider_Id_857,747
-Order_No_4769,User_Id_246,Bike,3,Business,1,5,1:44:57 PM,1,5,1:54:59 PM,1,5,2:22:59 PM,1,5,2:33:37 PM,1,5,3:01:27 PM,15,28.6,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_237,1670
-Order_No_20563,User_Id_510,Bike,3,Business,10,4,4:50:46 PM,10,4,4:51:12 PM,10,4,5:08:56 PM,10,4,5:17:31 PM,10,4,5:43:32 PM,13,23.4,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_418,1561
-Order_No_20098,User_Id_1884,Bike,1,Personal,20,4,12:17:40 PM,20,4,12:18:23 PM,20,4,12:27:30 PM,20,4,12:29:54 PM,20,4,12:51:21 PM,4,24.6,,-1.2807298,36.8299116,-1.3049316999999998,36.8225732,Rider_Id_338,1287
-Order_No_1373,User_Id_1964,Bike,3,Personal,7,4,12:24:08 PM,7,4,12:24:26 PM,7,4,12:39:21 PM,7,4,12:41:26 PM,7,4,1:21:44 PM,13,24.1,,-1.34364,36.8925345,-1.2802618000000001,36.8191684,Rider_Id_411,2418
-Order_No_8048,User_Id_2956,Bike,3,Business,25,5,10:31:56 AM,25,5,10:56:40 AM,25,5,10:56:49 AM,25,5,12:08:00 PM,25,5,12:50:19 PM,15,21.0,,-1.3562374,36.904295399999995,-1.290894,36.822971,Rider_Id_100,2539
-Order_No_13930,User_Id_3127,Bike,3,Business,20,3,12:11:53 PM,20,3,12:13:20 PM,20,3,12:21:59 PM,20,3,12:24:53 PM,20,3,12:41:40 PM,7,27.3,,-1.2641888000000001,36.7603984,-1.2918246,36.801584999999996,Rider_Id_517,1007
-Order_No_9946,User_Id_2415,Bike,1,Personal,15,6,9:45:41 AM,15,6,9:51:55 AM,15,6,10:02:39 AM,15,6,10:28:36 AM,15,6,10:46:29 AM,6,17.8,,-1.2699263,36.8468961,-1.2766473999999999,36.8155764,Rider_Id_189,1073
-Order_No_413,User_Id_2174,Bike,3,Business,31,4,10:45:04 AM,31,4,10:45:10 AM,31,4,10:45:53 AM,31,4,11:07:19 AM,31,4,11:28:48 AM,17,26.4,,-1.2482781,36.884091999999995,-1.1707617000000001,36.9675086,Rider_Id_510,1289
-Order_No_17857,User_Id_584,Bike,1,Personal,25,5,11:40:13 AM,25,5,12:01:27 PM,25,5,12:19:00 PM,25,5,12:33:01 PM,25,5,12:45:14 PM,3,21.2,,-1.2981102,36.8293513,-1.302559,36.819555,Rider_Id_749,733
-Order_No_7732,User_Id_910,Bike,3,Personal,13,1,11:38:23 AM,13,1,11:53:04 AM,13,1,11:59:09 AM,13,1,12:03:44 PM,13,1,1:12:34 PM,5,17.3,,-1.2506462,36.846258,-1.287588,36.820226,Rider_Id_265,4130
-Order_No_11673,User_Id_2909,Bike,3,Personal,1,6,9:49:08 AM,1,6,9:49:34 AM,1,6,10:00:28 AM,1,6,10:10:48 AM,1,6,10:41:12 AM,14,15.4,,-1.2850643999999998,36.8343657,-1.2780989,36.7286308,Rider_Id_857,1824
-Order_No_11418,User_Id_1620,Bike,1,Personal,9,6,10:13:39 AM,9,6,10:14:58 AM,9,6,10:20:15 AM,9,6,10:47:02 AM,9,6,11:08:25 AM,10,17.4,,-1.2871215,36.822053000000004,-1.227132,36.8374277,Rider_Id_379,1283
-Order_No_8872,User_Id_2883,Bike,2,Personal,18,2,11:32:07 AM,18,2,11:32:17 AM,18,2,11:38:01 AM,18,2,11:39:08 AM,18,2,11:49:26 AM,4,22.5,,-1.2847098999999997,36.772234499999996,-1.2854173999999998,36.7951152,Rider_Id_726,618
-Order_No_779,User_Id_2154,Bike,3,Business,10,1,12:45:05 PM,10,1,12:45:26 PM,10,1,1:19:02 PM,10,1,1:34:39 PM,10,1,1:58:45 PM,13,18.8,,-1.2156006000000001,36.8916865,-1.2765736,36.851364600000004,Rider_Id_522,1446
-Order_No_219,User_Id_3674,Bike,3,Business,26,2,9:50:04 AM,26,2,9:51:22 AM,26,2,10:05:28 AM,26,2,10:08:33 AM,26,2,10:34:30 AM,6,,,-1.2551895,36.7822034,-1.2765641,36.793782,Rider_Id_829,1557
-Order_No_26872,User_Id_111,Bike,3,Business,12,5,2:05:28 PM,12,5,2:08:04 PM,12,5,2:19:50 PM,12,5,2:28:27 PM,12,5,2:49:13 PM,10,,,-1.2551895,36.7822034,-1.2998418999999999,36.844024100000006,Rider_Id_843,1246
-Order_No_16824,User_Id_1389,Bike,3,Business,6,1,1:38:53 PM,6,1,1:45:18 PM,6,1,2:09:10 PM,6,1,2:14:28 PM,6,1,2:44:30 PM,16,,,-1.207546,36.7913976,-1.2551895,36.7822034,Rider_Id_96,1802
-Order_No_28067,User_Id_2987,Bike,3,Business,13,4,10:06:25 AM,13,4,10:07:04 AM,13,4,10:19:06 AM,13,4,10:27:14 AM,13,4,10:58:47 AM,15,18.0,2.0,-1.2702120000000001,36.82108770000001,-1.3284365,36.8970679,Rider_Id_419,1893
-Order_No_26228,User_Id_3149,Bike,3,Business,7,4,1:56:03 PM,7,4,1:57:15 PM,7,4,2:00:23 PM,7,4,2:31:03 PM,7,4,2:37:49 PM,6,30.9,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_411,406
-Order_No_13290,User_Id_2289,Bike,3,Business,18,3,3:43:23 PM,18,3,3:43:53 PM,18,3,3:51:27 PM,18,3,3:55:11 PM,18,3,4:20:31 PM,8,20.9,,-1.2551895,36.7822034,-1.2930037,36.812647399999996,Rider_Id_764,1520
-Order_No_24977,User_Id_3560,Bike,3,Business,24,1,2:59:03 PM,24,1,2:59:47 PM,24,1,3:05:48 PM,24,1,3:30:36 PM,24,1,3:30:39 PM,2,24.7,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_86,3
-Order_No_12352,User_Id_2781,Bike,3,Personal,22,2,11:38:44 AM,22,2,11:39:04 AM,22,2,11:49:09 AM,22,2,11:58:40 AM,22,2,12:36:17 PM,16,19.5,,-1.3043649,36.7816132,-1.3484124,36.6711807,Rider_Id_755,2257
-Order_No_7807,User_Id_1341,Bike,3,Business,13,3,2:38:40 PM,13,3,2:39:15 PM,13,3,2:45:00 PM,13,3,2:54:50 PM,13,3,2:58:09 PM,7,29.4,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_633,199
-Order_No_5034,User_Id_2374,Bike,2,Personal,27,3,12:39:18 PM,27,3,12:41:05 PM,27,3,1:05:10 PM,27,3,1:10:01 PM,27,3,1:29:10 PM,7,,,-1.2884354,36.760676600000004,-1.2707178999999997,36.7979713,Rider_Id_465,1149
-Order_No_22425,User_Id_3669,Bike,3,Business,25,1,11:17:34 AM,25,1,11:18:01 AM,25,1,11:22:10 AM,25,1,11:27:49 AM,25,1,11:46:51 AM,2,25.3,,-1.2615888000000002,36.7928732,-1.2564152,36.779280299999996,Rider_Id_576,1142
-Order_No_1032,User_Id_2063,Bike,3,Business,25,1,8:49:37 AM,25,1,8:50:30 AM,25,1,9:00:49 AM,25,1,9:14:19 AM,25,1,9:33:15 AM,8,21.0,,-1.2577288999999998,36.74107720000001,-1.2551895,36.7822034,Rider_Id_655,1136
-Order_No_2122,User_Id_3641,Bike,3,Business,3,1,3:27:19 PM,3,1,3:28:28 PM,3,1,4:08:10 PM,3,1,4:25:12 PM,3,1,5:08:08 PM,17,23.0,,-1.2551895,36.7822034,-1.2977643,36.8844591,Rider_Id_116,2576
-Order_No_14351,User_Id_306,Bike,1,Personal,9,6,1:05:07 PM,9,6,1:05:49 PM,9,6,1:21:33 PM,9,6,1:35:05 PM,9,6,2:01:47 PM,9,19.0,,-1.3157973,36.815148,-1.258321,36.795453,Rider_Id_549,1602
-Order_No_22230,User_Id_2523,Bike,3,Business,26,5,2:40:58 PM,26,5,2:41:16 PM,26,5,2:41:29 PM,26,5,2:53:52 PM,26,5,3:03:02 PM,8,,,-1.2551895,36.7822034,-1.2856664,36.755983799999996,Rider_Id_291,550
-Order_No_1543,User_Id_3560,Bike,3,Business,9,3,2:26:50 PM,9,3,2:28:45 PM,9,3,3:22:50 PM,9,3,3:24:45 PM,9,3,3:47:13 PM,9,25.5,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_801,1348
-Order_No_22762,User_Id_1346,Bike,3,Business,11,1,1:14:43 PM,11,1,1:14:49 PM,11,1,1:22:25 PM,11,1,1:26:05 PM,11,1,1:56:24 PM,19,18.2,,-1.2998129999999999,36.787139200000006,-1.3700383,36.9190174,Rider_Id_543,1819
-Order_No_9547,User_Id_2212,Bike,3,Business,13,3,10:54:53 AM,13,3,10:55:09 AM,13,3,11:00:19 AM,13,3,11:08:47 AM,13,3,11:27:07 AM,5,24.8,,-1.2571472,36.795063299999995,-1.2425149,36.789013700000005,Rider_Id_778,1100
-Order_No_22919,User_Id_1033,Bike,3,Personal,5,2,11:37:27 AM,5,2,11:37:58 AM,5,2,11:57:22 AM,5,2,11:59:36 AM,5,2,1:11:04 PM,12,25.3,,-1.2325706,36.808392299999994,-1.2958383,36.8073162,Rider_Id_547,4288
-Order_No_10698,User_Id_3492,Bike,3,Business,29,3,10:52:24 AM,29,3,10:52:50 AM,29,3,11:05:59 AM,29,3,11:15:19 AM,29,3,11:36:58 AM,7,,,-1.299207,36.8414337,-1.2728947,36.807942600000004,Rider_Id_2,1299
-Order_No_11276,User_Id_2066,Bike,3,Business,1,3,4:01:27 PM,1,3,4:02:28 PM,1,3,4:22:24 PM,1,3,4:25:36 PM,1,3,5:26:45 PM,17,24.1,,-1.316961,36.820337,-1.2541368999999998,36.756404499999995,Rider_Id_672,3669
-Order_No_13556,User_Id_2523,Bike,3,Business,8,2,12:49:29 PM,8,2,1:01:20 PM,8,2,1:09:30 PM,8,2,1:20:44 PM,8,2,1:36:32 PM,5,21.5,,-1.2559559999999999,36.789867,-1.2697379999999998,36.7965023,Rider_Id_535,948
-Order_No_1020,User_Id_2200,Bike,3,Business,29,1,8:25:35 AM,29,1,8:57:40 AM,29,1,9:14:38 AM,29,1,9:21:10 AM,29,1,9:39:40 AM,4,19.2,,-1.290894,36.822971,-1.3047026000000002,36.8172033,Rider_Id_171,1110
-Order_No_4601,User_Id_1721,Bike,3,Business,3,1,3:14:42 PM,3,1,3:15:53 PM,3,1,3:22:49 PM,3,1,3:30:36 PM,3,1,3:58:33 PM,14,23.2,,-1.2551895,36.7822034,-1.2558721000000002,36.7101188,Rider_Id_856,1677
-Order_No_8664,User_Id_2869,Bike,3,Business,12,2,10:48:32 AM,12,2,10:58:10 AM,12,2,11:48:10 AM,12,2,11:57:43 AM,12,2,12:36:55 PM,9,24.3,,-1.2845766,36.8245168,-1.3025826,36.7670807,Rider_Id_404,2352
-Order_No_4539,User_Id_517,Bike,3,Business,9,5,12:44:27 PM,9,5,12:53:44 PM,9,5,1:01:10 PM,9,5,1:07:46 PM,9,5,1:30:04 PM,10,25.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_555,1338
-Order_No_17979,User_Id_649,Bike,1,Business,12,4,10:47:28 AM,12,4,10:47:41 AM,12,4,10:48:19 AM,12,4,11:13:53 AM,12,4,11:52:53 AM,11,18.8,,-1.2551895,36.7822034,-1.2964396,36.7760609,Rider_Id_103,2340
-Order_No_18855,User_Id_186,Bike,3,Business,14,2,9:41:55 AM,14,2,9:42:44 AM,14,2,9:51:52 AM,14,2,9:55:24 AM,14,2,10:07:31 AM,4,,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_435,727
-Order_No_21508,User_Id_2100,Bike,3,Business,19,5,11:46:32 AM,19,5,11:46:40 AM,19,5,11:48:25 AM,19,5,11:59:30 AM,19,5,12:06:10 PM,8,24.7,,-1.2765736,36.851364600000004,-1.2948091999999998,36.8869203,Rider_Id_179,400
-Order_No_14416,User_Id_1304,Bike,3,Business,24,2,3:41:48 PM,24,2,3:41:59 PM,24,2,3:47:55 PM,24,2,3:50:52 PM,24,2,3:56:47 PM,3,18.4,,-1.2551895,36.7822034,-1.2562249,36.786866100000005,Rider_Id_519,355
-Order_No_4771,User_Id_3201,Bike,3,Business,23,5,11:34:41 AM,23,5,11:46:45 AM,23,5,11:47:38 AM,23,5,12:23:40 PM,23,5,1:00:42 PM,6,24.0,,-1.2584143,36.8048002,-1.2967701999999999,36.8040381,Rider_Id_101,2222
-Order_No_23974,User_Id_496,Bike,3,Business,9,2,11:29:18 AM,9,2,11:29:59 AM,9,2,11:41:46 AM,9,2,11:46:51 AM,9,2,11:56:37 AM,3,27.1,,-1.2551895,36.7822034,-1.273488,36.804158,Rider_Id_695,586
-Order_No_26141,User_Id_1926,Bike,3,Business,6,2,4:04:07 PM,6,2,4:23:45 PM,6,2,4:37:11 PM,6,2,4:51:30 PM,6,2,5:32:16 PM,10,,,-1.3332749,36.870814700000004,-1.2897368,36.8226783,Rider_Id_612,2446
-Order_No_5126,User_Id_393,Bike,3,Business,14,1,9:58:01 AM,14,1,9:58:22 AM,14,1,9:58:37 AM,14,1,10:41:51 AM,14,1,10:42:01 AM,11,23.5,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,10
-Order_No_10918,User_Id_1787,Bike,3,Business,24,7,2:42:06 PM,24,7,2:43:30 PM,24,7,3:12:12 PM,24,7,3:20:58 PM,24,7,3:52:12 PM,12,,,-1.2860183,36.8975338,-1.2283403,36.8822756,Rider_Id_668,1874
-Order_No_20559,User_Id_3247,Bike,3,Business,15,3,10:34:14 AM,15,3,10:36:12 AM,15,3,10:45:12 AM,15,3,10:54:41 AM,15,3,11:18:14 AM,10,,,-1.2571472,36.795063299999995,-1.31861,36.8113967,Rider_Id_205,1413
-Order_No_15768,User_Id_3635,Bike,1,Business,22,3,3:54:32 PM,22,3,3:54:50 PM,22,3,3:57:45 PM,22,3,4:25:20 PM,22,3,5:07:00 PM,23,25.8,,-1.2615888000000002,36.7928732,-1.3592118,36.6717545,Rider_Id_857,2500
-Order_No_3488,User_Id_3201,Bike,3,Business,24,3,9:40:56 AM,24,3,9:41:04 AM,24,3,9:54:23 AM,24,3,10:02:12 AM,24,3,10:14:32 AM,5,17.1,0.9,-1.2584143,36.8048002,-1.2559559999999999,36.772901,Rider_Id_620,740
-Order_No_21830,User_Id_1261,Bike,1,Personal,19,5,8:22:44 AM,19,5,8:23:01 AM,19,5,8:30:39 AM,19,5,8:32:32 AM,19,5,10:03:25 AM,18,20.1,,-1.2669736,36.8040985,-1.3232073999999998,36.89136679999999,Rider_Id_108,5453
-Order_No_20120,User_Id_2344,Bike,3,Business,5,2,4:32:51 PM,5,2,4:34:13 PM,5,2,4:34:59 PM,5,2,4:47:03 PM,5,2,5:25:44 PM,18,21.6,,-1.2553615,36.789537599999996,-1.3385790000000002,36.89630379999999,Rider_Id_379,2321
-Order_No_640,User_Id_1368,Bike,3,Business,11,1,2:48:11 PM,11,1,2:54:39 PM,11,1,3:02:16 PM,11,1,3:15:35 PM,11,1,3:59:09 PM,15,28.2,,-1.2571472,36.795063299999995,-1.3366311000000002,36.769092799999996,Rider_Id_205,2614
-Order_No_20211,User_Id_2212,Bike,3,Business,19,5,9:04:04 AM,19,5,9:42:00 AM,19,5,10:07:30 AM,19,5,10:13:06 AM,19,5,11:00:26 AM,5,20.0,,-1.2646084,36.8028645,-1.2841726,36.8179589,Rider_Id_829,2840
-Order_No_27681,User_Id_113,Bike,3,Business,18,3,2:30:34 PM,18,3,2:31:29 PM,18,3,2:44:35 PM,18,3,2:50:49 PM,18,3,3:39:28 PM,8,20.7,,-1.283998,36.819886,-1.2963097,36.7688221,Rider_Id_148,2919
-Order_No_18667,User_Id_3211,Bike,3,Personal,27,3,10:43:48 AM,27,3,10:54:20 AM,27,3,11:07:52 AM,27,3,11:22:49 AM,27,3,11:47:33 AM,12,,,-1.2783772,36.8201617,-1.2245407,36.7583778,Rider_Id_661,1484
-Order_No_13681,User_Id_2112,Bike,3,Business,25,4,11:26:38 AM,25,4,11:26:47 AM,25,4,11:27:12 AM,25,4,11:46:45 AM,25,4,12:16:17 PM,13,,,-1.2527958000000001,36.8003131,-1.2156006000000001,36.8916865,Rider_Id_89,1772
-Order_No_580,User_Id_2982,Bike,3,Business,10,1,9:33:54 AM,10,1,9:34:14 AM,10,1,9:41:40 AM,10,1,10:34:51 AM,10,1,11:21:38 AM,18,18.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_519,2807
-Order_No_24057,User_Id_2150,Bike,3,Business,2,2,2:26:27 PM,2,2,2:27:23 PM,2,2,2:55:22 PM,2,2,2:59:33 PM,2,2,3:31:30 PM,15,26.2,,-1.2872344,36.7773949,-1.3444459999999998,36.756597,Rider_Id_38,1917
-Order_No_17145,User_Id_1502,Bike,3,Business,4,1,3:46:32 PM,4,1,3:46:48 PM,4,1,3:56:41 PM,4,1,4:11:44 PM,4,1,4:57:03 PM,19,,,-1.3014461000000002,36.7661381,-1.2232123,36.8932547,Rider_Id_148,2719
-Order_No_21502,User_Id_1500,Bike,3,Business,18,4,11:25:34 AM,18,4,11:40:16 AM,18,4,11:46:45 AM,18,4,12:03:39 PM,18,4,12:26:18 PM,10,,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_327,1359
-Order_No_13675,User_Id_2701,Bike,3,Business,22,3,9:47:54 AM,22,3,9:48:09 AM,22,3,10:04:11 AM,22,3,10:06:37 AM,22,3,10:40:54 AM,7,15.3,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_932,2057
-Order_No_566,User_Id_496,Bike,3,Business,23,5,10:30:43 AM,23,5,10:31:07 AM,23,5,10:43:42 AM,23,5,10:51:00 AM,23,5,11:40:02 AM,23,24.3,,-1.374163,36.913444,-1.2551895,36.7822034,Rider_Id_880,2942
-Order_No_8983,User_Id_1882,Bike,3,Business,13,7,9:36:15 AM,13,7,9:36:27 AM,13,7,9:44:24 AM,13,7,9:49:28 AM,13,7,10:28:25 AM,14,20.1,,-1.2268871000000001,36.8073946,-1.2237773,36.8861711,Rider_Id_101,2337
-Order_No_8002,User_Id_3492,Bike,3,Business,28,2,12:59:58 PM,28,2,1:00:55 PM,28,2,1:15:43 PM,28,2,1:27:20 PM,28,2,1:49:03 PM,7,21.1,,-1.2728947,36.807942600000004,-1.299207,36.8414337,Rider_Id_907,1303
-Order_No_6854,User_Id_1389,Bike,3,Business,4,1,4:02:05 PM,4,1,4:02:28 PM,4,1,4:15:53 PM,4,1,4:34:25 PM,4,1,4:50:09 PM,5,21.0,,-1.2584143,36.8048002,-1.2885528999999998,36.816014100000004,Rider_Id_742,944
-Order_No_4366,User_Id_2803,Bike,3,Business,17,1,1:06:17 PM,17,1,1:25:40 PM,17,1,2:16:33 PM,17,1,2:17:41 PM,17,1,3:14:07 PM,7,24.7,,-1.2765736,36.851364600000004,-1.2963492,36.8093377,Rider_Id_436,3386
-Order_No_10520,User_Id_3080,Bike,3,Business,8,5,1:02:20 PM,8,5,2:41:58 PM,8,5,2:50:11 PM,8,5,2:59:24 PM,8,5,3:30:08 PM,22,30.5,,-1.2551895,36.7822034,-1.3342256000000001,36.9106741,Rider_Id_328,1844
-Order_No_8916,User_Id_3291,Bike,3,Business,3,4,8:49:38 AM,3,4,8:52:15 AM,3,4,9:03:16 AM,3,4,9:18:29 AM,3,4,9:40:40 AM,12,20.5,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_263,1331
-Order_No_4361,User_Id_146,Bike,3,Personal,3,4,11:29:55 AM,3,4,11:30:12 AM,3,4,11:46:03 AM,3,4,11:50:09 AM,3,4,12:31:10 PM,14,23.5,,-1.2985153,36.7876043,-1.3389278999999998,36.7092201,Rider_Id_846,2461
-Order_No_21639,User_Id_1927,Bike,3,Business,2,2,3:36:41 PM,2,2,3:41:06 PM,2,2,3:51:04 PM,2,2,4:44:16 PM,2,2,4:44:28 PM,14,28.6,,-1.3472066,36.7692638,-1.3263105,36.8622904,Rider_Id_947,12
-Order_No_9045,User_Id_1245,Bike,3,Business,9,4,3:30:59 PM,9,4,3:37:59 PM,9,4,3:39:33 PM,9,4,4:11:17 PM,9,4,4:33:43 PM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_297,1346
-Order_No_19954,User_Id_3599,Bike,3,Business,29,5,9:54:29 AM,29,5,10:02:06 AM,29,5,10:22:10 AM,29,5,11:04:26 AM,29,5,11:42:01 AM,15,26.6,,-1.3302996,36.8707935,-1.2612531999999999,36.7897977,Rider_Id_654,2255
-Order_No_3725,User_Id_3064,Bike,3,Business,25,4,11:40:34 AM,25,4,11:55:57 AM,25,4,11:56:14 AM,25,4,12:07:54 PM,25,4,12:30:55 PM,6,,,-1.2571472,36.795063299999995,-1.2990813,36.8034213,Rider_Id_61,1381
-Order_No_7520,User_Id_393,Bike,3,Business,6,4,11:59:26 AM,6,4,11:59:59 AM,6,4,12:02:21 PM,6,4,12:12:47 PM,6,4,12:26:08 PM,5,19.5,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_2,801
-Order_No_27229,User_Id_375,Bike,3,Business,11,3,10:33:31 AM,11,3,11:32:42 AM,11,3,12:22:01 PM,11,3,12:45:11 PM,11,3,1:27:50 PM,8,19.2,,-1.2870803,36.8152302,-1.2551895,36.7822034,Rider_Id_752,2559
-Order_No_6417,User_Id_3264,Bike,3,Business,24,4,4:01:36 PM,24,4,4:03:16 PM,24,4,4:10:28 PM,24,4,4:32:45 PM,24,4,4:32:49 PM,8,26.2,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_322,4
-Order_No_765,User_Id_1148,Bike,3,Business,13,4,2:56:52 PM,13,4,2:57:56 PM,13,4,3:08:28 PM,13,4,3:35:23 PM,13,4,4:06:31 PM,15,22.3,,-1.3100586,36.850032899999995,-1.2409761000000001,36.776006200000005,Rider_Id_676,1868
-Order_No_12737,User_Id_2008,Bike,3,Business,2,4,8:54:17 AM,2,4,8:54:58 AM,2,4,9:32:16 AM,2,4,9:58:16 AM,2,4,10:13:58 AM,6,,,-1.2508229,36.7895263,-1.285244,36.832159000000004,Rider_Id_119,942
-Order_No_21664,User_Id_700,Bike,3,Business,7,1,4:17:26 PM,7,1,4:18:07 PM,7,1,4:28:29 PM,7,1,4:33:18 PM,7,1,5:01:40 PM,11,24.2,,-1.323135,36.833139,-1.3014461000000002,36.7661381,Rider_Id_779,1702
-Order_No_23855,User_Id_2154,Bike,3,Business,27,3,12:27:05 PM,27,3,12:27:48 PM,27,3,12:40:41 PM,27,3,12:59:19 PM,27,3,1:08:09 PM,5,26.3,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_336,530
-Order_No_2237,User_Id_1500,Bike,3,Business,22,5,9:00:10 AM,22,5,9:01:34 AM,22,5,9:28:32 AM,22,5,9:35:12 AM,22,5,10:13:47 AM,10,22.2,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_327,2315
-Order_No_15867,User_Id_1927,Bike,3,Business,12,2,10:44:52 AM,12,2,10:45:14 AM,12,2,10:58:44 AM,12,2,11:42:06 AM,12,2,12:19:32 PM,23,24.5,,-1.2285834999999998,36.882646799999996,-1.3472066,36.7692638,Rider_Id_199,2246
-Order_No_7113,User_Id_3201,Bike,3,Business,9,3,12:59:39 PM,9,3,12:59:56 PM,9,3,1:12:00 PM,9,3,1:14:30 PM,9,3,1:24:20 PM,2,25.2,,-1.2584143,36.8048002,-1.2561334,36.7990458,Rider_Id_580,590
-Order_No_15592,User_Id_1882,Bike,3,Business,14,3,6:59:22 AM,14,3,8:25:50 AM,14,3,8:41:45 AM,14,3,8:53:21 AM,14,3,9:08:22 AM,5,18.5,,-1.2958783,36.7928125,-1.2877809999999998,36.817865999999995,Rider_Id_612,901
-Order_No_12325,User_Id_868,Bike,3,Business,13,4,8:45:54 AM,13,4,8:47:23 AM,13,4,9:43:02 AM,13,4,9:56:14 AM,13,4,10:10:58 AM,7,17.9,2.0,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_829,884
-Order_No_22390,User_Id_3647,Bike,3,Business,14,2,1:18:10 PM,14,2,1:18:29 PM,14,2,1:18:51 PM,14,2,1:26:13 PM,14,2,2:10:16 PM,18,,,-1.2726389999999999,36.794723,-1.344748,36.745184,Rider_Id_293,2643
-Order_No_9967,User_Id_1271,Bike,3,Business,4,2,9:58:52 AM,4,2,10:17:26 AM,4,2,10:37:43 AM,4,2,10:44:04 AM,4,2,11:25:49 AM,24,19.7,,-1.3244885,36.897791999999995,-1.2830073,36.7662294,Rider_Id_741,2505
-Order_No_19403,User_Id_2112,Bike,3,Business,18,2,10:39:36 AM,18,2,10:56:37 AM,18,2,11:02:52 AM,18,2,11:37:48 AM,18,2,11:56:57 AM,14,,,-1.2571472,36.795063299999995,-1.2156006000000001,36.8916865,Rider_Id_453,1149
-Order_No_21012,User_Id_3487,Bike,3,Business,3,5,1:24:31 PM,3,5,1:28:43 PM,3,5,1:34:22 PM,3,5,1:56:01 PM,3,5,2:15:18 PM,5,,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_953,1157
-Order_No_15477,User_Id_625,Bike,3,Business,2,3,12:46:39 PM,2,3,12:55:25 PM,2,3,1:32:59 PM,2,3,1:38:47 PM,2,3,1:49:56 PM,5,23.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_753,669
-Order_No_21921,User_Id_190,Bike,3,Business,23,4,2:43:07 PM,23,4,2:45:42 PM,23,4,3:12:11 PM,23,4,3:15:28 PM,23,4,3:49:38 PM,17,24.8,,-1.2459607,36.7661735,-1.3270347,36.866736200000005,Rider_Id_576,2050
-Order_No_16448,User_Id_1974,Bike,3,Personal,3,3,4:58:10 PM,3,3,4:58:27 PM,3,3,5:11:13 PM,3,3,5:27:56 PM,3,3,5:33:37 PM,9,27.4,,-1.3013101999999999,36.796370200000005,-1.2538731,36.7850223,Rider_Id_678,341
-Order_No_449,User_Id_3192,Bike,3,Personal,29,2,3:36:18 PM,29,2,3:36:31 PM,29,2,3:47:53 PM,29,2,4:14:25 PM,29,2,4:31:40 PM,8,23.6,,-1.3249978,36.8304773,-1.2658315,36.809049,Rider_Id_888,1035
-Order_No_19092,User_Id_2676,Bike,3,Business,24,4,4:57:57 PM,24,4,4:58:35 PM,24,4,5:09:39 PM,24,4,5:16:37 PM,24,4,5:35:10 PM,9,21.0,,-1.2963097,36.7688221,-1.2624229999999999,36.801647100000004,Rider_Id_103,1113
-Order_No_12159,User_Id_3517,Bike,3,Business,8,4,9:33:25 AM,8,4,9:34:04 AM,8,4,9:39:34 AM,8,4,10:04:15 AM,8,4,10:37:08 AM,15,20.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_329,1973
-Order_No_7670,User_Id_3091,Bike,3,Business,11,2,9:47:31 AM,11,2,9:49:39 AM,11,2,10:33:37 AM,11,2,10:42:48 AM,11,2,11:12:41 AM,9,19.6,,-1.279395,36.825364,-1.3025826,36.7670807,Rider_Id_819,1793
-Order_No_11971,User_Id_2523,Bike,3,Business,31,4,2:03:56 PM,31,4,2:04:53 PM,31,4,2:18:44 PM,31,4,2:25:56 PM,31,4,2:57:40 PM,5,28.8,,-1.2551895,36.7822034,-1.2657456,36.8087424,Rider_Id_75,1904
-Order_No_15338,User_Id_2763,Bike,3,Business,23,3,11:34:56 AM,23,3,11:35:35 AM,23,3,11:39:57 AM,23,3,11:54:51 AM,23,3,12:13:35 PM,6,,,-1.273733,36.818022,-1.2551895,36.7822034,Rider_Id_31,1124
-Order_No_1061,User_Id_3635,Bike,1,Business,18,4,10:31:43 AM,18,4,10:32:44 AM,18,4,10:57:10 AM,18,4,11:49:54 AM,18,4,12:21:59 PM,14,25.7,,-1.2615888000000002,36.7928732,-1.3158232,36.86453579999999,Rider_Id_478,1925
-Order_No_10544,User_Id_928,Bike,3,Business,20,3,11:50:06 AM,20,3,11:50:20 AM,20,3,11:55:10 AM,20,3,12:01:27 PM,20,3,12:29:56 PM,8,26.2,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_831,1709
-Order_No_10046,User_Id_2767,Bike,3,Business,23,5,4:09:04 PM,23,5,4:30:37 PM,23,5,4:44:27 PM,23,5,4:54:45 PM,23,5,5:22:23 PM,8,24.6,,-1.257773,36.795486,-1.233018,36.8004477,Rider_Id_445,1658
-Order_No_12640,User_Id_3222,Bike,3,Business,25,1,9:52:23 AM,25,1,9:52:44 AM,25,1,9:56:05 AM,25,1,9:57:50 AM,25,1,10:19:54 AM,10,,,-1.2551895,36.7822034,-1.3108783000000002,36.8392726,Rider_Id_958,1324
-Order_No_18766,User_Id_1927,Bike,3,Business,11,5,3:34:07 PM,11,5,3:34:15 PM,11,5,3:35:45 PM,11,5,3:37:02 PM,11,5,4:03:08 PM,12,26.4,,-1.3472066,36.7692638,-1.2866011999999998,36.8195691,Rider_Id_649,1566
-Order_No_14871,User_Id_286,Bike,3,Business,11,2,8:57:23 AM,11,2,8:57:50 AM,11,2,9:19:15 AM,11,2,9:23:03 AM,11,2,10:09:40 AM,17,16.8,,-1.2551895,36.7822034,-1.200709,36.8471472,Rider_Id_865,2797
-Order_No_12728,User_Id_3521,Bike,3,Business,6,2,7:24:38 AM,6,2,8:33:06 AM,6,2,8:34:21 AM,6,2,8:47:03 AM,6,2,9:22:53 AM,5,20.0,,-1.2768326,36.8073804,-1.302634,36.8110146,Rider_Id_699,2150
-Order_No_121,User_Id_3686,Bike,3,Business,15,5,10:57:04 AM,15,5,10:57:15 AM,15,5,10:59:24 AM,15,5,11:04:46 AM,15,5,11:14:07 AM,3,25.8,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_709,561
-Order_No_4193,User_Id_1245,Bike,3,Business,25,1,1:28:34 PM,25,1,1:28:45 PM,25,1,1:31:11 PM,25,1,2:04:54 PM,25,1,2:31:53 PM,16,27.8,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_267,1619
-Order_No_17598,User_Id_2727,Bike,3,Personal,7,1,2:17:39 PM,7,1,2:17:57 PM,7,1,2:19:10 PM,7,1,2:38:00 PM,7,1,3:25:23 PM,12,26.6,,-1.3164171,36.83271679999999,-1.2862949,36.7599986,Rider_Id_31,2843
-Order_No_21204,User_Id_2030,Bike,3,Business,29,2,1:41:30 PM,29,2,1:47:43 PM,29,2,2:15:00 PM,29,2,2:19:41 PM,29,2,2:54:01 PM,9,23.7,,-1.3261258999999999,36.904086,-1.3328365,36.8715943,Rider_Id_304,2060
-Order_No_18098,User_Id_1568,Bike,3,Business,4,2,2:31:33 PM,4,2,2:33:28 PM,4,2,2:37:38 PM,4,2,2:43:47 PM,4,2,3:10:50 PM,6,20.4,,-1.2897368,36.8226783,-1.303806,36.783041,Rider_Id_456,1623
-Order_No_26733,User_Id_2358,Bike,4,Business,18,4,9:33:27 AM,18,4,9:41:23 AM,18,4,10:02:37 AM,18,4,10:10:31 AM,18,4,10:17:33 AM,4,20.8,,-1.286708,36.823190000000004,-1.297503,36.817503,Rider_Id_477,422
-Order_No_358,User_Id_3546,Bike,3,Personal,3,1,1:47:25 PM,3,1,1:57:38 PM,3,1,2:38:18 PM,3,1,2:43:01 PM,3,1,3:20:31 PM,13,22.8,,-1.3316438,36.76916731,-1.293973,36.80169,Rider_Id_328,2250
-Order_No_966,User_Id_2956,Bike,3,Business,19,3,3:54:36 PM,19,3,3:59:36 PM,19,3,4:59:32 PM,19,3,5:03:04 PM,19,3,6:20:53 PM,5,22.6,,-1.2584143,36.8048002,-1.2857548,36.819062,Rider_Id_86,4669
-Order_No_2827,User_Id_765,Bike,3,Business,16,2,11:01:52 AM,16,2,11:31:51 AM,16,2,11:56:24 AM,16,2,12:05:25 PM,16,2,12:59:53 PM,27,,,-1.1648303,36.951368,-1.2643174,36.7794769,Rider_Id_8,3268
-Order_No_6282,User_Id_635,Bike,3,Business,25,6,10:32:54 AM,25,6,10:33:17 AM,25,6,10:48:27 AM,25,6,11:00:22 AM,25,6,11:18:29 AM,5,,,-1.2780878,36.846943599999996,-1.3081376,36.83297720000001,Rider_Id_158,1087
-Order_No_14543,User_Id_1447,Bike,2,Personal,14,4,9:11:28 AM,14,4,9:15:41 AM,14,4,9:59:44 AM,14,4,10:05:27 AM,14,4,10:26:49 AM,8,15.3,,-1.2838392,36.8842742,-1.2332121999999999,36.8733172,Rider_Id_812,1282
-Order_No_17663,User_Id_2587,Bike,3,Business,23,4,4:05:10 PM,23,4,4:05:29 PM,23,4,4:24:10 PM,23,4,4:27:16 PM,23,4,4:35:07 PM,2,20.7,,-1.28878,36.8168312,-1.2905033000000001,36.8096462,Rider_Id_88,471
-Order_No_18572,User_Id_909,Bike,3,Business,5,5,4:31:38 PM,5,5,4:32:01 PM,5,5,4:32:57 PM,5,5,4:34:09 PM,5,5,5:15:54 PM,8,26.3,,-1.225322,36.80855,-1.2684816,36.808562200000004,Rider_Id_697,2505
-Order_No_18493,User_Id_3674,Bike,3,Business,4,2,2:27:21 PM,4,2,2:31:59 PM,4,2,2:32:04 PM,4,2,3:27:07 PM,4,2,3:27:22 PM,7,22.5,,-1.2551895,36.7822034,-1.2867809,36.8218665,Rider_Id_855,15
-Order_No_27617,User_Id_868,Bike,3,Business,13,1,11:20:30 AM,13,1,11:26:27 AM,13,1,12:18:51 PM,13,1,12:28:29 PM,13,1,12:53:11 PM,10,,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_771,1482
-Order_No_18880,User_Id_457,Bike,3,Personal,11,1,3:35:12 PM,11,1,3:35:24 PM,11,1,3:38:28 PM,11,1,3:42:17 PM,11,1,3:52:16 PM,6,27.9,,-1.2985749,36.8088,-1.2697893,36.8124106,Rider_Id_250,599
-Order_No_6201,User_Id_3291,Bike,3,Business,23,3,3:14:06 PM,23,3,3:14:49 PM,23,3,3:23:44 PM,23,3,3:45:08 PM,23,3,4:07:21 PM,11,27.3,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_219,1333
-Order_No_15805,User_Id_3247,Bike,3,Business,16,3,9:42:05 AM,16,3,9:42:13 AM,16,3,9:59:51 AM,16,3,10:02:02 AM,16,3,10:40:52 AM,22,19.6,,-1.2571472,36.795063299999995,-1.298037,36.6934582,Rider_Id_353,2330
-Order_No_13631,User_Id_3404,Bike,3,Business,12,2,11:24:50 AM,12,2,11:37:21 AM,12,2,12:02:22 PM,12,2,12:10:36 PM,12,2,12:39:41 PM,5,28.2,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_831,1745
-Order_No_26366,User_Id_1724,Bike,3,Business,28,4,10:52:59 AM,28,4,10:53:19 AM,28,4,10:56:37 AM,28,4,11:10:04 AM,28,4,11:21:11 AM,7,28.2,,-1.2626488,36.823977299999996,-1.2551895,36.7822034,Rider_Id_62,667
-Order_No_19955,User_Id_1211,Bike,2,Personal,11,4,10:42:34 AM,11,4,10:43:17 AM,11,4,10:49:30 AM,11,4,10:53:43 AM,11,4,11:08:13 AM,6,23.7,,-1.2595421999999998,36.7871178,-1.2795549,36.7627011,Rider_Id_434,870
-Order_No_16135,User_Id_393,Bike,3,Business,29,1,12:54:33 PM,29,1,12:55:25 PM,29,1,1:02:37 PM,29,1,1:10:09 PM,29,1,1:18:15 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_438,486
-Order_No_4767,User_Id_552,Bike,3,Business,28,5,1:50:22 PM,28,5,2:05:53 PM,28,5,2:37:57 PM,28,5,2:57:09 PM,28,5,3:29:34 PM,23,25.4,,-1.3472066,36.7692638,-1.335477,36.930829700000004,Rider_Id_764,1945
-Order_No_7728,User_Id_1402,Bike,3,Business,24,3,1:54:07 PM,24,3,2:11:11 PM,24,3,2:21:23 PM,24,3,2:24:21 PM,24,3,2:53:36 PM,5,,,-1.2584143,36.8048002,-1.2813012,36.832396200000005,Rider_Id_411,1755
-Order_No_6613,User_Id_2330,Bike,3,Business,3,3,4:57:30 PM,3,3,4:58:27 PM,3,3,5:06:28 PM,3,3,5:11:56 PM,3,3,5:17:01 PM,2,27.4,,-1.2671943,36.8062533,-1.2536275,36.798793200000006,Rider_Id_205,305
-Order_No_16181,User_Id_1642,Bike,3,Business,31,3,11:30:20 AM,31,3,11:30:29 AM,31,3,11:56:43 AM,31,3,12:10:26 PM,31,3,12:49:04 PM,13,24.5,,-1.3316276000000002,36.8727316,-1.2728069,36.7962362,Rider_Id_336,2318
-Order_No_12091,User_Id_3287,Bike,3,Business,14,5,11:27:35 AM,14,5,11:27:51 AM,14,5,11:39:23 AM,14,5,12:11:09 PM,14,5,12:49:32 PM,18,21.7,,-1.2452664,36.802854100000005,-1.3169591,36.7000029,Rider_Id_45,2303
-Order_No_2602,User_Id_3222,Bike,3,Business,23,4,2:55:07 PM,23,4,3:01:45 PM,23,4,3:09:09 PM,23,4,3:20:32 PM,23,4,3:38:04 PM,8,24.5,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_448,1052
-Order_No_11336,User_Id_2866,Bike,3,Business,10,1,12:53:14 PM,10,1,12:54:03 PM,10,1,1:02:01 PM,10,1,1:08:28 PM,10,1,1:17:01 PM,4,23.6,,-1.2551895,36.7822034,-1.2658334999999998,36.804286700000006,Rider_Id_215,513
-Order_No_19386,User_Id_2457,Bike,3,Business,23,4,9:49:31 AM,23,4,9:50:52 AM,23,4,10:15:20 AM,23,4,10:18:37 AM,23,4,11:15:37 AM,11,,,-1.2551895,36.7822034,-1.3002865,36.7491504,Rider_Id_279,3420
-Order_No_26969,User_Id_2974,Bike,3,Business,25,5,12:01:52 PM,25,5,12:03:46 PM,25,5,12:13:41 PM,25,5,12:18:36 PM,25,5,12:31:59 PM,6,21.0,,-1.2621261000000001,36.8079755,-1.287428,36.7800821,Rider_Id_412,803
-Order_No_9773,User_Id_1578,Bike,3,Business,14,5,3:21:14 PM,14,5,5:11:27 PM,14,5,5:14:45 PM,14,5,5:57:04 PM,14,5,6:20:05 PM,7,22.8,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_267,1381
-Order_No_15627,User_Id_2195,Bike,1,Business,17,2,2:27:05 PM,17,2,2:27:44 PM,17,2,2:27:48 PM,17,2,2:46:30 PM,17,2,3:03:27 PM,11,24.6,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_886,1017
-Order_No_15690,User_Id_3291,Bike,3,Business,24,3,9:35:59 AM,24,3,10:10:28 AM,24,3,10:13:18 AM,24,3,10:24:38 AM,24,3,10:52:34 AM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_291,1676
-Order_No_3429,User_Id_238,Bike,3,Business,18,4,2:08:05 PM,18,4,2:10:29 PM,18,4,2:22:47 PM,18,4,2:38:09 PM,18,4,2:45:06 PM,5,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_61,417
-Order_No_18708,User_Id_412,Bike,3,Business,25,6,10:20:30 AM,25,6,10:30:59 AM,25,6,10:33:47 AM,25,6,10:47:08 AM,25,6,11:08:45 AM,2,,,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_27,1297
-Order_No_27774,User_Id_111,Bike,3,Business,13,5,12:36:44 PM,13,5,12:38:27 PM,13,5,12:58:53 PM,13,5,1:11:50 PM,13,5,1:27:43 PM,6,,,-1.27871,36.819573,-1.2551895,36.7822034,Rider_Id_159,953
-Order_No_25237,User_Id_830,Bike,3,Business,20,1,3:15:57 PM,20,1,3:16:28 PM,20,1,3:29:24 PM,20,1,3:35:54 PM,20,1,3:56:20 PM,9,,,-1.2551895,36.7822034,-1.2966785,36.7896469,Rider_Id_933,1226
-Order_No_26941,User_Id_2933,Bike,2,Personal,1,3,1:57:54 PM,1,3,1:58:48 PM,1,3,2:20:18 PM,1,3,2:33:06 PM,1,3,2:47:11 PM,6,23.7,,-1.311208,36.778743,-1.3052671999999998,36.753853899999996,Rider_Id_140,845
-Order_No_10150,User_Id_3314,Bike,1,Personal,24,5,9:23:41 AM,24,5,9:24:32 AM,24,5,10:15:28 AM,24,5,10:23:51 AM,24,5,10:52:21 AM,5,15.6,,-1.2858511000000001,36.7946184,-1.285709,36.8219844,Rider_Id_234,1710
-Order_No_5181,User_Id_136,Bike,3,Business,6,2,10:06:03 AM,6,2,10:15:19 AM,6,2,10:37:04 AM,6,2,10:41:09 AM,6,2,11:13:21 AM,16,24.8,,-1.3326173000000001,36.71462329999999,-1.2536041999999998,36.806489500000005,Rider_Id_46,1932
-Order_No_28100,User_Id_1205,Bike,3,Business,2,2,9:51:32 AM,2,2,9:52:42 AM,2,2,9:55:45 AM,2,2,10:02:39 AM,2,2,10:17:39 AM,7,20.5,,-1.2495931,36.80650870000001,-1.294598,36.781639,Rider_Id_484,900
-Order_No_8628,User_Id_970,Bike,3,Business,14,2,9:10:38 AM,14,2,9:11:11 AM,14,2,9:11:57 AM,14,2,9:22:15 AM,14,2,9:36:44 AM,4,,,-1.2551895,36.7822034,-1.2690931,36.8065779,Rider_Id_926,869
-Order_No_1595,User_Id_948,Bike,3,Business,13,1,9:34:12 AM,13,1,9:35:16 AM,13,1,9:48:16 AM,13,1,10:03:49 AM,13,1,10:24:47 AM,9,,,-1.297532,36.784932,-1.2696908000000002,36.81519,Rider_Id_624,1258
-Order_No_15861,User_Id_3391,Bike,1,Personal,19,7,10:28:38 AM,19,7,10:31:50 AM,19,7,10:38:15 AM,19,7,11:00:53 AM,19,7,11:09:29 AM,3,18.1,,-1.316948,36.824399,-1.3009926,36.8208532,Rider_Id_329,516
-Order_No_1856,User_Id_1484,Bike,3,Business,9,5,1:41:52 PM,9,5,1:42:43 PM,9,5,1:51:58 PM,9,5,2:00:38 PM,9,5,3:15:27 PM,2,26.0,,-1.2571472,36.795063299999995,-1.25896,36.8016691,Rider_Id_467,4489
-Order_No_2100,User_Id_2341,Bike,1,Personal,7,4,11:18:56 AM,7,4,12:34:35 PM,7,4,1:18:45 PM,7,4,1:26:42 PM,7,4,1:39:52 PM,4,16.9,,-1.2925919,36.81705770000001,-1.3041605,36.8164326,Rider_Id_752,790
-Order_No_19671,User_Id_1886,Bike,2,Personal,6,3,1:33:34 PM,6,3,1:41:36 PM,6,3,2:28:26 PM,6,3,2:35:24 PM,6,3,3:04:42 PM,12,22.0,,-1.2868973,36.8232984,-1.3145553,36.749384899999995,Rider_Id_116,1758
-Order_No_15520,User_Id_2215,Bike,1,Personal,3,5,11:06:57 AM,3,5,11:07:08 AM,3,5,11:18:50 AM,3,5,11:20:32 AM,3,5,11:58:23 AM,18,21.3,,-1.2062428,36.8379106,-1.3175119,36.8058513,Rider_Id_105,2271
-Order_No_5483,User_Id_2444,Bike,1,Personal,20,5,10:45:14 AM,20,5,11:06:39 AM,20,5,11:17:17 AM,20,5,11:30:28 AM,20,5,12:00:39 PM,10,18.4,,-1.2633254999999999,36.710569299999996,-1.264788,36.7883897,Rider_Id_313,1811
-Order_No_20433,User_Id_635,Bike,3,Business,26,2,2:10:59 PM,26,2,2:36:54 PM,26,2,2:48:05 PM,26,2,2:52:58 PM,26,2,3:22:52 PM,5,29.6,,-1.2892668999999999,36.8273814,-1.2602337,36.79905479999999,Rider_Id_959,1794
-Order_No_12324,User_Id_2200,Bike,3,Business,26,1,2:20:54 PM,26,1,3:06:59 PM,26,1,3:25:30 PM,26,1,3:46:54 PM,26,1,3:51:39 PM,5,22.0,0.7,-1.2710687,36.8142839,-1.290894,36.822971,Rider_Id_660,285
-Order_No_17993,User_Id_246,Bike,3,Business,20,2,1:50:56 PM,20,2,1:55:10 PM,20,2,2:02:29 PM,20,2,2:07:29 PM,20,2,2:28:06 PM,12,23.9,,-1.2584143,36.8048002,-1.2483726000000002,36.8901617,Rider_Id_237,1237
-Order_No_20849,User_Id_1346,Bike,3,Business,3,5,1:43:46 PM,3,5,1:44:17 PM,3,5,1:53:11 PM,3,5,2:00:35 PM,3,5,2:06:56 PM,2,25.1,,-1.2998129999999999,36.787139200000006,-1.301796,36.8012024,Rider_Id_798,381
-Order_No_6319,User_Id_2637,Bike,3,Personal,5,2,1:50:43 PM,5,2,1:54:17 PM,5,2,2:14:03 PM,5,2,2:17:06 PM,5,2,2:38:25 PM,4,27.3,,-1.2575907,36.8060203,-1.2762593,36.8272556,Rider_Id_953,1279
-Order_No_1851,User_Id_2900,Bike,3,Business,13,3,5:12:27 PM,13,3,5:12:40 PM,13,3,5:21:47 PM,13,3,6:36:30 PM,13,3,6:54:16 PM,8,26.1,,-1.2963097,36.7688221,-1.26365,36.719989,Rider_Id_71,1066
-Order_No_3664,User_Id_517,Bike,3,Business,4,2,11:27:10 AM,4,2,11:47:16 AM,4,2,11:50:25 AM,4,2,11:53:02 AM,4,2,12:31:38 PM,14,19.5,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2316
-Order_No_1653,User_Id_3621,Bike,3,Business,19,2,10:57:36 AM,19,2,10:58:25 AM,19,2,11:11:09 AM,19,2,11:17:19 AM,19,2,11:42:26 AM,5,27.7,,-1.2836583000000001,36.7886014,-1.2949722,36.7524325,Rider_Id_216,1507
-Order_No_16477,User_Id_2532,Bike,3,Business,4,3,9:51:58 AM,4,3,9:52:06 AM,4,3,9:52:32 AM,4,3,10:29:08 AM,4,3,10:38:26 AM,2,14.8,,-1.2579303999999998,36.8019667,-1.2753108999999998,36.8130185,Rider_Id_379,558
-Order_No_1193,User_Id_391,Bike,3,Business,24,1,1:03:59 PM,24,1,1:04:42 PM,24,1,1:22:18 PM,24,1,1:31:42 PM,24,1,2:00:36 PM,13,22.7,,-1.2867454,36.8259017,-1.2054954,36.8985954,Rider_Id_726,1734
-Order_No_13416,User_Id_2869,Bike,3,Business,20,1,1:16:16 PM,20,1,1:19:29 PM,20,1,1:21:03 PM,20,1,1:29:16 PM,20,1,1:46:48 PM,9,,,-1.3025826,36.7670807,-1.290894,36.822971,Rider_Id_855,1052
-Order_No_24758,User_Id_633,Bike,3,Business,20,3,3:24:42 PM,20,3,3:25:18 PM,20,3,3:26:52 PM,20,3,3:37:13 PM,20,3,3:46:42 PM,4,28.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_576,569
-Order_No_16332,User_Id_1085,Bike,3,Business,5,2,1:46:58 PM,5,2,1:52:42 PM,5,2,2:02:26 PM,5,2,2:17:37 PM,5,2,2:41:51 PM,16,,,-1.232864,36.808470799999995,-1.301908,36.7592822,Rider_Id_746,1454
-Order_No_1010,User_Id_1754,Bike,3,Business,6,2,9:50:17 AM,6,2,9:50:41 AM,6,2,9:54:39 AM,6,2,10:01:39 AM,6,2,10:28:42 AM,13,20.0,,-1.2283403,36.8822756,-1.2860183,36.8975338,Rider_Id_803,1623
-Order_No_11382,User_Id_2987,Bike,3,Business,22,3,1:38:51 PM,22,3,1:48:09 PM,22,3,1:48:42 PM,22,3,2:19:04 PM,22,3,2:27:12 PM,9,25.5,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_179,488
-Order_No_25722,User_Id_59,Bike,3,Business,21,3,10:14:54 AM,21,3,11:44:16 AM,21,3,11:46:24 AM,21,3,12:01:12 PM,21,3,12:17:22 PM,7,22.2,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_726,970
-Order_No_1408,User_Id_706,Bike,3,Business,14,1,3:59:34 PM,14,1,4:00:07 PM,14,1,4:05:45 PM,14,1,4:18:03 PM,14,1,4:25:04 PM,3,25.6,,-1.3077869,36.844320700000004,-1.3080091999999999,36.8284543,Rider_Id_781,421
-Order_No_7211,User_Id_2854,Bike,3,Business,19,6,2:35:34 PM,19,6,2:49:21 PM,19,6,3:02:48 PM,19,6,3:05:45 PM,19,6,3:21:34 PM,4,24.4,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_103,949
-Order_No_20538,User_Id_699,Bike,3,Personal,16,2,11:20:01 AM,16,2,11:25:15 AM,16,2,11:37:18 AM,16,2,11:39:00 AM,16,2,11:51:03 AM,4,23.5,,-1.2850643999999998,36.8343657,-1.2967701999999999,36.8040381,Rider_Id_22,723
-Order_No_5158,User_Id_635,Bike,3,Business,19,6,11:55:43 AM,19,6,11:56:56 AM,19,6,11:58:48 AM,19,6,12:42:36 PM,19,6,1:43:36 PM,13,25.4,,-1.2602337,36.79905479999999,-1.3288106000000002,36.8697344,Rider_Id_26,3660
-Order_No_23054,User_Id_198,Bike,3,Business,25,1,12:09:18 PM,25,1,12:09:47 PM,25,1,12:16:32 PM,25,1,12:20:42 PM,25,1,1:03:55 PM,13,25.6,,-1.3091799,36.831327200000004,-1.3396256000000002,36.8863135,Rider_Id_26,2593
-Order_No_23459,User_Id_248,Bike,3,Business,15,3,3:32:48 PM,15,3,3:33:11 PM,15,3,3:41:58 PM,15,3,3:45:29 PM,15,3,4:05:14 PM,7,25.8,,-1.304735,36.784605400000004,-1.2703801000000001,36.7985261,Rider_Id_649,1185
-Order_No_3241,User_Id_1959,Bike,3,Business,24,3,12:34:01 PM,24,3,12:34:21 PM,24,3,12:35:43 PM,24,3,12:44:15 PM,24,3,1:56:58 PM,21,,,-1.2551895,36.7822034,-1.2126245,36.6630919,Rider_Id_168,4363
-Order_No_3658,User_Id_3222,Bike,3,Business,25,5,2:09:51 PM,25,5,2:14:42 PM,25,5,2:25:48 PM,25,5,2:29:01 PM,25,5,2:52:54 PM,7,29.0,,-1.2551895,36.7822034,-1.2936941000000002,36.8156795,Rider_Id_512,1433
-Order_No_10987,User_Id_53,Bike,3,Business,25,1,2:08:43 PM,25,1,2:09:40 PM,25,1,2:16:40 PM,25,1,2:51:28 PM,25,1,3:07:18 PM,5,27.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_341,950
-Order_No_683,User_Id_2129,Bike,1,Personal,28,2,12:28:39 PM,28,2,1:42:07 PM,28,2,2:28:22 PM,28,2,2:36:07 PM,28,2,3:06:22 PM,7,19.5,,-1.2834899,36.820437,-1.2595421999999998,36.7871178,Rider_Id_107,1815
-Order_No_718,User_Id_1891,Bike,3,Business,29,2,10:43:50 AM,29,2,10:44:45 AM,29,2,10:50:09 AM,29,2,10:52:51 AM,29,2,11:12:13 AM,6,23.4,,-1.2606981000000002,36.808862700000006,-1.2506462,36.846258,Rider_Id_72,1162
-Order_No_20958,User_Id_909,Bike,3,Business,14,4,1:29:04 PM,14,4,1:31:27 PM,14,4,1:38:28 PM,14,4,1:46:24 PM,14,4,2:07:46 PM,7,27.4,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_600,1282
-Order_No_11652,User_Id_1500,Bike,3,Business,14,2,11:32:11 AM,14,2,11:32:42 AM,14,2,11:33:36 AM,14,2,11:56:16 AM,14,2,11:57:42 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_137,86
-Order_No_25904,User_Id_3557,Bike,3,Business,30,2,3:23:44 PM,30,2,3:24:02 PM,30,2,3:34:31 PM,30,2,3:44:17 PM,30,2,3:44:19 PM,4,27.3,,-1.2581837,36.801705600000005,-1.2622145,36.8087391,Rider_Id_380,2
-Order_No_4817,User_Id_1642,Bike,3,Business,15,1,10:18:43 AM,15,1,10:21:53 AM,15,1,10:42:42 AM,15,1,10:47:11 AM,15,1,11:00:45 AM,5,,,-1.2726506000000002,36.7960153,-1.2876365,36.816096,Rider_Id_438,814
-Order_No_16929,User_Id_1329,Bike,3,Business,16,6,12:02:09 PM,16,6,12:02:54 PM,16,6,12:12:20 PM,16,6,12:18:17 PM,16,6,12:38:04 PM,19,25.8,,-1.273056,36.811298,-1.1933233,36.935494299999995,Rider_Id_865,1187
-Order_No_6863,User_Id_393,Bike,3,Business,4,4,9:55:53 AM,4,4,10:11:05 AM,4,4,10:24:35 AM,4,4,10:39:33 AM,4,4,11:14:51 AM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,2118
-Order_No_19013,User_Id_867,Bike,3,Business,2,2,7:19:10 AM,2,2,7:24:02 AM,2,2,8:04:03 AM,2,2,8:14:31 AM,2,2,8:42:39 AM,10,20.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_206,1688
-Order_No_15404,User_Id_2627,Bike,3,Business,24,4,11:37:44 AM,24,4,11:37:54 AM,24,4,11:47:30 AM,24,4,12:39:43 PM,24,4,12:58:24 PM,15,24.7,,-1.2283403,36.8822756,-1.2571472,36.795063299999995,Rider_Id_75,1121
-Order_No_10138,User_Id_3698,Bike,3,Business,7,5,12:20:09 PM,7,5,12:20:37 PM,7,5,12:23:34 PM,7,5,12:48:24 PM,7,5,1:14:25 PM,11,,,-1.2600931999999998,36.8041689,-1.3108386,36.7555239,Rider_Id_383,1561
-Order_No_9088,User_Id_3320,Bike,3,Business,29,4,3:10:42 PM,29,4,3:14:15 PM,29,4,3:14:28 PM,29,4,3:32:01 PM,29,4,3:53:52 PM,9,24.9,,-1.3080091999999999,36.8284543,-1.2708331,36.790419299999996,Rider_Id_268,1311
-Order_No_18109,User_Id_346,Bike,3,Business,4,1,12:10:49 PM,4,1,12:13:20 PM,4,1,12:28:53 PM,4,1,12:46:53 PM,4,1,1:08:27 PM,2,26.5,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_311,1294
-Order_No_18789,User_Id_393,Bike,3,Business,16,2,9:28:45 AM,16,2,9:28:53 AM,16,2,9:32:14 AM,16,2,9:46:55 AM,16,2,9:55:11 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_168,496
-Order_No_7049,User_Id_432,Bike,3,Business,20,3,4:41:02 PM,20,3,4:44:39 PM,20,3,4:57:07 PM,20,3,5:03:47 PM,20,3,5:28:26 PM,8,27.4,,-1.3012008,36.764868,-1.2551895,36.7822034,Rider_Id_523,1479
-Order_No_11304,User_Id_1993,Bike,2,Personal,27,5,9:29:34 AM,27,5,9:29:55 AM,27,5,9:39:05 AM,27,5,9:42:52 AM,27,5,10:00:13 AM,5,16.3,,-1.2900726999999999,36.7890059,-1.2871938,36.8195031,Rider_Id_857,1041
-Order_No_22509,User_Id_1392,Bike,3,Business,13,3,10:00:51 AM,13,3,10:08:01 AM,13,3,10:21:34 AM,13,3,10:23:40 AM,13,3,10:43:00 AM,7,17.3,,-1.2551895,36.7822034,-1.2898178999999999,36.827084899999996,Rider_Id_452,1160
-Order_No_2482,User_Id_3621,Bike,3,Business,7,5,1:33:17 PM,7,5,1:34:08 PM,7,5,1:44:45 PM,7,5,1:48:13 PM,7,5,2:07:45 PM,10,,,-1.2836583000000001,36.7886014,-1.2614967,36.7420697,Rider_Id_161,1172
-Order_No_8061,User_Id_3731,Bike,1,Personal,8,2,10:17:37 AM,8,2,10:18:05 AM,8,2,10:33:35 AM,8,2,10:35:54 AM,8,2,10:55:07 AM,9,21.4,,-1.3244691000000002,36.804587299999994,-1.2948781,36.7844523,Rider_Id_543,1153
-Order_No_16764,User_Id_3701,Bike,3,Business,18,2,10:00:31 AM,18,2,10:10:27 AM,18,2,10:16:19 AM,18,2,10:20:04 AM,18,2,10:55:57 AM,11,19.6,,-1.2684239,36.79524620000001,-1.3111885,36.7364015,Rider_Id_379,2153
-Order_No_5556,User_Id_1075,Bike,3,Business,24,1,5:05:06 PM,24,1,5:05:16 PM,24,1,5:06:42 PM,24,1,5:12:10 PM,24,1,5:18:51 PM,1,22.7,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_685,401
-Order_No_8251,User_Id_2069,Bike,3,Business,25,6,12:48:07 PM,25,6,12:49:07 PM,25,6,1:01:03 PM,25,6,1:20:12 PM,25,6,1:32:59 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_8,767
-Order_No_23311,User_Id_540,Bike,3,Business,24,4,12:39:16 PM,24,4,12:39:25 PM,24,4,12:39:50 PM,24,4,12:44:07 PM,24,4,1:00:44 PM,4,24.4,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_876,997
-Order_No_9269,User_Id_2701,Bike,3,Business,24,4,10:42:20 AM,24,4,10:47:15 AM,24,4,11:02:58 AM,24,4,11:12:18 AM,24,4,11:30:11 AM,5,21.2,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_888,1073
-Order_No_3430,User_Id_3203,Bike,3,Business,19,3,12:31:33 PM,19,3,12:31:52 PM,19,3,12:41:49 PM,19,3,12:55:30 PM,19,3,1:21:28 PM,16,22.3,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_274,1558
-Order_No_4096,User_Id_1307,Bike,3,Business,3,5,8:16:44 AM,3,5,8:17:16 AM,3,5,8:42:44 AM,3,5,8:44:13 AM,3,5,8:48:06 AM,3,17.6,,-1.2844665000000002,36.7866113,-1.2759421999999998,36.7677551,Rider_Id_726,233
-Order_No_19173,User_Id_1304,Bike,3,Business,5,1,8:51:44 AM,5,1,8:51:52 AM,5,1,8:54:29 AM,5,1,9:00:13 AM,5,1,9:42:51 AM,15,18.8,,-1.2551895,36.7822034,-1.3354076000000001,36.86307,Rider_Id_630,2558
-Order_No_27861,User_Id_407,Bike,2,Personal,27,4,1:15:54 PM,27,4,1:16:46 PM,27,4,1:21:22 PM,27,4,1:26:55 PM,27,4,1:52:35 PM,21,23.0,,-1.301796,36.8012024,-1.3533402,36.9078924,Rider_Id_844,1540
-Order_No_24477,User_Id_747,Bike,3,Personal,23,3,4:38:14 PM,23,3,4:43:43 PM,23,3,5:03:15 PM,23,3,5:17:20 PM,23,3,5:19:58 PM,2,24.3,,-1.3334681000000002,36.7116093,-1.3421303999999998,36.718364,Rider_Id_226,158
-Order_No_25048,User_Id_1724,Bike,3,Business,17,5,3:40:56 PM,17,5,3:41:48 PM,17,5,3:53:31 PM,17,5,4:18:58 PM,17,5,4:19:03 PM,11,,,-1.32189,36.8589727,-1.2941648000000001,36.7977315,Rider_Id_921,5
-Order_No_19232,User_Id_2828,Bike,1,Business,20,1,12:18:38 PM,20,1,12:18:50 PM,20,1,12:20:12 PM,20,1,12:36:22 PM,20,1,12:44:10 PM,3,20.3,,-1.2642719999999998,36.7978791,-1.2615888000000002,36.7928732,Rider_Id_648,468
-Order_No_23686,User_Id_867,Bike,3,Business,10,1,1:44:48 PM,10,1,1:45:38 PM,10,1,1:45:47 PM,10,1,2:37:15 PM,10,1,2:37:17 PM,13,18.3,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_39,2
-Order_No_18223,User_Id_3239,Bike,3,Personal,19,6,2:06:21 PM,19,6,3:06:17 PM,19,6,3:14:58 PM,19,6,3:21:26 PM,19,6,3:43:33 PM,7,24.4,,-1.2839322,36.8292426,-1.2855921000000001,36.7788349,Rider_Id_281,1327
-Order_No_24111,User_Id_2216,Bike,3,Business,19,5,3:45:42 PM,19,5,4:22:18 PM,19,5,4:45:36 PM,19,5,5:06:25 PM,19,5,5:32:45 PM,8,24.3,,-1.2551895,36.7822034,-1.2966203,36.817837,Rider_Id_328,1580
-Order_No_6305,User_Id_1194,Bike,3,Personal,12,4,1:01:44 PM,12,4,1:02:43 PM,12,4,1:11:05 PM,12,4,1:14:21 PM,12,4,1:35:00 PM,7,18.7,,-1.3040899,36.7842136,-1.3099584,36.732364399999994,Rider_Id_951,1239
-Order_No_8686,User_Id_2344,Bike,3,Business,24,5,12:20:55 PM,24,5,12:21:12 PM,24,5,12:22:09 PM,24,5,12:38:09 PM,24,5,12:50:42 PM,4,20.8,,-1.2571472,36.795063299999995,-1.27871,36.819573,Rider_Id_648,753
-Order_No_10396,User_Id_3450,Bike,3,Business,7,5,10:59:53 AM,7,5,11:00:51 AM,7,5,11:05:40 AM,7,5,11:15:07 AM,7,5,11:38:12 AM,10,,,-1.2551895,36.7822034,-1.2942,36.7777614,Rider_Id_131,1385
-Order_No_28061,User_Id_510,Bike,3,Business,19,3,4:14:18 PM,19,3,5:26:06 PM,19,3,5:46:09 PM,19,3,5:48:46 PM,19,3,6:23:48 PM,5,22.3,,-1.2860183,36.8975338,-1.3142011,36.899387899999994,Rider_Id_532,2102
-Order_No_6732,User_Id_1500,Bike,3,Business,27,3,9:41:53 AM,27,3,9:42:37 AM,27,3,9:42:48 AM,27,3,10:20:28 AM,27,3,10:20:31 AM,8,21.2,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_613,3
-Order_No_12733,User_Id_1584,Bike,3,Business,13,1,12:46:29 PM,13,1,12:46:47 PM,13,1,12:59:30 PM,13,1,1:18:16 PM,13,1,1:40:41 PM,9,17.2,,-1.2623805000000001,36.7805404,-1.2969741000000001,36.7856612,Rider_Id_520,1345
-Order_No_4488,User_Id_2956,Bike,3,Business,11,4,4:28:34 PM,11,4,4:29:50 PM,11,4,4:33:37 PM,11,4,4:47:32 PM,11,4,5:07:31 PM,5,26.7,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_576,1199
-Order_No_24944,User_Id_3669,Bike,3,Business,24,3,10:42:51 AM,24,3,10:43:57 AM,24,3,11:02:17 AM,24,3,11:11:44 AM,24,3,12:02:24 PM,8,,,-1.3025826,36.7670807,-1.2615888000000002,36.7928732,Rider_Id_958,3040
-Order_No_22242,User_Id_3032,Bike,3,Business,19,3,9:46:03 AM,19,3,9:47:47 AM,19,3,10:32:40 AM,19,3,10:46:23 AM,19,3,11:21:52 AM,12,22.3,,-1.2571472,36.795063299999995,-1.3246118999999998,36.783333899999995,Rider_Id_362,2129
-Order_No_3613,User_Id_914,Bike,3,Business,6,2,2:46:33 PM,6,2,2:46:51 PM,6,2,2:59:19 PM,6,2,3:06:15 PM,6,2,3:20:05 PM,3,27.6,,-1.2670213,36.7994482,-1.2630521000000001,36.806801899999996,Rider_Id_503,830
-Order_No_18285,User_Id_1500,Bike,3,Business,13,4,8:30:33 AM,13,4,8:31:14 AM,13,4,8:31:17 AM,13,4,8:54:34 AM,13,4,8:58:41 AM,8,18.5,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_472,247
-Order_No_22931,User_Id_391,Bike,3,Business,30,6,10:40:26 AM,30,6,10:46:19 AM,30,6,10:55:32 AM,30,6,11:19:01 AM,30,6,12:05:02 PM,17,26.8,,-1.300921,36.828195,-1.1967675,36.8064464,Rider_Id_59,2761
-Order_No_10294,User_Id_3091,Bike,3,Business,21,5,2:28:05 PM,21,5,2:45:20 PM,21,5,2:47:16 PM,21,5,2:52:18 PM,21,5,3:03:00 PM,3,24.7,,-1.3012008,36.764868,-1.3002865,36.7491504,Rider_Id_770,642
-Order_No_2033,User_Id_65,Bike,1,Personal,6,1,12:44:42 PM,6,1,12:45:40 PM,6,1,12:54:05 PM,6,1,1:07:21 PM,6,1,1:51:08 PM,13,20.3,,-1.2662524,36.824479499999995,-1.3299358000000001,36.8524971,Rider_Id_320,2627
-Order_No_19223,User_Id_269,Bike,3,Business,17,1,3:52:53 PM,17,1,4:35:42 PM,17,1,4:45:36 PM,17,1,4:47:24 PM,17,1,5:15:48 PM,12,25.6,,-1.3332749,36.870814700000004,-1.2852569,36.8976121,Rider_Id_622,1704
-Order_No_12986,User_Id_186,Bike,3,Business,21,4,12:15:00 PM,21,4,12:15:52 PM,21,4,12:18:21 PM,21,4,12:31:37 PM,21,4,12:39:58 PM,5,25.3,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_205,501
-Order_No_22973,User_Id_3108,Bike,3,Business,7,4,11:11:23 AM,7,4,11:34:15 AM,7,4,12:05:08 PM,7,4,12:14:51 PM,7,4,12:31:58 PM,8,16.8,,-1.2551895,36.7822034,-1.2894795000000001,36.781853399999996,Rider_Id_900,1027
-Order_No_13628,User_Id_2112,Bike,3,Business,10,4,10:56:28 AM,10,4,10:56:54 AM,10,4,11:05:56 AM,10,4,11:07:43 AM,10,4,11:35:36 AM,16,24.9,,-1.2156006000000001,36.8916865,-1.3285517,36.9018,Rider_Id_685,1673
-Order_No_19570,User_Id_1927,Bike,3,Business,24,6,5:25:11 PM,24,6,5:48:29 PM,24,6,6:09:08 PM,24,6,6:25:00 PM,24,6,6:41:21 PM,10,22.4,,-1.305399,36.822184899999996,-1.3463041999999998,36.768164,Rider_Id_276,981
-Order_No_20401,User_Id_432,Bike,3,Business,22,3,4:04:00 PM,22,3,4:04:26 PM,22,3,4:04:35 PM,22,3,4:13:34 PM,22,3,4:27:09 PM,3,24.5,,-1.3012008,36.764868,-1.3002865,36.7491504,Rider_Id_161,815
-Order_No_16548,User_Id_1642,Bike,3,Business,8,1,11:11:09 AM,8,1,11:15:25 AM,8,1,11:25:03 AM,8,1,11:33:47 AM,8,1,11:50:45 AM,5,27.9,,-1.2728069,36.7962362,-1.288269,36.818275299999996,Rider_Id_291,1018
-Order_No_19696,User_Id_1932,Bike,3,Business,9,1,1:51:40 PM,9,1,1:53:19 PM,9,1,2:02:28 PM,9,1,2:10:14 PM,9,1,2:30:11 PM,7,20.9,,-1.3102963,36.8212746,-1.2922857,36.781327399999995,Rider_Id_844,1197
-Order_No_5103,User_Id_2894,Bike,3,Business,26,1,11:15:22 AM,26,1,11:19:10 AM,26,1,11:54:38 AM,26,1,11:57:21 AM,26,1,12:23:08 PM,13,23.0,,-1.290641,36.812940999999995,-1.2611078000000002,36.750701,Rider_Id_254,1547
-Order_No_4999,User_Id_1787,Bike,3,Business,13,6,5:57:55 PM,13,6,5:58:40 PM,13,6,6:12:13 PM,13,6,6:14:40 PM,13,6,6:31:12 PM,12,27.7,,-1.2527958000000001,36.8003131,-1.2283403,36.8822756,Rider_Id_484,992
-Order_No_27339,User_Id_633,Bike,3,Business,24,4,10:07:58 AM,24,4,10:35:44 AM,24,4,10:49:40 AM,24,4,10:52:11 AM,24,4,11:05:29 AM,5,24.3,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_576,798
-Order_No_16372,User_Id_1809,Bike,3,Business,25,2,10:02:00 AM,25,2,10:02:39 AM,25,2,10:14:14 AM,25,2,10:54:17 AM,25,2,11:07:37 AM,15,23.6,,-1.2283403,36.8822756,-1.2646084,36.8028645,Rider_Id_771,800
-Order_No_11059,User_Id_2627,Bike,3,Business,11,2,12:24:34 PM,11,2,12:27:03 PM,11,2,12:31:55 PM,11,2,12:51:16 PM,11,2,1:29:31 PM,17,19.6,,-1.3472066,36.7692638,-1.2571472,36.795063299999995,Rider_Id_205,2295
-Order_No_19212,User_Id_60,Bike,3,Business,17,3,3:23:15 PM,17,3,3:24:12 PM,17,3,3:38:04 PM,17,3,3:46:51 PM,17,3,4:01:56 PM,8,,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_166,905
-Order_No_7194,User_Id_3420,Bike,3,Business,23,2,3:28:25 PM,23,2,3:51:34 PM,23,2,4:09:42 PM,23,2,4:13:55 PM,23,2,4:38:08 PM,7,18.8,,-1.2678373,36.8359086,-1.3046007,36.821174299999996,Rider_Id_612,1453
-Order_No_15930,User_Id_1363,Bike,3,Business,17,3,3:22:37 PM,17,3,3:23:01 PM,17,3,3:32:13 PM,17,3,3:41:35 PM,17,3,3:49:09 PM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_506,454
-Order_No_16155,User_Id_469,Bike,3,Personal,27,7,4:41:19 PM,27,7,5:19:28 PM,27,7,5:34:29 PM,27,7,5:58:09 PM,27,7,6:17:50 PM,12,,,-1.291231,36.8344488,-1.3253826000000002,36.8822315,Rider_Id_206,1181
-Order_No_235,User_Id_2112,Bike,3,Business,4,1,10:20:48 AM,4,1,10:21:14 AM,4,1,10:29:06 AM,4,1,10:46:41 AM,4,1,11:00:22 AM,7,21.1,,-1.2765736,36.851364600000004,-1.306245,36.819297799999994,Rider_Id_124,821
-Order_No_15065,User_Id_26,Bike,1,Personal,2,3,10:23:11 AM,2,3,10:25:15 AM,2,3,10:32:21 AM,2,3,10:37:02 AM,2,3,10:49:09 AM,6,22.2,,-1.2598589,36.7470168,-1.2569503,36.78347279999999,Rider_Id_122,727
-Order_No_8670,User_Id_2423,Bike,3,Business,30,2,3:55:39 PM,30,2,4:06:12 PM,30,2,4:10:55 PM,30,2,4:19:32 PM,30,2,4:27:37 PM,3,,,-1.2551895,36.7822034,-1.2655477,36.8001682,Rider_Id_8,485
-Order_No_10019,User_Id_1500,Bike,3,Business,22,5,11:49:23 AM,22,5,11:52:53 AM,22,5,12:33:01 PM,22,5,12:35:19 PM,22,5,12:48:11 PM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_154,772
-Order_No_1852,User_Id_186,Bike,3,Business,10,5,9:06:10 AM,10,5,9:07:37 AM,10,5,10:44:52 AM,10,5,10:51:57 AM,10,5,11:22:52 AM,6,,,-1.2571472,36.795063299999995,-1.2904016,36.82263029999999,Rider_Id_582,1855
-Order_No_2281,User_Id_1976,Bike,3,Business,9,5,12:46:07 PM,9,5,12:49:55 PM,9,5,12:58:17 PM,9,5,12:59:39 PM,9,5,1:19:15 PM,9,25.5,,-1.257028,36.7810435,-1.2544761,36.8198826,Rider_Id_675,1176
-Order_No_25217,User_Id_2106,Bike,1,Personal,31,4,2:14:47 PM,31,4,2:16:45 PM,31,4,2:55:17 PM,31,4,3:01:36 PM,31,4,3:31:54 PM,13,23.1,,-1.2874648000000002,36.8271985,-1.1985673000000001,36.836368799999995,Rider_Id_116,1818
-Order_No_3806,User_Id_3349,Bike,3,Personal,27,4,4:52:46 PM,27,4,4:57:45 PM,27,4,4:59:05 PM,27,4,5:07:06 PM,27,4,5:14:37 PM,2,23.1,,-1.2850643999999998,36.8343657,-1.2712408000000002,36.832831899999995,Rider_Id_698,451
-Order_No_7638,User_Id_2212,Bike,3,Business,17,3,9:50:38 AM,17,3,9:51:25 AM,17,3,10:00:07 AM,17,3,10:01:27 AM,17,3,10:11:04 AM,5,20.9,,-1.2646084,36.8028645,-1.2425149,36.789013700000005,Rider_Id_453,577
-Order_No_2158,User_Id_2021,Bike,3,Business,3,4,11:44:46 AM,3,4,1:44:42 PM,3,4,1:59:50 PM,3,4,2:12:48 PM,3,4,2:24:22 PM,5,25.9,,-1.2951297,36.8335521,-1.2836832,36.8306719,Rider_Id_14,694
-Order_No_7608,User_Id_3521,Bike,3,Business,17,3,2:44:11 PM,17,3,2:45:09 PM,17,3,2:57:23 PM,17,3,3:25:09 PM,17,3,3:48:34 PM,5,28.2,,-1.2559559999999999,36.772901,-1.2768326,36.8073804,Rider_Id_727,1405
-Order_No_18815,User_Id_795,Bike,3,Personal,4,3,11:10:34 AM,4,3,11:11:08 AM,4,3,11:17:19 AM,4,3,11:39:44 AM,4,3,11:48:06 AM,1,16.9,,-1.2624531,36.8228476,-1.26278,36.826409999999996,Rider_Id_103,502
-Order_No_22275,User_Id_2200,Bike,3,Business,30,5,5:07:22 PM,30,5,5:47:18 PM,30,5,5:53:13 PM,30,5,6:31:15 PM,30,5,7:05:08 PM,7,21.8,,-1.2551895,36.7822034,-1.2412188999999998,36.7760433,Rider_Id_267,2033
-Order_No_13739,User_Id_1271,Bike,3,Business,24,1,3:51:23 PM,24,1,4:44:48 PM,24,1,5:03:09 PM,24,1,5:11:24 PM,24,1,5:31:46 PM,7,24.3,,-1.2927792,36.9053492,-1.3244885,36.897791999999995,Rider_Id_890,1222
-Order_No_13117,User_Id_3635,Bike,1,Business,24,1,12:57:09 PM,24,1,12:57:28 PM,24,1,12:57:34 PM,24,1,1:11:07 PM,24,1,1:20:49 PM,3,22.3,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_51,582
-Order_No_15002,User_Id_3647,Bike,3,Business,26,5,3:50:41 PM,26,5,3:52:24 PM,26,5,4:01:34 PM,26,5,4:05:48 PM,26,5,4:25:21 PM,4,,,-1.2726389999999999,36.794723,-1.2676758999999997,36.801804,Rider_Id_620,1173
-Order_No_21561,User_Id_393,Bike,3,Business,29,1,8:29:42 AM,29,1,8:32:00 AM,29,1,8:52:48 AM,29,1,8:59:53 AM,29,1,9:34:19 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_155,2066
-Order_No_1637,User_Id_570,Bike,3,Business,13,3,9:18:50 AM,13,3,9:19:18 AM,13,3,9:27:55 AM,13,3,9:34:44 AM,13,3,9:47:01 AM,6,17.1,,-1.2551895,36.7822034,-1.284382,36.774604499999995,Rider_Id_762,737
-Order_No_25806,User_Id_1271,Bike,3,Business,8,2,10:07:30 AM,8,2,10:07:46 AM,8,2,10:07:57 AM,8,2,10:51:15 AM,8,2,10:53:13 AM,4,21.4,,-1.3244885,36.897791999999995,-1.3266624,36.882035200000004,Rider_Id_546,118
-Order_No_7157,User_Id_2956,Bike,3,Business,19,1,12:15:49 PM,19,1,12:16:08 PM,19,1,12:17:10 PM,19,1,12:20:32 PM,19,1,12:29:52 PM,3,24.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_551,560
-Order_No_26056,User_Id_661,Bike,3,Business,15,3,3:22:35 PM,15,3,3:57:20 PM,15,3,4:11:19 PM,15,3,4:17:18 PM,15,3,4:40:48 PM,4,21.8,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_140,1410
-Order_No_20496,User_Id_3647,Bike,3,Business,2,3,5:10:25 PM,2,3,5:12:11 PM,2,3,5:20:45 PM,2,3,5:26:17 PM,2,3,5:36:16 PM,6,25.0,,-1.2726389999999999,36.794723,-1.2739249,36.819115100000005,Rider_Id_427,599
-Order_No_15434,User_Id_794,Bike,2,Personal,12,2,11:08:12 AM,12,2,11:08:31 AM,12,2,11:09:47 AM,12,2,11:18:56 AM,12,2,11:42:09 AM,9,21.0,,-1.2629944,36.7998816,-1.3141593,36.8101097,Rider_Id_379,1393
-Order_No_5639,User_Id_265,Bike,3,Business,21,1,3:34:43 PM,21,1,3:35:10 PM,21,1,3:47:11 PM,21,1,3:55:42 PM,21,1,4:42:20 PM,9,21.6,,-1.300921,36.828195,-1.2566636999999998,36.808857700000004,Rider_Id_280,2798
-Order_No_11530,User_Id_1329,Bike,3,Business,9,5,5:39:42 PM,9,5,5:42:45 PM,9,5,5:54:49 PM,9,5,6:04:57 PM,9,5,6:12:05 PM,2,25.1,,-1.273056,36.811298,-1.2721028,36.795348499999996,Rider_Id_427,428
-Order_No_23745,User_Id_3521,Bike,3,Business,17,1,1:26:44 PM,17,1,1:27:15 PM,17,1,1:32:32 PM,17,1,2:28:31 PM,17,1,2:37:53 PM,5,28.3,,-1.264945,36.784678,-1.2696908000000002,36.81519,Rider_Id_726,562
-Order_No_4832,User_Id_137,Bike,3,Personal,12,4,12:51:05 PM,12,4,12:54:37 PM,12,4,12:59:01 PM,12,4,1:00:46 PM,12,4,1:37:24 PM,5,18.9,,-1.2916556000000001,36.8262516,-1.2971709999999999,36.783278,Rider_Id_24,2198
-Order_No_15778,User_Id_279,Bike,1,Personal,17,5,4:24:23 PM,17,5,4:26:23 PM,17,5,4:28:15 PM,17,5,4:46:36 PM,17,5,5:04:59 PM,6,19.1,,-1.2824911,36.8218042,-1.2611642,36.7949932,Rider_Id_45,1103
-Order_No_3718,User_Id_522,Bike,1,Personal,2,2,9:00:10 AM,2,2,9:00:38 AM,2,2,10:07:46 AM,2,2,10:11:45 AM,2,2,11:08:10 AM,19,15.3,,-1.2643343,36.7447423,-1.2402455,36.8791753,Rider_Id_755,3385
-Order_No_5002,User_Id_2789,Bike,3,Business,21,4,10:15:12 AM,21,4,10:16:35 AM,21,4,10:26:30 AM,21,4,10:31:02 AM,21,4,10:42:13 AM,8,22.0,,-1.304735,36.784605400000004,-1.2623495,36.8049247,Rider_Id_125,671
-Order_No_11800,User_Id_2008,Bike,3,Business,4,4,3:48:39 PM,4,4,3:53:58 PM,4,4,4:02:04 PM,4,4,4:10:43 PM,4,4,4:37:35 PM,7,27.1,,-1.2508229,36.7895263,-1.285244,36.832159000000004,Rider_Id_907,1612
-Order_No_11504,User_Id_3647,Bike,3,Business,24,3,4:20:23 PM,24,3,4:20:40 PM,24,3,4:28:49 PM,24,3,4:41:30 PM,24,3,4:58:56 PM,6,,,-1.2726389999999999,36.794723,-1.2893233999999998,36.8100047,Rider_Id_161,1046
-Order_No_10656,User_Id_3291,Bike,3,Business,28,2,1:23:26 PM,28,2,1:26:01 PM,28,2,1:52:34 PM,28,2,2:13:54 PM,28,2,2:53:19 PM,18,23.5,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_674,2365
-Order_No_11455,User_Id_3127,Bike,3,Business,15,5,1:56:31 PM,15,5,1:58:34 PM,15,5,2:24:58 PM,15,5,2:27:27 PM,15,5,2:47:36 PM,5,28.0,,-1.2918246,36.801584999999996,-1.2964347999999999,36.8182757,Rider_Id_468,1209
-Order_No_856,User_Id_1245,Bike,3,Business,27,6,10:12:22 AM,27,6,10:12:43 AM,27,6,10:12:52 AM,27,6,10:36:32 AM,27,6,11:10:29 AM,16,,,-1.2602337,36.79905479999999,-1.3198615,36.7110323,Rider_Id_61,2037
-Order_No_18364,User_Id_1609,Bike,3,Business,7,1,12:35:50 PM,7,1,12:38:36 PM,7,1,12:49:25 PM,7,1,1:02:44 PM,7,1,1:26:29 PM,5,21.4,,-1.32589,36.831679,-1.3205163999999998,36.8098882,Rider_Id_543,1425
-Order_No_3232,User_Id_186,Bike,3,Business,9,4,12:24:21 PM,9,4,12:26:06 PM,9,4,12:32:25 PM,9,4,12:41:40 PM,9,4,1:00:34 PM,8,,,-1.2571472,36.795063299999995,-1.304078,36.83014,Rider_Id_383,1134
-Order_No_17109,User_Id_393,Bike,3,Business,28,2,9:03:13 AM,28,2,9:03:57 AM,28,2,9:36:08 AM,28,2,9:42:52 AM,28,2,10:20:54 AM,14,18.9,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_168,2282
-Order_No_9810,User_Id_393,Bike,3,Business,21,5,8:37:28 AM,21,5,9:10:54 AM,21,5,9:22:46 AM,21,5,9:56:15 AM,21,5,9:56:22 AM,4,19.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_555,7
-Order_No_3159,User_Id_2411,Bike,3,Business,10,4,4:44:32 PM,10,4,4:45:03 PM,10,4,4:46:10 PM,10,4,5:04:01 PM,10,4,5:35:00 PM,5,23.6,,-1.2615888000000002,36.7928732,-1.2763916000000002,36.8246826,Rider_Id_258,1859
-Order_No_13447,User_Id_1527,Bike,3,Business,6,6,10:53:20 AM,6,6,10:53:32 AM,6,6,11:17:41 AM,6,6,11:42:35 AM,6,6,11:43:11 AM,4,26.6,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_436,36
-Order_No_17785,User_Id_1317,Bike,3,Personal,25,5,12:58:02 PM,25,5,12:59:55 PM,25,5,1:09:21 PM,25,5,1:16:49 PM,25,5,1:41:39 PM,7,26.2,,-1.2924173,36.779781400000005,-1.2596241000000001,36.762407,Rider_Id_328,1490
-Order_No_23372,User_Id_1133,Bike,3,Business,26,5,10:41:22 AM,26,5,10:42:09 AM,26,5,11:19:29 AM,26,5,11:29:49 AM,26,5,12:00:30 PM,11,,,-1.2103609,36.7801748,-1.2571472,36.795063299999995,Rider_Id_353,1841
-Order_No_9974,User_Id_1405,Bike,3,Business,12,5,3:08:23 PM,12,5,3:14:14 PM,12,5,3:28:04 PM,12,5,3:42:53 PM,12,5,4:13:08 PM,16,26.9,,-1.2805198,36.827761200000005,-1.3478563000000001,36.8935824,Rider_Id_74,1815
-Order_No_4340,User_Id_1725,Bike,1,Business,2,4,2:10:51 PM,2,4,2:12:24 PM,2,4,2:20:27 PM,2,4,2:22:33 PM,2,4,2:40:16 PM,5,26.3,,-1.2652108,36.8056075,-1.2903067,36.8151026,Rider_Id_754,1063
-Order_No_24784,User_Id_3560,Bike,3,Business,5,2,2:34:42 PM,5,2,2:36:08 PM,5,2,2:54:33 PM,5,2,3:03:40 PM,5,2,3:09:38 PM,1,27.3,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_899,358
-Order_No_23712,User_Id_2174,Bike,3,Business,9,4,10:23:51 AM,9,4,10:23:56 AM,9,4,10:25:18 AM,9,4,10:39:16 AM,9,4,11:25:00 AM,15,23.9,,-1.2482781,36.884091999999995,-1.2979242,36.7902416,Rider_Id_82,2744
-Order_No_2008,User_Id_2956,Bike,3,Business,7,4,6:25:35 PM,7,4,6:27:21 PM,7,4,6:39:08 PM,7,4,6:43:46 PM,7,4,7:10:00 PM,9,25.5,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_541,1574
-Order_No_20550,User_Id_540,Bike,3,Business,22,3,7:50:42 AM,22,3,8:09:45 AM,22,3,8:54:39 AM,22,3,9:19:31 AM,22,3,9:26:50 AM,5,18.9,,-1.2753108999999998,36.8130185,-1.2551895,36.7822034,Rider_Id_96,439
-Order_No_14891,User_Id_393,Bike,3,Business,7,5,8:36:31 AM,7,5,8:39:51 AM,7,5,9:04:45 AM,7,5,9:31:40 AM,7,5,10:48:22 AM,8,,,-1.3228003000000002,36.8306435,-1.2770708,36.8231093,Rider_Id_271,4602
-Order_No_16511,User_Id_2436,Bike,3,Business,28,4,12:30:52 PM,28,4,12:31:04 PM,28,4,12:31:18 PM,28,4,12:44:24 PM,28,4,1:10:51 PM,15,23.7,,-1.2551895,36.7822034,-1.335515,36.864137,Rider_Id_26,1587
-Order_No_23744,User_Id_1162,Bike,3,Business,8,4,12:06:15 PM,8,4,12:08:59 PM,8,4,12:20:56 PM,8,4,12:28:02 PM,8,4,12:48:36 PM,8,26.5,,-1.3122610000000001,36.794658,-1.2719495,36.7728776,Rider_Id_726,1234
-Order_No_1471,User_Id_2881,Bike,1,Personal,20,7,10:03:10 AM,20,7,10:37:18 AM,20,7,10:54:11 AM,20,7,11:00:40 AM,20,7,11:12:52 AM,5,20.0,,-1.2851714,36.83182070000001,-1.3102963,36.8212746,Rider_Id_858,732
-Order_No_448,User_Id_186,Bike,3,Business,8,1,10:13:11 AM,8,1,10:13:17 AM,8,1,10:13:21 AM,8,1,10:15:19 AM,8,1,10:48:53 AM,23,21.7,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_953,2014
-Order_No_3276,User_Id_2956,Bike,3,Business,10,2,9:35:33 AM,10,2,9:46:01 AM,10,2,9:46:14 AM,10,2,9:56:09 AM,10,2,10:13:20 AM,11,16.2,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_51,1031
-Order_No_15131,User_Id_1955,Bike,3,Business,30,4,3:20:46 PM,30,4,3:21:46 PM,30,4,3:59:24 PM,30,4,4:19:43 PM,30,4,4:37:40 PM,7,,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_673,1077
-Order_No_18284,User_Id_3599,Bike,3,Business,25,2,1:07:50 PM,25,2,1:09:37 PM,25,2,1:09:46 PM,25,2,1:52:07 PM,25,2,2:12:20 PM,16,27.1,,-1.3302996,36.8707935,-1.2706815,36.7646286,Rider_Id_813,1213
-Order_No_16529,User_Id_510,Bike,3,Business,21,5,9:58:48 AM,21,5,10:32:29 AM,21,5,10:48:16 AM,21,5,10:59:36 AM,21,5,12:02:08 PM,12,22.6,,-1.2860183,36.8975338,-1.281754,36.8324229,Rider_Id_554,3752
-Order_No_26919,User_Id_2279,Bike,3,Business,22,3,10:57:39 AM,22,3,10:59:01 AM,22,3,11:09:55 AM,22,3,11:14:46 AM,22,3,11:32:32 AM,7,23.2,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_119,1066
-Order_No_926,User_Id_1246,Bike,3,Business,8,2,3:39:09 PM,8,2,3:57:24 PM,8,2,4:04:54 PM,8,2,4:16:59 PM,8,2,4:58:23 PM,23,25.4,,-1.3700383,36.9190174,-1.2756120000000002,36.76379779999999,Rider_Id_441,2484
-Order_No_11150,User_Id_1719,Bike,3,Business,22,5,12:49:51 PM,22,5,12:52:03 PM,22,5,12:52:21 PM,22,5,12:58:55 PM,22,5,1:47:10 PM,16,28.7,,-1.296797,36.776452500000005,-1.23966,36.75258,Rider_Id_709,2895
-Order_No_28077,User_Id_59,Bike,3,Business,8,5,4:55:17 PM,8,5,4:59:45 PM,8,5,5:03:14 PM,8,5,5:23:22 PM,8,5,6:19:52 PM,6,25.4,,-1.290894,36.822971,-1.3230781,36.8353276,Rider_Id_513,3390
-Order_No_5916,User_Id_1500,Bike,3,Business,8,5,3:32:57 PM,8,5,4:00:44 PM,8,5,4:17:57 PM,8,5,4:38:30 PM,8,5,5:01:56 PM,8,27.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_76,1406
-Order_No_24622,User_Id_3674,Bike,3,Business,7,1,10:03:39 AM,7,1,10:04:39 AM,7,1,10:20:40 AM,7,1,10:41:03 AM,7,1,11:05:09 AM,8,24.8,,-1.2551895,36.7822034,-1.2987976,36.829477000000004,Rider_Id_607,1446
-Order_No_11345,User_Id_1748,Bike,2,Personal,19,4,6:54:28 PM,19,4,6:54:51 PM,19,4,6:56:54 PM,19,4,7:05:05 PM,19,4,7:24:30 PM,10,19.7,,-1.2952108999999998,36.775706400000004,-1.2527958000000001,36.8003131,Rider_Id_74,1165
-Order_No_528,User_Id_20,Bike,3,Business,28,2,5:46:08 PM,28,2,5:46:36 PM,28,2,5:51:49 PM,28,2,6:00:02 PM,28,2,7:07:22 PM,17,21.4,,-1.2562796999999999,36.8075858,-1.3390024,36.8746657,Rider_Id_139,4040
-Order_No_26730,User_Id_2454,Bike,3,Business,27,2,3:15:08 PM,27,2,3:28:25 PM,27,2,3:47:14 PM,27,2,4:16:27 PM,27,2,5:03:53 PM,7,25.7,,-1.2733047,36.8109744,-1.2993231,36.7753372,Rider_Id_725,2846
-Order_No_7218,User_Id_435,Bike,3,Business,7,5,12:10:17 PM,7,5,1:38:56 PM,7,5,2:05:22 PM,7,5,2:08:05 PM,7,5,2:42:42 PM,12,,,-1.2784722,36.7556652,-1.300921,36.828195,Rider_Id_342,2077
-Order_No_6961,User_Id_346,Bike,3,Business,9,2,1:36:23 PM,9,2,1:37:26 PM,9,2,1:54:20 PM,9,2,2:04:07 PM,9,2,2:19:20 PM,8,30.4,,-1.3025826,36.7670807,-1.28878,36.8168312,Rider_Id_855,913
-Order_No_24516,User_Id_2587,Bike,3,Business,26,3,9:32:18 AM,26,3,9:33:35 AM,26,3,9:44:43 AM,26,3,9:47:10 AM,26,3,10:02:23 AM,5,19.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_847,913
-Order_No_14441,User_Id_627,Bike,3,Business,26,2,11:50:16 AM,26,2,11:51:28 AM,26,2,12:28:57 PM,26,2,12:44:10 PM,26,2,1:40:40 PM,18,26.1,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_172,3390
-Order_No_15439,User_Id_3669,Bike,3,Business,29,3,11:45:09 AM,29,3,11:46:13 AM,29,3,12:57:38 PM,29,3,1:14:45 PM,29,3,1:24:16 PM,2,,,-1.2615888000000002,36.7928732,-1.2560455,36.780086700000005,Rider_Id_427,571
-Order_No_26998,User_Id_2582,Bike,3,Business,23,4,3:56:02 PM,23,4,3:59:19 PM,23,4,4:03:09 PM,23,4,5:01:19 PM,23,4,5:17:55 PM,3,,,-1.2603403,36.7998848,-1.2551895,36.7822034,Rider_Id_427,996
-Order_No_3252,User_Id_2216,Bike,3,Business,3,1,2:48:46 PM,3,1,2:54:14 PM,3,1,3:10:45 PM,3,1,3:16:04 PM,3,1,3:16:06 PM,3,23.6,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_647,2
-Order_No_17378,User_Id_868,Bike,3,Business,4,2,11:36:14 AM,4,2,11:44:02 AM,4,2,11:47:15 AM,4,2,11:56:23 AM,4,2,12:06:38 PM,4,20.5,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_75,615
-Order_No_13186,User_Id_1724,Bike,3,Business,16,5,1:39:16 PM,16,5,1:39:23 PM,16,5,1:43:49 PM,16,5,1:56:06 PM,16,5,2:28:58 PM,21,,,-1.2551895,36.7822034,-1.1848195,36.915471999999994,Rider_Id_932,1972
-Order_No_15024,User_Id_1584,Bike,3,Business,4,1,2:03:34 PM,4,1,2:26:33 PM,4,1,2:30:21 PM,4,1,2:43:28 PM,4,1,2:57:38 PM,6,,,-1.2969741000000001,36.7856612,-1.2857524,36.819680600000005,Rider_Id_906,850
-Order_No_2724,User_Id_2654,Bike,1,Personal,4,4,2:44:31 PM,4,4,2:45:20 PM,4,4,2:52:14 PM,4,4,2:53:41 PM,4,4,3:09:53 PM,3,24.8,,-1.2943818,36.7858765,-1.2963097,36.7688221,Rider_Id_390,972
-Order_No_13966,User_Id_92,Bike,1,Personal,3,3,1:28:57 PM,3,3,1:39:45 PM,3,3,2:01:14 PM,3,3,2:27:38 PM,3,3,3:01:48 PM,14,26.9,,-1.1997066,36.7739715,-1.2715686000000002,36.8023526,Rider_Id_257,2050
-Order_No_5004,User_Id_867,Bike,3,Business,13,3,12:57:23 PM,13,3,12:58:10 PM,13,3,12:59:12 PM,13,3,1:44:57 PM,13,3,2:09:19 PM,10,29.2,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_206,1462
-Order_No_2607,User_Id_393,Bike,3,Business,17,3,8:46:14 AM,17,3,8:48:01 AM,17,3,8:55:15 AM,17,3,9:04:31 AM,17,3,9:16:16 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_728,705
-Order_No_20592,User_Id_2710,Bike,3,Personal,28,4,3:45:14 PM,28,4,3:45:23 PM,28,4,3:46:03 PM,28,4,3:56:47 PM,28,4,4:28:53 PM,14,27.2,,-1.2535486,36.7933541,-1.2374254999999998,36.8920631,Rider_Id_674,1926
-Order_No_16983,User_Id_3669,Bike,3,Business,20,3,5:02:25 PM,20,3,5:23:08 PM,20,3,5:44:34 PM,20,3,5:56:07 PM,20,3,6:15:25 PM,8,27.4,,-1.2615888000000002,36.7928732,-1.2434437,36.7487213,Rider_Id_269,1158
-Order_No_423,User_Id_1646,Bike,3,Business,20,6,3:05:49 PM,20,6,3:05:59 PM,20,6,3:23:35 PM,20,6,3:25:29 PM,20,6,3:44:48 PM,11,,,-1.2517448,36.7924971,-1.3039025,36.7715803,Rider_Id_161,1159
-Order_No_21116,User_Id_577,Bike,3,Personal,26,6,9:29:32 AM,26,6,9:30:38 AM,26,6,9:35:27 AM,26,6,9:43:39 AM,26,6,10:15:38 AM,12,20.5,,-1.3270347,36.866736200000005,-1.3628438999999999,36.9407567,Rider_Id_418,1919
-Order_No_18553,User_Id_3686,Bike,3,Business,28,4,10:16:08 AM,28,4,10:16:30 AM,28,4,10:32:12 AM,28,4,10:47:36 AM,28,4,11:38:56 AM,22,27.9,,-1.2551895,36.7822034,-1.333148,36.922453000000004,Rider_Id_26,3080
-Order_No_629,User_Id_2627,Bike,3,Business,7,5,11:02:05 AM,7,5,11:06:54 AM,7,5,11:41:48 AM,7,5,11:43:52 AM,7,5,12:08:43 PM,8,,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_259,1491
-Order_No_17161,User_Id_633,Bike,3,Business,25,1,2:03:49 PM,25,1,2:04:20 PM,25,1,2:06:52 PM,25,1,2:47:51 PM,25,1,3:12:26 PM,10,27.9,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_86,1475
-Order_No_5197,User_Id_2503,Bike,2,Business,16,1,4:01:23 PM,16,1,4:01:30 PM,16,1,4:06:17 PM,16,1,4:40:44 PM,16,1,4:45:03 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_116,259
-Order_No_6240,User_Id_3727,Bike,2,Business,19,2,4:11:13 PM,19,2,4:11:32 PM,19,2,4:17:24 PM,19,2,4:23:56 PM,19,2,4:50:33 PM,8,18.7,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_465,1597
-Order_No_26087,User_Id_2627,Bike,3,Business,22,4,10:44:12 AM,22,4,10:44:40 AM,22,4,10:49:55 AM,22,4,10:59:56 AM,22,4,11:01:19 AM,2,22.5,,-1.2571472,36.795063299999995,-1.2591018999999999,36.800577000000004,Rider_Id_353,83
-Order_No_11310,User_Id_391,Bike,3,Business,28,4,9:49:58 AM,28,4,9:50:51 AM,28,4,9:51:13 AM,28,4,10:25:58 AM,28,4,10:35:53 AM,17,22.7,,-1.2867454,36.8259017,-1.3518598,36.7324661,Rider_Id_137,595
-Order_No_12763,User_Id_3124,Bike,1,Personal,1,1,5:37:41 PM,1,1,5:38:03 PM,1,1,5:49:10 PM,1,1,5:51:36 PM,1,1,6:06:37 PM,8,23.7,,-1.2030691,36.8394744,-1.2157086000000001,36.870270399999995,Rider_Id_607,901
-Order_No_8476,User_Id_59,Bike,3,Business,6,4,12:25:25 PM,6,4,12:53:20 PM,6,4,1:10:49 PM,6,4,1:13:17 PM,6,4,2:24:49 PM,6,24.2,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_928,4292
-Order_No_1775,User_Id_2158,Bike,3,Personal,17,3,3:04:38 PM,17,3,3:06:12 PM,17,3,3:17:42 PM,17,3,3:47:57 PM,17,3,3:54:07 PM,7,,,-1.2888734,36.8268027,-1.25896,36.8016691,Rider_Id_843,370
-Order_No_13957,User_Id_3561,Bike,3,Personal,24,5,9:12:06 AM,24,5,9:13:33 AM,24,5,9:25:46 AM,24,5,9:29:53 AM,24,5,9:46:39 AM,5,15.7,,-1.2658315,36.809049,-1.2807298,36.8299116,Rider_Id_194,1006
-Order_No_22565,User_Id_2801,Bike,3,Business,8,3,10:10:52 AM,8,3,10:15:22 AM,8,3,10:42:45 AM,8,3,10:47:34 AM,8,3,11:01:31 AM,7,,,-1.2879652,36.8203441,-1.2674273999999999,36.787082700000006,Rider_Id_341,837
-Order_No_20126,User_Id_3487,Bike,3,Business,18,4,5:55:41 PM,18,4,5:55:59 PM,18,4,6:12:01 PM,18,4,6:14:33 PM,18,4,6:52:24 PM,12,,,-1.279395,36.825364,-1.2232123,36.8932547,Rider_Id_119,2271
-Order_No_25839,User_Id_3599,Bike,3,Business,14,2,10:42:40 AM,14,2,10:43:02 AM,14,2,11:02:01 AM,14,2,11:18:45 AM,14,2,11:49:22 AM,13,,,-1.3302996,36.8707935,-1.2568926,36.8130636,Rider_Id_675,1837
-Order_No_16635,User_Id_2360,Bike,3,Business,10,5,12:21:42 PM,10,5,12:22:00 PM,10,5,12:22:22 PM,10,5,12:31:28 PM,10,5,12:42:34 PM,5,,,-1.2551895,36.7822034,-1.2700552,36.817067,Rider_Id_576,666
-Order_No_6399,User_Id_1075,Bike,3,Business,11,4,11:42:41 AM,11,4,11:58:56 AM,11,4,12:07:10 PM,11,4,12:19:53 PM,11,4,1:05:26 PM,13,25.5,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_404,2733
-Order_No_13380,User_Id_2330,Bike,3,Business,30,4,3:34:34 PM,30,4,3:35:33 PM,30,4,3:50:05 PM,30,4,3:53:58 PM,30,4,4:31:03 PM,15,,,-1.2642903,36.80021370000001,-1.2524916000000001,36.8656196,Rider_Id_329,2225
-Order_No_25978,User_Id_3291,Bike,3,Business,7,4,2:01:56 PM,7,4,2:02:28 PM,7,4,2:05:52 PM,7,4,2:12:47 PM,7,4,2:22:22 PM,5,31.2,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_649,575
-Order_No_8305,User_Id_517,Bike,3,Business,9,6,11:05:21 AM,9,6,11:05:37 AM,9,6,11:05:42 AM,9,6,11:16:15 AM,9,6,11:35:24 AM,4,24.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_311,1149
-Order_No_9692,User_Id_633,Bike,3,Business,7,3,10:39:02 AM,7,3,10:43:33 AM,7,3,10:59:06 AM,7,3,11:43:15 AM,7,3,11:49:55 AM,4,25.1,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_684,400
-Order_No_8615,User_Id_2200,Bike,3,Business,12,1,11:54:16 AM,12,1,12:13:39 PM,12,1,12:15:29 PM,12,1,12:17:27 PM,12,1,12:17:41 PM,2,24.0,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_436,14
-Order_No_9257,User_Id_2195,Bike,3,Business,4,2,1:03:21 PM,4,2,1:27:35 PM,4,2,1:35:02 PM,4,2,1:50:31 PM,4,2,2:07:16 PM,12,22.6,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_75,1005
-Order_No_1689,User_Id_2956,Bike,3,Business,10,2,2:44:24 PM,10,2,4:46:18 PM,10,2,4:57:43 PM,10,2,5:06:52 PM,10,2,5:45:44 PM,7,20.8,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_467,2332
-Order_No_15763,User_Id_1280,Bike,3,Business,19,3,3:27:59 PM,19,3,5:04:41 PM,19,3,5:29:16 PM,19,3,5:36:28 PM,19,3,6:22:18 PM,5,22.3,,-1.2551895,36.7822034,-1.2684319,36.81658529999999,Rider_Id_178,2750
-Order_No_24787,User_Id_3291,Bike,3,Business,6,3,12:47:53 PM,6,3,12:48:46 PM,6,3,12:58:00 PM,6,3,1:07:04 PM,6,3,1:19:34 PM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_56,750
-Order_No_9450,User_Id_1787,Bike,3,Business,2,5,3:57:29 PM,2,5,4:05:40 PM,2,5,4:11:06 PM,2,5,4:23:54 PM,2,5,4:43:18 PM,10,24.9,,-1.2283403,36.8822756,-1.2684319,36.81658529999999,Rider_Id_704,1164
-Order_No_8687,User_Id_3430,Bike,1,Personal,10,4,12:48:13 PM,10,4,12:55:12 PM,10,4,1:08:40 PM,10,4,1:17:52 PM,10,4,1:39:51 PM,5,,,-1.3162537,36.819990600000004,-1.2899469,36.8078877,Rider_Id_782,1319
-Order_No_1271,User_Id_2078,Bike,1,Personal,5,5,10:30:40 AM,5,5,10:30:39 AM,5,5,10:38:04 AM,5,5,10:44:54 AM,5,5,11:08:31 AM,10,23.6,,-1.2332988999999999,36.8130698,-1.2567295,36.784224,Rider_Id_770,1417
-Order_No_3813,User_Id_3283,Bike,3,Business,8,3,2:25:59 PM,8,3,2:26:53 PM,8,3,2:52:01 PM,8,3,3:05:22 PM,8,3,3:29:16 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_787,1434
-Order_No_14314,User_Id_3355,Bike,3,Business,23,4,3:17:15 PM,23,4,3:21:08 PM,23,4,3:35:33 PM,23,4,4:29:35 PM,23,4,5:01:01 PM,10,20.6,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_882,1886
-Order_No_25158,User_Id_631,Bike,1,Personal,27,3,10:12:36 AM,27,3,10:31:49 AM,27,3,11:00:16 AM,27,3,11:03:51 AM,27,3,11:53:11 AM,19,,,-1.2247704,36.878966999999996,-1.3225156999999998,36.826241100000004,Rider_Id_812,2960
-Order_No_808,User_Id_1482,Bike,3,Business,16,4,1:12:40 PM,16,4,1:14:26 PM,16,4,1:35:54 PM,16,4,1:45:53 PM,16,4,2:06:44 PM,9,,,-1.3025666999999999,36.7803495,-1.2649605,36.798177700000004,Rider_Id_831,1251
-Order_No_6974,User_Id_478,Bike,3,Business,4,4,2:43:34 PM,4,4,2:44:18 PM,4,4,2:47:10 PM,4,4,2:49:43 PM,4,4,3:30:17 PM,8,24.8,,-1.2551895,36.7822034,-1.2864733,36.8239937,Rider_Id_31,2434
-Order_No_12151,User_Id_545,Bike,3,Business,16,4,2:34:29 PM,16,4,2:34:58 PM,16,4,2:35:17 PM,16,4,2:40:58 PM,16,4,3:19:48 PM,19,,,-1.2963097,36.7688221,-1.2417321000000001,36.8886277,Rider_Id_831,2330
-Order_No_18778,User_Id_2200,Bike,3,Business,11,2,1:13:17 PM,11,2,2:54:55 PM,11,2,2:57:36 PM,11,2,3:10:49 PM,11,2,4:26:28 PM,6,22.3,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_268,4539
-Order_No_14919,User_Id_2064,Bike,2,Personal,13,3,2:22:19 PM,13,3,2:24:26 PM,13,3,2:30:05 PM,13,3,3:45:14 PM,13,3,3:54:37 PM,2,23.5,,-1.2689559,36.8097614,-1.2664882,36.803337400000004,Rider_Id_116,563
-Order_No_24383,User_Id_1178,Bike,3,Business,9,3,8:12:37 PM,9,3,8:13:04 PM,9,3,8:19:18 PM,9,3,8:43:35 PM,9,3,8:51:10 PM,5,18.3,,-1.2862269,36.7841601,-1.2697858999999998,36.7946203,Rider_Id_355,455
-Order_No_12002,User_Id_2956,Bike,3,Business,20,1,3:50:29 PM,20,1,3:50:51 PM,20,1,4:02:25 PM,20,1,4:20:43 PM,20,1,4:25:45 PM,1,,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_448,302
-Order_No_25103,User_Id_1304,Bike,3,Business,30,2,1:06:45 PM,30,2,1:22:24 PM,30,2,1:26:41 PM,30,2,2:09:34 PM,30,2,2:41:54 PM,9,27.5,,-1.2864673999999998,36.820262,-1.3332749,36.870814700000004,Rider_Id_469,1940
-Order_No_3366,User_Id_540,Bike,3,Business,30,4,3:09:31 PM,30,4,3:09:51 PM,30,4,3:31:59 PM,30,4,3:41:35 PM,30,4,3:57:29 PM,5,,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_659,954
-Order_No_3814,User_Id_1803,Bike,1,Personal,28,1,12:35:51 PM,28,1,12:36:56 PM,28,1,1:08:23 PM,28,1,1:18:19 PM,28,1,1:58:35 PM,10,22.6,,-1.3081260000000001,36.8532641,-1.2673271000000002,36.8064451,Rider_Id_480,2416
-Order_No_16733,User_Id_2200,Bike,3,Business,23,2,2:25:57 PM,23,2,2:38:52 PM,23,2,2:55:10 PM,23,2,2:57:38 PM,23,2,3:22:08 PM,8,18.9,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_208,1470
-Order_No_23046,User_Id_353,Bike,3,Personal,27,3,3:45:56 PM,27,3,3:46:46 PM,27,3,3:52:58 PM,27,3,3:55:19 PM,27,3,4:41:45 PM,19,29.0,,-1.2649605,36.798177700000004,-1.3678944,36.7513257,Rider_Id_720,2786
-Order_No_22614,User_Id_1792,Bike,3,Business,23,2,10:50:02 AM,23,2,10:51:17 AM,23,2,11:20:35 AM,23,2,11:25:36 AM,23,2,11:47:22 AM,10,17.9,,-1.2809121,36.7852524,-1.3271684,36.8173021,Rider_Id_299,1306
-Order_No_20030,User_Id_1642,Bike,3,Business,4,1,3:03:51 PM,4,1,3:11:16 PM,4,1,3:48:33 PM,4,1,5:02:31 PM,4,1,5:02:33 PM,7,27.8,,-1.2852299999999999,36.8248292,-1.2726506000000002,36.7960153,Rider_Id_61,2
-Order_No_95,User_Id_2523,Bike,3,Business,6,4,2:07:22 PM,6,4,2:08:19 PM,6,4,2:13:52 PM,6,4,2:18:09 PM,6,4,2:23:47 PM,2,21.5,,-1.2551895,36.7822034,-1.2567251,36.798840999999996,Rider_Id_709,338
-Order_No_2400,User_Id_2658,Bike,3,Business,10,1,3:10:37 PM,10,1,3:34:27 PM,10,1,3:34:32 PM,10,1,3:49:22 PM,10,1,4:21:26 PM,7,18.2,,-1.2924639,36.77957979999999,-1.2813012,36.832396200000005,Rider_Id_89,1924
-Order_No_18301,User_Id_3547,Bike,3,Business,21,2,10:11:24 AM,21,2,10:12:44 AM,21,2,10:33:56 AM,21,2,10:38:45 AM,21,2,11:15:56 AM,13,22.7,,-1.2856319,36.767299799999996,-1.274374,36.8550291,Rider_Id_599,2231
-Order_No_23359,User_Id_1195,Bike,3,Business,11,1,3:48:09 PM,11,1,3:51:13 PM,11,1,3:51:17 PM,11,1,3:57:43 PM,11,1,4:07:21 PM,4,28.7,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_619,578
-Order_No_23857,User_Id_1874,Bike,3,Business,19,3,1:53:57 PM,19,3,1:54:53 PM,19,3,1:56:41 PM,19,3,2:03:45 PM,19,3,2:50:34 PM,27,25.5,,-1.2571472,36.795063299999995,-1.1305126,36.676700200000006,Rider_Id_74,2809
-Order_No_2775,User_Id_2686,Bike,3,Personal,21,6,10:42:07 AM,21,6,10:42:26 AM,21,6,10:45:44 AM,21,6,10:58:34 AM,21,6,11:20:06 AM,12,16.3,,-1.3404187,36.7614685,-1.316065,36.840398,Rider_Id_670,1292
-Order_No_1722,User_Id_265,Bike,3,Business,20,2,12:45:22 PM,20,2,12:49:08 PM,20,2,1:14:17 PM,20,2,1:32:26 PM,20,2,2:19:15 PM,7,24.0,,-1.2600931999999998,36.8041689,-1.300921,36.828195,Rider_Id_916,2809
-Order_No_10406,User_Id_2316,Bike,1,Personal,5,3,3:04:42 PM,5,3,3:05:38 PM,5,3,3:30:43 PM,5,3,3:42:39 PM,5,3,4:01:27 PM,9,23.7,,-1.2861764999999998,36.8209086,-1.3069389999999999,36.7727566,Rider_Id_882,1128
-Order_No_10229,User_Id_393,Bike,3,Business,3,3,4:42:53 PM,3,3,4:43:13 PM,3,3,4:43:20 PM,3,3,5:00:54 PM,3,3,5:33:53 PM,11,27.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_223,1979
-Order_No_2845,User_Id_1469,Bike,3,Business,11,5,2:45:41 PM,11,5,2:46:36 PM,11,5,3:18:28 PM,11,5,3:20:42 PM,11,5,4:35:38 PM,6,22.5,,-1.2579303999999998,36.8019667,-1.2924639,36.77957979999999,Rider_Id_290,4496
-Order_No_7962,User_Id_1399,Bike,3,Personal,26,4,2:08:00 PM,26,4,2:09:24 PM,26,4,2:12:59 PM,26,4,2:21:25 PM,26,4,2:26:25 PM,1,22.1,,-1.2985031999999999,36.7821448,-1.3043649,36.7816132,Rider_Id_101,300
-Order_No_16262,User_Id_1231,Bike,3,Business,1,4,2:09:37 PM,1,4,2:10:27 PM,1,4,2:51:53 PM,1,4,2:53:08 PM,1,4,3:39:55 PM,13,,,-1.2821421999999998,36.8161565,-1.2156006000000001,36.8916865,Rider_Id_736,2807
-Order_No_8650,User_Id_1047,Bike,3,Personal,17,1,10:10:52 AM,17,1,10:11:09 AM,17,1,10:22:40 AM,17,1,10:23:59 AM,17,1,10:41:34 AM,6,18.9,,-1.3077869,36.844320700000004,-1.3010058999999998,36.8216816,Rider_Id_281,1055
-Order_No_24198,User_Id_3674,Bike,3,Business,29,3,2:31:58 PM,29,3,2:44:43 PM,29,3,2:54:02 PM,29,3,2:56:48 PM,29,3,3:12:57 PM,10,21.0,,-1.2551895,36.7822034,-1.2323335,36.810035600000006,Rider_Id_96,969
-Order_No_4942,User_Id_111,Bike,3,Business,16,3,10:03:42 AM,16,3,10:04:30 AM,16,3,10:10:17 AM,16,3,10:14:00 AM,16,3,10:14:04 AM,4,19.8,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_932,4
-Order_No_13976,User_Id_2200,Bike,3,Business,20,2,11:12:43 AM,20,2,11:24:08 AM,20,2,11:32:20 AM,20,2,11:41:42 AM,20,2,12:12:51 PM,8,20.0,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_933,1869
-Order_No_17828,User_Id_2106,Bike,1,Personal,17,4,1:24:05 PM,17,4,1:25:47 PM,17,4,1:39:58 PM,17,4,2:00:24 PM,17,4,2:25:11 PM,12,24.1,,-1.2840605,36.8191699,-1.1985673000000001,36.836368799999995,Rider_Id_266,1487
-Order_No_23454,User_Id_2587,Bike,3,Business,16,2,2:25:06 PM,16,2,3:03:53 PM,16,2,3:22:34 PM,16,2,3:23:52 PM,16,2,3:41:24 PM,6,,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_438,1052
-Order_No_21892,User_Id_2212,Bike,3,Business,20,3,11:37:26 AM,20,3,11:37:39 AM,20,3,11:40:34 AM,20,3,12:09:27 PM,20,3,12:18:58 PM,2,26.4,,-1.2571472,36.795063299999995,-1.2733047,36.8109744,Rider_Id_948,571
-Order_No_20794,User_Id_283,Bike,3,Business,3,1,2:35:41 PM,3,1,2:36:17 PM,3,1,2:38:20 PM,3,1,3:05:04 PM,3,1,3:27:32 PM,4,23.6,,-1.2804227,36.8250375,-1.2715686000000002,36.8023526,Rider_Id_12,1348
-Order_No_13446,User_Id_269,Bike,3,Business,5,3,1:11:44 PM,5,3,2:22:02 PM,5,3,2:31:57 PM,5,3,2:48:04 PM,5,3,3:30:40 PM,15,25.0,,-1.2551895,36.7822034,-1.3226301999999999,36.8719901,Rider_Id_596,2556
-Order_No_18477,User_Id_718,Bike,3,Business,27,5,4:11:15 PM,27,5,4:11:39 PM,27,5,4:24:53 PM,27,5,4:29:12 PM,27,5,5:05:14 PM,9,23.8,,-1.2551895,36.7822034,-1.3001735,36.8042049,Rider_Id_519,2162
-Order_No_25604,User_Id_2457,Bike,3,Business,21,3,3:19:50 PM,21,3,3:36:50 PM,21,3,3:54:00 PM,21,3,3:57:25 PM,21,3,4:08:02 PM,8,23.2,2.9,-1.2551895,36.7822034,-1.2862436000000002,36.791017,Rider_Id_195,637
-Order_No_4022,User_Id_42,Bike,3,Business,29,1,3:40:48 PM,29,1,3:41:48 PM,29,1,4:12:19 PM,29,1,4:15:10 PM,29,1,4:27:46 PM,4,24.2,,-1.2564152,36.779280299999996,-1.2551895,36.7822034,Rider_Id_878,756
-Order_No_9745,User_Id_622,Bike,1,Business,4,5,3:38:47 PM,4,5,3:39:06 PM,4,5,3:54:24 PM,4,5,4:03:32 PM,4,5,4:34:20 PM,19,24.0,,-1.2190729,36.907120299999995,-1.2951362,36.7876989,Rider_Id_265,1848
-Order_No_19784,User_Id_635,Bike,3,Business,16,4,3:51:10 PM,16,4,3:51:36 PM,16,4,4:11:29 PM,16,4,4:44:15 PM,16,4,5:07:11 PM,8,,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_709,1376
-Order_No_19579,User_Id_755,Bike,1,Personal,26,3,11:46:24 AM,26,3,11:46:57 AM,26,3,11:53:41 AM,26,3,12:08:07 PM,26,3,1:04:38 PM,15,24.1,,-1.3297531,36.7063699,-1.2640571000000003,36.7883547,Rider_Id_125,3391
-Order_No_8360,User_Id_393,Bike,3,Business,23,3,1:45:57 PM,23,3,1:46:04 PM,23,3,1:48:40 PM,23,3,1:59:40 PM,23,3,2:06:42 PM,4,27.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,422
-Order_No_27631,User_Id_2789,Bike,3,Business,28,1,4:11:10 PM,28,1,4:13:07 PM,28,1,4:20:16 PM,28,1,4:32:42 PM,28,1,4:48:29 PM,4,25.3,,-1.304735,36.784605400000004,-1.2885364,36.7995016,Rider_Id_559,947
-Order_No_7477,User_Id_393,Bike,3,Business,28,2,8:51:53 AM,28,2,8:55:19 AM,28,2,9:08:48 AM,28,2,9:56:01 AM,28,2,10:18:35 AM,11,18.6,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_849,1354
-Order_No_1667,User_Id_302,Bike,3,Business,27,4,5:48:55 PM,27,4,5:49:00 PM,27,4,5:53:23 PM,27,4,6:00:40 PM,27,4,6:22:32 PM,2,22.8,,-1.2649605,36.798177700000004,-1.2544458,36.7897277,Rider_Id_275,1312
-Order_No_5466,User_Id_3470,Bike,3,Business,21,1,10:49:15 AM,21,1,10:50:58 AM,21,1,11:12:12 AM,21,1,11:22:49 AM,21,1,11:42:45 AM,8,20.0,,-1.2765736,36.851364600000004,-1.2601708999999999,36.8002238,Rider_Id_900,1196
-Order_No_20454,User_Id_2605,Bike,3,Business,4,3,3:28:05 PM,4,3,3:28:21 PM,4,3,3:44:04 PM,4,3,3:51:03 PM,4,3,4:21:12 PM,22,18.2,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_116,1809
-Order_No_20436,User_Id_2330,Bike,3,Business,28,2,3:32:20 PM,28,2,3:32:58 PM,28,2,3:37:31 PM,28,2,3:46:32 PM,28,2,4:14:53 PM,10,23.4,,-1.2645318,36.7981775,-1.3015197,36.7658456,Rider_Id_96,1701
-Order_No_5189,User_Id_1402,Bike,3,Business,8,3,9:04:49 AM,8,3,9:05:15 AM,8,3,9:13:43 AM,8,3,9:15:44 AM,8,3,9:27:44 AM,5,17.4,,-1.2669451,36.8033576,-1.2891921999999998,36.825189,Rider_Id_754,720
-Order_No_12385,User_Id_1850,Bike,3,Business,31,5,4:05:40 PM,31,5,4:25:48 PM,31,5,4:32:21 PM,31,5,4:41:12 PM,31,5,4:51:05 PM,4,23.9,,-1.2571472,36.795063299999995,-1.2816664,36.8226716,Rider_Id_491,593
-Order_No_17612,User_Id_3562,Bike,2,Personal,3,4,3:01:10 PM,3,4,3:02:52 PM,3,4,3:17:55 PM,3,4,3:24:23 PM,3,4,4:38:27 PM,16,25.2,,-1.2650181999999999,36.7926717,-1.2816507,36.8770543,Rider_Id_753,4444
-Order_No_11204,User_Id_1502,Bike,3,Business,8,4,10:26:17 AM,8,4,10:26:36 AM,8,4,10:33:44 AM,8,4,10:35:19 AM,8,4,11:27:21 AM,15,26.5,,-1.2232123,36.8932547,-1.2938211000000002,36.8270021,Rider_Id_685,3122
-Order_No_26301,User_Id_3698,Bike,3,Business,22,3,10:46:02 AM,22,3,10:46:36 AM,22,3,10:53:43 AM,22,3,11:14:32 AM,22,3,11:40:19 AM,17,23.3,,-1.2285995,36.901237200000004,-1.3244885,36.897791999999995,Rider_Id_291,1547
-Order_No_13572,User_Id_2066,Bike,3,Business,13,3,11:37:28 AM,13,3,12:08:01 PM,13,3,12:15:20 PM,13,3,12:29:54 PM,13,3,12:58:04 PM,6,20.0,,-1.316961,36.820337,-1.3288576,36.8465936,Rider_Id_844,1690
-Order_No_6411,User_Id_2587,Bike,3,Business,1,1,12:42:51 PM,1,1,12:43:18 PM,1,1,12:43:41 PM,1,1,12:59:41 PM,1,1,1:45:10 PM,9,24.0,,-1.28878,36.8168312,-1.2413316,36.878552500000005,Rider_Id_656,2729
-Order_No_19090,User_Id_3635,Bike,1,Business,25,2,9:50:15 AM,25,2,9:50:24 AM,25,2,9:51:55 AM,25,2,9:58:28 AM,25,2,10:07:21 AM,3,18.0,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_865,533
-Order_No_10909,User_Id_2183,Bike,3,Business,26,5,11:45:05 AM,26,5,1:00:05 PM,26,5,1:04:54 PM,26,5,1:09:51 PM,26,5,1:17:42 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_578,471
-Order_No_222,User_Id_136,Bike,3,Business,30,5,11:52:33 AM,30,5,11:52:51 AM,30,5,11:59:32 AM,30,5,12:02:54 PM,30,5,12:27:59 PM,3,23.7,,-1.3326173000000001,36.71462329999999,-1.3169591,36.7000029,Rider_Id_917,1505
-Order_No_23663,User_Id_517,Bike,3,Business,29,3,1:07:16 PM,29,3,1:07:39 PM,29,3,1:10:17 PM,29,3,1:22:25 PM,29,3,1:55:13 PM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_211,1968
-Order_No_19440,User_Id_2330,Bike,3,Business,20,3,2:59:38 PM,20,3,3:36:31 PM,20,3,3:46:25 PM,20,3,3:48:28 PM,20,3,4:06:22 PM,8,28.3,,-1.2124582,36.802894,-1.2324923,36.836377500000005,Rider_Id_131,1074
-Order_No_25099,User_Id_661,Bike,3,Business,29,2,2:48:17 PM,29,2,2:48:55 PM,29,2,3:05:58 PM,29,2,3:39:54 PM,29,2,3:58:29 PM,4,23.7,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_140,1115
-Order_No_4599,User_Id_1340,Bike,3,Business,13,4,10:05:21 AM,13,4,10:18:40 AM,13,4,10:33:26 AM,13,4,10:36:37 AM,13,4,10:53:17 AM,6,21.8,,-1.2945798000000002,36.7737755,-1.2707178999999997,36.7979713,Rider_Id_205,1000
-Order_No_13000,User_Id_1849,Bike,3,Business,25,3,2:09:49 PM,25,3,2:19:44 PM,25,3,2:35:49 PM,25,3,2:37:48 PM,25,3,3:13:12 PM,7,17.6,,-1.290894,36.822971,-1.2632534,36.799342700000004,Rider_Id_144,2124
-Order_No_11779,User_Id_3147,Bike,3,Business,19,1,2:47:07 PM,19,1,2:47:25 PM,19,1,2:51:45 PM,19,1,2:55:17 PM,19,1,3:32:29 PM,15,25.0,,-1.3472066,36.7692638,-1.2591018999999999,36.800577000000004,Rider_Id_519,2232
-Order_No_17685,User_Id_3201,Bike,3,Business,29,6,3:40:00 PM,29,6,3:48:59 PM,29,6,4:20:26 PM,29,6,4:26:44 PM,29,6,4:50:33 PM,17,26.5,,-1.3463041999999998,36.768164,-1.2584143,36.8048002,Rider_Id_237,1429
-Order_No_5029,User_Id_1271,Bike,3,Business,5,5,10:38:58 AM,5,5,10:48:40 AM,5,5,10:48:56 AM,5,5,10:55:13 AM,5,5,11:27:40 AM,18,24.7,,-1.3244885,36.897791999999995,-1.2987748,36.8024593,Rider_Id_564,1947
-Order_No_16830,User_Id_2200,Bike,3,Business,29,1,12:30:51 PM,29,1,12:36:12 PM,29,1,12:51:12 PM,29,1,1:16:31 PM,29,1,1:28:46 PM,5,,,-1.290894,36.822971,-1.2545163000000001,36.8243717,Rider_Id_336,735
-Order_No_1185,User_Id_3653,Bike,2,Personal,5,3,11:03:36 AM,5,3,11:04:46 AM,5,3,11:08:48 AM,5,3,11:22:45 AM,5,3,11:36:11 AM,5,22.1,,-1.2687582,36.8012024,-1.2867132,36.8118433,Rider_Id_379,806
-Order_No_19921,User_Id_868,Bike,3,Business,6,4,9:10:47 AM,6,4,9:12:12 AM,6,4,9:35:58 AM,6,4,9:42:14 AM,6,4,10:07:32 AM,8,18.8,3.0,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_937,1518
-Order_No_10680,User_Id_265,Bike,3,Business,10,5,11:04:04 AM,10,5,11:05:31 AM,10,5,11:33:23 AM,10,5,11:37:43 AM,10,5,12:21:42 PM,13,,,-1.2569714,36.7799637,-1.300921,36.828195,Rider_Id_411,2639
-Order_No_13528,User_Id_863,Bike,3,Personal,3,5,10:36:05 AM,3,5,10:37:43 AM,3,5,10:47:45 AM,3,5,10:50:53 AM,3,5,11:04:30 AM,5,,,-1.2945684,36.8154093,-1.2963097,36.7688221,Rider_Id_205,817
-Order_No_19065,User_Id_71,Bike,3,Business,6,5,2:29:34 PM,6,5,2:34:54 PM,6,5,2:51:38 PM,6,5,3:02:37 PM,6,5,3:27:53 PM,10,22.6,,-1.2891921999999998,36.825189,-1.293886,36.7596951,Rider_Id_629,1516
-Order_No_4270,User_Id_867,Bike,3,Business,21,4,11:36:38 AM,21,4,11:37:18 AM,21,4,11:41:11 AM,21,4,11:44:18 AM,21,4,12:02:43 PM,9,25.0,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_523,1105
-Order_No_8672,User_Id_3573,Bike,1,Personal,25,2,11:31:51 AM,25,2,11:34:05 AM,25,2,11:54:59 AM,25,2,11:57:39 AM,25,2,12:40:25 PM,6,23.8,,-1.2939186000000003,36.8069693,-1.2631940000000002,36.797624,Rider_Id_713,2566
-Order_No_26531,User_Id_2195,Bike,1,Business,6,5,10:04:51 AM,6,5,10:07:12 AM,6,5,10:16:31 AM,6,5,10:27:50 AM,6,5,11:20:38 AM,11,15.2,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_88,3168
-Order_No_23733,User_Id_2700,Bike,3,Business,17,6,2:32:31 PM,17,6,2:54:58 PM,17,6,3:13:53 PM,17,6,3:17:31 PM,17,6,4:08:01 PM,8,23.7,,-1.2987697,36.746851,-1.2845834999999999,36.8079121,Rider_Id_625,3030
-Order_No_27650,User_Id_3647,Bike,3,Business,31,4,12:51:03 PM,31,4,12:56:03 PM,31,4,1:04:12 PM,31,4,1:11:18 PM,31,4,1:25:37 PM,8,26.5,,-1.2726389999999999,36.794723,-1.2698167999999999,36.7421898,Rider_Id_247,859
-Order_No_20237,User_Id_1750,Bike,1,Personal,29,5,2:17:08 PM,29,5,2:17:18 PM,29,5,2:20:16 PM,29,5,3:12:23 PM,29,5,4:09:15 PM,34,21.3,,-1.4135469999999999,36.955076399999996,-1.2675351000000001,36.7456136,Rider_Id_919,3412
-Order_No_20770,User_Id_3338,Bike,3,Business,30,4,2:56:24 PM,30,4,2:56:42 PM,30,4,3:00:46 PM,30,4,3:04:57 PM,30,4,3:32:14 PM,5,,,-1.2551895,36.7822034,-1.2788888999999999,36.8100058,Rider_Id_161,1637
-Order_No_11112,User_Id_2412,Bike,3,Business,28,4,5:33:16 PM,28,4,6:02:22 PM,28,4,6:21:29 PM,28,4,6:35:41 PM,28,4,6:58:01 PM,5,,,-1.2156006000000001,36.8916865,-1.2297202,36.874550799999994,Rider_Id_808,1340
-Order_No_21507,User_Id_53,Bike,3,Business,11,6,11:26:15 AM,11,6,11:26:30 AM,11,6,12:01:45 PM,11,6,12:54:39 PM,11,6,1:16:55 PM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_155,1336
-Order_No_24127,User_Id_3283,Bike,3,Business,6,1,1:51:23 PM,6,1,1:55:25 PM,6,1,1:55:34 PM,6,1,2:25:15 PM,6,1,2:25:18 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_86,3
-Order_No_730,User_Id_1986,Bike,3,Personal,7,4,12:27:47 PM,7,4,12:28:46 PM,7,4,12:51:35 PM,7,4,12:55:37 PM,7,4,1:27:37 PM,14,24.2,,-1.2326041,36.8009029,-1.2980345,36.792122799999994,Rider_Id_79,1920
-Order_No_10063,User_Id_3599,Bike,3,Business,27,1,1:14:24 PM,27,1,1:31:39 PM,27,1,2:19:53 PM,27,1,2:34:44 PM,27,1,2:59:29 PM,11,23.3,,-1.3302996,36.8707935,-1.2744277,36.8104437,Rider_Id_427,1485
-Order_No_4803,User_Id_2691,Bike,3,Business,26,3,2:22:39 PM,26,3,2:23:44 PM,26,3,2:39:48 PM,26,3,2:45:13 PM,26,3,3:00:11 PM,8,27.2,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_726,898
-Order_No_14015,User_Id_2112,Bike,3,Business,29,5,9:25:13 AM,29,5,9:44:51 AM,29,5,9:46:44 AM,29,5,10:21:10 AM,29,5,10:28:36 AM,13,,,-1.2156006000000001,36.8916865,-1.2889576,36.81833579999999,Rider_Id_886,446
-Order_No_79,User_Id_1244,Bike,3,Business,9,6,10:42:31 AM,9,6,10:42:54 AM,9,6,10:45:59 AM,9,6,10:56:21 AM,9,6,11:13:50 AM,8,24.7,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_326,1049
-Order_No_24451,User_Id_928,Bike,3,Business,15,1,12:34:36 PM,15,1,12:35:34 PM,15,1,12:37:54 PM,15,1,12:44:22 PM,15,1,1:08:02 PM,15,,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_103,1420
-Order_No_2779,User_Id_3769,Bike,3,Personal,17,1,10:40:32 AM,17,1,10:41:54 AM,17,1,10:59:33 AM,17,1,11:04:10 AM,17,1,11:19:00 AM,9,25.2,,-1.2967403999999998,36.7361755,-1.2796191000000001,36.7995343,Rider_Id_88,890
-Order_No_26335,User_Id_1402,Bike,3,Business,18,4,9:47:16 AM,18,4,9:47:50 AM,18,4,10:15:24 AM,18,4,10:28:41 AM,18,4,11:47:37 AM,15,20.5,,-1.3417,36.73493,-1.2891921999999998,36.825189,Rider_Id_565,4736
-Order_No_4005,User_Id_3555,Bike,2,Personal,11,4,2:59:27 PM,11,4,2:59:48 PM,11,4,3:08:02 PM,11,4,3:16:25 PM,11,4,3:26:35 PM,4,26.7,,-1.2619538000000001,36.80876370000001,-1.2587023000000002,36.789848799999994,Rider_Id_932,610
-Order_No_14962,User_Id_2086,Bike,1,Personal,23,3,5:38:37 PM,23,3,5:41:33 PM,23,3,5:49:53 PM,23,3,5:55:01 PM,23,3,6:15:02 PM,4,20.4,,-1.2952843,36.7938766,-1.2994194,36.7647917,Rider_Id_919,1201
-Order_No_13800,User_Id_3061,Bike,3,Business,27,4,12:39:47 PM,27,4,1:17:18 PM,27,4,1:23:23 PM,27,4,1:36:28 PM,27,4,2:03:22 PM,13,23.6,,-1.3189978999999998,36.7104076,-1.2967701999999999,36.8040381,Rider_Id_101,1614
-Order_No_1826,User_Id_1882,Bike,3,Business,15,5,10:51:34 AM,15,5,10:51:56 AM,15,5,11:01:43 AM,15,5,11:16:12 AM,15,5,11:33:45 AM,10,26.5,,-1.2628638,36.8070399,-1.3014461000000002,36.7661381,Rider_Id_205,1053
-Order_No_9307,User_Id_1502,Bike,3,Business,7,5,11:59:37 AM,7,5,12:41:12 PM,7,5,12:50:10 PM,7,5,12:57:25 PM,7,5,1:19:29 PM,12,25.3,,-1.290894,36.822971,-1.2232123,36.8932547,Rider_Id_744,1324
-Order_No_21893,User_Id_646,Bike,3,Personal,27,4,2:31:25 PM,27,4,2:36:07 PM,27,4,2:54:13 PM,27,4,3:01:30 PM,27,4,3:13:22 PM,3,25.3,,-1.3272708999999998,36.711442,-1.3060040000000002,36.707987,Rider_Id_565,712
-Order_No_1904,User_Id_1209,Bike,3,Business,24,3,4:18:53 PM,24,3,4:19:15 PM,24,3,4:27:40 PM,24,3,4:34:41 PM,24,3,4:58:05 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_438,1404
-Order_No_671,User_Id_1329,Bike,3,Business,8,6,12:29:26 PM,8,6,12:29:55 PM,8,6,12:58:24 PM,8,6,1:01:51 PM,8,6,2:29:35 PM,19,20.0,,-1.273056,36.811298,-1.3103985,36.9114173,Rider_Id_498,5264
-Order_No_25463,User_Id_1724,Bike,3,Business,5,5,12:10:45 PM,5,5,12:11:17 PM,5,5,12:12:23 PM,5,5,12:26:05 PM,5,5,12:43:24 PM,2,23.9,,-1.2551895,36.7822034,-1.2571472,36.795063299999995,Rider_Id_663,1039
-Order_No_2559,User_Id_774,Bike,3,Business,14,4,3:30:07 PM,14,4,3:37:33 PM,14,4,3:59:33 PM,14,4,4:17:11 PM,14,4,4:28:55 PM,5,21.4,,-1.298574,36.793572,-1.2753902,36.808223,Rider_Id_474,704
-Order_No_2585,User_Id_868,Bike,3,Business,6,1,3:15:22 PM,6,1,3:16:21 PM,6,1,3:25:53 PM,6,1,3:40:00 PM,6,1,3:58:09 PM,9,24.5,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_116,1089
-Order_No_23142,User_Id_143,Bike,1,Personal,24,2,11:49:34 AM,24,2,11:50:03 AM,24,2,11:57:13 AM,24,2,12:00:49 PM,24,2,12:36:49 PM,16,17.5,,-1.2745912,36.818905900000004,-1.3170028,36.702096999999995,Rider_Id_951,2160
-Order_No_5824,User_Id_3201,Bike,3,Business,5,3,3:36:55 PM,5,3,3:37:32 PM,5,3,4:01:35 PM,5,3,4:10:38 PM,5,3,4:18:20 PM,3,25.1,,-1.2642719999999998,36.7978791,-1.2551895,36.7822034,Rider_Id_290,462
-Order_No_17366,User_Id_172,Bike,1,Personal,15,3,4:10:12 PM,15,3,4:10:57 PM,15,3,4:14:24 PM,15,3,4:19:16 PM,15,3,4:32:43 PM,3,21.8,,-1.286149,36.816157200000006,-1.2796633,36.826162700000005,Rider_Id_88,807
-Order_No_4607,User_Id_3517,Bike,3,Business,26,2,1:40:45 PM,26,2,1:41:35 PM,26,2,1:50:04 PM,26,2,2:08:20 PM,26,2,2:35:40 PM,15,30.8,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_218,1640
-Order_No_13759,User_Id_1500,Bike,3,Business,26,2,8:06:50 AM,26,2,8:07:26 AM,26,2,8:14:23 AM,26,2,8:47:30 AM,26,2,9:02:28 AM,8,22.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_842,898
-Order_No_13031,User_Id_1346,Bike,3,Business,10,1,3:26:29 PM,10,1,3:26:50 PM,10,1,3:47:49 PM,10,1,3:52:11 PM,10,1,4:29:12 PM,20,18.8,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_622,2221
-Order_No_23491,User_Id_1502,Bike,3,Business,6,3,1:43:14 PM,6,3,1:56:49 PM,6,3,1:57:12 PM,6,3,2:00:45 PM,6,3,2:38:46 PM,11,,,-1.2859912,36.8756811,-1.2232123,36.8932547,Rider_Id_885,2281
-Order_No_6525,User_Id_1796,Bike,3,Business,27,3,12:34:43 PM,27,3,12:36:18 PM,27,3,12:46:09 PM,27,3,12:50:46 PM,27,3,12:57:37 PM,7,26.2,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_524,411
-Order_No_18948,User_Id_2801,Bike,3,Business,12,2,2:13:14 PM,12,2,2:14:00 PM,12,2,2:26:42 PM,12,2,2:31:09 PM,12,2,3:01:27 PM,14,,,-1.3104506,36.8687235,-1.2674273999999999,36.787082700000006,Rider_Id_495,1818
-Order_No_10242,User_Id_2244,Bike,1,Personal,25,5,6:37:09 PM,25,5,6:58:34 PM,25,5,7:09:32 PM,25,5,7:20:15 PM,25,5,7:29:54 PM,5,21.1,,-1.2562249,36.786866100000005,-1.2681946000000002,36.8012069,Rider_Id_103,579
-Order_No_23106,User_Id_419,Bike,3,Business,21,5,11:32:03 AM,21,5,11:32:31 AM,21,5,11:47:19 AM,21,5,11:53:51 AM,21,5,12:05:06 PM,3,22.7,,-1.2571472,36.795063299999995,-1.2551895,36.7822034,Rider_Id_866,675
-Order_No_23762,User_Id_190,Bike,3,Business,22,3,4:26:50 PM,22,3,4:26:50 PM,22,3,4:27:08 PM,22,3,4:37:41 PM,22,3,4:37:44 PM,6,24.0,,-1.3275885,36.862155200000004,-1.3270347,36.866736200000005,Rider_Id_242,3
-Order_No_18695,User_Id_2906,Bike,2,Personal,27,4,10:38:03 AM,27,4,10:38:12 AM,27,4,10:43:15 AM,27,4,10:48:10 AM,27,4,11:01:50 AM,7,22.7,,-1.2649605,36.798177700000004,-1.2979147,36.785781400000005,Rider_Id_216,820
-Order_No_8593,User_Id_1724,Bike,3,Business,20,1,1:13:43 PM,20,1,1:13:57 PM,20,1,1:26:48 PM,20,1,1:29:31 PM,20,1,1:45:05 PM,6,,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_448,934
-Order_No_188,User_Id_3201,Bike,3,Business,13,3,4:01:10 PM,13,3,4:01:30 PM,13,3,4:05:22 PM,13,3,4:20:26 PM,13,3,4:38:14 PM,3,27.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_547,1068
-Order_No_10777,User_Id_2869,Bike,3,Business,18,4,12:50:43 PM,18,4,12:52:12 PM,18,4,1:24:08 PM,18,4,1:38:49 PM,18,4,2:04:18 PM,9,,,-1.279395,36.825364,-1.3025826,36.7670807,Rider_Id_338,1529
-Order_No_28065,User_Id_510,Bike,3,Business,21,5,11:08:39 AM,21,5,11:20:40 AM,21,5,11:50:55 AM,21,5,11:53:01 AM,21,5,12:49:32 PM,12,22.9,,-1.3052491000000002,36.8223899,-1.2860183,36.8975338,Rider_Id_31,3391
-Order_No_27746,User_Id_186,Bike,3,Business,31,2,9:51:44 AM,31,2,10:07:14 AM,31,2,10:23:09 AM,31,2,10:25:29 AM,31,2,11:05:40 AM,24,16.8,0.9,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_65,2411
-Order_No_24284,User_Id_1392,Bike,3,Business,11,4,1:47:24 PM,11,4,2:54:11 PM,11,4,2:54:59 PM,11,4,3:03:45 PM,11,4,3:27:12 PM,8,26.8,,-1.2551895,36.7822034,-1.295723,36.822691999999996,Rider_Id_245,1407
-Order_No_10088,User_Id_1724,Bike,3,Business,17,3,3:45:32 PM,17,3,3:48:00 PM,17,3,3:48:37 PM,17,3,4:03:05 PM,17,3,4:19:14 PM,7,,,-1.2551895,36.7822034,-1.2821883,36.826961100000005,Rider_Id_291,969
-Order_No_4413,User_Id_510,Bike,3,Business,20,2,9:53:32 AM,20,2,9:54:14 AM,20,2,9:58:03 AM,20,2,10:04:58 AM,20,2,10:30:38 AM,12,19.2,,-1.279395,36.825364,-1.2860183,36.8975338,Rider_Id_878,1540
-Order_No_12597,User_Id_649,Bike,3,Business,24,5,11:21:12 AM,24,5,11:21:45 AM,24,5,12:10:16 PM,24,5,12:18:45 PM,24,5,1:01:16 PM,16,20.9,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_268,2551
-Order_No_9166,User_Id_136,Bike,3,Business,12,5,11:37:30 AM,12,5,11:38:17 AM,12,5,11:53:38 AM,12,5,11:57:52 AM,12,5,12:54:35 PM,19,26.5,,-1.2546023000000002,36.8266773,-1.3254522,36.721658399999995,Rider_Id_520,3403
-Order_No_8909,User_Id_2982,Bike,3,Business,5,5,10:24:33 AM,5,5,10:24:55 AM,5,5,10:31:26 AM,5,5,10:52:12 AM,5,5,11:46:40 AM,21,24.3,,-1.300921,36.828195,-1.2239243,36.710118,Rider_Id_519,3268
-Order_No_15096,User_Id_3599,Bike,3,Business,14,5,10:37:37 AM,14,5,10:38:01 AM,14,5,10:56:12 AM,14,5,11:56:16 AM,14,5,11:56:18 AM,7,22.1,,-1.3302996,36.8707935,-1.3270347,36.866736200000005,Rider_Id_459,2
-Order_No_27309,User_Id_2530,Bike,1,Personal,22,2,4:46:23 PM,22,2,4:54:15 PM,22,2,5:03:18 PM,22,2,5:10:22 PM,22,2,5:23:43 PM,2,18.4,,-1.2886366000000002,36.8245436,-1.2990606000000002,36.8033028,Rider_Id_531,801
-Order_No_27008,User_Id_2514,Bike,3,Business,26,2,11:04:52 AM,26,2,11:05:36 AM,26,2,11:12:51 AM,26,2,11:17:51 AM,26,2,11:22:45 AM,1,,,-1.266294,36.8057971,-1.2684666999999998,36.8002891,Rider_Id_100,294
-Order_No_3334,User_Id_3647,Bike,3,Business,27,2,4:18:13 PM,27,2,4:18:40 PM,27,2,4:27:48 PM,27,2,4:29:34 PM,27,2,5:11:17 PM,27,25.4,,-1.2726389999999999,36.794723,-1.1523217,36.962048100000004,Rider_Id_647,2503
-Order_No_24023,User_Id_718,Bike,1,Business,4,3,12:34:28 PM,4,3,12:34:39 PM,4,3,12:40:32 PM,4,3,12:47:32 PM,4,3,12:55:59 PM,6,17.2,,-1.2551895,36.7822034,-1.2758541,36.773861600000004,Rider_Id_103,507
-Order_No_2206,User_Id_391,Bike,3,Business,12,1,9:41:23 AM,12,1,9:55:17 AM,12,1,10:08:25 AM,12,1,10:10:57 AM,12,1,10:35:37 AM,14,19.4,0.4,-1.2857573999999998,36.8295667,-1.3370583,36.884625299999996,Rider_Id_276,1480
-Order_No_5550,User_Id_3291,Bike,3,Business,28,4,9:22:02 AM,28,4,9:22:34 AM,28,4,9:31:19 AM,28,4,9:59:38 AM,28,4,10:14:48 AM,4,22.6,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_857,910
-Order_No_27390,User_Id_3647,Bike,3,Business,4,2,4:11:35 PM,4,2,4:21:33 PM,4,2,4:25:09 PM,4,2,4:29:27 PM,4,2,4:41:46 PM,5,22.5,,-1.2726389999999999,36.794723,-1.2560455,36.780086700000005,Rider_Id_655,739
-Order_No_26386,User_Id_2289,Bike,3,Business,27,3,12:36:24 PM,27,3,12:36:38 PM,27,3,12:39:16 PM,27,3,12:51:49 PM,27,3,1:18:29 PM,12,26.2,,-1.2551895,36.7822034,-1.3127877000000001,36.848198700000005,Rider_Id_206,1600
-Order_No_15649,User_Id_2523,Bike,3,Business,5,4,12:44:08 PM,5,4,12:46:02 PM,5,4,12:59:49 PM,5,4,1:04:43 PM,5,4,1:22:12 PM,8,,,-1.2551895,36.7822034,-1.303711,36.81549329999999,Rider_Id_636,1049
-Order_No_809,User_Id_816,Bike,3,Business,17,4,11:07:42 AM,17,4,11:08:40 AM,17,4,11:22:08 AM,17,4,11:55:54 AM,17,4,12:24:21 PM,6,25.2,,-1.2571472,36.795063299999995,-1.290894,36.822971,Rider_Id_358,1707
-Order_No_8738,User_Id_393,Bike,3,Business,14,4,9:41:02 AM,14,4,9:41:11 AM,14,4,9:49:12 AM,14,4,10:17:26 AM,14,4,10:17:27 AM,14,17.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,1
-Order_No_27341,User_Id_3560,Bike,3,Business,15,5,3:34:36 PM,15,5,3:34:59 PM,15,5,4:07:33 PM,15,5,4:19:00 PM,15,5,4:25:23 PM,1,17.7,,-1.290894,36.822971,-1.2871938,36.8195031,Rider_Id_456,383
-Order_No_27516,User_Id_540,Bike,3,Business,14,2,2:34:45 PM,14,2,2:36:02 PM,14,2,2:38:10 PM,14,2,2:58:58 PM,14,2,3:13:04 PM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_291,846
-Order_No_23971,User_Id_2069,Bike,3,Business,18,4,10:31:04 AM,18,4,10:31:36 AM,18,4,10:57:35 AM,18,4,11:14:58 AM,18,4,11:34:00 AM,9,,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_435,1142
-Order_No_17877,User_Id_1964,Bike,3,Personal,17,5,1:13:20 PM,17,5,1:14:42 PM,17,5,1:18:45 PM,17,5,1:26:42 PM,17,5,1:42:15 PM,11,,,-1.2977386999999998,36.803144700000004,-1.32671,36.8600138,Rider_Id_564,933
-Order_No_23853,User_Id_1787,Bike,3,Business,16,4,1:58:04 PM,16,4,2:00:20 PM,16,4,3:00:31 PM,16,4,3:18:42 PM,16,4,3:39:59 PM,11,,,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_907,1277
-Order_No_11334,User_Id_393,Bike,3,Business,20,3,3:50:05 PM,20,3,3:52:46 PM,20,3,3:52:54 PM,20,3,4:07:07 PM,20,3,4:07:16 PM,4,28.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,9
-Order_No_21712,User_Id_2330,Bike,3,Business,9,3,12:33:25 PM,9,3,12:33:40 PM,9,3,12:47:35 PM,9,3,12:52:12 PM,9,3,1:36:46 PM,23,25.4,,-1.3800951000000001,36.9297274,-1.2642903,36.80021370000001,Rider_Id_322,2674
-Order_No_15829,User_Id_3001,Bike,3,Business,31,5,4:25:29 PM,31,5,4:28:44 PM,31,5,4:47:16 PM,31,5,4:54:08 PM,31,5,5:13:35 PM,6,23.8,,-1.2846195,36.7958316,-1.2637515,36.78856629999999,Rider_Id_502,1167
-Order_No_27673,User_Id_867,Bike,3,Business,20,2,4:11:26 PM,20,2,4:12:45 PM,20,2,4:19:06 PM,20,2,4:41:44 PM,20,2,5:16:27 PM,10,23.9,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_640,2083
-Order_No_18945,User_Id_3186,Bike,3,Business,29,5,2:02:13 PM,29,5,2:23:41 PM,29,5,2:37:41 PM,29,5,2:41:38 PM,29,5,2:56:45 PM,5,28.9,,-1.2980228999999999,36.78865,-1.2949455,36.8195238,Rider_Id_564,907
-Order_No_25320,User_Id_1776,Bike,3,Personal,7,5,1:53:44 PM,7,5,1:56:43 PM,7,5,2:10:18 PM,7,5,2:12:04 PM,7,5,2:31:55 PM,7,,,-1.2578943,36.798921299999996,-1.2622285,36.8534706,Rider_Id_245,1191
-Order_No_10880,User_Id_2330,Bike,3,Business,12,2,5:36:27 PM,12,2,5:38:04 PM,12,2,6:19:03 PM,12,2,6:21:55 PM,12,2,6:43:50 PM,9,26.5,,-1.2326041,36.8009029,-1.2637056999999998,36.805170000000004,Rider_Id_755,1315
-Order_No_22546,User_Id_2801,Bike,3,Business,9,5,11:37:54 AM,9,5,11:44:26 AM,9,5,11:58:47 AM,9,5,12:18:32 PM,9,5,12:49:53 PM,8,25.8,,-1.304592,36.822007,-1.2595421999999998,36.7871178,Rider_Id_634,1881
-Order_No_17215,User_Id_309,Bike,3,Business,3,1,10:26:11 AM,3,1,10:27:03 AM,3,1,10:53:33 AM,3,1,10:55:32 AM,3,1,11:26:36 AM,17,21.4,,-1.2156006000000001,36.8916865,-1.2571472,36.795063299999995,Rider_Id_958,1864
-Order_No_21300,User_Id_2595,Bike,1,Personal,29,5,5:56:38 PM,29,5,5:58:20 PM,29,5,6:19:50 PM,29,5,6:21:40 PM,29,5,7:06:39 PM,12,20.7,,-1.2851603,36.824966700000004,-1.2777823999999998,36.8977804,Rider_Id_549,2699
-Order_No_8419,User_Id_478,Bike,3,Business,4,4,2:41:03 PM,4,4,2:41:41 PM,4,4,2:47:13 PM,4,4,2:58:21 PM,4,4,3:47:54 PM,13,25.0,,-1.2551895,36.7822034,-1.3233955,36.856294399999996,Rider_Id_805,2973
-Order_No_23828,User_Id_2924,Bike,3,Personal,17,1,11:15:00 AM,17,1,11:16:25 AM,17,1,11:31:41 AM,17,1,11:32:42 AM,17,1,11:46:57 AM,10,23.7,,-1.2808705,36.768482399999996,-1.2664227,36.7453671,Rider_Id_932,855
-Order_No_17755,User_Id_533,Bike,3,Business,21,2,12:12:31 PM,21,2,12:14:16 PM,21,2,12:14:19 PM,21,2,12:20:20 PM,21,2,12:31:03 PM,7,22.7,,-1.2551895,36.7822034,-1.2763494,36.7671783,Rider_Id_576,643
-Order_No_3881,User_Id_2523,Bike,3,Business,11,1,10:27:04 AM,11,1,10:28:28 AM,11,1,10:28:42 AM,11,1,10:35:33 AM,11,1,11:12:34 AM,17,26.4,,-1.2551895,36.7822034,-1.2702844,36.880320899999994,Rider_Id_576,2221
-Order_No_2511,User_Id_3599,Bike,3,Business,3,1,1:56:42 PM,3,1,2:22:17 PM,3,1,2:50:37 PM,3,1,3:03:16 PM,3,1,3:25:48 PM,12,23.2,,-1.3302996,36.8707935,-1.262492,36.80388370000001,Rider_Id_25,1352
-Order_No_22916,User_Id_3560,Bike,3,Business,8,4,3:20:04 PM,8,4,3:46:42 PM,8,4,3:56:04 PM,8,4,3:59:59 PM,8,4,4:27:19 PM,5,27.4,,-1.2656193,36.803458899999995,-1.279395,36.825364,Rider_Id_353,1640
-Order_No_1783,User_Id_2803,Bike,3,Business,27,3,12:41:21 PM,27,3,12:43:52 PM,27,3,12:53:26 PM,27,3,12:54:50 PM,27,3,1:10:09 PM,8,26.1,,-1.2765736,36.851364600000004,-1.2915971000000002,36.880902899999995,Rider_Id_933,919
-Order_No_6067,User_Id_2899,Bike,3,Personal,23,2,1:42:07 PM,23,2,1:44:31 PM,23,2,2:01:33 PM,23,2,2:03:30 PM,23,2,2:16:42 PM,5,,,-1.298762,36.763645600000004,-1.285355,36.795287,Rider_Id_641,792
-Order_No_27106,User_Id_2200,Bike,3,Business,12,5,11:54:05 AM,12,5,12:05:06 PM,12,5,12:08:52 PM,12,5,12:20:28 PM,12,5,1:27:18 PM,5,24.7,,-1.290894,36.822971,-1.3040651,36.798125399999996,Rider_Id_88,4010
-Order_No_2011,User_Id_2258,Bike,3,Business,31,3,2:44:13 PM,31,3,2:51:10 PM,31,3,2:52:53 PM,31,3,2:58:24 PM,31,3,3:26:41 PM,7,27.1,,-1.2571472,36.795063299999995,-1.3011768,36.818364,Rider_Id_46,1697
-Order_No_903,User_Id_868,Bike,3,Business,5,2,1:57:01 PM,5,2,2:09:13 PM,5,2,2:17:12 PM,5,2,2:34:51 PM,5,2,2:54:28 PM,7,22.5,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_265,1177
-Order_No_1484,User_Id_1927,Bike,3,Business,18,1,9:49:27 AM,18,1,9:49:48 AM,18,1,10:10:17 AM,18,1,11:03:30 AM,18,1,11:27:30 AM,19,27.9,,-1.3562374,36.904295399999995,-1.3472066,36.7692638,Rider_Id_952,1440
-Order_No_1521,User_Id_2701,Bike,3,Business,6,5,3:22:20 PM,6,5,3:33:24 PM,6,5,3:42:25 PM,6,5,3:51:36 PM,6,5,4:16:12 PM,5,22.7,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_159,1476
-Order_No_11717,User_Id_873,Bike,1,Personal,27,4,10:16:28 AM,27,4,10:21:30 AM,27,4,10:35:33 AM,27,4,10:43:45 AM,27,4,11:28:58 AM,5,22.8,,-1.3075266,36.8292369,-1.2890374,36.8165815,Rider_Id_328,2713
-Order_No_6012,User_Id_142,Bike,3,Business,22,3,3:38:08 PM,22,3,3:40:16 PM,22,3,4:28:06 PM,22,3,4:30:15 PM,22,3,4:55:19 PM,8,24.3,,-1.2963097,36.7688221,-1.2570328000000002,36.7869744,Rider_Id_269,1504
-Order_No_1635,User_Id_788,Bike,3,Business,25,1,1:55:38 PM,25,1,1:55:57 PM,25,1,2:13:56 PM,25,1,2:18:12 PM,25,1,2:55:25 PM,14,28.2,,-1.225322,36.80855,-1.2413316,36.878552500000005,Rider_Id_870,2233
-Order_No_6083,User_Id_346,Bike,3,Business,22,5,11:53:13 AM,22,5,11:59:43 AM,22,5,12:22:21 PM,22,5,12:26:55 PM,22,5,1:14:23 PM,14,,,-1.2860183,36.8975338,-1.28878,36.8168312,Rider_Id_569,2848
-Order_No_22567,User_Id_2317,Bike,1,Personal,28,5,12:04:43 PM,28,5,12:05:09 PM,28,5,12:21:26 PM,28,5,12:24:49 PM,28,5,1:01:41 PM,4,25.1,,-1.3003196000000001,36.8171416,-1.2966535,36.8123136,Rider_Id_532,2212
-Order_No_2308,User_Id_475,Bike,3,Business,20,4,12:38:15 PM,20,4,12:38:35 PM,20,4,12:50:33 PM,20,4,1:58:58 PM,20,4,2:35:46 PM,18,27.8,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_167,2208
-Order_No_27771,User_Id_2727,Bike,3,Personal,30,3,12:31:34 PM,30,3,12:32:44 PM,30,3,12:46:48 PM,30,3,1:31:28 PM,30,3,2:15:33 PM,12,24.9,,-1.3155246999999999,36.8484927,-1.2925556,36.7903149,Rider_Id_519,2645
-Order_No_25464,User_Id_1071,Bike,1,Personal,12,4,3:29:17 PM,12,4,3:29:36 PM,12,4,3:29:47 PM,12,4,3:32:09 PM,12,4,3:42:31 PM,2,21.5,,-1.2695287,36.798177700000004,-1.2728424999999999,36.791039000000005,Rider_Id_338,622
-Order_No_13351,User_Id_2200,Bike,3,Business,6,2,2:17:17 PM,6,2,2:20:51 PM,6,2,2:48:21 PM,6,2,2:50:13 PM,6,2,3:16:33 PM,10,27.5,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_895,1580
-Order_No_8425,User_Id_391,Bike,3,Business,6,3,11:53:20 AM,6,3,12:11:21 PM,6,3,12:34:20 PM,6,3,12:43:09 PM,6,3,1:30:10 PM,19,,,-1.3524201,36.9045107,-1.2784063,36.8222933,Rider_Id_436,2821
-Order_No_16896,User_Id_3569,Bike,3,Personal,14,2,1:55:38 PM,14,2,1:56:11 PM,14,2,1:56:56 PM,14,2,2:16:31 PM,14,2,3:05:12 PM,28,,,-1.2472011,36.880030700000006,-1.3858142,36.7641498,Rider_Id_75,2921
-Order_No_7536,User_Id_1795,Bike,3,Personal,22,2,2:58:14 PM,22,2,3:01:26 PM,22,2,3:15:08 PM,22,2,3:17:59 PM,22,2,3:41:22 PM,7,25.5,,-1.2744387,36.7849057,-1.2951169,36.823896000000005,Rider_Id_428,1403
-Order_No_21750,User_Id_1961,Bike,3,Business,30,2,3:07:23 PM,30,2,3:12:58 PM,30,2,3:50:59 PM,30,2,4:01:21 PM,30,2,4:20:37 PM,8,,,-1.3444459999999998,36.756597,-1.3181159,36.710155900000004,Rider_Id_864,1156
-Order_No_5389,User_Id_1771,Bike,1,Personal,8,2,6:04:51 PM,8,2,6:19:11 PM,8,2,6:35:27 PM,8,2,6:58:23 PM,8,2,7:38:14 PM,11,20.5,,-1.2821474,36.8317731,-1.3104826,36.903499,Rider_Id_347,2391
-Order_No_14233,User_Id_393,Bike,3,Business,10,4,2:19:59 PM,10,4,2:20:10 PM,10,4,2:20:17 PM,10,4,2:30:30 PM,10,4,2:44:10 PM,3,25.4,,-1.3231001,36.8283559,-1.3004062,36.829741,Rider_Id_518,820
-Order_No_6119,User_Id_3283,Bike,3,Business,4,1,1:22:48 PM,4,1,1:23:54 PM,4,1,1:39:54 PM,4,1,1:46:46 PM,4,1,2:01:57 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_205,911
-Order_No_20767,User_Id_391,Bike,3,Business,15,5,8:55:39 AM,15,5,8:55:50 AM,15,5,9:00:16 AM,15,5,9:31:37 AM,15,5,9:56:24 AM,5,19.2,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_119,1487
-Order_No_8054,User_Id_3420,Bike,3,Business,9,5,7:32:31 AM,9,5,8:11:55 AM,9,5,8:32:10 AM,9,5,8:34:10 AM,9,5,8:50:42 AM,6,20.4,,-1.3046007,36.821174299999996,-1.311706,36.802841,Rider_Id_114,992
-Order_No_17163,User_Id_1724,Bike,3,Business,29,1,2:55:12 PM,29,1,2:56:13 PM,29,1,3:21:45 PM,29,1,3:22:59 PM,29,1,4:01:30 PM,23,,,-1.2551895,36.7822034,-1.3724413,36.9284922,Rider_Id_940,2311
-Order_No_25364,User_Id_2112,Bike,3,Business,15,6,9:47:11 AM,15,6,10:04:34 AM,15,6,10:32:53 AM,15,6,10:55:57 AM,15,6,12:05:24 PM,23,,,-1.3472066,36.7692638,-1.2156006000000001,36.8916865,Rider_Id_541,4167
-Order_No_12860,User_Id_3525,Bike,3,Business,12,3,10:34:47 AM,12,3,10:47:28 AM,12,3,10:56:28 AM,12,3,12:29:35 PM,12,3,1:01:33 PM,15,19.4,,-1.3700383,36.9190174,-1.2981102,36.8293513,Rider_Id_436,1918
-Order_No_28079,User_Id_733,Bike,3,Business,6,3,12:27:38 PM,6,3,12:28:00 PM,6,3,12:36:02 PM,6,3,12:39:18 PM,6,3,1:29:22 PM,20,22.5,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_279,3004
-Order_No_11023,User_Id_53,Bike,3,Business,30,3,3:48:40 PM,30,3,3:49:06 PM,30,3,3:52:16 PM,30,3,3:58:27 PM,30,3,4:21:30 PM,5,28.9,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_518,1383
-Order_No_3914,User_Id_788,Bike,3,Business,28,5,2:28:16 PM,28,5,2:28:57 PM,28,5,2:29:12 PM,28,5,2:37:44 PM,28,5,2:54:55 PM,7,25.2,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_685,1031
-Order_No_9458,User_Id_2454,Bike,3,Business,27,3,3:19:43 PM,27,3,3:20:56 PM,27,3,3:34:53 PM,27,3,3:38:25 PM,27,3,3:53:24 PM,3,29.5,,-1.2993231,36.7753372,-1.2942023,36.7958179,Rider_Id_678,899
-Order_No_9469,User_Id_2627,Bike,3,Business,14,2,2:53:04 PM,14,2,2:53:31 PM,14,2,3:08:32 PM,14,2,3:16:17 PM,14,2,3:16:53 PM,1,,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_131,36
-Order_No_13513,User_Id_1594,Bike,3,Business,11,4,10:51:37 AM,11,4,10:51:45 AM,11,4,10:51:59 AM,11,4,10:54:52 AM,11,4,11:20:46 AM,16,25.6,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_539,1554
-Order_No_18942,User_Id_60,Bike,3,Business,11,2,3:16:40 PM,11,2,3:22:21 PM,11,2,3:24:20 PM,11,2,3:35:17 PM,11,2,4:11:50 PM,21,21.4,,-1.3444459999999998,36.756597,-1.2297799999999999,36.836215,Rider_Id_548,2193
-Order_No_26038,User_Id_3091,Bike,3,Business,9,4,5:44:39 PM,9,4,5:44:54 PM,9,4,5:45:08 PM,9,4,5:54:49 PM,9,4,6:23:18 PM,10,23.0,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_89,1709
-Order_No_4649,User_Id_1340,Bike,3,Business,5,2,10:45:32 AM,5,2,10:46:07 AM,5,2,10:53:45 AM,5,2,10:58:09 AM,5,2,11:26:53 AM,6,25.5,,-1.2707178999999997,36.7979713,-1.3017117,36.8149763,Rider_Id_953,1724
-Order_No_17879,User_Id_3291,Bike,3,Business,3,1,9:16:18 AM,3,1,9:16:59 AM,3,1,9:48:57 AM,3,1,10:17:31 AM,3,1,11:05:08 AM,5,18.3,5.0,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_895,2857
-Order_No_2966,User_Id_2627,Bike,3,Business,5,5,2:10:05 PM,5,5,2:13:10 PM,5,5,2:22:41 PM,5,5,2:28:31 PM,5,5,2:46:32 PM,11,27.7,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_276,1081
-Order_No_20289,User_Id_246,Bike,3,Business,17,4,1:48:22 PM,17,4,1:48:37 PM,17,4,1:51:07 PM,17,4,1:55:33 PM,17,4,2:07:40 PM,2,28.0,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_194,727
-Order_No_11940,User_Id_3222,Bike,3,Business,4,1,2:51:28 PM,4,1,2:52:27 PM,4,1,3:00:21 PM,4,1,3:07:42 PM,4,1,3:21:10 PM,6,,,-1.2551895,36.7822034,-1.2902869,36.8085396,Rider_Id_75,808
-Order_No_25961,User_Id_2037,Bike,1,Personal,5,2,11:03:57 AM,5,2,11:15:31 AM,5,2,11:23:58 AM,5,2,11:29:23 AM,5,2,11:40:54 AM,4,21.6,,-1.2864309999999999,36.818411,-1.296168,36.808564000000004,Rider_Id_852,691
-Order_No_11873,User_Id_2069,Bike,3,Business,23,2,12:58:08 PM,23,2,12:58:32 PM,23,2,1:23:33 PM,23,2,1:43:58 PM,23,2,1:53:55 PM,6,,,-1.2297799999999999,36.836215,-1.228911,36.881876,Rider_Id_8,597
-Order_No_28264,User_Id_816,Bike,3,Business,21,1,10:52:32 AM,21,1,10:54:19 AM,21,1,10:57:35 AM,21,1,11:02:32 AM,21,1,12:12:30 PM,4,25.4,,-1.2938806999999999,36.8129504,-1.290894,36.822971,Rider_Id_458,4198
-Order_No_25184,User_Id_2330,Bike,3,Business,1,5,1:29:28 PM,1,5,1:48:26 PM,1,5,2:10:43 PM,1,5,2:17:18 PM,1,5,3:00:39 PM,17,28.5,,-1.3340505,36.711903799999995,-1.2642903,36.80021370000001,Rider_Id_433,2601
-Order_No_12185,User_Id_2804,Bike,3,Business,24,3,3:28:11 PM,24,3,3:28:38 PM,24,3,3:32:52 PM,24,3,3:35:05 PM,24,3,4:04:38 PM,12,,,-1.2551895,36.7822034,-1.3270716,36.8406371,Rider_Id_349,1773
-Order_No_13381,User_Id_246,Bike,3,Business,11,1,9:31:23 AM,11,1,9:32:20 AM,11,1,9:42:29 AM,11,1,9:59:18 AM,11,1,10:06:14 AM,1,14.6,,-1.2579303999999998,36.8019667,-1.2584143,36.8048002,Rider_Id_770,416
-Order_No_13609,User_Id_2423,Bike,3,Business,21,5,9:44:54 AM,21,5,9:46:38 AM,21,5,9:59:56 AM,21,5,10:01:49 AM,21,5,10:20:57 AM,8,20.2,,-1.2551895,36.7822034,-1.2904049,36.8055154,Rider_Id_383,1148
-Order_No_187,User_Id_3414,Bike,1,Personal,10,1,8:55:56 AM,10,1,8:56:40 AM,10,1,9:19:48 AM,10,1,9:37:49 AM,10,1,10:05:37 AM,8,18.0,,-1.3173868999999998,36.8112044,-1.2993093999999998,36.777206899999996,Rider_Id_328,1668
-Order_No_18439,User_Id_2212,Bike,3,Business,11,1,12:25:14 PM,11,1,12:25:26 PM,11,1,12:35:19 PM,11,1,12:38:03 PM,11,1,12:49:45 PM,4,24.2,,-1.2571472,36.795063299999995,-1.2559559999999999,36.772901,Rider_Id_879,702
-Order_No_5816,User_Id_811,Bike,2,Personal,11,4,1:56:56 PM,11,4,1:57:09 PM,11,4,2:18:47 PM,11,4,2:21:36 PM,11,4,2:44:06 PM,10,26.7,,-1.3219579,36.8414053,-1.285355,36.795287,Rider_Id_662,1350
-Order_No_3605,User_Id_3647,Bike,3,Business,4,2,2:29:16 PM,4,2,2:30:07 PM,4,2,2:42:46 PM,4,2,2:51:32 PM,4,2,3:04:36 PM,5,23.6,11.0,-1.2726389999999999,36.794723,-1.262805,36.79424229999999,Rider_Id_259,784
-Order_No_16190,User_Id_1927,Bike,3,Business,17,1,2:45:53 PM,17,1,2:59:36 PM,17,1,3:06:51 PM,17,1,3:14:13 PM,17,1,3:43:13 PM,20,24.6,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_647,1740
-Order_No_25632,User_Id_2757,Bike,3,Business,6,2,5:20:25 PM,6,2,5:31:46 PM,6,2,5:47:03 PM,6,2,5:48:48 PM,6,2,6:17:27 PM,13,,,-1.2891921999999998,36.825189,-1.3332025,36.7789361,Rider_Id_237,1719
-Order_No_21221,User_Id_1899,Bike,3,Business,24,5,1:12:14 PM,24,5,1:13:07 PM,24,5,1:27:32 PM,24,5,1:38:14 PM,24,5,2:01:19 PM,13,23.4,,-1.293515,36.897607,-1.331619,36.847976,Rider_Id_676,1385
-Order_No_212,User_Id_2987,Bike,3,Business,3,4,3:15:42 PM,3,4,3:16:08 PM,3,4,3:18:45 PM,3,4,3:20:39 PM,3,4,3:49:10 PM,4,25.8,,-1.2702120000000001,36.82108770000001,-1.2534301,36.800098299999995,Rider_Id_829,1711
-Order_No_15910,User_Id_296,Bike,3,Business,8,5,12:58:49 PM,8,5,1:00:35 PM,8,5,1:41:26 PM,8,5,1:46:06 PM,8,5,2:34:49 PM,9,27.3,,-1.2976854,36.775358600000004,-1.2571472,36.795063299999995,Rider_Id_244,2923
-Order_No_2595,User_Id_2931,Bike,3,Business,28,1,5:06:43 PM,28,1,5:06:49 PM,28,1,5:07:06 PM,28,1,5:11:18 PM,28,1,6:09:21 PM,22,25.4,,-1.174484,36.914608,-1.2452664,36.802854100000005,Rider_Id_428,3483
-Order_No_27959,User_Id_2330,Bike,3,Business,29,3,2:59:11 PM,29,3,3:03:21 PM,29,3,3:17:50 PM,29,3,3:32:44 PM,29,3,3:53:41 PM,14,21.0,,-1.2212611999999998,36.8440033,-1.2645318,36.7981775,Rider_Id_8,1257
-Order_No_20256,User_Id_1363,Bike,3,Business,16,4,2:22:14 PM,16,4,2:23:12 PM,16,4,2:35:24 PM,16,4,2:45:16 PM,16,4,2:45:21 PM,4,,,-1.3004062,36.829741,-1.295036,36.814306099999996,Rider_Id_863,5
-Order_No_26466,User_Id_1075,Bike,3,Business,14,4,12:44:24 PM,14,4,12:46:05 PM,14,4,12:52:55 PM,14,4,1:02:57 PM,14,4,1:07:08 PM,1,25.2,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_807,251
-Order_No_22904,User_Id_393,Bike,3,Business,12,5,2:09:58 PM,12,5,2:13:40 PM,12,5,2:13:54 PM,12,5,3:10:13 PM,12,5,3:20:50 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_331,637
-Order_No_24448,User_Id_3525,Bike,3,Business,15,4,10:23:03 AM,15,4,10:23:48 AM,15,4,10:28:27 AM,15,4,10:37:45 AM,15,4,11:16:22 AM,13,24.5,,-1.3700383,36.9190174,-1.3239488000000001,36.831683500000004,Rider_Id_593,2317
-Order_No_19774,User_Id_2330,Bike,3,Business,11,2,3:00:54 PM,11,2,3:02:51 PM,11,2,3:07:59 PM,11,2,3:11:37 PM,11,2,3:35:50 PM,12,21.2,,-1.2284323,36.8757058,-1.2642903,36.80021370000001,Rider_Id_709,1453
-Order_No_3204,User_Id_2066,Bike,3,Business,13,4,1:35:48 PM,13,4,1:40:47 PM,13,4,1:51:23 PM,13,4,1:53:54 PM,13,4,2:11:36 PM,8,26.5,,-1.316961,36.820337,-1.3332749,36.870814700000004,Rider_Id_189,1062
-Order_No_19792,User_Id_3715,Bike,1,Personal,10,4,9:08:26 AM,10,4,10:08:54 AM,10,4,10:45:50 AM,10,4,10:49:49 AM,10,4,11:08:02 AM,7,,,-1.2794105,36.821565500000005,-1.3159197,36.8516724,Rider_Id_123,1093
-Order_No_2665,User_Id_3669,Bike,3,Business,17,4,11:10:46 AM,17,4,11:11:27 AM,17,4,11:17:49 AM,17,4,11:38:28 AM,17,4,12:07:02 PM,3,25.4,,-1.2590837,36.804902,-1.2615888000000002,36.7928732,Rider_Id_167,1714
-Order_No_4487,User_Id_1724,Bike,3,Business,25,4,8:43:19 AM,25,4,8:43:47 AM,25,4,8:55:04 AM,25,4,8:58:42 AM,25,4,9:14:01 AM,8,,,-1.2907853999999999,36.7774108,-1.2551895,36.7822034,Rider_Id_663,919
-Order_No_15908,User_Id_1075,Bike,3,Business,16,2,4:06:30 PM,16,2,4:07:15 PM,16,2,4:20:53 PM,16,2,4:25:51 PM,16,2,4:38:32 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_318,761
-Order_No_25393,User_Id_3104,Bike,3,Personal,17,4,12:39:38 PM,17,4,12:42:01 PM,17,4,1:02:04 PM,17,4,1:04:38 PM,17,4,1:15:27 PM,4,25.3,,-1.3421985,36.9040545,-1.3276436000000003,36.883396600000005,Rider_Id_559,649
-Order_No_875,User_Id_1166,Bike,2,Personal,25,2,5:38:47 PM,25,2,5:39:51 PM,25,2,5:53:31 PM,25,2,6:00:57 PM,25,2,6:41:00 PM,7,23.9,,-1.2884997,36.8183895,-1.2606291,36.796951,Rider_Id_96,2403
-Order_No_19744,User_Id_647,Bike,3,Business,29,5,10:14:19 AM,29,5,10:41:07 AM,29,5,11:13:02 AM,29,5,11:55:34 AM,29,5,11:55:36 AM,9,26.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_11,2
-Order_No_23846,User_Id_2982,Bike,3,Business,9,3,10:10:32 AM,9,3,10:11:08 AM,9,3,10:19:29 AM,9,3,10:53:52 AM,9,3,11:35:26 AM,18,25.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_114,2494
-Order_No_26529,User_Id_53,Bike,3,Business,5,2,1:33:12 PM,5,2,1:46:35 PM,5,2,1:56:49 PM,5,2,2:03:58 PM,5,2,2:19:47 PM,6,27.5,,-1.2770708,36.8231093,-1.2636051000000001,36.7850995,Rider_Id_267,949
-Order_No_8363,User_Id_2330,Bike,3,Business,2,2,1:07:16 PM,2,2,1:08:41 PM,2,2,1:26:03 PM,2,2,1:27:22 PM,2,2,1:40:53 PM,6,26.0,,-1.2533885,36.8165128,-1.2124582,36.802894,Rider_Id_783,811
-Order_No_16158,User_Id_186,Bike,3,Business,14,4,4:24:29 PM,14,4,4:39:47 PM,14,4,5:01:10 PM,14,4,5:09:27 PM,14,4,5:27:03 PM,4,26.3,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_548,1056
-Order_No_7714,User_Id_2066,Bike,3,Business,10,1,11:08:40 AM,10,1,11:10:20 AM,10,1,11:23:42 AM,10,1,11:30:44 AM,10,1,11:50:42 AM,10,,,-1.316961,36.820337,-1.2523488999999999,36.8006091,Rider_Id_88,1198
-Order_No_24223,User_Id_3595,Bike,1,Personal,13,5,4:09:29 PM,13,5,4:10:00 PM,13,5,4:15:07 PM,13,5,4:22:46 PM,13,5,5:08:42 PM,17,19.3,,-1.2596241000000001,36.762407,-1.2219928999999998,36.8349924,Rider_Id_101,2756
-Order_No_12971,User_Id_214,Bike,3,Personal,24,1,11:15:03 AM,24,1,11:16:31 AM,24,1,11:34:48 AM,24,1,11:42:14 AM,24,1,12:02:39 PM,6,22.7,,-1.2612801000000002,36.7870625,-1.2616213,36.8221128,Rider_Id_882,1225
-Order_No_7369,User_Id_1721,Bike,3,Business,13,3,2:10:17 PM,13,3,2:19:30 PM,13,3,2:47:31 PM,13,3,2:52:47 PM,13,3,3:12:22 PM,11,27.3,,-1.2551895,36.7822034,-1.2310607,36.803577600000004,Rider_Id_152,1175
-Order_No_7709,User_Id_1687,Bike,3,Business,27,3,12:42:03 PM,27,3,12:42:37 PM,27,3,12:50:55 PM,27,3,1:06:06 PM,27,3,1:18:01 PM,3,26.0,,-1.2626574,36.8014034,-1.2732803,36.794037700000004,Rider_Id_952,715
-Order_No_22527,User_Id_868,Bike,3,Business,13,2,1:28:51 PM,13,2,1:29:02 PM,13,2,1:39:40 PM,13,2,1:43:44 PM,13,2,1:53:33 PM,5,26.1,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_557,589
-Order_No_15990,User_Id_238,Bike,3,Business,29,2,2:07:58 PM,29,2,2:09:44 PM,29,2,2:25:43 PM,29,2,2:40:14 PM,29,2,2:53:16 PM,5,26.9,,-1.32671,36.8600138,-1.3077869,36.844320700000004,Rider_Id_26,782
-Order_No_88,User_Id_2358,Bike,4,Business,13,3,10:24:01 AM,13,3,10:24:10 AM,13,3,10:34:15 AM,13,3,10:44:48 AM,13,3,11:16:33 AM,21,20.0,,-1.3256903999999998,36.8747943,-1.2559559999999999,36.789867,Rider_Id_543,1905
-Order_No_21919,User_Id_1021,Bike,1,Personal,9,4,6:37:50 PM,9,4,6:53:03 PM,9,4,7:26:56 PM,9,4,7:43:40 PM,9,4,8:08:18 PM,19,18.9,,-1.2540928,36.69782,-1.2624632,36.8405321,Rider_Id_116,1478
-Order_No_22308,User_Id_1106,Bike,1,Personal,28,1,3:10:10 PM,28,1,3:10:56 PM,28,1,3:22:38 PM,28,1,3:47:28 PM,28,1,4:20:13 PM,13,24.5,,-1.3025043,36.753532899999996,-1.3529387,36.664256200000004,Rider_Id_101,1965
-Order_No_5818,User_Id_3521,Bike,3,Business,25,1,9:28:50 AM,25,1,9:29:15 AM,25,1,9:41:58 AM,25,1,9:46:26 AM,25,1,9:56:38 AM,2,,,-1.2701582,36.807443299999996,-1.2696908000000002,36.81519,Rider_Id_953,612
-Order_No_7528,User_Id_3645,Bike,1,Personal,19,5,2:20:38 PM,19,5,2:21:55 PM,19,5,3:04:40 PM,19,5,3:06:13 PM,19,5,3:31:42 PM,10,27.2,,-1.2849335,36.802275200000004,-1.2428734,36.8464149,Rider_Id_72,1529
-Order_No_26931,User_Id_3320,Bike,3,Business,29,5,1:20:20 PM,29,5,1:21:14 PM,29,5,1:44:06 PM,29,5,1:52:19 PM,29,5,2:36:09 PM,26,28.8,,-1.2708331,36.790419299999996,-1.3799485,36.9349923,Rider_Id_377,2630
-Order_No_1276,User_Id_2464,Bike,3,Business,25,6,10:01:43 AM,25,6,10:02:27 AM,25,6,10:18:35 AM,25,6,10:53:50 AM,25,6,11:52:10 AM,15,,,-1.279395,36.825364,-1.3185307,36.9001089,Rider_Id_921,3500
-Order_No_6404,User_Id_2016,Bike,1,Personal,8,3,10:06:58 AM,8,3,10:07:35 AM,8,3,10:09:59 AM,8,3,10:14:11 AM,8,3,10:26:31 AM,6,17.2,,-1.2671943,36.807601,-1.2871136,36.785507,Rider_Id_62,740
-Order_No_27155,User_Id_3599,Bike,3,Business,7,5,9:17:19 AM,7,5,9:35:00 AM,7,5,10:30:10 AM,7,5,10:37:19 AM,7,5,10:56:30 AM,7,25.3,,-1.3302996,36.8707935,-1.3270347,36.866736200000005,Rider_Id_810,1151
-Order_No_12788,User_Id_1773,Bike,1,Personal,5,6,11:58:02 AM,5,6,11:58:48 AM,5,6,12:11:49 PM,5,6,12:16:47 PM,5,6,12:46:42 PM,9,22.3,,-1.3102508,36.822997799999996,-1.2444408000000002,36.7934817,Rider_Id_779,1795
-Order_No_23747,User_Id_3407,Bike,3,Business,16,6,11:14:42 AM,16,6,11:15:33 AM,16,6,11:19:23 AM,16,6,11:25:17 AM,16,6,11:51:16 AM,5,25.6,,-1.2810888999999999,36.8247135,-1.3077517,36.838581700000006,Rider_Id_89,1559
-Order_No_8388,User_Id_1500,Bike,3,Business,20,3,2:51:47 PM,20,3,2:53:35 PM,20,3,3:01:42 PM,20,3,3:21:47 PM,20,3,3:50:10 PM,8,28.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_26,1703
-Order_No_27985,User_Id_322,Bike,3,Business,9,2,12:00:52 PM,9,2,12:01:15 PM,9,2,12:17:17 PM,9,2,12:42:54 PM,9,2,1:12:03 PM,13,21.8,,-1.2452664,36.802854100000005,-1.2300082,36.8867427,Rider_Id_882,1749
-Order_No_24842,User_Id_2859,Bike,1,Personal,13,1,9:50:12 AM,13,1,9:52:29 AM,13,1,10:15:20 AM,13,1,10:19:04 AM,13,1,10:51:44 AM,5,15.2,,-1.2527958000000001,36.8003131,-1.2834945,36.812937,Rider_Id_592,1960
-Order_No_26488,User_Id_1148,Bike,3,Business,11,5,2:25:40 PM,11,5,2:39:55 PM,11,5,3:03:10 PM,11,5,3:25:18 PM,11,5,4:04:24 PM,15,22.5,,-1.3100586,36.850032899999995,-1.2294985,36.8775495,Rider_Id_482,2346
-Order_No_17375,User_Id_2482,Bike,2,Personal,5,5,9:23:09 AM,5,5,9:25:16 AM,5,5,9:34:43 AM,5,5,9:42:05 AM,5,5,10:20:41 AM,5,17.7,,-1.2661263999999999,36.8030359,-1.2956951,36.812924200000005,Rider_Id_731,2316
-Order_No_6638,User_Id_393,Bike,3,Business,1,1,12:33:06 PM,1,1,12:34:12 PM,1,1,1:00:37 PM,1,1,1:03:51 PM,1,1,1:40:17 PM,14,23.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_743,2186
-Order_No_18807,User_Id_3222,Bike,3,Business,4,4,11:53:01 AM,4,4,11:53:59 AM,4,4,12:01:59 PM,4,4,12:07:58 PM,4,4,12:15:58 PM,3,25.4,,-1.2551895,36.7822034,-1.2676026000000002,36.810362899999994,Rider_Id_770,480
-Order_No_25772,User_Id_3291,Bike,3,Business,24,5,4:28:50 PM,24,5,4:29:10 PM,24,5,4:45:04 PM,24,5,4:46:26 PM,24,5,4:51:34 PM,2,,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_857,308
-Order_No_11156,User_Id_393,Bike,3,Business,21,5,10:12:34 AM,21,5,10:13:02 AM,21,5,10:13:06 AM,21,5,10:42:43 AM,21,5,11:22:18 AM,11,22.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,2375
-Order_No_7170,User_Id_1850,Bike,3,Business,18,1,12:21:49 PM,18,1,12:23:54 PM,18,1,12:29:36 PM,18,1,1:01:22 PM,18,1,1:01:39 PM,8,26.8,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_498,17
-Order_No_8421,User_Id_666,Bike,2,Personal,9,6,11:09:25 AM,9,6,11:39:05 AM,9,6,11:51:22 AM,9,6,11:57:56 AM,9,6,12:09:21 PM,4,17.3,,-1.2770071000000003,36.832167,-1.2597777000000001,36.823734,Rider_Id_852,685
-Order_No_2597,User_Id_2061,Bike,2,Personal,10,2,5:18:29 PM,10,2,5:28:01 PM,10,2,5:38:59 PM,10,2,5:42:11 PM,10,2,5:57:54 PM,9,20.7,,-1.2902141000000003,36.7714825,-1.2659148999999998,36.805432200000006,Rider_Id_312,943
-Order_No_9320,User_Id_248,Bike,3,Business,14,5,11:45:37 AM,14,5,11:49:00 AM,14,5,12:00:26 PM,14,5,12:03:03 PM,14,5,12:34:00 PM,12,21.6,,-1.304735,36.784605400000004,-1.3227037,36.847087200000004,Rider_Id_454,1857
-Order_No_20618,User_Id_1787,Bike,3,Business,4,1,1:13:15 PM,4,1,1:21:52 PM,4,1,1:32:13 PM,4,1,1:41:04 PM,4,1,2:03:10 PM,11,,,-1.2793686000000002,36.82592329999999,-1.2283403,36.8822756,Rider_Id_341,1326
-Order_No_14714,User_Id_2587,Bike,3,Business,2,3,11:41:11 AM,2,3,11:42:45 AM,2,3,12:01:02 PM,2,3,12:05:22 PM,2,3,12:16:30 PM,1,23.9,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_362,668
-Order_No_361,User_Id_1133,Bike,3,Business,8,2,10:39:35 AM,8,2,10:41:10 AM,8,2,11:06:12 AM,8,2,11:08:52 AM,8,2,11:35:43 AM,6,23.2,,-1.2929499,36.8180835,-1.2571472,36.795063299999995,Rider_Id_150,1611
-Order_No_9813,User_Id_3300,Bike,3,Business,11,4,10:21:25 AM,11,4,10:22:10 AM,11,4,10:35:58 AM,11,4,10:43:03 AM,11,4,11:11:28 AM,11,25.6,,-1.318062,36.849326,-1.303596,36.778378000000004,Rider_Id_154,1705
-Order_No_14860,User_Id_2587,Bike,3,Business,16,2,2:22:16 PM,16,2,3:29:13 PM,16,2,3:29:27 PM,16,2,3:58:05 PM,16,2,4:49:30 PM,16,,,-1.323615,36.700475,-1.28878,36.8168312,Rider_Id_322,3085
-Order_No_20364,User_Id_393,Bike,3,Business,15,2,2:19:30 PM,15,2,2:19:40 PM,15,2,2:20:46 PM,15,2,2:30:17 PM,15,2,2:50:00 PM,11,24.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_62,1183
-Order_No_1024,User_Id_186,Bike,3,Business,3,1,12:30:35 PM,3,1,12:32:05 PM,3,1,12:36:48 PM,3,1,12:51:45 PM,3,1,1:21:35 PM,20,21.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_168,1790
-Order_No_7878,User_Id_3706,Bike,2,Personal,10,3,7:32:28 AM,10,3,7:33:44 AM,10,3,8:07:13 AM,10,3,8:15:17 AM,10,3,8:29:52 AM,9,17.6,5.0,-1.2518056999999998,36.7997135,-1.2846195,36.7958316,Rider_Id_846,875
-Order_No_25169,User_Id_3203,Bike,3,Business,10,2,11:25:55 AM,10,2,11:26:18 AM,10,2,11:26:24 AM,10,2,11:50:14 AM,10,2,11:59:42 AM,9,20.1,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_406,568
-Order_No_25607,User_Id_724,Bike,1,Personal,28,5,3:10:16 PM,28,5,3:13:30 PM,28,5,3:32:42 PM,28,5,3:35:39 PM,28,5,4:00:21 PM,8,25.6,,-1.2619538000000001,36.80876370000001,-1.3023756000000002,36.7949276,Rider_Id_21,1482
-Order_No_16862,User_Id_2523,Bike,3,Business,8,2,10:53:09 AM,8,2,10:53:17 AM,8,2,10:57:02 AM,8,2,11:07:09 AM,8,2,11:44:16 AM,20,23.4,,-1.2551895,36.7822034,-1.3586568,36.911681200000004,Rider_Id_647,2227
-Order_No_3654,User_Id_1781,Bike,3,Business,5,3,1:56:48 PM,5,3,1:57:42 PM,5,3,2:21:46 PM,5,3,2:32:24 PM,5,3,3:10:22 PM,7,25.1,,-1.2571472,36.795063299999995,-1.3004062,36.829741,Rider_Id_439,2278
-Order_No_4397,User_Id_649,Bike,3,Business,30,4,2:46:10 PM,30,4,3:20:23 PM,30,4,3:40:22 PM,30,4,3:44:45 PM,30,4,4:09:57 PM,7,,,-1.2551895,36.7822034,-1.2854089,36.8284874,Rider_Id_868,1512
-Order_No_16382,User_Id_2028,Bike,1,Personal,1,1,1:54:31 PM,1,1,1:58:39 PM,1,1,2:12:06 PM,1,1,2:20:23 PM,1,1,2:51:52 PM,6,25.6,,-1.312469,36.815177,-1.2765737,36.8175399,Rider_Id_612,1889
-Order_No_21779,User_Id_3147,Bike,3,Business,13,3,3:10:32 PM,13,3,3:11:05 PM,13,3,3:14:51 PM,13,3,3:28:02 PM,13,3,3:59:00 PM,17,27.4,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_890,1858
-Order_No_9389,User_Id_3420,Bike,3,Business,19,2,9:00:22 AM,19,2,9:27:56 AM,19,2,9:34:26 AM,19,2,9:37:14 AM,19,2,9:52:34 AM,6,15.5,0.9,-1.3046007,36.821174299999996,-1.3257299999999999,36.801356,Rider_Id_208,920
-Order_No_25429,User_Id_776,Bike,1,Personal,6,5,5:14:00 PM,6,5,5:18:23 PM,6,5,5:28:01 PM,6,5,5:33:43 PM,6,5,6:05:55 PM,4,,,-1.2665773999999999,36.8069908,-1.2551895,36.7822034,Rider_Id_898,1932
-Order_No_1477,User_Id_2587,Bike,3,Business,2,5,2:36:28 PM,2,5,2:53:28 PM,2,5,3:12:46 PM,2,5,3:16:18 PM,2,5,3:42:26 PM,2,25.3,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_569,1568
-Order_No_22106,User_Id_633,Bike,3,Business,15,2,2:59:35 PM,15,2,2:59:43 PM,15,2,3:05:18 PM,15,2,3:06:31 PM,15,2,3:33:55 PM,11,24.7,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_523,1644
-Order_No_21097,User_Id_3669,Bike,3,Business,10,5,12:41:28 PM,10,5,12:48:08 PM,10,5,1:12:39 PM,10,5,1:27:19 PM,10,5,1:43:18 PM,6,,,-1.2793686000000002,36.82592329999999,-1.2615888000000002,36.7928732,Rider_Id_193,959
-Order_No_16316,User_Id_2701,Bike,3,Business,3,1,12:13:48 PM,3,1,12:21:35 PM,3,1,12:41:55 PM,3,1,12:52:44 PM,3,1,1:26:40 PM,8,21.4,,-1.2527958000000001,36.797624400000004,-1.2765736,36.851364600000004,Rider_Id_567,2036
-Order_No_27181,User_Id_1927,Bike,3,Business,29,5,12:44:38 PM,29,5,12:49:04 PM,29,5,1:39:42 PM,29,5,1:53:42 PM,29,5,2:35:40 PM,15,28.8,,-1.3472066,36.7692638,-1.2591018999999999,36.800577000000004,Rider_Id_500,2518
-Order_No_16538,User_Id_1245,Bike,3,Business,5,5,10:34:39 AM,5,5,10:35:25 AM,5,5,10:36:18 AM,5,5,11:05:38 AM,5,5,11:31:08 AM,15,24.7,,-1.300921,36.828195,-1.3523738,36.7266216,Rider_Id_61,1530
-Order_No_1992,User_Id_2106,Bike,3,Personal,1,1,12:34:47 PM,1,1,12:47:29 PM,1,1,12:56:59 PM,1,1,1:19:32 PM,1,1,1:59:12 PM,18,24.2,,-1.2840605,36.8191699,-1.1803358,36.8856922,Rider_Id_832,2380
-Order_No_16150,User_Id_1882,Bike,3,Business,2,4,2:32:11 PM,2,4,3:07:13 PM,2,4,3:28:11 PM,2,4,3:32:01 PM,2,4,4:07:53 PM,13,,,-1.323615,36.700475,-1.2956882,36.7861845,Rider_Id_831,2152
-Order_No_8140,User_Id_2676,Bike,3,Business,3,4,12:03:14 PM,3,4,12:03:42 PM,3,4,12:03:56 PM,3,4,12:11:56 PM,3,4,12:35:11 PM,7,22.0,,-1.2963097,36.7688221,-1.2865096,36.8169364,Rider_Id_101,1395
-Order_No_14278,User_Id_2150,Bike,3,Business,6,6,11:52:42 AM,6,6,11:52:56 AM,6,6,12:00:15 PM,6,6,12:02:59 PM,6,6,12:31:09 PM,18,26.8,,-1.2628473,36.781805,-1.3444459999999998,36.756597,Rider_Id_952,1690
-Order_No_706,User_Id_1205,Bike,3,Business,25,4,2:09:18 PM,25,4,2:09:30 PM,25,4,2:16:24 PM,25,4,2:25:28 PM,25,4,2:43:59 PM,8,,,-1.2495931,36.80650870000001,-1.2873232,36.765857200000006,Rider_Id_484,1111
-Order_No_15737,User_Id_3462,Bike,1,Personal,17,1,12:11:21 PM,17,1,12:14:03 PM,17,1,12:23:01 PM,17,1,12:26:01 PM,17,1,12:40:36 PM,6,24.9,,-1.2556829999999999,36.813771,-1.2842863000000002,36.7951109,Rider_Id_726,875
-Order_No_21476,User_Id_346,Bike,3,Business,10,4,11:01:26 AM,10,4,11:02:46 AM,10,4,11:26:20 AM,10,4,11:32:50 AM,10,4,11:57:43 AM,4,24.6,,-1.2571472,36.795063299999995,-1.28878,36.8168312,Rider_Id_362,1493
-Order_No_2910,User_Id_3291,Bike,3,Business,15,5,2:49:48 PM,15,5,2:52:22 PM,15,5,3:08:55 PM,15,5,3:19:00 PM,15,5,3:42:57 PM,11,17.7,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_8,1437
-Order_No_24884,User_Id_393,Bike,3,Business,11,5,2:40:31 PM,11,5,2:42:17 PM,11,5,2:42:20 PM,11,5,3:47:27 PM,11,5,3:47:29 PM,4,26.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,2
-Order_No_20132,User_Id_2030,Bike,3,Business,11,1,11:26:54 AM,11,1,11:31:16 AM,11,1,11:41:42 AM,11,1,11:55:43 AM,11,1,12:12:39 PM,5,24.2,,-1.3100586,36.850032899999995,-1.2965799999999998,36.823699,Rider_Id_72,1016
-Order_No_3862,User_Id_1976,Bike,3,Business,16,4,12:42:07 PM,16,4,12:43:11 PM,16,4,1:14:19 PM,16,4,1:20:16 PM,16,4,2:10:45 PM,10,14.8,,-1.2761055000000001,36.845617600000004,-1.257028,36.7810435,Rider_Id_452,3029
-Order_No_27438,User_Id_20,Bike,3,Business,13,4,2:52:49 PM,13,4,2:54:15 PM,13,4,3:14:37 PM,13,4,3:18:19 PM,13,4,4:12:11 PM,17,23.0,,-1.281336,36.824692,-1.3261836999999999,36.698124,Rider_Id_622,3232
-Order_No_15001,User_Id_136,Bike,3,Business,3,2,2:51:52 PM,3,2,3:00:07 PM,3,2,3:24:21 PM,3,2,3:26:53 PM,3,2,3:56:28 PM,10,20.2,,-1.2649605,36.798177700000004,-1.3025826,36.7670807,Rider_Id_726,1775
-Order_No_19109,User_Id_1974,Bike,3,Personal,17,5,1:08:22 PM,17,5,1:09:39 PM,17,5,1:17:56 PM,17,5,1:30:02 PM,17,5,1:54:45 PM,5,,,-1.2854988999999999,36.8218183,-1.3013101999999999,36.796370200000005,Rider_Id_768,1483
-Order_No_19794,User_Id_2283,Bike,2,Personal,15,1,11:08:46 AM,15,1,11:09:14 AM,15,1,11:09:53 AM,15,1,11:21:23 AM,15,1,11:21:40 AM,1,,,-1.2562796999999999,36.8075858,-1.2661958999999998,36.807544,Rider_Id_51,17
-Order_No_17522,User_Id_393,Bike,3,Business,7,2,11:51:16 AM,7,2,11:54:08 AM,7,2,12:13:10 PM,7,2,12:28:49 PM,7,2,12:57:49 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_349,1740
-Order_No_23950,User_Id_1809,Bike,3,Business,29,3,11:33:56 AM,29,3,11:34:05 AM,29,3,11:39:41 AM,29,3,11:53:15 AM,29,3,12:02:31 PM,2,18.5,,-1.2527958000000001,36.8003131,-1.2646084,36.8028645,Rider_Id_875,556
-Order_No_20589,User_Id_3599,Bike,3,Business,10,3,9:19:54 AM,10,3,9:20:10 AM,10,3,9:55:44 AM,10,3,10:03:08 AM,10,3,10:27:18 AM,8,21.7,,-1.3302996,36.8707935,-1.3064585,36.8119065,Rider_Id_760,1450
-Order_No_19632,User_Id_3149,Bike,3,Business,7,3,3:13:38 PM,7,3,3:15:56 PM,7,3,3:16:09 PM,7,3,3:37:24 PM,7,3,3:37:40 PM,6,28.4,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_98,16
-Order_No_24670,User_Id_1133,Bike,3,Business,13,4,10:25:37 AM,13,4,10:25:58 AM,13,4,10:29:36 AM,13,4,10:30:54 AM,13,4,10:37:34 AM,3,18.5,,-1.2571472,36.795063299999995,-1.2738936,36.8035805,Rider_Id_409,400
-Order_No_22366,User_Id_2008,Bike,3,Business,24,5,12:11:48 PM,24,5,12:13:37 PM,24,5,12:28:46 PM,24,5,1:00:31 PM,24,5,1:00:40 PM,6,21.6,,-1.2508229,36.7895263,-1.27948,36.830444799999995,Rider_Id_119,9
-Order_No_18453,User_Id_2804,Bike,3,Business,3,1,8:45:53 AM,3,1,8:50:21 AM,3,1,9:00:06 AM,3,1,9:06:54 AM,3,1,9:25:31 AM,9,18.1,5.1,-1.2624935,36.8434339,-1.2551895,36.7822034,Rider_Id_416,1117
-Order_No_18138,User_Id_2701,Bike,3,Business,18,2,1:04:05 PM,18,2,1:04:44 PM,18,2,1:05:50 PM,18,2,1:33:15 PM,18,2,1:41:55 PM,10,27.5,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_179,520
-Order_No_16650,User_Id_136,Bike,3,Business,12,5,11:20:11 AM,12,5,11:20:40 AM,12,5,1:06:54 PM,12,5,1:08:07 PM,12,5,1:48:15 PM,30,26.7,,-1.3094447,36.9107706,-1.3254522,36.721658399999995,Rider_Id_448,2408
-Order_No_20330,User_Id_2464,Bike,3,Business,29,3,4:02:16 PM,29,3,4:21:31 PM,29,3,4:32:06 PM,29,3,4:38:18 PM,29,3,4:56:35 PM,5,20.9,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_849,1097
-Order_No_9043,User_Id_3347,Bike,1,Personal,1,3,2:40:17 PM,1,3,2:42:17 PM,1,3,2:59:30 PM,1,3,3:05:37 PM,1,3,3:19:19 PM,5,23.9,,-1.280467,36.7859296,-1.2969293,36.8027733,Rider_Id_414,822
-Order_No_4694,User_Id_1672,Bike,2,Personal,17,5,11:25:51 AM,17,5,11:26:40 AM,17,5,11:41:49 AM,17,5,11:45:14 AM,17,5,12:12:42 PM,5,17.8,,-1.2804664,36.7701111,-1.2560455,36.780086700000005,Rider_Id_882,1648
-Order_No_2331,User_Id_3295,Bike,3,Business,28,2,9:26:07 AM,28,2,9:26:17 AM,28,2,9:26:53 AM,28,2,9:41:33 AM,28,2,10:13:19 AM,14,18.6,,-1.2571472,36.795063299999995,-1.3312396,36.857532299999995,Rider_Id_177,1906
-Order_No_5370,User_Id_246,Bike,3,Business,16,3,3:14:16 PM,16,3,3:15:17 PM,16,3,3:30:38 PM,16,3,3:33:29 PM,16,3,4:09:01 PM,5,26.8,,-1.2584143,36.8048002,-1.2653185,36.8358841,Rider_Id_879,2132
-Order_No_20890,User_Id_3718,Bike,3,Business,17,1,10:18:07 AM,17,1,10:18:17 AM,17,1,10:24:38 AM,17,1,10:27:19 AM,17,1,10:44:49 AM,3,18.9,,-1.2650027,36.8126242,-1.2726389999999999,36.794723,Rider_Id_259,1050
-Order_No_3022,User_Id_2366,Bike,1,Personal,26,6,12:09:29 PM,26,6,12:10:20 PM,26,6,12:12:51 PM,26,6,12:25:07 PM,26,6,12:49:07 PM,7,22.4,,-1.2750893,36.791218900000004,-1.2629393999999998,36.8275593,Rider_Id_379,1440
-Order_No_22453,User_Id_393,Bike,3,Business,12,2,10:13:29 AM,12,2,10:13:39 AM,12,2,10:13:47 AM,12,2,10:25:52 AM,12,2,11:15:37 AM,14,17.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_901,2985
-Order_No_2186,User_Id_1125,Bike,1,Personal,17,3,9:04:34 AM,17,3,9:13:52 AM,17,3,9:27:12 AM,17,3,9:32:00 AM,17,3,9:47:14 AM,4,21.2,,-1.2705629999999999,36.817397,-1.2892697,36.81545139999999,Rider_Id_268,914
-Order_No_19263,User_Id_2358,Bike,4,Business,4,2,4:48:48 PM,4,2,4:49:30 PM,4,2,4:54:31 PM,4,2,5:02:43 PM,4,2,5:25:55 PM,2,20.9,,-1.302222,36.75873,-1.296797,36.776452500000005,Rider_Id_249,1392
-Order_No_7350,User_Id_3647,Bike,3,Business,15,5,1:15:19 PM,15,5,1:16:19 PM,15,5,1:29:45 PM,15,5,1:32:23 PM,15,5,2:01:16 PM,17,28.1,,-1.2726389999999999,36.794723,-1.207763,36.8825151,Rider_Id_91,1733
-Order_No_22008,User_Id_20,Bike,3,Business,10,5,12:16:44 PM,10,5,12:17:23 PM,10,5,12:21:48 PM,10,5,12:28:13 PM,10,5,12:34:49 PM,1,,,-1.2616841,36.801999,-1.2629666000000002,36.8068369,Rider_Id_96,396
-Order_No_5359,User_Id_2970,Bike,1,Personal,6,1,5:13:12 PM,6,1,5:13:18 PM,6,1,5:28:40 PM,6,1,5:30:48 PM,6,1,6:08:37 PM,21,22.5,,-1.3681253,36.915529799999995,-1.2796151000000002,36.8910091,Rider_Id_629,2269
-Order_No_14583,User_Id_3283,Bike,3,Business,25,1,12:40:43 PM,25,1,12:41:02 PM,25,1,12:53:28 PM,25,1,1:12:26 PM,25,1,1:24:06 PM,6,25.6,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_459,700
-Order_No_5205,User_Id_2789,Bike,3,Business,3,3,11:52:15 AM,3,3,12:10:34 PM,3,3,12:21:26 PM,3,3,12:33:22 PM,3,3,12:40:02 PM,1,27.4,,-1.3019924999999999,36.7833825,-1.304735,36.784605400000004,Rider_Id_468,400
-Order_No_18951,User_Id_2174,Bike,3,Business,20,3,8:36:04 AM,20,3,9:17:48 AM,20,3,9:44:36 AM,20,3,10:08:58 AM,20,3,10:26:03 AM,10,,,-1.2482781,36.884091999999995,-1.270528,36.8153,Rider_Id_812,1025
-Order_No_10718,User_Id_393,Bike,3,Business,20,2,9:43:19 AM,20,2,9:54:12 AM,20,2,9:54:29 AM,20,2,10:23:00 AM,20,2,11:04:01 AM,14,18.9,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_39,2461
-Order_No_2837,User_Id_640,Bike,3,Business,21,3,8:48:51 AM,21,3,8:49:42 AM,21,3,8:57:20 AM,21,3,9:09:29 AM,21,3,9:12:36 AM,2,17.2,1.1,-1.304033,36.7848695,-1.2980228999999999,36.78865,Rider_Id_88,187
-Order_No_23171,User_Id_3291,Bike,3,Business,23,6,12:43:32 PM,23,6,12:43:55 PM,23,6,12:55:48 PM,23,6,1:03:42 PM,23,6,1:28:31 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_910,1489
-Order_No_14008,User_Id_2987,Bike,3,Business,17,2,12:46:52 PM,17,2,12:47:29 PM,17,2,12:47:37 PM,17,2,1:03:00 PM,17,2,1:14:04 PM,11,18.9,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_874,664
-Order_No_22208,User_Id_867,Bike,3,Business,21,4,8:12:18 AM,21,4,8:12:40 AM,21,4,8:16:09 AM,21,4,8:42:49 AM,21,4,9:09:15 AM,10,18.8,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_31,1586
-Order_No_18793,User_Id_704,Bike,1,Business,2,2,9:30:40 AM,2,2,9:32:38 AM,2,2,9:42:12 AM,2,2,9:46:17 AM,2,2,10:40:05 AM,11,15.2,,-1.2831203000000002,36.8316065,-1.2860183,36.8975338,Rider_Id_81,3228
-Order_No_24792,User_Id_2700,Bike,3,Business,23,7,7:09:48 PM,23,7,7:17:50 PM,23,7,7:30:00 PM,23,7,7:41:21 PM,23,7,7:58:11 PM,8,19.6,,-1.3052671999999998,36.753853899999996,-1.2845834999999999,36.8079121,Rider_Id_565,1010
-Order_No_37,User_Id_2082,Bike,3,Personal,6,1,4:52:49 PM,6,1,4:53:38 PM,6,1,4:54:03 PM,6,1,5:14:57 PM,6,1,5:53:33 PM,11,22.2,,-1.270054,36.815164,-1.27034,36.747764000000004,Rider_Id_406,2316
-Order_No_3959,User_Id_3222,Bike,3,Business,11,5,2:27:07 PM,11,5,2:27:14 PM,11,5,2:29:48 PM,11,5,2:36:10 PM,11,5,3:31:31 PM,34,26.6,,-1.2551895,36.7822034,-1.1105305,36.6461267,Rider_Id_822,3321
-Order_No_8724,User_Id_2987,Bike,3,Business,7,3,11:12:58 AM,7,3,11:19:32 AM,7,3,11:31:13 AM,7,3,11:34:33 AM,7,3,11:57:02 AM,8,25.2,,-1.2702120000000001,36.82108770000001,-1.3271374,36.8495534,Rider_Id_949,1349
-Order_No_23546,User_Id_1927,Bike,3,Business,7,4,1:57:09 PM,7,4,1:57:46 PM,7,4,2:38:39 PM,7,4,2:47:11 PM,7,4,3:26:15 PM,17,31.2,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_154,2344
-Order_No_4377,User_Id_2330,Bike,3,Business,31,4,10:40:08 AM,31,4,10:40:50 AM,31,4,10:47:48 AM,31,4,10:53:36 AM,31,4,11:15:38 AM,19,26.4,,-1.2642903,36.80021370000001,-1.185943,36.9276394,Rider_Id_648,1322
-Order_No_9361,User_Id_2521,Bike,3,Business,30,6,10:41:19 AM,30,6,10:41:31 AM,30,6,10:41:46 AM,30,6,11:11:59 AM,30,6,12:23:23 PM,14,26.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_25,4284
-Order_No_23673,User_Id_646,Bike,2,Personal,24,4,3:01:15 PM,24,4,3:03:55 PM,24,4,3:17:34 PM,24,4,3:18:42 PM,24,4,3:32:03 PM,5,22.8,,-1.2800815,36.775352399999996,-1.2661263999999999,36.8030359,Rider_Id_24,801
-Order_No_27675,User_Id_3204,Bike,1,Personal,8,3,8:01:56 AM,8,3,8:02:40 AM,8,3,8:14:16 AM,8,3,8:30:22 AM,8,3,8:53:28 AM,11,17.2,,-1.264788,36.7883897,-1.3076261999999998,36.8325382,Rider_Id_446,1386
-Order_No_3293,User_Id_3147,Bike,3,Business,12,2,12:37:29 PM,12,2,12:38:16 PM,12,2,1:22:16 PM,12,2,1:30:30 PM,12,2,1:42:02 PM,4,24.7,,-1.3472066,36.7692638,-1.3205111,36.7801016,Rider_Id_328,692
-Order_No_3690,User_Id_1651,Bike,3,Business,8,5,12:46:09 PM,8,5,12:49:12 PM,8,5,12:56:27 PM,8,5,1:04:11 PM,8,5,1:29:59 PM,14,28.3,,-1.2551895,36.7822034,-1.3263105,36.8622904,Rider_Id_205,1548
-Order_No_7809,User_Id_1209,Bike,3,Business,3,1,4:45:06 PM,3,1,4:46:13 PM,3,1,4:53:07 PM,3,1,4:54:33 PM,3,1,5:02:37 PM,4,17.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_2,484
-Order_No_17185,User_Id_1620,Bike,1,Personal,9,2,12:31:56 PM,9,2,12:32:10 PM,9,2,12:54:29 PM,9,2,12:58:18 PM,9,2,1:21:57 PM,10,22.1,,-1.2871215,36.822053000000004,-1.227132,36.8374277,Rider_Id_622,1419
-Order_No_17012,User_Id_1207,Bike,3,Business,10,1,8:12:49 PM,10,1,8:13:57 PM,10,1,8:15:55 PM,10,1,8:36:35 PM,10,1,9:21:53 PM,13,16.7,,-1.2629666000000002,36.8068369,-1.2162532,36.7095449,Rider_Id_907,2718
-Order_No_1889,User_Id_1340,Bike,3,Business,7,2,12:24:03 PM,7,2,12:24:29 PM,7,2,12:40:19 PM,7,2,1:03:38 PM,7,2,1:51:38 PM,20,23.3,,-1.2602386,36.7503491,-1.2628423999999998,36.8950641,Rider_Id_574,2880
-Order_No_17060,User_Id_909,Bike,3,Business,15,5,3:51:04 PM,15,5,4:16:54 PM,15,5,4:37:00 PM,15,5,5:08:55 PM,15,5,5:13:44 PM,7,27.6,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_588,289
-Order_No_12071,User_Id_391,Bike,3,Business,18,4,9:01:50 AM,18,4,9:02:11 AM,18,4,9:10:37 AM,18,4,9:23:51 AM,18,4,9:46:21 AM,10,,,-1.300921,36.828195,-1.2959666,36.76971629999999,Rider_Id_933,1350
-Order_No_12834,User_Id_1245,Bike,3,Business,10,1,1:35:43 PM,10,1,1:36:20 PM,10,1,1:54:53 PM,10,1,2:05:19 PM,10,1,2:32:35 PM,11,18.1,,-1.2948751,36.7938907,-1.3198615,36.7110323,Rider_Id_429,1636
-Order_No_7109,User_Id_3686,Bike,3,Business,22,5,7:49:40 AM,22,5,7:49:49 AM,22,5,8:07:42 AM,22,5,8:10:34 AM,22,5,8:20:15 AM,4,,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_353,581
-Order_No_23649,User_Id_2216,Bike,3,Business,3,5,3:57:38 PM,3,5,4:08:46 PM,3,5,4:22:28 PM,3,5,4:31:12 PM,3,5,4:50:38 PM,9,,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_177,1166
-Order_No_20528,User_Id_204,Bike,3,Personal,31,2,3:32:17 PM,31,2,3:32:43 PM,31,2,3:58:46 PM,31,2,4:03:02 PM,31,2,4:36:43 PM,16,22.3,,-1.2309879,36.8873186,-1.3043821000000002,36.7840586,Rider_Id_812,2021
-Order_No_16101,User_Id_242,Bike,3,Business,7,4,11:25:19 AM,7,4,11:31:13 AM,7,4,11:42:58 AM,7,4,11:49:34 AM,7,4,12:09:15 PM,9,16.8,,-1.2551895,36.7822034,-1.306245,36.819297799999994,Rider_Id_752,1181
-Order_No_15379,User_Id_3487,Bike,3,Business,10,3,10:17:11 AM,10,3,10:20:46 AM,10,3,10:24:51 AM,10,3,10:28:07 AM,10,3,10:58:59 AM,5,21.9,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_660,1852
-Order_No_12935,User_Id_3647,Bike,3,Business,3,5,5:05:22 PM,3,5,5:08:01 PM,3,5,5:10:59 PM,3,5,5:18:30 PM,3,5,5:43:01 PM,5,,,-1.2726389999999999,36.794723,-1.2748221000000002,36.7817865,Rider_Id_815,1471
-Order_No_19500,User_Id_3599,Bike,3,Business,25,2,11:48:32 AM,25,2,12:38:45 PM,25,2,1:26:35 PM,25,2,1:52:43 PM,25,2,2:18:29 PM,14,26.9,,-1.3302996,36.8707935,-1.2653185,36.8358841,Rider_Id_946,1546
-Order_No_25881,User_Id_1341,Bike,3,Business,15,1,12:03:18 PM,15,1,12:11:27 PM,15,1,12:30:35 PM,15,1,12:43:00 PM,15,1,1:00:29 PM,15,,,-1.3444459999999998,36.756597,-1.331619,36.847976,Rider_Id_628,1049
-Order_No_17181,User_Id_1976,Bike,3,Business,26,5,4:24:42 PM,26,5,4:31:17 PM,26,5,4:42:53 PM,26,5,4:46:01 PM,26,5,5:23:53 PM,18,,,-1.257028,36.7810435,-1.2156006000000001,36.8916865,Rider_Id_125,2272
-Order_No_547,User_Id_3688,Bike,3,Business,4,1,12:19:44 PM,4,1,12:20:37 PM,4,1,12:23:36 PM,4,1,12:27:44 PM,4,1,12:51:14 PM,8,25.8,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_291,1410
-Order_No_18490,User_Id_1471,Bike,3,Business,18,4,1:13:38 PM,18,4,1:14:48 PM,18,4,1:27:18 PM,18,4,1:36:36 PM,18,4,1:47:46 PM,7,,,-1.2853222,36.7767778,-1.2551895,36.7822034,Rider_Id_96,670
-Order_No_23961,User_Id_1471,Bike,3,Business,14,5,5:22:57 PM,14,5,6:00:47 PM,14,5,6:14:59 PM,14,5,6:42:26 PM,14,5,7:12:28 PM,13,21.6,,-1.2297202,36.881950700000004,-1.3037386999999998,36.8175661,Rider_Id_384,1802
-Order_No_19772,User_Id_2344,Bike,3,Business,9,1,9:35:11 AM,9,1,9:35:20 AM,9,1,9:38:46 AM,9,1,9:41:52 AM,9,1,9:48:22 AM,2,15.1,,-1.2551895,36.7822034,-1.2592428,36.7894541,Rider_Id_103,390
-Order_No_18866,User_Id_2866,Bike,3,Business,3,4,1:42:11 PM,3,4,1:42:35 PM,3,4,1:43:17 PM,3,4,2:15:21 PM,3,4,2:34:40 PM,11,25.8,,-1.322064,36.817157,-1.2551895,36.7822034,Rider_Id_449,1159
-Order_No_6897,User_Id_635,Bike,3,Business,5,5,1:04:37 PM,5,5,1:33:17 PM,5,5,2:00:55 PM,5,5,2:07:55 PM,5,5,2:18:33 PM,3,26.4,,-1.2534952,36.7825215,-1.2602337,36.79905479999999,Rider_Id_770,638
-Order_No_742,User_Id_3599,Bike,3,Business,6,1,10:37:32 AM,6,1,11:18:00 AM,6,1,11:23:28 AM,6,1,11:34:19 AM,6,1,12:06:19 PM,14,,,-1.3302996,36.8707935,-1.2868736,36.780588200000004,Rider_Id_581,1920
-Order_No_9107,User_Id_1113,Bike,1,Personal,9,2,2:00:31 PM,9,2,2:01:48 PM,9,2,2:09:09 PM,9,2,2:16:09 PM,9,2,2:42:21 PM,13,22.3,,-1.2649605,36.798177700000004,-1.2156006000000001,36.8916865,Rider_Id_259,1572
-Order_No_27725,User_Id_2330,Bike,3,Business,3,1,11:15:24 AM,3,1,11:16:05 AM,3,1,11:21:28 AM,3,1,11:25:40 AM,3,1,11:46:55 AM,9,21.7,,-1.2536275,36.798793200000006,-1.2687407,36.7373043,Rider_Id_161,1275
-Order_No_5594,User_Id_2956,Bike,3,Business,19,1,9:56:23 AM,19,1,9:57:24 AM,19,1,10:17:41 AM,19,1,10:27:48 AM,19,1,10:48:49 AM,9,19.5,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_425,1261
-Order_No_2457,User_Id_2222,Bike,3,Business,2,1,2:42:11 PM,2,1,2:55:51 PM,2,1,3:30:50 PM,2,1,3:41:34 PM,2,1,4:13:08 PM,7,20.2,,-1.2930128,36.8356869,-1.3259463,36.850258000000004,Rider_Id_844,1894
-Order_No_763,User_Id_3283,Bike,3,Business,3,1,12:05:18 PM,3,1,12:05:51 PM,3,1,12:14:03 PM,3,1,12:19:32 PM,3,1,12:29:07 PM,5,21.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_8,575
-Order_No_8275,User_Id_302,Bike,3,Business,15,6,2:10:14 PM,15,6,2:53:07 PM,15,6,3:05:51 PM,15,6,3:10:48 PM,15,6,3:30:13 PM,14,26.0,,-1.2649605,36.798177700000004,-1.3255287,36.7850772,Rider_Id_179,1165
-Order_No_10027,User_Id_928,Bike,3,Business,30,3,10:47:35 AM,30,3,10:48:45 AM,30,3,10:59:03 AM,30,3,11:10:07 AM,30,3,11:10:09 AM,5,24.8,,-1.2551895,36.7822034,-1.2657199,36.7914391,Rider_Id_647,2
-Order_No_25556,User_Id_633,Bike,3,Business,27,2,9:01:39 AM,27,2,9:09:46 AM,27,2,9:16:32 AM,27,2,9:27:21 AM,27,2,9:36:21 AM,4,18.7,2.0,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_535,540
-Order_No_17838,User_Id_656,Bike,2,Personal,4,3,5:56:58 PM,4,3,6:04:15 PM,4,3,6:31:47 PM,4,3,6:37:47 PM,4,3,7:08:51 PM,12,,,-1.3301576000000002,36.78302120000001,-1.2812997,36.798667200000004,Rider_Id_465,1864
-Order_No_24446,User_Id_71,Bike,3,Business,6,2,2:12:53 PM,6,2,4:16:29 PM,6,2,4:28:42 PM,6,2,4:42:06 PM,6,2,5:32:27 PM,8,,,-1.2891921999999998,36.825189,-1.2551895,36.7822034,Rider_Id_456,3021
-Order_No_8416,User_Id_3647,Bike,3,Business,7,5,2:12:33 PM,7,5,2:13:38 PM,7,5,2:15:17 PM,7,5,2:19:51 PM,7,5,2:20:41 PM,3,26.8,,-1.2726389999999999,36.794723,-1.2729843,36.8105717,Rider_Id_544,50
-Order_No_22145,User_Id_1967,Bike,1,Personal,15,2,10:27:24 AM,15,2,10:48:42 AM,15,2,11:06:01 AM,15,2,11:09:19 AM,15,2,11:34:13 AM,3,23.0,,-1.2740396,36.7993518,-1.2656584,36.809625700000005,Rider_Id_761,1494
-Order_No_21457,User_Id_496,Bike,3,Business,19,2,10:43:17 AM,19,2,10:54:36 AM,19,2,11:07:19 AM,19,2,11:18:23 AM,19,2,11:42:54 AM,10,26.7,,-1.2551895,36.7822034,-1.2583408999999999,36.7176324,Rider_Id_849,1471
-Order_No_11407,User_Id_635,Bike,3,Business,28,4,11:47:06 AM,28,4,11:47:44 AM,28,4,11:56:06 AM,28,4,11:58:15 AM,28,4,12:21:34 PM,7,23.5,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_686,1399
-Order_No_3850,User_Id_3463,Bike,2,Personal,29,6,2:55:19 PM,29,6,2:55:43 PM,29,6,3:04:09 PM,29,6,3:15:04 PM,29,6,3:21:43 PM,4,28.0,,-1.2872344,36.7773949,-1.2747549999999999,36.7906585,Rider_Id_750,399
-Order_No_12604,User_Id_1209,Bike,3,Business,28,1,4:06:56 PM,28,1,4:49:19 PM,28,1,4:49:24 PM,28,1,4:56:52 PM,28,1,5:24:19 PM,12,25.3,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_523,1647
-Order_No_22207,User_Id_1899,Bike,3,Business,30,4,11:28:31 AM,30,4,11:28:48 AM,30,4,11:42:12 AM,30,4,11:55:05 AM,30,4,12:24:20 PM,11,,,-1.2663631000000002,36.8163329,-1.331619,36.847976,Rider_Id_68,1755
-Order_No_7649,User_Id_868,Bike,3,Business,17,3,10:14:10 AM,17,3,10:14:27 AM,17,3,10:14:39 AM,17,3,10:34:56 AM,17,3,10:46:37 AM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_68,701
-Order_No_10301,User_Id_3283,Bike,3,Business,17,5,10:27:40 AM,17,5,10:30:06 AM,17,5,11:08:16 AM,17,5,11:23:45 AM,17,5,11:41:08 AM,7,17.8,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_179,1043
-Order_No_760,User_Id_1500,Bike,3,Business,2,2,3:50:04 PM,2,2,3:51:31 PM,2,2,3:56:53 PM,2,2,4:12:52 PM,2,2,4:32:24 PM,11,29.2,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_809,1172
-Order_No_13237,User_Id_1072,Bike,1,Personal,23,6,11:05:34 AM,23,6,11:06:21 AM,23,6,11:18:57 AM,23,6,11:24:44 AM,23,6,11:56:25 AM,9,,,-1.261849,36.808254,-1.3234472,36.8416613,Rider_Id_427,1901
-Order_No_8635,User_Id_3354,Bike,3,Business,23,3,12:08:48 PM,23,3,12:55:19 PM,23,3,12:57:28 PM,23,3,1:03:18 PM,23,3,1:24:52 PM,13,,,-1.3700383,36.9190174,-1.3153122,36.8965598,Rider_Id_932,1294
-Order_No_18200,User_Id_3621,Bike,3,Business,9,3,11:06:28 AM,9,3,11:06:39 AM,9,3,11:15:12 AM,9,3,11:22:59 AM,9,3,11:40:13 AM,7,21.1,,-1.2836583000000001,36.7886014,-1.2473215,36.794881200000006,Rider_Id_761,1034
-Order_No_5496,User_Id_972,Bike,3,Business,8,4,5:28:09 PM,8,4,5:56:26 PM,8,4,6:02:28 PM,8,4,6:09:08 PM,8,4,6:21:59 PM,6,25.6,,-1.2862033000000002,36.8152966,-1.2961968000000001,36.7801894,Rider_Id_510,771
-Order_No_8105,User_Id_2330,Bike,3,Business,8,3,9:55:00 AM,8,3,9:55:30 AM,8,3,9:57:36 AM,8,3,10:14:36 AM,8,3,10:33:28 AM,13,,,-1.2645318,36.7981775,-1.2284323,36.8757058,Rider_Id_576,1132
-Order_No_466,User_Id_2931,Bike,3,Business,14,4,2:40:11 PM,14,4,2:49:54 PM,14,4,3:01:10 PM,14,4,3:05:08 PM,14,4,3:28:23 PM,9,28.0,,-1.2452664,36.802854100000005,-1.2962832,36.784968299999996,Rider_Id_210,1395
-Order_No_14550,User_Id_970,Bike,3,Business,26,5,11:20:51 AM,26,5,11:21:38 AM,26,5,11:29:38 AM,26,5,11:32:05 AM,26,5,11:58:52 AM,9,,,-1.2551895,36.7822034,-1.3040651,36.798125399999996,Rider_Id_161,1607
-Order_No_22472,User_Id_2956,Bike,3,Business,21,2,5:47:50 PM,21,2,5:48:12 PM,21,2,5:49:01 PM,21,2,5:56:11 PM,21,2,6:02:30 PM,2,23.8,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_205,379
-Order_No_26269,User_Id_868,Bike,3,Business,8,1,2:11:28 PM,8,1,2:11:58 PM,8,1,2:22:15 PM,8,1,2:23:39 PM,8,1,3:04:35 PM,7,29.6,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_106,2456
-Order_No_2394,User_Id_1363,Bike,3,Business,29,5,10:23:58 AM,29,5,10:26:06 AM,29,5,10:39:34 AM,29,5,11:19:13 AM,29,5,11:19:16 AM,10,26.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_507,3
-Order_No_7529,User_Id_2330,Bike,3,Business,14,5,1:20:56 PM,14,5,1:28:32 PM,14,5,1:42:23 PM,14,5,1:44:28 PM,14,5,1:55:02 PM,4,23.6,,-1.3040812,36.7924217,-1.2868601,36.7738025,Rider_Id_847,634
-Order_No_3513,User_Id_2200,Bike,3,Business,13,1,9:54:13 AM,13,1,9:55:39 AM,13,1,10:02:07 AM,13,1,10:06:22 AM,13,1,10:21:20 AM,6,15.2,,-1.2649605,36.798177700000004,-1.290894,36.822971,Rider_Id_263,898
-Order_No_16215,User_Id_2587,Bike,3,Business,25,6,12:33:29 PM,25,6,12:33:44 PM,25,6,12:40:05 PM,25,6,12:57:15 PM,25,6,1:05:10 PM,2,,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_449,475
-Order_No_1415,User_Id_3686,Bike,3,Business,5,2,10:30:09 AM,5,2,10:30:52 AM,5,2,10:36:49 AM,5,2,10:42:03 AM,5,2,11:19:58 AM,8,25.6,,-1.2551895,36.7822034,-1.29817,36.800141700000005,Rider_Id_708,2275
-Order_No_5273,User_Id_1244,Bike,3,Business,6,3,3:18:41 PM,6,3,3:19:28 PM,6,3,3:21:28 PM,6,3,3:32:14 PM,6,3,3:53:00 PM,8,27.5,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_433,1246
-Order_No_1213,User_Id_3557,Bike,3,Business,4,4,9:12:38 AM,4,4,9:14:57 AM,4,4,9:29:56 AM,4,4,9:42:48 AM,4,4,10:00:23 AM,8,21.8,,-1.29387,36.7907813,-1.2546732,36.808679600000005,Rider_Id_445,1055
-Order_No_14525,User_Id_550,Bike,1,Personal,17,3,10:39:52 AM,17,3,10:40:29 AM,17,3,10:41:16 AM,17,3,10:48:37 AM,17,3,11:04:01 AM,7,25.6,,-1.2622591,36.7992934,-1.2927667999999999,36.789071,Rider_Id_215,924
-Order_No_13939,User_Id_2587,Bike,3,Business,31,4,3:28:18 PM,31,4,3:47:44 PM,31,4,4:00:23 PM,31,4,4:02:11 PM,31,4,4:23:31 PM,8,28.6,,-1.28878,36.8168312,-1.3215393999999998,36.836621,Rider_Id_276,1280
-Order_No_20001,User_Id_955,Bike,2,Personal,4,6,11:49:30 AM,4,6,11:49:58 AM,4,6,11:55:24 AM,4,6,12:02:05 PM,4,6,12:15:33 PM,4,19.6,,-1.2790653,36.7907262,-1.2675332000000001,36.808561,Rider_Id_452,808
-Order_No_7366,User_Id_2982,Bike,3,Business,21,1,1:33:01 PM,21,1,1:34:03 PM,21,1,1:38:52 PM,21,1,1:55:44 PM,21,1,2:15:46 PM,7,27.4,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_340,1202
-Order_No_22172,User_Id_3091,Bike,3,Business,13,5,3:56:44 PM,13,5,3:57:07 PM,13,5,3:57:31 PM,13,5,4:37:14 PM,13,5,5:05:46 PM,18,19.1,,-1.2297202,36.881950700000004,-1.3014461000000002,36.7661381,Rider_Id_886,1712
-Order_No_12843,User_Id_2162,Bike,3,Business,25,4,12:45:30 PM,25,4,12:57:56 PM,25,4,1:15:12 PM,25,4,1:28:24 PM,25,4,1:42:46 PM,8,20.0,,-1.2615888000000002,36.7928732,-1.2857671,36.7875614,Rider_Id_485,862
-Order_No_19362,User_Id_3080,Bike,3,Business,5,5,3:22:02 PM,5,5,3:22:15 PM,5,5,3:22:37 PM,5,5,3:34:37 PM,5,5,4:19:54 PM,16,26.4,,-1.2551895,36.7822034,-1.3194518,36.869396,Rider_Id_450,2717
-Order_No_22451,User_Id_867,Bike,3,Business,13,2,3:01:49 PM,13,2,3:02:14 PM,13,2,3:03:24 PM,13,2,3:31:27 PM,13,2,4:00:51 PM,14,26.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_619,1764
-Order_No_8638,User_Id_1882,Bike,3,Business,15,4,2:50:47 PM,15,4,3:04:09 PM,15,4,3:12:17 PM,15,4,3:21:41 PM,15,4,4:07:24 PM,13,27.5,,-1.2958783,36.7928125,-1.3175825,36.7004796,Rider_Id_612,2743
-Order_No_9723,User_Id_1329,Bike,3,Business,28,5,9:52:59 AM,28,5,9:54:09 AM,28,5,9:57:19 AM,28,5,9:58:35 AM,28,5,10:04:56 AM,3,19.3,,-1.273056,36.811298,-1.2567464,36.7915273,Rider_Id_337,381
-Order_No_23970,User_Id_972,Bike,3,Business,26,6,12:20:39 PM,26,6,12:22:06 PM,26,6,12:29:22 PM,26,6,12:34:30 PM,26,6,12:46:59 PM,4,22.8,,-1.2862033000000002,36.8152966,-1.3016043,36.8259264,Rider_Id_888,749
-Order_No_26124,User_Id_2658,Bike,3,Business,8,1,1:12:44 PM,8,1,1:14:39 PM,8,1,1:27:31 PM,8,1,1:38:30 PM,8,1,2:02:39 PM,7,29.5,,-1.2924639,36.77957979999999,-1.2813012,36.832396200000005,Rider_Id_378,1449
-Order_No_7569,User_Id_496,Bike,3,Business,1,1,2:41:14 PM,1,1,2:43:09 PM,1,1,2:53:47 PM,1,1,3:01:27 PM,1,1,3:15:28 PM,7,,,-1.2551895,36.7822034,-1.2802803,36.8271747,Rider_Id_903,841
-Order_No_23837,User_Id_2279,Bike,3,Business,23,4,2:07:05 PM,23,4,2:07:34 PM,23,4,2:16:27 PM,23,4,2:19:55 PM,23,4,2:35:02 PM,5,24.5,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_19,907
-Order_No_15594,User_Id_646,Bike,2,Personal,3,2,10:18:07 AM,3,2,10:20:48 AM,3,2,10:54:20 AM,3,2,11:10:45 AM,3,2,11:28:26 AM,7,18.5,,-1.284705,36.79287,-1.2661263999999999,36.8030359,Rider_Id_752,1061
-Order_No_4659,User_Id_3283,Bike,3,Business,20,2,10:17:44 AM,20,2,10:28:52 AM,20,2,10:36:41 AM,20,2,10:50:11 AM,20,2,11:16:35 AM,14,19.6,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_100,1584
-Order_No_4312,User_Id_3091,Bike,3,Business,9,2,10:56:27 AM,9,2,10:57:23 AM,9,2,11:16:07 AM,9,2,11:29:09 AM,9,2,11:50:19 AM,9,27.2,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_855,1270
-Order_No_16093,User_Id_3016,Bike,1,Personal,23,1,10:36:21 AM,23,1,10:36:31 AM,23,1,10:59:39 AM,23,1,11:02:01 AM,23,1,11:22:54 AM,9,18.3,,-1.2937028,36.7874493,-1.269199,36.8236418,Rider_Id_956,1253
-Order_No_23448,User_Id_295,Bike,3,Business,1,1,1:44:58 PM,1,1,1:59:22 PM,1,1,1:59:37 PM,1,1,2:06:03 PM,1,1,2:06:10 PM,2,,,-1.286925,36.821915999999995,-1.2878973,36.8225054,Rider_Id_571,7
-Order_No_5092,User_Id_2700,Bike,3,Business,30,2,4:43:19 PM,30,2,4:44:38 PM,30,2,5:12:29 PM,30,2,5:19:49 PM,30,2,5:27:37 PM,2,23.6,,-1.2885503999999999,36.8066908,-1.2845834999999999,36.8079121,Rider_Id_80,468
-Order_No_21299,User_Id_1957,Bike,2,Personal,2,4,10:51:40 AM,2,4,10:51:55 AM,2,4,10:54:41 AM,2,4,11:12:59 AM,2,4,11:20:22 AM,2,22.7,,-1.3043821000000002,36.7840586,-1.2976248,36.7809463,Rider_Id_105,443
-Order_No_12245,User_Id_1075,Bike,3,Business,14,4,11:51:26 AM,14,4,12:13:59 PM,14,4,12:16:08 PM,14,4,12:20:50 PM,14,4,12:46:20 PM,9,23.9,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_448,1530
-Order_No_9399,User_Id_3201,Bike,3,Business,7,5,1:43:54 PM,7,5,1:45:17 PM,7,5,1:46:04 PM,7,5,2:03:04 PM,7,5,2:18:37 PM,8,,,-1.2929489,36.8192715,-1.2584143,36.8048002,Rider_Id_320,933
-Order_No_26266,User_Id_2521,Bike,3,Business,11,6,9:47:45 AM,11,6,9:48:24 AM,11,6,9:54:21 AM,11,6,10:13:36 AM,11,6,10:28:49 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_843,913
-Order_No_4417,User_Id_635,Bike,3,Business,8,3,11:21:30 AM,8,3,11:23:38 AM,8,3,11:39:46 AM,8,3,12:10:52 PM,8,3,12:53:45 PM,6,22.4,,-1.300921,36.828195,-1.2709138,36.85387862,Rider_Id_301,2573
-Order_No_6693,User_Id_2457,Bike,3,Business,26,5,11:43:02 AM,26,5,1:09:49 PM,26,5,1:41:51 PM,26,5,1:47:27 PM,26,5,2:33:28 PM,8,,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_559,2761
-Order_No_17515,User_Id_1304,Bike,3,Business,24,3,11:01:52 AM,24,3,11:02:29 AM,24,3,11:14:01 AM,24,3,11:16:05 AM,24,3,11:53:42 AM,18,20.1,,-1.3332749,36.870814700000004,-1.2363747999999999,36.817750200000006,Rider_Id_737,2257
-Order_No_4100,User_Id_2143,Bike,3,Business,6,5,10:48:15 AM,6,5,10:49:30 AM,6,5,10:58:03 AM,6,5,11:10:34 AM,6,5,11:31:45 AM,9,20.3,,-1.296797,36.776452500000005,-1.3147848,36.833207200000004,Rider_Id_844,1271
-Order_No_17876,User_Id_640,Bike,3,Business,20,5,2:46:39 PM,20,5,2:46:41 PM,20,5,3:02:22 PM,20,5,3:26:52 PM,20,5,3:42:35 PM,5,20.6,,-1.3012902,36.80964,-1.304033,36.7848695,Rider_Id_229,943
-Order_No_16339,User_Id_491,Bike,1,Personal,29,5,4:49:50 PM,29,5,4:52:47 PM,29,5,5:06:42 PM,29,5,5:10:13 PM,29,5,5:42:44 PM,5,20.3,,-1.263987,36.798891,-1.2808482,36.8003686,Rider_Id_192,1951
-Order_No_23695,User_Id_2069,Bike,3,Business,27,3,2:51:40 PM,27,3,2:52:21 PM,27,3,2:55:25 PM,27,3,3:02:22 PM,27,3,3:26:10 PM,16,29.7,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_709,1428
-Order_No_23388,User_Id_706,Bike,3,Business,2,3,2:35:34 PM,2,3,2:43:20 PM,2,3,3:02:07 PM,2,3,3:03:42 PM,2,3,3:27:24 PM,4,26.1,,-1.3077869,36.844320700000004,-1.2958261,36.8304671,Rider_Id_858,1422
-Order_No_16189,User_Id_517,Bike,3,Business,6,3,8:24:31 AM,6,3,8:24:53 AM,6,3,8:24:59 AM,6,3,9:05:23 AM,6,3,9:05:26 AM,14,,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_239,3
-Order_No_25022,User_Id_2069,Bike,3,Business,18,4,9:51:30 AM,18,4,9:52:07 AM,18,4,10:01:02 AM,18,4,10:06:48 AM,18,4,10:30:47 AM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_204,1439
-Order_No_2649,User_Id_3647,Bike,3,Business,5,1,12:57:47 PM,5,1,12:58:55 PM,5,1,1:10:44 PM,5,1,1:14:16 PM,5,1,1:29:30 PM,4,24.4,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_905,914
-Order_No_21996,User_Id_302,Bike,3,Business,15,5,12:00:11 PM,15,5,12:00:37 PM,15,5,12:13:03 PM,15,5,12:16:48 PM,15,5,1:22:00 PM,1,26.0,,-1.2649605,36.798177700000004,-1.2637056999999998,36.805170000000004,Rider_Id_682,3912
-Order_No_10253,User_Id_707,Bike,1,Personal,3,3,2:50:38 PM,3,3,2:58:44 PM,3,3,3:09:49 PM,3,3,3:11:27 PM,3,3,3:31:04 PM,5,26.7,,-1.3018312,36.781749700000006,-1.2884799,36.82179179999999,Rider_Id_847,1177
-Order_No_12883,User_Id_706,Bike,3,Business,12,5,2:27:08 PM,12,5,2:35:39 PM,12,5,2:57:29 PM,12,5,3:17:51 PM,12,5,3:34:05 PM,6,27.2,,-1.3239488000000001,36.831683500000004,-1.3083266000000002,36.8494756,Rider_Id_299,974
-Order_No_9949,User_Id_3531,Bike,1,Personal,15,2,12:47:50 PM,15,2,12:49:18 PM,15,2,1:03:28 PM,15,2,1:17:25 PM,15,2,1:39:34 PM,15,22.8,,-1.2980228999999999,36.78865,-1.3311462,36.75230139999999,Rider_Id_919,1329
-Order_No_12631,User_Id_393,Bike,3,Business,31,3,10:16:27 AM,31,3,10:52:36 AM,31,3,10:53:34 AM,31,3,11:14:42 AM,31,3,11:36:23 AM,10,24.6,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_39,1301
-Order_No_7410,User_Id_3283,Bike,3,Business,3,3,12:16:17 PM,3,3,12:17:13 PM,3,3,12:27:39 PM,3,3,12:38:28 PM,3,3,12:51:21 PM,5,27.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_817,773
-Order_No_20526,User_Id_3222,Bike,3,Business,10,1,3:14:43 PM,10,1,3:27:08 PM,10,1,4:27:50 PM,10,1,4:29:54 PM,10,1,4:56:46 PM,8,19.0,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_268,1612
-Order_No_2945,User_Id_2454,Bike,3,Business,14,5,12:12:41 PM,14,5,12:29:44 PM,14,5,12:43:37 PM,14,5,12:48:57 PM,14,5,1:01:13 PM,3,21.5,,-1.2941791999999999,36.763363700000006,-1.2993231,36.7753372,Rider_Id_383,736
-Order_No_6317,User_Id_498,Bike,1,Personal,7,4,5:04:09 PM,7,4,5:04:51 PM,7,4,5:21:42 PM,7,4,5:23:48 PM,7,4,5:42:47 PM,2,17.8,,-1.2973052,36.770748,-1.3020513999999999,36.77482979999999,Rider_Id_215,1139
-Order_No_24735,User_Id_552,Bike,3,Business,19,2,9:34:19 AM,19,2,9:35:12 AM,19,2,10:00:39 AM,19,2,10:46:32 AM,19,2,11:40:14 AM,21,27.4,,-1.2571472,36.795063299999995,-1.335477,36.930829700000004,Rider_Id_588,3222
-Order_No_17219,User_Id_3291,Bike,3,Business,29,3,1:10:37 PM,29,3,1:11:12 PM,29,3,1:24:37 PM,29,3,1:49:50 PM,29,3,1:49:55 PM,12,20.9,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_788,5
-Order_No_20352,User_Id_136,Bike,3,Business,26,5,9:49:04 AM,26,5,9:52:02 AM,26,5,10:04:20 AM,26,5,10:16:43 AM,26,5,10:56:16 AM,11,,,-1.2965045,36.7913036,-1.3254522,36.721658399999995,Rider_Id_559,2373
-Order_No_3528,User_Id_2981,Bike,1,Personal,2,2,3:04:03 PM,2,2,3:36:15 PM,2,2,4:18:51 PM,2,2,4:25:29 PM,2,2,5:05:21 PM,10,26.3,,-1.2750798,36.8451936,-1.2571472,36.795063299999995,Rider_Id_755,2392
-Order_No_6676,User_Id_3610,Bike,1,Personal,17,5,2:24:12 PM,17,5,2:25:19 PM,17,5,2:35:17 PM,17,5,2:38:11 PM,17,5,3:06:41 PM,6,20.0,,-1.2853860000000001,36.8304605,-1.2940713,36.7890758,Rider_Id_552,1710
-Order_No_5368,User_Id_552,Bike,3,Business,14,4,1:59:42 PM,14,4,2:07:15 PM,14,4,2:55:15 PM,14,4,3:06:31 PM,14,4,3:58:56 PM,17,28.0,,-1.279395,36.825364,-1.3632162,36.9108253,Rider_Id_452,3145
-Order_No_23286,User_Id_633,Bike,3,Business,18,5,9:35:05 AM,18,5,9:44:19 AM,18,5,10:02:17 AM,18,5,10:06:57 AM,18,5,10:30:31 AM,5,20.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_436,1414
-Order_No_20840,User_Id_928,Bike,3,Business,20,1,10:53:25 AM,20,1,10:56:55 AM,20,1,10:58:12 AM,20,1,11:18:28 AM,20,1,11:32:19 AM,7,,,-1.2551895,36.7822034,-1.290724,36.81947,Rider_Id_438,831
-Order_No_21641,User_Id_3647,Bike,3,Business,12,1,3:59:15 PM,12,1,4:04:11 PM,12,1,4:11:16 PM,12,1,4:13:52 PM,12,1,4:24:41 PM,4,26.7,,-1.2726389999999999,36.794723,-1.2569503,36.78347279999999,Rider_Id_56,649
-Order_No_12295,User_Id_506,Bike,3,Business,26,2,11:31:13 AM,26,2,11:47:54 AM,26,2,12:20:36 PM,26,2,12:23:51 PM,26,2,1:31:06 PM,28,26.2,,-1.3939049,36.756519899999994,-1.2321358,36.872784700000004,Rider_Id_917,4035
-Order_No_17002,User_Id_635,Bike,3,Business,30,4,11:26:48 AM,30,4,11:56:57 AM,30,4,12:05:54 PM,30,4,12:22:23 PM,30,4,12:47:18 PM,7,,,-1.2602337,36.79905479999999,-1.2922966,36.8374042,Rider_Id_732,1495
-Order_No_965,User_Id_649,Bike,3,Business,12,2,9:42:43 AM,12,2,9:43:03 AM,12,2,9:48:15 AM,12,2,10:19:18 AM,12,2,11:02:41 AM,22,21.0,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_177,2603
-Order_No_14344,User_Id_432,Bike,3,Business,30,4,4:27:01 PM,30,4,4:28:35 PM,30,4,4:41:27 PM,30,4,4:49:48 PM,30,4,5:14:50 PM,9,,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_770,1502
-Order_No_17455,User_Id_1340,Bike,3,Business,26,2,12:31:58 PM,26,2,12:33:10 PM,26,2,12:38:14 PM,26,2,1:07:02 PM,26,2,1:15:19 PM,3,26.4,,-1.253966,36.7987692,-1.2707178999999997,36.7979713,Rider_Id_205,497
-Order_No_15701,User_Id_517,Bike,3,Business,18,6,9:59:58 AM,18,6,10:00:14 AM,18,6,10:05:32 AM,18,6,10:13:25 AM,18,6,10:21:50 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,505
-Order_No_19649,User_Id_1724,Bike,3,Business,3,1,3:23:05 PM,3,1,4:25:11 PM,3,1,4:53:14 PM,3,1,5:00:49 PM,3,1,5:42:44 PM,15,22.6,,-1.2551895,36.7822034,-1.3267688999999998,36.8756255,Rider_Id_427,2515
-Order_No_24460,User_Id_1363,Bike,3,Business,27,4,2:34:02 PM,27,4,2:38:00 PM,27,4,2:51:28 PM,27,4,2:53:28 PM,27,4,3:24:57 PM,10,25.0,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_612,1889
-Order_No_10865,User_Id_3283,Bike,3,Business,12,2,9:50:18 AM,12,2,10:12:21 AM,12,2,10:36:23 AM,12,2,10:43:31 AM,12,2,10:53:25 AM,4,24.5,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_268,594
-Order_No_23507,User_Id_2360,Bike,3,Business,26,2,10:52:45 AM,26,2,10:53:10 AM,26,2,11:03:14 AM,26,2,11:07:45 AM,26,2,11:22:50 AM,8,27.5,,-1.2551895,36.7822034,-1.2897215,36.7945959,Rider_Id_576,905
-Order_No_28241,User_Id_2400,Bike,2,Personal,21,1,1:03:28 PM,21,1,1:04:52 PM,21,1,1:14:43 PM,21,1,1:17:10 PM,21,1,1:25:43 PM,5,19.9,,-1.280951,36.7990799,-1.2887345,36.8225613,Rider_Id_441,513
-Order_No_25354,User_Id_1329,Bike,3,Business,8,5,12:12:32 PM,8,5,12:39:36 PM,8,5,12:49:14 PM,8,5,12:51:37 PM,8,5,1:28:52 PM,12,28.5,,-1.273056,36.811298,-1.2239241,36.8707489,Rider_Id_210,2235
-Order_No_25928,User_Id_704,Bike,3,Business,29,1,2:59:42 PM,29,1,2:59:47 PM,29,1,2:59:53 PM,29,1,3:02:51 PM,29,1,3:32:38 PM,18,24.4,,-1.2620912,36.8013562,-1.2860183,36.8975338,Rider_Id_408,1787
-Order_No_505,User_Id_1221,Bike,3,Personal,1,3,11:08:56 AM,1,3,11:09:45 AM,1,3,11:19:14 AM,1,3,11:35:45 AM,1,3,12:21:25 PM,18,19.9,,-1.2614329,36.8094365,-1.1800438,36.885822999999995,Rider_Id_107,2740
-Order_No_14099,User_Id_3283,Bike,3,Business,6,3,3:48:13 PM,6,3,3:53:21 PM,6,3,4:09:40 PM,6,3,4:20:27 PM,6,3,4:34:14 PM,7,27.4,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_156,827
-Order_No_21868,User_Id_867,Bike,3,Business,10,3,8:33:26 AM,10,3,8:37:40 AM,10,3,8:57:39 AM,10,3,9:10:42 AM,10,3,9:51:50 AM,9,21.7,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_155,2468
-Order_No_9341,User_Id_2553,Bike,1,Personal,11,1,5:19:58 PM,11,1,5:50:13 PM,11,1,5:59:47 PM,11,1,6:33:07 PM,11,1,6:40:49 PM,8,21.2,,-1.284382,36.774604499999995,-1.278612,36.8195876,Rider_Id_268,462
-Order_No_28085,User_Id_3599,Bike,3,Business,22,4,1:23:05 PM,22,4,1:23:18 PM,22,4,1:38:19 PM,22,4,2:04:15 PM,22,4,2:28:47 PM,12,26.6,,-1.3302996,36.8707935,-1.2858786,36.7888062,Rider_Id_268,1472
-Order_No_15129,User_Id_633,Bike,3,Business,12,6,12:37:33 PM,12,6,12:38:23 PM,12,6,12:44:39 PM,12,6,12:46:09 PM,12,6,12:55:20 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_518,551
-Order_No_9761,User_Id_1500,Bike,3,Business,17,5,1:47:00 PM,17,5,1:58:26 PM,17,5,2:17:33 PM,17,5,2:49:38 PM,17,5,3:13:06 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_868,1408
-Order_No_14710,User_Id_3426,Bike,3,Business,17,7,11:10:38 AM,17,7,11:16:07 AM,17,7,11:32:45 AM,17,7,12:07:15 PM,17,7,12:20:12 PM,9,28.5,,-1.290894,36.822971,-1.3012008,36.764868,Rider_Id_119,777
-Order_No_14503,User_Id_3547,Bike,3,Business,6,3,9:52:18 AM,6,3,9:52:38 AM,6,3,9:54:43 AM,6,3,10:09:21 AM,6,3,10:29:06 AM,7,16.9,,-1.2760915000000002,36.8081029,-1.2761408,36.7710839,Rider_Id_247,1185
-Order_No_13264,User_Id_635,Bike,3,Business,13,3,3:13:01 PM,13,3,3:13:26 PM,13,3,3:22:54 PM,13,3,4:01:22 PM,13,3,4:23:58 PM,10,27.1,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_283,1356
-Order_No_1491,User_Id_2154,Bike,3,Business,26,6,1:30:43 PM,26,6,1:31:15 PM,26,6,2:03:57 PM,26,6,2:20:29 PM,26,6,3:06:21 PM,16,23.8,,-1.3169591,36.7000029,-1.2885528999999998,36.816014100000004,Rider_Id_122,2752
-Order_No_11107,User_Id_1431,Bike,3,Personal,6,5,10:53:31 AM,6,5,10:55:48 AM,6,5,11:14:04 AM,6,5,11:19:35 AM,6,5,11:51:23 AM,10,20.5,,-1.3187067,36.8288886,-1.2779711,36.782234499999994,Rider_Id_131,1908
-Order_No_19685,User_Id_193,Bike,2,Personal,25,6,5:49:38 PM,25,6,5:49:52 PM,25,6,6:00:36 PM,25,6,6:03:35 PM,25,6,6:33:36 PM,16,,,-1.276245,36.836329299999996,-1.2672045,36.717206899999994,Rider_Id_622,1801
-Order_No_5379,User_Id_2956,Bike,3,Business,2,4,1:31:32 PM,2,4,1:41:15 PM,2,4,1:44:47 PM,2,4,1:48:31 PM,2,4,2:04:51 PM,6,,,-1.2584143,36.8048002,-1.2880128999999998,36.8308097,Rider_Id_250,980
-Order_No_2871,User_Id_540,Bike,3,Business,5,3,2:48:34 PM,5,3,3:16:51 PM,5,3,3:36:26 PM,5,3,3:43:31 PM,5,3,3:52:31 PM,5,25.1,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_857,540
-Order_No_21086,User_Id_3189,Bike,3,Personal,31,5,10:56:14 AM,31,5,10:57:32 AM,31,5,11:07:01 AM,31,5,11:31:20 AM,31,5,11:53:43 AM,11,21.9,,-1.2784798000000002,36.8315208,-1.2513528999999999,36.752171999999995,Rider_Id_206,1343
-Order_No_6933,User_Id_1044,Bike,2,Personal,18,6,1:39:21 PM,18,6,1:39:35 PM,18,6,1:44:49 PM,18,6,2:01:29 PM,18,6,2:06:34 PM,2,20.6,,-1.2882603000000001,36.818882099999996,-1.284835,36.8152125,Rider_Id_45,305
-Order_No_10117,User_Id_3187,Bike,2,Personal,9,3,8:58:07 AM,9,3,8:58:30 AM,9,3,9:13:37 AM,9,3,9:16:03 AM,9,3,9:48:10 AM,13,17.7,0.8,-1.3206958,36.8769553,-1.306245,36.819297799999994,Rider_Id_285,1927
-Order_No_6856,User_Id_3470,Bike,3,Business,30,5,3:01:17 PM,30,5,3:08:02 PM,30,5,3:18:08 PM,30,5,3:26:13 PM,30,5,4:13:38 PM,5,24.8,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_436,2845
-Order_No_12572,User_Id_3647,Bike,3,Business,20,4,9:42:26 AM,20,4,11:07:41 AM,20,4,11:16:12 AM,20,4,11:18:35 AM,20,4,11:44:37 AM,7,24.6,,-1.2726389999999999,36.794723,-1.2816546999999998,36.8283405,Rider_Id_156,1562
-Order_No_22699,User_Id_3283,Bike,3,Business,18,4,12:30:32 PM,18,4,12:38:50 PM,18,4,12:58:29 PM,18,4,1:07:29 PM,18,4,1:21:24 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_924,835
-Order_No_7541,User_Id_914,Bike,3,Business,8,4,2:21:27 PM,8,4,2:23:08 PM,8,4,2:36:09 PM,8,4,2:53:10 PM,8,4,3:49:32 PM,21,27.1,,-1.400779,36.763071000000004,-1.2670213,36.7994482,Rider_Id_845,3382
-Order_No_24125,User_Id_3201,Bike,3,Business,14,1,3:18:29 PM,14,1,3:25:34 PM,14,1,3:33:58 PM,14,1,3:59:26 PM,14,1,4:14:40 PM,5,25.6,,-1.2793686000000002,36.82592329999999,-1.2584143,36.8048002,Rider_Id_733,914
-Order_No_6885,User_Id_2457,Bike,3,Business,26,5,1:50:12 PM,26,5,1:52:35 PM,26,5,2:07:39 PM,26,5,2:11:56 PM,26,5,2:47:27 PM,8,,,-1.2551895,36.7822034,-1.3057159999999999,36.809882,Rider_Id_578,2131
-Order_No_8513,User_Id_393,Bike,3,Business,12,2,12:30:40 PM,12,2,12:30:50 PM,12,2,12:34:40 PM,12,2,12:41:54 PM,12,2,12:51:15 PM,4,24.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_88,561
-Order_No_476,User_Id_830,Bike,3,Business,5,1,5:48:45 PM,5,1,5:52:01 PM,5,1,5:53:43 PM,5,1,5:58:33 PM,5,1,6:19:10 PM,8,24.3,,-1.2551895,36.7822034,-1.2904016,36.82263029999999,Rider_Id_31,1237
-Order_No_14759,User_Id_3291,Bike,3,Business,26,5,9:07:28 AM,26,5,9:08:12 AM,26,5,9:09:09 AM,26,5,9:37:07 AM,26,5,9:53:30 AM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_402,983
-Order_No_1148,User_Id_767,Bike,1,Personal,25,5,9:06:47 AM,25,5,9:09:11 AM,25,5,9:30:22 AM,25,5,9:35:14 AM,25,5,10:09:31 AM,9,17.0,,-1.2657709,36.730574600000004,-1.2773014,36.7773015,Rider_Id_412,2057
-Order_No_5961,User_Id_913,Bike,3,Personal,4,4,12:30:51 PM,4,4,12:32:35 PM,4,4,12:41:10 PM,4,4,12:45:58 PM,4,4,1:02:36 PM,6,25.5,,-1.2692689,36.8055169,-1.3034413,36.777010600000004,Rider_Id_903,998
-Order_No_22889,User_Id_478,Bike,3,Business,13,1,9:56:06 AM,13,1,9:56:52 AM,13,1,10:10:20 AM,13,1,10:13:11 AM,13,1,10:29:45 AM,9,15.4,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_415,994
-Order_No_10231,User_Id_3091,Bike,1,Business,3,4,9:49:32 AM,3,4,9:51:16 AM,3,4,10:19:59 AM,3,4,10:29:32 AM,3,4,10:56:23 AM,12,17.2,16.1,-1.2843582,36.82298839999999,-1.2817919,36.7351687,Rider_Id_742,1611
-Order_No_8920,User_Id_1927,Bike,3,Business,2,2,11:48:19 AM,2,2,11:52:37 AM,2,2,12:05:59 PM,2,2,12:16:32 PM,2,2,12:36:43 PM,13,26.3,,-1.2765736,36.851364600000004,-1.3472066,36.7692638,Rider_Id_377,1211
-Order_No_17889,User_Id_928,Bike,3,Business,1,2,11:01:43 AM,1,2,11:02:24 AM,1,2,11:24:53 AM,1,2,11:34:13 AM,1,2,12:04:52 PM,10,21.8,,-1.2559559999999999,36.789867,-1.3064585,36.8119065,Rider_Id_479,1839
-Order_No_25045,User_Id_788,Bike,3,Business,27,3,2:04:44 PM,27,3,2:11:01 PM,27,3,2:23:42 PM,27,3,2:30:44 PM,27,3,2:58:33 PM,12,,,-1.2771683,36.820987200000005,-1.225322,36.80855,Rider_Id_844,1669
-Order_No_12101,User_Id_1304,Bike,3,Business,5,5,12:12:30 PM,5,5,12:14:02 PM,5,5,12:32:59 PM,5,5,12:35:30 PM,5,5,1:24:34 PM,7,23.7,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_279,2944
-Order_No_25447,User_Id_2509,Bike,3,Business,27,4,11:04:32 AM,27,4,11:04:57 AM,27,4,11:38:42 AM,27,4,11:49:13 AM,27,4,12:10:45 PM,16,23.0,,-1.2302228000000002,36.8692935,-1.2571472,36.795063299999995,Rider_Id_62,1292
-Order_No_12060,User_Id_2066,Bike,3,Business,28,4,4:35:08 PM,28,4,4:47:07 PM,28,4,5:06:43 PM,28,4,5:09:13 PM,28,4,5:55:58 PM,8,27.9,,-1.3210188999999999,36.841055100000005,-1.270518,36.8349244,Rider_Id_343,2805
-Order_No_18740,User_Id_2258,Bike,3,Business,24,3,10:06:11 AM,24,3,10:38:09 AM,24,3,11:01:25 AM,24,3,11:04:28 AM,24,3,11:50:55 AM,5,19.9,,-1.2571472,36.795063299999995,-1.2793642,36.8295717,Rider_Id_171,2787
-Order_No_24009,User_Id_3474,Bike,3,Personal,3,3,3:26:56 PM,3,3,3:36:26 PM,3,3,3:53:53 PM,3,3,4:06:52 PM,3,3,4:22:21 PM,8,27.0,,-1.2942764,36.7969749,-1.2653185,36.8358841,Rider_Id_427,929
-Order_No_23323,User_Id_3222,Bike,3,Business,10,5,8:59:05 AM,10,5,9:01:21 AM,10,5,9:08:48 AM,10,5,9:09:51 AM,10,5,9:31:16 AM,8,,,-1.2551895,36.7822034,-1.2998903,36.8179213,Rider_Id_70,1285
-Order_No_25725,User_Id_412,Bike,3,Business,4,1,3:12:22 PM,4,1,3:12:56 PM,4,1,3:14:49 PM,4,1,3:18:06 PM,4,1,3:50:59 PM,7,28.5,,-1.303596,36.778378000000004,-1.266837,36.799248999999996,Rider_Id_605,1973
-Order_No_4622,User_Id_2763,Bike,3,Business,4,1,3:53:54 PM,4,1,4:02:39 PM,4,1,4:06:30 PM,4,1,4:08:35 PM,4,1,4:59:54 PM,9,22.8,,-1.2553615,36.789537599999996,-1.300921,36.828195,Rider_Id_742,3079
-Order_No_10517,User_Id_680,Bike,2,Personal,12,5,10:20:56 AM,12,5,10:53:47 AM,12,5,11:26:05 AM,12,5,11:36:55 AM,12,5,11:50:18 AM,5,24.3,,-1.2883346000000002,36.8148489,-1.2762775,36.8173551,Rider_Id_299,803
-Order_No_1898,User_Id_1589,Bike,3,Business,5,3,12:58:12 PM,5,3,12:58:30 PM,5,3,1:55:17 PM,5,3,2:01:27 PM,5,3,2:32:46 PM,7,19.2,0.1,-1.207546,36.788708899999996,-1.225322,36.80855,Rider_Id_689,1879
-Order_No_22091,User_Id_3080,Bike,1,Business,1,5,1:40:18 PM,1,5,1:40:36 PM,1,5,1:46:40 PM,1,5,1:56:32 PM,1,5,2:21:28 PM,9,24.4,,-1.2559559999999999,36.772901,-1.2388687,36.7529026,Rider_Id_956,1496
-Order_No_25187,User_Id_2112,Bike,3,Business,25,4,12:41:21 PM,25,4,1:26:48 PM,25,4,1:29:36 PM,25,4,1:38:32 PM,25,4,1:54:52 PM,12,,,-1.28878,36.8168312,-1.2156006000000001,36.8916865,Rider_Id_411,980
-Order_No_3380,User_Id_1340,Bike,3,Business,25,5,11:06:37 AM,25,5,11:08:21 AM,25,5,11:23:57 AM,25,5,11:26:01 AM,25,5,11:53:24 AM,9,26.5,,-1.2707178999999997,36.7979713,-1.3142268999999998,36.839245,Rider_Id_660,1643
-Order_No_15336,User_Id_3763,Bike,3,Business,20,5,10:34:34 AM,20,5,10:35:35 AM,20,5,10:47:45 AM,20,5,10:51:37 AM,20,5,11:16:44 AM,5,18.6,,-1.2671001,36.7977185,-1.2866908000000001,36.82612329999999,Rider_Id_159,1507
-Order_No_27453,User_Id_1298,Bike,1,Personal,5,4,9:03:49 AM,5,4,9:04:03 AM,5,4,9:10:56 AM,5,4,9:23:46 AM,5,4,9:45:45 AM,10,,,-1.2633493999999998,36.7959696,-1.308819,36.8421201,Rider_Id_379,1319
-Order_No_4540,User_Id_3404,Bike,3,Business,29,6,12:10:24 PM,29,6,12:10:50 PM,29,6,12:13:44 PM,29,6,12:18:55 PM,29,6,12:34:25 PM,5,24.0,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_275,930
-Order_No_3743,User_Id_3264,Bike,3,Business,4,4,2:51:20 PM,4,4,2:57:25 PM,4,4,3:01:39 PM,4,4,3:11:17 PM,4,4,3:25:21 PM,6,27.3,,-1.2551895,36.7822034,-1.2882850000000001,36.815912,Rider_Id_61,844
-Order_No_10547,User_Id_2931,Bike,3,Business,21,1,12:08:36 PM,21,1,12:44:31 PM,21,1,1:04:59 PM,21,1,1:38:18 PM,21,1,2:35:17 PM,23,27.6,,-1.3433181,36.9138018,-1.2452664,36.802854100000005,Rider_Id_14,3419
-Order_No_4178,User_Id_706,Bike,3,Business,7,4,10:27:22 AM,7,4,10:28:02 AM,7,4,10:41:22 AM,7,4,11:38:13 AM,7,4,12:03:28 PM,12,24.1,,-1.3559093,36.910045200000006,-1.3077869,36.844320700000004,Rider_Id_863,1515
-Order_No_9717,User_Id_2238,Bike,1,Personal,30,4,10:33:34 AM,30,4,10:33:57 AM,30,4,10:34:25 AM,30,4,10:47:45 AM,30,4,11:11:37 AM,14,20.2,,-1.349133,36.759958600000004,-1.3006208000000001,36.77274439999999,Rider_Id_881,1432
-Order_No_24583,User_Id_2102,Bike,3,Business,26,2,9:01:35 AM,26,2,9:01:54 AM,26,2,9:56:17 AM,26,2,10:04:41 AM,26,2,10:33:26 AM,15,,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_430,1725
-Order_No_10992,User_Id_909,Bike,3,Business,10,5,4:03:11 PM,10,5,4:05:43 PM,10,5,4:48:19 PM,10,5,5:08:08 PM,10,5,5:55:23 PM,13,21.0,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_519,2835
-Order_No_28268,User_Id_2523,Bike,3,Business,20,2,12:50:23 PM,20,2,12:52:33 PM,20,2,1:15:03 PM,20,2,1:18:06 PM,20,2,2:24:18 PM,26,19.8,,-1.2551895,36.7822034,-1.1605816,36.8977958,Rider_Id_70,3972
-Order_No_15045,User_Id_2636,Bike,1,Personal,29,5,12:53:43 PM,29,5,1:00:36 PM,29,5,1:20:25 PM,29,5,1:21:33 PM,29,5,1:28:30 PM,2,19.9,,-1.2979631,36.7869859,-1.2898353999999999,36.787270299999996,Rider_Id_312,417
-Order_No_25966,User_Id_265,Bike,3,Business,15,5,11:56:51 AM,15,5,11:57:46 AM,15,5,12:12:31 PM,15,5,12:17:04 PM,15,5,12:44:45 PM,14,25.7,,-1.300921,36.828195,-1.3374393999999998,36.7339115,Rider_Id_675,1661
-Order_No_11825,User_Id_2044,Bike,2,Personal,5,6,5:26:54 PM,5,6,5:27:28 PM,5,6,5:44:04 PM,5,6,5:50:13 PM,5,6,5:55:50 PM,1,22.6,,-1.2615584,36.814950599999996,-1.2517392,36.82496079999999,Rider_Id_56,337
-Order_No_7729,User_Id_868,Bike,3,Business,30,2,1:26:14 PM,30,2,1:28:14 PM,30,2,1:36:45 PM,30,2,1:42:54 PM,30,2,2:01:07 PM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_484,1093
-Order_No_18003,User_Id_1329,Bike,3,Business,14,2,11:13:43 AM,14,2,11:14:10 AM,14,2,11:30:11 AM,14,2,11:44:02 AM,14,2,12:24:33 PM,12,21.6,,-1.3233955,36.856294399999996,-1.252973,36.8165107,Rider_Id_308,2431
-Order_No_20189,User_Id_136,Bike,3,Business,23,6,12:45:11 PM,23,6,12:45:23 PM,23,6,12:47:28 PM,23,6,1:00:00 PM,23,6,1:12:24 PM,2,26.4,,-1.2531983,36.8069669,-1.2629039,36.8048286,Rider_Id_547,744
-Order_No_15319,User_Id_3398,Bike,3,Business,2,2,2:53:26 PM,2,2,2:53:35 PM,2,2,3:01:09 PM,2,2,3:23:12 PM,2,2,4:21:56 PM,26,26.4,,-1.2297202,36.881950700000004,-1.3169591,36.7000029,Rider_Id_449,3524
-Order_No_22668,User_Id_393,Bike,3,Business,6,1,1:49:36 PM,6,1,1:50:20 PM,6,1,1:50:27 PM,6,1,2:04:01 PM,6,1,2:26:47 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_386,1366
-Order_No_18011,User_Id_2627,Bike,3,Business,11,2,3:11:55 PM,11,2,3:15:15 PM,11,2,3:32:07 PM,11,2,3:38:15 PM,11,2,3:52:33 PM,9,22.5,,-1.3014461000000002,36.7661381,-1.2571472,36.795063299999995,Rider_Id_279,858
-Order_No_13283,User_Id_20,Bike,3,Business,20,2,4:30:34 PM,20,2,4:43:24 PM,20,2,4:58:01 PM,20,2,5:03:23 PM,20,2,5:51:30 PM,13,24.0,,-1.2628638,36.8070399,-1.3343128999999998,36.863711200000004,Rider_Id_38,2887
-Order_No_8908,User_Id_1245,Bike,3,Business,12,3,9:16:44 AM,12,3,9:19:55 AM,12,3,9:23:44 AM,12,3,9:40:28 AM,12,3,10:13:47 AM,17,16.5,,-1.3198615,36.7110323,-1.2837254999999999,36.8220797,Rider_Id_726,1999
-Order_No_7303,User_Id_1471,Bike,3,Business,29,5,1:43:30 PM,29,5,1:46:14 PM,29,5,2:14:38 PM,29,5,2:26:04 PM,29,5,2:43:00 PM,8,21.1,,-1.2551895,36.7822034,-1.2864746999999999,36.797388399999996,Rider_Id_101,1016
-Order_No_20960,User_Id_2330,Bike,3,Business,26,5,12:52:29 PM,26,5,12:52:53 PM,26,5,12:57:26 PM,26,5,1:04:28 PM,26,5,1:24:25 PM,9,,,-1.2642903,36.80021370000001,-1.3015197,36.7658456,Rider_Id_484,1197
-Order_No_8564,User_Id_1761,Bike,3,Business,8,4,9:45:00 AM,8,4,10:00:15 AM,8,4,10:14:17 AM,8,4,10:22:31 AM,8,4,11:15:12 AM,26,20.4,,-1.3688433,36.7402114,-1.332624,36.913187,Rider_Id_387,3161
-Order_No_15962,User_Id_2778,Bike,2,Personal,3,5,9:17:28 AM,3,5,9:27:33 AM,3,5,9:41:01 AM,3,5,9:43:33 AM,3,5,9:58:49 AM,6,17.4,,-1.3038912,36.7961524,-1.2846475,36.8275903,Rider_Id_754,916
-Order_No_12117,User_Id_2523,Bike,3,Business,21,4,12:00:13 PM,21,4,12:00:48 PM,21,4,12:02:06 PM,21,4,12:19:12 PM,21,4,12:50:22 PM,11,28.0,,-1.2551895,36.7822034,-1.2310607,36.803577600000004,Rider_Id_26,1870
-Order_No_23570,User_Id_91,Bike,3,Personal,15,3,3:11:07 PM,15,3,3:11:44 PM,15,3,3:58:16 PM,15,3,4:13:59 PM,15,3,4:28:13 PM,9,25.8,,-1.2990731000000002,36.848805799999994,-1.2809133999999998,36.8145375,Rider_Id_819,854
-Order_No_13286,User_Id_3149,Bike,3,Business,10,5,3:08:19 PM,10,5,3:09:45 PM,10,5,3:14:12 PM,10,5,3:33:35 PM,10,5,3:44:25 PM,6,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_211,650
-Order_No_7025,User_Id_3203,Bike,3,Business,16,1,10:56:20 AM,16,1,10:56:37 AM,16,1,11:16:31 AM,16,1,11:24:54 AM,16,1,11:44:44 AM,12,19.2,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_116,1190
-Order_No_27653,User_Id_640,Bike,3,Business,15,3,2:50:44 PM,15,3,2:54:07 PM,15,3,3:11:14 PM,15,3,3:19:25 PM,15,3,3:48:44 PM,6,26.0,,-1.2857524,36.819680600000005,-1.2969741000000001,36.7856612,Rider_Id_158,1759
-Order_No_4561,User_Id_190,Bike,3,Business,7,2,1:20:49 PM,7,2,1:21:08 PM,7,2,1:22:25 PM,7,2,1:50:35 PM,7,2,2:13:41 PM,9,,,-1.2820996999999998,36.8163378,-1.3270347,36.866736200000005,Rider_Id_564,1386
-Order_No_5655,User_Id_2309,Bike,2,Personal,1,1,2:46:47 PM,1,1,3:20:25 PM,1,1,3:34:04 PM,1,1,3:56:52 PM,1,1,4:33:28 PM,20,25.6,,-1.3081770000000001,36.879807,-1.2248308,36.801257299999996,Rider_Id_584,2196
-Order_No_2514,User_Id_323,Bike,3,Business,31,3,11:56:40 AM,31,3,11:56:59 AM,31,3,12:00:32 PM,31,3,12:36:33 PM,31,3,1:13:05 PM,21,24.8,,-1.2551895,36.7822034,-1.3632162,36.9108253,Rider_Id_38,2192
-Order_No_18191,User_Id_2412,Bike,3,Business,10,6,4:12:54 PM,10,6,4:13:01 PM,10,6,4:45:33 PM,10,6,4:54:20 PM,10,6,5:31:27 PM,17,24.5,,-1.2297202,36.881950700000004,-1.159322,36.933658,Rider_Id_260,2227
-Order_No_25258,User_Id_3442,Bike,1,Personal,18,4,4:09:54 PM,18,4,4:58:34 PM,18,4,5:11:50 PM,18,4,5:35:00 PM,18,4,5:47:50 PM,6,23.4,,-1.2877303999999998,36.8294383,-1.2611642,36.7949932,Rider_Id_213,770
-Order_No_21498,User_Id_2271,Bike,3,Business,4,3,10:53:36 AM,4,3,10:53:44 AM,4,3,10:53:49 AM,4,3,11:19:45 AM,4,3,11:41:31 AM,14,17.0,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_900,1306
-Order_No_3927,User_Id_2112,Bike,3,Business,26,6,5:11:00 PM,26,6,6:20:39 PM,26,6,6:30:14 PM,26,6,6:43:45 PM,26,6,6:55:07 PM,5,22.3,,-1.2297202,36.874550799999994,-1.2156006000000001,36.8916865,Rider_Id_79,682
-Order_No_7681,User_Id_3264,Bike,3,Business,31,3,4:37:46 PM,31,3,4:40:37 PM,31,3,4:41:03 PM,31,3,4:45:09 PM,31,3,5:23:07 PM,14,24.4,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_38,2278
-Order_No_3576,User_Id_478,Bike,3,Business,29,3,11:50:59 AM,29,3,11:51:36 AM,29,3,12:02:38 PM,29,3,12:43:10 PM,29,3,12:48:59 PM,7,18.3,,-1.2924188,36.802439299999996,-1.2571472,36.795063299999995,Rider_Id_217,349
-Order_No_21101,User_Id_3440,Bike,1,Personal,8,3,8:43:09 AM,8,3,8:43:21 AM,8,3,8:54:40 AM,8,3,9:04:28 AM,8,3,9:24:27 AM,17,17.5,,-1.2637631999999999,36.820668299999994,-1.1926735000000002,36.931181200000005,Rider_Id_857,1199
-Order_No_2364,User_Id_3669,Bike,3,Business,15,1,3:05:02 PM,15,1,3:05:33 PM,15,1,3:07:17 PM,15,1,3:33:09 PM,15,1,3:33:25 PM,4,,,-1.2551895,36.7822034,-1.2657464,36.780114399999995,Rider_Id_436,16
-Order_No_20397,User_Id_1829,Bike,3,Personal,19,3,12:00:00 PM,19,3,12:00:41 PM,19,3,12:11:43 PM,19,3,12:18:38 PM,19,3,12:35:38 PM,8,,,-1.3056847,36.8101341,-1.2590031000000002,36.8078168,Rider_Id_648,1020
-Order_No_4866,User_Id_3426,Bike,3,Business,31,3,9:45:59 AM,31,3,9:50:46 AM,31,3,10:02:53 AM,31,3,10:04:37 AM,31,3,11:08:10 AM,10,19.3,,-1.3025826,36.7670807,-1.2517841,36.801338799999996,Rider_Id_596,3813
-Order_No_24062,User_Id_1103,Bike,3,Business,3,4,2:58:47 PM,3,4,2:59:55 PM,3,4,3:14:25 PM,3,4,3:27:29 PM,3,4,3:39:48 PM,7,25.8,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_404,739
-Order_No_1733,User_Id_1687,Bike,3,Business,24,5,12:03:27 PM,24,5,12:04:53 PM,24,5,12:10:38 PM,24,5,12:18:30 PM,24,5,1:06:19 PM,6,21.3,,-1.2626574,36.8014034,-1.2252229,36.8035587,Rider_Id_660,2869
-Order_No_13526,User_Id_3372,Bike,3,Business,14,6,10:57:05 AM,14,6,10:57:15 AM,14,6,11:18:44 AM,14,6,11:24:09 AM,14,6,12:07:47 PM,11,17.8,,-1.2969741000000001,36.7856612,-1.3339461000000001,36.821912899999994,Rider_Id_722,2618
-Order_No_24641,User_Id_3264,Bike,3,Business,12,5,2:48:05 PM,12,5,2:49:43 PM,12,5,3:17:52 PM,12,5,3:19:18 PM,12,5,3:41:06 PM,9,,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_99,1308
-Order_No_5603,User_Id_1245,Bike,3,Business,4,4,10:21:16 AM,4,4,10:21:41 AM,4,4,10:33:37 AM,4,4,10:39:58 AM,4,4,11:13:19 AM,17,25.6,,-1.2602337,36.79905479999999,-1.3198615,36.7110323,Rider_Id_793,2001
-Order_No_21238,User_Id_2587,Bike,3,Business,12,5,3:04:04 PM,12,5,3:16:28 PM,12,5,3:52:00 PM,12,5,4:03:14 PM,12,5,4:17:04 PM,2,27.3,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_656,830
-Order_No_12706,User_Id_1500,Bike,3,Business,14,1,12:12:18 PM,14,1,12:12:28 PM,14,1,12:16:36 PM,14,1,12:20:28 PM,14,1,12:34:55 PM,9,23.3,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_932,867
-Order_No_8735,User_Id_3291,Bike,3,Business,6,4,1:52:23 PM,6,4,1:52:53 PM,6,4,2:08:11 PM,6,4,2:13:45 PM,6,4,2:55:59 PM,11,21.4,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_419,2534
-Order_No_26710,User_Id_3647,Bike,3,Business,31,4,9:16:23 AM,31,4,9:18:08 AM,31,4,9:47:59 AM,31,4,9:49:03 AM,31,4,10:21:41 AM,5,20.6,,-1.2726389999999999,36.794723,-1.2548773000000002,36.80773189999999,Rider_Id_953,1958
-Order_No_4098,User_Id_2956,Bike,3,Business,13,3,10:20:01 AM,13,3,10:20:31 AM,13,3,10:46:00 AM,13,3,10:58:08 AM,13,3,11:23:23 AM,9,,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_831,1515
-Order_No_28023,User_Id_261,Bike,2,Personal,17,5,10:06:26 AM,17,5,10:07:02 AM,17,5,10:12:10 AM,17,5,10:29:53 AM,17,5,10:48:45 AM,8,14.2,,-1.2675332000000001,36.808561,-1.296797,36.776452500000005,Rider_Id_882,1132
-Order_No_14615,User_Id_3647,Bike,3,Business,29,4,3:40:21 PM,29,4,3:47:44 PM,29,4,3:59:20 PM,29,4,4:02:32 PM,29,4,4:29:12 PM,8,24.9,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_937,1600
-Order_No_20084,User_Id_3283,Bike,3,Business,2,6,9:45:29 AM,2,6,9:45:39 AM,2,6,9:52:31 AM,2,6,9:59:46 AM,2,6,10:08:06 AM,4,21.1,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_427,500
-Order_No_2732,User_Id_2532,Bike,3,Business,13,3,2:05:54 PM,13,3,2:11:17 PM,13,3,2:16:06 PM,13,3,2:20:33 PM,13,3,3:34:45 PM,9,27.1,,-1.2551895,36.7822034,-1.2942,36.7777614,Rider_Id_411,4452
-Order_No_6055,User_Id_295,Bike,3,Business,31,1,11:18:28 AM,31,1,11:39:44 AM,31,1,11:39:52 AM,31,1,11:45:28 AM,31,1,11:56:53 AM,6,20.9,,-1.286925,36.821915999999995,-1.2967108,36.787113500000004,Rider_Id_942,685
-Order_No_2329,User_Id_393,Bike,3,Business,25,1,9:59:28 AM,25,1,9:59:47 AM,25,1,10:07:11 AM,25,1,10:24:20 AM,25,1,10:48:52 AM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_411,1472
-Order_No_9174,User_Id_1402,Bike,3,Business,24,5,10:31:29 AM,24,5,11:32:39 AM,24,5,11:45:17 AM,24,5,12:05:05 PM,24,5,1:17:10 PM,16,21.3,,-1.3562374,36.904295399999995,-1.2813012,36.832396200000005,Rider_Id_418,4325
-Order_No_26302,User_Id_3145,Bike,3,Business,9,4,10:05:38 AM,9,4,10:05:53 AM,9,4,10:18:40 AM,9,4,10:22:30 AM,9,4,10:51:59 AM,11,,,-1.2726389999999999,36.794723,-1.22155,36.7848476,Rider_Id_147,1769
-Order_No_5386,User_Id_1271,Bike,3,Business,15,2,9:45:04 AM,15,2,9:45:17 AM,15,2,9:52:15 AM,15,2,10:05:11 AM,15,2,10:40:32 AM,25,20.5,,-1.3108386,36.7555239,-1.3244885,36.897791999999995,Rider_Id_493,2121
-Order_No_1566,User_Id_3201,Bike,3,Business,4,2,2:46:02 PM,4,2,2:48:09 PM,4,2,3:22:53 PM,4,2,3:24:03 PM,4,2,3:47:24 PM,3,22.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_162,1401
-Order_No_16503,User_Id_718,Bike,3,Business,18,4,3:38:29 PM,18,4,3:39:10 PM,18,4,3:43:00 PM,18,4,3:45:05 PM,18,4,4:49:41 PM,10,26.7,,-1.2551895,36.7822034,-1.3156451999999998,36.815802000000005,Rider_Id_268,3876
-Order_No_24148,User_Id_136,Bike,3,Business,29,6,12:06:22 PM,29,6,12:06:51 PM,29,6,12:16:16 PM,29,6,12:18:03 PM,29,6,12:52:54 PM,16,25.2,,-1.2652979,36.800039899999994,-1.3169591,36.7000029,Rider_Id_263,2091
-Order_No_11626,User_Id_635,Bike,3,Business,10,1,10:13:51 AM,10,1,11:21:10 AM,10,1,11:54:21 AM,10,1,11:57:57 AM,10,1,12:52:02 PM,28,18.4,,-1.2847613000000002,36.967084,-1.2602337,36.79905479999999,Rider_Id_907,3245
-Order_No_15025,User_Id_2169,Bike,2,Personal,14,6,9:22:46 AM,14,6,9:23:11 AM,14,6,9:39:32 AM,14,6,9:44:01 AM,14,6,10:20:19 AM,11,15.0,,-1.3205163999999998,36.8098882,-1.2539293,36.792074799999995,Rider_Id_88,2178
-Order_No_5817,User_Id_393,Bike,3,Business,25,4,1:34:40 PM,25,4,1:57:32 PM,25,4,2:09:49 PM,25,4,2:20:42 PM,25,4,2:54:52 PM,10,20.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_761,2050
-Order_No_23226,User_Id_1724,Bike,3,Business,15,5,11:01:47 AM,15,5,11:03:10 AM,15,5,11:21:04 AM,15,5,11:22:41 AM,15,5,12:10:14 PM,15,26.3,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_448,2853
-Order_No_20949,User_Id_1687,Bike,3,Business,28,4,11:04:12 AM,28,4,11:06:54 AM,28,4,11:22:25 AM,28,4,11:24:42 AM,28,4,11:54:48 AM,3,27.9,,-1.2626574,36.8014034,-1.2755629,36.813152200000005,Rider_Id_548,1806
-Order_No_4664,User_Id_594,Bike,1,Personal,28,1,12:56:16 PM,28,1,12:58:25 PM,28,1,1:33:34 PM,28,1,1:41:04 PM,28,1,1:58:30 PM,6,24.5,,-1.3024794,36.8228284,-1.297611,36.775445,Rider_Id_865,1046
-Order_No_3265,User_Id_3201,Bike,3,Business,15,1,3:11:30 PM,15,1,3:12:06 PM,15,1,3:15:29 PM,15,1,3:27:59 PM,15,1,3:33:37 PM,1,,,-1.2605938,36.807323499999995,-1.2584143,36.8048002,Rider_Id_191,338
-Order_No_16280,User_Id_713,Bike,1,Personal,4,2,2:18:00 PM,4,2,2:18:35 PM,4,2,2:45:12 PM,4,2,2:47:52 PM,4,2,3:00:52 PM,4,20.4,,-1.2868838,36.8284259,-1.2731101999999999,36.8133041,Rider_Id_531,780
-Order_No_11423,User_Id_1976,Bike,3,Business,21,1,10:01:09 AM,21,1,10:01:50 AM,21,1,10:08:11 AM,21,1,10:15:12 AM,21,1,10:31:05 AM,6,22.6,,-1.257028,36.7810435,-1.2905152,36.783498200000004,Rider_Id_881,953
-Order_No_11934,User_Id_1205,Bike,3,Business,1,1,2:01:04 PM,1,1,2:01:08 PM,1,1,2:07:13 PM,1,1,2:10:59 PM,1,1,2:47:45 PM,22,25.6,,-1.257309,36.806008399999996,-1.341891,36.761269,Rider_Id_105,2206
-Order_No_20239,User_Id_3404,Bike,3,Business,21,5,4:49:15 PM,21,5,4:50:10 PM,21,5,4:53:23 PM,21,5,4:55:06 PM,21,5,5:12:14 PM,5,23.2,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_879,1028
-Order_No_4739,User_Id_3718,Bike,3,Business,9,5,12:45:36 PM,9,5,12:47:31 PM,9,5,1:09:07 PM,9,5,1:20:08 PM,9,5,1:23:29 PM,1,25.5,,-1.2650027,36.8126242,-1.2675613000000001,36.8133001,Rider_Id_427,201
-Order_No_25865,User_Id_136,Bike,3,Business,17,3,9:38:40 AM,17,3,9:38:53 AM,17,3,9:49:42 AM,17,3,10:08:12 AM,17,3,10:46:59 AM,16,,,-1.2738073,36.813117299999995,-1.3254522,36.721658399999995,Rider_Id_363,2327
-Order_No_11170,User_Id_1469,Bike,3,Business,23,5,3:59:07 PM,23,5,4:54:20 PM,23,5,5:07:18 PM,23,5,5:08:54 PM,23,5,5:41:07 PM,8,24.8,,-1.2551895,36.7822034,-1.3007818000000002,36.8230554,Rider_Id_206,1933
-Order_No_28076,User_Id_2394,Bike,3,Personal,16,4,11:25:00 AM,16,4,11:25:07 AM,16,4,11:35:45 AM,16,4,11:40:14 AM,16,4,11:48:34 AM,6,14.9,,-1.2817856,36.788100899999996,-1.263987,36.798891,Rider_Id_281,500
-Order_No_3723,User_Id_2685,Bike,1,Personal,18,4,10:46:01 AM,18,4,10:46:20 AM,18,4,10:51:47 AM,18,4,11:57:52 AM,18,4,12:31:56 PM,22,25.5,,-1.3433181,36.9138018,-1.2899,36.7794355,Rider_Id_120,2044
-Order_No_3514,User_Id_3272,Bike,1,Personal,19,5,1:29:00 PM,19,5,1:29:10 PM,19,5,1:46:45 PM,19,5,2:47:21 PM,19,5,3:24:40 PM,12,27.6,,-1.306219,36.869912400000004,-1.2696341,36.8042045,Rider_Id_755,2239
-Order_No_15525,User_Id_3035,Bike,3,Business,2,6,10:14:03 AM,2,6,10:14:40 AM,2,6,10:26:13 AM,2,6,10:42:37 AM,2,6,10:50:01 AM,2,27.4,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_88,444
-Order_No_15479,User_Id_3135,Bike,3,Personal,12,2,5:44:28 PM,12,2,5:44:51 PM,12,2,6:08:35 PM,12,2,6:29:42 PM,12,2,7:24:58 PM,9,26.7,,-1.2803877000000001,36.8300596,-1.2856319,36.767299799999996,Rider_Id_953,3316
-Order_No_1530,User_Id_496,Bike,3,Business,1,3,11:12:23 AM,1,3,11:13:17 AM,1,3,11:20:30 AM,1,3,11:28:28 AM,1,3,12:11:52 PM,10,19.7,,-1.2551895,36.7822034,-1.3127303999999997,36.8189817,Rider_Id_519,2604
-Order_No_6137,User_Id_2195,Bike,3,Business,17,5,12:48:18 PM,17,5,12:49:40 PM,17,5,1:01:04 PM,17,5,1:19:01 PM,17,5,1:37:57 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_907,1136
-Order_No_6357,User_Id_540,Bike,3,Business,17,3,2:15:15 PM,17,3,2:19:47 PM,17,3,2:32:43 PM,17,3,3:18:04 PM,17,3,3:20:26 PM,5,,,-1.2571472,36.795063299999995,-1.2890893,36.8268633,Rider_Id_436,142
-Order_No_7606,User_Id_2987,Bike,3,Business,16,4,11:37:46 AM,16,4,11:38:12 AM,16,4,11:42:14 AM,16,4,11:48:18 AM,16,4,12:01:25 PM,7,14.5,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_754,787
-Order_No_17900,User_Id_388,Bike,1,Personal,29,5,4:47:43 PM,29,5,5:38:22 PM,29,5,5:49:55 PM,29,5,6:05:40 PM,29,5,6:35:59 PM,16,20.5,,-1.2115008999999999,36.8417431,-1.285996,36.797048,Rider_Id_101,1819
-Order_No_23637,User_Id_2956,Bike,3,Business,7,4,4:56:05 PM,7,4,4:56:28 PM,7,4,5:04:09 PM,7,4,5:06:20 PM,7,4,5:06:48 PM,3,25.5,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_250,28
-Order_No_24761,User_Id_432,Bike,3,Business,24,5,11:37:45 AM,24,5,1:29:17 PM,24,5,1:49:53 PM,24,5,1:51:06 PM,24,5,2:45:52 PM,11,23.2,,-1.2846863000000002,36.738305100000005,-1.269942,36.801095000000004,Rider_Id_372,3286
-Order_No_26389,User_Id_2828,Bike,3,Business,28,4,11:01:36 AM,28,4,11:02:09 AM,28,4,11:07:38 AM,28,4,11:12:02 AM,28,4,11:57:59 AM,13,,,-1.2615888000000002,36.7928732,-1.3332749,36.870814700000004,Rider_Id_326,2757
-Order_No_1956,User_Id_267,Bike,3,Personal,16,4,11:14:18 AM,16,4,11:14:24 AM,16,4,11:17:42 AM,16,4,11:22:05 AM,16,4,11:29:46 AM,4,14.7,,-1.2833106,36.82402379999999,-1.266294,36.8057971,Rider_Id_754,461
-Order_No_5440,User_Id_3283,Bike,3,Business,30,5,2:50:15 PM,30,5,2:53:32 PM,30,5,3:08:47 PM,30,5,3:49:56 PM,30,5,4:09:13 PM,14,24.4,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_307,1157
-Order_No_13897,User_Id_1796,Bike,3,Business,17,3,1:31:11 PM,17,3,1:31:49 PM,17,3,1:54:13 PM,17,3,2:06:11 PM,17,3,2:34:13 PM,18,,,-1.228911,36.881876,-1.331619,36.847976,Rider_Id_691,1682
-Order_No_13661,User_Id_113,Bike,1,Business,10,5,11:37:55 AM,10,5,11:38:45 AM,10,5,11:59:46 AM,10,5,12:19:58 PM,10,5,12:39:26 PM,8,19.3,,-1.2637056999999998,36.805170000000004,-1.2963097,36.7688221,Rider_Id_882,1168
-Order_No_19762,User_Id_2174,Bike,3,Business,16,4,3:27:34 PM,16,4,3:27:40 PM,16,4,3:28:35 PM,16,4,3:36:35 PM,16,4,4:08:44 PM,16,18.8,,-1.2482781,36.884091999999995,-1.2979242,36.7902416,Rider_Id_946,1929
-Order_No_357,User_Id_1882,Bike,3,Business,10,5,1:07:47 PM,10,5,1:14:06 PM,10,5,1:32:43 PM,10,5,1:38:30 PM,10,5,2:00:42 PM,9,,,-1.323615,36.700475,-1.3014461000000002,36.7661381,Rider_Id_572,1332
-Order_No_26589,User_Id_376,Bike,2,Personal,2,3,7:34:45 PM,2,3,7:50:32 PM,2,3,8:18:27 PM,2,3,8:22:33 PM,2,3,8:54:01 PM,10,17.1,16.1,-1.2655306,36.828148,-1.2655103,36.7686545,Rider_Id_103,1888
-Order_No_23482,User_Id_2112,Bike,3,Business,29,1,4:41:55 PM,29,1,4:46:35 PM,29,1,5:22:05 PM,29,1,5:33:44 PM,29,1,6:27:18 PM,30,23.4,,-1.3169591,36.7000029,-1.2156006000000001,36.8916865,Rider_Id_644,3214
-Order_No_10792,User_Id_2646,Bike,2,Personal,9,1,1:59:39 PM,9,1,2:00:53 PM,9,1,2:05:52 PM,9,1,2:07:32 PM,9,1,2:27:08 PM,5,20.9,,-1.273056,36.811298,-1.2858511000000001,36.7946184,Rider_Id_734,1176
-Order_No_20236,User_Id_1500,Bike,3,Business,14,2,4:01:30 PM,14,2,4:06:46 PM,14,2,4:20:25 PM,14,2,4:45:02 PM,14,2,5:18:36 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_709,2014
-Order_No_4467,User_Id_435,Bike,3,Business,31,5,11:26:34 AM,31,5,11:31:12 AM,31,5,11:39:34 AM,31,5,11:49:13 AM,31,5,12:07:16 PM,7,21.9,,-1.300921,36.828195,-1.2918159,36.8773479,Rider_Id_591,1083
-Order_No_11160,User_Id_1955,Bike,3,Business,17,1,9:05:13 AM,17,1,9:05:39 AM,17,1,9:23:52 AM,17,1,9:35:39 AM,17,1,10:05:35 AM,13,18.2,,-1.330929,36.835151,-1.2571472,36.795063299999995,Rider_Id_858,1796
-Order_No_15012,User_Id_3413,Bike,1,Personal,4,5,6:16:05 PM,4,5,6:17:18 PM,4,5,6:49:18 PM,4,5,7:11:10 PM,4,5,8:12:05 PM,15,22.4,,-1.3112653,36.826919700000005,-1.2598589,36.7470168,Rider_Id_948,3655
-Order_No_24969,User_Id_3647,Bike,3,Business,1,5,4:01:54 PM,1,5,4:02:31 PM,1,5,4:02:39 PM,1,5,4:17:47 PM,1,5,5:04:02 PM,20,28.4,,-1.2726389999999999,36.794723,-1.3770858,36.7546064,Rider_Id_31,2775
-Order_No_24247,User_Id_3158,Bike,2,Personal,24,4,4:26:29 PM,24,4,4:28:51 PM,24,4,4:45:59 PM,24,4,5:13:29 PM,24,4,5:37:17 PM,7,21.0,,-1.2855798999999999,36.817620899999994,-1.289274,36.7781339,Rider_Id_808,1428
-Order_No_4985,User_Id_1329,Bike,3,Business,28,3,10:23:39 AM,28,3,10:25:20 AM,28,3,10:31:03 AM,28,3,10:32:54 AM,28,3,10:48:33 AM,5,,,-1.273056,36.811298,-1.2849763,36.7841767,Rider_Id_663,939
-Order_No_597,User_Id_2824,Bike,1,Personal,6,6,12:02:14 PM,6,6,12:02:31 PM,6,6,12:14:00 PM,6,6,12:16:42 PM,6,6,12:39:17 PM,4,24.3,,-1.2924639,36.77957979999999,-1.3090343,36.7662134,Rider_Id_247,1355
-Order_No_11398,User_Id_1341,Bike,3,Business,18,6,9:08:01 AM,18,6,9:09:18 AM,18,6,9:15:26 AM,18,6,9:21:43 AM,18,6,9:33:10 AM,6,,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_943,687
-Order_No_4474,User_Id_3698,Bike,3,Business,18,4,1:40:24 PM,18,4,1:57:10 PM,18,4,2:19:36 PM,18,4,2:42:00 PM,18,4,3:50:40 PM,27,,,-1.3949989,36.7654761,-1.3244885,36.897791999999995,Rider_Id_572,4120
-Order_No_17880,User_Id_2071,Bike,1,Personal,31,4,7:06:19 PM,31,4,7:07:14 PM,31,4,7:21:24 PM,31,4,7:27:18 PM,31,4,8:07:22 PM,19,22.1,,-1.2812976,36.7625848,-1.2108864,36.78070220000001,Rider_Id_874,2404
-Order_No_10047,User_Id_3560,Bike,3,Business,4,2,9:48:30 AM,4,2,11:10:45 AM,4,2,11:14:41 AM,4,2,11:24:11 AM,4,2,11:44:26 AM,5,20.5,,-1.2527958000000001,36.797624400000004,-1.279395,36.825364,Rider_Id_139,1215
-Order_No_6970,User_Id_186,Bike,3,Business,31,5,3:44:20 PM,31,5,3:57:45 PM,31,5,3:57:53 PM,31,5,4:07:33 PM,31,5,4:39:46 PM,6,24.7,,-1.2889913999999998,36.820944299999994,-1.2571472,36.795063299999995,Rider_Id_531,1933
-Order_No_21289,User_Id_3291,Bike,3,Business,17,1,10:04:48 AM,17,1,10:04:58 AM,17,1,10:05:57 AM,17,1,10:21:16 AM,17,1,11:24:35 AM,12,19.1,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_669,3799
-Order_No_13400,User_Id_1500,Bike,3,Business,9,2,10:16:08 AM,9,2,10:16:56 AM,9,2,10:20:23 AM,9,2,10:53:44 AM,9,2,10:53:51 AM,8,27.1,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_507,7
-Order_No_3374,User_Id_3688,Bike,3,Business,6,3,1:02:10 PM,6,3,1:02:44 PM,6,3,1:24:52 PM,6,3,1:47:57 PM,6,3,1:58:39 PM,6,27.6,,-1.2736466,36.8164042,-1.2551895,36.7822034,Rider_Id_959,642
-Order_No_24454,User_Id_1920,Bike,3,Business,1,4,1:01:35 PM,1,4,1:02:33 PM,1,4,1:22:12 PM,1,4,1:34:55 PM,1,4,1:54:51 PM,9,,,-1.2551895,36.7822034,-1.2981352,36.8213822,Rider_Id_932,1196
-Order_No_26585,User_Id_2330,Bike,3,Business,29,1,3:53:07 PM,29,1,3:54:22 PM,29,1,4:05:03 PM,29,1,4:07:35 PM,29,1,4:27:01 PM,8,,,-1.294597,36.7742999,-1.2642903,36.80021370000001,Rider_Id_613,1166
-Order_No_6583,User_Id_3154,Bike,1,Business,11,2,3:18:47 PM,11,2,3:20:50 PM,11,2,3:51:21 PM,11,2,3:56:42 PM,11,2,5:12:55 PM,21,25.5,,-1.2914795000000001,36.824897299999996,-1.3326173000000001,36.71462329999999,Rider_Id_268,4573
-Order_No_13724,User_Id_1281,Bike,3,Business,31,5,10:31:09 AM,31,5,10:31:16 AM,31,5,10:48:51 AM,31,5,11:44:39 AM,31,5,12:14:31 PM,20,17.2,,-1.2854453000000001,36.818836299999994,-1.348697,36.710527,Rider_Id_45,1792
-Order_No_16927,User_Id_393,Bike,3,Business,11,2,11:37:24 AM,11,2,11:37:46 AM,11,2,11:39:37 AM,11,2,11:48:29 AM,11,2,12:11:09 PM,11,19.3,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_35,1360
-Order_No_25514,User_Id_3517,Bike,3,Business,30,2,9:57:30 AM,30,2,10:02:18 AM,30,2,10:38:05 AM,30,2,11:05:19 AM,30,2,11:44:24 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_787,2345
-Order_No_3478,User_Id_190,Bike,3,Business,27,1,3:23:18 PM,27,1,3:25:08 PM,27,1,3:33:06 PM,27,1,4:25:02 PM,27,1,4:29:18 PM,2,22.9,,-1.328762,36.8534414,-1.3270347,36.866736200000005,Rider_Id_402,256
-Order_No_27914,User_Id_1037,Bike,2,Personal,11,3,4:04:58 PM,11,3,4:08:29 PM,11,3,4:26:58 PM,11,3,4:32:12 PM,11,3,4:50:46 PM,5,19.8,,-1.2596651,36.8086697,-1.2873708000000001,36.814908,Rider_Id_752,1114
-Order_No_22279,User_Id_527,Bike,2,Personal,18,2,2:36:51 PM,18,2,2:50:43 PM,18,2,2:54:03 PM,18,2,3:29:59 PM,18,2,5:02:04 PM,13,27.3,,-1.2854453000000001,36.818836299999994,-1.3319425,36.894470899999995,Rider_Id_101,5525
-Order_No_27323,User_Id_3635,Bike,1,Business,28,2,9:21:40 AM,28,2,9:25:29 AM,28,2,9:35:31 AM,28,2,9:57:04 AM,28,2,10:05:57 AM,7,16.1,51.1,-1.22589,36.7822548,-1.225322,36.80855,Rider_Id_882,533
-Order_No_23775,User_Id_2987,Bike,3,Business,3,5,11:22:53 AM,3,5,11:23:27 AM,3,5,11:28:13 AM,3,5,11:32:36 AM,3,5,12:00:06 PM,9,21.2,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_427,1650
-Order_No_21887,User_Id_1141,Bike,1,Business,4,1,3:00:29 PM,4,1,3:17:05 PM,4,1,3:36:43 PM,4,1,3:45:06 PM,4,1,4:27:34 PM,5,22.5,,-1.2877645,36.837853499999994,-1.3102508,36.822997799999996,Rider_Id_844,2548
-Order_No_8014,User_Id_1246,Bike,3,Business,4,1,3:35:42 PM,4,1,3:52:25 PM,4,1,4:08:00 PM,4,1,4:13:05 PM,4,1,4:54:43 PM,22,,,-1.3700383,36.9190174,-1.2591485,36.789133500000005,Rider_Id_883,2498
-Order_No_18087,User_Id_867,Bike,3,Business,4,2,4:09:44 PM,4,2,4:09:48 PM,4,2,4:09:56 PM,4,2,4:19:31 PM,4,2,5:03:17 PM,14,23.7,11.0,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_619,2626
-Order_No_18117,User_Id_1424,Bike,3,Business,30,5,2:53:00 PM,30,5,3:00:03 PM,30,5,3:09:25 PM,30,5,3:13:44 PM,30,5,4:09:58 PM,11,24.5,,-1.3015700000000001,36.7871,-1.257984,36.745016,Rider_Id_685,3374
-Order_No_26259,User_Id_2801,Bike,3,Business,4,1,2:53:41 PM,4,1,2:54:09 PM,4,1,2:55:44 PM,4,1,3:04:46 PM,4,1,3:08:59 PM,3,28.8,,-1.2595421999999998,36.7871178,-1.2559559999999999,36.772901,Rider_Id_258,253
-Order_No_24686,User_Id_547,Bike,1,Personal,13,4,1:21:56 PM,13,4,1:22:22 PM,13,4,1:32:49 PM,13,4,1:35:23 PM,13,4,2:06:27 PM,19,26.3,,-1.320871,36.826126,-1.3713927,36.7213028,Rider_Id_543,1864
-Order_No_14178,User_Id_2521,Bike,3,Business,26,6,9:03:52 AM,26,6,9:04:06 AM,26,6,9:05:58 AM,26,6,9:24:27 AM,26,6,9:34:41 AM,4,20.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,614
-Order_No_12183,User_Id_136,Bike,3,Business,7,4,9:05:43 AM,7,4,9:05:51 AM,7,4,9:06:40 AM,7,4,9:28:31 AM,7,4,10:07:34 AM,28,,,-1.3254522,36.721658399999995,-1.3524201,36.9045107,Rider_Id_598,2343
-Order_No_23634,User_Id_1245,Bike,3,Business,31,5,1:28:34 PM,31,5,3:12:54 PM,31,5,3:12:58 PM,31,5,3:55:01 PM,31,5,4:01:46 PM,19,24.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_41,405
-Order_No_530,User_Id_113,Bike,1,Business,22,3,1:05:00 PM,22,3,1:05:40 PM,22,3,1:09:29 PM,22,3,1:17:00 PM,22,3,1:45:42 PM,8,23.0,,-1.2844653,36.8206676,-1.2963097,36.7688221,Rider_Id_414,1722
-Order_No_26090,User_Id_1927,Bike,3,Business,19,2,10:06:13 AM,19,2,10:06:23 AM,19,2,10:08:05 AM,19,2,10:40:52 AM,19,2,11:49:42 AM,19,26.7,,-1.2927792,36.9053492,-1.3472066,36.7692638,Rider_Id_900,4130
-Order_No_17775,User_Id_1787,Bike,3,Business,3,1,12:42:38 PM,3,1,12:43:09 PM,3,1,12:44:20 PM,3,1,1:15:10 PM,3,1,1:40:11 PM,15,21.4,,-1.2283403,36.8822756,-1.2571472,36.795063299999995,Rider_Id_248,1501
-Order_No_22062,User_Id_2423,Bike,3,Business,29,5,2:21:53 PM,29,5,2:29:25 PM,29,5,2:42:33 PM,29,5,2:45:03 PM,29,5,3:31:18 PM,8,21.0,,-1.2551895,36.7822034,-1.2945073999999999,36.8083195,Rider_Id_875,2775
-Order_No_15195,User_Id_3355,Bike,3,Business,20,4,12:11:24 PM,20,4,12:11:31 PM,20,4,12:20:56 PM,20,4,12:36:37 PM,20,4,12:53:36 PM,10,24.5,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_263,1019
-Order_No_21389,User_Id_3201,Bike,3,Business,25,1,12:11:40 PM,25,1,12:11:54 PM,25,1,12:13:54 PM,25,1,12:17:16 PM,25,1,12:25:29 PM,2,,,-1.2649605,36.798177700000004,-1.2632534,36.799342700000004,Rider_Id_103,493
-Order_No_4283,User_Id_393,Bike,3,Business,16,2,3:10:52 PM,16,2,3:11:24 PM,16,2,3:24:53 PM,16,2,3:50:05 PM,16,2,4:05:01 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_910,896
-Order_No_11191,User_Id_1035,Bike,1,Personal,3,3,12:46:08 PM,3,3,12:47:09 PM,3,3,12:49:31 PM,3,3,1:06:38 PM,3,3,1:31:35 PM,8,24.2,,-1.2626765,36.795598999999996,-1.2991116000000003,36.7960658,Rider_Id_215,1497
-Order_No_5326,User_Id_2523,Bike,3,Business,11,2,10:34:05 AM,11,2,11:11:15 AM,11,2,11:13:56 AM,11,2,11:15:34 AM,11,2,11:40:50 AM,8,19.2,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_532,1516
-Order_No_27927,User_Id_1553,Bike,1,Personal,11,6,3:48:15 PM,11,6,3:48:51 PM,11,6,4:10:31 PM,11,6,4:12:25 PM,11,6,4:33:18 PM,11,21.9,,-1.307774,36.840754100000005,-1.2343087,36.84749,Rider_Id_674,1253
-Order_No_5314,User_Id_2216,Bike,3,Business,19,2,10:50:56 AM,19,2,10:51:27 AM,19,2,11:01:11 AM,19,2,11:16:51 AM,19,2,11:24:31 AM,1,26.8,,-1.2551895,36.7822034,-1.2549259,36.7919494,Rider_Id_557,460
-Order_No_23348,User_Id_2037,Bike,1,Personal,18,2,12:24:51 PM,18,2,12:25:12 PM,18,2,12:27:52 PM,18,2,12:39:04 PM,18,2,12:52:51 PM,3,22.5,,-1.2864309999999999,36.818411,-1.2903277,36.80556,Rider_Id_785,827
-Order_No_12906,User_Id_2344,Bike,3,Business,8,2,10:51:01 AM,8,2,11:08:13 AM,8,2,11:18:27 AM,8,2,11:23:56 AM,8,2,11:42:09 AM,10,21.3,,-1.2559559999999999,36.789867,-1.2456401000000001,36.8096727,Rider_Id_589,1093
-Order_No_15715,User_Id_3282,Bike,3,Personal,6,1,3:26:07 PM,6,1,3:39:49 PM,6,1,4:15:12 PM,6,1,4:17:45 PM,6,1,4:54:07 PM,8,24.7,,-1.290059,36.7703475,-1.2861764999999998,36.8209086,Rider_Id_250,2182
-Order_No_1254,User_Id_496,Bike,3,Business,31,5,10:18:22 AM,31,5,10:19:13 AM,31,5,10:22:53 AM,31,5,10:32:03 AM,31,5,10:48:52 AM,10,21.8,,-1.2551895,36.7822034,-1.2518813999999998,36.859660999999996,Rider_Id_576,1009
-Order_No_3007,User_Id_1048,Bike,2,Personal,16,6,3:02:36 PM,16,6,3:04:25 PM,16,6,3:13:09 PM,16,6,3:28:20 PM,16,6,3:42:19 PM,7,23.1,,-1.2686365,36.794832899999996,-1.2892281,36.7535185,Rider_Id_427,839
-Order_No_18203,User_Id_1207,Bike,3,Business,15,5,5:36:17 PM,15,5,5:36:37 PM,15,5,5:39:50 PM,15,5,5:42:24 PM,15,5,6:13:26 PM,14,27.8,,-1.2628638,36.8070399,-1.207546,36.7913976,Rider_Id_858,1862
-Order_No_11520,User_Id_3517,Bike,3,Business,8,3,3:18:59 PM,8,3,3:20:39 PM,8,3,3:21:16 PM,8,3,3:29:25 PM,8,3,5:06:55 PM,9,,,-1.300921,36.828195,-1.2996819,36.778251700000006,Rider_Id_19,5850
-Order_No_27731,User_Id_1904,Bike,1,Personal,16,1,1:28:18 PM,16,1,1:29:57 PM,16,1,1:31:48 PM,16,1,1:34:43 PM,16,1,2:05:46 PM,9,22.6,,-1.3257473,36.859206799999995,-1.2904049,36.8055154,Rider_Id_88,1863
-Order_No_16420,User_Id_1647,Bike,3,Business,4,4,4:27:15 PM,4,4,4:31:23 PM,4,4,4:54:31 PM,4,4,5:01:26 PM,4,4,6:03:09 PM,13,25.8,,-1.2190317,36.8825092,-1.2849786,36.8322989,Rider_Id_570,3703
-Order_No_27315,User_Id_496,Bike,3,Business,20,4,5:31:14 PM,20,4,5:50:44 PM,20,4,6:06:50 PM,20,4,6:13:06 PM,20,4,6:44:32 PM,7,22.5,,-1.2551895,36.7822034,-1.2915968999999998,36.823763899999996,Rider_Id_320,1886
-Order_No_9184,User_Id_3075,Bike,1,Personal,28,6,10:00:45 AM,28,6,10:01:54 AM,28,6,10:07:51 AM,28,6,10:39:38 AM,28,6,11:03:12 AM,6,24.0,,-1.274165,36.814577,-1.304078,36.83014,Rider_Id_174,1414
-Order_No_16757,User_Id_2689,Bike,1,Business,26,6,5:57:51 PM,26,6,6:01:14 PM,26,6,6:11:00 PM,26,6,6:15:55 PM,26,6,6:37:05 PM,9,22.3,,-1.2675979,36.815565299999996,-1.3095666000000001,36.7698746,Rider_Id_186,1270
-Order_No_5154,User_Id_510,Bike,3,Business,4,1,4:27:44 PM,4,1,4:28:05 PM,4,1,4:39:55 PM,4,1,4:49:17 PM,4,1,5:17:07 PM,12,27.5,,-1.2283403,36.8822756,-1.2860183,36.8975338,Rider_Id_925,1670
-Order_No_20983,User_Id_442,Bike,3,Personal,18,1,3:28:18 PM,18,1,3:30:34 PM,18,1,3:44:37 PM,18,1,3:53:39 PM,18,1,4:09:11 PM,6,30.7,,-1.2885417,36.810052299999995,-1.2609745,36.7886626,Rider_Id_405,932
-Order_No_8962,User_Id_3072,Bike,2,Personal,3,3,4:31:02 PM,3,3,4:35:26 PM,3,3,4:41:30 PM,3,3,4:47:14 PM,3,3,4:59:29 PM,4,23.5,,-1.282543,36.824618,-1.2875012,36.7927159,Rider_Id_726,735
-Order_No_16936,User_Id_3264,Bike,3,Business,30,5,9:30:09 AM,30,5,9:32:22 AM,30,5,9:38:41 AM,30,5,9:45:18 AM,30,5,10:53:24 AM,8,19.2,,-1.2551895,36.7822034,-1.2995101,36.8185828,Rider_Id_322,4086
-Order_No_13072,User_Id_3300,Bike,3,Business,4,2,10:05:20 AM,4,2,10:59:06 AM,4,2,11:33:01 AM,4,2,11:44:33 AM,4,2,12:30:32 PM,18,20.3,,-1.293515,36.897607,-1.303596,36.778378000000004,Rider_Id_864,2759
-Order_No_15255,User_Id_1992,Bike,3,Business,2,4,11:45:26 AM,2,4,11:45:46 AM,2,4,11:48:32 AM,2,4,11:51:17 AM,2,4,12:30:47 PM,5,,,-1.2899816000000002,36.7846439,-1.3025826,36.7670807,Rider_Id_327,2370
-Order_No_14256,User_Id_909,Bike,3,Business,10,3,1:33:55 PM,10,3,1:35:42 PM,10,3,1:45:43 PM,10,3,2:05:58 PM,10,3,2:19:59 PM,8,30.7,,-1.225322,36.80855,-1.2675238000000002,36.8127148,Rider_Id_291,841
-Order_No_16045,User_Id_788,Bike,3,Business,11,2,12:01:22 PM,11,2,1:25:17 PM,11,2,1:26:14 PM,11,2,1:44:35 PM,11,2,2:07:49 PM,8,22.1,,-1.225322,36.80855,-1.2596969999999998,36.800802000000004,Rider_Id_596,1394
-Order_No_9540,User_Id_3674,Bike,3,Business,4,2,2:33:31 PM,4,2,2:48:01 PM,4,2,3:03:35 PM,4,2,3:07:55 PM,4,2,3:30:44 PM,7,22.5,,-1.2551895,36.7822034,-1.2880883,36.8227336,Rider_Id_894,1369
-Order_No_16423,User_Id_2216,Bike,3,Business,5,3,11:28:33 AM,5,3,11:44:09 AM,5,3,12:21:17 PM,5,3,12:25:55 PM,5,3,12:42:17 PM,5,22.6,,-1.2551895,36.7822034,-1.2658053,36.7920955,Rider_Id_167,982
-Order_No_8565,User_Id_1102,Bike,3,Business,1,1,2:42:49 PM,1,1,2:44:16 PM,1,1,2:44:35 PM,1,1,2:56:17 PM,1,1,3:06:28 PM,4,,,-1.3004062,36.829741,-1.2994771999999999,36.804825,Rider_Id_855,611
-Order_No_27237,User_Id_816,Bike,3,Business,20,3,12:46:21 PM,20,3,12:47:52 PM,20,3,1:09:56 PM,20,3,1:48:08 PM,20,3,1:48:16 PM,3,28.5,,-1.3052491000000002,36.8223899,-1.290894,36.822971,Rider_Id_127,8
-Order_No_23137,User_Id_2982,Bike,3,Business,3,4,8:33:57 AM,3,4,8:34:10 AM,3,4,8:39:55 AM,3,4,8:53:33 AM,3,4,9:22:52 AM,18,20.4,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_932,1759
-Order_No_764,User_Id_1091,Bike,1,Personal,13,3,12:52:05 PM,13,3,12:54:02 PM,13,3,1:01:02 PM,13,3,1:15:54 PM,13,3,1:37:19 PM,5,20.3,,-1.2660357,36.7898223,-1.2764786,36.81947879999999,Rider_Id_268,1285
-Order_No_25824,User_Id_1561,Bike,3,Business,29,2,11:39:31 AM,29,2,11:39:39 AM,29,2,12:05:17 PM,29,2,12:11:49 PM,29,2,12:59:07 PM,14,21.4,,-1.328259,36.860975,-1.2615584,36.814950599999996,Rider_Id_543,2838
-Order_No_27103,User_Id_3718,Bike,3,Business,13,2,11:09:37 AM,13,2,11:32:18 AM,13,2,11:49:32 AM,13,2,11:52:48 AM,13,2,12:08:07 PM,3,22.2,,-1.2650027,36.8126242,-1.265432,36.795034,Rider_Id_528,919
-Order_No_10799,User_Id_2502,Bike,2,Personal,27,5,1:50:49 PM,27,5,1:50:54 PM,27,5,2:04:14 PM,27,5,2:09:03 PM,27,5,2:26:27 PM,6,23.9,,-1.2651218000000002,36.8228765,-1.2559294,36.788465,Rider_Id_754,1044
-Order_No_18967,User_Id_186,Bike,3,Business,10,2,12:29:18 PM,10,2,12:30:39 PM,10,2,12:58:18 PM,10,2,1:05:41 PM,10,2,1:21:42 PM,6,20.2,,-1.2890399,36.8164942,-1.2571191000000002,36.8019652,Rider_Id_268,961
-Order_No_2075,User_Id_868,Bike,3,Business,30,4,10:48:05 AM,30,4,11:01:29 AM,30,4,11:01:48 AM,30,4,11:31:05 AM,30,4,12:01:53 PM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_427,1848
-Order_No_17229,User_Id_2685,Bike,1,Personal,15,1,2:20:50 PM,15,1,2:21:18 PM,15,1,2:23:53 PM,15,1,2:27:47 PM,15,1,2:46:41 PM,11,,,-1.2899,36.7794355,-1.3181152,36.70954570000001,Rider_Id_409,1134
-Order_No_23680,User_Id_2112,Bike,3,Business,8,6,1:15:50 PM,8,6,1:16:05 PM,8,6,1:43:21 PM,8,6,2:18:52 PM,8,6,3:07:22 PM,12,,,-1.28878,36.8168312,-1.2156006000000001,36.8916865,Rider_Id_739,2910
-Order_No_17767,User_Id_393,Bike,3,Business,7,4,1:07:10 PM,7,4,1:07:19 PM,7,4,1:07:52 PM,7,4,1:45:52 PM,7,4,1:45:54 PM,4,27.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_188,2
-Order_No_21477,User_Id_3017,Bike,3,Business,20,4,3:13:48 PM,20,4,3:28:44 PM,20,4,3:43:28 PM,20,4,3:49:20 PM,20,4,4:18:18 PM,8,28.1,,-1.2993231,36.7753372,-1.3472066,36.7692638,Rider_Id_532,1738
-Order_No_6634,User_Id_238,Bike,3,Business,22,5,9:49:25 AM,22,5,9:49:49 AM,22,5,10:03:13 AM,22,5,10:07:01 AM,22,5,10:49:29 AM,11,22.4,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_127,2548
-Order_No_19016,User_Id_2763,Bike,3,Business,8,5,9:21:05 AM,8,5,9:22:40 AM,8,5,9:30:59 AM,8,5,9:38:39 AM,8,5,10:30:26 AM,14,20.7,,-1.2551895,36.7822034,-1.3015998,36.8764939,Rider_Id_937,3107
-Order_No_17157,User_Id_786,Bike,1,Personal,4,4,10:53:49 AM,4,4,10:54:08 AM,4,4,11:03:59 AM,4,4,11:12:31 AM,4,4,11:33:46 AM,9,,,-1.3123271,36.814738299999995,-1.2884403999999998,36.7736747,Rider_Id_726,1275
-Order_No_24011,User_Id_868,Bike,3,Business,8,1,12:01:06 PM,8,1,12:02:12 PM,8,1,12:36:12 PM,8,1,12:38:29 PM,8,1,1:00:24 PM,4,27.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547,1315
-Order_No_21040,User_Id_3674,Bike,3,Business,10,1,2:51:55 PM,10,1,3:02:42 PM,10,1,3:09:00 PM,10,1,3:12:37 PM,10,1,3:38:04 PM,9,18.7,,-1.2551895,36.7822034,-1.3018312,36.781749700000006,Rider_Id_786,1527
-Order_No_11498,User_Id_1683,Bike,3,Business,9,2,1:49:59 PM,9,2,1:50:50 PM,9,2,2:03:50 PM,9,2,2:15:38 PM,9,2,2:53:56 PM,6,22.5,,-1.296694,36.787758000000004,-1.2675332000000001,36.808561,Rider_Id_131,2298
-Order_No_24492,User_Id_1154,Bike,3,Business,9,2,2:35:37 PM,9,2,2:35:50 PM,9,2,2:41:07 PM,9,2,2:51:51 PM,9,2,3:20:14 PM,21,30.6,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_877,1703
-Order_No_24911,User_Id_746,Bike,2,Personal,22,5,10:59:06 AM,22,5,11:01:19 AM,22,5,11:39:17 AM,22,5,11:47:56 AM,22,5,12:15:04 PM,9,21.9,,-1.2880771,36.8269099,-1.2867726000000002,36.7630367,Rider_Id_844,1628
-Order_No_14279,User_Id_2174,Bike,3,Business,21,1,11:54:03 AM,21,1,11:54:07 AM,21,1,11:57:02 AM,21,1,12:29:46 PM,21,1,1:02:14 PM,25,25.5,,-1.2482781,36.884091999999995,-1.4136642,36.957045,Rider_Id_12,1948
-Order_No_8557,User_Id_2112,Bike,3,Business,14,3,1:14:36 PM,14,3,2:06:51 PM,14,3,2:07:18 PM,14,3,2:18:47 PM,14,3,2:51:47 PM,17,25.5,,-1.2156006000000001,36.8916865,-1.2551895,36.7822034,Rider_Id_447,1980
-Order_No_16515,User_Id_3283,Bike,3,Business,29,5,11:14:39 AM,29,5,11:15:33 AM,29,5,11:24:22 AM,29,5,11:30:21 AM,29,5,11:51:48 AM,7,26.6,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_154,1287
-Order_No_70,User_Id_635,Bike,3,Business,10,5,10:05:38 AM,10,5,10:05:56 AM,10,5,10:16:31 AM,10,5,10:17:58 AM,10,5,10:24:38 AM,3,13.4,,-1.2534952,36.7825215,-1.2599563,36.799343900000004,Rider_Id_409,400
-Order_No_6769,User_Id_1329,Bike,3,Business,24,4,12:36:54 PM,24,4,12:39:11 PM,24,4,12:49:28 PM,24,4,12:55:10 PM,24,4,1:21:15 PM,6,21.1,,-1.273056,36.811298,-1.2946581,36.779292100000006,Rider_Id_742,1565
-Order_No_12036,User_Id_2763,Bike,3,Business,5,1,12:26:54 PM,5,1,12:27:15 PM,5,1,12:36:47 PM,5,1,12:42:31 PM,5,1,12:51:21 PM,4,24.3,,-1.2551895,36.7822034,-1.2717851,36.810536299999995,Rider_Id_822,530
-Order_No_12015,User_Id_3214,Bike,3,Business,6,4,2:39:01 PM,6,4,2:40:17 PM,6,4,3:13:54 PM,6,4,3:21:44 PM,6,4,4:22:30 PM,21,21.2,,-1.289795,36.7957132,-1.3190102,36.923069299999995,Rider_Id_411,3646
-Order_No_2458,User_Id_2681,Bike,1,Personal,7,1,3:20:14 PM,7,1,3:36:57 PM,7,1,3:45:05 PM,7,1,3:48:05 PM,7,1,3:58:19 PM,4,20.5,,-1.2854453000000001,36.818836299999994,-1.2720542,36.7910241,Rider_Id_100,614
-Order_No_11216,User_Id_2884,Bike,2,Personal,28,1,2:15:27 PM,28,1,2:27:51 PM,28,1,2:32:25 PM,28,1,2:41:49 PM,28,1,2:59:24 PM,9,24.5,,-1.2517448,36.7924971,-1.2764692,36.758011100000004,Rider_Id_379,1055
-Order_No_28130,User_Id_2195,Bike,3,Business,12,5,10:16:05 AM,12,5,10:16:39 AM,12,5,10:27:01 AM,12,5,10:45:36 AM,12,5,11:02:52 AM,9,26.7,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_547,1036
-Order_No_2748,User_Id_1549,Bike,1,Personal,26,4,1:11:23 PM,26,4,1:11:34 PM,26,4,1:26:22 PM,26,4,1:37:16 PM,26,4,1:58:42 PM,15,21.9,,-1.3223345,36.7195749,-1.3214562,36.810082799999996,Rider_Id_648,1286
-Order_No_8195,User_Id_2987,Bike,3,Business,4,2,12:45:02 PM,4,2,12:51:16 PM,4,2,1:05:04 PM,4,2,1:06:38 PM,4,2,1:20:14 PM,4,19.7,,-1.2702120000000001,36.82108770000001,-1.2513591,36.7947674,Rider_Id_658,816
-Order_No_27346,User_Id_346,Bike,3,Business,4,3,1:23:37 PM,4,3,1:25:21 PM,4,3,1:42:04 PM,4,3,2:06:18 PM,4,3,2:20:39 PM,2,18.1,,-1.2891921999999998,36.825189,-1.2885528999999998,36.816014100000004,Rider_Id_698,861
-Order_No_2681,User_Id_187,Bike,3,Business,3,6,2:24:40 PM,3,6,2:26:31 PM,3,6,2:28:32 PM,3,6,3:16:17 PM,3,6,3:44:03 PM,8,22.4,,-1.3027293,36.766197399999996,-1.3165542,36.700067,Rider_Id_774,1666
-Order_No_24902,User_Id_393,Bike,3,Business,17,3,2:50:01 PM,17,3,2:50:21 PM,17,3,2:59:10 PM,17,3,3:09:01 PM,17,3,3:31:30 PM,10,28.4,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1349
-Order_No_5828,User_Id_3283,Bike,3,Business,22,5,3:25:54 PM,22,5,3:27:42 PM,22,5,3:39:04 PM,22,5,3:47:18 PM,22,5,4:25:21 PM,10,27.5,,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_605,2283
-Order_No_19254,User_Id_3217,Bike,1,Business,15,3,1:57:25 PM,15,3,1:57:37 PM,15,3,2:12:08 PM,15,3,2:17:25 PM,15,3,3:19:10 PM,20,21.7,,-1.2143386999999999,36.7914548,-1.2216291000000001,36.9180415,Rider_Id_916,3705
-Order_No_5864,User_Id_2216,Bike,3,Business,18,1,10:05:44 AM,18,1,10:12:51 AM,18,1,10:18:00 AM,18,1,10:23:23 AM,18,1,10:44:50 AM,10,21.5,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_783,1287
-Order_No_11249,User_Id_2523,Bike,3,Business,12,2,11:00:11 AM,12,2,11:01:58 AM,12,2,11:19:13 AM,12,2,11:27:45 AM,12,2,11:59:46 AM,8,24.7,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_863,1921
-Order_No_3994,User_Id_517,Bike,3,Business,29,3,8:54:52 AM,29,3,8:55:54 AM,29,3,9:08:30 AM,29,3,9:24:31 AM,29,3,9:35:52 AM,11,17.7,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_706,681
-Order_No_13076,User_Id_1928,Bike,1,Business,9,1,12:15:38 PM,9,1,12:16:22 PM,9,1,12:27:47 PM,9,1,12:31:11 PM,9,1,1:07:50 PM,16,20.5,,-1.2571191000000002,36.8019652,-1.2808685,36.890694399999994,Rider_Id_74,2199
-Order_No_2825,User_Id_3641,Bike,3,Business,23,4,12:16:26 PM,23,4,12:16:48 PM,23,4,1:09:22 PM,23,4,1:10:29 PM,23,4,1:22:28 PM,8,23.5,,-1.2831463,36.76360220000001,-1.2551895,36.7822034,Rider_Id_451,719
-Order_No_26144,User_Id_2801,Bike,3,Business,22,2,4:22:35 PM,22,2,4:23:02 PM,22,2,4:38:57 PM,22,2,4:47:27 PM,22,2,5:08:05 PM,11,24.9,,-1.2595421999999998,36.7871178,-1.233433,36.800420200000005,Rider_Id_52,1238
-Order_No_179,User_Id_3201,Bike,3,Business,19,1,3:31:03 PM,19,1,3:31:27 PM,19,1,3:37:35 PM,19,1,3:40:58 PM,19,1,3:51:57 PM,3,25.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_167,659
-Order_No_22867,User_Id_2509,Bike,3,Business,19,5,10:26:45 AM,19,5,10:27:03 AM,19,5,10:33:56 AM,19,5,10:42:30 AM,19,5,11:14:16 AM,20,24.8,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_383,1906
-Order_No_17909,User_Id_868,Bike,3,Business,20,3,1:10:44 PM,20,3,1:10:54 PM,20,3,1:21:19 PM,20,3,1:37:01 PM,20,3,1:55:18 PM,9,29.5,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_291,1097
-Order_No_11433,User_Id_391,Bike,3,Business,7,4,2:01:01 PM,7,4,2:09:12 PM,7,4,3:09:06 PM,7,4,3:32:14 PM,7,4,3:54:38 PM,5,31.1,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_590,1344
-Order_No_2612,User_Id_3095,Bike,3,Personal,22,2,12:57:34 PM,22,2,12:59:12 PM,22,2,1:53:03 PM,22,2,2:15:11 PM,22,2,2:51:04 PM,12,25.6,,-1.3294531,36.8614019,-1.3015700000000001,36.7871,Rider_Id_520,2153
-Order_No_23215,User_Id_1410,Bike,3,Business,10,5,10:37:31 AM,10,5,10:42:15 AM,10,5,10:53:12 AM,10,5,11:22:53 AM,10,5,12:03:42 PM,22,,,-1.2847613000000002,36.967084,-1.2292756999999999,36.8704721,Rider_Id_506,2449
-Order_No_17295,User_Id_928,Bike,3,Business,9,5,3:06:27 PM,9,5,3:21:46 PM,9,5,3:23:50 PM,9,5,3:38:29 PM,9,5,4:13:05 PM,4,26.2,,-1.2551895,36.7822034,-1.2693662000000001,36.8085295,Rider_Id_668,2076
-Order_No_9089,User_Id_3291,Bike,3,Business,18,6,10:28:17 AM,18,6,10:29:00 AM,18,6,10:37:58 AM,18,6,11:01:44 AM,18,6,11:12:10 AM,5,19.2,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_882,626
-Order_No_4938,User_Id_2328,Bike,2,Personal,6,3,3:20:29 PM,6,3,3:22:53 PM,6,3,3:54:57 PM,6,3,4:07:34 PM,6,3,4:46:15 PM,12,21.9,,-1.2762513,36.7964048,-1.2423801,36.8478805,Rider_Id_536,2321
-Order_No_23446,User_Id_2154,Bike,3,Business,27,1,12:02:29 PM,27,1,12:24:22 PM,27,1,12:39:26 PM,27,1,12:52:51 PM,27,1,1:06:21 PM,6,20.8,,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_907,810
-Order_No_8074,User_Id_635,Bike,3,Business,3,4,9:13:26 AM,3,4,9:42:03 AM,3,4,9:49:14 AM,3,4,10:06:17 AM,3,4,10:17:35 AM,8,20.2,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_871,678
-Order_No_10079,User_Id_2423,Bike,3,Business,7,4,10:11:14 AM,7,4,10:12:23 AM,7,4,10:18:19 AM,7,4,10:34:41 AM,7,4,11:13:34 AM,8,27.5,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_520,2333
-Order_No_13170,User_Id_3115,Bike,3,Business,7,3,1:44:45 PM,7,3,1:45:16 PM,7,3,1:59:27 PM,7,3,2:04:22 PM,7,3,2:46:43 PM,17,28.1,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_131,2541
-Order_No_6062,User_Id_1500,Bike,3,Business,16,4,9:28:59 AM,16,4,9:30:11 AM,16,4,9:30:41 AM,16,4,10:38:23 AM,16,4,10:55:50 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_547,1047
-Order_No_23479,User_Id_1075,Bike,3,Business,13,3,1:11:18 PM,13,3,1:13:23 PM,13,3,1:27:40 PM,13,3,1:36:17 PM,13,3,1:58:29 PM,5,29.0,,-1.2527958000000001,36.8003131,-1.281385,36.8176006,Rider_Id_76,1332
-Order_No_1928,User_Id_1336,Bike,3,Business,16,2,12:23:29 PM,16,2,12:25:32 PM,16,2,12:32:06 PM,16,2,12:50:24 PM,16,2,12:56:24 PM,2,23.5,,-1.290894,36.822971,-1.2891921999999998,36.825189,Rider_Id_208,360
-Order_No_12852,User_Id_1874,Bike,3,Business,16,5,3:20:35 PM,16,5,4:25:16 PM,16,5,4:42:35 PM,16,5,4:52:04 PM,16,5,5:08:00 PM,5,26.2,,-1.2571472,36.795063299999995,-1.2431489,36.7957656,Rider_Id_427,956
-Order_No_9012,User_Id_1754,Bike,3,Business,10,4,1:03:40 PM,10,4,1:05:17 PM,10,4,1:19:58 PM,10,4,1:23:40 PM,10,4,1:55:54 PM,11,,,-1.312734,36.855414,-1.2860183,36.8975338,Rider_Id_648,1934
-Order_No_25884,User_Id_633,Bike,3,Business,7,4,9:58:42 AM,7,4,9:59:13 AM,7,4,10:02:26 AM,7,4,10:06:45 AM,7,4,10:19:30 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_611,765
-Order_No_11993,User_Id_1372,Bike,3,Business,14,4,11:23:24 AM,14,4,11:24:32 AM,14,4,11:39:16 AM,14,4,11:46:15 AM,14,4,12:06:01 PM,3,25.0,,-1.2875013000000002,36.8171787,-1.2885417,36.810052299999995,Rider_Id_30,1186
-Order_No_15476,User_Id_867,Bike,3,Business,18,2,11:39:06 AM,18,2,12:13:11 PM,18,2,12:15:50 PM,18,2,12:28:06 PM,18,2,12:54:05 PM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_555,1559
-Order_No_13653,User_Id_3426,Bike,3,Business,31,3,5:26:39 PM,31,3,5:27:12 PM,31,3,5:29:58 PM,31,3,5:37:21 PM,31,3,6:08:52 PM,10,24.4,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_647,1891
-Order_No_9493,User_Id_2523,Bike,3,Business,11,1,9:42:47 AM,11,1,9:43:00 AM,11,1,9:44:13 AM,11,1,9:51:12 AM,11,1,10:06:34 AM,9,18.1,,-1.2551895,36.7822034,-1.2978456,36.7537369,Rider_Id_379,922
-Order_No_6456,User_Id_839,Bike,1,Personal,24,1,3:30:15 PM,24,1,3:30:25 PM,24,1,3:33:13 PM,24,1,3:36:16 PM,24,1,3:52:29 PM,6,,,-1.2682178999999998,36.8009289,-1.3038156,36.812273299999994,Rider_Id_795,973
-Order_No_6213,User_Id_892,Bike,2,Personal,21,5,12:52:10 PM,21,5,12:52:58 PM,21,5,1:09:39 PM,21,5,1:28:31 PM,21,5,1:53:48 PM,9,19.9,,-1.2649605,36.798177700000004,-1.312475,36.8225778,Rider_Id_754,1517
-Order_No_9190,User_Id_41,Bike,2,Personal,20,4,5:48:39 PM,20,4,5:49:10 PM,20,4,6:06:53 PM,20,4,6:18:28 PM,20,4,6:45:15 PM,10,25.0,,-1.3171237,36.8393643,-1.2894031000000001,36.7745653,Rider_Id_848,1607
-Order_No_9535,User_Id_1809,Bike,3,Business,19,4,3:32:48 PM,19,4,3:33:01 PM,19,4,3:46:26 PM,19,4,3:50:04 PM,19,4,4:00:40 PM,1,20.7,,-1.2646084,36.8028645,-1.263987,36.798891,Rider_Id_898,636
-Order_No_25985,User_Id_706,Bike,3,Business,7,5,8:28:22 AM,7,5,8:32:11 AM,7,5,8:57:26 AM,7,5,9:09:53 AM,7,5,9:15:09 AM,4,20.4,1.1,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_477,316
-Order_No_22348,User_Id_1535,Bike,1,Business,3,5,2:52:07 PM,3,5,2:53:18 PM,3,5,3:19:30 PM,3,5,3:27:31 PM,3,5,4:02:58 PM,5,25.4,,-1.2897368,36.8226783,-1.2643993999999998,36.812919,Rider_Id_96,2127
-Order_No_25814,User_Id_186,Bike,3,Business,26,2,3:16:27 PM,26,2,4:27:01 PM,26,2,4:44:12 PM,26,2,4:48:34 PM,26,2,5:41:20 PM,23,28.5,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_519,3166
-Order_No_5597,User_Id_496,Bike,3,Business,9,2,11:30:13 AM,9,2,11:30:34 AM,9,2,11:32:05 AM,9,2,11:36:53 AM,9,2,12:43:58 PM,16,27.1,,-1.2551895,36.7822034,-1.275034,36.881586,Rider_Id_730,4025
-Order_No_2202,User_Id_3149,Bike,3,Business,20,1,2:19:03 PM,20,1,2:19:32 PM,20,1,2:35:30 PM,20,1,2:46:53 PM,20,1,3:01:45 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_206,892
-Order_No_18116,User_Id_3557,Bike,3,Business,3,3,4:01:06 PM,3,3,4:02:41 PM,3,3,4:04:25 PM,3,3,4:28:36 PM,3,3,4:38:42 PM,2,26.8,,-1.2523598999999999,36.8017648,-1.2546732,36.808679600000005,Rider_Id_574,606
-Order_No_13622,User_Id_1482,Bike,3,Business,23,2,12:17:21 PM,23,2,12:23:40 PM,23,2,12:24:02 PM,23,2,12:49:11 PM,23,2,1:38:49 PM,23,18.1,,-1.3385790000000002,36.89630379999999,-1.2311903,36.8102105,Rider_Id_747,2978
-Order_No_15615,User_Id_1329,Bike,3,Business,16,2,7:12:16 PM,16,2,7:15:03 PM,16,2,7:22:55 PM,16,2,7:25:04 PM,16,2,7:43:38 PM,6,,,-1.273056,36.811298,-1.2832105,36.7777664,Rider_Id_461,1114
-Order_No_15720,User_Id_393,Bike,3,Business,6,4,10:43:44 AM,6,4,10:44:42 AM,6,4,10:57:57 AM,6,4,11:06:48 AM,6,4,11:19:46 AM,4,19.9,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_35,778
-Order_No_2914,User_Id_1569,Bike,2,Personal,7,5,2:52:13 PM,7,5,2:52:42 PM,7,5,3:00:41 PM,7,5,3:03:54 PM,7,5,3:36:49 PM,19,24.9,,-1.2627937,36.815695700000006,-1.314193,36.916132,Rider_Id_74,1975
-Order_No_21414,User_Id_3317,Bike,2,Personal,16,4,2:55:52 PM,16,4,2:56:39 PM,16,4,3:05:44 PM,16,4,3:13:13 PM,16,4,3:31:54 PM,4,19.2,,-1.2946581,36.779292100000006,-1.3014461000000002,36.7661381,Rider_Id_4,1121
-Order_No_8006,User_Id_2987,Bike,3,Business,31,5,3:25:07 PM,31,5,3:54:07 PM,31,5,3:59:30 PM,31,5,4:10:24 PM,31,5,4:19:46 PM,3,20.2,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_179,562
-Order_No_26314,User_Id_2828,Bike,3,Business,1,5,9:47:13 AM,1,5,11:50:11 AM,1,5,11:58:29 AM,1,5,12:06:43 PM,1,5,1:22:04 PM,14,28.7,,-1.2710687,36.8142839,-1.2137605,36.8788137,Rider_Id_52,4521
-Order_No_12932,User_Id_2102,Bike,3,Business,18,1,12:29:42 PM,18,1,12:29:55 PM,18,1,12:38:13 PM,18,1,12:43:20 PM,18,1,1:11:25 PM,15,26.7,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_650,1685
-Order_No_4923,User_Id_553,Bike,3,Business,2,2,5:01:44 PM,2,2,5:02:19 PM,2,2,5:12:30 PM,2,2,5:14:09 PM,2,2,5:24:01 PM,3,23.6,,-1.2551895,36.7822034,-1.2654219999999998,36.8059349,Rider_Id_383,592
-Order_No_7329,User_Id_1250,Bike,1,Personal,24,5,12:15:39 PM,24,5,12:16:28 PM,24,5,12:26:33 PM,24,5,12:29:10 PM,24,5,12:55:32 PM,14,20.8,,-1.2699441999999999,36.8206553,-1.3324865,36.8938303,Rider_Id_676,1582
-Order_No_23691,User_Id_111,Bike,3,Business,27,1,3:33:15 PM,27,1,3:33:36 PM,27,1,3:33:41 PM,27,1,3:39:11 PM,27,1,3:52:23 PM,5,23.3,,-1.2551895,36.7822034,-1.2561398000000001,36.8194374,Rider_Id_576,792
-Order_No_13700,User_Id_1852,Bike,1,Personal,8,1,8:07:45 AM,8,1,8:08:11 AM,8,1,8:28:05 AM,8,1,8:33:10 AM,8,1,8:56:24 AM,8,20.0,,-1.2657709,36.730574600000004,-1.2922857,36.781327399999995,Rider_Id_74,1394
-Order_No_24890,User_Id_933,Bike,2,Personal,31,4,4:58:43 PM,31,4,4:58:54 PM,31,4,5:04:22 PM,31,4,5:06:44 PM,31,4,5:47:50 PM,24,22.5,,-1.2137756000000002,36.7996657,-1.3217538999999998,36.8734832,Rider_Id_103,2466
-Order_No_10933,User_Id_1207,Bike,1,Business,7,6,4:33:55 PM,7,6,4:34:10 PM,7,6,4:36:45 PM,7,6,5:22:29 PM,7,6,5:52:09 PM,18,20.6,,-1.2745321000000003,36.765088,-1.2293754,36.862432399999996,Rider_Id_45,1780
-Order_No_25896,User_Id_3283,Bike,3,Business,27,3,4:07:48 PM,27,3,4:08:07 PM,27,3,4:14:08 PM,27,3,4:32:45 PM,27,3,4:33:07 PM,5,27.7,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_318,22
-Order_No_16652,User_Id_3669,Bike,3,Business,1,4,10:52:52 AM,1,4,10:53:07 AM,1,4,10:54:32 AM,1,4,11:16:35 AM,1,4,11:25:02 AM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_648,507
-Order_No_5115,User_Id_136,Bike,3,Business,3,5,3:44:25 PM,3,5,3:52:58 PM,3,5,4:07:59 PM,3,5,4:09:43 PM,3,5,4:27:49 PM,9,,,-1.2654716000000001,36.7983635,-1.2313147,36.80168629999999,Rider_Id_205,1086
-Order_No_10497,User_Id_3517,Bike,3,Business,9,2,11:22:32 AM,9,2,11:24:10 AM,9,2,11:37:18 AM,9,2,11:42:30 AM,9,2,12:11:59 PM,19,26.9,,-1.3211823999999999,36.868387299999995,-1.3882008,36.7699445,Rider_Id_715,1769
-Order_No_22569,User_Id_868,Bike,3,Business,27,6,12:30:02 PM,27,6,12:39:17 PM,27,6,1:02:53 PM,27,6,1:10:58 PM,27,6,1:22:43 PM,10,,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_484,705
-Order_No_27307,User_Id_1329,Bike,3,Business,2,4,1:58:58 PM,2,4,2:00:02 PM,2,4,2:06:46 PM,2,4,2:08:24 PM,2,4,2:35:43 PM,8,,,-1.273056,36.811298,-1.296268,36.785084000000005,Rider_Id_660,1639
-Order_No_8448,User_Id_627,Bike,3,Business,16,2,4:39:40 PM,16,2,4:44:41 PM,16,2,4:52:01 PM,16,2,4:57:16 PM,16,2,5:10:07 PM,4,24.9,,-1.2551895,36.7822034,-1.2728947,36.807942600000004,Rider_Id_795,771
-Order_No_3316,User_Id_3080,Bike,3,Business,7,4,10:58:38 AM,7,4,10:58:51 AM,7,4,11:17:10 AM,7,4,11:24:15 AM,7,4,12:32:52 PM,15,27.6,,-1.2551895,36.7822034,-1.3231725,36.864017700000005,Rider_Id_118,4117
-Order_No_6185,User_Id_2982,Bike,3,Business,23,2,9:37:09 AM,23,2,9:37:18 AM,23,2,9:38:31 AM,23,2,10:06:00 AM,23,2,10:35:53 AM,21,,,-1.300921,36.828195,-1.2239243,36.710118,Rider_Id_923,1793
-Order_No_11154,User_Id_2330,Bike,3,Business,1,3,12:40:24 PM,1,3,12:40:57 PM,1,3,12:47:27 PM,1,3,12:48:50 PM,1,3,1:35:41 PM,12,,,-1.2921463,36.8029626,-1.2687407,36.7373043,Rider_Id_440,2811
-Order_No_11493,User_Id_1280,Bike,3,Business,18,4,2:27:02 PM,18,4,2:27:38 PM,18,4,2:32:42 PM,18,4,2:44:29 PM,18,4,3:00:54 PM,8,,,-1.2551895,36.7822034,-1.2903864999999999,36.7957267,Rider_Id_322,985
-Order_No_8341,User_Id_3584,Bike,2,Personal,19,5,2:39:29 PM,19,5,3:05:45 PM,19,5,3:20:46 PM,19,5,3:28:17 PM,19,5,3:50:12 PM,9,27.5,,-1.3172395000000001,36.839269,-1.2517571,36.81702670000001,Rider_Id_622,1315
-Order_No_18540,User_Id_2532,Bike,3,Business,17,5,12:47:29 PM,17,5,12:47:41 PM,17,5,12:48:56 PM,17,5,12:51:28 PM,17,5,1:17:17 PM,8,,,-1.2551895,36.7822034,-1.3024794,36.8228284,Rider_Id_709,1549
-Order_No_4768,User_Id_2601,Bike,3,Business,7,4,11:59:32 AM,7,4,11:59:41 AM,7,4,12:05:19 PM,7,4,12:12:34 PM,7,4,12:21:21 PM,3,24.4,,-1.2528485,36.7953722,-1.2732506000000001,36.8132437,Rider_Id_379,527
-Order_No_11708,User_Id_517,Bike,3,Business,30,4,10:14:36 AM,30,4,10:15:05 AM,30,4,10:27:30 AM,30,4,10:47:24 AM,30,4,10:59:48 AM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,744
-Order_No_18119,User_Id_312,Bike,3,Personal,28,6,10:10:24 AM,28,6,10:10:57 AM,28,6,10:21:33 AM,28,6,10:28:56 AM,28,6,10:49:41 AM,12,17.7,,-1.2846899999999999,36.826112,-1.3257473,36.859206799999995,Rider_Id_754,1245
-Order_No_15226,User_Id_186,Bike,3,Business,14,4,8:23:48 AM,14,4,8:35:27 AM,14,4,9:18:59 AM,14,4,9:36:30 AM,14,4,11:01:58 AM,6,15.3,,-1.2902869,36.8085396,-1.2579303999999998,36.8019667,Rider_Id_159,5128
-Order_No_14870,User_Id_1346,Bike,3,Business,8,2,3:33:40 PM,8,2,3:36:57 PM,8,2,3:54:51 PM,8,2,3:56:19 PM,8,2,4:10:54 PM,2,25.4,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_38,875
-Order_No_849,User_Id_2784,Bike,3,Business,22,5,3:54:17 PM,22,5,4:03:42 PM,22,5,4:15:56 PM,22,5,4:17:23 PM,22,5,4:36:06 PM,4,31.1,,-1.2963097,36.7688221,-1.2981858,36.747492,Rider_Id_306,1123
-Order_No_8631,User_Id_816,Bike,3,Business,14,4,5:21:13 PM,14,4,5:37:39 PM,14,4,5:40:48 PM,14,4,6:03:58 PM,14,4,6:35:35 PM,7,26.3,,-1.290894,36.822971,-1.2546732,36.7923244,Rider_Id_724,1897
-Order_No_20321,User_Id_3686,Bike,3,Business,19,3,9:57:31 AM,19,3,10:14:09 AM,19,3,10:20:28 AM,19,3,10:24:13 AM,19,3,11:00:16 AM,15,19.7,,-1.2551895,36.7822034,-1.2186206,36.856336299999995,Rider_Id_453,2163
-Order_No_26129,User_Id_2344,Bike,3,Business,31,2,3:41:30 PM,31,2,3:41:42 PM,31,2,3:47:26 PM,31,2,3:59:43 PM,31,2,4:13:53 PM,4,22.0,,-1.2571472,36.795063299999995,-1.2809913999999998,36.8123591,Rider_Id_882,850
-Order_No_9277,User_Id_3661,Bike,3,Business,27,3,9:37:23 AM,27,3,9:37:32 AM,27,3,10:13:05 AM,27,3,10:19:50 AM,27,3,11:01:52 AM,23,,,-1.2134646,36.8755266,-1.2651573999999999,36.7474347,Rider_Id_445,2522
-Order_No_232,User_Id_3291,Bike,3,Business,11,2,11:15:16 AM,11,2,11:16:09 AM,11,2,11:18:09 AM,11,2,11:44:02 AM,11,2,12:06:37 PM,8,24.2,,-1.2600926000000001,36.8088685,-1.2999309,36.830799799999994,Rider_Id_895,1355
-Order_No_2296,User_Id_136,Bike,3,Business,25,2,1:17:17 PM,25,2,1:17:49 PM,25,2,1:26:29 PM,25,2,1:43:05 PM,25,2,2:09:11 PM,9,27.3,,-1.2950413,36.7651697,-1.2773427,36.8266035,Rider_Id_846,1566
-Order_No_6749,User_Id_3420,Bike,3,Business,6,3,9:45:41 AM,6,3,10:02:14 AM,6,3,10:10:05 AM,6,3,10:12:32 AM,6,3,10:25:31 AM,6,16.1,14.0,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_852,779
-Order_No_5877,User_Id_53,Bike,3,Business,14,5,1:39:33 PM,14,5,1:39:51 PM,14,5,1:39:55 PM,14,5,2:10:08 PM,14,5,2:23:18 PM,5,23.2,,-1.2770708,36.8231093,-1.29508,36.796863,Rider_Id_921,790
-Order_No_4802,User_Id_246,Bike,3,Business,21,6,3:38:04 PM,21,6,3:38:50 PM,21,6,3:46:07 PM,21,6,3:58:16 PM,21,6,4:02:05 PM,2,18.5,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_427,229
-Order_No_27066,User_Id_1077,Bike,3,Personal,3,5,2:06:36 PM,3,5,2:10:09 PM,3,5,2:32:25 PM,3,5,2:40:12 PM,3,5,2:58:23 PM,6,25.2,,-1.2882411,36.827495500000005,-1.2631940000000002,36.797624,Rider_Id_229,1091
-Order_No_8708,User_Id_633,Bike,3,Business,15,5,10:39:59 AM,15,5,10:41:04 AM,15,5,10:48:28 AM,15,5,10:51:26 AM,15,5,10:59:58 AM,4,26.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_289,512
-Order_No_15845,User_Id_2801,Bike,3,Business,7,3,11:23:41 AM,7,3,11:24:42 AM,7,3,11:25:11 AM,7,3,11:33:58 AM,7,3,11:34:29 AM,2,24.9,,-1.2595421999999998,36.7871178,-1.2635744,36.781677,Rider_Id_215,31
-Order_No_14146,User_Id_757,Bike,1,Personal,24,1,2:54:16 PM,24,1,2:55:24 PM,24,1,3:15:54 PM,24,1,3:18:36 PM,24,1,4:18:43 PM,6,,,-1.2943818,36.7858765,-1.2863371000000001,36.8200261,Rider_Id_328,3607
-Order_No_9422,User_Id_2927,Bike,2,Personal,3,3,3:32:22 PM,3,3,3:36:57 PM,3,3,3:57:33 PM,3,3,4:05:36 PM,3,3,4:30:17 PM,12,26.8,,-1.3472066,36.7692638,-1.285382,36.8162878,Rider_Id_726,1481
-Order_No_6543,User_Id_3291,Bike,3,Business,10,1,10:50:32 AM,10,1,11:04:09 AM,10,1,11:23:36 AM,10,1,11:30:10 AM,10,1,11:49:00 AM,11,23.5,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_626,1130
-Order_No_2061,User_Id_20,Bike,3,Business,17,3,2:29:09 PM,17,3,2:30:50 PM,17,3,2:57:48 PM,17,3,2:59:10 PM,17,3,3:31:01 PM,8,28.5,,-1.3090533000000002,36.8371502,-1.2586742,36.801643799999994,Rider_Id_565,1911
-Order_No_5445,User_Id_1069,Bike,1,Personal,25,5,3:14:19 PM,25,5,3:32:25 PM,25,5,3:58:03 PM,25,5,4:03:44 PM,25,5,4:46:41 PM,6,23.4,,-1.2806773999999999,36.8370763,-1.3020624,36.7904837,Rider_Id_852,2577
-Order_No_8947,User_Id_635,Bike,3,Business,29,6,10:34:57 AM,29,6,10:37:15 AM,29,6,10:52:37 AM,29,6,11:03:49 AM,29,6,11:29:00 AM,8,24.0,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_436,1511
-Order_No_6358,User_Id_978,Bike,1,Personal,7,4,1:55:23 PM,7,4,2:08:09 PM,7,4,2:34:46 PM,7,4,2:39:18 PM,7,4,3:05:20 PM,5,20.1,,-1.3084067,36.83450139999999,-1.2805312,36.832668700000006,Rider_Id_669,1562
-Order_No_4018,User_Id_1746,Bike,3,Business,13,3,12:02:16 PM,13,3,12:02:44 PM,13,3,12:06:31 PM,13,3,12:12:35 PM,13,3,12:38:31 PM,7,,,-1.2850639,36.8179454,-1.2975066000000002,36.76894229999999,Rider_Id_378,1556
-Order_No_11712,User_Id_3283,Bike,3,Business,24,3,12:29:02 PM,24,3,12:30:01 PM,24,3,1:15:41 PM,24,3,1:22:05 PM,24,3,1:42:20 PM,8,19.7,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_559,1215
-Order_No_23120,User_Id_136,Bike,3,Business,7,5,12:28:36 PM,7,5,12:29:47 PM,7,5,12:35:42 PM,7,5,12:41:07 PM,7,5,12:59:00 PM,2,,,-1.2654716000000001,36.7983635,-1.2760462,36.807469700000006,Rider_Id_401,1073
-Order_No_28142,User_Id_12,Bike,3,Business,28,1,12:45:55 PM,28,1,12:46:52 PM,28,1,1:15:30 PM,28,1,1:33:53 PM,28,1,2:15:50 PM,18,26.1,,-1.2492578,36.734851500000005,-1.2400209999999998,36.849990000000005,Rider_Id_240,2517
-Order_No_673,User_Id_2330,Bike,3,Business,29,3,3:29:27 PM,29,3,3:32:48 PM,29,3,4:16:48 PM,29,3,4:18:09 PM,29,3,4:50:05 PM,4,21.2,,-1.2642903,36.80021370000001,-1.2575406999999998,36.7868197,Rider_Id_582,1916
-Order_No_14649,User_Id_2718,Bike,3,Business,4,3,4:44:07 PM,4,3,5:06:45 PM,4,3,5:45:01 PM,4,3,5:58:32 PM,4,3,6:50:59 PM,18,,,-1.3103985,36.9114173,-1.2601,36.796528,Rider_Id_248,3147
-Order_No_8608,User_Id_391,Bike,3,Business,18,1,12:41:56 PM,18,1,12:46:57 PM,18,1,12:59:54 PM,18,1,1:24:21 PM,18,1,2:20:30 PM,13,26.7,,-1.300921,36.828195,-1.2449522,36.7580025,Rider_Id_262,3369
-Order_No_14893,User_Id_991,Bike,2,Personal,23,3,8:20:02 AM,23,3,8:20:36 AM,23,3,8:32:25 AM,23,3,8:35:29 AM,23,3,8:43:55 AM,3,16.6,16.0,-1.2455165000000001,36.7909394,-1.2626765,36.795598999999996,Rider_Id_103,506
-Order_No_8918,User_Id_635,Bike,3,Business,30,4,3:05:22 PM,30,4,3:05:35 PM,30,4,3:05:45 PM,30,4,3:57:47 PM,30,4,4:05:23 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_70,456
-Order_No_14366,User_Id_970,Bike,3,Business,30,3,1:03:35 PM,30,3,1:04:56 PM,30,3,1:12:29 PM,30,3,1:19:29 PM,30,3,1:36:13 PM,6,25.0,,-1.2551895,36.7822034,-1.249526,36.7711579,Rider_Id_925,1004
-Order_No_26541,User_Id_1075,Bike,3,Business,18,1,12:43:35 PM,18,1,12:44:09 PM,18,1,12:47:22 PM,18,1,1:10:07 PM,18,1,1:17:06 PM,2,27.9,,-1.2571472,36.795063299999995,-1.2527958000000001,36.8003131,Rider_Id_131,419
-Order_No_27918,User_Id_215,Bike,3,Personal,24,2,5:07:58 PM,24,2,5:08:19 PM,24,2,5:28:17 PM,24,2,5:32:10 PM,24,2,5:45:32 PM,5,18.8,,-1.2833620000000001,36.8260149,-1.2735166,36.801255600000005,Rider_Id_670,802
-Order_No_4388,User_Id_540,Bike,3,Business,3,3,9:25:16 AM,3,3,9:25:46 AM,3,3,9:34:45 AM,3,3,10:29:41 AM,3,3,10:46:32 AM,8,18.3,,-1.2945684,36.8154093,-1.2551895,36.7822034,Rider_Id_12,1011
-Order_No_21670,User_Id_2987,Bike,3,Business,30,3,2:01:54 PM,30,3,2:05:20 PM,30,3,2:14:33 PM,30,3,2:16:04 PM,30,3,2:30:02 PM,4,25.3,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_379,838
-Order_No_11288,User_Id_1469,Bike,3,Business,20,1,8:23:00 AM,20,1,8:24:43 AM,20,1,8:56:25 AM,20,1,9:01:17 AM,20,1,9:23:36 AM,15,19.0,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_155,1339
-Order_No_17481,User_Id_867,Bike,3,Business,8,4,7:37:20 AM,8,4,8:41:41 AM,8,4,9:03:08 AM,8,4,9:34:23 AM,8,4,10:15:26 AM,10,20.7,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_208,2463
-Order_No_23468,User_Id_2587,Bike,3,Business,31,4,10:27:58 AM,31,4,10:32:48 AM,31,4,10:45:24 AM,31,4,11:02:10 AM,31,4,11:14:35 AM,5,26.6,,-1.2620912,36.8013562,-1.28878,36.8168312,Rider_Id_489,745
-Order_No_20541,User_Id_71,Bike,3,Business,19,1,5:21:19 PM,19,1,5:30:11 PM,19,1,5:44:34 PM,19,1,6:03:00 PM,19,1,6:05:32 PM,2,23.7,,-1.2793686000000002,36.82592329999999,-1.2891921999999998,36.825189,Rider_Id_237,152
-Order_No_1855,User_Id_2386,Bike,1,Personal,14,6,12:32:26 PM,14,6,12:33:30 PM,14,6,12:49:35 PM,14,6,12:55:56 PM,14,6,1:23:36 PM,6,17.6,,-1.2965169,36.793521999999996,-1.3186521999999998,36.769988399999995,Rider_Id_465,1660
-Order_No_22627,User_Id_972,Bike,3,Business,29,1,10:44:24 AM,29,1,10:44:51 AM,29,1,10:52:55 AM,29,1,10:54:01 AM,29,1,11:21:16 AM,10,,,-1.2862033000000002,36.8152966,-1.3211235000000001,36.7765164,Rider_Id_250,1635
-Order_No_9538,User_Id_2899,Bike,3,Personal,22,5,4:20:33 PM,22,5,4:24:49 PM,22,5,4:33:45 PM,22,5,4:35:02 PM,22,5,4:50:51 PM,3,28.6,,-1.2808705,36.768482399999996,-1.298762,36.763645600000004,Rider_Id_269,949
-Order_No_12443,User_Id_1255,Bike,1,Personal,15,1,1:46:56 PM,15,1,1:48:53 PM,15,1,2:19:01 PM,15,1,2:23:50 PM,15,1,2:42:17 PM,11,,,-1.2833895,36.8212908,-1.2218778000000001,36.820601700000005,Rider_Id_832,1107
-Order_No_20781,User_Id_633,Bike,3,Business,15,2,11:45:04 AM,15,2,11:52:56 AM,15,2,12:04:21 PM,15,2,12:08:26 PM,15,2,12:37:36 PM,9,24.3,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_80,1750
-Order_No_11559,User_Id_1243,Bike,1,Business,15,3,2:08:47 PM,15,3,2:09:24 PM,15,3,2:21:24 PM,15,3,2:25:23 PM,15,3,2:42:29 PM,7,21.8,,-1.304524,36.775371,-1.3034396000000001,36.832999799999996,Rider_Id_754,1026
-Order_No_13977,User_Id_2216,Bike,3,Business,10,3,11:41:28 AM,10,3,12:43:04 PM,10,3,12:45:35 PM,10,3,1:02:16 PM,10,3,1:02:58 PM,4,27.6,,-1.2551895,36.7822034,-1.2519787,36.780649100000005,Rider_Id_258,42
-Order_No_13269,User_Id_1597,Bike,1,Personal,28,1,11:51:49 AM,28,1,11:52:24 AM,28,1,11:59:51 AM,28,1,12:09:20 PM,28,1,12:28:03 PM,5,22.9,,-1.3075974,36.8100855,-1.3010058999999998,36.8216816,Rider_Id_749,1123
-Order_No_23152,User_Id_2200,Bike,3,Business,11,4,11:20:20 AM,11,4,11:25:08 AM,11,4,11:50:47 AM,11,4,12:02:45 PM,11,4,12:33:39 PM,9,23.9,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_622,1854
-Order_No_9678,User_Id_706,Bike,3,Business,18,2,3:31:39 PM,18,2,3:32:33 PM,18,2,3:32:42 PM,18,2,4:03:05 PM,18,2,4:03:08 PM,5,25.8,,-1.295723,36.822691999999996,-1.3077869,36.844320700000004,Rider_Id_848,3
-Order_No_12075,User_Id_1304,Bike,3,Business,22,5,3:35:58 PM,22,5,3:37:03 PM,22,5,3:49:32 PM,22,5,3:58:27 PM,22,5,4:19:19 PM,8,27.2,,-1.2551895,36.7822034,-1.2876803999999997,36.7802778,Rider_Id_831,1252
-Order_No_17537,User_Id_393,Bike,3,Business,7,5,9:38:17 AM,7,5,9:39:03 AM,7,5,9:40:10 AM,7,5,9:47:11 AM,7,5,9:57:19 AM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_35,608
-Order_No_23324,User_Id_3689,Bike,3,Business,6,4,1:29:44 PM,6,4,1:29:57 PM,6,4,1:40:24 PM,6,4,2:16:00 PM,6,4,2:34:36 PM,6,23.4,,-1.281754,36.8324229,-1.2527958000000001,36.8003131,Rider_Id_882,1116
-Order_No_22477,User_Id_3565,Bike,3,Business,8,5,11:53:52 AM,8,5,11:54:15 AM,8,5,12:00:37 PM,8,5,12:05:49 PM,8,5,12:38:51 PM,21,24.0,,-1.2844665000000002,36.7866113,-1.391222,36.760676000000004,Rider_Id_368,1982
-Order_No_22837,User_Id_1996,Bike,3,Business,4,4,2:41:03 PM,4,4,2:45:16 PM,4,4,3:08:01 PM,4,4,3:22:02 PM,4,4,3:43:51 PM,7,27.4,,-1.2946358999999998,36.8352718,-1.2640573,36.8085528,Rider_Id_686,1309
-Order_No_20459,User_Id_2222,Bike,3,Business,28,1,12:23:44 PM,28,1,12:26:40 PM,28,1,12:28:09 PM,28,1,12:34:34 PM,28,1,1:12:33 PM,10,22.8,,-1.2930128,36.8356869,-1.3277669,36.8975169,Rider_Id_633,2279
-Order_No_14089,User_Id_552,Bike,3,Business,13,3,11:06:42 AM,13,3,11:07:06 AM,13,3,12:03:01 PM,13,3,12:14:52 PM,13,3,12:51:22 PM,20,,,-1.3562374,36.904295399999995,-1.2569503,36.78347279999999,Rider_Id_300,2190
-Order_No_9532,User_Id_1882,Bike,3,Business,29,5,9:00:05 AM,29,5,9:01:18 AM,29,5,9:26:32 AM,29,5,9:30:28 AM,29,5,10:06:50 AM,17,20.7,,-1.2877809999999998,36.817865999999995,-1.323615,36.700475,Rider_Id_448,2182
-Order_No_15871,User_Id_1570,Bike,3,Personal,13,5,2:09:17 PM,13,5,2:12:51 PM,13,5,2:18:35 PM,13,5,2:37:22 PM,13,5,3:25:32 PM,7,19.4,,-1.2903841999999999,36.8110684,-1.2793831,36.7617573,Rider_Id_431,2890
-Order_No_3306,User_Id_265,Bike,3,Business,22,5,9:36:14 AM,22,5,9:36:56 AM,22,5,9:56:04 AM,22,5,9:58:04 AM,22,5,10:20:54 AM,13,22.0,,-1.3450936000000002,36.7457914,-1.300921,36.828195,Rider_Id_506,1370
-Order_No_17166,User_Id_635,Bike,3,Business,11,1,3:29:45 PM,11,1,3:34:02 PM,11,1,3:50:17 PM,11,1,4:09:38 PM,11,1,4:14:16 PM,4,28.8,,-1.2602337,36.79905479999999,-1.2691495000000002,36.7928193,Rider_Id_86,278
-Order_No_936,User_Id_2454,Bike,3,Business,6,3,5:11:10 PM,6,3,5:11:23 PM,6,3,5:20:13 PM,6,3,5:26:26 PM,6,3,5:35:43 PM,4,26.1,,-1.2993231,36.7753372,-1.296098,36.798225,Rider_Id_246,557
-Order_No_10730,User_Id_3299,Bike,3,Personal,30,4,12:13:31 PM,30,4,12:25:23 PM,30,4,12:38:57 PM,30,4,12:51:19 PM,30,4,1:31:14 PM,16,,,-1.3339943,36.7263409,-1.2669736,36.8040985,Rider_Id_538,2395
-Order_No_28248,User_Id_1075,Bike,3,Business,25,5,1:37:39 PM,25,5,1:38:50 PM,25,5,1:51:19 PM,25,5,1:54:18 PM,25,5,2:23:38 PM,5,29.2,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_268,1760
-Order_No_21599,User_Id_2649,Bike,3,Business,3,1,2:40:26 PM,3,1,2:40:42 PM,3,1,2:45:28 PM,3,1,2:48:36 PM,3,1,3:30:42 PM,14,23.1,,-1.2752846000000002,36.802702200000006,-1.2067510000000001,36.8356839,Rider_Id_706,2526
-Order_No_2573,User_Id_1417,Bike,3,Business,8,3,3:57:52 PM,8,3,3:58:45 PM,8,3,4:09:39 PM,8,3,4:14:22 PM,8,3,4:30:06 PM,5,,,-1.2985749,36.8088,-1.2741728,36.79691079999999,Rider_Id_607,944
-Order_No_3975,User_Id_3300,Bike,3,Business,6,4,3:41:51 PM,6,4,3:43:58 PM,6,4,3:54:24 PM,6,4,4:10:10 PM,6,4,4:36:44 PM,7,21.5,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_427,1594
-Order_No_17304,User_Id_393,Bike,3,Business,7,2,9:00:37 AM,7,2,9:02:10 AM,7,2,9:08:18 AM,7,2,9:13:40 AM,7,2,10:30:30 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_778,4610
-Order_No_26275,User_Id_1724,Bike,3,Business,20,3,4:41:07 PM,20,3,4:43:58 PM,20,3,5:24:11 PM,20,3,5:29:29 PM,20,3,6:10:06 PM,12,21.4,,-1.2551895,36.7822034,-1.3200587,36.838058000000004,Rider_Id_498,2437
-Order_No_14762,User_Id_882,Bike,1,Personal,4,5,11:32:16 AM,4,5,11:50:26 AM,4,5,11:57:20 AM,4,5,12:05:10 PM,4,5,12:29:34 PM,6,22.3,,-1.2862659,36.8264616,-1.2538731,36.7850223,Rider_Id_531,1464
-Order_No_19755,User_Id_3135,Bike,3,Personal,6,3,12:29:10 PM,6,3,12:31:58 PM,6,3,1:17:20 PM,6,3,1:20:39 PM,6,3,2:03:19 PM,18,20.2,,-1.277585,36.77145,-1.3194518,36.869396,Rider_Id_474,2560
-Order_No_4804,User_Id_2200,Bike,3,Business,31,5,5:35:00 PM,31,5,5:55:16 PM,31,5,6:10:22 PM,31,5,6:13:48 PM,31,5,6:41:30 PM,4,23.6,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_20,1662
-Order_No_26260,User_Id_2330,Bike,3,Business,7,5,9:42:11 AM,7,5,9:42:39 AM,7,5,9:45:38 AM,7,5,9:49:50 AM,7,5,9:55:44 AM,3,,,-1.2915246999999999,36.805575700000006,-1.3040812,36.7924217,Rider_Id_829,354
-Order_No_25116,User_Id_1882,Bike,3,Business,31,3,3:35:57 PM,31,3,3:51:04 PM,31,3,4:08:01 PM,31,3,4:24:06 PM,31,3,4:52:39 PM,9,27.2,,-1.3169591,36.7000029,-1.3014461000000002,36.7661381,Rider_Id_548,1713
-Order_No_24408,User_Id_2454,Bike,3,Business,28,4,5:08:07 PM,28,4,5:08:54 PM,28,4,5:31:25 PM,28,4,5:43:03 PM,28,4,6:17:45 PM,15,25.8,,-1.2286304,36.8422473,-1.2993231,36.7753372,Rider_Id_907,2082
-Order_No_7287,User_Id_2311,Bike,3,Business,9,3,10:03:10 AM,9,3,10:58:28 AM,9,3,11:02:25 AM,9,3,11:06:38 AM,9,3,11:27:08 AM,6,21.3,,-1.2844795,36.7817533,-1.3012902,36.80964,Rider_Id_676,1230
-Order_No_2614,User_Id_3295,Bike,3,Business,26,5,4:28:25 PM,26,5,4:28:34 PM,26,5,4:34:56 PM,26,5,4:36:46 PM,26,5,4:48:55 PM,4,,,-1.2760915000000002,36.8081029,-1.2571472,36.795063299999995,Rider_Id_620,729
-Order_No_15399,User_Id_2336,Bike,1,Personal,6,3,10:59:14 AM,6,3,11:13:24 AM,6,3,11:15:10 AM,6,3,11:35:55 AM,6,3,11:56:25 AM,10,20.0,,-1.2248308,36.801257299999996,-1.263099,36.7879186,Rider_Id_116,1230
-Order_No_24234,User_Id_393,Bike,3,Business,14,2,12:20:47 PM,14,2,12:21:49 PM,14,2,12:35:45 PM,14,2,12:46:12 PM,14,2,12:56:25 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,613
-Order_No_24089,User_Id_2200,Bike,3,Business,24,5,11:38:24 AM,24,5,11:38:35 AM,24,5,11:50:56 AM,24,5,11:59:27 AM,24,5,12:17:04 PM,6,20.9,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_88,1057
-Order_No_12755,User_Id_2174,Bike,3,Business,16,4,1:13:15 PM,16,4,1:17:24 PM,16,4,1:19:56 PM,16,4,1:46:12 PM,16,4,3:07:31 PM,10,18.8,,-1.2482781,36.884091999999995,-1.2782261000000001,36.822500899999994,Rider_Id_307,4879
-Order_No_14210,User_Id_2523,Bike,3,Business,10,5,2:58:41 PM,10,5,2:59:06 PM,10,5,3:00:49 PM,10,5,3:14:24 PM,10,5,3:14:30 PM,2,22.0,,-1.2551895,36.7822034,-1.2629944,36.7998816,Rider_Id_541,6
-Order_No_10349,User_Id_3647,Bike,3,Business,1,5,11:39:54 AM,1,5,11:41:20 AM,1,5,11:53:00 AM,1,5,12:00:14 PM,1,5,12:14:15 PM,4,26.6,,-1.2726389999999999,36.794723,-1.2753108999999998,36.8130185,Rider_Id_167,841
-Order_No_2729,User_Id_3551,Bike,1,Personal,9,3,12:54:49 PM,9,3,1:02:31 PM,9,3,1:08:46 PM,9,3,1:13:44 PM,9,3,1:40:34 PM,10,20.9,,-1.2850638,36.813096,-1.2295205,36.812042600000005,Rider_Id_865,1610
-Order_No_19264,User_Id_1148,Bike,3,Business,10,5,9:53:50 AM,10,5,9:57:55 AM,10,5,10:38:44 AM,10,5,10:53:55 AM,10,5,11:04:37 AM,3,19.2,,-1.3100586,36.850032899999995,-1.311818,36.8638544,Rider_Id_519,642
-Order_No_16725,User_Id_2423,Bike,3,Business,30,2,10:55:26 AM,30,2,10:58:34 AM,30,2,11:03:04 AM,30,2,11:18:25 AM,30,2,11:50:13 AM,4,,,-1.2551895,36.7822034,-1.2684437,36.810097,Rider_Id_291,1908
-Order_No_18358,User_Id_59,Bike,3,Business,29,1,4:54:49 PM,29,1,4:55:01 PM,29,1,5:00:49 PM,29,1,5:06:38 PM,29,1,5:34:05 PM,19,23.5,,-1.290894,36.822971,-1.1917753,36.9211989,Rider_Id_865,1647
-Order_No_470,User_Id_2358,Bike,4,Business,29,3,5:35:23 PM,29,3,5:36:36 PM,29,3,5:46:35 PM,29,3,5:58:38 PM,29,3,6:18:25 PM,2,19.2,,-1.302222,36.75873,-1.296797,36.776452500000005,Rider_Id_846,1187
-Order_No_6804,User_Id_633,Bike,3,Business,5,1,9:29:10 AM,5,1,10:17:33 AM,5,1,10:57:26 AM,5,1,11:57:10 AM,5,1,12:15:06 PM,4,24.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_593,1076
-Order_No_23804,User_Id_3718,Bike,3,Business,10,1,10:34:38 AM,10,1,10:48:49 AM,10,1,11:02:00 AM,10,1,11:39:49 AM,10,1,11:53:42 AM,5,23.5,,-1.2972834999999998,36.809687,-1.2650027,36.8126242,Rider_Id_50,833
-Order_No_25198,User_Id_533,Bike,3,Business,5,2,3:04:37 PM,5,2,3:49:28 PM,5,2,3:53:25 PM,5,2,4:08:30 PM,5,2,4:55:31 PM,17,,,-1.3637665,36.7269036,-1.2765492,36.7669809,Rider_Id_779,2821
-Order_No_16252,User_Id_2184,Bike,1,Personal,13,1,4:42:08 PM,13,1,4:42:36 PM,13,1,4:50:45 PM,13,1,4:57:53 PM,13,1,5:16:29 PM,7,19.4,,-1.2764786,36.81947879999999,-1.2832014999999999,36.7862218,Rider_Id_754,1116
-Order_No_27625,User_Id_567,Bike,3,Business,20,3,10:40:55 AM,20,3,10:42:27 AM,20,3,10:50:50 AM,20,3,10:53:20 AM,20,3,11:16:10 AM,8,26.5,,-1.2963097,36.7688221,-1.2753108999999998,36.8130185,Rider_Id_269,1370
-Order_No_8712,User_Id_1402,Bike,3,Business,10,1,11:51:45 AM,10,1,11:55:03 AM,10,1,12:13:51 PM,10,1,12:24:05 PM,10,1,12:44:35 PM,1,18.3,,-1.2793686000000002,36.82592329999999,-1.2813012,36.832396200000005,Rider_Id_739,1230
-Order_No_12195,User_Id_3221,Bike,3,Business,3,4,2:09:06 PM,3,4,2:09:55 PM,3,4,2:10:08 PM,3,4,3:15:53 PM,3,4,3:57:21 PM,25,25.9,,-1.199323,36.92053,-1.3064585,36.8119065,Rider_Id_387,2488
-Order_No_10678,User_Id_3517,Bike,3,Business,20,1,9:15:13 AM,20,1,9:15:39 AM,20,1,9:36:30 AM,20,1,10:08:39 AM,20,1,10:35:40 AM,7,14.2,,-1.2696795,36.8640088,-1.300921,36.828195,Rider_Id_467,1621
-Order_No_16554,User_Id_2308,Bike,3,Business,2,2,12:37:30 PM,2,2,12:37:55 PM,2,2,12:45:11 PM,2,2,1:03:01 PM,2,2,1:44:01 PM,20,21.1,,-1.2963097,36.7688221,-1.3123562,36.9157558,Rider_Id_247,2460
-Order_No_10298,User_Id_2631,Bike,1,Personal,5,4,11:38:28 AM,5,4,12:10:48 PM,5,4,12:18:54 PM,5,4,12:22:17 PM,5,4,12:33:58 PM,4,,,-1.2845871000000002,36.8234834,-1.269059,36.8108027,Rider_Id_629,701
-Order_No_19308,User_Id_1482,Bike,3,Business,17,6,12:39:14 PM,17,6,12:41:02 PM,17,6,12:55:06 PM,17,6,12:56:09 PM,17,6,1:40:22 PM,24,23.9,,-1.3385790000000002,36.89630379999999,-1.3981712,36.7673241,Rider_Id_751,2653
-Order_No_17274,User_Id_478,Bike,3,Business,2,4,10:25:19 AM,2,4,11:18:17 AM,2,4,11:30:05 AM,2,4,11:41:59 AM,2,4,12:01:28 PM,9,22.7,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_101,1169
-Order_No_11654,User_Id_627,Bike,3,Business,22,5,10:30:55 AM,22,5,10:35:56 AM,22,5,10:40:24 AM,22,5,10:44:36 AM,22,5,11:10:02 AM,7,28.5,,-1.2551895,36.7822034,-1.2821653,36.81997929999999,Rider_Id_709,1526
-Order_No_5072,User_Id_1990,Bike,3,Business,20,1,2:16:34 PM,20,1,2:21:59 PM,20,1,2:23:20 PM,20,1,2:58:07 PM,20,1,3:55:42 PM,9,,,-1.2854453000000001,36.818836299999994,-1.275442,36.766020700000006,Rider_Id_564,3455
-Order_No_24928,User_Id_900,Bike,2,Personal,3,3,10:42:05 AM,3,3,10:42:11 AM,3,3,10:49:27 AM,3,3,10:55:01 AM,3,3,11:36:46 AM,35,24.2,,-1.3508036,36.7093385,-1.3513864,36.9033371,Rider_Id_43,2505
-Order_No_26310,User_Id_1500,Bike,3,Business,15,2,3:45:24 PM,15,2,3:45:43 PM,15,2,3:47:16 PM,15,2,3:54:03 PM,15,2,4:34:05 PM,12,24.4,,-1.2574219,36.7927073,-1.32256,36.8396386,Rider_Id_931,2402
-Order_No_13543,User_Id_2271,Bike,3,Business,21,2,9:34:14 AM,21,2,9:35:29 AM,21,2,9:44:40 AM,21,2,9:56:32 AM,21,2,10:13:54 AM,8,,,-1.293515,36.897607,-1.3322116000000002,36.890378999999996,Rider_Id_377,1042
-Order_No_2507,User_Id_2700,Bike,3,Business,17,5,4:56:51 PM,17,5,5:09:07 PM,17,5,5:24:14 PM,17,5,5:32:38 PM,17,5,5:46:58 PM,3,19.1,,-1.293973,36.80169,-1.2867483,36.8196599,Rider_Id_79,860
-Order_No_4477,User_Id_2804,Bike,3,Business,25,4,12:57:22 PM,25,4,1:00:57 PM,25,4,1:16:17 PM,25,4,1:20:22 PM,25,4,1:43:56 PM,12,20.2,,-1.2551895,36.7822034,-1.2128205,36.8026175,Rider_Id_612,1414
-Order_No_595,User_Id_1304,Bike,3,Business,17,5,1:32:49 PM,17,5,1:43:24 PM,17,5,1:58:20 PM,17,5,2:03:34 PM,17,5,2:24:04 PM,6,,,-1.2551895,36.7822034,-1.2855666000000001,36.819492600000004,Rider_Id_709,1230
-Order_No_5304,User_Id_2200,Bike,3,Business,2,2,11:28:39 AM,2,2,11:31:48 AM,2,2,11:40:57 AM,2,2,12:44:11 PM,2,2,1:01:38 PM,6,26.3,,-1.2534301,36.800098299999995,-1.290894,36.822971,Rider_Id_436,1047
-Order_No_7736,User_Id_3647,Bike,3,Business,10,1,2:59:12 PM,10,1,3:20:43 PM,10,1,3:45:08 PM,10,1,3:52:36 PM,10,1,4:09:04 PM,5,18.2,,-1.2726389999999999,36.794723,-1.2833106,36.82402379999999,Rider_Id_155,988
-Order_No_3689,User_Id_419,Bike,3,Business,27,5,11:58:01 AM,27,5,11:59:40 AM,27,5,12:00:27 PM,27,5,12:24:34 PM,27,5,12:49:14 PM,14,22.4,,-1.2571472,36.795063299999995,-1.2238103,36.8772588,Rider_Id_536,1480
-Order_No_18620,User_Id_3239,Bike,3,Personal,8,5,11:36:20 AM,8,5,11:38:17 AM,8,5,11:42:07 AM,8,5,11:58:12 AM,8,5,12:33:39 PM,10,,,-1.2839322,36.8292426,-1.3332749,36.870814700000004,Rider_Id_846,2127
-Order_No_6249,User_Id_3060,Bike,3,Business,24,3,11:28:13 AM,24,3,11:29:55 AM,24,3,11:51:48 AM,24,3,12:39:01 PM,24,3,1:14:55 PM,5,,,-1.3024794,36.8228284,-1.2764098999999998,36.8208451,Rider_Id_155,2154
-Order_No_1379,User_Id_127,Bike,1,Personal,24,7,10:40:35 AM,24,7,10:43:31 AM,24,7,10:54:11 AM,24,7,11:05:07 AM,24,7,11:29:19 AM,12,,,-1.3068736,36.887004,-1.311943,36.833113399999995,Rider_Id_890,1452
-Order_No_9968,User_Id_633,Bike,3,Business,20,2,12:24:26 PM,20,2,12:38:11 PM,20,2,12:52:00 PM,20,2,12:56:00 PM,20,2,1:23:35 PM,9,19.9,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_219,1655
-Order_No_20918,User_Id_201,Bike,3,Business,4,2,8:38:52 AM,4,2,8:47:35 AM,4,2,8:58:15 AM,4,2,9:07:33 AM,4,2,9:21:46 AM,6,16.3,99.0,-1.2551895,36.7822034,-1.2816948000000001,36.8252304,Rider_Id_407,853
-Order_No_5880,User_Id_611,Bike,1,Personal,1,7,6:37:37 PM,1,7,6:48:06 PM,1,7,7:07:09 PM,1,7,7:13:33 PM,1,7,7:52:43 PM,12,20.9,,-1.29603,36.739171999999996,-1.3267091,36.778587200000004,Rider_Id_105,2350
-Order_No_13465,User_Id_124,Bike,3,Personal,29,2,1:16:37 PM,29,2,1:17:55 PM,29,2,1:26:38 PM,29,2,1:35:14 PM,29,2,2:01:31 PM,4,23.9,,-1.2714020000000001,36.8238135,-1.2580991000000001,36.794151899999996,Rider_Id_279,1577
-Order_No_22382,User_Id_3560,Bike,3,Business,28,4,3:19:18 PM,28,4,3:19:41 PM,28,4,3:52:58 PM,28,4,4:14:53 PM,28,4,4:15:02 PM,6,27.3,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_569,9
-Order_No_16949,User_Id_1705,Bike,3,Business,7,3,10:50:12 AM,7,3,11:25:54 AM,7,3,11:30:02 AM,7,3,11:33:27 AM,7,3,11:45:47 AM,6,25.2,,-1.2551895,36.7822034,-1.2884799,36.82179179999999,Rider_Id_88,740
-Order_No_7979,User_Id_2956,Bike,3,Business,28,2,3:45:58 PM,28,2,3:49:48 PM,28,2,3:53:06 PM,28,2,4:01:59 PM,28,2,4:20:54 PM,7,23.4,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_882,1135
-Order_No_15915,User_Id_1292,Bike,3,Business,10,1,11:17:39 AM,10,1,11:19:13 AM,10,1,11:26:30 AM,10,1,11:31:09 AM,10,1,11:52:41 AM,8,23.5,,-1.3025826,36.7670807,-1.3169591,36.7000029,Rider_Id_24,1292
-Order_No_8911,User_Id_3651,Bike,3,Business,14,4,8:14:27 AM,14,4,8:14:54 AM,14,4,8:17:32 AM,14,4,8:20:15 AM,14,4,8:42:35 AM,8,15.5,,-1.2579303999999998,36.8019667,-1.22556,36.80813,Rider_Id_726,1340
-Order_No_2157,User_Id_393,Bike,3,Business,6,3,11:19:36 AM,6,3,11:19:49 AM,6,3,11:20:20 AM,6,3,11:41:59 AM,6,3,11:44:13 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,134
-Order_No_21819,User_Id_3488,Bike,2,Personal,29,6,12:17:25 PM,29,6,12:17:48 PM,29,6,12:30:39 PM,29,6,12:37:47 PM,29,6,1:01:23 PM,8,25.1,,-1.3002897,36.76016370000001,-1.268478,36.800814200000005,Rider_Id_465,1416
-Order_No_13573,User_Id_3222,Bike,3,Business,26,5,9:49:45 AM,26,5,9:50:14 AM,26,5,10:10:09 AM,26,5,10:15:17 AM,26,5,10:49:34 AM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_155,2057
-Order_No_4158,User_Id_2216,Bike,3,Business,5,2,12:41:45 PM,5,2,1:00:32 PM,5,2,1:13:06 PM,5,2,1:49:34 PM,5,2,2:29:13 PM,17,,,-1.2867627000000001,36.883446,-1.2876803999999997,36.7802778,Rider_Id_699,2379
-Order_No_7192,User_Id_2200,Bike,3,Business,5,5,11:25:53 AM,5,5,11:27:07 AM,5,5,11:44:54 AM,5,5,11:46:00 AM,5,5,12:39:39 PM,6,24.6,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_831,3219
-Order_No_23508,User_Id_3064,Bike,3,Business,12,3,3:30:23 PM,12,3,3:34:36 PM,12,3,3:41:53 PM,12,3,3:46:17 PM,12,3,3:54:02 PM,3,20.9,,-1.2622145,36.8087391,-1.2571472,36.795063299999995,Rider_Id_205,465
-Order_No_18474,User_Id_2641,Bike,3,Business,20,3,10:32:13 AM,20,3,10:32:46 AM,20,3,10:51:33 AM,20,3,11:48:31 AM,20,3,12:10:36 PM,12,26.4,,-1.3103985,36.9114173,-1.3263105,36.8622904,Rider_Id_883,1325
-Order_No_28228,User_Id_2468,Bike,3,Business,23,3,9:06:40 AM,23,3,9:06:52 AM,23,3,9:57:35 AM,23,3,10:06:25 AM,23,3,10:44:28 AM,15,,,-1.2519268,36.695314200000006,-1.2857671,36.7875614,Rider_Id_64,2283
-Order_No_2714,User_Id_2195,Bike,3,Business,15,6,1:25:36 PM,15,6,1:25:54 PM,15,6,1:38:20 PM,15,6,1:41:47 PM,15,6,1:54:11 PM,6,21.4,,-1.2297799999999999,36.836215,-1.228911,36.881876,Rider_Id_8,744
-Order_No_9980,User_Id_3557,Bike,3,Business,25,4,3:34:51 PM,25,4,3:36:14 PM,25,4,3:48:12 PM,25,4,4:28:03 PM,25,4,4:30:29 PM,3,20.8,,-1.269534,36.80697,-1.2546732,36.808679600000005,Rider_Id_215,146
-Order_No_9142,User_Id_1271,Bike,3,Business,20,2,9:36:20 AM,20,2,9:52:35 AM,20,2,10:31:44 AM,20,2,10:53:21 AM,20,2,11:51:17 AM,32,19.7,,-1.3244885,36.897791999999995,-1.243638,36.684333,Rider_Id_669,3476
-Order_No_16964,User_Id_3064,Bike,3,Business,22,4,9:34:33 AM,22,4,10:02:30 AM,22,4,10:18:20 AM,22,4,10:26:37 AM,22,4,10:45:58 AM,6,19.8,3.1,-1.2571472,36.795063299999995,-1.3012618999999999,36.821369700000005,Rider_Id_114,1161
-Order_No_16091,User_Id_2100,Bike,3,Business,1,1,4:15:17 PM,1,1,4:17:24 PM,1,1,4:18:03 PM,1,1,4:20:43 PM,1,1,5:03:28 PM,15,25.8,,-1.3472066,36.7692638,-1.2765736,36.851364600000004,Rider_Id_857,2565
-Order_No_18044,User_Id_393,Bike,3,Business,4,2,12:29:29 PM,4,2,12:30:31 PM,4,2,12:34:28 PM,4,2,12:41:45 PM,4,2,1:08:23 PM,11,20.6,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_36,1598
-Order_No_17523,User_Id_302,Bike,3,Business,26,7,7:47:24 PM,26,7,7:47:45 PM,26,7,8:18:26 PM,26,7,8:21:27 PM,26,7,8:30:49 PM,2,19.5,,-1.2649605,36.798177700000004,-1.252973,36.8165107,Rider_Id_40,562
-Order_No_2242,User_Id_1642,Bike,3,Business,27,3,2:09:37 PM,27,3,2:10:05 PM,27,3,2:16:53 PM,27,3,2:18:17 PM,27,3,2:30:42 PM,5,29.0,,-1.2728069,36.7962362,-1.2846195,36.7958316,Rider_Id_576,745
-Order_No_15482,User_Id_499,Bike,3,Personal,16,2,5:35:55 PM,16,2,5:36:32 PM,16,2,5:46:20 PM,16,2,5:51:13 PM,16,2,6:19:36 PM,11,25.3,,-1.2652808999999998,36.808065899999995,-1.3196808999999998,36.8410572,Rider_Id_648,1703
-Order_No_285,User_Id_103,Bike,3,Business,27,6,8:37:33 AM,27,6,8:37:55 AM,27,6,8:50:45 AM,27,6,8:55:43 AM,27,6,9:29:51 AM,19,,,-1.2844665000000002,36.7866113,-1.3205566,36.914167799999994,Rider_Id_228,2048
-Order_No_14719,User_Id_624,Bike,3,Business,5,5,11:48:39 AM,5,5,11:59:25 AM,5,5,12:16:27 PM,5,5,12:41:10 PM,5,5,12:55:42 PM,6,23.7,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_900,872
-Order_No_26216,User_Id_2987,Bike,3,Business,20,5,1:27:13 PM,20,5,2:59:22 PM,20,5,3:05:24 PM,20,5,3:10:36 PM,20,5,3:28:55 PM,7,20.4,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_427,1099
-Order_No_4822,User_Id_2436,Bike,3,Business,22,5,2:31:02 PM,22,5,2:31:47 PM,22,5,2:32:58 PM,22,5,2:43:49 PM,22,5,2:43:51 PM,2,27.5,,-1.2551895,36.7822034,-1.2567464,36.7915273,Rider_Id_56,2
-Order_No_14388,User_Id_1850,Bike,3,Business,11,1,1:54:26 PM,11,1,1:56:27 PM,11,1,2:07:05 PM,11,1,2:25:35 PM,11,1,2:42:01 PM,6,28.9,,-1.2571472,36.795063299999995,-1.2896398999999998,36.812449799999996,Rider_Id_378,986
-Order_No_27092,User_Id_700,Bike,3,Business,10,1,3:37:03 PM,10,1,4:07:22 PM,10,1,4:33:06 PM,10,1,4:40:06 PM,10,1,5:19:55 PM,10,18.8,,-1.3205163999999998,36.8098882,-1.2640571000000003,36.7883547,Rider_Id_447,2389
-Order_No_27574,User_Id_1724,Bike,3,Business,15,1,10:09:27 AM,15,1,10:09:59 AM,15,1,10:10:20 AM,15,1,10:17:26 AM,15,1,10:27:32 AM,4,20.8,,-1.2551895,36.7822034,-1.2690013,36.8092872,Rider_Id_291,606
-Order_No_14242,User_Id_1500,Bike,3,Business,25,1,12:01:23 PM,25,1,12:03:06 PM,25,1,12:09:53 PM,25,1,12:17:15 PM,25,1,12:25:42 PM,6,26.9,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_156,507
-Order_No_23581,User_Id_3291,Bike,3,Business,10,3,1:08:41 PM,10,3,1:09:00 PM,10,3,1:10:04 PM,10,3,1:27:56 PM,10,3,1:34:06 PM,5,28.0,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_291,370
-Order_No_18906,User_Id_3470,Bike,3,Business,9,5,11:44:01 AM,9,5,11:45:22 AM,9,5,11:51:39 AM,9,5,12:31:14 PM,9,5,1:48:22 PM,15,25.7,,-1.28878,36.8168312,-1.2265676,36.9068373,Rider_Id_718,4628
-Order_No_16950,User_Id_3173,Bike,1,Personal,11,3,12:35:59 PM,11,3,12:36:05 PM,11,3,12:45:51 PM,11,3,12:50:50 PM,11,3,2:01:38 PM,27,19.1,,-1.2717608999999999,36.8400291,-1.3707479,36.6563289,Rider_Id_629,4248
-Order_No_14981,User_Id_1482,Bike,3,Business,7,3,12:18:19 PM,7,3,12:24:59 PM,7,3,12:38:57 PM,7,3,12:50:49 PM,7,3,1:16:15 PM,10,25.2,,-1.3122473999999997,36.8728544,-1.3385790000000002,36.89630379999999,Rider_Id_86,1526
-Order_No_67,User_Id_517,Bike,3,Business,30,4,1:36:35 PM,30,4,1:36:52 PM,30,4,1:39:52 PM,30,4,1:43:46 PM,30,4,2:13:26 PM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_349,1780
-Order_No_108,User_Id_2869,Bike,3,Business,18,4,1:11:55 PM,18,4,1:13:03 PM,18,4,1:22:08 PM,18,4,1:25:01 PM,18,4,2:10:47 PM,9,,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_92,2746
-Order_No_6683,User_Id_286,Bike,3,Business,27,2,1:39:13 PM,27,2,1:42:39 PM,27,2,1:55:24 PM,27,2,1:57:03 PM,27,2,2:21:48 PM,12,25.7,,-1.315611,36.844733899999994,-1.2551895,36.7822034,Rider_Id_920,1485
-Order_No_13406,User_Id_1754,Bike,3,Business,3,7,1:45:29 PM,3,7,1:46:10 PM,3,7,2:05:10 PM,3,7,2:18:35 PM,3,7,2:39:53 PM,11,22.8,,-1.2297202,36.874550799999994,-1.2860183,36.8975338,Rider_Id_408,1278
-Order_No_5470,User_Id_3214,Bike,3,Business,13,2,10:21:14 AM,13,2,10:32:29 AM,13,2,10:45:06 AM,13,2,10:50:42 AM,13,2,11:19:00 AM,11,22.4,,-1.308231,36.857306,-1.2604408,36.8206328,Rider_Id_675,1698
-Order_No_23,User_Id_1578,Bike,3,Business,11,2,4:34:22 PM,11,2,5:57:06 PM,11,2,6:06:57 PM,11,2,6:13:17 PM,11,2,7:14:23 PM,10,21.1,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_609,3666
-Order_No_12571,User_Id_1244,Bike,3,Business,25,1,11:53:30 AM,25,1,11:56:51 AM,25,1,12:11:20 PM,25,1,12:21:21 PM,25,1,1:11:52 PM,18,26.9,,-1.293515,36.897607,-1.303596,36.778378000000004,Rider_Id_126,3031
-Order_No_7623,User_Id_1724,Bike,3,Business,3,2,12:00:24 PM,3,2,12:04:53 PM,3,2,12:41:34 PM,3,2,12:52:25 PM,3,2,1:14:45 PM,10,18.4,,-1.2551895,36.7822034,-1.3066143,36.8161046,Rider_Id_312,1340
-Order_No_26192,User_Id_136,Bike,3,Business,3,1,2:59:22 PM,3,1,2:59:38 PM,3,1,3:30:38 PM,3,1,3:37:14 PM,3,1,4:40:59 PM,23,23.2,,-1.2248308,36.801257299999996,-1.3169591,36.7000029,Rider_Id_882,3825
-Order_No_9018,User_Id_393,Bike,3,Business,21,1,11:18:04 AM,21,1,11:18:10 AM,21,1,11:20:29 AM,21,1,11:33:37 AM,21,1,12:00:42 PM,14,25.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1625
-Order_No_8473,User_Id_1417,Bike,3,Business,23,3,9:10:25 AM,23,3,9:11:01 AM,23,3,9:13:22 AM,23,3,9:47:42 AM,23,3,10:04:01 AM,7,,,-1.2590946,36.7607115,-1.2741728,36.79691079999999,Rider_Id_880,979
-Order_No_11448,User_Id_3326,Bike,3,Business,16,4,9:23:00 AM,16,4,9:24:46 AM,16,4,9:52:46 AM,16,4,10:02:01 AM,16,4,10:17:18 AM,7,,,-1.2571472,36.795063299999995,-1.304344,36.812274,Rider_Id_205,917
-Order_No_14575,User_Id_1075,Bike,3,Business,28,1,1:46:21 PM,28,1,1:46:42 PM,28,1,1:55:59 PM,28,1,1:59:26 PM,28,1,2:29:13 PM,15,26.3,,-1.2156006000000001,36.8916865,-1.2534301,36.800098299999995,Rider_Id_691,1787
-Order_No_6427,User_Id_3300,Bike,3,Business,6,4,9:57:14 AM,6,4,9:57:45 AM,6,4,10:08:26 AM,6,4,10:17:29 AM,6,4,10:33:56 AM,5,16.2,4.1,-1.3040553,36.8092658,-1.303596,36.778378000000004,Rider_Id_277,987
-Order_No_23396,User_Id_510,Bike,3,Business,29,2,3:43:09 PM,29,2,3:46:32 PM,29,2,3:46:40 PM,29,2,3:55:36 PM,29,2,4:15:43 PM,12,23.6,,-1.2860183,36.8975338,-1.3332749,36.870814700000004,Rider_Id_397,1207
-Order_No_11218,User_Id_496,Bike,3,Business,15,5,1:00:10 PM,15,5,1:00:22 PM,15,5,1:08:55 PM,15,5,1:22:54 PM,15,5,2:17:10 PM,22,25.9,,-1.2571472,36.795063299999995,-1.374163,36.913444,Rider_Id_913,3256
-Order_No_24071,User_Id_552,Bike,3,Business,23,5,11:33:26 AM,23,5,11:38:39 AM,23,5,12:00:44 PM,23,5,12:37:55 PM,23,5,1:18:22 PM,22,24.1,,-1.3472066,36.7692638,-1.3632162,36.9108253,Rider_Id_581,2427
-Order_No_26067,User_Id_2869,Bike,3,Business,5,5,11:26:53 AM,5,5,11:27:38 AM,5,5,11:46:43 AM,5,5,12:17:33 PM,5,5,12:56:40 PM,18,24.7,,-1.2413316,36.878552500000005,-1.3025826,36.7670807,Rider_Id_691,2347
-Order_No_22398,User_Id_3495,Bike,1,Personal,26,4,2:50:23 PM,26,4,2:50:47 PM,26,4,2:59:44 PM,26,4,3:03:02 PM,26,4,3:10:51 PM,3,22.2,,-1.2866305,36.7754914,-1.2765492,36.7669809,Rider_Id_844,469
-Order_No_10969,User_Id_1245,Bike,3,Business,15,5,12:49:57 PM,15,5,1:17:33 PM,15,5,2:02:23 PM,15,5,2:24:51 PM,15,5,3:25:49 PM,21,27.8,,-1.2857763999999998,36.8236622,-1.3198615,36.7110323,Rider_Id_332,3658
-Order_No_574,User_Id_436,Bike,1,Personal,31,4,10:45:33 AM,31,4,10:45:41 AM,31,4,10:53:30 AM,31,4,11:05:15 AM,31,4,11:35:04 AM,19,21.9,,-1.2946528,36.8153656,-1.351703,36.907065,Rider_Id_101,1789
-Order_No_17020,User_Id_867,Bike,3,Business,10,5,11:52:20 AM,10,5,11:53:46 AM,10,5,12:22:25 PM,10,5,12:40:45 PM,10,5,1:00:40 PM,14,,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_544,1195
-Order_No_27779,User_Id_3684,Bike,2,Personal,30,7,12:38:07 PM,30,7,12:38:26 PM,30,7,12:55:53 PM,30,7,1:15:27 PM,30,7,1:56:41 PM,16,24.6,,-1.3039695,36.783826700000006,-1.2294985,36.8775495,Rider_Id_612,2474
-Order_No_4350,User_Id_3493,Bike,1,Personal,3,5,12:24:28 PM,3,5,12:24:34 PM,3,5,12:41:00 PM,3,5,12:43:01 PM,3,5,1:03:41 PM,8,21.5,,-1.288922,36.814826000000004,-1.2623805000000001,36.7805404,Rider_Id_857,1240
-Order_No_14331,User_Id_868,Bike,3,Business,30,2,10:09:42 AM,30,2,10:34:49 AM,30,2,10:42:53 AM,30,2,10:55:12 AM,30,2,11:06:42 AM,5,22.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_726,690
-Order_No_10410,User_Id_1179,Bike,3,Business,24,3,2:29:03 PM,24,3,2:45:09 PM,24,3,3:08:41 PM,24,3,3:15:21 PM,24,3,4:01:31 PM,21,18.5,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_675,2770
-Order_No_17310,User_Id_3586,Bike,3,Business,15,1,8:33:51 AM,15,1,8:43:45 AM,15,1,8:53:24 AM,15,1,9:07:05 AM,15,1,9:39:14 AM,11,,,-1.2914795000000001,36.824897299999996,-1.275442,36.766020700000006,Rider_Id_208,1929
-Order_No_803,User_Id_232,Bike,3,Personal,28,2,1:05:35 PM,28,2,1:05:40 PM,28,2,1:27:56 PM,28,2,1:30:34 PM,28,2,2:06:08 PM,21,17.4,,-1.329648,36.678007,-1.266294,36.8057971,Rider_Id_754,2134
-Order_No_17783,User_Id_1353,Bike,3,Personal,26,5,3:35:44 PM,26,5,3:37:19 PM,26,5,4:01:37 PM,26,5,4:12:54 PM,26,5,4:27:40 PM,7,,,-1.2917868000000001,36.7872675,-1.2753108999999998,36.8130185,Rider_Id_438,886
-Order_No_13581,User_Id_733,Bike,3,Business,7,4,12:19:29 PM,7,4,12:20:05 PM,7,4,12:24:01 PM,7,4,12:26:05 PM,7,4,12:46:22 PM,7,27.7,,-1.2551895,36.7822034,-1.2819415,36.7814934,Rider_Id_206,1217
-Order_No_3809,User_Id_1329,Bike,3,Business,24,3,9:59:22 AM,24,3,9:59:58 AM,24,3,10:06:37 AM,24,3,10:09:36 AM,24,3,10:20:52 AM,5,17.0,0.9,-1.273056,36.811298,-1.277783,36.7816464,Rider_Id_446,676
-Order_No_3972,User_Id_302,Bike,3,Business,4,1,12:43:57 PM,4,1,12:45:07 PM,4,1,12:45:37 PM,4,1,1:03:49 PM,4,1,1:26:00 PM,10,26.8,,-1.2649605,36.798177700000004,-1.3159197,36.8516724,Rider_Id_81,1331
-Order_No_14085,User_Id_3025,Bike,3,Business,14,3,2:43:43 PM,14,3,3:15:30 PM,14,3,4:05:44 PM,14,3,4:08:38 PM,14,3,4:25:06 PM,8,25.5,,-1.292462,36.7972911,-1.255081,36.802335,Rider_Id_612,988
-Order_No_603,User_Id_2718,Bike,3,Business,18,4,12:00:32 PM,18,4,12:02:40 PM,18,4,12:10:29 PM,18,4,12:17:35 PM,18,4,12:42:19 PM,4,25.4,,-1.2668815,36.8017244,-1.2904753,36.8144721,Rider_Id_299,1484
-Order_No_4741,User_Id_2956,Bike,3,Business,5,2,10:31:03 AM,5,2,10:31:32 AM,5,2,10:56:04 AM,5,2,11:01:23 AM,5,2,11:48:46 AM,13,21.5,,-1.2584143,36.8048002,-1.3281792,36.8532879,Rider_Id_207,2843
-Order_No_16164,User_Id_393,Bike,3,Business,14,1,11:29:38 AM,14,1,11:29:56 AM,14,1,11:32:01 AM,14,1,11:35:47 AM,14,1,11:43:01 AM,4,23.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,434
-Order_No_11645,User_Id_1927,Bike,3,Business,3,1,12:42:08 PM,3,1,12:42:21 PM,3,1,12:42:38 PM,3,1,12:44:18 PM,3,1,1:17:42 PM,26,21.4,,-1.3472066,36.7692638,-1.3916458999999999,36.93058920000001,Rider_Id_778,2004
-Order_No_1533,User_Id_2803,Bike,3,Business,15,4,10:27:15 AM,15,4,10:27:37 AM,15,4,10:34:47 AM,15,4,10:37:52 AM,15,4,11:06:28 AM,16,24.3,,-1.2765736,36.851364600000004,-1.269767,36.7351068,Rider_Id_62,1716
-Order_No_21569,User_Id_733,Bike,3,Business,30,3,12:18:08 PM,30,3,12:19:19 PM,30,3,12:29:23 PM,30,3,12:31:53 PM,30,3,12:31:54 PM,5,25.0,,-1.2551895,36.7822034,-1.2629461000000002,36.76531370000001,Rider_Id_647,1
-Order_No_19343,User_Id_2041,Bike,1,Business,11,4,11:56:08 AM,11,4,11:56:36 AM,11,4,12:09:39 PM,11,4,12:28:24 PM,11,4,12:41:21 PM,5,24.0,,-1.290894,36.822971,-1.29387,36.7907813,Rider_Id_265,777
-Order_No_9478,User_Id_1721,Bike,3,Business,10,3,1:45:52 PM,10,3,1:46:23 PM,10,3,1:48:33 PM,10,3,1:53:02 PM,10,3,2:21:52 PM,10,30.7,,-1.2551895,36.7822034,-1.2363747999999999,36.817750200000006,Rider_Id_709,1730
-Order_No_23144,User_Id_3635,Bike,1,Business,11,4,10:43:43 AM,11,4,10:43:52 AM,11,4,10:50:40 AM,11,4,11:09:51 AM,11,4,11:40:15 AM,17,23.7,,-1.2156006000000001,36.8916865,-1.2615888000000002,36.7928732,Rider_Id_704,1824
-Order_No_107,User_Id_1207,Bike,3,Business,31,3,1:35:09 PM,31,3,1:36:39 PM,31,3,2:15:21 PM,31,3,2:18:58 PM,31,3,3:05:14 PM,14,26.8,,-1.2629666000000002,36.8068369,-1.2180795,36.874970000000005,Rider_Id_880,2776
-Order_No_14073,User_Id_136,Bike,3,Business,18,1,8:51:44 AM,18,1,8:57:42 AM,18,1,9:21:22 AM,18,1,9:25:43 AM,18,1,9:41:32 AM,17,21.3,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_170,949
-Order_No_11775,User_Id_265,Bike,3,Business,20,3,2:27:13 PM,20,3,2:38:58 PM,20,3,2:53:20 PM,20,3,3:04:53 PM,20,3,3:20:10 PM,5,28.4,,-1.3049875,36.86334179999999,-1.300921,36.828195,Rider_Id_576,917
-Order_No_19060,User_Id_868,Bike,3,Business,29,5,10:03:27 AM,29,5,10:05:08 AM,29,5,10:21:33 AM,29,5,10:29:41 AM,29,5,11:09:46 AM,9,20.7,,-1.265715,36.823815,-1.2975066000000002,36.76894229999999,Rider_Id_907,2405
-Order_No_19733,User_Id_1381,Bike,2,Personal,10,2,12:49:54 PM,10,2,12:52:45 PM,10,2,1:46:21 PM,10,2,1:55:03 PM,10,2,2:19:18 PM,9,21.0,,-1.2811695,36.8200313,-1.22606,36.80039,Rider_Id_234,1455
-Order_No_18700,User_Id_3222,Bike,3,Business,21,2,2:01:38 PM,21,2,2:02:07 PM,21,2,2:03:02 PM,21,2,2:07:28 PM,21,2,2:09:12 PM,1,24.9,,-1.2551895,36.7822034,-1.2549259,36.7919494,Rider_Id_926,104
-Order_No_16532,User_Id_3418,Bike,3,Business,12,5,1:14:58 PM,12,5,1:18:03 PM,12,5,1:45:40 PM,12,5,1:51:25 PM,12,5,2:26:14 PM,9,,,-1.2828186000000001,36.823279799999995,-1.2887613000000002,36.77305379999999,Rider_Id_709,2089
-Order_No_5790,User_Id_478,Bike,3,Business,27,1,9:09:49 AM,27,1,9:10:21 AM,27,1,9:15:14 AM,27,1,9:20:12 AM,27,1,9:21:11 AM,3,18.0,0.9,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_708,59
-Order_No_3354,User_Id_2856,Bike,1,Personal,2,2,1:48:37 PM,2,2,1:49:26 PM,2,2,1:53:49 PM,2,2,2:05:39 PM,2,2,2:32:27 PM,13,26.0,,-1.3283152,36.862882,-1.3104166000000002,36.89683370000001,Rider_Id_543,1608
-Order_No_7127,User_Id_2344,Bike,3,Business,5,2,2:19:41 PM,5,2,2:20:05 PM,5,2,2:20:49 PM,5,2,2:28:10 PM,5,2,2:59:28 PM,15,22.7,,-1.2553615,36.789537599999996,-1.3319466999999998,36.776699799999996,Rider_Id_101,1878
-Order_No_16915,User_Id_1725,Bike,3,Business,2,3,11:16:49 AM,2,3,11:18:48 AM,2,3,11:19:02 AM,2,3,11:25:00 AM,2,3,11:38:34 AM,4,22.0,,-1.2652108,36.8056075,-1.2865096,36.8169364,Rider_Id_633,814
-Order_No_18662,User_Id_3631,Bike,3,Personal,10,5,12:17:47 PM,10,5,12:18:24 PM,10,5,12:25:32 PM,10,5,12:27:07 PM,10,5,12:34:42 PM,2,,,-1.2990817,36.7654866,-1.2963097,36.7688221,Rider_Id_327,455
-Order_No_1726,User_Id_3147,Bike,3,Business,5,5,9:57:14 AM,5,5,9:57:30 AM,5,5,10:19:58 AM,5,5,10:27:25 AM,5,5,10:55:58 AM,14,19.9,,-1.3014461000000002,36.7661381,-1.3472066,36.7692638,Rider_Id_4,1713
-Order_No_3618,User_Id_283,Bike,3,Business,1,3,2:51:24 PM,1,3,2:51:29 PM,1,3,2:59:01 PM,1,3,3:06:49 PM,1,3,3:46:54 PM,8,23.7,,-1.2804227,36.8250375,-1.3025666999999999,36.7803495,Rider_Id_462,2405
-Order_No_7320,User_Id_3662,Bike,1,Personal,25,5,10:37:40 AM,25,5,10:39:18 AM,25,5,10:50:02 AM,25,5,10:52:04 AM,25,5,11:20:01 AM,9,21.1,,-1.2819711,36.809793400000004,-1.3215473,36.8435326,Rider_Id_749,1677
-Order_No_4226,User_Id_970,Bike,3,Business,5,2,9:27:26 AM,5,2,9:28:02 AM,5,2,9:59:00 AM,5,2,10:05:18 AM,5,2,10:32:49 AM,8,18.2,,-1.2551895,36.7822034,-1.2927895,36.759123200000005,Rider_Id_290,1651
-Order_No_19053,User_Id_3269,Bike,1,Personal,4,3,11:59:49 AM,4,3,12:00:07 PM,4,3,12:04:22 PM,4,3,12:22:42 PM,4,3,1:05:16 PM,15,17.0,,-1.3039695,36.783826700000006,-1.2333,36.82226,Rider_Id_312,2554
-Order_No_21831,User_Id_1148,Bike,3,Business,8,5,3:38:37 PM,8,5,4:08:20 PM,8,5,4:29:58 PM,8,5,4:36:53 PM,8,5,5:09:40 PM,5,25.3,,-1.3100586,36.850032899999995,-1.3319197,36.850148299999994,Rider_Id_311,1967
-Order_No_10446,User_Id_978,Bike,1,Personal,20,3,12:48:38 PM,20,3,12:48:45 PM,20,3,12:56:31 PM,20,3,1:05:06 PM,20,3,1:37:29 PM,25,20.1,,-1.3084067,36.83450139999999,-1.4302983,36.9613684,Rider_Id_549,1943
-Order_No_20860,User_Id_2008,Bike,3,Business,12,4,1:34:09 PM,12,4,1:37:30 PM,12,4,1:52:23 PM,12,4,2:00:20 PM,12,4,3:05:12 PM,7,21.6,,-1.2508229,36.7895263,-1.2785858,36.82896829999999,Rider_Id_752,3892
-Order_No_429,User_Id_426,Bike,3,Business,25,2,9:16:12 AM,25,2,9:16:33 AM,25,2,9:32:02 AM,25,2,9:52:16 AM,25,2,11:08:34 AM,14,17.8,,-1.3323107,36.8692616,-1.2684319,36.81658529999999,Rider_Id_612,4578
-Order_No_22779,User_Id_2034,Bike,2,Personal,24,1,9:32:26 AM,24,1,9:32:40 AM,24,1,9:51:25 AM,24,1,10:04:47 AM,24,1,10:40:40 AM,17,15.6,,-1.1992343,36.788489399999996,-1.290499,36.784363,Rider_Id_764,2153
-Order_No_589,User_Id_633,Bike,3,Business,18,1,1:44:48 PM,18,1,1:47:08 PM,18,1,1:47:13 PM,18,1,1:52:16 PM,18,1,2:00:59 PM,4,29.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206,523
-Order_No_16466,User_Id_2360,Bike,3,Business,22,5,2:32:53 PM,22,5,2:41:58 PM,22,5,2:43:32 PM,22,5,3:09:33 PM,22,5,3:38:02 PM,19,,,-1.3384245000000001,36.8794338,-1.2551895,36.7822034,Rider_Id_874,1709
-Order_No_28238,User_Id_1133,Bike,3,Business,14,5,10:11:58 AM,14,5,10:12:09 AM,14,5,10:41:33 AM,14,5,10:50:25 AM,14,5,11:19:57 AM,17,24.2,,-1.2194214,36.891564200000005,-1.2571472,36.795063299999995,Rider_Id_447,1772
-Order_No_17213,User_Id_2587,Bike,3,Business,6,1,2:49:56 PM,6,1,2:53:26 PM,6,1,3:14:19 PM,6,1,3:27:08 PM,6,1,4:03:55 PM,16,,,-1.323615,36.700475,-1.28878,36.8168312,Rider_Id_564,2207
-Order_No_2831,User_Id_2956,Bike,3,Business,7,4,5:06:16 PM,7,4,5:07:09 PM,7,4,5:30:17 PM,7,4,5:50:52 PM,7,4,6:40:05 PM,20,29.2,,-1.2860183,36.8975338,-1.2584143,36.8048002,Rider_Id_697,2953
-Order_No_25663,User_Id_3609,Bike,2,Personal,2,2,12:37:15 PM,2,2,12:37:21 PM,2,2,12:39:00 PM,2,2,12:41:30 PM,2,2,1:27:30 PM,14,21.0,,-1.2676026000000002,36.810362899999994,-1.2850318,36.8907864,Rider_Id_789,2760
-Order_No_21331,User_Id_1178,Bike,3,Business,30,4,1:31:25 PM,30,4,1:32:20 PM,30,4,1:45:40 PM,30,4,1:53:29 PM,30,4,2:08:12 PM,7,20.7,,-1.2866544,36.784310999999995,-1.2627203000000002,36.7929743,Rider_Id_536,883
-Order_No_15604,User_Id_2516,Bike,1,Personal,18,4,9:11:44 AM,18,4,9:12:17 AM,18,4,9:20:29 AM,18,4,9:31:46 AM,18,4,10:08:44 AM,5,20.7,,-1.2862436000000002,36.791017,-1.2904753,36.8144721,Rider_Id_156,2218
-Order_No_3319,User_Id_1601,Bike,1,Personal,22,5,10:12:53 AM,22,5,10:13:11 AM,22,5,10:29:37 AM,22,5,10:31:59 AM,22,5,10:42:08 AM,5,16.3,,-1.2559559999999999,36.772901,-1.2613754,36.795771,Rider_Id_103,609
-Order_No_17029,User_Id_1327,Bike,3,Personal,21,5,1:18:32 PM,21,5,1:20:13 PM,21,5,1:24:48 PM,21,5,1:26:02 PM,21,5,1:57:51 PM,8,22.7,,-1.2615518000000001,36.7889052,-1.2924639,36.77957979999999,Rider_Id_866,1909
-Order_No_5278,User_Id_3291,Bike,3,Business,20,1,10:17:59 AM,20,1,10:18:41 AM,20,1,10:28:17 AM,20,1,11:20:20 AM,20,1,11:30:29 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_491,609
-Order_No_12818,User_Id_1899,Bike,3,Business,24,4,11:24:06 AM,24,4,11:26:21 AM,24,4,12:03:15 PM,24,4,12:09:04 PM,24,4,12:28:57 PM,14,21.2,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_543,1193
-Order_No_3375,User_Id_53,Bike,3,Business,12,1,2:42:37 PM,12,1,2:59:49 PM,12,1,3:05:16 PM,12,1,3:11:22 PM,12,1,3:44:31 PM,5,26.9,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_148,1989
-Order_No_14642,User_Id_3695,Bike,1,Personal,25,2,11:14:21 AM,25,2,11:14:56 AM,25,2,11:24:14 AM,25,2,11:41:40 AM,25,2,11:50:15 AM,3,23.6,,-1.2923919,36.7951531,-1.2894917,36.8056192,Rider_Id_754,515
-Order_No_19680,User_Id_759,Bike,1,Personal,4,5,2:44:38 PM,4,5,2:48:05 PM,4,5,3:00:15 PM,4,5,3:06:10 PM,4,5,4:03:13 PM,13,24.0,,-1.2736771,36.8123141,-1.303217,36.731466999999995,Rider_Id_196,3423
-Order_No_21530,User_Id_718,Bike,3,Business,14,4,3:13:37 PM,14,4,3:35:23 PM,14,4,3:51:18 PM,14,4,3:54:40 PM,14,4,4:20:10 PM,8,28.1,,-1.2551895,36.7822034,-1.2856664,36.755983799999996,Rider_Id_940,1530
-Order_No_10532,User_Id_1280,Bike,3,Business,9,2,9:40:39 AM,9,2,9:40:51 AM,9,2,9:42:30 AM,9,2,9:47:31 AM,9,2,9:57:47 AM,3,20.7,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_411,616
-Order_No_2154,User_Id_928,Bike,3,Business,24,5,1:40:52 PM,24,5,1:41:26 PM,24,5,1:56:53 PM,24,5,2:03:53 PM,24,5,2:44:38 PM,7,23.1,,-1.2551895,36.7822034,-1.290724,36.81947,Rider_Id_204,2445
-Order_No_15327,User_Id_1715,Bike,3,Business,6,5,2:49:31 PM,6,5,2:56:44 PM,6,5,3:15:28 PM,6,5,3:21:08 PM,6,5,4:37:31 PM,7,22.7,,-1.2608993,36.799246000000004,-1.3022591000000001,36.8074294,Rider_Id_140,4583
-Order_No_25307,User_Id_2330,Bike,3,Business,8,3,9:45:06 AM,8,3,9:45:27 AM,8,3,9:48:42 AM,8,3,9:50:07 AM,8,3,9:56:17 AM,3,,,-1.3040812,36.7924217,-1.2921463,36.8029626,Rider_Id_829,370
-Order_No_24202,User_Id_642,Bike,2,Personal,31,5,4:28:57 PM,31,5,4:32:29 PM,31,5,4:33:15 PM,31,5,4:51:36 PM,31,5,5:17:51 PM,15,19.7,,-1.2854453000000001,36.818836299999994,-1.3444745,36.7507427,Rider_Id_813,1575
-Order_No_20119,User_Id_1578,Bike,3,Business,20,4,12:17:24 PM,20,4,12:19:34 PM,20,4,12:27:56 PM,20,4,12:32:33 PM,20,4,12:54:45 PM,3,22.1,,-1.28878,36.8168312,-1.3012902,36.80964,Rider_Id_72,1332
-Order_No_2254,User_Id_1502,Bike,3,Business,28,4,9:58:26 AM,28,4,9:58:46 AM,28,4,10:31:47 AM,28,4,10:34:17 AM,28,4,11:02:28 AM,17,28.0,,-1.2232123,36.8932547,-1.2551895,36.7822034,Rider_Id_260,1691
-Order_No_2320,User_Id_3291,Bike,3,Business,8,6,9:58:07 AM,8,6,9:58:54 AM,8,6,10:25:56 AM,8,6,10:36:07 AM,8,6,10:51:35 AM,5,20.0,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_523,928
-Order_No_8673,User_Id_2041,Bike,1,Business,30,6,11:49:19 AM,30,6,12:04:28 PM,30,6,12:11:19 PM,30,6,12:23:22 PM,30,6,12:43:50 PM,2,21.8,,-1.290894,36.822971,-1.2848173999999999,36.826163,Rider_Id_21,1228
-Order_No_25630,User_Id_2410,Bike,1,Personal,15,1,2:42:42 PM,15,1,2:46:38 PM,15,1,2:56:44 PM,15,1,3:04:54 PM,15,1,3:20:02 PM,6,,,-1.2664428,36.809453000000005,-1.2962832,36.784968299999996,Rider_Id_383,908
-Order_No_9971,User_Id_1075,Bike,3,Business,4,4,12:35:08 PM,4,4,12:36:11 PM,4,4,12:44:38 PM,4,4,12:58:19 PM,4,4,1:09:34 PM,3,25.5,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_131,675
-Order_No_11019,User_Id_3487,Bike,3,Business,3,5,10:06:38 AM,3,5,10:08:06 AM,3,5,10:12:57 AM,3,5,10:16:43 AM,3,5,10:28:50 AM,5,,,-1.2658424,36.8101804,-1.279395,36.825364,Rider_Id_205,727
-Order_No_11505,User_Id_2330,Bike,3,Business,15,1,3:12:17 PM,15,1,3:14:07 PM,15,1,3:27:05 PM,15,1,3:31:00 PM,15,1,3:59:03 PM,11,,,-1.2524916000000001,36.8656196,-1.233018,36.8004477,Rider_Id_653,1683
-Order_No_3702,User_Id_2918,Bike,3,Business,27,4,10:18:58 AM,27,4,10:19:35 AM,27,4,10:34:05 AM,27,4,10:36:54 AM,27,4,11:16:24 AM,16,22.6,,-1.3339461000000001,36.821912899999994,-1.2684443,36.759878799999996,Rider_Id_16,2370
-Order_No_11335,User_Id_186,Bike,3,Business,14,2,11:45:46 AM,14,2,11:46:31 AM,14,2,11:50:02 AM,14,2,12:03:19 PM,14,2,12:11:08 PM,6,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_404,469
-Order_No_11790,User_Id_1363,Bike,3,Business,27,2,11:40:07 AM,27,2,11:46:05 AM,27,2,11:46:15 AM,27,2,11:50:16 AM,27,2,12:06:33 PM,5,24.0,,-1.3004062,36.829741,-1.2976558999999999,36.791414700000004,Rider_Id_539,977
-Order_No_21026,User_Id_2330,Bike,3,Business,4,2,10:58:50 AM,4,2,11:47:24 AM,4,2,11:53:32 AM,4,2,11:56:27 AM,4,2,12:44:00 PM,12,20.7,,-1.3339536,36.7181,-1.3040812,36.7924217,Rider_Id_204,2853
-Order_No_16444,User_Id_635,Bike,3,Business,27,3,2:59:48 PM,27,3,3:01:54 PM,27,3,3:04:09 PM,27,3,3:23:13 PM,27,3,3:46:09 PM,8,29.6,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_787,1376
-Order_No_24217,User_Id_1654,Bike,1,Personal,11,1,8:56:56 AM,11,1,9:09:40 AM,11,1,9:32:31 AM,11,1,9:34:20 AM,11,1,9:49:36 AM,5,14.6,,-1.3157973,36.815148,-1.2891116000000002,36.8106915,Rider_Id_322,916
-Order_No_6420,User_Id_1329,Bike,3,Business,12,2,3:25:19 PM,12,2,3:26:32 PM,12,2,3:34:14 PM,12,2,3:41:34 PM,12,2,4:05:54 PM,18,27.2,,-1.273056,36.811298,-1.3681253,36.915529799999995,Rider_Id_62,1460
-Order_No_12323,User_Id_3674,Bike,3,Business,8,2,9:27:37 AM,8,2,9:32:48 AM,8,2,10:21:00 AM,8,2,10:45:29 AM,8,2,11:07:45 AM,7,23.4,,-1.2551895,36.7822034,-1.2817196000000002,36.822194,Rider_Id_362,1336
-Order_No_8276,User_Id_1882,Bike,3,Business,3,1,6:13:45 PM,3,1,6:26:23 PM,3,1,6:44:42 PM,3,1,6:50:34 PM,3,1,7:21:42 PM,18,22.3,,-1.2273427,36.799468299999994,-1.2160906999999999,36.885194899999995,Rider_Id_743,1868
-Order_No_22922,User_Id_1058,Bike,1,Business,27,1,1:07:01 PM,27,1,1:10:18 PM,27,1,1:30:03 PM,27,1,1:44:28 PM,27,1,2:07:27 PM,16,,,-1.2251703,36.890471399999996,-1.2571472,36.795063299999995,Rider_Id_520,1379
-Order_No_9393,User_Id_3331,Bike,3,Business,14,1,2:22:08 PM,14,1,2:22:59 PM,14,1,2:29:46 PM,14,1,2:33:55 PM,14,1,3:04:40 PM,10,25.5,,-1.2559559999999999,36.789867,-1.3201096,36.8180968,Rider_Id_207,1845
-Order_No_10041,User_Id_928,Bike,3,Business,30,2,3:04:57 PM,30,2,3:17:35 PM,30,2,3:19:36 PM,30,2,3:29:25 PM,30,2,3:36:09 PM,3,,,-1.2551895,36.7822034,-1.256522,36.786413200000005,Rider_Id_16,404
-Order_No_17485,User_Id_296,Bike,3,Business,28,4,9:22:54 AM,28,4,9:23:29 AM,28,4,9:30:03 AM,28,4,9:43:07 AM,28,4,10:02:04 AM,5,19.4,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_24,1137
-Order_No_23363,User_Id_2190,Bike,3,Business,8,3,12:48:47 PM,8,3,12:48:59 PM,8,3,1:08:16 PM,8,3,1:16:41 PM,8,3,2:15:30 PM,15,,,-1.3019530000000001,36.788458,-1.2247423000000002,36.83409210000001,Rider_Id_599,3529
-Order_No_8679,User_Id_635,Bike,3,Business,21,4,2:19:09 PM,21,4,2:19:23 PM,21,4,2:19:40 PM,21,4,2:34:15 PM,21,4,2:51:32 PM,10,31.1,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_459,1037
-Order_No_24745,User_Id_635,Bike,3,Business,9,4,10:53:00 AM,9,4,10:55:26 AM,9,4,11:54:04 AM,9,4,11:56:14 AM,9,4,12:22:22 PM,5,23.6,,-1.2857763999999998,36.8236622,-1.2599563,36.799343900000004,Rider_Id_519,1568
-Order_No_5168,User_Id_2528,Bike,3,Business,11,1,9:33:16 AM,11,1,9:33:30 AM,11,1,9:34:07 AM,11,1,9:41:51 AM,11,1,9:49:31 AM,3,20.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_26,460
-Order_No_21532,User_Id_2330,Bike,3,Business,19,2,4:37:40 PM,19,2,4:39:04 PM,19,2,4:47:06 PM,19,2,4:51:00 PM,19,2,5:17:32 PM,8,28.4,,-1.3015197,36.7658456,-1.2649605,36.798177700000004,Rider_Id_129,1592
-Order_No_26784,User_Id_3372,Bike,3,Business,8,3,2:00:32 PM,8,3,2:00:52 PM,8,3,2:11:49 PM,8,3,2:16:14 PM,8,3,2:44:56 PM,11,25.7,,-1.3169591,36.7000029,-1.2969741000000001,36.7856612,Rider_Id_844,1722
-Order_No_27989,User_Id_3283,Bike,3,Business,3,4,9:22:48 AM,3,4,9:29:17 AM,3,4,9:30:13 AM,3,4,9:58:22 AM,3,4,10:18:38 AM,9,17.5,15.9,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_813,1216
-Order_No_17676,User_Id_111,Bike,3,Business,9,5,2:21:37 PM,9,5,2:38:16 PM,9,5,3:00:04 PM,9,5,3:51:55 PM,9,5,4:45:44 PM,9,26.0,,-1.2551895,36.7822034,-1.3001735,36.8042049,Rider_Id_303,3229
-Order_No_17159,User_Id_265,Bike,3,Business,2,5,1:22:07 PM,2,5,1:27:26 PM,2,5,1:38:05 PM,2,5,1:58:56 PM,2,5,2:25:22 PM,13,25.2,,-1.2311903,36.8102105,-1.300921,36.828195,Rider_Id_101,1586
-Order_No_26680,User_Id_419,Bike,3,Business,25,1,12:14:42 PM,25,1,12:15:49 PM,25,1,12:16:53 PM,25,1,12:29:53 PM,25,1,12:49:18 PM,5,25.7,,-1.2571472,36.795063299999995,-1.2897368,36.8226783,Rider_Id_576,1165
-Order_No_11325,User_Id_517,Bike,3,Business,29,4,10:21:21 AM,29,4,10:21:32 AM,29,4,10:30:05 AM,29,4,10:44:08 AM,29,4,10:44:10 AM,5,21.4,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_188,2
-Order_No_14696,User_Id_3647,Bike,3,Business,7,3,9:50:41 AM,7,3,9:51:29 AM,7,3,10:01:24 AM,7,3,10:06:36 AM,7,3,10:31:10 AM,16,,,-1.2726389999999999,36.794723,-1.3239725,36.8827383,Rider_Id_648,1474
-Order_No_14945,User_Id_706,Bike,3,Business,18,5,2:28:52 PM,18,5,2:29:14 PM,18,5,2:36:24 PM,18,5,2:45:58 PM,18,5,2:51:21 PM,3,26.8,,-1.3080091999999999,36.8284543,-1.3077869,36.844320700000004,Rider_Id_178,323
-Order_No_12056,User_Id_3182,Bike,2,Personal,27,7,5:36:47 PM,27,7,5:36:57 PM,27,7,5:53:27 PM,27,7,5:57:52 PM,27,7,6:09:11 PM,1,22.1,,-1.294042,36.766290999999995,-1.2901669,36.7685878,Rider_Id_148,679
-Order_No_11734,User_Id_1207,Bike,3,Business,14,4,10:10:47 AM,14,4,10:12:44 AM,14,4,10:36:29 AM,14,4,10:46:06 AM,14,4,11:01:13 AM,7,23.5,,-1.2745321000000003,36.765088,-1.2629666000000002,36.8068369,Rider_Id_101,907
-Order_No_12589,User_Id_111,Bike,3,Business,29,3,9:22:59 AM,29,3,9:29:40 AM,29,3,9:47:22 AM,29,3,10:02:58 AM,29,3,10:44:12 AM,8,17.7,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_245,2474
-Order_No_22362,User_Id_868,Bike,3,Business,23,6,2:49:32 PM,23,6,2:51:52 PM,23,6,2:58:15 PM,23,6,3:09:08 PM,23,6,3:39:06 PM,14,,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_855,1798
-Order_No_224,User_Id_3521,Bike,3,Business,2,1,8:24:55 AM,2,1,8:31:53 AM,2,1,8:38:33 AM,2,1,8:50:14 AM,2,1,8:59:08 AM,2,16.7,,-1.2684666999999998,36.8002891,-1.2696908000000002,36.81519,Rider_Id_103,534
-Order_No_15513,User_Id_3462,Bike,1,Personal,4,3,9:38:55 AM,4,3,9:39:51 AM,4,3,10:05:48 AM,4,3,10:08:00 AM,4,3,10:25:09 AM,5,14.8,,-1.2556829999999999,36.813771,-1.2677688999999999,36.7857969,Rider_Id_808,1029
-Order_No_7126,User_Id_1724,Bike,3,Business,15,5,11:03:18 AM,15,5,11:03:49 AM,15,5,11:08:51 AM,15,5,11:16:59 AM,15,5,11:18:49 AM,3,26.3,,-1.2551895,36.7822034,-1.2594538999999998,36.8067841,Rider_Id_881,110
-Order_No_23692,User_Id_1588,Bike,3,Business,23,5,4:25:30 PM,23,5,4:29:19 PM,23,5,4:30:56 PM,23,5,4:38:54 PM,23,5,4:51:17 PM,5,24.8,,-1.269534,36.80697,-1.2551895,36.7822034,Rider_Id_279,743
-Order_No_11234,User_Id_3599,Bike,3,Business,10,1,2:23:37 PM,10,1,2:39:55 PM,10,1,3:06:49 PM,10,1,3:27:29 PM,10,1,3:27:59 PM,7,18.4,,-1.3302996,36.8707935,-1.3117442,36.8737871,Rider_Id_376,30
-Order_No_19360,User_Id_2436,Bike,3,Business,8,5,2:21:33 PM,8,5,2:21:56 PM,8,5,2:40:02 PM,8,5,2:44:39 PM,8,5,3:14:38 PM,15,30.4,,-1.2551895,36.7822034,-1.3194911,36.720085299999994,Rider_Id_770,1799
-Order_No_5694,User_Id_1384,Bike,3,Business,29,5,9:21:59 AM,29,5,9:22:44 AM,29,5,9:38:38 AM,29,5,9:51:48 AM,29,5,10:32:54 AM,22,20.8,,-1.2517448,36.7924971,-1.2675827,36.927266100000004,Rider_Id_263,2466
-Order_No_8613,User_Id_84,Bike,3,Business,9,4,4:13:14 PM,9,4,4:13:33 PM,9,4,4:23:46 PM,9,4,4:35:45 PM,9,4,4:47:45 PM,3,23.2,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_259,720
-Order_No_12281,User_Id_2763,Bike,3,Business,8,5,1:09:07 PM,8,5,2:58:43 PM,8,5,3:10:45 PM,8,5,3:11:58 PM,8,5,3:47:23 PM,6,30.2,,-1.2551895,36.7822034,-1.2870705,36.82560170000001,Rider_Id_522,2125
-Order_No_9987,User_Id_2200,Bike,3,Business,24,6,11:05:24 AM,24,6,11:28:56 AM,24,6,11:54:14 AM,24,6,11:59:43 AM,24,6,12:21:12 PM,6,24.0,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_581,1289
-Order_No_19857,User_Id_265,Bike,3,Business,26,5,7:54:30 AM,26,5,7:54:58 AM,26,5,7:55:34 AM,26,5,8:36:50 AM,26,5,8:44:15 AM,5,,,-1.3273709999999999,36.840590999999996,-1.300921,36.828195,Rider_Id_69,445
-Order_No_24384,User_Id_1880,Bike,3,Business,30,4,2:49:45 PM,30,4,2:50:39 PM,30,4,3:00:59 PM,30,4,3:08:07 PM,30,4,3:37:41 PM,10,,,-1.2571472,36.795063299999995,-1.2919683000000002,36.8500706,Rider_Id_221,1774
-Order_No_6956,User_Id_391,Bike,3,Business,23,2,9:19:57 AM,23,2,9:20:39 AM,23,2,9:29:00 AM,23,2,10:25:30 AM,23,2,10:25:34 AM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_613,4
-Order_No_20553,User_Id_1205,Bike,3,Business,27,3,10:56:49 AM,27,3,10:57:32 AM,27,3,10:57:33 AM,27,3,11:11:49 AM,27,3,11:44:21 AM,17,26.2,,-1.2495931,36.80650870000001,-1.3218455,36.721300899999996,Rider_Id_831,1952
-Order_No_9409,User_Id_1419,Bike,1,Personal,19,5,12:33:14 PM,19,5,12:34:22 PM,19,5,12:40:28 PM,19,5,12:56:28 PM,19,5,1:14:31 PM,6,24.9,,-1.2728947,36.807942600000004,-1.2859690000000001,36.789204999999995,Rider_Id_857,1083
-Order_No_20713,User_Id_914,Bike,3,Business,28,4,3:19:44 PM,28,4,3:21:37 PM,28,4,3:43:49 PM,28,4,3:52:13 PM,28,4,4:47:06 PM,11,27.4,,-1.2670213,36.7994482,-1.3270716,36.8406371,Rider_Id_26,3293
-Order_No_20356,User_Id_2279,Bike,3,Business,27,1,10:16:58 AM,27,1,10:17:27 AM,27,1,10:41:29 AM,27,1,10:48:49 AM,27,1,10:48:57 AM,3,20.6,,-1.2980085,36.8044778,-1.2897368,36.8226783,Rider_Id_19,8
-Order_No_19897,User_Id_2171,Bike,2,Personal,25,6,2:27:01 PM,25,6,2:27:30 PM,25,6,2:32:07 PM,25,6,2:38:51 PM,25,6,2:58:14 PM,6,,,-1.2709579,36.81089620000001,-1.3009926,36.8208532,Rider_Id_427,1163
-Order_No_5462,User_Id_2112,Bike,3,Business,31,5,1:09:54 PM,31,5,1:20:13 PM,31,5,1:37:32 PM,31,5,1:47:45 PM,31,5,2:55:47 PM,12,24.8,,-1.28878,36.8168312,-1.2156006000000001,36.8916865,Rider_Id_272,4082
-Order_No_15102,User_Id_1469,Bike,3,Business,8,1,3:20:30 PM,8,1,3:22:34 PM,8,1,3:44:19 PM,8,1,3:52:58 PM,8,1,4:16:30 PM,12,24.7,,-1.2571472,36.795063299999995,-1.2402455,36.8791753,Rider_Id_882,1412
-Order_No_2985,User_Id_3711,Bike,3,Business,25,5,10:32:47 AM,25,5,10:37:59 AM,25,5,10:45:53 AM,25,5,10:55:04 AM,25,5,11:53:21 AM,9,26.4,,-1.3025826,36.7670807,-1.291768,36.8297208,Rider_Id_210,3497
-Order_No_17966,User_Id_868,Bike,3,Business,7,5,10:05:48 AM,7,5,10:34:45 AM,7,5,10:49:22 AM,7,5,11:19:28 AM,7,5,11:49:27 AM,9,25.0,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_829,1799
-Order_No_16700,User_Id_2273,Bike,1,Personal,16,4,4:04:46 PM,16,4,4:06:09 PM,16,4,4:26:01 PM,16,4,4:28:46 PM,16,4,5:26:29 PM,8,18.8,,-1.286198,36.8193847,-1.3215473,36.8435326,Rider_Id_519,3463
-Order_No_25801,User_Id_1341,Bike,3,Business,14,4,1:19:42 PM,14,4,1:21:56 PM,14,4,1:24:17 PM,14,4,1:35:53 PM,14,4,1:48:30 PM,7,27.6,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_519,757
-Order_No_6375,User_Id_659,Bike,2,Personal,3,3,7:55:55 PM,3,3,7:56:23 PM,3,3,8:01:11 PM,3,3,8:13:02 PM,3,3,8:34:17 PM,3,19.3,,-1.2852583999999998,36.8043641,-1.2812305,36.7791109,Rider_Id_578,1275
-Order_No_22424,User_Id_2627,Bike,3,Business,3,1,3:45:10 PM,3,1,3:50:57 PM,3,1,4:02:42 PM,3,1,4:10:44 PM,3,1,4:26:55 PM,5,23.2,,-1.2571472,36.795063299999995,-1.2857548,36.819062,Rider_Id_407,971
-Order_No_25333,User_Id_1029,Bike,2,Personal,9,1,3:23:01 PM,9,1,3:23:17 PM,9,1,3:25:42 PM,9,1,3:34:18 PM,9,1,3:45:19 PM,7,21.0,,-1.2639238000000002,36.7889646,-1.2507752,36.755662799999996,Rider_Id_379,661
-Order_No_3000,User_Id_1059,Bike,1,Personal,4,4,11:01:27 AM,4,4,11:01:40 AM,4,4,11:16:50 AM,4,4,11:19:53 AM,4,4,11:52:26 AM,14,,,-1.3303649,36.726018700000004,-1.2724885,36.785699900000004,Rider_Id_844,1953
-Order_No_20131,User_Id_2982,Bike,3,Business,30,4,1:52:51 PM,30,4,1:53:41 PM,30,4,2:01:47 PM,30,4,3:33:45 PM,30,4,4:23:41 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_340,2996
-Order_No_4341,User_Id_3050,Bike,3,Business,12,5,12:30:21 PM,12,5,12:30:48 PM,12,5,12:46:51 PM,12,5,12:51:10 PM,12,5,1:25:43 PM,17,24.5,,-1.2830989,36.8165739,-1.3398006000000002,36.717935,Rider_Id_731,2073
-Order_No_6353,User_Id_1754,Bike,3,Business,2,1,9:06:23 AM,2,1,9:06:33 AM,2,1,9:06:38 AM,2,1,9:24:58 AM,2,1,10:23:03 AM,17,16.4,,-1.2860183,36.8975338,-1.2661246000000002,36.7961775,Rider_Id_771,3485
-Order_No_13209,User_Id_1500,Bike,3,Business,19,2,1:40:22 PM,19,2,1:41:15 PM,19,2,1:54:58 PM,19,2,2:05:44 PM,19,2,2:48:42 PM,13,30.0,,-1.3083266000000002,36.8494756,-1.2638185,36.7930057,Rider_Id_522,2578
-Order_No_25849,User_Id_1271,Bike,3,Business,6,3,12:07:38 PM,6,3,12:07:55 PM,6,3,12:20:19 PM,6,3,12:32:52 PM,6,3,1:21:37 PM,22,,,-1.3108386,36.7555239,-1.2227615,36.8989174,Rider_Id_327,2925
-Order_No_10473,User_Id_510,Bike,3,Business,6,4,5:19:44 PM,6,4,5:20:56 PM,6,4,5:43:26 PM,6,4,5:45:30 PM,6,4,6:26:48 PM,17,22.0,,-1.2860183,36.8975338,-1.3251825000000002,36.78975870000001,Rider_Id_890,2478
-Order_No_22517,User_Id_53,Bike,3,Business,10,5,10:42:02 AM,10,5,10:42:56 AM,10,5,10:57:31 AM,10,5,11:16:24 AM,10,5,11:16:25 AM,3,,,-1.2770708,36.8231093,-1.2802781,36.8311292,Rider_Id_910,1
-Order_No_18918,User_Id_393,Bike,3,Business,14,1,3:40:29 PM,14,1,3:40:48 PM,14,1,3:40:54 PM,14,1,4:34:27 PM,14,1,4:34:29 PM,14,24.6,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_515,2
-Order_No_2882,User_Id_2200,Bike,3,Business,3,2,2:15:55 PM,3,2,2:30:14 PM,3,2,2:37:33 PM,3,2,2:41:48 PM,3,2,3:09:27 PM,7,20.1,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_88,1659
-Order_No_15596,User_Id_2154,Bike,3,Business,14,5,12:43:56 PM,14,5,12:45:36 PM,14,5,1:09:45 PM,14,5,1:19:11 PM,14,5,1:50:42 PM,17,21.7,,-1.269767,36.7351068,-1.2765736,36.851364600000004,Rider_Id_197,1891
-Order_No_18124,User_Id_186,Bike,3,Business,20,4,11:23:52 AM,20,4,11:24:00 AM,20,4,11:28:50 AM,20,4,11:49:00 AM,20,4,12:29:38 PM,20,22.3,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_647,2438
-Order_No_4191,User_Id_633,Bike,3,Business,25,5,3:52:02 PM,25,5,3:52:06 PM,25,5,3:52:09 PM,25,5,3:53:36 PM,25,5,4:09:14 PM,5,29.1,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_39,938
-Order_No_14691,User_Id_1271,Bike,3,Business,5,5,2:42:21 PM,5,5,2:43:29 PM,5,5,2:51:18 PM,5,5,3:08:53 PM,5,5,3:55:17 PM,23,27.8,,-1.3244885,36.897791999999995,-1.2784722,36.7556652,Rider_Id_96,2784
-Order_No_4370,User_Id_2030,Bike,3,Business,30,3,2:43:04 PM,30,3,2:44:20 PM,30,3,2:49:34 PM,30,3,3:05:06 PM,30,3,3:15:31 PM,3,29.1,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_862,625
-Order_No_15242,User_Id_893,Bike,3,Business,3,1,12:46:35 PM,3,1,12:47:40 PM,3,1,1:03:35 PM,3,1,1:11:24 PM,3,1,1:23:23 PM,5,21.4,,-1.2663194,36.809901,-1.2655306,36.8358511,Rider_Id_279,719
-Order_No_18624,User_Id_2497,Bike,3,Business,29,4,3:06:35 PM,29,4,3:36:04 PM,29,4,3:41:20 PM,29,4,3:58:19 PM,29,4,4:17:15 PM,2,24.6,,-1.2677588,36.8169359,-1.2782978,36.827323799999995,Rider_Id_320,1136
-Order_No_24886,User_Id_635,Bike,3,Business,13,1,10:08:15 AM,13,1,10:08:28 AM,13,1,10:21:58 AM,13,1,10:42:12 AM,13,1,11:07:30 AM,17,,,-1.177202,36.8351183,-1.2602337,36.79905479999999,Rider_Id_712,1518
-Order_No_2269,User_Id_3521,Bike,3,Business,3,5,9:55:51 AM,3,5,9:56:35 AM,3,5,10:34:13 AM,3,5,10:50:30 AM,3,5,11:13:41 AM,10,,,-1.3234708999999998,36.8493194,-1.2696908000000002,36.81519,Rider_Id_790,1391
-Order_No_26300,User_Id_393,Bike,3,Business,13,3,2:42:25 PM,13,3,2:43:10 PM,13,3,2:43:26 PM,13,3,3:14:57 PM,13,3,3:34:39 PM,11,29.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_796,1182
-Order_No_48,User_Id_627,Bike,3,Business,16,4,11:49:06 AM,16,4,11:50:26 AM,16,4,12:07:24 PM,16,4,12:13:20 PM,16,4,12:33:58 PM,9,,,-1.3012173,36.8093987,-1.2551895,36.7822034,Rider_Id_591,1238
-Order_No_14937,User_Id_1244,Bike,3,Business,3,4,1:14:15 PM,3,4,1:14:34 PM,3,4,1:14:46 PM,3,4,1:27:45 PM,3,4,1:47:44 PM,8,23.3,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_462,1199
-Order_No_325,User_Id_868,Bike,3,Business,8,3,10:47:04 AM,8,3,10:48:40 AM,8,3,10:54:28 AM,8,3,10:59:30 AM,8,3,11:09:30 AM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_829,600
-Order_No_23874,User_Id_391,Bike,3,Business,30,6,10:50:00 AM,30,6,10:51:05 AM,30,6,11:06:27 AM,30,6,11:38:04 AM,30,6,11:55:37 AM,5,22.1,,-1.3010429,36.835333500000004,-1.2777923999999998,36.8231577,Rider_Id_752,1053
-Order_No_10288,User_Id_429,Bike,3,Personal,29,5,2:39:50 PM,29,5,2:41:46 PM,29,5,3:01:47 PM,29,5,3:04:26 PM,29,5,3:47:26 PM,11,21.2,,-1.2633493999999998,36.7959696,-1.3258649,36.8000584,Rider_Id_208,2580
-Order_No_24584,User_Id_867,Bike,3,Business,7,2,12:05:45 PM,7,2,12:17:45 PM,7,2,12:35:24 PM,7,2,12:37:27 PM,7,2,1:29:07 PM,11,,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_469,3100
-Order_No_15213,User_Id_3186,Bike,3,Business,15,2,10:49:17 AM,15,2,10:50:18 AM,15,2,10:52:34 AM,15,2,11:09:04 AM,15,2,11:25:23 AM,7,24.3,,-1.2753108999999998,36.8130185,-1.2980228999999999,36.78865,Rider_Id_379,979
-Order_No_8860,User_Id_867,Bike,3,Business,23,6,9:03:55 AM,23,6,9:04:32 AM,23,6,9:09:49 AM,23,6,10:12:58 AM,23,6,10:33:05 AM,10,18.8,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_284,1207
-Order_No_15970,User_Id_2783,Bike,2,Personal,16,1,4:16:04 PM,16,1,4:20:12 PM,16,1,4:30:51 PM,16,1,4:59:11 PM,16,1,5:31:52 PM,8,,,-1.2859331,36.7865464,-1.2659913999999999,36.828365500000004,Rider_Id_215,1961
-Order_No_19563,User_Id_2112,Bike,3,Business,13,3,11:19:41 AM,13,3,11:21:26 AM,13,3,11:56:25 AM,13,3,12:03:32 PM,13,3,12:38:06 PM,16,20.2,,-1.225322,36.80855,-1.2156006000000001,36.8916865,Rider_Id_543,2074
-Order_No_12863,User_Id_2691,Bike,3,Business,15,5,9:32:31 AM,15,5,9:32:52 AM,15,5,9:47:56 AM,15,5,9:50:06 AM,15,5,10:26:12 AM,21,20.4,,-1.264865,36.810488,-1.1962799,36.9521972,Rider_Id_958,2166
-Order_No_16948,User_Id_733,Bike,3,Business,2,4,11:11:50 AM,2,4,11:12:41 AM,2,4,11:12:51 AM,2,4,11:25:09 AM,2,4,1:06:57 PM,15,,,-1.2551895,36.7822034,-1.3265961000000002,36.8718676,Rider_Id_349,6108
-Order_No_246,User_Id_867,Bike,3,Business,19,1,3:40:11 PM,19,1,3:49:45 PM,19,1,4:12:39 PM,19,1,4:20:13 PM,19,1,4:50:19 PM,14,25.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_828,1806
-Order_No_5303,User_Id_2154,Bike,3,Business,24,4,1:43:18 PM,24,4,1:44:44 PM,24,4,2:08:49 PM,24,4,2:24:37 PM,24,4,2:41:43 PM,5,28.4,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_899,1026
-Order_No_14399,User_Id_2627,Bike,3,Business,4,1,2:48:03 PM,4,1,2:49:01 PM,4,1,2:49:31 PM,4,1,3:13:07 PM,4,1,3:38:48 PM,14,28.5,,-1.2285834999999998,36.882646799999996,-1.2571472,36.795063299999995,Rider_Id_75,1541
-Order_No_20867,User_Id_1405,Bike,3,Business,24,4,2:29:38 PM,24,4,2:32:24 PM,24,4,3:02:25 PM,24,4,3:49:40 PM,24,4,3:56:37 PM,9,28.1,,-1.3194518,36.869396,-1.3478563000000001,36.8935824,Rider_Id_86,417
-Order_No_21471,User_Id_1882,Bike,3,Business,26,2,8:36:01 AM,26,2,8:37:48 AM,26,2,8:51:53 AM,26,2,9:11:42 AM,26,2,9:30:38 AM,7,,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_332,1136
-Order_No_1037,User_Id_1363,Bike,3,Business,18,6,9:27:59 AM,18,6,9:29:28 AM,18,6,10:03:32 AM,18,6,10:05:08 AM,18,6,10:18:43 AM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_448,815
-Order_No_18399,User_Id_393,Bike,3,Business,3,5,12:12:16 PM,3,5,12:12:43 PM,3,5,12:12:52 PM,3,5,12:33:44 PM,3,5,1:09:16 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,2132
-Order_No_25255,User_Id_1341,Bike,3,Business,15,5,9:50:18 AM,15,5,9:51:06 AM,15,5,10:48:23 AM,15,5,11:03:50 AM,15,5,11:22:54 AM,13,26.0,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_267,1144
-Order_No_20337,User_Id_2436,Bike,3,Business,2,2,12:30:04 PM,2,2,12:32:13 PM,2,2,1:24:52 PM,2,2,1:35:32 PM,2,2,1:53:25 PM,9,29.6,,-1.2931286,36.7584407,-1.2551895,36.7822034,Rider_Id_850,1073
-Order_No_23755,User_Id_2030,Bike,3,Business,13,4,2:18:16 PM,13,4,2:19:21 PM,13,4,2:29:40 PM,13,4,2:36:23 PM,13,4,2:55:24 PM,5,22.4,,-1.3379798999999999,36.883545299999994,-1.3226301999999999,36.8719901,Rider_Id_276,1141
-Order_No_4242,User_Id_2956,Bike,3,Business,18,2,6:04:15 PM,18,2,6:05:32 PM,18,2,6:19:04 PM,18,2,6:27:41 PM,18,2,7:04:00 PM,16,23.8,,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_932,2179
-Order_No_7560,User_Id_2279,Bike,3,Business,26,2,9:50:20 AM,26,2,9:51:19 AM,26,2,10:20:45 AM,26,2,10:22:03 AM,26,2,10:33:48 AM,8,21.6,,-1.2880883,36.8227336,-1.2571472,36.795063299999995,Rider_Id_448,705
-Order_No_1888,User_Id_2454,Bike,3,Business,20,3,1:59:57 PM,20,3,2:00:52 PM,20,3,2:05:04 PM,20,3,2:21:08 PM,20,3,2:34:28 PM,3,29.6,,-1.2941791999999999,36.763363700000006,-1.2993231,36.7753372,Rider_Id_129,800
-Order_No_9121,User_Id_970,Bike,3,Business,1,5,10:47:10 AM,1,5,10:51:44 AM,1,5,11:10:09 AM,1,5,11:16:40 AM,1,5,12:13:30 PM,19,26.7,,-1.2551895,36.7822034,-1.3459625,36.9073925,Rider_Id_831,3410
-Order_No_2175,User_Id_868,Bike,3,Business,18,2,1:10:02 PM,18,2,1:10:36 PM,18,2,1:10:41 PM,18,2,1:25:48 PM,18,2,1:45:58 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_40,1210
-Order_No_25911,User_Id_502,Bike,3,Personal,25,1,10:53:31 AM,25,1,11:09:01 AM,25,1,11:17:59 AM,25,1,11:36:50 AM,25,1,11:59:04 AM,6,,,-1.3019136999999998,36.7925105,-1.279085,36.819303000000005,Rider_Id_752,1334
-Order_No_27392,User_Id_893,Bike,3,Business,13,1,9:52:11 AM,13,1,9:52:44 AM,13,1,9:55:20 AM,13,1,9:59:42 AM,13,1,10:43:33 AM,7,,,-1.2663194,36.809901,-1.2847316,36.7658086,Rider_Id_99,2631
-Order_No_1700,User_Id_2090,Bike,2,Personal,19,4,10:03:17 AM,19,4,10:03:37 AM,19,4,10:07:56 AM,19,4,10:33:05 AM,19,4,11:04:57 AM,19,20.1,,-1.2912668,36.7656703,-1.3714408000000002,36.75819310000001,Rider_Id_895,1912
-Order_No_3407,User_Id_2154,Bike,3,Business,12,2,4:14:17 PM,12,2,4:18:15 PM,12,2,4:30:05 PM,12,2,4:34:58 PM,12,2,4:54:59 PM,6,28.6,,-1.2765736,36.851364600000004,-1.2878913,36.8089253,Rider_Id_291,1201
-Order_No_17749,User_Id_1469,Bike,3,Business,7,4,2:28:21 PM,7,4,2:28:39 PM,7,4,2:34:23 PM,7,4,2:47:18 PM,7,4,3:06:56 PM,8,27.8,,-1.2551895,36.7822034,-1.3040024,36.808296999999996,Rider_Id_267,1178
-Order_No_11481,User_Id_2330,Bike,3,Business,30,3,11:32:55 AM,30,3,11:34:00 AM,30,3,11:52:40 AM,30,3,12:03:45 PM,30,3,12:32:19 PM,8,25.1,,-1.3015197,36.7658456,-1.3270197,36.7037303,Rider_Id_622,1714
-Order_No_27973,User_Id_1918,Bike,1,Personal,27,4,11:35:45 AM,27,4,11:36:02 AM,27,4,11:36:24 AM,27,4,11:55:29 AM,27,4,11:55:35 AM,2,22.6,,-1.253332,36.80636,-1.2707373,36.805440999999995,Rider_Id_51,6
-Order_No_18526,User_Id_2733,Bike,3,Business,15,5,12:37:14 PM,15,5,12:38:16 PM,15,5,1:10:30 PM,15,5,1:15:35 PM,15,5,1:30:54 PM,6,25.7,,-1.311706,36.802841,-1.2963097,36.7688221,Rider_Id_269,919
-Order_No_16041,User_Id_154,Bike,2,Business,14,4,3:45:13 PM,14,4,3:48:43 PM,14,4,4:34:59 PM,14,4,4:41:08 PM,14,4,5:01:59 PM,9,21.3,,-1.2551895,36.7822034,-1.2984591,36.7855438,Rider_Id_312,1251
-Order_No_22581,User_Id_2986,Bike,3,Personal,14,5,10:00:13 AM,14,5,10:00:45 AM,14,5,10:13:36 AM,14,5,10:17:53 AM,14,5,10:41:12 AM,11,17.3,,-1.3255287,36.7850772,-1.2903886,36.8063847,Rider_Id_610,1399
-Order_No_14241,User_Id_3420,Bike,3,Business,21,5,2:38:36 PM,21,5,3:10:21 PM,21,5,3:16:15 PM,21,5,3:29:38 PM,21,5,4:02:11 PM,7,24.4,,-1.265981,36.793612100000004,-1.3046007,36.821174299999996,Rider_Id_607,1953
-Order_No_711,User_Id_157,Bike,3,Business,8,6,2:59:33 PM,8,6,3:03:19 PM,8,6,3:43:14 PM,8,6,3:47:29 PM,8,6,5:00:20 PM,18,23.4,1.9,-1.225322,36.80855,-1.2859912,36.8756811,Rider_Id_697,4371
-Order_No_13974,User_Id_1363,Bike,3,Business,12,5,9:19:30 AM,12,5,9:20:15 AM,12,5,9:28:21 AM,12,5,9:37:15 AM,12,5,9:56:10 AM,5,18.4,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_519,1135
-Order_No_14450,User_Id_2150,Bike,3,Business,12,3,3:05:31 PM,12,3,3:08:16 PM,12,3,3:53:13 PM,12,3,4:21:46 PM,12,3,4:21:54 PM,12,21.0,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_376,8
-Order_No_20954,User_Id_1746,Bike,3,Business,4,2,2:10:17 PM,4,2,2:48:17 PM,4,2,2:57:59 PM,4,2,3:23:10 PM,4,2,3:38:20 PM,5,20.2,,-1.2914364999999999,36.8580531,-1.2801014,36.821042299999995,Rider_Id_208,910
-Order_No_24192,User_Id_635,Bike,3,Business,27,2,1:01:23 PM,27,2,1:01:42 PM,27,2,1:02:12 PM,27,2,1:18:17 PM,27,2,1:37:46 PM,6,23.7,,-1.3016417,36.8271679,-1.3229345,36.8566974,Rider_Id_920,1169
-Order_No_11953,User_Id_788,Bike,3,Business,28,1,9:45:05 AM,28,1,9:51:33 AM,28,1,10:22:46 AM,28,1,10:34:51 AM,28,1,11:25:25 AM,10,23.9,,-1.279395,36.825364,-1.225322,36.80855,Rider_Id_332,3034
-Order_No_5923,User_Id_3560,Bike,3,Business,23,6,3:53:50 PM,23,6,3:58:48 PM,23,6,3:59:37 PM,23,6,4:07:45 PM,23,6,4:14:29 PM,1,,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_763,404
-Order_No_2608,User_Id_1350,Bike,2,Personal,2,3,9:55:47 AM,2,3,9:55:55 AM,2,3,10:11:43 AM,2,3,10:23:17 AM,2,3,10:42:04 AM,9,17.8,0.9,-1.3271684,36.8173021,-1.336875,36.754409,Rider_Id_196,1127
-Order_No_28179,User_Id_517,Bike,3,Business,28,5,9:42:24 AM,28,5,9:42:47 AM,28,5,9:46:14 AM,28,5,9:58:57 AM,28,5,10:12:40 AM,11,19.5,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,823
-Order_No_2503,User_Id_451,Bike,3,Personal,18,2,9:15:28 AM,18,2,9:15:45 AM,18,2,9:23:21 AM,18,2,9:27:53 AM,18,2,9:53:09 AM,10,16.3,,-1.2945178999999998,36.7685349,-1.2736466,36.8164042,Rider_Id_612,1516
-Order_No_22803,User_Id_3186,Bike,3,Business,6,4,2:13:43 PM,6,4,2:14:10 PM,6,4,2:30:31 PM,6,4,2:35:24 PM,6,4,2:41:44 PM,2,21.3,,-1.2980228999999999,36.78865,-1.2987732,36.7932128,Rider_Id_277,380
-Order_No_28134,User_Id_1527,Bike,3,Business,21,2,9:31:54 AM,21,2,9:32:57 AM,21,2,10:07:24 AM,21,2,10:11:37 AM,21,2,10:24:39 AM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_155,782
-Order_No_4644,User_Id_3517,Bike,3,Business,5,2,9:23:20 AM,5,2,9:23:46 AM,5,2,9:27:33 AM,5,2,10:40:34 AM,5,2,11:11:52 AM,16,25.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_519,1878
-Order_No_26149,User_Id_2216,Bike,3,Business,4,5,9:47:41 AM,4,5,9:48:04 AM,4,5,9:51:45 AM,4,5,10:26:57 AM,4,5,10:57:05 AM,15,17.3,0.3,-1.2867627000000001,36.883446,-1.2854173999999998,36.7951152,Rider_Id_557,1808
-Order_No_4690,User_Id_2423,Bike,3,Business,30,2,10:43:43 AM,30,2,10:53:50 AM,30,2,11:00:41 AM,30,2,11:07:00 AM,30,2,11:40:44 AM,8,,,-1.2551895,36.7822034,-1.3012173,36.8093987,Rider_Id_564,2024
-Order_No_19199,User_Id_2200,Bike,3,Business,23,2,9:57:20 AM,23,2,10:15:46 AM,23,2,10:40:48 AM,23,2,10:49:12 AM,23,2,11:07:13 AM,6,17.9,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_88,1081
-Order_No_3382,User_Id_3109,Bike,2,Personal,22,5,8:12:48 AM,22,5,8:13:20 AM,22,5,8:34:04 AM,22,5,9:00:48 AM,22,5,9:44:39 AM,9,16.3,,-1.2894847,36.7556088,-1.3066143,36.8161046,Rider_Id_140,2631
-Order_No_18876,User_Id_248,Bike,3,Business,5,4,2:15:18 PM,5,4,2:15:29 PM,5,4,2:16:27 PM,5,4,2:31:34 PM,5,4,2:49:08 PM,8,22.3,,-1.304735,36.784605400000004,-1.2562249,36.786866100000005,Rider_Id_101,1054
-Order_No_4559,User_Id_302,Bike,3,Business,21,3,3:24:47 PM,21,3,3:51:51 PM,21,3,4:01:24 PM,21,3,4:05:20 PM,21,3,4:23:19 PM,6,23.4,3.0,-1.2649605,36.798177700000004,-1.2755141,36.8323179,Rider_Id_434,1079
-Order_No_26339,User_Id_1238,Bike,3,Personal,11,5,12:30:49 PM,11,5,12:42:44 PM,11,5,1:06:52 PM,11,5,1:08:05 PM,11,5,1:37:11 PM,7,20.9,,-1.263419,36.8060348,-1.302088,36.831162,Rider_Id_268,1746
-Order_No_12888,User_Id_1295,Bike,2,Personal,28,4,11:21:41 AM,28,4,11:22:16 AM,28,4,11:26:49 AM,28,4,11:50:27 AM,28,4,12:04:21 PM,11,,,-1.2947459,36.775909999999996,-1.3339461000000001,36.821912899999994,Rider_Id_312,834
-Order_No_7869,User_Id_1637,Bike,3,Business,26,5,12:15:39 PM,26,5,12:28:02 PM,26,5,12:28:03 PM,26,5,12:57:56 PM,26,5,1:25:05 PM,3,,,-1.2649605,36.798177700000004,-1.2615888000000002,36.7928732,Rider_Id_755,1629
-Order_No_14363,User_Id_1209,Bike,3,Business,18,4,11:02:16 AM,18,4,11:03:07 AM,18,4,11:30:40 AM,18,4,11:38:31 AM,18,4,11:53:14 AM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_843,883
-Order_No_27964,User_Id_3291,Bike,3,Business,17,2,1:25:45 PM,17,2,1:54:47 PM,17,2,2:09:34 PM,17,2,2:19:00 PM,17,2,2:57:45 PM,13,24.3,,-1.3313643,36.857742200000004,-1.2600926000000001,36.8088685,Rider_Id_670,2325
-Order_No_23863,User_Id_1899,Bike,3,Business,27,1,1:47:48 PM,27,1,1:48:17 PM,27,1,1:48:41 PM,27,1,2:05:51 PM,27,1,2:19:19 PM,7,23.2,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_686,808
-Order_No_13340,User_Id_1310,Bike,2,Personal,15,1,10:59:58 AM,15,1,11:00:46 AM,15,1,11:12:53 AM,15,1,11:18:33 AM,15,1,11:32:02 AM,8,,,-1.325307,36.825157,-1.3038156,36.812273299999994,Rider_Id_72,809
-Order_No_7797,User_Id_111,Bike,3,Business,22,5,12:40:17 PM,22,5,12:40:41 PM,22,5,12:41:20 PM,22,5,12:46:42 PM,22,5,12:55:03 PM,4,,,-1.2551895,36.7822034,-1.2747247,36.8150014,Rider_Id_793,501
-Order_No_23786,User_Id_2956,Bike,3,Business,28,2,3:09:09 PM,28,2,3:09:20 PM,28,2,3:10:12 PM,28,2,3:14:21 PM,28,2,3:24:52 PM,3,19.3,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_207,631
-Order_No_6145,User_Id_1329,Bike,3,Business,9,3,1:34:32 PM,9,3,1:35:27 PM,9,3,2:08:57 PM,9,3,2:21:16 PM,9,3,2:37:32 PM,5,23.2,,-1.273056,36.811298,-1.303664,36.818777000000004,Rider_Id_159,976
-Order_No_17791,User_Id_1244,Bike,3,Business,16,5,2:48:27 PM,16,5,3:06:44 PM,16,5,3:07:34 PM,16,5,3:32:04 PM,16,5,3:52:59 PM,11,,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_623,1255
-Order_No_12505,User_Id_186,Bike,3,Business,23,3,4:04:25 PM,23,3,4:05:00 PM,23,3,4:17:24 PM,23,3,4:22:52 PM,23,3,4:45:14 PM,4,22.1,,-1.2579303999999998,36.8019667,-1.2890399,36.8164942,Rider_Id_148,1342
-Order_No_8146,User_Id_2042,Bike,3,Business,2,4,9:39:41 AM,2,4,9:39:56 AM,2,4,10:07:17 AM,2,4,10:18:19 AM,2,4,10:45:52 AM,13,13.2,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_297,1653
-Order_No_9011,User_Id_3064,Bike,3,Business,8,5,2:40:49 PM,8,5,2:49:01 PM,8,5,3:15:19 PM,8,5,3:22:54 PM,8,5,3:41:04 PM,8,30.5,,-1.2571472,36.795063299999995,-1.2969741000000001,36.7856612,Rider_Id_590,1090
-Order_No_15194,User_Id_231,Bike,1,Personal,28,5,1:06:56 PM,28,5,1:12:31 PM,28,5,1:17:25 PM,28,5,1:23:48 PM,28,5,1:37:41 PM,3,24.9,,-1.2872652,36.826761299999994,-1.2956951,36.812924200000005,Rider_Id_698,833
-Order_No_19037,User_Id_3391,Bike,1,Personal,7,5,1:02:05 PM,7,5,1:46:09 PM,7,5,1:48:05 PM,7,5,1:58:57 PM,7,5,2:19:59 PM,6,24.8,,-1.3009926,36.8208532,-1.2858757,36.843020700000004,Rider_Id_669,1262
-Order_No_19659,User_Id_2803,Bike,1,Business,25,2,8:48:04 AM,25,2,8:48:17 AM,25,2,8:57:55 AM,25,2,9:09:14 AM,25,2,9:48:24 AM,13,17.7,,-1.2573382,36.877362,-1.2571472,36.795063299999995,Rider_Id_727,2350
-Order_No_27161,User_Id_3501,Bike,1,Personal,10,1,5:05:08 PM,10,1,5:05:14 PM,10,1,5:05:45 PM,10,1,5:24:26 PM,10,1,6:02:43 PM,10,18.9,,-1.2855766000000002,36.7928037,-1.3159197,36.8516724,Rider_Id_929,2297
-Order_No_8612,User_Id_1304,Bike,3,Business,26,5,9:51:25 AM,26,5,10:06:42 AM,26,5,10:14:08 AM,26,5,10:18:11 AM,26,5,10:52:31 AM,10,,,-1.2551895,36.7822034,-1.2304909,36.821690999999994,Rider_Id_519,2060
-Order_No_19874,User_Id_2987,Bike,3,Business,7,3,11:04:08 AM,7,3,11:04:44 AM,7,3,11:07:40 AM,7,3,11:09:43 AM,7,3,11:09:46 AM,3,25.2,,-1.2702120000000001,36.82108770000001,-1.263987,36.798891,Rider_Id_390,3
-Order_No_8709,User_Id_3560,Bike,3,Business,12,2,11:16:39 AM,12,2,11:31:44 AM,12,2,11:41:55 AM,12,2,11:51:30 AM,12,2,12:29:29 PM,7,24.6,,-1.2657464,36.780114399999995,-1.2897368,36.8226783,Rider_Id_504,2279
-Order_No_26904,User_Id_2232,Bike,2,Personal,22,5,2:44:28 PM,22,5,2:49:46 PM,22,5,3:05:14 PM,22,5,3:19:24 PM,22,5,3:41:21 PM,10,,,-1.2846475,36.8275903,-1.2256671000000001,36.8148558,Rider_Id_812,1317
-Order_No_21451,User_Id_1966,Bike,3,Business,16,4,4:08:40 PM,16,4,4:25:37 PM,16,4,4:52:14 PM,16,4,5:03:05 PM,16,4,5:49:31 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_519,2786
-Order_No_11890,User_Id_3647,Bike,3,Business,29,2,1:37:33 PM,29,2,1:40:43 PM,29,2,1:56:08 PM,29,2,2:04:31 PM,29,2,2:27:34 PM,7,26.9,,-1.2726389999999999,36.794723,-1.2959613,36.7684114,Rider_Id_246,1383
-Order_No_19152,User_Id_3291,Bike,3,Business,30,2,9:39:30 AM,30,2,9:39:46 AM,30,2,10:20:50 AM,30,2,10:51:14 AM,30,2,11:16:33 AM,12,,,-1.318062,36.849326,-1.2600926000000001,36.8088685,Rider_Id_68,1519
-Order_No_20487,User_Id_2161,Bike,2,Personal,13,1,11:15:03 AM,13,1,11:15:53 AM,13,1,11:21:17 AM,13,1,11:46:29 AM,13,1,12:01:30 PM,9,17.2,,-1.3001432,36.7764912,-1.3149338,36.7135703,Rider_Id_754,901
-Order_No_17056,User_Id_3698,Bike,3,Business,29,3,2:29:15 PM,29,3,2:43:56 PM,29,3,2:48:24 PM,29,3,3:12:20 PM,29,3,3:26:44 PM,7,20.9,,-1.3244885,36.897791999999995,-1.34364,36.8925345,Rider_Id_856,864
-Order_No_21623,User_Id_111,Bike,3,Business,13,1,11:44:20 AM,13,1,11:44:34 AM,13,1,11:45:14 AM,13,1,12:03:43 PM,13,1,12:52:28 PM,12,,,-1.2551895,36.7822034,-1.3157450000000002,36.847565,Rider_Id_99,2925
-Order_No_25043,User_Id_3491,Bike,3,Business,22,2,3:42:05 PM,22,2,3:42:12 PM,22,2,3:45:25 PM,22,2,3:50:51 PM,22,2,5:09:04 PM,14,25.8,,-1.2285834999999998,36.882646799999996,-1.3244885,36.897791999999995,Rider_Id_882,4693
-Order_No_10959,User_Id_3283,Bike,3,Business,27,4,12:46:24 PM,27,4,12:46:43 PM,27,4,12:51:27 PM,27,4,1:48:50 PM,27,4,2:07:24 PM,10,23.9,,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_498,1114
-Order_No_27260,User_Id_3108,Bike,3,Business,22,5,11:25:14 AM,22,5,11:26:18 AM,22,5,11:26:34 AM,22,5,11:33:49 AM,22,5,11:53:07 AM,7,,,-1.2551895,36.7822034,-1.2894795000000001,36.781853399999996,Rider_Id_518,1158
-Order_No_20417,User_Id_1856,Bike,3,Business,4,4,2:40:03 PM,4,4,2:44:27 PM,4,4,2:56:30 PM,4,4,3:09:20 PM,4,4,3:33:08 PM,8,27.5,,-1.273009,36.802807,-1.3126476,36.831455,Rider_Id_709,1428
-Order_No_10218,User_Id_3295,Bike,3,Business,8,2,1:56:52 PM,8,2,1:57:13 PM,8,2,2:03:09 PM,8,2,2:16:08 PM,8,2,2:28:14 PM,3,25.5,,-1.2571472,36.795063299999995,-1.2753902,36.808223,Rider_Id_509,726
-Order_No_28170,User_Id_1342,Bike,3,Business,18,1,2:40:52 PM,18,1,2:41:06 PM,18,1,3:06:58 PM,18,1,3:14:14 PM,18,1,4:04:46 PM,18,17.4,,-1.3025826,36.7670807,-1.2486688999999997,36.874896899999996,Rider_Id_755,3032
-Order_No_6534,User_Id_1882,Bike,3,Business,11,4,10:51:40 AM,11,4,10:53:09 AM,11,4,11:02:11 AM,11,4,11:03:52 AM,11,4,11:23:54 AM,8,25.6,,-1.3014461000000002,36.7661381,-1.2877809999999998,36.817865999999995,Rider_Id_198,1202
-Order_No_12886,User_Id_3404,Bike,3,Business,8,2,1:29:27 PM,8,2,1:53:13 PM,8,2,1:57:46 PM,8,2,2:00:35 PM,8,2,2:14:45 PM,9,25.3,,-1.279395,36.825364,-1.2413316,36.878552500000005,Rider_Id_878,850
-Order_No_12483,User_Id_1329,Bike,3,Business,13,1,11:40:53 AM,13,1,11:41:38 AM,13,1,11:45:31 AM,13,1,11:50:11 AM,13,1,12:01:11 PM,3,,,-1.273056,36.811298,-1.2546732,36.808679600000005,Rider_Id_96,660
-Order_No_9364,User_Id_1557,Bike,3,Business,19,3,10:18:07 AM,19,3,10:18:23 AM,19,3,10:33:34 AM,19,3,10:36:57 AM,19,3,10:48:18 AM,6,,,-1.3147073999999999,36.811721999999996,-1.2942764,36.7969749,Rider_Id_612,681
-Order_No_24321,User_Id_810,Bike,3,Personal,26,3,2:58:04 PM,26,3,2:58:20 PM,26,3,3:01:08 PM,26,3,3:24:50 PM,26,3,3:36:13 PM,6,27.2,,-1.2704561,36.7688456,-1.2833534,36.8165865,Rider_Id_82,683
-Order_No_4966,User_Id_554,Bike,1,Personal,23,3,10:51:32 AM,23,3,10:52:48 AM,23,3,10:59:51 AM,23,3,11:04:35 AM,23,3,11:27:00 AM,6,21.9,,-1.269059,36.8108027,-1.2942764,36.7969749,Rider_Id_531,1345
-Order_No_13103,User_Id_2982,Bike,3,Business,3,3,3:59:54 PM,3,3,4:01:31 PM,3,3,4:09:27 PM,3,3,4:27:05 PM,3,3,5:07:01 PM,18,28.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_235,2396
-Order_No_10257,User_Id_393,Bike,3,Business,11,4,12:50:24 PM,11,4,12:51:40 PM,11,4,1:06:30 PM,11,4,1:57:03 PM,11,4,2:00:06 PM,4,27.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_787,183
-Order_No_20847,User_Id_2982,Bike,3,Business,10,1,11:37:26 AM,10,1,11:53:11 AM,10,1,12:12:53 PM,10,1,12:55:40 PM,10,1,1:32:37 PM,18,18.6,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_654,2217
-Order_No_21417,User_Id_3586,Bike,3,Business,9,3,4:33:12 PM,9,3,4:33:50 PM,9,3,4:41:09 PM,9,3,4:45:07 PM,9,3,5:06:18 PM,8,24.5,,-1.275442,36.766020700000006,-1.2760462,36.807469700000006,Rider_Id_205,1271
-Order_No_22991,User_Id_3291,Bike,3,Business,6,1,9:05:24 AM,6,1,9:05:40 AM,6,1,9:14:43 AM,6,1,9:22:13 AM,6,1,9:28:18 AM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_427,365
-Order_No_17076,User_Id_867,Bike,3,Business,5,2,9:45:47 AM,5,2,9:46:11 AM,5,2,9:55:34 AM,5,2,10:06:13 AM,5,2,10:36:18 AM,14,21.0,2.0,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_148,1805
-Order_No_23196,User_Id_1724,Bike,3,Business,18,1,10:30:12 AM,18,1,10:31:52 AM,18,1,10:46:14 AM,18,1,10:58:04 AM,18,1,11:40:43 AM,3,27.9,,-1.2551895,36.7822034,-1.2595466999999998,36.8113044,Rider_Id_448,2559
-Order_No_5954,User_Id_3599,Bike,3,Business,10,1,11:16:59 AM,10,1,11:17:00 AM,10,1,11:26:06 AM,10,1,11:48:10 AM,10,1,12:20:45 PM,14,18.3,,-1.3302996,36.8707935,-1.2819302,36.776979499999996,Rider_Id_675,1955
-Order_No_22564,User_Id_53,Bike,3,Business,8,4,2:36:34 PM,8,4,2:51:33 PM,8,4,3:04:06 PM,8,4,3:33:02 PM,8,4,3:52:52 PM,6,27.3,,-1.2770708,36.8231093,-1.2475788,36.82154379999999,Rider_Id_333,1190
-Order_No_17171,User_Id_1342,Bike,3,Business,6,2,11:44:39 AM,6,2,11:50:21 AM,6,2,11:50:45 AM,6,2,12:33:59 PM,6,2,1:25:42 PM,9,24.9,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_268,3103
-Order_No_2903,User_Id_2008,Bike,3,Business,31,2,1:11:53 PM,31,2,1:13:08 PM,31,2,1:23:47 PM,31,2,1:33:27 PM,31,2,1:49:13 PM,5,21.9,,-1.2508229,36.7895263,-1.2638898,36.8223638,Rider_Id_882,946
-Order_No_2080,User_Id_111,Bike,3,Business,28,4,3:22:04 PM,28,4,4:42:56 PM,28,4,4:46:19 PM,28,4,4:47:25 PM,28,4,5:14:28 PM,8,28.1,,-1.2551895,36.7822034,-1.2913205,36.822356899999996,Rider_Id_448,1623
-Order_No_9706,User_Id_906,Bike,1,Personal,26,2,8:49:52 AM,26,2,8:52:05 AM,26,2,9:08:02 AM,26,2,9:14:34 AM,26,2,9:36:02 AM,6,,,-1.2874313999999998,36.7764912,-1.2947777,36.8194497,Rider_Id_752,1288
-Order_No_20865,User_Id_286,Bike,3,Business,22,4,3:53:02 PM,22,4,4:27:39 PM,22,4,4:41:07 PM,22,4,4:46:53 PM,22,4,5:08:11 PM,7,24.1,,-1.2551895,36.7822034,-1.2962966999999999,36.8020231,Rider_Id_913,1278
-Order_No_3272,User_Id_1051,Bike,3,Business,29,1,11:20:33 AM,29,1,11:21:17 AM,29,1,11:21:25 AM,29,1,11:28:38 AM,29,1,12:00:23 PM,21,,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_397,1905
-Order_No_18447,User_Id_1500,Bike,3,Business,18,4,12:32:01 PM,18,4,12:32:47 PM,18,4,12:52:51 PM,18,4,1:02:27 PM,18,4,1:28:41 PM,11,,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_641,1574
-Order_No_26631,User_Id_2030,Bike,3,Business,17,1,2:09:32 PM,17,1,2:26:41 PM,17,1,2:46:59 PM,17,1,2:56:52 PM,17,1,3:23:17 PM,14,24.8,,-1.1490105,36.930741299999994,-1.2156006000000001,36.8916865,Rider_Id_40,1585
-Order_No_6736,User_Id_2509,Bike,3,Business,27,4,2:52:42 PM,27,4,2:52:52 PM,27,4,2:56:45 PM,27,4,3:00:27 PM,27,4,3:26:39 PM,20,23.8,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_685,1572
-Order_No_19233,User_Id_1482,Bike,3,Business,11,4,12:49:18 PM,11,4,12:51:27 PM,11,4,12:56:48 PM,11,4,12:57:56 PM,11,4,1:30:37 PM,17,24.1,,-1.3385790000000002,36.89630379999999,-1.260232,36.807334000000004,Rider_Id_543,1961
-Order_No_14340,User_Id_2360,Bike,3,Business,13,3,9:14:25 AM,13,3,9:14:59 AM,13,3,9:18:57 AM,13,3,9:51:12 AM,13,3,10:15:03 AM,7,20.4,,-1.2708857,36.8135766,-1.3207146,36.8305771,Rider_Id_678,1431
-Order_No_26285,User_Id_788,Bike,3,Business,10,4,2:06:19 PM,10,4,2:07:25 PM,10,4,2:16:12 PM,10,4,2:34:28 PM,10,4,2:53:25 PM,8,25.4,,-1.2571472,36.795063299999995,-1.225322,36.80855,Rider_Id_178,1137
-Order_No_14825,User_Id_909,Bike,3,Business,9,2,11:01:23 AM,9,2,11:04:52 AM,9,2,11:23:27 AM,9,2,11:25:43 AM,9,2,12:10:18 PM,10,27.2,,-1.225322,36.80855,-1.2880128999999998,36.8308097,Rider_Id_660,2675
-Order_No_4733,User_Id_1760,Bike,1,Personal,7,4,6:22:36 PM,7,4,6:23:09 PM,7,4,6:30:54 PM,7,4,6:38:45 PM,7,4,6:48:55 PM,1,17.6,,-1.264865,36.810488,-1.2593048999999998,36.802937299999996,Rider_Id_434,610
-Order_No_14434,User_Id_393,Bike,3,Business,27,3,12:17:10 PM,27,3,12:17:43 PM,27,3,12:17:50 PM,27,3,12:26:15 PM,27,3,12:50:56 PM,11,25.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_901,1481
-Order_No_15721,User_Id_3426,Bike,3,Business,4,1,1:35:09 PM,4,1,1:41:40 PM,4,1,1:57:50 PM,4,1,2:24:58 PM,4,1,2:42:50 PM,9,28.4,,-1.290894,36.822971,-1.3012008,36.764868,Rider_Id_124,1072
-Order_No_7236,User_Id_1402,Bike,3,Business,26,2,11:53:21 AM,26,2,11:53:55 AM,26,2,12:04:53 PM,26,2,12:27:23 PM,26,2,12:37:57 PM,1,27.3,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_855,634
-Order_No_9868,User_Id_1469,Bike,3,Business,15,3,9:31:15 AM,15,3,9:31:25 AM,15,3,9:31:29 AM,15,3,9:44:27 AM,15,3,9:56:49 AM,6,,,-1.2551895,36.7822034,-1.265432,36.795034,Rider_Id_576,742
-Order_No_21281,User_Id_2801,Bike,3,Business,14,5,4:00:19 PM,14,5,4:01:24 PM,14,5,4:15:26 PM,14,5,4:18:35 PM,14,5,4:31:53 PM,5,23.2,,-1.2674273999999999,36.787082700000006,-1.269534,36.80697,Rider_Id_446,798
-Order_No_624,User_Id_3339,Bike,3,Business,27,3,10:10:59 AM,27,3,10:11:40 AM,27,3,10:15:37 AM,27,3,10:19:52 AM,27,3,10:45:55 AM,8,,,-1.2551895,36.7822034,-1.2962618000000001,36.792933399999995,Rider_Id_85,1563
-Order_No_9424,User_Id_3647,Bike,3,Business,9,5,11:24:43 AM,9,5,11:25:04 AM,9,5,11:51:49 AM,9,5,11:53:06 AM,9,5,12:38:01 PM,16,25.9,,-1.2726389999999999,36.794723,-1.2701893999999998,36.90246,Rider_Id_620,2695
-Order_No_24443,User_Id_496,Bike,3,Business,7,2,10:12:40 AM,7,2,10:13:13 AM,7,2,11:00:24 AM,7,2,11:29:45 AM,7,2,12:10:27 PM,22,,,-1.2571472,36.795063299999995,-1.374163,36.913444,Rider_Id_520,2442
-Order_No_2172,User_Id_2850,Bike,1,Personal,8,5,5:49:17 PM,8,5,5:52:31 PM,8,5,6:17:41 PM,8,5,6:24:15 PM,8,5,6:56:29 PM,12,19.8,,-1.2813444999999999,36.83563970000001,-1.2257343,36.89335429999999,Rider_Id_852,1934
-Order_No_3059,User_Id_2587,Bike,3,Business,11,2,2:10:47 PM,11,2,2:18:57 PM,11,2,2:52:00 PM,11,2,3:04:14 PM,11,2,3:20:06 PM,2,25.6,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_268,952
-Order_No_16922,User_Id_909,Bike,3,Business,11,4,1:58:17 PM,11,4,1:58:33 PM,11,4,2:08:45 PM,11,4,2:17:09 PM,11,4,2:56:37 PM,13,26.5,,-1.225322,36.80855,-1.2857117,36.7968296,Rider_Id_191,2368
-Order_No_8203,User_Id_2587,Bike,3,Business,30,1,12:11:56 PM,30,1,12:12:05 PM,30,1,12:33:41 PM,30,1,12:48:59 PM,30,1,1:46:59 PM,14,22.7,,-1.3065978999999999,36.7186349,-1.2885528999999998,36.816014100000004,Rider_Id_846,3480
-Order_No_21904,User_Id_248,Bike,3,Business,21,2,4:10:41 PM,21,2,4:11:54 PM,21,2,4:23:29 PM,21,2,4:25:37 PM,21,2,4:34:11 PM,2,24.9,,-1.3050378999999999,36.7943452,-1.304735,36.784605400000004,Rider_Id_576,514
-Order_No_22201,User_Id_2383,Bike,3,Business,22,4,8:13:25 AM,22,4,8:13:59 AM,22,4,8:27:53 AM,22,4,8:30:49 AM,22,4,8:39:58 AM,4,19.6,3.1,-1.2608993,36.799246000000004,-1.273009,36.802807,Rider_Id_536,549
-Order_No_18313,User_Id_2330,Bike,3,Business,29,3,2:55:27 PM,29,3,3:35:04 PM,29,3,3:52:33 PM,29,3,4:16:26 PM,29,3,4:38:49 PM,10,20.9,,-1.2524916000000001,36.8656196,-1.2642903,36.80021370000001,Rider_Id_576,1343
-Order_No_15111,User_Id_3283,Bike,3,Business,13,3,12:27:19 PM,13,3,12:27:41 PM,13,3,12:31:45 PM,13,3,12:49:25 PM,13,3,1:03:58 PM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_215,873
-Order_No_15056,User_Id_1687,Bike,3,Business,15,3,2:41:59 PM,15,3,2:43:46 PM,15,3,2:56:12 PM,15,3,3:09:52 PM,15,3,3:28:08 PM,9,21.7,,-1.3011535,36.8437979,-1.3267367,36.8844852,Rider_Id_754,1096
-Order_No_5347,User_Id_504,Bike,1,Personal,27,4,12:57:45 PM,27,4,12:58:01 PM,27,4,1:08:33 PM,27,4,1:15:58 PM,27,4,1:40:25 PM,9,22.7,,-1.3104733999999998,36.7363698,-1.2650523,36.786679799999995,Rider_Id_565,1467
-Order_No_7774,User_Id_393,Bike,3,Business,19,2,9:46:49 AM,19,2,9:52:02 AM,19,2,9:59:43 AM,19,2,10:10:21 AM,19,2,10:19:01 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_15,520
-Order_No_11044,User_Id_867,Bike,3,Business,15,5,7:22:13 AM,15,5,7:34:36 AM,15,5,7:56:13 AM,15,5,8:18:01 AM,15,5,8:39:59 AM,10,19.1,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_177,1318
-Order_No_1744,User_Id_3621,Bike,3,Business,13,1,9:36:19 AM,13,1,9:37:02 AM,13,1,9:47:34 AM,13,1,9:50:18 AM,13,1,10:16:11 AM,13,,,-1.2836583000000001,36.7886014,-1.2355996,36.81607329999999,Rider_Id_155,1553
-Order_No_14201,User_Id_1874,Bike,3,Business,12,2,11:02:22 AM,12,2,11:18:03 AM,12,2,11:36:17 AM,12,2,11:40:25 AM,12,2,12:08:18 PM,5,24.7,,-1.2571472,36.795063299999995,-1.289925,36.820209000000006,Rider_Id_590,1673
-Order_No_23994,User_Id_2154,Bike,3,Business,19,1,1:47:01 PM,19,1,1:48:54 PM,19,1,1:49:06 PM,19,1,1:58:05 PM,19,1,2:33:39 PM,8,25.0,,-1.2584143,36.8048002,-1.2765736,36.851364600000004,Rider_Id_928,2134
-Order_No_27374,User_Id_2175,Bike,3,Business,19,2,1:31:46 PM,19,2,1:32:42 PM,19,2,1:41:11 PM,19,2,1:42:31 PM,19,2,2:04:23 PM,7,29.9,,-1.2885417,36.810052299999995,-1.2615888000000002,36.7928732,Rider_Id_445,1312
-Order_No_16016,User_Id_2436,Bike,3,Business,26,1,1:55:17 PM,26,1,1:56:38 PM,26,1,2:00:45 PM,26,1,2:21:05 PM,26,1,2:36:50 PM,6,22.3,0.7,-1.2551895,36.7822034,-1.2479361999999998,36.772759,Rider_Id_770,945
-Order_No_15946,User_Id_2987,Bike,3,Business,24,3,12:02:05 PM,24,3,12:03:52 PM,24,3,12:11:03 PM,24,3,12:13:53 PM,24,3,12:25:48 PM,3,19.7,,-1.2649605,36.798177700000004,-1.2702120000000001,36.82108770000001,Rider_Id_107,715
-Order_No_2944,User_Id_2956,Bike,3,Business,22,5,1:30:05 PM,22,5,1:30:28 PM,22,5,1:31:47 PM,22,5,1:38:52 PM,22,5,1:50:46 PM,3,27.3,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_778,714
-Order_No_22926,User_Id_640,Bike,3,Business,9,4,11:19:26 AM,9,4,11:20:16 AM,9,4,11:32:55 AM,9,4,11:35:34 AM,9,4,12:26:44 PM,7,,,-1.2969741000000001,36.7856612,-1.2641723999999999,36.8014198,Rider_Id_571,3070
-Order_No_780,User_Id_1075,Bike,3,Business,31,4,12:12:39 PM,31,4,12:14:01 PM,31,4,12:41:15 PM,31,4,12:46:21 PM,31,4,1:28:41 PM,14,26.7,,-1.3035886,36.8599564,-1.2527958000000001,36.8003131,Rider_Id_332,2540
-Order_No_26638,User_Id_1880,Bike,3,Business,12,2,2:09:14 PM,12,2,2:09:58 PM,12,2,2:12:58 PM,12,2,2:40:45 PM,12,2,2:56:22 PM,11,,,-1.2571472,36.795063299999995,-1.2662306,36.710789,Rider_Id_783,937
-Order_No_4461,User_Id_3432,Bike,3,Business,4,6,10:27:53 AM,4,6,10:38:31 AM,4,6,10:55:25 AM,4,6,11:07:38 AM,4,6,11:48:59 AM,21,,,-1.2672588,36.8000252,-1.3130876,36.68064879999999,Rider_Id_675,2481
-Order_No_12364,User_Id_3538,Bike,2,Personal,19,2,4:00:05 PM,19,2,4:00:24 PM,19,2,4:03:32 PM,19,2,4:09:08 PM,19,2,4:28:54 PM,17,18.3,,-1.2697141,36.798177700000004,-1.224119,36.8916459,Rider_Id_408,1186
-Order_No_6718,User_Id_393,Bike,3,Business,30,2,1:27:02 PM,30,2,1:32:58 PM,30,2,1:33:29 PM,30,2,1:40:55 PM,30,2,2:19:55 PM,14,27.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_239,2340
-Order_No_8692,User_Id_3291,Bike,3,Business,3,6,2:09:52 PM,3,6,2:11:02 PM,3,6,2:18:32 PM,3,6,2:40:25 PM,3,6,2:43:18 PM,4,22.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_137,173
-Order_No_23088,User_Id_2383,Bike,3,Business,21,3,8:41:23 AM,21,3,11:06:41 AM,21,3,11:20:46 AM,21,3,11:23:19 AM,21,3,11:51:18 AM,6,22.2,,-1.2608993,36.799246000000004,-1.292044,36.821938,Rider_Id_102,1679
-Order_No_21566,User_Id_647,Bike,3,Business,3,3,9:08:42 AM,3,3,9:15:27 AM,3,3,10:03:48 AM,3,3,10:23:01 AM,3,3,10:32:40 AM,4,21.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_763,579
-Order_No_20215,User_Id_635,Bike,3,Business,8,5,10:07:51 AM,8,5,10:08:13 AM,8,5,10:22:43 AM,8,5,10:24:01 AM,8,5,10:34:09 AM,5,23.2,,-1.2602337,36.79905479999999,-1.25681,36.7730826,Rider_Id_611,608
-Order_No_27408,User_Id_295,Bike,3,Business,2,4,12:44:00 PM,2,4,3:00:30 PM,2,4,3:45:42 PM,2,4,3:46:49 PM,2,4,3:57:02 PM,6,,,-1.2967108,36.787113500000004,-1.286925,36.821915999999995,Rider_Id_845,613
-Order_No_18429,User_Id_1936,Bike,1,Personal,28,1,12:54:56 PM,28,1,12:57:11 PM,28,1,1:28:42 PM,28,1,1:46:11 PM,28,1,2:21:45 PM,10,24.7,,-1.3118872,36.8039137,-1.2750282,36.85322360000001,Rider_Id_844,2134
-Order_No_9060,User_Id_3698,Bike,3,Business,23,4,12:00:53 PM,23,4,12:01:53 PM,23,4,12:08:10 PM,23,4,12:27:27 PM,23,4,1:06:26 PM,22,23.2,,-1.3107918,36.761794,-1.3244885,36.897791999999995,Rider_Id_709,2339
-Order_No_5530,User_Id_393,Bike,3,Business,13,3,10:38:32 AM,13,3,10:38:43 AM,13,3,10:38:47 AM,13,3,10:53:43 AM,13,3,11:21:14 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_901,1651
-Order_No_20818,User_Id_868,Bike,3,Business,24,4,3:55:02 PM,24,4,4:46:40 PM,24,4,4:55:30 PM,24,4,4:57:26 PM,24,4,5:21:22 PM,8,21.0,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_88,1436
-Order_No_3142,User_Id_346,Bike,3,Business,28,4,12:40:18 PM,28,4,1:54:55 PM,28,4,2:46:06 PM,28,4,2:51:43 PM,28,4,3:12:25 PM,5,29.8,,-1.28878,36.8168312,-1.2591018999999999,36.800577000000004,Rider_Id_304,1242
-Order_No_17151,User_Id_3547,Bike,3,Business,9,2,12:24:37 PM,9,2,12:24:45 PM,9,2,12:35:35 PM,9,2,12:40:28 PM,9,2,1:35:25 PM,22,27.0,,-1.2761408,36.7710839,-1.3160976999999998,36.913164200000004,Rider_Id_306,3297
-Order_No_23154,User_Id_3398,Bike,3,Business,12,3,6:07:10 PM,12,3,6:07:36 PM,12,3,6:31:08 PM,12,3,6:33:48 PM,12,3,7:11:04 PM,19,24.4,,-1.3052491000000002,36.8223899,-1.3169591,36.7000029,Rider_Id_328,2236
-Order_No_27587,User_Id_2828,Bike,3,Business,30,2,3:33:34 PM,30,2,3:33:53 PM,30,2,3:43:05 PM,30,2,3:58:54 PM,30,2,4:49:18 PM,14,27.3,,-1.2615888000000002,36.7928732,-1.3251383999999997,36.7860195,Rider_Id_793,3024
-Order_No_21501,User_Id_393,Bike,3,Business,1,1,8:37:38 AM,1,1,8:37:50 AM,1,1,8:38:01 AM,1,1,9:38:02 AM,1,1,9:38:11 AM,5,18.2,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_10,9
-Order_No_25350,User_Id_1959,Bike,3,Business,24,3,2:13:21 PM,24,3,2:13:53 PM,24,3,2:30:49 PM,24,3,2:35:20 PM,24,3,2:49:10 PM,6,,,-1.2647975,36.821518299999994,-1.2551895,36.7822034,Rider_Id_710,830
-Order_No_25953,User_Id_2684,Bike,1,Personal,10,3,3:37:20 PM,10,3,3:38:01 PM,10,3,4:04:00 PM,10,3,4:06:34 PM,10,3,4:28:25 PM,14,24.7,,-1.2429937,36.8754167,-1.2966203,36.817837,Rider_Id_217,1311
-Order_No_3666,User_Id_577,Bike,3,Personal,16,3,2:35:55 PM,16,3,2:36:06 PM,16,3,2:46:20 PM,16,3,2:55:06 PM,16,3,3:23:08 PM,12,26.8,,-1.3270347,36.866736200000005,-1.2662728,36.8151265,Rider_Id_698,1682
-Order_No_8066,User_Id_2330,Bike,3,Business,21,1,11:56:09 AM,21,1,11:56:32 AM,21,1,12:10:50 PM,21,1,12:12:48 PM,21,1,12:30:11 PM,9,25.5,,-1.2642903,36.80021370000001,-1.2326041,36.8009029,Rider_Id_611,1043
-Order_No_20035,User_Id_409,Bike,3,Personal,1,5,4:31:28 PM,1,5,5:49:44 PM,1,5,6:05:13 PM,1,5,6:39:23 PM,1,5,7:02:54 PM,11,30.2,,-1.2539848999999998,36.824720899999996,-1.304735,36.784605400000004,Rider_Id_427,1411
-Order_No_13841,User_Id_393,Bike,3,Business,7,5,10:08:09 AM,7,5,10:08:41 AM,7,5,10:20:18 AM,7,5,10:39:51 AM,7,5,10:40:25 AM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_740,34
-Order_No_20527,User_Id_2828,Bike,2,Business,5,4,1:36:34 PM,5,4,1:37:06 PM,5,4,1:37:12 PM,5,4,2:02:29 PM,5,4,2:02:40 PM,3,22.1,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_51,11
-Order_No_21474,User_Id_1575,Bike,3,Personal,12,5,11:17:56 AM,12,5,11:18:36 AM,12,5,11:33:13 AM,12,5,11:36:42 AM,12,5,11:41:51 AM,1,24.7,,-1.2562796999999999,36.8075858,-1.2670196000000002,36.807700200000006,Rider_Id_343,309
-Order_No_6946,User_Id_3383,Bike,1,Personal,1,3,9:28:47 AM,1,3,9:29:04 AM,1,3,9:32:52 AM,1,3,9:40:58 AM,1,3,9:47:56 AM,2,,,-1.2586742,36.801643799999994,-1.2632534,36.799342700000004,Rider_Id_726,418
-Order_No_16204,User_Id_1075,Bike,3,Business,4,5,11:46:42 AM,4,5,11:48:29 AM,4,5,12:19:08 PM,4,5,12:33:56 PM,4,5,12:54:37 PM,8,17.5,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_101,1241
-Order_No_26278,User_Id_868,Bike,3,Business,13,6,2:10:08 PM,13,6,2:11:03 PM,13,6,2:18:38 PM,13,6,2:33:55 PM,13,6,2:46:42 PM,5,28.1,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_383,767
-Order_No_16317,User_Id_3283,Bike,3,Business,9,4,10:27:47 AM,9,4,10:44:41 AM,9,4,11:36:25 AM,9,4,11:37:28 AM,9,4,12:21:37 PM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_591,2649
-Order_No_4956,User_Id_2006,Bike,3,Business,19,1,10:01:47 AM,19,1,10:02:08 AM,19,1,10:13:35 AM,19,1,10:15:12 AM,19,1,10:29:01 AM,5,19.2,,-1.2661263999999999,36.8030359,-1.2559559999999999,36.772901,Rider_Id_50,829
-Order_No_9608,User_Id_2523,Bike,3,Business,11,1,9:20:51 AM,11,1,9:28:41 AM,11,1,9:33:25 AM,11,1,9:37:10 AM,11,1,10:36:09 AM,8,18.1,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_551,3539
-Order_No_21482,User_Id_3650,Bike,3,Business,12,1,11:21:16 AM,12,1,11:21:46 AM,12,1,11:31:52 AM,12,1,11:33:59 AM,12,1,12:31:26 PM,21,23.9,,-1.2848376,36.802762200000004,-1.3930098999999998,36.743146,Rider_Id_30,3447
-Order_No_21991,User_Id_1372,Bike,3,Business,4,5,10:21:24 AM,4,5,10:21:36 AM,4,5,10:30:07 AM,4,5,10:45:07 AM,4,5,11:04:59 AM,11,17.3,,-1.2885417,36.810052299999995,-1.2361443,36.810851799999995,Rider_Id_343,1192
-Order_No_17463,User_Id_984,Bike,1,Personal,12,6,11:00:24 AM,12,6,11:00:41 AM,12,6,11:04:16 AM,12,6,11:20:20 AM,12,6,11:54:58 AM,18,23.0,,-1.2976971000000002,36.7911336,-1.2119232,36.899645899999996,Rider_Id_390,2078
-Order_No_14446,User_Id_1402,Bike,3,Business,13,1,11:34:36 AM,13,1,11:34:54 AM,13,1,11:58:40 AM,13,1,12:06:16 PM,13,1,12:53:30 PM,13,17.4,,-1.2156006000000001,36.8916865,-1.2891921999999998,36.825189,Rider_Id_633,2834
-Order_No_10674,User_Id_3599,Bike,3,Business,19,5,12:27:50 PM,19,5,12:33:56 PM,19,5,12:34:10 PM,19,5,1:25:15 PM,19,5,1:50:12 PM,15,25.0,,-1.3302996,36.8707935,-1.2912772,36.7734495,Rider_Id_874,1497
-Order_No_10776,User_Id_1075,Bike,3,Business,9,4,11:40:44 AM,9,4,11:42:27 AM,9,4,11:51:11 AM,9,4,11:54:56 AM,9,4,12:44:58 PM,18,,,-1.2860183,36.8975338,-1.2527958000000001,36.8003131,Rider_Id_48,3002
-Order_No_12676,User_Id_2866,Bike,3,Business,29,5,10:56:55 AM,29,5,10:57:26 AM,29,5,11:00:04 AM,29,5,11:10:12 AM,29,5,11:35:13 AM,10,19.6,,-1.2551895,36.7822034,-1.2953162,36.7828041,Rider_Id_16,1501
-Order_No_13514,User_Id_2200,Bike,3,Business,30,5,11:45:52 AM,30,5,11:46:49 AM,30,5,12:12:09 PM,30,5,12:18:11 PM,30,5,12:45:37 PM,12,23.6,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_928,1646
-Order_No_26460,User_Id_3326,Bike,3,Business,28,2,2:35:24 PM,28,2,2:36:45 PM,28,2,2:45:21 PM,28,2,2:54:09 PM,28,2,3:32:55 PM,8,23.3,,-1.2571472,36.795063299999995,-1.3080696000000003,36.8237191,Rider_Id_42,2326
-Order_No_1187,User_Id_3647,Bike,3,Business,18,2,4:04:27 PM,18,2,4:10:10 PM,18,2,4:16:38 PM,18,2,4:19:23 PM,18,2,4:40:10 PM,5,25.6,,-1.2726389999999999,36.794723,-1.2712521,36.818089,Rider_Id_660,1247
-Order_No_13769,User_Id_3689,Bike,3,Business,6,3,3:19:25 PM,6,3,3:31:52 PM,6,3,4:09:30 PM,6,3,4:12:02 PM,6,3,4:25:14 PM,3,21.6,,-1.2601708999999999,36.8002238,-1.2553615,36.789537599999996,Rider_Id_881,792
-Order_No_24618,User_Id_823,Bike,3,Personal,23,6,3:27:39 PM,23,6,3:31:04 PM,23,6,3:46:45 PM,23,6,3:52:16 PM,23,6,4:14:40 PM,10,28.1,,-1.3025826,36.7670807,-1.2495128999999998,36.788687599999996,Rider_Id_545,1344
-Order_No_21829,User_Id_3647,Bike,3,Business,31,4,1:00:34 PM,31,4,1:01:56 PM,31,4,1:18:16 PM,31,4,1:28:06 PM,31,4,1:48:34 PM,10,26.5,,-1.2726389999999999,36.794723,-1.3231001,36.8283559,Rider_Id_267,1228
-Order_No_6883,User_Id_2330,Bike,3,Business,13,1,12:40:28 PM,13,1,12:41:37 PM,13,1,1:07:45 PM,13,1,1:14:52 PM,13,1,2:00:47 PM,11,,,-1.293082,36.8278578,-1.2925641,36.897644899999996,Rider_Id_884,2755
-Order_No_10736,User_Id_1012,Bike,3,Personal,4,5,11:12:39 AM,4,5,11:14:33 AM,4,5,11:38:21 AM,4,5,11:43:58 AM,4,5,11:56:33 AM,6,17.1,,-1.2750975,36.8289032,-1.3108783000000002,36.8392726,Rider_Id_72,755
-Order_No_8169,User_Id_3018,Bike,2,Personal,17,1,10:01:13 AM,17,1,10:02:00 AM,17,1,10:10:58 AM,17,1,11:46:44 AM,17,1,12:09:20 PM,7,24.8,,-1.2913748999999999,36.8012314,-1.2615983,36.8083096,Rider_Id_785,1356
-Order_No_7599,User_Id_867,Bike,3,Business,15,1,10:12:03 AM,15,1,10:12:20 AM,15,1,10:14:50 AM,15,1,10:34:58 AM,15,1,10:36:35 AM,9,,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_245,97
-Order_No_11127,User_Id_614,Bike,1,Personal,26,2,11:12:43 AM,26,2,11:29:37 AM,26,2,11:39:06 AM,26,2,12:06:09 PM,26,2,12:27:43 PM,9,,,-1.3299358000000001,36.8524971,-1.2885364,36.7995016,Rider_Id_379,1294
-Order_No_327,User_Id_1205,Bike,3,Business,10,4,11:27:14 AM,10,4,11:27:47 AM,10,4,11:42:31 AM,10,4,12:08:00 PM,10,4,12:32:43 PM,16,24.8,,-1.2810807,36.8144229,-1.3395431999999998,36.72301829999999,Rider_Id_31,1483
-Order_No_23132,User_Id_393,Bike,3,Business,24,3,11:23:24 AM,24,3,11:31:07 AM,24,3,11:31:11 AM,24,3,11:45:03 AM,24,3,11:57:12 AM,5,19.7,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_10,729
-Order_No_11740,User_Id_393,Bike,3,Business,14,5,2:43:23 PM,14,5,2:43:33 PM,14,5,2:43:47 PM,14,5,2:55:15 PM,14,5,3:04:42 PM,4,23.0,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_168,567
-Order_No_22496,User_Id_2066,Bike,3,Business,14,3,5:57:09 PM,14,3,5:58:15 PM,14,3,6:14:11 PM,14,3,6:22:26 PM,14,3,7:00:43 PM,8,23.3,,-1.3270716,36.8406371,-1.2917003999999999,36.8802381,Rider_Id_554,2297
-Order_No_21552,User_Id_2956,Bike,3,Business,27,1,2:00:11 PM,27,1,2:01:00 PM,27,1,2:18:35 PM,27,1,2:28:17 PM,27,1,2:52:23 PM,7,22.9,,-1.2584143,36.8048002,-1.3008433000000001,36.8036256,Rider_Id_829,1446
-Order_No_23668,User_Id_2987,Bike,3,Business,15,3,12:09:14 PM,15,3,12:09:42 PM,15,3,12:20:42 PM,15,3,12:27:59 PM,15,3,12:53:31 PM,7,18.1,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_519,1532
-Order_No_15794,User_Id_3547,Bike,3,Business,4,1,10:48:59 AM,4,1,10:50:18 AM,4,1,11:07:37 AM,4,1,11:16:48 AM,4,1,12:10:40 PM,16,26.5,,-1.3194518,36.869396,-1.2717021000000002,36.7650838,Rider_Id_675,3232
-Order_No_20177,User_Id_868,Bike,3,Business,16,2,4:36:27 PM,16,2,4:37:47 PM,16,2,4:38:42 PM,16,2,4:49:00 PM,16,2,5:04:19 PM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_349,919
-Order_No_27878,User_Id_2572,Bike,1,Personal,3,2,4:55:04 PM,3,2,5:09:14 PM,3,2,5:40:52 PM,3,2,5:47:00 PM,3,2,5:57:12 PM,2,18.7,,-1.2747247,36.8150014,-1.2659148999999998,36.805432200000006,Rider_Id_951,612
-Order_No_15280,User_Id_2801,Bike,3,Business,14,3,4:01:42 PM,14,3,4:02:26 PM,14,3,4:13:40 PM,14,3,4:19:52 PM,14,3,4:26:52 PM,5,25.7,,-1.2595421999999998,36.7871178,-1.257773,36.795486,Rider_Id_839,420
-Order_No_8762,User_Id_203,Bike,3,Business,13,2,3:32:11 PM,13,2,3:32:44 PM,13,2,3:49:44 PM,13,2,3:52:15 PM,13,2,5:35:49 PM,17,26.2,,-1.3294489999999999,36.890451,-1.3039706,36.7688174,Rider_Id_559,6214
-Order_No_21942,User_Id_3291,Bike,3,Business,25,6,9:29:57 AM,25,6,9:48:41 AM,25,6,10:02:36 AM,25,6,10:12:05 AM,25,6,10:29:54 AM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_27,1069
-Order_No_7003,User_Id_20,Bike,3,Business,9,3,11:23:29 AM,9,3,11:24:01 AM,9,3,11:29:30 AM,9,3,11:33:20 AM,9,3,11:48:40 AM,8,21.0,,-1.2629666000000002,36.8068369,-1.3090533000000002,36.8371502,Rider_Id_100,920
-Order_No_19456,User_Id_1209,Bike,3,Business,12,5,12:59:58 PM,12,5,1:00:21 PM,12,5,1:00:29 PM,12,5,1:13:44 PM,12,5,1:28:33 PM,9,26.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_349,889
-Order_No_24658,User_Id_2209,Bike,3,Business,13,3,2:11:18 PM,13,3,2:14:29 PM,13,3,2:37:52 PM,13,3,2:42:13 PM,13,3,3:07:38 PM,7,23.5,,-1.2687680000000001,36.841871000000005,-1.3031805,36.818276000000004,Rider_Id_890,1525
-Order_No_5461,User_Id_640,Bike,3,Business,26,5,3:47:34 PM,26,5,3:47:44 PM,26,5,3:51:09 PM,26,5,3:56:32 PM,26,5,4:39:28 PM,17,,,-1.2969741000000001,36.7856612,-1.216212,36.841799,Rider_Id_96,2576
-Order_No_24816,User_Id_2956,Bike,3,Business,19,2,3:31:22 PM,19,2,3:32:08 PM,19,2,3:40:46 PM,19,2,3:47:14 PM,19,2,4:05:53 PM,10,29.9,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_407,1119
-Order_No_11043,User_Id_3629,Bike,3,Personal,28,4,9:52:50 AM,28,4,9:53:41 AM,28,4,10:21:18 AM,28,4,10:24:07 AM,28,4,10:49:56 AM,6,19.5,,-1.2852951000000001,36.81338529999999,-1.282937,36.781698399999996,Rider_Id_245,1549
-Order_No_13228,User_Id_2525,Bike,1,Personal,15,5,2:13:24 PM,15,5,2:16:48 PM,15,5,2:28:15 PM,15,5,2:37:59 PM,15,5,2:51:05 PM,7,22.7,,-1.3037587,36.8076138,-1.2765492,36.7669809,Rider_Id_409,786
-Order_No_5025,User_Id_2179,Bike,3,Business,5,2,12:57:19 PM,5,2,12:59:37 PM,5,2,1:17:53 PM,5,2,1:24:56 PM,5,2,1:49:01 PM,8,23.6,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_953,1445
-Order_No_24747,User_Id_1244,Bike,3,Business,29,5,12:31:16 PM,29,5,12:32:56 PM,29,5,12:44:14 PM,29,5,12:51:33 PM,29,5,1:15:18 PM,8,26.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_154,1425
-Order_No_1822,User_Id_733,Bike,3,Business,22,5,1:19:41 PM,22,5,1:20:00 PM,22,5,1:20:26 PM,22,5,1:27:50 PM,22,5,1:53:11 PM,16,,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_523,1521
-Order_No_24499,User_Id_3533,Bike,3,Business,5,2,12:00:45 PM,5,2,12:01:13 PM,5,2,12:20:33 PM,5,2,12:24:23 PM,5,2,12:36:14 PM,5,25.4,,-1.2615888000000002,36.7928732,-1.2790232,36.7923526,Rider_Id_958,711
-Order_No_3356,User_Id_3686,Bike,3,Business,15,1,12:32:18 PM,15,1,12:32:46 PM,15,1,12:33:53 PM,15,1,12:37:22 PM,15,1,1:20:29 PM,18,,,-1.2551895,36.7822034,-1.319048,36.8943118,Rider_Id_709,2587
-Order_No_5512,User_Id_944,Bike,1,Personal,26,4,11:26:06 AM,26,4,11:28:17 AM,26,4,12:01:27 PM,26,4,12:05:10 PM,26,4,12:25:13 PM,5,18.9,,-1.2555969999999999,36.830024,-1.2731241000000002,36.799281799999996,Rider_Id_849,1203
-Order_No_17365,User_Id_671,Bike,1,Personal,22,3,9:52:13 AM,22,3,9:55:13 AM,22,3,10:31:16 AM,22,3,10:34:58 AM,22,3,11:02:43 AM,10,23.2,,-1.2343087,36.84749,-1.302559,36.819555,Rider_Id_320,1665
-Order_No_7600,User_Id_2412,Bike,3,Business,30,4,10:35:04 AM,30,4,10:35:43 AM,30,4,10:43:56 AM,30,4,11:03:25 AM,30,4,11:22:08 AM,11,,,-1.2728947,36.807942600000004,-1.2283403,36.8822756,Rider_Id_245,1123
-Order_No_6343,User_Id_685,Bike,3,Business,26,5,3:11:26 PM,26,5,3:13:13 PM,26,5,3:28:35 PM,26,5,3:31:57 PM,26,5,3:42:39 PM,5,,,-1.2726506000000002,36.7960153,-1.2876365,36.816096,Rider_Id_291,642
-Order_No_27117,User_Id_2789,Bike,3,Business,1,1,11:45:34 AM,1,1,11:46:53 AM,1,1,11:57:06 AM,1,1,12:13:15 PM,1,1,12:24:20 PM,8,,,-1.304735,36.784605400000004,-1.2651643,36.807221399999996,Rider_Id_958,665
-Order_No_16790,User_Id_2886,Bike,3,Personal,24,1,5:48:04 PM,24,1,5:51:17 PM,24,1,6:10:34 PM,24,1,6:14:20 PM,24,1,6:33:40 PM,6,22.4,,-1.2576187,36.82933320000001,-1.2637591000000001,36.79038320000001,Rider_Id_275,1160
-Order_No_20657,User_Id_3647,Bike,3,Business,30,2,12:09:39 PM,30,2,12:10:04 PM,30,2,12:45:07 PM,30,2,12:46:31 PM,30,2,1:55:57 PM,26,22.1,,-1.2726389999999999,36.794723,-1.2790133,36.963059,Rider_Id_578,4166
-Order_No_20492,User_Id_3491,Bike,3,Business,9,3,9:18:30 AM,9,3,9:20:03 AM,9,3,9:36:02 AM,9,3,10:10:11 AM,9,3,10:36:33 AM,11,20.1,,-1.2781938999999998,36.8191922,-1.2283403,36.8822756,Rider_Id_72,1582
-Order_No_22654,User_Id_1081,Bike,3,Business,5,2,2:56:27 PM,5,2,2:59:29 PM,5,2,3:09:37 PM,5,2,3:19:40 PM,5,2,4:26:33 PM,9,27.3,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_210,4013
-Order_No_25782,User_Id_3194,Bike,3,Business,7,4,1:25:53 PM,7,4,1:27:07 PM,7,4,1:34:15 PM,7,4,1:46:05 PM,7,4,2:00:32 PM,7,31.0,,-1.2571472,36.795063299999995,-1.3025453000000002,36.832049700000006,Rider_Id_205,867
-Order_No_9642,User_Id_868,Bike,3,Business,7,4,10:27:50 AM,7,4,10:59:32 AM,7,4,11:05:03 AM,7,4,11:15:31 AM,7,4,11:30:32 AM,5,17.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_727,901
-Order_No_20662,User_Id_6,Bike,3,Personal,29,3,10:58:14 AM,29,3,11:01:58 AM,29,3,11:15:39 AM,29,3,11:20:44 AM,29,3,11:45:36 AM,10,18.4,,-1.322759,36.844803999999996,-1.2668734,36.8054026,Rider_Id_74,1492
-Order_No_5348,User_Id_1544,Bike,3,Business,3,1,4:20:08 PM,3,1,5:09:08 PM,3,1,5:14:30 PM,3,1,5:42:18 PM,3,1,5:48:29 PM,1,17.5,,-1.2887801,36.8297743,-1.2885029,36.825604999999996,Rider_Id_673,371
-Order_No_12754,User_Id_432,Bike,3,Business,2,4,3:42:47 PM,2,4,3:43:15 PM,2,4,3:50:36 PM,2,4,3:54:15 PM,2,4,4:32:16 PM,9,,,-1.2551895,36.7822034,-1.3012008,36.764868,Rider_Id_177,2281
-Order_No_2392,User_Id_2523,Bike,3,Business,11,1,12:41:36 PM,11,1,12:41:50 PM,11,1,12:42:27 PM,11,1,12:45:10 PM,11,1,1:16:42 PM,12,26.6,,-1.2551895,36.7822034,-1.2404576,36.868302299999996,Rider_Id_579,1892
-Order_No_13384,User_Id_1796,Bike,3,Business,26,2,1:59:04 PM,26,2,1:59:30 PM,26,2,2:21:29 PM,26,2,3:04:27 PM,26,2,3:04:32 PM,15,29.7,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_268,5
-Order_No_6538,User_Id_186,Bike,3,Business,20,3,4:16:08 PM,20,3,4:17:45 PM,20,3,4:38:11 PM,20,3,4:53:03 PM,20,3,4:53:12 PM,6,28.0,,-1.2571472,36.795063299999995,-1.2913246,36.8019522,Rider_Id_205,9
-Order_No_6628,User_Id_2523,Bike,3,Business,5,2,12:02:13 PM,5,2,12:04:09 PM,5,2,12:15:02 PM,5,2,12:22:26 PM,5,2,12:39:38 PM,8,25.6,,-1.2551895,36.7822034,-1.2876803999999997,36.7802778,Rider_Id_920,1032
-Order_No_24034,User_Id_2982,Bike,3,Business,14,4,4:06:38 PM,14,4,4:07:02 PM,14,4,4:07:20 PM,14,4,4:34:53 PM,14,4,5:19:30 PM,18,26.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_661,2677
-Order_No_15475,User_Id_393,Bike,3,Business,12,2,9:52:25 AM,12,2,9:53:34 AM,12,2,10:10:16 AM,12,2,10:22:49 AM,12,2,10:31:40 AM,4,20.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_127,531
-Order_No_13338,User_Id_672,Bike,2,Personal,7,2,2:15:44 PM,7,2,2:17:43 PM,7,2,2:39:01 PM,7,2,2:44:43 PM,7,2,3:08:34 PM,7,24.7,,-1.2901669,36.7685878,-1.2628938,36.7921146,Rider_Id_722,1431
-Order_No_1340,User_Id_3092,Bike,3,Personal,7,1,11:04:03 AM,7,1,11:04:20 AM,7,1,11:11:16 AM,7,1,11:13:06 AM,7,1,11:30:23 AM,12,24.9,,-1.2917868000000001,36.7872675,-1.249526,36.7711579,Rider_Id_216,1037
-Order_No_15511,User_Id_3154,Bike,1,Business,9,2,10:43:00 AM,9,2,10:43:59 AM,9,2,11:09:00 AM,9,2,11:11:40 AM,9,2,11:49:44 AM,17,21.8,,-1.3326173000000001,36.71462329999999,-1.2697141,36.798177700000004,Rider_Id_565,2284
-Order_No_1191,User_Id_1484,Bike,3,Business,18,1,11:56:24 AM,18,1,11:58:26 AM,18,1,12:12:40 PM,18,1,12:17:14 PM,18,1,12:40:39 PM,7,,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_865,1405
-Order_No_8032,User_Id_972,Bike,3,Business,11,4,3:06:23 PM,11,4,3:49:33 PM,11,4,4:06:49 PM,11,4,4:08:52 PM,11,4,4:29:36 PM,5,26.8,,-1.2862033000000002,36.8152966,-1.2946581,36.779292100000006,Rider_Id_208,1244
-Order_No_19482,User_Id_613,Bike,3,Business,11,6,4:35:04 PM,11,6,4:35:20 PM,11,6,4:37:53 PM,11,6,4:51:25 PM,11,6,5:04:13 PM,1,,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_435,768
-Order_No_10819,User_Id_3341,Bike,1,Personal,9,1,2:25:31 PM,9,1,2:29:33 PM,9,1,2:35:15 PM,9,1,2:45:04 PM,9,1,3:13:08 PM,5,21.3,,-1.2795796,36.819062,-1.2824577,36.7967953,Rider_Id_21,1684
-Order_No_27121,User_Id_2532,Bike,3,Business,16,4,11:17:22 AM,16,4,11:22:23 AM,16,4,11:28:09 AM,16,4,12:08:54 PM,16,4,12:33:30 PM,8,,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_868,1476
-Order_No_7062,User_Id_2836,Bike,1,Personal,5,3,1:40:07 PM,5,3,1:40:28 PM,5,3,1:42:00 PM,5,3,2:41:42 PM,5,3,2:55:59 PM,7,23.6,,-1.2883572,36.8446699,-1.3205163999999998,36.8098882,Rider_Id_179,857
-Order_No_4774,User_Id_393,Bike,3,Business,16,3,9:25:39 AM,16,3,9:26:01 AM,16,3,9:26:09 AM,16,3,9:46:06 AM,16,3,10:27:23 AM,14,19.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_702,2477
-Order_No_5378,User_Id_3641,Bike,3,Business,12,1,12:00:15 PM,12,1,12:04:08 PM,12,1,12:07:59 PM,12,1,12:13:42 PM,12,1,12:33:11 PM,8,23.8,,-1.2551895,36.7822034,-1.2998903,36.8179213,Rider_Id_518,1169
-Order_No_17684,User_Id_893,Bike,3,Business,24,3,12:01:06 PM,24,3,12:12:00 PM,24,3,12:28:39 PM,24,3,12:37:19 PM,24,3,1:01:25 PM,5,19.8,,-1.2663194,36.809901,-1.2872899,36.820434000000006,Rider_Id_477,1446
-Order_No_9969,User_Id_517,Bike,3,Business,7,5,12:03:26 PM,7,5,12:05:03 PM,7,5,12:07:45 PM,7,5,12:17:03 PM,7,5,12:53:31 PM,14,25.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2188
-Order_No_11298,User_Id_994,Bike,1,Personal,28,1,3:04:04 PM,28,1,3:18:15 PM,28,1,3:27:31 PM,28,1,3:29:56 PM,28,1,3:48:23 PM,9,24.3,,-1.3004062,36.829741,-1.2994194,36.7647917,Rider_Id_409,1107
-Order_No_3651,User_Id_2825,Bike,2,Personal,13,1,9:40:18 AM,13,1,9:40:22 AM,13,1,9:45:30 AM,13,1,9:48:23 AM,13,1,10:09:04 AM,13,15.1,,-1.2615048,36.8116724,-1.2156006000000001,36.8916865,Rider_Id_754,1241
-Order_No_12772,User_Id_111,Bike,3,Business,21,5,3:10:41 PM,21,5,3:11:52 PM,21,5,3:18:52 PM,21,5,3:22:16 PM,21,5,3:48:27 PM,10,27.1,,-1.2551895,36.7822034,-1.3201096,36.8180968,Rider_Id_848,1571
-Order_No_2641,User_Id_3470,Bike,3,Business,30,5,9:19:04 AM,30,5,9:21:12 AM,30,5,9:31:46 AM,30,5,9:55:49 AM,30,5,10:09:05 AM,3,19.0,,-1.28878,36.8168312,-1.3012902,36.80964,Rider_Id_101,796
-Order_No_20576,User_Id_2989,Bike,3,Business,29,2,2:37:02 PM,29,2,2:37:59 PM,29,2,3:01:47 PM,29,2,3:08:52 PM,29,2,3:34:38 PM,12,27.2,,-1.3472066,36.7692638,-1.300392,36.847352,Rider_Id_246,1546
-Order_No_359,User_Id_1578,Bike,3,Business,12,3,11:54:50 AM,12,3,11:56:01 AM,12,3,12:25:15 PM,12,3,12:34:06 PM,12,3,1:24:56 PM,10,24.3,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_630,3050
-Order_No_23265,User_Id_1271,Bike,3,Business,30,2,2:46:29 PM,30,2,2:55:53 PM,30,2,3:41:14 PM,30,2,3:43:26 PM,30,2,4:17:47 PM,26,27.5,,-1.3244885,36.897791999999995,-1.3949989,36.7654761,Rider_Id_699,2061
-Order_No_22943,User_Id_2956,Bike,3,Business,18,6,12:39:30 PM,18,6,12:40:18 PM,18,6,12:41:17 PM,18,6,12:56:39 PM,18,6,1:17:33 PM,5,19.0,,-1.2584143,36.8048002,-1.2857548,36.819062,Rider_Id_179,1254
-Order_No_13844,User_Id_1850,Bike,3,Business,18,4,2:11:35 PM,18,4,2:14:58 PM,18,4,2:20:59 PM,18,4,2:24:21 PM,18,4,2:52:22 PM,7,,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_250,1681
-Order_No_4851,User_Id_1205,Bike,1,Business,6,6,11:55:26 AM,6,6,11:55:54 AM,6,6,11:56:52 AM,6,6,12:03:54 PM,6,6,12:26:34 PM,6,24.2,,-1.2706815,36.7646286,-1.3009173,36.7959207,Rider_Id_574,1360
-Order_No_3314,User_Id_2271,Bike,3,Business,11,1,12:20:29 PM,11,1,12:20:51 PM,11,1,12:31:25 PM,11,1,12:51:09 PM,11,1,2:01:30 PM,20,26.7,,-1.2628473,36.781805,-1.293515,36.897607,Rider_Id_269,4221
-Order_No_20761,User_Id_2801,Bike,3,Business,21,1,2:03:21 PM,21,1,2:04:29 PM,21,1,2:27:18 PM,21,1,2:28:42 PM,21,1,2:45:02 PM,8,27.8,,-1.3028118,36.8174764,-1.2674273999999999,36.787082700000006,Rider_Id_359,980
-Order_No_1963,User_Id_661,Bike,3,Business,6,5,12:20:34 PM,6,5,12:23:27 PM,6,5,12:33:19 PM,6,5,12:42:06 PM,6,5,1:07:45 PM,4,20.5,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_549,1539
-Order_No_22478,User_Id_3017,Bike,3,Business,1,3,12:55:18 PM,1,3,12:55:48 PM,1,3,1:19:08 PM,1,3,1:23:02 PM,1,3,1:29:45 PM,2,20.1,,-1.299313,36.78314879999999,-1.2963097,36.7688221,Rider_Id_519,403
-Order_No_13405,User_Id_1966,Bike,3,Business,18,1,11:28:52 AM,18,1,11:29:08 AM,18,1,12:03:42 PM,18,1,12:06:02 PM,18,1,12:55:35 PM,21,26.8,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_385,2973
-Order_No_9120,User_Id_1611,Bike,3,Personal,3,1,10:44:48 AM,3,1,10:45:18 AM,3,1,10:49:21 AM,3,1,10:56:12 AM,3,1,11:02:38 AM,2,20.6,,-1.2618016,36.794202500000004,-1.2663135,36.7973895,Rider_Id_875,386
-Order_No_8093,User_Id_393,Bike,3,Business,6,4,10:08:45 AM,6,4,10:09:06 AM,6,4,10:13:23 AM,6,4,10:20:19 AM,6,4,10:30:47 AM,4,15.9,4.0,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_35,628
-Order_No_23306,User_Id_909,Bike,3,Business,14,2,2:18:30 PM,14,2,2:19:07 PM,14,2,2:37:21 PM,14,2,2:42:33 PM,14,2,2:42:48 PM,1,,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_445,15
-Order_No_4916,User_Id_186,Bike,3,Business,6,3,11:17:17 AM,6,3,11:18:02 AM,6,3,11:26:25 AM,6,3,11:45:36 AM,6,3,11:56:27 AM,4,,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_770,651
-Order_No_2938,User_Id_1500,Bike,3,Business,5,5,10:39:49 AM,5,5,10:40:39 AM,5,5,10:47:47 AM,5,5,10:51:38 AM,5,5,11:24:42 AM,13,24.4,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_436,1984
-Order_No_18495,User_Id_2978,Bike,3,Business,1,5,2:28:24 PM,1,5,2:29:18 PM,1,5,2:38:23 PM,1,5,2:50:34 PM,1,5,3:39:49 PM,12,28.5,,-1.2551895,36.7822034,-1.3256809999999999,36.7959072,Rider_Id_205,2955
-Order_No_23402,User_Id_1329,Bike,3,Business,29,1,3:25:00 PM,29,1,3:25:48 PM,29,1,3:31:58 PM,29,1,3:37:15 PM,29,1,3:46:22 PM,3,,,-1.273056,36.811298,-1.2672461000000002,36.7996289,Rider_Id_96,547
-Order_No_28003,User_Id_646,Bike,2,Personal,19,4,11:55:21 AM,19,4,11:55:42 AM,19,4,12:09:21 PM,19,4,12:19:55 PM,19,4,12:43:35 PM,27,20.3,,-1.2459593999999998,36.823297700000005,-1.1846355,36.948572799999994,Rider_Id_103,1420
-Order_No_2879,User_Id_1292,Bike,3,Business,15,4,12:01:16 PM,15,4,12:01:30 PM,15,4,12:13:10 PM,15,4,12:28:15 PM,15,4,1:20:32 PM,10,24.2,,-1.3025826,36.7670807,-1.2991139999999999,36.834835999999996,Rider_Id_124,3137
-Order_No_8220,User_Id_1557,Bike,3,Business,28,5,10:31:13 AM,28,5,10:44:16 AM,28,5,10:51:34 AM,28,5,11:02:58 AM,28,5,11:34:26 AM,5,25.0,,-1.3147073999999999,36.811721999999996,-1.3054934,36.80422670000001,Rider_Id_713,1888
-Order_No_26180,User_Id_393,Bike,3,Business,2,2,8:56:44 AM,2,2,9:48:09 AM,2,2,10:14:40 AM,2,2,10:34:37 AM,2,2,10:47:53 AM,4,26.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_92,796
-Order_No_16599,User_Id_3350,Bike,1,Personal,25,3,10:59:09 AM,25,3,10:59:30 AM,25,3,11:01:48 AM,25,3,11:03:18 AM,25,3,11:27:28 AM,6,,,-1.2551895,36.7822034,-1.2886385,36.8240195,Rider_Id_89,1450
-Order_No_1442,User_Id_872,Bike,3,Business,20,3,2:48:19 PM,20,3,2:49:27 PM,20,3,3:00:49 PM,20,3,3:08:39 PM,20,3,3:23:20 PM,5,23.0,,-1.2674001,36.7986537,-1.2807481,36.7765481,Rider_Id_322,881
-Order_No_21872,User_Id_1787,Bike,3,Business,29,3,3:26:24 PM,29,3,3:28:02 PM,29,3,3:46:33 PM,29,3,4:02:28 PM,29,3,4:21:10 PM,10,19.2,,-1.2872996,36.8310969,-1.2297202,36.881950700000004,Rider_Id_384,1122
-Order_No_22448,User_Id_2866,Bike,3,Business,6,1,4:04:58 PM,6,1,4:05:18 PM,6,1,4:08:08 PM,6,1,4:10:28 PM,6,1,4:43:27 PM,8,24.5,,-1.2551895,36.7822034,-1.2994217,36.8009779,Rider_Id_857,1979
-Order_No_75,User_Id_248,Bike,3,Business,21,4,9:48:43 AM,21,4,9:49:25 AM,21,4,10:05:01 AM,21,4,10:07:36 AM,21,4,10:48:37 AM,9,18.9,,-1.304735,36.784605400000004,-1.2588381000000002,36.791414700000004,Rider_Id_269,2461
-Order_No_5849,User_Id_2733,Bike,3,Business,8,1,11:28:33 AM,8,1,11:29:46 AM,8,1,12:06:03 PM,8,1,12:10:42 PM,8,1,12:33:01 PM,5,27.7,,-1.2963097,36.7688221,-1.2857723,36.808805799999995,Rider_Id_763,1339
-Order_No_17808,User_Id_269,Bike,3,Business,31,2,1:42:22 PM,31,2,2:39:44 PM,31,2,2:50:03 PM,31,2,2:57:01 PM,31,2,3:16:12 PM,10,22.1,,-1.3332749,36.870814700000004,-1.2932780000000001,36.827789,Rider_Id_409,1151
-Order_No_1054,User_Id_640,Bike,3,Business,19,1,10:32:14 AM,19,1,10:32:57 AM,19,1,10:38:33 AM,19,1,10:45:33 AM,19,1,10:59:31 AM,3,24.9,,-1.304033,36.7848695,-1.3005312,36.801878,Rider_Id_649,838
-Order_No_9711,User_Id_2346,Bike,1,Personal,7,4,3:34:41 PM,7,4,3:38:27 PM,7,4,4:13:10 PM,7,4,4:15:18 PM,7,4,4:38:37 PM,8,20.2,,-1.3054537,36.8284768,-1.274716,36.789582,Rider_Id_312,1399
-Order_No_10507,User_Id_2946,Bike,2,Personal,13,1,9:46:09 AM,13,1,9:46:35 AM,13,1,9:56:04 AM,13,1,10:02:03 AM,13,1,10:21:36 AM,8,15.0,,-1.2749662,36.7658781,-1.2983462,36.8139996,Rider_Id_74,1173
-Order_No_9705,User_Id_2330,Bike,3,Business,18,1,11:20:41 AM,18,1,11:21:55 AM,18,1,11:53:19 AM,18,1,11:56:09 AM,18,1,12:07:16 PM,5,28.1,,-1.3040812,36.7924217,-1.2890715,36.8157588,Rider_Id_223,667
-Order_No_6336,User_Id_2691,Bike,3,Business,19,4,9:25:28 AM,19,4,9:25:38 AM,19,4,9:30:29 AM,19,4,9:48:37 AM,19,4,10:03:53 AM,6,14.7,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_379,916
-Order_No_8463,User_Id_2763,Bike,3,Business,13,3,1:15:23 PM,13,3,1:16:43 PM,13,3,1:33:06 PM,13,3,1:48:06 PM,13,3,2:16:58 PM,13,27.0,,-1.2551895,36.7822034,-1.326825,36.864345,Rider_Id_931,1732
-Order_No_15198,User_Id_1500,Bike,3,Business,25,1,3:59:15 PM,25,1,4:00:34 PM,25,1,4:21:10 PM,25,1,4:23:46 PM,25,1,4:45:17 PM,9,27.9,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_8,1291
-Order_No_5425,User_Id_496,Bike,3,Business,21,4,4:15:07 PM,21,4,4:17:06 PM,21,4,4:29:13 PM,21,4,4:40:16 PM,21,4,4:50:05 PM,5,,,-1.2551895,36.7822034,-1.2842559999999998,36.812486299999996,Rider_Id_156,589
-Order_No_19879,User_Id_2174,Bike,3,Business,21,3,10:37:21 AM,21,3,10:37:29 AM,21,3,10:41:17 AM,21,3,10:53:15 AM,21,3,11:14:36 AM,10,22.0,,-1.2482781,36.884091999999995,-1.2782261000000001,36.822500899999994,Rider_Id_307,1281
-Order_No_5636,User_Id_627,Bike,3,Business,25,1,9:51:33 AM,25,1,9:52:32 AM,25,1,9:53:20 AM,25,1,10:03:52 AM,25,1,10:26:17 AM,9,21.2,,-1.2551895,36.7822034,-1.2986123,36.8076281,Rider_Id_783,1345
-Order_No_3030,User_Id_3613,Bike,3,Business,9,4,12:39:13 PM,9,4,12:42:08 PM,9,4,12:50:53 PM,9,4,12:53:36 PM,9,4,1:03:49 PM,4,24.0,,-1.2633493999999998,36.7959696,-1.286925,36.821915999999995,Rider_Id_857,613
-Order_No_25070,User_Id_2159,Bike,3,Business,14,4,8:39:50 AM,14,4,8:44:13 AM,14,4,9:07:37 AM,14,4,9:09:48 AM,14,4,9:32:20 AM,7,15.4,,-1.304735,36.784605400000004,-1.28101,36.8115102,Rider_Id_752,1352
-Order_No_26879,User_Id_1666,Bike,3,Personal,13,1,9:29:53 AM,13,1,9:31:15 AM,13,1,9:39:21 AM,13,1,9:45:14 AM,13,1,9:56:09 AM,5,15.1,,-1.2941023999999999,36.79578,-1.288269,36.818275299999996,Rider_Id_79,655
-Order_No_26699,User_Id_136,Bike,3,Business,6,2,5:39:41 PM,6,2,5:39:49 PM,6,2,5:41:16 PM,6,2,5:47:18 PM,6,2,6:41:28 PM,18,,,-1.3326173000000001,36.71462329999999,-1.256958,36.8144929,Rider_Id_45,3250
-Order_No_15359,User_Id_2523,Bike,3,Business,2,3,12:23:56 PM,2,3,12:32:34 PM,2,3,12:52:39 PM,2,3,12:55:19 PM,2,3,1:27:04 PM,14,21.9,,-1.2559559999999999,36.789867,-1.3203209999999999,36.7195522,Rider_Id_131,1905
-Order_No_5183,User_Id_1450,Bike,3,Personal,17,1,9:24:46 AM,17,1,9:26:04 AM,17,1,9:51:11 AM,17,1,9:58:59 AM,17,1,10:13:47 AM,6,18.2,,-1.2995135,36.793363299999996,-1.3108386,36.7555239,Rider_Id_328,888
-Order_No_13456,User_Id_1051,Bike,3,Business,26,1,11:23:33 AM,26,1,11:28:44 AM,26,1,11:40:26 AM,26,1,11:47:13 AM,26,1,12:30:53 PM,21,23.1,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_648,2620
-Order_No_20759,User_Id_2677,Bike,3,Business,25,4,12:06:58 PM,25,4,12:07:26 PM,25,4,12:19:57 PM,25,4,12:22:16 PM,25,4,12:51:55 PM,11,,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_953,1779
-Order_No_24634,User_Id_2801,Bike,3,Business,22,2,12:25:54 PM,22,2,12:30:34 PM,22,2,12:50:50 PM,22,2,12:54:22 PM,22,2,1:20:38 PM,9,24.2,,-1.3038408000000001,36.818713,-1.3104506,36.8687235,Rider_Id_332,1576
-Order_No_9267,User_Id_867,Bike,3,Business,27,2,4:26:05 PM,27,2,4:29:17 PM,27,2,4:29:25 PM,27,2,4:31:06 PM,27,2,4:50:44 PM,9,25.8,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_649,1178
-Order_No_8382,User_Id_3283,Bike,3,Business,30,4,2:51:38 PM,30,4,2:52:04 PM,30,4,2:57:38 PM,30,4,3:03:54 PM,30,4,3:27:27 PM,13,,,-1.318062,36.849326,-1.2628473,36.781805,Rider_Id_178,1413
-Order_No_18549,User_Id_3344,Bike,2,Business,26,3,12:13:57 PM,26,3,12:14:15 PM,26,3,12:35:22 PM,26,3,12:37:35 PM,26,3,1:12:49 PM,7,24.2,,-1.3245107,36.8049625,-1.2813370000000002,36.818968,Rider_Id_328,2114
-Order_No_1737,User_Id_2154,Bike,3,Business,21,1,3:33:21 PM,21,1,4:25:03 PM,21,1,4:34:39 PM,21,1,4:37:02 PM,21,1,5:37:16 PM,20,25.2,,-1.3165542,36.700067,-1.2765736,36.851364600000004,Rider_Id_509,3614
-Order_No_9433,User_Id_271,Bike,1,Personal,6,6,4:04:31 PM,6,6,4:05:18 PM,6,6,4:22:28 PM,6,6,4:39:10 PM,6,6,5:12:11 PM,22,25.1,,-1.2857763999999998,36.8236622,-1.2491258,36.689425299999996,Rider_Id_933,1981
-Order_No_20087,User_Id_2956,Bike,3,Business,16,4,9:21:02 AM,16,4,9:21:14 AM,16,4,9:21:41 AM,16,4,9:28:55 AM,16,4,10:12:24 AM,15,15.4,,-1.2584143,36.8048002,-1.3020824,36.8944672,Rider_Id_882,2609
-Order_No_11587,User_Id_1245,Bike,3,Business,31,3,12:32:37 PM,31,3,12:34:17 PM,31,3,1:04:28 PM,31,3,1:20:11 PM,31,3,2:38:46 PM,20,24.9,,-1.300921,36.828195,-1.3223908000000002,36.6773487,Rider_Id_64,4715
-Order_No_1211,User_Id_3222,Bike,3,Business,13,4,2:42:33 PM,13,4,2:43:54 PM,13,4,2:53:49 PM,13,4,2:58:13 PM,13,4,3:11:53 PM,3,22.3,,-1.2551895,36.7822034,-1.2676026000000002,36.810362899999994,Rider_Id_523,820
-Order_No_24224,User_Id_1363,Bike,3,Business,6,2,2:58:13 PM,6,2,3:21:48 PM,6,2,3:24:12 PM,6,2,3:37:30 PM,6,2,3:54:42 PM,8,27.7,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_188,1032
-Order_No_27539,User_Id_2378,Bike,2,Personal,26,3,8:26:17 AM,26,3,8:29:33 AM,26,3,8:40:01 AM,26,3,8:53:24 AM,26,3,9:17:54 AM,11,18.8,,-1.2669736,36.8040985,-1.309124,36.846829299999996,Rider_Id_279,1470
-Order_No_21543,User_Id_3727,Bike,3,Business,18,1,2:23:04 PM,18,1,2:23:17 PM,18,1,2:33:24 PM,18,1,2:37:19 PM,18,1,2:57:26 PM,8,30.9,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_952,1207
-Order_No_22232,User_Id_2956,Bike,3,Business,13,1,10:12:20 AM,13,1,10:12:55 AM,13,1,10:29:43 AM,13,1,10:40:13 AM,13,1,11:03:54 AM,14,,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_564,1421
-Order_No_17555,User_Id_393,Bike,3,Business,4,1,8:22:34 AM,4,1,8:22:57 AM,4,1,8:25:23 AM,4,1,8:32:11 AM,4,1,9:03:10 AM,14,21.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1859
-Order_No_3620,User_Id_1636,Bike,3,Business,25,1,12:52:16 PM,25,1,12:55:35 PM,25,1,1:08:53 PM,25,1,1:55:58 PM,25,1,2:29:54 PM,12,30.2,,-1.2472011,36.880030700000006,-1.2991919,36.8368475,Rider_Id_8,2036
-Order_No_7405,User_Id_18,Bike,3,Personal,23,1,11:50:11 AM,23,1,11:50:54 AM,23,1,11:53:29 AM,23,1,11:59:06 AM,23,1,12:20:34 PM,7,18.7,,-1.2960302,36.771131700000005,-1.3038156,36.812273299999994,Rider_Id_64,1288
-Order_No_25457,User_Id_2700,Bike,3,Business,27,2,1:45:22 PM,27,2,1:45:54 PM,27,2,1:56:49 PM,27,2,1:59:22 PM,27,2,2:04:14 PM,3,25.8,,-1.292943,36.808292,-1.295908,36.791433000000005,Rider_Id_322,292
-Order_No_15333,User_Id_2814,Bike,2,Personal,5,2,9:43:58 AM,5,2,9:44:58 AM,5,2,9:51:19 AM,5,2,10:11:32 AM,5,2,10:15:37 AM,1,17.7,4.0,-1.2909043,36.790780100000006,-1.2899,36.7794355,Rider_Id_24,245
-Order_No_8862,User_Id_2801,Bike,3,Business,23,3,4:45:05 PM,23,3,4:45:56 PM,23,3,4:53:38 PM,23,3,4:55:28 PM,23,3,5:06:57 PM,5,24.3,,-1.2637056999999998,36.805170000000004,-1.2595421999999998,36.7871178,Rider_Id_902,689
-Order_No_24995,User_Id_1927,Bike,3,Business,2,6,11:20:52 AM,2,6,11:21:53 AM,2,6,11:59:04 AM,2,6,12:08:49 PM,2,6,12:46:14 PM,19,27.4,,-1.2927792,36.9053492,-1.3472066,36.7692638,Rider_Id_237,2245
-Order_No_26455,User_Id_393,Bike,3,Business,15,1,2:13:12 PM,15,1,2:14:02 PM,15,1,2:17:11 PM,15,1,2:32:58 PM,15,1,2:35:42 PM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_403,164
-Order_No_513,User_Id_2109,Bike,2,Personal,4,4,8:18:52 AM,4,4,8:19:14 AM,4,4,8:32:18 AM,4,4,8:35:31 AM,4,4,9:07:26 AM,9,18.6,,-1.2915472,36.774696399999996,-1.3192808,36.7121888,Rider_Id_219,1915
-Order_No_18801,User_Id_2869,Bike,3,Business,22,3,1:14:51 PM,22,3,1:23:39 PM,22,3,1:40:27 PM,22,3,2:02:37 PM,22,3,2:31:19 PM,13,24.5,,-1.2907473999999999,36.8453439,-1.3025826,36.7670807,Rider_Id_161,1722
-Order_No_10692,User_Id_3149,Bike,3,Business,16,6,10:00:05 AM,16,6,10:00:45 AM,16,6,10:11:35 AM,16,6,10:38:48 AM,16,6,10:54:05 AM,5,25.9,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_448,917
-Order_No_24960,User_Id_1329,Bike,3,Business,20,4,3:37:26 PM,20,4,4:27:51 PM,20,4,4:45:32 PM,20,4,4:46:44 PM,20,4,5:06:45 PM,9,22.9,,-1.3257661,36.8275149,-1.273056,36.811298,Rider_Id_205,1201
-Order_No_19151,User_Id_633,Bike,3,Business,26,2,3:58:35 PM,26,2,4:14:42 PM,26,2,4:23:41 PM,26,2,4:26:24 PM,26,2,4:41:48 PM,5,29.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_953,924
-Order_No_16859,User_Id_3517,Bike,3,Business,7,5,1:51:13 PM,7,5,1:51:25 PM,7,5,1:51:36 PM,7,5,2:03:45 PM,7,5,2:30:14 PM,16,,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_340,1589
-Order_No_21714,User_Id_393,Bike,3,Business,17,5,11:01:07 AM,17,5,11:01:44 AM,17,5,11:04:32 AM,17,5,11:14:14 AM,17,5,11:45:24 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_211,1870
-Order_No_24882,User_Id_357,Bike,3,Business,26,2,8:48:37 AM,26,2,8:53:41 AM,26,2,8:59:19 AM,26,2,9:26:07 AM,26,2,9:45:52 AM,7,,,-1.2996681,36.806841299999995,-1.2945178999999998,36.7685349,Rider_Id_837,1185
-Order_No_3789,User_Id_1405,Bike,3,Business,18,2,10:32:01 AM,18,2,10:32:19 AM,18,2,10:56:21 AM,18,2,11:00:05 AM,18,2,11:34:18 AM,19,22.5,,-1.2986828000000001,36.7927414,-1.3478563000000001,36.8935824,Rider_Id_456,2053
-Order_No_1901,User_Id_2271,Bike,3,Business,7,1,1:37:48 PM,7,1,1:40:33 PM,7,1,1:50:50 PM,7,1,2:12:43 PM,7,1,2:40:55 PM,14,26.6,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_547,1692
-Order_No_11598,User_Id_2514,Bike,3,Business,25,4,10:10:11 AM,25,4,10:17:51 AM,25,4,10:23:34 AM,25,4,10:29:15 AM,25,4,10:42:34 AM,3,16.4,2.1,-1.266294,36.8057971,-1.28593,36.8151111,Rider_Id_289,799
-Order_No_3946,User_Id_3420,Bike,3,Business,8,2,2:51:59 PM,8,2,2:55:11 PM,8,2,3:03:08 PM,8,2,3:07:11 PM,8,2,3:21:23 PM,6,25.3,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_848,852
-Order_No_8383,User_Id_324,Bike,3,Personal,12,5,1:15:53 PM,12,5,1:16:51 PM,12,5,1:22:37 PM,12,5,1:25:06 PM,12,5,2:18:02 PM,15,26.6,,-1.2653676,36.79110120000001,-1.332942,36.714065000000005,Rider_Id_880,3176
-Order_No_1927,User_Id_2150,Bike,3,Business,13,4,9:28:47 AM,13,4,9:29:19 AM,13,4,9:55:04 AM,13,4,10:04:59 AM,13,4,10:43:03 AM,22,18.4,,-1.2296618000000001,36.84382720000001,-1.3444459999999998,36.756597,Rider_Id_427,2284
-Order_No_6711,User_Id_2582,Bike,3,Business,4,1,1:18:39 PM,4,1,1:19:01 PM,4,1,1:32:53 PM,4,1,1:37:41 PM,4,1,2:14:35 PM,19,,,-1.2551895,36.7822034,-1.3283746,36.8893241,Rider_Id_281,2214
-Order_No_15340,User_Id_1246,Bike,3,Business,31,3,11:26:00 AM,31,3,11:26:55 AM,31,3,11:36:57 AM,31,3,11:40:22 AM,31,3,11:50:20 AM,4,24.5,,-1.2615048,36.8116724,-1.2571472,36.795063299999995,Rider_Id_623,598
-Order_No_22184,User_Id_1075,Bike,3,Business,16,2,4:45:04 PM,16,2,4:50:19 PM,16,2,5:15:46 PM,16,2,5:19:45 PM,16,2,5:51:17 PM,8,,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_76,1892
-Order_No_27844,User_Id_3291,Bike,3,Business,28,2,1:50:21 PM,28,2,1:50:46 PM,28,2,2:01:14 PM,28,2,2:03:33 PM,28,2,2:13:54 PM,2,23.7,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_829,621
-Order_No_12143,User_Id_496,Bike,3,Business,22,3,2:24:53 PM,22,3,2:33:44 PM,22,3,2:43:15 PM,22,3,2:46:57 PM,22,3,3:13:31 PM,10,24.5,,-1.2551895,36.7822034,-1.2518813999999998,36.859660999999996,Rider_Id_933,1594
-Order_No_6984,User_Id_811,Bike,3,Personal,18,1,12:56:22 PM,18,1,1:00:57 PM,18,1,1:30:10 PM,18,1,1:34:56 PM,18,1,1:55:26 PM,7,30.8,,-1.298529,36.816482,-1.2963097,36.7688221,Rider_Id_903,1230
-Order_No_16176,User_Id_2956,Bike,3,Business,16,4,2:04:01 PM,16,4,2:04:13 PM,16,4,2:23:18 PM,16,4,2:25:59 PM,16,4,3:05:56 PM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_177,2397
-Order_No_23750,User_Id_1217,Bike,3,Personal,8,1,9:50:09 AM,8,1,9:54:10 AM,8,1,10:23:57 AM,8,1,10:36:20 AM,8,1,11:20:39 AM,12,24.4,,-1.2867809,36.8218665,-1.2251345,36.8835216,Rider_Id_86,2659
-Order_No_9477,User_Id_1502,Bike,3,Business,1,4,1:25:56 PM,1,4,1:39:18 PM,1,4,1:46:45 PM,1,4,2:02:11 PM,1,4,2:17:39 PM,12,,,-1.2874501,36.8261229,-1.2156006000000001,36.8916865,Rider_Id_320,928
-Order_No_2389,User_Id_2330,Bike,3,Business,10,5,5:54:30 PM,10,5,6:19:44 PM,10,5,6:40:52 PM,10,5,6:43:43 PM,10,5,7:18:40 PM,19,,,-1.1926151000000003,36.9347904,-1.2671943,36.8062533,Rider_Id_936,2097
-Order_No_2282,User_Id_1244,Bike,3,Business,5,2,10:08:59 AM,5,2,10:09:28 AM,5,2,10:22:57 AM,5,2,10:29:05 AM,5,2,10:48:21 AM,8,17.9,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_89,1156
-Order_No_9274,User_Id_2196,Bike,3,Personal,25,5,3:14:12 PM,25,5,3:14:38 PM,25,5,3:21:27 PM,25,5,3:24:36 PM,25,5,3:43:47 PM,9,23.2,,-1.2945178999999998,36.7685349,-1.2553615,36.789537599999996,Rider_Id_46,1151
-Order_No_21514,User_Id_941,Bike,1,Personal,6,6,10:49:45 AM,6,6,11:00:35 AM,6,6,11:14:32 AM,6,6,11:33:51 AM,6,6,11:53:52 AM,8,24.2,,-1.2807298,36.8299116,-1.2813649,36.7764385,Rider_Id_622,1201
-Order_No_702,User_Id_478,Bike,3,Business,8,5,11:32:15 AM,8,5,11:33:31 AM,8,5,11:39:23 AM,8,5,11:45:15 AM,8,5,12:01:04 PM,3,28.2,,-1.2551895,36.7822034,-1.2671001,36.7977185,Rider_Id_200,949
-Order_No_1572,User_Id_3599,Bike,3,Business,21,4,9:28:44 AM,21,4,9:30:38 AM,21,4,10:09:23 AM,21,4,10:26:36 AM,21,4,11:12:21 AM,12,21.7,,-1.3302996,36.8707935,-1.2618631999999999,36.8100226,Rider_Id_126,2745
-Order_No_7360,User_Id_3641,Bike,3,Business,17,3,1:31:12 PM,17,3,1:33:56 PM,17,3,1:46:10 PM,17,3,1:52:34 PM,17,3,2:15:14 PM,12,28.3,,-1.2551895,36.7822034,-1.3276043,36.836921999999994,Rider_Id_565,1360
-Order_No_16715,User_Id_2050,Bike,1,Personal,7,1,10:13:36 AM,7,1,10:14:16 AM,7,1,10:21:50 AM,7,1,10:28:56 AM,7,1,10:32:40 AM,2,18.2,,-1.2982033000000002,36.809263200000004,-1.285495,36.7981347,Rider_Id_100,224
-Order_No_22697,User_Id_419,Bike,3,Business,8,3,5:36:32 PM,8,3,5:37:10 PM,8,3,5:49:55 PM,8,3,6:07:43 PM,8,3,6:36:16 PM,9,24.2,,-1.3001813,36.811122,-1.2571472,36.795063299999995,Rider_Id_400,1713
-Order_No_7637,User_Id_1336,Bike,3,Business,28,2,4:32:18 PM,28,2,4:37:51 PM,28,2,4:43:12 PM,28,2,4:47:49 PM,28,2,4:58:51 PM,3,21.7,,-1.290894,36.822971,-1.2878913,36.8089253,Rider_Id_448,662
-Order_No_12851,User_Id_2523,Bike,3,Business,11,2,9:45:02 AM,11,2,9:46:03 AM,11,2,9:55:24 AM,11,2,9:57:18 AM,11,2,10:43:13 AM,23,16.6,,-1.2551895,36.7822034,-1.3532582,36.907305,Rider_Id_778,2755
-Order_No_3385,User_Id_391,Bike,3,Business,31,1,2:05:24 PM,31,1,2:24:04 PM,31,1,2:29:26 PM,31,1,2:32:25 PM,31,1,2:53:07 PM,6,24.1,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_511,1242
-Order_No_5627,User_Id_1222,Bike,3,Personal,15,1,11:34:31 AM,15,1,11:35:43 AM,15,1,11:44:38 AM,15,1,11:47:18 AM,15,1,12:28:50 PM,6,,,-1.2707178999999997,36.7979713,-1.3006208000000001,36.77274439999999,Rider_Id_131,2492
-Order_No_21164,User_Id_3420,Bike,3,Business,19,6,2:13:38 PM,19,6,2:14:54 PM,19,6,2:41:05 PM,19,6,2:43:39 PM,19,6,3:05:46 PM,9,27.5,,-1.3046007,36.821174299999996,-1.3272688,36.775839700000006,Rider_Id_622,1327
-Order_No_14524,User_Id_804,Bike,2,Personal,8,1,2:13:49 PM,8,1,2:15:38 PM,8,1,2:24:49 PM,8,1,2:35:56 PM,8,1,2:50:23 PM,7,24.7,,-1.2959783,36.7860021,-1.302625,36.8247507,Rider_Id_612,867
-Order_No_8811,User_Id_2271,Bike,3,Business,14,5,2:19:24 PM,14,5,2:19:47 PM,14,5,2:20:35 PM,14,5,2:39:01 PM,14,5,3:02:31 PM,11,23.1,,-1.307143,36.825009,-1.293515,36.897607,Rider_Id_686,1410
-Order_No_10566,User_Id_1281,Bike,3,Business,15,3,11:21:10 AM,15,3,11:21:19 AM,15,3,11:45:00 AM,15,3,11:51:41 AM,15,3,12:17:03 PM,13,18.3,,-1.2707742,36.7578609,-1.3184421,36.6911394,Rider_Id_179,1522
-Order_No_26219,User_Id_3326,Bike,3,Business,25,5,2:27:08 PM,25,5,2:28:37 PM,25,5,2:42:21 PM,25,5,3:12:48 PM,25,5,3:15:26 PM,8,29.3,,-1.2571472,36.795063299999995,-1.3090727,36.831526200000006,Rider_Id_436,158
-Order_No_10574,User_Id_1809,Bike,3,Business,19,2,9:27:13 AM,19,2,9:27:22 AM,19,2,9:34:46 AM,19,2,10:05:44 AM,19,2,10:11:46 AM,1,15.6,1.0,-1.2646084,36.8028645,-1.2620912,36.8013562,Rider_Id_290,362
-Order_No_13938,User_Id_2523,Bike,3,Business,19,3,9:52:09 AM,19,3,9:53:51 AM,19,3,10:03:37 AM,19,3,10:07:15 AM,19,3,10:30:06 AM,12,19.7,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_205,1371
-Order_No_9387,User_Id_176,Bike,1,Personal,6,1,1:07:20 PM,6,1,3:17:55 PM,6,1,3:53:51 PM,6,1,4:10:48 PM,6,1,4:44:23 PM,4,24.6,,-1.2854453000000001,36.818836299999994,-1.2666695,36.8328283,Rider_Id_519,2015
-Order_No_13680,User_Id_2102,Bike,3,Business,30,3,2:01:55 PM,30,3,2:02:16 PM,30,3,2:17:04 PM,30,3,2:22:41 PM,30,3,2:58:52 PM,15,29.0,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_456,2171
-Order_No_4730,User_Id_2183,Bike,2,Business,1,3,9:31:08 AM,1,3,9:38:56 AM,1,3,9:48:49 AM,1,3,9:53:31 AM,1,3,10:11:25 AM,6,,,-1.2551895,36.7822034,-1.2889396,36.81554860000001,Rider_Id_882,1074
-Order_No_26111,User_Id_2358,Bike,4,Business,3,3,11:56:00 AM,3,3,11:57:16 AM,3,3,12:03:37 PM,3,3,12:13:10 PM,3,3,12:19:34 PM,1,23.9,,-1.302222,36.75873,-1.2945498,36.754749100000005,Rider_Id_88,384
-Order_No_17899,User_Id_1473,Bike,3,Business,14,1,12:13:05 PM,14,1,12:13:21 PM,14,1,12:25:55 PM,14,1,12:31:26 PM,14,1,1:53:29 PM,32,23.6,,-1.3700383,36.9190174,-1.2489594,36.7358883,Rider_Id_620,4923
-Order_No_5879,User_Id_113,Bike,3,Business,19,4,9:12:14 AM,19,4,9:12:34 AM,19,4,9:38:57 AM,19,4,9:41:55 AM,19,4,10:12:02 AM,8,14.4,,-1.2844653,36.8206676,-1.2963097,36.7688221,Rider_Id_174,1807
-Order_No_23543,User_Id_1389,Bike,3,Business,5,2,1:30:36 PM,5,2,1:31:09 PM,5,2,1:38:36 PM,5,2,1:46:13 PM,5,2,2:07:11 PM,14,,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_205,1258
-Order_No_14930,User_Id_81,Bike,3,Business,20,4,11:30:03 AM,20,4,11:30:11 AM,20,4,11:32:15 AM,20,4,11:38:13 AM,20,4,11:43:09 AM,2,22.2,,-1.2596969999999998,36.800664000000005,-1.2641496,36.791413899999995,Rider_Id_720,296
-Order_No_23369,User_Id_3291,Bike,3,Business,31,1,9:53:13 AM,31,1,9:53:27 AM,31,1,9:54:19 AM,31,1,10:32:33 AM,31,1,10:51:27 AM,12,20.8,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_669,1134
-Order_No_6838,User_Id_389,Bike,1,Personal,22,5,1:08:40 PM,22,5,1:09:41 PM,22,5,1:19:06 PM,22,5,1:30:28 PM,22,5,1:32:37 PM,1,21.8,,-1.3007534,36.7964576,-1.2889003,36.784402899999996,Rider_Id_312,129
-Order_No_11515,User_Id_1412,Bike,3,Personal,25,3,12:18:08 PM,25,3,12:18:34 PM,25,3,12:31:08 PM,25,3,12:36:48 PM,25,3,1:12:25 PM,10,,,-1.238874,36.807889,-1.2591485,36.789133500000005,Rider_Id_208,2137
-Order_No_2278,User_Id_113,Bike,1,Business,22,3,11:26:38 AM,22,3,11:28:45 AM,22,3,11:36:35 AM,22,3,11:45:12 AM,22,3,12:01:57 PM,8,23.0,,-1.2963097,36.7688221,-1.2844653,36.8206676,Rider_Id_865,1005
-Order_No_20664,User_Id_3647,Bike,3,Business,10,1,5:30:41 PM,10,1,5:34:11 PM,10,1,5:50:06 PM,10,1,5:55:29 PM,10,1,6:13:14 PM,5,18.9,,-1.2726389999999999,36.794723,-1.292943,36.808292,Rider_Id_755,1065
-Order_No_24953,User_Id_2212,Bike,3,Business,11,5,2:43:46 PM,11,5,2:45:36 PM,11,5,2:48:34 PM,11,5,2:58:56 PM,11,5,3:19:07 PM,9,26.5,,-1.2571472,36.795063299999995,-1.2310607,36.803577600000004,Rider_Id_685,1211
-Order_No_18568,User_Id_2693,Bike,1,Personal,19,5,1:42:04 PM,19,5,1:54:34 PM,19,5,2:03:44 PM,19,5,2:13:34 PM,19,5,2:25:31 PM,4,27.5,,-1.2521181000000001,36.8104419,-1.2609186000000001,36.7938342,Rider_Id_467,717
-Order_No_17155,User_Id_3291,Bike,3,Business,5,3,12:07:29 PM,5,3,12:09:17 PM,5,3,12:13:13 PM,5,3,12:19:32 PM,5,3,1:19:11 PM,12,22.2,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_4,3579
-Order_No_19188,User_Id_391,Bike,3,Business,19,2,3:37:56 PM,19,2,3:48:35 PM,19,2,4:07:32 PM,19,2,4:12:44 PM,19,2,5:13:09 PM,5,29.9,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_928,3625
-Order_No_27873,User_Id_3718,Bike,3,Business,23,2,12:12:48 PM,23,2,12:14:05 PM,23,2,12:16:11 PM,23,2,12:28:38 PM,23,2,12:42:29 PM,6,18.0,,-1.2635744,36.781677,-1.2650027,36.8126242,Rider_Id_536,831
-Order_No_8133,User_Id_635,Bike,3,Business,13,4,12:03:44 PM,13,4,12:04:11 PM,13,4,12:04:35 PM,13,4,12:34:58 PM,13,4,12:51:48 PM,10,22.1,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_595,1010
-Order_No_20568,User_Id_944,Bike,1,Personal,11,3,1:33:58 PM,11,3,1:37:36 PM,11,3,2:07:48 PM,11,3,2:16:47 PM,11,3,2:53:14 PM,7,20.4,,-1.2555969999999999,36.830024,-1.264788,36.7883897,Rider_Id_726,2187
-Order_No_4023,User_Id_635,Bike,3,Business,9,6,10:06:30 AM,9,6,10:07:18 AM,9,6,10:17:57 AM,9,6,11:07:54 AM,9,6,11:34:56 AM,6,27.5,,-1.3081376,36.83297720000001,-1.3229345,36.8566974,Rider_Id_181,1622
-Order_No_22948,User_Id_2069,Bike,3,Business,27,3,11:26:52 AM,27,3,11:31:42 AM,27,3,11:44:31 AM,27,3,11:54:05 AM,27,3,12:05:58 PM,6,26.1,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_8,713
-Order_No_7417,User_Id_2631,Bike,1,Personal,4,3,1:04:32 PM,4,3,1:05:00 PM,4,3,1:13:44 PM,4,3,1:18:21 PM,4,3,1:32:21 PM,6,17.2,,-1.2845871000000002,36.8234834,-1.2889003,36.784402899999996,Rider_Id_629,840
-Order_No_9198,User_Id_868,Bike,3,Business,12,6,9:44:18 AM,12,6,9:44:30 AM,12,6,9:51:57 AM,12,6,9:56:04 AM,12,6,10:15:53 AM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_379,1189
-Order_No_1792,User_Id_177,Bike,3,Personal,22,5,12:31:57 PM,22,5,12:32:43 PM,22,5,12:51:06 PM,22,5,1:09:14 PM,22,5,1:44:01 PM,17,21.9,,-1.2749662,36.7658781,-1.2677303999999998,36.8779867,Rider_Id_620,2087
-Order_No_25007,User_Id_3689,Bike,3,Business,5,2,5:03:19 PM,5,2,5:56:19 PM,5,2,6:13:38 PM,5,2,6:29:18 PM,5,2,6:49:27 PM,6,21.4,,-1.2793686000000002,36.82592329999999,-1.2601708999999999,36.8002238,Rider_Id_742,1209
-Order_No_10879,User_Id_2987,Bike,3,Business,28,1,3:56:37 PM,28,1,4:16:29 PM,28,1,4:26:54 PM,28,1,4:34:54 PM,28,1,4:53:21 PM,10,22.6,,-1.2702120000000001,36.82108770000001,-1.3018758,36.75908629999999,Rider_Id_919,1107
-Order_No_22009,User_Id_3331,Bike,3,Business,11,2,10:58:28 AM,11,2,1:13:04 PM,11,2,1:49:06 PM,11,2,1:50:41 PM,11,2,2:29:52 PM,10,22.5,,-1.2551895,36.7822034,-1.2363747999999999,36.817750200000006,Rider_Id_755,2351
-Order_No_13836,User_Id_3264,Bike,3,Business,12,5,2:45:32 PM,12,5,2:47:08 PM,12,5,2:50:01 PM,12,5,2:53:28 PM,12,5,3:15:22 PM,6,,,-1.2551895,36.7822034,-1.2882462,36.81645379999999,Rider_Id_709,1314
-Order_No_443,User_Id_2857,Bike,3,Business,18,1,12:53:15 PM,18,1,12:54:14 PM,18,1,1:01:39 PM,18,1,1:15:55 PM,18,1,1:39:10 PM,7,,,-1.3034072,36.821803499999994,-1.2683619,36.806599299999995,Rider_Id_949,1395
-Order_No_13366,User_Id_3727,Bike,3,Business,29,5,10:58:51 AM,29,5,10:59:26 AM,29,5,10:59:36 AM,29,5,11:34:22 AM,29,5,11:35:26 AM,6,26.7,,-1.2963097,36.7688221,-1.2825816,36.8092346,Rider_Id_613,64
-Order_No_2869,User_Id_2285,Bike,1,Personal,8,1,9:59:36 AM,8,1,10:04:51 AM,8,1,10:18:36 AM,8,1,10:36:04 AM,8,1,10:59:53 AM,10,24.5,,-1.3324593999999998,36.861964,-1.2950041,36.814358299999995,Rider_Id_662,1429
-Order_No_6018,User_Id_2102,Bike,3,Business,29,1,3:32:21 PM,29,1,3:33:21 PM,29,1,3:48:13 PM,29,1,3:49:23 PM,29,1,4:23:46 PM,14,,,-1.3250048,36.869718799999994,-1.2613768,36.801941799999994,Rider_Id_863,2063
-Order_No_2787,User_Id_2828,Bike,1,Business,30,6,11:22:51 AM,30,6,11:23:19 AM,30,6,11:23:59 AM,30,6,11:54:54 AM,30,6,12:28:37 PM,16,22.1,,-1.2615888000000002,36.7928732,-1.3239725,36.8827383,Rider_Id_648,2023
-Order_No_9513,User_Id_2523,Bike,3,Business,26,2,2:42:32 PM,26,2,2:43:47 PM,26,2,2:57:19 PM,26,2,2:59:41 PM,26,2,3:22:32 PM,3,31.2,,-1.2551895,36.7822034,-1.2514315,36.7918586,Rider_Id_318,1371
-Order_No_22285,User_Id_2490,Bike,2,Personal,9,2,10:51:54 AM,9,2,11:44:35 AM,9,2,11:57:53 AM,9,2,12:06:42 PM,9,2,12:24:47 PM,6,22.0,,-1.2536209999999999,36.7991917,-1.2896398999999998,36.812449799999996,Rider_Id_38,1085
-Order_No_4986,User_Id_3300,Bike,3,Business,4,2,10:03:19 AM,4,2,10:03:52 AM,4,2,10:23:53 AM,4,2,10:42:18 AM,4,2,11:12:27 AM,17,20.3,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_75,1809
-Order_No_12006,User_Id_1336,Bike,3,Business,20,3,11:38:30 AM,20,3,11:50:32 AM,20,3,12:04:26 PM,20,3,12:05:40 PM,20,3,12:16:21 PM,3,26.2,,-1.2765736,36.851364600000004,-1.2885029,36.825604999999996,Rider_Id_953,641
-Order_No_15264,User_Id_2216,Bike,3,Business,11,5,9:54:13 AM,11,5,9:54:34 AM,11,5,9:56:09 AM,11,5,10:06:44 AM,11,5,10:11:37 AM,2,20.9,,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_449,293
-Order_No_18696,User_Id_1657,Bike,1,Personal,14,5,11:57:35 AM,14,5,11:58:18 AM,14,5,12:03:55 PM,14,5,12:12:53 PM,14,5,12:23:45 PM,4,24.4,,-1.2619756000000002,36.800139,-1.2673299999999998,36.823679999999996,Rider_Id_263,652
-Order_No_14218,User_Id_1367,Bike,3,Business,6,4,12:30:11 PM,6,4,12:30:31 PM,6,4,12:47:53 PM,6,4,1:00:38 PM,6,4,1:42:18 PM,26,24.2,,-1.275442,36.766020700000006,-1.3732935,36.919940600000004,Rider_Id_222,2500
-Order_No_22112,User_Id_3127,Bike,3,Business,24,4,3:19:32 PM,24,4,3:20:01 PM,24,4,3:51:44 PM,24,4,3:57:43 PM,24,4,4:05:34 PM,3,28.1,,-1.2918246,36.801584999999996,-1.302559,36.819555,Rider_Id_655,471
-Order_No_10060,User_Id_876,Bike,3,Business,14,1,1:38:47 PM,14,1,1:39:09 PM,14,1,1:59:57 PM,14,1,2:02:20 PM,14,1,2:15:32 PM,7,25.9,,-1.2746236,36.8254455,-1.2897215,36.7945959,Rider_Id_886,792
-Order_No_17222,User_Id_635,Bike,3,Business,29,1,12:21:16 PM,29,1,12:24:47 PM,29,1,12:31:09 PM,29,1,12:49:39 PM,29,1,1:20:39 PM,7,,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_435,1860
-Order_No_3879,User_Id_2804,Bike,3,Business,30,4,10:07:25 AM,30,4,11:36:55 AM,30,4,11:51:27 AM,30,4,12:00:13 PM,30,4,12:24:03 PM,6,,,-1.2551895,36.7822034,-1.282543,36.824618,Rider_Id_829,1430
-Order_No_7967,User_Id_3647,Bike,3,Business,21,4,2:43:52 PM,21,4,2:44:43 PM,21,4,2:55:38 PM,21,4,3:09:06 PM,21,4,3:38:16 PM,21,26.4,,-1.2726389999999999,36.794723,-1.3768823000000001,36.760893200000005,Rider_Id_446,1750
-Order_No_27702,User_Id_1568,Bike,3,Business,4,1,3:49:07 PM,4,1,3:50:06 PM,4,1,4:03:20 PM,4,1,4:35:53 PM,4,1,4:49:50 PM,6,25.9,,-1.2897368,36.8226783,-1.3017947,36.849789200000004,Rider_Id_187,837
-Order_No_22409,User_Id_868,Bike,3,Business,14,5,3:52:59 PM,14,5,3:53:30 PM,14,5,4:06:56 PM,14,5,4:15:13 PM,14,5,4:22:48 PM,3,23.2,,-1.2783848999999998,36.8163445,-1.2663631000000002,36.8163329,Rider_Id_462,455
-Order_No_26053,User_Id_2987,Bike,3,Business,27,4,11:21:04 AM,27,4,11:21:38 AM,27,4,11:31:16 AM,27,4,11:37:10 AM,27,4,11:58:11 AM,9,22.7,,-1.2702120000000001,36.82108770000001,-1.3280091,36.8524146,Rider_Id_622,1261
-Order_No_26984,User_Id_868,Bike,3,Business,7,5,10:31:06 AM,7,5,10:36:03 AM,7,5,10:47:04 AM,7,5,11:05:14 AM,7,5,11:25:58 AM,9,25.6,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_622,1244
-Order_No_21505,User_Id_1075,Bike,3,Business,13,1,9:57:52 AM,13,1,9:58:13 AM,13,1,10:29:59 AM,13,1,10:33:17 AM,13,1,10:48:30 AM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_361,913
-Order_No_11215,User_Id_2237,Bike,3,Business,13,2,2:45:20 PM,13,2,2:45:33 PM,13,2,2:55:28 PM,13,2,3:00:22 PM,13,2,3:30:59 PM,15,26.0,,-1.266571,36.801406,-1.2036664,36.819262200000004,Rider_Id_902,1837
-Order_No_8089,User_Id_136,Bike,3,Business,13,2,2:56:49 PM,13,2,3:27:51 PM,13,2,3:28:04 PM,13,2,3:34:23 PM,13,2,3:35:28 PM,3,26.2,,-1.3326173000000001,36.71462329999999,-1.3169591,36.7000029,Rider_Id_928,65
-Order_No_9996,User_Id_3557,Bike,3,Business,24,2,2:58:31 PM,24,2,3:00:17 PM,24,2,3:10:10 PM,24,2,3:16:59 PM,24,2,3:35:45 PM,5,18.2,,-1.2844083999999998,36.8106592,-1.2546732,36.808679600000005,Rider_Id_88,1126
-Order_No_18586,User_Id_897,Bike,2,Personal,5,2,10:46:02 AM,5,2,10:47:11 AM,5,2,10:55:50 AM,5,2,11:04:12 AM,5,2,11:38:34 AM,9,21.4,,-1.2795796,36.819062,-1.2295075,36.801697100000005,Rider_Id_949,2062
-Order_No_731,User_Id_635,Bike,3,Business,26,1,2:12:45 PM,26,1,2:18:25 PM,26,1,2:25:30 PM,26,1,2:28:43 PM,26,1,2:55:23 PM,13,22.3,0.9,-1.2599563,36.799343900000004,-1.3221141,36.8636835,Rider_Id_878,1600
-Order_No_21492,User_Id_2669,Bike,1,Personal,6,6,6:39:44 PM,6,6,6:56:43 PM,6,6,7:09:38 PM,6,6,7:14:49 PM,6,6,7:46:41 PM,10,25.1,,-1.2801206,36.817709,-1.3049726000000001,36.891238200000004,Rider_Id_148,1912
-Order_No_21251,User_Id_1500,Bike,3,Business,11,4,3:27:52 PM,11,4,4:12:49 PM,11,4,4:19:47 PM,11,4,4:24:58 PM,11,4,4:36:12 PM,6,27.0,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_291,674
-Order_No_24149,User_Id_1402,Bike,3,Business,22,5,11:08:56 AM,22,5,11:10:24 AM,22,5,11:39:47 AM,22,5,11:47:56 AM,22,5,11:50:28 AM,1,28.4,,-1.2813012,36.832396200000005,-1.2882247,36.8238821,Rider_Id_377,152
-Order_No_18604,User_Id_237,Bike,3,Business,30,5,10:42:03 AM,30,5,10:43:51 AM,30,5,10:49:49 AM,30,5,11:00:46 AM,30,5,11:16:33 AM,10,23.7,,-1.2597436000000002,36.8282526,-1.2551895,36.7822034,Rider_Id_453,947
-Order_No_16714,User_Id_1471,Bike,3,Business,15,5,12:29:34 PM,15,5,12:29:46 PM,15,5,12:42:05 PM,15,5,12:46:13 PM,15,5,2:23:00 PM,3,26.6,,-1.2551895,36.7822034,-1.2652279,36.8020159,Rider_Id_290,5807
-Order_No_7281,User_Id_2008,Bike,3,Business,10,1,5:00:40 PM,10,1,5:11:21 PM,10,1,6:01:04 PM,10,1,6:07:17 PM,10,1,6:47:35 PM,7,19.0,,-1.2508229,36.7895263,-1.2858501,36.8306293,Rider_Id_773,2418
-Order_No_18084,User_Id_393,Bike,3,Business,9,3,1:47:25 PM,9,3,1:47:44 PM,9,3,1:47:49 PM,9,3,2:13:50 PM,9,3,2:32:57 PM,11,25.5,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_62,1147
-Order_No_22326,User_Id_3517,Bike,3,Business,20,3,9:32:41 AM,20,3,9:33:06 AM,20,3,10:24:56 AM,20,3,11:25:28 AM,20,3,11:46:47 AM,15,27.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_817,1279
-Order_No_5005,User_Id_3781,Bike,3,Business,23,3,10:11:47 AM,23,3,10:12:06 AM,23,3,10:13:15 AM,23,3,10:23:40 AM,23,3,11:12:21 AM,20,,,-1.1855066,36.825464600000004,-1.2419938,36.768372899999996,Rider_Id_918,2921
-Order_No_17949,User_Id_2587,Bike,3,Business,23,1,11:30:58 AM,23,1,11:34:51 AM,23,1,11:46:03 AM,23,1,11:54:54 AM,23,1,12:10:33 PM,5,18.7,,-1.2527958000000001,36.8003131,-1.2885528999999998,36.816014100000004,Rider_Id_726,939
-Order_No_25372,User_Id_1597,Bike,1,Personal,22,5,2:24:45 PM,22,5,2:25:08 PM,22,5,2:44:35 PM,22,5,2:48:40 PM,22,5,3:10:20 PM,14,,,-1.3159617000000001,36.8117934,-1.3339943,36.7263409,Rider_Id_216,1300
-Order_No_8481,User_Id_3291,Bike,3,Business,8,6,2:26:46 PM,8,6,2:27:06 PM,8,6,2:48:55 PM,8,6,3:19:00 PM,8,6,3:19:03 PM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_309,3
-Order_No_721,User_Id_2609,Bike,1,Business,27,1,9:35:57 AM,27,1,9:36:58 AM,27,1,9:52:43 AM,27,1,9:56:07 AM,27,1,10:24:56 AM,14,16.4,,-1.32189,36.8589727,-1.2551895,36.7822034,Rider_Id_848,1729
-Order_No_2371,User_Id_1542,Bike,1,Personal,23,4,9:24:54 AM,23,4,9:26:11 AM,23,4,9:49:54 AM,23,4,10:26:53 AM,23,4,10:47:45 AM,6,15.5,,-1.2783547,36.8243545,-1.304969,36.846623,Rider_Id_754,1252
-Order_No_11291,User_Id_375,Bike,3,Business,12,2,2:57:39 PM,12,2,3:00:27 PM,12,2,3:11:38 PM,12,2,3:18:29 PM,12,2,3:49:04 PM,9,,,-1.2551895,36.7822034,-1.2346243000000001,36.8109003,Rider_Id_281,1835
-Order_No_11942,User_Id_909,Bike,3,Business,28,5,11:29:13 AM,28,5,12:02:23 PM,28,5,12:16:00 PM,28,5,12:22:23 PM,28,5,12:41:54 PM,9,25.1,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_453,1171
-Order_No_21044,User_Id_1926,Bike,3,Business,28,4,1:39:13 PM,28,4,1:41:14 PM,28,4,1:43:18 PM,28,4,1:58:42 PM,28,4,2:13:50 PM,3,30.1,,-1.2752846000000002,36.802702200000006,-1.2571472,36.795063299999995,Rider_Id_647,908
-Order_No_21970,User_Id_1245,Bike,3,Business,4,1,10:54:29 AM,4,1,11:10:36 AM,4,1,11:48:24 AM,4,1,12:04:13 PM,4,1,12:38:31 PM,14,26.7,,-1.3882008,36.7699445,-1.3198615,36.7110323,Rider_Id_246,2058
-Order_No_11072,User_Id_503,Bike,3,Business,13,3,2:39:56 PM,13,3,2:40:24 PM,13,3,2:55:39 PM,13,3,2:59:20 PM,13,3,3:07:51 PM,8,23.4,,-1.2859912,36.8756811,-1.2413316,36.878552500000005,Rider_Id_900,511
-Order_No_18426,User_Id_1724,Bike,3,Business,31,5,12:42:36 PM,31,5,12:57:33 PM,31,5,1:14:35 PM,31,5,1:17:49 PM,31,5,1:42:53 PM,7,21.9,,-1.2551895,36.7822034,-1.2870829,36.817074100000006,Rider_Id_341,1504
-Order_No_4766,User_Id_1246,Bike,3,Business,8,4,12:21:29 PM,8,4,12:21:55 PM,8,4,12:22:43 PM,8,4,12:57:14 PM,8,4,1:26:46 PM,2,26.5,,-1.2571472,36.795063299999995,-1.2618631999999999,36.8100226,Rider_Id_520,1772
-Order_No_7091,User_Id_3355,Bike,3,Business,30,7,4:41:01 PM,30,7,5:45:09 PM,30,7,6:01:01 PM,30,7,6:05:35 PM,30,7,6:28:56 PM,10,25.1,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_612,1401
-Order_No_2675,User_Id_3688,Bike,3,Business,28,1,3:12:46 PM,28,1,3:17:05 PM,28,1,3:54:40 PM,28,1,4:00:30 PM,28,1,4:10:27 PM,4,26.3,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_489,597
-Order_No_12507,User_Id_3517,Bike,3,Business,13,3,8:42:29 AM,13,3,8:42:41 AM,13,3,8:58:33 AM,13,3,10:03:08 AM,13,3,10:31:29 AM,16,17.5,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_487,1701
-Order_No_5632,User_Id_457,Bike,3,Personal,28,2,2:08:36 PM,28,2,2:09:36 PM,28,2,2:11:55 PM,28,2,2:19:55 PM,28,2,2:43:08 PM,10,23.6,,-1.2697893,36.8124106,-1.2990731000000002,36.848805799999994,Rider_Id_959,1393
-Order_No_9563,User_Id_1329,Bike,3,Business,11,2,6:00:32 PM,11,2,6:01:34 PM,11,2,6:10:18 PM,11,2,6:12:31 PM,11,2,6:16:28 PM,1,21.0,,-1.273056,36.811298,-1.2753108999999998,36.8130185,Rider_Id_685,237
-Order_No_9223,User_Id_2330,Bike,3,Business,21,4,2:58:28 PM,21,4,3:05:51 PM,21,4,3:15:13 PM,21,4,3:17:03 PM,21,4,3:38:23 PM,8,26.1,,-1.2291477,36.8440295,-1.2325706,36.808392299999994,Rider_Id_8,1280
-Order_No_9059,User_Id_3686,Bike,3,Business,11,2,2:14:51 PM,11,2,4:13:55 PM,11,2,4:23:27 PM,11,2,4:30:55 PM,11,2,5:06:39 PM,12,22.5,,-1.2551895,36.7822034,-1.318497,36.8296133,Rider_Id_362,2144
-Order_No_8725,User_Id_111,Bike,3,Business,29,5,2:04:38 PM,29,5,2:05:48 PM,29,5,2:34:18 PM,29,5,2:41:18 PM,29,5,3:02:55 PM,15,21.3,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_520,1297
-Order_No_22176,User_Id_186,Bike,3,Business,29,1,4:10:45 PM,29,1,4:11:17 PM,29,1,4:13:03 PM,29,1,4:25:55 PM,29,1,4:58:26 PM,17,24.5,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_703,1951
-Order_No_23355,User_Id_2780,Bike,1,Personal,21,5,11:10:23 AM,21,5,11:10:33 AM,21,5,11:45:13 AM,21,5,11:47:02 AM,21,5,12:02:42 PM,5,20.0,,-1.2854406,36.789409,-1.2739488,36.8060493,Rider_Id_722,940
-Order_No_17388,User_Id_1642,Bike,3,Business,10,4,3:04:30 PM,10,4,3:25:15 PM,10,4,3:45:56 PM,10,4,3:53:54 PM,10,4,4:10:25 PM,5,21.9,1.0,-1.2823851000000002,36.8186472,-1.2726283999999999,36.7882255,Rider_Id_100,991
-Order_No_17704,User_Id_3578,Bike,3,Business,4,2,1:37:17 PM,4,2,1:40:47 PM,4,2,2:00:34 PM,4,2,2:10:23 PM,4,2,2:28:59 PM,10,22.5,,-1.2440646999999998,36.7190288,-1.2527958000000001,36.797624400000004,Rider_Id_161,1116
-Order_No_9154,User_Id_475,Bike,3,Business,24,5,10:52:19 AM,24,5,10:53:40 AM,24,5,11:11:36 AM,24,5,11:20:20 AM,24,5,11:39:37 AM,7,21.6,,-1.2571472,36.795063299999995,-1.3024794,36.8228284,Rider_Id_155,1157
-Order_No_21223,User_Id_1341,Bike,3,Business,23,6,10:44:55 AM,23,6,10:45:38 AM,23,6,11:14:34 AM,23,6,11:16:12 AM,23,6,11:36:49 AM,12,,,-1.293515,36.897607,-1.331619,36.847976,Rider_Id_691,1237
-Order_No_1,User_Id_556,Bike,1,Personal,9,2,12:10:50 PM,9,2,12:12:24 PM,9,2,12:15:06 PM,9,2,12:20:43 PM,9,2,12:32:52 PM,4,21.8,,-1.2741728,36.79691079999999,-1.265971,36.766806,Rider_Id_848,729
-Order_No_22186,User_Id_393,Bike,3,Business,16,3,11:18:40 AM,16,3,11:18:57 AM,16,3,11:19:01 AM,16,3,11:32:36 AM,16,3,11:39:33 AM,4,22.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_555,417
-Order_No_12823,User_Id_2069,Bike,3,Business,11,1,12:30:09 PM,11,1,12:30:48 PM,11,1,12:43:21 PM,11,1,12:46:14 PM,11,1,1:20:20 PM,16,26.7,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_463,2046
-Order_No_21564,User_Id_865,Bike,3,Business,4,2,3:31:54 PM,4,2,3:45:28 PM,4,2,4:08:51 PM,4,2,4:25:33 PM,4,2,5:33:06 PM,4,22.7,,-1.2456412000000001,36.7955298,-1.2551895,36.7822034,Rider_Id_109,4053
-Order_No_22756,User_Id_3527,Bike,3,Personal,7,2,3:55:43 PM,7,2,3:57:16 PM,7,2,4:14:32 PM,7,2,4:36:17 PM,7,2,4:57:27 PM,7,,,-1.2640571000000003,36.7883547,-1.2935921000000001,36.7876166,Rider_Id_547,1270
-Order_No_6617,User_Id_1245,Bike,3,Business,1,5,8:47:34 AM,1,5,8:55:33 AM,1,5,9:06:30 AM,1,5,9:27:22 AM,1,5,9:58:04 AM,14,,,-1.300921,36.828195,-1.3616286,36.7452212,Rider_Id_416,1842
-Order_No_15555,User_Id_1363,Bike,3,Business,26,2,2:16:57 PM,26,2,2:19:13 PM,26,2,2:22:43 PM,26,2,2:52:37 PM,26,2,3:28:11 PM,6,30.9,,-1.3004062,36.829741,-1.2714767,36.8138291,Rider_Id_349,2134
-Order_No_24839,User_Id_2074,Bike,2,Personal,31,4,11:43:22 AM,31,4,11:43:36 AM,31,4,12:00:40 PM,31,4,12:25:05 PM,31,4,12:52:58 PM,9,21.9,,-1.2960063999999998,36.758236700000005,-1.2741728,36.79691079999999,Rider_Id_140,1673
-Order_No_17593,User_Id_186,Bike,3,Business,3,3,11:53:33 AM,3,3,11:55:17 AM,3,3,12:10:09 PM,3,3,12:23:58 PM,3,3,12:34:59 PM,4,27.4,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_448,661
-Order_No_15107,User_Id_2383,Bike,3,Business,13,4,9:12:28 AM,13,4,9:13:15 AM,13,4,9:38:38 AM,13,4,10:10:00 AM,13,4,10:30:27 AM,2,18.0,2.1,-1.264865,36.810488,-1.2608993,36.799246000000004,Rider_Id_112,1227
-Order_No_5082,User_Id_3317,Bike,1,Personal,27,7,4:10:39 PM,27,7,4:16:57 PM,27,7,4:39:55 PM,27,7,4:45:12 PM,27,7,5:10:00 PM,10,22.0,,-1.2517392,36.82496079999999,-1.2737136999999998,36.7716706,Rider_Id_281,1488
-Order_No_26107,User_Id_3091,Bike,3,Business,8,3,1:22:03 PM,8,3,1:23:43 PM,8,3,1:31:04 PM,8,3,1:48:12 PM,8,3,2:07:03 PM,7,,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_193,1131
-Order_No_27441,User_Id_3647,Bike,3,Business,16,2,4:35:46 PM,16,2,4:39:23 PM,16,2,5:02:17 PM,16,2,5:06:10 PM,16,2,5:30:36 PM,7,,,-1.2726389999999999,36.794723,-1.2950041,36.814358299999995,Rider_Id_815,1466
-Order_No_12534,User_Id_3326,Bike,3,Business,11,1,9:55:54 AM,11,1,9:58:01 AM,11,1,10:02:04 AM,11,1,10:10:46 AM,11,1,10:36:34 AM,9,21.1,,-1.2551895,36.7822034,-1.3080696000000003,36.8237191,Rider_Id_108,1548
-Order_No_3295,User_Id_3599,Bike,3,Business,13,1,12:39:00 PM,13,1,12:52:03 PM,13,1,1:01:47 PM,13,1,1:11:47 PM,13,1,1:35:49 PM,12,,,-1.3302996,36.8707935,-1.2738936,36.8035805,Rider_Id_222,1442
-Order_No_21190,User_Id_393,Bike,3,Business,13,4,9:56:54 AM,13,4,9:57:12 AM,13,4,9:59:28 AM,13,4,10:08:23 AM,13,4,10:50:18 AM,14,18.7,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_157,2515
-Order_No_28155,User_Id_60,Bike,3,Business,28,2,11:34:51 AM,28,2,11:35:13 AM,28,2,11:36:31 AM,28,2,11:42:29 AM,28,2,11:55:30 AM,7,21.2,,-1.2663631000000002,36.8163329,-1.2297799999999999,36.836215,Rider_Id_341,781
-Order_No_11239,User_Id_3211,Bike,3,Personal,8,1,11:42:05 AM,8,1,11:51:01 AM,8,1,12:08:32 PM,8,1,12:17:35 PM,8,1,12:35:33 PM,4,24.6,,-1.2783772,36.8201617,-1.2658315,36.809049,Rider_Id_956,1078
-Order_No_5853,User_Id_2330,Bike,3,Business,18,4,11:02:12 AM,18,4,11:02:26 AM,18,4,11:03:09 AM,18,4,11:26:42 AM,18,4,11:33:57 AM,12,,,-1.293082,36.8278578,-1.2284323,36.8757058,Rider_Id_61,435
-Order_No_3432,User_Id_649,Bike,3,Business,8,2,4:07:20 PM,8,2,4:10:47 PM,8,2,4:19:27 PM,8,2,4:26:44 PM,8,2,5:31:46 PM,22,25.2,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_512,3902
-Order_No_6878,User_Id_3035,Bike,3,Business,20,4,9:06:16 AM,20,4,9:07:00 AM,20,4,9:11:58 AM,20,4,9:13:49 AM,20,4,9:48:56 AM,11,18.3,0.1,-1.2963097,36.7688221,-1.257309,36.806008399999996,Rider_Id_571,2107
-Order_No_23995,User_Id_3487,Bike,3,Business,12,5,10:21:52 AM,12,5,10:22:27 AM,12,5,10:30:49 AM,12,5,10:37:51 AM,12,5,10:57:25 AM,9,26.7,,-1.279395,36.825364,-1.2295205,36.812042600000005,Rider_Id_341,1174
-Order_No_20216,User_Id_1075,Bike,3,Business,30,2,3:58:44 PM,30,2,4:39:02 PM,30,2,4:55:40 PM,30,2,5:15:03 PM,30,2,5:42:13 PM,5,,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_456,1630
-Order_No_24548,User_Id_2527,Bike,2,Personal,26,2,11:47:49 AM,26,2,11:48:26 AM,26,2,11:56:47 AM,26,2,11:59:35 AM,26,2,12:21:32 PM,4,,,-1.2603714,36.7893387,-1.2672588,36.8000252,Rider_Id_738,1317
-Order_No_2618,User_Id_3149,Bike,3,Business,15,5,2:37:39 PM,15,5,3:57:12 PM,15,5,4:06:18 PM,15,5,4:17:17 PM,15,5,4:30:04 PM,5,28.1,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_156,767
-Order_No_6468,User_Id_3599,Bike,3,Business,28,3,1:12:26 PM,28,3,3:23:08 PM,28,3,3:26:43 PM,28,3,3:38:42 PM,28,3,4:05:53 PM,12,,,-1.3302996,36.8707935,-1.296642,36.799207,Rider_Id_661,1631
-Order_No_1357,User_Id_136,Bike,3,Business,30,2,10:42:03 AM,30,2,10:51:02 AM,30,2,11:02:55 AM,30,2,11:37:09 AM,30,2,11:37:18 AM,10,,,-1.3254522,36.721658399999995,-1.2971024,36.7836598,Rider_Id_831,9
-Order_No_13197,User_Id_714,Bike,3,Business,5,2,2:30:27 PM,5,2,2:32:05 PM,5,2,3:08:54 PM,5,2,3:20:13 PM,5,2,3:31:21 PM,10,,,-1.2607331,36.7428881,-1.2628638,36.8070399,Rider_Id_269,668
-Order_No_22156,User_Id_393,Bike,3,Business,10,4,2:20:35 PM,10,4,2:21:05 PM,10,4,2:22:46 PM,10,4,2:29:45 PM,10,4,2:40:49 PM,3,25.3,,-1.3231001,36.8283559,-1.3004062,36.829741,Rider_Id_523,664
-Order_No_461,User_Id_631,Bike,1,Personal,14,4,10:24:09 AM,14,4,10:26:11 AM,14,4,10:57:42 AM,14,4,11:00:43 AM,14,4,11:39:36 AM,23,19.2,,-1.214428,36.883258000000005,-1.3215473,36.8435326,Rider_Id_124,2333
-Order_No_13484,User_Id_3326,Bike,3,Business,28,4,12:06:10 PM,28,4,12:06:22 PM,28,4,12:06:45 PM,28,4,12:10:42 PM,28,4,12:40:44 PM,4,23.7,,-1.2551895,36.7822034,-1.2752846000000002,36.802702200000006,Rider_Id_831,1802
-Order_No_19725,User_Id_2953,Bike,3,Business,23,2,3:04:29 PM,23,2,3:11:15 PM,23,2,3:52:14 PM,23,2,4:18:52 PM,23,2,4:43:14 PM,8,,,-1.3223238000000002,36.8320056,-1.3015700000000001,36.7871,Rider_Id_233,1462
-Order_No_19779,User_Id_1520,Bike,2,Personal,8,2,9:10:27 AM,8,2,9:16:47 AM,8,2,9:36:12 AM,8,2,9:41:27 AM,8,2,10:04:40 AM,9,,,-1.2988168,36.8198572,-1.2559559999999999,36.789867,Rider_Id_345,1393
-Order_No_6324,User_Id_1073,Bike,3,Business,6,4,4:54:28 PM,6,4,4:54:42 PM,6,4,5:07:54 PM,6,4,5:10:04 PM,6,4,5:25:39 PM,8,22.4,,-1.2491515,36.79076848,-1.2866305,36.7754914,Rider_Id_103,935
-Order_No_23823,User_Id_248,Bike,3,Business,3,3,11:35:32 AM,3,3,11:35:43 AM,3,3,11:37:36 AM,3,3,11:43:16 AM,3,3,12:23:40 PM,7,24.2,,-1.304735,36.784605400000004,-1.2603996000000002,36.79722720000001,Rider_Id_663,2424
-Order_No_19931,User_Id_3686,Bike,3,Business,9,4,9:53:29 AM,9,4,9:56:59 AM,9,4,10:26:54 AM,9,4,10:28:47 AM,9,4,11:24:40 AM,8,,,-1.2551895,36.7822034,-1.305146,36.830025,Rider_Id_435,3353
-Order_No_12972,User_Id_1874,Bike,3,Business,11,1,11:26:41 AM,11,1,11:26:52 AM,11,1,11:41:37 AM,11,1,11:46:58 AM,11,1,12:18:11 PM,20,23.9,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_28,1873
-Order_No_21028,User_Id_3578,Bike,3,Business,26,6,2:44:17 PM,26,6,2:45:24 PM,26,6,2:56:19 PM,26,6,3:10:19 PM,26,6,3:42:37 PM,13,28.2,,-1.2527958000000001,36.8003131,-1.2297488,36.86058320000001,Rider_Id_880,1938
-Order_No_22012,User_Id_2987,Bike,3,Business,29,1,1:08:07 PM,29,1,1:17:52 PM,29,1,1:27:59 PM,29,1,1:30:13 PM,29,1,1:43:54 PM,3,,,-1.2702120000000001,36.82108770000001,-1.2805757,36.8223948,Rider_Id_522,821
-Order_No_27068,User_Id_1245,Bike,3,Business,27,6,12:35:24 PM,27,6,12:37:27 PM,27,6,12:39:31 PM,27,6,12:52:46 PM,27,6,1:37:21 PM,19,,,-1.3198615,36.7110323,-1.3159197,36.8516724,Rider_Id_427,2675
-Order_No_22139,User_Id_2154,Bike,3,Business,18,5,2:25:15 PM,18,5,2:41:42 PM,18,5,3:04:19 PM,18,5,3:07:07 PM,18,5,3:31:45 PM,7,24.3,,-1.2885528999999998,36.816014100000004,-1.2626906,36.7827022,Rider_Id_742,1478
-Order_No_23796,User_Id_1280,Bike,3,Business,11,1,9:25:40 AM,11,1,9:26:10 AM,11,1,9:34:24 AM,11,1,9:43:04 AM,11,1,10:00:21 AM,4,21.1,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_245,1037
-Order_No_14872,User_Id_3283,Bike,3,Business,14,4,12:39:33 PM,14,4,12:41:07 PM,14,4,12:50:57 PM,14,4,1:03:28 PM,14,4,1:18:13 PM,6,25.0,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_268,885
-Order_No_9840,User_Id_213,Bike,1,Personal,6,1,8:25:33 AM,6,1,8:37:47 AM,6,1,8:53:10 AM,6,1,8:59:10 AM,6,1,9:43:01 AM,25,14.9,,-1.3724141,36.926992,-1.3009476999999998,36.768673299999996,Rider_Id_932,2631
-Order_No_1272,User_Id_1329,Bike,3,Business,28,5,9:51:09 AM,28,5,9:51:25 AM,28,5,10:11:28 AM,28,5,10:13:40 AM,28,5,10:32:01 AM,14,19.6,,-1.273056,36.811298,-1.222849,36.8973875,Rider_Id_698,1101
-Order_No_18853,User_Id_1145,Bike,2,Personal,11,3,5:09:07 PM,11,3,5:15:24 PM,11,3,5:24:04 PM,11,3,5:34:42 PM,11,3,5:58:40 PM,14,20.0,,-1.2934656999999998,36.8212737,-1.2061844,36.7922321,Rider_Id_629,1438
-Order_No_26660,User_Id_3201,Bike,3,Business,18,2,3:45:53 PM,18,2,3:46:17 PM,18,2,3:52:12 PM,18,2,3:54:40 PM,18,2,3:54:43 PM,3,25.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_647,3
-Order_No_5256,User_Id_237,Bike,3,Business,13,1,2:41:48 PM,13,1,2:42:04 PM,13,1,2:44:39 PM,13,1,2:51:25 PM,13,1,3:16:19 PM,10,,,-1.2551895,36.7822034,-1.3052469999999998,36.795638200000006,Rider_Id_422,1494
-Order_No_5584,User_Id_682,Bike,2,Personal,29,6,12:54:08 PM,29,6,12:56:55 PM,29,6,1:05:54 PM,29,6,1:14:34 PM,29,6,1:28:47 PM,5,24.9,,-1.3052671999999998,36.753853899999996,-1.2947685,36.778583399999995,Rider_Id_565,853
-Order_No_4830,User_Id_2827,Bike,3,Business,3,1,1:23:00 PM,3,1,1:40:40 PM,3,1,1:53:24 PM,3,1,1:57:34 PM,3,1,3:45:06 PM,8,23.4,,-1.2850643999999998,36.8343657,-1.238874,36.807889,Rider_Id_79,6452
-Order_No_24496,User_Id_3517,Bike,3,Business,28,5,3:50:20 PM,28,5,3:52:42 PM,28,5,3:59:00 PM,28,5,4:55:47 PM,28,5,6:07:52 PM,15,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_519,4325
-Order_No_9254,User_Id_552,Bike,3,Business,30,3,9:21:52 AM,30,3,9:23:03 AM,30,3,9:39:43 AM,30,3,9:44:44 AM,30,3,10:07:03 AM,14,19.6,,-1.2852569,36.8976121,-1.3562374,36.904295399999995,Rider_Id_622,1339
-Order_No_26214,User_Id_1329,Bike,3,Business,28,3,12:33:43 PM,28,3,12:34:02 PM,28,3,12:40:00 PM,28,3,12:46:43 PM,28,3,1:00:30 PM,5,,,-1.273056,36.811298,-1.2956951,36.812924200000005,Rider_Id_45,827
-Order_No_7212,User_Id_1544,Bike,3,Business,22,3,2:44:47 PM,22,3,3:34:05 PM,22,3,3:56:21 PM,22,3,3:57:58 PM,22,3,4:25:02 PM,5,24.3,,-1.2791223999999999,36.8306878,-1.297901,36.803489899999995,Rider_Id_673,1624
-Order_No_27244,User_Id_533,Bike,3,Business,5,3,12:39:39 PM,5,3,12:58:33 PM,5,3,1:02:38 PM,5,3,1:25:25 PM,5,3,1:59:51 PM,8,22.4,,-1.2803414,36.8265456,-1.2765492,36.7669809,Rider_Id_845,2066
-Order_No_6086,User_Id_3496,Bike,3,Business,7,4,4:02:05 PM,7,4,4:03:29 PM,7,4,4:12:40 PM,7,4,4:38:08 PM,7,4,5:12:15 PM,7,25.6,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_168,2047
-Order_No_4909,User_Id_1389,Bike,3,Business,10,6,12:57:49 PM,10,6,12:57:55 PM,10,6,12:58:04 PM,10,6,1:08:16 PM,10,6,2:12:51 PM,15,23.8,,-1.28878,36.8168312,-1.207546,36.7913976,Rider_Id_391,3875
-Order_No_14745,User_Id_510,Bike,3,Business,12,3,3:39:25 PM,12,3,3:39:33 PM,12,3,3:40:46 PM,12,3,4:12:15 PM,12,3,4:53:47 PM,12,26.3,,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_402,2492
-Order_No_15059,User_Id_909,Bike,3,Business,27,7,1:19:10 PM,27,7,2:39:29 PM,27,7,2:42:59 PM,27,7,2:54:17 PM,27,7,3:08:14 PM,7,27.9,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_105,837
-Order_No_23490,User_Id_269,Bike,3,Business,30,3,3:30:45 PM,30,3,4:11:45 PM,30,3,4:24:16 PM,30,3,4:27:12 PM,30,3,4:49:40 PM,12,25.2,,-1.3332749,36.870814700000004,-1.2927792,36.9053492,Rider_Id_742,1348
-Order_No_1651,User_Id_552,Bike,3,Business,31,3,1:05:25 PM,31,3,1:33:08 PM,31,3,2:12:10 PM,31,3,2:20:04 PM,31,3,3:05:27 PM,13,26.9,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_206,2723
-Order_No_25921,User_Id_1792,Bike,3,Business,11,1,3:52:39 PM,11,1,3:58:53 PM,11,1,4:13:00 PM,11,1,4:20:20 PM,11,1,4:55:18 PM,15,29.0,,-1.2809121,36.7852524,-1.2470928,36.8939232,Rider_Id_545,2098
-Order_No_21769,User_Id_1500,Bike,3,Business,29,5,1:56:11 PM,29,5,3:09:17 PM,29,5,3:38:47 PM,29,5,3:44:10 PM,29,5,4:02:25 PM,6,28.9,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_858,1095
-Order_No_17535,User_Id_1557,Bike,3,Business,21,5,12:33:39 PM,21,5,12:34:53 PM,21,5,12:56:03 PM,21,5,12:57:58 PM,21,5,1:10:57 PM,4,22.9,,-1.2694178,36.812692799999994,-1.2551895,36.7822034,Rider_Id_866,779
-Order_No_3450,User_Id_552,Bike,3,Business,26,2,4:04:18 PM,26,2,4:04:33 PM,26,2,4:16:52 PM,26,2,4:37:43 PM,26,2,5:13:46 PM,17,29.4,,-1.290894,36.822971,-1.3632162,36.9108253,Rider_Id_583,2163
-Order_No_7840,User_Id_868,Bike,3,Business,26,5,9:37:34 AM,26,5,9:37:43 AM,26,5,9:40:13 AM,26,5,9:49:52 AM,26,5,10:05:51 AM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_383,959
-Order_No_18859,User_Id_3032,Bike,3,Business,26,2,11:31:39 AM,26,2,11:32:10 AM,26,2,11:33:27 AM,26,2,11:43:19 AM,26,2,12:01:02 PM,8,27.5,,-1.2571472,36.795063299999995,-1.2320608999999998,36.752092,Rider_Id_268,1063
-Order_No_24515,User_Id_3521,Bike,3,Business,18,4,9:23:11 AM,18,4,9:26:40 AM,18,4,10:04:31 AM,18,4,10:16:46 AM,18,4,10:26:32 AM,2,20.4,,-1.2684666999999998,36.8002891,-1.2768326,36.8073804,Rider_Id_299,586
-Order_No_24461,User_Id_1687,Bike,3,Business,20,1,2:59:44 PM,20,1,3:00:15 PM,20,1,3:15:17 PM,20,1,3:31:36 PM,20,1,3:48:11 PM,7,,,-1.2626574,36.8014034,-1.2726754999999998,36.7754049,Rider_Id_956,995
-Order_No_9629,User_Id_517,Bike,3,Business,31,3,3:44:19 PM,31,3,3:53:53 PM,31,3,3:57:11 PM,31,3,4:03:37 PM,31,3,4:13:58 PM,6,26.9,,-1.3077869,36.844320700000004,-1.3177546999999998,36.8303703,Rider_Id_62,621
-Order_No_26740,User_Id_788,Bike,3,Business,22,2,1:35:04 PM,22,2,1:35:16 PM,22,2,1:39:09 PM,22,2,1:46:46 PM,22,2,2:02:44 PM,7,25.8,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_882,958
-Order_No_23958,User_Id_1484,Bike,3,Business,23,3,12:32:44 PM,23,3,12:33:38 PM,23,3,12:36:24 PM,23,3,12:48:19 PM,23,3,1:08:40 PM,13,,,-1.2571472,36.795063299999995,-1.2343236000000002,36.8861,Rider_Id_31,1221
-Order_No_13155,User_Id_3517,Bike,3,Business,7,5,10:06:35 AM,7,5,10:07:11 AM,7,5,10:15:37 AM,7,5,11:20:59 AM,7,5,11:40:40 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_557,1181
-Order_No_9370,User_Id_1194,Bike,1,Personal,16,2,2:06:49 PM,16,2,2:07:02 PM,16,2,2:12:34 PM,16,2,2:16:05 PM,16,2,2:45:40 PM,4,27.6,,-1.304192,36.739457,-1.2959724,36.7569331,Rider_Id_612,1775
-Order_No_5396,User_Id_3686,Bike,3,Business,13,4,2:22:15 PM,13,4,2:49:30 PM,13,4,3:02:28 PM,13,4,3:04:10 PM,13,4,3:27:19 PM,7,22.2,,-1.2551895,36.7822034,-1.2957631,36.8161042,Rider_Id_362,1389
-Order_No_9624,User_Id_2903,Bike,2,Personal,17,3,12:35:29 PM,17,3,1:00:46 PM,17,3,1:08:44 PM,17,3,1:11:37 PM,17,3,1:47:00 PM,8,25.4,,-1.2456412000000001,36.7955298,-1.3015998,36.816646500000004,Rider_Id_726,2123
-Order_No_1182,User_Id_111,Bike,3,Business,1,5,2:27:37 PM,1,5,2:28:27 PM,1,5,2:38:27 PM,1,5,2:50:39 PM,1,5,3:12:55 PM,10,28.5,,-1.2551895,36.7822034,-1.2948834999999999,36.7830474,Rider_Id_205,1336
-Order_No_24970,User_Id_2008,Bike,3,Business,17,5,9:59:34 AM,17,5,10:00:49 AM,17,5,10:07:05 AM,17,5,10:45:02 AM,17,5,10:50:31 AM,17,,,-1.2508229,36.7895263,-1.1723835,36.7587976,Rider_Id_953,329
-Order_No_9947,User_Id_2330,Bike,3,Business,5,2,12:12:26 PM,5,2,12:13:15 PM,5,2,12:21:24 PM,5,2,12:24:24 PM,5,2,12:42:49 PM,6,23.5,,-1.2921463,36.8029626,-1.2649605,36.798177700000004,Rider_Id_958,1105
-Order_No_19078,User_Id_1280,Bike,3,Business,10,3,10:43:40 AM,10,3,10:44:00 AM,10,3,10:49:36 AM,10,3,10:53:07 AM,10,3,11:26:40 AM,13,27.8,,-1.2551895,36.7822034,-1.3207951,36.8286902,Rider_Id_411,2013
-Order_No_6000,User_Id_1787,Bike,3,Business,9,4,10:13:41 AM,9,4,10:21:51 AM,9,4,10:50:57 AM,9,4,10:53:30 AM,9,4,11:08:23 AM,6,,,-1.2283403,36.8822756,-1.2255337,36.8371306,Rider_Id_930,893
-Order_No_23914,User_Id_136,Bike,3,Business,12,1,3:39:56 PM,12,1,4:31:29 PM,12,1,4:39:32 PM,12,1,4:41:10 PM,12,1,5:00:48 PM,8,23.8,,-1.3014461000000002,36.7661381,-1.3326173000000001,36.71462329999999,Rider_Id_470,1178
-Order_No_10720,User_Id_3647,Bike,3,Business,14,3,10:17:14 AM,14,3,10:19:21 AM,14,3,10:31:10 AM,14,3,10:33:19 AM,14,3,10:54:51 AM,7,24.5,,-1.2726389999999999,36.794723,-1.3012902,36.80964,Rider_Id_849,1292
-Order_No_23484,User_Id_970,Bike,3,Business,22,5,9:15:23 AM,22,5,9:15:37 AM,22,5,10:15:00 AM,22,5,10:17:38 AM,22,5,10:54:06 AM,15,,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_829,2188
-Order_No_28116,User_Id_1492,Bike,1,Personal,28,5,10:07:23 AM,28,5,10:08:48 AM,28,5,10:30:55 AM,28,5,10:35:59 AM,28,5,11:13:44 AM,15,25.1,,-1.2911796,36.821506799999995,-1.1855066,36.825464600000004,Rider_Id_96,2265
-Order_No_24877,User_Id_776,Bike,1,Personal,15,7,10:06:47 AM,15,7,10:40:30 AM,15,7,10:51:41 AM,15,7,10:56:14 AM,15,7,11:26:53 AM,12,18.1,,-1.278967,36.8236745,-1.2664227,36.7453671,Rider_Id_467,1839
-Order_No_27590,User_Id_1231,Bike,3,Business,3,1,10:27:52 AM,3,1,10:35:57 AM,3,1,10:48:55 AM,3,1,10:51:46 AM,3,1,11:20:57 AM,12,22.3,,-1.2821421999999998,36.8161565,-1.2283403,36.8822756,Rider_Id_677,1751
-Order_No_3104,User_Id_3222,Bike,3,Business,18,5,2:46:39 PM,18,5,2:48:38 PM,18,5,2:57:15 PM,18,5,2:59:58 PM,18,5,4:13:43 PM,6,26.9,,-1.2551895,36.7822034,-1.2407027,36.765004299999994,Rider_Id_131,4425
-Order_No_17484,User_Id_928,Bike,3,Business,22,3,2:50:58 PM,22,3,3:15:50 PM,22,3,4:02:15 PM,22,3,4:21:38 PM,22,3,4:44:48 PM,8,24.4,,-1.2878913,36.8089253,-1.2551895,36.7822034,Rider_Id_448,1390
-Order_No_24754,User_Id_1244,Bike,3,Business,26,2,9:35:07 AM,26,2,9:35:25 AM,26,2,9:44:40 AM,26,2,9:54:41 AM,26,2,10:16:43 AM,7,22.0,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_427,1322
-Order_No_4431,User_Id_3222,Bike,3,Business,8,5,2:17:57 PM,8,5,2:18:37 PM,8,5,2:20:25 PM,8,5,2:39:59 PM,8,5,3:18:21 PM,8,30.5,,-1.2551895,36.7822034,-1.3012618999999999,36.821369700000005,Rider_Id_630,2302
-Order_No_17326,User_Id_15,Bike,1,Personal,31,4,3:26:46 PM,31,4,3:33:43 PM,31,4,3:52:41 PM,31,4,3:56:02 PM,31,4,4:54:19 PM,15,22.9,,-1.2022306,36.7893305,-1.2808747999999999,36.8289823,Rider_Id_144,3497
-Order_No_27909,User_Id_2603,Bike,2,Personal,16,2,11:57:11 AM,16,2,12:03:37 PM,16,2,12:11:25 PM,16,2,12:14:38 PM,16,2,1:13:39 PM,19,23.6,,-1.3632162,36.9108253,-1.2852583999999998,36.8043641,Rider_Id_770,3541
-Order_No_7333,User_Id_517,Bike,3,Business,13,2,8:42:48 AM,13,2,9:58:24 AM,13,2,10:28:05 AM,13,2,10:33:48 AM,13,2,10:52:45 AM,6,22.3,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_95,1137
-Order_No_20839,User_Id_1141,Bike,3,Business,28,6,11:14:31 AM,28,6,11:14:47 AM,28,6,11:19:21 AM,28,6,11:23:06 AM,28,6,11:38:13 AM,6,24.2,,-1.2877645,36.837853499999994,-1.2643993999999998,36.812919,Rider_Id_629,907
-Order_No_16498,User_Id_1075,Bike,3,Business,23,6,3:01:26 PM,23,6,3:02:04 PM,23,6,3:12:47 PM,23,6,3:17:34 PM,23,6,3:24:21 PM,1,28.5,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_167,407
-Order_No_27275,User_Id_745,Bike,3,Personal,13,5,2:27:15 PM,13,5,2:51:25 PM,13,5,2:57:39 PM,13,5,3:01:37 PM,13,5,3:26:59 PM,7,19.1,,-1.2903643,36.8319433,-1.2962832,36.784968299999996,Rider_Id_832,1522
-Order_No_21264,User_Id_2509,Bike,3,Business,1,1,3:32:07 PM,1,1,3:33:44 PM,1,1,3:54:42 PM,1,1,4:01:15 PM,1,1,4:46:24 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_138,2709
-Order_No_10217,User_Id_2878,Bike,3,Personal,16,3,1:42:26 PM,16,3,1:42:53 PM,16,3,2:03:43 PM,16,3,2:43:38 PM,16,3,3:23:59 PM,13,25.5,,-1.2529598999999998,36.7609473,-1.2974381000000001,36.78212655,Rider_Id_290,2421
-Order_No_13701,User_Id_2391,Bike,3,Personal,12,2,4:54:04 PM,12,2,4:55:26 PM,12,2,5:09:17 PM,12,2,5:28:27 PM,12,2,5:30:10 PM,6,28.2,,-1.311706,36.802841,-1.2993093999999998,36.777206899999996,Rider_Id_678,103
-Order_No_4841,User_Id_3420,Bike,3,Business,10,4,11:04:46 AM,10,4,11:05:35 AM,10,4,11:15:49 AM,10,4,11:17:43 AM,10,4,11:46:27 AM,10,24.9,,-1.3046007,36.821174299999996,-1.2541928999999998,36.85150170000001,Rider_Id_30,1724
-Order_No_20578,User_Id_1280,Bike,3,Business,28,4,12:59:34 PM,28,4,12:59:58 PM,28,4,1:07:08 PM,28,4,1:23:33 PM,28,4,1:48:33 PM,10,23.8,,-1.2551895,36.7822034,-1.2975066000000002,36.76894229999999,Rider_Id_831,1500
-Order_No_20950,User_Id_2931,Bike,3,Business,28,4,10:22:43 AM,28,4,10:43:48 AM,28,4,11:43:09 AM,28,4,12:34:53 PM,28,4,2:05:29 PM,11,23.6,,-1.3433181,36.9138018,-1.287957,36.9550037,Rider_Id_507,5436
-Order_No_24635,User_Id_265,Bike,3,Business,10,4,3:09:20 PM,10,4,3:09:24 PM,10,4,3:12:49 PM,10,4,3:41:04 PM,10,4,4:46:17 PM,24,21.7,1.0,-1.300921,36.828195,-1.2491258,36.689425299999996,Rider_Id_105,3913
-Order_No_27126,User_Id_517,Bike,3,Business,3,1,10:56:47 AM,3,1,11:20:08 AM,3,1,11:36:11 AM,3,1,11:43:51 AM,3,1,12:15:05 PM,10,22.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_523,1874
-Order_No_21202,User_Id_1637,Bike,3,Business,14,3,8:43:26 AM,14,3,8:49:21 AM,14,3,8:53:39 AM,14,3,9:06:39 AM,14,3,9:10:43 AM,2,18.5,,-1.2615888000000002,36.7928732,-1.2571472,36.795063299999995,Rider_Id_708,244
-Order_No_10739,User_Id_517,Bike,3,Business,27,6,10:14:51 AM,27,6,10:27:06 AM,27,6,10:33:58 AM,27,6,10:45:47 AM,27,6,10:52:57 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_205,430
-Order_No_778,User_Id_1976,Bike,3,Business,2,1,1:31:15 PM,2,1,1:31:42 PM,2,1,1:46:39 PM,2,1,1:54:12 PM,2,1,2:16:19 PM,8,19.8,,-1.3019530000000001,36.788458,-1.257028,36.7810435,Rider_Id_312,1327
-Order_No_2390,User_Id_248,Bike,3,Business,6,4,11:08:29 AM,6,4,11:10:44 AM,6,4,11:26:22 AM,6,4,11:34:13 AM,6,4,11:51:28 AM,6,19.7,,-1.286937,36.824674200000004,-1.2988291,36.7795686,Rider_Id_821,1035
-Order_No_6955,User_Id_350,Bike,1,Personal,28,1,11:57:41 AM,28,1,11:58:06 AM,28,1,12:01:08 PM,28,1,12:04:13 PM,28,1,12:13:00 PM,3,22.9,,-1.2990606000000002,36.8033028,-1.3043649,36.7816132,Rider_Id_676,527
-Order_No_1056,User_Id_3291,Bike,3,Business,28,5,11:56:29 AM,28,5,11:57:15 AM,28,5,12:03:38 PM,28,5,12:05:47 PM,28,5,12:19:43 PM,4,25.0,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_882,836
-Order_No_9689,User_Id_3486,Bike,3,Business,20,3,11:16:31 AM,20,3,11:17:16 AM,20,3,11:25:51 AM,20,3,11:27:34 AM,20,3,12:08:32 PM,21,27.2,,-1.2551895,36.7822034,-1.3705766000000001,36.9120742,Rider_Id_876,2458
-Order_No_6140,User_Id_626,Bike,3,Personal,21,4,10:16:27 AM,21,4,10:17:23 AM,21,4,10:29:40 AM,21,4,10:31:01 AM,21,4,10:39:13 AM,3,19.1,,-1.2683391000000002,36.798379600000004,-1.269534,36.80697,Rider_Id_412,492
-Order_No_25997,User_Id_1942,Bike,1,Personal,14,4,9:42:01 AM,14,4,10:28:59 AM,14,4,10:29:37 AM,14,4,10:32:10 AM,14,4,11:38:22 AM,8,18.8,,-1.2963097,36.7688221,-1.3138646999999999,36.811896000000004,Rider_Id_698,3972
-Order_No_22162,User_Id_2379,Bike,2,Personal,13,5,5:25:18 PM,13,5,5:31:18 PM,13,5,5:55:03 PM,13,5,6:08:28 PM,13,5,6:53:50 PM,18,19.3,,-1.3632233,36.7339157,-1.2925236,36.7795556,Rider_Id_629,2722
-Order_No_13918,User_Id_788,Bike,3,Business,14,3,11:40:50 AM,14,3,11:41:05 AM,14,3,11:47:14 AM,14,3,11:54:00 AM,14,3,12:33:21 PM,8,24.5,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_345,2361
-Order_No_5669,User_Id_3283,Bike,3,Business,22,2,10:40:54 AM,22,2,10:49:31 AM,22,2,11:10:30 AM,22,2,11:33:12 AM,22,2,12:01:33 PM,9,19.5,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_844,1701
-Order_No_26252,User_Id_859,Bike,2,Personal,15,1,4:00:00 PM,15,1,4:30:08 PM,15,1,5:02:40 PM,15,1,5:05:48 PM,15,1,5:18:09 PM,5,,,-1.2939373,36.7807916,-1.3159617000000001,36.8117934,Rider_Id_565,741
-Order_No_26245,User_Id_1008,Bike,3,Personal,3,5,11:47:50 AM,3,5,11:48:56 AM,3,5,11:52:07 AM,3,5,12:01:26 PM,3,5,12:30:07 PM,5,,,-1.2868973,36.8232984,-1.3039934,36.8180494,Rider_Id_831,1721
-Order_No_17165,User_Id_633,Bike,3,Business,6,3,11:14:59 AM,6,3,11:15:17 AM,6,3,11:38:00 AM,6,3,11:41:30 AM,6,3,11:51:25 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,595
-Order_No_26358,User_Id_517,Bike,3,Business,31,5,10:20:53 AM,31,5,11:05:02 AM,31,5,11:05:06 AM,31,5,12:07:59 PM,31,5,12:08:04 PM,8,21.8,,-1.3228003000000002,36.8306435,-1.2770708,36.8231093,Rider_Id_10,5
-Order_No_6746,User_Id_2102,Bike,3,Business,27,3,3:29:37 PM,27,3,3:30:05 PM,27,3,3:40:22 PM,27,3,3:48:20 PM,27,3,4:19:30 PM,13,29.2,,-1.2613768,36.801941799999994,-1.3323107,36.8692616,Rider_Id_411,1870
-Order_No_21404,User_Id_3283,Bike,3,Business,24,6,11:32:18 AM,24,6,11:32:35 AM,24,6,11:52:54 AM,24,6,11:58:19 AM,24,6,12:15:07 PM,13,24.2,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_453,1008
-Order_No_3902,User_Id_2869,Bike,3,Business,26,2,9:06:24 AM,26,2,9:07:08 AM,26,2,9:32:31 AM,26,2,9:36:57 AM,26,2,10:47:33 AM,9,21.6,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_306,4236
-Order_No_5325,User_Id_2271,Bike,3,Business,25,3,11:23:17 AM,25,3,11:23:29 AM,25,3,11:23:35 AM,25,3,11:42:02 AM,25,3,12:05:57 PM,11,,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_900,1435
-Order_No_12773,User_Id_412,Bike,3,Business,3,1,2:40:09 PM,3,1,2:40:22 PM,3,1,2:40:48 PM,3,1,2:57:11 PM,3,1,2:57:21 PM,2,23.4,,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_574,10
-Order_No_24711,User_Id_3404,Bike,3,Business,22,6,10:21:15 AM,22,6,10:26:06 AM,22,6,10:35:56 AM,22,6,10:41:15 AM,22,6,11:06:58 AM,5,23.2,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_26,1543
-Order_No_27743,User_Id_286,Bike,3,Business,22,4,6:43:12 PM,22,4,6:49:06 PM,22,4,7:01:19 PM,22,4,7:10:26 PM,22,4,7:23:57 PM,7,24.2,,-1.2551895,36.7822034,-1.2962966999999999,36.8020231,Rider_Id_38,811
-Order_No_21807,User_Id_1280,Bike,3,Business,7,4,8:34:15 AM,7,4,8:34:38 AM,7,4,8:38:52 AM,7,4,8:58:49 AM,7,4,9:43:15 AM,13,,,-1.2551895,36.7822034,-1.3207951,36.8286902,Rider_Id_708,2666
-Order_No_18406,User_Id_1316,Bike,3,Business,15,5,11:12:30 AM,15,5,11:14:38 AM,15,5,11:26:21 AM,15,5,11:30:19 AM,15,5,11:50:38 AM,11,25.8,,-1.3173868999999998,36.8112044,-1.2551895,36.7822034,Rider_Id_205,1219
-Order_No_27782,User_Id_970,Bike,3,Business,13,3,7:25:09 AM,13,3,8:34:50 AM,13,3,8:42:23 AM,13,3,8:47:58 AM,13,3,9:13:50 AM,6,20.1,,-1.2551895,36.7822034,-1.2834816,36.8219031,Rider_Id_119,1552
-Order_No_16233,User_Id_507,Bike,1,Personal,27,5,2:39:13 PM,27,5,2:39:55 PM,27,5,2:55:28 PM,27,5,3:10:39 PM,27,5,3:28:08 PM,8,24.1,,-1.2875013000000002,36.8171787,-1.301093,36.7790314,Rider_Id_88,1049
-Order_No_18523,User_Id_142,Bike,3,Business,5,2,10:04:18 AM,5,2,10:04:40 AM,5,2,10:21:47 AM,5,2,10:26:26 AM,5,2,10:58:09 AM,8,17.9,3.9,-1.2595421999999998,36.7871178,-1.3039411,36.7695316,Rider_Id_735,1903
-Order_No_22801,User_Id_1500,Bike,3,Business,4,6,10:52:52 AM,4,6,10:56:29 AM,4,6,11:07:56 AM,4,6,11:11:42 AM,4,6,11:21:40 AM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_161,598
-Order_No_16191,User_Id_20,Bike,3,Business,2,2,2:25:33 PM,2,2,2:25:42 PM,2,2,2:45:22 PM,2,2,2:48:55 PM,2,2,3:27:54 PM,20,26.1,,-1.3342256000000001,36.9106741,-1.2628638,36.8070399,Rider_Id_62,2339
-Order_No_27823,User_Id_432,Bike,3,Business,2,2,1:46:30 PM,2,2,1:47:26 PM,2,2,2:07:59 PM,2,2,2:09:56 PM,2,2,3:07:47 PM,3,29.3,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_76,3471
-Order_No_13475,User_Id_1500,Bike,3,Business,5,1,4:01:37 PM,5,1,4:24:13 PM,5,1,4:24:40 PM,5,1,4:52:13 PM,5,1,5:14:35 PM,10,24.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_10,1342
-Order_No_16710,User_Id_1797,Bike,2,Personal,21,1,2:12:51 PM,21,1,2:58:53 PM,21,1,3:30:06 PM,21,1,3:41:37 PM,21,1,4:12:34 PM,7,21.5,,-1.2871938,36.8195031,-1.2827576,36.783024100000006,Rider_Id_216,1857
-Order_No_6763,User_Id_1164,Bike,1,Personal,31,2,1:55:23 PM,31,2,1:57:46 PM,31,2,2:33:14 PM,31,2,2:40:41 PM,31,2,3:12:38 PM,9,22.0,,-1.2889024,36.8187402,-1.2946908000000001,36.7549734,Rider_Id_882,1917
-Order_No_25355,User_Id_517,Bike,3,Business,28,4,3:30:44 PM,28,4,3:31:17 PM,28,4,3:32:55 PM,28,4,3:35:11 PM,28,4,3:43:50 PM,5,27.4,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_206,519
-Order_No_11053,User_Id_1850,Bike,3,Business,25,4,2:13:49 PM,25,4,2:14:34 PM,25,4,2:14:39 PM,25,4,2:21:22 PM,25,4,2:39:09 PM,8,,,-1.2551895,36.7822034,-1.2987440000000001,36.817823700000005,Rider_Id_576,1067
-Order_No_276,User_Id_1831,Bike,3,Business,23,3,11:55:34 AM,23,3,12:03:19 PM,23,3,12:09:08 PM,23,3,12:17:40 PM,23,3,12:53:47 PM,21,21.9,,-1.3562374,36.904295399999995,-1.2553615,36.789537599999996,Rider_Id_408,2167
-Order_No_25757,User_Id_478,Bike,3,Business,2,1,11:51:05 AM,2,1,11:52:05 AM,2,1,11:54:07 AM,2,1,12:08:42 PM,2,1,12:23:01 PM,9,19.3,,-1.3043821000000002,36.7840586,-1.2551895,36.7822034,Rider_Id_101,859
-Order_No_3685,User_Id_2587,Bike,3,Business,25,5,3:40:04 PM,25,5,3:40:17 PM,25,5,3:53:27 PM,25,5,3:58:00 PM,25,5,4:29:21 PM,8,23.6,,-1.225322,36.80855,-1.2579303999999998,36.8019667,Rider_Id_144,1881
-Order_No_18939,User_Id_2523,Bike,3,Business,19,2,9:36:42 AM,19,2,9:36:50 AM,19,2,9:37:31 AM,19,2,9:41:45 AM,19,2,9:51:41 AM,3,,,-1.2551895,36.7822034,-1.2676026000000002,36.810362899999994,Rider_Id_523,596
-Order_No_12514,User_Id_1235,Bike,1,Personal,28,5,10:36:26 AM,28,5,10:37:42 AM,28,5,10:49:32 AM,28,5,11:02:32 AM,28,5,11:34:05 AM,12,24.9,,-1.2884799,36.82179179999999,-1.2203754999999998,36.816571100000004,Rider_Id_208,1893
-Order_No_12936,User_Id_346,Bike,3,Business,5,2,12:52:24 PM,5,2,1:04:52 PM,5,2,1:10:49 PM,5,2,1:18:28 PM,5,2,1:32:29 PM,4,25.4,,-1.28878,36.8168312,-1.2979063,36.8373372,Rider_Id_448,841
-Order_No_12220,User_Id_2987,Bike,3,Business,31,3,11:08:00 AM,31,3,11:08:51 AM,31,3,11:18:16 AM,31,3,11:24:03 AM,31,3,11:39:11 AM,10,24.5,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_279,908
-Order_No_16009,User_Id_3560,Bike,3,Business,18,3,10:50:34 AM,18,3,10:51:45 AM,18,3,11:00:30 AM,18,3,11:16:20 AM,18,3,11:48:34 AM,8,20.0,,-1.3014461000000002,36.7661381,-1.279395,36.825364,Rider_Id_234,1934
-Order_No_3747,User_Id_3355,Bike,3,Business,29,4,2:41:38 PM,29,4,2:45:45 PM,29,4,2:50:22 PM,29,4,2:53:17 PM,29,4,3:07:30 PM,10,24.8,,-1.3052491000000002,36.8223899,-1.32544,36.8958753,Rider_Id_913,853
-Order_No_17631,User_Id_2200,Bike,3,Business,27,3,4:03:40 PM,27,3,4:08:23 PM,27,3,4:27:46 PM,27,3,4:39:56 PM,27,3,5:40:37 PM,4,27.4,,-1.290894,36.822971,-1.3041128999999998,36.810493900000004,Rider_Id_448,3641
-Order_No_10165,User_Id_1046,Bike,1,Personal,12,3,8:54:17 AM,12,3,8:55:40 AM,12,3,9:19:31 AM,12,3,9:21:45 AM,12,3,9:45:56 AM,4,16.5,,-1.3054537,36.8284768,-1.3007415,36.8356523,Rider_Id_328,1451
-Order_No_11655,User_Id_868,Bike,3,Business,26,4,12:39:26 PM,26,4,12:44:42 PM,26,4,1:01:05 PM,26,4,1:41:02 PM,26,4,2:05:29 PM,8,22.1,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_229,1467
-Order_No_17952,User_Id_393,Bike,3,Business,26,2,10:38:01 AM,26,2,10:38:10 AM,26,2,10:38:15 AM,26,2,10:42:53 AM,26,2,11:01:00 AM,4,27.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_519,1087
-Order_No_24038,User_Id_664,Bike,1,Personal,29,2,7:43:48 AM,29,2,8:20:46 AM,29,2,8:48:49 AM,29,2,8:57:02 AM,29,2,9:11:08 AM,5,16.6,,-1.2517392,36.82496079999999,-1.2633513,36.794152399999994,Rider_Id_379,846
-Order_No_19965,User_Id_3686,Bike,3,Business,25,1,1:36:12 PM,25,1,1:36:57 PM,25,1,1:44:49 PM,25,1,2:03:51 PM,25,1,2:13:46 PM,6,,,-1.2551895,36.7822034,-1.2729217,36.762482899999995,Rider_Id_101,595
-Order_No_7344,User_Id_868,Bike,3,Business,9,2,1:15:58 PM,9,2,1:19:35 PM,9,2,1:48:54 PM,9,2,2:00:21 PM,9,2,2:14:23 PM,9,30.7,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_525,842
-Order_No_17114,User_Id_248,Bike,3,Business,20,3,3:13:40 PM,20,3,3:14:03 PM,20,3,3:22:20 PM,20,3,3:24:37 PM,20,3,3:45:01 PM,10,28.5,,-1.304735,36.784605400000004,-1.2656051000000001,36.7826654,Rider_Id_123,1224
-Order_No_11173,User_Id_1280,Bike,3,Business,11,1,1:05:20 PM,11,1,1:05:43 PM,11,1,1:09:33 PM,11,1,1:14:44 PM,11,1,1:23:42 PM,5,24.2,,-1.2551895,36.7822034,-1.2647688,36.775086,Rider_Id_622,538
-Order_No_6842,User_Id_970,Bike,3,Business,2,2,12:11:06 PM,2,2,12:13:56 PM,2,2,12:19:22 PM,2,2,12:22:03 PM,2,2,12:44:00 PM,7,26.1,,-1.2551895,36.7822034,-1.2872652,36.826761299999994,Rider_Id_245,1317
-Order_No_11589,User_Id_2583,Bike,3,Personal,6,4,1:44:29 PM,6,4,1:45:24 PM,6,4,1:51:40 PM,6,4,1:57:02 PM,6,4,2:15:13 PM,6,21.4,,-1.2607568999999998,36.7747575,-1.258685,36.805292,Rider_Id_663,1091
-Order_No_13723,User_Id_2454,Bike,3,Business,18,4,10:34:29 AM,18,4,10:34:53 AM,18,4,10:55:54 AM,18,4,10:58:36 AM,18,4,11:29:20 AM,15,25.6,,-1.2219928999999998,36.8349924,-1.2993231,36.7753372,Rider_Id_829,1844
-Order_No_15143,User_Id_265,Bike,3,Business,28,1,12:05:49 PM,28,1,12:05:58 PM,28,1,12:11:09 PM,28,1,12:24:54 PM,28,1,1:28:53 PM,16,23.8,,-1.300921,36.828195,-1.2521562,36.926050700000005,Rider_Id_519,3839
-Order_No_13391,User_Id_2853,Bike,3,Business,10,4,4:23:12 PM,10,4,4:26:15 PM,10,4,4:32:19 PM,10,4,4:35:31 PM,10,4,5:32:29 PM,22,22.2,,-1.3700383,36.9190174,-1.2731553,36.796972600000004,Rider_Id_753,3418
-Order_No_17464,User_Id_1527,Bike,3,Business,8,1,3:10:39 PM,8,1,3:11:00 PM,8,1,3:29:36 PM,8,1,3:47:17 PM,8,1,4:05:44 PM,9,29.5,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_349,1107
-Order_No_16571,User_Id_867,Bike,3,Business,27,2,8:18:05 AM,27,2,9:13:52 AM,27,2,9:19:57 AM,27,2,9:42:05 AM,27,2,10:11:29 AM,10,18.6,1.9,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_631,1764
-Order_No_24472,User_Id_3201,Bike,3,Business,21,4,1:59:52 PM,21,4,2:01:12 PM,21,4,2:17:11 PM,21,4,2:24:02 PM,21,4,2:40:48 PM,11,30.8,,-1.2765736,36.851364600000004,-1.2584143,36.8048002,Rider_Id_276,1006
-Order_No_19163,User_Id_1990,Bike,3,Business,21,2,9:48:40 AM,21,2,9:50:09 AM,21,2,9:53:55 AM,21,2,10:12:44 AM,21,2,10:16:19 AM,2,,,-1.2763494,36.7671783,-1.275442,36.766020700000006,Rider_Id_205,215
-Order_No_26546,User_Id_3635,Bike,1,Business,28,5,3:46:41 PM,28,5,3:48:16 PM,28,5,3:58:09 PM,28,5,4:28:09 PM,28,5,5:26:08 PM,9,25.5,,-1.2584143,36.8048002,-1.3185736000000001,36.8258933,Rider_Id_178,3479
-Order_No_7054,User_Id_2801,Bike,3,Business,20,2,8:09:54 AM,20,2,8:10:29 AM,20,2,9:02:34 AM,20,2,9:07:35 AM,20,2,9:34:07 AM,10,19.1,,-1.2595421999999998,36.7871178,-1.2996681,36.806841299999995,Rider_Id_228,1592
-Order_No_12586,User_Id_3718,Bike,3,Business,14,5,3:02:56 PM,14,5,3:09:52 PM,14,5,3:29:08 PM,14,5,3:34:36 PM,14,5,3:40:54 PM,2,23.5,,-1.2650027,36.8126242,-1.2640299,36.813407399999996,Rider_Id_932,378
-Order_No_888,User_Id_1231,Bike,3,Business,7,3,2:16:10 PM,7,3,2:33:37 PM,7,3,2:33:44 PM,7,3,2:41:50 PM,7,3,2:41:56 PM,2,28.1,,-1.2821421999999998,36.8161565,-1.290894,36.822971,Rider_Id_351,6
-Order_No_11774,User_Id_868,Bike,3,Business,6,6,1:43:44 PM,6,6,1:43:58 PM,6,6,1:50:25 PM,6,6,2:01:10 PM,6,6,2:17:24 PM,6,28.2,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_686,974
-Order_No_8989,User_Id_3194,Bike,3,Business,14,4,12:53:05 PM,14,4,1:15:45 PM,14,4,2:04:23 PM,14,4,2:06:17 PM,14,4,2:17:49 PM,3,28.1,,-1.2551895,36.7822034,-1.2599805,36.798801399999995,Rider_Id_38,692
-Order_No_11417,User_Id_1363,Bike,3,Business,3,4,1:09:23 PM,3,4,1:10:11 PM,3,4,1:10:16 PM,3,4,1:19:43 PM,3,4,1:47:15 PM,12,23.4,,-1.3004062,36.829741,-1.238874,36.807889,Rider_Id_445,1652
-Order_No_22074,User_Id_3487,Bike,3,Business,7,2,10:09:07 AM,7,2,10:12:27 AM,7,2,10:18:42 AM,7,2,10:27:22 AM,7,2,10:36:36 AM,5,,,-1.2658424,36.8101804,-1.279395,36.825364,Rider_Id_205,554
-Order_No_4085,User_Id_1336,Bike,3,Business,29,4,9:38:22 AM,29,4,12:37:27 PM,29,4,1:03:23 PM,29,4,1:04:27 PM,29,4,1:49:48 PM,9,21.6,,-1.2891921999999998,36.825189,-1.2551895,36.7822034,Rider_Id_660,2721
-Order_No_10141,User_Id_3283,Bike,3,Business,30,6,11:00:16 AM,30,6,11:15:19 AM,30,6,11:35:00 AM,30,6,11:58:23 AM,30,6,11:58:25 AM,6,26.8,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_309,2
-Order_No_11223,User_Id_2183,Bike,3,Business,18,4,3:38:58 PM,18,4,3:39:29 PM,18,4,3:41:04 PM,18,4,3:44:32 PM,18,4,4:20:45 PM,9,26.9,,-1.2551895,36.7822034,-1.2920375,36.7632608,Rider_Id_268,2173
-Order_No_16648,User_Id_868,Bike,3,Business,20,1,2:06:43 PM,20,1,2:06:58 PM,20,1,2:12:38 PM,20,1,2:23:20 PM,20,1,2:34:14 PM,6,21.3,,-1.265715,36.823815,-1.2628473,36.781805,Rider_Id_932,654
-Order_No_20498,User_Id_346,Bike,3,Business,12,2,12:56:17 PM,12,2,12:59:22 PM,12,2,1:08:41 PM,12,2,1:11:07 PM,12,2,1:11:09 PM,4,24.6,,-1.28878,36.8168312,-1.3033063,36.811792100000005,Rider_Id_903,2
-Order_No_7288,User_Id_3017,Bike,3,Business,15,6,3:07:16 PM,15,6,4:15:34 PM,15,6,4:15:52 PM,15,6,4:24:16 PM,15,6,4:43:25 PM,11,26.0,,-1.2993231,36.7753372,-1.2669026,36.844527299999996,Rider_Id_179,1149
-Order_No_11853,User_Id_2739,Bike,1,Personal,20,1,5:15:53 PM,20,1,5:16:37 PM,20,1,5:24:17 PM,20,1,5:28:55 PM,20,1,6:12:20 PM,7,20.6,,-1.2674273999999999,36.787082700000006,-1.2615584,36.814950599999996,Rider_Id_727,2605
-Order_No_1125,User_Id_1279,Bike,2,Personal,7,4,12:42:22 PM,7,4,12:45:54 PM,7,4,12:56:21 PM,7,4,1:10:24 PM,7,4,1:24:10 PM,6,16.8,,-1.2882965,36.778957299999995,-1.2973781999999998,36.794233399999996,Rider_Id_116,826
-Order_No_18684,User_Id_2733,Bike,3,Business,28,2,10:07:01 AM,28,2,10:08:47 AM,28,2,10:27:30 AM,28,2,10:53:06 AM,28,2,11:20:13 AM,8,21.1,,-1.2813012,36.832396200000005,-1.2963097,36.7688221,Rider_Id_19,1627
-Order_No_13796,User_Id_2423,Bike,3,Business,30,2,10:45:19 AM,30,2,10:49:08 AM,30,2,11:00:10 AM,30,2,11:10:48 AM,30,2,11:41:51 AM,8,,,-1.2551895,36.7822034,-1.2995101,36.8185828,Rider_Id_245,1863
-Order_No_26468,User_Id_798,Bike,3,Business,21,4,10:39:45 AM,21,4,10:42:42 AM,21,4,11:04:06 AM,21,4,11:06:51 AM,21,4,11:26:10 AM,8,21.7,,-1.2594237,36.80008660000001,-1.304033,36.7848695,Rider_Id_88,1159
-Order_No_5532,User_Id_2736,Bike,2,Personal,21,4,12:23:35 PM,21,4,12:24:38 PM,21,4,1:19:14 PM,21,4,1:26:12 PM,21,4,1:40:26 PM,5,21.8,,-1.2741006000000001,36.8240907,-1.2655306,36.828148,Rider_Id_906,854
-Order_No_12312,User_Id_2056,Bike,2,Personal,4,3,11:14:34 AM,4,3,11:14:58 AM,4,3,11:29:45 AM,4,3,11:31:08 AM,4,3,11:58:58 AM,11,17.2,,-1.3036691,36.7526062,-1.2619538000000001,36.80876370000001,Rider_Id_449,1670
-Order_No_23741,User_Id_668,Bike,3,Business,11,1,11:51:24 AM,11,1,11:52:04 AM,11,1,12:19:01 PM,11,1,12:36:13 PM,11,1,12:59:12 PM,9,24.0,,-1.2628638,36.8070399,-1.2326041,36.8009029,Rider_Id_755,1379
-Order_No_18357,User_Id_1162,Bike,3,Business,6,4,9:30:13 AM,6,4,9:39:41 AM,6,4,9:48:22 AM,6,4,9:51:38 AM,6,4,10:14:26 AM,9,18.7,3.1,-1.3122610000000001,36.794658,-1.2626954,36.8273738,Rider_Id_345,1368
-Order_No_7615,User_Id_2195,Bike,1,Business,1,6,9:47:15 AM,1,6,9:48:03 AM,1,6,9:51:12 AM,1,6,9:56:37 AM,1,6,10:05:46 AM,6,15.3,,-1.307143,36.825009,-1.3313643,36.857742200000004,Rider_Id_670,549
-Order_No_11199,User_Id_3222,Bike,3,Business,8,5,5:13:34 PM,8,5,5:21:35 PM,8,5,5:33:08 PM,8,5,5:35:17 PM,8,5,6:08:33 PM,4,28.1,,-1.2551895,36.7822034,-1.261403,36.769224200000004,Rider_Id_300,1996
-Order_No_26628,User_Id_3698,Bike,3,Business,23,4,3:00:15 PM,23,4,3:00:46 PM,23,4,3:08:31 PM,23,4,3:16:04 PM,23,4,3:31:04 PM,7,24.6,,-1.3244885,36.897791999999995,-1.34364,36.8925345,Rider_Id_856,900
-Order_No_10136,User_Id_2942,Bike,3,Business,14,2,2:41:56 PM,14,2,2:54:15 PM,14,2,3:13:57 PM,14,2,3:24:46 PM,14,2,3:38:23 PM,5,24.2,,-1.2614346,36.8283124,-1.298529,36.816482,Rider_Id_427,817
-Order_No_26347,User_Id_1156,Bike,2,Personal,14,2,11:57:58 AM,14,2,11:58:14 AM,14,2,12:12:04 PM,14,2,12:17:52 PM,14,2,12:43:47 PM,6,21.3,,-1.2913178,36.795794799999996,-1.2793101,36.7660227,Rider_Id_229,1555
-Order_No_20903,User_Id_984,Bike,1,Personal,5,6,12:07:39 PM,5,6,12:08:22 PM,5,6,12:22:54 PM,5,6,12:40:13 PM,5,6,12:56:25 PM,8,22.2,,-1.3021315,36.821555700000005,-1.2659148999999998,36.805432200000006,Rider_Id_543,972
-Order_No_23587,User_Id_238,Bike,3,Business,18,4,2:36:09 PM,18,4,2:36:55 PM,18,4,2:49:43 PM,18,4,2:55:31 PM,18,4,4:11:04 PM,16,,,-1.2991441000000001,36.7528804,-1.3077869,36.844320700000004,Rider_Id_267,4533
-Order_No_12087,User_Id_393,Bike,3,Business,2,6,12:18:41 PM,2,6,12:18:50 PM,2,6,12:18:58 PM,2,6,12:44:11 PM,2,6,12:44:13 PM,4,27.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10,2
-Order_No_17926,User_Id_2154,Bike,3,Business,25,4,12:31:53 PM,25,4,1:25:19 PM,25,4,1:44:06 PM,25,4,1:49:37 PM,25,4,2:50:01 PM,9,,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_204,3624
-Order_No_6406,User_Id_1482,Bike,3,Business,22,5,8:07:13 AM,22,5,8:07:34 AM,22,5,8:08:58 AM,22,5,8:18:34 AM,22,5,8:56:33 AM,18,22.2,,-1.2649605,36.798177700000004,-1.3247309999999999,36.907109000000005,Rider_Id_793,2279
-Order_No_13929,User_Id_1892,Bike,2,Personal,25,2,11:13:31 AM,25,2,11:16:56 AM,25,2,11:28:17 AM,25,2,11:33:45 AM,25,2,12:00:30 PM,3,23.8,,-1.3081007,36.839634999999994,-1.3112653,36.826919700000005,Rider_Id_38,1605
-Order_No_18666,User_Id_2869,Bike,3,Business,8,3,3:08:48 PM,8,3,3:09:41 PM,8,3,3:23:13 PM,8,3,3:30:18 PM,8,3,4:26:36 PM,3,,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_211,3378
-Order_No_21989,User_Id_146,Bike,3,Personal,24,4,11:31:08 AM,24,4,11:31:29 AM,24,4,11:41:53 AM,24,4,11:46:50 AM,24,4,12:00:46 PM,4,24.4,,-1.3073663999999998,36.815839399999994,-1.2985153,36.7876043,Rider_Id_758,836
-Order_No_4428,User_Id_2344,Bike,3,Business,26,2,4:11:04 PM,26,2,4:11:25 PM,26,2,4:13:01 PM,26,2,4:54:22 PM,26,2,5:11:59 PM,7,28.6,,-1.2571472,36.795063299999995,-1.2857246999999998,36.789429299999995,Rider_Id_445,1057
-Order_No_2651,User_Id_1959,Bike,1,Business,11,1,10:02:39 AM,11,1,10:03:36 AM,11,1,10:18:54 AM,11,1,10:28:58 AM,11,1,10:54:34 AM,14,14.4,,-1.3312306,36.85636829999999,-1.2551895,36.7822034,Rider_Id_791,1536
-Order_No_15752,User_Id_1953,Bike,3,Personal,5,6,11:03:31 AM,5,6,11:04:04 AM,5,6,11:17:48 AM,5,6,11:54:35 AM,5,6,12:14:40 PM,7,22.6,,-1.2802576,36.8246917,-1.2915306999999998,36.790586499999996,Rider_Id_742,1205
-Order_No_17423,User_Id_3487,Bike,3,Business,27,6,10:32:56 AM,27,6,10:35:39 AM,27,6,10:51:13 AM,27,6,11:10:24 AM,27,6,11:59:49 AM,5,,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_3,2965
-Order_No_23056,User_Id_635,Bike,3,Business,23,4,1:35:37 PM,23,4,1:36:53 PM,23,4,1:43:52 PM,23,4,1:51:52 PM,23,4,2:31:16 PM,6,24.8,,-1.2602337,36.79905479999999,-1.2963367000000001,36.8094235,Rider_Id_119,2364
-Order_No_7014,User_Id_1336,Bike,3,Business,19,5,1:08:21 PM,19,5,1:28:10 PM,19,5,1:45:48 PM,19,5,1:53:53 PM,19,5,2:07:01 PM,2,27.5,,-1.2891921999999998,36.825189,-1.2909536,36.8407162,Rider_Id_857,788
-Order_No_5859,User_Id_858,Bike,1,Personal,2,4,9:39:15 AM,2,4,9:39:29 AM,2,4,10:03:48 AM,2,4,10:11:48 AM,2,4,11:12:56 AM,7,13.5,,-1.28856,36.827399,-1.2551895,36.7822034,Rider_Id_19,3668
-Order_No_404,User_Id_758,Bike,3,Business,22,5,11:11:38 AM,22,5,11:13:20 AM,22,5,11:23:19 AM,22,5,11:26:13 AM,22,5,12:30:48 PM,14,28.6,,-1.2917868000000001,36.7872675,-1.3194518,36.869396,Rider_Id_32,3875
-Order_No_7189,User_Id_186,Bike,3,Business,13,3,12:26:53 PM,13,3,12:27:09 PM,13,3,12:34:05 PM,13,3,12:43:03 PM,13,3,1:00:15 PM,7,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_343,1032
-Order_No_7217,User_Id_2216,Bike,3,Business,26,4,9:49:04 AM,26,4,10:01:53 AM,26,4,10:02:12 AM,26,4,10:11:25 AM,26,4,10:31:10 AM,7,15.2,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_101,1185
-Order_No_24051,User_Id_2956,Bike,3,Business,4,2,1:40:12 PM,4,2,1:41:28 PM,4,2,1:50:17 PM,4,2,1:55:00 PM,4,2,2:16:22 PM,3,22.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_155,1282
-Order_No_8026,User_Id_2200,Bike,3,Business,28,5,4:41:05 PM,28,5,4:41:55 PM,28,5,4:52:20 PM,28,5,5:02:51 PM,28,5,5:13:45 PM,2,,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_532,654
-Order_No_20307,User_Id_391,Bike,3,Business,20,3,2:44:55 PM,20,3,2:48:49 PM,20,3,3:02:31 PM,20,3,3:20:13 PM,20,3,3:52:13 PM,5,28.5,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_411,1920
-Order_No_6041,User_Id_393,Bike,3,Business,3,3,12:23:31 PM,3,3,12:40:26 PM,3,3,12:50:10 PM,3,3,1:05:54 PM,3,3,1:34:23 PM,10,24.2,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_62,1709
-Order_No_25850,User_Id_3263,Bike,2,Personal,16,1,11:48:15 AM,16,1,11:48:24 AM,16,1,11:51:43 AM,16,1,11:59:34 AM,16,1,12:30:49 PM,17,19.2,,-1.2559559999999999,36.772901,-1.1884361,36.7407215,Rider_Id_103,1875
-Order_No_21168,User_Id_3300,Bike,3,Business,28,2,2:02:50 PM,28,2,2:04:05 PM,28,2,2:16:01 PM,28,2,2:22:44 PM,28,2,2:41:51 PM,8,23.4,,-1.2663631000000002,36.8163329,-1.303596,36.778378000000004,Rider_Id_706,1147
-Order_No_23103,User_Id_1850,Bike,3,Business,2,4,1:33:46 PM,2,4,1:34:22 PM,2,4,2:00:25 PM,2,4,2:02:28 PM,2,4,2:24:48 PM,7,,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_138,1340
-Order_No_21330,User_Id_1724,Bike,3,Business,9,4,10:29:52 AM,9,4,10:30:10 AM,9,4,10:44:48 AM,9,4,10:48:46 AM,9,4,11:13:15 AM,5,,,-1.2633393,36.7853172,-1.2571472,36.795063299999995,Rider_Id_341,1469
-Order_No_27450,User_Id_763,Bike,2,Personal,2,4,3:58:57 PM,2,4,3:59:20 PM,2,4,4:09:51 PM,2,4,4:22:01 PM,2,4,4:33:26 PM,3,26.2,,-1.2875013000000002,36.8171787,-1.270054,36.815164,Rider_Id_754,685
-Order_No_15169,User_Id_635,Bike,3,Business,28,4,3:11:34 PM,28,4,3:12:13 PM,28,4,3:16:56 PM,28,4,3:31:53 PM,28,4,3:32:07 PM,2,27.5,,-1.2684816,36.808562200000004,-1.2602337,36.79905479999999,Rider_Id_541,14
-Order_No_5919,User_Id_266,Bike,1,Personal,5,2,1:24:37 PM,5,2,1:24:44 PM,5,2,1:36:20 PM,5,2,1:44:00 PM,5,2,2:14:51 PM,19,22.7,,-1.3278743,36.8868144,-1.3327068000000002,36.77972379999999,Rider_Id_543,1851
-Order_No_25594,User_Id_1899,Bike,3,Business,24,5,1:54:56 PM,24,5,1:55:33 PM,24,5,2:03:38 PM,24,5,2:14:13 PM,24,5,2:28:28 PM,10,23.3,,-1.2783848999999998,36.8163445,-1.331619,36.847976,Rider_Id_857,855
-Order_No_19547,User_Id_393,Bike,3,Business,12,6,9:20:04 AM,12,6,9:20:24 AM,12,6,9:20:32 AM,12,6,9:34:37 AM,12,6,9:52:53 AM,10,18.6,3.1,-1.3177546999999998,36.8303703,-1.2569006,36.7925141,Rider_Id_173,1096
-Order_No_3989,User_Id_517,Bike,3,Business,27,6,10:20:52 AM,27,6,10:26:28 AM,27,6,10:39:38 AM,27,6,11:06:25 AM,27,6,11:06:27 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_309,2
-Order_No_15826,User_Id_1749,Bike,3,Personal,13,4,11:12:21 AM,13,4,11:13:21 AM,13,4,11:15:49 AM,13,4,11:20:04 AM,13,4,11:34:43 AM,5,22.1,,-1.2621576,36.8103473,-1.288167,36.8181484,Rider_Id_727,879
-Order_No_7849,User_Id_53,Bike,3,Business,10,3,2:40:02 PM,10,3,2:43:05 PM,10,3,2:48:53 PM,10,3,3:20:29 PM,10,3,3:34:50 PM,5,30.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_756,861
-Order_No_26385,User_Id_640,Bike,3,Business,24,3,10:00:48 AM,24,3,10:00:56 AM,24,3,10:06:02 AM,24,3,10:18:04 AM,24,3,11:03:18 AM,21,17.1,0.9,-1.304033,36.7848695,-1.207546,36.7913976,Rider_Id_578,2714
-Order_No_22887,User_Id_2598,Bike,3,Personal,26,3,4:15:09 PM,26,3,4:16:01 PM,26,3,4:47:18 PM,26,3,5:01:29 PM,26,3,5:31:31 PM,15,22.8,,-1.294489,36.874652600000005,-1.308217,36.77413,Rider_Id_26,1802
-Order_No_15126,User_Id_2330,Bike,3,Business,30,3,11:06:38 AM,30,3,11:07:13 AM,30,3,11:52:50 AM,30,3,12:00:06 PM,30,3,12:32:30 PM,14,24.9,,-1.2994934999999999,36.8908744,-1.2649605,36.798177700000004,Rider_Id_48,1944
-Order_No_10256,User_Id_3635,Bike,3,Business,8,4,2:39:51 PM,8,4,2:40:55 PM,8,4,3:05:00 PM,8,4,3:15:27 PM,8,4,3:20:06 PM,2,27.3,,-1.2615888000000002,36.7928732,-1.2669099,36.79774329999999,Rider_Id_881,279
-Order_No_6219,User_Id_1741,Bike,1,Personal,11,6,4:06:28 PM,11,6,4:06:37 PM,11,6,4:18:13 PM,11,6,4:58:53 PM,11,6,5:38:53 PM,10,20.9,,-1.2976940000000001,36.749788,-1.2864396,36.8307935,Rider_Id_105,2400
-Order_No_21772,User_Id_2509,Bike,3,Business,29,5,10:38:12 AM,29,5,10:42:33 AM,29,5,11:02:00 AM,29,5,11:12:25 AM,29,5,11:29:21 AM,5,19.8,,-1.2909665000000001,36.8150406,-1.2857671,36.7875614,Rider_Id_726,1016
-Order_No_18588,User_Id_2076,Bike,1,Personal,4,6,1:46:55 PM,4,6,2:03:03 PM,4,6,2:42:15 PM,4,6,2:48:22 PM,4,6,3:23:01 PM,23,23.1,,-1.2687102,36.6721254,-1.2897215,36.7945959,Rider_Id_940,2079
-Order_No_13363,User_Id_1850,Bike,3,Business,12,5,12:26:39 PM,12,5,12:33:18 PM,12,5,12:52:51 PM,12,5,12:57:18 PM,12,5,1:48:28 PM,11,26.6,,-1.3173868999999998,36.8112044,-1.2566586000000002,36.792968200000004,Rider_Id_778,3070
-Order_No_9268,User_Id_2989,Bike,3,Business,7,1,8:15:02 AM,7,1,8:44:42 AM,7,1,9:17:37 AM,7,1,9:30:08 AM,7,1,10:03:23 AM,12,21.6,,-1.3472066,36.7692638,-1.2866011999999998,36.8195691,Rider_Id_412,1995
-Order_No_5563,User_Id_1402,Bike,3,Business,30,2,4:11:59 PM,30,2,4:20:37 PM,30,2,4:37:41 PM,30,2,5:12:50 PM,30,2,5:13:01 PM,1,,,-1.2803414,36.8265456,-1.2813012,36.832396200000005,Rider_Id_845,11
-Order_No_18834,User_Id_1850,Bike,3,Business,13,1,3:17:00 PM,13,1,3:18:19 PM,13,1,3:25:59 PM,13,1,3:47:45 PM,13,1,3:56:22 PM,7,,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_106,517
-Order_No_10793,User_Id_970,Bike,3,Business,28,4,2:38:57 PM,28,4,2:39:29 PM,28,4,2:54:13 PM,28,4,2:59:26 PM,28,4,3:07:41 PM,3,29.9,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_52,495
-Order_No_15501,User_Id_2066,Bike,3,Business,13,1,11:14:57 AM,13,1,11:17:10 AM,13,1,11:30:28 AM,13,1,11:33:57 AM,13,1,11:53:01 AM,8,17.0,,-1.316961,36.820337,-1.2655477,36.8001682,Rider_Id_726,1144
-Order_No_23940,User_Id_3201,Bike,3,Business,10,1,2:17:49 PM,10,1,2:19:43 PM,10,1,2:34:44 PM,10,1,2:45:12 PM,10,1,3:14:09 PM,9,18.2,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_155,1737
-Order_No_14009,User_Id_1389,Bike,3,Business,29,6,1:12:43 PM,29,6,1:12:51 PM,29,6,1:26:02 PM,29,6,1:46:55 PM,29,6,2:14:29 PM,14,27.8,,-1.2857919,36.8191749,-1.207546,36.7913976,Rider_Id_74,1654
-Order_No_3065,User_Id_3283,Bike,3,Business,21,3,9:04:53 AM,21,3,9:05:13 AM,21,3,9:42:10 AM,21,3,10:25:06 AM,21,3,10:50:03 AM,14,17.1,1.0,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_237,1497
-Order_No_22806,User_Id_3689,Bike,3,Business,19,1,12:02:16 PM,19,1,1:14:12 PM,19,1,1:30:27 PM,19,1,1:37:37 PM,19,1,1:53:17 PM,5,25.1,,-1.2793686000000002,36.82592329999999,-1.2527958000000001,36.8003131,Rider_Id_265,940
-Order_No_24481,User_Id_136,Bike,3,Business,21,2,1:02:31 PM,21,2,1:28:13 PM,21,2,1:28:56 PM,21,2,1:45:28 PM,21,2,2:19:49 PM,18,24.9,,-1.3254522,36.721658399999995,-1.2629838,36.8086926,Rider_Id_411,2061
-Order_No_14528,User_Id_517,Bike,3,Business,31,1,10:51:24 AM,31,1,10:51:40 AM,31,1,11:00:09 AM,31,1,11:05:10 AM,31,1,11:31:40 AM,14,20.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1590
-Order_No_9612,User_Id_569,Bike,3,Personal,22,3,1:46:36 PM,22,3,1:47:07 PM,22,3,1:51:31 PM,22,3,1:56:38 PM,22,3,2:47:04 PM,10,25.5,,-1.2871506000000001,36.75828320000001,-1.3188148000000002,36.809247799999994,Rider_Id_334,3026
-Order_No_7308,User_Id_3555,Bike,2,Personal,11,4,11:09:52 AM,11,4,11:10:04 AM,11,4,11:15:12 AM,11,4,11:42:02 AM,11,4,11:50:43 AM,4,24.1,,-1.2622145,36.8087391,-1.2587023000000002,36.789848799999994,Rider_Id_754,521
-Order_No_19209,User_Id_2154,Bike,3,Business,27,1,11:20:06 AM,27,1,11:21:13 AM,27,1,11:35:09 AM,27,1,11:37:36 AM,27,1,11:54:43 AM,10,20.9,,-1.2765736,36.851364600000004,-1.2551895,36.7822034,Rider_Id_709,1027
-Order_No_12791,User_Id_1955,Bike,3,Business,26,1,3:01:11 PM,26,1,3:02:15 PM,26,1,3:27:06 PM,26,1,3:28:08 PM,26,1,3:52:48 PM,11,22.4,0.7,-1.2859912,36.8756811,-1.2962731,36.811186299999996,Rider_Id_85,1480
-Order_No_23709,User_Id_2733,Bike,3,Business,28,5,3:16:37 PM,28,5,3:29:06 PM,28,5,3:37:09 PM,28,5,3:43:23 PM,28,5,3:54:46 PM,2,25.6,,-1.328259,36.860975,-1.3332749,36.870814700000004,Rider_Id_932,683
-Order_No_1624,User_Id_3635,Bike,1,Business,21,5,2:40:46 PM,21,5,2:41:05 PM,21,5,2:41:17 PM,21,5,2:52:57 PM,21,5,2:59:35 PM,3,27.1,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_647,398
-Order_No_16493,User_Id_3283,Bike,3,Business,1,1,1:53:21 PM,1,1,2:09:15 PM,1,1,2:10:29 PM,1,1,2:42:15 PM,1,1,2:57:47 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_61,932
-Order_No_20930,User_Id_90,Bike,1,Personal,15,1,1:29:30 PM,15,1,1:56:18 PM,15,1,2:06:48 PM,15,1,2:12:28 PM,15,1,2:31:07 PM,7,,,-1.256958,36.8144929,-1.294624,36.814873999999996,Rider_Id_72,1119
-Order_No_10007,User_Id_302,Bike,3,Business,17,4,7:49:23 PM,17,4,7:49:40 PM,17,4,7:55:39 PM,17,4,8:09:18 PM,17,4,8:19:29 PM,7,22.6,,-1.2649605,36.798177700000004,-1.2996632,36.789840999999996,Rider_Id_801,611
-Order_No_855,User_Id_3725,Bike,2,Personal,6,4,5:07:12 PM,6,4,5:07:32 PM,6,4,5:10:05 PM,6,4,5:14:21 PM,6,4,5:31:43 PM,4,22.7,,-1.2547348999999999,36.8065138,-1.2731645,36.7900343,Rider_Id_409,1042
-Order_No_21197,User_Id_1245,Bike,3,Business,13,3,1:04:33 PM,13,3,1:04:46 PM,13,3,1:22:15 PM,13,3,2:43:18 PM,13,3,3:45:16 PM,19,27.3,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519,3718
-Order_No_9076,User_Id_265,Bike,3,Business,29,1,1:40:19 PM,29,1,1:41:16 PM,29,1,1:59:56 PM,29,1,2:19:08 PM,29,1,3:22:54 PM,22,,,-1.300921,36.828195,-1.3223908000000002,36.6773487,Rider_Id_61,3826
-Order_No_9929,User_Id_1500,Bike,3,Business,13,3,3:11:50 PM,13,3,3:12:21 PM,13,3,3:20:51 PM,13,3,4:09:16 PM,13,3,4:33:51 PM,8,29.1,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_349,1475
-Order_No_17261,User_Id_2223,Bike,2,Personal,18,1,2:04:15 PM,18,1,2:05:46 PM,18,1,2:28:10 PM,18,1,2:33:17 PM,18,1,3:02:32 PM,7,17.0,,-1.3314048,36.8229367,-1.2896398999999998,36.812449799999996,Rider_Id_46,1755
-Order_No_3017,User_Id_2066,Bike,3,Business,30,4,12:20:49 PM,30,4,12:21:54 PM,30,4,12:32:26 PM,30,4,12:36:31 PM,30,4,1:00:07 PM,6,,,-1.3210188999999999,36.841055100000005,-1.2882850000000001,36.815912,Rider_Id_183,1416
-Order_No_2215,User_Id_2066,Bike,3,Business,23,6,11:30:30 AM,23,6,11:31:57 AM,23,6,12:04:51 PM,23,6,12:10:31 PM,23,6,12:39:32 PM,13,,,-1.3210188999999999,36.841055100000005,-1.3002993,36.9153417,Rider_Id_448,1741
-Order_No_27612,User_Id_1469,Bike,3,Business,14,4,9:36:36 AM,14,4,10:21:12 AM,14,4,10:21:40 AM,14,4,10:30:39 AM,14,4,10:56:34 AM,7,17.8,,-1.2551895,36.7822034,-1.2963492,36.8093377,Rider_Id_268,1555
-Order_No_13958,User_Id_3091,Bike,3,Business,5,3,10:42:56 AM,5,3,10:43:31 AM,5,3,11:09:13 AM,5,3,11:29:32 AM,5,3,12:17:50 PM,19,22.4,,-1.2859912,36.8756811,-1.3012008,36.764868,Rider_Id_320,2898
-Order_No_6263,User_Id_2718,Bike,3,Business,29,2,3:39:57 PM,29,2,4:10:10 PM,29,2,4:50:48 PM,29,2,5:00:32 PM,29,2,6:27:00 PM,20,,,-1.3415466999999999,36.6696294,-1.2668815,36.8017244,Rider_Id_680,5188
-Order_No_26390,User_Id_3194,Bike,3,Business,17,1,2:52:49 PM,17,1,3:02:26 PM,17,1,3:14:17 PM,17,1,3:18:59 PM,17,1,3:37:24 PM,6,24.6,,-1.3052491000000002,36.8223899,-1.2854173999999998,36.7951152,Rider_Id_829,1105
-Order_No_30,User_Id_419,Bike,3,Business,7,2,2:24:11 PM,7,2,2:24:33 PM,7,2,2:31:29 PM,7,2,2:37:30 PM,7,2,3:41:22 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_238,3832
-Order_No_22171,User_Id_635,Bike,3,Business,19,3,1:05:00 PM,19,3,1:05:14 PM,19,3,1:19:12 PM,19,3,1:21:08 PM,19,3,2:16:06 PM,26,22.0,,-1.2847613000000002,36.967084,-1.2599563,36.799343900000004,Rider_Id_551,3298
-Order_No_13866,User_Id_1500,Bike,3,Business,8,1,9:22:26 AM,8,1,9:23:17 AM,8,1,9:23:34 AM,8,1,10:11:02 AM,8,1,10:11:23 AM,11,21.8,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_239,21
-Order_No_23760,User_Id_1544,Bike,3,Business,11,2,3:20:35 PM,11,2,3:48:01 PM,11,2,3:48:12 PM,11,2,4:13:50 PM,11,2,4:48:00 PM,21,21.4,,-1.1932197,36.898385499999996,-1.2924173,36.779781400000005,Rider_Id_291,2050
-Order_No_14427,User_Id_909,Bike,3,Business,28,3,2:09:59 PM,28,3,2:46:05 PM,28,3,3:04:07 PM,28,3,3:11:45 PM,28,3,3:24:02 PM,7,,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_769,737
-Order_No_8332,User_Id_1882,Bike,3,Business,10,4,9:14:50 AM,10,4,9:15:00 AM,10,4,9:16:32 AM,10,4,9:54:11 AM,10,4,10:19:51 AM,18,19.3,,-1.2628638,36.8070399,-1.3499321000000002,36.9047554,Rider_Id_247,1540
-Order_No_24606,User_Id_3283,Bike,3,Business,31,4,1:22:51 PM,31,4,1:23:23 PM,31,4,1:29:30 PM,31,4,1:39:45 PM,31,4,1:50:57 PM,6,28.6,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_576,672
-Order_No_15578,User_Id_2174,Bike,3,Business,30,5,9:51:23 AM,30,5,9:51:32 AM,30,5,9:52:56 AM,30,5,9:59:31 AM,30,5,10:26:58 AM,10,19.1,,-1.2482781,36.884091999999995,-1.2868078,36.8219396,Rider_Id_53,1647
-Order_No_12517,User_Id_302,Bike,3,Business,26,5,2:46:48 PM,26,5,2:55:40 PM,26,5,3:03:38 PM,26,5,3:10:17 PM,26,5,3:32:01 PM,8,,,-1.2649605,36.798177700000004,-1.307376,36.813404,Rider_Id_662,1304
-Order_No_18569,User_Id_302,Bike,3,Business,27,2,5:43:20 PM,27,2,5:53:38 PM,27,2,6:05:04 PM,27,2,6:11:34 PM,27,2,6:37:14 PM,8,22.5,,-1.2649605,36.798177700000004,-1.3172395000000001,36.839269,Rider_Id_933,1540
-Order_No_7909,User_Id_3125,Bike,1,Personal,29,3,3:07:30 PM,29,3,3:09:02 PM,29,3,3:18:18 PM,29,3,3:22:56 PM,29,3,3:42:01 PM,8,19.0,,-1.2672212999999999,36.8275035,-1.3068448000000001,36.8348641,Rider_Id_423,1145
-Order_No_23444,User_Id_3560,Bike,3,Business,11,1,10:44:13 AM,11,1,10:45:27 AM,11,1,10:59:22 AM,11,1,11:01:08 AM,11,1,11:22:47 AM,6,26.7,,-1.279395,36.825364,-1.2560455,36.780086700000005,Rider_Id_311,1299
-Order_No_12707,User_Id_2330,Bike,3,Business,2,4,5:13:27 PM,2,4,5:14:10 PM,2,4,5:25:33 PM,2,4,5:30:01 PM,2,4,5:40:17 PM,3,,,-1.2921463,36.8029626,-1.3040812,36.7924217,Rider_Id_76,616
-Order_No_2534,User_Id_265,Bike,3,Business,12,5,12:45:35 PM,12,5,12:46:09 PM,12,5,12:52:21 PM,12,5,1:27:36 PM,12,5,1:41:39 PM,5,24.7,,-1.300921,36.828195,-1.3083777,36.85900770000001,Rider_Id_848,843
-Order_No_6544,User_Id_722,Bike,3,Business,4,1,10:51:50 AM,4,1,10:52:14 AM,4,1,10:56:14 AM,4,1,11:23:17 AM,4,1,12:05:58 PM,19,25.5,,-1.32943,36.894242999999996,-1.25896,36.8016691,Rider_Id_564,2561
-Order_No_13802,User_Id_633,Bike,3,Business,3,4,8:46:37 AM,3,4,9:02:27 AM,3,4,9:14:43 AM,3,4,9:21:11 AM,3,4,9:42:25 AM,9,20.2,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_576,1274
-Order_No_5000,User_Id_2956,Bike,3,Business,5,5,12:12:30 PM,5,5,12:13:13 PM,5,5,12:33:30 PM,5,5,12:37:30 PM,5,5,1:08:33 PM,14,24.5,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_353,1863
-Order_No_11192,User_Id_1408,Bike,1,Personal,9,2,10:21:16 AM,9,2,10:21:28 AM,9,2,10:25:09 AM,9,2,10:44:51 AM,9,2,11:04:37 AM,8,22.2,,-1.2862659,36.8264616,-1.293886,36.7596951,Rider_Id_88,1186
-Order_No_20445,User_Id_1471,Bike,3,Business,19,3,4:03:46 PM,19,3,4:04:14 PM,19,3,4:05:28 PM,19,3,4:08:13 PM,19,3,4:13:29 PM,3,24.4,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_708,316
-Order_No_27814,User_Id_136,Bike,3,Business,3,1,12:02:44 PM,3,1,12:03:17 PM,3,1,12:24:21 PM,3,1,12:26:11 PM,3,1,12:43:37 PM,3,22.1,,-1.3326173000000001,36.71462329999999,-1.3169591,36.7000029,Rider_Id_457,1046
-Order_No_7850,User_Id_1346,Bike,1,Business,9,4,5:02:29 PM,9,4,5:02:43 PM,9,4,5:10:27 PM,9,4,5:15:01 PM,9,4,5:35:29 PM,6,22.9,,-1.2849951000000002,36.824174600000006,-1.2998129999999999,36.787139200000006,Rider_Id_857,1228
-Order_No_9417,User_Id_1410,Bike,3,Business,8,1,3:33:12 PM,8,1,3:33:53 PM,8,1,3:39:12 PM,8,1,4:03:00 PM,8,1,5:09:47 PM,20,29.6,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_883,4007
-Order_No_28166,User_Id_268,Bike,3,Business,22,4,11:05:15 AM,22,4,11:06:01 AM,22,4,11:18:31 AM,22,4,11:46:03 AM,22,4,12:23:24 PM,15,22.6,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_793,2241
-Order_No_15018,User_Id_1341,Bike,3,Business,23,6,12:32:36 PM,23,6,12:32:50 PM,23,6,12:34:14 PM,23,6,12:38:27 PM,23,6,1:01:47 PM,13,,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_952,1400
-Order_No_21060,User_Id_1066,Bike,3,Personal,28,4,12:47:26 PM,28,4,12:47:59 PM,28,4,1:07:07 PM,28,4,1:11:57 PM,28,4,1:41:31 PM,5,27.9,,-1.2885029,36.825604999999996,-1.312469,36.815177,Rider_Id_863,1774
-Order_No_25279,User_Id_3517,Bike,3,Business,18,1,1:49:04 PM,18,1,1:49:45 PM,18,1,1:54:34 PM,18,1,2:57:05 PM,18,1,3:20:49 PM,16,31.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_580,1424
-Order_No_12875,User_Id_3326,Bike,3,Business,7,4,2:54:09 PM,7,4,2:54:40 PM,7,4,3:02:30 PM,7,4,3:06:25 PM,7,4,3:28:52 PM,8,27.5,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_295,1347
-Order_No_19318,User_Id_3222,Bike,3,Business,4,1,2:30:21 PM,4,1,2:34:24 PM,4,1,2:40:51 PM,4,1,2:49:05 PM,4,1,3:11:07 PM,10,28.5,,-1.2551895,36.7822034,-1.2903067,36.83756339999999,Rider_Id_219,1322
-Order_No_11189,User_Id_3331,Bike,1,Business,25,5,1:34:21 PM,25,5,1:35:35 PM,25,5,1:52:56 PM,25,5,2:05:04 PM,25,5,2:40:51 PM,12,23.6,,-1.2559559999999999,36.789867,-1.3083777,36.85900770000001,Rider_Id_888,2147
-Order_No_18264,User_Id_125,Bike,1,Personal,31,5,1:54:55 PM,31,5,1:55:05 PM,31,5,1:56:30 PM,31,5,2:09:13 PM,31,5,3:19:06 PM,17,19.8,,-1.287741,36.8175255,-1.2000518,36.7805946,Rider_Id_6,4193
-Order_No_10066,User_Id_868,Bike,3,Business,1,5,9:16:43 AM,1,5,9:17:10 AM,1,5,9:25:42 AM,1,5,9:38:33 AM,1,5,9:46:29 AM,4,23.4,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_576,476
-Order_No_13880,User_Id_2828,Bike,3,Business,16,6,12:06:52 PM,16,6,12:07:35 PM,16,6,12:10:41 PM,16,6,12:25:13 PM,16,6,12:34:30 PM,3,19.0,,-1.263987,36.798891,-1.2615888000000002,36.7928732,Rider_Id_598,557
-Order_No_25409,User_Id_3224,Bike,2,Personal,19,4,1:52:29 PM,19,4,1:53:06 PM,19,4,1:58:21 PM,19,4,2:02:40 PM,19,4,2:50:25 PM,4,20.3,,-1.269277,36.8068993,-1.2551895,36.7822034,Rider_Id_100,2865
-Order_No_2868,User_Id_1880,Bike,3,Business,26,4,1:05:45 PM,26,4,1:05:57 PM,26,4,1:11:01 PM,26,4,1:16:24 PM,26,4,1:58:20 PM,7,19.1,,-1.2571472,36.795063299999995,-1.2768753000000002,36.830352600000005,Rider_Id_415,2516
-Order_No_4151,User_Id_1244,Bike,3,Business,12,6,8:49:05 AM,12,6,8:49:41 AM,12,6,9:04:38 AM,12,6,9:17:27 AM,12,6,9:31:52 AM,8,,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_670,865
-Order_No_12376,User_Id_1329,Bike,3,Business,6,4,4:52:53 PM,6,4,5:03:02 PM,6,4,5:03:08 PM,6,4,5:12:28 PM,6,4,5:12:32 PM,3,21.8,,-1.273056,36.811298,-1.256033,36.791845,Rider_Id_75,4
-Order_No_8568,User_Id_1831,Bike,3,Business,11,5,9:15:57 AM,11,5,9:16:56 AM,11,5,9:24:38 AM,11,5,10:05:43 AM,11,5,10:08:04 AM,21,21.2,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_656,141
-Order_No_10164,User_Id_788,Bike,3,Business,14,4,8:44:45 AM,14,4,9:04:06 AM,14,4,9:25:01 AM,14,4,9:55:13 AM,14,4,10:24:19 AM,8,17.8,,-1.2615888000000002,36.7928732,-1.225322,36.80855,Rider_Id_545,1746
-Order_No_24900,User_Id_2804,Bike,3,Business,15,5,3:32:12 PM,15,5,3:34:44 PM,15,5,3:46:51 PM,15,5,4:02:16 PM,15,5,4:08:21 PM,3,28.0,,-1.2551895,36.7822034,-1.2659148999999998,36.805432200000006,Rider_Id_881,365
-Order_No_25903,User_Id_3283,Bike,3,Business,21,4,2:17:03 PM,21,4,2:17:18 PM,21,4,2:18:52 PM,21,4,2:28:56 PM,21,4,2:42:18 PM,5,31.0,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_575,802
-Order_No_12005,User_Id_3291,Bike,3,Business,28,4,9:38:06 AM,28,4,9:38:14 AM,28,4,9:38:18 AM,28,4,9:50:04 AM,28,4,10:03:55 AM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_345,831
-Order_No_6285,User_Id_1502,Bike,3,Business,16,5,2:37:38 PM,16,5,3:46:28 PM,16,5,4:13:46 PM,16,5,4:25:59 PM,16,5,5:11:20 PM,11,,,-1.3154433,36.806385999999996,-1.2915971000000002,36.880902899999995,Rider_Id_646,2721
-Order_No_7857,User_Id_928,Bike,3,Business,30,4,4:02:32 PM,30,4,4:04:05 PM,30,4,4:11:02 PM,30,4,4:18:43 PM,30,4,4:36:55 PM,8,,,-1.2551895,36.7822034,-1.2937193,36.8236214,Rider_Id_491,1092
-Order_No_2220,User_Id_868,Bike,3,Business,24,1,10:16:14 AM,24,1,10:16:33 AM,24,1,10:20:49 AM,24,1,10:32:16 AM,24,1,10:43:13 AM,4,22.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_453,657
-Order_No_9933,User_Id_3647,Bike,3,Business,2,4,3:31:38 PM,2,4,3:32:10 PM,2,4,3:48:15 PM,2,4,3:54:15 PM,2,4,4:09:00 PM,3,,,-1.2726389999999999,36.794723,-1.2747549999999999,36.7906585,Rider_Id_452,885
-Order_No_24426,User_Id_1148,Bike,3,Business,25,2,2:38:37 PM,25,2,2:39:49 PM,25,2,2:52:49 PM,25,2,3:08:10 PM,25,2,3:35:29 PM,11,27.3,,-1.3100586,36.850032899999995,-1.2748221000000002,36.7817865,Rider_Id_753,1639
-Order_No_15916,User_Id_2523,Bike,3,Business,13,3,2:48:22 PM,13,3,2:53:25 PM,13,3,2:54:52 PM,13,3,2:58:29 PM,13,3,3:27:50 PM,7,29.2,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_709,1761
-Order_No_16223,User_Id_3647,Bike,3,Business,7,3,10:25:16 AM,7,3,10:31:02 AM,7,3,11:18:14 AM,7,3,11:20:37 AM,7,3,11:45:19 AM,7,25.0,,-1.2549476000000002,36.8269576,-1.2726389999999999,36.794723,Rider_Id_685,1482
-Order_No_23699,User_Id_496,Bike,3,Business,31,3,12:11:37 PM,31,3,12:11:45 PM,31,3,12:17:35 PM,31,3,12:35:22 PM,31,3,1:18:02 PM,22,24.9,,-1.2571472,36.795063299999995,-1.374163,36.913444,Rider_Id_647,2560
-Order_No_7821,User_Id_738,Bike,3,Business,15,1,3:40:51 PM,15,1,3:47:09 PM,15,1,4:20:41 PM,15,1,4:29:39 PM,15,1,5:22:16 PM,10,,,-1.2881479999999998,36.7850276,-1.2601645000000001,36.7167656,Rider_Id_38,3157
-Order_No_11202,User_Id_1154,Bike,3,Business,13,1,10:06:54 AM,13,1,10:07:04 AM,13,1,10:09:37 AM,13,1,10:15:24 AM,13,1,10:46:34 AM,20,,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_900,1870
-Order_No_2351,User_Id_1589,Bike,3,Business,9,7,1:48:48 PM,9,7,2:06:24 PM,9,7,2:09:16 PM,9,7,2:10:47 PM,9,7,3:15:52 PM,18,18.0,,-1.225322,36.80855,-1.2835228,36.7437619,Rider_Id_60,3905
-Order_No_18336,User_Id_1245,Bike,3,Business,11,5,11:52:20 AM,11,5,12:00:00 PM,11,5,12:39:42 PM,11,5,12:54:06 PM,11,5,1:03:23 PM,4,20.8,,-1.300921,36.828195,-1.2904262,36.828083899999996,Rider_Id_196,557
-Order_No_9459,User_Id_148,Bike,3,Business,6,2,9:36:29 AM,6,2,9:41:08 AM,6,2,9:53:28 AM,6,2,10:18:05 AM,6,2,10:29:49 AM,7,19.7,,-1.2551895,36.7822034,-1.2927799,36.8250456,Rider_Id_101,704
-Order_No_1714,User_Id_1340,Bike,3,Business,12,2,2:36:54 PM,12,2,2:38:01 PM,12,2,2:38:05 PM,12,2,2:45:02 PM,12,2,3:19:18 PM,16,,,-1.2707178999999997,36.7979713,-1.312122,36.785771200000006,Rider_Id_756,2056
-Order_No_1132,User_Id_2279,Bike,3,Business,10,3,1:50:11 PM,10,3,1:55:28 PM,10,3,2:09:25 PM,10,3,2:49:36 PM,10,3,2:52:38 PM,7,30.6,,-1.2897368,36.8226783,-1.2551895,36.7822034,Rider_Id_61,182
-Order_No_4546,User_Id_1245,Bike,3,Business,29,6,10:40:23 AM,29,6,11:14:54 AM,29,6,11:37:27 AM,29,6,11:43:24 AM,29,6,12:10:16 PM,5,24.8,,-1.3198615,36.7110323,-1.3130876,36.68064879999999,Rider_Id_565,1612
-Order_No_5138,User_Id_1500,Bike,3,Business,7,5,12:48:00 PM,7,5,12:50:19 PM,7,5,12:57:12 PM,7,5,1:07:17 PM,7,5,1:26:56 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_36,1179
-Order_No_10186,User_Id_1927,Bike,3,Business,27,3,4:29:27 PM,27,3,4:30:22 PM,27,3,4:39:39 PM,27,3,5:30:59 PM,27,3,6:12:22 PM,19,27.6,,-1.2927792,36.9053492,-1.3472066,36.7692638,Rider_Id_883,2483
-Order_No_12969,User_Id_3437,Bike,1,Personal,18,3,3:50:27 PM,18,3,3:50:35 PM,18,3,3:58:51 PM,18,3,4:03:50 PM,18,3,4:19:03 PM,3,20.9,,-1.2627937,36.815695700000006,-1.2733003999999999,36.7943468,Rider_Id_616,913
-Order_No_6080,User_Id_393,Bike,3,Business,10,3,8:39:25 AM,10,3,8:40:10 AM,10,3,8:40:16 AM,10,3,9:29:19 AM,10,3,9:29:29 AM,11,21.8,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_239,10
-Order_No_7543,User_Id_3487,Bike,3,Business,29,1,3:09:32 PM,29,1,3:10:35 PM,29,1,3:14:46 PM,29,1,3:18:33 PM,29,1,3:29:03 PM,6,,,-1.279395,36.825364,-1.2615888000000002,36.7928732,Rider_Id_291,630
-Order_No_3412,User_Id_123,Bike,3,Business,29,1,11:50:23 AM,29,1,11:50:37 AM,29,1,12:18:33 PM,29,1,12:33:03 PM,29,1,1:11:41 PM,12,,,-1.2404912000000001,36.88477879999999,-1.2731645,36.7900343,Rider_Id_612,2318
-Order_No_6226,User_Id_2360,Bike,3,Business,17,1,10:46:05 AM,17,1,10:46:37 AM,17,1,10:59:49 AM,17,1,11:07:18 AM,17,1,11:23:59 AM,5,23.7,,-1.2962966999999999,36.8020231,-1.2708857,36.8135766,Rider_Id_958,1001
-Order_No_26287,User_Id_1368,Bike,3,Business,11,2,2:37:20 PM,11,2,2:51:24 PM,11,2,3:04:47 PM,11,2,3:20:15 PM,11,2,4:46:21 PM,10,21.5,,-1.2571472,36.795063299999995,-1.314128,36.777239,Rider_Id_109,5166
-Order_No_15101,User_Id_412,Bike,3,Business,8,6,12:16:30 PM,8,6,12:16:48 PM,8,6,12:16:53 PM,8,6,12:22:29 PM,8,6,12:26:07 PM,2,20.1,,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_531,218
-Order_No_16173,User_Id_393,Bike,3,Business,2,2,8:43:09 AM,2,2,8:43:42 AM,2,2,8:49:36 AM,2,2,9:01:18 AM,2,2,9:11:11 AM,4,20.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_263,593
-Order_No_24587,User_Id_3718,Bike,3,Business,15,4,12:33:45 PM,15,4,12:34:51 PM,15,4,12:56:34 PM,15,4,1:01:11 PM,15,4,1:31:27 PM,5,24.5,,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_322,1816
-Order_No_1766,User_Id_552,Bike,3,Business,26,2,12:54:54 PM,26,2,12:55:39 PM,26,2,1:11:54 PM,26,2,1:30:28 PM,26,2,1:54:38 PM,18,27.3,,-1.290894,36.822971,-1.3562374,36.904295399999995,Rider_Id_99,1450
-Order_No_24561,User_Id_53,Bike,3,Business,18,6,10:44:04 AM,18,6,10:46:52 AM,18,6,10:56:48 AM,18,6,11:15:35 AM,18,6,11:22:53 AM,4,21.0,,-1.2770708,36.8231093,-1.3024505,36.8243189,Rider_Id_843,438
-Order_No_8351,User_Id_1482,Bike,3,Business,4,2,3:38:34 PM,4,2,3:38:46 PM,4,2,3:43:46 PM,4,2,3:47:29 PM,4,2,4:16:35 PM,15,23.6,11.0,-1.3385790000000002,36.89630379999999,-1.2822773,36.795312100000004,Rider_Id_267,1746
-Order_No_13842,User_Id_2956,Bike,3,Business,26,2,1:36:22 PM,26,2,1:36:33 PM,26,2,1:46:52 PM,26,2,1:52:51 PM,26,2,1:58:48 PM,1,29.6,,-1.2584143,36.8048002,-1.2594538999999998,36.8067841,Rider_Id_205,357
-Order_No_9597,User_Id_718,Bike,3,Business,4,2,3:16:29 PM,4,2,3:22:24 PM,4,2,3:41:49 PM,4,2,4:14:41 PM,4,2,4:47:22 PM,3,22.7,,-1.2551895,36.7822034,-1.2702120000000001,36.8037249,Rider_Id_25,1961
-Order_No_2980,User_Id_3517,Bike,3,Business,13,3,11:36:46 AM,13,3,11:36:57 AM,13,3,11:44:01 AM,13,3,12:48:26 PM,13,3,1:14:02 PM,16,25.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_164,1536
-Order_No_987,User_Id_1471,Bike,3,Business,18,1,2:46:56 PM,18,1,2:52:02 PM,18,1,3:25:51 PM,18,1,3:49:26 PM,18,1,4:22:53 PM,12,30.8,,-1.2571472,36.795063299999995,-1.3078334999999999,36.8670353,Rider_Id_686,2007
-Order_No_15483,User_Id_3081,Bike,1,Personal,12,5,1:17:33 PM,12,5,1:17:44 PM,12,5,1:34:10 PM,12,5,1:36:58 PM,12,5,2:12:05 PM,14,27.3,,-1.2916556000000001,36.8262516,-1.2627007,36.913695600000004,Rider_Id_74,2107
-Order_No_24090,User_Id_552,Bike,3,Business,20,3,5:31:57 PM,20,3,5:32:05 PM,20,3,5:35:22 PM,20,3,5:41:32 PM,20,3,6:33:23 PM,21,27.4,,-1.2571472,36.795063299999995,-1.335477,36.930829700000004,Rider_Id_778,3111
-Order_No_1263,User_Id_1190,Bike,1,Personal,23,4,3:07:32 PM,23,4,3:18:11 PM,23,4,3:34:51 PM,23,4,3:44:29 PM,23,4,4:00:11 PM,6,20.9,,-1.257623,36.7463064,-1.2654136,36.7825146,Rider_Id_722,942
-Order_No_20736,User_Id_548,Bike,3,Business,6,6,11:27:25 AM,6,6,11:50:38 AM,6,6,12:15:55 PM,6,6,12:18:40 PM,6,6,12:40:55 PM,15,24.3,,-1.338484,36.883377,-1.2686413,36.8126976,Rider_Id_179,1335
-Order_No_14384,User_Id_868,Bike,3,Business,14,5,2:46:32 PM,14,5,2:55:53 PM,14,5,3:05:45 PM,14,5,3:12:56 PM,14,5,3:29:18 PM,4,23.1,,-1.259478,36.801781299999995,-1.2663631000000002,36.8163329,Rider_Id_245,982
-Order_No_15121,User_Id_2310,Bike,3,Business,10,4,3:52:44 PM,10,4,4:06:38 PM,10,4,4:27:12 PM,10,4,4:38:18 PM,10,4,4:53:10 PM,6,22.1,,-1.2765736,36.851364600000004,-1.2859912,36.8756811,Rider_Id_668,892
-Order_No_14672,User_Id_1740,Bike,3,Personal,14,5,10:36:45 AM,14,5,10:36:52 AM,14,5,10:46:22 AM,14,5,10:59:10 AM,14,5,11:51:46 AM,16,24.3,,-1.3385790000000002,36.89630379999999,-1.3019530000000001,36.788458,Rider_Id_932,3156
-Order_No_23715,User_Id_3426,Bike,3,Business,7,3,4:57:03 PM,7,3,4:58:36 PM,7,3,5:18:19 PM,7,3,5:24:55 PM,7,3,5:44:39 PM,5,25.3,,-1.3014461000000002,36.7661381,-1.2896933000000002,36.792903499999994,Rider_Id_920,1184
-Order_No_5091,User_Id_2112,Bike,3,Business,2,3,4:05:30 PM,2,3,4:05:43 PM,2,3,4:15:00 PM,2,3,5:28:02 PM,2,3,5:44:08 PM,14,25.0,,-1.2571472,36.795063299999995,-1.2156006000000001,36.8916865,Rider_Id_38,966
-Order_No_24794,User_Id_3153,Bike,3,Personal,18,2,11:18:10 AM,18,2,11:19:14 AM,18,2,11:23:48 AM,18,2,11:29:22 AM,18,2,11:57:01 AM,10,,,-1.268171,36.809199,-1.3031813,36.7576089,Rider_Id_4,1659
-Order_No_13969,User_Id_640,Bike,3,Business,30,4,10:49:10 AM,30,4,10:50:00 AM,30,4,10:55:10 AM,30,4,11:01:19 AM,30,4,11:47:10 AM,21,,,-1.304033,36.7848695,-1.207546,36.7913976,Rider_Id_815,2751
-Order_No_23729,User_Id_1500,Bike,3,Business,18,4,8:11:32 AM,18,4,8:21:36 AM,18,4,8:57:49 AM,18,4,9:00:43 AM,18,4,9:11:33 AM,6,20.4,,-1.2770708,36.8231093,-1.2574219,36.7927073,Rider_Id_208,650
-Order_No_24380,User_Id_3149,Bike,3,Business,27,3,3:24:12 PM,27,3,3:25:49 PM,27,3,3:41:26 PM,27,3,4:16:24 PM,27,3,4:32:57 PM,6,29.2,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_686,993
-Order_No_16702,User_Id_608,Bike,3,Business,7,3,8:54:44 AM,7,3,9:00:21 AM,7,3,9:24:22 AM,7,3,9:27:59 AM,7,3,9:53:59 AM,8,,,-1.2569503,36.78347279999999,-1.2995738000000001,36.8199719,Rider_Id_219,1560
-Order_No_19666,User_Id_2423,Bike,3,Business,10,3,3:16:27 PM,10,3,3:20:24 PM,10,3,3:22:25 PM,10,3,3:28:56 PM,10,3,3:58:27 PM,10,30.8,,-1.2551895,36.7822034,-1.3126476,36.831455,Rider_Id_548,1771
-Order_No_17538,User_Id_1570,Bike,3,Personal,30,1,1:36:57 PM,30,1,1:37:44 PM,30,1,1:47:39 PM,30,1,2:04:22 PM,30,1,2:23:08 PM,7,20.6,,-1.2793831,36.7617573,-1.2697141,36.798177700000004,Rider_Id_124,1126
-Order_No_13614,User_Id_1787,Bike,3,Business,5,2,1:39:31 PM,5,2,1:40:03 PM,5,2,1:52:22 PM,5,2,1:54:52 PM,5,2,2:34:36 PM,15,,,-1.2285834999999998,36.882646799999996,-1.2551895,36.7822034,Rider_Id_959,2384
-Order_No_7446,User_Id_484,Bike,3,Personal,21,1,2:14:56 PM,21,1,2:15:35 PM,21,1,2:45:27 PM,21,1,2:59:43 PM,21,1,3:30:06 PM,9,21.8,,-1.29387,36.7907813,-1.3271684,36.8173021,Rider_Id_417,1823
-Order_No_27505,User_Id_2159,Bike,3,Business,15,4,1:20:17 PM,15,4,1:23:49 PM,15,4,1:45:07 PM,15,4,1:46:24 PM,15,4,2:07:17 PM,8,27.6,,-1.304735,36.784605400000004,-1.2557778999999998,36.7879648,Rider_Id_425,1253
-Order_No_5163,User_Id_3194,Bike,3,Business,28,4,3:51:06 PM,28,4,4:20:55 PM,28,4,4:33:08 PM,28,4,4:37:34 PM,28,4,4:54:10 PM,4,25.6,,-1.2571472,36.795063299999995,-1.2738287,36.8182811,Rider_Id_167,996
-Order_No_9279,User_Id_3560,Bike,3,Business,16,1,4:50:50 PM,16,1,4:51:03 PM,16,1,5:09:36 PM,16,1,5:13:30 PM,16,1,5:24:31 PM,2,,,-1.2812845,36.8327127,-1.279395,36.825364,Rider_Id_670,661
-Order_No_3512,User_Id_760,Bike,3,Business,3,1,12:51:39 PM,3,1,12:53:22 PM,3,1,1:04:07 PM,3,1,1:17:33 PM,3,1,2:32:39 PM,18,21.4,,-1.2736695,36.8028996,-1.338558,36.9215488,Rider_Id_829,4506
-Order_No_17232,User_Id_2532,Bike,3,Business,2,2,11:58:29 AM,2,2,12:08:07 PM,2,2,12:08:13 PM,2,2,12:32:28 PM,2,2,1:29:00 PM,6,26.1,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_828,3392
-Order_No_20944,User_Id_412,Bike,3,Business,27,6,9:50:43 AM,27,6,9:51:10 AM,27,6,10:19:40 AM,27,6,10:33:15 AM,27,6,10:52:04 AM,10,,,-1.303596,36.778378000000004,-1.266837,36.799248999999996,Rider_Id_155,1129
-Order_No_6314,User_Id_1329,Bike,3,Business,4,5,9:26:35 AM,4,5,9:45:45 AM,4,5,10:27:31 AM,4,5,10:36:41 AM,4,5,10:46:58 AM,4,17.3,,-1.273056,36.811298,-1.284835,36.8152125,Rider_Id_489,617
-Order_No_845,User_Id_214,Bike,1,Personal,10,2,9:38:54 AM,10,2,9:39:45 AM,10,2,10:17:58 AM,10,2,10:23:40 AM,10,2,11:07:43 AM,12,16.2,,-1.2004858999999999,36.7782049,-1.2616213,36.8221128,Rider_Id_802,2643
-Order_No_950,User_Id_1329,Bike,3,Business,8,5,12:45:07 PM,8,5,1:15:19 PM,8,5,1:37:27 PM,8,5,1:38:31 PM,8,5,1:38:34 PM,4,27.3,,-1.273056,36.811298,-1.2878913,36.8089253,Rider_Id_779,3
-Order_No_4606,User_Id_2383,Bike,3,Business,21,2,9:27:41 AM,21,2,9:28:34 AM,21,2,9:32:46 AM,21,2,9:39:36 AM,21,2,9:47:14 AM,3,,,-1.2608993,36.799246000000004,-1.2558951,36.78602,Rider_Id_247,458
-Order_No_15042,User_Id_3607,Bike,3,Personal,14,5,12:17:28 PM,14,5,12:34:35 PM,14,5,12:54:58 PM,14,5,1:08:09 PM,14,5,1:21:06 PM,4,21.6,,-1.300298,36.791306299999995,-1.3012008,36.764868,Rider_Id_847,777
-Order_No_14359,User_Id_1754,Bike,3,Business,26,5,2:02:19 PM,26,5,3:13:55 PM,26,5,3:28:53 PM,26,5,3:39:45 PM,26,5,4:08:31 PM,12,,,-1.3226301999999999,36.8719901,-1.2860183,36.8975338,Rider_Id_890,1726
-Order_No_17209,User_Id_2869,Bike,3,Business,10,3,12:29:35 PM,10,3,12:29:59 PM,10,3,12:47:48 PM,10,3,12:55:51 PM,10,3,1:48:43 PM,3,28.0,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_327,3172
-Order_No_21944,User_Id_914,Bike,3,Business,15,4,12:46:24 PM,15,4,12:46:52 PM,15,4,1:17:34 PM,15,4,1:25:01 PM,15,4,2:06:50 PM,17,24.5,,-1.2670213,36.7994482,-1.216682,36.908577799999996,Rider_Id_470,2509
-Order_No_5706,User_Id_3621,Bike,3,Business,11,2,4:28:23 PM,11,2,4:31:00 PM,11,2,4:39:10 PM,11,2,4:42:14 PM,11,2,5:16:29 PM,7,20.5,,-1.2836583000000001,36.7886014,-1.2567546,36.780824100000004,Rider_Id_624,2055
-Order_No_25617,User_Id_1539,Bike,1,Personal,14,2,8:13:48 PM,14,2,8:14:34 PM,14,2,8:40:13 PM,14,2,8:41:28 PM,14,2,9:21:01 PM,6,18.8,,-1.2870003,36.8312206,-1.3148058999999999,36.844952500000005,Rider_Id_86,2373
-Order_No_23895,User_Id_2174,Bike,3,Business,23,6,8:56:48 AM,23,6,9:08:42 AM,23,6,9:39:27 AM,23,6,10:17:55 AM,23,6,10:52:32 AM,6,18.9,,-1.2482781,36.884091999999995,-1.2241625,36.8851115,Rider_Id_489,2077
-Order_No_27081,User_Id_510,Bike,3,Business,19,6,11:04:22 AM,19,6,11:05:06 AM,19,6,11:20:46 AM,19,6,11:26:03 AM,19,6,12:07:42 PM,11,25.2,,-1.2885029,36.825604999999996,-1.2860183,36.8975338,Rider_Id_456,2499
-Order_No_173,User_Id_1244,Bike,3,Business,21,5,9:32:57 AM,21,5,9:33:02 AM,21,5,9:33:07 AM,21,5,9:49:25 AM,21,5,10:04:01 AM,9,20.0,,-1.303596,36.778378000000004,-1.3204931,36.7043102,Rider_Id_874,876
-Order_No_16090,User_Id_2523,Bike,3,Business,30,3,9:33:13 AM,30,3,9:33:38 AM,30,3,9:36:58 AM,30,3,10:09:41 AM,30,3,10:30:01 AM,10,19.9,,-1.2551895,36.7822034,-1.3073569,36.82021629999999,Rider_Id_322,1220
-Order_No_1420,User_Id_286,Bike,3,Business,27,3,4:04:47 PM,27,3,4:23:32 PM,27,3,4:26:26 PM,27,3,4:32:20 PM,27,3,5:02:07 PM,8,,,-1.2962966999999999,36.8020231,-1.2551895,36.7822034,Rider_Id_268,1787
-Order_No_25942,User_Id_3578,Bike,3,Business,29,1,12:41:50 PM,29,1,12:42:38 PM,29,1,1:10:03 PM,29,1,1:19:49 PM,29,1,1:38:32 PM,6,,,-1.2936941000000002,36.8156795,-1.2527958000000001,36.8003131,Rider_Id_19,1123
-Order_No_27320,User_Id_164,Bike,3,Personal,1,5,10:35:55 AM,1,5,10:51:25 AM,1,5,11:26:13 AM,1,5,11:57:04 AM,1,5,12:26:18 PM,20,26.3,,-1.1993973,36.92993679999999,-1.2752846000000002,36.802702200000006,Rider_Id_75,1754
-Order_No_23275,User_Id_3359,Bike,3,Business,27,2,10:11:41 AM,27,2,11:13:42 AM,27,2,11:20:00 AM,27,2,11:24:42 AM,27,2,11:44:15 AM,8,23.8,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_379,1173
-Order_No_4394,User_Id_1172,Bike,3,Personal,29,3,11:46:55 AM,29,3,11:47:55 AM,29,3,11:59:06 AM,29,3,12:06:23 PM,29,3,12:26:49 PM,8,,,-1.32256,36.8396386,-1.2922364,36.78955379999999,Rider_Id_99,1226
-Order_No_13601,User_Id_1209,Bike,3,Business,18,4,3:43:08 PM,18,4,3:43:26 PM,18,4,3:51:53 PM,18,4,3:55:37 PM,18,4,4:17:35 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_422,1318
-Order_No_12535,User_Id_111,Bike,3,Business,24,3,12:46:23 PM,24,3,12:46:58 PM,24,3,12:47:01 PM,24,3,12:50:00 PM,24,3,1:24:49 PM,8,,,-1.2551895,36.7822034,-1.2879682,36.806261299999996,Rider_Id_576,2089
-Order_No_26786,User_Id_53,Bike,3,Business,1,5,10:11:01 AM,1,5,11:54:22 AM,1,5,12:05:00 PM,1,5,12:09:11 PM,1,5,12:30:10 PM,5,28.7,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_674,1259
-Order_No_2191,User_Id_2310,Bike,1,Business,12,5,4:30:20 PM,12,5,4:35:40 PM,12,5,4:35:55 PM,12,5,5:00:58 PM,12,5,5:10:39 PM,6,25.7,,-1.2927792,36.9053492,-1.2859912,36.8756811,Rider_Id_179,581
-Order_No_12738,User_Id_2008,Bike,3,Business,31,5,9:44:42 AM,31,5,9:45:38 AM,31,5,9:53:21 AM,31,5,10:04:02 AM,31,5,10:27:45 AM,9,,,-1.2508229,36.7895263,-1.2944584,36.7614555,Rider_Id_937,1423
-Order_No_27485,User_Id_1104,Bike,2,Business,19,5,8:26:13 AM,19,5,8:26:25 AM,19,5,9:00:31 AM,19,5,9:01:40 AM,19,5,9:34:01 AM,29,20.4,,-1.1874211000000001,36.928644899999995,-1.2917988999999999,36.7910266,Rider_Id_704,1941
-Order_No_17350,User_Id_1329,Bike,3,Business,7,2,10:54:47 AM,7,2,10:56:34 AM,7,2,11:04:30 AM,7,2,11:06:26 AM,7,2,11:32:14 AM,12,,,-1.273056,36.811298,-1.2672045,36.717206899999994,Rider_Id_228,1548
-Order_No_13428,User_Id_265,Bike,3,Business,6,3,12:42:59 PM,6,3,12:53:15 PM,6,3,1:15:08 PM,6,3,2:23:46 PM,6,3,2:48:07 PM,6,22.0,,-1.300921,36.828195,-1.3273709999999999,36.840590999999996,Rider_Id_526,1461
-Order_No_2335,User_Id_1850,Bike,3,Business,18,1,12:25:06 PM,18,1,12:26:06 PM,18,1,12:41:33 PM,18,1,12:47:18 PM,18,1,1:06:34 PM,2,26.9,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_215,1156
-Order_No_15735,User_Id_897,Bike,2,Personal,29,2,9:33:21 AM,29,2,9:34:47 AM,29,2,9:43:02 AM,29,2,9:51:24 AM,29,2,10:05:54 AM,9,16.7,,-1.2795796,36.819062,-1.2295075,36.801697100000005,Rider_Id_100,870
-Order_No_27123,User_Id_2423,Bike,3,Business,21,4,12:06:26 PM,21,4,12:06:38 PM,21,4,12:09:45 PM,21,4,12:18:29 PM,21,4,1:16:15 PM,10,28.0,,-1.2551895,36.7822034,-1.3126476,36.831455,Rider_Id_349,3466
-Order_No_19973,User_Id_1642,Bike,3,Business,6,3,3:47:23 PM,6,3,3:48:25 PM,6,3,4:03:03 PM,6,3,4:10:47 PM,6,3,4:25:41 PM,4,,,-1.267568,36.812335,-1.274731,36.7949226,Rider_Id_557,894
-Order_No_9549,User_Id_3450,Bike,3,Business,28,2,8:20:53 AM,28,2,8:22:05 AM,28,2,9:01:59 AM,28,2,9:06:48 AM,28,2,9:31:09 AM,6,18.6,,-1.2551895,36.7822034,-1.2834816,36.8219031,Rider_Id_92,1461
-Order_No_13056,User_Id_868,Bike,3,Business,7,5,11:29:07 AM,7,5,11:29:55 AM,7,5,11:59:11 AM,7,5,12:07:07 PM,7,5,12:28:36 PM,7,,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_427,1289
-Order_No_10366,User_Id_633,Bike,3,Business,27,2,10:13:32 AM,27,2,10:14:02 AM,27,2,10:17:10 AM,27,2,10:24:47 AM,27,2,10:43:26 AM,9,18.6,2.1,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_878,1119
-Order_No_1432,User_Id_3727,Bike,3,Business,13,2,11:06:54 AM,13,2,11:07:36 AM,13,2,11:16:01 AM,13,2,11:18:30 AM,13,2,11:44:41 AM,8,22.4,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_57,1571
-Order_No_1389,User_Id_2789,Bike,3,Business,5,5,8:27:30 AM,5,5,8:29:16 AM,5,5,8:56:36 AM,5,5,8:59:37 AM,5,5,9:20:28 AM,7,19.8,,-1.304735,36.784605400000004,-1.2603996000000002,36.79722720000001,Rider_Id_576,1251
-Order_No_10538,User_Id_867,Bike,3,Business,29,3,11:43:44 AM,29,3,11:44:08 AM,29,3,11:54:30 AM,29,3,12:25:35 PM,29,3,12:50:19 PM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_591,1484
-Order_No_5510,User_Id_2258,Bike,3,Business,6,2,9:09:57 AM,6,2,9:12:54 AM,6,2,9:24:55 AM,6,2,9:28:22 AM,6,2,9:52:17 AM,10,19.7,,-1.2571472,36.795063299999995,-1.2861447,36.7483206,Rider_Id_762,1435
-Order_No_24025,User_Id_3165,Bike,3,Personal,7,1,6:17:10 PM,7,1,6:25:12 PM,7,1,7:06:29 PM,7,1,7:11:26 PM,7,1,7:34:16 PM,3,20.2,,-1.293973,36.80169,-1.2883775,36.82804960000001,Rider_Id_159,1370
-Order_No_483,User_Id_3606,Bike,3,Business,12,2,3:44:41 PM,12,2,3:47:41 PM,12,2,4:02:01 PM,12,2,4:05:34 PM,12,2,4:47:57 PM,17,,,-1.3175825,36.7004796,-1.2628638,36.8070399,Rider_Id_691,2543
-Order_No_2395,User_Id_8,Bike,2,Personal,12,5,1:39:45 PM,12,5,1:41:02 PM,12,5,1:43:30 PM,12,5,1:47:55 PM,12,5,1:56:49 PM,3,27.2,,-1.2727979999999999,36.8124195,-1.2644008999999998,36.817796200000004,Rider_Id_795,534
-Order_No_2197,User_Id_3674,Bike,3,Business,23,2,4:13:53 PM,23,2,4:19:11 PM,23,2,4:37:14 PM,23,2,4:47:59 PM,23,2,5:07:16 PM,7,19.3,,-1.2551895,36.7822034,-1.2549476000000002,36.8269576,Rider_Id_565,1157
-Order_No_14793,User_Id_3496,Bike,3,Business,13,4,3:28:12 PM,13,4,3:48:31 PM,13,4,4:02:21 PM,13,4,4:12:49 PM,13,4,4:31:39 PM,8,22.6,,-1.300921,36.828195,-1.2516391000000002,36.8114886,Rider_Id_247,1130
-Order_No_10604,User_Id_239,Bike,1,Personal,15,6,1:45:12 PM,15,6,1:45:44 PM,15,6,1:59:44 PM,15,6,2:02:43 PM,15,6,2:25:23 PM,9,25.7,,-1.2836832,36.8306719,-1.32571,36.805533000000004,Rider_Id_674,1360
-Order_No_11994,User_Id_1370,Bike,3,Personal,10,4,11:23:04 AM,10,4,11:58:03 AM,10,4,12:08:50 PM,10,4,12:09:52 PM,10,4,12:21:03 PM,4,,,-1.261692,36.80161,-1.2867201000000001,36.8187204,Rider_Id_923,671
-Order_No_15633,User_Id_3291,Bike,3,Business,25,4,11:03:54 AM,25,4,11:04:36 AM,25,4,11:07:50 AM,25,4,11:17:34 AM,25,4,11:33:28 AM,5,20.1,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_737,954
-Order_No_4513,User_Id_412,Bike,3,Business,5,2,8:29:49 AM,5,2,8:30:14 AM,5,2,8:41:25 AM,5,2,9:06:21 AM,5,2,9:07:09 AM,2,21.1,2.0,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_61,48
-Order_No_17801,User_Id_439,Bike,3,Business,3,4,9:47:08 AM,3,4,9:47:31 AM,3,4,9:49:28 AM,3,4,10:02:16 AM,3,4,10:45:08 AM,11,20.3,,-1.2884799,36.82179179999999,-1.3323107,36.8692616,Rider_Id_88,2572
-Order_No_9874,User_Id_2200,Bike,3,Business,24,3,4:48:14 PM,24,3,5:32:21 PM,24,3,5:32:48 PM,24,3,5:50:49 PM,24,3,6:17:30 PM,8,18.5,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_927,1601
-Order_No_25378,User_Id_3496,Bike,3,Business,3,5,3:43:59 PM,3,5,3:44:20 PM,3,5,3:45:26 PM,3,5,4:12:01 PM,3,5,4:32:01 PM,9,25.3,,-1.300921,36.828195,-1.3007592,36.77541970000001,Rider_Id_925,1200
-Order_No_1204,User_Id_1471,Bike,3,Business,3,1,4:37:14 PM,3,1,4:42:51 PM,3,1,5:25:34 PM,3,1,5:31:15 PM,3,1,5:47:30 PM,9,17.8,,-1.2551895,36.7822034,-1.2917868000000001,36.7872675,Rider_Id_250,975
-Order_No_2847,User_Id_2763,Bike,3,Business,16,4,11:11:16 AM,16,4,11:12:53 AM,16,4,11:19:42 AM,16,4,11:27:54 AM,16,4,11:46:18 AM,8,14.8,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_229,1104
-Order_No_22613,User_Id_1796,Bike,3,Business,5,2,1:25:25 PM,5,2,1:26:54 PM,5,2,1:46:41 PM,5,2,1:57:09 PM,5,2,2:34:19 PM,15,27.6,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_812,2230
-Order_No_14738,User_Id_2216,Bike,3,Business,1,1,9:58:13 AM,1,1,9:58:27 AM,1,1,10:00:08 AM,1,1,10:08:10 AM,1,1,11:08:08 AM,10,21.4,,-1.2551895,36.7822034,-1.2363747999999999,36.817750200000006,Rider_Id_177,3598
-Order_No_21096,User_Id_3688,Bike,3,Business,3,3,3:45:33 PM,3,3,3:47:25 PM,3,3,4:13:23 PM,3,3,4:15:33 PM,3,3,4:51:37 PM,6,28.2,,-1.2736466,36.8164042,-1.2551895,36.7822034,Rider_Id_166,2164
-Order_No_7918,User_Id_1809,Bike,3,Business,3,1,8:08:46 AM,3,1,8:50:16 AM,3,1,9:09:58 AM,3,1,9:37:28 AM,3,1,10:10:45 AM,10,18.5,,-1.3100608999999999,36.820536499999996,-1.2571472,36.795063299999995,Rider_Id_338,1997
-Order_No_7902,User_Id_1329,Bike,3,Business,24,5,4:19:32 PM,24,5,4:23:15 PM,24,5,4:25:09 PM,24,5,4:32:57 PM,24,5,4:56:04 PM,8,19.9,,-1.273056,36.811298,-1.324009,36.845882700000004,Rider_Id_800,1387
-Order_No_686,User_Id_246,Bike,3,Business,11,1,4:09:36 PM,11,1,4:11:24 PM,11,1,4:12:30 PM,11,1,4:26:07 PM,11,1,4:59:45 PM,21,23.2,,-1.3562374,36.904295399999995,-1.2584143,36.8048002,Rider_Id_541,2018
-Order_No_12893,User_Id_186,Bike,3,Business,13,3,8:52:41 AM,13,3,8:52:59 AM,13,3,9:05:38 AM,13,3,9:10:47 AM,13,3,9:55:58 AM,23,17.4,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_427,2711
-Order_No_8361,User_Id_1716,Bike,3,Personal,17,4,10:06:21 AM,17,4,10:07:14 AM,17,4,10:12:03 AM,17,4,10:18:05 AM,17,4,10:31:24 AM,6,,,-1.2738936,36.8035805,-1.3056478999999999,36.7692294,Rider_Id_446,799
-Order_No_10585,User_Id_53,Bike,3,Business,23,4,10:26:39 AM,23,4,10:28:08 AM,23,4,10:54:49 AM,23,4,11:01:30 AM,23,4,11:18:59 AM,5,23.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_790,1049
-Order_No_3467,User_Id_111,Bike,3,Business,15,2,9:36:10 AM,15,2,9:48:26 AM,15,2,10:05:53 AM,15,2,10:20:04 AM,15,2,11:22:16 AM,8,18.1,2.0,-1.293399,36.7821943,-1.2626906,36.7827022,Rider_Id_782,3732
-Order_No_5953,User_Id_60,Bike,3,Business,10,1,3:23:30 PM,10,1,3:26:53 PM,10,1,4:20:54 PM,10,1,4:36:12 PM,10,1,5:04:54 PM,9,18.7,,-1.2783848999999998,36.8163445,-1.2296618000000001,36.84382720000001,Rider_Id_158,1722
-Order_No_2760,User_Id_970,Bike,3,Business,5,5,3:12:16 PM,5,5,3:12:36 PM,5,5,3:16:07 PM,5,5,3:24:03 PM,5,5,3:54:54 PM,15,27.7,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_690,1851
-Order_No_23830,User_Id_265,Bike,3,Business,5,5,2:29:31 PM,5,5,2:30:22 PM,5,5,2:30:47 PM,5,5,3:12:40 PM,5,5,3:12:45 PM,4,27.4,,-1.300921,36.828195,-1.3226514999999999,36.819568200000006,Rider_Id_510,5
-Order_No_5622,User_Id_2124,Bike,1,Personal,12,2,10:15:46 AM,12,2,10:19:56 AM,12,2,10:25:02 AM,12,2,10:33:03 AM,12,2,10:53:41 AM,10,21.0,,-1.2655477,36.8001682,-1.2379585,36.875789000000005,Rider_Id_820,1238
-Order_No_11874,User_Id_635,Bike,3,Business,30,3,12:04:29 PM,30,3,12:04:47 PM,30,3,12:16:04 PM,30,3,12:44:41 PM,30,3,1:03:03 PM,6,25.2,,-1.3077722999999999,36.8336065,-1.3229345,36.8566974,Rider_Id_540,1102
-Order_No_2047,User_Id_2154,Bike,3,Business,14,1,3:46:32 PM,14,1,3:47:02 PM,14,1,3:47:35 PM,14,1,4:05:22 PM,14,1,4:24:13 PM,7,25.5,,-1.2885528999999998,36.816014100000004,-1.2559559999999999,36.789867,Rider_Id_633,1131
-Order_No_9081,User_Id_635,Bike,3,Business,12,5,2:35:42 PM,12,5,2:48:45 PM,12,5,3:14:11 PM,12,5,3:35:37 PM,12,5,4:13:22 PM,7,,,-1.300921,36.828195,-1.2692689,36.8055169,Rider_Id_315,2265
-Order_No_8533,User_Id_867,Bike,3,Business,7,4,10:28:03 AM,7,4,10:31:15 AM,7,4,10:39:07 AM,7,4,11:24:32 AM,7,4,11:29:19 AM,10,27.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_137,287
-Order_No_21309,User_Id_2069,Bike,3,Business,17,1,11:02:02 AM,17,1,11:02:09 AM,17,1,11:02:16 AM,17,1,11:22:39 AM,17,1,11:26:58 AM,9,23.9,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_137,259
-Order_No_19766,User_Id_685,Bike,3,Business,12,3,11:02:04 AM,12,3,11:08:35 AM,12,3,11:29:14 AM,12,3,11:48:39 AM,12,3,12:44:34 PM,12,19.2,,-1.265432,36.795034,-1.3233955,36.856294399999996,Rider_Id_131,3355
-Order_No_12244,User_Id_1244,Bike,3,Business,16,3,3:52:22 PM,16,3,3:53:39 PM,16,3,4:03:50 PM,16,3,4:14:56 PM,16,3,4:31:23 PM,8,26.9,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_247,987
-Order_No_24997,User_Id_517,Bike,3,Business,3,4,11:24:05 AM,3,4,11:27:27 AM,3,4,11:29:33 AM,3,4,11:37:19 AM,3,4,11:45:15 AM,4,23.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,476
-Order_No_15250,User_Id_3355,Bike,3,Business,11,2,11:49:20 AM,11,2,11:51:16 AM,11,2,12:04:30 PM,11,2,12:11:49 PM,11,2,12:39:08 PM,10,19.6,,-1.3014461000000002,36.7661381,-1.3052491000000002,36.8223899,Rider_Id_457,1639
-Order_No_24529,User_Id_867,Bike,3,Business,22,5,12:12:55 PM,22,5,12:15:06 PM,22,5,12:25:01 PM,22,5,12:35:16 PM,22,5,1:07:09 PM,10,28.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_349,1913
-Order_No_5123,User_Id_3727,Bike,3,Business,28,2,12:15:06 PM,28,2,12:15:34 PM,28,2,12:16:28 PM,28,2,12:19:43 PM,28,2,12:30:42 PM,3,21.2,,-1.2963097,36.7688221,-1.293393,36.790237,Rider_Id_438,659
-Order_No_9914,User_Id_1500,Bike,3,Business,8,1,8:46:42 AM,8,1,8:47:10 AM,8,1,8:52:00 AM,8,1,9:11:47 AM,8,1,9:28:58 AM,8,21.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_507,1031
-Order_No_11875,User_Id_1758,Bike,1,Personal,17,5,7:34:03 PM,17,5,7:34:49 PM,17,5,7:35:21 PM,17,5,7:58:28 PM,17,5,8:19:27 PM,9,16.8,,-1.280753,36.8191175,-1.2387846000000002,36.7901393,Rider_Id_229,1259
-Order_No_3107,User_Id_3647,Bike,3,Business,18,5,10:01:51 AM,18,5,10:02:06 AM,18,5,10:14:06 AM,18,5,10:26:01 AM,18,5,10:31:56 AM,2,20.2,,-1.2726389999999999,36.794723,-1.2741728,36.79691079999999,Rider_Id_486,355
-Order_No_27560,User_Id_533,Bike,3,Business,14,5,5:06:06 PM,14,5,5:06:19 PM,14,5,5:09:53 PM,14,5,5:16:26 PM,14,5,5:32:07 PM,7,22.0,,-1.2597528999999998,36.806629,-1.2765492,36.7669809,Rider_Id_795,941
-Order_No_27304,User_Id_1307,Bike,3,Business,29,5,3:34:41 PM,29,5,3:35:32 PM,29,5,3:44:31 PM,29,5,4:01:10 PM,29,5,4:38:28 PM,16,21.3,,-1.2994934999999999,36.8908744,-1.2844665000000002,36.7866113,Rider_Id_618,2238
-Order_No_20720,User_Id_1450,Bike,3,Personal,4,1,11:54:11 AM,4,1,12:05:34 PM,4,1,12:52:49 PM,4,1,1:18:00 PM,4,1,1:49:38 PM,9,20.8,,-1.280753,36.8191175,-1.2912918,36.758140999999995,Rider_Id_929,1898
-Order_No_11809,User_Id_1207,Bike,3,Business,9,5,7:22:31 PM,9,5,7:24:17 PM,9,5,7:30:12 PM,9,5,7:47:43 PM,9,5,8:08:54 PM,8,20.7,,-1.2629666000000002,36.8068369,-1.2745321000000003,36.765088,Rider_Id_86,1271
-Order_No_9077,User_Id_3661,Bike,3,Business,7,2,11:14:38 AM,7,2,11:24:36 AM,7,2,11:45:01 AM,7,2,11:47:56 AM,7,2,12:06:58 PM,10,,,-1.2132866000000002,36.867827399999996,-1.2326041,36.8009029,Rider_Id_8,1142
-Order_No_9517,User_Id_2066,Bike,3,Business,15,3,5:45:30 PM,15,3,5:52:23 PM,15,3,6:07:14 PM,15,3,6:09:44 PM,15,3,6:26:19 PM,7,21.0,,-1.316961,36.820337,-1.2747143,36.806888799999996,Rider_Id_857,995
-Order_No_18380,User_Id_2933,Bike,2,Personal,29,7,2:14:24 PM,29,7,2:14:50 PM,29,7,2:39:05 PM,29,7,2:54:46 PM,29,7,3:11:02 PM,7,23.1,,-1.311208,36.778743,-1.2721028,36.795348499999996,Rider_Id_465,976
-Order_No_20940,User_Id_2691,Bike,3,Business,10,4,12:02:17 PM,10,4,12:02:22 PM,10,4,12:02:38 PM,10,4,12:14:41 PM,10,4,1:05:44 PM,23,24.6,,-1.2551895,36.7822034,-1.373915,36.923362,Rider_Id_120,3063
-Order_No_20173,User_Id_562,Bike,3,Business,13,4,3:54:53 PM,13,4,4:00:44 PM,13,4,4:18:44 PM,13,4,4:40:33 PM,13,4,4:56:22 PM,6,22.6,,-1.3227181000000001,36.8528097,-1.3233496,36.8139159,Rider_Id_156,949
-Order_No_27998,User_Id_867,Bike,3,Business,26,2,9:57:22 AM,26,2,9:57:34 AM,26,2,9:57:59 AM,26,2,10:07:28 AM,26,2,10:35:36 AM,14,22.0,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_901,1688
-Order_No_2510,User_Id_534,Bike,3,Personal,26,6,11:30:42 AM,26,6,11:30:51 AM,26,6,11:43:44 AM,26,6,11:51:32 AM,26,6,12:43:36 PM,16,26.2,,-1.2866298,36.8227916,-1.3103985,36.9114173,Rider_Id_758,3124
-Order_No_15711,User_Id_2230,Bike,3,Personal,11,4,12:20:51 PM,11,4,12:22:29 PM,11,4,12:41:10 PM,11,4,12:48:10 PM,11,4,1:21:16 PM,7,23.9,,-1.2450649,36.7869245,-1.2858996999999999,36.813863399999995,Rider_Id_844,1986
-Order_No_23025,User_Id_393,Bike,3,Business,7,3,9:55:51 AM,7,3,10:21:29 AM,7,3,10:21:45 AM,7,3,10:37:42 AM,7,3,11:03:21 AM,10,25.1,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_208,1539
-Order_No_16295,User_Id_635,Bike,3,Business,4,2,1:02:08 PM,4,2,1:11:39 PM,4,2,1:51:51 PM,4,2,2:02:46 PM,4,2,2:40:20 PM,17,22.4,,-1.177202,36.8351183,-1.2602337,36.79905479999999,Rider_Id_740,2254
-Order_No_12678,User_Id_1620,Bike,1,Personal,7,5,11:07:24 AM,7,5,11:10:50 AM,7,5,11:49:05 AM,7,5,11:55:22 AM,7,5,12:26:14 PM,16,25.7,,-1.227132,36.8374277,-1.2969741000000001,36.7856612,Rider_Id_124,1852
-Order_No_4028,User_Id_972,Bike,3,Business,1,4,11:06:23 AM,1,4,11:09:33 AM,1,4,11:24:57 AM,1,4,11:26:08 AM,1,4,11:45:11 AM,4,,,-1.2862033000000002,36.8152966,-1.2654348,36.8022464,Rider_Id_660,1143
-Order_No_27700,User_Id_2169,Bike,2,Personal,2,6,10:05:25 AM,2,6,10:06:50 AM,2,6,10:17:12 AM,2,6,10:33:52 AM,2,6,10:52:40 AM,6,23.8,,-1.3205163999999998,36.8098882,-1.3019352,36.816768100000004,Rider_Id_208,1128
-Order_No_25901,User_Id_2982,Bike,3,Business,15,6,9:05:30 AM,15,6,9:05:41 AM,15,6,9:07:27 AM,15,6,9:33:37 AM,15,6,10:10:32 AM,11,16.6,,-1.300921,36.828195,-1.3296956,36.878209999999996,Rider_Id_2,2215
-Order_No_21100,User_Id_2069,Bike,3,Business,27,4,11:53:49 AM,27,4,11:54:07 AM,27,4,11:54:14 AM,27,4,12:08:10 PM,27,4,12:48:26 PM,12,23.0,,-1.293515,36.897607,-1.228911,36.881876,Rider_Id_900,2416
-Order_No_616,User_Id_3048,Bike,1,Personal,15,6,7:02:44 PM,15,6,7:03:06 PM,15,6,7:19:41 PM,15,6,7:36:52 PM,15,6,8:09:28 PM,15,19.2,,-1.2957807,36.771395299999995,-1.2131370000000001,36.7900285,Rider_Id_74,1956
-Order_No_19071,User_Id_393,Bike,3,Business,18,2,2:04:34 PM,18,2,2:26:04 PM,18,2,2:27:35 PM,18,2,2:39:31 PM,18,2,3:07:09 PM,11,25.4,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_518,1658
-Order_No_1089,User_Id_2869,Bike,3,Business,14,5,4:59:08 PM,14,5,5:00:46 PM,14,5,5:05:12 PM,14,5,5:16:40 PM,14,5,5:44:39 PM,9,21.5,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_36,1679
-Order_No_3891,User_Id_928,Bike,3,Business,2,4,3:02:35 PM,2,4,3:08:13 PM,2,4,3:08:17 PM,2,4,3:45:53 PM,2,4,3:48:48 PM,7,,,-1.2551895,36.7822034,-1.290724,36.81947,Rider_Id_61,175
-Order_No_21693,User_Id_1781,Bike,3,Business,18,2,11:38:28 AM,18,2,12:00:33 PM,18,2,12:30:06 PM,18,2,12:36:21 PM,18,2,1:51:05 PM,17,,,-1.2571472,36.795063299999995,-1.2860183,36.8975338,Rider_Id_532,4484
-Order_No_1787,User_Id_2523,Bike,3,Business,25,4,11:55:08 AM,25,4,11:55:42 AM,25,4,12:02:21 PM,25,4,12:13:35 PM,25,4,12:40:21 PM,8,,,-1.2551895,36.7822034,-1.284877,36.787520799999996,Rider_Id_422,1606
-Order_No_12700,User_Id_59,Bike,3,Business,23,2,2:34:06 PM,23,2,2:34:43 PM,23,2,2:48:51 PM,23,2,2:58:36 PM,23,2,3:28:38 PM,18,19.0,,-1.290894,36.822971,-1.1933233,36.935494299999995,Rider_Id_148,1802
-Order_No_17154,User_Id_635,Bike,3,Business,9,2,9:47:39 AM,9,2,9:48:21 AM,9,2,10:15:45 AM,9,2,10:26:01 AM,9,2,10:56:28 AM,3,20.6,,-1.257069,36.81467,-1.2602337,36.79905479999999,Rider_Id_52,1827
-Order_No_26136,User_Id_1471,Bike,3,Business,28,5,1:10:55 PM,28,5,1:12:56 PM,28,5,1:30:30 PM,28,5,1:38:12 PM,28,5,2:18:11 PM,12,25.7,,-1.2551895,36.7822034,-1.2129211000000002,36.7963408,Rider_Id_96,2399
-Order_No_17340,User_Id_3402,Bike,3,Business,25,4,2:09:34 PM,25,4,2:15:40 PM,25,4,2:20:04 PM,25,4,2:27:37 PM,25,4,2:37:44 PM,5,20.6,,-1.3109868,36.8459114,-1.2966911,36.833342200000004,Rider_Id_848,607
-Order_No_17330,User_Id_867,Bike,3,Business,15,6,11:29:04 AM,15,6,11:29:13 AM,15,6,11:30:36 AM,15,6,11:37:41 AM,15,6,12:01:56 PM,9,19.1,,-1.2574219,36.7927073,-1.2991441000000001,36.7528804,Rider_Id_155,1455
-Order_No_12833,User_Id_2968,Bike,1,Business,3,2,5:18:15 PM,3,2,5:20:02 PM,3,2,5:48:48 PM,3,2,5:49:54 PM,3,2,6:14:07 PM,2,18.7,,-1.28283,36.8199739,-1.285442,36.831270700000005,Rider_Id_88,1453
-Order_No_859,User_Id_1209,Bike,3,Business,8,2,4:00:27 PM,8,2,4:00:55 PM,8,2,4:03:38 PM,8,2,4:16:40 PM,8,2,4:27:20 PM,4,25.2,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_523,640
-Order_No_7942,User_Id_868,Bike,3,Business,16,2,4:38:16 PM,16,2,4:38:55 PM,16,2,4:47:24 PM,16,2,4:52:20 PM,16,2,5:08:50 PM,6,,,-1.3033561000000002,36.8163631,-1.265715,36.823815,Rider_Id_570,990
-Order_No_20434,User_Id_1578,Bike,3,Business,16,2,1:07:10 PM,16,2,1:07:23 PM,16,2,1:23:04 PM,16,2,1:26:56 PM,16,2,1:49:58 PM,5,23.7,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_263,1382
-Order_No_8822,User_Id_1329,Bike,3,Business,28,2,1:16:56 PM,28,2,1:17:22 PM,28,2,1:20:07 PM,28,2,1:26:52 PM,28,2,1:56:11 PM,8,21.4,,-1.273056,36.811298,-1.3193233,36.840990999999995,Rider_Id_591,1759
-Order_No_3456,User_Id_3470,Bike,3,Business,5,2,3:08:38 PM,5,2,3:09:05 PM,5,2,3:19:47 PM,5,2,3:36:24 PM,5,2,3:44:45 PM,1,,,-1.28878,36.8168312,-1.2791714,36.825599700000005,Rider_Id_942,501
-Order_No_24721,User_Id_1028,Bike,1,Personal,28,4,9:17:36 AM,28,4,9:21:43 AM,28,4,9:47:45 AM,28,4,10:03:45 AM,28,4,10:31:24 AM,11,,,-1.2100103000000002,36.797424,-1.2684319,36.81658529999999,Rider_Id_101,1659
-Order_No_10539,User_Id_1342,Bike,3,Business,6,2,11:19:46 AM,6,2,11:21:56 AM,6,2,11:32:52 AM,6,2,11:41:08 AM,6,2,11:57:42 AM,9,24.9,,-1.2977705,36.81341389999999,-1.3025826,36.7670807,Rider_Id_726,994
-Order_No_772,User_Id_517,Bike,3,Business,4,3,1:49:15 PM,4,3,2:25:36 PM,4,3,2:29:28 PM,4,3,3:46:52 PM,4,3,3:48:24 PM,10,18.5,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_927,92
-Order_No_331,User_Id_1244,Bike,3,Business,10,4,9:32:03 AM,10,4,9:32:44 AM,10,4,10:03:16 AM,10,4,10:05:34 AM,10,4,10:57:51 AM,8,19.4,,-1.265715,36.823815,-1.303596,36.778378000000004,Rider_Id_918,3137
-Order_No_19576,User_Id_70,Bike,2,Personal,24,1,9:39:07 AM,24,1,9:39:30 AM,24,1,9:42:54 AM,24,1,9:47:38 AM,24,1,10:20:10 AM,8,15.4,,-1.2959749,36.7770396,-1.2824911,36.8218042,Rider_Id_281,1952
-Order_No_10309,User_Id_1342,Bike,3,Business,30,2,4:40:41 PM,30,2,4:40:53 PM,30,2,4:41:31 PM,30,2,4:50:02 PM,30,2,5:03:04 PM,4,23.6,,-1.3025826,36.7670807,-1.2882803,36.7815881,Rider_Id_785,782
-Order_No_14877,User_Id_1209,Bike,3,Business,23,2,1:25:41 PM,23,2,1:28:10 PM,23,2,1:47:53 PM,23,2,1:52:09 PM,23,2,2:18:21 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_306,1572
-Order_No_13672,User_Id_3036,Bike,3,Personal,29,5,8:15:40 AM,29,5,8:16:40 AM,29,5,8:26:31 AM,29,5,8:33:28 AM,29,5,8:42:14 AM,2,,,-1.2925792,36.790690000000005,-1.2976363000000002,36.7815901,Rider_Id_334,526
-Order_No_17216,User_Id_2900,Bike,3,Business,3,3,3:53:33 PM,3,3,3:55:45 PM,3,3,4:06:18 PM,3,3,4:10:22 PM,3,3,4:28:16 PM,10,26.6,,-1.2963097,36.7688221,-1.304969,36.846623,Rider_Id_216,1074
-Order_No_2230,User_Id_475,Bike,3,Business,11,1,12:25:54 PM,11,1,12:32:42 PM,11,1,12:38:46 PM,11,1,12:43:26 PM,11,1,12:58:53 PM,3,17.9,,-1.2551895,36.7822034,-1.2576857,36.779919899999996,Rider_Id_322,927
-Order_No_12214,User_Id_2523,Bike,3,Business,7,1,11:18:01 AM,7,1,11:22:35 AM,7,1,11:33:34 AM,7,1,11:35:29 AM,7,1,12:08:38 PM,7,24.7,,-1.2551895,36.7822034,-1.293082,36.8278578,Rider_Id_477,1989
-Order_No_17975,User_Id_627,Bike,3,Business,7,5,10:13:16 AM,7,5,10:13:22 AM,7,5,10:15:52 AM,7,5,10:22:27 AM,7,5,10:53:31 AM,19,20.6,0.9,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_647,1864
-Order_No_17305,User_Id_1724,Bike,3,Business,26,2,1:04:37 PM,26,2,1:06:54 PM,26,2,1:38:16 PM,26,2,1:42:15 PM,26,2,2:06:00 PM,8,29.9,,-1.2907853999999999,36.7774108,-1.2551895,36.7822034,Rider_Id_459,1425
-Order_No_21460,User_Id_2990,Bike,1,Personal,3,5,12:38:02 PM,3,5,12:38:25 PM,3,5,1:15:13 PM,3,5,1:18:54 PM,3,5,2:05:49 PM,9,21.4,,-1.2762743,36.815278899999996,-1.3183188999999997,36.8493704,Rider_Id_903,2815
-Order_No_4590,User_Id_3494,Bike,3,Personal,21,1,1:01:36 PM,21,1,1:06:16 PM,21,1,1:11:57 PM,21,1,1:26:21 PM,21,1,1:41:50 PM,7,19.8,,-1.2856419,36.826076799999996,-1.2963097,36.7688221,Rider_Id_680,929
-Order_No_26516,User_Id_3050,Bike,3,Business,8,5,1:39:42 PM,8,5,2:33:51 PM,8,5,2:37:45 PM,8,5,3:19:39 PM,8,5,3:20:56 PM,10,30.6,,-1.2945073999999999,36.8083195,-1.3104733999999998,36.7363698,Rider_Id_655,77
-Order_No_16908,User_Id_2956,Bike,3,Business,5,2,9:22:27 AM,5,2,9:22:37 AM,5,2,9:28:03 AM,5,2,9:42:41 AM,5,2,9:58:47 AM,9,20.8,2.0,-1.2571472,36.795063299999995,-1.273547,36.742313,Rider_Id_937,966
-Order_No_25539,User_Id_1075,Bike,3,Business,6,3,1:53:16 PM,6,3,1:53:33 PM,6,3,2:27:52 PM,6,3,2:29:39 PM,6,3,2:46:59 PM,3,27.3,,-1.2527958000000001,36.8003131,-1.2712193,36.8256876,Rider_Id_547,1040
-Order_No_26082,User_Id_1527,Bike,3,Business,9,4,12:39:44 PM,9,4,12:42:36 PM,9,4,12:43:18 PM,9,4,12:50:39 PM,9,4,1:13:24 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_206,1365
-Order_No_28037,User_Id_1370,Bike,3,Personal,16,3,12:50:28 PM,16,3,12:50:53 PM,16,3,12:51:02 PM,16,3,12:54:01 PM,16,3,1:33:31 PM,13,24.1,,-1.261692,36.80161,-1.3332749,36.870814700000004,Rider_Id_84,2370
-Order_No_19940,User_Id_478,Bike,3,Business,6,2,1:16:58 PM,6,2,1:17:34 PM,6,2,1:23:23 PM,6,2,1:28:16 PM,6,2,1:29:50 PM,3,25.1,,-1.2551895,36.7822034,-1.2630899,36.7800761,Rider_Id_881,94
-Order_No_22335,User_Id_3283,Bike,3,Business,30,2,3:14:41 PM,30,2,3:22:20 PM,30,2,3:31:36 PM,30,2,3:36:21 PM,30,2,3:57:25 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_548,1264
-Order_No_25813,User_Id_516,Bike,3,Business,11,1,3:55:16 PM,11,1,3:55:28 PM,11,1,3:59:52 PM,11,1,4:05:43 PM,11,1,4:32:48 PM,9,28.1,,-1.2551895,36.7822034,-1.2986123,36.8076281,Rider_Id_925,1625
-Order_No_20111,User_Id_2330,Bike,3,Business,15,5,3:36:45 PM,15,5,3:39:11 PM,15,5,3:45:03 PM,15,5,3:47:16 PM,15,5,3:56:06 PM,4,28.1,,-1.3040812,36.7924217,-1.3015197,36.7658456,Rider_Id_545,530
-Order_No_26673,User_Id_3291,Bike,3,Business,1,1,2:07:18 PM,1,1,2:07:39 PM,1,1,2:07:53 PM,1,1,2:23:13 PM,1,1,2:39:12 PM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_854,959
-Order_No_54,User_Id_3010,Bike,1,Personal,5,5,4:13:58 PM,5,5,4:14:32 PM,5,5,4:21:31 PM,5,5,4:30:30 PM,5,5,4:52:00 PM,6,26.4,,-1.2809121,36.7852524,-1.2590031000000002,36.8078168,Rider_Id_178,1290
-Order_No_26929,User_Id_1329,Bike,3,Business,12,5,11:49:58 AM,12,5,11:50:48 AM,12,5,11:59:00 AM,12,5,12:00:19 PM,12,5,12:38:49 PM,15,26.5,,-1.273056,36.811298,-1.2272148999999999,36.71197960000001,Rider_Id_660,2310
-Order_No_11531,User_Id_2415,Bike,1,Personal,22,5,2:23:46 PM,22,5,2:26:14 PM,22,5,2:55:37 PM,22,5,3:10:18 PM,22,5,3:22:11 PM,8,,,-1.2699263,36.8468961,-1.2697141,36.798177700000004,Rider_Id_919,713
-Order_No_19161,User_Id_1729,Bike,1,Personal,7,2,2:20:15 PM,7,2,2:20:41 PM,7,2,2:35:55 PM,7,2,2:37:26 PM,7,2,3:00:34 PM,6,24.7,,-1.2873152,36.8246342,-1.3235941,36.8237885,Rider_Id_857,1388
-Order_No_19081,User_Id_1271,Bike,3,Business,1,3,12:06:00 PM,1,3,12:06:07 PM,1,3,12:11:51 PM,1,3,12:30:39 PM,1,3,1:30:06 PM,13,19.7,,-1.3107918,36.761794,-1.3072409999999999,36.830236799999994,Rider_Id_216,3567
-Order_No_15208,User_Id_2412,Bike,3,Business,8,1,8:46:02 AM,8,1,8:46:36 AM,8,1,9:02:38 AM,8,1,9:15:05 AM,8,1,10:12:54 AM,12,19.7,,-1.2852569,36.8976121,-1.2297202,36.881950700000004,Rider_Id_532,3469
-Order_No_22664,User_Id_2066,Bike,3,Business,26,4,11:29:29 AM,26,4,11:30:38 AM,26,4,11:44:44 AM,26,4,11:48:02 AM,26,4,12:50:37 PM,10,19.1,,-1.316961,36.820337,-1.2566586000000002,36.792968200000004,Rider_Id_844,3755
-Order_No_6963,User_Id_2112,Bike,3,Business,13,3,2:39:52 PM,13,3,2:43:42 PM,13,3,2:45:38 PM,13,3,2:58:00 PM,13,3,3:20:23 PM,12,29.1,,-1.279395,36.825364,-1.2156006000000001,36.8916865,Rider_Id_845,1343
-Order_No_27463,User_Id_1849,Bike,3,Business,5,2,9:24:01 AM,5,2,9:35:12 AM,5,2,9:47:15 AM,5,2,9:54:35 AM,5,2,10:18:19 AM,9,17.9,4.1,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_808,1424
-Order_No_9489,User_Id_1578,Bike,3,Business,28,2,12:33:43 PM,28,2,1:13:07 PM,28,2,1:40:31 PM,28,2,1:46:47 PM,28,2,2:26:11 PM,9,19.4,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_447,2364
-Order_No_1819,User_Id_346,Bike,3,Business,5,2,11:47:39 AM,5,2,11:48:10 AM,5,2,11:48:21 AM,5,2,12:10:35 PM,5,2,12:32:16 PM,11,23.5,,-1.2283403,36.8822756,-1.28878,36.8168312,Rider_Id_925,1301
-Order_No_274,User_Id_2509,Bike,3,Business,11,4,12:28:16 PM,11,4,12:29:43 PM,11,4,12:33:03 PM,11,4,12:38:03 PM,11,4,12:53:44 PM,4,23.8,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_536,941
-Order_No_6259,User_Id_1329,Bike,3,Business,20,4,4:47:33 PM,20,4,4:56:13 PM,20,4,5:10:53 PM,20,4,5:14:14 PM,20,4,5:30:47 PM,7,22.9,,-1.273056,36.811298,-1.2957031,36.7654104,Rider_Id_86,993
-Order_No_8265,User_Id_1578,Bike,3,Business,20,1,4:51:10 PM,20,1,5:16:24 PM,20,1,5:33:15 PM,20,1,5:41:46 PM,20,1,5:57:59 PM,4,20.3,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_213,973
-Order_No_19330,User_Id_1927,Bike,3,Business,1,6,4:22:22 PM,1,6,4:47:47 PM,1,6,4:56:28 PM,1,6,5:09:26 PM,1,6,5:51:41 PM,21,21.1,,-1.3463041999999998,36.768164,-1.2685865,36.8850792,Rider_Id_345,2535
-Order_No_20138,User_Id_3091,Bike,3,Business,5,5,12:05:50 PM,5,5,12:07:01 PM,5,5,12:16:46 PM,5,5,12:26:06 PM,5,5,1:05:33 PM,7,24.3,,-1.290894,36.822971,-1.3117395,36.8592009,Rider_Id_910,2367
-Order_No_25655,User_Id_75,Bike,2,Personal,15,3,11:03:33 AM,15,3,11:03:40 AM,15,3,12:02:23 PM,15,3,12:12:46 PM,15,3,1:14:16 PM,29,18.1,,-1.1923911,36.944733299999996,-1.3014462,36.7607695,Rider_Id_734,3690
-Order_No_8795,User_Id_880,Bike,3,Personal,21,5,12:02:06 PM,21,5,12:03:08 PM,21,5,12:15:27 PM,21,5,12:22:26 PM,21,5,12:45:03 PM,4,19.9,,-1.2995135,36.793363299999996,-1.2892529,36.7740575,Rider_Id_312,1357
-Order_No_16268,User_Id_286,Bike,3,Business,18,4,5:12:19 PM,18,4,5:13:43 PM,18,4,5:40:37 PM,18,4,5:47:36 PM,18,4,6:09:47 PM,7,23.7,,-1.2551895,36.7822034,-1.2962966999999999,36.8020231,Rider_Id_208,1331
-Order_No_6995,User_Id_2828,Bike,2,Business,2,1,12:25:36 PM,2,1,12:26:33 PM,2,1,12:38:24 PM,2,1,12:53:19 PM,2,1,1:18:19 PM,3,19.0,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_908,1500
-Order_No_14270,User_Id_3496,Bike,3,Business,28,1,2:06:31 PM,28,1,2:07:51 PM,28,1,2:19:33 PM,28,1,2:27:08 PM,28,1,2:34:25 PM,2,24.4,,-1.300921,36.828195,-1.3092358,36.836384,Rider_Id_654,437
-Order_No_16034,User_Id_510,Bike,3,Business,7,4,5:31:45 PM,7,4,5:32:08 PM,7,4,5:32:42 PM,7,4,6:02:56 PM,7,4,6:38:10 PM,17,29.5,,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_865,2114
-Order_No_6371,User_Id_3506,Bike,3,Personal,19,2,12:40:42 PM,19,2,12:41:28 PM,19,2,12:52:43 PM,19,2,12:55:35 PM,19,2,1:06:50 PM,4,,,-1.2563531000000001,36.7896742,-1.2551895,36.7822034,Rider_Id_148,675
-Order_No_462,User_Id_2330,Bike,3,Business,19,2,11:45:56 AM,19,2,11:46:11 AM,19,2,12:09:02 PM,19,2,12:10:27 PM,19,2,12:38:42 PM,11,26.4,,-1.2524916000000001,36.8656196,-1.2642903,36.80021370000001,Rider_Id_248,1695
-Order_No_18623,User_Id_265,Bike,3,Business,21,4,11:19:04 AM,21,4,11:19:28 AM,21,4,11:28:18 AM,21,4,11:51:01 AM,21,4,12:38:20 PM,15,28.0,,-1.300921,36.828195,-1.2057494,36.837181,Rider_Id_519,2839
-Order_No_10241,User_Id_3404,Bike,3,Business,5,1,12:53:49 PM,5,1,1:14:01 PM,5,1,1:26:14 PM,5,1,1:30:58 PM,5,1,1:53:54 PM,5,24.5,,-1.2588181,36.8106709,-1.279395,36.825364,Rider_Id_660,1376
-Order_No_1719,User_Id_393,Bike,3,Business,28,4,8:25:20 AM,28,4,8:25:36 AM,28,4,8:25:43 AM,28,4,9:00:11 AM,28,4,9:00:13 AM,11,22.5,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,2
-Order_No_1454,User_Id_868,Bike,3,Business,23,5,1:45:26 PM,23,5,2:20:02 PM,23,5,2:39:27 PM,23,5,3:04:37 PM,23,5,3:04:44 PM,10,26.2,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_708,7
-Order_No_15670,User_Id_2383,Bike,3,Business,10,4,3:17:41 PM,10,4,3:18:30 PM,10,4,3:32:59 PM,10,4,3:39:32 PM,10,4,4:52:51 PM,20,25.4,,-1.2539424,36.806077200000004,-1.3124537,36.925365,Rider_Id_956,4399
-Order_No_7781,User_Id_3091,Bike,1,Business,28,6,12:31:08 PM,28,6,12:31:31 PM,28,6,12:39:47 PM,28,6,12:51:48 PM,28,6,1:10:57 PM,10,24.0,,-1.2536041999999998,36.806489500000005,-1.3014461000000002,36.7661381,Rider_Id_116,1149
-Order_No_20760,User_Id_2574,Bike,3,Personal,13,7,3:57:42 PM,13,7,4:08:58 PM,13,7,4:23:43 PM,13,7,4:25:22 PM,13,7,4:36:35 PM,4,26.2,,-1.2780823000000001,36.7960191,-1.2819131000000001,36.7813526,Rider_Id_749,673
-Order_No_22830,User_Id_2237,Bike,3,Business,1,5,3:46:03 PM,1,5,4:17:56 PM,1,5,4:36:15 PM,1,5,4:44:10 PM,1,5,5:04:44 PM,3,30.6,,-1.266571,36.801406,-1.2574087999999999,36.799712,Rider_Id_720,1234
-Order_No_25431,User_Id_2112,Bike,3,Business,6,4,2:11:04 PM,6,4,2:51:56 PM,6,4,3:18:27 PM,6,4,4:38:10 PM,6,4,4:57:24 PM,12,22.6,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_150,1154
-Order_No_11091,User_Id_2219,Bike,3,Business,23,2,1:41:45 PM,23,2,1:44:27 PM,23,2,1:48:33 PM,23,2,1:52:34 PM,23,2,2:13:33 PM,6,,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_654,1259
-Order_No_20405,User_Id_393,Bike,3,Business,14,1,8:21:41 AM,14,1,9:14:20 AM,14,1,9:26:59 AM,14,1,9:37:10 AM,14,1,10:02:25 AM,11,20.4,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_555,1515
-Order_No_7951,User_Id_2207,Bike,3,Business,2,5,11:15:12 AM,2,5,11:17:38 AM,2,5,11:22:13 AM,2,5,11:35:44 AM,2,5,12:27:31 PM,3,23.6,,-1.2768326,36.8073804,-1.28164,36.816866999999995,Rider_Id_88,3107
-Order_No_12089,User_Id_1690,Bike,3,Business,28,1,5:30:48 PM,28,1,5:31:39 PM,28,1,5:49:19 PM,28,1,5:51:58 PM,28,1,6:31:05 PM,7,22.5,,-1.2717851,36.810536299999995,-1.304033,36.7848695,Rider_Id_752,2347
-Order_No_3410,User_Id_3635,Bike,1,Business,29,3,2:27:03 PM,29,3,2:27:17 PM,29,3,2:30:24 PM,29,3,2:34:49 PM,29,3,2:44:24 PM,3,19.2,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_450,575
-Order_No_20422,User_Id_1847,Bike,1,Personal,26,3,12:57:54 PM,26,3,1:07:23 PM,26,3,1:30:56 PM,26,3,1:50:59 PM,26,3,2:12:26 PM,7,27.2,,-1.3109313,36.854418100000004,-1.3214562,36.810082799999996,Rider_Id_532,1287
-Order_No_1862,User_Id_3194,Bike,3,Business,30,5,1:09:57 PM,30,5,1:10:27 PM,30,5,1:11:16 PM,30,5,1:37:34 PM,30,5,1:57:15 PM,8,24.7,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_19,1181
-Order_No_20980,User_Id_2041,Bike,1,Business,12,3,11:46:59 AM,12,3,11:48:41 AM,12,3,12:06:34 PM,12,3,12:22:37 PM,12,3,12:37:40 PM,4,23.0,,-1.290894,36.822971,-1.2690931,36.8065779,Rider_Id_726,903
-Order_No_5889,User_Id_2330,Bike,3,Business,15,6,2:42:57 PM,15,6,2:43:16 PM,15,6,2:49:33 PM,15,6,3:12:07 PM,15,6,3:54:27 PM,21,23.5,,-1.2637056999999998,36.805170000000004,-1.3800951000000001,36.9297274,Rider_Id_720,2540
-Order_No_25127,User_Id_2133,Bike,1,Personal,12,6,8:42:21 PM,12,6,8:42:45 PM,12,6,9:08:24 PM,12,6,9:17:29 PM,12,6,9:52:24 PM,20,19.8,,-1.2691211000000002,36.824380600000005,-1.3673224,36.916197499999996,Rider_Id_95,2095
-Order_No_15180,User_Id_733,Bike,3,Business,6,2,12:21:10 PM,6,2,12:21:22 PM,6,2,12:36:12 PM,6,2,12:41:08 PM,6,2,1:01:12 PM,15,25.2,,-1.2551895,36.7822034,-1.2232396,36.893321500000006,Rider_Id_79,1204
-Order_No_23850,User_Id_1148,Bike,3,Business,11,2,12:01:47 PM,11,2,12:02:59 PM,11,2,12:12:01 PM,11,2,12:19:08 PM,11,2,12:39:03 PM,5,19.6,,-1.3099295,36.8436807,-1.3074264,36.822612299999996,Rider_Id_943,1195
-Order_No_24643,User_Id_2406,Bike,3,Business,3,1,10:46:08 AM,3,1,10:48:10 AM,3,1,11:07:56 AM,3,1,11:09:20 AM,3,1,11:23:31 AM,5,21.6,,-1.3064585,36.8119065,-1.2867573,36.812662599999996,Rider_Id_778,851
-Order_No_3221,User_Id_1976,Bike,3,Business,30,3,11:06:04 AM,30,3,11:06:34 AM,30,3,11:07:21 AM,30,3,11:22:09 AM,30,3,11:40:25 AM,7,25.2,,-1.257028,36.7810435,-1.287428,36.7800821,Rider_Id_101,1096
-Order_No_11394,User_Id_552,Bike,3,Business,9,3,3:10:56 PM,9,3,3:12:44 PM,9,3,3:18:42 PM,9,3,3:28:54 PM,9,3,4:05:34 PM,9,25.3,,-1.3329777,36.866203000000006,-1.335477,36.930829700000004,Rider_Id_31,2200
-Order_No_10281,User_Id_972,Bike,3,Business,5,6,11:49:53 AM,5,6,11:50:12 AM,5,6,12:01:20 PM,5,6,12:03:25 PM,5,6,12:21:21 PM,6,22.4,,-1.2862033000000002,36.8152966,-1.308819,36.8421201,Rider_Id_100,1076
-Order_No_17457,User_Id_627,Bike,3,Business,1,3,10:27:49 AM,1,3,10:27:59 AM,1,3,10:31:37 AM,1,3,10:41:15 AM,1,3,11:05:08 AM,6,19.7,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_882,1433
-Order_No_22834,User_Id_3291,Bike,3,Business,31,1,1:12:22 PM,31,1,1:14:27 PM,31,1,1:22:11 PM,31,1,1:27:51 PM,31,1,1:40:56 PM,4,21.1,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_362,785
-Order_No_1083,User_Id_20,Bike,3,Business,30,3,9:53:26 AM,30,3,9:53:32 AM,30,3,10:08:13 AM,30,3,10:20:23 AM,30,3,11:00:20 AM,14,17.5,,-1.2628638,36.8070399,-1.3346931,36.8661911,Rider_Id_290,2397
-Order_No_22433,User_Id_2069,Bike,3,Business,8,5,2:17:41 PM,8,5,2:18:10 PM,8,5,2:28:04 PM,8,5,2:35:04 PM,8,5,2:47:47 PM,9,30.5,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_52,763
-Order_No_17557,User_Id_1075,Bike,3,Business,10,5,11:00:32 AM,10,5,11:01:13 AM,10,5,11:08:27 AM,10,5,11:22:36 AM,10,5,11:46:20 AM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_427,1424
-Order_No_4686,User_Id_788,Bike,3,Business,13,3,4:43:38 PM,13,3,5:19:07 PM,13,3,5:19:54 PM,13,3,5:29:18 PM,13,3,6:01:33 PM,15,26.1,,-1.2156006000000001,36.8916865,-1.225322,36.80855,Rider_Id_131,1935
-Order_No_20444,User_Id_2802,Bike,1,Personal,9,1,12:09:19 PM,9,1,12:10:09 PM,9,1,12:24:33 PM,9,1,12:38:40 PM,9,1,12:54:44 PM,9,20.3,,-1.2765737,36.8175399,-1.2413316,36.878552500000005,Rider_Id_103,964
-Order_No_12781,User_Id_1329,Bike,3,Business,11,4,3:42:40 PM,11,4,3:46:49 PM,11,4,4:06:06 PM,11,4,4:14:17 PM,11,4,4:28:41 PM,8,27.0,,-1.273056,36.811298,-1.3217370000000002,36.81797879999999,Rider_Id_61,864
-Order_No_2438,User_Id_3217,Bike,3,Business,25,3,10:20:49 AM,25,3,10:33:24 AM,25,3,10:54:15 AM,25,3,11:04:51 AM,25,3,11:18:23 AM,4,,,-1.22556,36.80813,-1.214328,36.7911903,Rider_Id_208,812
-Order_No_20021,User_Id_1578,Bike,3,Business,31,3,8:35:59 AM,31,3,8:36:43 AM,31,3,8:59:14 AM,31,3,9:14:31 AM,31,3,9:41:07 AM,8,19.1,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_796,1596
-Order_No_15684,User_Id_393,Bike,3,Business,2,5,12:47:40 PM,2,5,1:27:10 PM,2,5,1:48:49 PM,2,5,1:55:50 PM,2,5,2:26:45 PM,14,25.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_623,1855
-Order_No_27860,User_Id_633,Bike,3,Business,31,5,8:52:12 AM,31,5,8:53:23 AM,31,5,9:13:00 AM,31,5,9:25:34 AM,31,5,9:39:18 AM,4,14.8,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_56,824
-Order_No_9303,User_Id_53,Bike,3,Business,28,4,10:09:26 AM,28,4,10:10:13 AM,28,4,10:22:41 AM,28,4,10:28:40 AM,28,4,10:53:56 AM,11,19.7,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_156,1516
-Order_No_4404,User_Id_1207,Bike,3,Business,15,1,4:20:11 PM,15,1,4:20:40 PM,15,1,4:24:41 PM,15,1,4:40:24 PM,15,1,5:25:34 PM,12,,,-1.2629666000000002,36.8068369,-1.2351541000000001,36.8748505,Rider_Id_519,2710
-Order_No_27,User_Id_577,Bike,1,Personal,26,3,12:34:55 PM,26,3,12:36:04 PM,26,3,12:47:43 PM,26,3,12:55:28 PM,26,3,1:53:21 PM,11,24.0,,-1.2662728,36.8151265,-1.3270347,36.866736200000005,Rider_Id_50,3473
-Order_No_3258,User_Id_1336,Bike,3,Business,8,4,10:36:58 AM,8,4,10:43:36 AM,8,4,10:48:40 AM,8,4,10:53:21 AM,8,4,11:08:56 AM,3,26.5,,-1.2866011999999998,36.8195691,-1.2891921999999998,36.825189,Rider_Id_615,935
-Order_No_1126,User_Id_2801,Bike,3,Business,21,2,1:18:26 PM,21,2,1:18:57 PM,21,2,1:28:20 PM,21,2,1:48:53 PM,21,2,1:57:18 PM,5,24.8,,-1.2566688999999998,36.779695200000006,-1.2674273999999999,36.787082700000006,Rider_Id_448,505
-Order_No_14335,User_Id_1787,Bike,3,Business,30,2,3:06:33 PM,30,2,3:14:15 PM,30,2,3:30:01 PM,30,2,3:34:57 PM,30,2,3:57:57 PM,15,,,-1.2283403,36.8822756,-1.2551895,36.7822034,Rider_Id_8,1380
-Order_No_10860,User_Id_552,Bike,3,Business,12,3,4:39:01 PM,12,3,5:09:34 PM,12,3,5:23:05 PM,12,3,5:27:31 PM,12,3,5:46:17 PM,7,24.0,,-1.3562374,36.904295399999995,-1.335477,36.930829700000004,Rider_Id_927,1126
-Order_No_4524,User_Id_246,Bike,3,Business,17,1,1:20:43 PM,17,1,1:22:11 PM,17,1,1:34:42 PM,17,1,1:36:46 PM,17,1,1:53:33 PM,10,24.6,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_598,1007
-Order_No_25525,User_Id_3647,Bike,3,Business,3,4,10:21:17 AM,3,4,10:22:33 AM,3,4,10:31:46 AM,3,4,10:34:05 AM,3,4,10:50:03 AM,6,21.9,,-1.2726389999999999,36.794723,-1.290789,36.8024859,Rider_Id_345,958
-Order_No_366,User_Id_1749,Bike,3,Personal,27,3,2:39:01 PM,27,3,2:40:04 PM,27,3,2:57:30 PM,27,3,2:58:57 PM,27,3,3:19:48 PM,4,29.2,,-1.2785858,36.82896829999999,-1.2621576,36.8103473,Rider_Id_61,1251
-Order_No_17234,User_Id_1990,Bike,3,Business,8,3,11:29:38 AM,8,3,11:30:21 AM,8,3,11:55:27 AM,8,3,12:10:27 PM,8,3,12:52:46 PM,10,,,-1.2558721000000002,36.722296,-1.275442,36.766020700000006,Rider_Id_165,2539
-Order_No_18497,User_Id_3352,Bike,1,Personal,27,5,8:30:57 AM,27,5,8:31:21 AM,27,5,8:46:33 AM,27,5,8:50:44 AM,27,5,9:09:47 AM,10,16.1,,-1.2981962,36.762347999999996,-1.3127303999999997,36.8189817,Rider_Id_449,1143
-Order_No_24012,User_Id_3300,Bike,3,Business,1,1,12:42:02 PM,1,1,1:05:36 PM,1,1,1:20:03 PM,1,1,1:45:32 PM,1,1,2:07:23 PM,8,,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_61,1311
-Order_No_18176,User_Id_1363,Bike,3,Business,18,4,8:35:31 AM,18,4,8:51:29 AM,18,4,9:18:10 AM,18,4,9:30:40 AM,18,4,9:53:47 AM,7,20.4,,-1.2732317,36.799558700000006,-1.3004062,36.829741,Rider_Id_646,1387
-Order_No_15636,User_Id_1840,Bike,1,Personal,22,2,12:23:19 PM,22,2,12:36:18 PM,22,2,1:25:41 PM,22,2,1:33:09 PM,22,2,1:54:48 PM,6,21.1,,-1.2559559999999999,36.772901,-1.2683271999999999,36.82103529999999,Rider_Id_812,1299
-Order_No_19391,User_Id_2869,Bike,3,Business,13,4,12:06:32 PM,13,4,12:12:26 PM,13,4,12:27:35 PM,13,4,12:37:09 PM,13,4,12:58:32 PM,9,22.1,,-1.2649605,36.798177700000004,-1.3025826,36.7670807,Rider_Id_344,1283
-Order_No_23522,User_Id_2451,Bike,1,Personal,2,2,11:24:37 AM,2,2,11:30:05 AM,2,2,11:38:07 AM,2,2,11:44:11 AM,2,2,11:58:09 AM,4,20.9,,-1.285714,36.830624,-1.2713865,36.8058221,Rider_Id_761,838
-Order_No_26726,User_Id_53,Bike,3,Business,30,2,11:58:43 AM,30,2,12:00:20 PM,30,2,12:00:29 PM,30,2,12:15:32 PM,30,2,1:26:21 PM,5,22.4,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_684,4249
-Order_No_25091,User_Id_3147,Bike,3,Business,13,3,10:26:09 AM,13,3,10:26:45 AM,13,3,10:47:09 AM,13,3,11:00:54 AM,13,3,11:33:34 AM,3,19.9,,-1.3404187,36.7614685,-1.346755,36.756349,Rider_Id_844,1960
-Order_No_15549,User_Id_3647,Bike,3,Business,8,3,11:04:55 AM,8,3,11:05:10 AM,8,3,11:17:20 AM,8,3,11:24:18 AM,8,3,11:45:26 AM,8,,,-1.2726389999999999,36.794723,-1.3041128999999998,36.810493900000004,Rider_Id_341,1268
-Order_No_18255,User_Id_1148,Bike,3,Business,23,6,9:18:32 AM,23,6,9:34:50 AM,23,6,9:49:14 AM,23,6,10:05:53 AM,23,6,10:16:35 AM,3,19.1,,-1.3100586,36.850032899999995,-1.3120711999999999,36.8567865,Rider_Id_332,642
-Order_No_5869,User_Id_393,Bike,3,Business,21,4,8:40:47 AM,21,4,9:21:57 AM,21,4,9:33:58 AM,21,4,9:36:30 AM,21,4,10:32:19 AM,14,21.7,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_314,3349
-Order_No_21286,User_Id_1209,Bike,3,Business,18,4,2:33:24 PM,18,4,2:33:44 PM,18,4,2:33:48 PM,18,4,2:49:25 PM,18,4,3:07:37 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_349,1092
-Order_No_20457,User_Id_432,Bike,3,Business,24,5,8:12:57 AM,24,5,8:14:00 AM,24,5,8:22:43 AM,24,5,8:27:03 AM,24,5,8:44:53 AM,3,18.3,,-1.3012008,36.764868,-1.2763494,36.7671783,Rider_Id_826,1070
-Order_No_345,User_Id_3558,Bike,2,Personal,4,2,9:13:33 AM,4,2,9:15:09 AM,4,2,9:35:03 AM,4,2,9:38:10 AM,4,2,9:57:41 AM,12,16.5,,-1.2691298,36.8254266,-1.2037968000000001,36.7802765,Rider_Id_453,1171
-Order_No_2204,User_Id_1633,Bike,1,Personal,29,6,4:25:31 PM,29,6,4:28:32 PM,29,6,4:37:26 PM,29,6,4:46:33 PM,29,6,5:16:32 PM,6,23.6,,-1.2671296,36.81257720000001,-1.292008,36.802784,Rider_Id_86,1799
-Order_No_536,User_Id_3766,Bike,2,Personal,20,5,3:02:48 PM,20,5,3:02:55 PM,20,5,3:20:12 PM,20,5,3:22:15 PM,20,5,3:34:18 PM,4,20.6,,-1.2765533000000002,36.82908629999999,-1.2670213,36.7994482,Rider_Id_616,723
-Order_No_27007,User_Id_393,Bike,3,Business,1,5,8:56:02 AM,1,5,9:18:11 AM,1,5,9:19:42 AM,1,5,10:08:02 AM,1,5,10:08:05 AM,11,23.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_39,3
-Order_No_15156,User_Id_3517,Bike,3,Business,4,5,2:44:54 PM,4,5,2:44:58 PM,4,5,2:46:55 PM,4,5,2:55:47 PM,4,5,3:20:46 PM,16,24.3,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_100,1499
-Order_No_13424,User_Id_299,Bike,3,Business,14,5,4:49:24 PM,14,5,4:49:58 PM,14,5,5:34:42 PM,14,5,5:49:20 PM,14,5,5:49:27 PM,15,21.7,,-1.2629666000000002,36.8068369,-1.3465623,36.7677838,Rider_Id_139,7
-Order_No_27247,User_Id_302,Bike,3,Business,16,6,7:24:39 PM,16,6,7:25:09 PM,16,6,7:35:06 PM,16,6,8:08:04 PM,16,6,8:08:31 PM,9,,,-1.2649605,36.798177700000004,-1.3112325,36.8146321,Rider_Id_314,27
-Order_No_12947,User_Id_1245,Bike,3,Business,15,1,2:44:45 PM,15,1,2:45:10 PM,15,1,2:53:08 PM,15,1,4:32:53 PM,15,1,5:23:03 PM,16,,,-1.300921,36.828195,-1.3441039,36.717774600000006,Rider_Id_206,3010
-Order_No_17790,User_Id_577,Bike,3,Personal,4,2,1:20:20 PM,4,2,1:22:16 PM,4,2,1:44:12 PM,4,2,1:49:55 PM,4,2,2:47:24 PM,13,22.4,,-1.3270347,36.866736200000005,-1.2740027,36.804917200000006,Rider_Id_343,3449
-Order_No_12410,User_Id_3201,Bike,3,Business,29,3,4:28:29 PM,29,3,4:28:47 PM,29,3,4:28:56 PM,29,3,4:40:01 PM,29,3,4:51:57 PM,3,19.2,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_822,716
-Order_No_27285,User_Id_391,Bike,3,Business,18,4,10:24:47 AM,18,4,10:33:56 AM,18,4,11:26:28 AM,18,4,11:47:04 AM,18,4,12:25:22 PM,18,,,-1.2491258,36.689425299999996,-1.2867454,36.8259017,Rider_Id_217,2298
-Order_No_19324,User_Id_393,Bike,3,Business,30,2,10:41:55 AM,30,2,10:43:06 AM,30,2,10:53:26 AM,30,2,11:10:08 AM,30,2,11:37:09 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_843,1621
-Order_No_11255,User_Id_3718,Bike,3,Business,4,2,12:25:55 PM,4,2,1:33:07 PM,4,2,2:26:19 PM,4,2,2:33:39 PM,4,2,2:44:00 PM,2,23.7,11.1,-1.2650027,36.8126242,-1.2810205,36.8248105,Rider_Id_404,621
-Order_No_15759,User_Id_2464,Bike,3,Business,7,5,1:16:50 PM,7,5,1:33:03 PM,7,5,2:05:30 PM,7,5,2:14:10 PM,7,5,2:33:42 PM,6,,,-1.279395,36.825364,-1.2640571000000003,36.7883547,Rider_Id_36,1172
-Order_No_18401,User_Id_53,Bike,3,Business,11,4,11:16:09 AM,11,4,11:17:51 AM,11,4,11:26:33 AM,11,4,11:56:21 AM,11,4,12:15:33 PM,5,25.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_99,1152
-Order_No_27704,User_Id_1724,Bike,3,Business,20,1,9:50:42 AM,20,1,9:50:52 AM,20,1,9:50:57 AM,20,1,9:55:43 AM,20,1,10:05:08 AM,3,18.9,,-1.2551895,36.7822034,-1.2595466999999998,36.8113044,Rider_Id_576,565
-Order_No_10953,User_Id_1831,Bike,3,Business,24,3,1:50:22 PM,24,3,1:53:31 PM,24,3,2:12:17 PM,24,3,2:14:15 PM,24,3,3:08:34 PM,21,18.3,,-1.3562374,36.904295399999995,-1.2571472,36.795063299999995,Rider_Id_283,3259
-Order_No_23326,User_Id_2467,Bike,3,Personal,23,4,1:53:51 PM,23,4,1:57:14 PM,23,4,2:24:02 PM,23,4,2:31:13 PM,23,4,2:56:09 PM,16,20.7,,-1.2635459999999998,36.716171,-1.311407,36.798891,Rider_Id_116,1496
-Order_No_14131,User_Id_3291,Bike,3,Business,4,1,10:47:18 AM,4,1,10:52:33 AM,4,1,10:56:22 AM,4,1,11:07:02 AM,4,1,11:15:41 AM,5,25.8,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_103,519
-Order_No_11456,User_Id_1334,Bike,2,Personal,16,4,11:23:01 AM,16,4,11:25:28 AM,16,4,11:32:08 AM,16,4,11:33:40 AM,16,4,11:43:20 AM,6,14.6,,-1.3086326000000001,36.8080886,-1.304033,36.7848695,Rider_Id_88,580
-Order_No_20152,User_Id_1179,Bike,3,Business,24,3,2:04:21 PM,24,3,2:07:53 PM,24,3,2:07:56 PM,24,3,2:30:20 PM,24,3,3:09:37 PM,21,18.4,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_345,2357
-Order_No_3132,User_Id_3599,Bike,3,Business,3,1,11:26:54 AM,3,1,11:36:22 AM,3,1,11:45:00 AM,3,1,11:55:03 AM,3,1,12:31:17 PM,21,21.4,,-1.3302996,36.8707935,-1.2153260000000001,36.891993799999995,Rider_Id_130,2174
-Order_No_24333,User_Id_496,Bike,3,Business,2,4,11:51:26 AM,2,4,11:53:03 AM,2,4,11:53:15 AM,2,4,12:01:26 PM,2,4,12:14:23 PM,6,,,-1.2551895,36.7822034,-1.2889024,36.8187402,Rider_Id_291,777
-Order_No_14504,User_Id_53,Bike,3,Business,31,2,12:38:18 PM,31,2,12:38:23 PM,31,2,12:40:03 PM,31,2,1:10:55 PM,31,2,1:21:49 PM,4,18.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_927,654
-Order_No_25987,User_Id_1882,Bike,3,Business,3,3,6:44:33 PM,3,3,6:45:14 PM,3,3,6:53:55 PM,3,3,7:13:25 PM,3,3,7:32:08 PM,8,23.7,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_257,1123
-Order_No_1842,User_Id_2066,Bike,3,Business,15,3,2:36:29 PM,15,3,2:37:25 PM,15,3,2:40:21 PM,15,3,2:43:37 PM,15,3,3:11:01 PM,11,21.4,,-1.316961,36.820337,-1.2618539,36.777240500000005,Rider_Id_393,1644
-Order_No_9910,User_Id_718,Bike,3,Business,13,1,2:24:23 PM,13,1,2:24:34 PM,13,1,2:24:39 PM,13,1,2:29:53 PM,13,1,2:44:42 PM,3,,,-1.2551895,36.7822034,-1.2669351,36.8058806,Rider_Id_291,889
-Order_No_22938,User_Id_3647,Bike,3,Business,19,3,3:05:40 PM,19,3,3:17:05 PM,19,3,3:20:09 PM,19,3,3:41:32 PM,19,3,3:41:56 PM,5,24.6,,-1.2726389999999999,36.794723,-1.266736,36.7989348,Rider_Id_813,24
-Order_No_8671,User_Id_2102,Bike,3,Business,30,4,10:21:42 AM,30,4,10:21:57 AM,30,4,10:58:52 AM,30,4,11:04:38 AM,30,4,11:33:10 AM,14,,,-1.3250048,36.869718799999994,-1.2613768,36.801941799999994,Rider_Id_25,1712
-Order_No_18618,User_Id_53,Bike,3,Business,18,1,10:16:51 AM,18,1,10:18:01 AM,18,1,10:25:25 AM,18,1,10:35:08 AM,18,1,11:00:40 AM,11,27.9,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_156,1532
-Order_No_8595,User_Id_393,Bike,3,Business,15,6,11:47:52 AM,15,6,11:54:02 AM,15,6,11:56:48 AM,15,6,12:04:37 PM,15,6,12:41:32 PM,14,21.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_555,2215
-Order_No_20074,User_Id_111,Bike,3,Business,15,3,3:17:14 PM,15,3,3:18:54 PM,15,3,3:31:18 PM,15,3,3:38:24 PM,15,3,4:14:04 PM,12,21.8,,-1.2551895,36.7822034,-1.318049,36.8556554,Rider_Id_882,2140
-Order_No_20991,User_Id_1721,Bike,3,Business,25,5,9:11:28 AM,25,5,9:12:41 AM,25,5,9:38:24 AM,25,5,9:54:32 AM,25,5,10:18:57 AM,6,20.9,,-1.2551895,36.7822034,-1.2560864999999999,36.829677000000004,Rider_Id_489,1465
-Order_No_4401,User_Id_1400,Bike,2,Personal,13,6,1:38:29 PM,13,6,1:50:02 PM,13,6,2:13:03 PM,13,6,2:26:18 PM,13,6,2:49:57 PM,11,28.1,,-1.2696795,36.8640088,-1.3194988,36.796668100000005,Rider_Id_248,1419
-Order_No_14822,User_Id_2677,Bike,3,Business,24,4,9:41:50 AM,24,4,9:42:10 AM,24,4,9:50:10 AM,24,4,10:02:28 AM,24,4,11:05:55 AM,14,19.2,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_216,3807
-Order_No_4638,User_Id_1207,Bike,3,Business,2,2,1:25:20 PM,2,2,1:26:03 PM,2,2,1:38:20 PM,2,2,2:03:09 PM,2,2,2:37:19 PM,9,26.0,,-1.2705661,36.808036799999996,-1.2745321000000003,36.765088,Rider_Id_612,2050
-Order_No_9635,User_Id_238,Bike,3,Business,12,2,8:20:57 AM,12,2,8:21:21 AM,12,2,8:25:30 AM,12,2,8:30:47 AM,12,2,8:55:58 AM,15,21.1,,-1.3077869,36.844320700000004,-1.362953,36.904214,Rider_Id_281,1511
-Order_No_2580,User_Id_1209,Bike,3,Business,24,3,11:05:49 AM,24,3,11:07:29 AM,24,3,11:13:57 AM,24,3,11:16:38 AM,24,3,12:01:20 PM,16,,,-1.3004062,36.829741,-1.3871626,36.7705016,Rider_Id_504,2682
-Order_No_17482,User_Id_2457,Bike,3,Business,12,2,2:53:58 PM,12,2,3:23:34 PM,12,2,3:37:13 PM,12,2,3:40:03 PM,12,2,4:14:29 PM,8,23.1,,-1.2551895,36.7822034,-1.3016151000000002,36.811735399999996,Rider_Id_726,2066
-Order_No_16590,User_Id_868,Bike,3,Business,19,6,9:51:41 AM,19,6,9:52:41 AM,19,6,9:57:30 AM,19,6,10:01:46 AM,19,6,10:06:53 AM,3,21.5,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_819,307
-Order_No_4799,User_Id_1503,Bike,3,Personal,26,3,8:50:44 AM,26,3,8:52:19 AM,26,3,9:09:39 AM,26,3,9:20:52 AM,26,3,9:40:25 AM,7,18.9,,-1.2671296,36.81257720000001,-1.2854173999999998,36.7951152,Rider_Id_882,1173
-Order_No_19678,User_Id_1004,Bike,3,Business,8,3,11:39:38 AM,8,3,11:40:18 AM,8,3,11:46:29 AM,8,3,12:12:43 PM,8,3,1:13:14 PM,15,,,-1.2586579,36.8137885,-1.3250048,36.869718799999994,Rider_Id_959,3631
-Order_No_16958,User_Id_2532,Bike,3,Business,11,5,3:39:00 PM,11,5,4:49:07 PM,11,5,5:04:22 PM,11,5,5:05:40 PM,11,5,5:27:35 PM,6,24.3,,-1.2551895,36.7822034,-1.285382,36.8162878,Rider_Id_899,1315
-Order_No_12386,User_Id_265,Bike,3,Business,8,4,12:19:46 PM,8,4,12:20:49 PM,8,4,12:28:47 PM,8,4,12:34:02 PM,8,4,12:40:29 PM,3,26.1,,-1.3108107,36.8244221,-1.300921,36.828195,Rider_Id_634,387
-Order_No_18210,User_Id_733,Bike,3,Business,7,2,12:48:10 PM,7,2,12:48:21 PM,7,2,12:50:46 PM,7,2,12:58:08 PM,7,2,1:21:38 PM,18,,,-1.2551895,36.7822034,-1.3277669,36.8975169,Rider_Id_411,1410
-Order_No_12474,User_Id_2216,Bike,3,Business,28,5,1:55:04 PM,28,5,2:25:28 PM,28,5,2:44:22 PM,28,5,2:47:54 PM,28,5,3:11:21 PM,12,25.3,,-1.2867627000000001,36.883446,-1.295156,36.809430600000006,Rider_Id_187,1407
-Order_No_25051,User_Id_393,Bike,3,Business,14,5,11:27:23 AM,14,5,11:44:03 AM,14,5,11:53:44 AM,14,5,12:06:42 PM,14,5,12:34:48 PM,10,24.6,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_188,1686
-Order_No_2995,User_Id_3201,Bike,3,Business,30,3,11:48:48 AM,30,3,11:49:24 AM,30,3,12:00:13 PM,30,3,12:08:21 PM,30,3,1:08:58 PM,17,24.8,,-1.3165542,36.700067,-1.2584143,36.8048002,Rider_Id_70,3637
-Order_No_19167,User_Id_867,Bike,3,Business,25,5,10:00:35 AM,25,5,10:45:44 AM,25,5,10:53:18 AM,25,5,11:25:00 AM,25,5,11:45:47 AM,10,26.6,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_555,1247
-Order_No_4287,User_Id_3108,Bike,3,Business,19,5,10:24:57 AM,19,5,10:25:06 AM,19,5,10:26:03 AM,19,5,12:03:37 PM,19,5,1:20:16 PM,20,24.9,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_101,4599
-Order_No_22846,User_Id_2733,Bike,3,Business,6,2,10:41:22 AM,6,2,10:43:13 AM,6,2,10:46:54 AM,6,2,11:11:27 AM,6,2,11:21:03 AM,2,24.9,,-1.2941448999999998,36.7592641,-1.2963097,36.7688221,Rider_Id_215,576
-Order_No_16236,User_Id_1329,Bike,3,Business,31,3,1:34:01 PM,31,3,1:34:07 PM,31,3,1:49:17 PM,31,3,3:10:33 PM,31,3,3:56:57 PM,20,27.1,,-1.332624,36.913187,-1.269534,36.80697,Rider_Id_647,2784
-Order_No_14724,User_Id_3517,Bike,3,Business,30,6,10:22:00 AM,30,6,10:22:28 AM,30,6,10:28:15 AM,30,6,10:46:46 AM,30,6,11:16:11 AM,21,26.8,,-1.3033328000000002,36.7561697,-1.3882008,36.7699445,Rider_Id_855,1765
-Order_No_13641,User_Id_1271,Bike,3,Business,25,1,4:25:23 PM,25,1,4:26:56 PM,25,1,5:11:04 PM,25,1,5:22:37 PM,25,1,5:40:47 PM,12,27.6,,-1.3244885,36.897791999999995,-1.3166679,36.824763,Rider_Id_26,1090
-Order_No_25981,User_Id_103,Bike,3,Business,31,3,10:24:56 AM,31,3,10:25:16 AM,31,3,10:31:21 AM,31,3,10:42:06 AM,31,3,11:15:03 AM,19,24.7,,-1.2844665000000002,36.7866113,-1.2162532,36.7095449,Rider_Id_735,1977
-Order_No_3495,User_Id_265,Bike,3,Business,11,4,12:52:00 PM,11,4,12:58:40 PM,11,4,1:07:38 PM,11,4,1:37:19 PM,11,4,2:14:21 PM,18,26.9,,-1.3204931,36.7043102,-1.300921,36.828195,Rider_Id_520,2222
-Order_No_16711,User_Id_2548,Bike,3,Business,18,3,1:20:15 PM,18,3,1:21:22 PM,18,3,1:49:19 PM,18,3,2:05:57 PM,18,3,2:24:34 PM,6,20.9,,-1.2768758999999998,36.8477557,-1.3096278000000001,36.8533243,Rider_Id_88,1117
-Order_No_17609,User_Id_2279,Bike,3,Business,30,2,3:08:29 PM,30,2,3:15:13 PM,30,2,3:40:22 PM,30,2,3:50:52 PM,30,2,4:23:55 PM,3,27.4,,-1.2897368,36.8226783,-1.3012902,36.80964,Rider_Id_206,1983
-Order_No_24943,User_Id_396,Bike,3,Personal,5,4,2:11:45 PM,5,4,2:12:01 PM,5,4,2:28:44 PM,5,4,2:32:18 PM,5,4,2:43:13 PM,3,22.0,,-1.2883946000000002,36.7728469,-1.2857671,36.7875614,Rider_Id_449,655
-Order_No_4576,User_Id_3686,Bike,3,Business,20,3,1:09:42 PM,20,3,1:10:10 PM,20,3,1:12:32 PM,20,3,1:20:32 PM,20,3,1:51:57 PM,11,27.3,,-1.2551895,36.7822034,-1.2381,36.732826,Rider_Id_709,1885
-Order_No_4791,User_Id_1363,Bike,3,Business,7,1,1:20:18 PM,7,1,1:33:25 PM,7,1,1:44:34 PM,7,1,1:46:07 PM,7,1,2:03:39 PM,5,26.8,,-1.3004062,36.829741,-1.2752846000000002,36.802702200000006,Rider_Id_436,1052
-Order_No_19923,User_Id_393,Bike,3,Business,30,2,11:46:28 AM,30,2,12:09:04 PM,30,2,12:22:04 PM,30,2,12:23:54 PM,30,2,12:36:40 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_438,766
-Order_No_17108,User_Id_1482,Bike,3,Business,1,4,11:49:44 AM,1,4,11:50:31 AM,1,4,11:53:39 AM,1,4,12:59:33 PM,1,4,1:27:19 PM,16,,,-1.260232,36.807334000000004,-1.3385790000000002,36.89630379999999,Rider_Id_648,1666
-Order_No_527,User_Id_2804,Bike,3,Business,25,1,2:39:22 PM,25,1,2:39:54 PM,25,1,3:08:28 PM,25,1,3:17:13 PM,25,1,3:54:14 PM,3,28.1,,-1.2669736,36.8040985,-1.2551895,36.7822034,Rider_Id_434,2221
-Order_No_12100,User_Id_1589,Bike,3,Business,9,7,12:28:59 PM,9,7,12:29:17 PM,9,7,12:37:55 PM,9,7,12:44:08 PM,9,7,1:38:30 PM,14,18.7,,-1.3014461000000002,36.7661381,-1.225322,36.80855,Rider_Id_371,3262
-Order_No_9839,User_Id_909,Bike,3,Business,11,1,12:25:13 PM,11,1,12:25:43 PM,11,1,12:34:29 PM,11,1,12:40:59 PM,11,1,1:11:47 PM,10,26.7,,-1.279395,36.825364,-1.225322,36.80855,Rider_Id_504,1848
-Order_No_21918,User_Id_2676,Bike,3,Business,25,1,10:13:50 AM,25,1,10:14:54 AM,25,1,10:43:57 AM,25,1,11:12:40 AM,25,1,11:12:44 AM,2,25.3,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_267,4
-Order_No_20645,User_Id_1524,Bike,1,Personal,8,6,9:35:08 AM,8,6,9:35:39 AM,8,6,9:46:32 AM,8,6,9:54:25 AM,8,6,11:22:24 AM,19,18.3,,-1.2943272,36.7667198,-1.2859194,36.900615,Rider_Id_312,5279
-Order_No_8021,User_Id_868,Bike,3,Business,6,3,1:08:45 PM,6,3,1:09:34 PM,6,3,1:25:33 PM,6,3,1:28:22 PM,6,3,1:42:19 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_920,837
-Order_No_28110,User_Id_2701,Bike,3,Business,24,4,2:50:09 PM,24,4,2:53:47 PM,24,4,3:05:37 PM,24,4,3:07:12 PM,24,4,3:14:13 PM,1,22.6,,-1.279395,36.825364,-1.2866011999999998,36.8195691,Rider_Id_79,421
-Order_No_4968,User_Id_3674,Bike,3,Business,14,3,10:53:34 AM,14,3,10:58:36 AM,14,3,11:04:18 AM,14,3,11:12:19 AM,14,3,11:32:28 AM,8,24.7,,-1.2551895,36.7822034,-1.2747604,36.8259205,Rider_Id_726,1209
-Order_No_1845,User_Id_2461,Bike,3,Business,9,3,3:10:47 PM,9,3,3:11:12 PM,9,3,3:24:08 PM,9,3,3:45:36 PM,9,3,4:43:13 PM,14,23.5,,-1.2377049999999998,36.7663283,-1.310627,36.819216,Rider_Id_949,3457
-Order_No_16289,User_Id_3686,Bike,3,Business,7,3,12:02:18 PM,7,3,12:05:30 PM,7,3,12:07:00 PM,7,3,12:14:05 PM,7,3,12:29:24 PM,6,25.2,,-1.2551895,36.7822034,-1.2843046,36.774613200000005,Rider_Id_107,919
-Order_No_4570,User_Id_1402,Bike,3,Business,30,2,2:54:37 PM,30,2,3:05:16 PM,30,2,3:18:04 PM,30,2,3:44:59 PM,30,2,3:45:06 PM,1,,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_953,7
-Order_No_27707,User_Id_2659,Bike,3,Business,29,5,12:38:11 PM,29,5,12:39:37 PM,29,5,12:52:54 PM,29,5,12:57:17 PM,29,5,1:12:40 PM,6,26.6,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_206,923
-Order_No_2745,User_Id_517,Bike,3,Business,31,5,1:45:43 PM,31,5,1:48:06 PM,31,5,1:51:31 PM,31,5,2:26:43 PM,31,5,2:26:45 PM,4,24.7,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_619,2
-Order_No_15109,User_Id_2174,Bike,3,Business,12,2,9:44:29 AM,12,2,9:44:39 AM,12,2,9:45:29 AM,12,2,10:37:46 AM,12,2,10:57:44 AM,12,24.5,,-1.2482781,36.884091999999995,-1.2981239,36.8367977,Rider_Id_510,1198
-Order_No_18771,User_Id_2989,Bike,3,Business,2,3,1:54:53 PM,2,3,1:55:12 PM,2,3,2:00:44 PM,2,3,2:09:38 PM,2,3,2:41:49 PM,20,26.2,,-1.2860183,36.8975338,-1.3472066,36.7692638,Rider_Id_31,1931
-Order_No_7743,User_Id_46,Bike,3,Business,13,4,3:19:38 PM,13,4,3:23:53 PM,13,4,3:28:29 PM,13,4,4:13:28 PM,13,4,4:17:41 PM,8,23.0,,-1.2996396000000001,36.8520799,-1.2788469999999998,36.825346200000006,Rider_Id_864,253
-Order_No_24168,User_Id_136,Bike,3,Business,22,5,3:54:08 PM,22,5,3:57:37 PM,22,5,4:02:03 PM,22,5,4:35:26 PM,22,5,4:35:29 PM,8,28.6,,-1.2560874,36.7931031,-1.2965045,36.7913036,Rider_Id_539,3
-Order_No_8148,User_Id_1363,Bike,3,Business,17,1,9:30:25 AM,17,1,9:30:35 AM,17,1,9:30:40 AM,17,1,9:36:40 AM,17,1,9:56:52 AM,10,19.2,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_871,1212
-Order_No_10552,User_Id_1882,Bike,3,Business,9,3,1:57:27 PM,9,3,2:16:01 PM,9,3,2:31:22 PM,9,3,2:34:52 PM,9,3,3:01:01 PM,12,25.1,,-1.277018,36.820337,-1.2160906999999999,36.885194899999995,Rider_Id_576,1569
-Order_No_12743,User_Id_158,Bike,3,Personal,27,1,1:34:00 PM,27,1,1:34:21 PM,27,1,1:37:31 PM,27,1,1:41:25 PM,27,1,1:52:42 PM,3,23.0,,-1.3205163999999998,36.8098882,-1.3041041000000002,36.817144899999995,Rider_Id_216,677
-Order_No_12498,User_Id_1304,Bike,3,Business,19,4,12:58:41 PM,19,4,1:02:47 PM,19,4,1:24:14 PM,19,4,1:48:42 PM,19,4,2:37:22 PM,10,20.4,,-1.2909536,36.8407162,-1.2551895,36.7822034,Rider_Id_234,2920
-Order_No_14718,User_Id_2701,Bike,3,Business,1,4,10:39:57 AM,1,4,10:50:41 AM,1,4,11:03:53 AM,1,4,11:34:03 AM,1,4,11:39:25 AM,11,,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_744,322
-Order_No_14194,User_Id_1128,Bike,1,Personal,22,3,4:27:27 PM,22,3,4:28:18 PM,22,3,4:32:08 PM,22,3,5:02:22 PM,22,3,6:21:36 PM,28,23.2,,-1.2571651000000001,36.803220700000004,-1.4127216999999999,36.9433886,Rider_Id_56,4754
-Order_No_8785,User_Id_2268,Bike,1,Personal,3,5,11:12:42 AM,3,5,11:18:31 AM,3,5,11:26:22 AM,3,5,11:36:44 AM,3,5,11:44:31 AM,4,21.6,,-1.3069436,36.8172066,-1.2882850000000001,36.815912,Rider_Id_754,467
-Order_No_12096,User_Id_3760,Bike,2,Personal,1,1,3:20:03 PM,1,1,3:20:14 PM,1,1,3:24:28 PM,1,1,3:33:35 PM,1,1,3:41:57 PM,2,25.8,,-1.2752846000000002,36.802702200000006,-1.2747111,36.80839829999999,Rider_Id_622,502
-Order_No_22607,User_Id_3291,Bike,3,Business,24,3,1:20:01 PM,24,3,1:27:02 PM,24,3,1:38:00 PM,24,3,2:01:55 PM,24,3,2:05:34 PM,8,18.3,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_70,219
-Order_No_8079,User_Id_1244,Bike,3,Business,7,4,2:50:23 PM,7,4,2:50:47 PM,7,4,2:59:16 PM,7,4,3:05:59 PM,7,4,3:36:03 PM,11,31.3,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_332,1804
-Order_No_24153,User_Id_3222,Bike,3,Business,27,2,10:31:21 AM,27,2,11:31:27 AM,27,2,11:34:33 AM,27,2,11:36:54 AM,27,2,11:58:17 AM,8,24.0,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_532,1283
-Order_No_27888,User_Id_1268,Bike,1,Personal,17,5,5:00:23 PM,17,5,5:03:11 PM,17,5,5:18:12 PM,17,5,5:35:26 PM,17,5,6:02:04 PM,9,18.9,,-1.2751745,36.765767600000004,-1.2983805,36.8198439,Rider_Id_213,1598
-Order_No_25263,User_Id_3727,Bike,3,Business,8,5,1:50:30 PM,8,5,1:51:18 PM,8,5,1:58:00 PM,8,5,2:02:57 PM,8,5,2:23:46 PM,8,27.0,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_263,1249
-Order_No_5536,User_Id_391,Bike,3,Business,11,6,12:00:54 PM,11,6,12:12:19 PM,11,6,12:45:50 PM,11,6,1:00:54 PM,11,6,1:23:23 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_491,1349
-Order_No_13131,User_Id_868,Bike,3,Business,4,2,10:43:56 AM,4,2,11:46:51 AM,4,2,11:47:36 AM,4,2,11:58:47 AM,4,2,1:00:05 PM,17,20.4,,-1.3188358999999998,36.703852600000005,-1.265715,36.823815,Rider_Id_821,3678
-Order_No_9559,User_Id_1527,Bike,3,Business,5,5,10:16:49 AM,5,5,10:17:17 AM,5,5,10:44:08 AM,5,5,11:04:22 AM,5,5,11:26:13 AM,9,24.7,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_245,1311
-Order_No_27623,User_Id_2717,Bike,3,Business,27,5,10:59:05 AM,27,5,11:01:09 AM,27,5,11:10:17 AM,27,5,11:16:17 AM,27,5,11:40:01 AM,6,22.3,,-1.2551895,36.7822034,-1.2834945,36.812937,Rider_Id_519,1424
-Order_No_1652,User_Id_3426,Bike,3,Business,6,4,4:39:51 PM,6,4,4:41:42 PM,6,4,4:59:25 PM,6,4,5:07:05 PM,6,4,5:28:16 PM,9,22.6,,-1.2551895,36.7822034,-1.3014461000000002,36.7661381,Rider_Id_762,1271
-Order_No_10904,User_Id_3283,Bike,3,Business,6,4,11:22:19 AM,6,4,11:23:37 AM,6,4,11:33:42 AM,6,4,11:37:58 AM,6,4,11:48:22 AM,5,19.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_279,624
-Order_No_20124,User_Id_238,Bike,3,Business,22,5,12:06:26 PM,22,5,12:08:10 PM,22,5,12:09:26 PM,22,5,12:46:36 PM,22,5,12:52:58 PM,4,28.4,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_576,382
-Order_No_3185,User_Id_3426,Bike,3,Business,7,5,3:02:49 PM,7,5,3:03:58 PM,7,5,3:13:18 PM,7,5,3:18:29 PM,7,5,3:43:05 PM,9,24.7,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_848,1476
-Order_No_22216,User_Id_1687,Bike,3,Business,18,1,12:23:18 PM,18,1,12:25:15 PM,18,1,12:40:42 PM,18,1,12:47:53 PM,18,1,1:31:29 PM,5,,,-1.2733003999999999,36.7943468,-1.2772438,36.8264231,Rider_Id_444,2616
-Order_No_9131,User_Id_393,Bike,3,Business,2,4,2:50:56 PM,2,4,2:52:21 PM,2,4,3:19:29 PM,2,4,3:33:54 PM,2,4,3:53:18 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_591,1164
-Order_No_25075,User_Id_2514,Bike,3,Business,27,3,3:53:19 PM,27,3,3:54:07 PM,27,3,4:08:23 PM,27,3,4:14:49 PM,27,3,4:47:23 PM,4,29.5,,-1.288086,36.821115999999996,-1.266294,36.8057971,Rider_Id_448,1954
-Order_No_4722,User_Id_1178,Bike,3,Business,9,4,12:03:25 PM,9,4,12:04:11 PM,9,4,12:12:02 PM,9,4,12:15:55 PM,9,4,12:29:42 PM,6,24.0,,-1.2665946000000001,36.7888833,-1.2862269,36.7841601,Rider_Id_107,827
-Order_No_19364,User_Id_3487,Bike,3,Business,26,5,9:42:18 AM,26,5,9:43:45 AM,26,5,9:59:40 AM,26,5,10:05:01 AM,26,5,11:00:58 AM,12,,,-1.3472066,36.7692638,-1.279395,36.825364,Rider_Id_506,3357
-Order_No_19830,User_Id_972,Bike,3,Business,21,3,3:25:45 PM,21,3,3:37:04 PM,21,3,3:45:33 PM,21,3,3:48:48 PM,21,3,4:21:23 PM,5,23.2,2.9,-1.2862033000000002,36.8152966,-1.2908072,36.7860517,Rider_Id_178,1955
-Order_No_22320,User_Id_2788,Bike,2,Personal,16,1,10:35:38 AM,16,1,10:38:03 AM,16,1,11:03:01 AM,16,1,11:07:12 AM,16,1,11:21:15 AM,3,19.5,,-1.2856017,36.8129897,-1.293973,36.80169,Rider_Id_848,843
-Order_No_6479,User_Id_246,Bike,3,Business,5,1,9:16:13 AM,5,1,9:17:41 AM,5,1,9:41:13 AM,5,1,9:57:58 AM,5,1,10:26:13 AM,10,18.4,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_531,1695
-Order_No_12778,User_Id_2344,Bike,3,Business,11,1,12:24:14 PM,11,1,12:25:08 PM,11,1,12:25:44 PM,11,1,12:41:19 PM,11,1,1:05:51 PM,12,17.9,,-1.2551895,36.7822034,-1.3275536000000001,36.837167,Rider_Id_379,1472
-Order_No_2799,User_Id_3404,Bike,3,Business,10,6,11:27:32 AM,10,6,11:32:28 AM,10,6,11:41:45 AM,10,6,11:43:16 AM,10,6,12:04:05 PM,10,24.1,,-1.2413316,36.878552500000005,-1.279395,36.825364,Rider_Id_504,1249
-Order_No_25096,User_Id_1245,Bike,3,Business,12,5,7:42:39 AM,12,5,7:42:46 AM,12,5,7:43:12 AM,12,5,8:43:19 AM,12,5,9:07:19 AM,17,21.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_416,1440
-Order_No_27430,User_Id_246,Bike,3,Business,22,2,12:53:17 PM,22,2,12:54:01 PM,22,2,1:11:34 PM,22,2,1:29:23 PM,22,2,1:46:49 PM,6,23.9,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_456,1046
-Order_No_21481,User_Id_3521,Bike,3,Business,10,1,2:05:42 PM,10,1,2:07:18 PM,10,1,2:47:10 PM,10,1,2:59:32 PM,10,1,3:16:41 PM,7,,,-1.2597436000000002,36.8282526,-1.2696908000000002,36.81519,Rider_Id_537,1029
-Order_No_12494,User_Id_633,Bike,3,Business,6,3,4:09:01 PM,6,3,4:10:30 PM,6,3,4:20:09 PM,6,3,4:29:26 PM,6,3,4:48:22 PM,11,27.6,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_576,1136
-Order_No_20588,User_Id_1081,Bike,3,Business,5,2,2:43:09 PM,5,2,2:47:56 PM,5,2,3:05:46 PM,5,2,3:14:25 PM,5,2,3:38:08 PM,11,27.3,,-1.2285834999999998,36.882646799999996,-1.290894,36.822971,Rider_Id_168,1423
-Order_No_1807,User_Id_1642,Bike,3,Business,2,4,2:35:28 PM,2,4,2:36:23 PM,2,4,2:45:35 PM,2,4,2:52:38 PM,2,4,3:11:11 PM,5,26.2,,-1.2876365,36.816096,-1.2728069,36.7962362,Rider_Id_616,1113
-Order_No_26876,User_Id_391,Bike,3,Business,1,5,11:12:39 AM,1,5,11:13:27 AM,1,5,11:23:11 AM,1,5,11:25:39 AM,1,5,11:54:32 AM,18,26.5,,-1.2867454,36.8259017,-1.3878504999999999,36.7692885,Rider_Id_237,1733
-Order_No_20499,User_Id_2789,Bike,3,Business,21,4,12:38:22 PM,21,4,12:38:53 PM,21,4,12:44:00 PM,21,4,12:52:01 PM,21,4,1:07:46 PM,5,21.9,,-1.304735,36.784605400000004,-1.300722,36.816865,Rider_Id_312,945
-Order_No_8062,User_Id_1792,Bike,3,Business,30,3,3:09:14 PM,30,3,3:10:53 PM,30,3,3:20:27 PM,30,3,3:21:59 PM,30,3,3:44:22 PM,10,25.2,,-1.288159,36.792527500000006,-1.243307,36.796957899999995,Rider_Id_898,1343
-Order_No_23241,User_Id_435,Bike,3,Business,4,2,9:09:04 AM,4,2,9:09:40 AM,4,2,9:44:20 AM,4,2,9:49:44 AM,4,2,10:47:12 AM,16,16.4,98.9,-1.3411844,36.7108768,-1.300921,36.828195,Rider_Id_831,3448
-Order_No_7282,User_Id_204,Bike,1,Personal,20,4,5:30:00 PM,20,4,5:30:25 PM,20,4,5:44:20 PM,20,4,5:46:21 PM,20,4,6:00:14 PM,6,24.9,,-1.3173868999999998,36.8112044,-1.2964924,36.7873649,Rider_Id_328,833
-Order_No_6054,User_Id_3080,Bike,3,Business,5,3,10:31:15 AM,5,3,10:40:05 AM,5,3,10:44:27 AM,5,3,10:49:23 AM,5,3,11:00:47 AM,4,22.5,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_436,684
-Order_No_8591,User_Id_868,Bike,3,Business,20,3,9:35:02 AM,20,3,9:35:36 AM,20,3,9:40:19 AM,20,3,10:10:49 AM,20,3,10:26:50 AM,8,20.9,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_299,961
-Order_No_21689,User_Id_2956,Bike,3,Business,9,4,4:34:01 PM,9,4,4:35:35 PM,9,4,4:44:46 PM,9,4,4:57:21 PM,9,4,5:33:45 PM,10,,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_930,2184
-Order_No_4353,User_Id_2112,Bike,3,Business,20,1,2:40:19 PM,20,1,2:40:55 PM,20,1,2:44:36 PM,20,1,3:05:20 PM,20,1,3:15:57 PM,5,,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_519,637
-Order_No_1111,User_Id_1792,Bike,3,Business,25,1,2:06:19 PM,25,1,2:06:47 PM,25,1,2:31:10 PM,25,1,2:56:28 PM,25,1,3:23:26 PM,12,28.2,,-1.34474,36.89794570000001,-1.3104506,36.8687235,Rider_Id_955,1618
-Order_No_21108,User_Id_975,Bike,1,Personal,18,4,9:14:23 AM,18,4,9:18:27 AM,18,4,9:26:45 AM,18,4,9:30:28 AM,18,4,9:46:36 AM,3,20.6,,-1.267452,36.806015,-1.2604326000000001,36.8063723,Rider_Id_453,968
-Order_No_27588,User_Id_619,Bike,1,Personal,12,2,11:07:15 AM,12,2,11:07:37 AM,12,2,11:12:24 AM,12,2,11:20:30 AM,12,2,11:27:23 AM,2,21.2,,-1.3014515,36.8120092,-1.2913826000000002,36.8006377,Rider_Id_676,413
-Order_No_3,User_Id_635,Bike,3,Business,29,3,3:00:12 PM,29,3,3:16:26 PM,29,3,3:51:52 PM,29,3,3:53:37 PM,29,3,4:30:03 PM,4,20.9,,-1.2602337,36.79905479999999,-1.2833335,36.8260084,Rider_Id_393,2186
-Order_No_8464,User_Id_640,Bike,3,Business,27,3,2:28:14 PM,27,3,2:42:14 PM,27,3,2:42:41 PM,27,3,3:01:56 PM,27,3,3:32:29 PM,6,,,-1.304033,36.7848695,-1.2833106,36.82402379999999,Rider_Id_431,1833
-Order_No_25543,User_Id_516,Bike,3,Business,7,5,3:25:44 PM,7,5,3:31:29 PM,7,5,3:34:01 PM,7,5,3:37:49 PM,7,5,4:22:11 PM,15,,,-1.2551895,36.7822034,-1.3190988000000001,36.86603970000001,Rider_Id_582,2662
-Order_No_20076,User_Id_802,Bike,3,Business,20,4,3:42:38 PM,20,4,3:46:47 PM,20,4,3:47:17 PM,20,4,4:38:33 PM,20,4,5:16:41 PM,10,22.7,,-1.3226301999999999,36.8719901,-1.2864963,36.8252086,Rider_Id_848,2288
-Order_No_13861,User_Id_3440,Bike,1,Personal,5,5,1:51:11 PM,5,5,1:53:52 PM,5,5,2:02:20 PM,5,5,2:09:21 PM,5,5,2:18:59 PM,3,26.8,,-1.2629818000000002,36.8135865,-1.2617573999999998,36.8025343,Rider_Id_407,578
-Order_No_20324,User_Id_393,Bike,3,Business,28,5,7:46:04 AM,28,5,9:54:31 AM,28,5,9:56:56 AM,28,5,10:11:46 AM,28,5,10:19:18 AM,4,20.7,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_731,452
-Order_No_19470,User_Id_2407,Bike,2,Personal,21,6,9:48:12 AM,21,6,9:48:59 AM,21,6,9:53:30 AM,21,6,9:57:44 AM,21,6,10:08:32 AM,4,13.9,,-1.2678924999999999,36.7629124,-1.2878783999999999,36.7662334,Rider_Id_379,648
-Order_No_24904,User_Id_136,Bike,3,Business,10,1,2:49:17 PM,10,1,2:53:12 PM,10,1,3:08:12 PM,10,1,3:23:30 PM,10,1,3:57:16 PM,8,18.2,,-1.3254522,36.721658399999995,-1.2957616000000003,36.7669835,Rider_Id_226,2026
-Order_No_20094,User_Id_1482,Bike,3,Business,15,4,2:26:53 PM,15,4,2:59:47 PM,15,4,3:14:17 PM,15,4,3:39:42 PM,15,4,3:55:37 PM,10,27.7,,-1.3122473999999997,36.8728544,-1.3385790000000002,36.89630379999999,Rider_Id_675,955
-Order_No_21143,User_Id_28,Bike,3,Business,5,3,9:10:57 AM,5,3,9:11:44 AM,5,3,9:22:37 AM,5,3,9:50:53 AM,5,3,10:16:53 AM,14,18.2,,-1.2935617,36.835057,-1.3384245000000001,36.8794338,Rider_Id_932,1560
-Order_No_26657,User_Id_393,Bike,3,Business,11,1,11:16:00 AM,11,1,11:16:24 AM,11,1,11:16:44 AM,11,1,11:26:38 AM,11,1,11:38:31 AM,5,26.8,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_154,713
-Order_No_23797,User_Id_1500,Bike,3,Business,20,4,8:42:38 AM,20,4,10:20:07 AM,20,4,10:20:14 AM,20,4,10:37:10 AM,20,4,10:56:56 AM,11,22.1,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_39,1186
-Order_No_826,User_Id_972,Bike,3,Business,26,3,9:01:52 AM,26,3,9:06:46 AM,26,3,9:17:21 AM,26,3,9:21:19 AM,26,3,9:39:37 AM,9,19.2,,-1.2862033000000002,36.8152966,-1.2953212,36.7632608,Rider_Id_726,1098
-Order_No_27573,User_Id_1008,Bike,3,Personal,15,3,3:25:53 PM,15,3,3:27:32 PM,15,3,3:33:49 PM,15,3,3:43:45 PM,15,3,4:00:23 PM,8,25.8,,-1.3039934,36.8180494,-1.2615584,36.814950599999996,Rider_Id_427,998
-Order_No_2025,User_Id_2523,Bike,3,Business,18,4,5:00:06 PM,18,4,5:00:21 PM,18,4,5:04:23 PM,18,4,5:09:35 PM,18,4,5:29:24 PM,4,,,-1.2551895,36.7822034,-1.2633048999999998,36.803125099999995,Rider_Id_168,1189
-Order_No_27793,User_Id_1306,Bike,2,Personal,6,6,2:35:48 PM,6,6,2:37:43 PM,6,6,2:48:38 PM,6,6,2:50:48 PM,6,6,3:02:25 PM,6,28.2,,-1.2839026000000002,36.80893,-1.274377,36.770452,Rider_Id_622,697
-Order_No_21975,User_Id_2767,Bike,3,Business,6,6,1:58:11 PM,6,6,2:06:00 PM,6,6,2:10:51 PM,6,6,2:20:11 PM,6,6,2:32:45 PM,3,27.9,,-1.257773,36.795486,-1.2590031000000002,36.8078168,Rider_Id_905,754
-Order_No_17293,User_Id_3752,Bike,3,Business,14,2,11:44:27 AM,14,2,11:44:50 AM,14,2,12:00:18 PM,14,2,12:06:34 PM,14,2,12:24:27 PM,2,,,-1.256594,36.8002785,-1.2753108999999998,36.8130185,Rider_Id_205,1073
-Order_No_15190,User_Id_269,Bike,3,Business,11,1,3:09:53 PM,11,1,3:11:37 PM,11,1,3:24:18 PM,11,1,3:31:22 PM,11,1,3:56:46 PM,15,23.1,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_726,1524
-Order_No_19581,User_Id_2828,Bike,2,Business,6,5,12:03:48 PM,6,5,12:04:10 PM,6,5,12:17:27 PM,6,5,12:47:48 PM,6,5,12:49:41 PM,3,20.4,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_486,113
-Order_No_25235,User_Id_972,Bike,3,Business,7,6,9:55:11 AM,7,6,9:55:47 AM,7,6,10:01:54 AM,7,6,10:05:07 AM,7,6,10:36:09 AM,14,13.9,,-1.2862033000000002,36.8152966,-1.3298923,36.7590619,Rider_Id_88,1862
-Order_No_10214,User_Id_2523,Bike,3,Business,10,3,9:51:10 AM,10,3,9:51:46 AM,10,3,10:02:10 AM,10,3,10:03:22 AM,10,3,10:22:25 AM,7,21.7,,-1.2551895,36.7822034,-1.282937,36.781698399999996,Rider_Id_828,1143
-Order_No_9455,User_Id_2174,Bike,3,Business,15,6,10:56:31 AM,15,6,10:56:45 AM,15,6,10:58:41 AM,15,6,11:30:12 AM,15,6,11:56:00 AM,6,,,-1.2482781,36.884091999999995,-1.2241625,36.8851115,Rider_Id_216,1548
-Order_No_20491,User_Id_2987,Bike,3,Business,10,1,1:10:16 PM,10,1,2:27:03 PM,10,1,2:34:18 PM,10,1,2:36:39 PM,10,1,2:51:25 PM,3,18.8,,-1.2702120000000001,36.82108770000001,-1.253654,36.8204391,Rider_Id_646,886
-Order_No_25021,User_Id_2069,Bike,3,Business,23,2,11:29:49 AM,23,2,11:31:09 AM,23,2,11:36:43 AM,23,2,11:44:40 AM,23,2,11:57:39 AM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_96,779
-Order_No_13288,User_Id_1216,Bike,3,Personal,3,2,9:05:25 AM,3,2,9:05:45 AM,3,2,9:07:37 AM,3,2,9:14:41 AM,3,2,9:28:50 AM,8,15.1,1.9,-1.2551895,36.7822034,-1.285355,36.795287,Rider_Id_103,849
-Order_No_28058,User_Id_3686,Bike,3,Business,2,5,2:39:09 PM,2,5,2:40:04 PM,2,5,2:43:35 PM,2,5,2:49:32 PM,2,5,2:59:09 PM,4,25.3,,-1.2551895,36.7822034,-1.2737013000000001,36.806912200000006,Rider_Id_720,577
-Order_No_21965,User_Id_3565,Bike,3,Business,27,7,3:50:41 PM,27,7,4:05:54 PM,27,7,4:29:41 PM,27,7,4:45:59 PM,27,7,5:12:19 PM,16,21.9,,-1.214328,36.7911903,-1.2844665000000002,36.7866113,Rider_Id_103,1580
-Order_No_27334,User_Id_242,Bike,3,Business,30,3,9:57:20 AM,30,3,9:57:26 AM,30,3,10:02:46 AM,30,3,10:08:10 AM,30,3,10:40:12 AM,18,17.4,,-1.2553615,36.789537599999996,-1.3385790000000002,36.89630379999999,Rider_Id_103,1922
-Order_No_6389,User_Id_116,Bike,3,Personal,7,4,11:22:42 AM,7,4,11:23:30 AM,7,4,12:22:00 PM,7,4,12:57:48 PM,7,4,2:20:16 PM,17,27.8,,-1.3433181,36.9138018,-1.2897399,36.8270975,Rider_Id_300,4948
-Order_No_15329,User_Id_528,Bike,2,Personal,22,2,1:45:01 PM,22,2,1:55:44 PM,22,2,2:01:52 PM,22,2,2:07:13 PM,22,2,3:00:34 PM,9,21.4,,-1.2866544,36.82179779999999,-1.3251825000000002,36.78975870000001,Rider_Id_123,3201
-Order_No_3447,User_Id_393,Bike,3,Business,30,2,2:35:46 PM,30,2,2:37:54 PM,30,2,2:38:03 PM,30,2,2:59:40 PM,30,2,2:59:44 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,4
-Order_No_17526,User_Id_2873,Bike,1,Personal,13,5,3:34:11 PM,13,5,3:35:44 PM,13,5,3:47:03 PM,13,5,3:52:54 PM,13,5,4:21:44 PM,13,19.1,,-1.2406196000000003,36.7736495,-1.2947442,36.7624888,Rider_Id_752,1730
-Order_No_20089,User_Id_280,Bike,1,Personal,12,6,6:57:30 PM,12,6,6:58:19 PM,12,6,7:16:58 PM,12,6,7:34:16 PM,12,6,8:15:03 PM,8,20.1,,-1.2696183,36.8021778,-1.2850645,36.84268370000001,Rider_Id_159,2447
-Order_No_21506,User_Id_782,Bike,1,Personal,20,4,10:42:17 AM,20,4,10:42:30 AM,20,4,10:45:19 AM,20,4,10:53:26 AM,20,4,11:52:58 AM,9,24.5,,-1.2655332000000001,36.8094648,-1.3134523999999999,36.8064495,Rider_Id_38,3572
-Order_No_27046,User_Id_3200,Bike,1,Personal,22,3,1:52:57 PM,22,3,2:08:03 PM,22,3,2:31:55 PM,22,3,2:39:44 PM,22,3,3:34:40 PM,36,25.6,,-1.15393,36.936186,-1.274381,36.7272265,Rider_Id_400,3296
-Order_No_27303,User_Id_2008,Bike,3,Business,26,2,2:53:18 PM,26,2,2:54:32 PM,26,2,3:03:28 PM,26,2,3:11:05 PM,26,2,3:28:54 PM,6,29.8,,-1.2508229,36.7895263,-1.2596241000000001,36.762407,Rider_Id_958,1069
-Order_No_2260,User_Id_136,Bike,3,Business,28,1,9:39:02 AM,28,1,9:39:29 AM,28,1,9:44:38 AM,28,1,9:54:31 AM,28,1,10:28:08 AM,15,19.4,,-1.2560874,36.7931031,-1.3254522,36.721658399999995,Rider_Id_428,2017
-Order_No_25005,User_Id_2956,Bike,3,Business,13,1,12:15:25 PM,13,1,12:16:00 PM,13,1,12:18:45 PM,13,1,12:22:04 PM,13,1,12:34:20 PM,4,,,-1.2584143,36.8048002,-1.2635051000000002,36.7829162,Rider_Id_931,736
-Order_No_8567,User_Id_2,Bike,1,Personal,15,3,9:55:25 AM,15,3,9:56:29 AM,15,3,10:06:17 AM,15,3,10:13:20 AM,15,3,10:42:50 AM,6,16.3,,-1.280467,36.7859296,-1.2916556000000001,36.8262516,Rider_Id_592,1770
-Order_No_28,User_Id_1840,Bike,1,Personal,13,3,4:54:06 PM,13,3,5:05:21 PM,13,3,5:08:41 PM,13,3,5:21:34 PM,13,3,5:57:16 PM,10,21.8,,-1.2559559999999999,36.772901,-1.2988168,36.8198572,Rider_Id_908,2142
-Order_No_6013,User_Id_2587,Bike,3,Business,7,1,12:24:56 PM,7,1,12:27:01 PM,7,1,1:15:37 PM,7,1,1:18:00 PM,7,1,1:40:11 PM,2,24.9,,-1.2896431000000002,36.8086201,-1.28878,36.8168312,Rider_Id_899,1331
-Order_No_438,User_Id_3291,Bike,3,Business,14,4,11:05:51 AM,14,4,11:06:09 AM,14,4,11:19:23 AM,14,4,11:33:08 AM,14,4,11:54:03 AM,12,23.9,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_260,1255
-Order_No_11459,User_Id_3647,Bike,3,Business,22,5,2:55:49 PM,22,5,2:56:34 PM,22,5,3:13:33 PM,22,5,3:18:22 PM,22,5,3:37:40 PM,8,31.0,,-1.2726389999999999,36.794723,-1.2972794,36.801481700000004,Rider_Id_205,1158
-Order_No_27163,User_Id_2677,Bike,3,Business,22,3,3:24:22 PM,22,3,3:38:50 PM,22,3,3:43:53 PM,22,3,3:52:45 PM,22,3,4:19:06 PM,15,24.4,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_291,1581
-Order_No_17153,User_Id_1724,Bike,3,Business,3,5,9:47:16 AM,3,5,9:47:46 AM,3,5,9:59:13 AM,3,5,10:07:16 AM,3,5,10:19:16 AM,5,,,-1.2638132,36.784977500000004,-1.2571472,36.795063299999995,Rider_Id_778,720
-Order_No_7844,User_Id_1304,Bike,3,Business,24,3,1:05:04 PM,24,3,1:05:24 PM,24,3,1:12:47 PM,24,3,1:18:34 PM,24,3,1:46:40 PM,16,19.9,,-1.3332749,36.870814700000004,-1.2564152,36.779280299999996,Rider_Id_620,1686
-Order_No_5110,User_Id_1363,Bike,3,Business,23,3,8:32:30 AM,23,3,8:32:38 AM,23,3,8:39:42 AM,23,3,8:41:42 AM,23,3,8:45:36 AM,2,,,-1.3004062,36.829741,-1.2945552,36.839749700000006,Rider_Id_842,234
-Order_No_5837,User_Id_658,Bike,2,Personal,30,1,10:07:30 AM,30,1,10:08:15 AM,30,1,10:35:12 AM,30,1,10:37:36 AM,30,1,12:48:59 PM,27,22.5,,-1.2827946000000001,36.768944899999994,-1.1560774,36.7067359,Rider_Id_857,7883
-Order_No_3843,User_Id_3334,Bike,1,Personal,9,4,3:54:32 PM,9,4,4:00:09 PM,9,4,4:24:48 PM,9,4,4:26:40 PM,9,4,4:44:54 PM,4,25.7,,-1.2699441999999999,36.8206553,-1.2640778999999998,36.8402848,Rider_Id_636,1094
-Order_No_14523,User_Id_3689,Bike,3,Business,17,5,6:02:43 PM,17,5,6:13:25 PM,17,5,6:14:56 PM,17,5,6:49:03 PM,17,5,7:21:12 PM,12,18.9,,-1.2297202,36.881950700000004,-1.2527958000000001,36.8003131,Rider_Id_272,1929
-Order_No_9410,User_Id_3647,Bike,3,Business,11,4,10:50:17 AM,11,4,10:50:35 AM,11,4,11:12:38 AM,11,4,11:18:08 AM,11,4,11:40:24 AM,6,25.6,,-1.2726389999999999,36.794723,-1.303108,36.791261999999996,Rider_Id_260,1336
-Order_No_17517,User_Id_640,Bike,3,Business,21,4,9:06:29 AM,21,4,9:51:34 AM,21,4,9:59:51 AM,21,4,10:32:07 AM,21,4,11:44:53 AM,23,21.7,,-1.3724413,36.9284922,-1.304033,36.7848695,Rider_Id_478,4366
-Order_No_11148,User_Id_393,Bike,3,Business,6,3,1:02:11 PM,6,3,1:03:09 PM,6,3,1:23:18 PM,6,3,1:41:34 PM,6,3,2:17:10 PM,14,27.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_559,2136
-Order_No_21932,User_Id_269,Bike,3,Business,25,4,9:14:19 AM,25,4,9:36:20 AM,25,4,9:42:02 AM,25,4,9:50:49 AM,25,4,10:32:40 AM,23,16.6,2.0,-1.3169591,36.7000029,-1.3332749,36.870814700000004,Rider_Id_905,2511
-Order_No_15757,User_Id_391,Bike,3,Business,19,2,3:13:39 PM,19,2,3:17:56 PM,19,2,3:26:23 PM,19,2,3:34:17 PM,19,2,3:51:34 PM,5,29.5,,-1.2602337,36.79905479999999,-1.2867454,36.8259017,Rider_Id_785,1037
-Order_No_7321,User_Id_913,Bike,3,Personal,16,2,6:12:56 PM,16,2,6:13:27 PM,16,2,6:32:40 PM,16,2,6:38:51 PM,16,2,6:48:00 PM,6,,,-1.3034413,36.777010600000004,-1.2692689,36.8055169,Rider_Id_678,549
-Order_No_14549,User_Id_59,Bike,3,Business,8,3,2:42:55 PM,8,3,2:43:15 PM,8,3,2:48:50 PM,8,3,2:55:40 PM,8,3,3:02:10 PM,3,26.0,,-1.290894,36.822971,-1.2673299999999998,36.823679999999996,Rider_Id_629,390
-Order_No_9881,User_Id_2956,Bike,3,Business,23,4,4:33:54 PM,23,4,4:34:12 PM,23,4,4:37:19 PM,23,4,4:40:44 PM,23,4,4:48:30 PM,3,20.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_726,466
-Order_No_4277,User_Id_265,Bike,3,Business,11,1,4:06:10 PM,11,1,4:06:21 PM,11,1,4:06:37 PM,11,1,4:17:48 PM,11,1,4:51:35 PM,12,28.0,,-1.300921,36.828195,-1.3253021999999999,36.8803995,Rider_Id_31,2027
-Order_No_5613,User_Id_478,Bike,3,Business,6,3,9:03:48 AM,6,3,9:04:19 AM,6,3,9:09:25 AM,6,3,9:12:37 AM,6,3,9:22:15 AM,3,,,-1.2551895,36.7822034,-1.2628638,36.8070399,Rider_Id_958,578
-Order_No_18407,User_Id_3711,Bike,3,Business,7,1,11:15:39 AM,7,1,11:15:47 AM,7,1,11:30:25 AM,7,1,11:35:31 AM,7,1,11:51:06 AM,3,24.7,,-1.3012008,36.764868,-1.2979147,36.785781400000005,Rider_Id_846,935
-Order_No_1705,User_Id_2154,Bike,3,Business,13,4,12:16:58 PM,13,4,12:23:32 PM,13,4,12:38:43 PM,13,4,12:41:58 PM,13,4,1:15:33 PM,5,22.0,,-1.2765736,36.851364600000004,-1.2885315,36.8230419,Rider_Id_323,2015
-Order_No_14361,User_Id_970,Bike,3,Business,10,1,2:06:19 PM,10,1,2:06:59 PM,10,1,2:19:07 PM,10,1,2:28:56 PM,10,1,2:36:32 PM,2,18.5,,-1.2551895,36.7822034,-1.2567251,36.798840999999996,Rider_Id_76,456
-Order_No_6813,User_Id_633,Bike,3,Business,21,4,10:28:16 AM,21,4,10:28:24 AM,21,4,10:29:54 AM,21,4,10:31:22 AM,21,4,10:39:08 AM,4,21.6,,-1.3004062,36.829741,-1.3173868999999998,36.8112044,Rider_Id_329,466
-Order_No_22645,User_Id_2107,Bike,1,Personal,24,1,10:56:52 AM,24,1,10:57:20 AM,24,1,11:01:14 AM,24,1,11:11:12 AM,24,1,11:41:07 AM,8,22.7,,-1.2972654,36.8330766,-1.2813481000000002,36.793093299999995,Rider_Id_848,1795
-Order_No_15062,User_Id_3222,Bike,3,Business,28,4,3:18:07 PM,28,4,3:19:42 PM,28,4,3:51:46 PM,28,4,3:55:26 PM,28,4,4:45:46 PM,19,27.4,,-1.2551895,36.7822034,-1.334474,36.88389,Rider_Id_770,3020
-Order_No_10554,User_Id_571,Bike,3,Business,23,3,11:01:42 AM,23,3,11:02:00 AM,23,3,11:06:02 AM,23,3,11:23:57 AM,23,3,11:50:53 AM,8,,,-1.3236332,36.701152,-1.2981318000000002,36.718714,Rider_Id_210,1616
-Order_No_25606,User_Id_1244,Bike,3,Business,24,1,9:12:10 AM,24,1,9:12:18 AM,24,1,9:15:36 AM,24,1,9:19:29 AM,24,1,9:34:24 AM,6,19.5,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_958,895
-Order_No_16446,User_Id_2754,Bike,3,Personal,14,4,2:51:33 PM,14,4,2:52:33 PM,14,4,3:02:41 PM,14,4,3:05:31 PM,14,4,3:22:41 PM,5,28.1,,-1.2580936000000003,36.7967408,-1.2885417,36.810052299999995,Rider_Id_498,1030
-Order_No_10026,User_Id_2989,Bike,3,Business,13,5,9:55:53 AM,13,5,9:58:58 AM,13,5,10:00:39 AM,13,5,10:06:47 AM,13,5,10:32:20 AM,18,15.4,,-1.3392837,36.76282898,-1.2551895,36.7822034,Rider_Id_103,1533
-Order_No_23736,User_Id_2344,Bike,3,Business,6,2,12:28:46 PM,6,2,12:35:20 PM,6,2,12:36:00 PM,6,2,12:50:07 PM,6,2,1:09:50 PM,10,25.2,,-1.2551895,36.7822034,-1.2999305,36.843617,Rider_Id_38,1183
-Order_No_20582,User_Id_2701,Bike,3,Business,1,4,9:17:01 AM,1,4,9:18:28 AM,1,4,9:30:54 AM,1,4,9:55:48 AM,1,4,10:06:21 AM,5,,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_171,633
-Order_No_20411,User_Id_700,Bike,3,Business,19,3,12:33:18 PM,19,3,1:20:24 PM,19,3,1:28:31 PM,19,3,1:56:39 PM,19,3,2:13:17 PM,5,24.2,,-1.302478,36.786003799999996,-1.3123652,36.796426600000004,Rider_Id_548,998
-Order_No_3799,User_Id_2030,Bike,3,Business,18,2,2:02:27 PM,18,2,2:34:25 PM,18,2,2:34:50 PM,18,2,2:58:59 PM,18,2,3:04:28 PM,7,25.5,,-1.2482465,36.8889062,-1.2156006000000001,36.8916865,Rider_Id_539,329
-Order_No_210,User_Id_3088,Bike,3,Business,21,4,6:51:45 PM,21,4,7:23:49 PM,21,4,8:04:25 PM,21,4,8:16:38 PM,21,4,8:45:33 PM,10,21.5,,-1.2696795,36.8640088,-1.2551358999999997,36.9062227,Rider_Id_436,1735
-Order_No_305,User_Id_391,Bike,3,Business,28,1,3:52:32 PM,28,1,4:29:09 PM,28,1,4:42:51 PM,28,1,4:48:00 PM,28,1,5:23:07 PM,5,25.2,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_819,2107
-Order_No_96,User_Id_2330,Bike,3,Business,31,4,4:10:26 PM,31,4,4:10:43 PM,31,4,4:19:54 PM,31,4,4:26:04 PM,31,4,4:49:25 PM,8,28.8,,-1.3270197,36.7037303,-1.3015197,36.7658456,Rider_Id_831,1401
-Order_No_2545,User_Id_3565,Bike,3,Business,11,7,9:33:18 AM,11,7,9:33:27 AM,11,7,9:45:49 AM,11,7,9:50:07 AM,11,7,10:29:30 AM,20,21.1,,-1.2844665000000002,36.7866113,-1.3681253,36.915529799999995,Rider_Id_805,2363
-Order_No_27493,User_Id_2212,Bike,3,Business,13,3,9:57:08 AM,13,3,9:59:16 AM,13,3,10:15:46 AM,13,3,10:33:25 AM,13,3,11:09:36 AM,8,24.6,,-1.2830276000000003,36.8297516,-1.2584143,36.8048002,Rider_Id_548,2171
-Order_No_15374,User_Id_2587,Bike,3,Business,11,2,3:18:08 PM,11,2,3:29:40 PM,11,2,3:30:46 PM,11,2,3:42:23 PM,11,2,4:05:13 PM,2,21.3,,-1.28878,36.8168312,-1.2880128999999998,36.8308097,Rider_Id_519,1370
-Order_No_10427,User_Id_248,Bike,3,Business,26,5,10:35:17 AM,26,5,10:35:40 AM,26,5,10:42:52 AM,26,5,10:47:41 AM,26,5,11:14:00 AM,10,,,-1.304735,36.784605400000004,-1.2623332,36.8091055,Rider_Id_168,1579
-Order_No_11931,User_Id_1209,Bike,3,Business,2,6,9:31:35 AM,2,6,10:47:18 AM,2,6,11:09:51 AM,2,6,11:19:33 AM,2,6,11:35:23 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_523,950
-Order_No_13163,User_Id_2423,Bike,3,Business,21,4,2:28:59 PM,21,4,2:32:34 PM,21,4,2:35:42 PM,21,4,2:43:22 PM,21,4,2:56:21 PM,8,26.2,,-1.2551895,36.7822034,-1.2964347999999999,36.8182757,Rider_Id_801,779
-Order_No_11787,User_Id_635,Bike,3,Business,21,4,7:45:27 AM,21,4,7:49:00 AM,21,4,8:06:01 AM,21,4,8:20:42 AM,21,4,10:06:15 AM,19,21.9,,-1.3081376,36.83297720000001,-1.223961,36.716941299999995,Rider_Id_419,6333
-Order_No_2530,User_Id_1874,Bike,3,Business,3,4,11:06:33 AM,3,4,11:06:49 AM,3,4,11:16:16 AM,3,4,11:19:01 AM,3,4,11:57:00 AM,16,22.0,,-1.2579303999999998,36.8019667,-1.3261882999999999,36.7202391,Rider_Id_486,2279
-Order_No_26539,User_Id_1075,Bike,3,Business,28,4,3:43:51 PM,28,4,3:44:04 PM,28,4,3:49:52 PM,28,4,3:57:56 PM,28,4,4:31:27 PM,10,27.6,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_831,2011
-Order_No_26812,User_Id_1584,Bike,3,Business,7,3,9:36:39 AM,7,3,11:47:25 AM,7,3,11:54:39 AM,7,3,12:04:36 PM,7,3,12:18:36 PM,7,25.2,,-1.2771155,36.826679999999996,-1.2969741000000001,36.7856612,Rider_Id_88,840
-Order_No_21960,User_Id_3071,Bike,1,Personal,31,4,5:40:07 PM,31,4,6:11:50 PM,31,4,6:32:05 PM,31,4,6:51:46 PM,31,4,7:30:48 PM,13,22.5,,-1.2384876,36.877810700000005,-1.3072409999999999,36.830236799999994,Rider_Id_116,2342
-Order_No_103,User_Id_2457,Bike,1,Business,21,5,7:36:54 AM,21,5,7:37:40 AM,21,5,8:04:08 AM,21,5,8:19:59 AM,21,5,8:28:15 AM,4,19.8,,-1.2559559999999999,36.789867,-1.269534,36.80697,Rider_Id_116,496
-Order_No_20290,User_Id_2022,Bike,1,Personal,21,1,4:39:45 PM,21,1,4:39:57 PM,21,1,4:42:17 PM,21,1,4:50:27 PM,21,1,5:04:14 PM,6,,,-1.2633493999999998,36.7959696,-1.2817125,36.8201761,Rider_Id_207,827
-Order_No_18581,User_Id_3222,Bike,3,Business,15,1,2:51:36 PM,15,1,2:54:37 PM,15,1,3:05:16 PM,15,1,3:16:26 PM,15,1,3:35:39 PM,13,,,-1.2551895,36.7822034,-1.2045978000000002,36.759561100000006,Rider_Id_449,1153
-Order_No_8891,User_Id_1525,Bike,3,Business,29,5,12:40:04 PM,29,5,12:40:22 PM,29,5,12:57:54 PM,29,5,1:01:24 PM,29,5,1:50:07 PM,22,26.9,,-1.3197021,36.9092821,-1.2597839,36.808196,Rider_Id_411,2923
-Order_No_1629,User_Id_2924,Bike,2,Personal,4,4,1:14:18 PM,4,4,1:14:56 PM,4,4,1:23:32 PM,4,4,1:41:34 PM,4,4,1:57:47 PM,6,24.6,,-1.266486,36.807675,-1.2504511,36.7724943,Rider_Id_857,973
-Order_No_14014,User_Id_3426,Bike,3,Business,5,1,4:10:53 PM,5,1,4:11:23 PM,5,1,4:26:05 PM,5,1,4:29:08 PM,5,1,4:59:25 PM,5,26.0,,-1.3014461000000002,36.7661381,-1.2817426,36.7846148,Rider_Id_343,1817
-Order_No_20642,User_Id_2183,Bike,3,Business,10,4,1:25:28 PM,10,4,1:26:00 PM,10,4,1:30:12 PM,10,4,1:41:20 PM,10,4,2:14:27 PM,16,21.7,1.1,-1.2559559999999999,36.789867,-1.3463041999999998,36.768164,Rider_Id_742,1987
-Order_No_19390,User_Id_1148,Bike,3,Business,8,1,11:41:44 AM,8,1,11:43:25 AM,8,1,11:48:40 AM,8,1,12:04:20 PM,8,1,12:28:53 PM,9,24.3,,-1.3100586,36.850032899999995,-1.2710687,36.8142839,Rider_Id_848,1473
-Order_No_668,User_Id_3599,Bike,3,Business,14,2,10:46:33 AM,14,2,11:04:17 AM,14,2,11:50:28 AM,14,2,12:05:05 PM,14,2,12:48:28 PM,20,,,-1.3302996,36.8707935,-1.3166316,36.713034,Rider_Id_519,2603
-Order_No_289,User_Id_2956,Bike,3,Business,27,6,2:23:57 PM,27,6,2:32:00 PM,27,6,2:44:01 PM,27,6,2:47:52 PM,27,6,3:13:36 PM,5,,,-1.2584143,36.8048002,-1.281754,36.8324229,Rider_Id_171,1544
-Order_No_13479,User_Id_346,Bike,3,Business,14,2,3:57:37 PM,14,2,4:03:10 PM,14,2,4:45:36 PM,14,2,5:19:22 PM,14,2,5:21:13 PM,10,,,-1.2274192,36.80083079999999,-1.28878,36.8168312,Rider_Id_193,111
-Order_No_21068,User_Id_637,Bike,1,Business,17,2,2:12:18 PM,17,2,2:14:36 PM,17,2,2:28:34 PM,17,2,2:49:58 PM,17,2,3:09:35 PM,8,24.6,,-1.2551895,36.7822034,-1.2577288999999998,36.74107720000001,Rider_Id_100,1177
-Order_No_8951,User_Id_1471,Bike,3,Business,3,1,12:08:03 PM,3,1,12:10:40 PM,3,1,12:22:39 PM,3,1,12:23:47 PM,3,1,1:06:58 PM,5,21.6,,-1.2551895,36.7822034,-1.2684319,36.81658529999999,Rider_Id_194,2591
-Order_No_5262,User_Id_3404,Bike,3,Business,7,4,12:23:09 PM,7,4,12:24:18 PM,7,4,12:32:14 PM,7,4,12:37:52 PM,7,4,1:31:20 PM,5,24.3,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_727,3208
-Order_No_10521,User_Id_1796,Bike,3,Business,25,4,11:30:37 AM,25,4,11:30:52 AM,25,4,11:35:50 AM,25,4,11:45:46 AM,25,4,12:06:15 PM,13,,,-1.2600926000000001,36.8088685,-1.331619,36.847976,Rider_Id_484,1229
-Order_No_22272,User_Id_3160,Bike,3,Business,16,3,1:58:36 PM,16,3,1:59:00 PM,16,3,2:10:16 PM,16,3,2:16:09 PM,16,3,2:52:25 PM,17,25.7,,-1.2844665000000002,36.7866113,-1.2698307,36.8801649,Rider_Id_789,2176
-Order_No_774,User_Id_2174,Bike,3,Business,16,3,12:42:26 PM,16,3,12:42:31 PM,16,3,12:42:43 PM,16,3,1:10:17 PM,16,3,1:23:12 PM,10,22.9,,-1.2482781,36.884091999999995,-1.261795,36.822333,Rider_Id_216,775
-Order_No_8256,User_Id_3674,Bike,3,Business,20,4,10:56:40 AM,20,4,10:58:09 AM,20,4,11:04:12 AM,20,4,11:09:55 AM,20,4,11:46:53 AM,15,22.3,,-1.2551895,36.7822034,-1.3323107,36.8692616,Rider_Id_205,2218
-Order_No_25878,User_Id_2879,Bike,3,Business,8,5,3:54:41 PM,8,5,3:59:40 PM,8,5,4:44:00 PM,8,5,4:58:03 PM,8,5,6:00:32 PM,18,19.9,,-1.2639198,36.8069299,-1.326207,36.889066,Rider_Id_268,3749
-Order_No_3602,User_Id_2801,Bike,3,Business,10,3,3:40:40 PM,10,3,3:41:13 PM,10,3,3:48:41 PM,10,3,4:33:59 PM,10,3,4:53:46 PM,9,27.1,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_297,1187
-Order_No_9988,User_Id_3560,Bike,3,Business,7,4,10:13:00 AM,7,4,10:14:30 AM,7,4,10:27:01 AM,7,4,10:31:13 AM,7,4,10:48:14 AM,6,,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_531,1021
-Order_No_15267,User_Id_1280,Bike,3,Business,9,4,8:57:33 AM,9,4,8:58:22 AM,9,4,9:05:39 AM,9,4,9:10:23 AM,9,4,9:35:12 AM,8,,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_953,1489
-Order_No_428,User_Id_627,Bike,3,Business,11,1,2:54:02 PM,11,1,2:54:28 PM,11,1,3:02:21 PM,11,1,3:15:39 PM,11,1,3:24:03 PM,3,28.0,,-1.2571472,36.795063299999995,-1.2635089,36.78873370000001,Rider_Id_205,504
-Order_No_22265,User_Id_2801,Bike,3,Business,5,2,4:26:58 PM,5,2,4:32:43 PM,5,2,4:45:07 PM,5,2,4:49:48 PM,5,2,5:04:12 PM,4,,,-1.2987276,36.81763370000001,-1.307726,36.839117,Rider_Id_85,864
-Order_No_17168,User_Id_2982,Bike,3,Business,9,5,11:30:57 AM,9,5,11:47:11 AM,9,5,12:31:07 PM,9,5,12:35:17 PM,9,5,1:36:27 PM,18,25.7,,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_810,3670
-Order_No_8923,User_Id_53,Bike,3,Business,27,1,3:04:06 PM,27,1,3:06:02 PM,27,1,3:14:07 PM,27,1,3:22:47 PM,27,1,3:55:16 PM,6,22.9,,-1.2770708,36.8231093,-1.2556732,36.7927849,Rider_Id_27,1949
-Order_No_20965,User_Id_3372,Bike,3,Business,29,7,5:37:36 PM,29,7,5:37:48 PM,29,7,5:59:39 PM,29,7,6:01:14 PM,29,7,6:55:26 PM,26,15.4,,-1.214328,36.7911903,-1.3169591,36.7000029,Rider_Id_208,3252
-Order_No_5601,User_Id_3027,Bike,1,Personal,1,7,5:49:08 PM,1,7,5:54:00 PM,1,7,6:00:07 PM,1,7,6:10:26 PM,1,7,6:26:07 PM,9,21.0,,-1.3100608999999999,36.820536499999996,-1.2785902,36.79315820000001,Rider_Id_105,941
-Order_No_23583,User_Id_2102,Bike,3,Business,10,1,12:46:06 PM,10,1,12:48:05 PM,10,1,12:57:09 PM,10,1,1:01:11 PM,10,1,1:29:55 PM,14,18.4,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_856,1724
-Order_No_25008,User_Id_814,Bike,3,Business,10,4,3:01:25 PM,10,4,3:01:38 PM,10,4,3:04:30 PM,10,4,3:10:48 PM,10,4,3:47:02 PM,9,25.4,,-1.3014461000000002,36.7661381,-1.2571472,36.795063299999995,Rider_Id_829,2174
-Order_No_7740,User_Id_2700,Bike,3,Business,19,5,9:21:21 AM,19,5,9:23:17 AM,19,5,9:41:09 AM,19,5,9:46:15 AM,19,5,10:04:24 AM,9,20.1,,-1.2845834999999999,36.8079121,-1.3080793,36.7505396,Rider_Id_726,1089
-Order_No_12887,User_Id_3017,Bike,3,Business,9,3,2:56:35 PM,9,3,2:56:54 PM,9,3,3:01:19 PM,9,3,3:07:52 PM,9,3,3:28:28 PM,2,23.1,,-1.2915271000000002,36.782702,-1.2963097,36.7688221,Rider_Id_663,1236
-Order_No_24858,User_Id_3674,Bike,3,Business,10,3,10:57:55 AM,10,3,10:58:13 AM,10,3,10:59:53 AM,10,3,11:09:18 AM,10,3,11:24:25 AM,8,27.9,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_291,907
-Order_No_15100,User_Id_3149,Bike,3,Business,25,4,1:22:40 PM,25,4,1:23:37 PM,25,4,1:27:28 PM,25,4,1:40:37 PM,25,4,1:48:26 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_877,469
-Order_No_20367,User_Id_3294,Bike,1,Personal,3,2,10:35:37 AM,3,2,10:51:42 AM,3,2,10:59:34 AM,3,2,11:03:10 AM,3,2,11:31:15 AM,7,18.3,,-1.265432,36.795034,-1.2985749,36.8088,Rider_Id_912,1685
-Order_No_7903,User_Id_2074,Bike,2,Personal,13,5,9:57:14 AM,13,5,10:00:34 AM,13,5,10:09:34 AM,13,5,10:12:01 AM,13,5,10:29:52 AM,5,15.4,,-1.2741728,36.79691079999999,-1.2949473999999999,36.8270407,Rider_Id_722,1071
-Order_No_17664,User_Id_3525,Bike,3,Business,8,1,10:06:16 AM,8,1,10:07:03 AM,8,1,10:47:03 AM,8,1,11:10:33 AM,8,1,11:46:47 AM,20,27.9,,-1.3533402,36.9078924,-1.2571472,36.795063299999995,Rider_Id_856,2174
-Order_No_16424,User_Id_496,Bike,3,Business,27,5,12:56:59 PM,27,5,12:57:38 PM,27,5,1:02:46 PM,27,5,1:10:08 PM,27,5,1:20:46 PM,6,22.4,,-1.2551895,36.7822034,-1.2843046,36.774613200000005,Rider_Id_445,638
-Order_No_25032,User_Id_393,Bike,3,Business,18,1,2:03:05 PM,18,1,2:03:56 PM,18,1,2:04:34 PM,18,1,2:17:42 PM,18,1,2:51:58 PM,14,29.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_828,2056
-Order_No_16954,User_Id_661,Bike,3,Business,22,2,3:25:52 PM,22,2,3:27:47 PM,22,2,3:53:04 PM,22,2,4:12:00 PM,22,2,4:26:42 PM,4,21.2,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_116,882
-Order_No_18993,User_Id_868,Bike,3,Business,22,3,5:00:09 PM,22,3,5:07:39 PM,22,3,5:33:52 PM,22,3,5:40:30 PM,22,3,5:57:29 PM,6,23.8,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_953,1019
-Order_No_3436,User_Id_1927,Bike,3,Business,10,1,3:34:07 PM,10,1,4:24:30 PM,10,1,4:34:37 PM,10,1,5:04:29 PM,10,1,5:34:19 PM,18,18.6,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_85,1790
-Order_No_20268,User_Id_69,Bike,3,Personal,16,4,3:16:07 PM,16,4,3:17:04 PM,16,4,3:21:37 PM,16,4,3:25:01 PM,16,4,4:02:06 PM,7,,,-1.2979147,36.785781400000005,-1.2698306000000001,36.7644059,Rider_Id_269,2225
-Order_No_7706,User_Id_517,Bike,3,Business,10,1,7:29:57 AM,10,1,8:34:29 AM,10,1,8:40:19 AM,10,1,9:09:14 AM,10,1,9:22:38 AM,4,18.7,1.9,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_623,804
-Order_No_5366,User_Id_736,Bike,3,Business,22,1,10:26:27 AM,22,1,10:27:36 AM,22,1,10:47:41 AM,22,1,10:51:51 AM,22,1,11:37:09 AM,13,24.7,,-1.2943818,36.7858765,-1.3332749,36.870814700000004,Rider_Id_532,2718
-Order_No_28150,User_Id_1482,Bike,3,Business,15,1,10:01:57 AM,15,1,10:02:13 AM,15,1,10:09:38 AM,15,1,10:14:30 AM,15,1,10:45:37 AM,14,,,-1.2750257,36.8157165,-1.3385790000000002,36.89630379999999,Rider_Id_167,1867
-Order_No_4581,User_Id_3300,Bike,3,Business,13,1,2:55:16 PM,13,1,2:55:32 PM,13,1,3:24:18 PM,13,1,3:37:52 PM,13,1,4:00:51 PM,13,19.6,,-1.3313643,36.857742200000004,-1.303596,36.778378000000004,Rider_Id_213,1379
-Order_No_21268,User_Id_1927,Bike,3,Business,16,5,3:50:03 PM,16,5,4:21:57 PM,16,5,4:27:08 PM,16,5,4:33:10 PM,16,5,5:17:58 PM,20,26.1,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_487,2688
-Order_No_25206,User_Id_2956,Bike,3,Business,13,3,12:29:28 PM,13,3,12:30:03 PM,13,3,12:35:07 PM,13,3,12:39:47 PM,13,3,12:49:13 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_318,566
-Order_No_15804,User_Id_814,Bike,3,Business,5,1,2:11:49 PM,5,1,2:14:34 PM,5,1,2:22:39 PM,5,1,2:39:17 PM,5,1,3:14:35 PM,7,26.0,,-1.2571472,36.795063299999995,-1.2878216,36.799597999999996,Rider_Id_770,2118
-Order_No_21603,User_Id_3315,Bike,3,Personal,16,1,2:07:19 PM,16,1,2:08:43 PM,16,1,2:32:34 PM,16,1,2:45:15 PM,16,1,3:13:32 PM,4,22.6,,-1.2939186000000003,36.8069693,-1.2845673,36.780133299999996,Rider_Id_532,1697
-Order_No_15394,User_Id_2350,Bike,1,Personal,4,1,3:30:52 PM,4,1,3:33:40 PM,4,1,3:52:00 PM,4,1,3:56:06 PM,4,1,4:18:03 PM,10,22.5,,-1.2946287,36.7585367,-1.3114711000000001,36.8088708,Rider_Id_312,1317
-Order_No_126,User_Id_1148,Bike,3,Business,23,5,11:22:52 AM,23,5,12:03:15 PM,23,5,12:03:25 PM,23,5,12:41:04 PM,23,5,1:16:25 PM,10,24.2,,-1.3100586,36.850032899999995,-1.2664428,36.809453000000005,Rider_Id_848,2121
-Order_No_11054,User_Id_3698,Bike,3,Business,6,1,3:58:13 PM,6,1,3:58:53 PM,6,1,4:53:21 PM,6,1,5:09:00 PM,6,1,5:51:14 PM,26,,,-1.3244885,36.897791999999995,-1.3108386,36.7555239,Rider_Id_218,2534
-Order_No_10011,User_Id_2330,Bike,3,Business,28,4,2:32:28 PM,28,4,2:38:25 PM,28,4,2:40:28 PM,28,4,2:45:24 PM,28,4,3:24:03 PM,13,30.0,,-1.2284323,36.8757058,-1.2649605,36.798177700000004,Rider_Id_101,2319
-Order_No_4004,User_Id_391,Bike,3,Business,15,5,10:18:09 AM,15,5,10:19:46 AM,15,5,10:47:12 AM,15,5,12:06:21 PM,15,5,12:30:12 PM,6,26.0,,-1.2789469,36.7796691,-1.2784063,36.8222933,Rider_Id_436,1431
-Order_No_26810,User_Id_1603,Bike,1,Personal,28,1,4:51:12 PM,28,1,4:57:15 PM,28,1,5:15:56 PM,28,1,5:17:27 PM,28,1,5:49:03 PM,7,22.5,,-1.2806773999999999,36.8370763,-1.3220191000000001,36.840016600000006,Rider_Id_208,1896
-Order_No_10389,User_Id_3222,Bike,3,Business,18,2,2:58:33 PM,18,2,3:05:12 PM,18,2,3:21:24 PM,18,2,3:44:15 PM,18,2,4:45:30 PM,13,25.8,,-1.2551895,36.7822034,-1.3122286,36.7659612,Rider_Id_685,3675
-Order_No_10392,User_Id_1178,Bike,3,Business,30,1,9:08:59 AM,30,1,9:09:44 AM,30,1,9:30:28 AM,30,1,9:35:54 AM,30,1,9:56:41 AM,9,16.6,2.0,-1.2717608999999999,36.8400291,-1.2862269,36.7841601,Rider_Id_857,1247
-Order_No_14753,User_Id_2330,Bike,3,Business,12,5,5:25:33 PM,12,5,5:25:57 PM,12,5,5:37:27 PM,12,5,5:40:12 PM,12,5,6:06:19 PM,10,26.7,,-1.2645318,36.7981775,-1.3015197,36.7658456,Rider_Id_177,1567
-Order_No_10893,User_Id_186,Bike,3,Business,27,2,11:11:27 AM,27,2,11:56:59 AM,27,2,12:04:44 PM,27,2,12:27:58 PM,27,2,12:40:25 PM,5,23.8,,-1.2902869,36.8085396,-1.2571472,36.795063299999995,Rider_Id_379,747
-Order_No_20837,User_Id_136,Bike,3,Business,12,1,11:23:24 AM,12,1,11:29:59 AM,12,1,11:36:34 AM,12,1,11:43:14 AM,12,1,12:09:39 PM,15,23.6,,-1.3254522,36.721658399999995,-1.2881035,36.8142095,Rider_Id_71,1585
-Order_No_10267,User_Id_1346,Bike,3,Business,14,3,4:06:11 PM,14,3,4:10:33 PM,14,3,4:37:16 PM,14,3,4:56:19 PM,14,3,5:00:23 PM,6,23.3,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_403,244
-Order_No_18162,User_Id_3053,Bike,3,Personal,15,5,11:07:49 AM,15,5,11:08:21 AM,15,5,11:20:16 AM,15,5,11:48:59 AM,15,5,12:21:10 PM,8,26.6,,-1.2534301,36.800098299999995,-1.23966,36.75258,Rider_Id_172,1931
-Order_No_4695,User_Id_2745,Bike,2,Personal,19,6,10:25:57 AM,19,6,10:26:19 AM,19,6,10:37:55 AM,19,6,11:00:46 AM,19,6,11:24:59 AM,5,21.5,,-1.2738167,36.8157821,-1.2817856,36.788100899999996,Rider_Id_338,1453
-Order_No_25321,User_Id_1882,Bike,1,Business,2,2,9:08:42 AM,2,2,9:08:57 AM,2,2,9:14:25 AM,2,2,9:37:47 AM,2,2,10:05:05 AM,8,15.2,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_432,1638
-Order_No_4120,User_Id_1439,Bike,3,Business,8,2,11:42:10 AM,8,2,11:43:17 AM,8,2,11:57:13 AM,8,2,12:01:06 PM,8,2,12:18:10 PM,6,23.3,,-1.2962486999999998,36.7772971,-1.2962437,36.8196435,Rider_Id_548,1024
-Order_No_909,User_Id_265,Bike,3,Business,6,6,11:01:00 AM,6,6,11:02:15 AM,6,6,11:15:33 AM,6,6,11:43:46 AM,6,6,12:05:01 PM,10,26.9,,-1.300921,36.828195,-1.3323949,36.8915219,Rider_Id_709,1275
-Order_No_21703,User_Id_286,Bike,3,Business,6,4,9:27:23 AM,6,4,9:29:12 AM,6,4,9:42:08 AM,6,4,9:58:09 AM,6,4,10:11:56 AM,3,18.9,2.9,-1.2809121,36.7852524,-1.2962966999999999,36.8020231,Rider_Id_404,827
-Order_No_6786,User_Id_1205,Bike,3,Business,7,2,3:24:35 PM,7,2,3:26:58 PM,7,2,3:39:13 PM,7,2,3:50:41 PM,7,2,4:27:24 PM,5,,,-1.2495931,36.80650870000001,-1.2810807,36.8144229,Rider_Id_155,2203
-Order_No_8076,User_Id_517,Bike,3,Business,22,3,3:38:49 PM,22,3,3:39:52 PM,22,3,3:39:59 PM,22,3,3:47:09 PM,22,3,3:47:15 PM,4,24.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_619,6
-Order_No_11338,User_Id_3586,Bike,3,Business,17,4,3:15:07 PM,17,4,3:16:01 PM,17,4,3:28:24 PM,17,4,3:37:10 PM,17,4,4:01:26 PM,9,27.8,,-1.2869041,36.8240057,-1.275442,36.766020700000006,Rider_Id_237,1456
-Order_No_15061,User_Id_3599,Bike,3,Business,3,5,11:42:51 AM,3,5,11:43:58 AM,3,5,11:59:14 AM,3,5,12:21:06 PM,3,5,12:54:02 PM,13,,,-1.3302996,36.8707935,-1.2567393,36.814323200000004,Rider_Id_675,1976
-Order_No_5538,User_Id_265,Bike,3,Business,4,4,3:57:16 PM,4,4,3:58:46 PM,4,4,4:00:30 PM,4,4,4:10:19 PM,4,4,4:16:37 PM,2,24.7,,-1.300921,36.828195,-1.310011,36.820824,Rider_Id_848,378
-Order_No_20787,User_Id_2701,Bike,3,Business,19,3,2:36:19 PM,19,3,3:46:42 PM,19,3,3:55:05 PM,19,3,4:23:20 PM,19,3,4:42:58 PM,5,24.5,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_31,1178
-Order_No_17382,User_Id_909,Bike,3,Business,5,2,2:01:25 PM,5,2,2:03:43 PM,5,2,2:30:34 PM,5,2,2:46:36 PM,5,2,3:08:04 PM,8,,,-1.2287313999999998,36.8367354,-1.225322,36.80855,Rider_Id_8,1288
-Order_No_3521,User_Id_914,Bike,3,Business,4,2,12:49:45 PM,4,2,1:15:55 PM,4,2,1:43:16 PM,4,2,1:54:57 PM,4,2,2:06:48 PM,3,23.9,11.1,-1.2571472,36.795063299999995,-1.2670213,36.7994482,Rider_Id_547,711
-Order_No_24070,User_Id_2271,Bike,3,Business,2,4,10:46:32 AM,2,4,10:57:25 AM,2,4,11:08:52 AM,2,4,11:19:17 AM,2,4,11:38:02 AM,15,22.4,,-1.2287313999999998,36.8367354,-1.293515,36.897607,Rider_Id_174,1125
-Order_No_17453,User_Id_53,Bike,3,Business,28,4,11:50:18 AM,28,4,11:51:19 AM,28,4,11:58:38 AM,28,4,12:22:52 PM,28,4,12:22:54 PM,5,27.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_953,2
-Order_No_19964,User_Id_2412,Bike,3,Business,15,6,12:24:15 PM,15,6,12:31:41 PM,15,6,12:51:27 PM,15,6,12:52:58 PM,15,6,1:53:00 PM,9,22.1,,-1.2859912,36.8756811,-1.2285834999999998,36.882646799999996,Rider_Id_547,3602
-Order_No_76,User_Id_3355,Bike,3,Business,28,3,12:23:54 PM,28,3,12:26:36 PM,28,3,12:36:11 PM,28,3,12:37:47 PM,28,3,12:44:09 PM,3,,,-1.3052491000000002,36.8223899,-1.3197098999999999,36.831231200000005,Rider_Id_679,382
-Order_No_6200,User_Id_627,Bike,3,Business,27,3,3:16:25 PM,27,3,3:16:40 PM,27,3,3:29:28 PM,27,3,3:37:17 PM,27,3,4:13:01 PM,21,28.9,,-1.2571472,36.795063299999995,-1.3440557,36.909965299999996,Rider_Id_206,2144
-Order_No_9021,User_Id_2330,Bike,3,Business,9,3,3:32:32 PM,9,3,3:32:50 PM,9,3,3:40:07 PM,9,3,3:47:59 PM,9,3,4:34:11 PM,17,25.4,,-1.2642903,36.80021370000001,-1.2927792,36.9053492,Rider_Id_512,2772
-Order_No_9216,User_Id_2701,Bike,3,Business,20,3,9:22:46 AM,20,3,9:26:12 AM,20,3,9:46:20 AM,20,3,9:54:46 AM,20,3,10:11:09 AM,5,20.8,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_61,983
-Order_No_23789,User_Id_1207,Bike,3,Business,26,2,9:45:51 AM,26,2,9:48:19 AM,26,2,10:08:22 AM,26,2,10:13:41 AM,26,2,10:39:05 AM,7,,,-1.2629666000000002,36.8068369,-1.2745321000000003,36.765088,Rider_Id_72,1524
-Order_No_20551,User_Id_3621,Bike,3,Business,2,6,4:59:56 PM,2,6,5:00:42 PM,2,6,5:13:18 PM,2,6,5:16:12 PM,2,6,6:09:46 PM,17,,,-1.2836583000000001,36.7886014,-1.356039,36.750176,Rider_Id_246,3214
-Order_No_28229,User_Id_2100,Bike,3,Business,28,3,12:44:45 PM,28,3,12:51:50 PM,28,3,12:53:17 PM,28,3,12:55:54 PM,28,3,1:14:20 PM,6,,,-1.2765736,36.851364600000004,-1.2905033000000001,36.8096462,Rider_Id_933,1106
-Order_No_21890,User_Id_2835,Bike,1,Personal,25,2,12:41:52 PM,25,2,12:42:31 PM,25,2,12:59:01 PM,25,2,1:04:55 PM,25,2,1:37:37 PM,15,23.9,,-1.2810157,36.802757799999995,-1.2100796999999999,36.811189,Rider_Id_848,1962
-Order_No_14170,User_Id_3339,Bike,3,Business,31,5,12:51:41 PM,31,5,1:12:49 PM,31,5,1:20:02 PM,31,5,1:33:08 PM,31,5,2:16:43 PM,6,24.7,,-1.2551895,36.7822034,-1.273412,36.8182057,Rider_Id_432,2615
-Order_No_14198,User_Id_265,Bike,3,Business,4,4,10:43:02 AM,4,4,10:44:21 AM,4,4,10:48:04 AM,4,4,11:05:19 AM,4,4,11:24:10 AM,8,,,-1.300921,36.828195,-1.2940853,36.786666499999995,Rider_Id_848,1131
-Order_No_14048,User_Id_2306,Bike,3,Business,24,5,2:57:56 PM,24,5,3:00:28 PM,24,5,3:16:50 PM,24,5,3:19:04 PM,24,5,3:54:19 PM,17,22.9,,-1.2271998,36.808490500000005,-1.3190962,36.8563967,Rider_Id_484,2115
-Order_No_5781,User_Id_1271,Bike,3,Business,23,5,4:21:21 PM,23,5,4:24:10 PM,23,5,4:38:00 PM,23,5,4:40:07 PM,23,5,4:53:05 PM,8,24.7,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_268,778
-Order_No_9312,User_Id_265,Bike,3,Business,25,4,2:48:56 PM,25,4,3:05:12 PM,25,4,3:35:50 PM,25,4,3:48:27 PM,25,4,4:44:43 PM,15,20.7,,-1.300921,36.828195,-1.3374229,36.9091381,Rider_Id_131,3376
-Order_No_13065,User_Id_1274,Bike,3,Personal,13,3,4:27:19 PM,13,3,4:28:29 PM,13,3,4:59:51 PM,13,3,5:37:35 PM,13,3,6:09:30 PM,9,25.8,,-1.2836832,36.8306719,-1.328762,36.8534414,Rider_Id_513,1915
-Order_No_18388,User_Id_2344,Bike,3,Business,22,4,11:23:43 AM,22,4,11:25:25 AM,22,4,11:29:57 AM,22,4,11:51:02 AM,22,4,12:15:30 PM,9,22.5,,-1.3024794,36.8228284,-1.2551895,36.7822034,Rider_Id_441,1468
-Order_No_24876,User_Id_1500,Bike,3,Business,11,5,1:16:54 PM,11,5,1:17:02 PM,11,5,1:17:06 PM,11,5,1:34:56 PM,11,5,1:57:23 PM,11,26.4,,-1.3228003000000002,36.8306435,-1.2574219,36.7927073,Rider_Id_555,1347
-Order_No_9048,User_Id_540,Bike,3,Business,8,4,2:12:49 PM,8,4,2:13:47 PM,8,4,2:18:22 PM,8,4,2:26:48 PM,8,4,2:26:49 PM,3,27.5,,-1.2551895,36.7822034,-1.2673725,36.8077775,Rider_Id_191,1
-Order_No_22353,User_Id_1966,Bike,3,Business,10,1,3:03:17 PM,10,1,3:20:22 PM,10,1,3:35:39 PM,10,1,3:50:56 PM,10,1,4:17:06 PM,21,18.4,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_675,1570
-Order_No_2350,User_Id_661,Bike,3,Business,7,3,2:27:01 PM,7,3,2:27:32 PM,7,3,2:46:04 PM,7,3,3:04:59 PM,7,3,3:13:04 PM,3,28.4,,-1.311208,36.778743,-1.2963097,36.7688221,Rider_Id_649,485
-Order_No_24141,User_Id_868,Bike,3,Business,16,3,12:55:44 PM,16,3,1:36:24 PM,16,3,1:53:30 PM,16,3,2:11:00 PM,16,3,2:28:41 PM,9,25.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_131,1061
-Order_No_3292,User_Id_246,Bike,3,Business,25,2,4:15:12 PM,25,2,4:15:38 PM,25,2,4:19:20 PM,25,2,4:27:41 PM,25,2,5:05:14 PM,20,26.9,,-1.2584143,36.8048002,-1.3632162,36.9108253,Rider_Id_648,2253
-Order_No_23534,User_Id_391,Bike,3,Business,27,2,1:07:12 PM,27,2,1:12:44 PM,27,2,1:17:20 PM,27,2,1:44:25 PM,27,2,2:07:14 PM,7,25.6,,-1.300921,36.828195,-1.2566892,36.815451700000004,Rider_Id_871,1369
-Order_No_14769,User_Id_3331,Bike,3,Business,27,2,9:25:02 AM,27,2,9:26:16 AM,27,2,9:29:22 AM,27,2,9:36:32 AM,27,2,10:09:15 AM,18,18.6,1.9,-1.2551895,36.7822034,-1.3254431999999998,36.8951705,Rider_Id_449,1963
-Order_No_3298,User_Id_1280,Bike,3,Business,28,5,5:17:35 PM,28,5,5:17:48 PM,28,5,5:19:13 PM,28,5,5:25:47 PM,28,5,5:38:20 PM,3,,,-1.2551895,36.7822034,-1.2647688,36.775086,Rider_Id_449,753
-Order_No_24353,User_Id_3060,Bike,3,Business,2,2,5:12:05 PM,2,2,5:13:30 PM,2,2,5:26:48 PM,2,2,5:29:13 PM,2,2,5:43:22 PM,4,28.3,,-1.2699491000000003,36.8135968,-1.2534301,36.800098299999995,Rider_Id_910,849
-Order_No_14138,User_Id_136,Bike,3,Business,29,5,9:10:16 AM,29,5,9:11:07 AM,29,5,9:24:29 AM,29,5,9:29:03 AM,29,5,10:00:31 AM,14,21.0,,-1.2848376,36.802762200000004,-1.3254522,36.721658399999995,Rider_Id_576,1888
-Order_No_1170,User_Id_640,Bike,3,Business,18,4,4:08:47 PM,18,4,4:12:55 PM,18,4,4:30:39 PM,18,4,4:39:36 PM,18,4,4:59:47 PM,5,,,-1.2969741000000001,36.7856612,-1.296965,36.806567799999996,Rider_Id_177,1211
-Order_No_12133,User_Id_1141,Bike,3,Business,24,3,12:09:39 PM,24,3,12:11:48 PM,24,3,12:19:01 PM,24,3,12:34:55 PM,24,3,12:54:34 PM,7,19.9,,-1.2951297,36.8335521,-1.2684319,36.81658529999999,Rider_Id_210,1179
-Order_No_25443,User_Id_2634,Bike,3,Business,30,4,11:39:15 AM,30,4,11:39:15 AM,30,4,11:43:12 AM,30,4,11:44:22 AM,30,4,12:14:36 PM,15,,,-1.2153260000000001,36.891993799999995,-1.3094235,36.87,Rider_Id_71,1814
-Order_No_26877,User_Id_3438,Bike,1,Personal,3,2,2:08:55 PM,3,2,2:11:00 PM,3,2,2:23:30 PM,3,2,2:28:29 PM,3,2,2:49:18 PM,4,20.0,,-1.2953803,36.8156295,-1.2810205,36.8248105,Rider_Id_88,1249
-Order_No_17630,User_Id_28,Bike,3,Business,28,2,3:47:20 PM,28,2,3:48:40 PM,28,2,4:06:27 PM,28,2,4:26:31 PM,28,2,4:41:34 PM,6,19.4,,-1.268027,36.8037005,-1.2998459999999998,36.7940155,Rider_Id_427,903
-Order_No_12636,User_Id_2503,Bike,2,Business,11,5,1:06:23 PM,11,5,1:07:30 PM,11,5,1:12:55 PM,11,5,1:17:17 PM,11,5,1:41:49 PM,8,20.6,,-1.2559559999999999,36.789867,-1.2858511000000001,36.7946184,Rider_Id_729,1472
-Order_No_15807,User_Id_3473,Bike,1,Personal,23,3,1:56:04 PM,23,3,2:02:34 PM,23,3,2:12:03 PM,23,3,2:17:30 PM,23,3,2:55:50 PM,9,22.4,,-1.3312306,36.85636829999999,-1.2901786,36.824294,Rider_Id_888,2300
-Order_No_2512,User_Id_1103,Bike,3,Business,31,3,3:38:17 PM,31,3,3:41:29 PM,31,3,3:48:55 PM,31,3,4:23:46 PM,31,3,4:43:51 PM,7,26.9,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_726,1205
-Order_No_16142,User_Id_2587,Bike,3,Business,3,5,9:29:09 AM,3,5,9:32:36 AM,3,5,9:46:19 AM,3,5,9:50:31 AM,3,5,9:51:51 AM,3,17.3,,-1.2720268999999997,36.8174107,-1.2885528999999998,36.816014100000004,Rider_Id_79,80
-Order_No_9486,User_Id_704,Bike,1,Business,18,3,9:45:05 AM,18,3,9:45:11 AM,18,3,9:49:05 AM,18,3,9:59:30 AM,18,3,10:25:49 AM,13,14.5,,-1.2860183,36.8975338,-1.320157,36.830887,Rider_Id_589,1579
-Order_No_24252,User_Id_2987,Bike,3,Business,12,5,3:02:40 PM,12,5,3:07:31 PM,12,5,3:07:35 PM,12,5,3:34:39 PM,12,5,3:45:48 PM,9,27.0,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_813,669
-Order_No_225,User_Id_3669,Bike,3,Business,27,1,10:04:40 AM,27,1,10:18:31 AM,27,1,10:41:20 AM,27,1,10:53:28 AM,27,1,12:46:54 PM,24,21.0,,-1.2615888000000002,36.7928732,-1.34364,36.8925345,Rider_Id_542,6806
-Order_No_19969,User_Id_635,Bike,3,Business,24,3,12:04:08 PM,24,3,12:09:34 PM,24,3,12:33:06 PM,24,3,1:10:57 PM,24,3,1:55:09 PM,7,19.7,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_722,2652
-Order_No_8555,User_Id_1324,Bike,2,Personal,1,3,12:46:49 PM,1,3,12:47:31 PM,1,3,1:01:25 PM,1,3,1:16:16 PM,1,3,2:02:34 PM,8,19.8,,-1.2611578,36.814530700000006,-1.3061562,36.8184362,Rider_Id_131,2778
-Order_No_22886,User_Id_661,Bike,3,Business,22,5,11:43:31 AM,22,5,11:44:56 AM,22,5,12:11:16 PM,22,5,12:39:53 PM,22,5,12:52:00 PM,4,21.7,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_312,727
-Order_No_26608,User_Id_1958,Bike,3,Business,12,3,10:50:10 AM,12,3,11:03:34 AM,12,3,11:09:16 AM,12,3,11:11:54 AM,12,3,12:13:45 PM,23,19.2,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_488,3711
-Order_No_8043,User_Id_2412,Bike,3,Business,11,1,1:54:58 PM,11,1,1:57:11 PM,11,1,2:08:34 PM,11,1,2:22:52 PM,11,1,2:47:36 PM,13,29.1,,-1.207546,36.7913976,-1.2283403,36.8822756,Rider_Id_205,1484
-Order_No_20809,User_Id_532,Bike,3,Personal,4,1,11:11:39 AM,4,1,11:11:53 AM,4,1,11:23:46 AM,4,1,11:44:26 AM,4,1,12:06:20 PM,14,21.1,,-1.2363091999999998,36.882361700000004,-1.2747549999999999,36.7906585,Rider_Id_735,1314
-Order_No_2697,User_Id_3647,Bike,3,Business,6,4,3:30:56 PM,6,4,3:31:54 PM,6,4,3:44:32 PM,6,4,3:47:39 PM,6,4,4:19:19 PM,8,23.5,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_279,1900
-Order_No_6437,User_Id_1880,Bike,3,Business,8,2,12:28:29 PM,8,2,12:29:32 PM,8,2,12:32:59 PM,8,2,12:52:02 PM,8,2,1:20:10 PM,12,23.2,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_937,1688
-Order_No_22467,User_Id_393,Bike,3,Business,16,2,2:44:51 PM,16,2,2:45:01 PM,16,2,2:50:04 PM,16,2,2:54:57 PM,16,2,3:21:28 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_168,1591
-Order_No_2452,User_Id_3147,Bike,3,Business,12,3,4:56:13 PM,12,3,4:57:25 PM,12,3,5:15:38 PM,12,3,5:25:28 PM,12,3,6:06:32 PM,17,20.5,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_890,2464
-Order_No_16735,User_Id_1500,Bike,3,Business,7,5,4:27:38 PM,7,5,4:30:30 PM,7,5,4:35:57 PM,7,5,4:44:55 PM,7,5,5:09:09 PM,10,21.6,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_161,1454
-Order_No_2981,User_Id_2195,Bike,1,Business,3,3,9:35:46 AM,3,3,9:37:02 AM,3,3,9:42:09 AM,3,3,9:52:26 AM,3,3,10:00:05 AM,6,18.4,,-1.307143,36.825009,-1.3313643,36.857742200000004,Rider_Id_890,459
-Order_No_25416,User_Id_3661,Bike,3,Business,9,3,9:37:06 AM,9,3,9:37:16 AM,9,3,9:48:15 AM,9,3,9:49:29 AM,9,3,10:28:36 AM,19,20.2,,-1.2134646,36.8755266,-1.3208162,36.8451209,Rider_Id_685,2347
-Order_No_8428,User_Id_2789,Bike,3,Business,20,3,3:16:26 PM,20,3,3:17:13 PM,20,3,3:36:59 PM,20,3,3:38:08 PM,20,3,4:14:44 PM,10,28.3,,-1.304735,36.784605400000004,-1.3169591,36.7000029,Rider_Id_448,2196
-Order_No_13491,User_Id_1500,Bike,3,Business,8,2,3:58:13 PM,8,2,3:58:31 PM,8,2,3:58:36 PM,8,2,4:29:44 PM,8,2,4:44:11 PM,8,25.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_925,867
-Order_No_11855,User_Id_1839,Bike,3,Personal,8,5,5:56:14 PM,8,5,5:56:22 PM,8,5,5:57:30 PM,8,5,6:03:13 PM,8,5,6:18:21 PM,4,25.0,,-1.2975066000000002,36.76894229999999,-1.302725,36.734783,Rider_Id_669,908
-Order_No_12709,User_Id_1471,Bike,3,Business,28,2,2:41:51 PM,28,2,2:42:01 PM,28,2,2:50:11 PM,28,2,3:02:52 PM,28,2,3:28:19 PM,12,23.5,,-1.2551895,36.7822034,-1.2987332,36.8463349,Rider_Id_411,1527
-Order_No_13709,User_Id_3222,Bike,3,Business,14,1,2:24:44 PM,14,1,2:24:52 PM,14,1,2:27:29 PM,14,1,2:33:07 PM,14,1,2:41:21 PM,4,26.0,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_620,494
-Order_No_24909,User_Id_3363,Bike,1,Personal,28,2,9:59:29 AM,28,2,10:00:56 AM,28,2,10:34:05 AM,28,2,10:42:09 AM,28,2,11:17:29 AM,7,17.6,,-1.279395,36.835624100000004,-1.297101,36.798243,Rider_Id_179,2120
-Order_No_27342,User_Id_3781,Bike,3,Business,23,5,9:17:56 AM,23,5,9:22:45 AM,23,5,9:26:29 AM,23,5,9:44:38 AM,23,5,9:57:35 AM,7,20.1,,-1.2419938,36.768372899999996,-1.26071,36.8206332,Rider_Id_783,777
-Order_No_4311,User_Id_2476,Bike,3,Business,22,5,9:40:49 AM,22,5,9:42:01 AM,22,5,9:56:58 AM,22,5,10:02:50 AM,22,5,10:40:12 AM,8,,,-1.2795594,36.847264700000004,-1.2649605,36.798177700000004,Rider_Id_467,2242
-Order_No_13440,User_Id_1146,Bike,3,Business,21,4,2:03:21 PM,21,4,2:04:49 PM,21,4,2:19:11 PM,21,4,2:39:56 PM,21,4,2:53:34 PM,7,26.5,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_25,818
-Order_No_25006,User_Id_1724,Bike,3,Business,24,5,11:47:37 AM,24,5,11:49:03 AM,24,5,12:00:55 PM,24,5,12:02:23 PM,24,5,12:49:57 PM,6,21.4,,-1.2551895,36.7822034,-1.2647975,36.821518299999994,Rider_Id_930,2854
-Order_No_14252,User_Id_2112,Bike,3,Business,28,4,12:41:40 PM,28,4,12:42:24 PM,28,4,12:55:23 PM,28,4,1:06:18 PM,28,4,1:35:02 PM,5,23.5,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_75,1724
-Order_No_24277,User_Id_635,Bike,3,Business,10,1,10:26:51 AM,10,1,10:27:05 AM,10,1,10:28:50 AM,10,1,11:16:03 AM,10,1,11:47:00 AM,10,,,-1.3016417,36.8271679,-1.240299,36.795942,Rider_Id_595,1857
-Order_No_24628,User_Id_173,Bike,3,Business,12,3,1:56:10 PM,12,3,1:58:00 PM,12,3,2:12:19 PM,12,3,2:19:34 PM,12,3,2:41:19 PM,11,25.8,,-1.2993231,36.7753372,-1.2545163000000001,36.8243717,Rider_Id_404,1305
-Order_No_10784,User_Id_2330,Bike,3,Business,24,4,11:06:10 AM,24,4,11:06:27 AM,24,4,11:15:10 AM,24,4,11:17:37 AM,24,4,11:32:53 AM,4,24.7,,-1.2326041,36.8009029,-1.2533885,36.8165128,Rider_Id_882,916
-Order_No_3096,User_Id_2330,Bike,3,Business,30,3,3:25:08 PM,30,3,3:25:56 PM,30,3,3:35:05 PM,30,3,3:38:33 PM,30,3,3:59:54 PM,8,29.0,,-1.2873232,36.765857200000006,-1.2649605,36.798177700000004,Rider_Id_246,1281
-Order_No_16461,User_Id_635,Bike,3,Business,25,6,10:39:27 AM,25,6,10:39:58 AM,25,6,10:40:57 AM,25,6,11:02:09 AM,25,6,11:49:30 AM,8,,,-1.3081376,36.83297720000001,-1.2945798000000002,36.7737755,Rider_Id_686,2841
-Order_No_18781,User_Id_1146,Bike,3,Business,15,5,9:30:54 AM,15,5,9:35:50 AM,15,5,9:35:58 AM,15,5,9:58:18 AM,15,5,10:06:29 AM,5,20.3,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_10,491
-Order_No_7742,User_Id_1500,Bike,3,Business,5,5,3:33:59 PM,5,5,3:36:14 PM,5,5,3:36:22 PM,5,5,4:12:16 PM,5,5,4:32:41 PM,8,27.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_842,1225
-Order_No_23669,User_Id_2869,Bike,3,Business,18,4,9:24:19 AM,18,4,9:25:27 AM,18,4,10:20:29 AM,18,4,10:33:17 AM,18,4,10:41:17 AM,3,,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_200,480
-Order_No_18777,User_Id_3757,Bike,3,Personal,8,1,12:22:21 PM,8,1,12:22:38 PM,8,1,12:29:20 PM,8,1,1:01:23 PM,8,1,1:19:05 PM,9,27.7,,-1.2655381,36.8354648,-1.3173868999999998,36.8112044,Rider_Id_576,1062
-Order_No_27319,User_Id_2212,Bike,3,Business,5,2,8:55:32 AM,5,2,9:08:16 AM,5,2,9:11:32 AM,5,2,9:25:32 AM,5,2,9:37:26 AM,4,17.9,,-1.2571472,36.795063299999995,-1.281767,36.8220791,Rider_Id_576,714
-Order_No_14075,User_Id_84,Bike,3,Business,14,1,9:06:44 AM,14,1,9:06:57 AM,14,1,9:44:19 AM,14,1,9:46:45 AM,14,1,10:33:04 AM,23,20.4,,-1.3632162,36.9108253,-1.2551895,36.7822034,Rider_Id_346,2779
-Order_No_2536,User_Id_1705,Bike,3,Business,7,5,12:07:08 PM,7,5,12:18:24 PM,7,5,12:18:49 PM,7,5,12:27:09 PM,7,5,1:07:01 PM,12,24.9,,-1.2551895,36.7822034,-1.3078018,36.849149,Rider_Id_848,2392
-Order_No_25028,User_Id_1073,Bike,3,Business,22,5,3:25:10 PM,22,5,3:26:28 PM,22,5,3:36:49 PM,22,5,3:38:56 PM,22,5,3:45:08 PM,4,27.3,,-1.2561964,36.7885001,-1.2596969999999998,36.800664000000005,Rider_Id_875,372
-Order_No_20466,User_Id_2587,Bike,3,Business,17,3,10:36:19 AM,17,3,10:37:01 AM,17,3,10:46:29 AM,17,3,10:48:26 AM,17,3,11:06:17 AM,6,25.2,,-1.28878,36.8168312,-1.2808667,36.7883545,Rider_Id_857,1071
-Order_No_12493,User_Id_2702,Bike,3,Personal,15,6,10:31:58 AM,15,6,10:32:08 AM,15,6,10:33:28 AM,15,6,10:48:51 AM,15,6,11:21:46 AM,10,21.8,,-1.2736646,36.814277399999995,-1.3174857,36.801993700000004,Rider_Id_89,1975
-Order_No_3118,User_Id_2658,Bike,3,Business,29,4,5:41:43 PM,29,4,5:42:10 PM,29,4,5:57:00 PM,29,4,5:58:15 PM,29,4,6:34:55 PM,12,20.8,,-1.2891921999999998,36.825189,-1.2711571000000002,36.901728799999994,Rider_Id_622,2200
-Order_No_24165,User_Id_393,Bike,3,Business,20,3,10:40:50 AM,20,3,10:41:14 AM,20,3,10:42:51 AM,20,3,11:13:16 AM,20,3,11:13:18 AM,7,26.5,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_953,2
-Order_No_23845,User_Id_3283,Bike,3,Business,12,4,11:25:40 AM,12,4,12:09:24 PM,12,4,12:19:23 PM,12,4,12:48:06 PM,12,4,1:25:23 PM,14,19.1,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_510,2237
-Order_No_7726,User_Id_3326,Bike,3,Business,12,3,3:40:07 PM,12,3,3:54:02 PM,12,3,4:26:50 PM,12,3,4:29:53 PM,12,3,5:45:35 PM,22,21.0,,-1.2551895,36.7822034,-1.3433296000000001,36.910612799999996,Rider_Id_155,4542
-Order_No_3650,User_Id_3149,Bike,3,Business,3,3,11:06:21 AM,3,3,11:07:05 AM,3,3,11:07:31 AM,3,3,11:28:43 AM,3,3,11:38:21 AM,6,24.2,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_848,578
-Order_No_24503,User_Id_3186,Bike,3,Business,26,4,3:38:33 PM,26,4,3:39:22 PM,26,4,3:46:32 PM,26,4,3:50:34 PM,26,4,4:02:37 PM,4,21.9,,-1.2980228999999999,36.78865,-1.298529,36.816482,Rider_Id_207,723
-Order_No_28088,User_Id_269,Bike,3,Business,24,5,1:06:20 PM,24,5,1:06:39 PM,24,5,1:54:16 PM,24,5,2:02:02 PM,24,5,2:18:04 PM,11,23.2,,-1.2810586000000002,36.883829399999996,-1.3332749,36.870814700000004,Rider_Id_549,962
-Order_No_24727,User_Id_302,Bike,3,Business,10,7,1:57:46 PM,10,7,1:58:10 PM,10,7,2:07:02 PM,10,7,2:12:38 PM,10,7,2:33:48 PM,8,,,-1.2649605,36.798177700000004,-1.307376,36.813404,Rider_Id_761,1270
-Order_No_10923,User_Id_1280,Bike,3,Business,26,5,4:18:01 PM,26,5,4:18:29 PM,26,5,4:19:04 PM,26,5,4:26:09 PM,26,5,4:40:31 PM,4,,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_531,862
-Order_No_10824,User_Id_1245,Bike,3,Business,17,5,11:41:48 AM,17,5,11:42:29 AM,17,5,11:45:09 AM,17,5,11:51:40 AM,17,5,12:51:01 PM,18,,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_855,3561
-Order_No_22218,User_Id_3247,Bike,3,Business,17,4,2:54:41 PM,17,4,3:35:47 PM,17,4,3:55:06 PM,17,4,3:57:37 PM,17,4,4:29:43 PM,5,28.0,,-1.2571472,36.795063299999995,-1.2790098,36.8270106,Rider_Id_899,1926
-Order_No_6047,User_Id_1859,Bike,3,Business,22,2,1:03:36 PM,22,2,1:04:05 PM,22,2,1:05:34 PM,22,2,1:12:49 PM,22,2,1:47:38 PM,13,19.6,,-1.2993666000000001,36.8232195,-1.2470959,36.8729823,Rider_Id_561,2089
-Order_No_27877,User_Id_743,Bike,1,Personal,29,3,4:47:24 PM,29,3,4:50:47 PM,29,3,5:15:18 PM,29,3,5:26:46 PM,29,3,6:08:23 PM,18,19.3,,-1.4099456,36.945811799999994,-1.32189,36.8589727,Rider_Id_350,2497
-Order_No_15928,User_Id_3326,Bike,3,Business,21,4,2:57:02 PM,21,4,2:57:47 PM,21,4,3:08:58 PM,21,4,3:23:08 PM,21,4,4:22:45 PM,22,26.4,,-1.2551895,36.7822034,-1.3433296000000001,36.910612799999996,Rider_Id_329,3577
-Order_No_24268,User_Id_393,Bike,3,Business,2,4,2:13:28 PM,2,4,3:26:08 PM,2,4,3:39:38 PM,2,4,4:09:40 PM,2,4,4:09:57 PM,8,26.4,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_606,17
-Order_No_7571,User_Id_3251,Bike,3,Business,3,4,1:15:01 PM,3,4,1:15:30 PM,3,4,1:39:51 PM,3,4,1:42:41 PM,3,4,2:06:53 PM,10,25.2,,-1.2703461999999999,36.7684896,-1.30797,36.82655,Rider_Id_486,1452
-Order_No_23574,User_Id_633,Bike,3,Business,5,2,3:22:34 PM,5,2,3:23:27 PM,5,2,3:27:46 PM,5,2,3:31:54 PM,5,2,3:41:20 PM,4,27.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_433,566
-Order_No_24707,User_Id_3017,Bike,1,Business,8,3,3:46:12 PM,8,3,3:47:32 PM,8,3,3:57:30 PM,8,3,4:01:10 PM,8,3,4:20:09 PM,8,25.8,,-1.3123782,36.8181528,-1.299313,36.78314879999999,Rider_Id_726,1139
-Order_No_1840,User_Id_970,Bike,3,Business,21,3,2:20:49 PM,21,3,2:26:33 PM,21,3,2:36:56 PM,21,3,2:38:39 PM,21,3,3:13:58 PM,7,23.0,2.9,-1.2551895,36.7822034,-1.2852,36.82397,Rider_Id_726,2119
-Order_No_14882,User_Id_1244,Bike,3,Business,16,6,2:55:03 PM,16,6,2:55:13 PM,16,6,3:07:57 PM,16,6,3:22:25 PM,16,6,3:40:06 PM,9,,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_427,1061
-Order_No_7372,User_Id_3108,Bike,3,Business,24,4,8:48:33 AM,24,4,8:48:53 AM,24,4,8:50:54 AM,24,4,8:54:58 AM,24,4,9:02:56 AM,1,19.2,,-1.2551895,36.7822034,-1.2606254,36.783056,Rider_Id_880,478
-Order_No_18034,User_Id_978,Bike,1,Personal,12,3,10:42:01 AM,12,3,10:46:50 AM,12,3,11:22:23 AM,12,3,11:29:52 AM,12,3,11:59:47 AM,6,23.4,,-1.3084067,36.83450139999999,-1.2780023,36.8241653,Rider_Id_336,1795
-Order_No_4319,User_Id_3283,Bike,3,Business,16,3,3:23:14 PM,16,3,3:23:59 PM,16,3,3:33:42 PM,16,3,3:46:29 PM,16,3,3:57:35 PM,5,27.2,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_46,666
-Order_No_2823,User_Id_1496,Bike,1,Personal,17,3,12:41:51 PM,17,3,12:49:35 PM,17,3,1:03:13 PM,17,3,1:17:34 PM,17,3,1:36:37 PM,12,25.2,,-1.207546,36.7913976,-1.2655306,36.828148,Rider_Id_623,1143
-Order_No_16759,User_Id_389,Bike,1,Personal,19,2,3:52:43 PM,19,2,4:02:32 PM,19,2,4:12:59 PM,19,2,4:26:26 PM,19,2,4:47:39 PM,6,18.6,,-1.294869,36.815327,-1.2806514,36.777435600000004,Rider_Id_322,1273
-Order_No_12223,User_Id_3686,Bike,3,Business,15,1,2:13:50 PM,15,1,2:14:07 PM,15,1,2:14:35 PM,15,1,2:20:05 PM,15,1,2:39:00 PM,12,,,-1.2551895,36.7822034,-1.318497,36.8296133,Rider_Id_848,1135
-Order_No_4959,User_Id_627,Bike,3,Business,22,5,1:59:56 PM,22,5,2:01:44 PM,22,5,2:10:31 PM,22,5,3:03:39 PM,22,5,3:03:55 PM,18,30.8,,-1.2571472,36.795063299999995,-1.3339943,36.7263409,Rider_Id_318,16
-Order_No_25384,User_Id_3557,Bike,3,Business,15,5,3:24:07 PM,15,5,3:25:46 PM,15,5,3:43:25 PM,15,5,4:21:02 PM,15,5,4:43:40 PM,14,17.9,,-1.207546,36.7913976,-1.2546732,36.808679600000005,Rider_Id_99,1358
-Order_No_26681,User_Id_1081,Bike,3,Business,5,2,2:42:21 PM,5,2,2:42:37 PM,5,2,2:54:05 PM,5,2,3:02:55 PM,5,2,3:32:20 PM,10,27.7,,-1.2413316,36.878552500000005,-1.290894,36.822971,Rider_Id_462,1765
-Order_No_1091,User_Id_393,Bike,3,Business,16,3,12:45:46 PM,16,3,12:46:05 PM,16,3,12:46:10 PM,16,3,1:27:42 PM,16,3,1:27:43 PM,4,22.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,1
-Order_No_18409,User_Id_2279,Bike,3,Business,18,1,1:46:32 PM,18,1,2:31:06 PM,18,1,2:38:33 PM,18,1,2:49:29 PM,18,1,3:15:17 PM,4,30.9,,-1.2897368,36.8226783,-1.2980155,36.797306400000004,Rider_Id_539,1548
-Order_No_17698,User_Id_3128,Bike,1,Personal,19,5,10:04:37 AM,19,5,10:04:45 AM,19,5,10:13:49 AM,19,5,10:18:00 AM,19,5,10:34:04 AM,12,20.0,,-1.2047753,36.7915359,-1.2604472,36.8365287,Rider_Id_676,964
-Order_No_24017,User_Id_2801,Bike,3,Business,8,1,5:11:55 PM,8,1,5:12:54 PM,8,1,5:16:50 PM,8,1,5:20:14 PM,8,1,5:52:21 PM,11,28.8,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_933,1927
-Order_No_21428,User_Id_1715,Bike,3,Business,22,3,10:19:11 AM,22,3,10:19:43 AM,22,3,10:31:31 AM,22,3,10:41:51 AM,22,3,10:55:36 AM,3,22.9,,-1.2747111,36.80839829999999,-1.253966,36.7987692,Rider_Id_213,825
-Order_No_493,User_Id_2150,Bike,3,Business,27,4,1:39:04 PM,27,4,1:41:46 PM,27,4,1:55:06 PM,27,4,2:14:32 PM,27,4,2:30:45 PM,12,23.9,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_533,973
-Order_No_20007,User_Id_134,Bike,3,Business,28,4,12:16:40 PM,28,4,12:17:47 PM,28,4,12:27:16 PM,28,4,12:32:47 PM,28,4,1:01:02 PM,16,27.9,,-1.2609745,36.7886626,-1.207546,36.7913976,Rider_Id_523,1695
-Order_No_4865,User_Id_1065,Bike,2,Personal,27,5,4:41:03 PM,27,5,4:41:59 PM,27,5,4:47:06 PM,27,5,4:50:45 PM,27,5,5:05:27 PM,8,17.7,,-1.2740027,36.804917200000006,-1.3016151000000002,36.811735399999996,Rider_Id_510,882
-Order_No_18724,User_Id_186,Bike,3,Business,9,3,8:36:42 AM,9,3,8:38:02 AM,9,3,8:51:08 AM,9,3,8:59:22 AM,9,3,9:11:03 AM,6,20.0,,-1.2571472,36.795063299999995,-1.298529,36.816482,Rider_Id_178,701
-Order_No_26510,User_Id_1363,Bike,3,Business,7,4,2:10:56 PM,7,4,2:11:58 PM,7,4,2:12:01 PM,7,4,2:34:57 PM,7,4,2:40:57 PM,7,27.5,,-1.3004062,36.829741,-1.2616841,36.801999,Rider_Id_137,360
-Order_No_12345,User_Id_186,Bike,3,Business,1,5,12:08:15 PM,1,5,12:17:27 PM,1,5,12:18:07 PM,1,5,12:25:24 PM,1,5,12:57:36 PM,20,26.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_647,1932
-Order_No_27351,User_Id_393,Bike,3,Business,14,4,1:20:53 PM,14,4,1:22:58 PM,14,4,1:32:05 PM,14,4,1:37:39 PM,14,4,1:45:03 PM,4,27.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_127,444
-Order_No_27635,User_Id_286,Bike,3,Business,1,6,10:42:16 AM,1,6,10:58:35 AM,1,6,11:42:52 AM,1,6,11:46:25 AM,1,6,12:57:39 PM,11,22.2,,-1.2551895,36.7822034,-1.2827084999999998,36.849582899999994,Rider_Id_362,4274
-Order_No_15926,User_Id_627,Bike,3,Business,16,1,12:24:20 PM,16,1,12:24:38 PM,16,1,12:26:39 PM,16,1,12:37:57 PM,16,1,1:04:53 PM,9,19.5,,-1.2551895,36.7822034,-1.2983462,36.8139996,Rider_Id_101,1616
-Order_No_18998,User_Id_478,Bike,3,Business,21,2,1:48:58 PM,21,2,1:50:16 PM,21,2,1:54:48 PM,21,2,2:03:18 PM,21,2,2:35:15 PM,6,25.0,,-1.2571472,36.795063299999995,-1.2950041,36.814358299999995,Rider_Id_386,1917
-Order_No_680,User_Id_2281,Bike,1,Personal,14,4,2:14:37 PM,14,4,2:40:56 PM,14,4,2:56:49 PM,14,4,3:51:10 PM,14,4,4:35:12 PM,9,21.2,,-1.2891901000000001,36.76200120000001,-1.2644742,36.808345700000004,Rider_Id_64,2642
-Order_No_26078,User_Id_2279,Bike,3,Business,27,1,3:51:22 PM,27,1,3:51:48 PM,27,1,3:56:25 PM,27,1,4:01:15 PM,27,1,4:22:16 PM,7,,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_698,1261
-Order_No_785,User_Id_2330,Bike,3,Business,10,3,6:32:49 PM,10,3,6:35:07 PM,10,3,6:53:05 PM,10,3,6:57:16 PM,10,3,7:44:41 PM,22,27.3,,-1.2124582,36.802894,-1.2854537,36.9051024,Rider_Id_72,2845
-Order_No_17898,User_Id_3746,Bike,1,Personal,25,5,6:02:40 PM,25,5,6:05:08 PM,25,5,6:22:04 PM,25,5,6:31:19 PM,25,5,6:49:38 PM,5,21.1,,-1.2646579,36.825321100000004,-1.2750247,36.7981039,Rider_Id_755,1099
-Order_No_6788,User_Id_1674,Bike,1,Personal,31,4,4:00:36 PM,31,4,4:01:26 PM,31,4,4:13:54 PM,31,4,4:39:14 PM,31,4,5:04:33 PM,12,22.4,,-1.2902869,36.8085396,-1.2343236000000002,36.8861,Rider_Id_379,1519
-Order_No_2139,User_Id_1245,Bike,3,Business,7,4,2:26:30 PM,7,4,2:26:45 PM,7,4,2:41:09 PM,7,4,2:53:48 PM,7,4,3:34:00 PM,21,31.2,,-1.300921,36.828195,-1.236927,36.702785999999996,Rider_Id_718,2412
-Order_No_14560,User_Id_2763,Bike,3,Business,22,5,11:50:10 AM,22,5,11:51:59 AM,22,5,12:01:35 PM,22,5,12:06:38 PM,22,5,12:25:17 PM,8,28.6,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_937,1119
-Order_No_20610,User_Id_393,Bike,3,Business,18,1,10:15:08 AM,18,1,10:17:28 AM,18,1,10:17:35 AM,18,1,10:49:17 AM,18,1,10:49:19 AM,11,27.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,2
-Order_No_3137,User_Id_1635,Bike,3,Personal,18,2,8:41:14 AM,18,2,8:42:21 AM,18,2,9:06:05 AM,18,2,9:21:04 AM,18,2,9:56:27 AM,11,19.7,,-1.2483726000000002,36.8901617,-1.2684816,36.808562200000004,Rider_Id_467,2123
-Order_No_21224,User_Id_3655,Bike,1,Personal,4,4,4:25:34 PM,4,4,4:28:37 PM,4,4,4:42:07 PM,4,4,4:49:42 PM,4,4,5:08:44 PM,4,,,-1.2913598,36.8025568,-1.3043649,36.7816132,Rider_Id_597,1142
-Order_No_3063,User_Id_2523,Bike,3,Business,16,4,9:24:25 AM,16,4,9:25:44 AM,16,4,9:36:04 AM,16,4,9:42:41 AM,16,4,10:09:46 AM,18,15.5,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_754,1625
-Order_No_20629,User_Id_3291,Bike,3,Business,18,1,11:03:17 AM,18,1,11:09:30 AM,18,1,11:13:55 AM,18,1,11:29:44 AM,18,1,11:45:26 AM,8,28.0,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_675,942
-Order_No_26957,User_Id_393,Bike,3,Business,18,2,10:26:03 AM,18,2,12:33:52 PM,18,2,12:40:51 PM,18,2,1:06:30 PM,18,2,1:30:30 PM,10,22.7,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1440
-Order_No_8461,User_Id_760,Bike,3,Business,29,5,4:15:01 PM,29,5,4:16:12 PM,29,5,4:23:29 PM,29,5,4:28:29 PM,29,5,4:45:43 PM,7,29.2,,-1.2886303,36.8026947,-1.2513025,36.809441899999996,Rider_Id_404,1034
-Order_No_3830,User_Id_246,Bike,3,Business,4,1,8:29:45 AM,4,1,8:32:07 AM,4,1,9:21:35 AM,4,1,9:30:42 AM,4,1,9:47:47 AM,10,18.0,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_906,1025
-Order_No_18136,User_Id_2304,Bike,1,Personal,5,5,12:43:54 PM,5,5,12:44:50 PM,5,5,12:58:24 PM,5,5,1:07:45 PM,5,5,1:38:22 PM,11,23.7,,-1.3091733,36.740809999999996,-1.2715686000000002,36.8023526,Rider_Id_328,1837
-Order_No_22137,User_Id_3718,Bike,3,Business,29,1,10:05:37 AM,29,1,10:07:32 AM,29,1,10:12:35 AM,29,1,10:17:28 AM,29,1,10:37:56 AM,5,18.8,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_596,1228
-Order_No_27796,User_Id_1459,Bike,3,Business,29,4,2:25:42 PM,29,4,2:34:44 PM,29,4,2:39:27 PM,29,4,2:42:42 PM,29,4,2:55:31 PM,7,25.0,,-1.264945,36.784678,-1.2479361999999998,36.772759,Rider_Id_123,769
-Order_No_6144,User_Id_1572,Bike,2,Personal,30,6,12:39:12 PM,30,6,12:54:54 PM,30,6,1:02:15 PM,30,6,1:06:44 PM,30,6,1:31:54 PM,7,22.0,,-1.286282,36.8217484,-1.2960302,36.771131700000005,Rider_Id_21,1510
-Order_No_24289,User_Id_3291,Bike,3,Business,25,5,10:26:31 AM,25,5,10:44:14 AM,25,5,11:04:23 AM,25,5,11:12:00 AM,25,5,11:28:18 AM,5,26.3,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_26,978
-Order_No_18478,User_Id_914,Bike,3,Business,16,2,3:23:02 PM,16,2,3:23:15 PM,16,2,3:34:50 PM,16,2,3:43:10 PM,16,2,4:11:49 PM,11,,,-1.2670213,36.7994482,-1.3083777,36.85900770000001,Rider_Id_99,1719
-Order_No_24810,User_Id_3686,Bike,3,Business,11,4,3:00:43 PM,11,4,4:04:39 PM,11,4,4:27:31 PM,11,4,4:30:53 PM,11,4,4:46:32 PM,4,26.8,,-1.261733,36.809971000000004,-1.2551895,36.7822034,Rider_Id_913,939
-Order_No_20095,User_Id_53,Bike,3,Business,12,5,11:42:48 AM,12,5,11:53:33 AM,12,5,11:54:08 AM,12,5,12:28:25 PM,12,5,12:56:48 PM,5,26.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_161,1703
-Order_No_11230,User_Id_3521,Bike,3,Business,4,1,1:08:46 PM,4,1,1:10:14 PM,4,1,1:25:34 PM,4,1,1:33:48 PM,4,1,1:42:05 PM,2,28.6,,-1.2682478000000001,36.8072852,-1.2696908000000002,36.81519,Rider_Id_547,497
-Order_No_5955,User_Id_3561,Bike,3,Personal,18,4,2:02:16 PM,18,4,2:03:01 PM,18,4,2:05:40 PM,18,4,2:08:55 PM,18,4,2:28:57 PM,3,26.8,,-1.2658315,36.809049,-1.2719878,36.790769399999995,Rider_Id_434,1202
-Order_No_19532,User_Id_2509,Bike,3,Business,6,3,4:51:50 PM,6,3,4:52:33 PM,6,3,5:00:54 PM,6,3,5:10:33 PM,6,3,6:21:39 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_579,4266
-Order_No_835,User_Id_2976,Bike,3,Business,25,4,8:58:18 AM,25,4,8:58:41 AM,25,4,9:16:00 AM,25,4,9:20:59 AM,25,4,9:33:14 AM,2,16.2,2.0,-1.2562796999999999,36.8075858,-1.2544418,36.80197253,Rider_Id_167,735
-Order_No_13075,User_Id_633,Bike,3,Business,22,5,10:30:45 AM,22,5,10:32:41 AM,22,5,10:43:31 AM,22,5,10:55:42 AM,22,5,11:24:18 AM,12,28.3,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_306,1716
-Order_No_21648,User_Id_2030,Bike,3,Business,7,4,12:19:56 PM,7,4,12:20:09 PM,7,4,12:27:23 PM,7,4,12:36:53 PM,7,4,12:39:52 PM,2,27.5,,-1.2495931,36.80650870000001,-1.2601197,36.7985271,Rider_Id_489,179
-Order_No_26387,User_Id_627,Bike,3,Business,8,3,4:41:30 PM,8,3,4:42:57 PM,8,3,4:46:53 PM,8,3,4:56:27 PM,8,3,6:11:08 PM,10,23.8,,-1.2551895,36.7822034,-1.221377,36.781314,Rider_Id_415,4481
-Order_No_3117,User_Id_3560,Bike,3,Business,29,1,1:13:33 PM,29,1,1:19:32 PM,29,1,1:25:59 PM,29,1,2:14:04 PM,29,1,2:14:11 PM,3,24.5,,-1.3052491000000002,36.8223899,-1.279395,36.825364,Rider_Id_358,7
-Order_No_27459,User_Id_2691,Bike,3,Business,19,3,10:39:56 AM,19,3,10:51:57 AM,19,3,11:16:12 AM,19,3,11:29:32 AM,19,3,11:50:59 AM,8,22.2,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_337,1287
-Order_No_18069,User_Id_1341,Bike,3,Business,17,3,11:32:52 AM,17,3,11:33:56 AM,17,3,11:38:24 AM,17,3,11:52:59 AM,17,3,12:13:11 PM,15,,,-1.3444459999999998,36.756597,-1.331619,36.847976,Rider_Id_327,1212
-Order_No_13757,User_Id_416,Bike,1,Business,20,1,2:52:48 PM,20,1,2:53:15 PM,20,1,3:03:24 PM,20,1,3:11:02 PM,20,1,3:22:12 PM,6,21.5,,-1.2846195,36.7958316,-1.2824911,36.8218042,Rider_Id_241,670
-Order_No_7630,User_Id_2701,Bike,3,Business,21,4,11:24:19 AM,21,4,11:24:36 AM,21,4,11:24:40 AM,21,4,12:14:28 PM,21,4,12:21:10 PM,11,25.4,,-1.2232123,36.8932547,-1.2765736,36.851364600000004,Rider_Id_633,402
-Order_No_9263,User_Id_3557,Bike,3,Business,19,2,4:08:51 PM,19,2,4:10:05 PM,19,2,4:10:57 PM,19,2,4:36:03 PM,19,2,4:53:32 PM,6,28.4,,-1.293973,36.80169,-1.2546732,36.808679600000005,Rider_Id_19,1049
-Order_No_6093,User_Id_2587,Bike,3,Business,12,1,2:57:44 PM,12,1,3:38:57 PM,12,1,3:39:35 PM,12,1,3:46:54 PM,12,1,4:01:44 PM,3,26.9,,-1.28878,36.8168312,-1.2896398999999998,36.812449799999996,Rider_Id_101,890
-Order_No_23003,User_Id_2208,Bike,2,Personal,31,2,11:02:52 AM,31,2,11:03:50 AM,31,2,11:14:40 AM,31,2,11:56:56 AM,31,2,12:21:25 PM,3,18.5,,-1.2738167,36.8157821,-1.2874292,36.823314,Rider_Id_882,1469
-Order_No_5739,User_Id_2982,Bike,3,Business,4,1,12:19:31 PM,4,1,12:19:49 PM,4,1,12:42:26 PM,4,1,12:59:55 PM,4,1,1:34:20 PM,18,25.6,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_715,2065
-Order_No_20341,User_Id_448,Bike,2,Personal,26,3,7:14:57 AM,26,3,7:22:40 AM,26,3,7:30:25 AM,26,3,7:37:44 AM,26,3,7:46:18 AM,4,19.2,,-1.2893745,36.80025620000001,-1.3075173,36.7845652,Rider_Id_722,514
-Order_No_25696,User_Id_1995,Bike,3,Business,2,4,4:48:47 PM,2,4,4:49:04 PM,2,4,4:49:20 PM,2,4,5:01:42 PM,2,4,5:13:28 PM,3,,,-1.2551895,36.7822034,-1.2677278,36.8001549,Rider_Id_695,706
-Order_No_9236,User_Id_393,Bike,3,Business,9,2,1:51:43 PM,9,2,1:52:04 PM,9,2,2:06:24 PM,9,2,2:10:00 PM,9,2,3:01:00 PM,8,30.7,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_155,3060
-Order_No_28001,User_Id_3686,Bike,3,Business,3,5,3:49:27 PM,3,5,3:52:36 PM,3,5,3:58:32 PM,3,5,4:00:37 PM,3,5,4:14:34 PM,4,,,-1.2551895,36.7822034,-1.25896,36.8016691,Rider_Id_206,837
-Order_No_25129,User_Id_2423,Bike,3,Business,19,3,12:26:44 PM,19,3,1:59:45 PM,19,3,2:09:30 PM,19,3,2:20:16 PM,19,3,3:12:12 PM,6,24.2,,-1.2571472,36.795063299999995,-1.2967701999999999,36.8040381,Rider_Id_147,3116
-Order_No_14576,User_Id_197,Bike,3,Business,7,4,2:42:30 PM,7,4,2:42:40 PM,7,4,2:54:57 PM,7,4,3:10:17 PM,7,4,3:43:46 PM,21,31.3,,-1.206696,36.847709,-1.3282758,36.8590281,Rider_Id_466,2009
-Order_No_13803,User_Id_3264,Bike,3,Business,17,4,7:32:05 AM,17,4,7:32:47 AM,17,4,8:06:40 AM,17,4,8:10:23 AM,17,4,9:03:52 AM,6,,,-1.2551895,36.7822034,-1.2628008999999998,36.8262921,Rider_Id_453,3209
-Order_No_18172,User_Id_2195,Bike,3,Business,15,6,11:52:27 AM,15,6,11:53:04 AM,15,6,12:01:30 PM,15,6,12:13:35 PM,15,6,12:26:58 PM,9,19.1,,-1.2663631000000002,36.8163329,-1.228911,36.881876,Rider_Id_775,803
-Order_No_9309,User_Id_629,Bike,1,Personal,13,5,5:49:52 PM,13,5,5:50:11 PM,13,5,5:59:06 PM,13,5,6:06:43 PM,13,5,6:27:54 PM,9,19.4,,-1.3043961000000002,36.7861511,-1.2615284,36.7897495,Rider_Id_806,1271
-Order_No_18081,User_Id_136,Bike,3,Business,7,5,1:00:14 PM,7,5,1:01:55 PM,7,5,1:22:44 PM,7,5,1:33:36 PM,7,5,2:05:22 PM,9,,,-1.2654716000000001,36.7983635,-1.2313147,36.80168629999999,Rider_Id_55,1906
-Order_No_4680,User_Id_2587,Bike,3,Business,16,2,2:12:46 PM,16,2,2:14:22 PM,16,2,2:42:31 PM,16,2,2:46:22 PM,16,2,3:04:56 PM,5,,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_394,1114
-Order_No_5351,User_Id_1927,Bike,3,Business,20,3,2:44:44 PM,20,3,2:49:42 PM,20,3,3:17:56 PM,20,3,3:22:48 PM,20,3,4:02:48 PM,20,28.6,,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_649,2400
-Order_No_22195,User_Id_635,Bike,3,Business,20,1,9:31:27 AM,20,1,9:32:17 AM,20,1,9:38:16 AM,20,1,10:21:25 AM,20,1,10:34:56 AM,8,19.0,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_538,811
-Order_No_26240,User_Id_1724,Bike,3,Business,15,1,1:30:21 PM,15,1,1:31:51 PM,15,1,1:40:15 PM,15,1,2:01:22 PM,15,1,2:24:04 PM,7,,,-1.3014461000000002,36.7661381,-1.286067,36.8146155,Rider_Id_306,1362
-Order_No_158,User_Id_868,Bike,3,Business,14,5,4:06:26 PM,14,5,4:15:54 PM,14,5,4:27:09 PM,14,5,4:38:48 PM,14,5,4:38:54 PM,3,23.1,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_510,6
-Order_No_22750,User_Id_421,Bike,3,Business,19,3,2:48:39 PM,19,3,4:02:51 PM,19,3,4:11:36 PM,19,3,4:25:18 PM,19,3,4:50:13 PM,8,24.3,,-1.2975066000000002,36.76894229999999,-1.2737591000000001,36.814252200000006,Rider_Id_607,1495
-Order_No_10854,User_Id_53,Bike,3,Business,28,5,1:07:54 PM,28,5,1:09:43 PM,28,5,1:14:36 PM,28,5,1:30:20 PM,28,5,1:43:44 PM,5,23.6,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_656,804
-Order_No_14646,User_Id_1976,Bike,3,Business,2,5,5:21:48 PM,2,5,5:37:27 PM,2,5,5:48:57 PM,2,5,5:50:54 PM,2,5,6:14:02 PM,8,24.0,,-1.257028,36.7810435,-1.301468,36.77465789,Rider_Id_208,1388
-Order_No_17632,User_Id_2602,Bike,3,Personal,13,4,2:27:20 PM,13,4,2:34:30 PM,13,4,2:45:43 PM,13,4,2:49:50 PM,13,4,3:10:15 PM,8,22.6,,-1.270766,36.810988,-1.3228162,36.8226414,Rider_Id_247,1225
-Order_No_15125,User_Id_1752,Bike,2,Personal,22,3,3:08:45 PM,22,3,3:09:48 PM,22,3,3:18:17 PM,22,3,3:24:22 PM,22,3,4:05:10 PM,20,25.9,,-1.2989758,36.77535770000001,-1.2191053,36.8995881,Rider_Id_334,2448
-Order_No_17329,User_Id_1027,Bike,3,Personal,11,4,11:48:21 AM,11,4,12:44:39 PM,11,4,12:59:26 PM,11,4,1:02:07 PM,11,4,1:17:24 PM,5,25.6,,-1.2728424999999999,36.791039000000005,-1.2797536,36.8223726,Rider_Id_576,917
-Order_No_19001,User_Id_546,Bike,3,Personal,28,1,11:25:25 AM,28,1,11:25:38 AM,28,1,11:34:11 AM,28,1,11:41:02 AM,28,1,12:11:14 PM,7,22.5,,-1.2656584,36.809625700000005,-1.2377049999999998,36.7663283,Rider_Id_103,1812
-Order_No_3788,User_Id_517,Bike,3,Business,16,6,10:38:36 AM,16,6,10:39:35 AM,16,6,10:53:22 AM,16,6,10:58:29 AM,16,6,11:15:11 AM,4,25.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_304,1002
-Order_No_8123,User_Id_2587,Bike,3,Business,7,7,10:22:21 AM,7,7,10:22:45 AM,7,7,10:29:42 AM,7,7,10:51:02 AM,7,7,10:55:58 AM,2,25.4,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_532,296
-Order_No_9488,User_Id_1805,Bike,2,Personal,28,2,1:45:29 PM,28,2,1:46:52 PM,28,2,1:55:20 PM,28,2,1:59:42 PM,28,2,2:33:11 PM,15,19.3,,-1.2979147,36.785781400000005,-1.3206958,36.8769553,Rider_Id_350,2009
-Order_No_18074,User_Id_893,Bike,3,Business,23,3,12:32:11 PM,23,3,12:33:00 PM,23,3,12:36:34 PM,23,3,12:39:13 PM,23,3,12:41:43 PM,2,,,-1.2663194,36.809901,-1.2675979,36.815565299999996,Rider_Id_498,150
-Order_No_27288,User_Id_3002,Bike,1,Personal,4,6,4:22:25 PM,4,6,4:24:12 PM,4,6,4:47:32 PM,4,6,4:51:53 PM,4,6,5:03:44 PM,6,22.5,,-1.2925428999999997,36.7799999,-1.2693017,36.8022778,Rider_Id_670,711
-Order_No_8245,User_Id_3050,Bike,3,Business,8,5,5:16:23 PM,8,5,5:17:15 PM,8,5,5:24:46 PM,8,5,6:24:53 PM,8,5,6:51:19 PM,14,25.3,,-1.2945073999999999,36.8083195,-1.3169591,36.7000029,Rider_Id_205,1586
-Order_No_5924,User_Id_2700,Bike,1,Business,28,1,1:46:20 PM,28,1,1:59:34 PM,28,1,2:13:01 PM,28,1,2:24:01 PM,28,1,2:44:45 PM,7,24.7,,-1.293973,36.80169,-1.2562249,36.786866100000005,Rider_Id_103,1244
-Order_No_26229,User_Id_723,Bike,1,Personal,16,3,1:50:31 PM,16,3,1:51:46 PM,16,3,2:07:05 PM,16,3,2:18:49 PM,16,3,2:45:02 PM,10,25.6,,-1.2569006,36.7925141,-1.3034735,36.7986213,Rider_Id_209,1573
-Order_No_21690,User_Id_3517,Bike,3,Business,24,3,9:50:29 AM,24,3,9:51:12 AM,24,3,10:32:41 AM,24,3,10:56:30 AM,24,3,11:29:15 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_950,1965
-Order_No_15894,User_Id_2653,Bike,1,Personal,1,3,4:46:55 PM,1,3,4:47:17 PM,1,3,4:58:31 PM,1,3,5:01:07 PM,1,3,5:17:03 PM,8,23.4,,-1.2860372,36.7549573,-1.2663135,36.7973895,Rider_Id_449,956
-Order_No_6341,User_Id_635,Bike,3,Business,31,5,2:28:49 PM,31,5,2:31:31 PM,31,5,2:52:44 PM,31,5,2:56:35 PM,31,5,3:24:13 PM,7,24.6,,-1.2599563,36.799343900000004,-1.3081376,36.83297720000001,Rider_Id_582,1658
-Order_No_16895,User_Id_1422,Bike,3,Business,17,1,4:18:25 PM,17,1,4:19:04 PM,17,1,4:30:32 PM,17,1,4:43:28 PM,17,1,4:43:33 PM,1,25.4,,-1.3015162,36.840095500000004,-1.310518,36.837305,Rider_Id_62,5
-Order_No_25958,User_Id_84,Bike,3,Business,4,2,12:45:29 PM,4,2,1:30:24 PM,4,2,1:42:49 PM,4,2,1:57:47 PM,4,2,3:18:06 PM,21,23.7,10.9,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_586,4819
-Order_No_26871,User_Id_1271,Bike,3,Business,10,3,3:41:44 PM,10,3,3:44:10 PM,10,3,3:54:52 PM,10,3,3:56:54 PM,10,3,5:06:52 PM,23,30.7,,-1.3244885,36.897791999999995,-1.2784722,36.7556652,Rider_Id_168,4198
-Order_No_19218,User_Id_60,Bike,3,Business,5,6,9:36:40 AM,5,6,9:37:47 AM,5,6,9:50:24 AM,5,6,10:05:21 AM,5,6,10:43:44 AM,18,18.0,4.0,-1.293515,36.897607,-1.2297799999999999,36.836215,Rider_Id_477,2303
-Order_No_24420,User_Id_1687,Bike,3,Business,12,2,9:51:29 AM,12,2,9:59:24 AM,12,2,10:01:02 AM,12,2,10:20:21 AM,12,2,10:20:26 AM,1,15.9,,-1.2962832,36.784968299999996,-1.3025299,36.7901963,Rider_Id_354,5
-Order_No_14472,User_Id_1471,Bike,3,Business,15,5,12:35:49 PM,15,5,12:35:59 PM,15,5,12:37:42 PM,15,5,12:46:26 PM,15,5,1:16:24 PM,13,26.6,,-1.2551895,36.7822034,-1.3206841999999999,36.8362301,Rider_Id_854,1798
-Order_No_4808,User_Id_3277,Bike,2,Personal,18,5,1:47:42 PM,18,5,2:15:21 PM,18,5,2:29:31 PM,18,5,2:35:33 PM,18,5,2:58:02 PM,5,24.4,,-1.2863177,36.8237972,-1.2913382,36.8018508,Rider_Id_89,1349
-Order_No_3637,User_Id_3272,Bike,2,Personal,2,1,12:21:52 PM,2,1,12:24:19 PM,2,1,12:33:47 PM,2,1,12:35:47 PM,2,1,1:35:10 PM,5,19.0,,-1.2696341,36.8042045,-1.2754476000000001,36.836381200000005,Rider_Id_46,3563
-Order_No_2349,User_Id_928,Bike,3,Business,30,2,3:18:46 PM,30,2,4:53:06 PM,30,2,4:53:48 PM,30,2,4:56:54 PM,30,2,5:34:43 PM,6,,,-1.2551895,36.7822034,-1.285324,36.812901000000004,Rider_Id_800,2269
-Order_No_7678,User_Id_3586,Bike,3,Business,13,2,2:30:18 PM,13,2,2:53:27 PM,13,2,3:00:01 PM,13,2,3:02:23 PM,13,2,4:08:09 PM,7,26.0,,-1.275442,36.766020700000006,-1.2820458000000001,36.813455700000006,Rider_Id_812,3946
-Order_No_22706,User_Id_1205,Bike,3,Business,14,5,3:06:42 PM,14,5,3:22:14 PM,14,5,3:27:33 PM,14,5,3:40:52 PM,14,5,3:59:05 PM,9,23.8,,-1.257309,36.806008399999996,-1.2954438,36.7667096,Rider_Id_541,1093
-Order_No_22349,User_Id_3647,Bike,3,Business,17,3,12:41:03 PM,17,3,1:06:22 PM,17,3,1:19:54 PM,17,3,1:23:24 PM,17,3,1:37:33 PM,5,,,-1.2726389999999999,36.794723,-1.2858996999999999,36.813863399999995,Rider_Id_709,849
-Order_No_18204,User_Id_3091,Bike,3,Business,28,5,4:03:54 PM,28,5,4:04:27 PM,28,5,4:17:47 PM,28,5,4:25:40 PM,28,5,5:10:05 PM,23,25.2,,-1.207546,36.7913976,-1.3014461000000002,36.7661381,Rider_Id_685,2665
-Order_No_1196,User_Id_432,Bike,3,Business,28,2,8:24:53 AM,28,2,8:25:26 AM,28,2,8:27:32 AM,28,2,8:31:44 AM,28,2,9:49:45 AM,9,18.9,,-1.3012008,36.764868,-1.2584143,36.8048002,Rider_Id_855,4681
-Order_No_22493,User_Id_3283,Bike,3,Business,18,1,11:20:08 AM,18,1,11:20:28 AM,18,1,11:25:54 AM,18,1,11:43:22 AM,18,1,11:43:28 AM,7,26.8,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_919,6
-Order_No_4831,User_Id_2330,Bike,3,Business,30,4,1:10:25 PM,30,4,1:11:07 PM,30,4,1:11:12 PM,30,4,1:50:36 PM,30,4,2:10:39 PM,10,,,-1.2642903,36.80021370000001,-1.2212611999999998,36.8440033,Rider_Id_704,1203
-Order_No_8741,User_Id_1568,Bike,3,Business,7,4,3:29:58 PM,7,4,3:30:32 PM,7,4,3:39:26 PM,7,4,3:44:59 PM,7,4,4:04:58 PM,8,20.3,,-1.2972916,36.7880497,-1.2579303999999998,36.8019667,Rider_Id_663,1199
-Order_No_18885,User_Id_2748,Bike,1,Personal,5,6,11:01:18 AM,5,6,11:02:25 AM,5,6,11:21:52 AM,5,6,11:23:30 AM,5,6,11:45:33 AM,9,22.4,,-1.269103,36.8223998,-1.2743743,36.7657321,Rider_Id_762,1323
-Order_No_767,User_Id_1244,Bike,3,Business,28,5,12:19:23 PM,28,5,12:20:06 PM,28,5,12:36:25 PM,28,5,12:41:15 PM,28,5,1:59:52 PM,8,23.9,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_511,4717
-Order_No_24964,User_Id_1927,Bike,3,Business,2,6,11:21:30 AM,2,6,11:23:00 AM,2,6,11:38:15 AM,2,6,11:43:41 AM,2,6,12:28:03 PM,9,27.5,,-1.3014461000000002,36.7661381,-1.3472066,36.7692638,Rider_Id_605,2662
-Order_No_11379,User_Id_53,Bike,3,Business,13,3,3:11:05 PM,13,3,3:12:41 PM,13,3,3:15:04 PM,13,3,3:23:49 PM,13,3,3:43:31 PM,5,27.0,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_26,1182
-Order_No_3912,User_Id_909,Bike,3,Business,29,6,3:31:47 PM,29,6,3:32:28 PM,29,6,3:41:21 PM,29,6,3:52:24 PM,29,6,4:13:30 PM,11,26.7,,-1.28878,36.8168312,-1.225322,36.80855,Rider_Id_489,1266
-Order_No_9834,User_Id_3201,Bike,3,Business,10,1,8:30:45 AM,10,1,8:55:25 AM,10,1,9:05:31 AM,10,1,9:10:54 AM,10,1,9:46:41 AM,6,16.6,,-1.305936,36.85095,-1.2897368,36.8226783,Rider_Id_211,2147
-Order_No_19397,User_Id_1642,Bike,3,Business,26,5,10:13:41 AM,26,5,10:14:10 AM,26,5,10:25:18 AM,26,5,10:40:37 AM,26,5,11:18:39 AM,16,,,-1.2726506000000002,36.7960153,-1.2046998,36.7839931,Rider_Id_306,2282
-Order_No_12522,User_Id_367,Bike,3,Personal,22,2,11:09:45 AM,22,2,11:40:52 AM,22,2,11:47:40 AM,22,2,11:54:02 AM,22,2,12:02:28 PM,2,19.5,,-1.2866269,36.82463679999999,-1.2824911,36.8218042,Rider_Id_742,506
-Order_No_11309,User_Id_540,Bike,3,Business,1,4,3:13:26 PM,1,4,3:14:08 PM,1,4,3:17:16 PM,1,4,3:19:58 PM,1,4,3:47:42 PM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_402,1664
-Order_No_151,User_Id_393,Bike,3,Business,5,1,8:20:30 AM,5,1,8:21:51 AM,5,1,8:46:59 AM,5,1,8:53:07 AM,5,1,9:28:06 AM,14,18.4,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,2099
-Order_No_25156,User_Id_1195,Bike,3,Business,21,2,10:48:48 AM,21,2,11:06:55 AM,21,2,11:20:23 AM,21,2,11:39:10 AM,21,2,12:03:01 PM,15,22.4,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_628,1431
-Order_No_16897,User_Id_1271,Bike,3,Business,8,1,12:04:50 PM,8,1,12:05:15 PM,8,1,12:10:02 PM,8,1,12:15:03 PM,8,1,12:55:56 PM,24,27.7,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_418,2453
-Order_No_14266,User_Id_265,Bike,3,Business,28,4,2:57:22 PM,28,4,3:29:17 PM,28,4,3:35:29 PM,28,4,3:59:57 PM,28,4,4:28:32 PM,6,29.8,,-1.300921,36.828195,-1.326825,36.864345,Rider_Id_6,1715
-Order_No_23962,User_Id_3186,Bike,3,Business,17,5,1:57:11 PM,17,5,1:58:16 PM,17,5,2:08:11 PM,17,5,2:12:51 PM,17,5,2:35:44 PM,3,,,-1.2980228999999999,36.78865,-1.2854406,36.789409,Rider_Id_815,1373
-Order_No_24680,User_Id_1329,Bike,3,Business,12,5,6:39:23 PM,12,5,6:40:36 PM,12,5,6:46:19 PM,12,5,6:51:46 PM,12,5,7:04:47 PM,6,26.6,,-1.273056,36.811298,-1.2832105,36.7777664,Rider_Id_484,781
-Order_No_4947,User_Id_2553,Bike,1,Personal,15,1,3:57:45 PM,15,1,4:16:34 PM,15,1,4:20:31 PM,15,1,4:31:18 PM,15,1,5:17:36 PM,9,,,-1.2918239,36.8235665,-1.284382,36.774604499999995,Rider_Id_674,2778
-Order_No_20251,User_Id_3149,Bike,3,Business,9,3,1:00:15 PM,9,3,1:00:40 PM,9,3,1:00:50 PM,9,3,1:18:07 PM,9,3,1:31:38 PM,6,25.1,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_518,811
-Order_No_26317,User_Id_635,Bike,3,Business,13,2,10:17:17 AM,13,2,10:32:07 AM,13,2,10:54:00 AM,13,2,11:00:57 AM,13,2,11:17:05 AM,9,22.4,,-1.2599563,36.799343900000004,-1.2948886999999998,36.7683938,Rider_Id_952,968
-Order_No_12207,User_Id_265,Bike,3,Business,14,2,1:14:46 PM,14,2,1:15:33 PM,14,2,1:49:12 PM,14,2,1:52:02 PM,14,2,2:28:54 PM,15,,,-1.3459625,36.9073925,-1.300921,36.828195,Rider_Id_856,2212
-Order_No_23607,User_Id_2833,Bike,2,Personal,12,5,4:43:33 PM,12,5,4:44:07 PM,12,5,4:45:29 PM,12,5,5:03:28 PM,12,5,6:08:53 PM,22,25.8,,-1.26919,36.79907,-1.392674,36.750337,Rider_Id_680,3925
-Order_No_20778,User_Id_1856,Bike,3,Business,15,2,11:52:01 AM,15,2,11:55:29 AM,15,2,12:07:20 PM,15,2,12:37:47 PM,15,2,12:38:16 PM,5,24.2,,-1.2738936,36.8035805,-1.2883698000000001,36.8269434,Rider_Id_862,29
-Order_No_5908,User_Id_2404,Bike,2,Personal,3,3,3:39:04 PM,3,3,3:48:35 PM,3,3,4:01:38 PM,3,3,4:40:33 PM,3,3,5:06:35 PM,8,23.4,,-1.3233955,36.856294399999996,-1.3057159999999999,36.809882,Rider_Id_565,1562
-Order_No_26324,User_Id_3439,Bike,3,Business,24,5,10:08:00 AM,24,5,10:09:53 AM,24,5,10:47:58 AM,24,5,10:53:12 AM,24,5,11:16:14 AM,11,21.3,,-1.2393157,36.766887700000005,-1.2466808999999999,36.8237453,Rider_Id_689,1382
-Order_No_19002,User_Id_1500,Bike,3,Business,12,2,11:28:20 AM,12,2,11:28:28 AM,12,2,11:30:42 AM,12,2,11:34:23 AM,12,2,11:56:05 AM,11,28.3,,-1.2638185,36.7930057,-1.2610133999999997,36.8506926,Rider_Id_885,1302
-Order_No_5866,User_Id_3264,Bike,3,Business,14,2,10:51:29 AM,14,2,10:52:07 AM,14,2,11:08:04 AM,14,2,11:09:26 AM,14,2,11:26:43 AM,3,,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_362,1037
-Order_No_14465,User_Id_2627,Bike,3,Business,24,3,2:54:13 PM,24,3,2:54:39 PM,24,3,3:04:25 PM,24,3,3:12:42 PM,24,3,3:20:12 PM,2,,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_96,450
-Order_No_2046,User_Id_3491,Bike,3,Business,9,3,1:29:28 PM,9,3,2:18:08 PM,9,3,2:49:31 PM,9,3,3:00:16 PM,9,3,3:46:45 PM,20,23.2,,-1.2297202,36.874550799999994,-1.312124,36.76162,Rider_Id_100,2789
-Order_No_17062,User_Id_496,Bike,3,Business,26,2,11:01:26 AM,26,2,11:01:58 AM,26,2,11:05:58 AM,26,2,11:09:09 AM,26,2,11:27:58 AM,8,27.4,,-1.2551895,36.7822034,-1.3007402,36.8108766,Rider_Id_634,1129
-Order_No_13903,User_Id_3560,Bike,3,Business,15,5,12:31:00 PM,15,5,12:32:12 PM,15,5,12:32:21 PM,15,5,12:54:21 PM,15,5,12:54:36 PM,1,26.1,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_320,15
-Order_No_5727,User_Id_3470,Bike,3,Business,26,1,3:45:53 PM,26,1,3:52:47 PM,26,1,4:00:43 PM,26,1,4:07:46 PM,26,1,4:19:15 PM,5,22.1,0.7,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_583,689
-Order_No_14665,User_Id_393,Bike,3,Business,20,1,10:27:07 AM,20,1,10:27:53 AM,20,1,10:45:42 AM,20,1,11:05:29 AM,20,1,11:12:07 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_710,398
-Order_No_19915,User_Id_1500,Bike,3,Business,8,1,10:09:59 AM,8,1,10:10:37 AM,8,1,10:13:35 AM,8,1,10:59:57 AM,8,1,11:15:14 AM,8,28.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,917
-Order_No_3858,User_Id_3689,Bike,3,Business,7,5,4:25:28 PM,7,5,4:39:48 PM,7,5,4:47:51 PM,7,5,4:51:15 PM,7,5,5:30:47 PM,7,22.9,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_382,2372
-Order_No_981,User_Id_393,Bike,3,Business,11,1,1:51:02 PM,11,1,1:51:15 PM,11,1,2:01:27 PM,11,1,2:09:08 PM,11,1,2:50:17 PM,14,27.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_571,2469
-Order_No_8786,User_Id_1178,Bike,3,Business,19,1,2:49:05 PM,19,1,2:51:31 PM,19,1,3:00:30 PM,19,1,3:05:31 PM,19,1,3:22:03 PM,9,25.0,,-1.2868515,36.7844247,-1.2401893,36.7961714,Rider_Id_649,992
-Order_No_26934,User_Id_3080,Bike,3,Business,30,1,10:59:03 AM,30,1,11:00:09 AM,30,1,11:10:24 AM,30,1,11:24:09 AM,30,1,11:42:45 AM,8,22.4,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_19,1116
-Order_No_27270,User_Id_2627,Bike,3,Business,15,5,12:04:04 PM,15,5,12:05:39 PM,15,5,12:22:58 PM,15,5,12:49:03 PM,15,5,2:13:02 PM,9,26.5,,-1.2813012,36.832396200000005,-1.2571472,36.795063299999995,Rider_Id_268,5039
-Order_No_4009,User_Id_562,Bike,3,Business,10,5,2:04:20 PM,10,5,2:05:50 PM,10,5,2:17:37 PM,10,5,2:25:16 PM,10,5,3:00:06 PM,14,,,-1.3227181000000001,36.8528097,-1.242902,36.870334,Rider_Id_206,2090
-Order_No_8616,User_Id_1787,Bike,3,Business,19,5,3:39:45 PM,19,5,3:39:58 PM,19,5,3:44:19 PM,19,5,3:50:32 PM,19,5,4:13:15 PM,12,,,-1.2527958000000001,36.8003131,-1.2283403,36.8822756,Rider_Id_697,1363
-Order_No_24010,User_Id_685,Bike,3,Business,22,3,2:21:01 PM,22,3,2:25:44 PM,22,3,2:27:44 PM,22,3,3:01:33 PM,22,3,3:16:24 PM,4,24.3,,-1.265432,36.795034,-1.2619756000000002,36.800139,Rider_Id_436,891
-Order_No_12122,User_Id_1075,Bike,3,Business,15,5,2:41:11 PM,15,5,2:42:56 PM,15,5,2:43:21 PM,15,5,2:55:43 PM,15,5,3:07:38 PM,1,28.1,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_210,715
-Order_No_19558,User_Id_1361,Bike,1,Personal,28,1,9:24:04 AM,28,1,9:24:35 AM,28,1,9:36:28 AM,28,1,9:39:56 AM,28,1,10:03:43 AM,8,17.8,,-1.2948784,36.776632799999994,-1.2688997,36.80146129999999,Rider_Id_726,1427
-Order_No_8031,User_Id_3718,Bike,3,Business,26,5,3:39:07 PM,26,5,3:52:15 PM,26,5,4:05:24 PM,26,5,4:07:29 PM,26,5,4:19:01 PM,5,,,-1.2650027,36.8126242,-1.2635089,36.78873370000001,Rider_Id_326,692
-Order_No_3770,User_Id_640,Bike,3,Business,2,5,3:05:53 PM,2,5,3:07:01 PM,2,5,3:08:59 PM,2,5,3:41:14 PM,2,5,3:48:02 PM,3,25.2,,-1.3016431,36.762006799999995,-1.304033,36.7848695,Rider_Id_568,408
-Order_No_10942,User_Id_2591,Bike,1,Personal,16,2,9:32:14 AM,16,2,10:00:36 AM,16,2,10:21:29 AM,16,2,10:25:01 AM,16,2,10:57:35 AM,16,,,-1.2311903,36.8102105,-1.3223238000000002,36.8320056,Rider_Id_85,1954
-Order_No_26762,User_Id_3637,Bike,2,Personal,5,3,4:09:12 PM,5,3,4:09:40 PM,5,3,4:26:26 PM,5,3,4:39:04 PM,5,3,5:06:19 PM,8,22.3,,-1.3001735,36.8042049,-1.3261286,36.8469625,Rider_Id_895,1635
-Order_No_23477,User_Id_2956,Bike,3,Business,6,6,9:06:00 AM,6,6,9:06:08 AM,6,6,9:59:50 AM,6,6,10:17:05 AM,6,6,11:31:05 AM,20,19.7,,-1.2860183,36.8975338,-1.2584143,36.8048002,Rider_Id_223,4440
-Order_No_7587,User_Id_1687,Bike,3,Business,26,5,2:41:14 PM,26,5,2:41:47 PM,26,5,2:57:48 PM,26,5,3:03:37 PM,26,5,3:13:28 PM,4,,,-1.2627937,36.815695700000006,-1.2732803,36.794037700000004,Rider_Id_959,591
-Order_No_20051,User_Id_3064,Bike,3,Business,16,4,10:27:53 AM,16,4,10:28:25 AM,16,4,10:39:49 AM,16,4,10:48:03 AM,16,4,11:07:34 AM,7,14.5,,-1.2571472,36.795063299999995,-1.2948834999999999,36.7830474,Rider_Id_329,1171
-Order_No_11413,User_Id_1329,Bike,3,Business,10,3,3:06:16 PM,10,3,3:06:58 PM,10,3,3:15:47 PM,10,3,3:17:06 PM,10,3,3:36:47 PM,4,30.5,,-1.273056,36.811298,-1.2925316999999998,36.8021114,Rider_Id_166,1181
-Order_No_125,User_Id_3490,Bike,2,Personal,31,5,9:31:44 AM,31,5,9:33:38 AM,31,5,9:54:08 AM,31,5,9:57:14 AM,31,5,10:11:46 AM,5,14.8,,-1.279461,36.7949088,-1.2994217,36.8009779,Rider_Id_85,872
-Order_No_22480,User_Id_60,Bike,3,Business,11,6,12:36:13 PM,11,6,12:38:03 PM,11,6,12:50:49 PM,11,6,1:13:27 PM,11,6,1:13:29 PM,8,,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_953,2
-Order_No_20458,User_Id_426,Bike,3,Business,7,5,12:08:16 PM,7,5,12:08:37 PM,7,5,1:20:04 PM,7,5,1:34:38 PM,7,5,2:07:12 PM,14,24.9,,-1.2968083000000001,36.787097499999994,-1.3323107,36.8692616,Rider_Id_328,1954
-Order_No_17493,User_Id_2454,Bike,3,Business,16,4,12:09:34 PM,16,4,12:10:51 PM,16,4,12:26:48 PM,16,4,12:31:48 PM,16,4,12:43:08 PM,4,,,-1.3034413,36.777010600000004,-1.2941413,36.7958937,Rider_Id_786,680
-Order_No_10188,User_Id_3283,Bike,3,Business,19,2,1:56:30 PM,19,2,1:57:08 PM,19,2,2:03:12 PM,19,2,2:12:48 PM,19,2,2:29:31 PM,9,18.7,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_851,1003
-Order_No_5227,User_Id_2330,Bike,3,Business,23,4,3:47:32 PM,23,4,3:47:50 PM,23,4,3:49:41 PM,23,4,3:51:17 PM,23,4,3:57:17 PM,4,20.7,,-1.3040812,36.7924217,-1.2868601,36.7738025,Rider_Id_207,360
-Order_No_22311,User_Id_909,Bike,3,Business,29,5,1:22:52 PM,29,5,1:32:01 PM,29,5,1:37:48 PM,29,5,1:43:43 PM,29,5,2:12:40 PM,13,21.4,,-1.2887938,36.828583200000004,-1.225322,36.80855,Rider_Id_844,1737
-Order_No_23415,User_Id_1268,Bike,1,Personal,21,4,5:18:57 PM,21,4,5:19:38 PM,21,4,5:28:37 PM,21,4,5:31:37 PM,21,4,5:58:06 PM,9,19.8,,-1.293554,36.763113,-1.2988168,36.8198572,Rider_Id_105,1589
-Order_No_18535,User_Id_2439,Bike,3,Business,11,5,1:46:56 PM,11,5,3:00:40 PM,11,5,3:07:17 PM,11,5,3:38:32 PM,11,5,3:57:25 PM,6,26.4,,-1.2615888000000002,36.7928732,-1.2886923000000001,36.8241519,Rider_Id_489,1133
-Order_No_5339,User_Id_3557,Bike,3,Business,29,1,1:39:13 PM,29,1,1:39:45 PM,29,1,1:48:04 PM,29,1,1:58:58 PM,29,1,2:20:21 PM,9,24.5,,-1.2981352,36.8213822,-1.2546732,36.808679600000005,Rider_Id_219,1283
-Order_No_21150,User_Id_2866,Bike,3,Business,9,3,9:42:20 AM,9,3,9:43:22 AM,9,3,9:47:19 AM,9,3,9:50:58 AM,9,3,10:02:24 AM,6,20.1,,-1.2551895,36.7822034,-1.265715,36.823815,Rider_Id_536,686
-Order_No_19242,User_Id_2801,Bike,3,Business,28,4,2:23:10 PM,28,4,2:23:50 PM,28,4,2:24:40 PM,28,4,2:31:33 PM,28,4,2:55:36 PM,10,29.9,,-1.2674273999999999,36.787082700000006,-1.3108386,36.7555239,Rider_Id_576,1443
-Order_No_12110,User_Id_2349,Bike,3,Business,4,5,9:56:33 AM,4,5,9:58:09 AM,4,5,10:02:44 AM,4,5,10:08:35 AM,4,5,10:46:59 AM,6,17.6,0.4,-1.2784495,36.7981729,-1.262471,36.8194216,Rider_Id_101,2304
-Order_No_27837,User_Id_1329,Bike,3,Business,10,5,12:48:11 PM,10,5,12:53:44 PM,10,5,1:40:43 PM,10,5,1:42:59 PM,10,5,1:53:54 PM,4,21.7,,-1.2795183,36.823808899999996,-1.273056,36.811298,Rider_Id_229,655
-Order_No_19367,User_Id_375,Bike,3,Business,13,3,11:53:35 AM,13,3,11:54:34 AM,13,3,11:56:28 AM,13,3,12:08:22 PM,13,3,12:47:25 PM,9,,,-1.2551895,36.7822034,-1.3041128999999998,36.810493900000004,Rider_Id_154,2343
-Order_No_15355,User_Id_2200,Bike,3,Business,16,3,1:21:38 PM,16,3,1:32:49 PM,16,3,1:54:13 PM,16,3,2:12:47 PM,16,3,3:01:50 PM,10,25.4,,-1.2859912,36.8756811,-1.279395,36.825364,Rider_Id_676,2943
-Order_No_15577,User_Id_391,Bike,3,Business,23,3,10:47:38 AM,23,3,10:50:55 AM,23,3,11:21:14 AM,23,3,11:30:42 AM,23,3,12:06:01 PM,6,,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_511,2119
-Order_No_5547,User_Id_2181,Bike,2,Personal,30,1,8:32:18 AM,30,1,10:01:20 AM,30,1,10:18:28 AM,30,1,10:22:32 AM,30,1,10:37:49 AM,8,16.5,1.9,-1.2665973999999998,36.7921181,-1.2932780000000001,36.827789,Rider_Id_648,917
-Order_No_14806,User_Id_3004,Bike,1,Personal,8,7,4:38:17 PM,8,7,4:38:48 PM,8,7,4:44:36 PM,8,7,4:48:32 PM,8,7,5:09:46 PM,10,20.8,,-1.2601903,36.8066946,-1.2325082,36.7304696,Rider_Id_478,1274
-Order_No_15786,User_Id_383,Bike,3,Business,18,2,1:06:14 PM,18,2,1:06:22 PM,18,2,1:13:19 PM,18,2,1:21:38 PM,18,2,1:39:07 PM,10,,,-1.3749536,36.9200703,-1.3241819,36.882342799999996,Rider_Id_647,1049
-Order_No_12224,User_Id_3108,Bike,3,Business,9,5,11:59:13 AM,9,5,12:00:25 PM,9,5,12:00:54 PM,9,5,12:47:21 PM,9,5,1:38:56 PM,15,25.5,,-1.2551895,36.7822034,-1.3348111,36.8614787,Rider_Id_107,3095
-Order_No_9595,User_Id_3686,Bike,3,Business,11,3,4:49:55 PM,11,3,4:52:00 PM,11,3,5:01:08 PM,11,3,5:08:18 PM,11,3,5:33:01 PM,10,20.2,,-1.2551895,36.7822034,-1.3068448000000001,36.8348641,Rider_Id_770,1483
-Order_No_7557,User_Id_2973,Bike,2,Personal,19,3,11:55:45 AM,19,3,11:56:09 AM,19,3,12:11:54 PM,19,3,12:16:40 PM,19,3,12:35:53 PM,5,,,-1.2824911,36.8218042,-1.2962832,36.784968299999996,Rider_Id_726,1153
-Order_No_17962,User_Id_53,Bike,3,Business,4,4,3:33:53 PM,4,4,3:38:14 PM,4,4,3:47:37 PM,4,4,4:09:23 PM,4,4,4:09:26 PM,6,27.5,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_740,3
-Order_No_14510,User_Id_3560,Bike,3,Business,23,6,11:57:59 AM,23,6,12:00:06 PM,23,6,12:09:14 PM,23,6,12:15:44 PM,23,6,12:37:23 PM,5,26.4,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_436,1299
-Order_No_22551,User_Id_1845,Bike,3,Business,25,4,12:13:41 PM,25,4,12:13:48 PM,25,4,12:14:27 PM,25,4,12:25:24 PM,25,4,12:56:21 PM,21,20.0,,-1.2917868000000001,36.7872675,-1.3705766000000001,36.9120742,Rider_Id_231,1857
-Order_No_16818,User_Id_1927,Bike,3,Business,14,4,4:59:01 PM,14,4,5:02:10 PM,14,4,5:22:24 PM,14,4,5:23:30 PM,14,4,6:26:19 PM,15,25.9,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_682,3769
-Order_No_22222,User_Id_2701,Bike,3,Business,1,4,9:17:59 AM,1,4,9:18:52 AM,1,4,9:58:49 AM,1,4,10:18:31 AM,1,4,10:47:59 AM,12,,,-1.3027293,36.766197399999996,-1.2765736,36.851364600000004,Rider_Id_588,1768
-Order_No_22109,User_Id_120,Bike,1,Personal,9,4,8:37:49 AM,9,4,8:38:04 AM,9,4,8:43:50 AM,9,4,8:47:59 AM,9,4,9:11:12 AM,12,17.0,,-1.2795197,36.8168287,-1.2907853999999999,36.89963710000001,Rider_Id_726,1393
-Order_No_4305,User_Id_2344,Bike,3,Business,4,1,4:23:29 PM,4,1,4:24:53 PM,4,1,4:25:42 PM,4,1,4:35:33 PM,4,1,4:53:25 PM,8,25.9,,-1.298529,36.816482,-1.2551895,36.7822034,Rider_Id_715,1072
-Order_No_18486,User_Id_888,Bike,2,Personal,30,1,8:54:23 AM,30,1,8:55:05 AM,30,1,9:33:16 AM,30,1,9:34:38 AM,30,1,10:10:02 AM,13,16.3,2.0,-1.250249,36.76639,-1.2995738000000001,36.8199719,Rider_Id_452,2124
-Order_No_18187,User_Id_1340,Bike,3,Business,5,2,4:05:52 PM,5,2,4:24:25 PM,5,2,4:32:46 PM,5,2,4:33:57 PM,5,2,4:51:26 PM,6,24.8,,-1.2707178999999997,36.7979713,-1.289882,36.8105888,Rider_Id_548,1049
-Order_No_11663,User_Id_1637,Bike,3,Business,13,4,2:35:23 PM,13,4,2:35:50 PM,13,4,2:46:27 PM,13,4,2:50:54 PM,13,4,3:16:51 PM,15,22.5,,-1.2615888000000002,36.7928732,-1.207546,36.7913976,Rider_Id_259,1557
-Order_No_18251,User_Id_3487,Bike,3,Business,8,3,11:53:49 AM,8,3,11:54:50 AM,8,3,11:59:58 AM,8,3,12:08:56 PM,8,3,12:23:17 PM,5,,,-1.279395,36.825364,-1.2917868000000001,36.7872675,Rider_Id_289,861
-Order_No_3616,User_Id_3621,Bike,3,Business,9,2,7:57:11 AM,9,2,7:57:36 AM,9,2,8:06:54 AM,9,2,8:15:25 AM,9,2,8:26:45 AM,4,17.4,4.0,-1.2836583000000001,36.7886014,-1.288167,36.8181484,Rider_Id_355,680
-Order_No_7808,User_Id_3080,Bike,3,Business,11,1,1:39:52 PM,11,1,1:40:54 PM,11,1,1:43:43 PM,11,1,1:51:42 PM,11,1,2:29:46 PM,22,29.1,,-1.2551895,36.7822034,-1.3342256000000001,36.9106741,Rider_Id_634,2284
-Order_No_16451,User_Id_1880,Bike,3,Business,23,3,12:48:06 PM,23,3,12:49:26 PM,23,3,1:12:06 PM,23,3,1:13:44 PM,23,3,1:35:18 PM,7,,,-1.2571472,36.795063299999995,-1.2981102,36.8293513,Rider_Id_660,1294
-Order_No_13241,User_Id_3091,Bike,3,Business,4,4,2:45:05 PM,4,4,2:45:14 PM,4,4,2:52:50 PM,4,4,2:55:25 PM,4,4,3:09:26 PM,2,24.7,,-1.3012008,36.764868,-1.2952639,36.756260600000005,Rider_Id_751,841
-Order_No_11667,User_Id_2289,Bike,3,Business,6,5,1:20:08 PM,6,5,1:39:04 PM,6,5,1:45:48 PM,6,5,1:56:11 PM,6,5,2:46:12 PM,7,22.4,,-1.2551895,36.7822034,-1.2950826000000002,36.801837899999995,Rider_Id_642,3001
-Order_No_5997,User_Id_1764,Bike,1,Personal,31,5,2:13:23 PM,31,5,2:14:42 PM,31,5,2:27:24 PM,31,5,2:35:02 PM,31,5,2:49:43 PM,7,20.0,,-1.3321038,36.8012564,-1.2887816,36.8088885,Rider_Id_249,881
-Order_No_26849,User_Id_3727,Bike,3,Business,6,4,3:29:41 PM,6,4,3:30:09 PM,6,4,3:43:27 PM,6,4,3:45:57 PM,6,4,4:00:31 PM,6,21.1,,-1.2963097,36.7688221,-1.2825816,36.8092346,Rider_Id_404,874
-Order_No_2397,User_Id_1796,Bike,3,Business,2,2,10:36:21 AM,2,2,10:37:42 AM,2,2,10:38:03 AM,2,2,10:46:22 AM,2,2,11:13:29 AM,21,26.1,,-1.331619,36.847976,-1.3181159,36.710155900000004,Rider_Id_61,1627
-Order_No_20695,User_Id_3201,Bike,3,Business,25,3,1:12:59 PM,25,3,1:13:09 PM,25,3,1:19:46 PM,25,3,1:25:44 PM,25,3,1:30:54 PM,1,,,-1.2527958000000001,36.8003131,-1.2649605,36.798177700000004,Rider_Id_103,310
-Order_No_24790,User_Id_2596,Bike,3,Business,18,1,2:45:40 PM,18,1,2:46:24 PM,18,1,2:53:47 PM,18,1,3:05:29 PM,18,1,3:34:16 PM,17,30.6,,-1.2963097,36.7688221,-1.2300082,36.8867427,Rider_Id_247,1727
-Order_No_14303,User_Id_3471,Bike,2,Personal,23,6,11:48:03 AM,23,6,11:53:35 AM,23,6,12:14:33 PM,23,6,12:29:22 PM,23,6,12:45:15 PM,6,,,-1.2873639,36.8256076,-1.2745187,36.787643599999996,Rider_Id_581,953
-Order_No_19069,User_Id_2132,Bike,3,Business,1,5,8:21:37 AM,1,5,8:22:31 AM,1,5,8:24:40 AM,1,5,8:36:01 AM,1,5,9:30:42 AM,27,23.0,,-1.2384056,36.8718698,-1.3858142,36.7641498,Rider_Id_75,3281
-Order_No_6567,User_Id_2801,Bike,3,Business,11,4,9:34:30 AM,11,4,9:36:07 AM,11,4,10:06:20 AM,11,4,10:26:37 AM,11,4,11:07:32 AM,22,18.3,,-1.3219125,36.714515000000006,-1.3104506,36.8687235,Rider_Id_565,2455
-Order_No_1899,User_Id_265,Bike,3,Business,30,2,12:39:25 PM,30,2,12:42:44 PM,30,2,12:49:34 PM,30,2,12:53:03 PM,30,2,1:00:38 PM,3,,,-1.3082842,36.843419399999995,-1.300921,36.828195,Rider_Id_155,455
-Order_No_1214,User_Id_3426,Bike,3,Business,16,5,1:31:13 PM,16,5,1:44:04 PM,16,5,1:51:34 PM,16,5,2:00:16 PM,16,5,2:11:45 PM,2,,,-1.3014461000000002,36.7661381,-1.3069389999999999,36.7727566,Rider_Id_952,689
-Order_No_8821,User_Id_640,Bike,3,Business,1,1,11:25:20 AM,1,1,11:25:47 AM,1,1,11:30:41 AM,1,1,11:33:54 AM,1,1,12:03:28 PM,8,24.0,,-1.304033,36.7848695,-1.2656079,36.8114464,Rider_Id_731,1774
-Order_No_28129,User_Id_393,Bike,3,Business,22,5,10:03:34 AM,22,5,10:04:51 AM,22,5,10:29:40 AM,22,5,10:46:39 AM,22,5,10:56:22 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_399,583
-Order_No_17290,User_Id_1527,Bike,3,Business,4,4,12:24:57 PM,4,4,12:26:05 PM,4,4,1:42:50 PM,4,4,2:02:11 PM,4,4,2:02:17 PM,4,27.5,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_843,6
-Order_No_1731,User_Id_3674,Bike,3,Business,28,5,9:27:44 AM,28,5,10:36:05 AM,28,5,10:50:39 AM,28,5,10:51:51 AM,28,5,11:19:50 AM,14,24.9,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_612,1679
-Order_No_247,User_Id_2523,Bike,3,Business,13,5,2:57:08 PM,13,5,2:57:19 PM,13,5,2:59:11 PM,13,5,3:12:31 PM,13,5,3:29:01 PM,8,19.1,,-1.2551895,36.7822034,-1.2860372,36.7549573,Rider_Id_101,990
-Order_No_13943,User_Id_1158,Bike,3,Personal,25,1,9:09:34 AM,25,1,9:09:45 AM,25,1,9:30:44 AM,25,1,9:32:56 AM,25,1,9:42:27 AM,4,14.9,,-1.2790803,36.8156909,-1.2661263999999999,36.8030359,Rider_Id_89,571
-Order_No_12598,User_Id_2956,Bike,3,Business,27,1,10:53:56 AM,27,1,10:55:31 AM,27,1,11:19:29 AM,27,1,11:23:37 AM,27,1,11:51:50 AM,13,20.8,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_541,1693
-Order_No_3307,User_Id_1899,Bike,3,Business,14,2,12:38:40 PM,14,2,12:39:15 PM,14,2,12:48:07 PM,14,2,1:39:55 PM,14,2,2:13:58 PM,13,24.0,,-1.2600926000000001,36.8088685,-1.331619,36.847976,Rider_Id_882,2043
-Order_No_3845,User_Id_2066,Bike,3,Business,12,5,10:27:57 AM,12,5,10:28:21 AM,12,5,10:39:23 AM,12,5,10:53:21 AM,12,5,11:09:09 AM,12,26.3,,-1.3210188999999999,36.841055100000005,-1.369541,36.907455,Rider_Id_126,948
-Order_No_27375,User_Id_1209,Bike,3,Business,8,3,11:22:09 AM,8,3,11:23:42 AM,8,3,11:26:57 AM,8,3,11:45:53 AM,8,3,12:00:03 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_411,850
-Order_No_2165,User_Id_1500,Bike,3,Business,30,3,12:46:21 PM,30,3,1:06:47 PM,30,3,1:07:08 PM,30,3,1:54:12 PM,30,3,2:11:19 PM,8,28.8,,-1.3004062,36.829741,-1.2636051000000001,36.7850995,Rider_Id_111,1027
-Order_No_25702,User_Id_2469,Bike,3,Business,11,4,1:17:19 PM,11,4,3:01:41 PM,11,4,3:21:17 PM,11,4,3:28:34 PM,11,4,3:49:08 PM,15,27.1,,-1.303924,36.8758792,-1.369541,36.907455,Rider_Id_166,1234
-Order_No_19445,User_Id_2801,Bike,3,Business,27,2,3:23:32 PM,27,2,3:24:22 PM,27,2,3:40:49 PM,27,2,3:44:47 PM,27,2,4:14:36 PM,10,25.5,,-1.2595421999999998,36.7871178,-1.303187,36.824195700000004,Rider_Id_498,1789
-Order_No_10598,User_Id_136,Bike,3,Business,11,5,1:23:20 PM,11,5,1:24:14 PM,11,5,1:28:47 PM,11,5,1:42:20 PM,11,5,2:29:51 PM,18,22.7,,-1.2914795000000001,36.824897299999996,-1.3326173000000001,36.71462329999999,Rider_Id_805,2851
-Order_No_25089,User_Id_1827,Bike,3,Business,19,2,3:49:31 PM,19,2,4:25:48 PM,19,2,4:46:39 PM,19,2,5:18:02 PM,19,2,5:31:55 PM,11,19.6,,-1.307143,36.825009,-1.293515,36.897607,Rider_Id_549,833
-Order_No_8964,User_Id_1062,Bike,3,Business,24,1,5:53:54 PM,24,1,5:54:16 PM,24,1,5:57:00 PM,24,1,6:03:37 PM,24,1,6:21:11 PM,7,22.3,,-1.238874,36.807889,-1.266116,36.8088644,Rider_Id_674,1054
-Order_No_27379,User_Id_1591,Bike,2,Personal,12,5,2:45:48 PM,12,5,3:50:14 PM,12,5,3:52:29 PM,12,5,3:58:25 PM,12,5,4:22:50 PM,5,27.2,,-1.2655306,36.828148,-1.2813370000000002,36.818968,Rider_Id_101,1465
-Order_No_10430,User_Id_1958,Bike,3,Business,13,4,11:44:47 AM,13,4,11:45:20 AM,13,4,11:48:28 AM,13,4,12:14:18 PM,13,4,12:48:49 PM,21,21.9,,-1.2551895,36.7822034,-1.3700383,36.9190174,Rider_Id_952,2071
-Order_No_9754,User_Id_3583,Bike,3,Personal,8,2,10:11:57 AM,8,2,10:12:23 AM,8,2,10:39:34 AM,8,2,10:44:10 AM,8,2,11:40:57 AM,12,21.4,,-1.3288576,36.8465936,-1.2842588,36.902807,Rider_Id_549,3407
-Order_No_4827,User_Id_3213,Bike,1,Personal,30,4,12:31:45 PM,30,4,12:32:00 PM,30,4,12:46:10 PM,30,4,12:50:23 PM,30,4,1:28:10 PM,13,20.1,,-1.3007534,36.7964576,-1.3413428,36.7022135,Rider_Id_249,2267
-Order_No_9584,User_Id_811,Bike,2,Personal,19,5,12:22:37 PM,19,5,12:22:51 PM,19,5,12:23:32 PM,19,5,12:26:03 PM,19,5,12:37:26 PM,4,25.0,,-1.2963097,36.7688221,-1.2793831,36.7617573,Rider_Id_352,683
-Order_No_5829,User_Id_1209,Bike,3,Business,7,1,9:41:45 AM,7,1,9:46:33 AM,7,1,9:51:58 AM,7,1,9:57:16 AM,7,1,10:05:23 AM,4,21.6,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_523,487
-Order_No_17285,User_Id_1697,Bike,3,Personal,3,1,3:29:37 PM,3,1,3:29:57 PM,3,1,3:31:54 PM,3,1,3:48:46 PM,3,1,4:32:43 PM,12,23.0,,-1.2977386999999998,36.803144700000004,-1.3192808,36.7121888,Rider_Id_179,2637
-Order_No_26572,User_Id_1304,Bike,3,Business,3,3,4:08:13 PM,3,3,4:08:29 PM,3,3,4:09:01 PM,3,3,4:14:29 PM,3,3,5:03:07 PM,23,27.0,,-1.2551895,36.7822034,-1.3440557,36.909965299999996,Rider_Id_102,2918
-Order_No_23737,User_Id_136,Bike,3,Business,12,5,11:46:32 AM,12,5,11:47:04 AM,12,5,12:05:11 PM,12,5,12:20:48 PM,12,5,1:25:12 PM,28,24.3,,-1.3094447,36.9107706,-1.3326173000000001,36.71462329999999,Rider_Id_328,3864
-Order_No_371,User_Id_3647,Bike,3,Business,6,2,11:56:38 AM,6,2,11:57:49 AM,6,2,12:05:26 PM,6,2,12:10:43 PM,6,2,12:26:30 PM,11,24.8,,-1.2726389999999999,36.794723,-1.235348,36.760016,Rider_Id_574,947
-Order_No_25234,User_Id_3647,Bike,3,Business,21,4,3:41:55 PM,21,4,3:42:05 PM,21,4,3:53:09 PM,21,4,3:59:49 PM,21,4,5:02:27 PM,19,26.2,,-1.2726389999999999,36.794723,-1.3206433000000002,36.9117533,Rider_Id_523,3758
-Order_No_9523,User_Id_868,Bike,3,Business,2,6,11:33:59 AM,2,6,11:44:19 AM,2,6,11:55:33 AM,2,6,12:12:21 PM,2,6,12:23:32 PM,7,23.7,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_752,671
-Order_No_3549,User_Id_393,Bike,3,Business,8,5,2:23:07 PM,8,5,2:55:41 PM,8,5,3:01:51 PM,8,5,3:04:40 PM,8,5,3:43:34 PM,14,21.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_294,2334
-Order_No_26470,User_Id_2956,Bike,3,Business,23,6,1:15:08 PM,23,6,1:17:12 PM,23,6,1:21:15 PM,23,6,1:27:20 PM,23,6,1:40:44 PM,2,,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_318,804
-Order_No_22389,User_Id_547,Bike,1,Personal,3,5,10:59:14 AM,3,5,11:00:53 AM,3,5,11:27:44 AM,3,5,11:39:16 AM,3,5,12:10:07 PM,4,21.6,,-1.2652108,36.8056075,-1.2916556000000001,36.8262516,Rider_Id_700,1851
-Order_No_22473,User_Id_3599,Bike,3,Business,21,2,10:32:26 AM,21,2,10:34:56 AM,21,2,10:46:50 AM,21,2,11:13:11 AM,21,2,11:52:29 AM,9,22.6,,-1.3302996,36.8707935,-1.2911796,36.821506799999995,Rider_Id_48,2358
-Order_No_22823,User_Id_1932,Bike,3,Business,5,2,3:42:23 PM,5,2,3:42:55 PM,5,2,3:49:37 PM,5,2,3:54:29 PM,5,2,4:08:08 PM,7,22.6,,-1.3100608999999999,36.820536499999996,-1.2922857,36.781327399999995,Rider_Id_116,819
-Order_No_3474,User_Id_1882,Bike,3,Business,10,5,3:33:10 PM,10,5,3:35:55 PM,10,5,4:11:05 PM,10,5,4:28:40 PM,10,5,4:49:02 PM,8,21.6,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_263,1222
-Order_No_26910,User_Id_622,Bike,1,Business,6,4,10:09:12 AM,6,4,10:10:07 AM,6,4,10:24:45 AM,6,4,10:29:32 AM,6,4,10:52:39 AM,5,17.9,,-1.2951362,36.7876989,-1.2778903999999998,36.775924100000005,Rider_Id_849,1387
-Order_No_21325,User_Id_2532,Bike,3,Business,17,4,9:57:16 AM,17,4,10:04:09 AM,17,4,10:21:09 AM,17,4,10:38:28 AM,17,4,11:10:52 AM,7,25.4,,-1.2551895,36.7822034,-1.2870235,36.820198600000005,Rider_Id_725,1944
-Order_No_24762,User_Id_1746,Bike,3,Business,10,5,11:34:03 AM,10,5,11:34:14 AM,10,5,12:18:46 PM,10,5,12:32:57 PM,10,5,1:13:53 PM,19,19.3,,-1.2156006000000001,36.8916865,-1.296797,36.776452500000005,Rider_Id_384,2456
-Order_No_2070,User_Id_2707,Bike,3,Business,13,4,12:31:44 PM,13,4,12:34:37 PM,13,4,12:50:35 PM,13,4,1:04:34 PM,13,4,1:33:53 PM,9,22.0,,-1.2654831,36.82936289999999,-1.2963097,36.7688221,Rider_Id_382,1759
-Order_No_10478,User_Id_22,Bike,2,Personal,22,7,2:00:41 PM,22,7,2:01:59 PM,22,7,2:11:44 PM,22,7,2:21:59 PM,22,7,2:57:55 PM,8,18.9,,-1.3112325,36.8146321,-1.2713370000000002,36.786712,Rider_Id_465,2156
-Order_No_24819,User_Id_1850,Bike,3,Business,9,4,8:54:25 AM,9,4,9:00:55 AM,9,4,9:19:04 AM,9,4,9:22:01 AM,9,4,9:36:50 AM,6,16.9,,-1.2551895,36.7822034,-1.2802266000000002,36.8164714,Rider_Id_882,889
-Order_No_21747,User_Id_1705,Bike,3,Business,19,4,10:08:46 AM,19,4,10:53:55 AM,19,4,10:56:36 AM,19,4,11:02:28 AM,19,4,11:36:25 AM,7,20.1,,-1.2551895,36.7822034,-1.2882803,36.7815881,Rider_Id_752,2037
-Order_No_20730,User_Id_1500,Bike,3,Business,23,4,10:08:29 AM,23,4,10:09:55 AM,23,4,10:14:03 AM,23,4,10:15:07 AM,23,4,10:28:44 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_193,817
-Order_No_2506,User_Id_1271,Bike,3,Business,17,3,12:09:21 PM,17,3,12:15:01 PM,17,3,12:39:25 PM,17,3,1:04:09 PM,17,3,1:14:44 PM,8,25.6,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_946,635
-Order_No_17762,User_Id_3560,Bike,3,Business,18,5,10:01:17 AM,18,5,10:02:30 AM,18,5,10:25:13 AM,18,5,10:41:49 AM,18,5,11:19:30 AM,11,19.9,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_253,2261
-Order_No_19687,User_Id_1925,Bike,2,Personal,16,3,3:34:43 PM,16,3,3:34:49 PM,16,3,3:35:05 PM,16,3,3:40:50 PM,16,3,4:19:15 PM,20,25.5,,-1.2860183,36.8975338,-1.3337097,36.7659945,Rider_Id_742,2305
-Order_No_21940,User_Id_2184,Bike,1,Personal,23,1,4:45:34 PM,23,1,4:45:43 PM,23,1,5:23:14 PM,23,1,5:24:14 PM,23,1,5:51:47 PM,7,19.4,,-1.2764786,36.81947879999999,-1.2924639,36.77957979999999,Rider_Id_229,1653
-Order_No_242,User_Id_1443,Bike,1,Personal,27,3,9:37:16 AM,27,3,9:40:57 AM,27,3,9:59:32 AM,27,3,10:12:04 AM,27,3,10:58:19 AM,6,,,-1.3154433,36.806385999999996,-1.2962832,36.784968299999996,Rider_Id_846,2775
-Order_No_23787,User_Id_1753,Bike,2,Business,28,5,4:52:12 PM,28,5,4:53:56 PM,28,5,5:00:58 PM,28,5,5:11:41 PM,28,5,5:34:46 PM,10,,,-1.2551895,36.7822034,-1.301769,36.757993,Rider_Id_905,1385
-Order_No_13403,User_Id_1453,Bike,1,Business,6,1,4:33:52 PM,6,1,4:34:18 PM,6,1,4:45:12 PM,6,1,4:51:30 PM,6,1,5:13:34 PM,8,22.5,,-1.3025826,36.7670807,-1.3169591,36.7000029,Rider_Id_770,1324
-Order_No_11912,User_Id_496,Bike,3,Business,5,3,2:45:58 PM,5,3,2:46:19 PM,5,3,2:58:15 PM,5,3,3:10:24 PM,5,3,3:34:26 PM,12,23.6,,-1.3197098999999999,36.831231200000005,-1.2551895,36.7822034,Rider_Id_946,1442
-Order_No_20984,User_Id_1148,Bike,3,Business,22,5,12:49:21 PM,22,5,1:22:31 PM,22,5,1:45:51 PM,22,5,2:00:51 PM,22,5,2:21:11 PM,5,31.2,,-1.3100586,36.850032899999995,-1.3319197,36.850148299999994,Rider_Id_787,1220
-Order_No_5985,User_Id_2195,Bike,3,Business,16,7,10:37:12 AM,16,7,10:38:22 AM,16,7,11:13:06 AM,16,7,11:15:06 AM,16,7,11:38:54 AM,12,21.3,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_201,1428
-Order_No_19743,User_Id_2924,Bike,3,Personal,2,3,7:59:49 PM,2,3,8:07:42 PM,2,3,9:01:49 PM,2,3,9:09:00 PM,2,3,9:28:28 PM,10,20.1,,-1.2664227,36.7453671,-1.2661657,36.8015748,Rider_Id_288,1168
-Order_No_14171,User_Id_2523,Bike,3,Business,29,1,1:58:54 PM,29,1,1:59:55 PM,29,1,2:06:52 PM,29,1,2:10:28 PM,29,1,2:49:30 PM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_161,2342
-Order_No_24213,User_Id_2213,Bike,2,Personal,4,5,3:32:53 PM,4,5,3:37:34 PM,4,5,3:44:23 PM,4,5,4:11:40 PM,4,5,4:48:50 PM,17,24.2,,-1.32943,36.894242999999996,-1.2966785,36.7896469,Rider_Id_101,2230
-Order_No_11225,User_Id_1280,Bike,3,Business,7,2,10:13:49 AM,7,2,10:14:00 AM,7,2,10:14:07 AM,7,2,10:18:42 AM,7,2,10:23:39 AM,3,,,-1.2551895,36.7822034,-1.2575406999999998,36.7868197,Rider_Id_576,297
-Order_No_22376,User_Id_633,Bike,3,Business,16,6,11:31:34 AM,16,6,11:31:47 AM,16,6,11:33:44 AM,16,6,11:39:26 AM,16,6,11:50:48 AM,5,25.6,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_206,682
-Order_No_3955,User_Id_982,Bike,3,Business,21,4,11:21:18 AM,21,4,11:22:18 AM,21,4,11:57:50 AM,21,4,12:27:09 PM,21,4,1:33:38 PM,26,27.9,,-1.3087008,36.8565578,-1.2517871,36.6795716,Rider_Id_787,3989
-Order_No_19131,User_Id_999,Bike,1,Personal,24,4,10:24:07 AM,24,4,10:24:24 AM,24,4,10:35:10 AM,24,4,10:41:51 AM,24,4,11:01:19 AM,19,21.1,,-1.2853965,36.8183118,-1.1983841999999998,36.928906700000006,Rider_Id_103,1168
-Order_No_11486,User_Id_1724,Bike,3,Business,21,3,1:20:35 PM,21,3,1:21:33 PM,21,3,1:33:42 PM,21,3,1:41:41 PM,21,3,2:05:27 PM,7,23.4,3.1,-1.2861764999999998,36.8209086,-1.2551895,36.7822034,Rider_Id_477,1426
-Order_No_2950,User_Id_435,Bike,3,Business,12,3,2:24:11 PM,12,3,2:27:41 PM,12,3,2:29:40 PM,12,3,2:44:25 PM,12,3,3:02:14 PM,3,20.9,,-1.2974510000000001,36.8210544,-1.300921,36.828195,Rider_Id_462,1069
-Order_No_8806,User_Id_2700,Bike,3,Business,6,2,11:22:13 AM,6,2,11:27:59 AM,6,2,11:35:00 AM,6,2,11:56:25 AM,6,2,12:36:59 PM,5,25.1,,-1.2757858,36.788684100000005,-1.2897368,36.8226783,Rider_Id_956,2434
-Order_No_18947,User_Id_867,Bike,3,Business,9,6,9:05:17 AM,9,6,9:06:15 AM,9,6,9:26:02 AM,9,6,9:37:37 AM,9,6,10:07:18 AM,10,20.2,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_468,1781
-Order_No_18377,User_Id_2523,Bike,3,Business,2,4,3:33:16 PM,2,4,3:34:53 PM,2,4,3:37:07 PM,2,4,3:41:51 PM,2,4,4:09:55 PM,11,,,-1.2551895,36.7822034,-1.3169651000000002,36.8340857,Rider_Id_764,1684
-Order_No_8994,User_Id_186,Bike,3,Business,22,5,3:22:04 PM,22,5,3:24:29 PM,22,5,3:42:04 PM,22,5,3:58:54 PM,22,5,4:42:02 PM,20,30.8,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_154,2588
-Order_No_21812,User_Id_2801,Bike,3,Business,24,1,11:39:24 AM,24,1,11:40:04 AM,24,1,11:58:10 AM,24,1,11:59:31 AM,24,1,12:16:06 PM,7,22.3,,-1.2595421999999998,36.7871178,-1.281577,36.79382929999999,Rider_Id_38,995
-Order_No_7997,User_Id_970,Bike,3,Business,29,2,1:28:16 PM,29,2,1:29:23 PM,29,2,1:38:32 PM,29,2,1:40:47 PM,29,2,1:55:39 PM,7,26.8,,-1.2551895,36.7822034,-1.2795549,36.7627011,Rider_Id_333,892
-Order_No_13020,User_Id_2587,Bike,3,Business,21,4,2:51:43 PM,21,4,2:58:06 PM,21,4,3:11:47 PM,21,4,3:19:28 PM,21,4,3:19:31 PM,5,26.4,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_70,3
-Order_No_13249,User_Id_1500,Bike,3,Business,4,1,10:29:58 AM,4,1,11:06:07 AM,4,1,11:19:06 AM,4,1,11:26:11 AM,4,1,11:48:23 AM,8,26.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_559,1332
-Order_No_7257,User_Id_3727,Bike,3,Business,21,4,12:52:39 PM,21,4,12:55:28 PM,21,4,1:04:13 PM,21,4,1:05:40 PM,21,4,1:35:51 PM,8,25.3,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_269,1811
-Order_No_25177,User_Id_265,Bike,3,Business,11,1,11:39:21 AM,11,1,11:40:00 AM,11,1,11:51:18 AM,11,1,11:56:30 AM,11,1,12:12:04 PM,12,26.4,,-1.329285,36.88678529999999,-1.300921,36.828195,Rider_Id_402,934
-Order_No_4496,User_Id_3434,Bike,3,Business,26,5,8:44:59 AM,26,5,8:46:30 AM,26,5,9:18:02 AM,26,5,9:23:11 AM,26,5,9:41:22 AM,5,,,-1.2963097,36.7688221,-1.3041128999999998,36.810493900000004,Rider_Id_855,1091
-Order_No_18797,User_Id_3291,Bike,3,Business,4,2,12:03:39 PM,4,2,12:04:18 PM,4,2,12:08:04 PM,4,2,12:17:45 PM,4,2,12:46:21 PM,9,20.7,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_2,1716
-Order_No_8614,User_Id_3222,Bike,3,Business,8,1,2:53:04 PM,8,1,2:53:39 PM,8,1,2:58:40 PM,8,1,3:03:00 PM,8,1,3:12:55 PM,4,29.5,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_520,595
-Order_No_19332,User_Id_431,Bike,1,Personal,30,1,11:13:05 AM,30,1,11:13:09 AM,30,1,11:35:10 AM,30,1,11:38:33 AM,30,1,12:00:39 PM,8,22.6,,-1.2733186,36.826633799999996,-1.3108368000000001,36.8226308,Rider_Id_229,1326
-Order_No_19622,User_Id_2190,Bike,3,Business,10,5,10:57:47 AM,10,5,10:58:21 AM,10,5,11:07:47 AM,10,5,11:18:12 AM,10,5,11:57:06 AM,11,19.4,,-1.3019530000000001,36.788458,-1.2477886999999999,36.7960085,Rider_Id_279,2334
-Order_No_25331,User_Id_1341,Bike,3,Business,25,6,9:52:52 AM,25,6,9:53:18 AM,25,6,9:55:30 AM,25,6,10:04:25 AM,25,6,10:14:49 AM,7,,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_591,624
-Order_No_3550,User_Id_867,Bike,3,Business,27,3,9:42:08 AM,27,3,9:43:02 AM,27,3,9:48:39 AM,27,3,10:03:06 AM,27,3,10:31:03 AM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_154,1677
-Order_No_14370,User_Id_868,Bike,3,Business,28,2,10:54:39 AM,28,2,10:55:13 AM,28,2,10:59:20 AM,28,2,11:12:35 AM,28,2,11:25:21 AM,5,21.4,,-1.259478,36.801781299999995,-1.265715,36.823815,Rider_Id_341,766
-Order_No_20019,User_Id_1768,Bike,3,Personal,30,4,2:18:40 PM,30,4,2:19:32 PM,30,4,2:41:30 PM,30,4,2:49:18 PM,30,4,3:49:41 PM,8,,,-1.2750257,36.8157165,-1.2977326,36.7711606,Rider_Id_539,3623
-Order_No_24704,User_Id_2132,Bike,3,Business,14,1,11:47:14 AM,14,1,12:03:09 PM,14,1,12:38:36 PM,14,1,1:02:37 PM,14,1,1:40:14 PM,11,23.3,,-1.2384056,36.8718698,-1.3095309,36.8658586,Rider_Id_332,2257
-Order_No_24339,User_Id_2012,Bike,3,Personal,18,4,7:04:34 AM,18,4,7:05:35 AM,18,4,7:26:00 AM,18,4,7:30:11 AM,18,4,8:02:22 AM,23,,,-1.2715686000000002,36.8023526,-1.375192,36.9361554,Rider_Id_880,1931
-Order_No_15127,User_Id_972,Bike,3,Business,23,5,1:06:29 PM,23,5,1:13:30 PM,23,5,1:34:21 PM,23,5,1:38:23 PM,23,5,2:02:56 PM,4,26.4,,-1.2862033000000002,36.8152966,-1.2768326,36.8073804,Rider_Id_554,1473
-Order_No_8753,User_Id_20,Bike,3,Business,5,1,9:10:01 AM,5,1,9:30:57 AM,5,1,9:36:54 AM,5,1,9:43:49 AM,5,1,10:16:52 AM,5,18.7,,-1.2628638,36.8070399,-1.2929499,36.8180835,Rider_Id_268,1983
-Order_No_25756,User_Id_645,Bike,1,Personal,18,3,3:47:18 PM,18,3,3:50:03 PM,18,3,4:07:32 PM,18,3,4:23:23 PM,18,3,5:07:54 PM,8,20.8,,-1.2655306,36.828148,-1.31064,36.8223153,Rider_Id_722,2671
-Order_No_11042,User_Id_2789,Bike,3,Business,5,3,10:54:22 AM,5,3,10:55:31 AM,5,3,11:05:45 AM,5,3,11:09:57 AM,5,3,11:22:07 AM,6,22.5,,-1.304735,36.784605400000004,-1.2902869,36.8085396,Rider_Id_315,730
-Order_No_2235,User_Id_1642,Bike,3,Business,6,2,10:39:25 AM,6,2,10:43:17 AM,6,2,11:02:40 AM,6,2,11:05:02 AM,6,2,11:31:35 AM,5,24.9,,-1.2728069,36.7962362,-1.2834816,36.8219031,Rider_Id_88,1593
-Order_No_18901,User_Id_3391,Bike,1,Personal,28,5,1:37:25 PM,28,5,1:45:05 PM,28,5,2:01:37 PM,28,5,2:03:34 PM,28,5,2:17:51 PM,4,25.3,,-1.2998789,36.816896299999996,-1.2857248,36.8228262,Rider_Id_79,857
-Order_No_21111,User_Id_60,Bike,3,Business,19,6,10:48:08 AM,19,6,10:48:27 AM,19,6,10:55:29 AM,19,6,11:46:17 AM,19,6,12:04:19 PM,6,25.4,,-1.228911,36.881876,-1.2296618000000001,36.84382720000001,Rider_Id_669,1082
-Order_No_21985,User_Id_2330,Bike,3,Business,17,5,2:16:09 PM,17,5,2:16:40 PM,17,5,2:23:10 PM,17,5,2:29:12 PM,17,5,3:09:03 PM,17,,,-1.2987263999999998,36.8831224,-1.2326041,36.8009029,Rider_Id_907,2391
-Order_No_14072,User_Id_3557,Bike,3,Business,12,2,10:50:00 AM,12,2,10:50:30 AM,12,2,10:54:40 AM,12,2,11:15:47 AM,12,2,11:28:19 AM,2,24.4,,-1.2652108,36.8056075,-1.2546732,36.808679600000005,Rider_Id_531,752
-Order_No_8787,User_Id_3326,Bike,3,Business,18,1,10:28:12 AM,18,1,10:28:39 AM,18,1,10:37:50 AM,18,1,10:42:54 AM,18,1,11:37:56 AM,22,28.0,,-1.2551895,36.7822034,-1.341804,36.920576000000004,Rider_Id_147,3302
-Order_No_25519,User_Id_970,Bike,3,Business,28,4,9:40:30 AM,28,4,9:40:57 AM,28,4,9:41:39 AM,28,4,9:49:02 AM,28,4,9:49:55 AM,2,22.7,,-1.2551895,36.7822034,-1.2567251,36.798840999999996,Rider_Id_523,53
-Order_No_18061,User_Id_1329,Bike,3,Business,9,6,5:07:47 PM,9,6,5:08:48 PM,9,6,5:20:06 PM,9,6,5:21:46 PM,9,6,5:41:29 PM,4,,,-1.273056,36.811298,-1.284835,36.8152125,Rider_Id_605,1183
-Order_No_106,User_Id_1363,Bike,3,Business,11,4,9:10:15 AM,11,4,9:11:10 AM,11,4,9:22:44 AM,11,4,9:24:41 AM,11,4,9:53:13 AM,10,22.1,,-1.3004062,36.829741,-1.2529484,36.8651347,Rider_Id_85,1712
-Order_No_7273,User_Id_2609,Bike,1,Business,6,3,11:53:39 AM,6,3,12:07:14 PM,6,3,12:12:48 PM,6,3,12:16:47 PM,6,3,12:40:52 PM,14,20.2,,-1.2553615,36.789537599999996,-1.3332749,36.870814700000004,Rider_Id_4,1445
-Order_No_9867,User_Id_2330,Bike,3,Business,19,2,2:22:56 PM,19,2,2:24:08 PM,19,2,2:48:15 PM,19,2,2:50:09 PM,19,2,3:07:58 PM,8,29.4,,-1.2325706,36.808392299999994,-1.2637056999999998,36.805170000000004,Rider_Id_318,1069
-Order_No_11002,User_Id_2030,Bike,3,Business,10,3,9:26:03 AM,10,3,9:26:41 AM,10,3,9:26:48 AM,10,3,9:31:53 AM,10,3,9:38:09 AM,2,21.7,,-1.2963097,36.7688221,-1.2969741000000001,36.7856612,Rider_Id_438,376
-Order_No_15639,User_Id_2523,Bike,3,Business,13,1,9:15:23 AM,13,1,9:18:07 AM,13,1,9:18:55 AM,13,1,9:32:38 AM,13,1,9:53:01 AM,6,,,-1.2551895,36.7822034,-1.2721619,36.769064799999995,Rider_Id_215,1223
-Order_No_2918,User_Id_535,Bike,3,Business,11,6,12:08:06 PM,11,6,12:08:45 PM,11,6,12:15:16 PM,11,6,12:25:51 PM,11,6,1:11:12 PM,18,,,-1.2549259,36.7919494,-1.2715944,36.9114173,Rider_Id_438,2721
-Order_No_15019,User_Id_3517,Bike,3,Business,9,2,11:28:54 AM,9,2,11:29:05 AM,9,2,11:29:08 AM,9,2,11:36:56 AM,9,2,12:09:12 PM,16,27.1,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_871,1936
-Order_No_14591,User_Id_2829,Bike,3,Business,16,2,11:57:24 AM,16,2,12:09:32 PM,16,2,12:22:13 PM,16,2,12:23:44 PM,16,2,1:43:25 PM,16,,,-1.2571472,36.795063299999995,-1.3165542,36.700067,Rider_Id_279,4781
-Order_No_7470,User_Id_1896,Bike,2,Personal,19,4,10:53:57 AM,19,4,10:54:59 AM,19,4,11:08:17 AM,19,4,11:14:00 AM,19,4,11:26:08 AM,6,20.2,,-1.2950611,36.794790899999995,-1.3051283999999999,36.8252627,Rider_Id_465,728
-Order_No_26348,User_Id_3309,Bike,3,Personal,28,4,1:01:09 PM,28,4,1:02:51 PM,28,4,1:12:46 PM,28,4,1:22:42 PM,28,4,1:50:12 PM,5,,,-1.2995135,36.793363299999996,-1.3108386,36.7555239,Rider_Id_898,1650
-Order_No_12035,User_Id_2523,Bike,3,Business,17,2,4:55:26 PM,17,2,4:55:35 PM,17,2,4:55:52 PM,17,2,5:10:28 PM,17,2,5:35:11 PM,9,24.0,,-1.2551895,36.7822034,-1.2977565,36.8025534,Rider_Id_510,1483
-Order_No_27847,User_Id_1392,Bike,3,Business,2,2,12:24:06 PM,2,2,12:25:28 PM,2,2,12:31:17 PM,2,2,12:34:40 PM,2,2,12:51:22 PM,7,26.4,,-1.2551895,36.7822034,-1.2898178999999999,36.827084899999996,Rider_Id_96,1002
-Order_No_6572,User_Id_1085,Bike,3,Business,21,5,5:19:47 PM,21,5,6:01:19 PM,21,5,6:31:50 PM,21,5,6:37:13 PM,21,5,6:53:45 PM,7,22.8,,-1.2534301,36.800098299999995,-1.2985749,36.8088,Rider_Id_489,992
-Order_No_1045,User_Id_265,Bike,3,Business,7,1,1:06:52 PM,7,1,1:07:46 PM,7,1,1:23:09 PM,7,1,1:34:24 PM,7,1,2:07:04 PM,15,20.4,,-1.300921,36.828195,-1.2606476999999998,36.722469399999994,Rider_Id_654,1960
-Order_No_6483,User_Id_1363,Bike,3,Business,14,4,2:54:00 PM,14,4,3:08:37 PM,14,4,3:22:52 PM,14,4,3:27:59 PM,14,4,3:43:39 PM,1,28.0,,-1.3004062,36.829741,-1.3068209,36.8282524,Rider_Id_311,940
-Order_No_23368,User_Id_811,Bike,2,Personal,10,5,2:26:31 PM,10,5,2:26:56 PM,10,5,2:46:58 PM,10,5,2:52:00 PM,10,5,3:11:18 PM,7,22.0,,-1.278612,36.8195876,-1.296797,36.776452500000005,Rider_Id_213,1158
-Order_No_19877,User_Id_3283,Bike,3,Business,11,4,11:55:18 AM,11,4,11:55:39 AM,11,4,11:58:53 AM,11,4,12:08:39 PM,11,4,12:14:04 PM,4,25.6,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_147,325
-Order_No_20628,User_Id_391,Bike,3,Business,3,5,3:05:08 PM,3,5,4:10:16 PM,3,5,4:16:17 PM,3,5,4:27:25 PM,3,5,4:45:28 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_438,1083
-Order_No_25065,User_Id_1544,Bike,3,Business,1,6,12:25:30 PM,1,6,12:25:30 PM,1,6,12:26:03 PM,1,6,12:59:57 PM,1,6,1:06:20 PM,4,,,-1.2994816999999999,36.819316,-1.2923738,36.7987236,Rider_Id_206,383
-Order_No_15583,User_Id_2435,Bike,2,Personal,11,2,1:30:47 PM,11,2,1:34:05 PM,11,2,1:44:11 PM,11,2,1:51:54 PM,11,2,2:26:46 PM,14,25.4,,-1.3272343,36.7110502,-1.2646846999999999,36.7906823,Rider_Id_147,2092
-Order_No_26447,User_Id_246,Bike,3,Business,12,1,1:00:55 PM,12,1,1:01:23 PM,12,1,1:03:29 PM,12,1,1:09:03 PM,12,1,1:11:38 PM,1,23.9,,-1.2584143,36.8048002,-1.2597528999999998,36.806629,Rider_Id_875,155
-Order_No_15586,User_Id_1502,Bike,3,Business,22,1,1:00:35 PM,22,1,1:04:18 PM,22,1,1:45:43 PM,22,1,2:37:37 PM,22,1,2:56:28 PM,12,26.4,,-1.28878,36.8168312,-1.2232123,36.8932547,Rider_Id_223,1131
-Order_No_25310,User_Id_230,Bike,1,Personal,1,3,3:59:06 PM,1,3,3:59:37 PM,1,3,4:08:14 PM,1,3,4:10:22 PM,1,3,4:41:12 PM,9,23.8,,-1.2952784,36.7793271,-1.307774,36.840754100000005,Rider_Id_521,1850
-Order_No_21174,User_Id_2772,Bike,3,Personal,13,6,1:30:56 PM,13,6,1:33:58 PM,13,6,1:50:17 PM,13,6,1:54:21 PM,13,6,2:48:59 PM,18,28.0,,-1.216212,36.841799,-1.292188,36.764855,Rider_Id_882,3278
-Order_No_21740,User_Id_302,Bike,3,Business,19,5,2:02:25 PM,19,5,2:02:42 PM,19,5,2:07:00 PM,19,5,2:21:32 PM,19,5,2:32:07 PM,2,27.6,,-1.2649605,36.798177700000004,-1.25896,36.8016691,Rider_Id_578,635
-Order_No_2038,User_Id_1927,Bike,3,Business,28,3,1:46:37 PM,28,3,1:46:42 PM,28,3,1:47:08 PM,28,3,2:09:08 PM,28,3,2:28:16 PM,15,,,-1.3332749,36.870814700000004,-1.3463041999999998,36.768164,Rider_Id_622,1148
-Order_No_6753,User_Id_1940,Bike,1,Personal,5,6,9:40:21 AM,5,6,9:40:45 AM,5,6,9:59:23 AM,5,6,10:09:19 AM,5,6,10:19:26 AM,4,19.1,,-1.2473038,36.788470700000005,-1.2670196000000002,36.807700200000006,Rider_Id_219,607
-Order_No_23610,User_Id_1646,Bike,3,Business,9,2,2:31:32 PM,9,2,2:31:54 PM,9,2,2:57:13 PM,9,2,3:02:26 PM,9,2,3:38:08 PM,10,30.6,,-1.2894616,36.782653100000005,-1.2517448,36.7924971,Rider_Id_76,2142
-Order_No_12919,User_Id_1300,Bike,3,Personal,22,5,4:28:14 PM,22,5,4:32:48 PM,22,5,4:40:49 PM,22,5,5:51:32 PM,22,5,6:28:52 PM,7,28.5,,-1.2795819,36.8307141,-1.277783,36.7816464,Rider_Id_276,2240
-Order_No_3527,User_Id_3449,Bike,1,Personal,19,2,8:47:02 AM,19,2,8:50:16 AM,19,2,9:12:35 AM,19,2,9:34:02 AM,19,2,9:51:23 AM,10,15.4,1.1,-1.2689559,36.8097614,-1.266748,36.741316,Rider_Id_101,1041
-Order_No_25506,User_Id_868,Bike,3,Business,13,6,2:16:44 PM,13,6,2:17:05 PM,13,6,2:33:17 PM,13,6,2:37:00 PM,13,6,2:50:09 PM,4,27.9,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547,789
-Order_No_7561,User_Id_2946,Bike,2,Personal,31,4,3:28:23 PM,31,4,3:31:25 PM,31,4,4:10:04 PM,31,4,4:13:19 PM,31,4,4:40:52 PM,10,22.7,,-1.2749662,36.7658781,-1.289521,36.819072399999996,Rider_Id_124,1653
-Order_No_4696,User_Id_510,Bike,3,Business,22,5,10:05:50 AM,22,5,10:06:08 AM,22,5,10:14:38 AM,22,5,10:16:53 AM,22,5,10:41:46 AM,13,16.3,,-1.2860183,36.8975338,-1.3332749,36.870814700000004,Rider_Id_397,1493
-Order_No_25402,User_Id_1195,Bike,3,Business,5,5,12:05:46 PM,5,5,12:06:04 PM,5,5,12:34:31 PM,5,5,12:52:13 PM,5,5,1:28:28 PM,6,24.4,,-1.2922966,36.8374042,-1.3228003000000002,36.8306435,Rider_Id_910,2175
-Order_No_19934,User_Id_2085,Bike,3,Personal,11,4,10:28:15 AM,11,4,10:28:41 AM,11,4,10:40:30 AM,11,4,10:45:37 AM,11,4,11:18:59 AM,12,25.5,,-1.3270347,36.866736200000005,-1.2678813999999998,36.815757399999995,Rider_Id_459,2002
-Order_No_8141,User_Id_2344,Bike,3,Business,2,4,9:35:35 AM,2,4,9:36:31 AM,2,4,9:48:49 AM,2,4,9:52:42 AM,2,4,10:18:01 AM,7,13.5,,-1.2551895,36.7822034,-1.298529,36.816482,Rider_Id_519,1519
-Order_No_1559,User_Id_2587,Bike,3,Business,14,1,3:11:34 PM,14,1,4:30:21 PM,14,1,4:33:33 PM,14,1,4:39:03 PM,14,1,5:16:33 PM,10,24.6,,-1.2413316,36.878552500000005,-1.28878,36.8168312,Rider_Id_48,2250
-Order_No_3792,User_Id_3331,Bike,3,Business,23,4,1:09:03 PM,23,4,1:09:14 PM,23,4,1:12:56 PM,23,4,1:16:58 PM,23,4,1:27:42 PM,4,18.7,,-1.2551895,36.7822034,-1.2728947,36.807942600000004,Rider_Id_415,644
-Order_No_25465,User_Id_2330,Bike,3,Business,14,4,3:54:55 PM,14,4,3:58:45 PM,14,4,4:08:33 PM,14,4,4:24:25 PM,14,4,4:37:55 PM,9,27.8,,-1.2642903,36.80021370000001,-1.2326041,36.8009029,Rider_Id_611,810
-Order_No_17318,User_Id_864,Bike,1,Personal,5,3,9:23:35 AM,5,3,9:25:29 AM,5,3,10:09:14 AM,5,3,10:30:03 AM,5,3,11:14:35 AM,12,18.5,,-1.2409807,36.8144219,-1.3008471000000001,36.8099068,Rider_Id_937,2672
-Order_No_17884,User_Id_391,Bike,3,Business,7,1,2:46:20 PM,7,1,2:46:55 PM,7,1,3:06:39 PM,7,1,3:20:49 PM,7,1,3:52:20 PM,13,20.6,,-1.300921,36.828195,-1.3477328000000002,36.744707500000004,Rider_Id_742,1891
-Order_No_19125,User_Id_265,Bike,3,Business,18,5,2:53:56 PM,18,5,2:55:59 PM,18,5,3:17:47 PM,18,5,3:20:57 PM,18,5,3:38:19 PM,4,27.0,,-1.305791,36.811162700000004,-1.300921,36.828195,Rider_Id_654,1042
-Order_No_10786,User_Id_3194,Bike,3,Business,14,4,12:50:43 PM,14,4,12:54:26 PM,14,4,12:54:44 PM,14,4,12:58:46 PM,14,4,1:03:06 PM,3,23.7,,-1.2551895,36.7822034,-1.2659148999999998,36.805432200000006,Rider_Id_56,260
-Order_No_8315,User_Id_2347,Bike,3,Personal,13,3,2:18:20 PM,13,3,2:18:44 PM,13,3,2:28:50 PM,13,3,2:53:26 PM,13,3,2:53:33 PM,3,29.0,,-1.2944292,36.779356899999996,-1.292278,36.797717999999996,Rider_Id_678,7
-Order_No_791,User_Id_3686,Bike,3,Business,9,5,3:42:25 PM,9,5,4:04:37 PM,9,5,4:09:55 PM,9,5,4:32:49 PM,9,5,4:48:37 PM,9,25.1,,-1.2551895,36.7822034,-1.2899733,36.7908611,Rider_Id_881,948
-Order_No_214,User_Id_2890,Bike,3,Personal,7,5,7:05:12 PM,7,5,7:05:42 PM,7,5,7:21:11 PM,7,5,7:24:08 PM,7,5,7:39:46 PM,7,22.3,,-1.2615584,36.814950599999996,-1.2670847,36.7858231,Rider_Id_598,938
-Order_No_9430,User_Id_3517,Bike,3,Business,26,5,4:25:59 PM,26,5,4:27:24 PM,26,5,5:02:03 PM,26,5,5:04:40 PM,26,5,5:41:00 PM,18,,,-1.3139988999999999,36.841878,-1.3882008,36.7699445,Rider_Id_710,2180
-Order_No_8783,User_Id_824,Bike,3,Business,26,1,3:30:03 PM,26,1,3:30:42 PM,26,1,3:49:02 PM,26,1,3:54:12 PM,26,1,4:40:32 PM,32,22.4,0.8,-1.207546,36.7913976,-1.3169591,36.7000029,Rider_Id_403,2780
-Order_No_19814,User_Id_302,Bike,3,Business,28,6,2:27:30 PM,28,6,2:27:54 PM,28,6,2:38:41 PM,28,6,3:00:21 PM,28,6,3:13:02 PM,6,25.1,,-1.2649605,36.798177700000004,-1.2909633999999999,36.7813995,Rider_Id_415,761
-Order_No_27424,User_Id_640,Bike,3,Business,9,4,3:45:03 PM,9,4,4:29:11 PM,9,4,4:40:20 PM,9,4,4:43:50 PM,9,4,4:59:18 PM,3,,,-1.2969741000000001,36.7856612,-1.2878914,36.7728222,Rider_Id_92,928
-Order_No_4718,User_Id_2595,Bike,3,Personal,20,1,12:00:36 PM,20,1,12:02:19 PM,20,1,12:18:08 PM,20,1,12:27:04 PM,20,1,12:38:55 PM,10,20.4,,-1.2656922,36.813694399999996,-1.2478187,36.8844611,Rider_Id_207,711
-Order_No_4253,User_Id_3621,Bike,3,Business,15,4,3:45:53 PM,15,4,3:46:58 PM,15,4,3:54:31 PM,15,4,3:59:43 PM,15,4,4:17:54 PM,5,27.4,,-1.2836583000000001,36.7886014,-1.290724,36.81947,Rider_Id_510,1091
-Order_No_10832,User_Id_3264,Bike,3,Business,26,1,11:50:09 AM,26,1,12:27:54 PM,26,1,12:41:10 PM,26,1,12:42:54 PM,26,1,1:16:41 PM,8,22.9,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_441,2027
-Order_No_20081,User_Id_3222,Bike,3,Business,24,4,10:11:22 AM,24,4,10:11:33 AM,24,4,10:33:36 AM,24,4,10:38:12 AM,24,4,10:49:38 AM,5,24.7,,-1.2768326,36.8073804,-1.2551895,36.7822034,Rider_Id_755,686
-Order_No_8620,User_Id_615,Bike,3,Business,31,5,10:45:43 AM,31,5,10:45:58 AM,31,5,10:55:53 AM,31,5,10:58:50 AM,31,5,11:21:06 AM,9,21.9,,-1.2802043,36.76500789999999,-1.3066143,36.8161046,Rider_Id_761,1336
-Order_No_248,User_Id_2430,Bike,1,Personal,23,6,11:21:40 AM,23,6,11:22:06 AM,23,6,11:33:33 AM,23,6,11:37:08 AM,23,6,11:56:06 AM,9,,,-1.2677646,36.8298466,-1.2372752,36.888538700000005,Rider_Id_116,1138
-Order_No_18095,User_Id_3264,Bike,3,Business,9,5,10:42:24 AM,9,5,10:42:52 AM,9,5,10:52:54 AM,9,5,10:54:12 AM,9,5,11:09:30 AM,9,25.9,,-1.2551895,36.7822034,-1.2962618000000001,36.792933399999995,Rider_Id_623,918
-Order_No_15004,User_Id_1961,Bike,3,Business,3,5,8:36:39 AM,3,5,8:37:04 AM,3,5,8:47:16 AM,3,5,9:07:17 AM,3,5,9:49:04 AM,20,,,-1.331619,36.847976,-1.3181159,36.710155900000004,Rider_Id_519,2507
-Order_No_26177,User_Id_706,Bike,3,Business,17,4,9:35:01 AM,17,4,9:35:29 AM,17,4,9:43:14 AM,17,4,10:04:22 AM,17,4,10:21:50 AM,6,18.2,7.0,-1.3077869,36.844320700000004,-1.3177546999999998,36.8303703,Rider_Id_148,1048
-Order_No_21475,User_Id_1131,Bike,3,Business,29,3,5:15:16 PM,29,3,5:16:35 PM,29,3,5:19:59 PM,29,3,5:27:49 PM,29,3,5:59:02 PM,13,21.2,,-1.2527958000000001,36.8003131,-1.207546,36.7913976,Rider_Id_139,1873
-Order_No_11488,User_Id_1336,Bike,3,Business,10,1,2:11:27 PM,10,1,3:01:35 PM,10,1,3:18:42 PM,10,1,3:23:55 PM,10,1,3:37:26 PM,2,18.9,,-1.2885029,36.825604999999996,-1.2815459999999999,36.819875,Rider_Id_812,811
-Order_No_5848,User_Id_3621,Bike,3,Business,8,6,9:11:18 AM,8,6,9:14:45 AM,8,6,9:56:32 AM,8,6,9:58:48 AM,8,6,10:33:56 AM,16,16.7,3.9,-1.2836583000000001,36.7886014,-1.2178791000000002,36.891137,Rider_Id_309,2108
-Order_No_24074,User_Id_1584,Bike,3,Business,25,1,11:54:50 AM,25,1,11:55:09 AM,25,1,12:07:19 PM,25,1,12:10:10 PM,25,1,12:23:48 PM,4,27.3,,-1.2969741000000001,36.7856612,-1.2857117,36.7968296,Rider_Id_138,818
-Order_No_13080,User_Id_3041,Bike,1,Personal,14,5,1:55:20 PM,14,5,1:55:46 PM,14,5,2:03:14 PM,14,5,2:15:40 PM,14,5,2:23:59 PM,4,27.2,,-1.2550097,36.8045852,-1.2684239,36.79524620000001,Rider_Id_56,499
-Order_No_19417,User_Id_3686,Bike,3,Business,9,4,3:49:21 PM,9,4,4:07:30 PM,9,4,4:09:47 PM,9,4,4:32:46 PM,9,4,4:51:47 PM,11,,,-1.2551895,36.7822034,-1.2529484,36.8651347,Rider_Id_291,1141
-Order_No_25812,User_Id_2523,Bike,3,Business,6,3,12:42:47 PM,6,3,12:42:57 PM,6,3,12:45:00 PM,6,3,12:49:43 PM,6,3,1:23:13 PM,17,22.6,,-1.2551895,36.7822034,-1.3117864,36.6973045,Rider_Id_576,2010
-Order_No_11508,User_Id_2956,Bike,3,Business,3,1,3:54:05 PM,3,1,4:02:10 PM,3,1,4:13:11 PM,3,1,4:18:00 PM,3,1,4:40:28 PM,14,23.0,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_959,1348
-Order_No_14931,User_Id_510,Bike,3,Business,24,3,11:22:48 AM,24,3,11:22:58 AM,24,3,11:23:02 AM,24,3,11:24:57 AM,24,3,12:12:07 PM,28,20.1,,-1.2860183,36.8975338,-1.3192808,36.7121888,Rider_Id_408,2830
-Order_No_20144,User_Id_1899,Bike,3,Business,5,3,12:17:56 PM,5,3,12:18:13 PM,5,3,12:20:30 PM,5,3,1:02:07 PM,5,3,1:20:16 PM,13,22.4,,-1.293515,36.897607,-1.331619,36.847976,Rider_Id_900,1089
-Order_No_22715,User_Id_20,Bike,3,Business,6,2,3:32:55 PM,6,2,3:34:08 PM,6,2,4:15:26 PM,6,2,4:40:02 PM,6,2,5:13:28 PM,11,,,-1.3140948000000001,36.850972,-1.258693,36.801757,Rider_Id_399,2006
-Order_No_13470,User_Id_3064,Bike,3,Business,17,3,9:45:06 AM,17,3,9:45:30 AM,17,3,9:49:31 AM,17,3,9:57:28 AM,17,3,10:08:36 AM,6,21.0,,-1.2571472,36.795063299999995,-1.263989,36.765174,Rider_Id_620,668
-Order_No_18868,User_Id_265,Bike,3,Business,27,4,3:45:11 PM,27,4,3:45:44 PM,27,4,3:50:07 PM,27,4,3:59:11 PM,27,4,4:22:36 PM,10,24.9,,-1.300921,36.828195,-1.2873232,36.765857200000006,Rider_Id_100,1405
-Order_No_8661,User_Id_265,Bike,3,Business,30,2,10:59:48 AM,30,2,11:00:25 AM,30,2,11:10:15 AM,30,2,11:25:43 AM,30,2,12:12:12 PM,40,22.5,,-1.300921,36.828195,-1.1481468000000001,36.651587899999996,Rider_Id_619,2789
-Order_No_7339,User_Id_60,Bike,3,Business,19,2,12:30:44 PM,19,2,12:34:58 PM,19,2,12:36:42 PM,19,2,1:20:02 PM,19,2,2:04:16 PM,10,26.5,,-1.2600926000000001,36.8088685,-1.2296618000000001,36.84382720000001,Rider_Id_206,2654
-Order_No_16050,User_Id_136,Bike,3,Business,22,2,1:00:42 PM,22,2,1:05:36 PM,22,2,1:37:33 PM,22,2,1:45:55 PM,22,2,3:07:39 PM,3,25.4,,-1.2965045,36.7913036,-1.290641,36.812940999999995,Rider_Id_847,4904
-Order_No_13345,User_Id_478,Bike,3,Business,13,3,10:19:30 AM,13,3,10:20:08 AM,13,3,10:34:46 AM,13,3,11:44:11 AM,13,3,12:15:16 PM,12,25.0,,-1.3099295,36.8436807,-1.2571472,36.795063299999995,Rider_Id_194,1865
-Order_No_13322,User_Id_747,Bike,1,Personal,4,4,1:50:41 PM,4,4,1:51:47 PM,4,4,2:05:15 PM,4,4,2:10:52 PM,4,4,2:42:56 PM,10,24.6,,-1.3009173,36.7959207,-1.2698167999999999,36.7421898,Rider_Id_328,1924
-Order_No_4809,User_Id_1389,Bike,3,Business,29,6,3:41:36 PM,29,6,3:41:57 PM,29,6,3:42:17 PM,29,6,4:11:25 PM,29,6,4:20:40 PM,14,28.1,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_720,555
-Order_No_5617,User_Id_788,Bike,3,Business,5,1,4:16:27 PM,5,1,4:25:50 PM,5,1,4:36:42 PM,5,1,4:42:12 PM,5,1,5:08:55 PM,11,24.2,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_909,1603
-Order_No_1614,User_Id_1882,Bike,3,Business,10,4,12:12:13 PM,10,4,12:12:27 PM,10,4,12:13:57 PM,10,4,12:32:01 PM,10,4,1:57:21 PM,18,24.8,,-1.2628638,36.8070399,-1.3499321000000002,36.9047554,Rider_Id_447,5120
-Order_No_13060,User_Id_391,Bike,3,Business,4,2,3:23:17 PM,4,2,5:11:55 PM,4,2,5:12:01 PM,4,2,6:10:33 PM,4,2,6:10:35 PM,5,22.6,,-1.2913864,36.7949496,-1.2784063,36.8222933,Rider_Id_855,2
-Order_No_8704,User_Id_1502,Bike,3,Business,14,1,3:32:59 PM,14,1,3:34:00 PM,14,1,3:36:32 PM,14,1,3:50:06 PM,14,1,4:11:33 PM,13,25.8,,-1.2584143,36.8048002,-1.2232123,36.8932547,Rider_Id_685,1287
-Order_No_17991,User_Id_1500,Bike,3,Business,17,1,9:11:24 AM,17,1,9:12:51 AM,17,1,9:21:24 AM,17,1,9:28:32 AM,17,1,9:54:27 AM,9,18.1,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_345,1555
-Order_No_3560,User_Id_283,Bike,3,Business,30,1,9:43:43 AM,30,1,9:56:01 AM,30,1,10:13:23 AM,30,1,10:18:30 AM,30,1,10:39:11 AM,4,16.3,2.0,-1.2804227,36.8250375,-1.2624632,36.8405321,Rider_Id_229,1241
-Order_No_8914,User_Id_516,Bike,3,Business,24,5,12:31:55 PM,24,5,12:33:24 PM,24,5,12:50:54 PM,24,5,1:02:43 PM,24,5,1:41:53 PM,5,21.6,,-1.2571472,36.795063299999995,-1.2540684,36.823951799999996,Rider_Id_167,2350
-Order_No_4662,User_Id_136,Bike,3,Business,8,5,2:04:00 PM,8,5,2:05:00 PM,8,5,2:14:53 PM,8,5,2:20:21 PM,8,5,2:46:22 PM,16,27.0,,-1.3233496,36.8139159,-1.3254522,36.721658399999995,Rider_Id_523,1561
-Order_No_5418,User_Id_2523,Bike,3,Business,23,5,9:27:34 AM,23,5,10:34:30 AM,23,5,10:42:28 AM,23,5,10:53:20 AM,23,5,11:12:13 AM,8,24.1,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_726,1133
-Order_No_12264,User_Id_2200,Bike,3,Business,22,3,11:56:40 AM,22,3,11:58:44 AM,22,3,12:12:01 PM,22,3,12:27:09 PM,22,3,12:54:41 PM,10,23.2,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_213,1652
-Order_No_5270,User_Id_2587,Bike,3,Business,6,4,10:58:58 AM,6,4,10:59:13 AM,6,4,11:28:25 AM,6,4,11:36:23 AM,6,4,12:07:26 PM,18,24.2,,-1.3065978999999999,36.7186349,-1.28878,36.8168312,Rider_Id_647,1863
-Order_No_2259,User_Id_393,Bike,3,Business,14,2,12:56:16 PM,14,2,12:57:07 PM,14,2,1:04:42 PM,14,2,1:11:19 PM,14,2,1:42:31 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_328,1872
-Order_No_13876,User_Id_3674,Bike,3,Business,3,5,4:04:33 PM,3,5,4:07:56 PM,3,5,4:16:25 PM,3,5,4:20:40 PM,3,5,5:16:28 PM,8,,,-1.2551895,36.7822034,-1.2922992,36.798303499999996,Rider_Id_343,3348
-Order_No_10043,User_Id_1417,Bike,3,Business,12,2,10:16:05 AM,12,2,10:17:23 AM,12,2,10:26:09 AM,12,2,10:33:53 AM,12,2,11:02:17 AM,14,24.4,,-1.2741728,36.79691079999999,-1.3323107,36.8692616,Rider_Id_50,1704
-Order_No_10205,User_Id_1179,Bike,3,Business,10,1,3:02:21 PM,10,1,3:30:37 PM,10,1,4:20:16 PM,10,1,4:37:17 PM,10,1,5:12:26 PM,21,18.8,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_647,2109
-Order_No_23451,User_Id_868,Bike,3,Business,26,5,1:51:35 PM,26,5,1:52:14 PM,26,5,2:00:18 PM,26,5,2:25:01 PM,26,5,2:25:10 PM,3,,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_907,9
-Order_No_16273,User_Id_136,Bike,3,Business,16,6,10:09:25 AM,16,6,10:12:26 AM,16,6,10:33:28 AM,16,6,10:37:35 AM,16,6,11:39:05 AM,24,25.6,,-1.3254522,36.721658399999995,-1.2313147,36.80168629999999,Rider_Id_545,3690
-Order_No_17397,User_Id_3222,Bike,3,Business,17,1,2:46:57 PM,17,1,2:47:14 PM,17,1,2:47:32 PM,17,1,2:53:34 PM,17,1,2:56:10 PM,3,24.8,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_107,156
-Order_No_14742,User_Id_868,Bike,3,Business,11,4,10:54:23 AM,11,4,10:54:40 AM,11,4,10:57:42 AM,11,4,11:21:42 AM,11,4,11:43:05 AM,8,25.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_831,1283
-Order_No_4187,User_Id_1502,Bike,3,Business,11,2,2:51:40 PM,11,2,2:52:01 PM,11,2,2:59:35 PM,11,2,3:05:00 PM,11,2,3:47:43 PM,19,22.1,,-1.3014461000000002,36.7661381,-1.2232123,36.8932547,Rider_Id_65,2563
-Order_No_26401,User_Id_1618,Bike,1,Personal,11,4,12:01:14 PM,11,4,12:13:57 PM,11,4,12:45:24 PM,11,4,12:47:31 PM,11,4,1:19:30 PM,10,23.9,,-1.2797071000000002,36.82240770000001,-1.2810437,36.904236499999996,Rider_Id_532,1919
-Order_No_8828,User_Id_3517,Bike,3,Business,21,3,3:12:35 PM,21,3,3:12:46 PM,21,3,3:13:03 PM,21,3,4:14:44 PM,21,3,4:41:21 PM,15,23.4,3.0,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_293,1597
-Order_No_17346,User_Id_3565,Bike,3,Business,1,5,5:08:50 PM,1,5,5:08:56 PM,1,5,5:18:27 PM,1,5,5:25:51 PM,1,5,5:42:09 PM,6,22.7,,-1.2844665000000002,36.7866113,-1.2949473999999999,36.8270407,Rider_Id_735,978
-Order_No_12592,User_Id_2423,Bike,3,Business,19,3,2:51:22 PM,19,3,4:03:52 PM,19,3,4:11:07 PM,19,3,4:18:34 PM,19,3,4:38:27 PM,8,24.4,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_920,1193
-Order_No_24118,User_Id_1271,Bike,3,Business,8,1,12:33:45 PM,8,1,12:37:28 PM,8,1,12:48:03 PM,8,1,1:03:36 PM,8,1,1:47:50 PM,28,24.7,,-1.3244885,36.897791999999995,-1.3921858999999999,36.740959000000004,Rider_Id_86,2654
-Order_No_25303,User_Id_1329,Bike,3,Business,30,3,9:40:31 AM,30,3,9:43:47 AM,30,3,9:58:56 AM,30,3,10:01:45 AM,30,3,10:14:35 AM,2,19.6,,-1.273056,36.811298,-1.2600931999999998,36.8041689,Rider_Id_206,770
-Order_No_25949,User_Id_393,Bike,3,Business,26,5,7:51:08 AM,26,5,7:51:18 AM,26,5,7:51:29 AM,26,5,9:11:18 AM,26,5,9:17:31 AM,4,,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_97,373
-Order_No_243,User_Id_1500,Bike,3,Business,2,6,10:22:33 AM,2,6,10:23:41 AM,2,6,10:23:45 AM,2,6,11:44:23 AM,2,6,11:44:28 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_515,5
-Order_No_2134,User_Id_1500,Bike,3,Business,4,5,9:04:39 AM,4,5,9:06:25 AM,4,5,9:40:14 AM,4,5,9:46:28 AM,4,5,10:31:25 AM,11,19.3,51.9,-1.3077869,36.844320700000004,-1.2569006,36.7925141,Rider_Id_543,2697
-Order_No_11185,User_Id_1363,Bike,3,Business,8,2,12:09:17 PM,8,2,12:09:52 PM,8,2,12:15:24 PM,8,2,12:39:46 PM,8,2,12:53:08 PM,5,23.2,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_576,802
-Order_No_16494,User_Id_3688,Bike,3,Business,19,3,4:06:21 PM,19,3,4:11:49 PM,19,3,4:13:57 PM,19,3,4:21:03 PM,19,3,4:33:10 PM,4,24.2,,-1.2551895,36.7822034,-1.2658334999999998,36.804286700000006,Rider_Id_574,727
-Order_No_26336,User_Id_475,Bike,3,Business,31,4,2:14:41 PM,31,4,2:15:21 PM,31,4,2:24:27 PM,31,4,3:00:57 PM,31,4,3:22:39 PM,6,22.7,,-1.2579303999999998,36.8019667,-1.3024794,36.8228284,Rider_Id_555,1302
-Order_No_25305,User_Id_3689,Bike,3,Business,14,5,4:20:51 PM,14,5,4:23:32 PM,14,5,4:54:00 PM,14,5,4:55:52 PM,14,5,5:19:54 PM,8,21.7,,-1.2527958000000001,36.8003131,-1.306245,36.819297799999994,Rider_Id_537,1442
-Order_No_9859,User_Id_1882,Bike,3,Business,3,7,9:05:40 AM,3,7,9:21:56 AM,3,7,9:39:13 AM,3,7,10:17:01 AM,3,7,10:44:08 AM,7,21.8,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_605,1627
-Order_No_2465,User_Id_1959,Bike,3,Business,2,2,2:23:55 PM,2,2,2:25:32 PM,2,2,2:36:46 PM,2,2,4:01:33 PM,2,2,4:42:36 PM,10,29.5,,-1.2551895,36.7822034,-1.2664227,36.7453671,Rider_Id_850,2463
-Order_No_23806,User_Id_3291,Bike,3,Business,28,2,2:07:04 PM,28,2,2:07:17 PM,28,2,2:22:53 PM,28,2,2:30:14 PM,28,2,3:05:58 PM,17,23.6,,-1.3188358999999998,36.703852600000005,-1.2600926000000001,36.8088685,Rider_Id_524,2144
-Order_No_14026,User_Id_1944,Bike,1,Personal,8,6,6:20:02 PM,8,6,6:20:48 PM,8,6,6:46:07 PM,8,6,6:49:28 PM,8,6,7:28:39 PM,15,23.1,,-1.2909633999999999,36.7813995,-1.2049707,36.785895700000005,Rider_Id_465,2351
-Order_No_26479,User_Id_1382,Bike,1,Personal,22,6,2:42:16 PM,22,6,2:43:38 PM,22,6,2:57:24 PM,22,6,3:02:36 PM,22,6,3:27:29 PM,10,,,-1.2985073,36.7493901,-1.3188148000000002,36.809247799999994,Rider_Id_328,1493
-Order_No_15523,User_Id_815,Bike,2,Personal,29,3,2:21:21 PM,29,3,2:23:14 PM,29,3,2:48:31 PM,29,3,2:57:04 PM,29,3,3:15:02 PM,5,18.8,,-1.2962832,36.784968299999996,-1.2657469,36.802822799999994,Rider_Id_670,1078
-Order_No_17719,User_Id_238,Bike,3,Business,3,6,10:31:29 AM,3,6,10:31:48 AM,3,6,10:38:18 AM,3,6,10:46:23 AM,3,6,11:16:33 AM,15,23.6,,-1.3177546999999998,36.8303703,-1.362953,36.904214,Rider_Id_487,1810
-Order_No_20133,User_Id_1561,Bike,3,Business,3,3,3:44:07 PM,3,3,3:55:53 PM,3,3,4:15:55 PM,3,3,4:27:58 PM,3,3,4:43:42 PM,7,28.3,,-1.233018,36.8004477,-1.257227,36.825389200000004,Rider_Id_7,944
-Order_No_5626,User_Id_868,Bike,3,Business,5,5,11:01:02 AM,5,5,11:01:25 AM,5,5,11:09:14 AM,5,5,11:28:16 AM,5,5,11:36:45 AM,4,24.6,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_484,509
-Order_No_23968,User_Id_1424,Bike,3,Business,31,2,10:42:51 AM,31,2,10:43:11 AM,31,2,10:49:00 AM,31,2,11:04:48 AM,31,2,11:18:29 AM,5,18.5,,-1.3015700000000001,36.7871,-1.3014461000000002,36.7661381,Rider_Id_903,821
-Order_No_19902,User_Id_496,Bike,3,Business,2,4,2:53:14 PM,2,4,2:57:39 PM,2,4,3:00:27 PM,2,4,3:28:23 PM,2,4,3:49:59 PM,8,,,-1.2571472,36.795063299999995,-1.3004846,36.790159100000004,Rider_Id_681,1296
-Order_No_14268,User_Id_1927,Bike,3,Business,4,2,2:15:31 PM,4,2,3:06:45 PM,4,2,3:22:37 PM,4,2,3:39:12 PM,4,2,4:39:52 PM,20,23.7,11.1,-1.3472066,36.7692638,-1.2413316,36.878552500000005,Rider_Id_937,3640
-Order_No_4368,User_Id_2454,Bike,3,Business,17,4,10:26:45 AM,17,4,10:53:05 AM,17,4,10:53:52 AM,17,4,11:31:54 AM,17,4,11:39:11 AM,6,22.5,,-1.2865096,36.8169364,-1.299313,36.78314879999999,Rider_Id_805,437
-Order_No_26523,User_Id_1439,Bike,3,Business,17,1,2:15:38 PM,17,1,2:18:01 PM,17,1,2:30:21 PM,17,1,2:46:19 PM,17,1,3:15:06 PM,3,24.8,,-1.297324,36.787863200000004,-1.2962486999999998,36.7772971,Rider_Id_847,1727
-Order_No_10589,User_Id_3674,Bike,3,Business,29,3,2:30:11 PM,29,3,2:33:06 PM,29,3,2:43:08 PM,29,3,2:54:54 PM,29,3,4:12:35 PM,20,21.2,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_162,4661
-Order_No_14224,User_Id_2112,Bike,3,Business,18,1,9:00:13 AM,18,1,9:00:51 AM,18,1,9:12:02 AM,18,1,9:38:06 AM,18,1,9:46:51 AM,5,22.2,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_445,525
-Order_No_14602,User_Id_1500,Bike,3,Business,27,2,3:17:39 PM,27,2,3:22:26 PM,27,2,3:27:50 PM,27,2,4:04:19 PM,27,2,4:26:32 PM,8,25.5,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_660,1333
-Order_No_20475,User_Id_635,Bike,3,Business,23,3,2:56:22 PM,23,3,2:57:21 PM,23,3,3:13:30 PM,23,3,3:29:18 PM,23,3,3:50:10 PM,6,27.4,,-1.3081376,36.83297720000001,-1.3229345,36.8566974,Rider_Id_332,1252
-Order_No_18827,User_Id_46,Bike,3,Business,16,6,10:50:03 AM,16,6,10:51:13 AM,16,6,11:07:53 AM,16,6,11:18:10 AM,16,6,11:51:09 AM,7,25.9,,-1.2836832,36.8306719,-1.3056581,36.8445565,Rider_Id_954,1979
-Order_No_8937,User_Id_2216,Bike,3,Business,1,4,12:59:41 PM,1,4,1:00:12 PM,1,4,1:08:45 PM,1,4,1:12:19 PM,1,4,1:41:25 PM,18,,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_470,1746
-Order_No_2670,User_Id_425,Bike,1,Personal,9,6,3:36:46 PM,9,6,3:45:00 PM,9,6,4:06:01 PM,9,6,4:16:45 PM,9,6,4:40:41 PM,18,19.4,,-1.3581253,36.7444904,-1.3238698999999998,36.8488766,Rider_Id_852,1436
-Order_No_5703,User_Id_909,Bike,3,Business,11,1,5:40:15 PM,11,1,6:08:26 PM,11,1,6:26:50 PM,11,1,6:37:24 PM,11,1,7:12:56 PM,14,,,-1.225322,36.80855,-1.2486688999999997,36.874896899999996,Rider_Id_383,2132
-Order_No_16182,User_Id_1271,Bike,3,Business,8,1,4:08:44 PM,8,1,4:09:17 PM,8,1,4:19:04 PM,8,1,4:29:07 PM,8,1,4:57:19 PM,24,29.6,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_418,1692
-Order_No_10184,User_Id_570,Bike,3,Business,2,2,2:29:09 PM,2,2,2:31:16 PM,2,2,2:31:32 PM,2,2,2:33:09 PM,2,2,3:17:44 PM,7,29.4,,-1.2861764999999998,36.8209086,-1.2551895,36.7822034,Rider_Id_779,2675
-Order_No_10013,User_Id_1824,Bike,3,Business,31,3,3:43:55 PM,31,3,3:53:57 PM,31,3,4:01:38 PM,31,3,4:13:11 PM,31,3,4:35:49 PM,6,26.8,,-1.2674001,36.7986537,-1.297213,36.7862115,Rider_Id_19,1358
-Order_No_14461,User_Id_393,Bike,3,Business,14,3,9:57:05 AM,14,3,10:52:36 AM,14,3,11:07:16 AM,14,3,11:19:10 AM,14,3,11:50:58 AM,10,24.8,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_649,1908
-Order_No_4317,User_Id_380,Bike,2,Personal,27,3,10:35:13 AM,27,3,10:48:54 AM,27,3,11:05:31 AM,27,3,11:08:37 AM,27,3,12:05:41 PM,28,,,-1.3864951,36.6841026,-1.2615888000000002,36.7928732,Rider_Id_844,3424
-Order_No_16552,User_Id_1285,Bike,3,Business,30,4,1:55:09 PM,30,4,1:55:55 PM,30,4,2:15:18 PM,30,4,2:20:38 PM,30,4,2:38:16 PM,4,,,-1.3024794,36.8228284,-1.2787447,36.8182891,Rider_Id_694,1058
-Order_No_8414,User_Id_441,Bike,1,Personal,28,2,5:26:43 PM,28,2,5:27:01 PM,28,2,5:45:31 PM,28,2,5:50:15 PM,28,2,6:18:06 PM,4,18.3,,-1.2561913999999998,36.794654200000004,-1.2615584,36.814950599999996,Rider_Id_447,1671
-Order_No_10139,User_Id_1502,Bike,3,Business,6,1,9:56:50 AM,6,1,9:57:26 AM,6,1,10:52:32 AM,6,1,11:06:33 AM,6,1,11:22:00 AM,12,,,-1.28878,36.8168312,-1.2232123,36.8932547,Rider_Id_341,927
-Order_No_11364,User_Id_2789,Bike,3,Business,19,2,10:05:20 AM,19,2,10:05:32 AM,19,2,10:11:26 AM,19,2,10:13:17 AM,19,2,10:34:18 AM,8,,,-1.304735,36.784605400000004,-1.2559559999999999,36.772901,Rider_Id_958,1261
-Order_No_375,User_Id_1538,Bike,1,Personal,29,3,3:37:23 PM,29,3,3:38:12 PM,29,3,4:08:09 PM,29,3,4:12:34 PM,29,3,4:33:41 PM,8,18.8,,-1.2996681,36.806841299999995,-1.3260468,36.8015034,Rider_Id_421,1267
-Order_No_9291,User_Id_3222,Bike,3,Business,14,2,3:39:50 PM,14,2,3:40:09 PM,14,2,3:40:34 PM,14,2,3:48:44 PM,14,2,4:11:11 PM,12,,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_291,1347
-Order_No_14051,User_Id_2200,Bike,3,Business,8,6,12:10:43 PM,8,6,1:03:03 PM,8,6,1:10:52 PM,8,6,1:17:45 PM,8,6,1:23:36 PM,2,18.1,,-1.290894,36.822971,-1.2848173999999999,36.826163,Rider_Id_781,351
-Order_No_5857,User_Id_788,Bike,3,Business,8,5,5:01:08 PM,8,5,5:02:32 PM,8,5,5:07:13 PM,8,5,5:11:57 PM,8,5,5:39:06 PM,7,25.3,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_903,1629
-Order_No_11412,User_Id_3422,Bike,3,Business,3,1,3:50:27 PM,3,1,3:58:46 PM,3,1,4:00:14 PM,3,1,4:17:28 PM,3,1,4:20:13 PM,5,23.2,,-1.3069855,36.8255528,-1.3276043,36.836921999999994,Rider_Id_813,165
-Order_No_9141,User_Id_3669,Bike,3,Business,20,4,12:02:03 PM,20,4,12:02:36 PM,20,4,12:17:42 PM,20,4,12:48:30 PM,20,4,12:48:41 PM,3,24.5,,-1.2685707,36.806496100000004,-1.2615888000000002,36.7928732,Rider_Id_51,11
-Order_No_2783,User_Id_439,Bike,3,Business,3,1,3:14:22 PM,3,1,3:43:13 PM,3,1,3:49:29 PM,3,1,4:02:28 PM,3,1,4:23:25 PM,4,23.6,,-1.2613768,36.801941799999994,-1.2778316,36.7922222,Rider_Id_407,1257
-Order_No_3500,User_Id_752,Bike,3,Personal,24,4,9:30:01 AM,24,4,9:40:32 AM,24,4,9:56:29 AM,24,4,10:02:15 AM,24,4,10:29:40 AM,13,16.8,,-1.2872996,36.8310969,-1.3191788,36.8895401,Rider_Id_88,1645
-Order_No_28054,User_Id_2700,Bike,3,Business,22,4,1:30:49 PM,22,4,1:44:16 PM,22,4,1:51:01 PM,22,4,2:38:01 PM,22,4,2:56:05 PM,7,26.5,,-1.301809,36.75643,-1.2845834999999999,36.8079121,Rider_Id_237,1084
-Order_No_15116,User_Id_2110,Bike,1,Personal,20,6,10:27:20 AM,20,6,10:27:37 AM,20,6,10:44:33 AM,20,6,10:52:22 AM,20,6,11:19:20 AM,15,25.6,,-1.2987520000000001,36.735415,-1.2537505,36.8326484,Rider_Id_565,1618
-Order_No_22417,User_Id_552,Bike,3,Business,23,5,3:02:56 PM,23,5,3:04:38 PM,23,5,3:05:38 PM,23,5,3:27:21 PM,23,5,4:37:21 PM,21,26.2,,-1.2551895,36.7822034,-1.3632162,36.9108253,Rider_Id_19,4200
-Order_No_27925,User_Id_3669,Bike,3,Business,11,4,4:05:59 PM,11,4,4:21:56 PM,11,4,4:32:05 PM,11,4,4:38:37 PM,11,4,5:33:12 PM,8,26.6,,-1.2615888000000002,36.7928732,-1.2922992,36.798303499999996,Rider_Id_613,3275
-Order_No_12131,User_Id_2195,Bike,3,Business,20,6,1:15:34 PM,20,6,1:15:55 PM,20,6,1:19:41 PM,20,6,1:28:41 PM,20,6,1:53:47 PM,16,,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_422,1506
-Order_No_23281,User_Id_3159,Bike,1,Personal,9,6,1:46:48 PM,9,6,2:34:19 PM,9,6,2:41:47 PM,9,6,2:46:11 PM,9,6,3:06:18 PM,9,19.3,,-1.2870003,36.8312206,-1.334168,36.811671600000004,Rider_Id_852,1207
-Order_No_27737,User_Id_412,Bike,3,Business,17,4,3:30:44 PM,17,4,3:30:53 PM,17,4,3:30:55 PM,17,4,3:53:55 PM,17,4,4:03:07 PM,3,27.9,,-1.272828,36.816608,-1.266837,36.799248999999996,Rider_Id_886,552
-Order_No_6489,User_Id_3749,Bike,1,Personal,21,1,3:22:34 PM,21,1,3:22:38 PM,21,1,3:46:39 PM,21,1,3:49:43 PM,21,1,4:16:58 PM,10,21.7,,-1.279285,36.8255639,-1.3314446000000002,36.8264081,Rider_Id_116,1635
-Order_No_1369,User_Id_2330,Bike,3,Business,17,3,12:58:45 PM,17,3,12:59:35 PM,17,3,1:07:40 PM,17,3,1:13:27 PM,17,3,1:30:57 PM,9,,,-1.2645318,36.7981775,-1.2326041,36.8009029,Rider_Id_953,1050
-Order_No_17728,User_Id_2423,Bike,3,Business,26,2,12:49:21 PM,26,2,12:49:37 PM,26,2,12:49:46 PM,26,2,1:06:27 PM,26,2,1:15:11 PM,4,26.0,,-1.2571472,36.795063299999995,-1.2763916000000002,36.8246826,Rider_Id_576,524
-Order_No_16551,User_Id_1724,Bike,3,Business,29,5,4:17:18 PM,29,5,4:20:31 PM,29,5,4:36:42 PM,29,5,4:38:44 PM,29,5,4:52:53 PM,7,27.8,,-1.2551895,36.7822034,-1.2871164,36.7652395,Rider_Id_933,849
-Order_No_8597,User_Id_1809,Bike,3,Business,22,2,3:32:08 PM,22,2,3:32:31 PM,22,2,3:37:54 PM,22,2,3:49:41 PM,22,2,3:55:13 PM,2,25.5,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_167,332
-Order_No_8879,User_Id_517,Bike,3,Business,13,2,7:52:04 AM,13,2,11:03:33 AM,13,2,11:17:16 AM,13,2,11:39:03 AM,13,2,11:53:02 AM,4,22.5,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_654,839
-Order_No_9664,User_Id_1724,Bike,3,Business,8,5,3:13:43 PM,8,5,3:38:01 PM,8,5,3:48:52 PM,8,5,3:51:54 PM,8,5,4:43:32 PM,7,30.2,,-1.2551895,36.7822034,-1.2789469,36.7796691,Rider_Id_576,3098
-Order_No_12426,User_Id_3306,Bike,1,Personal,14,2,10:05:48 AM,14,2,10:06:34 AM,14,2,10:43:19 AM,14,2,10:47:51 AM,14,2,11:27:23 AM,17,21.7,,-1.3296591,36.7572949,-1.2697141,36.798177700000004,Rider_Id_465,2372
-Order_No_8387,User_Id_2956,Bike,3,Business,10,5,6:19:55 PM,10,5,6:24:03 PM,10,5,6:41:10 PM,10,5,7:40:15 PM,10,5,7:40:22 PM,9,,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_567,7
-Order_No_4352,User_Id_1162,Bike,3,Business,30,3,2:17:17 PM,30,3,2:18:19 PM,30,3,2:48:39 PM,30,3,3:01:36 PM,30,3,3:50:42 PM,16,28.9,,-1.21275,36.8051314,-1.3122610000000001,36.794658,Rider_Id_362,2946
-Order_No_12985,User_Id_393,Bike,3,Business,11,5,11:58:17 AM,11,5,11:58:36 AM,11,5,11:58:42 AM,11,5,12:14:32 PM,11,5,12:43:26 PM,14,21.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_555,1734
-Order_No_24112,User_Id_1584,Bike,3,Business,7,3,2:04:37 PM,7,3,2:08:29 PM,7,3,2:31:52 PM,7,3,3:21:19 PM,7,3,3:56:00 PM,7,28.4,,-1.2842021000000001,36.8281144,-1.304033,36.7848695,Rider_Id_554,2081
-Order_No_10166,User_Id_3635,Bike,1,Business,17,3,12:50:42 PM,17,3,12:58:46 PM,17,3,1:22:33 PM,17,3,1:23:33 PM,17,3,2:42:55 PM,9,25.5,,-1.2615888000000002,36.7928732,-1.2837908000000002,36.739837200000004,Rider_Id_726,4762
-Order_No_6816,User_Id_3560,Bike,3,Business,10,1,2:12:15 PM,10,1,2:45:49 PM,10,1,3:29:47 PM,10,1,3:44:55 PM,10,1,5:02:18 PM,12,18.7,,-1.2852569,36.8976121,-1.279395,36.825364,Rider_Id_697,4643
-Order_No_3267,User_Id_1927,Bike,3,Business,14,5,3:24:15 PM,14,5,3:24:27 PM,14,5,3:34:23 PM,14,5,3:43:39 PM,14,5,4:55:46 PM,15,27.0,,-1.3463041999999998,36.768164,-1.2591018999999999,36.800577000000004,Rider_Id_328,4327
-Order_No_3338,User_Id_2190,Bike,3,Business,4,2,1:44:56 PM,4,2,1:49:22 PM,4,2,2:13:21 PM,4,2,2:23:46 PM,4,2,3:18:01 PM,9,22.3,,-1.3019530000000001,36.788458,-1.2513025,36.809441899999996,Rider_Id_502,3255
-Order_No_8225,User_Id_833,Bike,1,Personal,19,3,5:36:42 PM,19,3,5:37:39 PM,19,3,5:58:24 PM,19,3,6:17:59 PM,19,3,7:11:39 PM,15,23.2,,-1.2248308,36.801257299999996,-1.2273133999999999,36.871462,Rider_Id_895,3220
-Order_No_21515,User_Id_649,Bike,3,Business,21,4,2:30:44 PM,21,4,2:31:25 PM,21,4,2:33:38 PM,21,4,2:43:09 PM,21,4,2:51:02 PM,3,26.5,,-1.2568549999999998,36.804132,-1.2551895,36.7822034,Rider_Id_647,473
-Order_No_20683,User_Id_1927,Bike,3,Business,19,2,8:56:59 AM,19,2,8:57:17 AM,19,2,9:21:09 AM,19,2,9:41:57 AM,19,2,10:16:46 AM,15,21.1,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_207,2089
-Order_No_28041,User_Id_43,Bike,1,Personal,20,4,1:50:40 PM,20,4,1:51:16 PM,20,4,2:12:54 PM,20,4,2:20:46 PM,20,4,2:33:45 PM,4,27.9,,-1.2816451000000002,36.8145398,-1.2753108999999998,36.8130185,Rider_Id_336,779
-Order_No_7408,User_Id_1342,Bike,3,Business,14,5,3:07:35 PM,14,5,3:08:57 PM,14,5,3:21:36 PM,14,5,3:30:30 PM,14,5,4:23:41 PM,9,27.2,,-1.3025826,36.7670807,-1.257309,36.806008399999996,Rider_Id_124,3191
-Order_No_24780,User_Id_1993,Bike,2,Personal,31,5,12:06:24 PM,31,5,12:06:59 PM,31,5,12:22:03 PM,31,5,12:31:05 PM,31,5,12:45:20 PM,10,17.1,,-1.2871938,36.8195031,-1.2467896,36.869873299999995,Rider_Id_865,855
-Order_No_3581,User_Id_3283,Bike,3,Business,20,1,2:41:48 PM,20,1,2:46:01 PM,20,1,3:33:36 PM,20,1,3:38:25 PM,20,1,4:29:28 PM,19,,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_697,3063
-Order_No_19548,User_Id_2665,Bike,3,Business,6,3,9:12:57 AM,6,3,9:15:41 AM,6,3,9:15:53 AM,6,3,9:27:10 AM,6,3,9:52:25 AM,12,17.0,,-1.3241728000000001,36.7210093,-1.3021506,36.799944399999994,Rider_Id_515,1515
-Order_No_1770,User_Id_136,Bike,3,Business,9,1,5:05:56 PM,9,1,5:06:38 PM,9,1,5:19:50 PM,9,1,5:21:34 PM,9,1,5:49:12 PM,7,20.4,,-1.2649605,36.798177700000004,-1.2943272,36.7667198,Rider_Id_434,1658
-Order_No_28089,User_Id_198,Bike,3,Business,11,1,11:27:35 AM,11,1,11:27:55 AM,11,1,11:29:03 AM,11,1,11:37:46 AM,11,1,12:01:28 PM,17,26.5,,-1.3396256000000002,36.8863135,-1.3255287,36.7850772,Rider_Id_145,1422
-Order_No_6121,User_Id_3647,Bike,3,Business,27,4,9:46:56 AM,27,4,9:48:32 AM,27,4,9:59:54 AM,27,4,10:06:02 AM,27,4,10:26:11 AM,6,18.0,,-1.2726389999999999,36.794723,-1.2884243,36.820724799999994,Rider_Id_26,1209
-Order_No_19153,User_Id_248,Bike,3,Business,30,5,8:57:13 AM,30,5,8:57:53 AM,30,5,9:01:58 AM,30,5,9:06:54 AM,30,5,9:31:16 AM,10,19.1,,-1.304735,36.784605400000004,-1.3297531,36.7063699,Rider_Id_356,1462
-Order_No_4655,User_Id_2523,Bike,3,Business,21,4,4:33:13 PM,21,4,4:33:37 PM,21,4,4:33:55 PM,21,4,4:38:08 PM,21,4,4:47:56 PM,4,28.7,,-1.2551895,36.7822034,-1.2679513999999998,36.8095213,Rider_Id_576,588
-Order_No_21631,User_Id_3781,Bike,3,Business,14,3,11:58:06 AM,14,3,12:05:46 PM,14,3,12:17:24 PM,14,3,12:25:30 PM,14,3,12:44:21 PM,9,24.8,,-1.2308211000000002,36.867904100000004,-1.26071,36.8206332,Rider_Id_761,1131
-Order_No_26406,User_Id_3297,Bike,3,Business,19,4,2:43:54 PM,19,4,2:45:24 PM,19,4,2:58:09 PM,19,4,3:01:06 PM,19,4,3:44:02 PM,14,20.7,,-1.3077027,36.81635429999999,-1.2274192,36.80083079999999,Rider_Id_614,2576
-Order_No_24007,User_Id_970,Bike,3,Business,12,5,2:19:03 PM,12,5,2:20:19 PM,12,5,2:27:06 PM,12,5,2:34:22 PM,12,5,2:49:42 PM,8,,,-1.2571472,36.795063299999995,-1.3080696000000003,36.8237191,Rider_Id_576,920
-Order_No_15390,User_Id_2757,Bike,3,Business,25,1,5:42:43 PM,25,1,5:43:45 PM,25,1,5:55:08 PM,25,1,5:58:30 PM,25,1,6:14:07 PM,8,28.3,,-1.314437,36.712353,-1.2963097,36.7688221,Rider_Id_484,937
-Order_No_24363,User_Id_3452,Bike,1,Personal,11,5,2:20:03 PM,11,5,2:23:19 PM,11,5,2:25:19 PM,11,5,2:29:31 PM,11,5,2:52:11 PM,9,22.7,,-1.2815826000000001,36.8205195,-1.2592902,36.7634987,Rider_Id_101,1360
-Order_No_9124,User_Id_1363,Bike,3,Business,21,4,3:55:21 PM,21,4,3:55:55 PM,21,4,4:01:59 PM,21,4,4:03:11 PM,21,4,4:10:50 PM,5,26.1,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_136,459
-Order_No_5503,User_Id_2335,Bike,3,Business,8,4,10:28:38 AM,8,4,10:38:41 AM,8,4,11:46:33 AM,8,4,11:53:38 AM,8,4,12:38:15 PM,8,26.2,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_187,2677
-Order_No_14768,User_Id_1450,Bike,3,Personal,7,5,3:17:17 PM,7,5,3:19:54 PM,7,5,3:32:04 PM,7,5,3:38:08 PM,7,5,4:19:58 PM,13,,,-1.2583017,36.8023526,-1.3108386,36.7555239,Rider_Id_161,2510
-Order_No_27526,User_Id_1179,Bike,3,Business,7,5,11:48:36 AM,7,5,11:59:13 AM,7,5,12:14:06 PM,7,5,12:33:12 PM,7,5,1:18:32 PM,21,25.2,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_807,2720
-Order_No_485,User_Id_598,Bike,1,Business,19,6,1:27:25 PM,19,6,1:27:50 PM,19,6,1:34:56 PM,19,6,1:38:51 PM,19,6,2:20:55 PM,24,24.0,,-1.2248308,36.801257299999996,-1.3157001,36.7075432,Rider_Id_103,2524
-Order_No_27469,User_Id_914,Bike,3,Business,24,3,2:39:27 PM,24,3,3:23:05 PM,24,3,3:53:20 PM,24,3,3:57:32 PM,24,3,4:09:31 PM,4,18.3,,-1.2670213,36.7994482,-1.2816233999999997,36.8239846,Rider_Id_171,719
-Order_No_9227,User_Id_2801,Bike,3,Business,10,1,9:33:05 AM,10,1,9:34:20 AM,10,1,9:49:15 AM,10,1,9:55:57 AM,10,1,11:11:20 AM,16,16.5,,-1.3104506,36.8687235,-1.2674273999999999,36.787082700000006,Rider_Id_195,4523
-Order_No_5720,User_Id_2523,Bike,3,Business,27,3,9:43:07 AM,27,3,9:43:20 AM,27,3,9:44:17 AM,27,3,9:57:50 AM,27,3,10:26:40 AM,10,,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_908,1730
-Order_No_4579,User_Id_186,Bike,3,Business,14,3,3:36:51 PM,14,3,3:37:00 PM,14,3,3:49:10 PM,14,3,3:51:25 PM,14,3,4:16:37 PM,18,25.7,,-1.3700383,36.9190174,-1.2902869,36.8085396,Rider_Id_647,1512
-Order_No_15741,User_Id_1154,Bike,3,Business,19,3,12:01:52 PM,19,3,12:02:42 PM,19,3,12:30:57 PM,19,3,12:37:50 PM,19,3,1:11:06 PM,20,22.4,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_440,1996
-Order_No_19682,User_Id_733,Bike,3,Business,12,2,1:05:28 PM,12,2,1:05:43 PM,12,2,1:06:07 PM,12,2,1:11:20 PM,12,2,1:56:09 PM,21,24.6,,-1.2551895,36.7822034,-1.3407764,36.9100521,Rider_Id_630,2689
-Order_No_25684,User_Id_472,Bike,3,Personal,22,5,8:35:39 AM,22,5,8:36:33 AM,22,5,8:49:38 AM,22,5,8:58:02 AM,22,5,9:49:44 AM,7,,,-1.293393,36.790237,-1.2684319,36.81658529999999,Rider_Id_332,3102
-Order_No_26024,User_Id_111,Bike,3,Business,12,5,2:04:41 PM,12,5,2:04:55 PM,12,5,2:04:56 PM,12,5,2:08:19 PM,12,5,2:13:27 PM,2,,,-1.2551895,36.7822034,-1.2575555,36.798705299999995,Rider_Id_576,308
-Order_No_7024,User_Id_426,Bike,3,Business,2,5,11:26:01 AM,2,5,11:26:48 AM,2,5,11:32:27 AM,2,5,11:43:03 AM,2,5,12:18:51 PM,18,23.7,,-1.3323107,36.8692616,-1.2534952,36.7825215,Rider_Id_130,2148
-Order_No_18164,User_Id_1642,Bike,3,Business,21,4,9:45:11 AM,21,4,9:45:40 AM,21,4,9:57:14 AM,21,4,10:11:01 AM,21,4,10:28:44 AM,5,21.7,,-1.2726506000000002,36.7960153,-1.2808159,36.8160545,Rider_Id_76,1063
-Order_No_17585,User_Id_128,Bike,1,Personal,5,4,11:48:19 AM,5,4,11:51:30 AM,5,4,12:36:53 PM,5,4,12:42:55 PM,5,4,12:57:14 PM,8,,,-1.3098463,36.8054139,-1.3246122999999999,36.790226700000005,Rider_Id_101,859
-Order_No_13291,User_Id_2696,Bike,3,Personal,3,5,8:57:20 AM,3,5,8:57:55 AM,3,5,8:59:48 AM,3,5,9:04:56 AM,3,5,9:22:00 AM,5,,,-1.3039706,36.7688174,-1.2896431000000002,36.8086201,Rider_Id_829,1024
-Order_No_4485,User_Id_1722,Bike,1,Personal,25,2,12:37:31 PM,25,2,12:39:27 PM,25,2,12:43:27 PM,25,2,12:54:58 PM,25,2,1:09:42 PM,7,23.8,,-1.268501,36.801065,-1.2948385,36.776807899999994,Rider_Id_116,884
-Order_No_18246,User_Id_1823,Bike,2,Personal,15,2,5:37:24 PM,15,2,5:37:38 PM,15,2,5:43:44 PM,15,2,5:55:33 PM,15,2,6:09:38 PM,3,23.7,,-1.2563646000000002,36.793149,-1.2613999,36.788205600000005,Rider_Id_467,845
-Order_No_23683,User_Id_393,Bike,3,Business,6,1,1:12:22 PM,6,1,1:13:31 PM,6,1,1:31:05 PM,6,1,1:49:11 PM,6,1,1:49:39 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_328,28
-Order_No_15824,User_Id_1368,Bike,3,Business,29,4,2:07:39 PM,29,4,2:17:34 PM,29,4,2:40:00 PM,29,4,2:47:24 PM,29,4,3:08:21 PM,8,24.9,,-1.2571472,36.795063299999995,-1.2905978999999999,36.844258200000006,Rider_Id_404,1257
-Order_No_3443,User_Id_1068,Bike,1,Personal,19,5,10:18:25 AM,19,5,10:18:47 AM,19,5,10:30:14 AM,19,5,10:42:57 AM,19,5,11:06:07 AM,7,24.7,,-1.2615006000000002,36.801272499999996,-1.277585,36.77145,Rider_Id_611,1390
-Order_No_16270,User_Id_187,Bike,3,Business,30,2,11:06:00 AM,30,2,11:14:42 AM,30,2,11:26:47 AM,30,2,11:31:18 AM,30,2,11:51:46 AM,8,22.2,,-1.3472066,36.7692638,-1.3169591,36.7000029,Rider_Id_905,1228
-Order_No_27294,User_Id_1792,Bike,3,Business,7,4,10:20:42 AM,7,4,10:24:04 AM,7,4,10:43:31 AM,7,4,11:05:46 AM,7,4,11:40:45 AM,18,16.9,,-1.3610501,36.7520889,-1.288159,36.792527500000006,Rider_Id_379,2099
-Order_No_18140,User_Id_1304,Bike,3,Business,19,3,1:56:16 PM,19,3,1:56:36 PM,19,3,2:33:09 PM,19,3,2:34:56 PM,19,3,3:13:49 PM,14,24.3,,-1.2859912,36.8756811,-1.2287282,36.8115304,Rider_Id_46,2333
-Order_No_4941,User_Id_2829,Bike,3,Business,17,3,1:55:33 PM,17,3,1:55:53 PM,17,3,2:06:19 PM,17,3,2:14:01 PM,17,3,2:23:43 PM,2,,,-1.2571472,36.795063299999995,-1.2631614,36.810602,Rider_Id_68,582
-Order_No_25752,User_Id_2718,Bike,3,Business,4,2,12:25:49 PM,4,2,12:30:54 PM,4,2,12:42:59 PM,4,2,12:53:04 PM,4,2,1:06:27 PM,4,19.7,,-1.269534,36.80697,-1.2668815,36.8017244,Rider_Id_671,803
-Order_No_2785,User_Id_1291,Bike,3,Personal,25,4,5:33:28 PM,25,4,5:34:50 PM,25,4,5:40:41 PM,25,4,5:48:29 PM,25,4,6:30:51 PM,7,,,-1.2864836000000002,36.82788660000001,-1.2963097,36.7688221,Rider_Id_148,2542
-Order_No_27800,User_Id_2330,Bike,3,Business,10,5,9:27:52 AM,10,5,9:29:17 AM,10,5,9:42:47 AM,10,5,9:44:59 AM,10,5,10:15:28 AM,14,,,-1.2925641,36.897644899999996,-1.3798085,36.9290528,Rider_Id_690,1829
-Order_No_18932,User_Id_1220,Bike,3,Personal,4,3,10:25:11 AM,4,3,10:25:20 AM,4,3,10:26:48 AM,4,3,10:46:48 AM,4,3,11:14:54 AM,14,17.1,,-1.2567464,36.7915273,-1.2156006000000001,36.8916865,Rider_Id_116,1686
-Order_No_11502,User_Id_2876,Bike,1,Personal,25,1,11:48:03 AM,25,1,11:48:29 AM,25,1,11:48:35 AM,25,1,11:58:22 AM,25,1,12:11:13 PM,6,,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_379,771
-Order_No_2493,User_Id_1075,Bike,3,Business,11,5,2:02:34 PM,11,5,2:02:43 PM,11,5,2:08:17 PM,11,5,2:29:20 PM,11,5,2:59:20 PM,10,26.5,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_52,1800
-Order_No_26474,User_Id_2066,Bike,3,Business,25,3,3:43:39 PM,25,3,3:45:01 PM,25,3,3:58:02 PM,25,3,4:00:16 PM,25,3,4:23:55 PM,16,17.6,,-1.316961,36.820337,-1.3319686000000002,36.911555299999996,Rider_Id_409,1419
-Order_No_16674,User_Id_3434,Bike,3,Business,6,1,9:17:02 AM,6,1,9:18:49 AM,6,1,9:25:05 AM,6,1,9:33:22 AM,6,1,9:54:11 AM,8,,,-1.2963097,36.7688221,-1.2813012,36.832396200000005,Rider_Id_99,1249
-Order_No_9382,User_Id_3470,Bike,3,Business,26,1,3:44:04 PM,26,1,3:48:56 PM,26,1,3:49:48 PM,26,1,4:08:33 PM,26,1,4:49:34 PM,21,22.4,0.8,-1.3165542,36.700067,-1.28878,36.8168312,Rider_Id_647,2461
-Order_No_21072,User_Id_71,Bike,3,Business,10,6,10:58:11 AM,10,6,10:58:26 AM,10,6,11:05:30 AM,10,6,11:13:59 AM,10,6,11:40:23 AM,13,24.1,,-1.339046,36.762596,-1.2891921999999998,36.825189,Rider_Id_110,1584
-Order_No_13196,User_Id_1650,Bike,1,Personal,19,7,10:57:57 AM,19,7,11:06:00 AM,19,7,11:30:43 AM,19,7,11:35:47 AM,19,7,11:48:48 AM,6,18.1,,-1.2707583,36.8237131,-1.3069436,36.8172066,Rider_Id_956,781
-Order_No_10984,User_Id_411,Bike,3,Business,17,1,4:13:35 PM,17,1,4:14:06 PM,17,1,4:40:03 PM,17,1,5:29:30 PM,17,1,6:25:48 PM,11,22.8,,-1.3111623,36.8436727,-1.2768326,36.8073804,Rider_Id_206,3378
-Order_No_6727,User_Id_3203,Bike,3,Business,2,6,10:41:44 AM,2,6,10:42:24 AM,2,6,10:44:14 AM,2,6,10:55:54 AM,2,6,11:06:05 AM,9,23.6,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_852,611
-Order_No_4400,User_Id_3404,Bike,3,Business,23,3,5:22:04 PM,23,3,5:23:00 PM,23,3,5:31:18 PM,23,3,5:42:32 PM,23,3,6:17:18 PM,15,24.1,,-1.279395,36.825364,-1.3560290000000002,36.744491499999995,Rider_Id_237,2086
-Order_No_12200,User_Id_1278,Bike,2,Personal,6,3,11:53:30 AM,6,3,11:53:40 AM,6,3,12:04:51 PM,6,3,12:06:52 PM,6,3,12:34:16 PM,15,19.9,,-1.2899733,36.7908611,-1.2297202,36.874550799999994,Rider_Id_726,1644
-Order_No_14599,User_Id_196,Bike,3,Business,7,1,8:14:42 AM,7,1,8:14:47 AM,7,1,8:27:03 AM,7,1,8:28:09 AM,7,1,9:11:20 AM,11,21.6,,-1.2575690000000002,36.842164000000004,-1.2070018999999998,36.78374,Rider_Id_534,2591
-Order_No_3698,User_Id_1363,Bike,3,Business,19,2,9:03:25 AM,19,2,9:07:01 AM,19,2,9:11:39 AM,19,2,9:12:46 AM,19,2,9:27:28 AM,7,21.2,,-1.3004062,36.829741,-1.3015998,36.8764939,Rider_Id_57,882
-Order_No_12033,User_Id_2436,Bike,3,Business,5,5,2:16:25 PM,5,5,2:16:42 PM,5,5,2:31:18 PM,5,5,2:33:14 PM,5,5,3:19:22 PM,29,26.6,,-1.2551895,36.7822034,-1.13707,36.971041,Rider_Id_188,2768
-Order_No_1607,User_Id_741,Bike,3,Business,18,5,9:39:35 AM,18,5,10:00:41 AM,18,5,10:22:08 AM,18,5,11:05:51 AM,18,5,11:28:57 AM,6,21.2,,-1.2833620000000001,36.8260149,-1.3043961000000002,36.7861511,Rider_Id_196,1386
-Order_No_12865,User_Id_1787,Bike,3,Business,25,1,9:29:05 AM,25,1,9:31:01 AM,25,1,9:38:17 AM,25,1,9:52:45 AM,25,1,10:16:07 AM,13,21.3,,-1.2967839,36.8128576,-1.2283403,36.8822756,Rider_Id_858,1402
-Order_No_5022,User_Id_1471,Bike,3,Business,21,4,2:11:31 PM,21,4,2:15:55 PM,21,4,2:28:33 PM,21,4,2:37:51 PM,21,4,2:53:22 PM,8,26.1,,-1.2551895,36.7822034,-1.3021707,36.816844399999994,Rider_Id_336,931
-Order_No_5063,User_Id_1500,Bike,3,Business,13,4,3:19:36 PM,13,4,3:22:46 PM,13,4,3:28:29 PM,13,4,3:32:24 PM,13,4,3:43:55 PM,6,23.0,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_291,691
-Order_No_21881,User_Id_3426,Bike,3,Business,7,5,8:33:12 AM,7,5,8:33:38 AM,7,5,8:38:49 AM,7,5,8:44:02 AM,7,5,9:07:55 AM,8,18.5,,-1.3014461000000002,36.7661381,-1.2605938,36.807323499999995,Rider_Id_74,1433
-Order_No_10005,User_Id_1340,Bike,3,Business,13,3,10:17:37 AM,13,3,10:18:08 AM,13,3,10:26:29 AM,13,3,10:38:51 AM,13,3,11:23:22 AM,18,25.0,,-1.3285987,36.903742799999996,-1.2707178999999997,36.7979713,Rider_Id_411,2671
-Order_No_16914,User_Id_3291,Bike,3,Business,22,5,9:41:09 AM,22,5,9:41:33 AM,22,5,10:18:29 AM,22,5,10:41:58 AM,22,5,11:22:45 AM,18,,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_377,2447
-Order_No_10555,User_Id_2154,Bike,3,Business,4,4,2:59:36 PM,4,4,3:00:11 PM,4,4,3:16:28 PM,4,4,3:31:59 PM,4,4,4:03:32 PM,20,27.1,,-1.3705766000000001,36.9120742,-1.2765736,36.851364600000004,Rider_Id_877,1893
-Order_No_25773,User_Id_2982,Bike,3,Business,7,1,4:02:24 PM,7,1,4:02:37 PM,7,1,4:23:30 PM,7,1,4:39:23 PM,7,1,5:19:02 PM,18,24.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_516,2379
-Order_No_15849,User_Id_1148,Bike,3,Business,27,6,8:39:11 AM,27,6,8:55:28 AM,27,6,9:16:12 AM,27,6,9:22:20 AM,27,6,9:49:18 AM,14,,,-1.3100586,36.850032899999995,-1.2294985,36.8775495,Rider_Id_715,1618
-Order_No_21600,User_Id_3175,Bike,3,Business,5,4,2:29:21 PM,5,4,2:29:51 PM,5,4,2:30:32 PM,5,4,2:48:04 PM,5,4,2:48:15 PM,2,22.1,,-1.2681003999999998,36.7952081,-1.2575406999999998,36.7868197,Rider_Id_51,11
-Order_No_2026,User_Id_893,Bike,3,Business,1,1,1:11:53 PM,1,1,1:13:28 PM,1,1,1:23:07 PM,1,1,1:24:56 PM,1,1,1:47:40 PM,9,24.0,,-1.2664491000000002,36.8102685,-1.2698167999999999,36.7421898,Rider_Id_72,1364
-Order_No_2088,User_Id_3091,Bike,3,Business,8,2,3:35:08 PM,8,2,3:40:58 PM,8,2,3:48:46 PM,8,2,3:53:02 PM,8,2,4:30:50 PM,8,25.4,,-1.3012008,36.764868,-1.2551895,36.7822034,Rider_Id_489,2268
-Order_No_5457,User_Id_722,Bike,3,Business,10,4,12:16:47 PM,10,4,12:16:59 PM,10,4,12:23:14 PM,10,4,12:41:27 PM,10,4,1:08:26 PM,15,24.9,,-1.32943,36.894242999999996,-1.3041128999999998,36.810493900000004,Rider_Id_418,1619
-Order_No_5107,User_Id_3468,Bike,1,Personal,10,2,2:27:11 PM,10,2,2:30:21 PM,10,2,2:50:22 PM,10,2,2:55:16 PM,10,2,3:17:48 PM,10,21.3,,-1.3248575,36.8038345,-1.2764568,36.7939953,Rider_Id_844,1352
-Order_No_10603,User_Id_3686,Bike,3,Business,13,4,3:51:57 PM,13,4,3:53:01 PM,13,4,3:59:24 PM,13,4,4:06:07 PM,13,4,4:20:53 PM,6,23.3,,-1.2551895,36.7822034,-1.2765641,36.793782,Rider_Id_538,886
-Order_No_15938,User_Id_84,Bike,3,Business,14,4,3:05:33 PM,14,4,3:24:21 PM,14,4,3:31:28 PM,14,4,3:32:33 PM,14,4,3:56:27 PM,15,28.2,,-1.2551895,36.7822034,-1.2283403,36.8822756,Rider_Id_14,1434
-Order_No_21227,User_Id_635,Bike,3,Business,16,3,2:47:23 PM,16,3,2:49:16 PM,16,3,3:09:09 PM,16,3,3:22:43 PM,16,3,3:36:49 PM,8,27.1,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_828,846
-Order_No_1951,User_Id_1810,Bike,1,Personal,24,4,11:29:38 AM,24,4,11:54:35 AM,24,4,12:10:09 PM,24,4,12:14:43 PM,24,4,12:32:48 PM,6,21.0,,-1.3233586000000002,36.814356599999996,-1.2854453000000001,36.818836299999994,Rider_Id_808,1085
-Order_No_27211,User_Id_2227,Bike,3,Personal,9,3,9:07:34 AM,9,3,9:08:51 AM,9,3,9:21:20 AM,9,3,9:23:17 AM,9,3,9:42:12 AM,6,20.2,,-1.2541275,36.8263055,-1.2741728,36.79691079999999,Rider_Id_332,1135
-Order_No_25140,User_Id_3146,Bike,1,Personal,26,4,2:35:09 PM,26,4,2:35:28 PM,26,4,2:42:43 PM,26,4,3:06:31 PM,26,4,3:25:12 PM,6,21.8,,-1.299313,36.78314879999999,-1.3175119,36.8058513,Rider_Id_101,1121
-Order_No_14908,User_Id_3560,Bike,3,Business,28,4,3:23:53 PM,28,4,3:26:21 PM,28,4,4:02:49 PM,28,4,4:14:37 PM,28,4,4:40:52 PM,5,27.5,,-1.279395,36.825364,-1.3072409999999999,36.830236799999994,Rider_Id_96,1575
-Order_No_14462,User_Id_635,Bike,3,Business,28,3,3:53:30 PM,28,3,4:16:13 PM,28,3,4:23:34 PM,28,3,4:44:07 PM,28,3,5:03:20 PM,6,23.3,,-1.3016417,36.8271679,-1.3229345,36.8566974,Rider_Id_675,1153
-Order_No_880,User_Id_77,Bike,1,Personal,30,7,3:18:26 PM,30,7,3:44:55 PM,30,7,4:05:23 PM,30,7,4:20:31 PM,30,7,4:52:01 PM,8,27.0,,-1.3064585,36.8119065,-1.2886090000000001,36.782939299999995,Rider_Id_632,1890
-Order_No_11782,User_Id_2008,Bike,3,Business,19,1,10:30:18 AM,19,1,10:30:28 AM,19,1,10:38:08 AM,19,1,11:31:28 AM,19,1,12:01:42 PM,17,24.6,,-1.2508229,36.7895263,-1.2482377,36.6673782,Rider_Id_634,1814
-Order_No_3561,User_Id_1329,Bike,3,Business,9,6,10:36:06 AM,9,6,10:36:50 AM,9,6,10:37:10 AM,9,6,10:56:27 AM,9,6,11:15:42 AM,10,25.0,,-1.273056,36.811298,-1.2879162,36.754825399999994,Rider_Id_40,1155
-Order_No_19545,User_Id_246,Bike,3,Business,17,4,1:11:41 PM,17,4,1:11:52 PM,17,4,1:17:31 PM,17,4,1:22:33 PM,17,4,1:42:15 PM,7,25.5,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_428,1182
-Order_No_12116,User_Id_2851,Bike,1,Personal,16,4,2:44:56 PM,16,4,3:03:30 PM,16,4,3:15:07 PM,16,4,3:23:56 PM,16,4,4:24:09 PM,12,19.1,,-1.2967403999999998,36.7361755,-1.2615983,36.8083096,Rider_Id_50,3613
-Order_No_2178,User_Id_2649,Bike,3,Business,3,1,11:57:14 AM,3,1,11:58:16 AM,3,1,12:09:08 PM,3,1,12:12:22 PM,3,1,12:39:22 PM,13,21.5,,-1.2752846000000002,36.802702200000006,-1.2030691,36.8394744,Rider_Id_706,1620
-Order_No_11257,User_Id_3283,Bike,3,Business,22,5,1:34:34 PM,22,5,1:54:11 PM,22,5,2:13:12 PM,22,5,2:20:36 PM,22,5,2:44:00 PM,13,31.0,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_770,1404
-Order_No_10424,User_Id_2677,Bike,3,Business,2,3,9:34:37 AM,2,3,9:34:55 AM,2,3,9:50:22 AM,2,3,9:56:31 AM,2,3,10:20:16 AM,14,19.9,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_263,1425
-Order_No_18379,User_Id_1482,Bike,3,Business,21,5,11:13:17 AM,21,5,11:29:01 AM,21,5,11:39:21 AM,21,5,11:41:56 AM,21,5,12:16:16 PM,7,22.8,,-1.2966600000000001,36.828668,-1.2534301,36.800098299999995,Rider_Id_899,2060
-Order_No_2462,User_Id_2801,Bike,3,Business,5,5,1:45:02 PM,5,5,1:48:16 PM,5,5,1:54:23 PM,5,5,2:05:02 PM,5,5,2:42:57 PM,9,27.5,,-1.2674273999999999,36.787082700000006,-1.3024794,36.8228284,Rider_Id_755,2275
-Order_No_26650,User_Id_3647,Bike,3,Business,18,4,11:58:18 AM,18,4,11:59:15 AM,18,4,12:08:28 PM,18,4,12:11:01 PM,18,4,12:21:17 PM,4,,,-1.2569503,36.78347279999999,-1.2726389999999999,36.794723,Rider_Id_937,616
-Order_No_6772,User_Id_1927,Bike,3,Business,20,2,9:35:16 AM,20,2,9:35:22 AM,20,2,9:41:49 AM,20,2,9:44:37 AM,20,2,10:27:03 AM,15,19.1,,-1.2584143,36.8048002,-1.3463041999999998,36.768164,Rider_Id_329,2546
-Order_No_7012,User_Id_1544,Bike,3,Business,17,5,1:35:32 PM,17,5,1:36:23 PM,17,5,2:03:03 PM,17,5,2:08:46 PM,17,5,3:10:11 PM,18,,,-1.2538731,36.7850223,-1.1855066,36.825464600000004,Rider_Id_198,3685
-Order_No_25834,User_Id_2069,Bike,3,Business,22,1,1:39:27 PM,22,1,1:39:43 PM,22,1,1:52:28 PM,22,1,1:59:13 PM,22,1,2:19:46 PM,16,26.4,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_86,1233
-Order_No_7508,User_Id_2523,Bike,3,Business,4,1,11:03:50 AM,4,1,11:05:32 AM,4,1,11:11:10 AM,4,1,11:16:21 AM,4,1,11:27:50 AM,6,25.7,,-1.2551895,36.7822034,-1.2357748,36.7900524,Rider_Id_178,689
-Order_No_6852,User_Id_3434,Bike,3,Business,13,4,11:21:28 AM,13,4,11:22:15 AM,13,4,11:31:57 AM,13,4,11:38:15 AM,13,4,12:21:34 PM,17,23.4,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_548,2599
-Order_No_23268,User_Id_2143,Bike,3,Business,6,4,12:19:23 PM,6,4,12:20:37 PM,6,4,12:30:58 PM,6,4,12:46:11 PM,6,4,12:59:46 PM,2,24.2,,-1.3016513,36.7649321,-1.2975066000000002,36.76894229999999,Rider_Id_527,815
-Order_No_6888,User_Id_2330,Bike,3,Business,24,5,1:03:03 PM,24,5,1:04:36 PM,24,5,1:08:01 PM,24,5,1:22:41 PM,24,5,1:55:52 PM,9,21.4,,-1.3015197,36.7658456,-1.2645318,36.7981775,Rider_Id_155,1991
-Order_No_24138,User_Id_265,Bike,3,Business,16,2,2:53:03 PM,16,2,2:53:26 PM,16,2,3:23:53 PM,16,2,3:27:37 PM,16,2,3:51:33 PM,4,27.6,,-1.300921,36.828195,-1.2956951,36.812924200000005,Rider_Id_736,1436
-Order_No_24134,User_Id_136,Bike,3,Business,4,2,3:37:06 PM,4,2,4:00:32 PM,4,2,4:00:51 PM,4,2,4:21:38 PM,4,2,4:21:40 PM,9,23.6,11.0,-1.2326041,36.8009029,-1.2649605,36.798177700000004,Rider_Id_598,2
-Order_No_25232,User_Id_1533,Bike,2,Business,8,1,10:26:53 AM,8,1,10:27:38 AM,8,1,10:55:09 AM,8,1,10:58:16 AM,8,1,11:30:38 AM,12,24.7,,-1.2152204,36.8563184,-1.2932780000000001,36.827789,Rider_Id_532,1942
-Order_No_17135,User_Id_1075,Bike,3,Business,7,4,2:11:19 PM,7,4,2:12:08 PM,7,4,2:12:35 PM,7,4,2:28:01 PM,7,4,2:28:03 PM,2,31.0,,-1.2527958000000001,36.8003131,-1.2685707,36.806496100000004,Rider_Id_807,2
-Order_No_6975,User_Id_3127,Bike,3,Business,19,2,2:53:01 PM,19,2,2:54:03 PM,19,2,3:44:18 PM,19,2,3:55:31 PM,19,2,4:17:46 PM,4,29.8,,-1.2964347999999999,36.8182757,-1.2918246,36.801584999999996,Rider_Id_62,1335
-Order_No_7792,User_Id_1075,Bike,3,Business,15,6,12:21:18 PM,15,6,12:40:08 PM,15,6,1:24:26 PM,15,6,1:32:53 PM,15,6,1:58:07 PM,10,23.2,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_205,1514
-Order_No_6382,User_Id_909,Bike,3,Business,24,2,10:16:41 AM,24,2,10:16:50 AM,24,2,10:38:53 AM,24,2,10:43:24 AM,24,2,11:23:54 AM,20,17.6,,-1.2859912,36.8756811,-1.225322,36.80855,Rider_Id_549,2430
-Order_No_739,User_Id_3689,Bike,3,Business,29,4,12:25:49 PM,29,4,12:40:28 PM,29,4,12:52:35 PM,29,4,1:01:04 PM,29,4,1:09:54 PM,1,21.3,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_222,530
-Order_No_21591,User_Id_2509,Bike,3,Business,4,1,11:25:11 AM,4,1,11:25:20 AM,4,1,11:25:37 AM,4,1,11:42:49 AM,4,1,12:24:16 PM,20,25.4,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_647,2487
-Order_No_22374,User_Id_2982,Bike,3,Business,16,5,1:17:26 PM,16,5,1:18:33 PM,16,5,1:29:31 PM,16,5,1:46:02 PM,16,5,2:09:49 PM,7,,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_581,1427
-Order_No_11749,User_Id_3727,Bike,3,Business,11,2,11:49:27 AM,11,2,11:56:56 AM,11,2,12:23:38 PM,11,2,12:54:21 PM,11,2,1:18:37 PM,11,19.6,,-1.2514753,36.7870287,-1.2963097,36.7688221,Rider_Id_762,1456
-Order_No_6713,User_Id_1548,Bike,1,Personal,30,3,9:51:12 AM,30,3,9:56:24 AM,30,3,10:07:25 AM,30,3,10:08:33 AM,30,3,10:20:13 AM,7,17.3,,-1.2950408000000002,36.809817200000005,-1.32571,36.805533000000004,Rider_Id_941,700
-Order_No_22331,User_Id_82,Bike,1,Personal,9,2,9:36:39 AM,9,2,9:54:45 AM,9,2,10:03:39 AM,9,2,10:16:28 AM,9,2,11:02:37 AM,13,17.5,3.9,-1.2449603999999999,36.796515500000005,-1.2802794,36.865390600000005,Rider_Id_882,2769
-Order_No_486,User_Id_2112,Bike,3,Business,5,1,3:17:31 PM,5,1,3:47:36 PM,5,1,4:08:01 PM,5,1,4:18:55 PM,5,1,4:48:58 PM,16,26.1,,-1.2156006000000001,36.8916865,-1.3285517,36.9018,Rider_Id_101,1803
-Order_No_11166,User_Id_3599,Bike,3,Business,31,3,9:17:22 AM,31,3,9:33:25 AM,31,3,9:49:24 AM,31,3,10:21:29 AM,31,3,11:02:00 AM,15,19.2,,-1.3302996,36.8707935,-1.2912772,36.7734495,Rider_Id_712,2431
-Order_No_17592,User_Id_3300,Bike,3,Business,2,3,9:52:50 AM,2,3,10:16:01 AM,2,3,10:31:09 AM,2,3,10:46:17 AM,2,3,11:10:41 AM,11,22.1,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_753,1464
-Order_No_21487,User_Id_897,Bike,2,Personal,25,5,9:05:12 AM,25,5,9:05:31 AM,25,5,9:13:42 AM,25,5,9:21:34 AM,25,5,9:47:50 AM,9,16.8,,-1.2295075,36.801697100000005,-1.2740396,36.810590000000005,Rider_Id_644,1576
-Order_No_9231,User_Id_2696,Bike,3,Personal,30,4,8:39:48 AM,30,4,8:40:04 AM,30,4,8:42:20 AM,30,4,8:54:26 AM,30,4,9:42:04 AM,19,16.4,,-1.2778523000000002,36.90945429999999,-1.296797,36.776452500000005,Rider_Id_848,2858
-Order_No_11693,User_Id_2494,Bike,3,Business,9,2,10:39:01 AM,9,2,10:39:30 AM,9,2,11:10:21 AM,9,2,11:11:40 AM,9,2,11:54:18 AM,17,22.0,,-1.3294489999999999,36.890451,-1.2963097,36.7688221,Rider_Id_532,2558
-Order_No_8380,User_Id_540,Bike,3,Business,20,4,10:04:58 AM,20,4,10:48:18 AM,20,4,11:03:15 AM,20,4,11:11:18 AM,20,4,11:33:38 AM,7,22.2,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_300,1340
-Order_No_20140,User_Id_2085,Bike,3,Personal,5,4,8:56:22 AM,5,4,9:05:35 AM,5,4,9:16:48 AM,5,4,9:21:44 AM,5,4,9:59:49 AM,7,,,-1.296797,36.776452500000005,-1.2876365,36.816096,Rider_Id_636,2285
-Order_No_18609,User_Id_868,Bike,3,Business,9,3,12:25:26 PM,9,3,12:25:46 PM,9,3,12:38:34 PM,9,3,12:45:59 PM,9,3,12:58:43 PM,9,25.4,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_86,764
-Order_No_6614,User_Id_2677,Bike,3,Business,20,1,9:01:33 AM,20,1,9:02:07 AM,20,1,9:16:22 AM,20,1,9:22:41 AM,20,1,10:05:04 AM,12,19.0,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_942,2543
-Order_No_8055,User_Id_3283,Bike,3,Business,8,2,9:35:46 AM,8,2,9:58:02 AM,8,2,10:39:31 AM,8,2,10:41:59 AM,8,2,11:22:39 AM,19,23.3,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_457,2440
-Order_No_10847,User_Id_2069,Bike,3,Business,22,5,10:47:08 AM,22,5,11:18:18 AM,22,5,11:52:14 AM,22,5,12:05:25 PM,22,5,12:22:01 PM,6,28.5,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_309,996
-Order_No_13670,User_Id_1500,Bike,3,Business,23,3,12:11:53 PM,23,3,12:27:26 PM,23,3,12:40:34 PM,23,3,12:55:52 PM,23,3,1:21:29 PM,11,,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_518,1537
-Order_No_15366,User_Id_925,Bike,3,Business,28,3,3:06:16 PM,28,3,3:15:21 PM,28,3,3:41:56 PM,28,3,3:45:01 PM,28,3,4:13:39 PM,10,,,-1.2582514,36.8130701,-1.3239488000000001,36.831683500000004,Rider_Id_857,1718
-Order_No_21160,User_Id_2531,Bike,3,Personal,25,5,1:26:06 PM,25,5,1:35:14 PM,25,5,1:38:58 PM,25,5,1:45:59 PM,25,5,2:21:15 PM,10,23.6,,-1.2861764999999998,36.8209086,-1.2884354,36.760676600000004,Rider_Id_393,2116
-Order_No_23989,User_Id_2030,Bike,3,Business,11,1,12:57:49 PM,11,1,1:17:51 PM,11,1,2:09:35 PM,11,1,2:34:50 PM,11,1,2:50:29 PM,2,23.3,,-1.2970941,36.7789326,-1.295908,36.791433000000005,Rider_Id_616,939
-Order_No_6822,User_Id_978,Bike,1,Personal,11,1,1:59:45 PM,11,1,2:01:14 PM,11,1,2:09:45 PM,11,1,2:28:52 PM,11,1,2:49:30 PM,10,23.1,,-1.3084067,36.83450139999999,-1.3295083,36.884402200000004,Rider_Id_726,1238
-Order_No_6550,User_Id_1363,Bike,3,Business,25,1,2:33:03 PM,25,1,2:33:24 PM,25,1,2:38:23 PM,25,1,2:39:27 PM,25,1,3:03:26 PM,13,29.9,,-1.3004062,36.829741,-1.242902,36.870334,Rider_Id_181,1439
-Order_No_1839,User_Id_1346,Bike,3,Business,11,2,12:54:46 PM,11,2,12:55:25 PM,11,2,1:05:45 PM,11,2,1:07:58 PM,11,2,1:17:53 PM,2,19.2,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_356,595
-Order_No_8415,User_Id_2150,Bike,3,Business,7,5,11:09:31 AM,7,5,11:59:08 AM,7,5,12:02:37 PM,7,5,12:20:43 PM,7,5,12:33:00 PM,7,,,-1.3188358999999998,36.703852600000005,-1.3444459999999998,36.756597,Rider_Id_628,737
-Order_No_12752,User_Id_3647,Bike,3,Business,26,3,4:38:26 PM,26,3,4:38:33 PM,26,3,4:55:11 PM,26,3,5:01:23 PM,26,3,5:57:21 PM,19,19.4,,-1.2726389999999999,36.794723,-1.3443916999999999,36.6700333,Rider_Id_38,3358
-Order_No_6166,User_Id_2069,Bike,3,Business,20,1,12:30:36 PM,20,1,1:34:10 PM,20,1,1:40:24 PM,20,1,2:02:23 PM,20,1,2:38:21 PM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_519,2158
-Order_No_26990,User_Id_3266,Bike,3,Business,20,1,8:47:25 AM,20,1,8:53:40 AM,20,1,9:04:13 AM,20,1,9:05:34 AM,20,1,9:27:58 AM,18,14.0,,-1.3339943,36.7263409,-1.321181,36.83695,Rider_Id_249,1344
-Order_No_27492,User_Id_3647,Bike,3,Business,18,4,12:28:45 PM,18,4,12:29:25 PM,18,4,12:30:54 PM,18,4,12:36:44 PM,18,4,1:04:03 PM,13,,,-1.2726389999999999,36.794723,-1.3326173000000001,36.71462329999999,Rider_Id_770,1639
-Order_No_1462,User_Id_1544,Bike,3,Business,13,4,1:01:26 PM,13,4,1:08:06 PM,13,4,1:27:05 PM,13,4,1:31:05 PM,13,4,2:00:31 PM,8,22.0,,-1.2851603,36.824966700000004,-1.284382,36.774604499999995,Rider_Id_491,1766
-Order_No_8767,User_Id_1500,Bike,3,Business,17,4,2:02:11 PM,17,4,2:02:27 PM,17,4,2:02:52 PM,17,4,2:05:26 PM,17,4,2:20:37 PM,9,28.1,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_518,911
-Order_No_27457,User_Id_1927,Bike,3,Business,20,2,11:25:12 AM,20,2,11:25:21 AM,20,2,11:40:04 AM,20,2,11:42:07 AM,20,2,12:36:07 PM,20,19.7,,-1.2927792,36.9053492,-1.3463041999999998,36.768164,Rider_Id_699,3240
-Order_No_25683,User_Id_3647,Bike,3,Business,3,2,3:57:57 PM,3,2,4:03:07 PM,3,2,4:22:11 PM,3,2,4:25:21 PM,3,2,4:37:50 PM,6,20.2,,-1.2726389999999999,36.794723,-1.2583976000000001,36.798046500000005,Rider_Id_103,749
-Order_No_732,User_Id_186,Bike,3,Business,7,5,10:56:39 AM,7,5,10:57:37 AM,7,5,11:14:45 AM,7,5,11:19:46 AM,7,5,12:00:02 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_475,2416
-Order_No_12198,User_Id_2869,Bike,3,Business,15,5,2:44:24 PM,15,5,2:49:49 PM,15,5,3:05:28 PM,15,5,3:25:08 PM,15,5,3:51:13 PM,9,17.9,,-1.3169591,36.7000029,-1.3014461000000002,36.7661381,Rider_Id_392,1565
-Order_No_10490,User_Id_2829,Bike,3,Business,26,5,11:29:28 AM,26,5,11:30:35 AM,26,5,11:41:10 AM,26,5,11:46:59 AM,26,5,12:05:38 PM,6,,,-1.2571472,36.795063299999995,-1.2981625,36.813014200000005,Rider_Id_96,1119
-Order_No_14185,User_Id_186,Bike,3,Business,2,2,4:23:24 PM,2,2,4:23:31 PM,2,2,4:37:13 PM,2,2,4:39:12 PM,2,2,4:56:08 PM,4,23.5,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_254,1016
-Order_No_9983,User_Id_3283,Bike,3,Business,29,1,2:35:35 PM,29,1,2:35:57 PM,29,1,2:41:19 PM,29,1,2:50:51 PM,29,1,3:10:35 PM,7,24.5,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_208,1184
-Order_No_13590,User_Id_2423,Bike,3,Business,14,5,12:41:54 PM,14,5,12:42:52 PM,14,5,12:52:46 PM,14,5,1:06:25 PM,14,5,1:33:40 PM,7,21.9,,-1.2571472,36.795063299999995,-1.3012173,36.8093987,Rider_Id_829,1635
-Order_No_13840,User_Id_3560,Bike,3,Business,9,4,2:28:46 PM,9,4,2:29:58 PM,9,4,2:40:15 PM,9,4,2:49:23 PM,9,4,3:20:33 PM,12,25.6,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_174,1870
-Order_No_4840,User_Id_1777,Bike,3,Personal,13,1,12:05:39 PM,13,1,12:12:00 PM,13,1,12:37:39 PM,13,1,12:39:49 PM,13,1,1:00:16 PM,9,17.2,,-1.2505691,36.7531862,-1.270066,36.8039766,Rider_Id_882,1227
-Order_No_14657,User_Id_2852,Bike,1,Personal,18,2,1:03:14 PM,18,2,1:03:42 PM,18,2,1:07:04 PM,18,2,1:12:40 PM,18,2,1:21:29 PM,3,22.9,,-1.2571472,36.795063299999995,-1.2564152,36.779280299999996,Rider_Id_881,529
-Order_No_15648,User_Id_3688,Bike,3,Business,31,3,12:51:57 PM,31,3,1:20:27 PM,31,3,1:33:45 PM,31,3,1:36:17 PM,31,3,1:59:16 PM,12,27.1,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_147,1379
-Order_No_6707,User_Id_3686,Bike,3,Business,19,3,1:42:24 PM,19,3,1:43:02 PM,19,3,1:53:21 PM,19,3,1:55:08 PM,19,3,2:14:55 PM,9,24.5,,-1.2551895,36.7822034,-1.2315181,36.814054299999995,Rider_Id_913,1187
-Order_No_6608,User_Id_2112,Bike,3,Business,24,5,3:27:29 PM,24,5,3:29:05 PM,24,5,3:40:22 PM,24,5,3:49:34 PM,24,5,4:20:19 PM,19,23.2,,-1.2156006000000001,36.8916865,-1.2629461000000002,36.76531370000001,Rider_Id_539,1845
-Order_No_651,User_Id_3521,Bike,3,Business,18,4,2:59:15 PM,18,4,3:01:37 PM,18,4,3:19:05 PM,18,4,3:23:40 PM,18,4,3:43:16 PM,7,,,-1.2597649,36.775631,-1.2696908000000002,36.81519,Rider_Id_166,1176
-Order_No_15919,User_Id_2164,Bike,3,Business,9,2,12:15:58 PM,9,2,12:16:34 PM,9,2,12:20:15 PM,9,2,12:29:09 PM,9,2,12:50:37 PM,4,26.9,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_630,1288
-Order_No_18165,User_Id_3615,Bike,1,Personal,5,3,2:25:36 PM,5,3,2:26:49 PM,5,3,2:49:22 PM,5,3,2:53:32 PM,5,3,3:07:49 PM,10,23.6,,-1.266362,36.796939,-1.2896693000000001,36.8412299,Rider_Id_86,857
-Order_No_18479,User_Id_3688,Bike,3,Business,30,3,10:29:22 AM,30,3,10:31:26 AM,30,3,10:33:07 AM,30,3,10:44:42 AM,30,3,11:03:02 AM,9,25.0,,-1.2551895,36.7822034,-1.2917868000000001,36.7872675,Rider_Id_124,1100
-Order_No_1580,User_Id_136,Bike,3,Business,5,5,12:20:33 PM,5,5,1:19:50 PM,5,5,1:42:34 PM,5,5,1:48:10 PM,5,5,2:18:32 PM,9,26.6,,-1.3326173000000001,36.71462329999999,-1.3601056999999999,36.767584,Rider_Id_565,1822
-Order_No_17575,User_Id_2195,Bike,3,Business,17,3,3:22:23 PM,17,3,3:28:03 PM,17,3,3:57:22 PM,17,3,4:03:54 PM,17,3,4:23:17 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_394,1163
-Order_No_16058,User_Id_2154,Bike,3,Business,24,4,1:01:48 PM,24,4,1:12:58 PM,24,4,1:21:32 PM,24,4,1:44:53 PM,24,4,2:10:03 PM,5,28.5,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_156,1510
-Order_No_11260,User_Id_2512,Bike,3,Personal,31,2,1:19:21 PM,31,2,1:20:09 PM,31,2,1:21:28 PM,31,2,1:39:47 PM,31,2,1:51:43 PM,2,22.3,,-1.2583748,36.8011466,-1.2538731,36.7850223,Rider_Id_536,716
-Order_No_4950,User_Id_136,Bike,3,Business,17,1,10:00:57 AM,17,1,10:01:08 AM,17,1,10:06:33 AM,17,1,10:14:49 AM,17,1,11:34:42 AM,26,19.0,,-1.246585,36.880373,-1.3254522,36.721658399999995,Rider_Id_685,4793
-Order_No_21896,User_Id_510,Bike,3,Business,4,5,12:51:12 PM,4,5,1:32:09 PM,4,5,1:45:43 PM,4,5,2:02:02 PM,4,5,3:15:24 PM,16,,,-1.3190102,36.923069299999995,-1.2860183,36.8975338,Rider_Id_518,4402
-Order_No_21516,User_Id_393,Bike,3,Business,30,2,1:26:27 PM,30,2,1:27:24 PM,30,2,1:27:33 PM,30,2,2:03:33 PM,30,2,2:03:40 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_239,7
-Order_No_21935,User_Id_2454,Bike,3,Business,29,5,3:08:24 PM,29,5,3:09:30 PM,29,5,3:27:13 PM,29,5,3:34:19 PM,29,5,4:05:21 PM,8,28.8,,-1.2570328000000002,36.7869744,-1.2993231,36.7753372,Rider_Id_829,1862
-Order_No_28194,User_Id_2523,Bike,3,Business,22,4,2:25:26 PM,22,4,2:31:24 PM,22,4,2:31:27 PM,22,4,2:41:59 PM,22,4,2:50:45 PM,5,26.3,,-1.2551895,36.7822034,-1.2585695,36.769754600000006,Rider_Id_450,526
-Order_No_23336,User_Id_3149,Bike,3,Business,20,3,11:52:56 AM,20,3,11:53:25 AM,20,3,11:53:31 AM,20,3,12:15:11 PM,20,3,12:18:06 PM,6,20.2,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_397,175
-Order_No_20476,User_Id_3184,Bike,3,Business,12,1,9:06:42 AM,12,1,9:06:52 AM,12,1,9:22:57 AM,12,1,9:32:14 AM,12,1,9:48:24 AM,13,19.3,0.3,-1.3396256000000002,36.8863135,-1.285908,36.8980126,Rider_Id_31,970
-Order_No_22917,User_Id_3689,Bike,3,Business,10,1,1:41:22 PM,10,1,1:42:36 PM,10,1,1:59:10 PM,10,1,2:06:20 PM,10,1,3:00:29 PM,19,18.1,,-1.2571472,36.795063299999995,-1.1720874,36.8219039,Rider_Id_856,3249
-Order_No_5184,User_Id_2539,Bike,3,Business,17,5,3:05:28 PM,17,5,3:07:38 PM,17,5,3:19:36 PM,17,5,3:39:39 PM,17,5,3:57:06 PM,6,,,-1.2930037,36.812647399999996,-1.2760462,36.807469700000006,Rider_Id_410,1047
-Order_No_4988,User_Id_875,Bike,1,Personal,12,6,4:48:16 PM,12,6,4:49:51 PM,12,6,5:06:48 PM,12,6,5:27:22 PM,12,6,5:56:53 PM,10,23.5,,-1.2865677,36.825334600000005,-1.300106,36.7584546,Rider_Id_103,1771
-Order_No_27015,User_Id_2271,Bike,3,Business,24,3,1:10:23 PM,24,3,1:24:00 PM,24,3,1:50:17 PM,24,3,2:12:50 PM,24,3,2:36:13 PM,10,,,-1.3346773,36.8842734,-1.293515,36.897607,Rider_Id_855,1403
-Order_No_24623,User_Id_81,Bike,3,Business,12,4,1:04:06 PM,12,4,1:06:45 PM,12,4,1:17:13 PM,12,4,1:18:49 PM,12,4,1:39:18 PM,7,19.0,,-1.2641496,36.791413899999995,-1.2596651,36.8086697,Rider_Id_415,1229
-Order_No_12742,User_Id_2150,Bike,3,Business,14,4,9:18:41 AM,14,4,9:27:43 AM,14,4,10:04:34 AM,14,4,10:14:58 AM,14,4,10:34:46 AM,13,18.9,,-1.3033561000000002,36.8163631,-1.3444459999999998,36.756597,Rider_Id_332,1188
-Order_No_2899,User_Id_1179,Bike,3,Business,8,2,2:56:06 PM,8,2,2:56:20 PM,8,2,2:56:40 PM,8,2,3:32:54 PM,8,2,4:20:19 PM,21,25.2,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_191,2845
-Order_No_8370,User_Id_3579,Bike,3,Business,6,1,11:36:42 AM,6,1,11:38:01 AM,6,1,11:48:31 AM,6,1,11:52:12 AM,6,1,12:29:31 PM,22,,,-1.2615584,36.814950599999996,-1.1479335,36.8841725,Rider_Id_205,2239
-Order_No_10975,User_Id_635,Bike,3,Business,29,2,9:02:46 AM,29,2,9:10:49 AM,29,2,9:21:55 AM,29,2,10:05:12 AM,29,2,10:27:47 AM,8,16.4,,-1.3016417,36.8271679,-1.2668093,36.7966542,Rider_Id_888,1355
-Order_No_25778,User_Id_3032,Bike,3,Business,15,2,1:26:48 PM,15,2,1:26:59 PM,15,2,1:42:51 PM,15,2,2:44:48 PM,15,2,3:09:28 PM,19,24.4,,-1.2812821,36.8891847,-1.2579303999999998,36.8019667,Rider_Id_621,1480
-Order_No_15948,User_Id_3398,Bike,3,Business,6,4,4:07:36 PM,6,4,5:30:53 PM,6,4,5:41:40 PM,6,4,5:51:06 PM,6,4,6:30:34 PM,17,22.8,,-1.3169591,36.7000029,-1.259919,36.808244,Rider_Id_187,2368
-Order_No_11494,User_Id_1329,Bike,3,Business,16,2,4:47:53 PM,16,2,4:48:27 PM,16,2,4:52:26 PM,16,2,4:53:43 PM,16,2,5:35:03 PM,19,,,-1.273056,36.811298,-1.3103985,36.9114173,Rider_Id_953,2480
-Order_No_3863,User_Id_2523,Bike,3,Business,16,1,12:26:19 PM,16,1,12:26:48 PM,16,1,12:42:25 PM,16,1,12:44:03 PM,16,1,1:07:13 PM,8,19.3,,-1.2551895,36.7822034,-1.2860372,36.7549573,Rider_Id_279,1390
-Order_No_15066,User_Id_3762,Bike,1,Personal,11,1,10:10:15 AM,11,1,10:11:11 AM,11,1,10:11:40 AM,11,1,10:20:37 AM,11,1,10:38:58 AM,9,14.7,,-1.3028861,36.794382899999995,-1.2631940000000002,36.797624,Rider_Id_56,1101
-Order_No_26895,User_Id_342,Bike,2,Personal,3,5,9:00:24 AM,3,5,9:00:35 AM,3,5,9:14:34 AM,3,5,9:17:02 AM,3,5,9:42:22 AM,9,17.2,,-1.2916155,36.84079129999999,-1.264788,36.7883897,Rider_Id_194,1520
-Order_No_995,User_Id_1502,Bike,1,Business,8,1,11:59:30 AM,8,1,12:07:28 PM,8,1,12:19:07 PM,8,1,12:34:29 PM,8,1,12:54:44 PM,17,24.5,,-1.2156006000000001,36.8916865,-1.2551895,36.7822034,Rider_Id_731,1215
-Order_No_17294,User_Id_2241,Bike,1,Personal,25,6,1:31:17 PM,25,6,1:31:42 PM,25,6,1:59:08 PM,25,6,2:05:16 PM,25,6,2:33:27 PM,12,,,-1.2248308,36.801257299999996,-1.2555129999999999,36.752421999999996,Rider_Id_400,1691
-Order_No_19293,User_Id_3547,Bike,3,Business,15,1,12:49:46 PM,15,1,12:51:00 PM,15,1,1:25:19 PM,15,1,1:34:03 PM,15,1,1:48:17 PM,3,,,-1.2761408,36.7710839,-1.270424,36.779289,Rider_Id_523,854
-Order_No_13767,User_Id_758,Bike,3,Business,18,5,9:53:20 AM,18,5,9:54:40 AM,18,5,10:05:10 AM,18,5,10:07:27 AM,18,5,10:37:30 AM,8,17.8,,-1.2998129999999999,36.787139200000006,-1.2696908000000002,36.81519,Rider_Id_16,1803
-Order_No_26552,User_Id_3080,Bike,3,Business,7,5,10:30:03 AM,7,5,10:30:11 AM,7,5,10:40:04 AM,7,5,10:47:15 AM,7,5,11:24:42 AM,18,25.2,,-1.2551895,36.7822034,-1.3389278999999998,36.7092201,Rider_Id_449,2247
-Order_No_17227,User_Id_1642,Bike,3,Business,28,5,9:47:40 AM,28,5,9:48:33 AM,28,5,10:07:29 AM,28,5,10:12:09 AM,28,5,10:23:20 AM,5,20.5,,-1.281892,36.7834972,-1.2728069,36.7962362,Rider_Id_612,671
-Order_No_15510,User_Id_2463,Bike,2,Personal,25,2,10:38:40 AM,25,2,10:38:54 AM,25,2,10:39:20 AM,25,2,11:35:09 AM,25,2,11:46:54 AM,14,24.0,,-1.3007646000000002,36.838754200000004,-1.2433667,36.76094929999999,Rider_Id_41,705
-Order_No_28055,User_Id_1073,Bike,3,Business,30,4,2:54:06 PM,30,4,2:55:10 PM,30,4,3:06:15 PM,30,4,3:12:24 PM,30,4,3:24:51 PM,8,20.7,,-1.290641,36.812940999999995,-1.2563242000000001,36.791156799999996,Rider_Id_216,747
-Order_No_22221,User_Id_633,Bike,3,Business,12,2,10:30:24 AM,12,2,10:32:08 AM,12,2,10:45:30 AM,12,2,10:52:44 AM,12,2,11:15:20 AM,9,28.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_448,1356
-Order_No_5705,User_Id_3517,Bike,3,Business,14,2,8:14:22 AM,14,2,8:14:45 AM,14,2,8:18:42 AM,14,2,8:58:03 AM,14,2,9:30:16 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_211,1933
-Order_No_14900,User_Id_2365,Bike,3,Personal,26,2,12:57:06 PM,26,2,12:57:30 PM,26,2,1:07:26 PM,26,2,1:11:40 PM,26,2,1:25:35 PM,12,26.3,,-1.3299925,36.879832,-1.3277315,36.8374612,Rider_Id_49,835
-Order_No_10632,User_Id_718,Bike,3,Business,3,5,4:04:19 PM,3,5,4:18:34 PM,3,5,4:26:28 PM,3,5,4:33:29 PM,3,5,4:44:00 PM,5,,,-1.2551895,36.7822034,-1.2764073,36.795211200000004,Rider_Id_247,631
-Order_No_21069,User_Id_1880,Bike,3,Business,9,4,12:20:58 PM,9,4,12:21:34 PM,9,4,12:23:20 PM,9,4,12:42:07 PM,9,4,1:13:45 PM,12,,,-1.2571472,36.795063299999995,-1.3167553,36.869485,Rider_Id_925,1898
-Order_No_10658,User_Id_3264,Bike,3,Business,30,3,11:34:14 AM,30,3,11:36:28 AM,30,3,11:59:26 AM,30,3,12:04:11 PM,30,3,12:24:26 PM,6,22.4,,-1.2553615,36.789537599999996,-1.2687962,36.825707,Rider_Id_742,1215
-Order_No_26850,User_Id_1244,Bike,3,Business,7,6,12:40:06 PM,7,6,12:40:25 PM,7,6,12:53:24 PM,7,6,1:04:42 PM,7,6,1:22:26 PM,8,18.8,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_427,1064
-Order_No_25221,User_Id_3222,Bike,3,Business,27,3,9:40:58 AM,27,3,9:42:00 AM,27,3,9:50:40 AM,27,3,9:55:57 AM,27,3,10:13:32 AM,8,,,-1.2551895,36.7822034,-1.2964347999999999,36.8182757,Rider_Id_953,1055
-Order_No_24511,User_Id_1899,Bike,3,Business,12,3,11:55:18 AM,12,3,12:01:14 PM,12,3,12:01:28 PM,12,3,12:17:02 PM,12,3,1:06:42 PM,11,19.3,,-1.2663631000000002,36.8163329,-1.331619,36.847976,Rider_Id_338,2980
-Order_No_24522,User_Id_136,Bike,3,Business,9,5,9:06:44 AM,9,5,9:08:44 AM,9,5,9:22:20 AM,9,5,10:01:01 AM,9,5,10:07:02 AM,15,20.4,,-1.3254522,36.721658399999995,-1.2560874,36.7931031,Rider_Id_288,361
-Order_No_23434,User_Id_1646,Bike,3,Business,2,2,9:19:16 AM,2,2,9:20:09 AM,2,2,9:52:32 AM,2,2,10:04:33 AM,2,2,10:14:44 AM,7,20.5,,-1.3006954,36.7416164,-1.3207446000000003,36.6969684,Rider_Id_855,611
-Order_No_25299,User_Id_868,Bike,3,Business,21,6,11:39:23 AM,21,6,11:39:41 AM,21,6,11:47:04 AM,21,6,12:00:43 PM,21,6,12:18:03 PM,6,16.3,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_670,1040
-Order_No_10305,User_Id_498,Bike,1,Personal,29,2,12:00:47 PM,29,2,12:00:58 PM,29,2,12:08:27 PM,29,2,12:29:49 PM,29,2,12:59:38 PM,17,21.5,,-1.3321944,36.889232899999996,-1.2642903,36.80021370000001,Rider_Id_103,1789
-Order_No_26618,User_Id_198,Bike,3,Business,19,3,12:52:00 PM,19,3,12:52:25 PM,19,3,1:13:21 PM,19,3,1:17:10 PM,19,3,2:00:00 PM,17,22.2,,-1.3396256000000002,36.8863135,-1.2568001,36.8215083,Rider_Id_822,2570
-Order_No_22582,User_Id_2804,Bike,3,Business,25,4,11:46:52 AM,25,4,11:47:47 AM,25,4,11:59:11 AM,25,4,12:02:50 PM,25,4,1:40:32 PM,12,19.9,,-1.2551895,36.7822034,-1.2128205,36.8026175,Rider_Id_290,5862
-Order_No_18156,User_Id_3001,Bike,3,Business,25,4,1:42:21 PM,25,4,1:44:41 PM,25,4,1:49:44 PM,25,4,1:59:36 PM,25,4,2:24:18 PM,7,,,-1.2795216,36.77067520000001,-1.269534,36.80697,Rider_Id_572,1482
-Order_No_5106,User_Id_1557,Bike,3,Business,5,1,2:05:48 PM,5,1,2:56:27 PM,5,1,3:04:26 PM,5,1,3:22:39 PM,5,1,3:33:55 PM,1,25.9,,-1.288269,36.818275299999996,-1.2815072,36.82096,Rider_Id_281,676
-Order_No_16254,User_Id_2008,Bike,3,Business,4,5,2:56:51 PM,4,5,2:57:16 PM,4,5,3:05:13 PM,4,5,3:17:00 PM,4,5,3:59:13 PM,17,,,-1.2508229,36.7895263,-1.2485923,36.65909620000001,Rider_Id_283,2533
-Order_No_2564,User_Id_1419,Bike,3,Personal,24,5,2:22:55 PM,24,5,2:24:46 PM,24,5,2:37:16 PM,24,5,2:41:54 PM,24,5,3:37:22 PM,21,23.2,,-1.2737903,36.789124,-1.314193,36.916132,Rider_Id_468,3328
-Order_No_14409,User_Id_2330,Bike,3,Business,13,3,6:04:09 PM,13,3,6:05:32 PM,13,3,6:07:03 PM,13,3,6:18:17 PM,13,3,6:39:11 PM,9,27.4,,-1.2649605,36.798177700000004,-1.2124582,36.802894,Rider_Id_52,1254
-Order_No_5902,User_Id_1894,Bike,1,Personal,30,1,3:25:52 PM,30,1,3:28:21 PM,30,1,4:19:01 PM,30,1,4:34:57 PM,30,1,5:13:23 PM,11,16.1,,-1.2315379,36.8156795,-1.2913246,36.8019522,Rider_Id_937,2306
-Order_No_5888,User_Id_2523,Bike,3,Business,27,4,1:03:08 PM,27,4,1:35:33 PM,27,4,1:53:46 PM,27,4,2:10:09 PM,27,4,2:28:05 PM,9,23.8,,-1.2516167,36.8678844,-1.2830057,36.81754779999999,Rider_Id_477,1076
-Order_No_26147,User_Id_2154,Bike,3,Business,10,1,8:15:53 AM,10,1,8:23:19 AM,10,1,8:58:03 AM,10,1,9:03:52 AM,10,1,9:48:14 AM,5,16.7,,-1.2765736,36.851364600000004,-1.2869041,36.8240057,Rider_Id_271,2662
-Order_No_16901,User_Id_3688,Bike,3,Business,12,2,10:55:43 AM,12,2,10:56:02 AM,12,2,10:56:14 AM,12,2,11:20:32 AM,12,2,11:42:17 AM,12,24.7,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_579,1305
-Order_No_23972,User_Id_3194,Bike,3,Business,15,2,3:10:52 PM,15,2,3:54:11 PM,15,2,4:06:30 PM,15,2,4:30:42 PM,15,2,5:45:20 PM,7,24.6,,-1.2811695,36.8200313,-1.2626906,36.7827022,Rider_Id_881,4478
-Order_No_20611,User_Id_1271,Bike,3,Business,29,5,2:55:43 PM,29,5,2:56:11 PM,29,5,3:03:44 PM,29,5,3:08:09 PM,29,5,3:43:41 PM,18,29.2,,-1.3244885,36.897791999999995,-1.2600931999999998,36.8041689,Rider_Id_675,2132
-Order_No_12074,User_Id_262,Bike,3,Business,28,1,2:12:10 PM,28,1,2:16:00 PM,28,1,2:31:37 PM,28,1,2:41:34 PM,28,1,2:51:14 PM,2,24.3,,-1.2679971,36.808893700000006,-1.2696908000000002,36.81519,Rider_Id_726,580
-Order_No_20807,User_Id_677,Bike,3,Business,4,2,1:06:32 PM,4,2,1:08:49 PM,4,2,1:19:36 PM,4,2,1:29:43 PM,4,2,2:31:42 PM,9,20.6,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_787,3719
-Order_No_3211,User_Id_136,Bike,3,Business,19,1,2:13:52 PM,19,1,2:14:03 PM,19,1,2:51:19 PM,19,1,2:57:15 PM,19,1,3:33:53 PM,11,25.1,,-1.3326173000000001,36.71462329999999,-1.2857671,36.7875614,Rider_Id_874,2198
-Order_No_4268,User_Id_593,Bike,1,Personal,3,2,3:46:42 PM,3,2,5:38:27 PM,3,2,6:10:08 PM,3,2,6:11:31 PM,3,2,6:40:49 PM,9,18.8,,-1.2713370000000002,36.786712,-1.2665476999999998,36.7410165,Rider_Id_465,1758
-Order_No_16396,User_Id_1182,Bike,2,Personal,11,1,3:11:17 PM,11,1,3:12:05 PM,11,1,3:46:05 PM,11,1,3:48:04 PM,11,1,5:01:36 PM,19,23.1,,-1.2686620000000002,36.8016018,-1.3489543,36.7175503,Rider_Id_919,4412
-Order_No_26920,User_Id_298,Bike,3,Personal,5,5,11:34:59 AM,5,5,12:02:54 PM,5,5,12:10:35 PM,5,5,12:17:46 PM,5,5,12:56:00 PM,21,24.5,,-1.3561118,36.904582399999995,-1.2658315,36.809049,Rider_Id_373,2294
-Order_No_13823,User_Id_2956,Bike,3,Business,27,3,7:08:19 PM,27,3,7:08:41 PM,27,3,7:11:43 PM,27,3,7:17:00 PM,27,3,7:27:50 PM,2,27.3,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_172,650
-Order_No_16563,User_Id_3432,Bike,3,Business,25,1,5:13:44 PM,25,1,5:15:40 PM,25,1,5:35:15 PM,25,1,5:52:29 PM,25,1,6:31:53 PM,13,27.5,,-1.2672588,36.8000252,-1.338217,36.784572,Rider_Id_459,2364
-Order_No_12254,User_Id_2801,Bike,3,Business,21,1,11:39:31 AM,21,1,11:40:08 AM,21,1,12:00:42 PM,21,1,12:29:04 PM,21,1,12:59:33 PM,8,25.3,,-1.3024794,36.8228284,-1.2595421999999998,36.7871178,Rider_Id_899,1829
-Order_No_25202,User_Id_391,Bike,3,Business,18,1,1:47:31 PM,18,1,1:49:01 PM,18,1,2:44:03 PM,18,1,3:06:30 PM,18,1,3:28:07 PM,5,29.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_668,1297
-Order_No_19765,User_Id_3149,Bike,3,Business,16,3,3:14:00 PM,16,3,3:14:53 PM,16,3,3:22:49 PM,16,3,4:07:05 PM,16,3,4:17:07 PM,4,25.5,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_543,602
-Order_No_10373,User_Id_1471,Bike,3,Business,13,3,1:28:29 PM,13,3,1:29:23 PM,13,3,1:31:19 PM,13,3,1:41:42 PM,13,3,2:01:19 PM,4,27.0,,-1.2551895,36.7822034,-1.2618631999999999,36.8100226,Rider_Id_279,1177
-Order_No_10269,User_Id_867,Bike,3,Business,14,4,3:32:59 PM,14,4,3:35:13 PM,14,4,4:09:38 PM,14,4,4:48:34 PM,14,4,5:11:15 PM,10,26.2,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_233,1361
-Order_No_26771,User_Id_412,Bike,3,Business,11,1,12:05:20 PM,11,1,12:05:55 PM,11,1,12:11:25 PM,11,1,12:16:24 PM,11,1,12:30:36 PM,8,24.1,,-1.266837,36.799248999999996,-1.303596,36.778378000000004,Rider_Id_62,852
-Order_No_13875,User_Id_3283,Bike,3,Business,24,5,1:44:32 PM,24,5,1:46:09 PM,24,5,1:55:15 PM,24,5,2:05:55 PM,24,5,2:28:14 PM,7,22.8,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_709,1339
-Order_No_5832,User_Id_2516,Bike,1,Personal,28,1,12:19:11 PM,28,1,12:20:08 PM,28,1,12:24:22 PM,28,1,12:32:22 PM,28,1,12:53:11 PM,6,22.8,,-1.2860203000000001,36.7832538,-1.2934656999999998,36.8212737,Rider_Id_409,1249
-Order_No_10571,User_Id_1850,Bike,3,Business,17,3,3:30:34 PM,17,3,3:30:50 PM,17,3,3:36:15 PM,17,3,3:38:14 PM,17,3,3:49:14 PM,2,,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_690,660
-Order_No_9104,User_Id_3149,Bike,3,Business,9,5,10:24:37 AM,9,5,10:27:35 AM,9,5,10:27:47 AM,9,5,11:55:07 AM,9,5,12:04:20 PM,6,25.7,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_69,553
-Order_No_8681,User_Id_1425,Bike,3,Business,7,4,11:46:29 AM,7,4,11:46:51 AM,7,4,11:59:32 AM,7,4,12:04:27 PM,7,4,12:39:40 PM,4,27.7,,-1.2803877000000001,36.8300596,-1.3004062,36.829741,Rider_Id_590,2113
-Order_No_25955,User_Id_1271,Bike,3,Business,7,5,3:59:13 PM,7,5,3:59:33 PM,7,5,4:01:38 PM,7,5,4:25:44 PM,7,5,5:04:33 PM,23,26.8,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_46,2329
-Order_No_16482,User_Id_1500,Bike,3,Business,26,2,12:48:28 PM,26,2,12:48:42 PM,26,2,12:57:23 PM,26,2,1:05:48 PM,26,2,1:24:25 PM,8,27.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_56,1117
-Order_No_22333,User_Id_3686,Bike,3,Business,26,5,9:22:42 AM,26,5,9:22:59 AM,26,5,9:23:38 AM,26,5,9:27:15 AM,26,5,9:27:36 AM,2,,,-1.2551895,36.7822034,-1.255392,36.792539,Rider_Id_876,21
-Order_No_25905,User_Id_1677,Bike,3,Personal,7,5,4:11:53 PM,7,5,4:15:05 PM,7,5,4:22:19 PM,7,5,4:31:37 PM,7,5,4:49:44 PM,10,26.8,,-1.2963097,36.7688221,-1.2601903,36.8066946,Rider_Id_178,1087
-Order_No_16645,User_Id_1107,Bike,2,Personal,24,1,1:47:00 PM,24,1,1:47:06 PM,24,1,2:01:18 PM,24,1,2:04:39 PM,24,1,2:19:47 PM,4,,,-1.2728058,36.8049903,-1.288167,36.8181484,Rider_Id_336,908
-Order_No_26473,User_Id_285,Bike,1,Personal,16,1,4:20:16 PM,16,1,4:21:39 PM,16,1,4:29:32 PM,16,1,4:37:02 PM,16,1,4:56:49 PM,11,,,-1.2862987,36.7674295,-1.3194988,36.796668100000005,Rider_Id_312,1187
-Order_No_11473,User_Id_1471,Bike,3,Business,18,2,11:39:38 AM,18,2,11:39:57 AM,18,2,12:18:25 PM,18,2,12:33:41 PM,18,2,1:31:44 PM,23,,,-1.2551895,36.7822034,-1.3374736,36.9207076,Rider_Id_222,3483
-Order_No_318,User_Id_2900,Bike,3,Business,19,1,12:44:28 PM,19,1,1:01:09 PM,19,1,1:11:38 PM,19,1,1:24:57 PM,19,1,2:04:50 PM,10,24.9,,-1.295324,36.839628999999995,-1.2963097,36.7688221,Rider_Id_848,2393
-Order_No_23614,User_Id_2423,Bike,3,Business,3,4,1:07:00 PM,3,4,1:07:11 PM,3,4,1:20:11 PM,3,4,1:22:41 PM,3,4,1:58:51 PM,6,21.9,,-1.2559559999999999,36.789867,-1.2793148,36.8148394,Rider_Id_676,2170
-Order_No_16565,User_Id_238,Bike,3,Business,26,5,3:21:56 PM,26,5,3:24:27 PM,26,5,3:39:05 PM,26,5,3:41:22 PM,26,5,3:51:40 PM,3,,,-1.3077869,36.844320700000004,-1.3166811999999999,36.859460799999994,Rider_Id_161,618
-Order_No_7957,User_Id_53,Bike,3,Business,2,6,9:42:33 AM,2,6,9:43:06 AM,2,6,9:43:16 AM,2,6,9:57:10 AM,2,6,10:15:42 AM,8,20.9,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_842,1112
-Order_No_12829,User_Id_1244,Bike,3,Business,28,5,8:50:28 AM,28,5,8:50:34 AM,28,5,8:55:04 AM,28,5,10:21:22 AM,28,5,11:15:54 AM,17,19.3,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_669,3272
-Order_No_12007,User_Id_2829,Bike,3,Business,17,5,2:18:24 PM,17,5,2:19:48 PM,17,5,3:10:10 PM,17,5,3:41:00 PM,17,5,4:15:10 PM,18,,,-1.3289016000000002,36.8765964,-1.2571472,36.795063299999995,Rider_Id_865,2050
-Order_No_11827,User_Id_393,Bike,3,Business,20,4,8:51:34 AM,20,4,9:01:41 AM,20,4,9:03:01 AM,20,4,9:29:45 AM,20,4,10:38:48 AM,11,18.5,0.2,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_188,4143
-Order_No_9553,User_Id_2789,Bike,3,Business,8,1,10:08:42 AM,8,1,10:09:12 AM,8,1,10:29:38 AM,8,1,10:45:13 AM,8,1,11:12:01 AM,7,27.8,,-1.304735,36.784605400000004,-1.265432,36.795034,Rider_Id_269,1608
-Order_No_21861,User_Id_1927,Bike,3,Business,23,2,12:17:33 PM,23,2,12:17:49 PM,23,2,12:46:53 PM,23,2,12:48:44 PM,23,2,1:15:25 PM,12,,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_411,1601
-Order_No_5198,User_Id_1363,Bike,3,Business,21,2,2:49:47 PM,21,2,3:02:28 PM,21,2,3:07:18 PM,21,2,3:11:19 PM,21,2,3:16:01 PM,2,25.0,,-1.3004062,36.829741,-1.2945128000000001,36.8396881,Rider_Id_953,282
-Order_No_16797,User_Id_2583,Bike,3,Personal,16,4,12:46:19 PM,16,4,12:47:27 PM,16,4,12:59:08 PM,16,4,1:03:50 PM,16,4,1:22:32 PM,6,,,-1.2607568999999998,36.7747575,-1.2613768,36.801941799999994,Rider_Id_7,1122
-Order_No_15602,User_Id_517,Bike,3,Business,3,4,11:28:16 AM,3,4,11:41:09 AM,3,4,11:41:17 AM,3,4,11:46:04 AM,3,4,12:10:00 PM,11,23.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_555,1436
-Order_No_21531,User_Id_3486,Bike,3,Business,20,4,12:54:37 PM,20,4,12:55:00 PM,20,4,12:55:36 PM,20,4,1:02:46 PM,20,4,1:34:34 PM,18,22.2,,-1.2551895,36.7822034,-1.2320538,36.6769615,Rider_Id_43,1908
-Order_No_10766,User_Id_2466,Bike,1,Personal,3,4,9:56:07 AM,3,4,10:03:45 AM,3,4,10:16:12 AM,3,4,10:30:45 AM,3,4,11:07:37 AM,7,17.5,16.1,-1.2877645,36.837853499999994,-1.2562366000000003,36.8038565,Rider_Id_549,2212
-Order_No_27481,User_Id_3149,Bike,3,Business,21,3,12:41:43 PM,21,3,12:42:52 PM,21,3,12:42:58 PM,21,3,1:32:12 PM,21,3,1:32:15 PM,5,23.4,3.0,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_10,3
-Order_No_24573,User_Id_2444,Bike,1,Personal,22,3,8:38:50 AM,22,3,8:51:19 AM,22,3,9:33:51 AM,22,3,9:36:05 AM,22,3,9:58:07 AM,10,15.1,,-1.2633254999999999,36.710569299999996,-1.2640571000000003,36.7883547,Rider_Id_4,1322
-Order_No_1240,User_Id_655,Bike,1,Personal,17,3,12:49:26 PM,17,3,12:50:06 PM,17,3,12:52:07 PM,17,3,12:57:34 PM,17,3,1:11:05 PM,4,25.2,,-1.2860785,36.8107713,-1.2836827,36.7988758,Rider_Id_754,811
-Order_No_3024,User_Id_1527,Bike,3,Business,20,1,10:00:25 AM,20,1,10:00:55 AM,20,1,10:03:15 AM,20,1,10:13:46 AM,20,1,10:27:32 AM,5,19.1,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_591,826
-Order_No_18438,User_Id_788,Bike,3,Business,8,2,8:29:16 AM,8,2,8:46:09 AM,8,2,9:04:31 AM,8,2,9:33:36 AM,8,2,9:48:10 AM,7,19.2,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_607,874
-Order_No_12112,User_Id_552,Bike,3,Business,30,3,11:51:09 AM,30,3,11:51:37 AM,30,3,11:59:23 AM,30,3,12:05:01 PM,30,3,12:51:58 PM,18,25.1,,-1.2793686000000002,36.82592329999999,-1.3562374,36.904295399999995,Rider_Id_237,2817
-Order_No_8575,User_Id_3606,Bike,3,Business,2,4,3:18:56 PM,2,4,3:19:54 PM,2,4,3:30:54 PM,2,4,4:05:00 PM,2,4,4:20:27 PM,7,,,-1.2628638,36.8070399,-1.2623805000000001,36.7805404,Rider_Id_119,927
-Order_No_15979,User_Id_2987,Bike,3,Business,10,5,1:04:50 PM,10,5,1:05:21 PM,10,5,1:22:18 PM,10,5,1:23:47 PM,10,5,1:42:54 PM,7,19.4,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_541,1147
-Order_No_19014,User_Id_3458,Bike,3,Business,29,1,11:14:31 AM,29,1,11:16:05 AM,29,1,11:28:20 AM,29,1,11:34:27 AM,29,1,11:54:21 AM,8,,,-1.3025299,36.7901963,-1.2970761999999998,36.8453541,Rider_Id_46,1194
-Order_No_19826,User_Id_1849,Bike,3,Business,5,3,10:10:11 AM,5,3,10:15:07 AM,5,3,11:06:42 AM,5,3,11:09:59 AM,5,3,11:28:26 AM,8,22.4,,-1.2551895,36.7822034,-1.290894,36.822971,Rider_Id_928,1107
-Order_No_5570,User_Id_1245,Bike,3,Business,29,3,10:55:16 AM,29,3,10:55:44 AM,29,3,11:01:22 AM,29,3,11:26:58 AM,29,3,11:47:46 AM,5,,,-1.300921,36.828195,-1.3234104,36.7989459,Rider_Id_863,1248
-Order_No_18027,User_Id_2627,Bike,3,Business,20,3,12:04:33 PM,20,3,12:05:17 PM,20,3,12:22:53 PM,20,3,12:35:43 PM,20,3,12:35:45 PM,2,27.1,,-1.2571472,36.795063299999995,-1.2591018999999999,36.800577000000004,Rider_Id_647,2
-Order_No_15293,User_Id_3044,Bike,3,Business,2,5,4:07:18 PM,2,5,4:22:23 PM,2,5,4:23:01 PM,2,5,4:38:05 PM,2,5,4:55:27 PM,13,24.1,,-1.3257893,36.8940194,-1.3177546999999998,36.8303703,Rider_Id_813,1042
-Order_No_20647,User_Id_2483,Bike,1,Business,16,1,12:33:26 PM,16,1,12:34:17 PM,16,1,12:36:12 PM,16,1,12:48:06 PM,16,1,1:53:55 PM,12,19.4,,-1.2559559999999999,36.789867,-1.2842685,36.7397756,Rider_Id_536,3949
-Order_No_10976,User_Id_2330,Bike,3,Business,28,4,3:42:10 PM,28,4,3:46:18 PM,28,4,3:46:24 PM,28,4,4:34:32 PM,28,4,5:46:46 PM,9,25.8,,-1.2649605,36.798177700000004,-1.3015197,36.7658456,Rider_Id_926,4334
-Order_No_7717,User_Id_3525,Bike,3,Business,14,3,10:00:11 AM,14,3,10:01:10 AM,14,3,10:17:38 AM,14,3,10:20:43 AM,14,3,10:37:46 AM,15,18.4,,-1.309835,36.84727,-1.3700383,36.9190174,Rider_Id_231,1023
-Order_No_6687,User_Id_435,Bike,3,Business,11,2,12:18:39 PM,11,2,12:18:56 PM,11,2,12:26:47 PM,11,2,1:03:50 PM,11,2,1:27:19 PM,10,19.4,,-1.300921,36.828195,-1.2539848999999998,36.824720899999996,Rider_Id_158,1409
-Order_No_11786,User_Id_2523,Bike,3,Business,20,3,3:31:42 PM,20,3,3:33:14 PM,20,3,3:44:25 PM,20,3,3:49:54 PM,20,3,4:12:36 PM,7,29.4,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_506,1362
-Order_No_11279,User_Id_111,Bike,3,Business,11,2,12:57:48 PM,11,2,12:58:03 PM,11,2,1:04:42 PM,11,2,1:06:38 PM,11,2,1:30:01 PM,12,24.4,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_279,1403
-Order_No_4436,User_Id_1721,Bike,3,Business,6,4,2:56:37 PM,6,4,2:57:40 PM,6,4,3:00:41 PM,6,4,3:03:57 PM,6,4,3:29:38 PM,9,21.5,,-1.2551895,36.7822034,-1.2972988,36.7894461,Rider_Id_16,1541
-Order_No_15825,User_Id_1924,Bike,3,Personal,21,4,2:51:05 PM,21,4,2:51:45 PM,21,4,2:55:31 PM,21,4,3:13:20 PM,21,4,3:49:36 PM,18,22.4,,-1.2916738,36.790105700000005,-1.2540898999999999,36.70427279999999,Rider_Id_449,2176
-Order_No_25151,User_Id_1205,Bike,3,Business,3,3,1:35:47 PM,3,3,1:39:17 PM,3,3,1:41:31 PM,3,3,1:48:02 PM,3,3,1:54:17 PM,2,26.7,,-1.2921114,36.819219,-1.2862457,36.8214792,Rider_Id_456,375
-Order_No_26478,User_Id_1912,Bike,3,Business,29,3,4:27:36 PM,29,3,4:30:07 PM,29,3,4:41:20 PM,29,3,4:46:39 PM,29,3,5:01:29 PM,5,21.2,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_907,890
-Order_No_6825,User_Id_3235,Bike,1,Personal,17,5,9:50:13 AM,17,5,9:50:28 AM,17,5,10:00:31 AM,17,5,10:05:24 AM,17,5,10:22:30 AM,6,14.1,,-1.310011,36.820824,-1.2677588,36.8169359,Rider_Id_956,1026
-Order_No_10911,User_Id_2614,Bike,1,Personal,17,5,4:29:48 PM,17,5,4:30:06 PM,17,5,4:34:48 PM,17,5,4:42:10 PM,17,5,4:47:13 PM,1,19.2,,-1.26559,36.8027,-1.2750393,36.8151527,Rider_Id_452,303
-Order_No_782,User_Id_3420,Bike,3,Business,30,5,9:25:18 AM,30,5,9:45:47 AM,30,5,9:58:28 AM,30,5,10:01:30 AM,30,5,11:05:39 AM,6,18.8,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_607,3849
-Order_No_18418,User_Id_2695,Bike,1,Personal,5,5,10:52:56 AM,5,5,11:15:22 AM,5,5,11:32:27 AM,5,5,11:36:48 AM,5,5,11:51:52 AM,6,23.8,,-1.2997169,36.7486486,-1.2809264,36.7842675,Rider_Id_565,904
-Order_No_1711,User_Id_552,Bike,3,Business,23,2,12:14:23 PM,23,2,12:15:29 PM,23,2,12:44:49 PM,23,2,12:46:17 PM,23,2,1:21:55 PM,17,18.2,,-1.279395,36.825364,-1.3632162,36.9108253,Rider_Id_329,2138
-Order_No_14211,User_Id_1342,Bike,3,Business,26,4,1:49:20 PM,26,4,1:50:00 PM,26,4,1:59:52 PM,26,4,2:01:03 PM,26,4,2:28:29 PM,9,22.0,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_520,1646
-Order_No_6149,User_Id_1073,Bike,3,Business,19,2,11:11:31 AM,19,2,11:12:41 AM,19,2,11:30:42 AM,19,2,11:34:55 AM,19,2,12:06:17 PM,16,26.7,,-1.290683,36.871041,-1.2561964,36.7885001,Rider_Id_377,1882
-Order_No_3935,User_Id_393,Bike,3,Business,29,1,10:51:11 AM,29,1,10:51:25 AM,29,1,10:53:23 AM,29,1,11:28:56 AM,29,1,11:49:48 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_168,1252
-Order_No_24804,User_Id_1880,Bike,3,Business,30,4,11:51:03 AM,30,4,11:52:05 AM,30,4,12:02:13 PM,30,4,12:07:08 PM,30,4,12:34:31 PM,13,,,-1.2571472,36.795063299999995,-1.302453,36.8668919,Rider_Id_96,1643
-Order_No_22153,User_Id_1205,Bike,3,Business,12,2,9:41:05 AM,12,2,9:59:31 AM,12,2,10:07:53 AM,12,2,10:13:06 AM,12,2,10:50:28 AM,9,16.1,,-1.2810807,36.8144229,-1.2475136,36.7703179,Rider_Id_635,2242
-Order_No_15167,User_Id_1340,Bike,3,Business,12,1,1:45:10 PM,12,1,1:46:52 PM,12,1,2:06:18 PM,12,1,3:11:58 PM,12,1,3:47:01 PM,19,26.7,,-1.3277669,36.8975169,-1.2707178999999997,36.7979713,Rider_Id_276,2103
-Order_No_27614,User_Id_598,Bike,1,Business,22,3,4:39:41 PM,22,3,4:41:58 PM,22,3,5:38:33 PM,22,3,5:39:34 PM,22,3,6:03:49 PM,8,23.2,,-1.2329501,36.808385200000004,-1.2536797,36.7995085,Rider_Id_384,1455
-Order_No_16723,User_Id_2200,Bike,3,Business,10,1,3:02:10 PM,10,1,3:13:43 PM,10,1,3:36:36 PM,10,1,3:50:49 PM,10,1,4:46:01 PM,10,,,-1.290894,36.822971,-1.2413316,36.878552500000005,Rider_Id_537,3312
-Order_No_11978,User_Id_393,Bike,3,Business,13,4,11:12:12 AM,13,4,11:12:23 AM,13,4,11:12:30 AM,13,4,11:22:33 AM,13,4,12:02:50 PM,11,21.8,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_39,2417
-Order_No_16959,User_Id_2894,Bike,3,Business,26,5,4:04:38 PM,26,5,4:08:46 PM,26,5,4:14:19 PM,26,5,4:21:05 PM,26,5,4:57:26 PM,8,,,-1.2974913000000001,36.805429600000004,-1.2549476000000002,36.8269576,Rider_Id_289,2181
-Order_No_8617,User_Id_186,Bike,3,Business,15,4,11:23:19 AM,15,4,12:07:16 PM,15,4,12:14:59 PM,15,4,12:17:30 PM,15,4,1:18:15 PM,18,24.3,,-1.3700383,36.9190174,-1.2902869,36.8085396,Rider_Id_222,3645
-Order_No_13881,User_Id_1921,Bike,1,Personal,13,6,12:21:56 PM,13,6,12:23:07 PM,13,6,12:50:19 PM,13,6,12:53:09 PM,13,6,1:17:11 PM,6,25.2,,-1.2728947,36.807942600000004,-1.2968658000000002,36.7776584,Rider_Id_393,1442
-Order_No_16374,User_Id_2676,Bike,3,Business,6,1,10:13:23 AM,6,1,10:13:41 AM,6,1,10:20:59 AM,6,1,10:41:26 AM,6,1,10:51:23 AM,2,20.4,,-1.3047482,36.784967200000004,-1.2963097,36.7688221,Rider_Id_636,597
-Order_No_2883,User_Id_816,Bike,3,Business,17,4,10:34:31 AM,17,4,10:44:00 AM,17,4,10:48:54 AM,17,4,11:03:29 AM,17,4,11:32:32 AM,11,25.5,,-1.2326041,36.8009029,-1.290894,36.822971,Rider_Id_237,1743
-Order_No_12009,User_Id_1245,Bike,3,Business,17,3,2:43:28 PM,17,3,2:43:39 PM,17,3,2:46:37 PM,17,3,3:30:20 PM,17,3,3:58:36 PM,10,28.5,,-1.300921,36.828195,-1.3455099,36.768275700000004,Rider_Id_662,1696
-Order_No_3198,User_Id_1724,Bike,3,Business,14,2,10:46:38 AM,14,2,10:47:55 AM,14,2,10:57:37 AM,14,2,11:03:22 AM,14,2,11:14:10 AM,4,,,-1.2551895,36.7822034,-1.2690013,36.8092872,Rider_Id_247,648
-Order_No_25272,User_Id_2069,Bike,3,Business,27,1,2:54:04 PM,27,1,2:54:37 PM,27,1,2:54:54 PM,27,1,3:01:30 PM,27,1,3:29:43 PM,12,22.9,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_72,1693
-Order_No_19113,User_Id_421,Bike,3,Business,20,4,5:01:10 PM,20,4,5:17:23 PM,20,4,5:33:42 PM,20,4,5:35:04 PM,20,4,5:56:21 PM,5,22.8,,-1.2975066000000002,36.76894229999999,-1.2745551000000002,36.7979797,Rider_Id_576,1277
-Order_No_19556,User_Id_2763,Bike,3,Business,2,4,3:22:17 PM,2,4,3:23:30 PM,2,4,3:29:01 PM,2,4,3:33:08 PM,2,4,4:08:42 PM,12,,,-1.2551895,36.7822034,-1.3200694,36.793130600000005,Rider_Id_205,2134
-Order_No_10429,User_Id_2306,Bike,3,Business,11,1,12:06:04 PM,11,1,12:09:29 PM,11,1,12:25:04 PM,11,1,1:00:57 PM,11,1,1:28:16 PM,8,26.7,,-1.2684761,36.83489470000001,-1.2271998,36.808490500000005,Rider_Id_76,1639
-Order_No_14729,User_Id_412,Bike,3,Business,26,2,8:57:43 AM,26,2,8:58:54 AM,26,2,9:55:08 AM,26,2,10:11:26 AM,26,2,12:03:56 PM,12,22.0,,-1.3313643,36.857742200000004,-1.266837,36.799248999999996,Rider_Id_787,6750
-Order_No_13508,User_Id_635,Bike,3,Business,10,4,8:50:12 AM,10,4,8:50:40 AM,10,4,8:57:12 AM,10,4,9:09:26 AM,10,4,9:51:05 AM,17,19.3,,-1.2599563,36.799343900000004,-1.177202,36.8351183,Rider_Id_427,2499
-Order_No_8986,User_Id_535,Bike,3,Business,11,6,10:51:09 AM,11,6,11:29:21 AM,11,6,11:33:58 AM,11,6,12:53:17 PM,11,6,1:52:28 PM,18,,,-1.2549259,36.7919494,-1.2715944,36.9114173,Rider_Id_778,3551
-Order_No_8250,User_Id_3525,Bike,3,Business,20,3,11:18:51 AM,20,3,11:19:14 AM,20,3,11:31:05 AM,20,3,11:34:06 AM,20,3,11:56:44 AM,13,27.3,,-1.3700383,36.9190174,-1.3239488000000001,36.831683500000004,Rider_Id_584,1358
-Order_No_10978,User_Id_3647,Bike,3,Business,14,2,1:15:51 PM,14,2,1:17:24 PM,14,2,1:27:54 PM,14,2,1:30:58 PM,14,2,1:43:31 PM,4,,,-1.2726389999999999,36.794723,-1.2674482,36.8076994,Rider_Id_959,753
-Order_No_27663,User_Id_2330,Bike,3,Business,20,1,6:20:20 PM,20,1,6:20:52 PM,20,1,6:28:03 PM,20,1,6:38:17 PM,20,1,7:07:22 PM,10,,,-1.2536275,36.798793200000006,-1.3015197,36.7658456,Rider_Id_155,1745
-Order_No_4079,User_Id_3647,Bike,3,Business,20,4,2:21:06 PM,20,4,2:22:22 PM,20,4,2:45:38 PM,20,4,2:49:13 PM,20,4,2:58:18 PM,5,25.1,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_290,545
-Order_No_21827,User_Id_2258,Bike,3,Business,14,2,2:27:27 PM,14,2,2:27:46 PM,14,2,2:29:55 PM,14,2,3:28:03 PM,14,2,3:28:12 PM,2,24.1,,-1.2571472,36.795063299999995,-1.263161,36.8059854,Rider_Id_875,9
-Order_No_15644,User_Id_136,Bike,3,Business,2,2,9:03:28 AM,2,2,9:03:59 AM,2,2,9:17:58 AM,2,2,9:22:52 AM,2,2,10:47:03 AM,18,20.7,,-1.2633232,36.830506799999995,-1.3254522,36.721658399999995,Rider_Id_530,5051
-Order_No_19738,User_Id_633,Bike,3,Business,24,4,1:02:47 PM,24,4,1:03:59 PM,24,4,1:12:54 PM,24,4,1:15:55 PM,24,4,1:23:40 PM,4,24.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_576,465
-Order_No_4049,User_Id_2528,Bike,3,Business,8,5,2:41:37 PM,8,5,2:47:58 PM,8,5,2:49:58 PM,8,5,2:51:30 PM,8,5,2:57:46 PM,3,30.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_520,376
-Order_No_18018,User_Id_391,Bike,3,Business,22,4,1:25:17 PM,22,4,1:29:05 PM,22,4,1:43:19 PM,22,4,1:47:07 PM,22,4,2:08:08 PM,5,26.2,,-1.2602337,36.79905479999999,-1.2777923999999998,36.8231577,Rider_Id_114,1261
-Order_No_18258,User_Id_3147,Bike,3,Business,12,2,3:50:02 PM,12,2,4:11:12 PM,12,2,4:13:26 PM,12,2,4:17:08 PM,12,2,4:51:51 PM,18,23.1,,-1.3404187,36.7614685,-1.2551895,36.7822034,Rider_Id_635,2083
-Order_No_1708,User_Id_1209,Bike,3,Business,15,1,1:18:07 PM,15,1,1:19:07 PM,15,1,1:30:08 PM,15,1,1:34:28 PM,15,1,1:59:02 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_327,1474
-Order_No_19592,User_Id_868,Bike,3,Business,14,6,9:30:07 AM,14,6,9:48:32 AM,14,6,10:07:57 AM,14,6,10:16:56 AM,14,6,10:31:28 AM,8,15.0,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_752,872
-Order_No_26815,User_Id_346,Bike,3,Business,30,4,10:04:43 AM,30,4,10:06:22 AM,30,4,10:18:00 AM,30,4,10:33:16 AM,30,4,10:36:54 AM,4,,,-1.28878,36.8168312,-1.2979063,36.8373372,Rider_Id_19,218
-Order_No_3403,User_Id_3184,Bike,3,Business,2,5,9:32:23 AM,2,5,9:32:38 AM,2,5,9:32:40 AM,2,5,9:43:20 AM,2,5,10:09:06 AM,17,18.2,17.0,-1.3396256000000002,36.8863135,-1.2673786,36.8081593,Rider_Id_813,1546
-Order_No_25645,User_Id_2523,Bike,3,Business,30,3,9:38:34 AM,30,3,9:38:56 AM,30,3,9:54:46 AM,30,3,10:13:22 AM,30,3,10:14:21 AM,4,19.8,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_14,59
-Order_No_2441,User_Id_1705,Bike,3,Business,9,4,2:47:02 PM,9,4,2:47:44 PM,9,4,3:05:52 PM,9,4,3:07:19 PM,9,4,3:33:16 PM,15,25.9,,-1.2551895,36.7822034,-1.2239216,36.8792146,Rider_Id_379,1557
-Order_No_7724,User_Id_1500,Bike,3,Business,6,3,10:57:31 AM,6,3,10:57:54 AM,6,3,11:02:03 AM,6,3,11:34:06 AM,6,3,12:01:23 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_304,1637
-Order_No_14617,User_Id_1205,Bike,3,Business,13,3,12:02:37 PM,13,3,12:13:25 PM,13,3,12:21:22 PM,13,3,12:31:32 PM,13,3,1:02:36 PM,6,,,-1.2513025,36.809441899999996,-1.290724,36.81947,Rider_Id_291,1864
-Order_No_17408,User_Id_2330,Bike,3,Business,22,5,6:28:00 PM,22,5,6:28:24 PM,22,5,6:29:23 PM,22,5,6:37:33 PM,22,5,6:55:05 PM,8,28.5,,-1.3192788999999998,36.711299100000005,-1.3015197,36.7658456,Rider_Id_881,1052
-Order_No_10553,User_Id_893,Bike,3,Business,19,1,10:58:35 AM,19,1,10:59:38 AM,19,1,11:12:00 AM,19,1,11:15:49 AM,19,1,11:33:25 AM,5,24.6,,-1.2594473999999998,36.8089122,-1.2850523,36.7859591,Rider_Id_547,1056
-Order_No_4131,User_Id_3755,Bike,1,Personal,17,4,11:47:47 AM,17,4,11:47:51 AM,17,4,11:48:19 AM,17,4,12:00:26 PM,17,4,12:21:26 PM,7,22.6,,-1.2965004999999998,36.7791835,-1.28249,36.81909829999999,Rider_Id_789,1260
-Order_No_9037,User_Id_1346,Bike,3,Business,5,2,4:06:19 PM,5,2,4:08:17 PM,5,2,4:13:45 PM,5,2,4:19:34 PM,5,2,4:42:43 PM,2,,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_801,1389
-Order_No_23998,User_Id_2330,Bike,3,Business,11,1,3:28:12 PM,11,1,3:31:20 PM,11,1,3:31:24 PM,11,1,3:40:54 PM,11,1,4:00:37 PM,7,29.0,,-1.2325706,36.808392299999994,-1.2649605,36.798177700000004,Rider_Id_206,1183
-Order_No_5307,User_Id_113,Bike,1,Business,21,1,11:01:29 AM,21,1,11:02:36 AM,21,1,11:09:34 AM,21,1,11:21:09 AM,21,1,11:38:18 AM,8,20.1,,-1.2844653,36.8206676,-1.2963097,36.7688221,Rider_Id_406,1029
-Order_No_9779,User_Id_868,Bike,3,Business,28,6,12:24:01 PM,28,6,12:24:08 PM,28,6,12:37:36 PM,28,6,12:48:26 PM,28,6,1:18:15 PM,14,24.1,,-1.265715,36.823815,-1.293515,36.897607,Rider_Id_670,1789
-Order_No_20709,User_Id_2883,Bike,2,Personal,28,2,4:10:34 PM,28,2,4:12:16 PM,28,2,4:32:16 PM,28,2,4:33:40 PM,28,2,5:13:36 PM,8,18.2,,-1.2847098999999997,36.772234499999996,-1.2472601,36.7859285,Rider_Id_447,2396
-Order_No_15923,User_Id_1367,Bike,3,Business,12,3,11:20:33 AM,12,3,11:20:33 AM,12,3,12:11:27 PM,12,3,12:22:34 PM,12,3,12:53:22 PM,10,19.3,,-1.2733311,36.8133602,-1.275442,36.766020700000006,Rider_Id_247,1848
-Order_No_13028,User_Id_2017,Bike,3,Business,22,2,11:21:48 AM,22,2,11:24:49 AM,22,2,11:39:51 AM,22,2,11:43:49 AM,22,2,12:04:16 PM,7,19.2,,-1.2865096,36.8169364,-1.2963097,36.7688221,Rider_Id_412,1227
-Order_No_28062,User_Id_1920,Bike,3,Business,23,5,12:08:30 PM,23,5,12:09:42 PM,23,5,12:14:40 PM,23,5,12:19:12 PM,23,5,12:59:48 PM,28,24.1,,-1.2551895,36.7822034,-1.1838303,36.9543295,Rider_Id_913,2436
-Order_No_27474,User_Id_2468,Bike,3,Business,20,3,1:15:11 PM,20,3,1:16:18 PM,20,3,1:23:11 PM,20,3,1:29:34 PM,20,3,2:33:43 PM,7,27.1,,-1.2821883,36.826961100000005,-1.2931104,36.7677484,Rider_Id_404,3849
-Order_No_21609,User_Id_3635,Bike,1,Business,7,5,10:35:00 AM,7,5,10:52:14 AM,7,5,11:06:24 AM,7,5,11:19:57 AM,7,5,11:52:46 AM,14,25.3,,-1.2615888000000002,36.7928732,-1.2297202,36.881950700000004,Rider_Id_802,1969
-Order_No_1971,User_Id_2000,Bike,3,Business,4,1,10:30:04 AM,4,1,10:35:36 AM,4,1,10:38:44 AM,4,1,10:57:52 AM,4,1,11:15:50 AM,9,26.7,,-1.2551895,36.7822034,-1.2697053999999999,36.8519724,Rider_Id_103,1078
-Order_No_21247,User_Id_3181,Bike,3,Personal,11,4,9:42:33 AM,11,4,9:46:31 AM,11,4,9:50:16 AM,11,4,10:07:50 AM,11,4,10:17:51 AM,6,18.5,,-1.299787,36.8335941,-1.3233955,36.856294399999996,Rider_Id_179,601
-Order_No_21816,User_Id_2154,Bike,3,Business,2,5,4:28:00 PM,2,5,4:35:19 PM,2,5,4:50:37 PM,2,5,4:58:00 PM,2,5,5:14:55 PM,11,24.0,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_414,1015
-Order_No_6175,User_Id_1552,Bike,1,Personal,15,6,8:57:44 AM,15,6,8:58:32 AM,15,6,9:10:03 AM,15,6,9:18:40 AM,15,6,9:20:33 AM,2,17.7,,-1.2668093,36.7966542,-1.2676091,36.786255499999996,Rider_Id_172,113
-Order_No_13924,User_Id_2867,Bike,1,Personal,20,1,2:51:03 PM,20,1,2:51:55 PM,20,1,3:06:15 PM,20,1,3:32:35 PM,20,1,3:49:39 PM,6,21.3,,-1.2882850000000001,36.815912,-1.2635485,36.790465600000005,Rider_Id_338,1024
-Order_No_23375,User_Id_2383,Bike,3,Business,4,2,3:43:51 PM,4,2,3:56:24 PM,4,2,4:11:12 PM,4,2,4:12:55 PM,4,2,4:22:24 PM,2,22.6,,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_138,569
-Order_No_6024,User_Id_3434,Bike,3,Business,18,1,12:11:04 PM,18,1,12:22:21 PM,18,1,12:28:38 PM,18,1,12:30:50 PM,18,1,12:58:53 PM,10,,,-1.2963097,36.7688221,-1.304969,36.846623,Rider_Id_937,1683
-Order_No_8186,User_Id_3283,Bike,3,Business,6,3,9:42:45 AM,6,3,9:54:59 AM,6,3,10:01:14 AM,6,3,10:17:14 AM,6,3,10:42:02 AM,13,15.9,13.9,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_379,1488
-Order_No_1243,User_Id_2735,Bike,3,Personal,30,3,10:47:39 AM,30,3,11:26:16 AM,30,3,11:29:57 AM,30,3,11:44:39 AM,30,3,12:01:58 PM,6,22.5,,-1.2523598999999999,36.8017648,-1.2966453000000002,36.8191342,Rider_Id_379,1039
-Order_No_17077,User_Id_393,Bike,3,Business,26,2,10:22:53 AM,26,2,10:24:23 AM,26,2,10:30:48 AM,26,2,10:45:08 AM,26,2,10:57:28 AM,4,27.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_409,740
-Order_No_2946,User_Id_1693,Bike,3,Business,8,3,10:13:07 AM,8,3,10:13:39 AM,8,3,10:25:03 AM,8,3,11:10:07 AM,8,3,11:31:29 AM,9,22.1,,-1.3272264999999999,36.8231497,-1.2574087999999999,36.799712,Rider_Id_938,1282
-Order_No_20906,User_Id_1527,Bike,3,Business,5,5,11:55:51 AM,5,5,11:58:35 AM,5,5,12:09:18 PM,5,5,12:15:37 PM,5,5,12:28:28 PM,4,24.7,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_358,771
-Order_No_17628,User_Id_533,Bike,3,Business,23,5,9:45:58 AM,23,5,9:49:00 AM,23,5,10:02:42 AM,23,5,10:10:00 AM,23,5,10:25:11 AM,6,20.4,,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_290,911
-Order_No_22620,User_Id_2094,Bike,2,Personal,22,2,2:31:08 PM,22,2,2:32:54 PM,22,2,2:54:13 PM,22,2,3:05:10 PM,22,2,3:34:47 PM,8,21.2,,-1.2922364,36.78955379999999,-1.2884996000000002,36.74222520000001,Rider_Id_116,1777
-Order_No_28094,User_Id_393,Bike,3,Business,1,1,10:40:42 AM,1,1,10:41:31 AM,1,1,10:50:38 AM,1,1,11:05:37 AM,1,1,11:12:15 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_901,398
-Order_No_4369,User_Id_1363,Bike,3,Business,21,1,3:10:59 PM,21,1,3:11:09 PM,21,1,3:11:19 PM,21,1,3:14:37 PM,21,1,3:27:28 PM,3,27.7,,-1.3004062,36.829741,-1.3064585,36.8119065,Rider_Id_518,771
-Order_No_21742,User_Id_3743,Bike,3,Business,11,1,5:19:46 PM,11,1,5:42:51 PM,11,1,6:03:19 PM,11,1,6:22:14 PM,11,1,6:47:31 PM,14,21.0,,-1.3223393,36.886731700000006,-1.320157,36.830887,Rider_Id_543,1517
-Order_No_5258,User_Id_3149,Bike,3,Business,6,4,11:15:13 AM,6,4,11:17:16 AM,6,4,11:17:36 AM,6,4,11:43:10 AM,6,4,11:50:17 AM,6,19.8,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_2,427
-Order_No_17544,User_Id_3062,Bike,3,Personal,4,5,5:23:03 PM,4,5,5:25:57 PM,4,5,5:47:14 PM,4,5,6:12:20 PM,4,5,6:44:11 PM,7,21.6,,-1.2791223999999999,36.8306878,-1.2765625,36.780930100000006,Rider_Id_332,1911
-Order_No_18839,User_Id_3069,Bike,1,Personal,17,4,4:59:36 PM,17,4,5:01:23 PM,17,4,5:18:58 PM,17,4,5:24:43 PM,17,4,6:00:15 PM,13,22.2,,-1.2786893999999998,36.8032321,-1.329568,36.8520825,Rider_Id_340,2132
-Order_No_5971,User_Id_3149,Bike,3,Business,4,2,12:49:48 PM,4,2,12:57:31 PM,4,2,12:57:37 PM,4,2,2:34:22 PM,4,2,2:44:08 PM,6,23.7,10.9,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_848,586
-Order_No_25374,User_Id_435,Bike,3,Business,23,4,9:04:12 AM,23,4,9:05:24 AM,23,4,9:25:25 AM,23,4,9:37:47 AM,23,4,9:52:57 AM,7,,,-1.2908072,36.7860517,-1.300921,36.828195,Rider_Id_119,910
-Order_No_12796,User_Id_1226,Bike,2,Personal,31,2,9:52:11 AM,31,2,9:53:26 AM,31,2,10:10:41 AM,31,2,10:17:47 AM,31,2,10:31:34 AM,9,17.0,1.0,-1.3291184,36.7892374,-1.2981625,36.813014200000005,Rider_Id_857,827
-Order_No_15645,User_Id_1724,Bike,3,Business,5,2,3:25:35 PM,5,2,3:26:07 PM,5,2,4:07:16 PM,5,2,4:21:08 PM,5,2,4:33:45 PM,3,,,-1.259382,36.807204999999996,-1.2551895,36.7822034,Rider_Id_755,757
-Order_No_17220,User_Id_718,Bike,3,Business,26,1,12:52:25 PM,26,1,12:53:50 PM,26,1,1:07:54 PM,26,1,1:12:25 PM,26,1,1:34:11 PM,8,23.0,,-1.2551895,36.7822034,-1.298094,36.803149,Rider_Id_290,1306
-Order_No_18374,User_Id_2931,Bike,3,Business,15,2,2:16:59 PM,15,2,2:17:13 PM,15,2,2:26:50 PM,15,2,2:50:46 PM,15,2,3:33:46 PM,23,24.6,,-1.3433181,36.9138018,-1.2452664,36.802854100000005,Rider_Id_504,2580
-Order_No_26425,User_Id_393,Bike,3,Business,10,4,8:36:08 AM,10,4,8:36:15 AM,10,4,8:37:21 AM,10,4,8:52:58 AM,10,4,9:24:55 AM,14,19.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1917
-Order_No_20386,User_Id_3599,Bike,3,Business,4,2,12:34:17 PM,4,2,12:44:53 PM,4,2,2:11:27 PM,4,2,2:38:21 PM,4,2,3:56:50 PM,15,22.4,,-1.3302996,36.8707935,-1.2610066,36.7851622,Rider_Id_459,4709
-Order_No_16549,User_Id_671,Bike,1,Personal,6,4,12:12:43 PM,6,4,12:19:41 PM,6,4,12:48:10 PM,6,4,12:58:35 PM,6,4,1:16:18 PM,8,22.1,,-1.2343087,36.84749,-1.2855798999999999,36.817620899999994,Rider_Id_882,1063
-Order_No_13568,User_Id_3240,Bike,2,Personal,27,4,9:25:01 AM,27,4,9:25:09 AM,27,4,9:29:46 AM,27,4,9:31:32 AM,27,4,10:01:41 AM,12,19.0,2.9,-1.3212168,36.82760579999999,-1.2551895,36.7822034,Rider_Id_62,1809
-Order_No_13399,User_Id_1384,Bike,3,Business,6,1,9:22:00 AM,6,1,9:22:52 AM,6,1,9:37:11 AM,6,1,9:38:48 AM,6,1,11:03:41 AM,16,,,-1.2517448,36.7924971,-1.3202111,36.8880995,Rider_Id_131,5093
-Order_No_5722,User_Id_972,Bike,3,Business,13,3,1:44:45 PM,13,3,1:52:40 PM,13,3,2:13:43 PM,13,3,2:16:17 PM,13,3,2:30:17 PM,4,23.6,,-1.2862033000000002,36.8152966,-1.2610959,36.828102200000004,Rider_Id_844,840
-Order_No_25224,User_Id_478,Bike,3,Business,5,2,2:23:51 PM,5,2,2:58:24 PM,5,2,3:29:53 PM,5,2,4:26:25 PM,5,2,4:49:08 PM,4,,,-1.2844516000000001,36.8245932,-1.2571472,36.795063299999995,Rider_Id_660,1363
-Order_No_4348,User_Id_635,Bike,3,Business,12,4,11:02:39 AM,12,4,11:03:05 AM,12,4,11:14:35 AM,12,4,11:20:48 AM,12,4,11:48:49 AM,8,18.9,,-1.2599563,36.799343900000004,-1.2443443,36.8085667,Rider_Id_519,1681
-Order_No_28196,User_Id_3355,Bike,3,Business,12,1,3:33:54 PM,12,1,3:44:42 PM,12,1,4:05:07 PM,12,1,4:06:34 PM,12,1,4:30:14 PM,9,26.8,,-1.2859912,36.8756811,-1.3052491000000002,36.8223899,Rider_Id_31,1420
-Order_No_3886,User_Id_1469,Bike,3,Business,15,1,4:11:30 PM,15,1,4:11:39 PM,15,1,4:11:51 PM,15,1,4:16:25 PM,15,1,4:43:47 PM,14,,,-1.2551895,36.7822034,-1.3263105,36.8622904,Rider_Id_96,1642
-Order_No_840,User_Id_1927,Bike,3,Business,21,2,1:24:08 PM,21,2,1:31:26 PM,21,2,1:47:11 PM,21,2,1:52:47 PM,21,2,2:39:12 PM,10,25.2,,-1.323615,36.700475,-1.3472066,36.7692638,Rider_Id_211,2785
-Order_No_20064,User_Id_2509,Bike,3,Business,3,3,4:12:47 PM,3,3,4:14:31 PM,3,3,4:38:36 PM,3,3,4:46:06 PM,3,3,5:38:12 PM,14,23.7,,-1.2571472,36.795063299999995,-1.2238103,36.8772588,Rider_Id_578,3126
-Order_No_15580,User_Id_2091,Bike,1,Personal,23,6,1:59:39 PM,23,6,2:00:10 PM,23,6,2:13:56 PM,23,6,2:22:11 PM,23,6,3:01:30 PM,14,,,-1.2731645,36.7902246,-1.3371926,36.7250933,Rider_Id_858,2359
-Order_No_19553,User_Id_3326,Bike,3,Business,29,2,1:15:18 PM,29,2,1:15:43 PM,29,2,1:20:57 PM,29,2,1:30:42 PM,29,2,1:30:45 PM,3,23.7,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_647,3
-Order_No_16162,User_Id_1271,Bike,3,Business,5,5,2:59:29 PM,5,5,3:00:25 PM,5,5,3:46:43 PM,5,5,3:59:32 PM,5,5,5:13:01 PM,20,27.4,,-1.3107918,36.761794,-1.2702844,36.880320899999994,Rider_Id_435,4409
-Order_No_9390,User_Id_2112,Bike,3,Business,19,3,1:04:51 PM,19,3,1:21:47 PM,19,3,1:43:03 PM,19,3,1:46:20 PM,19,3,2:34:52 PM,16,24.6,,-1.2156006000000001,36.8916865,-1.3038543,36.83810510000001,Rider_Id_75,2912
-Order_No_15041,User_Id_3557,Bike,3,Business,28,3,4:27:55 PM,28,3,4:34:54 PM,28,3,4:35:35 PM,28,3,4:52:31 PM,28,3,4:52:35 PM,4,23.3,,-1.2546732,36.808679600000005,-1.2569503,36.78347279999999,Rider_Id_848,4
-Order_No_25965,User_Id_722,Bike,3,Business,14,4,3:17:22 PM,14,4,3:21:55 PM,14,4,3:39:06 PM,14,4,3:47:35 PM,14,4,5:16:20 PM,19,27.7,,-1.32943,36.894242999999996,-1.2655306,36.828148,Rider_Id_64,5325
-Order_No_13873,User_Id_517,Bike,3,Business,2,3,8:19:48 AM,2,3,8:21:26 AM,2,3,8:24:19 AM,2,3,8:32:48 AM,2,3,8:39:52 AM,4,20.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,424
-Order_No_12809,User_Id_283,Bike,3,Business,28,4,2:11:53 PM,28,4,2:13:44 PM,28,4,2:36:23 PM,28,4,2:38:53 PM,28,4,3:01:48 PM,7,,,-1.2804227,36.8250375,-1.2850048,36.79233789999999,Rider_Id_88,1375
-Order_No_21676,User_Id_635,Bike,3,Business,26,1,12:27:48 PM,26,1,12:37:46 PM,26,1,12:46:58 PM,26,1,1:11:29 PM,26,1,1:26:10 PM,6,23.0,,-1.300921,36.828195,-1.3229345,36.8566974,Rider_Id_406,881
-Order_No_27042,User_Id_393,Bike,3,Business,30,5,7:55:42 AM,30,5,10:52:46 AM,30,5,10:54:52 AM,30,5,11:09:31 AM,30,5,11:33:38 AM,4,23.9,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_523,1447
-Order_No_22449,User_Id_53,Bike,3,Business,27,3,11:04:03 AM,27,3,11:05:54 AM,27,3,11:13:28 AM,27,3,11:26:59 AM,27,3,11:42:07 AM,5,25.9,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_858,908
-Order_No_17649,User_Id_393,Bike,3,Business,10,1,11:53:35 AM,10,1,1:10:27 PM,10,1,1:11:05 PM,10,1,1:40:14 PM,10,1,2:14:46 PM,11,18.9,,-1.3228003000000002,36.8306435,-1.2574219,36.7927073,Rider_Id_39,2072
-Order_No_27391,User_Id_3621,Bike,3,Business,30,6,3:59:51 PM,30,6,4:00:15 PM,30,6,4:10:50 PM,30,6,4:16:54 PM,30,6,4:42:28 PM,15,28.4,,-1.2836583000000001,36.7886014,-1.3289028,36.8719683,Rider_Id_678,1534
-Order_No_19703,User_Id_1724,Bike,3,Business,5,1,12:01:05 PM,5,1,12:01:16 PM,5,1,12:08:28 PM,5,1,12:10:39 PM,5,1,12:20:39 PM,3,24.4,,-1.2593061,36.8071016,-1.2551895,36.7822034,Rider_Id_660,600
-Order_No_18062,User_Id_2617,Bike,1,Personal,15,1,11:45:52 AM,15,1,11:47:41 AM,15,1,12:02:06 PM,15,1,12:08:10 PM,15,1,12:24:22 PM,6,,,-1.2961742,36.8192864,-1.2721466000000001,36.8155359,Rider_Id_268,972
-Order_No_22048,User_Id_2008,Bike,3,Business,11,4,3:22:16 PM,11,4,3:30:22 PM,11,4,4:00:16 PM,11,4,4:06:52 PM,11,4,4:24:22 PM,9,27.0,,-1.2508229,36.7895263,-1.3121752,36.8083282,Rider_Id_829,1050
-Order_No_11634,User_Id_3239,Bike,3,Personal,3,5,12:25:01 PM,3,5,12:25:08 PM,3,5,12:27:55 PM,3,5,12:37:47 PM,3,5,1:26:39 PM,22,21.2,,-1.2836832,36.8306719,-1.3886344,36.9372718,Rider_Id_414,2932
-Order_No_27462,User_Id_302,Bike,3,Business,12,2,1:27:04 PM,12,2,1:28:44 PM,12,2,1:38:45 PM,12,2,1:44:25 PM,12,2,2:02:10 PM,6,26.8,,-1.2652979,36.800039899999994,-1.2974472,36.8156997,Rider_Id_576,1065
-Order_No_27125,User_Id_1075,Bike,3,Business,6,3,1:02:28 PM,6,3,1:02:46 PM,6,3,1:03:40 PM,6,3,1:11:11 PM,6,3,1:41:13 PM,13,,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_210,1802
-Order_No_9132,User_Id_733,Bike,3,Business,23,2,3:46:31 PM,23,2,3:59:51 PM,23,2,3:59:54 PM,23,2,4:04:40 PM,23,2,4:25:30 PM,8,,,-1.2551895,36.7822034,-1.2913246,36.8019522,Rider_Id_576,1250
-Order_No_9243,User_Id_2956,Bike,3,Business,2,5,1:45:58 PM,2,5,1:46:22 PM,2,5,1:48:05 PM,2,5,2:11:17 PM,2,5,3:15:49 PM,17,25.0,,-1.3165542,36.700067,-1.2584143,36.8048002,Rider_Id_105,3872
-Order_No_24407,User_Id_295,Bike,3,Business,15,5,2:59:13 PM,15,5,3:43:56 PM,15,5,3:44:02 PM,15,5,3:59:35 PM,15,5,4:13:56 PM,6,27.9,,-1.2967108,36.787113500000004,-1.286925,36.821915999999995,Rider_Id_942,861
-Order_No_238,User_Id_735,Bike,1,Personal,3,5,9:51:38 AM,3,5,9:51:45 AM,3,5,10:22:59 AM,3,5,10:32:06 AM,3,5,11:06:30 AM,20,21.4,,-1.3029371,36.860933700000004,-1.3091733,36.740809999999996,Rider_Id_402,2064
-Order_No_5099,User_Id_3222,Bike,3,Business,9,4,12:07:24 PM,9,4,12:07:42 PM,9,4,12:09:33 PM,9,4,12:17:33 PM,9,4,12:34:13 PM,5,,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_161,1000
-Order_No_3868,User_Id_1368,Bike,3,Business,28,4,4:44:11 PM,28,4,4:45:11 PM,28,4,4:45:37 PM,28,4,4:57:11 PM,28,4,5:14:48 PM,5,25.5,,-1.2571472,36.795063299999995,-1.2818812,36.82681420000001,Rider_Id_330,1057
-Order_No_601,User_Id_3295,Bike,3,Business,22,4,2:58:34 PM,22,4,3:00:42 PM,22,4,3:08:53 PM,22,4,3:12:50 PM,22,4,3:30:45 PM,8,26.2,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_199,1075
-Order_No_26636,User_Id_363,Bike,1,Personal,16,3,10:11:59 AM,16,3,10:26:30 AM,16,3,10:44:21 AM,16,3,10:50:39 AM,16,3,11:14:51 AM,9,24.0,,-1.3314048,36.8229367,-1.2658315,36.809049,Rider_Id_100,1452
-Order_No_796,User_Id_2956,Bike,3,Business,3,5,3:41:35 PM,3,5,3:44:47 PM,3,5,4:17:11 PM,3,5,4:19:39 PM,3,5,4:41:57 PM,10,25.0,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_764,1338
-Order_No_15920,User_Id_731,Bike,3,Business,11,1,12:20:25 PM,11,1,12:21:14 PM,11,1,12:31:51 PM,11,1,12:34:37 PM,11,1,12:58:46 PM,11,24.0,,-1.2574087999999999,36.799712,-1.3166778000000001,36.8492927,Rider_Id_279,1449
-Order_No_18213,User_Id_142,Bike,3,Business,11,2,2:20:07 PM,11,2,2:41:54 PM,11,2,2:52:05 PM,11,2,3:12:03 PM,11,2,3:35:37 PM,10,22.4,,-1.2517448,36.7924971,-1.2963097,36.7688221,Rider_Id_536,1414
-Order_No_24390,User_Id_633,Bike,3,Business,20,3,2:32:43 PM,20,3,2:34:41 PM,20,3,2:47:28 PM,20,3,2:51:02 PM,20,3,3:19:22 PM,11,29.4,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_692,1700
-Order_No_10696,User_Id_3108,Bike,3,Business,10,4,12:35:04 PM,10,4,12:36:52 PM,10,4,12:51:31 PM,10,4,12:53:46 PM,10,4,1:26:12 PM,14,24.8,,-1.2551895,36.7822034,-1.3020905,36.876917600000006,Rider_Id_343,1946
-Order_No_4879,User_Id_1033,Bike,3,Personal,15,5,2:59:46 PM,15,5,3:41:22 PM,15,5,3:58:38 PM,15,5,4:07:36 PM,15,5,4:43:02 PM,11,28.1,,-1.2326041,36.8009029,-1.2902539,36.8211322,Rider_Id_839,2126
-Order_No_7620,User_Id_3283,Bike,3,Business,18,5,9:33:47 AM,18,5,9:33:57 AM,18,5,9:39:42 AM,18,5,9:55:39 AM,18,5,10:07:35 AM,5,20.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_205,716
-Order_No_18904,User_Id_928,Bike,3,Business,30,3,1:47:52 PM,30,3,1:49:28 PM,30,3,1:49:33 PM,30,3,1:54:25 PM,30,3,2:21:47 PM,6,29.0,,-1.2551895,36.7822034,-1.2902552,36.8179349,Rider_Id_876,1642
-Order_No_4682,User_Id_1363,Bike,3,Business,7,5,10:46:28 AM,7,5,10:53:57 AM,7,5,11:07:14 AM,7,5,11:11:13 AM,7,5,11:29:32 AM,3,25.6,,-1.3004062,36.829741,-1.291768,36.8297208,Rider_Id_447,1099
-Order_No_15686,User_Id_755,Bike,1,Personal,29,6,2:55:27 PM,29,6,2:55:51 PM,29,6,3:13:03 PM,29,6,3:28:10 PM,29,6,3:54:25 PM,12,28.0,,-1.2640571000000003,36.7883547,-1.2270685,36.760680900000004,Rider_Id_178,1575
-Order_No_4383,User_Id_3711,Bike,3,Business,29,4,5:05:46 PM,29,4,5:12:39 PM,29,4,5:18:07 PM,29,4,5:29:11 PM,29,4,5:52:46 PM,9,20.8,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_320,1415
-Order_No_583,User_Id_2706,Bike,3,Personal,4,1,1:47:55 PM,4,1,1:49:55 PM,4,1,2:01:21 PM,4,1,2:02:28 PM,4,1,2:19:51 PM,6,22.5,,-1.2603014,36.8069828,-1.2858511000000001,36.7946184,Rider_Id_116,1043
-Order_No_22050,User_Id_263,Bike,1,Personal,3,2,8:44:35 AM,3,2,8:55:17 AM,3,2,9:16:42 AM,3,2,9:31:00 AM,3,2,10:00:11 AM,6,15.5,1.9,-1.2624632,36.8405321,-1.300722,36.816865,Rider_Id_208,1751
-Order_No_5511,User_Id_1482,Bike,3,Business,15,6,9:53:40 AM,15,6,9:54:02 AM,15,6,9:59:35 AM,15,6,10:06:06 AM,15,6,10:29:44 AM,15,17.0,,-1.2573921000000001,36.7992507,-1.3385790000000002,36.89630379999999,Rider_Id_341,1418
-Order_No_12775,User_Id_2041,Bike,3,Business,12,3,1:39:07 PM,12,3,1:54:01 PM,12,3,1:54:07 PM,12,3,2:27:04 PM,12,3,3:12:45 PM,1,26.3,,-1.290894,36.822971,-1.2932780000000001,36.827789,Rider_Id_692,2741
-Order_No_1119,User_Id_3360,Bike,2,Personal,18,3,12:54:56 PM,18,3,1:07:11 PM,18,3,1:35:12 PM,18,3,1:52:06 PM,18,3,2:38:05 PM,13,20.9,,-1.328128,36.8719,-1.2555811000000001,36.802267,Rider_Id_848,2759
-Order_No_18954,User_Id_1358,Bike,3,Personal,12,2,12:34:15 PM,12,2,12:36:25 PM,12,2,12:51:20 PM,12,2,12:59:51 PM,12,2,1:17:18 PM,10,28.0,,-1.2927064,36.832885499999996,-1.2315379,36.8156795,Rider_Id_906,1047
-Order_No_16976,User_Id_2677,Bike,3,Business,16,4,9:57:49 AM,16,4,9:58:33 AM,16,4,9:58:39 AM,16,4,10:29:05 AM,16,4,11:08:18 AM,10,,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_99,2353
-Order_No_11315,User_Id_633,Bike,3,Business,24,3,3:23:26 PM,24,3,3:50:40 PM,24,3,3:50:55 PM,24,3,4:15:49 PM,24,3,4:22:36 PM,4,18.4,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_515,407
-Order_No_12361,User_Id_3426,Bike,3,Business,4,2,11:29:43 AM,4,2,11:44:10 AM,4,2,12:10:19 PM,4,2,12:12:50 PM,4,2,12:52:58 PM,9,19.4,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_382,2408
-Order_No_14954,User_Id_1621,Bike,2,Personal,9,2,9:12:30 AM,9,2,9:12:37 AM,9,2,9:23:36 AM,9,2,9:28:13 AM,9,2,10:25:46 AM,17,17.7,4.0,-1.2840461,36.7934021,-1.3327653999999998,36.8853117,Rider_Id_805,3453
-Order_No_16056,User_Id_3147,Bike,3,Business,24,3,9:22:48 AM,24,3,9:32:11 AM,24,3,9:44:47 AM,24,3,10:05:46 AM,24,3,10:46:00 AM,15,16.8,1.1,-1.3332749,36.870814700000004,-1.3472066,36.7692638,Rider_Id_611,2414
-Order_No_12440,User_Id_1500,Bike,3,Business,20,1,9:27:27 AM,20,1,9:28:18 AM,20,1,9:31:29 AM,20,1,10:00:00 AM,20,1,10:01:28 AM,8,18.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_137,88
-Order_No_27387,User_Id_1925,Bike,2,Personal,15,2,9:20:24 AM,15,2,9:38:28 AM,15,2,10:05:03 AM,15,2,10:12:49 AM,15,2,10:45:18 AM,12,17.8,2.0,-1.2494503000000001,36.8531852,-1.2860183,36.8975338,Rider_Id_397,1949
-Order_No_1189,User_Id_2200,Bike,3,Business,12,1,8:42:54 AM,12,1,8:47:34 AM,12,1,8:54:02 AM,12,1,9:00:57 AM,12,1,9:06:18 AM,2,19.4,0.5,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_88,321
-Order_No_26065,User_Id_2330,Bike,3,Business,12,3,5:39:21 PM,12,3,5:43:26 PM,12,3,6:18:16 PM,12,3,6:20:17 PM,12,3,7:18:41 PM,6,20.7,,-1.2921463,36.8029626,-1.3015197,36.7658456,Rider_Id_233,3504
-Order_No_22064,User_Id_2330,Bike,3,Business,24,3,1:31:28 PM,24,3,1:33:06 PM,24,3,1:51:33 PM,24,3,2:02:25 PM,24,3,2:30:54 PM,7,,,-1.2687407,36.7373043,-1.3015197,36.7658456,Rider_Id_523,1709
-Order_No_12401,User_Id_1244,Bike,3,Business,23,6,1:19:52 PM,23,6,1:23:06 PM,23,6,1:33:38 PM,23,6,2:08:52 PM,23,6,2:08:57 PM,11,,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_459,5
-Order_No_27022,User_Id_3127,Bike,3,Business,13,4,3:47:51 PM,13,4,3:48:23 PM,13,4,4:02:37 PM,13,4,4:11:59 PM,13,4,4:25:42 PM,4,26.6,,-1.2789156000000002,36.8171348,-1.2918246,36.801584999999996,Rider_Id_847,823
-Order_No_16963,User_Id_867,Bike,3,Business,11,2,12:47:14 PM,11,2,12:50:13 PM,11,2,12:53:20 PM,11,2,1:22:15 PM,11,2,1:51:14 PM,10,19.5,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_36,1739
-Order_No_7954,User_Id_1188,Bike,1,Personal,4,1,10:14:27 AM,4,1,10:15:19 AM,4,1,10:21:31 AM,4,1,10:23:25 AM,4,1,10:38:16 AM,8,18.0,,-1.2791714,36.825599700000005,-1.3277531999999999,36.829790100000004,Rider_Id_360,891
-Order_No_26412,User_Id_1927,Bike,3,Business,7,1,3:04:33 PM,7,1,4:00:54 PM,7,1,4:16:38 PM,7,1,4:26:37 PM,7,1,5:06:18 PM,11,20.4,,-1.3404187,36.7614685,-1.2965678999999999,36.8158653,Rider_Id_874,2381
-Order_No_15037,User_Id_3560,Bike,3,Business,7,1,1:15:37 PM,7,1,1:18:09 PM,7,1,1:39:05 PM,7,1,1:42:52 PM,7,1,2:01:00 PM,1,26.8,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_210,1088
-Order_No_23356,User_Id_400,Bike,3,Business,3,4,2:12:21 PM,3,4,2:12:46 PM,3,4,2:18:31 PM,3,4,2:22:50 PM,3,4,2:42:05 PM,4,25.9,,-1.275442,36.766020700000006,-1.2559559999999999,36.772901,Rider_Id_846,1155
-Order_No_4068,User_Id_2587,Bike,3,Business,27,4,2:49:52 PM,27,4,3:07:43 PM,27,4,3:22:20 PM,27,4,3:27:44 PM,27,4,3:27:53 PM,2,25.3,,-1.281754,36.8324229,-1.28878,36.8168312,Rider_Id_284,9
-Order_No_9285,User_Id_2260,Bike,1,Personal,12,5,3:59:32 PM,12,5,4:29:58 PM,12,5,4:31:49 PM,12,5,4:57:19 PM,12,5,5:37:03 PM,7,25.5,,-1.2882850000000001,36.815912,-1.2559559999999999,36.772901,Rider_Id_101,2384
-Order_No_15764,User_Id_393,Bike,3,Business,20,3,10:41:06 AM,20,3,10:41:26 AM,20,3,10:42:50 AM,20,3,10:56:11 AM,20,3,11:13:44 AM,7,26.6,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_145,1053
-Order_No_13870,User_Id_517,Bike,3,Business,9,5,10:26:50 AM,9,5,10:27:38 AM,9,5,10:46:29 AM,9,5,10:54:22 AM,9,5,11:30:31 AM,14,25.5,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_608,2169
-Order_No_18760,User_Id_2982,Bike,3,Business,15,4,12:51:18 PM,15,4,12:51:59 PM,15,4,12:59:07 PM,15,4,1:21:46 PM,15,4,2:23:25 PM,19,24.2,,-1.300921,36.828195,-1.2239243,36.710118,Rider_Id_95,3699
-Order_No_13025,User_Id_2982,Bike,3,Business,13,4,3:03:48 PM,13,4,3:32:48 PM,13,4,3:43:36 PM,13,4,3:55:43 PM,13,4,4:29:51 PM,13,22.4,,-1.300921,36.828195,-1.2252722,36.8756717,Rider_Id_522,2048
-Order_No_7385,User_Id_2801,Bike,3,Business,14,5,12:40:36 PM,14,5,1:07:02 PM,14,5,1:13:05 PM,14,5,1:19:38 PM,14,5,1:49:02 PM,8,21.7,,-1.2674273999999999,36.787082700000006,-1.2879652,36.8203441,Rider_Id_709,1764
-Order_No_5851,User_Id_1932,Bike,3,Business,2,1,12:41:32 PM,2,1,12:42:23 PM,2,1,12:49:38 PM,2,1,1:05:51 PM,2,1,1:14:49 PM,2,19.4,,-1.2874711,36.764620799999996,-1.2985986999999999,36.7809119,Rider_Id_192,538
-Order_No_9856,User_Id_3599,Bike,3,Business,3,5,2:03:02 PM,3,5,2:27:21 PM,3,5,3:00:27 PM,3,5,3:18:54 PM,3,5,3:56:50 PM,14,,,-1.3302996,36.8707935,-1.2886434,36.770986799999996,Rider_Id_519,2276
-Order_No_2735,User_Id_2200,Bike,3,Business,29,7,11:50:29 AM,29,7,11:55:25 AM,29,7,12:11:06 PM,29,7,12:18:53 PM,29,7,12:44:28 PM,12,22.0,,-1.290894,36.822971,-1.235348,36.760016,Rider_Id_105,1535
-Order_No_291,User_Id_552,Bike,3,Business,10,1,4:57:32 PM,10,1,5:17:02 PM,10,1,5:48:16 PM,10,1,5:58:08 PM,10,1,6:34:16 PM,23,19.0,,-1.3472066,36.7692638,-1.3562374,36.904295399999995,Rider_Id_55,2168
-Order_No_22113,User_Id_1340,Bike,3,Business,25,5,11:33:44 AM,25,5,11:33:54 AM,25,5,11:35:52 AM,25,5,11:42:21 AM,25,5,12:24:10 PM,17,26.4,,-1.2707178999999997,36.7979713,-1.3167218,36.8852763,Rider_Id_19,2509
-Order_No_23706,User_Id_113,Bike,1,Business,7,1,12:46:14 PM,7,1,12:46:32 PM,7,1,12:46:38 PM,7,1,12:56:43 PM,7,1,12:56:52 PM,2,21.6,,-1.3018758,36.75908629999999,-1.2963097,36.7688221,Rider_Id_900,9
-Order_No_12819,User_Id_2557,Bike,1,Personal,19,5,10:03:55 AM,19,5,10:16:34 AM,19,5,10:22:19 AM,19,5,10:26:05 AM,19,5,10:42:09 AM,3,20.0,,-1.2944457,36.7923516,-1.2969293,36.8027733,Rider_Id_299,964
-Order_No_19677,User_Id_62,Bike,3,Business,15,1,1:31:52 PM,15,1,2:21:22 PM,15,1,2:35:22 PM,15,1,2:39:23 PM,15,1,2:51:57 PM,6,,,-1.2967108,36.787113500000004,-1.286925,36.821915999999995,Rider_Id_845,754
-Order_No_11714,User_Id_868,Bike,3,Business,3,3,3:20:38 PM,3,3,3:21:14 PM,3,3,3:28:44 PM,3,3,3:40:10 PM,3,3,3:54:57 PM,5,27.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_65,887
-Order_No_9438,User_Id_704,Bike,3,Business,18,1,1:27:53 PM,18,1,1:28:00 PM,18,1,1:28:04 PM,18,1,1:42:57 PM,18,1,2:32:23 PM,17,17.4,,-1.2860183,36.8975338,-1.2592428,36.7894541,Rider_Id_397,2966
-Order_No_22421,User_Id_1724,Bike,3,Business,29,2,4:13:22 PM,29,2,4:14:34 PM,29,2,4:26:12 PM,29,2,4:27:25 PM,29,2,4:46:13 PM,7,23.8,,-1.2553615,36.789537599999996,-1.2729643999999998,36.7938653,Rider_Id_937,1128
-Order_No_10896,User_Id_2869,Bike,3,Business,3,1,1:02:22 PM,3,1,1:04:12 PM,3,1,1:09:47 PM,3,1,1:20:09 PM,3,1,1:36:18 PM,3,21.4,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_870,969
-Order_No_8799,User_Id_2828,Bike,1,Business,9,1,3:59:12 PM,9,1,3:59:59 PM,9,1,4:12:09 PM,9,1,5:00:36 PM,9,1,5:09:39 PM,10,20.2,,-1.2976940000000001,36.749788,-1.2644742,36.808345700000004,Rider_Id_541,543
-Order_No_24228,User_Id_1329,Bike,3,Business,2,5,4:51:45 PM,2,5,5:09:41 PM,2,5,5:21:46 PM,2,5,5:23:01 PM,2,5,7:09:32 PM,5,24.1,,-1.273056,36.811298,-1.283016,36.801959499999995,Rider_Id_287,6391
-Order_No_1817,User_Id_3521,Bike,3,Business,25,1,9:25:49 AM,25,1,9:26:41 AM,25,1,9:31:48 AM,25,1,9:39:10 AM,25,1,10:01:32 AM,5,,,-1.2696908000000002,36.81519,-1.2615888000000002,36.7928732,Rider_Id_205,1342
-Order_No_17019,User_Id_867,Bike,3,Business,13,4,12:20:28 PM,13,4,12:21:25 PM,13,4,12:41:24 PM,13,4,12:49:31 PM,13,4,1:28:33 PM,9,22.1,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_827,2342
-Order_No_5950,User_Id_2532,Bike,3,Business,13,2,4:19:37 PM,13,2,4:28:18 PM,13,2,5:05:42 PM,13,2,5:37:13 PM,13,2,6:36:54 PM,8,23.7,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_469,3581
-Order_No_12249,User_Id_136,Bike,3,Business,21,1,3:26:32 PM,21,1,3:46:08 PM,21,1,4:14:25 PM,21,1,4:21:00 PM,21,1,5:03:26 PM,14,21.8,,-1.3326173000000001,36.71462329999999,-1.3233496,36.8139159,Rider_Id_791,2546
-Order_No_5711,User_Id_2066,Bike,3,Business,18,4,9:41:11 AM,18,4,9:44:22 AM,18,4,9:52:40 AM,18,4,10:06:22 AM,18,4,10:25:52 AM,7,,,-1.3210188999999999,36.841055100000005,-1.2795796,36.819062,Rider_Id_96,1170
-Order_No_18923,User_Id_412,Bike,3,Business,13,4,2:30:23 PM,13,4,2:30:47 PM,13,4,2:33:23 PM,13,4,2:41:54 PM,13,4,3:01:07 PM,8,23.2,,-1.307143,36.825009,-1.266837,36.799248999999996,Rider_Id_211,1153
-Order_No_28111,User_Id_346,Bike,3,Business,28,4,10:50:43 AM,28,4,10:53:55 AM,28,4,11:03:26 AM,28,4,11:06:50 AM,28,4,11:28:19 AM,6,,,-1.2885528999999998,36.816014100000004,-1.3039695,36.783826700000006,Rider_Id_21,1289
-Order_No_12430,User_Id_3686,Bike,3,Business,11,4,2:35:54 PM,11,4,2:36:17 PM,11,4,2:50:35 PM,11,4,2:53:04 PM,11,4,3:10:27 PM,4,27.2,,-1.2551895,36.7822034,-1.2747247,36.8150014,Rider_Id_903,1043
-Order_No_14274,User_Id_187,Bike,3,Business,7,1,11:49:07 AM,7,1,12:08:55 PM,7,1,12:23:29 PM,7,1,12:42:24 PM,7,1,12:57:37 PM,8,24.8,,-1.3472066,36.7692638,-1.3165542,36.700067,Rider_Id_874,913
-Order_No_18026,User_Id_3331,Bike,3,Business,16,5,2:50:38 PM,16,5,3:13:29 PM,16,5,3:52:55 PM,16,5,3:56:31 PM,16,5,5:04:22 PM,8,,,-1.2551895,36.7822034,-1.2987976,36.829477000000004,Rider_Id_554,4071
-Order_No_2185,User_Id_432,Bike,3,Business,24,5,9:49:22 AM,24,5,9:49:37 AM,24,5,9:50:56 AM,24,5,10:37:11 AM,24,5,10:37:19 AM,3,21.4,,-1.3012008,36.764868,-1.2846863000000002,36.738305100000005,Rider_Id_855,8
-Order_No_4046,User_Id_2086,Bike,1,Personal,30,6,11:27:24 AM,30,6,11:28:50 AM,30,6,11:47:12 AM,30,6,11:50:36 AM,30,6,12:05:15 PM,7,22.2,,-1.2994194,36.7647917,-1.2657709,36.730574600000004,Rider_Id_312,879
-Order_No_4039,User_Id_1336,Bike,3,Business,22,4,1:06:02 PM,22,4,1:25:22 PM,22,4,1:49:35 PM,22,4,1:53:11 PM,22,4,2:48:35 PM,8,26.5,,-1.2891921999999998,36.825189,-1.2615888000000002,36.7928732,Rider_Id_554,3324
-Order_No_22022,User_Id_248,Bike,3,Business,19,3,12:06:30 PM,19,3,12:08:50 PM,19,3,12:19:10 PM,19,3,12:44:48 PM,19,3,1:00:25 PM,6,22.2,,-1.2971877,36.8270159,-1.304735,36.784605400000004,Rider_Id_518,937
-Order_No_8017,User_Id_552,Bike,3,Business,15,5,4:29:12 PM,15,5,4:29:25 PM,15,5,4:29:29 PM,15,5,4:40:19 PM,15,5,5:06:33 PM,7,27.6,,-1.3562374,36.904295399999995,-1.335477,36.930829700000004,Rider_Id_519,1574
-Order_No_663,User_Id_1404,Bike,2,Personal,11,5,10:03:14 AM,11,5,10:04:09 AM,11,5,10:26:21 AM,11,5,10:35:25 AM,11,5,10:58:49 AM,10,20.8,,-1.3211235000000001,36.7765164,-1.2869041,36.8240057,Rider_Id_338,1404
-Order_No_18260,User_Id_3291,Bike,3,Business,6,3,1:14:39 PM,6,3,1:16:11 PM,6,3,1:25:53 PM,6,3,1:35:06 PM,6,3,1:48:44 PM,4,27.6,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_535,818
-Order_No_19114,User_Id_2685,Bike,1,Personal,18,1,8:53:30 AM,18,1,8:53:52 AM,18,1,8:58:43 AM,18,1,9:14:42 AM,18,1,10:05:07 AM,21,15.0,0.3,-1.2972816999999999,36.7795831,-1.3433181,36.9138018,Rider_Id_354,3025
-Order_No_25434,User_Id_3108,Bike,3,Business,4,3,12:26:05 PM,4,3,12:26:17 PM,4,3,12:39:07 PM,4,3,1:13:42 PM,4,3,1:55:26 PM,20,17.0,,-1.2551895,36.7822034,-1.1672795,36.8245972,Rider_Id_116,2504
-Order_No_10258,User_Id_3201,Bike,3,Business,6,2,2:42:08 PM,6,2,2:59:04 PM,6,2,3:08:05 PM,6,2,3:10:00 PM,6,2,3:32:55 PM,14,27.4,,-1.2232123,36.8932547,-1.2649605,36.798177700000004,Rider_Id_456,1375
-Order_No_850,User_Id_393,Bike,3,Business,6,3,1:37:11 PM,6,3,1:44:23 PM,6,3,1:53:20 PM,6,3,2:01:34 PM,6,3,2:19:59 PM,11,27.6,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_330,1105
-Order_No_10922,User_Id_3283,Bike,3,Business,19,4,12:58:08 PM,19,4,12:58:32 PM,19,4,1:04:20 PM,19,4,1:19:27 PM,19,4,1:28:45 PM,5,20.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_101,558
-Order_No_12625,User_Id_3201,Bike,3,Business,18,1,4:21:30 PM,18,1,4:22:38 PM,18,1,4:38:29 PM,18,1,4:46:28 PM,18,1,5:13:21 PM,3,28.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_647,1613
-Order_No_26736,User_Id_2701,Bike,3,Business,24,1,1:34:20 PM,24,1,1:35:10 PM,24,1,1:48:55 PM,24,1,2:01:39 PM,24,1,2:33:07 PM,12,,,-1.303031,36.758577,-1.2765736,36.851364600000004,Rider_Id_88,1888
-Order_No_18867,User_Id_3599,Bike,3,Business,5,5,2:32:19 PM,5,5,2:34:21 PM,5,5,2:46:36 PM,5,5,3:05:05 PM,5,5,3:50:08 PM,13,27.4,,-1.3302996,36.8707935,-1.2567393,36.814323200000004,Rider_Id_548,2703
-Order_No_10974,User_Id_1559,Bike,2,Personal,11,4,1:13:04 PM,11,4,1:13:39 PM,11,4,1:28:18 PM,11,4,1:48:13 PM,11,4,2:16:48 PM,14,26.7,,-1.2514753,36.7870287,-1.2287936000000002,36.826978999999994,Rider_Id_882,1715
-Order_No_19903,User_Id_635,Bike,3,Business,6,4,4:39:56 PM,6,4,4:40:39 PM,6,4,4:48:04 PM,6,4,4:49:58 PM,6,4,5:01:42 PM,5,22.4,,-1.2857763999999998,36.8236622,-1.2599563,36.799343900000004,Rider_Id_676,704
-Order_No_28049,User_Id_2737,Bike,1,Personal,18,4,5:42:01 PM,18,4,5:44:05 PM,18,4,5:44:31 PM,18,4,6:01:36 PM,18,4,6:12:03 PM,3,23.7,,-1.2666534,36.7935346,-1.2664491000000002,36.8102685,Rider_Id_519,627
-Order_No_18721,User_Id_1352,Bike,3,Personal,5,2,9:37:19 AM,5,2,9:37:41 AM,5,2,9:46:18 AM,5,2,9:55:51 AM,5,2,10:33:03 AM,19,17.9,4.1,-1.2655306,36.828148,-1.3459625,36.9073925,Rider_Id_813,2232
-Order_No_14291,User_Id_867,Bike,3,Business,19,2,12:48:34 PM,19,2,12:49:52 PM,19,2,12:55:21 PM,19,2,1:34:36 PM,19,2,1:34:39 PM,10,29.9,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_953,3
-Order_No_24848,User_Id_909,Bike,3,Business,10,2,10:51:36 AM,10,2,10:57:48 AM,10,2,11:15:16 AM,10,2,11:18:02 AM,10,2,11:37:13 AM,7,20.1,,-1.225322,36.80855,-1.2597034,36.8003065,Rider_Id_726,1151
-Order_No_27748,User_Id_1209,Bike,3,Business,17,4,1:17:28 PM,17,4,1:40:29 PM,17,4,1:57:35 PM,17,4,2:13:26 PM,17,4,2:18:54 PM,4,28.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_714,328
-Order_No_21396,User_Id_2323,Bike,1,Personal,20,4,1:55:58 PM,20,4,1:59:15 PM,20,4,2:00:57 PM,20,4,2:06:03 PM,20,4,2:21:17 PM,6,27.8,,-1.2621576,36.8103473,-1.2985749,36.8088,Rider_Id_88,914
-Order_No_12815,User_Id_718,Bike,1,Business,6,4,1:04:37 PM,6,4,1:04:57 PM,6,4,1:05:19 PM,6,4,1:17:26 PM,6,4,1:53:26 PM,14,21.9,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_79,2160
-Order_No_1741,User_Id_877,Bike,2,Personal,13,7,7:20:33 PM,13,7,7:20:42 PM,13,7,7:29:56 PM,13,7,7:39:09 PM,13,7,7:46:14 PM,4,19.0,0.3,-1.2580765,36.8024166,-1.2407613000000002,36.7807351,Rider_Id_103,425
-Order_No_3783,User_Id_3647,Bike,3,Business,23,2,3:01:58 PM,23,2,3:19:26 PM,23,2,3:29:03 PM,23,2,3:33:50 PM,23,2,3:57:16 PM,4,,,-1.2726389999999999,36.794723,-1.2674482,36.8076994,Rider_Id_155,1406
-Order_No_25713,User_Id_1195,Bike,3,Business,4,2,11:55:11 AM,4,2,12:41:41 PM,4,2,12:42:38 PM,4,2,12:50:17 PM,4,2,1:28:23 PM,15,19.4,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_523,2286
-Order_No_19012,User_Id_1642,Bike,3,Business,27,2,12:07:45 PM,27,2,12:07:58 PM,27,2,12:11:57 PM,27,2,12:31:23 PM,27,2,12:36:18 PM,3,23.6,,-1.2672782,36.804143599999996,-1.2726506000000002,36.7960153,Rider_Id_56,295
-Order_No_2437,User_Id_640,Bike,3,Business,17,3,10:31:43 AM,17,3,10:32:06 AM,17,3,10:39:51 AM,17,3,10:44:35 AM,17,3,10:54:32 AM,3,,,-1.2969741000000001,36.7856612,-1.2945106000000002,36.7728398,Rider_Id_819,597
-Order_No_13142,User_Id_1927,Bike,3,Business,22,5,3:56:07 PM,22,5,4:23:06 PM,22,5,4:47:30 PM,22,5,4:51:50 PM,22,5,5:28:59 PM,23,28.4,,-1.3472066,36.7692638,-1.3562374,36.904295399999995,Rider_Id_343,2229
-Order_No_5856,User_Id_2026,Bike,3,Business,17,3,7:06:53 PM,17,3,7:07:15 PM,17,3,7:20:45 PM,17,3,8:09:18 PM,17,3,8:09:24 PM,12,,,-1.2584143,36.8048002,-1.3094481000000002,36.8652486,Rider_Id_119,6
-Order_No_26661,User_Id_1205,Bike,3,Business,16,2,3:15:59 PM,16,2,3:16:25 PM,16,2,3:26:11 PM,16,2,3:37:20 PM,16,2,3:49:31 PM,3,,,-1.2495931,36.80650870000001,-1.2549259,36.7919494,Rider_Id_858,731
-Order_No_3525,User_Id_393,Bike,3,Business,25,3,11:53:11 AM,25,3,11:55:29 AM,25,3,12:00:33 PM,25,3,12:26:01 PM,25,3,1:14:26 PM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_687,2905
-Order_No_8051,User_Id_567,Bike,3,Business,31,4,8:54:10 AM,31,4,8:55:53 AM,31,4,9:06:01 AM,31,4,9:09:46 AM,31,4,9:23:45 AM,8,17.7,,-1.296797,36.776452500000005,-1.2658334999999998,36.804286700000006,Rider_Id_735,839
-Order_No_10609,User_Id_1715,Bike,3,Business,29,5,3:43:45 PM,29,5,3:46:11 PM,29,5,4:12:09 PM,29,5,4:14:05 PM,29,5,4:31:23 PM,4,21.1,,-1.2608993,36.799246000000004,-1.2764851000000002,36.8007126,Rider_Id_407,1038
-Order_No_12125,User_Id_3291,Bike,3,Business,22,6,1:18:23 PM,22,6,1:18:45 PM,22,6,1:29:10 PM,22,6,1:30:35 PM,22,6,1:43:04 PM,4,21.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_103,749
-Order_No_20674,User_Id_633,Bike,3,Business,24,4,1:34:41 PM,24,4,1:40:20 PM,24,4,1:55:19 PM,24,4,1:57:19 PM,24,4,2:08:18 PM,5,28.1,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_576,659
-Order_No_18732,User_Id_3091,Bike,3,Business,30,2,1:27:38 PM,30,2,1:28:12 PM,30,2,1:30:14 PM,30,2,1:49:27 PM,30,2,3:04:50 PM,6,,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_812,4523
-Order_No_26503,User_Id_393,Bike,3,Business,4,2,3:35:51 PM,4,2,3:53:03 PM,4,2,3:58:55 PM,4,2,4:01:50 PM,4,2,4:17:01 PM,5,22.3,,-1.2983042,36.8261141,-1.3228003000000002,36.8306435,Rider_Id_211,911
-Order_No_10790,User_Id_1971,Bike,3,Personal,30,4,3:34:05 PM,30,4,3:38:38 PM,30,4,4:09:54 PM,30,4,4:16:07 PM,30,4,4:28:06 PM,3,,,-1.3142268999999998,36.839245,-1.3202459999999998,36.836938399999994,Rider_Id_643,719
-Order_No_27899,User_Id_393,Bike,3,Business,2,4,12:17:09 PM,2,4,12:17:19 PM,2,4,12:17:23 PM,2,4,1:37:03 PM,2,4,1:37:05 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_619,2
-Order_No_13422,User_Id_1500,Bike,3,Business,4,1,8:54:53 AM,4,1,8:55:52 AM,4,1,9:28:47 AM,4,1,9:42:57 AM,4,1,10:12:48 AM,8,21.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_590,1791
-Order_No_9979,User_Id_265,Bike,3,Business,1,4,9:05:09 AM,1,4,9:05:46 AM,1,4,9:13:12 AM,1,4,9:14:33 AM,1,4,9:26:56 AM,7,,,-1.2996632,36.789840999999996,-1.300921,36.828195,Rider_Id_88,743
-Order_No_11540,User_Id_1049,Bike,3,Personal,5,2,10:01:34 AM,5,2,10:02:36 AM,5,2,10:12:05 AM,5,2,10:29:52 AM,5,2,10:47:18 AM,8,21.1,2.1,-1.300334,36.797495700000006,-1.258685,36.805292,Rider_Id_267,1046
-Order_No_5863,User_Id_306,Bike,1,Personal,13,5,2:46:20 PM,13,5,2:47:41 PM,13,5,3:06:13 PM,13,5,3:10:21 PM,13,5,3:55:50 PM,11,19.2,,-1.3157973,36.815148,-1.2473038,36.788470700000005,Rider_Id_427,2729
-Order_No_25387,User_Id_1568,Bike,3,Business,14,5,10:53:22 AM,14,5,10:53:41 AM,14,5,11:02:17 AM,14,5,11:12:59 AM,14,5,11:30:18 AM,7,24.4,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_726,1039
-Order_No_26567,User_Id_2330,Bike,3,Business,31,4,12:47:16 PM,31,4,1:35:04 PM,31,4,2:15:19 PM,31,4,2:18:24 PM,31,4,2:56:03 PM,5,28.9,,-1.2671943,36.8062533,-1.293082,36.8278578,Rider_Id_362,2259
-Order_No_21021,User_Id_517,Bike,3,Business,28,5,1:21:34 PM,28,5,1:23:11 PM,28,5,1:23:15 PM,28,5,2:21:36 PM,28,5,2:21:42 PM,4,25.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,6
-Order_No_9501,User_Id_633,Bike,3,Business,14,1,2:58:15 PM,14,1,3:00:53 PM,14,1,3:00:59 PM,14,1,3:15:03 PM,14,1,3:15:07 PM,4,25.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_239,4
-Order_No_19963,User_Id_2763,Bike,3,Business,10,5,3:09:22 PM,10,5,4:48:14 PM,10,5,5:04:25 PM,10,5,5:09:05 PM,10,5,5:28:07 PM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_815,1142
-Order_No_3827,User_Id_3506,Bike,1,Personal,2,2,1:09:38 PM,2,2,1:10:10 PM,2,2,1:11:01 PM,2,2,1:26:19 PM,2,2,1:51:04 PM,14,20.9,,-1.2563531000000001,36.7896742,-1.3104733999999998,36.7363698,Rider_Id_446,1485
-Order_No_657,User_Id_3222,Bike,3,Business,1,1,3:13:25 PM,1,1,3:14:14 PM,1,1,3:15:59 PM,1,1,3:17:23 PM,1,1,3:58:44 PM,8,25.6,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_447,2481
-Order_No_24430,User_Id_265,Bike,3,Business,27,1,12:28:54 PM,27,1,12:29:18 PM,27,1,12:33:32 PM,27,1,1:46:55 PM,27,1,1:47:12 PM,21,,,-1.300921,36.828195,-1.2434951,36.6760986,Rider_Id_927,17
-Order_No_23423,User_Id_1222,Bike,3,Personal,15,1,10:42:25 AM,15,1,10:42:38 AM,15,1,10:45:26 AM,15,1,10:49:41 AM,15,1,11:01:45 AM,6,,,-1.2707178999999997,36.7979713,-1.2969293,36.8027733,Rider_Id_467,724
-Order_No_1937,User_Id_1209,Bike,3,Business,10,5,9:55:11 AM,10,5,9:55:29 AM,10,5,10:10:37 AM,10,5,10:14:27 AM,10,5,10:21:34 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,427
-Order_No_24965,User_Id_2371,Bike,3,Personal,5,3,12:10:29 PM,5,3,12:12:18 PM,5,3,12:20:19 PM,5,3,1:41:10 PM,5,3,2:08:02 PM,12,18.9,0.3,-1.2844072,36.824729600000005,-1.2636435,36.9087594,Rider_Id_359,1612
-Order_No_4555,User_Id_3674,Bike,3,Business,29,5,11:01:07 AM,29,5,11:02:42 AM,29,5,11:05:11 AM,29,5,11:15:55 AM,29,5,11:43:14 AM,10,26.6,,-1.2551895,36.7822034,-1.307376,36.813404,Rider_Id_926,1639
-Order_No_18736,User_Id_2154,Bike,3,Business,26,5,2:58:32 PM,26,5,3:13:04 PM,26,5,3:25:50 PM,26,5,3:35:35 PM,26,5,4:34:55 PM,6,,,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_890,3560
-Order_No_21320,User_Id_3647,Bike,3,Business,21,4,5:00:09 PM,21,4,5:01:07 PM,21,4,5:34:01 PM,21,4,5:37:06 PM,21,4,6:11:00 PM,10,28.5,,-1.2726389999999999,36.794723,-1.315343,36.817992,Rider_Id_269,2034
-Order_No_4407,User_Id_2509,Bike,3,Business,17,1,2:32:52 PM,17,1,2:35:45 PM,17,1,3:01:36 PM,17,1,3:48:08 PM,17,1,4:18:03 PM,6,24.8,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_795,1795
-Order_No_9375,User_Id_3283,Bike,3,Business,18,1,11:40:56 AM,18,1,11:41:21 AM,18,1,11:42:43 AM,18,1,12:07:04 PM,18,1,12:18:23 PM,5,28.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_547,679
-Order_No_20801,User_Id_1369,Bike,2,Personal,13,5,3:45:37 PM,13,5,3:46:34 PM,13,5,3:56:17 PM,13,5,4:11:57 PM,13,5,4:31:08 PM,7,19.1,,-1.2915306999999998,36.790586499999996,-1.3011736999999999,36.743672,Rider_Id_312,1151
-Order_No_25244,User_Id_136,Bike,3,Business,25,2,10:47:30 AM,25,2,10:47:36 AM,25,2,10:58:48 AM,25,2,11:04:41 AM,25,2,11:55:12 AM,29,23.9,,-1.3326173000000001,36.71462329999999,-1.3094447,36.9107706,Rider_Id_465,3031
-Order_No_4634,User_Id_1402,Bike,3,Business,27,1,11:27:36 AM,27,1,11:32:48 AM,27,1,12:05:20 PM,27,1,12:07:15 PM,27,1,12:07:17 PM,2,21.0,,-1.2813012,36.832396200000005,-1.2868950000000001,36.8150045,Rider_Id_27,2
-Order_No_13037,User_Id_71,Bike,3,Business,31,1,9:02:48 AM,31,1,9:45:28 AM,31,1,9:51:45 AM,31,1,10:03:57 AM,31,1,11:29:31 AM,5,18.5,3.0,-1.281754,36.8324229,-1.2689015,36.7986106,Rider_Id_237,5134
-Order_No_27582,User_Id_3689,Bike,3,Business,12,2,12:54:15 PM,12,2,12:54:29 PM,12,2,1:00:17 PM,12,2,1:08:12 PM,12,2,2:03:16 PM,3,21.0,,-1.2601708999999999,36.8002238,-1.2697141,36.798177700000004,Rider_Id_334,3304
-Order_No_19566,User_Id_3496,Bike,3,Business,9,3,4:34:06 PM,9,3,4:34:19 PM,9,3,4:49:02 PM,9,3,4:51:32 PM,9,3,5:03:55 PM,5,24.3,,-1.300921,36.828195,-1.3052838,36.85455379999999,Rider_Id_95,743
-Order_No_8713,User_Id_2313,Bike,2,Personal,28,5,4:29:26 PM,28,5,4:29:52 PM,28,5,4:39:44 PM,28,5,4:42:49 PM,28,5,5:01:53 PM,12,,,-1.2736442,36.8149232,-1.2238103,36.8772588,Rider_Id_754,1144
-Order_No_12412,User_Id_1362,Bike,1,Personal,10,4,7:27:32 PM,10,4,7:27:53 PM,10,4,7:46:17 PM,10,4,7:49:09 PM,10,4,8:13:57 PM,11,18.1,3.9,-1.2627555,36.792955299999996,-1.2105983999999999,36.8109774,Rider_Id_881,1488
-Order_No_3929,User_Id_868,Bike,3,Business,24,3,11:52:29 AM,24,3,12:02:31 PM,24,3,12:05:03 PM,24,3,12:24:10 PM,24,3,12:33:37 PM,6,,,-1.3033561000000002,36.8163631,-1.265715,36.823815,Rider_Id_19,567
-Order_No_9272,User_Id_2454,Bike,3,Business,7,5,3:02:10 PM,7,5,3:08:41 PM,7,5,3:19:59 PM,7,5,3:22:21 PM,7,5,3:48:58 PM,4,,,-1.2942023,36.7958179,-1.2867538,36.7765986,Rider_Id_429,1597
-Order_No_17562,User_Id_1342,Bike,3,Business,23,2,2:24:18 PM,23,2,2:27:06 PM,23,2,2:27:14 PM,23,2,3:02:32 PM,23,2,3:36:58 PM,9,18.9,,-1.28878,36.8168312,-1.3025826,36.7670807,Rider_Id_928,2066
-Order_No_17968,User_Id_3560,Bike,3,Business,28,4,3:00:30 PM,28,4,3:02:13 PM,28,4,3:22:18 PM,28,4,3:37:53 PM,28,4,3:42:37 PM,1,27.4,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_411,284
-Order_No_14066,User_Id_1389,Bike,3,Business,12,2,9:53:17 AM,12,2,9:53:35 AM,12,2,10:18:44 AM,12,2,10:45:59 AM,12,2,11:15:02 AM,22,28.0,,-1.3014461000000002,36.7661381,-1.207546,36.7913976,Rider_Id_315,1743
-Order_No_16831,User_Id_517,Bike,3,Business,7,5,8:56:12 AM,7,5,8:56:25 AM,7,5,8:58:50 AM,7,5,9:03:27 AM,7,5,9:36:42 AM,14,20.8,0.9,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,1995
-Order_No_3645,User_Id_2933,Bike,2,Personal,28,5,9:33:17 AM,28,5,9:34:05 AM,28,5,9:54:40 AM,28,5,10:01:32 AM,28,5,10:23:03 AM,6,20.4,,-1.2721028,36.795348499999996,-1.288002,36.827379,Rider_Id_208,1291
-Order_No_11133,User_Id_3506,Bike,1,Personal,29,5,3:25:09 PM,29,5,3:36:10 PM,29,5,3:51:42 PM,29,5,3:53:27 PM,29,5,4:15:51 PM,16,21.2,,-1.3052671999999998,36.753853899999996,-1.329568,36.8520825,Rider_Id_312,1344
-Order_No_16373,User_Id_3194,Bike,3,Business,29,3,11:04:02 AM,29,3,11:08:09 AM,29,3,11:20:02 AM,29,3,11:31:02 AM,29,3,12:10:59 PM,9,,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_42,2397
-Order_No_7926,User_Id_3283,Bike,3,Business,20,1,4:10:03 PM,20,1,4:10:38 PM,20,1,4:17:33 PM,20,1,4:20:38 PM,20,1,4:32:19 PM,5,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_138,701
-Order_No_3679,User_Id_3439,Bike,3,Business,23,4,9:22:20 AM,23,4,9:23:10 AM,23,4,9:30:46 AM,23,4,9:34:40 AM,23,4,10:27:10 AM,25,,,-1.2393157,36.766887700000005,-1.1481468000000001,36.651587899999996,Rider_Id_198,3150
-Order_No_12066,User_Id_302,Bike,3,Business,21,5,8:27:20 PM,21,5,8:37:55 PM,21,5,8:55:46 PM,21,5,9:06:16 PM,21,5,9:25:10 PM,3,18.0,,-1.2649605,36.798177700000004,-1.2513861000000002,36.8174281,Rider_Id_489,1134
-Order_No_26654,User_Id_3586,Bike,3,Business,15,1,9:12:19 AM,15,1,9:12:45 AM,15,1,9:14:20 AM,15,1,9:33:54 AM,15,1,9:49:58 AM,7,,,-1.2591018999999999,36.800577000000004,-1.275442,36.766020700000006,Rider_Id_467,964
-Order_No_24274,User_Id_2528,Bike,3,Business,3,1,1:10:12 PM,3,1,1:15:01 PM,3,1,1:27:45 PM,3,1,1:32:55 PM,3,1,1:51:46 PM,6,23.3,,-1.2551895,36.7822034,-1.281223,36.8239,Rider_Id_790,1131
-Order_No_12126,User_Id_374,Bike,2,Personal,10,7,8:03:09 PM,10,7,8:04:15 PM,10,7,8:25:11 PM,10,7,8:27:28 PM,10,7,9:02:50 PM,5,17.4,,-1.2831463,36.76360220000001,-1.2900726999999999,36.7890059,Rider_Id_334,2122
-Order_No_24739,User_Id_2271,Bike,3,Business,7,5,4:03:15 PM,7,5,4:09:30 PM,7,5,4:22:46 PM,7,5,4:27:04 PM,7,5,5:31:59 PM,17,,,-1.303596,36.778378000000004,-1.293515,36.897607,Rider_Id_709,3895
-Order_No_7445,User_Id_2344,Bike,3,Business,14,1,12:11:07 PM,14,1,12:12:49 PM,14,1,12:57:05 PM,14,1,12:58:34 PM,14,1,1:26:19 PM,8,23.4,,-1.2551895,36.7822034,-1.291282,36.798229,Rider_Id_817,1665
-Order_No_6691,User_Id_1502,Bike,3,Business,20,3,12:24:47 PM,20,3,12:25:42 PM,20,3,1:55:42 PM,20,3,2:08:46 PM,20,3,2:40:55 PM,11,28.4,,-1.2859912,36.8756811,-1.2232123,36.8932547,Rider_Id_900,1929
-Order_No_16411,User_Id_537,Bike,2,Personal,10,4,8:57:00 AM,10,4,8:57:51 AM,10,4,9:10:12 AM,10,4,9:16:12 AM,10,4,9:57:13 AM,9,18.5,,-1.2702844,36.880320899999994,-1.3100117,36.8226483,Rider_Id_618,2461
-Order_No_19244,User_Id_2504,Bike,3,Business,28,2,11:55:02 AM,28,2,11:55:07 AM,28,2,12:08:58 PM,28,2,12:18:01 PM,28,2,12:26:27 PM,5,17.4,,-1.328259,36.860975,-1.3332749,36.870814700000004,Rider_Id_213,506
-Order_No_14134,User_Id_867,Bike,3,Business,6,2,1:32:41 PM,6,2,1:32:58 PM,6,2,1:36:38 PM,6,2,1:38:14 PM,6,2,2:20:34 PM,14,27.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_515,2540
-Order_No_21598,User_Id_1920,Bike,3,Business,13,2,1:31:10 PM,13,2,1:48:26 PM,13,2,1:51:30 PM,13,2,1:59:29 PM,13,2,2:25:12 PM,10,26.3,,-1.2551895,36.7822034,-1.2982578000000002,36.84439520000001,Rider_Id_518,1543
-Order_No_13978,User_Id_3426,Bike,3,Business,9,6,10:54:28 AM,9,6,10:55:00 AM,9,6,11:00:47 AM,9,6,11:06:11 AM,9,6,11:19:45 AM,4,27.5,,-1.3012008,36.764868,-1.2985559,36.783791,Rider_Id_132,814
-Order_No_10263,User_Id_2140,Bike,1,Personal,4,2,4:34:19 PM,4,2,4:59:24 PM,4,2,5:08:54 PM,4,2,5:34:38 PM,4,2,5:40:30 PM,4,21.3,,-1.2790175,36.8151778,-1.2545163000000001,36.8243717,Rider_Id_620,352
-Order_No_15984,User_Id_685,Bike,3,Business,6,1,3:33:33 PM,6,1,3:36:41 PM,6,1,3:44:31 PM,6,1,3:48:13 PM,6,1,4:21:53 PM,13,,,-1.3255316000000001,36.711542,-1.2726506000000002,36.7960153,Rider_Id_654,2020
-Order_No_3600,User_Id_3091,Bike,3,Business,31,4,1:09:20 PM,31,4,1:09:53 PM,31,4,1:13:13 PM,31,4,1:59:59 PM,31,4,2:21:46 PM,9,28.7,,-1.3012008,36.764868,-1.2571472,36.795063299999995,Rider_Id_40,1307
-Order_No_3635,User_Id_970,Bike,3,Business,24,4,11:17:16 AM,24,4,11:17:46 AM,24,4,11:26:23 AM,24,4,11:31:53 AM,24,4,12:06:44 PM,17,24.7,,-1.2551895,36.7822034,-1.2977643,36.8844591,Rider_Id_620,2091
-Order_No_13618,User_Id_3647,Bike,3,Business,7,4,4:28:22 PM,7,4,4:28:43 PM,7,4,4:39:25 PM,7,4,4:42:59 PM,7,4,5:05:51 PM,5,29.2,,-1.2726389999999999,36.794723,-1.2962832,36.784968299999996,Rider_Id_452,1372
-Order_No_18612,User_Id_379,Bike,1,Personal,7,2,1:59:23 PM,7,2,1:59:54 PM,7,2,2:23:42 PM,7,2,2:39:04 PM,7,2,3:42:29 PM,8,24.8,,-1.2655332000000001,36.8094648,-1.2729217,36.762482899999995,Rider_Id_229,3805
-Order_No_7986,User_Id_3524,Bike,2,Personal,10,2,12:45:50 PM,10,2,12:46:50 PM,10,2,12:50:55 PM,10,2,12:56:39 PM,10,2,1:38:59 PM,14,20.4,,-1.2809666000000002,36.8765961,-1.2841951999999999,36.8030328,Rider_Id_618,2540
-Order_No_17634,User_Id_478,Bike,3,Business,24,3,3:28:53 PM,24,3,3:34:28 PM,24,3,3:42:55 PM,24,3,3:48:32 PM,24,3,4:18:25 PM,18,,,-1.2174537,36.880190899999995,-1.2551895,36.7822034,Rider_Id_484,1793
-Order_No_8367,User_Id_868,Bike,3,Business,30,3,10:11:43 AM,30,3,10:12:43 AM,30,3,10:35:03 AM,30,3,10:46:13 AM,30,3,11:02:08 AM,9,24.8,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_377,955
-Order_No_14058,User_Id_212,Bike,3,Personal,31,2,11:18:09 AM,31,2,11:18:31 AM,31,2,11:23:42 AM,31,2,11:29:27 AM,31,2,11:45:16 AM,6,18.4,,-1.2666153,36.8015124,-1.2607568999999998,36.7747575,Rider_Id_519,949
-Order_No_17460,User_Id_970,Bike,3,Business,15,5,1:11:07 PM,15,5,1:13:47 PM,15,5,1:16:04 PM,15,5,1:30:20 PM,15,5,1:35:04 PM,3,26.2,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_446,284
-Order_No_14028,User_Id_1724,Bike,3,Business,8,4,3:34:18 PM,8,4,3:40:40 PM,8,4,4:39:15 PM,8,4,4:46:15 PM,8,4,5:03:26 PM,6,25.4,,-1.2551895,36.7822034,-1.2655306,36.828148,Rider_Id_646,1031
-Order_No_4294,User_Id_1724,Bike,3,Business,7,2,12:53:40 PM,7,2,1:06:24 PM,7,2,1:07:09 PM,7,2,1:19:56 PM,7,2,1:24:50 PM,4,23.0,,-1.2658373,36.8018598,-1.2551895,36.7822034,Rider_Id_51,294
-Order_No_22572,User_Id_3686,Bike,3,Business,27,3,9:34:21 AM,27,3,9:34:42 AM,27,3,9:37:53 AM,27,3,9:56:14 AM,27,3,11:04:39 AM,22,,,-1.2551895,36.7822034,-1.3190102,36.923069299999995,Rider_Id_800,4105
-Order_No_3291,User_Id_140,Bike,3,Personal,23,2,4:24:08 PM,23,2,4:34:39 PM,23,2,4:44:35 PM,23,2,4:49:38 PM,23,2,5:19:22 PM,9,,,-1.2944292,36.779356899999996,-1.2546209,36.8016025,Rider_Id_245,1784
-Order_No_23002,User_Id_2582,Bike,3,Business,27,3,9:15:08 AM,27,3,9:15:16 AM,27,3,9:20:17 AM,27,3,9:32:56 AM,27,3,10:07:57 AM,18,,,-1.3356493,36.892766200000004,-1.2551895,36.7822034,Rider_Id_379,2101
-Order_No_4423,User_Id_3669,Bike,3,Business,16,3,10:13:30 AM,16,3,10:20:08 AM,16,3,10:27:13 AM,16,3,10:48:44 AM,16,3,11:36:03 AM,19,23.0,,-1.2615888000000002,36.7928732,-1.2708104,36.910537,Rider_Id_379,2839
-Order_No_5788,User_Id_2982,Bike,3,Business,26,5,3:35:59 PM,26,5,3:42:16 PM,26,5,3:56:08 PM,26,5,4:02:01 PM,26,5,4:21:36 PM,7,,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_551,1175
-Order_No_22649,User_Id_1271,Bike,3,Business,20,3,11:28:58 AM,20,3,11:29:12 AM,20,3,11:39:12 AM,20,3,12:06:00 PM,20,3,12:48:20 PM,24,27.3,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_218,2540
-Order_No_22963,User_Id_835,Bike,3,Business,19,1,3:39:10 PM,19,1,3:43:20 PM,19,1,4:06:33 PM,19,1,4:13:35 PM,19,1,4:22:48 PM,1,25.3,,-1.284982,36.7863938,-1.2917868000000001,36.7872675,Rider_Id_322,553
-Order_No_23954,User_Id_706,Bike,3,Business,19,3,3:59:35 PM,19,3,4:00:30 PM,19,3,4:10:33 PM,19,3,4:19:32 PM,19,3,4:27:36 PM,4,25.6,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_632,484
-Order_No_26842,User_Id_2718,Bike,3,Business,13,2,2:51:47 PM,13,2,2:51:54 PM,13,2,3:06:02 PM,13,2,3:09:57 PM,13,2,3:21:45 PM,3,26.0,,-1.2668815,36.8017244,-1.2768326,36.8073804,Rider_Id_727,708
-Order_No_11743,User_Id_1500,Bike,3,Business,5,2,12:06:45 PM,5,2,12:08:33 PM,5,2,12:20:01 PM,5,2,12:24:36 PM,5,2,12:45:04 PM,10,25.5,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_411,1228
-Order_No_13437,User_Id_868,Bike,3,Business,13,4,3:52:09 PM,13,4,3:52:38 PM,13,4,3:54:35 PM,13,4,4:04:29 PM,13,4,4:16:28 PM,4,23.2,,-1.266837,36.799248999999996,-1.2663631000000002,36.8163329,Rider_Id_427,719
-Order_No_8156,User_Id_990,Bike,3,Business,8,1,11:30:41 AM,8,1,11:30:58 AM,8,1,11:50:28 AM,8,1,12:05:04 PM,8,1,12:50:29 PM,17,24.3,,-1.3164339,36.857888200000005,-1.3624272,36.7427843,Rider_Id_328,2725
-Order_No_3471,User_Id_2423,Bike,3,Business,26,5,8:54:33 AM,26,5,8:55:19 AM,26,5,9:05:03 AM,26,5,9:18:21 AM,26,5,9:34:11 AM,7,,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_205,950
-Order_No_24916,User_Id_868,Bike,3,Business,15,2,11:41:55 AM,15,2,11:43:51 AM,15,2,11:44:09 AM,15,2,12:00:51 PM,15,2,12:18:28 PM,9,22.8,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_408,1057
-Order_No_1720,User_Id_2605,Bike,3,Business,30,3,2:31:57 PM,30,3,2:32:31 PM,30,3,2:40:07 PM,30,3,2:45:02 PM,30,3,3:05:53 PM,10,29.1,,-1.3066143,36.8161046,-1.2571472,36.795063299999995,Rider_Id_933,1251
-Order_No_5447,User_Id_3135,Bike,3,Personal,23,1,11:03:02 AM,23,1,11:03:09 AM,23,1,11:04:14 AM,23,1,11:07:29 AM,23,1,12:00:47 PM,16,18.5,,-1.2470959,36.8729823,-1.3194518,36.869396,Rider_Id_260,3198
-Order_No_1392,User_Id_393,Bike,3,Business,13,3,1:35:06 PM,13,3,1:35:16 PM,13,3,1:35:20 PM,13,3,2:21:00 PM,13,3,2:21:12 PM,14,29.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515,12
-Order_No_27127,User_Id_3201,Bike,3,Business,6,4,4:15:38 PM,6,4,4:16:04 PM,6,4,4:21:42 PM,6,4,4:33:07 PM,6,4,4:57:10 PM,15,21.7,,-1.2285834999999998,36.882646799999996,-1.2584143,36.8048002,Rider_Id_8,1443
-Order_No_13619,User_Id_412,Bike,3,Business,20,3,2:46:37 PM,20,3,2:47:29 PM,20,3,2:55:53 PM,20,3,3:03:24 PM,20,3,3:19:29 PM,7,29.6,,-1.303596,36.778378000000004,-1.266837,36.799248999999996,Rider_Id_71,965
-Order_No_16736,User_Id_2423,Bike,3,Business,10,3,3:33:27 PM,10,3,3:34:05 PM,10,3,3:34:24 PM,10,3,3:54:18 PM,10,3,4:00:14 PM,1,30.5,,-1.2571472,36.795063299999995,-1.2655477,36.8001682,Rider_Id_720,356
-Order_No_10110,User_Id_3291,Bike,3,Business,14,2,11:43:35 AM,14,2,11:45:06 AM,14,2,11:50:53 AM,14,2,12:03:32 PM,14,2,12:15:35 PM,5,,,-1.2566688999999998,36.779695200000006,-1.2600926000000001,36.8088685,Rider_Id_379,723
-Order_No_15297,User_Id_1340,Bike,3,Business,22,5,3:12:26 PM,22,5,3:14:22 PM,22,5,3:30:20 PM,22,5,3:35:04 PM,22,5,3:53:02 PM,7,27.2,,-1.2943795,36.759124799999995,-1.2707178999999997,36.7979713,Rider_Id_315,1078
-Order_No_8198,User_Id_393,Bike,3,Business,1,1,8:17:41 AM,1,1,8:17:51 AM,1,1,8:32:59 AM,1,1,8:35:32 AM,1,1,9:06:17 AM,14,18.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,1845
-Order_No_14466,User_Id_3647,Bike,3,Business,10,3,10:26:41 AM,10,3,10:28:05 AM,10,3,10:36:57 AM,10,3,10:40:31 AM,10,3,10:58:13 AM,8,27.8,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_576,1062
-Order_No_22277,User_Id_1329,Bike,3,Business,1,4,9:06:01 AM,1,4,9:07:06 AM,1,4,9:20:53 AM,1,4,9:23:57 AM,1,4,9:55:13 AM,14,,,-1.273056,36.811298,-1.2970848,36.887822899999996,Rider_Id_279,1876
-Order_No_17984,User_Id_635,Bike,3,Business,24,1,11:06:52 AM,24,1,11:08:40 AM,24,1,11:19:28 AM,24,1,11:23:43 AM,24,1,11:43:27 AM,8,22.9,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_340,1184
-Order_No_25470,User_Id_3052,Bike,3,Personal,13,4,4:11:43 PM,13,4,4:12:44 PM,13,4,4:33:21 PM,13,4,4:39:45 PM,13,4,5:47:57 PM,25,22.6,,-1.2603185,36.7400255,-1.3686008,36.6579557,Rider_Id_800,4092
-Order_No_25919,User_Id_357,Bike,3,Business,4,4,2:29:04 PM,4,4,2:33:23 PM,4,4,3:05:31 PM,4,4,3:17:52 PM,4,4,3:38:28 PM,7,27.3,,-1.2951833000000001,36.8195615,-1.2945178999999998,36.7685349,Rider_Id_855,1236
-Order_No_4861,User_Id_3291,Bike,3,Business,22,2,4:17:07 PM,22,2,4:17:53 PM,22,2,4:27:17 PM,22,2,4:47:56 PM,22,2,5:12:40 PM,12,25.3,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_75,1484
-Order_No_10867,User_Id_439,Bike,3,Business,23,4,11:40:54 AM,23,4,11:50:04 AM,23,4,12:03:06 PM,23,4,12:17:41 PM,23,4,12:35:43 PM,9,23.4,,-1.3250048,36.869718799999994,-1.333148,36.922453000000004,Rider_Id_591,1082
-Order_No_4852,User_Id_2978,Bike,3,Business,10,1,3:53:03 PM,10,1,3:53:26 PM,10,1,4:00:58 PM,10,1,4:05:36 PM,10,1,4:16:46 PM,4,18.7,,-1.2551895,36.7822034,-1.2761764,36.8016961,Rider_Id_881,670
-Order_No_22716,User_Id_2408,Bike,3,Personal,25,1,3:18:46 PM,25,1,3:19:57 PM,25,1,3:23:59 PM,25,1,3:36:16 PM,25,1,4:26:05 PM,6,,,-1.2972834999999998,36.809687,-1.2655477,36.8001682,Rider_Id_192,2989
-Order_No_1168,User_Id_3091,Bike,3,Business,18,5,2:55:07 PM,18,5,2:56:04 PM,18,5,3:08:00 PM,18,5,3:09:20 PM,18,5,3:26:17 PM,3,27.0,,-1.3012008,36.764868,-1.3002865,36.7491504,Rider_Id_548,1017
-Order_No_21420,User_Id_706,Bike,3,Business,14,1,10:44:53 AM,14,1,10:45:26 AM,14,1,10:52:13 AM,14,1,10:54:18 AM,14,1,11:00:44 AM,3,23.5,,-1.3077869,36.844320700000004,-1.3080091999999999,36.8284543,Rider_Id_589,386
-Order_No_13540,User_Id_393,Bike,3,Business,19,3,9:50:05 AM,19,3,9:59:43 AM,19,3,9:59:49 AM,19,3,10:27:17 AM,19,3,10:49:58 AM,11,19.8,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_619,1361
-Order_No_12047,User_Id_393,Bike,3,Business,29,1,11:06:20 AM,29,1,12:20:56 PM,29,1,12:21:01 PM,29,1,12:32:30 PM,29,1,1:16:39 PM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_702,2649
-Order_No_16338,User_Id_3273,Bike,2,Personal,28,5,1:36:52 PM,28,5,1:41:11 PM,28,5,1:52:37 PM,28,5,2:22:32 PM,28,5,3:15:22 PM,26,25.3,,-1.3156678,36.713569,-1.2175221,36.8246637,Rider_Id_565,3170
-Order_No_2492,User_Id_2523,Bike,3,Business,7,1,10:10:25 AM,7,1,10:11:46 AM,7,1,10:41:16 AM,7,1,10:42:45 AM,7,1,11:18:08 AM,6,24.8,,-1.2551895,36.7822034,-1.2889218999999998,36.8256084,Rider_Id_795,2123
-Order_No_15188,User_Id_1927,Bike,3,Business,17,4,2:22:10 PM,17,4,2:22:20 PM,17,4,2:39:03 PM,17,4,2:50:37 PM,17,4,3:38:05 PM,23,27.9,,-1.3472066,36.7692638,-1.2232123,36.8932547,Rider_Id_669,2848
-Order_No_10605,User_Id_3249,Bike,2,Personal,10,5,4:06:22 PM,10,5,4:06:34 PM,10,5,4:21:17 PM,10,5,4:33:58 PM,10,5,5:18:43 PM,20,21.0,,-1.3168075000000001,36.8626441,-1.2593591999999998,36.7775322,Rider_Id_248,2685
-Order_No_20024,User_Id_2454,Bike,3,Business,22,2,1:03:55 PM,22,2,1:05:26 PM,22,2,1:15:08 PM,22,2,1:17:44 PM,22,2,1:24:48 PM,2,23.9,,-1.2993231,36.7753372,-1.293716,36.772628000000005,Rider_Id_847,424
-Order_No_3481,User_Id_632,Bike,1,Personal,10,3,4:22:35 PM,10,3,4:23:19 PM,10,3,4:36:36 PM,10,3,4:49:36 PM,10,3,5:11:01 PM,12,23.0,,-1.2951409999999999,36.769918,-1.2688502000000002,36.8357356,Rider_Id_612,1285
-Order_No_5749,User_Id_302,Bike,3,Business,4,2,4:07:22 PM,4,2,4:21:27 PM,4,2,4:28:30 PM,4,2,5:28:24 PM,4,2,5:58:06 PM,8,21.1,,-1.2815287,36.853420299999996,-1.2649605,36.798177700000004,Rider_Id_882,1782
-Order_No_25486,User_Id_867,Bike,3,Business,26,2,8:37:20 AM,26,2,8:37:35 AM,26,2,8:41:35 AM,26,2,9:05:43 AM,26,2,9:31:18 AM,10,21.9,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_99,1535
-Order_No_11183,User_Id_3246,Bike,3,Business,26,1,11:42:22 AM,26,1,11:42:33 AM,26,1,11:57:26 AM,26,1,12:21:05 PM,26,1,1:24:39 PM,16,23.2,,-1.2551895,36.7822034,-1.3165542,36.700067,Rider_Id_395,3814
-Order_No_9110,User_Id_867,Bike,3,Business,29,1,2:34:07 PM,29,1,2:35:11 PM,29,1,2:38:59 PM,29,1,3:07:55 PM,29,1,3:29:37 PM,10,,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_245,1302
-Order_No_15342,User_Id_517,Bike,3,Business,29,3,3:40:08 PM,29,3,3:42:22 PM,29,3,3:47:59 PM,29,3,3:53:54 PM,29,3,4:14:50 PM,11,20.9,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_158,1256
-Order_No_26517,User_Id_3372,Bike,3,Business,25,1,1:00:32 PM,25,1,1:00:51 PM,25,1,1:05:10 PM,25,1,1:09:11 PM,25,1,1:41:29 PM,3,,,-1.2969741000000001,36.7856612,-1.3002897,36.76016370000001,Rider_Id_312,1938
-Order_No_27023,User_Id_1469,Bike,3,Business,4,4,3:22:36 PM,4,4,3:22:55 PM,4,4,3:49:41 PM,4,4,3:52:04 PM,4,4,4:19:57 PM,15,24.7,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_755,1673
-Order_No_10299,User_Id_510,Bike,3,Business,25,4,2:33:11 PM,25,4,2:34:32 PM,25,4,2:56:04 PM,25,4,3:21:25 PM,25,4,4:08:37 PM,12,,,-1.290894,36.822971,-1.2860183,36.8975338,Rider_Id_422,2832
-Order_No_4904,User_Id_2102,Bike,3,Business,20,1,9:17:48 AM,20,1,9:18:30 AM,20,1,10:18:52 AM,20,1,10:22:20 AM,20,1,11:09:07 AM,15,18.9,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_25,2807
-Order_No_6714,User_Id_2829,Bike,3,Business,7,3,3:19:39 PM,7,3,3:20:20 PM,7,3,3:30:10 PM,7,3,3:32:15 PM,7,3,4:17:17 PM,5,28.5,,-1.2571472,36.795063299999995,-1.2791714,36.825599700000005,Rider_Id_879,2702
-Order_No_6894,User_Id_2587,Bike,3,Business,2,3,11:42:29 AM,2,3,11:54:42 AM,2,3,12:05:18 PM,2,3,12:09:25 PM,2,3,12:39:39 PM,12,24.2,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_31,1814
-Order_No_23223,User_Id_3091,Bike,3,Business,19,7,1:53:25 PM,19,7,1:54:31 PM,19,7,2:09:31 PM,19,7,2:11:55 PM,19,7,2:35:51 PM,9,21.1,,-1.3052491000000002,36.8223899,-1.3014461000000002,36.7661381,Rider_Id_400,1436
-Order_No_24326,User_Id_1033,Bike,3,Personal,13,3,4:49:57 PM,13,3,4:53:57 PM,13,3,5:07:55 PM,13,3,5:29:46 PM,13,3,5:58:18 PM,11,26.0,,-1.2326041,36.8009029,-1.2086858999999999,36.859699,Rider_Id_247,1712
-Order_No_13674,User_Id_635,Bike,3,Business,13,3,9:35:07 AM,13,3,9:36:27 AM,13,3,9:43:28 AM,13,3,10:37:20 AM,13,3,10:59:39 AM,7,24.9,,-1.300921,36.828195,-1.2585521000000002,36.7945765,Rider_Id_483,1339
-Order_No_3854,User_Id_3222,Bike,3,Business,27,1,12:03:21 PM,27,1,12:06:27 PM,27,1,12:20:52 PM,27,1,12:50:55 PM,27,1,1:12:21 PM,14,20.7,,-1.3261878999999999,36.86017929999999,-1.2551895,36.7822034,Rider_Id_856,1286
-Order_No_7953,User_Id_3547,Bike,3,Business,12,2,10:26:13 AM,12,2,10:26:20 AM,12,2,10:34:07 AM,12,2,10:41:10 AM,12,2,11:20:29 AM,18,24.4,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_25,2359
-Order_No_11386,User_Id_3091,Bike,3,Business,3,3,8:44:23 AM,3,3,8:44:42 AM,3,3,8:55:48 AM,3,3,9:01:32 AM,3,3,9:24:23 AM,13,18.7,,-1.3012008,36.764868,-1.3293565,36.786702500000004,Rider_Id_648,1371
-Order_No_3014,User_Id_1509,Bike,3,Personal,2,4,4:55:42 PM,2,4,4:56:24 PM,2,4,5:12:15 PM,2,4,5:15:49 PM,2,4,5:33:20 PM,4,23.7,,-1.2701765,36.8109424,-1.2709086,36.7929516,Rider_Id_338,1051
-Order_No_21288,User_Id_303,Bike,3,Business,10,1,2:05:10 PM,10,1,3:36:43 PM,10,1,3:49:50 PM,10,1,4:02:23 PM,10,1,4:47:26 PM,11,18.6,,-1.2857524,36.819680600000005,-1.2245407,36.7583778,Rider_Id_265,2703
-Order_No_20814,User_Id_2154,Bike,3,Business,13,4,3:22:14 PM,13,4,3:40:44 PM,13,4,4:15:36 PM,13,4,4:32:24 PM,13,4,5:46:23 PM,29,22.6,,-1.225322,36.80855,-1.2599673999999998,36.959099200000004,Rider_Id_547,4439
-Order_No_12928,User_Id_251,Bike,1,Personal,26,6,3:13:31 PM,26,6,3:17:24 PM,26,6,3:29:48 PM,26,6,3:33:58 PM,26,6,3:49:14 PM,7,24.1,,-1.3041617,36.7627771,-1.3099145,36.802277399999994,Rider_Id_852,916
-Order_No_13810,User_Id_635,Bike,3,Business,13,3,11:06:52 AM,13,3,11:08:39 AM,13,3,11:46:54 AM,13,3,11:50:14 AM,13,3,12:04:50 PM,5,,,-1.2602337,36.79905479999999,-1.2793503000000002,36.8039715,Rider_Id_732,876
-Order_No_8658,User_Id_635,Bike,3,Business,23,5,8:38:16 AM,23,5,8:39:23 AM,23,5,8:49:05 AM,23,5,9:11:47 AM,23,5,9:33:50 AM,8,20.0,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_322,1323
-Order_No_21790,User_Id_1920,Bike,3,Business,1,4,12:18:16 PM,1,4,12:18:44 PM,1,4,12:32:53 PM,1,4,12:37:44 PM,1,4,12:46:53 PM,1,,,-1.2551895,36.7822034,-1.261842,36.784859999999995,Rider_Id_620,549
-Order_No_6034,User_Id_1482,Bike,3,Business,17,5,3:03:59 PM,17,5,3:06:13 PM,17,5,3:20:10 PM,17,5,3:49:37 PM,17,5,4:07:42 PM,11,,,-1.2409346,36.874305299999996,-1.2649605,36.798177700000004,Rider_Id_419,1085
-Order_No_3900,User_Id_1340,Bike,3,Business,19,2,3:06:31 PM,19,2,3:12:45 PM,19,2,3:21:58 PM,19,2,3:26:11 PM,19,2,3:37:09 PM,4,30.2,,-1.2735999,36.8218131,-1.2707178999999997,36.7979713,Rider_Id_268,658
-Order_No_417,User_Id_2229,Bike,3,Business,14,5,2:13:08 PM,14,5,2:13:42 PM,14,5,2:17:10 PM,14,5,2:24:30 PM,14,5,3:09:31 PM,23,23.2,,-1.3247683000000001,36.85803,-1.2128205,36.8026175,Rider_Id_4,2701
-Order_No_4196,User_Id_3674,Bike,3,Business,21,4,12:34:42 PM,21,4,12:36:26 PM,21,4,12:44:03 PM,21,4,12:48:42 PM,21,4,1:13:37 PM,12,25.4,,-1.2551895,36.7822034,-1.3224102,36.83072070000001,Rider_Id_188,1495
-Order_No_25653,User_Id_3283,Bike,3,Business,19,2,12:20:10 PM,19,2,12:39:07 PM,19,2,12:51:46 PM,19,2,1:03:43 PM,19,2,1:18:47 PM,6,26.7,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_75,904
-Order_No_17098,User_Id_2216,Bike,3,Business,30,2,9:14:19 AM,30,2,9:14:54 AM,30,2,9:21:51 AM,30,2,9:27:13 AM,30,2,9:35:36 AM,4,,,-1.2551895,36.7822034,-1.2737013000000001,36.806912200000006,Rider_Id_245,503
-Order_No_12867,User_Id_2216,Bike,3,Business,12,2,2:52:33 PM,12,2,3:22:35 PM,12,2,3:38:02 PM,12,2,3:40:48 PM,12,2,3:58:13 PM,7,23.2,,-1.2551895,36.7822034,-1.2945159,36.7870611,Rider_Id_726,1045
-Order_No_4375,User_Id_3091,Bike,1,Business,28,6,12:23:11 PM,28,6,12:23:31 PM,28,6,12:36:46 PM,28,6,12:48:22 PM,28,6,1:49:06 PM,21,24.1,,-1.2927792,36.9053492,-1.3014461000000002,36.7661381,Rider_Id_951,3644
-Order_No_22877,User_Id_778,Bike,3,Business,11,2,1:12:54 PM,11,2,1:14:16 PM,11,2,2:04:07 PM,11,2,2:06:34 PM,11,2,2:44:36 PM,9,22.3,,-1.2535486,36.7933541,-1.2290218000000002,36.8000283,Rider_Id_755,2282
-Order_No_10089,User_Id_635,Bike,3,Business,15,4,2:23:52 PM,15,4,2:32:33 PM,15,4,2:41:42 PM,15,4,3:03:58 PM,15,4,3:55:08 PM,7,27.7,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_726,3070
-Order_No_27613,User_Id_186,Bike,3,Business,26,5,1:05:43 PM,26,5,1:14:22 PM,26,5,1:41:52 PM,26,5,1:47:37 PM,26,5,2:36:04 PM,25,,,-1.3700383,36.9190174,-1.2622851000000002,36.7893989,Rider_Id_547,2907
-Order_No_26290,User_Id_393,Bike,3,Business,25,1,4:08:24 PM,25,1,4:08:54 PM,25,1,4:09:02 PM,25,1,4:42:06 PM,25,1,4:42:18 PM,14,27.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,12
-Order_No_15211,User_Id_1719,Bike,3,Business,4,5,11:59:41 AM,4,5,12:01:28 PM,4,5,12:26:53 PM,4,5,12:28:09 PM,4,5,1:13:13 PM,14,17.1,,-1.2931728,36.7888371,-1.2330694,36.745451,Rider_Id_362,2704
-Order_No_17784,User_Id_3222,Bike,3,Business,30,5,3:07:22 PM,30,5,3:07:50 PM,30,5,3:51:20 PM,30,5,3:57:05 PM,30,5,4:30:46 PM,14,24.5,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_523,2021
-Order_No_15151,User_Id_3420,Bike,3,Business,7,1,10:08:53 AM,7,1,10:08:58 AM,7,1,10:18:15 AM,7,1,10:30:06 AM,7,1,10:55:25 AM,6,18.3,,-1.265981,36.793612100000004,-1.3046007,36.821174299999996,Rider_Id_948,1519
-Order_No_14590,User_Id_3032,Bike,3,Business,21,5,10:31:08 AM,21,5,10:34:18 AM,21,5,10:41:48 AM,21,5,10:46:14 AM,21,5,11:40:02 AM,18,23.0,,-1.2571472,36.795063299999995,-1.3251247,36.899592600000005,Rider_Id_557,3228
-Order_No_13932,User_Id_3063,Bike,2,Personal,3,2,4:33:25 PM,3,2,4:42:50 PM,3,2,4:48:45 PM,3,2,4:50:50 PM,3,2,5:04:24 PM,5,18.5,,-1.2926094,36.7904435,-1.31471,36.8110941,Rider_Id_312,814
-Order_No_15378,User_Id_772,Bike,3,Business,22,4,1:32:52 PM,22,4,1:37:43 PM,22,4,1:48:17 PM,22,4,2:00:09 PM,22,4,2:34:35 PM,14,26.4,,-1.2844665000000002,36.7866113,-1.2580531000000001,36.865462799999996,Rider_Id_882,2066
-Order_No_6679,User_Id_2509,Bike,3,Business,27,3,11:12:46 AM,27,3,11:13:00 AM,27,3,11:13:39 AM,27,3,11:24:00 AM,27,3,12:22:08 PM,20,26.0,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_470,3488
-Order_No_19118,User_Id_419,Bike,3,Business,18,1,2:14:28 PM,18,1,2:16:58 PM,18,1,2:58:17 PM,18,1,3:02:17 PM,18,1,3:40:44 PM,5,29.4,,-1.2571472,36.795063299999995,-1.2886923000000001,36.8241519,Rider_Id_118,2307
-Order_No_22978,User_Id_3647,Bike,3,Business,12,5,9:43:42 AM,12,5,9:44:20 AM,12,5,9:54:01 AM,12,5,10:00:24 AM,12,5,10:08:51 AM,4,21.6,,-1.2628638,36.8070399,-1.2726389999999999,36.794723,Rider_Id_607,507
-Order_No_7937,User_Id_269,Bike,3,Business,7,1,10:43:50 AM,7,1,10:45:21 AM,7,1,10:58:46 AM,7,1,11:02:19 AM,7,1,11:23:07 AM,5,24.8,,-1.3076233999999998,36.8393399,-1.3226301999999999,36.8719901,Rider_Id_543,1248
-Order_No_20385,User_Id_1796,Bike,3,Business,12,2,2:12:48 PM,12,2,2:14:14 PM,12,2,2:14:48 PM,12,2,3:08:11 PM,12,2,3:10:43 PM,11,27.2,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_41,152
-Order_No_13753,User_Id_633,Bike,3,Business,27,3,2:46:37 PM,27,3,2:48:57 PM,27,3,3:00:36 PM,27,3,3:19:50 PM,27,3,3:40:12 PM,9,29.3,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_448,1222
-Order_No_3800,User_Id_1886,Bike,2,Personal,13,5,5:42:02 PM,13,5,5:42:46 PM,13,5,5:43:35 PM,13,5,5:55:34 PM,13,5,6:04:41 PM,4,19.0,,-1.2248308,36.801257299999996,-1.214328,36.7911903,Rider_Id_735,547
-Order_No_14913,User_Id_49,Bike,3,Business,15,4,4:11:40 PM,15,4,4:14:51 PM,15,4,4:25:45 PM,15,4,4:42:33 PM,15,4,5:19:41 PM,15,25.3,,-1.3294489999999999,36.890451,-1.2906533000000002,36.783068799999995,Rider_Id_64,2228
-Order_No_17752,User_Id_3283,Bike,3,Business,10,4,3:30:40 PM,10,4,3:31:15 PM,10,4,3:35:37 PM,10,4,3:45:20 PM,10,4,3:58:48 PM,6,25.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_439,808
-Order_No_6517,User_Id_3496,Bike,3,Business,27,4,10:24:15 AM,27,4,10:24:19 AM,27,4,10:24:37 AM,27,4,10:56:40 AM,27,4,11:27:27 AM,15,23.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_167,1847
-Order_No_19278,User_Id_633,Bike,3,Business,4,5,10:46:00 AM,4,5,10:46:07 AM,4,5,10:46:11 AM,4,5,11:06:43 AM,4,5,11:06:45 AM,6,17.4,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_515,2
-Order_No_3855,User_Id_3686,Bike,3,Business,10,4,10:21:09 AM,10,4,10:21:59 AM,10,4,10:31:58 AM,10,4,10:34:51 AM,10,4,11:13:14 AM,10,,,-1.2559559999999999,36.789867,-1.2287282,36.8115304,Rider_Id_729,2303
-Order_No_22262,User_Id_3647,Bike,3,Business,24,5,3:00:20 PM,24,5,3:01:24 PM,24,5,3:15:35 PM,24,5,3:23:04 PM,24,5,4:03:37 PM,18,23.0,,-1.2726389999999999,36.794723,-1.3532483999999998,36.7249576,Rider_Id_217,2433
-Order_No_23395,User_Id_3283,Bike,3,Business,9,2,12:21:01 PM,9,2,12:22:00 PM,9,2,12:30:10 PM,9,2,12:41:50 PM,9,2,12:50:42 PM,5,21.9,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_875,532
-Order_No_11868,User_Id_3587,Bike,1,Personal,18,6,12:01:18 PM,18,6,12:01:33 PM,18,6,12:05:54 PM,18,6,12:09:29 PM,18,6,12:17:30 PM,2,18.9,,-1.2941791999999999,36.763363700000006,-1.2864865,36.76614620000001,Rider_Id_449,481
-Order_No_13561,User_Id_718,Bike,3,Business,28,1,3:38:58 PM,28,1,3:39:48 PM,28,1,3:43:31 PM,28,1,3:46:01 PM,28,1,4:17:25 PM,24,26.1,,-1.2551895,36.7822034,-1.178056,36.9312342,Rider_Id_882,1884
-Order_No_16137,User_Id_2150,Bike,3,Business,20,1,1:51:34 PM,20,1,1:53:47 PM,20,1,2:11:35 PM,20,1,2:50:43 PM,20,1,2:58:39 PM,9,,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_119,476
-Order_No_6288,User_Id_84,Bike,3,Business,22,2,12:35:08 PM,22,2,12:35:46 PM,22,2,12:37:19 PM,22,2,1:02:37 PM,22,2,1:31:27 PM,11,24.1,,-1.2551895,36.7822034,-1.3141836999999998,36.8163275,Rider_Id_379,1730
-Order_No_2613,User_Id_2349,Bike,3,Business,27,1,9:40:46 AM,27,1,9:42:12 AM,27,1,9:58:46 AM,27,1,10:04:46 AM,27,1,10:27:35 AM,12,18.1,0.9,-1.2784229,36.791051,-1.311687,36.866857,Rider_Id_438,1369
-Order_No_26418,User_Id_2102,Bike,3,Business,9,5,3:18:41 PM,9,5,3:23:33 PM,9,5,3:38:16 PM,9,5,3:49:13 PM,9,5,4:44:40 PM,15,26.3,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_914,3327
-Order_No_17849,User_Id_3291,Bike,3,Business,1,3,2:13:18 PM,1,3,2:21:14 PM,1,3,2:39:38 PM,1,3,2:49:43 PM,1,3,3:08:55 PM,11,23.9,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_273,1152
-Order_No_18961,User_Id_198,Bike,3,Business,1,5,10:26:44 AM,1,5,10:28:08 AM,1,5,10:59:40 AM,1,5,11:02:13 AM,1,5,11:16:32 AM,8,28.7,,-1.3396256000000002,36.8863135,-1.3705766000000001,36.9120742,Rider_Id_242,859
-Order_No_17606,User_Id_190,Bike,3,Business,21,4,4:35:05 PM,21,4,4:35:11 PM,21,4,4:37:33 PM,21,4,4:41:45 PM,21,4,5:14:57 PM,16,28.3,,-1.2404309999999998,36.8739331,-1.3270347,36.866736200000005,Rider_Id_910,1992
-Order_No_21548,User_Id_3517,Bike,3,Business,30,2,4:46:20 PM,30,2,4:53:21 PM,30,2,5:09:06 PM,30,2,5:15:40 PM,30,2,6:01:00 PM,15,,,-1.300921,36.828195,-1.2156006000000001,36.8916865,Rider_Id_155,2720
-Order_No_15950,User_Id_2521,Bike,3,Business,25,5,10:50:17 AM,25,5,10:50:33 AM,25,5,10:52:46 AM,25,5,10:59:45 AM,25,5,11:29:50 AM,11,26.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,1805
-Order_No_2488,User_Id_3491,Bike,3,Business,5,5,5:18:46 PM,5,5,5:18:54 PM,5,5,5:27:27 PM,5,5,5:32:32 PM,5,5,5:42:38 PM,3,26.3,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_484,606
-Order_No_18327,User_Id_3420,Bike,3,Business,13,3,3:28:03 PM,13,3,3:28:38 PM,13,3,3:29:56 PM,13,3,3:31:02 PM,13,3,4:23:20 PM,6,27.2,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_143,3138
-Order_No_6640,User_Id_1642,Bike,3,Business,12,2,3:30:55 PM,12,2,3:40:27 PM,12,2,3:53:32 PM,12,2,3:56:48 PM,12,2,4:20:52 PM,9,26.9,,-1.2726506000000002,36.7960153,-1.3054333,36.8373808,Rider_Id_576,1444
-Order_No_16629,User_Id_2112,Bike,3,Business,24,6,2:48:54 PM,24,6,2:58:17 PM,24,6,3:32:35 PM,24,6,3:34:43 PM,24,6,4:12:40 PM,14,27.1,,-1.207546,36.7913976,-1.2156006000000001,36.8916865,Rider_Id_626,2277
-Order_No_5064,User_Id_3404,Bike,3,Business,8,5,12:04:44 PM,8,5,12:05:30 PM,8,5,12:14:20 PM,8,5,12:16:18 PM,8,5,12:31:34 PM,4,24.4,,-1.279395,36.825364,-1.2596969999999998,36.800802000000004,Rider_Id_692,916
-Order_No_6139,User_Id_3283,Bike,3,Business,25,5,2:18:32 PM,25,5,2:20:34 PM,25,5,2:30:20 PM,25,5,2:47:54 PM,25,5,2:52:32 PM,5,23.2,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_116,278
-Order_No_26720,User_Id_496,Bike,3,Business,30,2,12:03:40 PM,30,2,12:08:27 PM,30,2,12:16:14 PM,30,2,12:33:46 PM,30,2,12:33:56 PM,5,,,-1.2551895,36.7822034,-1.2592947,36.7698085,Rider_Id_660,10
-Order_No_19243,User_Id_432,Bike,3,Business,15,5,11:46:54 AM,15,5,11:47:24 AM,15,5,11:59:13 AM,15,5,12:11:24 PM,15,5,12:28:38 PM,7,26.5,,-1.2968981000000002,36.8118792,-1.3012008,36.764868,Rider_Id_559,1034
-Order_No_3040,User_Id_3291,Bike,3,Business,5,2,10:59:40 AM,5,2,11:00:19 AM,5,2,11:00:26 AM,5,2,11:13:37 AM,5,2,11:25:34 AM,5,25.5,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_523,717
-Order_No_21992,User_Id_517,Bike,3,Business,31,5,4:08:15 PM,31,5,4:10:23 PM,31,5,4:26:15 PM,31,5,4:51:10 PM,31,5,4:51:11 PM,11,23.8,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_863,1
-Order_No_20536,User_Id_733,Bike,3,Business,22,3,11:17:41 AM,22,3,11:20:27 AM,22,3,11:25:11 AM,22,3,11:29:07 AM,22,3,11:44:20 AM,8,23.2,,-1.2551895,36.7822034,-1.2937193,36.8236214,Rider_Id_709,913
-Order_No_8120,User_Id_3621,Bike,3,Business,19,2,5:02:22 PM,19,2,5:02:38 PM,19,2,5:09:12 PM,19,2,5:18:35 PM,19,2,5:29:25 PM,5,28.2,,-1.2836583000000001,36.7886014,-1.2949722,36.7524325,Rider_Id_855,650
-Order_No_21261,User_Id_1535,Bike,1,Business,12,5,10:47:29 AM,12,5,11:13:27 AM,12,5,11:26:55 AM,12,5,11:36:14 AM,12,5,11:45:27 AM,2,24.6,,-1.2897368,36.8226783,-1.288709,36.802659000000006,Rider_Id_662,553
-Order_No_14920,User_Id_2000,Bike,3,Business,14,3,12:10:44 PM,14,3,12:10:50 PM,14,3,12:13:49 PM,14,3,12:20:23 PM,14,3,12:42:46 PM,14,24.5,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_649,1343
-Order_No_21253,User_Id_972,Bike,3,Business,15,5,2:23:54 PM,15,5,2:24:00 PM,15,5,2:24:12 PM,15,5,2:47:08 PM,15,5,3:12:43 PM,13,22.7,,-1.2862033000000002,36.8152966,-1.2953535,36.901262599999995,Rider_Id_886,1535
-Order_No_17006,User_Id_2795,Bike,1,Personal,22,5,11:31:19 AM,22,5,11:34:34 AM,22,5,11:45:16 AM,22,5,11:53:38 AM,22,5,12:22:33 PM,12,21.8,,-1.3108386,36.7555239,-1.263419,36.8060348,Rider_Id_116,1735
-Order_No_27150,User_Id_970,Bike,3,Business,11,1,2:33:29 PM,11,1,2:37:21 PM,11,1,2:46:54 PM,11,1,2:56:00 PM,11,1,3:07:27 PM,6,28.1,,-1.2551895,36.7822034,-1.266533,36.765838200000005,Rider_Id_932,687
-Order_No_17924,User_Id_3426,Bike,3,Business,28,1,12:37:58 PM,28,1,12:39:12 PM,28,1,12:42:50 PM,28,1,12:53:59 PM,28,1,1:10:48 PM,8,23.6,,-1.3012008,36.764868,-1.2551895,36.7822034,Rider_Id_829,1009
-Order_No_252,User_Id_2211,Bike,2,Personal,14,5,10:29:35 AM,14,5,10:30:02 AM,14,5,10:37:58 AM,14,5,10:43:27 AM,14,5,10:59:08 AM,5,24.5,,-1.2598002000000001,36.7963379,-1.2658011,36.7919358,Rider_Id_822,941
-Order_No_11798,User_Id_3404,Bike,3,Business,29,6,1:29:57 PM,29,6,1:30:59 PM,29,6,1:37:51 PM,29,6,1:42:37 PM,29,6,1:51:46 PM,2,26.7,,-1.2882183999999999,36.826346,-1.279395,36.825364,Rider_Id_237,549
-Order_No_4302,User_Id_1363,Bike,3,Business,15,5,8:54:40 AM,15,5,8:58:15 AM,15,5,9:15:12 AM,15,5,9:28:59 AM,15,5,9:41:18 AM,5,20.1,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_448,739
-Order_No_23186,User_Id_151,Bike,3,Business,25,4,2:57:16 PM,25,4,3:00:21 PM,25,4,3:06:59 PM,25,4,3:16:02 PM,25,4,3:36:08 PM,8,20.8,,-1.2620725,36.802651700000006,-1.2969741000000001,36.7856612,Rider_Id_726,1206
-Order_No_27855,User_Id_1405,Bike,3,Business,7,3,11:51:13 AM,7,3,11:56:10 AM,7,3,12:05:00 PM,7,3,12:14:29 PM,7,3,12:41:49 PM,9,24.9,,-1.3194518,36.869396,-1.3478563000000001,36.8935824,Rider_Id_276,1640
-Order_No_13099,User_Id_2317,Bike,1,Personal,17,3,2:25:18 PM,17,3,2:25:42 PM,17,3,2:26:06 PM,17,3,2:40:41 PM,17,3,3:32:07 PM,24,28.2,,-1.3003196000000001,36.8171416,-1.2492584,36.6689141,Rider_Id_478,3086
-Order_No_24860,User_Id_2101,Bike,1,Personal,20,5,12:34:28 PM,20,5,12:35:04 PM,20,5,12:40:00 PM,20,5,12:45:53 PM,20,5,1:30:55 PM,15,18.3,,-1.291216,36.8746657,-1.2866521,36.780966,Rider_Id_618,2702
-Order_No_26235,User_Id_2310,Bike,3,Business,9,3,4:55:58 PM,9,3,4:56:45 PM,9,3,5:14:02 PM,9,3,5:25:11 PM,9,3,5:51:54 PM,6,21.6,,-1.2860183,36.8975338,-1.2859912,36.8756811,Rider_Id_543,1603
-Order_No_21908,User_Id_527,Bike,2,Personal,15,2,12:42:50 PM,15,2,12:47:17 PM,15,2,1:03:50 PM,15,2,1:14:19 PM,15,2,1:31:49 PM,12,22.6,,-1.279448,36.823568,-1.3295139,36.8985677,Rider_Id_393,1050
-Order_No_3376,User_Id_3363,Bike,1,Personal,21,6,3:22:57 PM,21,6,3:23:05 PM,21,6,3:28:50 PM,21,6,3:57:34 PM,21,6,4:38:06 PM,18,18.5,,-1.2536041999999998,36.806489500000005,-1.307725,36.69941,Rider_Id_858,2432
-Order_No_1985,User_Id_1075,Bike,3,Business,24,7,9:15:35 AM,24,7,9:32:22 AM,24,7,10:01:46 AM,24,7,10:27:43 AM,24,7,10:57:30 AM,1,20.5,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_119,1787
-Order_No_12559,User_Id_3355,Bike,3,Business,29,4,4:09:17 PM,29,4,4:55:14 PM,29,4,5:03:25 PM,29,4,5:08:53 PM,29,4,5:28:03 PM,8,20.8,,-1.3226301999999999,36.8719901,-1.3052491000000002,36.8223899,Rider_Id_932,1150
-Order_No_22826,User_Id_1482,Bike,3,Business,30,5,9:16:45 AM,30,5,9:17:37 AM,30,5,9:30:37 AM,30,5,9:37:26 AM,30,5,10:24:30 AM,14,18.8,,-1.2782261000000001,36.822500899999994,-1.3385790000000002,36.89630379999999,Rider_Id_31,2824
-Order_No_14191,User_Id_1787,Bike,3,Business,13,6,11:40:33 AM,13,6,11:41:27 AM,13,6,12:01:08 PM,13,6,12:59:22 PM,13,6,1:26:33 PM,9,26.0,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_435,1631
-Order_No_25763,User_Id_1271,Bike,3,Business,30,5,10:46:46 AM,30,5,10:51:46 AM,30,5,11:06:11 AM,30,5,11:10:38 AM,30,5,12:06:30 PM,29,23.5,,-1.3244885,36.897791999999995,-1.1404395,36.9668024,Rider_Id_254,3352
-Order_No_10949,User_Id_2989,Bike,3,Business,31,3,11:07:56 AM,31,3,11:31:58 AM,31,3,11:32:17 AM,31,3,11:55:08 AM,31,3,12:15:58 PM,10,24.6,,-1.3169591,36.7000029,-1.3463041999999998,36.768164,Rider_Id_631,1250
-Order_No_19593,User_Id_3015,Bike,1,Personal,1,2,5:14:52 PM,1,2,5:15:07 PM,1,2,6:01:23 PM,1,2,6:16:34 PM,1,2,6:29:19 PM,13,20.0,,-1.3026355,36.801298100000004,-1.3264635,36.888388899999995,Rider_Id_95,765
-Order_No_18008,User_Id_1500,Bike,3,Business,2,4,2:01:16 PM,2,4,2:08:00 PM,2,4,3:11:09 PM,2,4,3:21:46 PM,2,4,4:14:28 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_778,3162
-Order_No_16409,User_Id_346,Bike,3,Business,25,6,12:13:50 PM,25,6,12:14:42 PM,25,6,12:23:19 PM,25,6,12:55:03 PM,25,6,1:19:09 PM,12,,,-1.2283403,36.8822756,-1.28878,36.8168312,Rider_Id_291,1446
-Order_No_258,User_Id_3331,Bike,3,Business,4,2,10:41:41 AM,4,2,11:21:43 AM,4,2,11:33:11 AM,4,2,11:37:09 AM,4,2,12:09:34 PM,9,19.3,,-1.2551895,36.7822034,-1.2956171,36.7888097,Rider_Id_817,1945
-Order_No_16840,User_Id_2948,Bike,1,Personal,18,3,4:49:55 PM,18,3,4:50:10 PM,18,3,4:51:42 PM,18,3,5:04:49 PM,18,3,5:36:24 PM,10,20.3,,-1.3014461000000002,36.7661381,-1.3194988,36.796668100000005,Rider_Id_668,1895
-Order_No_13160,User_Id_1916,Bike,1,Personal,10,1,8:29:50 AM,10,1,8:30:32 AM,10,1,8:35:45 AM,10,1,9:51:47 AM,10,1,10:05:23 AM,4,17.9,,-1.280465,36.820715,-1.2983805,36.8198439,Rider_Id_88,816
-Order_No_20169,User_Id_2412,Bike,3,Business,4,4,10:52:37 AM,4,4,11:20:00 AM,4,4,11:30:22 AM,4,4,11:37:01 AM,4,4,11:54:58 AM,10,,,-1.2696795,36.8640088,-1.2297202,36.881950700000004,Rider_Id_100,1077
-Order_No_18958,User_Id_1304,Bike,3,Business,27,2,3:38:14 PM,27,2,3:41:23 PM,27,2,3:53:23 PM,27,2,3:56:43 PM,27,2,4:54:23 PM,19,25.7,,-1.3226301999999999,36.8719901,-1.3104733999999998,36.7363698,Rider_Id_62,3460
-Order_No_20669,User_Id_3635,Bike,1,Business,25,2,3:51:17 PM,25,2,3:51:32 PM,25,2,3:55:28 PM,25,2,4:07:36 PM,25,2,4:13:11 PM,3,27.2,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_446,335
-Order_No_1626,User_Id_3115,Bike,3,Business,20,1,10:42:43 AM,20,1,10:43:43 AM,20,1,10:47:05 AM,20,1,10:59:19 AM,20,1,11:34:05 AM,14,,,-1.3189252,36.861726899999994,-1.2963097,36.7688221,Rider_Id_459,2086
-Order_No_11709,User_Id_3265,Bike,1,Personal,25,6,11:01:15 AM,25,6,11:01:23 AM,25,6,11:15:42 AM,25,6,11:23:09 AM,25,6,11:47:55 AM,14,,,-1.322999,36.783263299999994,-1.2529072,36.8525066,Rider_Id_216,1486
-Order_No_12468,User_Id_562,Bike,3,Business,6,4,1:17:54 PM,6,4,1:30:13 PM,6,4,2:18:29 PM,6,4,2:25:12 PM,6,4,2:59:05 PM,7,,,-1.3227181000000001,36.8528097,-1.2838296999999999,36.810923200000005,Rider_Id_511,2033
-Order_No_4272,User_Id_1245,Bike,3,Business,11,4,12:16:41 PM,11,4,12:16:56 PM,11,4,12:19:05 PM,11,4,1:11:59 PM,11,4,1:26:11 PM,15,25.8,,-1.300921,36.828195,-1.2174265,36.8122001,Rider_Id_436,852
-Order_No_23094,User_Id_1072,Bike,1,Personal,11,1,2:50:40 PM,11,1,2:54:48 PM,11,1,3:01:13 PM,11,1,3:02:39 PM,11,1,3:12:19 PM,3,23.2,,-1.261849,36.808254,-1.2790653,36.7907262,Rider_Id_379,580
-Order_No_27440,User_Id_2642,Bike,3,Personal,18,5,3:41:17 PM,18,5,3:41:30 PM,18,5,4:07:16 PM,18,5,4:13:37 PM,18,5,5:01:43 PM,20,24.5,,-1.326774,36.78780689999999,-1.3562374,36.904295399999995,Rider_Id_432,2886
-Order_No_2065,User_Id_2342,Bike,2,Personal,12,3,1:30:37 PM,12,3,1:53:34 PM,12,3,2:13:39 PM,12,3,2:18:07 PM,12,3,2:39:41 PM,6,26.3,,-1.3233991000000003,36.842189399999995,-1.2872899,36.820434000000006,Rider_Id_698,1294
-Order_No_9406,User_Id_2469,Bike,3,Business,18,2,3:40:13 PM,18,2,3:40:21 PM,18,2,3:52:40 PM,18,2,5:29:07 PM,18,2,6:10:13 PM,20,23.7,,-1.3802726,36.9442993,-1.308231,36.857306,Rider_Id_43,2466
-Order_No_754,User_Id_265,Bike,3,Business,12,2,2:09:31 PM,12,2,2:26:25 PM,12,2,2:40:16 PM,12,2,2:59:18 PM,12,2,3:26:01 PM,16,27.2,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_858,1603
-Order_No_23785,User_Id_3420,Bike,3,Business,27,3,9:31:28 AM,27,3,9:31:42 AM,27,3,9:41:08 AM,27,3,9:44:45 AM,27,3,10:08:18 AM,6,21.2,,-1.3046007,36.821174299999996,-1.275885,36.8066908,Rider_Id_448,1413
-Order_No_1717,User_Id_893,Bike,3,Business,7,5,10:08:06 AM,7,5,10:09:16 AM,7,5,10:12:25 AM,7,5,10:24:22 AM,7,5,10:57:58 AM,21,18.5,,-1.2664491000000002,36.8102685,-1.343439,36.912065000000005,Rider_Id_86,2016
-Order_No_20478,User_Id_547,Bike,1,Personal,8,3,4:05:56 PM,8,3,4:07:34 PM,8,3,4:26:46 PM,8,3,4:32:20 PM,8,3,4:54:24 PM,10,23.9,,-1.2840605,36.8191699,-1.3180993,36.8676304,Rider_Id_636,1324
-Order_No_13705,User_Id_2289,Bike,3,Business,4,1,3:35:38 PM,4,1,3:36:25 PM,4,1,3:36:44 PM,4,1,3:45:55 PM,4,1,4:19:57 PM,6,,,-1.2551895,36.7822034,-1.2869955,36.8202749,Rider_Id_291,2042
-Order_No_4777,User_Id_602,Bike,3,Personal,20,1,10:09:44 AM,20,1,10:09:58 AM,20,1,10:16:20 AM,20,1,10:23:56 AM,20,1,11:09:22 AM,22,19.3,,-1.3214562,36.810082799999996,-1.235002,36.9289349,Rider_Id_183,2726
-Order_No_28102,User_Id_3027,Bike,1,Personal,8,2,2:42:39 PM,8,2,4:27:36 PM,8,2,4:48:42 PM,8,2,5:12:37 PM,8,2,6:10:38 PM,5,20.8,,-1.2827651000000002,36.843383100000004,-1.2705886999999998,36.829210100000005,Rider_Id_101,3481
-Order_No_16312,User_Id_3355,Bike,3,Business,4,2,3:45:55 PM,4,2,3:46:23 PM,4,2,4:00:59 PM,4,2,4:02:14 PM,4,2,4:26:58 PM,12,24.0,11.1,-1.2860183,36.8975338,-1.3052491000000002,36.8223899,Rider_Id_31,1484
-Order_No_15091,User_Id_567,Bike,3,Business,17,1,3:36:13 PM,17,1,3:36:46 PM,17,1,3:42:03 PM,17,1,3:57:11 PM,17,1,4:03:13 PM,2,28.1,,-1.2954956,36.760385799999995,-1.2963097,36.7688221,Rider_Id_65,362
-Order_No_19924,User_Id_2271,Bike,3,Business,27,1,10:43:57 AM,27,1,10:44:16 AM,27,1,10:48:36 AM,27,1,10:56:41 AM,27,1,11:24:06 AM,12,24.1,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_805,1645
-Order_No_27965,User_Id_393,Bike,3,Business,11,1,1:24:39 PM,11,1,1:24:47 PM,11,1,1:24:51 PM,11,1,1:59:27 PM,11,1,1:59:29 PM,11,27.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,2
-Order_No_2213,User_Id_2112,Bike,3,Business,27,4,12:50:26 PM,27,4,12:50:51 PM,27,4,1:08:55 PM,27,4,1:16:25 PM,27,4,1:34:02 PM,5,23.0,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_649,1057
-Order_No_7984,User_Id_1271,Bike,3,Business,9,5,12:48:41 PM,9,5,12:56:37 PM,9,5,1:15:59 PM,9,5,1:36:40 PM,9,5,2:22:53 PM,27,26.3,,-1.3244885,36.897791999999995,-1.3201913,36.7047971,Rider_Id_625,2773
-Order_No_4043,User_Id_3070,Bike,3,Business,17,3,12:15:16 PM,17,3,12:16:06 PM,17,3,1:32:08 PM,17,3,1:41:27 PM,17,3,2:35:46 PM,7,,,-1.3127833999999998,36.869639899999996,-1.2837469,36.8309294,Rider_Id_48,3259
-Order_No_15906,User_Id_346,Bike,3,Business,9,2,1:50:38 PM,9,2,1:51:44 PM,9,2,2:28:39 PM,9,2,2:37:01 PM,9,2,3:46:59 PM,16,30.5,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_7,4198
-Order_No_26791,User_Id_1594,Bike,3,Business,20,3,2:13:57 PM,20,3,2:15:06 PM,20,3,2:29:46 PM,20,3,2:45:44 PM,20,3,3:11:49 PM,15,29.6,,-1.2325706,36.808392299999994,-1.2156006000000001,36.8916865,Rider_Id_205,1565
-Order_No_25471,User_Id_1073,Bike,3,Business,19,2,11:28:28 AM,19,2,11:29:46 AM,19,2,11:49:09 AM,19,2,11:52:58 AM,19,2,12:33:46 PM,8,27.5,,-1.2561964,36.7885001,-1.2860152,36.826003,Rider_Id_831,2448
-Order_No_24523,User_Id_2587,Bike,3,Business,15,1,3:10:45 PM,15,1,3:17:16 PM,15,1,3:32:58 PM,15,1,3:48:28 PM,15,1,4:38:13 PM,7,,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_58,2985
-Order_No_8381,User_Id_3201,Bike,3,Business,5,2,8:47:50 AM,5,2,8:48:55 AM,5,2,8:59:05 AM,5,2,9:07:59 AM,5,2,9:33:00 AM,10,20.9,1.9,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_708,1501
-Order_No_20631,User_Id_1880,Bike,3,Business,7,4,11:27:50 AM,7,4,11:42:35 AM,7,4,12:03:47 PM,7,4,12:19:00 PM,7,4,1:24:41 PM,25,27.6,,-1.2156006000000001,36.8916865,-1.3310785,36.8933109,Rider_Id_75,3941
-Order_No_10920,User_Id_3718,Bike,3,Business,4,4,3:03:03 PM,4,4,3:06:51 PM,4,4,3:07:20 PM,4,4,3:34:30 PM,4,4,3:46:48 PM,3,25.0,,-1.2707373,36.805440999999995,-1.2608073999999998,36.828373,Rider_Id_51,738
-Order_No_7438,User_Id_1075,Bike,3,Business,3,5,1:13:22 PM,3,5,1:13:42 PM,3,5,1:51:31 PM,3,5,1:56:54 PM,3,5,2:02:12 PM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_106,318
-Order_No_6699,User_Id_1973,Bike,2,Personal,17,1,10:47:37 AM,17,1,10:48:04 AM,17,1,10:55:38 AM,17,1,11:03:13 AM,17,1,11:25:14 AM,7,25.2,,-1.3034231,36.7650287,-1.2655477,36.8001682,Rider_Id_328,1321
-Order_No_18194,User_Id_1379,Bike,1,Personal,9,7,8:26:48 AM,9,7,8:36:36 AM,9,7,8:50:31 AM,9,7,8:54:30 AM,9,7,9:36:29 AM,17,19.0,,-1.3178903,36.841449100000006,-1.2363091999999998,36.882361700000004,Rider_Id_543,2519
-Order_No_7818,User_Id_633,Bike,3,Business,25,1,10:08:26 AM,25,1,10:08:42 AM,25,1,10:23:40 AM,25,1,10:28:20 AM,25,1,10:55:10 AM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_545,1610
-Order_No_9280,User_Id_2436,Bike,3,Business,25,3,12:11:23 PM,25,3,12:11:34 PM,25,3,12:12:11 PM,25,3,12:21:53 PM,25,3,1:01:31 PM,10,,,-1.2551895,36.7822034,-1.2304909,36.821690999999994,Rider_Id_519,2378
-Order_No_8,User_Id_3661,Bike,3,Business,15,1,1:11:46 PM,15,1,1:15:42 PM,15,1,1:30:22 PM,15,1,1:39:06 PM,15,1,2:11:45 PM,17,,,-1.2132866000000002,36.867827399999996,-1.2987332,36.8463349,Rider_Id_8,1959
-Order_No_17887,User_Id_2069,Bike,3,Business,27,3,11:25:01 AM,27,3,11:25:12 AM,27,3,11:28:34 AM,27,3,11:34:27 AM,27,3,12:03:56 PM,16,25.8,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_306,1769
-Order_No_244,User_Id_3291,Bike,3,Business,17,5,11:19:25 AM,17,5,11:19:44 AM,17,5,11:21:22 AM,17,5,11:30:10 AM,17,5,12:01:39 PM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_958,1889
-Order_No_10054,User_Id_1927,Bike,3,Business,16,4,4:22:55 PM,16,4,5:02:24 PM,16,4,5:07:04 PM,16,4,5:10:27 PM,16,4,5:50:39 PM,14,,,-1.3472066,36.7692638,-1.3106338,36.680976799999996,Rider_Id_821,2412
-Order_No_25080,User_Id_3426,Bike,3,Business,31,1,1:48:49 PM,31,1,1:49:19 PM,31,1,1:58:30 PM,31,1,2:10:42 PM,31,1,2:34:00 PM,9,23.9,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_576,1398
-Order_No_13708,User_Id_2457,Bike,3,Business,7,2,8:37:57 AM,7,2,8:38:07 AM,7,2,8:44:43 AM,7,2,8:49:30 AM,7,2,9:15:42 AM,7,16.6,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_228,1572
-Order_No_15201,User_Id_1019,Bike,1,Personal,10,4,9:23:09 AM,10,4,9:24:40 AM,10,4,9:40:28 AM,10,4,9:52:35 AM,10,4,10:22:29 AM,9,18.4,,-1.29387,36.7907813,-1.2515637,36.8119508,Rider_Id_486,1794
-Order_No_13462,User_Id_2989,Bike,3,Business,2,5,11:33:48 AM,2,5,11:35:09 AM,2,5,11:47:05 AM,2,5,12:34:30 PM,2,5,1:10:04 PM,15,23.5,,-1.2571472,36.795063299999995,-1.3463041999999998,36.768164,Rider_Id_715,2134
-Order_No_1107,User_Id_1530,Bike,3,Personal,8,2,2:26:54 PM,8,2,2:27:58 PM,8,2,2:35:17 PM,8,2,2:36:40 PM,8,2,3:17:46 PM,10,25.3,,-1.2924173,36.779781400000005,-1.3178263000000001,36.7141818,Rider_Id_674,2466
-Order_No_14319,User_Id_3355,Bike,3,Business,23,3,10:28:52 AM,23,3,10:35:48 AM,23,3,10:51:17 AM,23,3,11:20:25 AM,23,3,11:35:10 AM,7,22.0,,-1.2620912,36.8013562,-1.3052491000000002,36.8223899,Rider_Id_397,885
-Order_No_6120,User_Id_700,Bike,3,Business,18,1,10:10:39 AM,18,1,10:28:17 AM,18,1,11:23:57 AM,18,1,11:33:13 AM,18,1,12:33:37 PM,8,26.7,,-1.323135,36.833139,-1.2617028000000001,36.7983849,Rider_Id_332,3624
-Order_No_14067,User_Id_1472,Bike,1,Personal,16,2,12:11:32 PM,16,2,12:13:09 PM,16,2,12:19:26 PM,16,2,12:32:56 PM,16,2,12:44:16 PM,5,23.5,,-1.2686289,36.8073469,-1.2776982,36.83126,Rider_Id_100,680
-Order_No_21907,User_Id_3355,Bike,3,Business,3,3,2:09:24 PM,3,3,2:10:55 PM,3,3,2:36:59 PM,3,3,2:45:05 PM,3,3,3:49:07 PM,10,27.0,,-1.2859912,36.8756811,-1.3052491000000002,36.8223899,Rider_Id_333,3842
-Order_No_13908,User_Id_3686,Bike,3,Business,18,3,10:05:40 AM,18,3,10:06:54 AM,18,3,10:21:01 AM,18,3,10:23:32 AM,18,3,10:47:18 AM,9,14.5,,-1.2551895,36.7822034,-1.2857117,36.7968296,Rider_Id_131,1426
-Order_No_20853,User_Id_1245,Bike,3,Business,14,1,9:11:24 AM,14,1,9:25:06 AM,14,1,9:35:00 AM,14,1,10:06:06 AM,14,1,10:36:04 AM,17,20.4,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_412,1798
-Order_No_8647,User_Id_2886,Bike,3,Personal,15,6,1:43:04 PM,15,6,1:43:26 PM,15,6,1:56:19 PM,15,6,2:12:55 PM,15,6,2:25:43 PM,6,23.5,,-1.280549,36.820640999999995,-1.2637591000000001,36.79038320000001,Rider_Id_812,768
-Order_No_21891,User_Id_3718,Bike,3,Business,15,1,2:48:32 PM,15,1,2:50:05 PM,15,1,3:01:33 PM,15,1,3:08:22 PM,15,1,3:32:17 PM,5,,,-1.2650027,36.8126242,-1.2877501,36.814659899999995,Rider_Id_519,1435
-Order_No_11750,User_Id_1482,Bike,3,Business,13,2,3:11:59 PM,13,2,3:16:11 PM,13,2,3:28:14 PM,13,2,3:30:46 PM,13,2,3:58:39 PM,8,26.2,,-1.2966600000000001,36.828668,-1.2848964,36.777431,Rider_Id_338,1673
-Order_No_16684,User_Id_3103,Bike,1,Personal,12,4,8:04:59 AM,12,4,8:06:04 AM,12,4,8:24:10 AM,12,4,8:40:20 AM,12,4,9:00:15 AM,11,16.0,,-1.2939186000000003,36.8069693,-1.2379585,36.875789000000005,Rider_Id_159,1195
-Order_No_24276,User_Id_2159,Bike,3,Business,22,2,1:02:39 PM,22,2,1:03:33 PM,22,2,1:16:18 PM,22,2,1:20:57 PM,22,2,2:01:32 PM,6,19.3,,-1.304735,36.784605400000004,-1.2953659,36.82494620000001,Rider_Id_680,2435
-Order_No_8155,User_Id_3222,Bike,3,Business,8,3,3:31:45 PM,8,3,3:32:58 PM,8,3,3:34:05 PM,8,3,3:37:52 PM,8,3,4:04:44 PM,13,,,-1.2551895,36.7822034,-1.3133713,36.8541174,Rider_Id_168,1612
-Order_No_14160,User_Id_1994,Bike,3,Business,18,1,12:03:35 PM,18,1,12:05:09 PM,18,1,12:25:48 PM,18,1,12:34:17 PM,18,1,12:51:39 PM,3,28.2,,-1.273733,36.818022,-1.286925,36.821915999999995,Rider_Id_763,1042
-Order_No_6998,User_Id_2066,Bike,3,Business,25,3,8:27:38 AM,25,3,8:29:35 AM,25,3,8:47:04 AM,25,3,8:53:03 AM,25,3,9:03:27 AM,6,13.3,,-1.316961,36.820337,-1.3048468999999998,36.7876088,Rider_Id_726,624
-Order_No_26706,User_Id_393,Bike,3,Business,24,5,3:14:26 PM,24,5,3:15:35 PM,24,5,3:25:03 PM,24,5,3:37:30 PM,24,5,3:50:29 PM,4,23.1,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_2,779
-Order_No_5524,User_Id_909,Bike,3,Business,15,5,3:59:23 PM,15,5,4:14:05 PM,15,5,4:50:50 PM,15,5,5:04:21 PM,15,5,5:23:31 PM,8,27.7,,-1.225322,36.80855,-1.255392,36.792539,Rider_Id_52,1150
-Order_No_3956,User_Id_393,Bike,3,Business,17,1,8:06:12 AM,17,1,10:04:58 AM,17,1,10:22:06 AM,17,1,10:35:27 AM,17,1,10:44:16 AM,4,23.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_254,529
-Order_No_20194,User_Id_1389,Bike,3,Business,16,2,3:55:07 PM,16,2,4:05:14 PM,16,2,4:30:38 PM,16,2,4:36:07 PM,16,2,4:59:23 PM,16,,,-1.207546,36.7913976,-1.2765736,36.851364600000004,Rider_Id_801,1396
-Order_No_19506,User_Id_2746,Bike,1,Personal,1,2,10:41:49 AM,1,2,10:43:49 AM,1,2,11:05:49 AM,1,2,11:13:22 AM,1,2,11:21:33 AM,4,21.7,,-1.2784495,36.7981729,-1.2789476999999998,36.8122359,Rider_Id_396,491
-Order_No_26264,User_Id_1004,Bike,3,Business,7,5,12:17:40 PM,7,5,12:42:38 PM,7,5,12:51:08 PM,7,5,1:02:20 PM,7,5,1:22:40 PM,5,,,-1.3250048,36.869718799999994,-1.3142909999999999,36.8478646,Rider_Id_829,1220
-Order_No_13505,User_Id_3648,Bike,3,Personal,5,2,10:04:09 AM,5,2,10:05:53 AM,5,2,10:12:13 AM,5,2,10:43:25 AM,5,2,11:10:49 AM,16,23.5,,-1.2987276,36.81763370000001,-1.3566495,36.7482787,Rider_Id_34,1644
-Order_No_10708,User_Id_2462,Bike,3,Personal,28,5,2:51:36 PM,28,5,3:05:46 PM,28,5,3:12:30 PM,28,5,3:41:34 PM,28,5,4:06:31 PM,17,25.4,,-1.2778747,36.883182899999994,-1.3211235000000001,36.7765164,Rider_Id_900,1497
-Order_No_27509,User_Id_1075,Bike,3,Business,28,4,10:43:00 AM,28,4,10:50:35 AM,28,4,11:16:14 AM,28,4,11:22:33 AM,28,4,12:15:09 PM,20,23.8,,-1.3562374,36.904295399999995,-1.2527958000000001,36.8003131,Rider_Id_883,3156
-Order_No_12717,User_Id_3222,Bike,3,Business,27,2,11:17:43 AM,27,2,11:18:58 AM,27,2,11:19:20 AM,27,2,11:37:49 AM,27,2,11:46:22 AM,5,23.6,,-1.2977565,36.8025534,-1.2821378,36.7740428,Rider_Id_390,513
-Order_No_5754,User_Id_1809,Bike,3,Business,6,3,1:22:05 PM,6,3,1:23:07 PM,6,3,1:36:08 PM,6,3,1:37:23 PM,6,3,1:58:37 PM,9,,,-1.2964063,36.8281083,-1.2571472,36.795063299999995,Rider_Id_99,1274
-Order_No_3671,User_Id_635,Bike,3,Business,15,5,12:05:42 PM,15,5,12:07:24 PM,15,5,12:53:54 PM,15,5,1:13:52 PM,15,5,1:33:26 PM,7,25.8,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_76,1174
-Order_No_13921,User_Id_790,Bike,1,Personal,16,2,9:46:56 AM,16,2,9:47:39 AM,16,2,9:54:50 AM,16,2,10:17:49 AM,16,2,10:51:40 AM,15,,,-1.2590946,36.7607115,-1.3190714,36.8016277,Rider_Id_568,2031
-Order_No_4064,User_Id_2861,Bike,1,Personal,2,2,9:46:12 AM,2,2,9:46:50 AM,2,2,10:06:01 AM,2,2,10:12:48 AM,2,2,10:27:59 AM,9,15.3,,-1.2942632,36.8113036,-1.2560455,36.780086700000005,Rider_Id_100,911
-Order_No_3873,User_Id_3689,Bike,3,Business,5,2,1:48:07 PM,5,2,1:48:28 PM,5,2,1:48:35 PM,5,2,1:49:40 PM,5,2,2:07:05 PM,6,22.6,,-1.2601708999999999,36.8002238,-1.2904016,36.82263029999999,Rider_Id_379,1045
-Order_No_21456,User_Id_835,Bike,3,Business,9,2,10:35:46 AM,9,2,10:35:56 AM,9,2,10:45:34 AM,9,2,10:47:17 AM,9,2,11:20:37 AM,20,27.2,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_548,2000
-Order_No_16941,User_Id_1502,Bike,3,Business,14,4,1:51:01 PM,14,4,3:36:00 PM,14,4,3:52:52 PM,14,4,3:56:56 PM,14,4,4:26:43 PM,12,28.0,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_377,1787
-Order_No_12317,User_Id_868,Bike,3,Business,28,2,2:43:51 PM,28,2,2:44:02 PM,28,2,2:44:13 PM,28,2,3:05:44 PM,28,2,3:18:58 PM,5,23.7,,-1.259478,36.801781299999995,-1.265715,36.823815,Rider_Id_72,794
-Order_No_9330,User_Id_435,Bike,3,Business,29,3,12:00:34 PM,29,3,12:01:37 PM,29,3,12:23:35 PM,29,3,12:34:19 PM,29,3,1:09:56 PM,8,,,-1.3081376,36.83297720000001,-1.2567295,36.784224,Rider_Id_2,2137
-Order_No_6366,User_Id_176,Bike,1,Personal,5,2,1:07:25 PM,5,2,1:10:08 PM,5,2,1:30:26 PM,5,2,1:43:54 PM,5,2,2:08:30 PM,15,22.9,,-1.330427,36.893170500000004,-1.2666695,36.8328283,Rider_Id_906,1476
-Order_No_9002,User_Id_3635,Bike,1,Business,20,4,11:08:57 AM,20,4,11:09:05 AM,20,4,11:09:21 AM,20,4,11:15:58 AM,20,4,11:16:17 AM,3,24.6,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_51,19
-Order_No_3152,User_Id_1606,Bike,1,Personal,15,1,10:15:36 AM,15,1,10:16:17 AM,15,1,10:20:29 AM,15,1,10:21:38 AM,15,1,10:40:27 AM,9,,,-1.2666153,36.8015124,-1.3240024,36.811902200000006,Rider_Id_208,1129
-Order_No_24318,User_Id_323,Bike,1,Business,12,3,10:29:24 AM,12,3,10:49:03 AM,12,3,11:03:43 AM,12,3,11:09:03 AM,12,3,11:17:38 AM,4,23.4,,-1.3632162,36.9108253,-1.3861826000000002,36.9349923,Rider_Id_343,515
-Order_No_27222,User_Id_12,Bike,3,Business,28,4,1:56:19 PM,28,4,2:12:28 PM,28,4,2:34:31 PM,28,4,2:39:27 PM,28,4,3:00:46 PM,12,,,-1.2400209999999998,36.849990000000005,-1.3031315,36.8175661,Rider_Id_851,1279
-Order_No_20318,User_Id_3246,Bike,1,Business,16,2,8:46:51 AM,16,2,8:47:16 AM,16,2,9:32:53 AM,16,2,9:36:45 AM,16,2,10:17:08 AM,16,,,-1.3165542,36.700067,-1.2615888000000002,36.7928732,Rider_Id_565,2423
-Order_No_15859,User_Id_2066,Bike,3,Business,14,2,12:54:10 PM,14,2,12:54:46 PM,14,2,1:05:34 PM,14,2,1:11:31 PM,14,2,1:34:35 PM,8,21.5,,-1.316961,36.820337,-1.2654199,36.8008276,Rider_Id_213,1384
-Order_No_993,User_Id_496,Bike,3,Business,2,2,4:50:20 PM,2,2,4:52:11 PM,2,2,4:56:55 PM,2,2,5:03:00 PM,2,2,5:26:32 PM,10,28.6,,-1.2551895,36.7822034,-1.2948533000000002,36.799161600000005,Rider_Id_85,1412
-Order_No_14183,User_Id_2542,Bike,3,Personal,30,6,12:33:23 PM,30,6,12:46:57 PM,30,6,1:13:29 PM,30,6,1:34:34 PM,30,6,2:38:08 PM,5,23.9,,-1.2883158000000001,36.8199114,-1.2899816000000002,36.7846439,Rider_Id_95,3814
-Order_No_7806,User_Id_2961,Bike,3,Personal,4,5,11:33:02 AM,4,5,11:33:55 AM,4,5,12:50:27 PM,4,5,1:07:36 PM,4,5,2:17:47 PM,26,17.3,,-1.3251247,36.899592600000005,-1.2138959999999999,36.7980885,Rider_Id_26,4211
-Order_No_27893,User_Id_3674,Bike,3,Business,18,2,1:18:20 PM,18,2,1:18:43 PM,18,2,1:30:26 PM,18,2,1:34:24 PM,18,2,1:43:19 PM,4,25.4,,-1.2551895,36.7822034,-1.2651643,36.807221399999996,Rider_Id_727,535
-Order_No_4507,User_Id_3752,Bike,3,Business,15,3,1:29:31 PM,15,3,1:30:24 PM,15,3,1:37:22 PM,15,3,1:43:32 PM,15,3,2:28:02 PM,12,26.0,,-1.2963097,36.7688221,-1.2919683000000002,36.8500706,Rider_Id_854,2670
-Order_No_7880,User_Id_1363,Bike,3,Business,18,1,9:40:39 AM,18,1,9:52:32 AM,18,1,9:54:05 AM,18,1,9:57:57 AM,18,1,10:21:41 AM,10,22.1,,-1.3004062,36.829741,-1.249738,36.863184000000004,Rider_Id_178,1424
-Order_No_15802,User_Id_635,Bike,3,Business,13,4,9:34:39 AM,13,4,9:39:34 AM,13,4,9:45:11 AM,13,4,10:34:30 AM,13,4,10:52:14 AM,11,21.9,,-1.2403615,36.872332,-1.2599563,36.799343900000004,Rider_Id_925,1064
-Order_No_15839,User_Id_246,Bike,3,Business,18,2,5:57:58 PM,18,2,5:58:56 PM,18,2,6:09:59 PM,18,2,6:26:45 PM,18,2,6:49:35 PM,8,,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_736,1370
-Order_No_19988,User_Id_3621,Bike,3,Business,29,6,12:11:46 PM,29,6,12:12:41 PM,29,6,12:34:06 PM,29,6,12:37:40 PM,29,6,1:21:40 PM,15,24.2,,-1.2836583000000001,36.7886014,-1.2778747,36.883182899999994,Rider_Id_854,2640
-Order_No_21934,User_Id_1500,Bike,3,Business,4,4,10:14:21 AM,4,4,10:16:29 AM,4,4,10:16:37 AM,4,4,10:21:01 AM,4,4,10:42:17 AM,13,21.8,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_576,1276
-Order_No_7358,User_Id_2123,Bike,1,Personal,10,5,10:00:07 AM,10,5,10:00:14 AM,10,5,10:04:48 AM,10,5,10:09:45 AM,10,5,10:31:18 AM,14,13.6,,-1.2870736999999999,36.8327911,-1.3382857,36.887071500000005,Rider_Id_414,1293
-Order_No_6068,User_Id_3521,Bike,3,Business,20,1,3:29:43 PM,20,1,3:30:28 PM,20,1,3:34:00 PM,20,1,3:59:49 PM,20,1,4:03:19 PM,1,,,-1.2701582,36.807443299999996,-1.2594473999999998,36.8089122,Rider_Id_68,210
-Order_No_10836,User_Id_2212,Bike,3,Business,31,5,3:16:52 PM,31,5,3:22:40 PM,31,5,3:58:24 PM,31,5,4:02:49 PM,31,5,4:24:19 PM,5,24.8,,-1.2571472,36.795063299999995,-1.2896398999999998,36.812449799999996,Rider_Id_817,1290
-Order_No_24527,User_Id_1642,Bike,3,Business,21,1,2:57:29 PM,21,1,2:57:55 PM,21,1,3:08:11 PM,21,1,3:11:41 PM,21,1,4:15:34 PM,5,27.6,,-1.2897399,36.8270975,-1.2726506000000002,36.7960153,Rider_Id_819,3833
-Order_No_16166,User_Id_71,Bike,3,Business,24,3,2:26:09 PM,24,3,2:34:10 PM,24,3,2:43:12 PM,24,3,3:08:41 PM,24,3,3:34:01 PM,8,18.2,,-1.2891921999999998,36.825189,-1.2584143,36.8048002,Rider_Id_436,1520
-Order_No_7835,User_Id_391,Bike,3,Business,7,5,3:03:55 PM,7,5,3:25:20 PM,7,5,3:25:25 PM,7,5,4:18:30 PM,7,5,4:18:33 PM,4,,,-1.2867454,36.8259017,-1.300921,36.828195,Rider_Id_855,3
-Order_No_16989,User_Id_1959,Bike,3,Business,10,5,10:06:17 AM,10,5,10:06:40 AM,10,5,10:17:50 AM,10,5,10:19:53 AM,10,5,10:40:16 AM,6,,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_868,1223
-Order_No_2632,User_Id_2467,Bike,1,Personal,13,4,9:19:07 AM,13,4,9:20:18 AM,13,4,9:35:12 AM,13,4,9:37:01 AM,13,4,9:52:53 AM,6,18.4,,-1.2880152,36.7716734,-1.3111883999999998,36.80644960000001,Rider_Id_749,952
-Order_No_3459,User_Id_2277,Bike,3,Business,27,5,11:18:33 AM,27,5,11:21:25 AM,27,5,11:21:45 AM,27,5,11:50:38 AM,27,5,12:45:00 PM,4,22.2,,-1.2894521,36.826366799999995,-1.3030546,36.8101808,Rider_Id_698,3262
-Order_No_21978,User_Id_1500,Bike,3,Business,10,3,11:47:49 AM,10,3,11:48:18 AM,10,3,11:48:36 AM,10,3,12:12:23 PM,10,3,12:36:56 PM,8,27.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_258,1473
-Order_No_648,User_Id_3283,Bike,3,Business,2,2,9:14:14 AM,2,2,9:20:02 AM,2,2,9:32:54 AM,2,2,9:46:55 AM,2,2,9:59:55 AM,6,20.7,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_427,780
-Order_No_5023,User_Id_3355,Bike,3,Business,13,3,2:58:22 PM,13,3,3:08:23 PM,13,3,3:09:01 PM,13,3,3:15:25 PM,13,3,3:32:03 PM,8,23.6,,-1.3052491000000002,36.8223899,-1.2615888000000002,36.7928732,Rider_Id_379,998
-Order_No_8714,User_Id_1903,Bike,3,Personal,9,3,1:41:49 PM,9,3,1:42:01 PM,9,3,1:53:09 PM,9,3,1:59:49 PM,9,3,2:55:39 PM,26,25.3,,-1.2960063999999998,36.758236700000005,-1.3533402,36.9078924,Rider_Id_457,3350
-Order_No_28056,User_Id_3689,Bike,3,Business,14,3,1:09:19 PM,14,3,1:09:28 PM,14,3,1:16:34 PM,14,3,1:19:45 PM,14,3,1:40:17 PM,5,24.4,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_237,1232
-Order_No_21121,User_Id_2801,Bike,3,Business,15,3,4:15:27 PM,15,3,4:17:06 PM,15,3,4:28:11 PM,15,3,4:41:10 PM,15,3,4:56:30 PM,6,25.0,,-1.2566586000000002,36.792968200000004,-1.289081,36.8300409,Rider_Id_378,920
-Order_No_18875,User_Id_2330,Bike,3,Business,27,1,5:59:09 PM,27,1,5:59:56 PM,27,1,6:08:18 PM,27,1,6:12:06 PM,27,1,6:32:26 PM,8,,,-1.2536275,36.798793200000006,-1.2336843000000002,36.800725899999996,Rider_Id_96,1220
-Order_No_11991,User_Id_1856,Bike,3,Business,8,4,9:09:49 AM,8,4,9:21:50 AM,8,4,9:55:14 AM,8,4,9:59:00 AM,8,4,10:18:55 AM,5,20.5,,-1.273009,36.802807,-1.2930705,36.827943299999994,Rider_Id_114,1195
-Order_No_15497,User_Id_887,Bike,3,Personal,22,5,8:54:44 AM,22,5,9:43:09 AM,22,5,9:59:35 AM,22,5,10:01:24 AM,22,5,10:21:47 AM,8,16.1,,-1.311706,36.802841,-1.2574087999999999,36.799712,Rider_Id_65,1223
-Order_No_17527,User_Id_970,Bike,3,Business,23,2,3:22:03 PM,23,2,3:23:39 PM,23,2,3:47:50 PM,23,2,3:52:30 PM,23,2,4:22:08 PM,10,,,-1.2551895,36.7822034,-1.3089235000000001,36.8372446,Rider_Id_96,1778
-Order_No_1939,User_Id_1329,Bike,3,Business,20,4,4:07:04 PM,20,4,4:19:50 PM,20,4,4:26:01 PM,20,4,4:28:43 PM,20,4,4:58:00 PM,15,25.3,,-1.273056,36.811298,-1.2521158000000001,36.689862899999994,Rider_Id_105,1757
-Order_No_21199,User_Id_3714,Bike,2,Personal,15,3,11:18:32 AM,15,3,11:18:51 AM,15,3,11:33:34 AM,15,3,11:34:40 AM,15,3,12:17:03 PM,17,18.1,,-1.2938694,36.7629838,-1.2311903,36.8102105,Rider_Id_334,2543
-Order_No_6114,User_Id_2412,Bike,3,Business,20,2,3:58:35 PM,20,2,4:08:56 PM,20,2,4:10:58 PM,20,2,4:13:30 PM,20,2,4:41:18 PM,14,23.8,,-1.2297202,36.881950700000004,-1.3024794,36.8228284,Rider_Id_685,1668
-Order_No_21524,User_Id_3404,Bike,3,Business,26,2,5:03:30 PM,26,2,5:04:37 PM,26,2,5:09:25 PM,26,2,5:14:50 PM,26,2,5:40:08 PM,13,29.4,,-1.2232123,36.8932547,-1.279395,36.825364,Rider_Id_26,1518
-Order_No_8921,User_Id_3356,Bike,3,Business,3,1,12:47:07 PM,3,1,12:47:30 PM,3,1,1:18:52 PM,3,1,1:20:02 PM,3,1,1:54:17 PM,19,21.3,,-1.2036664,36.819262200000004,-1.2973364,36.7916296,Rider_Id_7,2055
-Order_No_14076,User_Id_2523,Bike,3,Business,10,4,10:54:42 AM,10,4,10:55:02 AM,10,4,11:02:00 AM,10,4,11:08:25 AM,10,4,11:41:56 AM,18,24.7,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_523,2011
-Order_No_23672,User_Id_391,Bike,3,Business,7,2,10:08:55 AM,7,2,10:09:47 AM,7,2,10:19:56 AM,7,2,10:26:29 AM,7,2,10:40:50 AM,5,16.5,,-1.2777923999999998,36.8231577,-1.3010429,36.835333500000004,Rider_Id_414,861
-Order_No_21472,User_Id_2066,Bike,3,Business,27,5,1:32:29 PM,27,5,1:32:58 PM,27,5,1:52:25 PM,27,5,1:56:13 PM,27,5,2:13:48 PM,9,24.0,,-1.316961,36.820337,-1.2655306,36.828148,Rider_Id_105,1055
-Order_No_11850,User_Id_2008,Bike,3,Business,20,4,9:25:54 AM,20,4,9:44:01 AM,20,4,10:27:25 AM,20,4,10:37:16 AM,20,4,12:21:17 PM,16,22.0,,-1.2508229,36.7895263,-1.2434951,36.6760986,Rider_Id_88,6241
-Order_No_13773,User_Id_2330,Bike,3,Business,25,1,5:10:57 PM,25,1,5:11:23 PM,25,1,5:17:51 PM,25,1,5:20:07 PM,25,1,5:44:48 PM,9,27.3,,-1.2671943,36.8062533,-1.3015197,36.7658456,Rider_Id_795,1481
-Order_No_3448,User_Id_3701,Bike,3,Business,25,5,4:00:35 PM,25,5,4:00:42 PM,25,5,4:07:56 PM,25,5,4:18:50 PM,25,5,4:22:10 PM,2,29.0,,-1.2612573999999999,36.7879622,-1.2643693999999999,36.785413500000004,Rider_Id_56,200
-Order_No_13566,User_Id_3426,Bike,3,Business,12,2,5:46:11 PM,12,2,5:48:09 PM,12,2,5:58:37 PM,12,2,6:03:57 PM,12,2,6:22:24 PM,2,28.3,,-1.3012008,36.764868,-1.2918688,36.7603541,Rider_Id_952,1107
-Order_No_15266,User_Id_3744,Bike,3,Business,3,4,1:26:32 PM,3,4,1:27:36 PM,3,4,1:39:04 PM,3,4,1:45:05 PM,3,4,2:28:52 PM,20,25.9,,-1.2701978999999999,36.8138177,-1.3361484,36.91611279999999,Rider_Id_812,2627
-Order_No_10365,User_Id_3080,Bike,3,Business,24,4,12:07:15 PM,24,4,12:11:42 PM,24,4,12:28:55 PM,24,4,12:31:08 PM,24,4,12:46:51 PM,6,21.3,,-1.2553615,36.789537599999996,-1.282543,36.824618,Rider_Id_159,943
-Order_No_9300,User_Id_3462,Bike,1,Personal,6,6,9:45:18 AM,6,6,9:45:56 AM,6,6,10:03:43 AM,6,6,10:06:27 AM,6,6,10:22:32 AM,6,19.3,,-1.2556829999999999,36.813771,-1.2842863000000002,36.7951109,Rider_Id_427,965
-Order_No_5167,User_Id_1368,Bike,3,Business,5,4,2:28:20 PM,5,4,2:28:36 PM,5,4,2:33:30 PM,5,4,2:34:47 PM,5,4,2:54:31 PM,8,22.2,,-1.2579303999999998,36.8019667,-1.2903624,36.787524600000005,Rider_Id_379,1184
-Order_No_22361,User_Id_558,Bike,2,Personal,28,5,8:33:54 AM,28,5,8:35:36 AM,28,5,8:51:35 AM,28,5,9:23:48 AM,28,5,9:59:02 AM,15,20.7,,-1.3322618,36.8045412,-1.3189978999999998,36.7104076,Rider_Id_532,2114
-Order_No_6469,User_Id_2587,Bike,3,Business,20,4,12:50:33 PM,20,4,12:51:42 PM,20,4,1:08:12 PM,20,4,1:21:46 PM,20,4,1:58:37 PM,15,24.5,,-1.28878,36.8168312,-1.216682,36.908577799999996,Rider_Id_736,2211
-Order_No_21988,User_Id_1384,Bike,3,Business,27,3,2:28:33 PM,27,3,2:29:20 PM,27,3,2:38:20 PM,27,3,2:43:43 PM,27,3,3:15:46 PM,19,29.5,,-1.2517448,36.7924971,-1.2352916,36.9162792,Rider_Id_125,1923
-Order_No_14914,User_Id_3264,Bike,3,Business,10,5,3:30:50 PM,10,5,3:32:47 PM,10,5,3:43:37 PM,10,5,3:44:57 PM,10,5,4:48:30 PM,19,,,-1.2551895,36.7822034,-1.1845436000000003,36.8282795,Rider_Id_690,3813
-Order_No_13007,User_Id_3203,Bike,3,Business,28,1,9:59:43 AM,28,1,10:01:07 AM,28,1,10:12:48 AM,28,1,10:19:53 AM,28,1,10:35:04 AM,7,18.0,,-1.2297799999999999,36.836215,-1.228911,36.881876,Rider_Id_852,911
-Order_No_21923,User_Id_336,Bike,1,Personal,11,5,10:52:20 AM,11,5,11:09:36 AM,11,5,11:29:37 AM,11,5,11:36:30 AM,11,5,12:01:47 PM,5,20.8,,-1.2844941,36.822762100000006,-1.3061448,36.7978023,Rider_Id_338,1517
-Order_No_20038,User_Id_2987,Bike,3,Business,22,1,3:16:42 PM,22,1,3:40:09 PM,22,1,3:49:24 PM,22,1,3:51:18 PM,22,1,4:19:13 PM,9,26.1,,-1.2702120000000001,36.82108770000001,-1.3280091,36.8524146,Rider_Id_482,1675
-Order_No_19523,User_Id_1687,Bike,3,Business,21,3,10:31:43 AM,21,3,10:32:00 AM,21,3,10:41:49 AM,21,3,10:49:04 AM,21,3,11:24:07 AM,13,22.0,,-1.3169591,36.7000029,-1.2962832,36.784968299999996,Rider_Id_247,2103
-Order_No_26044,User_Id_393,Bike,3,Business,9,3,11:55:42 AM,9,3,11:56:58 AM,9,3,12:21:56 PM,9,3,12:40:02 PM,9,3,1:04:28 PM,11,25.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_725,1466
-Order_No_24238,User_Id_391,Bike,3,Business,14,4,10:35:16 AM,14,4,10:54:58 AM,14,4,11:14:11 AM,14,4,11:17:44 AM,14,4,12:19:50 PM,18,23.9,,-1.2867454,36.8259017,-1.3878504999999999,36.7692885,Rider_Id_365,3726
-Order_No_10910,User_Id_1271,Bike,3,Business,6,3,12:18:06 PM,6,3,12:20:09 PM,6,3,12:46:29 PM,6,3,1:46:00 PM,6,3,2:32:08 PM,17,27.7,,-1.2285995,36.901237200000004,-1.3244885,36.897791999999995,Rider_Id_75,2768
-Order_No_11482,User_Id_1688,Bike,3,Business,13,4,11:45:39 AM,13,4,11:46:14 AM,13,4,11:46:43 AM,13,4,12:03:10 PM,13,4,12:43:00 PM,18,21.9,,-1.300921,36.828195,-1.3855778,36.9269619,Rider_Id_686,2390
-Order_No_6128,User_Id_1721,Bike,3,Business,11,2,10:07:20 AM,11,2,10:08:00 AM,11,2,10:10:36 AM,11,2,10:41:23 AM,11,2,11:25:26 AM,15,19.4,,-1.2551895,36.7822034,-1.3194911,36.720085299999994,Rider_Id_133,2643
-Order_No_20279,User_Id_478,Bike,3,Business,6,3,10:38:04 AM,6,3,10:39:00 AM,6,3,10:46:27 AM,6,3,11:01:05 AM,6,3,11:13:52 AM,3,,,-1.2551895,36.7822034,-1.2665558000000001,36.8056479,Rider_Id_245,767
-Order_No_14784,User_Id_2383,Bike,3,Business,17,3,11:34:28 AM,17,3,11:36:08 AM,17,3,11:40:55 AM,17,3,11:46:30 AM,17,3,11:53:39 AM,1,25.4,,-1.2666153,36.8015124,-1.2608993,36.799246000000004,Rider_Id_726,429
-Order_No_25908,User_Id_552,Bike,3,Business,20,1,1:27:30 PM,20,1,1:27:53 PM,20,1,1:32:31 PM,20,1,1:42:09 PM,20,1,2:15:40 PM,23,,,-1.3472066,36.7692638,-1.335477,36.930829700000004,Rider_Id_411,2011
-Order_No_10422,User_Id_1329,Bike,3,Business,14,3,8:56:21 AM,14,3,9:00:42 AM,14,3,9:19:18 AM,14,3,9:22:19 AM,14,3,10:00:22 AM,8,18.4,,-1.273056,36.811298,-1.3223922,36.837965999999994,Rider_Id_88,2283
-Order_No_13574,User_Id_3509,Bike,1,Personal,7,5,11:36:01 AM,7,5,11:46:39 AM,7,5,12:01:31 PM,7,5,12:06:05 PM,7,5,12:43:58 PM,8,25.5,,-1.290499,36.784363,-1.282772,36.82599929999999,Rider_Id_726,2273
-Order_No_737,User_Id_1859,Bike,3,Business,31,4,12:24:37 PM,31,4,12:25:58 PM,31,4,12:43:24 PM,31,4,1:07:50 PM,31,4,1:28:29 PM,8,21.6,,-1.3037386999999998,36.8175661,-1.2626765,36.795598999999996,Rider_Id_188,1239
-Order_No_2901,User_Id_2587,Bike,3,Business,26,6,10:49:14 AM,26,6,10:50:08 AM,26,6,11:05:19 AM,26,6,11:13:50 AM,26,6,11:46:00 AM,8,25.8,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_800,1930
-Order_No_615,User_Id_186,Bike,3,Business,20,1,5:17:04 PM,20,1,5:17:19 PM,20,1,5:41:21 PM,20,1,5:45:50 PM,20,1,5:55:16 PM,3,,,-1.2571472,36.795063299999995,-1.2551895,36.7822034,Rider_Id_933,566
-Order_No_10948,User_Id_2021,Bike,3,Business,12,5,11:06:33 AM,12,5,11:07:36 AM,12,5,11:38:20 AM,12,5,11:39:34 AM,12,5,12:10:47 PM,18,26.7,,-1.377114,36.726465999999995,-1.29475,36.83421,Rider_Id_572,1873
-Order_No_8543,User_Id_2701,Bike,3,Business,14,3,10:08:42 AM,14,3,10:09:24 AM,14,3,10:24:05 AM,14,3,10:26:10 AM,14,3,11:38:24 AM,11,18.6,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_248,4334
-Order_No_13664,User_Id_733,Bike,3,Business,29,1,3:59:01 PM,29,1,3:59:53 PM,29,1,4:15:46 PM,29,1,4:18:40 PM,29,1,4:46:03 PM,15,,,-1.2551895,36.7822034,-1.3265997,36.8721129,Rider_Id_263,1643
-Order_No_25375,User_Id_2285,Bike,3,Personal,12,5,11:16:16 AM,12,5,11:23:21 AM,12,5,11:40:22 AM,12,5,11:44:09 AM,12,5,12:17:22 PM,16,26.4,,-1.351453,36.899315,-1.2950041,36.814358299999995,Rider_Id_856,1993
-Order_No_19746,User_Id_2756,Bike,3,Business,6,1,5:20:10 PM,6,1,5:22:29 PM,6,1,5:49:06 PM,6,1,5:50:28 PM,6,1,6:14:12 PM,5,,,-1.2544051,36.7982934,-1.2814609,36.826593200000005,Rider_Id_624,1424
-Order_No_5266,User_Id_776,Bike,1,Personal,1,5,4:41:34 PM,1,5,4:44:41 PM,1,5,4:56:10 PM,1,5,5:08:59 PM,1,5,5:21:29 PM,6,22.8,,-1.278967,36.8236745,-1.3027038,36.7814225,Rider_Id_103,750
-Order_No_24538,User_Id_2627,Bike,3,Business,17,3,2:38:31 PM,17,3,2:38:56 PM,17,3,2:45:41 PM,17,3,2:47:18 PM,17,3,2:47:20 PM,2,,,-1.2571472,36.795063299999995,-1.2591018999999999,36.800577000000004,Rider_Id_40,2
-Order_No_28149,User_Id_265,Bike,3,Business,10,5,11:07:38 AM,10,5,11:09:25 AM,10,5,11:19:01 AM,10,5,11:34:17 AM,10,5,11:51:50 AM,8,,,-1.2731645,36.7900343,-1.300921,36.828195,Rider_Id_298,1053
-Order_No_3699,User_Id_1304,Bike,3,Business,13,2,9:31:37 AM,13,2,9:32:58 AM,13,2,9:40:28 AM,13,2,9:43:43 AM,13,2,10:25:36 AM,18,19.8,8.9,-1.3226301999999999,36.8719901,-1.2343236000000002,36.8861,Rider_Id_63,2513
-Order_No_4142,User_Id_91,Bike,3,Personal,19,2,12:29:38 PM,19,2,12:30:27 PM,19,2,1:06:06 PM,19,2,1:10:07 PM,19,2,1:38:38 PM,4,27.4,,-1.2775276000000002,36.8140021,-1.2611523,36.806922899999996,Rider_Id_484,1711
-Order_No_5862,User_Id_718,Bike,3,Business,14,2,10:41:22 AM,14,2,10:44:45 AM,14,2,10:56:39 AM,14,2,11:05:12 AM,14,2,11:40:54 AM,12,,,-1.2551895,36.7822034,-1.3114499,36.7425836,Rider_Id_3,2142
-Order_No_24078,User_Id_1245,Bike,3,Business,6,3,3:36:22 PM,6,3,3:37:02 PM,6,3,3:53:45 PM,6,3,4:01:27 PM,6,3,4:47:43 PM,16,27.4,,-1.2599563,36.799343900000004,-1.3198615,36.7110323,Rider_Id_631,2776
-Order_No_24251,User_Id_1089,Bike,1,Personal,10,5,4:24:57 PM,10,5,4:25:13 PM,10,5,4:30:10 PM,10,5,4:38:20 PM,10,5,5:39:07 PM,23,21.3,,-1.2799394,36.8192374,-1.259447,36.673921299999996,Rider_Id_414,3647
-Order_No_22249,User_Id_868,Bike,3,Business,10,3,9:41:47 AM,10,3,9:41:55 AM,10,3,9:44:37 AM,10,3,10:26:00 AM,10,3,10:35:23 AM,4,22.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_205,563
-Order_No_24846,User_Id_567,Bike,3,Business,22,5,11:09:22 AM,22,5,11:09:50 AM,22,5,11:11:31 AM,22,5,11:17:47 AM,22,5,11:48:10 AM,8,,,-1.2963097,36.7688221,-1.2753108999999998,36.8130185,Rider_Id_354,1823
-Order_No_1941,User_Id_393,Bike,3,Business,8,1,11:05:34 AM,8,1,11:05:45 AM,8,1,11:07:50 AM,8,1,11:24:14 AM,8,1,11:56:20 AM,14,28.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_168,1926
-Order_No_6085,User_Id_2479,Bike,1,Personal,20,7,12:57:27 PM,20,7,12:57:44 PM,20,7,1:10:49 PM,20,7,1:16:02 PM,20,7,1:37:41 PM,18,20.0,,-1.271672,36.783604,-1.320722,36.8803076,Rider_Id_103,1299
-Order_No_20721,User_Id_3689,Bike,3,Business,4,2,3:01:18 PM,4,2,3:32:54 PM,4,2,3:54:43 PM,4,2,4:01:17 PM,4,2,4:22:52 PM,9,23.8,11.1,-1.2962832,36.784968299999996,-1.2527958000000001,36.8003131,Rider_Id_847,1295
-Order_No_8940,User_Id_3599,Bike,3,Business,2,4,11:59:15 AM,2,4,11:59:55 AM,2,4,12:15:33 PM,2,4,12:25:46 PM,2,4,12:46:29 PM,12,,,-1.3302996,36.8707935,-1.3713751,36.934862,Rider_Id_461,1243
-Order_No_22503,User_Id_2066,Bike,3,Business,18,1,12:06:06 PM,18,1,12:06:53 PM,18,1,12:19:42 PM,18,1,12:24:20 PM,18,1,12:50:10 PM,11,26.7,,-1.3210188999999999,36.841055100000005,-1.3124537,36.925365,Rider_Id_675,1550
-Order_No_3322,User_Id_3289,Bike,3,Personal,1,5,1:08:42 PM,1,5,1:11:22 PM,1,5,1:49:24 PM,1,5,3:14:24 PM,1,5,4:07:47 PM,15,28.5,,-1.3216456,36.7145156,-1.2707373,36.805440999999995,Rider_Id_226,3203
-Order_No_19409,User_Id_3291,Bike,3,Business,10,1,9:38:01 AM,10,1,9:38:27 AM,10,1,9:59:14 AM,10,1,10:09:33 AM,10,1,10:18:18 AM,4,16.7,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_829,525
-Order_No_21484,User_Id_704,Bike,3,Business,14,5,9:46:42 AM,14,5,10:31:21 AM,14,5,10:40:26 AM,14,5,11:22:14 AM,14,5,12:08:27 PM,12,21.3,,-1.290894,36.822971,-1.2860183,36.8975338,Rider_Id_718,2773
-Order_No_13913,User_Id_3296,Bike,1,Personal,3,1,2:34:33 PM,3,1,2:34:57 PM,3,1,2:45:39 PM,3,1,2:48:50 PM,3,1,3:08:44 PM,7,23.2,,-1.253332,36.80636,-1.2981625,36.813014200000005,Rider_Id_857,1194
-Order_No_9861,User_Id_2315,Bike,1,Personal,22,7,9:49:21 AM,22,7,10:21:19 AM,22,7,10:39:26 AM,22,7,10:56:44 AM,22,7,11:13:58 AM,8,17.4,,-1.2962832,36.784968299999996,-1.2649605,36.798177700000004,Rider_Id_465,1034
-Order_No_22129,User_Id_653,Bike,3,Business,17,3,4:21:47 PM,17,3,4:23:04 PM,17,3,4:30:15 PM,17,3,4:32:58 PM,17,3,5:01:59 PM,15,24.8,,-1.279395,36.825364,-1.3251247,36.899592600000005,Rider_Id_31,1741
-Order_No_4051,User_Id_3035,Bike,3,Business,1,4,12:25:03 PM,1,4,12:30:44 PM,1,4,12:46:47 PM,1,4,12:51:04 PM,1,4,1:11:17 PM,8,,,-1.2853965,36.8183118,-1.2963097,36.7688221,Rider_Id_477,1213
-Order_No_11807,User_Id_2306,Bike,3,Business,18,1,9:46:24 AM,18,1,9:49:02 AM,18,1,10:23:07 AM,18,1,10:31:44 AM,18,1,11:24:38 AM,17,22.1,,-1.3241108,36.8692908,-1.2271998,36.808490500000005,Rider_Id_611,3174
-Order_No_8688,User_Id_1215,Bike,2,Personal,5,7,10:23:40 AM,5,7,10:23:58 AM,5,7,10:36:54 AM,5,7,10:41:13 AM,5,7,11:09:49 AM,11,19.3,,-1.3049101,36.7873232,-1.3254093999999998,36.7084902,Rider_Id_105,1716
-Order_No_15043,User_Id_1500,Bike,3,Business,18,4,12:37:59 PM,18,4,12:38:54 PM,18,4,12:53:27 PM,18,4,1:13:57 PM,18,4,1:40:44 PM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_349,1607
-Order_No_1403,User_Id_2017,Bike,3,Business,19,2,2:16:16 PM,19,2,2:16:50 PM,19,2,2:30:59 PM,19,2,2:56:20 PM,19,2,3:17:31 PM,15,18.7,,-1.2365885,36.8898127,-1.262492,36.800973,Rider_Id_455,1271
-Order_No_8300,User_Id_2733,Bike,3,Business,5,3,3:00:40 PM,5,3,3:00:46 PM,5,3,3:02:11 PM,5,3,3:04:16 PM,5,3,3:35:57 PM,5,24.8,,-1.2963097,36.7688221,-1.2995132,36.8043032,Rider_Id_445,1901
-Order_No_6326,User_Id_722,Bike,3,Business,18,4,1:46:32 PM,18,4,1:46:46 PM,18,4,2:04:22 PM,18,4,2:21:20 PM,18,4,3:02:11 PM,19,,,-1.32943,36.894242999999996,-1.2647975,36.821518299999994,Rider_Id_126,2451
-Order_No_16539,User_Id_2523,Bike,3,Business,20,1,11:33:52 AM,20,1,11:37:01 AM,20,1,11:41:26 AM,20,1,11:46:39 AM,20,1,11:54:05 AM,3,20.3,,-1.2551895,36.7822034,-1.2631940000000002,36.797624,Rider_Id_415,446
-Order_No_12290,User_Id_1280,Bike,3,Business,2,4,1:58:03 PM,2,4,1:58:19 PM,2,4,2:09:02 PM,2,4,2:25:13 PM,2,4,2:25:15 PM,3,,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_193,2
-Order_No_15678,User_Id_893,Bike,3,Business,17,5,4:25:16 PM,17,5,4:25:30 PM,17,5,4:29:10 PM,17,5,4:41:43 PM,17,5,5:06:51 PM,11,18.9,,-1.2663194,36.809901,-1.2224231,36.8560094,Rider_Id_882,1508
-Order_No_6395,User_Id_3669,Bike,3,Business,25,4,3:15:29 PM,25,4,3:16:05 PM,25,4,3:20:29 PM,25,4,3:27:40 PM,25,4,3:45:47 PM,9,,,-1.2615888000000002,36.7928732,-1.3025826,36.7670807,Rider_Id_576,1087
-Order_No_27025,User_Id_3651,Bike,3,Business,25,2,11:51:08 AM,25,2,11:51:16 AM,25,2,12:09:38 PM,25,2,12:19:36 PM,25,2,12:30:39 PM,2,24.0,,-1.2571472,36.795063299999995,-1.2662728,36.8151265,Rider_Id_116,663
-Order_No_23609,User_Id_2828,Bike,1,Business,5,6,12:19:43 PM,5,6,12:19:56 PM,5,6,1:01:09 PM,5,6,1:12:32 PM,5,6,1:41:03 PM,13,22.4,,-1.2141875,36.791039000000005,-1.2615888000000002,36.7928732,Rider_Id_658,1711
-Order_No_28081,User_Id_3647,Bike,3,Business,7,5,4:42:30 PM,7,5,5:01:45 PM,7,5,5:38:54 PM,7,5,5:42:37 PM,7,5,6:17:48 PM,9,21.5,,-1.2726389999999999,36.794723,-1.235348,36.760016,Rider_Id_435,2111
-Order_No_351,User_Id_3426,Bike,3,Business,3,4,3:02:57 PM,3,4,3:03:18 PM,3,4,3:31:52 PM,3,4,3:36:58 PM,3,4,4:53:22 PM,9,26.1,,-1.279395,36.825364,-1.3014461000000002,36.7661381,Rider_Id_831,4584
-Order_No_496,User_Id_3738,Bike,3,Business,1,3,5:57:58 PM,1,3,5:58:12 PM,1,3,5:59:23 PM,1,3,6:15:00 PM,1,3,6:31:50 PM,7,23.4,,-1.2584143,36.8048002,-1.2857671,36.7875614,Rider_Id_648,1010
-Order_No_16942,User_Id_728,Bike,2,Personal,20,1,1:28:37 PM,20,1,1:30:02 PM,20,1,1:38:43 PM,20,1,1:46:32 PM,20,1,1:55:13 PM,4,21.5,,-1.2854453000000001,36.818836299999994,-1.2884634,36.8128356,Rider_Id_88,521
-Order_No_4688,User_Id_2330,Bike,3,Business,3,1,12:06:28 PM,3,1,12:08:00 PM,3,1,12:12:41 PM,3,1,12:15:55 PM,3,1,12:30:22 PM,11,21.7,,-1.3143328,36.8137434,-1.2671943,36.8062533,Rider_Id_168,867
-Order_No_13357,User_Id_1976,Bike,3,Business,9,5,10:35:18 AM,9,5,10:35:36 AM,9,5,10:46:55 AM,9,5,10:49:12 AM,9,5,11:33:34 AM,23,25.5,,-1.257028,36.7810435,-1.3103985,36.9114173,Rider_Id_381,2662
-Order_No_22890,User_Id_3177,Bike,3,Business,19,3,2:09:47 PM,19,3,2:10:12 PM,19,3,2:18:45 PM,19,3,2:40:06 PM,19,3,3:24:13 PM,18,24.2,,-1.3253106000000001,36.8903296,-1.3019530000000001,36.788458,Rider_Id_457,2647
-Order_No_4250,User_Id_1329,Bike,3,Business,23,3,10:48:10 AM,23,3,10:50:12 AM,23,3,11:01:22 AM,23,3,11:11:54 AM,23,3,11:23:29 AM,5,21.7,,-1.273056,36.811298,-1.2840353999999998,36.786742700000005,Rider_Id_888,695
-Order_No_28175,User_Id_839,Bike,1,Personal,22,3,10:08:32 AM,22,3,10:08:47 AM,22,3,10:16:00 AM,22,3,10:18:18 AM,22,3,10:41:36 AM,6,15.1,,-1.2682178999999998,36.8009289,-1.3038156,36.812273299999994,Rider_Id_754,1398
-Order_No_22845,User_Id_250,Bike,2,Personal,23,3,2:05:19 PM,23,3,2:05:57 PM,23,3,2:14:00 PM,23,3,2:16:43 PM,23,3,2:29:35 PM,2,22.2,,-1.2642903,36.80021370000001,-1.269534,36.80697,Rider_Id_473,772
-Order_No_9808,User_Id_1500,Bike,3,Business,22,3,3:20:00 PM,22,3,3:40:52 PM,22,3,3:47:31 PM,22,3,3:51:28 PM,22,3,4:15:07 PM,9,24.4,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_263,1419
-Order_No_6069,User_Id_2677,Bike,3,Business,15,5,9:34:26 AM,15,5,9:35:05 AM,15,5,9:49:47 AM,15,5,10:05:07 AM,15,5,11:13:59 AM,14,19.1,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_300,4132
-Order_No_3058,User_Id_788,Bike,3,Business,6,4,10:29:56 AM,6,4,10:30:29 AM,6,4,10:30:32 AM,6,4,10:58:40 AM,6,4,11:29:41 AM,16,23.9,,-1.3014461000000002,36.7661381,-1.225322,36.80855,Rider_Id_450,1861
-Order_No_28213,User_Id_3291,Bike,3,Business,1,1,2:31:59 PM,1,1,2:34:16 PM,1,1,2:46:40 PM,1,1,3:10:41 PM,1,1,3:24:58 PM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_306,857
-Order_No_5541,User_Id_867,Bike,3,Business,7,1,12:02:29 PM,7,1,12:03:09 PM,7,1,12:15:02 PM,7,1,12:16:59 PM,7,1,12:42:08 PM,14,24.9,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_555,1509
-Order_No_7730,User_Id_661,Bike,3,Business,29,4,9:40:52 AM,29,4,9:40:58 AM,29,4,9:41:50 AM,29,4,9:43:11 AM,29,4,10:29:57 AM,20,18.7,4.0,-1.3244885,36.897791999999995,-1.2963097,36.7688221,Rider_Id_164,2806
-Order_No_6903,User_Id_326,Bike,3,Personal,17,1,9:37:54 AM,17,1,9:41:32 AM,17,1,9:50:50 AM,17,1,9:54:07 AM,17,1,10:06:25 AM,4,19.1,,-1.2641662,36.8136393,-1.2718101000000002,36.79176,Rider_Id_112,738
-Order_No_2907,User_Id_3295,Bike,3,Business,28,4,1:50:18 PM,28,4,1:51:05 PM,28,4,2:04:13 PM,28,4,2:44:47 PM,28,4,3:00:28 PM,13,29.9,,-1.3126476,36.831455,-1.2571472,36.795063299999995,Rider_Id_365,941
-Order_No_25601,User_Id_2691,Bike,3,Business,29,1,12:56:01 PM,29,1,12:56:28 PM,29,1,1:18:16 PM,29,1,1:28:47 PM,29,1,2:06:15 PM,6,,,-1.281223,36.8239,-1.2857671,36.7875614,Rider_Id_812,2248
-Order_No_7176,User_Id_3647,Bike,3,Business,28,3,1:47:32 PM,28,3,1:47:40 PM,28,3,1:52:38 PM,28,3,1:57:06 PM,28,3,3:00:02 PM,19,,,-1.2726389999999999,36.794723,-1.3443916999999999,36.6700333,Rider_Id_393,3776
-Order_No_25889,User_Id_2412,Bike,3,Business,21,2,9:29:05 AM,21,2,9:29:34 AM,21,2,9:49:59 AM,21,2,10:04:43 AM,21,2,10:57:15 AM,11,,,-1.2283403,36.8822756,-1.290894,36.822971,Rider_Id_686,3152
-Order_No_26668,User_Id_186,Bike,3,Business,11,2,3:00:08 PM,11,2,3:58:07 PM,11,2,3:58:54 PM,11,2,4:03:25 PM,11,2,4:36:13 PM,16,21.4,,-1.3700383,36.9190174,-1.2852569,36.8976121,Rider_Id_937,1968
-Order_No_7422,User_Id_2975,Bike,2,Personal,3,5,11:46:49 AM,3,5,11:49:19 AM,3,5,11:53:44 AM,3,5,12:05:53 PM,3,5,12:22:35 PM,7,21.4,,-1.2809913999999998,36.8123591,-1.2632928999999997,36.785618,Rider_Id_754,1002
-Order_No_28191,User_Id_1244,Bike,3,Business,30,4,10:08:04 AM,30,4,10:08:17 AM,30,4,10:31:48 AM,30,4,10:45:10 AM,30,4,11:09:41 AM,8,20.3,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_447,1471
-Order_No_7820,User_Id_393,Bike,3,Business,14,4,8:50:01 AM,14,4,8:50:17 AM,14,4,8:50:23 AM,14,4,9:07:32 AM,14,4,9:28:17 AM,11,19.1,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_111,1245
-Order_No_22437,User_Id_627,Bike,3,Business,4,4,11:42:04 AM,4,4,11:42:39 AM,4,4,11:46:00 AM,4,4,12:01:26 PM,4,4,12:16:12 PM,7,,,-1.2571472,36.795063299999995,-1.2876155,36.78684439999999,Rider_Id_882,886
-Order_No_23437,User_Id_868,Bike,3,Business,31,3,3:46:37 PM,31,3,3:48:22 PM,31,3,3:59:10 PM,31,3,4:03:22 PM,31,3,4:03:27 PM,3,27.0,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_456,5
-Order_No_20832,User_Id_1415,Bike,2,Personal,23,3,1:11:39 PM,23,3,1:14:58 PM,23,3,1:59:43 PM,23,3,2:01:58 PM,23,3,3:13:00 PM,11,22.3,,-1.2252107,36.8300026,-1.2631940000000002,36.797624,Rider_Id_742,4262
-Order_No_16814,User_Id_1205,Bike,3,Business,30,6,11:20:17 AM,30,6,11:20:50 AM,30,6,11:28:48 AM,30,6,11:30:18 AM,30,6,11:47:05 AM,3,22.1,,-1.2707742,36.7578609,-1.2860804,36.761295600000004,Rider_Id_636,1007
-Order_No_24404,User_Id_1687,Bike,3,Business,4,1,10:50:01 AM,4,1,10:51:31 AM,4,1,11:03:32 AM,4,1,11:09:11 AM,4,1,11:18:49 AM,3,25.8,,-1.2732803,36.794037700000004,-1.2626574,36.8014034,Rider_Id_397,578
-Order_No_2023,User_Id_2582,Bike,3,Business,7,4,3:47:19 PM,7,4,3:47:45 PM,7,4,3:56:11 PM,7,4,4:02:24 PM,7,4,4:24:40 PM,10,30.9,,-1.2985642,36.7707474,-1.2551895,36.7822034,Rider_Id_341,1336
-Order_No_26174,User_Id_393,Bike,3,Business,22,2,2:53:55 PM,22,2,2:53:59 PM,22,2,2:56:44 PM,22,2,3:07:33 PM,22,2,3:37:17 PM,14,25.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1784
-Order_No_21902,User_Id_3291,Bike,3,Business,9,3,3:32:05 PM,9,3,3:38:59 PM,9,3,3:45:14 PM,9,3,4:00:08 PM,9,3,4:17:19 PM,11,25.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_101,1031
-Order_No_15430,User_Id_517,Bike,3,Business,17,6,10:47:39 AM,17,6,11:09:05 AM,17,6,11:21:28 AM,17,6,11:26:17 AM,17,6,11:44:43 AM,10,24.0,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_854,1106
-Order_No_6992,User_Id_1075,Bike,3,Business,13,3,12:17:53 PM,13,3,12:18:56 PM,13,3,12:43:23 PM,13,3,1:29:07 PM,13,3,1:36:40 PM,19,24.6,,-1.2527958000000001,36.8003131,-1.2422942,36.6669445,Rider_Id_805,453
-Order_No_13951,User_Id_2869,Bike,3,Business,29,5,1:55:04 PM,29,5,1:55:28 PM,29,5,1:59:22 PM,29,5,2:01:36 PM,29,5,2:21:17 PM,9,29.1,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_658,1181
-Order_No_19819,User_Id_3426,Bike,3,Business,2,6,11:07:53 AM,2,6,11:08:34 AM,2,6,11:16:37 AM,2,6,11:33:36 AM,2,6,12:00:40 PM,9,27.4,,-1.3165542,36.700067,-1.3012008,36.764868,Rider_Id_831,1624
-Order_No_21375,User_Id_2704,Bike,1,Personal,29,6,11:38:23 AM,29,6,11:38:58 AM,29,6,11:53:13 AM,29,6,11:58:45 AM,29,6,1:00:27 PM,29,25.0,,-1.207546,36.7913976,-1.3091733,36.740809999999996,Rider_Id_247,3702
-Order_No_8871,User_Id_1927,Bike,3,Business,13,3,5:03:14 PM,13,3,5:03:58 PM,13,3,5:18:22 PM,13,3,5:42:13 PM,13,3,6:26:32 PM,23,27.6,,-1.2285834999999998,36.882646799999996,-1.3472066,36.7692638,Rider_Id_76,2659
-Order_No_6841,User_Id_1366,Bike,3,Business,4,2,11:01:39 AM,4,2,11:11:50 AM,4,2,11:17:38 AM,4,2,11:19:20 AM,4,2,11:45:05 AM,8,19.5,,-1.3138646999999999,36.811896000000004,-1.2701385,36.8235208,Rider_Id_933,1545
-Order_No_9926,User_Id_733,Bike,3,Business,2,4,1:57:30 PM,2,4,1:57:38 PM,2,4,1:57:42 PM,2,4,2:11:03 PM,2,4,2:45:01 PM,23,,,-1.2551895,36.7822034,-1.3452448000000001,36.910166600000004,Rider_Id_438,2038
-Order_No_10032,User_Id_635,Bike,3,Business,21,2,12:11:11 PM,21,2,12:11:52 PM,21,2,12:13:24 PM,21,2,12:58:51 PM,21,2,1:56:35 PM,8,22.6,,-1.3081376,36.83297720000001,-1.2575139,36.8338121,Rider_Id_686,3464
-Order_No_14786,User_Id_3009,Bike,1,Personal,30,1,11:47:48 AM,30,1,11:48:15 AM,30,1,12:02:31 PM,30,1,12:41:34 PM,30,1,12:53:32 PM,8,22.4,,-1.3034413,36.777010600000004,-1.2576952,36.7969797,Rider_Id_465,718
-Order_No_5657,User_Id_1304,Bike,3,Business,6,2,9:04:51 AM,6,2,9:05:06 AM,6,2,9:13:57 AM,6,2,9:16:35 AM,6,2,9:54:13 AM,13,20.0,,-1.3332749,36.870814700000004,-1.2695287,36.798177700000004,Rider_Id_611,2258
-Order_No_21749,User_Id_1051,Bike,3,Business,11,4,11:58:15 AM,11,4,12:01:14 PM,11,4,12:25:56 PM,11,4,12:43:16 PM,11,4,1:00:40 PM,8,25.4,,-1.2963097,36.7688221,-1.2852299999999999,36.8248292,Rider_Id_855,1044
-Order_No_4743,User_Id_2828,Bike,3,Business,26,3,2:16:08 PM,26,3,2:16:54 PM,26,3,2:24:30 PM,26,3,2:32:09 PM,26,3,2:44:07 PM,5,27.0,,-1.3346773,36.8842734,-1.3207349,36.8819157,Rider_Id_770,718
-Order_No_23084,User_Id_2801,Bike,3,Business,20,3,8:21:31 AM,20,3,8:22:25 AM,20,3,9:09:19 AM,20,3,9:17:09 AM,20,3,10:26:44 AM,16,20.8,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_373,4175
-Order_No_8128,User_Id_1976,Bike,3,Business,25,1,9:35:03 AM,25,1,9:35:23 AM,25,1,9:39:39 AM,25,1,9:40:55 AM,25,1,9:53:23 AM,10,,,-1.257028,36.7810435,-1.2607621999999998,36.8227608,Rider_Id_315,748
-Order_No_24732,User_Id_1271,Bike,3,Business,29,5,10:21:26 AM,29,5,10:24:34 AM,29,5,10:41:32 AM,29,5,11:58:57 AM,29,5,12:14:48 PM,17,19.5,,-1.2545163000000001,36.8243717,-1.3244885,36.897791999999995,Rider_Id_644,951
-Order_No_6891,User_Id_1281,Bike,3,Business,2,2,11:40:07 AM,2,2,11:40:49 AM,2,2,12:19:35 PM,2,2,1:02:18 PM,2,2,1:16:50 PM,11,20.8,,-1.2558953000000002,36.7339105,-1.2706815,36.7646286,Rider_Id_578,872
-Order_No_436,User_Id_136,Bike,3,Business,13,4,1:20:35 PM,13,4,1:24:24 PM,13,4,1:55:28 PM,13,4,1:57:02 PM,13,4,2:26:51 PM,16,23.0,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_572,1789
-Order_No_24233,User_Id_3291,Bike,3,Business,22,5,11:32:09 AM,22,5,11:32:52 AM,22,5,11:58:59 AM,22,5,12:16:15 PM,22,5,12:25:22 PM,4,,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_52,547
-Order_No_3423,User_Id_3686,Bike,3,Business,24,4,12:02:45 PM,24,4,12:03:23 PM,24,4,12:09:39 PM,24,4,12:15:43 PM,24,4,12:50:04 PM,6,24.3,,-1.2551895,36.7822034,-1.2729291999999999,36.7679079,Rider_Id_755,2061
-Order_No_21686,User_Id_427,Bike,3,Personal,18,1,2:25:48 PM,18,1,2:26:21 PM,18,1,2:26:34 PM,18,1,2:36:12 PM,18,1,2:50:11 PM,7,30.9,,-1.300921,36.828195,-1.269534,36.80697,Rider_Id_472,839
-Order_No_10381,User_Id_3701,Bike,3,Business,23,4,2:03:44 PM,23,4,2:04:16 PM,23,4,2:18:41 PM,23,4,2:33:48 PM,23,4,2:55:07 PM,8,24.6,,-1.2975098,36.8397842,-1.2612573999999999,36.7879622,Rider_Id_448,1279
-Order_No_9990,User_Id_186,Bike,3,Business,18,2,1:13:13 PM,18,2,1:22:18 PM,18,2,1:23:55 PM,18,2,1:31:50 PM,18,2,2:27:33 PM,17,,,-1.3700383,36.9190174,-1.2899256000000001,36.815551,Rider_Id_345,3343
-Order_No_4133,User_Id_3647,Bike,3,Business,26,3,4:18:50 PM,26,3,4:19:10 PM,26,3,4:29:35 PM,26,3,4:30:51 PM,26,3,5:21:32 PM,11,27.2,,-1.2726389999999999,36.794723,-1.3184421,36.8312624,Rider_Id_755,3041
-Order_No_10611,User_Id_1075,Bike,3,Business,26,2,10:15:33 AM,26,2,10:18:00 AM,26,2,10:44:40 AM,26,2,11:44:44 AM,26,2,11:57:41 AM,6,27.4,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_953,777
-Order_No_2000,User_Id_1410,Bike,3,Business,22,3,12:53:09 PM,22,3,12:53:41 PM,22,3,1:16:31 PM,22,3,1:18:08 PM,22,3,2:17:05 PM,21,23.0,,-1.2847613000000002,36.967084,-1.300921,36.828195,Rider_Id_175,3537
-Order_No_17763,User_Id_1724,Bike,3,Business,2,4,5:08:23 PM,2,4,5:23:44 PM,2,4,5:44:50 PM,2,4,6:03:56 PM,2,4,7:22:42 PM,11,23.7,,-1.2610117,36.7236153,-1.2941648000000001,36.7977315,Rider_Id_279,4726
-Order_No_3730,User_Id_438,Bike,3,Personal,29,2,9:49:54 AM,29,2,9:50:09 AM,29,2,10:17:17 AM,29,2,10:19:39 AM,29,2,10:53:56 AM,4,16.8,,-1.2559294,36.788465,-1.2578072,36.816705600000006,Rider_Id_752,2057
-Order_No_16604,User_Id_868,Bike,3,Business,4,6,11:22:09 AM,4,6,11:22:50 AM,4,6,11:41:28 AM,4,6,11:55:00 AM,4,6,12:31:25 PM,14,,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_377,2185
-Order_No_8790,User_Id_2867,Bike,1,Personal,3,2,10:13:28 AM,3,2,10:36:37 AM,3,2,10:43:51 AM,3,2,10:46:57 AM,3,2,11:18:30 AM,4,18.3,,-1.2753108999999998,36.8130185,-1.2813849,36.8193667,Rider_Id_726,1893
-Order_No_2660,User_Id_393,Bike,3,Business,22,2,1:29:22 PM,22,2,1:31:10 PM,22,2,1:51:59 PM,22,2,1:57:21 PM,22,2,2:27:49 PM,14,25.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_518,1828
-Order_No_6202,User_Id_2789,Bike,3,Business,8,2,2:38:53 PM,8,2,3:07:56 PM,8,2,3:24:40 PM,8,2,3:33:01 PM,8,2,3:50:27 PM,5,25.3,,-1.2947777,36.8194497,-1.304735,36.784605400000004,Rider_Id_819,1046
-Order_No_24417,User_Id_2330,Bike,3,Business,15,1,4:01:04 PM,15,1,5:14:59 PM,15,1,5:28:08 PM,15,1,5:36:05 PM,15,1,5:45:10 PM,8,,,-1.185943,36.9276394,-1.2284323,36.8757058,Rider_Id_291,545
-Order_No_18219,User_Id_3647,Bike,3,Business,16,2,10:40:00 AM,16,2,10:45:42 AM,16,2,11:22:53 AM,16,2,11:27:57 AM,16,2,11:49:20 AM,5,,,-1.2726389999999999,36.794723,-1.2883,36.8167842,Rider_Id_709,1283
-Order_No_2021,User_Id_2169,Bike,2,Personal,2,1,5:13:29 PM,2,1,5:13:48 PM,2,1,5:28:39 PM,2,1,5:40:19 PM,2,1,6:10:57 PM,11,19.5,,-1.311208,36.778743,-1.3634667,36.7389097,Rider_Id_449,1838
-Order_No_23505,User_Id_2069,Bike,3,Business,25,6,4:29:55 PM,25,6,4:30:09 PM,25,6,4:30:24 PM,25,6,4:39:22 PM,25,6,5:01:13 PM,12,,,-1.259478,36.801781299999995,-1.228911,36.881876,Rider_Id_178,1311
-Order_No_22698,User_Id_2309,Bike,2,Personal,12,5,3:55:25 PM,12,5,3:56:37 PM,12,5,4:08:15 PM,12,5,4:54:48 PM,12,5,5:29:32 PM,17,25.5,,-1.3081770000000001,36.879807,-1.233018,36.8004477,Rider_Id_871,2084
-Order_No_17942,User_Id_2330,Bike,3,Business,31,5,10:52:24 AM,31,5,10:53:39 AM,31,5,11:02:21 AM,31,5,11:03:28 AM,31,5,11:20:23 AM,8,21.8,,-1.294597,36.7742999,-1.2645318,36.7981775,Rider_Id_176,1015
-Order_No_13184,User_Id_3647,Bike,3,Business,8,5,12:48:28 PM,8,5,12:49:20 PM,8,5,12:54:01 PM,8,5,1:00:22 PM,8,5,1:18:09 PM,7,24.3,,-1.2971877,36.8270159,-1.2726389999999999,36.794723,Rider_Id_875,1067
-Order_No_18770,User_Id_867,Bike,3,Business,10,1,12:33:42 PM,10,1,12:38:55 PM,10,1,12:48:05 PM,10,1,1:04:49 PM,10,1,1:42:07 PM,13,18.3,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_155,2238
-Order_No_11145,User_Id_635,Bike,3,Business,26,2,11:09:51 AM,26,2,11:10:23 AM,26,2,11:16:50 AM,26,2,11:26:34 AM,26,2,11:33:59 AM,5,27.4,,-1.2602337,36.79905479999999,-1.25681,36.7730826,Rider_Id_177,445
-Order_No_16727,User_Id_1927,Bike,3,Business,19,2,5:13:56 PM,19,2,5:15:00 PM,19,2,5:28:53 PM,19,2,5:36:28 PM,19,2,6:02:19 PM,12,28.2,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_26,1551
-Order_No_23159,User_Id_1014,Bike,1,Business,28,5,9:01:28 AM,28,5,9:03:52 AM,28,5,9:15:33 AM,28,5,9:23:24 AM,28,5,9:49:34 AM,12,20.5,,-1.3234104,36.7989459,-1.2733546,36.7696069,Rider_Id_188,1570
-Order_No_27759,User_Id_1336,Bike,3,Business,24,4,2:43:49 PM,24,4,2:44:05 PM,24,4,2:45:01 PM,24,4,3:22:10 PM,24,4,3:31:46 PM,2,28.4,,-1.28878,36.8168312,-1.2885029,36.825604999999996,Rider_Id_156,576
-Order_No_21084,User_Id_3647,Bike,3,Business,8,3,1:59:49 PM,8,3,2:01:03 PM,8,3,2:16:50 PM,8,3,2:27:43 PM,8,3,2:56:22 PM,6,,,-1.2726389999999999,36.794723,-1.2944584,36.7614555,Rider_Id_427,1719
-Order_No_2110,User_Id_286,Bike,3,Business,30,5,3:32:23 PM,30,5,4:20:06 PM,30,5,4:43:40 PM,30,5,4:52:09 PM,30,5,5:32:19 PM,9,21.9,,-1.2551895,36.7822034,-1.3069127,36.835137200000005,Rider_Id_685,2410
-Order_No_23909,User_Id_2190,Bike,3,Business,16,2,2:42:56 PM,16,2,2:44:36 PM,16,2,3:02:41 PM,16,2,3:05:14 PM,16,2,3:28:21 PM,8,27.3,,-1.3019530000000001,36.788458,-1.2624343,36.817086100000004,Rider_Id_649,1387
-Order_No_17706,User_Id_3557,Bike,3,Business,28,3,4:03:05 PM,28,3,4:03:35 PM,28,3,4:25:47 PM,28,3,4:28:45 PM,28,3,4:58:12 PM,10,,,-1.317411,36.818222,-1.2546732,36.808679600000005,Rider_Id_343,1767
-Order_No_17494,User_Id_3097,Bike,3,Personal,7,1,12:01:25 PM,7,1,12:01:44 PM,7,1,12:02:47 PM,7,1,12:46:08 PM,7,1,1:33:28 PM,28,24.8,,-1.280549,36.829244200000005,-1.4242042,36.717817,Rider_Id_124,2840
-Order_No_8816,User_Id_346,Bike,3,Business,3,3,12:12:10 PM,3,3,12:13:29 PM,3,3,12:47:40 PM,3,3,12:52:48 PM,3,3,1:36:14 PM,13,27.2,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_76,2606
-Order_No_19432,User_Id_111,Bike,3,Business,24,3,12:47:31 PM,24,3,12:48:26 PM,24,3,12:48:28 PM,24,3,12:58:52 PM,24,3,1:13:28 PM,7,,,-1.2551895,36.7822034,-1.2841836000000002,36.806550200000004,Rider_Id_291,876
-Order_No_1046,User_Id_1089,Bike,1,Personal,6,3,2:03:46 PM,6,3,2:27:34 PM,6,3,3:31:09 PM,6,3,3:42:09 PM,6,3,4:06:50 PM,6,21.9,,-1.2871938,36.8195031,-1.2536041999999998,36.806489500000005,Rider_Id_192,1481
-Order_No_28036,User_Id_1402,Bike,3,Business,5,5,3:37:28 PM,5,5,3:38:25 PM,5,5,3:45:07 PM,5,5,3:49:22 PM,5,5,4:08:18 PM,1,27.7,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_790,1136
-Order_No_22512,User_Id_2504,Bike,3,Business,20,2,4:50:00 PM,20,2,4:50:28 PM,20,2,5:14:50 PM,20,2,5:19:24 PM,20,2,5:39:52 PM,8,23.7,,-1.2854453000000001,36.818836299999994,-1.2963097,36.7688221,Rider_Id_622,1228
-Order_No_22121,User_Id_1580,Bike,2,Personal,20,6,4:54:19 PM,20,6,4:55:03 PM,20,6,4:56:02 PM,20,6,5:08:43 PM,20,6,5:19:48 PM,4,23.8,,-1.2959829999999999,36.782284000000004,-1.2825581,36.7775416,Rider_Id_568,665
-Order_No_27290,User_Id_1207,Bike,3,Business,23,6,11:28:37 AM,23,6,11:28:54 AM,23,6,11:33:18 AM,23,6,11:37:19 AM,23,6,12:00:12 PM,10,,,-1.2628638,36.8070399,-1.3178671999999998,36.812166299999994,Rider_Id_484,1373
-Order_No_5279,User_Id_2587,Bike,3,Business,30,2,12:07:50 PM,30,2,12:22:58 PM,30,2,12:31:45 PM,30,2,12:50:24 PM,30,2,1:18:17 PM,16,22.5,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_414,1673
-Order_No_17459,User_Id_2112,Bike,3,Business,15,2,2:33:27 PM,15,2,3:04:08 PM,15,2,3:14:37 PM,15,2,3:27:14 PM,15,2,4:39:55 PM,12,24.3,,-1.290894,36.822971,-1.2237773,36.8861711,Rider_Id_320,4361
-Order_No_26116,User_Id_3282,Bike,3,Personal,1,5,3:36:47 PM,1,5,3:37:02 PM,1,5,3:44:05 PM,1,5,3:46:31 PM,1,5,4:14:12 PM,5,28.4,,-1.290059,36.7703475,-1.2925374,36.7995411,Rider_Id_588,1661
-Order_No_7102,User_Id_2423,Bike,3,Business,24,2,2:50:16 PM,24,2,2:50:24 PM,24,2,2:51:23 PM,24,2,2:58:52 PM,24,2,3:19:59 PM,6,18.4,,-1.2551895,36.7822034,-1.2884308,36.821751,Rider_Id_415,1267
-Order_No_21875,User_Id_1469,Bike,3,Business,15,1,9:51:54 AM,15,1,9:53:44 AM,15,1,10:05:10 AM,15,1,10:10:15 AM,15,1,11:17:36 AM,7,,,-1.2551895,36.7822034,-1.2822277,36.826854299999994,Rider_Id_646,4041
-Order_No_1207,User_Id_2174,Bike,3,Business,28,4,2:11:33 PM,28,4,2:11:42 PM,28,4,2:11:51 PM,28,4,2:42:19 PM,28,4,3:28:55 PM,23,30.2,,-1.2482781,36.884091999999995,-1.3446058,36.760517799999995,Rider_Id_86,2796
-Order_No_11332,User_Id_951,Bike,1,Personal,5,3,3:57:06 PM,5,3,3:57:21 PM,5,3,4:02:06 PM,5,3,4:07:42 PM,5,3,4:29:36 PM,15,23.8,,-1.256731,36.7525716,-1.229805,36.803534,Rider_Id_116,1314
-Order_No_3736,User_Id_1983,Bike,3,Personal,6,5,11:15:01 AM,6,5,11:15:40 AM,6,5,11:19:05 AM,6,5,11:57:03 AM,6,5,12:26:17 PM,14,20.2,,-1.2235201,36.80206079999999,-1.2929576999999999,36.7955434,Rider_Id_100,1754
-Order_No_60,User_Id_2587,Bike,3,Business,9,5,2:33:51 PM,9,5,2:37:01 PM,9,5,2:37:40 PM,9,5,3:00:54 PM,9,5,3:22:06 PM,5,26.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_403,1272
-Order_No_762,User_Id_3108,Bike,3,Business,13,3,2:11:54 PM,13,3,2:12:23 PM,13,3,2:21:55 PM,13,3,2:23:28 PM,13,3,2:53:09 PM,25,27.2,,-1.2551895,36.7822034,-1.1648303,36.951368,Rider_Id_598,1781
-Order_No_12536,User_Id_302,Bike,3,Business,13,6,6:37:31 PM,13,6,6:56:46 PM,13,6,7:09:44 PM,13,6,7:16:10 PM,13,6,7:44:49 PM,7,26.5,,-1.2649605,36.798177700000004,-1.2696795,36.8640088,Rider_Id_775,1719
-Order_No_27130,User_Id_1500,Bike,3,Business,5,2,10:33:59 AM,5,2,10:34:17 AM,5,2,10:38:18 AM,5,2,10:54:46 AM,5,2,11:15:57 AM,8,25.5,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_154,1271
-Order_No_12722,User_Id_2066,Bike,3,Business,14,2,1:32:24 PM,14,2,1:33:38 PM,14,2,1:40:06 PM,14,2,1:42:09 PM,14,2,1:56:23 PM,6,24.2,,-1.316961,36.820337,-1.2967108,36.787113500000004,Rider_Id_663,854
-Order_No_26686,User_Id_1436,Bike,3,Personal,9,4,9:03:30 PM,9,4,9:12:05 PM,9,4,9:22:13 PM,9,4,9:24:24 PM,9,4,9:32:58 PM,2,,,-1.2963097,36.7688221,-1.2994194,36.7647917,Rider_Id_299,514
-Order_No_13740,User_Id_3647,Bike,3,Business,10,1,12:45:58 PM,10,1,12:50:56 PM,10,1,1:00:19 PM,10,1,1:04:50 PM,10,1,1:51:21 PM,14,18.7,,-1.2726389999999999,36.794723,-1.3121154,36.7109299,Rider_Id_576,2791
-Order_No_3212,User_Id_635,Bike,3,Business,25,6,12:06:19 PM,25,6,12:08:08 PM,25,6,12:25:09 PM,25,6,12:37:21 PM,25,6,1:50:34 PM,13,,,-1.3081376,36.83297720000001,-1.2332536,36.8135721,Rider_Id_416,4393
-Order_No_1451,User_Id_647,Bike,3,Business,14,5,1:33:02 PM,14,5,1:34:55 PM,14,5,1:39:13 PM,14,5,1:51:49 PM,14,5,2:08:02 PM,4,22.9,,-1.3004062,36.829741,-1.292163,36.8000995,Rider_Id_243,973
-Order_No_28160,User_Id_978,Bike,1,Personal,16,2,3:32:54 PM,16,2,3:59:13 PM,16,2,4:14:45 PM,16,2,4:25:06 PM,16,2,5:03:40 PM,5,27.6,,-1.3084067,36.83450139999999,-1.2805312,36.832668700000006,Rider_Id_942,2314
-Order_No_19508,User_Id_1789,Bike,3,Business,27,1,3:13:17 PM,27,1,3:14:03 PM,27,1,3:31:03 PM,27,1,3:34:06 PM,27,1,4:00:02 PM,12,23.0,,-1.2917868000000001,36.7872675,-1.3002926000000001,36.8668523,Rider_Id_68,1556
-Order_No_16835,User_Id_3291,Bike,3,Business,15,5,11:34:01 AM,15,5,11:34:25 AM,15,5,11:53:40 AM,15,5,12:06:28 PM,15,5,12:56:22 PM,18,25.8,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_856,2994
-Order_No_10530,User_Id_346,Bike,3,Business,8,5,11:21:00 AM,8,5,11:29:21 AM,8,5,11:33:39 AM,8,5,11:40:33 AM,8,5,11:40:42 AM,2,28.4,,-1.2813012,36.832396200000005,-1.28878,36.8168312,Rider_Id_266,9
-Order_No_12691,User_Id_1304,Bike,3,Business,27,2,3:36:39 PM,27,2,3:53:53 PM,27,2,4:01:56 PM,27,2,4:07:18 PM,27,2,4:07:47 PM,7,25.5,,-1.3226301999999999,36.8719901,-1.3327653999999998,36.8853117,Rider_Id_522,29
-Order_No_1764,User_Id_867,Bike,3,Business,10,3,2:33:36 PM,10,3,2:33:51 PM,10,3,2:33:55 PM,10,3,2:38:40 PM,10,3,3:23:54 PM,16,30.4,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_394,2714
-Order_No_13679,User_Id_1768,Bike,3,Personal,19,2,9:26:56 AM,19,2,9:27:49 AM,19,2,9:34:01 AM,19,2,9:40:45 AM,19,2,10:01:38 AM,5,,,-1.2750257,36.8157165,-1.2935617,36.835057,Rider_Id_34,1253
-Order_No_8413,User_Id_2956,Bike,3,Business,28,1,10:00:47 AM,28,1,10:38:30 AM,28,1,10:57:17 AM,28,1,11:28:33 AM,28,1,11:53:41 AM,12,22.8,,-1.339046,36.762596,-1.290894,36.822971,Rider_Id_56,1508
-Order_No_27535,User_Id_3647,Bike,3,Business,17,3,10:20:25 AM,17,3,10:20:39 AM,17,3,10:29:41 AM,17,3,10:35:31 AM,17,3,11:11:56 AM,21,,,-1.2726389999999999,36.794723,-1.3319686000000002,36.911555299999996,Rider_Id_829,2185
-Order_No_10669,User_Id_1500,Bike,3,Business,8,5,9:43:52 AM,8,5,9:44:34 AM,8,5,9:45:04 AM,8,5,10:20:18 AM,8,5,10:43:04 AM,10,23.0,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_803,1366
-Order_No_7875,User_Id_517,Bike,3,Business,6,4,2:03:49 PM,6,4,2:03:56 PM,6,4,2:04:39 PM,6,4,2:12:14 PM,6,4,2:51:32 PM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2358
-Order_No_19690,User_Id_2956,Bike,3,Business,11,2,1:38:51 PM,11,2,1:39:20 PM,11,2,1:49:25 PM,11,2,1:55:00 PM,11,2,1:57:05 PM,2,22.1,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_575,125
-Order_No_20755,User_Id_758,Bike,3,Business,3,4,1:41:57 PM,3,4,1:42:03 PM,3,4,1:42:11 PM,3,4,1:45:48 PM,3,4,2:00:58 PM,4,26.2,,-1.2996681,36.806841299999995,-1.2917868000000001,36.7872675,Rider_Id_322,910
-Order_No_17174,User_Id_238,Bike,3,Business,22,3,3:03:43 PM,22,3,3:23:04 PM,22,3,3:35:35 PM,22,3,3:58:41 PM,22,3,4:08:35 PM,3,24.2,,-1.3077869,36.844320700000004,-1.3166811999999999,36.859460799999994,Rider_Id_2,594
-Order_No_9682,User_Id_2271,Bike,3,Business,17,3,3:09:27 PM,17,3,3:11:05 PM,17,3,3:38:22 PM,17,3,4:06:37 PM,17,3,4:31:01 PM,9,28.4,,-1.3343128999999998,36.885417499999996,-1.293515,36.897607,Rider_Id_844,1464
-Order_No_1618,User_Id_3621,Bike,3,Business,9,2,3:36:25 PM,9,2,3:36:50 PM,9,2,3:39:36 PM,9,2,4:19:41 PM,9,2,4:19:56 PM,16,30.4,,-1.2836583000000001,36.7886014,-1.2640196000000001,36.8850599,Rider_Id_709,15
-Order_No_12573,User_Id_570,Bike,3,Business,13,2,9:01:47 AM,13,2,9:22:02 AM,13,2,9:34:44 AM,13,2,9:43:17 AM,13,2,9:58:47 AM,6,19.7,9.1,-1.2551895,36.7822034,-1.2765492,36.7669809,Rider_Id_778,930
-Order_No_8546,User_Id_1856,Bike,3,Business,2,5,1:09:58 PM,2,5,1:10:40 PM,2,5,1:24:19 PM,2,5,1:46:38 PM,2,5,1:55:51 PM,5,25.1,,-1.273009,36.802807,-1.2637421,36.7814816,Rider_Id_337,553
-Order_No_5998,User_Id_111,Bike,3,Business,15,4,2:46:24 PM,15,4,2:47:02 PM,15,4,2:56:07 PM,15,4,2:57:39 PM,15,4,3:11:15 PM,4,27.4,,-1.2551895,36.7822034,-1.2707177,36.8009828,Rider_Id_259,816
-Order_No_3533,User_Id_1500,Bike,3,Business,30,2,3:58:13 PM,30,2,4:07:51 PM,30,2,4:18:27 PM,30,2,4:40:16 PM,30,2,5:07:56 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_910,1660
-Order_No_4432,User_Id_136,Bike,3,Business,16,2,2:40:24 PM,16,2,2:40:51 PM,16,2,2:57:09 PM,16,2,3:14:52 PM,16,2,3:54:50 PM,30,,,-1.3094447,36.9107706,-1.3254522,36.721658399999995,Rider_Id_377,2398
-Order_No_2481,User_Id_618,Bike,1,Personal,10,5,2:27:07 PM,10,5,2:27:59 PM,10,5,2:45:27 PM,10,5,2:49:07 PM,10,5,3:24:58 PM,8,21.8,,-1.2840605,36.8191699,-1.2853885,36.775643,Rider_Id_519,2151
-Order_No_1310,User_Id_1014,Bike,1,Business,13,6,8:49:30 PM,13,6,8:51:02 PM,13,6,9:03:10 PM,13,6,9:10:37 PM,13,6,10:09:23 PM,5,22.3,,-1.3043821000000002,36.7840586,-1.2830073,36.7662294,Rider_Id_299,3526
-Order_No_22431,User_Id_1502,Bike,3,Business,18,4,10:30:59 AM,18,4,10:31:23 AM,18,4,10:41:41 AM,18,4,10:43:34 AM,18,4,11:10:18 AM,13,,,-1.2584143,36.8048002,-1.2232123,36.8932547,Rider_Id_797,1604
-Order_No_11657,User_Id_635,Bike,3,Business,31,4,12:47:40 PM,31,4,12:48:02 PM,31,4,12:53:32 PM,31,4,1:04:50 PM,31,4,1:29:52 PM,4,26.5,,-1.3012141000000002,36.826223999999996,-1.2907473999999999,36.8453439,Rider_Id_206,1502
-Order_No_27716,User_Id_2523,Bike,3,Business,8,5,3:54:46 PM,8,5,4:02:53 PM,8,5,4:24:40 PM,8,5,4:28:29 PM,8,5,4:47:22 PM,7,27.4,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_660,1133
-Order_No_17442,User_Id_2968,Bike,1,Business,20,1,5:42:33 PM,20,1,5:43:12 PM,20,1,6:02:11 PM,20,1,6:10:01 PM,20,1,6:40:44 PM,11,20.5,,-1.3049726000000001,36.891238200000004,-1.2782978,36.827323799999995,Rider_Id_456,1843
-Order_No_3563,User_Id_3080,Bike,3,Business,24,2,4:22:55 PM,24,2,4:23:06 PM,24,2,4:30:01 PM,24,2,4:33:35 PM,24,2,4:59:53 PM,4,18.7,,-1.2551895,36.7822034,-1.2699441999999999,36.8206553,Rider_Id_519,1578
-Order_No_12652,User_Id_1473,Bike,3,Business,16,3,12:56:20 PM,16,3,12:56:28 PM,16,3,1:06:37 PM,16,3,1:08:25 PM,16,3,2:29:41 PM,23,24.1,,-1.3700383,36.9190174,-1.2946581,36.779292100000006,Rider_Id_89,4876
-Order_No_13426,User_Id_2207,Bike,3,Business,22,5,11:58:13 AM,22,5,11:58:44 AM,22,5,12:07:49 PM,22,5,12:14:04 PM,22,5,1:00:09 PM,9,,,-1.3107818000000002,36.8477324,-1.2696908000000002,36.81519,Rider_Id_778,2765
-Order_No_5487,User_Id_1304,Bike,3,Business,31,5,3:03:12 PM,31,5,3:03:31 PM,31,5,3:05:11 PM,31,5,3:18:53 PM,31,5,4:23:38 PM,19,20.0,,-1.2551895,36.7822034,-1.3601056999999999,36.767584,Rider_Id_720,3885
-Order_No_20846,User_Id_2956,Bike,3,Business,7,5,2:21:05 PM,7,5,2:31:51 PM,7,5,2:49:43 PM,7,5,2:55:27 PM,7,5,3:30:35 PM,14,26.9,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_103,2108
-Order_No_13586,User_Id_186,Bike,3,Business,22,1,3:53:52 PM,22,1,3:55:08 PM,22,1,4:02:37 PM,22,1,4:09:19 PM,22,1,4:44:56 PM,20,26.0,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_178,2137
-Order_No_3287,User_Id_1500,Bike,3,Business,6,3,10:54:21 AM,6,3,10:55:45 AM,6,3,11:04:21 AM,6,3,11:07:51 AM,6,3,11:18:50 AM,6,,,-1.2836832,36.8306719,-1.2638185,36.7930057,Rider_Id_728,659
-Order_No_16822,User_Id_1341,Bike,3,Business,24,5,10:20:48 AM,24,5,10:21:13 AM,24,5,10:27:41 AM,24,5,10:43:03 AM,24,5,11:15:48 AM,17,20.9,,-1.2600926000000001,36.8088685,-1.3393547,36.8910035,Rider_Id_882,1965
-Order_No_26346,User_Id_3599,Bike,3,Business,29,5,9:59:46 AM,29,5,10:14:56 AM,29,5,10:50:28 AM,29,5,11:09:35 AM,29,5,11:38:29 AM,13,26.7,,-1.3302996,36.8707935,-1.26348,36.800986,Rider_Id_61,1734
-Order_No_3507,User_Id_3635,Bike,1,Business,27,4,12:55:05 PM,27,4,12:55:16 PM,27,4,12:59:07 PM,27,4,1:01:46 PM,27,4,1:13:54 PM,3,22.6,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_96,728
-Order_No_21871,User_Id_635,Bike,3,Business,29,5,3:06:46 PM,29,5,3:07:50 PM,29,5,3:13:37 PM,29,5,3:18:15 PM,29,5,3:44:13 PM,16,29.1,,-1.2602337,36.79905479999999,-1.2434951,36.6760986,Rider_Id_279,1558
-Order_No_21946,User_Id_1085,Bike,3,Business,11,5,11:44:13 AM,11,5,11:45:23 AM,11,5,11:52:17 AM,11,5,11:57:08 AM,11,5,12:18:25 PM,7,20.9,,-1.2628638,36.8070399,-1.232864,36.808470799999995,Rider_Id_279,1277
-Order_No_18159,User_Id_587,Bike,1,Personal,22,5,3:07:41 PM,22,5,3:18:25 PM,22,5,3:49:58 PM,22,5,4:01:08 PM,22,5,4:09:20 PM,5,,,-1.2582194,36.819468900000004,-1.2371615,36.8084033,Rider_Id_616,492
-Order_No_26480,User_Id_478,Bike,3,Business,19,3,11:22:54 AM,19,3,12:06:07 PM,19,3,12:26:16 PM,19,3,12:28:08 PM,19,3,12:44:25 PM,4,22.3,,-1.2844516000000001,36.8245932,-1.2571472,36.795063299999995,Rider_Id_660,977
-Order_No_27148,User_Id_2767,Bike,3,Business,13,4,1:02:27 PM,13,4,1:03:24 PM,13,4,1:14:24 PM,13,4,1:17:07 PM,13,4,1:27:28 PM,1,22.0,,-1.257773,36.795486,-1.2580991000000001,36.794151899999996,Rider_Id_279,621
-Order_No_20609,User_Id_1874,Bike,3,Business,22,4,11:23:18 AM,22,4,11:26:16 AM,22,4,11:54:33 AM,22,4,11:55:58 AM,22,4,12:09:18 PM,7,22.7,,-1.2571472,36.795063299999995,-1.2950041,36.814358299999995,Rider_Id_744,800
-Order_No_5628,User_Id_2330,Bike,3,Business,28,4,2:41:10 PM,28,4,2:42:09 PM,28,4,2:54:27 PM,28,4,2:55:33 PM,28,4,3:08:24 PM,3,29.8,,-1.3015197,36.7658456,-1.2873232,36.765857200000006,Rider_Id_348,771
-Order_No_22084,User_Id_3529,Bike,1,Personal,28,1,2:52:58 PM,28,1,3:10:26 PM,28,1,3:24:50 PM,28,1,3:30:57 PM,28,1,3:43:43 PM,5,24.5,,-1.298529,36.816482,-1.2791714,36.825599700000005,Rider_Id_852,766
-Order_No_2509,User_Id_3403,Bike,3,Business,6,3,9:39:56 AM,6,3,9:41:40 AM,6,3,9:47:19 AM,6,3,9:51:52 AM,6,3,10:11:21 AM,8,16.2,14.0,-1.2553615,36.789537599999996,-1.2866816,36.787186299999995,Rider_Id_727,1169
-Order_No_4107,User_Id_2335,Bike,3,Business,18,4,10:58:22 AM,18,4,10:58:40 AM,18,4,11:04:26 AM,18,4,11:11:12 AM,18,4,12:13:58 PM,22,25.6,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_393,3766
-Order_No_12825,User_Id_2587,Bike,3,Business,25,4,12:04:11 PM,25,4,12:30:37 PM,25,4,12:34:31 PM,25,4,12:39:14 PM,25,4,12:44:30 PM,1,19.8,,-1.28878,36.8168312,-1.2789476999999998,36.8122359,Rider_Id_692,316
-Order_No_14843,User_Id_1075,Bike,3,Business,21,1,1:22:56 PM,21,1,2:07:48 PM,21,1,2:26:33 PM,21,1,2:40:43 PM,21,1,3:34:49 PM,17,27.8,,-1.2852569,36.8976121,-1.2527958000000001,36.8003131,Rider_Id_19,3246
-Order_No_11724,User_Id_2675,Bike,1,Personal,19,6,2:13:46 PM,19,6,2:14:11 PM,19,6,2:23:27 PM,19,6,2:46:04 PM,19,6,3:01:29 PM,9,24.1,,-1.2675132,36.8014826,-1.3145124,36.840001799999996,Rider_Id_753,925
-Order_No_20681,User_Id_2763,Bike,3,Business,13,1,1:56:55 PM,13,1,1:57:10 PM,13,1,1:59:12 PM,13,1,2:10:27 PM,13,1,2:45:46 PM,13,19.6,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_45,2119
-Order_No_13089,User_Id_3202,Bike,2,Personal,10,1,3:35:49 PM,10,1,3:36:36 PM,10,1,3:45:39 PM,10,1,3:50:36 PM,10,1,4:13:54 PM,8,,,-1.2817763,36.7792738,-1.26005,36.82108,Rider_Id_722,1398
-Order_No_21668,User_Id_1882,Bike,3,Business,12,6,12:49:24 PM,12,6,12:51:13 PM,12,6,1:43:03 PM,12,6,2:46:13 PM,12,6,3:29:16 PM,9,25.2,,-1.2475028000000001,36.877469700000006,-1.277512,36.820013,Rider_Id_75,2583
-Order_No_7913,User_Id_2306,Bike,3,Business,14,4,2:42:21 PM,14,4,2:43:40 PM,14,4,3:05:49 PM,14,4,3:24:09 PM,14,4,4:06:13 PM,19,28.2,,-1.2271998,36.808490500000005,-1.3241108,36.8692908,Rider_Id_703,2524
-Order_No_24026,User_Id_3517,Bike,3,Business,27,6,10:27:12 AM,27,6,10:27:36 AM,27,6,10:43:08 AM,27,6,11:14:09 AM,27,6,11:40:06 AM,15,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_171,1557
-Order_No_927,User_Id_909,Bike,3,Business,6,6,12:05:19 PM,6,6,12:05:40 PM,6,6,12:19:11 PM,6,6,1:06:09 PM,6,6,1:09:55 PM,16,24.2,,-1.225322,36.80855,-1.3014461000000002,36.7661381,Rider_Id_137,226
-Order_No_11841,User_Id_3606,Bike,3,Business,25,2,11:38:52 AM,25,2,11:39:58 AM,25,2,11:51:47 AM,25,2,11:56:10 AM,25,2,12:22:02 PM,6,24.0,,-1.291623,36.814209999999996,-1.2628638,36.8070399,Rider_Id_88,1552
-Order_No_11528,User_Id_633,Bike,3,Business,19,2,3:09:24 PM,19,2,3:09:55 PM,19,2,3:16:25 PM,19,2,3:29:03 PM,19,2,3:36:50 PM,4,29.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_206,467
-Order_No_12021,User_Id_3547,Bike,3,Business,10,1,11:28:37 AM,10,1,11:29:07 AM,10,1,11:31:53 AM,10,1,12:01:17 PM,10,1,12:21:15 PM,9,18.3,,-1.3285517,36.9018,-1.3194518,36.869396,Rider_Id_856,1198
-Order_No_9348,User_Id_2289,Bike,3,Business,20,4,2:32:48 PM,20,4,2:33:07 PM,20,4,2:43:24 PM,20,4,3:12:25 PM,20,4,3:27:27 PM,21,28.2,,-1.2571472,36.795063299999995,-1.343439,36.912065000000005,Rider_Id_405,902
-Order_No_17341,User_Id_1484,Bike,3,Business,7,1,2:51:58 PM,7,1,2:52:52 PM,7,1,2:53:54 PM,7,1,2:56:38 PM,7,1,3:26:40 PM,8,20.3,,-1.2559559999999999,36.789867,-1.2935303,36.7988142,Rider_Id_95,1802
-Order_No_7750,User_Id_2935,Bike,3,Business,29,5,11:53:22 AM,29,5,11:55:04 AM,29,5,12:02:47 PM,29,5,12:11:43 PM,29,5,12:35:50 PM,17,19.7,,-1.3657873999999999,36.734604299999994,-1.2961922,36.791994,Rider_Id_636,1447
-Order_No_15269,User_Id_2685,Bike,1,Personal,31,2,1:39:12 PM,31,2,1:42:18 PM,31,2,2:12:45 PM,31,2,2:42:58 PM,31,2,3:21:42 PM,22,22.0,,-1.3433181,36.9138018,-1.2899,36.7794355,Rider_Id_456,2324
-Order_No_12735,User_Id_136,Bike,3,Business,6,4,11:11:30 AM,6,4,11:15:01 AM,6,4,12:26:44 PM,6,4,12:35:15 PM,6,4,1:24:13 PM,12,24.0,,-1.2738338,36.782403,-1.3319268999999998,36.7142325,Rider_Id_200,2938
-Order_No_27570,User_Id_789,Bike,1,Personal,6,5,3:46:06 PM,6,5,3:47:27 PM,6,5,4:02:09 PM,6,5,4:04:50 PM,6,5,4:15:54 PM,8,22.6,,-1.2924639,36.77957979999999,-1.2671001,36.7977185,Rider_Id_312,664
-Order_No_27279,User_Id_2789,Bike,3,Business,26,2,12:09:45 PM,26,2,12:10:53 PM,26,2,12:21:46 PM,26,2,12:32:32 PM,26,2,12:51:39 PM,5,27.4,,-1.2981625,36.813014200000005,-1.304735,36.784605400000004,Rider_Id_177,1147
-Order_No_8303,User_Id_2405,Bike,1,Personal,10,1,9:31:22 AM,10,1,9:31:36 AM,10,1,9:36:51 AM,10,1,9:50:01 AM,10,1,10:25:21 AM,16,18.0,,-1.2960265,36.8780276,-1.2551895,36.7822034,Rider_Id_848,2120
-Order_No_15835,User_Id_2075,Bike,1,Personal,7,5,9:40:23 AM,7,5,9:40:31 AM,7,5,9:48:29 AM,7,5,9:54:04 AM,7,5,10:39:28 AM,21,18.5,,-1.3045487,36.7764687,-1.2143386999999999,36.7914548,Rider_Id_644,2724
-Order_No_24279,User_Id_2978,Bike,3,Business,15,5,9:05:01 AM,15,5,9:05:48 AM,15,5,9:08:40 AM,15,5,9:14:22 AM,15,5,9:26:24 AM,3,19.1,,-1.2551895,36.7822034,-1.2664428,36.809453000000005,Rider_Id_576,722
-Order_No_28098,User_Id_1787,Bike,3,Business,13,3,3:39:15 PM,13,3,3:39:38 PM,13,3,3:58:26 PM,13,3,4:13:12 PM,13,3,4:59:33 PM,18,29.2,,-1.3014461000000002,36.7661381,-1.2283403,36.8822756,Rider_Id_708,2781
-Order_No_8596,User_Id_3211,Bike,1,Personal,21,1,2:25:59 PM,21,1,2:54:06 PM,21,1,3:23:13 PM,21,1,3:24:29 PM,21,1,4:14:32 PM,5,21.8,,-1.2844551000000002,36.825175200000004,-1.2983462,36.8139996,Rider_Id_844,3003
-Order_No_27789,User_Id_2207,Bike,3,Business,26,5,8:45:59 AM,26,5,9:27:03 AM,26,5,9:47:31 AM,26,5,9:54:18 AM,26,5,9:58:38 AM,2,,,-1.2679971,36.808893700000006,-1.2768326,36.8073804,Rider_Id_770,260
-Order_No_8549,User_Id_1471,Bike,3,Business,10,1,1:07:36 PM,10,1,1:08:44 PM,10,1,1:19:32 PM,10,1,1:28:40 PM,10,1,1:54:47 PM,15,18.6,,-1.2551895,36.7822034,-1.2238103,36.8772588,Rider_Id_894,1567
-Order_No_28221,User_Id_1205,Bike,3,Business,26,1,11:53:10 AM,26,1,11:58:39 AM,26,1,12:23:58 PM,26,1,12:38:39 PM,26,1,1:29:46 PM,9,23.1,,-1.2873232,36.765857200000006,-1.257309,36.806008399999996,Rider_Id_303,3067
-Order_No_25923,User_Id_2885,Bike,1,Personal,20,4,4:45:24 PM,20,4,4:45:44 PM,20,4,4:50:59 PM,20,4,5:03:09 PM,20,4,5:21:44 PM,6,24.8,,-1.2671296,36.81257720000001,-1.29817,36.800141700000005,Rider_Id_612,1115
-Order_No_649,User_Id_2801,Bike,3,Business,1,4,4:14:13 PM,1,4,4:15:27 PM,1,4,4:29:51 PM,1,4,4:37:02 PM,1,4,5:50:34 PM,9,,,-1.2595421999999998,36.7871178,-1.3021707,36.816844399999994,Rider_Id_684,4412
-Order_No_13752,User_Id_393,Bike,3,Business,10,1,12:04:12 PM,10,1,12:04:18 PM,10,1,12:04:24 PM,10,1,12:19:47 PM,10,1,12:48:32 PM,14,23.7,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_619,1725
-Order_No_18185,User_Id_635,Bike,3,Business,8,5,9:00:53 AM,8,5,9:02:34 AM,8,5,9:13:14 AM,8,5,9:23:12 AM,8,5,9:35:00 AM,7,23.0,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_119,708
-Order_No_5137,User_Id_3661,Bike,3,Business,20,3,12:07:29 PM,20,3,12:08:39 PM,20,3,12:28:33 PM,20,3,12:32:18 PM,20,3,12:53:43 PM,18,27.4,,-1.2132866000000002,36.867827399999996,-1.3195936000000001,36.916169700000005,Rider_Id_683,1285
-Order_No_20513,User_Id_23,Bike,2,Personal,28,2,1:20:45 PM,28,2,1:20:54 PM,28,2,1:33:03 PM,28,2,1:55:01 PM,28,2,2:37:12 PM,21,19.7,,-1.2914795000000001,36.824897299999996,-1.3254522,36.721658399999995,Rider_Id_320,2531
-Order_No_18469,User_Id_111,Bike,3,Business,10,2,3:28:38 PM,10,2,3:29:03 PM,10,2,3:40:06 PM,10,2,3:43:32 PM,10,2,4:30:54 PM,10,21.1,,-1.2551895,36.7822034,-1.2380935,36.80729470000001,Rider_Id_467,2842
-Order_No_23104,User_Id_1714,Bike,3,Personal,24,4,11:42:31 AM,24,4,11:43:02 AM,24,4,11:54:37 AM,24,4,12:16:19 PM,24,4,12:44:57 PM,7,24.7,,-1.3012618999999999,36.821369700000005,-1.2555969999999999,36.830024,Rider_Id_622,1718
-Order_No_24250,User_Id_3588,Bike,3,Business,20,2,11:07:07 AM,20,2,11:07:34 AM,20,2,11:12:50 AM,20,2,11:26:16 AM,20,2,11:52:01 AM,20,19.8,,-1.3632421000000001,36.9183909,-1.2985749,36.8088,Rider_Id_598,1545
-Order_No_20777,User_Id_2854,Bike,3,Business,25,2,10:23:46 AM,25,2,10:24:10 AM,25,2,10:49:43 AM,25,2,11:08:24 AM,25,2,11:38:02 AM,17,24.0,,-1.2482465,36.8889062,-1.2963097,36.7688221,Rider_Id_851,1778
-Order_No_6016,User_Id_1271,Bike,3,Business,14,3,12:25:34 PM,14,3,1:56:46 PM,14,3,1:56:52 PM,14,3,2:13:58 PM,14,3,2:33:47 PM,9,25.6,,-1.3244885,36.897791999999995,-1.2812821,36.8891847,Rider_Id_179,1189
-Order_No_15851,User_Id_60,Bike,3,Business,13,3,2:31:58 PM,13,3,2:32:17 PM,13,3,2:45:12 PM,13,3,2:50:22 PM,13,3,3:02:32 PM,9,29.0,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_8,730
-Order_No_4932,User_Id_496,Bike,3,Business,4,1,10:48:05 AM,4,1,10:48:50 AM,4,1,10:48:56 AM,4,1,11:13:44 AM,4,1,11:28:17 AM,5,21.0,,-1.2553615,36.789537599999996,-1.2677588,36.8169359,Rider_Id_379,873
-Order_No_14509,User_Id_598,Bike,2,Business,28,5,3:58:21 PM,28,5,3:58:47 PM,28,5,3:59:33 PM,28,5,4:01:54 PM,28,5,4:33:58 PM,10,25.7,,-1.2536797,36.7995085,-1.2108864,36.78070220000001,Rider_Id_882,1924
-Order_No_5780,User_Id_3599,Bike,3,Business,2,2,11:27:36 AM,2,2,11:56:32 AM,2,2,12:35:27 PM,2,2,12:42:32 PM,2,2,1:31:58 PM,12,20.9,,-1.3302996,36.8707935,-1.2874367,36.880444700000005,Rider_Id_810,2966
-Order_No_1384,User_Id_3343,Bike,3,Personal,27,1,12:16:56 PM,27,1,12:17:39 PM,27,1,12:59:02 PM,27,1,1:01:03 PM,27,1,1:54:47 PM,24,23.9,,-1.3620656999999998,36.7431685,-1.2358386000000001,36.767204,Rider_Id_350,3224
-Order_No_5164,User_Id_265,Bike,3,Business,22,1,8:21:50 AM,22,1,8:22:19 AM,22,1,8:32:14 AM,22,1,8:37:42 AM,22,1,9:28:22 AM,21,20.1,,-1.300921,36.828195,-1.2434951,36.6760986,Rider_Id_216,3040
-Order_No_21466,User_Id_1446,Bike,1,Personal,17,3,9:54:33 AM,17,3,9:55:50 AM,17,3,10:04:56 AM,17,3,10:08:28 AM,17,3,11:01:47 AM,17,20.9,,-1.3688433,36.7402114,-1.276805,36.829187700000006,Rider_Id_726,3199
-Order_No_18186,User_Id_111,Bike,3,Business,30,2,11:37:53 AM,30,2,11:38:52 AM,30,2,11:46:09 AM,30,2,11:50:20 AM,30,2,1:53:27 PM,13,22.3,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_937,7387
-Order_No_3083,User_Id_640,Bike,3,Business,18,4,9:16:28 AM,18,4,9:26:29 AM,18,4,9:37:20 AM,18,4,9:44:51 AM,18,4,10:05:19 AM,6,,,-1.2969741000000001,36.7856612,-1.2962731,36.811186299999996,Rider_Id_778,1228
-Order_No_10057,User_Id_3326,Bike,3,Business,12,2,2:42:05 PM,12,2,2:43:01 PM,12,2,2:58:47 PM,12,2,3:08:21 PM,12,2,3:37:45 PM,8,27.1,,-1.2551895,36.7822034,-1.2977386999999998,36.803144700000004,Rider_Id_315,1764
-Order_No_2261,User_Id_970,Bike,3,Business,22,5,12:49:32 PM,22,5,12:49:53 PM,22,5,12:55:06 PM,22,5,1:04:21 PM,22,5,1:54:53 PM,19,,,-1.2571472,36.795063299999995,-1.3325582,36.740669700000005,Rider_Id_798,3032
-Order_No_18112,User_Id_3211,Bike,1,Personal,7,4,2:13:25 PM,7,4,2:35:46 PM,7,4,2:53:39 PM,7,4,4:06:30 PM,7,4,4:22:59 PM,6,20.1,,-1.2783772,36.8201617,-1.2886090000000001,36.782939299999995,Rider_Id_431,989
-Order_No_16893,User_Id_1508,Bike,2,Personal,19,3,9:08:51 AM,19,3,9:09:37 AM,19,3,9:09:43 AM,19,3,9:40:09 AM,19,3,10:16:56 AM,21,,,-1.226537,36.91563,-1.2874215,36.7767292,Rider_Id_813,2207
-Order_No_9609,User_Id_867,Bike,3,Business,5,2,1:10:25 PM,5,2,1:10:52 PM,5,2,1:10:58 PM,5,2,1:21:06 PM,5,2,1:42:24 PM,9,25.7,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_523,1278
-Order_No_22641,User_Id_2066,Bike,3,Business,16,2,5:41:59 PM,16,2,5:43:57 PM,16,2,5:52:14 PM,16,2,7:05:51 PM,16,2,7:23:28 PM,10,,,-1.3210188999999999,36.841055100000005,-1.2699958,36.795639,Rider_Id_438,1057
-Order_No_12560,User_Id_3213,Bike,1,Personal,30,4,9:24:41 AM,30,4,9:25:23 AM,30,4,9:28:27 AM,30,4,10:20:21 AM,30,4,10:41:55 AM,8,16.3,,-1.2630828,36.805053799999996,-1.3007534,36.7964576,Rider_Id_131,1294
-Order_No_1915,User_Id_393,Bike,3,Business,28,3,8:35:18 AM,28,3,8:35:42 AM,28,3,8:38:23 AM,28,3,8:49:06 AM,28,3,9:31:44 AM,14,20.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_523,2558
-Order_No_8182,User_Id_2154,Bike,3,Business,28,4,4:30:25 PM,28,4,4:36:34 PM,28,4,5:06:04 PM,28,4,5:16:12 PM,28,4,5:47:32 PM,13,,,-1.2765736,36.851364600000004,-1.2860372,36.7549573,Rider_Id_752,1880
-Order_No_11161,User_Id_2523,Bike,3,Business,3,1,2:39:54 PM,3,1,2:43:59 PM,3,1,3:04:54 PM,3,1,3:09:35 PM,3,1,3:34:48 PM,13,23.2,,-1.2551895,36.7822034,-1.3024388,36.859699,Rider_Id_96,1513
-Order_No_23072,User_Id_3482,Bike,1,Personal,8,3,9:21:59 AM,8,3,9:23:38 AM,8,3,9:46:04 AM,8,3,9:53:59 AM,8,3,10:44:00 AM,25,17.4,,-1.3681253,36.915529799999995,-1.2944931,36.769838899999996,Rider_Id_379,3001
-Order_No_13336,User_Id_1329,Bike,3,Business,13,4,10:49:51 AM,13,4,10:50:26 AM,13,4,10:50:39 AM,13,4,10:54:28 AM,13,4,11:39:46 AM,21,21.9,,-1.273056,36.811298,-1.2995252,36.6871615,Rider_Id_576,2718
-Order_No_1497,User_Id_3326,Bike,3,Business,14,4,4:03:40 PM,14,4,4:12:42 PM,14,4,4:55:24 PM,14,4,4:57:28 PM,14,4,5:28:09 PM,7,21.1,,-1.2551895,36.7822034,-1.2862269,36.7841601,Rider_Id_752,1841
-Order_No_20577,User_Id_432,Bike,3,Business,20,1,6:37:37 PM,20,1,6:42:54 PM,20,1,6:53:05 PM,20,1,6:57:18 PM,20,1,7:14:39 PM,3,,,-1.3012008,36.764868,-1.2763494,36.7671783,Rider_Id_959,1041
-Order_No_18389,User_Id_3484,Bike,1,Personal,17,1,12:57:25 PM,17,1,12:57:36 PM,17,1,1:05:43 PM,17,1,1:08:20 PM,17,1,1:27:58 PM,15,24.9,,-1.2343236000000002,36.8861,-1.2927600000000001,36.783799,Rider_Id_320,1178
-Order_No_2374,User_Id_3345,Bike,1,Personal,7,6,3:33:37 PM,7,6,3:34:15 PM,7,6,3:36:45 PM,7,6,3:49:54 PM,7,6,4:04:58 PM,6,21.3,,-1.3043821000000002,36.7840586,-1.269974,36.77537279999999,Rider_Id_449,904
-Order_No_21387,User_Id_302,Bike,3,Business,10,1,3:10:16 PM,10,1,3:12:18 PM,10,1,3:16:57 PM,10,1,3:29:54 PM,10,1,3:42:18 PM,4,,,-1.2649605,36.798177700000004,-1.2767473,36.80953710000001,Rider_Id_882,744
-Order_No_19507,User_Id_1715,Bike,3,Business,16,1,9:06:02 AM,16,1,9:08:21 AM,16,1,9:16:10 AM,16,1,9:19:27 AM,16,1,9:37:42 AM,7,14.6,,-1.2608993,36.799246000000004,-1.2892435,36.8221852,Rider_Id_808,1095
-Order_No_3044,User_Id_2344,Bike,3,Business,14,4,12:57:29 PM,14,4,2:24:35 PM,14,4,2:26:43 PM,14,4,2:37:47 PM,14,4,3:02:03 PM,6,27.9,,-1.2551895,36.7822034,-1.2812704,36.81735379999999,Rider_Id_863,1456
-Order_No_8213,User_Id_1787,Bike,3,Business,10,4,1:21:17 PM,10,4,1:21:36 PM,10,4,1:44:40 PM,10,4,1:51:55 PM,10,4,2:16:14 PM,9,25.4,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_81,1459
-Order_No_24215,User_Id_3283,Bike,3,Business,19,6,8:53:00 AM,19,6,9:30:20 AM,19,6,9:47:21 AM,19,6,9:55:54 AM,19,6,10:19:36 AM,6,16.7,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_327,1422
-Order_No_497,User_Id_2330,Bike,3,Business,20,3,9:23:06 AM,20,3,9:23:26 AM,20,3,9:31:38 AM,20,3,9:35:50 AM,20,3,9:46:42 AM,8,20.8,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_786,652
-Order_No_8773,User_Id_540,Bike,3,Business,8,2,11:43:47 AM,8,2,11:45:22 AM,8,2,11:52:22 AM,8,2,11:59:47 AM,8,2,12:11:20 PM,6,23.3,,-1.2571472,36.795063299999995,-1.2967701999999999,36.8040381,Rider_Id_247,693
-Order_No_2314,User_Id_1329,Bike,3,Business,14,5,3:58:02 PM,14,5,4:02:58 PM,14,5,4:17:59 PM,14,5,4:20:27 PM,14,5,4:44:56 PM,4,23.6,,-1.273056,36.811298,-1.2560874,36.7931031,Rider_Id_547,1469
-Order_No_2548,User_Id_1271,Bike,3,Business,20,4,10:01:57 AM,20,4,10:02:19 AM,20,4,11:41:28 AM,20,4,11:42:33 AM,20,4,12:21:18 PM,24,22.0,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_48,2325
-Order_No_18643,User_Id_3420,Bike,3,Business,2,4,11:53:14 AM,2,4,11:53:47 AM,2,4,11:58:45 AM,2,4,12:07:43 PM,2,4,12:23:39 PM,6,,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_106,956
-Order_No_25694,User_Id_435,Bike,3,Business,27,1,12:49:43 PM,27,1,12:52:09 PM,27,1,12:59:58 PM,27,1,1:09:59 PM,27,1,1:25:05 PM,6,20.9,,-1.266294,36.8057971,-1.300921,36.828195,Rider_Id_168,906
-Order_No_20461,User_Id_2596,Bike,3,Business,18,2,12:14:23 PM,18,2,12:15:10 PM,18,2,12:21:45 PM,18,2,12:29:46 PM,18,2,12:40:28 PM,2,,,-1.2975066000000002,36.76894229999999,-1.3014461000000002,36.7661381,Rider_Id_649,642
-Order_No_26491,User_Id_2200,Bike,3,Business,6,2,12:46:06 PM,6,2,12:51:28 PM,6,2,1:06:02 PM,6,2,1:12:35 PM,6,2,2:02:07 PM,8,25.0,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_548,2972
-Order_No_11516,User_Id_352,Bike,3,Personal,29,2,8:52:02 AM,29,2,9:15:04 AM,29,2,9:34:15 AM,29,2,9:38:02 AM,29,2,10:42:56 AM,7,16.7,,-1.2866269,36.82463679999999,-1.3202277,36.8223638,Rider_Id_906,3894
-Order_No_16480,User_Id_3359,Bike,3,Business,18,2,4:41:38 PM,18,2,4:41:48 PM,18,2,4:46:01 PM,18,2,5:01:58 PM,18,2,5:14:23 PM,4,,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_116,745
-Order_No_27487,User_Id_3239,Bike,3,Personal,24,1,10:38:26 AM,24,1,10:39:33 AM,24,1,10:45:05 AM,24,1,10:53:29 AM,24,1,11:08:14 AM,14,22.9,,-1.2836832,36.8306719,-1.2124618999999999,36.8739332,Rider_Id_237,885
-Order_No_22385,User_Id_2423,Bike,3,Business,28,4,3:12:36 PM,28,4,3:13:54 PM,28,4,3:19:54 PM,28,4,3:50:26 PM,28,4,4:11:10 PM,12,27.6,,-1.2571472,36.795063299999995,-1.329568,36.8520825,Rider_Id_484,1244
-Order_No_18047,User_Id_1787,Bike,3,Business,20,3,11:53:11 AM,20,3,11:53:38 AM,20,3,12:04:28 PM,20,3,12:25:36 PM,20,3,12:43:01 PM,10,27.3,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_168,1045
-Order_No_27024,User_Id_3118,Bike,1,Personal,26,3,3:30:27 PM,26,3,3:38:32 PM,26,3,3:46:11 PM,26,3,3:56:06 PM,26,3,4:12:54 PM,5,27.2,,-1.2861764999999998,36.8209086,-1.3072409999999999,36.830236799999994,Rider_Id_698,1008
-Order_No_24392,User_Id_2593,Bike,3,Business,7,2,10:57:21 AM,7,2,10:57:41 AM,7,2,11:00:55 AM,7,2,11:04:57 AM,7,2,11:11:26 AM,1,23.3,,-1.2615888000000002,36.7928732,-1.2551895,36.7822034,Rider_Id_449,389
-Order_No_26001,User_Id_3222,Bike,3,Business,19,2,9:57:21 AM,19,2,9:57:40 AM,19,2,10:11:00 AM,19,2,10:14:46 AM,19,2,10:39:40 AM,4,,,-1.2551895,36.7822034,-1.2695287,36.798177700000004,Rider_Id_259,1494
-Order_No_12677,User_Id_2229,Bike,3,Business,12,3,10:28:48 AM,12,3,10:33:11 AM,12,3,10:43:52 AM,12,3,10:46:56 AM,12,3,11:09:35 AM,10,19.6,,-1.3247683000000001,36.85803,-1.301893,36.815245000000004,Rider_Id_243,1359
-Order_No_3231,User_Id_3250,Bike,3,Business,9,5,11:50:11 AM,9,5,12:06:10 PM,9,5,12:38:32 PM,9,5,12:43:24 PM,9,5,1:31:21 PM,28,25.6,,-1.3827933000000001,36.7626219,-1.3433181,36.9138018,Rider_Id_858,2877
-Order_No_9738,User_Id_633,Bike,3,Business,28,4,2:41:39 PM,28,4,2:42:41 PM,28,4,2:46:04 PM,28,4,2:59:07 PM,28,4,3:27:31 PM,12,30.2,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_584,1704
-Order_No_3160,User_Id_2000,Bike,3,Business,11,1,3:19:37 PM,11,1,3:20:53 PM,11,1,3:33:04 PM,11,1,3:35:37 PM,11,1,4:00:30 PM,8,28.1,,-1.2551895,36.7822034,-1.293393,36.790237,Rider_Id_631,1493
-Order_No_19627,User_Id_2271,Bike,3,Business,16,2,4:18:26 PM,16,2,4:18:47 PM,16,2,4:23:32 PM,16,2,4:37:40 PM,16,2,5:14:31 PM,14,,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_96,2211
-Order_No_12306,User_Id_284,Bike,1,Personal,22,5,12:22:59 PM,22,5,12:23:12 PM,22,5,12:44:40 PM,22,5,12:53:13 PM,22,5,1:18:23 PM,10,21.6,,-1.2602386,36.7503491,-1.2796316,36.8250415,Rider_Id_844,1510
-Order_No_26003,User_Id_3434,Bike,3,Business,14,5,12:12:05 PM,14,5,12:12:58 PM,14,5,12:13:03 PM,14,5,12:26:28 PM,14,5,1:02:42 PM,20,24.3,,-1.2963097,36.7688221,-1.3123562,36.9157558,Rider_Id_886,2174
-Order_No_3599,User_Id_1271,Bike,3,Business,3,1,1:28:59 PM,3,1,1:49:36 PM,3,1,2:07:01 PM,3,1,2:24:17 PM,3,1,3:02:59 PM,12,23.6,,-1.2927792,36.9053492,-1.3226301999999999,36.8719901,Rider_Id_589,2322
-Order_No_17038,User_Id_2454,Bike,3,Business,14,1,3:06:13 PM,14,1,3:38:11 PM,14,1,4:25:20 PM,14,1,4:29:49 PM,14,1,5:30:11 PM,30,25.6,,-1.3491978999999998,36.9647412,-1.2993231,36.7753372,Rider_Id_829,3622
-Order_No_5141,User_Id_1131,Bike,3,Business,29,1,12:58:41 PM,29,1,12:59:14 PM,29,1,1:12:21 PM,29,1,1:14:25 PM,29,1,1:48:58 PM,18,,,-1.207546,36.7913976,-1.2842308,36.7886575,Rider_Id_878,2073
-Order_No_25055,User_Id_3701,Bike,3,Business,26,2,11:28:53 AM,26,2,11:29:27 AM,26,2,11:57:55 AM,26,2,12:01:16 PM,26,2,12:22:48 PM,6,27.6,,-1.2612573999999999,36.7879622,-1.2611818000000001,36.8271669,Rider_Id_448,1292
-Order_No_27243,User_Id_53,Bike,3,Business,3,1,11:24:41 AM,3,1,11:31:37 AM,3,1,11:44:25 AM,3,1,12:09:04 PM,3,1,12:26:41 PM,5,21.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_158,1057
-Order_No_14842,User_Id_2132,Bike,3,Business,3,3,3:44:23 PM,3,3,3:45:32 PM,3,3,3:54:18 PM,3,3,4:32:04 PM,3,3,5:47:35 PM,12,27.2,,-1.2384056,36.8718698,-1.2954051000000002,36.8364161,Rider_Id_448,4531
-Order_No_20054,User_Id_3201,Bike,3,Business,27,3,1:27:37 PM,27,3,1:28:03 PM,27,3,1:30:32 PM,27,3,1:43:19 PM,27,3,1:48:13 PM,1,29.3,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_647,294
-Order_No_26535,User_Id_142,Bike,3,Business,19,2,12:54:53 PM,19,2,12:55:20 PM,19,2,1:10:32 PM,19,2,1:19:53 PM,19,2,1:30:05 PM,4,26.4,,-1.2795216,36.77067520000001,-1.2963097,36.7688221,Rider_Id_778,612
-Order_No_2418,User_Id_1075,Bike,3,Business,22,5,2:56:38 PM,22,5,3:08:44 PM,22,5,3:25:07 PM,22,5,3:28:23 PM,22,5,3:52:58 PM,13,30.9,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_520,1475
-Order_No_21099,User_Id_1551,Bike,1,Personal,29,5,12:39:27 PM,29,5,12:41:36 PM,29,5,12:53:50 PM,29,5,1:01:49 PM,29,5,1:18:59 PM,5,19.6,,-1.2964347999999999,36.8182757,-1.2833808,36.827197999999996,Rider_Id_208,1030
-Order_No_13304,User_Id_439,Bike,3,Business,14,5,9:35:29 AM,14,5,10:32:04 AM,14,5,10:45:20 AM,14,5,10:57:38 AM,14,5,11:51:11 AM,15,21.7,,-1.251511,36.8148595,-1.3323107,36.8692616,Rider_Id_93,3213
-Order_No_8262,User_Id_635,Bike,3,Business,12,2,2:30:22 PM,12,2,2:33:06 PM,12,2,2:42:19 PM,12,2,3:00:33 PM,12,2,3:18:03 PM,6,,,-1.3081376,36.83297720000001,-1.3229345,36.8566974,Rider_Id_154,1050
-Order_No_15746,User_Id_1972,Bike,3,Business,13,4,11:35:02 AM,13,4,11:35:17 AM,13,4,11:46:50 AM,13,4,11:55:57 AM,13,4,12:29:29 PM,8,22.2,,-1.3142268999999998,36.839245,-1.2620725,36.802651700000006,Rider_Id_399,2012
-Order_No_15405,User_Id_516,Bike,3,Business,2,4,2:49:10 PM,2,4,2:53:39 PM,2,4,3:04:23 PM,2,4,3:12:32 PM,2,4,4:15:48 PM,6,,,-1.2551895,36.7822034,-1.2540684,36.823951799999996,Rider_Id_178,3796
-Order_No_12827,User_Id_2200,Bike,3,Business,12,2,12:54:19 PM,12,2,12:55:10 PM,12,2,1:14:35 PM,12,2,1:49:18 PM,12,2,2:03:03 PM,1,22.8,,-1.2840466999999998,36.8222701,-1.290894,36.822971,Rider_Id_431,825
-Order_No_8331,User_Id_2112,Bike,3,Business,1,6,3:52:02 PM,1,6,3:52:08 PM,1,6,3:52:13 PM,1,6,3:57:42 PM,1,6,4:22:41 PM,18,22.0,,-1.2156006000000001,36.8916865,-1.2962832,36.784968299999996,Rider_Id_704,1499
-Order_No_9326,User_Id_3710,Bike,3,Business,5,3,1:48:50 PM,5,3,2:05:45 PM,5,3,2:12:36 PM,5,3,2:16:07 PM,5,3,2:28:46 PM,4,24.8,,-1.2551895,36.7822034,-1.2689924,36.798157,Rider_Id_167,759
-Order_No_24491,User_Id_393,Bike,3,Business,16,3,4:23:14 PM,16,3,4:26:45 PM,16,3,4:26:50 PM,16,3,4:28:47 PM,16,3,4:45:40 PM,4,27.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_576,1013
-Order_No_26626,User_Id_1634,Bike,1,Personal,12,4,12:40:36 PM,12,4,12:40:47 PM,12,4,12:49:06 PM,12,4,12:52:45 PM,12,4,1:28:40 PM,16,19.1,,-1.2219305,36.90221,-1.2601,36.796528,Rider_Id_384,2155
-Order_No_3983,User_Id_3640,Bike,2,Personal,3,2,7:56:53 PM,3,2,7:57:35 PM,3,2,8:16:13 PM,3,2,8:24:01 PM,3,2,8:57:29 PM,14,17.1,,-1.286411,36.7925973,-1.3089969,36.7064138,Rider_Id_95,2008
-Order_No_19423,User_Id_467,Bike,3,Business,7,2,9:38:28 AM,7,2,9:38:42 AM,7,2,9:42:45 AM,7,2,9:47:13 AM,7,2,10:54:07 AM,36,,,-1.304735,36.784605400000004,-1.1247063,36.6795585,Rider_Id_952,4014
-Order_No_857,User_Id_393,Bike,3,Business,18,5,10:19:12 AM,18,5,10:25:38 AM,18,5,10:50:26 AM,18,5,11:03:01 AM,18,5,11:25:17 AM,8,20.3,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_725,1336
-Order_No_18591,User_Id_1952,Bike,1,Personal,14,1,2:20:52 PM,14,1,2:36:57 PM,14,1,3:27:57 PM,14,1,3:37:59 PM,14,1,4:42:39 PM,24,25.5,,-1.2654663000000002,36.9090598,-1.2877226000000002,36.758563099999996,Rider_Id_581,3880
-Order_No_18675,User_Id_1959,Bike,3,Business,31,5,3:32:45 PM,31,5,3:34:07 PM,31,5,3:47:53 PM,31,5,3:50:48 PM,31,5,4:05:51 PM,10,24.8,,-1.2551895,36.7822034,-1.2664227,36.7453671,Rider_Id_96,903
-Order_No_18155,User_Id_103,Bike,3,Business,26,5,6:16:39 PM,26,5,6:35:00 PM,26,5,7:22:58 PM,26,5,7:26:48 PM,26,5,8:00:01 PM,21,,,-1.1668055,36.830444799999995,-1.2844665000000002,36.7866113,Rider_Id_222,1993
-Order_No_15704,User_Id_1500,Bike,3,Business,2,3,3:09:42 PM,2,3,3:11:04 PM,2,3,3:15:58 PM,2,3,3:44:15 PM,2,3,3:44:18 PM,13,26.2,,-1.2574219,36.7927073,-1.3228003000000002,36.8306435,Rider_Id_725,3
-Order_No_3680,User_Id_970,Bike,3,Business,9,1,11:38:40 AM,9,1,11:54:17 AM,9,1,12:17:27 PM,9,1,12:25:32 PM,9,1,12:39:40 PM,8,20.2,,-1.2551895,36.7822034,-1.2377323,36.80599829999999,Rider_Id_101,848
-Order_No_6452,User_Id_3295,Bike,3,Business,25,5,9:12:08 AM,25,5,9:14:08 AM,25,5,9:14:53 AM,25,5,9:27:34 AM,25,5,10:15:58 AM,21,20.9,,-1.2571472,36.795063299999995,-1.3908673999999999,36.7674236,Rider_Id_536,2904
-Order_No_23203,User_Id_393,Bike,3,Business,17,5,9:43:44 AM,17,5,9:44:18 AM,17,5,9:44:25 AM,17,5,10:39:26 AM,17,5,10:51:31 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_843,725
-Order_No_12588,User_Id_2412,Bike,3,Business,11,2,12:49:44 PM,11,2,12:52:51 PM,11,2,12:53:53 PM,11,2,12:57:13 PM,11,2,1:20:51 PM,11,19.6,,-1.2285834999999998,36.882646799999996,-1.290894,36.822971,Rider_Id_320,1418
-Order_No_23185,User_Id_868,Bike,3,Business,6,6,1:43:26 PM,6,6,1:43:45 PM,6,6,1:47:04 PM,6,6,2:00:19 PM,6,6,2:13:30 PM,7,28.3,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_695,791
-Order_No_989,User_Id_1120,Bike,3,Personal,25,4,12:19:00 PM,25,4,1:24:20 PM,25,4,1:34:58 PM,25,4,1:36:37 PM,25,4,1:45:43 PM,5,,,-1.2807,36.808066,-1.2564222,36.7937091,Rider_Id_576,546
-Order_No_7317,User_Id_3669,Bike,3,Business,15,6,11:40:18 AM,15,6,11:49:58 AM,15,6,12:02:29 PM,15,6,12:21:18 PM,15,6,12:50:32 PM,10,22.1,,-1.2527958000000001,36.8003131,-1.2429937,36.8754167,Rider_Id_477,1754
-Order_No_15110,User_Id_1447,Bike,2,Personal,13,4,7:13:29 PM,13,4,7:14:05 PM,13,4,7:37:55 PM,13,4,7:42:21 PM,13,4,8:07:24 PM,17,20.0,,-1.2838392,36.8842742,-1.299947,36.76854470000001,Rider_Id_179,1503
-Order_No_20427,User_Id_3276,Bike,2,Personal,27,4,5:06:45 PM,27,4,5:07:25 PM,27,4,5:07:45 PM,27,4,5:19:58 PM,27,4,5:20:07 PM,2,22.8,,-1.3053113,36.842438200000004,-1.3001072,36.8499697,Rider_Id_179,9
-Order_No_4456,User_Id_136,Bike,3,Business,10,4,3:46:19 PM,10,4,3:50:13 PM,10,4,4:03:33 PM,10,4,4:17:23 PM,10,4,4:30:56 PM,8,25.0,,-1.3009676000000001,36.7657189,-1.3254522,36.721658399999995,Rider_Id_906,813
-Order_No_8840,User_Id_3222,Bike,3,Business,5,5,3:35:03 PM,5,5,3:35:19 PM,5,5,3:37:33 PM,5,5,3:40:22 PM,5,5,3:46:41 PM,3,27.7,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_708,379
-Order_No_3594,User_Id_2701,Bike,3,Business,6,3,12:37:45 PM,6,3,12:38:00 PM,6,3,12:38:05 PM,6,3,1:05:59 PM,6,3,1:16:50 PM,5,,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_906,651
-Order_No_9170,User_Id_3675,Bike,1,Personal,16,1,1:49:35 PM,16,1,1:49:56 PM,16,1,1:54:02 PM,16,1,2:12:01 PM,16,1,2:28:15 PM,6,22.4,,-1.2682178999999998,36.8009289,-1.29387,36.7907813,Rider_Id_519,974
-Order_No_4623,User_Id_20,Bike,3,Business,3,1,11:19:49 AM,3,1,11:20:15 AM,3,1,11:26:27 AM,3,1,11:32:58 AM,3,1,11:45:13 AM,9,21.6,,-1.2607331,36.7428881,-1.2629666000000002,36.8068369,Rider_Id_269,735
-Order_No_14789,User_Id_793,Bike,3,Personal,8,3,11:11:05 AM,8,3,11:11:49 AM,8,3,11:18:27 AM,8,3,11:30:20 AM,8,3,11:36:38 AM,6,22.4,,-1.2681211000000001,36.779247399999996,-1.2559294,36.788465,Rider_Id_116,378
-Order_No_23279,User_Id_2066,Bike,3,Business,5,3,11:47:18 AM,5,3,11:47:59 AM,5,3,12:00:32 PM,5,3,12:05:48 PM,5,3,12:58:28 PM,10,22.6,,-1.3210188999999999,36.841055100000005,-1.2790807,36.78259723,Rider_Id_19,3160
-Order_No_21443,User_Id_2000,Bike,3,Business,11,5,8:50:26 AM,11,5,8:52:35 AM,11,5,9:17:35 AM,11,5,9:20:51 AM,11,5,9:45:01 AM,7,18.0,5.1,-1.2559559999999999,36.789867,-1.292044,36.821938,Rider_Id_159,1450
-Order_No_9550,User_Id_3734,Bike,3,Personal,23,7,3:59:54 PM,23,7,4:01:02 PM,23,7,4:14:11 PM,23,7,4:19:10 PM,23,7,4:48:13 PM,12,25.0,,-1.280951,36.7990799,-1.2235201,36.80206079999999,Rider_Id_920,1743
-Order_No_25971,User_Id_3548,Bike,1,Personal,20,4,11:07:08 AM,20,4,11:11:11 AM,20,4,11:43:35 AM,20,4,11:46:15 AM,20,4,12:20:32 PM,9,24.6,,-1.3112754999999998,36.743061100000006,-1.29817,36.800141700000005,Rider_Id_482,2057
-Order_No_9890,User_Id_1869,Bike,1,Personal,12,5,3:07:05 PM,12,5,3:08:40 PM,12,5,3:22:43 PM,12,5,3:29:35 PM,12,5,4:04:49 PM,13,27.2,,-1.3304954,36.87272670000001,-1.3039695,36.783826700000006,Rider_Id_532,2114
-Order_No_11866,User_Id_2523,Bike,3,Business,27,1,11:19:14 AM,27,1,11:23:30 AM,27,1,11:26:20 AM,27,1,11:31:54 AM,27,1,12:09:29 PM,13,20.6,,-1.2551895,36.7822034,-1.3024388,36.859699,Rider_Id_875,2255
-Order_No_2208,User_Id_63,Bike,3,Personal,19,3,9:48:30 AM,19,3,9:49:58 AM,19,3,9:50:04 AM,19,3,10:04:08 AM,19,3,10:26:14 AM,13,19.6,,-1.2737091,36.813153299999996,-1.2762878999999998,36.902493299999996,Rider_Id_403,1326
-Order_No_16846,User_Id_3689,Bike,3,Business,26,1,11:08:51 AM,26,1,11:28:29 AM,26,1,11:36:32 AM,26,1,11:41:10 AM,26,1,12:07:49 PM,9,22.9,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_629,1599
-Order_No_7063,User_Id_2423,Bike,3,Business,30,3,9:08:04 AM,30,3,9:11:16 AM,30,3,9:21:28 AM,30,3,9:28:55 AM,30,3,9:52:16 AM,8,19.8,,-1.2551895,36.7822034,-1.2945073999999999,36.8083195,Rider_Id_343,1401
-Order_No_4674,User_Id_3091,Bike,3,Business,21,2,11:56:45 AM,21,2,11:57:21 AM,21,2,12:07:16 PM,21,2,12:10:32 PM,21,2,12:34:31 PM,9,19.6,,-1.2642719999999998,36.7978791,-1.3014461000000002,36.7661381,Rider_Id_452,1439
-Order_No_12787,User_Id_2008,Bike,3,Business,1,5,9:14:39 AM,1,5,9:14:59 AM,1,5,9:15:22 AM,1,5,9:36:01 AM,1,5,9:56:51 AM,14,23.0,,-1.2508229,36.7895263,-1.2210264,36.8893138,Rider_Id_90,1250
-Order_No_9918,User_Id_2803,Bike,3,Business,4,5,10:33:51 AM,4,5,11:03:06 AM,4,5,12:25:59 PM,4,5,12:31:29 PM,4,5,12:50:58 PM,3,17.2,,-1.2765736,36.851364600000004,-1.2677415,36.83484179999999,Rider_Id_48,1169
-Order_No_8632,User_Id_1856,Bike,3,Business,20,4,11:09:55 AM,20,4,11:11:16 AM,20,4,11:16:57 AM,20,4,11:26:40 AM,20,4,11:39:08 AM,4,22.2,,-1.2731645,36.7900343,-1.2649605,36.798177700000004,Rider_Id_453,748
-Order_No_13843,User_Id_633,Bike,3,Business,15,1,8:08:17 AM,15,1,8:08:55 AM,15,1,8:31:15 AM,15,1,8:32:17 AM,15,1,8:42:33 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_208,616
-Order_No_28051,User_Id_3239,Bike,3,Personal,26,6,10:05:57 AM,26,6,10:06:54 AM,26,6,10:22:29 AM,26,6,10:28:27 AM,26,6,11:03:11 AM,8,18.6,,-1.2839322,36.8292426,-1.2873232,36.765857200000006,Rider_Id_480,2084
-Order_No_19626,User_Id_2701,Bike,3,Business,12,3,1:57:04 PM,12,3,1:57:33 PM,12,3,2:04:42 PM,12,3,2:06:12 PM,12,3,2:43:02 PM,8,26.3,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_407,2210
-Order_No_14408,User_Id_1809,Bike,3,Business,26,1,3:37:18 PM,26,1,4:18:05 PM,26,1,4:27:45 PM,26,1,4:33:51 PM,26,1,4:53:01 PM,11,19.8,,-1.2571472,36.795063299999995,-1.2413316,36.878552500000005,Rider_Id_607,1150
-Order_No_17416,User_Id_2112,Bike,3,Business,29,5,2:55:16 PM,29,5,3:54:44 PM,29,5,4:12:21 PM,29,5,4:25:03 PM,29,5,4:47:40 PM,5,21.1,,-1.2156006000000001,36.8916865,-1.206228,36.917467200000004,Rider_Id_752,1357
-Order_No_11534,User_Id_868,Bike,3,Business,5,5,11:48:19 AM,5,5,11:49:14 AM,5,5,11:58:36 AM,5,5,12:06:41 PM,5,5,12:16:32 PM,5,23.6,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_857,591
-Order_No_691,User_Id_868,Bike,3,Business,15,1,12:11:28 PM,15,1,12:13:13 PM,15,1,12:54:03 PM,15,1,1:01:41 PM,15,1,1:07:52 PM,3,,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_341,371
-Order_No_17211,User_Id_867,Bike,3,Business,26,2,2:02:47 PM,26,2,2:03:09 PM,26,2,2:03:27 PM,26,2,2:14:34 PM,26,2,2:48:13 PM,15,29.8,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_154,2019
-Order_No_10958,User_Id_2801,Bike,3,Business,10,1,2:14:04 PM,10,1,2:14:37 PM,10,1,2:27:09 PM,10,1,2:39:35 PM,10,1,3:10:07 PM,6,18.5,,-1.2674273999999999,36.787082700000006,-1.2896398999999998,36.812449799999996,Rider_Id_158,1832
-Order_No_21136,User_Id_3338,Bike,3,Business,26,2,2:43:39 PM,26,2,2:44:02 PM,26,2,2:46:52 PM,26,2,3:13:19 PM,26,2,3:35:10 PM,9,31.2,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_187,1311
-Order_No_4140,User_Id_2523,Bike,3,Business,28,2,11:42:19 AM,28,2,11:42:41 AM,28,2,11:44:45 AM,28,2,11:52:30 AM,28,2,12:05:38 PM,4,21.3,,-1.2551895,36.7822034,-1.2689015,36.7986106,Rider_Id_709,788
-Order_No_7361,User_Id_393,Bike,3,Business,31,3,3:35:11 PM,31,3,3:35:40 PM,31,3,3:35:47 PM,31,3,4:08:33 PM,31,3,4:30:49 PM,14,27.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_39,1336
-Order_No_4497,User_Id_3091,Bike,3,Business,14,5,3:21:18 PM,14,5,3:23:41 PM,14,5,3:24:15 PM,14,5,3:43:35 PM,14,5,4:07:56 PM,9,23.1,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_36,1461
-Order_No_1838,User_Id_3355,Bike,3,Business,15,6,1:53:51 PM,15,6,2:11:31 PM,15,6,2:11:48 PM,15,6,2:40:05 PM,15,6,2:55:28 PM,7,23.2,,-1.2584143,36.8048002,-1.3052491000000002,36.8223899,Rider_Id_669,923
-Order_No_19812,User_Id_1271,Bike,3,Business,24,1,11:58:56 AM,24,1,11:59:12 AM,24,1,12:47:03 PM,24,1,1:19:41 PM,24,1,1:38:40 PM,9,22.9,,-1.3244885,36.897791999999995,-1.2698307,36.8801649,Rider_Id_26,1139
-Order_No_23913,User_Id_393,Bike,3,Business,2,6,8:37:49 AM,2,6,8:47:29 AM,2,6,9:02:37 AM,2,6,9:22:25 AM,2,6,9:22:27 AM,4,21.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_26,2
-Order_No_8807,User_Id_2682,Bike,1,Personal,27,4,12:05:46 PM,27,4,12:05:56 PM,27,4,12:10:52 PM,27,4,12:22:46 PM,27,4,12:38:19 PM,6,22.9,,-1.2950408000000002,36.809817200000005,-1.2753108999999998,36.8130185,Rider_Id_932,933
-Order_No_19727,User_Id_2024,Bike,2,Personal,19,5,11:43:02 AM,19,5,11:43:35 AM,19,5,11:54:06 AM,19,5,12:18:46 PM,19,5,12:57:50 PM,19,25.0,,-1.324282,36.86625,-1.2298784999999999,36.7819851,Rider_Id_178,2344
-Order_No_26432,User_Id_393,Bike,3,Business,15,1,11:40:29 AM,15,1,11:40:51 AM,15,1,11:41:11 AM,15,1,12:24:31 PM,15,1,12:47:18 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_935,1367
-Order_No_24207,User_Id_2200,Bike,3,Business,7,5,9:28:43 AM,7,5,9:45:52 AM,7,5,10:05:02 AM,7,5,10:11:54 AM,7,5,10:39:45 AM,9,20.4,1.1,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_404,1671
-Order_No_16526,User_Id_1297,Bike,2,Personal,18,4,6:39:00 PM,18,4,6:39:11 PM,18,4,6:41:25 PM,18,4,6:57:04 PM,18,4,7:08:38 PM,7,23.5,,-1.2649605,36.798177700000004,-1.2983805,36.8198439,Rider_Id_266,694
-Order_No_10148,User_Id_2154,Bike,3,Business,23,2,10:38:13 AM,23,2,10:39:57 AM,23,2,10:57:05 AM,23,2,10:59:15 AM,23,2,11:21:02 AM,8,,,-1.2765736,36.851364600000004,-1.2585521000000002,36.7945765,Rider_Id_281,1307
-Order_No_21547,User_Id_1336,Bike,3,Business,19,2,12:40:41 PM,19,2,12:42:35 PM,19,2,12:44:45 PM,19,2,1:00:40 PM,19,2,1:33:45 PM,18,26.5,,-1.2885029,36.825604999999996,-1.3425573,36.71025039999999,Rider_Id_800,1985
-Order_No_16110,User_Id_3647,Bike,3,Business,3,4,2:10:54 PM,3,4,2:14:00 PM,3,4,2:21:45 PM,3,4,2:24:39 PM,3,4,2:44:01 PM,8,26.0,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_434,1162
-Order_No_20788,User_Id_2587,Bike,3,Business,27,4,3:21:26 PM,27,4,3:22:05 PM,27,4,3:24:45 PM,27,4,3:29:41 PM,27,4,4:18:30 PM,5,23.8,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_86,2929
-Order_No_8645,User_Id_3201,Bike,3,Business,28,4,10:16:09 AM,28,4,10:16:28 AM,28,4,10:17:54 AM,28,4,10:23:07 AM,28,4,10:24:17 AM,1,22.3,,-1.2584143,36.8048002,-1.2594538999999998,36.8067841,Rider_Id_579,70
-Order_No_20430,User_Id_2803,Bike,3,Business,14,4,9:54:16 AM,14,4,9:56:52 AM,14,4,10:19:42 AM,14,4,10:22:55 AM,14,4,10:40:32 AM,6,17.7,,-1.2765736,36.851364600000004,-1.2568264,36.86531289999999,Rider_Id_448,1057
-Order_No_16227,User_Id_2008,Bike,3,Business,10,5,3:50:02 PM,10,5,4:07:25 PM,10,5,4:24:35 PM,10,5,4:29:22 PM,10,5,4:56:58 PM,6,,,-1.2508229,36.7895263,-1.2788257,36.82877,Rider_Id_843,1656
-Order_No_4818,User_Id_2030,Bike,3,Business,11,2,3:38:33 PM,11,2,3:42:08 PM,11,2,3:50:17 PM,11,2,3:53:17 PM,11,2,4:53:34 PM,3,22.5,,-1.2969741000000001,36.7856612,-1.295908,36.791433000000005,Rider_Id_596,3617
-Order_No_1583,User_Id_1955,Bike,3,Business,22,4,4:35:12 PM,22,4,4:57:58 PM,22,4,5:07:23 PM,22,4,5:18:06 PM,22,4,5:48:45 PM,17,24.2,,-1.2859912,36.8756811,-1.2955333999999998,36.7742857,Rider_Id_456,1839
-Order_No_8594,User_Id_2625,Bike,1,Personal,9,6,3:12:54 PM,9,6,3:17:39 PM,9,6,3:45:13 PM,9,6,4:10:41 PM,9,6,4:51:11 PM,12,19.1,,-1.2248308,36.801257299999996,-1.2432,36.7414006,Rider_Id_301,2430
-Order_No_19321,User_Id_2903,Bike,2,Personal,18,4,12:41:55 PM,18,4,12:42:19 PM,18,4,12:43:14 PM,18,4,12:54:47 PM,18,4,1:13:00 PM,10,25.3,,-1.3000978,36.7658352,-1.2456412000000001,36.7955298,Rider_Id_848,1093
-Order_No_5979,User_Id_3444,Bike,1,Personal,28,2,2:56:14 PM,28,2,2:56:41 PM,28,2,3:06:51 PM,28,2,3:22:44 PM,28,2,4:00:40 PM,14,19.6,,-1.263419,36.8060348,-1.2884487,36.8912556,Rider_Id_86,2276
-Order_No_2523,User_Id_246,Bike,3,Business,12,4,2:35:05 PM,12,4,2:35:20 PM,12,4,2:42:48 PM,12,4,3:01:35 PM,12,4,3:25:21 PM,2,21.6,,-1.2615888000000002,36.7928732,-1.2584143,36.8048002,Rider_Id_951,1426
-Order_No_237,User_Id_1591,Bike,2,Personal,11,4,6:10:31 PM,11,4,6:14:57 PM,11,4,6:41:46 PM,11,4,6:49:42 PM,11,4,7:45:52 PM,9,23.9,,-1.2655306,36.828148,-1.3096450000000002,36.808864,Rider_Id_949,3370
-Order_No_14471,User_Id_640,Bike,3,Business,13,1,11:22:05 AM,13,1,11:24:45 AM,13,1,12:06:48 PM,13,1,12:15:06 PM,13,1,1:16:55 PM,14,,,-1.2969741000000001,36.7856612,-1.3246455,36.8754415,Rider_Id_907,3709
-Order_No_4919,User_Id_2008,Bike,3,Business,10,5,2:34:08 PM,10,5,3:03:29 PM,10,5,3:16:34 PM,10,5,3:25:48 PM,10,5,3:57:31 PM,6,,,-1.2508229,36.7895263,-1.2881035,36.8142095,Rider_Id_815,1903
-Order_No_22535,User_Id_2066,Bike,3,Business,22,5,2:01:56 PM,22,5,2:02:04 PM,22,5,2:26:18 PM,22,5,2:30:47 PM,22,5,2:58:14 PM,17,,,-1.316961,36.820337,-1.3332322,36.9289405,Rider_Id_581,1647
-Order_No_14436,User_Id_1194,Bike,3,Personal,12,4,4:00:10 PM,12,4,4:06:58 PM,12,4,4:22:50 PM,12,4,4:27:30 PM,12,4,4:56:20 PM,9,21.9,,-1.304192,36.739457,-1.2906958999999998,36.7961042,Rider_Id_140,1730
-Order_No_28010,User_Id_1500,Bike,3,Business,1,1,3:12:26 PM,1,1,3:16:57 PM,1,1,3:26:09 PM,1,1,3:29:32 PM,1,1,3:42:21 PM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_349,769
-Order_No_7789,User_Id_1419,Bike,3,Personal,14,4,2:45:15 PM,14,4,3:23:33 PM,14,4,3:23:57 PM,14,4,3:31:31 PM,14,4,4:46:50 PM,5,21.6,,-1.2738167,36.8157821,-1.2811395,36.7897609,Rider_Id_467,4519
-Order_No_7353,User_Id_3091,Bike,3,Business,9,6,2:52:37 PM,9,6,3:23:40 PM,9,6,3:29:28 PM,9,6,3:42:37 PM,9,6,3:55:08 PM,6,27.3,,-1.290894,36.822971,-1.2962832,36.784968299999996,Rider_Id_276,751
-Order_No_2391,User_Id_2219,Bike,3,Business,18,4,11:52:50 AM,18,4,11:53:35 AM,18,4,11:59:17 AM,18,4,12:07:56 PM,18,4,12:20:54 PM,6,,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_85,778
-Order_No_5153,User_Id_686,Bike,1,Personal,9,2,10:35:14 AM,9,2,10:35:41 AM,9,2,10:38:35 AM,9,2,11:03:13 AM,9,2,11:46:33 AM,21,22.0,,-1.366466,36.91584,-1.2615348999999998,36.8068294,Rider_Id_848,2600
-Order_No_5900,User_Id_3547,Bike,3,Business,8,3,9:56:20 AM,8,3,10:37:14 AM,8,3,10:57:09 AM,8,3,11:06:54 AM,8,3,11:45:04 AM,18,,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_11,2290
-Order_No_20470,User_Id_1927,Bike,3,Business,20,1,3:43:48 PM,20,1,3:44:05 PM,20,1,3:47:23 PM,20,1,4:12:17 PM,20,1,4:31:34 PM,10,,,-1.3472066,36.7692638,-1.3260023,36.6886989,Rider_Id_815,1157
-Order_No_18765,User_Id_1342,Bike,3,Business,25,4,12:51:20 PM,25,4,1:15:34 PM,25,4,1:26:44 PM,25,4,1:27:46 PM,25,4,1:51:28 PM,9,20.2,,-1.2551895,36.7822034,-1.3025826,36.7670807,Rider_Id_263,1422
-Order_No_23645,User_Id_478,Bike,3,Business,15,1,2:50:03 PM,15,1,2:51:39 PM,15,1,3:08:12 PM,15,1,3:22:53 PM,15,1,3:34:36 PM,3,,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_177,703
-Order_No_3318,User_Id_3149,Bike,3,Business,18,6,10:27:56 AM,18,6,10:28:13 AM,18,6,10:29:27 AM,18,6,10:42:45 AM,18,6,10:51:45 AM,5,21.3,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_591,540
-Order_No_4732,User_Id_3321,Bike,3,Personal,28,1,2:27:59 PM,28,1,2:28:20 PM,28,1,2:33:45 PM,28,1,2:40:05 PM,28,1,3:37:27 PM,7,26.1,,-1.2723552,36.7881306,-1.2945073999999999,36.8083195,Rider_Id_510,3442
-Order_No_12114,User_Id_3647,Bike,3,Business,2,5,11:28:21 AM,2,5,12:16:19 PM,2,5,12:33:33 PM,2,5,12:39:17 PM,2,5,1:05:10 PM,6,23.7,,-1.2726389999999999,36.794723,-1.2874632,36.8243956,Rider_Id_477,1553
-Order_No_2077,User_Id_1072,Bike,1,Personal,9,6,9:40:14 AM,9,6,9:41:08 AM,9,6,9:51:17 AM,9,6,9:55:22 AM,9,6,10:07:26 AM,7,15.4,,-1.261849,36.808254,-1.2966323,36.7875842,Rider_Id_103,724
-Order_No_27101,User_Id_197,Bike,3,Business,13,3,3:37:43 PM,13,3,3:39:36 PM,13,3,3:48:43 PM,13,3,3:57:37 PM,13,3,4:19:26 PM,13,27.2,,-1.2664491000000002,36.8102685,-1.3282758,36.8590281,Rider_Id_880,1309
-Order_No_24208,User_Id_391,Bike,3,Business,23,6,11:45:52 AM,23,6,11:46:55 AM,23,6,11:54:11 AM,23,6,12:12:16 PM,23,6,12:26:40 PM,5,26.1,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_378,864
-Order_No_8972,User_Id_2956,Bike,3,Business,3,5,9:52:07 AM,3,5,9:53:29 AM,3,5,9:56:58 AM,3,5,10:22:50 AM,3,5,11:22:53 AM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_187,3603
-Order_No_14452,User_Id_1976,Bike,3,Business,11,4,12:57:37 PM,11,4,12:58:56 PM,11,4,1:12:24 PM,11,4,1:16:23 PM,11,4,1:45:58 PM,12,23.8,,-1.257028,36.7810435,-1.2738136,36.8232881,Rider_Id_219,1775
-Order_No_11410,User_Id_1246,Bike,3,Business,25,5,3:42:43 PM,25,5,3:43:09 PM,25,5,3:58:42 PM,25,5,4:19:52 PM,25,5,4:25:20 PM,3,29.3,,-1.2571472,36.795063299999995,-1.2515637,36.8119508,Rider_Id_822,328
-Order_No_15040,User_Id_393,Bike,3,Business,8,3,9:06:14 AM,8,3,9:07:05 AM,8,3,9:07:11 AM,8,3,9:47:20 AM,8,3,9:47:25 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,5
-Order_No_18506,User_Id_972,Bike,3,Business,1,1,10:03:48 AM,1,1,10:04:00 AM,1,1,10:09:39 AM,1,1,10:11:03 AM,1,1,10:28:53 AM,17,18.3,,-1.2862033000000002,36.8152966,-1.341891,36.761269,Rider_Id_74,1070
-Order_No_19667,User_Id_3222,Bike,3,Business,6,4,9:33:58 AM,6,4,9:53:01 AM,6,4,10:10:54 AM,6,4,10:18:58 AM,6,4,10:38:59 AM,10,18.8,3.1,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_383,1201
-Order_No_12653,User_Id_567,Bike,3,Business,16,2,12:53:19 PM,16,2,12:54:17 PM,16,2,1:27:24 PM,16,2,1:37:35 PM,16,2,1:55:42 PM,6,,,-1.2707178999999997,36.7979713,-1.2963097,36.7688221,Rider_Id_324,1087
-Order_No_27974,User_Id_3141,Bike,2,Personal,10,5,1:05:13 PM,10,5,1:05:43 PM,10,5,1:21:18 PM,10,5,1:41:54 PM,10,5,2:21:10 PM,18,21.8,,-1.2696183,36.8021778,-1.3103985,36.9114173,Rider_Id_791,2356
-Order_No_1386,User_Id_2150,Bike,3,Business,17,3,3:26:33 PM,17,3,3:30:22 PM,17,3,3:36:09 PM,17,3,3:50:18 PM,17,3,4:02:46 PM,6,,,-1.3181159,36.710155900000004,-1.3444459999999998,36.756597,Rider_Id_340,748
-Order_No_26230,User_Id_1792,Bike,3,Business,20,2,3:45:11 PM,20,2,3:50:35 PM,20,2,3:56:28 PM,20,2,4:00:46 PM,20,2,5:07:04 PM,13,24.1,,-1.2809121,36.7852524,-1.256055,36.7695771,Rider_Id_268,3978
-Order_No_27313,User_Id_3686,Bike,3,Business,12,2,11:33:01 AM,12,2,11:34:02 AM,12,2,11:44:11 AM,12,2,11:45:38 AM,12,2,12:30:26 PM,6,28.0,,-1.2551895,36.7822034,-1.2743253,36.7721049,Rider_Id_167,2688
-Order_No_13828,User_Id_1874,Bike,3,Business,15,4,2:45:16 PM,15,4,2:53:50 PM,15,4,3:33:51 PM,15,4,3:37:47 PM,15,4,4:00:23 PM,6,27.5,,-1.2571472,36.795063299999995,-1.2575690000000002,36.842164000000004,Rider_Id_522,1356
-Order_No_13088,User_Id_1651,Bike,3,Business,31,5,10:55:48 AM,31,5,10:56:28 AM,31,5,11:11:24 AM,31,5,11:15:21 AM,31,5,11:49:36 AM,21,21.9,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_96,2055
-Order_No_3016,User_Id_265,Bike,3,Business,15,2,11:42:39 AM,15,2,12:13:58 PM,15,2,12:44:51 PM,15,2,12:52:02 PM,15,2,1:33:51 PM,10,24.1,,-1.300921,36.828195,-1.240299,36.795942,Rider_Id_870,2509
-Order_No_10668,User_Id_3621,Bike,3,Business,8,1,2:32:29 PM,8,1,2:33:13 PM,8,1,2:37:57 PM,8,1,2:40:34 PM,8,1,4:13:19 PM,20,29.7,,-1.2836583000000001,36.7886014,-1.230842,36.914967,Rider_Id_778,5565
-Order_No_20560,User_Id_496,Bike,3,Business,10,5,11:03:43 AM,10,5,11:05:36 AM,10,5,11:12:46 AM,10,5,11:17:54 AM,10,5,11:55:11 AM,7,,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_411,2237
-Order_No_16160,User_Id_1148,Bike,3,Business,7,2,2:58:29 PM,7,2,2:59:15 PM,7,2,3:08:05 PM,7,2,3:22:22 PM,7,2,4:11:56 PM,14,24.8,,-1.3100586,36.850032899999995,-1.2294985,36.8775495,Rider_Id_471,2974
-Order_No_6032,User_Id_393,Bike,3,Business,18,4,9:53:27 AM,18,4,9:53:39 AM,18,4,9:53:44 AM,18,4,10:39:53 AM,18,4,10:39:56 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_39,3
-Order_No_18758,User_Id_2200,Bike,3,Business,8,4,1:16:43 PM,8,4,1:17:03 PM,8,4,1:56:06 PM,8,4,2:02:13 PM,8,4,2:26:59 PM,2,27.5,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_569,1486
-Order_No_18113,User_Id_733,Bike,3,Business,28,4,3:53:04 PM,28,4,3:53:27 PM,28,4,4:08:28 PM,28,4,4:22:20 PM,28,4,4:22:53 PM,4,27.4,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_79,33
-Order_No_4566,User_Id_3434,Bike,3,Business,2,3,1:10:16 PM,2,3,1:13:37 PM,2,3,1:31:04 PM,2,3,1:48:03 PM,2,3,2:37:45 PM,22,23.6,,-1.3261258999999999,36.904086,-1.2963097,36.7688221,Rider_Id_791,2982
-Order_No_17694,User_Id_1392,Bike,3,Business,13,1,3:36:41 PM,13,1,4:34:37 PM,13,1,5:27:19 PM,13,1,5:35:24 PM,13,1,5:55:55 PM,8,,,-1.2551895,36.7822034,-1.3050332,36.8100437,Rider_Id_716,1231
-Order_No_24209,User_Id_3355,Bike,3,Business,3,5,2:32:10 PM,3,5,2:37:09 PM,3,5,2:56:20 PM,3,5,3:08:43 PM,3,5,3:27:23 PM,6,25.0,,-1.2765736,36.851364600000004,-1.3052491000000002,36.8223899,Rider_Id_754,1120
-Order_No_5932,User_Id_1019,Bike,1,Personal,4,3,2:43:25 PM,4,3,2:44:38 PM,4,3,2:55:46 PM,4,3,3:05:45 PM,4,3,3:39:36 PM,16,18.5,,-1.29387,36.7907813,-1.214328,36.7911903,Rider_Id_334,2031
-Order_No_6629,User_Id_2148,Bike,1,Personal,10,1,9:15:16 AM,10,1,9:15:21 AM,10,1,9:23:00 AM,10,1,9:58:16 AM,10,1,10:20:31 AM,12,18.3,,-1.3261306000000002,36.850596,-1.303596,36.778378000000004,Rider_Id_85,1335
-Order_No_669,User_Id_2532,Bike,3,Business,24,1,12:03:51 PM,24,1,12:05:33 PM,24,1,12:15:37 PM,24,1,12:18:09 PM,24,1,12:41:07 PM,8,22.6,,-1.2551895,36.7822034,-1.2987440000000001,36.817823700000005,Rider_Id_43,1378
-Order_No_2257,User_Id_1469,Bike,3,Business,30,2,3:26:05 PM,30,2,4:02:44 PM,30,2,4:21:39 PM,30,2,4:29:30 PM,30,2,4:53:35 PM,4,,,-1.2551895,36.7822034,-1.2752846000000002,36.802702200000006,Rider_Id_269,1445
-Order_No_14136,User_Id_2154,Bike,3,Business,9,2,3:01:43 PM,9,2,3:02:20 PM,9,2,3:07:59 PM,9,2,3:15:30 PM,9,2,3:30:18 PM,6,30.8,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_291,888
-Order_No_19148,User_Id_824,Bike,3,Business,5,2,8:38:21 AM,5,2,8:59:30 AM,5,2,9:15:11 AM,5,2,9:20:14 AM,5,2,9:54:05 AM,15,21.2,2.0,-1.2886923000000001,36.8241519,-1.207546,36.7913976,Rider_Id_206,2031
-Order_No_24723,User_Id_3698,Bike,3,Business,30,4,5:01:25 PM,30,4,5:05:04 PM,30,4,5:36:23 PM,30,4,5:39:32 PM,30,4,5:57:16 PM,11,,,-1.3244885,36.897791999999995,-1.3975387,36.9377463,Rider_Id_126,1064
-Order_No_9707,User_Id_465,Bike,2,Personal,22,5,2:01:30 PM,22,5,2:05:16 PM,22,5,3:06:13 PM,22,5,3:18:16 PM,22,5,3:55:20 PM,7,,,-1.2551973,36.778174299999996,-1.2765298999999999,36.822030600000005,Rider_Id_268,2224
-Order_No_3846,User_Id_3517,Bike,3,Business,5,2,9:39:19 AM,5,2,9:39:44 AM,5,2,9:40:16 AM,5,2,10:24:00 AM,5,2,11:04:14 AM,16,21.1,1.9,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_332,2414
-Order_No_6529,User_Id_2066,Bike,3,Business,23,5,1:14:14 PM,23,5,1:16:11 PM,23,5,1:47:49 PM,23,5,1:53:53 PM,23,5,2:42:02 PM,14,26.6,,-1.3210188999999999,36.841055100000005,-1.2751598,36.922987400000004,Rider_Id_31,2889
-Order_No_313,User_Id_3020,Bike,1,Personal,24,4,12:41:36 PM,24,4,12:41:49 PM,24,4,1:13:11 PM,24,4,1:16:01 PM,24,4,2:15:18 PM,5,21.4,,-1.3090115,36.8078826,-1.292044,36.821938,Rider_Id_338,3557
-Order_No_1774,User_Id_2344,Bike,3,Business,19,1,3:27:52 PM,19,1,3:28:11 PM,19,1,3:46:48 PM,19,1,3:51:30 PM,19,1,5:05:14 PM,21,25.1,,-1.2571472,36.795063299999995,-1.3363040000000002,36.9132662,Rider_Id_956,4424
-Order_No_14064,User_Id_793,Bike,3,Personal,25,1,12:25:20 PM,25,1,12:26:09 PM,25,1,1:02:57 PM,25,1,1:19:14 PM,25,1,1:57:54 PM,12,27.0,,-1.2837254999999999,36.8220797,-1.2651573999999999,36.7474347,Rider_Id_26,2320
-Order_No_4290,User_Id_246,Bike,3,Business,13,4,10:50:01 AM,13,4,2:10:26 PM,13,4,2:30:06 PM,13,4,2:33:24 PM,13,4,2:51:01 PM,4,22.6,,-1.2584143,36.8048002,-1.2821421999999998,36.8161565,Rider_Id_928,1057
-Order_No_8777,User_Id_635,Bike,3,Business,4,4,2:47:00 PM,4,4,2:51:06 PM,4,4,3:19:18 PM,4,4,3:24:42 PM,4,4,3:31:49 PM,2,27.5,,-1.2602337,36.79905479999999,-1.2618264,36.8141179,Rider_Id_85,427
-Order_No_14624,User_Id_3291,Bike,3,Business,7,3,1:13:07 PM,7,3,1:13:47 PM,7,3,1:14:00 PM,7,3,1:17:36 PM,7,3,1:32:10 PM,7,24.9,,-1.2628473,36.781805,-1.272828,36.816608,Rider_Id_633,874
-Order_No_10462,User_Id_1020,Bike,1,Personal,2,4,2:47:17 PM,2,4,2:50:49 PM,2,4,3:15:01 PM,2,4,3:17:59 PM,2,4,3:37:02 PM,7,26.2,,-1.2764786,36.81947879999999,-1.312942,36.8152651,Rider_Id_754,1143
-Order_No_27864,User_Id_2774,Bike,1,Personal,22,6,2:22:37 PM,22,6,2:22:50 PM,22,6,2:55:03 PM,22,6,3:01:59 PM,22,6,3:39:03 PM,24,,,-1.2921513,36.752547,-1.3061785,36.90941420000001,Rider_Id_100,2224
-Order_No_16249,User_Id_1464,Bike,2,Personal,10,5,12:31:16 PM,10,5,12:31:28 PM,10,5,1:06:52 PM,10,5,1:13:54 PM,10,5,1:51:51 PM,17,19.4,,-1.2912419,36.877528399999996,-1.301774,36.771106599999996,Rider_Id_234,2277
-Order_No_19749,User_Id_2207,Bike,3,Business,22,5,9:06:22 AM,22,5,9:07:33 AM,22,5,9:15:00 AM,22,5,9:19:11 AM,22,5,9:34:52 AM,7,22.4,,-1.3012237,36.8261656,-1.2696908000000002,36.81519,Rider_Id_206,941
-Order_No_17868,User_Id_708,Bike,3,Personal,1,5,1:34:05 PM,1,5,1:35:04 PM,1,5,1:51:58 PM,1,5,1:55:16 PM,1,5,2:11:31 PM,8,32.1,,-1.2635051000000002,36.7829162,-1.2969293,36.8027733,Rider_Id_856,975
-Order_No_6649,User_Id_3283,Bike,3,Business,5,5,1:25:59 PM,5,5,1:26:20 PM,5,5,1:51:29 PM,5,5,1:52:50 PM,5,5,2:10:49 PM,7,27.6,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_177,1079
-Order_No_24962,User_Id_3560,Bike,3,Business,5,1,3:02:38 PM,5,1,3:10:43 PM,5,1,3:16:19 PM,5,1,3:27:25 PM,5,1,3:40:50 PM,1,26.1,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_935,805
-Order_No_9209,User_Id_928,Bike,3,Business,8,5,1:01:43 PM,8,5,1:06:34 PM,8,5,1:08:51 PM,8,5,2:47:29 PM,8,5,3:25:11 PM,12,27.2,,-1.2551895,36.7822034,-1.3127877000000001,36.848198700000005,Rider_Id_451,2262
-Order_No_9362,User_Id_1882,Bike,3,Business,13,2,11:11:08 AM,13,2,12:09:52 PM,13,2,12:29:23 PM,13,2,12:30:26 PM,13,2,12:49:55 PM,10,22.4,,-1.2847654,36.820105,-1.2273427,36.799468299999994,Rider_Id_615,1169
-Order_No_11061,User_Id_1075,Bike,3,Business,4,4,3:46:14 PM,4,4,3:46:26 PM,4,4,3:47:57 PM,4,4,3:50:49 PM,4,4,4:01:45 PM,3,27.3,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_318,656
-Order_No_27660,User_Id_1571,Bike,3,Personal,10,3,12:28:42 PM,10,3,12:29:10 PM,10,3,1:03:47 PM,10,3,1:08:38 PM,10,3,1:32:09 PM,14,27.7,,-1.2924639,36.77957979999999,-1.2349573,36.869367499999996,Rider_Id_205,1411
-Order_No_23933,User_Id_3222,Bike,3,Business,2,5,3:04:57 PM,2,5,3:07:22 PM,2,5,3:07:25 PM,2,5,3:26:03 PM,2,5,3:40:13 PM,9,25.2,,-1.2551895,36.7822034,-1.2961742,36.8192864,Rider_Id_345,850
-Order_No_25699,User_Id_3470,Bike,3,Business,30,5,4:01:03 PM,30,5,4:33:24 PM,30,5,4:43:22 PM,30,5,4:47:07 PM,30,5,5:00:48 PM,5,22.1,,-1.28878,36.8168312,-1.2591018999999999,36.800577000000004,Rider_Id_522,821
-Order_No_6953,User_Id_265,Bike,3,Business,4,6,8:58:35 AM,4,6,8:58:49 AM,4,6,9:02:32 AM,4,6,9:21:12 AM,4,6,9:39:38 AM,8,15.7,,-1.2945798000000002,36.7737755,-1.300921,36.828195,Rider_Id_636,1106
-Order_No_7255,User_Id_2200,Bike,3,Business,24,5,11:18:13 AM,24,5,11:18:56 AM,24,5,11:52:09 AM,24,5,12:20:01 PM,24,5,12:20:09 PM,17,21.6,,-1.290894,36.822971,-1.3124537,36.925365,Rider_Id_819,8
-Order_No_19424,User_Id_1746,Bike,3,Business,10,1,5:39:18 PM,10,1,6:11:50 PM,10,1,6:17:24 PM,10,1,6:24:36 PM,10,1,6:43:33 PM,6,18.9,,-1.296797,36.776452500000005,-1.2819174,36.821821,Rider_Id_848,1137
-Order_No_10770,User_Id_1960,Bike,3,Personal,8,5,10:20:38 AM,8,5,10:24:30 AM,8,5,10:41:44 AM,8,5,10:45:31 AM,8,5,11:03:36 AM,3,,,-1.2886434,36.770986799999996,-1.3023756000000002,36.7949276,Rider_Id_844,1085
-Order_No_25098,User_Id_3647,Bike,3,Business,5,1,4:18:21 PM,5,1,4:26:59 PM,5,1,4:38:36 PM,5,1,5:07:46 PM,5,1,5:13:58 PM,8,24.6,,-1.2726389999999999,36.794723,-1.305399,36.822184899999996,Rider_Id_403,372
-Order_No_16763,User_Id_1389,Bike,3,Business,30,2,3:14:24 PM,30,2,3:24:44 PM,30,2,3:36:40 PM,30,2,3:46:06 PM,30,2,4:11:47 PM,15,27.2,,-1.207546,36.7913976,-1.2591018999999999,36.800577000000004,Rider_Id_754,1541
-Order_No_15581,User_Id_3517,Bike,3,Business,1,1,9:34:24 AM,1,1,9:35:05 AM,1,1,10:03:59 AM,1,1,10:36:29 AM,1,1,11:21:12 AM,20,,,-1.3882008,36.7699445,-1.2964987,36.7741132,Rider_Id_559,2683
-Order_No_25959,User_Id_816,Bike,3,Business,20,3,12:38:31 PM,20,3,12:44:46 PM,20,3,1:02:49 PM,20,3,1:13:26 PM,20,3,1:50:28 PM,6,26.2,,-1.2527958000000001,36.8003131,-1.290894,36.822971,Rider_Id_569,2222
-Order_No_7879,User_Id_2565,Bike,3,Personal,3,5,2:30:35 PM,3,5,2:32:13 PM,3,5,2:42:34 PM,3,5,2:59:01 PM,3,5,3:21:15 PM,7,25.2,,-1.296797,36.776452500000005,-1.3090066,36.8136512,Rider_Id_846,1334
-Order_No_5042,User_Id_2412,Bike,3,Business,17,5,3:07:51 PM,17,5,3:08:01 PM,17,5,3:15:28 PM,17,5,3:22:04 PM,17,5,3:22:14 PM,5,,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_126,10
-Order_No_1221,User_Id_517,Bike,3,Business,29,4,10:55:40 AM,29,4,11:01:18 AM,29,4,11:12:57 AM,29,4,11:16:15 AM,29,4,11:31:03 AM,5,21.7,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_581,888
-Order_No_4021,User_Id_2330,Bike,3,Business,16,3,11:37:02 AM,16,3,11:37:08 AM,16,3,11:37:22 AM,16,3,11:39:04 AM,16,3,11:59:26 AM,9,22.6,,-1.3040812,36.7924217,-1.2642903,36.80021370000001,Rider_Id_253,1222
-Order_No_23894,User_Id_2457,Bike,3,Business,6,2,11:32:23 AM,6,2,12:37:19 PM,6,2,12:55:37 PM,6,2,1:01:02 PM,6,2,1:34:07 PM,7,25.2,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_671,1985
-Order_No_3041,User_Id_3129,Bike,1,Personal,31,4,2:24:39 PM,31,4,2:26:34 PM,31,4,2:54:53 PM,31,4,2:58:59 PM,31,4,3:22:28 PM,8,23.1,,-1.2930197,36.7904356,-1.2536041999999998,36.806489500000005,Rider_Id_103,1409
-Order_No_27511,User_Id_2789,Bike,3,Business,3,3,12:04:27 PM,3,3,12:04:57 PM,3,3,12:19:17 PM,3,3,12:25:50 PM,3,3,12:55:53 PM,8,27.6,,-1.304735,36.784605400000004,-1.2623495,36.8049247,Rider_Id_693,1803
-Order_No_7427,User_Id_238,Bike,3,Business,25,1,1:00:35 PM,25,1,1:00:54 PM,25,1,1:10:23 PM,25,1,1:12:36 PM,25,1,1:37:54 PM,15,27.1,,-1.3077869,36.844320700000004,-1.362953,36.904214,Rider_Id_943,1518
-Order_No_3717,User_Id_2423,Bike,3,Business,13,3,1:09:51 PM,13,3,1:11:24 PM,13,3,1:15:48 PM,13,3,1:24:33 PM,13,3,1:39:16 PM,4,24.8,,-1.2551895,36.7822034,-1.2618631999999999,36.8100226,Rider_Id_379,883
-Order_No_8740,User_Id_2523,Bike,3,Business,27,3,12:16:44 PM,27,3,12:16:58 PM,27,3,12:20:48 PM,27,3,12:26:06 PM,27,3,12:47:21 PM,8,26.0,,-1.2551895,36.7822034,-1.2866381000000002,36.7963662,Rider_Id_26,1275
-Order_No_6266,User_Id_3718,Bike,3,Business,9,2,9:58:51 AM,9,2,9:59:17 AM,9,2,10:19:28 AM,9,2,10:21:51 AM,9,2,11:26:48 AM,19,17.6,4.0,-1.2650027,36.8126242,-1.3339943,36.7263409,Rider_Id_948,3897
-Order_No_19673,User_Id_3233,Bike,1,Personal,14,4,11:44:38 AM,14,4,11:45:17 AM,14,4,12:04:15 PM,14,4,12:10:41 PM,14,4,12:47:26 PM,10,19.1,,-1.214328,36.7911903,-1.2574645,36.805504,Rider_Id_802,2205
-Order_No_887,User_Id_115,Bike,3,Business,15,6,1:54:16 PM,15,6,1:55:36 PM,15,6,2:29:38 PM,15,6,2:34:17 PM,15,6,3:21:29 PM,6,21.8,,-1.281854,36.8246889,-1.313971,36.84652020000001,Rider_Id_109,2832
-Order_No_8652,User_Id_37,Bike,3,Business,13,3,4:51:18 PM,13,3,4:51:56 PM,13,3,5:13:40 PM,13,3,5:42:02 PM,13,3,5:42:04 PM,15,27.6,,-1.3263105,36.8622904,-1.3472066,36.7692638,Rider_Id_411,2
-Order_No_17048,User_Id_2946,Bike,2,Personal,5,4,9:45:39 AM,5,4,9:45:49 AM,5,4,9:59:10 AM,5,4,10:01:49 AM,5,4,10:39:26 AM,8,,,-1.2749662,36.7658781,-1.2806591999999999,36.8298077,Rider_Id_844,2257
-Order_No_8353,User_Id_1439,Bike,3,Business,3,3,9:51:46 AM,3,3,9:52:58 AM,3,3,10:00:32 AM,3,3,10:20:42 AM,3,3,10:56:52 AM,16,21.4,,-1.2913642,36.87108189999999,-1.2962486999999998,36.7772971,Rider_Id_247,2170
-Order_No_12285,User_Id_3496,Bike,3,Business,14,5,11:26:13 AM,14,5,11:28:47 AM,14,5,11:51:12 AM,14,5,12:24:53 PM,14,5,12:36:15 PM,4,21.7,,-1.300921,36.828195,-1.3173868999999998,36.8112044,Rider_Id_267,682
-Order_No_13554,User_Id_244,Bike,2,Personal,6,4,8:49:11 AM,6,4,9:04:21 AM,6,4,9:27:05 AM,6,4,9:36:44 AM,6,4,10:33:57 AM,28,17.7,,-1.3191527,36.6758827,-1.3038645,36.8684596,Rider_Id_844,3433
-Order_No_21833,User_Id_1189,Bike,2,Personal,21,1,1:07:02 PM,21,1,1:09:35 PM,21,1,1:24:28 PM,21,1,1:28:58 PM,21,1,2:46:21 PM,4,20.2,,-1.2842665,36.7967626,-1.2815530000000002,36.8185334,Rider_Id_844,4643
-Order_No_21025,User_Id_3647,Bike,3,Business,23,3,3:27:11 PM,23,3,3:27:19 PM,23,3,3:28:47 PM,23,3,3:36:30 PM,23,3,3:57:38 PM,6,27.3,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_865,1268
-Order_No_11144,User_Id_2987,Bike,3,Business,17,4,4:36:01 PM,17,4,4:36:11 PM,17,4,4:40:27 PM,17,4,4:46:58 PM,17,4,5:33:10 PM,15,21.9,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_101,2772
-Order_No_18741,User_Id_3686,Bike,3,Business,15,4,3:44:25 PM,15,4,4:03:07 PM,15,4,4:09:05 PM,15,4,4:13:48 PM,15,4,4:38:05 PM,8,27.6,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_254,1457
-Order_No_27798,User_Id_1209,Bike,3,Business,20,4,1:21:46 PM,20,4,1:32:52 PM,20,4,1:46:43 PM,20,4,2:04:16 PM,20,4,2:45:27 PM,9,25.2,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_812,2471
-Order_No_12618,User_Id_393,Bike,3,Business,6,1,8:51:53 AM,6,1,8:52:03 AM,6,1,8:52:08 AM,6,1,9:37:09 AM,6,1,9:37:13 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_10,4
-Order_No_18971,User_Id_1927,Bike,3,Business,27,1,2:08:55 PM,27,1,2:10:01 PM,27,1,2:29:47 PM,27,1,2:53:51 PM,27,1,3:23:04 PM,17,23.0,,-1.2859912,36.8756811,-1.3472066,36.7692638,Rider_Id_126,1753
-Order_No_11294,User_Id_2436,Bike,3,Business,8,5,3:20:42 PM,8,5,3:40:07 PM,8,5,3:46:32 PM,8,5,3:51:39 PM,8,5,4:14:52 PM,9,30.6,,-1.2551895,36.7822034,-1.2945128000000001,36.8396881,Rider_Id_576,1393
-Order_No_23151,User_Id_265,Bike,3,Business,4,4,1:25:21 PM,4,4,1:27:19 PM,4,4,1:43:20 PM,4,4,1:54:32 PM,4,4,2:50:20 PM,7,24.8,,-1.300921,36.828195,-1.260039,36.8159152,Rider_Id_19,3348
-Order_No_21128,User_Id_3686,Bike,3,Business,16,3,3:56:42 PM,16,3,3:56:49 PM,16,3,4:01:39 PM,16,3,4:03:54 PM,16,3,4:08:11 PM,3,26.9,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_932,257
-Order_No_17282,User_Id_1146,Bike,3,Business,21,2,2:25:42 PM,21,2,2:26:37 PM,21,2,2:37:31 PM,21,2,2:57:24 PM,21,2,3:20:45 PM,7,25.1,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_321,1401
-Order_No_8343,User_Id_868,Bike,3,Business,29,5,8:58:36 AM,29,5,9:45:00 AM,29,5,10:16:24 AM,29,5,10:23:37 AM,29,5,10:54:52 AM,9,20.9,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_787,1875
-Order_No_5707,User_Id_1471,Bike,3,Business,27,3,1:12:23 PM,27,3,1:12:47 PM,27,3,1:28:53 PM,27,3,1:45:47 PM,27,3,1:56:58 PM,3,,,-1.2551895,36.7822034,-1.2655477,36.8001682,Rider_Id_259,671
-Order_No_13137,User_Id_2587,Bike,3,Business,17,5,1:06:06 PM,17,5,1:06:19 PM,17,5,1:06:32 PM,17,5,1:59:43 PM,17,5,2:15:23 PM,6,20.4,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_213,940
-Order_No_16443,User_Id_3599,Bike,3,Business,4,4,12:11:28 PM,4,4,12:28:19 PM,4,4,1:23:23 PM,4,4,1:56:53 PM,4,4,2:46:40 PM,11,24.9,,-1.3302996,36.8707935,-1.296342,36.810352,Rider_Id_117,2987
-Order_No_14819,User_Id_2956,Bike,3,Business,6,3,11:27:38 AM,6,3,11:28:05 AM,6,3,12:09:26 PM,6,3,12:13:43 PM,6,3,12:34:28 PM,10,20.2,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_742,1245
-Order_No_15836,User_Id_2150,Bike,3,Business,5,2,10:18:19 AM,5,2,10:21:10 AM,5,2,10:31:26 AM,5,2,10:33:16 AM,5,2,10:54:43 AM,9,25.3,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_801,1287
-Order_No_10417,User_Id_868,Bike,3,Business,13,4,9:43:57 AM,13,4,9:53:32 AM,13,4,9:55:58 AM,13,4,10:01:09 AM,13,4,10:12:46 AM,5,18.1,2.1,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_937,697
-Order_No_10815,User_Id_3498,Bike,2,Personal,22,2,11:18:22 AM,22,2,11:18:34 AM,22,2,11:31:17 AM,22,2,11:41:58 AM,22,2,12:06:57 PM,7,19.6,,-1.2735236,36.7992196,-1.2915968999999998,36.823763899999996,Rider_Id_116,1499
-Order_No_27775,User_Id_2305,Bike,1,Personal,17,5,1:32:32 PM,17,5,1:33:46 PM,17,5,1:48:17 PM,17,5,1:59:56 PM,17,5,2:40:00 PM,17,20.4,,-1.2486621,36.8874559,-1.3007592,36.77541970000001,Rider_Id_851,2404
-Order_No_22939,User_Id_2192,Bike,1,Personal,1,1,3:39:35 PM,1,1,3:39:53 PM,1,1,3:45:15 PM,1,1,3:53:02 PM,1,1,4:44:32 PM,14,25.6,,-1.2975066000000002,36.76894229999999,-1.2481864,36.8644269,Rider_Id_754,3090
-Order_No_19751,User_Id_2383,Bike,3,Business,4,2,2:12:30 PM,4,2,2:14:39 PM,4,2,3:31:06 PM,4,2,3:43:12 PM,4,2,3:56:10 PM,2,23.8,11.0,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_547,778
-Order_No_27300,User_Id_598,Bike,1,Business,18,5,11:08:56 AM,18,5,11:26:50 AM,18,5,11:48:53 AM,18,5,11:50:03 AM,18,5,12:36:53 PM,16,21.2,,-1.3178263000000001,36.7141818,-1.2536797,36.7995085,Rider_Id_938,2810
-Order_No_2776,User_Id_2956,Bike,3,Business,20,3,11:56:55 AM,20,3,11:57:59 AM,20,3,12:03:49 PM,20,3,12:17:26 PM,20,3,12:28:59 PM,6,26.3,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_764,693
-Order_No_2311,User_Id_1527,Bike,3,Business,9,2,3:46:10 PM,9,2,3:46:36 PM,9,2,4:28:09 PM,9,2,4:50:42 PM,9,2,4:59:44 PM,4,27.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_763,542
-Order_No_8834,User_Id_2001,Bike,3,Personal,20,3,3:54:38 PM,20,3,3:55:09 PM,20,3,3:58:49 PM,20,3,4:20:08 PM,20,3,4:20:17 PM,3,28.6,,-1.2584143,36.8048002,-1.2752846000000002,36.802702200000006,Rider_Id_953,9
-Order_No_7500,User_Id_169,Bike,1,Personal,8,1,9:42:16 AM,8,1,9:56:52 AM,8,1,10:01:31 AM,8,1,10:04:56 AM,8,1,10:16:36 AM,7,19.8,,-1.3100608999999999,36.820536499999996,-1.2658315,36.809049,Rider_Id_88,700
-Order_No_15881,User_Id_142,Bike,3,Business,4,4,11:55:21 AM,4,4,11:56:04 AM,4,4,12:12:43 PM,4,4,12:21:19 PM,4,4,12:39:22 PM,6,,,-1.2857723,36.808805799999995,-1.2963097,36.7688221,Rider_Id_328,1083
-Order_No_17657,User_Id_2523,Bike,3,Business,29,3,2:49:40 PM,29,3,2:49:58 PM,29,3,2:50:20 PM,29,3,2:55:53 PM,29,3,4:15:10 PM,16,18.8,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_629,4757
-Order_No_19968,User_Id_1402,Bike,3,Business,24,4,3:36:31 PM,24,4,3:36:53 PM,24,4,3:43:07 PM,24,4,4:15:46 PM,24,4,4:24:39 PM,1,28.4,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_365,533
-Order_No_12331,User_Id_635,Bike,3,Business,15,4,7:58:20 AM,15,4,8:00:32 AM,15,4,8:24:42 AM,15,4,9:07:39 AM,15,4,9:26:51 AM,7,19.7,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_778,1152
-Order_No_3615,User_Id_640,Bike,3,Business,24,1,2:30:43 PM,24,1,2:31:00 PM,24,1,2:34:32 PM,24,1,2:42:04 PM,24,1,3:08:34 PM,3,,,-1.304033,36.7848695,-1.2945106000000002,36.7728398,Rider_Id_312,1590
-Order_No_25879,User_Id_286,Bike,3,Business,1,6,9:38:12 AM,1,6,9:43:14 AM,1,6,9:51:15 AM,1,6,10:07:20 AM,1,6,10:31:43 AM,11,17.7,2.9,-1.2551895,36.7822034,-1.3080097,36.7494062,Rider_Id_322,1463
-Order_No_2974,User_Id_3752,Bike,3,Business,12,3,2:36:02 PM,12,3,2:38:28 PM,12,3,2:39:05 PM,12,3,3:17:52 PM,12,3,3:17:54 PM,2,20.9,,-1.256594,36.8002785,-1.2753108999999998,36.8130185,Rider_Id_531,2
-Order_No_21658,User_Id_2195,Bike,1,Business,9,2,10:01:39 AM,9,2,10:12:23 AM,9,2,10:28:44 AM,9,2,10:34:29 AM,9,2,11:21:20 AM,14,22.2,,-1.3444459999999998,36.756597,-1.3313643,36.857742200000004,Rider_Id_844,2811
-Order_No_13188,User_Id_2523,Bike,3,Business,25,1,2:03:54 PM,25,1,2:04:20 PM,25,1,2:24:10 PM,25,1,2:32:25 PM,25,1,3:06:33 PM,17,,,-1.2551895,36.7822034,-1.3339943,36.7263409,Rider_Id_648,2048
-Order_No_26459,User_Id_393,Bike,3,Business,8,4,4:00:09 PM,8,4,4:09:27 PM,8,4,4:35:10 PM,8,4,4:48:39 PM,8,4,5:15:04 PM,14,25.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_708,1585
-Order_No_7593,User_Id_2112,Bike,3,Business,6,1,2:51:14 PM,6,1,2:51:23 PM,6,1,3:05:46 PM,6,1,3:08:37 PM,6,1,3:49:17 PM,16,24.5,,-1.2156006000000001,36.8916865,-1.3285517,36.9018,Rider_Id_384,2440
-Order_No_2407,User_Id_2987,Bike,3,Business,28,5,12:56:21 PM,28,5,12:56:48 PM,28,5,12:58:56 PM,28,5,1:13:56 PM,28,5,1:21:47 PM,12,25.3,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_886,471
-Order_No_22460,User_Id_1500,Bike,3,Business,22,5,8:03:48 AM,22,5,8:04:00 AM,22,5,8:06:05 AM,22,5,9:34:12 AM,22,5,9:34:34 AM,8,22.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_119,22
-Order_No_19008,User_Id_53,Bike,3,Business,25,4,10:59:02 AM,25,4,12:18:42 PM,25,4,12:22:23 PM,25,4,1:29:30 PM,25,4,1:58:32 PM,11,19.8,,-1.306378,36.7519845,-1.2770708,36.8231093,Rider_Id_731,1742
-Order_No_26041,User_Id_346,Bike,3,Business,14,4,8:52:58 AM,14,4,8:54:24 AM,14,4,9:55:14 AM,14,4,10:01:47 AM,14,4,10:27:40 AM,6,17.9,,-1.2615888000000002,36.7928732,-1.28878,36.8168312,Rider_Id_280,1553
-Order_No_4992,User_Id_1245,Bike,3,Business,4,2,1:40:28 PM,4,2,1:41:35 PM,4,2,1:56:32 PM,4,2,2:04:11 PM,4,2,2:56:23 PM,17,22.4,,-1.2602337,36.79905479999999,-1.3198615,36.7110323,Rider_Id_155,3132
-Order_No_4001,User_Id_3291,Bike,3,Business,9,6,1:32:43 PM,9,6,1:33:18 PM,9,6,1:34:05 PM,9,6,1:45:02 PM,9,6,1:53:19 PM,5,30.0,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_720,497
-Order_No_8882,User_Id_3326,Bike,3,Business,31,4,1:01:34 PM,31,4,1:03:45 PM,31,4,1:14:10 PM,31,4,1:16:04 PM,31,4,1:50:25 PM,8,26.5,,-1.2551895,36.7822034,-1.304344,36.812274,Rider_Id_857,2061
-Order_No_26027,User_Id_2523,Bike,3,Business,21,5,2:58:55 PM,21,5,3:12:13 PM,21,5,3:22:42 PM,21,5,3:24:25 PM,21,5,4:24:12 PM,21,24.4,,-1.2551895,36.7822034,-1.3688433,36.7402114,Rider_Id_596,3587
-Order_No_3997,User_Id_3686,Bike,3,Business,27,2,2:43:44 PM,27,2,2:47:32 PM,27,2,2:59:54 PM,27,2,3:04:54 PM,27,2,3:28:09 PM,8,25.8,,-1.2551895,36.7822034,-1.287428,36.7800821,Rider_Id_958,1395
-Order_No_5367,User_Id_3318,Bike,3,Business,29,5,3:24:03 PM,29,5,3:24:49 PM,29,5,3:35:03 PM,29,5,3:36:14 PM,29,5,3:55:12 PM,4,29.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_343,1138
-Order_No_5172,User_Id_510,Bike,3,Business,4,2,3:41:57 PM,4,2,3:46:30 PM,4,2,4:16:09 PM,4,2,4:24:03 PM,4,2,5:04:37 PM,21,23.8,11.1,-1.2551895,36.7822034,-1.2860183,36.8975338,Rider_Id_38,2434
-Order_No_15087,User_Id_3674,Bike,3,Business,3,1,10:33:19 AM,3,1,10:33:32 AM,3,1,10:34:49 AM,3,1,10:41:32 AM,3,1,11:01:18 AM,7,21.7,,-1.2551895,36.7822034,-1.2388691,36.7579496,Rider_Id_161,1186
-Order_No_8357,User_Id_238,Bike,3,Business,14,2,2:19:43 PM,14,2,2:19:57 PM,14,2,2:20:09 PM,14,2,2:26:23 PM,14,2,2:47:18 PM,13,,,-1.2638185,36.7930057,-1.3077869,36.844320700000004,Rider_Id_576,1255
-Order_No_28045,User_Id_2587,Bike,3,Business,7,1,3:16:34 PM,7,1,3:17:36 PM,7,1,3:23:44 PM,7,1,3:28:51 PM,7,1,3:43:58 PM,2,26.7,,-1.281754,36.8324229,-1.28878,36.8168312,Rider_Id_379,907
-Order_No_27377,User_Id_3661,Bike,3,Business,8,4,9:38:37 AM,8,4,9:38:45 AM,8,4,10:22:51 AM,8,4,10:25:08 AM,8,4,10:56:14 AM,19,20.6,,-1.2134646,36.8755266,-1.3208162,36.8451209,Rider_Id_265,1866
-Order_No_11346,User_Id_2982,Bike,3,Business,26,2,3:45:17 PM,26,2,3:46:24 PM,26,2,3:51:53 PM,26,2,4:34:51 PM,26,2,5:26:17 PM,18,29.4,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_697,3086
-Order_No_19629,User_Id_3517,Bike,3,Business,2,2,2:07:07 PM,2,2,2:07:20 PM,2,2,2:07:22 PM,2,2,2:56:02 PM,2,2,3:27:01 PM,15,29.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_595,1859
-Order_No_4765,User_Id_2150,Bike,3,Business,24,5,9:29:45 AM,24,5,9:29:53 AM,24,5,9:38:46 AM,24,5,9:40:02 AM,24,5,10:03:10 AM,15,15.8,,-1.3444459999999998,36.756597,-1.265715,36.823815,Rider_Id_74,1388
-Order_No_23726,User_Id_3149,Bike,3,Business,7,2,11:44:35 AM,7,2,11:45:11 AM,7,2,11:54:15 AM,7,2,12:14:39 PM,7,2,12:40:38 PM,10,,,-1.3167113000000001,36.8301563,-1.2690834,36.7994156,Rider_Id_910,1559
-Order_No_7845,User_Id_635,Bike,3,Business,14,4,9:52:34 AM,14,4,10:06:13 AM,14,4,10:20:53 AM,14,4,10:26:22 AM,14,4,10:43:48 AM,7,17.8,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_724,1046
-Order_No_24212,User_Id_640,Bike,3,Business,27,1,10:48:01 AM,27,1,10:49:58 AM,27,1,11:02:33 AM,27,1,11:10:13 AM,27,1,11:29:05 AM,8,20.8,,-1.2709035,36.8168523,-1.304033,36.7848695,Rider_Id_341,1132
-Order_No_16743,User_Id_421,Bike,3,Business,18,2,1:20:01 PM,18,2,1:20:29 PM,18,2,1:40:20 PM,18,2,1:43:39 PM,18,2,2:42:58 PM,28,25.7,,-1.2975066000000002,36.76894229999999,-1.1605816,36.8977958,Rider_Id_457,3559
-Order_No_17910,User_Id_3641,Bike,3,Business,8,3,1:49:37 PM,8,3,1:50:03 PM,8,3,1:52:05 PM,8,3,2:07:03 PM,8,3,2:59:58 PM,18,,,-1.2551895,36.7822034,-1.276292,36.90082,Rider_Id_952,3175
-Order_No_17014,User_Id_2412,Bike,3,Business,31,1,1:08:09 PM,31,1,1:18:50 PM,31,1,1:27:18 PM,31,1,1:37:28 PM,31,1,1:52:24 PM,3,23.9,,-1.2156006000000001,36.8916865,-1.2285834999999998,36.882646799999996,Rider_Id_199,896
-Order_No_2517,User_Id_3647,Bike,3,Business,18,4,1:35:29 PM,18,4,1:36:36 PM,18,4,1:48:09 PM,18,4,2:00:03 PM,18,4,2:05:22 PM,2,,,-1.2726389999999999,36.794723,-1.2741728,36.79691079999999,Rider_Id_161,319
-Order_No_16500,User_Id_868,Bike,3,Business,15,3,11:59:45 AM,15,3,12:51:32 PM,15,3,1:26:42 PM,15,3,1:58:46 PM,15,3,2:16:41 PM,6,26.1,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_787,1075
-Order_No_13858,User_Id_2285,Bike,3,Personal,4,2,12:03:22 PM,4,2,12:04:10 PM,4,2,12:18:17 PM,4,2,12:28:05 PM,4,2,12:52:30 PM,7,20.4,,-1.2690837,36.8006261,-1.2950041,36.814358299999995,Rider_Id_123,1465
-Order_No_23367,User_Id_1271,Bike,3,Business,9,2,9:21:29 AM,9,2,9:21:37 AM,9,2,9:46:37 AM,9,2,10:22:24 AM,9,2,11:06:38 AM,25,17.4,4.1,-1.3244885,36.897791999999995,-1.1708105,36.8336102,Rider_Id_598,2654
-Order_No_10245,User_Id_1578,Bike,3,Business,27,2,10:32:43 AM,27,2,11:13:27 AM,27,2,11:33:29 AM,27,2,11:43:57 AM,27,2,12:00:16 PM,5,23.7,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_928,979
-Order_No_25538,User_Id_2330,Bike,3,Business,11,1,5:29:51 PM,11,1,5:30:02 PM,11,1,5:34:40 PM,11,1,5:39:58 PM,11,1,5:49:48 PM,2,,,-1.2873232,36.765857200000006,-1.3015197,36.7658456,Rider_Id_368,590
-Order_No_2337,User_Id_3621,Bike,3,Business,6,4,6:08:32 PM,6,4,6:10:23 PM,6,4,6:58:28 PM,6,4,7:03:52 PM,6,4,7:15:42 PM,4,22.6,,-1.2836583000000001,36.7886014,-1.2721028,36.795348499999996,Rider_Id_103,710
-Order_No_21166,User_Id_3698,Bike,3,Business,23,4,9:35:25 AM,23,4,9:35:42 AM,23,4,9:46:57 AM,23,4,9:59:15 AM,23,4,10:46:10 AM,22,,,-1.3244885,36.897791999999995,-1.3014461000000002,36.7661381,Rider_Id_675,2815
-Order_No_661,User_Id_2987,Bike,3,Business,22,4,12:37:25 PM,22,4,12:38:58 PM,22,4,12:47:07 PM,22,4,12:48:20 PM,22,4,1:16:45 PM,15,22.4,,-1.2702120000000001,36.82108770000001,-1.3284365,36.8970679,Rider_Id_359,1705
-Order_No_1528,User_Id_2664,Bike,1,Personal,21,1,11:43:07 AM,21,1,11:46:18 AM,21,1,12:21:21 PM,21,1,12:24:36 PM,21,1,12:35:22 PM,6,19.9,,-1.2626906,36.7827022,-1.2816664,36.8226716,Rider_Id_406,646
-Order_No_27162,User_Id_3517,Bike,3,Business,25,4,10:58:26 AM,25,4,11:00:27 AM,25,4,11:35:38 AM,25,4,11:59:41 AM,25,4,12:29:34 PM,15,19.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_340,1793
-Order_No_21860,User_Id_357,Bike,3,Business,24,3,9:59:14 AM,24,3,9:59:51 AM,24,3,10:09:34 AM,24,3,11:36:01 AM,24,3,11:56:55 AM,9,19.8,,-1.2657992,36.8036825,-1.2945178999999998,36.7685349,Rider_Id_649,1254
-Order_No_18475,User_Id_3561,Bike,3,Personal,24,5,11:41:35 AM,24,5,11:42:52 AM,24,5,12:08:36 PM,24,5,12:19:11 PM,24,5,12:30:22 PM,6,21.1,,-1.2807298,36.8299116,-1.2658315,36.809049,Rider_Id_857,671
-Order_No_20828,User_Id_3175,Bike,3,Business,6,2,3:37:43 PM,6,2,3:38:11 PM,6,2,4:13:00 PM,6,2,4:14:30 PM,6,2,4:27:46 PM,4,27.3,,-1.2681003999999998,36.7952081,-1.263419,36.8060348,Rider_Id_434,796
-Order_No_11015,User_Id_49,Bike,3,Business,18,2,5:16:24 PM,18,2,5:17:32 PM,18,2,5:18:36 PM,18,2,5:38:48 PM,18,2,6:16:47 PM,7,23.7,,-1.2798346999999999,36.8219092,-1.3010773000000002,36.7752554,Rider_Id_469,2279
-Order_No_10055,User_Id_1402,Bike,3,Business,3,5,9:38:57 AM,3,5,9:40:59 AM,3,5,10:11:20 AM,3,5,10:12:25 AM,3,5,10:40:12 AM,8,,,-1.2813012,36.832396200000005,-1.2551895,36.7822034,Rider_Id_624,1667
-Order_No_9206,User_Id_1995,Bike,3,Business,3,5,4:24:46 PM,3,5,4:28:02 PM,3,5,4:38:53 PM,3,5,4:44:54 PM,3,5,5:12:17 PM,3,,,-1.2677278,36.8001549,-1.2551895,36.7822034,Rider_Id_281,1643
-Order_No_12065,User_Id_914,Bike,3,Business,19,2,3:45:16 PM,19,2,3:51:39 PM,19,2,4:06:23 PM,19,2,4:12:31 PM,19,2,4:44:05 PM,11,29.3,,-1.2670213,36.7994482,-1.3083777,36.85900770000001,Rider_Id_708,1894
-Order_No_17890,User_Id_2982,Bike,3,Business,18,5,4:11:26 PM,18,5,4:28:30 PM,18,5,5:01:39 PM,18,5,5:16:02 PM,18,5,6:02:23 PM,20,25.6,,-1.177202,36.8351183,-1.2804349,36.7802593,Rider_Id_75,2781
-Order_No_24240,User_Id_3222,Bike,3,Business,8,3,3:31:03 PM,8,3,3:33:46 PM,8,3,3:45:59 PM,8,3,3:48:35 PM,8,3,3:56:43 PM,3,,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_337,488
-Order_No_13093,User_Id_3291,Bike,3,Business,21,1,10:05:30 AM,21,1,10:05:59 AM,21,1,10:11:36 AM,21,1,10:26:27 AM,21,1,10:38:41 AM,5,22.6,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_322,734
-Order_No_5519,User_Id_393,Bike,3,Business,25,6,12:00:28 PM,25,6,12:16:19 PM,25,6,12:23:45 PM,25,6,12:27:46 PM,25,6,12:39:47 PM,5,,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_168,721
-Order_No_9596,User_Id_246,Bike,3,Business,27,2,1:16:40 PM,27,2,1:28:41 PM,27,2,1:30:31 PM,27,2,1:42:05 PM,27,2,1:56:01 PM,4,25.8,,-1.2584143,36.8048002,-1.2866011999999998,36.8195691,Rider_Id_521,836
-Order_No_1307,User_Id_3339,Bike,3,Business,5,1,9:48:47 AM,5,1,9:50:16 AM,5,1,9:56:07 AM,5,1,10:04:03 AM,5,1,10:31:17 AM,9,18.4,,-1.2551895,36.7822034,-1.2904476999999999,36.7912763,Rider_Id_770,1634
-Order_No_27697,User_Id_2200,Bike,3,Business,1,4,1:55:01 PM,1,4,2:08:11 PM,1,4,2:17:40 PM,1,4,2:26:43 PM,1,4,2:40:44 PM,6,,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_878,841
-Order_No_13364,User_Id_3283,Bike,3,Business,20,3,10:12:19 AM,20,3,10:47:04 AM,20,3,11:01:01 AM,20,3,11:06:33 AM,20,3,11:15:59 AM,6,27.3,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_240,566
-Order_No_20265,User_Id_868,Bike,3,Business,30,6,12:53:07 PM,30,6,12:53:27 PM,30,6,12:57:15 PM,30,6,1:01:15 PM,30,6,1:18:57 PM,7,26.7,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_709,1062
-Order_No_455,User_Id_2701,Bike,3,Business,20,3,10:34:00 AM,20,3,10:40:41 AM,20,3,11:21:49 AM,20,3,11:22:59 AM,20,3,11:38:10 AM,2,20.1,,-1.279395,36.825364,-1.293082,36.8278578,Rider_Id_786,911
-Order_No_14432,User_Id_1341,Bike,3,Business,9,4,3:34:42 PM,9,4,3:52:41 PM,9,4,3:56:48 PM,9,4,4:02:58 PM,9,4,4:25:48 PM,15,,,-1.2628473,36.781805,-1.331619,36.847976,Rider_Id_411,1370
-Order_No_27311,User_Id_2154,Bike,3,Business,15,3,1:40:02 PM,15,3,1:40:31 PM,15,3,1:51:04 PM,15,3,1:58:27 PM,15,3,2:15:36 PM,5,25.7,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_20,1029
-Order_No_8091,User_Id_1075,Bike,3,Business,28,1,2:33:41 PM,28,1,2:35:13 PM,28,1,2:49:08 PM,28,1,2:53:17 PM,28,1,3:10:37 PM,8,26.2,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_263,1040
-Order_No_10172,User_Id_1502,Bike,3,Business,29,3,2:32:11 PM,29,3,2:44:09 PM,29,3,3:22:25 PM,29,3,3:25:11 PM,29,3,3:51:02 PM,3,21.0,,-1.2232123,36.8932547,-1.2198413000000001,36.9054958,Rider_Id_27,1551
-Order_No_4635,User_Id_733,Bike,3,Business,24,5,11:47:30 AM,24,5,12:12:43 PM,24,5,12:33:36 PM,24,5,12:44:04 PM,24,5,1:07:21 PM,7,21.5,,-1.2551895,36.7822034,-1.2857241000000001,36.820348100000004,Rider_Id_490,1397
-Order_No_16931,User_Id_136,Bike,3,Business,3,4,12:17:39 PM,3,4,12:19:25 PM,3,4,12:19:50 PM,3,4,12:37:35 PM,3,4,12:48:04 PM,4,23.3,,-1.3165103999999999,36.707772600000006,-1.3254522,36.721658399999995,Rider_Id_644,629
-Order_No_8034,User_Id_1146,Bike,3,Business,15,3,4:53:12 PM,15,3,4:53:41 PM,15,3,5:10:14 PM,15,3,5:28:33 PM,15,3,5:46:36 PM,7,25.0,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_76,1083
-Order_No_12670,User_Id_242,Bike,3,Business,31,4,3:57:10 PM,31,4,4:21:36 PM,31,4,4:33:03 PM,31,4,4:37:18 PM,31,4,4:37:40 PM,4,22.3,,-1.2553615,36.789537599999996,-1.268027,36.8037005,Rider_Id_51,22
-Order_No_8445,User_Id_983,Bike,1,Personal,18,2,2:34:36 PM,18,2,2:45:58 PM,18,2,2:49:29 PM,18,2,2:57:32 PM,18,2,3:17:25 PM,8,27.5,,-1.2753108999999998,36.8130185,-1.2969741000000001,36.7856612,Rider_Id_216,1193
-Order_No_18963,User_Id_816,Bike,3,Business,2,3,10:22:32 AM,2,3,10:23:16 AM,2,3,10:23:25 AM,2,3,10:31:14 AM,2,3,10:39:16 AM,2,20.1,,-1.2793686000000002,36.82592329999999,-1.290894,36.822971,Rider_Id_456,482
-Order_No_21200,User_Id_2358,Bike,4,Business,18,4,12:34:58 PM,18,4,12:35:03 PM,18,4,12:36:47 PM,18,4,12:48:39 PM,18,4,1:07:46 PM,12,25.5,,-1.346286,36.768793,-1.293973,36.80169,Rider_Id_622,1147
-Order_No_1404,User_Id_1075,Bike,3,Business,12,2,10:35:26 AM,12,2,10:42:46 AM,12,2,11:42:30 AM,12,2,11:43:43 AM,12,2,12:10:56 PM,5,28.2,,-1.2527958000000001,36.8003131,-1.2880128999999998,36.8308097,Rider_Id_378,1633
-Order_No_26295,User_Id_2789,Bike,3,Business,24,4,12:40:03 PM,24,4,12:41:29 PM,24,4,12:54:28 PM,24,4,12:56:37 PM,24,4,1:10:04 PM,7,24.3,,-1.304735,36.784605400000004,-1.3044313,36.818504,Rider_Id_432,807
-Order_No_21846,User_Id_186,Bike,3,Business,9,4,9:50:54 AM,9,4,9:51:27 AM,9,4,9:57:12 AM,9,4,10:06:13 AM,9,4,10:20:05 AM,6,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_819,832
-Order_No_15937,User_Id_2392,Bike,3,Personal,6,4,1:03:21 PM,6,4,1:05:47 PM,6,4,1:14:16 PM,6,4,2:17:27 PM,6,4,2:50:21 PM,18,21.3,,-1.2365885,36.8898127,-1.3013101999999999,36.796370200000005,Rider_Id_8,1974
-Order_No_6451,User_Id_238,Bike,3,Business,29,4,9:38:59 AM,29,4,9:40:04 AM,29,4,9:40:13 AM,29,4,9:46:42 AM,29,4,9:47:04 AM,4,18.5,3.9,-1.3080091999999999,36.8284543,-1.3077869,36.844320700000004,Rider_Id_848,22
-Order_No_6095,User_Id_517,Bike,3,Business,28,5,10:42:44 AM,28,5,10:42:50 AM,28,5,10:44:52 AM,28,5,10:50:47 AM,28,5,11:01:05 AM,4,23.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,618
-Order_No_9891,User_Id_265,Bike,3,Business,17,1,2:40:07 PM,17,1,2:58:25 PM,17,1,3:33:38 PM,17,1,3:55:59 PM,17,1,4:13:17 PM,5,24.5,,-1.3042113000000002,36.8683022,-1.300921,36.828195,Rider_Id_47,1038
-Order_No_807,User_Id_393,Bike,3,Business,22,5,1:42:47 PM,22,5,1:42:58 PM,22,5,1:45:02 PM,22,5,1:51:59 PM,22,5,2:01:58 PM,4,27.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_842,599
-Order_No_22338,User_Id_1502,Bike,3,Business,3,4,1:08:03 PM,3,4,1:08:52 PM,3,4,1:25:12 PM,3,4,1:28:14 PM,3,4,1:49:36 PM,17,21.9,,-1.2237773,36.8861711,-1.2559559999999999,36.789867,Rider_Id_742,1282
-Order_No_10440,User_Id_2801,Bike,3,Business,29,3,12:14:43 PM,29,3,12:15:51 PM,29,3,12:21:54 PM,29,3,12:25:10 PM,29,3,1:03:43 PM,26,,,-1.2674273999999999,36.787082700000006,-1.34364,36.8925345,Rider_Id_432,2313
-Order_No_23496,User_Id_28,Bike,3,Business,26,4,9:23:36 AM,26,4,9:23:51 AM,26,4,9:28:57 AM,26,4,9:34:24 AM,26,4,10:07:34 AM,18,15.2,,-1.2998459999999998,36.7940155,-1.347383,36.879001200000005,Rider_Id_449,1990
-Order_No_14232,User_Id_3201,Bike,3,Business,15,5,2:52:53 PM,15,5,2:53:21 PM,15,5,2:55:39 PM,15,5,3:14:26 PM,15,5,3:29:19 PM,2,28.0,,-1.2584143,36.8048002,-1.265715,36.808525,Rider_Id_466,893
-Order_No_16272,User_Id_2457,Bike,3,Business,26,5,1:24:11 PM,26,5,2:21:13 PM,26,5,2:43:11 PM,26,5,2:45:58 PM,26,5,3:01:31 PM,6,,,-1.2551895,36.7822034,-1.2811504999999999,36.8145398,Rider_Id_253,933
-Order_No_21878,User_Id_299,Bike,3,Business,15,4,10:35:56 AM,15,4,10:38:20 AM,15,4,10:43:47 AM,15,4,10:46:42 AM,15,4,12:03:51 PM,18,24.3,,-1.3175825,36.7004796,-1.2628638,36.8070399,Rider_Id_125,4629
-Order_No_4303,User_Id_3283,Bike,3,Business,7,1,11:27:43 AM,7,1,11:27:53 AM,7,1,11:27:55 AM,7,1,11:53:58 AM,7,1,12:18:32 PM,13,24.6,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_111,1474
-Order_No_7938,User_Id_3365,Bike,2,Personal,10,5,4:05:02 PM,10,5,4:06:12 PM,10,5,4:27:59 PM,10,5,5:15:29 PM,10,5,5:38:02 PM,3,20.9,,-1.2816136,36.83286270000001,-1.2860198,36.8176562,Rider_Id_229,1353
-Order_No_16371,User_Id_478,Bike,3,Business,29,5,3:13:29 PM,29,5,3:13:44 PM,29,5,3:25:18 PM,29,5,3:47:24 PM,29,5,4:19:55 PM,14,21.4,,-1.3060302,36.7665044,-1.2579303999999998,36.8019667,Rider_Id_247,1951
-Order_No_16955,User_Id_1500,Bike,3,Business,22,3,7:45:06 AM,22,3,7:45:45 AM,22,3,8:02:25 AM,22,3,8:19:37 AM,22,3,8:36:52 AM,8,18.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_2,1035
-Order_No_15904,User_Id_1502,Bike,3,Business,15,5,4:51:02 PM,15,5,4:51:42 PM,15,5,5:02:44 PM,15,5,5:11:04 PM,15,5,5:39:59 PM,17,27.6,,-1.2232123,36.8932547,-1.2551895,36.7822034,Rider_Id_8,1735
-Order_No_4058,User_Id_3517,Bike,3,Business,26,1,9:31:39 AM,26,1,9:32:00 AM,26,1,9:32:02 AM,26,1,9:58:35 AM,26,1,10:21:47 AM,15,17.6,4.9,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_120,1392
-Order_No_6738,User_Id_516,Bike,3,Business,10,5,4:02:44 PM,10,5,4:04:23 PM,10,5,4:04:52 PM,10,5,4:45:46 PM,10,5,5:25:37 PM,13,,,-1.2571472,36.795063299999995,-1.2224188,36.8868824,Rider_Id_291,2391
-Order_No_15484,User_Id_3326,Bike,3,Business,14,4,1:55:21 PM,14,4,2:03:41 PM,14,4,2:21:58 PM,14,4,2:25:29 PM,14,4,2:50:49 PM,8,27.8,,-1.2551895,36.7822034,-1.2976576000000002,36.80812529999999,Rider_Id_660,1520
-Order_No_15793,User_Id_2330,Bike,3,Business,10,1,1:26:07 PM,10,1,1:58:07 PM,10,1,2:03:32 PM,10,1,2:05:15 PM,10,1,2:21:00 PM,7,18.1,,-1.2212611999999998,36.8440033,-1.2336843000000002,36.800725899999996,Rider_Id_740,945
-Order_No_16839,User_Id_635,Bike,3,Business,3,4,2:45:02 PM,3,4,2:45:28 PM,3,4,2:49:36 PM,3,4,3:12:58 PM,3,4,3:29:07 PM,8,26.2,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_90,969
-Order_No_12956,User_Id_3326,Bike,3,Business,12,3,11:18:42 AM,12,3,11:25:58 AM,12,3,11:47:27 AM,12,3,11:49:51 AM,12,3,12:12:09 PM,9,19.3,,-1.2551895,36.7822034,-1.3017794,36.8240888,Rider_Id_394,1338
-Order_No_20884,User_Id_627,Bike,3,Business,4,4,12:46:50 PM,4,4,12:47:18 PM,4,4,12:59:37 PM,4,4,1:01:20 PM,4,4,1:08:44 PM,3,25.7,,-1.2551895,36.7822034,-1.2632076,36.774424200000006,Rider_Id_191,444
-Order_No_12016,User_Id_1500,Bike,3,Business,29,3,2:20:18 PM,29,3,2:36:34 PM,29,3,2:42:58 PM,29,3,2:47:55 PM,29,3,3:13:17 PM,9,21.1,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_155,1522
-Order_No_15689,User_Id_1754,Bike,3,Business,28,4,10:12:13 AM,28,4,10:12:19 AM,28,4,10:12:24 AM,28,4,10:16:53 AM,28,4,10:32:47 AM,12,,,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_408,954
-Order_No_26645,User_Id_1527,Bike,3,Business,3,3,2:55:55 PM,3,3,2:56:02 PM,3,3,2:56:07 PM,3,3,3:03:40 PM,3,3,3:03:41 PM,5,28.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_515,1
-Order_No_21605,User_Id_3175,Bike,3,Business,24,4,9:15:21 AM,24,4,9:15:35 AM,24,4,9:25:37 AM,24,4,9:33:48 AM,24,4,9:41:26 AM,4,16.9,,-1.2562796999999999,36.8075858,-1.2612417,36.7958868,Rider_Id_116,458
-Order_No_1782,User_Id_2200,Bike,3,Business,15,6,12:04:56 PM,15,6,12:05:43 PM,15,6,12:40:25 PM,15,6,1:09:17 PM,15,6,1:19:20 PM,2,22.1,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_698,603
-Order_No_22204,User_Id_2000,Bike,3,Business,10,3,3:03:05 PM,10,3,3:03:33 PM,10,3,3:06:09 PM,10,3,3:12:08 PM,10,3,4:08:27 PM,10,30.8,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_709,3379
-Order_No_24398,User_Id_926,Bike,3,Personal,19,2,10:08:14 AM,19,2,10:11:34 AM,19,2,10:20:36 AM,19,2,10:38:21 AM,19,2,11:11:54 AM,6,,,-1.2560455,36.780086700000005,-1.2637056999999998,36.805170000000004,Rider_Id_116,2013
-Order_No_6717,User_Id_2330,Bike,3,Business,10,3,6:32:20 PM,10,3,6:32:32 PM,10,3,6:50:12 PM,10,3,6:58:20 PM,10,3,7:44:44 PM,18,27.4,,-1.2536275,36.798793200000006,-1.2854537,36.9051024,Rider_Id_854,2784
-Order_No_9342,User_Id_480,Bike,1,Personal,8,1,4:03:24 PM,8,1,4:19:32 PM,8,1,4:32:09 PM,8,1,4:41:06 PM,8,1,4:55:48 PM,5,18.6,,-1.3321944,36.889232899999996,-1.3278522,36.8711134,Rider_Id_932,882
-Order_No_15189,User_Id_1469,Bike,3,Business,30,2,1:09:40 PM,30,2,1:13:18 PM,30,2,2:00:05 PM,30,2,2:03:03 PM,30,2,3:00:32 PM,12,,,-1.2551895,36.7822034,-1.3127877000000001,36.848198700000005,Rider_Id_938,3449
-Order_No_1745,User_Id_1642,Bike,3,Business,9,3,11:34:35 AM,9,3,12:09:49 PM,9,3,12:19:46 PM,9,3,12:35:11 PM,9,3,1:10:23 PM,5,25.2,,-1.288269,36.818275299999996,-1.2728069,36.7962362,Rider_Id_425,2112
-Order_No_21849,User_Id_2987,Bike,3,Business,28,3,4:12:35 PM,28,3,4:31:38 PM,28,3,4:39:43 PM,28,3,4:45:49 PM,28,3,5:31:07 PM,4,23.1,,-1.2702120000000001,36.82108770000001,-1.2534301,36.800098299999995,Rider_Id_279,2718
-Order_No_8314,User_Id_868,Bike,3,Business,26,6,10:07:53 AM,26,6,10:08:07 AM,26,6,10:10:45 AM,26,6,10:18:49 AM,26,6,10:31:36 AM,5,20.6,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_607,767
-Order_No_12659,User_Id_3354,Bike,3,Business,3,3,11:07:01 AM,3,3,11:12:26 AM,3,3,11:19:39 AM,3,3,11:22:53 AM,3,3,11:39:08 AM,12,24.1,,-1.3700383,36.9190174,-1.3234708999999998,36.8493194,Rider_Id_890,975
-Order_No_5966,User_Id_3295,Bike,3,Business,28,4,9:12:25 AM,28,4,9:12:42 AM,28,4,10:13:13 AM,28,4,10:18:40 AM,28,4,11:13:38 AM,20,22.7,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_295,3298
-Order_No_12088,User_Id_3295,Bike,3,Business,14,2,10:07:55 AM,14,2,10:08:16 AM,14,2,10:14:05 AM,14,2,10:20:07 AM,14,2,10:29:00 AM,3,15.6,,-1.2571472,36.795063299999995,-1.2634530000000002,36.7955612,Rider_Id_882,533
-Order_No_13500,User_Id_1363,Bike,3,Business,18,4,2:13:53 PM,18,4,2:14:28 PM,18,4,2:16:48 PM,18,4,2:24:21 PM,18,4,2:35:36 PM,5,26.8,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_519,675
-Order_No_27976,User_Id_811,Bike,3,Personal,10,5,5:32:49 PM,10,5,5:35:16 PM,10,5,5:47:19 PM,10,5,5:56:07 PM,10,5,6:01:43 PM,2,,,-1.2980228999999999,36.78865,-1.2963097,36.7688221,Rider_Id_692,336
-Order_No_20889,User_Id_1051,Bike,3,Business,20,4,11:58:57 AM,20,4,12:00:50 PM,20,4,12:10:06 PM,20,4,12:13:17 PM,20,4,1:04:41 PM,30,22.4,,-1.2963097,36.7688221,-1.1538233,36.950874,Rider_Id_548,3084
-Order_No_9833,User_Id_1146,Bike,3,Business,3,1,11:21:32 AM,3,1,11:23:21 AM,3,1,11:46:38 AM,3,1,12:16:30 PM,3,1,12:31:42 PM,7,21.5,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_643,912
-Order_No_8831,User_Id_3628,Bike,1,Personal,9,6,4:50:53 PM,9,6,4:51:49 PM,9,6,4:58:51 PM,9,6,5:04:32 PM,9,6,5:34:58 PM,15,18.9,,-1.3009097,36.764109399999995,-1.2312682,36.7524906,Rider_Id_74,1826
-Order_No_21977,User_Id_1502,Bike,3,Business,5,2,11:12:25 AM,5,2,11:13:12 AM,5,2,12:13:00 PM,5,2,12:18:23 PM,5,2,12:34:21 PM,11,25.5,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_550,958
-Order_No_11684,User_Id_3175,Bike,3,Business,5,1,1:49:04 PM,5,1,1:50:41 PM,5,1,2:17:56 PM,5,1,2:37:24 PM,5,1,2:37:57 PM,3,26.2,,-1.2628638,36.8070399,-1.2681003999999998,36.7952081,Rider_Id_108,33
-Order_No_3255,User_Id_1544,Bike,3,Business,3,4,10:56:18 AM,3,4,11:19:31 AM,3,4,11:55:49 AM,3,4,12:04:46 PM,3,4,12:28:20 PM,10,21.9,,-1.2870774,36.8259832,-1.3052671999999998,36.753853899999996,Rider_Id_409,1414
-Order_No_19951,User_Id_649,Bike,3,Business,10,1,9:53:48 AM,10,1,10:33:16 AM,10,1,10:33:28 AM,10,1,10:40:27 AM,10,1,11:09:02 AM,14,,,-1.20079,36.77198,-1.2551895,36.7822034,Rider_Id_546,1715
-Order_No_28157,User_Id_2045,Bike,3,Personal,5,5,10:33:27 AM,5,5,10:33:44 AM,5,5,10:44:08 AM,5,5,10:51:10 AM,5,5,11:50:16 AM,26,24.0,,-1.3272343,36.7110502,-1.2248308,36.801257299999996,Rider_Id_789,3546
-Order_No_14682,User_Id_633,Bike,3,Business,13,3,12:02:48 PM,13,3,12:03:53 PM,13,3,12:21:00 PM,13,3,12:23:23 PM,13,3,12:39:19 PM,6,25.0,,-1.3083266000000002,36.8494756,-1.3004062,36.829741,Rider_Id_260,956
-Order_No_18216,User_Id_1138,Bike,1,Personal,11,2,2:53:59 PM,11,2,2:54:20 PM,11,2,2:59:06 PM,11,2,3:02:53 PM,11,2,3:16:45 PM,6,25.7,,-1.3052671999999998,36.753853899999996,-1.2925236,36.7795556,Rider_Id_64,832
-Order_No_27824,User_Id_3355,Bike,3,Business,24,1,11:27:45 AM,24,1,11:27:59 AM,24,1,11:39:19 AM,24,1,11:51:30 AM,24,1,12:10:33 PM,15,22.6,,-1.2283403,36.8822756,-1.3052491000000002,36.8223899,Rider_Id_812,1143
-Order_No_24047,User_Id_2801,Bike,3,Business,10,3,11:20:23 AM,10,3,11:21:13 AM,10,3,11:34:52 AM,10,3,11:36:02 AM,10,3,12:20:14 PM,20,27.6,,-1.2595421999999998,36.7871178,-1.3206433000000002,36.9117533,Rider_Id_290,2652
-Order_No_23809,User_Id_635,Bike,3,Business,30,4,1:14:41 PM,30,4,1:19:07 PM,30,4,1:33:16 PM,30,4,2:04:35 PM,30,4,2:23:25 PM,4,,,-1.300921,36.828195,-1.284497,36.8286605,Rider_Id_567,1130
-Order_No_15509,User_Id_2112,Bike,3,Business,9,2,1:41:08 PM,9,2,1:41:20 PM,9,2,2:14:47 PM,9,2,2:20:46 PM,9,2,2:48:33 PM,17,30.4,,-1.2156006000000001,36.8916865,-1.2551895,36.7822034,Rider_Id_8,1667
-Order_No_18369,User_Id_1341,Bike,3,Business,15,6,11:33:04 AM,15,6,11:33:09 AM,15,6,11:39:10 AM,15,6,11:47:08 AM,15,6,12:07:03 PM,11,19.0,,-1.303596,36.778378000000004,-1.3306113000000002,36.848052100000004,Rider_Id_109,1195
-Order_No_9016,User_Id_867,Bike,3,Business,7,4,1:52:05 PM,7,4,1:52:30 PM,7,4,1:53:18 PM,7,4,2:08:35 PM,7,4,2:32:24 PM,10,27.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_914,1429
-Order_No_20679,User_Id_1336,Bike,3,Business,25,5,11:38:13 AM,25,5,11:39:23 AM,25,5,11:53:06 AM,25,5,12:03:21 PM,25,5,12:22:36 PM,8,26.5,,-1.2885029,36.825604999999996,-1.2615888000000002,36.7928732,Rider_Id_336,1155
-Order_No_14022,User_Id_3417,Bike,1,Personal,1,6,2:39:36 PM,1,6,2:40:03 PM,1,6,2:41:15 PM,1,6,3:01:12 PM,1,6,3:36:59 PM,23,19.3,,-1.3773235000000001,36.9365672,-1.2826971999999999,36.970281299999996,Rider_Id_890,2147
-Order_No_10315,User_Id_1034,Bike,1,Personal,20,1,12:45:48 PM,20,1,12:46:12 PM,20,1,1:05:47 PM,20,1,1:10:24 PM,20,1,1:23:56 PM,4,20.4,,-1.26559,36.8027,-1.2946764,36.8184646,Rider_Id_88,812
-Order_No_21345,User_Id_2457,Bike,3,Business,30,4,12:48:05 PM,30,4,12:48:28 PM,30,4,1:01:15 PM,30,4,1:05:47 PM,30,4,1:20:00 PM,6,20.4,,-1.2551895,36.7822034,-1.2825773999999999,36.8191403,Rider_Id_105,853
-Order_No_10433,User_Id_2562,Bike,1,Personal,21,6,4:13:40 PM,21,6,4:30:46 PM,21,6,4:46:14 PM,21,6,4:50:37 PM,21,6,5:08:15 PM,8,17.8,,-1.2854718,36.871219700000005,-1.3299841000000001,36.8466903,Rider_Id_890,1058
-Order_No_10361,User_Id_3222,Bike,3,Business,25,1,4:25:35 PM,25,1,4:26:42 PM,25,1,4:27:41 PM,25,1,4:28:54 PM,25,1,4:54:07 PM,7,27.8,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_468,1513
-Order_No_23349,User_Id_1280,Bike,3,Business,8,5,12:40:40 PM,8,5,12:43:01 PM,8,5,1:24:09 PM,8,5,1:31:08 PM,8,5,2:10:15 PM,5,28.5,,-1.2551895,36.7822034,-1.2694398,36.762448299999996,Rider_Id_228,2347
-Order_No_11630,User_Id_391,Bike,3,Business,14,3,9:12:10 AM,14,3,11:21:57 AM,14,3,11:31:02 AM,14,3,11:33:31 AM,14,3,11:59:41 AM,5,24.4,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_436,1570
-Order_No_2286,User_Id_2150,Bike,3,Business,8,6,10:26:51 AM,8,6,10:27:09 AM,8,6,10:55:00 AM,8,6,11:02:29 AM,8,6,11:27:09 AM,13,20.0,,-1.3040553,36.8092658,-1.3444459999999998,36.756597,Rider_Id_790,1480
-Order_No_1600,User_Id_478,Bike,3,Business,6,1,11:43:06 AM,6,1,11:43:52 AM,6,1,11:43:59 AM,6,1,11:52:43 AM,6,1,12:13:28 PM,11,,,-1.2551895,36.7822034,-1.3142701,36.853769299999996,Rider_Id_576,1245
-Order_No_2527,User_Id_2677,Bike,3,Business,5,1,9:13:02 AM,5,1,9:13:16 AM,5,1,9:13:31 AM,5,1,10:01:53 AM,5,1,10:09:38 AM,14,18.8,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_70,465
-Order_No_330,User_Id_1329,Bike,3,Business,30,5,1:27:49 PM,30,5,1:55:45 PM,30,5,2:03:14 PM,30,5,2:05:22 PM,30,5,2:22:23 PM,7,24.8,,-1.273056,36.811298,-1.2771886000000001,36.8419032,Rider_Id_14,1021
-Order_No_20368,User_Id_3291,Bike,3,Business,28,4,12:14:04 PM,28,4,12:31:55 PM,28,4,12:36:42 PM,28,4,12:56:14 PM,28,4,1:06:46 PM,5,27.9,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_269,632
-Order_No_3794,User_Id_269,Bike,3,Business,17,3,9:24:53 AM,17,3,9:27:28 AM,17,3,9:44:51 AM,17,3,9:51:09 AM,17,3,10:26:13 AM,11,21.0,,-1.3332749,36.870814700000004,-1.294869,36.815327,Rider_Id_857,2104
-Order_No_6392,User_Id_1850,Bike,3,Business,24,3,2:56:06 PM,24,3,2:57:55 PM,24,3,2:58:15 PM,24,3,3:04:53 PM,24,3,3:26:10 PM,6,,,-1.2551895,36.7822034,-1.2785434,36.821532700000006,Rider_Id_709,1277
-Order_No_2118,User_Id_2763,Bike,3,Business,5,2,9:33:21 AM,5,2,9:33:56 AM,5,2,9:55:01 AM,5,2,10:10:41 AM,5,2,10:43:36 AM,6,18.0,,-1.2551895,36.7822034,-1.2870705,36.82560170000001,Rider_Id_88,1975
-Order_No_20034,User_Id_2956,Bike,3,Business,26,2,12:39:38 PM,26,2,12:39:55 PM,26,2,12:40:58 PM,26,2,1:02:38 PM,26,2,1:04:33 PM,1,26.3,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_875,115
-Order_No_20226,User_Id_1850,Bike,3,Business,16,2,3:38:30 PM,16,2,3:40:52 PM,16,2,4:10:44 PM,16,2,4:12:41 PM,16,2,4:31:17 PM,7,27.2,,-1.2551895,36.7822034,-1.2897399,36.8270975,Rider_Id_208,1116
-Order_No_24140,User_Id_3201,Bike,3,Business,15,1,4:40:57 PM,15,1,4:41:17 PM,15,1,5:00:06 PM,15,1,5:14:32 PM,15,1,5:25:18 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_557,646
-Order_No_2238,User_Id_3291,Bike,3,Business,13,3,11:15:32 AM,13,3,11:15:49 AM,13,3,11:23:57 AM,13,3,11:40:21 AM,13,3,12:09:50 PM,12,24.8,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_199,1769
-Order_No_19380,User_Id_3517,Bike,3,Business,30,4,1:59:04 PM,30,4,1:59:19 PM,30,4,2:04:26 PM,30,4,2:26:23 PM,30,4,3:05:13 PM,15,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_211,2330
-Order_No_18338,User_Id_3487,Bike,3,Business,29,1,10:06:00 AM,29,1,10:06:44 AM,29,1,10:16:15 AM,29,1,10:18:16 AM,29,1,10:37:49 AM,5,,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_819,1173
-Order_No_2983,User_Id_677,Bike,3,Business,19,5,12:43:04 PM,19,5,1:02:36 PM,19,5,1:20:36 PM,19,5,1:24:08 PM,19,5,1:50:44 PM,12,,,-1.290894,36.822971,-1.2363091999999998,36.882361700000004,Rider_Id_910,1596
-Order_No_16322,User_Id_970,Bike,3,Business,7,5,3:23:35 PM,7,5,3:38:39 PM,7,5,3:49:08 PM,7,5,3:52:46 PM,7,5,4:29:36 PM,19,,,-1.2551895,36.7822034,-1.3459625,36.9073925,Rider_Id_155,2210
-Order_No_293,User_Id_265,Bike,3,Business,25,6,12:55:43 PM,25,6,12:56:00 PM,25,6,1:05:44 PM,25,6,1:22:04 PM,25,6,2:02:25 PM,14,,,-1.300921,36.828195,-1.3616286,36.7452212,Rider_Id_414,2421
-Order_No_4426,User_Id_2469,Bike,3,Business,28,3,4:22:00 PM,28,3,4:41:09 PM,28,3,5:30:56 PM,28,3,5:38:04 PM,28,3,6:03:57 PM,20,23.2,,-1.3802928,36.93644929999999,-1.308231,36.857306,Rider_Id_72,1553
-Order_No_14435,User_Id_3711,Bike,3,Business,24,4,4:48:30 PM,24,4,4:48:55 PM,24,4,4:53:41 PM,24,4,5:03:00 PM,24,4,5:18:12 PM,1,26.0,,-1.3012008,36.764868,-1.2943746,36.7687604,Rider_Id_445,912
-Order_No_4610,User_Id_393,Bike,3,Business,21,5,1:23:26 PM,21,5,1:30:12 PM,21,5,1:30:17 PM,21,5,1:50:35 PM,21,5,1:50:39 PM,4,24.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,4
-Order_No_14911,User_Id_3091,Bike,3,Business,25,1,9:05:47 AM,25,1,9:09:14 AM,25,1,9:22:30 AM,25,1,9:56:35 AM,25,1,10:53:20 AM,5,,,-1.290894,36.822971,-1.262073,36.7986087,Rider_Id_127,3405
-Order_No_8258,User_Id_2587,Bike,3,Business,6,3,3:32:01 PM,6,3,3:33:08 PM,6,3,3:42:08 PM,6,3,3:47:43 PM,6,3,4:04:17 PM,5,,,-1.28878,36.8168312,-1.2922364,36.78955379999999,Rider_Id_452,994
-Order_No_9265,User_Id_1500,Bike,3,Business,21,2,10:41:24 AM,21,2,10:41:37 AM,21,2,10:43:53 AM,21,2,10:45:57 AM,21,2,10:58:08 AM,5,22.7,,-1.2638185,36.7930057,-1.282937,36.781698399999996,Rider_Id_709,731
-Order_No_516,User_Id_1920,Bike,3,Business,29,4,9:36:01 AM,29,4,9:36:12 AM,29,4,9:42:12 AM,29,4,9:48:01 AM,29,4,10:01:12 AM,4,18.8,4.1,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_720,791
-Order_No_15011,User_Id_2634,Bike,3,Business,31,5,11:26:37 AM,31,5,11:29:06 AM,31,5,11:29:13 AM,31,5,11:33:16 AM,31,5,12:17:07 PM,21,21.6,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_330,2631
-Order_No_18860,User_Id_3560,Bike,3,Business,21,4,4:32:47 PM,21,4,4:36:38 PM,21,4,4:44:59 PM,21,4,4:47:08 PM,21,4,5:16:02 PM,5,,,-1.279395,36.825364,-1.3072409999999999,36.830236799999994,Rider_Id_448,1734
-Order_No_17214,User_Id_1750,Bike,1,Personal,19,5,9:40:52 AM,19,5,9:57:31 AM,19,5,10:09:49 AM,19,5,10:17:48 AM,19,5,10:45:37 AM,11,20.4,,-1.3339943,36.7263409,-1.3009721,36.7689325,Rider_Id_208,1669
-Order_No_16407,User_Id_2801,Bike,3,Business,9,2,1:06:17 PM,9,2,1:07:07 PM,9,2,1:27:12 PM,9,2,1:47:45 PM,9,2,2:10:42 PM,9,30.4,,-1.307726,36.839117,-1.2595421999999998,36.7871178,Rider_Id_695,1377
-Order_No_2119,User_Id_1271,Bike,3,Business,25,5,1:04:54 PM,25,5,1:05:28 PM,25,5,1:19:51 PM,25,5,2:12:22 PM,25,5,2:49:44 PM,11,29.0,,-1.3107918,36.761794,-1.30393,36.826136,Rider_Id_540,2242
-Order_No_22309,User_Id_1205,Bike,3,Business,20,2,2:07:50 PM,20,2,2:11:29 PM,20,2,2:28:43 PM,20,2,2:46:49 PM,20,2,3:03:18 PM,7,24.1,,-1.257309,36.806008399999996,-1.2958383,36.8073162,Rider_Id_829,989
-Order_No_16079,User_Id_2380,Bike,1,Personal,23,6,6:51:41 PM,23,6,7:07:48 PM,23,6,7:30:27 PM,23,6,7:33:09 PM,23,6,8:20:59 PM,21,,,-1.2591497999999999,36.7137624,-1.3249066999999999,36.8015554,Rider_Id_898,2870
-Order_No_21098,User_Id_1687,Bike,3,Business,13,3,2:05:07 PM,13,3,2:05:20 PM,13,3,2:11:38 PM,13,3,2:23:48 PM,13,3,2:38:46 PM,7,29.3,,-1.2626574,36.8014034,-1.3014732,36.7781201,Rider_Id_622,898
-Order_No_15023,User_Id_393,Bike,3,Business,26,2,2:31:22 PM,26,2,2:31:44 PM,26,2,2:34:36 PM,26,2,2:43:55 PM,26,2,2:56:19 PM,4,31.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_519,744
-Order_No_21455,User_Id_2216,Bike,3,Business,15,4,11:27:05 AM,15,4,12:28:03 PM,15,4,12:35:44 PM,15,4,1:08:07 PM,15,4,1:27:50 PM,7,24.6,,-1.2551895,36.7822034,-1.2308883,36.7698236,Rider_Id_518,1183
-Order_No_3708,User_Id_2739,Bike,1,Personal,20,5,2:14:56 PM,20,5,2:15:04 PM,20,5,2:23:24 PM,20,5,2:28:07 PM,20,5,2:49:34 PM,7,20.6,,-1.2595421999999998,36.7871178,-1.2962832,36.784968299999996,Rider_Id_550,1287
-Order_No_4395,User_Id_2701,Bike,3,Business,28,2,10:10:29 AM,28,2,10:11:35 AM,28,2,10:40:47 AM,28,2,10:55:21 AM,28,2,11:22:45 AM,11,17.8,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_409,1644
-Order_No_24092,User_Id_3022,Bike,1,Personal,14,2,11:54:01 AM,14,2,11:54:17 AM,14,2,12:00:11 PM,14,2,12:07:04 PM,14,2,12:31:46 PM,10,21.4,,-1.2595885,36.8088463,-1.3087372,36.840374700000005,Rider_Id_592,1482
-Order_No_28004,User_Id_1304,Bike,3,Business,15,4,5:38:26 PM,15,4,5:55:53 PM,15,4,6:15:05 PM,15,4,6:17:51 PM,15,4,6:35:40 PM,5,25.2,,-1.298141,36.891382,-1.2702844,36.880320899999994,Rider_Id_622,1069
-Order_No_2849,User_Id_2069,Bike,3,Business,25,1,11:17:32 AM,25,1,11:18:31 AM,25,1,11:25:39 AM,25,1,11:34:38 AM,25,1,12:05:01 PM,16,27.0,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_210,1823
-Order_No_27679,User_Id_391,Bike,3,Business,26,2,10:18:41 AM,26,2,10:24:36 AM,26,2,10:34:03 AM,26,2,10:36:45 AM,26,2,11:12:49 AM,5,27.3,,-1.2602337,36.79905479999999,-1.2867454,36.8259017,Rider_Id_76,2164
-Order_No_22571,User_Id_136,Bike,3,Business,13,1,3:24:35 PM,13,1,3:24:48 PM,13,1,3:29:08 PM,13,1,3:34:37 PM,13,1,4:14:10 PM,24,,,-1.2313147,36.80168629999999,-1.3165103999999999,36.707772600000006,Rider_Id_72,2373
-Order_No_199,User_Id_1927,Bike,3,Business,27,2,5:48:10 PM,27,2,5:48:49 PM,27,2,5:51:47 PM,27,2,5:54:29 PM,27,2,6:33:41 PM,15,22.1,,-1.2584143,36.8048002,-1.3463041999999998,36.768164,Rider_Id_626,2352
-Order_No_11104,User_Id_1304,Bike,3,Business,25,5,2:55:15 PM,25,5,2:57:01 PM,25,5,3:05:10 PM,25,5,3:09:22 PM,25,5,3:14:37 PM,3,23.5,,-1.2553615,36.789537599999996,-1.2607765,36.8080021,Rider_Id_103,315
-Order_No_8067,User_Id_1075,Bike,3,Business,18,2,11:54:06 AM,18,2,11:54:46 AM,18,2,12:00:10 PM,18,2,12:03:36 PM,18,2,12:15:02 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_658,686
-Order_No_3149,User_Id_540,Bike,3,Business,16,3,10:39:18 AM,16,3,10:48:42 AM,16,3,10:52:48 AM,16,3,11:03:18 AM,16,3,11:40:07 AM,7,22.6,,-1.2551895,36.7822034,-1.2878180000000001,36.828623,Rider_Id_26,2209
-Order_No_7723,User_Id_3326,Bike,3,Business,5,1,3:03:52 PM,5,1,3:21:39 PM,5,1,3:42:03 PM,5,1,3:43:37 PM,5,1,4:37:25 PM,9,25.8,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_878,3228
-Order_No_13766,User_Id_3677,Bike,1,Personal,5,4,3:13:00 PM,5,4,3:16:19 PM,5,4,3:24:08 PM,5,4,3:50:12 PM,5,4,4:08:36 PM,6,22.2,,-1.2925792,36.790690000000005,-1.2717851,36.810536299999995,Rider_Id_379,1104
-Order_No_11683,User_Id_1245,Bike,3,Business,5,2,2:28:25 PM,5,2,2:29:04 PM,5,2,2:54:13 PM,5,2,3:51:01 PM,5,2,4:29:01 PM,17,27.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_75,2280
-Order_No_15710,User_Id_3291,Bike,3,Business,8,5,4:17:11 PM,8,5,4:20:37 PM,8,5,4:34:50 PM,8,5,4:39:13 PM,8,5,4:52:57 PM,4,28.2,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_427,824
-Order_No_16222,User_Id_3426,Bike,3,Business,12,1,9:04:51 AM,12,1,9:06:01 AM,12,1,9:14:22 AM,12,1,9:17:37 AM,12,1,10:09:01 AM,8,19.5,0.4,-1.3025826,36.7670807,-1.2615888000000002,36.7928732,Rider_Id_559,3084
-Order_No_24592,User_Id_2030,Bike,3,Business,11,5,2:54:35 PM,11,5,2:54:52 PM,11,5,3:01:45 PM,11,5,3:06:30 PM,11,5,3:18:02 PM,3,26.5,,-1.2768326,36.8073804,-1.2601197,36.7985271,Rider_Id_289,692
-Order_No_13993,User_Id_1522,Bike,3,Business,11,2,9:38:45 AM,11,2,9:43:05 AM,11,2,9:58:56 AM,11,2,10:27:30 AM,11,2,10:59:04 AM,17,16.8,,-1.215909,36.886055999999996,-1.2584143,36.8048002,Rider_Id_60,1894
-Order_No_26471,User_Id_1246,Bike,3,Business,21,1,11:16:48 AM,21,1,11:18:16 AM,21,1,11:30:20 AM,21,1,11:39:31 AM,21,1,11:50:45 AM,5,25.5,,-1.2571472,36.795063299999995,-1.2619524,36.823471999999995,Rider_Id_741,674
-Order_No_6106,User_Id_970,Bike,3,Business,24,4,11:20:32 AM,24,4,11:21:19 AM,24,4,11:23:28 AM,24,4,11:26:44 AM,24,4,11:55:57 AM,10,24.7,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_828,1753
-Order_No_13053,User_Id_868,Bike,3,Business,4,1,10:48:56 AM,4,1,10:49:10 AM,4,1,11:36:32 AM,4,1,11:48:51 AM,4,1,12:11:19 PM,9,25.6,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_127,1348
-Order_No_5037,User_Id_2066,Bike,3,Business,26,5,5:15:28 PM,26,5,5:15:52 PM,26,5,5:27:50 PM,26,5,5:29:25 PM,26,5,5:50:06 PM,9,,,-1.3210188999999999,36.841055100000005,-1.2971024,36.7836598,Rider_Id_448,1241
-Order_No_20099,User_Id_1042,Bike,1,Personal,7,2,3:35:59 PM,7,2,3:36:29 PM,7,2,3:42:10 PM,7,2,3:45:24 PM,7,2,4:34:28 PM,16,24.6,,-1.2882462,36.81645379999999,-1.20079,36.77198,Rider_Id_857,2944
-Order_No_25358,User_Id_246,Bike,3,Business,11,1,2:46:13 PM,11,1,2:50:04 PM,11,1,2:50:28 PM,11,1,3:01:52 PM,11,1,3:19:19 PM,9,23.2,,-1.2584143,36.8048002,-1.272975,36.748237700000004,Rider_Id_268,1047
-Order_No_842,User_Id_2066,Bike,3,Business,29,5,9:53:12 AM,29,5,9:54:55 AM,29,5,10:07:32 AM,29,5,10:09:15 AM,29,5,10:32:56 AM,6,,,-1.316961,36.820337,-1.2937028,36.7874493,Rider_Id_752,1421
-Order_No_22073,User_Id_246,Bike,3,Business,12,1,12:57:12 PM,12,1,12:57:33 PM,12,1,1:03:55 PM,12,1,1:08:29 PM,12,1,1:12:35 PM,2,24.0,,-1.2584143,36.8048002,-1.2591018999999999,36.800577000000004,Rider_Id_756,246
-Order_No_25831,User_Id_1502,Bike,3,Business,6,4,5:04:27 PM,6,4,5:07:17 PM,6,4,5:35:03 PM,6,4,5:41:21 PM,6,4,6:23:47 PM,19,22.8,,-1.3014461000000002,36.7661381,-1.2232123,36.8932547,Rider_Id_596,2546
-Order_No_9391,User_Id_1927,Bike,3,Business,15,3,1:28:08 PM,15,3,1:28:58 PM,15,3,1:35:18 PM,15,3,1:38:48 PM,15,3,2:06:02 PM,15,25.7,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_953,1634
-Order_No_1813,User_Id_680,Bike,2,Personal,13,1,8:45:08 AM,13,1,8:45:19 AM,13,1,9:00:58 AM,13,1,9:02:49 AM,13,1,9:26:06 AM,14,15.0,,-1.3255736,36.7275277,-1.2883346000000002,36.8148489,Rider_Id_249,1397
-Order_No_27781,User_Id_142,Bike,3,Business,13,2,8:31:38 AM,13,2,8:56:42 AM,13,2,9:28:06 AM,13,2,9:29:26 AM,13,2,9:39:56 AM,6,19.9,9.0,-1.2857723,36.808805799999995,-1.2963097,36.7688221,Rider_Id_486,630
-Order_No_15909,User_Id_3355,Bike,3,Business,6,5,12:21:12 PM,6,5,12:21:30 PM,6,5,12:26:46 PM,6,5,12:35:24 PM,6,5,12:55:30 PM,8,20.2,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_726,1206
-Order_No_13378,User_Id_1927,Bike,3,Business,21,2,2:01:51 PM,21,2,2:02:34 PM,21,2,2:27:09 PM,21,2,2:42:56 PM,21,2,3:06:16 PM,12,25.2,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_341,1400
-Order_No_10419,User_Id_2869,Bike,3,Business,4,2,12:18:18 PM,4,2,12:22:57 PM,4,2,12:44:03 PM,4,2,12:54:09 PM,4,2,1:21:07 PM,9,20.3,,-1.279395,36.825364,-1.3025826,36.7670807,Rider_Id_709,1618
-Order_No_4577,User_Id_391,Bike,3,Business,20,1,11:17:16 AM,20,1,11:22:30 AM,20,1,11:34:26 AM,20,1,11:47:01 AM,20,1,12:22:07 PM,5,,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_829,2106
-Order_No_20319,User_Id_2383,Bike,3,Business,20,2,8:43:47 AM,20,2,9:40:54 AM,20,2,10:03:18 AM,20,2,10:11:53 AM,20,2,10:31:26 AM,5,19.2,,-1.2855666000000001,36.819492600000004,-1.2608993,36.799246000000004,Rider_Id_290,1173
-Order_No_4065,User_Id_1209,Bike,3,Business,13,4,8:16:06 AM,13,4,8:31:36 AM,13,4,8:50:37 AM,13,4,9:00:44 AM,13,4,9:09:44 AM,4,18.0,2.0,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_656,540
-Order_No_368,User_Id_1311,Bike,3,Business,27,2,2:16:34 PM,27,2,2:28:21 PM,27,2,2:50:46 PM,27,2,3:01:29 PM,27,2,3:24:07 PM,7,25.6,,-1.2827651000000002,36.843383100000004,-1.2358419,36.85641470000001,Rider_Id_343,1358
-Order_No_787,User_Id_393,Bike,3,Business,19,2,12:46:32 PM,19,2,12:47:04 PM,19,2,1:00:39 PM,19,2,1:07:42 PM,19,2,1:28:08 PM,5,27.6,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_855,1226
-Order_No_5194,User_Id_2412,Bike,3,Business,11,1,11:22:50 AM,11,1,11:24:09 AM,11,1,11:30:00 AM,11,1,11:39:35 AM,11,1,12:31:32 PM,12,26.8,,-1.2584143,36.8048002,-1.2283403,36.8822756,Rider_Id_383,3117
-Order_No_25175,User_Id_393,Bike,3,Business,25,1,11:38:00 AM,25,1,11:38:14 AM,25,1,11:38:21 AM,25,1,11:58:45 AM,25,1,11:58:46 AM,4,27.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,1
-Order_No_3812,User_Id_1719,Bike,3,Business,22,3,2:41:21 PM,22,3,3:07:41 PM,22,3,3:09:49 PM,22,3,3:16:31 PM,22,3,4:07:22 PM,13,24.5,,-1.2963097,36.7688221,-1.23966,36.75258,Rider_Id_819,3051
-Order_No_19976,User_Id_101,Bike,2,Personal,13,3,1:19:12 PM,13,3,1:27:32 PM,13,3,1:34:18 PM,13,3,2:21:43 PM,13,3,3:23:47 PM,7,23.7,,-1.2868973,36.8232984,-1.2872058000000002,36.7802742,Rider_Id_351,3724
-Order_No_6160,User_Id_890,Bike,3,Personal,2,1,2:36:37 PM,2,1,2:40:25 PM,2,1,2:53:42 PM,2,1,3:08:49 PM,2,1,3:39:22 PM,12,20.2,,-1.2658315,36.809049,-1.3400922,36.7839494,Rider_Id_379,1833
-Order_No_27667,User_Id_2706,Bike,3,Personal,23,1,3:15:21 PM,23,1,3:15:40 PM,23,1,3:23:06 PM,23,1,3:24:33 PM,23,1,3:52:55 PM,3,,,-1.2603014,36.8069828,-1.2606445,36.8185586,Rider_Id_519,1702
-Order_No_21969,User_Id_59,Bike,3,Business,11,1,1:43:28 PM,11,1,1:43:40 PM,11,1,1:58:00 PM,11,1,2:14:45 PM,11,1,2:50:17 PM,5,28.2,,-1.290894,36.822971,-1.2664491000000002,36.8102685,Rider_Id_336,2132
-Order_No_9734,User_Id_857,Bike,1,Personal,23,4,11:01:21 AM,23,4,11:02:48 AM,23,4,11:16:01 AM,23,4,11:18:03 AM,23,4,11:37:55 AM,8,18.7,,-1.2850643999999998,36.8343657,-1.314945,36.8411437,Rider_Id_85,1192
-Order_No_14894,User_Id_2425,Bike,1,Personal,6,1,12:45:22 PM,6,1,12:45:33 PM,6,1,12:50:24 PM,6,1,12:53:49 PM,6,1,1:04:51 PM,5,20.7,,-1.3195185,36.8180902,-1.2840002000000001,36.829921999999996,Rider_Id_343,662
-Order_No_25815,User_Id_3264,Bike,3,Business,3,4,12:31:32 PM,3,4,12:31:50 PM,3,4,12:32:09 PM,3,4,12:42:44 PM,3,4,1:16:27 PM,20,23.6,,-1.2551895,36.7822034,-1.330608,36.677485,Rider_Id_450,2023
-Order_No_24431,User_Id_700,Bike,3,Business,28,2,12:43:50 PM,28,2,12:44:08 PM,28,2,12:59:03 PM,28,2,1:00:05 PM,28,2,1:14:14 PM,4,17.8,,-1.2884227,36.772450899999995,-1.2900726999999999,36.7890059,Rider_Id_919,849
-Order_No_26530,User_Id_2978,Bike,3,Business,5,2,9:07:18 AM,5,2,9:07:42 AM,5,2,9:16:23 AM,5,2,9:21:53 AM,5,2,9:33:02 AM,2,17.8,,-1.263499,36.801454799999995,-1.2551895,36.7822034,Rider_Id_854,669
-Order_No_11603,User_Id_3494,Bike,3,Personal,7,4,2:58:25 PM,7,4,2:58:39 PM,7,4,2:59:05 PM,7,4,3:13:42 PM,7,4,3:18:03 PM,2,20.3,,-1.3016513,36.7649321,-1.2963097,36.7688221,Rider_Id_100,261
-Order_No_8459,User_Id_2008,Bike,3,Business,15,4,11:09:04 AM,15,4,11:10:14 AM,15,4,11:45:34 AM,15,4,12:52:27 PM,15,4,12:52:30 PM,22,24.3,,-1.2508229,36.7895263,-1.2070577,36.9206406,Rider_Id_717,3
-Order_No_6559,User_Id_1245,Bike,3,Business,3,3,2:59:05 PM,3,3,2:59:25 PM,3,3,3:16:17 PM,3,3,3:23:02 PM,3,3,4:31:59 PM,19,28.2,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_923,4137
-Order_No_5001,User_Id_3557,Bike,3,Business,15,5,11:12:19 AM,15,5,11:16:21 AM,15,5,11:29:33 AM,15,5,11:37:30 AM,15,5,12:31:22 PM,7,25.7,,-1.2546732,36.808679600000005,-1.2817763,36.7792738,Rider_Id_709,3232
-Order_No_12064,User_Id_661,Bike,3,Business,3,1,1:05:30 PM,3,1,1:14:23 PM,3,1,1:24:24 PM,3,1,1:28:12 PM,3,1,2:14:10 PM,17,22.4,,-1.2963097,36.7688221,-1.2224188,36.8868824,Rider_Id_267,2758
-Order_No_3998,User_Id_2207,Bike,3,Business,30,5,9:45:43 AM,30,5,9:45:51 AM,30,5,10:11:17 AM,30,5,10:13:56 AM,30,5,10:48:08 AM,16,18.8,,-1.207546,36.7913976,-1.2768326,36.8073804,Rider_Id_733,2052
-Order_No_20267,User_Id_186,Bike,3,Business,25,4,10:27:43 AM,25,4,10:29:23 AM,25,4,10:40:42 AM,25,4,10:42:17 AM,25,4,11:16:22 AM,4,20.2,,-1.2571472,36.795063299999995,-1.2890399,36.8164942,Rider_Id_147,2045
-Order_No_14835,User_Id_1363,Bike,3,Business,10,1,2:55:00 PM,10,1,2:57:17 PM,10,1,3:07:57 PM,10,1,3:21:14 PM,10,1,3:29:46 PM,4,18.3,,-1.3004062,36.829741,-1.2825816,36.8092346,Rider_Id_106,512
-Order_No_6504,User_Id_2523,Bike,3,Business,25,4,12:40:26 PM,25,4,12:40:59 PM,25,4,12:59:44 PM,25,4,1:02:57 PM,25,4,1:45:24 PM,23,20.2,,-1.2551895,36.7822034,-1.3447079,36.91105,Rider_Id_541,2547
-Order_No_19454,User_Id_1000,Bike,1,Personal,4,1,2:50:52 PM,4,1,2:51:56 PM,4,1,3:02:17 PM,4,1,3:04:59 PM,4,1,3:09:38 PM,2,22.8,,-1.3090188999999999,36.772010200000004,-1.298043,36.771853,Rider_Id_312,279
-Order_No_6621,User_Id_302,Bike,3,Business,21,5,8:34:22 PM,21,5,8:57:24 PM,21,5,9:05:59 PM,21,5,9:22:41 PM,21,5,9:36:00 PM,3,18.2,,-1.2649605,36.798177700000004,-1.2763916000000002,36.8246826,Rider_Id_607,799
-Order_No_13123,User_Id_2279,Bike,3,Business,25,5,10:18:01 AM,25,5,10:45:39 AM,25,5,11:00:28 AM,25,5,11:16:54 AM,25,5,11:29:26 AM,5,21.2,,-1.2897368,36.8226783,-1.2631940000000002,36.797624,Rider_Id_742,752
-Order_No_2318,User_Id_3091,Bike,3,Business,28,5,12:33:14 PM,28,5,12:33:58 PM,28,5,12:55:49 PM,28,5,1:00:30 PM,28,5,1:32:29 PM,5,25.2,,-1.2894649,36.787753,-1.3012008,36.764868,Rider_Id_431,1919
-Order_No_4870,User_Id_3705,Bike,2,Personal,11,2,12:03:37 PM,11,2,12:04:31 PM,11,2,12:22:27 PM,11,2,12:36:51 PM,11,2,12:46:12 PM,5,24.0,,-1.2771886000000001,36.8419032,-1.2855798999999999,36.817620899999994,Rider_Id_409,561
-Order_No_9123,User_Id_3701,Bike,3,Business,2,2,2:32:53 PM,2,2,2:33:24 PM,2,2,3:06:30 PM,2,2,3:07:48 PM,2,2,3:38:34 PM,17,29.2,,-1.2612573999999999,36.7879622,-1.3253037,36.8942439,Rider_Id_576,1846
-Order_No_6874,User_Id_3423,Bike,1,Personal,9,7,1:53:31 PM,9,7,2:19:26 PM,9,7,2:25:52 PM,9,7,2:28:05 PM,9,7,2:44:17 PM,7,25.9,,-1.2590031000000002,36.8078168,-1.3056847,36.8101341,Rider_Id_103,972
-Order_No_14071,User_Id_1245,Bike,3,Business,24,3,11:32:20 AM,24,3,11:59:46 AM,24,3,12:14:22 PM,24,3,12:29:49 PM,24,3,12:47:32 PM,6,20.0,,-1.300921,36.828195,-1.3009173,36.7959207,Rider_Id_596,1063
-Order_No_18067,User_Id_53,Bike,3,Business,28,4,3:02:10 PM,28,4,3:04:25 PM,28,4,3:18:55 PM,28,4,3:22:01 PM,28,4,3:37:33 PM,7,29.8,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_448,932
-Order_No_5274,User_Id_503,Bike,3,Business,14,4,10:26:27 AM,14,4,10:29:59 AM,14,4,10:40:03 AM,14,4,10:45:48 AM,14,4,12:16:07 PM,15,19.1,,-1.2601708999999999,36.8002238,-1.2859912,36.8756811,Rider_Id_865,5419
-Order_No_11485,User_Id_635,Bike,3,Business,7,4,3:31:13 PM,7,4,3:32:57 PM,7,4,3:39:45 PM,7,4,4:03:07 PM,7,4,4:18:31 PM,6,20.1,,-1.2599563,36.799343900000004,-1.264621,36.778904,Rider_Id_726,924
-Order_No_8456,User_Id_2987,Bike,3,Business,8,4,11:54:24 AM,8,4,11:57:30 AM,8,4,12:02:56 PM,8,4,12:06:11 PM,8,4,12:22:26 PM,7,26.1,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_744,975
-Order_No_9366,User_Id_2789,Bike,3,Business,22,3,2:39:14 PM,22,3,3:05:27 PM,22,3,3:11:56 PM,22,3,3:14:55 PM,22,3,3:32:04 PM,7,24.2,,-1.304735,36.784605400000004,-1.2764851000000002,36.8007126,Rider_Id_177,1029
-Order_No_16167,User_Id_981,Bike,3,Business,20,3,4:01:51 PM,20,3,4:11:07 PM,20,3,4:38:48 PM,20,3,4:42:01 PM,20,3,5:06:15 PM,8,21.8,,-1.2815969999999999,36.826482,-1.3030793999999999,36.777176399999995,Rider_Id_88,1454
-Order_No_4360,User_Id_2731,Bike,1,Personal,14,5,9:43:08 AM,14,5,9:51:11 AM,14,5,10:20:02 AM,14,5,10:21:44 AM,14,5,10:33:34 AM,5,18.2,,-1.2855798999999999,36.817620899999994,-1.2859201999999998,36.78941210000001,Rider_Id_726,710
-Order_No_9903,User_Id_3667,Bike,3,Personal,4,1,9:43:58 AM,4,1,9:44:53 AM,4,1,9:50:06 AM,4,1,9:54:35 AM,4,1,10:04:42 AM,4,21.8,,-1.2690931,36.8065779,-1.2612573999999999,36.7879622,Rider_Id_205,607
-Order_No_16826,User_Id_1363,Bike,3,Business,14,5,11:24:39 AM,14,5,11:28:24 AM,14,5,11:51:28 AM,14,5,11:52:47 AM,14,5,12:04:56 PM,5,21.6,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_267,729
-Order_No_19935,User_Id_793,Bike,3,Personal,27,3,12:44:50 PM,27,3,1:16:36 PM,27,3,1:47:03 PM,27,3,2:02:51 PM,27,3,3:22:44 PM,12,29.5,,-1.2651573999999999,36.7474347,-1.2837254999999999,36.8220797,Rider_Id_411,4793
-Order_No_23345,User_Id_2750,Bike,3,Business,8,4,2:31:25 PM,8,4,2:33:11 PM,8,4,2:46:07 PM,8,4,2:47:43 PM,8,4,3:03:39 PM,4,27.1,,-1.275442,36.766020700000006,-1.2559559999999999,36.772901,Rider_Id_548,956
-Order_No_15954,User_Id_136,Bike,3,Business,27,1,12:24:43 PM,27,1,12:25:19 PM,27,1,12:53:42 PM,27,1,12:55:39 PM,27,1,1:10:06 PM,4,20.8,,-1.2654716000000001,36.7983635,-1.2553627,36.819628200000004,Rider_Id_344,867
-Order_No_3344,User_Id_2582,Bike,3,Business,6,3,9:04:57 AM,6,3,9:05:07 AM,6,3,9:09:06 AM,6,3,9:24:33 AM,6,3,10:00:27 AM,13,16.7,,-1.2551895,36.7822034,-1.2470959,36.8729823,Rider_Id_876,2154
-Order_No_8887,User_Id_3434,Bike,3,Business,6,3,3:35:45 PM,6,3,3:36:04 PM,6,3,3:41:27 PM,6,3,3:45:46 PM,6,3,4:03:26 PM,6,,,-1.2963097,36.7688221,-1.302559,36.819555,Rider_Id_341,1060
-Order_No_4332,User_Id_101,Bike,2,Personal,18,5,2:09:43 PM,18,5,2:37:55 PM,18,5,2:43:16 PM,18,5,2:52:29 PM,18,5,3:12:41 PM,6,24.2,,-1.2868973,36.8232984,-1.2872058000000002,36.7802742,Rider_Id_644,1212
-Order_No_1038,User_Id_2459,Bike,1,Personal,22,3,7:10:46 AM,22,3,7:11:44 AM,22,3,7:45:17 AM,22,3,7:54:02 AM,22,3,8:32:30 AM,21,15.3,,-1.2082468000000002,36.7691703,-1.3012173,36.8093987,Rider_Id_875,2308
-Order_No_16357,User_Id_1402,Bike,3,Business,4,4,8:01:19 AM,4,4,8:16:12 AM,4,4,8:54:27 AM,4,4,9:03:43 AM,4,4,9:29:53 AM,16,21.6,,-1.3374393999999998,36.7339115,-1.2813012,36.832396200000005,Rider_Id_855,1570
-Order_No_8042,User_Id_3726,Bike,1,Personal,27,5,6:59:07 PM,27,5,6:59:10 PM,27,5,7:13:25 PM,27,5,7:16:20 PM,27,5,7:24:13 PM,2,17.8,,-1.2621576,36.8103473,-1.2612278000000001,36.8181679,Rider_Id_229,473
-Order_No_24690,User_Id_1340,Bike,3,Business,5,4,2:58:22 PM,5,4,2:58:35 PM,5,4,3:09:02 PM,5,4,3:14:56 PM,5,4,3:33:10 PM,9,22.1,,-1.2707178999999997,36.7979713,-1.2602386,36.7503491,Rider_Id_951,1094
-Order_No_102,User_Id_1589,Bike,3,Business,14,5,10:17:15 AM,14,5,10:17:55 AM,14,5,10:24:36 AM,14,5,10:35:13 AM,14,5,10:49:52 AM,4,22.1,,-1.225322,36.798761299999995,-1.2391616,36.8101823,Rider_Id_781,879
-Order_No_790,User_Id_791,Bike,3,Business,5,5,8:46:10 AM,5,5,8:46:21 AM,5,5,9:00:41 AM,5,5,9:03:40 AM,5,5,10:08:18 AM,17,17.8,,-1.2931446000000002,36.7613971,-1.3610501,36.7520889,Rider_Id_680,3878
-Order_No_25012,User_Id_3432,Bike,3,Business,23,4,4:20:37 PM,23,4,4:37:07 PM,23,4,4:54:23 PM,23,4,5:23:34 PM,23,4,5:24:57 PM,2,,,-1.2672588,36.8000252,-1.258606,36.800887,Rider_Id_119,83
-Order_No_1934,User_Id_1486,Bike,1,Personal,17,4,3:21:51 PM,17,4,4:20:47 PM,17,4,4:21:32 PM,17,4,4:36:02 PM,17,4,5:32:40 PM,5,21.8,,-1.2864733,36.8239937,-1.3149256999999999,36.8441635,Rider_Id_644,3398
-Order_No_7751,User_Id_265,Bike,3,Business,31,3,11:24:33 AM,31,3,11:27:49 AM,31,3,11:41:17 AM,31,3,12:16:41 PM,31,3,1:04:04 PM,6,24.7,,-1.2699491000000003,36.8135968,-1.300921,36.828195,Rider_Id_555,2843
-Order_No_10761,User_Id_3091,Bike,3,Business,5,5,2:51:48 PM,5,5,3:15:42 PM,5,5,3:26:43 PM,5,5,3:30:07 PM,5,5,4:21:53 PM,3,27.8,,-1.290894,36.822971,-1.2866078,36.808873299999995,Rider_Id_910,3106
-Order_No_6291,User_Id_2150,Bike,3,Business,2,3,9:56:18 AM,2,3,9:57:04 AM,2,3,9:57:06 AM,2,3,10:18:29 AM,2,3,10:38:06 AM,15,20.2,,-1.265715,36.823815,-1.3444459999999998,36.756597,Rider_Id_886,1177
-Order_No_18929,User_Id_3688,Bike,3,Business,2,2,10:41:41 AM,2,2,10:43:42 AM,2,2,10:50:40 AM,2,2,10:57:32 AM,2,2,11:12:54 AM,9,26.1,,-1.3004846,36.790159100000004,-1.2571472,36.795063299999995,Rider_Id_315,922
-Order_No_7825,User_Id_3728,Bike,2,Personal,3,4,10:37:50 AM,3,4,10:38:07 AM,3,4,10:57:57 AM,3,4,11:02:58 AM,3,4,11:38:03 AM,5,22.0,,-1.2839026000000002,36.80893,-1.2661263999999999,36.8030359,Rider_Id_753,2105
-Order_No_7298,User_Id_2330,Bike,3,Business,21,4,6:09:40 PM,21,4,6:10:18 PM,21,4,6:17:26 PM,21,4,6:19:43 PM,21,4,6:56:09 PM,7,,,-1.2642903,36.80021370000001,-1.2873232,36.765857200000006,Rider_Id_354,2186
-Order_No_6154,User_Id_2980,Bike,1,Personal,12,4,12:11:07 PM,12,4,12:13:27 PM,12,4,12:31:27 PM,12,4,12:35:57 PM,12,4,12:50:12 PM,7,19.1,,-1.2669099,36.79774329999999,-1.2969741000000001,36.7856612,Rider_Id_951,855
-Order_No_5729,User_Id_3723,Bike,1,Personal,15,1,10:42:10 AM,15,1,10:42:30 AM,15,1,10:47:46 AM,15,1,10:51:49 AM,15,1,11:02:52 AM,7,,,-1.300722,36.816865,-1.2628638,36.8070399,Rider_Id_726,663
-Order_No_4549,User_Id_426,Bike,3,Business,18,1,3:05:23 PM,18,1,3:05:37 PM,18,1,3:15:32 PM,18,1,3:20:54 PM,18,1,4:06:39 PM,18,17.4,,-1.3323107,36.8692616,-1.2600616000000002,36.787018200000006,Rider_Id_192,2745
-Order_No_822,User_Id_633,Bike,3,Business,23,6,8:45:26 AM,23,6,8:47:12 AM,23,6,8:47:19 AM,23,6,9:02:16 AM,23,6,9:15:38 AM,9,18.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_576,802
-Order_No_16679,User_Id_1809,Bike,3,Business,1,4,11:21:09 AM,1,4,11:21:26 AM,1,4,11:27:46 AM,1,4,11:30:12 AM,1,4,11:30:39 AM,2,,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_647,27
-Order_No_7084,User_Id_1850,Bike,3,Business,9,4,11:41:33 AM,9,4,11:42:19 AM,9,4,11:45:05 AM,9,4,11:47:47 AM,9,4,12:09:48 PM,8,,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_92,1321
-Order_No_21909,User_Id_3194,Bike,3,Business,8,5,3:03:15 PM,8,5,3:05:18 PM,8,5,3:56:56 PM,8,5,4:01:04 PM,8,5,4:01:51 PM,8,30.4,,-1.2551895,36.7822034,-1.2832259,36.795837299999995,Rider_Id_425,47
-Order_No_973,User_Id_2582,Bike,3,Business,1,4,12:10:30 PM,1,4,12:10:40 PM,1,4,12:22:10 PM,1,4,12:35:17 PM,1,4,1:10:14 PM,19,,,-1.2825223,36.901456599999996,-1.2551895,36.7822034,Rider_Id_699,2097
-Order_No_4887,User_Id_2412,Bike,3,Business,17,3,2:00:54 PM,17,3,2:03:08 PM,17,3,2:17:29 PM,17,3,2:20:56 PM,17,3,2:48:44 PM,10,,,-1.2765736,36.851364600000004,-1.2283403,36.8822756,Rider_Id_204,1668
-Order_No_17941,User_Id_421,Bike,1,Business,1,1,12:36:03 PM,1,1,12:36:23 PM,1,1,12:38:00 PM,1,1,12:40:38 PM,1,1,1:07:34 PM,7,24.0,,-1.2975066000000002,36.76894229999999,-1.303217,36.731466999999995,Rider_Id_532,1616
-Order_No_10870,User_Id_3688,Bike,3,Business,15,5,1:03:19 PM,15,5,1:03:48 PM,15,5,1:30:11 PM,15,5,1:37:37 PM,15,5,2:06:08 PM,16,28.2,,-1.2551895,36.7822034,-1.2153260000000001,36.891993799999995,Rider_Id_200,1711
-Order_No_5185,User_Id_3647,Bike,3,Business,18,5,4:06:01 PM,18,5,4:19:58 PM,18,5,4:48:43 PM,18,5,4:53:53 PM,18,5,5:14:37 PM,5,25.4,,-1.2726389999999999,36.794723,-1.2797536,36.8223726,Rider_Id_725,1244
-Order_No_6207,User_Id_570,Bike,3,Business,14,1,10:01:24 AM,14,1,10:02:04 AM,14,1,10:04:56 AM,14,1,10:08:35 AM,14,1,10:24:51 AM,2,18.6,0.3,-1.2559559999999999,36.789867,-1.263483,36.8005343,Rider_Id_798,976
-Order_No_8644,User_Id_1178,Bike,3,Business,5,3,4:41:02 PM,5,3,4:44:51 PM,5,3,4:51:54 PM,5,3,5:46:36 PM,5,3,5:52:23 PM,5,22.3,,-1.2868515,36.7844247,-1.2677371000000002,36.7729723,Rider_Id_882,347
-Order_No_18946,User_Id_2344,Bike,3,Business,5,2,4:33:23 PM,5,2,4:33:30 PM,5,2,4:34:49 PM,5,2,4:46:58 PM,5,2,5:45:36 PM,23,21.6,,-1.2553615,36.789537599999996,-1.3447079,36.91105,Rider_Id_379,3518
-Order_No_21835,User_Id_630,Bike,3,Personal,21,1,10:34:38 AM,21,1,10:34:58 AM,21,1,10:39:05 AM,21,1,10:41:39 AM,21,1,11:16:35 AM,22,20.1,,-1.2664331,36.800047799999994,-1.3361484,36.91611279999999,Rider_Id_167,2096
-Order_No_22003,User_Id_3291,Bike,3,Business,6,6,12:30:20 PM,6,6,12:31:18 PM,6,6,12:39:27 PM,6,6,12:54:02 PM,6,6,1:15:21 PM,8,26.8,,-1.2999309,36.830799799999994,-1.2600926000000001,36.8088685,Rider_Id_206,1279
-Order_No_4418,User_Id_2866,Bike,3,Business,13,4,12:41:53 PM,13,4,12:42:14 PM,13,4,1:12:31 PM,13,4,1:24:59 PM,13,4,1:38:46 PM,4,22.2,,-1.2551895,36.7822034,-1.2675979,36.815565299999996,Rider_Id_193,827
-Order_No_19586,User_Id_2627,Bike,3,Business,5,2,2:19:47 PM,5,2,2:20:02 PM,5,2,2:23:38 PM,5,2,2:43:49 PM,5,2,2:47:04 PM,2,,,-1.2571472,36.795063299999995,-1.2591018999999999,36.800577000000004,Rider_Id_937,195
-Order_No_25787,User_Id_3092,Bike,3,Personal,28,4,10:07:52 AM,28,4,10:09:07 AM,28,4,10:33:33 AM,28,4,10:35:00 AM,28,4,10:56:25 AM,6,,,-1.2675979,36.815565299999996,-1.2978459,36.7912964,Rider_Id_467,1285
-Order_No_15446,User_Id_970,Bike,3,Business,11,1,2:34:08 PM,11,1,3:20:33 PM,11,1,3:30:05 PM,11,1,3:32:08 PM,11,1,3:49:27 PM,8,28.1,,-1.2551895,36.7822034,-1.2852,36.82397,Rider_Id_210,1039
-Order_No_15676,User_Id_2069,Bike,3,Business,25,2,11:46:51 AM,25,2,11:47:29 AM,25,2,12:01:24 PM,25,2,12:22:30 PM,25,2,1:04:22 PM,13,24.0,,-1.307143,36.825009,-1.228911,36.881876,Rider_Id_882,2512
-Order_No_11125,User_Id_246,Bike,3,Business,6,3,7:28:11 PM,6,3,7:28:30 PM,6,3,7:28:38 PM,6,3,7:29:46 PM,6,3,7:33:41 PM,2,19.9,,-1.2601708999999999,36.8002238,-1.2584143,36.8048002,Rider_Id_103,235
-Order_No_11884,User_Id_913,Bike,3,Personal,4,2,3:20:59 PM,4,2,3:24:37 PM,4,2,3:36:53 PM,4,2,3:46:38 PM,4,2,4:07:03 PM,5,22.5,,-1.2857315,36.7953424,-1.2692689,36.8055169,Rider_Id_826,1225
-Order_No_13953,User_Id_393,Bike,3,Business,8,5,1:35:28 PM,8,5,1:36:36 PM,8,5,1:36:45 PM,8,5,1:52:45 PM,8,5,2:01:40 PM,4,27.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_901,535
-Order_No_26951,User_Id_2412,Bike,3,Business,24,5,2:12:35 PM,24,5,2:16:09 PM,24,5,2:16:34 PM,24,5,2:35:27 PM,24,5,3:01:39 PM,15,22.8,,-1.2283403,36.8822756,-1.2551895,36.7822034,Rider_Id_788,1572
-Order_No_21785,User_Id_2532,Bike,3,Business,17,4,9:58:48 AM,17,4,10:01:38 AM,17,4,10:11:13 AM,17,4,10:12:58 AM,17,4,10:31:31 AM,8,,,-1.2551895,36.7822034,-1.294869,36.815327,Rider_Id_131,1113
-Order_No_5909,User_Id_3201,Bike,3,Business,21,2,11:48:37 AM,21,2,11:49:18 AM,21,2,12:00:23 PM,21,2,12:04:18 PM,21,2,12:18:21 PM,6,22.7,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_341,843
-Order_No_4598,User_Id_552,Bike,3,Business,25,1,1:11:21 PM,25,1,1:11:35 PM,25,1,1:27:56 PM,25,1,1:35:59 PM,25,1,2:01:38 PM,16,27.9,,-1.2859912,36.8756811,-1.3562374,36.904295399999995,Rider_Id_108,1539
-Order_No_3286,User_Id_1724,Bike,3,Business,5,5,8:26:46 AM,5,5,8:27:09 AM,5,5,8:48:36 AM,5,5,9:05:38 AM,5,5,9:33:03 AM,5,19.6,,-1.2638132,36.784977500000004,-1.2551895,36.7822034,Rider_Id_155,1645
-Order_No_10577,User_Id_1707,Bike,3,Personal,30,2,4:50:54 PM,30,2,5:02:20 PM,30,2,5:10:53 PM,30,2,5:15:44 PM,30,2,5:49:29 PM,17,,,-1.2921233,36.806385999999996,-1.1793857,36.8311248,Rider_Id_674,2025
-Order_No_25439,User_Id_1075,Bike,3,Business,13,3,12:00:48 PM,13,3,12:01:20 PM,13,3,12:12:53 PM,13,3,12:17:19 PM,13,3,12:49:17 PM,13,,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_8,1918
-Order_No_5497,User_Id_1500,Bike,3,Business,27,4,7:48:08 AM,27,4,8:20:05 AM,27,4,8:25:42 AM,27,4,8:56:25 AM,27,4,9:06:32 AM,8,17.8,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_424,607
-Order_No_5319,User_Id_2964,Bike,3,Business,30,2,9:54:59 AM,30,2,9:55:15 AM,30,2,9:57:25 AM,30,2,10:18:06 AM,30,2,10:35:04 AM,11,,,-1.2551895,36.7822034,-1.2381,36.732826,Rider_Id_245,1018
-Order_No_16261,User_Id_3017,Bike,3,Business,20,4,5:14:46 PM,20,4,5:15:10 PM,20,4,5:29:12 PM,20,4,5:33:06 PM,20,4,6:14:46 PM,15,25.1,,-1.2993231,36.7753372,-1.244847,36.6830249,Rider_Id_465,2500
-Order_No_13041,User_Id_2112,Bike,3,Business,24,4,6:26:53 PM,24,4,6:43:45 PM,24,4,7:02:19 PM,24,4,7:35:45 PM,24,4,7:55:36 PM,14,20.8,,-1.207546,36.7913976,-1.2156006000000001,36.8916865,Rider_Id_607,1191
-Order_No_1314,User_Id_198,Bike,3,Business,15,3,9:38:13 AM,15,3,9:53:36 AM,15,3,10:18:05 AM,15,3,10:19:50 AM,15,3,10:36:34 AM,11,,,-1.3396256000000002,36.8863135,-1.344994,36.91328,Rider_Id_448,1004
-Order_No_17357,User_Id_478,Bike,3,Business,15,1,12:14:58 PM,15,1,12:15:07 PM,15,1,12:15:37 PM,15,1,12:26:05 PM,15,1,12:54:28 PM,17,,,-1.2551895,36.7822034,-1.3339943,36.7263409,Rider_Id_291,1703
-Order_No_10877,User_Id_3647,Bike,3,Business,9,2,10:39:49 AM,9,2,10:40:31 AM,9,2,11:02:57 AM,9,2,11:11:12 AM,9,2,11:19:55 AM,5,27.2,,-1.2963316,36.7863289,-1.2726389999999999,36.794723,Rider_Id_829,523
-Order_No_24347,User_Id_3291,Bike,3,Business,19,2,3:20:46 PM,19,2,3:21:14 PM,19,2,3:31:16 PM,19,2,3:36:14 PM,19,2,3:55:11 PM,8,29.3,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_291,1137
-Order_No_6210,User_Id_517,Bike,3,Business,30,3,1:07:22 PM,30,3,1:09:28 PM,30,3,1:18:11 PM,30,3,1:26:26 PM,30,3,1:57:09 PM,14,24.9,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_523,1843
-Order_No_28251,User_Id_435,Bike,3,Business,7,5,12:34:24 PM,7,5,12:38:41 PM,7,5,12:50:41 PM,7,5,12:54:50 PM,7,5,1:18:39 PM,8,,,-1.2969741000000001,36.7856612,-1.300921,36.828195,Rider_Id_826,1429
-Order_No_17652,User_Id_633,Bike,3,Business,26,2,3:27:01 PM,26,2,3:30:05 PM,26,2,3:30:57 PM,26,2,3:47:00 PM,26,2,4:05:23 PM,4,30.8,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_80,1103
-Order_No_22099,User_Id_1148,Bike,3,Business,8,4,1:11:54 PM,8,4,1:13:57 PM,8,4,1:34:37 PM,8,4,1:40:05 PM,8,4,2:04:21 PM,9,27.3,,-1.3100586,36.850032899999995,-1.2764098999999998,36.8208451,Rider_Id_810,1456
-Order_No_14158,User_Id_1133,Bike,3,Business,15,3,2:34:54 PM,15,3,2:35:17 PM,15,3,2:41:12 PM,15,3,2:42:57 PM,15,3,2:52:01 PM,3,25.7,,-1.2681262,36.788234100000004,-1.2571472,36.795063299999995,Rider_Id_520,544
-Order_No_26966,User_Id_265,Bike,3,Business,28,4,12:55:43 PM,28,4,1:09:19 PM,28,4,1:32:41 PM,28,4,1:42:26 PM,28,4,2:33:04 PM,16,30.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_275,3038
-Order_No_19590,User_Id_1271,Bike,3,Business,8,5,10:51:35 AM,8,5,10:51:51 AM,8,5,11:18:17 AM,8,5,11:27:48 AM,8,5,12:10:54 PM,26,24.1,,-1.3244885,36.897791999999995,-1.3949989,36.7654761,Rider_Id_411,2586
-Order_No_26863,User_Id_1926,Bike,3,Business,8,2,12:01:52 PM,8,2,12:32:50 PM,8,2,12:39:01 PM,8,2,12:50:08 PM,8,2,12:59:14 PM,3,21.6,,-1.2897368,36.8226783,-1.267548,36.803249,Rider_Id_100,546
-Order_No_23377,User_Id_633,Bike,3,Business,20,3,2:42:31 PM,20,3,2:42:50 PM,20,3,2:43:09 PM,20,3,2:47:49 PM,20,3,2:47:52 PM,5,28.6,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_39,3
-Order_No_21058,User_Id_2063,Bike,3,Business,21,1,2:53:20 PM,21,1,2:54:25 PM,21,1,2:58:23 PM,21,1,3:08:44 PM,21,1,3:34:13 PM,18,27.8,,-1.2551895,36.7822034,-1.2868018,36.890606299999995,Rider_Id_40,1529
-Order_No_573,User_Id_3291,Bike,3,Business,8,6,11:10:07 AM,8,6,11:13:10 AM,8,6,11:13:17 AM,8,6,11:28:38 AM,8,6,11:44:21 AM,5,20.0,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_698,943
-Order_No_27652,User_Id_2801,Bike,3,Business,11,1,11:19:56 AM,11,1,11:20:10 AM,11,1,11:25:12 AM,11,1,11:28:29 AM,11,1,11:53:58 AM,7,26.6,,-1.2674273999999999,36.787082700000006,-1.2809846999999999,36.8274169,Rider_Id_709,1529
-Order_No_14159,User_Id_2896,Bike,3,Business,10,6,11:55:29 AM,10,6,11:57:07 AM,10,6,12:26:21 PM,10,6,12:30:59 PM,10,6,1:46:30 PM,18,23.9,,-1.3338005,36.863026,-1.2336076,36.8336102,Rider_Id_114,4531
-Order_No_22530,User_Id_3405,Bike,2,Personal,12,5,6:28:40 PM,12,5,6:28:57 PM,12,5,6:39:23 PM,12,5,6:44:32 PM,12,5,7:21:38 PM,15,25.8,,-1.2709086,36.7929516,-1.1992502,36.8294932,Rider_Id_105,2226
-Order_No_7306,User_Id_1882,Bike,3,Business,14,4,8:38:07 PM,14,4,8:38:43 PM,14,4,8:58:28 PM,14,4,9:37:54 PM,14,4,9:47:26 PM,6,22.1,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_678,572
-Order_No_6289,User_Id_1685,Bike,1,Personal,30,4,2:56:09 PM,30,4,2:58:37 PM,30,4,3:35:40 PM,30,4,3:48:06 PM,30,4,4:28:51 PM,5,20.8,,-1.2802476999999999,36.8141141,-1.2857671,36.7875614,Rider_Id_268,2445
-Order_No_26878,User_Id_187,Bike,3,Business,26,1,12:51:29 PM,26,1,1:05:11 PM,26,1,1:31:26 PM,26,1,1:43:14 PM,26,1,2:45:42 PM,22,22.4,0.8,-1.3226301999999999,36.8719901,-1.3165542,36.700067,Rider_Id_928,3748
-Order_No_9984,User_Id_1363,Bike,3,Business,2,5,9:00:32 AM,2,5,9:01:12 AM,2,5,9:01:22 AM,2,5,9:09:14 AM,2,5,9:18:32 AM,3,17.9,16.9,-1.3004062,36.829741,-1.3047132,36.808955299999994,Rider_Id_69,558
-Order_No_3920,User_Id_111,Bike,3,Business,7,5,2:40:36 PM,7,5,2:41:01 PM,7,5,2:49:18 PM,7,5,3:00:55 PM,7,5,3:10:47 PM,4,,,-1.2551895,36.7822034,-1.2681518,36.809816399999995,Rider_Id_624,592
-Order_No_2301,User_Id_265,Bike,3,Business,24,3,2:52:41 PM,24,3,3:20:53 PM,24,3,3:42:17 PM,24,3,3:44:35 PM,24,3,4:22:59 PM,7,,,-1.300921,36.828195,-1.3154845,36.7912475,Rider_Id_843,2304
-Order_No_11286,User_Id_2457,Bike,3,Business,15,2,3:44:58 PM,15,2,3:57:06 PM,15,2,4:11:13 PM,15,2,4:12:24 PM,15,2,4:31:59 PM,8,24.7,,-1.2626906,36.7827022,-1.2995132,36.8043032,Rider_Id_782,1175
-Order_No_10198,User_Id_53,Bike,3,Business,10,5,9:30:04 AM,10,5,9:35:03 AM,10,5,9:42:01 AM,10,5,9:48:41 AM,10,5,10:01:21 AM,7,,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_768,760
-Order_No_8623,User_Id_868,Bike,3,Business,23,3,9:47:17 AM,23,3,9:48:01 AM,23,3,10:02:17 AM,23,3,10:18:33 AM,23,3,10:27:45 AM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_937,552
-Order_No_16212,User_Id_2532,Bike,3,Business,7,5,12:47:54 PM,7,5,12:48:02 PM,7,5,12:51:34 PM,7,5,12:53:41 PM,7,5,12:56:25 PM,3,25.2,,-1.2551895,36.7822034,-1.256522,36.786413200000005,Rider_Id_353,164
-Order_No_5149,User_Id_2982,Bike,3,Business,27,3,10:16:53 AM,27,3,10:16:59 AM,27,3,10:17:40 AM,27,3,10:57:45 AM,27,3,11:17:16 AM,10,,,-1.300921,36.828195,-1.2797184,36.7811448,Rider_Id_771,1171
-Order_No_7230,User_Id_1244,Bike,3,Business,24,1,11:00:14 AM,24,1,11:02:13 AM,24,1,11:58:44 AM,24,1,12:23:47 PM,24,1,1:21:56 PM,17,22.7,,-1.228911,36.881876,-1.303596,36.778378000000004,Rider_Id_101,3489
-Order_No_27314,User_Id_868,Bike,3,Business,15,4,10:10:35 AM,15,4,10:12:38 AM,15,4,10:18:36 AM,15,4,10:24:35 AM,15,4,10:35:55 AM,9,19.4,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_871,680
-Order_No_11650,User_Id_2564,Bike,3,Business,25,6,9:20:11 AM,25,6,9:21:12 AM,25,6,9:58:53 AM,25,6,10:13:46 AM,25,6,10:36:47 AM,9,,,-1.2795195,36.82346939999999,-1.2271998,36.808490500000005,Rider_Id_119,1381
-Order_No_27236,User_Id_238,Bike,3,Business,12,5,9:41:33 AM,12,5,9:43:08 AM,12,5,9:43:12 AM,12,5,10:13:44 AM,12,5,10:22:20 AM,4,21.8,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_239,516
-Order_No_8234,User_Id_2330,Bike,3,Business,25,5,5:22:25 PM,25,5,5:22:55 PM,25,5,5:31:05 PM,25,5,5:33:37 PM,25,5,5:59:39 PM,8,26.6,,-1.3192788999999998,36.711299100000005,-1.3015197,36.7658456,Rider_Id_269,1562
-Order_No_3601,User_Id_2767,Bike,3,Business,12,1,1:39:08 PM,12,1,2:31:58 PM,12,1,2:49:23 PM,12,1,3:01:31 PM,12,1,3:16:08 PM,8,27.0,,-1.257773,36.795486,-1.2230813,36.8014169,Rider_Id_147,877
-Order_No_18101,User_Id_1155,Bike,3,Personal,28,4,11:26:26 AM,28,4,11:32:06 AM,28,4,11:49:29 AM,28,4,11:55:59 AM,28,4,12:22:38 PM,5,28.1,,-1.287079,36.7811277,-1.2897747,36.81599179999999,Rider_Id_223,1599
-Order_No_8285,User_Id_1469,Bike,3,Business,15,1,9:49:07 AM,15,1,9:50:54 AM,15,1,9:58:38 AM,15,1,10:02:39 AM,15,1,10:16:12 AM,7,,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_453,813
-Order_No_6521,User_Id_3494,Bike,3,Personal,24,1,12:37:19 PM,24,1,12:37:29 PM,24,1,12:37:54 PM,24,1,1:30:32 PM,24,1,1:49:10 PM,10,22.3,,-1.2892959,36.837867100000004,-1.2963097,36.7688221,Rider_Id_179,1118
-Order_No_23399,User_Id_1410,Bike,3,Business,25,1,1:15:32 PM,25,1,1:32:28 PM,25,1,2:32:34 PM,25,1,2:40:35 PM,25,1,3:40:10 PM,33,30.3,,-1.3882008,36.7699445,-1.2847613000000002,36.967084,Rider_Id_168,3575
-Order_No_13496,User_Id_1162,Bike,3,Business,5,3,9:59:38 AM,5,3,10:19:10 AM,5,3,10:26:29 AM,5,3,10:32:15 AM,5,3,10:47:24 AM,5,22.4,,-1.2733047,36.8109744,-1.2958383,36.8073162,Rider_Id_19,909
-Order_No_28128,User_Id_570,Bike,3,Business,6,4,12:59:30 PM,6,4,1:14:50 PM,6,4,1:38:14 PM,6,4,2:34:50 PM,6,4,3:51:34 PM,22,23.6,,-1.3342256000000001,36.9106741,-1.2551895,36.7822034,Rider_Id_6,4604
-Order_No_1090,User_Id_2956,Bike,3,Business,15,3,9:55:51 AM,15,3,10:00:02 AM,15,3,10:04:45 AM,15,3,10:07:26 AM,15,3,10:54:43 AM,6,,,-1.2584143,36.8048002,-1.2854151000000003,36.8233848,Rider_Id_921,2837
-Order_No_11246,User_Id_814,Bike,3,Business,26,2,2:26:53 PM,26,2,2:27:09 PM,26,2,2:30:55 PM,26,2,2:56:22 PM,26,2,4:19:29 PM,14,,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_908,4987
-Order_No_10142,User_Id_1334,Bike,2,Personal,30,4,11:30:17 AM,30,4,11:50:02 AM,30,4,12:00:27 PM,30,4,12:04:14 PM,30,4,12:16:10 PM,6,20.2,,-1.3086326000000001,36.8080886,-1.304033,36.7848695,Rider_Id_848,716
-Order_No_14787,User_Id_3008,Bike,1,Personal,9,1,9:28:31 AM,9,1,9:28:55 AM,9,1,9:36:02 AM,9,1,9:44:57 AM,9,1,10:37:18 AM,16,15.3,,-1.2882968000000001,36.7768414,-1.3124921999999999,36.880497399999996,Rider_Id_228,3141
-Order_No_1630,User_Id_1014,Bike,1,Business,8,1,12:35:17 PM,8,1,12:38:42 PM,8,1,12:41:48 PM,8,1,12:52:49 PM,8,1,1:13:23 PM,6,24.7,,-1.2590031000000002,36.8078168,-1.2733546,36.7696069,Rider_Id_848,1234
-Order_No_27088,User_Id_3689,Bike,3,Business,18,4,12:36:32 PM,18,4,1:25:27 PM,18,4,1:30:15 PM,18,4,2:28:28 PM,18,4,2:41:40 PM,5,26.8,,-1.28878,36.8168312,-1.2527958000000001,36.8003131,Rider_Id_19,792
-Order_No_24799,User_Id_214,Bike,1,Personal,13,3,9:20:31 AM,13,3,9:24:07 AM,13,3,9:32:42 AM,13,3,9:47:12 AM,13,3,10:09:07 AM,5,16.1,,-1.2616213,36.8221128,-1.2538731,36.7850223,Rider_Id_808,1315
-Order_No_12288,User_Id_53,Bike,3,Business,14,2,12:57:46 PM,14,2,12:59:39 PM,14,2,1:23:42 PM,14,2,1:45:15 PM,14,2,2:29:08 PM,8,,,-1.2770708,36.8231093,-1.2769540000000001,36.765926,Rider_Id_436,2633
-Order_No_27292,User_Id_3291,Bike,3,Business,25,1,9:34:04 AM,25,1,9:34:53 AM,25,1,9:39:52 AM,25,1,9:51:06 AM,25,1,10:03:16 AM,5,20.9,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_831,730
-Order_No_15113,User_Id_20,Bike,3,Business,20,5,8:25:34 AM,20,5,8:25:59 AM,20,5,8:34:56 AM,20,5,8:41:24 AM,20,5,9:05:13 AM,14,14.5,,-1.2628638,36.8070399,-1.3346931,36.8661911,Rider_Id_116,1429
-Order_No_507,User_Id_2132,Bike,3,Business,3,4,10:03:46 AM,3,4,10:04:36 AM,3,4,10:34:42 AM,3,4,10:45:45 AM,3,4,11:55:17 AM,27,23.3,,-1.2384056,36.8718698,-1.3858142,36.7641498,Rider_Id_925,4172
-Order_No_9740,User_Id_2330,Bike,3,Business,8,5,1:14:19 PM,8,5,1:35:24 PM,8,5,1:37:08 PM,8,5,1:41:40 PM,8,5,2:02:10 PM,10,27.3,,-1.2524916000000001,36.8656196,-1.2637056999999998,36.805170000000004,Rider_Id_845,1230
-Order_No_9014,User_Id_173,Bike,3,Business,14,5,5:12:04 PM,14,5,5:12:14 PM,14,5,5:20:41 PM,14,5,5:25:57 PM,14,5,6:03:04 PM,11,22.9,,-1.3207146,36.8305771,-1.2993231,36.7753372,Rider_Id_712,2227
-Order_No_28120,User_Id_1882,Bike,3,Business,13,1,4:40:35 PM,13,1,4:40:53 PM,13,1,4:41:34 PM,13,1,5:16:38 PM,13,1,5:33:30 PM,7,,,-1.2603403,36.7998848,-1.2268871000000001,36.8073946,Rider_Id_547,1012
-Order_No_15562,User_Id_1850,Bike,3,Business,13,3,4:00:36 PM,13,3,4:02:56 PM,13,3,4:08:56 PM,13,3,4:21:30 PM,13,3,5:13:02 PM,8,29.4,,-1.2551895,36.7822034,-1.3033012,36.8170864,Rider_Id_865,3092
-Order_No_112,User_Id_391,Bike,3,Business,20,1,1:58:18 PM,20,1,2:02:17 PM,20,1,2:15:12 PM,20,1,2:41:36 PM,20,1,3:38:59 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_139,3443
-Order_No_15038,User_Id_3647,Bike,3,Business,11,1,1:25:37 PM,11,1,1:26:44 PM,11,1,1:34:04 PM,11,1,1:46:18 PM,11,1,2:33:01 PM,29,29.1,,-1.2726389999999999,36.794723,-1.2905202,36.97740579999999,Rider_Id_932,2803
-Order_No_7582,User_Id_2555,Bike,3,Business,21,7,4:03:49 PM,21,7,4:05:09 PM,21,7,4:24:47 PM,21,7,4:29:11 PM,21,7,5:01:49 PM,12,,,-1.2885398000000001,36.767025,-1.2677415,36.83484179999999,Rider_Id_324,1958
-Order_No_930,User_Id_2382,Bike,1,Personal,21,4,12:47:25 PM,21,4,12:56:55 PM,21,4,1:30:04 PM,21,4,1:33:09 PM,21,4,1:48:04 PM,8,22.5,,-1.3188148000000002,36.809247799999994,-1.2736094999999998,36.8011282,Rider_Id_312,895
-Order_No_18022,User_Id_2457,Bike,3,Business,7,2,7:57:43 AM,7,2,7:58:37 AM,7,2,8:21:52 AM,7,2,8:25:54 AM,7,2,9:01:13 AM,19,,,-1.2551895,36.7822034,-1.324753,36.89748,Rider_Id_865,2119
-Order_No_12824,User_Id_1363,Bike,3,Business,1,5,2:16:19 PM,1,5,2:18:28 PM,1,5,3:04:46 PM,1,5,3:05:48 PM,1,5,3:18:20 PM,4,32.0,,-1.3004062,36.829741,-1.2974913000000001,36.805429600000004,Rider_Id_89,752
-Order_No_7270,User_Id_3647,Bike,3,Business,13,4,2:34:16 PM,13,4,2:37:51 PM,13,4,2:41:35 PM,13,4,2:56:19 PM,13,4,3:15:48 PM,4,23.1,,-1.2762743,36.815278899999996,-1.2726389999999999,36.794723,Rider_Id_75,1169
-Order_No_4216,User_Id_1004,Bike,3,Business,21,2,2:11:30 PM,21,2,2:17:45 PM,21,2,2:25:06 PM,21,2,2:40:46 PM,21,2,2:52:14 PM,9,24.9,,-1.3250048,36.869718799999994,-1.333148,36.922453000000004,Rider_Id_404,688
-Order_No_27095,User_Id_2066,Bike,3,Business,6,4,1:04:26 PM,6,4,1:05:58 PM,6,4,1:17:46 PM,6,4,1:19:17 PM,6,4,1:27:13 PM,3,21.9,,-1.316961,36.820337,-1.3044313,36.818504,Rider_Id_844,476
-Order_No_19856,User_Id_111,Bike,3,Business,16,3,2:58:16 PM,16,3,2:59:37 PM,16,3,3:07:17 PM,16,3,3:11:08 PM,16,3,3:32:30 PM,11,26.8,,-1.2551895,36.7822034,-1.3100586,36.850032899999995,Rider_Id_553,1282
-Order_No_25390,User_Id_1687,Bike,3,Business,19,2,4:18:02 PM,19,2,4:24:25 PM,19,2,4:47:26 PM,19,2,5:13:45 PM,19,2,5:37:50 PM,4,28.1,,-1.2871938,36.8195031,-1.3030659,36.8135093,Rider_Id_448,1445
-Order_No_2894,User_Id_1450,Bike,3,Personal,31,5,4:33:45 PM,31,5,4:35:39 PM,31,5,5:18:14 PM,31,5,5:24:04 PM,31,5,5:57:27 PM,6,19.7,,-1.2995135,36.793363299999996,-1.3108386,36.7555239,Rider_Id_447,2003
-Order_No_12914,User_Id_640,Bike,3,Business,12,5,12:14:10 PM,12,5,12:14:58 PM,12,5,12:19:35 PM,12,5,12:21:57 PM,12,5,12:34:26 PM,6,24.5,,-1.304033,36.7848695,-1.2962731,36.811186299999996,Rider_Id_932,749
-Order_No_25979,User_Id_136,Bike,3,Business,2,2,11:03:17 AM,2,2,11:08:47 AM,2,2,11:16:22 AM,2,2,11:18:38 AM,2,2,11:50:43 AM,20,26.1,,-1.3254522,36.721658399999995,-1.2352013000000002,36.7776638,Rider_Id_205,1925
-Order_No_8948,User_Id_2532,Bike,3,Business,13,3,2:06:56 PM,13,3,2:10:53 PM,13,3,2:16:52 PM,13,3,2:20:22 PM,13,3,2:47:57 PM,8,27.3,,-1.2551895,36.7822034,-1.2904049,36.8055154,Rider_Id_411,1655
-Order_No_24550,User_Id_633,Bike,3,Business,5,2,2:10:45 PM,5,2,2:11:11 PM,5,2,2:28:40 PM,5,2,2:32:41 PM,5,2,2:39:37 PM,4,27.6,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_433,416
-Order_No_12048,User_Id_111,Bike,3,Business,31,5,2:20:27 PM,31,5,2:22:36 PM,31,5,2:48:18 PM,31,5,2:52:03 PM,31,5,3:01:42 PM,4,24.6,,-1.2551895,36.7822034,-1.2747247,36.8150014,Rider_Id_490,579
-Order_No_12873,User_Id_632,Bike,1,Personal,9,4,1:56:21 PM,9,4,1:56:33 PM,9,4,1:58:38 PM,9,4,3:20:35 PM,9,4,3:43:19 PM,7,25.8,,-1.2688502000000002,36.8357356,-1.2736094999999998,36.8011282,Rider_Id_633,1364
-Order_No_12147,User_Id_1484,Bike,3,Business,17,4,11:16:42 AM,17,4,11:17:18 AM,17,4,11:26:43 AM,17,4,11:31:59 AM,17,4,11:44:42 AM,1,25.6,,-1.2642719999999998,36.7978791,-1.2571472,36.795063299999995,Rider_Id_428,763
-Order_No_1632,User_Id_496,Bike,3,Business,20,3,12:19:40 PM,20,3,12:21:30 PM,20,3,12:28:17 PM,20,3,12:39:55 PM,20,3,1:23:53 PM,10,20.3,,-1.2551895,36.7822034,-1.3201096,36.8180968,Rider_Id_101,2638
-Order_No_27649,User_Id_391,Bike,3,Business,13,1,3:15:14 PM,13,1,3:20:42 PM,13,1,3:30:47 PM,13,1,4:22:41 PM,13,1,4:22:44 PM,5,,,-1.300921,36.828195,-1.2839729,36.8115146,Rider_Id_843,3
-Order_No_381,User_Id_914,Bike,3,Business,28,4,3:30:08 PM,28,4,3:36:17 PM,28,4,3:51:02 PM,28,4,3:55:18 PM,28,4,4:24:17 PM,7,27.3,,-1.2670213,36.7994482,-1.3023305,36.826051899999996,Rider_Id_99,1739
-Order_No_27223,User_Id_517,Bike,3,Business,29,2,3:07:14 PM,29,2,3:15:01 PM,29,2,3:16:29 PM,29,2,3:29:48 PM,29,2,3:43:08 PM,4,27.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_515,800
-Order_No_1202,User_Id_635,Bike,3,Business,7,2,11:19:24 AM,7,2,11:20:09 AM,7,2,11:38:54 AM,7,2,11:53:18 AM,7,2,12:12:24 PM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_7,1146
-Order_No_13436,User_Id_1724,Bike,3,Business,11,4,9:03:25 AM,11,4,9:04:32 AM,11,4,9:08:56 AM,11,4,9:30:19 AM,11,4,9:42:07 AM,5,22.0,,-1.2571472,36.795063299999995,-1.286067,36.8146155,Rider_Id_953,708
-Order_No_14044,User_Id_1500,Bike,3,Business,26,5,2:31:43 PM,26,5,2:32:21 PM,26,5,2:38:17 PM,26,5,2:50:51 PM,26,5,3:12:54 PM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_193,1323
-Order_No_19120,User_Id_1908,Bike,1,Personal,6,1,4:11:10 PM,6,1,4:11:48 PM,6,1,4:20:28 PM,6,1,4:36:18 PM,6,1,5:17:21 PM,11,22.2,,-1.2877907,36.830065000000005,-1.29241,36.90652,Rider_Id_347,2463
-Order_No_20945,User_Id_3001,Bike,3,Business,7,1,3:58:44 PM,7,1,4:07:19 PM,7,1,4:40:59 PM,7,1,4:48:12 PM,7,1,5:08:26 PM,7,20.0,,-1.2637515,36.78856629999999,-1.2991542999999999,36.7827912,Rider_Id_100,1214
-Order_No_21964,User_Id_868,Bike,3,Business,7,5,10:02:06 AM,7,5,10:02:27 AM,7,5,10:06:16 AM,7,5,10:45:37 AM,7,5,11:07:35 AM,6,,,-1.307143,36.825009,-1.2663631000000002,36.8163329,Rider_Id_591,1318
-Order_No_3782,User_Id_1329,Bike,3,Business,10,3,10:31:21 AM,10,3,10:33:55 AM,10,3,10:55:48 AM,10,3,11:25:58 AM,10,3,11:29:02 AM,12,27.9,,-1.273056,36.811298,-1.3302996,36.8707935,Rider_Id_855,184
-Order_No_9673,User_Id_136,Bike,3,Business,12,3,9:56:56 AM,12,3,9:59:56 AM,12,3,10:06:36 AM,12,3,10:11:19 AM,12,3,11:00:36 AM,14,,,-1.3254522,36.721658399999995,-1.2662138,36.7997481,Rider_Id_647,2957
-Order_No_3905,User_Id_633,Bike,3,Business,14,3,3:13:10 PM,14,3,3:34:51 PM,14,3,3:46:40 PM,14,3,3:54:31 PM,14,3,4:29:09 PM,12,25.5,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_854,2078
-Order_No_14411,User_Id_3201,Bike,3,Business,2,2,12:34:57 PM,2,2,12:55:21 PM,2,2,1:08:01 PM,2,2,1:19:41 PM,2,2,1:35:47 PM,6,20.8,,-1.2891921999999998,36.825189,-1.2649605,36.798177700000004,Rider_Id_532,966
-Order_No_19526,User_Id_393,Bike,3,Business,20,3,3:28:36 PM,20,3,3:28:47 PM,20,3,3:29:12 PM,20,3,3:48:41 PM,20,3,4:14:38 PM,14,29.2,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_15,1557
-Order_No_3435,User_Id_1241,Bike,2,Personal,10,5,12:51:09 PM,10,5,12:52:00 PM,10,5,1:00:59 PM,10,5,1:05:31 PM,10,5,1:41:21 PM,13,19.0,,-1.261842,36.784859999999995,-1.2323371,36.864816999999995,Rider_Id_45,2150
-Order_No_23573,User_Id_111,Bike,3,Business,4,5,1:37:21 PM,4,5,1:37:48 PM,4,5,1:55:51 PM,4,5,2:11:06 PM,4,5,3:08:34 PM,13,,,-1.2551895,36.7822034,-1.3215393999999998,36.836621,Rider_Id_167,3448
-Order_No_17933,User_Id_3064,Bike,3,Business,21,1,2:35:36 PM,21,1,2:36:20 PM,21,1,2:40:01 PM,21,1,2:45:00 PM,21,1,2:54:49 PM,4,27.7,,-1.2571472,36.795063299999995,-1.245477,36.785037,Rider_Id_205,589
-Order_No_9691,User_Id_1207,Bike,3,Business,23,6,12:04:50 PM,23,6,12:05:25 PM,23,6,12:07:51 PM,23,6,12:13:46 PM,23,6,12:42:12 PM,13,,,-1.2629666000000002,36.8068369,-1.2156006000000001,36.8916865,Rider_Id_119,1706
-Order_No_22170,User_Id_393,Bike,3,Business,28,4,10:29:30 AM,28,4,10:31:38 AM,28,4,10:31:42 AM,28,4,11:14:54 AM,28,4,11:14:56 AM,6,28.2,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_515,2
-Order_No_16015,User_Id_2858,Bike,3,Business,17,3,7:56:27 AM,17,3,7:57:18 AM,17,3,8:36:35 AM,17,3,9:12:14 AM,17,3,9:39:23 AM,16,,,-1.2884243,36.820724799999994,-1.3164106000000002,36.6952197,Rider_Id_99,1629
-Order_No_14424,User_Id_2801,Bike,3,Business,14,2,1:05:28 PM,14,2,1:06:49 PM,14,2,1:31:21 PM,14,2,1:32:29 PM,14,2,1:52:07 PM,8,,,-1.289081,36.8300409,-1.2674273999999999,36.787082700000006,Rider_Id_276,1178
-Order_No_4222,User_Id_2454,Bike,3,Business,10,3,2:00:54 PM,10,3,2:15:11 PM,10,3,2:22:39 PM,10,3,2:25:05 PM,10,3,2:30:46 PM,2,30.5,,-1.2941413,36.7958937,-1.2985749,36.8088,Rider_Id_783,341
-Order_No_23305,User_Id_259,Bike,3,Personal,7,4,12:36:06 PM,7,4,2:24:10 PM,7,4,2:30:12 PM,7,4,2:34:50 PM,7,4,3:42:02 PM,6,20.3,,-1.304033,36.7848695,-1.3066143,36.8161046,Rider_Id_312,4032
-Order_No_2141,User_Id_2956,Bike,3,Business,5,2,1:19:57 PM,5,2,1:20:31 PM,5,2,1:20:55 PM,5,2,1:37:38 PM,5,2,1:43:12 PM,1,22.6,,-1.2584143,36.8048002,-1.2601708999999999,36.8002238,Rider_Id_379,334
-Order_No_14652,User_Id_467,Bike,3,Business,30,4,4:11:38 PM,30,4,4:12:25 PM,30,4,4:27:09 PM,30,4,4:31:21 PM,30,4,4:40:09 PM,2,,,-1.304735,36.784605400000004,-1.2977326,36.7711606,Rider_Id_158,528
-Order_No_14770,User_Id_3560,Bike,3,Business,14,4,1:17:09 PM,14,4,1:19:42 PM,14,4,1:34:25 PM,14,4,1:37:04 PM,14,4,2:04:44 PM,7,27.4,,-1.279395,36.825364,-1.3222535,36.8246176,Rider_Id_291,1660
-Order_No_14116,User_Id_3686,Bike,3,Business,18,4,2:38:03 PM,18,4,2:38:45 PM,18,4,2:43:15 PM,18,4,2:48:56 PM,18,4,3:22:28 PM,11,26.9,,-1.2551895,36.7822034,-1.2217966999999998,36.810990000000004,Rider_Id_770,2012
-Order_No_3991,User_Id_2989,Bike,3,Business,24,1,2:59:33 PM,24,1,3:09:56 PM,24,1,3:56:25 PM,24,1,4:05:31 PM,24,1,4:27:17 PM,15,24.8,,-1.3472066,36.7692638,-1.2591018999999999,36.800577000000004,Rider_Id_925,1306
-Order_No_11837,User_Id_188,Bike,1,Personal,24,5,12:11:54 PM,24,5,12:13:05 PM,24,5,12:21:25 PM,24,5,12:31:54 PM,24,5,12:44:09 PM,5,21.1,,-1.3078623999999999,36.7697658,-1.2994771999999999,36.804825,Rider_Id_805,735
-Order_No_17765,User_Id_3404,Bike,3,Business,2,5,2:22:02 PM,2,5,3:52:33 PM,2,5,3:58:37 PM,2,5,3:59:37 PM,2,5,4:27:29 PM,11,25.0,,-1.279395,36.825364,-1.3104733999999998,36.7363698,Rider_Id_878,1672
-Order_No_4156,User_Id_533,Bike,3,Business,6,3,11:11:24 AM,6,3,11:17:21 AM,6,3,11:33:35 AM,6,3,11:53:45 AM,6,3,12:41:00 PM,8,22.6,,-1.2765492,36.7669809,-1.2811503,36.8235884,Rider_Id_277,2835
-Order_No_22912,User_Id_3149,Bike,3,Business,26,5,2:52:37 PM,26,5,2:59:56 PM,26,5,3:02:25 PM,26,5,3:28:36 PM,26,5,3:35:01 PM,5,,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_97,385
-Order_No_9358,User_Id_1575,Bike,1,Personal,4,4,10:44:27 AM,4,4,10:44:38 AM,4,4,10:51:12 AM,4,4,11:13:27 AM,4,4,11:29:25 AM,12,,,-1.2682178999999998,36.8009289,-1.2646374,36.723567100000004,Rider_Id_785,958
-Order_No_22770,User_Id_2330,Bike,3,Business,22,3,5:23:19 PM,22,3,5:24:02 PM,22,3,5:33:48 PM,22,3,5:48:46 PM,22,3,6:12:23 PM,10,24.2,,-1.3339536,36.7181,-1.294597,36.7742999,Rider_Id_362,1417
-Order_No_18712,User_Id_1754,Bike,3,Business,15,5,9:27:37 AM,15,5,9:27:51 AM,15,5,9:45:24 AM,15,5,9:52:01 AM,15,5,10:11:07 AM,12,19.2,,-1.2283403,36.8822756,-1.2860183,36.8975338,Rider_Id_8,1146
-Order_No_24337,User_Id_2701,Bike,3,Business,4,4,10:58:31 AM,4,4,11:15:19 AM,4,4,11:22:06 AM,4,4,11:45:24 AM,4,4,11:51:25 AM,7,,,-1.2765736,36.851364600000004,-1.3218497999999999,36.8615286,Rider_Id_137,361
-Order_No_15799,User_Id_2828,Bike,3,Business,16,1,10:09:40 AM,16,1,10:09:57 AM,16,1,10:34:24 AM,16,1,10:38:59 AM,16,1,10:58:53 AM,7,19.1,,-1.2966453000000002,36.8191342,-1.2615888000000002,36.7928732,Rider_Id_764,1194
-Order_No_24660,User_Id_393,Bike,3,Business,13,3,9:30:56 AM,13,3,11:09:04 AM,13,3,11:19:40 AM,13,3,12:45:05 PM,13,3,12:46:40 PM,10,19.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_333,95
-Order_No_8993,User_Id_1271,Bike,3,Business,16,4,9:44:54 AM,16,4,9:45:11 AM,16,4,9:58:00 AM,16,4,10:08:31 AM,16,4,11:16:59 AM,17,15.6,,-1.3244885,36.897791999999995,-1.2699441999999999,36.8206553,Rider_Id_213,4108
-Order_No_25448,User_Id_3565,Bike,3,Business,3,7,10:56:39 AM,3,7,10:56:50 AM,3,7,11:03:03 AM,3,7,11:05:29 AM,3,7,11:38:16 AM,18,27.8,,-1.2844665000000002,36.7866113,-1.207546,36.7913976,Rider_Id_368,1967
-Order_No_14357,User_Id_71,Bike,3,Business,26,1,4:45:18 PM,26,1,5:04:18 PM,26,1,5:15:11 PM,26,1,5:23:53 PM,26,1,5:35:06 PM,2,20.0,,-1.2793686000000002,36.82592329999999,-1.2891921999999998,36.825189,Rider_Id_268,673
-Order_No_8791,User_Id_2150,Bike,3,Business,3,5,2:25:11 PM,3,5,2:52:29 PM,3,5,3:03:30 PM,3,5,4:00:14 PM,3,5,4:12:19 PM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_459,725
-Order_No_1812,User_Id_598,Bike,1,Business,20,1,4:03:57 PM,20,1,4:07:20 PM,20,1,4:14:37 PM,20,1,4:18:31 PM,20,1,4:26:06 PM,2,21.6,,-1.2536797,36.7995085,-1.255392,36.792539,Rider_Id_882,455
-Order_No_15351,User_Id_640,Bike,3,Business,16,2,4:13:24 PM,16,2,4:15:45 PM,16,2,4:27:47 PM,16,2,4:32:07 PM,16,2,4:47:50 PM,2,,,-1.289809,36.7938311,-1.2969741000000001,36.7856612,Rider_Id_245,943
-Order_No_9648,User_Id_776,Bike,1,Personal,1,7,10:04:22 AM,1,7,10:30:12 AM,1,7,10:30:29 AM,1,7,11:07:51 AM,1,7,12:11:15 PM,11,20.3,,-1.278967,36.8236745,-1.2711571000000002,36.901728799999994,Rider_Id_805,3804
-Order_No_14840,User_Id_3550,Bike,1,Personal,9,2,4:00:56 PM,9,2,4:02:07 PM,9,2,4:34:18 PM,9,2,4:36:17 PM,9,2,4:43:11 PM,2,19.2,,-1.286797,36.773786,-1.2998201999999999,36.764399700000006,Rider_Id_565,414
-Order_No_13557,User_Id_1527,Bike,3,Business,29,3,10:15:43 AM,29,3,10:15:56 AM,29,3,10:26:48 AM,29,3,10:31:32 AM,29,3,11:02:36 AM,6,18.0,,-1.3004062,36.829741,-1.3267168,36.8050359,Rider_Id_255,1864
-Order_No_15729,User_Id_393,Bike,3,Business,9,3,12:18:05 PM,9,3,12:18:15 PM,9,3,12:20:34 PM,9,3,12:28:56 PM,9,3,12:39:58 PM,4,25.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,662
-Order_No_5226,User_Id_2154,Bike,3,Business,16,3,4:33:40 PM,16,3,4:34:15 PM,16,3,4:44:38 PM,16,3,4:51:55 PM,16,3,5:11:43 PM,6,23.2,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_31,1188
-Order_No_26438,User_Id_1502,Bike,3,Business,6,1,3:01:12 PM,6,1,3:01:54 PM,6,1,3:20:34 PM,6,1,3:40:27 PM,6,1,4:02:06 PM,17,,,-1.2232123,36.8932547,-1.2571472,36.795063299999995,Rider_Id_8,1299
-Order_No_14833,User_Id_393,Bike,3,Business,1,1,1:41:45 PM,1,1,1:42:53 PM,1,1,1:42:57 PM,1,1,2:15:30 PM,1,1,2:37:46 PM,10,25.5,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_555,1336
-Order_No_18531,User_Id_3291,Bike,3,Business,24,4,1:45:52 PM,24,4,1:46:55 PM,24,4,1:53:33 PM,24,4,2:07:28 PM,24,4,2:07:35 PM,2,28.1,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_4,7
-Order_No_7159,User_Id_2412,Bike,3,Business,27,2,2:12:20 PM,27,2,2:12:48 PM,27,2,2:40:01 PM,27,2,2:48:34 PM,27,2,2:57:10 PM,5,25.5,,-1.2156006000000001,36.8916865,-1.2297202,36.881950700000004,Rider_Id_358,516
-Order_No_2702,User_Id_1225,Bike,2,Personal,17,2,8:39:43 AM,17,2,9:18:56 AM,17,2,9:36:38 AM,17,2,9:43:32 AM,17,2,10:48:27 AM,23,14.6,,-1.3700383,36.9190174,-1.297651,36.791839200000005,Rider_Id_668,3895
-Order_No_8377,User_Id_3697,Bike,3,Personal,2,2,12:12:25 PM,2,2,12:14:02 PM,2,2,12:26:04 PM,2,2,12:55:11 PM,2,2,1:16:03 PM,6,26.2,,-1.2845871000000002,36.8234834,-1.2575555,36.798705299999995,Rider_Id_119,1252
-Order_No_710,User_Id_1502,Bike,3,Business,13,7,10:58:08 AM,13,7,11:14:05 AM,13,7,11:25:43 AM,13,7,11:36:13 AM,13,7,12:06:57 PM,14,23.8,,-1.225322,36.80855,-1.2237773,36.8861711,Rider_Id_101,1844
-Order_No_15621,User_Id_1398,Bike,3,Business,8,5,2:06:34 PM,8,5,2:17:56 PM,8,5,2:42:42 PM,8,5,2:54:37 PM,8,5,4:09:16 PM,5,27.1,,-1.2615584,36.814950599999996,-1.2788306,36.808170700000005,Rider_Id_404,4479
-Order_No_26408,User_Id_1363,Bike,3,Business,4,1,4:05:36 PM,4,1,4:07:32 PM,4,1,4:16:04 PM,4,1,4:19:38 PM,4,1,4:39:27 PM,4,28.5,,-1.3004062,36.829741,-1.2902869,36.8085396,Rider_Id_668,1189
-Order_No_26787,User_Id_1578,Bike,3,Business,24,4,3:38:30 PM,24,4,4:27:26 PM,24,4,4:35:48 PM,24,4,4:44:19 PM,24,4,5:56:08 PM,7,25.8,,-1.28878,36.8168312,-1.3083495,36.840715,Rider_Id_34,4309
-Order_No_12372,User_Id_3525,Bike,3,Business,7,5,10:15:21 AM,7,5,10:15:37 AM,7,5,10:53:10 AM,7,5,10:59:25 AM,7,5,12:14:05 PM,18,25.2,,-1.2038216999999998,36.87945070000001,-1.2571472,36.795063299999995,Rider_Id_812,4480
-Order_No_12049,User_Id_393,Bike,3,Business,26,2,12:33:32 PM,26,2,12:34:59 PM,26,2,12:35:03 PM,26,2,12:40:01 PM,26,2,12:57:03 PM,3,26.3,,-1.3167113000000001,36.8301563,-1.3210541,36.8169608,Rider_Id_901,1022
-Order_No_24578,User_Id_633,Bike,3,Business,8,5,2:49:01 PM,8,5,2:55:56 PM,8,5,3:17:36 PM,8,5,3:28:10 PM,8,5,3:37:34 PM,4,27.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_114,564
-Order_No_23110,User_Id_3372,Bike,3,Business,10,5,4:31:17 PM,10,5,4:31:47 PM,10,5,4:45:21 PM,10,5,4:53:46 PM,10,5,5:57:08 PM,26,21.3,,-1.2143386999999999,36.7914548,-1.3169591,36.7000029,Rider_Id_144,3802
-Order_No_18687,User_Id_2005,Bike,3,Business,11,6,2:55:28 PM,11,6,2:56:01 PM,11,6,3:07:37 PM,11,6,3:19:31 PM,11,6,3:32:50 PM,7,21.6,,-1.2872996,36.8310969,-1.3043649,36.7816132,Rider_Id_229,799
-Order_No_18907,User_Id_1594,Bike,3,Business,10,5,12:28:37 PM,10,5,12:30:27 PM,10,5,12:44:40 PM,10,5,12:55:11 PM,10,5,1:20:56 PM,9,,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_607,1545
-Order_No_20213,User_Id_2740,Bike,3,Personal,15,2,10:37:35 AM,15,2,10:38:50 AM,15,2,10:45:13 AM,15,2,10:52:09 AM,15,2,11:09:01 AM,7,24.3,,-1.3127303999999997,36.8189817,-1.2944292,36.779356899999996,Rider_Id_237,1012
-Order_No_11491,User_Id_1863,Bike,3,Business,3,3,7:19:21 PM,3,3,7:19:40 PM,3,3,7:36:52 PM,3,3,7:47:36 PM,3,3,7:53:47 PM,2,19.4,,-1.259488,36.800101,-1.2627937,36.815695700000006,Rider_Id_407,371
-Order_No_7275,User_Id_3381,Bike,3,Business,1,4,5:00:36 PM,1,4,5:19:19 PM,1,4,5:37:22 PM,1,4,5:43:18 PM,1,4,6:01:43 PM,5,,,-1.279662,36.814902000000004,-1.3172551000000001,36.8208701,Rider_Id_554,1105
-Order_No_19758,User_Id_3404,Bike,3,Business,24,3,1:03:21 PM,24,3,1:26:12 PM,24,3,1:33:10 PM,24,3,1:50:56 PM,24,3,2:09:37 PM,8,18.6,,-1.279395,36.825364,-1.3196808999999998,36.8410572,Rider_Id_237,1121
-Order_No_9823,User_Id_1796,Bike,3,Business,6,3,11:23:44 AM,6,3,11:24:03 AM,6,3,11:25:47 AM,6,3,12:06:10 PM,6,3,12:17:27 PM,5,22.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_26,677
-Order_No_1416,User_Id_1502,Bike,3,Business,19,1,5:44:12 PM,19,1,5:45:50 PM,19,1,6:00:36 PM,19,1,6:06:19 PM,19,1,6:24:23 PM,13,23.5,,-1.2584143,36.8048002,-1.2232123,36.8932547,Rider_Id_932,1084
-Order_No_8857,User_Id_2030,Bike,3,Business,5,5,10:08:21 AM,5,5,10:09:47 AM,5,5,10:40:27 AM,5,5,10:41:43 AM,5,5,11:30:47 AM,13,24.4,,-1.2963097,36.7688221,-1.3263105,36.8622904,Rider_Id_599,2944
-Order_No_20972,User_Id_2069,Bike,3,Business,9,3,10:44:46 AM,9,3,10:44:58 AM,9,3,10:45:26 AM,9,3,10:52:49 AM,9,3,11:21:56 AM,10,25.2,,-1.228911,36.881876,-1.272828,36.816608,Rider_Id_178,1747
-Order_No_6867,User_Id_2088,Bike,1,Personal,29,5,2:08:20 PM,29,5,2:18:21 PM,29,5,2:26:38 PM,29,5,2:37:15 PM,29,5,2:50:42 PM,3,21.1,,-1.2884997,36.8183895,-1.2966535,36.8123136,Rider_Id_212,807
-Order_No_4681,User_Id_295,Bike,3,Business,26,5,11:28:24 AM,26,5,1:05:39 PM,26,5,1:06:53 PM,26,5,1:33:25 PM,26,5,1:55:00 PM,6,,,-1.2967108,36.787113500000004,-1.2868598999999998,36.821542,Rider_Id_330,1295
-Order_No_18004,User_Id_868,Bike,3,Business,22,6,2:27:34 PM,22,6,2:28:24 PM,22,6,2:43:03 PM,22,6,2:52:15 PM,22,6,3:04:02 PM,4,25.9,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_427,707
-Order_No_12266,User_Id_2789,Bike,3,Business,3,1,12:30:59 PM,3,1,1:02:20 PM,3,1,1:27:19 PM,3,1,1:57:07 PM,3,1,2:39:35 PM,6,23.4,,-1.2890399,36.8164942,-1.304735,36.784605400000004,Rider_Id_783,2548
-Order_No_7790,User_Id_2521,Bike,3,Business,26,6,10:33:34 AM,26,6,10:34:21 AM,26,6,10:45:18 AM,26,6,10:48:24 AM,26,6,11:17:16 AM,14,26.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1732
-Order_No_10725,User_Id_2330,Bike,3,Business,31,5,2:08:57 PM,31,5,2:14:56 PM,31,5,2:57:40 PM,31,5,3:17:01 PM,31,5,4:39:29 PM,19,24.6,,-1.2524916000000001,36.8656196,-1.3015197,36.7658456,Rider_Id_42,4948
-Order_No_20002,User_Id_3647,Bike,3,Business,25,4,9:09:32 AM,25,4,9:09:58 AM,25,4,9:18:48 AM,25,4,9:24:31 AM,25,4,9:39:01 AM,4,,,-1.2726389999999999,36.794723,-1.2674482,36.8076994,Rider_Id_306,870
-Order_No_1698,User_Id_1705,Bike,3,Business,10,4,1:40:58 PM,10,4,1:42:40 PM,10,4,1:46:53 PM,10,4,1:56:12 PM,10,4,2:13:42 PM,7,25.1,,-1.2571472,36.795063299999995,-1.2404104,36.8104486,Rider_Id_268,1050
-Order_No_21666,User_Id_3621,Bike,3,Business,26,6,7:40:18 AM,26,6,7:40:59 AM,26,6,7:53:48 AM,26,6,7:59:53 AM,26,6,8:30:31 AM,13,20.3,,-1.2836583000000001,36.7886014,-1.2987419,36.8740842,Rider_Id_31,1838
-Order_No_12575,User_Id_2457,Bike,3,Business,19,4,3:22:26 PM,19,4,3:22:35 PM,19,4,3:23:38 PM,19,4,3:29:36 PM,19,4,3:41:35 PM,4,20.5,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_726,719
-Order_No_13483,User_Id_1620,Bike,1,Personal,28,6,8:42:13 AM,28,6,8:42:43 AM,28,6,9:00:55 AM,28,6,9:14:00 AM,28,6,9:34:05 AM,12,17.7,,-1.235348,36.760016,-1.227132,36.8374277,Rider_Id_103,1205
-Order_No_15348,User_Id_3635,Bike,1,Business,25,6,12:15:11 PM,25,6,12:15:53 PM,25,6,12:21:16 PM,25,6,12:35:27 PM,25,6,12:44:15 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_770,528
-Order_No_27799,User_Id_2866,Bike,3,Business,7,5,4:13:42 PM,7,5,4:19:47 PM,7,5,4:49:37 PM,7,5,4:53:50 PM,7,5,6:00:43 PM,23,21.4,,-1.2551895,36.7822034,-1.3448483,36.9004081,Rider_Id_831,4013
-Order_No_10230,User_Id_868,Bike,3,Business,29,3,5:10:01 PM,29,3,5:11:37 PM,29,3,5:15:53 PM,29,3,5:33:12 PM,29,3,5:48:01 PM,7,21.1,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_576,889
-Order_No_12152,User_Id_962,Bike,3,Personal,14,4,4:24:27 PM,14,4,4:25:02 PM,14,4,4:39:23 PM,14,4,4:48:23 PM,14,4,5:25:45 PM,16,26.6,,-1.3391863000000002,36.7689931,-1.2658941000000001,36.7966665,Rider_Id_623,2242
-Order_No_9062,User_Id_3291,Bike,3,Business,21,5,10:42:43 AM,21,5,10:51:03 AM,21,5,10:52:57 AM,21,5,11:14:04 AM,21,5,11:33:49 AM,11,22.8,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_449,1185
-Order_No_4888,User_Id_3496,Bike,3,Business,31,2,3:14:01 PM,31,2,3:14:08 PM,31,2,3:14:16 PM,31,2,3:49:52 PM,31,2,4:02:37 PM,13,22.3,,-1.332175,36.8819641,-1.300921,36.828195,Rider_Id_813,765
-Order_No_26823,User_Id_3647,Bike,3,Business,7,4,11:55:54 AM,7,4,11:57:18 AM,7,4,12:07:30 PM,7,4,1:07:43 PM,7,4,1:08:06 PM,12,27.7,,-1.2726389999999999,36.794723,-1.3065271999999999,36.86049860000001,Rider_Id_436,23
-Order_No_11926,User_Id_111,Bike,3,Business,16,2,3:05:44 PM,16,2,3:06:22 PM,16,2,3:10:41 PM,16,2,3:12:23 PM,16,2,3:33:05 PM,3,,,-1.2551895,36.7822034,-1.2648164,36.8057234,Rider_Id_570,1242
-Order_No_6942,User_Id_20,Bike,3,Business,12,1,12:07:19 PM,12,1,12:07:59 PM,12,1,12:15:45 PM,12,1,12:22:31 PM,12,1,12:47:33 PM,18,23.6,,-1.2628638,36.8070399,-1.3244885,36.897791999999995,Rider_Id_501,1502
-Order_No_15222,User_Id_809,Bike,1,Personal,16,4,7:52:03 AM,16,4,7:52:21 AM,16,4,7:57:58 AM,16,4,8:08:33 AM,16,4,8:17:50 AM,4,15.3,,-1.2661263999999999,36.8030359,-1.2815072,36.82096,Rider_Id_857,557
-Order_No_16519,User_Id_1850,Bike,3,Business,5,2,4:02:08 PM,5,2,4:04:09 PM,5,2,4:10:37 PM,5,2,4:12:19 PM,5,2,4:37:02 PM,7,27.3,,-1.2551895,36.7822034,-1.2824744,36.804724,Rider_Id_359,1483
-Order_No_24913,User_Id_2509,Bike,3,Business,24,1,12:56:20 PM,24,1,12:56:27 PM,24,1,12:59:42 PM,24,1,1:44:11 PM,24,1,2:22:19 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_290,2288
-Order_No_9432,User_Id_2884,Bike,2,Personal,6,3,4:57:08 PM,6,3,4:58:21 PM,6,3,5:14:32 PM,6,3,5:34:46 PM,6,3,5:50:53 PM,3,19.9,,-1.2873232,36.765857200000006,-1.2764692,36.758011100000004,Rider_Id_499,967
-Order_No_22568,User_Id_3283,Bike,3,Business,9,4,12:37:22 PM,9,4,12:39:01 PM,9,4,1:01:06 PM,9,4,1:22:06 PM,9,4,1:36:17 PM,5,23.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_700,851
-Order_No_21358,User_Id_3599,Bike,3,Business,27,1,12:51:26 PM,27,1,12:53:29 PM,27,1,1:04:01 PM,27,1,1:55:52 PM,27,1,2:20:32 PM,13,23.3,,-1.3302996,36.8707935,-1.3189908000000001,36.777074,Rider_Id_211,1480
-Order_No_12854,User_Id_2216,Bike,3,Business,12,2,9:18:37 AM,12,2,9:18:54 AM,12,2,9:23:53 AM,12,2,9:44:13 AM,12,2,10:50:48 AM,19,20.9,,-1.2551895,36.7822034,-1.3253021999999999,36.8803995,Rider_Id_690,3995
-Order_No_25660,User_Id_2283,Bike,2,Personal,15,6,5:16:45 PM,15,6,5:17:50 PM,15,6,5:35:12 PM,15,6,5:40:49 PM,15,6,5:49:43 PM,3,25.0,,-1.2661958999999998,36.807544,-1.2696341,36.791664000000004,Rider_Id_622,534
-Order_No_3878,User_Id_830,Bike,3,Business,25,1,11:10:38 AM,25,1,11:10:55 AM,25,1,11:24:53 AM,25,1,11:31:06 AM,25,1,11:31:12 AM,8,25.3,,-1.2551895,36.7822034,-1.271672,36.783604,Rider_Id_56,6
-Order_No_490,User_Id_529,Bike,2,Personal,18,3,1:28:44 PM,18,3,1:28:57 PM,18,3,1:35:05 PM,18,3,1:42:11 PM,18,3,1:54:07 PM,4,20.8,,-1.2709603999999999,36.8162138,-1.2676163999999999,36.8075243,Rider_Id_844,716
-Order_No_3233,User_Id_3599,Bike,3,Business,10,3,9:21:27 AM,10,3,9:28:05 AM,10,3,10:16:46 AM,10,3,10:26:57 AM,10,3,11:26:52 AM,14,21.9,,-1.3302996,36.8707935,-1.2850523,36.7859591,Rider_Id_448,3595
-Order_No_6393,User_Id_2137,Bike,1,Personal,28,2,3:36:16 PM,28,2,3:36:49 PM,28,2,3:43:17 PM,28,2,3:47:43 PM,28,2,4:09:28 PM,8,19.4,,-1.2661263999999999,36.8030359,-1.276902,36.76611,Rider_Id_334,1305
-Order_No_18649,User_Id_510,Bike,3,Business,9,1,3:18:49 PM,9,1,3:18:53 PM,9,1,3:22:32 PM,9,1,3:50:33 PM,9,1,4:42:02 PM,19,20.9,,-1.2551895,36.7822034,-1.2860183,36.8975338,Rider_Id_951,3089
-Order_No_2819,User_Id_2801,Bike,3,Business,20,3,11:07:30 AM,20,3,11:08:13 AM,20,3,11:09:53 AM,20,3,11:15:55 AM,20,3,11:52:44 AM,9,26.6,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_518,2209
-Order_No_13763,User_Id_2102,Bike,3,Business,28,4,4:27:47 PM,28,4,4:28:26 PM,28,4,4:41:52 PM,28,4,4:45:51 PM,28,4,5:28:15 PM,15,27.9,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_548,2544
-Order_No_5480,User_Id_2216,Bike,3,Business,18,4,3:15:26 PM,18,4,3:15:39 PM,18,4,3:16:57 PM,18,4,3:27:05 PM,18,4,3:27:13 PM,4,,,-1.2551895,36.7822034,-1.2566688999999998,36.779695200000006,Rider_Id_99,8
-Order_No_24107,User_Id_1527,Bike,3,Business,4,4,10:15:52 AM,4,4,10:16:55 AM,4,4,10:16:56 AM,4,4,10:25:35 AM,4,4,10:38:40 AM,4,21.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_943,785
-Order_No_8313,User_Id_477,Bike,1,Personal,18,1,4:17:47 PM,18,1,5:26:18 PM,18,1,5:39:46 PM,18,1,5:44:17 PM,18,1,6:49:41 PM,24,,,-1.3308191,36.9019697,-1.2358386000000001,36.767204,Rider_Id_584,3924
-Order_No_757,User_Id_391,Bike,3,Business,10,5,1:52:35 PM,10,5,1:56:42 PM,10,5,2:10:24 PM,10,5,2:27:24 PM,10,5,2:51:58 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_709,1474
-Order_No_10181,User_Id_1081,Bike,3,Business,18,1,12:42:55 PM,18,1,12:43:21 PM,18,1,12:56:26 PM,18,1,1:07:36 PM,18,1,1:50:29 PM,9,26.9,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_448,2573
-Order_No_14063,User_Id_391,Bike,3,Business,13,1,10:41:50 AM,13,1,11:16:36 AM,13,1,11:28:01 AM,13,1,11:39:47 AM,13,1,12:12:44 PM,6,17.4,,-1.3223238000000002,36.8320056,-1.2857573999999998,36.8295667,Rider_Id_213,1977
-Order_No_12423,User_Id_1271,Bike,3,Business,23,1,11:20:47 AM,23,1,11:21:17 AM,23,1,11:29:26 AM,23,1,11:38:00 AM,23,1,12:01:32 PM,18,18.3,,-1.2591018999999999,36.800577000000004,-1.3244885,36.897791999999995,Rider_Id_103,1412
-Order_No_9604,User_Id_20,Bike,3,Business,22,4,11:22:28 AM,22,4,11:30:11 AM,22,4,11:37:29 AM,22,4,11:49:57 AM,22,4,12:11:45 PM,9,22.4,,-1.2648363,36.802375399999995,-1.3125593,36.8430941,Rider_Id_878,1308
-Order_No_27284,User_Id_2030,Bike,3,Business,22,2,12:03:56 PM,22,2,12:09:14 PM,22,2,12:23:29 PM,22,2,12:28:21 PM,22,2,12:40:07 PM,3,24.2,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_862,706
-Order_No_22429,User_Id_2370,Bike,2,Personal,26,6,11:38:57 AM,26,6,11:39:44 AM,26,6,11:58:31 AM,26,6,12:09:41 PM,26,6,1:40:04 PM,16,22.4,,-1.3023387,36.9066847,-1.2840461,36.7934021,Rider_Id_618,5423
-Order_No_12557,User_Id_3547,Bike,3,Business,10,1,11:31:02 AM,10,1,11:32:06 AM,10,1,12:12:52 PM,10,1,12:18:52 PM,10,1,12:37:57 PM,9,18.5,,-1.3285517,36.9018,-1.3194518,36.869396,Rider_Id_567,1145
-Order_No_12567,User_Id_2956,Bike,3,Business,9,4,8:47:56 AM,9,4,8:48:42 AM,9,4,8:56:04 AM,9,4,9:14:42 AM,9,4,9:33:38 AM,2,,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_720,1136
-Order_No_11409,User_Id_2582,Bike,3,Business,17,5,8:09:14 AM,17,5,8:09:32 AM,17,5,8:09:49 AM,17,5,9:09:44 AM,17,5,9:33:07 AM,24,,,-1.3343128999999998,36.885417499999996,-1.2551895,36.7822034,Rider_Id_746,1403
-Order_No_15942,User_Id_2601,Bike,1,Personal,1,1,8:14:31 AM,1,1,8:15:25 AM,1,1,8:43:53 AM,1,1,8:51:03 AM,1,1,8:58:13 AM,3,18.6,,-1.300334,36.797495700000006,-1.2963097,36.7688221,Rider_Id_726,430
-Order_No_17660,User_Id_2828,Bike,1,Business,4,1,2:20:18 PM,4,1,2:33:07 PM,4,1,2:48:13 PM,4,1,3:03:56 PM,4,1,3:28:09 PM,14,22.7,,-1.3207349,36.8819157,-1.2909665000000001,36.8150406,Rider_Id_789,1453
-Order_No_9467,User_Id_3647,Bike,3,Business,20,1,10:04:30 AM,20,1,10:05:42 AM,20,1,10:21:13 AM,20,1,10:29:09 AM,20,1,10:46:42 AM,8,19.1,,-1.2726389999999999,36.794723,-1.2575690000000002,36.842164000000004,Rider_Id_829,1053
-Order_No_1957,User_Id_3686,Bike,3,Business,2,4,10:57:05 AM,2,4,10:57:39 AM,2,4,11:00:46 AM,2,4,11:04:13 AM,2,4,11:18:36 AM,3,,,-1.2551895,36.7822034,-1.2672588,36.8000252,Rider_Id_695,863
-Order_No_24521,User_Id_2030,Bike,3,Business,21,2,2:25:57 PM,21,2,2:26:40 PM,21,2,2:37:32 PM,21,2,2:40:06 PM,21,2,2:48:23 PM,2,24.8,,-1.2839651,36.789170399999996,-1.295908,36.791433000000005,Rider_Id_277,497
-Order_No_13218,User_Id_186,Bike,3,Business,7,5,3:46:54 PM,7,5,4:13:16 PM,7,5,4:13:19 PM,7,5,4:32:21 PM,7,5,5:04:16 PM,4,21.4,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_19,1915
-Order_No_16468,User_Id_3521,Bike,3,Business,4,4,9:06:48 AM,4,4,9:07:04 AM,4,4,9:18:23 AM,4,4,9:22:43 AM,4,4,9:50:08 AM,8,21.8,,-1.3031971000000002,36.786314600000004,-1.2696908000000002,36.81519,Rider_Id_548,1645
-Order_No_9466,User_Id_2183,Bike,3,Business,6,2,12:36:10 PM,6,2,12:37:05 PM,6,2,12:48:15 PM,6,2,12:55:27 PM,6,2,1:17:52 PM,6,24.9,,-1.2551895,36.7822034,-1.2825816,36.8092346,Rider_Id_503,1345
-Order_No_6480,User_Id_2112,Bike,3,Business,20,7,3:11:40 PM,20,7,3:34:59 PM,20,7,3:45:28 PM,20,7,3:47:59 PM,20,7,4:30:21 PM,16,20.3,,-1.3052491000000002,36.8223899,-1.2156006000000001,36.8916865,Rider_Id_581,2542
-Order_No_14516,User_Id_2112,Bike,3,Business,29,4,3:50:03 PM,29,4,3:50:46 PM,29,4,4:08:39 PM,29,4,4:15:44 PM,29,4,4:43:47 PM,15,24.8,,-1.3277669,36.8975169,-1.2156006000000001,36.8916865,Rider_Id_675,1683
-Order_No_17184,User_Id_1280,Bike,3,Business,20,3,3:45:42 PM,20,3,3:46:02 PM,20,3,3:46:28 PM,20,3,3:50:04 PM,20,3,4:03:51 PM,4,29.4,,-1.2551895,36.7822034,-1.2768326,36.8073804,Rider_Id_576,827
-Order_No_24954,User_Id_1869,Bike,3,Personal,27,1,11:49:43 AM,27,1,11:50:41 AM,27,1,11:56:46 AM,27,1,12:20:32 PM,27,1,12:37:23 PM,7,23.9,,-1.3039695,36.783826700000006,-1.3291184,36.7892374,Rider_Id_749,1011
-Order_No_7960,User_Id_3291,Bike,3,Business,11,6,3:20:17 PM,11,6,3:20:53 PM,11,6,3:34:18 PM,11,6,3:38:29 PM,11,6,3:49:54 PM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_256,685
-Order_No_20532,User_Id_2000,Bike,3,Business,21,4,3:03:02 PM,21,4,3:03:29 PM,21,4,3:03:46 PM,21,4,3:11:48 PM,21,4,3:29:32 PM,8,31.1,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_576,1064
-Order_No_7001,User_Id_893,Bike,3,Business,3,1,10:57:40 AM,3,1,10:59:25 AM,3,1,11:06:54 AM,3,1,11:09:50 AM,3,1,11:30:42 AM,7,22.3,,-1.2594473999999998,36.8089122,-1.2847316,36.7658086,Rider_Id_147,1252
-Order_No_19656,User_Id_2866,Bike,3,Business,14,3,2:33:37 PM,14,3,2:34:25 PM,14,3,2:43:22 PM,14,3,2:47:04 PM,14,3,3:32:20 PM,26,25.8,,-1.2551895,36.7822034,-1.3916458999999999,36.93058920000001,Rider_Id_353,2716
-Order_No_17041,User_Id_688,Bike,3,Personal,22,2,2:16:31 PM,22,2,2:22:24 PM,22,2,2:32:32 PM,22,2,2:50:07 PM,22,2,3:11:33 PM,6,21.5,,-1.299313,36.78314879999999,-1.2701356000000001,36.769429200000005,Rider_Id_898,1286
-Order_No_25084,User_Id_3080,Bike,3,Business,26,5,10:59:27 AM,26,5,12:10:24 PM,26,5,12:16:36 PM,26,5,12:17:53 PM,26,5,1:34:32 PM,7,,,-1.2551895,36.7822034,-1.2879618,36.82008629999999,Rider_Id_903,4599
-Order_No_23333,User_Id_2871,Bike,1,Personal,25,1,1:27:19 PM,25,1,1:27:31 PM,25,1,1:27:50 PM,25,1,1:41:31 PM,25,1,1:47:10 PM,4,,,-1.2616743000000001,36.7852711,-1.2623805000000001,36.7805404,Rider_Id_379,339
-Order_No_10523,User_Id_265,Bike,3,Business,14,1,12:11:51 PM,14,1,12:12:37 PM,14,1,12:17:56 PM,14,1,12:29:44 PM,14,1,12:46:15 PM,8,23.3,,-1.2633493999999998,36.7959696,-1.300921,36.828195,Rider_Id_882,991
-Order_No_19178,User_Id_3091,Bike,3,Business,5,3,12:13:10 PM,5,3,12:27:07 PM,5,3,12:32:51 PM,5,3,12:45:29 PM,5,3,1:38:49 PM,6,22.4,,-1.3025826,36.7670807,-1.2694398,36.762448299999996,Rider_Id_210,3200
-Order_No_27449,User_Id_3647,Bike,3,Business,25,4,3:22:40 PM,25,4,3:25:48 PM,25,4,3:35:31 PM,25,4,4:46:47 PM,25,4,5:11:21 PM,5,,,-1.2726389999999999,36.794723,-1.264917,36.8150651,Rider_Id_774,1474
-Order_No_8802,User_Id_136,Bike,3,Business,4,2,4:45:00 PM,4,2,4:52:45 PM,4,2,5:34:10 PM,4,2,6:25:32 PM,4,2,6:26:01 PM,9,22.6,,-1.2654716000000001,36.7983635,-1.2313147,36.80168629999999,Rider_Id_739,29
-Order_No_13546,User_Id_1383,Bike,1,Personal,21,4,10:58:37 AM,21,4,11:07:53 AM,21,4,11:18:59 AM,21,4,11:36:09 AM,21,4,12:59:47 PM,20,21.5,,-1.3015998,36.8764939,-1.2707742,36.7578609,Rider_Id_101,5018
-Order_No_6612,User_Id_1926,Bike,3,Business,13,3,3:55:45 PM,13,3,3:55:57 PM,13,3,4:07:05 PM,13,3,4:15:59 PM,13,3,4:49:25 PM,16,29.3,,-1.3263105,36.8622904,-1.2571472,36.795063299999995,Rider_Id_19,2006
-Order_No_2363,User_Id_2869,Bike,3,Business,3,3,2:11:49 PM,3,3,2:16:34 PM,3,3,2:37:07 PM,3,3,2:42:20 PM,3,3,3:17:46 PM,9,28.1,,-1.3025826,36.7670807,-1.2551895,36.7822034,Rider_Id_477,2126
-Order_No_22835,User_Id_3426,Bike,3,Business,18,2,9:58:43 AM,18,2,9:59:15 AM,18,2,10:09:44 AM,18,2,10:30:22 AM,18,2,10:56:10 AM,9,16.7,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_726,1548
-Order_No_7652,User_Id_437,Bike,3,Business,5,1,2:38:06 PM,5,1,2:42:52 PM,5,1,2:53:11 PM,5,1,3:22:09 PM,5,1,3:46:59 PM,8,26.0,,-1.2952784,36.7793271,-1.2551895,36.7822034,Rider_Id_660,1490
-Order_No_4707,User_Id_1392,Bike,3,Business,7,5,1:36:47 PM,7,5,1:40:01 PM,7,5,1:53:01 PM,7,5,1:57:29 PM,7,5,2:42:44 PM,12,,,-1.2551895,36.7822034,-1.3272115,36.7924151,Rider_Id_709,2715
-Order_No_863,User_Id_3201,Bike,3,Business,29,4,10:47:06 AM,29,4,10:54:31 AM,29,4,11:03:04 AM,29,4,11:08:00 AM,29,4,11:35:30 AM,3,21.5,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_345,1650
-Order_No_11471,User_Id_1719,Bike,3,Business,10,5,12:56:34 PM,10,5,12:58:08 PM,10,5,1:06:46 PM,10,5,1:11:20 PM,10,5,1:17:36 PM,2,,,-1.2969741000000001,36.7856612,-1.3039706,36.7688174,Rider_Id_256,376
-Order_No_80,User_Id_3017,Bike,3,Business,10,5,7:13:41 PM,10,5,7:13:59 PM,10,5,7:22:47 PM,10,5,7:46:19 PM,10,5,8:09:57 PM,3,17.9,,-1.299313,36.78314879999999,-1.2801129,36.7787822,Rider_Id_238,1418
-Order_No_1510,User_Id_478,Bike,3,Business,1,1,2:47:27 PM,1,1,2:47:54 PM,1,1,3:01:45 PM,1,1,3:12:38 PM,1,1,3:34:35 PM,8,,,-1.3045333000000001,36.82280720000001,-1.2534301,36.800098299999995,Rider_Id_907,1317
-Order_No_15680,User_Id_3203,Bike,3,Business,9,6,10:53:30 AM,9,6,10:55:06 AM,9,6,11:05:19 AM,9,6,11:36:14 AM,9,6,11:49:45 AM,13,17.0,,-1.307143,36.825009,-1.228911,36.881876,Rider_Id_900,811
-Order_No_15629,User_Id_2933,Bike,2,Personal,5,3,4:05:28 PM,5,3,4:09:18 PM,5,3,4:25:57 PM,5,3,4:37:20 PM,5,3,5:07:26 PM,5,22.1,,-1.2721028,36.795348499999996,-1.2947685,36.778583399999995,Rider_Id_848,1806
-Order_No_22864,User_Id_1990,Bike,3,Business,24,5,2:26:54 PM,24,5,2:27:26 PM,24,5,2:27:46 PM,24,5,2:54:27 PM,24,5,3:12:08 PM,4,22.8,,-1.2767064,36.8227148,-1.2658315,36.809049,Rider_Id_704,1061
-Order_No_5454,User_Id_1926,Bike,3,Business,6,6,12:56:29 PM,6,6,1:03:22 PM,6,6,1:19:38 PM,6,6,1:21:53 PM,6,6,1:42:33 PM,14,24.4,,-1.3044764,36.8843623,-1.2156006000000001,36.8916865,Rider_Id_31,1240
-Order_No_1341,User_Id_2066,Bike,3,Business,24,3,4:04:12 PM,24,3,4:04:38 PM,24,3,4:13:32 PM,24,3,4:16:40 PM,24,3,5:10:41 PM,18,,,-1.3210188999999999,36.841055100000005,-1.2153260000000001,36.891993799999995,Rider_Id_233,3241
-Order_No_18729,User_Id_2017,Bike,3,Business,11,1,3:17:23 PM,11,1,3:40:59 PM,11,1,3:49:31 PM,11,1,4:06:02 PM,11,1,4:11:56 PM,2,23.0,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_216,354
-Order_No_21228,User_Id_897,Bike,2,Personal,11,1,8:46:12 AM,11,1,8:46:58 AM,11,1,9:08:03 AM,11,1,9:10:42 AM,11,1,9:34:38 AM,10,14.3,,-1.2295075,36.801697100000005,-1.2665946000000001,36.7888833,Rider_Id_434,1436
-Order_No_23066,User_Id_1363,Bike,3,Business,15,5,8:53:34 AM,15,5,8:54:55 AM,15,5,8:55:00 AM,15,5,9:51:16 AM,15,5,10:17:36 AM,10,20.5,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_828,1580
-Order_No_25211,User_Id_1527,Bike,3,Business,30,4,12:10:35 PM,30,4,12:11:36 PM,30,4,12:32:33 PM,30,4,12:57:57 PM,30,4,1:28:35 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_948,1838
-Order_No_12242,User_Id_3222,Bike,3,Business,22,2,3:03:41 PM,22,2,3:04:15 PM,22,2,3:09:26 PM,22,2,3:12:46 PM,22,2,3:22:16 PM,3,25.8,,-1.2551895,36.7822034,-1.2610837,36.8021315,Rider_Id_446,570
-Order_No_14292,User_Id_633,Bike,3,Business,24,4,10:47:15 AM,24,4,11:08:37 AM,24,4,11:08:42 AM,24,4,11:15:11 AM,24,4,11:30:22 AM,5,24.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_576,911
-Order_No_21627,User_Id_478,Bike,3,Business,12,3,12:44:30 PM,12,3,12:49:16 PM,12,3,12:52:02 PM,12,3,12:54:07 PM,12,3,1:26:25 PM,11,19.3,,-1.2551895,36.7822034,-1.3142701,36.853769299999996,Rider_Id_168,1938
-Order_No_10303,User_Id_2310,Bike,1,Business,30,6,9:48:39 AM,30,6,9:50:18 AM,30,6,10:16:57 AM,30,6,10:22:59 AM,30,6,10:45:35 AM,7,17.8,,-1.285382,36.8162878,-1.29928,36.85631,Rider_Id_752,1356
-Order_No_15695,User_Id_868,Bike,3,Business,10,3,3:04:00 PM,10,3,3:04:35 PM,10,3,3:13:46 PM,10,3,3:25:37 PM,10,3,3:50:56 PM,8,30.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_306,1519
-Order_No_18972,User_Id_811,Bike,3,Personal,12,3,1:07:37 PM,12,3,1:15:51 PM,12,3,1:28:47 PM,12,3,1:41:32 PM,12,3,1:57:27 PM,7,26.7,,-1.2868973,36.8232984,-1.296797,36.776452500000005,Rider_Id_676,955
-Order_No_10623,User_Id_1075,Bike,3,Business,31,1,3:53:09 PM,31,1,4:43:42 PM,31,1,4:54:31 PM,31,1,5:02:59 PM,31,1,5:17:01 PM,1,22.5,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_216,842
-Order_No_12457,User_Id_1329,Bike,3,Business,12,5,3:40:11 PM,12,5,3:40:32 PM,12,5,3:45:12 PM,12,5,4:09:47 PM,12,5,5:27:37 PM,18,27.3,,-1.273056,36.811298,-1.3103985,36.9114173,Rider_Id_519,4670
-Order_No_13888,User_Id_2454,Bike,3,Business,5,6,4:41:55 PM,5,6,4:44:07 PM,5,6,4:49:05 PM,5,6,4:56:14 PM,5,6,5:02:59 PM,2,21.9,,-1.2993231,36.7753372,-1.2863547,36.789537599999996,Rider_Id_649,405
-Order_No_23315,User_Id_2457,Bike,3,Business,30,2,2:54:57 PM,30,2,3:07:23 PM,30,2,3:18:00 PM,30,2,3:23:13 PM,30,2,3:43:51 PM,7,27.2,,-1.2551895,36.7822034,-1.2915484,36.8297516,Rider_Id_462,1238
-Order_No_9302,User_Id_1502,Bike,1,Business,7,1,12:23:55 PM,7,1,12:32:10 PM,7,1,12:36:58 PM,7,1,1:00:20 PM,7,1,1:14:57 PM,4,21.7,,-1.2913996,36.8274442,-1.3101624,36.818915000000004,Rider_Id_742,877
-Order_No_8819,User_Id_2200,Bike,3,Business,5,2,1:56:13 PM,5,2,2:12:58 PM,5,2,2:32:07 PM,5,2,2:34:23 PM,5,2,2:50:49 PM,4,,,-1.2765736,36.851364600000004,-1.290894,36.822971,Rider_Id_448,986
-Order_No_15307,User_Id_693,Bike,2,Personal,15,1,8:38:40 AM,15,1,8:39:19 AM,15,1,8:51:17 AM,15,1,8:58:02 AM,15,1,9:08:10 AM,5,,,-1.2384447,36.7919924,-1.2580991000000001,36.794151899999996,Rider_Id_453,608
-Order_No_23675,User_Id_2412,Bike,3,Business,28,5,12:36:50 PM,28,5,12:37:39 PM,28,5,12:44:55 PM,28,5,12:58:41 PM,28,5,1:17:50 PM,13,25.0,,-1.2571472,36.795063299999995,-1.2297202,36.881950700000004,Rider_Id_726,1149
-Order_No_13174,User_Id_2801,Bike,3,Business,11,1,1:52:33 PM,11,1,1:52:57 PM,11,1,2:00:26 PM,11,1,2:08:46 PM,11,1,2:37:07 PM,12,27.8,,-1.2595421999999998,36.7871178,-1.3288576,36.8465936,Rider_Id_622,1701
-Order_No_24362,User_Id_53,Bike,3,Business,21,4,1:01:35 PM,21,4,1:03:02 PM,21,4,1:15:21 PM,21,4,1:58:16 PM,21,4,2:17:41 PM,5,26.3,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_660,1165
-Order_No_3622,User_Id_635,Bike,3,Business,12,2,3:52:06 PM,12,2,4:06:22 PM,12,2,4:09:44 PM,12,2,4:15:59 PM,12,2,4:33:12 PM,9,27.1,,-1.2599563,36.799343900000004,-1.2293243999999999,36.7839539,Rider_Id_510,1033
-Order_No_10829,User_Id_540,Bike,3,Business,7,1,12:59:58 PM,7,1,1:08:19 PM,7,1,1:16:06 PM,7,1,1:18:46 PM,7,1,1:46:38 PM,8,24.8,,-1.2551895,36.7822034,-1.3027475,36.8178196,Rider_Id_512,1672
-Order_No_17958,User_Id_2523,Bike,3,Business,29,1,2:01:10 PM,29,1,2:01:47 PM,29,1,2:02:07 PM,29,1,2:05:33 PM,29,1,2:27:25 PM,14,,,-1.2551895,36.7822034,-1.3147071000000001,36.8644977,Rider_Id_564,1312
-Order_No_23852,User_Id_2627,Bike,3,Business,28,1,12:19:39 PM,28,1,12:33:26 PM,28,1,12:49:46 PM,28,1,12:58:42 PM,28,1,1:20:08 PM,15,23.9,,-1.2283403,36.8822756,-1.2571472,36.795063299999995,Rider_Id_276,1286
-Order_No_8260,User_Id_2803,Bike,3,Business,14,4,9:47:01 AM,14,4,9:53:22 AM,14,4,10:16:18 AM,14,4,10:19:06 AM,14,4,11:06:07 AM,6,18.0,,-1.2765736,36.851364600000004,-1.2989287,36.81630820000001,Rider_Id_436,2821
-Order_No_6606,User_Id_2154,Bike,3,Business,5,4,8:24:07 AM,5,4,8:57:40 AM,5,4,9:26:33 AM,5,4,9:50:37 AM,5,4,10:16:17 AM,5,,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_159,1540
-Order_No_27862,User_Id_3560,Bike,3,Business,27,3,2:00:19 PM,27,3,2:00:46 PM,27,3,2:06:26 PM,27,3,2:13:33 PM,27,3,2:27:01 PM,5,29.1,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_436,808
-Order_No_22577,User_Id_2691,Bike,3,Business,27,2,4:20:03 PM,27,2,4:21:49 PM,27,2,4:29:33 PM,27,2,4:35:58 PM,27,2,4:48:50 PM,8,22.3,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_247,772
-Order_No_1995,User_Id_1850,Bike,3,Business,13,4,11:38:09 AM,13,4,11:39:24 AM,13,4,11:50:07 AM,13,4,11:54:14 AM,13,4,12:11:02 PM,7,21.9,,-1.2551895,36.7822034,-1.2785434,36.821532700000006,Rider_Id_365,1008
-Order_No_4092,User_Id_3257,Bike,1,Personal,4,4,11:56:24 AM,4,4,11:56:34 AM,4,4,12:01:35 PM,4,4,12:07:59 PM,4,4,12:29:31 PM,8,,,-1.2661958999999998,36.807544,-1.3016653,36.8323628,Rider_Id_247,1292
-Order_No_4465,User_Id_1880,Bike,3,Business,13,4,3:06:17 PM,13,4,3:06:50 PM,13,4,3:20:14 PM,13,4,3:29:18 PM,13,4,3:39:54 PM,3,23.0,,-1.2571472,36.795063299999995,-1.2608816,36.7824431,Rider_Id_770,636
-Order_No_12059,User_Id_60,Bike,3,Business,8,3,9:39:28 AM,8,3,9:40:04 AM,8,3,9:46:30 AM,8,3,10:03:35 AM,8,3,10:15:23 AM,8,17.3,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_174,708
-Order_No_3795,User_Id_868,Bike,3,Business,26,5,1:13:48 PM,26,5,1:14:04 PM,26,5,1:19:04 PM,26,5,1:33:21 PM,26,5,1:53:13 PM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_349,1192
-Order_No_10774,User_Id_3214,Bike,3,Business,26,2,10:15:34 AM,26,2,10:15:48 AM,26,2,10:20:21 AM,26,2,10:24:15 AM,26,2,10:35:54 AM,3,22.0,,-1.289795,36.7957132,-1.304735,36.784605400000004,Rider_Id_829,699
-Order_No_23631,User_Id_2195,Bike,1,Business,6,4,10:25:36 AM,6,4,10:26:03 AM,6,4,10:40:36 AM,6,4,10:47:08 AM,6,4,11:31:53 AM,10,22.2,,-1.2667076000000002,36.80543529999999,-1.3313643,36.857742200000004,Rider_Id_216,2685
-Order_No_17302,User_Id_1350,Bike,3,Personal,22,6,12:47:11 PM,22,6,12:48:35 PM,22,6,12:56:29 PM,22,6,1:07:27 PM,22,6,1:22:34 PM,6,23.2,,-1.2962832,36.784968299999996,-1.3094838,36.8206907,Rider_Id_205,907
-Order_No_13476,User_Id_1002,Bike,1,Personal,24,1,7:15:33 PM,24,1,7:15:53 PM,24,1,7:18:06 PM,24,1,7:23:04 PM,24,1,7:57:55 PM,17,24.3,,-1.2931104,36.7677484,-1.3205334999999998,36.8789718,Rider_Id_336,2091
-Order_No_14839,User_Id_391,Bike,3,Business,26,5,7:57:43 AM,26,5,7:59:19 AM,26,5,7:59:25 AM,26,5,8:23:59 AM,26,5,8:24:06 AM,5,,,-1.2867454,36.8259017,-1.2602337,36.79905479999999,Rider_Id_61,7
-Order_No_12215,User_Id_3222,Bike,3,Business,30,4,1:17:29 PM,30,4,1:20:54 PM,30,4,1:28:27 PM,30,4,1:32:53 PM,30,4,1:43:33 PM,4,,,-1.2551895,36.7822034,-1.2676593,36.8103939,Rider_Id_576,640
-Order_No_21180,User_Id_1469,Bike,3,Business,14,3,12:19:04 PM,14,3,12:19:43 PM,14,3,12:34:12 PM,14,3,12:44:12 PM,14,3,1:15:38 PM,8,24.5,,-1.2551895,36.7822034,-1.300722,36.816865,Rider_Id_579,1886
-Order_No_2993,User_Id_60,Bike,3,Business,4,5,9:40:05 AM,4,5,9:40:15 AM,4,5,9:45:52 AM,4,5,10:33:40 AM,4,5,10:41:36 AM,6,17.3,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_445,476
-Order_No_9721,User_Id_3635,Bike,1,Business,29,6,9:57:57 AM,29,6,9:58:06 AM,29,6,10:28:55 AM,29,6,10:37:05 AM,29,6,11:47:38 AM,44,25.2,,-1.290894,36.822971,-1.0352624,36.9131317,Rider_Id_661,4233
-Order_No_16169,User_Id_3201,Bike,3,Business,9,3,5:02:25 PM,9,3,5:02:52 PM,9,3,5:07:02 PM,9,3,5:18:04 PM,9,3,5:18:07 PM,1,24.5,,-1.2605938,36.807323499999995,-1.2584143,36.8048002,Rider_Id_685,3
-Order_No_7355,User_Id_3300,Bike,3,Business,8,1,1:38:24 PM,8,1,1:39:11 PM,8,1,1:43:25 PM,8,1,1:52:35 PM,8,1,2:23:37 PM,7,29.6,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_7,1862
-Order_No_10128,User_Id_1051,Bike,3,Business,21,5,2:42:09 PM,21,5,3:28:17 PM,21,5,3:29:49 PM,21,5,3:31:58 PM,21,5,4:25:46 PM,8,24.5,,-1.2963097,36.7688221,-1.2647206,36.7259313,Rider_Id_725,3228
-Order_No_389,User_Id_1179,Bike,3,Business,20,4,2:30:50 PM,20,4,2:30:56 PM,20,4,2:31:16 PM,20,4,2:42:39 PM,20,4,3:15:41 PM,21,27.8,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_64,1982
-Order_No_21307,User_Id_198,Bike,3,Business,19,3,11:57:55 AM,19,3,11:58:07 AM,19,3,12:07:53 PM,19,3,12:15:32 PM,19,3,12:54:00 PM,17,,,-1.3396256000000002,36.8863135,-1.2705296999999998,36.7987172,Rider_Id_265,2308
-Order_No_10035,User_Id_2040,Bike,3,Personal,30,6,9:35:41 AM,30,6,9:36:06 AM,30,6,9:47:02 AM,30,6,10:18:33 AM,30,6,11:01:31 AM,21,17.9,,-1.3321944,36.889232899999996,-1.2485190000000002,36.783656,Rider_Id_74,2578
-Order_No_11096,User_Id_2532,Bike,3,Business,18,1,9:17:00 AM,18,1,9:20:09 AM,18,1,9:20:58 AM,18,1,9:23:35 AM,18,1,9:39:06 AM,8,14.8,0.3,-1.2551895,36.7822034,-1.2941848999999999,36.7895754,Rider_Id_379,931
-Order_No_9500,User_Id_1500,Bike,3,Business,2,6,10:42:40 AM,2,6,11:01:22 AM,2,6,11:19:01 AM,2,6,11:22:34 AM,2,6,11:44:05 AM,8,27.4,,-1.3004062,36.829741,-1.2636051000000001,36.7850995,Rider_Id_362,1291
-Order_No_19752,User_Id_3017,Bike,3,Business,21,2,8:29:52 PM,21,2,10:28:59 PM,21,2,10:34:36 PM,21,2,10:37:32 PM,21,2,10:39:12 PM,5,16.0,,-1.299313,36.78314879999999,-1.3071979999999999,36.7502243,Rider_Id_517,100
-Order_No_20783,User_Id_393,Bike,3,Business,8,3,10:04:21 AM,8,3,10:04:56 AM,8,3,10:10:52 AM,8,3,10:30:49 AM,8,3,10:34:49 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_411,240
-Order_No_17104,User_Id_3630,Bike,2,Personal,29,2,5:36:59 PM,29,2,5:37:37 PM,29,2,5:52:36 PM,29,2,6:09:16 PM,29,2,6:45:18 PM,14,22.2,,-1.2886923000000001,36.8241519,-1.214328,36.7911903,Rider_Id_478,2162
-Order_No_933,User_Id_1268,Bike,1,Personal,12,5,9:49:10 AM,12,5,9:55:23 AM,12,5,10:23:36 AM,12,5,10:26:45 AM,12,5,11:02:36 AM,9,18.6,,-1.273757,36.821634,-1.293554,36.763113,Rider_Id_532,2151
-Order_No_12413,User_Id_1687,Bike,3,Business,3,1,8:29:09 AM,3,1,8:29:43 AM,3,1,8:37:57 AM,3,1,8:45:05 AM,3,1,8:57:59 AM,6,15.1,,-1.2888684,36.816866999999995,-1.2915472,36.774696399999996,Rider_Id_726,774
-Order_No_12708,User_Id_393,Bike,3,Business,21,5,2:09:03 PM,21,5,2:09:28 PM,21,5,2:09:39 PM,21,5,2:23:10 PM,21,5,2:53:02 PM,14,24.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_10,1792
-Order_No_27975,User_Id_186,Bike,3,Business,8,2,12:40:28 PM,8,2,12:56:14 PM,8,2,1:09:12 PM,8,2,1:14:33 PM,8,2,1:57:56 PM,23,23.2,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_114,2603
-Order_No_26453,User_Id_1271,Bike,3,Business,3,4,11:40:08 AM,3,4,1:26:13 PM,3,4,1:42:38 PM,3,4,1:57:49 PM,3,4,2:27:08 PM,11,25.9,,-1.3244885,36.897791999999995,-1.2669247,36.908649700000005,Rider_Id_501,1759
-Order_No_10947,User_Id_3727,Bike,3,Business,9,3,12:17:07 PM,9,3,12:17:35 PM,9,3,12:18:58 PM,9,3,12:27:01 PM,9,3,12:44:54 PM,3,25.1,,-1.2963097,36.7688221,-1.2860203000000001,36.7832538,Rider_Id_952,1073
-Order_No_3301,User_Id_393,Bike,3,Business,19,3,8:57:01 AM,19,3,9:13:22 AM,19,3,9:20:59 AM,19,3,9:29:48 AM,19,3,10:22:07 AM,14,19.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_188,3139
-Order_No_11753,User_Id_1284,Bike,1,Personal,16,4,10:24:55 AM,16,4,10:25:03 AM,16,4,10:32:04 AM,16,4,10:39:15 AM,16,4,10:58:47 AM,9,14.7,,-1.2672018,36.8071919,-1.3185736000000001,36.8258933,Rider_Id_857,1172
-Order_No_26796,User_Id_3355,Bike,3,Business,16,3,10:50:27 AM,16,3,11:31:23 AM,16,3,11:46:20 AM,16,3,12:16:40 PM,16,3,1:09:13 PM,13,23.9,,-1.3562374,36.904295399999995,-1.3052491000000002,36.8223899,Rider_Id_711,3153
-Order_No_18368,User_Id_1271,Bike,3,Business,13,4,11:14:40 AM,13,4,11:15:09 AM,13,4,11:34:35 AM,13,4,11:49:33 AM,13,4,12:49:43 PM,24,22.0,,-1.3107918,36.761794,-1.2209733,36.904574600000004,Rider_Id_268,3610
-Order_No_25459,User_Id_3149,Bike,3,Business,3,1,2:57:47 PM,3,1,3:05:04 PM,3,1,3:09:19 PM,3,1,3:18:16 PM,3,1,3:35:25 PM,5,23.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_158,1029
-Order_No_24332,User_Id_419,Bike,3,Business,8,1,1:46:43 PM,8,1,1:48:28 PM,8,1,1:48:37 PM,8,1,1:55:10 PM,8,1,2:19:50 PM,12,24.7,,-1.2571472,36.795063299999995,-1.3332749,36.870814700000004,Rider_Id_45,1480
-Order_No_2302,User_Id_2763,Bike,3,Business,14,5,2:37:52 PM,14,5,2:45:10 PM,14,5,2:53:38 PM,14,5,3:04:35 PM,14,5,3:28:14 PM,11,22.8,,-1.2551895,36.7822034,-1.3250103000000002,36.8388817,Rider_Id_896,1419
-Order_No_1950,User_Id_2532,Bike,3,Business,12,5,10:23:31 AM,12,5,10:24:04 AM,12,5,10:28:37 AM,12,5,10:34:15 AM,12,5,10:59:12 AM,4,26.3,,-1.2551895,36.7822034,-1.273792,36.806401,Rider_Id_958,1497
-Order_No_13277,User_Id_248,Bike,3,Business,5,2,9:19:25 AM,5,2,9:19:43 AM,5,2,9:20:01 AM,5,2,9:45:20 AM,5,2,10:30:05 AM,9,17.9,4.0,-1.304735,36.784605400000004,-1.2592947,36.7698085,Rider_Id_354,2685
-Order_No_7893,User_Id_1146,Bike,3,Business,28,5,10:52:01 AM,28,5,10:53:32 AM,28,5,10:53:36 AM,28,5,11:02:24 AM,28,5,11:13:02 AM,6,23.8,,-1.3167113000000001,36.8301563,-1.3012841999999998,36.8561033,Rider_Id_619,638
-Order_No_8700,User_Id_2801,Bike,3,Business,27,4,12:51:17 PM,27,4,12:51:24 PM,27,4,12:56:55 PM,27,4,1:01:45 PM,27,4,1:26:21 PM,9,22.8,,-1.2595421999999998,36.7871178,-1.2839049999999999,36.824927200000005,Rider_Id_726,1476
-Order_No_18881,User_Id_1246,Bike,3,Business,14,3,10:39:02 AM,14,3,10:39:18 AM,14,3,10:46:08 AM,14,3,10:50:33 AM,14,3,11:05:28 AM,2,24.5,,-1.2571472,36.795063299999995,-1.2682478000000001,36.8072852,Rider_Id_566,895
-Order_No_6752,User_Id_893,Bike,3,Business,19,1,4:28:04 PM,19,1,4:28:21 PM,19,1,4:39:43 PM,19,1,4:44:35 PM,19,1,5:13:38 PM,11,23.7,,-1.2146739,36.8560568,-1.2641579,36.8216792,Rider_Id_685,1743
-Order_No_14699,User_Id_393,Bike,3,Business,7,2,9:35:43 AM,7,2,9:37:08 AM,7,2,9:49:20 AM,7,2,10:26:30 AM,7,2,10:44:55 AM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_155,1105
-Order_No_19999,User_Id_3355,Bike,3,Business,23,2,1:40:14 PM,23,2,1:40:23 PM,23,2,1:53:53 PM,23,2,1:55:37 PM,23,2,2:05:09 PM,2,19.1,,-1.3052491000000002,36.8223899,-1.2983805,36.8198439,Rider_Id_612,572
-Order_No_25068,User_Id_3517,Bike,3,Business,15,6,8:30:24 AM,15,6,8:31:04 AM,15,6,9:04:10 AM,15,6,10:20:53 AM,15,6,10:50:25 AM,15,17.6,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_208,1772
-Order_No_5335,User_Id_788,Bike,3,Business,23,5,9:25:06 AM,23,5,9:44:23 AM,23,5,9:49:48 AM,23,5,10:02:08 AM,23,5,10:45:22 AM,10,20.4,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_436,2594
-Order_No_12279,User_Id_1787,Bike,3,Business,25,4,5:16:49 PM,25,4,5:22:02 PM,25,4,5:24:16 PM,25,4,5:29:07 PM,25,4,5:46:10 PM,5,,,-1.2156006000000001,36.8916865,-1.2297202,36.881950700000004,Rider_Id_79,1023
-Order_No_7309,User_Id_1695,Bike,1,Personal,16,2,10:23:44 AM,16,2,10:23:53 AM,16,2,10:27:22 AM,16,2,10:30:12 AM,16,2,10:46:08 AM,8,,,-1.257309,36.806008399999996,-1.2793831,36.7617573,Rider_Id_453,956
-Order_No_8039,User_Id_1500,Bike,3,Business,22,3,11:20:26 AM,22,3,11:20:45 AM,22,3,11:21:11 AM,22,3,11:59:53 AM,22,3,12:00:12 PM,10,23.1,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_515,19
-Order_No_18716,User_Id_633,Bike,3,Business,21,1,3:35:36 PM,21,1,3:41:55 PM,21,1,3:50:11 PM,21,1,4:12:56 PM,21,1,4:39:18 PM,10,27.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_518,1582
-Order_No_27398,User_Id_2676,Bike,3,Business,18,1,11:26:53 AM,18,1,11:27:32 AM,18,1,11:49:50 AM,18,1,11:54:01 AM,18,1,12:16:27 PM,9,27.1,,-1.2612974,36.7993448,-1.2963097,36.7688221,Rider_Id_279,1346
-Order_No_14970,User_Id_3477,Bike,3,Business,28,1,11:53:39 AM,28,1,11:59:36 AM,28,1,12:04:17 PM,28,1,12:18:57 PM,28,1,12:36:52 PM,4,23.9,,-1.2791714,36.825599700000005,-1.304127,36.805743,Rider_Id_360,1075
-Order_No_15455,User_Id_3287,Bike,3,Business,15,2,10:42:11 AM,15,2,10:43:12 AM,15,2,10:52:34 AM,15,2,10:58:09 AM,15,2,11:35:57 AM,11,24.3,,-1.2452664,36.802854100000005,-1.2969741000000001,36.7856612,Rider_Id_56,2268
-Order_No_23782,User_Id_3500,Bike,3,Business,11,3,8:58:58 AM,11,3,8:59:11 AM,11,3,9:09:01 AM,11,3,9:16:29 AM,11,3,9:41:10 AM,13,15.5,0.9,-1.2621828000000002,36.7949276,-1.3248423,36.8530567,Rider_Id_752,1481
-Order_No_10116,User_Id_3599,Bike,3,Business,24,5,2:18:59 PM,24,5,2:19:21 PM,24,5,2:42:41 PM,24,5,3:33:04 PM,24,5,3:42:03 PM,5,22.8,,-1.3302996,36.8707935,-1.3184778999999998,36.8373109,Rider_Id_675,539
-Order_No_15155,User_Id_3727,Bike,2,Business,3,3,11:11:58 AM,3,3,11:14:29 AM,3,3,11:33:34 AM,3,3,11:34:54 AM,3,3,11:55:15 AM,8,24.2,,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_328,1221
-Order_No_21031,User_Id_1081,Bike,3,Business,20,3,11:46:51 AM,20,3,11:49:41 AM,20,3,12:13:04 PM,20,3,12:22:28 PM,20,3,12:37:01 PM,5,26.3,,-1.304127,36.805743,-1.290894,36.822971,Rider_Id_206,873
-Order_No_14483,User_Id_3569,Bike,3,Personal,8,5,1:59:45 PM,8,5,2:27:23 PM,8,5,2:53:49 PM,8,5,2:59:48 PM,8,5,3:58:37 PM,20,30.6,,-1.2586742,36.810996,-1.3858142,36.7641498,Rider_Id_622,3529
-Order_No_1767,User_Id_540,Bike,3,Business,8,2,11:45:46 AM,8,2,11:46:08 AM,8,2,11:47:33 AM,8,2,11:55:07 AM,8,2,12:05:48 PM,4,23.6,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_19,641
-Order_No_7241,User_Id_2767,Bike,3,Business,9,4,12:58:56 PM,9,4,1:00:22 PM,9,4,1:16:15 PM,9,4,1:17:16 PM,9,4,1:41:31 PM,9,23.9,,-1.257773,36.795486,-1.2885712999999999,36.764981799999994,Rider_Id_263,1455
-Order_No_14172,User_Id_3060,Bike,3,Business,15,5,2:01:47 PM,15,5,2:02:18 PM,15,5,2:29:37 PM,15,5,2:35:16 PM,15,5,2:52:21 PM,4,27.9,,-1.2764098999999998,36.8208451,-1.2534301,36.800098299999995,Rider_Id_434,1025
-Order_No_12954,User_Id_3251,Bike,3,Business,8,3,1:20:40 PM,8,3,1:23:19 PM,8,3,1:45:19 PM,8,3,1:54:21 PM,8,3,2:29:27 PM,6,26.0,,-1.2478668,36.8069224,-1.2833534,36.8165865,Rider_Id_88,2106
-Order_No_13032,User_Id_1025,Bike,3,Business,16,2,2:18:54 PM,16,2,2:19:34 PM,16,2,2:28:28 PM,16,2,3:26:25 PM,16,2,3:27:09 PM,2,,,-1.2969741000000001,36.7856612,-1.3039706,36.7688174,Rider_Id_324,44
-Order_No_24793,User_Id_1792,Bike,3,Business,15,4,11:48:36 AM,15,4,11:49:26 AM,15,4,11:54:35 AM,15,4,12:04:20 PM,15,4,12:22:17 PM,5,24.2,,-1.2619756000000002,36.800139,-1.2809121,36.7852524,Rider_Id_259,1077
-Order_No_15419,User_Id_53,Bike,3,Business,8,4,9:58:23 AM,8,4,10:12:59 AM,8,4,10:50:06 AM,8,4,10:51:39 AM,8,4,11:15:01 AM,6,26.4,,-1.2574219,36.7927073,-1.2770708,36.8231093,Rider_Id_432,1402
-Order_No_948,User_Id_3113,Bike,3,Personal,19,4,3:54:44 PM,19,4,3:58:37 PM,19,4,4:08:54 PM,19,4,4:13:35 PM,19,4,5:06:28 PM,8,20.3,,-1.281614,36.8275424,-1.287428,36.7800821,Rider_Id_174,3173
-Order_No_426,User_Id_186,Bike,3,Business,19,2,4:20:20 PM,19,2,4:21:15 PM,19,2,4:33:06 PM,19,2,4:52:21 PM,19,2,4:58:23 PM,4,28.5,,-1.2571472,36.795063299999995,-1.2825816,36.8092346,Rider_Id_678,362
-Order_No_5778,User_Id_391,Bike,3,Business,28,4,8:07:05 AM,28,4,8:48:09 AM,28,4,8:57:16 AM,28,4,9:04:13 AM,28,4,9:20:54 AM,5,,,-1.2599563,36.799343900000004,-1.2857573999999998,36.8295667,Rider_Id_752,1001
-Order_No_1229,User_Id_1976,Bike,3,Business,11,5,2:42:16 PM,11,5,2:42:53 PM,11,5,2:46:30 PM,11,5,2:49:34 PM,11,5,2:58:24 PM,4,26.2,,-1.267311,36.809196,-1.257028,36.7810435,Rider_Id_648,530
-Order_No_27261,User_Id_2987,Bike,3,Business,3,1,11:12:40 AM,3,1,11:12:52 AM,3,1,11:14:14 AM,3,1,11:27:56 AM,3,1,11:41:12 AM,9,20.6,,-1.2702120000000001,36.82108770000001,-1.3280091,36.8524146,Rider_Id_179,796
-Order_No_24110,User_Id_2237,Bike,3,Business,11,1,3:40:09 PM,11,1,3:40:40 PM,11,1,3:49:56 PM,11,1,4:05:31 PM,11,1,4:21:00 PM,5,28.2,,-1.2772389,36.7855864,-1.2574087999999999,36.799712,Rider_Id_523,929
-Order_No_17541,User_Id_2587,Bike,3,Business,25,1,12:41:46 PM,25,1,12:42:31 PM,25,1,12:45:08 PM,25,1,12:53:47 PM,25,1,12:53:52 PM,2,25.5,,-1.28878,36.8168312,-1.2880128999999998,36.8308097,Rider_Id_686,5
-Order_No_3945,User_Id_3201,Bike,3,Business,4,1,2:38:18 PM,4,1,4:14:19 PM,4,1,4:56:26 PM,4,1,5:12:26 PM,4,1,5:33:26 PM,5,27.6,,-1.2584143,36.8048002,-1.2857548,36.819062,Rider_Id_448,1260
-Order_No_11823,User_Id_832,Bike,1,Personal,16,1,2:21:42 PM,16,1,2:21:59 PM,16,1,2:39:45 PM,16,1,3:14:51 PM,16,1,3:30:23 PM,10,22.5,,-1.2994194,36.7647917,-1.3070122,36.8287218,Rider_Id_312,932
-Order_No_16729,User_Id_1687,Bike,3,Business,8,3,10:50:30 AM,8,3,10:51:46 AM,8,3,10:59:22 AM,8,3,11:09:55 AM,8,3,11:33:06 AM,11,,,-1.2626574,36.8014034,-1.281531,36.740864,Rider_Id_96,1391
-Order_No_14113,User_Id_246,Bike,3,Business,18,4,9:17:04 AM,18,4,9:19:47 AM,18,4,9:54:14 AM,18,4,10:08:06 AM,18,4,10:36:40 AM,6,20.4,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_736,1714
-Order_No_27809,User_Id_3326,Bike,3,Business,13,4,4:16:05 PM,13,4,4:16:23 PM,13,4,5:10:03 PM,13,4,5:14:41 PM,13,4,5:14:45 PM,3,22.7,,-1.2551895,36.7822034,-1.256522,36.786413200000005,Rider_Id_536,4
-Order_No_8479,User_Id_1880,Bike,3,Business,24,4,11:22:31 AM,24,4,11:22:43 AM,24,4,11:23:07 AM,24,4,11:31:40 AM,24,4,12:01:56 PM,7,24.5,,-1.2571472,36.795063299999995,-1.2948834999999999,36.7830474,Rider_Id_586,1816
-Order_No_2010,User_Id_635,Bike,3,Business,25,1,11:55:55 AM,25,1,12:11:44 PM,25,1,12:12:19 PM,25,1,12:32:54 PM,25,1,12:52:20 PM,7,25.4,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_518,1166
-Order_No_17731,User_Id_286,Bike,3,Business,30,5,5:13:23 PM,30,5,5:15:17 PM,30,5,5:16:19 PM,30,5,5:23:24 PM,30,5,5:33:34 PM,3,22.0,,-1.2551895,36.7822034,-1.2666153,36.8015124,Rider_Id_259,610
-Order_No_19761,User_Id_2801,Bike,3,Business,26,3,2:11:31 PM,26,3,3:36:25 PM,26,3,4:04:29 PM,26,3,4:11:23 PM,26,3,4:23:13 PM,7,27.2,,-1.281577,36.79382929999999,-1.2595421999999998,36.7871178,Rider_Id_875,710
-Order_No_542,User_Id_1329,Bike,3,Business,6,4,3:55:00 PM,6,4,4:17:27 PM,6,4,4:32:32 PM,6,4,4:37:52 PM,6,4,4:51:08 PM,4,21.7,,-1.273056,36.811298,-1.2477846000000001,36.8023807,Rider_Id_75,796
-Order_No_20875,User_Id_1200,Bike,1,Personal,25,2,9:44:15 AM,25,2,9:44:50 AM,25,2,9:58:29 AM,25,2,10:07:20 AM,25,2,10:16:26 AM,6,17.8,,-1.2703282,36.7642563,-1.2655477,36.8001682,Rider_Id_116,546
-Order_No_10178,User_Id_3082,Bike,1,Personal,26,6,10:19:39 AM,26,6,10:20:06 AM,26,6,10:21:59 AM,26,6,10:25:31 AM,26,6,10:39:48 AM,6,18.5,,-1.2803499,36.827340299999996,-1.307774,36.840754100000005,Rider_Id_100,857
-Order_No_21000,User_Id_567,Bike,3,Business,21,5,11:50:17 AM,21,5,12:14:57 PM,21,5,12:43:06 PM,21,5,1:11:36 PM,21,5,1:13:38 PM,5,22.6,,-1.2745551000000002,36.7979797,-1.2795796,36.819062,Rider_Id_72,122
-Order_No_16946,User_Id_635,Bike,3,Business,11,4,2:34:17 PM,11,4,2:35:41 PM,11,4,2:52:19 PM,11,4,3:13:28 PM,11,4,4:02:11 PM,8,27.2,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_155,2923
-Order_No_24290,User_Id_2008,Bike,3,Business,18,5,12:35:07 PM,18,5,12:35:22 PM,18,5,12:37:30 PM,18,5,12:53:13 PM,18,5,1:29:55 PM,17,20.2,,-1.2508229,36.7895263,-1.2482377,36.6673782,Rider_Id_882,2202
-Order_No_4853,User_Id_2828,Bike,3,Business,23,6,11:56:23 AM,23,6,11:56:38 AM,23,6,11:57:20 AM,23,6,12:24:27 PM,23,6,12:28:58 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_446,271
-Order_No_1859,User_Id_2523,Bike,3,Business,8,5,1:36:03 PM,8,5,1:38:01 PM,8,5,2:06:49 PM,8,5,2:16:16 PM,8,5,3:36:38 PM,7,27.0,,-1.2861764999999998,36.8209086,-1.2551895,36.7822034,Rider_Id_206,4822
-Order_No_14485,User_Id_246,Bike,3,Business,21,1,2:04:56 PM,21,1,2:05:24 PM,21,1,2:12:09 PM,21,1,2:16:32 PM,21,1,2:24:50 PM,3,27.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_205,498
-Order_No_23089,User_Id_1724,Bike,3,Business,25,4,9:31:27 AM,25,4,9:33:25 AM,25,4,9:44:05 AM,25,4,9:45:38 AM,25,4,10:11:13 AM,10,,,-1.3122786,36.8098477,-1.2551895,36.7822034,Rider_Id_709,1535
-Order_No_11801,User_Id_3318,Bike,3,Business,24,3,11:39:28 AM,24,3,11:40:36 AM,24,3,11:55:22 AM,24,3,11:57:50 AM,24,3,11:58:00 AM,4,,,-1.3004062,36.829741,-1.2825816,36.8092346,Rider_Id_19,10
-Order_No_25680,User_Id_788,Bike,3,Business,31,1,9:13:39 AM,31,1,9:18:07 AM,31,1,9:22:26 AM,31,1,9:27:59 AM,31,1,9:43:10 AM,7,18.5,3.0,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_453,911
-Order_No_27412,User_Id_970,Bike,3,Business,24,3,9:30:41 AM,24,3,9:30:50 AM,24,3,9:33:26 AM,24,3,9:41:31 AM,24,3,10:01:13 AM,12,,,-1.2551895,36.7822034,-1.3181546,36.837326399999995,Rider_Id_291,1182
-Order_No_17217,User_Id_391,Bike,3,Business,16,2,12:55:10 PM,16,2,1:13:32 PM,16,2,1:36:51 PM,16,2,1:43:35 PM,16,2,2:37:00 PM,15,27.3,,-1.319613,36.711055,-1.2784063,36.8222933,Rider_Id_328,3205
-Order_No_11435,User_Id_1146,Bike,3,Business,12,2,12:22:12 PM,12,2,12:23:09 PM,12,2,12:23:28 PM,12,2,1:00:13 PM,12,2,1:11:26 PM,5,24.5,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_469,673
-Order_No_3404,User_Id_1386,Bike,2,Personal,26,3,12:07:26 PM,26,3,12:08:16 PM,26,3,12:17:51 PM,26,3,12:21:06 PM,26,3,12:35:16 PM,6,24.2,,-1.2940713,36.7890758,-1.2796103,36.8127301,Rider_Id_674,850
-Order_No_21312,User_Id_3560,Bike,3,Business,14,3,1:53:26 PM,14,3,2:16:41 PM,14,3,2:25:54 PM,14,3,2:28:10 PM,14,3,3:22:11 PM,9,25.6,,-1.279395,36.825364,-1.3242351,36.8202498,Rider_Id_237,3241
-Order_No_9643,User_Id_1414,Bike,3,Personal,20,4,1:27:22 PM,20,4,2:17:57 PM,20,4,2:38:43 PM,20,4,3:28:35 PM,20,4,4:28:35 PM,7,25.0,,-1.2905666,36.853309700000004,-1.2786493,36.825228700000004,Rider_Id_436,3600
-Order_No_25637,User_Id_3291,Bike,3,Business,8,5,11:51:07 AM,8,5,11:52:40 AM,8,5,12:52:05 PM,8,5,1:02:01 PM,8,5,1:20:08 PM,4,28.3,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_690,1087
-Order_No_11882,User_Id_3147,Bike,3,Business,19,2,9:35:17 AM,19,2,9:35:26 AM,19,2,9:39:11 AM,19,2,10:04:49 AM,19,2,10:40:39 AM,15,,,-1.2571472,36.795063299999995,-1.3472066,36.7692638,Rider_Id_770,2150
-Order_No_1921,User_Id_1151,Bike,2,Personal,5,3,3:24:55 PM,5,3,3:25:21 PM,5,3,3:28:17 PM,5,3,3:52:12 PM,5,3,4:24:25 PM,15,23.7,,-1.2696795,36.8640088,-1.304842,36.90576,Rider_Id_182,1933
-Order_No_17584,User_Id_3355,Bike,3,Business,4,1,1:55:09 PM,4,1,1:56:05 PM,4,1,2:06:00 PM,4,1,2:18:12 PM,4,1,2:33:01 PM,6,28.8,,-1.3052491000000002,36.8223899,-1.2897215,36.7945959,Rider_Id_210,889
-Order_No_17848,User_Id_1329,Bike,3,Business,28,3,10:24:16 AM,28,3,10:27:10 AM,28,3,10:36:01 AM,28,3,10:41:25 AM,28,3,11:35:10 AM,4,,,-1.273056,36.811298,-1.2877501,36.814659899999995,Rider_Id_829,3225
-Order_No_7454,User_Id_1402,Bike,3,Business,18,1,10:13:43 AM,18,1,10:18:08 AM,18,1,10:35:19 AM,18,1,10:53:50 AM,18,1,11:46:00 AM,11,27.8,,-1.225322,36.80855,-1.2813012,36.832396200000005,Rider_Id_763,3130
-Order_No_6183,User_Id_3283,Bike,3,Business,28,4,10:35:10 AM,28,4,10:36:28 AM,28,4,10:57:50 AM,28,4,11:06:38 AM,28,4,11:27:17 AM,13,23.9,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_590,1239
-Order_No_5677,User_Id_517,Bike,3,Business,9,6,11:14:38 AM,9,6,11:14:48 AM,9,6,11:15:01 AM,9,6,11:24:32 AM,9,6,11:24:37 AM,5,24.7,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_619,5
-Order_No_5588,User_Id_592,Bike,1,Personal,3,3,2:59:45 PM,3,3,3:00:38 PM,3,3,3:01:12 PM,3,3,3:26:12 PM,3,3,3:34:35 PM,2,26.6,,-1.2678628,36.794366499999995,-1.2615348999999998,36.8068294,Rider_Id_875,503
-Order_No_100,User_Id_1787,Bike,3,Business,8,2,9:53:55 AM,8,2,10:00:28 AM,8,2,10:08:01 AM,8,2,10:15:49 AM,8,2,10:43:12 AM,7,18.8,,-1.2609721,36.8480813,-1.2283403,36.8822756,Rider_Id_79,1643
-Order_No_19876,User_Id_971,Bike,1,Personal,6,4,4:16:47 PM,6,4,4:17:09 PM,6,4,4:34:36 PM,6,4,4:38:38 PM,6,4,5:16:43 PM,11,22.4,,-1.3193233,36.840990999999995,-1.257309,36.806008399999996,Rider_Id_548,2285
-Order_No_8487,User_Id_868,Bike,3,Business,22,3,3:16:54 PM,22,3,3:32:30 PM,22,3,4:07:00 PM,22,3,4:10:08 PM,22,3,4:10:09 PM,5,24.1,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_849,1
-Order_No_20092,User_Id_1205,Bike,3,Business,24,3,3:14:04 PM,24,3,3:14:34 PM,24,3,3:27:59 PM,24,3,3:31:51 PM,24,3,3:44:49 PM,3,18.3,,-1.257309,36.806008399999996,-1.2657148,36.815146399999996,Rider_Id_727,778
-Order_No_10665,User_Id_944,Bike,1,Personal,17,3,9:46:16 AM,17,3,9:46:49 AM,17,3,9:49:20 AM,17,3,9:59:12 AM,17,3,10:19:12 AM,7,21.0,,-1.2555969999999999,36.830024,-1.2964347999999999,36.8182757,Rider_Id_338,1200
-Order_No_12993,User_Id_3493,Bike,1,Personal,2,1,2:15:58 PM,2,1,2:19:37 PM,2,1,2:30:11 PM,2,1,2:38:35 PM,2,1,2:51:10 PM,7,19.8,,-1.2912668,36.7656703,-1.2623805000000001,36.7805404,Rider_Id_116,755
-Order_No_24878,User_Id_1500,Bike,3,Business,12,5,2:01:06 PM,12,5,2:01:30 PM,12,5,2:01:38 PM,12,5,2:28:56 PM,12,5,2:51:37 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_349,1361
-Order_No_19533,User_Id_3647,Bike,3,Business,5,3,11:09:18 AM,5,3,11:10:46 AM,5,3,11:21:19 AM,5,3,11:26:09 AM,5,3,12:19:18 PM,15,22.6,,-1.2726389999999999,36.794723,-1.3194518,36.869396,Rider_Id_547,3189
-Order_No_9850,User_Id_435,Bike,3,Business,29,3,2:48:39 PM,29,3,2:48:39 PM,29,3,2:49:27 PM,29,3,3:16:53 PM,29,3,3:39:28 PM,15,21.0,,-1.300921,36.828195,-1.242265,36.88087279999999,Rider_Id_521,1355
-Order_No_14231,User_Id_2423,Bike,3,Business,3,3,3:32:53 PM,3,3,3:33:02 PM,3,3,3:41:45 PM,3,3,4:35:51 PM,3,3,4:46:08 PM,4,27.4,,-1.2571472,36.795063299999995,-1.2676851,36.8209429,Rider_Id_279,617
-Order_No_27004,User_Id_3565,Bike,3,Business,25,5,10:03:59 AM,25,5,10:04:30 AM,25,5,10:15:35 AM,25,5,10:35:54 AM,25,5,11:22:48 AM,35,21.1,,-1.2844665000000002,36.7866113,-1.1140488000000002,37.012030200000005,Rider_Id_908,2814
-Order_No_4837,User_Id_3647,Bike,3,Business,15,1,2:07:10 PM,15,1,2:08:02 PM,15,1,2:14:53 PM,15,1,2:16:36 PM,15,1,2:35:52 PM,8,,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_434,1156
-Order_No_27282,User_Id_2987,Bike,3,Business,26,1,10:18:29 AM,26,1,11:05:33 AM,26,1,11:30:56 AM,26,1,11:34:30 AM,26,1,11:49:48 AM,4,22.9,,-1.2702120000000001,36.82108770000001,-1.2534301,36.800098299999995,Rider_Id_322,918
-Order_No_17068,User_Id_2046,Bike,2,Personal,2,4,4:39:42 PM,2,4,4:41:22 PM,2,4,4:58:32 PM,2,4,5:08:13 PM,2,4,5:28:49 PM,4,23.7,,-1.283994,36.773977,-1.2965169,36.793521999999996,Rider_Id_229,1236
-Order_No_10259,User_Id_116,Bike,3,Personal,10,1,3:15:45 PM,10,1,3:19:02 PM,10,1,3:35:00 PM,10,1,3:38:58 PM,10,1,4:10:57 PM,14,18.1,,-1.2201256999999999,36.8115035,-1.2929587,36.783318,Rider_Id_856,1919
-Order_No_20345,User_Id_3727,Bike,3,Business,13,2,11:01:05 AM,13,2,11:02:10 AM,13,2,11:11:19 AM,13,2,11:21:43 AM,13,2,11:50:44 AM,7,22.5,,-1.2963097,36.7688221,-1.268276,36.800139,Rider_Id_147,1741
-Order_No_27656,User_Id_467,Bike,3,Business,10,5,12:02:31 PM,10,5,12:02:59 PM,10,5,12:07:28 PM,10,5,12:18:46 PM,10,5,12:36:31 PM,3,,,-1.304735,36.784605400000004,-1.2850048,36.79233789999999,Rider_Id_468,1065
-Order_No_6292,User_Id_393,Bike,3,Business,29,5,2:21:53 PM,29,5,2:22:17 PM,29,5,2:22:25 PM,29,5,2:29:04 PM,29,5,2:57:23 PM,11,29.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_349,1699
-Order_No_19449,User_Id_187,Bike,3,Business,31,3,5:21:52 PM,31,3,6:25:12 PM,31,3,6:31:17 PM,31,3,6:39:13 PM,31,3,7:15:47 PM,17,24.6,,-1.3169591,36.7000029,-1.2584143,36.8048002,Rider_Id_647,2194
-Order_No_13280,User_Id_311,Bike,3,Business,29,2,2:16:07 PM,29,2,2:17:03 PM,29,2,2:19:41 PM,29,2,2:29:26 PM,29,2,3:12:20 PM,20,27.1,,-1.2917868000000001,36.7872675,-1.3705766000000001,36.9120742,Rider_Id_656,2574
-Order_No_24797,User_Id_925,Bike,3,Business,7,3,2:26:29 PM,7,3,2:42:53 PM,7,3,2:53:07 PM,7,3,2:54:07 PM,7,3,3:14:09 PM,7,28.1,,-1.3253336000000002,36.8305745,-1.3097713,36.849494,Rider_Id_436,1202
-Order_No_6774,User_Id_1195,Bike,3,Business,14,4,9:21:42 AM,14,4,9:22:01 AM,14,4,9:43:38 AM,14,4,10:04:48 AM,14,4,10:37:00 AM,14,17.6,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_326,1932
-Order_No_25318,User_Id_2523,Bike,3,Business,21,4,4:31:45 PM,21,4,4:32:20 PM,21,4,4:38:29 PM,21,4,4:43:32 PM,21,4,5:12:33 PM,9,28.7,,-1.2551895,36.7822034,-1.3074264,36.822612299999996,Rider_Id_26,1741
-Order_No_15516,User_Id_3599,Bike,3,Business,21,2,10:22:12 AM,21,2,10:55:13 AM,21,2,11:26:50 AM,21,2,11:36:34 AM,21,2,12:08:35 PM,8,22.7,,-1.3302996,36.8707935,-1.3116855,36.8233653,Rider_Id_332,1921
-Order_No_20791,User_Id_3355,Bike,3,Business,8,5,1:13:23 PM,8,5,1:13:31 PM,8,5,1:22:58 PM,8,5,1:42:48 PM,8,5,2:05:13 PM,17,27.0,,-1.2859912,36.8756811,-1.2551895,36.7822034,Rider_Id_839,1345
-Order_No_21680,User_Id_391,Bike,3,Business,22,5,10:49:11 AM,22,5,10:50:36 AM,22,5,10:51:20 AM,22,5,11:29:17 AM,22,5,11:39:50 AM,7,28.3,,-1.300921,36.828195,-1.2897108999999998,36.8715037,Rider_Id_595,633
-Order_No_10572,User_Id_517,Bike,3,Business,3,1,11:21:06 AM,3,1,11:21:49 AM,3,1,11:28:45 AM,3,1,11:39:26 AM,3,1,11:58:33 AM,11,21.4,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_155,1147
-Order_No_15178,User_Id_3222,Bike,3,Business,28,2,12:49:04 PM,28,2,12:50:22 PM,28,2,12:55:50 PM,28,2,1:07:46 PM,28,2,1:51:04 PM,8,21.1,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_154,2598
-Order_No_23297,User_Id_1329,Bike,3,Business,28,2,12:20:31 PM,28,2,12:21:05 PM,28,2,12:39:10 PM,28,2,12:41:22 PM,28,2,1:00:00 PM,6,21.1,,-1.311452,36.82182,-1.273056,36.811298,Rider_Id_591,1118
-Order_No_17179,User_Id_2910,Bike,3,Business,10,1,3:40:57 PM,10,1,3:45:01 PM,10,1,4:03:56 PM,10,1,4:37:13 PM,10,1,5:00:30 PM,8,18.6,,-1.2601197,36.7985271,-1.296797,36.776452500000005,Rider_Id_382,1397
-Order_No_7756,User_Id_2691,Bike,3,Business,10,4,11:26:51 AM,10,4,11:30:19 AM,10,4,11:37:39 AM,10,4,11:49:46 AM,10,4,12:08:42 PM,8,24.6,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_319,1136
-Order_No_20942,User_Id_2069,Bike,3,Business,29,4,12:50:12 PM,29,4,12:51:09 PM,29,4,12:59:53 PM,29,4,1:20:35 PM,29,4,1:45:36 PM,12,21.6,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_646,1501
-Order_No_7980,User_Id_1075,Bike,3,Business,30,6,11:14:17 AM,30,6,11:14:29 AM,30,6,11:45:05 AM,30,6,11:50:45 AM,30,6,12:17:36 PM,15,26.5,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_510,1611
-Order_No_16242,User_Id_2330,Bike,3,Business,18,1,4:06:03 PM,18,1,4:06:11 PM,18,1,4:21:31 PM,18,1,4:23:34 PM,18,1,4:34:39 PM,6,30.6,,-1.3015197,36.7658456,-1.2921463,36.8029626,Rider_Id_510,665
-Order_No_21119,User_Id_3517,Bike,3,Business,13,2,12:21:44 PM,13,2,12:22:00 PM,13,2,12:49:03 PM,13,2,1:06:25 PM,13,2,1:31:04 PM,15,22.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_550,1479
-Order_No_11569,User_Id_1118,Bike,1,Personal,11,3,12:02:21 PM,11,3,12:38:32 PM,11,3,12:57:01 PM,11,3,1:04:24 PM,11,3,1:47:19 PM,7,19.0,,-1.2857902,36.8316231,-1.3226567,36.8221416,Rider_Id_281,2575
-Order_No_15142,User_Id_865,Bike,3,Business,14,2,9:01:39 AM,14,2,9:02:35 AM,14,2,9:15:08 AM,14,2,9:18:27 AM,14,2,10:03:29 AM,22,,,-1.2456412000000001,36.7955298,-1.3442152,36.9199984,Rider_Id_155,2702
-Order_No_17172,User_Id_3201,Bike,3,Business,2,4,3:04:13 PM,2,4,3:05:12 PM,2,4,3:08:56 PM,2,4,3:11:54 PM,2,4,3:21:43 PM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_881,589
-Order_No_2352,User_Id_3547,Bike,3,Business,20,3,12:16:51 PM,20,3,12:17:07 PM,20,3,1:14:26 PM,20,3,1:24:23 PM,20,3,1:59:45 PM,18,27.0,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_48,2122
-Order_No_21211,User_Id_1959,Bike,1,Business,22,3,10:34:19 AM,22,3,10:34:27 AM,22,3,10:57:32 AM,22,3,11:00:49 AM,22,3,11:34:17 AM,9,22.9,,-1.257873,36.843612,-1.2559559999999999,36.789867,Rider_Id_54,2008
-Order_No_21317,User_Id_2271,Bike,3,Business,18,6,10:52:17 AM,18,6,10:54:01 AM,18,6,11:04:37 AM,18,6,11:12:35 AM,18,6,11:35:06 AM,11,21.1,,-1.318062,36.849326,-1.293515,36.897607,Rider_Id_709,1351
-Order_No_12391,User_Id_928,Bike,3,Business,30,3,10:05:41 AM,30,3,10:15:14 AM,30,3,11:19:01 AM,30,3,11:23:33 AM,30,3,11:50:34 AM,8,25.2,,-1.2551895,36.7822034,-1.2866381000000002,36.7963662,Rider_Id_829,1621
-Order_No_20782,User_Id_419,Bike,3,Business,6,3,2:55:15 PM,6,3,2:55:46 PM,6,3,3:12:26 PM,6,3,3:18:49 PM,6,3,3:49:37 PM,21,27.7,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_717,1848
-Order_No_1998,User_Id_2195,Bike,3,Business,29,1,3:59:51 PM,29,1,4:00:50 PM,29,1,4:23:57 PM,29,1,4:53:04 PM,29,1,5:09:22 PM,11,,,-1.2783848999999998,36.8163445,-1.228911,36.881876,Rider_Id_815,978
-Order_No_2917,User_Id_3080,Bike,3,Business,8,5,2:20:53 PM,8,5,2:21:30 PM,8,5,2:41:44 PM,8,5,2:51:59 PM,8,5,3:02:26 PM,5,30.2,,-1.2551895,36.7822034,-1.2644443,36.7734547,Rider_Id_452,627
-Order_No_18564,User_Id_3469,Bike,3,Personal,23,1,12:11:58 PM,23,1,12:12:21 PM,23,1,12:23:48 PM,23,1,12:33:45 PM,23,1,12:57:59 PM,13,18.6,,-1.2521881000000001,36.7537237,-1.2900075,36.8032564,Rider_Id_334,1454
-Order_No_2398,User_Id_2306,Bike,3,Business,26,2,11:21:38 AM,26,2,11:21:47 AM,26,2,11:45:54 AM,26,2,11:48:21 AM,26,2,12:34:28 PM,19,27.4,,-1.2271998,36.808490500000005,-1.3241108,36.8692908,Rider_Id_6,2767
-Order_No_26900,User_Id_302,Bike,3,Business,5,1,3:53:40 PM,5,1,4:20:59 PM,5,1,4:29:44 PM,5,1,4:40:49 PM,5,1,4:53:54 PM,7,24.5,,-1.2649605,36.798177700000004,-1.2916738,36.790105700000005,Rider_Id_315,785
-Order_No_10105,User_Id_2817,Bike,1,Personal,25,1,10:01:30 AM,25,1,10:02:08 AM,25,1,10:03:04 AM,25,1,10:15:15 AM,25,1,10:29:54 AM,7,15.2,,-1.264788,36.7883897,-1.2941557,36.7792012,Rider_Id_379,879
-Order_No_15614,User_Id_2344,Bike,3,Business,19,2,4:13:06 PM,19,2,4:14:46 PM,19,2,4:26:08 PM,19,2,4:32:40 PM,19,2,5:03:57 PM,8,28.2,,-1.298529,36.816482,-1.2551895,36.7822034,Rider_Id_372,1877
-Order_No_6059,User_Id_1205,Bike,3,Business,5,3,12:39:53 PM,5,3,12:40:01 PM,5,3,12:50:11 PM,5,3,1:05:01 PM,5,3,1:47:17 PM,21,22.3,,-1.2810807,36.8144229,-1.3802928,36.93644929999999,Rider_Id_105,2536
-Order_No_22443,User_Id_2688,Bike,2,Personal,22,3,1:46:52 PM,22,3,1:48:30 PM,22,3,1:49:00 PM,22,3,2:11:13 PM,22,3,2:35:43 PM,4,25.8,,-1.296797,36.776452500000005,-1.2897215,36.7945959,Rider_Id_414,1470
-Order_No_2422,User_Id_1482,Bike,3,Business,12,3,11:26:05 AM,12,3,11:36:16 AM,12,3,11:51:40 AM,12,3,12:02:00 PM,12,3,12:58:20 PM,12,19.6,,-1.2079306,36.770537700000006,-1.2573921000000001,36.7992507,Rider_Id_959,3380
-Order_No_26023,User_Id_3404,Bike,3,Business,11,1,10:47:58 AM,11,1,10:50:29 AM,11,1,10:57:12 AM,11,1,11:02:33 AM,11,1,11:50:02 AM,11,26.6,,-1.279395,36.825364,-1.3206958,36.8769553,Rider_Id_564,2849
-Order_No_19655,User_Id_1706,Bike,2,Personal,19,3,4:11:42 PM,19,3,4:12:57 PM,19,3,4:18:46 PM,19,3,4:32:36 PM,19,3,4:40:44 PM,2,23.2,,-1.3018873,36.7766167,-1.2908158,36.7784546,Rider_Id_312,488
-Order_No_21974,User_Id_2423,Bike,3,Business,3,3,3:13:31 PM,3,3,3:18:30 PM,3,3,3:43:30 PM,3,3,3:53:59 PM,3,3,4:15:13 PM,7,28.0,,-1.2571472,36.795063299999995,-1.2963546000000001,36.8037035,Rider_Id_177,1274
-Order_No_16841,User_Id_3426,Bike,3,Business,26,5,1:30:53 PM,26,5,1:38:35 PM,26,5,1:54:26 PM,26,5,2:10:13 PM,26,5,2:52:59 PM,9,,,-1.2615888000000002,36.7928732,-1.3014461000000002,36.7661381,Rider_Id_548,2566
-Order_No_25551,User_Id_3295,Bike,3,Business,22,3,11:27:17 AM,22,3,11:29:20 AM,22,3,12:07:04 PM,22,3,12:10:15 PM,22,3,12:51:08 PM,7,23.3,,-1.2571472,36.795063299999995,-1.2965924,36.8179314,Rider_Id_139,2453
-Order_No_3660,User_Id_2656,Bike,2,Personal,5,6,10:11:12 AM,5,6,10:11:39 AM,5,6,10:54:28 AM,5,6,11:09:49 AM,5,6,11:44:40 AM,26,22.4,,-1.1874211000000001,36.928644899999995,-1.2925505,36.7953715,Rider_Id_813,2091
-Order_No_8167,User_Id_1075,Bike,3,Business,6,3,1:51:47 PM,6,3,2:08:46 PM,6,3,2:22:32 PM,6,3,2:46:19 PM,6,3,3:14:47 PM,15,27.7,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_958,1708
-Order_No_27950,User_Id_1500,Bike,3,Business,4,4,2:58:56 PM,4,4,2:59:12 PM,4,4,2:59:32 PM,4,4,3:05:07 PM,4,4,3:30:41 PM,9,27.3,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_349,1534
-Order_No_26676,User_Id_2963,Bike,1,Personal,18,1,10:39:31 AM,18,1,10:43:11 AM,18,1,10:49:05 AM,18,1,11:00:15 AM,18,1,11:07:13 AM,2,,,-1.3007534,36.7964576,-1.2860203000000001,36.7832538,Rider_Id_865,418
-Order_No_17105,User_Id_2150,Bike,3,Business,17,3,9:17:35 AM,17,3,9:28:59 AM,17,3,9:39:18 AM,17,3,9:44:36 AM,17,3,10:09:18 AM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_263,1482
-Order_No_24253,User_Id_3136,Bike,1,Personal,28,4,9:16:16 AM,28,4,9:17:11 AM,28,4,9:23:33 AM,28,4,9:32:28 AM,28,4,9:40:34 AM,4,,,-1.2696183,36.8021778,-1.264788,36.7883897,Rider_Id_116,486
-Order_No_35,User_Id_2801,Bike,3,Business,8,3,1:27:38 PM,8,3,1:27:56 PM,8,3,2:10:35 PM,8,3,2:20:26 PM,8,3,2:38:36 PM,7,,,-1.2674273999999999,36.787082700000006,-1.2827545,36.79361289999999,Rider_Id_921,1090
-Order_No_6657,User_Id_1402,Bike,3,Business,7,4,3:29:29 PM,7,4,3:32:05 PM,7,4,4:00:26 PM,7,4,4:09:26 PM,7,4,4:32:07 PM,2,30.9,,-1.2813012,36.832396200000005,-1.2848173999999999,36.826163,Rider_Id_653,1361
-Order_No_10355,User_Id_97,Bike,3,Business,14,1,11:07:40 AM,14,1,11:08:23 AM,14,1,11:12:43 AM,14,1,11:23:09 AM,14,1,11:58:27 AM,6,23.6,,-1.2879618,36.82008629999999,-1.2969741000000001,36.7856612,Rider_Id_178,2118
-Order_No_22590,User_Id_1141,Bike,3,Business,20,2,2:21:54 PM,20,2,2:58:26 PM,20,2,3:14:45 PM,20,2,3:22:47 PM,20,2,4:04:02 PM,8,24.2,,-1.2877645,36.837853499999994,-1.2979147,36.785781400000005,Rider_Id_263,2475
-Order_No_25016,User_Id_393,Bike,3,Business,22,5,12:09:42 PM,22,5,12:09:56 PM,22,5,12:10:43 PM,22,5,12:23:02 PM,22,5,12:58:04 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_26,2102
-Order_No_3899,User_Id_1329,Bike,3,Business,2,5,4:48:03 PM,2,5,4:57:51 PM,2,5,5:14:09 PM,2,5,5:19:53 PM,2,5,5:36:39 PM,6,24.1,,-1.273056,36.811298,-1.3005349,36.7873912,Rider_Id_147,1006
-Order_No_21611,User_Id_649,Bike,1,Business,19,4,11:14:53 AM,19,4,11:15:53 AM,19,4,11:21:09 AM,19,4,11:36:26 AM,19,4,11:49:30 AM,9,20.1,,-1.3034396000000001,36.832999799999996,-1.2551895,36.7822034,Rider_Id_629,784
-Order_No_22065,User_Id_393,Bike,3,Business,14,4,8:56:34 AM,14,4,8:56:58 AM,14,4,9:07:48 AM,14,4,9:17:22 AM,14,4,9:43:02 AM,4,19.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,1540
-Order_No_22229,User_Id_3002,Bike,1,Personal,27,1,10:23:47 AM,27,1,10:29:33 AM,27,1,10:44:57 AM,27,1,10:47:13 AM,27,1,11:01:16 AM,7,23.9,,-1.2527469,36.7904333,-1.2868838,36.8284259,Rider_Id_754,843
-Order_No_24085,User_Id_1071,Bike,1,Personal,6,1,10:41:03 AM,6,1,10:41:16 AM,6,1,10:50:12 AM,6,1,11:00:29 AM,6,1,11:24:13 AM,11,20.3,,-1.3301743000000001,36.8487987,-1.2689015,36.7986106,Rider_Id_105,1424
-Order_No_197,User_Id_2330,Bike,3,Business,20,3,4:12:03 PM,20,3,4:12:26 PM,20,3,4:16:36 PM,20,3,4:17:56 PM,20,3,5:03:06 PM,13,28.5,,-1.2533885,36.8165128,-1.2284323,36.8757058,Rider_Id_660,2710
-Order_No_10495,User_Id_437,Bike,3,Business,8,4,11:16:32 AM,8,4,11:17:04 AM,8,4,11:26:08 AM,8,4,11:40:16 AM,8,4,12:37:46 PM,18,26.3,,-1.2551895,36.7822034,-1.2902161,36.893618100000005,Rider_Id_328,3450
-Order_No_21333,User_Id_1205,Bike,3,Business,28,4,10:39:28 AM,28,4,10:40:22 AM,28,4,10:52:00 AM,28,4,11:01:21 AM,28,4,11:43:06 AM,3,,,-1.2810807,36.8144229,-1.2920693,36.8026415,Rider_Id_88,2505
-Order_No_16691,User_Id_3070,Bike,3,Business,22,3,2:08:20 PM,22,3,2:09:51 PM,22,3,2:26:21 PM,22,3,2:39:26 PM,22,3,3:25:11 PM,19,24.5,,-1.3127833999999998,36.869639899999996,-1.2521881000000001,36.7537237,Rider_Id_691,2745
-Order_No_18587,User_Id_435,Bike,3,Business,27,1,2:34:02 PM,27,1,2:34:45 PM,27,1,3:22:01 PM,27,1,3:37:44 PM,27,1,4:33:54 PM,10,23.2,,-1.3000978,36.7658352,-1.300921,36.828195,Rider_Id_92,3370
-Order_No_6850,User_Id_1106,Bike,1,Personal,13,4,9:19:42 AM,13,4,9:20:27 AM,13,4,9:26:48 AM,13,4,9:38:47 AM,13,4,10:06:48 AM,12,18.8,,-1.2684666999999998,36.8002891,-1.3353385,36.7778749,Rider_Id_882,1681
-Order_No_15846,User_Id_60,Bike,3,Business,24,1,10:51:19 AM,24,1,10:51:43 AM,24,1,11:03:42 AM,24,1,11:14:35 AM,24,1,11:28:12 AM,8,22.6,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_427,817
-Order_No_8199,User_Id_909,Bike,3,Business,5,5,1:14:34 PM,5,5,1:14:54 PM,5,5,1:34:54 PM,5,5,1:42:53 PM,5,5,2:22:39 PM,14,27.8,,-1.2156006000000001,36.8916865,-1.225322,36.80855,Rider_Id_709,2386
-Order_No_2475,User_Id_1792,Bike,3,Business,24,1,10:15:58 AM,24,1,10:16:25 AM,24,1,10:22:28 AM,24,1,10:26:36 AM,24,1,10:33:51 AM,4,15.4,,-1.2809121,36.7852524,-1.2756466999999998,36.807989500000005,Rider_Id_446,435
-Order_No_21016,User_Id_2523,Bike,3,Business,11,1,10:43:38 AM,11,1,10:44:06 AM,11,1,10:48:45 AM,11,1,10:54:50 AM,11,1,11:08:44 AM,4,26.6,,-1.2551895,36.7822034,-1.2754343000000001,36.8127158,Rider_Id_520,834
-Order_No_5493,User_Id_393,Bike,3,Business,24,5,8:53:47 AM,24,5,8:54:01 AM,24,5,9:07:08 AM,24,5,9:27:18 AM,24,5,9:31:29 AM,1,18.3,,-1.3002553999999997,36.8256566,-1.3004062,36.829741,Rider_Id_211,251
-Order_No_13260,User_Id_1246,Bike,3,Business,25,4,8:38:47 AM,25,4,8:41:54 AM,25,4,9:20:53 AM,25,4,9:23:08 AM,25,4,10:16:22 AM,18,16.2,2.0,-1.3700383,36.9190174,-1.2824744,36.804724,Rider_Id_487,3194
-Order_No_13891,User_Id_81,Bike,3,Business,30,3,11:08:22 AM,30,3,11:10:15 AM,30,3,11:16:08 AM,30,3,11:37:46 AM,30,3,12:10:17 PM,16,24.9,,-1.2156006000000001,36.8916865,-1.2641496,36.791413899999995,Rider_Id_869,1951
-Order_No_2923,User_Id_788,Bike,3,Business,23,1,9:54:43 AM,23,1,9:54:49 AM,23,1,10:01:14 AM,23,1,10:04:59 AM,23,1,10:28:58 AM,11,14.4,,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_616,1439
-Order_No_20282,User_Id_3464,Bike,3,Business,23,6,10:43:34 AM,23,6,10:44:50 AM,23,6,10:50:41 AM,23,6,11:01:09 AM,23,6,11:30:36 AM,9,26.2,,-1.3014461000000002,36.7661381,-1.2885029,36.825604999999996,Rider_Id_800,1767
-Order_No_12800,User_Id_2869,Bike,3,Business,19,2,9:27:47 AM,19,2,9:28:15 AM,19,2,9:44:01 AM,19,2,9:45:15 AM,19,2,10:00:48 AM,9,20.9,,-1.2551895,36.7822034,-1.3025826,36.7670807,Rider_Id_931,933
-Order_No_4778,User_Id_2587,Bike,3,Business,31,1,12:58:26 PM,31,1,12:58:32 PM,31,1,12:59:14 PM,31,1,1:26:26 PM,31,1,1:38:15 PM,6,21.2,,-1.28878,36.8168312,-1.256522,36.786413200000005,Rider_Id_449,709
-Order_No_2473,User_Id_2008,Bike,3,Business,17,3,10:13:34 AM,17,3,10:15:05 AM,17,3,10:31:36 AM,17,3,10:38:20 AM,17,3,10:59:45 AM,6,,,-1.2508229,36.7895263,-1.2674404,36.823165100000004,Rider_Id_422,1285
-Order_No_2396,User_Id_640,Bike,3,Business,7,3,10:49:42 AM,7,3,11:17:12 AM,7,3,11:39:22 AM,7,3,11:44:55 AM,7,3,12:16:16 PM,10,24.9,,-1.304033,36.7848695,-1.2644886,36.8018968,Rider_Id_208,1881
-Order_No_17313,User_Id_3647,Bike,3,Business,5,5,3:00:26 PM,5,5,3:01:40 PM,5,5,3:10:35 PM,5,5,3:20:11 PM,5,5,3:39:14 PM,7,27.6,,-1.2726389999999999,36.794723,-1.3004844,36.7704915,Rider_Id_99,1143
-Order_No_1781,User_Id_73,Bike,1,Personal,11,3,1:13:51 PM,11,3,1:14:08 PM,11,3,1:31:26 PM,11,3,1:59:15 PM,11,3,2:34:44 PM,10,20.3,,-1.2665773999999999,36.8069908,-1.3075119,36.8527811,Rider_Id_951,2129
-Order_No_25004,User_Id_3727,Bike,3,Business,18,2,3:08:12 PM,18,2,3:19:22 PM,18,2,3:26:32 PM,18,2,3:36:16 PM,18,2,4:57:12 PM,3,25.5,,-1.2963097,36.7688221,-1.29663,36.7912971,Rider_Id_829,4856
-Order_No_12771,User_Id_478,Bike,3,Business,7,1,7:35:54 AM,7,1,7:53:03 AM,7,1,8:10:02 AM,7,1,8:26:40 AM,7,1,8:51:33 AM,6,21.6,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_72,1493
-Order_No_11556,User_Id_3030,Bike,3,Business,24,5,11:48:24 AM,24,5,11:58:04 AM,24,5,12:07:53 PM,24,5,12:36:44 PM,24,5,12:47:09 PM,5,20.9,,-1.263441,36.840808,-1.2866011999999998,36.8195691,Rider_Id_229,625
-Order_No_508,User_Id_1417,Bike,3,Business,2,2,2:47:44 PM,2,2,2:47:58 PM,2,2,2:57:12 PM,2,2,3:31:55 PM,2,2,4:19:42 PM,20,29.4,,-1.3362908000000002,36.915456799999994,-1.2741728,36.79691079999999,Rider_Id_863,2867
-Order_No_5006,User_Id_972,Bike,3,Business,19,1,2:20:40 PM,19,1,3:37:07 PM,19,1,3:41:23 PM,19,1,3:46:34 PM,19,1,4:18:04 PM,14,25.4,,-1.2862033000000002,36.8152966,-1.2927792,36.9053492,Rider_Id_403,1890
-Order_No_27460,User_Id_214,Bike,3,Personal,13,1,2:43:12 PM,13,1,2:43:32 PM,13,1,2:58:48 PM,13,1,3:03:47 PM,13,1,3:48:19 PM,3,19.3,,-1.2616213,36.8221128,-1.262229,36.802597999999996,Rider_Id_407,2672
-Order_No_10408,User_Id_2866,Bike,3,Business,5,1,11:17:06 AM,5,1,11:17:40 AM,5,1,11:20:09 AM,5,1,11:26:50 AM,5,1,11:35:23 AM,4,24.6,,-1.2551895,36.7822034,-1.2658334999999998,36.804286700000006,Rider_Id_65,513
-Order_No_26279,User_Id_3686,Bike,3,Business,16,2,9:58:20 AM,16,2,10:00:37 AM,16,2,10:13:04 AM,16,2,10:38:34 AM,16,2,10:49:05 AM,5,23.3,,-1.2551895,36.7822034,-1.2684319,36.81658529999999,Rider_Id_726,631
-Order_No_27065,User_Id_2789,Bike,3,Business,28,4,2:22:45 PM,28,4,2:23:13 PM,28,4,2:37:55 PM,28,4,3:07:55 PM,28,4,3:20:52 PM,3,27.3,,-1.2929747,36.76463579999999,-1.304735,36.784605400000004,Rider_Id_682,777
-Order_No_26077,User_Id_3686,Bike,3,Business,7,5,12:49:24 PM,7,5,12:49:55 PM,7,5,12:50:30 PM,7,5,12:55:18 PM,7,5,1:05:12 PM,3,25.2,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_630,594
-Order_No_22780,User_Id_2627,Bike,3,Business,19,2,1:16:53 PM,19,2,1:17:23 PM,19,2,1:19:34 PM,19,2,1:46:48 PM,19,2,1:52:08 PM,2,29.8,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_205,320
-Order_No_19259,User_Id_1502,Bike,3,Business,7,3,11:00:50 AM,7,3,11:02:44 AM,7,3,11:13:37 AM,7,3,11:29:53 AM,7,3,11:59:52 AM,13,25.1,,-1.2822934,36.8226529,-1.2232123,36.8932547,Rider_Id_669,1799
-Order_No_4815,User_Id_700,Bike,3,Business,7,5,8:37:22 AM,7,5,8:40:38 AM,7,5,9:09:48 AM,7,5,9:16:15 AM,7,5,9:31:05 AM,7,18.3,,-1.2844520000000001,36.8300885,-1.3205163999999998,36.8098882,Rider_Id_88,890
-Order_No_9135,User_Id_3689,Bike,3,Business,13,1,3:50:45 PM,13,1,3:50:57 PM,13,1,3:51:19 PM,13,1,5:14:35 PM,13,1,5:15:45 PM,1,19.4,,-1.2642719999999998,36.7978791,-1.2527958000000001,36.8003131,Rider_Id_875,70
-Order_No_19996,User_Id_2423,Bike,3,Business,18,4,1:11:48 PM,18,4,1:15:50 PM,18,4,1:23:31 PM,18,4,1:34:57 PM,18,4,1:57:39 PM,7,,,-1.2571472,36.795063299999995,-1.3028118,36.8174764,Rider_Id_61,1362
-Order_No_13213,User_Id_3521,Bike,3,Business,31,2,1:44:23 PM,31,2,1:45:03 PM,31,2,2:00:23 PM,31,2,2:27:10 PM,31,2,3:00:20 PM,8,22.3,,-1.3031971000000002,36.786314600000004,-1.2696908000000002,36.81519,Rider_Id_851,1990
-Order_No_13457,User_Id_1927,Bike,3,Business,17,3,1:33:31 PM,17,3,1:33:42 PM,17,3,1:34:19 PM,17,3,1:51:31 PM,17,3,2:32:51 PM,20,,,-1.2852569,36.8976121,-1.3472066,36.7692638,Rider_Id_584,2480
-Order_No_3551,User_Id_3502,Bike,2,Personal,22,5,3:44:23 PM,22,5,3:45:13 PM,22,5,3:50:12 PM,22,5,4:15:34 PM,22,5,4:53:17 PM,10,,,-1.2930931,36.7559267,-1.2893883999999998,36.828207,Rider_Id_846,2263
-Order_No_12503,User_Id_3250,Bike,3,Business,27,3,1:07:16 PM,27,3,1:25:54 PM,27,3,2:09:18 PM,27,3,2:13:18 PM,27,3,2:46:48 PM,14,29.3,,-1.3827933000000001,36.7626219,-1.3034451,36.752312599999996,Rider_Id_269,2010
-Order_No_26765,User_Id_1245,Bike,3,Business,6,3,9:44:08 AM,6,3,9:44:23 AM,6,3,9:45:01 AM,6,3,10:18:53 AM,6,3,10:45:10 AM,17,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_167,1577
-Order_No_3648,User_Id_778,Bike,3,Business,4,1,10:08:55 AM,4,1,10:09:19 AM,4,1,10:24:53 AM,4,1,10:27:29 AM,4,1,10:42:47 AM,7,21.7,,-1.2535486,36.7933541,-1.2717174,36.775388299999996,Rider_Id_103,918
-Order_No_18962,User_Id_2175,Bike,3,Business,24,4,2:01:01 PM,24,4,2:01:41 PM,24,4,2:02:03 PM,24,4,2:09:45 PM,24,4,2:57:49 PM,19,28.5,,-1.2551895,36.7822034,-1.2860183,36.8975338,Rider_Id_651,2884
-Order_No_7291,User_Id_2987,Bike,3,Business,14,5,1:20:16 PM,14,5,1:20:20 PM,14,5,1:21:06 PM,14,5,1:29:29 PM,14,5,1:49:35 PM,12,24.4,,-1.2297202,36.881950700000004,-1.2649605,36.798177700000004,Rider_Id_584,1206
-Order_No_26805,User_Id_972,Bike,3,Business,7,2,1:09:55 PM,7,2,1:10:25 PM,7,2,1:22:44 PM,7,2,1:23:46 PM,7,2,1:43:55 PM,4,23.0,,-1.2862033000000002,36.8152966,-1.2996681,36.806841299999995,Rider_Id_674,1209
-Order_No_7395,User_Id_1995,Bike,3,Business,11,5,1:38:16 PM,11,5,1:41:12 PM,11,5,1:50:14 PM,11,5,1:57:26 PM,11,5,2:17:32 PM,9,22.6,,-1.2559559999999999,36.789867,-1.2765736,36.851364600000004,Rider_Id_742,1206
-Order_No_26822,User_Id_1245,Bike,3,Business,18,2,10:59:04 AM,18,2,11:01:25 AM,18,2,11:12:12 AM,18,2,11:29:52 AM,18,2,12:06:35 PM,19,,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519,2203
-Order_No_11117,User_Id_640,Bike,3,Business,14,2,11:35:39 AM,14,2,11:36:17 AM,14,2,11:45:30 AM,14,2,11:48:52 AM,14,2,12:29:46 PM,21,,,-1.2969741000000001,36.7856612,-1.207546,36.7913976,Rider_Id_299,2454
-Order_No_488,User_Id_533,Bike,3,Business,31,3,12:07:00 PM,31,3,12:07:58 PM,31,3,12:15:19 PM,31,3,12:23:56 PM,31,3,12:42:23 PM,7,24.8,,-1.2571472,36.795063299999995,-1.2765492,36.7669809,Rider_Id_937,1107
-Order_No_17789,User_Id_498,Bike,1,Personal,25,5,12:04:36 PM,25,5,12:04:48 PM,25,5,12:15:36 PM,25,5,12:18:36 PM,25,5,12:38:09 PM,7,21.2,,-1.3018758,36.75908629999999,-1.3046965,36.8016456,Rider_Id_101,1173
-Order_No_24849,User_Id_346,Bike,3,Business,4,3,9:44:33 AM,4,3,10:00:09 AM,4,3,10:12:30 AM,4,3,10:16:23 AM,4,3,11:01:07 AM,4,15.1,,-1.2885528999999998,36.816014100000004,-1.2770549999999998,36.813027000000005,Rider_Id_698,2684
-Order_No_2969,User_Id_3470,Bike,3,Business,23,5,11:55:12 AM,23,5,12:16:47 PM,23,5,12:25:56 PM,23,5,12:28:45 PM,23,5,12:46:08 PM,7,23.9,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_250,1043
-Order_No_2531,User_Id_867,Bike,3,Business,31,3,11:21:45 AM,31,3,11:24:06 AM,31,3,11:47:59 AM,31,3,11:57:59 AM,31,3,12:24:16 PM,10,24.8,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_532,1577
-Order_No_24870,User_Id_393,Bike,3,Business,11,2,11:12:50 AM,11,2,1:09:57 PM,11,2,1:10:29 PM,11,2,1:34:15 PM,11,2,2:07:44 PM,11,22.5,,-1.3228003000000002,36.8306435,-1.2574219,36.7927073,Rider_Id_914,2009
-Order_No_25882,User_Id_2869,Bike,3,Business,24,3,4:57:51 PM,24,3,4:59:46 PM,24,3,5:07:43 PM,24,3,5:17:39 PM,24,3,5:41:22 PM,10,,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_377,1423
-Order_No_19945,User_Id_2956,Bike,3,Business,26,2,9:38:30 AM,26,2,9:39:38 AM,26,2,9:47:46 AM,26,2,9:52:01 AM,26,2,10:49:05 AM,6,21.6,,-1.2584143,36.8048002,-1.2931121,36.8224572,Rider_Id_378,3424
-Order_No_3667,User_Id_3674,Bike,3,Business,26,5,6:08:31 PM,26,5,6:52:38 PM,26,5,6:53:38 PM,26,5,7:15:59 PM,26,5,7:24:55 PM,6,,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_855,536
-Order_No_19798,User_Id_346,Bike,3,Business,7,4,3:45:47 PM,7,4,4:44:27 PM,7,4,4:58:13 PM,7,4,5:03:51 PM,7,4,5:49:56 PM,16,29.2,,-1.3165542,36.700067,-1.28878,36.8168312,Rider_Id_831,2765
-Order_No_6940,User_Id_3339,Bike,3,Business,17,1,10:04:09 AM,17,1,10:07:05 AM,17,1,10:18:38 AM,17,1,10:23:32 AM,17,1,10:44:04 AM,9,19.2,,-1.2551895,36.7822034,-1.3084468999999999,36.8263054,Rider_Id_247,1232
-Order_No_5836,User_Id_3105,Bike,3,Personal,3,1,5:52:58 PM,3,1,6:09:39 PM,3,1,6:24:42 PM,3,1,6:29:25 PM,3,1,6:48:35 PM,7,17.6,,-1.2793172,36.827656700000006,-1.256522,36.786413200000005,Rider_Id_341,1150
-Order_No_14664,User_Id_1355,Bike,2,Personal,23,7,6:52:40 PM,23,7,6:52:58 PM,23,7,7:16:14 PM,23,7,7:21:41 PM,23,7,7:57:47 PM,18,23.8,,-1.2125712,36.796415100000004,-1.2939882,36.759119899999995,Rider_Id_103,2166
-Order_No_1699,User_Id_3222,Bike,3,Business,18,5,2:40:21 PM,18,5,2:41:09 PM,18,5,2:51:21 PM,18,5,3:01:06 PM,18,5,3:22:04 PM,8,26.9,,-1.2551895,36.7822034,-1.2922992,36.798303499999996,Rider_Id_801,1258
-Order_No_1748,User_Id_3331,Bike,3,Business,9,3,10:09:58 AM,9,3,10:11:12 AM,9,3,10:14:51 AM,9,3,10:17:04 AM,9,3,10:27:02 AM,4,20.4,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_439,598
-Order_No_20600,User_Id_2930,Bike,1,Personal,20,4,2:26:43 PM,20,4,2:28:01 PM,20,4,3:03:04 PM,20,4,3:19:28 PM,20,4,4:00:08 PM,9,28.2,,-1.2242978999999998,36.7647642,-1.2261183999999998,36.8094779,Rider_Id_810,2440
-Order_No_7110,User_Id_265,Bike,3,Business,12,1,1:59:37 PM,12,1,2:01:11 PM,12,1,2:10:15 PM,12,1,3:04:30 PM,12,1,3:09:53 PM,16,26.8,,-1.300921,36.828195,-1.2285995,36.901237200000004,Rider_Id_70,323
-Order_No_4834,User_Id_2700,Bike,3,Business,27,4,4:18:52 PM,27,4,4:19:38 PM,27,4,4:30:48 PM,27,4,4:34:03 PM,27,4,5:10:06 PM,8,23.0,,-1.261232,36.7795039,-1.2845834999999999,36.8079121,Rider_Id_167,2163
-Order_No_1738,User_Id_20,Bike,3,Business,6,4,11:24:02 AM,6,4,11:24:33 AM,6,4,11:26:06 AM,6,4,11:41:08 AM,6,4,11:41:22 AM,1,19.5,,-1.2652279,36.8020159,-1.2628638,36.8070399,Rider_Id_354,14
-Order_No_13256,User_Id_393,Bike,3,Business,8,2,11:23:19 AM,8,2,11:24:02 AM,8,2,11:31:27 AM,8,2,11:41:26 AM,8,2,11:53:50 AM,4,23.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,744
-Order_No_13695,User_Id_1207,Bike,3,Business,16,3,12:25:37 PM,16,3,12:26:35 PM,16,3,12:30:21 PM,16,3,12:36:15 PM,16,3,1:05:04 PM,19,22.8,,-1.2648363,36.802375399999995,-1.317461,36.707031,Rider_Id_634,1729
-Order_No_14273,User_Id_391,Bike,3,Business,17,5,3:50:48 PM,17,5,4:08:49 PM,17,5,4:19:13 PM,17,5,4:39:01 PM,17,5,4:39:31 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_855,30
-Order_No_23865,User_Id_1500,Bike,3,Business,18,1,9:46:22 AM,18,1,9:48:50 AM,18,1,9:57:51 AM,18,1,10:16:05 AM,18,1,10:42:58 AM,8,22.2,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_311,1613
-Order_No_26083,User_Id_635,Bike,3,Business,30,4,1:50:01 PM,30,4,1:50:41 PM,30,4,2:11:56 PM,30,4,2:27:44 PM,30,4,2:52:45 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_686,1501
-Order_No_10851,User_Id_633,Bike,3,Business,27,3,8:15:29 AM,27,3,8:17:02 AM,27,3,8:22:46 AM,27,3,8:40:47 AM,27,3,8:40:49 AM,5,21.2,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_472,2
-Order_No_24785,User_Id_914,Bike,3,Business,24,3,10:09:17 AM,24,3,10:10:13 AM,24,3,10:31:39 AM,24,3,10:36:47 AM,24,3,11:06:10 AM,6,,,-1.2670213,36.7994482,-1.298194,36.800998,Rider_Id_441,1763
-Order_No_6653,User_Id_238,Bike,3,Business,27,3,8:38:02 AM,27,3,8:38:30 AM,27,3,8:40:55 AM,27,3,8:51:26 AM,27,3,8:58:37 AM,4,21.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,431
-Order_No_18613,User_Id_1964,Bike,3,Personal,22,3,12:41:53 PM,22,3,1:33:42 PM,22,3,1:35:44 PM,22,3,1:43:19 PM,22,3,2:30:51 PM,14,24.4,,-1.1867518999999997,36.8369389,-1.2791238,36.8200368,Rider_Id_709,2852
-Order_No_20688,User_Id_553,Bike,3,Business,9,2,3:05:45 PM,9,2,3:06:05 PM,9,2,3:06:11 PM,9,2,3:47:17 PM,9,2,4:10:00 PM,17,22.5,,-1.2183760000000001,36.8821822,-1.2551895,36.7822034,Rider_Id_813,1363
-Order_No_22465,User_Id_2987,Bike,3,Business,15,1,3:22:23 PM,15,1,3:34:54 PM,15,1,3:42:19 PM,15,1,3:58:52 PM,15,1,4:13:37 PM,4,,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_519,885
-Order_No_19121,User_Id_75,Bike,2,Personal,17,2,12:28:35 PM,17,2,12:29:18 PM,17,2,12:39:27 PM,17,2,12:43:02 PM,17,2,1:23:03 PM,13,19.0,,-1.3014462,36.7607695,-1.261247,36.826993,Rider_Id_427,2401
-Order_No_22299,User_Id_198,Bike,3,Business,9,2,10:05:20 AM,9,2,10:06:24 AM,9,2,10:19:02 AM,9,2,10:20:46 AM,9,2,11:37:44 AM,33,20.9,,-1.3396256000000002,36.8863135,-1.1427075,36.9541727,Rider_Id_418,4618
-Order_No_7959,User_Id_3557,Bike,3,Business,12,5,9:33:01 AM,12,5,9:34:20 AM,12,5,9:43:30 AM,12,5,9:46:05 AM,12,5,10:11:05 AM,7,18.6,,-1.2546732,36.808679600000005,-1.2662639999999998,36.762418,Rider_Id_379,1500
-Order_No_6533,User_Id_133,Bike,1,Personal,25,6,11:48:36 AM,25,6,11:48:39 AM,25,6,11:58:06 AM,25,6,12:05:59 PM,25,6,12:23:31 PM,4,,,-1.2678813999999998,36.815757399999995,-1.2728069,36.7962362,Rider_Id_882,1052
-Order_No_21338,User_Id_1792,Bike,3,Business,22,1,10:31:43 AM,22,1,10:32:25 AM,22,1,10:46:43 AM,22,1,10:49:18 AM,22,1,11:32:58 AM,14,25.1,,-1.2809121,36.7852524,-1.3339943,36.7263409,Rider_Id_565,2620
-Order_No_10737,User_Id_2691,Bike,3,Business,24,4,9:19:48 AM,24,4,9:22:28 AM,24,4,10:08:02 AM,24,4,10:42:25 AM,24,4,11:01:39 AM,9,21.1,,-1.2609700000000001,36.787286,-1.2677415,36.83484179999999,Rider_Id_844,1154
-Order_No_16115,User_Id_2330,Bike,3,Business,20,3,4:04:30 PM,20,3,4:04:59 PM,20,3,4:06:02 PM,20,3,4:13:56 PM,20,3,4:58:54 PM,17,28.5,,-1.2649605,36.798177700000004,-1.3340505,36.711903799999995,Rider_Id_914,2698
-Order_No_1641,User_Id_1333,Bike,2,Personal,10,7,12:59:53 PM,10,7,1:00:51 PM,10,7,1:23:29 PM,10,7,1:30:49 PM,10,7,2:00:58 PM,21,17.8,,-1.2151769,36.8846524,-1.2927531,36.7651386,Rider_Id_906,1809
-Order_No_8500,User_Id_3724,Bike,1,Personal,3,3,4:19:00 PM,3,3,4:28:54 PM,3,3,4:46:06 PM,3,3,4:53:12 PM,3,3,4:58:52 PM,3,23.4,,-1.2547780000000002,36.790006,-1.2697141,36.798177700000004,Rider_Id_147,340
-Order_No_24653,User_Id_3115,Bike,3,Business,7,2,7:47:59 AM,7,2,7:58:18 AM,7,2,8:00:37 AM,7,2,8:03:55 AM,7,2,8:12:28 AM,2,,,-1.2963097,36.7688221,-1.2969741000000001,36.7856612,Rider_Id_298,513
-Order_No_1932,User_Id_2102,Bike,3,Business,7,4,9:24:45 AM,7,4,9:24:57 AM,7,4,9:25:12 AM,7,4,9:33:13 AM,7,4,10:01:29 AM,15,,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_430,1696
-Order_No_1011,User_Id_1827,Bike,3,Business,23,4,12:07:59 PM,23,4,12:11:13 PM,23,4,12:14:47 PM,23,4,12:42:43 PM,23,4,12:59:24 PM,14,18.7,,-1.265715,36.823815,-1.293515,36.897607,Rider_Id_179,1001
-Order_No_26329,User_Id_830,Bike,3,Business,5,5,12:39:44 PM,5,5,12:40:00 PM,5,5,12:48:11 PM,5,5,12:56:59 PM,5,5,1:10:31 PM,3,23.8,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_857,812
-Order_No_21644,User_Id_3359,Bike,3,Business,15,5,11:00:36 AM,15,5,11:11:35 AM,15,5,11:26:01 AM,15,5,11:32:48 AM,15,5,12:09:00 PM,7,26.5,,-1.2791223999999999,36.8306878,-1.2551895,36.7822034,Rider_Id_351,2172
-Order_No_22643,User_Id_1209,Bike,3,Business,24,3,2:48:55 PM,24,3,3:08:35 PM,24,3,3:09:11 PM,24,3,3:15:27 PM,24,3,3:31:43 PM,6,,,-1.3004062,36.829741,-1.2703478000000001,36.8106896,Rider_Id_921,976
-Order_No_19862,User_Id_3621,Bike,3,Business,11,2,9:35:06 AM,11,2,9:35:14 AM,11,2,9:42:52 AM,11,2,9:43:55 AM,11,2,9:58:47 AM,5,18.6,1.9,-1.2836583000000001,36.7886014,-1.2795796,36.819062,Rider_Id_648,892
-Order_No_27362,User_Id_635,Bike,3,Business,24,4,12:16:41 PM,24,4,12:25:23 PM,24,4,1:09:16 PM,24,4,1:19:53 PM,24,4,1:27:43 PM,3,21.0,,-1.2777923999999998,36.8231577,-1.26639,36.834883000000005,Rider_Id_844,470
-Order_No_4697,User_Id_2327,Bike,1,Personal,9,1,12:52:17 PM,9,1,1:02:48 PM,9,1,1:28:51 PM,9,1,2:10:21 PM,9,1,2:17:26 PM,2,21.2,,-1.3106677,36.82331370000001,-1.3004062,36.829741,Rider_Id_939,425
-Order_No_5277,User_Id_1787,Bike,3,Business,18,4,4:02:55 PM,18,4,4:03:17 PM,18,4,4:04:30 PM,18,4,4:24:59 PM,18,4,4:37:15 PM,3,27.0,,-1.2232123,36.8932547,-1.2283403,36.8822756,Rider_Id_704,736
-Order_No_5008,User_Id_1787,Bike,3,Business,21,2,9:59:39 AM,21,2,9:59:51 AM,21,2,10:04:05 AM,21,2,10:17:36 AM,21,2,10:57:22 AM,18,,,-1.3014461000000002,36.7661381,-1.2283403,36.8822756,Rider_Id_161,2386
-Order_No_21388,User_Id_2523,Bike,3,Business,8,2,9:43:47 AM,8,2,9:44:13 AM,8,2,9:48:13 AM,8,2,9:59:20 AM,8,2,9:59:21 AM,6,19.1,,-1.2551895,36.7822034,-1.270424,36.779289,Rider_Id_647,1
-Order_No_1235,User_Id_3704,Bike,3,Personal,14,4,9:10:51 AM,14,4,9:11:45 AM,14,4,9:28:25 AM,14,4,9:31:04 AM,14,4,9:40:38 AM,5,18.8,,-1.2917868000000001,36.7872675,-1.272704,36.793127899999995,Rider_Id_798,574
-Order_No_17247,User_Id_265,Bike,3,Business,13,2,4:23:34 PM,13,2,4:49:22 PM,13,2,4:57:03 PM,13,2,4:59:06 PM,13,2,5:10:28 PM,4,23.7,,-1.300921,36.828195,-1.2974472,36.8156997,Rider_Id_685,682
-Order_No_5771,User_Id_867,Bike,3,Business,28,3,3:39:05 PM,28,3,3:39:19 PM,28,3,3:39:26 PM,28,3,3:41:27 PM,28,3,4:07:22 PM,11,,,-1.306378,36.7519845,-1.2996533000000001,36.829262899999996,Rider_Id_854,1555
-Order_No_4061,User_Id_2982,Bike,3,Business,5,2,3:56:18 PM,5,2,3:56:40 PM,5,2,3:57:42 PM,5,2,4:22:41 PM,5,2,5:04:13 PM,18,27.7,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_583,2492
-Order_No_20888,User_Id_1246,Bike,3,Business,29,3,3:25:24 PM,29,3,3:45:01 PM,29,3,3:51:32 PM,29,3,3:53:20 PM,29,3,4:21:23 PM,14,21.2,,-1.3700383,36.9190174,-1.307182,36.850321,Rider_Id_938,1683
-Order_No_19653,User_Id_1781,Bike,1,Business,22,3,5:50:51 PM,22,3,5:54:14 PM,22,3,6:14:22 PM,22,3,6:38:03 PM,22,3,7:27:07 PM,19,23.0,,-1.3206928999999998,36.8621776,-1.2239216,36.8792146,Rider_Id_272,2944
-Order_No_25112,User_Id_3291,Bike,3,Business,16,6,3:49:48 PM,16,6,3:50:27 PM,16,6,4:02:23 PM,16,6,4:45:38 PM,16,6,4:45:43 PM,8,27.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_267,5
-Order_No_1815,User_Id_1161,Bike,1,Personal,1,5,6:18:48 PM,1,5,6:19:10 PM,1,5,6:26:17 PM,1,5,6:38:24 PM,1,5,6:56:40 PM,6,22.5,,-1.2879041000000002,36.8217926,-1.2705296999999998,36.7987172,Rider_Id_103,1096
-Order_No_14495,User_Id_3283,Bike,3,Business,18,4,1:06:08 PM,18,4,1:06:25 PM,18,4,1:17:10 PM,18,4,1:25:43 PM,18,4,1:41:15 PM,6,25.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_532,932
-Order_No_13412,User_Id_2767,Bike,3,Business,13,4,1:50:42 PM,13,4,1:57:58 PM,13,4,2:24:13 PM,13,4,3:10:37 PM,13,4,3:50:25 PM,11,22.5,,-1.3076233999999998,36.8393399,-1.257773,36.795486,Rider_Id_511,2388
-Order_No_12040,User_Id_393,Bike,3,Business,12,2,8:38:14 AM,12,2,8:39:59 AM,12,2,8:51:07 AM,12,2,8:57:33 AM,12,2,9:28:44 AM,11,20.7,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_728,1871
-Order_No_22914,User_Id_393,Bike,3,Business,4,1,3:58:50 PM,4,1,3:59:12 PM,4,1,3:59:17 PM,4,1,4:34:25 PM,4,1,4:34:27 PM,14,27.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_239,2
-Order_No_24679,User_Id_1051,Bike,3,Business,27,1,4:36:01 PM,27,1,4:36:01 PM,27,1,4:36:10 PM,27,1,4:37:32 PM,27,1,4:51:34 PM,8,,,-1.2963097,36.7688221,-1.2753108999999998,36.8130185,Rider_Id_71,842
-Order_No_20714,User_Id_2066,Bike,3,Business,5,2,12:15:18 PM,5,2,12:17:16 PM,5,2,12:58:47 PM,5,2,1:15:57 PM,5,2,1:51:48 PM,9,25.6,,-1.3210188999999999,36.841055100000005,-1.2768326,36.8073804,Rider_Id_701,2151
-Order_No_13734,User_Id_913,Bike,3,Personal,10,5,5:52:14 PM,10,5,6:06:35 PM,10,5,6:21:19 PM,10,5,6:22:46 PM,10,5,6:34:43 PM,4,,,-1.2823746999999999,36.8258855,-1.2617028000000001,36.7983849,Rider_Id_843,717
-Order_No_25733,User_Id_1792,Bike,3,Business,5,4,11:16:18 AM,5,4,11:16:57 AM,5,4,11:22:24 AM,5,4,11:24:01 AM,5,4,11:49:09 AM,15,,,-1.2924639,36.77957979999999,-1.2212785,36.808606700000006,Rider_Id_103,1508
-Order_No_2970,User_Id_867,Bike,3,Business,14,4,1:21:32 PM,14,4,2:08:28 PM,14,4,2:09:26 PM,14,4,2:14:46 PM,14,4,2:33:03 PM,9,28.0,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_156,1097
-Order_No_17,User_Id_2346,Bike,1,Personal,9,3,1:50:16 PM,9,3,1:50:30 PM,9,3,1:58:35 PM,9,3,2:01:42 PM,9,3,2:50:22 PM,25,23.3,,-1.2857072,36.822586,-1.2480844,36.6596223,Rider_Id_742,2920
-Order_No_5938,User_Id_142,Bike,3,Business,8,4,7:09:50 AM,8,4,7:10:36 AM,8,4,7:44:34 AM,8,4,7:50:14 AM,8,4,8:33:55 AM,11,20.4,,-1.2963097,36.7688221,-1.3314048,36.8229367,Rider_Id_303,2621
-Order_No_18817,User_Id_1527,Bike,3,Business,6,4,12:27:55 PM,6,4,12:28:40 PM,6,4,12:28:42 PM,6,4,12:38:22 PM,6,4,12:38:23 PM,4,19.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_293,1
-Order_No_4412,User_Id_2190,Bike,3,Business,27,1,3:02:24 PM,27,1,3:03:36 PM,27,1,3:18:11 PM,27,1,3:26:05 PM,27,1,3:43:30 PM,7,23.2,,-1.3019530000000001,36.788458,-1.302088,36.831162,Rider_Id_826,1045
-Order_No_10180,User_Id_265,Bike,3,Business,23,2,10:50:17 AM,23,2,10:50:26 AM,23,2,11:02:06 AM,23,2,11:09:24 AM,23,2,11:28:10 AM,4,,,-1.3218459999999999,36.82826,-1.300921,36.828195,Rider_Id_349,1126
-Order_No_26764,User_Id_627,Bike,3,Business,6,4,9:40:42 AM,6,4,9:41:27 AM,6,4,9:45:36 AM,6,4,9:50:32 AM,6,4,10:09:42 AM,3,16.3,4.1,-1.2551895,36.7822034,-1.2671943,36.807601,Rider_Id_205,1150
-Order_No_22726,User_Id_893,Bike,3,Business,13,2,10:59:16 AM,13,2,11:10:18 AM,13,2,11:27:40 AM,13,2,11:39:15 AM,13,2,12:30:41 PM,8,22.4,,-1.3019530000000001,36.788458,-1.2594473999999998,36.8089122,Rider_Id_578,3086
-Order_No_26667,User_Id_3599,Bike,3,Business,29,4,10:50:39 AM,29,4,11:05:46 AM,29,4,11:21:51 AM,29,4,11:23:58 AM,29,4,11:57:58 AM,11,21.6,,-1.3302996,36.8707935,-1.3136451999999998,36.7974014,Rider_Id_609,2040
-Order_No_1648,User_Id_1642,Bike,3,Business,28,6,11:02:44 AM,28,6,11:02:54 AM,28,6,11:03:20 AM,28,6,11:33:12 AM,28,6,11:40:28 AM,5,23.9,,-1.2862269,36.7841601,-1.2728069,36.7962362,Rider_Id_74,436
-Order_No_10778,User_Id_1502,Bike,3,Business,9,2,1:45:50 PM,9,2,2:06:36 PM,9,2,2:55:52 PM,9,2,2:59:36 PM,9,2,3:03:53 PM,7,30.8,,-1.2232123,36.8932547,-1.2525335,36.875113899999995,Rider_Id_300,257
-Order_No_26148,User_Id_111,Bike,3,Business,15,5,12:54:46 PM,15,5,1:00:00 PM,15,5,1:45:31 PM,15,5,1:46:37 PM,15,5,3:01:55 PM,11,28.2,,-1.2551895,36.7822034,-1.2432427,36.8642382,Rider_Id_448,4518
-Order_No_15878,User_Id_1976,Bike,3,Business,29,1,11:08:48 AM,29,1,11:09:07 AM,29,1,11:20:01 AM,29,1,11:27:15 AM,29,1,12:04:23 PM,15,,,-1.257028,36.7810435,-1.3274778999999999,36.7814225,Rider_Id_731,2228
-Order_No_19371,User_Id_393,Bike,3,Business,8,1,4:36:40 PM,8,1,4:37:26 PM,8,1,4:50:59 PM,8,1,4:53:37 PM,8,1,5:17:15 PM,11,28.8,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_570,1418
-Order_No_13703,User_Id_265,Bike,3,Business,28,4,11:19:48 AM,28,4,11:20:08 AM,28,4,11:26:06 AM,28,4,11:53:56 AM,28,4,12:34:53 PM,16,28.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_519,2457
-Order_No_12490,User_Id_517,Bike,3,Business,31,5,11:36:17 AM,31,5,11:37:01 AM,31,5,11:45:02 AM,31,5,12:36:37 PM,31,5,12:41:06 PM,1,21.7,,-1.3002553999999997,36.8256566,-1.3004062,36.829741,Rider_Id_158,269
-Order_No_2765,User_Id_265,Bike,3,Business,7,2,3:50:37 PM,7,2,3:51:57 PM,7,2,4:01:46 PM,7,2,4:19:08 PM,7,2,4:35:17 PM,7,,,-1.2673786,36.8081593,-1.300921,36.828195,Rider_Id_856,969
-Order_No_20886,User_Id_2701,Bike,3,Business,1,4,11:02:28 AM,1,4,11:11:07 AM,1,4,11:29:13 AM,1,4,11:55:32 AM,1,4,12:19:08 PM,6,,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_477,1416
-Order_No_24204,User_Id_2030,Bike,3,Business,20,2,11:10:38 AM,20,2,1:14:05 PM,20,2,1:31:19 PM,20,2,1:47:05 PM,20,2,3:02:31 PM,22,24.0,,-1.1612808000000001,36.9563278,-1.2145529,36.790778,Rider_Id_770,4526
-Order_No_13965,User_Id_3101,Bike,1,Personal,12,3,6:53:21 AM,12,3,6:57:00 AM,12,3,7:23:31 AM,12,3,7:27:51 AM,12,3,8:05:07 AM,4,14.3,,-1.3061448,36.7978023,-1.2973948999999998,36.816038,Rider_Id_328,2236
-Order_No_27519,User_Id_3326,Bike,3,Business,12,3,11:21:45 AM,12,3,11:28:19 AM,12,3,11:39:35 AM,12,3,12:14:23 PM,12,3,12:19:56 PM,8,19.4,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_139,333
-Order_No_1429,User_Id_733,Bike,3,Business,29,1,2:46:38 PM,29,1,2:47:58 PM,29,1,2:56:54 PM,29,1,3:01:49 PM,29,1,3:29:41 PM,7,,,-1.2551895,36.7822034,-1.2939186000000003,36.8069693,Rider_Id_205,1672
-Order_No_3379,User_Id_1797,Bike,2,Personal,28,2,7:24:02 PM,28,2,7:24:44 PM,28,2,7:54:48 PM,28,2,7:59:09 PM,28,2,9:01:53 PM,13,17.5,,-1.2827576,36.783024100000006,-1.2418,36.84778,Rider_Id_578,3764
-Order_No_26399,User_Id_776,Bike,1,Personal,15,5,11:37:10 AM,15,5,11:58:06 AM,15,5,11:58:34 AM,15,5,12:06:46 PM,15,5,12:31:16 PM,8,19.9,,-1.278967,36.8236745,-1.2907222,36.8843012,Rider_Id_84,1470
-Order_No_11512,User_Id_2066,Bike,3,Business,29,2,1:51:04 PM,29,2,1:59:59 PM,29,2,2:21:23 PM,29,2,2:28:23 PM,29,2,2:53:07 PM,8,27.0,,-1.3210188999999999,36.841055100000005,-1.2856902,36.830616299999996,Rider_Id_336,1484
-Order_No_22192,User_Id_2103,Bike,2,Personal,2,2,1:40:26 PM,2,2,1:42:36 PM,2,2,2:06:38 PM,2,2,2:11:26 PM,2,2,2:59:13 PM,7,26.0,,-1.2557778999999998,36.7879648,-1.282319,36.819008200000006,Rider_Id_844,2867
-Order_No_11622,User_Id_3331,Bike,3,Business,25,5,12:53:14 PM,25,5,12:53:39 PM,25,5,12:55:49 PM,25,5,12:58:38 PM,25,5,1:13:08 PM,9,26.4,,-1.2551895,36.7822034,-1.2786703000000001,36.826044,Rider_Id_512,870
-Order_No_23542,User_Id_970,Bike,3,Business,18,4,10:25:23 AM,18,4,10:26:03 AM,18,4,10:35:29 AM,18,4,10:47:12 AM,18,4,11:21:26 AM,4,,,-1.2571472,36.795063299999995,-1.2904753,36.8144721,Rider_Id_422,2054
-Order_No_27108,User_Id_246,Bike,3,Business,5,3,5:46:01 PM,5,3,5:46:18 PM,5,3,5:51:30 PM,5,3,5:56:05 PM,5,3,6:03:54 PM,3,22.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_247,469
-Order_No_16957,User_Id_2589,Bike,3,Personal,9,2,12:25:02 PM,9,2,12:27:42 PM,9,2,12:55:20 PM,9,2,12:57:16 PM,9,2,1:12:55 PM,4,22.0,,-1.2825816,36.8092346,-1.289795,36.7957132,Rider_Id_749,939
-Order_No_22599,User_Id_2701,Bike,3,Business,30,3,3:52:04 PM,30,3,4:07:23 PM,30,3,4:18:04 PM,30,3,4:21:18 PM,30,3,4:40:53 PM,7,25.3,,-1.279395,36.825364,-1.311208,36.778743,Rider_Id_844,1175
-Order_No_1129,User_Id_2200,Bike,3,Business,1,4,3:42:27 PM,1,4,3:56:13 PM,1,4,4:01:05 PM,1,4,4:47:37 PM,1,4,4:53:45 PM,2,,,-1.28878,36.8168312,-1.290894,36.822971,Rider_Id_31,368
-Order_No_8147,User_Id_2627,Bike,3,Business,5,3,10:54:01 AM,5,3,10:54:07 AM,5,3,11:12:44 AM,5,3,11:16:05 AM,5,3,11:41:00 AM,17,22.5,,-1.2156006000000001,36.8916865,-1.2571472,36.795063299999995,Rider_Id_685,1495
-Order_No_670,User_Id_867,Bike,3,Business,21,2,10:56:53 AM,21,2,11:04:41 AM,21,2,11:17:02 AM,21,2,11:19:09 AM,21,2,11:38:52 AM,9,22.3,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_709,1183
-Order_No_4208,User_Id_1882,Bike,3,Business,12,2,10:01:59 AM,12,2,11:35:01 AM,12,2,11:51:36 AM,12,2,12:06:29 PM,12,2,12:29:55 PM,7,24.6,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_332,1406
-Order_No_20682,User_Id_3599,Bike,3,Business,14,2,10:54:24 AM,14,2,12:13:08 PM,14,2,12:33:46 PM,14,2,12:53:57 PM,14,2,1:46:07 PM,13,,,-1.3302996,36.8707935,-1.2536508999999998,36.8149295,Rider_Id_591,3130
-Order_No_1445,User_Id_1207,Bike,1,Business,10,7,2:53:56 PM,10,7,2:54:35 PM,10,7,3:13:48 PM,10,7,3:16:16 PM,10,7,3:40:08 PM,11,20.8,,-1.2667935,36.902148600000004,-1.2351541000000001,36.8748505,Rider_Id_589,1432
-Order_No_25268,User_Id_1363,Bike,3,Business,31,3,10:32:55 AM,31,3,10:33:28 AM,31,3,12:13:12 PM,31,3,12:22:54 PM,31,3,12:41:47 PM,7,24.5,,-1.2649605,36.798177700000004,-1.3004062,36.829741,Rider_Id_276,1133
-Order_No_13756,User_Id_2582,Bike,3,Business,11,1,2:36:29 PM,11,1,2:39:11 PM,11,1,2:48:02 PM,11,1,2:50:45 PM,11,1,3:13:59 PM,15,29.0,,-1.2551895,36.7822034,-1.2447780000000002,36.6725425,Rider_Id_836,1394
-Order_No_13461,User_Id_2523,Bike,3,Business,30,3,9:37:31 AM,30,3,9:48:53 AM,30,3,10:06:34 AM,30,3,10:11:17 AM,30,3,11:05:37 AM,8,19.6,,-1.2551895,36.7822034,-1.2924639,36.77957979999999,Rider_Id_761,3260
-Order_No_13706,User_Id_2200,Bike,3,Business,31,1,3:23:04 PM,31,1,4:44:44 PM,31,1,4:54:20 PM,31,1,4:59:53 PM,31,1,5:14:08 PM,2,22.6,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_338,855
-Order_No_27256,User_Id_53,Bike,3,Business,11,1,3:22:55 PM,11,1,3:53:11 PM,11,1,4:10:12 PM,11,1,4:22:07 PM,11,1,4:35:50 PM,5,28.1,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_953,823
-Order_No_792,User_Id_1271,Bike,3,Business,28,3,2:09:12 PM,28,3,2:14:16 PM,28,3,2:28:41 PM,28,3,3:13:46 PM,28,3,3:25:37 PM,8,,,-1.303924,36.8758792,-1.3244885,36.897791999999995,Rider_Id_675,711
-Order_No_8160,User_Id_606,Bike,1,Personal,10,1,11:06:38 AM,10,1,11:18:52 AM,10,1,11:29:48 AM,10,1,11:32:29 AM,10,1,11:44:46 AM,8,,,-1.2143386999999999,36.7914548,-1.2152204,36.8563184,Rider_Id_453,737
-Order_No_7558,User_Id_391,Bike,3,Business,24,3,8:49:10 AM,24,3,9:19:07 AM,24,3,9:35:46 AM,24,3,9:43:36 AM,24,3,11:00:17 AM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_106,4601
-Order_No_23738,User_Id_633,Bike,3,Business,7,4,9:00:14 AM,7,4,9:00:43 AM,7,4,9:17:47 AM,7,4,9:21:15 AM,7,4,9:40:05 AM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_106,1130
-Order_No_3511,User_Id_136,Bike,3,Business,23,6,10:59:05 AM,23,6,11:08:53 AM,23,6,11:16:48 AM,23,6,11:18:01 AM,23,6,11:27:29 AM,4,,,-1.3165103999999999,36.707772600000006,-1.3254522,36.721658399999995,Rider_Id_575,568
-Order_No_18619,User_Id_635,Bike,3,Business,16,3,2:55:03 PM,16,3,3:01:30 PM,16,3,3:09:05 PM,16,3,3:17:54 PM,16,3,3:29:10 PM,7,27.1,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_675,676
-Order_No_4981,User_Id_1179,Bike,3,Business,22,4,2:33:37 PM,22,4,2:41:16 PM,22,4,2:56:47 PM,22,4,3:03:36 PM,22,4,4:14:16 PM,21,26.5,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_498,4240
-Order_No_7684,User_Id_3175,Bike,3,Business,15,4,11:50:26 AM,15,4,11:50:47 AM,15,4,11:56:17 AM,15,4,11:58:03 AM,15,4,12:26:15 PM,12,24.3,,-1.2681003999999998,36.7952081,-1.2101621,36.787859399999995,Rider_Id_432,1692
-Order_No_2126,User_Id_3647,Bike,3,Business,13,4,3:19:56 PM,13,4,3:20:53 PM,13,4,3:22:50 PM,13,4,3:30:07 PM,13,4,3:46:53 PM,6,23.0,,-1.2921656000000001,36.81041810000001,-1.2726389999999999,36.794723,Rider_Id_564,1006
-Order_No_348,User_Id_3283,Bike,3,Business,5,2,9:15:05 AM,5,2,9:26:43 AM,5,2,10:22:40 AM,5,2,10:31:41 AM,5,2,11:14:15 AM,19,21.2,1.9,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_699,2554
-Order_No_26891,User_Id_640,Bike,3,Business,24,3,9:52:29 AM,24,3,9:52:41 AM,24,3,9:56:31 AM,24,3,10:04:04 AM,24,3,10:17:06 AM,3,16.9,0.9,-1.304033,36.7848695,-1.2945106000000002,36.7728398,Rider_Id_612,782
-Order_No_14812,User_Id_1968,Bike,1,Personal,16,2,11:02:49 AM,16,2,11:26:59 AM,16,2,11:41:48 AM,16,2,11:44:57 AM,16,2,12:15:39 PM,11,23.6,,-1.2670582000000001,36.7680056,-1.3173868999999998,36.8112044,Rider_Id_38,1842
-Order_No_15808,User_Id_2099,Bike,3,Business,26,1,12:27:49 PM,26,1,12:30:36 PM,26,1,12:45:10 PM,26,1,12:53:21 PM,26,1,1:22:53 PM,10,23.1,,-1.2326041,36.8009029,-1.2682608,36.797315600000005,Rider_Id_387,1772
-Order_No_18043,User_Id_1991,Bike,3,Personal,18,2,6:38:37 PM,18,2,6:38:49 PM,18,2,6:41:29 PM,18,2,7:04:28 PM,18,2,8:03:18 PM,12,23.8,,-1.2727268999999999,36.8298864,-1.2987787,36.7448429,Rider_Id_569,3530
-Order_No_12869,User_Id_1304,Bike,3,Business,13,2,10:30:03 AM,13,2,10:47:34 AM,13,2,11:00:17 AM,13,2,11:03:25 AM,13,2,11:43:54 AM,12,22.2,,-1.3332749,36.870814700000004,-1.2693662000000001,36.8085295,Rider_Id_559,2429
-Order_No_4587,User_Id_2384,Bike,3,Personal,16,3,1:20:10 PM,16,3,1:24:20 PM,16,3,1:43:31 PM,16,3,1:47:56 PM,16,3,2:15:20 PM,13,26.8,,-1.2295205,36.812042600000005,-1.2857671,36.7875614,Rider_Id_932,1644
-Order_No_2684,User_Id_2801,Bike,3,Business,3,1,1:38:18 PM,3,1,1:41:05 PM,3,1,1:51:34 PM,3,1,1:54:53 PM,3,1,2:15:27 PM,7,23.4,,-1.2674273999999999,36.787082700000006,-1.281577,36.79382929999999,Rider_Id_162,1234
-Order_No_27242,User_Id_2642,Bike,3,Personal,1,3,8:27:08 AM,1,3,8:42:13 AM,1,3,9:15:20 AM,1,3,9:22:49 AM,1,3,9:49:23 AM,19,,,-1.3700383,36.9190174,-1.2736083999999999,36.9101609,Rider_Id_406,1594
-Order_No_8417,User_Id_2174,Bike,3,Business,12,5,9:45:13 AM,12,5,9:45:18 AM,12,5,9:53:20 AM,12,5,9:59:39 AM,12,5,10:34:12 AM,21,18.6,,-1.2482781,36.884091999999995,-1.1456993,36.9499598,Rider_Id_12,2073
-Order_No_5210,User_Id_909,Bike,3,Business,16,4,3:37:34 PM,16,4,3:45:54 PM,16,4,4:31:13 PM,16,4,4:33:03 PM,16,4,4:59:52 PM,8,,,-1.225322,36.80855,-1.2596969999999998,36.800802000000004,Rider_Id_815,1609
-Order_No_5490,User_Id_393,Bike,3,Business,9,2,8:13:58 AM,9,2,8:14:07 AM,9,2,8:14:13 AM,9,2,9:11:54 AM,9,2,9:52:28 AM,14,17.4,3.9,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_702,2434
-Order_No_23051,User_Id_393,Bike,3,Business,8,5,2:09:58 PM,8,5,2:10:25 PM,8,5,2:11:52 PM,8,5,2:51:45 PM,8,5,2:51:46 PM,11,27.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,1
-Order_No_13420,User_Id_2102,Bike,3,Business,31,5,11:33:40 AM,31,5,11:36:05 AM,31,5,11:39:31 AM,31,5,11:50:57 AM,31,5,12:24:08 PM,11,21.5,,-1.3323107,36.8692616,-1.296965,36.806567799999996,Rider_Id_856,1991
-Order_No_3726,User_Id_1500,Bike,3,Business,29,4,1:06:22 PM,29,4,1:56:39 PM,29,4,2:06:36 PM,29,4,2:31:01 PM,29,4,2:55:36 PM,8,24.6,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_345,1475
-Order_No_12424,User_Id_1502,Bike,3,Business,17,5,2:46:15 PM,17,5,2:46:28 PM,17,5,2:50:00 PM,17,5,2:53:09 PM,17,5,3:10:21 PM,6,,,-1.2232123,36.8932547,-1.2404912000000001,36.88477879999999,Rider_Id_7,1032
-Order_No_6162,User_Id_3218,Bike,3,Business,4,1,10:59:18 AM,4,1,11:01:27 AM,4,1,11:18:29 AM,4,1,11:47:12 AM,4,1,12:06:19 PM,11,20.8,,-1.2156006000000001,36.8916865,-1.2765736,36.851364600000004,Rider_Id_248,1147
-Order_No_21665,User_Id_2523,Bike,3,Business,4,1,11:01:35 AM,4,1,11:02:18 AM,4,1,11:15:55 AM,4,1,11:21:30 AM,4,1,11:33:15 AM,7,25.5,,-1.2551895,36.7822034,-1.2908303,36.804951200000005,Rider_Id_322,705
-Order_No_1335,User_Id_1721,Bike,3,Business,11,2,10:04:49 AM,11,2,10:05:20 AM,11,2,10:16:50 AM,11,2,10:27:36 AM,11,2,11:02:11 AM,17,16.6,,-1.2571472,36.795063299999995,-1.3194911,36.720085299999994,Rider_Id_279,2075
-Order_No_15130,User_Id_2154,Bike,3,Business,15,2,1:35:18 PM,15,2,1:39:20 PM,15,2,1:39:41 PM,15,2,1:53:28 PM,15,2,2:14:55 PM,6,24.7,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_26,1287
-Order_No_1096,User_Id_867,Bike,3,Business,22,5,11:41:39 AM,22,5,11:43:38 AM,22,5,12:08:57 PM,22,5,12:22:48 PM,22,5,12:51:44 PM,9,28.5,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_118,1736
-Order_No_21968,User_Id_1329,Bike,3,Business,8,2,12:48:21 PM,8,2,12:48:57 PM,8,2,12:52:38 PM,8,2,12:56:27 PM,8,2,1:27:18 PM,9,23.5,,-1.273056,36.811298,-1.3191318,36.8042377,Rider_Id_427,1851
-Order_No_14697,User_Id_3283,Bike,3,Business,5,2,1:05:32 PM,5,2,1:05:49 PM,5,2,1:11:51 PM,5,2,1:26:30 PM,5,2,1:42:49 PM,14,21.4,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_216,979
-Order_No_25828,User_Id_649,Bike,3,Business,26,2,10:26:43 AM,26,2,10:27:53 AM,26,2,10:39:00 AM,26,2,11:43:09 AM,26,2,12:04:44 PM,9,,,-1.3034396000000001,36.832999799999996,-1.2551895,36.7822034,Rider_Id_791,1295
-Order_No_13638,User_Id_2154,Bike,3,Business,16,4,3:17:01 PM,16,4,3:19:22 PM,16,4,3:45:28 PM,16,4,3:49:09 PM,16,4,4:09:07 PM,5,,,-1.2765736,36.851364600000004,-1.2857548,36.819062,Rider_Id_204,1198
-Order_No_8418,User_Id_2207,Bike,3,Business,16,4,2:52:33 PM,16,4,2:53:08 PM,16,4,2:58:29 PM,16,4,3:06:04 PM,16,4,3:53:20 PM,1,19.2,,-1.2696908000000002,36.81519,-1.2724096,36.8135361,Rider_Id_498,2836
-Order_No_12837,User_Id_198,Bike,3,Business,28,4,2:57:18 PM,28,4,3:03:58 PM,28,4,3:53:27 PM,28,4,4:04:31 PM,28,4,4:33:35 PM,11,27.3,,-1.3396256000000002,36.8863135,-1.3047852,36.8512721,Rider_Id_507,1744
-Order_No_1779,User_Id_1271,Bike,3,Business,21,3,10:37:40 AM,21,3,10:37:51 AM,21,3,10:38:01 AM,21,3,10:55:44 AM,21,3,11:52:27 AM,19,21.8,,-1.3244885,36.897791999999995,-1.254191,36.809913200000004,Rider_Id_402,3403
-Order_No_4293,User_Id_393,Bike,3,Business,12,2,11:13:22 AM,12,2,11:13:31 AM,12,2,11:20:38 AM,12,2,11:29:43 AM,12,2,12:02:51 PM,14,24.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1988
-Order_No_9720,User_Id_2801,Bike,3,Business,24,5,5:25:16 PM,24,5,5:25:41 PM,24,5,5:35:17 PM,24,5,5:38:18 PM,24,5,6:05:20 PM,8,20.2,,-1.2674273999999999,36.787082700000006,-1.2951355,36.768931,Rider_Id_930,1622
-Order_No_10101,User_Id_1402,Bike,3,Business,10,3,10:07:31 AM,10,3,10:08:33 AM,10,3,10:52:36 AM,10,3,11:05:32 AM,10,3,11:55:29 AM,1,27.8,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_819,2997
-Order_No_3187,User_Id_1209,Bike,3,Business,18,4,12:26:23 PM,18,4,12:31:46 PM,18,4,12:48:42 PM,18,4,1:05:12 PM,18,4,1:16:11 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_281,659
-Order_No_9276,User_Id_627,Bike,3,Business,12,3,10:07:42 AM,12,3,10:09:07 AM,12,3,10:11:56 AM,12,3,10:24:33 AM,12,3,10:56:19 AM,17,,,-1.2551895,36.7822034,-1.2387934999999999,36.671595700000005,Rider_Id_409,1906
-Order_No_2009,User_Id_393,Bike,3,Business,10,1,4:40:41 PM,10,1,4:50:40 PM,10,1,5:05:13 PM,10,1,5:15:40 PM,10,1,5:46:06 PM,14,18.9,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_623,1826
-Order_No_3413,User_Id_496,Bike,3,Business,30,2,3:21:59 PM,30,2,3:30:13 PM,30,2,3:50:35 PM,30,2,3:55:58 PM,30,2,4:16:33 PM,4,,,-1.2551895,36.7822034,-1.266294,36.8057971,Rider_Id_856,1235
-Order_No_2084,User_Id_2200,Bike,3,Business,7,5,2:12:09 PM,7,5,4:52:43 PM,7,5,4:59:07 PM,7,5,5:24:50 PM,7,5,5:40:58 PM,6,22.9,,-1.2649605,36.798177700000004,-1.290894,36.822971,Rider_Id_267,968
-Order_No_19645,User_Id_1419,Bike,3,Personal,17,1,3:14:32 PM,17,1,4:01:12 PM,17,1,4:23:25 PM,17,1,4:34:38 PM,17,1,4:46:53 PM,6,22.4,,-1.2728947,36.807942600000004,-1.2859690000000001,36.789204999999995,Rider_Id_818,735
-Order_No_15491,User_Id_517,Bike,3,Business,5,3,8:11:10 AM,5,3,9:51:58 AM,5,3,9:57:07 AM,5,3,10:03:31 AM,5,3,10:42:57 AM,10,17.9,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_523,2366
-Order_No_7510,User_Id_2941,Bike,1,Personal,8,2,9:54:26 AM,8,2,9:54:48 AM,8,2,10:08:36 AM,8,2,10:16:27 AM,8,2,10:50:21 AM,13,,,-1.3108368000000001,36.8226308,-1.2283331,36.8562419,Rider_Id_340,2034
-Order_No_8876,User_Id_1798,Bike,3,Business,26,5,10:17:59 AM,26,5,10:21:54 AM,26,5,10:28:29 AM,26,5,10:31:48 AM,26,5,11:42:57 AM,10,,,-1.2615888000000002,36.7928732,-1.3080097,36.7494062,Rider_Id_708,4269
-Order_No_6794,User_Id_868,Bike,3,Business,31,4,9:30:05 AM,31,4,9:31:01 AM,31,4,9:40:37 AM,31,4,9:51:46 AM,31,4,10:06:50 AM,6,17.5,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_100,904
-Order_No_3727,User_Id_2436,Bike,3,Business,2,2,10:48:38 AM,2,2,10:51:17 AM,2,2,11:12:10 AM,2,2,11:15:41 AM,2,2,12:03:50 PM,6,26.1,,-1.2571472,36.795063299999995,-1.300722,36.816865,Rider_Id_177,2889
-Order_No_27455,User_Id_242,Bike,3,Business,8,5,12:13:39 PM,8,5,1:57:47 PM,8,5,2:23:24 PM,8,5,2:29:37 PM,8,5,3:10:00 PM,8,21.3,,-1.2551895,36.7822034,-1.2965924,36.8179314,Rider_Id_312,2423
-Order_No_21696,User_Id_3201,Bike,3,Business,19,3,11:12:50 AM,19,3,11:13:06 AM,19,3,11:15:55 AM,19,3,11:20:30 AM,19,3,11:27:19 AM,1,,,-1.2649605,36.798177700000004,-1.2597528999999998,36.806629,Rider_Id_882,409
-Order_No_2210,User_Id_203,Bike,3,Business,25,1,4:42:33 PM,25,1,4:43:10 PM,25,1,4:51:54 PM,25,1,4:54:48 PM,25,1,5:35:20 PM,6,28.5,,-1.2963097,36.7688221,-1.2826463,36.728912,Rider_Id_306,2432
-Order_No_17930,User_Id_2038,Bike,1,Personal,15,3,11:43:06 AM,15,3,11:43:17 AM,15,3,11:52:10 AM,15,3,11:55:55 AM,15,3,12:21:15 PM,13,18.0,,-1.26559,36.8027,-1.2136206,36.89126029999999,Rider_Id_882,1520
-Order_No_11275,User_Id_2150,Bike,3,Business,28,4,4:16:11 PM,28,4,4:16:53 PM,28,4,4:20:07 PM,28,4,4:35:45 PM,28,4,4:58:25 PM,11,25.8,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_119,1360
-Order_No_12976,User_Id_1209,Bike,3,Business,4,6,11:20:37 AM,4,6,11:24:36 AM,4,6,11:24:49 AM,4,6,11:45:23 AM,4,6,11:55:50 AM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_68,627
-Order_No_27596,User_Id_393,Bike,3,Business,7,5,11:45:51 AM,7,5,11:46:40 AM,7,5,11:46:51 AM,7,5,12:54:23 PM,7,5,12:54:29 PM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_855,6
-Order_No_26689,User_Id_3194,Bike,3,Business,4,2,2:01:06 PM,4,2,2:01:52 PM,4,2,2:03:51 PM,4,2,2:08:09 PM,4,2,2:24:49 PM,4,22.5,,-1.2551895,36.7822034,-1.2651643,36.807221399999996,Rider_Id_868,1000
-Order_No_8071,User_Id_139,Bike,3,Personal,24,4,2:06:52 PM,24,4,2:07:56 PM,24,4,2:30:05 PM,24,4,3:01:03 PM,24,4,3:10:10 PM,4,28.5,,-1.300722,36.816865,-1.2923412,36.795197200000004,Rider_Id_783,547
-Order_No_13262,User_Id_2532,Bike,3,Business,24,4,1:59:36 PM,24,4,2:00:01 PM,24,4,2:14:46 PM,24,4,2:21:21 PM,24,4,2:46:27 PM,8,28.3,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_436,1506
-Order_No_16323,User_Id_478,Bike,3,Business,2,2,11:08:02 AM,2,2,11:18:33 AM,2,2,12:06:27 PM,2,2,12:22:42 PM,2,2,1:07:51 PM,4,26.1,,-1.2844516000000001,36.8245932,-1.2571472,36.795063299999995,Rider_Id_819,2709
-Order_No_3928,User_Id_1724,Bike,3,Business,20,4,3:02:32 PM,20,4,4:05:41 PM,20,4,4:21:28 PM,20,4,4:22:46 PM,20,4,4:48:36 PM,9,25.1,,-1.2907853999999999,36.7774108,-1.2647206,36.7259313,Rider_Id_829,1550
-Order_No_16688,User_Id_2899,Bike,3,Personal,17,1,4:23:21 PM,17,1,4:23:37 PM,17,1,4:41:01 PM,17,1,4:49:47 PM,17,1,4:58:16 PM,1,25.7,,-1.3014461000000002,36.7661381,-1.298762,36.763645600000004,Rider_Id_101,509
-Order_No_10801,User_Id_2200,Bike,3,Business,6,4,12:26:37 PM,6,4,1:48:28 PM,6,4,2:03:04 PM,6,4,2:05:52 PM,6,4,2:31:50 PM,12,,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_404,1558
-Order_No_17080,User_Id_2700,Bike,1,Business,9,3,12:04:31 PM,9,3,12:06:01 PM,9,3,12:19:02 PM,9,3,12:36:41 PM,9,3,12:51:16 PM,4,20.9,,-1.2814037,36.815031299999994,-1.292083,36.802646,Rider_Id_751,875
-Order_No_5439,User_Id_53,Bike,3,Business,23,5,10:28:16 AM,23,5,10:45:56 AM,23,5,10:46:13 AM,23,5,11:05:17 AM,23,5,11:43:55 AM,5,24.1,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_748,2318
-Order_No_16356,User_Id_3085,Bike,1,Personal,25,2,10:29:04 AM,25,2,10:29:55 AM,25,2,10:41:42 AM,25,2,10:52:40 AM,25,2,11:04:24 AM,6,23.6,,-1.3012013999999998,36.8043289,-1.2789683,36.8284768,Rider_Id_88,704
-Order_No_7083,User_Id_391,Bike,3,Business,29,2,3:01:41 PM,29,2,3:02:30 PM,29,2,3:10:09 PM,29,2,3:22:10 PM,29,2,4:01:37 PM,11,27.2,,-1.2784063,36.8222933,-1.2295969999999998,36.8678736,Rider_Id_462,2367
-Order_No_1464,User_Id_2412,Bike,3,Business,11,1,11:03:20 AM,11,1,11:36:00 AM,11,1,11:49:11 AM,11,1,12:01:03 PM,11,1,12:25:18 PM,12,26.6,,-1.2283403,36.8822756,-1.2789476999999998,36.8122359,Rider_Id_291,1455
-Order_No_27350,User_Id_1245,Bike,3,Business,11,1,3:02:27 PM,11,1,3:02:37 PM,11,1,3:05:19 PM,11,1,3:22:40 PM,11,1,3:55:27 PM,18,27.8,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_846,1967
-Order_No_5935,User_Id_1966,Bike,3,Business,3,5,3:01:53 PM,3,5,3:12:54 PM,3,5,3:26:46 PM,3,5,3:41:59 PM,3,5,4:20:47 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_595,2328
-Order_No_2912,User_Id_3517,Bike,3,Business,5,2,10:36:27 AM,5,2,10:41:49 AM,5,2,11:24:25 AM,5,2,11:50:37 AM,5,2,12:23:43 PM,10,23.5,,-1.300921,36.828195,-1.3231726000000001,36.891603700000005,Rider_Id_899,1986
-Order_No_28052,User_Id_2423,Bike,3,Business,6,4,9:57:23 AM,6,4,9:57:57 AM,6,4,10:00:51 AM,6,4,10:11:14 AM,6,4,11:21:08 AM,7,18.7,3.0,-1.2571472,36.795063299999995,-1.3012173,36.8093987,Rider_Id_441,4194
-Order_No_24382,User_Id_1304,Bike,3,Business,17,5,3:39:15 PM,17,5,3:39:45 PM,17,5,3:42:12 PM,17,5,3:51:06 PM,17,5,3:58:51 PM,3,,,-1.2551895,36.7822034,-1.2707332,36.7970816,Rider_Id_178,465
-Order_No_11243,User_Id_2330,Bike,3,Business,25,5,11:10:55 AM,25,5,11:11:37 AM,25,5,11:18:35 AM,25,5,11:23:12 AM,25,5,11:33:52 AM,3,26.5,,-1.3015197,36.7658456,-1.2873232,36.765857200000006,Rider_Id_246,640
-Order_No_18202,User_Id_706,Bike,3,Business,21,1,7:48:51 AM,21,1,7:52:33 AM,21,1,8:03:27 AM,21,1,8:08:43 AM,21,1,8:42:52 AM,4,16.8,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_805,2049
-Order_No_11644,User_Id_1522,Bike,3,Business,11,2,9:36:58 AM,11,2,9:37:36 AM,11,2,9:38:02 AM,11,2,9:47:22 AM,11,2,10:18:28 AM,2,16.4,,-1.2584143,36.8048002,-1.268885,36.805758000000004,Rider_Id_258,1866
-Order_No_18169,User_Id_3291,Bike,3,Business,14,3,12:53:52 PM,14,3,1:00:01 PM,14,3,1:07:06 PM,14,3,1:22:15 PM,14,3,1:30:37 PM,5,24.4,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_131,502
-Order_No_2283,User_Id_3005,Bike,3,Personal,8,2,1:04:59 PM,8,2,1:05:18 PM,8,2,1:11:32 PM,8,2,1:22:35 PM,8,2,1:33:12 PM,5,21.2,,-1.2753108999999998,36.8130185,-1.2650042,36.790392700000005,Rider_Id_100,637
-Order_No_17556,User_Id_321,Bike,1,Personal,4,6,11:21:39 AM,4,6,11:22:04 AM,4,6,11:30:28 AM,4,6,11:44:07 AM,4,6,12:13:42 PM,13,20.0,,-1.3246122999999999,36.790226700000005,-1.2749209,36.7857781,Rider_Id_798,1775
-Order_No_20380,User_Id_868,Bike,3,Business,2,2,11:41:20 AM,2,2,11:45:14 AM,2,2,11:53:34 AM,2,2,12:22:31 PM,2,2,1:04:22 PM,9,26.3,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_233,2511
-Order_No_9201,User_Id_393,Bike,3,Business,27,1,8:49:31 AM,27,1,8:50:25 AM,27,1,9:07:12 AM,27,1,9:44:49 AM,27,1,10:06:27 AM,11,18.2,1.0,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_194,1298
-Order_No_25534,User_Id_2216,Bike,3,Business,14,1,4:20:15 PM,14,1,4:32:36 PM,14,1,4:35:27 PM,14,1,4:37:21 PM,14,1,4:55:34 PM,5,23.3,,-1.2559559999999999,36.789867,-1.2650523,36.786679799999995,Rider_Id_633,1093
-Order_No_22044,User_Id_2154,Bike,3,Business,17,3,3:41:59 PM,17,3,3:43:16 PM,17,3,3:49:02 PM,17,3,4:16:16 PM,17,3,4:43:18 PM,5,,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_910,1622
-Order_No_6182,User_Id_412,Bike,3,Business,19,3,12:33:55 PM,19,3,12:35:39 PM,19,3,12:38:45 PM,19,3,12:44:25 PM,19,3,12:44:27 PM,2,22.2,,-1.2600926000000001,36.8088685,-1.266837,36.799248999999996,Rider_Id_517,2
-Order_No_18315,User_Id_731,Bike,3,Business,28,5,12:42:16 PM,28,5,12:42:59 PM,28,5,12:49:37 PM,28,5,12:59:47 PM,28,5,1:19:04 PM,7,25.1,,-1.2574087999999999,36.799712,-1.2878081000000001,36.8200516,Rider_Id_844,1157
-Order_No_6920,User_Id_3283,Bike,3,Business,25,5,12:54:49 PM,25,5,1:07:29 PM,25,5,1:07:55 PM,25,5,1:21:34 PM,25,5,1:46:00 PM,5,21.1,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_51,1466
-Order_No_6290,User_Id_393,Bike,3,Business,29,1,10:19:17 AM,29,1,10:19:45 AM,29,1,10:22:40 AM,29,1,10:34:18 AM,29,1,10:52:32 AM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1094
-Order_No_7772,User_Id_1843,Bike,1,Personal,17,1,9:24:21 AM,17,1,9:24:50 AM,17,1,9:50:31 AM,17,1,9:53:59 AM,17,1,10:22:16 AM,12,18.4,,-1.2655332000000001,36.8094648,-1.2174537,36.880190899999995,Rider_Id_503,1697
-Order_No_18013,User_Id_3149,Bike,3,Business,18,6,9:42:53 AM,18,6,9:43:13 AM,18,6,9:44:16 AM,18,6,9:59:48 AM,18,6,10:08:26 AM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_591,518
-Order_No_15991,User_Id_3647,Bike,3,Business,18,5,4:51:20 PM,18,5,4:51:37 PM,18,5,5:03:23 PM,18,5,5:07:37 PM,18,5,5:09:10 PM,2,25.5,,-1.2726389999999999,36.794723,-1.2741728,36.79691079999999,Rider_Id_881,93
-Order_No_11766,User_Id_3578,Bike,3,Business,3,1,11:13:00 AM,3,1,11:14:52 AM,3,1,11:30:33 AM,3,1,11:43:55 AM,3,1,12:22:02 PM,7,21.5,,-1.2878913,36.8089253,-1.2527958000000001,36.8003131,Rider_Id_228,2287
-Order_No_8772,User_Id_1038,Bike,1,Business,14,6,4:40:48 PM,14,6,4:41:03 PM,14,6,4:46:29 PM,14,6,4:50:40 PM,14,6,5:04:35 PM,6,19.0,,-1.2962893,36.7708484,-1.3099145,36.802277399999994,Rider_Id_465,835
-Order_No_12961,User_Id_3326,Bike,3,Business,28,2,3:07:41 PM,28,2,3:08:34 PM,28,2,3:28:31 PM,28,2,3:54:33 PM,28,2,4:17:43 PM,7,23.7,,-1.2571472,36.795063299999995,-1.3038156,36.812273299999994,Rider_Id_524,1390
-Order_No_15626,User_Id_3647,Bike,3,Business,27,2,10:51:35 AM,27,2,10:51:43 AM,27,2,11:17:10 AM,27,2,11:19:16 AM,27,2,11:33:12 AM,6,23.8,,-1.2726389999999999,36.794723,-1.2655477,36.8001682,Rider_Id_50,836
-Order_No_23884,User_Id_3291,Bike,3,Business,4,6,12:57:29 PM,4,6,12:57:46 PM,4,6,1:00:36 PM,4,6,1:15:41 PM,4,6,1:51:49 PM,17,,,-1.3181159,36.710155900000004,-1.2600926000000001,36.8088685,Rider_Id_205,2168
-Order_No_8733,User_Id_2507,Bike,2,Personal,3,4,9:34:52 AM,3,4,9:35:57 AM,3,4,9:58:16 AM,3,4,10:00:54 AM,3,4,10:33:22 AM,14,17.1,16.1,-1.2930931,36.7559267,-1.3170903999999999,36.8509938,Rider_Id_648,1948
-Order_No_2854,User_Id_1880,Bike,3,Business,29,5,12:50:48 PM,29,5,12:51:03 PM,29,5,12:53:52 PM,29,5,1:09:59 PM,29,5,1:55:05 PM,7,19.6,,-1.2551895,36.7822034,-1.2924886,36.8084039,Rider_Id_727,2706
-Order_No_24604,User_Id_1976,Bike,3,Business,2,4,9:46:13 AM,2,4,9:46:28 AM,2,4,9:50:29 AM,2,4,10:01:51 AM,2,4,10:16:41 AM,11,,,-1.257028,36.7810435,-1.264627,36.842259000000006,Rider_Id_828,890
-Order_No_27111,User_Id_1469,Bike,3,Business,23,4,12:39:54 PM,23,4,12:40:40 PM,23,4,12:46:53 PM,23,4,1:00:33 PM,23,4,1:29:04 PM,13,23.1,,-1.2571472,36.795063299999995,-1.2343236000000002,36.8861,Rider_Id_490,1711
-Order_No_13867,User_Id_2072,Bike,3,Personal,4,4,2:59:49 PM,4,4,3:01:54 PM,4,4,3:14:40 PM,4,4,3:29:33 PM,4,4,3:59:17 PM,4,24.6,,-1.2586742,36.801643799999994,-1.2640571000000003,36.7883547,Rider_Id_802,1784
-Order_No_17226,User_Id_3291,Bike,3,Business,2,4,12:23:03 PM,2,4,12:23:19 PM,2,4,12:36:26 PM,2,4,12:55:52 PM,2,4,1:03:22 PM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_448,450
-Order_No_17758,User_Id_3647,Bike,3,Business,25,4,3:58:12 PM,25,4,4:01:57 PM,25,4,4:13:25 PM,25,4,5:06:38 PM,25,4,5:21:48 PM,6,,,-1.2726389999999999,36.794723,-1.3023756000000002,36.7949276,Rider_Id_119,910
-Order_No_14139,User_Id_3599,Bike,3,Business,3,1,11:39:31 AM,3,1,11:39:56 AM,3,1,11:46:01 AM,3,1,11:53:17 AM,3,1,12:38:06 PM,19,22.4,,-1.3302996,36.8707935,-1.3095379999999999,36.7353407,Rider_Id_654,2689
-Order_No_8598,User_Id_2804,Bike,3,Business,14,1,3:47:12 PM,14,1,3:47:28 PM,14,1,3:51:25 PM,14,1,4:03:01 PM,14,1,4:22:29 PM,8,25.6,,-1.2852139,36.751683,-1.2551895,36.7822034,Rider_Id_932,1168
-Order_No_24413,User_Id_847,Bike,3,Business,8,3,12:12:01 PM,8,3,12:16:21 PM,8,3,12:37:25 PM,8,3,12:49:47 PM,8,3,1:21:00 PM,13,,,-1.3298648000000002,36.8774718,-1.287353,36.826992,Rider_Id_705,1873
-Order_No_5281,User_Id_1378,Bike,2,Personal,22,5,1:23:08 PM,22,5,1:23:49 PM,22,5,1:35:27 PM,22,5,1:40:32 PM,22,5,1:56:23 PM,4,,,-1.2628638,36.8070399,-1.2611642,36.7949932,Rider_Id_147,951
-Order_No_4962,User_Id_3641,Bike,3,Business,15,5,1:10:34 PM,15,5,1:11:28 PM,15,5,1:14:16 PM,15,5,1:27:15 PM,15,5,1:43:08 PM,6,26.1,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_85,953
-Order_No_21419,User_Id_2041,Bike,3,Business,28,1,4:02:26 PM,28,1,4:26:50 PM,28,1,4:41:21 PM,28,1,4:56:57 PM,28,1,5:25:29 PM,13,25.2,,-1.290894,36.822971,-1.3285517,36.9018,Rider_Id_540,1712
-Order_No_9457,User_Id_478,Bike,3,Business,30,2,2:17:19 PM,30,2,2:19:19 PM,30,2,2:33:15 PM,30,2,2:56:01 PM,30,2,3:22:53 PM,6,,,-1.2551895,36.7822034,-1.280753,36.8191175,Rider_Id_291,1612
-Order_No_12895,User_Id_1340,Bike,3,Business,3,4,12:15:55 PM,3,4,12:52:06 PM,3,4,1:12:02 PM,3,4,1:20:58 PM,3,4,2:22:56 PM,21,22.1,,-1.3972661,36.7594584,-1.2707178999999997,36.7979713,Rider_Id_74,3718
-Order_No_20388,User_Id_2523,Bike,3,Business,14,1,10:43:17 AM,14,1,10:43:28 AM,14,1,11:01:46 AM,14,1,11:30:52 AM,14,1,11:37:31 AM,15,23.3,,-1.2516167,36.8678844,-1.2729291999999999,36.7679079,Rider_Id_685,399
-Order_No_12298,User_Id_633,Bike,3,Business,26,2,10:11:21 AM,26,2,10:13:04 AM,26,2,10:18:43 AM,26,2,10:21:15 AM,26,2,10:36:05 AM,5,21.9,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_910,890
-Order_No_10676,User_Id_3175,Bike,3,Business,14,4,9:39:43 AM,14,4,9:39:54 AM,14,4,9:59:28 AM,14,4,10:00:43 AM,14,4,10:33:19 AM,13,15.3,,-1.2612417,36.7958868,-1.214328,36.7911903,Rider_Id_881,1956
-Order_No_21927,User_Id_157,Bike,3,Business,23,3,2:51:22 PM,23,3,2:56:02 PM,23,3,3:17:21 PM,23,3,3:49:00 PM,23,3,4:04:17 PM,10,22.1,,-1.3043061000000002,36.780373499999996,-1.2642719999999998,36.7978791,Rider_Id_103,917
-Order_No_5161,User_Id_391,Bike,3,Business,14,2,10:36:48 AM,14,2,10:38:05 AM,14,2,10:55:07 AM,14,2,11:16:17 AM,14,2,11:31:27 AM,11,,,-1.2867454,36.8259017,-1.2226661,36.8877816,Rider_Id_291,910
-Order_No_18154,User_Id_2477,Bike,2,Personal,24,5,12:45:27 PM,24,5,12:45:46 PM,24,5,1:00:45 PM,24,5,1:15:13 PM,24,5,1:34:28 PM,17,21.0,,-1.2889751,36.815404799999996,-1.3610501,36.7520889,Rider_Id_249,1155
-Order_No_27579,User_Id_1081,Bike,3,Business,15,5,11:35:38 AM,15,5,11:36:12 AM,15,5,11:42:24 AM,15,5,11:51:12 AM,15,5,12:17:05 PM,11,26.2,,-1.2551895,36.7822034,-1.3034451,36.752312599999996,Rider_Id_250,1553
-Order_No_15365,User_Id_393,Bike,3,Business,4,1,9:43:53 AM,4,1,9:44:17 AM,4,1,9:46:12 AM,4,1,10:34:00 AM,4,1,10:34:22 AM,14,25.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_97,22
-Order_No_2720,User_Id_1402,Bike,3,Business,16,4,2:41:11 PM,16,4,2:42:12 PM,16,4,3:10:12 PM,16,4,3:14:28 PM,16,4,3:25:54 PM,1,,,-1.290894,36.822971,-1.2813012,36.832396200000005,Rider_Id_617,686
-Order_No_14698,User_Id_136,Bike,3,Business,20,3,12:22:31 PM,20,3,12:22:53 PM,20,3,12:29:57 PM,20,3,12:33:21 PM,20,3,1:15:35 PM,19,26.3,,-1.3254522,36.721658399999995,-1.3074566,36.8614804,Rider_Id_778,2534
-Order_No_17542,User_Id_1402,Bike,3,Business,20,3,2:16:03 PM,20,3,2:17:19 PM,20,3,2:29:00 PM,20,3,2:33:16 PM,20,3,3:14:45 PM,8,29.6,,-1.2813012,36.832396200000005,-1.2551895,36.7822034,Rider_Id_506,2489
-Order_No_25654,User_Id_124,Bike,3,Personal,8,2,2:58:06 PM,8,2,3:00:10 PM,8,2,3:17:47 PM,8,2,3:25:56 PM,8,2,3:47:30 PM,7,25.1,,-1.2714020000000001,36.8238135,-1.3008471000000001,36.8099068,Rider_Id_281,1294
-Order_No_27849,User_Id_238,Bike,3,Business,30,6,11:49:41 AM,30,6,11:49:50 AM,30,6,11:49:54 AM,30,6,12:16:47 PM,30,6,12:25:34 PM,5,26.4,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_842,527
-Order_No_13460,User_Id_2523,Bike,3,Business,24,1,4:50:03 PM,24,1,4:50:11 PM,24,1,4:51:14 PM,24,1,4:57:24 PM,24,1,5:22:38 PM,8,24.3,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_38,1514
-Order_No_18397,User_Id_2801,Bike,3,Business,13,3,10:07:04 AM,13,3,10:09:33 AM,13,3,10:26:18 AM,13,3,10:35:01 AM,13,3,10:45:18 AM,5,20.0,,-1.2753108999999998,36.8130185,-1.2595421999999998,36.7871178,Rider_Id_379,617
-Order_No_914,User_Id_286,Bike,3,Business,22,4,2:42:07 PM,22,4,2:43:25 PM,22,4,2:55:51 PM,22,4,2:59:42 PM,22,4,3:30:38 PM,12,26.2,,-1.2551895,36.7822034,-1.3223238000000002,36.8320056,Rider_Id_250,1856
-Order_No_4416,User_Id_2982,Bike,3,Business,2,4,12:16:46 PM,2,4,12:36:40 PM,2,4,12:45:16 PM,2,4,1:24:59 PM,2,4,2:03:30 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_538,2311
-Order_No_16394,User_Id_1207,Bike,3,Business,9,5,7:08:16 PM,9,5,7:08:22 PM,9,5,7:08:36 PM,9,5,7:21:28 PM,9,5,7:54:57 PM,12,25.0,,-1.2629666000000002,36.8068369,-1.2351541000000001,36.8748505,Rider_Id_584,2009
-Order_No_9360,User_Id_3647,Bike,3,Business,16,4,3:04:30 PM,16,4,3:07:12 PM,16,4,3:29:00 PM,16,4,3:34:18 PM,16,4,3:46:50 PM,4,,,-1.2726389999999999,36.794723,-1.2615983,36.8083096,Rider_Id_930,752
-Order_No_27345,User_Id_868,Bike,3,Business,14,2,3:05:39 PM,14,2,3:06:28 PM,14,2,3:52:01 PM,14,2,4:04:40 PM,14,2,4:31:21 PM,9,,,-1.318062,36.849326,-1.265715,36.823815,Rider_Id_686,1601
-Order_No_12121,User_Id_346,Bike,3,Business,7,4,3:42:05 PM,7,4,3:44:46 PM,7,4,3:57:36 PM,7,4,4:14:11 PM,7,4,4:21:45 PM,8,30.9,,-1.28878,36.8168312,-1.3215393999999998,36.836621,Rider_Id_61,454
-Order_No_20073,User_Id_2216,Bike,3,Business,5,5,1:33:24 PM,5,5,1:33:56 PM,5,5,1:40:01 PM,5,5,1:46:32 PM,5,5,1:52:08 PM,2,27.6,,-1.2551895,36.7822034,-1.2668093,36.7966542,Rider_Id_952,336
-Order_No_15552,User_Id_3647,Bike,3,Business,1,1,11:28:36 AM,1,1,11:29:32 AM,1,1,11:39:41 AM,1,1,11:43:28 AM,1,1,12:18:37 PM,18,,,-1.2726389999999999,36.794723,-1.298037,36.6934582,Rider_Id_147,2109
-Order_No_977,User_Id_2982,Bike,3,Business,5,3,9:57:01 AM,5,3,10:06:09 AM,5,3,10:34:14 AM,5,3,10:57:10 AM,5,3,11:41:44 AM,18,22.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_661,2674
-Order_No_1983,User_Id_718,Bike,3,Business,13,1,12:03:00 PM,13,1,12:03:29 PM,13,1,12:03:36 PM,13,1,12:08:11 PM,13,1,12:15:37 PM,3,,,-1.2551895,36.7822034,-1.2665327,36.8108799,Rider_Id_576,446
-Order_No_26368,User_Id_733,Bike,3,Business,5,5,12:09:33 PM,5,5,12:10:47 PM,5,5,12:27:01 PM,5,5,12:33:32 PM,5,5,12:50:56 PM,5,24.7,,-1.2571472,36.795063299999995,-1.2793503000000002,36.8039715,Rider_Id_576,1044
-Order_No_13636,User_Id_1469,Bike,3,Business,30,2,3:20:22 PM,30,2,3:48:39 PM,30,2,3:54:32 PM,30,2,4:01:18 PM,30,2,4:05:25 PM,3,,,-1.2551895,36.7822034,-1.2646568999999999,36.780944399999996,Rider_Id_125,247
-Order_No_10196,User_Id_1500,Bike,3,Business,10,1,9:20:16 AM,10,1,9:28:14 AM,10,1,9:44:28 AM,10,1,9:54:07 AM,10,1,10:41:06 AM,6,16.6,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_779,2819
-Order_No_4854,User_Id_2946,Bike,2,Personal,18,3,12:39:16 PM,18,3,12:39:25 PM,18,3,12:52:20 PM,18,3,12:55:53 PM,18,3,1:13:24 PM,8,19.8,,-1.2749662,36.7658781,-1.2983462,36.8139996,Rider_Id_634,1051
-Order_No_23588,User_Id_1932,Bike,3,Business,29,2,2:37:39 PM,29,2,2:38:50 PM,29,2,3:02:40 PM,29,2,3:23:26 PM,29,2,3:29:49 PM,2,23.9,,-1.2873232,36.765857200000006,-1.2922857,36.781327399999995,Rider_Id_919,383
-Order_No_22971,User_Id_186,Bike,3,Business,28,2,3:21:02 PM,28,2,3:23:18 PM,28,2,3:38:18 PM,28,2,4:07:42 PM,28,2,4:17:41 PM,5,23.5,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_448,599
-Order_No_7687,User_Id_1500,Bike,3,Business,11,4,3:48:34 PM,11,4,3:48:53 PM,11,4,3:49:03 PM,11,4,4:22:46 PM,11,4,4:22:52 PM,10,27.2,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_515,6
-Order_No_12304,User_Id_439,Bike,3,Business,23,5,11:36:04 AM,23,5,11:36:46 AM,23,5,11:37:37 AM,23,5,11:46:50 AM,23,5,12:42:44 PM,15,23.9,,-1.2696823999999998,36.8026164,-1.3323107,36.8692616,Rider_Id_120,3354
-Order_No_5506,User_Id_3517,Bike,3,Business,1,1,1:20:10 PM,1,1,1:21:32 PM,1,1,1:39:12 PM,1,1,1:59:22 PM,1,1,2:12:53 PM,8,,,-1.300921,36.828195,-1.332175,36.8819641,Rider_Id_127,811
-Order_No_19636,User_Id_704,Bike,1,Business,9,2,10:26:34 AM,9,2,10:34:46 AM,9,2,10:40:34 AM,9,2,12:12:38 PM,9,2,12:35:33 PM,12,22.1,,-1.3332749,36.870814700000004,-1.2860183,36.8975338,Rider_Id_478,1375
-Order_No_10961,User_Id_3291,Bike,3,Business,6,6,12:44:15 PM,6,6,12:44:40 PM,6,6,1:02:31 PM,6,6,1:26:17 PM,6,6,1:51:10 PM,11,26.5,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_855,1493
-Order_No_3588,User_Id_1477,Bike,2,Personal,7,1,12:22:57 PM,7,1,12:23:37 PM,7,1,12:27:26 PM,7,1,12:48:57 PM,7,1,1:10:14 PM,11,21.6,,-1.2560455,36.780086700000005,-1.3057159999999999,36.809882,Rider_Id_446,1277
-Order_No_7258,User_Id_173,Bike,3,Business,30,5,6:41:35 PM,30,5,6:41:56 PM,30,5,6:49:03 PM,30,5,6:59:29 PM,30,5,7:05:55 PM,2,22.0,,-1.2993231,36.7753372,-1.2963097,36.7688221,Rider_Id_539,386
-Order_No_16806,User_Id_3491,Bike,3,Business,26,6,1:06:34 PM,26,6,1:07:03 PM,26,6,1:29:53 PM,26,6,1:32:54 PM,26,6,1:56:18 PM,12,28.2,,-1.2860183,36.8975338,-1.2283403,36.8822756,Rider_Id_547,1404
-Order_No_2817,User_Id_3727,Bike,3,Business,8,5,10:11:20 AM,8,5,10:11:37 AM,8,5,10:20:48 AM,8,5,10:24:05 AM,8,5,10:47:58 AM,9,20.3,,-1.2963097,36.7688221,-1.252433,36.7919418,Rider_Id_755,1433
-Order_No_24526,User_Id_2301,Bike,2,Personal,11,2,3:24:40 PM,11,2,3:25:53 PM,11,2,3:38:05 PM,11,2,3:41:36 PM,11,2,3:54:56 PM,5,25.5,,-1.3031315,36.8175661,-1.2855707,36.788852,Rider_Id_85,800
-Order_No_6526,User_Id_1880,Bike,3,Business,11,2,2:18:12 PM,11,2,4:55:52 PM,11,2,4:59:18 PM,11,2,5:17:01 PM,11,2,5:47:08 PM,10,20.9,,-1.311173,36.827569,-1.2551895,36.7822034,Rider_Id_671,1807
-Order_No_9178,User_Id_186,Bike,3,Business,27,3,11:37:04 AM,27,3,11:38:21 AM,27,3,11:38:46 AM,27,3,11:48:48 AM,27,3,11:48:50 AM,5,25.9,,-1.2902869,36.8085396,-1.2571472,36.795063299999995,Rider_Id_779,2
-Order_No_664,User_Id_3525,Bike,3,Business,30,5,12:49:50 PM,30,5,1:24:55 PM,30,5,1:33:35 PM,30,5,2:15:19 PM,30,5,2:35:00 PM,10,24.6,,-1.2886923000000001,36.8241519,-1.303924,36.8758792,Rider_Id_812,1181
-Order_No_27132,User_Id_733,Bike,3,Business,11,4,4:36:12 PM,11,4,4:39:20 PM,11,4,4:39:49 PM,11,4,4:47:09 PM,11,4,5:10:34 PM,11,26.4,,-1.2571472,36.795063299999995,-1.3257473,36.859206799999995,Rider_Id_881,1405
-Order_No_18830,User_Id_246,Bike,3,Business,22,2,11:50:41 AM,22,2,11:50:58 AM,22,2,11:51:07 AM,22,2,12:04:20 PM,22,2,12:32:32 PM,15,23.8,,-1.2283403,36.8822756,-1.2584143,36.8048002,Rider_Id_669,1692
-Order_No_7065,User_Id_60,Bike,3,Business,26,5,12:47:55 PM,26,5,1:00:14 PM,26,5,1:44:48 PM,26,5,1:46:22 PM,26,5,2:51:58 PM,21,,,-1.3444459999999998,36.756597,-1.2296618000000001,36.84382720000001,Rider_Id_867,3936
-Order_No_783,User_Id_53,Bike,3,Business,7,1,3:07:55 PM,7,1,3:25:12 PM,7,1,3:25:15 PM,7,1,4:16:49 PM,7,1,4:16:59 PM,5,26.6,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_188,10
-Order_No_4848,User_Id_3517,Bike,3,Business,1,5,4:16:34 PM,1,5,4:16:57 PM,1,5,4:17:09 PM,1,5,4:37:52 PM,1,5,5:11:51 PM,15,26.6,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_925,2039
-Order_No_4921,User_Id_2330,Bike,3,Business,2,5,1:09:40 PM,2,5,1:11:03 PM,2,5,1:33:04 PM,2,5,1:34:11 PM,2,5,2:09:55 PM,5,24.9,,-1.2846026000000001,36.8219425,-1.2642903,36.80021370000001,Rider_Id_268,2144
-Order_No_25341,User_Id_3291,Bike,3,Business,8,6,3:01:26 PM,8,6,3:28:49 PM,8,6,3:39:30 PM,8,6,3:55:34 PM,8,6,4:16:23 PM,9,23.6,1.9,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_658,1249
-Order_No_17270,User_Id_3599,Bike,3,Business,27,1,1:04:55 PM,27,1,1:05:55 PM,27,1,1:53:36 PM,27,1,2:17:10 PM,27,1,2:55:57 PM,19,23.2,,-1.3302996,36.8707935,-1.3668652000000001,36.730459,Rider_Id_772,2327
-Order_No_8865,User_Id_3283,Bike,3,Business,8,1,11:58:33 AM,8,1,12:21:04 PM,8,1,12:29:25 PM,8,1,12:33:59 PM,8,1,12:53:06 PM,13,27.9,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_8,1147
-Order_No_7316,User_Id_1075,Bike,3,Business,13,3,1:13:12 PM,13,3,1:13:27 PM,13,3,1:25:32 PM,13,3,1:33:04 PM,13,3,1:42:58 PM,3,29.0,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_205,594
-Order_No_149,User_Id_1154,Bike,3,Business,6,3,11:48:04 AM,6,3,11:48:20 AM,6,3,11:50:35 AM,6,3,12:04:42 PM,6,3,12:30:39 PM,20,,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_932,1557
-Order_No_13349,User_Id_226,Bike,3,Personal,9,4,9:29:41 AM,9,4,9:46:11 AM,9,4,10:18:54 AM,9,4,10:25:55 AM,9,4,11:12:01 AM,22,,,-1.2300427999999999,36.6683516,-1.2929796,36.8329533,Rider_Id_299,2766
-Order_No_20263,User_Id_1798,Bike,3,Business,4,4,11:17:40 AM,4,4,11:22:27 AM,4,4,11:32:14 AM,4,4,11:41:46 AM,4,4,11:58:33 AM,6,,,-1.2615888000000002,36.7928732,-1.278612,36.8195876,Rider_Id_857,1007
-Order_No_10480,User_Id_2207,Bike,3,Business,18,1,11:02:42 AM,18,1,11:03:21 AM,18,1,11:14:05 AM,18,1,11:19:24 AM,18,1,11:41:02 AM,3,28.0,,-1.2547101,36.809327200000006,-1.2696908000000002,36.81519,Rider_Id_205,1298
-Order_No_17452,User_Id_2678,Bike,2,Personal,18,6,9:10:42 AM,18,6,9:10:48 AM,18,6,9:27:27 AM,18,6,9:29:36 AM,18,6,9:47:05 AM,12,15.6,,-1.3223068,36.7929881,-1.2891901000000001,36.76200120000001,Rider_Id_249,1049
-Order_No_25736,User_Id_1500,Bike,3,Business,29,1,4:03:33 PM,29,1,4:15:47 PM,29,1,4:16:50 PM,29,1,4:55:33 PM,29,1,4:58:06 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,153
-Order_No_11995,User_Id_1687,Bike,3,Business,22,2,3:37:06 PM,22,2,3:38:23 PM,22,2,4:16:49 PM,22,2,4:21:43 PM,22,2,5:24:49 PM,9,25.5,,-1.3011535,36.8437979,-1.2729802,36.793734,Rider_Id_26,3786
-Order_No_1827,User_Id_3291,Bike,3,Business,14,2,11:02:41 AM,14,2,11:08:54 AM,14,2,11:42:20 AM,14,2,12:12:30 PM,14,2,12:41:39 PM,12,21.3,,-1.318062,36.849326,-1.2600926000000001,36.8088685,Rider_Id_179,1749
-Order_No_15572,User_Id_3080,Bike,3,Business,15,4,3:07:06 PM,15,4,3:08:53 PM,15,4,3:29:45 PM,15,4,3:33:17 PM,15,4,4:07:54 PM,15,27.4,,-1.2551895,36.7822034,-1.1992502,36.8294932,Rider_Id_607,2077
-Order_No_12646,User_Id_2789,Bike,3,Business,7,5,11:34:11 AM,7,5,11:36:09 AM,7,5,11:43:32 AM,7,5,11:47:01 AM,7,5,12:04:11 PM,6,,,-1.304735,36.784605400000004,-1.2611728000000002,36.7753701,Rider_Id_709,1030
-Order_No_6318,User_Id_3222,Bike,3,Business,18,2,9:31:53 AM,18,2,9:32:58 AM,18,2,9:47:43 AM,18,2,9:49:05 AM,18,2,10:18:33 AM,6,19.8,,-1.2551895,36.7822034,-1.2479361999999998,36.772759,Rider_Id_651,1768
-Order_No_18707,User_Id_914,Bike,3,Business,24,3,3:24:10 PM,24,3,3:24:30 PM,24,3,3:24:47 PM,24,3,3:34:54 PM,24,3,3:57:18 PM,11,,,-1.2670213,36.7994482,-1.3270716,36.8406371,Rider_Id_81,1344
-Order_No_7160,User_Id_1471,Bike,3,Business,13,3,1:07:11 PM,13,3,1:10:11 PM,13,3,1:16:04 PM,13,3,1:27:15 PM,13,3,2:03:06 PM,9,24.9,,-1.2551895,36.7822034,-1.2864746999999999,36.797388399999996,Rider_Id_379,2151
-Order_No_27871,User_Id_3434,Bike,3,Business,30,4,12:22:39 PM,30,4,12:22:51 PM,30,4,12:23:23 PM,30,4,12:28:52 PM,30,4,1:11:14 PM,20,,,-1.2963097,36.7688221,-1.3244885,36.897791999999995,Rider_Id_958,2542
-Order_No_1023,User_Id_517,Bike,3,Business,27,6,10:33:09 AM,27,6,10:33:59 AM,27,6,10:44:56 AM,27,6,10:52:58 AM,27,6,11:16:40 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_329,1422
-Order_No_5813,User_Id_2763,Bike,3,Business,4,1,9:19:37 AM,4,1,9:19:47 AM,4,1,9:28:05 AM,4,1,9:38:09 AM,4,1,10:13:15 AM,13,21.8,,-1.2551895,36.7822034,-1.3270347,36.866736200000005,Rider_Id_958,2106
-Order_No_2998,User_Id_2987,Bike,3,Business,5,4,3:31:50 PM,5,4,3:37:59 PM,5,4,3:38:03 PM,5,4,3:50:03 PM,5,4,4:29:28 PM,10,22.4,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_406,2365
-Order_No_2723,User_Id_393,Bike,3,Business,27,3,2:49:25 PM,27,3,2:49:38 PM,27,3,2:50:09 PM,27,3,2:56:04 PM,27,3,3:15:42 PM,4,29.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,1178
-Order_No_5618,User_Id_3647,Bike,3,Business,10,4,3:47:13 PM,10,4,4:14:35 PM,10,4,4:18:04 PM,10,4,4:23:50 PM,10,4,4:35:46 PM,5,21.9,1.1,-1.2726389999999999,36.794723,-1.2737591000000001,36.814252200000006,Rider_Id_100,716
-Order_No_2459,User_Id_627,Bike,3,Business,10,1,3:21:45 PM,10,1,3:22:09 PM,10,1,3:30:04 PM,10,1,3:36:00 PM,10,1,3:45:14 PM,2,18.3,,-1.2551895,36.7822034,-1.2575555,36.798705299999995,Rider_Id_591,554
-Order_No_12460,User_Id_3496,Bike,3,Business,30,2,3:10:28 PM,30,2,3:18:18 PM,30,2,3:23:53 PM,30,2,4:35:41 PM,30,2,4:45:58 PM,7,,,-1.300921,36.828195,-1.2600034,36.807763,Rider_Id_613,617
-Order_No_4174,User_Id_3320,Bike,3,Business,10,4,7:54:58 AM,10,4,7:55:45 AM,10,4,7:56:16 AM,10,4,8:14:09 AM,10,4,8:56:17 AM,26,19.0,,-1.2708331,36.790419299999996,-1.3799485,36.9349923,Rider_Id_822,2528
-Order_No_25314,User_Id_3147,Bike,3,Business,27,2,8:30:11 AM,27,2,9:35:02 AM,27,2,9:53:35 AM,27,2,9:57:41 AM,27,2,10:27:36 AM,17,18.9,2.1,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_71,1795
-Order_No_22189,User_Id_2330,Bike,3,Business,20,4,11:39:04 AM,20,4,12:01:55 PM,20,4,12:17:10 PM,20,4,12:18:53 PM,20,4,12:33:17 PM,11,22.4,,-1.2212611999999998,36.8440033,-1.2199518,36.803213299999996,Rider_Id_870,864
-Order_No_21077,User_Id_3403,Bike,3,Business,2,1,2:26:53 PM,2,1,2:30:05 PM,2,1,2:49:26 PM,2,1,2:50:31 PM,2,1,3:37:03 PM,15,19.9,,-1.2551895,36.7822034,-1.3192808,36.7121888,Rider_Id_105,2792
-Order_No_21550,User_Id_2063,Bike,3,Business,25,5,2:36:09 PM,25,5,2:38:30 PM,25,5,3:08:37 PM,25,5,3:53:24 PM,25,5,4:57:36 PM,18,29.3,,-1.3289016000000002,36.8765964,-1.2551895,36.7822034,Rider_Id_697,3852
-Order_No_11839,User_Id_2216,Bike,3,Business,1,4,2:25:09 PM,1,4,2:25:54 PM,1,4,2:30:50 PM,1,4,2:38:30 PM,1,4,3:05:37 PM,14,,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_450,1627
-Order_No_8530,User_Id_3201,Bike,3,Business,28,4,1:24:38 PM,28,4,1:24:59 PM,28,4,1:25:04 PM,28,4,1:35:50 PM,28,4,2:42:54 PM,17,27.6,,-1.3165542,36.700067,-1.2584143,36.8048002,Rider_Id_876,4024
-Order_No_11731,User_Id_2066,Bike,3,Business,2,4,4:51:56 PM,2,4,4:53:50 PM,2,4,5:06:03 PM,2,4,5:09:39 PM,2,4,5:32:52 PM,9,,,-1.3210188999999999,36.841055100000005,-1.2684319,36.81658529999999,Rider_Id_709,1393
-Order_No_27818,User_Id_640,Bike,3,Business,21,2,1:10:44 PM,21,2,1:10:56 PM,21,2,1:17:33 PM,21,2,1:19:14 PM,21,2,2:02:20 PM,12,22.6,,-1.2969741000000001,36.7856612,-1.2546502,36.782863299999995,Rider_Id_958,2586
-Order_No_24981,User_Id_633,Bike,3,Business,26,2,7:36:22 AM,26,2,7:37:34 AM,26,2,7:44:44 AM,26,2,8:11:31 AM,26,2,8:31:02 AM,9,21.7,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_99,1171
-Order_No_12354,User_Id_2956,Bike,3,Business,28,2,4:28:54 PM,28,2,4:30:08 PM,28,2,4:57:39 PM,28,2,4:59:11 PM,28,2,5:42:30 PM,14,21.5,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_58,2599
-Order_No_18404,User_Id_1500,Bike,3,Business,5,2,10:15:12 AM,5,2,10:15:58 AM,5,2,10:22:13 AM,5,2,10:35:02 AM,5,2,10:59:09 AM,10,23.7,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_829,1447
-Order_No_17448,User_Id_2803,Bike,3,Business,1,4,10:29:35 AM,1,4,10:30:07 AM,1,4,10:37:06 AM,1,4,10:38:13 AM,1,4,11:01:48 AM,7,,,-1.2765736,36.851364600000004,-1.2896398999999998,36.812449799999996,Rider_Id_660,1415
-Order_No_18001,User_Id_393,Bike,3,Business,19,3,8:44:51 AM,19,3,8:48:06 AM,19,3,8:48:09 AM,19,3,9:10:40 AM,19,3,9:38:12 AM,11,19.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,1652
-Order_No_6246,User_Id_1231,Bike,3,Business,29,1,10:23:41 AM,29,1,10:25:07 AM,29,1,10:32:02 AM,29,1,10:36:45 AM,29,1,11:11:29 AM,13,,,-1.2821421999999998,36.8161565,-1.2156006000000001,36.8916865,Rider_Id_612,2084
-Order_No_16651,User_Id_2956,Bike,3,Business,19,2,9:10:55 AM,19,2,9:11:05 AM,19,2,9:11:35 AM,19,2,9:23:33 AM,19,2,9:23:36 AM,2,,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_523,3
-Order_No_22831,User_Id_3635,Bike,1,Business,12,3,3:40:03 PM,12,3,3:40:15 PM,12,3,3:57:13 PM,12,3,4:07:21 PM,12,3,4:37:02 PM,13,26.7,,-1.2095044,36.783206400000005,-1.2615888000000002,36.7928732,Rider_Id_307,1781
-Order_No_1650,User_Id_3515,Bike,1,Personal,13,4,10:44:27 AM,13,4,10:50:48 AM,13,4,11:08:01 AM,13,4,11:16:11 AM,13,4,11:51:29 AM,6,23.3,,-1.2946696000000002,36.85165870000001,-1.3053865,36.891733200000004,Rider_Id_189,2118
-Order_No_20050,User_Id_635,Bike,3,Business,21,2,1:17:41 PM,21,2,1:26:55 PM,21,2,1:35:48 PM,21,2,2:01:00 PM,21,2,2:19:56 PM,10,24.8,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_591,1136
-Order_No_11135,User_Id_1245,Bike,3,Business,28,4,10:14:47 AM,28,4,10:15:03 AM,28,4,10:15:11 AM,28,4,10:34:30 AM,28,4,11:09:28 AM,17,28.2,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_581,2098
-Order_No_8926,User_Id_1075,Bike,3,Business,11,6,12:02:41 PM,11,6,12:07:50 PM,11,6,12:20:23 PM,11,6,12:30:21 PM,11,6,1:08:03 PM,5,,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_422,2262
-Order_No_10864,User_Id_633,Bike,3,Business,9,3,3:04:03 PM,9,3,3:04:26 PM,9,3,3:04:32 PM,9,3,3:08:22 PM,9,3,3:22:34 PM,5,25.2,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_555,852
-Order_No_21156,User_Id_3621,Bike,3,Business,1,3,9:14:25 AM,1,3,9:14:45 AM,1,3,9:35:11 AM,1,3,9:38:56 AM,1,3,10:30:58 AM,21,,,-1.2836583000000001,36.7886014,-1.3559786,36.6547515,Rider_Id_155,3122
-Order_No_20911,User_Id_3291,Bike,3,Business,21,2,11:15:46 AM,21,2,11:16:04 AM,21,2,11:22:59 AM,21,2,11:32:00 AM,21,2,11:54:46 AM,11,22.4,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_675,1366
-Order_No_5288,User_Id_393,Bike,3,Business,26,2,10:14:36 AM,26,2,10:14:47 AM,26,2,10:14:52 AM,26,2,10:19:46 AM,26,2,10:30:24 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_15,638
-Order_No_1661,User_Id_1813,Bike,2,Personal,11,1,7:13:42 AM,11,1,7:45:10 AM,11,1,7:59:04 AM,11,1,8:01:56 AM,11,1,8:10:29 AM,3,14.5,,-1.2697141,36.798177700000004,-1.2560455,36.780086700000005,Rider_Id_116,513
-Order_No_23242,User_Id_1329,Bike,3,Business,26,2,6:30:58 PM,26,2,6:31:40 PM,26,2,6:38:21 PM,26,2,6:46:42 PM,26,2,7:07:53 PM,6,29.4,,-1.273056,36.811298,-1.2882120000000001,36.778334,Rider_Id_907,1271
-Order_No_24119,User_Id_2743,Bike,1,Personal,14,2,1:54:08 PM,14,2,1:54:09 PM,14,2,2:09:17 PM,14,2,2:14:01 PM,14,2,2:35:45 PM,8,24.1,,-1.3107918,36.761794,-1.2971378999999998,36.8019794,Rider_Id_754,1304
-Order_No_10483,User_Id_970,Bike,3,Business,4,2,12:58:06 PM,4,2,1:34:26 PM,4,2,1:51:22 PM,4,2,2:15:34 PM,4,2,3:05:19 PM,6,23.7,11.0,-1.2571472,36.795063299999995,-1.2852,36.82397,Rider_Id_404,2985
-Order_No_19987,User_Id_3565,Bike,3,Business,3,7,12:38:08 PM,3,7,12:38:21 PM,3,7,12:38:30 PM,3,7,12:42:56 PM,3,7,1:19:25 PM,21,28.0,,-1.2844665000000002,36.7866113,-1.391222,36.760676000000004,Rider_Id_379,2189
-Order_No_18640,User_Id_2065,Bike,3,Personal,21,4,4:02:24 PM,21,4,4:03:48 PM,21,4,4:16:25 PM,21,4,4:18:27 PM,21,4,4:34:51 PM,4,22.7,,-1.2961951999999999,36.7876396,-1.2883346000000002,36.8148489,Rider_Id_322,984
-Order_No_27036,User_Id_13,Bike,3,Personal,12,3,3:27:38 PM,12,3,3:31:19 PM,12,3,3:57:40 PM,12,3,4:14:41 PM,12,3,4:58:31 PM,13,21.2,,-1.3247683000000001,36.85803,-1.3402212,36.7696184,Rider_Id_119,2630
-Order_No_3778,User_Id_20,Bike,3,Business,30,3,10:22:27 AM,30,3,10:22:48 AM,30,3,10:33:50 AM,30,3,10:38:16 AM,30,3,10:50:07 AM,4,22.7,,-1.2629666000000002,36.8068369,-1.2730516,36.813544799999995,Rider_Id_208,711
-Order_No_1259,User_Id_3184,Bike,3,Business,28,5,11:26:35 AM,28,5,11:27:05 AM,28,5,11:39:17 AM,28,5,11:47:42 AM,28,5,12:11:36 PM,17,25.0,,-1.3396256000000002,36.8863135,-1.2682178999999998,36.8009289,Rider_Id_345,1434
-Order_No_409,User_Id_2801,Bike,3,Business,25,3,9:44:25 AM,25,3,9:44:37 AM,25,3,9:48:19 AM,25,3,9:53:57 AM,25,3,10:21:57 AM,11,13.5,,-1.2595421999999998,36.7871178,-1.307726,36.839117,Rider_Id_951,1680
-Order_No_14421,User_Id_2002,Bike,3,Personal,10,4,12:18:21 PM,10,4,12:19:02 PM,10,4,12:39:06 PM,10,4,12:45:47 PM,10,4,1:05:30 PM,12,,,-1.276154,36.822416499999996,-1.2101737,36.870661600000005,Rider_Id_100,1183
-Order_No_23501,User_Id_758,Bike,3,Business,24,4,2:01:00 PM,24,4,2:01:33 PM,24,4,2:05:05 PM,24,4,2:15:08 PM,24,4,2:48:29 PM,14,28.4,,-1.2917868000000001,36.7872675,-1.3194518,36.869396,Rider_Id_620,2001
-Order_No_7617,User_Id_661,Bike,3,Business,15,3,12:02:27 PM,15,3,12:02:59 PM,15,3,12:13:00 PM,15,3,12:42:43 PM,15,3,1:26:01 PM,13,18.3,,-1.2963097,36.7688221,-1.3299358000000001,36.8524971,Rider_Id_140,2598
-Order_No_9913,User_Id_3757,Bike,3,Personal,10,3,12:36:11 PM,10,3,12:52:55 PM,10,3,1:15:56 PM,10,3,1:38:58 PM,10,3,1:58:18 PM,9,30.5,,-1.2655381,36.8354648,-1.3173868999999998,36.8112044,Rider_Id_383,1160
-Order_No_23858,User_Id_635,Bike,3,Business,8,5,10:39:42 AM,8,5,10:41:03 AM,8,5,10:45:33 AM,8,5,11:17:51 AM,8,5,11:17:57 AM,3,24.2,,-1.2602337,36.79905479999999,-1.272704,36.793127899999995,Rider_Id_727,6
-Order_No_8082,User_Id_2330,Bike,3,Business,27,1,11:49:17 AM,27,1,11:55:57 AM,27,1,12:05:15 PM,27,1,12:06:35 PM,27,1,12:38:08 PM,10,20.8,,-1.3015197,36.7658456,-1.2687407,36.7373043,Rider_Id_599,1893
-Order_No_403,User_Id_2701,Bike,3,Business,21,3,9:37:40 AM,21,3,9:41:38 AM,21,3,9:52:14 AM,21,3,9:53:20 AM,21,3,10:18:38 AM,6,16.8,1.0,-1.2765736,36.851364600000004,-1.2885315,36.8230419,Rider_Id_878,1518
-Order_No_15368,User_Id_57,Bike,2,Personal,28,6,6:59:58 AM,28,6,7:00:52 AM,28,6,7:20:50 AM,28,6,7:35:42 AM,28,6,7:57:29 AM,6,17.3,,-1.284879,36.824597100000005,-1.2999578999999999,36.7990742,Rider_Id_399,1307
-Order_No_5846,User_Id_3696,Bike,1,Personal,16,6,9:38:47 AM,16,6,9:43:11 AM,16,6,9:53:42 AM,16,6,10:11:51 AM,16,6,10:34:12 AM,8,15.6,,-1.2790653,36.7907262,-1.317899,36.8307061,Rider_Id_88,1341
-Order_No_9607,User_Id_1500,Bike,3,Business,8,1,9:41:40 AM,8,1,9:42:14 AM,8,1,10:14:43 AM,8,1,10:16:57 AM,8,1,10:47:27 AM,6,22.0,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_378,1830
-Order_No_25025,User_Id_3186,Bike,3,Business,8,5,9:35:03 AM,8,5,9:36:03 AM,8,5,9:59:50 AM,8,5,10:02:23 AM,8,5,10:15:40 AM,5,22.9,,-1.3060302,36.7665044,-1.2980228999999999,36.78865,Rider_Id_783,797
-Order_No_12979,User_Id_2763,Bike,3,Business,6,2,1:26:41 PM,6,2,1:31:46 PM,6,2,1:44:10 PM,6,2,1:48:10 PM,6,2,2:12:24 PM,8,27.7,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_829,1454
-Order_No_25420,User_Id_306,Bike,1,Personal,6,5,12:47:52 PM,6,5,12:50:26 PM,6,5,1:17:49 PM,6,5,1:55:41 PM,6,5,2:06:07 PM,6,22.7,,-1.2907062,36.791020700000004,-1.3157973,36.815148,Rider_Id_726,626
-Order_No_2515,User_Id_3283,Bike,3,Business,12,2,10:40:24 AM,12,2,10:40:50 AM,12,2,10:49:47 AM,12,2,11:06:38 AM,12,2,11:29:53 AM,6,24.6,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_332,1395
-Order_No_18481,User_Id_517,Bike,3,Business,5,6,10:57:46 AM,5,6,10:57:59 AM,5,6,11:00:02 AM,5,6,11:03:32 AM,5,6,11:11:46 AM,4,22.5,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_543,494
-Order_No_17812,User_Id_889,Bike,3,Business,23,2,8:11:50 AM,23,2,9:03:04 AM,23,2,9:54:12 AM,23,2,9:58:40 AM,23,2,10:11:47 AM,3,15.2,7.1,-1.2985153,36.7876043,-1.2938713,36.7917214,Rider_Id_89,787
-Order_No_17126,User_Id_2987,Bike,3,Business,10,2,9:41:33 AM,10,2,9:51:15 AM,10,2,9:56:52 AM,10,2,10:00:16 AM,10,2,10:35:06 AM,13,16.4,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_131,2090
-Order_No_4693,User_Id_1809,Bike,3,Business,19,3,11:35:19 AM,19,3,11:37:39 AM,19,3,11:38:55 AM,19,3,11:49:23 AM,19,3,12:06:58 PM,8,22.4,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_56,1055
-Order_No_19374,User_Id_867,Bike,3,Business,13,2,9:27:29 AM,13,2,9:28:20 AM,13,2,9:50:08 AM,13,2,9:54:20 AM,13,2,10:17:25 AM,12,19.7,8.9,-1.306378,36.7519845,-1.2996533000000001,36.829262899999996,Rider_Id_188,1385
-Order_No_12243,User_Id_635,Bike,3,Business,8,1,11:55:16 AM,8,1,11:55:47 AM,8,1,12:01:10 PM,8,1,12:24:07 PM,8,1,1:08:45 PM,10,28.0,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_923,2678
-Order_No_14251,User_Id_2956,Bike,3,Business,20,3,4:01:31 PM,20,3,4:02:02 PM,20,3,4:06:27 PM,20,3,4:26:00 PM,20,3,4:32:31 PM,3,23.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_116,391
-Order_No_13711,User_Id_1389,Bike,3,Business,13,3,12:41:12 PM,13,3,12:47:39 PM,13,3,12:54:23 PM,13,3,1:02:22 PM,13,3,1:33:24 PM,15,24.7,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_576,1862
-Order_No_1494,User_Id_552,Bike,3,Business,30,2,9:50:20 AM,30,2,9:51:22 AM,30,2,10:32:53 AM,30,2,10:39:39 AM,30,2,11:00:17 AM,13,22.3,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_397,1238
-Order_No_3693,User_Id_2987,Bike,3,Business,29,1,4:35:38 PM,29,1,4:49:09 PM,29,1,4:58:22 PM,29,1,4:59:49 PM,29,1,5:42:43 PM,7,23.6,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_143,2574
-Order_No_25507,User_Id_246,Bike,3,Business,31,1,12:20:41 PM,31,1,12:21:32 PM,31,1,12:27:47 PM,31,1,12:51:28 PM,31,1,12:51:33 PM,6,20.9,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_456,5
-Order_No_1203,User_Id_970,Bike,3,Business,29,1,12:59:22 PM,29,1,12:59:34 PM,29,1,1:02:42 PM,29,1,1:37:14 PM,29,1,1:37:16 PM,10,,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_193,2
-Order_No_25114,User_Id_1594,Bike,3,Business,22,1,2:04:37 PM,22,1,2:04:55 PM,22,1,2:11:26 PM,22,1,2:28:05 PM,22,1,2:39:12 PM,5,,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_691,667
-Order_No_20937,User_Id_186,Bike,3,Business,13,4,3:39:39 PM,13,4,3:40:20 PM,13,4,3:44:28 PM,13,4,3:50:00 PM,13,4,4:04:28 PM,7,23.3,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_407,868
-Order_No_15460,User_Id_718,Bike,3,Business,29,3,8:45:43 AM,29,3,8:46:11 AM,29,3,8:53:45 AM,29,3,9:02:06 AM,29,3,9:19:25 AM,7,17.8,,-1.2551895,36.7822034,-1.2861453999999999,36.827251399999994,Rider_Id_491,1039
-Order_No_295,User_Id_2274,Bike,2,Personal,9,3,10:23:53 AM,9,3,10:38:58 AM,9,3,11:05:15 AM,9,3,11:10:40 AM,9,3,11:28:35 AM,5,20.9,,-1.2657273999999998,36.827338700000006,-1.2592148,36.7996449,Rider_Id_782,1075
-Order_No_6853,User_Id_1994,Bike,3,Business,10,1,11:45:38 AM,10,1,12:07:43 PM,10,1,12:17:16 PM,10,1,12:19:07 PM,10,1,12:35:55 PM,7,18.5,,-1.3031412,36.7610533,-1.288167,36.8181484,Rider_Id_344,1008
-Order_No_26642,User_Id_3669,Bike,3,Business,25,5,10:04:38 AM,25,5,10:04:52 AM,25,5,10:05:25 AM,25,5,10:15:21 AM,25,5,10:43:02 AM,13,20.7,,-1.2615888000000002,36.7928732,-1.3339166,36.8692482,Rider_Id_379,1661
-Order_No_5763,User_Id_302,Bike,3,Business,8,6,5:31:52 PM,8,6,5:32:18 PM,8,6,5:38:24 PM,8,6,5:49:59 PM,8,6,6:08:58 PM,7,22.2,,-1.2649605,36.798177700000004,-1.2510146999999998,36.7522023,Rider_Id_112,1139
-Order_No_28204,User_Id_2828,Bike,3,Business,2,6,9:30:12 AM,2,6,10:27:43 AM,2,6,11:09:22 AM,2,6,11:18:56 AM,2,6,11:39:26 AM,15,,,-1.2778747,36.883182899999994,-1.3319295,36.7924359,Rider_Id_675,1230
-Order_No_6355,User_Id_2069,Bike,3,Business,6,3,10:28:44 AM,6,3,10:28:55 AM,6,3,10:37:45 AM,6,3,10:41:03 AM,6,3,11:16:28 AM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_599,2125
-Order_No_8630,User_Id_3426,Bike,3,Business,31,3,8:50:58 AM,31,3,8:51:29 AM,31,3,8:57:22 AM,31,3,9:09:49 AM,31,3,9:48:18 AM,11,19.0,,-1.3014461000000002,36.7661381,-1.2905978999999999,36.844258200000006,Rider_Id_761,2309
-Order_No_18582,User_Id_2523,Bike,3,Business,8,3,10:05:55 AM,8,3,10:07:01 AM,8,3,10:16:15 AM,8,3,10:18:04 AM,8,3,10:49:48 AM,15,17.5,,-1.2551895,36.7822034,-1.218673,36.8888713,Rider_Id_415,1904
-Order_No_3021,User_Id_2701,Bike,3,Business,21,4,12:54:04 PM,21,4,12:54:28 PM,21,4,12:54:40 PM,21,4,2:14:50 PM,21,4,2:14:55 PM,5,26.4,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_31,5
-Order_No_26781,User_Id_1938,Bike,1,Personal,11,3,12:22:25 PM,11,3,12:23:25 PM,11,3,1:09:11 PM,11,3,1:15:54 PM,11,3,1:43:36 PM,5,18.8,,-1.2709579,36.81089620000001,-1.2905152,36.783498200000004,Rider_Id_795,1662
-Order_No_14261,User_Id_3291,Bike,3,Business,15,6,9:52:59 AM,15,6,9:54:10 AM,15,6,10:06:21 AM,15,6,10:16:26 AM,15,6,10:47:58 AM,11,16.9,,-1.2296618000000001,36.84382720000001,-1.2600926000000001,36.8088685,Rider_Id_158,1892
-Order_No_10379,User_Id_1442,Bike,3,Personal,20,4,9:32:19 AM,20,4,9:32:57 AM,20,4,9:33:01 AM,20,4,9:55:17 AM,20,4,10:11:38 AM,8,18.5,0.3,-1.2840605,36.8191699,-1.2963097,36.7688221,Rider_Id_886,981
-Order_No_1117,User_Id_220,Bike,1,Personal,6,3,11:40:25 AM,6,3,11:53:05 AM,6,3,12:04:51 PM,6,3,12:12:44 PM,6,3,12:53:08 PM,14,19.8,,-1.2559559999999999,36.789867,-1.2918163999999999,36.8773248,Rider_Id_852,2424
-Order_No_3005,User_Id_2260,Bike,1,Personal,26,3,11:28:47 AM,26,3,11:29:39 AM,26,3,11:57:23 AM,26,3,12:00:09 PM,26,3,1:06:08 PM,2,23.8,,-1.3064585,36.8119065,-1.3090663,36.813686499999996,Rider_Id_87,3959
-Order_No_16879,User_Id_2803,Bike,3,Business,14,4,9:09:13 AM,14,4,9:20:30 AM,14,4,9:32:13 AM,14,4,9:33:13 AM,14,4,9:58:43 AM,9,17.7,,-1.2765736,36.851364600000004,-1.2868018,36.890606299999995,Rider_Id_590,1530
-Order_No_7039,User_Id_867,Bike,3,Business,18,1,2:31:32 PM,18,1,2:32:40 PM,18,1,2:32:48 PM,18,1,2:34:54 PM,18,1,3:06:31 PM,14,29.4,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_831,1897
-Order_No_22498,User_Id_2423,Bike,3,Business,6,4,10:59:49 AM,6,4,11:00:21 AM,6,4,11:10:08 AM,6,4,11:11:09 AM,6,4,11:37:02 AM,14,24.3,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_764,1553
-Order_No_16837,User_Id_3091,Bike,3,Business,18,2,12:54:35 PM,18,2,1:04:07 PM,18,2,1:18:59 PM,18,2,1:21:46 PM,18,2,1:38:03 PM,3,,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_548,977
-Order_No_22543,User_Id_3688,Bike,3,Business,27,3,2:34:43 PM,27,3,2:35:17 PM,27,3,3:05:05 PM,27,3,3:12:49 PM,27,3,3:52:01 PM,17,29.3,,-1.3463041999999998,36.768164,-1.2551895,36.7822034,Rider_Id_168,2352
-Order_No_19062,User_Id_2584,Bike,1,Personal,23,4,3:34:00 PM,23,4,4:54:05 PM,23,4,4:57:47 PM,23,4,5:04:07 PM,23,4,5:18:20 PM,6,19.9,,-1.2560455,36.780086700000005,-1.2679786,36.8126862,Rider_Id_726,853
-Order_No_3177,User_Id_2982,Bike,3,Business,20,1,10:37:12 AM,20,1,10:37:26 AM,20,1,10:39:10 AM,20,1,11:28:26 AM,20,1,12:19:21 PM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_519,3055
-Order_No_3608,User_Id_3462,Bike,1,Personal,16,2,9:51:54 AM,16,2,9:52:16 AM,16,2,10:05:00 AM,16,2,10:13:45 AM,16,2,10:46:56 AM,14,,,-1.2556829999999999,36.813771,-1.2137605,36.8788137,Rider_Id_789,1991
-Order_No_4111,User_Id_393,Bike,3,Business,11,4,11:03:17 AM,11,4,11:03:51 AM,11,4,11:03:54 AM,11,4,11:35:21 AM,11,4,12:06:18 PM,14,25.6,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_293,1857
-Order_No_27879,User_Id_830,Bike,3,Business,15,1,4:50:50 PM,15,1,4:51:22 PM,15,1,5:00:36 PM,15,1,5:10:54 PM,15,1,5:45:37 PM,21,,,-1.2551895,36.7822034,-1.3632162,36.9108253,Rider_Id_812,2083
-Order_No_1321,User_Id_733,Bike,3,Business,5,5,12:31:29 PM,5,5,12:31:39 PM,5,5,12:32:59 PM,5,5,12:35:50 PM,5,5,1:09:32 PM,15,24.5,,-1.2551895,36.7822034,-1.2224188,36.8868824,Rider_Id_709,2022
-Order_No_5693,User_Id_905,Bike,1,Personal,21,2,9:05:11 AM,21,2,9:05:31 AM,21,2,9:46:45 AM,21,2,9:53:04 AM,21,2,10:10:56 AM,13,15.5,,-1.2850643999999998,36.8343657,-1.2119232,36.899645899999996,Rider_Id_208,1072
-Order_No_5253,User_Id_3557,Bike,3,Business,22,1,9:09:00 AM,22,1,9:10:05 AM,22,1,9:14:54 AM,22,1,9:24:20 AM,22,1,9:32:36 AM,3,19.9,,-1.2546732,36.808679600000005,-1.2665065,36.7970081,Rider_Id_268,496
-Order_No_7332,User_Id_346,Bike,3,Business,10,1,1:05:41 PM,10,1,1:07:40 PM,10,1,1:14:41 PM,10,1,1:39:04 PM,10,1,2:01:59 PM,1,18.4,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_300,1375
-Order_No_15709,User_Id_2031,Bike,1,Personal,25,6,4:46:57 PM,25,6,4:47:12 PM,25,6,5:01:04 PM,25,6,5:04:16 PM,25,6,5:59:08 PM,23,,,-1.2317292,36.796820000000004,-1.3317192,36.7537532,Rider_Id_881,3292
-Order_No_22103,User_Id_2417,Bike,2,Personal,11,5,9:24:58 AM,11,5,9:25:19 AM,11,5,9:38:38 AM,11,5,10:21:06 AM,11,5,10:48:31 AM,24,17.9,4.9,-1.3243811,36.8761802,-1.3958588,36.7701901,Rider_Id_663,1645
-Order_No_2298,User_Id_393,Bike,3,Business,15,3,11:26:44 AM,15,3,11:27:08 AM,15,3,11:27:12 AM,15,3,12:04:18 PM,15,3,12:05:02 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_39,44
-Order_No_11584,User_Id_3688,Bike,3,Business,18,1,12:11:07 PM,18,1,12:11:29 PM,18,1,12:17:22 PM,18,1,12:19:31 PM,18,1,12:19:57 PM,5,27.1,,-1.2551895,36.7822034,-1.2629296,36.78787979999999,Rider_Id_498,26
-Order_No_18689,User_Id_2030,Bike,3,Business,12,1,9:07:49 AM,12,1,9:08:01 AM,12,1,9:10:25 AM,12,1,9:53:40 AM,12,1,9:53:42 AM,4,19.5,0.5,-1.2482781,36.884091999999995,-1.256828,36.855895000000004,Rider_Id_86,2
-Order_No_15955,User_Id_2330,Bike,3,Business,6,3,1:19:34 PM,6,3,1:20:21 PM,6,3,1:32:19 PM,6,3,1:35:54 PM,6,3,1:53:07 PM,8,,,-1.2291477,36.8440295,-1.2325706,36.808392299999994,Rider_Id_8,1033
-Order_No_8973,User_Id_633,Bike,3,Business,22,2,3:45:52 PM,22,2,3:46:20 PM,22,2,3:46:27 PM,22,2,4:00:34 PM,22,2,4:19:54 PM,4,25.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_39,1160
-Order_No_17433,User_Id_1596,Bike,1,Personal,12,4,1:16:43 PM,12,4,1:16:54 PM,12,4,1:31:12 PM,12,4,1:48:17 PM,12,4,2:00:46 PM,8,21.6,,-1.2918239,36.8235665,-1.25537,36.7756,Rider_Id_629,749
-Order_No_18184,User_Id_3496,Bike,3,Business,19,4,12:53:18 PM,19,4,1:30:59 PM,19,4,2:02:50 PM,19,4,2:48:49 PM,19,4,3:16:12 PM,9,20.4,,-1.2949652,36.7687874,-1.300921,36.828195,Rider_Id_88,1643
-Order_No_10934,User_Id_165,Bike,1,Personal,26,4,12:52:31 PM,26,4,12:55:56 PM,26,4,1:08:37 PM,26,4,1:28:19 PM,26,4,1:53:25 PM,9,19.1,,-1.2261183999999998,36.8094779,-1.2621118999999998,36.803669,Rider_Id_208,1506
-Order_No_6914,User_Id_20,Bike,3,Business,1,4,11:27:46 AM,1,4,11:28:30 AM,1,4,11:36:14 AM,1,4,11:45:03 AM,1,4,12:22:44 PM,20,,,-1.2616841,36.801999,-1.369541,36.907455,Rider_Id_578,2261
-Order_No_7466,User_Id_3661,Bike,3,Business,16,2,9:40:36 AM,16,2,9:40:48 AM,16,2,10:26:36 AM,16,2,10:28:25 AM,16,2,11:03:19 AM,15,,,-1.2132866000000002,36.867827399999996,-1.3090727,36.831526200000006,Rider_Id_266,2094
-Order_No_4933,User_Id_2763,Bike,3,Business,22,5,12:49:39 PM,22,5,12:49:59 PM,22,5,1:00:12 PM,22,5,1:04:40 PM,22,5,1:36:24 PM,13,,,-1.2551895,36.7822034,-1.326825,36.864345,Rider_Id_452,1904
-Order_No_9847,User_Id_357,Bike,3,Business,27,2,11:44:53 AM,27,2,12:10:50 PM,27,2,12:50:15 PM,27,2,12:57:21 PM,27,2,1:09:24 PM,7,23.8,,-1.2803870000000002,36.802981,-1.2945178999999998,36.7685349,Rider_Id_72,723
-Order_No_13106,User_Id_1209,Bike,3,Business,27,1,3:35:08 PM,27,1,3:36:53 PM,27,1,3:42:11 PM,27,1,4:06:11 PM,27,1,4:15:49 PM,4,23.2,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_817,578
-Order_No_3791,User_Id_1329,Bike,3,Business,24,5,4:23:38 PM,24,5,5:26:36 PM,24,5,6:02:31 PM,24,5,6:16:49 PM,24,5,6:45:25 PM,11,20.0,,-1.273056,36.811298,-1.3170773999999998,36.856955,Rider_Id_427,1716
-Order_No_847,User_Id_319,Bike,2,Personal,7,4,1:23:06 PM,7,4,1:25:02 PM,7,4,1:39:12 PM,7,4,1:41:47 PM,7,4,2:13:55 PM,8,20.0,,-1.3043649,36.7816132,-1.280122,36.8309201,Rider_Id_116,1928
-Order_No_25204,User_Id_84,Bike,3,Business,10,1,4:13:39 PM,10,1,4:18:11 PM,10,1,4:44:19 PM,10,1,4:46:44 PM,10,1,5:34:53 PM,22,18.9,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_150,2889
-Order_No_14247,User_Id_2521,Bike,3,Business,5,6,9:24:41 AM,5,6,9:25:09 AM,5,6,9:27:29 AM,5,6,9:29:58 AM,5,6,9:56:41 AM,14,17.9,3.9,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1603
-Order_No_4839,User_Id_3729,Bike,1,Personal,9,3,2:25:28 PM,9,3,2:25:36 PM,9,3,2:25:56 PM,9,3,2:40:28 PM,9,3,3:21:42 PM,15,23.2,,-1.2196545,36.88401939999999,-1.2733223,36.795100500000004,Rider_Id_100,2474
-Order_No_23639,User_Id_245,Bike,2,Personal,11,4,5:26:08 PM,11,4,5:26:19 PM,11,4,5:30:22 PM,11,4,5:40:19 PM,11,4,5:59:33 PM,6,24.1,,-1.2861764999999998,36.8209086,-1.3243280000000002,36.8383843,Rider_Id_156,1154
-Order_No_7518,User_Id_2627,Bike,3,Business,11,2,12:29:02 PM,11,2,12:40:51 PM,11,2,12:52:44 PM,11,2,1:23:59 PM,11,2,2:38:32 PM,21,19.2,,-1.3562374,36.904295399999995,-1.2571472,36.795063299999995,Rider_Id_202,4473
-Order_No_1216,User_Id_265,Bike,3,Business,13,1,10:08:11 AM,13,1,10:09:45 AM,13,1,10:29:23 AM,13,1,11:36:44 AM,13,1,12:33:29 PM,9,,,-1.2955041999999999,36.77146870000001,-1.300921,36.828195,Rider_Id_451,3405
-Order_No_17835,User_Id_302,Bike,3,Business,21,5,6:06:40 PM,21,5,6:17:17 PM,21,5,6:24:57 PM,21,5,6:31:16 PM,21,5,6:58:07 PM,8,23.2,,-1.2649605,36.798177700000004,-1.307376,36.813404,Rider_Id_576,1611
-Order_No_5629,User_Id_3518,Bike,3,Business,28,5,9:21:02 AM,28,5,9:21:09 AM,28,5,9:22:49 AM,28,5,9:27:00 AM,28,5,9:49:59 AM,14,20.7,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_449,1379
-Order_No_22237,User_Id_2615,Bike,1,Personal,13,3,3:22:15 PM,13,3,3:39:23 PM,13,3,4:02:35 PM,13,3,4:28:48 PM,13,3,5:02:34 PM,9,23.4,,-1.336582,36.871736999999996,-1.302559,36.819555,Rider_Id_755,2026
-Order_No_26770,User_Id_3647,Bike,3,Business,27,2,2:00:01 PM,27,2,2:03:09 PM,27,2,2:16:44 PM,27,2,2:20:29 PM,27,2,2:40:26 PM,6,25.7,,-1.2726389999999999,36.794723,-1.2828362,36.818923600000005,Rider_Id_477,1197
-Order_No_14068,User_Id_1651,Bike,3,Business,6,5,12:40:26 PM,6,5,12:41:09 PM,6,5,12:42:47 PM,6,5,12:52:28 PM,6,5,1:08:27 PM,8,20.4,,-1.2551895,36.7822034,-1.2965678999999999,36.8158653,Rider_Id_908,959
-Order_No_17797,User_Id_2956,Bike,3,Business,2,2,5:07:48 PM,2,2,5:08:34 PM,2,2,5:21:53 PM,2,2,5:32:16 PM,2,2,5:50:59 PM,7,28.3,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_330,1123
-Order_No_21914,User_Id_2956,Bike,3,Business,26,2,11:51:58 AM,26,2,12:13:25 PM,26,2,12:47:49 PM,26,2,12:50:41 PM,26,2,2:10:36 PM,14,27.3,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_547,4795
-Order_No_10108,User_Id_230,Bike,1,Personal,6,3,3:30:51 PM,6,3,3:33:08 PM,6,3,3:49:12 PM,6,3,3:59:52 PM,6,3,4:55:56 PM,9,21.6,,-1.2952784,36.7793271,-1.307774,36.840754100000005,Rider_Id_148,3364
-Order_No_21324,User_Id_1245,Bike,3,Business,28,5,3:23:20 PM,28,5,3:23:53 PM,28,5,3:42:37 PM,28,5,3:57:53 PM,28,5,4:39:23 PM,18,25.5,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_496,2490
-Order_No_28254,User_Id_2828,Bike,3,Business,1,5,8:43:51 AM,1,5,8:46:00 AM,1,5,9:38:10 AM,1,5,10:23:25 AM,1,5,11:12:00 AM,14,23.4,,-1.2710687,36.8142839,-1.2137605,36.8788137,Rider_Id_44,2915
-Order_No_6829,User_Id_3496,Bike,3,Business,13,4,12:40:16 PM,13,4,12:40:57 PM,13,4,1:28:21 PM,13,4,1:46:44 PM,13,4,2:39:23 PM,23,22.6,,-1.300921,36.828195,-1.1956524,36.948806899999994,Rider_Id_925,3159
-Order_No_2563,User_Id_635,Bike,3,Business,16,6,12:42:52 PM,16,6,12:43:40 PM,16,6,12:46:49 PM,16,6,12:48:41 PM,16,6,1:09:28 PM,2,25.8,,-1.2599563,36.799343900000004,-1.2618264,36.8141179,Rider_Id_326,1247
-Order_No_20892,User_Id_3635,Bike,1,Business,10,3,9:15:54 AM,10,3,9:16:07 AM,10,3,9:20:57 AM,10,3,10:39:05 AM,10,3,10:49:14 AM,3,22.5,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_453,609
-Order_No_19571,User_Id_393,Bike,3,Business,1,5,9:57:27 AM,1,5,10:43:46 AM,1,5,10:44:01 AM,1,5,10:51:03 AM,1,5,11:22:44 AM,11,26.4,,-1.3167113000000001,36.8301563,-1.2636051000000001,36.7850995,Rider_Id_523,1901
-Order_No_3311,User_Id_478,Bike,3,Business,8,5,1:07:15 PM,8,5,1:07:33 PM,8,5,1:11:38 PM,8,5,1:23:17 PM,8,5,2:31:29 PM,8,24.0,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_26,4092
-Order_No_26923,User_Id_265,Bike,3,Business,9,4,2:45:32 PM,9,4,2:46:13 PM,9,4,2:53:34 PM,9,4,3:01:31 PM,9,4,3:11:08 PM,3,25.9,,-1.3034072,36.821803499999994,-1.300921,36.828195,Rider_Id_939,577
-Order_No_619,User_Id_1058,Bike,1,Business,25,2,2:59:42 PM,25,2,2:59:57 PM,25,2,3:34:02 PM,25,2,3:35:57 PM,25,2,3:56:29 PM,16,27.1,,-1.2251703,36.890471399999996,-1.2571472,36.795063299999995,Rider_Id_498,1232
-Order_No_25505,User_Id_186,Bike,3,Business,23,5,3:35:47 PM,23,5,3:41:19 PM,23,5,3:47:08 PM,23,5,4:03:55 PM,23,5,4:36:58 PM,20,26.4,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_107,1983
-Order_No_10808,User_Id_635,Bike,3,Business,5,2,1:13:30 PM,5,2,1:16:13 PM,5,2,1:21:02 PM,5,2,1:28:38 PM,5,2,1:45:36 PM,7,21.4,,-1.2599563,36.799343900000004,-1.3016417,36.8271679,Rider_Id_808,1018
-Order_No_26413,User_Id_972,Bike,3,Business,19,3,12:23:02 PM,19,3,1:28:25 PM,19,3,1:37:07 PM,19,3,1:39:12 PM,19,3,1:55:20 PM,10,25.8,,-1.2862033000000002,36.8152966,-1.2295205,36.812042600000005,Rider_Id_726,968
-Order_No_17902,User_Id_3291,Bike,3,Business,16,6,12:03:54 PM,16,6,12:13:35 PM,16,6,12:29:31 PM,16,6,1:29:04 PM,16,6,1:29:55 PM,8,25.7,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_778,51
-Order_No_16605,User_Id_46,Bike,3,Business,8,3,10:04:18 AM,8,3,10:08:53 AM,8,3,10:25:47 AM,8,3,10:36:30 AM,8,3,10:51:37 AM,6,,,-1.2852299999999999,36.8248292,-1.2996396000000001,36.8520799,Rider_Id_291,907
-Order_No_12462,User_Id_391,Bike,3,Business,25,5,2:40:41 PM,25,5,2:44:52 PM,25,5,2:53:08 PM,25,5,3:35:16 PM,25,5,3:38:22 PM,7,29.2,,-1.300921,36.828195,-1.2908072,36.7860517,Rider_Id_925,186
-Order_No_7439,User_Id_3698,Bike,3,Business,16,2,10:05:58 AM,16,2,10:06:10 AM,16,2,10:21:48 AM,16,2,10:38:31 AM,16,2,11:22:01 AM,28,,,-1.3244885,36.897791999999995,-1.1506305000000001,36.9643513,Rider_Id_955,2610
-Order_No_2628,User_Id_663,Bike,3,Business,15,6,2:00:47 PM,15,6,2:01:13 PM,15,6,2:02:21 PM,15,6,2:36:19 PM,15,6,2:56:23 PM,6,21.6,,-1.2689785,36.8082713,-1.2705886999999998,36.829210100000005,Rider_Id_25,1204
-Order_No_4589,User_Id_865,Bike,3,Business,27,3,4:42:43 PM,27,3,4:43:04 PM,27,3,4:43:11 PM,27,3,4:49:55 PM,27,3,5:04:28 PM,2,27.4,,-1.2452664,36.802854100000005,-1.2654219999999998,36.8059349,Rider_Id_524,873
-Order_No_13362,User_Id_3599,Bike,3,Business,21,2,12:15:17 PM,21,2,12:17:32 PM,21,2,12:39:46 PM,21,2,12:42:13 PM,21,2,1:30:25 PM,14,22.4,,-1.3302996,36.8707935,-1.2568865,36.7945489,Rider_Id_321,2892
-Order_No_15184,User_Id_2069,Bike,3,Business,25,6,1:05:10 PM,25,6,1:05:23 PM,25,6,1:05:54 PM,25,6,1:15:51 PM,25,6,1:44:01 PM,12,,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_607,1690
-Order_No_11476,User_Id_136,Bike,3,Business,10,3,8:58:20 AM,10,3,9:06:30 AM,10,3,9:40:51 AM,10,3,9:43:52 AM,10,3,10:22:41 AM,21,21.7,,-1.3254522,36.721658399999995,-1.2549476000000002,36.8269576,Rider_Id_855,2329
-Order_No_13054,User_Id_393,Bike,3,Business,11,5,3:51:26 PM,11,5,3:52:11 PM,11,5,3:52:18 PM,11,5,4:26:48 PM,11,5,4:26:49 PM,4,26.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,1
-Order_No_11339,User_Id_1437,Bike,3,Personal,9,3,12:40:34 PM,9,3,12:42:29 PM,9,3,12:57:45 PM,9,3,1:04:05 PM,9,3,1:42:30 PM,9,25.2,,-1.2890635,36.83185220000001,-1.2850318,36.8907864,Rider_Id_48,2305
-Order_No_25595,User_Id_733,Bike,3,Business,18,2,10:38:54 AM,18,2,10:39:57 AM,18,2,11:19:30 AM,18,2,11:22:20 AM,18,2,11:59:37 AM,16,22.5,,-1.2604833,36.7352777,-1.323682,36.708787,Rider_Id_268,2237
-Order_No_20640,User_Id_3194,Bike,3,Business,14,4,9:52:40 AM,14,4,9:52:53 AM,14,4,9:53:12 AM,14,4,10:16:21 AM,14,4,10:45:50 AM,14,18.9,,-1.2551895,36.7822034,-1.312745,36.875715,Rider_Id_26,1769
-Order_No_19419,User_Id_3432,Bike,3,Business,19,6,11:01:05 AM,19,6,11:01:32 AM,19,6,11:10:25 AM,19,6,11:23:32 AM,19,6,11:47:24 AM,3,25.4,,-1.2672588,36.8000252,-1.2527469,36.7904333,Rider_Id_190,1432
-Order_No_18048,User_Id_868,Bike,3,Business,13,4,1:25:33 PM,13,4,2:13:04 PM,13,4,2:30:16 PM,13,4,2:47:50 PM,13,4,3:12:08 PM,8,23.1,,-1.303596,36.778378000000004,-1.2663631000000002,36.8163329,Rider_Id_838,1458
-Order_No_7697,User_Id_892,Bike,2,Personal,11,3,10:29:56 AM,11,3,10:31:28 AM,11,3,10:39:00 AM,11,3,10:44:53 AM,11,3,11:11:12 AM,8,18.9,,-1.2680641000000001,36.807869700000005,-1.3203841,36.8290563,Rider_Id_808,1579
-Order_No_18847,User_Id_1329,Bike,3,Business,22,4,10:37:40 AM,22,4,10:38:06 AM,22,4,10:46:03 AM,22,4,10:54:07 AM,22,4,11:22:15 AM,18,22.4,,-1.273056,36.811298,-1.3242283000000001,36.701819,Rider_Id_74,1688
-Order_No_19539,User_Id_1432,Bike,1,Personal,24,1,11:08:39 AM,24,1,11:08:56 AM,24,1,11:17:15 AM,24,1,11:21:15 AM,24,1,11:33:06 AM,4,22.6,,-1.2950408000000002,36.809817200000005,-1.2793148,36.8148394,Rider_Id_88,711
-Order_No_24537,User_Id_1882,Bike,3,Business,22,5,9:23:55 AM,22,5,9:36:44 AM,22,5,9:44:24 AM,22,5,9:47:35 AM,22,5,10:01:59 AM,7,,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_783,864
-Order_No_25104,User_Id_381,Bike,2,Personal,31,2,1:12:10 PM,31,2,1:12:57 PM,31,2,1:21:00 PM,31,2,1:32:07 PM,31,2,1:43:03 PM,5,22.3,,-1.2632268999999998,36.803261600000006,-1.2534992,36.7965565,Rider_Id_379,656
-Order_No_928,User_Id_2383,Bike,3,Business,9,2,8:54:14 AM,9,2,8:54:45 AM,9,2,9:10:39 AM,9,2,9:22:03 AM,9,2,9:34:34 AM,5,20.6,,-1.2696819,36.8028694,-1.2608993,36.799246000000004,Rider_Id_520,751
-Order_No_8636,User_Id_2912,Bike,2,Personal,27,1,1:05:49 PM,27,1,1:06:18 PM,27,1,1:10:55 PM,27,1,1:16:38 PM,27,1,1:43:00 PM,17,24.0,,-1.2601,36.796528,-1.2036664,36.819262200000004,Rider_Id_898,1582
-Order_No_2926,User_Id_3222,Bike,3,Business,6,3,4:10:12 PM,6,3,4:11:00 PM,6,3,4:17:23 PM,6,3,4:23:00 PM,6,3,4:35:14 PM,5,,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_523,734
-Order_No_17776,User_Id_1195,Bike,3,Business,19,3,10:25:03 AM,19,3,10:38:09 AM,19,3,10:47:31 AM,19,3,10:56:26 AM,19,3,11:44:27 AM,15,22.4,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_404,2881
-Order_No_17733,User_Id_3565,Bike,3,Business,23,2,10:44:57 AM,23,2,10:48:33 AM,23,2,11:24:44 AM,23,2,11:33:06 AM,23,2,12:09:57 PM,11,18.2,,-1.2770168,36.8331774,-1.3142011,36.899387899999994,Rider_Id_532,2211
-Order_No_19392,User_Id_1075,Bike,3,Business,20,3,11:36:18 AM,20,3,11:36:44 AM,20,3,11:44:40 AM,20,3,11:53:30 AM,20,3,12:00:40 PM,1,27.3,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_205,430
-Order_No_24076,User_Id_391,Bike,3,Business,26,2,2:10:38 PM,26,2,2:12:16 PM,26,2,2:13:03 PM,26,2,2:22:13 PM,26,2,2:30:22 PM,4,29.8,,-1.2867454,36.8259017,-1.290641,36.812940999999995,Rider_Id_845,489
-Order_No_16550,User_Id_391,Bike,3,Business,18,4,11:50:16 AM,18,4,11:51:49 AM,18,4,11:58:49 AM,18,4,12:17:48 PM,18,4,12:44:56 PM,14,,,-1.300921,36.828195,-1.3688433,36.7402114,Rider_Id_206,1628
-Order_No_10937,User_Id_2008,Bike,3,Business,30,1,2:18:57 PM,30,1,2:21:15 PM,30,1,2:40:12 PM,30,1,2:43:24 PM,30,1,3:19:11 PM,6,20.4,,-1.2508229,36.7895263,-1.2810198999999998,36.8150453,Rider_Id_754,2147
-Order_No_22858,User_Id_187,Bike,3,Business,16,4,2:02:53 PM,16,4,2:04:45 PM,16,4,2:11:28 PM,16,4,2:26:39 PM,16,4,3:07:35 PM,17,18.8,,-1.3169591,36.7000029,-1.2863535,36.81939570000001,Rider_Id_74,2456
-Order_No_28148,User_Id_1304,Bike,3,Business,24,4,10:50:39 AM,24,4,10:53:50 AM,24,4,11:39:43 AM,24,4,11:43:03 AM,24,4,12:04:02 PM,8,21.2,,-1.2553615,36.789537599999996,-1.2965824,36.815302200000005,Rider_Id_752,1259
-Order_No_10006,User_Id_1075,Bike,3,Business,18,4,11:48:11 AM,18,4,11:48:40 AM,18,4,11:55:22 AM,18,4,12:03:55 PM,18,4,12:14:15 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_484,620
-Order_No_4052,User_Id_302,Bike,3,Business,23,6,12:44:22 PM,23,6,12:46:11 PM,23,6,12:53:49 PM,23,6,1:04:10 PM,23,6,1:57:08 PM,8,26.3,,-1.2649605,36.798177700000004,-1.3117749,36.82246,Rider_Id_928,3178
-Order_No_15276,User_Id_2412,Bike,3,Business,15,1,2:45:39 PM,15,1,2:45:50 PM,15,1,2:48:57 PM,15,1,3:03:43 PM,15,1,3:29:10 PM,12,,,-1.2534301,36.800098299999995,-1.2283403,36.8822756,Rider_Id_247,1527
-Order_No_21800,User_Id_1045,Bike,1,Personal,22,3,4:14:18 PM,22,3,4:16:34 PM,22,3,4:42:17 PM,22,3,4:58:57 PM,22,3,5:34:39 PM,9,23.0,,-1.3169651000000002,36.8340857,-1.3039695,36.783826700000006,Rider_Id_521,2142
-Order_No_13078,User_Id_517,Bike,3,Business,28,5,8:43:35 AM,28,5,8:43:48 AM,28,5,8:44:00 AM,28,5,9:05:04 AM,28,5,9:05:10 AM,11,19.5,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_39,6
-Order_No_8395,User_Id_2956,Bike,3,Business,13,1,9:33:52 AM,13,1,9:34:01 AM,13,1,9:49:06 AM,13,1,9:54:07 AM,13,1,9:57:10 AM,2,15.2,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_520,183
-Order_No_17283,User_Id_393,Bike,3,Business,21,1,12:52:22 PM,21,1,1:00:38 PM,21,1,1:11:24 PM,21,1,1:16:58 PM,21,1,1:45:13 PM,14,25.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1695
-Order_No_18775,User_Id_590,Bike,3,Business,10,1,12:51:44 PM,10,1,12:52:05 PM,10,1,1:00:16 PM,10,1,1:07:51 PM,10,1,1:27:01 PM,5,23.6,,-1.2734594,36.8170558,-1.2674273999999999,36.787082700000006,Rider_Id_646,1150
-Order_No_22118,User_Id_3283,Bike,3,Business,24,3,1:47:07 PM,24,3,2:08:35 PM,24,3,2:25:15 PM,24,3,2:40:22 PM,24,3,2:58:38 PM,13,18.3,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_775,1096
-Order_No_21165,User_Id_2691,Bike,3,Business,15,5,11:56:18 AM,15,5,11:57:06 AM,15,5,12:13:27 PM,15,5,12:26:14 PM,15,5,12:54:37 PM,10,26.3,,-1.2551895,36.7822034,-1.3159617000000001,36.8117934,Rider_Id_331,1703
-Order_No_7016,User_Id_3283,Bike,3,Business,26,5,10:07:31 AM,26,5,10:07:42 AM,26,5,10:19:23 AM,26,5,10:38:00 AM,26,5,10:51:37 AM,6,,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_76,817
-Order_No_6212,User_Id_2605,Bike,3,Business,9,3,3:39:03 PM,9,3,3:47:13 PM,9,3,4:00:21 PM,9,3,4:02:52 PM,9,3,4:49:24 PM,22,23.5,,-1.2559559999999999,36.789867,-1.3562374,36.904295399999995,Rider_Id_742,2792
-Order_No_25064,User_Id_1346,Bike,3,Business,20,4,11:49:36 AM,20,4,11:59:26 AM,20,4,12:11:25 PM,20,4,12:15:54 PM,20,4,12:33:11 PM,6,22.2,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_382,1037
-Order_No_26202,User_Id_2956,Bike,3,Business,9,2,12:30:36 PM,9,2,12:32:04 PM,9,2,12:39:06 PM,9,2,12:44:49 PM,9,2,1:09:19 PM,5,27.2,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_435,1470
-Order_No_12449,User_Id_387,Bike,1,Personal,11,4,2:27:35 PM,11,4,2:33:01 PM,11,4,2:33:22 PM,11,4,2:47:40 PM,11,4,3:02:26 PM,12,26.9,,-1.3246455,36.8754415,-1.3802928,36.93644929999999,Rider_Id_179,886
-Order_No_15079,User_Id_393,Bike,3,Business,25,6,11:13:40 AM,25,6,11:13:52 AM,25,6,11:13:59 AM,25,6,11:26:58 AM,25,6,12:00:35 PM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_349,2017
-Order_No_18879,User_Id_97,Bike,3,Business,20,3,5:39:07 PM,20,3,5:40:05 PM,20,3,5:48:09 PM,20,3,6:08:37 PM,20,3,6:26:06 PM,4,27.8,,-1.2751745,36.765767600000004,-1.2969741000000001,36.7856612,Rider_Id_129,1049
-Order_No_19159,User_Id_2676,Bike,3,Business,25,1,2:50:42 PM,25,1,2:50:52 PM,25,1,2:50:59 PM,25,1,3:06:18 PM,25,1,4:07:48 PM,2,28.1,,-1.2963097,36.7688221,-1.3014461000000002,36.7661381,Rider_Id_425,3690
-Order_No_19811,User_Id_2523,Bike,3,Business,24,3,10:35:42 AM,24,3,10:35:49 AM,24,3,10:43:15 AM,24,3,10:44:55 AM,24,3,11:02:19 AM,10,20.1,,-1.2551895,36.7822034,-1.2299678,36.81530789999999,Rider_Id_370,1044
-Order_No_4596,User_Id_2112,Bike,3,Business,29,2,3:35:03 PM,29,2,3:35:17 PM,29,2,3:43:17 PM,29,2,3:57:03 PM,29,2,4:22:28 PM,14,23.8,,-1.2524976,36.8062935,-1.2156006000000001,36.8916865,Rider_Id_543,1525
-Order_No_22727,User_Id_346,Bike,3,Business,28,4,4:17:44 PM,28,4,4:29:50 PM,28,4,4:52:54 PM,28,4,6:08:56 PM,28,4,6:09:18 PM,12,25.8,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_436,22
-Order_No_18114,User_Id_1329,Bike,3,Business,7,2,4:24:36 PM,7,2,4:26:24 PM,7,2,4:35:37 PM,7,2,4:40:31 PM,7,2,5:25:34 PM,5,,,-1.273056,36.811298,-1.3028118,36.8174764,Rider_Id_309,2703
-Order_No_7346,User_Id_3621,Bike,3,Business,13,4,9:16:12 AM,13,4,9:16:52 AM,13,4,9:36:02 AM,13,4,9:38:35 AM,13,4,10:07:07 AM,11,18.5,,-1.2836583000000001,36.7886014,-1.323186,36.8319281,Rider_Id_427,1712
-Order_No_19319,User_Id_2474,Bike,1,Personal,7,6,3:51:07 PM,7,6,3:51:40 PM,7,6,4:16:01 PM,7,6,4:32:11 PM,7,6,5:11:34 PM,16,20.9,,-1.2780878,36.846943599999996,-1.2643343,36.7447423,Rider_Id_6,2363
-Order_No_7191,User_Id_478,Bike,3,Business,29,1,1:02:40 PM,29,1,1:02:48 PM,29,1,1:04:26 PM,29,1,1:07:07 PM,29,1,1:41:17 PM,9,,,-1.2551895,36.7822034,-1.2985548999999998,36.788208700000006,Rider_Id_815,2050
-Order_No_14926,User_Id_1500,Bike,3,Business,21,4,9:11:34 AM,21,4,9:11:47 AM,21,4,9:11:54 AM,21,4,9:53:29 AM,21,4,9:55:29 AM,8,21.8,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_41,120
-Order_No_12905,User_Id_552,Bike,3,Business,20,4,12:41:57 PM,20,4,12:42:33 PM,20,4,1:11:36 PM,20,4,1:13:41 PM,20,4,2:00:12 PM,20,22.4,,-1.2584143,36.8048002,-1.3632162,36.9108253,Rider_Id_496,2791
-Order_No_2959,User_Id_3283,Bike,3,Business,3,1,12:53:18 PM,3,1,12:53:57 PM,3,1,1:02:37 PM,3,1,1:09:44 PM,3,1,1:19:43 PM,6,20.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_726,599
-Order_No_8778,User_Id_3017,Bike,3,Business,11,3,3:10:33 PM,11,3,3:16:45 PM,11,3,3:23:52 PM,11,3,3:29:12 PM,11,3,3:38:03 PM,4,20.4,,-1.299313,36.78314879999999,-1.2940768999999999,36.749157399999994,Rider_Id_806,531
-Order_No_22001,User_Id_1965,Bike,1,Personal,3,1,10:02:12 AM,3,1,10:02:43 AM,3,1,10:05:18 AM,3,1,10:12:30 AM,3,1,10:45:30 AM,14,15.2,,-1.2697141,36.798177700000004,-1.2143386999999999,36.7914548,Rider_Id_620,1980
-Order_No_24317,User_Id_3331,Bike,3,Business,4,2,12:52:07 PM,4,2,1:21:13 PM,4,2,1:28:50 PM,4,2,1:31:09 PM,4,2,1:53:22 PM,8,19.5,,-1.2551895,36.7822034,-1.2876155,36.78684439999999,Rider_Id_338,1333
-Order_No_14898,User_Id_1576,Bike,3,Business,7,1,1:53:03 PM,7,1,1:53:45 PM,7,1,1:59:26 PM,7,1,2:04:00 PM,7,1,2:29:07 PM,7,20.7,,-1.3100608999999999,36.820536499999996,-1.2677588,36.8169359,Rider_Id_742,1507
-Order_No_24619,User_Id_3201,Bike,3,Business,20,3,9:36:13 AM,20,3,9:36:47 AM,20,3,9:37:02 AM,20,3,9:48:14 AM,20,3,9:56:17 AM,2,,,-1.2584143,36.8048002,-1.2561334,36.7990458,Rider_Id_31,483
-Order_No_25229,User_Id_1809,Bike,3,Business,8,1,11:29:22 AM,8,1,11:29:44 AM,8,1,11:34:39 AM,8,1,11:46:35 AM,8,1,11:56:48 AM,2,24.6,,-1.2527958000000001,36.8003131,-1.2646084,36.8028645,Rider_Id_407,613
-Order_No_11695,User_Id_1402,Bike,3,Business,18,4,2:38:19 PM,18,4,2:39:02 PM,18,4,2:45:27 PM,18,4,2:56:22 PM,18,4,3:16:38 PM,11,,,-1.2285834999999998,36.882646799999996,-1.2813012,36.832396200000005,Rider_Id_438,1216
-Order_No_5660,User_Id_265,Bike,3,Business,23,3,11:16:45 AM,23,3,11:16:52 AM,23,3,11:21:31 AM,23,3,11:36:41 AM,23,3,12:02:05 PM,18,,,-1.3081376,36.83297720000001,-1.3117864,36.6973045,Rider_Id_871,1524
-Order_No_20053,User_Id_2174,Bike,3,Business,14,5,1:02:06 PM,14,5,1:02:28 PM,14,5,1:02:50 PM,14,5,1:48:08 PM,14,5,2:30:02 PM,23,23.9,,-1.2482781,36.884091999999995,-1.3446058,36.760517799999995,Rider_Id_12,2514
-Order_No_16815,User_Id_20,Bike,3,Business,17,3,12:30:19 PM,17,3,12:49:09 PM,17,3,12:55:54 PM,17,3,1:00:16 PM,17,3,1:53:47 PM,7,25.6,,-1.2586742,36.801643799999994,-1.3090533000000002,36.8371502,Rider_Id_280,3211
-Order_No_4670,User_Id_1402,Bike,3,Business,23,2,11:50:38 AM,23,2,2:08:33 PM,23,2,2:08:41 PM,23,2,2:24:30 PM,23,2,2:38:46 PM,1,,,-1.279395,36.825364,-1.2813012,36.832396200000005,Rider_Id_79,856
-Order_No_9877,User_Id_269,Bike,3,Business,30,3,3:26:55 PM,30,3,3:53:04 PM,30,3,3:58:45 PM,30,3,4:02:48 PM,30,3,4:23:29 PM,14,25.2,,-1.3332749,36.870814700000004,-1.3404187,36.7614685,Rider_Id_890,1241
-Order_No_23407,User_Id_2350,Bike,1,Personal,19,5,12:37:03 PM,19,5,12:46:22 PM,19,5,1:01:52 PM,19,5,1:23:11 PM,19,5,1:35:28 PM,4,25.1,,-1.2844083999999998,36.8106592,-1.2745107,36.797450299999994,Rider_Id_844,737
-Order_No_4862,User_Id_1927,Bike,3,Business,11,1,5:50:52 PM,11,1,6:16:19 PM,11,1,6:50:00 PM,11,1,7:00:43 PM,11,1,7:41:16 PM,19,,,-1.2927792,36.9053492,-1.3472066,36.7692638,Rider_Id_377,2433
-Order_No_25681,User_Id_1390,Bike,1,Personal,28,5,2:07:53 PM,28,5,2:08:50 PM,28,5,2:29:39 PM,28,5,2:31:34 PM,28,5,2:57:05 PM,5,25.3,,-1.2994217,36.8009779,-1.3080783999999999,36.776235,Rider_Id_722,1531
-Order_No_25011,User_Id_391,Bike,3,Business,5,3,2:54:55 PM,5,3,3:13:42 PM,5,3,3:44:17 PM,5,3,3:53:46 PM,5,3,4:48:06 PM,21,25.0,,-1.2847613000000002,36.967084,-1.2867454,36.8259017,Rider_Id_345,3260
-Order_No_1784,User_Id_2080,Bike,3,Personal,21,2,11:11:44 AM,21,2,11:12:50 AM,21,2,11:23:24 AM,21,2,11:29:56 AM,21,2,11:57:07 AM,9,22.6,,-1.2948998,36.789164,-1.2554961000000002,36.8020345,Rider_Id_548,1631
-Order_No_3779,User_Id_1329,Bike,3,Business,13,4,10:13:21 AM,13,4,10:14:25 AM,13,4,10:23:08 AM,13,4,10:24:31 AM,13,4,10:41:34 AM,4,18.6,,-1.273056,36.811298,-1.2808066999999999,36.791472799999994,Rider_Id_435,1023
-Order_No_24021,User_Id_2627,Bike,3,Business,5,2,9:19:06 AM,5,2,9:19:20 AM,5,2,10:14:20 AM,5,2,10:21:14 AM,5,2,11:05:34 AM,24,18.1,,-1.3190102,36.923069299999995,-1.2571472,36.795063299999995,Rider_Id_404,2660
-Order_No_6354,User_Id_633,Bike,3,Business,12,2,3:31:54 PM,12,2,3:46:33 PM,12,2,4:06:30 PM,12,2,5:07:12 PM,12,2,5:13:41 PM,12,28.3,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_436,389
-Order_No_6782,User_Id_3115,Bike,3,Business,19,3,3:45:44 PM,19,3,3:48:05 PM,19,3,3:49:58 PM,19,3,3:52:06 PM,19,3,3:57:30 PM,3,24.2,,-1.2765492,36.7669809,-1.2949438999999998,36.7604214,Rider_Id_723,324
-Order_No_19322,User_Id_1269,Bike,3,Personal,10,5,6:13:03 PM,10,5,6:13:14 PM,10,5,6:33:15 PM,10,5,6:35:34 PM,10,5,7:07:06 PM,14,21.0,,-1.3271155000000001,36.733289,-1.2817426,36.7846148,Rider_Id_144,1892
-Order_No_15652,User_Id_635,Bike,3,Business,31,3,10:52:41 AM,31,3,10:53:23 AM,31,3,10:56:49 AM,31,3,11:18:09 AM,31,3,11:31:58 AM,8,24.6,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_283,829
-Order_No_9074,User_Id_3599,Bike,3,Business,20,1,2:33:27 PM,20,1,3:22:24 PM,20,1,3:42:58 PM,20,1,4:00:30 PM,20,1,4:47:36 PM,17,,,-1.3302996,36.8707935,-1.2949438999999998,36.7604214,Rider_Id_591,2826
-Order_No_5406,User_Id_2150,Bike,3,Business,20,1,2:38:59 PM,20,1,3:51:33 PM,20,1,4:05:46 PM,20,1,4:24:20 PM,20,1,4:59:14 PM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_2,2094
-Order_No_9780,User_Id_1271,Bike,3,Business,22,5,2:40:06 PM,22,5,2:40:43 PM,22,5,2:56:21 PM,22,5,2:59:36 PM,22,5,3:53:47 PM,23,,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_890,3251
-Order_No_20881,User_Id_2924,Bike,2,Personal,4,6,8:47:21 AM,4,6,8:47:46 AM,4,6,9:22:29 AM,4,6,9:52:46 AM,4,6,10:24:07 AM,15,15.7,,-1.2827651000000002,36.843383100000004,-1.2606665000000001,36.7473715,Rider_Id_95,1881
-Order_No_25846,User_Id_3647,Bike,3,Business,8,1,3:00:43 PM,8,1,3:01:07 PM,8,1,3:11:10 PM,8,1,3:18:25 PM,8,1,4:01:52 PM,18,29.7,,-1.2726389999999999,36.794723,-1.298037,36.6934582,Rider_Id_762,2607
-Order_No_22198,User_Id_2523,Bike,3,Business,10,1,10:26:36 AM,10,1,10:27:10 AM,10,1,10:34:34 AM,10,1,10:39:40 AM,10,1,10:51:28 AM,2,,,-1.2551895,36.7822034,-1.2557777,36.7994091,Rider_Id_820,708
-Order_No_5649,User_Id_1873,Bike,3,Business,13,3,9:49:24 AM,13,3,9:50:17 AM,13,3,10:24:55 AM,13,3,10:26:23 AM,13,3,11:16:11 AM,17,20.4,,-1.3100117,36.8226483,-1.2253996,36.8875735,Rider_Id_519,2988
-Order_No_1067,User_Id_2509,Bike,3,Business,13,4,11:48:03 AM,13,4,12:34:56 PM,13,4,12:50:40 PM,13,4,1:53:28 PM,13,4,3:10:58 PM,15,22.5,,-1.2571472,36.795063299999995,-1.2778747,36.883182899999994,Rider_Id_194,4650
-Order_No_25458,User_Id_393,Bike,3,Business,20,3,4:38:58 PM,20,3,4:39:20 PM,20,3,4:40:45 PM,20,3,5:13:49 PM,20,3,5:31:29 PM,4,27.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_303,1060
-Order_No_24702,User_Id_1768,Bike,3,Personal,3,4,10:27:07 AM,3,4,10:27:22 AM,3,4,10:33:31 AM,3,4,10:38:07 AM,3,4,11:03:58 AM,22,23.4,,-1.2750257,36.8157165,-1.1683312,36.95548,Rider_Id_237,1551
-Order_No_18006,User_Id_2122,Bike,3,Business,7,7,12:06:25 PM,7,7,12:27:52 PM,7,7,12:47:12 PM,7,7,12:54:51 PM,7,7,1:09:49 PM,3,25.6,,-1.2924173,36.779781400000005,-1.2963097,36.7688221,Rider_Id_532,898
-Order_No_24424,User_Id_53,Bike,3,Business,19,2,11:54:36 AM,19,2,11:59:23 AM,19,2,12:21:07 PM,19,2,12:34:25 PM,19,2,1:01:47 PM,8,26.5,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_154,1642
-Order_No_13781,User_Id_1363,Bike,3,Business,12,1,2:31:44 PM,12,1,2:33:40 PM,12,1,2:44:19 PM,12,1,2:48:56 PM,12,1,3:16:17 PM,10,26.9,,-1.3004062,36.829741,-1.2529484,36.8651347,Rider_Id_708,1641
-Order_No_13270,User_Id_3621,Bike,3,Business,10,6,7:50:29 AM,10,6,7:50:55 AM,10,6,7:56:58 AM,10,6,8:03:06 AM,10,6,8:13:19 AM,5,20.7,,-1.2836583000000001,36.7886014,-1.2857701000000001,36.7591723,Rider_Id_45,613
-Order_No_6454,User_Id_3688,Bike,3,Business,7,5,3:47:17 PM,7,5,3:52:40 PM,7,5,3:57:55 PM,7,5,3:59:31 PM,7,5,4:26:26 PM,10,,,-1.2551895,36.7822034,-1.3050378999999999,36.7943452,Rider_Id_903,1615
-Order_No_19453,User_Id_633,Bike,3,Business,25,5,9:44:43 AM,25,5,9:46:02 AM,25,5,9:55:28 AM,25,5,10:17:17 AM,25,5,10:26:43 AM,5,20.8,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_580,566
-Order_No_1105,User_Id_633,Bike,3,Business,15,2,1:40:02 PM,15,2,1:43:03 PM,15,2,1:52:08 PM,15,2,1:53:55 PM,15,2,2:08:12 PM,5,24.7,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_576,857
-Order_No_14748,User_Id_2429,Bike,1,Personal,4,2,3:44:54 PM,4,2,3:47:07 PM,4,2,3:57:48 PM,4,2,4:00:25 PM,4,2,4:11:21 PM,5,20.5,,-1.2789493,36.81922479999999,-1.276282,36.786421999999995,Rider_Id_857,656
-Order_No_18902,User_Id_265,Bike,3,Business,17,1,2:20:41 PM,17,1,2:55:35 PM,17,1,3:07:33 PM,17,1,3:53:40 PM,17,1,4:41:22 PM,18,28.1,,-1.300921,36.828195,-1.3593378,36.9433184,Rider_Id_482,2862
-Order_No_20187,User_Id_1471,Bike,3,Business,30,2,11:17:20 AM,30,2,11:27:56 AM,30,2,11:37:21 AM,30,2,11:41:07 AM,30,2,12:40:15 PM,8,,,-1.2551895,36.7822034,-1.3021707,36.816844399999994,Rider_Id_68,3548
-Order_No_13371,User_Id_20,Bike,3,Business,14,3,10:04:55 AM,14,3,10:05:09 AM,14,3,10:08:40 AM,14,3,10:17:27 AM,14,3,10:56:17 AM,17,18.7,,-1.2628638,36.8070399,-1.3390024,36.8746657,Rider_Id_620,2330
-Order_No_3296,User_Id_868,Bike,3,Business,18,4,10:54:59 AM,18,4,10:55:14 AM,18,4,10:55:20 AM,18,4,11:01:01 AM,18,4,11:14:11 AM,9,,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_7,790
-Order_No_5147,User_Id_3647,Bike,3,Business,9,4,3:13:01 PM,9,4,3:16:23 PM,9,4,3:55:45 PM,9,4,4:15:07 PM,9,4,5:23:03 PM,18,,,-1.2726389999999999,36.794723,-1.3768823000000001,36.760893200000005,Rider_Id_815,4076
-Order_No_2014,User_Id_816,Bike,3,Business,17,4,7:58:52 AM,17,4,8:17:36 AM,17,4,8:32:51 AM,17,4,8:41:33 AM,17,4,8:53:35 AM,1,,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_725,722
-Order_No_13941,User_Id_2200,Bike,3,Business,28,5,1:23:08 PM,28,5,1:35:31 PM,28,5,1:43:56 PM,28,5,2:34:09 PM,28,5,3:09:48 PM,17,25.3,,-1.3700383,36.9190174,-1.290894,36.822971,Rider_Id_498,2139
-Order_No_26579,User_Id_2677,Bike,3,Business,8,4,2:55:09 PM,8,4,2:55:43 PM,8,4,3:11:24 PM,8,4,3:20:31 PM,8,4,4:28:59 PM,30,27.4,,-1.306378,36.7519845,-1.3440557,36.909965299999996,Rider_Id_30,4108
-Order_No_3440,User_Id_3291,Bike,3,Business,7,5,2:15:55 PM,7,5,2:16:28 PM,7,5,2:29:38 PM,7,5,2:39:28 PM,7,5,2:54:25 PM,4,,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_309,897
-Order_No_1000,User_Id_2721,Bike,1,Personal,8,5,12:21:44 PM,8,5,12:29:38 PM,8,5,12:58:10 PM,8,5,1:15:14 PM,8,5,1:47:43 PM,20,,,-1.3178524,36.7139804,-1.3205669,36.836216799999995,Rider_Id_107,1949
-Order_No_6220,User_Id_190,Bike,3,Business,27,3,12:23:28 PM,27,3,12:23:48 PM,27,3,12:25:40 PM,27,3,12:27:08 PM,27,3,12:41:30 PM,8,26.3,,-1.3270347,36.866736200000005,-1.332624,36.913187,Rider_Id_813,862
-Order_No_3080,User_Id_2457,Bike,3,Business,23,4,2:46:15 PM,23,4,2:46:38 PM,23,4,2:47:11 PM,23,4,3:02:51 PM,23,4,3:25:48 PM,10,24.7,,-1.2551895,36.7822034,-1.3025299,36.7901963,Rider_Id_19,1377
-Order_No_8572,User_Id_1073,Bike,3,Business,25,3,1:14:49 PM,25,3,1:15:00 PM,25,3,1:34:27 PM,25,3,1:39:29 PM,25,3,2:00:05 PM,5,17.5,,-1.2559294,36.788465,-1.2658534,36.8242971,Rider_Id_88,1236
-Order_No_13393,User_Id_2200,Bike,3,Business,12,2,2:49:52 PM,12,2,2:52:02 PM,12,2,3:12:57 PM,12,2,3:35:07 PM,12,2,3:35:13 PM,2,,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_314,6
-Order_No_9005,User_Id_1363,Bike,3,Business,20,3,4:18:43 PM,20,3,4:19:18 PM,20,3,4:20:34 PM,20,3,4:56:44 PM,20,3,4:57:09 PM,8,27.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_619,25
-Order_No_1082,User_Id_2538,Bike,1,Personal,15,1,9:54:45 AM,15,1,9:55:57 AM,15,1,10:04:30 AM,15,1,10:11:46 AM,15,1,10:26:31 AM,5,,,-1.2575406999999998,36.7868197,-1.2848928,36.777887,Rider_Id_467,885
-Order_No_24484,User_Id_1329,Bike,3,Business,6,4,1:43:09 PM,6,4,1:43:54 PM,6,4,2:17:09 PM,6,4,2:30:22 PM,6,4,4:08:00 PM,20,,,-1.273056,36.811298,-1.1998362,36.929287200000005,Rider_Id_64,5858
-Order_No_3856,User_Id_1882,Bike,3,Business,8,2,3:36:40 PM,8,2,3:53:40 PM,8,2,3:56:52 PM,8,2,3:59:10 PM,8,2,4:27:43 PM,3,25.3,,-1.2588774999999999,36.8079744,-1.2663778,36.8076024,Rider_Id_932,1713
-Order_No_1591,User_Id_3772,Bike,1,Personal,11,6,11:24:26 AM,11,6,11:24:41 AM,11,6,11:30:15 AM,11,6,11:33:17 AM,11,6,11:44:47 AM,6,20.9,,-1.2971834,36.829507299999996,-1.276245,36.836329299999996,Rider_Id_249,690
-Order_No_8170,User_Id_3763,Bike,3,Business,26,3,12:30:56 PM,26,3,12:45:28 PM,26,3,12:45:56 PM,26,3,1:00:27 PM,26,3,1:27:20 PM,11,24.1,,-1.2671001,36.7977185,-1.3039222,36.8534707,Rider_Id_857,1613
-Order_No_14701,User_Id_391,Bike,3,Business,2,6,3:05:05 PM,2,6,3:21:03 PM,2,6,3:34:38 PM,2,6,3:36:10 PM,2,6,4:34:45 PM,19,30.8,,-1.2867454,36.8259017,-1.3981712,36.7673241,Rider_Id_291,3515
-Order_No_27981,User_Id_1900,Bike,3,Personal,7,2,12:43:27 PM,7,2,12:44:19 PM,7,2,12:44:37 PM,7,2,12:55:02 PM,7,2,1:18:38 PM,6,,,-1.3040651,36.798125399999996,-1.2764851000000002,36.8007126,Rider_Id_647,1416
-Order_No_26547,User_Id_868,Bike,3,Business,2,6,11:59:58 AM,2,6,1:17:51 PM,2,6,1:39:28 PM,2,6,1:54:41 PM,2,6,2:21:32 PM,14,31.2,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_411,1611
-Order_No_6243,User_Id_393,Bike,3,Business,14,4,12:00:54 PM,14,4,12:01:21 PM,14,4,12:01:28 PM,14,4,1:14:42 PM,14,4,1:14:44 PM,14,23.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515,2
-Order_No_9392,User_Id_867,Bike,3,Business,5,6,10:36:15 AM,5,6,10:36:20 AM,5,6,10:36:23 AM,5,6,10:40:14 AM,5,6,11:10:26 AM,10,22.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_914,1812
-Order_No_1339,User_Id_972,Bike,3,Business,28,6,1:56:59 PM,28,6,1:57:38 PM,28,6,2:03:27 PM,28,6,2:04:49 PM,28,6,2:18:15 PM,4,25.1,,-1.2862033000000002,36.8152966,-1.3041128999999998,36.810493900000004,Rider_Id_844,806
-Order_No_608,User_Id_393,Bike,3,Business,4,1,11:22:27 AM,4,1,11:23:07 AM,4,1,11:32:47 AM,4,1,11:36:34 AM,4,1,12:03:38 PM,14,26.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1624
-Order_No_4793,User_Id_3108,Bike,3,Business,15,5,10:06:00 AM,15,5,10:13:09 AM,15,5,10:18:23 AM,15,5,10:24:10 AM,15,5,10:47:48 AM,10,20.5,,-1.2551895,36.7822034,-1.3156451999999998,36.815802000000005,Rider_Id_326,1418
-Order_No_16199,User_Id_1188,Bike,3,Personal,31,4,8:35:37 AM,31,4,8:35:44 AM,31,4,8:52:46 AM,31,4,8:55:34 AM,31,4,9:07:41 AM,7,17.6,,-1.3039402,36.776293200000005,-1.2804375,36.764612799999995,Rider_Id_648,727
-Order_No_11139,User_Id_640,Bike,3,Business,30,1,12:45:24 PM,30,1,12:45:43 PM,30,1,12:46:50 PM,30,1,12:59:32 PM,30,1,1:08:51 PM,3,22.5,,-1.304033,36.7848695,-1.3026036,36.79348770000001,Rider_Id_247,559
-Order_No_10492,User_Id_1148,Bike,3,Business,8,5,2:40:30 PM,8,5,2:41:59 PM,8,5,2:49:06 PM,8,5,2:54:28 PM,8,5,3:13:57 PM,5,27.2,,-1.3100586,36.850032899999995,-1.2960687,36.8167909,Rider_Id_148,1169
-Order_No_20764,User_Id_1304,Bike,3,Business,9,1,4:28:57 PM,9,1,4:30:07 PM,9,1,4:36:47 PM,9,1,4:48:33 PM,9,1,5:39:14 PM,15,20.2,,-1.2551895,36.7822034,-1.3038645,36.8684596,Rider_Id_279,3041
-Order_No_20344,User_Id_3517,Bike,3,Business,4,5,1:36:09 PM,4,5,1:39:14 PM,4,5,1:46:40 PM,4,5,2:10:13 PM,4,5,2:36:46 PM,16,24.0,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_100,1593
-Order_No_4876,User_Id_3239,Bike,3,Personal,11,1,11:16:56 AM,11,1,11:28:24 AM,11,1,11:58:18 AM,11,1,12:00:19 PM,11,1,12:20:39 PM,6,18.0,,-1.2839322,36.8292426,-1.312469,36.815177,Rider_Id_752,1220
-Order_No_23572,User_Id_1839,Bike,1,Personal,12,7,1:11:44 PM,12,7,1:12:00 PM,12,7,1:20:46 PM,12,7,1:28:15 PM,12,7,1:55:31 PM,10,19.5,,-1.288621,36.778846,-1.3314048,36.8229367,Rider_Id_465,1636
-Order_No_6932,User_Id_157,Bike,3,Business,17,1,1:18:49 PM,17,1,1:19:08 PM,17,1,1:23:36 PM,17,1,1:27:16 PM,17,1,1:47:05 PM,10,24.9,,-1.2642719999999998,36.7978791,-1.2248308,36.801257299999996,Rider_Id_882,1189
-Order_No_14645,User_Id_647,Bike,3,Business,7,5,12:24:43 PM,7,5,12:25:39 PM,7,5,12:27:00 PM,7,5,12:37:44 PM,7,5,12:58:43 PM,6,,,-1.3004062,36.829741,-1.274291,36.8334934,Rider_Id_2,1259
-Order_No_8295,User_Id_393,Bike,3,Business,8,2,1:23:03 PM,8,2,1:23:13 PM,8,2,1:36:41 PM,8,2,1:48:21 PM,8,2,1:58:13 PM,4,25.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_62,592
-Order_No_23848,User_Id_2462,Bike,3,Personal,8,2,4:09:36 PM,8,2,4:13:41 PM,8,2,4:52:06 PM,8,2,4:57:54 PM,8,2,5:40:09 PM,17,23.6,,-1.2778747,36.883182899999994,-1.2832281,36.9706668,Rider_Id_584,2535
-Order_No_3579,User_Id_478,Bike,3,Business,8,5,12:36:58 PM,8,5,12:38:16 PM,8,5,12:48:06 PM,8,5,12:54:01 PM,8,5,1:36:01 PM,15,24.3,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_205,2520
-Order_No_15285,User_Id_3652,Bike,1,Personal,28,4,3:01:23 PM,28,4,3:09:01 PM,28,4,3:15:30 PM,28,4,3:18:57 PM,28,4,3:34:56 PM,4,,,-1.3014515,36.8120092,-1.2856826000000001,36.8255265,Rider_Id_851,959
-Order_No_4460,User_Id_2042,Bike,3,Business,7,2,11:31:58 AM,7,2,11:32:32 AM,7,2,11:38:23 AM,7,2,11:46:19 AM,7,2,12:32:14 PM,19,23.1,,-1.2860183,36.8975338,-1.2551895,36.7822034,Rider_Id_900,2755
-Order_No_13565,User_Id_1329,Bike,3,Business,15,5,10:45:51 AM,15,5,10:51:37 AM,15,5,10:58:32 AM,15,5,11:03:28 AM,15,5,11:30:42 AM,6,25.7,,-1.273056,36.811298,-1.2991481000000002,36.8043449,Rider_Id_657,1634
-Order_No_322,User_Id_136,Bike,3,Business,14,5,3:03:03 PM,14,5,3:12:13 PM,14,5,3:22:51 PM,14,5,3:27:22 PM,14,5,4:24:49 PM,14,27.3,,-1.3326173000000001,36.71462329999999,-1.2896398999999998,36.812449799999996,Rider_Id_312,3447
-Order_No_22871,User_Id_3647,Bike,3,Business,9,3,10:15:12 AM,9,3,10:15:27 AM,9,3,10:21:07 AM,9,3,10:27:02 AM,9,3,10:28:51 AM,4,20.4,,-1.2726389999999999,36.794723,-1.2651643,36.807221399999996,Rider_Id_829,109
-Order_No_2642,User_Id_1271,Bike,3,Business,18,2,1:57:14 PM,18,2,1:57:19 PM,18,2,2:02:28 PM,18,2,2:05:58 PM,18,2,2:39:18 PM,24,25.8,,-1.3244885,36.897791999999995,-1.3108386,36.7555239,Rider_Id_675,2000
-Order_No_19905,User_Id_567,Bike,3,Business,28,4,3:03:49 PM,28,4,3:14:27 PM,28,4,3:25:52 PM,28,4,3:41:51 PM,28,4,4:15:42 PM,10,30.1,,-1.3108783000000002,36.8392726,-1.2963097,36.7688221,Rider_Id_607,2031
-Order_No_26803,User_Id_3547,Bike,3,Business,31,5,12:45:08 PM,31,5,12:53:54 PM,31,5,1:32:40 PM,31,5,1:46:19 PM,31,5,2:18:06 PM,11,24.7,,-1.2856319,36.767299799999996,-1.2549476000000002,36.8269576,Rider_Id_269,1907
-Order_No_23899,User_Id_255,Bike,2,Personal,11,4,4:03:39 PM,11,4,4:04:07 PM,11,4,4:13:09 PM,11,4,4:19:37 PM,11,4,4:39:04 PM,5,26.7,,-1.3014461000000002,36.7661381,-1.2694398,36.762448299999996,Rider_Id_328,1167
-Order_No_12368,User_Id_718,Bike,3,Business,7,5,9:42:57 AM,7,5,9:52:31 AM,7,5,10:09:39 AM,7,5,10:14:01 AM,7,5,10:48:05 AM,8,20.5,0.9,-1.2551895,36.7822034,-1.2987976,36.829477000000004,Rider_Id_725,2044
-Order_No_12436,User_Id_2227,Bike,3,Personal,19,3,10:28:57 AM,19,3,10:29:48 AM,19,3,10:36:19 AM,19,3,10:43:38 AM,19,3,11:10:08 AM,8,22.4,,-1.2541275,36.8263055,-1.297101,36.798243,Rider_Id_630,1590
-Order_No_25166,User_Id_71,Bike,3,Business,21,1,9:43:26 AM,21,1,9:56:41 AM,21,1,10:03:06 AM,21,1,10:11:10 AM,21,1,11:07:33 AM,7,17.0,,-1.2821108,36.8250555,-1.32751,36.828812,Rider_Id_742,3383
-Order_No_25085,User_Id_1104,Bike,2,Business,31,2,2:32:13 PM,31,2,2:32:36 PM,31,2,2:34:09 PM,31,2,3:21:59 PM,31,2,4:24:33 PM,10,22.2,,-1.2551895,36.7822034,-1.2976940000000001,36.749788,Rider_Id_101,3754
-Order_No_12830,User_Id_1222,Bike,3,Personal,21,4,1:21:33 PM,21,4,1:37:12 PM,21,4,1:57:02 PM,21,4,2:00:19 PM,21,4,2:24:50 PM,8,22.4,,-1.2898709,36.751918200000006,-1.2707178999999997,36.7979713,Rider_Id_465,1471
-Order_No_24678,User_Id_1455,Bike,1,Personal,19,4,11:31:16 AM,19,4,11:48:29 AM,19,4,12:16:51 PM,19,4,12:19:17 PM,19,4,12:44:52 PM,6,20.4,,-1.2897399,36.8270975,-1.2609721,36.8480813,Rider_Id_752,1535
-Order_No_24672,User_Id_2987,Bike,3,Business,25,4,3:53:04 PM,25,4,3:53:23 PM,25,4,3:56:22 PM,25,4,4:00:39 PM,25,4,5:04:23 PM,12,20.6,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_698,3824
-Order_No_8654,User_Id_397,Bike,3,Business,21,5,1:41:59 PM,21,5,1:42:10 PM,21,5,1:59:45 PM,21,5,2:08:57 PM,21,5,2:40:21 PM,18,24.6,,-1.3103985,36.9114173,-1.3307985,36.8036471,Rider_Id_420,1884
-Order_No_5871,User_Id_3130,Bike,1,Personal,8,1,2:59:23 PM,8,1,3:01:14 PM,8,1,3:11:30 PM,8,1,3:18:18 PM,8,1,3:46:11 PM,13,25.1,,-1.2128205,36.8026175,-1.2885417,36.810052299999995,Rider_Id_578,1673
-Order_No_6662,User_Id_557,Bike,3,Business,27,6,7:12:58 PM,27,6,7:13:28 PM,27,6,7:51:59 PM,27,6,7:56:34 PM,27,6,8:34:47 PM,24,,,-1.2809423999999998,36.8950141,-1.1630034,36.8263292,Rider_Id_461,2293
-Order_No_19267,User_Id_246,Bike,3,Business,5,3,10:20:01 AM,5,3,10:20:14 AM,5,3,10:28:17 AM,5,3,10:34:24 AM,5,3,10:40:55 AM,2,22.5,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_289,391
-Order_No_4029,User_Id_3441,Bike,3,Business,5,2,12:09:31 PM,5,2,12:09:56 PM,5,2,12:25:42 PM,5,2,12:35:14 PM,5,2,12:54:32 PM,8,25.4,,-1.300722,36.7882234,-1.2551895,36.7822034,Rider_Id_19,1158
-Order_No_13367,User_Id_2271,Bike,3,Business,2,6,10:53:27 AM,2,6,10:54:30 AM,2,6,11:09:15 AM,2,6,11:20:10 AM,2,6,11:44:31 AM,13,24.0,,-1.2850325,36.822932,-1.293515,36.897607,Rider_Id_297,1461
-Order_No_5429,User_Id_1335,Bike,3,Personal,25,5,3:24:16 PM,25,5,3:24:49 PM,25,5,3:32:07 PM,25,5,3:36:28 PM,25,5,3:59:54 PM,5,23.4,,-1.2611987,36.80609129999999,-1.2536855,36.8245276,Rider_Id_795,1406
-Order_No_16621,User_Id_311,Bike,3,Business,11,1,2:53:11 PM,11,1,3:11:32 PM,11,1,3:29:02 PM,11,1,3:46:36 PM,11,1,4:09:06 PM,21,27.9,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_874,1350
-Order_No_8797,User_Id_1280,Bike,3,Business,6,4,4:24:10 PM,6,4,4:24:41 PM,6,4,4:25:01 PM,6,4,4:34:23 PM,6,4,5:14:55 PM,5,22.3,,-1.2551895,36.7822034,-1.2708788,36.766115500000005,Rider_Id_875,2432
-Order_No_9327,User_Id_76,Bike,1,Personal,25,6,2:38:25 PM,25,6,2:39:02 PM,25,6,3:01:32 PM,25,6,3:04:29 PM,25,6,3:22:52 PM,8,,,-1.260232,36.807334000000004,-1.2962832,36.784968299999996,Rider_Id_598,1103
-Order_No_7966,User_Id_361,Bike,1,Personal,11,3,11:26:41 AM,11,3,11:27:13 AM,11,3,11:32:16 AM,11,3,11:36:32 AM,11,3,11:48:13 AM,5,18.9,,-1.2709086,36.7929516,-1.2817083999999999,36.8143646,Rider_Id_812,701
-Order_No_16906,User_Id_393,Bike,3,Business,17,1,11:42:33 AM,17,1,12:06:40 PM,17,1,12:22:16 PM,17,1,12:30:47 PM,17,1,12:59:44 PM,14,25.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_200,1737
-Order_No_19517,User_Id_393,Bike,3,Business,20,4,1:45:32 PM,20,4,1:45:20 PM,20,4,1:45:26 PM,20,4,1:47:43 PM,20,4,2:22:49 PM,14,28.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,2106
-Order_No_27456,User_Id_2415,Bike,3,Personal,15,5,1:00:30 PM,15,5,1:16:52 PM,15,5,1:23:48 PM,15,5,1:37:09 PM,15,5,1:49:54 PM,5,22.3,,-1.2863177,36.8237972,-1.2988168,36.8198572,Rider_Id_64,765
-Order_No_6385,User_Id_3300,Bike,3,Business,12,3,2:04:22 PM,12,3,2:07:54 PM,12,3,2:08:47 PM,12,3,2:33:27 PM,12,3,2:39:29 PM,5,20.8,,-1.3040553,36.8092658,-1.303596,36.778378000000004,Rider_Id_564,362
-Order_No_3271,User_Id_2271,Bike,3,Business,30,6,3:35:07 PM,30,6,3:57:29 PM,30,6,3:57:35 PM,30,6,4:17:41 PM,30,6,4:56:01 PM,14,23.8,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_886,2300
-Order_No_9394,User_Id_1075,Bike,3,Business,4,5,4:42:16 PM,4,5,4:44:02 PM,4,5,4:57:06 PM,4,5,5:02:11 PM,4,5,5:34:56 PM,10,21.8,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_254,1965
-Order_No_6079,User_Id_2523,Bike,3,Business,4,5,2:31:09 PM,4,5,2:31:45 PM,4,5,2:33:45 PM,4,5,2:35:30 PM,4,5,3:29:13 PM,14,,,-1.3204931,36.7043102,-1.3781602,36.7562761,Rider_Id_457,3223
-Order_No_11768,User_Id_136,Bike,3,Business,25,1,9:10:05 AM,25,1,9:10:19 AM,25,1,9:52:36 AM,25,1,9:54:21 AM,25,1,10:29:20 AM,17,,,-1.3254522,36.721658399999995,-1.290641,36.812940999999995,Rider_Id_831,2099
-Order_No_19697,User_Id_2200,Bike,3,Business,8,4,11:55:42 AM,8,4,11:57:43 AM,8,4,12:24:51 PM,8,4,12:32:08 PM,8,4,1:18:24 PM,7,26.3,,-1.290894,36.822971,-1.2546732,36.7923244,Rider_Id_512,2776
-Order_No_21857,User_Id_653,Bike,3,Business,24,1,12:36:25 PM,24,1,12:36:42 PM,24,1,12:45:48 PM,24,1,12:47:59 PM,24,1,1:07:15 PM,5,22.5,,-1.279395,36.825364,-1.303105,36.819134999999996,Rider_Id_148,1156
-Order_No_2807,User_Id_1245,Bike,3,Business,20,4,2:12:39 PM,20,4,2:55:33 PM,20,4,3:24:36 PM,20,4,4:05:49 PM,20,4,4:37:08 PM,20,25.3,,-1.300921,36.828195,-1.3494207,36.70254129999999,Rider_Id_456,1879
-Order_No_19713,User_Id_496,Bike,3,Business,12,1,3:33:16 PM,12,1,3:35:15 PM,12,1,3:40:19 PM,12,1,3:49:44 PM,12,1,4:23:48 PM,22,26.9,,-1.2571472,36.795063299999995,-1.374163,36.913444,Rider_Id_770,2044
-Order_No_14545,User_Id_1527,Bike,3,Business,15,3,3:37:24 PM,15,3,3:37:57 PM,15,3,3:44:44 PM,15,3,3:50:32 PM,15,3,3:57:40 PM,4,25.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_910,428
-Order_No_24541,User_Id_71,Bike,3,Business,13,3,2:45:12 PM,13,3,2:45:34 PM,13,3,2:51:50 PM,13,3,3:48:02 PM,13,3,4:12:00 PM,1,23.6,,-1.2864673999999998,36.820262,-1.2821108,36.8250555,Rider_Id_338,1438
-Order_No_20596,User_Id_393,Bike,3,Business,30,2,2:49:41 PM,30,2,2:52:40 PM,30,2,2:52:57 PM,30,2,3:00:01 PM,30,2,3:20:08 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_438,1207
-Order_No_1913,User_Id_798,Bike,3,Business,26,3,10:14:43 AM,26,3,10:15:51 AM,26,3,10:22:27 AM,26,3,10:36:57 AM,26,3,11:18:20 AM,11,24.2,,-1.304033,36.7848695,-1.258348,36.844901799999995,Rider_Id_100,2483
-Order_No_5482,User_Id_868,Bike,3,Business,7,2,11:28:39 AM,7,2,11:32:04 AM,7,2,11:47:08 AM,7,2,11:53:37 AM,7,2,12:05:08 PM,5,23.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_105,691
-Order_No_1658,User_Id_1081,Bike,3,Business,6,3,10:15:26 AM,6,3,10:15:55 AM,6,3,10:35:15 AM,6,3,10:47:48 AM,6,3,11:15:04 AM,10,22.6,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_681,1636
-Order_No_20932,User_Id_1183,Bike,3,Personal,25,5,3:24:43 PM,25,5,3:24:53 PM,25,5,3:26:49 PM,25,5,3:38:21 PM,25,5,4:00:45 PM,12,29.3,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_871,1344
-Order_No_3572,User_Id_3621,Bike,3,Business,23,2,9:00:15 AM,23,2,9:00:54 AM,23,2,9:12:57 AM,23,2,9:17:07 AM,23,2,9:32:41 AM,4,15.3,7.0,-1.2836583000000001,36.7886014,-1.2833106,36.82402379999999,Rider_Id_571,934
-Order_No_2704,User_Id_1927,Bike,3,Business,9,3,3:11:58 PM,9,3,3:12:13 PM,9,3,3:37:18 PM,9,3,3:48:17 PM,9,3,4:35:33 PM,17,25.3,,-1.3467388999999999,36.7690312,-1.2551895,36.7822034,Rider_Id_246,2836
-Order_No_18717,User_Id_3560,Bike,3,Business,12,2,3:32:37 PM,12,2,3:33:28 PM,12,2,3:37:16 PM,12,2,3:42:59 PM,12,2,3:54:46 PM,5,,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_903,707
-Order_No_16193,User_Id_1384,Bike,3,Business,15,5,7:47:33 AM,15,5,7:48:45 AM,15,5,7:57:07 AM,15,5,8:01:27 AM,15,5,8:59:38 AM,12,19.1,,-1.2517448,36.7924971,-1.3192764,36.8028521,Rider_Id_108,3491
-Order_No_21496,User_Id_1557,Bike,3,Business,17,1,11:43:39 AM,17,1,11:43:52 AM,17,1,11:51:39 AM,17,1,12:10:54 PM,17,1,12:21:24 PM,7,24.8,,-1.2808155,36.8136842,-1.2551895,36.7822034,Rider_Id_189,630
-Order_No_8682,User_Id_323,Bike,3,Business,22,3,11:12:53 AM,22,3,11:13:08 AM,22,3,11:20:47 AM,22,3,11:54:25 AM,22,3,1:21:01 PM,20,23.0,,-1.2571472,36.795063299999995,-1.3632162,36.9108253,Rider_Id_19,5196
-Order_No_16966,User_Id_3295,Bike,3,Business,8,1,11:33:31 AM,8,1,11:34:08 AM,8,1,11:46:51 AM,8,1,12:00:29 PM,8,1,12:28:08 PM,5,28.0,,-1.2571472,36.795063299999995,-1.2815072,36.82096,Rider_Id_769,1659
-Order_No_15277,User_Id_1363,Bike,3,Business,30,4,10:28:11 AM,30,4,10:29:51 AM,30,4,10:30:07 AM,30,4,10:42:45 AM,30,4,10:55:13 AM,7,,,-1.3004062,36.829741,-1.331619,36.847976,Rider_Id_89,748
-Order_No_9127,User_Id_3370,Bike,3,Business,7,2,11:22:20 AM,7,2,11:22:53 AM,7,2,11:23:06 AM,7,2,11:33:25 AM,7,2,11:39:18 AM,3,23.0,,-1.2527958000000001,36.8003131,-1.2615888000000002,36.7928732,Rider_Id_51,353
-Order_No_11707,User_Id_2218,Bike,1,Personal,8,1,1:58:14 PM,8,1,1:59:15 PM,8,1,2:05:18 PM,8,1,2:12:03 PM,8,1,2:35:52 PM,13,24.9,,-1.2600219,36.8019631,-1.2239754999999999,36.7087131,Rider_Id_932,1429
-Order_No_22104,User_Id_478,Bike,3,Business,10,4,9:37:05 AM,10,4,9:37:37 AM,10,4,9:37:57 AM,10,4,9:42:22 AM,10,4,10:17:48 AM,10,19.3,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_26,2126
-Order_No_23743,User_Id_1502,Bike,3,Business,23,2,11:54:05 AM,23,2,11:55:23 AM,23,2,12:16:40 PM,23,2,12:29:26 PM,23,2,12:49:10 PM,11,,,-1.2765736,36.851364600000004,-1.2232123,36.8932547,Rider_Id_686,1184
-Order_No_20597,User_Id_633,Bike,3,Business,11,5,10:34:45 AM,11,5,10:37:52 AM,11,5,10:49:45 AM,11,5,10:54:26 AM,11,5,11:04:49 AM,4,20.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_523,623
-Order_No_3966,User_Id_939,Bike,2,Personal,4,2,9:32:42 AM,4,2,9:33:09 AM,4,2,9:55:57 AM,4,2,9:57:08 AM,4,2,10:09:05 AM,5,16.5,,-1.268499,36.746882,-1.281722,36.762926,Rider_Id_116,717
-Order_No_18511,User_Id_700,Bike,3,Business,3,1,12:49:19 PM,3,1,12:53:54 PM,3,1,1:17:01 PM,3,1,1:25:08 PM,3,1,1:39:01 PM,11,22.2,,-1.3319858,36.7533,-1.3205163999999998,36.8098882,Rider_Id_24,833
-Order_No_9249,User_Id_647,Bike,3,Business,6,4,8:54:21 AM,6,4,8:56:52 AM,6,4,9:08:23 AM,6,4,9:29:57 AM,6,4,9:43:31 AM,10,16.2,4.1,-1.3004062,36.829741,-1.2555317,36.8562313,Rider_Id_70,814
-Order_No_5058,User_Id_3149,Bike,3,Business,21,2,11:22:01 AM,21,2,11:22:27 AM,21,2,11:40:02 AM,21,2,12:06:05 PM,21,2,12:15:52 PM,6,22.4,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_409,587
-Order_No_8097,User_Id_2956,Bike,3,Business,23,4,1:24:21 PM,23,4,1:28:09 PM,23,4,1:30:20 PM,23,4,1:43:58 PM,23,4,2:05:09 PM,2,24.7,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_547,1271
-Order_No_19023,User_Id_246,Bike,3,Business,28,1,11:51:32 AM,28,1,12:15:49 PM,28,1,12:26:50 PM,28,1,12:40:18 PM,28,1,1:30:54 PM,17,22.7,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_101,3036
-Order_No_14115,User_Id_2658,Bike,3,Business,19,1,11:31:10 AM,19,1,11:49:21 AM,19,1,12:23:03 PM,19,1,12:25:49 PM,19,1,12:35:08 PM,2,24.5,,-1.2891921999999998,36.825189,-1.2868546,36.824811,Rider_Id_456,559
-Order_No_402,User_Id_2829,Bike,3,Business,27,5,2:11:03 PM,27,5,2:11:10 PM,27,5,2:13:08 PM,27,5,2:21:56 PM,27,5,2:57:35 PM,20,24.2,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_415,2139
-Order_No_22810,User_Id_2200,Bike,3,Business,3,4,2:19:40 PM,3,4,2:23:34 PM,3,4,2:38:51 PM,3,4,2:45:19 PM,3,4,3:03:02 PM,5,25.8,,-1.2658334999999998,36.804286700000006,-1.290894,36.822971,Rider_Id_576,1063
-Order_No_14564,User_Id_3589,Bike,1,Personal,22,5,9:56:44 AM,22,5,9:56:52 AM,22,5,9:59:36 AM,22,5,10:11:33 AM,22,5,10:42:05 AM,12,16.3,,-1.2579303999999998,36.8019667,-1.3080793,36.7505396,Rider_Id_228,1832
-Order_No_13747,User_Id_2190,Bike,3,Business,16,4,2:38:16 PM,16,4,2:38:31 PM,16,4,2:49:08 PM,16,4,3:01:57 PM,16,4,3:17:20 PM,5,18.9,,-1.3019530000000001,36.788458,-1.2931446000000002,36.7613971,Rider_Id_754,923
-Order_No_4391,User_Id_567,Bike,3,Business,29,1,10:57:55 AM,29,1,10:58:39 AM,29,1,11:17:32 AM,29,1,11:24:17 AM,29,1,11:35:13 AM,3,,,-1.2745551000000002,36.7979797,-1.295908,36.791433000000005,Rider_Id_559,656
-Order_No_18903,User_Id_1154,Bike,3,Business,15,3,9:39:45 AM,15,3,9:39:57 AM,15,3,9:40:49 AM,15,3,9:44:38 AM,15,3,10:11:26 AM,13,,,-1.2917868000000001,36.7872675,-1.332454,36.8835186,Rider_Id_829,1608
-Order_No_11879,User_Id_2587,Bike,3,Business,9,2,12:02:07 PM,9,2,12:12:28 PM,9,2,12:26:04 PM,9,2,12:29:13 PM,9,2,12:45:38 PM,5,21.8,,-1.28878,36.8168312,-1.2596969999999998,36.800802000000004,Rider_Id_85,985
-Order_No_5642,User_Id_909,Bike,3,Business,30,2,1:05:16 PM,30,2,2:04:19 PM,30,2,2:12:51 PM,30,2,2:29:21 PM,30,2,2:38:09 PM,7,27.5,,-1.225322,36.80855,-1.2527958000000001,36.8003131,Rider_Id_101,528
-Order_No_22528,User_Id_1245,Bike,3,Business,21,2,11:05:26 AM,21,2,11:12:58 AM,21,2,11:14:40 AM,21,2,11:21:03 AM,21,2,12:17:21 PM,14,22.7,,-1.3198615,36.7110323,-1.2668611,36.795638700000005,Rider_Id_315,3378
-Order_No_5925,User_Id_2144,Bike,3,Personal,21,1,2:22:36 PM,21,1,2:32:31 PM,21,1,2:49:50 PM,21,1,3:04:55 PM,21,1,3:37:49 PM,10,21.4,,-1.2655306,36.828148,-1.214328,36.7911903,Rider_Id_770,1974
-Order_No_8316,User_Id_867,Bike,3,Business,9,2,2:38:46 PM,9,2,2:39:08 PM,9,2,2:41:28 PM,9,2,3:34:03 PM,9,2,3:34:05 PM,10,30.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_206,2
-Order_No_28078,User_Id_2700,Bike,3,Business,23,5,1:21:12 PM,23,5,3:41:41 PM,23,5,3:42:37 PM,23,5,3:54:08 PM,23,5,4:11:13 PM,4,26.2,,-1.2845902,36.825472399999995,-1.2845834999999999,36.8079121,Rider_Id_102,1025
-Order_No_7666,User_Id_3647,Bike,3,Business,5,1,4:09:34 PM,5,1,4:38:20 PM,5,1,4:46:40 PM,5,1,4:49:49 PM,5,1,5:04:28 PM,6,24.6,,-1.2726389999999999,36.794723,-1.2902141000000003,36.7714825,Rider_Id_722,879
-Order_No_9672,User_Id_63,Bike,2,Personal,2,7,3:55:18 PM,2,7,4:31:49 PM,2,7,4:43:32 PM,2,7,4:52:37 PM,2,7,6:10:31 PM,14,,,-1.2647469,36.8157706,-1.2846933,36.9059033,Rider_Id_654,4674
-Order_No_7755,User_Id_238,Bike,3,Business,13,3,11:43:53 AM,13,3,11:44:24 AM,13,3,11:49:47 AM,13,3,11:51:16 AM,13,3,12:19:11 PM,15,,,-1.3077869,36.844320700000004,-1.362953,36.904214,Rider_Id_168,1675
-Order_No_10270,User_Id_3283,Bike,3,Business,10,4,1:00:48 PM,10,4,2:00:57 PM,10,4,2:23:01 PM,10,4,2:27:58 PM,10,4,2:51:23 PM,7,25.0,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_553,1405
-Order_No_22254,User_Id_3647,Bike,3,Business,9,2,10:35:42 AM,9,2,10:38:54 AM,9,2,10:55:35 AM,9,2,11:05:38 AM,9,2,12:21:40 PM,5,26.9,,-1.2726389999999999,36.794723,-1.2828362,36.818923600000005,Rider_Id_435,4562
-Order_No_5315,User_Id_20,Bike,3,Business,4,2,4:33:25 PM,4,2,4:41:19 PM,4,2,4:59:52 PM,4,2,5:09:56 PM,4,2,5:30:39 PM,5,21.0,,-1.278353,36.824045,-1.2629666000000002,36.8068369,Rider_Id_857,1243
-Order_No_18593,User_Id_2990,Bike,1,Personal,16,4,11:47:58 AM,16,4,11:48:30 AM,16,4,11:55:14 AM,16,4,12:22:29 PM,16,4,12:32:30 PM,4,14.9,,-1.2814031000000001,36.82016470000001,-1.3047026000000002,36.8172033,Rider_Id_906,601
-Order_No_19194,User_Id_332,Bike,1,Personal,3,3,11:07:41 AM,3,3,11:07:52 AM,3,3,11:15:12 AM,3,3,11:18:44 AM,3,3,11:45:36 AM,22,24.1,,-1.314193,36.916132,-1.3535476999999998,36.7602697,Rider_Id_449,1612
-Order_No_17590,User_Id_60,Bike,3,Business,15,3,9:53:17 AM,15,3,9:53:27 AM,15,3,9:59:53 AM,15,3,10:09:29 AM,15,3,10:34:50 AM,14,,,-1.2628473,36.781805,-1.2296618000000001,36.84382720000001,Rider_Id_3,1521
-Order_No_8270,User_Id_432,Bike,3,Business,2,2,3:30:34 PM,2,2,3:51:19 PM,2,2,4:15:51 PM,2,2,4:35:56 PM,2,2,5:36:21 PM,9,28.5,,-1.290894,36.822971,-1.3012008,36.764868,Rider_Id_760,3625
-Order_No_5963,User_Id_393,Bike,3,Business,2,5,7:55:19 AM,2,5,8:27:18 AM,2,5,9:02:09 AM,2,5,9:48:58 AM,2,5,10:00:46 AM,4,18.2,17.0,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_208,708
-Order_No_14396,User_Id_3661,Bike,3,Business,22,2,12:43:24 PM,22,2,12:46:32 PM,22,2,1:18:23 PM,22,2,1:19:33 PM,22,2,1:33:05 PM,6,24.2,,-1.2134646,36.8755266,-1.2413316,36.878552500000005,Rider_Id_111,812
-Order_No_17449,User_Id_2066,Bike,3,Business,4,1,1:27:25 PM,4,1,1:27:45 PM,4,1,1:40:48 PM,4,1,2:01:18 PM,4,1,2:19:17 PM,9,28.6,,-1.3210188999999999,36.841055100000005,-1.2658315,36.809049,Rider_Id_513,1079
-Order_No_4175,User_Id_635,Bike,3,Business,17,5,9:54:47 AM,17,5,10:17:14 AM,17,5,11:03:11 AM,17,5,11:19:28 AM,17,5,11:45:37 AM,17,17.8,,-1.177202,36.8351183,-1.2599563,36.799343900000004,Rider_Id_86,1569
-Order_No_14612,User_Id_60,Bike,3,Business,8,3,1:39:20 PM,8,3,1:51:55 PM,8,3,2:01:39 PM,8,3,2:11:26 PM,8,3,2:25:53 PM,7,,,-1.228911,36.881876,-1.2296618000000001,36.84382720000001,Rider_Id_276,867
-Order_No_18720,User_Id_286,Bike,3,Business,6,4,11:30:18 AM,6,4,12:15:03 PM,6,4,12:20:40 PM,6,4,12:38:28 PM,6,4,1:43:16 PM,7,23.9,,-1.2551895,36.7822034,-1.2962966999999999,36.8020231,Rider_Id_518,3888
-Order_No_26745,User_Id_246,Bike,3,Business,20,5,9:55:49 AM,20,5,10:11:08 AM,20,5,10:32:17 AM,20,5,10:39:45 AM,20,5,10:59:53 AM,17,18.7,,-1.2156006000000001,36.8916865,-1.2584143,36.8048002,Rider_Id_334,1208
-Order_No_7611,User_Id_646,Bike,3,Personal,19,3,12:22:08 PM,19,3,12:39:30 PM,19,3,12:42:22 PM,19,3,12:47:24 PM,19,3,12:51:45 PM,2,22.1,,-1.2809121,36.7852524,-1.2884066,36.7866984,Rider_Id_881,261
-Order_No_22629,User_Id_2248,Bike,1,Personal,31,4,6:53:54 PM,31,4,7:48:23 PM,31,4,8:07:24 PM,31,4,8:11:41 PM,31,4,8:26:40 PM,8,17.9,,-1.3066143,36.8161046,-1.2825655,36.787261,Rider_Id_116,899
-Order_No_9583,User_Id_3108,Bike,3,Business,9,5,11:56:08 AM,9,5,11:59:28 AM,9,5,12:12:24 PM,9,5,12:47:30 PM,9,5,1:11:03 PM,17,25.6,,-1.2551895,36.7822034,-1.332642,36.890212299999995,Rider_Id_215,1413
-Order_No_324,User_Id_3283,Bike,3,Business,5,3,9:59:03 AM,5,3,10:06:38 AM,5,3,10:17:02 AM,5,3,10:23:32 AM,5,3,10:58:52 AM,10,17.8,,-1.2628473,36.781805,-1.300337,36.829327,Rider_Id_175,2120
-Order_No_24991,User_Id_265,Bike,3,Business,7,3,3:17:13 PM,7,3,3:50:35 PM,7,3,4:07:29 PM,7,3,4:16:41 PM,7,3,4:41:29 PM,8,28.1,,-1.3006208000000001,36.77274439999999,-1.300921,36.828195,Rider_Id_532,1488
-Order_No_700,User_Id_955,Bike,2,Personal,6,1,4:36:38 PM,6,1,4:38:49 PM,6,1,5:10:04 PM,6,1,5:42:00 PM,6,1,5:54:16 PM,5,22.3,,-1.2901786,36.824294,-1.2579171999999998,36.79383370000001,Rider_Id_857,736
-Order_No_6224,User_Id_749,Bike,1,Personal,4,6,2:36:43 PM,4,6,2:49:13 PM,4,6,3:10:42 PM,4,6,3:21:56 PM,4,6,3:42:04 PM,6,22.9,,-1.2795197,36.8168287,-1.2773014,36.7773015,Rider_Id_19,1208
-Order_No_14390,User_Id_1091,Bike,1,Personal,30,3,8:00:46 AM,30,3,8:07:47 AM,30,3,8:39:23 AM,30,3,8:45:35 AM,30,3,9:06:15 AM,6,17.4,,-1.2764786,36.81947879999999,-1.2994217,36.8009779,Rider_Id_208,1240
-Order_No_23951,User_Id_3264,Bike,3,Business,25,1,4:00:06 PM,25,1,4:00:26 PM,25,1,4:02:02 PM,25,1,4:05:11 PM,25,1,4:26:04 PM,9,30.2,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_709,1253
-Order_No_13001,User_Id_1271,Bike,3,Business,14,4,5:28:29 PM,14,4,5:30:38 PM,14,4,5:52:57 PM,14,4,5:56:10 PM,14,4,6:16:06 PM,11,21.3,,-1.3244885,36.897791999999995,-1.326825,36.864345,Rider_Id_549,1196
-Order_No_13621,User_Id_2330,Bike,3,Business,20,4,11:15:40 AM,20,4,11:17:58 AM,20,4,11:30:59 AM,20,4,11:33:30 AM,20,4,11:49:27 AM,7,22.4,,-1.2921463,36.8029626,-1.2642903,36.80021370000001,Rider_Id_275,957
-Order_No_16601,User_Id_868,Bike,3,Business,27,6,10:47:25 AM,27,6,10:47:58 AM,27,6,11:02:26 AM,27,6,11:10:17 AM,27,6,11:32:00 AM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_641,1303
-Order_No_2044,User_Id_1245,Bike,3,Business,22,3,8:01:28 AM,22,3,8:13:35 AM,22,3,8:33:30 AM,22,3,8:50:07 AM,22,3,9:25:05 AM,25,15.4,,-1.4332561000000001,36.9562993,-1.300921,36.828195,Rider_Id_213,2098
-Order_No_22315,User_Id_2216,Bike,3,Business,28,3,3:43:35 PM,28,3,4:00:12 PM,28,3,4:01:50 PM,28,3,4:06:08 PM,28,3,4:30:46 PM,4,,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_222,1478
-Order_No_9017,User_Id_1500,Bike,3,Business,3,3,3:28:42 PM,3,3,3:33:15 PM,3,3,3:45:22 PM,3,3,4:02:31 PM,3,3,4:17:21 PM,8,28.2,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_576,890
-Order_No_4159,User_Id_2412,Bike,3,Business,14,4,9:41:00 AM,14,4,10:30:46 AM,14,4,10:58:11 AM,14,4,11:05:47 AM,14,4,11:31:19 AM,11,18.9,,-1.290894,36.822971,-1.2297202,36.874550799999994,Rider_Id_752,1532
-Order_No_451,User_Id_1469,Bike,3,Business,29,1,5:27:36 PM,29,1,5:28:06 PM,29,1,5:28:20 PM,29,1,5:38:01 PM,29,1,5:45:14 PM,4,,,-1.2566688999999998,36.779695200000006,-1.2551895,36.7822034,Rider_Id_843,433
-Order_No_6655,User_Id_2195,Bike,3,Business,13,4,1:02:35 PM,13,4,1:03:16 PM,13,4,1:10:26 PM,13,4,1:25:56 PM,13,4,2:16:37 PM,16,22.1,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_492,3041
-Order_No_15288,User_Id_2383,Bike,3,Business,21,1,10:34:16 AM,21,1,10:34:54 AM,21,1,10:38:37 AM,21,1,10:42:13 AM,21,1,11:09:34 AM,7,25.3,,-1.2608993,36.799246000000004,-1.2989933999999999,36.813669399999995,Rider_Id_205,1641
-Order_No_6588,User_Id_1750,Bike,1,Personal,3,3,11:56:27 AM,3,3,11:56:47 AM,3,3,12:04:00 PM,3,3,12:11:21 PM,3,3,1:02:28 PM,11,24.0,,-1.3339943,36.7263409,-1.3009721,36.7689325,Rider_Id_805,3067
-Order_No_2939,User_Id_2344,Bike,3,Business,6,4,11:18:56 AM,6,4,11:25:11 AM,6,4,11:26:54 AM,6,4,11:34:20 AM,6,4,12:32:26 PM,8,24.0,,-1.2551895,36.7822034,-1.3050332,36.8100437,Rider_Id_510,3486
-Order_No_25130,User_Id_3466,Bike,2,Personal,21,5,11:01:45 AM,21,5,11:02:10 AM,21,5,11:30:45 AM,21,5,11:40:29 AM,21,5,11:54:48 AM,6,19.8,,-1.2245407,36.7583778,-1.2533103,36.8057423,Rider_Id_144,859
-Order_No_18448,User_Id_540,Bike,3,Business,10,1,2:09:49 PM,10,1,2:11:51 PM,10,1,2:20:31 PM,10,1,2:24:25 PM,10,1,2:58:53 PM,8,18.8,,-1.2551895,36.7822034,-1.2940713,36.7890758,Rider_Id_205,2068
-Order_No_6233,User_Id_53,Bike,3,Business,21,4,9:07:27 AM,21,4,9:07:41 AM,21,4,9:14:08 AM,21,4,9:49:49 AM,21,4,10:04:32 AM,4,18.9,,-1.3004062,36.829741,-1.29508,36.796863,Rider_Id_311,883
-Order_No_18941,User_Id_1075,Bike,3,Business,4,1,1:15:44 PM,4,1,1:16:43 PM,4,1,1:20:21 PM,4,1,1:22:33 PM,4,1,1:39:49 PM,3,25.5,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_377,1036
-Order_No_2319,User_Id_649,Bike,3,Business,18,5,1:53:24 PM,18,5,2:06:52 PM,18,5,2:20:49 PM,18,5,2:23:26 PM,18,5,2:43:26 PM,11,24.5,,-1.2964396,36.7760609,-1.2626906,36.7827022,Rider_Id_782,1200
-Order_No_7167,User_Id_1389,Bike,3,Business,1,3,2:45:43 PM,1,3,2:46:11 PM,1,3,2:53:05 PM,1,3,3:18:14 PM,1,3,3:52:57 PM,13,,,-1.2283403,36.8822756,-1.207546,36.7913976,Rider_Id_691,2083
-Order_No_13120,User_Id_2982,Bike,3,Business,27,4,12:54:07 PM,27,4,12:54:13 PM,27,4,1:10:19 PM,27,4,1:13:15 PM,27,4,2:13:52 PM,18,23.2,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_569,3637
-Order_No_26950,User_Id_471,Bike,1,Personal,27,7,12:52:53 PM,27,7,12:53:11 PM,27,7,1:00:18 PM,27,7,1:08:54 PM,27,7,1:40:10 PM,25,20.3,,-1.2943666999999999,36.7815346,-1.3722577,36.9415667,Rider_Id_103,1876
-Order_No_4740,User_Id_2632,Bike,1,Personal,9,2,8:31:16 AM,9,2,8:31:47 AM,9,2,8:47:45 AM,9,2,8:51:20 AM,9,2,9:21:55 AM,12,17.5,4.1,-1.2871085,36.8303905,-1.265952,36.8842106,Rider_Id_532,1835
-Order_No_14317,User_Id_286,Bike,3,Business,14,2,6:00:26 PM,14,2,6:02:14 PM,14,2,6:16:13 PM,14,2,6:19:54 PM,14,2,6:43:24 PM,7,,,-1.2551895,36.7822034,-1.2876092,36.802742200000004,Rider_Id_953,1410
-Order_No_17144,User_Id_393,Bike,3,Business,9,4,1:02:37 PM,9,4,1:03:53 PM,9,4,1:12:44 PM,9,4,1:30:32 PM,9,4,1:39:51 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_411,559
-Order_No_20651,User_Id_3264,Bike,3,Business,17,3,4:35:34 PM,17,3,5:05:08 PM,17,3,5:23:01 PM,17,3,5:24:55 PM,17,3,5:43:32 PM,6,,,-1.2551895,36.7822034,-1.2882462,36.81645379999999,Rider_Id_96,1117
-Order_No_19613,User_Id_393,Bike,3,Business,22,3,10:36:46 AM,22,3,10:44:03 AM,22,3,10:44:40 AM,22,3,11:56:45 AM,22,3,12:00:35 PM,11,23.3,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_436,230
-Order_No_20347,User_Id_3341,Bike,1,Personal,29,5,11:48:15 AM,29,5,12:12:44 PM,29,5,12:12:55 PM,29,5,12:16:51 PM,29,5,1:01:27 PM,4,19.8,,-1.2863177,36.8237972,-1.2994217,36.8009779,Rider_Id_756,2676
-Order_No_21352,User_Id_835,Bike,3,Business,15,5,1:50:14 PM,15,5,1:54:37 PM,15,5,2:16:04 PM,15,5,2:18:38 PM,15,5,3:00:43 PM,20,17.9,,-1.300722,36.7882234,-1.3700383,36.9190174,Rider_Id_154,2525
-Order_No_7646,User_Id_60,Bike,3,Business,14,5,4:28:49 PM,14,5,4:29:30 PM,14,5,4:29:47 PM,14,5,4:47:38 PM,14,5,5:16:00 PM,14,21.6,,-1.2628473,36.781805,-1.2296618000000001,36.84382720000001,Rider_Id_564,1702
-Order_No_22623,User_Id_1920,Bike,3,Business,2,5,8:39:35 AM,2,5,8:39:44 AM,2,5,8:47:01 AM,2,5,8:50:03 AM,2,5,9:27:03 AM,4,18.0,17.1,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_228,2220
-Order_No_28030,User_Id_1389,Bike,3,Business,7,5,4:29:13 PM,7,5,4:51:50 PM,7,5,5:06:06 PM,7,5,5:28:50 PM,7,5,6:05:18 PM,19,23.2,,-1.2859912,36.8756811,-1.207546,36.7913976,Rider_Id_685,2188
-Order_No_2150,User_Id_3127,Bike,3,Business,5,2,4:43:00 PM,5,2,4:44:57 PM,5,2,4:59:32 PM,5,2,5:05:42 PM,5,2,5:19:32 PM,5,25.2,,-1.2795796,36.819062,-1.2918246,36.801584999999996,Rider_Id_513,830
-Order_No_16413,User_Id_1384,Bike,3,Business,2,4,3:14:08 PM,2,4,3:16:20 PM,2,4,3:34:57 PM,2,4,3:38:07 PM,2,4,4:46:12 PM,22,,,-1.2517448,36.7924971,-1.25217,36.9303345,Rider_Id_538,4085
-Order_No_24547,User_Id_2582,Bike,3,Business,11,1,10:36:27 AM,11,1,10:36:59 AM,11,1,10:53:08 AM,11,1,10:56:09 AM,11,1,11:43:43 AM,18,26.5,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_118,2854
-Order_No_18644,User_Id_867,Bike,3,Business,5,3,9:07:27 AM,5,3,9:57:30 AM,5,3,10:10:03 AM,5,3,10:16:24 AM,5,3,10:48:21 AM,9,18.0,,-1.2574219,36.7927073,-1.306378,36.7519845,Rider_Id_631,1917
-Order_No_23478,User_Id_346,Bike,3,Business,8,5,12:53:11 PM,8,5,1:22:56 PM,8,5,1:34:51 PM,8,5,1:40:05 PM,8,5,1:51:58 PM,3,21.7,,-1.2885528999999998,36.816014100000004,-1.3040024,36.808296999999996,Rider_Id_217,713
-Order_No_25666,User_Id_706,Bike,3,Business,27,4,11:08:47 AM,27,4,11:11:40 AM,27,4,11:22:19 AM,27,4,11:33:28 AM,27,4,11:41:41 AM,4,23.0,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_659,493
-Order_No_9397,User_Id_136,Bike,3,Business,26,5,2:27:56 PM,26,5,2:38:44 PM,26,5,2:59:08 PM,26,5,3:02:26 PM,26,5,3:33:12 PM,8,,,-1.3326173000000001,36.71462329999999,-1.3025826,36.7670807,Rider_Id_593,1846
-Order_No_996,User_Id_3621,Bike,3,Business,20,4,11:03:05 AM,20,4,11:04:39 AM,20,4,11:21:52 AM,20,4,11:24:38 AM,20,4,11:34:46 AM,5,22.1,,-1.2836583000000001,36.7886014,-1.2991481000000002,36.8043449,Rider_Id_379,608
-Order_No_27210,User_Id_3291,Bike,3,Business,23,3,11:46:09 AM,23,3,11:48:03 AM,23,3,12:07:16 PM,23,3,12:20:36 PM,23,3,12:30:27 PM,5,21.8,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_360,591
-Order_No_26777,User_Id_2363,Bike,1,Personal,3,2,4:48:48 PM,3,2,4:50:30 PM,3,2,4:59:36 PM,3,2,5:06:53 PM,3,2,5:30:50 PM,11,18.8,,-1.2649605,36.798177700000004,-1.2095044,36.783206400000005,Rider_Id_103,1437
-Order_No_10963,User_Id_2523,Bike,3,Business,4,1,2:53:59 PM,4,1,2:54:38 PM,4,1,3:01:33 PM,4,1,3:04:41 PM,4,1,3:42:33 PM,12,28.7,,-1.2551895,36.7822034,-1.3184778999999998,36.8373109,Rider_Id_953,2272
-Order_No_666,User_Id_2931,Bike,3,Business,27,3,2:50:05 PM,27,3,3:07:58 PM,27,3,3:33:31 PM,27,3,3:48:16 PM,27,3,4:26:30 PM,23,29.3,,-1.3433181,36.9138018,-1.2452664,36.802854100000005,Rider_Id_524,2294
-Order_No_6983,User_Id_3291,Bike,3,Business,5,2,10:05:53 AM,5,2,10:06:24 AM,5,2,10:11:57 AM,5,2,10:17:38 AM,5,2,10:46:29 AM,13,18.2,,-1.3313643,36.857742200000004,-1.2600926000000001,36.8088685,Rider_Id_926,1731
-Order_No_22370,User_Id_246,Bike,3,Business,28,1,11:50:15 AM,28,1,12:06:43 PM,28,1,12:21:00 PM,28,1,1:04:07 PM,28,1,1:24:51 PM,23,22.7,,-1.311342,36.913268,-1.2584143,36.8048002,Rider_Id_832,1244
-Order_No_5841,User_Id_3689,Bike,3,Business,9,3,4:40:57 PM,9,3,4:41:08 PM,9,3,4:45:39 PM,9,3,4:52:37 PM,9,3,5:15:02 PM,2,24.4,,-1.2527958000000001,36.8003131,-1.2562809,36.792382,Rider_Id_909,1345
-Order_No_27530,User_Id_3334,Bike,1,Personal,8,3,11:46:46 AM,8,3,11:46:55 AM,8,3,11:51:32 AM,8,3,11:55:18 AM,8,3,12:13:37 PM,7,22.0,,-1.2699441999999999,36.8206553,-1.2951264,36.798382000000004,Rider_Id_754,1099
-Order_No_23862,User_Id_3621,Bike,3,Business,24,6,5:15:03 PM,24,6,5:15:31 PM,24,6,5:21:03 PM,24,6,5:26:35 PM,24,6,5:44:24 PM,9,22.5,,-1.2836583000000001,36.7886014,-1.3231061000000002,36.84552242,Rider_Id_427,1069
-Order_No_3234,User_Id_1329,Bike,3,Business,12,3,3:25:33 PM,12,3,3:52:34 PM,12,3,3:57:18 PM,12,3,4:08:59 PM,12,3,4:20:32 PM,4,20.8,,-1.273056,36.811298,-1.2854453000000001,36.818836299999994,Rider_Id_291,693
-Order_No_24084,User_Id_2523,Bike,3,Business,11,2,2:16:33 PM,11,2,2:54:31 PM,11,2,3:02:10 PM,11,2,3:04:07 PM,11,2,3:53:09 PM,8,22.1,,-1.2551895,36.7822034,-1.2944292,36.779356899999996,Rider_Id_646,2942
-Order_No_6976,User_Id_2069,Bike,3,Business,20,3,10:56:46 AM,20,3,10:57:28 AM,20,3,11:30:52 AM,20,3,11:45:38 AM,20,3,12:02:25 PM,9,27.0,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_683,1007
-Order_No_3073,User_Id_868,Bike,3,Business,30,3,1:19:56 PM,30,3,1:22:12 PM,30,3,1:30:48 PM,30,3,1:39:16 PM,30,3,1:54:31 PM,5,25.3,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_742,915
-Order_No_20278,User_Id_393,Bike,3,Business,28,1,4:10:02 PM,28,1,4:10:33 PM,28,1,4:10:37 PM,28,1,4:16:32 PM,28,1,4:46:44 PM,14,26.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1812
-Order_No_23264,User_Id_1961,Bike,3,Business,29,3,12:10:51 PM,29,3,12:11:26 PM,29,3,12:18:07 PM,29,3,12:28:22 PM,29,3,12:58:19 PM,17,,,-1.2663631000000002,36.8163329,-1.3188358999999998,36.703852600000005,Rider_Id_675,1797
-Order_No_26086,User_Id_3184,Bike,3,Business,8,2,2:26:52 PM,8,2,2:34:53 PM,8,2,2:35:37 PM,8,2,2:45:45 PM,8,2,3:08:38 PM,14,23.4,,-1.2958383,36.8073162,-1.3457877,36.8811894,Rider_Id_633,1373
-Order_No_7928,User_Id_238,Bike,3,Business,28,3,1:39:14 PM,28,3,1:40:58 PM,28,3,1:41:03 PM,28,3,1:45:34 PM,28,3,1:45:41 PM,4,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_39,7
-Order_No_14651,User_Id_2138,Bike,2,Personal,13,4,2:33:07 PM,13,4,2:33:24 PM,13,4,2:37:32 PM,13,4,2:45:14 PM,13,4,3:17:39 PM,17,26.6,,-1.29702,36.788216,-1.2016352,36.8200903,Rider_Id_465,1945
-Order_No_18382,User_Id_3686,Bike,3,Business,12,2,10:55:45 AM,12,2,10:56:45 AM,12,2,10:56:54 AM,12,2,11:05:03 AM,12,2,11:28:16 AM,9,28.1,,-1.2551895,36.7822034,-1.3090727,36.831526200000006,Rider_Id_518,1393
-Order_No_19281,User_Id_1970,Bike,1,Personal,29,6,11:18:39 AM,29,6,11:19:31 AM,29,6,11:26:56 AM,29,6,12:29:46 PM,29,6,12:40:39 PM,6,25.2,,-1.3099295,36.8436807,-1.2862033000000002,36.8152966,Rider_Id_74,653
-Order_No_25264,User_Id_2504,Bike,3,Business,15,1,10:59:27 AM,15,1,10:59:55 AM,15,1,11:12:04 AM,15,1,11:28:52 AM,15,1,12:23:14 PM,8,,,-1.2963097,36.7688221,-1.2736169,36.8078703,Rider_Id_148,3262
-Order_No_14580,User_Id_393,Bike,3,Business,1,5,2:24:46 PM,1,5,2:25:08 PM,1,5,2:25:15 PM,1,5,3:12:19 PM,1,5,3:12:21 PM,14,28.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_239,2
-Order_No_12546,User_Id_3641,Bike,3,Business,30,2,8:45:25 AM,30,2,8:45:55 AM,30,2,8:55:54 AM,30,2,8:59:09 AM,30,2,9:22:09 AM,10,,,-1.2551895,36.7822034,-1.3019688999999999,36.774497,Rider_Id_953,1380
-Order_No_9636,User_Id_1646,Bike,3,Business,23,2,10:55:44 AM,23,2,10:56:02 AM,23,2,10:58:15 AM,23,2,11:15:27 AM,23,2,12:30:21 PM,24,,,-1.3006954,36.7416164,-1.2285834999999998,36.882646799999996,Rider_Id_800,4494
-Order_No_18571,User_Id_1584,Bike,3,Business,20,5,10:13:25 AM,20,5,10:14:53 AM,20,5,10:33:20 AM,20,5,10:37:49 AM,20,5,11:00:26 AM,8,18.6,,-1.26559,36.8027,-1.2969741000000001,36.7856612,Rider_Id_379,1357
-Order_No_18032,User_Id_3560,Bike,3,Business,25,1,3:42:50 PM,25,1,3:43:04 PM,25,1,3:43:15 PM,25,1,3:44:42 PM,25,1,4:14:32 PM,18,30.2,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_448,1790
-Order_No_27722,User_Id_3097,Bike,3,Personal,7,1,8:25:16 AM,7,1,8:40:44 AM,7,1,9:20:44 AM,7,1,9:28:29 AM,7,1,10:20:22 AM,29,21.5,,-1.4242042,36.717817,-1.2795044,36.7849319,Rider_Id_559,3113
-Order_No_14188,User_Id_2174,Bike,3,Business,30,6,12:05:00 PM,30,6,12:05:09 PM,30,6,12:08:28 PM,30,6,12:56:49 PM,30,6,1:39:38 PM,22,26.4,,-1.2482781,36.884091999999995,-1.3470008999999998,36.761945399999995,Rider_Id_82,2569
-Order_No_6458,User_Id_978,Bike,1,Personal,20,1,12:46:51 PM,20,1,1:23:47 PM,20,1,1:36:04 PM,20,1,2:05:15 PM,20,1,2:32:26 PM,6,21.5,,-1.3084067,36.83450139999999,-1.2763950000000002,36.825707,Rider_Id_213,1631
-Order_No_15631,User_Id_3635,Bike,1,Business,27,4,6:29:40 PM,27,4,6:30:47 PM,27,4,6:30:53 PM,27,4,7:13:46 PM,27,4,8:05:40 PM,23,23.0,,-1.2615888000000002,36.7928732,-1.2056712,36.9221784,Rider_Id_813,3114
-Order_No_26746,User_Id_2570,Bike,3,Personal,21,5,9:27:59 AM,21,5,9:28:18 AM,21,5,9:38:27 AM,21,5,9:44:16 AM,21,5,11:25:29 AM,18,19.6,,-1.2305936000000002,36.87549979999999,-1.3226301999999999,36.8719901,Rider_Id_89,6073
-Order_No_23843,User_Id_1075,Bike,3,Business,25,1,12:17:02 PM,25,1,12:17:32 PM,25,1,12:25:51 PM,25,1,12:29:49 PM,25,1,12:37:25 PM,1,27.1,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_547,456
-Order_No_13559,User_Id_393,Bike,3,Business,7,5,9:35:13 AM,7,5,9:35:34 AM,7,5,9:35:38 AM,7,5,10:26:03 AM,7,5,10:26:05 AM,14,,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_39,2
-Order_No_26223,User_Id_269,Bike,3,Business,19,3,9:02:54 AM,19,3,9:22:49 AM,19,3,9:38:14 AM,19,3,9:57:20 AM,19,3,10:17:53 AM,8,19.7,,-1.3562374,36.904295399999995,-1.3226301999999999,36.8719901,Rider_Id_300,1233
-Order_No_1515,User_Id_2097,Bike,2,Personal,21,5,9:32:49 AM,21,5,9:33:37 AM,21,5,9:43:20 AM,21,5,9:57:45 AM,21,5,10:37:05 AM,8,19.9,,-1.2874711,36.764620799999996,-1.2854089,36.8284874,Rider_Id_785,2360
-Order_No_23917,User_Id_2330,Bike,3,Business,13,1,11:47:49 AM,13,1,11:48:21 AM,13,1,11:48:55 AM,13,1,11:56:36 AM,13,1,12:13:16 PM,8,,,-1.2124582,36.802894,-1.2536275,36.798793200000006,Rider_Id_843,1000
-Order_No_5774,User_Id_3426,Bike,3,Business,11,7,11:40:48 AM,11,7,11:59:02 AM,11,7,12:10:30 PM,11,7,12:43:54 PM,11,7,1:25:06 PM,9,25.0,,-1.290894,36.822971,-1.3014461000000002,36.7661381,Rider_Id_303,2472
-Order_No_7492,User_Id_393,Bike,3,Business,15,6,9:04:27 AM,15,6,9:04:42 AM,15,6,9:04:47 AM,15,6,9:12:44 AM,15,6,9:31:55 AM,11,16.9,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_619,1151
-Order_No_21346,User_Id_2523,Bike,3,Business,6,3,2:47:33 PM,6,3,2:49:12 PM,6,3,2:58:43 PM,6,3,3:20:19 PM,6,3,3:20:27 PM,8,27.3,,-1.2551895,36.7822034,-1.2856233999999997,36.7865464,Rider_Id_953,8
-Order_No_12405,User_Id_2987,Bike,3,Business,6,2,5:06:48 PM,6,2,5:07:39 PM,6,2,5:08:18 PM,6,2,5:11:27 PM,6,2,5:56:44 PM,8,,,-1.2649605,36.798177700000004,-1.2946558000000001,36.7908316,Rider_Id_188,2717
-Order_No_23012,User_Id_3674,Bike,3,Business,14,2,10:43:16 AM,14,2,10:43:59 AM,14,2,10:49:04 AM,14,2,10:53:00 AM,14,2,12:06:36 PM,10,21.7,,-1.2551895,36.7822034,-1.314342,36.84233,Rider_Id_882,4416
-Order_No_1672,User_Id_2639,Bike,3,Personal,14,5,12:38:04 PM,14,5,12:38:57 PM,14,5,12:55:09 PM,14,5,12:59:33 PM,14,5,1:54:42 PM,18,21.8,,-1.32943,36.894242999999996,-1.2661981000000002,36.815471200000005,Rider_Id_856,3309
-Order_No_10570,User_Id_2730,Bike,1,Personal,6,3,4:39:54 PM,6,3,4:51:07 PM,6,3,5:08:39 PM,6,3,5:11:11 PM,6,3,5:46:00 PM,5,20.1,,-1.2824911,36.8218042,-1.2790653,36.7907262,Rider_Id_531,2089
-Order_No_5033,User_Id_635,Bike,3,Business,27,6,12:11:29 PM,27,6,12:13:13 PM,27,6,12:13:28 PM,27,6,1:07:00 PM,27,6,1:19:07 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_404,727
-Order_No_8507,User_Id_3621,Bike,3,Business,21,4,2:00:33 PM,21,4,2:00:49 PM,21,4,2:10:25 PM,21,4,2:26:35 PM,21,4,2:48:57 PM,12,31.0,,-1.2836583000000001,36.7886014,-1.329568,36.8520825,Rider_Id_678,1342
-Order_No_13169,User_Id_2150,Bike,3,Business,16,4,11:43:44 AM,16,4,11:44:00 AM,16,4,11:46:41 AM,16,4,11:57:48 AM,16,4,12:18:40 PM,11,14.8,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_648,1252
-Order_No_160,User_Id_1899,Bike,3,Business,9,4,1:14:19 PM,9,4,1:16:22 PM,9,4,1:40:18 PM,9,4,1:56:23 PM,9,4,2:12:43 PM,6,25.8,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_882,980
-Order_No_3960,User_Id_868,Bike,3,Business,28,2,8:50:45 AM,28,2,8:51:34 AM,28,2,9:03:44 AM,28,2,9:20:43 AM,28,2,9:37:19 AM,6,18.6,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_2,996
-Order_No_26816,User_Id_1500,Bike,3,Business,4,4,10:32:32 AM,4,4,10:33:20 AM,4,4,10:42:31 AM,4,4,10:55:41 AM,4,4,11:22:31 AM,10,25.3,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_926,1610
-Order_No_9665,User_Id_2847,Bike,3,Personal,5,5,5:12:06 PM,5,5,5:22:51 PM,5,5,5:37:30 PM,5,5,6:03:47 PM,5,5,6:32:08 PM,6,24.6,,-1.2564799,36.8112138,-1.2822773,36.795312100000004,Rider_Id_755,1701
-Order_No_1954,User_Id_1482,Bike,3,Business,13,2,11:07:25 AM,13,2,11:08:07 AM,13,2,11:25:51 AM,13,2,11:38:29 AM,13,2,12:33:03 PM,17,22.1,,-1.3385790000000002,36.89630379999999,-1.2979242,36.7902416,Rider_Id_565,3274
-Order_No_28020,User_Id_3283,Bike,3,Business,5,5,2:13:55 PM,5,5,2:14:19 PM,5,5,2:21:04 PM,5,5,2:34:41 PM,5,5,2:55:34 PM,14,27.7,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_8,1253
-Order_No_2856,User_Id_725,Bike,1,Personal,19,6,3:54:04 PM,19,6,3:55:02 PM,19,6,4:28:04 PM,19,6,4:33:32 PM,19,6,4:53:58 PM,6,21.6,,-1.2879618,36.82008629999999,-1.2772632,36.792225099999996,Rider_Id_916,1226
-Order_No_19691,User_Id_186,Bike,3,Business,3,3,3:35:25 PM,3,3,3:35:39 PM,3,3,3:37:51 PM,3,3,3:45:50 PM,3,3,4:20:36 PM,23,26.7,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_31,2086
-Order_No_4006,User_Id_53,Bike,3,Business,10,1,2:48:31 PM,10,1,2:55:29 PM,10,1,2:55:57 PM,10,1,2:58:40 PM,10,1,3:27:58 PM,12,18.5,,-1.3167113000000001,36.8301563,-1.2769540000000001,36.765926,Rider_Id_800,1758
-Order_No_5609,User_Id_393,Bike,3,Business,29,1,1:17:49 PM,29,1,1:19:55 PM,29,1,1:28:21 PM,29,1,1:36:07 PM,29,1,1:46:34 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_570,627
-Order_No_20639,User_Id_1209,Bike,3,Business,25,4,10:32:19 AM,25,4,10:32:48 AM,25,4,10:35:56 AM,25,4,10:45:54 AM,25,4,10:53:21 AM,5,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_206,447
-Order_No_17257,User_Id_393,Bike,3,Business,7,2,9:34:59 AM,7,2,9:35:10 AM,7,2,9:51:13 AM,7,2,10:28:21 AM,7,2,11:01:12 AM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_910,1971
-Order_No_19920,User_Id_889,Bike,3,Business,21,4,10:57:26 AM,21,4,10:58:08 AM,21,4,11:08:54 AM,21,4,11:14:00 AM,21,4,11:26:44 AM,7,21.5,,-1.2985153,36.7876043,-1.2664956,36.7788652,Rider_Id_312,764
-Order_No_7145,User_Id_1544,Bike,3,Business,24,5,1:29:14 PM,24,5,1:33:26 PM,24,5,1:47:03 PM,24,5,1:58:54 PM,24,5,3:30:30 PM,8,22.9,,-1.2697053999999999,36.8519724,-1.2603403,36.7998848,Rider_Id_921,5496
-Order_No_7823,User_Id_1721,Bike,3,Business,17,4,9:41:40 AM,17,4,9:42:00 AM,17,4,9:53:45 AM,17,4,10:08:41 AM,17,4,10:34:27 AM,10,,,-1.2551895,36.7822034,-1.2323335,36.810035600000006,Rider_Id_755,1546
-Order_No_23452,User_Id_1500,Bike,3,Business,17,3,8:38:11 AM,17,3,8:39:59 AM,17,3,8:54:20 AM,17,3,9:42:24 AM,17,3,10:04:32 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_8,1328
-Order_No_11631,User_Id_60,Bike,3,Business,6,1,12:12:25 PM,6,1,12:12:41 PM,6,1,12:23:02 PM,6,1,12:31:33 PM,6,1,1:14:23 PM,16,20.4,,-1.331619,36.847976,-1.2296618000000001,36.84382720000001,Rider_Id_519,2570
-Order_No_7509,User_Id_647,Bike,3,Business,3,3,10:35:48 AM,3,3,10:36:40 AM,3,3,10:55:23 AM,3,3,11:05:03 AM,3,3,11:22:47 AM,4,27.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_96,1064
-Order_No_6686,User_Id_1742,Bike,1,Personal,15,6,6:20:16 PM,15,6,6:20:48 PM,15,6,6:41:50 PM,15,6,6:45:30 PM,15,6,6:59:39 PM,4,24.8,,-1.2966785,36.7896469,-1.280467,36.7859296,Rider_Id_465,849
-Order_No_26381,User_Id_1500,Bike,3,Business,26,5,10:18:54 AM,26,5,10:19:19 AM,26,5,10:19:56 AM,26,5,10:30:24 AM,26,5,10:51:55 AM,5,,,-1.2638185,36.7930057,-1.2615048,36.8116724,Rider_Id_576,1291
-Order_No_14035,User_Id_3201,Bike,3,Business,5,2,8:28:47 AM,5,2,8:30:40 AM,5,2,8:43:21 AM,5,2,8:57:42 AM,5,2,9:09:46 AM,6,20.9,2.0,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_156,724
-Order_No_8485,User_Id_627,Bike,3,Business,12,3,10:01:47 AM,12,3,10:02:11 AM,12,3,10:02:17 AM,12,3,10:14:21 AM,12,3,10:32:50 AM,10,,,-1.2551895,36.7822034,-1.3131471000000001,36.817656799999995,Rider_Id_619,1109
-Order_No_25952,User_Id_393,Bike,3,Business,26,5,8:19:27 AM,26,5,9:12:16 AM,26,5,9:18:34 AM,26,5,9:35:01 AM,26,5,10:15:23 AM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,2422
-Order_No_12154,User_Id_2763,Bike,3,Business,16,2,2:24:39 PM,16,2,2:24:50 PM,16,2,2:32:09 PM,16,2,2:33:20 PM,16,2,2:45:24 PM,7,,,-1.2551895,36.7822034,-1.2653185,36.8358841,Rider_Id_349,724
-Order_No_13126,User_Id_53,Bike,3,Business,3,3,11:52:52 AM,3,3,11:57:54 AM,3,3,12:24:08 PM,3,3,12:32:45 PM,3,3,12:55:45 PM,5,27.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_306,1380
-Order_No_27979,User_Id_2801,Bike,3,Business,22,3,11:57:54 AM,22,3,11:58:14 AM,22,3,12:02:50 PM,22,3,12:05:30 PM,22,3,12:14:45 PM,3,23.3,,-1.2674273999999999,36.787082700000006,-1.255392,36.792539,Rider_Id_854,555
-Order_No_2940,User_Id_2216,Bike,3,Business,30,2,2:40:35 PM,30,2,2:43:33 PM,30,2,3:02:07 PM,30,2,3:08:32 PM,30,2,3:55:37 PM,7,,,-1.2551895,36.7822034,-1.2603178999999998,36.8305747,Rider_Id_517,2825
-Order_No_25751,User_Id_2066,Bike,3,Business,20,3,3:21:51 PM,20,3,3:22:57 PM,20,3,3:38:33 PM,20,3,3:42:17 PM,20,3,4:13:33 PM,13,28.3,,-1.3210188999999999,36.841055100000005,-1.2871375,36.8898931,Rider_Id_856,1876
-Order_No_21525,User_Id_503,Bike,1,Business,29,6,11:42:56 AM,29,6,11:51:45 AM,29,6,11:52:43 AM,29,6,12:21:40 PM,29,6,12:43:26 PM,13,25.2,,-1.285382,36.8162878,-1.2859912,36.8756811,Rider_Id_497,1306
-Order_No_16987,User_Id_186,Bike,3,Business,7,2,2:58:26 PM,7,2,3:07:49 PM,7,2,3:21:13 PM,7,2,3:28:00 PM,7,2,3:42:54 PM,7,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_177,894
-Order_No_24595,User_Id_1719,Bike,3,Business,6,2,10:03:42 AM,6,2,10:15:52 AM,6,2,10:22:33 AM,6,2,10:25:56 AM,6,2,10:53:33 AM,8,19.8,,-1.2768687,36.8209066,-1.296797,36.776452500000005,Rider_Id_858,1657
-Order_No_18387,User_Id_3195,Bike,1,Personal,4,1,9:08:49 AM,4,1,9:24:44 AM,4,1,9:43:29 AM,4,1,9:50:03 AM,4,1,10:14:44 AM,17,17.9,,-1.2004287,36.83215560000001,-1.2945743,36.7732602,Rider_Id_116,1481
-Order_No_1642,User_Id_867,Bike,3,Business,12,5,1:37:54 PM,12,5,1:39:21 PM,12,5,1:47:52 PM,12,5,2:05:53 PM,12,5,2:30:28 PM,12,,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_926,1475
-Order_No_26826,User_Id_1287,Bike,3,Business,7,2,3:04:58 PM,7,2,3:05:33 PM,7,2,3:07:03 PM,7,2,3:13:38 PM,7,2,3:21:49 PM,3,,,-1.2581837,36.801705600000005,-1.2551895,36.7822034,Rider_Id_520,491
-Order_No_4614,User_Id_2523,Bike,3,Business,31,4,12:44:42 PM,31,4,12:44:56 PM,31,4,12:45:25 PM,31,4,12:55:18 PM,31,4,1:27:23 PM,13,26.6,,-1.2551895,36.7822034,-1.3155299999999999,36.863732,Rider_Id_770,1925
-Order_No_26241,User_Id_1271,Bike,3,Business,1,5,9:10:51 AM,1,5,9:11:38 AM,1,5,9:26:10 AM,1,5,9:39:26 AM,1,5,9:52:55 AM,7,23.3,,-1.3244885,36.897791999999995,-1.2911643,36.8772933,Rider_Id_459,809
-Order_No_11175,User_Id_53,Bike,3,Business,28,2,9:38:40 AM,28,2,9:39:24 AM,28,2,9:56:26 AM,28,2,10:11:30 AM,28,2,10:31:00 AM,5,18.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_448,1170
-Order_No_14506,User_Id_814,Bike,3,Business,31,3,7:23:50 AM,31,3,7:24:10 AM,31,3,7:27:21 AM,31,3,7:29:12 AM,31,3,7:50:31 AM,10,15.4,,-1.2571472,36.795063299999995,-1.3014461000000002,36.7661381,Rider_Id_731,1279
-Order_No_4037,User_Id_391,Bike,3,Business,25,3,12:19:18 PM,25,3,12:25:36 PM,25,3,12:50:00 PM,25,3,12:57:20 PM,25,3,1:19:30 PM,8,,,-1.2857573999999998,36.8295667,-1.238874,36.807889,Rider_Id_144,1330
-Order_No_955,User_Id_1482,Bike,3,Business,7,5,11:11:34 AM,7,5,11:12:19 AM,7,5,11:13:52 AM,7,5,11:25:13 AM,7,5,11:48:44 AM,10,25.0,,-1.2966600000000001,36.828668,-1.3385790000000002,36.89630379999999,Rider_Id_519,1411
-Order_No_25046,User_Id_1088,Bike,1,Personal,17,1,2:48:56 PM,17,1,2:49:04 PM,17,1,3:09:06 PM,17,1,3:10:09 PM,17,1,3:31:13 PM,14,28.2,,-1.3175119,36.8058513,-1.3281885,36.8937091,Rider_Id_409,1264
-Order_No_6060,User_Id_2701,Bike,3,Business,6,3,11:08:55 AM,6,3,11:16:27 AM,6,3,11:22:30 AM,6,3,11:27:38 AM,6,3,11:41:52 AM,5,22.5,,-1.279395,36.825364,-1.2765736,36.851364600000004,Rider_Id_953,854
-Order_No_15265,User_Id_1927,Bike,3,Business,13,3,2:16:03 PM,13,3,2:16:33 PM,13,3,2:30:05 PM,13,3,2:40:13 PM,13,3,3:13:03 PM,14,29.0,,-1.3014461000000002,36.7661381,-1.3467388999999999,36.7690312,Rider_Id_154,1970
-Order_No_23357,User_Id_2634,Bike,3,Business,3,5,11:26:01 AM,3,5,11:27:25 AM,3,5,11:35:09 AM,3,5,11:41:13 AM,3,5,12:22:11 PM,21,,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_205,2458
-Order_No_638,User_Id_393,Bike,3,Business,9,4,1:57:16 PM,9,4,1:58:02 PM,9,4,2:15:58 PM,9,4,2:20:59 PM,9,4,3:10:14 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_411,2955
-Order_No_7843,User_Id_552,Bike,3,Business,25,2,1:03:48 PM,25,2,1:05:24 PM,25,2,1:32:48 PM,25,2,1:47:47 PM,25,2,1:59:37 PM,8,27.0,,-1.3332749,36.870814700000004,-1.3632162,36.9108253,Rider_Id_38,710
-Order_No_2199,User_Id_498,Bike,1,Personal,2,1,4:07:03 PM,2,1,4:10:20 PM,2,1,4:25:00 PM,2,1,4:28:06 PM,2,1,4:48:55 PM,9,20.2,,-1.3046965,36.8016456,-1.2642903,36.80021370000001,Rider_Id_726,1249
-Order_No_13994,User_Id_2691,Bike,3,Business,8,5,12:17:55 PM,8,5,1:40:34 PM,8,5,1:58:43 PM,8,5,2:16:53 PM,8,5,2:34:50 PM,6,21.3,,-1.269534,36.80697,-1.2857671,36.7875614,Rider_Id_726,1077
-Order_No_372,User_Id_1075,Bike,3,Business,17,3,3:03:25 PM,17,3,3:04:32 PM,17,3,3:25:05 PM,17,3,3:31:05 PM,17,3,4:36:06 PM,7,,,-1.2527958000000001,36.8003131,-1.2861471000000002,36.7903723,Rider_Id_641,3901
-Order_No_10508,User_Id_1637,Bike,3,Business,30,4,11:33:10 AM,30,4,11:33:47 AM,30,4,11:49:42 AM,30,4,12:24:41 PM,30,4,12:52:43 PM,9,20.5,,-1.2551895,36.7822034,-1.3014461000000002,36.7661381,Rider_Id_548,1682
-Order_No_24613,User_Id_960,Bike,1,Personal,4,2,1:32:21 PM,4,2,1:33:45 PM,4,2,1:33:56 PM,4,2,1:51:12 PM,4,2,2:19:41 PM,16,20.5,,-1.2646700000000002,36.778734,-1.3287648,36.744573200000005,Rider_Id_874,1709
-Order_No_13334,User_Id_1882,Bike,3,Business,19,2,3:53:11 PM,19,2,3:54:22 PM,19,2,3:55:49 PM,19,2,4:04:34 PM,19,2,5:18:02 PM,6,29.3,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_436,4408
-Order_No_11075,User_Id_1500,Bike,3,Business,17,3,10:55:52 AM,17,3,10:56:07 AM,17,3,10:56:33 AM,17,3,11:28:20 AM,17,3,11:43:45 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_41,925
-Order_No_12551,User_Id_1179,Bike,3,Business,29,2,2:44:43 PM,29,2,2:45:10 PM,29,2,3:01:02 PM,29,2,3:12:26 PM,29,2,3:49:26 PM,23,24.0,,-1.3700383,36.9190174,-1.2998129999999999,36.787139200000006,Rider_Id_890,2220
-Order_No_13738,User_Id_1410,Bike,3,Business,11,2,11:26:07 AM,11,2,11:26:28 AM,11,2,11:26:31 AM,11,2,12:07:59 PM,11,2,12:37:03 PM,20,19.3,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_871,1744
-Order_No_18884,User_Id_2700,Bike,3,Business,10,6,12:01:15 PM,10,6,12:02:02 PM,10,6,12:11:58 PM,10,6,12:19:30 PM,10,6,12:26:16 PM,3,24.0,,-1.2812376,36.8264821,-1.2845834999999999,36.8079121,Rider_Id_726,406
-Order_No_10328,User_Id_1471,Bike,3,Business,14,4,8:54:21 AM,14,4,8:54:45 AM,14,4,9:11:34 AM,14,4,9:16:11 AM,14,4,10:10:47 AM,4,17.9,,-1.2551895,36.7822034,-1.2752846000000002,36.802702200000006,Rider_Id_747,3276
-Order_No_14430,User_Id_649,Bike,3,Business,18,1,11:58:48 AM,18,1,11:59:16 AM,18,1,12:13:08 PM,18,1,12:28:12 PM,18,1,1:00:44 PM,17,,,-1.2551895,36.7822034,-1.3404187,36.7614685,Rider_Id_159,1952
-Order_No_26458,User_Id_1397,Bike,3,Business,9,6,9:52:03 AM,9,6,9:52:40 AM,9,6,9:56:33 AM,9,6,10:05:07 AM,9,6,10:24:47 AM,11,21.0,,-1.2648363,36.802375399999995,-1.2351541000000001,36.8748505,Rider_Id_828,1180
-Order_No_20129,User_Id_2132,Bike,3,Business,3,4,12:05:37 PM,3,4,12:05:47 PM,3,4,12:11:06 PM,3,4,12:21:54 PM,3,4,12:52:37 PM,13,23.5,,-1.2384056,36.8718698,-1.3108783000000002,36.8392726,Rider_Id_237,1843
-Order_No_9080,User_Id_20,Bike,3,Business,6,6,3:22:11 PM,6,6,3:22:32 PM,6,6,3:30:22 PM,6,6,3:37:46 PM,6,6,4:10:08 PM,13,28.0,,-1.2628638,36.8070399,-1.2264343999999998,36.70713610000001,Rider_Id_131,1942
-Order_No_24961,User_Id_1245,Bike,3,Business,14,4,10:49:41 AM,14,4,10:49:49 AM,14,4,10:49:58 AM,14,4,11:02:57 AM,14,4,11:30:34 AM,19,24.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_728,1657
-Order_No_9727,User_Id_2178,Bike,1,Personal,10,5,12:13:28 PM,10,5,12:15:24 PM,10,5,12:22:37 PM,10,5,12:27:07 PM,10,5,12:48:58 PM,6,19.1,,-1.2998551999999999,36.818707700000004,-1.2781391000000002,36.8267148,Rider_Id_249,1311
-Order_No_18149,User_Id_1922,Bike,3,Personal,22,3,10:04:05 AM,22,3,10:04:29 AM,22,3,10:31:48 AM,22,3,10:37:25 AM,22,3,11:06:24 AM,12,23.3,,-1.2443975,36.7757308,-1.2960687,36.8167909,Rider_Id_959,1739
-Order_No_2365,User_Id_2335,Bike,3,Business,31,4,1:29:34 PM,31,4,1:30:17 PM,31,4,1:34:47 PM,31,4,1:38:31 PM,31,4,2:00:30 PM,15,28.9,,-1.2551895,36.7822034,-1.3323107,36.8692616,Rider_Id_871,1319
-Order_No_13501,User_Id_1271,Bike,3,Business,21,4,9:45:13 AM,21,4,9:45:54 AM,21,4,10:01:26 AM,21,4,10:11:22 AM,21,4,11:00:08 AM,15,21.7,,-1.3244885,36.897791999999995,-1.2864733,36.8239937,Rider_Id_26,2926
-Order_No_24871,User_Id_718,Bike,3,Business,30,5,9:45:25 AM,30,5,9:47:39 AM,30,5,10:17:34 AM,30,5,10:19:34 AM,30,5,10:38:59 AM,10,19.0,,-1.2551895,36.7822034,-1.2363747999999999,36.817750200000006,Rider_Id_453,1165
-Order_No_3139,User_Id_1769,Bike,2,Personal,23,4,9:38:15 PM,23,4,9:55:02 PM,23,4,10:13:42 PM,23,4,10:19:19 PM,23,4,10:37:14 PM,12,18.3,,-1.2577288999999998,36.74107720000001,-1.2782978,36.827323799999995,Rider_Id_116,1075
-Order_No_9407,User_Id_1927,Bike,3,Business,4,2,2:27:53 PM,4,2,2:28:59 PM,4,2,2:42:02 PM,4,2,2:47:18 PM,4,2,3:23:47 PM,17,22.5,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_675,2189
-Order_No_228,User_Id_1500,Bike,3,Business,7,4,10:52:53 AM,7,4,10:54:07 AM,7,4,11:11:25 AM,7,4,11:16:24 AM,7,4,11:52:40 AM,8,27.6,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_436,2176
-Order_No_1116,User_Id_1754,Bike,3,Business,24,1,5:19:05 PM,24,1,5:28:21 PM,24,1,5:28:25 PM,24,1,5:34:32 PM,24,1,6:07:16 PM,17,22.5,,-1.2860183,36.8975338,-1.3191788,36.8895401,Rider_Id_114,1964
-Order_No_8312,User_Id_868,Bike,3,Business,4,2,12:02:08 PM,4,2,12:04:07 PM,4,2,12:11:38 PM,4,2,12:26:09 PM,4,2,12:46:43 PM,5,18.7,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_208,1234
-Order_No_3811,User_Id_1207,Bike,3,Business,8,6,11:13:13 AM,8,6,11:15:39 AM,8,6,11:34:32 AM,8,6,12:03:24 PM,8,6,12:48:44 PM,14,18.2,,-1.2628638,36.8070399,-1.2156718999999998,36.7990906,Rider_Id_523,2720
-Order_No_22053,User_Id_84,Bike,3,Business,27,4,10:18:52 AM,27,4,10:25:17 AM,27,4,10:40:31 AM,27,4,10:52:50 AM,27,4,11:29:18 AM,21,23.0,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_436,2188
-Order_No_7779,User_Id_1500,Bike,3,Business,3,3,1:40:03 PM,3,3,1:40:25 PM,3,3,2:01:56 PM,3,3,2:48:40 PM,3,3,2:48:50 PM,8,28.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_216,10
-Order_No_3974,User_Id_3557,Bike,3,Business,26,3,1:04:14 PM,26,3,1:04:57 PM,26,3,1:23:44 PM,26,3,1:44:07 PM,26,3,1:58:21 PM,6,27.2,,-1.287079,36.7811277,-1.2546732,36.808679600000005,Rider_Id_882,854
-Order_No_14392,User_Id_3149,Bike,3,Business,21,4,1:08:17 PM,21,4,1:09:43 PM,21,4,1:25:24 PM,21,4,1:37:30 PM,21,4,1:50:34 PM,6,30.9,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_855,784
-Order_No_17878,User_Id_2701,Bike,3,Business,24,4,2:31:43 PM,24,4,2:34:33 PM,24,4,2:43:27 PM,24,4,2:50:32 PM,24,4,3:11:47 PM,10,22.5,,-1.279395,36.825364,-1.2478187,36.8844611,Rider_Id_124,1275
-Order_No_6574,User_Id_2436,Bike,3,Business,22,5,1:30:23 PM,22,5,1:31:53 PM,22,5,1:38:13 PM,22,5,1:47:03 PM,22,5,2:08:43 PM,9,31.0,,-1.2551895,36.7822034,-1.2315181,36.814054299999995,Rider_Id_576,1300
-Order_No_5109,User_Id_269,Bike,3,Business,15,4,2:36:14 PM,15,4,2:43:11 PM,15,4,3:02:43 PM,15,4,4:04:13 PM,15,4,5:05:12 PM,16,27.5,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_593,3659
-Order_No_16111,User_Id_1005,Bike,1,Personal,23,3,9:16:57 AM,23,3,9:39:53 AM,23,3,9:51:25 AM,23,3,10:10:34 AM,23,3,10:37:30 AM,8,16.6,16.1,-1.298437,36.781971,-1.2763916000000002,36.8246826,Rider_Id_116,1616
-Order_No_16419,User_Id_3186,Bike,3,Business,12,2,2:34:28 PM,12,2,2:36:36 PM,12,2,2:54:02 PM,12,2,2:58:36 PM,12,2,3:50:35 PM,7,,,-1.2980228999999999,36.78865,-1.2581837,36.801705600000005,Rider_Id_831,3119
-Order_No_23876,User_Id_1724,Bike,3,Business,27,3,1:11:33 PM,27,3,1:12:07 PM,27,3,1:19:50 PM,27,3,1:24:12 PM,27,3,1:38:37 PM,8,,,-1.2905046,36.7828277,-1.2551895,36.7822034,Rider_Id_345,865
-Order_No_10863,User_Id_928,Bike,3,Business,11,1,12:37:00 PM,11,1,12:38:59 PM,11,1,1:01:35 PM,11,1,1:04:13 PM,11,1,1:39:43 PM,15,17.9,,-1.2551895,36.7822034,-1.3319466999999998,36.776699799999996,Rider_Id_749,2130
-Order_No_10131,User_Id_635,Bike,3,Business,4,1,11:35:47 AM,4,1,11:40:36 AM,4,1,12:03:12 PM,4,1,12:39:11 PM,4,1,1:00:12 PM,6,26.6,,-1.3081376,36.83297720000001,-1.3229345,36.8566974,Rider_Id_513,1261
-Order_No_13520,User_Id_3651,Bike,3,Business,31,5,8:01:35 AM,31,5,8:01:55 AM,31,5,8:03:59 AM,31,5,8:20:18 AM,31,5,8:37:49 AM,11,14.7,,-1.2571472,36.795063299999995,-1.298784,36.8643814,Rider_Id_179,1051
-Order_No_3964,User_Id_25,Bike,1,Personal,21,6,11:00:34 AM,21,6,11:01:25 AM,21,6,11:32:41 AM,21,6,11:39:03 AM,21,6,12:24:52 PM,6,16.3,,-1.2982391000000002,36.7950991,-1.2805233,36.7494508,Rider_Id_465,2749
-Order_No_14426,User_Id_238,Bike,3,Business,29,4,11:24:55 AM,29,4,11:25:08 AM,29,4,11:25:39 AM,29,4,11:30:04 AM,29,4,11:53:40 AM,15,21.4,,-1.3077869,36.844320700000004,-1.3559093,36.910045200000006,Rider_Id_10,1416
-Order_No_13748,User_Id_3656,Bike,3,Personal,10,1,1:31:15 PM,10,1,1:31:37 PM,10,1,1:42:21 PM,10,1,1:45:35 PM,10,1,2:24:49 PM,8,,,-1.2551895,36.7822034,-1.2983124,36.8104456,Rider_Id_820,2354
-Order_No_8241,User_Id_2523,Bike,3,Business,3,2,1:38:00 PM,3,2,1:40:17 PM,3,2,1:49:19 PM,3,2,1:54:11 PM,3,2,2:12:41 PM,8,20.0,,-1.2551895,36.7822034,-1.304394,36.818995,Rider_Id_88,1110
-Order_No_4067,User_Id_2465,Bike,1,Personal,2,2,9:51:26 AM,2,2,9:52:55 AM,2,2,10:23:36 AM,2,2,10:29:37 AM,2,2,10:43:03 AM,6,15.0,,-1.3398265,36.739982399999995,-1.3234611,36.7270436,Rider_Id_412,806
-Order_No_10844,User_Id_635,Bike,3,Business,8,4,9:35:25 AM,8,4,10:28:43 AM,8,4,10:39:38 AM,8,4,10:47:16 AM,8,4,11:09:29 AM,6,26.1,,-1.2904695,36.8243257,-1.2599563,36.799343900000004,Rider_Id_276,1333
-Order_No_10120,User_Id_1363,Bike,3,Business,24,4,2:48:27 PM,24,4,2:52:35 PM,24,4,2:52:39 PM,24,4,3:07:45 PM,24,4,3:07:46 PM,5,28.2,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_619,1
-Order_No_26840,User_Id_3017,Bike,1,Business,13,5,5:14:52 PM,13,5,5:15:35 PM,13,5,5:26:49 PM,13,5,5:32:05 PM,13,5,5:37:01 PM,2,19.2,,-1.299313,36.78314879999999,-1.3063901000000002,36.787367200000006,Rider_Id_431,296
-Order_No_12267,User_Id_2069,Bike,3,Business,5,6,10:47:44 AM,5,6,10:47:54 AM,5,6,10:49:00 AM,5,6,10:59:37 AM,5,6,11:17:50 AM,6,22.4,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_477,1093
-Order_No_10630,User_Id_868,Bike,3,Business,24,3,3:20:29 PM,24,3,3:22:55 PM,24,3,4:02:37 PM,24,3,4:18:10 PM,24,3,5:05:31 PM,14,,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_126,2841
-Order_No_17321,User_Id_2789,Bike,3,Business,7,4,5:31:36 PM,7,4,5:32:00 PM,7,4,5:34:57 PM,7,4,5:40:00 PM,7,4,6:09:13 PM,6,29.4,,-1.304735,36.784605400000004,-1.2787766999999999,36.8171894,Rider_Id_411,1753
-Order_No_23308,User_Id_3283,Bike,3,Business,5,3,9:06:43 AM,5,3,9:10:18 AM,5,3,9:19:30 AM,5,3,9:25:56 AM,5,3,10:28:36 AM,13,18.2,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_677,3760
-Order_No_5176,User_Id_1271,Bike,3,Business,12,3,4:08:16 PM,12,3,4:50:30 PM,12,3,4:52:24 PM,12,3,5:04:14 PM,12,3,5:15:52 PM,8,24.4,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_116,698
-Order_No_18964,User_Id_1482,Bike,3,Business,2,2,5:11:07 PM,2,2,5:13:46 PM,2,2,5:37:04 PM,2,2,5:44:01 PM,2,2,6:06:34 PM,7,28.4,,-1.2822773,36.795312100000004,-1.2664135,36.8322682,Rider_Id_99,1353
-Order_No_18922,User_Id_3320,Bike,3,Business,2,5,2:08:42 PM,2,5,2:23:50 PM,2,5,2:23:53 PM,2,5,2:37:21 PM,2,5,2:53:14 PM,8,25.0,,-1.3080091999999999,36.8284543,-1.271823,36.789215000000006,Rider_Id_874,953
-Order_No_17735,User_Id_1500,Bike,3,Business,4,4,9:17:05 AM,4,4,9:17:37 AM,4,4,10:24:59 AM,4,4,10:52:29 AM,4,4,11:08:26 AM,11,25.5,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_127,957
-Order_No_2404,User_Id_2216,Bike,3,Business,31,3,12:58:31 PM,31,3,1:23:16 PM,31,3,1:23:21 PM,31,3,1:31:47 PM,31,3,1:49:10 PM,8,24.6,,-1.2551895,36.7822034,-1.2859355,36.784364700000005,Rider_Id_536,1043
-Order_No_23114,User_Id_788,Bike,3,Business,12,1,12:01:17 PM,12,1,12:02:38 PM,12,1,12:14:45 PM,12,1,12:23:28 PM,12,1,12:59:59 PM,15,23.6,,-1.3052491000000002,36.8223899,-1.225322,36.80855,Rider_Id_778,2191
-Order_No_8512,User_Id_393,Bike,3,Business,7,4,2:54:12 PM,7,4,2:57:52 PM,7,4,3:15:22 PM,7,4,3:31:21 PM,7,4,3:55:44 PM,6,27.6,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_168,1463
-Order_No_1514,User_Id_1428,Bike,3,Personal,13,3,7:58:31 AM,13,3,7:59:16 AM,13,3,8:43:47 AM,13,3,8:56:59 AM,13,3,9:18:11 AM,7,17.5,,-1.300106,36.7584546,-1.3222617,36.7164539,Rider_Id_774,1272
-Order_No_28177,User_Id_3194,Bike,3,Business,15,5,1:15:58 PM,15,5,1:26:07 PM,15,5,1:37:07 PM,15,5,1:48:04 PM,15,5,2:13:13 PM,7,17.8,,-1.2551895,36.7822034,-1.2857548,36.819062,Rider_Id_119,1509
-Order_No_21699,User_Id_2216,Bike,3,Business,29,2,10:18:30 AM,29,2,10:20:50 AM,29,2,10:43:28 AM,29,2,10:48:32 AM,29,2,11:06:45 AM,10,21.5,,-1.2553615,36.789537599999996,-1.2363747999999999,36.817750200000006,Rider_Id_116,1093
-Order_No_26596,User_Id_3222,Bike,3,Business,21,4,10:50:24 AM,21,4,10:50:41 AM,21,4,10:51:46 AM,21,4,10:55:52 AM,21,4,12:02:52 PM,19,27.9,,-1.2551895,36.7822034,-1.334474,36.88389,Rider_Id_793,4020
-Order_No_15787,User_Id_154,Bike,2,Business,5,2,4:24:32 PM,5,2,4:36:16 PM,5,2,5:06:06 PM,5,2,5:08:34 PM,5,2,5:25:43 PM,6,21.3,,-1.2553615,36.789537599999996,-1.2878148999999999,36.8146245,Rider_Id_742,1029
-Order_No_26161,User_Id_136,Bike,3,Business,9,4,12:36:32 PM,9,4,12:38:01 PM,9,4,12:43:36 PM,9,4,12:51:15 PM,9,4,1:18:00 PM,17,,,-1.2531983,36.8069669,-1.3254522,36.721658399999995,Rider_Id_438,1605
-Order_No_17189,User_Id_3590,Bike,3,Business,9,3,9:35:33 AM,9,3,9:38:12 AM,9,3,9:49:05 AM,9,3,9:57:09 AM,9,3,10:29:43 AM,3,20.3,,-1.266294,36.8057971,-1.2881035,36.8142095,Rider_Id_88,1954
-Order_No_20728,User_Id_2154,Bike,3,Business,8,2,2:32:46 PM,8,2,2:37:04 PM,8,2,2:50:13 PM,8,2,2:55:21 PM,8,2,3:08:38 PM,9,25.4,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_383,797
-Order_No_17317,User_Id_1796,Bike,3,Business,28,4,9:04:42 AM,28,4,9:05:52 AM,28,4,9:24:56 AM,28,4,9:37:13 AM,28,4,10:11:51 AM,11,19.5,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_907,2078
-Order_No_5670,User_Id_2330,Bike,3,Business,27,1,4:52:10 PM,27,1,4:52:47 PM,27,1,5:08:39 PM,27,1,5:23:20 PM,27,1,5:43:26 PM,15,,,-1.2336843000000002,36.800725899999996,-1.2687407,36.7373043,Rider_Id_484,1206
-Order_No_5012,User_Id_393,Bike,3,Business,13,1,11:20:21 AM,13,1,11:20:38 AM,13,1,11:24:04 AM,13,1,11:26:39 AM,13,1,11:56:17 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,1778
-Order_No_15531,User_Id_391,Bike,3,Business,29,4,9:36:15 AM,29,4,10:48:24 AM,29,4,10:54:18 AM,29,4,11:13:14 AM,29,4,11:50:55 AM,5,21.6,,-1.2777923999999998,36.8231577,-1.3016417,36.8271679,Rider_Id_725,2261
-Order_No_27040,User_Id_868,Bike,3,Business,28,2,9:34:21 AM,28,2,9:34:58 AM,28,2,9:37:27 AM,28,2,9:49:33 AM,28,2,9:59:57 AM,5,18.5,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_576,624
-Order_No_16902,User_Id_2513,Bike,3,Personal,16,4,1:32:52 PM,16,4,1:33:38 PM,16,4,1:57:30 PM,16,4,2:05:59 PM,16,4,2:21:08 PM,4,18.9,,-1.2865507999999999,36.8238701,-1.292163,36.8000995,Rider_Id_754,909
-Order_No_16821,User_Id_3355,Bike,3,Business,17,4,2:56:45 PM,17,4,2:57:27 PM,17,4,2:59:36 PM,17,4,3:07:26 PM,17,4,3:31:47 PM,13,27.9,,-1.3052491000000002,36.8223899,-1.32943,36.894242999999996,Rider_Id_779,1461
-Order_No_115,User_Id_1898,Bike,3,Personal,18,4,2:24:25 PM,18,4,2:25:26 PM,18,4,2:33:33 PM,18,4,2:58:07 PM,18,4,3:19:37 PM,11,26.9,,-1.2988291,36.7795686,-1.3181843,36.839482700000005,Rider_Id_568,1290
-Order_No_3095,User_Id_2267,Bike,2,Personal,3,1,12:20:48 PM,3,1,12:22:57 PM,3,1,12:53:30 PM,3,1,1:11:00 PM,3,1,1:48:20 PM,12,20.4,,-1.2956951,36.812924200000005,-1.2598589,36.7470168,Rider_Id_447,2240
-Order_No_3925,User_Id_432,Bike,3,Business,24,5,2:10:37 PM,24,5,2:19:08 PM,24,5,2:24:05 PM,24,5,2:28:33 PM,24,5,2:49:38 PM,8,22.8,,-1.3012008,36.764868,-1.2819053999999999,36.8237089,Rider_Id_404,1265
-Order_No_26785,User_Id_3291,Bike,3,Business,31,1,9:42:29 AM,31,1,9:42:59 AM,31,1,9:46:12 AM,31,1,9:56:22 AM,31,1,10:05:46 AM,4,18.7,3.1,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_670,564
-Order_No_11514,User_Id_1179,Bike,3,Business,31,3,4:31:17 PM,31,3,4:43:24 PM,31,3,4:53:24 PM,31,3,5:18:27 PM,31,3,5:52:11 PM,15,24.3,,-1.3700383,36.9190174,-1.298905,36.8538783,Rider_Id_588,2024
-Order_No_12334,User_Id_2330,Bike,3,Business,7,5,11:09:53 AM,7,5,11:46:04 AM,7,5,11:46:21 AM,7,5,11:51:50 AM,7,5,12:40:27 PM,23,25.3,,-1.3376825,36.7575489,-1.2524916000000001,36.8656196,Rider_Id_648,2917
-Order_No_5715,User_Id_391,Bike,3,Business,22,4,2:20:50 PM,22,4,3:03:47 PM,22,4,3:25:54 PM,22,4,3:43:35 PM,22,4,4:04:46 PM,5,26.2,,-1.300921,36.828195,-1.2850694,36.8236625,Rider_Id_812,1271
-Order_No_27464,User_Id_1482,Bike,3,Business,1,4,12:05:18 PM,1,4,12:05:40 PM,1,4,12:07:43 PM,1,4,12:12:32 PM,1,4,12:46:40 PM,17,,,-1.3385790000000002,36.89630379999999,-1.2583017,36.8023526,Rider_Id_771,2048
-Order_No_22095,User_Id_2691,Bike,3,Business,21,4,4:35:09 PM,21,4,4:35:45 PM,21,4,4:42:08 PM,21,4,4:45:21 PM,21,4,5:29:31 PM,8,,,-1.2551895,36.7822034,-1.2857671,36.7875614,Rider_Id_686,2650
-Order_No_22631,User_Id_391,Bike,3,Business,26,5,1:28:50 PM,26,5,1:32:46 PM,26,5,1:39:34 PM,26,5,2:18:01 PM,26,5,3:09:22 PM,8,,,-1.2867454,36.8259017,-1.2935549,36.7703935,Rider_Id_800,3081
-Order_No_15251,User_Id_701,Bike,3,Personal,14,5,2:50:09 PM,14,5,2:56:31 PM,14,5,3:29:36 PM,14,5,3:45:30 PM,14,5,4:23:44 PM,10,23.7,,-1.3080793,36.7505396,-1.2868608999999998,36.8265752,Rider_Id_858,2294
-Order_No_10742,User_Id_1642,Bike,3,Business,30,3,1:54:17 PM,30,3,1:55:26 PM,30,3,2:19:15 PM,30,3,2:27:17 PM,30,3,2:53:18 PM,5,29.0,,-1.278327,36.823771,-1.2728069,36.7962362,Rider_Id_696,1561
-Order_No_7064,User_Id_2174,Bike,3,Business,31,5,12:14:09 PM,31,5,12:14:09 PM,31,5,12:24:27 PM,31,5,1:25:07 PM,31,5,1:25:12 PM,16,21.8,,-1.2482781,36.884091999999995,-1.2016076999999998,36.930397,Rider_Id_86,5
-Order_No_25076,User_Id_1336,Bike,3,Business,29,1,3:55:23 PM,29,1,4:14:43 PM,29,1,4:28:14 PM,29,1,4:31:41 PM,29,1,4:58:59 PM,2,24.3,,-1.2891921999999998,36.825189,-1.2761677,36.81855,Rider_Id_522,1638
-Order_No_1558,User_Id_778,Bike,3,Business,8,5,9:15:55 AM,8,5,9:17:04 AM,8,5,9:25:52 AM,8,5,9:29:29 AM,8,5,9:47:57 AM,8,23.0,,-1.2535486,36.7933541,-1.2490885,36.8313166,Rider_Id_885,1108
-Order_No_7753,User_Id_3661,Bike,3,Business,12,1,9:40:20 AM,12,1,9:43:35 AM,12,1,9:58:59 AM,12,1,10:01:41 AM,12,1,10:36:11 AM,17,19.6,0.3,-1.2134646,36.8755266,-1.2987332,36.8463349,Rider_Id_726,2070
-Order_No_16037,User_Id_2423,Bike,3,Business,2,2,3:43:38 PM,2,2,3:45:01 PM,2,2,3:49:48 PM,2,2,3:52:33 PM,2,2,4:40:37 PM,8,26.4,,-1.2551895,36.7822034,-1.303664,36.818777000000004,Rider_Id_131,2884
-Order_No_22945,User_Id_3420,Bike,3,Business,23,6,10:17:54 AM,23,6,10:19:06 AM,23,6,10:29:43 AM,23,6,10:34:03 AM,23,6,10:49:33 AM,6,26.2,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_459,930
-Order_No_11633,User_Id_2200,Bike,3,Business,2,2,11:32:50 AM,2,2,11:42:11 AM,2,2,12:07:41 PM,2,2,12:25:08 PM,2,2,12:56:57 PM,12,20.8,,-1.225322,36.80855,-1.290894,36.822971,Rider_Id_726,1909
-Order_No_22665,User_Id_2694,Bike,1,Personal,24,1,12:09:50 PM,24,1,12:14:25 PM,24,1,12:20:58 PM,24,1,12:32:20 PM,24,1,1:06:48 PM,13,22.5,,-1.2023462999999999,36.8282909,-1.2881295,36.8473281,Rider_Id_622,2068
-Order_No_4072,User_Id_2801,Bike,3,Business,8,5,10:32:24 AM,8,5,10:38:31 AM,8,5,10:57:00 AM,8,5,11:06:07 AM,8,5,11:36:24 AM,7,28.6,,-1.2809846999999999,36.8274169,-1.2674273999999999,36.787082700000006,Rider_Id_76,1817
-Order_No_22772,User_Id_2988,Bike,3,Personal,29,3,4:35:44 PM,29,3,4:47:56 PM,29,3,4:54:57 PM,29,3,4:57:51 PM,29,3,5:20:36 PM,7,21.0,,-1.2802803,36.8271747,-1.2567409,36.7798977,Rider_Id_183,1365
-Order_No_5182,User_Id_1363,Bike,3,Business,6,4,4:10:14 PM,6,4,4:12:28 PM,6,4,4:25:51 PM,6,4,5:49:21 PM,6,4,5:49:25 PM,10,21.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_271,4
-Order_No_5933,User_Id_2007,Bike,1,Business,22,5,10:54:22 AM,22,5,10:57:02 AM,22,5,11:29:54 AM,22,5,11:39:06 AM,22,5,12:01:19 PM,7,21.9,,-1.282543,36.824618,-1.2579303999999998,36.8019667,Rider_Id_124,1333
-Order_No_20449,User_Id_635,Bike,3,Business,2,5,9:15:46 AM,2,5,9:37:48 AM,2,5,10:37:49 AM,2,5,10:43:13 AM,2,5,11:37:14 AM,17,23.6,,-1.177202,36.8351183,-1.2599563,36.799343900000004,Rider_Id_802,3241
-Order_No_23623,User_Id_2335,Bike,3,Business,2,4,9:41:24 AM,2,4,9:41:30 AM,2,4,9:58:31 AM,2,4,10:02:22 AM,2,4,10:39:35 AM,17,13.2,,-1.2551895,36.7822034,-1.2859912,36.8756811,Rider_Id_452,2233
-Order_No_10712,User_Id_2866,Bike,3,Business,25,1,11:51:57 AM,25,1,11:52:18 AM,25,1,11:52:33 AM,25,1,12:02:00 PM,25,1,12:13:04 PM,3,27.1,,-1.2551895,36.7822034,-1.2495931,36.80650870000001,Rider_Id_576,664
-Order_No_16513,User_Id_868,Bike,3,Business,5,3,8:55:19 AM,5,3,10:01:42 AM,5,3,10:13:32 AM,5,3,10:14:49 AM,5,3,10:52:43 AM,8,18.2,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_89,2274
-Order_No_27867,User_Id_2789,Bike,3,Business,26,2,2:58:02 PM,26,2,2:58:21 PM,26,2,3:06:30 PM,26,2,3:17:54 PM,26,2,3:43:20 PM,12,29.9,,-1.304735,36.784605400000004,-1.2567511999999998,36.8236011,Rider_Id_361,1526
-Order_No_4119,User_Id_412,Bike,3,Business,6,1,2:51:57 PM,6,1,2:56:09 PM,6,1,3:02:25 PM,6,1,3:12:54 PM,6,1,3:33:47 PM,5,,,-1.2783848999999998,36.8163445,-1.266837,36.799248999999996,Rider_Id_531,1253
-Order_No_14208,User_Id_2801,Bike,3,Business,16,2,11:40:43 AM,16,2,11:41:10 AM,16,2,11:46:41 AM,16,2,12:01:06 PM,16,2,12:13:14 PM,6,23.6,,-1.2595421999999998,36.7871178,-1.2896398999999998,36.812449799999996,Rider_Id_116,728
-Order_No_3784,User_Id_717,Bike,1,Personal,1,3,1:28:21 PM,1,3,1:28:41 PM,1,3,1:38:56 PM,1,3,1:41:09 PM,1,3,2:15:21 PM,12,23.7,,-1.3067965,36.849351,-1.2583976000000001,36.798046500000005,Rider_Id_648,2052
-Order_No_10282,User_Id_68,Bike,1,Personal,1,2,1:51:54 PM,1,2,2:29:33 PM,1,2,2:37:36 PM,1,2,3:00:21 PM,1,2,3:24:18 PM,9,20.4,0.9,-1.2141875,36.791039000000005,-1.2161358,36.842929299999994,Rider_Id_648,1437
-Order_No_8629,User_Id_945,Bike,3,Personal,30,3,4:40:56 PM,30,3,4:41:07 PM,30,3,4:48:26 PM,30,3,4:52:29 PM,30,3,5:16:00 PM,14,27.4,,-1.3154523999999999,36.7022978,-1.376634,36.7613498,Rider_Id_105,1411
-Order_No_14971,User_Id_1363,Bike,3,Business,24,4,2:58:50 PM,24,4,3:00:05 PM,24,4,3:06:09 PM,24,4,3:08:34 PM,24,4,3:34:48 PM,10,28.3,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_576,1574
-Order_No_343,User_Id_1500,Bike,3,Business,30,5,9:32:19 AM,30,5,10:15:10 AM,30,5,10:31:55 AM,30,5,10:36:21 AM,30,5,10:53:42 AM,11,23.8,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_878,1041
-Order_No_11405,User_Id_1370,Bike,3,Personal,18,5,3:45:04 PM,18,5,4:00:09 PM,18,5,4:23:47 PM,18,5,4:29:19 PM,18,5,5:10:08 PM,8,24.6,,-1.261692,36.80161,-1.3108674,36.795817,Rider_Id_749,2449
-Order_No_23622,User_Id_2373,Bike,1,Personal,29,5,4:23:39 PM,29,5,4:23:56 PM,29,5,4:33:59 PM,29,5,4:41:47 PM,29,5,5:16:05 PM,15,20.6,,-1.2681257,36.8079664,-1.200688,36.7891976,Rider_Id_427,2058
-Order_No_18651,User_Id_2216,Bike,3,Business,15,5,2:42:32 PM,15,5,2:43:34 PM,15,5,2:51:17 PM,15,5,3:23:53 PM,15,5,3:23:56 PM,19,28.2,,-1.2551895,36.7822034,-1.3327653999999998,36.8853117,Rider_Id_953,3
-Order_No_15487,User_Id_1169,Bike,1,Personal,4,1,2:15:58 PM,4,1,2:32:25 PM,4,1,2:32:33 PM,4,1,2:49:45 PM,4,1,3:10:08 PM,4,22.6,,-1.2977386999999998,36.803144700000004,-1.2868838,36.8284259,Rider_Id_777,1223
-Order_No_24196,User_Id_2801,Bike,3,Business,7,3,10:27:00 AM,7,3,10:50:29 AM,7,3,11:04:34 AM,7,3,11:25:12 AM,7,3,11:35:50 AM,4,25.1,,-1.2809846999999999,36.8274169,-1.2696908000000002,36.81519,Rider_Id_909,638
-Order_No_6716,User_Id_1768,Bike,3,Personal,4,5,11:42:41 AM,4,5,11:53:03 AM,4,5,12:05:59 PM,4,5,12:18:21 PM,4,5,12:30:07 PM,3,17.4,,-1.2750257,36.8157165,-1.2886923000000001,36.8241519,Rider_Id_206,706
-Order_No_13109,User_Id_2869,Bike,3,Business,3,3,12:10:45 PM,3,3,12:14:07 PM,3,3,12:24:38 PM,3,3,12:27:25 PM,3,3,1:20:40 PM,9,27.3,,-1.28878,36.8168312,-1.3025826,36.7670807,Rider_Id_441,3195
-Order_No_22609,User_Id_1927,Bike,3,Business,22,5,1:30:00 PM,22,5,1:34:45 PM,22,5,2:12:16 PM,22,5,2:19:09 PM,22,5,2:51:52 PM,15,31.1,,-1.3472066,36.7692638,-1.2949438999999998,36.7604214,Rider_Id_118,1963
-Order_No_10201,User_Id_2207,Bike,3,Business,18,1,11:51:23 AM,18,1,11:52:54 AM,18,1,12:23:10 PM,18,1,12:27:45 PM,18,1,12:57:54 PM,13,28.2,,-1.3151858,36.854623,-1.2696908000000002,36.81519,Rider_Id_127,1809
-Order_No_4301,User_Id_1484,Bike,3,Business,15,5,3:38:25 PM,15,5,3:45:49 PM,15,5,4:13:59 PM,15,5,4:18:51 PM,15,5,4:49:36 PM,9,18.0,,-1.225322,36.80855,-1.2551895,36.7822034,Rider_Id_61,1845
-Order_No_2231,User_Id_635,Bike,3,Business,19,6,9:08:58 AM,19,6,9:09:15 AM,19,6,9:16:03 AM,19,6,9:41:10 AM,19,6,9:57:28 AM,7,21.4,,-1.3081376,36.83297720000001,-1.2599563,36.799343900000004,Rider_Id_15,978
-Order_No_14673,User_Id_1051,Bike,3,Business,29,1,11:27:15 AM,29,1,11:33:00 AM,29,1,11:33:54 AM,29,1,12:04:06 PM,29,1,12:32:16 PM,15,,,-1.2804792,36.838507799999995,-1.3160976999999998,36.913164200000004,Rider_Id_456,1690
-Order_No_24067,User_Id_1882,Bike,3,Business,3,3,11:15:06 AM,3,3,11:15:24 AM,3,3,11:38:41 AM,3,3,11:42:45 AM,3,3,11:52:33 AM,1,27.5,,-1.2588774999999999,36.8079744,-1.253784,36.79907,Rider_Id_194,588
-Order_No_21335,User_Id_3599,Bike,3,Business,2,4,12:33:50 PM,2,4,12:52:23 PM,2,4,1:17:57 PM,2,4,1:38:25 PM,2,4,2:10:11 PM,13,,,-1.3302996,36.8707935,-1.2644742,36.808345700000004,Rider_Id_427,1906
-Order_No_11905,User_Id_3326,Bike,3,Business,6,4,2:00:43 PM,6,4,2:01:02 PM,6,4,2:01:27 PM,6,4,2:09:01 PM,6,4,2:18:53 PM,5,21.5,,-1.2551895,36.7822034,-1.2658315,36.809049,Rider_Id_168,592
-Order_No_6074,User_Id_2683,Bike,2,Personal,17,1,2:07:36 PM,17,1,3:20:32 PM,17,1,3:32:35 PM,17,1,3:46:14 PM,17,1,3:52:30 PM,8,28.2,,-1.2887269,36.869311200000006,-1.3273709999999999,36.840590999999996,Rider_Id_179,376
-Order_No_6306,User_Id_1500,Bike,3,Business,18,2,9:05:33 AM,18,2,10:19:22 AM,18,2,10:39:20 AM,18,2,10:44:02 AM,18,2,10:59:17 AM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_914,915
-Order_No_20697,User_Id_1796,Bike,3,Business,28,4,11:35:48 AM,28,4,11:36:03 AM,28,4,11:43:15 AM,28,4,11:46:09 AM,28,4,11:57:50 AM,7,23.7,,-1.307143,36.825009,-1.331619,36.847976,Rider_Id_127,701
-Order_No_14382,User_Id_2205,Bike,2,Personal,27,1,8:33:43 AM,27,1,8:34:00 AM,27,1,8:35:06 AM,27,1,8:51:16 AM,27,1,9:22:05 AM,17,16.6,,-1.2697858999999998,36.7946203,-1.2119232,36.899645899999996,Rider_Id_379,1849
-Order_No_1869,User_Id_3080,Bike,3,Business,13,3,3:00:04 PM,13,3,3:00:31 PM,13,3,3:11:54 PM,13,3,3:18:27 PM,13,3,3:50:17 PM,17,27.3,,-1.2551895,36.7822034,-1.3339943,36.7263409,Rider_Id_267,1910
-Order_No_1858,User_Id_1500,Bike,3,Business,3,1,11:20:06 AM,3,1,11:20:24 AM,3,1,11:27:02 AM,3,1,11:30:42 AM,3,1,12:05:28 PM,12,21.7,,-1.2638185,36.7930057,-1.3167113000000001,36.8301563,Rider_Id_411,2086
-Order_No_26075,User_Id_2987,Bike,3,Business,7,1,12:33:58 PM,7,1,12:34:36 PM,7,1,12:40:22 PM,7,1,12:44:16 PM,7,1,1:09:23 PM,11,21.6,,-1.2702120000000001,36.82108770000001,-1.3018758,36.75908629999999,Rider_Id_923,1507
-Order_No_10935,User_Id_324,Bike,3,Personal,20,3,9:07:47 AM,20,3,9:08:44 AM,20,3,9:30:46 AM,20,3,9:33:14 AM,20,3,9:56:35 AM,9,,,-1.2823611000000001,36.735454299999994,-1.332942,36.714065000000005,Rider_Id_205,1401
-Order_No_16385,User_Id_2330,Bike,3,Business,1,1,2:46:01 PM,1,1,2:48:15 PM,1,1,3:03:02 PM,1,1,3:06:29 PM,1,1,3:36:40 PM,11,,,-1.293082,36.8278578,-1.233018,36.8004477,Rider_Id_272,1811
-Order_No_20748,User_Id_2801,Bike,3,Business,6,3,10:07:53 AM,6,3,10:08:06 AM,6,3,10:08:51 AM,6,3,10:16:48 AM,6,3,10:48:45 AM,15,16.6,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_501,1917
-Order_No_16633,User_Id_2801,Bike,3,Business,27,2,4:31:56 PM,27,2,4:34:08 PM,27,2,5:00:07 PM,27,2,5:03:36 PM,27,2,5:22:10 PM,4,22.3,,-1.2595421999999998,36.7871178,-1.2664428,36.809453000000005,Rider_Id_875,1114
-Order_No_14731,User_Id_2778,Bike,2,Personal,27,7,9:27:47 AM,27,7,9:45:56 AM,27,7,10:27:14 AM,27,7,10:34:00 AM,27,7,10:45:34 AM,9,20.6,,-1.2865726,36.764218400000004,-1.2647469,36.8157706,Rider_Id_103,694
-Order_No_23045,User_Id_2436,Bike,3,Business,9,2,12:30:25 PM,9,2,12:31:05 PM,9,2,1:03:14 PM,9,2,1:20:23 PM,9,2,2:17:21 PM,12,21.8,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_328,3418
-Order_No_6414,User_Id_928,Bike,3,Business,28,4,11:37:34 AM,28,4,11:41:54 AM,28,4,11:50:23 AM,28,4,11:56:19 AM,28,4,12:16:55 PM,9,27.9,,-1.2551895,36.7822034,-1.2529072,36.8525066,Rider_Id_576,1236
-Order_No_19165,User_Id_635,Bike,3,Business,5,3,9:49:55 AM,5,3,9:50:04 AM,5,3,9:56:14 AM,5,3,10:42:11 AM,5,3,10:54:47 AM,6,22.4,,-1.300921,36.828195,-1.3229345,36.8566974,Rider_Id_857,756
-Order_No_12610,User_Id_53,Bike,3,Business,13,4,9:43:19 AM,13,4,9:44:12 AM,13,4,9:58:35 AM,13,4,10:23:01 AM,13,4,10:43:12 AM,5,18.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_76,1211
-Order_No_703,User_Id_2982,Bike,3,Business,1,4,10:13:49 AM,1,4,10:14:01 AM,1,4,10:31:13 AM,1,4,10:41:41 AM,1,4,11:48:42 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_593,4021
-Order_No_12496,User_Id_478,Bike,3,Business,24,4,7:49:10 AM,24,4,7:49:41 AM,24,4,7:52:51 AM,24,4,8:03:20 AM,24,4,8:34:39 AM,15,19.5,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_427,1879
-Order_No_16349,User_Id_186,Bike,3,Business,15,3,12:00:24 PM,15,3,12:01:49 PM,15,3,12:07:49 PM,15,3,12:14:37 PM,15,3,12:24:54 PM,6,,,-1.2902869,36.8085396,-1.2571472,36.795063299999995,Rider_Id_235,617
-Order_No_9248,User_Id_3291,Bike,3,Business,15,1,1:34:15 PM,15,1,1:35:11 PM,15,1,1:43:33 PM,15,1,2:02:35 PM,15,1,2:27:28 PM,12,,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_532,1493
-Order_No_27184,User_Id_2008,Bike,3,Business,26,5,11:30:46 AM,26,5,11:53:50 AM,26,5,12:03:26 PM,26,5,12:10:36 PM,26,5,12:34:29 PM,7,,,-1.2508229,36.7895263,-1.285244,36.832159000000004,Rider_Id_713,1433
-Order_No_10163,User_Id_899,Bike,1,Personal,6,6,1:09:42 PM,6,6,1:11:55 PM,6,6,1:15:25 PM,6,6,1:19:59 PM,6,6,1:34:53 PM,9,24.2,,-1.2854453000000001,36.818836299999994,-1.3213011000000001,36.860956,Rider_Id_216,894
-Order_No_26457,User_Id_2436,Bike,3,Business,3,3,3:44:52 PM,3,3,3:45:42 PM,3,3,3:55:01 PM,3,3,4:00:24 PM,3,3,5:19:21 PM,9,28.0,,-1.2551895,36.7822034,-1.2931286,36.7584407,Rider_Id_32,4737
-Order_No_16553,User_Id_1329,Bike,3,Business,5,3,10:20:33 AM,5,3,11:00:36 AM,5,3,11:19:43 AM,5,3,11:22:12 AM,5,3,11:34:21 AM,4,22.3,,-1.273056,36.811298,-1.2615888000000002,36.7928732,Rider_Id_611,729
-Order_No_20479,User_Id_3688,Bike,3,Business,30,3,10:26:18 AM,30,3,10:29:40 AM,30,3,10:37:00 AM,30,3,10:42:23 AM,30,3,11:01:54 AM,8,25.1,,-1.2551895,36.7822034,-1.3038156,36.812273299999994,Rider_Id_205,1171
-Order_No_17413,User_Id_496,Bike,3,Business,9,4,2:56:34 PM,9,4,3:00:47 PM,9,4,3:10:49 PM,9,4,3:16:54 PM,9,4,3:41:10 PM,4,,,-1.2551895,36.7822034,-1.2649114,36.8134191,Rider_Id_427,1456
-Order_No_17732,User_Id_1329,Bike,3,Business,30,6,3:17:05 PM,30,6,3:18:27 PM,30,6,3:27:13 PM,30,6,3:32:43 PM,30,6,3:46:57 PM,9,28.6,,-1.273056,36.811298,-1.252726,36.7563739,Rider_Id_383,854
-Order_No_3289,User_Id_2587,Bike,3,Business,6,3,3:33:00 PM,6,3,3:33:42 PM,6,3,3:41:57 PM,6,3,3:47:49 PM,6,3,4:58:31 PM,17,,,-1.28878,36.8168312,-1.3169591,36.7000029,Rider_Id_452,4242
-Order_No_15674,User_Id_2330,Bike,3,Business,8,5,1:16:15 PM,8,5,1:36:26 PM,8,5,2:07:40 PM,8,5,2:34:43 PM,8,5,3:11:47 PM,8,27.3,,-1.293082,36.8278578,-1.3015197,36.7658456,Rider_Id_206,2224
-Order_No_18269,User_Id_3291,Bike,3,Business,6,6,12:13:48 PM,6,6,12:14:34 PM,6,6,12:15:31 PM,6,6,1:30:49 PM,6,6,1:57:42 PM,12,26.5,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_459,1613
-Order_No_3486,User_Id_517,Bike,3,Business,9,6,8:53:30 AM,9,6,8:54:00 AM,9,6,8:54:22 AM,9,6,9:20:48 AM,9,6,9:39:00 AM,7,19.9,,-1.3167113000000001,36.8301563,-1.3012841999999998,36.8561033,Rider_Id_842,1092
-Order_No_16669,User_Id_1011,Bike,1,Personal,8,6,5:25:03 PM,8,6,5:25:18 PM,8,6,5:44:08 PM,8,6,5:53:58 PM,8,6,6:17:52 PM,13,23.0,,-1.3071754,36.777376200000006,-1.2321733,36.7974377,Rider_Id_208,1434
-Order_No_25256,User_Id_1120,Bike,3,Personal,19,2,2:04:57 PM,19,2,2:05:14 PM,19,2,2:22:04 PM,19,2,2:34:05 PM,19,2,2:48:08 PM,2,29.5,,-1.2643693999999999,36.785413500000004,-1.2561824,36.7931555,Rider_Id_177,843
-Order_No_25609,User_Id_2830,Bike,1,Personal,15,1,7:07:35 AM,15,1,7:07:48 AM,15,1,7:16:19 AM,15,1,7:19:20 AM,15,1,7:51:44 AM,12,16.7,,-1.2636863,36.753893299999994,-1.2549476000000002,36.8269576,Rider_Id_726,1944
-Order_No_23182,User_Id_2676,Bike,1,Business,29,3,8:31:01 AM,29,3,8:31:41 AM,29,3,8:43:02 AM,29,3,9:02:08 AM,29,3,9:09:53 AM,2,15.3,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_88,465
-Order_No_6167,User_Id_939,Bike,2,Personal,17,5,4:29:25 PM,17,5,4:29:37 PM,17,5,4:36:58 PM,17,5,4:50:50 PM,17,5,5:31:09 PM,13,19.2,,-1.3254093999999998,36.7084902,-1.277783,36.7816464,Rider_Id_858,2419
-Order_No_23314,User_Id_633,Bike,3,Business,1,5,1:24:25 PM,1,5,1:48:31 PM,1,5,2:25:01 PM,1,5,2:33:22 PM,1,5,2:53:22 PM,5,28.4,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_26,1200
-Order_No_1095,User_Id_661,Bike,3,Business,6,4,11:12:53 AM,6,4,11:13:22 AM,6,4,11:30:24 AM,6,4,11:54:45 AM,6,4,12:13:07 PM,4,21.8,,-1.3092046,36.7458722,-1.2963097,36.7688221,Rider_Id_548,1102
-Order_No_4849,User_Id_3647,Bike,3,Business,27,4,5:05:45 PM,27,4,5:06:11 PM,27,4,5:16:07 PM,27,4,5:17:21 PM,27,4,5:33:12 PM,5,23.1,,-1.2726389999999999,36.794723,-1.2538731,36.7850223,Rider_Id_566,951
-Order_No_15492,User_Id_453,Bike,1,Personal,22,3,3:06:10 PM,22,3,3:06:27 PM,22,3,3:10:54 PM,22,3,3:34:29 PM,22,3,3:49:35 PM,5,25.9,,-1.275075,36.766391,-1.2970281000000001,36.7903141,Rider_Id_898,906
-Order_No_5069,User_Id_1642,Bike,3,Business,8,3,2:31:11 PM,8,3,2:31:52 PM,8,3,2:56:15 PM,8,3,2:59:04 PM,8,3,3:18:06 PM,5,,,-1.2728069,36.7962362,-1.2684816,36.808562200000004,Rider_Id_422,1142
-Order_No_14219,User_Id_3647,Bike,3,Business,17,4,9:55:21 AM,17,4,9:56:09 AM,17,4,10:15:10 AM,17,4,10:30:20 AM,17,4,10:30:30 AM,6,,,-1.2726389999999999,36.794723,-1.2963555,36.8084065,Rider_Id_818,10
-Order_No_19448,User_Id_2956,Bike,3,Business,1,6,4:51:04 PM,1,6,5:09:12 PM,1,6,5:14:44 PM,1,6,5:19:21 PM,1,6,5:28:32 PM,4,20.7,,-1.2584143,36.8048002,-1.2597367,36.7848482,Rider_Id_647,551
-Order_No_24956,User_Id_3340,Bike,3,Business,6,2,11:38:05 AM,6,2,11:44:59 AM,6,2,11:53:40 AM,6,2,12:21:37 PM,6,2,1:32:16 PM,30,25.2,,-1.3510811000000003,36.894806,-1.2095044,36.783206400000005,Rider_Id_518,4239
-Order_No_8609,User_Id_1244,Bike,3,Business,29,2,9:16:40 AM,29,2,9:17:27 AM,29,2,9:19:45 AM,29,2,9:44:01 AM,29,2,10:03:30 AM,9,18.5,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_178,1169
-Order_No_25745,User_Id_510,Bike,3,Business,31,4,4:17:11 PM,31,4,4:17:23 PM,31,4,4:17:28 PM,31,4,4:18:56 PM,31,4,4:35:56 PM,11,22.9,,-1.2860183,36.8975338,-1.2413316,36.878552500000005,Rider_Id_771,1020
-Order_No_12780,User_Id_1148,Bike,3,Business,27,3,2:25:11 PM,27,3,2:25:46 PM,27,3,2:39:02 PM,27,3,2:54:44 PM,27,3,3:30:27 PM,5,29.1,,-1.3100586,36.850032899999995,-1.3241185,36.857821200000004,Rider_Id_154,2143
-Order_No_15844,User_Id_3752,Bike,3,Business,13,1,3:48:55 PM,13,1,3:50:47 PM,13,1,4:05:13 PM,13,1,4:06:49 PM,13,1,5:43:20 PM,4,,,-1.335316,36.8845063,-1.3263105,36.8622904,Rider_Id_56,5791
-Order_No_3999,User_Id_1559,Bike,2,Personal,16,4,1:45:12 PM,16,4,1:45:20 PM,16,4,1:56:19 PM,16,4,2:27:45 PM,16,4,3:36:52 PM,13,18.9,,-1.3026696000000002,36.8418734,-1.221696,36.8270195,Rider_Id_229,4147
-Order_No_27671,User_Id_592,Bike,1,Personal,12,4,3:16:22 PM,12,4,3:20:40 PM,12,4,3:21:26 PM,12,4,3:49:42 PM,12,4,4:51:29 PM,11,21.5,,-1.2884354,36.760676600000004,-1.3100117,36.8226483,Rider_Id_672,3707
-Order_No_3952,User_Id_478,Bike,3,Business,17,3,10:48:03 AM,17,3,11:34:34 AM,17,3,11:51:45 AM,17,3,12:02:30 PM,17,3,12:22:56 PM,8,25.4,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_147,1226
-Order_No_13185,User_Id_3517,Bike,3,Business,21,4,2:40:30 PM,21,4,2:40:43 PM,21,4,2:48:25 PM,21,4,2:57:21 PM,21,4,3:25:33 PM,15,31.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_362,1692
-Order_No_19300,User_Id_635,Bike,3,Business,7,4,11:15:51 AM,7,4,11:16:01 AM,7,4,11:22:58 AM,7,4,11:33:36 AM,7,4,12:15:00 PM,14,27.6,,-1.3081376,36.83297720000001,-1.2603185,36.7400255,Rider_Id_329,2484
-Order_No_5607,User_Id_1435,Bike,3,Personal,24,1,4:46:38 PM,24,1,4:47:08 PM,24,1,4:58:15 PM,24,1,5:05:34 PM,24,1,5:28:16 PM,19,22.5,,-1.2948091999999998,36.8869203,-1.3014461000000002,36.7661381,Rider_Id_440,1362
-Order_No_3428,User_Id_867,Bike,3,Business,8,2,2:03:38 PM,8,2,2:04:12 PM,8,2,2:16:27 PM,8,2,2:17:50 PM,8,2,2:49:52 PM,14,25.1,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_254,1922
-Order_No_10514,User_Id_3283,Bike,3,Business,3,4,9:51:28 AM,3,4,9:56:29 AM,3,4,10:09:20 AM,3,4,10:19:36 AM,3,4,10:54:07 AM,7,20.2,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_14,2071
-Order_No_4165,User_Id_3291,Bike,3,Business,27,1,10:55:58 AM,27,1,10:56:17 AM,27,1,11:04:26 AM,27,1,11:22:31 AM,27,1,11:42:59 AM,11,20.9,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_708,1228
-Order_No_14855,User_Id_990,Bike,3,Business,6,3,11:05:35 AM,6,3,11:18:32 AM,6,3,11:28:27 AM,6,3,11:30:29 AM,6,3,11:38:58 AM,4,20.0,,-1.3343138,36.70867929999999,-1.3266489,36.703369099999996,Rider_Id_478,509
-Order_No_16707,User_Id_2175,Bike,3,Business,17,3,5:35:23 PM,17,3,5:35:57 PM,17,3,5:45:28 PM,17,3,5:51:04 PM,17,3,6:12:12 PM,8,,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_953,1268
-Order_No_7095,User_Id_265,Bike,3,Business,14,4,12:57:17 PM,14,4,1:27:06 PM,14,4,1:32:59 PM,14,4,1:48:54 PM,14,4,3:21:04 PM,48,27.8,,-1.300921,36.828195,-1.0302254000000002,37.016779299999996,Rider_Id_102,5530
-Order_No_13658,User_Id_814,Bike,3,Business,10,4,1:19:29 PM,10,4,1:20:09 PM,10,4,1:25:08 PM,10,4,1:26:58 PM,10,4,2:39:42 PM,20,,,-1.2579303999999998,36.8019667,-1.3303979,36.913818299999996,Rider_Id_865,4364
-Order_No_3251,User_Id_1329,Bike,3,Business,30,5,9:56:52 AM,30,5,10:12:30 AM,30,5,10:31:38 AM,30,5,10:36:26 AM,30,5,10:55:52 AM,7,23.6,,-1.273056,36.811298,-1.2771886000000001,36.8419032,Rider_Id_646,1166
-Order_No_18181,User_Id_1246,Bike,3,Business,15,5,11:19:57 AM,15,5,11:20:13 AM,15,5,11:24:51 AM,15,5,11:36:39 AM,15,5,12:14:22 PM,20,26.5,,-1.3700383,36.9190174,-1.2978908000000002,36.796321500000005,Rider_Id_551,2263
-Order_No_3720,User_Id_2962,Bike,1,Personal,9,1,11:10:57 AM,9,1,11:21:02 AM,9,1,11:48:29 AM,9,1,11:49:32 AM,9,1,12:04:26 PM,8,20.4,,-1.2789902,36.7650115,-1.2886923000000001,36.8241519,Rider_Id_102,894
-Order_No_25624,User_Id_393,Bike,3,Business,5,5,10:20:27 AM,5,5,10:20:51 AM,5,5,10:21:04 AM,5,5,10:54:59 AM,5,5,10:55:01 AM,14,24.5,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_515,2
-Order_No_8497,User_Id_3599,Bike,3,Business,12,5,11:06:24 AM,12,5,11:25:43 AM,12,5,11:27:42 AM,12,5,11:54:54 AM,12,5,12:22:04 PM,11,24.6,,-1.3302996,36.8707935,-1.3103985,36.9114173,Rider_Id_533,1630
-Order_No_28038,User_Id_2395,Bike,1,Personal,18,3,12:56:42 PM,18,3,12:58:56 PM,18,3,1:07:18 PM,18,3,1:44:38 PM,18,3,2:25:42 PM,5,20.6,,-1.2864298,36.8224345,-1.2657456,36.8087424,Rider_Id_268,2464
-Order_No_24435,User_Id_3123,Bike,1,Personal,9,4,5:14:10 PM,9,4,5:15:33 PM,9,4,5:52:09 PM,9,4,6:10:10 PM,9,4,6:26:47 PM,3,23.0,,-1.2739208,36.8310153,-1.2859576000000001,36.8197604,Rider_Id_229,997
-Order_No_7603,User_Id_2190,Bike,3,Business,10,5,4:39:43 PM,10,5,4:40:03 PM,10,5,4:40:51 PM,10,5,4:55:40 PM,10,5,5:37:30 PM,20,21.0,,-1.3019530000000001,36.788458,-1.3664581,36.9177807,Rider_Id_215,2510
-Order_No_4458,User_Id_1075,Bike,3,Business,2,2,12:28:04 PM,2,2,12:28:20 PM,2,2,12:34:16 PM,2,2,12:38:49 PM,2,2,12:46:55 PM,2,26.1,,-1.2527958000000001,36.8003131,-1.2571472,36.795063299999995,Rider_Id_484,486
-Order_No_12350,User_Id_3295,Bike,3,Business,7,5,2:28:35 PM,7,5,2:28:47 PM,7,5,2:34:08 PM,7,5,2:43:53 PM,7,5,3:01:30 PM,7,24.7,,-1.2571472,36.795063299999995,-1.2966203,36.817837,Rider_Id_882,1057
-Order_No_2476,User_Id_2260,Bike,1,Personal,19,5,4:12:56 PM,19,5,4:16:19 PM,19,5,4:30:34 PM,19,5,4:39:21 PM,19,5,5:10:58 PM,7,24.5,,-1.2562796999999999,36.8075858,-1.2981352,36.8213822,Rider_Id_432,1897
-Order_No_5770,User_Id_1850,Bike,3,Business,6,4,3:30:06 PM,6,4,3:32:01 PM,6,4,3:34:37 PM,6,4,3:36:58 PM,6,4,3:40:46 PM,2,23.3,,-1.2551895,36.7822034,-1.2638185,36.7832391,Rider_Id_116,228
-Order_No_5516,User_Id_635,Bike,3,Business,3,5,3:15:10 PM,3,5,3:25:36 PM,3,5,3:35:16 PM,3,5,4:24:24 PM,3,5,5:29:30 PM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_591,3906
-Order_No_23870,User_Id_136,Bike,3,Business,22,5,3:04:00 PM,22,5,3:05:00 PM,22,5,3:17:58 PM,22,5,3:22:04 PM,22,5,3:46:35 PM,7,,,-1.2536041999999998,36.806489500000005,-1.2505657,36.8211047,Rider_Id_549,1471
-Order_No_3473,User_Id_1873,Bike,3,Business,5,5,1:16:49 PM,5,5,1:22:57 PM,5,5,1:33:20 PM,5,5,1:51:35 PM,5,5,2:21:57 PM,8,26.6,,-1.225322,36.80855,-1.2571472,36.795063299999995,Rider_Id_410,1822
-Order_No_6881,User_Id_2077,Bike,2,Personal,2,2,4:27:58 PM,2,2,4:33:25 PM,2,2,4:57:18 PM,2,2,5:04:31 PM,2,2,5:32:39 PM,9,23.7,,-1.3251583999999998,36.8199384,-1.2946581,36.779292100000006,Rider_Id_532,1688
-Order_No_5697,User_Id_246,Bike,3,Business,4,2,2:32:40 PM,4,2,3:50:45 PM,4,2,4:25:31 PM,4,2,4:27:56 PM,4,2,4:51:41 PM,10,23.8,11.0,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_685,1425
-Order_No_17896,User_Id_3404,Bike,3,Business,3,1,9:31:50 AM,3,1,10:01:09 AM,3,1,10:35:52 AM,3,1,11:21:43 AM,3,1,11:32:37 AM,1,22.3,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_299,654
-Order_No_24470,User_Id_478,Bike,3,Business,8,5,11:18:49 AM,8,5,11:19:42 AM,8,5,11:19:49 AM,8,5,11:32:06 AM,8,5,12:06:18 PM,12,28.2,,-1.2551895,36.7822034,-1.318566,36.837210999999996,Rider_Id_523,2052
-Order_No_12105,User_Id_393,Bike,3,Business,13,3,12:30:14 PM,13,3,12:30:47 PM,13,3,12:30:56 PM,13,3,12:50:54 PM,13,3,12:59:58 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_120,544
-Order_No_6545,User_Id_2523,Bike,3,Business,21,5,2:57:15 PM,21,5,2:57:34 PM,21,5,3:00:13 PM,21,5,3:02:43 PM,21,5,3:11:20 PM,3,24.7,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_658,517
-Order_No_5202,User_Id_2175,Bike,3,Business,17,3,3:21:50 PM,17,3,3:22:35 PM,17,3,3:26:45 PM,17,3,3:43:18 PM,17,3,4:33:35 PM,6,,,-1.2551895,36.7822034,-1.2775226999999998,36.8174812,Rider_Id_709,3017
-Order_No_16105,User_Id_507,Bike,3,Personal,8,3,9:13:31 AM,8,3,9:13:55 AM,8,3,9:21:25 AM,8,3,9:24:00 AM,8,3,9:42:08 AM,5,,,-1.301093,36.7790314,-1.28467,36.794438,Rider_Id_161,1088
-Order_No_7548,User_Id_2344,Bike,3,Business,18,3,9:18:26 AM,18,3,9:18:53 AM,18,3,9:21:24 AM,18,3,9:32:16 AM,18,3,9:39:50 AM,3,14.4,,-1.2571472,36.795063299999995,-1.2551895,36.7822034,Rider_Id_726,454
-Order_No_11244,User_Id_2330,Bike,3,Business,11,2,2:23:09 PM,11,2,2:26:30 PM,11,2,2:31:43 PM,11,2,2:51:07 PM,11,2,2:51:21 PM,9,21.2,,-1.2642903,36.80021370000001,-1.2687407,36.7373043,Rider_Id_139,14
-Order_No_4619,User_Id_1792,Bike,3,Business,21,3,9:57:42 AM,21,3,10:09:45 AM,21,3,10:20:58 AM,21,3,10:22:24 AM,21,3,10:41:05 AM,6,17.2,1.1,-1.2809121,36.7852524,-1.2619756000000002,36.800139,Rider_Id_114,1121
-Order_No_13970,User_Id_3647,Bike,3,Business,29,1,3:32:55 PM,29,1,3:33:31 PM,29,1,3:42:16 PM,29,1,3:52:58 PM,29,1,4:17:37 PM,13,,,-1.2726389999999999,36.794723,-1.29928,36.85631,Rider_Id_291,1479
-Order_No_7195,User_Id_1245,Bike,3,Business,9,3,1:03:59 PM,9,3,1:04:49 PM,9,3,1:16:51 PM,9,3,2:04:39 PM,9,3,2:33:00 PM,19,25.4,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_622,1701
-Order_No_5976,User_Id_2801,Bike,3,Business,21,3,11:09:09 AM,21,3,11:10:03 AM,21,3,11:14:12 AM,21,3,11:16:30 AM,21,3,11:42:37 AM,8,22.1,,-1.2595421999999998,36.7871178,-1.2925919,36.81705770000001,Rider_Id_274,1567
-Order_No_16125,User_Id_3506,Bike,1,Personal,29,2,3:58:54 PM,29,2,4:01:06 PM,29,2,4:19:32 PM,29,2,4:21:42 PM,29,2,4:39:28 PM,11,23.9,,-1.3052671999999998,36.753853899999996,-1.3516579,36.729836299999995,Rider_Id_103,1066
-Order_No_12758,User_Id_1926,Bike,3,Business,11,5,4:14:50 PM,11,5,4:41:03 PM,11,5,5:05:11 PM,11,5,5:10:49 PM,11,5,5:39:30 PM,6,20.3,,-1.2897368,36.8226783,-1.3036637,36.7853135,Rider_Id_929,1721
-Order_No_10802,User_Id_805,Bike,3,Personal,23,3,9:36:40 AM,23,3,9:36:58 AM,23,3,9:43:11 AM,23,3,9:47:54 AM,23,3,10:14:10 AM,15,,,-1.3088044,36.810644700000005,-1.398531,36.767365000000005,Rider_Id_355,1576
-Order_No_7207,User_Id_1271,Bike,3,Business,16,3,10:18:43 AM,16,3,10:19:40 AM,16,3,10:52:20 AM,16,3,11:00:37 AM,16,3,11:35:08 AM,14,22.9,,-1.3244885,36.897791999999995,-1.238018,36.870424,Rider_Id_457,2071
-Order_No_3701,User_Id_2330,Bike,3,Business,12,5,2:43:08 PM,12,5,2:43:47 PM,12,5,3:05:22 PM,12,5,3:14:18 PM,12,5,3:38:21 PM,20,,,-1.2505902,36.865112599999996,-1.380892,36.923378,Rider_Id_8,1443
-Order_No_517,User_Id_393,Bike,3,Business,20,3,3:54:57 PM,20,3,3:55:15 PM,20,3,4:13:45 PM,20,3,4:29:09 PM,20,3,4:41:50 PM,4,29.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,761
-Order_No_11771,User_Id_1792,Bike,3,Business,24,5,5:31:10 PM,24,5,5:31:29 PM,24,5,5:31:56 PM,24,5,5:38:39 PM,24,5,6:11:27 PM,14,,,-1.2809121,36.7852524,-1.2291315,36.7852269,Rider_Id_215,1968
-Order_No_4500,User_Id_2195,Bike,3,Business,11,4,2:51:54 PM,11,4,2:53:05 PM,11,4,3:31:08 PM,11,4,3:55:02 PM,11,4,4:12:10 PM,6,26.9,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_8,1028
-Order_No_5365,User_Id_14,Bike,3,Personal,19,3,3:17:33 PM,19,3,3:37:19 PM,19,3,3:45:07 PM,19,3,3:49:44 PM,19,3,4:13:33 PM,10,24.6,,-1.279228,36.7746478,-1.3070122,36.8287218,Rider_Id_685,1429
-Order_No_4637,User_Id_3352,Bike,3,Personal,21,4,9:22:13 AM,21,4,9:22:25 AM,21,4,9:31:46 AM,21,4,9:46:03 AM,21,4,10:03:44 AM,10,21.8,,-1.2946939,36.8043582,-1.2533491,36.861591100000005,Rider_Id_291,1061
-Order_No_25351,User_Id_3051,Bike,1,Personal,3,1,2:16:12 PM,3,1,2:17:34 PM,3,1,2:32:16 PM,3,1,2:41:29 PM,3,1,3:05:16 PM,6,23.0,,-1.2702333000000001,36.80065,-1.303664,36.818777000000004,Rider_Id_447,1427
-Order_No_1022,User_Id_3434,Bike,3,Business,19,3,11:46:46 AM,19,3,11:47:16 AM,19,3,12:12:21 PM,19,3,12:26:19 PM,19,3,1:00:50 PM,16,22.2,,-1.3022899,36.869332,-1.2963097,36.7688221,Rider_Id_895,2071
-Order_No_16229,User_Id_3496,Bike,3,Business,11,5,3:17:10 PM,11,5,3:33:18 PM,11,5,4:05:09 PM,11,5,4:40:21 PM,11,5,5:16:27 PM,21,24.2,,-1.2847613000000002,36.967084,-1.300921,36.828195,Rider_Id_207,2166
-Order_No_21393,User_Id_3283,Bike,3,Business,27,3,11:32:48 AM,27,3,11:33:56 AM,27,3,11:43:50 AM,27,3,12:05:41 PM,27,3,12:49:44 PM,19,25.8,,-1.293515,36.897607,-1.2628473,36.781805,Rider_Id_292,2643
-Order_No_10596,User_Id_86,Bike,1,Personal,3,5,8:22:53 AM,3,5,8:23:43 AM,3,5,8:41:54 AM,3,5,8:47:32 AM,3,5,9:07:03 AM,10,17.3,,-1.3014461000000002,36.7661381,-1.2598589,36.7470168,Rider_Id_105,1171
-Order_No_13390,User_Id_1753,Bike,2,Business,25,2,1:53:07 PM,25,2,1:53:33 PM,25,2,1:54:13 PM,25,2,2:04:06 PM,25,2,2:41:53 PM,16,27.3,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_498,2267
-Order_No_4890,User_Id_3404,Bike,3,Business,21,5,3:49:13 PM,21,5,3:49:34 PM,21,5,4:09:08 PM,21,5,4:42:02 PM,21,5,4:47:00 PM,1,23.2,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_909,298
-Order_No_6234,User_Id_1329,Bike,3,Business,4,4,9:50:49 AM,4,4,9:53:17 AM,4,4,10:01:20 AM,4,4,10:03:29 AM,4,4,10:25:37 AM,6,21.6,,-1.273056,36.811298,-1.2942023,36.7958179,Rider_Id_85,1328
-Order_No_20518,User_Id_2523,Bike,3,Business,10,3,9:45:44 AM,10,3,9:45:58 AM,10,3,9:46:33 AM,10,3,10:03:02 AM,10,3,10:11:54 AM,5,21.6,,-1.2551895,36.7822034,-1.2619185,36.759662,Rider_Id_108,532
-Order_No_8009,User_Id_136,Bike,3,Business,25,5,12:34:48 PM,25,5,12:34:53 PM,25,5,12:38:05 PM,25,5,12:53:47 PM,25,5,1:47:27 PM,14,26.5,,-1.3254522,36.721658399999995,-1.274716,36.789582,Rider_Id_831,3220
-Order_No_8868,User_Id_1410,Bike,3,Business,11,1,3:39:30 PM,11,1,3:43:42 PM,11,1,3:53:37 PM,11,1,4:16:16 PM,11,1,4:54:33 PM,27,29.0,,-1.300921,36.828195,-1.15135,36.9566534,Rider_Id_8,2297
-Order_No_14282,User_Id_136,Bike,3,Business,31,4,8:45:56 AM,31,4,8:46:21 AM,31,4,9:29:56 AM,31,4,9:32:19 AM,31,4,10:47:57 AM,16,20.6,,-1.3254522,36.721658399999995,-1.281614,36.8275424,Rider_Id_831,4538
-Order_No_25395,User_Id_3222,Bike,3,Business,29,3,1:47:21 PM,29,3,1:48:09 PM,29,3,1:51:40 PM,29,3,2:06:35 PM,29,3,2:33:50 PM,7,21.3,,-1.2551895,36.7822034,-1.2887816,36.8088885,Rider_Id_330,1635
-Order_No_9152,User_Id_20,Bike,3,Business,14,5,12:23:02 PM,14,5,12:24:03 PM,14,5,1:31:26 PM,14,5,1:32:35 PM,14,5,1:53:47 PM,8,23.0,,-1.3008826,36.8378114,-1.2680478000000002,36.8013518,Rider_Id_567,1272
-Order_No_8378,User_Id_1719,Bike,3,Business,22,5,11:23:26 AM,22,5,11:25:11 AM,22,5,12:08:09 PM,22,5,12:23:29 PM,22,5,12:57:14 PM,18,28.6,,-1.23118,36.72678,-1.296797,36.776452500000005,Rider_Id_217,2025
-Order_No_13173,User_Id_2175,Bike,3,Business,24,4,2:04:16 PM,24,4,2:05:12 PM,24,4,2:35:43 PM,24,4,2:39:53 PM,24,4,2:58:32 PM,5,28.1,,-1.2551895,36.7822034,-1.2790175,36.8151778,Rider_Id_268,1119
-Order_No_7386,User_Id_2030,Bike,3,Business,28,1,2:53:30 PM,28,1,2:54:38 PM,28,1,3:06:00 PM,28,1,3:08:27 PM,28,1,3:20:51 PM,3,26.3,,-1.304735,36.784605400000004,-1.295908,36.791433000000005,Rider_Id_548,744
-Order_No_14477,User_Id_432,Bike,3,Business,16,4,9:54:48 AM,16,4,9:55:30 AM,16,4,10:01:06 AM,16,4,10:09:36 AM,16,4,10:29:21 AM,10,,,-1.3012008,36.764868,-1.3002553999999997,36.8256566,Rider_Id_377,1185
-Order_No_24857,User_Id_3621,Bike,3,Business,13,4,12:49:35 PM,13,4,12:50:28 PM,13,4,1:21:19 PM,13,4,1:24:36 PM,13,4,2:25:04 PM,23,22.2,,-1.2836583000000001,36.7886014,-1.1931364,36.9476629,Rider_Id_570,3628
-Order_No_20088,User_Id_3152,Bike,3,Personal,15,6,2:51:46 PM,15,6,2:51:53 PM,15,6,3:04:32 PM,15,6,3:10:32 PM,15,6,4:17:44 PM,24,23.3,,-1.2975438999999998,36.756396,-1.306416,36.916820200000004,Rider_Id_200,4032
-Order_No_3116,User_Id_830,Bike,3,Business,27,4,2:26:55 PM,27,4,2:34:02 PM,27,4,2:36:04 PM,27,4,2:54:40 PM,27,4,3:21:47 PM,7,25.1,,-1.2551895,36.7822034,-1.2822909,36.8193456,Rider_Id_96,1627
-Order_No_22297,User_Id_2801,Bike,3,Business,13,4,9:36:58 AM,13,4,9:37:14 AM,13,4,9:38:01 AM,13,4,9:51:51 AM,13,4,10:12:09 AM,17,18.5,,-1.307726,36.839117,-1.3483945,36.9074283,Rider_Id_205,1218
-Order_No_24738,User_Id_20,Bike,3,Business,20,1,12:56:06 PM,20,1,12:56:49 PM,20,1,1:02:13 PM,20,1,1:04:59 PM,20,1,1:33:07 PM,13,,,-1.2628638,36.8070399,-1.3343128999999998,36.863711200000004,Rider_Id_807,1688
-Order_No_11963,User_Id_2987,Bike,3,Business,11,2,11:12:33 AM,11,2,11:16:23 AM,11,2,11:39:36 AM,11,2,11:40:51 AM,11,2,11:56:44 AM,12,24.0,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_598,953
-Order_No_24920,User_Id_3517,Bike,3,Business,6,6,9:18:54 AM,6,6,9:19:17 AM,6,6,9:26:22 AM,6,6,9:56:30 AM,6,6,10:21:28 AM,15,19.8,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_843,1498
-Order_No_9419,User_Id_941,Bike,1,Personal,6,6,5:27:41 PM,6,6,5:27:51 PM,6,6,5:51:50 PM,6,6,5:57:52 PM,6,6,6:31:22 PM,19,25.2,,-1.2807298,36.8299116,-1.282399,36.957033,Rider_Id_208,2010
-Order_No_1433,User_Id_55,Bike,1,Personal,3,3,3:15:38 PM,3,3,3:16:23 PM,3,3,3:39:32 PM,3,3,4:14:02 PM,3,3,4:28:26 PM,11,26.7,,-1.3173868999999998,36.8112044,-1.2551895,36.7822034,Rider_Id_789,864
-Order_No_20864,User_Id_3354,Bike,3,Business,8,5,12:38:09 PM,8,5,12:41:46 PM,8,5,1:01:23 PM,8,5,1:03:22 PM,8,5,1:40:13 PM,18,28.2,,-1.2571472,36.795063299999995,-1.2738108,36.9167268,Rider_Id_343,2211
-Order_No_13911,User_Id_3674,Bike,3,Business,26,2,3:45:53 PM,26,2,3:46:58 PM,26,2,3:55:10 PM,26,2,3:57:54 PM,26,2,4:03:00 PM,3,29.9,,-1.2663135,36.7973895,-1.2551895,36.7822034,Rider_Id_801,306
-Order_No_22600,User_Id_3404,Bike,3,Business,25,5,12:52:42 PM,25,5,12:52:55 PM,25,5,12:54:16 PM,25,5,1:10:39 PM,25,5,1:10:51 PM,1,26.6,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_845,12
-Order_No_11306,User_Id_265,Bike,3,Business,17,5,9:51:03 AM,17,5,9:51:51 AM,17,5,9:57:51 AM,17,5,10:07:25 AM,17,5,10:24:15 AM,7,14.5,,-1.2642903,36.80021370000001,-1.300921,36.828195,Rider_Id_857,1010
-Order_No_3049,User_Id_111,Bike,3,Business,20,1,2:36:43 PM,20,1,2:37:00 PM,20,1,2:54:03 PM,20,1,2:59:29 PM,20,1,3:09:58 PM,8,,,-1.2551895,36.7822034,-1.2856664,36.755983799999996,Rider_Id_448,629
-Order_No_8212,User_Id_627,Bike,3,Business,6,1,3:15:16 PM,6,1,3:16:14 PM,6,1,3:33:58 PM,6,1,3:37:09 PM,6,1,4:16:07 PM,18,,,-1.2551895,36.7822034,-1.3491472,36.75997879999999,Rider_Id_770,2338
-Order_No_17138,User_Id_2763,Bike,3,Business,1,4,1:21:36 PM,1,4,1:21:43 PM,1,4,1:37:25 PM,1,4,1:42:51 PM,1,4,1:57:37 PM,2,,,-1.3014461000000002,36.7661381,-1.2934638,36.7668028,Rider_Id_477,886
-Order_No_25974,User_Id_590,Bike,3,Business,13,2,10:35:55 AM,13,2,10:59:02 AM,13,2,11:40:12 AM,13,2,11:46:27 AM,13,2,12:03:07 PM,3,22.2,,-1.2479361999999998,36.772759,-1.257773,36.795486,Rider_Id_866,1000
-Order_No_14451,User_Id_20,Bike,3,Business,1,3,3:44:08 PM,1,3,3:44:52 PM,1,3,4:20:29 PM,1,3,4:28:44 PM,1,3,4:52:42 PM,5,24.0,,-1.2767064,36.8227148,-1.2629666000000002,36.8068369,Rider_Id_229,1438
-Order_No_2428,User_Id_1346,Bike,3,Business,18,4,1:51:40 PM,18,4,1:52:23 PM,18,4,2:09:08 PM,18,4,2:11:48 PM,18,4,2:52:09 PM,20,,,-1.2917868000000001,36.7872675,-1.3700383,36.9190174,Rider_Id_710,2421
-Order_No_10158,User_Id_2216,Bike,3,Business,21,2,10:02:07 AM,21,2,10:02:55 AM,21,2,10:03:20 AM,21,2,10:09:04 AM,21,2,10:38:29 AM,10,,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_576,1765
-Order_No_8201,User_Id_1500,Bike,3,Business,15,2,11:34:28 AM,15,2,11:35:48 AM,15,2,12:19:58 PM,15,2,12:25:08 PM,15,2,1:09:07 PM,13,24.4,,-1.2574219,36.7927073,-1.3228003000000002,36.8306435,Rider_Id_725,2639
-Order_No_1864,User_Id_1764,Bike,1,Personal,7,4,9:29:22 AM,7,4,10:01:11 AM,7,4,10:19:33 AM,7,4,10:27:34 AM,7,4,11:02:40 AM,10,14.3,0.9,-1.3307951,36.79775479999999,-1.2930705,36.827943299999994,Rider_Id_635,2106
-Order_No_4038,User_Id_393,Bike,3,Business,23,3,12:36:04 PM,23,3,12:36:53 PM,23,3,12:43:18 PM,23,3,1:31:33 PM,23,3,2:04:59 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_555,2006
-Order_No_10874,User_Id_2190,Bike,3,Business,20,3,2:52:32 PM,20,3,2:52:43 PM,20,3,3:08:41 PM,20,3,4:18:27 PM,20,3,4:51:50 PM,19,28.6,,-1.3239725,36.8827383,-1.3019530000000001,36.788458,Rider_Id_886,2003
-Order_No_8059,User_Id_2279,Bike,3,Business,18,2,10:07:47 AM,18,2,10:08:36 AM,18,2,10:27:21 AM,18,2,10:40:47 AM,18,2,11:10:29 AM,12,22.8,,-1.3196808999999998,36.8410572,-1.2571472,36.795063299999995,Rider_Id_956,1782
-Order_No_9474,User_Id_2701,Bike,3,Business,14,1,5:03:02 PM,14,1,5:20:13 PM,14,1,5:29:45 PM,14,1,5:47:18 PM,14,1,6:11:25 PM,7,23.1,,-1.279395,36.825364,-1.2559559999999999,36.789867,Rider_Id_761,1447
-Order_No_17905,User_Id_1927,Bike,3,Business,3,1,11:25:20 AM,3,1,11:26:37 AM,3,1,11:37:47 AM,3,1,12:15:07 PM,3,1,12:18:31 PM,12,21.3,,-1.279395,36.825364,-1.3472066,36.7692638,Rider_Id_778,204
-Order_No_4911,User_Id_3496,Bike,3,Business,7,4,2:36:52 PM,7,4,2:37:42 PM,7,4,2:46:12 PM,7,4,3:02:47 PM,7,4,3:40:54 PM,14,27.5,,-1.3081376,36.83297720000001,-1.282907,36.738903,Rider_Id_105,2287
-Order_No_15212,User_Id_640,Bike,3,Business,16,5,2:42:03 PM,16,5,2:45:43 PM,16,5,3:04:07 PM,16,5,3:08:46 PM,16,5,3:27:30 PM,3,,,-1.304033,36.7848695,-1.2945106000000002,36.7728398,Rider_Id_353,1124
-Order_No_4924,User_Id_3320,Bike,3,Business,26,5,10:40:35 AM,26,5,11:05:54 AM,26,5,11:07:55 AM,26,5,11:12:00 AM,26,5,11:37:28 AM,8,,,-1.3080091999999999,36.8284543,-1.271823,36.789215000000006,Rider_Id_866,1528
-Order_No_25207,User_Id_2676,Bike,3,Business,31,2,11:44:13 AM,31,2,11:45:45 AM,31,2,11:53:16 AM,31,2,11:56:31 AM,31,2,12:25:41 PM,4,18.3,,-1.2963097,36.7688221,-1.2764692,36.758011100000004,Rider_Id_253,1750
-Order_No_13294,User_Id_3517,Bike,3,Business,24,3,1:43:53 PM,24,3,1:45:24 PM,24,3,1:58:03 PM,24,3,2:21:45 PM,24,3,2:52:54 PM,15,18.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_581,1869
-Order_No_9228,User_Id_1363,Bike,3,Business,1,5,9:27:46 AM,1,5,9:28:03 AM,1,5,9:31:24 AM,1,5,9:35:21 AM,1,5,10:01:19 AM,7,,,-1.3004062,36.829741,-1.2546732,36.808679600000005,Rider_Id_206,1558
-Order_No_25553,User_Id_2803,Bike,3,Business,6,1,11:03:51 AM,6,1,11:04:47 AM,6,1,11:16:59 AM,6,1,11:26:09 AM,6,1,11:57:10 AM,5,20.4,,-1.2765736,36.851364600000004,-1.2951169,36.823896000000005,Rider_Id_700,1861
-Order_No_24352,User_Id_3420,Bike,3,Business,6,3,10:01:24 AM,6,3,10:02:10 AM,6,3,10:06:08 AM,6,3,10:08:20 AM,6,3,10:19:33 AM,6,,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_231,673
-Order_No_15522,User_Id_439,Bike,3,Business,10,1,1:41:49 PM,10,1,1:43:27 PM,10,1,1:57:35 PM,10,1,2:10:08 PM,10,1,3:14:41 PM,8,18.9,,-1.3323107,36.8692616,-1.2935617,36.835057,Rider_Id_404,3873
-Order_No_16220,User_Id_2314,Bike,2,Personal,26,4,6:48:49 PM,26,4,6:49:33 PM,26,4,6:54:04 PM,26,4,6:58:15 PM,26,4,7:26:53 PM,12,19.5,,-1.2911123,36.7921078,-1.2482518,36.7550829,Rider_Id_937,1718
-Order_No_8744,User_Id_816,Bike,3,Business,16,3,10:43:09 AM,16,3,10:43:28 AM,16,3,11:04:11 AM,16,3,11:19:08 AM,16,3,12:11:30 PM,9,22.8,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_725,3142
-Order_No_22228,User_Id_1329,Bike,3,Business,5,3,9:18:31 AM,5,3,9:19:31 AM,5,3,9:35:41 AM,5,3,9:41:42 AM,5,3,10:12:15 AM,6,18.0,,-1.273056,36.811298,-1.3005349,36.7873912,Rider_Id_379,1833
-Order_No_2736,User_Id_2789,Bike,3,Business,15,5,12:06:45 PM,15,5,12:07:09 PM,15,5,12:15:06 PM,15,5,12:21:59 PM,15,5,12:54:13 PM,13,25.8,,-1.304735,36.784605400000004,-1.3024388,36.859699,Rider_Id_468,1934
-Order_No_23091,User_Id_3147,Bike,3,Business,28,5,1:41:57 PM,28,5,2:05:47 PM,28,5,2:29:14 PM,28,5,2:37:49 PM,28,5,3:13:00 PM,22,25.2,,-1.3632162,36.9108253,-1.3472066,36.7692638,Rider_Id_711,2111
-Order_No_24901,User_Id_830,Bike,3,Business,1,3,2:59:09 PM,1,3,2:59:15 PM,1,3,2:59:23 PM,1,3,3:16:44 PM,1,3,3:34:47 PM,7,24.1,,-1.2584143,36.8048002,-1.271672,36.783604,Rider_Id_777,1083
-Order_No_25950,User_Id_695,Bike,1,Personal,20,1,3:58:49 PM,20,1,4:00:27 PM,20,1,4:15:07 PM,20,1,4:19:57 PM,20,1,5:07:20 PM,16,21.2,,-1.2108499,36.781936200000004,-1.2977768,36.8110285,Rider_Id_384,2843
-Order_No_17913,User_Id_3686,Bike,3,Business,21,5,11:59:00 AM,21,5,12:02:32 PM,21,5,12:19:37 PM,21,5,12:27:12 PM,21,5,12:59:40 PM,12,22.8,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_436,1948
-Order_No_20469,User_Id_478,Bike,3,Business,8,5,11:17:25 AM,8,5,11:18:38 AM,8,5,11:26:03 AM,8,5,11:32:52 AM,8,5,12:08:35 PM,13,28.5,,-1.2551895,36.7822034,-1.3164474,36.857586100000006,Rider_Id_131,2143
-Order_No_9524,User_Id_3291,Bike,3,Business,16,4,9:46:04 AM,16,4,9:46:30 AM,16,4,9:53:05 AM,16,4,9:59:13 AM,16,4,10:23:30 AM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_204,1457
-Order_No_16325,User_Id_1075,Bike,3,Business,18,1,12:35:57 PM,18,1,12:37:06 PM,18,1,12:48:41 PM,18,1,12:50:37 PM,18,1,1:01:12 PM,1,27.8,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_318,635
-Order_No_13376,User_Id_635,Bike,3,Business,14,4,3:34:52 PM,14,4,3:38:32 PM,14,4,3:59:15 PM,14,4,4:13:32 PM,14,4,4:49:18 PM,17,28.1,,-1.177202,36.8351183,-1.2602337,36.79905479999999,Rider_Id_56,2146
-Order_No_16044,User_Id_2804,Bike,1,Business,11,5,9:52:49 AM,11,5,9:59:42 AM,11,5,10:01:13 AM,11,5,11:07:01 AM,11,5,11:16:40 AM,7,20.6,,-1.2811695,36.8200313,-1.2559559999999999,36.789867,Rider_Id_644,579
-Order_No_18415,User_Id_3517,Bike,3,Business,28,2,3:47:10 PM,28,2,3:47:36 PM,28,2,3:47:55 PM,28,2,4:40:27 PM,28,2,4:51:08 PM,15,21.6,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137,641
-Order_No_12314,User_Id_2130,Bike,3,Personal,10,1,1:02:45 PM,10,1,1:08:23 PM,10,1,1:35:04 PM,10,1,1:37:27 PM,10,1,2:28:56 PM,12,18.2,,-1.2310607,36.803577600000004,-1.2981625,36.813014200000005,Rider_Id_272,3089
-Order_No_13330,User_Id_3647,Bike,3,Business,14,4,1:20:13 PM,14,4,1:22:21 PM,14,4,1:34:45 PM,14,4,1:39:43 PM,14,4,1:55:18 PM,6,27.6,,-1.2726389999999999,36.794723,-1.298437,36.781971,Rider_Id_576,935
-Order_No_4152,User_Id_517,Bike,3,Business,22,4,8:01:20 AM,22,4,8:12:27 AM,22,4,9:05:25 AM,22,4,9:07:55 AM,22,4,9:44:43 AM,14,19.7,2.9,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,2208
-Order_No_22767,User_Id_3283,Bike,3,Business,8,5,1:42:21 PM,8,5,4:13:31 PM,8,5,4:23:47 PM,8,5,4:37:37 PM,8,5,5:21:53 PM,13,20.1,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_192,2656
-Order_No_20660,User_Id_3232,Bike,3,Business,26,5,10:28:13 AM,26,5,12:29:26 PM,26,5,12:39:37 PM,26,5,12:43:06 PM,26,5,1:29:29 PM,6,,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_101,2783
-Order_No_13449,User_Id_3064,Bike,3,Business,9,2,3:32:10 PM,9,2,3:35:50 PM,9,2,3:37:54 PM,9,2,3:49:33 PM,9,2,5:05:31 PM,5,30.7,,-1.2571472,36.795063299999995,-1.2788257,36.82877,Rider_Id_668,4558
-Order_No_27149,User_Id_2412,Bike,3,Business,21,2,11:57:20 AM,21,2,11:57:41 AM,21,2,11:59:02 AM,21,2,12:08:39 PM,21,2,12:25:04 PM,12,22.7,,-1.2584143,36.8048002,-1.2283403,36.8822756,Rider_Id_383,985
-Order_No_1923,User_Id_633,Bike,3,Business,4,4,3:21:42 PM,4,4,3:58:11 PM,4,4,4:16:39 PM,4,4,4:22:26 PM,4,4,4:37:17 PM,4,24.6,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_532,891
-Order_No_27017,User_Id_1831,Bike,3,Business,11,1,2:54:59 PM,11,1,2:58:02 PM,11,1,3:08:58 PM,11,1,3:15:54 PM,11,1,4:01:11 PM,18,29.0,,-1.282543,36.824618,-1.3562374,36.904295399999995,Rider_Id_583,2717
-Order_No_14917,User_Id_2436,Bike,3,Business,4,2,4:45:08 PM,4,2,4:49:24 PM,4,2,4:49:57 PM,4,2,5:00:33 PM,4,2,5:41:34 PM,14,22.4,,-1.2551895,36.7822034,-1.242902,36.870334,Rider_Id_651,2461
-Order_No_4927,User_Id_1271,Bike,3,Business,18,1,2:35:47 PM,18,1,2:37:37 PM,18,1,2:46:12 PM,18,1,3:02:20 PM,18,1,3:21:19 PM,15,29.3,,-1.3244885,36.897791999999995,-1.2824911,36.8218042,Rider_Id_863,1139
-Order_No_19395,User_Id_1753,Bike,2,Business,5,5,12:47:08 PM,5,5,12:48:09 PM,5,5,12:49:02 PM,5,5,12:54:29 PM,5,5,1:34:14 PM,17,23.6,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_574,2385
-Order_No_18441,User_Id_633,Bike,3,Business,20,3,1:11:52 PM,20,3,1:12:11 PM,20,3,1:13:18 PM,20,3,1:16:38 PM,20,3,2:13:23 PM,4,27.0,,-1.3004062,36.829741,-1.304127,36.805743,Rider_Id_349,3405
-Order_No_27188,User_Id_1243,Bike,1,Business,18,4,8:45:42 AM,18,4,8:47:15 AM,18,4,8:54:34 AM,18,4,9:12:18 AM,18,4,9:34:06 AM,10,20.4,,-1.304524,36.775371,-1.2551895,36.7822034,Rider_Id_571,1308
-Order_No_25452,User_Id_2661,Bike,2,Personal,15,1,1:45:09 PM,15,1,1:48:23 PM,15,1,2:00:15 PM,15,1,2:01:34 PM,15,1,2:22:08 PM,11,,,-1.3472066,36.7692638,-1.2905138,36.829301,Rider_Id_328,1234
-Order_No_18460,User_Id_3517,Bike,3,Business,31,3,2:59:43 PM,31,3,2:59:58 PM,31,3,3:08:28 PM,31,3,3:19:05 PM,31,3,4:09:10 PM,15,26.9,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_95,3005
-Order_No_26224,User_Id_1500,Bike,3,Business,8,5,11:21:06 AM,8,5,11:22:52 AM,8,5,11:23:56 AM,8,5,12:21:07 PM,8,5,12:21:10 PM,8,24.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_10,3
-Order_No_5301,User_Id_2174,Bike,3,Business,19,2,10:26:15 AM,19,2,10:44:59 AM,19,2,11:14:25 AM,19,2,11:16:35 AM,19,2,12:01:08 PM,15,26.4,,-1.2482781,36.884091999999995,-1.214071,36.7985052,Rider_Id_152,2673
-Order_No_26763,User_Id_1500,Bike,3,Business,14,2,9:58:41 AM,14,2,10:00:15 AM,14,2,10:04:25 AM,14,2,10:05:40 AM,14,2,10:58:19 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_298,3159
-Order_No_9028,User_Id_620,Bike,2,Personal,4,4,1:47:58 PM,4,4,2:22:17 PM,4,4,2:34:29 PM,4,4,2:57:13 PM,4,4,4:27:04 PM,11,24.6,,-1.2293754,36.862432399999996,-1.2815530000000002,36.8185334,Rider_Id_86,5391
-Order_No_11984,User_Id_2306,Bike,3,Business,4,1,11:57:05 AM,4,1,11:58:09 AM,4,1,12:21:27 PM,4,1,12:31:32 PM,4,1,12:59:16 PM,13,25.5,,-1.2271998,36.808490500000005,-1.296416,36.828281,Rider_Id_268,1664
-Order_No_22675,User_Id_3621,Bike,3,Business,25,7,11:46:59 AM,25,7,11:47:57 AM,25,7,12:02:23 PM,25,7,12:06:31 PM,25,7,12:39:07 PM,9,23.2,,-1.2836583000000001,36.7886014,-1.254443,36.825857,Rider_Id_805,1956
-Order_No_15021,User_Id_3194,Bike,3,Business,13,3,3:38:30 PM,13,3,3:39:26 PM,13,3,3:47:08 PM,13,3,3:51:55 PM,13,3,4:16:23 PM,4,29.1,,-1.2811503,36.8235884,-1.2885503999999999,36.8066908,Rider_Id_341,1468
-Order_No_7537,User_Id_53,Bike,3,Business,5,2,10:15:44 AM,5,2,10:22:14 AM,5,2,11:17:48 AM,5,2,11:55:00 AM,5,2,11:56:41 AM,5,25.6,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_436,101
-Order_No_17284,User_Id_635,Bike,3,Business,26,3,2:45:44 PM,26,3,2:47:42 PM,26,3,3:00:57 PM,26,3,3:22:46 PM,26,3,3:43:23 PM,7,27.1,,-1.3016417,36.8271679,-1.2618264,36.8141179,Rider_Id_402,1237
-Order_No_13839,User_Id_2216,Bike,3,Business,5,5,1:31:30 PM,5,5,1:32:02 PM,5,5,1:32:38 PM,5,5,1:44:29 PM,5,5,2:03:47 PM,8,27.7,,-1.2551895,36.7822034,-1.2964347999999999,36.8182757,Rider_Id_576,1158
-Order_No_18921,User_Id_2454,Bike,3,Business,23,2,9:42:31 AM,23,2,9:42:44 AM,23,2,9:50:12 AM,23,2,9:58:23 AM,23,2,10:10:18 AM,5,,,-1.3090343,36.7662134,-1.2942023,36.7958179,Rider_Id_306,715
-Order_No_20902,User_Id_3283,Bike,3,Business,18,4,10:49:55 AM,18,4,10:50:07 AM,18,4,10:51:32 AM,18,4,11:12:35 AM,18,4,11:36:20 AM,13,,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_710,1425
-Order_No_24731,User_Id_3647,Bike,3,Business,6,1,2:52:02 PM,6,1,2:52:24 PM,6,1,2:54:35 PM,6,1,2:58:46 PM,6,1,4:07:20 PM,16,,,-1.2726389999999999,36.794723,-1.298037,36.6934582,Rider_Id_831,4114
-Order_No_23427,User_Id_788,Bike,3,Business,10,1,1:59:18 PM,10,1,2:03:49 PM,10,1,2:15:42 PM,10,1,2:33:47 PM,10,1,2:59:03 PM,8,18.6,,-1.2571472,36.795063299999995,-1.225322,36.80855,Rider_Id_755,1516
-Order_No_26284,User_Id_1425,Bike,3,Business,9,2,10:33:33 AM,9,2,10:34:20 AM,9,2,10:36:38 AM,9,2,10:56:06 AM,9,2,11:06:12 AM,5,27.2,,-1.3193819,36.8743424,-1.338283,36.8871775,Rider_Id_394,606
-Order_No_12319,User_Id_2226,Bike,3,Personal,9,1,9:36:24 AM,9,1,10:05:07 AM,9,1,10:16:05 AM,9,1,10:24:55 AM,9,1,10:56:54 AM,19,15.5,,-1.2372853000000001,36.7611857,-1.3104733999999998,36.7363698,Rider_Id_103,1919
-Order_No_25895,User_Id_1841,Bike,3,Business,26,5,1:21:58 PM,26,5,1:23:14 PM,26,5,1:28:35 PM,26,5,1:39:35 PM,26,5,1:56:23 PM,10,,,-1.3148396,36.8202498,-1.2571472,36.795063299999995,Rider_Id_650,1008
-Order_No_7223,User_Id_1098,Bike,1,Personal,27,7,11:03:15 AM,27,7,11:03:32 AM,27,7,11:24:59 AM,27,7,11:34:14 AM,27,7,12:06:16 PM,12,20.3,,-1.2128896999999998,36.8910107,-1.1653207,36.9415164,Rider_Id_858,1922
-Order_No_10224,User_Id_868,Bike,3,Business,9,4,3:14:51 PM,9,4,3:15:45 PM,9,4,3:31:59 PM,9,4,3:36:35 PM,9,4,3:45:24 PM,3,26.0,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_882,529
-Order_No_18750,User_Id_474,Bike,3,Business,6,5,3:11:00 PM,6,5,3:12:30 PM,6,5,3:20:54 PM,6,5,3:23:48 PM,6,5,3:46:21 PM,4,22.3,,-1.264945,36.784678,-1.2567848999999998,36.814054299999995,Rider_Id_228,1353
-Order_No_6950,User_Id_1271,Bike,3,Business,4,2,1:31:03 PM,4,2,1:42:27 PM,4,2,1:55:06 PM,4,2,2:01:32 PM,4,2,3:00:15 PM,24,23.8,10.9,-1.3244885,36.897791999999995,-1.2821825,36.7588985,Rider_Id_829,3523
-Order_No_26328,User_Id_178,Bike,2,Personal,31,4,12:38:34 PM,31,4,12:40:28 PM,31,4,1:04:26 PM,31,4,1:18:01 PM,31,4,1:39:06 PM,5,21.7,,-1.2874648000000002,36.8271985,-1.2966785,36.7896469,Rider_Id_844,1265
-Order_No_8075,User_Id_2165,Bike,1,Personal,6,3,10:38:04 AM,6,3,10:38:44 AM,6,3,11:05:09 AM,6,3,11:17:17 AM,6,3,12:01:01 PM,16,19.9,,-1.2966785,36.7896469,-1.2161475,36.8828534,Rider_Id_334,2624
-Order_No_24807,User_Id_3647,Bike,3,Business,26,2,2:30:31 PM,26,2,2:30:46 PM,26,2,2:41:15 PM,26,2,2:46:01 PM,26,2,3:00:41 PM,5,29.9,,-1.2726389999999999,36.794723,-1.2896563,36.782282200000004,Rider_Id_576,880
-Order_No_13774,User_Id_1075,Bike,3,Business,18,1,4:47:49 PM,18,1,4:48:19 PM,18,1,5:20:24 PM,18,1,5:41:32 PM,18,1,6:16:05 PM,6,29.3,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_127,2073
-Order_No_12907,User_Id_3222,Bike,3,Business,13,4,3:47:53 PM,13,4,3:48:58 PM,13,4,4:01:00 PM,13,4,4:05:52 PM,13,4,4:34:01 PM,12,22.9,,-1.2551895,36.7822034,-1.3203917,36.8334751,Rider_Id_161,1689
-Order_No_26526,User_Id_2439,Bike,3,Business,19,2,1:28:35 PM,19,2,1:30:16 PM,19,2,1:35:20 PM,19,2,1:47:56 PM,19,2,1:54:28 PM,5,29.2,,-1.2534301,36.800098299999995,-1.2886923000000001,36.8241519,Rider_Id_812,392
-Order_No_24972,User_Id_3194,Bike,3,Business,10,2,12:25:27 PM,10,2,12:26:04 PM,10,2,12:32:35 PM,10,2,12:39:14 PM,10,2,1:21:48 PM,8,20.3,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_799,2554
-Order_No_17817,User_Id_3222,Bike,3,Business,11,1,1:51:32 PM,11,1,1:53:03 PM,11,1,1:53:27 PM,11,1,2:18:00 PM,11,1,2:18:07 PM,3,29.1,,-1.2551895,36.7822034,-1.2638132,36.784977500000004,Rider_Id_242,7
-Order_No_26020,User_Id_503,Bike,1,Business,13,5,3:34:26 PM,13,5,3:35:05 PM,13,5,3:36:25 PM,13,5,3:48:37 PM,13,5,4:06:17 PM,8,19.0,,-1.2859912,36.8756811,-1.3024388,36.859699,Rider_Id_890,1060
-Order_No_5459,User_Id_1304,Bike,3,Business,3,1,4:05:17 PM,3,1,4:06:00 PM,3,1,4:10:06 PM,3,1,4:22:21 PM,3,1,4:48:09 PM,6,23.6,,-1.3226301999999999,36.8719901,-1.329568,36.8520825,Rider_Id_439,1548
-Order_No_15032,User_Id_1834,Bike,3,Business,16,1,4:38:06 PM,16,1,4:39:35 PM,16,1,4:39:41 PM,16,1,4:50:29 PM,16,1,5:25:50 PM,6,,,-1.2643683000000001,36.7777614,-1.2693101999999998,36.798177700000004,Rider_Id_908,2121
-Order_No_19510,User_Id_2834,Bike,3,Business,12,3,12:39:49 PM,12,3,12:44:08 PM,12,3,12:52:42 PM,12,3,1:01:48 PM,12,3,1:35:46 PM,6,19.4,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_42,2038
-Order_No_21597,User_Id_393,Bike,3,Business,15,6,9:09:49 AM,15,6,10:48:00 AM,15,6,10:48:14 AM,15,6,11:12:20 AM,15,6,11:31:40 AM,11,22.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_619,1160
-Order_No_6659,User_Id_1075,Bike,3,Business,1,5,9:08:50 AM,1,5,9:09:25 AM,1,5,9:18:45 AM,1,5,9:20:16 AM,1,5,9:28:51 AM,1,,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_822,515
-Order_No_22828,User_Id_173,Bike,3,Business,12,1,9:43:06 AM,12,1,9:43:33 AM,12,1,9:45:31 AM,12,1,9:52:48 AM,12,1,9:56:30 AM,2,19.2,0.3,-1.303806,36.783041,-1.2993231,36.7753372,Rider_Id_786,222
-Order_No_13232,User_Id_53,Bike,3,Business,17,4,3:07:22 PM,17,4,3:09:00 PM,17,4,3:21:06 PM,17,4,3:50:39 PM,17,4,4:23:24 PM,5,27.8,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_725,1965
-Order_No_3098,User_Id_136,Bike,3,Business,6,1,4:55:31 PM,6,1,4:57:11 PM,6,1,5:16:59 PM,6,1,5:38:54 PM,6,1,6:07:47 PM,7,22.3,,-1.2248308,36.801257299999996,-1.2536041999999998,36.806489500000005,Rider_Id_882,1733
-Order_No_24955,User_Id_3214,Bike,3,Business,20,1,3:03:12 PM,20,1,3:04:15 PM,20,1,3:33:08 PM,20,1,3:41:07 PM,20,1,4:12:34 PM,21,,,-1.289795,36.7957132,-1.3190102,36.923069299999995,Rider_Id_845,1887
-Order_No_25619,User_Id_3647,Bike,3,Business,5,2,12:51:48 PM,5,2,12:52:20 PM,5,2,1:11:34 PM,5,2,1:18:38 PM,5,2,1:43:49 PM,13,23.6,,-1.2726389999999999,36.794723,-1.2309704,36.8688593,Rider_Id_26,1511
-Order_No_21796,User_Id_3686,Bike,3,Business,30,2,11:37:24 AM,30,2,11:38:33 AM,30,2,11:47:35 AM,30,2,11:50:14 AM,30,2,12:03:47 PM,5,22.5,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_770,813
-Order_No_23611,User_Id_3439,Bike,3,Business,24,5,12:18:25 PM,24,5,12:19:37 PM,24,5,12:34:34 PM,24,5,12:52:53 PM,24,5,1:30:58 PM,7,21.5,,-1.282937,36.781698399999996,-1.2393157,36.766887700000005,Rider_Id_55,2285
-Order_No_25301,User_Id_1500,Bike,3,Business,18,1,11:16:01 AM,18,1,11:16:12 AM,18,1,11:16:15 AM,18,1,11:26:03 AM,18,1,11:55:52 AM,10,26.9,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_576,1789
-Order_No_26160,User_Id_2117,Bike,1,Personal,19,4,10:29:56 AM,19,4,10:33:28 AM,19,4,10:40:19 AM,19,4,10:54:52 AM,19,4,11:08:28 AM,5,20.3,,-1.2897215,36.7945959,-1.2787795,36.8258525,Rider_Id_692,816
-Order_No_15406,User_Id_868,Bike,3,Business,23,6,2:26:38 PM,23,6,2:26:56 PM,23,6,2:29:26 PM,23,6,2:46:32 PM,23,6,3:02:10 PM,7,28.1,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_489,938
-Order_No_4749,User_Id_1724,Bike,3,Business,8,5,1:43:19 PM,8,5,2:41:06 PM,8,5,2:46:51 PM,8,5,2:57:41 PM,8,5,3:28:45 PM,3,30.6,,-1.259382,36.807204999999996,-1.2551895,36.7822034,Rider_Id_657,1864
-Order_No_20667,User_Id_733,Bike,3,Business,17,3,4:43:23 PM,17,3,5:03:42 PM,17,3,5:03:47 PM,17,3,5:10:50 PM,17,3,5:34:41 PM,15,,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_576,1431
-Order_No_9078,User_Id_3002,Bike,1,Personal,27,4,11:27:19 AM,27,4,11:27:32 AM,27,4,11:40:37 AM,27,4,11:41:43 AM,27,4,12:03:06 PM,8,22.8,,-1.2969275,36.78678789999999,-1.2528485,36.7953722,Rider_Id_933,1283
-Order_No_17988,User_Id_3404,Bike,3,Business,15,4,12:33:20 PM,15,4,12:56:51 PM,15,4,12:56:58 PM,15,4,1:17:56 PM,15,4,1:38:32 PM,8,24.3,,-1.279395,36.825364,-1.3270716,36.8406371,Rider_Id_925,1236
-Order_No_16637,User_Id_635,Bike,3,Business,28,2,2:27:20 PM,28,2,2:30:23 PM,28,2,2:35:32 PM,28,2,3:36:10 PM,28,2,3:47:30 PM,10,23.7,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_68,680
-Order_No_26776,User_Id_393,Bike,3,Business,10,4,10:14:40 AM,10,4,10:15:15 AM,10,4,10:17:30 AM,10,4,10:24:31 AM,10,4,10:48:26 AM,11,19.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,1435
-Order_No_12674,User_Id_265,Bike,3,Business,21,1,12:48:34 PM,21,1,12:48:55 PM,21,1,12:49:20 PM,21,1,1:15:37 PM,21,1,1:47:08 PM,13,25.5,,-1.2482465,36.8889062,-1.300921,36.828195,Rider_Id_75,1891
-Order_No_28163,User_Id_2701,Bike,3,Business,22,2,3:18:22 PM,22,2,3:21:19 PM,22,2,3:43:11 PM,22,2,3:51:26 PM,22,2,4:05:38 PM,10,21.3,,-1.279395,36.825364,-1.2478187,36.8844611,Rider_Id_687,852
-Order_No_17751,User_Id_1146,Bike,3,Business,4,4,1:03:55 PM,4,4,1:05:27 PM,4,4,1:10:35 PM,4,4,1:22:40 PM,4,4,1:36:12 PM,5,25.7,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_136,812
-Order_No_27267,User_Id_949,Bike,1,Personal,8,6,11:00:23 AM,8,6,11:00:43 AM,8,6,11:13:17 AM,8,6,11:15:43 AM,8,6,11:20:39 AM,1,22.6,,-1.298574,36.793572,-1.2944457,36.7923516,Rider_Id_105,296
-Order_No_19434,User_Id_1131,Bike,3,Business,24,1,10:09:05 AM,24,1,10:09:28 AM,24,1,10:25:40 AM,24,1,11:22:11 AM,24,1,12:19:25 PM,15,22.4,,-1.2644742,36.808345700000004,-1.2143386999999999,36.7914548,Rider_Id_101,3434
-Order_No_24564,User_Id_2876,Bike,1,Personal,5,3,11:50:27 AM,5,3,11:50:36 AM,5,3,12:02:09 PM,5,3,12:06:48 PM,5,3,12:22:16 PM,6,22.0,,-1.2684239,36.79524620000001,-1.2944292,36.779356899999996,Rider_Id_16,928
-Order_No_17053,User_Id_59,Bike,3,Business,14,4,10:47:44 AM,14,4,10:50:18 AM,14,4,11:01:19 AM,14,4,11:08:03 AM,14,4,11:18:01 AM,1,19.0,,-1.290894,36.822971,-1.2885528999999998,36.816014100000004,Rider_Id_444,598
-Order_No_9445,User_Id_3291,Bike,3,Business,28,2,9:45:18 AM,28,2,9:46:24 AM,28,2,10:01:06 AM,28,2,10:15:22 AM,28,2,10:28:06 AM,6,18.8,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_341,764
-Order_No_24117,User_Id_338,Bike,1,Personal,18,6,10:56:49 AM,18,6,10:57:21 AM,18,6,11:03:28 AM,18,6,11:10:39 AM,18,6,11:17:01 AM,4,19.1,,-1.3041098999999998,36.7867709,-1.282986,36.767414,Rider_Id_676,382
-Order_No_2155,User_Id_1363,Bike,3,Business,12,2,3:41:03 PM,12,2,3:43:37 PM,12,2,3:43:40 PM,12,2,3:51:20 PM,12,2,4:14:36 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_925,1396
-Order_No_18360,User_Id_2671,Bike,1,Personal,20,6,9:48:34 AM,20,6,9:48:43 AM,20,6,9:57:54 AM,20,6,10:49:53 AM,20,6,11:27:02 AM,18,25.7,,-1.2975438999999998,36.756396,-1.2070473999999998,36.7854119,Rider_Id_299,2229
-Order_No_3076,User_Id_1073,Bike,3,Business,1,1,2:58:27 PM,1,1,2:58:39 PM,1,1,3:07:21 PM,1,1,3:09:51 PM,1,1,3:33:37 PM,16,,,-1.2561964,36.7885001,-1.22386,36.8887256,Rider_Id_147,1426
-Order_No_18037,User_Id_3017,Bike,3,Business,8,5,4:13:33 PM,8,5,4:14:40 PM,8,5,4:22:32 PM,8,5,4:28:31 PM,8,5,4:37:38 PM,1,21.5,,-1.299313,36.78314879999999,-1.2940038999999999,36.7843369,Rider_Id_535,547
-Order_No_4121,User_Id_3272,Bike,2,Personal,17,5,3:29:30 PM,17,5,3:31:16 PM,17,5,3:36:19 PM,17,5,3:40:15 PM,17,5,3:57:42 PM,5,20.1,,-1.2696341,36.8042045,-1.2577628,36.835733000000005,Rider_Id_754,1047
-Order_No_20988,User_Id_2195,Bike,3,Business,15,1,11:52:17 AM,15,1,11:52:38 AM,15,1,12:00:50 PM,15,1,12:26:10 PM,15,1,1:00:41 PM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_923,2071
-Order_No_22206,User_Id_393,Bike,3,Business,5,2,12:16:29 PM,5,2,12:16:39 PM,5,2,12:16:47 PM,5,2,12:43:11 PM,5,2,12:52:07 PM,4,23.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,536
-Order_No_8045,User_Id_1724,Bike,3,Business,11,4,9:12:35 AM,11,4,9:13:03 AM,11,4,9:23:54 AM,11,4,9:38:14 AM,11,4,10:01:22 AM,6,22.0,,-1.2551895,36.7822034,-1.2655306,36.828148,Rider_Id_155,1388
-Order_No_13770,User_Id_2858,Bike,3,Business,25,1,8:06:33 PM,25,1,8:26:25 PM,25,1,9:00:20 PM,25,1,9:06:41 PM,25,1,9:19:46 PM,9,22.7,,-1.3019797,36.8033961,-1.3280121999999999,36.7861446,Rider_Id_801,785
-Order_No_17425,User_Id_738,Bike,3,Business,19,3,1:56:38 PM,19,3,1:58:00 PM,19,3,2:20:33 PM,19,3,2:39:05 PM,19,3,3:14:18 PM,10,25.5,,-1.2881479999999998,36.7850276,-1.2601219,36.71530511,Rider_Id_612,2113
-Order_No_11321,User_Id_2804,Bike,3,Business,18,4,2:20:53 PM,18,4,2:22:24 PM,18,4,2:43:07 PM,18,4,2:48:17 PM,18,4,2:56:29 PM,1,26.7,,-1.262805,36.79424229999999,-1.2571472,36.795063299999995,Rider_Id_893,492
-Order_No_26518,User_Id_2956,Bike,3,Business,8,3,4:23:26 PM,8,3,4:27:12 PM,8,3,4:41:12 PM,8,3,4:46:22 PM,8,3,5:57:35 PM,10,,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_96,4273
-Order_No_22157,User_Id_3145,Bike,3,Business,6,4,10:44:26 AM,6,4,10:45:38 AM,6,4,10:58:59 AM,6,4,11:02:22 AM,6,4,11:48:56 AM,9,24.0,,-1.2726389999999999,36.794723,-1.2835228,36.7437619,Rider_Id_322,2794
-Order_No_13194,User_Id_3291,Bike,3,Business,22,2,2:50:00 PM,22,2,2:50:24 PM,22,2,3:04:16 PM,22,2,3:16:50 PM,22,2,3:33:33 PM,5,25.8,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_365,1003
-Order_No_10998,User_Id_1688,Bike,3,Business,1,4,4:08:34 PM,1,4,4:09:35 PM,1,4,4:23:21 PM,1,4,4:40:30 PM,1,4,5:32:19 PM,15,,,-1.300921,36.828195,-1.369541,36.907455,Rider_Id_402,3109
-Order_No_24211,User_Id_323,Bike,3,Business,28,4,3:37:13 PM,28,4,3:37:27 PM,28,4,3:45:20 PM,28,4,4:06:01 PM,28,4,4:34:49 PM,12,,,-1.3207146,36.8305771,-1.3632162,36.9108253,Rider_Id_494,1728
-Order_No_27062,User_Id_2123,Bike,1,Personal,24,4,3:38:54 PM,24,4,3:40:58 PM,24,4,3:59:18 PM,24,4,4:18:14 PM,24,4,4:33:23 PM,4,22.9,,-1.2853860000000001,36.8304605,-1.2758847,36.808589700000006,Rider_Id_808,909
-Order_No_7549,User_Id_3452,Bike,1,Personal,24,4,2:26:45 PM,24,4,2:28:48 PM,24,4,2:32:51 PM,24,4,2:44:32 PM,24,4,2:53:36 PM,3,22.7,,-1.2666153,36.8015124,-1.2545422,36.7902075,Rider_Id_45,544
-Order_No_2863,User_Id_1351,Bike,1,Personal,17,3,10:20:03 AM,17,3,10:20:07 AM,17,3,11:00:57 AM,17,3,11:06:48 AM,17,3,11:49:55 AM,22,25.3,,-1.323682,36.708787,-1.2854453000000001,36.818836299999994,Rider_Id_680,2587
-Order_No_15981,User_Id_346,Bike,3,Business,10,4,10:37:26 AM,10,4,10:38:03 AM,10,4,10:46:14 AM,10,4,11:06:21 AM,10,4,11:12:41 AM,2,,,-1.290894,36.822971,-1.2885528999999998,36.816014100000004,Rider_Id_393,380
-Order_No_18270,User_Id_2634,Bike,3,Business,31,4,11:02:48 AM,31,4,11:46:30 AM,31,4,12:44:13 PM,31,4,12:48:35 PM,31,4,1:14:07 PM,13,26.6,,-1.26816,36.915079999999996,-1.2482465,36.8889062,Rider_Id_589,1532
-Order_No_23605,User_Id_3404,Bike,3,Business,21,4,11:18:38 AM,21,4,11:19:34 AM,21,4,11:51:02 AM,21,4,12:00:23 PM,21,4,12:06:59 PM,7,28.0,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_61,396
-Order_No_17314,User_Id_3661,Bike,3,Business,10,5,9:53:00 AM,10,5,9:54:17 AM,10,5,10:11:12 AM,10,5,10:16:03 AM,10,5,10:55:29 AM,17,,,-1.2132866000000002,36.867827399999996,-1.2955912,36.794701200000006,Rider_Id_291,2366
-Order_No_20150,User_Id_1787,Bike,3,Business,4,2,1:32:36 PM,4,2,1:44:44 PM,4,2,2:11:02 PM,4,2,2:13:37 PM,4,2,2:33:00 PM,3,22.6,,-1.215909,36.886055999999996,-1.2283403,36.8822756,Rider_Id_402,1163
-Order_No_24889,User_Id_1469,Bike,3,Business,29,2,11:27:14 AM,29,2,11:32:56 AM,29,2,11:42:49 AM,29,2,11:47:04 AM,29,2,12:05:39 PM,8,23.5,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_345,1115
-Order_No_27104,User_Id_517,Bike,3,Business,18,5,2:10:10 PM,18,5,2:49:43 PM,18,5,2:52:08 PM,18,5,3:52:32 PM,18,5,3:58:58 PM,5,24.5,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_56,386
-Order_No_10599,User_Id_343,Bike,3,Personal,29,2,7:42:45 AM,29,2,7:58:59 AM,29,2,8:33:05 AM,29,2,8:41:26 AM,29,2,9:06:29 AM,14,18.4,,-1.1855066,36.825464600000004,-1.2638898,36.8223638,Rider_Id_101,1503
-Order_No_28211,User_Id_1075,Bike,3,Business,14,4,12:51:53 PM,14,4,12:53:11 PM,14,4,1:08:01 PM,14,4,1:20:44 PM,14,4,1:38:22 PM,8,25.0,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_318,1058
-Order_No_10209,User_Id_2069,Bike,3,Business,10,5,10:48:44 AM,10,5,10:48:57 AM,10,5,10:55:30 AM,10,5,11:01:08 AM,10,5,11:23:42 AM,16,,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_536,1354
-Order_No_15667,User_Id_391,Bike,3,Business,15,5,1:29:11 PM,15,5,1:45:49 PM,15,5,2:14:47 PM,15,5,3:18:01 PM,15,5,3:21:38 PM,19,17.9,,-1.3204931,36.7043102,-1.2867454,36.8259017,Rider_Id_314,217
-Order_No_25308,User_Id_1457,Bike,1,Personal,16,4,12:11:06 PM,16,4,12:11:41 PM,16,4,12:36:41 PM,16,4,12:46:00 PM,16,4,1:17:09 PM,11,14.7,,-1.2755887,36.8742523,-1.3047026000000002,36.8172033,Rider_Id_932,1869
-Order_No_26527,User_Id_2701,Bike,3,Business,31,4,1:26:05 PM,31,4,1:35:51 PM,31,4,2:20:41 PM,31,4,2:25:37 PM,31,4,2:39:13 PM,10,23.1,,-1.279395,36.825364,-1.2478187,36.8844611,Rider_Id_589,816
-Order_No_27070,User_Id_1473,Bike,3,Business,30,5,8:55:26 AM,30,5,9:02:02 AM,30,5,9:11:37 AM,30,5,9:28:07 AM,30,5,10:08:59 AM,23,19.0,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_364,2452
-Order_No_15421,User_Id_250,Bike,2,Personal,20,3,1:09:54 PM,20,3,1:10:57 PM,20,3,1:27:00 PM,20,3,1:32:25 PM,20,3,1:56:18 PM,5,23.0,,-1.2874313999999998,36.7764912,-1.269534,36.80697,Rider_Id_465,1433
-Order_No_7388,User_Id_3404,Bike,3,Business,17,3,10:43:27 AM,17,3,10:43:35 AM,17,3,10:47:39 AM,17,3,10:57:17 AM,17,3,11:12:00 AM,3,25.2,,-1.3052491000000002,36.8223899,-1.279395,36.825364,Rider_Id_208,883
-Order_No_18068,User_Id_3355,Bike,3,Business,7,1,9:15:31 AM,7,1,9:16:00 AM,7,1,9:34:12 AM,7,1,10:06:57 AM,7,1,10:36:49 AM,9,18.4,,-1.3025826,36.7670807,-1.3052491000000002,36.8223899,Rider_Id_874,1792
-Order_No_2439,User_Id_3291,Bike,3,Business,8,1,10:34:03 AM,8,1,10:36:07 AM,8,1,10:51:24 AM,8,1,11:12:10 AM,8,1,11:30:18 AM,11,27.8,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_576,1088
-Order_No_26364,User_Id_1471,Bike,3,Business,21,3,12:47:31 PM,21,3,12:47:35 PM,21,3,12:47:47 PM,21,3,12:54:08 PM,21,3,1:42:35 PM,25,22.0,,-1.2551895,36.7822034,-1.1648303,36.951368,Rider_Id_351,2907
-Order_No_5903,User_Id_3295,Bike,3,Business,7,5,2:01:42 PM,7,5,2:03:37 PM,7,5,2:11:48 PM,7,5,2:21:10 PM,7,5,2:36:48 PM,6,,,-1.2571472,36.795063299999995,-1.2927403,36.8031436,Rider_Id_407,938
-Order_No_20448,User_Id_393,Bike,3,Business,20,1,12:52:28 PM,20,1,12:59:49 PM,20,1,1:25:52 PM,20,1,1:35:27 PM,20,1,2:00:39 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_163,1512
-Order_No_5409,User_Id_3222,Bike,3,Business,13,1,2:10:42 PM,13,1,2:11:10 PM,13,1,2:12:05 PM,13,1,2:18:22 PM,13,1,2:45:38 PM,10,,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_607,1636
-Order_No_3695,User_Id_1544,Bike,3,Business,18,6,1:05:19 PM,18,6,1:09:10 PM,18,6,1:16:19 PM,18,6,1:21:19 PM,18,6,1:57:22 PM,7,21.0,,-1.2873431,36.8236273,-1.2924173,36.779781400000005,Rider_Id_868,2163
-Order_No_9241,User_Id_2150,Bike,3,Business,6,3,1:41:43 PM,6,3,1:54:40 PM,6,3,2:08:04 PM,6,3,2:32:20 PM,6,3,2:56:43 PM,15,22.0,,-1.265715,36.823815,-1.3444459999999998,36.756597,Rider_Id_852,1463
-Order_No_12667,User_Id_2529,Bike,1,Personal,4,4,6:58:33 PM,4,4,7:12:06 PM,4,4,7:38:35 PM,4,4,7:46:38 PM,4,4,8:13:35 PM,11,,,-1.2242978999999998,36.7647642,-1.2000518,36.7805946,Rider_Id_101,1617
-Order_No_6117,User_Id_928,Bike,3,Business,30,2,1:38:52 PM,30,2,1:50:38 PM,30,2,2:13:15 PM,30,2,2:34:19 PM,30,2,4:08:20 PM,12,,,-1.2551895,36.7822034,-1.2632236000000001,36.871289600000004,Rider_Id_178,5641
-Order_No_10008,User_Id_3689,Bike,3,Business,17,1,12:22:19 PM,17,1,12:23:10 PM,17,1,12:32:39 PM,17,1,12:44:05 PM,17,1,12:51:22 PM,1,23.6,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_937,437
-Order_No_6506,User_Id_1162,Bike,3,Business,19,2,9:17:42 AM,19,2,9:18:18 AM,19,2,9:35:44 AM,19,2,9:37:06 AM,19,2,9:59:37 AM,8,,,-1.3122610000000001,36.794658,-1.2733047,36.8109744,Rider_Id_548,1351
-Order_No_19070,User_Id_909,Bike,3,Business,21,1,1:01:56 PM,21,1,1:02:21 PM,21,1,1:20:24 PM,21,1,1:29:19 PM,21,1,1:47:13 PM,8,25.3,,-1.225322,36.80855,-1.2665132,36.8009939,Rider_Id_125,1074
-Order_No_8342,User_Id_970,Bike,3,Business,28,5,3:28:20 PM,28,5,3:34:58 PM,28,5,3:42:13 PM,28,5,4:06:39 PM,28,5,4:37:51 PM,8,25.6,,-1.2958383,36.8073162,-1.2551895,36.7822034,Rider_Id_713,1872
-Order_No_4786,User_Id_2733,Bike,3,Business,5,5,1:01:02 PM,5,5,1:01:08 PM,5,5,1:02:53 PM,5,5,1:06:36 PM,5,5,1:18:55 PM,3,24.5,,-1.2963097,36.7688221,-1.2886166000000001,36.78368029999999,Rider_Id_24,739
-Order_No_25437,User_Id_1402,Bike,3,Business,15,1,11:23:16 AM,15,1,12:09:14 PM,15,1,12:34:14 PM,15,1,1:20:48 PM,15,1,1:21:13 PM,13,,,-1.339046,36.762596,-1.2813012,36.832396200000005,Rider_Id_613,25
-Order_No_2034,User_Id_3547,Bike,3,Business,11,4,11:54:06 AM,11,4,11:54:15 AM,11,4,12:02:51 PM,11,4,12:04:39 PM,11,4,12:43:33 PM,17,25.6,,-1.2761408,36.7710839,-1.3194518,36.869396,Rider_Id_541,2334
-Order_No_20878,User_Id_3201,Bike,3,Business,25,3,10:35:27 AM,25,3,10:35:35 AM,25,3,10:42:48 AM,25,3,11:04:43 AM,25,3,11:16:33 AM,7,,,-1.3052491000000002,36.8223899,-1.2649605,36.798177700000004,Rider_Id_951,710
-Order_No_6315,User_Id_868,Bike,3,Business,24,4,8:55:27 AM,24,4,9:23:51 AM,24,4,9:39:26 AM,24,4,9:50:19 AM,24,4,10:16:18 AM,9,17.1,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_742,1559
-Order_No_15271,User_Id_2601,Bike,3,Personal,14,4,12:54:46 PM,14,4,1:01:33 PM,14,4,1:15:26 PM,14,4,1:18:33 PM,14,4,1:38:37 PM,12,23.6,,-1.2852609,36.8313394,-1.2927792,36.9053492,Rider_Id_483,1204
-Order_No_4247,User_Id_1075,Bike,3,Business,1,5,3:33:26 PM,1,5,3:34:39 PM,1,5,3:41:52 PM,1,5,3:47:17 PM,1,5,4:08:58 PM,9,28.4,,-1.2527958000000001,36.8003131,-1.2934638,36.7668028,Rider_Id_315,1301
-Order_No_24568,User_Id_60,Bike,3,Business,2,6,10:04:09 AM,2,6,10:23:55 AM,2,6,10:34:41 AM,2,6,10:43:52 AM,2,6,10:55:41 AM,8,,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_383,709
-Order_No_24726,User_Id_3689,Bike,3,Business,2,5,4:24:44 PM,2,5,4:25:55 PM,2,5,4:47:13 PM,2,5,4:51:20 PM,2,5,5:02:23 PM,3,24.1,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_237,663
-Order_No_3353,User_Id_868,Bike,3,Business,4,4,9:46:12 AM,4,4,9:46:51 AM,4,4,9:50:18 AM,4,4,9:58:23 AM,4,4,10:20:00 AM,6,21.8,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_690,1297
-Order_No_23166,User_Id_1142,Bike,1,Personal,5,4,10:39:44 AM,5,4,10:41:26 AM,5,4,10:52:21 AM,5,4,11:16:25 AM,5,4,11:55:52 AM,11,,,-1.2801233,36.830010200000004,-1.291264,36.888704100000005,Rider_Id_844,2367
-Order_No_24549,User_Id_3517,Bike,3,Business,9,2,10:00:30 AM,9,2,10:00:59 AM,9,2,10:01:07 AM,9,2,10:34:44 AM,9,2,11:02:07 AM,15,27.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_450,1643
-Order_No_14401,User_Id_2418,Bike,2,Personal,29,3,8:46:48 AM,29,3,8:48:35 AM,29,3,8:54:31 AM,29,3,9:02:17 AM,29,3,9:24:30 AM,7,15.5,,-1.2630118000000001,36.802074299999994,-1.301259,36.842244,Rider_Id_726,1333
-Order_No_20595,User_Id_2030,Bike,3,Business,20,3,12:51:15 PM,20,3,12:53:01 PM,20,3,1:07:50 PM,20,3,1:25:52 PM,20,3,1:32:29 PM,1,20.4,,-1.289498,36.818878000000005,-1.2814451999999998,36.8253735,Rider_Id_5,397
-Order_No_190,User_Id_2383,Bike,3,Business,21,4,3:47:52 PM,21,4,3:48:27 PM,21,4,3:57:30 PM,21,4,4:12:43 PM,21,4,4:37:41 PM,11,31.1,,-1.2608993,36.799246000000004,-1.2987697,36.746851,Rider_Id_318,1498
-Order_No_17539,User_Id_3661,Bike,3,Business,7,5,9:32:17 AM,7,5,9:32:24 AM,7,5,9:52:20 AM,7,5,9:56:37 AM,7,5,10:34:56 AM,19,20.6,1.1,-1.2134646,36.8755266,-1.3208162,36.8451209,Rider_Id_685,2299
-Order_No_26039,User_Id_421,Bike,3,Business,18,2,2:09:19 PM,18,2,2:13:46 PM,18,2,2:22:37 PM,18,2,2:42:37 PM,18,2,3:11:32 PM,9,25.6,,-1.2975066000000002,36.76894229999999,-1.2551895,36.7822034,Rider_Id_548,1735
-Order_No_2744,User_Id_540,Bike,3,Business,13,3,1:48:40 PM,13,3,1:49:13 PM,13,3,2:03:19 PM,13,3,2:18:54 PM,13,3,2:35:12 PM,10,27.3,,-1.2551895,36.7822034,-1.2976368,36.751982700000006,Rider_Id_862,978
-Order_No_4207,User_Id_2330,Bike,3,Business,24,3,1:30:25 PM,24,3,1:35:41 PM,24,3,1:55:43 PM,24,3,1:58:35 PM,24,3,1:58:58 PM,9,,,-1.2326041,36.8009029,-1.2645318,36.7981775,Rider_Id_634,23
-Order_No_8902,User_Id_2463,Bike,2,Personal,18,5,2:36:39 PM,18,5,2:38:18 PM,18,5,2:54:37 PM,18,5,3:01:11 PM,18,5,3:37:16 PM,14,24.5,,-1.3082165000000001,36.8320031,-1.2368654,36.7534908,Rider_Id_791,2165
-Order_No_26509,User_Id_1363,Bike,3,Business,10,3,3:16:47 PM,10,3,3:17:29 PM,10,3,3:22:39 PM,10,3,3:32:05 PM,10,3,3:57:37 PM,10,30.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_728,1532
-Order_No_21076,User_Id_1363,Bike,3,Business,7,1,8:46:53 AM,7,1,8:47:57 AM,7,1,8:51:39 AM,7,1,8:58:35 AM,7,1,9:05:57 AM,4,18.6,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_397,442
-Order_No_4084,User_Id_552,Bike,3,Business,3,4,3:48:50 PM,3,4,3:49:30 PM,3,4,3:50:47 PM,3,4,4:53:06 PM,3,4,5:26:22 PM,17,23.6,,-1.290894,36.822971,-1.3632162,36.9108253,Rider_Id_703,1996
-Order_No_14164,User_Id_393,Bike,3,Business,25,1,12:13:13 PM,25,1,12:13:19 PM,25,1,12:13:24 PM,25,1,12:24:56 PM,25,1,1:00:58 PM,14,25.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_925,2162
-Order_No_20870,User_Id_1,Bike,3,Business,12,2,3:44:34 PM,12,2,3:48:42 PM,12,2,3:57:35 PM,12,2,4:06:31 PM,12,2,4:39:35 PM,8,23.0,,-1.2551895,36.7822034,-1.3034396000000001,36.832999799999996,Rider_Id_494,1984
-Order_No_3890,User_Id_517,Bike,3,Business,3,4,12:11:48 PM,3,4,12:13:03 PM,3,4,12:13:07 PM,3,4,12:20:27 PM,3,4,12:35:10 PM,7,23.3,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_619,883
-Order_No_2664,User_Id_635,Bike,3,Business,26,6,12:07:00 PM,26,6,12:09:09 PM,26,6,12:25:46 PM,26,6,12:28:05 PM,26,6,12:41:53 PM,5,22.4,,-1.2857763999999998,36.8236622,-1.2599563,36.799343900000004,Rider_Id_88,828
-Order_No_21782,User_Id_2436,Bike,3,Business,2,5,3:33:12 PM,2,5,3:37:17 PM,2,5,3:37:28 PM,2,5,3:53:45 PM,2,5,4:13:30 PM,8,25.1,,-1.2551895,36.7822034,-1.2925919,36.81705770000001,Rider_Id_596,1185
-Order_No_12012,User_Id_2828,Bike,2,Business,2,1,9:25:46 AM,2,1,9:26:07 AM,2,1,9:35:16 AM,2,1,9:42:20 AM,2,1,9:51:23 AM,3,16.5,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_71,543
-Order_No_24184,User_Id_1631,Bike,2,Personal,24,2,1:00:04 PM,24,2,1:00:12 PM,24,2,1:07:35 PM,24,2,1:28:18 PM,24,2,1:45:25 PM,6,17.2,,-1.2876575000000001,36.8272947,-1.270186,36.802279299999995,Rider_Id_144,1027
-Order_No_16979,User_Id_393,Bike,3,Business,30,2,4:23:11 PM,30,2,4:23:25 PM,30,2,4:28:21 PM,30,2,4:36:36 PM,30,2,5:11:23 PM,14,23.7,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_208,2087
-Order_No_19474,User_Id_2112,Bike,3,Business,25,4,11:56:30 AM,25,4,11:57:04 AM,25,4,12:04:34 PM,25,4,12:11:08 PM,25,4,12:37:51 PM,16,19.8,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_490,1603
-Order_No_4211,User_Id_633,Bike,3,Business,9,5,9:35:46 AM,9,5,9:40:10 AM,9,5,10:04:47 AM,9,5,10:27:30 AM,9,5,10:39:55 AM,4,20.4,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_432,745
-Order_No_26737,User_Id_2801,Bike,3,Business,8,3,2:10:30 PM,8,3,2:12:18 PM,8,3,2:19:40 PM,8,3,2:26:02 PM,8,3,2:40:49 PM,8,,,-1.2653185,36.8358841,-1.2674273999999999,36.787082700000006,Rider_Id_338,887
-Order_No_13722,User_Id_1882,Bike,3,Business,11,4,11:56:26 AM,11,4,11:56:44 AM,11,4,12:02:57 PM,11,4,12:36:38 PM,11,4,12:53:36 PM,7,25.5,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_832,1018
-Order_No_26267,User_Id_2931,Bike,3,Business,15,3,2:41:17 PM,15,3,2:46:38 PM,15,3,3:03:47 PM,15,3,3:14:23 PM,15,3,3:37:23 PM,7,25.7,,-1.2802803,36.8271747,-1.2452664,36.802854100000005,Rider_Id_907,1380
-Order_No_21926,User_Id_3355,Bike,3,Business,6,2,12:33:36 PM,6,2,12:56:50 PM,6,2,1:23:24 PM,6,2,1:30:25 PM,6,2,1:45:47 PM,4,25.0,,-1.279395,36.825364,-1.3052491000000002,36.8223899,Rider_Id_895,922
-Order_No_27508,User_Id_3601,Bike,3,Personal,19,4,3:02:08 PM,19,4,3:03:26 PM,19,4,3:14:08 PM,19,4,3:22:49 PM,19,4,3:41:21 PM,10,20.5,,-1.2678485000000002,36.8019541,-1.2746933,36.7396167,Rider_Id_131,1112
-Order_No_5635,User_Id_2587,Bike,3,Business,24,3,12:44:25 PM,24,3,12:57:19 PM,24,3,1:13:54 PM,24,3,1:17:30 PM,24,3,1:38:18 PM,6,20.0,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_764,1248
-Order_No_25803,User_Id_1927,Bike,3,Business,14,5,5:27:28 PM,14,5,5:28:30 PM,14,5,5:41:30 PM,14,5,5:54:58 PM,14,5,6:18:51 PM,12,21.5,,-1.290894,36.822971,-1.3472066,36.7692638,Rider_Id_336,1433
-Order_No_5120,User_Id_3647,Bike,3,Business,19,5,10:22:30 AM,19,5,10:22:59 AM,19,5,10:28:07 AM,19,5,10:31:57 AM,19,5,10:55:58 AM,13,20.1,,-1.2726389999999999,36.794723,-1.3216456,36.7145156,Rider_Id_22,1441
-Order_No_5665,User_Id_3517,Bike,3,Business,22,2,2:27:49 PM,22,2,2:28:12 PM,22,2,2:28:44 PM,22,2,3:37:08 PM,22,2,4:12:22 PM,15,25.6,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_686,2114
-Order_No_28217,User_Id_884,Bike,3,Personal,8,1,12:15:06 PM,8,1,12:22:05 PM,8,1,12:32:25 PM,8,1,12:35:08 PM,8,1,1:20:58 PM,20,27.9,,-1.1855927,36.843477,-1.2615909,36.76151289999999,Rider_Id_7,2750
-Order_No_14037,User_Id_3264,Bike,3,Business,8,2,4:17:06 PM,8,2,4:17:19 PM,8,2,4:19:40 PM,8,2,4:21:48 PM,8,2,4:30:39 PM,3,25.5,,-1.2551895,36.7822034,-1.2642665000000002,36.778828600000004,Rider_Id_829,531
-Order_No_5572,User_Id_2576,Bike,3,Personal,24,5,9:09:31 AM,24,5,9:10:48 AM,24,5,9:40:04 AM,24,5,9:42:14 AM,24,5,10:39:36 AM,18,18.0,,-1.3190988000000001,36.86603970000001,-1.297131,36.7604242,Rider_Id_68,3442
-Order_No_10551,User_Id_3647,Bike,3,Business,4,5,5:24:34 PM,4,5,5:25:19 PM,4,5,5:27:33 PM,4,5,5:42:18 PM,4,5,5:46:37 PM,4,21.6,,-1.2726389999999999,36.794723,-1.2676758999999997,36.801804,Rider_Id_427,259
-Order_No_25628,User_Id_2066,Bike,3,Business,13,4,3:33:31 PM,13,4,3:33:56 PM,13,4,3:43:06 PM,13,4,3:46:52 PM,13,4,4:12:00 PM,10,22.9,,-1.3210188999999999,36.841055100000005,-1.2649605,36.798177700000004,Rider_Id_344,1508
-Order_No_24144,User_Id_1380,Bike,3,Business,3,5,9:08:10 AM,3,5,11:21:42 AM,3,5,11:46:19 AM,3,5,12:25:57 PM,3,5,1:00:20 PM,12,,,-1.2261716,36.8005617,-1.2994162,36.833488700000004,Rider_Id_68,2063
-Order_No_2867,User_Id_2066,Bike,3,Business,23,4,3:42:38 PM,23,4,3:43:52 PM,23,4,3:48:49 PM,23,4,3:50:15 PM,23,4,4:25:51 PM,12,24.7,,-1.2502493000000001,36.7923169,-1.3270716,36.8406371,Rider_Id_205,2136
-Order_No_16975,User_Id_886,Bike,3,Personal,15,3,3:41:45 PM,15,3,3:42:44 PM,15,3,3:52:03 PM,15,3,3:54:48 PM,15,3,4:06:11 PM,4,21.7,,-1.2918246,36.801584999999996,-1.2818812,36.82681420000001,Rider_Id_857,683
-Order_No_15966,User_Id_3184,Bike,3,Business,9,5,9:59:07 AM,9,5,9:59:22 AM,9,5,10:21:34 AM,9,5,10:24:45 AM,9,5,10:53:19 AM,13,20.1,,-1.3090366000000002,36.880424100000006,-1.3396256000000002,36.8863135,Rider_Id_237,1714
-Order_No_8165,User_Id_3647,Bike,3,Business,17,4,1:56:33 PM,17,4,1:57:03 PM,17,4,2:10:19 PM,17,4,2:13:33 PM,17,4,2:59:36 PM,8,27.8,,-1.2726389999999999,36.794723,-1.2981102,36.8293513,Rider_Id_875,2763
-Order_No_1398,User_Id_1809,Bike,3,Business,27,5,11:45:37 AM,27,5,11:45:51 AM,27,5,11:54:00 AM,27,5,12:01:19 PM,27,5,12:19:04 PM,2,22.5,,-1.2527958000000001,36.8003131,-1.2646084,36.8028645,Rider_Id_933,1065
-Order_No_22399,User_Id_2216,Bike,3,Business,10,3,2:14:54 PM,10,3,2:15:29 PM,10,3,2:19:13 PM,10,3,3:23:36 PM,10,3,4:17:37 PM,6,30.6,,-1.2551895,36.7822034,-1.2843046,36.774613200000005,Rider_Id_451,3241
-Order_No_14537,User_Id_2330,Bike,3,Business,10,5,2:11:50 PM,10,5,2:13:30 PM,10,5,2:19:31 PM,10,5,2:43:10 PM,10,5,3:22:43 PM,9,,,-1.2671943,36.8062533,-1.2124582,36.802894,Rider_Id_547,2373
-Order_No_22040,User_Id_3727,Bike,3,Business,21,1,1:23:50 PM,21,1,1:24:32 PM,21,1,1:34:19 PM,21,1,1:35:38 PM,21,1,1:50:49 PM,3,27.6,,-1.2963097,36.7688221,-1.2860203000000001,36.7832538,Rider_Id_355,911
-Order_No_12127,User_Id_1103,Bike,3,Business,8,1,9:10:42 AM,8,1,9:17:20 AM,8,1,9:34:05 AM,8,1,9:47:57 AM,8,1,10:03:45 AM,7,19.8,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_656,948
-Order_No_14355,User_Id_478,Bike,3,Business,11,1,12:35:36 PM,11,1,12:45:48 PM,11,1,1:46:16 PM,11,1,1:58:31 PM,11,1,2:43:24 PM,8,28.7,,-1.2551895,36.7822034,-1.2830276000000003,36.8297516,Rider_Id_119,2693
-Order_No_15299,User_Id_1079,Bike,1,Personal,6,4,1:11:24 PM,6,4,1:39:34 PM,6,4,2:07:07 PM,6,4,2:39:10 PM,6,4,2:57:57 PM,18,23.3,,-1.2782208000000002,36.706262200000005,-1.2562796999999999,36.8075858,Rider_Id_482,1127
-Order_No_14950,User_Id_3194,Bike,3,Business,13,3,8:49:43 AM,13,3,8:51:12 AM,13,3,9:12:45 AM,13,3,9:18:06 AM,13,3,9:53:41 AM,5,20.3,,-1.2850638,36.813096,-1.312469,36.815177,Rider_Id_304,2135
-Order_No_23632,User_Id_2673,Bike,3,Business,9,3,1:19:12 PM,9,3,1:21:03 PM,9,3,1:22:21 PM,9,3,1:29:53 PM,9,3,2:26:02 PM,9,21.2,,-1.2865096,36.8169364,-1.264384,36.767412799999995,Rider_Id_345,3369
-Order_No_5372,User_Id_2212,Bike,3,Business,27,5,10:50:34 AM,27,5,10:50:42 AM,27,5,10:59:41 AM,27,5,11:02:05 AM,27,5,11:11:41 AM,5,22.4,,-1.2646084,36.8028645,-1.2559559999999999,36.772901,Rider_Id_415,576
-Order_No_8657,User_Id_418,Bike,1,Personal,23,3,12:57:05 PM,23,3,12:58:51 PM,23,3,1:00:14 PM,23,3,1:09:39 PM,23,3,2:03:32 PM,6,21.9,,-1.304033,36.7848695,-1.2816233999999997,36.8239846,Rider_Id_79,3233
-Order_No_10444,User_Id_1799,Bike,1,Personal,29,2,3:16:02 PM,29,2,3:16:23 PM,29,2,3:30:26 PM,29,2,3:39:38 PM,29,2,3:51:46 PM,7,23.6,,-1.2566078,36.7832089,-1.2658315,36.809049,Rider_Id_259,728
-Order_No_3373,User_Id_3318,Bike,3,Business,30,2,3:47:28 PM,30,2,3:57:28 PM,30,2,4:06:12 PM,30,2,4:22:34 PM,30,2,4:46:44 PM,10,,,-1.338283,36.8871775,-1.3004062,36.829741,Rider_Id_422,1450
-Order_No_1506,User_Id_733,Bike,3,Business,29,1,1:57:50 PM,29,1,1:58:14 PM,29,1,2:05:38 PM,29,1,2:24:55 PM,29,1,2:45:03 PM,29,,,-1.2551895,36.7822034,-1.1728272,36.9575076,Rider_Id_411,1208
-Order_No_7594,User_Id_1280,Bike,3,Business,20,3,2:45:01 PM,20,3,2:45:14 PM,20,3,2:45:37 PM,20,3,2:57:45 PM,20,3,3:05:20 PM,3,29.2,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_958,455
-Order_No_27384,User_Id_2227,Bike,3,Personal,5,2,12:35:59 PM,5,2,12:36:45 PM,5,2,12:49:31 PM,5,2,12:50:50 PM,5,2,1:12:52 PM,10,23.5,,-1.2541275,36.8263055,-1.2441236,36.881280700000005,Rider_Id_622,1322
-Order_No_16358,User_Id_869,Bike,3,Personal,10,4,11:35:41 AM,10,4,11:36:24 AM,10,4,11:40:39 AM,10,4,11:57:26 AM,10,4,12:03:31 PM,2,,,-1.2775276000000002,36.8140021,-1.2895261,36.8164407,Rider_Id_100,365
-Order_No_18806,User_Id_246,Bike,3,Business,11,1,2:52:37 PM,11,1,2:55:55 PM,11,1,2:58:21 PM,11,1,3:26:32 PM,11,1,3:27:00 PM,3,23.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_354,28
-Order_No_5517,User_Id_3283,Bike,3,Business,11,2,12:48:59 PM,11,2,12:52:37 PM,11,2,1:03:53 PM,11,2,1:12:46 PM,11,2,1:29:52 PM,6,19.4,,-1.2663631000000002,36.8163329,-1.2628473,36.781805,Rider_Id_75,1026
-Order_No_26326,User_Id_633,Bike,3,Business,6,2,10:03:25 AM,6,2,10:20:47 AM,6,2,10:40:10 AM,6,2,10:49:50 AM,6,2,11:28:45 AM,10,24.9,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_614,2335
-Order_No_23840,User_Id_256,Bike,2,Personal,26,6,3:19:25 PM,26,6,3:19:52 PM,26,6,3:29:16 PM,26,6,3:33:44 PM,26,6,4:04:07 PM,9,23.9,,-1.280572,36.826162,-1.3314048,36.8229367,Rider_Id_949,1823
-Order_No_10132,User_Id_2178,Bike,1,Personal,22,5,1:38:35 PM,22,5,1:42:01 PM,22,5,2:01:17 PM,22,5,2:25:17 PM,22,5,2:42:03 PM,7,,,-1.2248308,36.801257299999996,-1.2615584,36.814950599999996,Rider_Id_101,1006
-Order_No_8323,User_Id_1329,Bike,3,Business,29,5,7:39:07 PM,29,5,7:39:19 PM,29,5,7:39:37 PM,29,5,7:49:59 PM,29,5,7:59:54 PM,4,,,-1.273056,36.811298,-1.2686211,36.7898525,Rider_Id_119,595
-Order_No_1041,User_Id_640,Bike,3,Business,9,2,11:42:44 AM,9,2,11:45:01 AM,9,2,12:02:11 PM,9,2,12:26:12 PM,9,2,12:27:08 PM,7,26.8,,-1.2874796999999998,36.8307277,-1.304033,36.7848695,Rider_Id_76,56
-Order_No_9286,User_Id_3511,Bike,3,Personal,18,1,11:05:23 AM,18,1,11:21:27 AM,18,1,11:35:59 AM,18,1,11:42:21 AM,18,1,12:01:38 PM,6,,,-1.3339461000000001,36.821912899999994,-1.3127303999999997,36.8189817,Rider_Id_46,1157
-Order_No_1944,User_Id_393,Bike,3,Business,8,4,8:11:57 AM,8,4,8:17:38 AM,8,4,8:17:47 AM,8,4,9:10:57 AM,8,4,9:11:50 AM,4,20.5,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_128,53
-Order_No_310,User_Id_893,Bike,3,Business,14,5,12:49:23 PM,14,5,2:34:49 PM,14,5,3:10:48 PM,14,5,3:12:55 PM,14,5,3:47:48 PM,8,23.9,,-1.2663194,36.809901,-1.2985153,36.7876043,Rider_Id_441,2093
-Order_No_22314,User_Id_3674,Bike,3,Business,19,3,9:58:38 AM,19,3,10:16:40 AM,19,3,10:26:07 AM,19,3,10:34:43 AM,19,3,10:53:11 AM,8,22.3,,-1.2551895,36.7822034,-1.2944292,36.779356899999996,Rider_Id_105,1108
-Order_No_25491,User_Id_1336,Bike,3,Business,27,3,1:51:00 PM,27,3,1:54:51 PM,27,3,2:07:01 PM,27,3,3:15:17 PM,27,3,3:15:31 PM,9,29.4,,-1.2885029,36.825604999999996,-1.3263105,36.8622904,Rider_Id_613,14
-Order_No_22820,User_Id_649,Bike,3,Business,30,3,12:46:13 PM,30,3,12:46:47 PM,30,3,12:51:31 PM,30,3,1:11:11 PM,30,3,2:10:36 PM,9,22.5,,-1.2964396,36.7760609,-1.2553615,36.789537599999996,Rider_Id_474,3565
-Order_No_5565,User_Id_393,Bike,3,Business,23,3,10:30:23 AM,23,3,11:51:47 AM,23,3,12:17:53 PM,23,3,12:39:31 PM,23,3,1:08:53 PM,14,21.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_543,1762
-Order_No_19035,User_Id_3283,Bike,3,Business,30,1,10:00:06 AM,30,1,10:00:25 AM,30,1,10:02:25 AM,30,1,10:21:23 AM,30,1,10:31:14 AM,6,16.3,2.0,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_174,591
-Order_No_26070,User_Id_1653,Bike,3,Business,19,3,12:39:27 PM,19,3,12:39:50 PM,19,3,1:01:41 PM,19,3,1:03:42 PM,19,3,1:25:49 PM,5,,,-1.3186548999999999,36.8280233,-1.2970761999999998,36.8453541,Rider_Id_328,1327
-Order_No_25907,User_Id_3213,Bike,1,Personal,15,3,12:18:19 PM,15,3,12:18:33 PM,15,3,12:27:01 PM,15,3,12:30:31 PM,15,3,12:49:23 PM,7,18.1,,-1.3007534,36.7964576,-1.269656,36.767011,Rider_Id_754,1132
-Order_No_28080,User_Id_3309,Bike,3,Personal,5,4,12:36:50 PM,5,4,12:40:31 PM,5,4,12:57:49 PM,5,4,1:15:45 PM,5,4,1:30:08 PM,4,,,-1.2950408000000002,36.809817200000005,-1.2995135,36.793363299999996,Rider_Id_844,863
-Order_No_9993,User_Id_972,Bike,3,Business,24,6,12:14:24 PM,24,6,12:40:07 PM,24,6,12:53:34 PM,24,6,12:57:34 PM,24,6,1:13:08 PM,6,24.0,,-1.2862033000000002,36.8152966,-1.3205163999999998,36.8098882,Rider_Id_89,934
-Order_No_16988,User_Id_2234,Bike,2,Personal,6,3,5:40:21 PM,6,3,5:51:24 PM,6,3,6:26:41 PM,6,3,6:29:04 PM,6,3,6:41:43 PM,5,20.0,,-1.3199976,36.836921600000004,-1.3222881000000002,36.82753889999999,Rider_Id_890,759
-Order_No_15924,User_Id_1498,Bike,1,Business,24,4,12:25:09 PM,24,4,12:25:41 PM,24,4,12:32:35 PM,24,4,12:45:13 PM,24,4,1:03:26 PM,11,21.1,,-1.2753108999999998,36.8130185,-1.3332749,36.870814700000004,Rider_Id_360,1093
-Order_No_14049,User_Id_2159,Bike,3,Business,12,1,10:17:12 AM,12,1,10:18:01 AM,12,1,10:33:39 AM,12,1,10:38:18 AM,12,1,11:12:16 AM,13,23.7,,-1.304735,36.784605400000004,-1.3668652000000001,36.730459,Rider_Id_425,2038
-Order_No_26043,User_Id_2178,Bike,1,Personal,5,6,9:12:11 PM,5,6,9:13:09 PM,5,6,9:26:51 PM,5,6,9:35:24 PM,5,6,9:56:23 PM,11,19.1,,-1.276975,36.8231488,-1.3010017,36.8974511,Rider_Id_103,1259
-Order_No_8115,User_Id_2676,Bike,3,Business,1,5,9:56:53 AM,1,5,9:57:40 AM,1,5,10:17:24 AM,1,5,10:41:54 AM,1,5,10:54:08 AM,2,28.8,,-1.304033,36.7848695,-1.2963097,36.7688221,Rider_Id_343,734
-Order_No_25069,User_Id_3686,Bike,3,Business,26,5,9:23:36 AM,26,5,9:23:47 AM,26,5,9:27:03 AM,26,5,9:44:56 AM,26,5,9:56:52 AM,10,,,-1.2551895,36.7822034,-1.3039706,36.7688174,Rider_Id_245,716
-Order_No_25050,User_Id_111,Bike,3,Business,27,3,11:12:27 AM,27,3,11:13:06 AM,27,3,11:23:33 AM,27,3,11:25:52 AM,27,3,11:56:01 AM,12,26.3,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_154,1809
-Order_No_23441,User_Id_3562,Bike,2,Personal,13,1,2:00:39 PM,13,1,2:01:30 PM,13,1,2:05:29 PM,13,1,2:22:48 PM,13,1,2:34:46 PM,8,19.5,,-1.2995132,36.8043032,-1.2551895,36.7822034,Rider_Id_101,718
-Order_No_24203,User_Id_816,Bike,3,Business,11,1,1:34:31 PM,11,1,1:35:12 PM,11,1,1:49:36 PM,11,1,2:07:11 PM,11,1,2:26:31 PM,6,28.8,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_156,1160
-Order_No_14367,User_Id_2292,Bike,3,Business,20,6,1:05:45 PM,20,6,1:06:47 PM,20,6,1:21:22 PM,20,6,1:25:08 PM,20,6,1:53:29 PM,21,,,-1.2790196000000003,36.8160867,-1.375192,36.9361554,Rider_Id_276,1701
-Order_No_10606,User_Id_1594,Bike,3,Business,16,2,3:31:11 PM,16,2,3:31:56 PM,16,2,3:37:15 PM,16,2,3:43:09 PM,16,2,3:58:50 PM,7,,,-1.2156006000000001,36.8916865,-1.2467896,36.869873299999995,Rider_Id_388,941
-Order_No_20044,User_Id_3496,Bike,3,Business,30,2,4:40:30 PM,30,2,4:48:38 PM,30,2,5:01:05 PM,30,2,5:09:37 PM,30,2,5:32:23 PM,12,,,-1.300921,36.828195,-1.3347898999999999,36.7391053,Rider_Id_328,1366
-Order_No_9633,User_Id_3517,Bike,3,Business,18,4,1:34:37 PM,18,4,1:34:54 PM,18,4,1:52:55 PM,18,4,2:28:48 PM,18,4,2:42:58 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137,850
-Order_No_12822,User_Id_393,Bike,3,Business,30,2,9:34:12 AM,30,2,9:34:33 AM,30,2,9:34:37 AM,30,2,10:47:29 AM,30,2,10:47:33 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_39,4
-Order_No_11357,User_Id_909,Bike,3,Business,13,4,2:41:07 PM,13,4,3:06:36 PM,13,4,3:16:57 PM,13,4,3:20:56 PM,13,4,3:52:03 PM,13,22.2,,-1.225322,36.80855,-1.1668055,36.830444799999995,Rider_Id_762,1867
-Order_No_10289,User_Id_1124,Bike,1,Business,15,1,7:53:21 AM,15,1,7:54:22 AM,15,1,8:34:34 AM,15,1,8:44:55 AM,15,1,9:09:30 AM,11,,,-1.269018,36.796803000000004,-1.2812601000000001,36.73402720000001,Rider_Id_232,1475
-Order_No_10099,User_Id_1329,Bike,3,Business,24,5,2:35:55 PM,24,5,2:36:19 PM,24,5,2:40:47 PM,24,5,2:46:51 PM,24,5,3:04:38 PM,4,23.1,,-1.273056,36.811298,-1.256958,36.8144929,Rider_Id_761,1067
-Order_No_9750,User_Id_635,Bike,3,Business,26,3,9:20:15 AM,26,3,9:24:05 AM,26,3,9:33:32 AM,26,3,10:16:45 AM,26,3,10:38:04 AM,7,19.2,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_279,1279
-Order_No_16200,User_Id_393,Bike,3,Business,20,4,9:41:38 AM,20,4,10:24:14 AM,20,4,10:35:24 AM,20,4,10:43:53 AM,20,4,11:10:03 AM,11,22.4,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_931,1570
-Order_No_15385,User_Id_1329,Bike,3,Business,2,2,5:02:55 PM,2,2,5:03:56 PM,2,2,5:08:00 PM,2,2,5:09:12 PM,2,2,5:41:54 PM,18,28.6,,-1.273056,36.811298,-1.3557944,36.747136700000006,Rider_Id_250,1962
-Order_No_15882,User_Id_633,Bike,3,Business,31,4,2:43:41 PM,31,4,3:02:15 PM,31,4,3:34:27 PM,31,4,3:37:12 PM,31,4,4:05:14 PM,11,28.9,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_523,1682
-Order_No_1092,User_Id_2066,Bike,3,Business,5,3,12:20:47 PM,5,3,12:48:15 PM,5,3,12:57:17 PM,5,3,1:12:35 PM,5,3,1:21:38 PM,4,22.2,,-1.316961,36.820337,-1.2990238,36.7984903,Rider_Id_754,543
-Order_No_26292,User_Id_3561,Bike,3,Personal,12,2,1:00:07 PM,12,2,1:00:22 PM,12,2,1:09:05 PM,12,2,1:13:02 PM,12,2,1:42:48 PM,15,28.3,,-1.2658315,36.809049,-1.3242107,36.7212891,Rider_Id_885,1786
-Order_No_1656,User_Id_3686,Bike,3,Business,11,1,10:13:37 AM,11,1,10:14:09 AM,11,1,10:17:19 AM,11,1,10:26:02 AM,11,1,10:50:28 AM,7,20.7,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_378,1466
-Order_No_13327,User_Id_868,Bike,3,Business,28,4,12:18:07 PM,28,4,12:18:40 PM,28,4,1:10:43 PM,28,4,1:20:00 PM,28,4,1:54:19 PM,14,28.1,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_513,2059
-Order_No_17178,User_Id_2066,Bike,3,Business,3,5,4:06:51 PM,3,5,4:06:57 PM,3,5,4:12:22 PM,3,5,4:16:55 PM,3,5,4:30:32 PM,2,25.3,,-1.316961,36.820337,-1.3113209,36.8230282,Rider_Id_857,817
-Order_No_21888,User_Id_2596,Bike,3,Business,14,1,12:59:45 PM,14,1,12:59:55 PM,14,1,1:00:23 PM,14,1,1:11:58 PM,14,1,1:45:00 PM,19,23.5,,-1.2963097,36.7688221,-1.3379798999999999,36.883545299999994,Rider_Id_906,1982
-Order_No_2379,User_Id_1797,Bike,1,Personal,26,4,3:39:04 PM,26,4,3:40:09 PM,26,4,3:52:53 PM,26,4,3:54:50 PM,26,4,4:04:41 PM,5,22.2,,-1.2902633,36.782965000000004,-1.301796,36.8012024,Rider_Id_312,591
-Order_No_25173,User_Id_2309,Bike,2,Personal,12,5,9:52:18 AM,12,5,9:53:38 AM,12,5,10:05:09 AM,12,5,10:14:24 AM,12,5,10:29:41 AM,8,18.5,,-1.2561398000000001,36.8194374,-1.233018,36.8004477,Rider_Id_453,917
-Order_No_3896,User_Id_2801,Bike,3,Business,27,1,12:14:14 PM,27,1,12:14:59 PM,27,1,12:48:46 PM,27,1,12:56:24 PM,27,1,1:38:04 PM,9,21.0,,-1.2674273999999999,36.787082700000006,-1.294869,36.815327,Rider_Id_155,2500
-Order_No_1367,User_Id_1424,Bike,3,Business,28,3,11:18:45 AM,28,3,11:19:01 AM,28,3,11:24:07 AM,28,3,11:26:59 AM,28,3,11:53:09 AM,11,,,-1.3015700000000001,36.7871,-1.257984,36.745016,Rider_Id_750,1570
-Order_No_3377,User_Id_2801,Bike,3,Business,21,1,3:26:33 PM,21,1,3:27:05 PM,21,1,3:48:47 PM,21,1,4:12:51 PM,21,1,4:54:19 PM,16,27.8,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_143,2488
-Order_No_18537,User_Id_1927,Bike,3,Business,20,4,2:42:14 PM,20,4,2:57:51 PM,20,4,2:57:58 PM,20,4,3:17:00 PM,20,4,4:10:20 PM,15,25.4,,-1.2597528999999998,36.806629,-1.3472066,36.7692638,Rider_Id_704,3200
-Order_No_22284,User_Id_265,Bike,3,Business,2,2,11:41:28 AM,2,2,12:04:54 PM,2,2,12:20:04 PM,2,2,12:24:02 PM,2,2,12:49:39 PM,7,21.2,,-1.300921,36.828195,-1.260039,36.8159152,Rider_Id_496,1537
-Order_No_6361,User_Id_2378,Bike,3,Personal,16,2,1:21:21 PM,16,2,1:22:17 PM,16,2,1:29:50 PM,16,2,1:38:41 PM,16,2,2:21:52 PM,18,27.6,,-1.3336562,36.9144339,-1.2950041,36.814358299999995,Rider_Id_409,2591
-Order_No_9328,User_Id_633,Bike,3,Business,22,5,3:22:56 PM,22,5,3:31:02 PM,22,5,4:17:40 PM,22,5,4:22:31 PM,22,5,4:32:37 PM,4,30.8,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_682,606
-Order_No_26445,User_Id_1644,Bike,1,Personal,14,5,12:23:26 PM,14,5,12:23:45 PM,14,5,12:29:06 PM,14,5,12:46:16 PM,14,5,12:59:10 PM,8,24.2,,-1.2850851,36.82176,-1.2551895,36.7822034,Rider_Id_124,774
-Order_No_25113,User_Id_393,Bike,3,Business,18,1,9:57:57 AM,18,1,9:58:30 AM,18,1,10:03:53 AM,18,1,10:19:37 AM,18,1,10:41:31 AM,11,21.4,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_590,1314
-Order_No_27564,User_Id_3547,Bike,3,Business,11,2,1:01:11 PM,11,2,1:37:09 PM,11,2,1:37:17 PM,11,2,2:50:56 PM,11,2,3:08:19 PM,7,22.2,,-1.300921,36.828195,-1.3194518,36.869396,Rider_Id_260,1043
-Order_No_16341,User_Id_3621,Bike,3,Business,5,5,9:25:32 AM,5,5,9:26:25 AM,5,5,9:39:32 AM,5,5,9:43:08 AM,5,5,10:30:47 AM,16,19.8,,-1.2836583000000001,36.7886014,-1.3610501,36.7520889,Rider_Id_778,2859
-Order_No_910,User_Id_3147,Bike,3,Business,26,2,9:54:30 AM,26,2,10:00:03 AM,26,2,10:24:16 AM,26,2,10:29:26 AM,26,2,10:45:34 AM,7,21.8,,-1.3173868999999998,36.8112044,-1.3472066,36.7692638,Rider_Id_411,968
-Order_No_22341,User_Id_928,Bike,3,Business,30,2,11:06:53 AM,30,2,11:08:32 AM,30,2,11:37:44 AM,30,2,11:41:35 AM,30,2,11:57:22 AM,5,,,-1.2551895,36.7822034,-1.2657199,36.7914391,Rider_Id_570,947
-Order_No_23004,User_Id_2526,Bike,3,Personal,15,5,3:58:58 PM,15,5,4:00:10 PM,15,5,4:40:17 PM,15,5,4:46:34 PM,15,5,4:56:48 PM,4,27.9,,-1.3009097,36.764109399999995,-1.3104733999999998,36.7363698,Rider_Id_448,614
-Order_No_26511,User_Id_1932,Bike,3,Business,9,1,12:29:02 PM,9,1,12:29:14 PM,9,1,12:44:49 PM,9,1,12:50:52 PM,9,1,12:56:32 PM,2,20.3,,-1.2873232,36.765857200000006,-1.2985986999999999,36.7809119,Rider_Id_312,340
-Order_No_11130,User_Id_3212,Bike,1,Personal,20,5,12:02:31 PM,20,5,12:02:44 PM,20,5,12:24:29 PM,20,5,12:29:08 PM,20,5,1:26:52 PM,20,18.7,,-1.3273028,36.8739571,-1.2536209999999999,36.7991917,Rider_Id_379,3464
-Order_No_38,User_Id_3673,Bike,1,Personal,5,5,1:26:11 PM,5,5,1:52:28 PM,5,5,2:03:30 PM,5,5,2:50:49 PM,5,5,3:03:03 PM,6,26.5,,-1.300405,36.83562620000001,-1.32571,36.805533000000004,Rider_Id_263,734
-Order_No_22354,User_Id_1743,Bike,2,Personal,25,6,11:38:58 AM,25,6,11:51:30 AM,25,6,12:29:12 PM,25,6,12:36:10 PM,25,6,1:05:30 PM,18,,,-1.300215,36.6942878,-1.2677559999999999,36.794266,Rider_Id_832,1760
-Order_No_20999,User_Id_867,Bike,3,Business,18,4,8:23:31 AM,18,4,8:25:01 AM,18,4,8:33:17 AM,18,4,9:03:55 AM,18,4,9:22:34 AM,9,,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_166,1119
-Order_No_20887,User_Id_3621,Bike,3,Business,13,6,11:29:13 AM,13,6,11:34:02 AM,13,6,11:46:33 AM,13,6,11:50:53 AM,13,6,12:03:52 PM,5,26.0,,-1.2836583000000001,36.7886014,-1.2870081,36.818474200000004,Rider_Id_404,779
-Order_No_20398,User_Id_2587,Bike,3,Business,27,4,11:58:03 AM,27,4,11:58:13 AM,27,4,12:00:22 PM,27,4,12:17:30 PM,27,4,12:24:43 PM,1,22.9,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_31,433
-Order_No_10020,User_Id_3686,Bike,3,Business,7,5,9:35:08 AM,7,5,9:39:49 AM,7,5,9:47:45 AM,7,5,9:53:58 AM,7,5,10:20:44 AM,4,20.5,1.0,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_646,1606
-Order_No_3121,User_Id_2412,Bike,3,Business,11,1,8:20:19 AM,11,1,8:22:14 AM,11,1,8:33:10 AM,11,1,8:37:17 AM,11,1,8:50:26 AM,10,14.3,,-1.2873639,36.8256076,-1.2297202,36.874550799999994,Rider_Id_808,789
-Order_No_12916,User_Id_894,Bike,1,Personal,15,7,5:39:32 PM,15,7,6:03:54 PM,15,7,6:17:01 PM,15,7,6:32:37 PM,15,7,7:03:38 PM,7,20.3,,-1.346168,36.766149,-1.3757053999999997,36.7581013,Rider_Id_465,1861
-Order_No_11115,User_Id_635,Bike,3,Business,26,3,11:18:39 AM,26,3,11:21:33 AM,26,3,11:27:45 AM,26,3,12:10:06 PM,26,3,12:23:13 PM,7,24.2,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_179,787
-Order_No_10908,User_Id_1209,Bike,3,Business,3,3,8:14:50 AM,3,3,8:17:23 AM,3,3,8:21:15 AM,3,3,8:30:32 AM,3,3,8:58:41 AM,5,21.4,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_690,1689
-Order_No_15197,User_Id_2523,Bike,3,Business,18,1,9:52:51 AM,18,1,9:53:15 AM,18,1,9:53:44 AM,18,1,9:59:48 AM,18,1,10:29:02 AM,11,21.5,,-1.2551895,36.7822034,-1.2310607,36.803577600000004,Rider_Id_793,1754
-Order_No_12595,User_Id_3091,Bike,3,Business,18,2,1:53:41 PM,18,2,2:19:39 PM,18,2,2:23:23 PM,18,2,2:24:25 PM,18,2,3:23:14 PM,16,25.5,,-1.225322,36.80855,-1.3012008,36.764868,Rider_Id_353,3529
-Order_No_2814,User_Id_2801,Bike,3,Business,8,5,9:44:56 AM,8,5,9:46:07 AM,8,5,9:52:07 AM,8,5,9:53:36 AM,8,5,10:18:11 AM,5,20.6,,-1.2674273999999999,36.787082700000006,-1.2712193,36.8256876,Rider_Id_576,1475
-Order_No_8408,User_Id_2066,Bike,3,Business,11,2,2:12:36 PM,11,2,2:12:46 PM,11,2,2:15:06 PM,11,2,2:18:04 PM,11,2,2:28:23 PM,5,25.4,,-1.316961,36.820337,-1.2885503999999999,36.8066908,Rider_Id_676,619
-Order_No_27477,User_Id_1245,Bike,3,Business,27,3,1:06:30 PM,27,3,1:06:55 PM,27,3,1:13:09 PM,27,3,2:02:56 PM,27,3,2:41:29 PM,19,29.7,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519,2313
-Order_No_11536,User_Id_1837,Bike,1,Personal,1,1,11:56:18 AM,1,1,11:56:37 AM,1,1,12:04:42 PM,1,1,12:12:51 PM,1,1,12:55:51 PM,15,24.1,,-1.2772589,36.819022100000005,-1.3563742,36.7580286,Rider_Id_31,2580
-Order_No_25291,User_Id_1178,Bike,3,Business,30,4,12:35:35 PM,30,4,12:36:12 PM,30,4,12:51:30 PM,30,4,12:59:50 PM,30,4,1:21:46 PM,7,20.3,,-1.301028,36.809368,-1.2866544,36.784310999999995,Rider_Id_882,1316
-Order_No_22033,User_Id_1051,Bike,3,Business,24,4,10:04:22 AM,24,4,10:04:42 AM,24,4,10:14:56 AM,24,4,10:36:11 AM,24,4,11:34:50 AM,11,21.4,,-1.2963097,36.7688221,-1.304969,36.846623,Rider_Id_644,3519
-Order_No_28016,User_Id_391,Bike,3,Business,13,1,3:24:11 PM,13,1,3:46:45 PM,13,1,3:57:06 PM,13,1,4:12:27 PM,13,1,4:25:34 PM,5,,,-1.2602337,36.79905479999999,-1.2784063,36.8222933,Rider_Id_291,787
-Order_No_25916,User_Id_3326,Bike,3,Business,23,5,3:17:20 PM,23,5,3:31:33 PM,23,5,3:43:36 PM,23,5,3:48:20 PM,23,5,4:07:55 PM,8,26.2,,-1.2551895,36.7822034,-1.2812624,36.7714609,Rider_Id_675,1175
-Order_No_6405,User_Id_2150,Bike,3,Business,31,4,1:55:56 PM,31,4,1:56:36 PM,31,4,1:56:54 PM,31,4,2:14:34 PM,31,4,2:56:53 PM,15,29.0,,-1.3444459999999998,36.756597,-1.265715,36.823815,Rider_Id_925,2539
-Order_No_17766,User_Id_1851,Bike,3,Personal,2,4,12:31:06 PM,2,4,12:31:44 PM,2,4,12:48:31 PM,2,4,1:26:39 PM,2,4,2:29:44 PM,22,22.4,,-1.2765688999999998,36.8406151,-1.3149338,36.7135703,Rider_Id_174,3785
-Order_No_18824,User_Id_3264,Bike,3,Business,11,5,10:16:41 AM,11,5,10:16:58 AM,11,5,10:31:02 AM,11,5,10:32:27 AM,11,5,10:54:47 AM,9,20.9,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_269,1340
-Order_No_18273,User_Id_3355,Bike,3,Business,24,3,5:49:17 PM,24,3,5:54:38 PM,24,3,6:36:36 PM,24,3,6:43:17 PM,24,3,6:58:45 PM,9,18.6,,-1.2551895,36.7822034,-1.3052491000000002,36.8223899,Rider_Id_578,928
-Order_No_25786,User_Id_393,Bike,3,Business,17,3,8:40:54 AM,17,3,8:41:01 AM,17,3,8:43:26 AM,17,3,9:30:19 AM,17,3,9:30:28 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_10,9
-Order_No_21850,User_Id_3723,Bike,1,Personal,10,4,12:41:37 PM,10,4,12:42:22 PM,10,4,12:54:21 PM,10,4,12:58:21 PM,10,4,1:14:27 PM,9,,,-1.300722,36.816865,-1.2562249,36.786866100000005,Rider_Id_742,966
-Order_No_18071,User_Id_2066,Bike,3,Business,4,2,11:37:30 AM,4,2,11:38:08 AM,4,2,11:47:20 AM,4,2,11:52:24 AM,4,2,12:11:42 PM,12,19.0,,-1.316961,36.820337,-1.2592947,36.7698085,Rider_Id_88,1158
-Order_No_18305,User_Id_3201,Bike,3,Business,7,4,11:22:24 AM,7,4,11:23:49 AM,7,4,11:39:25 AM,7,4,11:46:53 AM,7,4,12:37:55 PM,9,27.4,,-1.2765736,36.851364600000004,-1.2584143,36.8048002,Rider_Id_79,3062
-Order_No_22377,User_Id_2691,Bike,3,Business,26,2,2:32:51 PM,26,2,2:34:40 PM,26,2,2:45:28 PM,26,2,2:51:25 PM,26,2,3:08:07 PM,8,,,-1.2857671,36.7875614,-1.2579303999999998,36.8019667,Rider_Id_355,1002
-Order_No_5446,User_Id_1724,Bike,3,Business,31,3,8:57:16 AM,31,3,8:57:34 AM,31,3,8:58:52 AM,31,3,9:00:28 AM,31,3,9:59:33 AM,15,19.2,,-1.2551895,36.7822034,-1.3221141,36.8636835,Rider_Id_62,3545
-Order_No_16132,User_Id_2358,Bike,4,Business,28,1,11:44:13 AM,28,1,11:46:17 AM,28,1,12:07:12 PM,28,1,12:13:30 PM,28,1,12:30:01 PM,12,22.8,,-1.2805198,36.827761200000005,-1.2154586,36.8830064,Rider_Id_208,991
-Order_No_10194,User_Id_3525,Bike,3,Business,21,4,11:42:33 AM,21,4,11:50:54 AM,21,4,12:01:20 PM,21,4,12:05:41 PM,21,4,12:29:59 PM,18,27.8,,-1.3700383,36.9190174,-1.3038156,36.812273299999994,Rider_Id_856,1458
-Order_No_11620,User_Id_2570,Bike,3,Personal,20,1,3:03:58 PM,20,1,3:04:47 PM,20,1,3:13:39 PM,20,1,3:16:20 PM,20,1,3:37:39 PM,12,,,-1.25896,36.8016691,-1.2295296999999998,36.883309499999996,Rider_Id_96,1279
-Order_No_19661,User_Id_483,Bike,1,Personal,20,3,3:06:26 PM,20,3,3:07:07 PM,20,3,3:13:30 PM,20,3,3:22:13 PM,20,3,3:37:22 PM,12,23.1,,-1.2689559,36.8097614,-1.2156006000000001,36.8916865,Rider_Id_589,909
-Order_No_7139,User_Id_419,Bike,3,Business,13,5,2:00:06 PM,13,5,2:00:18 PM,13,5,2:16:30 PM,13,5,2:27:10 PM,13,5,3:02:48 PM,20,19.1,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_806,2138
-Order_No_20041,User_Id_1075,Bike,3,Business,19,3,12:19:59 PM,19,3,1:35:43 PM,19,3,1:48:12 PM,19,3,1:58:07 PM,19,3,2:18:44 PM,13,24.5,,-1.207546,36.7913976,-1.2527958000000001,36.8003131,Rider_Id_315,1237
-Order_No_12799,User_Id_186,Bike,1,Business,24,5,12:00:52 PM,24,5,12:00:57 PM,24,5,12:12:56 PM,24,5,12:20:20 PM,24,5,12:38:16 PM,3,21.2,,-1.2890399,36.8164942,-1.2994217,36.8009779,Rider_Id_498,1076
-Order_No_26444,User_Id_3718,Bike,3,Business,5,3,5:01:20 PM,5,3,5:02:03 PM,5,3,5:10:09 PM,5,3,5:19:18 PM,5,3,5:24:29 PM,1,22.5,,-1.2650027,36.8126242,-1.2690013,36.8092872,Rider_Id_881,311
-Order_No_13688,User_Id_3560,Bike,3,Business,8,2,11:36:53 AM,8,2,11:37:24 AM,8,2,11:48:58 AM,8,2,11:58:09 AM,8,2,12:08:57 PM,2,23.4,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_720,648
-Order_No_17677,User_Id_2066,Bike,3,Business,9,2,5:14:26 PM,9,2,5:24:59 PM,9,2,5:41:24 PM,9,2,5:44:41 PM,9,2,6:19:34 PM,6,19.0,,-1.316961,36.820337,-1.3111839,36.84268370000001,Rider_Id_662,2093
-Order_No_25054,User_Id_1882,Bike,1,Business,4,4,7:04:41 PM,4,4,7:12:25 PM,4,4,7:30:03 PM,4,4,7:44:34 PM,4,4,8:01:13 PM,8,,,-1.2268871000000001,36.8073946,-1.2591018999999999,36.800577000000004,Rider_Id_178,999
-Order_No_3918,User_Id_2677,Bike,3,Business,30,2,9:41:47 AM,30,2,9:42:05 AM,30,2,9:52:20 AM,30,2,9:54:23 AM,30,2,10:32:12 AM,14,17.2,,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_905,2269
-Order_No_14337,User_Id_393,Bike,3,Business,26,5,1:59:49 PM,26,5,2:01:03 PM,26,5,2:30:29 PM,26,5,2:58:57 PM,26,5,3:33:00 PM,14,,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_583,2043
-Order_No_5872,User_Id_1304,Bike,3,Business,16,3,11:25:01 AM,16,3,11:26:02 AM,16,3,11:29:17 AM,16,3,11:40:36 AM,16,3,12:07:28 PM,9,24.1,,-1.2626906,36.7827022,-1.2973028,36.787614399999995,Rider_Id_650,1612
-Order_No_16787,User_Id_248,Bike,3,Business,21,4,12:46:22 PM,21,4,12:46:53 PM,21,4,12:53:31 PM,21,4,12:57:17 PM,21,4,1:24:58 PM,10,25.1,,-1.304735,36.784605400000004,-1.3264988000000002,36.710870899999996,Rider_Id_545,1661
-Order_No_11546,User_Id_3147,Bike,3,Business,9,2,6:06:33 PM,9,2,6:07:11 PM,9,2,6:17:37 PM,9,2,6:22:50 PM,9,2,6:59:08 PM,17,27.8,,-1.2859912,36.8756811,-1.3467388999999999,36.7690312,Rider_Id_709,2178
-Order_No_16075,User_Id_3232,Bike,3,Business,25,4,4:56:16 PM,25,4,4:56:46 PM,25,4,4:58:27 PM,25,4,5:15:34 PM,25,4,5:46:31 PM,16,,,-1.2551895,36.7822034,-1.2156006000000001,36.8916865,Rider_Id_137,1857
-Order_No_10451,User_Id_2735,Bike,3,Personal,20,4,8:28:38 AM,20,4,8:28:49 AM,20,4,8:49:19 AM,20,4,8:53:04 AM,20,4,10:23:38 AM,21,18.6,0.1,-1.25896,36.8016691,-1.3434861999999999,36.6895464,Rider_Id_395,5434
-Order_No_1300,User_Id_704,Bike,1,Business,22,3,1:54:19 PM,22,3,1:54:35 PM,22,3,2:06:32 PM,22,3,2:51:28 PM,22,3,3:56:41 PM,21,25.7,,-1.2564152,36.779280299999996,-1.2860183,36.8975338,Rider_Id_228,3913
-Order_No_24717,User_Id_2532,Bike,3,Business,7,5,2:52:01 PM,7,5,2:52:29 PM,7,5,2:57:51 PM,7,5,3:03:50 PM,7,5,3:37:53 PM,14,,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_690,2043
-Order_No_5667,User_Id_1075,Bike,3,Business,23,3,12:24:18 PM,23,3,12:24:28 PM,23,3,12:52:08 PM,23,3,12:54:23 PM,23,3,1:26:03 PM,15,,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_631,1900
-Order_No_25535,User_Id_60,Bike,3,Business,13,1,1:29:07 PM,13,1,1:32:54 PM,13,1,2:13:41 PM,13,1,2:24:53 PM,13,1,3:07:15 PM,16,,,-1.293515,36.897607,-1.2296618000000001,36.84382720000001,Rider_Id_408,2542
-Order_No_16240,User_Id_1329,Bike,3,Business,10,4,12:02:33 PM,10,4,12:03:00 PM,10,4,12:17:55 PM,10,4,12:21:21 PM,10,4,12:53:40 PM,2,24.6,,-1.273056,36.811298,-1.2641662,36.8136393,Rider_Id_467,1939
-Order_No_9848,User_Id_988,Bike,3,Personal,6,5,4:09:57 PM,6,5,4:56:27 PM,6,5,5:20:25 PM,6,5,5:28:00 PM,6,5,5:50:22 PM,14,,,-1.2113559999999999,36.905189799999995,-1.2796513999999999,36.819573,Rider_Id_906,1342
-Order_No_626,User_Id_887,Bike,2,Personal,24,4,4:18:25 PM,24,4,4:19:14 PM,24,4,4:30:32 PM,24,4,4:36:54 PM,24,4,4:50:31 PM,4,21.1,,-1.2606381000000002,36.8028749,-1.2595421999999998,36.7871178,Rider_Id_147,817
-Order_No_10929,User_Id_1075,Bike,3,Business,18,1,11:36:02 AM,18,1,11:40:48 AM,18,1,11:51:45 AM,18,1,1:02:59 PM,18,1,1:27:35 PM,8,26.7,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_686,1476
-Order_No_24306,User_Id_1746,Bike,3,Business,23,3,9:42:26 AM,23,3,9:42:40 AM,23,3,9:51:56 AM,23,3,10:12:54 AM,23,3,10:42:06 AM,11,,,-1.3108783000000002,36.8392726,-1.2975066000000002,36.76894229999999,Rider_Id_817,1752
-Order_No_23449,User_Id_3238,Bike,3,Business,7,5,2:50:58 PM,7,5,2:51:17 PM,7,5,3:03:20 PM,7,5,3:10:16 PM,7,5,4:10:41 PM,25,,,-1.3976138,36.782965999999995,-1.2539646,36.836242600000006,Rider_Id_291,3625
-Order_No_10529,User_Id_661,Bike,3,Business,7,6,2:03:50 PM,7,6,2:05:07 PM,7,6,2:15:32 PM,7,6,2:32:11 PM,7,6,2:45:34 PM,4,21.5,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_327,803
-Order_No_16152,User_Id_393,Bike,3,Business,20,1,3:09:19 PM,20,1,3:09:34 PM,20,1,3:10:13 PM,20,1,3:27:18 PM,20,1,3:43:19 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_206,961
-Order_No_17043,User_Id_393,Bike,3,Business,7,5,1:04:21 PM,7,5,1:04:59 PM,7,5,2:03:25 PM,7,5,2:07:01 PM,7,5,2:50:26 PM,11,,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_155,2605
-Order_No_5637,User_Id_3404,Bike,3,Business,15,5,2:23:51 PM,15,5,2:25:04 PM,15,5,2:54:15 PM,15,5,3:06:31 PM,15,5,3:37:56 PM,6,28.0,,-1.2551895,36.7822034,-1.279395,36.825364,Rider_Id_268,1885
-Order_No_2997,User_Id_1500,Bike,3,Business,9,4,2:21:57 PM,9,4,2:45:43 PM,9,4,2:49:15 PM,9,4,2:53:51 PM,9,4,3:13:41 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_448,1190
-Order_No_21403,User_Id_3107,Bike,3,Business,7,2,2:41:41 PM,7,2,2:59:01 PM,7,2,3:03:05 PM,7,2,3:19:01 PM,7,2,3:39:24 PM,12,24.6,,-1.2930128,36.8356869,-1.3191788,36.8895401,Rider_Id_449,1223
-Order_No_3345,User_Id_3517,Bike,3,Business,13,3,1:50:53 PM,13,3,1:51:34 PM,13,3,1:59:38 PM,13,3,2:22:47 PM,13,3,2:45:49 PM,15,29.2,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_130,1382
-Order_No_17120,User_Id_552,Bike,3,Business,12,2,2:25:45 PM,12,2,2:43:19 PM,12,2,3:14:59 PM,12,2,3:20:34 PM,12,2,4:10:25 PM,15,27.2,,-1.2859912,36.8756811,-1.3632162,36.9108253,Rider_Id_231,2991
-Order_No_13942,User_Id_633,Bike,3,Business,14,3,11:49:44 AM,14,3,11:50:06 AM,14,3,11:50:13 AM,14,3,12:00:47 PM,14,3,12:07:59 PM,4,24.7,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_239,432
-Order_No_15969,User_Id_2733,Bike,3,Business,16,4,10:59:10 AM,16,4,10:59:12 AM,16,4,11:05:31 AM,16,4,11:30:07 AM,16,4,11:41:31 AM,2,,,-1.3014461000000002,36.7661381,-1.2963097,36.7688221,Rider_Id_24,684
-Order_No_26945,User_Id_1245,Bike,3,Business,14,1,12:43:39 PM,14,1,12:44:01 PM,14,1,12:46:56 PM,14,1,1:23:01 PM,14,1,1:49:20 PM,17,23.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_340,1579
-Order_No_3032,User_Id_393,Bike,3,Business,11,1,2:33:43 PM,11,1,2:35:33 PM,11,1,2:36:03 PM,11,1,2:55:54 PM,11,1,3:13:15 PM,11,28.7,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_809,1041
-Order_No_18637,User_Id_1578,Bike,3,Business,11,2,8:37:15 AM,11,2,9:49:08 AM,11,2,10:00:26 AM,11,2,10:09:47 AM,11,2,10:23:43 AM,2,16.2,43.0,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_819,836
-Order_No_7859,User_Id_517,Bike,3,Business,4,5,10:24:48 AM,4,5,11:12:39 AM,4,5,11:14:52 AM,4,5,11:25:05 AM,4,5,11:32:29 AM,4,17.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,444
-Order_No_20118,User_Id_198,Bike,3,Business,14,1,11:29:05 AM,14,1,11:29:36 AM,14,1,11:41:56 AM,14,1,12:08:46 PM,14,1,12:26:01 PM,8,23.5,,-1.3396256000000002,36.8863135,-1.318729,36.8401589,Rider_Id_31,1035
-Order_No_20752,User_Id_1792,Bike,3,Business,14,2,4:28:18 PM,14,2,4:28:34 PM,14,2,4:35:57 PM,14,2,4:41:39 PM,14,2,4:49:31 PM,3,23.3,,-1.2805537,36.7920739,-1.2791535,36.8041561,Rider_Id_427,472
-Order_No_18239,User_Id_3283,Bike,3,Business,4,1,1:49:49 PM,4,1,1:50:19 PM,4,1,2:01:14 PM,4,1,2:25:29 PM,4,1,2:48:30 PM,14,,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_75,1381
-Order_No_19471,User_Id_635,Bike,3,Business,31,5,9:52:45 AM,31,5,9:52:45 AM,31,5,9:57:13 AM,31,5,10:39:07 AM,31,5,11:01:41 AM,10,21.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_582,1354
-Order_No_168,User_Id_1586,Bike,3,Personal,14,4,2:11:12 PM,14,4,2:13:37 PM,14,4,2:20:35 PM,14,4,2:22:31 PM,14,4,3:08:37 PM,18,28.2,,-1.3324865,36.8938303,-1.2896972,36.808035700000005,Rider_Id_205,2766
-Order_No_6075,User_Id_2869,Bike,3,Business,21,4,9:06:20 AM,21,4,9:08:03 AM,21,4,9:18:42 AM,21,4,9:28:45 AM,21,4,10:09:41 AM,11,21.8,,-1.2675238000000002,36.8127148,-1.3025826,36.7670807,Rider_Id_424,2456
-Order_No_22522,User_Id_391,Bike,3,Business,24,3,2:59:43 PM,24,3,4:48:17 PM,24,3,4:48:43 PM,24,3,5:01:32 PM,24,3,5:22:36 PM,5,18.4,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_878,1264
-Order_No_10666,User_Id_1410,Bike,3,Business,28,1,12:09:36 PM,28,1,12:09:50 PM,28,1,12:32:46 PM,28,1,1:03:08 PM,28,1,1:25:25 PM,20,22.9,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_888,1337
-Order_No_7201,User_Id_517,Bike,3,Business,6,4,3:52:14 PM,6,4,4:09:36 PM,6,4,4:16:13 PM,6,4,4:25:36 PM,6,4,5:03:39 PM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_523,2283
-Order_No_16598,User_Id_173,Bike,3,Business,13,4,11:17:47 AM,13,4,11:19:27 AM,13,4,11:25:15 AM,13,4,11:26:23 AM,13,4,11:34:18 AM,2,21.9,,-1.2993231,36.7753372,-1.2812624,36.7714609,Rider_Id_368,475
-Order_No_8209,User_Id_2057,Bike,2,Personal,15,1,10:29:28 AM,15,1,1:12:29 PM,15,1,1:22:21 PM,15,1,1:27:27 PM,15,1,1:47:06 PM,11,,,-1.2976368,36.751982700000006,-1.2630521000000001,36.806801899999996,Rider_Id_933,1179
-Order_No_6784,User_Id_2469,Bike,3,Business,26,1,5:33:17 PM,26,1,5:46:07 PM,26,1,5:55:58 PM,26,1,6:02:32 PM,26,1,6:15:52 PM,5,20.1,,-1.303924,36.8758792,-1.308231,36.857306,Rider_Id_675,800
-Order_No_20452,User_Id_1850,Bike,3,Business,10,1,2:21:59 PM,10,1,2:28:37 PM,10,1,2:35:22 PM,10,1,3:21:20 PM,10,1,3:34:42 PM,7,18.5,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_24,802
-Order_No_5017,User_Id_393,Bike,3,Business,8,2,3:38:49 PM,8,2,3:39:29 PM,8,2,3:42:20 PM,8,2,3:56:09 PM,8,2,4:26:22 PM,11,25.3,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_330,1813
-Order_No_13728,User_Id_633,Bike,3,Business,17,4,3:42:10 PM,17,4,3:42:21 PM,17,4,3:53:29 PM,17,4,3:56:22 PM,17,4,4:09:08 PM,4,27.8,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_523,766
-Order_No_6830,User_Id_1630,Bike,1,Personal,28,2,9:28:45 AM,28,2,9:29:43 AM,28,2,9:45:36 AM,28,2,9:53:04 AM,28,2,10:36:43 AM,20,15.9,51.0,-1.290059,36.7703475,-1.2902161,36.893618100000005,Rider_Id_548,2619
-Order_No_4308,User_Id_1075,Bike,3,Business,23,6,10:01:27 AM,23,6,10:04:04 AM,23,6,10:04:29 AM,23,6,10:11:17 AM,23,6,10:18:30 AM,1,20.0,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_576,433
-Order_No_11741,User_Id_3560,Bike,3,Business,20,3,1:14:35 PM,20,3,1:29:49 PM,20,3,1:31:09 PM,20,3,1:35:04 PM,20,3,2:08:17 PM,5,28.4,,-1.279395,36.825364,-1.3072409999999999,36.830236799999994,Rider_Id_448,1993
-Order_No_9179,User_Id_1360,Bike,1,Personal,3,3,4:26:24 PM,3,3,4:40:02 PM,3,3,4:46:13 PM,3,3,5:25:36 PM,3,3,5:32:56 PM,4,23.7,,-1.25789,36.79268,-1.2768326,36.8073804,Rider_Id_86,440
-Order_No_18029,User_Id_791,Bike,3,Business,24,3,1:59:36 PM,24,3,2:16:02 PM,24,3,2:57:50 PM,24,3,3:01:04 PM,24,3,3:37:09 PM,17,18.5,,-1.3671327,36.75642379999999,-1.2931446000000002,36.7613971,Rider_Id_857,2165
-Order_No_18575,User_Id_1796,Bike,3,Business,2,2,9:09:50 AM,2,2,9:24:06 AM,2,2,9:49:27 AM,2,2,10:04:07 AM,2,2,10:21:24 AM,13,20.3,,-1.303596,36.778378000000004,-1.331619,36.847976,Rider_Id_61,1037
-Order_No_9772,User_Id_811,Bike,3,Personal,4,3,10:37:41 AM,4,3,10:37:57 AM,4,3,10:45:21 AM,4,3,10:50:07 AM,4,3,11:02:45 AM,2,17.2,,-1.3014461000000002,36.7661381,-1.2963097,36.7688221,Rider_Id_74,758
-Order_No_18630,User_Id_1231,Bike,3,Business,23,4,9:15:33 AM,23,4,9:16:48 AM,23,4,9:28:56 AM,23,4,9:30:12 AM,23,4,9:54:17 AM,8,15.8,,-1.2821421999999998,36.8161565,-1.3014461000000002,36.7661381,Rider_Id_79,1445
-Order_No_5768,User_Id_256,Bike,3,Personal,4,5,12:25:38 PM,4,5,12:26:34 PM,4,5,12:47:58 PM,4,5,12:55:59 PM,4,5,1:19:43 PM,6,22.6,,-1.286837,36.823308000000004,-1.2941023999999999,36.79578,Rider_Id_425,1424
-Order_No_2043,User_Id_1850,Bike,3,Business,25,1,1:49:33 PM,25,1,1:53:46 PM,25,1,1:58:15 PM,25,1,2:24:38 PM,25,1,3:00:37 PM,7,30.2,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_686,2159
-Order_No_16528,User_Id_3560,Bike,3,Business,9,4,1:19:56 PM,9,4,1:22:50 PM,9,4,1:31:59 PM,9,4,1:55:54 PM,9,4,1:55:57 PM,1,,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_843,3
-Order_No_11468,User_Id_3500,Bike,3,Business,23,5,2:38:06 PM,23,5,2:56:59 PM,23,5,2:59:27 PM,23,5,3:09:04 PM,23,5,3:28:06 PM,2,26.3,,-1.2621828000000002,36.7949276,-1.2567464,36.788594599999996,Rider_Id_536,1142
-Order_No_25825,User_Id_424,Bike,1,Personal,3,1,9:05:30 AM,3,1,9:06:16 AM,3,1,9:39:22 AM,3,1,9:43:26 AM,3,1,9:58:23 AM,8,15.1,,-1.322999,36.783263299999994,-1.2985749,36.8088,Rider_Id_88,897
-Order_No_4030,User_Id_393,Bike,3,Business,1,1,7:29:51 AM,1,1,8:13:06 AM,1,1,8:14:31 AM,1,1,9:06:16 AM,1,1,9:17:35 AM,4,18.4,,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_87,679
-Order_No_11287,User_Id_1081,Bike,3,Business,5,2,2:35:21 PM,5,2,2:43:41 PM,5,2,2:55:38 PM,5,2,3:01:21 PM,5,2,3:36:25 PM,9,27.6,,-1.291768,36.8297208,-1.3025826,36.7670807,Rider_Id_906,2104
-Order_No_23707,User_Id_1882,Bike,3,Business,13,2,9:57:53 AM,13,2,10:09:11 AM,13,2,10:19:35 AM,13,2,10:22:02 AM,13,2,10:33:36 AM,5,19.9,9.1,-1.2877809999999998,36.817865999999995,-1.2591018999999999,36.800577000000004,Rider_Id_612,694
-Order_No_10320,User_Id_517,Bike,3,Business,27,4,2:51:46 PM,27,4,2:52:01 PM,27,4,2:54:20 PM,27,4,3:00:02 PM,27,4,3:15:29 PM,11,23.9,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,927
-Order_No_14943,User_Id_3291,Bike,3,Business,30,6,9:47:29 AM,30,6,9:47:42 AM,30,6,9:48:00 AM,30,6,11:13:30 AM,30,6,11:43:09 AM,12,26.5,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_260,1779
-Order_No_12945,User_Id_3517,Bike,3,Business,16,4,11:22:06 AM,16,4,11:22:33 AM,16,4,11:22:47 AM,16,4,12:01:37 PM,16,4,12:12:04 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_137,627
-Order_No_10078,User_Id_2514,Bike,3,Business,26,5,12:18:41 PM,26,5,12:31:12 PM,26,5,12:48:35 PM,26,5,1:00:41 PM,26,5,1:04:10 PM,1,,,-1.2684666999999998,36.8002891,-1.266294,36.8057971,Rider_Id_101,209
-Order_No_22584,User_Id_72,Bike,1,Personal,30,3,12:34:08 PM,30,3,1:15:47 PM,30,3,1:42:16 PM,30,3,1:57:27 PM,30,3,2:55:50 PM,7,24.9,,-1.3222489,36.786702500000004,-1.2932803,36.774089000000004,Rider_Id_480,3503
-Order_No_26109,User_Id_828,Bike,2,Personal,14,4,12:45:01 PM,14,4,12:48:54 PM,14,4,1:04:20 PM,14,4,1:11:48 PM,14,4,1:21:08 PM,4,19.0,,-1.2889037,36.7677152,-1.2817426,36.7846148,Rider_Id_312,560
-Order_No_4575,User_Id_2150,Bike,3,Business,10,5,9:07:48 AM,10,5,9:08:16 AM,10,5,9:20:36 AM,10,5,9:31:00 AM,10,5,10:00:41 AM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_376,1781
-Order_No_14041,User_Id_1342,Bike,3,Business,28,4,5:33:10 PM,28,4,5:33:24 PM,28,4,5:38:06 PM,28,4,5:47:53 PM,28,4,6:10:45 PM,11,,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_616,1372
-Order_No_18622,User_Id_3521,Bike,3,Business,18,2,12:13:16 PM,18,2,12:13:27 PM,18,2,12:22:25 PM,18,2,12:45:12 PM,18,2,1:06:38 PM,9,22.7,,-1.3201096,36.8180968,-1.2696908000000002,36.81519,Rider_Id_676,1286
-Order_No_20656,User_Id_3436,Bike,1,Personal,18,6,1:54:14 PM,18,6,1:54:41 PM,18,6,2:01:29 PM,18,6,2:17:50 PM,18,6,2:51:13 PM,11,20.6,,-1.250476,36.802013,-1.2973033999999999,36.7756907,Rider_Id_865,2003
-Order_No_24,User_Id_238,Bike,3,Business,15,1,3:25:37 PM,15,1,3:26:34 PM,15,1,3:31:52 PM,15,1,4:04:29 PM,15,1,4:36:33 PM,16,,,-1.2991441000000001,36.7528804,-1.3077869,36.844320700000004,Rider_Id_628,1924
-Order_No_1597,User_Id_393,Bike,3,Business,7,1,3:11:31 PM,7,1,3:24:14 PM,7,1,3:35:04 PM,7,1,3:42:01 PM,7,1,4:03:35 PM,11,26.8,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_436,1294
-Order_No_14362,User_Id_3739,Bike,1,Personal,19,2,3:02:11 PM,19,2,3:03:00 PM,19,2,3:14:14 PM,19,2,3:22:53 PM,19,2,3:47:59 PM,8,18.7,,-1.2706948,36.7844665,-1.2963018999999998,36.8120859,Rider_Id_956,1506
-Order_No_23235,User_Id_1724,Bike,3,Business,18,2,11:21:47 AM,18,2,11:43:47 AM,18,2,12:14:24 PM,18,2,12:16:27 PM,18,2,1:22:51 PM,15,,,-1.2551895,36.7822034,-1.206696,36.847709,Rider_Id_899,3984
-Order_No_9373,User_Id_1673,Bike,2,Personal,22,6,1:48:59 PM,22,6,1:49:05 PM,22,6,1:53:29 PM,22,6,2:12:33 PM,22,6,2:34:02 PM,11,,,-1.2222198999999998,36.8209432,-1.2583017,36.8023526,Rider_Id_6,1289
-Order_No_27996,User_Id_1934,Bike,2,Personal,21,6,9:47:59 AM,21,6,9:48:52 AM,21,6,9:49:40 AM,21,6,10:05:32 AM,21,6,10:43:05 AM,13,13.8,,-1.2559294,36.788465,-1.2207656,36.8282605,Rider_Id_215,2253
-Order_No_25513,User_Id_1927,Bike,3,Business,1,5,2:47:18 PM,1,5,2:47:47 PM,1,5,3:17:40 PM,1,5,3:28:05 PM,1,5,3:53:32 PM,6,24.5,,-1.3404187,36.7614685,-1.3411202,36.7271342,Rider_Id_549,1527
-Order_No_28190,User_Id_2174,Bike,3,Business,5,5,9:44:59 AM,5,5,9:45:37 AM,5,5,9:46:52 AM,5,5,10:18:34 AM,5,5,11:05:03 AM,12,17.8,,-1.2482781,36.884091999999995,-1.2981239,36.8367977,Rider_Id_216,2789
-Order_No_7699,User_Id_2330,Bike,3,Business,4,1,1:55:49 PM,4,1,1:56:14 PM,4,1,2:00:48 PM,4,1,2:04:45 PM,4,1,2:15:00 PM,4,28.7,,-1.2642903,36.80021370000001,-1.265432,36.795034,Rider_Id_79,615
-Order_No_1342,User_Id_760,Bike,3,Business,4,2,3:37:21 PM,4,2,3:53:59 PM,4,2,4:13:46 PM,4,2,4:18:14 PM,4,2,4:46:51 PM,5,22.5,,-1.2736695,36.8028996,-1.3023305,36.826051899999996,Rider_Id_539,1717
-Order_No_21145,User_Id_3450,Bike,3,Business,10,4,8:15:36 AM,10,4,8:16:08 AM,10,4,8:16:18 AM,10,4,8:27:37 AM,10,4,8:54:13 AM,10,19.3,,-1.2551895,36.7822034,-1.304023,36.788389,Rider_Id_579,1596
-Order_No_25671,User_Id_2330,Bike,3,Business,24,4,12:55:11 PM,24,4,12:56:09 PM,24,4,1:14:41 PM,24,4,1:17:09 PM,24,4,1:34:42 PM,11,24.5,,-1.2642903,36.80021370000001,-1.2355732,36.878606299999994,Rider_Id_38,1053
-Order_No_21217,User_Id_3491,Bike,3,Business,4,2,9:16:48 AM,4,2,10:11:25 AM,4,2,10:21:35 AM,4,2,10:27:49 AM,4,2,11:35:58 AM,11,15.8,83.0,-1.2781938999999998,36.8191922,-1.2285834999999998,36.882646799999996,Rider_Id_235,4089
-Order_No_2135,User_Id_1368,Bike,3,Business,26,2,3:12:34 PM,26,2,3:14:15 PM,26,2,3:33:39 PM,26,2,3:36:08 PM,26,2,3:56:51 PM,7,31.1,,-1.2571472,36.795063299999995,-1.2968917,36.7990526,Rider_Id_545,1243
-Order_No_26005,User_Id_432,Bike,3,Business,11,2,3:01:50 PM,11,2,3:04:18 PM,11,2,3:28:09 PM,11,2,3:33:09 PM,11,2,3:54:00 PM,3,21.4,,-1.3012008,36.764868,-1.2763494,36.7671783,Rider_Id_133,1251
-Order_No_27856,User_Id_1964,Bike,3,Personal,4,2,12:08:49 PM,4,2,12:10:13 PM,4,2,12:12:11 PM,4,2,12:24:00 PM,4,2,12:42:37 PM,14,20.7,,-1.32671,36.8600138,-1.2670196000000002,36.807700200000006,Rider_Id_359,1117
-Order_No_16784,User_Id_3127,Bike,3,Business,22,4,10:55:12 AM,22,4,11:02:36 AM,22,4,11:21:44 AM,22,4,11:27:40 AM,22,4,11:51:05 AM,8,22.7,,-1.2623805000000001,36.7805404,-1.2918246,36.801584999999996,Rider_Id_532,1405
-Order_No_25223,User_Id_2330,Bike,3,Business,25,1,3:42:06 PM,25,1,3:42:26 PM,25,1,3:42:34 PM,25,1,4:07:38 PM,25,1,4:07:47 PM,12,30.0,,-1.2536275,36.798793200000006,-1.2524916000000001,36.8656196,Rider_Id_61,9
-Order_No_15634,User_Id_3560,Bike,3,Business,24,5,3:10:32 PM,24,5,3:12:10 PM,24,5,3:12:59 PM,24,5,3:17:43 PM,24,5,4:42:48 PM,18,22.8,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_564,5105
-Order_No_15292,User_Id_1527,Bike,3,Business,3,1,11:36:00 AM,3,1,11:37:08 AM,3,1,11:44:11 AM,3,1,12:05:52 PM,3,1,12:11:52 PM,4,21.5,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_790,360
-Order_No_24180,User_Id_2216,Bike,3,Business,10,1,2:38:56 PM,10,1,2:39:24 PM,10,1,2:58:05 PM,10,1,3:11:43 PM,10,1,3:19:42 PM,9,18.4,,-1.2551895,36.7822034,-1.305791,36.811162700000004,Rider_Id_921,479
-Order_No_398,User_Id_1417,Bike,3,Business,7,2,1:04:26 PM,7,2,1:06:50 PM,7,2,1:16:13 PM,7,2,1:25:49 PM,7,2,1:35:22 PM,4,,,-1.2884243,36.820724799999994,-1.2741728,36.79691079999999,Rider_Id_942,573
-Order_No_7563,User_Id_1500,Bike,3,Business,4,4,11:24:43 AM,4,4,11:27:03 AM,4,4,11:58:31 AM,4,4,12:22:02 PM,4,4,12:40:24 PM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_219,1102
-Order_No_14343,User_Id_2330,Bike,3,Business,27,3,11:24:23 AM,27,3,11:24:52 AM,27,3,11:36:01 AM,27,3,11:47:29 AM,27,3,11:56:46 AM,8,26.1,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_119,557
-Order_No_22369,User_Id_1205,Bike,3,Business,19,1,11:13:10 AM,19,1,11:14:30 AM,19,1,11:37:18 AM,19,1,11:43:01 AM,19,1,12:15:41 PM,8,24.7,,-1.257309,36.806008399999996,-1.2873232,36.765857200000006,Rider_Id_219,1960
-Order_No_4017,User_Id_552,Bike,3,Business,6,5,11:55:02 AM,6,5,11:55:29 AM,6,5,12:00:33 PM,6,5,12:44:12 PM,6,5,1:03:55 PM,17,20.3,,-1.2816233999999997,36.8239846,-1.3632162,36.9108253,Rider_Id_397,1183
-Order_No_15090,User_Id_664,Bike,1,Personal,7,2,1:21:22 PM,7,2,1:23:08 PM,7,2,1:34:43 PM,7,2,1:39:47 PM,7,2,1:48:55 PM,4,24.8,,-1.2517392,36.82496079999999,-1.28093,36.8120044,Rider_Id_427,548
-Order_No_7612,User_Id_1281,Bike,3,Business,22,4,1:34:30 PM,22,4,1:40:47 PM,22,4,2:19:24 PM,22,4,2:22:49 PM,22,4,2:37:36 PM,8,26.2,,-1.2599502,36.79978,-1.2675333999999998,36.7658246,Rider_Id_685,887
-Order_No_14059,User_Id_2985,Bike,2,Personal,27,1,12:55:39 PM,27,1,12:55:49 PM,27,1,1:03:13 PM,27,1,1:10:10 PM,27,1,1:33:12 PM,14,23.9,,-1.3122610000000001,36.794658,-1.22966,36.84401,Rider_Id_249,1382
-Order_No_27044,User_Id_265,Bike,3,Business,29,5,11:47:01 AM,29,5,11:48:01 AM,29,5,12:17:03 PM,29,5,12:21:49 PM,29,5,1:23:09 PM,11,26.9,,-1.300921,36.828195,-1.2697200000000002,36.765853299999996,Rider_Id_507,3680
-Order_No_13332,User_Id_136,Bike,3,Business,16,6,12:50:58 PM,16,6,12:51:35 PM,16,6,12:59:03 PM,16,6,1:01:44 PM,16,6,1:09:14 PM,3,25.9,,-1.3254522,36.721658399999995,-1.3165103999999999,36.707772600000006,Rider_Id_831,450
-Order_No_8409,User_Id_2423,Bike,3,Business,26,2,11:46:09 AM,26,2,11:47:36 AM,26,2,11:58:23 AM,26,2,12:13:58 PM,26,2,12:33:51 PM,6,27.4,,-1.2571472,36.795063299999995,-1.300722,36.816865,Rider_Id_245,1193
-Order_No_8022,User_Id_567,Bike,3,Business,25,2,12:47:59 PM,25,2,1:08:25 PM,25,2,1:12:09 PM,25,2,1:37:20 PM,25,2,1:58:27 PM,7,26.9,,-1.280027,36.825293,-1.2963097,36.7688221,Rider_Id_571,1267
-Order_No_26016,User_Id_972,Bike,3,Business,4,4,11:25:01 AM,4,4,12:49:37 PM,4,4,12:59:19 PM,4,4,1:01:23 PM,4,4,1:18:07 PM,6,,,-1.2862033000000002,36.8152966,-1.3127303999999997,36.8189817,Rider_Id_844,1004
-Order_No_208,User_Id_2700,Bike,3,Business,8,6,5:56:18 PM,8,6,7:09:20 PM,8,6,7:24:02 PM,8,6,7:50:12 PM,8,6,8:09:25 PM,8,17.8,1.9,-1.3052671999999998,36.753853899999996,-1.2845834999999999,36.8079121,Rider_Id_267,1153
-Order_No_21234,User_Id_2443,Bike,1,Personal,9,4,7:38:56 PM,9,4,7:40:44 PM,9,4,8:02:40 PM,9,4,8:08:51 PM,9,4,8:44:29 PM,9,18.9,,-1.2611139999999998,36.806388,-1.3014461000000002,36.7661381,Rider_Id_213,2138
-Order_No_22273,User_Id_2509,Bike,3,Business,4,4,9:15:14 AM,4,4,9:26:08 AM,4,4,9:46:14 AM,4,4,9:55:31 AM,4,4,10:29:18 AM,23,21.7,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_901,2027
-Order_No_2694,User_Id_1687,Bike,3,Business,11,4,9:27:12 AM,11,4,9:28:00 AM,11,4,9:34:50 AM,11,4,9:47:49 AM,11,4,10:32:20 AM,8,18.4,,-1.2626574,36.8014034,-1.225441,36.763899,Rider_Id_882,2671
-Order_No_27709,User_Id_2470,Bike,1,Personal,8,3,9:40:07 AM,8,3,9:40:33 AM,8,3,9:53:24 AM,8,3,10:01:12 AM,8,3,10:29:34 AM,12,17.1,,-1.3323107,36.8692616,-1.297701,36.806896,Rider_Id_791,1702
-Order_No_10109,User_Id_3698,Bike,3,Business,15,3,9:45:05 AM,15,3,9:45:38 AM,15,3,10:06:02 AM,15,3,10:07:04 AM,15,3,10:21:40 AM,5,,,-1.3103985,36.9114173,-1.3244885,36.897791999999995,Rider_Id_411,876
-Order_No_17055,User_Id_2089,Bike,3,Business,11,1,10:51:52 AM,11,1,10:55:50 AM,11,1,11:04:44 AM,11,1,11:13:13 AM,11,1,11:34:21 AM,7,18.2,,-1.2615584,36.814950599999996,-1.3010058999999998,36.8216816,Rider_Id_116,1268
-Order_No_8956,User_Id_3557,Bike,3,Business,29,4,11:24:21 AM,29,4,11:25:40 AM,29,4,11:29:52 AM,29,4,11:35:54 AM,29,4,11:46:08 AM,2,21.4,,-1.2546732,36.808679600000005,-1.2682478000000001,36.8072852,Rider_Id_56,614
-Order_No_10693,User_Id_3565,Bike,3,Business,4,7,10:20:39 AM,4,7,10:20:46 AM,4,7,10:22:13 AM,4,7,10:29:47 AM,4,7,11:07:11 AM,35,17.7,3.0,-1.2844665000000002,36.7866113,-1.1140488000000002,37.012030200000005,Rider_Id_45,2244
-Order_No_10831,User_Id_1500,Bike,3,Business,2,3,12:56:15 PM,2,3,12:59:13 PM,2,3,1:06:09 PM,2,3,1:11:41 PM,2,3,1:29:15 PM,8,24.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_518,1054
-Order_No_26723,User_Id_2412,Bike,3,Business,31,2,5:53:29 PM,31,2,5:53:38 PM,31,2,5:53:43 PM,31,2,5:59:10 PM,31,2,6:11:10 PM,5,19.8,,-1.2156006000000001,36.8916865,-1.2297202,36.881950700000004,Rider_Id_531,720
-Order_No_1077,User_Id_3688,Bike,3,Business,12,2,3:49:08 PM,12,2,4:08:21 PM,12,2,4:29:07 PM,12,2,4:31:42 PM,12,2,5:11:56 PM,7,27.1,,-1.2551895,36.7822034,-1.2802803,36.8271747,Rider_Id_452,2414
-Order_No_13710,User_Id_868,Bike,3,Business,2,2,2:29:11 PM,2,2,2:30:10 PM,2,2,2:34:05 PM,2,2,3:27:34 PM,2,2,3:31:53 PM,10,29.5,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_445,259
-Order_No_8553,User_Id_2523,Bike,3,Business,2,3,11:57:39 AM,2,3,11:57:56 AM,2,3,12:06:05 PM,2,3,12:09:15 PM,2,3,12:48:59 PM,19,24.0,,-1.2551895,36.7822034,-1.2658003,36.8983864,Rider_Id_647,2384
-Order_No_25167,User_Id_3599,Bike,3,Business,27,3,1:29:38 PM,27,3,1:30:06 PM,27,3,1:46:46 PM,27,3,2:17:00 PM,27,3,2:45:42 PM,20,29.3,,-1.3302996,36.8707935,-1.3636899,36.9468353,Rider_Id_715,1722
-Order_No_1828,User_Id_2946,Bike,2,Personal,7,2,10:16:27 AM,7,2,10:17:31 AM,7,2,10:26:52 AM,7,2,10:35:34 AM,7,2,10:56:17 AM,8,23.2,,-1.2749662,36.7658781,-1.2983462,36.8139996,Rider_Id_229,1243
-Order_No_9837,User_Id_868,Bike,3,Business,10,1,12:28:54 PM,10,1,12:31:17 PM,10,1,12:31:23 PM,10,1,12:47:05 PM,10,1,1:07:29 PM,7,18.7,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_864,1224
-Order_No_17867,User_Id_3203,Bike,3,Business,17,4,10:09:28 AM,17,4,10:09:52 AM,17,4,10:20:22 AM,17,4,10:32:33 AM,17,4,11:02:20 AM,17,22.4,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_397,1787
-Order_No_1860,User_Id_393,Bike,3,Business,13,4,4:00:44 PM,13,4,4:00:56 PM,13,4,4:01:27 PM,13,4,4:37:46 PM,13,4,4:46:38 PM,4,22.3,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_10,532
-Order_No_1984,User_Id_265,Bike,3,Business,15,2,12:48:58 PM,15,2,12:51:18 PM,15,2,1:30:03 PM,15,2,2:54:23 PM,15,2,3:47:05 PM,7,24.4,,-1.2642978,36.819261,-1.300921,36.828195,Rider_Id_554,3162
-Order_No_16994,User_Id_2748,Bike,1,Personal,4,1,5:12:18 PM,4,1,5:41:15 PM,4,1,6:01:10 PM,4,1,6:03:34 PM,4,1,6:27:16 PM,8,20.8,,-1.269103,36.8223998,-1.2740995000000002,36.76767879999999,Rider_Id_192,1422
-Order_No_19303,User_Id_3426,Bike,3,Business,17,4,8:58:30 AM,17,4,8:59:21 AM,17,4,9:05:48 AM,17,4,9:15:39 AM,17,4,9:16:28 AM,2,,,-1.3012008,36.764868,-1.2949438999999998,36.7604214,Rider_Id_322,49
-Order_No_1696,User_Id_176,Bike,1,Personal,18,4,11:18:33 AM,18,4,11:20:39 AM,18,4,11:34:51 AM,18,4,11:37:19 AM,18,4,11:50:09 AM,4,25.5,,-1.278612,36.8195876,-1.2666695,36.8328283,Rider_Id_477,770
-Order_No_24350,User_Id_3280,Bike,2,Personal,22,2,12:17:40 PM,22,2,12:18:15 PM,22,2,12:22:37 PM,22,2,12:35:36 PM,22,2,12:45:54 PM,3,19.2,,-1.275001,36.766323,-1.290239,36.7620412,Rider_Id_334,618
-Order_No_21748,User_Id_1594,Bike,3,Business,25,1,3:31:40 PM,25,1,4:02:03 PM,25,1,4:21:27 PM,25,1,4:36:15 PM,25,1,5:02:26 PM,16,28.3,,-1.2156006000000001,36.8916865,-1.3285517,36.9018,Rider_Id_520,1571
-Order_No_406,User_Id_1146,Bike,3,Business,18,1,3:47:42 PM,18,1,3:48:14 PM,18,1,4:02:13 PM,18,1,4:16:54 PM,18,1,4:28:22 PM,6,30.6,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_26,688
-Order_No_6227,User_Id_2989,Bike,3,Business,2,4,2:17:12 PM,2,4,2:33:34 PM,2,4,2:56:37 PM,2,4,3:14:11 PM,2,4,3:32:29 PM,12,26.4,,-1.3463041999999998,36.768164,-1.2863535,36.81939570000001,Rider_Id_101,1098
-Order_No_15898,User_Id_1469,Bike,3,Business,17,3,3:45:56 PM,17,3,3:46:37 PM,17,3,3:55:31 PM,17,3,4:00:16 PM,17,3,4:20:34 PM,4,28.3,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_565,1218
-Order_No_7752,User_Id_3647,Bike,3,Business,9,3,9:15:10 AM,9,3,9:25:40 AM,9,3,9:33:10 AM,9,3,9:59:35 AM,9,3,10:27:00 AM,6,20.3,,-1.2726389999999999,36.794723,-1.2897399,36.8270975,Rider_Id_828,1645
-Order_No_9739,User_Id_552,Bike,3,Business,27,4,11:56:05 AM,27,4,11:56:11 AM,27,4,12:05:22 PM,27,4,12:09:13 PM,27,4,12:48:26 PM,23,22.8,,-1.3014461000000002,36.7661381,-1.3632162,36.9108253,Rider_Id_199,2353
-Order_No_2733,User_Id_635,Bike,3,Business,11,2,4:02:35 PM,11,2,4:09:07 PM,11,2,4:19:04 PM,11,2,4:20:45 PM,11,2,4:44:23 PM,9,21.2,,-1.300921,36.828195,-1.2643174,36.7794769,Rider_Id_583,1418
-Order_No_1525,User_Id_2423,Bike,3,Business,18,1,10:30:08 AM,18,1,10:34:00 AM,18,1,10:38:47 AM,18,1,10:41:29 AM,18,1,11:31:50 AM,14,,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_322,3021
-Order_No_12798,User_Id_2840,Bike,1,Personal,13,5,12:04:11 PM,13,5,12:04:18 PM,13,5,12:06:21 PM,13,5,12:18:59 PM,13,5,12:42:47 PM,14,,,-1.289788,36.824911,-1.1994789,36.81673320000001,Rider_Id_629,1428
-Order_No_23831,User_Id_2523,Bike,3,Business,22,5,3:12:00 PM,22,5,3:12:20 PM,22,5,3:28:12 PM,22,5,3:34:08 PM,22,5,4:14:50 PM,15,,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_192,2442
-Order_No_13302,User_Id_53,Bike,3,Business,18,1,2:41:18 PM,18,1,2:42:46 PM,18,1,2:49:31 PM,18,1,3:06:30 PM,18,1,3:27:03 PM,8,29.5,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_349,1233
-Order_No_11274,User_Id_1500,Bike,3,Business,18,5,9:07:03 AM,18,5,9:27:06 AM,18,5,9:41:25 AM,18,5,10:18:15 AM,18,5,10:36:00 AM,8,20.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_254,1065
-Order_No_18821,User_Id_635,Bike,3,Business,12,1,2:17:56 PM,12,1,2:28:46 PM,12,1,2:28:54 PM,12,1,2:33:52 PM,12,1,2:56:36 PM,8,26.7,,-1.2599563,36.799343900000004,-1.2780878,36.846943599999996,Rider_Id_31,1364
-Order_No_2638,User_Id_2956,Bike,3,Business,23,5,11:56:32 AM,23,5,12:04:15 PM,23,5,12:50:26 PM,23,5,12:56:20 PM,23,5,1:27:17 PM,14,24.2,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_870,1857
-Order_No_4399,User_Id_1209,Bike,3,Business,17,3,10:21:10 AM,17,3,10:21:42 AM,17,3,10:26:29 AM,17,3,10:32:51 AM,17,3,10:58:24 AM,9,,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_709,1533
-Order_No_18992,User_Id_2797,Bike,1,Personal,6,1,1:50:18 PM,6,1,1:52:21 PM,6,1,2:16:20 PM,6,1,2:28:52 PM,6,1,2:49:55 PM,5,24.8,,-1.2864298,36.8224345,-1.2696795,36.8640088,Rider_Id_629,1263
-Order_No_17150,User_Id_2174,Bike,3,Business,15,2,10:04:50 AM,15,2,10:04:54 AM,15,2,10:05:06 AM,15,2,11:25:11 AM,15,2,11:52:41 AM,16,24.2,,-1.2482781,36.884091999999995,-1.1668055,36.830444799999995,Rider_Id_82,1650
-Order_No_18393,User_Id_3686,Bike,3,Business,24,3,10:05:54 AM,24,3,10:18:16 AM,24,3,10:19:42 AM,24,3,10:21:06 AM,24,3,10:57:28 AM,8,16.8,1.1,-1.2551895,36.7822034,-1.296814,36.8162263,Rider_Id_263,2182
-Order_No_22932,User_Id_2214,Bike,2,Personal,27,3,2:15:29 PM,27,3,2:16:12 PM,27,3,2:25:35 PM,27,3,2:27:46 PM,27,3,3:03:43 PM,8,,,-1.3165209999999998,36.869310999999996,-1.2750975,36.8289032,Rider_Id_192,2157
-Order_No_23959,User_Id_1500,Bike,3,Business,21,4,8:36:45 AM,21,4,8:37:14 AM,21,4,9:17:35 AM,21,4,9:23:27 AM,21,4,9:52:11 AM,11,22.0,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_763,1724
-Order_No_14312,User_Id_2289,Bike,3,Business,19,1,2:20:58 PM,19,1,2:23:05 PM,19,1,2:39:25 PM,19,1,2:44:35 PM,19,1,3:08:19 PM,6,25.1,,-1.2551895,36.7822034,-1.2799996999999999,36.8237091,Rider_Id_660,1424
-Order_No_26135,User_Id_3599,Bike,3,Business,14,3,1:16:25 PM,14,3,1:23:30 PM,14,3,1:34:38 PM,14,3,2:08:17 PM,14,3,2:46:04 PM,11,25.7,,-1.3302996,36.8707935,-1.3329031999999998,36.7888802,Rider_Id_62,2267
-Order_No_24133,User_Id_3621,Bike,3,Business,13,3,2:32:55 PM,13,3,2:33:43 PM,13,3,2:42:40 PM,13,3,2:50:21 PM,13,3,3:18:23 PM,9,29.4,,-1.2836583000000001,36.7886014,-1.2700382,36.8426718,Rider_Id_206,1682
-Order_No_18168,User_Id_246,Bike,3,Business,20,5,11:26:12 AM,20,5,11:26:23 AM,20,5,11:27:26 AM,20,5,11:42:15 AM,20,5,12:09:48 PM,10,18.5,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_229,1653
-Order_No_7746,User_Id_391,Bike,3,Business,25,4,1:44:35 PM,25,4,2:32:25 PM,25,4,2:35:46 PM,25,4,2:49:19 PM,25,4,3:13:01 PM,7,,,-1.2867454,36.8259017,-1.3203917,36.8334751,Rider_Id_855,1422
-Order_No_20844,User_Id_706,Bike,3,Business,7,1,10:23:20 AM,7,1,10:26:08 AM,7,1,10:26:38 AM,7,1,10:30:40 AM,7,1,10:35:46 AM,3,21.6,,-1.3077869,36.844320700000004,-1.3080091999999999,36.8284543,Rider_Id_627,306
-Order_No_17231,User_Id_1249,Bike,3,Business,24,6,2:08:26 PM,24,6,2:08:41 PM,24,6,2:18:51 PM,24,6,3:42:52 PM,24,6,4:11:51 PM,11,27.1,,-1.3681253,36.915529799999995,-1.3189252,36.861726899999994,Rider_Id_612,1739
-Order_No_286,User_Id_3283,Bike,3,Business,4,1,9:24:58 AM,4,1,9:26:11 AM,4,1,9:29:53 AM,4,1,9:35:26 AM,4,1,9:45:24 AM,4,22.0,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_242,598
-Order_No_18432,User_Id_2307,Bike,3,Business,6,4,6:12:18 PM,6,4,6:12:48 PM,6,4,6:28:57 PM,6,4,6:40:15 PM,6,4,7:11:55 PM,14,21.7,,-1.3359536,36.853478700000004,-1.2513591,36.7947674,Rider_Id_910,1900
-Order_No_11190,User_Id_3406,Bike,1,Personal,7,1,3:56:10 PM,7,1,3:58:23 PM,7,1,4:18:59 PM,7,1,4:31:01 PM,7,1,5:36:09 PM,8,20.3,,-1.2798711,36.8207118,-1.3004944999999999,36.7727891,Rider_Id_56,3908
-Order_No_12602,User_Id_1342,Bike,3,Business,4,1,11:17:18 AM,4,1,11:31:28 AM,4,1,11:38:43 AM,4,1,11:48:47 AM,4,1,12:03:01 PM,5,21.0,,-1.2962832,36.784968299999996,-1.3014461000000002,36.7661381,Rider_Id_322,854
-Order_No_836,User_Id_1342,Bike,3,Business,14,6,3:17:20 PM,14,6,3:17:50 PM,14,6,3:20:45 PM,14,6,3:31:06 PM,14,6,3:46:49 PM,10,19.3,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_116,943
-Order_No_18104,User_Id_1316,Bike,1,Business,12,3,12:20:01 PM,12,3,12:20:30 PM,12,3,12:21:19 PM,12,3,12:37:54 PM,12,3,12:53:46 PM,7,23.4,,-1.2551895,36.7822034,-1.2793503000000002,36.8039715,Rider_Id_45,952
-Order_No_14302,User_Id_633,Bike,3,Business,19,2,3:35:11 PM,19,2,3:40:01 PM,19,2,3:54:57 PM,19,2,3:59:53 PM,19,2,4:16:23 PM,4,29.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_448,990
-Order_No_6332,User_Id_3204,Bike,1,Personal,18,3,2:03:54 PM,18,3,2:04:08 PM,18,3,2:14:40 PM,18,3,2:21:46 PM,18,3,3:23:01 PM,26,20.7,,-1.2585705,36.7853303,-1.3002993,36.9153417,Rider_Id_519,3675
-Order_No_7185,User_Id_265,Bike,3,Business,23,6,9:44:20 AM,23,6,9:44:29 AM,23,6,9:44:51 AM,23,6,9:53:04 AM,23,6,10:12:17 AM,9,19.0,,-1.300921,36.828195,-1.2812624,36.7714609,Rider_Id_576,1153
-Order_No_5965,User_Id_2102,Bike,3,Business,14,1,3:00:32 PM,14,1,3:00:45 PM,14,1,3:13:57 PM,14,1,3:17:20 PM,14,1,4:08:33 PM,15,25.8,,-1.3323107,36.8692616,-1.2613768,36.801941799999994,Rider_Id_75,3073
-Order_No_23118,User_Id_1719,Bike,3,Business,26,1,10:24:34 AM,26,1,10:24:55 AM,26,1,10:49:44 AM,26,1,11:07:15 AM,26,1,11:31:49 AM,15,22.8,,-1.2395125,36.7455843,-1.296797,36.776452500000005,Rider_Id_101,1474
-Order_No_10578,User_Id_1017,Bike,3,Business,14,2,9:14:53 AM,14,2,9:15:17 AM,14,2,9:19:05 AM,14,2,9:21:08 AM,14,2,9:29:14 AM,3,15.6,,-1.2551895,36.7822034,-1.259382,36.807204999999996,Rider_Id_726,486
-Order_No_2484,User_Id_391,Bike,3,Business,23,4,3:53:22 PM,23,4,3:57:52 PM,23,4,4:01:43 PM,23,4,4:06:02 PM,23,4,5:05:26 PM,7,24.7,,-1.2789469,36.7796691,-1.2784063,36.8222933,Rider_Id_19,3564
-Order_No_15730,User_Id_2956,Bike,3,Business,12,5,11:11:30 AM,12,5,11:11:57 AM,12,5,11:12:15 AM,12,5,11:24:06 AM,12,5,12:26:16 PM,3,24.6,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_215,3730
-Order_No_18570,User_Id_3291,Bike,3,Business,23,6,3:21:32 PM,23,6,3:22:51 PM,23,6,3:45:56 PM,23,6,3:47:18 PM,23,6,4:06:50 PM,5,28.5,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_605,1172
-Order_No_16087,User_Id_3621,Bike,3,Business,7,5,11:34:08 AM,7,5,11:57:26 AM,7,5,12:14:15 PM,7,5,12:18:32 PM,7,5,12:51:50 PM,8,,,-1.2836583000000001,36.7886014,-1.3331687,36.7848599,Rider_Id_643,1998
-Order_No_18422,User_Id_510,Bike,3,Business,11,3,2:24:25 PM,11,3,2:24:32 PM,11,3,2:24:39 PM,11,3,2:25:53 PM,11,3,2:49:10 PM,13,20.3,,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_771,1397
-Order_No_7198,User_Id_2160,Bike,2,Personal,24,5,1:27:44 PM,24,5,1:28:00 PM,24,5,1:28:32 PM,24,5,1:41:13 PM,24,5,1:53:46 PM,5,23.1,,-1.2881035,36.8142095,-1.2549653,36.797868,Rider_Id_74,753
-Order_No_24771,User_Id_517,Bike,3,Business,9,1,9:32:33 AM,9,1,9:33:54 AM,9,1,9:34:16 AM,9,1,10:18:16 AM,9,1,10:18:23 AM,8,15.1,,-1.3177546999999998,36.8303703,-1.2770708,36.8231093,Rider_Id_333,7
-Order_No_22898,User_Id_2517,Bike,1,Personal,27,1,7:02:32 PM,27,1,7:03:12 PM,27,1,7:20:57 PM,27,1,7:24:30 PM,27,1,7:54:23 PM,7,17.7,,-1.2682178999999998,36.8009289,-1.2979147,36.785781400000005,Rider_Id_770,1793
-Order_No_28113,User_Id_1529,Bike,1,Personal,14,5,1:34:08 PM,14,5,1:34:55 PM,14,5,1:48:52 PM,14,5,1:51:01 PM,14,5,2:15:25 PM,8,26.9,,-1.2873232,36.765857200000006,-1.274381,36.7272265,Rider_Id_548,1464
-Order_No_24864,User_Id_1341,Bike,3,Business,20,1,12:45:02 PM,20,1,12:46:00 PM,20,1,1:00:40 PM,20,1,1:17:35 PM,20,1,1:34:54 PM,9,,,-1.3033561000000002,36.8163631,-1.331619,36.847976,Rider_Id_831,1039
-Order_No_16809,User_Id_1453,Bike,3,Business,5,2,9:02:17 AM,5,2,9:03:33 AM,5,2,9:09:15 AM,5,2,9:31:11 AM,5,2,9:50:56 AM,8,17.5,4.0,-1.2273427,36.799468299999994,-1.2591018999999999,36.800577000000004,Rider_Id_865,1185
-Order_No_26833,User_Id_635,Bike,3,Business,4,4,11:40:41 AM,4,4,11:42:12 AM,4,4,11:42:15 AM,4,4,12:29:06 PM,4,4,12:46:45 PM,10,25.7,,-1.3079083,36.82703370000001,-1.2602337,36.79905479999999,Rider_Id_293,1059
-Order_No_22339,User_Id_2100,Bike,3,Business,8,6,2:58:25 PM,8,6,4:08:34 PM,8,6,4:16:15 PM,8,6,4:22:09 PM,8,6,4:48:30 PM,6,23.3,2.0,-1.2859912,36.8756811,-1.2765736,36.851364600000004,Rider_Id_849,1581
-Order_No_10107,User_Id_2828,Bike,2,Business,26,4,11:38:51 AM,26,4,11:39:03 AM,26,4,12:03:17 PM,26,4,12:12:57 PM,26,4,12:36:10 PM,14,19.0,,-1.2429937,36.8754167,-1.2615888000000002,36.7928732,Rider_Id_510,1393
-Order_No_23739,User_Id_3291,Bike,3,Business,29,6,9:07:54 AM,29,6,9:08:32 AM,29,6,9:16:08 AM,29,6,9:27:13 AM,29,6,9:33:57 AM,2,19.2,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_453,404
-Order_No_12527,User_Id_868,Bike,3,Business,14,2,4:05:45 PM,14,2,4:06:08 PM,14,2,4:11:47 PM,14,2,4:16:54 PM,14,2,4:34:03 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_697,1029
-Order_No_2625,User_Id_3758,Bike,1,Personal,26,2,3:49:21 PM,26,2,4:13:08 PM,26,2,4:24:19 PM,26,2,4:28:15 PM,26,2,5:03:17 PM,4,,,-1.2793430000000001,36.824395,-1.2771886000000001,36.8419032,Rider_Id_698,2102
-Order_No_3596,User_Id_3091,Bike,3,Business,26,3,10:08:30 AM,26,3,10:08:51 AM,26,3,10:20:33 AM,26,3,10:52:36 AM,26,3,11:38:55 AM,12,24.1,,-1.2765736,36.851364600000004,-1.3012008,36.764868,Rider_Id_123,2779
-Order_No_2812,User_Id_1798,Bike,3,Business,11,4,4:00:53 PM,11,4,4:04:21 PM,11,4,4:16:32 PM,11,4,4:24:36 PM,11,4,4:42:02 PM,3,27.2,,-1.2696183,36.8021778,-1.2615888000000002,36.7928732,Rider_Id_349,1046
-Order_No_10162,User_Id_3074,Bike,3,Business,26,6,2:47:52 PM,26,6,2:48:42 PM,26,6,3:02:03 PM,26,6,3:28:16 PM,26,6,3:40:46 PM,5,28.1,,-1.3097597,36.8316597,-1.31471,36.8110941,Rider_Id_925,750
-Order_No_11636,User_Id_2523,Bike,3,Business,11,5,11:11:09 AM,11,5,11:11:23 AM,11,5,11:23:18 AM,11,5,11:29:56 AM,11,5,11:35:08 AM,4,21.2,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_190,312
-Order_No_4248,User_Id_1250,Bike,1,Personal,9,3,2:49:46 PM,9,3,2:51:25 PM,9,3,3:05:57 PM,9,3,3:24:15 PM,9,3,4:02:08 PM,6,23.1,,-1.2743743,36.7657321,-1.303382,36.790658,Rider_Id_279,2273
-Order_No_14103,User_Id_2461,Bike,3,Business,26,6,1:39:14 PM,26,6,1:39:58 PM,26,6,2:00:30 PM,26,6,2:05:09 PM,26,6,2:32:30 PM,10,24.2,,-1.2844229999999999,36.820545,-1.2377049999999998,36.7663283,Rider_Id_888,1641
-Order_No_21349,User_Id_1329,Bike,3,Business,30,6,3:16:35 PM,30,6,3:17:20 PM,30,6,3:30:18 PM,30,6,3:31:29 PM,30,6,3:59:08 PM,8,28.6,,-1.273056,36.811298,-1.2261879999999998,36.7997968,Rider_Id_166,1659
-Order_No_24150,User_Id_1038,Bike,1,Business,13,5,9:04:45 AM,13,5,9:06:22 AM,13,5,9:24:38 AM,13,5,9:25:48 AM,13,5,10:01:44 AM,8,15.6,,-1.3099145,36.802277399999994,-1.326774,36.78780689999999,Rider_Id_465,2156
-Order_No_6500,User_Id_1502,Bike,3,Business,12,2,11:53:17 AM,12,2,11:55:50 AM,12,2,12:08:03 PM,12,2,12:19:58 PM,12,2,12:46:26 PM,5,24.4,,-1.2283403,36.8822756,-1.2232123,36.8932547,Rider_Id_206,1588
-Order_No_25073,User_Id_1850,Bike,3,Business,28,1,11:41:08 AM,28,1,11:41:48 AM,28,1,11:48:43 AM,28,1,11:56:03 AM,28,1,12:16:37 PM,8,23.5,,-1.2551895,36.7822034,-1.3033012,36.8170864,Rider_Id_674,1234
-Order_No_8469,User_Id_647,Bike,3,Business,5,5,10:10:06 AM,5,5,10:10:41 AM,5,5,10:10:47 AM,5,5,10:19:36 AM,5,5,10:31:47 AM,4,19.8,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_349,731
-Order_No_19493,User_Id_413,Bike,1,Personal,11,2,1:18:15 PM,11,2,1:23:56 PM,11,2,1:53:58 PM,11,2,2:11:40 PM,11,2,2:25:59 PM,9,25.4,,-1.289195,36.7817368,-1.3173868999999998,36.8112044,Rider_Id_933,859
-Order_No_5358,User_Id_3091,Bike,3,Business,11,2,1:23:28 PM,11,2,1:27:08 PM,11,2,1:46:54 PM,11,2,1:58:49 PM,11,2,2:23:54 PM,11,21.2,,-1.2860183,36.8975338,-1.290894,36.822971,Rider_Id_20,1505
-Order_No_23376,User_Id_186,Bike,3,Business,3,3,10:25:27 AM,3,3,12:05:23 PM,3,3,12:05:44 PM,3,3,12:09:06 PM,3,3,12:52:06 PM,23,27.3,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_831,2580
-Order_No_10362,User_Id_3599,Bike,3,Business,14,2,10:52:09 AM,14,2,11:09:44 AM,14,2,12:00:25 PM,14,2,12:12:37 PM,14,2,12:36:33 PM,10,,,-1.3302996,36.8707935,-1.2990606000000002,36.8033028,Rider_Id_427,1436
-Order_No_5220,User_Id_1329,Bike,3,Business,30,3,1:48:20 PM,30,3,1:48:51 PM,30,3,1:55:57 PM,30,3,1:57:21 PM,30,3,2:09:20 PM,3,28.8,,-1.273056,36.811298,-1.2657053999999999,36.7930278,Rider_Id_259,719
-Order_No_13118,User_Id_2804,Bike,3,Business,1,5,12:28:14 PM,1,5,12:28:40 PM,1,5,12:34:07 PM,1,5,12:59:29 PM,1,5,1:06:52 PM,1,26.6,,-1.2571472,36.795063299999995,-1.2642903,36.80021370000001,Rider_Id_822,443
-Order_No_15553,User_Id_1502,Bike,3,Business,11,5,4:11:31 PM,11,5,4:12:06 PM,11,5,4:27:56 PM,11,5,4:38:54 PM,11,5,5:00:49 PM,17,20.3,,-1.2237773,36.8861711,-1.2559559999999999,36.789867,Rider_Id_813,1315
-Order_No_1587,User_Id_186,Bike,3,Business,19,2,2:46:32 PM,19,2,2:46:50 PM,19,2,2:59:32 PM,19,2,3:03:37 PM,19,2,3:20:08 PM,7,29.2,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_763,991
-Order_No_8702,User_Id_85,Bike,2,Business,3,2,2:20:44 PM,3,2,2:21:13 PM,3,2,2:27:20 PM,3,2,2:40:36 PM,3,2,2:57:00 PM,11,20.1,,-1.2579303999999998,36.8019667,-1.3014461000000002,36.7661381,Rider_Id_103,984
-Order_No_26866,User_Id_2383,Bike,3,Business,16,4,4:35:47 PM,16,4,4:36:03 PM,16,4,4:41:29 PM,16,4,4:50:37 PM,16,4,5:10:33 PM,10,,,-1.2608993,36.799246000000004,-1.295319,36.760178,Rider_Id_484,1196
-Order_No_3441,User_Id_12,Bike,3,Business,5,3,9:39:19 AM,5,3,10:03:39 AM,5,3,10:48:01 AM,5,3,10:53:45 AM,5,3,11:11:35 AM,9,22.1,,-1.2400209999999998,36.849990000000005,-1.2864963,36.8252086,Rider_Id_320,1070
-Order_No_26970,User_Id_868,Bike,3,Business,28,5,9:58:52 AM,28,5,9:59:20 AM,28,5,10:12:09 AM,28,5,10:37:57 AM,28,5,10:59:12 AM,4,23.6,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_427,1275
-Order_No_26684,User_Id_2335,Bike,3,Business,5,3,1:39:21 PM,5,3,1:39:28 PM,5,3,1:39:39 PM,5,3,1:46:09 PM,5,3,2:20:22 PM,15,24.9,,-1.2551895,36.7822034,-1.3226301999999999,36.8719901,Rider_Id_828,2053
-Order_No_18525,User_Id_1075,Bike,3,Business,12,2,9:46:22 AM,12,2,9:49:38 AM,12,2,10:19:37 AM,12,2,10:24:14 AM,12,2,10:55:50 AM,6,20.7,,-1.2813012,36.832396200000005,-1.2527958000000001,36.8003131,Rider_Id_590,1896
-Order_No_4961,User_Id_3496,Bike,3,Business,10,5,11:04:52 AM,10,5,11:05:00 AM,10,5,11:05:13 AM,10,5,11:39:52 AM,10,5,12:01:05 PM,16,19.4,,-1.3190102,36.923069299999995,-1.300921,36.828195,Rider_Id_813,1273
-Order_No_20154,User_Id_3201,Bike,3,Business,30,5,8:15:51 AM,30,5,8:16:15 AM,30,5,8:45:18 AM,30,5,8:55:51 AM,30,5,9:13:58 AM,7,18.9,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_101,1087
-Order_No_12377,User_Id_186,Bike,3,Business,10,1,4:29:28 PM,10,1,4:31:26 PM,10,1,4:38:51 PM,10,1,4:47:58 PM,10,1,4:59:59 PM,5,18.8,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_576,721
-Order_No_16389,User_Id_3338,Bike,3,Business,31,5,12:20:38 PM,31,5,12:27:22 PM,31,5,12:29:17 PM,31,5,12:55:34 PM,31,5,1:49:46 PM,18,21.7,,-1.2551895,36.7822034,-1.3301121,36.89331,Rider_Id_926,3252
-Order_No_24705,User_Id_496,Bike,3,Business,22,3,10:20:46 AM,22,3,10:23:24 AM,22,3,10:33:36 AM,22,3,10:41:42 AM,22,3,11:21:29 AM,5,23.2,,-1.2571472,36.795063299999995,-1.2845748,36.821766600000004,Rider_Id_467,2387
-Order_No_12213,User_Id_2828,Bike,3,Business,8,1,10:52:21 AM,8,1,10:52:33 AM,8,1,11:19:59 AM,8,1,12:09:18 PM,8,1,12:22:53 PM,4,27.8,,-1.2727979999999999,36.8124195,-1.2615888000000002,36.7928732,Rider_Id_161,815
-Order_No_16734,User_Id_393,Bike,3,Business,14,4,10:49:27 AM,14,4,10:49:48 AM,14,4,10:54:23 AM,14,4,11:24:31 AM,14,4,11:56:00 AM,14,23.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_724,1889
-Order_No_2410,User_Id_1470,Bike,1,Personal,1,6,11:43:16 AM,1,6,11:54:30 AM,1,6,12:05:28 PM,1,6,12:08:55 PM,1,6,12:35:25 PM,9,18.5,,-1.2864673999999998,36.820262,-1.2810586000000002,36.883829399999996,Rider_Id_387,1590
-Order_No_14205,User_Id_1500,Bike,3,Business,18,5,11:40:32 AM,18,5,11:40:41 AM,18,5,11:41:16 AM,18,5,11:46:10 AM,18,5,12:15:29 PM,13,20.0,,-1.2574219,36.7927073,-1.3228003000000002,36.8306435,Rider_Id_518,1759
-Order_No_16834,User_Id_1205,Bike,3,Business,25,4,11:49:46 AM,25,4,11:50:12 AM,25,4,11:58:23 AM,25,4,12:02:28 PM,25,4,12:17:27 PM,4,,,-1.2495931,36.80650870000001,-1.2738936,36.8035805,Rider_Id_547,899
-Order_No_1574,User_Id_2692,Bike,3,Business,6,4,5:07:15 PM,6,4,5:09:10 PM,6,4,5:15:46 PM,6,4,5:26:56 PM,6,4,5:44:03 PM,6,22.7,,-1.2842286,36.8150609,-1.3064585,36.8119065,Rider_Id_940,1027
-Order_No_20163,User_Id_269,Bike,3,Business,30,3,3:24:45 PM,30,3,3:29:27 PM,30,3,3:33:27 PM,30,3,3:40:15 PM,30,3,4:52:31 PM,22,25.0,,-1.3332749,36.870814700000004,-1.3169591,36.7000029,Rider_Id_890,4336
-Order_No_5767,User_Id_3701,Bike,3,Business,26,3,3:42:07 PM,26,3,3:43:08 PM,26,3,3:51:40 PM,26,3,3:53:11 PM,26,3,4:11:28 PM,7,27.3,,-1.2862436000000002,36.7719109,-1.2684239,36.79524620000001,Rider_Id_337,1097
-Order_No_8765,User_Id_1500,Bike,3,Business,8,4,8:45:19 AM,8,4,8:48:59 AM,8,4,9:01:30 AM,8,4,9:37:50 AM,8,4,9:53:56 AM,8,20.4,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_620,966
-Order_No_1538,User_Id_393,Bike,3,Business,27,4,8:22:28 AM,27,4,8:22:49 AM,27,4,8:22:56 AM,27,4,8:57:14 AM,27,4,9:29:18 AM,14,18.9,3.1,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_10,1924
-Order_No_2146,User_Id_3585,Bike,1,Personal,22,6,1:43:06 PM,22,6,1:43:19 PM,22,6,1:52:36 PM,22,6,1:58:19 PM,22,6,2:09:03 PM,6,,,-1.2823757,36.7935809,-1.3010476,36.766442100000006,Rider_Id_612,644
-Order_No_12922,User_Id_458,Bike,3,Business,2,3,8:51:56 AM,2,3,8:52:22 AM,2,3,9:05:32 AM,2,3,9:18:28 AM,2,3,9:39:08 AM,12,18.0,0.9,-1.2475247999999999,36.881344399999996,-1.2729324,36.811562200000004,Rider_Id_408,1240
-Order_No_20562,User_Id_1768,Bike,1,Personal,3,3,11:04:51 AM,3,3,11:05:34 AM,3,3,11:15:05 AM,3,3,11:24:05 AM,3,3,11:47:38 AM,10,23.8,,-1.301809,36.75643,-1.2750257,36.8157165,Rider_Id_905,1413
-Order_No_5525,User_Id_1271,Bike,3,Business,10,1,2:33:55 PM,10,1,2:34:32 PM,10,1,2:44:34 PM,10,1,3:05:23 PM,10,1,4:20:20 PM,23,18.8,,-1.3107918,36.761794,-1.2927792,36.9053492,Rider_Id_279,4497
-Order_No_14167,User_Id_393,Bike,3,Business,31,4,12:07:44 PM,31,4,12:08:11 PM,31,4,12:08:18 PM,31,4,12:19:01 PM,31,4,12:50:42 PM,14,26.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_518,1901
-Order_No_3506,User_Id_3222,Bike,3,Business,14,5,2:35:56 PM,14,5,2:54:10 PM,14,5,2:57:04 PM,14,5,3:00:23 PM,14,5,3:21:59 PM,4,23.5,,-1.2551895,36.7822034,-1.2668469,36.804169099999996,Rider_Id_112,1296
-Order_No_27559,User_Id_3291,Bike,3,Business,14,3,11:11:56 AM,14,3,11:12:15 AM,14,3,11:17:12 AM,14,3,11:25:57 AM,14,3,11:38:21 AM,4,24.4,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_685,744
-Order_No_2706,User_Id_3669,Bike,3,Business,8,4,11:25:47 AM,8,4,11:26:13 AM,8,4,11:28:55 AM,8,4,11:45:49 AM,8,4,11:53:37 AM,2,26.3,,-1.2669099,36.79774329999999,-1.2615888000000002,36.7928732,Rider_Id_647,468
-Order_No_14043,User_Id_1075,Bike,3,Business,22,6,4:41:28 PM,22,6,5:23:03 PM,22,6,5:23:28 PM,22,6,5:55:23 PM,22,6,6:12:21 PM,5,23.9,,-1.2777926000000002,36.8215788,-1.2527958000000001,36.8003131,Rider_Id_40,1018
-Order_No_24513,User_Id_3326,Bike,3,Business,18,4,11:51:37 AM,18,4,11:52:22 AM,18,4,12:04:03 PM,18,4,12:14:27 PM,18,4,1:07:21 PM,21,,,-1.2527958000000001,36.8003131,-1.3351305,36.9184161,Rider_Id_166,3174
-Order_No_3371,User_Id_2207,Bike,3,Business,31,3,2:01:32 PM,31,3,2:14:37 PM,31,3,2:29:47 PM,31,3,4:06:18 PM,31,3,4:10:15 PM,4,26.9,,-1.28164,36.816866999999995,-1.2768326,36.8073804,Rider_Id_333,237
-Order_No_11069,User_Id_1363,Bike,3,Business,4,5,9:28:20 AM,4,5,9:42:50 AM,4,5,9:48:54 AM,4,5,9:49:56 AM,4,5,10:12:17 AM,4,19.6,51.9,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_753,1341
-Order_No_12328,User_Id_2868,Bike,1,Personal,2,4,9:17:29 AM,2,4,9:17:36 AM,2,4,9:19:29 AM,2,4,9:34:41 AM,2,4,9:42:19 AM,2,13.4,,-1.2943371000000001,36.8267,-1.3044313,36.818504,Rider_Id_414,458
-Order_No_25041,User_Id_2523,Bike,3,Business,22,4,11:39:11 AM,22,4,12:06:33 PM,22,4,12:07:37 PM,22,4,12:09:10 PM,22,4,1:06:27 PM,8,22.5,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_829,3437
-Order_No_18467,User_Id_3686,Bike,3,Business,24,4,12:03:08 PM,24,4,12:03:45 PM,24,4,12:09:47 PM,24,4,12:15:50 PM,24,4,12:24:25 PM,1,24.4,,-1.2551895,36.7822034,-1.2606254,36.783056,Rider_Id_755,515
-Order_No_5065,User_Id_2195,Bike,3,Business,3,1,10:34:12 AM,3,1,10:34:35 AM,3,1,10:44:21 AM,3,1,11:09:26 AM,3,1,11:59:49 AM,12,21.6,,-1.266837,36.799248999999996,-1.228911,36.881876,Rider_Id_829,3023
-Order_No_27811,User_Id_1094,Bike,1,Business,28,4,1:44:17 PM,28,4,2:09:40 PM,28,4,2:23:25 PM,28,4,2:24:46 PM,28,4,2:43:56 PM,2,,,-1.2897368,36.8226783,-1.2795299,36.8223098,Rider_Id_320,1150
-Order_No_18846,User_Id_2066,Bike,3,Business,1,1,11:47:55 AM,1,1,11:49:38 AM,1,1,12:06:08 PM,1,1,12:09:07 PM,1,1,12:31:43 PM,5,,,-1.3210188999999999,36.841055100000005,-1.3209207,36.869369899999995,Rider_Id_435,1356
-Order_No_22911,User_Id_1075,Bike,3,Business,13,3,3:22:48 PM,13,3,3:24:36 PM,13,3,3:53:46 PM,13,3,3:56:49 PM,13,3,4:26:48 PM,13,29.0,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_63,1799
-Order_No_21409,User_Id_627,Bike,3,Business,16,4,3:40:47 PM,16,4,3:41:10 PM,16,4,3:42:05 PM,16,4,3:44:59 PM,16,4,4:08:33 PM,8,,,-1.2551895,36.7822034,-1.2858059,36.7876302,Rider_Id_177,1414
-Order_No_13732,User_Id_393,Bike,3,Business,11,1,8:41:34 AM,11,1,8:42:04 AM,11,1,8:42:17 AM,11,1,9:37:13 AM,11,1,9:37:17 AM,14,18.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_10,4
-Order_No_27935,User_Id_3001,Bike,3,Business,22,4,7:57:37 PM,22,4,8:28:32 PM,22,4,8:45:08 PM,22,4,8:58:29 PM,22,4,9:15:41 PM,5,20.6,,-1.2701316,36.7824436,-1.2894456,36.778094700000004,Rider_Id_427,1032
-Order_No_27431,User_Id_1927,Bike,3,Business,8,5,11:35:27 AM,8,5,11:36:21 AM,8,5,11:45:16 AM,8,5,11:47:28 AM,8,5,12:28:09 PM,14,28.4,,-1.3472066,36.7692638,-1.265715,36.808525,Rider_Id_263,2441
-Order_No_20332,User_Id_3331,Bike,3,Business,7,4,3:17:49 PM,7,4,3:24:55 PM,7,4,3:37:45 PM,7,4,3:39:17 PM,7,4,3:52:56 PM,6,27.5,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_903,819
-Order_No_3623,User_Id_136,Bike,3,Business,15,2,12:39:31 PM,15,2,12:39:40 PM,15,2,12:55:44 PM,15,2,1:05:23 PM,15,2,1:35:20 PM,15,24.2,,-1.2943371000000001,36.8267,-1.3254522,36.721658399999995,Rider_Id_45,1797
-Order_No_17730,User_Id_1304,Bike,3,Business,5,1,10:09:19 AM,5,1,10:10:42 AM,5,1,10:11:49 AM,5,1,10:28:48 AM,5,1,11:04:58 AM,14,18.8,,-1.3332749,36.870814700000004,-1.2636813999999998,36.802996500000006,Rider_Id_108,2170
-Order_No_14844,User_Id_1845,Bike,3,Business,30,2,9:00:17 AM,30,2,9:00:27 AM,30,2,9:16:24 AM,30,2,9:49:07 AM,30,2,10:27:19 AM,21,17.5,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_769,2292
-Order_No_16221,User_Id_1736,Bike,3,Personal,14,5,2:48:18 PM,14,5,2:49:25 PM,14,5,3:08:20 PM,14,5,3:30:53 PM,14,5,4:04:31 PM,14,27.1,,-1.296485,36.7931959,-1.3189252,36.861726899999994,Rider_Id_890,2018
-Order_No_11593,User_Id_533,Bike,3,Business,12,1,2:18:35 PM,12,1,3:23:55 PM,12,1,3:24:59 PM,12,1,3:42:41 PM,12,1,3:53:49 PM,6,26.7,,-1.2765492,36.7669809,-1.2551895,36.7822034,Rider_Id_45,668
-Order_No_3281,User_Id_2014,Bike,2,Personal,30,4,12:45:10 PM,30,4,12:46:02 PM,30,4,12:58:28 PM,30,4,1:06:15 PM,30,4,1:30:47 PM,3,20.1,,-1.2813973,36.828920399999994,-1.2666695,36.8328283,Rider_Id_167,1472
-Order_No_1922,User_Id_3403,Bike,3,Business,2,2,4:04:55 PM,2,2,4:08:12 PM,2,2,4:12:54 PM,2,2,4:23:16 PM,2,2,4:41:40 PM,7,29.3,,-1.2551895,36.7822034,-1.2889751,36.815404799999996,Rider_Id_676,1104
-Order_No_14473,User_Id_3701,Bike,3,Business,4,2,4:06:37 PM,4,2,4:09:47 PM,4,2,4:27:51 PM,4,2,4:42:47 PM,4,2,5:04:08 PM,8,22.4,,-1.301735,36.761081,-1.2684239,36.79524620000001,Rider_Id_612,1281
-Order_No_16263,User_Id_2587,Bike,3,Business,7,1,3:14:05 PM,7,1,3:14:44 PM,7,1,3:18:03 PM,7,1,3:45:30 PM,7,1,4:03:33 PM,6,26.6,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_205,1083
-Order_No_14955,User_Id_1850,Bike,3,Business,4,1,2:21:23 PM,4,1,2:21:42 PM,4,1,2:21:52 PM,4,1,3:06:58 PM,4,1,3:22:41 PM,9,28.7,,-1.3047132,36.808955299999994,-1.2551895,36.7822034,Rider_Id_704,943
-Order_No_20625,User_Id_2200,Bike,3,Business,6,2,3:18:33 PM,6,2,4:20:59 PM,6,2,4:42:58 PM,6,2,4:59:00 PM,6,2,6:00:22 PM,7,,,-1.290894,36.822971,-1.2546732,36.7923244,Rider_Id_925,3682
-Order_No_3312,User_Id_2509,Bike,3,Business,11,1,2:41:03 PM,11,1,2:42:35 PM,11,1,2:53:12 PM,11,1,2:58:49 PM,11,1,4:05:03 PM,20,27.8,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_404,3974
-Order_No_22843,User_Id_3590,Bike,3,Business,8,2,9:39:38 AM,8,2,9:42:21 AM,8,2,10:10:04 AM,8,2,10:12:53 AM,8,2,11:31:11 AM,5,19.0,,-1.266294,36.8057971,-1.300722,36.816865,Rider_Id_379,4698
-Order_No_24239,User_Id_3689,Bike,3,Business,31,3,8:44:48 AM,31,3,8:46:06 AM,31,3,9:19:52 AM,31,3,9:27:48 AM,31,3,9:55:26 AM,9,19.3,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_817,1658
-Order_No_27901,User_Id_2587,Bike,3,Business,21,5,10:57:00 AM,21,5,11:00:27 AM,21,5,11:05:50 AM,21,5,11:15:41 AM,21,5,11:19:03 AM,1,22.6,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_819,202
-Order_No_15693,User_Id_635,Bike,3,Business,12,5,2:32:13 PM,12,5,2:33:16 PM,12,5,2:33:46 PM,12,5,3:48:47 PM,12,5,4:00:25 PM,10,,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_828,698
-Order_No_4427,User_Id_475,Bike,3,Business,21,1,2:52:15 PM,21,1,2:54:46 PM,21,1,3:11:35 PM,21,1,3:13:07 PM,21,1,3:39:23 PM,9,21.8,,-1.2626906,36.7827022,-1.2983462,36.8139996,Rider_Id_761,1576
-Order_No_24275,User_Id_1246,Bike,3,Business,9,5,3:12:32 PM,9,5,3:15:01 PM,9,5,3:20:00 PM,9,5,3:29:02 PM,9,5,3:34:15 PM,1,26.3,,-1.2571472,36.795063299999995,-1.2642903,36.80021370000001,Rider_Id_875,313
-Order_No_15860,User_Id_3500,Bike,3,Business,14,5,2:41:01 PM,14,5,2:42:08 PM,14,5,2:52:18 PM,14,5,3:12:42 PM,14,5,3:34:15 PM,8,23.0,,-1.2973948999999998,36.816038,-1.2621828000000002,36.7949276,Rider_Id_167,1293
-Order_No_24892,User_Id_2360,Bike,3,Business,5,5,3:32:36 PM,5,5,3:33:20 PM,5,5,3:43:39 PM,5,5,3:54:07 PM,5,5,4:09:25 PM,4,27.4,,-1.2708857,36.8135766,-1.2551895,36.7822034,Rider_Id_383,918
-Order_No_2412,User_Id_393,Bike,3,Business,17,4,8:20:14 AM,17,4,8:20:20 AM,17,4,8:23:05 AM,17,4,8:31:15 AM,17,4,9:04:13 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_523,1978
-Order_No_13023,User_Id_1205,Bike,3,Business,12,3,4:35:15 PM,12,3,4:35:31 PM,12,3,4:38:07 PM,12,3,4:42:31 PM,12,3,5:24:37 PM,17,24.0,,-1.2810807,36.8144229,-1.3085443,36.6884314,Rider_Id_266,2526
-Order_No_184,User_Id_3291,Bike,3,Business,31,2,1:38:29 PM,31,2,1:39:17 PM,31,2,1:49:34 PM,31,2,2:01:18 PM,31,2,2:24:09 PM,11,22.1,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_250,1371
-Order_No_11368,User_Id_3517,Bike,3,Business,6,6,12:22:05 PM,6,6,12:22:13 PM,6,6,12:31:08 PM,6,6,12:33:52 PM,6,6,1:08:13 PM,15,24.1,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_43,2061
-Order_No_16039,User_Id_813,Bike,3,Personal,14,1,10:23:54 AM,14,1,10:24:17 AM,14,1,10:28:50 AM,14,1,10:34:17 AM,14,1,10:51:54 AM,6,23.3,,-1.299207,36.8414337,-1.2986123,36.8076281,Rider_Id_111,1057
-Order_No_6205,User_Id_3718,Bike,3,Business,11,2,11:52:34 AM,11,2,12:43:54 PM,11,2,1:00:25 PM,11,2,1:07:51 PM,11,2,1:21:52 PM,2,19.3,,-1.2650027,36.8126242,-1.2684319,36.81658529999999,Rider_Id_268,841
-Order_No_27763,User_Id_478,Bike,3,Business,8,5,12:48:02 PM,8,5,1:42:22 PM,8,5,2:03:44 PM,8,5,2:06:09 PM,8,5,2:59:30 PM,9,27.4,,-1.2551895,36.7822034,-1.29387,36.7907813,Rider_Id_521,3201
-Order_No_25621,User_Id_183,Bike,1,Personal,11,5,1:43:54 PM,11,5,1:44:50 PM,11,5,1:47:52 PM,11,5,1:59:59 PM,11,5,2:09:15 PM,3,22.7,,-1.2783619,36.8183313,-1.293973,36.80169,Rider_Id_865,556
-Order_No_21948,User_Id_3686,Bike,3,Business,11,1,12:19:47 PM,11,1,12:20:21 PM,11,1,12:24:15 PM,11,1,12:41:36 PM,11,1,12:52:43 PM,4,26.7,,-1.2551895,36.7822034,-1.2519787,36.780649100000005,Rider_Id_885,667
-Order_No_21109,User_Id_1459,Bike,3,Business,16,2,4:16:27 PM,16,2,4:18:35 PM,16,2,4:32:01 PM,16,2,4:44:35 PM,16,2,4:56:18 PM,6,25.1,,-1.2764786,36.81947879999999,-1.264945,36.784678,Rider_Id_427,703
-Order_No_10586,User_Id_1594,Bike,3,Business,16,2,3:11:54 PM,16,2,3:12:47 PM,16,2,3:22:56 PM,16,2,3:38:43 PM,16,2,4:07:34 PM,12,,,-1.28878,36.8168312,-1.2156006000000001,36.8916865,Rider_Id_709,1731
-Order_No_1991,User_Id_3283,Bike,3,Business,25,1,11:14:00 AM,25,1,11:17:51 AM,25,1,12:00:44 PM,25,1,12:22:02 PM,25,1,12:46:45 PM,18,27.1,,-1.3444459999999998,36.756597,-1.2628473,36.781805,Rider_Id_628,1483
-Order_No_24942,User_Id_393,Bike,3,Business,25,1,12:01:27 PM,25,1,12:01:50 PM,25,1,12:04:58 PM,25,1,12:16:08 PM,25,1,12:42:29 PM,14,25.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_839,1581
-Order_No_11227,User_Id_1244,Bike,3,Business,15,2,10:20:06 AM,15,2,10:21:13 AM,15,2,10:30:19 AM,15,2,10:38:30 AM,15,2,10:57:54 AM,6,24.1,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_279,1164
-Order_No_3546,User_Id_478,Bike,3,Business,30,2,10:44:52 AM,30,2,10:51:06 AM,30,2,11:05:53 AM,30,2,11:11:25 AM,30,2,11:48:43 AM,7,,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_598,2238
-Order_No_24106,User_Id_2361,Bike,3,Personal,10,1,11:32:46 AM,10,1,11:45:04 AM,10,1,11:54:10 AM,10,1,11:56:16 AM,10,1,12:32:29 PM,7,18.6,,-1.2823843000000001,36.7734371,-1.289207,36.817558899999995,Rider_Id_310,2173
-Order_No_6133,User_Id_2763,Bike,3,Business,4,4,12:59:05 PM,4,4,1:00:49 PM,4,4,1:00:53 PM,4,4,1:30:08 PM,4,4,1:54:13 PM,8,25.5,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_508,1445
-Order_No_18262,User_Id_3686,Bike,3,Business,4,3,12:38:09 PM,4,3,12:40:52 PM,4,3,1:06:42 PM,4,3,1:11:47 PM,4,3,1:41:03 PM,8,17.2,,-1.2551895,36.7822034,-1.3024794,36.8228284,Rider_Id_908,1756
-Order_No_6595,User_Id_269,Bike,3,Business,5,3,11:44:23 AM,5,3,11:44:31 AM,5,3,11:45:18 AM,5,3,11:53:00 AM,5,3,12:35:18 PM,16,22.1,,-1.3332749,36.870814700000004,-1.2571472,36.795063299999995,Rider_Id_647,2538
-Order_No_25433,User_Id_2829,Bike,3,Business,4,1,1:51:18 PM,4,1,1:53:06 PM,4,1,2:29:03 PM,4,1,2:34:03 PM,4,1,2:51:20 PM,4,22.8,,-1.2579303999999998,36.8019667,-1.2833106,36.82402379999999,Rider_Id_586,1037
-Order_No_13626,User_Id_3283,Bike,3,Business,19,2,9:11:21 AM,19,2,9:11:41 AM,19,2,9:35:38 AM,19,2,10:04:29 AM,19,2,10:25:49 AM,14,21.1,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_75,1280
-Order_No_21930,User_Id_2102,Bike,3,Business,3,3,8:10:41 AM,3,3,8:11:36 AM,3,3,8:16:43 AM,3,3,8:28:24 AM,3,3,8:47:58 AM,13,18.5,,-1.2613768,36.801941799999994,-1.3323107,36.8692616,Rider_Id_726,1174
-Order_No_27801,User_Id_3470,Bike,3,Business,30,5,2:11:42 PM,30,5,2:22:01 PM,30,5,2:30:32 PM,30,5,2:38:06 PM,30,5,3:19:44 PM,1,24.6,,-1.28878,36.8168312,-1.2858424,36.8190802,Rider_Id_267,2498
-Order_No_11450,User_Id_1987,Bike,1,Personal,4,3,11:49:44 AM,4,3,11:51:32 AM,4,3,12:04:31 PM,4,3,12:06:15 PM,4,3,12:20:54 PM,3,17.1,,-1.3044313,36.818504,-1.2858996999999999,36.813863399999995,Rider_Id_629,879
-Order_No_13857,User_Id_246,Bike,3,Business,9,6,3:00:50 PM,9,6,3:01:06 PM,9,6,3:10:15 PM,9,6,3:17:52 PM,9,6,3:37:39 PM,7,19.4,,-1.225322,36.80855,-1.2584143,36.8048002,Rider_Id_116,1187
-Order_No_3719,User_Id_1482,Bike,3,Business,13,3,11:58:59 AM,13,3,11:59:27 AM,13,3,11:59:31 AM,13,3,12:03:45 PM,13,3,12:35:23 PM,24,25.0,,-1.3385790000000002,36.89630379999999,-1.22589,36.7822548,Rider_Id_886,1898
-Order_No_17429,User_Id_2768,Bike,3,Business,10,1,11:03:14 AM,10,1,11:03:59 AM,10,1,11:27:13 AM,10,1,12:04:42 PM,10,1,1:11:39 PM,38,18.3,,-1.3344999,36.8966374,-1.1344906000000001,36.9123061,Rider_Id_418,4017
-Order_No_23143,User_Id_3698,Bike,3,Business,11,4,3:30:22 PM,11,4,4:22:01 PM,11,4,4:41:47 PM,11,4,4:48:24 PM,11,4,5:13:15 PM,10,26.4,,-1.3107918,36.761794,-1.2863906999999999,36.8194287,Rider_Id_205,1491
-Order_No_9659,User_Id_2763,Bike,3,Business,9,4,4:21:12 PM,9,4,4:21:36 PM,9,4,4:32:15 PM,9,4,4:39:30 PM,9,4,4:59:07 PM,7,23.2,,-1.2551895,36.7822034,-1.2885417,36.810052299999995,Rider_Id_229,1177
-Order_No_6168,User_Id_867,Bike,3,Business,26,6,9:22:39 AM,26,6,9:25:34 AM,26,6,9:38:35 AM,26,6,9:45:48 AM,26,6,10:16:19 AM,11,18.8,,-1.2770708,36.8231093,-1.306378,36.7519845,Rider_Id_208,1831
-Order_No_2541,User_Id_742,Bike,3,Personal,14,1,10:56:44 AM,14,1,10:57:27 AM,14,1,11:01:37 AM,14,1,11:04:26 AM,14,1,11:33:42 AM,18,23.0,,-1.2804664,36.7701111,-1.2343236000000002,36.8861,Rider_Id_756,1756
-Order_No_11250,User_Id_3393,Bike,1,Personal,22,3,3:20:56 PM,22,3,3:26:21 PM,22,3,3:32:55 PM,22,3,3:56:43 PM,22,3,4:06:19 PM,5,25.8,,-1.278612,36.8195876,-1.2682178999999998,36.8009289,Rider_Id_956,576
-Order_No_183,User_Id_635,Bike,3,Business,5,2,2:08:15 PM,5,2,2:10:03 PM,5,2,2:24:52 PM,5,2,2:44:16 PM,5,2,3:07:33 PM,6,22.6,,-1.2857763999999998,36.8236622,-1.278197,36.7842562,Rider_Id_616,1397
-Order_No_11263,User_Id_2804,Bike,3,Business,15,1,1:57:16 PM,15,1,3:05:41 PM,15,1,3:12:55 PM,15,1,3:15:29 PM,15,1,3:37:49 PM,6,,,-1.2551895,36.7822034,-1.2825341000000001,36.819078499999996,Rider_Id_330,1340
-Order_No_20707,User_Id_2066,Bike,3,Business,17,5,12:22:25 PM,17,5,12:22:47 PM,17,5,12:26:02 PM,17,5,12:33:30 PM,17,5,12:45:19 PM,6,17.7,,-1.316961,36.820337,-1.2790803,36.8156909,Rider_Id_857,709
-Order_No_15442,User_Id_1075,Bike,3,Business,29,5,11:58:21 AM,29,5,11:59:00 AM,29,5,12:30:54 PM,29,5,12:40:49 PM,29,5,1:06:15 PM,5,26.8,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_291,1526
-Order_No_26703,User_Id_3745,Bike,3,Personal,28,1,11:42:53 AM,28,1,11:47:37 AM,28,1,11:58:21 AM,28,1,12:01:09 PM,28,1,12:33:42 PM,11,22.6,,-1.2450466999999998,36.7544464,-1.2741728,36.79691079999999,Rider_Id_103,1953
-Order_No_8443,User_Id_2465,Bike,1,Personal,3,1,4:07:50 PM,3,1,4:08:38 PM,3,1,4:28:22 PM,3,1,5:05:35 PM,3,1,5:27:56 PM,16,22.4,,-1.3214137,36.8569475,-1.3398265,36.739982399999995,Rider_Id_669,1341
-Order_No_42,User_Id_2069,Bike,3,Business,28,2,3:35:04 PM,28,2,3:36:01 PM,28,2,3:54:00 PM,28,2,3:56:59 PM,28,2,4:14:36 PM,9,23.4,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_233,1057
-Order_No_24327,User_Id_2271,Bike,3,Business,31,4,1:29:35 PM,31,4,1:32:53 PM,31,4,1:53:01 PM,31,4,2:00:33 PM,31,4,2:23:42 PM,11,28.8,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_416,1389
-Order_No_12286,User_Id_2066,Bike,3,Business,4,2,12:44:03 PM,4,2,12:44:51 PM,4,2,12:48:58 PM,4,2,12:51:16 PM,4,2,1:15:40 PM,5,18.9,,-1.316961,36.820337,-1.285382,36.8162878,Rider_Id_402,1464
-Order_No_25976,User_Id_3259,Bike,3,Business,12,2,1:31:25 PM,12,2,2:50:43 PM,12,2,3:10:19 PM,12,2,3:18:02 PM,12,2,3:38:00 PM,10,22.9,,-1.2956517,36.8036292,-1.326825,36.864345,Rider_Id_379,1198
-Order_No_22302,User_Id_2150,Bike,3,Business,4,4,9:16:19 AM,4,4,9:16:26 AM,4,4,9:24:41 AM,4,4,9:34:47 AM,4,4,10:12:29 AM,11,21.6,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_693,2262
-Order_No_26977,User_Id_3091,Bike,3,Business,22,5,3:24:24 PM,22,5,3:33:37 PM,22,5,3:57:46 PM,22,5,4:03:59 PM,22,5,4:28:30 PM,9,27.3,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_448,1471
-Order_No_6721,User_Id_2982,Bike,3,Business,7,3,9:42:40 AM,7,3,9:43:55 AM,7,3,9:44:04 AM,7,3,10:50:03 AM,7,3,11:17:21 AM,22,25.1,,-1.300921,36.828195,-1.1933341,36.9271667,Rider_Id_179,1638
-Order_No_7118,User_Id_265,Bike,3,Business,25,2,3:26:00 PM,25,2,3:26:05 PM,25,2,3:26:09 PM,25,2,4:03:35 PM,25,2,4:13:16 PM,13,27.0,,-1.3321944,36.889232899999996,-1.300921,36.828195,Rider_Id_874,581
-Order_No_21899,User_Id_478,Bike,3,Business,2,4,12:02:13 PM,2,4,12:03:10 PM,2,4,12:10:06 PM,2,4,12:14:00 PM,2,4,12:37:38 PM,9,22.6,,-1.2551895,36.7822034,-1.2985031999999999,36.7808727,Rider_Id_452,1418
-Order_No_26320,User_Id_2823,Bike,3,Business,6,5,12:46:50 PM,6,5,12:59:22 PM,6,5,1:03:59 PM,6,5,1:16:42 PM,6,5,1:31:06 PM,8,20.2,,-1.2585521000000002,36.7945765,-1.3014461000000002,36.7661381,Rider_Id_103,864
-Order_No_5951,User_Id_1484,Bike,3,Business,4,1,10:09:20 AM,4,1,10:10:04 AM,4,1,10:38:49 AM,4,1,10:54:36 AM,4,1,11:20:02 AM,15,21.2,,-1.2343236000000002,36.8861,-1.2553615,36.789537599999996,Rider_Id_467,1526
-Order_No_22633,User_Id_1329,Bike,3,Business,1,4,12:39:47 PM,1,4,12:42:30 PM,1,4,12:51:23 PM,1,4,1:01:09 PM,1,4,1:27:00 PM,6,,,-1.273056,36.811298,-1.2611728000000002,36.7753701,Rider_Id_427,1551
-Order_No_27077,User_Id_2801,Bike,3,Business,29,4,12:51:03 PM,29,4,12:51:39 PM,29,4,12:53:02 PM,29,4,1:16:39 PM,29,4,1:27:02 PM,5,21.7,,-1.2595421999999998,36.7871178,-1.2753108999999998,36.8130185,Rider_Id_322,623
-Order_No_24764,User_Id_2330,Bike,3,Business,29,3,3:25:15 PM,29,3,3:28:32 PM,29,3,3:49:31 PM,29,3,3:56:59 PM,29,3,4:34:19 PM,14,21.3,,-1.2925641,36.897644899999996,-1.3798085,36.9290528,Rider_Id_660,2240
-Order_No_14065,User_Id_286,Bike,3,Business,6,4,9:47:32 AM,6,4,10:41:41 AM,6,4,11:40:59 AM,6,4,11:54:59 AM,6,4,12:39:33 PM,11,24.2,,-1.3206928999999998,36.8621776,-1.2962966999999999,36.8020231,Rider_Id_677,2674
-Order_No_21184,User_Id_2587,Bike,3,Business,12,1,10:35:24 AM,12,1,10:58:04 AM,12,1,11:10:54 AM,12,1,11:20:37 AM,12,1,12:11:26 PM,8,23.8,,-1.3014461000000002,36.7661381,-1.28878,36.8168312,Rider_Id_436,3049
-Order_No_23052,User_Id_3312,Bike,3,Business,9,3,11:19:34 AM,9,3,11:20:36 AM,9,3,11:32:10 AM,9,3,11:38:33 AM,9,3,12:14:55 PM,13,25.3,,-1.2583017,36.8023526,-1.2159575,36.831182899999995,Rider_Id_205,2182
-Order_No_25153,User_Id_3295,Bike,3,Business,21,5,8:45:40 AM,21,5,8:45:59 AM,21,5,8:51:20 AM,21,5,9:02:47 AM,21,5,9:44:15 AM,20,20.0,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_620,2488
-Order_No_11267,User_Id_1370,Bike,3,Personal,3,4,4:03:19 PM,3,4,4:04:12 PM,3,4,4:18:39 PM,3,4,4:23:00 PM,3,4,4:31:03 PM,1,24.8,,-1.261692,36.80161,-1.2655143999999998,36.81273770000001,Rider_Id_290,483
-Order_No_19186,User_Id_3752,Bike,3,Business,17,5,3:04:45 PM,17,5,3:04:57 PM,17,5,3:08:07 PM,17,5,4:06:43 PM,17,5,4:15:23 PM,2,,,-1.2649605,36.798177700000004,-1.2753108999999998,36.8130185,Rider_Id_361,520
-Order_No_118,User_Id_3688,Bike,3,Business,14,4,2:42:58 PM,14,4,2:44:37 PM,14,4,3:05:00 PM,14,4,3:28:59 PM,14,4,4:13:52 PM,12,27.5,,-1.2551895,36.7822034,-1.2613229,36.8657183,Rider_Id_432,2693
-Order_No_27986,User_Id_393,Bike,3,Business,5,2,4:16:51 PM,5,2,4:17:40 PM,5,2,4:17:43 PM,5,2,4:30:13 PM,5,2,4:30:14 PM,6,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_39,1
-Order_No_18738,User_Id_3232,Bike,3,Business,26,5,12:53:07 PM,26,5,12:54:36 PM,26,5,1:46:01 PM,26,5,2:42:42 PM,26,5,3:43:10 PM,17,,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_755,3628
-Order_No_21265,User_Id_3560,Bike,3,Business,15,1,12:48:55 PM,15,1,12:50:27 PM,15,1,12:52:50 PM,15,1,12:57:10 PM,15,1,12:58:16 PM,2,,,-1.2813012,36.832396200000005,-1.279395,36.825364,Rider_Id_99,66
-Order_No_9047,User_Id_972,Bike,3,Business,4,6,10:28:00 AM,4,6,10:28:19 AM,4,6,10:44:15 AM,4,6,10:48:18 AM,4,6,11:34:05 AM,12,,,-1.2862033000000002,36.8152966,-1.3030532,36.8899952,Rider_Id_448,2747
-Order_No_1706,User_Id_1397,Bike,3,Business,26,2,3:14:48 PM,26,2,3:15:24 PM,26,2,3:20:39 PM,26,2,3:26:08 PM,26,2,3:56:16 PM,18,29.5,,-1.2648363,36.802375399999995,-1.317461,36.707031,Rider_Id_875,1808
-Order_No_12241,User_Id_520,Bike,2,Personal,8,1,11:19:02 AM,8,1,11:24:21 AM,8,1,11:24:50 AM,8,1,11:40:48 AM,8,1,12:02:22 PM,12,24.7,,-1.28878,36.8168312,-1.2238103,36.8772588,Rider_Id_731,1294
-Order_No_9100,User_Id_3691,Bike,1,Business,11,5,3:28:45 PM,11,5,3:29:41 PM,11,5,3:44:41 PM,11,5,3:59:42 PM,11,5,4:18:54 PM,6,22.5,,-1.2559559999999999,36.789867,-1.2651218000000002,36.8228765,Rider_Id_100,1152
-Order_No_24229,User_Id_1724,Bike,3,Business,16,4,2:57:49 PM,16,4,3:00:46 PM,16,4,3:15:34 PM,16,4,3:39:56 PM,16,4,3:47:29 PM,7,,,-1.2551895,36.7822034,-1.2823851000000002,36.8186472,Rider_Id_709,453
-Order_No_11209,User_Id_2330,Bike,3,Business,5,5,11:55:06 AM,5,5,11:56:29 AM,5,5,12:09:12 PM,5,5,12:15:08 PM,5,5,12:28:50 PM,2,24.3,,-1.3015197,36.7658456,-1.2856664,36.755983799999996,Rider_Id_548,822
-Order_No_7619,User_Id_2216,Bike,3,Business,18,1,12:03:15 PM,18,1,12:03:26 PM,18,1,12:06:08 PM,18,1,12:10:47 PM,18,1,1:13:24 PM,25,27.9,,-1.2551895,36.7822034,-1.3888629,36.935984000000005,Rider_Id_931,3757
-Order_No_13161,User_Id_2956,Bike,3,Business,21,2,12:58:52 PM,21,2,1:00:00 PM,21,2,1:01:07 PM,21,2,1:10:43 PM,21,2,1:20:51 PM,2,22.4,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_520,608
-Order_No_16507,User_Id_803,Bike,3,Business,30,3,1:24:47 PM,30,3,1:24:51 PM,30,3,1:42:12 PM,30,3,2:04:33 PM,30,3,2:37:39 PM,20,24.9,,-1.3321944,36.889232899999996,-1.2485190000000002,36.783656,Rider_Id_543,1986
-Order_No_17407,User_Id_977,Bike,1,Personal,31,2,7:52:46 AM,31,2,10:26:50 AM,31,2,10:38:20 AM,31,2,10:52:35 AM,31,2,11:14:07 AM,9,18.5,,-1.2952476000000002,36.75392539999999,-1.2892350000000001,36.8135805,Rider_Id_844,1292
-Order_No_4626,User_Id_3355,Bike,3,Business,7,6,4:44:16 PM,7,6,4:49:25 PM,7,6,5:00:35 PM,7,6,5:08:17 PM,7,6,5:28:13 PM,11,20.9,,-1.339046,36.762596,-1.3052491000000002,36.8223899,Rider_Id_629,1196
-Order_No_7238,User_Id_2987,Bike,3,Business,19,1,10:28:37 AM,19,1,11:33:26 AM,19,1,11:40:05 AM,19,1,11:54:37 AM,19,1,12:16:30 PM,12,24.6,,-1.3271374,36.8495534,-1.2649605,36.798177700000004,Rider_Id_675,1313
-Order_No_18615,User_Id_2748,Bike,1,Personal,13,7,11:22:27 AM,13,7,11:53:33 AM,13,7,12:30:22 PM,13,7,12:33:02 PM,13,7,1:20:30 PM,21,23.4,,-1.269103,36.8223998,-1.2544686999999999,36.9485028,Rider_Id_95,2848
-Order_No_1833,User_Id_3517,Bike,3,Business,16,6,9:32:52 AM,16,6,9:33:05 AM,16,6,9:36:52 AM,16,6,10:32:04 AM,16,6,11:04:46 AM,15,25.7,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_858,1962
-Order_No_4636,User_Id_1281,Bike,3,Business,1,3,12:23:29 PM,1,3,12:23:49 PM,1,3,12:28:10 PM,1,3,12:34:29 PM,1,3,12:53:31 PM,8,19.8,,-1.2707742,36.7578609,-1.2728058,36.8049903,Rider_Id_735,1142
-Order_No_20410,User_Id_1402,Bike,3,Business,12,5,10:56:02 AM,12,5,10:56:13 AM,12,5,11:19:10 AM,12,5,11:24:06 AM,12,5,11:49:03 AM,13,26.4,,-1.2232123,36.8932547,-1.2813012,36.832396200000005,Rider_Id_76,1497
-Order_No_5020,User_Id_3091,Bike,3,Business,14,1,2:56:20 PM,14,1,2:56:43 PM,14,1,3:02:19 PM,14,1,3:07:27 PM,14,1,3:24:03 PM,5,25.6,,-1.2846195,36.7958316,-1.3012008,36.764868,Rider_Id_516,996
-Order_No_11895,User_Id_1363,Bike,3,Business,12,5,2:31:52 PM,12,5,2:40:17 PM,12,5,2:56:17 PM,12,5,3:17:12 PM,12,5,3:40:09 PM,8,,,-1.2545163000000001,36.8243717,-1.3004062,36.829741,Rider_Id_435,1377
-Order_No_5999,User_Id_2548,Bike,3,Business,23,3,10:48:06 AM,23,3,10:48:20 AM,23,3,10:50:26 AM,23,3,10:59:07 AM,23,3,12:34:23 PM,16,,,-1.3031652,36.8466816,-1.3435721999999999,36.751409499999994,Rider_Id_231,5716
-Order_No_20535,User_Id_3207,Bike,1,Personal,15,3,2:14:50 PM,15,3,2:15:41 PM,15,3,2:25:48 PM,15,3,2:31:20 PM,15,3,2:54:36 PM,3,21.4,,-1.3014461000000002,36.7661381,-1.3009680000000001,36.772732500000004,Rider_Id_140,1396
-Order_No_27687,User_Id_1011,Bike,1,Personal,1,1,11:43:56 AM,1,1,11:44:02 AM,1,1,11:45:46 AM,1,1,12:09:44 PM,1,1,12:32:06 PM,10,24.2,,-1.2915306999999998,36.790586499999996,-1.2321733,36.7974377,Rider_Id_648,1342
-Order_No_19580,User_Id_1687,Bike,3,Business,11,2,2:20:16 PM,11,2,2:23:13 PM,11,2,2:42:41 PM,11,2,2:54:07 PM,11,2,3:17:33 PM,12,22.4,,-1.2626574,36.8014034,-1.2533046,36.886093,Rider_Id_38,1406
-Order_No_9562,User_Id_2066,Bike,3,Business,30,1,11:12:06 AM,30,1,11:42:40 AM,30,1,12:01:44 PM,30,1,12:03:28 PM,30,1,12:45:45 PM,16,22.8,,-1.316961,36.820337,-1.3319686000000002,36.911555299999996,Rider_Id_520,2537
-Order_No_20349,User_Id_297,Bike,2,Personal,16,2,10:06:40 AM,16,2,10:06:58 AM,16,2,10:14:33 AM,16,2,10:18:02 AM,16,2,10:57:26 AM,6,,,-1.2852908,36.7557439,-1.2977386999999998,36.803144700000004,Rider_Id_612,2364
-Order_No_25132,User_Id_792,Bike,2,Personal,24,2,10:07:06 AM,24,2,10:07:15 AM,24,2,10:08:49 AM,24,2,10:22:39 AM,24,2,10:32:45 AM,3,14.1,,-1.2738167,36.8157821,-1.2617458000000001,36.825144099999996,Rider_Id_88,606
-Order_No_20547,User_Id_893,Bike,3,Business,13,3,10:12:40 AM,13,3,10:13:08 AM,13,3,10:23:52 AM,13,3,10:25:05 AM,13,3,10:36:52 AM,4,17.1,,-1.2663194,36.809901,-1.2607621999999998,36.8227608,Rider_Id_660,707
-Order_No_7698,User_Id_1885,Bike,1,Personal,4,1,10:03:08 AM,4,1,11:17:44 AM,4,1,11:24:31 AM,4,1,11:28:05 AM,4,1,12:01:56 PM,19,21.1,,-1.2862033000000002,36.8152966,-1.2475223,36.937088,Rider_Id_539,2031
-Order_No_20403,User_Id_1051,Bike,3,Business,5,2,10:49:10 AM,5,2,10:49:44 AM,5,2,10:53:52 AM,5,2,10:57:49 AM,5,2,11:40:43 AM,14,21.6,,-1.2963097,36.7688221,-1.313483,36.872126,Rider_Id_846,2574
-Order_No_5296,User_Id_968,Bike,2,Personal,26,2,9:16:07 AM,26,2,9:16:15 AM,26,2,9:16:23 AM,26,2,10:02:18 AM,26,2,10:12:59 AM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_379,641
-Order_No_9158,User_Id_3090,Bike,2,Personal,18,2,10:23:34 AM,18,2,10:23:51 AM,18,2,10:37:46 AM,18,2,10:39:14 AM,18,2,11:07:56 AM,8,22.7,,-1.2602191999999999,36.7878612,-1.2885503999999999,36.8066908,Rider_Id_532,1722
-Order_No_15414,User_Id_2436,Bike,3,Business,29,1,5:08:19 PM,29,1,5:09:06 PM,29,1,5:12:03 PM,29,1,5:15:53 PM,29,1,5:16:04 PM,3,23.4,,-1.2551895,36.7822034,-1.268027,36.8037005,Rider_Id_541,11
-Order_No_11752,User_Id_1305,Bike,1,Personal,21,4,4:42:36 PM,21,4,4:43:02 PM,21,4,4:56:24 PM,21,4,5:04:01 PM,21,4,5:33:49 PM,17,19.8,,-1.2915306999999998,36.790586499999996,-1.2297202,36.874550799999994,Rider_Id_116,1788
-Order_No_20616,User_Id_1271,Bike,3,Business,14,3,12:30:42 PM,14,3,12:46:06 PM,14,3,1:02:45 PM,14,3,1:09:46 PM,14,3,1:38:01 PM,27,24.7,,-1.3949989,36.7654761,-1.3244885,36.897791999999995,Rider_Id_179,1695
-Order_No_7037,User_Id_788,Bike,3,Business,10,1,1:55:27 PM,10,1,1:55:36 PM,10,1,2:02:44 PM,10,1,2:20:01 PM,10,1,2:45:26 PM,13,,,-1.290894,36.822971,-1.225322,36.80855,Rider_Id_692,1525
-Order_No_24267,User_Id_3557,Bike,3,Business,3,3,2:10:02 PM,3,3,2:12:20 PM,3,3,3:07:02 PM,3,3,3:22:34 PM,3,3,3:46:58 PM,7,26.9,,-1.3076261999999998,36.8325382,-1.2546732,36.808679600000005,Rider_Id_731,1464
-Order_No_3962,User_Id_190,Bike,3,Business,29,3,2:17:29 PM,29,3,2:20:23 PM,29,3,2:27:00 PM,29,3,2:33:51 PM,29,3,3:13:15 PM,11,21.3,,-1.3270347,36.866736200000005,-1.2897399,36.8270975,Rider_Id_709,2364
-Order_No_22328,User_Id_3436,Bike,3,Personal,11,5,1:15:17 PM,11,5,1:16:28 PM,11,5,1:26:55 PM,11,5,1:28:32 PM,11,5,2:16:34 PM,9,20.9,,-1.2571472,36.795063299999995,-1.3133622,36.78763370000001,Rider_Id_362,2882
-Order_No_8210,User_Id_3300,Bike,3,Business,11,4,10:21:50 AM,11,4,10:22:04 AM,11,4,10:40:55 AM,11,4,10:53:35 AM,11,4,11:24:43 AM,19,25.4,,-1.293515,36.897607,-1.303596,36.778378000000004,Rider_Id_377,1868
-Order_No_18057,User_Id_111,Bike,3,Business,10,5,2:10:11 PM,10,5,2:10:30 PM,10,5,2:10:34 PM,10,5,2:14:53 PM,10,5,2:27:00 PM,5,,,-1.2551895,36.7822034,-1.2726925,36.7951829,Rider_Id_576,727
-Order_No_22317,User_Id_3635,Bike,1,Business,12,3,5:31:43 PM,12,3,5:32:46 PM,12,3,5:33:11 PM,12,3,6:05:45 PM,12,3,6:30:26 PM,12,24.2,,-1.2615888000000002,36.7928732,-1.2095044,36.783206400000005,Rider_Id_272,1481
-Order_No_10575,User_Id_1417,Bike,3,Business,12,2,3:10:03 PM,12,2,3:10:54 PM,12,2,3:25:19 PM,12,2,3:42:02 PM,12,2,4:11:36 PM,6,27.0,,-1.2950041,36.814358299999995,-1.2741728,36.79691079999999,Rider_Id_216,1774
-Order_No_15635,User_Id_393,Bike,3,Business,13,3,9:36:39 AM,13,3,9:36:49 AM,13,3,9:36:56 AM,13,3,9:51:54 AM,13,3,10:17:07 AM,14,17.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1513
-Order_No_4338,User_Id_1329,Bike,3,Business,6,4,10:24:25 AM,6,4,11:25:31 AM,6,4,11:53:16 AM,6,4,11:56:52 AM,6,4,1:04:30 PM,6,24.3,,-1.273056,36.811298,-1.3085436000000001,36.791414700000004,Rider_Id_669,4058
-Order_No_11387,User_Id_393,Bike,3,Business,28,1,1:53:12 PM,28,1,2:38:49 PM,28,1,2:39:38 PM,28,1,2:53:42 PM,28,1,3:22:33 PM,11,26.4,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_809,1731
-Order_No_8848,User_Id_432,Bike,3,Business,7,4,10:07:47 AM,7,4,10:08:30 AM,7,4,10:15:36 AM,7,4,10:16:55 AM,7,4,11:01:00 AM,4,,,-1.3012008,36.764868,-1.2962832,36.784968299999996,Rider_Id_493,2645
-Order_No_2128,User_Id_635,Bike,3,Business,22,5,4:03:53 PM,22,5,4:09:00 PM,22,5,4:35:02 PM,22,5,4:48:41 PM,22,5,5:52:23 PM,5,28.3,,-1.2857763999999998,36.8236622,-1.2602337,36.79905479999999,Rider_Id_910,3822
-Order_No_23586,User_Id_718,Bike,3,Business,10,5,10:53:10 AM,10,5,11:03:42 AM,10,5,11:42:23 AM,10,5,12:07:45 PM,10,5,12:09:41 PM,7,,,-1.2551895,36.7822034,-1.2897288999999998,36.8271257,Rider_Id_603,116
-Order_No_22183,User_Id_2701,Bike,3,Business,10,1,12:51:03 PM,10,1,1:12:36 PM,10,1,1:12:39 PM,10,1,1:41:37 PM,10,1,2:02:22 PM,8,18.1,,-1.2571472,36.795063299999995,-1.2765736,36.851364600000004,Rider_Id_79,1245
-Order_No_27575,User_Id_1769,Bike,2,Personal,6,4,1:48:22 PM,6,4,1:48:48 PM,6,4,1:49:40 PM,6,4,2:08:11 PM,6,4,2:56:17 PM,12,23.5,,-1.2551895,36.7822034,-1.3223228,36.8532545,Rider_Id_116,2886
-Order_No_18341,User_Id_1471,Bike,3,Business,1,4,7:33:16 AM,1,4,7:33:29 AM,1,4,8:27:41 AM,1,4,9:02:24 AM,1,4,9:22:41 AM,21,,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_656,1217
-Order_No_23766,User_Id_286,Bike,3,Business,5,3,3:51:53 PM,5,3,4:13:10 PM,5,3,4:36:37 PM,5,3,4:48:27 PM,5,3,5:05:33 PM,6,22.4,,-1.257314,36.805209999999995,-1.2962966999999999,36.8020231,Rider_Id_434,1026
-Order_No_7006,User_Id_2089,Bike,3,Business,1,5,1:22:33 PM,1,5,1:22:57 PM,1,5,1:43:45 PM,1,5,1:48:44 PM,1,5,2:19:17 PM,11,31.8,,-1.3322618,36.8045412,-1.2610721999999999,36.814707399999996,Rider_Id_164,1833
-Order_No_23083,User_Id_478,Bike,3,Business,14,5,1:59:04 PM,14,5,2:06:42 PM,14,5,2:12:25 PM,14,5,2:19:10 PM,14,5,2:43:14 PM,10,23.5,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_952,1444
-Order_No_18811,User_Id_1719,Bike,3,Business,11,1,2:23:01 PM,11,1,2:23:40 PM,11,1,2:35:03 PM,11,1,2:38:54 PM,11,1,3:08:29 PM,12,28.0,,-1.2859206,36.78882720000001,-1.23966,36.75258,Rider_Id_958,1775
-Order_No_18489,User_Id_706,Bike,3,Business,4,4,3:21:31 PM,4,4,3:21:35 PM,4,4,3:21:43 PM,4,4,3:29:13 PM,4,4,3:34:22 PM,2,24.9,,-1.3077869,36.844320700000004,-1.3006971,36.831413500000004,Rider_Id_848,309
-Order_No_4983,User_Id_972,Bike,3,Business,23,5,12:25:09 PM,23,5,12:25:26 PM,23,5,12:27:31 PM,23,5,12:37:07 PM,23,5,1:13:05 PM,21,24.0,,-1.2862033000000002,36.8152966,-1.3606809,36.9427964,Rider_Id_456,2158
-Order_No_8710,User_Id_111,Bike,3,Business,12,5,4:03:22 PM,12,5,4:04:09 PM,12,5,4:11:09 PM,12,5,4:13:12 PM,12,5,4:35:12 PM,9,,,-1.2551895,36.7822034,-1.2864746999999999,36.797388399999996,Rider_Id_708,1320
-Order_No_10625,User_Id_3194,Bike,3,Business,24,4,3:38:58 PM,24,4,3:40:08 PM,24,4,4:01:03 PM,24,4,4:08:51 PM,24,4,4:43:59 PM,18,28.5,,-1.2551895,36.7822034,-1.3441039,36.717774600000006,Rider_Id_862,2108
-Order_No_14455,User_Id_1402,Bike,3,Business,31,5,2:17:08 PM,31,5,2:18:39 PM,31,5,2:19:01 PM,31,5,3:15:30 PM,31,5,3:15:35 PM,2,24.8,,-1.2813012,36.832396200000005,-1.2882073,36.82376729999999,Rider_Id_458,5
-Order_No_22163,User_Id_2528,Bike,3,Business,15,5,5:54:58 PM,15,5,5:55:20 PM,15,5,5:57:50 PM,15,5,6:06:07 PM,15,5,6:27:58 PM,17,28.0,,-1.2551895,36.7822034,-1.3261836999999999,36.698124,Rider_Id_690,1311
-Order_No_6118,User_Id_302,Bike,3,Business,29,7,6:55:09 PM,29,7,7:26:49 PM,29,7,7:50:53 PM,29,7,7:57:53 PM,29,7,8:20:22 PM,7,14.6,2.0,-1.2649605,36.798177700000004,-1.2696795,36.8640088,Rider_Id_452,1349
-Order_No_19990,User_Id_3147,Bike,3,Business,28,1,1:29:38 PM,28,1,1:30:16 PM,28,1,1:43:01 PM,28,1,1:44:23 PM,28,1,1:59:42 PM,10,26.5,,-1.3052491000000002,36.8223899,-1.3467388999999999,36.7690312,Rider_Id_581,919
-Order_No_22464,User_Id_867,Bike,3,Business,7,1,11:04:12 AM,7,1,11:58:00 AM,7,1,12:17:41 PM,7,1,12:26:41 PM,7,1,12:53:52 PM,10,24.9,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_576,1631
-Order_No_6827,User_Id_3239,Bike,3,Personal,22,2,10:33:28 AM,22,2,10:34:02 AM,22,2,11:09:36 AM,22,2,11:19:28 AM,22,2,11:46:32 AM,14,19.3,,-1.2839322,36.8292426,-1.3116423000000001,36.9008384,Rider_Id_248,1624
-Order_No_7285,User_Id_2789,Bike,3,Business,15,4,11:30:44 AM,15,4,11:31:02 AM,15,4,11:38:49 AM,15,4,11:40:22 AM,15,4,12:38:37 PM,35,24.5,,-1.304735,36.784605400000004,-1.1192788,36.7373695,Rider_Id_917,3495
-Order_No_7969,User_Id_934,Bike,2,Personal,29,6,4:32:47 PM,29,6,4:45:38 PM,29,6,4:59:17 PM,29,6,5:05:03 PM,29,6,5:18:14 PM,7,23.8,,-1.2804309999999999,36.83332720000001,-1.2343087,36.84749,Rider_Id_208,791
-Order_No_25145,User_Id_1146,Bike,3,Business,9,3,11:41:38 AM,9,3,11:42:16 AM,9,3,11:42:29 AM,9,3,12:03:54 PM,9,3,12:03:57 PM,7,25.2,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_39,3
-Order_No_11739,User_Id_1195,Bike,3,Business,6,6,11:45:52 AM,6,6,11:46:18 AM,6,6,11:57:01 AM,6,6,12:07:41 PM,6,6,12:43:14 PM,15,24.1,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_31,2133
-Order_No_14281,User_Id_685,Bike,3,Business,13,4,9:46:03 AM,13,4,9:46:34 AM,13,4,9:52:58 AM,13,4,10:03:40 AM,13,4,10:33:52 AM,14,18.7,,-1.265432,36.795034,-1.3278522,36.8711134,Rider_Id_131,1812
-Order_No_9440,User_Id_761,Bike,3,Business,22,3,12:58:11 PM,22,3,12:58:20 PM,22,3,1:05:25 PM,22,3,1:14:36 PM,22,3,2:30:37 PM,23,23.0,,-1.2693101999999998,36.798177700000004,-1.2927239,36.946421,Rider_Id_676,4561
-Order_No_498,User_Id_3370,Bike,3,Business,31,2,10:33:11 AM,31,2,10:33:30 AM,31,2,10:41:00 AM,31,2,10:57:07 AM,31,2,11:36:32 AM,9,18.7,,-1.3038156,36.812273299999994,-1.2615888000000002,36.7928732,Rider_Id_580,2365
-Order_No_13745,User_Id_1646,Bike,3,Business,6,1,1:24:23 PM,6,1,1:24:50 PM,6,1,1:35:08 PM,6,1,1:41:40 PM,6,1,1:54:23 PM,6,,,-1.2517448,36.7924971,-1.2264893,36.7584117,Rider_Id_484,763
-Order_No_26961,User_Id_478,Bike,3,Business,30,2,11:42:49 AM,30,2,11:49:20 AM,30,2,12:00:27 PM,30,2,12:01:56 PM,30,2,12:26:07 PM,4,,,-1.2551895,36.7822034,-1.2728058,36.8049903,Rider_Id_953,1451
-Order_No_2957,User_Id_1245,Bike,3,Business,6,1,1:53:20 PM,6,1,1:53:41 PM,6,1,1:56:18 PM,6,1,2:41:54 PM,6,1,3:10:00 PM,22,,,-1.300921,36.828195,-1.3050342,36.668021100000004,Rider_Id_206,1686
-Order_No_23821,User_Id_142,Bike,3,Business,29,2,1:29:10 PM,29,2,1:34:57 PM,29,2,1:43:43 PM,29,2,1:48:11 PM,29,2,2:07:46 PM,8,26.8,,-1.2963097,36.7688221,-1.2570328000000002,36.7869744,Rider_Id_56,1175
-Order_No_9668,User_Id_1500,Bike,3,Business,16,3,8:19:18 AM,16,3,8:20:14 AM,16,3,8:28:50 AM,16,3,8:44:10 AM,16,3,9:04:05 AM,8,19.6,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_436,1195
-Order_No_26848,User_Id_2105,Bike,1,Personal,23,1,1:11:14 PM,23,1,1:11:21 PM,23,1,1:21:49 PM,23,1,1:29:09 PM,23,1,1:46:47 PM,8,18.6,,-1.2463211,36.789487,-1.2883829,36.8177296,Rider_Id_144,1058
-Order_No_17834,User_Id_1342,Bike,3,Business,23,3,12:10:52 PM,23,3,12:11:27 PM,23,3,12:19:40 PM,23,3,12:45:09 PM,23,3,12:58:41 PM,8,22.0,,-1.2962832,36.784968299999996,-1.2553615,36.789537599999996,Rider_Id_676,812
-Order_No_18024,User_Id_302,Bike,3,Business,24,7,8:50:55 PM,24,7,8:51:15 PM,24,7,9:06:58 PM,24,7,9:09:57 PM,24,7,9:29:16 PM,2,,,-1.2649605,36.798177700000004,-1.2598002000000001,36.7963379,Rider_Id_332,1159
-Order_No_24765,User_Id_1394,Bike,3,Personal,30,1,3:46:06 PM,30,1,3:46:34 PM,30,1,3:59:25 PM,30,1,4:08:51 PM,30,1,4:42:38 PM,21,20.4,,-1.2710687,36.8142839,-1.3463428999999998,36.9069888,Rider_Id_561,2027
-Order_No_21390,User_Id_2676,Bike,3,Business,8,2,10:49:27 AM,8,2,10:50:34 AM,8,2,11:11:56 AM,8,2,11:12:57 AM,8,2,11:47:14 AM,3,21.2,,-1.2631674,36.793583,-1.262492,36.800973,Rider_Id_733,2057
-Order_No_3131,User_Id_2330,Bike,3,Business,18,4,1:44:26 PM,18,4,1:45:22 PM,18,4,1:47:04 PM,18,4,1:50:42 PM,18,4,2:00:01 PM,6,,,-1.2890715,36.8157588,-1.2645318,36.7981775,Rider_Id_253,559
-Order_No_25590,User_Id_3262,Bike,3,Personal,19,2,4:01:38 PM,19,2,4:02:34 PM,19,2,4:19:28 PM,19,2,4:24:04 PM,19,2,5:51:15 PM,34,29.2,,-1.2876007999999999,36.838724600000006,-1.0921598999999997,37.005025,Rider_Id_697,5231
-Order_No_21882,User_Id_478,Bike,3,Business,3,3,11:41:03 AM,3,3,11:42:00 AM,3,3,11:55:54 AM,3,3,11:57:32 AM,3,3,12:31:31 PM,15,24.1,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_279,2039
-Order_No_5408,User_Id_635,Bike,3,Business,7,2,11:13:58 AM,7,2,11:17:16 AM,7,2,11:45:50 AM,7,2,11:56:24 AM,7,2,12:23:03 PM,5,,,-1.2857763999999998,36.8236622,-1.2602337,36.79905479999999,Rider_Id_624,1599
-Order_No_3822,User_Id_393,Bike,3,Business,10,4,9:37:55 AM,10,4,9:38:46 AM,10,4,9:48:11 AM,10,4,9:52:05 AM,10,4,10:01:03 AM,4,19.0,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_358,538
-Order_No_14902,User_Id_1329,Bike,3,Business,19,2,10:52:33 AM,19,2,10:53:21 AM,19,2,11:17:17 AM,19,2,11:25:56 AM,19,2,12:00:59 PM,9,27.5,,-1.273056,36.811298,-1.3314048,36.8229367,Rider_Id_697,2103
-Order_No_21654,User_Id_510,Bike,3,Business,9,5,5:42:04 PM,9,5,5:42:13 PM,9,5,5:48:09 PM,9,5,5:49:23 PM,9,5,6:09:34 PM,6,24.8,,-1.2860183,36.8975338,-1.3195936000000001,36.916169700000005,Rider_Id_675,1211
-Order_No_4960,User_Id_2083,Bike,3,Personal,24,4,4:14:06 PM,24,4,4:14:44 PM,24,4,4:18:23 PM,24,4,4:43:11 PM,24,4,5:03:24 PM,8,26.1,,-1.3012173,36.8093987,-1.2615888000000002,36.7928732,Rider_Id_315,1213
-Order_No_14244,User_Id_3445,Bike,1,Personal,31,2,12:17:53 PM,31,2,12:18:07 PM,31,2,12:22:43 PM,31,2,12:26:27 PM,31,2,12:36:51 PM,4,18.6,,-1.2615983,36.8083096,-1.2516793,36.786481,Rider_Id_726,624
-Order_No_27917,User_Id_2924,Bike,3,Personal,14,5,3:08:43 PM,14,5,3:13:23 PM,14,5,3:29:56 PM,14,5,4:05:24 PM,14,5,4:41:57 PM,16,23.6,,-1.2827651000000002,36.843383100000004,-1.2664227,36.7453671,Rider_Id_359,2193
-Order_No_6977,User_Id_1856,Bike,3,Business,20,4,12:52:00 PM,20,4,12:52:27 PM,20,4,1:25:13 PM,20,4,1:33:13 PM,20,4,1:56:57 PM,5,28.0,,-1.273009,36.802807,-1.2918698,36.7701974,Rider_Id_857,1424
-Order_No_25547,User_Id_461,Bike,1,Personal,12,3,10:34:25 AM,12,3,10:48:08 AM,12,3,11:20:19 AM,12,3,11:30:03 AM,12,3,12:47:43 PM,15,23.1,,-1.3178263000000001,36.7141818,-1.2932780000000001,36.827789,Rider_Id_101,4660
-Order_No_24115,User_Id_2523,Bike,3,Business,19,2,10:15:58 AM,19,2,10:17:03 AM,19,2,10:24:36 AM,19,2,10:30:05 AM,19,2,10:48:51 AM,4,21.2,,-1.2551895,36.7822034,-1.2747247,36.8150014,Rider_Id_131,1126
-Order_No_24014,User_Id_1831,Bike,3,Business,16,3,1:37:00 PM,16,3,1:52:42 PM,16,3,2:13:32 PM,16,3,2:17:35 PM,16,3,2:55:00 PM,21,26.9,,-1.3632162,36.9108253,-1.263987,36.798891,Rider_Id_675,2245
-Order_No_16921,User_Id_909,Bike,3,Business,2,2,3:47:18 PM,2,2,3:51:56 PM,2,2,4:11:03 PM,2,2,4:12:54 PM,2,2,4:41:26 PM,14,29.4,,-1.225322,36.80855,-1.304023,36.788389,Rider_Id_205,1712
-Order_No_18809,User_Id_635,Bike,3,Business,15,5,9:59:31 AM,15,5,10:02:58 AM,15,5,10:12:20 AM,15,5,10:14:47 AM,15,5,10:34:06 AM,7,19.1,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_291,1159
-Order_No_13899,User_Id_3517,Bike,3,Business,30,6,11:17:56 AM,30,6,11:18:06 AM,30,6,11:19:24 AM,30,6,11:20:25 AM,30,6,11:42:03 AM,8,26.6,,-1.3882008,36.7699445,-1.4139362,36.7059881,Rider_Id_716,1298
-Order_No_9338,User_Id_3222,Bike,3,Business,24,3,10:50:36 AM,24,3,10:51:11 AM,24,3,10:54:36 AM,24,3,10:57:34 AM,24,3,11:29:50 AM,6,19.7,,-1.2551895,36.7822034,-1.2608848,36.755160700000005,Rider_Id_405,1936
-Order_No_1946,User_Id_1544,Bike,3,Business,3,4,1:35:40 PM,3,4,1:56:16 PM,3,4,2:20:18 PM,3,4,2:26:03 PM,3,4,2:44:32 PM,4,25.0,,-1.278656,36.825694899999995,-1.30479,36.8129524,Rider_Id_268,1109
-Order_No_8364,User_Id_2200,Bike,3,Business,12,2,11:09:31 AM,12,2,11:10:12 AM,12,2,11:18:12 AM,12,2,11:31:19 AM,12,2,11:37:35 AM,1,28.0,,-1.290894,36.822971,-1.2880128999999998,36.8308097,Rider_Id_377,376
-Order_No_26932,User_Id_1473,Bike,3,Business,14,1,3:35:05 PM,14,1,3:41:20 PM,14,1,3:48:01 PM,14,1,3:53:31 PM,14,1,4:22:44 PM,16,25.9,,-1.3700383,36.9190174,-1.3044458,36.8233667,Rider_Id_442,1753
-Order_No_22146,User_Id_3487,Bike,3,Business,29,1,12:21:55 PM,29,1,12:23:28 PM,29,1,12:30:32 PM,29,1,12:37:14 PM,29,1,12:49:09 PM,6,,,-1.279395,36.825364,-1.2615888000000002,36.7928732,Rider_Id_205,715
-Order_No_4594,User_Id_3496,Bike,3,Business,11,4,3:15:59 PM,11,4,3:32:45 PM,11,4,3:58:19 PM,11,4,4:01:15 PM,11,4,4:07:42 PM,2,26.7,,-1.300921,36.828195,-1.2929108999999999,36.83157670000001,Rider_Id_88,387
-Order_No_26821,User_Id_2625,Bike,1,Personal,6,5,4:22:42 PM,6,5,4:23:16 PM,6,5,4:32:06 PM,6,5,4:40:42 PM,6,5,4:50:17 PM,3,,,-1.263987,36.798891,-1.2670213,36.7994482,Rider_Id_312,575
-Order_No_28154,User_Id_432,Bike,3,Business,10,1,1:42:45 PM,10,1,1:44:24 PM,10,1,1:53:23 PM,10,1,2:09:01 PM,10,1,3:10:12 PM,15,18.1,,-1.3012008,36.764868,-1.3083777,36.85900770000001,Rider_Id_161,3671
-Order_No_26901,User_Id_2066,Bike,3,Business,1,6,1:06:53 PM,1,6,1:07:01 PM,1,6,1:17:28 PM,1,6,1:21:53 PM,1,6,1:55:28 PM,10,18.5,,-1.316961,36.820337,-1.256958,36.8144929,Rider_Id_329,2015
-Order_No_11203,User_Id_393,Bike,3,Business,13,4,1:00:55 PM,13,4,1:20:35 PM,13,4,1:25:40 PM,13,4,1:29:49 PM,13,4,1:44:47 PM,5,22.3,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_538,898
-Order_No_12070,User_Id_2718,Bike,3,Business,27,5,10:08:43 AM,27,5,10:09:39 AM,27,5,10:14:50 AM,27,5,10:35:30 AM,27,5,10:40:31 AM,5,22.4,,-1.2601,36.796528,-1.2729620000000001,36.808820000000004,Rider_Id_116,301
-Order_No_929,User_Id_1500,Bike,3,Business,25,1,2:32:17 PM,25,1,2:37:05 PM,25,1,2:53:52 PM,25,1,2:55:24 PM,25,1,3:18:11 PM,8,29.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_956,1367
-Order_No_23310,User_Id_635,Bike,3,Business,28,4,2:27:51 PM,28,4,2:28:37 PM,28,4,3:15:05 PM,28,4,3:17:18 PM,28,4,3:53:28 PM,8,30.2,,-1.2780878,36.846943599999996,-1.2602337,36.79905479999999,Rider_Id_439,2170
-Order_No_16149,User_Id_1500,Bike,3,Business,2,5,3:41:12 PM,2,5,3:44:57 PM,2,5,3:47:54 PM,2,5,4:09:01 PM,2,5,5:05:10 PM,8,25.0,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_445,3369
-Order_No_15164,User_Id_2066,Bike,3,Business,6,3,11:14:07 AM,6,3,11:14:31 AM,6,3,11:30:15 AM,6,3,11:37:51 AM,6,3,12:09:36 PM,19,22.3,,-1.3210188999999999,36.841055100000005,-1.3218455,36.721300899999996,Rider_Id_523,1905
-Order_No_27352,User_Id_2055,Bike,3,Business,18,1,12:05:44 PM,18,1,12:20:48 PM,18,1,12:34:35 PM,18,1,12:37:39 PM,18,1,1:08:54 PM,13,,,-1.2895164,36.825984000000005,-1.214328,36.7911903,Rider_Id_330,1875
-Order_No_1958,User_Id_2515,Bike,1,Personal,21,5,9:48:35 AM,21,5,9:50:35 AM,21,5,10:04:37 AM,21,5,10:11:40 AM,21,5,10:29:39 AM,6,20.2,,-1.3057159999999999,36.809882,-1.2673299999999998,36.823679999999996,Rider_Id_65,1079
-Order_No_1793,User_Id_53,Bike,3,Business,27,1,12:12:23 PM,27,1,12:13:06 PM,27,1,12:13:14 PM,27,1,12:47:48 PM,27,1,12:52:44 PM,5,20.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_472,296
-Order_No_18347,User_Id_635,Bike,3,Business,18,4,2:23:52 PM,18,4,2:24:08 PM,18,4,2:28:07 PM,18,4,2:47:38 PM,18,4,3:00:03 PM,1,,,-1.2567464,36.7915273,-1.2602337,36.79905479999999,Rider_Id_641,745
-Order_No_25243,User_Id_1329,Bike,3,Business,12,3,1:30:33 PM,12,3,1:33:20 PM,12,3,2:02:55 PM,12,3,2:06:29 PM,12,3,2:21:09 PM,5,21.1,,-1.273056,36.811298,-1.2772389,36.7855864,Rider_Id_690,880
-Order_No_17093,User_Id_1081,Bike,3,Business,13,3,11:01:40 AM,13,3,11:02:06 AM,13,3,11:02:07 AM,13,3,11:12:40 AM,13,3,12:08:54 PM,23,24.9,,-1.2551895,36.7822034,-1.3450191999999999,36.9211859,Rider_Id_579,3374
-Order_No_27179,User_Id_2605,Bike,3,Business,10,2,12:09:17 PM,10,2,12:10:56 PM,10,2,12:11:07 PM,10,2,12:42:03 PM,10,2,1:20:35 PM,21,20.4,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_397,2312
-Order_No_15193,User_Id_3186,Bike,3,Business,15,3,4:11:08 PM,15,3,4:11:45 PM,15,3,4:22:22 PM,15,3,4:24:05 PM,15,3,5:08:54 PM,14,26.0,,-1.2980228999999999,36.78865,-1.3250048,36.869718799999994,Rider_Id_158,2689
-Order_No_11126,User_Id_3669,Bike,3,Business,17,3,10:33:08 AM,17,3,10:33:34 AM,17,3,10:52:50 AM,17,3,10:55:33 AM,17,3,11:45:48 AM,10,,,-1.2825379,36.843502,-1.2615888000000002,36.7928732,Rider_Id_660,3015
-Order_No_2252,User_Id_3300,Bike,3,Business,8,6,11:04:06 AM,8,6,11:04:54 AM,8,6,11:35:02 AM,8,6,11:41:16 AM,8,6,12:08:20 PM,8,19.9,,-1.2663631000000002,36.8163329,-1.303596,36.778378000000004,Rider_Id_109,1624
-Order_No_26976,User_Id_1976,Bike,3,Business,21,4,10:40:53 AM,21,4,10:41:27 AM,21,4,10:47:56 AM,21,4,10:49:46 AM,21,4,11:15:19 AM,7,25.1,,-1.257028,36.7810435,-1.2778153,36.7955703,Rider_Id_523,1533
-Order_No_8980,User_Id_1523,Bike,1,Personal,12,4,3:24:41 PM,12,4,3:28:27 PM,12,4,3:37:24 PM,12,4,3:47:14 PM,12,4,3:57:02 PM,4,21.7,,-1.26249,36.799715,-1.25537,36.7756,Rider_Id_281,588
-Order_No_3192,User_Id_3689,Bike,3,Business,20,2,5:44:35 PM,20,2,5:44:56 PM,20,2,5:55:11 PM,20,2,6:06:31 PM,20,2,6:32:12 PM,10,23.9,,-1.3014461000000002,36.7661381,-1.2527958000000001,36.8003131,Rider_Id_427,1541
-Order_No_17319,User_Id_2383,Bike,3,Business,14,2,12:32:17 PM,14,2,12:32:35 PM,14,2,12:38:09 PM,14,2,12:41:43 PM,14,2,12:46:47 PM,3,,,-1.2608993,36.799246000000004,-1.2654447,36.797867100000005,Rider_Id_634,304
-Order_No_20378,User_Id_1205,Bike,3,Business,26,2,3:57:11 PM,26,2,4:01:22 PM,26,2,4:10:27 PM,26,2,4:15:29 PM,26,2,4:35:00 PM,3,30.8,,-1.2495931,36.80650870000001,-1.2628937,36.798536999999996,Rider_Id_484,1171
-Order_No_10266,User_Id_2457,Bike,3,Business,26,5,11:44:35 AM,26,5,12:58:34 PM,26,5,1:07:10 PM,26,5,1:12:01 PM,26,5,2:02:29 PM,7,,,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_849,3028
-Order_No_10561,User_Id_53,Bike,3,Business,23,6,10:47:16 AM,23,6,10:47:49 AM,23,6,10:49:19 AM,23,6,11:22:58 AM,23,6,11:44:27 AM,5,26.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_953,1289
-Order_No_27057,User_Id_2200,Bike,3,Business,12,2,2:11:18 PM,12,2,2:11:56 PM,12,2,2:13:21 PM,12,2,2:23:23 PM,12,2,2:37:56 PM,4,,,-1.290894,36.822971,-1.2668734,36.8054026,Rider_Id_156,873
-Order_No_10081,User_Id_3291,Bike,3,Business,29,2,2:31:32 PM,29,2,2:32:07 PM,29,2,2:58:03 PM,29,2,3:44:29 PM,29,2,4:31:13 PM,18,27.1,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_328,2804
-Order_No_885,User_Id_2423,Bike,3,Business,14,4,3:35:52 PM,14,4,4:11:00 PM,14,4,4:18:46 PM,14,4,4:21:25 PM,14,4,4:38:57 PM,4,28.1,,-1.2551895,36.7822034,-1.2707979,36.8065786,Rider_Id_19,1052
-Order_No_25727,User_Id_633,Bike,3,Business,5,2,1:40:54 PM,5,2,2:26:13 PM,5,2,2:40:12 PM,5,2,3:10:29 PM,5,2,3:31:41 PM,5,27.3,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_959,1272
-Order_No_1540,User_Id_435,Bike,3,Business,7,5,12:20:40 PM,7,5,2:33:18 PM,7,5,2:47:11 PM,7,5,2:58:10 PM,7,5,3:32:13 PM,10,,,-1.300921,36.828195,-1.3472066,36.7692638,Rider_Id_732,2043
-Order_No_14002,User_Id_510,Bike,3,Business,11,1,9:51:33 AM,11,1,9:51:44 AM,11,1,9:51:48 AM,11,1,9:55:16 AM,11,1,11:05:57 AM,12,14.5,,-1.2860183,36.8975338,-1.325808,36.861664000000005,Rider_Id_589,4241
-Order_No_22015,User_Id_666,Bike,2,Personal,24,4,10:44:05 AM,24,4,10:46:48 AM,24,4,11:18:39 AM,24,4,11:21:59 AM,24,4,11:37:00 AM,5,21.4,,-1.2770071000000003,36.832167,-1.2624229999999999,36.801647100000004,Rider_Id_844,901
-Order_No_5797,User_Id_2412,Bike,3,Business,31,4,8:47:43 AM,31,4,8:48:10 AM,31,4,8:58:49 AM,31,4,9:03:43 AM,31,4,9:32:56 AM,14,17.5,,-1.2297202,36.874550799999994,-1.2553615,36.789537599999996,Rider_Id_95,1753
-Order_No_17264,User_Id_3599,Bike,3,Business,28,4,2:31:17 PM,28,4,2:32:11 PM,28,4,3:31:41 PM,28,4,3:54:16 PM,28,4,3:55:53 PM,7,27.5,,-1.3302996,36.8707935,-1.3094481000000002,36.8652486,Rider_Id_459,97
-Order_No_4823,User_Id_2929,Bike,3,Business,11,2,11:44:46 AM,11,2,1:31:36 PM,11,2,1:32:02 PM,11,2,1:51:06 PM,11,2,1:51:20 PM,5,25.7,,-1.269534,36.80697,-1.2551895,36.7822034,Rider_Id_185,14
-Order_No_16114,User_Id_2871,Bike,1,Personal,22,7,5:59:05 PM,22,7,5:59:22 PM,22,7,6:15:35 PM,22,7,6:26:08 PM,22,7,6:46:53 PM,10,19.1,,-1.2759432,36.7892706,-1.3172903,36.8059349,Rider_Id_465,1245
-Order_No_17646,User_Id_633,Bike,3,Business,10,4,7:20:44 AM,10,4,7:21:00 AM,10,4,7:44:32 AM,10,4,7:47:58 AM,10,4,8:15:59 AM,11,19.2,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_523,1681
-Order_No_1273,User_Id_2828,Bike,2,Business,28,4,2:25:34 PM,28,4,2:25:45 PM,28,4,2:41:00 PM,28,4,2:50:30 PM,28,4,2:59:11 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_865,521
-Order_No_8302,User_Id_3355,Bike,3,Business,29,5,3:11:08 PM,29,5,3:47:29 PM,29,5,4:00:34 PM,29,5,4:01:36 PM,29,5,4:07:50 PM,3,21.0,,-1.3052491000000002,36.8223899,-1.3207146,36.8305771,Rider_Id_549,374
-Order_No_12509,User_Id_3283,Bike,3,Business,30,6,2:58:29 PM,30,6,2:58:47 PM,30,6,3:00:22 PM,30,6,3:18:25 PM,30,6,3:38:20 PM,14,28.4,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_855,1195
-Order_No_5208,User_Id_136,Bike,3,Business,16,3,4:10:18 PM,16,3,4:25:33 PM,16,3,4:42:42 PM,16,3,4:47:15 PM,16,3,5:48:11 PM,24,20.2,,-1.3326173000000001,36.71462329999999,-1.2248308,36.801257299999996,Rider_Id_742,3656
-Order_No_21078,User_Id_1535,Bike,1,Business,23,4,10:02:51 AM,23,4,10:03:28 AM,23,4,10:09:07 AM,23,4,10:14:14 AM,23,4,10:34:52 AM,7,15.8,,-1.2897368,36.8226783,-1.2571472,36.795063299999995,Rider_Id_71,1238
-Order_No_21614,User_Id_2350,Bike,1,Personal,5,2,2:08:11 PM,5,2,2:09:29 PM,5,2,2:22:56 PM,5,2,2:28:28 PM,5,2,2:34:30 PM,2,22.9,,-1.2745107,36.797450299999994,-1.2631296,36.79567,Rider_Id_407,362
-Order_No_21315,User_Id_2561,Bike,3,Business,29,4,9:14:41 AM,29,4,9:22:52 AM,29,4,9:37:02 AM,29,4,9:42:07 AM,29,4,10:25:11 AM,7,18.5,3.9,-1.2551895,36.7822034,-1.2880771,36.8269099,Rider_Id_726,2584
-Order_No_2852,User_Id_419,Bike,3,Business,3,2,5:03:42 PM,3,2,5:10:04 PM,3,2,5:11:35 PM,3,2,5:22:39 PM,3,2,5:50:23 PM,9,18.8,,-1.3001813,36.811122,-1.2579303999999998,36.8019667,Rider_Id_192,1664
-Order_No_872,User_Id_1792,Bike,3,Business,5,5,12:21:35 PM,5,5,12:24:54 PM,5,5,12:40:28 PM,5,5,12:46:12 PM,5,5,1:03:26 PM,6,23.9,,-1.2809121,36.7852524,-1.2690931,36.8065779,Rider_Id_178,1034
-Order_No_1327,User_Id_586,Bike,3,Personal,15,2,8:30:28 AM,15,2,8:30:39 AM,15,2,8:57:01 AM,15,2,9:03:34 AM,15,2,9:16:59 AM,5,18.2,2.0,-1.2895261,36.8164407,-1.3058877,36.8349874,Rider_Id_196,805
-Order_No_11792,User_Id_3222,Bike,3,Business,4,2,12:02:16 PM,4,2,12:18:38 PM,4,2,12:30:17 PM,4,2,12:33:00 PM,4,2,1:05:20 PM,7,19.7,,-1.2551895,36.7822034,-1.2388691,36.7579496,Rider_Id_547,1940
-Order_No_3245,User_Id_1959,Bike,3,Business,18,4,11:03:54 AM,18,4,11:04:26 AM,18,4,11:17:08 AM,18,4,11:18:54 AM,18,4,11:48:52 AM,15,,,-1.2551895,36.7822034,-1.2286931,36.881603000000005,Rider_Id_779,1798
-Order_No_15328,User_Id_1920,Bike,3,Business,2,5,5:15:27 PM,2,5,5:24:38 PM,2,5,5:27:30 PM,2,5,5:29:00 PM,2,5,5:54:31 PM,10,24.1,,-1.2551895,36.7822034,-1.2982578000000002,36.84439520000001,Rider_Id_237,1531
-Order_No_6409,User_Id_3686,Bike,3,Business,26,2,9:59:22 AM,26,2,9:59:37 AM,26,2,10:00:51 AM,26,2,10:10:17 AM,26,2,10:22:43 AM,3,22.0,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_876,746
-Order_No_18331,User_Id_3411,Bike,1,Personal,15,3,11:41:26 AM,15,3,11:41:35 AM,15,3,11:49:06 AM,15,3,11:53:33 AM,15,3,12:29:46 PM,10,18.2,,-1.2919323,36.7659602,-1.3052641,36.8294145,Rider_Id_452,2173
-Order_No_1280,User_Id_2956,Bike,3,Business,16,6,8:35:09 AM,16,6,8:43:52 AM,16,6,9:18:53 AM,16,6,9:51:59 AM,16,6,9:55:40 AM,5,20.1,,-1.2584143,36.8048002,-1.2857548,36.819062,Rider_Id_436,221
-Order_No_15481,User_Id_136,Bike,3,Business,17,3,11:04:30 AM,17,3,11:05:40 AM,17,3,11:09:40 AM,17,3,11:12:34 AM,17,3,11:24:56 AM,6,,,-1.3254522,36.721658399999995,-1.3287648,36.744573200000005,Rider_Id_327,742
-Order_No_16654,User_Id_1787,Bike,3,Business,13,4,2:41:59 PM,13,4,2:42:10 PM,13,4,2:43:36 PM,13,4,2:46:14 PM,13,4,3:04:54 PM,3,22.9,,-1.2283403,36.8822756,-1.2343236000000002,36.8861,Rider_Id_788,1120
-Order_No_27139,User_Id_2866,Bike,3,Business,6,4,12:46:14 PM,6,4,12:47:09 PM,6,4,1:17:32 PM,6,4,1:27:01 PM,6,4,2:44:56 PM,16,19.7,,-1.3562374,36.904295399999995,-1.2913246,36.8019522,Rider_Id_200,4675
-Order_No_23849,User_Id_432,Bike,3,Business,13,1,1:19:39 PM,13,1,1:23:09 PM,13,1,1:39:38 PM,13,1,1:54:29 PM,13,1,2:29:56 PM,9,,,-1.279395,36.825364,-1.3012008,36.764868,Rider_Id_155,2127
-Order_No_22027,User_Id_2982,Bike,3,Business,27,6,12:16:16 PM,27,6,12:31:57 PM,27,6,12:51:29 PM,27,6,1:01:54 PM,27,6,1:53:25 PM,13,,,-1.300921,36.828195,-1.2235201,36.80206079999999,Rider_Id_184,3091
-Order_No_4188,User_Id_3752,Bike,3,Business,15,3,1:36:56 PM,15,3,1:37:45 PM,15,3,1:37:49 PM,15,3,1:54:06 PM,15,3,2:04:44 PM,7,25.8,,-1.3302996,36.8707935,-1.3263105,36.8622904,Rider_Id_955,638
-Order_No_8359,User_Id_1073,Bike,3,Business,7,3,9:32:08 AM,7,3,9:33:37 AM,7,3,9:47:47 AM,7,3,9:56:15 AM,7,3,10:19:07 AM,9,,,-1.2559185,36.79171804,-1.2698167999999999,36.7421898,Rider_Id_770,1372
-Order_No_21411,User_Id_265,Bike,3,Business,25,4,12:23:03 PM,25,4,1:24:20 PM,25,4,1:34:57 PM,25,4,1:37:53 PM,25,4,2:01:58 PM,8,,,-1.303407,36.776230299999995,-1.300921,36.828195,Rider_Id_548,1445
-Order_No_19998,User_Id_540,Bike,3,Business,24,3,12:36:44 PM,24,3,12:38:19 PM,24,3,12:59:40 PM,24,3,1:27:24 PM,24,3,1:49:25 PM,9,19.7,,-1.2978908000000002,36.796321500000005,-1.2551895,36.7822034,Rider_Id_565,1321
-Order_No_12890,User_Id_2008,Bike,3,Business,1,1,2:10:30 PM,1,1,2:10:49 PM,1,1,2:22:52 PM,1,1,2:26:54 PM,1,1,2:45:27 PM,6,25.6,,-1.2508229,36.7895263,-1.2608591999999998,36.831197700000004,Rider_Id_607,1113
-Order_No_15777,User_Id_53,Bike,3,Business,7,5,12:32:14 PM,7,5,12:33:44 PM,7,5,12:53:25 PM,7,5,1:00:19 PM,7,5,1:26:35 PM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_133,1576
-Order_No_19041,User_Id_2008,Bike,3,Business,27,1,1:16:28 PM,27,1,1:16:42 PM,27,1,1:36:36 PM,27,1,1:42:59 PM,27,1,2:36:35 PM,33,22.9,,-1.2508229,36.7895263,-1.1137700000000001,37.002043,Rider_Id_161,3216
-Order_No_18782,User_Id_3300,Bike,3,Business,30,4,9:22:00 AM,30,4,9:23:07 AM,30,4,9:35:38 AM,30,4,9:41:05 AM,30,4,10:06:50 AM,9,19.5,,-1.259478,36.801781299999995,-1.303596,36.778378000000004,Rider_Id_341,1545
-Order_No_15154,User_Id_1126,Bike,1,Personal,19,5,10:47:36 AM,19,5,10:48:19 AM,19,5,10:59:03 AM,19,5,11:03:33 AM,19,5,11:37:02 AM,16,25.1,,-1.2309778999999998,36.8565814,-1.2979633999999998,36.7719936,Rider_Id_812,2009
-Order_No_11219,User_Id_393,Bike,3,Business,19,1,11:31:42 AM,19,1,11:33:49 AM,19,1,11:37:15 AM,19,1,12:10:35 PM,19,1,12:42:47 PM,14,24.5,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_515,1932
-Order_No_18528,User_Id_20,Bike,3,Business,20,5,4:08:05 PM,20,5,4:27:44 PM,20,5,4:52:45 PM,20,5,5:15:42 PM,20,5,5:56:05 PM,13,20.2,,-1.3346931,36.8661911,-1.2628638,36.8070399,Rider_Id_808,2423
-Order_No_13648,User_Id_1051,Bike,3,Business,25,5,4:54:16 PM,25,5,4:54:22 PM,25,5,4:54:25 PM,25,5,4:55:42 PM,25,5,5:08:05 PM,2,26.8,,-1.2963097,36.7688221,-1.2969741000000001,36.7856612,Rider_Id_24,743
-Order_No_16812,User_Id_3647,Bike,3,Business,22,5,10:01:42 AM,22,5,10:02:36 AM,22,5,10:21:31 AM,22,5,10:25:57 AM,22,5,10:44:31 AM,6,22.4,,-1.2726389999999999,36.794723,-1.2946558000000001,36.760775,Rider_Id_520,1114
-Order_No_22140,User_Id_598,Bike,2,Business,14,7,11:45:49 AM,14,7,11:48:27 AM,14,7,11:55:01 AM,14,7,12:02:25 PM,14,7,12:48:12 PM,24,25.2,,-1.2329501,36.808385200000004,-1.3157001,36.7075432,Rider_Id_453,2747
-Order_No_12103,User_Id_2392,Bike,3,Personal,3,5,3:49:52 PM,3,5,3:55:11 PM,3,5,4:33:53 PM,3,5,4:49:12 PM,3,5,5:01:23 PM,4,,,-1.2955715,36.7970842,-1.2830989,36.8165739,Rider_Id_427,731
-Order_No_22304,User_Id_1923,Bike,2,Personal,14,7,2:56:57 PM,14,7,2:57:15 PM,14,7,3:01:29 PM,14,7,3:12:24 PM,14,7,3:29:11 PM,4,28.5,,-1.2919665,36.796108700000005,-1.3086197,36.8167787,Rider_Id_858,1007
-Order_No_23232,User_Id_1683,Bike,3,Business,14,2,3:44:38 PM,14,2,3:46:23 PM,14,2,4:09:44 PM,14,2,4:21:28 PM,14,2,4:35:21 PM,5,23.8,,-1.278525,36.8265351,-1.2765334,36.80150510000001,Rider_Id_213,833
-Order_No_25843,User_Id_2801,Bike,3,Business,26,3,9:01:31 AM,26,3,9:15:57 AM,26,3,9:21:10 AM,26,3,9:22:30 AM,26,3,9:45:56 AM,6,19.1,,-1.2595421999999998,36.7871178,-1.2896398999999998,36.812449799999996,Rider_Id_215,1406
-Order_No_15229,User_Id_3774,Bike,2,Personal,18,1,3:51:47 PM,18,1,3:58:15 PM,18,1,4:20:17 PM,18,1,4:33:27 PM,18,1,5:15:51 PM,8,,,-1.248177,36.7766125,-1.2731645,36.7902246,Rider_Id_322,2544
-Order_No_13122,User_Id_1329,Bike,3,Business,19,6,10:39:38 AM,19,6,10:39:48 AM,19,6,10:46:05 AM,19,6,10:47:14 AM,19,6,10:58:02 AM,5,25.2,,-1.273056,36.811298,-1.2855921000000001,36.7788349,Rider_Id_902,648
-Order_No_3844,User_Id_1363,Bike,3,Business,12,2,12:54:43 PM,12,2,12:57:16 PM,12,2,1:02:24 PM,12,2,1:39:36 PM,12,2,2:25:41 PM,8,23.0,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_297,2765
-Order_No_27868,User_Id_1329,Bike,3,Business,4,6,1:29:18 PM,4,6,1:30:00 PM,4,6,1:42:10 PM,4,6,1:43:56 PM,4,6,2:16:17 PM,8,,,-1.273056,36.811298,-1.3178938999999998,36.834883500000004,Rider_Id_815,1941
-Order_No_20898,User_Id_928,Bike,3,Business,24,4,3:14:45 PM,24,4,3:17:02 PM,24,4,3:31:20 PM,24,4,3:33:13 PM,24,4,3:58:40 PM,10,28.4,,-1.2551895,36.7822034,-1.2963097,36.7688221,Rider_Id_269,1527
-Order_No_16709,User_Id_2330,Bike,3,Business,10,5,2:36:02 PM,10,5,3:03:35 PM,10,5,3:11:40 PM,10,5,3:23:41 PM,10,5,3:41:58 PM,10,,,-1.2524916000000001,36.8656196,-1.2642903,36.80021370000001,Rider_Id_438,1097
-Order_No_17623,User_Id_2114,Bike,2,Personal,11,2,1:03:04 PM,11,2,1:05:42 PM,11,2,1:22:30 PM,11,2,1:57:19 PM,11,2,2:09:22 PM,5,25.6,,-1.3133974,36.8018636,-1.2834945,36.812937,Rider_Id_851,723
-Order_No_25475,User_Id_3517,Bike,3,Business,14,1,3:40:41 PM,14,1,3:45:48 PM,14,1,4:07:25 PM,14,1,5:16:34 PM,14,1,5:16:38 PM,5,24.5,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_26,4
-Order_No_27268,User_Id_633,Bike,3,Business,11,5,3:04:27 PM,11,5,3:21:14 PM,11,5,3:21:53 PM,11,5,3:24:56 PM,11,5,3:50:32 PM,11,26.3,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_914,1536
-Order_No_3339,User_Id_1304,Bike,3,Business,8,1,9:43:07 AM,8,1,11:05:46 AM,8,1,11:30:07 AM,8,1,11:39:05 AM,8,1,11:56:17 AM,4,24.6,,-1.3332749,36.870814700000004,-1.3385790000000002,36.89630379999999,Rider_Id_805,1032
-Order_No_14092,User_Id_3483,Bike,1,Personal,2,4,8:48:27 AM,2,4,8:49:57 AM,2,4,9:03:28 AM,2,4,9:06:15 AM,2,4,9:16:07 AM,3,13.6,,-1.2630521000000001,36.806801899999996,-1.2787096000000002,36.8157336,Rider_Id_857,592
-Order_No_7096,User_Id_1205,Bike,3,Business,18,3,9:50:03 AM,18,3,9:51:14 AM,18,3,9:55:16 AM,18,3,9:59:57 AM,18,3,10:14:46 AM,3,14.6,,-1.2810807,36.8144229,-1.298529,36.816482,Rider_Id_208,889
-Order_No_14535,User_Id_738,Bike,3,Business,5,5,1:00:49 PM,5,5,1:02:08 PM,5,5,1:20:41 PM,5,5,1:28:06 PM,5,5,1:46:41 PM,6,23.6,,-1.2881479999999998,36.7850276,-1.2838129999999999,36.73857279999999,Rider_Id_38,1115
-Order_No_8917,User_Id_3201,Bike,3,Business,26,3,12:54:55 PM,26,3,12:55:13 PM,26,3,1:04:56 PM,26,3,1:07:09 PM,26,3,1:50:06 PM,15,23.8,,-1.2649605,36.798177700000004,-1.337784,36.758651799999996,Rider_Id_914,2577
-Order_No_8423,User_Id_391,Bike,3,Business,1,1,10:40:33 AM,1,1,11:13:11 AM,1,1,11:27:53 AM,1,1,11:48:39 AM,1,1,12:18:18 PM,18,,,-1.3958588,36.7701901,-1.2867454,36.8259017,Rider_Id_411,1779
-Order_No_16990,User_Id_1329,Bike,3,Business,20,2,9:47:13 AM,20,2,9:57:41 AM,20,2,10:07:26 AM,20,2,10:09:46 AM,20,2,10:26:37 AM,6,19.1,,-1.273056,36.811298,-1.2832105,36.7777664,Rider_Id_131,1011
-Order_No_23078,User_Id_1346,Bike,3,Business,10,3,1:50:58 PM,10,3,1:51:31 PM,10,3,2:21:47 PM,10,3,2:24:48 PM,10,3,2:42:06 PM,6,30.6,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_263,1038
-Order_No_12303,User_Id_269,Bike,3,Business,22,5,11:26:17 AM,22,5,11:35:30 AM,22,5,11:41:30 AM,22,5,11:49:32 AM,22,5,12:15:09 PM,15,21.8,,-1.3332749,36.870814700000004,-1.2551895,36.7822034,Rider_Id_663,1537
-Order_No_19473,User_Id_755,Bike,1,Personal,21,6,12:39:26 PM,21,6,12:39:50 PM,21,6,12:48:45 PM,21,6,1:29:38 PM,21,6,2:34:48 PM,13,16.7,,-1.282357,36.782501,-1.2289456,36.765112200000004,Rider_Id_329,3910
-Order_No_3609,User_Id_867,Bike,3,Business,24,4,11:34:30 AM,24,4,11:36:53 AM,24,4,11:44:49 AM,24,4,12:58:10 PM,24,4,1:29:32 PM,10,24.7,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_331,1882
-Order_No_9140,User_Id_706,Bike,3,Business,21,1,4:30:55 PM,21,1,4:31:37 PM,21,1,4:42:57 PM,21,1,4:50:21 PM,21,1,5:01:31 PM,4,,,-1.298331,36.822614,-1.3077869,36.844320700000004,Rider_Id_116,670
-Order_No_24951,User_Id_3647,Bike,3,Business,24,4,1:36:58 PM,24,4,1:37:38 PM,24,4,1:51:01 PM,24,4,1:54:58 PM,24,4,2:08:17 PM,6,28.3,,-1.2726389999999999,36.794723,-1.2628638,36.8070399,Rider_Id_228,799
-Order_No_562,User_Id_246,Bike,3,Business,24,1,1:06:20 PM,24,1,1:08:11 PM,24,1,1:16:04 PM,24,1,1:21:25 PM,24,1,1:41:13 PM,18,22.9,,-1.2859912,36.8756811,-1.2584143,36.8048002,Rider_Id_404,1188
-Order_No_15346,User_Id_402,Bike,3,Business,7,5,3:29:49 PM,7,5,3:34:33 PM,7,5,3:43:29 PM,7,5,3:47:15 PM,7,5,5:00:35 PM,10,,,-1.2527958000000001,36.797624400000004,-1.3038543,36.83810510000001,Rider_Id_309,4400
-Order_No_25984,User_Id_2561,Bike,3,Business,18,1,10:17:50 AM,18,1,10:21:22 AM,18,1,10:24:02 AM,18,1,10:28:57 AM,18,1,10:59:46 AM,8,21.3,,-1.2551895,36.7822034,-1.3016151000000002,36.811735399999996,Rider_Id_690,1849
-Order_No_27680,User_Id_1245,Bike,3,Business,11,1,1:19:23 PM,11,1,1:20:19 PM,11,1,1:49:54 PM,11,1,2:04:45 PM,11,1,3:06:01 PM,19,28.8,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_787,3676
-Order_No_27445,User_Id_3234,Bike,1,Business,15,6,5:05:51 PM,15,6,5:33:06 PM,15,6,5:42:27 PM,15,6,6:22:59 PM,15,6,6:40:05 PM,8,24.9,,-1.3196808999999998,36.8410572,-1.2977386999999998,36.803144700000004,Rider_Id_539,1026
-Order_No_4760,User_Id_393,Bike,3,Business,13,3,10:57:44 AM,13,3,10:57:51 AM,13,3,10:58:03 AM,13,3,11:18:17 AM,13,3,11:42:18 AM,14,25.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_901,1441
-Order_No_1350,User_Id_540,Bike,3,Business,25,4,3:25:03 PM,25,4,3:25:12 PM,25,4,3:26:50 PM,25,4,3:28:20 PM,25,4,3:44:49 PM,5,20.9,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_531,989
-Order_No_16302,User_Id_3232,Bike,3,Business,6,1,12:37:05 PM,6,1,12:38:17 PM,6,1,12:51:26 PM,6,1,1:02:03 PM,6,1,1:23:56 PM,8,20.4,,-1.2551895,36.7822034,-1.2846195,36.7958316,Rider_Id_520,1313
-Order_No_22844,User_Id_972,Bike,3,Business,26,4,4:02:43 PM,26,4,4:05:07 PM,26,4,4:14:13 PM,26,4,4:17:01 PM,26,4,4:38:47 PM,11,21.9,,-1.2862033000000002,36.8152966,-1.3206958,36.8769553,Rider_Id_857,1306
-Order_No_11924,User_Id_1642,Bike,3,Business,17,4,11:15:53 AM,17,4,11:16:44 AM,17,4,11:31:18 AM,17,4,11:37:15 AM,17,4,11:59:18 AM,6,22.3,,-1.2728069,36.7962362,-1.2914681000000001,36.80128870000001,Rider_Id_782,1323
-Order_No_5910,User_Id_2480,Bike,3,Personal,3,5,9:58:47 AM,3,5,9:59:04 AM,3,5,10:17:42 AM,3,5,10:37:25 AM,3,5,10:59:45 AM,14,21.6,,-1.3508657,36.9019028,-1.3024794,36.8228284,Rider_Id_620,1340
-Order_No_6035,User_Id_3647,Bike,3,Business,29,1,2:46:45 PM,29,1,2:47:42 PM,29,1,2:58:00 PM,29,1,3:04:17 PM,29,1,3:12:02 PM,4,,,-1.2726389999999999,36.794723,-1.2609186000000001,36.7938342,Rider_Id_315,465
-Order_No_3954,User_Id_3289,Bike,3,Personal,1,5,7:53:36 PM,1,5,7:54:12 PM,1,5,8:02:23 PM,1,5,8:07:35 PM,1,5,8:14:54 PM,3,21.4,,-1.3002726999999998,36.7668028,-1.2974406,36.7765953,Rider_Id_862,439
-Order_No_24703,User_Id_3355,Bike,3,Business,31,1,11:09:43 AM,31,1,11:21:58 AM,31,1,11:25:41 AM,31,1,11:26:48 AM,31,1,12:11:26 PM,17,21.1,,-1.2156006000000001,36.8916865,-1.3052491000000002,36.8223899,Rider_Id_845,2678
-Order_No_3304,User_Id_2619,Bike,2,Personal,5,4,10:53:41 AM,5,4,10:53:58 AM,5,4,11:10:32 AM,5,4,11:48:39 AM,5,4,12:28:33 PM,5,,,-1.2795796,36.819062,-1.2649605,36.798177700000004,Rider_Id_951,2394
-Order_No_27145,User_Id_2982,Bike,3,Business,26,2,1:14:01 PM,26,2,2:19:34 PM,26,2,2:23:50 PM,26,2,3:05:03 PM,26,2,3:15:35 PM,8,,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_101,632
-Order_No_14751,User_Id_2701,Bike,3,Business,6,3,1:55:23 PM,6,3,1:56:20 PM,6,3,2:20:38 PM,6,3,2:28:10 PM,6,3,2:46:59 PM,7,22.0,,-1.279395,36.825364,-1.2553615,36.789537599999996,Rider_Id_531,1129
-Order_No_23751,User_Id_2358,Bike,4,Business,18,1,2:04:10 PM,18,1,2:04:48 PM,18,1,2:09:55 PM,18,1,2:15:25 PM,18,1,2:43:31 PM,31,17.3,,-1.3256903999999998,36.8747943,-1.2847178,36.6896108,Rider_Id_543,1686
-Order_No_22235,User_Id_3557,Bike,3,Business,4,1,9:10:56 AM,4,1,9:11:11 AM,4,1,9:18:41 AM,4,1,9:37:10 AM,4,1,9:52:00 AM,7,21.9,,-1.3047132,36.808955299999994,-1.2546732,36.808679600000005,Rider_Id_72,890
-Order_No_25195,User_Id_2828,Bike,1,Business,20,1,3:23:24 PM,20,1,3:25:19 PM,20,1,3:37:56 PM,20,1,3:42:04 PM,20,1,3:57:17 PM,5,21.3,,-1.2696183,36.8021778,-1.2772389,36.7855864,Rider_Id_407,913
-Order_No_24838,User_Id_265,Bike,3,Business,14,3,9:50:03 AM,14,3,10:19:32 AM,14,3,10:37:25 AM,14,3,10:52:15 AM,14,3,11:11:30 AM,16,24.6,,-1.3429554,36.7262559,-1.300921,36.828195,Rider_Id_555,1155
-Order_No_13854,User_Id_393,Bike,3,Business,28,4,4:44:33 PM,28,4,4:45:58 PM,28,4,4:46:03 PM,28,4,4:55:53 PM,28,4,5:04:31 PM,6,28.0,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_523,518
-Order_No_2432,User_Id_1210,Bike,1,Personal,6,7,11:35:51 AM,6,7,11:36:52 AM,6,7,12:00:57 PM,6,7,12:04:05 PM,6,7,12:23:21 PM,5,21.9,,-1.2867132,36.8118433,-1.2747671,36.8139951,Rider_Id_159,1156
-Order_No_5071,User_Id_2112,Bike,3,Business,25,1,1:32:20 PM,25,1,1:43:18 PM,25,1,1:58:07 PM,25,1,2:02:36 PM,25,1,2:49:47 PM,6,30.3,,-1.2156006000000001,36.8916865,-1.2413316,36.878552500000005,Rider_Id_660,2831
-Order_No_7412,User_Id_393,Bike,3,Business,7,1,4:00:35 PM,7,1,4:16:04 PM,7,1,4:16:10 PM,7,1,4:28:50 PM,7,1,4:44:29 PM,4,26.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_39,939
-Order_No_26750,User_Id_3251,Bike,3,Business,31,4,2:15:57 PM,31,4,2:21:00 PM,31,4,2:28:36 PM,31,4,2:35:50 PM,31,4,2:52:03 PM,6,22.8,,-1.2833534,36.8165865,-1.287139,36.768308000000005,Rider_Id_100,973
-Order_No_3575,User_Id_265,Bike,3,Business,20,3,10:04:44 AM,20,3,10:05:14 AM,20,3,10:08:57 AM,20,3,10:13:29 AM,20,3,10:27:34 AM,3,21.0,,-1.300921,36.828195,-1.3230539,36.8464688,Rider_Id_206,845
-Order_No_20199,User_Id_1169,Bike,1,Personal,29,5,4:12:16 PM,29,5,4:24:51 PM,29,5,4:52:34 PM,29,5,4:56:03 PM,29,5,5:13:03 PM,4,20.5,,-1.2902539,36.8211322,-1.2996713,36.79458579999999,Rider_Id_88,1020
-Order_No_6378,User_Id_3211,Bike,1,Personal,7,4,12:56:06 PM,7,4,1:38:24 PM,7,4,1:46:14 PM,7,4,1:50:54 PM,7,4,2:08:29 PM,3,20.0,,-1.2783772,36.8201617,-1.2883346000000002,36.8148489,Rider_Id_742,1055
-Order_No_5885,User_Id_2801,Bike,3,Business,25,1,10:27:03 AM,25,1,10:28:51 AM,25,1,10:40:26 AM,25,1,10:44:56 AM,25,1,11:04:00 AM,7,25.6,,-1.2674273999999999,36.787082700000006,-1.2809846999999999,36.8274169,Rider_Id_660,1144
-Order_No_10238,User_Id_1961,Bike,3,Business,30,2,2:02:11 PM,30,2,2:08:16 PM,30,2,2:23:43 PM,30,2,2:34:22 PM,30,2,3:10:28 PM,21,,,-1.331619,36.847976,-1.3181159,36.710155900000004,Rider_Id_420,2166
-Order_No_11997,User_Id_978,Bike,1,Personal,17,2,1:59:54 PM,17,2,2:08:48 PM,17,2,2:30:42 PM,17,2,2:56:04 PM,17,2,3:25:56 PM,4,24.4,,-1.3084067,36.83450139999999,-1.2805312,36.832668700000006,Rider_Id_939,1792
-Order_No_27905,User_Id_2714,Bike,1,Personal,8,5,2:22:34 PM,8,5,2:39:54 PM,8,5,3:37:23 PM,8,5,3:41:42 PM,8,5,4:45:46 PM,27,21.7,,-1.3518598,36.7324661,-1.2235201,36.80206079999999,Rider_Id_421,3844
-Order_No_10731,User_Id_2154,Bike,3,Business,8,1,11:34:56 AM,8,1,11:36:20 AM,8,1,11:39:43 AM,8,1,11:44:59 AM,8,1,12:04:30 PM,8,28.1,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_576,1171
-Order_No_15862,User_Id_2987,Bike,3,Business,30,2,11:38:01 AM,30,2,11:38:22 AM,30,2,11:51:46 AM,30,2,12:01:29 PM,30,2,12:12:27 PM,3,22.1,,-1.253654,36.8204391,-1.2702120000000001,36.82108770000001,Rider_Id_125,658
-Order_No_17303,User_Id_2936,Bike,2,Personal,16,1,5:23:22 PM,16,1,5:23:30 PM,16,1,5:26:10 PM,16,1,5:47:29 PM,16,1,6:14:52 PM,12,,,-1.3169591,36.7000029,-1.3662774,36.760781099999996,Rider_Id_672,1643
-Order_No_23353,User_Id_246,Bike,3,Business,6,3,3:34:25 PM,6,3,3:44:28 PM,6,3,3:44:35 PM,6,3,3:58:44 PM,6,3,5:10:50 PM,19,22.0,,-1.2584143,36.8048002,-1.311342,36.913268,Rider_Id_345,4326
-Order_No_19146,User_Id_1329,Bike,3,Business,14,5,4:51:01 PM,14,5,4:56:19 PM,14,5,5:02:23 PM,14,5,5:04:40 PM,14,5,5:54:16 PM,19,22.9,,-1.273056,36.811298,-1.3677142,36.9136314,Rider_Id_573,2976
-Order_No_24749,User_Id_3194,Bike,3,Business,3,3,12:35:25 PM,3,3,12:36:16 PM,3,3,12:40:28 PM,3,3,12:44:36 PM,3,3,12:55:58 PM,5,27.4,,-1.2551895,36.7822034,-1.270721,36.786231,Rider_Id_250,682
-Order_No_11503,User_Id_393,Bike,3,Business,29,1,10:52:23 AM,29,1,10:53:17 AM,29,1,11:08:55 AM,29,1,11:29:34 AM,29,1,11:29:37 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_99,3
-Order_No_22352,User_Id_2982,Bike,3,Business,6,3,4:35:17 PM,6,3,4:35:55 PM,6,3,4:37:03 PM,6,3,4:58:01 PM,6,3,5:52:39 PM,23,,,-1.300921,36.828195,-1.2588448,36.6907159,Rider_Id_571,3278
-Order_No_23403,User_Id_3621,Bike,3,Business,22,7,3:05:05 PM,22,7,3:05:27 PM,22,7,3:09:15 PM,22,7,3:20:57 PM,22,7,3:44:27 PM,14,19.2,,-1.2836583000000001,36.7886014,-1.328751,36.8818019,Rider_Id_670,1410
-Order_No_7210,User_Id_3291,Bike,3,Business,29,3,10:15:02 AM,29,3,10:15:21 AM,29,3,10:29:22 AM,29,3,10:35:50 AM,29,3,10:59:20 AM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_709,1410
-Order_No_6135,User_Id_346,Bike,3,Business,14,4,10:17:27 AM,14,4,10:17:48 AM,14,4,10:34:47 AM,14,4,10:40:26 AM,14,4,12:45:17 PM,15,25.2,,-1.28878,36.8168312,-1.216682,36.908577799999996,Rider_Id_119,7491
-Order_No_11987,User_Id_44,Bike,1,Personal,12,4,11:44:31 AM,12,4,11:45:00 AM,12,4,11:51:34 AM,12,4,12:01:32 PM,12,4,12:21:40 PM,15,18.9,,-1.2536725,36.680491499999995,-1.2560455,36.780086700000005,Rider_Id_735,1208
-Order_No_1865,User_Id_1439,Bike,3,Business,22,5,2:15:35 PM,22,5,2:15:54 PM,22,5,2:19:34 PM,22,5,2:23:19 PM,22,5,2:37:46 PM,5,27.3,,-1.2962486999999998,36.7772971,-1.2821752,36.7338676,Rider_Id_354,867
-Order_No_8739,User_Id_136,Bike,3,Business,31,4,11:17:53 AM,31,4,11:18:22 AM,31,4,11:29:18 AM,31,4,11:32:10 AM,31,4,12:04:50 PM,3,26.4,,-1.2965045,36.7913036,-1.2906104999999999,36.805327899999995,Rider_Id_206,1960
-Order_No_12695,User_Id_3647,Bike,3,Business,1,1,3:50:12 PM,1,1,3:52:59 PM,1,1,4:11:12 PM,1,1,4:18:42 PM,1,1,5:10:42 PM,17,,,-1.2726389999999999,36.794723,-1.2113548,36.8814114,Rider_Id_686,3120
-Order_No_1679,User_Id_2010,Bike,2,Personal,10,1,10:16:52 AM,10,1,10:19:07 AM,10,1,10:39:34 AM,10,1,10:51:16 AM,10,1,11:06:31 AM,6,,,-1.2820581,36.8284909,-1.3173868999999998,36.8112044,Rider_Id_88,915
-Order_No_20701,User_Id_3647,Bike,3,Business,2,3,11:36:46 AM,2,3,11:37:08 AM,2,3,11:47:18 AM,2,3,11:52:51 AM,2,3,12:15:07 PM,10,24.2,,-1.2726389999999999,36.794723,-1.3231001,36.8283559,Rider_Id_446,1336
-Order_No_17102,User_Id_481,Bike,3,Personal,21,2,9:02:19 AM,21,2,9:03:28 AM,21,2,9:36:15 AM,21,2,9:41:43 AM,21,2,10:04:29 AM,4,,,-1.3081376,36.83297720000001,-1.2802618000000001,36.8191684,Rider_Id_448,1366
-Order_No_8659,User_Id_1578,Bike,3,Business,6,2,11:23:41 AM,6,2,11:26:57 AM,6,2,11:31:32 AM,6,2,11:56:56 AM,6,2,12:52:53 PM,10,25.0,,-1.28878,36.8168312,-1.2413316,36.878552500000005,Rider_Id_101,3357
-Order_No_11763,User_Id_49,Bike,3,Business,29,6,10:55:14 AM,29,6,10:56:02 AM,29,6,10:59:27 AM,29,6,11:04:02 AM,29,6,11:55:00 AM,13,24.2,,-1.2790196000000003,36.8160867,-1.2155671000000001,36.8898931,Rider_Id_698,3058
-Order_No_16192,User_Id_2123,Bike,1,Personal,9,6,1:57:54 PM,9,6,1:58:44 PM,9,6,2:24:40 PM,9,6,2:29:51 PM,9,6,3:39:14 PM,20,19.2,,-1.2770766000000002,36.81961939999999,-1.3929388,36.760163899999995,Rider_Id_852,4163
-Order_No_10271,User_Id_3727,Bike,3,Business,6,4,9:52:28 AM,6,4,9:54:50 AM,6,4,10:00:05 AM,6,4,10:07:04 AM,6,4,10:25:58 AM,8,18.7,3.0,-1.2963097,36.7688221,-1.2600616000000002,36.787018200000006,Rider_Id_517,1134
-Order_No_23579,User_Id_3432,Bike,3,Business,24,4,2:40:25 PM,24,4,2:41:20 PM,24,4,3:08:01 PM,24,4,3:16:03 PM,24,4,4:27:55 PM,12,28.1,,-1.3139898,36.867698600000004,-1.2672588,36.8000252,Rider_Id_332,4312
-Order_No_15945,User_Id_2330,Bike,3,Business,20,3,1:00:21 PM,20,3,1:01:44 PM,20,3,1:07:40 PM,20,3,1:10:24 PM,20,3,1:46:19 PM,9,20.4,,-1.2671943,36.8062533,-1.2243841,36.8024757,Rider_Id_268,2155
-Order_No_11832,User_Id_1824,Bike,3,Business,9,1,5:32:13 PM,9,1,5:42:54 PM,9,1,5:55:02 PM,9,1,6:06:16 PM,9,1,6:19:03 PM,4,20.6,,-1.3043821000000002,36.7840586,-1.3099145,36.802277399999994,Rider_Id_465,767
-Order_No_11697,User_Id_1230,Bike,1,Personal,12,3,4:39:57 PM,12,3,4:47:30 PM,12,3,4:59:43 PM,12,3,5:03:09 PM,12,3,5:19:01 PM,5,24.2,,-1.2535139,36.8299659,-1.2855798999999999,36.8227554,Rider_Id_263,952
-Order_No_3617,User_Id_3647,Bike,3,Business,5,3,9:45:28 AM,5,3,9:46:07 AM,5,3,9:59:21 AM,5,3,10:02:48 AM,5,3,10:09:38 AM,2,18.2,,-1.2726389999999999,36.794723,-1.2746436,36.803682,Rider_Id_50,410
-Order_No_19042,User_Id_2440,Bike,3,Personal,21,1,8:04:31 PM,21,1,8:23:08 PM,21,1,8:46:29 PM,21,1,8:50:06 PM,21,1,9:17:18 PM,12,18.5,,-1.22589,36.7822548,-1.2676889999999998,36.7673638,Rider_Id_144,1632
-Order_No_13886,User_Id_1477,Bike,2,Personal,7,2,11:59:06 AM,7,2,12:00:49 PM,7,2,12:10:10 PM,7,2,12:20:59 PM,7,2,12:42:47 PM,8,23.4,,-1.2560455,36.780086700000005,-1.28159,36.8143758,Rider_Id_882,1308
-Order_No_18656,User_Id_3283,Bike,3,Business,20,2,10:08:00 AM,20,2,10:11:05 AM,20,2,10:23:18 AM,20,2,10:48:39 AM,20,2,10:54:47 AM,6,20.0,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_472,368
-Order_No_522,User_Id_1191,Bike,1,Personal,24,5,1:41:46 PM,24,5,1:42:14 PM,24,5,1:47:19 PM,24,5,2:06:17 PM,24,5,2:17:21 PM,7,23.3,,-1.32589,36.831679,-1.3282758,36.8590281,Rider_Id_932,664
-Order_No_20495,User_Id_868,Bike,3,Business,2,6,1:36:42 PM,2,6,1:37:01 PM,2,6,1:40:01 PM,2,6,1:55:26 PM,2,6,2:09:34 PM,7,29.1,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_427,848
-Order_No_14263,User_Id_867,Bike,3,Business,3,1,1:06:54 PM,3,1,3:50:14 PM,3,1,3:55:48 PM,3,1,4:05:23 PM,3,1,4:30:48 PM,10,23.4,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_762,1525
-Order_No_9765,User_Id_2200,Bike,3,Business,11,6,12:47:26 PM,11,6,12:48:48 PM,11,6,12:49:42 PM,11,6,1:03:07 PM,11,6,1:48:55 PM,11,,,-1.2852569,36.8976121,-1.290894,36.822971,Rider_Id_895,2748
-Order_No_13043,User_Id_3179,Bike,1,Personal,15,3,11:17:06 AM,15,3,11:17:13 AM,15,3,11:24:24 AM,15,3,11:35:04 AM,15,3,11:54:06 AM,7,17.9,,-1.2566078,36.7832089,-1.2404575,36.7960814,Rider_Id_229,1142
-Order_No_9578,User_Id_3560,Bike,3,Business,23,6,11:38:15 AM,23,6,11:39:18 AM,23,6,11:47:55 AM,23,6,11:56:25 AM,23,6,12:14:31 PM,5,,,-1.2591018999999999,36.800577000000004,-1.279395,36.825364,Rider_Id_456,1086
-Order_No_12532,User_Id_478,Bike,3,Business,6,1,2:00:36 PM,6,1,2:01:32 PM,6,1,2:26:59 PM,6,1,2:34:23 PM,6,1,3:02:58 PM,18,,,-1.2174537,36.880190899999995,-1.2551895,36.7822034,Rider_Id_691,1715
-Order_No_8467,User_Id_909,Bike,3,Business,28,1,1:49:34 PM,28,1,1:50:01 PM,28,1,2:04:50 PM,28,1,2:07:24 PM,28,1,2:23:52 PM,8,26.4,,-1.269161,36.841626399999996,-1.225322,36.80855,Rider_Id_627,988
-Order_No_24812,User_Id_402,Bike,3,Business,15,3,3:23:30 PM,15,3,3:29:44 PM,15,3,4:05:52 PM,15,3,4:15:41 PM,15,3,5:13:33 PM,6,25.7,,-1.290894,36.822971,-1.2527958000000001,36.8003131,Rider_Id_868,3472
-Order_No_16928,User_Id_1278,Bike,3,Personal,17,1,12:16:19 PM,17,1,12:17:32 PM,17,1,12:32:52 PM,17,1,12:40:27 PM,17,1,1:04:30 PM,10,23.6,,-1.3226301999999999,36.8719901,-1.303105,36.819134999999996,Rider_Id_498,1443
-Order_No_16107,User_Id_2789,Bike,3,Business,22,4,8:30:57 AM,22,4,8:31:57 AM,22,4,8:41:37 AM,22,4,8:46:48 AM,22,4,8:59:02 AM,5,19.4,2.9,-1.304735,36.784605400000004,-1.2946126,36.805964200000005,Rider_Id_726,734
-Order_No_21043,User_Id_2195,Bike,3,Business,26,2,3:46:53 PM,26,2,3:48:00 PM,26,2,3:54:35 PM,26,2,4:15:09 PM,26,2,4:40:41 PM,14,31.0,,-1.3033561000000002,36.8163631,-1.228911,36.881876,Rider_Id_177,1532
-Order_No_23593,User_Id_2398,Bike,1,Personal,31,5,12:59:36 PM,31,5,1:01:03 PM,31,5,1:03:26 PM,31,5,1:10:25 PM,31,5,1:32:48 PM,9,17.0,,-1.2574087999999999,36.799712,-1.3004846,36.790159100000004,Rider_Id_937,1343
-Order_No_23980,User_Id_186,Bike,3,Business,30,3,1:55:47 PM,30,3,2:15:34 PM,30,3,2:26:06 PM,30,3,2:28:39 PM,30,3,2:54:57 PM,20,24.9,,-1.3700383,36.9190174,-1.2902869,36.8085396,Rider_Id_520,1578
-Order_No_21842,User_Id_2188,Bike,2,Personal,19,4,12:21:13 PM,19,4,12:21:44 PM,19,4,12:35:44 PM,19,4,12:47:40 PM,19,4,1:18:42 PM,8,20.0,,-1.2970278999999998,36.761356,-1.269534,36.80697,Rider_Id_140,1862
-Order_No_17415,User_Id_2277,Bike,3,Business,13,2,12:28:19 PM,13,2,12:30:47 PM,13,2,12:35:47 PM,13,2,12:58:27 PM,13,2,1:18:10 PM,8,22.1,,-1.2389424999999998,36.8775718,-1.2915328000000001,36.8632896,Rider_Id_237,1183
-Order_No_12671,User_Id_136,Bike,3,Business,8,1,11:56:38 AM,8,1,11:59:17 AM,8,1,12:10:15 PM,8,1,12:11:53 PM,8,1,12:44:02 PM,14,27.8,,-1.3254522,36.721658399999995,-1.2963367000000001,36.8094235,Rider_Id_628,1929
-Order_No_7853,User_Id_3647,Bike,3,Business,12,5,12:43:57 PM,12,5,12:44:44 PM,12,5,1:02:00 PM,12,5,1:06:19 PM,12,5,1:20:49 PM,3,24.6,,-1.2726389999999999,36.794723,-1.2655477,36.8001682,Rider_Id_755,870
-Order_No_1021,User_Id_393,Bike,3,Business,11,2,12:48:54 PM,11,2,1:40:41 PM,11,2,1:40:43 PM,11,2,1:57:11 PM,11,2,2:30:58 PM,14,22.4,,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_619,2027
-Order_No_10228,User_Id_1288,Bike,1,Personal,28,1,11:37:53 AM,28,1,11:38:14 AM,28,1,11:38:25 AM,28,1,11:43:24 AM,28,1,12:26:08 PM,8,22.8,,-1.2861764999999998,36.8209086,-1.2586145,36.7782729,Rider_Id_546,2564
-Order_No_8509,User_Id_778,Bike,3,Business,20,1,9:58:38 AM,20,1,10:02:31 AM,20,1,10:15:39 AM,20,1,10:22:53 AM,20,1,10:44:07 AM,7,14.4,,-1.2535486,36.7933541,-1.2655712,36.768511700000005,Rider_Id_882,1274
-Order_No_11258,User_Id_420,Bike,3,Business,21,5,3:43:28 PM,21,5,4:00:36 PM,21,5,4:45:19 PM,21,5,5:04:06 PM,21,5,5:44:23 PM,22,25.2,,-1.3981712,36.7673241,-1.2709895,36.7799719,Rider_Id_312,2417
-Order_No_283,User_Id_3303,Bike,3,Business,15,6,9:08:57 AM,15,6,9:09:28 AM,15,6,9:54:39 AM,15,6,10:02:31 AM,15,6,10:16:36 AM,5,16.6,,-1.318411,36.8059305,-1.3122178999999998,36.823696000000005,Rider_Id_610,845
-Order_No_21244,User_Id_3491,Bike,3,Business,11,1,1:16:00 PM,11,1,1:27:26 PM,11,1,1:35:41 PM,11,1,1:54:38 PM,11,1,2:55:20 PM,10,29.1,,-1.2859912,36.8756811,-1.2283403,36.8822756,Rider_Id_771,3642
-Order_No_10763,User_Id_393,Bike,3,Business,28,2,12:37:00 PM,28,2,12:51:51 PM,28,2,1:01:35 PM,28,2,1:14:09 PM,28,2,1:25:17 PM,5,21.3,,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_2,668
-Order_No_23891,User_Id_868,Bike,3,Business,18,1,12:13:16 PM,18,1,12:15:38 PM,18,1,12:44:12 PM,18,1,12:54:18 PM,18,1,1:18:42 PM,8,27.8,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_652,1464
-Order_No_12702,User_Id_1329,Bike,3,Business,1,1,5:08:58 PM,1,1,5:10:09 PM,1,1,5:14:44 PM,1,1,5:17:52 PM,1,1,6:00:15 PM,8,27.4,,-1.273056,36.811298,-1.2315181,36.814054299999995,Rider_Id_166,2543
-Order_No_11452,User_Id_2532,Bike,3,Business,23,5,8:47:21 AM,23,5,8:49:19 AM,23,5,9:05:09 AM,23,5,9:07:34 AM,23,5,9:43:12 AM,14,20.0,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_933,2138
-Order_No_27299,User_Id_238,Bike,3,Business,29,5,2:27:44 PM,29,5,2:28:21 PM,29,5,2:38:18 PM,29,5,2:40:57 PM,29,5,2:47:10 PM,3,29.0,,-1.3077869,36.844320700000004,-1.3166811999999999,36.859460799999994,Rider_Id_519,373
-Order_No_13148,User_Id_59,Bike,3,Business,18,4,10:06:51 AM,18,4,10:26:47 AM,18,4,10:44:14 AM,18,4,10:56:48 AM,18,4,12:11:10 PM,4,,,-1.290894,36.822971,-1.3047026000000002,36.8172033,Rider_Id_394,4462
-Order_No_3731,User_Id_2132,Bike,3,Business,14,4,2:19:23 PM,14,4,2:20:17 PM,14,4,2:27:57 PM,14,4,3:05:35 PM,14,4,3:31:32 PM,13,27.9,,-1.2384056,36.8718698,-1.3160089,36.8551312,Rider_Id_75,1557
-Order_No_19349,User_Id_779,Bike,1,Personal,28,5,3:37:05 PM,28,5,3:38:25 PM,28,5,3:47:51 PM,28,5,3:55:03 PM,28,5,4:22:31 PM,9,25.7,,-1.2714948,36.8166789,-1.2905113000000001,36.759113799999994,Rider_Id_107,1648
-Order_No_27683,User_Id_402,Bike,3,Business,1,6,2:13:45 PM,1,6,2:15:36 PM,1,6,2:21:23 PM,1,6,2:34:05 PM,1,6,2:34:10 PM,1,26.4,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_819,5
-Order_No_22848,User_Id_1346,Bike,3,Business,19,2,2:52:47 PM,19,2,2:53:04 PM,19,2,2:57:36 PM,19,2,3:01:25 PM,19,2,3:01:58 PM,2,30.1,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_634,33
-Order_No_22988,User_Id_2933,Bike,2,Personal,19,4,3:31:40 PM,19,4,3:32:35 PM,19,4,3:37:15 PM,19,4,3:42:34 PM,19,4,4:05:30 PM,9,20.3,,-1.3052671999999998,36.753853899999996,-1.2721028,36.795348499999996,Rider_Id_412,1376
-Order_No_8277,User_Id_186,Bike,3,Business,3,5,12:02:22 PM,3,5,12:03:09 PM,3,5,12:07:40 PM,3,5,12:21:03 PM,3,5,12:52:49 PM,9,,,-1.2571472,36.795063299999995,-1.2326041,36.8009029,Rider_Id_660,1906
-Order_No_9498,User_Id_1271,Bike,3,Business,21,4,1:11:24 PM,21,4,1:29:03 PM,21,4,2:05:08 PM,21,4,2:13:53 PM,21,4,2:59:43 PM,27,31.0,,-1.3949989,36.7654761,-1.3244885,36.897791999999995,Rider_Id_628,2750
-Order_No_19009,User_Id_2609,Bike,1,Business,21,1,4:01:46 PM,21,1,4:02:06 PM,21,1,4:03:01 PM,21,1,4:25:18 PM,21,1,4:25:41 PM,3,21.5,,-1.2601708999999999,36.8002238,-1.2626906,36.7827022,Rider_Id_51,23
-Order_No_8607,User_Id_186,Bike,3,Business,18,1,9:17:07 AM,18,1,9:17:23 AM,18,1,9:26:49 AM,18,1,9:31:17 AM,18,1,10:07:12 AM,20,21.5,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_520,2155
-Order_No_7866,User_Id_3486,Bike,3,Business,19,3,12:08:02 PM,19,3,12:49:20 PM,19,3,12:54:54 PM,19,3,12:58:54 PM,19,3,1:58:30 PM,7,22.1,,-1.2551895,36.7822034,-1.2836183,36.8297208,Rider_Id_906,3576
-Order_No_5708,User_Id_2521,Bike,3,Business,25,5,3:02:17 PM,25,5,3:02:37 PM,25,5,3:02:41 PM,25,5,3:16:10 PM,25,5,3:23:53 PM,4,29.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,463
-Order_No_10895,User_Id_1850,Bike,3,Business,4,1,2:53:18 PM,4,1,2:53:40 PM,4,1,2:54:34 PM,4,1,3:14:19 PM,4,1,3:28:38 PM,4,28.5,,-1.2551895,36.7822034,-1.2649114,36.8134191,Rider_Id_451,859
-Order_No_7088,User_Id_1473,Bike,3,Business,20,1,12:09:43 PM,20,1,12:10:17 PM,20,1,12:29:30 PM,20,1,12:42:32 PM,20,1,1:25:09 PM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_131,2557
-Order_No_27770,User_Id_1179,Bike,3,Business,6,4,2:17:05 PM,6,4,2:25:56 PM,6,4,3:08:37 PM,6,4,3:33:22 PM,6,4,4:14:04 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_569,2442
-Order_No_7836,User_Id_1346,Bike,3,Business,1,4,11:25:51 AM,1,4,11:26:26 AM,1,4,11:27:00 AM,1,4,11:32:38 AM,1,4,11:42:18 AM,2,,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_390,580
-Order_No_21880,User_Id_3210,Bike,1,Personal,2,2,12:37:34 PM,2,2,12:39:18 PM,2,2,12:45:57 PM,2,2,12:51:46 PM,2,2,1:21:06 PM,8,20.8,,-1.2559559999999999,36.772901,-1.2795261,36.826018499999996,Rider_Id_449,1760
-Order_No_7586,User_Id_1439,Bike,3,Business,6,3,1:56:13 PM,6,3,1:56:42 PM,6,3,2:15:14 PM,6,3,2:18:36 PM,6,3,2:31:52 PM,5,27.7,,-1.2962486999999998,36.7772971,-1.261403,36.769224200000004,Rider_Id_246,796
-Order_No_19079,User_Id_868,Bike,3,Business,1,5,10:22:29 AM,1,5,10:47:26 AM,1,5,10:49:37 AM,1,5,11:06:52 AM,1,5,11:29:32 AM,8,26.3,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_210,1360
-Order_No_7364,User_Id_1402,Bike,3,Business,24,1,8:49:40 AM,24,1,8:51:07 AM,24,1,9:21:37 AM,24,1,9:30:22 AM,24,1,9:50:27 AM,13,19.6,,-1.2664227,36.7453671,-1.281754,36.8324229,Rider_Id_576,1205
-Order_No_2201,User_Id_811,Bike,3,Personal,3,5,2:35:21 PM,3,5,2:38:03 PM,3,5,2:44:24 PM,3,5,3:04:10 PM,3,5,3:30:49 PM,4,,,-1.2963097,36.7688221,-1.285355,36.795287,Rider_Id_599,1599
-Order_No_22470,User_Id_2069,Bike,3,Business,3,1,2:03:48 PM,3,1,2:28:17 PM,3,1,2:36:13 PM,3,1,2:52:12 PM,3,1,3:10:37 PM,12,23.1,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_675,1105
-Order_No_532,User_Id_1961,Bike,3,Business,4,6,8:38:00 AM,4,6,8:38:12 AM,4,6,8:46:56 AM,4,6,9:18:50 AM,4,6,10:01:24 AM,14,,,-1.2628473,36.781805,-1.3181159,36.710155900000004,Rider_Id_155,2554
-Order_No_5249,User_Id_3647,Bike,3,Business,15,3,11:35:15 AM,15,3,11:35:47 AM,15,3,11:40:44 AM,15,3,11:48:11 AM,15,3,12:27:18 PM,15,,,-1.2726389999999999,36.794723,-1.3339943,36.7263409,Rider_Id_710,2347
-Order_No_4625,User_Id_53,Bike,3,Business,25,1,12:29:33 PM,25,1,12:31:22 PM,25,1,12:32:27 PM,25,1,12:56:08 PM,25,1,1:14:41 PM,5,25.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_863,1113
-Order_No_10326,User_Id_2027,Bike,1,Personal,25,1,8:21:19 AM,25,1,8:22:09 AM,25,1,8:27:32 AM,25,1,8:29:29 AM,25,1,8:41:44 AM,8,15.2,,-1.2551895,36.7822034,-1.2962832,36.784968299999996,Rider_Id_379,735
-Order_No_7100,User_Id_3147,Bike,3,Business,31,4,11:53:45 AM,31,4,12:16:55 PM,31,4,12:32:06 PM,31,4,12:52:05 PM,31,4,1:17:48 PM,18,26.7,,-1.3456196000000002,36.9070132,-1.3467388999999999,36.7690312,Rider_Id_191,1543
-Order_No_3446,User_Id_3647,Bike,3,Business,7,4,12:35:16 PM,7,4,12:36:44 PM,7,4,12:54:22 PM,7,4,12:56:39 PM,7,4,1:28:38 PM,13,24.1,,-1.2726389999999999,36.794723,-1.3017226999999998,36.856131899999994,Rider_Id_426,1919
-Order_No_7444,User_Id_2271,Bike,3,Business,8,2,3:12:04 PM,8,2,3:12:27 PM,8,2,3:29:01 PM,8,2,3:34:37 PM,8,2,4:18:11 PM,17,25.5,,-1.2560183999999999,36.7934342,-1.293515,36.897607,Rider_Id_244,2614
-Order_No_26152,User_Id_2330,Bike,3,Business,26,5,2:13:19 PM,26,5,2:14:08 PM,26,5,2:48:33 PM,26,5,2:52:42 PM,26,5,3:09:18 PM,7,,,-1.2326041,36.8009029,-1.2291477,36.8440295,Rider_Id_263,996
-Order_No_1788,User_Id_2145,Bike,3,Personal,7,5,9:38:17 AM,7,5,9:38:35 AM,7,5,9:50:01 AM,7,5,9:54:36 AM,7,5,10:23:52 AM,19,,,-1.2752846000000002,36.802702200000006,-1.2540898999999999,36.70427279999999,Rider_Id_875,1756
-Order_No_24915,User_Id_2866,Bike,3,Business,8,4,8:48:51 AM,8,4,8:49:13 AM,8,4,8:49:20 AM,8,4,10:28:54 AM,8,4,11:16:27 AM,28,20.7,,-1.2551895,36.7822034,-1.162368,36.91076029999999,Rider_Id_227,2853
-Order_No_16057,User_Id_1617,Bike,1,Personal,22,5,1:30:29 PM,22,5,1:30:33 PM,22,5,1:30:43 PM,22,5,1:45:19 PM,22,5,2:12:31 PM,20,,,-1.2908102,36.7944473,-1.3673224,36.916197499999996,Rider_Id_813,1632
-Order_No_2006,User_Id_1597,Bike,1,Personal,16,4,1:21:28 PM,16,4,1:21:42 PM,16,4,1:36:36 PM,16,4,1:51:29 PM,16,4,2:41:04 PM,14,18.8,,-1.2322870000000001,36.801636,-1.3159617000000001,36.8117934,Rider_Id_208,2975
-Order_No_15413,User_Id_393,Bike,3,Business,8,5,8:43:05 AM,8,5,10:16:12 AM,8,5,10:16:17 AM,8,5,10:28:58 AM,8,5,10:40:13 AM,4,22.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_901,675
-Order_No_14287,User_Id_1500,Bike,3,Business,16,6,8:59:55 AM,16,6,9:00:17 AM,16,6,9:07:54 AM,16,6,9:22:49 AM,16,6,9:29:31 AM,8,20.4,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_119,402
-Order_No_16474,User_Id_722,Bike,3,Business,28,1,1:25:10 PM,28,1,1:26:09 PM,28,1,1:27:39 PM,28,1,1:55:21 PM,28,1,2:29:36 PM,9,26.3,,-1.32943,36.894242999999996,-1.3251409,36.886934499999995,Rider_Id_800,2055
-Order_No_15997,User_Id_1500,Bike,3,Business,9,2,3:23:54 PM,9,2,3:24:38 PM,9,2,3:27:03 PM,9,2,4:04:44 PM,9,2,4:28:22 PM,8,30.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_686,1418
-Order_No_22127,User_Id_3404,Bike,3,Business,29,2,3:37:08 PM,29,2,3:44:24 PM,29,2,3:56:08 PM,29,2,3:59:48 PM,29,2,4:20:37 PM,7,27.1,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_862,1249
-Order_No_3765,User_Id_3291,Bike,3,Business,29,1,1:35:35 PM,29,1,1:41:11 PM,29,1,1:55:53 PM,29,1,2:02:33 PM,29,1,2:14:57 PM,4,24.4,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_842,744
-Order_No_15789,User_Id_2209,Bike,3,Business,21,1,11:40:38 AM,21,1,11:41:15 AM,21,1,11:47:15 AM,21,1,11:51:53 AM,21,1,12:16:35 PM,19,25.6,,-1.2551895,36.7822034,-1.2408053999999997,36.663456700000005,Rider_Id_379,1482
-Order_No_9253,User_Id_795,Bike,3,Personal,17,4,4:49:42 PM,17,4,4:50:40 PM,17,4,5:07:43 PM,17,4,5:11:38 PM,17,4,5:31:49 PM,12,26.1,,-1.3104733999999998,36.7363698,-1.3637665,36.7269036,Rider_Id_74,1211
-Order_No_17764,User_Id_2680,Bike,3,Business,7,5,3:17:00 PM,7,5,3:22:42 PM,7,5,3:22:52 PM,7,5,3:35:38 PM,7,5,3:54:18 PM,7,,,-1.2882073,36.82376729999999,-1.3039706,36.7688174,Rider_Id_906,1120
-Order_No_18322,User_Id_2828,Bike,3,Business,27,3,3:48:46 PM,27,3,3:50:22 PM,27,3,4:04:02 PM,27,3,4:13:14 PM,27,3,4:36:03 PM,10,29.3,,-1.2615888000000002,36.7928732,-1.3149256999999999,36.8441635,Rider_Id_787,1369
-Order_No_18390,User_Id_2956,Bike,3,Business,31,3,8:43:30 AM,31,3,8:43:50 AM,31,3,8:59:09 AM,31,3,9:05:41 AM,31,3,9:10:59 AM,3,19.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_655,318
-Order_No_15976,User_Id_3621,Bike,3,Business,15,1,11:18:29 AM,15,1,11:18:42 AM,15,1,11:22:12 AM,15,1,11:26:04 AM,15,1,11:55:56 AM,12,,,-1.2836583000000001,36.7886014,-1.2998684999999999,36.8643907,Rider_Id_363,1792
-Order_No_20416,User_Id_1015,Bike,1,Personal,7,1,4:01:21 PM,7,1,4:19:41 PM,7,1,4:41:46 PM,7,1,5:05:05 PM,7,1,5:38:09 PM,9,20.0,,-1.3331402,36.8925275,-1.3793096000000002,36.931042100000006,Rider_Id_521,1984
-Order_No_18427,User_Id_1209,Bike,3,Business,17,5,2:19:12 PM,17,5,2:19:25 PM,17,5,2:19:34 PM,17,5,3:00:24 PM,17,5,3:21:46 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_349,1282
-Order_No_251,User_Id_1969,Bike,1,Personal,6,5,6:35:28 PM,6,5,6:35:47 PM,6,5,7:07:39 PM,6,5,7:11:51 PM,6,5,7:33:00 PM,11,,,-1.3211235000000001,36.7765164,-1.2799878999999998,36.8114341,Rider_Id_465,1269
-Order_No_10813,User_Id_868,Bike,3,Business,17,5,11:00:13 AM,17,5,11:01:14 AM,17,5,11:11:58 AM,17,5,11:29:44 AM,17,5,11:31:29 AM,7,,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_541,105
-Order_No_24750,User_Id_2030,Bike,3,Business,18,4,3:43:29 PM,18,4,3:56:09 PM,18,4,4:15:29 PM,18,4,4:18:12 PM,18,4,4:30:00 PM,3,,,-1.304735,36.784605400000004,-1.295908,36.791433000000005,Rider_Id_548,708
-Order_No_19494,User_Id_2207,Bike,3,Business,30,4,11:02:09 AM,30,4,11:02:29 AM,30,4,11:08:04 AM,30,4,11:30:35 AM,30,4,11:39:47 AM,1,20.5,,-1.2630521000000001,36.806801899999996,-1.2696908000000002,36.81519,Rider_Id_520,552
-Order_No_24183,User_Id_1357,Bike,1,Personal,22,3,11:04:42 AM,22,3,11:04:53 AM,22,3,11:31:05 AM,22,3,11:39:28 AM,22,3,12:23:11 PM,20,23.0,,-1.2897215,36.7945959,-1.2705293,36.6736142,Rider_Id_50,2623
-Order_No_14964,User_Id_402,Bike,3,Business,30,4,2:49:03 PM,30,4,2:49:37 PM,30,4,3:13:03 PM,30,4,3:25:26 PM,30,4,3:34:34 PM,3,,,-1.2551895,36.7822034,-1.2527958000000001,36.8003131,Rider_Id_279,548
-Order_No_17118,User_Id_1363,Bike,3,Business,10,3,3:18:49 PM,10,3,3:19:24 PM,10,3,3:20:17 PM,10,3,3:26:57 PM,10,3,3:33:38 PM,5,30.8,,-1.3004062,36.829741,-1.3239488000000001,36.831683500000004,Rider_Id_206,401
-Order_No_8770,User_Id_1363,Bike,3,Business,31,3,3:40:52 PM,31,3,3:41:38 PM,31,3,3:44:12 PM,31,3,3:48:46 PM,31,3,3:59:12 PM,6,27.0,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_408,626
-Order_No_5261,User_Id_203,Bike,3,Business,28,1,4:14:20 PM,28,1,4:14:30 PM,28,1,4:23:13 PM,28,1,4:58:17 PM,28,1,5:23:03 PM,9,25.4,,-1.2580991000000001,36.794151899999996,-1.2963097,36.7688221,Rider_Id_858,1486
-Order_No_24016,User_Id_391,Bike,3,Business,20,3,11:39:09 AM,20,3,11:55:52 AM,20,3,12:13:55 PM,20,3,12:30:35 PM,20,3,12:31:34 PM,5,27.1,,-1.2867454,36.8259017,-1.3010208,36.860918299999994,Rider_Id_61,59
-Order_No_27021,User_Id_3635,Bike,1,Business,28,2,9:33:29 AM,28,2,9:34:39 AM,28,2,9:43:48 AM,28,2,9:49:53 AM,28,2,10:24:31 AM,12,16.2,51.1,-1.2615888000000002,36.7928732,-1.2108499,36.781936200000004,Rider_Id_116,2078
-Order_No_19119,User_Id_3507,Bike,3,Personal,20,4,11:54:16 AM,20,4,11:54:42 AM,20,4,12:07:19 PM,20,4,12:10:47 PM,20,4,12:25:47 PM,9,24.4,,-1.2944628,36.7522739,-1.310792,36.821127000000004,Rider_Id_612,900
-Order_No_3916,User_Id_1479,Bike,1,Personal,6,1,10:58:58 AM,6,1,11:00:41 AM,6,1,11:14:02 AM,6,1,11:21:28 AM,6,1,11:35:46 AM,8,20.4,,-1.2883946000000002,36.7728469,-1.312469,36.815177,Rider_Id_636,858
-Order_No_10694,User_Id_1500,Bike,3,Business,23,2,4:11:30 PM,23,2,4:34:22 PM,23,2,4:40:53 PM,23,2,4:48:07 PM,23,2,5:09:06 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,1259
-Order_No_5844,User_Id_2956,Bike,3,Business,28,4,3:43:15 PM,28,4,3:43:32 PM,28,4,3:46:00 PM,28,4,4:00:14 PM,28,4,4:29:56 PM,6,29.9,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_913,1782
-Order_No_24774,User_Id_190,Bike,3,Business,29,1,3:30:02 PM,29,1,3:35:22 PM,29,1,4:03:43 PM,29,1,4:05:19 PM,29,1,4:19:42 PM,6,,,-1.3275885,36.862155200000004,-1.3270347,36.866736200000005,Rider_Id_211,863
-Order_No_26435,User_Id_755,Bike,1,Personal,6,1,3:12:57 PM,6,1,3:19:34 PM,6,1,3:43:32 PM,6,1,3:57:51 PM,6,1,4:10:42 PM,4,24.6,,-1.282357,36.782501,-1.2892762,36.764129,Rider_Id_770,771
-Order_No_6141,User_Id_1637,Bike,3,Business,23,5,1:04:00 PM,23,5,1:06:12 PM,23,5,1:21:38 PM,23,5,1:33:42 PM,23,5,1:55:59 PM,8,26.4,,-1.2615888000000002,36.7928732,-1.225322,36.80855,Rider_Id_733,1337
-Order_No_961,User_Id_2190,Bike,3,Business,26,5,11:26:39 AM,26,5,11:27:21 AM,26,5,11:40:37 AM,26,5,11:51:37 AM,26,5,12:23:08 PM,7,,,-1.3019530000000001,36.788458,-1.2694398,36.762448299999996,Rider_Id_613,1891
-Order_No_26693,User_Id_391,Bike,3,Business,29,1,8:35:14 AM,29,1,10:08:24 AM,29,1,10:21:57 AM,29,1,10:30:38 AM,29,1,11:11:50 AM,18,18.8,,-1.3882008,36.7699445,-1.2784063,36.8222933,Rider_Id_781,2472
-Order_No_16005,User_Id_3328,Bike,1,Personal,28,6,6:05:56 PM,28,6,6:06:33 PM,28,6,6:23:22 PM,28,6,6:29:06 PM,28,6,6:49:24 PM,10,22.9,,-1.2905065,36.856907899999996,-1.2780823000000001,36.7960191,Rider_Id_343,1218
-Order_No_5752,User_Id_643,Bike,2,Personal,3,3,5:38:44 PM,3,3,5:39:36 PM,3,3,5:50:03 PM,3,3,5:53:28 PM,3,3,6:23:54 PM,16,23.7,,-1.2956583000000002,36.77445170000001,-1.2580056999999998,36.6893887,Rider_Id_105,1826
-Order_No_10526,User_Id_3753,Bike,1,Personal,28,4,11:35:36 AM,28,4,11:40:10 AM,28,4,11:48:35 AM,28,4,11:50:26 AM,28,4,12:08:58 PM,5,,,-1.2898353999999999,36.787270299999996,-1.2959817,36.8168902,Rider_Id_21,1112
-Order_No_4874,User_Id_1271,Bike,3,Business,18,1,9:20:47 AM,18,1,9:21:56 AM,18,1,10:28:58 AM,18,1,10:30:04 AM,18,1,11:07:43 AM,22,21.3,,-1.3244885,36.897791999999995,-1.261403,36.769224200000004,Rider_Id_580,2259
-Order_No_8760,User_Id_633,Bike,3,Business,6,4,2:43:23 PM,6,4,3:38:40 PM,6,4,3:49:04 PM,6,4,3:54:36 PM,6,4,4:07:39 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_95,783
-Order_No_12458,User_Id_1329,Bike,3,Business,10,1,10:58:22 AM,10,1,10:58:54 AM,10,1,11:19:37 AM,10,1,11:21:21 AM,10,1,11:32:20 AM,3,23.4,,-1.273056,36.811298,-1.2646084,36.8028645,Rider_Id_407,659
-Order_No_26450,User_Id_2987,Bike,3,Business,25,5,11:33:46 AM,25,5,11:47:12 AM,25,5,12:05:26 PM,25,5,12:07:52 PM,25,5,12:19:19 PM,3,21.1,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_103,687
-Order_No_4169,User_Id_1786,Bike,2,Personal,23,4,8:40:51 AM,23,4,8:42:16 AM,23,4,8:51:20 AM,23,4,9:00:21 AM,23,4,9:20:26 AM,5,15.9,,-1.2965924,36.8179314,-1.2775226999999998,36.8174812,Rider_Id_857,1205
-Order_No_22992,User_Id_1646,Bike,3,Business,23,4,1:21:16 PM,23,4,1:22:12 PM,23,4,1:31:38 PM,23,4,1:45:01 PM,23,4,1:51:45 PM,3,24.5,,-1.2611987,36.80609129999999,-1.2517448,36.7924971,Rider_Id_344,404
-Order_No_2802,User_Id_633,Bike,3,Business,9,3,7:41:01 AM,9,3,7:46:07 AM,9,3,8:04:54 AM,9,3,8:09:59 AM,9,3,8:31:19 AM,9,20.4,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_436,1280
-Order_No_3148,User_Id_1850,Bike,3,Business,2,4,4:16:17 PM,2,4,4:16:39 PM,2,4,4:17:11 PM,2,4,4:24:02 PM,2,4,4:29:24 PM,2,,,-1.2551895,36.7822034,-1.2566888999999999,36.7930784,Rider_Id_695,322
-Order_No_19292,User_Id_265,Bike,3,Business,2,2,12:44:07 PM,2,2,12:46:49 PM,2,2,12:57:59 PM,2,2,1:03:51 PM,2,2,1:33:13 PM,9,26.3,,-1.300921,36.828195,-1.2748221000000002,36.7817865,Rider_Id_168,1762
-Order_No_7870,User_Id_540,Bike,3,Business,10,1,9:50:51 AM,10,1,10:01:47 AM,10,1,10:01:57 AM,10,1,10:08:15 AM,10,1,10:33:23 AM,7,18.4,2.1,-1.2551895,36.7822034,-1.289498,36.818878000000005,Rider_Id_268,1508
-Order_No_14375,User_Id_3420,Bike,3,Business,9,2,9:21:53 AM,9,2,9:22:11 AM,9,2,9:27:15 AM,9,2,9:29:37 AM,9,2,9:47:02 AM,6,20.7,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_487,1045
-Order_No_5334,User_Id_346,Bike,3,Business,28,4,4:28:29 PM,28,4,4:30:20 PM,28,4,4:47:02 PM,28,4,4:56:58 PM,28,4,5:25:59 PM,5,25.5,,-1.2527958000000001,36.8003131,-1.28878,36.8168312,Rider_Id_177,1741
-Order_No_18631,User_Id_3222,Bike,3,Business,27,2,3:00:17 PM,27,2,3:04:56 PM,27,2,3:22:47 PM,27,2,3:24:31 PM,27,2,3:35:14 PM,4,25.5,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_547,643
-Order_No_26222,User_Id_893,Bike,3,Business,14,2,12:33:13 PM,14,2,12:34:27 PM,14,2,12:48:31 PM,14,2,12:57:39 PM,14,2,2:08:41 PM,7,21.7,,-1.2973827,36.794287100000005,-1.2663194,36.809901,Rider_Id_268,4262
-Order_No_25410,User_Id_186,Bike,3,Business,2,4,8:25:14 AM,2,4,8:25:27 AM,2,4,8:46:16 AM,2,4,8:51:13 AM,2,4,9:31:54 AM,25,,,-1.3700383,36.9190174,-1.2285834999999998,36.882646799999996,Rider_Id_349,2441
-Order_No_13799,User_Id_1207,Bike,3,Business,1,6,7:19:43 PM,1,6,7:27:50 PM,1,6,7:50:18 PM,1,6,7:57:28 PM,1,6,8:25:24 PM,13,18.5,,-1.2629666000000002,36.8068369,-1.2156006000000001,36.8916865,Rider_Id_95,1676
-Order_No_15968,User_Id_2330,Bike,3,Business,6,4,4:03:08 PM,6,4,4:03:18 PM,6,4,4:06:59 PM,6,4,4:16:15 PM,6,4,4:32:56 PM,9,,,-1.2868601,36.7738025,-1.2671943,36.8062533,Rider_Id_847,1001
-Order_No_19461,User_Id_2587,Bike,3,Business,23,6,11:52:19 AM,23,6,11:52:58 AM,23,6,11:58:28 AM,23,6,12:04:54 PM,23,6,12:33:30 PM,14,26.4,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_411,1716
-Order_No_15779,User_Id_1075,Bike,3,Business,24,1,12:40:22 PM,24,1,12:40:41 PM,24,1,12:43:54 PM,24,1,12:48:03 PM,24,1,12:56:12 PM,1,22.9,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_79,489
-Order_No_10234,User_Id_295,Bike,3,Business,12,2,2:28:02 PM,12,2,3:10:35 PM,12,2,3:13:37 PM,12,2,3:23:40 PM,12,2,3:35:30 PM,6,26.9,,-1.282206,36.815958,-1.2967108,36.787113500000004,Rider_Id_330,710
-Order_No_705,User_Id_510,Bike,3,Business,27,3,4:06:27 PM,27,3,4:07:08 PM,27,3,4:30:51 PM,27,3,4:34:55 PM,27,3,4:58:02 PM,13,27.5,,-1.2860183,36.8975338,-1.3263105,36.8622904,Rider_Id_26,1387
-Order_No_22434,User_Id_186,Bike,3,Business,18,4,12:19:27 PM,18,4,12:20:37 PM,18,4,12:24:36 PM,18,4,12:41:01 PM,18,4,12:53:42 PM,5,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_322,761
-Order_No_14497,User_Id_970,Bike,3,Business,13,3,7:23:59 AM,13,3,7:37:41 AM,13,3,8:06:04 AM,13,3,8:09:31 AM,13,3,8:25:06 AM,4,20.2,,-1.2551895,36.7822034,-1.2525704,36.7955754,Rider_Id_580,935
-Order_No_578,User_Id_1237,Bike,1,Personal,26,3,2:25:41 PM,26,3,2:26:21 PM,26,3,2:27:19 PM,26,3,3:12:43 PM,26,3,3:12:56 PM,2,27.2,,-1.2741728,36.79691079999999,-1.2599563,36.799343900000004,Rider_Id_51,13
-Order_No_26739,User_Id_2344,Bike,3,Business,21,3,12:33:41 PM,21,3,12:37:48 PM,21,3,12:43:37 PM,21,3,12:55:21 PM,21,3,1:59:38 PM,4,21.9,,-1.2571472,36.795063299999995,-1.285324,36.812901000000004,Rider_Id_265,3857
-Order_No_21724,User_Id_2561,Bike,3,Business,5,5,12:33:21 PM,5,5,12:36:49 PM,5,5,1:31:37 PM,5,5,1:37:07 PM,5,5,1:58:29 PM,7,27.4,,-1.2551895,36.7822034,-1.2871938,36.8195031,Rider_Id_448,1282
-Order_No_16144,User_Id_467,Bike,3,Business,14,5,1:01:40 PM,14,5,1:03:10 PM,14,5,1:19:44 PM,14,5,1:25:13 PM,14,5,2:07:53 PM,16,21.9,,-1.304735,36.784605400000004,-1.332175,36.8819641,Rider_Id_564,2560
-Order_No_2535,User_Id_3201,Bike,3,Business,31,4,9:50:14 AM,31,4,9:50:54 AM,31,4,9:55:01 AM,31,4,10:02:59 AM,31,4,10:06:21 AM,2,20.4,,-1.2584143,36.8048002,-1.2591018999999999,36.800577000000004,Rider_Id_607,202
-Order_No_11496,User_Id_1927,Bike,3,Business,28,2,2:54:41 PM,28,2,2:58:30 PM,28,2,3:10:24 PM,28,2,3:44:58 PM,28,2,4:12:56 PM,12,23.5,,-1.28878,36.8168312,-1.3472066,36.7692638,Rider_Id_158,1678
-Order_No_13662,User_Id_3404,Bike,3,Business,19,3,5:36:36 PM,19,3,5:39:28 PM,19,3,5:51:59 PM,19,3,5:54:58 PM,19,3,6:02:41 PM,2,22.6,,-1.2882183999999999,36.826346,-1.279395,36.825364,Rider_Id_150,463
-Order_No_11830,User_Id_517,Bike,3,Business,31,5,12:44:24 PM,31,5,12:52:39 PM,31,5,12:53:56 PM,31,5,1:03:42 PM,31,5,1:38:10 PM,14,21.5,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_349,2068
-Order_No_20117,User_Id_635,Bike,3,Business,9,3,10:40:08 AM,9,3,10:45:14 AM,9,3,10:50:45 AM,9,3,11:39:27 AM,9,3,11:59:38 AM,8,25.3,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_926,1211
-Order_No_16121,User_Id_269,Bike,3,Business,22,2,1:57:51 PM,22,2,2:03:30 PM,22,2,2:12:22 PM,22,2,2:31:30 PM,22,2,3:05:05 PM,15,21.3,,-1.3332749,36.870814700000004,-1.2626906,36.7827022,Rider_Id_543,2015
-Order_No_12729,User_Id_2956,Bike,3,Business,5,2,11:56:58 AM,5,2,11:57:46 AM,5,2,12:11:02 PM,5,2,12:13:04 PM,5,2,1:05:03 PM,10,21.6,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_752,3119
-Order_No_18865,User_Id_788,Bike,3,Business,27,2,10:48:07 AM,27,2,10:48:19 AM,27,2,11:32:25 AM,27,2,11:34:48 AM,27,2,12:06:34 PM,14,24.0,,-1.2156006000000001,36.8916865,-1.225322,36.80855,Rider_Id_199,1906
-Order_No_24245,User_Id_2801,Bike,3,Business,6,4,2:59:29 PM,6,4,3:00:09 PM,6,4,3:05:38 PM,6,4,3:11:08 PM,6,4,3:51:51 PM,10,23.3,,-1.2595421999999998,36.7871178,-1.2981102,36.8293513,Rider_Id_651,2443
-Order_No_1006,User_Id_1584,Bike,3,Business,5,3,11:47:36 AM,5,3,11:47:53 AM,5,3,11:49:21 AM,5,3,12:01:13 PM,5,3,12:39:45 PM,4,22.0,,-1.304033,36.7848695,-1.2973654,36.8105527,Rider_Id_851,2312
-Order_No_10750,User_Id_2532,Bike,3,Business,16,4,9:35:34 AM,16,4,9:36:00 AM,16,4,9:36:41 AM,16,4,9:59:51 AM,16,4,10:39:38 AM,8,,,-1.2551895,36.7822034,-1.3024794,36.8228284,Rider_Id_926,2387
-Order_No_439,User_Id_1178,Bike,3,Business,27,2,4:02:45 PM,27,2,4:12:13 PM,27,2,4:21:21 PM,27,2,4:31:17 PM,27,2,5:02:07 PM,13,25.7,,-1.2261885000000001,36.8206948,-1.2868515,36.7844247,Rider_Id_648,1850
-Order_No_20684,User_Id_2801,Bike,3,Business,3,3,3:20:16 PM,3,3,3:22:46 PM,3,3,3:31:53 PM,3,3,4:00:57 PM,3,3,4:17:01 PM,8,28.1,,-1.2925919,36.81705770000001,-1.2595421999999998,36.7871178,Rider_Id_206,964
-Order_No_28117,User_Id_3647,Bike,3,Business,14,2,11:12:39 AM,14,2,11:13:15 AM,14,2,11:23:14 AM,14,2,11:29:45 AM,14,2,11:57:35 AM,12,,,-1.2726389999999999,36.794723,-1.2287313999999998,36.8367354,Rider_Id_161,1670
-Order_No_6907,User_Id_1568,Bike,3,Business,28,1,2:30:40 PM,28,1,2:51:25 PM,28,1,3:09:19 PM,28,1,3:12:50 PM,28,1,3:39:42 PM,9,26.5,,-1.2897368,36.8226783,-1.293049,36.879020000000004,Rider_Id_276,1612
-Order_No_6706,User_Id_3326,Bike,3,Business,11,4,12:01:39 PM,11,4,12:02:09 PM,11,4,12:10:39 PM,11,4,12:18:13 PM,11,4,12:56:38 PM,15,25.6,,-1.2551895,36.7822034,-1.221085,36.8587279,Rider_Id_690,2305
-Order_No_18544,User_Id_2789,Bike,3,Business,31,4,9:31:07 AM,31,4,9:31:31 AM,31,4,9:38:26 AM,31,4,9:42:08 AM,31,4,10:04:51 AM,5,20.7,,-1.304735,36.784605400000004,-1.2958383,36.8073162,Rider_Id_468,1363
-Order_No_1552,User_Id_788,Bike,3,Business,19,3,12:03:42 PM,19,3,12:05:37 PM,19,3,12:19:09 PM,19,3,1:09:39 PM,19,3,1:30:16 PM,9,22.1,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_125,1237
-Order_No_10703,User_Id_861,Bike,3,Personal,29,6,10:40:16 AM,29,6,10:40:29 AM,29,6,10:48:09 AM,29,6,10:49:37 AM,29,6,11:24:23 AM,14,24.8,,-1.2661263999999999,36.8030359,-1.2088598999999998,36.773843799999995,Rider_Id_178,2086
-Order_No_8338,User_Id_1385,Bike,1,Personal,4,2,2:57:17 PM,4,2,2:57:48 PM,4,2,3:05:47 PM,4,2,3:45:37 PM,4,2,4:19:27 PM,12,20.5,,-1.2474857,36.8737362,-1.2578616000000002,36.8012906,Rider_Id_674,2030
-Order_No_268,User_Id_1342,Bike,3,Business,25,2,1:53:15 PM,25,2,2:14:30 PM,25,2,2:35:38 PM,25,2,2:38:43 PM,25,2,2:54:12 PM,9,27.0,,-1.290894,36.822971,-1.3025826,36.7670807,Rider_Id_88,929
-Order_No_12362,User_Id_1831,Bike,3,Business,13,1,9:37:55 AM,13,1,9:38:16 AM,13,1,9:52:28 AM,13,1,10:51:46 AM,13,1,10:59:38 AM,21,,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_863,472
-Order_No_12711,User_Id_3208,Bike,3,Personal,11,1,12:12:52 PM,11,1,12:40:49 PM,11,1,12:57:11 PM,11,1,1:02:34 PM,11,1,1:28:42 PM,7,17.9,,-1.3068448000000001,36.8348641,-1.3123652,36.796426600000004,Rider_Id_791,1568
-Order_No_27357,User_Id_2987,Bike,3,Business,4,2,1:09:47 PM,4,2,1:11:39 PM,4,2,1:25:46 PM,4,2,1:30:02 PM,4,2,1:42:13 PM,10,18.6,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_116,731
-Order_No_12420,User_Id_1271,Bike,3,Business,19,2,3:07:07 PM,19,2,3:07:32 PM,19,2,3:17:35 PM,19,2,3:20:05 PM,19,2,3:37:28 PM,8,29.2,,-1.3244885,36.897791999999995,-1.2875442,36.891335,Rider_Id_675,1043
-Order_No_15106,User_Id_3647,Bike,3,Business,16,2,4:18:27 PM,16,2,4:20:06 PM,16,2,4:39:34 PM,16,2,4:52:46 PM,16,2,5:08:45 PM,6,25.1,,-1.2726389999999999,36.794723,-1.2957807,36.771395299999995,Rider_Id_536,959
-Order_No_15282,User_Id_552,Bike,3,Business,11,1,11:02:50 AM,11,1,11:11:04 AM,11,1,11:37:21 AM,11,1,12:02:14 PM,11,1,12:54:51 PM,21,26.4,,-1.2571472,36.795063299999995,-1.335477,36.930829700000004,Rider_Id_935,3157
-Order_No_12258,User_Id_1882,Bike,3,Business,21,4,6:38:31 PM,21,4,7:00:49 PM,21,4,7:22:48 PM,21,4,7:28:55 PM,21,4,7:47:56 PM,7,28.4,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_269,1141
-Order_No_18405,User_Id_3355,Bike,3,Business,4,2,12:11:00 PM,4,2,1:06:32 PM,4,2,1:12:54 PM,4,2,1:18:10 PM,4,2,1:31:07 PM,10,19.6,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_932,777
-Order_No_27059,User_Id_868,Bike,3,Business,3,3,10:30:48 AM,3,3,10:43:45 AM,3,3,10:52:04 AM,3,3,10:53:20 AM,3,3,10:53:22 AM,3,27.5,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_728,2
-Order_No_17265,User_Id_3647,Bike,3,Business,21,4,2:24:59 PM,21,4,2:25:45 PM,21,4,2:52:58 PM,21,4,2:56:03 PM,21,4,3:26:36 PM,8,30.9,,-1.2726389999999999,36.794723,-1.2960063999999998,36.758236700000005,Rider_Id_682,1833
-Order_No_1315,User_Id_2058,Bike,1,Personal,13,3,10:44:03 AM,13,3,10:45:10 AM,13,3,10:55:57 AM,13,3,10:59:23 AM,13,3,11:37:50 AM,18,20.3,,-1.2551895,36.7822034,-1.320819,36.888501299999994,Rider_Id_791,2307
-Order_No_27528,User_Id_3201,Bike,3,Business,18,5,3:09:48 PM,18,5,3:10:05 PM,18,5,3:44:03 PM,18,5,3:51:39 PM,18,5,3:58:13 PM,2,27.0,,-1.2584143,36.8048002,-1.2591018999999999,36.800577000000004,Rider_Id_19,394
-Order_No_27231,User_Id_868,Bike,3,Business,5,6,12:44:57 PM,5,6,12:54:08 PM,5,6,12:58:23 PM,5,6,1:15:46 PM,5,6,1:59:29 PM,9,22.4,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_487,2623
-Order_No_11930,User_Id_1363,Bike,3,Business,9,2,11:17:29 AM,9,2,11:21:16 AM,9,2,11:32:25 AM,9,2,11:38:53 AM,9,2,11:58:36 AM,8,22.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_882,1183
-Order_No_5558,User_Id_2207,Bike,3,Business,19,1,9:09:24 AM,19,1,9:11:00 AM,19,1,9:59:07 AM,19,1,10:03:54 AM,19,1,10:32:57 AM,12,19.2,,-1.3142866000000002,36.8618036,-1.2768326,36.8073804,Rider_Id_699,1743
-Order_No_16977,User_Id_2195,Bike,3,Business,9,2,10:13:52 AM,9,2,10:14:06 AM,9,2,10:19:27 AM,9,2,10:30:31 AM,9,2,10:54:11 AM,16,20.8,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_299,1420
-Order_No_23386,User_Id_3688,Bike,3,Business,9,2,12:01:33 PM,9,2,12:01:38 PM,9,2,12:03:11 PM,9,2,12:25:48 PM,9,2,12:41:06 PM,10,27.0,,-1.2551895,36.7822034,-1.3050378999999999,36.7943452,Rider_Id_438,918
-Order_No_2839,User_Id_2763,Bike,3,Business,7,4,12:06:46 PM,7,4,12:07:13 PM,7,4,12:21:18 PM,7,4,12:22:50 PM,7,4,12:31:15 PM,5,24.2,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_383,505
-Order_No_2964,User_Id_3647,Bike,3,Business,10,1,4:16:46 PM,10,1,4:21:23 PM,10,1,4:34:55 PM,10,1,4:40:08 PM,10,1,5:31:08 PM,9,19.0,,-1.2726389999999999,36.794723,-1.282907,36.738903,Rider_Id_706,3060
-Order_No_26838,User_Id_2330,Bike,3,Business,7,2,3:21:43 PM,7,2,3:23:34 PM,7,2,3:23:58 PM,7,2,3:25:42 PM,7,2,3:52:40 PM,7,,,-1.2890715,36.8157588,-1.294597,36.7742999,Rider_Id_796,1618
-Order_No_8287,User_Id_3222,Bike,3,Business,4,2,4:01:38 PM,4,2,4:58:00 PM,4,2,5:02:40 PM,4,2,5:11:07 PM,4,2,5:41:49 PM,7,22.6,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_673,1842
-Order_No_28244,User_Id_3295,Bike,3,Business,19,2,12:19:17 PM,19,2,12:19:54 PM,19,2,12:51:45 PM,19,2,1:01:19 PM,19,2,1:32:52 PM,5,27.7,,-1.2571472,36.795063299999995,-1.2803414,36.8265456,Rider_Id_118,1893
-Order_No_26614,User_Id_2804,Bike,3,Business,23,2,12:51:14 PM,23,2,12:51:52 PM,23,2,12:59:09 PM,23,2,1:08:00 PM,23,2,1:15:17 PM,6,,,-1.281613,36.8084651,-1.2571472,36.795063299999995,Rider_Id_291,437
-Order_No_7215,User_Id_1066,Bike,3,Personal,30,3,12:42:35 PM,30,3,12:43:30 PM,30,3,12:59:16 PM,30,3,1:06:38 PM,30,3,1:24:48 PM,5,25.2,,-1.2527958000000001,36.8003131,-1.2728069,36.7962362,Rider_Id_205,1090
-Order_No_17509,User_Id_246,Bike,3,Business,13,4,8:25:23 AM,13,4,8:25:34 AM,13,4,8:33:10 AM,13,4,8:38:33 AM,13,4,9:25:32 AM,6,18.6,,-1.2584143,36.8048002,-1.2597436000000002,36.8282526,Rider_Id_847,2819
-Order_No_25108,User_Id_627,Bike,3,Business,28,4,5:11:32 PM,28,4,5:13:07 PM,28,4,5:20:14 PM,28,4,5:23:20 PM,28,4,6:40:51 PM,12,28.1,,-1.2551895,36.7822034,-1.2416156,36.8736721,Rider_Id_793,4651
-Order_No_1208,User_Id_2535,Bike,2,Personal,22,5,10:48:26 AM,22,5,10:48:55 AM,22,5,10:51:53 AM,22,5,11:01:50 AM,22,5,11:27:50 AM,9,21.9,,-1.2622087,36.788087700000005,-1.2942209999999998,36.7719395,Rider_Id_116,1560
-Order_No_28245,User_Id_33,Bike,3,Personal,15,5,3:08:54 PM,15,5,3:11:08 PM,15,5,3:21:32 PM,15,5,3:34:29 PM,15,5,3:44:14 PM,6,17.8,,-1.2814609,36.826593200000005,-1.2544051,36.7982934,Rider_Id_61,585
-Order_No_15402,User_Id_2030,Bike,3,Business,16,5,2:24:54 PM,16,5,2:43:13 PM,16,5,3:04:17 PM,16,5,3:06:31 PM,16,5,3:12:40 PM,7,,,-1.3302996,36.8707935,-1.3333725,36.871761299999996,Rider_Id_839,369
-Order_No_10287,User_Id_1280,Bike,3,Business,7,4,2:15:55 PM,7,4,2:16:18 PM,7,4,2:17:49 PM,7,4,2:28:31 PM,7,4,2:59:27 PM,15,27.8,,-1.2551895,36.7822034,-1.3288106000000002,36.8697344,Rider_Id_958,1856
-Order_No_4958,User_Id_3283,Bike,3,Business,4,2,3:02:18 PM,4,2,3:03:49 PM,4,2,3:04:32 PM,4,2,3:26:59 PM,4,2,4:00:01 PM,14,22.6,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_27,1982
-Order_No_21759,User_Id_2150,Bike,3,Business,3,1,2:12:43 PM,3,1,2:15:02 PM,3,1,2:30:45 PM,3,1,2:40:15 PM,3,1,3:30:08 PM,15,23.1,,-1.2663631000000002,36.8163329,-1.3444459999999998,36.756597,Rider_Id_27,2993
-Order_No_23833,User_Id_1245,Bike,3,Business,8,4,2:01:33 PM,8,4,2:06:47 PM,8,4,2:16:04 PM,8,4,3:04:00 PM,8,4,3:45:38 PM,5,27.4,,-1.300921,36.828195,-1.295408,36.802892,Rider_Id_949,2498
-Order_No_13017,User_Id_1081,Bike,3,Business,5,2,3:09:20 PM,5,2,3:10:09 PM,5,2,3:27:59 PM,5,2,3:39:38 PM,5,2,4:10:49 PM,9,27.6,,-1.3014461000000002,36.7661381,-1.3472066,36.7692638,Rider_Id_328,1871
-Order_No_14284,User_Id_3621,Bike,3,Business,6,1,1:48:40 PM,6,1,1:49:14 PM,6,1,1:55:02 PM,6,1,1:57:43 PM,6,1,2:36:36 PM,14,,,-1.2836583000000001,36.7886014,-1.3181159,36.710155900000004,Rider_Id_161,2333
-Order_No_4430,User_Id_3450,Bike,3,Business,28,3,3:05:05 PM,28,3,4:06:58 PM,28,3,4:14:04 PM,28,3,4:21:26 PM,28,3,4:32:26 PM,9,,,-1.2551895,36.7822034,-1.2692149,36.74492,Rider_Id_550,660
-Order_No_16967,User_Id_633,Bike,3,Business,14,1,2:28:09 PM,14,1,2:28:37 PM,14,1,2:28:43 PM,14,1,2:35:39 PM,14,1,2:45:22 PM,4,25.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_39,583
-Order_No_24809,User_Id_3355,Bike,3,Business,25,4,1:18:13 PM,25,4,1:21:47 PM,25,4,1:28:56 PM,25,4,1:33:01 PM,25,4,2:49:58 PM,16,20.7,,-1.305399,36.822184899999996,-1.3427269,36.9194847,Rider_Id_867,4617
-Order_No_7950,User_Id_2169,Bike,2,Personal,6,5,8:58:52 AM,6,5,9:08:53 AM,6,5,9:21:07 AM,6,5,9:31:47 AM,6,5,9:53:16 AM,8,15.1,,-1.2857763999999998,36.8236622,-1.311208,36.778743,Rider_Id_808,1289
-Order_No_3045,User_Id_1397,Bike,3,Business,15,5,10:31:21 AM,15,5,10:47:24 AM,15,5,10:57:52 AM,15,5,11:03:35 AM,15,5,11:57:26 AM,27,26.5,,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_932,3231
-Order_No_10894,User_Id_3283,Bike,3,Business,29,3,12:56:36 PM,29,3,12:57:11 PM,29,3,1:02:46 PM,29,3,1:07:45 PM,29,3,1:21:22 PM,5,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_183,817
-Order_No_15361,User_Id_1552,Bike,1,Personal,11,2,11:50:15 AM,11,2,11:51:14 AM,11,2,12:10:48 PM,11,2,12:19:34 PM,11,2,12:34:23 PM,12,24.4,,-1.2668093,36.7966542,-1.2405791000000002,36.863429100000005,Rider_Id_857,889
-Order_No_27435,User_Id_2344,Bike,3,Business,21,4,3:59:31 PM,21,4,4:00:24 PM,21,4,4:00:31 PM,21,4,4:41:50 PM,21,4,4:52:31 PM,5,19.8,,-1.2551895,36.7822034,-1.2657199,36.7914391,Rider_Id_379,641
-Order_No_27401,User_Id_2198,Bike,1,Personal,1,1,3:51:26 PM,1,1,3:59:13 PM,1,1,4:14:40 PM,1,1,4:18:49 PM,1,1,4:46:14 PM,10,25.4,,-1.2248308,36.801257299999996,-1.2875013000000002,36.8171787,Rider_Id_156,1645
-Order_No_24035,User_Id_3542,Bike,2,Personal,15,1,3:21:14 PM,15,1,3:27:53 PM,15,1,3:31:35 PM,15,1,3:36:00 PM,15,1,4:40:37 PM,8,,,-1.28164,36.816866999999995,-1.311818,36.8638544,Rider_Id_268,3877
-Order_No_18638,User_Id_2457,Bike,3,Business,5,1,4:50:40 PM,5,1,4:53:00 PM,5,1,5:05:50 PM,5,1,5:09:44 PM,5,1,5:21:07 PM,4,24.5,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_279,683
-Order_No_15017,User_Id_2627,Bike,3,Business,14,5,11:38:51 AM,14,5,11:39:22 AM,14,5,11:43:26 AM,14,5,11:53:53 AM,14,5,11:56:13 AM,2,21.9,,-1.2584143,36.8048002,-1.2571472,36.795063299999995,Rider_Id_607,140
-Order_No_6379,User_Id_2069,Bike,3,Business,30,6,2:08:21 PM,30,6,2:09:04 PM,30,6,2:15:42 PM,30,6,2:26:58 PM,30,6,2:46:27 PM,9,28.6,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_855,1169
-Order_No_6764,User_Id_2982,Bike,3,Business,4,1,12:18:15 PM,4,1,12:42:41 PM,4,1,1:05:31 PM,4,1,1:25:06 PM,4,1,2:21:42 PM,23,21.1,,-1.177202,36.8351183,-1.2962790000000002,36.760463,Rider_Id_852,3396
-Order_No_15536,User_Id_3698,Bike,3,Business,24,3,10:01:07 AM,24,3,10:01:35 AM,24,3,10:13:56 AM,24,3,10:22:34 AM,24,3,11:34:31 AM,28,,,-1.3244885,36.897791999999995,-1.15135,36.9566534,Rider_Id_418,4317
-Order_No_27810,User_Id_627,Bike,3,Business,6,3,12:32:58 PM,6,3,12:33:38 PM,6,3,12:53:04 PM,6,3,12:54:37 PM,6,3,12:54:39 PM,3,,,-1.2551895,36.7822034,-1.2569383,36.7880021,Rider_Id_647,2
-Order_No_17203,User_Id_1363,Bike,3,Business,14,5,2:56:10 PM,14,5,2:56:20 PM,14,5,2:59:41 PM,14,5,3:09:51 PM,14,5,3:17:05 PM,5,23.2,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_766,434
-Order_No_44,User_Id_3201,Bike,3,Business,5,3,9:52:27 AM,5,3,9:52:40 AM,5,3,9:58:33 AM,5,3,10:04:17 AM,5,3,10:21:24 AM,3,18.3,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_611,1027
-Order_No_5676,User_Id_92,Bike,1,Personal,29,2,1:56:11 PM,29,2,1:57:54 PM,29,2,2:38:40 PM,29,2,2:41:13 PM,29,2,3:16:47 PM,17,23.8,,-1.214328,36.7911903,-1.2846195,36.7958316,Rider_Id_147,2134
-Order_No_2449,User_Id_3291,Bike,3,Business,8,5,10:53:59 AM,8,5,10:54:29 AM,8,5,11:03:37 AM,8,5,11:11:13 AM,8,5,11:28:24 AM,4,28.6,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_448,1031
-Order_No_9334,User_Id_2330,Bike,3,Business,28,4,9:16:16 AM,28,4,9:18:37 AM,28,4,9:26:47 AM,28,4,9:30:00 AM,28,4,9:53:01 AM,6,22.7,,-1.2990401000000003,36.8116114,-1.2642903,36.80021370000001,Rider_Id_803,1381
-Order_No_1251,User_Id_3557,Bike,3,Business,12,4,9:38:59 AM,12,4,9:39:45 AM,12,4,9:56:09 AM,12,4,10:04:06 AM,12,4,10:22:49 AM,8,16.2,,-1.2793831,36.7617573,-1.2546732,36.808679600000005,Rider_Id_735,1123
-Order_No_20214,User_Id_2676,Bike,3,Business,18,5,12:07:25 PM,18,5,12:07:37 PM,18,5,12:12:10 PM,18,5,12:47:36 PM,18,5,1:04:24 PM,2,20.0,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_559,1008
-Order_No_15546,User_Id_3404,Bike,3,Business,12,2,9:09:27 AM,12,2,9:12:40 AM,12,2,9:31:24 AM,12,2,9:42:45 AM,12,2,10:23:13 AM,5,20.7,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_309,2428
-Order_No_22415,User_Id_1389,Bike,3,Business,31,3,9:56:38 AM,31,3,9:57:52 AM,31,3,10:22:22 AM,31,3,10:34:50 AM,31,3,11:50:09 AM,18,24.9,,-1.2914308,36.801886700000004,-1.207546,36.7913976,Rider_Id_289,4519
-Order_No_10661,User_Id_1724,Bike,3,Business,13,2,12:33:32 PM,13,2,12:37:51 PM,13,2,12:59:14 PM,13,2,1:05:08 PM,13,2,1:28:11 PM,9,22.2,,-1.2941648000000001,36.7977315,-1.2551895,36.7822034,Rider_Id_276,1383
-Order_No_24950,User_Id_3434,Bike,3,Business,23,2,11:06:24 AM,23,2,11:08:38 AM,23,2,11:24:41 AM,23,2,11:46:20 AM,23,2,12:30:00 PM,16,18.2,,-1.2969741000000001,36.7856612,-1.2482465,36.8889062,Rider_Id_268,2620
-Order_No_17385,User_Id_2987,Bike,3,Business,6,5,3:21:01 PM,6,5,4:22:45 PM,6,5,4:27:43 PM,6,5,4:31:38 PM,6,5,4:42:38 PM,4,22.6,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_851,660
-Order_No_370,User_Id_3757,Bike,3,Personal,15,1,11:57:40 AM,15,1,11:58:15 AM,15,1,12:13:57 PM,15,1,12:39:30 PM,15,1,1:00:56 PM,9,,,-1.2655381,36.8354648,-1.3173868999999998,36.8112044,Rider_Id_907,1286
-Order_No_26702,User_Id_391,Bike,3,Business,8,5,2:23:25 PM,8,5,2:27:51 PM,8,5,2:52:18 PM,8,5,2:53:41 PM,8,5,3:09:37 PM,5,30.6,,-1.2784063,36.8222933,-1.300921,36.828195,Rider_Id_448,956
-Order_No_894,User_Id_3283,Bike,3,Business,18,4,10:50:47 AM,18,4,10:50:57 AM,18,4,10:53:15 AM,18,4,10:57:23 AM,18,4,11:15:21 AM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_99,1078
-Order_No_13910,User_Id_3353,Bike,1,Personal,11,2,10:37:15 AM,11,2,10:38:36 AM,11,2,10:57:41 AM,11,2,11:03:03 AM,11,2,11:23:30 AM,9,24.1,,-1.2248308,36.801257299999996,-1.275941,36.807443400000004,Rider_Id_857,1227
-Order_No_1777,User_Id_142,Bike,3,Business,4,4,11:17:17 AM,4,4,11:18:17 AM,4,4,11:29:04 AM,4,4,11:31:42 AM,4,4,12:16:19 PM,17,25.6,,-1.2963097,36.7688221,-1.2871375,36.8898931,Rider_Id_411,2677
-Order_No_23604,User_Id_3669,Bike,3,Business,10,5,1:50:49 PM,10,5,2:42:58 PM,10,5,2:50:26 PM,10,5,3:00:54 PM,10,5,3:35:22 PM,12,,,-1.2615888000000002,36.7928732,-1.2095044,36.783206400000005,Rider_Id_553,2068
-Order_No_11859,User_Id_2895,Bike,3,Business,13,2,10:52:58 AM,13,2,10:54:00 AM,13,2,10:59:32 AM,13,2,11:05:39 AM,13,2,11:58:29 AM,23,22.2,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_200,3170
-Order_No_8475,User_Id_2066,Bike,3,Business,30,4,4:59:54 PM,30,4,5:01:39 PM,30,4,5:16:46 PM,30,4,5:22:15 PM,30,4,5:41:48 PM,8,20.3,,-1.316961,36.820337,-1.2849815,36.7767751,Rider_Id_101,1173
-Order_No_11330,User_Id_1724,Bike,3,Business,30,4,11:08:54 AM,30,4,11:09:17 AM,30,4,11:47:16 AM,30,4,11:48:58 AM,30,4,12:18:41 PM,15,,,-1.2551895,36.7822034,-1.3267688999999998,36.8756255,Rider_Id_57,1783
-Order_No_14757,User_Id_241,Bike,3,Business,23,3,11:24:55 AM,23,3,11:26:24 AM,23,3,11:32:16 AM,23,3,11:44:16 AM,23,3,11:55:27 AM,5,21.7,,-1.2695287,36.798177700000004,-1.2895261,36.8164407,Rider_Id_726,671
-Order_No_19483,User_Id_2008,Bike,3,Business,4,6,9:54:34 AM,4,6,9:54:45 AM,4,6,10:04:52 AM,4,6,10:12:02 AM,4,6,10:44:47 AM,13,,,-1.2508229,36.7895263,-1.2417321000000001,36.8886277,Rider_Id_205,1965
-Order_No_23594,User_Id_2523,Bike,3,Business,18,2,9:27:47 AM,18,2,9:30:52 AM,18,2,9:45:18 AM,18,2,9:50:47 AM,18,2,10:12:09 AM,11,19.8,,-1.2551895,36.7822034,-1.2310607,36.803577600000004,Rider_Id_453,1282
-Order_No_6297,User_Id_2763,Bike,3,Business,13,3,3:24:32 PM,13,3,3:25:09 PM,13,3,3:30:44 PM,13,3,3:34:17 PM,13,3,3:46:00 PM,6,29.4,,-1.2551895,36.7822034,-1.2843046,36.774613200000005,Rider_Id_177,703
-Order_No_9618,User_Id_3599,Bike,3,Business,25,4,10:03:19 AM,25,4,11:08:38 AM,25,4,11:33:09 AM,25,4,11:51:19 AM,25,4,12:47:25 PM,12,20.2,,-1.3302996,36.8707935,-1.2672002,36.8245542,Rider_Id_675,3366
-Order_No_20655,User_Id_704,Bike,3,Business,8,5,3:57:17 PM,8,5,4:20:38 PM,8,5,4:20:49 PM,8,5,4:26:33 PM,8,5,5:04:51 PM,13,21.7,,-1.2860183,36.8975338,-1.3700383,36.9190174,Rider_Id_852,2298
-Order_No_25567,User_Id_3222,Bike,3,Business,3,5,10:30:02 AM,3,5,10:31:30 AM,3,5,10:37:58 AM,3,5,10:39:28 AM,3,5,11:05:48 AM,8,,,-1.2551895,36.7822034,-1.2824577,36.7967953,Rider_Id_570,1580
-Order_No_27428,User_Id_909,Bike,3,Business,12,2,4:57:33 PM,12,2,4:58:05 PM,12,2,5:17:50 PM,12,2,5:25:49 PM,12,2,5:53:04 PM,11,21.0,,-1.2615888000000002,36.7928732,-1.225322,36.80855,Rider_Id_101,1635
-Order_No_21681,User_Id_1047,Bike,3,Personal,5,5,10:18:45 AM,5,5,10:37:08 AM,5,5,10:46:26 AM,5,5,10:48:15 AM,5,5,11:05:42 AM,9,23.7,,-1.3077869,36.844320700000004,-1.269534,36.80697,Rider_Id_178,1047
-Order_No_10374,User_Id_2008,Bike,3,Business,26,5,2:30:37 PM,26,5,2:32:05 PM,26,5,2:48:59 PM,26,5,2:59:13 PM,26,5,3:22:54 PM,6,,,-1.2508229,36.7895263,-1.2745551000000002,36.7979797,Rider_Id_797,1421
-Order_No_4053,User_Id_1099,Bike,3,Business,7,3,11:43:23 AM,7,3,11:44:52 AM,7,3,11:59:26 AM,7,3,12:11:21 PM,7,3,12:42:39 PM,11,25.3,,-1.2764098999999998,36.8208451,-1.303924,36.8758792,Rider_Id_200,1878
-Order_No_15672,User_Id_2655,Bike,1,Personal,18,1,8:38:36 AM,18,1,8:46:16 AM,18,1,9:00:17 AM,18,1,9:03:44 AM,18,1,9:13:18 AM,2,14.9,0.3,-1.260039,36.8159152,-1.2515167,36.812185,Rider_Id_56,574
-Order_No_21395,User_Id_3669,Bike,3,Business,18,1,5:42:04 PM,18,1,5:47:20 PM,18,1,5:55:34 PM,18,1,6:24:30 PM,18,1,6:59:27 PM,22,28.9,,-1.2615888000000002,36.7928732,-1.3984711,36.76731029999999,Rider_Id_103,2097
-Order_No_25634,User_Id_1237,Bike,3,Personal,23,3,8:56:18 AM,23,3,8:56:33 AM,23,3,9:04:36 AM,23,3,9:08:35 AM,23,3,9:26:53 AM,3,16.9,15.9,-1.2741728,36.79691079999999,-1.2611987,36.80609129999999,Rider_Id_50,1098
-Order_No_19544,User_Id_762,Bike,3,Personal,25,1,9:24:58 AM,25,1,9:26:25 AM,25,1,9:33:56 AM,25,1,9:47:50 AM,25,1,10:10:07 AM,7,21.2,,-1.2793831,36.762057399999996,-1.2994771999999999,36.804825,Rider_Id_269,1337
-Order_No_2383,User_Id_393,Bike,3,Business,27,3,8:46:17 AM,27,3,8:46:29 AM,27,3,8:51:05 AM,27,3,9:02:57 AM,27,3,9:37:24 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_858,2067
-Order_No_6941,User_Id_186,Bike,3,Business,14,5,4:40:14 PM,14,5,4:42:42 PM,14,5,4:46:32 PM,14,5,4:50:12 PM,14,5,5:08:42 PM,7,21.3,,-1.2883158000000001,36.8199114,-1.2571472,36.795063299999995,Rider_Id_467,1110
-Order_No_23566,User_Id_3426,Bike,3,Business,30,3,6:06:47 PM,30,3,6:07:26 PM,30,3,6:08:36 PM,30,3,6:17:34 PM,30,3,6:50:27 PM,3,27.6,,-1.3012008,36.764868,-1.2765492,36.7669809,Rider_Id_925,1973
-Order_No_13583,User_Id_2066,Bike,3,Business,12,6,1:04:38 PM,12,6,1:05:02 PM,12,6,1:20:18 PM,12,6,1:28:37 PM,12,6,1:45:05 PM,8,22.9,,-1.316961,36.820337,-1.264865,36.810488,Rider_Id_103,988
-Order_No_21014,User_Id_867,Bike,3,Business,15,2,2:13:31 PM,15,2,2:21:07 PM,15,2,2:29:30 PM,15,2,2:35:42 PM,15,2,2:54:51 PM,9,24.4,,-1.2574219,36.7927073,-1.2991441000000001,36.7528804,Rider_Id_523,1149
-Order_No_2780,User_Id_2195,Bike,3,Business,17,5,10:05:58 AM,17,5,10:09:05 AM,17,5,10:18:27 AM,17,5,10:27:24 AM,17,5,10:47:25 AM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_660,1201
-Order_No_24781,User_Id_175,Bike,3,Personal,10,5,3:00:45 PM,10,5,3:00:59 PM,10,5,3:06:14 PM,10,5,3:09:17 PM,10,5,3:24:42 PM,7,22.0,,-1.2850643999999998,36.8343657,-1.2937528999999999,36.7894339,Rider_Id_414,925
-Order_No_14177,User_Id_718,Bike,3,Business,10,1,12:56:41 PM,10,1,12:58:51 PM,10,1,1:13:27 PM,10,1,1:27:58 PM,10,1,1:48:50 PM,8,18.4,,-1.2551895,36.7822034,-1.305146,36.830025,Rider_Id_907,1252
-Order_No_5248,User_Id_1075,Bike,3,Business,4,2,9:43:50 AM,4,2,11:07:47 AM,4,2,11:08:10 AM,4,2,11:13:26 AM,4,2,11:22:54 AM,3,20.3,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_205,568
-Order_No_14613,User_Id_3222,Bike,3,Business,17,4,3:01:59 PM,17,4,3:02:11 PM,17,4,3:11:29 PM,17,4,3:22:17 PM,17,4,4:00:35 PM,2,27.9,,-1.2551895,36.7822034,-1.2580991000000001,36.794151899999996,Rider_Id_399,2298
-Order_No_18991,User_Id_1149,Bike,3,Personal,15,2,10:29:38 AM,15,2,11:02:56 AM,15,2,11:03:09 AM,15,2,11:11:14 AM,15,2,11:24:21 AM,4,22.8,,-1.279285,36.826057399999996,-1.291899,36.809705,Rider_Id_100,787
-Order_No_24429,User_Id_2150,Bike,3,Business,2,4,4:07:28 PM,2,4,4:08:07 PM,2,4,4:12:48 PM,2,4,4:22:51 PM,2,4,4:58:43 PM,15,,,-1.272828,36.816608,-1.3444459999999998,36.756597,Rider_Id_235,2152
-Order_No_18621,User_Id_868,Bike,3,Business,21,5,10:28:19 AM,21,5,10:34:09 AM,21,5,11:11:59 AM,21,5,11:13:42 AM,21,5,11:29:00 AM,6,22.9,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_436,918
-Order_No_26773,User_Id_3521,Bike,3,Business,19,3,7:51:36 AM,19,3,9:58:20 AM,19,3,10:24:54 AM,19,3,10:30:48 AM,19,3,10:49:04 AM,4,,,-1.2768326,36.8073804,-1.2956951,36.812924200000005,Rider_Id_565,1096
-Order_No_26288,User_Id_1787,Bike,3,Business,9,2,2:31:17 PM,9,2,2:34:33 PM,9,2,2:46:46 PM,9,2,3:05:19 PM,9,2,4:02:34 PM,13,22.7,,-1.3052491000000002,36.8223899,-1.2297202,36.881950700000004,Rider_Id_932,3435
-Order_No_14096,User_Id_3326,Bike,3,Business,6,5,2:28:39 PM,6,5,3:28:20 PM,6,5,3:44:30 PM,6,5,4:19:55 PM,6,5,4:51:26 PM,21,22.6,,-1.2579303999999998,36.8019667,-1.3427269,36.9194847,Rider_Id_116,1891
-Order_No_17210,User_Id_3404,Bike,3,Business,5,2,12:50:46 PM,5,2,1:14:10 PM,5,2,1:21:51 PM,5,2,1:30:07 PM,5,2,1:58:48 PM,11,23.4,,-1.225322,36.80855,-1.279395,36.825364,Rider_Id_365,1721
-Order_No_9182,User_Id_496,Bike,3,Business,25,1,7:30:02 AM,25,1,9:37:09 AM,25,1,9:37:20 AM,25,1,9:52:36 AM,25,1,10:08:21 AM,6,,,-1.2551895,36.7822034,-1.2864,36.8277,Rider_Id_576,945
-Order_No_6438,User_Id_3115,Bike,3,Business,22,3,4:42:42 PM,22,3,4:43:10 PM,22,3,4:58:38 PM,22,3,5:05:16 PM,22,3,5:26:49 PM,2,23.9,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_708,1293
-Order_No_25586,User_Id_2131,Bike,3,Personal,21,4,8:51:02 PM,21,4,8:51:36 PM,21,4,8:56:58 PM,21,4,9:01:14 PM,21,4,9:12:27 PM,4,24.7,,-1.2963097,36.7688221,-1.2981858,36.747492,Rider_Id_678,673
-Order_No_9631,User_Id_517,Bike,3,Business,3,4,12:02:48 PM,3,4,12:03:15 PM,3,4,12:05:25 PM,3,4,12:09:59 PM,3,4,12:40:26 PM,14,23.5,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1827
-Order_No_22346,User_Id_3371,Bike,3,Personal,3,4,9:38:19 AM,3,4,9:43:33 AM,3,4,9:57:05 AM,3,4,10:23:26 AM,3,4,11:03:11 AM,17,17.4,16.1,-1.1971261,36.806307000000004,-1.2988168,36.8198572,Rider_Id_56,2385
-Order_No_25249,User_Id_3517,Bike,3,Business,20,3,8:17:38 AM,20,3,8:43:14 AM,20,3,8:49:03 AM,20,3,8:52:58 AM,20,3,9:28:39 AM,14,,,-1.3198615,36.7110323,-1.3882008,36.7699445,Rider_Id_824,2141
-Order_No_18727,User_Id_2836,Bike,1,Personal,9,2,11:22:06 AM,9,2,11:45:35 AM,9,2,12:05:40 PM,9,2,12:14:20 PM,9,2,12:23:21 PM,7,22.0,,-1.2883572,36.8446699,-1.3205163999999998,36.8098882,Rider_Id_336,541
-Order_No_1694,User_Id_1831,Bike,3,Business,15,5,5:03:36 PM,15,5,5:08:35 PM,15,5,5:12:17 PM,15,5,5:22:00 PM,15,5,6:18:19 PM,17,27.7,,-1.279448,36.823568,-1.3632162,36.9108253,Rider_Id_953,3379
-Order_No_16612,User_Id_3732,Bike,1,Personal,19,7,12:05:10 PM,19,7,12:05:34 PM,19,7,12:22:15 PM,19,7,12:25:11 PM,19,7,1:09:50 PM,18,18.1,,-1.2790653,36.7907262,-1.2092548,36.8043842,Rider_Id_452,2679
-Order_No_27195,User_Id_3091,Bike,3,Business,8,2,4:52:48 PM,8,2,5:38:07 PM,8,2,5:52:34 PM,8,2,6:14:08 PM,8,2,6:37:01 PM,9,23.6,,-1.3165542,36.700067,-1.3012008,36.764868,Rider_Id_237,1373
-Order_No_19871,User_Id_2956,Bike,3,Business,17,3,2:10:44 PM,17,3,2:32:26 PM,17,3,2:49:53 PM,17,3,3:04:51 PM,17,3,3:46:49 PM,6,28.6,,-1.2584143,36.8048002,-1.290894,36.822971,Rider_Id_88,2518
-Order_No_20202,User_Id_1014,Bike,3,Business,16,5,6:29:24 PM,16,5,6:30:23 PM,16,5,6:59:24 PM,16,5,7:19:24 PM,16,5,7:40:01 PM,7,26.0,,-1.2733546,36.7696069,-1.2647469,36.8157706,Rider_Id_187,1237
-Order_No_7013,User_Id_2823,Bike,3,Business,14,4,11:02:06 AM,14,4,11:11:26 AM,14,4,11:20:08 AM,14,4,11:23:31 AM,14,4,1:06:06 PM,9,19.1,,-1.3014461000000002,36.7661381,-1.2551895,36.7822034,Rider_Id_46,6155
-Order_No_26591,User_Id_3647,Bike,3,Business,15,5,9:09:09 AM,15,5,9:09:52 AM,15,5,9:40:31 AM,15,5,9:41:33 AM,15,5,10:54:49 AM,18,20.4,,-1.2726389999999999,36.794723,-1.3073803,36.6825842,Rider_Id_4,4396
-Order_No_27664,User_Id_2066,Bike,3,Business,18,4,11:24:45 AM,18,4,11:25:09 AM,18,4,11:35:20 AM,18,4,11:42:05 AM,18,4,12:16:56 PM,8,,,-1.3210188999999999,36.841055100000005,-1.2960265,36.8780276,Rider_Id_233,2091
-Order_No_6254,User_Id_3222,Bike,3,Business,28,2,9:01:04 AM,28,2,9:02:17 AM,28,2,9:02:37 AM,28,2,9:07:02 AM,28,2,9:27:14 AM,6,18.6,,-1.2551895,36.7822034,-1.2357748,36.7900524,Rider_Id_576,1212
-Order_No_19171,User_Id_1271,Bike,3,Business,4,3,2:26:10 PM,4,3,2:54:47 PM,4,3,2:58:20 PM,4,3,3:25:26 PM,4,3,3:35:34 PM,6,18.5,,-1.3244885,36.897791999999995,-1.3332749,36.870814700000004,Rider_Id_543,608
-Order_No_8569,User_Id_1642,Bike,3,Business,1,5,3:22:17 PM,1,5,3:23:11 PM,1,5,3:46:54 PM,1,5,3:53:23 PM,1,5,4:06:56 PM,8,28.7,,-1.2728069,36.7962362,-1.310144,36.7741554,Rider_Id_237,813
-Order_No_6535,User_Id_1780,Bike,3,Personal,16,3,10:29:55 AM,16,3,10:30:19 AM,16,3,10:31:45 AM,16,3,10:33:09 AM,16,3,11:39:23 AM,15,22.8,,-1.3103985,36.9114173,-1.298529,36.816482,Rider_Id_237,3974
-Order_No_8238,User_Id_2523,Bike,3,Business,2,4,12:24:47 PM,2,4,12:28:32 PM,2,4,12:55:22 PM,2,4,1:03:12 PM,2,4,1:17:18 PM,9,,,-1.2551895,36.7822034,-1.2959604999999998,36.7873854,Rider_Id_855,846
-Order_No_19193,User_Id_635,Bike,3,Business,3,3,2:17:09 PM,3,3,2:18:07 PM,3,3,2:21:37 PM,3,3,3:07:59 PM,3,3,3:23:17 PM,7,28.3,,-1.300921,36.828195,-1.2675613000000001,36.8133001,Rider_Id_205,918
-Order_No_25230,User_Id_2200,Bike,3,Business,31,1,5:17:18 PM,31,1,5:17:42 PM,31,1,5:49:25 PM,31,1,6:04:55 PM,31,1,6:15:31 PM,2,22.7,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_338,636
-Order_No_24539,User_Id_567,Bike,3,Business,27,4,3:38:47 PM,27,4,3:40:00 PM,27,4,3:48:35 PM,27,4,3:59:47 PM,27,4,4:18:36 PM,6,25.1,,-1.2633493999999998,36.7959696,-1.2963097,36.7688221,Rider_Id_932,1129
-Order_No_14982,User_Id_265,Bike,3,Business,1,5,3:29:06 PM,1,5,3:30:51 PM,1,5,3:50:01 PM,1,5,3:53:56 PM,1,5,4:13:37 PM,3,28.6,,-1.300921,36.828195,-1.3204504,36.840638299999995,Rider_Id_362,1181
-Order_No_5826,User_Id_2458,Bike,3,Business,7,2,1:10:15 PM,7,2,1:11:21 PM,7,2,1:21:46 PM,7,2,1:24:25 PM,7,2,1:41:58 PM,6,23.0,,-1.2857671,36.7875614,-1.2911796,36.821506799999995,Rider_Id_636,1053
-Order_No_7868,User_Id_1970,Bike,1,Personal,28,5,2:35:34 PM,28,5,2:37:51 PM,28,5,3:02:41 PM,28,5,3:24:16 PM,28,5,4:03:55 PM,6,25.7,,-1.3099295,36.8436807,-1.2862033000000002,36.8152966,Rider_Id_399,2379
-Order_No_24893,User_Id_2066,Bike,3,Business,30,5,8:34:15 AM,30,5,8:38:36 AM,30,5,8:56:41 AM,30,5,9:00:44 AM,30,5,9:19:48 AM,6,19.1,,-1.3210188999999999,36.841055100000005,-1.3291155000000001,36.8861,Rider_Id_114,1144
-Order_No_26639,User_Id_1880,Bike,3,Business,8,2,12:25:49 PM,8,2,12:26:11 PM,8,2,12:44:26 PM,8,2,12:50:21 PM,8,2,1:04:26 PM,3,23.5,,-1.2571472,36.795063299999995,-1.2663778,36.8076024,Rider_Id_425,845
-Order_No_27550,User_Id_3017,Bike,3,Business,24,4,6:24:15 PM,24,4,6:25:44 PM,24,4,6:34:14 PM,24,4,6:36:10 PM,24,4,6:41:06 PM,2,21.2,,-1.299313,36.78314879999999,-1.294598,36.781639,Rider_Id_334,296
-Order_No_4059,User_Id_517,Bike,3,Business,29,4,7:16:01 AM,29,4,8:46:48 AM,29,4,8:50:12 AM,29,4,8:57:10 AM,29,4,9:16:22 AM,4,18.9,4.0,-1.3177546999999998,36.8303703,-1.3004062,36.829741,Rider_Id_523,1152
-Order_No_14414,User_Id_2956,Bike,3,Business,6,3,1:34:50 PM,6,3,1:37:32 PM,6,3,1:48:31 PM,6,3,2:01:28 PM,6,3,2:24:01 PM,14,,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_669,1353
-Order_No_11102,User_Id_2207,Bike,3,Business,4,1,8:47:31 AM,4,1,8:48:13 AM,4,1,8:49:31 AM,4,1,9:05:30 AM,4,1,9:16:28 AM,6,21.9,,-1.2891116000000002,36.810438,-1.2696908000000002,36.81519,Rider_Id_955,658
-Order_No_3865,User_Id_635,Bike,3,Business,28,2,12:15:46 PM,28,2,12:16:32 PM,28,2,12:27:26 PM,28,2,12:33:40 PM,28,2,12:48:46 PM,5,21.2,,-1.2602337,36.79905479999999,-1.2823851000000002,36.8186472,Rider_Id_843,906
-Order_No_26641,User_Id_2931,Bike,3,Business,4,2,2:14:20 PM,4,2,2:34:21 PM,4,2,3:13:31 PM,4,2,3:29:34 PM,4,2,4:23:48 PM,13,23.8,11.1,-1.3081087,36.7405924,-1.2452664,36.802854100000005,Rider_Id_677,3254
-Order_No_1186,User_Id_2112,Bike,3,Business,21,3,2:53:25 PM,21,3,2:53:55 PM,21,3,2:58:51 PM,21,3,3:11:22 PM,21,3,3:20:14 PM,4,23.0,2.9,-1.2297202,36.881950700000004,-1.2156006000000001,36.8916865,Rider_Id_237,532
-Order_No_20670,User_Id_1874,Bike,3,Business,28,4,2:58:13 PM,28,4,2:59:22 PM,28,4,3:02:37 PM,28,4,3:12:39 PM,28,4,3:36:16 PM,18,27.4,,-1.2571472,36.795063299999995,-1.3251247,36.899592600000005,Rider_Id_855,1417
-Order_No_22401,User_Id_2828,Bike,3,Business,3,5,12:43:38 PM,3,5,12:45:37 PM,3,5,1:01:09 PM,3,5,1:03:44 PM,3,5,1:47:53 PM,8,,,-1.2615888000000002,36.7928732,-1.289556,36.797317,Rider_Id_147,2649
-Order_No_17721,User_Id_2066,Bike,3,Business,27,5,11:07:04 AM,27,5,11:08:20 AM,27,5,11:18:46 AM,27,5,11:21:34 AM,27,5,11:38:15 AM,5,22.3,,-1.316961,36.820337,-1.3181843,36.839482700000005,Rider_Id_844,1001
-Order_No_3801,User_Id_733,Bike,3,Business,24,5,2:15:54 PM,24,5,2:30:40 PM,24,5,2:39:14 PM,24,5,2:44:41 PM,24,5,3:07:00 PM,7,22.9,,-1.2551895,36.7822034,-1.2891614,36.8163497,Rider_Id_709,1339
-Order_No_5753,User_Id_2794,Bike,3,Personal,12,5,12:51:37 PM,12,5,12:52:59 PM,12,5,1:04:07 PM,12,5,1:24:53 PM,12,5,1:43:42 PM,10,26.6,,-1.284289,36.822292,-1.2482781,36.884091999999995,Rider_Id_341,1129
-Order_No_9068,User_Id_1389,Bike,3,Business,3,6,3:02:09 PM,3,6,3:02:47 PM,3,6,3:13:14 PM,3,6,3:39:07 PM,3,6,4:02:52 PM,14,22.2,,-1.2584143,36.8048002,-1.207546,36.7913976,Rider_Id_913,1425
-Order_No_1507,User_Id_1195,Bike,3,Business,12,5,3:29:05 PM,12,5,3:31:15 PM,12,5,3:51:49 PM,12,5,3:54:48 PM,12,5,4:02:43 PM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_304,475
-Order_No_7933,User_Id_3621,Bike,3,Business,20,2,8:11:54 AM,20,2,8:12:14 AM,20,2,8:20:54 AM,20,2,8:24:34 AM,20,2,8:43:53 AM,10,18.9,,-1.2836583000000001,36.7886014,-1.2664227,36.7453671,Rider_Id_446,1159
-Order_No_4453,User_Id_2008,Bike,3,Business,18,1,9:26:53 AM,18,1,9:27:36 AM,18,1,9:36:47 AM,18,1,9:43:12 AM,18,1,10:12:04 AM,11,22.0,,-1.2508229,36.7895263,-1.3173868999999998,36.8112044,Rider_Id_778,1732
-Order_No_21334,User_Id_3027,Bike,1,Personal,22,5,9:18:56 AM,22,5,9:19:07 AM,22,5,9:19:39 AM,22,5,9:41:34 AM,22,5,10:17:02 AM,13,16.2,,-1.2450205,36.8853338,-1.3037178999999999,36.8373602,Rider_Id_521,2128
-Order_No_8284,User_Id_2164,Bike,3,Business,22,5,2:45:39 PM,22,5,2:47:31 PM,22,5,2:55:38 PM,22,5,3:06:29 PM,22,5,3:30:55 PM,6,27.1,,-1.2551895,36.7822034,-1.281223,36.8239,Rider_Id_953,1466
-Order_No_19616,User_Id_2801,Bike,3,Business,14,4,2:10:14 PM,14,4,2:15:05 PM,14,4,2:42:06 PM,14,4,2:47:10 PM,14,4,3:11:50 PM,7,27.7,,-1.2595421999999998,36.7871178,-1.2884231000000002,36.779456599999996,Rider_Id_259,1480
-Order_No_7324,User_Id_2783,Bike,2,Personal,6,3,7:50:32 AM,6,3,7:58:31 AM,6,3,8:23:31 AM,6,3,8:31:30 AM,6,3,8:45:14 AM,6,15.9,13.9,-1.272704,36.793127899999995,-1.2989758,36.77535770000001,Rider_Id_116,824
-Order_No_2695,User_Id_475,Bike,3,Business,7,4,12:40:52 PM,7,4,12:44:17 PM,7,4,12:55:35 PM,7,4,1:42:49 PM,7,4,1:49:10 PM,7,31.2,,-1.2571472,36.795063299999995,-1.2965824,36.815302200000005,Rider_Id_653,381
-Order_No_16008,User_Id_3277,Bike,2,Personal,10,1,10:07:34 AM,10,1,10:14:05 AM,10,1,10:50:20 AM,10,1,10:58:31 AM,10,1,11:34:05 AM,9,,,-1.3376959,36.7605707,-1.3096061,36.8158239,Rider_Id_328,2134
-Order_No_22669,User_Id_53,Bike,3,Business,28,4,2:20:14 PM,28,4,2:27:36 PM,28,4,2:46:44 PM,28,4,2:56:11 PM,28,4,3:13:50 PM,5,27.3,,-1.2770708,36.8231093,-1.29508,36.796863,Rider_Id_126,1059
-Order_No_12716,User_Id_412,Bike,3,Business,25,1,3:49:59 PM,25,1,3:50:14 PM,25,1,4:07:22 PM,25,1,4:10:16 PM,25,1,4:21:15 PM,4,30.3,,-1.266837,36.799248999999996,-1.272828,36.816608,Rider_Id_52,659
-Order_No_4756,User_Id_868,Bike,3,Business,6,4,4:37:16 PM,6,4,5:00:54 PM,6,4,5:01:05 PM,6,4,5:31:51 PM,6,4,6:05:03 PM,9,22.6,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_345,1992
-Order_No_11039,User_Id_3635,Bike,1,Business,1,1,12:29:20 PM,1,1,12:30:00 PM,1,1,12:37:05 PM,1,1,12:43:44 PM,1,1,1:55:27 PM,8,23.8,,-1.3052491000000002,36.8223899,-1.2615888000000002,36.7928732,Rider_Id_644,4303
-Order_No_18433,User_Id_2159,Bike,3,Business,18,4,9:24:09 AM,18,4,9:25:09 AM,18,4,9:30:15 AM,18,4,9:34:17 AM,18,4,9:55:55 AM,8,20.5,,-1.304735,36.784605400000004,-1.2557778999999998,36.7879648,Rider_Id_612,1298
-Order_No_2774,User_Id_186,Bike,3,Business,8,4,4:02:40 PM,8,4,4:03:24 PM,8,4,4:18:39 PM,8,4,4:26:24 PM,8,4,5:03:45 PM,20,27.2,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_38,2241
-Order_No_4481,User_Id_2271,Bike,3,Business,24,4,2:25:42 PM,24,4,2:44:18 PM,24,4,2:50:10 PM,24,4,2:57:01 PM,24,4,3:31:46 PM,19,28.5,,-1.303596,36.778378000000004,-1.293515,36.897607,Rider_Id_556,2085
-Order_No_6633,User_Id_3307,Bike,3,Personal,29,1,10:12:48 AM,29,1,10:13:14 AM,29,1,10:21:55 AM,29,1,10:27:13 AM,29,1,10:37:46 AM,4,,,-1.3098299999999998,36.819618,-1.2933302,36.8298015,Rider_Id_343,633
-Order_No_23134,User_Id_893,Bike,3,Business,7,5,12:26:20 PM,7,5,12:28:10 PM,7,5,12:56:42 PM,7,5,1:02:56 PM,7,5,1:37:32 PM,21,25.1,,-1.2663194,36.809901,-1.343439,36.912065000000005,Rider_Id_580,2076
-Order_No_18337,User_Id_1410,Bike,3,Business,8,6,9:18:59 AM,8,6,9:21:20 AM,8,6,10:19:00 AM,8,6,10:37:43 AM,8,6,11:13:25 AM,20,18.2,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_167,2142
-Order_No_24439,User_Id_432,Bike,3,Business,15,5,11:39:51 AM,15,5,11:40:27 AM,15,5,11:46:06 AM,15,5,11:57:26 AM,15,5,12:08:36 PM,3,26.5,,-1.2948834999999999,36.7830474,-1.3012008,36.764868,Rider_Id_545,670
-Order_No_16726,User_Id_2216,Bike,3,Business,22,2,3:42:25 PM,22,2,3:43:03 PM,22,2,3:58:04 PM,22,2,4:06:04 PM,22,2,4:47:17 PM,14,25.5,,-1.2867627000000001,36.883446,-1.3448483,36.9004081,Rider_Id_620,2473
-Order_No_13920,User_Id_880,Bike,3,Personal,9,4,2:10:26 PM,9,4,2:16:28 PM,9,4,2:25:23 PM,9,4,2:29:45 PM,9,4,2:43:39 PM,5,25.8,,-1.2868546,36.824811,-1.2995135,36.793363299999996,Rider_Id_343,834
-Order_No_2003,User_Id_2436,Bike,3,Business,31,5,5:40:06 PM,31,5,5:40:33 PM,31,5,5:45:18 PM,31,5,5:57:05 PM,31,5,6:46:59 PM,18,19.7,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_720,2994
-Order_No_8718,User_Id_3064,Bike,3,Business,23,3,9:15:20 AM,23,3,9:17:05 AM,23,3,10:03:33 AM,23,3,10:25:05 AM,23,3,10:44:46 AM,9,16.8,16.0,-1.2545924,36.8410221,-1.2579303999999998,36.8019667,Rider_Id_589,1181
-Order_No_24677,User_Id_3326,Bike,3,Business,23,3,3:55:44 PM,23,3,3:55:56 PM,23,3,3:56:00 PM,23,3,3:57:02 PM,23,3,4:16:35 PM,9,27.5,,-1.304344,36.812274,-1.2551895,36.7822034,Rider_Id_31,1173
-Order_No_4621,User_Id_3641,Bike,3,Business,11,2,2:29:09 PM,11,2,3:22:49 PM,11,2,3:23:04 PM,11,2,3:44:23 PM,11,2,4:07:18 PM,15,21.5,,-1.2571472,36.795063299999995,-1.2257343,36.89335429999999,Rider_Id_320,1375
-Order_No_27546,User_Id_1384,Bike,3,Business,15,3,11:03:42 AM,15,3,11:03:59 AM,15,3,11:07:43 AM,15,3,11:12:05 AM,15,3,11:48:32 AM,14,,,-1.2517448,36.7924971,-1.3022584,36.7343665,Rider_Id_959,2187
-Order_No_8362,User_Id_60,Bike,3,Business,3,4,11:25:12 AM,3,4,11:25:46 AM,3,4,11:35:44 AM,3,4,11:39:33 AM,3,4,11:57:59 AM,6,23.4,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_75,1106
-Order_No_11501,User_Id_3557,Bike,3,Business,30,5,9:38:54 AM,30,5,9:39:53 AM,30,5,9:46:42 AM,30,5,9:51:20 AM,30,5,10:13:07 AM,9,19.2,,-1.2546732,36.808679600000005,-1.3066143,36.8161046,Rider_Id_449,1307
-Order_No_26095,User_Id_393,Bike,3,Business,11,2,1:35:54 PM,11,2,1:37:43 PM,11,2,1:40:24 PM,11,2,1:46:14 PM,11,2,2:16:54 PM,13,21.5,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_211,1840
-Order_No_5157,User_Id_661,Bike,3,Business,18,4,10:54:40 AM,18,4,10:55:33 AM,18,4,11:03:00 AM,18,4,11:11:19 AM,18,4,11:51:20 AM,17,25.7,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_749,2401
-Order_No_576,User_Id_517,Bike,3,Business,8,6,10:07:55 AM,8,6,10:09:28 AM,8,6,10:14:55 AM,8,6,10:20:42 AM,8,6,10:50:20 AM,5,16.6,3.9,-1.3228003000000002,36.8306435,-1.3077869,36.844320700000004,Rider_Id_863,1778
-Order_No_456,User_Id_2089,Bike,3,Business,21,3,1:04:44 PM,21,3,1:04:58 PM,21,3,1:08:56 PM,21,3,1:13:01 PM,21,3,1:39:48 PM,7,22.2,,-1.2655823999999998,36.8013576,-1.2610721999999999,36.814707399999996,Rider_Id_38,1607
-Order_No_9372,User_Id_3201,Bike,3,Business,26,1,3:29:12 PM,26,1,3:29:54 PM,26,1,3:32:42 PM,26,1,3:45:05 PM,26,1,3:55:38 PM,3,22.4,0.8,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_393,633
-Order_No_2268,User_Id_868,Bike,3,Business,9,2,10:19:02 AM,9,2,10:19:12 AM,9,2,10:20:09 AM,9,2,10:33:54 AM,9,2,10:52:29 AM,8,26.8,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_484,1115
-Order_No_3309,User_Id_2804,Bike,3,Business,27,3,3:18:10 PM,27,3,3:18:35 PM,27,3,3:30:04 PM,27,3,3:40:55 PM,27,3,4:36:37 PM,20,,,-1.2579303999999998,36.8019667,-1.333148,36.922453000000004,Rider_Id_618,3342
-Order_No_600,User_Id_3350,Bike,1,Personal,18,5,10:13:08 AM,18,5,10:55:59 AM,18,5,11:11:55 AM,18,5,11:16:38 AM,18,5,12:00:45 PM,6,21.3,,-1.2626906,36.7827022,-1.2886385,36.8240195,Rider_Id_742,2647
-Order_No_16720,User_Id_2304,Bike,1,Personal,21,5,5:35:51 PM,21,5,5:36:37 PM,21,5,5:57:52 PM,21,5,6:14:07 PM,21,5,6:44:38 PM,14,25.0,,-1.2946768999999998,36.7501951,-1.3549418,36.7280528,Rider_Id_268,1831
-Order_No_8978,User_Id_3259,Bike,3,Business,13,2,11:13:46 AM,13,2,11:23:19 AM,13,2,11:36:38 AM,13,2,11:41:41 AM,13,2,12:00:06 PM,8,22.3,,-1.2956517,36.8036292,-1.263441,36.840808,Rider_Id_744,1105
-Order_No_23092,User_Id_816,Bike,3,Business,23,5,7:48:24 AM,23,5,9:01:02 AM,23,5,9:22:59 AM,23,5,9:26:54 AM,23,5,9:43:08 AM,2,20.3,,-1.290894,36.822971,-1.2864733,36.8239937,Rider_Id_436,974
-Order_No_6698,User_Id_1887,Bike,1,Personal,3,3,9:34:00 AM,3,3,9:34:20 AM,3,3,10:02:24 AM,3,3,10:05:18 AM,3,3,10:16:07 AM,3,18.5,,-1.2787600000000001,36.812651,-1.2732412,36.811767700000004,Rider_Id_88,649
-Order_No_15576,User_Id_3689,Bike,3,Business,9,3,3:11:52 PM,9,3,3:12:27 PM,9,3,3:17:47 PM,9,3,3:23:13 PM,9,3,3:45:22 PM,6,25.3,,-1.2527958000000001,36.8003131,-1.2623805000000001,36.7805404,Rider_Id_205,1329
-Order_No_26964,User_Id_3560,Bike,3,Business,22,5,2:03:02 PM,22,5,2:03:31 PM,22,5,2:21:23 PM,22,5,2:47:39 PM,22,5,2:58:14 PM,8,27.4,,-1.279395,36.825364,-1.2765492,36.7669809,Rider_Id_156,635
-Order_No_18844,User_Id_3557,Bike,3,Business,13,3,8:44:55 AM,13,3,8:46:19 AM,13,3,8:53:13 AM,13,3,9:06:51 AM,13,3,9:26:50 AM,6,20.2,,-1.2729758,36.8284446,-1.2546732,36.808679600000005,Rider_Id_697,1199
-Order_No_19957,User_Id_633,Bike,3,Business,28,3,10:32:54 AM,28,3,10:52:43 AM,28,3,11:03:33 AM,28,3,11:07:24 AM,28,3,11:28:27 AM,11,,,-1.306378,36.7519845,-1.3004062,36.829741,Rider_Id_124,1263
-Order_No_5009,User_Id_2600,Bike,2,Personal,22,3,1:36:38 PM,22,3,1:37:04 PM,22,3,1:45:48 PM,22,3,2:00:26 PM,22,3,2:24:02 PM,8,25.5,,-1.2654199,36.8008276,-1.27339,36.7712735,Rider_Id_882,1416
-Order_No_23864,User_Id_53,Bike,3,Business,30,4,2:05:34 PM,30,4,2:05:51 PM,30,4,2:06:12 PM,30,4,2:34:54 PM,30,4,2:49:30 PM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_751,876
-Order_No_13071,User_Id_2420,Bike,3,Personal,19,2,5:13:46 PM,19,2,5:14:13 PM,19,2,5:24:07 PM,19,2,5:45:15 PM,19,2,5:45:25 PM,4,28.3,,-1.3103512,36.814996,-1.3028118,36.8174764,Rider_Id_686,10
-Order_No_11106,User_Id_3674,Bike,3,Business,15,2,3:31:22 PM,15,2,3:31:39 PM,15,2,3:32:02 PM,15,2,3:35:12 PM,15,2,3:54:18 PM,5,24.4,,-1.2551895,36.7822034,-1.2711982,36.824019899999996,Rider_Id_518,1146
-Order_No_21327,User_Id_1250,Bike,1,Personal,17,2,12:31:15 PM,17,2,12:39:22 PM,17,2,1:02:15 PM,17,2,1:09:10 PM,17,2,1:24:12 PM,5,18.8,,-1.3014461000000002,36.7661381,-1.2675333999999998,36.7658246,Rider_Id_851,902
-Order_No_1628,User_Id_867,Bike,3,Business,22,5,2:19:48 PM,22,5,2:20:26 PM,22,5,2:26:02 PM,22,5,2:30:52 PM,22,5,2:57:19 PM,9,31.0,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_177,1587
-Order_No_16495,User_Id_2714,Bike,1,Personal,11,1,4:57:25 PM,11,1,5:00:39 PM,11,1,5:27:04 PM,11,1,5:33:54 PM,11,1,6:06:33 PM,12,20.9,,-1.3043821000000002,36.7840586,-1.3518598,36.7324661,Rider_Id_614,1959
-Order_No_6880,User_Id_393,Bike,3,Business,17,5,12:02:13 PM,17,5,12:03:17 PM,17,5,12:25:41 PM,17,5,12:42:52 PM,17,5,1:34:58 PM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_2,3126
-Order_No_12252,User_Id_3450,Bike,3,Business,7,2,1:18:54 PM,7,2,1:19:57 PM,7,2,2:08:58 PM,7,2,2:16:25 PM,7,2,2:55:28 PM,14,,,-1.3352545,36.8659639,-1.2657464,36.780114399999995,Rider_Id_911,2343
-Order_No_21725,User_Id_432,Bike,3,Business,2,2,5:13:04 PM,2,2,5:13:55 PM,2,2,5:17:19 PM,2,2,5:36:48 PM,2,2,6:03:11 PM,9,28.4,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_484,1583
-Order_No_4291,User_Id_2066,Bike,3,Business,25,1,2:10:01 PM,25,1,2:12:16 PM,25,1,2:26:06 PM,25,1,2:34:18 PM,25,1,2:52:09 PM,4,27.9,,-1.3210188999999999,36.841055100000005,-1.3263105,36.8622904,Rider_Id_154,1071
-Order_No_18017,User_Id_3443,Bike,3,Personal,19,4,3:18:51 PM,19,4,3:36:41 PM,19,4,4:07:07 PM,19,4,4:12:11 PM,19,4,4:52:15 PM,14,20.3,,-1.318049,36.713231,-1.2875228,36.816767999999996,Rider_Id_79,2404
-Order_No_15214,User_Id_3404,Bike,3,Business,15,6,4:02:56 PM,15,6,4:05:02 PM,15,6,4:29:02 PM,15,6,4:36:40 PM,15,6,4:51:27 PM,2,21.1,,-1.2882183999999999,36.826346,-1.279395,36.825364,Rider_Id_725,887
-Order_No_26076,User_Id_185,Bike,1,Personal,9,1,9:22:25 AM,9,1,9:22:58 AM,9,1,9:27:47 AM,9,1,9:32:39 AM,9,1,9:55:36 AM,15,15.5,,-1.3069127,36.835137200000005,-1.2624998,36.75164229999999,Rider_Id_216,1377
-Order_No_309,User_Id_2229,Bike,3,Business,13,1,4:22:57 PM,13,1,4:25:34 PM,13,1,4:49:17 PM,13,1,4:54:05 PM,13,1,5:15:56 PM,5,,,-1.3247683000000001,36.85803,-1.3324865,36.8938303,Rider_Id_27,1311
-Order_No_3674,User_Id_716,Bike,3,Personal,6,1,11:15:20 AM,6,1,11:40:30 AM,6,1,11:54:46 AM,6,1,11:57:44 AM,6,1,12:22:04 PM,14,20.7,,-1.257309,36.806008399999996,-1.218673,36.8888713,Rider_Id_726,1460
-Order_No_26569,User_Id_1402,Bike,3,Business,18,6,10:06:51 AM,18,6,10:07:15 AM,18,6,10:30:35 AM,18,6,10:36:11 AM,18,6,11:25:09 AM,12,21.4,,-1.2242728999999999,36.8880921,-1.2813012,36.832396200000005,Rider_Id_158,2938
-Order_No_18995,User_Id_701,Bike,3,Personal,13,4,2:34:55 PM,13,4,2:35:07 PM,13,4,2:42:01 PM,13,4,2:47:35 PM,13,4,3:16:03 PM,14,22.3,,-1.2946581,36.779292100000006,-1.3231725,36.864017700000005,Rider_Id_205,1708
-Order_No_10587,User_Id_633,Bike,3,Business,15,5,3:54:05 PM,15,5,3:54:44 PM,15,5,4:07:47 PM,15,5,4:09:05 PM,15,5,4:20:49 PM,4,17.6,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_349,704
-Order_No_19484,User_Id_3291,Bike,3,Business,13,3,3:10:37 PM,13,3,3:16:48 PM,13,3,3:41:34 PM,13,3,4:00:32 PM,13,3,4:22:26 PM,12,29.1,,-1.3259463,36.850258000000004,-1.2600926000000001,36.8088685,Rider_Id_377,1314
-Order_No_11123,User_Id_510,Bike,3,Business,20,1,12:58:08 PM,20,1,1:00:05 PM,20,1,1:01:27 PM,20,1,1:18:57 PM,20,1,1:57:27 PM,15,20.3,,-1.2860183,36.8975338,-1.2668734,36.8054026,Rider_Id_179,2310
-Order_No_24555,User_Id_1245,Bike,3,Business,5,2,12:10:01 PM,5,2,12:10:24 PM,5,2,12:10:26 PM,5,2,1:34:06 PM,5,2,2:04:27 PM,17,27.6,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_595,1821
-Order_No_17689,User_Id_2869,Bike,3,Business,24,5,6:12:25 PM,24,5,6:13:48 PM,24,5,6:46:54 PM,24,5,6:59:29 PM,24,5,7:37:35 PM,9,20.0,,-1.291768,36.8297208,-1.3025826,36.7670807,Rider_Id_567,2286
-Order_No_5663,User_Id_136,Bike,3,Business,5,5,8:41:54 AM,5,5,8:48:15 AM,5,5,9:17:28 AM,5,5,9:44:44 AM,5,5,10:16:06 AM,15,17.8,,-1.3326173000000001,36.71462329999999,-1.3322618,36.8045412,Rider_Id_593,1882
-Order_No_3523,User_Id_2578,Bike,1,Personal,19,5,3:54:49 PM,19,5,4:03:03 PM,19,5,4:19:15 PM,19,5,4:22:26 PM,19,5,4:46:38 PM,5,27.6,,-1.2808667,36.7883545,-1.2939501,36.801818,Rider_Id_708,1452
-Order_No_6781,User_Id_3718,Bike,3,Business,21,3,10:14:51 AM,21,3,10:36:27 AM,21,3,10:50:00 AM,21,3,10:53:56 AM,21,3,11:05:07 AM,5,21.8,,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_744,671
-Order_No_18135,User_Id_1642,Bike,3,Business,25,1,1:57:20 PM,25,1,2:12:14 PM,25,1,2:27:30 PM,25,1,2:30:00 PM,25,1,2:43:28 PM,5,29.9,,-1.2808159,36.8160545,-1.2728069,36.7962362,Rider_Id_910,808
-Order_No_1475,User_Id_3688,Bike,3,Business,21,5,12:45:32 PM,21,5,1:04:23 PM,21,5,1:15:08 PM,21,5,1:18:12 PM,21,5,1:51:56 PM,9,22.8,,-1.2551895,36.7822034,-1.3001735,36.8042049,Rider_Id_548,2024
-Order_No_10906,User_Id_2989,Bike,3,Business,1,4,2:55:39 PM,1,4,2:55:48 PM,1,4,3:06:11 PM,1,4,3:26:47 PM,1,4,3:58:36 PM,18,,,-1.2859912,36.8756811,-1.3463041999999998,36.768164,Rider_Id_699,1909
-Order_No_6265,User_Id_2271,Bike,3,Business,2,4,3:35:51 PM,2,4,3:36:52 PM,2,4,4:22:01 PM,2,4,4:37:12 PM,2,4,5:05:19 PM,12,23.6,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_549,1687
-Order_No_27160,User_Id_2441,Bike,3,Business,11,5,12:53:41 PM,11,5,12:54:01 PM,11,5,1:11:08 PM,11,5,1:12:44 PM,11,5,1:28:37 PM,9,20.7,,-1.2559559999999999,36.789867,-1.2720146,36.8278376,Rider_Id_207,953
-Order_No_12651,User_Id_1075,Bike,3,Business,22,2,11:48:04 AM,22,2,11:48:22 AM,22,2,12:33:48 PM,22,2,12:36:46 PM,22,2,12:42:43 PM,3,24.1,,-1.2527958000000001,36.8003131,-1.2615888000000002,36.7928732,Rider_Id_56,357
-Order_No_8385,User_Id_393,Bike,3,Business,17,1,8:07:49 AM,17,1,10:23:17 AM,17,1,10:48:04 AM,17,1,11:19:28 AM,17,1,11:25:04 AM,4,23.8,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,336
-Order_No_26091,User_Id_2523,Bike,3,Business,23,5,9:30:36 AM,23,5,9:37:02 AM,23,5,9:53:53 AM,23,5,9:57:17 AM,23,5,10:39:59 AM,18,20.3,,-1.2551895,36.7822034,-1.3382857,36.887071500000005,Rider_Id_131,2562
-Order_No_19572,User_Id_868,Bike,3,Business,9,2,1:15:32 PM,9,2,1:15:53 PM,9,2,1:18:59 PM,9,2,1:25:23 PM,9,2,1:39:56 PM,8,27.0,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_438,873
-Order_No_8136,User_Id_3432,Bike,3,Business,10,3,3:52:03 PM,10,3,3:53:16 PM,10,3,4:21:26 PM,10,3,4:29:35 PM,10,3,4:36:38 PM,6,30.6,,-1.3139898,36.867698600000004,-1.3299358000000001,36.8524971,Rider_Id_218,423
-Order_No_13272,User_Id_3222,Bike,3,Business,6,3,2:41:36 PM,6,3,2:41:46 PM,6,3,2:43:40 PM,6,3,2:46:49 PM,6,3,3:13:39 PM,8,27.6,,-1.2551895,36.7822034,-1.3021707,36.816844399999994,Rider_Id_416,1610
-Order_No_2717,User_Id_136,Bike,3,Business,25,5,2:35:46 PM,25,5,2:36:37 PM,25,5,2:50:35 PM,25,5,3:04:53 PM,25,5,3:27:28 PM,8,29.4,,-1.3009676000000001,36.7657189,-1.3165103999999999,36.707772600000006,Rider_Id_831,1355
-Order_No_14924,User_Id_2982,Bike,3,Business,25,1,9:38:46 AM,25,1,9:38:55 AM,25,1,9:49:51 AM,25,1,10:05:16 AM,25,1,10:44:30 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_399,2354
-Order_No_12120,User_Id_346,Bike,3,Business,11,4,3:15:51 PM,11,4,3:37:36 PM,11,4,3:48:51 PM,11,4,3:52:55 PM,11,4,4:04:07 PM,5,27.0,,-1.28878,36.8168312,-1.2922364,36.78955379999999,Rider_Id_438,672
-Order_No_27977,User_Id_2738,Bike,3,Business,11,2,12:38:12 PM,11,2,12:47:43 PM,11,2,1:00:37 PM,11,2,1:12:00 PM,11,2,2:12:25 PM,27,19.4,,-1.3888629,36.935984000000005,-1.2528485,36.7953722,Rider_Id_654,3625
-Order_No_22869,User_Id_1244,Bike,3,Business,10,4,12:42:10 PM,10,4,12:43:15 PM,10,4,12:58:39 PM,10,4,1:07:13 PM,10,4,1:20:55 PM,6,24.8,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_52,822
-Order_No_13397,User_Id_3283,Bike,3,Business,7,4,1:45:08 PM,7,4,1:45:50 PM,7,4,1:51:26 PM,7,4,1:55:27 PM,7,4,2:02:30 PM,4,27.6,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_46,423
-Order_No_20859,User_Id_393,Bike,3,Business,18,4,4:31:50 PM,18,4,4:32:13 PM,18,4,4:43:19 PM,18,4,4:56:24 PM,18,4,4:56:32 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_710,8
-Order_No_632,User_Id_3557,Bike,3,Business,8,1,5:03:00 PM,8,1,5:03:30 PM,8,1,5:08:21 PM,8,1,5:10:59 PM,8,1,5:15:56 PM,2,28.9,,-1.2495931,36.80650870000001,-1.2546732,36.808679600000005,Rider_Id_383,297
-Order_No_10004,User_Id_1927,Bike,3,Business,30,4,5:01:01 PM,30,4,5:08:44 PM,30,4,5:27:37 PM,30,4,5:33:38 PM,30,4,6:06:18 PM,12,,,-1.2886923000000001,36.8241519,-1.3472066,36.7692638,Rider_Id_751,1960
-Order_No_15136,User_Id_3355,Bike,3,Business,15,3,3:45:16 PM,15,3,3:47:50 PM,15,3,4:00:31 PM,15,3,4:20:33 PM,15,3,5:02:57 PM,9,25.9,,-1.290894,36.822971,-1.2859912,36.8756811,Rider_Id_907,2544
-Order_No_26143,User_Id_2896,Bike,3,Business,27,6,9:02:05 AM,27,6,9:02:30 AM,27,6,9:08:12 AM,27,6,9:17:18 AM,27,6,9:35:17 AM,6,,,-1.3201406999999998,36.854043299999994,-1.3338005,36.863026,Rider_Id_263,1079
-Order_No_11958,User_Id_2724,Bike,3,Personal,18,4,10:04:25 AM,18,4,10:05:06 AM,18,4,10:13:06 AM,18,4,10:22:52 AM,18,4,10:41:34 AM,9,,,-1.2665327,36.8108799,-1.3239488000000001,36.831683500000004,Rider_Id_68,1122
-Order_No_27365,User_Id_1102,Bike,3,Business,28,2,12:58:52 PM,28,2,12:59:19 PM,28,2,1:29:47 PM,28,2,1:34:20 PM,28,2,2:01:41 PM,15,19.6,,-1.3004062,36.829741,-1.2630428999999999,36.7518974,Rider_Id_207,1641
-Order_No_25403,User_Id_1120,Bike,3,Personal,20,4,3:44:49 PM,20,4,4:04:25 PM,20,4,4:24:12 PM,20,4,4:32:53 PM,20,4,4:44:00 PM,5,24.8,,-1.2832123,36.826081200000004,-1.2627019,36.7937751,Rider_Id_86,667
-Order_No_6761,User_Id_391,Bike,3,Business,5,2,10:48:25 AM,5,2,10:49:45 AM,5,2,11:08:23 AM,5,2,11:42:40 AM,5,2,11:42:48 AM,5,21.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_297,8
-Order_No_9235,User_Id_1959,Bike,1,Business,7,4,4:38:45 PM,7,4,5:26:07 PM,7,4,5:39:45 PM,7,4,5:46:58 PM,7,4,6:11:05 PM,8,17.7,,-1.289788,36.824911,-1.2551895,36.7822034,Rider_Id_742,1447
-Order_No_3226,User_Id_2150,Bike,3,Business,31,5,9:43:42 AM,31,5,9:44:22 AM,31,5,9:44:30 AM,31,5,10:01:20 AM,31,5,10:12:09 AM,7,,,-1.3188358999999998,36.703852600000005,-1.3444459999999998,36.756597,Rider_Id_855,649
-Order_No_18005,User_Id_498,Bike,1,Personal,20,7,12:42:04 PM,20,7,1:00:12 PM,20,7,1:19:19 PM,20,7,1:23:59 PM,20,7,1:34:26 PM,3,20.0,,-1.3046965,36.8016456,-1.3131897,36.8180215,Rider_Id_956,627
-Order_No_20055,User_Id_913,Bike,3,Personal,30,3,7:06:56 PM,30,3,7:07:28 PM,30,3,7:07:33 PM,30,3,7:16:26 PM,30,3,7:34:13 PM,7,27.2,,-1.3039695,36.783826700000006,-1.2707178999999997,36.7979713,Rider_Id_925,1067
-Order_No_26542,User_Id_2271,Bike,3,Business,2,3,1:24:25 PM,2,3,1:27:12 PM,2,3,1:32:25 PM,2,3,2:00:15 PM,2,3,2:24:51 PM,14,26.2,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_670,1476
-Order_No_23939,User_Id_633,Bike,3,Business,16,6,10:13:32 AM,16,6,10:14:27 AM,16,6,10:19:07 AM,16,6,10:44:44 AM,16,6,10:57:24 AM,5,25.5,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_206,760
-Order_No_12206,User_Id_1809,Bike,3,Business,25,3,2:07:19 PM,25,3,2:07:28 PM,25,3,2:21:12 PM,25,3,2:41:09 PM,25,3,2:49:30 PM,2,17.5,,-1.2527958000000001,36.8003131,-1.2646084,36.8028645,Rider_Id_616,501
-Order_No_16854,User_Id_3291,Bike,3,Business,20,3,3:38:56 PM,20,3,3:50:44 PM,20,3,3:59:41 PM,20,3,4:18:54 PM,20,3,4:28:57 PM,4,29.4,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_780,603
-Order_No_25739,User_Id_2216,Bike,3,Business,3,1,2:32:27 PM,3,1,4:29:32 PM,3,1,4:31:52 PM,3,1,4:45:59 PM,3,1,5:15:14 PM,9,22.6,,-1.2867627000000001,36.883446,-1.3265961000000002,36.8718676,Rider_Id_19,1755
-Order_No_19413,User_Id_3091,Bike,3,Business,28,5,1:59:59 PM,28,5,2:01:55 PM,28,5,2:11:39 PM,28,5,2:24:35 PM,28,5,2:41:07 PM,5,25.4,,-1.3012008,36.764868,-1.2717174,36.775388299999996,Rider_Id_576,992
-Order_No_24945,User_Id_1271,Bike,3,Business,12,3,10:02:46 AM,12,3,10:09:57 AM,12,3,10:13:12 AM,12,3,10:43:11 AM,12,3,11:35:08 AM,19,23.3,,-1.3244885,36.897791999999995,-1.2236966,36.9290782,Rider_Id_413,3117
-Order_No_23812,User_Id_2412,Bike,3,Business,13,1,5:05:23 PM,13,1,5:06:06 PM,13,1,5:09:30 PM,13,1,5:27:36 PM,13,1,5:36:02 PM,11,,,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_137,506
-Order_No_22995,User_Id_2812,Bike,3,Personal,10,7,11:56:50 AM,10,7,11:59:25 AM,10,7,12:15:39 PM,10,7,12:23:57 PM,10,7,12:42:30 PM,9,17.7,,-1.2577288999999998,36.74107720000001,-1.2905046,36.7828277,Rider_Id_334,1113
-Order_No_12335,User_Id_2066,Bike,3,Business,15,1,9:46:08 AM,15,1,9:47:09 AM,15,1,9:55:08 AM,15,1,10:02:23 AM,15,1,10:14:46 AM,5,,,-1.316961,36.820337,-1.2942764,36.7969749,Rider_Id_720,743
-Order_No_2380,User_Id_1850,Bike,3,Business,25,4,3:39:58 PM,25,4,3:45:25 PM,25,4,3:57:12 PM,25,4,4:04:13 PM,25,4,4:22:00 PM,8,,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_710,1067
-Order_No_2107,User_Id_3024,Bike,2,Personal,26,3,10:07:29 AM,26,3,10:07:50 AM,26,3,10:18:53 AM,26,3,10:21:38 AM,26,3,10:45:25 AM,7,19.1,,-1.2535486,36.7933541,-1.2713370000000002,36.786712,Rider_Id_247,1427
-Order_No_1668,User_Id_1502,Bike,3,Business,15,4,9:23:31 AM,15,4,9:25:45 AM,15,4,9:41:14 AM,15,4,9:59:04 AM,15,4,10:33:01 AM,20,19.4,,-1.2232123,36.8932547,-1.3257473,36.859206799999995,Rider_Id_561,2037
-Order_No_7229,User_Id_3194,Bike,3,Business,26,3,10:16:03 AM,26,3,10:16:13 AM,26,3,10:17:22 AM,26,3,10:42:17 AM,26,3,10:53:23 AM,8,23.9,,-1.2551895,36.7822034,-1.2854173999999998,36.7951152,Rider_Id_116,666
-Order_No_18148,User_Id_1961,Bike,3,Business,15,3,3:37:46 PM,15,3,3:38:12 PM,15,3,3:46:02 PM,15,3,3:52:55 PM,15,3,4:27:43 PM,19,25.8,,-1.2600926000000001,36.8088685,-1.3181159,36.710155900000004,Rider_Id_484,2088
-Order_No_3595,User_Id_3175,Bike,3,Business,2,5,4:16:43 PM,2,5,4:18:21 PM,2,5,4:28:10 PM,2,5,4:42:57 PM,2,5,5:18:13 PM,16,23.9,,-1.2217966,36.917575,-1.2947307,36.845738399999995,Rider_Id_217,2116
-Order_No_28015,User_Id_2869,Bike,3,Business,21,4,9:15:26 AM,21,4,9:15:36 AM,21,4,9:50:16 AM,21,4,9:55:00 AM,21,4,10:43:08 AM,20,21.6,,-1.2283403,36.8822756,-1.3014461000000002,36.7661381,Rider_Id_442,2888
-Order_No_12874,User_Id_393,Bike,3,Business,10,1,10:02:50 AM,10,1,10:04:02 AM,10,1,10:16:15 AM,10,1,10:27:28 AM,10,1,10:52:19 AM,11,16.6,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_155,1491
-Order_No_18685,User_Id_2092,Bike,1,Personal,27,3,7:34:28 PM,27,3,7:35:44 PM,27,3,7:46:18 PM,27,3,7:58:10 PM,27,3,8:13:12 PM,5,,,-1.2912918,36.758140999999995,-1.2657709,36.730574600000004,Rider_Id_832,902
-Order_No_2604,User_Id_1592,Bike,1,Personal,28,5,6:34:29 PM,28,5,6:35:40 PM,28,5,6:53:21 PM,28,5,7:11:14 PM,28,5,7:29:54 PM,7,,,-1.3207146,36.8305771,-1.3291155000000001,36.8861,Rider_Id_85,1120
-Order_No_10308,User_Id_733,Bike,3,Business,29,1,3:00:01 PM,29,1,3:01:14 PM,29,1,3:08:47 PM,29,1,3:12:02 PM,29,1,3:43:50 PM,8,,,-1.2551895,36.7822034,-1.290641,36.812940999999995,Rider_Id_674,1908
-Order_No_25864,User_Id_20,Bike,3,Business,9,3,11:57:05 AM,9,3,11:57:21 AM,9,3,12:01:09 PM,9,3,12:08:24 PM,9,3,12:59:09 PM,8,21.0,,-1.3090533000000002,36.8371502,-1.2629666000000002,36.8068369,Rider_Id_100,3045
-Order_No_26442,User_Id_1612,Bike,1,Personal,8,5,9:15:22 AM,8,5,9:54:52 AM,8,5,10:17:08 AM,8,5,10:24:37 AM,8,5,10:51:56 AM,9,15.4,1.0,-1.260232,36.807334000000004,-1.3066143,36.8161046,Rider_Id_908,1639
-Order_No_1279,User_Id_346,Bike,3,Business,29,5,1:22:28 PM,29,5,1:23:22 PM,29,5,1:57:04 PM,29,5,2:05:42 PM,29,5,2:23:03 PM,7,29.1,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_907,1041
-Order_No_11077,User_Id_3264,Bike,3,Business,24,1,9:44:11 AM,24,1,9:48:20 AM,24,1,9:58:13 AM,24,1,10:03:14 AM,24,1,10:25:34 AM,9,15.7,,-1.2551895,36.7822034,-1.2963316,36.7863289,Rider_Id_531,1340
-Order_No_22119,User_Id_868,Bike,3,Business,25,1,3:21:36 PM,25,1,3:22:08 PM,25,1,3:23:16 PM,25,1,4:06:23 PM,25,1,4:17:52 PM,5,28.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_332,689
-Order_No_946,User_Id_1329,Bike,3,Business,27,3,12:32:33 PM,27,3,12:33:18 PM,27,3,12:37:30 PM,27,3,12:39:26 PM,27,3,12:52:03 PM,5,26.0,,-1.273056,36.811298,-1.2950408000000002,36.809817200000005,Rider_Id_783,757
-Order_No_19602,User_Id_758,Bike,3,Business,29,3,4:20:33 PM,29,3,4:24:40 PM,29,3,4:41:43 PM,29,3,4:46:56 PM,29,3,5:03:01 PM,7,21.3,,-1.2917868000000001,36.7872675,-1.2684319,36.81658529999999,Rider_Id_146,965
-Order_No_11793,User_Id_978,Bike,1,Personal,25,3,12:13:18 PM,25,3,12:15:00 PM,25,3,12:37:44 PM,25,3,12:56:23 PM,25,3,1:39:47 PM,5,,,-1.3084067,36.83450139999999,-1.2767856000000002,36.8285595,Rider_Id_616,2604
-Order_No_16104,User_Id_393,Bike,3,Business,28,4,9:25:55 AM,28,4,9:26:05 AM,28,4,9:26:21 AM,28,4,9:42:21 AM,28,4,10:15:38 AM,14,19.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_349,1997
-Order_No_698,User_Id_1500,Bike,3,Business,8,5,11:36:59 AM,8,5,11:41:22 AM,8,5,12:02:34 PM,8,5,12:20:46 PM,8,5,12:28:14 PM,6,28.4,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_119,448
-Order_No_23901,User_Id_1244,Bike,3,Business,12,6,12:25:40 PM,12,6,12:26:04 PM,12,6,12:26:14 PM,12,6,12:36:08 PM,12,6,1:07:04 PM,6,,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_828,1856
-Order_No_1044,User_Id_393,Bike,3,Business,18,5,11:37:51 AM,18,5,11:38:00 AM,18,5,11:41:25 AM,18,5,12:03:02 PM,18,5,12:15:40 PM,4,20.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_188,758
-Order_No_20100,User_Id_2112,Bike,3,Business,25,1,4:40:29 PM,25,1,4:40:38 PM,25,1,4:41:08 PM,25,1,5:06:14 PM,25,1,5:36:49 PM,5,27.7,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_649,1835
-Order_No_14315,User_Id_309,Bike,3,Personal,1,2,6:48:24 PM,1,2,7:04:54 PM,1,2,7:28:51 PM,1,2,7:36:56 PM,1,2,7:51:09 PM,10,18.6,,-1.3314506000000002,36.8408164,-1.2750415000000002,36.7965813,Rider_Id_404,853
-Order_No_17246,User_Id_1209,Bike,3,Business,3,5,4:13:19 PM,3,5,4:17:35 PM,3,5,4:19:21 PM,3,5,4:33:01 PM,3,5,4:33:03 PM,4,,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,2
-Order_No_10796,User_Id_1874,Bike,3,Business,23,5,10:05:58 AM,23,5,10:15:31 AM,23,5,10:24:22 AM,23,5,10:33:33 AM,23,5,10:56:42 AM,16,24.0,,-1.2571472,36.795063299999995,-1.2434951,36.6760986,Rider_Id_595,1389
-Order_No_2593,User_Id_2457,Bike,3,Business,18,1,9:40:36 AM,18,1,9:46:09 AM,18,1,9:53:00 AM,18,1,9:56:34 AM,18,1,10:25:25 AM,8,15.2,0.1,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_409,1731
-Order_No_20962,User_Id_3544,Bike,2,Personal,18,5,6:52:38 PM,18,5,7:28:20 PM,18,5,7:42:19 PM,18,5,8:01:32 PM,18,5,8:21:28 PM,4,19.3,,-1.25537,36.7756,-1.271377,36.7884294,Rider_Id_103,1196
-Order_No_15951,User_Id_1583,Bike,3,Business,8,2,11:50:24 AM,8,2,11:50:51 AM,8,2,12:02:14 PM,8,2,12:05:20 PM,8,2,12:58:15 PM,20,21.3,,-1.2998129999999999,36.787139200000006,-1.3700383,36.9190174,Rider_Id_543,3175
-Order_No_10966,User_Id_1500,Bike,3,Business,8,2,1:35:08 PM,8,2,1:37:35 PM,8,2,1:55:16 PM,8,2,1:59:32 PM,8,2,2:19:05 PM,11,25.3,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_576,1173
-Order_No_10290,User_Id_2691,Bike,3,Business,11,4,11:27:36 AM,11,4,11:28:42 AM,11,4,11:39:54 AM,11,4,11:45:19 AM,11,4,12:03:10 PM,8,24.0,,-1.2857671,36.7875614,-1.2571472,36.795063299999995,Rider_Id_933,1071
-Order_No_26428,User_Id_838,Bike,3,Business,19,2,10:08:49 AM,19,2,10:25:32 AM,19,2,10:58:51 AM,19,2,11:12:21 AM,19,2,12:29:49 PM,33,27.3,,-1.1648303,36.951368,-1.329568,36.8520825,Rider_Id_447,4648
-Order_No_19882,User_Id_1500,Bike,3,Business,15,1,4:01:16 PM,15,1,4:03:34 PM,15,1,4:09:48 PM,15,1,4:33:27 PM,15,1,4:37:54 PM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_812,267
-Order_No_26691,User_Id_2174,Bike,3,Business,12,5,12:43:51 PM,12,5,12:44:08 PM,12,5,12:44:19 PM,12,5,1:56:58 PM,12,5,3:06:24 PM,14,,,-1.2482781,36.884091999999995,-1.2016076999999998,36.930397,Rider_Id_86,4166
-Order_No_15769,User_Id_868,Bike,3,Business,1,1,8:48:42 AM,1,1,8:48:58 AM,1,1,8:55:53 AM,1,1,9:13:02 AM,1,1,9:21:15 AM,4,21.3,,-1.266837,36.799248999999996,-1.265715,36.823815,Rider_Id_427,493
-Order_No_3877,User_Id_967,Bike,3,Personal,26,2,3:20:08 PM,26,2,3:20:48 PM,26,2,3:37:35 PM,26,2,3:43:04 PM,26,2,4:10:07 PM,8,29.5,,-1.2795261,36.826018499999996,-1.3193416,36.793060499999996,Rider_Id_341,1623
-Order_No_9007,User_Id_1500,Bike,3,Business,28,5,2:23:25 PM,28,5,2:32:01 PM,28,5,2:39:10 PM,28,5,2:40:31 PM,28,5,3:01:12 PM,11,25.7,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_188,1241
-Order_No_27910,User_Id_3266,Bike,3,Business,30,1,9:30:48 AM,30,1,9:40:05 AM,30,1,9:41:02 AM,30,1,9:58:11 AM,30,1,10:31:11 AM,15,16.3,2.0,-1.3495948,36.7190665,-1.2885417,36.810052299999995,Rider_Id_726,1980
-Order_No_11608,User_Id_3091,Bike,3,Business,29,4,2:57:22 PM,29,4,5:13:39 PM,29,4,5:18:27 PM,29,4,5:29:25 PM,29,4,5:52:36 PM,9,20.9,,-1.290894,36.822971,-1.3012008,36.764868,Rider_Id_124,1391
-Order_No_24378,User_Id_391,Bike,3,Business,2,4,9:00:44 AM,2,4,9:01:45 AM,2,4,9:13:52 AM,2,4,9:57:15 AM,2,4,10:19:22 AM,16,,,-1.2089041999999999,36.7699002,-1.2867454,36.8259017,Rider_Id_193,1327
-Order_No_7461,User_Id_2436,Bike,3,Business,13,4,4:44:22 PM,13,4,4:45:45 PM,13,4,4:56:49 PM,13,4,4:59:46 PM,13,4,5:51:23 PM,7,24.4,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_727,3097
-Order_No_10029,User_Id_842,Bike,2,Personal,11,1,9:48:40 AM,11,1,10:04:26 AM,11,1,10:37:41 AM,11,1,10:43:51 AM,11,1,12:16:54 PM,5,18.0,,-1.296817,36.7884565,-1.2934656999999998,36.8212737,Rider_Id_844,5583
-Order_No_1234,User_Id_391,Bike,3,Business,20,4,2:00:05 PM,20,4,2:08:23 PM,20,4,2:08:46 PM,20,4,2:50:09 PM,20,4,4:01:39 PM,18,25.4,,-1.3882008,36.7699445,-1.2867454,36.8259017,Rider_Id_728,4290
-Order_No_10644,User_Id_2982,Bike,3,Business,13,4,8:46:05 AM,13,4,9:01:25 AM,13,4,9:29:49 AM,13,4,9:54:20 AM,13,4,10:34:37 AM,18,18.3,1.9,-1.177202,36.8351183,-1.300921,36.828195,Rider_Id_31,2417
-Order_No_24776,User_Id_552,Bike,3,Business,24,1,2:52:26 PM,24,1,2:53:12 PM,24,1,3:06:20 PM,24,1,3:09:00 PM,24,1,3:35:31 PM,17,24.8,,-1.279395,36.825364,-1.3632162,36.9108253,Rider_Id_194,1591
-Order_No_23986,User_Id_71,Bike,3,Business,9,5,12:48:54 PM,9,5,12:55:18 PM,9,5,1:06:45 PM,9,5,1:11:40 PM,9,5,1:27:02 PM,4,25.5,,-1.2765736,36.851364600000004,-1.2891921999999998,36.825189,Rider_Id_554,922
-Order_No_2821,User_Id_650,Bike,1,Personal,5,4,1:14:35 PM,5,4,1:16:16 PM,5,4,1:38:17 PM,5,4,2:06:47 PM,5,4,2:23:41 PM,5,22.1,,-1.2971709999999999,36.783278,-1.2713370000000002,36.786712,Rider_Id_844,1014
-Order_No_24557,User_Id_393,Bike,3,Business,25,1,11:37:33 AM,25,1,11:37:48 AM,25,1,11:37:52 AM,25,1,11:57:27 AM,25,1,11:57:32 AM,4,27.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_25,5
-Order_No_11558,User_Id_1384,Bike,3,Business,30,4,4:33:33 PM,30,4,4:39:11 PM,30,4,4:48:55 PM,30,4,4:57:07 PM,30,4,5:47:43 PM,8,,,-1.2517448,36.7924971,-1.2707534,36.837893,Rider_Id_930,3036
-Order_No_7359,User_Id_14,Bike,2,Personal,14,4,8:25:48 AM,14,4,8:44:19 AM,14,4,8:50:50 AM,14,4,8:57:38 AM,14,4,9:17:35 AM,3,15.7,,-1.3154433,36.806385999999996,-1.3070122,36.8287218,Rider_Id_635,1197
-Order_No_15879,User_Id_2956,Bike,3,Business,9,3,2:16:39 PM,9,3,2:17:59 PM,9,3,2:24:32 PM,9,3,2:29:32 PM,9,3,2:29:53 PM,2,23.5,,-1.291768,36.8297208,-1.2820175,36.8223974,Rider_Id_358,21
-Order_No_17123,User_Id_1990,Bike,3,Business,31,5,4:05:08 PM,31,5,4:10:12 PM,31,5,4:49:14 PM,31,5,5:01:46 PM,31,5,6:20:09 PM,20,23.5,,-1.3294489999999999,36.890451,-1.275442,36.766020700000006,Rider_Id_25,4703
-Order_No_24896,User_Id_2330,Bike,3,Business,31,3,1:28:43 PM,31,3,1:28:48 PM,31,3,1:36:03 PM,31,3,1:43:21 PM,31,3,1:55:27 PM,9,27.1,,-1.257901,36.79843820000001,-1.2681093,36.7438917,Rider_Id_237,726
-Order_No_5156,User_Id_1537,Bike,3,Personal,19,4,11:36:43 AM,19,4,11:48:15 AM,19,4,12:27:11 PM,19,4,12:31:42 PM,19,4,1:10:22 PM,6,20.3,,-1.2812458999999998,36.8302055,-1.3117438999999997,36.8108257,Rider_Id_752,2320
-Order_No_8611,User_Id_1693,Bike,1,Business,22,2,3:29:36 PM,22,2,3:45:04 PM,22,2,3:54:16 PM,22,2,4:09:04 PM,22,2,4:20:59 PM,4,21.3,,-1.2903886,36.8063847,-1.2729324,36.811562200000004,Rider_Id_726,715
-Order_No_16342,User_Id_1205,Bike,3,Business,13,1,9:56:13 AM,13,1,9:56:44 AM,13,1,10:21:15 AM,13,1,10:34:07 AM,13,1,10:59:21 AM,9,17.0,,-1.2707742,36.7578609,-1.2989933999999999,36.813669399999995,Rider_Id_726,1514
-Order_No_18674,User_Id_867,Bike,3,Business,11,4,12:14:33 PM,11,4,12:20:51 PM,11,4,12:32:49 PM,11,4,1:13:18 PM,11,4,1:45:51 PM,15,24.0,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_606,1953
-Order_No_25990,User_Id_2587,Bike,3,Business,21,5,10:59:20 AM,21,5,11:03:43 AM,21,5,11:27:58 AM,21,5,11:39:58 AM,21,5,12:47:19 PM,12,22.7,,-1.2859912,36.8756811,-1.28878,36.8168312,Rider_Id_404,4041
-Order_No_22536,User_Id_1384,Bike,3,Business,9,2,4:52:26 PM,9,2,4:52:36 PM,9,2,5:06:49 PM,9,2,5:12:35 PM,9,2,5:39:17 PM,13,28.0,,-1.2517448,36.7924971,-1.2518265,36.8697426,Rider_Id_89,1602
-Order_No_13481,User_Id_2952,Bike,1,Personal,7,5,2:06:19 PM,7,5,2:55:54 PM,7,5,3:01:46 PM,7,5,3:10:59 PM,7,5,3:46:17 PM,9,24.8,,-1.2629818000000002,36.8135865,-1.302546,36.7773717,Rider_Id_789,2118
-Order_No_27324,User_Id_2066,Bike,3,Business,5,4,3:34:30 PM,5,4,3:35:12 PM,5,4,3:50:16 PM,5,4,3:51:55 PM,5,4,4:26:35 PM,14,22.2,,-1.316961,36.820337,-1.3123977,36.902807,Rider_Id_347,2080
-Order_No_16499,User_Id_378,Bike,2,Personal,1,6,3:28:17 PM,1,6,3:33:07 PM,1,6,3:48:54 PM,1,6,3:54:38 PM,1,6,4:12:34 PM,5,19.6,,-1.2862718,36.824329,-1.2543163999999998,36.831860999999996,Rider_Id_400,1076
-Order_No_2768,User_Id_909,Bike,3,Business,25,4,2:29:39 PM,25,4,2:30:10 PM,25,4,2:42:31 PM,25,4,2:52:44 PM,25,4,3:19:04 PM,13,,,-1.225322,36.80855,-1.2506561,36.7522989,Rider_Id_72,1580
-Order_No_10195,User_Id_1148,Bike,3,Business,4,2,12:24:50 PM,4,2,12:25:10 PM,4,2,12:33:59 PM,4,2,12:51:35 PM,4,2,1:08:56 PM,10,19.0,,-1.3100586,36.850032899999995,-1.2676758999999997,36.801804,Rider_Id_116,1041
-Order_No_17580,User_Id_970,Bike,3,Business,13,2,10:00:32 AM,13,2,10:36:14 AM,13,2,10:46:46 AM,13,2,10:50:12 AM,13,2,10:53:43 AM,3,22.5,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_932,211
-Order_No_17008,User_Id_2030,Bike,3,Business,13,3,1:54:10 PM,13,3,1:54:23 PM,13,3,2:03:52 PM,13,3,2:13:37 PM,13,3,2:23:17 PM,1,29.0,,-1.2628638,36.8070399,-1.2601197,36.7985271,Rider_Id_575,580
-Order_No_3653,User_Id_75,Bike,2,Personal,28,5,11:37:34 AM,28,5,11:38:07 AM,28,5,11:47:40 AM,28,5,11:54:00 AM,28,5,12:07:50 PM,7,25.2,,-1.2939132,36.8081606,-1.3014462,36.7607695,Rider_Id_412,830
-Order_No_5952,User_Id_2978,Bike,3,Business,21,4,3:05:31 PM,21,4,3:08:13 PM,21,4,3:20:12 PM,21,4,3:21:40 PM,21,4,3:41:19 PM,7,26.4,,-1.2551895,36.7822034,-1.2812264,36.8204146,Rider_Id_330,1179
-Order_No_11434,User_Id_3365,Bike,2,Personal,19,3,2:42:23 PM,19,3,3:28:04 PM,19,3,3:38:16 PM,19,3,4:00:26 PM,19,3,5:16:35 PM,9,25.4,,-1.3197098999999999,36.831231200000005,-1.2684933999999999,36.8249394,Rider_Id_179,4569
-Order_No_15932,User_Id_478,Bike,3,Business,21,2,1:45:19 PM,21,2,1:46:13 PM,21,2,1:57:50 PM,21,2,2:00:11 PM,21,2,2:27:10 PM,11,24.8,,-1.2551895,36.7822034,-1.3142701,36.853769299999996,Rider_Id_548,1619
-Order_No_3769,User_Id_3547,Bike,3,Business,4,4,11:13:38 AM,4,4,11:15:21 AM,4,4,11:28:47 AM,4,4,11:35:34 AM,4,4,12:32:46 PM,6,25.3,,-1.2761408,36.7710839,-1.2592428,36.7894541,Rider_Id_119,3432
-Order_No_16843,User_Id_1882,Bike,3,Business,16,6,2:54:33 PM,16,6,2:55:08 PM,16,6,3:03:57 PM,16,6,3:32:17 PM,16,6,3:45:09 PM,6,28.6,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_855,772
-Order_No_18502,User_Id_2685,Bike,1,Personal,22,6,10:03:29 AM,22,6,10:03:45 AM,22,6,10:28:25 AM,22,6,10:44:02 AM,22,6,11:26:19 AM,28,21.3,,-1.3433181,36.9138018,-1.3329386,36.7087809,Rider_Id_504,2537
-Order_No_8576,User_Id_3537,Bike,2,Personal,4,3,8:34:39 AM,4,3,8:51:30 AM,4,3,9:18:08 AM,4,3,9:25:33 AM,4,3,9:52:32 AM,5,15.1,,-1.2881035,36.8142095,-1.2749209,36.7857781,Rider_Id_208,1619
-Order_No_12994,User_Id_265,Bike,3,Business,12,2,10:44:05 AM,12,2,10:44:38 AM,12,2,11:11:15 AM,12,2,11:23:18 AM,12,2,11:54:52 AM,14,28.0,,-1.2171851999999999,36.8145508,-1.300921,36.828195,Rider_Id_882,1894
-Order_No_15952,User_Id_1766,Bike,2,Personal,7,5,9:11:59 AM,7,5,9:12:29 AM,7,5,9:19:45 AM,7,5,9:33:24 AM,7,5,9:41:37 AM,2,18.4,,-1.2580991000000001,36.794151899999996,-1.2663135,36.7973895,Rider_Id_882,493
-Order_No_13859,User_Id_2987,Bike,3,Business,8,3,4:25:03 PM,8,3,4:25:37 PM,8,3,4:34:39 PM,8,3,4:41:27 PM,8,3,4:51:19 PM,3,24.1,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_427,592
-Order_No_25377,User_Id_2091,Bike,3,Personal,23,4,10:28:24 AM,23,4,10:29:38 AM,23,4,10:38:32 AM,23,4,10:55:10 AM,23,4,11:17:59 AM,10,18.6,,-1.2731645,36.7902246,-1.3047606,36.7361825,Rider_Id_279,1369
-Order_No_598,User_Id_2030,Bike,3,Business,7,5,11:13:34 AM,7,5,11:17:10 AM,7,5,11:25:00 AM,7,5,11:33:16 AM,7,5,11:52:04 AM,5,25.2,,-1.3100586,36.850032899999995,-1.2965799999999998,36.823699,Rider_Id_685,1128
-Order_No_17279,User_Id_2763,Bike,3,Business,22,2,2:36:44 PM,22,2,2:36:50 PM,22,2,3:01:21 PM,22,2,3:04:21 PM,22,2,3:07:59 PM,2,25.8,,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_205,218
-Order_No_25602,User_Id_1850,Bike,3,Business,11,2,10:16:37 AM,11,2,10:36:21 AM,11,2,10:57:01 AM,11,2,11:04:17 AM,11,2,11:18:42 AM,7,19.2,,-1.2551895,36.7822034,-1.2785434,36.821532700000006,Rider_Id_790,865
-Order_No_24659,User_Id_1880,Bike,3,Business,17,4,11:14:29 AM,17,4,11:14:48 AM,17,4,11:17:33 AM,17,4,11:24:37 AM,17,4,11:34:26 AM,7,25.2,,-1.2571472,36.795063299999995,-1.2935403,36.7796304,Rider_Id_881,589
-Order_No_20819,User_Id_635,Bike,3,Business,26,5,9:04:07 AM,26,5,9:04:40 AM,26,5,9:10:35 AM,26,5,9:17:34 AM,26,5,9:34:48 AM,6,,,-1.2602337,36.79905479999999,-1.2925023000000002,36.8259661,Rider_Id_291,1034
-Order_No_16450,User_Id_2330,Bike,3,Business,16,3,10:26:54 AM,16,3,10:27:25 AM,16,3,10:32:39 AM,16,3,10:39:50 AM,16,3,10:51:56 AM,6,22.7,,-1.3015197,36.7658456,-1.2915246999999999,36.805575700000006,Rider_Id_246,726
-Order_No_8782,User_Id_3674,Bike,3,Business,26,2,11:10:04 AM,26,2,11:10:38 AM,26,2,11:19:33 AM,26,2,11:22:29 AM,26,2,11:37:39 AM,4,26.4,,-1.2551895,36.7822034,-1.2663525,36.812833700000006,Rider_Id_49,910
-Order_No_12118,User_Id_3647,Bike,3,Business,29,3,1:03:12 PM,29,3,1:03:53 PM,29,3,1:06:10 PM,29,3,1:28:46 PM,29,3,1:28:49 PM,1,,,-1.2726389999999999,36.794723,-1.2709841,36.782452299999996,Rider_Id_855,3
-Order_No_19059,User_Id_296,Bike,3,Business,22,5,11:44:41 AM,22,5,11:45:00 AM,22,5,11:53:55 AM,22,5,11:58:11 AM,22,5,12:39:09 PM,20,21.5,,-1.2579303999999998,36.8019667,-1.3700383,36.9190174,Rider_Id_192,2458
-Order_No_7974,User_Id_2149,Bike,1,Personal,19,5,6:01:24 PM,19,5,6:02:39 PM,19,5,6:08:26 PM,19,5,6:13:31 PM,19,5,6:35:54 PM,11,24.2,,-1.2675332000000001,36.808561,-1.2127344999999998,36.7903289,Rider_Id_116,1343
-Order_No_12191,User_Id_53,Bike,3,Business,1,5,2:32:47 PM,1,5,2:37:55 PM,1,5,3:01:12 PM,1,5,3:07:27 PM,1,5,3:44:41 PM,5,32.1,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_17,2234
-Order_No_19768,User_Id_1329,Bike,3,Business,30,2,10:45:16 AM,30,2,10:46:12 AM,30,2,10:57:24 AM,30,2,11:01:15 AM,30,2,11:15:56 AM,3,22.4,,-1.273056,36.811298,-1.2767473,36.80953710000001,Rider_Id_580,881
-Order_No_17351,User_Id_1724,Bike,3,Business,7,3,1:06:31 PM,7,3,1:23:08 PM,7,3,1:40:29 PM,7,3,1:58:38 PM,7,3,2:18:55 PM,12,28.5,,-1.2941648000000001,36.7977315,-1.32189,36.8589727,Rider_Id_744,1217
-Order_No_22956,User_Id_1271,Bike,3,Business,28,3,11:22:13 AM,28,3,11:38:21 AM,28,3,11:48:23 AM,28,3,12:04:22 PM,28,3,1:02:16 PM,23,,,-1.3244885,36.897791999999995,-1.2022216000000001,36.9198343,Rider_Id_712,3474
-Order_No_21155,User_Id_3669,Bike,3,Business,24,4,12:10:10 PM,24,4,12:11:17 PM,24,4,12:23:30 PM,24,4,12:24:38 PM,24,4,12:50:21 PM,6,24.3,,-1.2615888000000002,36.7928732,-1.286553,36.8273374,Rider_Id_511,1543
-Order_No_19468,User_Id_1402,Bike,3,Business,14,2,3:48:02 PM,14,2,3:51:17 PM,14,2,4:07:14 PM,14,2,4:20:26 PM,14,2,5:12:36 PM,12,,,-1.2813012,36.832396200000005,-1.3091733,36.740809999999996,Rider_Id_247,3130
-Order_No_6010,User_Id_3091,Bike,3,Business,4,4,10:29:32 AM,4,4,10:40:20 AM,4,4,10:50:35 AM,4,4,11:00:35 AM,4,4,11:39:52 AM,14,25.5,,-1.2283403,36.8822756,-1.290894,36.822971,Rider_Id_7,2357
-Order_No_11710,User_Id_3372,Bike,3,Business,3,5,9:50:42 AM,3,5,9:50:48 AM,3,5,9:53:48 AM,3,5,9:56:57 AM,3,5,10:37:09 AM,18,17.5,,-1.304033,36.7848695,-1.2143386999999999,36.7914548,Rider_Id_414,2412
-Order_No_515,User_Id_1363,Bike,3,Business,3,4,3:02:30 PM,3,4,3:11:55 PM,3,4,3:20:12 PM,3,4,3:24:26 PM,3,4,3:36:13 PM,5,24.8,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_479,707
-Order_No_970,User_Id_3434,Bike,3,Business,9,5,12:24:20 PM,9,5,12:24:49 PM,9,5,12:36:24 PM,9,5,12:44:29 PM,9,5,1:55:54 PM,28,25.8,,-1.2963097,36.7688221,-1.1612808000000001,36.9563278,Rider_Id_958,4285
-Order_No_2294,User_Id_3291,Bike,3,Business,27,6,10:28:00 AM,27,6,10:28:39 AM,27,6,10:40:29 AM,27,6,10:45:52 AM,27,6,11:14:54 AM,17,,,-1.3444459999999998,36.756597,-1.2600926000000001,36.8088685,Rider_Id_790,1742
-Order_No_22999,User_Id_71,Bike,3,Business,28,1,12:44:00 PM,28,1,12:44:13 PM,28,1,12:55:44 PM,28,1,12:58:20 PM,28,1,1:26:41 PM,8,23.9,,-1.2813012,36.832396200000005,-1.2571472,36.795063299999995,Rider_Id_365,1701
-Order_No_23693,User_Id_948,Bike,3,Business,25,4,11:34:44 AM,25,4,11:36:51 AM,25,4,11:51:16 AM,25,4,12:27:28 PM,25,4,12:41:09 PM,7,,,-1.2959812,36.797232,-1.2696908000000002,36.81519,Rider_Id_855,821
-Order_No_13388,User_Id_2763,Bike,3,Business,16,3,11:15:31 AM,16,3,11:16:23 AM,16,3,11:24:36 AM,16,3,11:48:41 AM,16,3,12:18:54 PM,12,22.7,,-1.3215393999999998,36.836621,-1.2551895,36.7822034,Rider_Id_523,1813
-Order_No_3539,User_Id_1423,Bike,1,Personal,17,4,6:11:25 PM,17,4,6:21:57 PM,17,4,6:43:31 PM,17,4,7:09:09 PM,17,4,7:30:24 PM,12,21.8,,-1.2025741,36.786551700000004,-1.2701978999999999,36.8138177,Rider_Id_623,1275
-Order_No_20895,User_Id_2154,Bike,3,Business,3,4,9:58:55 AM,3,4,10:19:15 AM,3,4,10:33:16 AM,3,4,10:43:49 AM,3,4,11:11:32 AM,6,21.8,,-1.3036637,36.7853135,-1.2885528999999998,36.816014100000004,Rider_Id_753,1663
-Order_No_14581,User_Id_1642,Bike,3,Business,25,2,3:16:45 PM,25,2,3:17:06 PM,25,2,3:23:52 PM,25,2,3:28:57 PM,25,2,3:38:26 PM,3,27.2,,-1.2672782,36.804143599999996,-1.2726506000000002,36.7960153,Rider_Id_434,569
-Order_No_12682,User_Id_540,Bike,3,Business,9,2,9:10:04 AM,9,2,9:10:24 AM,9,2,9:10:27 AM,9,2,9:27:49 AM,9,2,9:35:04 AM,5,20.9,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_291,435
-Order_No_27069,User_Id_3777,Bike,1,Personal,25,1,7:28:44 PM,25,1,7:30:45 PM,25,1,7:35:55 PM,25,1,7:46:12 PM,25,1,7:58:05 PM,6,,,-1.2925792,36.790690000000005,-1.267452,36.806015,Rider_Id_86,713
-Order_No_19024,User_Id_2066,Bike,3,Business,22,5,11:42:09 AM,22,5,11:42:22 AM,22,5,11:55:40 AM,22,5,11:59:54 AM,22,5,12:24:41 PM,9,,,-1.3210188999999999,36.841055100000005,-1.3044764,36.8843623,Rider_Id_25,1487
-Order_No_1712,User_Id_1329,Bike,3,Business,14,5,4:39:33 PM,14,5,4:40:17 PM,14,5,4:47:53 PM,14,5,4:49:38 PM,14,5,4:57:36 PM,3,23.1,,-1.273056,36.811298,-1.2639556,36.798809399999996,Rider_Id_925,478
-Order_No_14090,User_Id_3283,Bike,3,Business,12,6,11:10:01 AM,12,6,11:10:32 AM,12,6,11:20:01 AM,12,6,11:25:24 AM,12,6,11:47:51 AM,7,,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_79,1347
-Order_No_25808,User_Id_2330,Bike,3,Business,31,5,5:59:56 PM,31,5,6:02:39 PM,31,5,6:08:47 PM,31,5,6:10:06 PM,31,5,6:29:47 PM,8,23.5,,-1.2536275,36.798793200000006,-1.2336843000000002,36.800725899999996,Rider_Id_139,1181
-Order_No_11253,User_Id_3372,Bike,3,Business,9,1,11:07:06 AM,9,1,11:07:32 AM,9,1,11:08:38 AM,9,1,11:22:36 AM,9,1,11:48:22 AM,11,20.1,,-1.3169591,36.7000029,-1.2969741000000001,36.7856612,Rider_Id_101,1546
-Order_No_3163,User_Id_393,Bike,3,Business,14,3,2:44:40 PM,14,3,3:10:13 PM,14,3,3:10:19 PM,14,3,3:43:06 PM,14,3,4:07:22 PM,10,25.7,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_97,1456
-Order_No_6217,User_Id_3647,Bike,3,Business,20,1,11:23:10 AM,20,1,11:23:56 AM,20,1,11:33:52 AM,20,1,11:40:46 AM,20,1,11:50:26 AM,4,,,-1.2726389999999999,36.794723,-1.2786915,36.7633571,Rider_Id_56,580
-Order_No_10220,User_Id_111,Bike,3,Business,21,3,3:16:25 PM,21,3,3:31:36 PM,21,3,3:43:58 PM,21,3,3:55:04 PM,21,3,4:08:09 PM,3,23.0,3.1,-1.2551895,36.7822034,-1.2715686000000002,36.8023526,Rider_Id_57,785
-Order_No_15663,User_Id_2132,Bike,3,Business,17,3,10:44:20 AM,17,3,11:13:05 AM,17,3,11:33:49 AM,17,3,11:40:34 AM,17,3,12:16:28 PM,13,25.5,,-1.2384056,36.8718698,-1.3172593000000001,36.8558936,Rider_Id_775,2154
-Order_No_17233,User_Id_2803,Bike,3,Business,14,3,9:29:32 AM,14,3,9:30:02 AM,14,3,9:53:04 AM,14,3,10:06:00 AM,14,3,10:37:54 AM,17,18.3,,-1.269767,36.7351068,-1.2765736,36.851364600000004,Rider_Id_536,1914
-Order_No_2610,User_Id_2069,Bike,3,Business,29,2,10:36:08 AM,29,2,10:38:19 AM,29,2,10:51:22 AM,29,2,11:12:51 AM,29,2,11:30:57 AM,9,23.7,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_332,1086
-Order_No_20012,User_Id_138,Bike,3,Business,27,3,3:27:43 PM,27,3,3:32:25 PM,27,3,4:23:33 PM,27,3,4:28:01 PM,27,3,5:02:07 PM,7,29.7,,-1.2615888000000002,36.7928732,-1.2813012,36.832396200000005,Rider_Id_855,2046
-Order_No_18555,User_Id_1191,Bike,1,Personal,23,4,10:58:17 AM,23,4,11:02:36 AM,23,4,11:26:00 AM,23,4,11:34:35 AM,23,4,11:46:19 AM,4,18.3,,-1.3282758,36.8590281,-1.32589,36.831679,Rider_Id_777,704
-Order_No_6423,User_Id_1363,Bike,3,Business,4,4,2:49:51 PM,4,4,2:50:01 PM,4,4,2:51:07 PM,4,4,3:03:08 PM,4,4,3:41:48 PM,13,27.4,,-1.3004062,36.829741,-1.238805,36.884373,Rider_Id_871,2320
-Order_No_4089,User_Id_3435,Bike,1,Business,3,3,12:08:37 PM,3,3,12:27:46 PM,3,3,1:03:01 PM,3,3,1:28:39 PM,3,3,2:16:40 PM,24,24.1,,-1.3454328000000002,36.66478060000001,-1.2696341,36.8042045,Rider_Id_88,2881
-Order_No_14056,User_Id_3647,Bike,3,Business,28,5,12:23:44 PM,28,5,12:24:08 PM,28,5,12:30:25 PM,28,5,12:33:54 PM,28,5,12:44:52 PM,5,23.7,,-1.2726389999999999,36.794723,-1.2904993,36.80438770000001,Rider_Id_940,658
-Order_No_9000,User_Id_1075,Bike,3,Business,9,4,11:37:12 AM,9,4,11:38:55 AM,9,4,11:59:19 AM,9,4,12:09:07 PM,9,4,12:30:29 PM,8,,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_435,1282
-Order_No_22954,User_Id_2159,Bike,3,Business,13,2,11:07:59 AM,13,2,11:09:56 AM,13,2,11:25:26 AM,13,2,11:32:37 AM,13,2,11:50:35 AM,7,22.3,,-1.304735,36.784605400000004,-1.3082357,36.8161482,Rider_Id_518,1078
-Order_No_6668,User_Id_928,Bike,3,Business,29,2,3:12:47 PM,29,2,3:13:28 PM,29,2,3:20:06 PM,29,2,3:33:05 PM,29,2,3:51:26 PM,8,26.8,,-1.2551895,36.7822034,-1.2945041000000002,36.8151527,Rider_Id_191,1101
-Order_No_25631,User_Id_3326,Bike,3,Business,21,4,9:28:29 AM,21,4,9:28:53 AM,21,4,9:33:45 AM,21,4,9:38:07 AM,21,4,10:19:53 AM,18,21.8,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_854,2506
-Order_No_14077,User_Id_1209,Bike,3,Business,8,3,10:27:32 AM,8,3,10:28:00 AM,8,3,10:28:04 AM,8,3,11:20:10 AM,8,3,11:32:19 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_258,729
-Order_No_15098,User_Id_3291,Bike,3,Business,4,2,11:58:15 AM,4,2,12:41:31 PM,4,2,12:51:37 PM,4,2,12:55:22 PM,4,2,1:58:52 PM,12,20.7,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_362,3810
-Order_No_27400,User_Id_1471,Bike,3,Business,29,2,5:15:20 PM,29,2,5:27:45 PM,29,2,5:31:49 PM,29,2,5:37:14 PM,29,2,5:48:05 PM,2,22.1,,-1.2553615,36.789537599999996,-1.2632534,36.799342700000004,Rider_Id_898,651
-Order_No_8874,User_Id_1222,Bike,3,Personal,29,3,9:44:45 AM,29,3,9:46:39 AM,29,3,10:02:41 AM,29,3,10:16:41 AM,29,3,10:45:19 AM,8,18.1,,-1.2566688999999998,36.779695200000006,-1.2876365,36.816096,Rider_Id_155,1718
-Order_No_6581,User_Id_432,Bike,3,Business,20,1,6:26:07 PM,20,1,6:57:13 PM,20,1,7:14:56 PM,20,1,7:18:44 PM,20,1,8:10:48 PM,3,,,-1.3012008,36.764868,-1.2763494,36.7671783,Rider_Id_868,3124
-Order_No_12017,User_Id_1342,Bike,3,Business,25,4,10:59:43 AM,25,4,11:00:41 AM,25,4,11:00:55 AM,25,4,11:26:45 AM,25,4,11:54:11 AM,12,20.0,,-1.3006353000000002,36.8462857,-1.3025826,36.7670807,Rider_Id_191,1646
-Order_No_26571,User_Id_3683,Bike,3,Business,30,2,2:55:35 PM,30,2,2:58:40 PM,30,2,3:00:07 PM,30,2,3:10:47 PM,30,2,4:04:03 PM,4,27.6,,-1.2897368,36.8226783,-1.2682478000000001,36.8072852,Rider_Id_928,3196
-Order_No_5713,User_Id_560,Bike,1,Personal,24,1,12:22:14 PM,24,1,12:22:34 PM,24,1,12:27:46 PM,24,1,12:41:21 PM,24,1,1:50:05 PM,15,22.7,,-1.2242368000000001,36.814868100000005,-1.2962832,36.784968299999996,Rider_Id_101,4124
-Order_No_23364,User_Id_111,Bike,3,Business,8,2,11:52:09 AM,8,2,11:52:36 AM,8,2,11:58:04 AM,8,2,11:59:57 AM,8,2,12:08:26 PM,1,23.6,,-1.2551895,36.7822034,-1.2538731,36.7850223,Rider_Id_512,509
-Order_No_17167,User_Id_1004,Bike,3,Business,30,4,2:15:02 PM,30,4,2:16:26 PM,30,4,2:26:08 PM,30,4,2:37:46 PM,30,4,2:56:55 PM,6,,,-1.3088295,36.8385975,-1.3250048,36.869718799999994,Rider_Id_890,1149
-Order_No_15228,User_Id_3372,Bike,3,Business,22,5,10:41:15 AM,22,5,10:47:22 AM,22,5,11:01:47 AM,22,5,11:10:45 AM,22,5,11:39:49 AM,16,21.8,,-1.2297202,36.874550799999994,-1.2969741000000001,36.7856612,Rider_Id_334,1744
-Order_No_2496,User_Id_3283,Bike,3,Business,8,6,12:36:26 PM,8,6,12:37:30 PM,8,6,12:56:13 PM,8,6,1:04:33 PM,8,6,1:22:46 PM,14,20.0,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_8,1093
-Order_No_397,User_Id_2651,Bike,3,Business,8,3,2:16:56 PM,8,3,2:44:05 PM,8,3,2:44:09 PM,8,3,3:43:25 PM,8,3,3:50:23 PM,6,,,-1.2818521999999999,36.8197495,-1.2974014999999999,36.7772406,Rider_Id_61,418
-Order_No_24416,User_Id_1482,Bike,3,Business,27,4,9:58:29 AM,27,4,9:58:46 AM,27,4,10:18:38 AM,27,4,10:29:55 AM,27,4,11:20:31 AM,30,18.9,3.1,-1.3385790000000002,36.89630379999999,-1.1627665,36.8216182,Rider_Id_427,3036
-Order_No_20504,User_Id_3222,Bike,3,Business,13,3,10:39:44 AM,13,3,10:40:00 AM,13,3,10:49:43 AM,13,3,10:52:59 AM,13,3,11:05:35 AM,3,,,-1.2551895,36.7822034,-1.2546732,36.808679600000005,Rider_Id_448,756
-Order_No_25137,User_Id_2150,Bike,3,Business,30,6,9:32:09 AM,30,6,9:33:03 AM,30,6,9:45:18 AM,30,6,10:17:54 AM,30,6,10:34:54 AM,12,,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_218,1020
-Order_No_25623,User_Id_2330,Bike,3,Business,4,1,4:15:46 PM,4,1,4:16:24 PM,4,1,4:31:14 PM,4,1,4:33:02 PM,4,1,4:58:00 PM,9,25.9,,-1.2326041,36.8009029,-1.2649605,36.798177700000004,Rider_Id_244,1498
-Order_No_27206,User_Id_1958,Bike,3,Business,31,4,3:31:25 PM,31,4,3:51:32 PM,31,4,4:00:11 PM,31,4,4:03:45 PM,31,4,4:46:44 PM,23,28.9,,-1.3700383,36.9190174,-1.2551895,36.7822034,Rider_Id_813,2579
-Order_No_3655,User_Id_478,Bike,1,Business,25,3,12:02:49 PM,25,3,12:03:12 PM,25,3,12:12:57 PM,25,3,12:22:01 PM,25,3,12:40:07 PM,9,,,-1.2551895,36.7822034,-1.2985031999999999,36.7808727,Rider_Id_726,1086
-Order_No_7242,User_Id_3300,Bike,3,Business,30,4,9:48:29 AM,30,4,10:01:00 AM,30,4,10:05:02 AM,30,4,10:13:38 AM,30,4,10:37:23 AM,9,19.6,,-1.3444459999999998,36.756597,-1.303596,36.778378000000004,Rider_Id_815,1425
-Order_No_20830,User_Id_868,Bike,3,Business,11,1,3:30:35 PM,11,1,3:33:44 PM,11,1,3:45:01 PM,11,1,4:00:50 PM,11,1,4:12:59 PM,4,28.8,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_205,729
-Order_No_17551,User_Id_733,Bike,3,Business,18,1,11:46:03 AM,18,1,11:46:40 AM,18,1,11:54:50 AM,18,1,12:06:53 PM,18,1,12:17:19 PM,4,27.9,,-1.2551895,36.7822034,-1.2692689,36.8055169,Rider_Id_205,626
-Order_No_13654,User_Id_265,Bike,3,Business,7,3,3:14:21 PM,7,3,3:16:25 PM,7,3,3:26:12 PM,7,3,3:38:42 PM,7,3,4:04:36 PM,10,28.5,,-1.300921,36.828195,-1.3014461000000002,36.7661381,Rider_Id_936,1554
-Order_No_19170,User_Id_517,Bike,3,Business,9,6,11:43:34 AM,9,6,11:43:42 AM,9,6,11:46:26 AM,9,6,11:55:52 AM,9,6,11:55:56 AM,4,24.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,4
-Order_No_14296,User_Id_2676,Bike,3,Business,18,1,1:47:49 PM,18,1,1:52:32 PM,18,1,2:15:48 PM,18,1,2:19:36 PM,18,1,2:53:12 PM,7,30.8,,-1.3047473,36.830562,-1.2963097,36.7688221,Rider_Id_448,2016
-Order_No_21867,User_Id_3283,Bike,3,Business,17,4,10:50:37 AM,17,4,10:51:04 AM,17,4,11:01:37 AM,17,4,11:17:54 AM,17,4,11:37:44 AM,13,25.6,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_902,1190
-Order_No_7231,User_Id_2532,Bike,3,Business,18,2,10:19:51 AM,18,2,10:29:32 AM,18,2,10:30:53 AM,18,2,11:05:30 AM,18,2,11:48:04 AM,8,,,-1.2551895,36.7822034,-1.2967701999999999,36.8040381,Rider_Id_19,2554
-Order_No_21107,User_Id_771,Bike,1,Personal,10,5,10:35:10 AM,10,5,10:35:19 AM,10,5,10:47:14 AM,10,5,10:52:27 AM,10,5,11:27:49 AM,21,19.0,,-1.3176268,36.8741735,-1.3146278,36.7196956,Rider_Id_213,2122
-Order_No_26035,User_Id_633,Bike,3,Business,12,6,11:50:51 AM,12,6,11:51:09 AM,12,6,11:51:38 AM,12,6,12:02:07 PM,12,6,12:02:09 PM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_10,2
-Order_No_9374,User_Id_496,Bike,3,Business,22,4,9:19:01 AM,22,4,9:19:38 AM,22,4,9:22:52 AM,22,4,9:28:12 AM,22,4,9:29:55 AM,2,19.6,3.1,-1.2551895,36.7822034,-1.2561334,36.7990458,Rider_Id_803,103
-Order_No_14936,User_Id_542,Bike,3,Personal,18,4,1:06:21 PM,18,4,1:06:28 PM,18,4,1:11:58 PM,18,4,1:18:20 PM,18,4,1:42:10 PM,17,25.5,,-1.3340618,36.710894200000006,-1.283527,36.82365120000001,Rider_Id_409,1430
-Order_No_22144,User_Id_2066,Bike,3,Business,22,3,9:54:51 AM,22,3,9:55:20 AM,22,3,10:08:05 AM,22,3,10:10:06 AM,22,3,10:26:14 AM,10,15.3,,-1.316961,36.820337,-1.2850113,36.7610856,Rider_Id_676,968
-Order_No_22135,User_Id_1739,Bike,1,Personal,8,3,12:37:38 PM,8,3,12:38:08 PM,8,3,12:55:24 PM,8,3,1:08:14 PM,8,3,1:28:58 PM,12,22.0,,-1.3232881,36.8537357,-1.2855153,36.7834515,Rider_Id_101,1244
-Order_No_19790,User_Id_1469,Bike,3,Business,29,2,11:04:33 AM,29,2,11:07:24 AM,29,2,11:10:23 AM,29,2,11:12:52 AM,29,2,11:42:21 AM,8,23.5,,-1.2551895,36.7822034,-1.2931812,36.8176469,Rider_Id_881,1769
-Order_No_22994,User_Id_2069,Bike,3,Business,12,3,10:45:58 AM,12,3,11:20:39 AM,12,3,12:07:09 PM,12,3,12:08:34 PM,12,3,12:24:24 PM,6,19.6,,-1.2297799999999999,36.836215,-1.228911,36.881876,Rider_Id_27,950
-Order_No_22759,User_Id_1578,Bike,3,Business,28,1,1:35:01 PM,28,1,2:01:09 PM,28,1,2:07:05 PM,28,1,2:28:35 PM,28,1,2:45:43 PM,17,26.1,,-1.3632162,36.9108253,-1.2859912,36.8756811,Rider_Id_295,1028
-Order_No_24151,User_Id_1705,Bike,3,Business,9,4,2:47:52 PM,9,4,2:48:31 PM,9,4,2:54:05 PM,9,4,2:59:49 PM,9,4,3:21:59 PM,8,25.6,,-1.2551895,36.7822034,-1.2900272,36.7935096,Rider_Id_415,1330
-Order_No_18541,User_Id_111,Bike,3,Business,12,3,1:40:54 PM,12,3,1:43:00 PM,12,3,1:43:47 PM,12,3,1:50:15 PM,12,3,2:08:03 PM,4,20.8,,-1.2551895,36.7822034,-1.2707177,36.8009828,Rider_Id_827,1068
-Order_No_12732,User_Id_186,Bike,3,Business,9,2,11:37:27 AM,9,2,11:38:05 AM,9,2,11:41:41 AM,9,2,12:06:48 PM,9,2,12:42:38 PM,20,27.2,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_770,2150
-Order_No_1394,User_Id_1927,Bike,3,Business,10,1,2:45:32 PM,10,1,2:45:57 PM,10,1,2:48:17 PM,10,1,2:57:55 PM,10,1,3:23:48 PM,10,18.4,,-1.3165542,36.700067,-1.3472066,36.7692638,Rider_Id_864,1553
-Order_No_16299,User_Id_3450,Bike,3,Business,28,3,2:31:24 PM,28,3,2:33:01 PM,28,3,2:42:35 PM,28,3,2:50:22 PM,28,3,3:28:09 PM,14,,,-1.2551895,36.7822034,-1.3332749,36.870814700000004,Rider_Id_65,2267
-Order_No_5782,User_Id_2996,Bike,2,Personal,22,2,12:15:56 PM,22,2,12:16:06 PM,22,2,12:25:52 PM,22,2,12:32:19 PM,22,2,1:04:06 PM,14,19.4,,-1.2637817,36.8764568,-1.3057159999999999,36.809882,Rider_Id_654,1907
-Order_No_12479,User_Id_302,Bike,3,Business,30,4,4:44:02 PM,30,4,4:52:46 PM,30,4,5:22:08 PM,30,4,5:31:14 PM,30,4,6:18:22 PM,10,20.4,,-1.2649605,36.798177700000004,-1.3273625,36.8300883,Rider_Id_447,2828
-Order_No_1028,User_Id_1340,Bike,3,Business,8,5,5:06:54 PM,8,5,5:13:17 PM,8,5,5:17:09 PM,8,5,5:19:52 PM,8,5,5:33:09 PM,3,27.9,,-1.2707178999999997,36.7979713,-1.2734594,36.8170558,Rider_Id_829,797
-Order_No_26367,User_Id_835,Bike,3,Business,4,4,11:12:10 AM,4,4,11:15:26 AM,4,4,11:50:35 AM,4,4,11:57:41 AM,4,4,12:21:13 PM,5,25.7,,-1.2917868000000001,36.7872675,-1.2867843,36.8134769,Rider_Id_119,1412
-Order_No_20506,User_Id_1541,Bike,3,Business,31,5,3:32:06 PM,31,5,3:33:26 PM,31,5,3:49:46 PM,31,5,3:52:32 PM,31,5,4:52:38 PM,6,24.8,,-1.2675557,36.803061,-1.2840461,36.7934021,Rider_Id_269,3606
-Order_No_19841,User_Id_3521,Bike,3,Business,18,4,8:55:51 AM,18,4,8:56:20 AM,18,4,9:05:10 AM,18,4,9:12:10 AM,18,4,9:17:51 AM,2,,,-1.2701354,36.807216700000005,-1.2696908000000002,36.81519,Rider_Id_607,341
-Order_No_22950,User_Id_2829,Bike,3,Business,30,2,11:02:59 AM,30,2,11:03:18 AM,30,2,11:06:41 AM,30,2,11:11:25 AM,30,2,11:32:08 AM,8,,,-1.2571472,36.795063299999995,-1.3055447,36.82332,Rider_Id_253,1243
-Order_No_14181,User_Id_3557,Bike,3,Business,16,2,2:43:40 PM,16,2,2:43:48 PM,16,2,2:58:31 PM,16,2,3:08:42 PM,16,2,3:23:43 PM,3,27.4,,-1.269534,36.80697,-1.2546732,36.808679600000005,Rider_Id_434,901
-Order_No_2050,User_Id_927,Bike,3,Personal,2,2,3:40:41 PM,2,2,3:44:32 PM,2,2,4:06:32 PM,2,2,4:21:25 PM,2,2,4:34:04 PM,3,26.2,,-1.2985749,36.8088,-1.2917868000000001,36.7872675,Rider_Id_328,759
-Order_No_16253,User_Id_375,Bike,3,Business,30,2,2:21:55 PM,30,2,4:08:29 PM,30,2,4:22:30 PM,30,2,4:25:05 PM,30,2,5:31:50 PM,8,,,-1.2551895,36.7822034,-1.2969293,36.8027733,Rider_Id_548,4005
-Order_No_22167,User_Id_868,Bike,3,Business,6,6,12:48:14 PM,6,6,12:48:44 PM,6,6,12:53:18 PM,6,6,12:58:55 PM,6,6,1:13:25 PM,7,26.9,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_690,870
-Order_No_4632,User_Id_136,Bike,3,Business,18,2,3:03:36 PM,18,2,3:03:46 PM,18,2,3:08:06 PM,18,2,3:12:54 PM,18,2,4:21:57 PM,16,27.5,,-1.3326173000000001,36.71462329999999,-1.263987,36.798891,Rider_Id_644,4143
-Order_No_25706,User_Id_1244,Bike,3,Business,25,4,2:17:01 PM,25,4,2:17:19 PM,25,4,2:37:26 PM,25,4,2:48:20 PM,25,4,3:12:04 PM,13,,,-1.3444459999999998,36.756597,-1.303596,36.778378000000004,Rider_Id_940,1424
-Order_No_10489,User_Id_2360,Bike,3,Business,7,4,12:31:47 PM,7,4,12:32:14 PM,7,4,12:39:45 PM,7,4,12:41:58 PM,7,4,1:20:20 PM,3,24.4,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_660,2302
-Order_No_19334,User_Id_2816,Bike,1,Personal,11,4,12:33:04 PM,11,4,12:33:32 PM,11,4,12:50:55 PM,11,4,12:55:39 PM,11,4,1:04:42 PM,7,23.8,,-1.2916556000000001,36.8262516,-1.3288576,36.8465936,Rider_Id_662,543
-Order_No_19096,User_Id_1131,Bike,3,Business,26,5,10:37:53 AM,26,5,10:38:59 AM,26,5,10:45:16 AM,26,5,10:53:55 AM,26,5,11:22:33 AM,15,,,-1.2869041,36.8240057,-1.207546,36.7913976,Rider_Id_456,1718
-Order_No_16567,User_Id_3149,Bike,3,Business,4,5,1:04:43 PM,4,5,1:04:58 PM,4,5,1:08:46 PM,4,5,1:19:35 PM,4,5,1:45:11 PM,13,17.2,,-1.2574219,36.7927073,-1.3228003000000002,36.8306435,Rider_Id_237,1536
-Order_No_5940,User_Id_419,Bike,3,Business,6,2,5:37:21 PM,6,2,5:41:38 PM,6,2,5:54:49 PM,6,2,6:00:52 PM,6,2,6:18:02 PM,6,,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_427,1030
-Order_No_12212,User_Id_1502,Bike,3,Business,6,3,1:43:49 PM,6,3,1:44:31 PM,6,3,1:46:15 PM,6,3,1:47:43 PM,6,3,2:23:26 PM,13,,,-1.2527958000000001,36.8003131,-1.2232123,36.8932547,Rider_Id_210,2143
-Order_No_16020,User_Id_496,Bike,3,Business,10,1,1:25:07 PM,10,1,1:25:18 PM,10,1,1:31:30 PM,10,1,1:33:48 PM,10,1,2:52:29 PM,23,18.6,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_498,4721
-Order_No_5016,User_Id_3184,Bike,3,Business,13,2,2:38:59 PM,13,2,2:39:09 PM,13,2,2:51:45 PM,13,2,3:17:55 PM,13,2,4:08:06 PM,15,26.4,,-1.2877831,36.8146624,-1.3396256000000002,36.8863135,Rider_Id_330,3011
-Order_No_6918,User_Id_754,Bike,3,Business,10,3,1:01:48 PM,10,3,1:02:10 PM,10,3,1:08:12 PM,10,3,2:30:33 PM,10,3,2:38:18 PM,5,30.4,,-1.3173868999999998,36.8112044,-1.2977386999999998,36.803144700000004,Rider_Id_438,465
-Order_No_17487,User_Id_435,Bike,3,Business,24,5,2:32:00 PM,24,5,2:33:36 PM,24,5,2:46:11 PM,24,5,2:54:43 PM,24,5,3:23:00 PM,8,23.0,,-1.2580991000000001,36.794151899999996,-1.300921,36.828195,Rider_Id_155,1697
-Order_No_5915,User_Id_1527,Bike,3,Business,10,3,8:47:27 AM,10,3,8:48:03 AM,10,3,8:56:47 AM,10,3,9:04:54 AM,10,3,9:14:52 AM,4,21.9,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_709,598
-Order_No_13582,User_Id_1389,Bike,3,Business,7,4,1:08:32 PM,7,4,1:08:44 PM,7,4,1:12:02 PM,7,4,1:21:49 PM,7,4,1:48:52 PM,14,27.8,,-1.2232123,36.8932547,-1.207546,36.7913976,Rider_Id_147,1623
-Order_No_4907,User_Id_2943,Bike,2,Personal,16,4,5:42:20 PM,16,4,5:47:03 PM,16,4,6:30:40 PM,16,4,6:35:23 PM,16,4,7:12:45 PM,9,19.3,,-1.3032455,36.7691712,-1.2482518,36.7550829,Rider_Id_229,2242
-Order_No_23956,User_Id_1304,Bike,3,Business,29,4,11:15:07 AM,29,4,11:33:03 AM,29,4,11:41:26 AM,29,4,11:43:49 AM,29,4,12:04:49 PM,10,21.6,,-1.3226301999999999,36.8719901,-1.295156,36.809430600000006,Rider_Id_195,1260
-Order_No_18294,User_Id_3470,Bike,3,Business,27,3,1:00:43 PM,27,3,1:04:23 PM,27,3,1:15:42 PM,27,3,1:30:57 PM,27,3,2:00:58 PM,4,25.8,,-1.28878,36.8168312,-1.2636813999999998,36.802996500000006,Rider_Id_329,1801
-Order_No_23291,User_Id_2066,Bike,3,Business,25,4,3:46:32 PM,25,4,3:50:08 PM,25,4,3:58:29 PM,25,4,4:01:24 PM,25,4,4:25:08 PM,9,20.9,,-1.316961,36.820337,-1.25454,36.839220000000005,Rider_Id_257,1424
-Order_No_1070,User_Id_1471,Bike,3,Business,24,5,3:02:20 PM,24,5,3:05:22 PM,24,5,3:42:51 PM,24,5,3:46:56 PM,24,5,4:06:21 PM,1,23.1,,-1.2635433,36.7970804,-1.2571472,36.795063299999995,Rider_Id_223,1165
-Order_No_26581,User_Id_60,Bike,3,Business,11,1,11:14:38 AM,11,1,11:14:56 AM,11,1,11:15:06 AM,11,1,11:59:18 AM,11,1,12:37:16 PM,15,23.8,,-1.303596,36.778378000000004,-1.2296618000000001,36.84382720000001,Rider_Id_26,2278
-Order_No_1520,User_Id_867,Bike,3,Business,6,3,7:35:58 AM,6,3,7:37:03 AM,6,3,7:50:08 AM,6,3,7:55:57 AM,6,3,8:18:43 AM,9,16.8,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_523,1366
-Order_No_16363,User_Id_3560,Bike,3,Business,2,5,12:53:24 PM,2,5,1:10:08 PM,2,5,1:41:24 PM,2,5,1:47:45 PM,2,5,2:03:35 PM,2,24.9,,-1.281754,36.8324229,-1.279395,36.825364,Rider_Id_569,950
-Order_No_25300,User_Id_1271,Bike,3,Business,26,2,2:54:19 PM,26,2,2:54:39 PM,26,2,2:54:43 PM,26,2,3:07:33 PM,26,2,3:32:32 PM,8,30.9,,-1.3244885,36.897791999999995,-1.3263105,36.8622904,Rider_Id_258,1499
-Order_No_20480,User_Id_788,Bike,3,Business,27,4,11:54:01 AM,27,4,12:00:22 PM,27,4,12:23:37 PM,27,4,12:26:05 PM,27,4,12:49:39 PM,7,22.9,,-1.225322,36.80855,-1.2605938,36.807323499999995,Rider_Id_477,1414
-Order_No_18324,User_Id_2587,Bike,3,Business,24,3,3:44:21 PM,24,3,4:02:02 PM,24,3,4:12:53 PM,24,3,4:16:37 PM,24,3,4:30:13 PM,5,18.5,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_88,816
-Order_No_25093,User_Id_1396,Bike,2,Personal,24,1,4:16:49 PM,24,1,4:19:23 PM,24,1,4:30:57 PM,24,1,4:41:07 PM,24,1,5:03:30 PM,8,24.6,,-1.2768829,36.7965104,-1.3223922,36.837965999999994,Rider_Id_857,1343
-Order_No_2637,User_Id_2828,Bike,3,Business,20,6,7:34:52 AM,20,6,9:49:37 AM,20,6,10:11:42 AM,20,6,10:14:32 AM,20,6,10:23:33 AM,4,19.6,,-1.2710687,36.8142839,-1.2546636999999998,36.8248917,Rider_Id_88,541
-Order_No_16148,User_Id_391,Bike,3,Business,29,1,2:12:44 PM,29,1,2:16:59 PM,29,1,2:28:06 PM,29,1,2:54:24 PM,29,1,2:54:28 PM,5,,,-1.2784063,36.8222933,-1.3081376,36.83297720000001,Rider_Id_843,4
-Order_No_1222,User_Id_1996,Bike,3,Business,21,4,10:02:38 AM,21,4,10:03:34 AM,21,4,10:22:55 AM,21,4,10:38:40 AM,21,4,10:55:12 AM,6,28.0,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_206,992
-Order_No_26687,User_Id_852,Bike,2,Personal,5,4,12:42:16 PM,5,4,12:47:28 PM,5,4,12:56:02 PM,5,4,1:03:59 PM,5,4,1:26:23 PM,8,,,-1.298066,36.776709499999996,-1.264788,36.7883897,Rider_Id_24,1344
-Order_No_12297,User_Id_1460,Bike,1,Personal,4,4,7:27:17 AM,4,4,7:27:49 AM,4,4,7:49:54 AM,4,4,7:57:14 AM,4,4,8:55:03 AM,17,18.5,,-1.200059,36.7665764,-1.285709,36.8219844,Rider_Id_101,3469
-Order_No_20635,User_Id_1425,Bike,3,Business,25,1,12:12:34 PM,25,1,12:12:45 PM,25,1,12:17:34 PM,25,1,12:28:57 PM,25,1,12:43:35 PM,12,27.3,,-1.3004062,36.829741,-1.338283,36.8871775,Rider_Id_863,878
-Order_No_15016,User_Id_2969,Bike,2,Personal,6,5,4:52:09 PM,6,5,4:53:24 PM,6,5,4:59:20 PM,6,5,5:05:40 PM,6,5,5:26:14 PM,6,,,-1.290109,36.824918,-1.2650181999999999,36.7926717,Rider_Id_914,1234
-Order_No_7140,User_Id_1927,Bike,3,Business,27,3,3:15:44 PM,27,3,3:31:50 PM,27,3,3:45:20 PM,27,3,3:58:24 PM,27,3,4:42:08 PM,17,29.7,,-1.3472066,36.7692638,-1.2620912,36.8013562,Rider_Id_755,2624
-Order_No_3150,User_Id_2916,Bike,2,Personal,12,4,10:48:03 AM,12,4,10:51:41 AM,12,4,11:08:58 AM,12,4,11:21:37 AM,12,4,12:38:40 PM,14,19.1,,-1.2287313999999998,36.8367354,-1.2924639,36.77957979999999,Rider_Id_752,4623
-Order_No_10576,User_Id_1425,Bike,3,Business,31,5,2:16:09 PM,31,5,2:18:04 PM,31,5,2:22:39 PM,31,5,2:53:34 PM,31,5,3:26:18 PM,10,24.9,,-1.338283,36.8871775,-1.3004062,36.829741,Rider_Id_383,1964
-Order_No_7450,User_Id_3186,Bike,3,Business,30,4,1:37:09 PM,30,4,1:37:23 PM,30,4,1:42:31 PM,30,4,1:45:18 PM,30,4,1:58:28 PM,4,,,-1.2980228999999999,36.78865,-1.2950041,36.814358299999995,Rider_Id_209,790
-Order_No_170,User_Id_431,Bike,1,Personal,5,5,1:11:49 PM,5,5,1:17:35 PM,5,5,1:51:19 PM,5,5,1:55:25 PM,5,5,2:18:35 PM,8,26.5,,-1.3108368000000001,36.8226308,-1.2575370000000001,36.843066,Rider_Id_844,1390
-Order_No_3039,User_Id_1426,Bike,1,Personal,8,6,9:20:01 AM,8,6,9:20:12 AM,8,6,9:27:38 AM,8,6,10:06:13 AM,8,6,10:24:14 AM,10,18.2,,-1.2574308,36.796051,-1.2941988000000002,36.7685658,Rider_Id_379,1081
-Order_No_4597,User_Id_2982,Bike,3,Business,25,1,11:13:59 AM,25,1,11:14:19 AM,25,1,11:29:37 AM,25,1,11:58:33 AM,25,1,12:51:45 PM,18,25.3,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_448,3192
-Order_No_20,User_Id_2412,Bike,3,Business,3,4,2:47:47 PM,3,4,2:48:10 PM,3,4,3:03:27 PM,3,4,3:08:17 PM,3,4,3:19:16 PM,3,26.1,,-1.2156006000000001,36.8916865,-1.2285834999999998,36.882646799999996,Rider_Id_854,659
-Order_No_27170,User_Id_3283,Bike,3,Business,31,5,9:05:31 AM,31,5,9:10:08 AM,31,5,9:25:06 AM,31,5,9:33:06 AM,31,5,9:47:05 AM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_542,839
-Order_No_21181,User_Id_1260,Bike,3,Business,6,4,1:30:23 PM,6,4,1:32:41 PM,6,4,2:02:34 PM,6,4,2:10:41 PM,6,4,2:33:43 PM,6,21.5,,-1.2551895,36.7822034,-1.2803414,36.8265456,Rider_Id_183,1382
-Order_No_3247,User_Id_2578,Bike,1,Personal,5,2,10:46:22 AM,5,2,10:47:08 AM,5,2,11:02:03 AM,5,2,11:05:16 AM,5,2,11:38:32 AM,14,21.5,,-1.332175,36.8819641,-1.2892350000000001,36.8135805,Rider_Id_543,1996
-Order_No_7362,User_Id_3201,Bike,3,Business,28,4,9:18:34 AM,28,4,9:19:19 AM,28,4,9:50:59 AM,28,4,10:01:10 AM,28,4,11:30:44 AM,15,19.3,,-1.2285834999999998,36.882646799999996,-1.2584143,36.8048002,Rider_Id_686,5374
-Order_No_15508,User_Id_2306,Bike,3,Business,21,2,10:22:11 AM,21,2,10:23:58 AM,21,2,11:15:46 AM,21,2,11:30:20 AM,21,2,12:04:42 PM,14,22.5,,-1.2271998,36.808490500000005,-1.3019530000000001,36.788458,Rider_Id_870,2062
-Order_No_22435,User_Id_633,Bike,3,Business,23,3,1:50:43 PM,23,3,1:52:19 PM,23,3,1:55:12 PM,23,3,2:00:29 PM,23,3,2:15:23 PM,5,27.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_714,894
-Order_No_4035,User_Id_136,Bike,3,Business,11,1,12:57:19 PM,11,1,12:58:00 PM,11,1,1:12:37 PM,11,1,1:18:13 PM,11,1,1:38:34 PM,11,23.8,,-1.2313147,36.80168629999999,-1.2560874,36.7931031,Rider_Id_959,1221
-Order_No_4105,User_Id_747,Bike,3,Personal,23,4,1:40:27 PM,23,4,1:40:52 PM,23,4,1:47:23 PM,23,4,1:48:41 PM,23,4,1:54:50 PM,3,20.7,,-1.2925236,36.7795556,-1.2998882,36.7895711,Rider_Id_64,369
-Order_No_21380,User_Id_3557,Bike,3,Business,24,3,3:44:14 PM,24,3,3:45:01 PM,24,3,4:00:22 PM,24,3,4:11:35 PM,24,3,4:11:53 PM,3,18.3,,-1.2655306,36.828148,-1.2546732,36.808679600000005,Rider_Id_847,18
-Order_No_16395,User_Id_238,Bike,3,Business,26,2,8:41:33 AM,26,2,8:42:23 AM,26,2,8:56:23 AM,26,2,9:03:34 AM,26,2,9:15:20 AM,4,22.0,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_709,706
-Order_No_6027,User_Id_1469,Bike,3,Business,21,5,12:41:58 PM,21,5,12:48:50 PM,21,5,1:09:15 PM,21,5,1:23:54 PM,21,5,1:24:10 PM,4,22.9,,-1.2571472,36.795063299999995,-1.2731645,36.7900343,Rider_Id_793,16
-Order_No_10804,User_Id_3222,Bike,3,Business,27,3,2:56:07 PM,27,3,2:57:38 PM,27,3,3:06:19 PM,27,3,3:09:46 PM,27,3,4:05:17 PM,6,29.6,,-1.2551895,36.7822034,-1.2869955,36.8202749,Rider_Id_156,3331
-Order_No_17204,User_Id_1787,Bike,3,Business,22,5,4:51:51 PM,22,5,4:57:16 PM,22,5,5:01:20 PM,22,5,5:03:00 PM,22,5,5:29:21 PM,12,28.6,,-1.2527958000000001,36.8003131,-1.2283403,36.8822756,Rider_Id_318,1581
-Order_No_2520,User_Id_496,Bike,3,Business,31,3,11:41:22 AM,31,3,11:41:40 AM,31,3,11:41:59 AM,31,3,11:52:21 AM,31,3,12:37:15 PM,23,24.6,,-1.2551895,36.7822034,-1.374163,36.913444,Rider_Id_450,2694
-Order_No_18635,User_Id_2514,Bike,3,Business,31,2,9:56:05 AM,31,2,9:56:31 AM,31,2,10:02:04 AM,31,2,10:07:01 AM,31,2,10:31:18 AM,6,16.8,1.1,-1.266294,36.8057971,-1.2983462,36.8139996,Rider_Id_882,1457
-Order_No_27072,User_Id_884,Bike,3,Personal,21,1,12:55:58 PM,21,1,12:56:29 PM,21,1,12:58:23 PM,21,1,1:08:55 PM,21,1,1:34:22 PM,19,25.3,,-1.2640571000000003,36.7883547,-1.1855927,36.843477,Rider_Id_45,1527
-Order_No_6642,User_Id_478,Bike,3,Business,30,2,2:45:49 PM,30,2,3:16:48 PM,30,2,3:19:26 PM,30,2,3:29:44 PM,30,2,3:51:47 PM,3,,,-1.2551895,36.7822034,-1.2640571000000003,36.7883547,Rider_Id_535,1323
-Order_No_25388,User_Id_2154,Bike,3,Business,25,5,12:58:51 PM,25,5,12:59:07 PM,25,5,12:59:34 PM,25,5,1:32:41 PM,25,5,1:46:56 PM,4,23.5,,-1.2885528999999998,36.816014100000004,-1.2651643,36.807221399999996,Rider_Id_100,855
-Order_No_27047,User_Id_2523,Bike,3,Business,29,3,11:27:24 AM,29,3,11:27:41 AM,29,3,11:28:08 AM,29,3,11:33:19 AM,29,3,11:51:46 AM,8,,,-1.2551895,36.7822034,-1.2876155,36.78684439999999,Rider_Id_535,1107
-Order_No_21945,User_Id_2216,Bike,3,Business,10,1,5:47:20 PM,10,1,5:48:29 PM,10,1,6:04:08 PM,10,1,6:33:25 PM,10,1,6:47:44 PM,7,18.8,,-1.2551895,36.7822034,-1.2890847,36.8048724,Rider_Id_139,859
-Order_No_17705,User_Id_3149,Bike,3,Business,20,3,3:44:05 PM,20,3,3:44:33 PM,20,3,3:44:38 PM,20,3,4:30:01 PM,20,3,4:38:36 PM,5,28.5,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_809,515
-Order_No_27760,User_Id_1469,Bike,3,Business,20,4,2:52:13 PM,20,4,3:13:15 PM,20,4,3:24:32 PM,20,4,3:26:25 PM,20,4,4:11:51 PM,5,25.1,,-1.2551895,36.7822034,-1.2764851000000002,36.8007126,Rider_Id_259,2726
-Order_No_15891,User_Id_793,Bike,3,Personal,6,3,10:54:37 AM,6,3,10:55:32 AM,6,3,11:04:31 AM,6,3,11:13:35 AM,6,3,11:29:01 AM,6,,,-1.2651573999999999,36.7474347,-1.256522,36.786413200000005,Rider_Id_518,926
-Order_No_12774,User_Id_186,Bike,3,Business,8,1,9:19:45 AM,8,1,9:20:20 AM,8,1,9:25:32 AM,8,1,9:45:26 AM,8,1,10:08:53 AM,17,22.1,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_953,1407
-Order_No_6938,User_Id_1346,Bike,3,Business,18,4,2:03:24 PM,18,4,2:03:57 PM,18,4,2:15:13 PM,18,4,2:22:30 PM,18,4,2:44:14 PM,6,,,-1.2695287,36.798177700000004,-1.2917868000000001,36.7872675,Rider_Id_523,1304
-Order_No_22778,User_Id_2174,Bike,3,Business,23,3,8:29:57 AM,23,3,8:30:05 AM,23,3,8:30:49 AM,23,3,9:21:03 AM,23,3,9:48:49 AM,15,,,-1.2482781,36.884091999999995,-1.214071,36.7985052,Rider_Id_75,1666
-Order_No_3364,User_Id_3778,Bike,3,Personal,17,1,12:13:51 PM,17,1,12:14:09 PM,17,1,12:24:48 PM,17,1,12:37:54 PM,17,1,1:06:22 PM,20,25.2,,-1.2775276000000002,36.8140021,-1.1825557,36.9331107,Rider_Id_105,1708
-Order_No_19872,User_Id_2330,Bike,1,Business,25,2,8:27:44 PM,25,2,8:27:58 PM,25,2,8:34:37 PM,25,2,8:38:42 PM,25,2,8:56:34 PM,6,21.5,,-1.30176,36.765658,-1.2915246999999999,36.805575700000006,Rider_Id_148,1072
-Order_No_8796,User_Id_1340,Bike,3,Business,7,2,12:47:28 PM,7,2,12:48:09 PM,7,2,1:08:30 PM,7,2,1:21:03 PM,7,2,1:53:48 PM,13,23.1,,-1.2707178999999997,36.7979713,-1.2628423999999998,36.8950641,Rider_Id_937,1965
-Order_No_1452,User_Id_816,Bike,3,Business,29,2,4:20:33 PM,29,2,4:23:32 PM,29,2,4:23:50 PM,29,2,4:40:33 PM,29,2,4:54:02 PM,5,,,-1.2977386999999998,36.803144700000004,-1.290894,36.822971,Rider_Id_237,809
-Order_No_23366,User_Id_1899,Bike,3,Business,31,5,2:13:03 PM,31,5,2:13:21 PM,31,5,2:20:01 PM,31,5,2:38:35 PM,31,5,2:56:52 PM,11,20.2,,-1.272828,36.816608,-1.331619,36.847976,Rider_Id_116,1097
-Order_No_4176,User_Id_3618,Bike,3,Personal,7,1,9:21:52 AM,7,1,9:23:38 AM,7,1,9:51:02 AM,7,1,9:56:00 AM,7,1,10:08:26 AM,2,18.3,,-1.279285,36.826057399999996,-1.2842286,36.8150609,Rider_Id_742,746
-Order_No_12175,User_Id_3576,Bike,2,Personal,19,6,12:34:19 PM,19,6,12:35:19 PM,19,6,12:38:49 PM,19,6,12:47:35 PM,19,6,1:15:54 PM,8,21.9,,-1.281223,36.8239,-1.2987748,36.767741799999996,Rider_Id_742,1699
-Order_No_11132,User_Id_553,Bike,3,Business,21,5,4:48:03 PM,21,5,5:10:32 PM,21,5,5:19:30 PM,21,5,5:25:26 PM,21,5,6:07:58 PM,14,23.2,,-1.2551895,36.7822034,-1.2279357,36.8680021,Rider_Id_519,2552
-Order_No_11369,User_Id_1573,Bike,1,Personal,4,1,7:41:14 PM,4,1,7:41:33 PM,4,1,7:44:34 PM,4,1,7:51:29 PM,4,1,7:59:12 PM,6,18.9,,-1.273056,36.811298,-1.297657,36.7995692,Rider_Id_116,463
-Order_No_10995,User_Id_142,Bike,3,Business,17,3,1:01:48 PM,17,3,1:02:11 PM,17,3,1:27:30 PM,17,3,1:31:38 PM,17,3,1:50:40 PM,8,,,-1.2963097,36.7688221,-1.2570328000000002,36.7869744,Rider_Id_68,1142
-Order_No_8584,User_Id_3108,Bike,3,Business,11,2,8:29:00 AM,11,2,8:29:25 AM,11,2,8:33:25 AM,11,2,8:36:18 AM,11,2,9:05:50 AM,13,16.5,,-1.2551895,36.7822034,-1.3149620000000002,36.865528000000005,Rider_Id_894,1772
-Order_No_26566,User_Id_1207,Bike,3,Business,4,2,9:59:58 AM,4,2,10:00:18 AM,4,2,10:22:25 AM,4,2,10:31:07 AM,4,2,11:26:53 AM,18,16.3,,-1.317461,36.707031,-1.2629666000000002,36.8068369,Rider_Id_789,3346
-Order_No_7418,User_Id_147,Bike,3,Personal,14,5,12:51:03 PM,14,5,1:00:16 PM,14,5,2:08:01 PM,14,5,2:19:34 PM,14,5,3:06:44 PM,18,23.6,,-1.311208,36.778743,-1.2222487,36.80814229999999,Rider_Id_457,2830
-Order_No_11124,User_Id_3222,Bike,3,Business,20,3,9:38:52 AM,20,3,9:39:17 AM,20,3,9:39:51 AM,20,3,9:46:23 AM,20,3,10:10:05 AM,10,20.9,,-1.2551895,36.7822034,-1.307376,36.813404,Rider_Id_576,1422
-Order_No_12524,User_Id_2829,Bike,3,Business,29,1,10:20:51 AM,29,1,10:24:45 AM,29,1,10:24:48 AM,29,1,10:37:33 AM,29,1,10:57:44 AM,6,,,-1.2571472,36.795063299999995,-1.3012902,36.80964,Rider_Id_291,1211
-Order_No_10999,User_Id_826,Bike,3,Personal,20,1,9:10:34 AM,20,1,9:19:59 AM,20,1,9:30:41 AM,20,1,9:35:23 AM,20,1,9:45:09 AM,3,14.1,,-1.2945041000000002,36.8151527,-1.2987440000000001,36.817823700000005,Rider_Id_338,586
-Order_No_7862,User_Id_393,Bike,3,Business,21,2,1:28:35 PM,21,2,1:28:50 PM,21,2,1:29:52 PM,21,2,1:31:29 PM,21,2,1:43:09 PM,4,22.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,700
-Order_No_12052,User_Id_1724,Bike,3,Business,29,2,4:12:59 PM,29,2,4:17:28 PM,29,2,4:32:25 PM,29,2,4:40:56 PM,29,2,5:19:42 PM,8,22.4,,-1.2553615,36.789537599999996,-1.2879618,36.82008629999999,Rider_Id_644,2326
-Order_No_27169,User_Id_419,Bike,3,Business,7,2,1:45:37 PM,7,2,1:47:12 PM,7,2,1:56:41 PM,7,2,2:01:50 PM,7,2,2:21:57 PM,6,24.8,,-1.2571472,36.795063299999995,-1.3001813,36.811122,Rider_Id_414,1207
-Order_No_627,User_Id_3239,Bike,3,Personal,8,2,10:27:56 AM,8,2,10:51:35 AM,8,2,11:06:35 AM,8,2,11:12:22 AM,8,2,11:27:03 AM,9,21.3,,-1.276154,36.822416499999996,-1.2477465,36.8783014,Rider_Id_669,881
-Order_No_681,User_Id_1882,Bike,3,Business,29,2,4:51:40 PM,29,2,4:52:53 PM,29,2,4:54:10 PM,29,2,5:14:08 PM,29,2,5:28:18 PM,4,,,-1.2958783,36.7928125,-1.3011335,36.7658946,Rider_Id_322,850
-Order_No_1153,User_Id_1882,Bike,1,Business,4,4,9:00:49 AM,4,4,9:02:44 AM,4,4,9:18:22 AM,4,4,9:19:47 AM,4,4,9:35:03 AM,9,18.6,,-1.2847654,36.820105,-1.3014461000000002,36.7661381,Rider_Id_754,916
-Order_No_4675,User_Id_635,Bike,3,Business,27,1,12:48:58 PM,27,1,12:49:19 PM,27,1,12:53:50 PM,27,1,1:26:10 PM,27,1,1:47:02 PM,10,20.9,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_2,1252
-Order_No_17720,User_Id_1544,Bike,3,Business,11,6,9:58:18 AM,11,6,10:00:04 AM,11,6,10:23:46 AM,11,6,10:26:11 AM,11,6,10:57:58 AM,10,,,-1.2813012,36.832396200000005,-1.234228,36.881122999999995,Rider_Id_448,1907
-Order_No_17276,User_Id_81,Bike,3,Business,11,2,12:51:13 PM,11,2,12:52:27 PM,11,2,12:52:58 PM,11,2,12:58:54 PM,11,2,12:58:58 PM,2,19.2,,-1.2641496,36.791413899999995,-1.2652039,36.7734799,Rider_Id_848,4
-Order_No_4951,User_Id_1329,Bike,3,Business,21,3,4:09:31 PM,21,3,5:16:09 PM,21,3,5:22:45 PM,21,3,5:32:44 PM,21,3,5:54:08 PM,6,,,-1.273056,36.811298,-1.3005349,36.7873912,Rider_Id_268,1284
-Order_No_3360,User_Id_1170,Bike,2,Personal,31,2,10:03:08 AM,31,2,10:09:27 AM,31,2,10:14:28 AM,31,2,10:29:18 AM,31,2,10:50:42 AM,6,17.2,1.0,-1.2559559999999999,36.772901,-1.3014461000000002,36.7661381,Rider_Id_101,1284
-Order_No_12475,User_Id_1544,Bike,3,Business,2,4,10:53:41 AM,2,4,10:58:20 AM,2,4,11:15:09 AM,2,4,11:25:10 AM,2,4,11:50:11 AM,7,,,-1.2924173,36.779781400000005,-1.2813012,36.832396200000005,Rider_Id_953,1501
-Order_No_22746,User_Id_193,Bike,2,Personal,18,5,9:41:28 AM,18,5,9:42:55 AM,18,5,10:01:05 AM,18,5,10:08:03 AM,18,5,10:19:23 AM,6,17.6,,-1.2562249,36.786866100000005,-1.2698167999999999,36.7421898,Rider_Id_216,680
-Order_No_7363,User_Id_3308,Bike,1,Personal,2,3,6:19:17 PM,2,3,6:25:16 PM,2,3,6:51:43 PM,2,3,6:53:44 PM,2,3,7:31:20 PM,11,18.2,,-1.231492,36.804054,-1.256688,36.772448,Rider_Id_103,2256
-Order_No_18249,User_Id_737,Bike,1,Personal,28,1,4:57:58 PM,28,1,5:10:43 PM,28,1,5:31:16 PM,28,1,5:41:11 PM,28,1,6:19:05 PM,14,22.6,,-1.2801958,36.8847721,-1.291779,36.802679,Rider_Id_116,2274
-Order_No_25893,User_Id_2330,Bike,3,Business,17,4,10:12:48 AM,17,4,10:13:17 AM,17,4,10:17:45 AM,17,4,10:46:46 AM,17,4,11:05:48 AM,10,25.4,,-1.2649605,36.798177700000004,-1.3015197,36.7658456,Rider_Id_647,1142
-Order_No_4167,User_Id_1500,Bike,3,Business,9,5,9:23:16 AM,9,5,9:51:59 AM,9,5,9:52:07 AM,9,5,9:57:27 AM,9,5,10:17:35 AM,9,20.4,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_208,1208
-Order_No_22037,User_Id_2382,Bike,1,Personal,7,6,1:48:44 PM,7,6,2:16:57 PM,7,6,2:46:47 PM,7,6,2:49:50 PM,7,6,3:03:18 PM,8,21.3,,-1.3188148000000002,36.809247799999994,-1.2752917,36.826687899999996,Rider_Id_629,808
-Order_No_19032,User_Id_1244,Bike,3,Business,2,4,9:46:11 AM,2,4,9:47:03 AM,2,4,9:49:56 AM,2,4,10:21:25 AM,2,4,10:38:16 AM,9,13.3,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_101,1011
-Order_No_10982,User_Id_3525,Bike,3,Business,27,1,1:02:32 PM,27,1,2:15:42 PM,27,1,2:26:12 PM,27,1,2:31:11 PM,27,1,3:27:55 PM,22,23.3,,-1.3700383,36.9190174,-1.2674273999999999,36.787082700000006,Rider_Id_868,3404
-Order_No_7222,User_Id_2828,Bike,3,Business,6,2,6:26:05 PM,6,2,6:26:44 PM,6,2,6:36:11 PM,6,2,6:46:04 PM,6,2,8:21:24 PM,31,,,-1.2615888000000002,36.7928732,-1.4196538,36.69308,Rider_Id_38,5720
-Order_No_8731,User_Id_3291,Bike,3,Business,16,4,3:00:10 PM,16,4,3:01:37 PM,16,4,3:03:18 PM,16,4,3:37:15 PM,16,4,3:47:56 PM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_686,641
-Order_No_20103,User_Id_3336,Bike,2,Personal,16,2,2:52:13 PM,16,2,2:54:08 PM,16,2,3:06:10 PM,16,2,3:15:16 PM,16,2,3:26:03 PM,5,27.5,,-1.2800617,36.7883831,-1.2959768999999999,36.7853374,Rider_Id_299,647
-Order_No_15393,User_Id_135,Bike,1,Personal,14,4,2:26:58 PM,14,4,3:09:47 PM,14,4,3:39:20 PM,14,4,3:47:05 PM,14,4,4:00:47 PM,6,21.3,,-1.2631187,36.788965399999995,-1.2919323,36.7659602,Rider_Id_45,822
-Order_No_873,User_Id_3686,Bike,3,Business,6,2,12:01:52 PM,6,2,12:22:51 PM,6,2,12:28:45 PM,6,2,12:33:23 PM,6,2,1:08:54 PM,10,25.0,,-1.2551895,36.7822034,-1.3156451999999998,36.815802000000005,Rider_Id_31,2131
-Order_No_24364,User_Id_3080,Bike,3,Business,19,1,7:44:44 AM,19,1,7:44:56 AM,19,1,7:45:07 AM,19,1,8:21:12 AM,19,1,8:38:30 AM,9,19.4,,-1.2624935,36.8434339,-1.2551895,36.7822034,Rider_Id_143,1038
-Order_No_15092,User_Id_3647,Bike,3,Business,7,2,4:33:32 PM,7,2,4:33:49 PM,7,2,4:35:46 PM,7,2,4:39:32 PM,7,2,5:02:26 PM,8,,,-1.2726389999999999,36.794723,-1.3068209,36.8282524,Rider_Id_660,1374
-Order_No_16278,User_Id_1818,Bike,3,Business,21,3,12:22:28 PM,21,3,12:23:31 PM,21,3,12:33:10 PM,21,3,12:39:35 PM,21,3,1:11:27 PM,11,21.8,,-1.304524,36.775371,-1.2551895,36.7822034,Rider_Id_596,1912
-Order_No_20309,User_Id_3264,Bike,3,Business,13,4,2:28:50 PM,13,4,2:31:13 PM,13,4,2:45:10 PM,13,4,2:48:44 PM,13,4,3:34:46 PM,24,22.4,,-1.2551895,36.7822034,-1.364732,36.658943,Rider_Id_846,2762
-Order_No_13801,User_Id_1280,Bike,3,Business,4,1,3:08:18 PM,4,1,3:10:19 PM,4,1,3:22:48 PM,4,1,3:26:38 PM,4,1,4:01:26 PM,13,,,-1.2551895,36.7822034,-1.3208221000000002,36.829573499999995,Rider_Id_647,2088
-Order_No_24673,User_Id_3123,Bike,1,Personal,20,5,5:14:34 PM,20,5,5:14:56 PM,20,5,5:17:44 PM,20,5,5:29:34 PM,20,5,6:14:06 PM,16,20.0,,-1.2859576000000001,36.8197604,-1.3169591,36.7000029,Rider_Id_648,2672
-Order_No_8358,User_Id_2677,Bike,3,Business,14,1,12:02:43 PM,14,1,12:03:02 PM,14,1,12:14:53 PM,14,1,12:27:10 PM,14,1,1:34:04 PM,16,23.4,,-1.306378,36.7519845,-1.2555317,36.8562313,Rider_Id_829,4014
-Order_No_14585,User_Id_3517,Bike,3,Business,5,3,4:05:26 PM,5,3,4:15:25 PM,5,3,4:33:02 PM,5,3,4:49:04 PM,5,3,4:49:10 PM,2,22.3,,-1.278956,36.8375411,-1.2867454,36.8259017,Rider_Id_179,6
-Order_No_17950,User_Id_1004,Bike,3,Business,17,5,2:13:04 PM,17,5,2:13:57 PM,17,5,2:29:39 PM,17,5,2:36:46 PM,17,5,3:08:14 PM,14,,,-1.3250048,36.869718799999994,-1.2603996000000002,36.79722720000001,Rider_Id_829,1888
-Order_No_2555,User_Id_2523,Bike,3,Business,23,4,9:38:52 AM,23,4,9:46:36 AM,23,4,9:53:13 AM,23,4,10:01:55 AM,23,4,10:24:55 AM,7,,,-1.2551895,36.7822034,-1.2915968999999998,36.823763899999996,Rider_Id_96,1380
-Order_No_18800,User_Id_1329,Bike,3,Business,13,1,3:18:01 PM,13,1,3:22:55 PM,13,1,3:33:57 PM,13,1,3:37:17 PM,13,1,3:59:04 PM,6,,,-1.273056,36.811298,-1.2370629,36.79819620000001,Rider_Id_161,1307
-Order_No_26940,User_Id_943,Bike,2,Personal,14,5,12:49:49 PM,14,5,12:56:48 PM,14,5,1:21:15 PM,14,5,1:29:59 PM,14,5,2:00:36 PM,7,24.5,,-1.3143165,36.848038200000005,-1.2841214,36.8293514,Rider_Id_844,1837
-Order_No_25748,User_Id_2066,Bike,3,Business,23,4,2:02:27 PM,23,4,2:13:43 PM,23,4,2:26:21 PM,23,4,2:28:07 PM,23,4,3:25:37 PM,7,24.7,,-1.3210188999999999,36.841055100000005,-1.2871085,36.8303905,Rider_Id_436,3450
-Order_No_24104,User_Id_1059,Bike,1,Personal,11,2,10:05:43 AM,11,2,10:07:17 AM,11,2,10:18:52 AM,11,2,10:23:45 AM,11,2,10:49:00 AM,11,18.4,1.9,-1.3303649,36.726018700000004,-1.296567,36.7813901,Rider_Id_263,1515
-Order_No_15323,User_Id_3688,Bike,3,Business,14,4,4:00:59 PM,14,4,4:02:55 PM,14,4,4:25:13 PM,14,4,4:30:46 PM,14,4,4:53:22 PM,6,27.6,,-1.3147073999999999,36.811721999999996,-1.2897399,36.8270975,Rider_Id_156,1356
-Order_No_10509,User_Id_2066,Bike,3,Business,8,5,4:33:59 PM,8,5,4:38:05 PM,8,5,4:58:32 PM,8,5,5:00:06 PM,8,5,5:28:21 PM,12,25.2,,-1.3210188999999999,36.841055100000005,-1.3664581,36.9177807,Rider_Id_675,1695
-Order_No_22865,User_Id_2801,Bike,3,Business,6,3,12:39:47 PM,6,3,12:40:40 PM,6,3,12:43:11 PM,6,3,12:49:43 PM,6,3,1:00:34 PM,4,22.5,,-1.2689186000000001,36.80016810000001,-1.2674273999999999,36.787082700000006,Rider_Id_807,651
-Order_No_27061,User_Id_3135,Bike,3,Personal,24,1,2:33:27 PM,24,1,2:33:52 PM,24,1,2:39:41 PM,24,1,2:48:00 PM,24,1,3:00:42 PM,4,,,-1.277585,36.77145,-1.265432,36.795034,Rider_Id_734,762
-Order_No_10667,User_Id_517,Bike,3,Business,2,3,1:39:24 PM,2,3,1:39:31 PM,2,3,1:44:12 PM,2,3,1:47:43 PM,2,3,2:14:47 PM,14,25.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1624
-Order_No_17061,User_Id_1207,Bike,3,Business,17,6,1:06:12 PM,17,6,1:23:22 PM,17,6,1:23:41 PM,17,6,1:32:10 PM,17,6,1:49:56 PM,14,23.9,,-1.2629666000000002,36.8068369,-1.207546,36.7913976,Rider_Id_541,1066
-Order_No_1850,User_Id_2154,Bike,3,Business,28,3,12:40:05 PM,28,3,12:40:19 PM,28,3,12:44:27 PM,28,3,12:47:06 PM,28,3,1:16:14 PM,16,,,-1.346557,36.769515000000006,-1.2765736,36.851364600000004,Rider_Id_456,1748
-Order_No_15662,User_Id_1271,Bike,3,Business,1,1,1:02:17 PM,1,1,1:09:35 PM,1,1,1:14:34 PM,1,1,1:39:41 PM,1,1,2:00:26 PM,11,25.7,,-1.2864733,36.8239937,-1.2927792,36.9053492,Rider_Id_785,1245
-Order_No_15048,User_Id_1066,Bike,1,Personal,15,1,9:29:04 AM,15,1,9:31:14 AM,15,1,9:46:13 AM,15,1,9:49:37 AM,15,1,10:17:30 AM,7,,,-1.2527958000000001,36.8003131,-1.3047026000000002,36.8172033,Rider_Id_751,1673
-Order_No_13082,User_Id_265,Bike,3,Business,3,5,12:39:51 PM,3,5,12:40:19 PM,3,5,12:41:36 PM,3,5,1:01:08 PM,3,5,1:25:41 PM,4,21.4,,-1.300921,36.828195,-1.3173868999999998,36.8112044,Rider_Id_471,1473
-Order_No_1581,User_Id_1465,Bike,3,Personal,22,6,10:33:08 AM,22,6,10:34:17 AM,22,6,10:56:41 AM,22,6,10:58:43 AM,22,6,11:22:20 AM,12,23.0,,-1.30482,36.756678,-1.2561298,36.7963749,Rider_Id_368,1417
-Order_No_18408,User_Id_2523,Bike,3,Business,29,3,3:19:26 PM,29,3,3:20:15 PM,29,3,3:28:11 PM,29,3,3:29:54 PM,29,3,3:49:21 PM,8,18.8,,-1.2551895,36.7822034,-1.2821378,36.7740428,Rider_Id_474,1167
-Order_No_2040,User_Id_1587,Bike,1,Personal,4,1,9:16:26 AM,4,1,9:18:12 AM,4,1,9:28:05 AM,4,1,9:34:05 AM,4,1,9:55:05 AM,6,17.8,,-1.2684961000000001,36.8141099,-1.3019025,36.791784299999996,Rider_Id_752,1260
-Order_No_12162,User_Id_3283,Bike,3,Business,29,2,1:25:42 PM,29,2,1:28:50 PM,29,2,1:30:51 PM,29,2,2:28:55 PM,29,2,2:40:13 PM,7,27.1,,-1.2872649999999999,36.821585999999996,-1.2628473,36.781805,Rider_Id_124,678
-Order_No_1328,User_Id_2216,Bike,3,Business,25,1,12:09:22 PM,25,1,12:09:47 PM,25,1,12:11:54 PM,25,1,12:22:32 PM,25,1,12:36:29 PM,8,27.2,,-1.2551895,36.7822034,-1.2922992,36.798303499999996,Rider_Id_267,837
-Order_No_16345,User_Id_3291,Bike,3,Business,22,5,12:15:52 PM,22,5,12:19:50 PM,22,5,12:35:56 PM,22,5,12:47:04 PM,22,5,1:06:38 PM,12,28.7,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_459,1174
-Order_No_18802,User_Id_2069,Bike,3,Business,22,4,4:29:33 PM,22,4,4:30:30 PM,22,4,4:35:59 PM,22,4,4:42:29 PM,22,4,5:06:53 PM,9,24.4,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_660,1464
-Order_No_9055,User_Id_627,Bike,3,Business,24,3,1:57:02 PM,24,3,1:58:13 PM,24,3,2:08:13 PM,24,3,2:20:49 PM,24,3,3:08:44 PM,11,,,-1.2571472,36.795063299999995,-1.2416156,36.8736721,Rider_Id_24,2875
-Order_No_3321,User_Id_2150,Bike,3,Business,13,1,2:06:23 PM,13,1,2:07:10 PM,13,1,2:14:22 PM,13,1,2:46:03 PM,13,1,3:28:01 PM,9,19.6,,-1.303596,36.778378000000004,-1.3444459999999998,36.756597,Rider_Id_140,2518
-Order_No_25527,User_Id_2219,Bike,3,Business,15,1,11:56:47 AM,15,1,11:57:04 AM,15,1,12:14:43 PM,15,1,12:16:09 PM,15,1,12:32:16 PM,6,,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_206,967
-Order_No_11897,User_Id_1869,Bike,1,Personal,9,3,2:40:49 PM,9,3,2:46:33 PM,9,3,3:15:46 PM,9,3,3:18:06 PM,9,3,3:38:32 PM,6,23.5,,-1.2976313999999998,36.777256,-1.2778976,36.8166591,Rider_Id_101,1226
-Order_No_5650,User_Id_2757,Bike,3,Business,5,1,5:02:52 PM,5,1,5:13:20 PM,5,1,5:49:14 PM,5,1,6:00:09 PM,5,1,6:12:50 PM,4,24.4,,-1.2891921999999998,36.825189,-1.2765737,36.8175399,Rider_Id_237,761
-Order_No_22785,User_Id_867,Bike,3,Business,1,6,9:58:17 AM,1,6,10:09:29 AM,1,6,10:29:16 AM,1,6,10:38:46 AM,1,6,11:06:24 AM,10,22.4,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_878,1658
-Order_No_4396,User_Id_517,Bike,3,Business,10,1,8:38:53 AM,10,1,8:38:58 AM,10,1,8:41:55 AM,10,1,8:46:36 AM,10,1,9:20:57 AM,14,18.3,2.0,-1.3228003000000002,36.8306435,-1.306378,36.7519845,Rider_Id_523,2061
-Order_No_7030,User_Id_2009,Bike,2,Personal,13,6,10:38:59 AM,13,6,10:39:19 AM,13,6,10:42:24 AM,13,6,10:46:54 AM,13,6,10:59:25 AM,6,24.9,,-1.3009173,36.7959207,-1.2851847,36.8314086,Rider_Id_409,751
-Order_No_18296,User_Id_3295,Bike,3,Business,15,3,11:02:30 AM,15,3,11:03:07 AM,15,3,11:09:44 AM,15,3,11:17:50 AM,15,3,11:44:57 AM,12,,,-1.2571472,36.795063299999995,-1.3029578000000002,36.871099,Rider_Id_770,1627
-Order_No_22690,User_Id_3517,Bike,3,Business,15,2,2:42:07 PM,15,2,2:42:14 PM,15,2,2:48:01 PM,15,2,3:16:49 PM,15,2,3:52:49 PM,15,24.7,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_340,2160
-Order_No_9453,User_Id_3560,Bike,3,Business,21,3,10:25:34 AM,21,3,11:45:34 AM,21,3,11:47:51 AM,21,3,11:50:36 AM,21,3,12:03:07 PM,4,21.9,,-1.279395,36.825364,-1.2597528999999998,36.806629,Rider_Id_237,751
-Order_No_22098,User_Id_3689,Bike,3,Business,30,3,11:14:48 AM,30,3,11:16:22 AM,30,3,11:17:07 AM,30,3,11:41:42 AM,30,3,11:46:52 AM,2,22.7,,-1.2579303999999998,36.8019667,-1.2601708999999999,36.8002238,Rider_Id_51,310
-Order_No_11524,User_Id_633,Bike,3,Business,27,4,12:42:02 PM,27,4,12:43:23 PM,27,4,12:52:22 PM,27,4,12:56:27 PM,27,4,1:06:57 PM,5,22.9,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_518,630
-Order_No_24143,User_Id_3234,Bike,1,Business,1,3,3:57:50 PM,1,3,4:00:44 PM,1,3,4:59:27 PM,1,3,5:43:50 PM,1,3,6:38:49 PM,10,23.1,,-1.2862659,36.8264616,-1.3180993,36.8676304,Rider_Id_700,3299
-Order_No_11147,User_Id_2982,Bike,3,Business,9,3,11:31:32 AM,9,3,11:31:42 AM,9,3,11:38:21 AM,9,3,12:02:13 PM,9,3,12:39:32 PM,18,25.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_148,2239
-Order_No_24990,User_Id_1767,Bike,3,Personal,9,3,2:55:50 PM,9,3,2:58:10 PM,9,3,3:20:21 PM,9,3,3:27:43 PM,9,3,3:35:52 PM,3,25.3,,-1.2834669,36.8297516,-1.2950041,36.814358299999995,Rider_Id_489,489
-Order_No_13872,User_Id_2691,Bike,3,Business,13,3,11:26:41 AM,13,3,11:27:04 AM,13,3,11:56:02 AM,13,3,12:02:24 PM,13,3,12:32:43 PM,8,19.9,,-1.2857671,36.7875614,-1.2551895,36.7822034,Rider_Id_755,1819
-Order_No_28009,User_Id_2062,Bike,1,Personal,15,7,2:43:20 PM,15,7,2:44:04 PM,15,7,3:02:23 PM,15,7,3:06:11 PM,15,7,3:36:11 PM,17,19.7,,-1.2873232,36.765857200000006,-1.3267367,36.8844852,Rider_Id_74,1800
-Order_No_22961,User_Id_1075,Bike,3,Business,19,3,11:28:35 AM,19,3,11:29:01 AM,19,3,11:33:39 AM,19,3,11:37:28 AM,19,3,12:11:34 PM,2,22.2,,-1.2527958000000001,36.8003131,-1.2561334,36.7990458,Rider_Id_453,2046
-Order_No_682,User_Id_2330,Bike,3,Business,29,1,4:31:00 PM,29,1,4:31:38 PM,29,1,4:38:48 PM,29,1,4:44:40 PM,29,1,5:19:42 PM,10,,,-1.2124582,36.802894,-1.2685423,36.8078703,Rider_Id_7,2102
-Order_No_15270,User_Id_1322,Bike,3,Business,12,2,1:41:02 PM,12,2,1:41:57 PM,12,2,2:40:49 PM,12,2,2:45:15 PM,12,2,3:12:49 PM,7,27.1,,-1.2638132,36.784977500000004,-1.2948751,36.7938907,Rider_Id_194,1654
-Order_No_10581,User_Id_2195,Bike,1,Business,15,6,8:55:54 AM,15,6,8:59:22 AM,15,6,9:05:29 AM,15,6,9:13:53 AM,15,6,9:27:49 AM,10,17.7,,-1.265715,36.823815,-1.3313643,36.857742200000004,Rider_Id_882,836
-Order_No_6905,User_Id_2344,Bike,3,Business,23,5,4:13:39 PM,23,5,4:21:10 PM,23,5,4:31:38 PM,23,5,4:34:15 PM,23,5,4:44:05 PM,3,24.7,,-1.2953162,36.7828041,-1.2857246999999998,36.789429299999995,Rider_Id_750,590
-Order_No_15470,User_Id_1946,Bike,1,Personal,25,3,1:17:47 PM,25,3,1:19:47 PM,25,3,1:22:46 PM,25,3,1:30:26 PM,25,3,1:45:22 PM,5,,,-1.2781519,36.832874100000005,-1.2913382,36.8018508,Rider_Id_393,896
-Order_No_10568,User_Id_323,Bike,3,Business,4,2,9:45:18 AM,4,2,9:45:28 AM,4,2,10:12:21 AM,4,2,10:23:24 AM,4,2,11:24:18 AM,26,16.4,,-1.2659447,36.7508667,-1.3632162,36.9108253,Rider_Id_832,3654
-Order_No_1798,User_Id_3727,Bike,3,Business,7,2,5:09:12 PM,7,2,5:09:25 PM,7,2,5:09:36 PM,7,2,5:11:42 PM,7,2,5:32:08 PM,4,23.2,,-1.2963097,36.7688221,-1.2764692,36.758011100000004,Rider_Id_939,1226
-Order_No_5271,User_Id_635,Bike,3,Business,25,6,1:21:33 PM,25,6,1:23:15 PM,25,6,1:28:13 PM,25,6,1:48:18 PM,25,6,2:19:34 PM,7,,,-1.2602337,36.79905479999999,-1.2976287,36.7744015,Rider_Id_617,1876
-Order_No_20604,User_Id_1154,Bike,3,Business,31,3,8:56:07 AM,31,3,8:58:28 AM,31,3,9:12:50 AM,31,3,9:16:14 AM,31,3,9:55:44 AM,5,19.0,,-1.2917868000000001,36.7872675,-1.3021707,36.816844399999994,Rider_Id_88,2370
-Order_No_11081,User_Id_3177,Bike,3,Business,18,4,10:05:43 AM,18,4,10:24:16 AM,18,4,10:36:20 AM,18,4,10:40:43 AM,18,4,11:11:04 AM,9,,,-1.3019530000000001,36.788458,-1.2566688999999998,36.779695200000006,Rider_Id_299,1821
-Order_No_756,User_Id_53,Bike,3,Business,3,1,9:00:51 AM,3,1,9:02:21 AM,3,1,9:02:33 AM,3,1,9:17:54 AM,3,1,9:19:35 AM,5,18.2,5.0,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_70,101
-Order_No_7112,User_Id_2982,Bike,3,Business,14,2,2:50:33 PM,14,2,3:53:19 PM,14,2,3:58:53 PM,14,2,4:11:24 PM,14,2,4:31:06 PM,7,,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_675,1182
-Order_No_4473,User_Id_391,Bike,3,Business,14,3,9:28:02 AM,14,3,9:44:15 AM,14,3,10:02:23 AM,14,3,10:46:02 AM,14,3,11:13:29 AM,22,24.5,,-1.2847613000000002,36.967084,-1.2867454,36.8259017,Rider_Id_813,1647
-Order_No_27005,User_Id_393,Bike,3,Business,30,2,9:43:36 AM,30,2,9:43:55 AM,30,2,9:48:53 AM,30,2,10:09:35 AM,30,2,10:17:59 AM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_843,504
-Order_No_8333,User_Id_3560,Bike,3,Business,27,1,4:06:38 PM,27,1,4:08:02 PM,27,1,4:17:13 PM,27,1,4:21:37 PM,27,1,4:38:23 PM,5,23.1,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_386,1006
-Order_No_25203,User_Id_3548,Bike,3,Personal,15,5,2:14:40 PM,15,5,2:15:02 PM,15,5,2:26:33 PM,15,5,2:27:37 PM,15,5,2:44:32 PM,5,28.0,,-1.2948385,36.776807899999994,-1.3104733999999998,36.7363698,Rider_Id_328,1015
-Order_No_2828,User_Id_2801,Bike,3,Business,27,3,12:24:54 PM,27,3,12:25:16 PM,27,3,12:33:09 PM,27,3,12:37:21 PM,27,3,1:03:34 PM,11,25.9,,-1.2674273999999999,36.787082700000006,-1.3090727,36.831526200000006,Rider_Id_177,1573
-Order_No_28151,User_Id_1959,Bike,3,Business,30,2,3:56:54 PM,30,2,4:08:18 PM,30,2,4:21:50 PM,30,2,4:30:47 PM,30,2,4:44:03 PM,3,,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_318,796
-Order_No_15364,User_Id_111,Bike,3,Business,6,1,3:07:46 PM,6,1,3:08:01 PM,6,1,3:08:37 PM,6,1,3:19:01 PM,6,1,3:55:56 PM,16,,,-1.2551895,36.7822034,-1.3194518,36.869396,Rider_Id_386,2215
-Order_No_13600,User_Id_3133,Bike,2,Personal,24,1,2:32:41 PM,24,1,2:40:19 PM,24,1,2:54:47 PM,24,1,2:57:27 PM,24,1,3:26:18 PM,9,,,-1.2562988000000002,36.8129456,-1.3200117,36.8324011,Rider_Id_38,1731
-Order_No_10291,User_Id_3203,Bike,3,Business,9,6,11:08:04 AM,9,6,11:20:54 AM,9,6,11:38:39 AM,9,6,12:22:49 PM,9,6,12:36:20 PM,9,17.3,,-1.272828,36.816608,-1.228911,36.881876,Rider_Id_379,811
-Order_No_2270,User_Id_1245,Bike,3,Business,8,5,1:35:24 PM,8,5,2:38:06 PM,8,5,2:38:16 PM,8,5,3:02:08 PM,8,5,3:35:19 PM,17,30.3,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_120,1991
-Order_No_11660,User_Id_426,Bike,3,Business,28,4,12:15:53 PM,28,4,12:17:15 PM,28,4,12:29:28 PM,28,4,12:32:51 PM,28,4,1:26:49 PM,17,,,-1.3323107,36.8692616,-1.2635744,36.781677,Rider_Id_326,3238
-Order_No_27354,User_Id_1402,Bike,3,Business,4,2,10:36:27 AM,4,2,10:37:25 AM,4,2,10:55:04 AM,4,2,10:57:43 AM,4,2,11:18:06 AM,8,20.5,,-1.2813012,36.832396200000005,-1.2551895,36.7822034,Rider_Id_582,1223
-Order_No_13011,User_Id_136,Bike,3,Business,15,5,9:10:58 AM,15,5,9:11:47 AM,15,5,9:33:06 AM,15,5,9:55:38 AM,15,5,10:35:32 AM,30,20.3,,-1.3043416,36.907419899999994,-1.3254522,36.721658399999995,Rider_Id_856,2394
-Order_No_24113,User_Id_3599,Bike,3,Business,14,2,12:37:19 PM,14,2,1:49:03 PM,14,2,1:58:39 PM,14,2,2:16:11 PM,14,2,2:20:09 PM,4,,,-1.3302996,36.8707935,-1.3295558,36.8390471,Rider_Id_218,238
-Order_No_24063,User_Id_867,Bike,3,Business,11,1,2:09:26 PM,11,1,2:19:46 PM,11,1,2:28:44 PM,11,1,2:38:06 PM,11,1,3:01:35 PM,11,28.7,,-1.2991441000000001,36.7528804,-1.2760462,36.807469700000006,Rider_Id_85,1409
-Order_No_9537,User_Id_3744,Bike,3,Business,11,2,2:53:27 PM,11,2,2:55:29 PM,11,2,3:10:45 PM,11,2,3:16:49 PM,11,2,3:35:44 PM,6,22.2,,-1.2672290000000002,36.806399,-1.2807336,36.7791105,Rider_Id_646,1135
-Order_No_11318,User_Id_718,Bike,3,Business,5,5,12:28:28 PM,5,5,12:29:14 PM,5,5,12:29:20 PM,5,5,12:43:42 PM,5,5,1:08:34 PM,12,23.9,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_871,1492
-Order_No_1495,User_Id_60,Bike,3,Business,14,2,3:10:59 PM,14,2,3:15:10 PM,14,2,3:36:58 PM,14,2,3:49:31 PM,14,2,4:05:43 PM,7,,,-1.272828,36.816608,-1.2297799999999999,36.836215,Rider_Id_660,972
-Order_No_5847,User_Id_3291,Bike,3,Business,16,1,12:24:34 PM,16,1,12:25:05 PM,16,1,12:38:13 PM,16,1,12:46:55 PM,16,1,1:00:32 PM,11,19.2,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_851,817
-Order_No_26417,User_Id_3686,Bike,3,Business,22,5,3:36:14 PM,22,5,3:48:04 PM,22,5,3:58:35 PM,22,5,4:19:53 PM,22,5,4:27:30 PM,6,27.1,,-1.2551895,36.7822034,-1.2819608,36.8109939,Rider_Id_411,457
-Order_No_22133,User_Id_2330,Bike,3,Business,5,2,12:43:55 PM,5,2,1:14:41 PM,5,2,1:27:23 PM,5,2,1:34:29 PM,5,2,2:07:34 PM,11,27.4,,-1.2284323,36.8757058,-1.293082,36.8278578,Rider_Id_622,1985
-Order_No_11950,User_Id_1882,Bike,3,Business,3,4,1:42:26 PM,3,4,2:20:06 PM,3,4,2:33:05 PM,3,4,2:36:56 PM,3,4,2:54:24 PM,7,25.8,,-1.2273427,36.799468299999994,-1.2630700000000001,36.796969,Rider_Id_26,1048
-Order_No_6092,User_Id_136,Bike,3,Business,5,3,9:02:46 AM,5,3,9:03:04 AM,5,3,9:08:21 AM,5,3,9:17:16 AM,5,3,9:42:51 AM,12,18.5,,-1.2738338,36.782403,-1.3326173000000001,36.71462329999999,Rider_Id_74,1535
-Order_No_17512,User_Id_3560,Bike,3,Business,5,6,10:46:31 AM,5,6,10:46:47 AM,5,6,11:02:15 AM,5,6,11:27:24 AM,5,6,11:34:31 AM,1,22.3,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_88,427
-Order_No_7482,User_Id_393,Bike,3,Business,2,6,8:37:10 AM,2,6,8:37:19 AM,2,6,8:38:10 AM,2,6,8:51:28 AM,2,6,9:20:39 AM,14,20.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1751
-Order_No_3127,User_Id_3560,Bike,3,Business,6,2,2:46:30 PM,6,2,3:58:01 PM,6,2,4:16:46 PM,6,2,4:34:59 PM,6,2,4:44:04 PM,2,,,-1.2882183999999999,36.826346,-1.279395,36.825364,Rider_Id_237,545
-Order_No_16400,User_Id_3599,Bike,3,Business,27,1,1:07:27 PM,27,1,1:25:32 PM,27,1,2:16:38 PM,27,1,2:35:08 PM,27,1,2:59:47 PM,12,22.9,,-1.3302996,36.8707935,-1.2922992,36.798303499999996,Rider_Id_675,1479
-Order_No_814,User_Id_393,Bike,3,Business,15,2,12:59:12 PM,15,2,12:59:40 PM,15,2,12:59:47 PM,15,2,1:10:55 PM,15,2,1:42:24 PM,14,24.1,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_583,1889
-Order_No_22439,User_Id_3201,Bike,3,Business,12,3,9:06:03 AM,12,3,9:07:28 AM,12,3,9:07:31 AM,12,3,9:21:05 AM,12,3,9:33:53 AM,5,,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_698,768
-Order_No_8957,User_Id_639,Bike,3,Business,30,5,5:04:41 PM,30,5,5:30:41 PM,30,5,5:42:31 PM,30,5,5:47:39 PM,30,5,6:44:23 PM,17,22.2,,-1.3169591,36.7000029,-1.2551895,36.7822034,Rider_Id_205,3404
-Order_No_15896,User_Id_136,Bike,3,Business,29,4,4:24:07 PM,29,4,4:24:34 PM,29,4,4:33:44 PM,29,4,4:49:24 PM,29,4,5:03:29 PM,4,20.8,,-1.2965045,36.7913036,-1.3009676000000001,36.7657189,Rider_Id_427,845
-Order_No_4886,User_Id_2154,Bike,3,Business,11,5,3:01:20 PM,11,5,3:05:40 PM,11,5,3:17:55 PM,11,5,3:19:27 PM,11,5,3:38:17 PM,6,26.3,,-1.2765736,36.851364600000004,-1.2877544,36.815450899999995,Rider_Id_15,1130
-Order_No_27370,User_Id_2828,Bike,3,Business,8,6,9:15:29 AM,8,6,10:19:52 AM,8,6,11:09:34 AM,8,6,11:18:17 AM,8,6,11:55:28 AM,22,18.4,,-1.2344098,36.7290354,-1.2156006000000001,36.8916865,Rider_Id_937,2231
-Order_No_17044,User_Id_2200,Bike,3,Business,14,4,3:06:35 PM,14,4,3:18:06 PM,14,4,3:26:32 PM,14,4,3:41:57 PM,14,4,3:47:07 PM,4,27.8,,-1.290894,36.822971,-1.3041128999999998,36.810493900000004,Rider_Id_336,310
-Order_No_3051,User_Id_1396,Bike,2,Personal,3,1,5:46:48 PM,3,1,5:47:26 PM,3,1,5:58:28 PM,3,1,6:15:25 PM,3,1,6:33:54 PM,5,22.4,,-1.2449603999999999,36.796515500000005,-1.2768829,36.7965104,Rider_Id_208,1109
-Order_No_21938,User_Id_2174,Bike,3,Business,27,1,1:34:32 PM,27,1,1:34:32 PM,27,1,1:36:02 PM,27,1,2:36:30 PM,27,1,2:36:34 PM,11,23.1,,-1.2482781,36.884091999999995,-1.261795,36.822333,Rider_Id_86,4
-Order_No_14883,User_Id_867,Bike,3,Business,9,2,9:22:27 AM,9,2,9:23:06 AM,9,2,9:32:44 AM,9,2,9:48:31 AM,9,2,9:59:02 AM,11,20.6,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_819,631
-Order_No_9515,User_Id_3491,Bike,3,Business,25,5,11:07:22 AM,25,5,11:07:34 AM,25,5,11:23:29 AM,25,5,11:42:45 AM,25,5,12:21:38 PM,23,21.2,,-1.3562374,36.904295399999995,-1.2297202,36.874550799999994,Rider_Id_543,2333
-Order_No_16568,User_Id_1402,Bike,3,Business,10,1,9:34:26 AM,10,1,9:35:18 AM,10,1,9:52:51 AM,10,1,10:25:09 AM,10,1,10:48:39 AM,13,16.7,,-1.2156006000000001,36.8916865,-1.2813012,36.832396200000005,Rider_Id_76,1410
-Order_No_20029,User_Id_3686,Bike,3,Business,15,5,9:33:37 AM,15,5,9:34:08 AM,15,5,9:45:21 AM,15,5,9:51:00 AM,15,5,10:42:20 AM,22,20.4,,-1.2551895,36.7822034,-1.3692201000000002,36.9252718,Rider_Id_932,3080
-Order_No_18230,User_Id_635,Bike,3,Business,17,4,12:48:02 PM,17,4,12:48:22 PM,17,4,12:54:42 PM,17,4,1:37:35 PM,17,4,2:08:13 PM,11,28.1,,-1.3081376,36.83297720000001,-1.2771412,36.766146,Rider_Id_936,1838
-Order_No_5395,User_Id_1291,Bike,3,Personal,3,3,9:50:12 AM,3,3,9:50:33 AM,3,3,10:05:07 AM,3,3,10:15:51 AM,3,3,10:36:20 AM,8,18.6,,-1.2664491000000002,36.8102685,-1.2963097,36.7688221,Rider_Id_219,1229
-Order_No_24605,User_Id_1133,Bike,3,Business,25,5,7:52:06 AM,25,5,8:53:24 AM,25,5,9:04:58 AM,25,5,9:10:58 AM,25,5,10:29:16 AM,10,20.8,,-1.3323107,36.8692616,-1.2844342,36.8225105,Rider_Id_26,4698
-Order_No_21555,User_Id_868,Bike,3,Business,28,4,12:59:59 PM,28,4,1:00:48 PM,28,4,1:21:02 PM,28,4,2:22:10 PM,28,4,2:47:19 PM,13,30.0,,-1.2600926000000001,36.8088685,-1.331619,36.847976,Rider_Id_26,1509
-Order_No_2716,User_Id_2330,Bike,3,Business,13,1,12:20:22 PM,13,1,12:22:41 PM,13,1,12:41:05 PM,13,1,12:42:15 PM,13,1,1:06:11 PM,5,,,-1.294597,36.7742999,-1.2915246999999999,36.805575700000006,Rider_Id_831,1436
-Order_No_8124,User_Id_3044,Bike,3,Business,8,5,4:30:04 PM,8,5,4:45:24 PM,8,5,4:56:10 PM,8,5,4:58:25 PM,8,5,5:12:58 PM,6,28.2,,-1.3228003000000002,36.8306435,-1.3127877000000001,36.848198700000005,Rider_Id_127,873
-Order_No_7990,User_Id_2159,Bike,3,Business,14,2,3:05:24 PM,14,2,3:05:37 PM,14,2,3:09:25 PM,14,2,3:40:44 PM,14,2,4:00:35 PM,12,24.1,,-1.3354923,36.859815600000005,-1.304735,36.784605400000004,Rider_Id_882,1191
-Order_No_11011,User_Id_3222,Bike,3,Business,15,5,4:38:22 PM,15,5,4:38:33 PM,15,5,4:41:08 PM,15,5,4:46:53 PM,15,5,4:54:33 PM,3,27.9,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_690,460
-Order_No_13285,User_Id_3689,Bike,3,Business,20,4,1:58:30 PM,20,4,2:38:33 PM,20,4,3:07:06 PM,20,4,3:09:51 PM,20,4,3:32:26 PM,8,25.2,,-1.2765736,36.851364600000004,-1.2527958000000001,36.8003131,Rider_Id_660,1355
-Order_No_23642,User_Id_3517,Bike,3,Business,26,5,2:19:33 PM,26,5,2:54:29 PM,26,5,3:17:30 PM,26,5,3:43:23 PM,26,5,4:21:02 PM,20,,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_61,2259
-Order_No_16870,User_Id_1281,Bike,3,Business,1,3,12:21:21 PM,1,3,12:22:14 PM,1,3,1:12:35 PM,1,3,2:17:53 PM,1,3,2:41:53 PM,9,23.8,,-1.2869041,36.8240057,-1.2707742,36.7578609,Rider_Id_229,1440
-Order_No_9660,User_Id_3283,Bike,3,Business,2,2,2:58:25 PM,2,2,2:59:02 PM,2,2,3:10:40 PM,2,2,3:26:31 PM,2,2,3:33:24 PM,5,26.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_882,413
-Order_No_14140,User_Id_53,Bike,3,Business,15,3,8:47:53 AM,15,3,8:49:13 AM,15,3,8:54:47 AM,15,3,9:11:33 AM,15,3,9:28:33 AM,6,,,-1.3080091999999999,36.8284543,-1.2770708,36.8231093,Rider_Id_921,1020
-Order_No_20696,User_Id_868,Bike,3,Business,13,6,11:01:31 AM,13,6,11:01:45 AM,13,6,11:11:08 AM,13,6,11:25:46 AM,13,6,11:55:16 AM,19,25.9,,-1.3181159,36.710155900000004,-1.265715,36.823815,Rider_Id_291,1770
-Order_No_18361,User_Id_3032,Bike,3,Business,19,3,9:52:12 AM,19,3,9:52:29 AM,19,3,9:59:38 AM,19,3,10:14:26 AM,19,3,10:55:10 AM,18,19.5,,-1.2571472,36.795063299999995,-1.3398006000000002,36.717935,Rider_Id_858,2444
-Order_No_12380,User_Id_1329,Bike,3,Business,31,4,9:52:06 AM,31,4,10:07:50 AM,31,4,10:15:54 AM,31,4,10:17:36 AM,31,4,10:29:55 AM,2,20.7,,-1.273056,36.811298,-1.2641662,36.8136393,Rider_Id_228,739
-Order_No_15099,User_Id_2956,Bike,3,Business,24,2,1:45:53 PM,24,2,1:46:04 PM,24,2,2:09:06 PM,24,2,2:12:01 PM,24,2,2:27:16 PM,4,18.6,,-1.2584143,36.8048002,-1.2562249,36.786866100000005,Rider_Id_415,915
-Order_No_12898,User_Id_393,Bike,3,Business,15,4,11:53:53 AM,15,4,12:22:56 PM,15,4,12:58:04 PM,15,4,1:06:47 PM,15,4,1:29:26 PM,10,24.4,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_532,1359
-Order_No_26309,User_Id_3698,Bike,3,Business,30,2,1:03:09 PM,30,2,1:06:40 PM,30,2,1:17:18 PM,30,2,1:23:35 PM,30,2,1:47:53 PM,7,,,-1.3244885,36.897791999999995,-1.34364,36.8925345,Rider_Id_164,1458
-Order_No_13257,User_Id_136,Bike,3,Business,20,1,11:08:37 AM,20,1,11:08:53 AM,20,1,11:22:47 AM,20,1,11:24:33 AM,20,1,11:40:45 AM,6,,,-1.3376931,36.751266799999996,-1.3254522,36.721658399999995,Rider_Id_815,972
-Order_No_8440,User_Id_3580,Bike,3,Personal,21,4,3:35:17 PM,21,4,3:38:23 PM,21,4,4:45:25 PM,21,4,4:53:00 PM,21,4,5:40:57 PM,20,20.0,,-1.2870003,36.8312206,-1.3745958999999999,36.9276225,Rider_Id_654,2877
-Order_No_23320,User_Id_517,Bike,3,Business,27,4,10:48:24 AM,27,4,11:35:24 AM,27,4,11:39:40 AM,27,4,11:53:06 AM,27,4,12:07:56 PM,4,23.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,890
-Order_No_22225,User_Id_2748,Bike,1,Personal,6,3,2:40:30 PM,6,3,3:01:11 PM,6,3,3:15:58 PM,6,3,3:19:04 PM,6,3,3:32:40 PM,6,21.6,,-1.269103,36.8223998,-1.2933649999999999,36.7961955,Rider_Id_726,816
-Order_No_6386,User_Id_2464,Bike,3,Business,3,1,2:49:22 PM,3,1,3:15:10 PM,3,1,3:32:36 PM,3,1,3:34:29 PM,3,1,4:23:51 PM,13,23.0,,-1.279395,36.825364,-1.2715944,36.9114173,Rider_Id_709,2962
-Order_No_25876,User_Id_685,Bike,3,Business,13,4,9:20:00 AM,13,4,9:21:05 AM,13,4,9:30:52 AM,13,4,9:35:29 AM,13,4,9:56:09 AM,8,18.4,,-1.265432,36.795034,-1.2960687,36.8167909,Rider_Id_291,1240
-Order_No_16417,User_Id_761,Bike,3,Business,18,3,9:37:11 AM,18,3,9:37:51 AM,18,3,9:45:47 AM,18,3,9:50:05 AM,18,3,10:27:10 AM,25,14.8,,-1.2693101999999998,36.798177700000004,-1.2740083999999998,36.96216810000001,Rider_Id_103,2225
-Order_No_2784,User_Id_788,Bike,3,Business,16,3,10:26:35 AM,16,3,10:27:22 AM,16,3,10:28:20 AM,16,3,10:42:35 AM,16,3,11:00:41 AM,10,24.2,,-1.28871,36.825125299999996,-1.225322,36.80855,Rider_Id_408,1086
-Order_No_22114,User_Id_1500,Bike,3,Business,25,1,9:20:22 AM,25,1,9:21:59 AM,25,1,9:28:42 AM,25,1,10:16:42 AM,25,1,10:32:39 AM,8,21.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_399,957
-Order_No_610,User_Id_3560,Bike,3,Business,25,1,11:41:59 AM,25,1,11:42:31 AM,25,1,11:56:10 AM,25,1,11:57:37 AM,25,1,12:39:46 PM,5,25.7,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_686,2529
-Order_No_4206,User_Id_867,Bike,3,Business,3,3,2:20:16 PM,3,3,2:25:24 PM,3,3,2:29:52 PM,3,3,3:10:47 PM,3,3,3:46:21 PM,10,28.4,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_686,2134
-Order_No_12619,User_Id_3080,Bike,3,Business,22,3,11:44:54 AM,22,3,11:45:18 AM,22,3,11:46:21 AM,22,3,11:52:33 AM,22,3,12:23:51 PM,18,22.9,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_576,1878
-Order_No_19681,User_Id_3708,Bike,1,Personal,1,1,1:48:03 PM,1,1,1:48:23 PM,1,1,2:09:43 PM,1,1,2:14:24 PM,1,1,2:54:11 PM,20,25.7,,-1.3120337,36.914762100000004,-1.2790653,36.7907262,Rider_Id_543,2387
-Order_No_14057,User_Id_3283,Bike,3,Business,28,6,2:26:39 PM,28,6,2:39:11 PM,28,6,2:43:51 PM,28,6,2:52:41 PM,28,6,3:24:03 PM,20,24.8,,-1.2628473,36.781805,-1.293515,36.897607,Rider_Id_101,1882
-Order_No_6356,User_Id_2066,Bike,3,Business,29,1,5:03:16 PM,29,1,5:03:46 PM,29,1,5:12:46 PM,29,1,5:22:16 PM,29,1,5:35:38 PM,3,23.2,,-1.316961,36.820337,-1.3124791999999998,36.829166,Rider_Id_208,802
-Order_No_12607,User_Id_2750,Bike,3,Business,13,1,10:55:02 AM,13,1,10:55:32 AM,13,1,11:00:47 AM,13,1,11:05:00 AM,13,1,11:14:23 AM,6,,,-1.2675333999999998,36.7658246,-1.2551895,36.7822034,Rider_Id_99,563
-Order_No_3347,User_Id_1787,Bike,3,Business,2,7,12:24:04 PM,2,7,1:21:54 PM,2,7,1:22:00 PM,2,7,1:35:13 PM,2,7,2:03:14 PM,17,25.1,,-1.2283403,36.8822756,-1.15135,36.9566534,Rider_Id_517,1681
-Order_No_17336,User_Id_1209,Bike,3,Business,6,3,11:15:42 AM,6,3,11:16:21 AM,6,3,11:29:39 AM,6,3,11:49:15 AM,6,3,12:10:36 PM,10,22.6,,-1.3004062,36.829741,-1.3263285,36.8926624,Rider_Id_11,1281
-Order_No_13864,User_Id_478,Bike,3,Business,8,5,11:20:09 AM,8,5,11:21:16 AM,8,5,11:45:25 AM,8,5,12:04:56 PM,8,5,12:20:31 PM,7,28.4,,-1.2551895,36.7822034,-1.2894795000000001,36.781853399999996,Rider_Id_205,935
-Order_No_27446,User_Id_955,Bike,2,Personal,10,5,2:08:10 PM,10,5,2:09:12 PM,10,5,2:25:44 PM,10,5,3:02:46 PM,10,5,3:13:41 PM,3,21.6,,-1.3142701,36.853769299999996,-1.322764,36.861414,Rider_Id_329,655
-Order_No_23966,User_Id_868,Bike,3,Business,20,3,1:04:53 PM,20,3,1:05:14 PM,20,3,1:12:08 PM,20,3,1:19:50 PM,20,3,1:37:06 PM,8,27.4,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_952,1036
-Order_No_20514,User_Id_1341,Bike,3,Business,27,1,10:31:36 AM,27,1,10:32:29 AM,27,1,10:41:24 AM,27,1,10:49:50 AM,27,1,11:00:09 AM,9,20.8,,-1.3033561000000002,36.8163631,-1.331619,36.847976,Rider_Id_591,619
-Order_No_11870,User_Id_2330,Bike,3,Business,15,5,12:16:37 PM,15,5,12:16:58 PM,15,5,12:27:12 PM,15,5,12:29:25 PM,15,5,12:35:05 PM,3,26.3,,-1.3040812,36.7924217,-1.2915246999999999,36.805575700000006,Rider_Id_862,340
-Order_No_14966,User_Id_1564,Bike,1,Personal,16,2,9:51:18 AM,16,2,9:51:55 AM,16,2,9:57:44 AM,16,2,9:59:08 AM,16,2,10:05:52 AM,3,,,-1.2452735000000001,36.7911588,-1.2670196000000002,36.807700200000006,Rider_Id_453,404
-Order_No_12492,User_Id_2803,Bike,3,Business,18,4,2:38:52 PM,18,4,2:39:02 PM,18,4,2:57:45 PM,18,4,2:59:20 PM,18,4,3:33:51 PM,14,,,-1.2765736,36.851364600000004,-1.2707623,36.7581285,Rider_Id_910,2071
-Order_No_25998,User_Id_3396,Bike,3,Business,16,2,7:25:33 PM,16,2,7:25:45 PM,16,2,7:32:33 PM,16,2,7:45:08 PM,16,2,8:16:52 PM,2,,,-1.2963097,36.7688221,-1.301809,36.75643,Rider_Id_197,1904
-Order_No_24344,User_Id_2286,Bike,3,Personal,31,7,1:49:26 PM,31,7,1:54:33 PM,31,7,2:40:46 PM,31,7,2:55:02 PM,31,7,3:11:43 PM,10,30.3,,-1.2073966,36.7917836,-1.2261247,36.793301899999996,Rider_Id_870,1001
-Order_No_12480,User_Id_1271,Bike,3,Business,3,2,4:16:46 PM,3,2,4:18:21 PM,3,2,4:54:32 PM,3,2,4:57:51 PM,3,2,5:16:05 PM,12,18.6,,-1.3244885,36.897791999999995,-1.3280091,36.8524146,Rider_Id_895,1094
-Order_No_2372,User_Id_1500,Bike,3,Business,28,4,12:56:43 PM,28,4,12:57:12 PM,28,4,12:59:55 PM,28,4,1:35:09 PM,28,4,1:35:26 PM,8,27.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_953,17
-Order_No_17883,User_Id_3599,Bike,3,Business,4,2,12:09:00 PM,4,2,12:42:22 PM,4,2,1:25:49 PM,4,2,1:34:34 PM,4,2,2:03:58 PM,12,22.3,,-1.3302996,36.8707935,-1.297101,36.798243,Rider_Id_416,1764
-Order_No_20295,User_Id_3557,Bike,3,Business,22,3,12:38:00 PM,22,3,12:42:52 PM,22,3,12:54:58 PM,22,3,1:41:51 PM,22,3,1:41:57 PM,6,24.4,,-1.286937,36.824674200000004,-1.2546732,36.808679600000005,Rider_Id_921,6
-Order_No_10543,User_Id_1665,Bike,1,Personal,21,4,1:56:32 PM,21,4,1:57:26 PM,21,4,2:25:55 PM,21,4,2:39:46 PM,21,4,3:15:31 PM,16,22.4,,-1.3207708,36.8685919,-1.2862269,36.7841601,Rider_Id_844,2145
-Order_No_18833,User_Id_868,Bike,3,Business,25,4,3:09:56 PM,25,4,3:10:07 PM,25,4,3:10:12 PM,25,4,3:19:53 PM,25,4,3:43:53 PM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_709,1440
-Order_No_427,User_Id_1961,Bike,3,Business,6,1,9:37:41 AM,6,1,9:38:01 AM,6,1,9:49:11 AM,6,1,10:20:57 AM,6,1,10:47:09 AM,22,,,-1.318062,36.849326,-1.3181159,36.710155900000004,Rider_Id_819,1572
-Order_No_5209,User_Id_868,Bike,3,Business,9,6,10:43:05 AM,9,6,10:43:30 AM,9,6,10:53:05 AM,9,6,11:11:23 AM,9,6,11:29:40 AM,8,24.7,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_263,1097
-Order_No_17489,User_Id_640,Bike,3,Business,21,2,10:22:45 AM,21,2,10:24:38 AM,21,2,10:58:38 AM,21,2,11:05:39 AM,21,2,11:34:40 AM,7,22.5,,-1.2969741000000001,36.7856612,-1.2821883,36.826961100000005,Rider_Id_831,1741
-Order_No_3390,User_Id_393,Bike,3,Business,25,4,12:44:20 PM,25,4,1:24:48 PM,25,4,1:28:25 PM,25,4,1:42:45 PM,25,4,1:53:17 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_211,632
-Order_No_22168,User_Id_62,Bike,3,Business,27,1,11:26:59 AM,27,1,12:33:01 PM,27,1,12:45:32 PM,27,1,1:04:40 PM,27,1,2:22:34 PM,11,20.9,,-1.2967108,36.787113500000004,-1.324859,36.859367,Rider_Id_812,4674
-Order_No_10402,User_Id_3647,Bike,3,Business,22,3,1:22:37 PM,22,3,1:24:44 PM,22,3,1:38:39 PM,22,3,1:41:05 PM,22,3,2:12:40 PM,16,24.5,,-1.298037,36.6934582,-1.2726389999999999,36.794723,Rider_Id_692,1895
-Order_No_9304,User_Id_3045,Bike,1,Personal,18,6,2:10:25 PM,18,6,2:10:48 PM,18,6,2:21:47 PM,18,6,2:32:35 PM,18,6,2:47:29 PM,5,20.5,,-1.2859116000000002,36.809803499999994,-1.2735236,36.7992196,Rider_Id_414,894
-Order_No_20351,User_Id_3560,Bike,3,Business,17,3,10:28:43 AM,17,3,10:29:54 AM,17,3,10:43:02 AM,17,3,11:04:35 AM,17,3,11:20:08 AM,10,25.2,,-1.2297202,36.881950700000004,-1.279395,36.825364,Rider_Id_897,933
-Order_No_13307,User_Id_1402,Bike,3,Business,16,1,1:00:37 PM,16,1,1:01:47 PM,16,1,1:20:35 PM,16,1,1:29:07 PM,16,1,1:44:01 PM,9,19.4,,-1.2891921999999998,36.825189,-1.2584143,36.8048002,Rider_Id_358,894
-Order_No_6871,User_Id_270,Bike,2,Personal,14,4,1:25:21 PM,14,4,1:33:16 PM,14,4,1:40:04 PM,14,4,2:02:43 PM,14,4,2:11:45 PM,4,21.6,,-1.31522,36.837006,-1.3312467,36.8321722,Rider_Id_890,542
-Order_No_2463,User_Id_1245,Bike,3,Business,17,5,9:47:15 AM,17,5,9:47:59 AM,17,5,10:42:01 AM,17,5,11:00:59 AM,17,5,11:26:49 AM,10,17.6,,-1.300921,36.828195,-1.271672,36.783604,Rider_Id_223,1550
-Order_No_284,User_Id_1856,Bike,3,Business,15,3,3:16:33 PM,15,3,3:17:10 PM,15,3,3:23:35 PM,15,3,3:41:47 PM,15,3,3:51:37 PM,4,21.7,,-1.273009,36.802807,-1.2866011999999998,36.8195691,Rider_Id_281,590
-Order_No_22694,User_Id_269,Bike,3,Business,6,4,4:29:27 PM,6,4,4:31:11 PM,6,4,4:36:02 PM,6,4,5:00:21 PM,6,4,5:43:21 PM,9,22.4,,-1.3332749,36.870814700000004,-1.3562374,36.904295399999995,Rider_Id_447,2580
-Order_No_4683,User_Id_2691,Bike,3,Business,20,3,10:05:16 AM,20,3,10:05:52 AM,20,3,10:35:46 AM,20,3,10:42:01 AM,20,3,11:10:20 AM,8,27.4,,-1.2857671,36.7875614,-1.2551895,36.7822034,Rider_Id_647,1699
-Order_No_4543,User_Id_53,Bike,3,Business,30,3,8:37:27 AM,30,3,9:59:33 AM,30,3,10:05:58 AM,30,3,10:18:44 AM,30,3,10:34:19 AM,5,19.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_576,935
-Order_No_10151,User_Id_972,Bike,3,Business,18,6,10:17:33 AM,18,6,10:20:50 AM,18,6,10:27:35 AM,18,6,10:29:37 AM,18,6,11:04:29 AM,7,15.7,,-1.2862033000000002,36.8152966,-1.32256,36.8396386,Rider_Id_857,2092
-Order_No_14708,User_Id_450,Bike,1,Personal,23,1,10:06:06 AM,23,1,10:06:25 AM,23,1,10:09:48 AM,23,1,10:17:15 AM,23,1,10:26:07 AM,4,14.4,,-1.2663778,36.8076024,-1.2562796999999999,36.8075858,Rider_Id_379,532
-Order_No_13234,User_Id_1389,Bike,3,Business,8,4,3:44:52 PM,8,4,3:47:06 PM,8,4,4:04:21 PM,8,4,4:45:43 PM,8,4,5:49:27 PM,24,25.6,,-1.3103985,36.9114173,-1.207546,36.7913976,Rider_Id_532,3824
-Order_No_8318,User_Id_148,Bike,3,Business,2,1,3:59:35 PM,2,1,4:01:45 PM,2,1,4:08:15 PM,2,1,4:14:52 PM,2,1,4:23:13 PM,2,20.2,,-1.2551895,36.7822034,-1.2579303999999998,36.8019667,Rider_Id_770,501
-Order_No_22989,User_Id_2956,Bike,3,Business,6,4,1:48:40 PM,6,4,1:49:25 PM,6,4,1:49:47 PM,6,4,2:02:51 PM,6,4,2:22:58 PM,10,23.2,,-1.2584143,36.8048002,-1.3134514,36.844629299999994,Rider_Id_179,1207
-Order_No_2072,User_Id_3291,Bike,3,Business,31,2,1:08:26 PM,31,2,1:09:06 PM,31,2,1:19:57 PM,31,2,1:35:26 PM,31,2,1:44:55 PM,4,22.1,,-1.265715,36.823815,-1.2600926000000001,36.8088685,Rider_Id_726,569
-Order_No_861,User_Id_2523,Bike,3,Business,30,2,9:58:47 AM,30,2,10:02:49 AM,30,2,10:13:05 AM,30,2,11:05:59 AM,30,2,11:06:01 AM,17,,,-1.2551895,36.7822034,-1.2778747,36.883182899999994,Rider_Id_953,2
-Order_No_17692,User_Id_517,Bike,3,Business,8,2,8:50:52 AM,8,2,8:51:10 AM,8,2,8:55:29 AM,8,2,9:02:05 AM,8,2,9:31:54 AM,14,19.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1789
-Order_No_10891,User_Id_1033,Bike,3,Personal,15,5,10:50:21 AM,15,5,10:51:11 AM,15,5,11:02:58 AM,15,5,11:09:10 AM,15,5,11:32:14 AM,11,26.4,,-1.2326041,36.8009029,-1.2830057,36.81754779999999,Rider_Id_669,1384
-Order_No_25487,User_Id_3647,Bike,3,Business,8,4,9:40:28 AM,8,4,9:40:34 AM,8,4,10:00:56 AM,8,4,10:04:46 AM,8,4,10:26:20 AM,13,20.3,,-1.2726389999999999,36.794723,-1.326496,36.702379,Rider_Id_932,1294
-Order_No_10124,User_Id_1389,Bike,3,Business,3,4,4:30:38 PM,3,4,4:40:37 PM,3,4,4:56:06 PM,3,4,5:02:23 PM,3,4,5:21:17 PM,5,18.1,,-1.281223,36.8239,-1.2668734,36.8054026,Rider_Id_692,1134
-Order_No_5260,User_Id_2523,Bike,3,Business,10,1,2:33:11 PM,10,1,2:33:49 PM,10,1,2:40:41 PM,10,1,2:47:38 PM,10,1,3:17:42 PM,5,18.5,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_205,1804
-Order_No_17468,User_Id_2216,Bike,3,Business,29,1,10:41:23 AM,29,1,10:41:54 AM,29,1,10:42:57 AM,29,1,10:57:09 AM,29,1,11:11:47 AM,5,,,-1.2551895,36.7822034,-1.2745107,36.797450299999994,Rider_Id_593,878
-Order_No_26768,User_Id_3758,Bike,1,Personal,14,6,9:26:13 AM,14,6,9:26:28 AM,14,6,9:58:39 AM,14,6,10:12:13 AM,14,6,11:00:35 AM,12,15.0,,-1.3083851,36.905855200000005,-1.2782058,36.8311888,Rider_Id_329,2902
-Order_No_3300,User_Id_517,Bike,3,Business,27,4,12:02:34 PM,27,4,12:14:56 PM,27,4,12:25:29 PM,27,4,12:29:58 PM,27,4,12:45:12 PM,11,23.1,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,914
-Order_No_20355,User_Id_3221,Bike,3,Business,11,4,10:11:54 AM,11,4,10:12:03 AM,11,4,10:16:34 AM,11,4,10:27:19 AM,11,4,11:14:31 AM,5,18.3,,-1.2764851000000002,36.8007126,-1.2551895,36.7822034,Rider_Id_345,2832
-Order_No_23618,User_Id_1359,Bike,2,Personal,20,5,1:56:23 PM,20,5,2:28:02 PM,20,5,2:52:19 PM,20,5,3:00:24 PM,20,5,4:17:21 PM,17,20.5,,-1.3688433,36.7402114,-1.3025666999999999,36.7803495,Rider_Id_939,4617
-Order_No_4725,User_Id_993,Bike,1,Personal,20,1,3:37:13 PM,20,1,3:38:04 PM,20,1,3:47:22 PM,20,1,4:33:36 PM,20,1,4:55:16 PM,11,20.2,,-1.2800881000000002,36.8147037,-1.2643343,36.7447423,Rider_Id_229,1300
-Order_No_13584,User_Id_2701,Bike,3,Business,12,3,10:44:31 AM,12,3,10:49:00 AM,12,3,11:03:46 AM,12,3,11:07:14 AM,12,3,11:43:10 AM,9,19.4,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_827,2156
-Order_No_11314,User_Id_1950,Bike,1,Personal,15,1,10:21:46 AM,15,1,10:22:34 AM,15,1,10:32:20 AM,15,1,10:36:57 AM,15,1,10:52:44 AM,7,,,-1.2413316,36.878552500000005,-1.2257343,36.89335429999999,Rider_Id_704,947
-Order_No_9708,User_Id_3621,Bike,3,Business,17,5,12:24:34 PM,17,5,12:28:54 PM,17,5,12:47:32 PM,17,5,12:51:06 PM,17,5,1:05:06 PM,5,,,-1.2836583000000001,36.7886014,-1.2945921999999999,36.8255247,Rider_Id_158,840
-Order_No_25294,User_Id_685,Bike,3,Business,7,2,2:48:13 PM,7,2,2:48:52 PM,7,2,3:07:14 PM,7,2,3:11:25 PM,7,2,4:27:02 PM,24,,,-1.2726506000000002,36.7960153,-1.34364,36.8925345,Rider_Id_161,4537
-Order_No_20020,User_Id_3199,Bike,1,Personal,31,2,4:28:51 PM,31,2,4:29:27 PM,31,2,4:36:07 PM,31,2,4:37:59 PM,31,2,4:59:54 PM,7,19.9,,-1.2630978000000002,36.7832596,-1.2994194,36.7647917,Rider_Id_519,1315
-Order_No_16855,User_Id_868,Bike,3,Business,20,3,10:37:53 AM,20,3,10:47:45 AM,20,3,10:58:27 AM,20,3,11:13:46 AM,20,3,11:24:48 AM,10,20.3,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_222,662
-Order_No_1655,User_Id_136,Bike,3,Business,17,5,11:11:54 AM,17,5,11:12:09 AM,17,5,11:18:35 AM,17,5,11:22:20 AM,17,5,11:30:25 AM,2,,,-1.2649605,36.798177700000004,-1.2657988000000002,36.812624,Rider_Id_96,485
-Order_No_14368,User_Id_393,Bike,3,Business,1,5,8:56:28 AM,1,5,8:56:48 AM,1,5,8:57:01 AM,1,5,9:33:18 AM,1,5,9:33:23 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,5
-Order_No_23470,User_Id_517,Bike,3,Business,3,4,9:49:04 AM,3,4,9:49:47 AM,3,4,9:58:11 AM,3,4,10:05:55 AM,3,4,10:22:32 AM,11,20.4,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_576,997
-Order_No_1640,User_Id_391,Bike,3,Business,10,3,2:47:28 PM,10,3,2:53:52 PM,10,3,3:28:40 PM,10,3,3:34:34 PM,10,3,4:26:32 PM,4,30.4,,-1.2867454,36.8259017,-1.2760462,36.807469700000006,Rider_Id_787,3118
-Order_No_13105,User_Id_277,Bike,1,Personal,27,1,1:09:00 PM,27,1,1:09:09 PM,27,1,1:14:07 PM,27,1,1:26:32 PM,27,1,1:53:50 PM,12,24.1,,-1.3066837,36.817750200000006,-1.2662639999999998,36.762418,Rider_Id_676,1638
-Order_No_17197,User_Id_516,Bike,3,Business,30,2,12:27:00 PM,30,2,12:58:38 PM,30,2,1:03:44 PM,30,2,1:21:22 PM,30,2,1:56:19 PM,17,,,-1.2551895,36.7822034,-1.3339943,36.7263409,Rider_Id_427,2097
-Order_No_4437,User_Id_1245,Bike,3,Business,13,2,2:34:18 PM,13,2,2:51:02 PM,13,2,2:59:29 PM,13,2,3:35:36 PM,13,2,4:11:42 PM,8,26.1,,-1.300921,36.828195,-1.282937,36.781698399999996,Rider_Id_829,2166
-Order_No_17000,User_Id_3017,Bike,3,Business,9,7,6:33:00 PM,9,7,6:36:40 PM,9,7,6:57:43 PM,9,7,6:58:43 PM,9,7,7:55:18 PM,15,24.3,,-1.2993231,36.7753372,-1.3206958,36.8769553,Rider_Id_328,3395
-Order_No_189,User_Id_1687,Bike,3,Business,30,2,10:22:12 AM,30,2,10:23:13 AM,30,2,10:28:23 AM,30,2,10:33:21 AM,30,2,11:33:29 AM,13,22.1,,-1.2649605,36.798177700000004,-1.3147071000000001,36.8644977,Rider_Id_676,3608
-Order_No_25257,User_Id_3752,Bike,3,Business,23,2,12:31:42 PM,23,2,1:21:34 PM,23,2,1:38:10 PM,23,2,1:45:02 PM,23,2,2:02:46 PM,14,,,-1.1648303,36.951368,-1.2285834999999998,36.882646799999996,Rider_Id_710,1064
-Order_No_11131,User_Id_3050,Bike,3,Business,22,5,4:51:15 PM,22,5,4:56:37 PM,22,5,5:10:32 PM,22,5,5:12:50 PM,22,5,5:13:19 PM,4,28.7,,-1.2945073999999999,36.8083195,-1.304214,36.8276533,Rider_Id_19,29
-Order_No_998,User_Id_1482,Bike,3,Business,31,3,3:14:59 PM,31,3,3:15:27 PM,31,3,3:25:14 PM,31,3,3:33:11 PM,31,3,3:57:13 PM,13,27.1,,-1.2971701,36.8288201,-1.2310607,36.803577600000004,Rider_Id_31,1442
-Order_No_5823,User_Id_2190,Bike,3,Business,3,1,2:03:56 PM,3,1,2:05:39 PM,3,1,2:20:52 PM,3,1,2:31:52 PM,3,1,3:15:38 PM,18,23.5,,-1.3019530000000001,36.788458,-1.260964,36.711857,Rider_Id_519,2626
-Order_No_25917,User_Id_3372,Bike,3,Business,4,3,4:21:02 PM,4,3,4:27:36 PM,4,3,4:31:37 PM,4,3,4:40:28 PM,4,3,5:12:58 PM,11,,,-1.3169591,36.7000029,-1.304033,36.7848695,Rider_Id_45,1950
-Order_No_8907,User_Id_2335,Bike,3,Business,4,4,12:07:31 PM,4,4,12:08:14 PM,4,4,12:29:16 PM,4,4,12:38:28 PM,4,4,1:09:25 PM,10,25.4,,-1.2551895,36.7822034,-1.3128307,36.825154299999994,Rider_Id_576,1857
-Order_No_2663,User_Id_3752,Bike,3,Business,14,2,11:48:14 AM,14,2,11:48:56 AM,14,2,12:13:38 PM,14,2,12:18:40 PM,14,2,12:34:44 PM,3,,,-1.304081,36.777099,-1.2962832,36.784968299999996,Rider_Id_952,964
-Order_No_20151,User_Id_3599,Bike,3,Business,7,5,9:23:47 AM,7,5,10:24:45 AM,7,5,10:44:58 AM,7,5,11:11:49 AM,7,5,11:32:03 AM,11,25.3,,-1.3302996,36.8707935,-1.3293565,36.786702500000004,Rider_Id_454,1214
-Order_No_14985,User_Id_2638,Bike,1,Personal,25,1,10:36:03 AM,25,1,10:48:38 AM,25,1,11:09:53 AM,25,1,11:16:51 AM,25,1,11:30:34 AM,4,,,-1.2861764999999998,36.8209086,-1.2536855,36.8245276,Rider_Id_531,823
-Order_No_25037,User_Id_2423,Bike,3,Business,24,4,1:08:21 PM,24,4,1:12:54 PM,24,4,1:20:16 PM,24,4,1:26:35 PM,24,4,1:45:42 PM,8,24.7,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_937,1147
-Order_No_11324,User_Id_1293,Bike,1,Personal,1,3,1:19:29 PM,1,3,1:19:38 PM,1,3,1:26:15 PM,1,3,1:32:18 PM,1,3,2:10:24 PM,18,24.1,,-1.3152336000000002,36.711640100000004,-1.2543163999999998,36.831860999999996,Rider_Id_726,2286
-Order_No_7428,User_Id_2577,Bike,2,Personal,18,6,5:11:13 PM,18,6,5:11:21 PM,18,6,5:36:43 PM,18,6,5:52:46 PM,18,6,6:22:00 PM,16,19.2,,-1.1891303,36.896427,-1.2007275,36.7771863,Rider_Id_384,1754
-Order_No_6988,User_Id_2312,Bike,2,Personal,12,6,5:09:27 PM,12,6,5:17:39 PM,12,6,5:35:08 PM,12,6,5:41:10 PM,12,6,5:58:05 PM,6,23.7,,-1.2918163999999999,36.8773248,-1.2962888000000001,36.8892359,Rider_Id_742,1015
-Order_No_12807,User_Id_2150,Bike,3,Business,14,5,9:28:19 AM,14,5,9:30:08 AM,14,5,9:47:27 AM,14,5,9:55:04 AM,14,5,10:56:42 AM,15,18.3,,-1.272828,36.816608,-1.3444459999999998,36.756597,Rider_Id_882,3698
-Order_No_9103,User_Id_635,Bike,3,Business,17,5,1:25:58 PM,17,5,1:26:26 PM,17,5,1:27:02 PM,17,5,1:50:54 PM,17,5,2:08:55 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_206,1081
-Order_No_10353,User_Id_3420,Bike,3,Business,26,5,12:34:29 PM,26,5,12:36:49 PM,26,5,12:49:21 PM,26,5,12:50:33 PM,26,5,1:04:36 PM,6,,,-1.3046007,36.821174299999996,-1.265981,36.793612100000004,Rider_Id_848,843
-Order_No_6602,User_Id_670,Bike,3,Personal,9,6,3:23:44 PM,9,6,3:23:56 PM,9,6,3:24:23 PM,9,6,4:17:35 PM,9,6,4:43:21 PM,6,27.0,,-1.273056,36.811298,-1.294562,36.7877173,Rider_Id_469,1546
-Order_No_15174,User_Id_3765,Bike,1,Personal,13,1,1:55:11 PM,13,1,1:55:17 PM,13,1,2:01:43 PM,13,1,2:08:34 PM,13,1,2:27:00 PM,5,19.3,,-1.279161,36.7692332,-1.2858511000000001,36.7946184,Rider_Id_329,1106
-Order_No_15831,User_Id_1527,Bike,3,Business,11,2,2:52:54 PM,11,2,2:53:59 PM,11,2,2:54:19 PM,11,2,3:06:35 PM,11,2,3:13:39 PM,4,21.3,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_891,424
-Order_No_24599,User_Id_635,Bike,3,Business,24,4,3:05:36 PM,24,4,3:32:00 PM,24,4,3:41:37 PM,24,4,3:57:08 PM,24,4,4:19:19 PM,8,22.8,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_661,1331
-Order_No_25481,User_Id_3283,Bike,3,Business,13,3,11:46:30 AM,13,3,12:31:31 PM,13,3,12:47:51 PM,13,3,12:54:39 PM,13,3,1:17:33 PM,13,,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_633,1374
-Order_No_25063,User_Id_393,Bike,3,Business,25,2,10:52:55 AM,25,2,10:53:03 AM,25,2,10:55:17 AM,25,2,11:34:42 AM,25,2,12:26:51 PM,14,24.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,3129
-Order_No_905,User_Id_2069,Bike,3,Business,1,1,10:57:58 AM,1,1,11:00:43 AM,1,1,11:10:28 AM,1,1,11:18:05 AM,1,1,11:29:19 AM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_7,674
-Order_No_7473,User_Id_1352,Bike,3,Personal,30,4,11:01:42 AM,30,4,11:02:25 AM,30,4,11:19:03 AM,30,4,11:26:38 AM,30,4,11:48:28 AM,6,20.2,,-1.2655306,36.828148,-1.298529,36.816482,Rider_Id_882,1310
-Order_No_20666,User_Id_1687,Bike,3,Business,28,3,1:17:51 PM,28,3,1:24:07 PM,28,3,1:41:35 PM,28,3,1:44:23 PM,28,3,2:06:16 PM,7,,,-1.2626574,36.8014034,-1.3014732,36.7781201,Rider_Id_147,1313
-Order_No_14341,User_Id_1803,Bike,1,Personal,30,4,10:25:35 AM,30,4,10:28:58 AM,30,4,10:52:58 AM,30,4,11:04:01 AM,30,4,11:34:59 AM,13,20.5,,-1.2057795,36.7747958,-1.2538731,36.7850223,Rider_Id_409,1858
-Order_No_18647,User_Id_3115,Bike,3,Business,20,3,12:15:01 PM,20,3,12:15:31 PM,20,3,12:54:37 PM,20,3,1:07:24 PM,20,3,2:09:27 PM,20,20.0,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_354,3723
-Order_No_17013,User_Id_3698,Bike,3,Business,31,5,12:32:35 PM,31,5,12:37:47 PM,31,5,12:37:56 PM,31,5,12:51:56 PM,31,5,1:09:11 PM,7,21.7,,-1.3244885,36.897791999999995,-1.34364,36.8925345,Rider_Id_692,1035
-Order_No_24328,User_Id_1263,Bike,2,Personal,7,7,5:10:09 PM,7,7,5:21:28 PM,7,7,5:33:41 PM,7,7,5:46:15 PM,7,7,6:12:25 PM,10,23.3,,-1.3185736000000001,36.8258933,-1.2697858999999998,36.7946203,Rider_Id_612,1570
-Order_No_13255,User_Id_3635,Bike,1,Business,16,2,8:55:48 AM,16,2,8:56:21 AM,16,2,9:02:57 AM,16,2,9:30:46 AM,16,2,9:37:29 AM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_453,403
-Order_No_8391,User_Id_393,Bike,3,Business,4,6,8:41:04 AM,4,6,8:42:38 AM,4,6,8:49:14 AM,4,6,8:56:51 AM,4,6,9:17:34 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_448,1243
-Order_No_9217,User_Id_3283,Bike,3,Business,29,1,11:33:09 AM,29,1,11:36:57 AM,29,1,11:49:56 AM,29,1,12:04:23 PM,29,1,12:19:15 PM,7,,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_327,892
-Order_No_19465,User_Id_1304,Bike,3,Business,25,1,11:08:59 AM,25,1,11:09:12 AM,25,1,11:15:28 AM,25,1,11:36:01 AM,25,1,11:41:11 AM,3,,,-1.2551895,36.7822034,-1.2559559999999999,36.772901,Rider_Id_498,310
-Order_No_3146,User_Id_562,Bike,3,Business,31,5,12:52:52 PM,31,5,1:02:40 PM,31,5,1:22:52 PM,31,5,1:29:46 PM,31,5,2:02:49 PM,17,21.8,,-1.3227181000000001,36.8528097,-1.378256,36.747862,Rider_Id_675,1983
-Order_No_23771,User_Id_652,Bike,1,Personal,19,4,10:39:22 AM,19,4,10:40:46 AM,19,4,10:46:04 AM,19,4,10:50:06 AM,19,4,11:10:50 AM,4,20.2,,-1.2982033000000002,36.809263200000004,-1.2808159,36.8160545,Rider_Id_174,1244
-Order_No_22243,User_Id_635,Bike,3,Business,15,3,2:54:05 PM,15,3,3:12:07 PM,15,3,3:43:22 PM,15,3,3:56:28 PM,15,3,4:56:01 PM,28,26.0,,-1.2847613000000002,36.967084,-1.2602337,36.79905479999999,Rider_Id_864,3573
-Order_No_13848,User_Id_2777,Bike,3,Business,7,4,4:47:44 PM,7,4,4:48:34 PM,7,4,4:54:25 PM,7,4,5:35:54 PM,7,4,5:55:51 PM,3,29.4,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_925,1197
-Order_No_5341,User_Id_779,Bike,1,Personal,5,6,11:54:12 AM,5,6,11:56:23 AM,5,6,12:27:49 PM,5,6,12:44:54 PM,5,6,1:05:44 PM,8,22.3,,-1.2714948,36.8166789,-1.3127303999999997,36.8189817,Rider_Id_753,1250
-Order_No_21134,User_Id_3557,Bike,3,Business,15,1,3:42:49 PM,15,1,3:46:47 PM,15,1,4:00:46 PM,15,1,4:11:22 PM,15,1,4:41:17 PM,8,,,-1.3047132,36.808955299999994,-1.2546732,36.808679600000005,Rider_Id_177,1795
-Order_No_7634,User_Id_1154,Bike,3,Business,10,1,9:22:36 AM,10,1,9:25:59 AM,10,1,9:56:45 AM,10,1,9:58:08 AM,10,1,10:16:45 AM,5,16.6,,-1.2917868000000001,36.7872675,-1.2825615,36.82271970000001,Rider_Id_429,1117
-Order_No_16706,User_Id_517,Bike,3,Business,28,5,11:54:52 AM,28,5,12:01:03 PM,28,5,12:12:32 PM,28,5,12:18:20 PM,28,5,12:45:23 PM,14,23.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1623
-Order_No_5038,User_Id_2216,Bike,3,Business,14,2,1:10:12 PM,14,2,2:16:12 PM,14,2,2:52:48 PM,14,2,3:12:13 PM,14,2,4:00:45 PM,12,,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_930,2912
-Order_No_27952,User_Id_3108,Bike,3,Business,8,3,9:12:13 AM,8,3,9:12:32 AM,8,3,9:21:38 AM,8,3,9:24:13 AM,8,3,9:58:27 AM,10,17.1,,-1.2551895,36.7822034,-1.3069587,36.837873200000004,Rider_Id_452,2054
-Order_No_22852,User_Id_2789,Bike,3,Business,3,1,8:58:27 AM,3,1,8:59:21 AM,3,1,9:25:16 AM,3,1,9:31:15 AM,3,1,9:40:39 AM,5,18.4,,-1.304735,36.784605400000004,-1.2939186000000003,36.8069693,Rider_Id_942,564
-Order_No_10548,User_Id_2154,Bike,3,Business,18,5,11:14:17 AM,18,5,11:48:29 AM,18,5,11:58:07 AM,18,5,12:10:10 PM,18,5,12:18:34 PM,2,21.6,,-1.290894,36.822971,-1.2885528999999998,36.816014100000004,Rider_Id_478,504
-Order_No_27177,User_Id_393,Bike,3,Business,22,5,4:17:03 PM,22,5,4:17:15 PM,22,5,4:17:19 PM,22,5,4:29:41 PM,22,5,4:36:14 PM,4,27.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_901,393
-Order_No_2031,User_Id_3459,Bike,1,Personal,10,7,10:48:26 AM,10,7,10:49:54 AM,10,7,11:07:50 AM,10,7,11:11:22 AM,10,7,11:36:28 AM,6,17.7,,-1.3170773999999998,36.856955,-1.3255193,36.825871899999996,Rider_Id_890,1506
-Order_No_3101,User_Id_2523,Bike,3,Business,12,2,3:03:54 PM,12,2,3:06:10 PM,12,2,3:13:45 PM,12,2,3:21:51 PM,12,2,3:53:10 PM,8,,,-1.2551895,36.7822034,-1.3021707,36.816844399999994,Rider_Id_495,1879
-Order_No_9296,User_Id_136,Bike,3,Business,6,3,12:27:49 PM,6,3,12:28:29 PM,6,3,12:38:28 PM,6,3,12:40:44 PM,6,3,1:13:34 PM,16,22.6,,-1.3254522,36.721658399999995,-1.3233496,36.8139159,Rider_Id_205,1970
-Order_No_1676,User_Id_3674,Bike,3,Business,18,4,3:44:27 PM,18,4,4:15:12 PM,18,4,4:33:30 PM,18,4,4:37:27 PM,18,4,5:20:09 PM,9,,,-1.2551895,36.7822034,-1.2786703000000001,36.826044,Rider_Id_709,2562
-Order_No_11836,User_Id_2330,Bike,3,Business,8,1,3:42:33 PM,8,1,3:42:57 PM,8,1,4:04:52 PM,8,1,4:08:36 PM,8,1,4:26:32 PM,6,29.4,,-1.2642903,36.80021370000001,-1.2944081,36.7897046,Rider_Id_709,1076
-Order_No_24861,User_Id_970,Bike,3,Business,12,3,2:17:43 PM,12,3,2:18:01 PM,12,3,2:22:33 PM,12,3,2:43:36 PM,12,3,3:04:06 PM,3,20.9,,-1.2551895,36.7822034,-1.2629594,36.8077626,Rider_Id_591,1230
-Order_No_15646,User_Id_1148,Bike,3,Business,22,4,2:05:03 PM,22,4,2:08:17 PM,22,4,2:27:18 PM,22,4,2:51:43 PM,22,4,3:53:44 PM,14,26.4,,-1.3100586,36.850032899999995,-1.2441236,36.881280700000005,Rider_Id_519,3721
-Order_No_3147,User_Id_1363,Bike,3,Business,18,2,3:55:13 PM,18,2,4:00:13 PM,18,2,4:06:47 PM,18,2,4:09:59 PM,18,2,4:46:16 PM,10,25.8,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_343,2177
-Order_No_2596,User_Id_3381,Bike,3,Business,19,1,9:53:50 AM,19,1,11:21:17 AM,19,1,11:40:44 AM,19,1,11:51:48 AM,19,1,12:09:19 PM,8,24.9,,-1.2559559999999999,36.772901,-1.279662,36.814902000000004,Rider_Id_612,1051
-Order_No_26061,User_Id_997,Bike,3,Business,9,5,7:38:31 AM,9,5,7:56:53 AM,9,5,8:25:00 AM,9,5,8:27:40 AM,9,5,9:04:03 AM,9,20.3,,-1.3110438,36.785000399999994,-1.2947678,36.8416866,Rider_Id_412,2183
-Order_No_4735,User_Id_3291,Bike,3,Business,12,3,2:43:14 PM,12,3,2:45:55 PM,12,3,2:52:02 PM,12,3,3:01:50 PM,12,3,3:17:27 PM,5,21.1,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_290,937
-Order_No_16662,User_Id_3445,Bike,1,Personal,21,4,4:10:11 PM,21,4,4:11:03 PM,21,4,4:15:05 PM,21,4,4:18:58 PM,21,4,4:32:49 PM,4,22.3,,-1.2615983,36.8083096,-1.2516793,36.786481,Rider_Id_216,831
-Order_No_23580,User_Id_3418,Bike,3,Business,6,4,1:32:15 PM,6,4,1:32:21 PM,6,4,1:53:58 PM,6,4,2:04:58 PM,6,4,2:20:47 PM,5,23.3,,-1.2828186000000001,36.823279799999995,-1.2595399999999999,36.802977,Rider_Id_336,949
-Order_No_20261,User_Id_2009,Bike,2,Personal,26,3,10:52:00 AM,26,3,10:52:07 AM,26,3,10:56:28 AM,26,3,11:03:08 AM,26,3,11:51:57 AM,3,23.9,,-1.2956882,36.7861845,-1.2868736,36.780588200000004,Rider_Id_750,2929
-Order_No_10727,User_Id_1085,Bike,3,Business,5,1,10:38:28 AM,5,1,10:38:34 AM,5,1,10:43:20 AM,5,1,10:47:51 AM,5,1,11:26:39 AM,18,24.6,,-1.2628638,36.8070399,-1.3169591,36.7000029,Rider_Id_902,2328
-Order_No_2935,User_Id_2691,Bike,3,Business,27,3,11:55:06 AM,27,3,11:55:55 AM,27,3,12:08:41 PM,27,3,12:19:28 PM,27,3,12:39:02 PM,8,,,-1.2857671,36.7875614,-1.2551895,36.7822034,Rider_Id_101,1174
-Order_No_1973,User_Id_2521,Bike,3,Business,5,5,1:35:21 PM,5,5,1:35:36 PM,5,5,1:37:02 PM,5,5,1:52:53 PM,5,5,2:48:38 PM,14,27.6,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_910,3345
-Order_No_8016,User_Id_914,Bike,3,Business,5,3,4:19:26 PM,5,3,4:20:18 PM,5,3,4:27:42 PM,5,3,4:36:37 PM,5,3,4:45:21 PM,3,22.7,,-1.2670213,36.7994482,-1.2630521000000001,36.806801899999996,Rider_Id_518,524
-Order_No_2951,User_Id_1260,Bike,3,Business,31,4,11:01:08 AM,31,4,11:01:33 AM,31,4,11:02:11 AM,31,4,11:03:33 AM,31,4,11:34:51 AM,12,26.3,,-1.2551895,36.7822034,-1.3270716,36.8406371,Rider_Id_518,1878
-Order_No_25657,User_Id_3647,Bike,3,Business,18,5,1:08:13 PM,18,5,1:09:21 PM,18,5,1:25:05 PM,18,5,1:31:56 PM,18,5,1:42:32 PM,5,19.9,,-1.2726389999999999,36.794723,-1.2592947,36.7698085,Rider_Id_205,636
-Order_No_12891,User_Id_1500,Bike,3,Business,31,5,4:11:57 PM,31,5,4:15:21 PM,31,5,4:24:42 PM,31,5,4:30:06 PM,31,5,4:56:22 PM,8,24.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_591,1576
-Order_No_9905,User_Id_1500,Bike,3,Business,22,3,12:02:36 PM,22,3,12:04:19 PM,22,3,12:10:16 PM,22,3,12:42:35 PM,22,3,12:43:38 PM,7,23.1,,-1.2638185,36.7930057,-1.2770708,36.8231093,Rider_Id_436,63
-Order_No_12715,User_Id_393,Bike,3,Business,4,4,8:50:16 AM,4,4,8:50:34 AM,4,4,8:54:33 AM,4,4,9:37:49 AM,4,4,10:03:08 AM,10,18.3,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1519
-Order_No_26508,User_Id_2869,Bike,3,Business,10,5,4:55:36 PM,10,5,4:58:39 PM,10,5,5:05:59 PM,10,5,5:11:08 PM,10,5,5:58:05 PM,3,,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_211,2817
-Order_No_16748,User_Id_2200,Bike,3,Business,7,4,12:39:49 PM,7,4,12:40:01 PM,7,4,12:41:05 PM,7,4,12:55:12 PM,7,4,1:01:49 PM,1,27.8,,-1.2813012,36.832396200000005,-1.290894,36.822971,Rider_Id_686,397
-Order_No_13935,User_Id_2619,Bike,2,Personal,26,3,10:41:49 AM,26,3,10:42:34 AM,26,3,10:48:35 AM,26,3,10:52:36 AM,26,3,11:04:35 AM,5,24.2,,-1.2649605,36.798177700000004,-1.2795796,36.819062,Rider_Id_726,719
-Order_No_1018,User_Id_1584,Bike,3,Business,14,5,1:56:54 PM,14,5,1:59:52 PM,14,5,2:18:22 PM,14,5,2:24:39 PM,14,5,3:26:51 PM,17,23.9,,-1.2969741000000001,36.7856612,-1.2970848,36.887822899999996,Rider_Id_272,3732
-Order_No_2384,User_Id_1724,Bike,3,Business,9,5,9:29:23 AM,9,5,9:50:53 AM,9,5,9:57:14 AM,9,5,10:03:00 AM,9,5,10:16:43 AM,8,20.4,,-1.2941648000000001,36.7977315,-1.2608591999999998,36.831197700000004,Rider_Id_315,823
-Order_No_24668,User_Id_2330,Bike,3,Business,13,4,10:52:26 AM,13,4,10:53:17 AM,13,4,11:10:57 AM,13,4,11:17:36 AM,13,4,11:39:03 AM,9,22.2,,-1.2645318,36.7981775,-1.2124582,36.802894,Rider_Id_362,1287
-Order_No_26994,User_Id_2454,Bike,3,Business,17,4,3:15:05 PM,17,4,3:28:10 PM,17,4,3:51:19 PM,17,4,3:54:03 PM,17,4,4:33:36 PM,10,28.0,,-1.3264333000000001,36.71071,-1.2993231,36.7753372,Rider_Id_831,2373
-Order_No_4147,User_Id_2330,Bike,3,Business,10,5,12:34:49 PM,10,5,12:35:21 PM,10,5,12:51:28 PM,10,5,12:57:51 PM,10,5,1:24:17 PM,18,,,-1.3443583000000001,36.7571528,-1.2645318,36.7981775,Rider_Id_267,1586
-Order_No_25852,User_Id_131,Bike,2,Personal,8,2,11:07:24 AM,8,2,11:26:17 AM,8,2,11:46:04 AM,8,2,12:05:37 PM,8,2,12:22:22 PM,8,21.5,,-1.2868838,36.8284259,-1.3206841999999999,36.8362301,Rider_Id_101,1005
-Order_No_5577,User_Id_2216,Bike,3,Business,23,5,4:15:06 PM,23,5,4:17:45 PM,23,5,4:21:25 PM,23,5,4:23:50 PM,23,5,4:33:17 PM,3,26.6,,-1.2649605,36.798177700000004,-1.2551895,36.7822034,Rider_Id_191,567
-Order_No_9806,User_Id_3687,Bike,1,Personal,28,5,8:54:36 AM,28,5,8:54:51 AM,28,5,9:11:08 AM,28,5,9:35:36 AM,28,5,10:27:23 AM,24,20.5,,-1.207546,36.7913976,-1.3074581,36.7434,Rider_Id_101,3107
-Order_No_21368,User_Id_3115,Bike,3,Business,8,3,6:40:43 PM,8,3,6:43:48 PM,8,3,6:50:15 PM,8,3,7:05:57 PM,8,3,7:16:24 PM,2,,,-1.2969741000000001,36.7856612,-1.2963097,36.7688221,Rider_Id_226,627
-Order_No_16732,User_Id_45,Bike,3,Business,4,1,11:27:56 AM,4,1,11:28:43 AM,4,1,11:36:18 AM,4,1,11:42:19 AM,4,1,11:47:48 AM,2,21.0,,-1.2945041000000002,36.8151527,-1.3057159999999999,36.809882,Rider_Id_124,329
-Order_No_13826,User_Id_349,Bike,1,Personal,28,6,3:42:13 PM,28,6,3:42:17 PM,28,6,3:45:12 PM,28,6,4:02:50 PM,28,6,4:16:59 PM,5,25.0,,-1.2821211000000001,36.8222021,-1.2707178999999997,36.7979713,Rider_Id_229,849
-Order_No_25424,User_Id_868,Bike,3,Business,11,2,9:54:10 AM,11,2,9:55:01 AM,11,2,10:26:09 AM,11,2,10:37:14 AM,11,2,10:48:41 AM,7,19.4,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_570,687
-Order_No_5295,User_Id_60,Bike,3,Business,2,4,9:39:25 AM,2,4,9:40:33 AM,2,4,9:45:07 AM,2,4,10:03:46 AM,2,4,10:19:10 AM,8,,,-1.272828,36.816608,-1.2296618000000001,36.84382720000001,Rider_Id_461,924
-Order_No_23666,User_Id_1245,Bike,3,Business,2,6,8:50:08 AM,2,6,8:50:54 AM,2,6,9:08:26 AM,2,6,9:25:03 AM,2,6,10:06:43 AM,17,21.0,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_468,2500
-Order_No_3823,User_Id_868,Bike,3,Business,23,5,2:17:39 PM,23,5,2:19:45 PM,23,5,2:29:16 PM,23,5,2:41:52 PM,23,5,2:53:48 PM,5,26.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_547,716
-Order_No_27043,User_Id_2174,Bike,3,Business,13,4,2:29:55 PM,13,4,2:29:55 PM,13,4,2:33:42 PM,13,4,3:33:54 PM,13,4,3:33:58 PM,12,22.9,,-1.2489964,36.8771594,-1.2649605,36.798177700000004,Rider_Id_86,4
-Order_No_16719,User_Id_2069,Bike,3,Business,6,4,10:42:08 AM,6,4,10:42:35 AM,6,4,10:45:05 AM,6,4,10:54:06 AM,6,4,11:14:57 AM,12,24.3,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_31,1251
-Order_No_18207,User_Id_3487,Bike,3,Business,28,2,1:48:32 PM,28,2,1:49:42 PM,28,2,2:08:16 PM,28,2,2:25:01 PM,28,2,2:45:40 PM,5,23.4,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_921,1239
-Order_No_19407,User_Id_2092,Bike,1,Personal,26,4,12:52:06 PM,26,4,12:53:49 PM,26,4,1:16:09 PM,26,4,1:30:25 PM,26,4,2:02:32 PM,20,18.8,,-1.2932312,36.897600700000005,-1.2912918,36.758140999999995,Rider_Id_103,1927
-Order_No_25488,User_Id_1405,Bike,3,Business,17,4,9:53:01 AM,17,4,9:54:32 AM,17,4,9:57:31 AM,17,4,10:28:19 AM,17,4,10:50:34 AM,9,,,-1.3194518,36.869396,-1.3478563000000001,36.8935824,Rider_Id_178,1335
-Order_No_16984,User_Id_60,Bike,3,Business,31,5,11:03:37 AM,31,5,11:04:20 AM,31,5,11:32:45 AM,31,5,11:38:57 AM,31,5,12:21:17 PM,7,21.9,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_691,2540
-Order_No_17250,User_Id_437,Bike,3,Business,29,4,11:39:32 AM,29,4,11:42:57 AM,29,4,11:47:03 AM,29,4,11:53:28 AM,29,4,12:25:38 PM,7,21.5,,-1.2551895,36.7822034,-1.2822909,36.8193456,Rider_Id_510,1930
-Order_No_17386,User_Id_2195,Bike,3,Business,20,6,1:12:05 PM,20,6,1:12:23 PM,20,6,1:29:31 PM,20,6,1:35:13 PM,20,6,1:50:09 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_309,896
-Order_No_21047,User_Id_2116,Bike,3,Business,23,3,10:41:22 AM,23,3,10:44:48 AM,23,3,11:06:02 AM,23,3,11:44:52 AM,23,3,12:07:58 PM,15,,,-1.1896438,36.9126935,-1.2271998,36.808490500000005,Rider_Id_857,1386
-Order_No_2740,User_Id_2956,Bike,3,Business,31,3,12:46:45 PM,31,3,12:46:52 PM,31,3,12:53:11 PM,31,3,1:03:13 PM,31,3,1:03:14 PM,1,24.8,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_79,1
-Order_No_22038,User_Id_184,Bike,1,Personal,9,1,4:35:40 PM,9,1,4:36:43 PM,9,1,4:43:53 PM,9,1,4:59:14 PM,9,1,5:14:07 PM,6,20.4,,-1.2879697,36.8020506,-1.3240024,36.811902200000006,Rider_Id_312,893
-Order_No_15053,User_Id_729,Bike,1,Personal,25,2,12:13:51 PM,25,2,12:14:05 PM,25,2,12:21:39 PM,25,2,12:30:15 PM,25,2,12:58:15 PM,6,23.9,,-1.2926879999999998,36.774472700000004,-1.2697141,36.798177700000004,Rider_Id_650,1680
-Order_No_16380,User_Id_3201,Bike,3,Business,4,1,1:14:29 PM,4,1,1:34:13 PM,4,1,1:47:48 PM,4,1,2:17:15 PM,4,1,2:38:15 PM,6,,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_452,1260
-Order_No_12693,User_Id_3295,Bike,3,Business,13,4,8:58:12 AM,13,4,8:58:46 AM,13,4,9:18:36 AM,13,4,9:33:21 AM,13,4,9:49:16 AM,7,18.5,,-1.2571472,36.795063299999995,-1.2965924,36.8179314,Rider_Id_576,955
-Order_No_13629,User_Id_3326,Bike,3,Business,22,5,12:15:10 PM,22,5,12:18:20 PM,22,5,12:23:18 PM,22,5,12:34:47 PM,22,5,12:55:41 PM,7,28.5,,-1.2897368,36.8226783,-1.2551895,36.7822034,Rider_Id_583,1254
-Order_No_22888,User_Id_1640,Bike,2,Personal,8,6,11:27:43 AM,8,6,11:28:01 AM,8,6,11:29:39 AM,8,6,12:16:26 PM,8,6,12:34:59 PM,17,22.2,,-1.3172701,36.8661154,-1.3671327,36.75642379999999,Rider_Id_765,1113
-Order_No_18452,User_Id_3171,Bike,3,Personal,11,1,11:40:58 AM,11,1,11:41:23 AM,11,1,11:48:53 AM,11,1,11:49:54 AM,11,1,12:43:33 PM,4,26.4,,-1.2807406000000001,36.8308915,-1.2913246,36.8019522,Rider_Id_378,3219
-Order_No_15637,User_Id_324,Bike,3,Personal,18,1,1:22:00 PM,18,1,1:22:11 PM,18,1,1:24:19 PM,18,1,1:25:53 PM,18,1,1:55:19 PM,15,27.9,,-1.2653676,36.79110120000001,-1.332942,36.714065000000005,Rider_Id_520,1766
-Order_No_17578,User_Id_2956,Bike,3,Business,2,4,4:18:03 PM,2,4,4:19:25 PM,2,4,4:33:08 PM,2,4,4:43:34 PM,2,4,5:12:20 PM,14,,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_78,1726
-Order_No_18955,User_Id_186,Bike,3,Business,11,5,1:51:42 PM,11,5,1:51:57 PM,11,5,2:10:28 PM,11,5,2:14:44 PM,11,5,2:36:18 PM,14,26.2,,-1.2571472,36.795063299999995,-1.2156006000000001,36.8916865,Rider_Id_512,1294
-Order_No_17903,User_Id_1591,Bike,2,Personal,25,6,12:19:17 PM,25,6,12:19:48 PM,25,6,12:33:33 PM,25,6,12:40:59 PM,25,6,12:50:23 PM,3,,,-1.301893,36.815245000000004,-1.292943,36.808292,Rider_Id_85,564
-Order_No_24822,User_Id_3110,Bike,3,Business,11,1,10:37:33 AM,11,1,10:37:51 AM,11,1,11:18:33 AM,11,1,11:22:01 AM,11,1,11:46:59 AM,15,26.8,,-1.2598589,36.7470168,-1.2420055,36.8144424,Rider_Id_393,1498
-Order_No_2930,User_Id_3283,Bike,3,Business,19,1,1:25:44 PM,19,1,1:44:48 PM,19,1,1:46:51 PM,19,1,1:57:43 PM,19,1,2:19:14 PM,10,25.1,,-1.307143,36.825009,-1.2628473,36.781805,Rider_Id_19,1291
-Order_No_25933,User_Id_2271,Bike,3,Business,10,3,10:00:35 AM,10,3,10:00:42 AM,10,3,10:52:00 AM,10,3,11:03:37 AM,10,3,11:47:55 AM,21,27.6,,-1.3444459999999998,36.756597,-1.293515,36.897607,Rider_Id_155,2658
-Order_No_28231,User_Id_496,Bike,3,Business,15,5,12:51:38 PM,15,5,12:52:08 PM,15,5,12:55:53 PM,15,5,1:00:23 PM,15,5,1:35:53 PM,14,26.0,,-1.2551895,36.7822034,-1.323481,36.792840600000005,Rider_Id_709,2130
-Order_No_1042,User_Id_867,Bike,3,Business,1,5,10:35:57 AM,1,5,10:36:06 AM,1,5,10:36:32 AM,1,5,10:38:57 AM,1,5,11:08:31 AM,10,28.6,,-1.2996533000000001,36.829262899999996,-1.2991441000000001,36.7528804,Rider_Id_248,1774
-Order_No_20946,User_Id_1329,Bike,3,Business,11,1,3:50:51 PM,11,1,3:51:32 PM,11,1,3:55:07 PM,11,1,4:08:32 PM,11,1,4:45:56 PM,16,28.2,,-1.273056,36.811298,-1.1855066,36.825464600000004,Rider_Id_584,2244
-Order_No_9854,User_Id_2990,Bike,1,Personal,10,5,1:56:15 PM,10,5,1:56:31 PM,10,5,2:10:14 PM,10,5,2:34:05 PM,10,5,3:03:22 PM,17,21.6,,-1.281342,36.8196838,-1.3668652000000001,36.730459,Rider_Id_449,1757
-Order_No_24210,User_Id_2100,Bike,3,Business,12,5,4:34:28 PM,12,5,4:57:35 PM,12,5,4:58:32 PM,12,5,5:08:22 PM,12,5,5:31:33 PM,10,25.9,,-1.2765736,36.851364600000004,-1.2571472,36.795063299999995,Rider_Id_89,1391
-Order_No_1052,User_Id_733,Bike,3,Business,16,3,11:40:16 AM,16,3,12:23:25 PM,16,3,12:40:03 PM,16,3,12:57:14 PM,16,3,1:52:19 PM,19,24.1,,-1.2579303999999998,36.8019667,-1.3160976999999998,36.913164200000004,Rider_Id_499,3305
-Order_No_17715,User_Id_2216,Bike,3,Business,8,1,10:36:58 AM,8,1,10:37:26 AM,8,1,10:38:38 AM,8,1,11:17:30 AM,8,1,11:19:38 AM,4,28.1,,-1.2551895,36.7822034,-1.2519787,36.780649100000005,Rider_Id_507,128
-Order_No_8227,User_Id_248,Bike,3,Business,27,3,2:15:05 PM,27,3,2:15:25 PM,27,3,2:41:38 PM,27,3,2:46:41 PM,27,3,3:15:43 PM,9,29.6,,-1.304735,36.784605400000004,-1.2635089,36.78873370000001,Rider_Id_314,1742
-Order_No_17932,User_Id_1705,Bike,3,Business,15,5,11:18:37 AM,15,5,11:19:50 AM,15,5,11:36:52 AM,15,5,11:39:50 AM,15,5,12:32:04 PM,9,26.3,,-1.2551895,36.7822034,-1.2325706,36.808392299999994,Rider_Id_436,3134
-Order_No_4194,User_Id_685,Bike,3,Business,13,1,2:33:01 PM,13,1,2:42:44 PM,13,1,2:48:51 PM,13,1,2:52:26 PM,13,1,3:28:08 PM,14,,,-1.2726506000000002,36.7960153,-1.323615,36.700475,Rider_Id_177,2142
-Order_No_8518,User_Id_1584,Bike,3,Business,30,2,9:21:17 AM,30,2,9:21:56 AM,30,2,9:27:18 AM,30,2,9:35:34 AM,30,2,9:52:33 AM,6,,,-1.2824911,36.8218042,-1.2969741000000001,36.7856612,Rider_Id_698,1019
-Order_No_4434,User_Id_3689,Bike,3,Business,4,3,2:38:17 PM,4,3,2:41:29 PM,4,3,3:09:31 PM,4,3,3:25:54 PM,4,3,3:49:30 PM,12,18.1,,-1.2527958000000001,36.8003131,-1.2647206,36.7259313,Rider_Id_844,1416
-Order_No_19989,User_Id_393,Bike,3,Business,18,4,9:51:20 AM,18,4,9:51:31 AM,18,4,9:51:38 AM,18,4,10:05:44 AM,18,4,10:37:21 AM,14,,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_349,1897
-Order_No_45,User_Id_1271,Bike,3,Business,23,5,3:30:32 PM,23,5,3:51:05 PM,23,5,4:35:53 PM,23,5,4:52:36 PM,23,5,5:36:41 PM,20,24.5,,-1.3244885,36.897791999999995,-1.2915472,36.774696399999996,Rider_Id_322,2645
-Order_No_13318,User_Id_747,Bike,3,Personal,26,3,11:16:01 AM,26,3,11:20:25 AM,26,3,11:45:17 AM,26,3,11:47:37 AM,26,3,12:03:02 PM,5,24.2,,-1.291768,36.8297208,-1.3009173,36.7959207,Rider_Id_343,925
-Order_No_22132,User_Id_2112,Bike,3,Business,23,6,5:04:32 PM,23,6,5:11:57 PM,23,6,5:24:59 PM,23,6,6:08:35 PM,23,6,6:41:45 PM,12,,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_787,1990
-Order_No_23481,User_Id_1500,Bike,3,Business,8,3,10:49:31 AM,8,3,11:03:38 AM,8,3,11:23:27 AM,8,3,11:29:51 AM,8,3,11:43:53 AM,6,,,-1.2770708,36.8231093,-1.2638185,36.7930057,Rider_Id_155,842
-Order_No_24630,User_Id_3283,Bike,3,Business,9,3,9:40:26 AM,9,3,9:40:53 AM,9,3,9:44:17 AM,9,3,9:59:21 AM,9,3,10:09:16 AM,5,20.4,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_550,595
-Order_No_5140,User_Id_1934,Bike,2,Personal,14,2,3:06:56 PM,14,2,3:07:26 PM,14,2,3:50:01 PM,14,2,3:52:03 PM,14,2,4:28:26 PM,9,24.0,,-1.304033,36.7848695,-1.3149338,36.7135703,Rider_Id_334,2183
-Order_No_2846,User_Id_2157,Bike,1,Personal,14,6,3:32:41 PM,14,6,3:32:54 PM,14,6,3:39:44 PM,14,6,4:08:26 PM,14,6,4:31:30 PM,16,19.2,,-1.291696,36.862768100000004,-1.214428,36.883258000000005,Rider_Id_629,1384
-Order_No_6767,User_Id_2457,Bike,3,Business,8,4,2:40:04 PM,8,4,2:40:52 PM,8,4,2:41:47 PM,8,4,2:54:29 PM,8,4,3:03:33 PM,3,27.1,,-1.2551895,36.7822034,-1.259919,36.808244,Rider_Id_40,544
-Order_No_5654,User_Id_2874,Bike,2,Personal,23,4,5:45:17 PM,23,4,5:45:21 PM,23,4,5:55:11 PM,23,4,5:59:10 PM,23,4,6:57:53 PM,24,20.0,,-1.2250582,36.8639193,-1.3561488000000002,36.7589267,Rider_Id_213,3523
-Order_No_11811,User_Id_234,Bike,2,Personal,10,5,12:54:27 PM,10,5,12:55:26 PM,10,5,1:25:03 PM,10,5,1:31:02 PM,10,5,1:56:24 PM,11,19.4,,-1.3234104,36.7989459,-1.3015998,36.8764939,Rider_Id_882,1522
-Order_No_7744,User_Id_753,Bike,3,Business,21,1,1:58:02 PM,21,1,1:58:12 PM,21,1,2:00:44 PM,21,1,2:29:08 PM,21,1,2:54:17 PM,27,27.6,,-1.3285517,36.9018,-1.3297531,36.7063699,Rider_Id_881,1509
-Order_No_23975,User_Id_2271,Bike,3,Business,28,5,10:34:03 AM,28,5,10:34:14 AM,28,5,10:40:38 AM,28,5,10:56:11 AM,28,5,11:31:25 AM,14,23.9,,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_829,2114
-Order_No_8217,User_Id_3066,Bike,1,Personal,14,1,10:12:55 AM,14,1,10:13:34 AM,14,1,10:14:26 AM,14,1,10:33:59 AM,14,1,10:52:24 AM,10,22.8,,-1.2791763,36.825770899999995,-1.2945319,36.761894500000004,Rider_Id_100,1105
-Order_No_14342,User_Id_2640,Bike,3,Personal,7,2,4:33:12 PM,7,2,4:35:11 PM,7,2,4:42:25 PM,7,2,4:48:39 PM,7,2,6:11:41 PM,4,22.8,,-1.2920693,36.8026415,-1.2795197,36.8168287,Rider_Id_519,4982
-Order_No_5291,User_Id_2285,Bike,3,Personal,18,4,2:19:47 PM,18,4,2:21:58 PM,18,4,2:44:50 PM,18,4,2:46:48 PM,18,4,3:10:22 PM,5,,,-1.3008826,36.8378114,-1.2950041,36.814358299999995,Rider_Id_924,1414
-Order_No_723,User_Id_2676,Bike,3,Business,8,5,5:10:16 PM,8,5,5:10:38 PM,8,5,5:15:06 PM,8,5,5:27:51 PM,8,5,5:45:10 PM,6,25.1,,-1.2963097,36.7688221,-1.2812549,36.7236622,Rider_Id_829,1039
-Order_No_3099,User_Id_2409,Bike,3,Personal,6,1,9:33:00 AM,6,1,9:33:10 AM,6,1,9:57:22 AM,6,1,10:03:56 AM,6,1,10:32:41 AM,11,15.1,,-1.2990731000000002,36.848805799999994,-1.2630521000000001,36.806801899999996,Rider_Id_402,1725
-Order_No_2455,User_Id_2804,Bike,3,Business,29,2,8:55:35 AM,29,2,8:55:55 AM,29,2,9:01:34 AM,29,2,9:06:30 AM,29,2,9:15:49 AM,4,18.4,,-1.2551895,36.7822034,-1.25681,36.7730826,Rider_Id_50,559
-Order_No_25855,User_Id_3561,Bike,3,Personal,11,3,11:46:57 AM,11,3,12:29:07 PM,11,3,12:43:05 PM,11,3,12:55:03 PM,11,3,2:08:10 PM,16,19.0,,-1.3255736,36.7275277,-1.2658315,36.809049,Rider_Id_334,4387
-Order_No_4836,User_Id_2415,Bike,1,Personal,11,3,1:34:02 PM,11,3,1:45:53 PM,11,3,2:36:53 PM,11,3,2:42:25 PM,11,3,2:58:07 PM,6,20.7,,-1.2766473999999999,36.8155764,-1.2899733,36.7908611,Rider_Id_752,942
-Order_No_22946,User_Id_970,Bike,3,Business,12,2,10:13:19 AM,12,2,10:17:59 AM,12,2,10:27:08 AM,12,2,10:31:05 AM,12,2,10:53:39 AM,9,17.9,,-1.2551895,36.7822034,-1.2953162,36.7828041,Rider_Id_535,1354
-Order_No_1297,User_Id_893,Bike,3,Business,5,1,10:21:12 AM,5,1,10:21:59 AM,5,1,10:25:13 AM,5,1,10:31:12 AM,5,1,10:47:46 AM,9,18.4,,-1.2594473999999998,36.8089122,-1.2273124,36.81636029999999,Rider_Id_101,994
-Order_No_24558,User_Id_3032,Bike,3,Business,9,5,10:29:32 AM,9,5,10:29:47 AM,9,5,10:46:01 AM,9,5,10:50:50 AM,9,5,11:03:54 AM,8,25.6,,-1.2924639,36.77957979999999,-1.2571472,36.795063299999995,Rider_Id_88,784
-Order_No_27723,User_Id_552,Bike,3,Business,9,4,3:08:18 PM,9,4,3:08:23 PM,9,4,3:08:45 PM,9,4,3:24:37 PM,9,4,4:00:42 PM,15,25.6,,-1.3562374,36.904295399999995,-1.3173868999999998,36.8112044,Rider_Id_539,2165
-Order_No_72,User_Id_3206,Bike,1,Personal,7,6,10:27:25 AM,7,6,10:27:49 AM,7,6,10:50:20 AM,7,6,10:52:33 AM,7,6,11:49:03 AM,14,19.2,,-1.2962888000000001,36.8892359,-1.3136451999999998,36.7974014,Rider_Id_423,3390
-Order_No_12878,User_Id_1850,Bike,3,Business,9,4,11:08:47 AM,9,4,11:12:25 AM,9,4,11:33:23 AM,9,4,11:58:58 AM,9,4,12:20:25 PM,5,,,-1.2571472,36.795063299999995,-1.289498,36.818878000000005,Rider_Id_764,1287
-Order_No_25111,User_Id_2330,Bike,3,Business,3,3,1:48:48 PM,3,3,1:51:57 PM,3,3,2:12:19 PM,3,3,2:24:12 PM,3,3,2:43:02 PM,10,28.2,,-1.3015197,36.7658456,-1.2536275,36.798793200000006,Rider_Id_576,1130
-Order_No_4857,User_Id_635,Bike,3,Business,1,4,2:08:57 PM,1,4,2:09:27 PM,1,4,2:23:40 PM,1,4,2:33:01 PM,1,4,2:59:40 PM,5,,,-1.2549476000000002,36.8269576,-1.2599563,36.799343900000004,Rider_Id_755,1599
-Order_No_6548,User_Id_516,Bike,3,Business,25,1,12:18:52 PM,25,1,12:19:45 PM,25,1,12:32:09 PM,25,1,12:35:31 PM,25,1,12:52:59 PM,8,27.2,,-1.2551895,36.7822034,-1.2985749,36.8088,Rider_Id_937,1048
-Order_No_13482,User_Id_3426,Bike,3,Business,12,6,11:18:20 AM,12,6,11:18:41 AM,12,6,11:25:51 AM,12,6,11:37:44 AM,12,6,11:59:15 AM,2,,,-1.3012008,36.764868,-1.2873232,36.765857200000006,Rider_Id_246,1291
-Order_No_20824,User_Id_1502,Bike,3,Business,7,5,12:42:54 PM,7,5,1:24:20 PM,7,5,1:26:26 PM,7,5,1:49:43 PM,7,5,2:05:21 PM,12,26.9,,-1.2856017,36.8129897,-1.2232123,36.8932547,Rider_Id_450,938
-Order_No_2442,User_Id_1721,Bike,3,Business,30,2,1:01:18 PM,30,2,1:13:35 PM,30,2,2:01:48 PM,30,2,2:02:52 PM,30,2,2:26:10 PM,9,,,-1.2551895,36.7822034,-1.304023,36.788389,Rider_Id_938,1398
-Order_No_23722,User_Id_2956,Bike,3,Business,10,5,3:42:38 PM,10,5,3:44:20 PM,10,5,3:50:44 PM,10,5,3:53:48 PM,10,5,4:24:23 PM,4,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_96,1835
-Order_No_11164,User_Id_3295,Bike,3,Business,14,4,2:25:55 PM,14,4,2:28:53 PM,14,4,2:34:46 PM,14,4,2:43:17 PM,14,4,3:10:18 PM,12,28.1,,-1.2571472,36.795063299999995,-1.3236648000000002,36.846181,Rider_Id_870,1621
-Order_No_27552,User_Id_1051,Bike,3,Business,17,3,11:16:17 AM,17,3,11:16:47 AM,17,3,11:24:11 AM,17,3,11:33:27 AM,17,3,12:51:36 PM,21,25.3,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_328,4689
-Order_No_12273,User_Id_3686,Bike,3,Business,17,2,10:39:14 AM,17,2,10:39:22 AM,17,2,11:08:59 AM,17,2,11:11:24 AM,17,2,12:04:45 PM,15,19.1,,-1.2551895,36.7822034,-1.3152796999999998,36.861145799999996,Rider_Id_951,3201
-Order_No_18711,User_Id_3599,Bike,3,Business,7,5,9:15:30 AM,7,5,10:48:12 AM,7,5,10:48:27 AM,7,5,11:04:05 AM,7,5,11:34:56 AM,9,25.2,,-1.3302996,36.8707935,-1.2939517,36.84467670000001,Rider_Id_609,1851
-Order_No_7601,User_Id_2521,Bike,3,Business,25,5,8:29:52 AM,25,5,8:30:19 AM,25,5,8:30:23 AM,25,5,8:34:05 AM,25,5,8:34:13 AM,4,20.7,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_239,8
-Order_No_25078,User_Id_598,Bike,1,Business,20,1,12:48:50 PM,20,1,1:14:31 PM,20,1,1:39:24 PM,20,1,1:49:09 PM,20,1,2:49:43 PM,24,21.4,,-1.2138373,36.799857200000005,-1.3103985,36.9114173,Rider_Id_882,3634
-Order_No_1062,User_Id_1687,Bike,3,Business,21,1,11:07:31 AM,21,1,11:08:09 AM,21,1,11:33:35 AM,21,1,11:38:18 AM,21,1,12:22:17 PM,16,19.9,,-1.2883354,36.81648846,-1.3194525,36.6983896,Rider_Id_455,2639
-Order_No_396,User_Id_3686,Bike,3,Business,18,3,4:24:04 PM,18,3,4:24:15 PM,18,3,4:28:08 PM,18,3,4:35:06 PM,18,3,5:10:27 PM,19,20.3,,-1.2551895,36.7822034,-1.3285517,36.9018,Rider_Id_726,2121
-Order_No_24182,User_Id_3283,Bike,3,Business,16,3,10:30:17 AM,16,3,10:31:33 AM,16,3,10:57:29 AM,16,3,11:07:24 AM,16,3,11:25:28 AM,13,22.9,,-1.2296618000000001,36.84382720000001,-1.2628473,36.781805,Rider_Id_52,1084
-Order_No_14634,User_Id_1363,Bike,3,Business,6,5,10:17:59 AM,6,5,10:18:30 AM,6,5,10:18:38 AM,6,5,10:22:02 AM,6,5,10:33:57 AM,3,15.2,,-1.3004062,36.829741,-1.2872899,36.820434000000006,Rider_Id_531,715
-Order_No_27583,User_Id_1179,Bike,3,Business,28,3,11:45:40 AM,28,3,11:46:17 AM,28,3,12:12:39 PM,28,3,12:16:45 PM,28,3,1:18:35 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_179,3710
-Order_No_25578,User_Id_302,Bike,3,Business,25,7,11:19:29 AM,25,7,11:19:42 AM,25,7,11:30:40 AM,25,7,12:03:24 PM,25,7,12:21:44 PM,10,22.8,,-1.2976313999999998,36.777256,-1.2649605,36.798177700000004,Rider_Id_612,1100
-Order_No_22482,User_Id_1341,Bike,3,Business,23,4,9:28:37 AM,23,4,9:29:29 AM,23,4,9:30:08 AM,23,4,9:45:54 AM,23,4,10:41:55 AM,11,,,-1.2663631000000002,36.8163329,-1.331619,36.847976,Rider_Id_99,3361
-Order_No_4997,User_Id_3404,Bike,3,Business,7,4,2:08:53 PM,7,4,2:19:29 PM,7,4,2:48:38 PM,7,4,2:55:52 PM,7,4,3:26:11 PM,8,27.6,,-1.279395,36.825364,-1.2765492,36.7669809,Rider_Id_778,1819
-Order_No_18899,User_Id_246,Bike,3,Business,7,5,2:40:36 PM,7,5,2:44:23 PM,7,5,2:56:44 PM,7,5,3:00:06 PM,7,5,3:04:05 PM,15,25.0,,-1.2297202,36.881950700000004,-1.2584143,36.8048002,Rider_Id_704,239
-Order_No_10207,User_Id_1404,Bike,2,Personal,26,6,3:00:30 PM,26,6,3:01:05 PM,26,6,3:17:29 PM,26,6,3:22:42 PM,26,6,3:38:27 PM,8,24.1,,-1.2956928,36.80259,-1.25454,36.839220000000005,Rider_Id_100,945
-Order_No_2190,User_Id_1469,Bike,3,Business,30,2,5:26:24 PM,30,2,5:34:01 PM,30,2,6:00:37 PM,30,2,6:03:54 PM,30,2,6:16:08 PM,4,,,-1.2551895,36.7822034,-1.2737314,36.807269399999996,Rider_Id_547,734
-Order_No_4462,User_Id_1454,Bike,3,Personal,11,1,9:47:25 AM,11,1,10:01:19 AM,11,1,10:17:31 AM,11,1,10:31:44 AM,11,1,10:53:44 AM,8,14.5,,-1.2624229999999999,36.801647100000004,-1.2961951999999999,36.7876396,Rider_Id_147,1320
-Order_No_13878,User_Id_186,Bike,3,Business,21,4,12:30:06 PM,21,4,12:30:43 PM,21,4,12:40:03 PM,21,4,12:42:29 PM,21,4,1:14:23 PM,5,28.1,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_819,1914
-Order_No_23267,User_Id_1582,Bike,3,Business,7,3,11:23:52 AM,7,3,11:24:13 AM,7,3,11:35:44 AM,7,3,11:51:32 AM,7,3,12:25:35 PM,12,25.3,,-1.3178711,36.84423066,-1.2508229,36.7895263,Rider_Id_649,2043
-Order_No_14712,User_Id_24,Bike,1,Personal,12,2,1:28:34 PM,12,2,1:40:46 PM,12,2,1:49:58 PM,12,2,2:13:26 PM,12,2,2:38:35 PM,12,22.8,,-1.303924,36.8758792,-1.2962041000000002,36.810829299999995,Rider_Id_616,1509
-Order_No_12802,User_Id_516,Bike,3,Business,16,4,2:20:34 PM,16,4,2:21:24 PM,16,4,2:22:13 PM,16,4,2:44:09 PM,16,4,2:53:54 PM,4,,,-1.2571472,36.795063299999995,-1.265432,36.795034,Rider_Id_147,585
-Order_No_19127,User_Id_635,Bike,3,Business,11,5,3:24:10 PM,11,5,4:42:25 PM,11,5,4:51:14 PM,11,5,5:11:27 PM,11,5,5:34:58 PM,7,20.6,,-1.3007481,36.8341778,-1.2602337,36.79905479999999,Rider_Id_720,1411
-Order_No_13190,User_Id_2330,Bike,3,Business,13,3,11:25:02 AM,13,3,11:25:18 AM,13,3,11:28:42 AM,13,3,11:32:03 AM,13,3,12:04:31 PM,12,24.6,,-1.3443583000000001,36.7571528,-1.2915246999999999,36.805575700000006,Rider_Id_798,1948
-Order_No_23585,User_Id_1934,Bike,2,Personal,19,3,2:17:05 PM,19,3,2:18:22 PM,19,3,2:28:46 PM,19,3,2:32:38 PM,19,3,2:52:38 PM,6,25.4,,-1.2877911,36.816449,-1.304033,36.7848695,Rider_Id_698,1200
-Order_No_26030,User_Id_1363,Bike,3,Business,2,3,8:19:13 AM,2,3,8:20:43 AM,2,3,8:23:24 AM,2,3,8:25:51 AM,2,3,8:43:12 AM,10,20.0,,-1.3004062,36.829741,-1.2991441000000001,36.7528804,Rider_Id_670,1041
-Order_No_15694,User_Id_1299,Bike,3,Business,8,4,10:09:46 AM,8,4,10:14:31 AM,8,4,10:32:39 AM,8,4,11:05:24 AM,8,4,11:22:32 AM,4,26.5,,-1.2713865,36.8058221,-1.282543,36.824618,Rider_Id_477,1028
-Order_No_10681,User_Id_1584,Bike,3,Business,8,5,4:15:39 PM,8,5,4:16:47 PM,8,5,4:29:49 PM,8,5,5:01:33 PM,8,5,5:01:35 PM,8,25.3,,-1.2969741000000001,36.7856612,-1.2715686000000002,36.8023526,Rider_Id_953,2
-Order_No_2382,User_Id_1500,Bike,3,Business,15,6,10:59:20 AM,15,6,11:52:53 AM,15,6,12:09:48 PM,15,6,12:28:27 PM,15,6,12:53:05 PM,8,22.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_523,1478
-Order_No_22561,User_Id_246,Bike,3,Business,6,1,3:47:36 PM,6,1,3:48:03 PM,6,1,3:48:10 PM,6,1,3:58:40 PM,6,1,4:14:28 PM,5,24.5,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_531,948
-Order_No_21722,User_Id_412,Bike,3,Business,25,1,8:58:24 AM,25,1,8:59:06 AM,25,1,9:18:24 AM,25,1,9:27:49 AM,25,1,9:47:42 AM,10,21.3,,-1.303596,36.778378000000004,-1.266837,36.799248999999996,Rider_Id_354,1193
-Order_No_3619,User_Id_2271,Bike,3,Business,2,2,2:56:19 PM,2,2,3:09:30 PM,2,2,3:20:02 PM,2,2,4:28:49 PM,2,2,4:50:59 PM,9,29.3,,-1.3343128999999998,36.885417499999996,-1.293515,36.897607,Rider_Id_855,1330
-Order_No_13589,User_Id_3291,Bike,3,Business,23,4,1:14:17 PM,23,4,1:14:46 PM,23,4,1:32:34 PM,23,4,1:39:26 PM,23,4,1:56:31 PM,5,24.4,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_290,1025
-Order_No_25614,User_Id_3560,Bike,3,Business,25,1,4:37:08 PM,25,1,4:38:47 PM,25,1,4:53:55 PM,25,1,5:01:17 PM,25,1,5:06:36 PM,1,27.3,,-1.2899256000000001,36.815551,-1.279395,36.825364,Rider_Id_48,319
-Order_No_21495,User_Id_2069,Bike,3,Business,30,3,4:43:03 PM,30,3,4:43:47 PM,30,3,5:02:03 PM,30,3,5:10:01 PM,30,3,5:48:08 PM,17,27.6,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_691,2287
-Order_No_7050,User_Id_3222,Bike,3,Business,19,3,12:34:47 PM,19,3,12:37:15 PM,19,3,1:15:21 PM,19,3,1:20:56 PM,19,3,1:47:33 PM,5,22.0,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_399,1597
-Order_No_17376,User_Id_2175,Bike,3,Business,13,2,12:04:17 PM,13,2,12:58:02 PM,13,2,1:16:27 PM,13,2,1:24:25 PM,13,2,2:09:22 PM,8,22.1,,-1.2551895,36.7822034,-1.2950408000000002,36.809817200000005,Rider_Id_436,2697
-Order_No_10833,User_Id_2701,Bike,3,Business,4,2,10:59:33 AM,4,2,11:00:05 AM,4,2,11:15:13 AM,4,2,11:28:53 AM,4,2,12:04:58 PM,10,20.3,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_788,2165
-Order_No_8008,User_Id_2358,Bike,4,Business,2,2,2:48:38 PM,2,2,2:49:13 PM,2,2,2:59:46 PM,2,2,3:13:13 PM,2,2,3:42:17 PM,18,26.3,,-1.3256903999999998,36.8747943,-1.2151769,36.8846524,Rider_Id_207,1744
-Order_No_20063,User_Id_1527,Bike,3,Business,10,3,11:12:41 AM,10,3,11:16:23 AM,10,3,11:42:39 AM,10,3,11:59:26 AM,10,3,11:59:28 AM,4,27.6,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_61,2
-Order_No_2460,User_Id_2523,Bike,3,Business,5,2,12:01:16 PM,5,2,12:01:42 PM,5,2,12:09:38 PM,5,2,12:14:22 PM,5,2,1:05:54 PM,28,25.6,,-1.2551895,36.7822034,-1.1429062,36.9300994,Rider_Id_154,3092
-Order_No_18012,User_Id_635,Bike,3,Business,8,6,10:30:32 AM,8,6,10:31:02 AM,8,6,10:38:11 AM,8,6,11:28:00 AM,8,6,11:43:40 AM,10,20.2,,-1.300921,36.828195,-1.2602337,36.79905479999999,Rider_Id_570,940
-Order_No_17746,User_Id_3647,Bike,3,Business,27,3,12:50:45 PM,27,3,12:51:28 PM,27,3,1:05:59 PM,27,3,1:10:07 PM,27,3,1:31:01 PM,6,25.9,,-1.2726389999999999,36.794723,-1.2951386,36.797008899999994,Rider_Id_576,1254
-Order_No_11142,User_Id_1004,Bike,3,Business,13,1,3:08:26 PM,13,1,3:22:19 PM,13,1,4:13:05 PM,13,1,4:19:03 PM,13,1,4:54:12 PM,12,,,-1.3250048,36.869718799999994,-1.277551,36.8273641,Rider_Id_364,2109
-Order_No_7249,User_Id_527,Bike,2,Personal,8,5,1:07:30 PM,8,5,1:08:58 PM,8,5,1:50:20 PM,8,5,2:28:08 PM,8,5,3:20:30 PM,13,21.4,,-1.2861764999999998,36.8209086,-1.3302565,36.898639200000005,Rider_Id_216,3142
-Order_No_26565,User_Id_970,Bike,3,Business,16,4,8:09:15 AM,16,4,8:11:28 AM,16,4,8:26:08 AM,16,4,8:27:19 AM,16,4,8:59:13 AM,6,,,-1.2551895,36.7822034,-1.280753,36.8191175,Rider_Id_617,1914
-Order_No_25649,User_Id_2330,Bike,3,Business,3,5,3:51:30 PM,3,5,3:53:32 PM,3,5,4:18:54 PM,3,5,4:31:57 PM,3,5,4:46:27 PM,3,,,-1.3040812,36.7924217,-1.294597,36.7742999,Rider_Id_197,870
-Order_No_28263,User_Id_238,Bike,3,Business,16,2,11:14:54 AM,16,2,12:08:52 PM,16,2,12:17:39 PM,16,2,12:19:02 PM,16,2,12:25:37 PM,3,,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_299,395
-Order_No_19665,User_Id_1363,Bike,3,Business,17,5,2:24:11 PM,17,5,2:25:30 PM,17,5,2:28:15 PM,17,5,2:29:25 PM,17,5,2:47:35 PM,4,,,-1.3004062,36.829741,-1.2890399,36.8164942,Rider_Id_211,1090
-Order_No_26907,User_Id_1243,Bike,3,Business,30,1,12:08:18 PM,30,1,12:08:36 PM,30,1,12:09:07 PM,30,1,12:18:13 PM,30,1,12:48:13 PM,16,22.8,,-1.2551895,36.7822034,-1.226855,36.8857626,Rider_Id_539,1800
-Order_No_10892,User_Id_2718,Bike,3,Business,28,1,4:14:59 PM,28,1,4:15:25 PM,28,1,4:32:34 PM,28,1,4:35:03 PM,28,1,4:44:00 PM,2,22.6,,-1.2670219999999999,36.807941,-1.2601,36.796528,Rider_Id_37,537
-Order_No_6476,User_Id_3300,Bike,3,Business,25,6,1:22:20 PM,25,6,1:22:48 PM,25,6,1:23:04 PM,25,6,1:34:02 PM,25,6,1:51:14 PM,9,,,-1.307143,36.825009,-1.303596,36.778378000000004,Rider_Id_155,1032
-Order_No_27512,User_Id_3510,Bike,1,Business,25,5,4:37:18 PM,25,5,4:42:42 PM,25,5,5:04:40 PM,25,5,5:13:49 PM,25,5,5:37:27 PM,6,21.1,,-1.2472211000000002,36.7826109,-1.2702719,36.7688649,Rider_Id_888,1418
-Order_No_7289,User_Id_1329,Bike,3,Business,2,3,3:42:54 PM,2,3,3:44:50 PM,2,3,4:10:33 PM,2,3,4:14:29 PM,2,3,4:29:10 PM,2,23.2,,-1.273056,36.811298,-1.2628066999999998,36.8140689,Rider_Id_676,881
-Order_No_6342,User_Id_928,Bike,3,Business,30,3,10:04:28 AM,30,3,10:22:04 AM,30,3,10:36:09 AM,30,3,10:37:16 AM,30,3,11:07:32 AM,7,25.0,,-1.2551895,36.7822034,-1.2872652,36.826761299999994,Rider_Id_206,1816
-Order_No_16619,User_Id_1463,Bike,3,Personal,9,3,11:51:32 AM,9,3,11:53:22 AM,9,3,12:17:06 PM,9,3,12:21:39 PM,9,3,12:46:49 PM,7,25.5,,-1.2931121,36.8224572,-1.2538731,36.7850223,Rider_Id_534,1510
-Order_No_19683,User_Id_1280,Bike,3,Business,3,1,10:33:54 AM,3,1,10:34:12 AM,3,1,10:35:23 AM,3,1,10:54:39 AM,3,1,11:29:25 AM,18,22.3,,-1.2551895,36.7822034,-1.3343128999999998,36.885417499999996,Rider_Id_120,2086
-Order_No_22744,User_Id_1556,Bike,3,Business,13,2,1:08:57 PM,13,2,1:11:16 PM,13,2,1:28:10 PM,13,2,1:40:04 PM,13,2,1:55:21 PM,2,26.4,,-1.2584143,36.8048002,-1.2701354,36.807216700000005,Rider_Id_446,917
-Order_No_18314,User_Id_186,Bike,3,Business,19,2,1:13:20 PM,19,2,1:14:51 PM,19,2,1:21:51 PM,19,2,1:31:48 PM,19,2,1:52:10 PM,6,27.4,,-1.2571472,36.795063299999995,-1.2985749,36.8088,Rider_Id_318,1222
-Order_No_15457,User_Id_302,Bike,3,Business,18,4,8:20:04 PM,18,4,8:21:24 PM,18,4,8:36:38 PM,18,4,8:38:36 PM,18,4,9:01:03 PM,5,20.0,,-1.2649605,36.798177700000004,-1.2844072,36.824729600000005,Rider_Id_299,1347
-Order_No_23249,User_Id_1363,Bike,3,Business,28,2,3:15:48 PM,28,2,3:34:19 PM,28,2,3:34:50 PM,28,2,3:38:05 PM,28,2,4:08:28 PM,3,23.7,,-1.3004062,36.829741,-1.281223,36.8239,Rider_Id_686,1823
-Order_No_17844,User_Id_481,Bike,3,Personal,6,4,1:33:42 PM,6,4,1:34:22 PM,6,4,1:41:40 PM,6,4,1:50:38 PM,6,4,2:24:05 PM,8,23.2,,-1.3016417,36.8271679,-1.2619756000000002,36.800139,Rider_Id_844,2007
-Order_No_17368,User_Id_2691,Bike,3,Business,7,1,10:00:12 AM,7,1,10:04:51 AM,7,1,10:12:43 AM,7,1,10:15:29 AM,7,1,10:38:40 AM,8,18.2,,-1.2857671,36.7875614,-1.2559559999999999,36.789867,Rider_Id_216,1391
-Order_No_11458,User_Id_2373,Bike,1,Personal,28,4,10:58:45 AM,28,4,10:59:22 AM,28,4,11:23:23 AM,28,4,11:24:28 AM,28,4,12:07:15 PM,22,,,-1.2789537,36.828639200000005,-1.228357,36.670860100000006,Rider_Id_494,2567
-Order_No_26217,User_Id_1557,Bike,3,Business,26,4,10:12:12 AM,26,4,10:21:52 AM,26,4,10:53:37 AM,26,4,11:06:20 AM,26,4,11:28:29 AM,10,19.0,,-1.3147073999999999,36.811721999999996,-1.3038645,36.8684596,Rider_Id_101,1329
-Order_No_21445,User_Id_2763,Bike,3,Business,6,3,9:17:53 AM,6,3,9:19:00 AM,6,3,9:19:06 AM,6,3,9:24:03 AM,6,3,9:36:02 AM,6,,,-1.2551895,36.7822034,-1.2843046,36.774613200000005,Rider_Id_523,719
-Order_No_21235,User_Id_142,Bike,3,Business,28,5,12:21:45 PM,28,5,12:29:01 PM,28,5,12:43:50 PM,28,5,12:48:04 PM,28,5,1:02:45 PM,6,25.2,,-1.2857723,36.808805799999995,-1.2963097,36.7688221,Rider_Id_932,881
-Order_No_13979,User_Id_1578,Bike,3,Business,5,1,3:20:54 PM,5,1,3:22:01 PM,5,1,3:43:03 PM,5,1,3:48:49 PM,5,1,4:13:36 PM,5,25.8,,-1.28878,36.8168312,-1.2597528999999998,36.806629,Rider_Id_795,1487
-Order_No_25183,User_Id_2982,Bike,3,Business,14,4,2:40:20 PM,14,4,4:00:22 PM,14,4,4:07:12 PM,14,4,4:21:53 PM,14,4,4:47:16 PM,7,21.2,,-1.300921,36.828195,-1.325469,36.862403,Rider_Id_549,1523
-Order_No_26554,User_Id_402,Bike,3,Business,1,6,1:47:49 PM,1,6,1:50:11 PM,1,6,2:14:12 PM,1,6,2:33:15 PM,1,6,3:02:57 PM,6,26.6,,-1.290894,36.822971,-1.2527958000000001,36.8003131,Rider_Id_502,1782
-Order_No_14993,User_Id_868,Bike,3,Business,12,2,4:57:05 PM,12,2,4:57:39 PM,12,2,5:10:31 PM,12,2,5:12:18 PM,12,2,5:31:03 PM,4,28.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_849,1125
-Order_No_1414,User_Id_393,Bike,3,Business,30,2,1:38:36 PM,30,2,1:41:55 PM,30,2,1:47:16 PM,30,2,2:06:35 PM,30,2,2:15:40 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_448,545
-Order_No_7089,User_Id_391,Bike,3,Business,8,5,8:17:45 AM,8,5,8:23:06 AM,8,5,8:42:16 AM,8,5,8:55:20 AM,8,5,9:09:56 AM,5,20.5,,-1.2602337,36.79905479999999,-1.2867454,36.8259017,Rider_Id_899,876
-Order_No_18552,User_Id_2801,Bike,3,Business,27,3,12:07:27 PM,27,3,12:11:23 PM,27,3,12:30:16 PM,27,3,12:39:22 PM,27,3,1:08:03 PM,8,26.2,,-1.289081,36.8300409,-1.2674273999999999,36.787082700000006,Rider_Id_61,1721
-Order_No_5724,User_Id_3771,Bike,3,Personal,17,3,2:18:32 PM,17,3,2:23:49 PM,17,3,2:40:08 PM,17,3,3:35:56 PM,17,3,4:03:55 PM,6,28.5,,-1.273808,36.8402896,-1.2768326,36.8073804,Rider_Id_432,1679
-Order_No_14973,User_Id_2154,Bike,3,Business,12,2,12:42:19 PM,12,2,12:52:42 PM,12,2,1:11:17 PM,12,2,1:12:45 PM,12,2,1:32:03 PM,7,28.2,,-1.2765736,36.851364600000004,-1.2591018999999999,36.800577000000004,Rider_Id_291,1158
-Order_No_23038,User_Id_840,Bike,1,Personal,20,1,5:14:08 PM,20,1,5:15:41 PM,20,1,5:29:11 PM,20,1,5:36:05 PM,20,1,5:53:59 PM,3,20.3,,-1.283527,36.82365120000001,-1.270054,36.815164,Rider_Id_698,1074
-Order_No_10239,User_Id_2174,Bike,3,Business,23,6,10:30:45 AM,23,6,10:31:07 AM,23,6,10:31:10 AM,23,6,11:08:28 AM,23,6,11:26:58 AM,22,,,-1.2482781,36.884091999999995,-1.3470008999999998,36.761945399999995,Rider_Id_86,1110
-Order_No_5159,User_Id_2493,Bike,1,Personal,21,4,3:09:31 PM,21,4,3:10:03 PM,21,4,3:26:27 PM,21,4,3:31:16 PM,21,4,4:03:03 PM,9,22.6,,-1.31471,36.8110941,-1.2658334999999998,36.804286700000006,Rider_Id_865,1907
-Order_No_1114,User_Id_170,Bike,3,Personal,20,1,1:19:45 PM,20,1,1:20:05 PM,20,1,1:41:37 PM,20,1,1:43:11 PM,20,1,2:57:26 PM,20,21.2,,-1.3223238000000002,36.8320056,-1.2081811000000002,36.794591499999996,Rider_Id_938,4455
-Order_No_26732,User_Id_1422,Bike,3,Business,3,4,3:31:39 PM,3,4,3:47:31 PM,3,4,4:15:07 PM,3,4,4:26:44 PM,3,4,4:48:29 PM,5,26.0,,-1.308985,36.835623,-1.2856826000000001,36.8255265,Rider_Id_436,1305
-Order_No_6554,User_Id_3147,Bike,3,Business,5,2,11:39:08 AM,5,2,11:39:19 AM,5,2,11:43:55 AM,5,2,11:47:17 AM,5,2,12:10:31 PM,16,23.5,,-1.2765492,36.7669809,-1.3472066,36.7692638,Rider_Id_368,1394
-Order_No_28173,User_Id_552,Bike,3,Business,12,3,11:22:06 AM,12,3,11:29:06 AM,12,3,11:38:04 AM,12,3,11:40:25 AM,12,3,12:06:31 PM,18,19.2,,-1.279395,36.825364,-1.3562374,36.904295399999995,Rider_Id_403,1566
-Order_No_2205,User_Id_1329,Bike,3,Business,13,2,5:52:07 PM,13,2,6:03:26 PM,13,2,6:21:39 PM,13,2,6:29:30 PM,13,2,6:54:41 PM,6,23.5,,-1.273056,36.811298,-1.3084722,36.8224665,Rider_Id_276,1511
-Order_No_2584,User_Id_2544,Bike,3,Personal,2,2,12:25:03 PM,2,2,12:27:08 PM,2,2,12:31:27 PM,2,2,12:40:16 PM,2,2,1:11:58 PM,19,26.2,,-1.2580991000000001,36.794151899999996,-1.3287648,36.744573200000005,Rider_Id_575,1902
-Order_No_16995,User_Id_2271,Bike,3,Business,2,3,10:10:51 AM,2,3,10:10:56 AM,2,3,10:11:03 AM,2,3,10:27:52 AM,2,3,10:47:51 AM,16,18.1,1.1,-1.272828,36.816608,-1.293515,36.897607,Rider_Id_397,1199
-Order_No_16523,User_Id_1926,Bike,3,Business,9,4,1:19:14 PM,9,4,1:19:36 PM,9,4,1:31:34 PM,9,4,1:40:58 PM,9,4,2:03:21 PM,15,25.6,,-1.3332749,36.870814700000004,-1.346168,36.766149,Rider_Id_74,1343
-Order_No_26692,User_Id_635,Bike,3,Business,20,1,1:33:28 PM,20,1,1:42:50 PM,20,1,2:21:54 PM,20,1,2:53:46 PM,20,1,3:20:40 PM,8,21.5,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_427,1614
-Order_No_5471,User_Id_2162,Bike,3,Business,7,1,1:38:41 PM,7,1,1:44:38 PM,7,1,2:08:57 PM,7,1,2:23:06 PM,7,1,2:55:53 PM,12,20.3,,-1.256624,36.870684600000004,-1.2615888000000002,36.7928732,Rider_Id_479,1967
-Order_No_4380,User_Id_3149,Bike,3,Business,30,5,2:59:30 PM,30,5,3:45:47 PM,30,5,3:45:48 PM,30,5,4:10:22 PM,30,5,4:48:03 PM,15,24.8,,-1.2991441000000001,36.7528804,-1.3177546999999998,36.8303703,Rider_Id_848,2261
-Order_No_14582,User_Id_136,Bike,3,Business,8,5,8:54:17 AM,8,5,9:06:05 AM,8,5,9:15:39 AM,8,5,9:18:53 AM,8,5,9:59:56 AM,19,20.7,,-1.2768704,36.8251777,-1.3254522,36.721658399999995,Rider_Id_758,2463
-Order_No_19408,User_Id_590,Bike,3,Business,7,4,6:07:24 PM,7,4,6:15:39 PM,7,4,6:30:22 PM,7,4,6:35:19 PM,7,4,7:47:40 PM,11,25.4,,-1.2815072,36.82096,-1.2296129,36.7641955,Rider_Id_436,4341
-Order_No_16585,User_Id_364,Bike,3,Personal,15,5,4:19:48 PM,15,5,4:25:05 PM,15,5,4:38:44 PM,15,5,4:50:41 PM,15,5,5:01:24 PM,5,27.7,,-1.2665082,36.7963034,-1.2812014999999999,36.82612460000001,Rider_Id_576,643
-Order_No_5282,User_Id_666,Bike,2,Personal,17,4,8:33:28 AM,17,4,9:05:11 AM,17,4,9:27:12 AM,17,4,9:29:54 AM,17,4,9:54:08 AM,5,17.9,7.0,-1.2770071000000003,36.832167,-1.2583017,36.8023526,Rider_Id_805,1454
-Order_No_14474,User_Id_1500,Bike,3,Business,7,4,10:35:24 AM,7,4,10:35:46 AM,7,4,10:53:58 AM,7,4,11:07:40 AM,7,4,11:37:10 AM,11,27.4,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_448,1770
-Order_No_4773,User_Id_2412,Bike,3,Business,21,4,12:25:22 PM,21,4,12:26:43 PM,21,4,12:52:21 PM,21,4,12:53:35 PM,21,4,1:11:02 PM,11,25.4,,-1.2843633999999997,36.8279416,-1.2285834999999998,36.882646799999996,Rider_Id_411,1047
-Order_No_17309,User_Id_868,Bike,3,Business,9,2,10:19:36 AM,9,2,10:20:09 AM,9,2,10:24:28 AM,9,2,10:28:51 AM,9,2,11:01:28 AM,7,20.9,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_570,1957
-Order_No_14566,User_Id_2200,Bike,3,Business,30,2,1:15:23 PM,30,2,2:51:27 PM,30,2,3:03:42 PM,30,2,3:26:26 PM,30,2,4:08:23 PM,9,27.4,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_399,2517
-Order_No_694,User_Id_3698,Bike,3,Business,13,4,1:38:31 PM,13,4,1:45:57 PM,13,4,2:13:10 PM,13,4,2:14:21 PM,13,4,2:57:05 PM,24,23.3,,-1.3244885,36.897791999999995,-1.3108386,36.7555239,Rider_Id_218,2564
-Order_No_3179,User_Id_3676,Bike,3,Personal,30,4,9:21:55 AM,30,4,9:23:51 AM,30,4,10:13:17 AM,30,4,10:23:01 AM,30,4,11:10:41 AM,22,19.2,,-1.353656,36.75865,-1.2263155000000001,36.7994199,Rider_Id_538,2860
-Order_No_2140,User_Id_2523,Bike,3,Business,30,3,9:41:34 AM,30,3,9:43:11 AM,30,3,9:46:36 AM,30,3,10:01:50 AM,30,3,10:26:21 AM,15,19.6,,-1.2551895,36.7822034,-1.3231725,36.864017700000005,Rider_Id_541,1471
-Order_No_16331,User_Id_3491,Bike,3,Business,22,5,9:49:38 AM,22,5,9:50:07 AM,22,5,10:06:32 AM,22,5,10:17:33 AM,22,5,10:58:39 AM,21,22.2,,-1.2283403,36.8822756,-1.3104733999999998,36.7363698,Rider_Id_75,2466
-Order_No_823,User_Id_2820,Bike,3,Personal,15,3,9:23:37 AM,15,3,9:31:27 AM,15,3,10:10:28 AM,15,3,10:14:53 AM,15,3,10:32:29 AM,9,16.4,,-1.3291184,36.7892374,-1.2903421,36.7885282,Rider_Id_208,1056
-Order_No_16900,User_Id_166,Bike,2,Personal,30,3,4:49:54 PM,30,3,4:53:03 PM,30,3,5:17:06 PM,30,3,5:35:52 PM,30,3,6:25:01 PM,17,,,-1.2819025,36.878684,-1.3026657,36.7748881,Rider_Id_742,2949
-Order_No_23776,User_Id_2987,Bike,3,Business,19,2,1:46:02 PM,19,2,1:47:25 PM,19,2,2:12:00 PM,19,2,2:19:12 PM,19,2,2:30:07 PM,4,18.3,,-1.2702120000000001,36.82108770000001,-1.2649605,36.798177700000004,Rider_Id_434,655
-Order_No_22625,User_Id_970,Bike,3,Business,20,3,9:30:34 AM,20,3,9:33:45 AM,20,3,9:43:52 AM,20,3,9:46:54 AM,20,3,9:57:04 AM,5,16.4,0.4,-1.2551895,36.7822034,-1.2477886999999999,36.7960085,Rider_Id_409,610
-Order_No_22310,User_Id_393,Bike,3,Business,9,4,10:56:54 AM,9,4,11:23:38 AM,9,4,11:30:07 AM,9,4,12:02:06 PM,9,4,12:11:53 PM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_168,587
-Order_No_8060,User_Id_1329,Bike,3,Business,20,3,11:34:04 AM,20,3,11:35:36 AM,20,3,11:44:13 AM,20,3,11:50:32 AM,20,3,12:08:27 PM,8,27.2,,-1.273056,36.811298,-1.248365,36.7530645,Rider_Id_484,1075
-Order_No_5312,User_Id_1085,Bike,3,Business,27,3,5:59:25 PM,27,3,6:00:14 PM,27,3,6:05:10 PM,27,3,6:18:07 PM,27,3,6:35:12 PM,7,27.5,,-1.2628638,36.8070399,-1.232864,36.808470799999995,Rider_Id_178,1025
-Order_No_12093,User_Id_2523,Bike,3,Business,8,4,9:54:46 AM,8,4,9:56:03 AM,8,4,10:09:13 AM,8,4,10:11:22 AM,8,4,11:28:08 AM,19,20.7,,-1.2551895,36.7822034,-1.3285517,36.9018,Rider_Id_715,4606
-Order_No_21683,User_Id_2509,Bike,3,Business,28,4,11:05:06 AM,28,4,11:06:42 AM,28,4,11:28:00 AM,28,4,11:29:55 AM,28,4,12:19:13 PM,12,,,-1.2579303999999998,36.8019667,-1.3332749,36.870814700000004,Rider_Id_520,2958
-Order_No_556,User_Id_1207,Bike,3,Business,23,5,5:56:16 PM,23,5,5:57:37 PM,23,5,6:11:22 PM,23,5,6:14:48 PM,23,5,6:44:37 PM,8,24.7,,-1.2628638,36.8070399,-1.2745321000000003,36.765088,Rider_Id_829,1789
-Order_No_9743,User_Id_2956,Bike,3,Business,16,4,10:22:21 AM,16,4,10:22:35 AM,16,4,10:28:47 AM,16,4,10:56:48 AM,16,4,10:56:54 AM,2,,,-1.2615888000000002,36.7928732,-1.2584143,36.8048002,Rider_Id_161,6
-Order_No_15613,User_Id_242,Bike,3,Business,2,3,12:19:38 PM,2,3,12:26:22 PM,2,3,1:06:23 PM,2,3,1:07:45 PM,2,3,1:30:00 PM,7,22.2,,-1.2559559999999999,36.789867,-1.2834669,36.8297516,Rider_Id_350,1335
-Order_No_7647,User_Id_200,Bike,3,Personal,3,7,11:39:34 AM,3,7,11:59:20 AM,3,7,12:10:29 PM,3,7,12:13:18 PM,3,7,12:47:52 PM,6,28.4,,-1.2857315,36.7953424,-1.3173868999999998,36.8112044,Rider_Id_17,2074
-Order_No_11557,User_Id_1597,Bike,1,Personal,31,4,6:23:57 PM,31,4,6:25:44 PM,31,4,6:57:02 PM,31,4,7:04:37 PM,31,4,8:26:05 PM,20,22.4,,-1.2322870000000001,36.801636,-1.3222489,36.786702500000004,Rider_Id_301,4888
-Order_No_14180,User_Id_186,Bike,3,Business,16,2,8:57:03 AM,16,2,8:57:35 AM,16,2,9:06:40 AM,16,2,9:12:06 AM,16,2,9:43:54 AM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_147,1908
-Order_No_13467,User_Id_2987,Bike,3,Business,1,3,1:49:07 PM,1,3,1:53:59 PM,1,3,1:59:18 PM,1,3,2:06:23 PM,1,3,2:29:29 PM,4,23.7,,-1.2702120000000001,36.82108770000001,-1.2513591,36.7947674,Rider_Id_754,1386
-Order_No_14111,User_Id_1502,Bike,3,Business,25,1,5:56:39 PM,25,1,5:57:12 PM,25,1,6:01:56 PM,25,1,6:08:31 PM,25,1,6:34:56 PM,13,27.4,,-1.2527958000000001,36.8003131,-1.2232123,36.8932547,Rider_Id_72,1585
-Order_No_23053,User_Id_1329,Bike,3,Business,4,5,10:00:43 AM,4,5,10:01:18 AM,4,5,10:23:40 AM,4,5,10:26:51 AM,4,5,10:42:22 AM,6,17.2,0.4,-1.273056,36.811298,-1.2652706,36.765246399999995,Rider_Id_332,931
-Order_No_3183,User_Id_393,Bike,3,Business,15,2,10:10:38 AM,15,2,10:10:43 AM,15,2,10:14:58 AM,15,2,10:28:01 AM,15,2,10:59:00 AM,14,20.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1859
-Order_No_21526,User_Id_2776,Bike,3,Personal,3,3,4:45:19 PM,3,3,4:48:43 PM,3,3,4:51:12 PM,3,3,5:12:16 PM,3,3,5:38:58 PM,5,23.5,,-1.2567848999999998,36.814054299999995,-1.2762513,36.7964048,Rider_Id_519,1602
-Order_No_15852,User_Id_2828,Bike,3,Business,22,5,9:20:32 AM,22,5,9:20:50 AM,22,5,9:23:21 AM,22,5,9:49:13 AM,22,5,10:10:40 AM,16,,,-1.2615888000000002,36.7928732,-1.2155671000000001,36.8898931,Rider_Id_555,1287
-Order_No_2896,User_Id_788,Bike,3,Business,16,3,8:05:05 AM,16,3,8:06:11 AM,16,3,8:24:51 AM,16,3,8:35:00 AM,16,3,9:01:24 AM,9,19.8,,-1.225322,36.80855,-1.2626906,36.7827022,Rider_Id_720,1584
-Order_No_19875,User_Id_3291,Bike,3,Business,22,3,11:21:19 AM,22,3,11:22:33 AM,22,3,11:32:04 AM,22,3,11:40:03 AM,22,3,11:49:53 AM,5,22.9,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_651,590
-Order_No_17492,User_Id_1133,Bike,3,Business,16,3,11:00:37 AM,16,3,11:09:43 AM,16,3,11:19:07 AM,16,3,11:22:46 AM,16,3,11:36:15 AM,4,23.0,,-1.3323107,36.8692616,-1.335957,36.871741799999995,Rider_Id_559,809
-Order_No_19619,User_Id_3283,Bike,3,Business,5,6,3:50:43 PM,5,6,3:51:23 PM,5,6,3:58:23 PM,5,6,4:06:14 PM,5,6,4:14:04 PM,6,22.2,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_178,470
-Order_No_18784,User_Id_1131,Bike,3,Business,30,4,4:11:02 PM,30,4,4:11:02 PM,30,4,4:11:23 PM,30,4,4:37:54 PM,30,4,4:41:59 PM,13,,,-1.2156006000000001,36.8916865,-1.207546,36.7913976,Rider_Id_193,245
-Order_No_4648,User_Id_3239,Bike,3,Personal,4,3,9:11:33 AM,4,3,9:19:55 AM,4,3,10:03:43 AM,4,3,10:07:58 AM,4,3,10:34:13 AM,9,14.9,,-1.2835524999999999,36.829638200000005,-1.2912419,36.877528399999996,Rider_Id_752,1575
-Order_No_15820,User_Id_1724,Bike,3,Business,5,1,1:23:56 PM,5,1,1:24:31 PM,5,1,1:40:55 PM,5,1,1:49:54 PM,5,1,2:03:14 PM,8,26.0,,-1.2907853999999999,36.7774108,-1.2551895,36.7822034,Rider_Id_952,800
-Order_No_3466,User_Id_3701,Bike,3,Business,6,2,8:50:26 AM,6,2,8:50:33 AM,6,2,9:02:39 AM,6,2,9:08:32 AM,6,2,9:26:25 AM,8,19.6,,-1.2684239,36.79524620000001,-1.3044313,36.818504,Rider_Id_952,1073
-Order_No_22227,User_Id_3326,Bike,3,Business,13,4,4:13:38 PM,13,4,4:14:55 PM,13,4,4:20:14 PM,13,4,4:31:26 PM,13,4,5:07:53 PM,8,23.0,,-1.2551895,36.7822034,-1.292278,36.797717999999996,Rider_Id_259,2187
-Order_No_12584,User_Id_362,Bike,1,Personal,22,3,2:56:52 PM,22,3,2:57:06 PM,22,3,3:07:00 PM,22,3,3:12:07 PM,22,3,3:32:41 PM,5,25.7,,-1.2942023,36.7958179,-1.2873232,36.765857200000006,Rider_Id_54,1234
-Order_No_6078,User_Id_2008,Bike,3,Business,4,5,3:28:10 PM,4,5,3:29:22 PM,4,5,3:42:04 PM,4,5,3:52:15 PM,4,5,4:06:27 PM,9,,,-1.2508229,36.7895263,-1.2696795,36.8640088,Rider_Id_307,852
-Order_No_12090,User_Id_1078,Bike,3,Business,18,4,8:03:42 AM,18,4,8:03:54 AM,18,4,8:35:32 AM,18,4,8:41:05 AM,18,4,9:05:07 AM,15,,,-1.2902348999999997,36.7850769,-1.2417321000000001,36.8886277,Rider_Id_576,1442
-Order_No_7977,User_Id_1561,Bike,3,Business,8,5,11:44:06 AM,8,5,11:44:49 AM,8,5,11:59:17 AM,8,5,12:05:19 PM,8,5,12:37:23 PM,14,,,-1.328259,36.860975,-1.2615584,36.814950599999996,Rider_Id_865,1924
-Order_No_4706,User_Id_633,Bike,3,Business,1,5,2:58:54 PM,1,5,2:59:02 PM,1,5,2:59:31 PM,1,5,3:14:23 PM,1,5,3:22:30 PM,4,28.5,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_523,487
-Order_No_6743,User_Id_1992,Bike,3,Business,18,4,10:15:25 PM,18,4,10:15:36 PM,18,4,10:18:03 PM,18,4,10:19:15 PM,18,4,10:19:17 PM,1,,,-1.3611971,36.6538278,-1.3620355,36.6533375,Rider_Id_214,2
-Order_No_19773,User_Id_1583,Bike,3,Business,31,5,3:02:27 PM,31,5,3:22:28 PM,31,5,4:19:11 PM,31,5,4:57:11 PM,31,5,5:19:34 PM,21,23.6,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_856,1343
-Order_No_23652,User_Id_1405,Bike,3,Business,27,4,12:00:22 PM,27,4,12:20:59 PM,27,4,12:23:47 PM,27,4,12:39:07 PM,27,4,1:01:22 PM,6,22.6,,-1.3275350000000001,36.89546,-1.3478563000000001,36.8935824,Rider_Id_543,1335
-Order_No_3001,User_Id_3647,Bike,3,Business,2,4,12:12:10 PM,2,4,12:13:30 PM,2,4,12:24:01 PM,2,4,12:34:26 PM,2,4,1:00:33 PM,9,,,-1.2726389999999999,36.794723,-1.3103512,36.814996,Rider_Id_709,1567
-Order_No_1659,User_Id_20,Bike,3,Business,22,4,4:45:25 PM,22,4,4:45:33 PM,22,4,4:50:20 PM,22,4,5:32:53 PM,22,4,6:20:05 PM,27,24.2,,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_886,2832
-Order_No_23509,User_Id_435,Bike,3,Business,21,2,2:04:35 PM,21,2,2:05:31 PM,21,2,2:15:32 PM,21,2,2:29:02 PM,21,2,2:56:00 PM,13,24.8,,-1.300921,36.828195,-1.2271237,36.8253963,Rider_Id_675,1618
-Order_No_5549,User_Id_1642,Bike,3,Business,3,3,2:13:56 PM,3,3,2:14:58 PM,3,3,2:24:20 PM,3,3,2:31:06 PM,3,3,3:09:11 PM,17,28.0,,-1.2726506000000002,36.7960153,-1.2000415,36.7771159,Rider_Id_691,2285
-Order_No_19916,User_Id_3683,Bike,3,Business,5,3,3:38:51 PM,5,3,3:39:19 PM,5,3,3:41:05 PM,5,3,3:55:19 PM,5,3,4:22:20 PM,11,23.7,,-1.2897368,36.8226783,-1.2391616,36.8101823,Rider_Id_726,1621
-Order_No_24852,User_Id_870,Bike,3,Personal,8,5,10:14:26 AM,8,5,10:15:07 AM,8,5,10:24:10 AM,8,5,10:33:52 AM,8,5,11:02:45 AM,13,,,-1.263419,36.8060348,-1.2810586000000002,36.883829399999996,Rider_Id_207,1733
-Order_No_19307,User_Id_3647,Bike,3,Business,8,1,11:00:26 AM,8,1,11:02:10 AM,8,1,11:13:18 AM,8,1,11:16:14 AM,8,1,11:53:10 AM,16,27.7,,-1.2726389999999999,36.794723,-1.2155671000000001,36.8898931,Rider_Id_245,2216
-Order_No_12269,User_Id_2018,Bike,1,Personal,1,1,3:21:52 PM,1,1,3:26:18 PM,1,1,3:52:54 PM,1,1,3:57:14 PM,1,1,5:58:31 PM,5,25.4,,-1.2871384,36.822796200000006,-1.2954364,36.849036100000006,Rider_Id_612,7277
-Order_No_5383,User_Id_393,Bike,3,Business,2,5,11:01:37 AM,2,5,11:27:40 AM,2,5,11:55:01 AM,2,5,11:57:01 AM,2,5,12:39:38 PM,14,23.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,2557
-Order_No_15432,User_Id_3404,Bike,3,Business,22,4,4:13:40 PM,22,4,4:39:39 PM,22,4,4:46:08 PM,22,4,4:51:34 PM,22,4,5:29:02 PM,4,24.4,,-1.279395,36.825364,-1.2790653,36.7907262,Rider_Id_379,2248
-Order_No_12756,User_Id_3080,Bike,3,Business,14,2,3:21:07 PM,14,2,3:22:25 PM,14,2,3:35:23 PM,14,2,3:41:29 PM,14,2,4:51:57 PM,5,24.2,,-1.2551895,36.7822034,-1.2620741000000002,36.815814700000004,Rider_Id_268,4228
-Order_No_14423,User_Id_286,Bike,3,Business,24,6,9:34:02 AM,24,6,9:41:04 AM,24,6,9:58:12 AM,24,6,9:59:26 AM,24,6,10:26:42 AM,19,18.6,,-1.2414003,36.8094718,-1.3206928999999998,36.8621776,Rider_Id_878,1636
-Order_No_22879,User_Id_1502,Bike,3,Business,15,4,11:33:24 AM,15,4,11:33:56 AM,15,4,11:34:07 AM,15,4,11:46:17 AM,15,4,12:38:20 PM,13,24.6,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_633,3123
-Order_No_11877,User_Id_2174,Bike,3,Business,23,5,10:12:59 AM,23,5,10:13:24 AM,23,5,10:21:36 AM,23,5,10:39:31 AM,23,5,11:26:24 AM,11,24.1,,-1.2482781,36.884091999999995,-1.261795,36.822333,Rider_Id_82,2813
-Order_No_24894,User_Id_136,Bike,3,Business,10,4,1:46:27 PM,10,4,1:48:08 PM,10,4,1:48:28 PM,10,4,2:09:43 PM,10,4,2:23:16 PM,4,25.3,,-1.3165103999999999,36.707772600000006,-1.3254522,36.721658399999995,Rider_Id_829,813
-Order_No_11716,User_Id_2154,Bike,3,Business,30,2,3:56:23 PM,30,2,3:57:03 PM,30,2,4:11:53 PM,30,2,4:24:12 PM,30,2,4:38:10 PM,5,27.6,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_865,838
-Order_No_10129,User_Id_1245,Bike,3,Business,5,2,11:03:20 AM,5,2,11:03:33 AM,5,2,11:26:54 AM,5,2,11:49:46 AM,5,2,12:52:13 PM,27,25.3,,-1.300921,36.828195,-1.364732,36.658943,Rider_Id_127,3747
-Order_No_6643,User_Id_190,Bike,3,Business,17,3,2:26:21 PM,17,3,2:26:39 PM,17,3,2:32:17 PM,17,3,2:38:14 PM,17,3,2:47:43 PM,8,,,-1.3270347,36.866736200000005,-1.332624,36.913187,Rider_Id_373,569
-Order_No_7614,User_Id_1500,Bike,3,Business,6,3,11:56:02 AM,6,3,11:56:50 AM,6,3,12:33:58 PM,6,3,12:36:57 PM,6,3,12:59:59 PM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_123,1382
-Order_No_10016,User_Id_3385,Bike,1,Personal,17,5,1:19:21 PM,17,5,1:19:48 PM,17,5,1:26:32 PM,17,5,1:31:28 PM,17,5,1:52:00 PM,9,17.7,,-1.2590031000000002,36.8078168,-1.2248308,36.801257299999996,Rider_Id_726,1232
-Order_No_6808,User_Id_1927,Bike,3,Business,10,1,12:33:41 PM,10,1,12:35:14 PM,10,1,1:08:21 PM,10,1,1:11:05 PM,10,1,1:26:29 PM,5,18.6,,-1.3223068,36.7929881,-1.3472066,36.7692638,Rider_Id_643,924
-Order_No_5390,User_Id_46,Bike,3,Business,3,1,8:28:17 AM,3,1,8:30:48 AM,3,1,9:42:02 AM,3,1,9:54:55 AM,3,1,10:54:37 AM,8,18.3,5.1,-1.2996396000000001,36.8520799,-1.2852299999999999,36.8248292,Rider_Id_567,3582
-Order_No_1031,User_Id_3698,Bike,3,Business,16,4,4:18:13 PM,16,4,4:18:48 PM,16,4,4:26:21 PM,16,4,4:29:35 PM,16,4,4:50:23 PM,15,,,-1.3244885,36.897791999999995,-1.3188148000000002,36.809247799999994,Rider_Id_712,1248
-Order_No_6173,User_Id_136,Bike,3,Business,27,4,3:49:12 PM,27,4,3:49:24 PM,27,4,4:31:48 PM,27,4,4:44:35 PM,27,4,5:29:36 PM,27,22.8,,-1.3165103999999999,36.707772600000006,-1.2313147,36.80168629999999,Rider_Id_789,2701
-Order_No_25297,User_Id_346,Bike,3,Business,14,2,4:02:32 PM,14,2,4:04:55 PM,14,2,4:07:39 PM,14,2,4:12:39 PM,14,2,4:39:49 PM,10,,,-1.28878,36.8168312,-1.3246455,36.8754415,Rider_Id_276,1630
-Order_No_3419,User_Id_3264,Bike,3,Business,22,2,12:14:46 PM,22,2,12:14:57 PM,22,2,12:16:53 PM,22,2,12:19:55 PM,22,2,12:36:06 PM,8,23.9,,-1.2551895,36.7822034,-1.3027475,36.8178196,Rider_Id_231,971
-Order_No_16141,User_Id_286,Bike,3,Business,18,1,12:04:27 PM,18,1,12:08:38 PM,18,1,12:39:35 PM,18,1,12:41:11 PM,18,1,1:08:00 PM,7,27.1,,-1.2551895,36.7822034,-1.2875013000000002,36.8171787,Rider_Id_169,1609
-Order_No_17974,User_Id_1502,Bike,3,Business,12,2,8:52:16 AM,12,2,8:52:34 AM,12,2,9:16:30 AM,12,2,9:17:56 AM,12,2,9:49:41 AM,17,15.9,,-1.2156006000000001,36.8916865,-1.2551895,36.7822034,Rider_Id_808,1905
-Order_No_7881,User_Id_393,Bike,3,Business,16,4,11:12:31 AM,16,4,11:13:32 AM,16,4,11:30:12 AM,16,4,11:33:49 AM,16,4,11:41:23 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_910,454
-Order_No_21033,User_Id_2587,Bike,3,Business,2,4,11:41:53 AM,2,4,11:43:33 AM,2,4,12:12:43 PM,2,4,12:19:35 PM,2,4,12:33:44 PM,2,,,-1.290894,36.822971,-1.28878,36.8168312,Rider_Id_585,849
-Order_No_15953,User_Id_2174,Bike,3,Business,21,1,12:59:41 PM,21,1,1:00:00 PM,21,1,1:00:41 PM,21,1,1:31:16 PM,21,1,2:02:38 PM,18,19.8,,-1.2482781,36.884091999999995,-1.2948802,36.766374,Rider_Id_423,1882
-Order_No_10222,User_Id_1972,Bike,3,Business,14,4,2:47:25 PM,14,4,2:48:59 PM,14,4,3:27:40 PM,14,4,3:29:06 PM,14,4,4:18:16 PM,8,27.5,,-1.3142268999999998,36.839245,-1.2649114,36.8134191,Rider_Id_127,2950
-Order_No_12282,User_Id_1300,Bike,3,Personal,5,2,4:01:31 PM,5,2,4:03:21 PM,5,2,4:35:55 PM,5,2,4:37:28 PM,5,2,4:59:47 PM,7,24.8,,-1.2795819,36.8307141,-1.277783,36.7816464,Rider_Id_959,1339
-Order_No_6566,User_Id_286,Bike,3,Business,24,6,8:47:01 AM,24,6,8:47:45 AM,24,6,9:03:25 AM,24,6,9:08:03 AM,24,6,9:32:36 AM,12,18.6,,-1.2414003,36.8094718,-1.2970034,36.833321399999996,Rider_Id_276,1473
-Order_No_11767,User_Id_2903,Bike,2,Personal,16,2,1:05:19 PM,16,2,1:05:47 PM,16,2,1:15:20 PM,16,2,1:17:07 PM,16,2,1:24:54 PM,3,23.7,,-1.2637056999999998,36.805170000000004,-1.2456412000000001,36.7955298,Rider_Id_22,467
-Order_No_10112,User_Id_2008,Bike,3,Business,10,3,11:26:11 AM,10,3,11:32:16 AM,10,3,12:08:12 PM,10,3,12:17:17 PM,10,3,12:50:26 PM,6,28.0,,-1.2508229,36.7895263,-1.2788257,36.82877,Rider_Id_76,1989
-Order_No_3359,User_Id_3487,Bike,3,Business,21,2,9:31:18 AM,21,2,9:34:00 AM,21,2,9:54:57 AM,21,2,9:59:53 AM,21,2,10:55:54 AM,5,,,-1.279395,36.825364,-1.2923412,36.795197200000004,Rider_Id_155,3361
-Order_No_17236,User_Id_111,Bike,3,Business,9,4,9:28:27 AM,9,4,9:38:17 AM,9,4,9:42:14 AM,9,4,9:51:20 AM,9,4,10:26:48 AM,6,16.9,,-1.2551895,36.7822034,-1.2813370000000002,36.818968,Rider_Id_74,2128
-Order_No_23469,User_Id_1051,Bike,3,Business,11,1,2:30:01 PM,11,1,2:30:40 PM,11,1,3:14:16 PM,11,1,3:19:32 PM,11,1,3:58:30 PM,22,27.9,,-1.3564684999999999,36.9049642,-1.2963097,36.7688221,Rider_Id_488,2338
-Order_No_27142,User_Id_136,Bike,3,Business,29,2,9:33:42 AM,29,2,9:34:15 AM,29,2,9:35:50 AM,29,2,9:41:31 AM,29,2,10:03:08 AM,11,18.4,,-1.2965045,36.7913036,-1.3254522,36.721658399999995,Rider_Id_449,1297
-Order_No_2295,User_Id_3434,Bike,1,Business,25,3,11:31:04 AM,25,3,1:12:16 PM,25,3,1:23:20 PM,25,3,1:57:04 PM,25,3,2:48:20 PM,30,17.8,,-1.175952,36.9417,-1.2963097,36.7688221,Rider_Id_848,3076
-Order_No_16838,User_Id_2456,Bike,3,Business,4,1,9:33:24 AM,4,1,9:34:42 AM,4,1,10:06:08 AM,4,1,10:21:20 AM,4,1,10:51:59 AM,13,22.1,,-1.2679513999999998,36.8095213,-1.3189252,36.861726899999994,Rider_Id_275,1839
-Order_No_23026,User_Id_2362,Bike,3,Personal,22,5,3:17:45 PM,22,5,3:18:02 PM,22,5,3:33:47 PM,22,5,3:40:14 PM,22,5,4:36:28 PM,21,27.2,,-1.3447657,36.900965299999996,-1.238018,36.870424,Rider_Id_145,3374
-Order_No_2101,User_Id_635,Bike,3,Business,3,5,8:44:55 AM,3,5,8:45:31 AM,3,5,9:00:37 AM,3,5,9:51:16 AM,3,5,9:51:19 AM,11,,,-1.2857763999999998,36.8236622,-1.2121659,36.811243700000006,Rider_Id_921,3
-Order_No_18772,User_Id_813,Bike,1,Personal,8,2,5:04:20 PM,8,2,5:07:53 PM,8,2,5:31:22 PM,8,2,5:34:35 PM,8,2,6:12:13 PM,11,20.6,,-1.299787,36.8335941,-1.2561231000000002,36.794099700000004,Rider_Id_549,2258
-Order_No_21618,User_Id_1693,Bike,3,Business,30,5,12:18:14 PM,30,5,12:18:23 PM,30,5,12:23:55 PM,30,5,12:26:14 PM,30,5,12:37:42 PM,4,23.8,,-1.2574087999999999,36.799712,-1.2657456,36.8087424,Rider_Id_50,688
-Order_No_6890,User_Id_130,Bike,3,Personal,8,1,3:37:46 PM,8,1,3:39:04 PM,8,1,3:51:52 PM,8,1,3:58:42 PM,8,1,4:18:17 PM,6,29.6,,-1.2575690000000002,36.842164000000004,-1.2761337,36.8011565,Rider_Id_343,1175
-Order_No_19455,User_Id_2828,Bike,3,Business,4,2,12:38:50 PM,4,2,12:39:23 PM,4,2,12:52:50 PM,4,2,1:09:41 PM,4,2,1:33:29 PM,4,19.0,,-1.2727979999999999,36.8124195,-1.2615888000000002,36.7928732,Rider_Id_447,1428
-Order_No_12953,User_Id_49,Bike,3,Business,27,4,2:49:57 PM,27,4,2:56:02 PM,27,4,3:04:43 PM,27,4,3:08:39 PM,27,4,3:36:08 PM,10,23.7,,-1.2790196000000003,36.8160867,-1.2906539,36.7523571,Rider_Id_21,1649
-Order_No_14269,User_Id_393,Bike,3,Business,10,4,11:39:49 AM,10,4,11:50:38 AM,10,4,11:51:07 AM,10,4,11:54:04 AM,10,4,12:25:33 PM,4,24.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_518,1889
-Order_No_10100,User_Id_1205,Bike,3,Business,20,3,2:30:40 PM,20,3,2:30:55 PM,20,3,2:34:42 PM,20,3,2:40:50 PM,20,3,2:58:22 PM,6,28.2,,-1.2495931,36.80650870000001,-1.2586964,36.775008500000006,Rider_Id_205,1052
-Order_No_13474,User_Id_3291,Bike,3,Business,4,2,12:30:41 PM,4,2,12:34:55 PM,4,2,12:35:21 PM,4,2,1:06:47 PM,4,2,1:17:49 PM,4,19.4,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_598,662
-Order_No_811,User_Id_283,Bike,3,Business,24,4,12:07:08 PM,24,4,12:41:52 PM,24,4,12:54:00 PM,24,4,1:04:25 PM,24,4,1:19:32 PM,8,21.3,,-1.2804227,36.8250375,-1.315086,36.83801,Rider_Id_808,907
-Order_No_8604,User_Id_1744,Bike,2,Personal,4,5,12:44:03 PM,4,5,2:42:23 PM,4,5,2:59:14 PM,4,5,3:02:11 PM,4,5,3:24:03 PM,6,24.1,,-1.2516322,36.8250834,-1.2857902,36.8316231,Rider_Id_753,1312
-Order_No_13787,User_Id_3417,Bike,1,Personal,31,2,1:02:44 PM,31,2,1:03:04 PM,31,2,1:12:51 PM,31,2,1:25:55 PM,31,2,1:55:13 PM,14,18.4,,-1.3240062,36.8490097,-1.3851908000000002,36.942831899999995,Rider_Id_520,1758
-Order_No_18428,User_Id_635,Bike,3,Business,23,5,12:18:50 PM,23,5,1:52:00 PM,23,5,2:06:15 PM,23,5,2:12:12 PM,23,5,2:31:51 PM,6,26.5,,-1.3016417,36.8271679,-1.3229345,36.8566974,Rider_Id_810,1179
-Order_No_23817,User_Id_1856,Bike,3,Business,28,1,9:26:06 AM,28,1,9:28:16 AM,28,1,9:48:58 AM,28,1,9:52:22 AM,28,1,10:09:47 AM,11,18.0,,-1.273009,36.802807,-1.2359565000000001,36.76227279999999,Rider_Id_103,1045
-Order_No_6564,User_Id_1882,Bike,3,Business,15,5,2:54:55 PM,15,5,3:33:45 PM,15,5,4:14:04 PM,15,5,4:53:05 PM,15,5,4:53:12 PM,4,27.8,,-1.2672588,36.8000252,-1.2877809999999998,36.817865999999995,Rider_Id_314,7
-Order_No_20704,User_Id_2587,Bike,3,Business,13,4,2:05:24 PM,13,4,2:06:54 PM,13,4,2:21:13 PM,13,4,2:27:24 PM,13,4,2:55:30 PM,5,22.9,,-1.28878,36.8168312,-1.2922364,36.78955379999999,Rider_Id_819,1686
-Order_No_19179,User_Id_2154,Bike,3,Business,13,2,3:32:13 PM,13,2,3:32:40 PM,13,2,3:45:35 PM,13,2,3:50:17 PM,13,2,4:10:38 PM,11,26.4,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_191,1221
-Order_No_13094,User_Id_3291,Bike,3,Business,27,6,10:26:13 AM,27,6,10:26:46 AM,27,6,10:31:21 AM,27,6,10:34:39 AM,27,6,10:41:24 AM,2,,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_655,405
-Order_No_25700,User_Id_2030,Bike,3,Business,14,5,11:39:48 AM,14,5,11:40:31 AM,14,5,11:41:15 AM,14,5,11:43:01 AM,14,5,11:56:56 AM,3,24.2,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_786,835
-Order_No_4414,User_Id_2523,Bike,3,Business,20,4,2:42:04 PM,20,4,2:48:30 PM,20,4,2:49:03 PM,20,4,2:55:16 PM,20,4,3:23:32 PM,18,25.4,,-1.2551895,36.7822034,-1.2198413000000001,36.9054958,Rider_Id_387,1696
-Order_No_10559,User_Id_81,Bike,3,Business,28,1,2:54:09 PM,28,1,3:00:45 PM,28,1,3:09:27 PM,28,1,3:16:05 PM,28,1,3:30:40 PM,4,26.1,,-1.2641496,36.791413899999995,-1.258606,36.800887,Rider_Id_880,875
-Order_No_21870,User_Id_37,Bike,3,Business,26,5,11:36:06 AM,26,5,11:36:20 AM,26,5,11:38:28 AM,26,5,11:54:08 AM,26,5,12:22:36 PM,21,,,-1.2571472,36.795063299999995,-1.34364,36.8925345,Rider_Id_880,1708
-Order_No_20725,User_Id_478,Bike,3,Business,6,2,9:46:34 AM,6,2,9:47:38 AM,6,2,9:48:06 AM,6,2,9:52:41 AM,6,2,10:20:35 AM,15,20.0,,-1.2551895,36.7822034,-1.3216456,36.7145156,Rider_Id_651,1674
-Order_No_17255,User_Id_835,Bike,3,Business,16,3,10:49:04 AM,16,3,10:49:11 AM,16,3,10:56:41 AM,16,3,11:13:34 AM,16,3,11:45:29 AM,23,23.0,,-1.2953892,36.7581624,-1.3700383,36.9190174,Rider_Id_238,1915
-Order_No_15317,User_Id_3615,Bike,1,Personal,18,3,3:09:38 PM,18,3,3:11:00 PM,18,3,3:20:53 PM,18,3,3:22:34 PM,18,3,4:04:03 PM,16,20.9,,-1.2853755,36.8133883,-1.2705913,36.910334399999996,Rider_Id_456,2489
-Order_No_10633,User_Id_2523,Bike,3,Business,22,2,3:46:26 PM,22,2,3:54:15 PM,22,2,4:42:59 PM,22,2,4:53:41 PM,22,2,5:19:10 PM,17,18.1,,-1.2626906,36.7827022,-1.206542,36.901262599999995,Rider_Id_844,1529
-Order_No_3505,User_Id_3149,Bike,3,Business,27,2,2:27:37 PM,27,2,2:33:56 PM,27,2,2:38:58 PM,27,2,3:20:18 PM,27,2,3:28:18 PM,5,25.4,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_555,480
-Order_No_5199,User_Id_3283,Bike,3,Business,13,3,3:15:51 PM,13,3,4:12:32 PM,13,3,4:22:00 PM,13,3,4:29:17 PM,13,3,5:04:17 PM,14,29.3,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_691,2100
-Order_No_25517,User_Id_1384,Bike,3,Business,3,5,11:23:15 AM,3,5,11:24:15 AM,3,5,11:39:17 AM,3,5,11:41:55 AM,3,5,12:13:10 PM,17,,,-1.2517448,36.7924971,-1.2035268,36.885113200000006,Rider_Id_575,1875
-Order_No_6899,User_Id_733,Bike,3,Business,6,1,5:30:07 PM,6,1,5:30:31 PM,6,1,5:30:34 PM,6,1,5:34:40 PM,6,1,5:41:00 PM,3,,,-1.2551895,36.7822034,-1.2649605,36.798177700000004,Rider_Id_576,380
-Order_No_10488,User_Id_3161,Bike,1,Personal,3,1,5:48:21 PM,3,1,5:50:00 PM,3,1,5:59:16 PM,3,1,6:26:51 PM,3,1,7:04:57 PM,13,22.4,,-1.3434861999999999,36.6895464,-1.2710754,36.6757137,Rider_Id_857,2286
-Order_No_10485,User_Id_1342,Bike,3,Business,5,5,10:43:38 AM,5,5,10:43:56 AM,5,5,10:49:44 AM,5,5,10:58:30 AM,5,5,11:12:04 AM,3,23.8,,-1.3025826,36.7670807,-1.2765492,36.7669809,Rider_Id_565,814
-Order_No_11046,User_Id_59,Bike,3,Business,19,1,10:22:52 AM,19,1,11:03:28 AM,19,1,11:03:56 AM,19,1,12:31:10 PM,19,1,12:52:53 PM,7,24.6,,-1.290894,36.822971,-1.2571472,36.795063299999995,Rider_Id_659,1303
-Order_No_957,User_Id_2310,Bike,1,Business,23,4,8:51:29 AM,23,4,8:52:03 AM,23,4,9:06:50 AM,23,4,9:12:17 AM,23,4,9:39:44 AM,11,15.6,,-1.3332749,36.870814700000004,-1.2859912,36.8756811,Rider_Id_281,1647
-Order_No_10197,User_Id_3547,Bike,3,Business,7,4,11:13:07 AM,7,4,11:13:21 AM,7,4,11:28:36 AM,7,4,11:32:30 AM,7,4,12:38:25 PM,18,24.5,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_876,3955
-Order_No_4295,User_Id_2150,Bike,3,Business,4,1,12:49:28 PM,4,1,1:02:52 PM,4,1,1:26:03 PM,4,1,1:27:39 PM,4,1,2:15:06 PM,15,26.7,,-1.3444459999999998,36.756597,-1.265715,36.823815,Rider_Id_605,2847
-Order_No_19654,User_Id_1063,Bike,2,Personal,24,5,11:03:25 AM,24,5,11:04:09 AM,24,5,11:12:19 AM,24,5,11:22:21 AM,24,5,11:44:32 AM,7,21.0,,-1.2707373,36.805440999999995,-1.2929576999999999,36.7955434,Rider_Id_754,1331
-Order_No_6530,User_Id_350,Bike,1,Personal,5,3,11:53:55 AM,5,3,11:54:07 AM,5,3,11:57:51 AM,5,3,12:14:09 PM,5,3,12:34:19 PM,11,22.1,,-1.2268906000000002,36.83731029999999,-1.2915199,36.8289158,Rider_Id_726,1210
-Order_No_8310,User_Id_1013,Bike,1,Personal,8,1,10:12:24 AM,8,1,10:25:57 AM,8,1,10:39:09 AM,8,1,10:42:28 AM,8,1,11:02:39 AM,8,24.5,,-1.2915472,36.774696399999996,-1.3173868999999998,36.8112044,Rider_Id_288,1211
-Order_No_16011,User_Id_868,Bike,3,Business,22,4,12:21:14 PM,22,4,1:05:16 PM,22,4,1:18:16 PM,22,4,1:26:19 PM,22,4,1:49:16 PM,8,22.7,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_937,1377
-Order_No_23461,User_Id_1724,Bike,3,Business,7,4,3:51:03 PM,7,4,3:51:49 PM,7,4,4:08:06 PM,7,4,4:09:48 PM,7,4,4:35:37 PM,8,31.3,,-1.2907853999999999,36.7774108,-1.2551895,36.7822034,Rider_Id_123,1549
-Order_No_28053,User_Id_186,Bike,3,Business,9,5,3:17:00 PM,9,5,3:35:09 PM,9,5,3:38:04 PM,9,5,3:42:11 PM,9,5,4:08:56 PM,18,26.4,,-1.3700383,36.9190174,-1.2902869,36.8085396,Rider_Id_647,1605
-Order_No_21987,User_Id_3599,Bike,3,Business,29,4,10:42:51 AM,29,4,10:43:15 AM,29,4,10:59:47 AM,29,4,11:21:59 AM,29,4,12:02:09 PM,14,21.3,,-1.3302996,36.8707935,-1.2622933,36.8303558,Rider_Id_419,2410
-Order_No_15472,User_Id_1304,Bike,3,Business,18,4,10:30:18 AM,18,4,10:32:06 AM,18,4,10:47:02 AM,18,4,11:02:01 AM,18,4,11:52:36 AM,14,25.5,,-1.3332749,36.870814700000004,-1.2770652,36.795423,Rider_Id_662,3035
-Order_No_21647,User_Id_517,Bike,3,Business,16,6,9:42:31 AM,16,6,9:42:45 AM,16,6,9:42:52 AM,16,6,10:31:22 AM,16,6,10:31:26 AM,14,20.4,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_39,4
-Order_No_6255,User_Id_868,Bike,3,Business,25,4,2:24:51 PM,25,4,2:24:59 PM,25,4,2:25:04 PM,25,4,2:31:46 PM,25,4,2:44:50 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_709,784
-Order_No_22682,User_Id_3619,Bike,2,Personal,11,4,5:05:06 PM,11,4,5:05:38 PM,11,4,5:15:13 PM,11,4,5:21:45 PM,11,4,6:00:44 PM,8,23.8,,-1.2989758,36.77535770000001,-1.278612,36.8195876,Rider_Id_105,2339
-Order_No_21301,User_Id_393,Bike,3,Business,27,4,10:42:37 AM,27,4,10:42:55 AM,27,4,10:48:21 AM,27,4,10:52:23 AM,27,4,11:24:58 AM,14,22.9,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,1955
-Order_No_19381,User_Id_1280,Bike,3,Business,10,5,5:06:45 PM,10,5,5:08:21 PM,10,5,5:09:00 PM,10,5,5:39:24 PM,10,5,5:39:26 PM,3,,,-1.2551895,36.7822034,-1.2653379999999999,36.8088155,Rider_Id_411,2
-Order_No_20016,User_Id_3521,Bike,3,Business,6,4,4:31:32 PM,6,4,4:32:48 PM,6,4,4:34:51 PM,6,4,4:41:36 PM,6,4,4:54:02 PM,3,21.6,,-1.2594473999999998,36.8089122,-1.2560874,36.7931031,Rider_Id_536,746
-Order_No_26338,User_Id_3686,Bike,3,Business,26,2,3:17:11 PM,26,2,3:17:42 PM,26,2,3:22:08 PM,26,2,3:53:29 PM,26,2,3:54:31 PM,8,29.5,,-1.2551895,36.7822034,-1.2964547,36.81781229999999,Rider_Id_275,62
-Order_No_22402,User_Id_1500,Bike,3,Business,28,4,10:50:59 AM,28,4,10:51:11 AM,28,4,10:54:22 AM,28,4,10:59:32 AM,28,4,11:37:32 AM,13,28.1,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_708,2280
-Order_No_23172,User_Id_1287,Bike,3,Business,10,3,1:41:35 PM,10,3,1:42:02 PM,10,3,1:44:18 PM,10,3,1:54:53 PM,10,3,1:54:55 PM,4,30.7,,-1.2639238000000002,36.7889646,-1.2551895,36.7822034,Rider_Id_871,2
-Order_No_24975,User_Id_2808,Bike,3,Business,10,1,1:19:52 PM,10,1,2:13:51 PM,10,1,3:00:28 PM,10,1,3:16:55 PM,10,1,3:35:24 PM,7,19.0,,-1.263499,36.801454799999995,-1.2966535,36.8123136,Rider_Id_574,1109
-Order_No_18692,User_Id_633,Bike,3,Business,23,2,2:46:25 PM,23,2,3:22:52 PM,23,2,3:36:45 PM,23,2,3:38:38 PM,23,2,4:02:45 PM,8,19.1,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_518,1447
-Order_No_27146,User_Id_1280,Bike,3,Business,3,3,4:31:37 PM,3,3,4:32:07 PM,3,3,4:32:34 PM,3,3,4:41:04 PM,3,3,4:54:17 PM,5,27.4,,-1.2551895,36.7822034,-1.2684319,36.81658529999999,Rider_Id_576,793
-Order_No_14122,User_Id_489,Bike,1,Personal,21,7,11:34:56 AM,21,7,11:36:17 AM,21,7,11:40:50 AM,21,7,11:48:48 AM,21,7,12:08:56 PM,9,24.9,,-1.2852583999999998,36.8043641,-1.319536,36.779328899999996,Rider_Id_282,1208
-Order_No_23093,User_Id_3491,Bike,3,Business,6,4,2:08:03 PM,6,4,2:11:18 PM,6,4,2:11:55 PM,6,4,2:40:59 PM,6,4,2:57:19 PM,12,23.6,,-1.2927792,36.9053492,-1.2297202,36.881950700000004,Rider_Id_589,980
-Order_No_23662,User_Id_136,Bike,3,Business,8,1,11:54:41 AM,8,1,11:54:58 AM,8,1,12:37:17 PM,8,1,12:46:41 PM,8,1,1:37:32 PM,21,27.8,,-1.336582,36.871736999999996,-1.3254522,36.721658399999995,Rider_Id_760,3051
-Order_No_10329,User_Id_2464,Bike,3,Business,3,1,4:04:56 PM,3,1,4:15:15 PM,3,1,4:30:22 PM,3,1,4:33:27 PM,3,1,4:45:57 PM,7,17.6,,-1.279395,36.825364,-1.2551895,36.7822034,Rider_Id_344,750
-Order_No_6209,User_Id_1758,Bike,2,Personal,1,6,8:23:08 AM,1,6,8:26:43 AM,1,6,8:33:14 AM,1,6,8:41:14 AM,1,6,8:58:55 AM,7,15.1,,-1.280753,36.8191175,-1.2896722,36.771240899999995,Rider_Id_726,1061
-Order_No_1212,User_Id_1205,Bike,3,Business,15,1,9:11:33 AM,15,1,9:11:51 AM,15,1,9:22:07 AM,15,1,9:28:03 AM,15,1,10:04:47 AM,20,,,-1.257309,36.806008399999996,-1.3085443,36.6884314,Rider_Id_754,2204
-Order_No_6913,User_Id_3599,Bike,3,Business,26,1,10:58:10 AM,26,1,11:13:03 AM,26,1,11:21:37 AM,26,1,12:21:03 PM,26,1,1:00:04 PM,15,22.9,,-1.3302996,36.8707935,-1.2912772,36.7734495,Rider_Id_937,2341
-Order_No_13694,User_Id_60,Bike,3,Business,3,4,12:01:10 PM,3,4,12:22:43 PM,3,4,12:34:03 PM,3,4,12:59:31 PM,3,4,1:16:34 PM,10,23.4,,-1.2600926000000001,36.8088685,-1.2297799999999999,36.836215,Rider_Id_14,1023
-Order_No_25316,User_Id_246,Bike,3,Business,26,5,4:57:38 PM,26,5,4:57:56 PM,26,5,5:03:04 PM,26,5,5:06:40 PM,26,5,5:36:08 PM,2,,,-1.2584143,36.8048002,-1.2617028000000001,36.7983849,Rider_Id_620,1768
-Order_No_6089,User_Id_1469,Bike,3,Business,13,3,3:57:07 PM,13,3,3:59:00 PM,13,3,4:03:41 PM,13,3,4:09:08 PM,13,3,4:41:01 PM,7,29.3,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_276,1913
-Order_No_16122,User_Id_1746,Bike,3,Business,2,4,2:29:00 PM,2,4,2:29:29 PM,2,4,2:41:46 PM,2,4,2:51:57 PM,2,4,3:05:25 PM,3,26.4,,-1.2962832,36.784968299999996,-1.296797,36.776452500000005,Rider_Id_548,808
-Order_No_11495,User_Id_186,Bike,3,Business,7,3,3:12:03 PM,7,3,3:12:48 PM,7,3,3:23:16 PM,7,3,3:28:00 PM,7,3,4:19:55 PM,23,28.2,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_208,3115
-Order_No_4441,User_Id_1590,Bike,2,Personal,21,5,3:11:56 PM,21,5,3:12:41 PM,21,5,3:13:00 PM,21,5,3:20:31 PM,21,5,3:28:42 PM,3,27.3,,-1.2854453000000001,36.818836299999994,-1.2861874,36.8128448,Rider_Id_320,491
-Order_No_27618,User_Id_2989,Bike,3,Business,6,2,4:19:47 PM,6,2,4:41:00 PM,6,2,4:55:08 PM,6,2,4:57:20 PM,6,2,5:19:23 PM,10,,,-1.3165542,36.700067,-1.3472066,36.7692638,Rider_Id_747,1323
-Order_No_1967,User_Id_2982,Bike,3,Business,25,5,11:04:00 AM,25,5,11:05:00 AM,25,5,11:29:57 AM,25,5,11:36:30 AM,25,5,12:09:52 PM,18,26.5,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_263,2002
-Order_No_20570,User_Id_868,Bike,3,Business,15,4,2:24:41 PM,15,4,3:16:15 PM,15,4,3:33:03 PM,15,4,3:36:09 PM,15,4,3:56:03 PM,8,27.7,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_343,1194
-Order_No_6303,User_Id_1304,Bike,3,Business,21,3,4:07:21 PM,21,3,4:12:54 PM,21,3,5:03:16 PM,21,3,5:07:10 PM,21,3,6:20:28 PM,14,,,-1.2551895,36.7822034,-1.2998684999999999,36.8643907,Rider_Id_547,4398
-Order_No_25208,User_Id_3291,Bike,3,Business,11,4,12:57:44 PM,11,4,12:59:21 PM,11,4,1:25:15 PM,11,4,1:52:00 PM,11,4,2:23:15 PM,18,26.8,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_675,1875
-Order_No_19634,User_Id_1901,Bike,2,Personal,12,5,11:35:34 AM,12,5,11:36:58 AM,12,5,11:44:30 AM,12,5,11:46:35 AM,12,5,12:24:08 PM,6,24.4,,-1.2544358,36.7974683,-1.2897215,36.7945959,Rider_Id_578,2253
-Order_No_13085,User_Id_865,Bike,3,Business,4,2,3:35:47 PM,4,2,3:45:55 PM,4,2,3:57:09 PM,4,2,3:58:37 PM,4,2,4:28:14 PM,6,22.7,,-1.2456412000000001,36.7955298,-1.2765641,36.793782,Rider_Id_706,1777
-Order_No_11307,User_Id_439,Bike,3,Business,29,4,1:19:25 PM,29,4,1:20:43 PM,29,4,1:25:08 PM,29,4,1:41:12 PM,29,4,1:58:49 PM,8,24.7,,-1.3323107,36.8692616,-1.3002148,36.835310299999996,Rider_Id_410,1057
-Order_No_3967,User_Id_517,Bike,3,Business,31,5,4:21:03 PM,31,5,4:24:45 PM,31,5,4:24:52 PM,31,5,4:45:39 PM,31,5,5:00:07 PM,4,23.5,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,868
-Order_No_20183,User_Id_2271,Bike,3,Business,2,2,4:07:12 PM,2,2,4:08:00 PM,2,2,4:29:48 PM,2,2,4:42:09 PM,2,2,5:16:11 PM,12,23.6,,-1.331619,36.847976,-1.293515,36.897607,Rider_Id_761,2042
-Order_No_16758,User_Id_63,Bike,2,Personal,5,5,8:40:15 AM,5,5,8:43:01 AM,5,5,8:51:28 AM,5,5,9:26:16 AM,5,5,9:51:52 AM,13,18.0,,-1.2737091,36.813153299999996,-1.2846933,36.9059033,Rider_Id_403,1536
-Order_No_17148,User_Id_3091,Bike,3,Business,17,5,1:29:38 PM,17,5,1:29:44 PM,17,5,1:40:43 PM,17,5,1:46:28 PM,17,5,2:03:19 PM,7,20.3,,-1.3014461000000002,36.7661381,-1.2902869,36.8085396,Rider_Id_249,1011
-Order_No_11933,User_Id_3570,Bike,2,Personal,18,6,7:34:30 PM,18,6,7:35:07 PM,18,6,8:14:47 PM,18,6,8:21:32 PM,18,6,8:42:58 PM,8,16.8,,-1.2732868999999998,36.832159000000004,-1.2817426,36.7846148,Rider_Id_4,1286
-Order_No_18629,User_Id_2801,Bike,3,Business,13,3,9:17:33 AM,13,3,9:18:01 AM,13,3,9:30:15 AM,13,3,9:33:10 AM,13,3,10:08:45 AM,23,20.3,,-1.2674273999999999,36.787082700000006,-1.3546228,36.9013783,Rider_Id_690,2135
-Order_No_9679,User_Id_391,Bike,3,Business,28,4,4:03:43 PM,28,4,4:10:05 PM,28,4,4:18:18 PM,28,4,4:21:25 PM,28,4,4:37:18 PM,5,,,-1.2777923999999998,36.8231577,-1.3010429,36.835333500000004,Rider_Id_752,953
-Order_No_12795,User_Id_3689,Bike,3,Business,20,4,9:28:23 AM,20,4,9:28:33 AM,20,4,9:31:12 AM,20,4,9:44:56 AM,20,4,9:49:05 AM,1,18.5,0.3,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_56,249
-Order_No_26993,User_Id_1473,Bike,3,Business,8,4,12:06:32 PM,8,4,12:07:10 PM,8,4,12:29:00 PM,8,4,12:30:50 PM,8,4,1:28:25 PM,18,26.3,,-1.3700383,36.9190174,-1.298701,36.8192575,Rider_Id_101,3455
-Order_No_1914,User_Id_2330,Bike,3,Business,9,3,12:38:21 PM,9,3,12:39:04 PM,9,3,12:45:47 PM,9,3,12:47:07 PM,9,3,1:01:21 PM,10,25.1,,-1.2687407,36.7373043,-1.2642903,36.80021370000001,Rider_Id_512,854
-Order_No_26592,User_Id_1724,Bike,3,Business,29,1,11:08:48 AM,29,1,11:18:15 AM,29,1,11:27:18 AM,29,1,11:28:41 AM,29,1,11:43:00 AM,7,,,-1.2861764999999998,36.8209086,-1.2551895,36.7822034,Rider_Id_336,859
-Order_No_4629,User_Id_3165,Bike,3,Personal,23,1,9:04:04 AM,23,1,9:04:34 AM,23,1,9:22:13 AM,23,1,9:29:07 AM,23,1,10:02:03 AM,6,14.3,,-1.2610837,36.8021315,-1.293973,36.80169,Rider_Id_486,1976
-Order_No_11197,User_Id_103,Bike,3,Business,27,6,8:41:05 AM,27,6,8:42:01 AM,27,6,9:09:30 AM,27,6,9:46:37 AM,27,6,10:28:39 AM,23,,,-1.2844665000000002,36.7866113,-1.3922456,36.7372289,Rider_Id_368,2522
-Order_No_27360,User_Id_2789,Bike,3,Business,9,4,8:43:27 AM,9,4,8:47:30 AM,9,4,9:09:11 AM,9,4,9:18:49 AM,9,4,9:36:09 AM,8,,,-1.2569503,36.78347279999999,-1.304735,36.784605400000004,Rider_Id_99,1040
-Order_No_24689,User_Id_972,Bike,3,Business,2,2,3:40:40 PM,2,2,3:41:00 PM,2,2,3:55:51 PM,2,2,3:58:44 PM,2,2,4:20:21 PM,5,26.3,,-1.2862033000000002,36.8152966,-1.3087917,36.8286342,Rider_Id_583,1297
-Order_No_11562,User_Id_2154,Bike,3,Business,2,5,1:34:44 PM,2,5,2:08:09 PM,2,5,2:13:38 PM,2,5,3:00:24 PM,2,5,3:39:09 PM,5,25.3,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_320,2325
-Order_No_8226,User_Id_635,Bike,3,Business,3,4,11:14:17 AM,3,4,11:14:34 AM,3,4,11:34:27 AM,3,4,11:53:02 AM,3,4,12:04:37 PM,8,23.5,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_809,695
-Order_No_15574,User_Id_2200,Bike,3,Business,7,2,2:10:27 PM,7,2,2:16:12 PM,7,2,2:32:30 PM,7,2,2:58:00 PM,7,2,3:11:31 PM,7,,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_448,811
-Order_No_1329,User_Id_3283,Bike,3,Business,18,4,12:54:29 PM,18,4,12:55:14 PM,18,4,12:59:42 PM,18,4,1:16:55 PM,18,4,1:23:25 PM,5,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_484,390
-Order_No_23551,User_Id_2718,Bike,3,Business,11,2,4:09:48 PM,11,2,4:11:23 PM,11,2,4:23:03 PM,11,2,4:26:50 PM,11,2,4:39:11 PM,2,22.1,,-1.2741118,36.802009399999996,-1.2657456,36.8087424,Rider_Id_112,741
-Order_No_25383,User_Id_2956,Bike,3,Business,4,6,9:28:20 AM,4,6,9:28:33 AM,4,6,9:36:58 AM,4,6,9:51:57 AM,4,6,10:01:25 AM,7,,,-1.2881035,36.8142095,-1.2584143,36.8048002,Rider_Id_341,568
-Order_No_20771,User_Id_2987,Bike,3,Business,14,2,1:05:58 PM,14,2,1:06:07 PM,14,2,1:06:11 PM,14,2,1:26:49 PM,14,2,1:46:32 PM,10,21.7,,-1.2702120000000001,36.82108770000001,-1.3280091,36.8524146,Rider_Id_874,1183
-Order_No_25035,User_Id_265,Bike,3,Business,5,1,11:14:49 AM,5,1,11:21:00 AM,5,1,11:46:12 AM,5,1,11:48:00 AM,5,1,12:10:34 PM,7,24.4,,-1.2962832,36.784968299999996,-1.300921,36.828195,Rider_Id_248,1354
-Order_No_6510,User_Id_2982,Bike,3,Business,30,2,8:56:35 AM,30,2,8:57:45 AM,30,2,9:13:19 AM,30,2,10:01:40 AM,30,2,10:38:31 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_61,2211
-Order_No_24325,User_Id_2069,Bike,3,Business,27,3,11:06:06 AM,27,3,11:07:19 AM,27,3,11:26:44 AM,27,3,11:31:44 AM,27,3,11:48:43 AM,9,26.3,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_269,1019
-Order_No_3926,User_Id_3525,Bike,3,Business,21,4,12:40:07 PM,21,4,12:46:10 PM,21,4,12:55:30 PM,21,4,1:03:16 PM,21,4,1:40:57 PM,18,25.3,,-1.2038216999999998,36.87945070000001,-1.2571472,36.795063299999995,Rider_Id_8,2261
-Order_No_14896,User_Id_2216,Bike,3,Business,15,1,11:52:12 AM,15,1,11:52:29 AM,15,1,11:56:14 AM,15,1,11:58:08 AM,15,1,12:14:06 PM,7,,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_958,958
-Order_No_25633,User_Id_3647,Bike,3,Business,22,4,10:04:53 AM,22,4,10:19:11 AM,22,4,10:28:13 AM,22,4,10:33:03 AM,22,4,11:13:18 AM,7,22.6,,-1.2726389999999999,36.794723,-1.29502,36.8086721,Rider_Id_441,2415
-Order_No_13502,User_Id_527,Bike,2,Personal,29,6,1:15:43 PM,29,6,1:16:17 PM,29,6,1:27:52 PM,29,6,1:58:34 PM,29,6,2:25:11 PM,12,28.1,,-1.2861764999999998,36.8209086,-1.3319425,36.894470899999995,Rider_Id_465,1597
-Order_No_15785,User_Id_722,Bike,3,Business,5,2,4:57:48 PM,5,2,5:02:36 PM,5,2,5:03:26 PM,5,2,5:41:48 PM,5,2,6:28:12 PM,20,,,-1.32943,36.894242999999996,-1.2492395,36.7986032,Rider_Id_404,2784
-Order_No_12641,User_Id_916,Bike,3,Business,22,2,9:33:05 AM,22,2,9:33:28 AM,22,2,9:49:35 AM,22,2,10:02:16 AM,22,2,10:26:36 AM,11,17.7,,-1.2909665000000001,36.8150406,-1.3321944,36.889232899999996,Rider_Id_888,1460
-Order_No_4826,User_Id_3285,Bike,2,Personal,15,6,11:19:36 AM,15,6,11:20:15 AM,15,6,11:31:13 AM,15,6,11:47:32 AM,15,6,11:52:48 AM,3,,,-1.2650181999999999,36.7926717,-1.2635425,36.8021963,Rider_Id_882,316
-Order_No_11854,User_Id_635,Bike,3,Business,8,3,3:01:40 PM,8,3,3:06:50 PM,8,3,3:12:39 PM,8,3,3:40:01 PM,8,3,4:02:34 PM,16,,,-1.3081376,36.83297720000001,-1.1999766,36.841524799999995,Rider_Id_564,1353
-Order_No_12396,User_Id_553,Bike,3,Business,5,4,2:11:58 PM,5,4,2:33:25 PM,5,4,2:39:57 PM,5,4,2:51:42 PM,5,4,3:11:32 PM,7,22.1,,-1.2551895,36.7822034,-1.2815072,36.82096,Rider_Id_629,1190
-Order_No_25552,User_Id_1589,Bike,3,Business,3,1,11:32:48 AM,3,1,11:34:25 AM,3,1,11:40:03 AM,3,1,11:51:32 AM,3,1,12:19:36 PM,14,21.3,,-1.3014461000000002,36.7661381,-1.225322,36.80855,Rider_Id_205,1684
-Order_No_1275,User_Id_2030,Bike,3,Business,4,5,11:22:52 AM,4,5,11:24:27 AM,4,5,11:54:57 AM,4,5,11:58:29 AM,4,5,1:02:39 PM,2,17.3,,-1.2508229,36.7895263,-1.2601197,36.7985271,Rider_Id_916,3850
-Order_No_22217,User_Id_606,Bike,1,Personal,1,3,2:08:32 PM,1,3,2:08:54 PM,1,3,2:15:42 PM,1,3,2:19:44 PM,1,3,2:27:08 PM,3,23.7,,-1.290908,36.7578336,-1.2934527,36.7755139,Rider_Id_273,444
-Order_No_18455,User_Id_3629,Bike,3,Personal,22,3,4:18:16 PM,22,3,4:19:47 PM,22,3,4:29:38 PM,22,3,4:35:07 PM,22,3,5:45:14 PM,6,23.9,,-1.2852951000000001,36.81338529999999,-1.2594002,36.7888767,Rider_Id_183,4207
-Order_No_21620,User_Id_2216,Bike,3,Business,7,5,9:42:59 AM,7,5,9:45:03 AM,7,5,9:52:40 AM,7,5,9:54:59 AM,7,5,10:26:16 AM,11,20.4,0.9,-1.2551895,36.7822034,-1.3185736000000001,36.8258933,Rider_Id_778,1877
-Order_No_16310,User_Id_3147,Bike,3,Business,12,1,8:13:48 AM,12,1,9:02:07 AM,12,1,9:10:30 AM,12,1,9:22:18 AM,12,1,9:36:32 AM,2,19.2,0.5,-1.3472066,36.7692638,-1.3332025,36.7789361,Rider_Id_328,854
-Order_No_9317,User_Id_2335,Bike,3,Business,18,5,9:34:49 AM,18,5,9:34:56 AM,18,5,9:39:21 AM,18,5,9:43:34 AM,18,5,10:21:12 AM,22,20.0,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_932,2258
-Order_No_16066,User_Id_3080,Bike,3,Business,7,5,9:53:00 AM,7,5,9:54:32 AM,7,5,10:18:50 AM,7,5,10:20:21 AM,7,5,10:57:40 AM,17,,,-1.2551895,36.7822034,-1.3227393,36.705093,Rider_Id_827,2239
-Order_No_21171,User_Id_1271,Bike,3,Business,12,2,3:54:29 PM,12,2,4:01:14 PM,12,2,4:10:06 PM,12,2,4:23:53 PM,12,2,5:10:33 PM,19,23.0,,-1.3244885,36.897791999999995,-1.296139,36.780316,Rider_Id_379,2800
-Order_No_14283,User_Id_640,Bike,3,Business,26,2,3:37:39 PM,26,2,3:44:09 PM,26,2,3:56:41 PM,26,2,4:12:50 PM,26,2,4:42:00 PM,6,29.9,,-1.2969741000000001,36.7856612,-1.28359,36.823091999999995,Rider_Id_61,1750
-Order_No_16739,User_Id_2677,Bike,3,Business,22,1,3:01:38 PM,22,1,3:07:24 PM,22,1,3:37:27 PM,22,1,4:12:44 PM,22,1,4:59:49 PM,14,26.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_328,2825
-Order_No_20396,User_Id_2996,Bike,2,Personal,22,2,11:28:44 AM,22,2,11:30:32 AM,22,2,11:33:17 AM,22,2,11:39:36 AM,22,2,11:52:37 AM,7,19.2,,-1.2294985,36.8775495,-1.1878027,36.9033431,Rider_Id_266,781
-Order_No_23936,User_Id_517,Bike,3,Business,29,3,10:16:11 AM,29,3,10:19:56 AM,29,3,10:25:37 AM,29,3,10:38:32 AM,29,3,11:01:59 AM,11,,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_16,1407
-Order_No_10917,User_Id_2987,Bike,3,Business,7,4,11:23:47 AM,7,4,11:33:32 AM,7,4,11:46:02 AM,7,4,11:52:11 AM,7,4,12:45:33 PM,10,16.8,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_771,3202
-Order_No_5827,User_Id_2330,Bike,3,Business,12,2,3:09:46 PM,12,2,3:12:35 PM,12,2,3:40:10 PM,12,2,3:43:38 PM,12,2,4:02:43 PM,9,,,-1.2326041,36.8009029,-1.2649605,36.798177700000004,Rider_Id_547,1145
-Order_No_11301,User_Id_2260,Bike,1,Personal,21,5,11:56:30 AM,21,5,11:56:53 AM,21,5,12:07:15 PM,21,5,12:14:55 PM,21,5,12:47:12 PM,6,20.2,,-1.2978908000000002,36.796321500000005,-1.315756,36.813058000000005,Rider_Id_612,1937
-Order_No_18195,User_Id_2474,Bike,1,Personal,3,2,8:51:58 AM,3,2,8:52:07 AM,3,2,8:58:24 AM,3,2,9:03:08 AM,3,2,9:22:35 AM,12,15.1,1.9,-1.304033,36.7848695,-1.2643343,36.7447423,Rider_Id_355,1167
-Order_No_24620,User_Id_3711,Bike,3,Business,13,4,12:58:36 PM,13,4,1:11:54 PM,13,4,1:39:36 PM,13,4,1:53:28 PM,13,4,2:15:34 PM,9,22.3,,-1.279395,36.825364,-1.3025826,36.7670807,Rider_Id_580,1326
-Order_No_8847,User_Id_2944,Bike,3,Personal,10,1,2:39:06 PM,10,1,2:41:06 PM,10,1,3:17:49 PM,10,1,4:03:20 PM,10,1,4:08:33 PM,10,18.1,,-1.2772989,36.8526035,-1.2642333000000001,36.779548299999995,Rider_Id_139,313
-Order_No_2800,User_Id_1402,Bike,3,Business,17,1,2:34:37 PM,18,2,1:16:53 PM,18,2,1:59:45 PM,18,2,2:01:52 PM,18,2,2:45:52 PM,11,25.6,,-1.225322,36.80855,-1.281754,36.8324229,Rider_Id_928,2640
-Order_No_20747,User_Id_1131,Bike,3,Business,9,4,3:58:47 PM,9,4,4:13:36 PM,9,4,4:23:08 PM,9,4,4:34:45 PM,9,4,5:05:47 PM,17,,,-1.207546,36.7913976,-1.2888734,36.8268027,Rider_Id_263,1862
-Order_No_25271,User_Id_186,Bike,3,Business,8,3,3:07:47 PM,8,3,3:07:55 PM,8,3,3:08:14 PM,8,3,3:11:28 PM,8,3,3:50:25 PM,23,,,-1.3700383,36.9190174,-1.2571472,36.795063299999995,Rider_Id_875,2337
-Order_No_26097,User_Id_2956,Bike,3,Business,26,2,7:25:09 AM,26,2,7:51:10 AM,26,2,8:03:08 AM,26,2,8:19:32 AM,26,2,9:24:30 AM,6,21.8,,-1.2584143,36.8048002,-1.2931121,36.8224572,Rider_Id_119,3898
-Order_No_22853,User_Id_1029,Bike,2,Personal,20,1,12:22:04 PM,20,1,12:22:34 PM,20,1,12:35:27 PM,20,1,12:37:43 PM,20,1,1:20:45 PM,10,20.0,,-1.2507752,36.755662799999996,-1.2925792,36.790690000000005,Rider_Id_770,2582
-Order_No_22253,User_Id_391,Bike,3,Business,19,2,2:14:55 PM,19,2,2:15:44 PM,19,2,2:32:57 PM,19,2,2:35:45 PM,19,2,3:11:57 PM,11,30.2,,-1.2867454,36.8259017,-1.2134633,36.8166402,Rider_Id_660,2172
-Order_No_25186,User_Id_2523,Bike,3,Business,13,1,11:29:39 AM,13,1,11:33:17 AM,13,1,11:44:59 AM,13,1,11:47:53 AM,13,1,12:02:59 PM,6,17.3,,-1.2551895,36.7822034,-1.2687962,36.825707,Rider_Id_249,906
-Order_No_8249,User_Id_1754,Bike,3,Business,19,5,12:16:45 PM,19,5,12:17:05 PM,19,5,12:17:12 PM,19,5,12:30:10 PM,19,5,12:37:20 PM,12,25.0,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_179,430
-Order_No_4346,User_Id_2587,Bike,3,Business,12,1,2:54:04 PM,12,1,2:55:35 PM,12,1,3:12:06 PM,12,1,3:19:06 PM,12,1,3:35:10 PM,4,27.0,,-1.28878,36.8168312,-1.2979063,36.8373372,Rider_Id_237,964
-Order_No_16768,User_Id_3283,Bike,3,Business,21,1,12:02:48 PM,21,1,12:04:20 PM,21,1,12:16:37 PM,21,1,12:26:48 PM,21,1,12:38:45 PM,7,25.3,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_336,717
-Order_No_13113,User_Id_391,Bike,3,Business,29,5,4:38:51 PM,29,5,4:47:10 PM,29,5,5:15:00 PM,29,5,5:19:04 PM,29,5,5:44:44 PM,2,27.8,,-1.2784063,36.8222933,-1.2870774,36.8259832,Rider_Id_291,1540
-Order_No_17162,User_Id_2039,Bike,2,Personal,29,5,3:57:15 PM,29,5,4:00:53 PM,29,5,4:49:19 PM,29,5,4:51:00 PM,29,5,5:15:25 PM,7,20.4,,-1.2755226,36.7811904,-1.2649605,36.798177700000004,Rider_Id_536,1465
-Order_No_18454,User_Id_540,Bike,3,Business,30,2,4:06:31 PM,30,2,4:14:09 PM,30,2,4:32:57 PM,30,2,4:37:23 PM,30,2,4:45:41 PM,4,,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_315,498
-Order_No_24161,User_Id_1527,Bike,3,Business,16,4,12:41:50 PM,16,4,12:43:16 PM,16,4,12:45:54 PM,16,4,1:29:40 PM,16,4,1:47:17 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_161,1057
-Order_No_23731,User_Id_635,Bike,3,Business,13,3,2:24:37 PM,13,3,2:26:45 PM,13,3,2:36:51 PM,13,3,2:39:06 PM,13,3,2:51:36 PM,5,29.2,,-1.2857763999999998,36.8236622,-1.2602337,36.79905479999999,Rider_Id_291,750
-Order_No_7882,User_Id_2008,Bike,3,Business,15,4,8:56:18 AM,15,4,8:57:19 AM,15,4,9:06:02 AM,15,4,9:10:23 AM,15,4,9:36:35 AM,13,19.8,,-1.2508229,36.7895263,-1.3270347,36.866736200000005,Rider_Id_387,1572
-Order_No_630,User_Id_2150,Bike,3,Business,29,5,9:27:25 AM,29,5,9:37:13 AM,29,5,9:54:59 AM,29,5,10:03:48 AM,29,5,10:23:45 AM,12,20.8,,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_459,1197
-Order_No_3207,User_Id_3689,Bike,3,Business,8,4,9:42:10 AM,8,4,9:42:24 AM,8,4,9:51:05 AM,8,4,10:12:13 AM,8,4,10:27:28 AM,1,20.4,,-1.2584143,36.8048002,-1.2527958000000001,36.8003131,Rider_Id_611,915
-Order_No_1002,User_Id_3599,Bike,3,Business,27,2,10:25:25 AM,27,2,11:00:15 AM,27,2,11:20:03 AM,27,2,11:29:46 AM,27,2,12:12:41 PM,15,23.8,,-1.3302996,36.8707935,-1.2658003,36.8983864,Rider_Id_675,2575
-Order_No_24562,User_Id_59,Bike,3,Business,18,4,10:40:42 AM,18,4,10:57:33 AM,18,4,11:15:08 AM,18,4,11:35:00 AM,18,4,12:03:48 PM,6,,,-1.3147073999999999,36.811721999999996,-1.290894,36.822971,Rider_Id_76,1728
-Order_No_1818,User_Id_2069,Bike,3,Business,3,5,3:15:11 PM,3,5,3:17:44 PM,3,5,3:59:53 PM,3,5,4:07:00 PM,3,5,5:16:28 PM,16,,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_155,4168
-Order_No_15064,User_Id_3127,Bike,3,Business,7,5,7:01:38 PM,7,5,7:11:37 PM,7,5,7:35:14 PM,7,5,7:41:05 PM,7,5,7:57:05 PM,6,17.5,,-1.3016281,36.805521500000005,-1.260997,36.8097857,Rider_Id_19,960
-Order_No_8851,User_Id_2412,Bike,3,Business,14,4,1:21:56 PM,14,4,1:29:15 PM,14,4,1:45:58 PM,14,4,1:54:23 PM,14,4,2:03:39 PM,3,27.8,,-1.2156006000000001,36.8916865,-1.2285834999999998,36.882646799999996,Rider_Id_75,556
-Order_No_23360,User_Id_2423,Bike,3,Business,13,3,1:11:26 PM,13,3,1:12:14 PM,13,3,1:45:13 PM,13,3,1:49:18 PM,13,3,2:31:35 PM,7,27.1,,-1.2551895,36.7822034,-1.2896171,36.8241291,Rider_Id_452,2537
-Order_No_8610,User_Id_2066,Bike,3,Business,15,4,12:09:28 PM,15,4,12:09:47 PM,15,4,12:11:45 PM,15,4,12:44:41 PM,15,4,1:53:54 PM,11,24.2,,-1.3270716,36.8406371,-1.307764,36.895431,Rider_Id_519,4153
-Order_No_6862,User_Id_517,Bike,3,Business,28,5,11:32:03 AM,28,5,11:32:22 AM,28,5,11:32:26 AM,28,5,11:47:44 AM,28,5,12:10:58 PM,11,23.6,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_10,1394
-Order_No_23378,User_Id_3569,Bike,3,Personal,1,1,2:03:23 PM,1,1,2:14:30 PM,1,1,2:24:55 PM,1,1,2:39:54 PM,1,1,3:26:14 PM,28,,,-1.2472011,36.880030700000006,-1.3858142,36.7641498,Rider_Id_691,2780
-Order_No_6793,User_Id_640,Bike,3,Business,24,3,8:57:42 AM,24,3,8:59:36 AM,24,3,9:19:17 AM,24,3,9:22:59 AM,24,3,9:34:54 AM,3,16.8,1.1,-1.2945106000000002,36.7728398,-1.304033,36.7848695,Rider_Id_655,715
-Order_No_12453,User_Id_3228,Bike,1,Personal,3,3,9:08:22 AM,3,3,9:09:55 AM,3,3,9:30:45 AM,3,3,9:36:59 AM,3,3,10:04:40 AM,10,18.7,,-1.2756751,36.814426700000006,-1.2630428999999999,36.7518974,Rider_Id_532,1661
-Order_No_3062,User_Id_53,Bike,3,Business,15,2,12:28:02 PM,15,2,12:50:56 PM,15,2,1:00:03 PM,15,2,1:04:27 PM,15,2,1:23:15 PM,7,24.4,,-1.2574219,36.7927073,-1.2770708,36.8231093,Rider_Id_576,1128
-Order_No_11232,User_Id_3247,Bike,3,Business,8,5,12:26:05 PM,8,5,12:26:26 PM,8,5,12:27:49 PM,8,5,12:41:11 PM,8,5,1:17:23 PM,23,24.3,,-1.2571472,36.795063299999995,-1.3433071,36.904307700000004,Rider_Id_584,2172
-Order_No_23271,User_Id_3283,Bike,3,Business,19,2,10:27:55 AM,19,2,10:29:27 AM,19,2,10:44:25 AM,19,2,10:54:31 AM,19,2,11:12:26 AM,7,27.3,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_831,1075
-Order_No_6337,User_Id_3404,Bike,3,Business,14,5,11:50:23 AM,14,5,2:18:57 PM,14,5,2:30:31 PM,14,5,2:38:19 PM,14,5,3:03:02 PM,5,23.6,,-1.2588181,36.8106709,-1.279395,36.825364,Rider_Id_108,1483
-Order_No_20047,User_Id_1209,Bike,3,Business,8,1,2:50:28 PM,8,1,2:51:16 PM,8,1,3:07:41 PM,8,1,3:22:54 PM,8,1,3:32:01 PM,5,29.7,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_126,547
-Order_No_4129,User_Id_1927,Bike,3,Business,7,4,8:39:55 AM,7,4,8:40:33 AM,7,4,8:45:34 AM,7,4,8:57:54 AM,7,4,9:44:42 AM,15,,,-1.2584143,36.8048002,-1.3472066,36.7692638,Rider_Id_131,2808
-Order_No_6096,User_Id_53,Bike,3,Business,24,5,11:58:34 AM,24,5,12:05:45 PM,24,5,12:20:25 PM,24,5,12:34:56 PM,24,5,12:59:12 PM,8,21.4,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_2,1456
-Order_No_17202,User_Id_1363,Bike,3,Business,14,2,11:39:42 AM,14,2,11:39:57 AM,14,2,11:50:01 AM,14,2,11:51:52 AM,14,2,12:02:06 PM,5,21.3,,-1.3004062,36.829741,-1.3215393999999998,36.836621,Rider_Id_249,614
-Order_No_24040,User_Id_3149,Bike,3,Business,4,5,1:06:16 PM,4,5,1:07:06 PM,4,5,1:24:23 PM,4,5,1:35:57 PM,4,5,2:06:00 PM,15,,,-1.306378,36.7519845,-1.3228003000000002,36.8306435,Rider_Id_114,1803
-Order_No_27940,User_Id_2523,Bike,3,Business,29,3,11:25:52 AM,29,3,11:26:07 AM,29,3,11:27:13 AM,29,3,11:35:11 AM,29,3,11:54:35 AM,4,,,-1.2551895,36.7822034,-1.2689015,36.7986106,Rider_Id_161,1164
-Order_No_14107,User_Id_111,Bike,3,Business,13,4,9:25:33 AM,13,4,9:26:30 AM,13,4,9:34:55 AM,13,4,9:37:28 AM,13,4,10:01:58 AM,9,18.7,,-1.2551895,36.7822034,-1.2879222,36.7747099,Rider_Id_259,1470
-Order_No_23211,User_Id_640,Bike,3,Business,6,1,9:08:41 AM,6,1,9:09:43 AM,6,1,9:59:30 AM,6,1,10:09:08 AM,6,1,10:29:40 AM,3,,,-1.2969741000000001,36.7856612,-1.2858578,36.7747046,Rider_Id_155,1232
-Order_No_23671,User_Id_2289,Bike,3,Business,11,5,4:35:32 PM,11,5,4:38:24 PM,11,5,4:48:42 PM,11,5,5:01:45 PM,11,5,5:10:23 PM,7,24.1,,-1.2551895,36.7822034,-1.2936941000000002,36.8156795,Rider_Id_72,518
-Order_No_25967,User_Id_3688,Bike,3,Business,4,4,3:51:50 PM,4,4,3:52:31 PM,4,4,3:56:26 PM,4,4,5:01:35 PM,4,4,5:02:22 PM,15,26.0,,-1.2285834999999998,36.882646799999996,-1.2519787,36.780649100000005,Rider_Id_307,47
-Order_No_14818,User_Id_2106,Bike,1,Personal,20,3,8:22:42 AM,20,3,9:35:49 AM,20,3,9:46:49 AM,20,3,10:08:39 AM,20,3,10:28:42 AM,9,16.3,0.3,-1.2266581,36.844552,-1.2874648000000002,36.8271985,Rider_Id_661,1203
-Order_No_18909,User_Id_3688,Bike,3,Business,9,5,12:26:46 PM,9,5,12:27:49 PM,9,5,12:55:46 PM,9,5,1:23:54 PM,9,5,2:13:36 PM,10,25.7,,-1.2551895,36.7822034,-1.3072605,36.84744570000001,Rider_Id_518,2982
-Order_No_27937,User_Id_552,Bike,3,Business,7,1,12:28:11 PM,7,1,12:32:14 PM,7,1,12:52:13 PM,7,1,12:54:34 PM,7,1,1:31:47 PM,23,25.0,,-1.3632162,36.9108253,-1.2413316,36.878552500000005,Rider_Id_505,2233
-Order_No_21263,User_Id_2503,Bike,2,Business,20,1,4:00:55 PM,20,1,4:33:05 PM,20,1,5:03:00 PM,20,1,5:15:08 PM,20,1,5:44:51 PM,7,20.4,,-1.2824911,36.8218042,-1.2551895,36.7822034,Rider_Id_532,1783
-Order_No_14862,User_Id_1882,Bike,3,Business,2,2,7:34:29 PM,2,2,7:36:43 PM,2,2,7:42:37 PM,2,2,7:58:20 PM,2,2,8:02:29 PM,5,23.2,,-1.2628638,36.8070399,-1.2877809999999998,36.817865999999995,Rider_Id_119,249
-Order_No_12023,User_Id_1402,Bike,3,Business,6,3,11:00:39 AM,6,3,11:04:50 AM,6,3,11:27:15 AM,6,3,11:32:48 AM,6,3,11:55:39 AM,6,22.3,,-1.2571472,36.795063299999995,-1.2813012,36.832396200000005,Rider_Id_679,1371
-Order_No_20082,User_Id_391,Bike,3,Business,7,2,2:57:52 PM,7,2,3:00:19 PM,7,2,3:16:04 PM,7,2,3:55:10 PM,7,2,3:55:11 PM,4,,,-1.2839729,36.8115146,-1.300921,36.828195,Rider_Id_119,1
-Order_No_8663,User_Id_3283,Bike,3,Business,23,3,4:10:29 PM,23,3,4:19:02 PM,23,3,4:52:02 PM,23,3,4:59:07 PM,23,3,5:19:55 PM,9,20.7,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_116,1248
-Order_No_26415,User_Id_1245,Bike,3,Business,29,5,4:29:06 PM,29,5,4:30:06 PM,29,5,4:40:00 PM,29,5,5:01:31 PM,29,5,5:37:09 PM,17,27.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_691,2138
-Order_No_18257,User_Id_517,Bike,3,Business,30,4,9:27:21 AM,30,4,9:27:51 AM,30,4,9:30:22 AM,30,4,9:34:26 AM,30,4,9:49:25 AM,7,19.5,,-1.3228003000000002,36.8306435,-1.3012841999999998,36.8561033,Rider_Id_709,899
-Order_No_27447,User_Id_2330,Bike,1,Business,28,5,4:41:24 PM,28,5,4:42:16 PM,28,5,5:38:41 PM,28,5,5:46:22 PM,28,5,6:24:26 PM,19,,,-1.254191,36.809913200000004,-1.185943,36.9276394,Rider_Id_532,2284
-Order_No_6153,User_Id_2112,Bike,3,Business,3,4,3:48:17 PM,3,4,3:54:56 PM,3,4,3:55:16 PM,3,4,4:11:17 PM,3,4,4:39:11 PM,15,26.0,,-1.3277669,36.8975169,-1.2156006000000001,36.8916865,Rider_Id_31,1674
-Order_No_24566,User_Id_156,Bike,1,Personal,11,4,10:36:02 AM,11,4,10:36:38 AM,11,4,10:48:40 AM,11,4,10:58:04 AM,11,4,11:17:27 AM,9,24.1,,-1.2943538,36.7898148,-1.2574219,36.7927073,Rider_Id_519,1163
-Order_No_13281,User_Id_1724,Bike,3,Business,5,4,11:41:38 AM,5,4,11:41:50 AM,5,4,11:44:28 AM,5,4,11:54:33 AM,5,4,12:49:59 PM,19,,,-1.2579303999999998,36.8019667,-1.3339943,36.7263409,Rider_Id_379,3326
-Order_No_12635,User_Id_3064,Bike,3,Business,28,1,10:42:31 AM,28,1,10:42:53 AM,28,1,10:43:20 AM,28,1,10:51:25 AM,28,1,11:07:33 AM,6,23.9,,-1.2571472,36.795063299999995,-1.2985749,36.8088,Rider_Id_191,968
-Order_No_6475,User_Id_2174,Bike,3,Business,28,6,11:05:18 AM,28,6,11:06:42 AM,28,6,11:07:15 AM,28,6,11:55:51 AM,28,6,12:17:54 PM,6,23.8,,-1.2482781,36.884091999999995,-1.2241625,36.8851115,Rider_Id_307,1323
-Order_No_14425,User_Id_552,Bike,3,Business,21,3,11:02:11 AM,21,3,11:02:26 AM,21,3,11:17:32 AM,21,3,11:21:09 AM,21,3,12:46:14 PM,22,22.0,,-1.3472066,36.7692638,-1.3632162,36.9108253,Rider_Id_533,5105
-Order_No_16960,User_Id_1402,Bike,3,Business,14,4,12:11:40 PM,14,4,12:12:40 PM,14,4,1:08:26 PM,14,4,1:10:24 PM,14,4,1:27:33 PM,3,25.0,,-1.2813012,36.832396200000005,-1.2733311,36.820489,Rider_Id_448,1029
-Order_No_1064,User_Id_3599,Bike,3,Business,30,6,9:46:58 AM,30,6,9:47:29 AM,30,6,10:11:14 AM,30,6,10:20:38 AM,30,6,10:45:35 AM,12,,,-1.3302996,36.8707935,-1.3251825000000002,36.78975870000001,Rider_Id_675,1497
-Order_No_19398,User_Id_393,Bike,3,Business,30,2,8:18:21 AM,30,2,8:53:39 AM,30,2,8:53:59 AM,30,2,9:44:21 AM,30,2,10:29:47 AM,14,17.1,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_98,2726
-Order_No_18894,User_Id_1856,Bike,3,Business,16,2,3:21:19 PM,16,2,3:21:51 PM,16,2,3:34:06 PM,16,2,3:38:49 PM,16,2,4:08:57 PM,4,,,-1.273009,36.802807,-1.2684319,36.81658529999999,Rider_Id_161,1808
-Order_No_6569,User_Id_1932,Bike,3,Business,5,2,10:26:24 AM,5,2,10:26:49 AM,5,2,10:33:02 AM,5,2,10:36:53 AM,5,2,10:44:31 AM,2,21.4,,-1.2873232,36.765857200000006,-1.2922857,36.781327399999995,Rider_Id_65,458
-Order_No_924,User_Id_3674,Bike,3,Business,14,4,3:59:57 PM,14,4,4:02:38 PM,14,4,4:04:49 PM,14,4,4:13:36 PM,14,4,4:37:34 PM,8,27.6,,-1.2551895,36.7822034,-1.3053489999999999,36.829708000000004,Rider_Id_26,1438
-Order_No_4125,User_Id_246,Bike,3,Business,28,2,1:45:15 PM,28,2,1:45:20 PM,28,2,1:45:23 PM,28,2,2:26:34 PM,28,2,2:53:24 PM,17,19.3,,-1.3169591,36.7000029,-1.2584143,36.8048002,Rider_Id_406,1610
-Order_No_5342,User_Id_3107,Bike,3,Business,4,1,11:17:56 AM,4,1,11:18:10 AM,4,1,11:22:47 AM,4,1,11:34:20 AM,4,1,11:52:50 AM,11,21.0,,-1.3059346,36.834933899999996,-1.3277669,36.8975169,Rider_Id_852,1110
-Order_No_4678,User_Id_1502,Bike,3,Business,19,1,11:35:07 AM,19,1,11:42:44 AM,19,1,11:42:47 AM,19,1,11:52:38 AM,19,1,12:08:25 PM,13,24.9,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_886,947
-Order_No_8171,User_Id_3283,Bike,3,Business,18,5,10:42:25 AM,18,5,10:42:38 AM,18,5,10:47:09 AM,18,5,10:51:16 AM,18,5,10:58:11 AM,5,20.0,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_72,415
-Order_No_10359,User_Id_246,Bike,3,Business,28,5,10:31:06 AM,28,5,10:31:15 AM,28,5,10:42:26 AM,28,5,10:58:01 AM,28,5,11:09:09 AM,3,23.8,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_275,668
-Order_No_8963,User_Id_1146,Bike,3,Business,13,4,12:42:21 PM,13,4,1:08:25 PM,13,4,1:13:11 PM,13,4,1:25:40 PM,13,4,1:39:17 PM,7,22.1,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_2,817
-Order_No_28167,User_Id_2008,Bike,3,Business,16,2,1:47:21 PM,16,2,1:47:47 PM,16,2,1:57:57 PM,16,2,2:06:59 PM,16,2,2:42:55 PM,8,,,-1.2508229,36.7895263,-1.296814,36.8162263,Rider_Id_56,2156
-Order_No_21035,User_Id_309,Bike,3,Business,28,2,9:21:22 AM,28,2,9:21:40 AM,28,2,9:21:55 AM,28,2,9:32:28 AM,28,2,10:11:52 AM,17,18.5,,-1.2156006000000001,36.8916865,-1.3038408000000001,36.818713,Rider_Id_266,2364
-Order_No_14814,User_Id_1402,Bike,3,Business,9,2,11:37:56 AM,9,2,12:57:24 PM,9,2,1:26:26 PM,9,2,1:36:13 PM,9,2,2:40:40 PM,7,30.5,,-1.2813012,36.832396200000005,-1.3024388,36.859699,Rider_Id_709,3867
-Order_No_13721,User_Id_676,Bike,2,Personal,3,2,2:49:53 PM,3,2,2:56:01 PM,3,2,3:38:18 PM,3,2,4:17:51 PM,3,2,5:00:39 PM,20,20.1,,-1.3381926000000002,36.9205612,-1.2753108999999998,36.8130185,Rider_Id_238,2568
-Order_No_19884,User_Id_665,Bike,1,Business,5,5,1:15:39 PM,5,5,1:17:32 PM,5,5,1:39:26 PM,5,5,1:49:06 PM,5,5,2:26:54 PM,18,26.6,,-1.2859912,36.8756811,-1.279767,36.7695313,Rider_Id_900,2268
-Order_No_28029,User_Id_1482,Bike,3,Business,26,1,2:46:44 PM,26,1,2:50:38 PM,26,1,3:21:43 PM,26,1,4:24:50 PM,26,1,4:37:40 PM,8,22.4,0.7,-1.3214137,36.8569475,-1.3385790000000002,36.89630379999999,Rider_Id_661,770
-Order_No_12996,User_Id_1500,Bike,3,Business,6,1,3:26:05 PM,6,1,3:27:15 PM,6,1,3:29:15 PM,6,1,3:55:33 PM,6,1,4:15:34 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_298,1201
-Order_No_4993,User_Id_1205,Bike,3,Business,21,4,12:38:52 PM,21,4,12:39:35 PM,21,4,12:46:50 PM,21,4,12:50:08 PM,21,4,1:06:49 PM,4,25.0,,-1.2495931,36.80650870000001,-1.2575406999999998,36.7868197,Rider_Id_428,1001
-Order_No_25472,User_Id_2000,Bike,3,Business,11,1,12:49:27 PM,11,1,12:49:40 PM,11,1,12:50:02 PM,11,1,12:52:00 PM,11,1,12:52:11 PM,3,23.9,,-1.2551895,36.7822034,-1.2562796999999999,36.8075858,Rider_Id_108,11
-Order_No_20085,User_Id_1271,Bike,3,Business,20,4,3:26:59 PM,20,4,3:27:24 PM,20,4,3:45:29 PM,20,4,4:04:24 PM,20,4,5:07:31 PM,19,25.2,,-1.3244885,36.897791999999995,-1.3049238,36.7841898,Rider_Id_548,3787
-Order_No_13239,User_Id_1893,Bike,3,Personal,7,5,12:08:11 PM,7,5,12:10:04 PM,7,5,12:16:02 PM,7,5,12:18:33 PM,7,5,12:59:42 PM,4,25.7,,-1.2668071,36.8068157,-1.2934656999999998,36.8212737,Rider_Id_857,2469
-Order_No_20691,User_Id_144,Bike,3,Personal,26,2,12:18:26 PM,26,2,12:18:45 PM,26,2,12:19:12 PM,26,2,12:26:42 PM,26,2,12:58:26 PM,6,26.3,,-1.2658334999999998,36.804286700000006,-1.2961938999999998,36.7837661,Rider_Id_758,1904
-Order_No_12511,User_Id_393,Bike,3,Business,12,2,4:03:19 PM,12,2,4:03:39 PM,12,2,4:03:42 PM,12,2,4:12:08 PM,12,2,4:24:21 PM,4,26.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_751,733
-Order_No_5046,User_Id_2804,Bike,3,Business,15,2,1:55:24 PM,15,2,1:56:34 PM,15,2,2:03:55 PM,15,2,2:22:09 PM,15,2,2:43:56 PM,14,24.8,,-1.2551895,36.7822034,-1.2450205,36.8853338,Rider_Id_517,1307
-Order_No_9112,User_Id_2412,Bike,3,Business,14,5,1:41:48 PM,14,5,1:42:21 PM,14,5,1:49:48 PM,14,5,1:54:22 PM,14,5,2:25:25 PM,5,23.2,,-1.2283403,36.8822756,-1.2467896,36.869873299999995,Rider_Id_739,1863
-Order_No_20816,User_Id_635,Bike,3,Business,25,4,1:09:12 PM,25,4,1:48:46 PM,25,4,1:53:34 PM,25,4,2:00:52 PM,25,4,2:27:13 PM,7,,,-1.2602337,36.79905479999999,-1.234958,36.8104235,Rider_Id_461,1581
-Order_No_15843,User_Id_824,Bike,3,Business,28,2,10:27:33 AM,28,2,10:27:41 AM,28,2,10:27:50 AM,28,2,10:52:09 AM,28,2,11:04:04 AM,4,17.5,,-1.2248308,36.801257299999996,-1.2143386999999999,36.7914548,Rider_Id_882,715
-Order_No_26724,User_Id_393,Bike,3,Business,11,1,2:17:12 PM,11,1,2:19:03 PM,11,1,2:25:12 PM,11,1,2:33:47 PM,11,1,3:03:16 PM,14,29.0,,-1.3167113000000001,36.8301563,-1.2991441000000001,36.7528804,Rider_Id_650,1769
-Order_No_10371,User_Id_391,Bike,3,Business,14,2,10:34:06 AM,14,2,10:55:15 AM,14,2,11:09:58 AM,14,2,11:18:35 AM,14,2,11:42:00 AM,15,,,-1.300921,36.828195,-1.2154586,36.8830064,Rider_Id_411,1405
-Order_No_16046,User_Id_2627,Bike,3,Business,2,3,11:20:54 AM,2,3,11:21:15 AM,2,3,11:27:29 AM,2,3,11:29:36 AM,2,3,11:46:29 AM,5,22.2,,-1.2646084,36.8028645,-1.2857548,36.819062,Rider_Id_264,1013
-Order_No_25749,User_Id_816,Bike,3,Business,11,6,1:03:28 PM,11,6,1:12:45 PM,11,6,1:30:05 PM,11,6,1:36:12 PM,11,6,2:09:44 PM,8,22.0,,-1.3014461000000002,36.7661381,-1.290894,36.822971,Rider_Id_592,2012
-Order_No_8394,User_Id_237,Bike,3,Business,26,1,12:32:33 PM,26,1,1:27:23 PM,26,1,1:31:56 PM,26,1,1:37:19 PM,26,1,1:54:38 PM,6,22.3,0.9,-1.2551895,36.7822034,-1.276957,36.824521000000004,Rider_Id_208,1039
-Order_No_23111,User_Id_3626,Bike,2,Personal,22,3,3:04:54 PM,22,3,3:04:59 PM,22,3,3:27:58 PM,22,3,3:33:38 PM,22,3,4:46:59 PM,32,25.6,,-1.3216456,36.7145156,-1.3492870000000001,36.9390989,Rider_Id_226,4401
-Order_No_4047,User_Id_3689,Bike,3,Business,3,3,6:07:43 PM,3,3,6:08:02 PM,3,3,6:10:56 PM,3,3,6:15:48 PM,3,3,6:43:03 PM,10,23.8,,-1.2527958000000001,36.8003131,-1.2248308,36.801257299999996,Rider_Id_178,1635
-Order_No_24577,User_Id_1329,Bike,3,Business,2,4,12:31:15 PM,2,4,12:31:38 PM,2,4,12:49:28 PM,2,4,1:03:14 PM,2,4,1:10:43 PM,1,,,-1.273056,36.811298,-1.2698334,36.8078,Rider_Id_411,449
-Order_No_11966,User_Id_3291,Bike,3,Business,24,2,10:28:10 AM,24,2,10:28:20 AM,24,2,10:32:12 AM,24,2,10:43:23 AM,24,2,10:52:23 AM,2,17.2,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_116,540
-Order_No_16740,User_Id_2866,Bike,3,Business,7,5,3:40:46 PM,7,5,3:56:31 PM,7,5,4:14:58 PM,7,5,4:17:53 PM,7,5,4:55:48 PM,8,,,-1.2551895,36.7822034,-1.2913246,36.8019522,Rider_Id_937,2275
-Order_No_1532,User_Id_3149,Bike,3,Business,21,3,3:57:02 PM,21,3,3:58:42 PM,21,3,3:59:56 PM,21,3,4:47:27 PM,21,3,5:01:02 PM,5,,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_925,815
-Order_No_16737,User_Id_3688,Bike,3,Business,22,4,4:47:16 PM,22,4,4:50:57 PM,22,4,5:37:01 PM,22,4,5:43:27 PM,22,4,6:03:54 PM,6,24.0,,-1.2551895,36.7822034,-1.2655306,36.828148,Rider_Id_547,1227
-Order_No_14557,User_Id_190,Bike,3,Business,12,3,3:14:04 PM,12,3,3:14:04 PM,12,3,3:40:22 PM,12,3,4:19:19 PM,12,3,5:08:54 PM,9,21.2,,-1.2897399,36.8270975,-1.3270347,36.866736200000005,Rider_Id_573,2975
-Order_No_14262,User_Id_1976,Bike,3,Business,7,3,10:10:35 AM,7,3,10:10:51 AM,7,3,10:13:26 AM,7,3,10:20:04 AM,7,3,10:52:01 AM,24,,,-1.257028,36.7810435,-1.3632421000000001,36.9183909,Rider_Id_387,1917
-Order_No_15558,User_Id_2828,Bike,1,Business,24,4,1:31:06 PM,24,4,1:33:40 PM,24,4,2:10:01 PM,24,4,2:11:04 PM,24,4,2:34:39 PM,9,22.8,,-1.2615888000000002,36.7928732,-1.307774,36.840754100000005,Rider_Id_742,1415
-Order_No_23791,User_Id_346,Bike,3,Business,8,5,3:47:16 PM,8,5,4:07:39 PM,8,5,4:28:59 PM,8,5,4:31:14 PM,8,5,4:43:51 PM,6,30.4,,-1.28878,36.8168312,-1.304023,36.788389,Rider_Id_378,757
-Order_No_3340,User_Id_2457,Bike,3,Business,26,5,4:18:11 PM,26,5,5:38:49 PM,26,5,5:47:06 PM,26,5,5:54:21 PM,26,5,6:15:56 PM,6,,,-1.2551895,36.7822034,-1.285763,36.82412,Rider_Id_521,1295
-Order_No_8662,User_Id_328,Bike,1,Personal,25,5,12:07:43 PM,25,5,12:09:13 PM,25,5,12:20:35 PM,25,5,12:28:25 PM,25,5,12:52:02 PM,16,21.1,,-1.2740396,36.7993518,-1.201875,36.89931370000001,Rider_Id_517,1417
-Order_No_13101,User_Id_2069,Bike,3,Business,14,2,12:36:48 PM,14,2,12:37:56 PM,14,2,12:44:11 PM,14,2,12:56:42 PM,14,2,1:08:26 PM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_845,704
-Order_No_11758,User_Id_3689,Bike,3,Business,23,4,3:07:27 PM,23,4,3:24:09 PM,23,4,3:30:54 PM,23,4,3:39:53 PM,23,4,3:59:03 PM,15,20.9,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_857,1150
-Order_No_23076,User_Id_1471,Bike,3,Business,15,5,2:34:18 PM,15,5,2:37:10 PM,15,5,2:46:22 PM,15,5,2:56:34 PM,15,5,3:18:09 PM,8,28.1,,-1.2551895,36.7822034,-1.2972916,36.7880497,Rider_Id_669,1295
-Order_No_26483,User_Id_893,Bike,3,Business,11,5,10:01:02 AM,11,5,10:03:43 AM,11,5,10:15:17 AM,11,5,10:19:16 AM,11,5,10:42:13 AM,7,17.8,4.9,-1.2663194,36.809901,-1.3001909,36.816362700000006,Rider_Id_207,1377
-Order_No_25582,User_Id_2510,Bike,1,Business,27,4,5:04:55 PM,27,4,5:05:33 PM,27,4,5:26:20 PM,27,4,5:32:57 PM,27,4,6:19:00 PM,14,23.0,,-1.303097,36.846517,-1.2541432,36.7707094,Rider_Id_96,2763
-Order_No_4244,User_Id_3283,Bike,3,Business,21,2,1:39:15 PM,21,2,1:40:54 PM,21,2,1:52:47 PM,21,2,1:59:03 PM,21,2,2:14:09 PM,7,25.0,,-1.303596,36.778378000000004,-1.2628473,36.781805,Rider_Id_138,906
-Order_No_6948,User_Id_2523,Bike,3,Business,8,2,10:53:57 AM,8,2,10:54:31 AM,8,2,10:56:05 AM,8,2,11:10:40 AM,8,2,1:09:43 PM,10,23.5,,-1.2551895,36.7822034,-1.3108783000000002,36.8392726,Rider_Id_268,7143
-Order_No_27217,User_Id_1754,Bike,3,Business,6,2,9:51:51 AM,6,2,10:49:01 AM,6,2,10:58:16 AM,6,2,11:29:35 AM,6,2,11:50:19 AM,16,25.2,,-1.3632162,36.9108253,-1.2860183,36.8975338,Rider_Id_136,1244
-Order_No_22674,User_Id_1676,Bike,2,Personal,15,2,1:10:14 PM,15,2,1:16:47 PM,15,2,1:20:54 PM,15,2,1:34:22 PM,15,2,2:23:30 PM,4,24.6,,-1.3057159999999999,36.809882,-1.2885364,36.7995016,Rider_Id_441,2948
-Order_No_21820,User_Id_2513,Bike,3,Personal,10,2,3:28:25 PM,10,2,3:30:31 PM,10,2,3:48:19 PM,10,2,4:01:37 PM,10,2,4:24:01 PM,5,21.3,,-1.2787293,36.825622700000004,-1.2922364,36.78955379999999,Rider_Id_268,1344
-Order_No_20225,User_Id_2866,Bike,3,Business,4,4,11:31:44 AM,4,4,11:36:05 AM,4,4,11:55:06 AM,4,4,12:06:02 PM,4,4,12:22:47 PM,8,25.3,,-1.2551895,36.7822034,-1.2894649,36.80484860000001,Rider_Id_855,1005
-Order_No_23696,User_Id_2601,Bike,3,Personal,16,3,9:23:30 AM,16,3,9:23:38 AM,16,3,9:25:15 AM,16,3,9:28:05 AM,16,3,9:34:02 AM,2,19.4,,-1.2963097,36.7688221,-1.3014461000000002,36.7661381,Rider_Id_425,357
-Order_No_24903,User_Id_2066,Bike,3,Business,2,4,1:11:11 PM,2,4,1:14:29 PM,2,4,1:29:06 PM,2,4,1:40:23 PM,2,4,1:59:53 PM,5,,,-1.3210188999999999,36.841055100000005,-1.3316276000000002,36.8727316,Rider_Id_709,1170
-Order_No_10641,User_Id_1245,Bike,3,Business,22,2,2:26:54 PM,22,2,2:27:36 PM,22,2,2:37:32 PM,22,2,2:50:10 PM,22,2,3:33:18 PM,14,25.7,,-1.300921,36.828195,-1.355664,36.743009,Rider_Id_551,2588
-Order_No_27876,User_Id_3291,Bike,3,Business,18,2,2:45:45 PM,18,2,2:46:43 PM,18,2,2:54:00 PM,18,2,2:59:40 PM,18,2,3:05:03 PM,2,25.7,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_24,323
-Order_No_26121,User_Id_420,Bike,3,Business,31,5,3:40:34 PM,31,5,3:42:24 PM,31,5,4:02:24 PM,31,5,4:24:51 PM,31,5,5:02:43 PM,13,20.0,,-1.3129578000000002,36.8463197,-1.2517448,36.7924971,Rider_Id_754,2272
-Order_No_17230,User_Id_3521,Bike,3,Business,18,1,8:50:47 AM,18,1,8:51:35 AM,18,1,9:01:01 AM,18,1,9:07:23 AM,18,1,9:19:35 AM,3,21.6,,-1.2696908000000002,36.81519,-1.2741728,36.79691079999999,Rider_Id_99,732
-Order_No_17370,User_Id_893,Bike,3,Business,12,1,12:52:30 PM,12,1,12:55:24 PM,12,1,1:26:41 PM,12,1,1:32:55 PM,12,1,1:32:59 PM,3,27.1,,-1.2664491000000002,36.8102685,-1.2663778,36.8076024,Rider_Id_486,4
-Order_No_7149,User_Id_994,Bike,1,Personal,10,5,3:31:37 PM,10,5,3:33:57 PM,10,5,4:14:43 PM,10,5,4:27:03 PM,10,5,4:52:25 PM,9,21.6,,-1.2854718,36.871219700000005,-1.286553,36.8273374,Rider_Id_179,1522
-Order_No_10286,User_Id_1178,Bike,3,Business,15,6,11:24:08 AM,15,6,11:24:29 AM,15,6,11:26:04 AM,15,6,12:12:20 PM,15,6,12:15:51 PM,4,,,-1.261587,36.796781,-1.2868515,36.7844247,Rider_Id_51,211
-Order_No_28239,User_Id_2548,Bike,2,Business,1,6,3:41:55 PM,1,6,3:42:40 PM,1,6,3:54:50 PM,1,6,4:14:57 PM,1,6,4:31:34 PM,8,19.3,,-1.2868973,36.8232984,-1.2957616000000003,36.7669835,Rider_Id_465,997
-Order_No_28197,User_Id_540,Bike,3,Business,12,2,3:40:12 PM,12,2,3:43:08 PM,12,2,3:44:02 PM,12,2,4:10:33 PM,12,2,4:22:00 PM,5,,,-1.2551895,36.7822034,-1.2665065,36.7970081,Rider_Id_242,687
-Order_No_18035,User_Id_1859,Bike,3,Business,12,5,10:25:03 AM,12,5,10:25:53 AM,12,5,10:34:56 AM,12,5,10:39:54 AM,12,5,11:02:56 AM,7,24.3,,-1.2626765,36.795598999999996,-1.2993666000000001,36.8232195,Rider_Id_89,1382
-Order_No_25940,User_Id_1329,Bike,3,Business,18,1,10:53:09 AM,18,1,10:53:23 AM,18,1,11:10:10 AM,18,1,11:11:48 AM,18,1,11:35:20 AM,6,28.2,,-1.273056,36.811298,-1.3012173,36.8093987,Rider_Id_343,1412
-Order_No_15901,User_Id_3001,Bike,3,Business,3,1,1:17:19 PM,3,1,1:19:27 PM,3,1,1:33:40 PM,3,1,1:41:34 PM,3,1,2:03:08 PM,7,23.1,,-1.2846195,36.7958316,-1.2675613000000001,36.8133001,Rider_Id_161,1294
-Order_No_1075,User_Id_1882,Bike,3,Business,30,6,2:04:52 PM,30,6,2:05:15 PM,30,6,2:08:38 PM,30,6,2:28:14 PM,30,6,2:45:50 PM,7,28.3,,-1.2956882,36.7861845,-1.2591018999999999,36.800577000000004,Rider_Id_383,1056
-Order_No_11649,User_Id_2804,Bike,3,Business,21,3,10:22:45 AM,21,3,10:24:25 AM,21,3,10:28:14 AM,21,3,10:44:00 AM,21,3,11:06:21 AM,8,22.0,,-1.2551895,36.7822034,-1.2996363000000002,36.8186864,Rider_Id_19,1341
-Order_No_20149,User_Id_1588,Bike,3,Business,8,5,3:44:30 PM,8,5,4:22:51 PM,8,5,5:02:37 PM,8,5,5:56:30 PM,8,5,6:28:05 PM,7,28.0,,-1.2897399,36.8270975,-1.2551895,36.7822034,Rider_Id_448,1895
-Order_No_11167,User_Id_1329,Bike,3,Business,9,6,5:21:53 PM,9,6,5:22:43 PM,9,6,5:28:08 PM,9,6,5:31:05 PM,9,6,5:53:23 PM,8,,,-1.273056,36.811298,-1.304033,36.7848695,Rider_Id_26,1338
-Order_No_18551,User_Id_3404,Bike,3,Business,19,3,2:24:11 PM,19,3,2:36:36 PM,19,3,2:53:49 PM,19,3,2:59:07 PM,19,3,3:41:47 PM,12,24.3,,-1.2860183,36.8975338,-1.279395,36.825364,Rider_Id_31,2560
-Order_No_5360,User_Id_1205,Bike,3,Business,1,3,12:07:41 PM,1,3,12:08:15 PM,1,3,12:20:12 PM,1,3,12:24:04 PM,1,3,12:47:17 PM,4,20.0,,-1.2810807,36.8144229,-1.2654228,36.8033218,Rider_Id_754,1393
-Order_No_17548,User_Id_632,Bike,1,Personal,4,2,1:29:25 PM,4,2,1:29:53 PM,4,2,1:38:25 PM,4,2,1:48:42 PM,4,2,2:07:22 PM,6,20.4,,-1.2869354,36.7708614,-1.2736094999999998,36.8011282,Rider_Id_231,1120
-Order_No_16680,User_Id_1927,Bike,3,Business,17,5,2:15:50 PM,17,5,2:16:04 PM,17,5,2:22:57 PM,17,5,2:35:54 PM,17,5,3:10:53 PM,22,,,-1.3472066,36.7692638,-1.3103985,36.9114173,Rider_Id_533,2099
-Order_No_4873,User_Id_3686,Bike,3,Business,16,4,9:32:19 AM,16,4,9:33:23 AM,16,4,9:35:14 AM,16,4,10:22:20 AM,16,4,10:41:30 AM,8,,,-1.2551895,36.7822034,-1.3053489999999999,36.829708000000004,Rider_Id_291,1150
-Order_No_25937,User_Id_3491,Bike,3,Business,10,3,11:44:34 AM,10,3,11:44:44 AM,10,3,11:53:12 AM,10,3,11:56:57 AM,10,3,12:17:56 PM,13,22.1,,-1.2248308,36.801257299999996,-1.2297202,36.881950700000004,Rider_Id_453,1259
-Order_No_16003,User_Id_1209,Bike,3,Business,18,4,10:24:06 AM,18,4,10:24:30 AM,18,4,10:35:51 AM,18,4,10:52:07 AM,18,4,10:52:34 AM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_61,27
-Order_No_20058,User_Id_2956,Bike,3,Business,4,1,3:27:06 PM,4,1,3:28:04 PM,4,1,3:36:44 PM,4,1,3:39:49 PM,4,1,3:52:27 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_318,758
-Order_No_20048,User_Id_3741,Bike,2,Personal,27,5,1:59:35 PM,27,5,2:04:18 PM,27,5,2:47:15 PM,27,5,3:05:20 PM,27,5,3:28:40 PM,6,24.2,,-1.2892898000000002,36.7874315,-1.269534,36.80697,Rider_Id_836,1400
-Order_No_3839,User_Id_20,Bike,3,Business,7,2,12:21:49 PM,7,2,12:23:38 PM,7,2,12:35:39 PM,7,2,12:44:40 PM,7,2,12:58:41 PM,6,23.0,,-1.2628638,36.8070399,-1.2804456,36.82295070000001,Rider_Id_857,841
-Order_No_12439,User_Id_1329,Bike,3,Business,16,6,10:30:38 AM,16,6,10:31:26 AM,16,6,10:35:26 AM,16,6,10:39:46 AM,16,6,11:05:58 AM,8,25.9,,-1.273056,36.811298,-1.2949438999999998,36.7604214,Rider_Id_25,1572
-Order_No_21418,User_Id_2066,Bike,3,Business,4,2,11:43:39 AM,4,2,11:45:57 AM,4,2,12:01:51 PM,4,2,12:06:38 PM,4,2,12:30:03 PM,13,18.7,,-1.316961,36.820337,-1.3270790000000001,36.881469,Rider_Id_320,1405
-Order_No_26047,User_Id_517,Bike,3,Business,27,4,8:51:34 AM,27,4,9:27:58 AM,27,4,9:28:19 AM,27,4,9:36:08 AM,27,4,9:56:39 AM,11,17.7,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_515,1231
-Order_No_19323,User_Id_847,Bike,3,Business,3,5,7:26:54 AM,3,5,7:27:07 AM,3,5,7:54:36 AM,3,5,7:56:28 AM,3,5,8:40:22 AM,13,,,-1.3290948999999999,36.8767589,-1.287353,36.826992,Rider_Id_956,2634
-Order_No_27887,User_Id_248,Bike,3,Business,6,5,10:43:46 AM,6,5,10:44:40 AM,6,5,10:56:35 AM,6,5,10:58:49 AM,6,5,11:16:07 AM,4,20.4,,-1.304735,36.784605400000004,-1.289379,36.792949,Rider_Id_465,1038
-Order_No_23060,User_Id_2030,Bike,3,Business,11,5,2:45:16 PM,11,5,2:47:06 PM,11,5,3:17:18 PM,11,5,3:25:41 PM,11,5,3:51:13 PM,9,26.4,,-1.22589,36.7822548,-1.2753108999999998,36.8130185,Rider_Id_547,1532
-Order_No_16194,User_Id_635,Bike,3,Business,20,3,4:06:24 PM,20,3,4:06:46 PM,20,3,4:10:58 PM,20,3,4:26:09 PM,20,3,4:47:56 PM,8,29.4,,-1.2599563,36.799343900000004,-1.3123271,36.814738299999995,Rider_Id_76,1307
-Order_No_20969,User_Id_1838,Bike,3,Business,31,4,3:30:25 PM,31,4,3:33:50 PM,31,4,4:03:51 PM,31,4,4:08:33 PM,31,4,4:43:21 PM,10,22.9,,-1.2649605,36.798177700000004,-1.214328,36.7911903,Rider_Id_103,2088
-Order_No_13451,User_Id_3109,Bike,2,Personal,6,5,11:08:48 AM,6,5,11:17:22 AM,6,5,11:29:54 AM,6,5,11:34:29 AM,6,5,11:36:53 AM,7,20.3,,-1.2551895,36.7822034,-1.2921114,36.819219,Rider_Id_621,144
-Order_No_15449,User_Id_1207,Bike,3,Business,15,4,3:58:36 PM,15,4,3:58:49 PM,15,4,4:03:55 PM,15,4,4:18:10 PM,15,4,4:26:07 PM,3,27.3,,-1.2628638,36.8070399,-1.2681003999999998,36.7952081,Rider_Id_446,477
-Order_No_17005,User_Id_2828,Bike,3,Business,1,5,9:01:23 AM,1,5,9:29:38 AM,1,5,9:38:12 AM,1,5,11:00:03 AM,1,5,11:25:11 AM,13,28.7,,-1.2710687,36.8142839,-1.2778747,36.883182899999994,Rider_Id_52,1508
-Order_No_16036,User_Id_1196,Bike,2,Business,7,4,2:50:15 PM,7,4,2:54:40 PM,7,4,3:21:23 PM,7,4,3:34:01 PM,7,4,4:01:50 PM,8,20.1,,-1.2932780000000001,36.827789,-1.2551895,36.7822034,Rider_Id_752,1669
-Order_No_22583,User_Id_3203,Bike,3,Business,30,2,1:53:30 PM,30,2,1:55:17 PM,30,2,1:55:45 PM,30,2,2:35:34 PM,30,2,3:10:34 PM,17,27.3,,-1.331619,36.847976,-1.228911,36.881876,Rider_Id_770,2100
-Order_No_2786,User_Id_2021,Bike,3,Business,9,2,10:01:40 AM,9,2,10:01:48 AM,9,2,10:36:29 AM,9,2,10:40:45 AM,9,2,11:09:24 AM,13,27.2,,-1.29475,36.83421,-1.2967635,36.7527447,Rider_Id_448,1719
-Order_No_25239,User_Id_1620,Bike,1,Personal,19,5,10:07:19 AM,19,5,10:07:52 AM,19,5,10:11:53 AM,19,5,10:43:03 AM,19,5,11:08:31 AM,13,25.1,,-1.2686837,36.8891847,-1.227132,36.8374277,Rider_Id_408,1528
-Order_No_16452,User_Id_3326,Bike,3,Business,11,4,3:29:11 PM,11,4,3:33:31 PM,11,4,4:04:16 PM,11,4,4:07:22 PM,11,4,4:21:07 PM,4,27.2,,-1.2551895,36.7822034,-1.2585521000000002,36.7945765,Rider_Id_547,825
-Order_No_24375,User_Id_3689,Bike,3,Business,5,1,1:35:17 PM,5,1,1:49:15 PM,5,1,1:58:30 PM,5,1,2:01:39 PM,5,1,2:18:48 PM,5,26.1,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_634,1029
-Order_No_26465,User_Id_3355,Bike,3,Business,18,2,9:08:05 AM,18,2,9:08:25 AM,18,2,9:15:39 AM,18,2,9:17:41 AM,18,2,9:33:47 AM,8,16.5,,-1.3052491000000002,36.8223899,-1.2551895,36.7822034,Rider_Id_674,966
-Order_No_7247,User_Id_3291,Bike,3,Business,24,3,2:42:19 PM,24,3,3:20:59 PM,24,3,3:41:28 PM,24,3,3:50:03 PM,24,3,4:19:02 PM,11,,,-1.2297799999999999,36.836215,-1.2600926000000001,36.8088685,Rider_Id_68,1739
-Order_No_16586,User_Id_517,Bike,3,Business,27,4,12:03:12 PM,27,4,12:17:14 PM,27,4,12:17:20 PM,27,4,1:18:15 PM,27,4,1:18:29 PM,4,23.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_10,14
-Order_No_15702,User_Id_1500,Bike,3,Business,7,2,9:19:30 AM,7,2,9:20:51 AM,7,2,9:31:53 AM,7,2,9:56:01 AM,7,2,9:57:50 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,109
-Order_No_2689,User_Id_136,Bike,3,Business,28,2,10:18:17 AM,28,2,10:38:33 AM,28,2,10:48:06 AM,28,2,10:50:33 AM,28,2,11:43:29 AM,17,21.1,,-1.3254522,36.721658399999995,-1.266022,36.8269774,Rider_Id_572,3176
-Order_No_5328,User_Id_1620,Bike,3,Personal,29,2,4:09:57 PM,29,2,4:11:21 PM,29,2,4:23:22 PM,29,2,4:31:14 PM,29,2,5:09:21 PM,11,23.7,,-1.261849,36.808254,-1.227132,36.8374277,Rider_Id_852,2287
-Order_No_8116,User_Id_3686,Bike,3,Business,30,4,9:41:47 AM,30,4,9:42:22 AM,30,4,9:48:29 AM,30,4,9:53:03 AM,30,4,10:05:37 AM,4,16.2,,-1.2551895,36.7822034,-1.2695287,36.798177700000004,Rider_Id_882,754
-Order_No_18371,User_Id_54,Bike,2,Personal,2,2,5:53:31 PM,2,2,5:54:12 PM,2,2,6:14:41 PM,2,2,6:22:25 PM,2,2,6:34:28 PM,2,23.6,,-1.2870045,36.773709600000004,-1.2991247,36.7642354,Rider_Id_328,723
-Order_No_15112,User_Id_1995,Bike,3,Business,20,2,4:05:30 PM,20,2,4:10:25 PM,20,2,4:17:30 PM,20,2,4:22:26 PM,20,2,4:32:46 PM,3,23.9,,-1.2657464,36.780114399999995,-1.2765492,36.7669809,Rider_Id_536,620
-Order_No_20654,User_Id_3291,Bike,3,Business,21,1,12:16:53 PM,21,1,12:20:10 PM,21,1,12:28:22 PM,21,1,1:04:15 PM,21,1,1:27:02 PM,9,20.0,,-1.307143,36.825009,-1.2600926000000001,36.8088685,Rider_Id_888,1367
-Order_No_17840,User_Id_1148,Bike,3,Business,15,3,2:42:43 PM,15,3,2:43:09 PM,15,3,2:54:55 PM,15,3,3:01:31 PM,15,3,3:11:06 PM,3,25.7,,-1.3104867,36.8495593,-1.311818,36.8638544,Rider_Id_715,575
-Order_No_7570,User_Id_3426,Bike,3,Business,9,5,4:25:40 PM,9,5,4:28:20 PM,9,5,4:56:09 PM,9,5,5:02:28 PM,9,5,5:42:01 PM,11,24.9,,-1.2584143,36.8048002,-1.3025826,36.7670807,Rider_Id_40,2373
-Order_No_12639,User_Id_704,Bike,3,Business,23,3,12:20:26 PM,23,3,12:59:35 PM,23,3,1:01:34 PM,23,3,1:03:35 PM,23,3,1:31:38 PM,13,21.6,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_103,1683
-Order_No_3774,User_Id_3450,Bike,3,Business,10,1,4:12:40 PM,10,1,4:13:00 PM,10,1,4:16:48 PM,10,1,4:23:47 PM,10,1,4:58:33 PM,8,18.4,,-1.2551895,36.7822034,-1.3044313,36.818504,Rider_Id_591,2086
-Order_No_828,User_Id_1329,Bike,3,Business,6,4,1:46:02 PM,6,4,1:47:35 PM,6,4,2:04:37 PM,6,4,2:06:27 PM,6,4,2:18:24 PM,3,21.2,,-1.273056,36.811298,-1.2702333000000001,36.80065,Rider_Id_579,717
-Order_No_27399,User_Id_2330,Bike,3,Business,10,3,1:10:12 PM,10,3,1:10:56 PM,10,3,1:21:22 PM,10,3,1:26:40 PM,10,3,1:58:55 PM,1,27.8,,-1.2642903,36.80021370000001,-1.2658486,36.8021534,Rider_Id_547,1935
-Order_No_27289,User_Id_53,Bike,3,Business,14,4,12:44:57 PM,14,4,12:47:46 PM,14,4,1:04:08 PM,14,4,1:06:58 PM,14,4,1:36:24 PM,11,25.1,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_99,1766
-Order_No_6015,User_Id_1280,Bike,3,Business,13,4,10:56:06 AM,13,4,10:56:29 AM,13,4,11:04:42 AM,13,4,11:10:30 AM,13,4,11:25:24 AM,5,22.3,,-1.2551895,36.7822034,-1.2708788,36.766115500000005,Rider_Id_139,894
-Order_No_11270,User_Id_1876,Bike,1,Personal,6,3,8:48:57 AM,6,3,9:22:11 AM,6,3,9:49:16 AM,6,3,9:58:38 AM,6,3,10:18:09 AM,8,16.2,13.9,-1.289498,36.818878000000005,-1.2999633,36.853268299999996,Rider_Id_846,1171
-Order_No_27553,User_Id_3647,Bike,3,Business,16,2,1:55:50 PM,16,2,1:56:29 PM,16,2,2:05:46 PM,16,2,2:09:16 PM,16,2,2:24:41 PM,8,27.4,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_22,925
-Order_No_12155,User_Id_3737,Bike,1,Personal,20,1,12:43:19 PM,20,1,12:44:43 PM,20,1,1:21:10 PM,20,1,1:28:24 PM,20,1,1:55:14 PM,11,20.4,,-1.2248308,36.801257299999996,-1.2797664,36.826966999999996,Rider_Id_754,1610
-Order_No_18691,User_Id_1205,Bike,3,Business,1,5,10:08:19 AM,1,5,10:08:49 AM,1,5,10:21:19 AM,1,5,10:28:52 AM,1,5,10:43:20 AM,6,23.3,,-1.2495931,36.80650870000001,-1.2449522,36.7580025,Rider_Id_607,868
-Order_No_9736,User_Id_3149,Bike,3,Business,21,3,9:45:14 AM,21,3,9:45:32 AM,21,3,9:58:13 AM,21,3,10:14:29 AM,21,3,10:42:33 AM,15,17.0,0.9,-1.306378,36.7519845,-1.3177546999999998,36.8303703,Rider_Id_85,1684
-Order_No_17916,User_Id_248,Bike,3,Business,14,4,3:40:12 PM,14,4,3:44:04 PM,14,4,3:50:53 PM,14,4,3:55:55 PM,14,4,5:18:41 PM,10,21.3,,-1.304735,36.784605400000004,-1.2556717,36.810087599999996,Rider_Id_89,4966
-Order_No_17688,User_Id_393,Bike,3,Business,15,1,4:28:54 PM,15,1,4:29:13 PM,15,1,4:30:59 PM,15,1,4:40:09 PM,15,1,5:01:29 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_168,1280
-Order_No_24596,User_Id_393,Bike,3,Business,12,2,11:58:39 AM,12,2,11:59:28 AM,12,2,12:00:20 PM,12,2,1:06:35 PM,12,2,1:08:40 PM,14,21.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_927,125
-Order_No_21716,User_Id_2030,Bike,3,Business,1,1,9:56:10 AM,1,1,9:59:14 AM,1,1,10:17:00 AM,1,1,10:27:15 AM,1,1,10:40:09 AM,7,18.6,,-1.3321944,36.889232899999996,-1.3333725,36.871761299999996,Rider_Id_622,774
-Order_No_7883,User_Id_3239,Bike,1,Personal,8,6,8:58:19 AM,8,6,8:58:41 AM,8,6,9:33:53 AM,8,6,9:35:28 AM,8,6,10:21:16 AM,13,18.1,,-1.2844072,36.824729600000005,-1.2613676999999999,36.9067336,Rider_Id_208,2748
-Order_No_11076,User_Id_3097,Bike,3,Personal,17,4,2:19:41 PM,17,4,2:19:58 PM,17,4,2:48:06 PM,17,4,2:50:58 PM,17,4,3:24:03 PM,23,27.9,,-1.175308,36.950746,-1.271033,36.799323,Rider_Id_48,1985
-Order_No_6560,User_Id_1004,Bike,3,Business,9,4,2:50:48 PM,9,4,2:52:46 PM,9,4,3:10:18 PM,9,4,3:39:16 PM,9,4,4:32:02 PM,22,,,-1.2152528999999999,36.7911336,-1.3250048,36.869718799999994,Rider_Id_7,3166
-Order_No_19551,User_Id_3376,Bike,1,Personal,3,1,2:59:04 PM,3,1,2:59:20 PM,3,1,3:29:13 PM,3,1,3:32:25 PM,3,1,4:12:28 PM,16,22.9,,-1.2119232,36.899645899999996,-1.289498,36.818878000000005,Rider_Id_384,2403
-Order_No_12373,User_Id_3339,Bike,3,Business,8,1,2:33:08 PM,8,1,2:33:41 PM,8,1,2:35:04 PM,8,1,2:49:05 PM,8,1,3:09:28 PM,14,25.1,,-1.2551895,36.7822034,-1.2297202,36.881950700000004,Rider_Id_100,1223
-Order_No_983,User_Id_2069,Bike,3,Business,22,1,10:21:29 AM,22,1,10:22:24 AM,22,1,10:31:23 AM,22,1,11:04:08 AM,22,1,11:23:40 AM,13,25.1,,-1.307143,36.825009,-1.228911,36.881876,Rider_Id_878,1172
-Order_No_7654,User_Id_3291,Bike,3,Business,25,6,9:03:20 AM,25,6,9:03:51 AM,25,6,9:18:13 AM,25,6,9:41:34 AM,25,6,9:55:06 AM,12,,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_75,812
-Order_No_24100,User_Id_2523,Bike,3,Business,9,2,2:29:01 PM,9,2,2:29:10 PM,9,2,2:31:40 PM,9,2,2:39:38 PM,9,2,2:44:46 PM,3,30.7,,-1.2551895,36.7822034,-1.2560455,36.780086700000005,Rider_Id_690,308
-Order_No_20792,User_Id_3702,Bike,1,Personal,1,6,12:27:00 PM,1,6,12:43:58 PM,1,6,1:15:00 PM,1,6,1:18:30 PM,1,6,1:38:25 PM,7,18.7,,-1.2824577,36.7967953,-1.28365,36.828013,Rider_Id_857,1195
-Order_No_22128,User_Id_1966,Bike,3,Business,14,2,1:08:32 PM,14,2,1:33:15 PM,14,2,1:41:23 PM,14,2,1:58:20 PM,14,2,2:32:31 PM,21,,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_877,2051
-Order_No_11097,User_Id_3035,Bike,3,Business,24,3,12:51:07 PM,24,3,1:15:57 PM,24,3,1:34:39 PM,24,3,1:37:39 PM,24,3,2:43:15 PM,8,18.6,,-1.2853965,36.8183118,-1.2963097,36.7688221,Rider_Id_328,3936
-Order_No_26234,User_Id_533,Bike,3,Business,29,1,6:03:02 PM,29,1,6:03:37 PM,29,1,6:18:55 PM,29,1,6:29:27 PM,29,1,6:44:22 PM,6,23.2,,-1.2765492,36.7669809,-1.2551895,36.7822034,Rider_Id_171,895
-Order_No_7392,User_Id_1329,Bike,3,Business,7,2,11:15:19 AM,7,2,11:46:28 AM,7,2,12:04:24 PM,7,2,12:16:09 PM,7,2,1:57:46 PM,9,,,-1.273056,36.811298,-1.2940768999999999,36.749157399999994,Rider_Id_864,6097
-Order_No_4351,User_Id_71,Bike,3,Business,24,1,5:44:38 PM,24,1,5:46:15 PM,24,1,6:02:56 PM,24,1,6:07:34 PM,24,1,6:32:41 PM,9,24.7,,-1.2891921999999998,36.825189,-1.3014461000000002,36.7661381,Rider_Id_263,1507
-Order_No_20175,User_Id_393,Bike,3,Business,19,2,9:46:30 AM,19,2,9:51:26 AM,19,2,9:59:23 AM,19,2,10:20:11 AM,19,2,10:26:06 AM,8,,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_26,355
-Order_No_27769,User_Id_2602,Bike,3,Personal,25,1,3:01:22 PM,25,1,3:02:27 PM,25,1,3:12:27 PM,25,1,3:21:46 PM,25,1,3:43:49 PM,7,30.2,,-1.270766,36.810988,-1.304735,36.784605400000004,Rider_Id_763,1323
-Order_No_26913,User_Id_633,Bike,3,Business,21,4,2:27:28 PM,21,4,2:36:12 PM,21,4,2:38:25 PM,21,4,3:08:50 PM,21,4,3:08:56 PM,4,31.0,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_436,6
-Order_No_6042,User_Id_510,Bike,3,Business,26,2,12:34:09 PM,26,2,2:37:40 PM,26,2,2:52:06 PM,26,2,2:59:36 PM,26,2,3:32:33 PM,12,31.0,,-1.290894,36.822971,-1.2860183,36.8975338,Rider_Id_953,1977
-Order_No_277,User_Id_1875,Bike,3,Personal,14,5,3:07:04 PM,14,5,3:34:46 PM,14,5,3:44:08 PM,14,5,3:49:38 PM,14,5,3:59:19 PM,2,23.7,,-1.3236332,36.701152,-1.326647,36.710044200000006,Rider_Id_205,581
-Order_No_12072,User_Id_269,Bike,1,Business,4,4,4:01:56 PM,4,4,4:16:03 PM,4,4,4:29:45 PM,4,4,4:36:08 PM,4,4,5:04:03 PM,12,,,-1.3332749,36.870814700000004,-1.3886344,36.9372718,Rider_Id_731,1675
-Order_No_13230,User_Id_2658,Bike,3,Business,1,5,3:16:10 PM,1,5,3:24:15 PM,1,5,3:51:17 PM,1,5,3:58:30 PM,1,5,5:05:24 PM,8,28.6,,-1.2885029,36.825604999999996,-1.2963097,36.7688221,Rider_Id_88,4014
-Order_No_10773,User_Id_3194,Bike,3,Business,29,3,12:27:51 PM,29,3,12:28:59 PM,29,3,12:29:34 PM,29,3,12:35:34 PM,29,3,1:12:11 PM,9,,,-1.2551895,36.7822034,-1.249541,36.83297720000001,Rider_Id_709,2197
-Order_No_17374,User_Id_113,Bike,3,Business,9,4,1:02:23 PM,9,4,1:04:44 PM,9,4,1:24:14 PM,9,4,1:40:46 PM,9,4,2:12:10 PM,8,25.6,,-1.2781187,36.8284492,-1.3039411,36.7695316,Rider_Id_329,1884
-Order_No_8044,User_Id_186,Bike,3,Business,2,2,9:28:40 AM,2,2,9:29:27 AM,2,2,9:29:37 AM,2,2,9:34:48 AM,2,2,9:55:55 AM,8,20.6,,-1.2571472,36.795063299999995,-1.2765736,36.851364600000004,Rider_Id_108,1267
-Order_No_24158,User_Id_3354,Bike,3,Business,29,4,2:40:11 PM,29,4,2:49:13 PM,29,4,3:03:06 PM,29,4,3:10:45 PM,29,4,3:34:14 PM,9,24.8,,-1.3004846,36.790159100000004,-1.2571472,36.795063299999995,Rider_Id_50,1409
-Order_No_25426,User_Id_3291,Bike,3,Business,10,5,10:02:19 AM,10,5,10:03:02 AM,10,5,10:26:34 AM,10,5,10:40:41 AM,10,5,10:56:00 AM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_427,919
-Order_No_25701,User_Id_1500,Bike,3,Business,9,6,8:31:50 AM,9,6,8:32:49 AM,9,6,8:56:49 AM,9,6,9:12:34 AM,9,6,9:33:55 AM,8,19.9,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_206,1281
-Order_No_1173,User_Id_1075,Bike,3,Business,28,2,9:36:28 AM,28,2,9:36:52 AM,28,2,9:40:07 AM,28,2,9:43:16 AM,28,2,9:51:26 AM,1,18.5,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_96,490
-Order_No_14456,User_Id_1478,Bike,3,Personal,24,4,4:24:28 PM,24,4,4:39:03 PM,24,4,5:00:28 PM,24,4,5:08:22 PM,24,4,5:43:47 PM,9,21.4,,-1.2769975,36.8139588,-1.2545665000000001,36.75978189999999,Rider_Id_208,2125
-Order_No_24164,User_Id_3525,Bike,3,Business,27,2,10:05:52 AM,27,2,11:57:40 AM,27,2,11:57:43 AM,27,2,12:14:26 PM,27,2,12:26:01 PM,12,24.0,,-1.3700383,36.9190174,-1.3194518,36.869396,Rider_Id_345,695
-Order_No_24006,User_Id_3354,Bike,3,Business,30,2,12:14:45 PM,30,2,12:20:35 PM,30,2,1:24:15 PM,30,2,1:53:14 PM,30,2,2:15:47 PM,3,,,-1.2571472,36.795063299999995,-1.2612111000000001,36.783151600000004,Rider_Id_547,1353
-Order_No_10685,User_Id_1075,Bike,3,Business,20,6,12:24:08 PM,20,6,12:25:10 PM,20,6,12:33:54 PM,20,6,12:48:23 PM,20,6,1:08:21 PM,13,,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_8,1198
-Order_No_5247,User_Id_265,Bike,3,Business,11,5,2:39:54 PM,11,5,2:43:04 PM,11,5,3:01:40 PM,11,5,3:07:31 PM,11,5,3:26:03 PM,6,26.5,,-1.2678690000000001,36.813379,-1.300921,36.828195,Rider_Id_48,1112
-Order_No_8812,User_Id_946,Bike,2,Personal,29,3,2:53:01 PM,29,3,3:01:33 PM,29,3,3:05:17 PM,29,3,3:06:48 PM,29,3,3:43:52 PM,17,19.0,,-1.2844665000000002,36.7866113,-1.2143386999999999,36.7914548,Rider_Id_536,2224
-Order_No_10807,User_Id_1245,Bike,3,Business,27,3,2:55:38 PM,27,3,2:55:59 PM,27,3,2:59:03 PM,27,3,3:05:39 PM,27,3,3:27:00 PM,6,29.4,,-1.3198615,36.7110323,-1.3532483999999998,36.7249576,Rider_Id_519,1281
-Order_No_17476,User_Id_3282,Bike,2,Personal,14,5,11:57:59 AM,14,5,11:58:26 AM,14,5,12:11:38 PM,14,5,12:26:51 PM,14,5,12:49:49 PM,5,24.5,,-1.2925374,36.7995411,-1.290059,36.7703475,Rider_Id_805,1378
-Order_No_7943,User_Id_3565,Bike,3,Business,7,5,9:17:23 AM,7,5,9:17:38 AM,7,5,9:17:45 AM,7,5,9:29:11 AM,7,5,9:58:27 AM,16,20.7,0.9,-1.2844665000000002,36.7866113,-1.1992502,36.8294932,Rider_Id_886,1756
-Order_No_853,User_Id_868,Bike,3,Business,29,6,12:26:48 PM,29,6,12:27:08 PM,29,6,12:28:59 PM,29,6,12:50:08 PM,29,6,1:01:55 PM,4,24.2,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_427,707
-Order_No_10637,User_Id_2829,Bike,3,Business,26,1,11:55:46 AM,26,1,11:57:32 AM,26,1,11:57:59 AM,26,1,12:03:52 PM,26,1,12:20:48 PM,7,23.2,,-1.2571472,36.795063299999995,-1.2857671,36.7875614,Rider_Id_89,1016
-Order_No_8018,User_Id_2801,Bike,3,Business,12,4,3:23:33 PM,12,4,3:23:52 PM,12,4,3:31:49 PM,12,4,3:34:45 PM,12,4,4:01:00 PM,14,21.5,,-1.2595421999999998,36.7871178,-1.298227,36.8530125,Rider_Id_71,1575
-Order_No_26494,User_Id_1217,Bike,3,Personal,5,2,12:31:04 PM,5,2,12:46:51 PM,5,2,1:02:48 PM,5,2,1:08:52 PM,5,2,2:10:15 PM,14,23.5,,-1.2175583,36.884343,-1.2880883,36.8227336,Rider_Id_589,3683
-Order_No_21246,User_Id_3669,Bike,3,Business,14,3,5:30:00 PM,14,3,5:30:53 PM,14,3,5:45:36 PM,14,3,6:01:20 PM,14,3,6:01:52 PM,2,23.7,,-1.2615888000000002,36.7928732,-1.2685707,36.806496100000004,Rider_Id_642,32
-Order_No_15480,User_Id_3554,Bike,3,Personal,24,4,12:19:06 PM,24,4,12:20:02 PM,24,4,12:34:24 PM,24,4,1:01:28 PM,24,4,1:51:09 PM,14,21.0,,-1.2656584,36.809625700000005,-1.3301798999999999,36.857524100000006,Rider_Id_479,2981
-Order_No_10845,User_Id_496,Bike,3,Business,27,1,11:00:26 AM,27,1,11:00:49 AM,27,1,11:00:52 AM,27,1,11:06:38 AM,27,1,11:16:33 AM,4,20.8,,-1.2551895,36.7822034,-1.2662728,36.8151265,Rider_Id_576,595
-Order_No_26375,User_Id_540,Bike,3,Business,26,5,9:33:06 AM,26,5,9:34:02 AM,26,5,9:39:55 AM,26,5,9:44:02 AM,26,5,9:53:21 AM,5,,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_306,559
-Order_No_22965,User_Id_2066,Bike,3,Business,1,5,11:41:45 AM,1,5,11:43:10 AM,1,5,11:51:35 AM,1,5,11:57:05 AM,1,5,12:12:46 PM,7,28.7,,-1.3210188999999999,36.841055100000005,-1.3429626000000001,36.8816956,Rider_Id_411,941
-Order_No_14422,User_Id_3373,Bike,1,Personal,14,2,3:07:50 PM,14,2,3:08:05 PM,14,2,3:08:33 PM,14,2,3:34:50 PM,14,2,4:40:38 PM,15,23.8,,-1.296797,36.776452500000005,-1.3581253,36.7444904,Rider_Id_179,3948
-Order_No_23127,User_Id_1418,Bike,1,Personal,14,4,3:19:17 PM,14,4,3:19:32 PM,14,4,3:41:05 PM,14,4,3:43:31 PM,14,4,3:52:26 PM,1,21.5,,-1.2915306999999998,36.790586499999996,-1.29362,36.78745,Rider_Id_312,535
-Order_No_20440,User_Id_2580,Bike,1,Personal,4,1,3:24:26 PM,4,1,3:54:51 PM,4,1,3:55:00 PM,4,1,4:05:00 PM,4,1,5:18:01 PM,6,22.6,,-1.289498,36.818878000000005,-1.3028861,36.794382899999995,Rider_Id_672,4381
-Order_No_12349,User_Id_760,Bike,3,Business,16,4,11:46:20 AM,16,4,11:46:33 AM,16,4,11:53:01 AM,16,4,11:56:40 AM,16,4,12:12:40 PM,4,,,-1.2736695,36.8028996,-1.2983375,36.8161099,Rider_Id_291,960
-Order_No_19462,User_Id_3291,Bike,3,Business,14,5,9:42:35 AM,14,5,9:42:56 AM,14,5,10:16:03 AM,14,5,10:30:59 AM,14,5,11:07:09 AM,16,18.0,,-1.293515,36.897607,-1.2600926000000001,36.8088685,Rider_Id_297,2170
-Order_No_10857,User_Id_393,Bike,3,Business,23,2,9:56:28 AM,23,2,9:58:00 AM,23,2,10:43:05 AM,23,2,11:00:10 AM,23,2,11:37:38 AM,14,18.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_56,2248
-Order_No_24173,User_Id_1402,Bike,3,Business,24,5,1:45:54 PM,24,5,1:47:46 PM,24,5,1:53:39 PM,24,5,2:02:11 PM,24,5,2:21:30 PM,1,23.2,,-1.279395,36.825364,-1.2813012,36.832396200000005,Rider_Id_790,1159
-Order_No_18192,User_Id_970,Bike,3,Business,11,1,11:22:28 AM,11,1,11:22:46 AM,11,1,11:47:53 AM,11,1,12:11:29 PM,11,1,1:10:23 PM,23,23.8,,-1.3459625,36.9073925,-1.2551895,36.7822034,Rider_Id_519,3534
-Order_No_21933,User_Id_868,Bike,3,Business,20,4,10:50:27 AM,20,4,10:51:37 AM,20,4,11:11:34 AM,20,4,11:23:24 AM,20,4,11:41:33 AM,6,22.1,,-1.300722,36.816865,-1.265715,36.823815,Rider_Id_407,1089
-Order_No_26173,User_Id_1467,Bike,3,Personal,2,2,3:10:15 PM,2,2,3:10:56 PM,2,2,3:21:38 PM,2,2,3:25:07 PM,2,2,3:31:16 PM,2,26.0,,-1.2984591,36.7855438,-1.2892529,36.7740575,Rider_Id_312,369
-Order_No_23847,User_Id_1711,Bike,3,Business,7,6,1:43:51 PM,7,6,1:43:59 PM,7,6,1:46:18 PM,7,6,2:09:06 PM,7,6,2:17:43 PM,2,21.6,,-1.2699491000000003,36.8135968,-1.2689559,36.8097614,Rider_Id_379,517
-Order_No_23100,User_Id_2169,Bike,2,Personal,9,6,12:16:39 PM,9,6,12:16:46 PM,9,6,12:28:34 PM,9,6,12:49:01 PM,9,6,1:01:51 PM,8,17.0,,-1.3205163999999998,36.8098882,-1.3025826,36.7670807,Rider_Id_844,770
-Order_No_27872,User_Id_2112,Bike,3,Business,25,1,2:05:44 PM,25,1,2:06:10 PM,25,1,2:27:38 PM,25,1,2:29:08 PM,25,1,2:47:44 PM,6,28.0,,-1.2156006000000001,36.8916865,-1.2413316,36.878552500000005,Rider_Id_206,1116
-Order_No_23867,User_Id_2200,Bike,3,Business,18,1,12:47:06 PM,18,1,12:48:19 PM,18,1,12:48:24 PM,18,1,1:07:32 PM,18,1,1:07:36 PM,1,27.8,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_906,4
-Order_No_15029,User_Id_1363,Bike,3,Business,31,4,1:49:49 PM,31,4,1:52:20 PM,31,4,1:52:47 PM,31,4,2:20:27 PM,31,4,2:48:20 PM,3,28.7,,-1.3004062,36.829741,-1.2887216000000001,36.808732299999996,Rider_Id_267,1673
-Order_No_12963,User_Id_691,Bike,1,Personal,9,3,10:00:33 AM,9,3,10:00:49 AM,9,3,10:19:43 AM,9,3,10:30:19 AM,9,3,11:30:50 AM,19,17.5,0.8,-1.2844303,36.8781656,-1.2046777,36.85686870000001,Rider_Id_248,3631
-Order_No_23976,User_Id_2330,Bike,3,Business,7,4,4:19:44 PM,7,4,4:20:03 PM,7,4,4:23:32 PM,7,4,4:30:40 PM,7,4,4:57:47 PM,10,27.6,,-1.3040812,36.7924217,-1.3192788999999998,36.711299100000005,Rider_Id_487,1627
-Order_No_16475,User_Id_2528,Bike,3,Business,16,2,1:42:06 PM,16,2,1:42:20 PM,16,2,1:56:07 PM,16,2,1:59:12 PM,16,2,2:06:11 PM,3,,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_575,419
-Order_No_9844,User_Id_633,Bike,3,Business,14,1,12:19:31 PM,14,1,1:25:29 PM,14,1,1:49:16 PM,14,1,1:58:53 PM,14,1,2:14:18 PM,5,25.9,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_935,925
-Order_No_22054,User_Id_412,Bike,3,Business,4,2,10:48:25 AM,4,2,11:03:23 AM,4,2,11:09:33 AM,4,2,11:11:12 AM,4,2,11:20:04 AM,4,19.5,,-1.266837,36.799248999999996,-1.272828,36.816608,Rider_Id_878,532
-Order_No_956,User_Id_1484,Bike,3,Business,19,4,2:18:09 PM,19,4,2:18:26 PM,19,4,2:28:28 PM,19,4,2:30:18 PM,19,4,2:52:23 PM,7,20.3,,-1.2551895,36.7822034,-1.2880351,36.828638899999994,Rider_Id_752,1325
-Order_No_9935,User_Id_1951,Bike,1,Personal,26,3,2:43:20 PM,26,3,2:44:14 PM,26,3,3:01:36 PM,26,3,3:05:46 PM,26,3,3:42:54 PM,5,27.3,,-1.297684,36.800348,-1.3077197,36.7919466,Rider_Id_565,2228
-Order_No_5212,User_Id_1642,Bike,3,Business,12,5,12:48:15 PM,12,5,1:32:14 PM,12,5,1:52:30 PM,12,5,1:53:41 PM,12,5,2:18:32 PM,5,27.0,,-1.2728069,36.7962362,-1.2887076000000002,36.825465799999996,Rider_Id_844,1491
-Order_No_57,User_Id_1336,Bike,3,Business,3,4,11:19:59 AM,3,4,11:28:05 AM,3,4,12:09:40 PM,3,4,12:13:08 PM,3,4,1:11:49 PM,8,23.5,,-1.2885029,36.825604999999996,-1.3038645,36.8684596,Rider_Id_436,3521
-Order_No_2327,User_Id_1462,Bike,3,Personal,13,4,10:30:45 AM,13,4,10:31:51 AM,13,4,10:38:47 AM,13,4,10:49:19 AM,13,4,11:02:18 AM,4,22.1,,-1.2665065,36.7970081,-1.2628638,36.8070399,Rider_Id_412,779
-Order_No_26098,User_Id_3222,Bike,3,Business,14,2,3:37:08 PM,14,2,3:38:56 PM,14,2,3:57:06 PM,14,2,3:59:17 PM,14,2,4:29:45 PM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_815,1828
-Order_No_20227,User_Id_517,Bike,3,Business,7,1,11:05:29 AM,7,1,11:06:18 AM,7,1,11:06:44 AM,7,1,12:01:01 PM,7,1,12:01:20 PM,14,24.7,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_515,19
-Order_No_25109,User_Id_1237,Bike,1,Personal,17,3,7:36:06 AM,17,3,7:36:13 AM,17,3,7:47:58 AM,17,3,7:58:07 AM,17,3,8:03:42 AM,4,21.1,,-1.2812305,36.7791109,-1.2741728,36.79691079999999,Rider_Id_829,335
-Order_No_8895,User_Id_3291,Bike,3,Business,4,2,11:51:28 AM,4,2,11:52:06 AM,4,2,11:53:30 AM,4,2,12:04:25 PM,4,2,12:10:13 PM,2,20.3,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_344,348
-Order_No_19086,User_Id_1363,Bike,3,Business,30,2,3:44:39 PM,30,2,3:44:46 PM,30,2,3:47:44 PM,30,2,3:51:37 PM,30,2,4:02:23 PM,5,27.5,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_208,646
-Order_No_2332,User_Id_868,Bike,3,Business,30,3,2:54:45 PM,30,3,3:32:57 PM,30,3,3:42:34 PM,30,3,3:52:40 PM,30,3,4:24:30 PM,15,25.0,,-1.3444459999999998,36.756597,-1.265715,36.823815,Rider_Id_852,1910
-Order_No_4801,User_Id_393,Bike,3,Business,17,4,12:01:07 PM,17,4,12:01:31 PM,17,4,12:07:37 PM,17,4,12:11:36 PM,17,4,12:36:47 PM,14,25.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_623,1511
-Order_No_26402,User_Id_59,Bike,3,Business,29,5,11:25:51 AM,29,5,12:37:37 PM,29,5,12:51:51 PM,29,5,12:53:09 PM,29,5,1:46:56 PM,5,26.6,,-1.3001909,36.816362700000006,-1.290894,36.822971,Rider_Id_682,3227
-Order_No_5170,User_Id_3647,Bike,3,Business,15,4,1:37:39 PM,15,4,1:39:45 PM,15,4,1:55:27 PM,15,4,1:58:09 PM,15,4,2:38:45 PM,8,27.6,,-1.2726389999999999,36.794723,-1.2653185,36.8358841,Rider_Id_532,2436
-Order_No_6497,User_Id_1482,Bike,3,Business,23,5,12:21:43 PM,23,5,12:25:11 PM,23,5,12:30:14 PM,23,5,12:33:29 PM,23,5,1:12:09 PM,19,24.1,,-1.3385790000000002,36.89630379999999,-1.263736,36.780353000000005,Rider_Id_882,2320
-Order_No_7221,User_Id_269,Bike,3,Business,29,2,3:41:59 PM,29,2,4:45:04 PM,29,2,5:00:25 PM,29,2,5:02:50 PM,29,2,5:25:27 PM,9,22.1,,-1.3332749,36.870814700000004,-1.335477,36.930829700000004,Rider_Id_543,1357
-Order_No_1481,User_Id_675,Bike,2,Personal,25,6,10:41:43 AM,25,6,10:42:20 AM,25,6,10:45:34 AM,25,6,10:49:53 AM,25,6,11:01:58 AM,7,,,-1.2595466999999998,36.8113044,-1.2894031000000001,36.7745653,Rider_Id_103,725
-Order_No_907,User_Id_186,Bike,3,Business,16,3,10:10:53 AM,16,3,10:11:35 AM,16,3,10:24:41 AM,16,3,10:30:19 AM,16,3,10:43:33 AM,7,19.8,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_26,794
-Order_No_20442,User_Id_1557,Bike,3,Business,26,4,1:45:40 PM,26,4,1:46:25 PM,26,4,1:59:37 PM,26,4,2:04:34 PM,26,4,2:21:51 PM,11,22.1,,-1.3147073999999999,36.811721999999996,-1.2551895,36.7822034,Rider_Id_629,1037
-Order_No_3869,User_Id_1849,Bike,3,Business,28,3,8:46:05 AM,28,3,9:01:17 AM,28,3,9:21:20 AM,28,3,9:39:21 AM,28,3,10:14:54 AM,7,20.6,,-1.290894,36.822971,-1.2551895,36.7822034,Rider_Id_89,2133
-Order_No_25587,User_Id_2330,Bike,3,Business,24,5,2:48:42 PM,24,5,2:53:28 PM,24,5,3:24:52 PM,24,5,3:57:34 PM,24,5,4:10:55 PM,5,22.8,,-1.294597,36.7742999,-1.3040812,36.7924217,Rider_Id_277,801
-Order_No_26758,User_Id_3701,Bike,3,Business,30,1,11:41:18 AM,30,1,11:41:43 AM,30,1,11:48:57 AM,30,1,11:53:17 AM,30,1,12:11:11 PM,5,22.4,,-1.2684239,36.79524620000001,-1.2762743,36.815278899999996,Rider_Id_519,1074
-Order_No_9546,User_Id_2523,Bike,3,Business,29,1,1:59:50 PM,29,1,2:01:06 PM,29,1,2:06:34 PM,29,1,2:10:40 PM,29,1,2:33:55 PM,7,,,-1.2551895,36.7822034,-1.2972834999999998,36.809687,Rider_Id_161,1395
-Order_No_13200,User_Id_3283,Bike,3,Business,7,1,1:54:53 PM,7,1,2:30:30 PM,7,1,2:47:29 PM,7,1,2:55:24 PM,7,1,2:56:16 PM,6,27.0,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_574,52
-Order_No_1713,User_Id_868,Bike,3,Business,5,5,11:42:28 AM,5,5,11:42:46 AM,5,5,11:52:35 AM,5,5,11:57:44 AM,5,5,12:13:27 PM,7,24.6,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_598,943
-Order_No_12344,User_Id_1746,Bike,3,Business,22,3,3:28:50 PM,22,3,3:31:45 PM,22,3,4:16:05 PM,22,3,4:45:44 PM,22,3,5:15:11 PM,8,23.0,,-1.2893219,36.8401475,-1.296797,36.776452500000005,Rider_Id_452,1767
-Order_No_4906,User_Id_633,Bike,3,Business,4,1,9:43:19 AM,4,1,9:43:40 AM,4,1,9:47:03 AM,4,1,9:51:33 AM,4,1,10:07:09 AM,9,21.7,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_154,936
-Order_No_19948,User_Id_1033,Bike,3,Personal,1,5,1:15:04 PM,1,5,1:16:35 PM,1,5,1:35:37 PM,1,5,1:40:52 PM,1,5,2:00:49 PM,7,28.7,,-1.2326041,36.8009029,-1.26071,36.8206332,Rider_Id_253,1197
-Order_No_16542,User_Id_633,Bike,3,Business,13,3,11:58:16 AM,13,3,11:59:42 AM,13,3,12:16:53 PM,13,3,12:18:31 PM,13,3,12:32:08 PM,4,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_763,817
-Order_No_15196,User_Id_2222,Bike,3,Business,2,4,3:13:20 PM,2,4,4:15:17 PM,2,4,4:28:45 PM,2,4,4:33:48 PM,2,4,4:58:30 PM,10,23.8,,-1.2930128,36.8356869,-1.3191788,36.8895401,Rider_Id_88,1482
-Order_No_11354,User_Id_1357,Bike,1,Personal,18,1,10:08:22 AM,18,1,10:10:29 AM,18,1,10:37:10 AM,18,1,10:39:28 AM,18,1,11:09:46 AM,30,,,-1.2776622,36.8202213,-1.1552028,36.9605628,Rider_Id_88,1818
-Order_No_28237,User_Id_3635,Bike,1,Business,23,4,1:37:54 PM,23,4,1:38:44 PM,23,4,1:46:27 PM,23,4,1:51:19 PM,23,4,1:57:41 PM,1,20.9,,-1.2551895,36.7822034,-1.2615888000000002,36.7928732,Rider_Id_857,382
-Order_No_9260,User_Id_1450,Bike,3,Personal,11,1,3:35:31 PM,11,1,3:39:56 PM,11,1,3:52:34 PM,11,1,3:55:36 PM,11,1,4:25:54 PM,12,28.8,,-1.3108386,36.7555239,-1.2663351,36.8099425,Rider_Id_445,1818
-Order_No_26756,User_Id_659,Bike,2,Personal,1,3,12:22:07 PM,1,3,12:22:10 PM,1,3,12:31:57 PM,1,3,12:36:22 PM,1,3,12:44:40 PM,3,19.8,,-1.2852583999999998,36.8043641,-1.2898176000000001,36.7782389,Rider_Id_229,498
-Order_No_26851,User_Id_3560,Bike,3,Business,14,3,2:13:09 PM,14,3,2:18:33 PM,14,3,3:07:54 PM,14,3,3:35:47 PM,14,3,3:46:36 PM,5,25.8,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_878,649
-Order_No_23728,User_Id_3283,Bike,3,Business,17,3,2:32:54 PM,17,3,2:37:46 PM,17,3,2:40:42 PM,17,3,2:50:00 PM,17,3,2:56:21 PM,5,,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_383,381
-Order_No_19821,User_Id_53,Bike,3,Business,4,6,10:28:48 AM,4,6,10:30:07 AM,4,6,10:30:23 AM,4,6,11:00:52 AM,4,6,11:13:05 AM,5,,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_538,733
-Order_No_9819,User_Id_1329,Bike,3,Business,26,2,5:32:01 PM,26,2,5:32:24 PM,26,2,5:34:48 PM,26,2,5:38:42 PM,26,2,7:02:20 PM,6,29.6,,-1.273056,36.811298,-1.3084722,36.8224665,Rider_Id_119,5018
-Order_No_8592,User_Id_2686,Bike,1,Personal,14,2,3:42:30 PM,14,2,3:59:39 PM,14,2,4:03:25 PM,14,2,4:06:43 PM,14,2,4:33:10 PM,12,24.1,,-1.3465623,36.7677838,-1.2791223999999999,36.8306878,Rider_Id_249,1587
-Order_No_17438,User_Id_432,Bike,3,Business,28,2,1:26:39 PM,28,2,1:28:35 PM,28,2,2:24:35 PM,28,2,2:43:53 PM,28,2,3:12:41 PM,12,23.6,,-1.2765736,36.851364600000004,-1.3012008,36.764868,Rider_Id_855,1728
-Order_No_14705,User_Id_3575,Bike,3,Personal,10,5,9:07:07 AM,10,5,9:07:32 AM,10,5,9:16:19 AM,10,5,9:22:46 AM,10,5,9:32:47 AM,4,13.6,,-1.269534,36.80697,-1.293973,36.80169,Rider_Id_754,601
-Order_No_21517,User_Id_2474,Bike,1,Personal,4,4,1:52:08 PM,4,4,1:52:38 PM,4,4,2:04:50 PM,4,4,2:12:06 PM,4,4,2:31:44 PM,14,24.9,,-1.2780878,36.846943599999996,-1.2614838,36.7530984,Rider_Id_207,1178
-Order_No_3405,User_Id_1205,Bike,3,Business,30,2,11:06:50 AM,30,2,11:24:34 AM,30,2,11:39:11 AM,30,2,12:20:24 PM,30,2,12:43:46 PM,5,22.2,,-1.257309,36.806008399999996,-1.2814178,36.8154096,Rider_Id_303,1402
-Order_No_18980,User_Id_419,Bike,3,Business,29,3,2:35:28 PM,29,3,2:38:02 PM,29,3,2:46:05 PM,29,3,2:48:35 PM,29,3,3:59:40 PM,20,21.3,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_911,4265
-Order_No_27524,User_Id_2587,Bike,3,Business,21,1,1:53:44 PM,21,1,1:54:03 PM,21,1,2:16:27 PM,21,1,2:26:37 PM,21,1,2:50:59 PM,15,21.8,,-1.2151769,36.8846524,-1.225322,36.80855,Rider_Id_397,1462
-Order_No_27120,User_Id_2200,Bike,3,Business,14,2,4:34:49 PM,14,2,5:13:19 PM,14,2,5:23:42 PM,14,2,5:43:44 PM,14,2,6:06:42 PM,11,,,-1.2285834999999998,36.882646799999996,-1.290894,36.822971,Rider_Id_567,1378
-Order_No_6622,User_Id_1329,Bike,3,Business,6,4,2:47:53 PM,6,4,2:51:23 PM,6,4,2:58:36 PM,6,4,3:15:42 PM,6,4,3:38:54 PM,3,,,-1.273056,36.811298,-1.2657053999999999,36.7930278,Rider_Id_925,1392
-Order_No_6006,User_Id_2412,Bike,3,Business,21,2,3:41:04 PM,21,2,3:43:11 PM,21,2,3:50:14 PM,21,2,4:16:30 PM,21,2,4:16:41 PM,12,25.2,,-1.2584143,36.8048002,-1.2283403,36.8822756,Rider_Id_517,11
-Order_No_5840,User_Id_462,Bike,1,Personal,28,4,10:48:22 AM,28,4,10:58:08 AM,28,4,11:18:16 AM,28,4,11:21:18 AM,28,4,11:38:39 AM,7,,,-1.2626954,36.8273738,-1.2560455,36.780086700000005,Rider_Id_846,1041
-Order_No_17576,User_Id_760,Bike,3,Business,29,2,10:46:41 AM,29,2,10:47:35 AM,29,2,11:42:55 AM,29,2,11:48:36 AM,29,2,12:27:58 PM,9,23.4,,-1.2801075000000002,36.8100471,-1.3088205,36.853849,Rider_Id_26,2362
-Order_No_13324,User_Id_824,Bike,1,Business,14,7,1:49:56 PM,14,7,1:50:37 PM,14,7,2:06:44 PM,14,7,2:09:55 PM,14,7,2:21:14 PM,8,28.6,,-1.2248308,36.801257299999996,-1.207546,36.7913976,Rider_Id_743,679
-Order_No_16536,User_Id_2335,Bike,3,Business,24,4,8:32:24 AM,24,4,8:33:16 AM,24,4,8:42:15 AM,24,4,8:50:43 AM,24,4,9:25:16 AM,21,16.9,,-1.2553615,36.789537599999996,-1.3632162,36.9108253,Rider_Id_40,2073
-Order_No_27240,User_Id_2030,Bike,3,Business,14,3,12:41:18 PM,14,3,12:42:28 PM,14,3,12:54:55 PM,14,3,1:06:05 PM,14,3,1:36:24 PM,5,24.6,,-1.304969,36.846623,-1.2965799999999998,36.823699,Rider_Id_11,1819
-Order_No_11367,User_Id_3215,Bike,3,Personal,9,4,1:45:22 PM,9,4,2:44:23 PM,9,4,2:59:37 PM,9,4,3:00:41 PM,9,4,3:21:48 PM,7,,,-1.2454036,36.7866654,-1.2884146,36.810766799999996,Rider_Id_953,1267
-Order_No_506,User_Id_3355,Bike,3,Business,21,1,12:25:12 PM,21,1,12:27:24 PM,21,1,1:01:48 PM,21,1,1:02:51 PM,21,1,1:28:50 PM,13,19.8,,-1.3052491000000002,36.8223899,-1.2413316,36.878552500000005,Rider_Id_102,1559
-Order_No_28215,User_Id_393,Bike,3,Business,17,3,8:40:49 AM,17,3,8:40:58 AM,17,3,8:44:15 AM,17,3,9:17:43 AM,17,3,9:47:44 AM,14,21.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,1801
-Order_No_8081,User_Id_517,Bike,3,Business,8,6,11:59:37 AM,8,6,12:00:44 PM,8,6,12:07:43 PM,8,6,12:46:21 PM,8,6,12:53:26 PM,13,20.0,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_624,425
-Order_No_17799,User_Id_1803,Bike,1,Personal,6,6,11:29:10 AM,6,6,11:32:49 AM,6,6,11:51:06 AM,6,6,11:55:58 AM,6,6,12:11:05 PM,5,24.5,,-1.2857929,36.82408289999999,-1.2966785,36.7896469,Rider_Id_844,907
-Order_No_5255,User_Id_2216,Bike,3,Business,25,1,5:18:11 PM,25,1,5:18:29 PM,25,1,5:19:32 PM,25,1,5:24:17 PM,25,1,5:40:44 PM,4,28.2,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_291,987
-Order_No_16981,User_Id_3718,Bike,3,Business,15,4,9:35:15 AM,15,4,9:49:13 AM,15,4,10:14:09 AM,15,4,10:18:13 AM,15,4,10:42:19 AM,5,19.8,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_247,1446
-Order_No_13845,User_Id_496,Bike,3,Business,30,2,12:19:39 PM,30,2,12:32:05 PM,30,2,1:01:33 PM,30,2,1:05:28 PM,30,2,1:23:34 PM,15,,,-1.2551895,36.7822034,-1.2283403,36.8822756,Rider_Id_247,1086
-Order_No_11129,User_Id_2154,Bike,3,Business,6,3,3:11:15 PM,6,3,3:11:25 PM,6,3,3:19:29 PM,6,3,3:29:40 PM,6,3,3:52:17 PM,9,,,-1.2283403,36.8822756,-1.2765736,36.851364600000004,Rider_Id_521,1357
-Order_No_10463,User_Id_2956,Bike,3,Business,19,1,10:42:36 AM,19,1,10:42:47 AM,19,1,10:48:39 AM,19,1,10:52:17 AM,19,1,10:58:06 AM,1,24.6,,-1.2584143,36.8048002,-1.2597528999999998,36.806629,Rider_Id_247,349
-Order_No_28069,User_Id_3510,Bike,1,Business,25,3,12:46:00 PM,25,3,12:46:29 PM,25,3,1:02:08 PM,25,3,1:06:05 PM,25,3,1:35:08 PM,5,,,-1.2472211000000002,36.7826109,-1.267452,36.806015,Rider_Id_956,1743
-Order_No_6684,User_Id_2330,Bike,3,Business,20,1,12:24:21 PM,20,1,12:24:34 PM,20,1,12:28:20 PM,20,1,12:30:48 PM,20,1,12:56:01 PM,9,,,-1.2645318,36.7981775,-1.2326041,36.8009029,Rider_Id_183,1513
-Order_No_2035,User_Id_2200,Bike,3,Business,9,2,3:20:32 PM,9,2,3:25:22 PM,9,2,3:34:25 PM,9,2,4:06:19 PM,9,2,4:15:38 PM,2,22.4,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_330,559
-Order_No_17188,User_Id_1941,Bike,3,Personal,6,3,1:25:52 PM,6,3,1:43:16 PM,6,3,1:47:10 PM,6,3,2:01:21 PM,6,3,2:11:51 PM,5,21.9,,-1.2776072,36.8227604,-1.2762383999999998,36.7929522,Rider_Id_116,630
-Order_No_18236,User_Id_2412,Bike,3,Business,3,4,3:58:45 PM,3,4,3:59:09 PM,3,4,4:15:06 PM,3,4,4:16:41 PM,3,4,4:29:48 PM,5,26.2,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_854,787
-Order_No_2225,User_Id_1357,Bike,1,Personal,17,5,1:04:22 PM,17,5,1:07:36 PM,17,5,1:12:07 PM,17,5,1:20:06 PM,17,5,1:33:58 PM,5,17.4,,-1.2663525,36.812833700000006,-1.2897215,36.7945959,Rider_Id_857,832
-Order_No_7399,User_Id_2657,Bike,3,Personal,28,2,9:28:59 AM,28,2,9:30:01 AM,28,2,9:31:02 AM,28,2,9:51:34 AM,28,2,10:46:26 AM,13,18.7,,-1.2752846000000002,36.802702200000006,-1.2422865,36.7278485,Rider_Id_99,3292
-Order_No_28018,User_Id_3686,Bike,3,Business,8,5,9:24:14 AM,8,5,9:26:46 AM,8,5,9:31:10 AM,8,5,9:38:45 AM,8,5,10:15:12 AM,3,20.4,,-1.2551895,36.7822034,-1.2655477,36.8001682,Rider_Id_875,2187
-Order_No_21034,User_Id_187,Bike,3,Business,1,6,3:37:23 PM,1,6,4:01:13 PM,1,6,4:11:18 PM,1,6,4:28:50 PM,1,6,4:49:42 PM,8,21.8,,-1.3014461000000002,36.7661381,-1.3169591,36.7000029,Rider_Id_267,1252
-Order_No_196,User_Id_435,Bike,3,Business,30,4,11:58:47 AM,30,4,11:59:39 AM,30,4,12:09:52 PM,30,4,12:13:22 PM,30,4,12:42:41 PM,13,,,-1.2569714,36.7799637,-1.300921,36.828195,Rider_Id_533,1759
-Order_No_15543,User_Id_2523,Bike,3,Business,6,1,9:53:35 AM,6,1,9:57:12 AM,6,1,10:06:57 AM,6,1,10:11:19 AM,6,1,10:39:33 AM,7,,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_96,1694
-Order_No_20784,User_Id_868,Bike,3,Business,10,1,12:29:45 PM,10,1,12:31:56 PM,10,1,12:34:16 PM,10,1,12:46:36 PM,10,1,1:07:14 PM,7,18.5,,-1.2628473,36.781805,-1.2663631000000002,36.8163329,Rider_Id_864,1238
-Order_No_20820,User_Id_59,Bike,3,Business,22,5,9:34:28 AM,22,5,9:37:23 AM,22,5,10:07:17 AM,22,5,10:39:49 AM,22,5,10:46:29 AM,6,28.4,,-1.29387,36.7907813,-1.290894,36.822971,Rider_Id_119,400
-Order_No_10092,User_Id_1502,Bike,3,Business,8,2,1:30:09 PM,8,2,1:31:07 PM,8,2,1:41:31 PM,8,2,1:48:29 PM,8,2,2:24:02 PM,13,25.4,,-1.2527958000000001,36.8003131,-1.2232123,36.8932547,Rider_Id_715,2133
-Order_No_9892,User_Id_3517,Bike,3,Business,1,1,10:52:53 AM,1,1,11:03:18 AM,1,1,11:30:41 AM,1,1,12:08:40 PM,1,1,12:46:10 PM,17,,,-1.3882008,36.7699445,-1.2896431000000002,36.8086201,Rider_Id_519,2250
-Order_No_4325,User_Id_633,Bike,3,Business,27,4,3:03:58 PM,27,4,3:39:07 PM,27,4,3:54:35 PM,27,4,3:57:45 PM,27,4,4:07:57 PM,4,25.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_532,612
-Order_No_10967,User_Id_1075,Bike,3,Business,30,4,3:38:05 PM,30,4,3:38:43 PM,30,4,3:47:19 PM,30,4,3:57:15 PM,30,4,4:05:38 PM,2,,,-1.2571472,36.795063299999995,-1.2527958000000001,36.8003131,Rider_Id_147,503
-Order_No_11029,User_Id_2587,Bike,3,Business,3,3,1:54:27 PM,3,3,2:02:27 PM,3,3,2:15:31 PM,3,3,3:05:12 PM,3,3,3:21:07 PM,8,28.3,,-1.3025826,36.7670807,-1.28878,36.8168312,Rider_Id_291,955
-Order_No_19279,User_Id_3372,Bike,3,Business,27,5,3:26:34 PM,27,5,3:27:31 PM,27,5,3:37:42 PM,27,5,3:55:35 PM,27,5,4:11:05 PM,6,24.1,,-1.2668611,36.795638700000005,-1.2969741000000001,36.7856612,Rider_Id_116,930
-Order_No_15281,User_Id_3686,Bike,3,Business,14,4,11:14:05 AM,14,4,11:58:58 AM,14,4,12:24:01 PM,14,4,12:25:20 PM,14,4,12:25:27 PM,6,23.7,,-1.2551895,36.7822034,-1.2912287,36.8178977,Rider_Id_634,7
-Order_No_8676,User_Id_1500,Bike,3,Business,2,4,3:57:52 PM,2,4,3:58:21 PM,2,4,4:05:37 PM,2,4,4:45:21 PM,2,4,4:45:23 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_403,2
-Order_No_25711,User_Id_2289,Bike,3,Business,25,5,11:48:08 AM,25,5,11:48:37 AM,25,5,11:58:14 AM,25,5,12:01:11 PM,25,5,12:20:11 PM,8,20.9,,-1.2553615,36.789537599999996,-1.2956951,36.812924200000005,Rider_Id_742,1140
-Order_No_21601,User_Id_3109,Bike,2,Personal,10,5,10:28:05 AM,10,5,10:28:20 AM,10,5,10:43:09 AM,10,5,11:13:34 AM,10,5,11:52:58 AM,11,19.4,,-1.3107918,36.761794,-1.2916556000000001,36.8262516,Rider_Id_452,2364
-Order_No_7032,User_Id_3528,Bike,3,Business,31,5,11:06:16 AM,31,5,11:28:15 AM,31,5,11:47:53 AM,31,5,11:51:41 AM,31,5,12:20:47 PM,3,21.5,,-1.3014515,36.8120092,-1.2904753,36.8144721,Rider_Id_502,1746
-Order_No_2232,User_Id_391,Bike,3,Business,3,3,9:41:13 AM,3,3,9:57:29 AM,3,3,10:15:20 AM,3,3,10:23:39 AM,3,3,11:14:22 AM,15,21.2,,-1.177202,36.8351183,-1.2784063,36.8222933,Rider_Id_102,3043
-Order_No_10094,User_Id_3599,Bike,3,Business,23,5,11:02:54 AM,23,5,12:08:01 PM,23,5,12:20:25 PM,23,5,12:37:43 PM,23,5,1:20:01 PM,13,24.1,,-1.3302996,36.8707935,-1.2662453000000002,36.8219775,Rider_Id_550,2538
-Order_No_7015,User_Id_1194,Bike,1,Personal,18,4,11:56:41 AM,18,4,12:19:15 PM,18,4,12:48:15 PM,18,4,1:10:24 PM,18,4,1:10:35 PM,6,25.6,,-1.287353,36.826992,-1.2895311,36.864337299999995,Rider_Id_662,11
-Order_No_16589,User_Id_868,Bike,3,Business,26,5,9:50:06 AM,26,5,9:50:58 AM,26,5,10:21:43 AM,26,5,10:28:14 AM,26,5,10:47:24 AM,8,,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_435,1150
-Order_No_18944,User_Id_3372,Bike,3,Business,7,4,12:44:53 PM,7,4,12:47:29 PM,7,4,1:13:02 PM,7,4,1:18:06 PM,7,4,1:44:50 PM,16,16.8,,-1.304033,36.7848695,-1.2297202,36.874550799999994,Rider_Id_543,1604
-Order_No_10762,User_Id_76,Bike,1,Personal,19,5,6:46:58 PM,19,5,6:47:10 PM,19,5,6:48:50 PM,19,5,7:15:56 PM,19,5,7:39:52 PM,6,24.3,,-1.2962832,36.784968299999996,-1.260232,36.807334000000004,Rider_Id_519,1436
-Order_No_28253,User_Id_909,Bike,3,Business,29,1,3:36:47 PM,29,1,3:37:15 PM,29,1,3:48:49 PM,29,1,3:59:00 PM,29,1,4:37:33 PM,13,,,-1.2283403,36.8822756,-1.225322,36.80855,Rider_Id_272,2313
-Order_No_3737,User_Id_2207,Bike,3,Business,15,5,2:14:36 PM,15,5,2:15:53 PM,15,5,2:27:26 PM,15,5,2:34:22 PM,15,5,3:34:55 PM,9,28.1,,-1.2325706,36.808392299999994,-1.2696908000000002,36.81519,Rider_Id_244,3633
-Order_No_19021,User_Id_909,Bike,3,Business,25,4,6:07:59 PM,25,4,6:09:12 PM,25,4,6:28:31 PM,25,4,6:35:35 PM,25,4,7:18:51 PM,10,,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_108,2596
-Order_No_18664,User_Id_1502,Bike,3,Business,14,1,4:38:08 PM,14,1,4:38:27 PM,14,1,4:46:53 PM,14,1,4:56:29 PM,14,1,5:27:36 PM,17,24.5,,-1.2232123,36.8932547,-1.2571472,36.795063299999995,Rider_Id_685,1867
-Order_No_3919,User_Id_893,Bike,3,Business,7,4,12:53:22 PM,7,4,12:54:14 PM,7,4,1:08:08 PM,7,4,1:11:27 PM,7,4,1:21:56 PM,8,27.7,,-1.2663194,36.809901,-1.2455906,36.817496899999995,Rider_Id_315,629
-Order_No_27472,User_Id_1075,Bike,3,Business,2,6,2:28:49 PM,2,6,2:53:23 PM,2,6,3:16:50 PM,2,6,3:25:50 PM,2,6,3:54:22 PM,7,30.9,,-1.2325706,36.808392299999994,-1.2527958000000001,36.8003131,Rider_Id_318,1712
-Order_No_12357,User_Id_972,Bike,3,Business,25,4,6:13:05 PM,25,4,6:13:24 PM,25,4,6:24:50 PM,25,4,6:26:54 PM,25,4,6:45:52 PM,5,,,-1.2862033000000002,36.8152966,-1.2785902,36.79315820000001,Rider_Id_612,1138
-Order_No_12803,User_Id_598,Bike,1,Business,7,4,1:15:10 PM,7,4,1:18:32 PM,7,4,1:31:05 PM,7,4,1:32:14 PM,7,4,2:02:41 PM,7,20.2,,-1.2536797,36.7995085,-1.2844520000000001,36.8300885,Rider_Id_31,1827
-Order_No_5354,User_Id_1471,Bike,3,Business,12,1,11:50:09 AM,12,1,11:51:52 AM,12,1,11:59:47 AM,12,1,12:13:25 PM,12,1,12:36:49 PM,9,23.7,,-1.2925919,36.81705770000001,-1.2551895,36.7822034,Rider_Id_519,1404
-Order_No_2965,User_Id_1995,Bike,3,Business,14,3,9:51:59 AM,14,3,9:52:11 AM,14,3,9:58:00 AM,14,3,10:00:33 AM,14,3,10:36:01 AM,22,18.6,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_793,2128
-Order_No_14834,User_Id_1527,Bike,3,Business,13,4,12:40:08 PM,13,4,12:40:45 PM,13,4,12:42:30 PM,13,4,12:55:59 PM,13,4,1:02:29 PM,5,22.1,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_2,390
-Order_No_12718,User_Id_2344,Bike,3,Business,24,3,4:30:54 PM,24,3,4:32:51 PM,24,3,4:40:11 PM,24,3,4:44:44 PM,24,3,4:44:47 PM,4,18.4,,-1.2619756000000002,36.800139,-1.2551895,36.7822034,Rider_Id_647,3
-Order_No_23147,User_Id_393,Bike,3,Business,10,4,3:39:29 PM,10,4,3:39:47 PM,10,4,3:47:17 PM,10,4,3:55:30 PM,10,4,4:35:23 PM,13,25.4,,-1.3231001,36.8283559,-1.306378,36.7519845,Rider_Id_523,2393
-Order_No_11594,User_Id_1469,Bike,3,Business,23,4,3:15:37 PM,23,4,3:16:12 PM,23,4,3:21:22 PM,23,4,3:32:35 PM,23,4,3:58:54 PM,16,24.5,,-1.2551895,36.7822034,-1.3194518,36.869396,Rider_Id_484,1579
-Order_No_16531,User_Id_2492,Bike,3,Personal,5,2,12:52:20 PM,5,2,2:10:28 PM,5,2,2:38:56 PM,5,2,2:43:25 PM,5,2,3:07:25 PM,4,27.3,,-1.2778447,36.830721399999995,-1.2927045,36.827994700000005,Rider_Id_686,1440
-Order_No_9661,User_Id_3432,Bike,3,Business,5,6,11:11:46 AM,5,6,11:12:13 AM,5,6,11:19:15 AM,5,6,11:24:29 AM,5,6,11:55:44 AM,14,22.5,,-1.2672588,36.8000252,-1.3291155000000001,36.8861,Rider_Id_103,1875
-Order_No_1476,User_Id_1502,Bike,3,Business,16,3,1:03:15 PM,16,3,1:03:36 PM,16,3,1:13:57 PM,16,3,1:25:09 PM,16,3,2:13:05 PM,14,22.6,,-1.3052491000000002,36.8223899,-1.2232123,36.8932547,Rider_Id_408,2876
-Order_No_8266,User_Id_3613,Bike,3,Business,7,5,9:22:28 AM,7,5,9:23:01 AM,7,5,9:29:21 AM,7,5,9:32:20 AM,7,5,10:24:39 AM,3,,,-1.2802043,36.76500789999999,-1.2601873,36.7559637,Rider_Id_161,3139
-Order_No_12320,User_Id_1162,Bike,3,Business,8,5,1:32:45 PM,8,5,1:47:42 PM,8,5,1:59:34 PM,8,5,2:16:54 PM,8,5,2:49:28 PM,8,27.1,,-1.2618811,36.8041786,-1.3122610000000001,36.794658,Rider_Id_953,1954
-Order_No_15550,User_Id_1044,Bike,2,Personal,4,6,5:09:27 PM,4,6,5:10:19 PM,4,6,5:21:23 PM,4,6,5:30:21 PM,4,6,5:41:19 PM,4,22.6,,-1.288621,36.778846,-1.284835,36.8152125,Rider_Id_427,658
-Order_No_20161,User_Id_1148,Bike,3,Business,31,5,2:35:21 PM,31,5,3:36:32 PM,31,5,4:07:35 PM,31,5,4:26:35 PM,31,5,4:28:00 PM,3,19.9,,-1.3100586,36.850032899999995,-1.311818,36.8638544,Rider_Id_890,85
-Order_No_18919,User_Id_2423,Bike,3,Business,9,2,2:53:52 PM,9,2,2:54:35 PM,9,2,3:13:02 PM,9,2,3:15:45 PM,9,2,3:25:39 PM,4,22.6,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_38,594
-Order_No_12392,User_Id_1544,Bike,3,Business,3,4,8:40:58 AM,3,4,8:42:00 AM,3,4,8:59:09 AM,3,4,9:09:16 AM,3,4,9:33:26 AM,9,17.2,15.9,-1.2874632,36.8243956,-1.2222127,36.843280799999995,Rider_Id_549,1450
-Order_No_4558,User_Id_337,Bike,1,Personal,5,4,9:15:52 AM,5,4,9:17:29 AM,5,4,9:17:33 AM,5,4,9:31:54 AM,5,4,9:59:04 AM,6,,,-1.273056,36.811298,-1.2983462,36.8139996,Rider_Id_874,1630
-Order_No_8205,User_Id_633,Bike,3,Business,19,1,12:29:12 PM,19,1,1:31:01 PM,19,1,1:34:11 PM,19,1,1:38:24 PM,19,1,2:01:58 PM,4,25.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_661,1414
-Order_No_13314,User_Id_1642,Bike,3,Business,18,1,12:04:56 PM,18,1,12:05:38 PM,18,1,12:06:24 PM,18,1,12:18:15 PM,18,1,12:33:41 PM,6,26.7,,-1.2728069,36.7962362,-1.2941023999999999,36.79578,Rider_Id_576,926
-Order_No_28153,User_Id_84,Bike,3,Business,24,4,3:53:39 PM,24,4,3:53:46 PM,24,4,4:04:37 PM,24,4,4:07:53 PM,24,4,4:54:01 PM,8,22.7,,-1.2553615,36.789537599999996,-1.2962832,36.784968299999996,Rider_Id_71,2768
-Order_No_26729,User_Id_2989,Bike,3,Business,23,1,3:08:39 PM,23,1,3:08:47 PM,23,1,3:15:04 PM,23,1,3:48:35 PM,23,1,4:29:29 PM,15,,,-1.2571472,36.795063299999995,-1.3404187,36.7614685,Rider_Id_770,2454
-Order_No_23400,User_Id_3521,Bike,3,Business,3,5,12:36:48 PM,3,5,12:37:06 PM,3,5,12:49:31 PM,3,5,1:42:40 PM,3,5,1:59:12 PM,2,25.2,,-1.2682478000000001,36.8072852,-1.2696908000000002,36.81519,Rider_Id_402,992
-Order_No_9829,User_Id_947,Bike,2,Personal,8,2,8:52:41 AM,8,2,8:52:58 AM,8,2,9:10:44 AM,8,2,9:15:55 AM,8,2,9:49:18 AM,14,,,-1.287428,36.7800821,-1.2696795,36.8640088,Rider_Id_942,2003
-Order_No_3460,User_Id_1363,Bike,3,Business,9,5,8:44:32 AM,9,5,8:45:03 AM,9,5,8:45:05 AM,9,5,8:47:38 AM,9,5,8:56:23 AM,5,20.4,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_595,525
-Order_No_6350,User_Id_53,Bike,3,Business,11,5,1:20:23 PM,11,5,1:21:42 PM,11,5,1:29:36 PM,11,5,1:58:42 PM,11,5,2:10:26 PM,6,26.6,,-1.3077869,36.844320700000004,-1.2770708,36.8231093,Rider_Id_576,704
-Order_No_17822,User_Id_3215,Bike,3,Personal,25,4,2:53:28 PM,25,4,2:54:24 PM,25,4,3:07:35 PM,25,4,3:09:51 PM,25,4,3:35:02 PM,10,,,-1.2454036,36.7866654,-1.29387,36.7907813,Rider_Id_411,1511
-Order_No_18066,User_Id_617,Bike,3,Personal,26,2,9:38:15 AM,26,2,12:07:19 PM,26,2,12:37:48 PM,26,2,12:43:48 PM,26,2,1:11:51 PM,7,,,-1.3222627,36.823409600000005,-1.2823301,36.8266816,Rider_Id_752,1683
-Order_No_13408,User_Id_3683,Bike,3,Business,10,4,11:21:08 AM,10,4,11:25:52 AM,10,4,11:36:24 AM,10,4,11:49:02 AM,10,4,12:00:57 PM,7,,,-1.2897368,36.8226783,-1.2579303999999998,36.8019667,Rider_Id_900,715
-Order_No_10516,User_Id_1544,Bike,3,Business,16,4,11:04:37 AM,16,4,11:05:30 AM,16,4,11:15:12 AM,16,4,11:21:47 AM,16,4,11:36:11 AM,3,,,-1.2851603,36.824966700000004,-1.2625042,36.812478000000006,Rider_Id_843,864
-Order_No_12381,User_Id_1800,Bike,1,Personal,7,1,9:48:52 AM,7,1,9:54:22 AM,7,1,10:01:03 AM,7,1,10:12:28 AM,7,1,10:31:43 AM,10,18.5,,-1.3250048,36.869718799999994,-1.285029,36.824926,Rider_Id_478,1155
-Order_No_26125,User_Id_313,Bike,2,Personal,3,2,8:02:33 AM,3,2,8:02:45 AM,3,2,8:18:03 AM,3,2,8:23:54 AM,3,2,9:15:31 AM,17,15.4,2.0,-1.3339943,36.7263409,-1.2811695,36.8200313,Rider_Id_844,3097
-Order_No_7653,User_Id_2262,Bike,1,Personal,29,2,2:13:53 PM,29,2,2:24:47 PM,29,2,3:09:41 PM,29,2,3:18:56 PM,29,2,3:44:50 PM,6,23.6,,-1.2877907,36.830065000000005,-1.2897215,36.7945959,Rider_Id_752,1554
-Order_No_15030,User_Id_2066,Bike,3,Business,3,5,2:30:43 PM,3,5,2:30:53 PM,3,5,2:39:08 PM,3,5,2:45:23 PM,3,5,3:18:22 PM,10,25.0,,-1.316961,36.820337,-1.2650413,36.798013700000006,Rider_Id_865,1979
-Order_No_26697,User_Id_2069,Bike,3,Business,2,2,5:08:48 PM,2,2,5:10:03 PM,2,2,5:16:07 PM,2,2,5:19:49 PM,2,2,6:04:30 PM,16,28.6,,-1.303596,36.778378000000004,-1.228911,36.881876,Rider_Id_682,2681
-Order_No_27693,User_Id_3547,Bike,3,Business,28,4,10:58:23 AM,28,4,10:58:43 AM,28,4,11:11:14 AM,28,4,11:15:16 AM,28,4,11:58:50 AM,19,28.1,,-1.3189252,36.861726899999994,-1.2856319,36.767299799999996,Rider_Id_418,2614
-Order_No_2850,User_Id_1271,Bike,3,Business,11,1,11:18:18 AM,11,1,11:18:39 AM,11,1,11:32:00 AM,11,1,11:52:32 AM,11,1,1:07:23 PM,17,24.2,,-1.3244885,36.897791999999995,-1.2285995,36.901237200000004,Rider_Id_411,4491
-Order_No_5606,User_Id_475,Bike,3,Business,20,4,11:37:04 AM,20,4,11:37:52 AM,20,4,11:44:01 AM,20,4,11:52:22 AM,20,4,11:59:34 AM,2,22.3,,-1.2551895,36.7822034,-1.2573514,36.794641799999994,Rider_Id_937,432
-Order_No_24433,User_Id_3426,Bike,3,Business,26,5,3:41:10 PM,26,5,4:13:23 PM,26,5,4:26:03 PM,26,5,4:33:00 PM,26,5,4:52:53 PM,10,,,-1.2584143,36.8048002,-1.3014461000000002,36.7661381,Rider_Id_743,1193
-Order_No_18175,User_Id_1724,Bike,3,Business,27,3,3:12:13 PM,27,3,3:13:28 PM,27,3,3:14:09 PM,27,3,3:43:51 PM,27,3,4:11:26 PM,13,,,-1.2551895,36.7822034,-1.3043951999999999,36.766413799999995,Rider_Id_101,1655
-Order_No_24258,User_Id_393,Bike,3,Business,18,2,8:03:17 AM,18,2,10:32:16 AM,18,2,10:41:02 AM,18,2,10:44:39 AM,18,2,11:00:52 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_436,973
-Order_No_24345,User_Id_113,Bike,3,Business,18,4,5:21:01 PM,18,4,5:22:20 PM,18,4,5:30:13 PM,18,4,5:36:54 PM,18,4,6:07:40 PM,7,23.7,,-1.2618802,36.8068534,-1.2963097,36.7688221,Rider_Id_330,1846
-Order_No_18841,User_Id_136,Bike,3,Business,12,3,12:53:22 PM,12,3,12:57:24 PM,12,3,1:20:42 PM,12,3,1:26:29 PM,12,3,2:04:31 PM,15,19.2,,-1.2949455,36.8195238,-1.3254522,36.721658399999995,Rider_Id_161,2282
-Order_No_19337,User_Id_909,Bike,3,Business,12,6,3:48:02 PM,12,6,3:49:24 PM,12,6,4:18:05 PM,12,6,4:23:35 PM,12,6,5:03:06 PM,11,25.2,,-1.279395,36.825364,-1.225322,36.80855,Rider_Id_899,2371
-Order_No_23800,User_Id_3291,Bike,3,Business,3,7,10:31:14 AM,3,7,10:31:27 AM,3,7,10:31:51 AM,3,7,11:42:24 AM,3,7,11:44:26 AM,12,28.5,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_510,122
-Order_No_1284,User_Id_262,Bike,3,Business,24,4,10:28:10 AM,24,4,10:28:30 AM,24,4,10:40:26 AM,24,4,10:56:35 AM,24,4,11:38:40 AM,14,21.2,,-1.320673,36.865660999999996,-1.2631940000000002,36.797624,Rider_Id_620,2525
-Order_No_8841,User_Id_2330,Bike,3,Business,9,4,12:41:17 PM,9,4,1:00:55 PM,9,4,1:14:45 PM,9,4,1:24:15 PM,9,4,1:50:49 PM,12,,,-1.2284323,36.8757058,-1.2925641,36.897644899999996,Rider_Id_856,1594
-Order_No_15389,User_Id_2330,Bike,3,Business,4,2,4:55:34 PM,4,2,5:04:18 PM,4,2,5:43:56 PM,4,2,5:46:49 PM,4,2,6:26:49 PM,14,22.7,,-1.2464559,36.816985700000004,-1.2687407,36.7373043,Rider_Id_309,2400
-Order_No_27135,User_Id_706,Bike,3,Business,25,5,7:55:32 AM,25,5,7:56:38 AM,25,5,8:19:01 AM,25,5,8:23:57 AM,25,5,8:53:39 AM,15,17.2,,-1.2991441000000001,36.7528804,-1.3077869,36.844320700000004,Rider_Id_105,1782
-Order_No_23955,User_Id_2455,Bike,3,Business,19,2,10:45:12 AM,19,2,10:45:38 AM,19,2,10:48:02 AM,19,2,11:09:25 AM,19,2,11:24:29 AM,4,27.5,,-1.2943272,36.7667198,-1.275442,36.766020700000006,Rider_Id_269,904
-Order_No_13030,User_Id_2733,Bike,3,Business,19,2,10:16:12 AM,19,2,10:56:24 AM,19,2,11:35:03 AM,19,2,11:41:07 AM,19,2,12:08:37 PM,8,27.5,,-1.2857521,36.82774570000001,-1.2963097,36.7688221,Rider_Id_683,1650
-Order_No_7078,User_Id_84,Bike,3,Business,28,4,9:56:33 AM,28,4,9:57:08 AM,28,4,9:59:48 AM,28,4,10:04:35 AM,28,4,11:23:37 AM,17,22.3,,-1.2551895,36.7822034,-1.3472066,36.7692638,Rider_Id_880,4742
-Order_No_10452,User_Id_1754,Bike,3,Business,8,6,2:44:55 PM,8,6,2:45:29 PM,8,6,2:55:55 PM,8,6,3:00:18 PM,8,6,3:47:43 PM,16,23.5,2.1,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_547,2845
-Order_No_9898,User_Id_3201,Bike,3,Business,23,3,2:17:57 PM,23,3,2:21:32 PM,23,3,2:26:35 PM,23,3,2:34:50 PM,23,3,2:53:41 PM,10,27.4,,-1.2584143,36.8048002,-1.2413316,36.878552500000005,Rider_Id_279,1131
-Order_No_6652,User_Id_1271,Bike,3,Business,6,1,3:08:25 PM,6,1,3:11:33 PM,6,1,3:11:38 PM,6,1,3:56:55 PM,6,1,4:19:10 PM,13,24.4,,-1.2927792,36.9053492,-1.3332749,36.870814700000004,Rider_Id_589,1335
-Order_No_26058,User_Id_1927,Bike,3,Business,14,4,2:44:10 PM,14,4,2:46:59 PM,14,4,3:00:14 PM,14,4,3:05:29 PM,14,4,3:34:19 PM,13,27.4,,-1.2765736,36.851364600000004,-1.3472066,36.7692638,Rider_Id_953,1730
-Order_No_18279,User_Id_3689,Bike,3,Business,27,3,3:37:48 PM,27,3,3:39:49 PM,27,3,3:41:06 PM,27,3,3:50:05 PM,27,3,4:12:52 PM,7,29.0,,-1.2527958000000001,36.8003131,-1.225322,36.80855,Rider_Id_547,1367
-Order_No_17092,User_Id_3700,Bike,3,Personal,23,7,9:40:50 AM,23,7,9:46:23 AM,23,7,10:23:00 AM,23,7,10:24:45 AM,23,7,11:10:04 AM,23,18.8,,-1.3105877000000001,36.817283,-1.200723,36.9219198,Rider_Id_184,2719
-Order_No_18611,User_Id_3475,Bike,1,Personal,10,5,11:30:18 AM,10,5,11:39:27 AM,10,5,12:50:50 PM,10,5,12:55:39 PM,10,5,1:26:47 PM,14,19.3,,-1.2778747,36.883182899999994,-1.2644742,36.808345700000004,Rider_Id_700,1868
-Order_No_11881,User_Id_2358,Bike,4,Business,13,3,4:33:23 PM,13,3,4:33:37 PM,13,3,4:55:56 PM,13,3,5:08:18 PM,13,3,5:48:49 PM,20,22.1,,-1.3256903999999998,36.8747943,-1.2688521000000001,36.8359672,Rider_Id_192,2431
-Order_No_7848,User_Id_2412,Bike,3,Business,6,6,11:36:11 AM,6,6,11:37:21 AM,6,6,11:54:37 AM,6,6,12:03:29 PM,6,6,12:41:46 PM,22,26.5,,-1.2283403,36.8822756,-1.1269423,36.768821,Rider_Id_75,2297
-Order_No_12879,User_Id_2212,Bike,3,Business,22,4,12:19:53 PM,22,4,12:22:24 PM,22,4,12:47:45 PM,22,4,12:56:20 PM,22,4,1:04:09 PM,4,22.5,,-1.2571472,36.795063299999995,-1.282435,36.817051,Rider_Id_71,469
-Order_No_3887,User_Id_1280,Bike,3,Business,8,5,11:52:18 AM,8,5,11:55:55 AM,8,5,12:15:23 PM,8,5,12:23:07 PM,8,5,12:34:55 PM,5,28.5,,-1.2551895,36.7822034,-1.2731645,36.7900343,Rider_Id_103,708
-Order_No_13392,User_Id_1304,Bike,3,Business,31,5,9:36:53 AM,31,5,9:37:33 AM,31,5,9:43:32 AM,31,5,9:47:47 AM,31,5,10:19:46 AM,8,,,-1.2551895,36.7822034,-1.3020973,36.8166312,Rider_Id_161,1919
-Order_No_28225,User_Id_138,Bike,3,Business,27,1,11:42:28 AM,27,1,11:43:43 AM,27,1,11:55:19 AM,27,1,11:58:48 AM,27,1,12:16:14 PM,6,21.0,,-1.2883,36.8167842,-1.2615888000000002,36.7928732,Rider_Id_341,1046
-Order_No_4505,User_Id_1809,Bike,3,Business,6,3,1:23:13 PM,6,3,1:23:36 PM,6,3,1:45:21 PM,6,3,1:55:06 PM,6,3,2:24:14 PM,17,,,-1.2156006000000001,36.8916865,-1.2571472,36.795063299999995,Rider_Id_75,1748
-Order_No_14967,User_Id_393,Bike,3,Business,18,1,11:07:58 AM,18,1,11:09:35 AM,18,1,11:17:57 AM,18,1,11:20:30 AM,18,1,11:32:33 AM,4,28.2,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,723
-Order_No_12654,User_Id_2869,Bike,3,Business,8,3,2:19:13 PM,8,3,2:20:53 PM,8,3,2:54:23 PM,8,3,3:21:49 PM,8,3,4:05:55 PM,21,,,-1.2860183,36.8975338,-1.3025826,36.7670807,Rider_Id_883,2646
-Order_No_19088,User_Id_439,Bike,3,Business,18,1,12:12:14 PM,18,1,12:12:41 PM,18,1,12:47:04 PM,18,1,12:51:00 PM,18,1,2:02:39 PM,25,26.7,,-1.3250048,36.869718799999994,-1.2633397,36.7454238,Rider_Id_418,4299
-Order_No_24197,User_Id_1075,Bike,3,Business,3,5,11:58:29 AM,3,5,11:58:59 AM,3,5,12:02:38 PM,3,5,12:08:52 PM,3,5,12:14:43 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_484,351
-Order_No_14888,User_Id_1090,Bike,3,Personal,15,1,12:27:09 PM,15,1,12:29:09 PM,15,1,12:50:26 PM,15,1,12:55:29 PM,15,1,1:18:38 PM,10,,,-1.3277531999999999,36.829790100000004,-1.2655332000000001,36.8094648,Rider_Id_38,1389
-Order_No_6618,User_Id_868,Bike,3,Business,25,4,1:09:39 PM,25,4,1:23:59 PM,25,4,1:44:05 PM,25,4,1:52:28 PM,25,4,2:06:34 PM,5,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_709,846
-Order_No_22566,User_Id_3772,Bike,1,Personal,29,6,7:38:52 AM,29,6,7:39:47 AM,29,6,8:16:18 AM,29,6,8:19:16 AM,29,6,8:32:51 AM,9,18.0,,-1.3054537,36.8284768,-1.2668093,36.7966542,Rider_Id_208,815
-Order_No_19351,User_Id_1620,Bike,1,Personal,14,2,4:24:07 PM,14,2,4:30:26 PM,14,2,5:12:09 PM,14,2,5:17:14 PM,14,2,5:47:50 PM,18,23.4,,-1.227132,36.8374277,-1.290059,36.7703475,Rider_Id_541,1836
-Order_No_19111,User_Id_2306,Bike,3,Business,11,1,10:16:17 AM,11,1,10:17:18 AM,11,1,10:32:00 AM,11,1,10:41:35 AM,11,1,11:28:21 AM,21,26.4,,-1.2271998,36.808490500000005,-1.3503196000000002,36.7666408,Rider_Id_882,2806
-Order_No_193,User_Id_2523,Bike,3,Business,18,2,12:41:41 PM,18,2,12:48:01 PM,18,2,12:51:57 PM,18,2,12:54:56 PM,18,2,1:18:39 PM,8,,,-1.2551895,36.7822034,-1.304344,36.812274,Rider_Id_698,1423
-Order_No_6896,User_Id_627,Bike,3,Business,1,5,2:25:48 PM,1,5,2:26:50 PM,1,5,2:31:04 PM,1,5,2:38:09 PM,1,5,2:55:47 PM,7,28.5,,-1.2571472,36.795063299999995,-1.2924639,36.77957979999999,Rider_Id_903,1058
-Order_No_26701,User_Id_830,Bike,3,Business,15,3,4:31:20 PM,15,3,4:31:45 PM,15,3,4:36:15 PM,15,3,4:39:40 PM,15,3,4:42:35 PM,3,24.9,,-1.2551895,36.7822034,-1.2603403,36.7998848,Rider_Id_177,175
-Order_No_20808,User_Id_1809,Bike,3,Business,10,5,3:13:36 PM,10,5,3:15:37 PM,10,5,3:23:41 PM,10,5,3:35:52 PM,10,5,3:44:13 PM,2,,,-1.2603403,36.7998848,-1.2571472,36.795063299999995,Rider_Id_882,501
-Order_No_410,User_Id_2801,Bike,3,Business,24,4,11:25:48 AM,24,4,11:25:57 AM,24,4,11:26:43 AM,24,4,11:48:09 AM,24,4,12:19:21 PM,16,24.7,,-1.3104506,36.8687235,-1.2595421999999998,36.7871178,Rider_Id_669,1872
-Order_No_26521,User_Id_3560,Bike,3,Business,4,1,3:41:12 PM,4,1,4:44:22 PM,4,1,4:49:41 PM,4,1,5:20:48 PM,4,1,5:48:13 PM,18,27.7,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_48,1645
-Order_No_9963,User_Id_2587,Bike,3,Business,3,1,12:34:31 PM,3,1,12:55:25 PM,3,1,1:00:34 PM,3,1,1:21:00 PM,3,1,1:41:42 PM,7,22.1,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_454,1242
-Order_No_6284,User_Id_1118,Bike,1,Personal,26,2,11:50:42 AM,26,2,11:51:44 AM,26,2,12:04:25 PM,26,2,12:23:26 PM,26,2,12:52:41 PM,9,,,-1.3005044,36.7871991,-1.3226567,36.8221416,Rider_Id_101,1755
-Order_No_4527,User_Id_60,Bike,3,Business,9,2,10:42:01 AM,9,2,10:42:15 AM,9,2,11:11:51 AM,9,2,11:29:36 AM,9,2,12:04:24 PM,16,26.8,,-1.331619,36.847976,-1.2296618000000001,36.84382720000001,Rider_Id_126,2088
-Order_No_7277,User_Id_302,Bike,3,Business,11,1,8:05:01 PM,11,1,8:06:04 PM,11,1,8:42:39 PM,11,1,8:44:36 PM,11,1,9:07:31 PM,8,22.8,,-1.2649605,36.798177700000004,-1.3162537,36.819990600000004,Rider_Id_598,1375
-Order_No_445,User_Id_1444,Bike,1,Personal,25,5,5:00:03 PM,25,5,5:00:46 PM,25,5,5:11:53 PM,25,5,5:19:46 PM,25,5,5:33:00 PM,3,21.4,,-1.2536041999999998,36.806489500000005,-1.269534,36.80697,Rider_Id_89,794
-Order_No_25484,User_Id_2828,Bike,3,Business,6,3,9:16:05 AM,6,3,9:20:40 AM,6,3,9:40:53 AM,6,3,9:52:44 AM,6,3,10:33:45 AM,6,,,-1.2615888000000002,36.7928732,-1.28878,36.8168312,Rider_Id_372,2461
-Order_No_25558,User_Id_3686,Bike,3,Business,5,1,12:36:36 PM,5,1,12:37:28 PM,5,1,12:43:16 PM,5,1,12:46:48 PM,5,1,1:31:52 PM,8,24.7,,-1.2551895,36.7822034,-1.3012618999999999,36.821369700000005,Rider_Id_770,2704
-Order_No_14400,User_Id_867,Bike,3,Business,5,1,1:08:21 PM,5,1,1:22:16 PM,5,1,1:38:36 PM,5,1,1:41:18 PM,5,1,2:16:21 PM,10,26.0,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_661,2103
-Order_No_2402,User_Id_1196,Bike,3,Business,29,2,1:28:03 PM,29,2,1:44:37 PM,29,2,2:03:09 PM,29,2,2:09:19 PM,29,2,2:25:25 PM,7,24.0,,-1.2553615,36.789537599999996,-1.2932780000000001,36.827789,Rider_Id_752,966
-Order_No_27997,User_Id_1329,Bike,3,Business,11,2,10:27:03 AM,11,2,10:34:25 AM,11,2,10:39:08 AM,11,2,10:40:54 AM,11,2,11:18:57 AM,3,19.5,,-1.273056,36.811298,-1.2631940000000002,36.797624,Rider_Id_881,2283
-Order_No_13761,User_Id_1500,Bike,3,Business,5,2,9:43:04 AM,5,2,9:44:04 AM,5,2,9:49:41 AM,5,2,10:05:41 AM,5,2,10:28:45 AM,11,20.9,2.0,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_611,1384
-Order_No_2627,User_Id_3470,Bike,3,Business,9,5,11:33:53 AM,9,5,12:41:24 PM,9,5,12:56:23 PM,9,5,1:17:21 PM,9,5,1:34:10 PM,6,25.5,,-1.2551895,36.7822034,-1.28878,36.8168312,Rider_Id_477,1009
-Order_No_20139,User_Id_972,Bike,3,Business,3,1,10:39:18 AM,3,1,10:39:27 AM,3,1,10:46:11 AM,3,1,10:48:30 AM,3,1,11:05:55 AM,10,20.6,,-1.2862033000000002,36.8152966,-1.3241819,36.882342799999996,Rider_Id_74,1045
-Order_No_3556,User_Id_2206,Bike,3,Business,4,2,10:54:34 AM,4,2,10:55:26 AM,4,2,11:29:05 AM,4,2,11:31:30 AM,4,2,12:04:26 PM,3,20.6,,-1.2615888000000002,36.7928732,-1.2697893,36.8124106,Rider_Id_259,1976
-Order_No_20556,User_Id_1527,Bike,3,Business,25,6,11:45:56 AM,25,6,11:47:23 AM,25,6,11:51:43 AM,25,6,12:06:03 PM,25,6,12:47:39 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_448,2496
-Order_No_2144,User_Id_124,Bike,3,Personal,15,2,12:13:05 PM,15,2,12:13:21 PM,15,2,12:24:00 PM,15,2,12:33:55 PM,15,2,1:16:59 PM,21,22.9,,-1.3192846,36.920516799999994,-1.2714020000000001,36.8238135,Rider_Id_543,2584
-Order_No_4657,User_Id_889,Bike,3,Business,20,2,8:09:19 AM,20,2,8:16:57 AM,20,2,8:32:07 AM,20,2,8:36:10 AM,20,2,9:07:24 AM,8,19.0,,-1.2985153,36.7876043,-1.2664491000000002,36.8102685,Rider_Id_612,1874
-Order_No_15575,User_Id_1346,Bike,3,Business,29,2,2:31:55 PM,29,2,2:32:43 PM,29,2,2:45:25 PM,29,2,2:48:23 PM,29,2,3:00:20 PM,2,27.0,,-1.2917868000000001,36.7872675,-1.2985749,36.8088,Rider_Id_860,717
-Order_No_10421,User_Id_1469,Bike,3,Business,26,2,11:46:52 AM,26,2,11:47:43 AM,26,2,11:50:45 AM,26,2,11:53:47 AM,26,2,12:02:16 PM,3,27.5,,-1.2551895,36.7822034,-1.273488,36.804158,Rider_Id_576,509
-Order_No_25493,User_Id_733,Bike,3,Business,9,5,12:24:22 PM,9,5,12:24:45 PM,9,5,12:48:11 PM,9,5,1:18:37 PM,9,5,1:31:09 PM,15,25.6,,-1.2551895,36.7822034,-1.214071,36.7985052,Rider_Id_811,752
-Order_No_1267,User_Id_3599,Bike,3,Business,30,2,10:27:47 AM,30,2,11:16:11 AM,30,2,11:40:32 AM,30,2,11:44:57 AM,30,2,12:21:24 PM,12,,,-1.3302996,36.8707935,-1.2829828,36.797206200000005,Rider_Id_853,2187
-Order_No_22143,User_Id_2066,Bike,3,Business,30,6,1:58:55 PM,30,6,1:59:16 PM,30,6,2:10:05 PM,30,6,2:14:06 PM,30,6,2:50:21 PM,12,28.7,,-1.3210188999999999,36.841055100000005,-1.3664581,36.9177807,Rider_Id_843,2175
-Order_No_2728,User_Id_3635,Bike,1,Business,17,3,1:49:54 PM,17,3,1:50:31 PM,17,3,1:53:39 PM,17,3,2:02:27 PM,17,3,2:35:32 PM,23,28.6,,-1.2615888000000002,36.7928732,-1.1923911,36.944733299999996,Rider_Id_648,1985
-Order_No_26615,User_Id_633,Bike,3,Business,22,5,7:31:30 AM,22,5,7:31:42 AM,22,5,7:35:32 AM,22,5,8:00:25 AM,22,5,8:00:49 AM,4,22.2,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_119,24
-Order_No_9831,User_Id_2200,Bike,3,Business,9,4,12:38:16 PM,9,4,12:40:04 PM,9,4,12:59:40 PM,9,4,1:15:46 PM,9,4,1:34:43 PM,6,,,-1.2571472,36.795063299999995,-1.290894,36.822971,Rider_Id_263,1137
-Order_No_16939,User_Id_2150,Bike,3,Business,22,5,4:17:20 PM,22,5,4:21:00 PM,22,5,4:30:03 PM,22,5,4:47:36 PM,22,5,5:09:18 PM,11,28.5,,-1.307143,36.825009,-1.3444459999999998,36.756597,Rider_Id_206,1302
-Order_No_25214,User_Id_3718,Bike,3,Business,5,1,11:39:16 AM,5,1,11:40:00 AM,5,1,12:09:45 PM,5,1,12:15:44 PM,5,1,12:23:04 PM,3,24.3,,-1.2650027,36.8126242,-1.26071,36.8206332,Rider_Id_315,440
-Order_No_20382,User_Id_29,Bike,3,Personal,25,6,11:00:19 AM,25,6,11:00:19 AM,25,6,11:08:01 AM,25,6,11:16:49 AM,25,6,11:55:32 AM,24,,,-1.2852951000000001,36.81338529999999,-1.2636813,36.669025399999995,Rider_Id_206,2323
-Order_No_10803,User_Id_1402,Bike,3,Business,3,3,8:10:57 AM,3,3,8:13:43 AM,3,3,8:32:07 AM,3,3,8:40:00 AM,3,3,9:24:29 AM,7,21.3,,-1.2813012,36.832396200000005,-1.3024388,36.859699,Rider_Id_119,2669
-Order_No_21617,User_Id_3565,Bike,3,Business,11,5,12:00:31 PM,11,5,12:01:34 PM,11,5,12:21:28 PM,11,5,12:30:59 PM,11,5,12:53:18 PM,15,20.7,,-1.2844665000000002,36.7866113,-1.2297202,36.874550799999994,Rider_Id_100,1339
-Order_No_11914,User_Id_1927,Bike,3,Business,10,5,12:20:43 PM,10,5,12:21:26 PM,10,5,12:34:55 PM,10,5,12:43:25 PM,10,5,1:06:38 PM,10,,,-1.3165542,36.700067,-1.3472066,36.7692638,Rider_Id_831,1393
-Order_No_5231,User_Id_248,Bike,3,Business,12,2,11:14:17 AM,12,2,11:14:38 AM,12,2,11:43:40 AM,12,2,11:52:33 AM,12,2,12:35:35 PM,17,28.2,,-1.2101671999999999,36.8336094,-1.304735,36.784605400000004,Rider_Id_8,2582
-Order_No_23098,User_Id_793,Bike,2,Personal,30,3,8:14:38 AM,30,3,8:15:46 AM,30,3,8:30:06 AM,30,3,9:19:41 AM,30,3,9:36:46 AM,10,17.2,,-1.2464366000000002,36.8170136,-1.2681211000000001,36.779247399999996,Rider_Id_103,1025
-Order_No_12022,User_Id_2289,Bike,3,Business,18,2,9:07:03 AM,18,2,9:10:25 AM,18,2,9:15:00 AM,18,2,9:26:15 AM,18,2,10:13:53 AM,7,19.6,,-1.2551895,36.7822034,-1.2887216000000001,36.808732299999996,Rider_Id_47,2858
-Order_No_26744,User_Id_2416,Bike,1,Personal,5,4,9:13:31 AM,5,4,9:15:00 AM,5,4,9:32:26 AM,5,4,9:39:28 AM,5,4,9:52:38 AM,6,,,-1.2980228999999999,36.78865,-1.266362,36.796939,Rider_Id_726,790
-Order_No_5743,User_Id_3222,Bike,3,Business,8,5,2:32:03 PM,8,5,2:33:17 PM,8,5,2:53:25 PM,8,5,3:02:36 PM,8,5,4:04:34 PM,19,30.6,,-1.2551895,36.7822034,-1.3253021999999999,36.8803995,Rider_Id_103,3718
-Order_No_22902,User_Id_3689,Bike,3,Business,1,4,2:12:50 PM,1,4,2:13:20 PM,1,4,2:14:08 PM,1,4,2:26:37 PM,1,4,2:27:16 PM,3,,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_731,39
-Order_No_26351,User_Id_867,Bike,3,Business,19,1,9:01:08 AM,19,1,9:01:26 AM,19,1,9:21:43 AM,19,1,9:34:30 AM,19,1,10:03:30 AM,11,19.3,,-1.306378,36.7519845,-1.2996533000000001,36.829262899999996,Rider_Id_258,1740
-Order_No_2161,User_Id_3355,Bike,3,Business,10,4,4:09:58 PM,10,4,4:19:16 PM,10,4,4:42:45 PM,10,4,4:52:43 PM,10,4,5:39:25 PM,12,23.4,,-1.2860183,36.8975338,-1.3052491000000002,36.8223899,Rider_Id_697,2802
-Order_No_20235,User_Id_228,Bike,3,Business,11,4,12:41:57 PM,11,4,12:43:12 PM,11,4,1:03:13 PM,11,4,1:08:17 PM,11,4,1:31:07 PM,12,25.6,,-1.2842665,36.7967626,-1.238874,36.807889,Rider_Id_8,1370
-Order_No_26902,User_Id_1245,Bike,3,Business,15,5,2:38:37 PM,15,5,2:39:43 PM,15,5,2:41:25 PM,15,5,3:52:04 PM,15,5,3:52:12 PM,18,17.8,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_404,8
-Order_No_2287,User_Id_2854,Bike,3,Business,6,3,3:17:14 PM,6,3,3:41:34 PM,6,3,4:06:32 PM,6,3,4:10:00 PM,6,3,4:29:40 PM,7,22.0,,-1.2868868,36.8145777,-1.2963097,36.7688221,Rider_Id_846,1180
-Order_No_2739,User_Id_661,Bike,3,Business,22,5,11:42:55 AM,22,5,11:45:03 AM,22,5,12:11:22 PM,22,5,12:39:59 PM,22,5,12:52:05 PM,4,21.5,,-1.2963097,36.7688221,-1.2950044,36.7512209,Rider_Id_312,726
-Order_No_16062,User_Id_1502,Bike,3,Business,15,5,5:18:28 PM,15,5,5:20:25 PM,15,5,5:26:19 PM,15,5,5:29:52 PM,15,5,5:52:15 PM,6,27.9,,-1.2232123,36.8932547,-1.1898857,36.9103054,Rider_Id_291,1343
-Order_No_24486,User_Id_53,Bike,3,Business,28,4,1:49:05 PM,28,4,1:49:34 PM,28,4,1:49:40 PM,28,4,2:15:17 PM,28,4,2:33:05 PM,5,27.2,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_809,1068
-Order_No_13830,User_Id_1724,Bike,3,Business,8,5,2:12:46 PM,8,5,2:13:16 PM,8,5,2:15:25 PM,8,5,2:30:51 PM,8,5,3:09:16 PM,10,30.3,,-1.2551895,36.7822034,-1.3090111,36.768327500000005,Rider_Id_576,2305
-Order_No_16292,User_Id_2436,Bike,3,Business,28,4,4:00:42 PM,28,4,4:01:28 PM,28,4,4:25:48 PM,28,4,4:26:48 PM,28,4,5:04:19 PM,3,30.2,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_536,2251
-Order_No_3449,User_Id_1568,Bike,3,Business,26,2,3:58:40 PM,26,2,4:00:43 PM,26,2,4:15:26 PM,26,2,4:24:05 PM,26,2,4:52:38 PM,9,29.5,,-1.2346044,36.8461804,-1.2421591,36.8939721,Rider_Id_8,1713
-Order_No_8589,User_Id_1721,Bike,3,Business,10,1,9:46:09 AM,10,1,9:48:01 AM,10,1,10:17:55 AM,10,1,10:44:36 AM,10,1,10:59:36 AM,7,18.7,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_2,900
-Order_No_20101,User_Id_136,Bike,3,Business,22,5,4:25:28 PM,22,5,4:30:27 PM,22,5,4:40:33 PM,22,5,4:42:04 PM,22,5,5:57:23 PM,16,28.4,,-1.3254522,36.721658399999995,-1.2654716000000001,36.7983635,Rider_Id_326,4519
-Order_No_18245,User_Id_666,Bike,2,Personal,26,6,11:44:05 AM,26,6,11:47:44 AM,26,6,11:53:26 AM,26,6,11:59:20 AM,26,6,12:15:49 PM,9,22.4,,-1.2770071000000003,36.832167,-1.2867182,36.7579445,Rider_Id_409,989
-Order_No_1599,User_Id_633,Bike,3,Business,7,4,10:50:22 AM,7,4,10:50:48 AM,7,4,11:34:04 AM,7,4,11:40:30 AM,7,4,11:49:46 AM,4,24.4,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_842,556
-Order_No_24152,User_Id_2956,Bike,3,Business,14,5,9:37:49 AM,14,5,10:31:55 AM,14,5,10:33:00 AM,14,5,11:09:45 AM,14,5,11:40:22 AM,9,21.6,,-1.281754,36.8324229,-1.2584143,36.8048002,Rider_Id_300,1837
-Order_No_19995,User_Id_90,Bike,1,Personal,18,3,1:53:10 PM,18,3,1:54:15 PM,18,3,1:58:38 PM,18,3,2:04:39 PM,18,3,2:33:28 PM,7,20.5,,-1.256958,36.8144929,-1.294624,36.814873999999996,Rider_Id_415,1729
-Order_No_14163,User_Id_2271,Bike,3,Business,19,2,12:26:22 PM,19,2,12:26:44 PM,19,2,12:27:57 PM,19,2,1:01:32 PM,19,2,1:01:34 PM,11,27.7,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_377,2
-Order_No_17718,User_Id_3295,Bike,3,Business,19,2,12:10:36 PM,19,2,12:14:08 PM,19,2,12:58:30 PM,19,2,1:18:47 PM,19,2,1:27:45 PM,6,27.6,,-1.2814233,36.8251177,-1.2571472,36.795063299999995,Rider_Id_119,538
-Order_No_12057,User_Id_807,Bike,2,Personal,20,3,2:37:50 PM,20,3,2:38:25 PM,20,3,2:53:14 PM,20,3,3:05:16 PM,20,3,3:23:27 PM,7,23.4,,-1.312643,36.80326710000001,-1.2891063999999999,36.7743692,Rider_Id_101,1091
-Order_No_1673,User_Id_2677,Bike,3,Business,17,4,10:03:57 AM,17,4,10:04:20 AM,17,4,10:19:27 AM,17,4,10:30:05 AM,17,4,11:02:39 AM,4,,,-1.2784722,36.7556652,-1.306378,36.7519845,Rider_Id_761,1954
-Order_No_3444,User_Id_1500,Bike,3,Business,24,4,11:00:59 AM,24,4,11:02:00 AM,24,4,11:12:02 AM,24,4,11:15:29 AM,24,4,11:35:12 AM,11,24.4,,-1.3077869,36.844320700000004,-1.2574219,36.7927073,Rider_Id_523,1183
-Order_No_65,User_Id_721,Bike,2,Personal,6,5,5:29:04 PM,6,5,5:29:20 PM,6,5,5:34:12 PM,6,5,5:40:36 PM,6,5,5:47:38 PM,2,,,-1.2874192,36.7938634,-1.2979147,36.785781400000005,Rider_Id_449,422
-Order_No_13555,User_Id_1471,Bike,3,Business,30,2,11:11:49 AM,30,2,11:15:37 AM,30,2,12:13:12 PM,30,2,12:22:41 PM,30,2,1:03:37 PM,9,,,-1.2551895,36.7822034,-1.2983462,36.8139996,Rider_Id_394,2456
-Order_No_2872,User_Id_2175,Bike,3,Business,29,3,2:39:57 PM,29,3,3:05:51 PM,29,3,3:24:57 PM,29,3,3:32:18 PM,29,3,4:03:44 PM,7,21.3,,-1.2551895,36.7822034,-1.2857548,36.819062,Rider_Id_624,1886
-Order_No_24947,User_Id_2212,Bike,3,Business,17,5,10:21:01 AM,17,5,10:22:14 AM,17,5,10:36:13 AM,17,5,10:40:34 AM,17,5,10:55:51 AM,3,,,-1.2571472,36.795063299999995,-1.2611642,36.7949932,Rider_Id_172,917
-Order_No_3409,User_Id_3283,Bike,3,Business,7,2,2:39:57 PM,7,2,2:40:48 PM,7,2,2:41:13 PM,7,2,2:44:15 PM,7,2,2:58:40 PM,5,25.0,,-1.266837,36.799248999999996,-1.2628473,36.781805,Rider_Id_666,865
-Order_No_25845,User_Id_867,Bike,3,Business,2,4,12:51:47 PM,2,4,12:54:23 PM,2,4,1:09:29 PM,2,4,1:13:16 PM,2,4,1:40:46 PM,12,,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_99,1650
-Order_No_28024,User_Id_1584,Bike,3,Business,1,1,2:59:28 PM,1,1,3:08:23 PM,1,1,3:48:30 PM,1,1,3:55:36 PM,1,1,4:25:36 PM,7,,,-1.2842021000000001,36.8281144,-1.2969741000000001,36.7856612,Rider_Id_507,1800
-Order_No_24091,User_Id_3469,Bike,3,Personal,18,5,10:40:09 AM,18,5,10:44:08 AM,18,5,11:13:39 AM,18,5,11:24:31 AM,18,5,11:52:35 AM,13,21.6,,-1.2583393,36.7464632,-1.2900075,36.8032564,Rider_Id_51,1684
-Order_No_11480,User_Id_53,Bike,3,Business,16,6,10:22:51 AM,16,6,10:24:28 AM,16,6,10:32:12 AM,16,6,10:54:33 AM,16,6,11:10:49 AM,5,25.5,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_674,976
-Order_No_8756,User_Id_855,Bike,1,Personal,26,7,11:33:04 AM,26,7,11:33:17 AM,26,7,12:16:05 PM,26,7,12:22:23 PM,26,7,12:54:38 PM,15,,,-1.309513,36.811345,-1.2727258999999997,36.9100807,Rider_Id_644,1935
-Order_No_24610,User_Id_249,Bike,1,Personal,6,5,10:19:39 AM,6,5,10:19:57 AM,6,5,10:30:02 AM,6,5,10:45:49 AM,6,5,11:21:52 AM,20,20.3,,-1.345742,36.761259,-1.2959669,36.8958092,Rider_Id_412,2163
-Order_No_2843,User_Id_1927,Bike,3,Business,1,5,5:37:50 PM,1,5,5:39:10 PM,1,5,6:15:05 PM,1,5,6:25:38 PM,1,5,7:12:07 PM,25,26.6,,-1.2232123,36.8932547,-1.3472066,36.7692638,Rider_Id_19,2789
-Order_No_4358,User_Id_1959,Bike,3,Business,10,1,1:07:20 PM,10,1,1:08:31 PM,10,1,1:19:19 PM,10,1,1:28:21 PM,10,1,2:33:43 PM,8,18.6,,-1.2551895,36.7822034,-1.2943272,36.7667198,Rider_Id_894,3922
-Order_No_26696,User_Id_3355,Bike,3,Business,13,5,12:01:01 PM,13,5,12:02:33 PM,13,5,12:21:21 PM,13,5,12:29:49 PM,13,5,12:44:19 PM,3,,,-1.314309,36.8392657,-1.3052491000000002,36.8223899,Rider_Id_939,870
-Order_No_12158,User_Id_3291,Bike,3,Business,17,5,10:49:04 AM,17,5,10:50:09 AM,17,5,11:05:02 AM,17,5,11:19:26 AM,17,5,11:19:28 AM,5,,,-1.2783848999999998,36.8163445,-1.2600926000000001,36.8088685,Rider_Id_76,2
-Order_No_22656,User_Id_1004,Bike,3,Business,14,2,2:55:36 PM,14,2,2:59:21 PM,14,2,3:17:37 PM,14,2,3:19:03 PM,14,2,4:09:42 PM,18,,,-1.3250048,36.869718799999994,-1.2235201,36.80206079999999,Rider_Id_183,3039
-Order_No_6826,User_Id_2350,Bike,1,Personal,18,1,12:38:46 PM,18,1,1:01:47 PM,18,1,1:22:08 PM,18,1,1:30:52 PM,18,1,1:52:28 PM,10,,,-1.2946287,36.7585367,-1.3114711000000001,36.8088708,Rider_Id_898,1296
-Order_No_25411,User_Id_2523,Bike,3,Business,2,2,9:08:47 AM,2,2,9:09:41 AM,2,2,9:11:49 AM,2,2,9:18:13 AM,2,2,9:45:49 AM,9,20.4,,-1.2551895,36.7822034,-1.3015661,36.8239003,Rider_Id_177,1656
-Order_No_25932,User_Id_2436,Bike,3,Business,1,4,12:45:48 PM,1,4,1:36:16 PM,1,4,1:52:17 PM,1,4,2:21:39 PM,1,4,2:46:31 PM,7,,,-1.2854453000000001,36.818836299999994,-1.26284,36.774046000000006,Rider_Id_131,1492
-Order_No_27869,User_Id_2330,Bike,3,Business,27,3,12:13:37 PM,27,3,12:14:10 PM,27,3,12:25:28 PM,27,3,12:28:55 PM,27,3,12:46:09 PM,5,25.9,,-1.3015197,36.7658456,-1.2915246999999999,36.805575700000006,Rider_Id_119,1034
-Order_No_15803,User_Id_758,Bike,3,Business,5,2,12:49:26 PM,5,2,12:49:53 PM,5,2,1:02:26 PM,5,2,1:04:46 PM,5,2,1:23:27 PM,8,23.5,,-1.2917868000000001,36.7872675,-1.2676026000000002,36.810362899999994,Rider_Id_368,1121
-Order_No_19157,User_Id_375,Bike,3,Business,6,1,3:00:06 PM,6,1,3:00:49 PM,6,1,3:05:45 PM,6,1,3:10:29 PM,6,1,3:35:50 PM,8,24.6,,-1.2551895,36.7822034,-1.2939132,36.8081606,Rider_Id_726,1521
-Order_No_5689,User_Id_3291,Bike,3,Business,21,4,1:50:51 PM,21,4,1:51:11 PM,21,4,1:54:31 PM,21,4,1:58:04 PM,21,4,2:09:09 PM,5,26.2,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_854,665
-Order_No_19378,User_Id_1340,Bike,3,Business,14,5,4:27:34 PM,14,5,4:46:02 PM,14,5,4:58:38 PM,14,5,5:05:52 PM,14,5,5:54:19 PM,19,23.1,,-1.2707178999999997,36.7979713,-1.3138705,36.894434000000004,Rider_Id_622,2907
-Order_No_9586,User_Id_1249,Bike,3,Business,1,1,4:58:47 PM,1,1,4:59:03 PM,1,1,5:03:22 PM,1,1,5:40:13 PM,1,1,5:40:14 PM,11,27.4,,-1.3681253,36.915529799999995,-1.3189252,36.861726899999994,Rider_Id_613,1
-Order_No_13900,User_Id_1787,Bike,3,Business,12,2,2:02:42 PM,12,2,2:03:04 PM,12,2,2:03:14 PM,12,2,2:15:03 PM,12,2,2:30:19 PM,11,,,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_812,916
-Order_No_19509,User_Id_868,Bike,3,Business,20,4,10:47:15 AM,20,4,10:47:56 AM,20,4,10:52:21 AM,20,4,10:58:01 AM,20,4,11:41:15 AM,7,22.3,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_268,2594
-Order_No_27891,User_Id_1329,Bike,3,Business,12,3,2:15:35 PM,12,3,2:17:47 PM,12,3,2:38:35 PM,12,3,2:41:13 PM,12,3,3:15:36 PM,19,21.1,,-1.273056,36.811298,-1.3334681000000002,36.7116093,Rider_Id_622,2063
-Order_No_26396,User_Id_1798,Bike,3,Business,7,4,10:15:47 AM,7,4,10:17:40 AM,7,4,10:20:59 AM,7,4,10:31:38 AM,7,4,10:52:07 AM,6,19.6,,-1.2899256000000001,36.815551,-1.2615888000000002,36.7928732,Rider_Id_86,1229
-Order_No_2948,User_Id_3674,Bike,3,Business,8,5,9:28:07 AM,8,5,9:28:42 AM,8,5,9:40:07 AM,8,5,9:51:05 AM,8,5,10:04:44 AM,6,20.5,,-1.2551895,36.7822034,-1.2750415000000002,36.7965813,Rider_Id_862,819
-Order_No_24130,User_Id_248,Bike,3,Business,7,5,2:23:06 PM,7,5,2:23:24 PM,7,5,2:29:52 PM,7,5,2:35:28 PM,7,5,2:57:17 PM,6,24.7,,-1.3064665000000002,36.825197700000004,-1.304735,36.784605400000004,Rider_Id_848,1309
-Order_No_19415,User_Id_2509,Bike,3,Business,20,5,11:07:09 AM,20,5,11:07:31 AM,20,5,11:07:46 AM,20,5,11:15:38 AM,20,5,11:48:12 AM,20,18.7,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_648,1954
-Order_No_23890,User_Id_412,Bike,3,Business,24,3,11:03:11 AM,24,3,11:04:14 AM,24,3,11:34:36 AM,24,3,11:49:21 AM,24,3,12:26:58 PM,11,,,-1.331619,36.847976,-1.266837,36.799248999999996,Rider_Id_675,2257
-Order_No_14277,User_Id_1329,Bike,3,Business,13,4,2:52:41 PM,13,4,3:26:36 PM,13,4,3:27:16 PM,13,4,3:54:08 PM,13,4,4:18:49 PM,9,23.2,,-1.273056,36.811298,-1.3217618,36.8568179,Rider_Id_75,1481
-Order_No_17990,User_Id_3560,Bike,3,Business,23,3,1:01:30 PM,23,3,1:02:28 PM,23,3,1:14:26 PM,23,3,1:37:45 PM,23,3,1:48:55 PM,1,27.5,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_26,670
-Order_No_16917,User_Id_391,Bike,3,Business,3,4,7:29:00 AM,3,4,7:46:00 AM,3,4,7:59:06 AM,3,4,8:10:16 AM,3,4,8:21:02 AM,5,17.1,15.9,-1.2777923999999998,36.8231577,-1.3007481,36.8341778,Rider_Id_644,646
-Order_No_14484,User_Id_3283,Bike,3,Business,6,3,8:46:36 AM,6,3,8:46:54 AM,6,3,8:50:14 AM,6,3,9:09:02 AM,6,3,9:18:06 AM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_72,544
-Order_No_9775,User_Id_2987,Bike,3,Business,23,3,12:05:18 PM,23,3,12:05:52 PM,23,3,12:19:18 PM,23,3,12:23:16 PM,23,3,12:54:06 PM,13,22.0,,-1.2702120000000001,36.82108770000001,-1.2927792,36.9053492,Rider_Id_103,1850
-Order_No_19038,User_Id_3295,Bike,3,Business,19,2,11:51:52 AM,19,2,11:53:05 AM,19,2,12:06:49 PM,19,2,12:14:04 PM,19,2,12:37:32 PM,14,27.6,,-1.2571472,36.795063299999995,-1.2706945,36.8786243,Rider_Id_404,1408
-Order_No_6766,User_Id_2804,Bike,3,Business,15,2,1:14:38 PM,15,2,1:14:45 PM,15,2,1:15:48 PM,15,2,1:17:03 PM,15,2,1:44:03 PM,19,24.5,,-1.2551895,36.7822034,-1.2927792,36.9053492,Rider_Id_903,1620
-Order_No_22703,User_Id_865,Bike,3,Business,19,2,3:32:52 PM,19,2,4:01:13 PM,19,2,4:12:16 PM,19,2,4:37:36 PM,19,2,5:03:25 PM,7,28.1,,-1.2452664,36.802854100000005,-1.2855653999999999,36.8244835,Rider_Id_336,1549
-Order_No_27368,User_Id_2789,Bike,3,Business,8,5,10:05:49 AM,8,5,10:06:13 AM,8,5,10:20:25 AM,8,5,10:26:41 AM,8,5,10:44:32 AM,10,20.7,,-1.304735,36.784605400000004,-1.3239248,36.7091136,Rider_Id_649,1071
-Order_No_21616,User_Id_535,Bike,3,Business,15,3,3:55:35 PM,15,3,3:56:50 PM,15,3,4:22:53 PM,15,3,4:35:48 PM,15,3,5:16:49 PM,20,24.8,,-1.3206958,36.8769553,-1.2549259,36.7919494,Rider_Id_870,2461
-Order_No_4509,User_Id_3621,Bike,3,Business,1,1,2:40:30 PM,1,1,2:41:37 PM,1,1,3:00:31 PM,1,1,3:02:57 PM,1,1,3:17:39 PM,5,25.5,,-1.2836583000000001,36.7886014,-1.2802803,36.8271747,Rider_Id_456,882
-Order_No_26905,User_Id_3647,Bike,3,Business,24,3,11:40:54 AM,24,3,11:41:20 AM,24,3,11:52:18 AM,24,3,11:58:58 AM,24,3,12:19:37 PM,8,,,-1.2726389999999999,36.794723,-1.302559,36.819555,Rider_Id_177,1239
-Order_No_5085,User_Id_1825,Bike,1,Personal,3,4,8:48:59 AM,3,4,8:50:40 AM,3,4,9:11:18 AM,3,4,9:38:14 AM,3,4,10:06:42 AM,8,17.3,15.9,-1.3015998,36.8764939,-1.3055221000000001,36.821676399999994,Rider_Id_753,1708
-Order_No_13389,User_Id_115,Bike,3,Business,14,5,4:23:44 PM,14,5,4:27:21 PM,14,5,4:56:10 PM,14,5,4:59:29 PM,14,5,6:14:01 PM,21,21.7,,-1.281854,36.8246889,-1.2773974,36.9678677,Rider_Id_436,4472
-Order_No_4440,User_Id_3295,Bike,3,Business,12,2,3:48:05 PM,12,2,3:53:27 PM,12,2,4:04:17 PM,12,2,4:06:01 PM,12,2,4:21:30 PM,8,26.9,,-1.2571472,36.795063299999995,-1.3025666999999999,36.7803495,Rider_Id_489,929
-Order_No_12864,User_Id_1271,Bike,3,Business,14,4,10:37:49 AM,14,4,10:38:07 AM,14,4,10:42:50 AM,14,4,10:51:14 AM,14,4,11:33:02 AM,24,23.9,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_411,2508
-Order_No_8883,User_Id_1500,Bike,3,Business,23,3,12:46:53 PM,23,3,12:48:50 PM,23,3,12:49:05 PM,23,3,12:59:00 PM,23,3,1:26:35 PM,10,,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_931,1655
-Order_No_26722,User_Id_2523,Bike,3,Business,12,2,12:34:54 PM,12,2,12:39:57 PM,12,2,1:01:02 PM,12,2,1:03:50 PM,12,2,1:31:12 PM,12,20.8,,-1.2551895,36.7822034,-1.3083777,36.85900770000001,Rider_Id_752,1642
-Order_No_26799,User_Id_2000,Bike,3,Business,1,4,2:12:53 PM,1,4,2:37:01 PM,1,4,2:39:24 PM,1,4,2:43:50 PM,1,4,3:00:08 PM,7,,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_477,978
-Order_No_26665,User_Id_1329,Bike,3,Business,25,4,3:28:08 PM,25,4,3:32:29 PM,25,4,3:40:56 PM,25,4,3:47:31 PM,25,4,4:03:51 PM,9,,,-1.273056,36.811298,-1.3210621999999999,36.802019,Rider_Id_484,980
-Order_No_15160,User_Id_868,Bike,3,Business,12,1,1:13:05 PM,12,1,1:16:49 PM,12,1,1:30:26 PM,12,1,1:34:55 PM,12,1,1:53:41 PM,8,26.9,,-1.265715,36.823815,-1.303596,36.778378000000004,Rider_Id_279,1126
-Order_No_6221,User_Id_412,Bike,3,Business,30,2,1:06:20 PM,30,2,1:24:25 PM,30,2,1:29:48 PM,30,2,1:45:15 PM,30,2,1:59:44 PM,3,,,-1.272828,36.816608,-1.266837,36.799248999999996,Rider_Id_461,869
-Order_No_25931,User_Id_914,Bike,3,Business,17,1,10:18:17 AM,17,1,10:18:24 AM,17,1,10:25:00 AM,17,1,10:31:05 AM,17,1,10:41:27 AM,3,18.0,,-1.2670213,36.7994482,-1.2630521000000001,36.806801899999996,Rider_Id_858,622
-Order_No_20765,User_Id_1425,Bike,3,Business,25,4,11:27:40 AM,25,4,11:27:54 AM,25,4,11:33:17 AM,25,4,11:37:30 AM,25,4,12:22:03 PM,12,,,-1.3004062,36.829741,-1.338283,36.8871775,Rider_Id_223,2673
-Order_No_21291,User_Id_2330,Bike,3,Business,15,5,3:34:17 PM,15,5,3:34:35 PM,15,5,3:46:43 PM,15,5,3:50:51 PM,15,5,4:53:57 PM,13,17.6,,-1.2199518,36.803213299999996,-1.2284323,36.8757058,Rider_Id_936,3786
-Order_No_23530,User_Id_2487,Bike,2,Personal,8,6,11:11:59 AM,8,6,11:12:55 AM,8,6,11:38:20 AM,8,6,11:42:22 AM,8,6,11:59:34 AM,9,22.4,,-1.2556618,36.8178427,-1.2197008999999999,36.82696179999999,Rider_Id_882,1032
-Order_No_17191,User_Id_162,Bike,3,Business,6,3,1:17:09 PM,6,3,1:20:51 PM,6,3,1:38:00 PM,6,3,1:51:08 PM,6,3,3:01:05 PM,28,27.6,,-1.3510811000000003,36.894806,-1.2023973,36.913955,Rider_Id_411,4197
-Order_No_11735,User_Id_1500,Bike,3,Business,10,1,8:37:06 AM,10,1,8:37:53 AM,10,1,8:38:41 AM,10,1,9:24:39 AM,10,1,9:38:33 AM,8,16.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_2,834
-Order_No_21967,User_Id_393,Bike,3,Business,11,2,10:13:30 AM,11,2,10:14:20 AM,11,2,10:14:31 AM,11,2,10:27:18 AM,11,2,10:47:54 AM,11,16.5,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_891,1236
-Order_No_20803,User_Id_1329,Bike,3,Business,26,5,1:42:08 PM,26,5,1:43:01 PM,26,5,1:48:21 PM,26,5,1:49:41 PM,26,5,2:10:16 PM,10,,,-1.273056,36.811298,-1.3333155,36.8065341,Rider_Id_96,1235
-Order_No_11128,User_Id_3291,Bike,3,Business,28,4,9:03:38 AM,28,4,9:04:11 AM,28,4,9:18:21 AM,28,4,9:26:11 AM,28,4,9:36:13 AM,5,22.5,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_269,602
-Order_No_1377,User_Id_238,Bike,3,Business,24,4,9:34:30 AM,24,4,9:34:40 AM,24,4,9:34:42 AM,24,4,10:58:24 AM,24,4,11:06:17 AM,5,24.7,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_450,473
-Order_No_433,User_Id_1909,Bike,3,Business,12,5,9:14:18 AM,12,5,9:16:39 AM,12,5,9:23:51 AM,12,5,9:28:02 AM,12,5,10:18:35 AM,8,18.4,,-1.2653185,36.8358841,-1.3054597,36.8119025,Rider_Id_726,3033
-Order_No_12746,User_Id_2190,Bike,3,Business,26,3,2:51:20 PM,26,3,2:51:59 PM,26,3,2:57:59 PM,26,3,3:12:23 PM,26,3,4:02:58 PM,17,27.2,,-1.3019530000000001,36.788458,-1.2701893999999998,36.90246,Rider_Id_731,3035
-Order_No_25841,User_Id_3599,Bike,3,Business,25,2,10:05:44 AM,25,2,10:59:57 AM,25,2,11:17:06 AM,25,2,11:59:29 AM,25,2,12:11:16 PM,13,23.7,,-1.3302996,36.8707935,-1.335477,36.930829700000004,Rider_Id_179,707
-Order_No_5436,User_Id_2869,Bike,3,Business,18,4,8:21:37 AM,18,4,8:21:57 AM,18,4,8:41:29 AM,18,4,8:57:13 AM,18,4,9:29:45 AM,3,,,-1.3025826,36.7670807,-1.2763494,36.7671783,Rider_Id_155,1952
-Order_No_9741,User_Id_521,Bike,1,Personal,29,7,1:33:37 PM,29,7,1:34:07 PM,29,7,1:46:44 PM,29,7,1:51:36 PM,29,7,2:14:25 PM,9,23.1,,-1.257585,36.799859999999995,-1.2884343999999999,36.76056989999999,Rider_Id_858,1369
-Order_No_23659,User_Id_3686,Bike,3,Business,8,5,4:11:47 PM,8,5,4:31:22 PM,8,5,4:49:48 PM,8,5,4:51:57 PM,8,5,5:40:35 PM,13,28.2,,-1.2551895,36.7822034,-1.2095044,36.783206400000005,Rider_Id_318,2918
-Order_No_5901,User_Id_2956,Bike,3,Business,22,5,2:42:32 PM,22,5,2:46:02 PM,22,5,3:08:40 PM,22,5,3:14:42 PM,22,5,3:29:46 PM,6,27.3,,-1.2584143,36.8048002,-1.2854151000000003,36.8233848,Rider_Id_489,904
-Order_No_15791,User_Id_2330,Bike,3,Business,26,6,9:43:14 AM,26,6,9:43:25 AM,26,6,10:18:32 AM,26,6,10:24:41 AM,26,6,11:13:37 AM,19,20.5,,-1.185943,36.9276394,-1.2671943,36.8062533,Rider_Id_209,2936
-Order_No_15934,User_Id_238,Bike,3,Business,23,4,11:07:46 AM,23,4,11:08:29 AM,23,4,11:13:20 AM,23,4,11:17:28 AM,23,4,11:35:28 AM,4,23.1,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_216,1080
-Order_No_25717,User_Id_208,Bike,3,Personal,22,5,2:39:01 PM,22,5,2:43:45 PM,22,5,3:11:39 PM,22,5,3:15:51 PM,22,5,3:50:16 PM,11,27.5,,-1.2583017,36.8023526,-1.3108386,36.7555239,Rider_Id_25,2065
-Order_No_6981,User_Id_1081,Bike,3,Business,22,2,12:19:25 PM,22,2,12:19:56 PM,22,2,12:28:05 PM,22,2,12:30:37 PM,22,2,12:39:31 PM,4,23.8,,-1.2944363,36.7983424,-1.2963097,36.7688221,Rider_Id_845,534
-Order_No_10994,User_Id_2154,Bike,3,Business,4,4,10:53:02 AM,4,4,11:01:09 AM,4,4,11:13:57 AM,4,4,11:32:46 AM,4,4,11:57:30 AM,8,25.5,,-1.2527958000000001,36.8003131,-1.2765736,36.851364600000004,Rider_Id_907,1484
-Order_No_10210,User_Id_283,Bike,3,Business,18,2,2:26:30 PM,18,2,2:28:12 PM,18,2,2:43:31 PM,18,2,2:46:26 PM,18,2,3:00:17 PM,3,27.4,,-1.2804227,36.8250375,-1.2883158000000001,36.8199114,Rider_Id_207,831
-Order_No_13419,User_Id_393,Bike,3,Business,23,2,4:00:20 PM,23,2,4:34:08 PM,23,2,4:34:18 PM,23,2,4:44:35 PM,23,2,5:16:49 PM,14,19.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_10,1934
-Order_No_13100,User_Id_379,Bike,1,Personal,9,4,8:33:46 AM,9,4,8:34:39 AM,9,4,8:46:01 AM,9,4,8:50:05 AM,9,4,9:23:57 AM,9,17.1,,-1.2831463,36.76360220000001,-1.2655332000000001,36.8094648,Rider_Id_322,2032
-Order_No_182,User_Id_510,Bike,3,Business,25,4,8:03:59 AM,25,4,8:05:29 AM,25,4,8:18:12 AM,25,4,8:23:03 AM,25,4,8:43:36 AM,7,,,-1.2860183,36.8975338,-1.3078334999999999,36.8670353,Rider_Id_148,1233
-Order_No_14608,User_Id_1502,Bike,3,Business,16,3,1:01:47 PM,16,3,1:36:41 PM,16,3,1:52:04 PM,16,3,1:56:08 PM,16,3,2:24:43 PM,10,26.9,,-1.2859912,36.8756811,-1.2232123,36.8932547,Rider_Id_817,1715
-Order_No_17754,User_Id_111,Bike,3,Business,16,3,10:04:25 AM,16,3,10:05:25 AM,16,3,10:05:47 AM,16,3,10:15:45 AM,16,3,10:36:05 AM,10,19.8,,-1.2551895,36.7822034,-1.2295205,36.812042600000005,Rider_Id_108,1220
-Order_No_2443,User_Id_302,Bike,3,Business,23,4,4:29:56 PM,23,4,4:38:00 PM,23,4,4:54:28 PM,23,4,5:23:21 PM,23,4,5:49:13 PM,5,,,-1.2649605,36.798177700000004,-1.2757943,36.819750299999995,Rider_Id_119,1552
-Order_No_20561,User_Id_1915,Bike,2,Personal,4,1,5:40:44 PM,4,1,5:43:16 PM,4,1,5:57:44 PM,4,1,6:06:18 PM,4,1,6:24:48 PM,7,21.2,,-1.28621,36.825633,-1.2622212,36.7865781,Rider_Id_116,1110
-Order_No_25218,User_Id_1075,Bike,3,Business,21,1,1:08:51 PM,21,1,1:20:37 PM,21,1,1:46:06 PM,21,1,1:55:33 PM,21,1,2:15:39 PM,8,27.5,,-1.2326041,36.8009029,-1.2527958000000001,36.8003131,Rider_Id_263,1206
-Order_No_15171,User_Id_718,Bike,3,Business,15,1,4:10:02 PM,15,1,5:23:05 PM,15,1,5:36:54 PM,15,1,5:38:55 PM,15,1,6:22:49 PM,14,,,-1.2551895,36.7822034,-1.2515931,36.8754396,Rider_Id_383,2634
-Order_No_15200,User_Id_393,Bike,3,Business,15,5,1:49:54 PM,15,5,1:55:06 PM,15,5,2:00:03 PM,15,5,2:12:33 PM,15,5,2:28:49 PM,4,17.9,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_233,976
-Order_No_10314,User_Id_2931,Bike,3,Business,1,5,2:58:22 PM,1,5,3:02:28 PM,1,5,3:31:08 PM,1,5,3:50:13 PM,1,5,4:33:10 PM,29,28.5,,-1.3433181,36.9138018,-1.3169591,36.7000029,Rider_Id_548,2577
-Order_No_17372,User_Id_2986,Bike,3,Personal,12,3,12:48:11 PM,12,3,12:52:29 PM,12,3,1:09:00 PM,12,3,1:17:26 PM,12,3,1:35:50 PM,4,19.5,,-1.2878773000000001,36.7818102,-1.2903886,36.8063847,Rider_Id_582,1104
-Order_No_9434,User_Id_435,Bike,3,Business,6,4,3:50:07 PM,6,4,3:50:23 PM,6,4,3:50:39 PM,6,4,4:04:36 PM,6,4,4:44:07 PM,16,21.1,,-1.300921,36.828195,-1.2016802,36.819022499999996,Rider_Id_293,2371
-Order_No_6077,User_Id_3239,Bike,1,Personal,5,5,9:34:50 AM,5,5,9:36:02 AM,5,5,9:54:54 AM,5,5,9:59:14 AM,5,5,10:17:11 AM,9,17.6,,-1.2844072,36.824729600000005,-1.3194988,36.796668100000005,Rider_Id_736,1077
-Order_No_231,User_Id_3761,Bike,3,Personal,27,1,4:00:52 PM,27,1,4:12:16 PM,27,1,4:29:00 PM,27,1,5:16:36 PM,27,1,5:16:38 PM,7,,,-1.2888734,36.8268027,-1.2615888000000002,36.7928732,Rider_Id_193,2
-Order_No_3607,User_Id_1873,Bike,3,Business,1,1,9:58:09 AM,1,1,9:58:50 AM,1,1,9:59:20 AM,1,1,10:53:48 AM,1,1,10:53:50 AM,4,,,-1.2571472,36.795063299999995,-1.2889396,36.81554860000001,Rider_Id_613,2
-Order_No_21728,User_Id_64,Bike,1,Personal,27,1,3:03:29 PM,27,1,3:04:16 PM,27,1,3:12:44 PM,27,1,3:35:02 PM,27,1,3:47:36 PM,5,,,-1.301159,36.784757299999995,-1.2965924,36.8179314,Rider_Id_86,754
-Order_No_23117,User_Id_2718,Bike,3,Business,4,1,3:04:47 PM,4,1,3:10:58 PM,4,1,3:15:42 PM,4,1,3:23:46 PM,4,1,3:33:46 PM,4,22.5,,-1.2601,36.796528,-1.2670219999999999,36.807941,Rider_Id_727,600
-Order_No_6190,User_Id_893,Bike,3,Business,8,2,2:24:50 PM,8,2,2:26:38 PM,8,2,2:32:33 PM,8,2,2:43:37 PM,8,2,3:03:49 PM,7,25.5,,-1.2664491000000002,36.8102685,-1.2847316,36.7658086,Rider_Id_205,1212
-Order_No_27624,User_Id_3239,Bike,3,Personal,7,1,10:41:28 AM,7,1,10:49:27 AM,7,1,10:59:38 AM,7,1,11:16:13 AM,7,1,11:51:30 AM,6,21.5,,-1.276154,36.822416499999996,-1.3146122,36.8322723,Rider_Id_742,2117
-Order_No_27710,User_Id_1500,Bike,3,Business,7,2,11:46:45 AM,7,2,11:49:03 AM,7,2,11:58:40 AM,7,2,12:37:46 PM,7,2,12:37:51 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_863,5
-Order_No_2408,User_Id_625,Bike,3,Business,22,6,12:06:02 PM,22,6,12:06:28 PM,22,6,12:10:52 PM,22,6,12:23:08 PM,22,6,1:04:54 PM,7,21.2,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_103,2506
-Order_No_11566,User_Id_835,Bike,3,Business,4,4,9:09:52 AM,4,4,9:27:19 AM,4,4,10:09:38 AM,4,4,10:32:51 AM,4,4,11:17:27 AM,21,25.5,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_883,2676
-Order_No_18288,User_Id_3410,Bike,3,Business,12,3,10:58:19 AM,12,3,11:14:17 AM,12,3,12:31:04 PM,12,3,12:34:59 PM,12,3,1:26:54 PM,21,23.3,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_753,3115
-Order_No_2348,User_Id_1527,Bike,3,Business,8,1,4:05:44 PM,8,1,4:12:23 PM,8,1,4:30:08 PM,8,1,4:52:28 PM,8,1,4:58:41 PM,4,29.0,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_349,373
-Order_No_4710,User_Id_2545,Bike,2,Personal,10,4,9:26:21 AM,10,4,9:27:52 AM,10,4,9:31:35 AM,10,4,9:57:14 AM,10,4,10:13:41 AM,6,18.4,,-1.2797184,36.7811448,-1.2573403,36.806074200000005,Rider_Id_379,987
-Order_No_19006,User_Id_1329,Bike,3,Business,6,4,3:21:59 PM,6,4,3:23:42 PM,6,4,3:32:00 PM,6,4,3:33:55 PM,6,4,3:53:42 PM,13,21.3,,-1.273056,36.811298,-1.2186819,36.892153,Rider_Id_481,1187
-Order_No_21013,User_Id_867,Bike,3,Business,22,5,3:01:55 PM,22,5,3:02:17 PM,22,5,3:12:04 PM,22,5,3:18:38 PM,22,5,3:34:55 PM,9,27.3,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_785,977
-Order_No_14711,User_Id_2559,Bike,3,Personal,13,1,11:28:14 AM,13,1,11:30:25 AM,13,1,11:34:24 AM,13,1,12:32:12 PM,13,1,1:17:39 PM,16,,,-1.2948886999999998,36.7683938,-1.2196223999999998,36.7881376,Rider_Id_855,2727
-Order_No_19736,User_Id_2008,Bike,3,Business,5,5,4:40:13 PM,5,5,4:42:26 PM,5,5,5:09:15 PM,5,5,5:10:51 PM,5,5,5:39:32 PM,7,26.3,,-1.2508229,36.7895263,-1.285244,36.832159000000004,Rider_Id_507,1721
-Order_No_22909,User_Id_1670,Bike,1,Personal,19,5,12:42:52 PM,19,5,12:49:05 PM,19,5,1:03:59 PM,19,5,1:09:47 PM,19,5,1:29:35 PM,7,24.8,,-1.290239,36.7620412,-1.2662639999999998,36.762418,Rider_Id_565,1188
-Order_No_24828,User_Id_2587,Bike,3,Business,17,7,1:16:09 PM,17,7,2:28:41 PM,17,7,2:43:28 PM,17,7,2:46:13 PM,17,7,3:07:22 PM,11,29.1,,-1.2283403,36.8822756,-1.28878,36.8168312,Rider_Id_8,1269
-Order_No_8442,User_Id_2043,Bike,1,Personal,3,1,11:14:52 AM,3,1,11:15:38 AM,3,1,11:27:37 AM,3,1,11:33:59 AM,3,1,11:42:34 AM,5,20.7,,-1.3099145,36.802277399999994,-1.2946581,36.779292100000006,Rider_Id_465,515
-Order_No_12161,User_Id_1724,Bike,3,Business,10,1,12:13:45 PM,10,1,12:15:31 PM,10,1,12:16:28 PM,10,1,12:17:47 PM,10,1,12:26:58 PM,6,23.5,,-1.2551895,36.7822034,-1.2756120000000002,36.76379779999999,Rider_Id_685,551
-Order_No_6861,User_Id_3291,Bike,3,Business,7,3,10:07:52 AM,7,3,10:08:08 AM,7,3,10:13:14 AM,7,3,10:14:26 AM,7,3,10:17:33 AM,2,,,-1.2673934999999998,36.8030328,-1.2600926000000001,36.8088685,Rider_Id_620,187
-Order_No_20746,User_Id_647,Bike,3,Business,15,1,3:07:30 PM,15,1,3:09:07 PM,15,1,3:25:16 PM,15,1,3:43:35 PM,15,1,4:00:56 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_790,1041
-Order_No_27712,User_Id_143,Bike,1,Personal,3,3,12:10:57 PM,3,3,12:11:04 PM,3,3,12:24:13 PM,3,3,12:26:24 PM,3,3,12:56:27 PM,10,23.9,,-1.2736771,36.8123141,-1.3255854999999999,36.86476270000001,Rider_Id_755,1803
-Order_No_7162,User_Id_1995,Bike,3,Business,31,5,12:38:58 PM,31,5,12:47:14 PM,31,5,1:06:17 PM,31,5,1:21:18 PM,31,5,1:21:23 PM,4,21.8,,-1.2764568,36.7939953,-1.2615888000000002,36.7928732,Rider_Id_344,5
-Order_No_25837,User_Id_1329,Bike,3,Business,29,2,3:28:03 PM,29,2,4:08:10 PM,29,2,4:12:10 PM,29,2,4:18:59 PM,29,2,4:48:35 PM,10,27.2,,-1.273056,36.811298,-1.2835228,36.7437619,Rider_Id_607,1776
-Order_No_8472,User_Id_1329,Bike,3,Business,27,3,5:13:27 PM,27,3,5:14:37 PM,27,3,5:38:51 PM,27,3,5:43:48 PM,27,3,6:18:57 PM,7,27.6,,-1.273056,36.811298,-1.276616,36.766094,Rider_Id_907,2109
-Order_No_4442,User_Id_1411,Bike,1,Personal,28,2,9:30:05 AM,28,2,9:29:52 AM,28,2,9:38:48 AM,28,2,9:51:11 AM,28,2,9:59:30 AM,3,16.2,51.0,-1.3083777,36.85900770000001,-1.3159197,36.8516724,Rider_Id_329,499
-Order_No_13311,User_Id_503,Bike,1,Business,22,3,4:37:38 PM,22,3,4:41:52 PM,22,3,4:46:52 PM,22,3,5:14:06 PM,22,3,6:42:32 PM,27,23.1,,-1.3169591,36.7000029,-1.2859912,36.8756811,Rider_Id_86,5306
-Order_No_6250,User_Id_816,Bike,3,Business,31,4,12:03:03 PM,31,4,12:04:15 PM,31,4,12:27:38 PM,31,4,1:27:40 PM,31,4,1:48:18 PM,7,26.3,,-1.3104159,36.8657803,-1.290894,36.822971,Rider_Id_88,1238
-Order_No_18434,User_Id_1584,Bike,3,Business,31,3,3:05:52 PM,31,3,3:06:58 PM,31,3,3:10:27 PM,31,3,3:14:44 PM,31,3,3:35:00 PM,6,27.1,,-1.2969741000000001,36.7856612,-1.2857524,36.819680600000005,Rider_Id_414,1216
-Order_No_6020,User_Id_323,Bike,3,Business,7,4,4:15:21 PM,7,4,4:15:30 PM,7,4,4:15:44 PM,7,4,4:29:52 PM,7,4,5:25:51 PM,22,31.2,,-1.2551895,36.7822034,-1.3562374,36.904295399999995,Rider_Id_584,3359
-Order_No_5624,User_Id_868,Bike,3,Business,2,4,3:30:40 PM,2,4,4:32:27 PM,2,4,4:39:47 PM,2,4,4:54:06 PM,2,4,5:25:46 PM,14,23.6,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_676,1900
-Order_No_27185,User_Id_1046,Bike,1,Personal,8,6,1:50:19 PM,8,6,1:54:56 PM,8,6,2:11:12 PM,8,6,2:12:58 PM,8,6,2:33:52 PM,7,24.8,,-1.3054537,36.8284768,-1.290881,36.8559893,Rider_Id_208,1254
-Order_No_2668,User_Id_478,Bike,3,Business,30,2,10:39:32 AM,30,2,10:48:18 AM,30,2,10:56:05 AM,30,2,11:03:23 AM,30,2,11:09:57 AM,2,,,-1.2551895,36.7822034,-1.2571472,36.795063299999995,Rider_Id_690,394
-Order_No_26593,User_Id_653,Bike,3,Business,12,5,12:28:06 PM,12,5,12:32:08 PM,12,5,1:11:15 PM,12,5,1:15:15 PM,12,5,1:22:51 PM,6,24.5,,-1.279395,36.825364,-1.2571472,36.795063299999995,Rider_Id_409,456
-Order_No_18563,User_Id_2436,Bike,3,Business,8,4,3:08:05 PM,8,4,3:16:28 PM,8,4,3:24:39 PM,8,4,3:34:31 PM,8,4,3:43:41 PM,3,27.1,,-1.262073,36.7986087,-1.2551895,36.7822034,Rider_Id_222,550
-Order_No_19813,User_Id_2330,Bike,3,Business,8,3,4:13:15 PM,8,3,4:15:19 PM,8,3,4:21:48 PM,8,3,4:26:30 PM,8,3,4:42:07 PM,9,,,-1.3040812,36.7924217,-1.2645318,36.7981775,Rider_Id_520,937
-Order_No_23253,User_Id_635,Bike,3,Business,27,4,3:37:18 PM,27,4,3:37:35 PM,27,4,3:51:15 PM,27,4,4:03:41 PM,27,4,4:21:36 PM,9,25.3,,-1.2365703000000001,36.805436799999995,-1.2602337,36.79905479999999,Rider_Id_882,1075
-Order_No_14932,User_Id_2330,Bike,3,Business,30,3,12:29:20 PM,30,3,12:30:08 PM,30,3,12:41:14 PM,30,3,12:45:27 PM,30,3,1:25:42 PM,12,24.9,,-1.2124582,36.802894,-1.2890715,36.8157588,Rider_Id_933,2415
-Order_No_24968,User_Id_1363,Bike,3,Business,1,5,2:04:26 PM,1,5,2:04:43 PM,1,5,2:05:09 PM,1,5,2:10:00 PM,1,5,2:33:47 PM,11,28.6,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_523,1427
-Order_No_23040,User_Id_3091,Bike,3,Business,28,5,11:27:03 AM,28,5,11:27:25 AM,28,5,11:27:42 AM,28,5,11:54:37 AM,28,5,12:26:14 PM,18,25.0,,-1.2283403,36.8822756,-1.3012008,36.764868,Rider_Id_179,1897
-Order_No_18108,User_Id_1205,Bike,3,Business,18,2,10:51:20 AM,18,2,10:51:59 AM,18,2,11:02:03 AM,18,2,11:09:23 AM,18,2,12:07:24 PM,8,22.7,,-1.2706815,36.7646286,-1.2810807,36.8144229,Rider_Id_844,3481
-Order_No_12626,User_Id_635,Bike,3,Business,5,3,11:50:26 AM,5,3,11:52:16 AM,5,3,12:37:52 PM,5,3,12:56:20 PM,5,3,1:10:27 PM,11,22.5,,-1.2599563,36.799343900000004,-1.3229345,36.8566974,Rider_Id_813,847
-Order_No_14890,User_Id_2523,Bike,3,Business,13,3,11:10:41 AM,13,3,11:11:05 AM,13,3,11:11:53 AM,13,3,11:19:32 AM,13,3,11:50:50 AM,4,24.9,,-1.2551895,36.7822034,-1.2516124,36.8018759,Rider_Id_708,1878
-Order_No_19777,User_Id_3283,Bike,3,Business,7,4,11:20:18 AM,7,4,11:35:03 AM,7,4,11:42:22 AM,7,4,11:44:47 AM,7,4,12:02:58 PM,6,27.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_563,1091
-Order_No_9569,User_Id_2897,Bike,1,Personal,3,5,10:52:40 AM,3,5,10:56:16 AM,3,5,11:19:07 AM,3,5,11:25:37 AM,3,5,12:36:35 PM,23,21.4,,-1.1683284,36.9487572,-1.2690947,36.7992995,Rider_Id_447,4258
-Order_No_27278,User_Id_635,Bike,3,Business,1,4,2:40:12 PM,1,4,2:40:53 PM,1,4,2:49:21 PM,1,4,3:33:32 PM,1,4,3:53:01 PM,8,,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_848,1169
-Order_No_15643,User_Id_136,Bike,3,Business,6,1,8:45:18 AM,6,1,9:21:54 AM,6,1,9:38:13 AM,6,1,9:40:38 AM,6,1,10:18:24 AM,22,,,-1.3254522,36.721658399999995,-1.23966,36.75258,Rider_Id_572,2266
-Order_No_14761,User_Id_2102,Bike,3,Business,26,1,12:41:05 PM,26,1,12:42:14 PM,26,1,12:50:03 PM,26,1,1:16:26 PM,26,1,1:35:32 PM,11,23.0,,-1.291623,36.814209999999996,-1.3323107,36.8692616,Rider_Id_573,1146
-Order_No_11241,User_Id_867,Bike,3,Business,3,4,1:16:41 PM,3,4,1:18:17 PM,3,4,1:29:10 PM,3,4,1:30:13 PM,3,4,1:52:11 PM,11,23.7,,-1.2991441000000001,36.7528804,-1.2996533000000001,36.829262899999996,Rider_Id_518,1318
-Order_No_5077,User_Id_2523,Bike,3,Business,25,4,2:27:55 PM,25,4,2:28:20 PM,25,4,2:28:41 PM,25,4,2:40:02 PM,25,4,2:55:31 PM,10,,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_291,929
-Order_No_2326,User_Id_1051,Bike,3,Business,6,4,11:16:22 AM,6,4,11:32:10 AM,6,4,12:01:09 PM,6,4,12:14:41 PM,6,4,1:27:28 PM,31,24.2,,-1.1723908,36.9461248,-1.2963097,36.7688221,Rider_Id_447,4367
-Order_No_8763,User_Id_2431,Bike,3,Business,17,6,11:16:49 AM,17,6,11:20:30 AM,17,6,11:25:52 AM,17,6,11:29:55 AM,17,6,11:41:53 AM,5,23.7,,-1.2679971,36.808893700000006,-1.276245,36.836329299999996,Rider_Id_646,718
-Order_No_16088,User_Id_2454,Bike,3,Business,20,1,4:13:45 PM,20,1,4:15:08 PM,20,1,4:51:21 PM,20,1,5:01:13 PM,20,1,5:32:03 PM,12,,,-1.3264333000000001,36.71071,-1.2942023,36.7958179,Rider_Id_855,1850
-Order_No_22838,User_Id_3621,Bike,3,Business,12,2,12:07:09 PM,12,2,12:07:56 PM,12,2,12:21:22 PM,12,2,12:24:08 PM,12,2,12:46:41 PM,5,28.1,,-1.2836583000000001,36.7886014,-1.2821883,36.826961100000005,Rider_Id_118,1353
-Order_No_4143,User_Id_1329,Bike,3,Business,18,5,10:14:54 AM,18,5,10:15:55 AM,18,5,10:21:10 AM,18,5,10:25:26 AM,18,5,10:57:01 AM,13,20.3,,-1.273056,36.811298,-1.1989344,36.836088700000005,Rider_Id_205,1895
-Order_No_14199,User_Id_3560,Bike,3,Business,23,6,1:30:14 PM,23,6,1:30:37 PM,23,6,1:39:14 PM,23,6,1:41:11 PM,23,6,1:44:28 PM,1,28.2,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_102,197
-Order_No_16607,User_Id_265,Bike,3,Business,6,4,10:49:51 AM,6,4,10:50:19 AM,6,4,11:12:44 AM,6,4,12:21:54 PM,6,4,12:40:27 PM,14,22.1,,-1.2813288999999999,36.754903399999996,-1.300921,36.828195,Rider_Id_116,1113
-Order_No_1463,User_Id_3647,Bike,3,Business,1,1,12:44:30 PM,1,1,12:47:18 PM,1,1,12:57:48 PM,1,1,1:00:47 PM,1,1,1:14:23 PM,7,24.2,,-1.2726389999999999,36.794723,-1.301809,36.75643,Rider_Id_45,816
-Order_No_22173,User_Id_2008,Bike,3,Business,19,3,8:49:18 AM,19,3,8:49:47 AM,19,3,8:58:54 AM,19,3,9:05:17 AM,19,3,9:40:13 AM,10,19.6,,-1.2508229,36.7895263,-1.301809,36.75643,Rider_Id_854,2096
-Order_No_17856,User_Id_20,Bike,3,Business,25,4,3:54:53 PM,25,4,3:59:26 PM,25,4,4:13:20 PM,25,4,4:23:07 PM,25,4,5:02:55 PM,18,,,-1.2628638,36.8070399,-1.3390024,36.8746657,Rider_Id_276,2388
-Order_No_6415,User_Id_3781,Bike,3,Business,25,5,2:47:53 PM,25,5,3:29:00 PM,25,5,3:36:51 PM,25,5,3:40:36 PM,25,5,3:56:02 PM,5,23.4,,-1.2610959,36.828102200000004,-1.2889576,36.81833579999999,Rider_Id_874,926
-Order_No_22836,User_Id_2310,Bike,1,Business,8,3,11:48:54 AM,8,3,11:49:22 AM,8,3,12:09:00 PM,8,3,12:10:43 PM,8,3,12:11:22 PM,11,22.3,,-1.2859912,36.8756811,-1.2479903,36.9579838,Rider_Id_397,39
-Order_No_18515,User_Id_718,Bike,3,Business,4,1,3:21:37 PM,4,1,3:21:44 PM,4,1,3:28:18 PM,4,1,3:29:32 PM,4,1,4:45:56 PM,23,,,-1.2551895,36.7822034,-1.352856,36.660647499999996,Rider_Id_885,4584
-Order_No_27678,User_Id_265,Bike,3,Business,24,3,10:21:37 AM,24,3,10:22:46 AM,24,3,10:28:00 AM,24,3,10:31:22 AM,24,3,10:58:36 AM,9,,,-1.2569503,36.78347279999999,-1.300921,36.828195,Rider_Id_411,1634
-Order_No_9816,User_Id_712,Bike,2,Personal,19,2,3:00:07 PM,19,2,3:00:35 PM,19,2,3:04:50 PM,19,2,3:09:21 PM,19,2,3:31:08 PM,10,18.3,,-1.2850107,36.810614,-1.238874,36.807889,Rider_Id_478,1307
-Order_No_23701,User_Id_1271,Bike,3,Business,11,2,4:07:23 PM,11,2,4:18:19 PM,11,2,4:30:08 PM,11,2,4:36:46 PM,11,2,5:20:20 PM,10,21.2,,-1.3107918,36.761794,-1.2805757,36.8223948,Rider_Id_267,2614
-Order_No_174,User_Id_1809,Bike,3,Business,16,2,3:14:23 PM,16,2,3:31:23 PM,16,2,3:43:28 PM,16,2,4:00:54 PM,16,2,4:24:48 PM,8,27.3,,-1.3052491000000002,36.8223899,-1.2646084,36.8028645,Rider_Id_263,1434
-Order_No_22606,User_Id_1329,Bike,3,Business,27,1,5:43:51 PM,27,1,5:47:30 PM,27,1,5:57:36 PM,27,1,6:05:27 PM,27,1,6:35:35 PM,9,,,-1.3193233,36.840990999999995,-1.273056,36.811298,Rider_Id_495,1808
-Order_No_13815,User_Id_333,Bike,1,Personal,11,2,1:58:06 PM,11,2,2:02:09 PM,11,2,2:16:21 PM,11,2,2:22:43 PM,11,2,2:37:43 PM,5,25.4,,-1.3024794,36.8228284,-1.2854173999999998,36.7951152,Rider_Id_148,900
-Order_No_18673,User_Id_1527,Bike,3,Business,20,1,3:42:37 PM,20,1,3:43:05 PM,20,1,3:43:18 PM,20,1,4:21:14 PM,20,1,4:47:12 PM,12,,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_871,1558
-Order_No_14383,User_Id_2112,Bike,3,Business,8,2,1:37:06 PM,8,2,1:40:21 PM,8,2,1:46:03 PM,8,2,2:26:41 PM,8,2,2:30:13 PM,12,25.2,,-1.290894,36.822971,-1.2156006000000001,36.8916865,Rider_Id_333,212
-Order_No_16231,User_Id_3285,Bike,2,Personal,3,2,9:16:35 AM,3,2,9:32:36 AM,3,2,10:04:38 AM,3,2,10:08:24 AM,3,2,10:22:33 AM,4,15.5,1.9,-1.2817083999999999,36.8143646,-1.2635425,36.8021963,Rider_Id_752,849
-Order_No_27432,User_Id_604,Bike,3,Business,16,4,8:37:08 AM,16,4,8:45:59 AM,16,4,8:54:47 AM,16,4,8:58:46 AM,16,4,9:08:51 AM,9,,,-1.2551895,36.7822034,-1.256731,36.7525716,Rider_Id_103,605
-Order_No_14726,User_Id_1104,Bike,2,Business,20,5,11:25:32 AM,20,5,11:26:20 AM,20,5,11:29:46 AM,20,5,11:38:02 AM,20,5,11:50:48 AM,4,18.5,,-1.2551895,36.7822034,-1.2736498,36.8061911,Rider_Id_951,766
-Order_No_4164,User_Id_3404,Bike,3,Business,1,5,10:25:18 AM,1,5,10:26:05 AM,1,5,10:33:45 AM,1,5,10:50:25 AM,1,5,10:58:52 AM,1,26.3,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_206,507
-Order_No_1460,User_Id_2733,Bike,3,Business,12,1,10:38:44 AM,12,1,11:08:00 AM,12,1,11:16:29 AM,12,1,11:31:07 AM,12,1,11:35:06 AM,2,23.6,,-1.3047482,36.784967200000004,-1.2963097,36.7688221,Rider_Id_19,239
-Order_No_21053,User_Id_2956,Bike,3,Business,3,1,9:07:13 AM,3,1,9:07:56 AM,3,1,9:16:17 AM,3,1,9:21:34 AM,3,1,9:46:59 AM,9,18.6,,-1.2584143,36.8048002,-1.2624329,36.752581299999996,Rider_Id_407,1525
-Order_No_19944,User_Id_393,Bike,3,Business,4,6,8:44:14 AM,4,6,8:44:37 AM,4,6,8:46:30 AM,4,6,9:20:21 AM,4,6,9:20:27 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_349,6
-Order_No_11089,User_Id_3718,Bike,3,Business,31,3,12:05:44 PM,31,3,12:06:54 PM,31,3,12:07:19 PM,31,3,12:20:06 PM,31,3,12:45:01 PM,10,24.9,,-1.2650027,36.8126242,-1.3205669,36.836216799999995,Rider_Id_208,1495
-Order_No_5977,User_Id_3283,Bike,3,Business,13,4,1:32:15 PM,13,4,1:33:50 PM,13,4,1:54:56 PM,13,4,2:47:29 PM,13,4,2:58:58 PM,7,23.3,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_55,689
-Order_No_22264,User_Id_2075,Bike,1,Personal,31,2,5:17:22 PM,31,2,5:17:54 PM,31,2,5:26:12 PM,31,2,5:42:28 PM,31,2,6:03:47 PM,8,20.0,,-1.2791714,36.825599700000005,-1.3052084,36.769138700000006,Rider_Id_105,1279
-Order_No_16175,User_Id_1329,Bike,3,Business,24,5,4:20:19 PM,24,5,4:59:29 PM,24,5,5:01:28 PM,24,5,5:04:53 PM,24,5,5:41:02 PM,9,20.2,,-1.273056,36.811298,-1.3226514999999999,36.819568200000006,Rider_Id_576,2169
-Order_No_22255,User_Id_928,Bike,3,Business,15,1,11:39:24 AM,15,1,11:39:50 AM,15,1,11:46:37 AM,15,1,11:55:21 AM,15,1,12:37:43 PM,8,,,-1.2551895,36.7822034,-1.2958383,36.8073162,Rider_Id_565,2542
-Order_No_22446,User_Id_2697,Bike,2,Personal,6,6,11:29:49 AM,6,6,11:30:04 AM,6,6,11:56:19 AM,6,6,11:59:25 AM,6,6,12:15:33 PM,7,24.1,,-1.2720806,36.7751959,-1.2622444,36.7596633,Rider_Id_496,968
-Order_No_5983,User_Id_496,Bike,3,Business,14,4,4:54:21 PM,14,4,4:56:18 PM,14,4,5:06:27 PM,14,4,5:34:35 PM,14,4,5:53:19 PM,6,25.9,,-1.2551895,36.7822034,-1.280753,36.8191175,Rider_Id_281,1124
-Order_No_5114,User_Id_646,Bike,2,Personal,18,4,11:01:43 AM,18,4,11:03:39 AM,18,4,11:10:08 AM,18,4,11:16:41 AM,18,4,11:34:35 AM,4,25.3,,-1.2661263999999999,36.8030359,-1.2904753,36.8144721,Rider_Id_268,1074
-Order_No_28139,User_Id_136,Bike,3,Business,5,2,9:46:12 AM,5,2,10:37:34 AM,5,2,10:42:53 AM,5,2,10:53:10 AM,5,2,11:02:15 AM,4,21.6,,-1.3169591,36.7000029,-1.3326173000000001,36.71462329999999,Rider_Id_116,545
-Order_No_3215,User_Id_3688,Bike,3,Business,11,2,2:52:14 PM,11,2,2:53:35 PM,11,2,2:56:05 PM,11,2,3:02:13 PM,11,2,3:49:30 PM,12,21.4,,-1.2551895,36.7822034,-1.3104733999999998,36.7363698,Rider_Id_161,2837
-Order_No_22271,User_Id_121,Bike,1,Personal,15,3,8:23:00 AM,15,3,8:23:20 AM,15,3,8:28:56 AM,15,3,8:31:45 AM,15,3,8:52:30 AM,10,16.2,,-1.2367559,36.8082168,-1.2733079999999999,36.802847,Rider_Id_208,1245
-Order_No_4746,User_Id_391,Bike,3,Business,18,1,10:26:43 AM,18,1,10:28:30 AM,18,1,11:40:27 AM,18,1,12:32:37 PM,18,1,12:33:25 PM,5,27.9,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_314,48
-Order_No_22289,User_Id_2544,Bike,3,Personal,23,4,2:27:55 PM,23,4,2:28:28 PM,23,4,2:43:08 PM,23,4,2:48:35 PM,23,4,2:57:23 PM,4,24.7,,-1.2664677,36.779325799999995,-1.2580991000000001,36.794151899999996,Rider_Id_269,528
-Order_No_13996,User_Id_3621,Bike,3,Business,3,6,10:20:15 AM,3,6,10:24:53 AM,3,6,10:36:17 AM,3,6,10:37:19 AM,3,6,10:46:26 AM,4,23.6,,-1.2836583000000001,36.7886014,-1.2899256000000001,36.815551,Rider_Id_250,547
-Order_No_122,User_Id_2989,Bike,3,Business,19,4,2:24:58 PM,19,4,2:25:08 PM,19,4,2:30:38 PM,19,4,2:37:05 PM,19,4,3:06:57 PM,17,20.4,,-1.3463041999999998,36.768164,-1.2551895,36.7822034,Rider_Id_726,1792
-Order_No_27226,User_Id_173,Bike,3,Business,24,3,4:20:07 PM,24,3,4:21:17 PM,24,3,4:26:56 PM,24,3,4:31:04 PM,24,3,4:48:00 PM,3,18.6,,-1.2993231,36.7753372,-1.2846104999999999,36.788157399999996,Rider_Id_548,1016
-Order_No_5475,User_Id_2154,Bike,3,Business,26,2,11:30:30 AM,26,2,11:31:31 AM,26,2,11:50:03 AM,26,2,12:00:34 PM,26,2,12:12:30 PM,5,27.5,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_293,716
-Order_No_21362,User_Id_393,Bike,3,Business,23,2,11:03:21 AM,23,2,11:03:34 AM,23,2,11:03:40 AM,23,2,11:35:07 AM,23,2,11:35:18 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,11
-Order_No_11422,User_Id_3283,Bike,3,Business,29,2,12:58:51 PM,29,2,12:59:09 PM,29,2,1:02:15 PM,29,2,1:10:20 PM,29,2,1:21:27 PM,5,23.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_178,667
-Order_No_14322,User_Id_393,Bike,3,Business,4,2,4:27:15 PM,4,2,4:27:15 PM,4,2,4:28:52 PM,4,2,4:48:22 PM,4,2,4:48:24 PM,4,22.3,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_239,2
-Order_No_27355,User_Id_816,Bike,3,Business,22,5,10:24:31 AM,22,5,10:24:42 AM,22,5,10:26:39 AM,22,5,11:22:17 AM,22,5,11:45:15 AM,9,,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_248,1378
-Order_No_9514,User_Id_2947,Bike,1,Personal,17,1,3:57:34 PM,17,1,3:57:42 PM,17,1,4:02:23 PM,17,1,4:09:00 PM,17,1,4:19:19 PM,3,28.1,,-1.26249,36.799715,-1.2731477,36.795080299999995,Rider_Id_727,619
-Order_No_15781,User_Id_1393,Bike,1,Business,13,3,4:04:22 PM,13,3,5:09:10 PM,13,3,5:12:06 PM,13,3,5:25:12 PM,13,3,5:50:38 PM,12,22.2,,-1.2297202,36.874550799999994,-1.2601708999999999,36.8002238,Rider_Id_101,1526
-Order_No_24369,User_Id_1399,Bike,3,Personal,3,5,1:58:40 PM,3,5,1:59:29 PM,3,5,2:04:55 PM,3,5,2:08:15 PM,3,5,2:12:48 PM,1,25.1,,-1.2985031999999999,36.7821448,-1.3043649,36.7816132,Rider_Id_726,273
-Order_No_11600,User_Id_3076,Bike,1,Personal,6,1,7:59:08 PM,6,1,8:00:29 PM,6,1,8:34:14 PM,6,1,8:40:39 PM,6,1,8:52:35 PM,9,18.3,,-1.2606978000000002,36.7512297,-1.2629701,36.7936628,Rider_Id_116,716
-Order_No_1181,User_Id_3599,Bike,3,Business,31,3,12:14:26 PM,31,3,12:53:16 PM,31,3,1:02:56 PM,31,3,1:15:47 PM,31,3,1:39:01 PM,14,24.7,,-1.3302996,36.8707935,-1.3374736,36.9207076,Rider_Id_62,1394
-Order_No_7300,User_Id_3701,Bike,3,Business,12,1,11:28:30 AM,12,1,11:35:02 AM,12,1,11:42:28 AM,12,1,11:49:55 AM,12,1,12:12:01 PM,8,23.7,,-1.2903643,36.8319433,-1.2684239,36.79524620000001,Rider_Id_403,1326
-Order_No_1754,User_Id_710,Bike,1,Personal,13,1,3:24:44 PM,13,1,3:25:16 PM,13,1,3:45:46 PM,13,1,4:05:51 PM,13,1,4:22:17 PM,11,19.7,,-1.2482781,36.884091999999995,-1.2610837,36.8021315,Rider_Id_144,986
-Order_No_25205,User_Id_2057,Bike,2,Personal,3,2,9:47:33 AM,3,2,9:49:32 AM,3,2,10:08:12 AM,3,2,10:17:15 AM,3,2,11:31:34 AM,9,15.5,2.1,-1.2630521000000001,36.806801899999996,-1.2976368,36.751982700000006,Rider_Id_379,4459
-Order_No_20800,User_Id_1509,Bike,3,Personal,8,2,10:54:44 AM,8,2,11:10:26 AM,8,2,11:55:13 AM,8,2,12:01:26 PM,8,2,12:34:01 PM,18,21.3,,-1.355948,36.7440908,-1.2713222,36.792263299999995,Rider_Id_226,1955
-Order_No_19559,User_Id_2207,Bike,3,Business,28,2,2:07:26 PM,28,2,2:07:38 PM,28,2,2:27:51 PM,28,2,2:39:22 PM,28,2,3:14:29 PM,12,19.7,,-1.2100103000000002,36.797424,-1.2696908000000002,36.81519,Rider_Id_882,2107
-Order_No_4529,User_Id_1873,Bike,3,Business,5,2,2:25:25 PM,5,2,2:26:10 PM,5,2,2:28:20 PM,5,2,2:35:39 PM,5,2,3:46:23 PM,21,,,-1.2571472,36.795063299999995,-1.3190102,36.923069299999995,Rider_Id_620,4244
-Order_No_2276,User_Id_1075,Bike,3,Business,5,6,11:47:19 AM,5,6,11:47:31 AM,5,6,11:52:33 AM,5,6,11:58:19 AM,5,6,12:04:22 PM,1,22.5,,-1.2649605,36.798177700000004,-1.2527958000000001,36.8003131,Rider_Id_634,363
-Order_No_17850,User_Id_2089,Bike,3,Business,8,4,5:05:26 PM,8,4,5:19:04 PM,8,4,5:42:55 PM,8,4,6:11:36 PM,8,4,6:29:10 PM,12,25.6,,-1.2213302,36.8494323,-1.2610721999999999,36.814707399999996,Rider_Id_200,1054
-Order_No_741,User_Id_2457,Bike,3,Business,9,4,2:46:16 PM,9,4,2:58:24 PM,9,4,3:08:21 PM,9,4,3:15:05 PM,9,4,3:50:21 PM,22,,,-1.2551895,36.7822034,-1.3692201000000002,36.9252718,Rider_Id_422,2116
-Order_No_10173,User_Id_2532,Bike,3,Business,13,3,11:42:50 AM,13,3,11:45:10 AM,13,3,11:52:10 AM,13,3,11:56:48 AM,13,3,1:03:38 PM,8,25.0,,-1.2551895,36.7822034,-1.2904049,36.8055154,Rider_Id_778,4010
-Order_No_11431,User_Id_186,Bike,3,Business,17,5,3:36:46 PM,17,5,3:36:59 PM,17,5,3:37:27 PM,17,5,3:45:14 PM,17,5,4:06:36 PM,5,,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_564,1282
-Order_No_8748,User_Id_2306,Bike,3,Business,12,2,10:27:58 AM,12,2,10:28:19 AM,12,2,10:48:45 AM,12,2,10:56:35 AM,12,2,11:31:27 AM,10,28.0,,-1.2271998,36.808490500000005,-1.2787076000000002,36.8315314,Rider_Id_785,2092
-Order_No_19430,User_Id_435,Bike,3,Business,27,1,12:18:45 PM,27,1,12:20:44 PM,27,1,12:40:43 PM,27,1,1:35:00 PM,27,1,1:44:40 PM,5,23.2,,-1.300921,36.828195,-1.2797623,36.805068299999995,Rider_Id_139,580
-Order_No_27788,User_Id_1642,Bike,3,Business,10,3,3:00:12 PM,10,3,3:04:15 PM,10,3,3:24:11 PM,10,3,3:29:00 PM,10,3,3:43:25 PM,5,30.7,,-1.2728069,36.7962362,-1.2876365,36.816096,Rider_Id_709,865
-Order_No_9986,User_Id_2763,Bike,3,Business,10,3,11:20:10 AM,10,3,11:21:52 AM,10,3,11:25:12 AM,10,3,11:27:19 AM,10,3,12:06:31 PM,14,27.6,,-1.2551895,36.7822034,-1.3015998,36.8764939,Rider_Id_835,2352
-Order_No_13523,User_Id_3686,Bike,3,Business,13,3,11:20:24 AM,13,3,11:20:32 AM,13,3,11:20:54 AM,13,3,11:25:49 AM,13,3,11:59:45 AM,11,25.0,,-1.2551895,36.7822034,-1.3197323,36.8120386,Rider_Id_751,2036
-Order_No_3803,User_Id_393,Bike,3,Business,8,1,8:35:17 AM,8,1,8:35:55 AM,8,1,8:47:31 AM,8,1,9:04:15 AM,8,1,9:27:15 AM,11,22.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_690,1380
-Order_No_22090,User_Id_1724,Bike,3,Business,21,4,7:58:08 AM,21,4,7:59:38 AM,21,4,8:03:31 AM,21,4,8:25:32 AM,21,4,9:26:01 AM,15,22.0,,-1.2551895,36.7822034,-1.3267688999999998,36.8756255,Rider_Id_875,3629
-Order_No_20826,User_Id_2801,Bike,3,Business,9,2,11:24:14 AM,9,2,11:24:52 AM,9,2,11:35:13 AM,9,2,11:41:32 AM,9,2,12:00:16 PM,5,27.2,,-1.2595421999999998,36.7871178,-1.269534,36.80697,Rider_Id_690,1124
-Order_No_8904,User_Id_3517,Bike,3,Business,24,3,3:21:12 PM,24,3,3:21:53 PM,24,3,3:22:35 PM,24,3,4:10:16 PM,24,3,4:41:18 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_206,1862
-Order_No_27962,User_Id_936,Bike,1,Personal,19,4,12:50:46 PM,19,4,12:50:51 PM,19,4,12:50:56 PM,19,4,1:12:15 PM,19,4,2:03:15 PM,14,20.4,,-1.307376,36.813404,-1.2698167999999999,36.7421898,Rider_Id_248,3060
-Order_No_22086,User_Id_1882,Bike,3,Business,31,4,8:34:24 AM,31,4,8:51:59 AM,31,4,9:15:22 AM,31,4,9:33:53 AM,31,4,9:45:39 AM,7,20.5,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_412,706
-Order_No_749,User_Id_240,Bike,2,Personal,23,4,11:10:16 AM,23,4,11:10:37 AM,23,4,11:33:57 AM,23,4,11:38:11 AM,23,4,12:14:23 PM,10,18.7,,-1.2931446000000002,36.7613971,-1.3094562,36.8167806,Rider_Id_393,2172
-Order_No_17687,User_Id_1357,Bike,1,Personal,14,5,10:03:41 AM,14,5,10:05:35 AM,14,5,10:19:14 AM,14,5,10:26:15 AM,14,5,10:40:41 AM,5,18.2,,-1.2663525,36.812833700000006,-1.2897215,36.7945959,Rider_Id_858,866
-Order_No_10118,User_Id_2763,Bike,3,Business,4,1,2:31:33 PM,4,1,2:34:46 PM,4,1,2:47:27 PM,4,1,2:57:05 PM,4,1,3:52:47 PM,13,,,-1.2551895,36.7822034,-1.3110966,36.856707,Rider_Id_44,3342
-Order_No_18845,User_Id_3557,Bike,3,Business,19,3,11:36:42 AM,19,3,11:37:02 AM,19,3,11:45:43 AM,19,3,11:58:59 AM,19,3,12:11:50 PM,3,,,-1.2740396,36.7993518,-1.2546732,36.808679600000005,Rider_Id_882,771
-Order_No_3892,User_Id_1085,Bike,3,Business,30,5,11:16:46 AM,30,5,11:18:50 AM,30,5,11:26:57 AM,30,5,11:40:09 AM,30,5,12:48:01 PM,18,23.8,,-1.316539,36.706856200000004,-1.2536275,36.798793200000006,Rider_Id_655,4072
-Order_No_16264,User_Id_733,Bike,3,Business,11,1,2:30:36 PM,11,1,2:33:11 PM,11,1,2:36:32 PM,11,1,2:42:29 PM,11,1,2:51:11 PM,3,29.1,,-1.2551895,36.7822034,-1.2649605,36.798177700000004,Rider_Id_576,522
-Order_No_23685,User_Id_635,Bike,3,Business,1,1,2:29:32 PM,1,1,2:35:25 PM,1,1,3:01:33 PM,1,1,3:10:05 PM,1,1,3:41:59 PM,8,,,-1.2780878,36.846943599999996,-1.2602337,36.79905479999999,Rider_Id_118,1914
-Order_No_21001,User_Id_2069,Bike,3,Business,25,1,10:02:21 AM,25,1,10:02:50 AM,25,1,10:26:34 AM,25,1,10:36:00 AM,25,1,10:58:00 AM,16,25.7,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_426,1320
-Order_No_1352,User_Id_2629,Bike,2,Personal,14,5,12:36:22 PM,14,5,12:37:32 PM,14,5,12:44:43 PM,14,5,12:45:45 PM,14,5,12:53:25 PM,5,24.6,,-1.2925316999999998,36.8021114,-1.2753108999999998,36.8130185,Rider_Id_726,460
-Order_No_16238,User_Id_203,Bike,3,Business,28,4,7:09:46 PM,28,4,7:16:21 PM,28,4,7:23:10 PM,28,4,7:29:35 PM,28,4,7:47:58 PM,8,28.1,,-1.2963097,36.7688221,-1.2622145,36.8087391,Rider_Id_164,1103
-Order_No_11715,User_Id_635,Bike,3,Business,24,3,3:06:18 PM,24,3,3:59:19 PM,24,3,4:11:11 PM,24,3,5:03:37 PM,24,3,5:04:45 PM,7,18.2,,-1.3016417,36.8271679,-1.2599563,36.799343900000004,Rider_Id_775,68
-Order_No_15505,User_Id_1081,Bike,3,Business,13,3,2:17:27 PM,13,3,2:17:42 PM,13,3,2:31:25 PM,13,3,2:36:53 PM,13,3,3:07:45 PM,3,27.4,,-1.2551895,36.7822034,-1.25537,36.7756,Rider_Id_875,1852
-Order_No_26388,User_Id_1341,Bike,3,Business,27,1,1:27:37 PM,27,1,1:28:20 PM,27,1,1:34:27 PM,27,1,1:54:30 PM,27,1,2:07:47 PM,9,23.3,,-1.3040553,36.8092658,-1.331619,36.847976,Rider_Id_72,797
-Order_No_16078,User_Id_3510,Bike,1,Business,31,2,1:29:02 PM,31,2,1:30:01 PM,31,2,1:39:45 PM,31,2,1:54:17 PM,31,2,2:17:00 PM,9,22.1,,-1.2472211000000002,36.7826109,-1.2892898000000002,36.7874315,Rider_Id_273,1363
-Order_No_9670,User_Id_3698,Bike,3,Business,18,4,1:41:46 PM,18,4,1:46:57 PM,18,4,2:18:40 PM,18,4,2:23:35 PM,18,4,3:00:17 PM,11,,,-1.263279,36.909609,-1.3244885,36.897791999999995,Rider_Id_697,2202
-Order_No_21052,User_Id_978,Bike,1,Personal,31,4,1:14:07 PM,31,4,1:39:43 PM,31,4,1:51:17 PM,31,4,2:07:57 PM,31,4,2:27:52 PM,6,23.1,,-1.3084067,36.83450139999999,-1.2763950000000002,36.825707,Rider_Id_116,1195
-Order_No_16458,User_Id_2412,Bike,3,Business,10,1,8:22:50 AM,10,1,8:30:30 AM,10,1,8:47:44 AM,10,1,8:57:13 AM,10,1,9:24:01 AM,11,16.6,,-1.2283403,36.8822756,-1.28878,36.8168312,Rider_Id_845,1608
-Order_No_24895,User_Id_633,Bike,3,Business,19,2,11:32:18 AM,19,2,11:32:36 AM,19,2,11:48:54 AM,19,2,12:14:29 PM,19,2,12:36:27 PM,11,27.5,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_855,1318
-Order_No_3182,User_Id_393,Bike,3,Business,16,2,1:02:57 PM,16,2,1:15:37 PM,16,2,1:25:35 PM,16,2,2:01:47 PM,16,2,2:02:03 PM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_709,16
-Order_No_21838,User_Id_2994,Bike,1,Personal,10,3,5:24:13 PM,10,3,5:37:11 PM,10,3,5:49:23 PM,10,3,6:01:00 PM,10,3,6:09:17 PM,2,23.0,,-1.2860557,36.8232389,-1.2896659,36.808642600000006,Rider_Id_148,497
-Order_No_1302,User_Id_3641,Bike,3,Business,4,4,3:18:54 PM,4,4,3:19:07 PM,4,4,3:24:59 PM,4,4,3:34:19 PM,4,4,4:11:10 PM,5,24.8,,-1.2551895,36.7822034,-1.2661639999999998,36.800460799999996,Rider_Id_178,2211
-Order_No_6912,User_Id_3621,Bike,3,Business,14,2,10:26:09 AM,14,2,10:27:12 AM,14,2,10:35:04 AM,14,2,10:38:21 AM,14,2,10:48:55 AM,4,,,-1.2836583000000001,36.7886014,-1.2877378000000002,36.809892600000005,Rider_Id_709,634
-Order_No_26293,User_Id_2956,Bike,3,Business,6,1,10:53:03 AM,6,1,10:53:13 AM,6,1,11:00:31 AM,6,1,11:10:30 AM,6,1,11:36:41 AM,6,20.6,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_19,1571
-Order_No_7788,User_Id_1955,Bike,3,Business,19,3,4:20:34 PM,19,3,4:21:48 PM,19,3,4:29:50 PM,19,3,4:35:24 PM,19,3,4:54:15 PM,8,23.3,,-1.2854406,36.789409,-1.2571472,36.795063299999995,Rider_Id_410,1131
-Order_No_22281,User_Id_1620,Bike,1,Personal,9,2,4:07:14 PM,9,2,4:10:28 PM,9,2,5:40:57 PM,9,2,5:45:01 PM,9,2,6:30:15 PM,13,19.2,,-1.227132,36.8374277,-1.300921,36.828195,Rider_Id_427,2714
-Order_No_16916,User_Id_2561,Bike,1,Business,30,1,3:56:03 PM,30,1,4:00:17 PM,30,1,4:22:26 PM,30,1,4:24:25 PM,30,1,4:52:26 PM,6,20.6,,-1.2551895,36.7822034,-1.2833106,36.82402379999999,Rider_Id_882,1681
-Order_No_11479,User_Id_3291,Bike,3,Business,28,3,1:04:27 PM,28,3,1:27:16 PM,28,3,1:28:04 PM,28,3,1:51:57 PM,28,3,2:05:35 PM,11,,,-1.303596,36.778378000000004,-1.2600926000000001,36.8088685,Rider_Id_179,818
-Order_No_16819,User_Id_1246,Bike,3,Business,11,4,9:33:40 AM,11,4,9:33:50 AM,11,4,10:32:34 AM,11,4,10:56:17 AM,11,4,11:36:11 AM,26,25.5,,-1.3700383,36.9190174,-1.27596,36.766263,Rider_Id_863,2394
-Order_No_5968,User_Id_2931,Bike,3,Business,28,4,1:21:11 PM,28,4,1:22:21 PM,28,4,1:31:48 PM,28,4,1:36:57 PM,28,4,1:57:19 PM,8,30.0,,-1.2452664,36.802854100000005,-1.29817,36.800141700000005,Rider_Id_383,1222
-Order_No_7315,User_Id_238,Bike,3,Business,18,1,3:27:11 PM,18,1,3:28:59 PM,18,1,3:41:20 PM,18,1,3:43:21 PM,18,1,4:10:00 PM,15,30.7,,-1.3077869,36.844320700000004,-1.3559093,36.910045200000006,Rider_Id_418,1599
-Order_No_13678,User_Id_2701,Bike,3,Business,15,6,1:44:08 PM,15,6,2:00:51 PM,15,6,2:00:59 PM,15,6,2:24:58 PM,15,6,2:50:06 PM,6,23.4,,-1.28878,36.8168312,-1.2765736,36.851364600000004,Rider_Id_26,1508
-Order_No_24777,User_Id_2523,Bike,3,Business,23,2,9:29:00 AM,23,2,9:29:13 AM,23,2,9:33:13 AM,23,2,9:39:44 AM,23,2,10:09:15 AM,14,,,-1.2551895,36.7822034,-1.329568,36.8520825,Rider_Id_958,1771
-Order_No_17443,User_Id_3547,Bike,3,Business,17,3,11:56:38 AM,17,3,11:57:51 AM,17,3,12:05:40 PM,17,3,1:08:53 PM,17,3,2:24:54 PM,21,,,-1.3285517,36.9018,-1.2761408,36.7710839,Rider_Id_883,4561
-Order_No_22647,User_Id_3517,Bike,3,Business,19,2,9:39:08 AM,19,2,9:39:29 AM,19,2,9:49:25 AM,19,2,10:25:07 AM,19,2,11:27:08 AM,27,21.2,,-1.300921,36.828195,-1.15135,36.9566534,Rider_Id_519,3721
-Order_No_2558,User_Id_478,Bike,3,Business,7,2,3:00:25 PM,7,2,3:02:03 PM,7,2,3:15:30 PM,7,2,3:25:46 PM,7,2,3:48:51 PM,6,,,-1.2853965,36.8183118,-1.3072409999999999,36.830236799999994,Rider_Id_843,1385
-Order_No_27329,User_Id_60,Bike,3,Business,26,5,9:57:16 AM,26,5,9:57:28 AM,26,5,10:34:32 AM,26,5,10:42:20 AM,26,5,10:54:57 AM,7,,,-1.228911,36.881876,-1.2297799999999999,36.836215,Rider_Id_952,757
-Order_No_13455,User_Id_2066,Bike,3,Business,6,5,1:33:47 PM,6,5,1:35:23 PM,6,5,1:40:07 PM,6,5,1:44:55 PM,6,5,1:57:59 PM,5,22.5,,-1.316961,36.820337,-1.2896398999999998,36.812449799999996,Rider_Id_535,784
-Order_No_24050,User_Id_2195,Bike,3,Business,15,6,10:38:01 AM,15,6,10:38:13 AM,15,6,10:44:54 AM,15,6,11:02:38 AM,15,6,11:16:02 AM,9,19.4,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_591,804
-Order_No_25721,User_Id_1882,Bike,3,Business,11,1,2:27:42 PM,11,1,2:31:20 PM,11,1,2:45:08 PM,11,1,3:18:08 PM,11,1,3:23:40 PM,8,29.0,,-1.3014461000000002,36.7661381,-1.2628638,36.8070399,Rider_Id_678,332
-Order_No_23358,User_Id_3283,Bike,3,Business,9,4,9:39:17 AM,9,4,9:39:40 AM,9,4,9:51:05 AM,9,4,9:59:59 AM,9,4,10:14:23 AM,5,,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_959,864
-Order_No_8722,User_Id_2330,Bike,3,Business,7,5,10:47:39 AM,7,5,10:49:59 AM,7,5,11:29:25 AM,7,5,11:43:43 AM,7,5,11:43:51 AM,3,,,-1.2890715,36.8157588,-1.2915246999999999,36.805575700000006,Rider_Id_271,8
-Order_No_19582,User_Id_2113,Bike,3,Personal,13,1,10:48:58 AM,13,1,10:49:43 AM,13,1,11:10:51 AM,13,1,11:19:29 AM,13,1,11:36:59 AM,12,17.4,,-1.2624632,36.8405321,-1.275442,36.766020700000006,Rider_Id_174,1050
-Order_No_22728,User_Id_2587,Bike,3,Business,16,2,2:21:27 PM,16,2,2:24:23 PM,16,2,2:41:02 PM,16,2,2:46:25 PM,16,2,3:01:22 PM,5,,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_438,897
-Order_No_20125,User_Id_190,Bike,3,Business,29,1,4:11:04 PM,29,1,4:12:11 PM,29,1,4:28:57 PM,29,1,4:43:21 PM,29,1,5:02:34 PM,15,,,-1.3456714,36.919369200000006,-1.3270347,36.866736200000005,Rider_Id_940,1153
-Order_No_23757,User_Id_1500,Bike,3,Business,10,5,10:18:08 AM,10,5,10:18:21 AM,10,5,10:25:37 AM,10,5,10:53:10 AM,10,5,11:08:44 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_843,934
-Order_No_10659,User_Id_1245,Bike,3,Business,17,5,1:04:44 PM,17,5,1:05:01 PM,17,5,1:15:24 PM,17,5,1:22:36 PM,17,5,2:01:59 PM,11,,,-1.3198615,36.7110323,-1.2929937,36.7815349,Rider_Id_204,2363
-Order_No_22986,User_Id_136,Bike,3,Business,8,5,11:47:18 AM,8,5,11:48:07 AM,8,5,12:03:13 PM,8,5,12:27:36 PM,8,5,1:04:47 PM,20,24.3,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_440,2231
-Order_No_3880,User_Id_3203,Bike,3,Business,25,3,2:17:10 PM,25,3,2:17:18 PM,25,3,2:25:21 PM,25,3,2:31:41 PM,25,3,2:58:28 PM,16,17.6,,-1.2628473,36.781805,-1.228911,36.881876,Rider_Id_399,1607
-Order_No_24640,User_Id_2390,Bike,2,Personal,16,1,11:28:41 AM,16,1,11:29:10 AM,16,1,11:33:48 AM,16,1,11:38:10 AM,16,1,11:49:16 AM,5,19.2,,-1.27401,36.819196999999996,-1.2676163999999999,36.8075243,Rider_Id_726,666
-Order_No_62,User_Id_1948,Bike,1,Personal,5,2,11:27:24 AM,5,2,11:27:30 AM,5,2,11:57:31 AM,5,2,12:11:41 PM,5,2,12:34:51 PM,16,21.3,,-1.3312306,36.85636829999999,-1.2454036,36.7866654,Rider_Id_886,1390
-Order_No_6264,User_Id_1086,Bike,3,Business,21,5,2:34:34 PM,21,5,2:34:44 PM,21,5,2:42:05 PM,21,5,2:45:09 PM,21,5,3:06:56 PM,6,24.8,,-1.2653185,36.8358841,-1.259213,36.811003,Rider_Id_231,1307
-Order_No_5842,User_Id_1787,Bike,3,Business,6,4,9:56:28 AM,6,4,9:56:52 AM,6,4,10:38:23 AM,6,4,10:49:39 AM,6,4,11:23:27 AM,15,23.9,,-1.2283403,36.8822756,-1.2551895,36.7822034,Rider_Id_669,2028
-Order_No_9082,User_Id_1017,Bike,3,Business,29,3,4:40:13 PM,29,3,4:42:32 PM,29,3,4:50:55 PM,29,3,4:52:59 PM,29,3,5:19:48 PM,3,21.3,,-1.2551895,36.7822034,-1.273488,36.804158,Rider_Id_751,1609
-Order_No_5990,User_Id_2908,Bike,3,Personal,9,4,10:55:37 AM,9,4,10:55:55 AM,9,4,10:56:07 AM,9,4,11:03:45 AM,9,4,11:15:25 AM,6,,,-1.2890399,36.8164942,-1.2929747,36.76463579999999,Rider_Id_564,700
-Order_No_14176,User_Id_2562,Bike,1,Personal,26,6,9:49:32 AM,26,6,10:39:23 AM,26,6,10:50:15 AM,26,6,10:53:20 AM,26,6,11:48:52 AM,7,22.7,,-1.2830603,36.8298283,-1.2894957,36.8709153,Rider_Id_351,3332
-Order_No_6540,User_Id_1329,Bike,3,Business,1,4,12:20:05 PM,1,4,12:21:54 PM,1,4,12:31:25 PM,1,4,12:34:45 PM,1,4,12:49:46 PM,5,,,-1.273056,36.811298,-1.293973,36.80169,Rider_Id_674,901
-Order_No_518,User_Id_3201,Bike,3,Business,31,4,1:27:11 PM,31,4,1:28:12 PM,31,4,1:46:07 PM,31,4,1:55:26 PM,31,4,2:15:32 PM,3,28.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_467,1206
-Order_No_18060,User_Id_3495,Bike,1,Personal,11,6,10:41:30 AM,11,6,10:42:12 AM,11,6,10:49:19 AM,11,6,10:52:10 AM,11,6,11:01:19 AM,7,21.1,,-1.3047132,36.808955299999994,-1.2866305,36.7754914,Rider_Id_207,549
-Order_No_11510,User_Id_3106,Bike,1,Personal,22,5,9:18:38 AM,22,5,9:18:48 AM,22,5,9:41:49 AM,22,5,9:50:00 AM,22,5,10:44:14 AM,22,16.5,,-1.2763120000000001,36.882742,-1.1850563,36.768693,Rider_Id_549,3254
-Order_No_20608,User_Id_393,Bike,3,Business,6,3,4:13:20 PM,6,3,4:13:37 PM,6,3,4:16:19 PM,6,3,4:20:30 PM,6,3,4:42:25 PM,3,,,-1.3167113000000001,36.8301563,-1.3210541,36.8169608,Rider_Id_842,1315
-Order_No_22880,User_Id_2240,Bike,1,Personal,8,3,1:38:06 PM,8,3,1:39:40 PM,8,3,1:41:11 PM,8,3,1:51:42 PM,8,3,1:58:53 PM,3,25.6,,-1.2607786,36.7916657,-1.2628638,36.8070399,Rider_Id_574,431
-Order_No_1567,User_Id_186,Bike,3,Business,5,2,3:37:08 PM,5,2,3:42:48 PM,5,2,4:09:12 PM,5,2,4:14:13 PM,5,2,4:36:57 PM,6,27.7,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_85,1364
-Order_No_4843,User_Id_2069,Bike,3,Business,7,5,3:51:27 PM,7,5,3:56:24 PM,7,5,4:12:58 PM,7,5,4:15:10 PM,7,5,4:42:28 PM,6,,,-1.2296618000000001,36.84382720000001,-1.228911,36.881876,Rider_Id_834,1638
-Order_No_14743,User_Id_3331,Bike,3,Business,19,4,10:29:55 AM,19,4,10:53:59 AM,19,4,10:57:09 AM,19,4,11:00:36 AM,19,4,11:48:43 AM,18,20.4,,-1.2551895,36.7822034,-1.353683,36.7611562,Rider_Id_147,2887
-Order_No_5399,User_Id_1724,Bike,3,Business,3,3,1:48:38 PM,3,3,1:49:21 PM,3,3,1:55:38 PM,3,3,1:58:38 PM,3,3,3:38:31 PM,26,28.1,,-1.303031,36.758577,-1.1848195,36.915471999999994,Rider_Id_599,5993
-Order_No_17363,User_Id_393,Bike,3,Business,28,4,11:24:21 AM,28,4,11:24:37 AM,28,4,11:28:40 AM,28,4,11:45:20 AM,28,4,12:07:36 PM,11,23.8,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_901,1336
-Order_No_10780,User_Id_393,Bike,3,Business,14,4,12:01:43 PM,14,4,12:10:22 PM,14,4,12:21:58 PM,14,4,12:44:50 PM,14,4,12:59:53 PM,5,23.9,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_856,903
-Order_No_27548,User_Id_2786,Bike,1,Personal,31,2,2:33:27 PM,31,2,2:33:52 PM,31,2,2:53:01 PM,31,2,2:59:22 PM,31,2,3:28:36 PM,14,21.9,,-1.316637,36.7009304,-1.2738348,36.7852209,Rider_Id_670,1754
-Order_No_7171,User_Id_970,Bike,3,Business,16,4,9:46:33 AM,16,4,9:49:23 AM,16,4,9:58:02 AM,16,4,10:05:05 AM,16,4,10:20:36 AM,7,,,-1.2551895,36.7822034,-1.2902589,36.8219257,Rider_Id_344,931
-Order_No_14574,User_Id_3091,Bike,3,Business,5,3,11:54:43 AM,5,3,1:32:04 PM,5,3,1:50:27 PM,5,3,1:59:23 PM,5,3,2:42:57 PM,19,25.1,,-1.2859912,36.8756811,-1.3012008,36.764868,Rider_Id_510,2614
-Order_No_7644,User_Id_2627,Bike,3,Business,24,5,3:36:56 PM,24,5,3:37:34 PM,24,5,3:47:33 PM,24,5,3:59:36 PM,24,5,4:04:10 PM,1,22.8,,-1.2571472,36.795063299999995,-1.2584143,36.8048002,Rider_Id_341,274
-Order_No_10580,User_Id_2523,Bike,3,Business,25,4,12:39:47 PM,25,4,12:56:42 PM,25,4,1:10:05 PM,25,4,1:11:10 PM,25,4,1:55:59 PM,7,19.8,,-1.2551895,36.7822034,-1.2989933999999999,36.813669399999995,Rider_Id_578,2689
-Order_No_25970,User_Id_265,Bike,3,Business,5,2,1:55:21 PM,5,2,1:57:58 PM,5,2,2:14:55 PM,5,2,2:22:53 PM,5,2,2:50:31 PM,11,27.6,,-1.2433673,36.7849565,-1.300921,36.828195,Rider_Id_148,1658
-Order_No_1334,User_Id_265,Bike,3,Business,9,4,2:56:03 PM,9,4,3:11:10 PM,9,4,3:29:00 PM,9,4,3:36:10 PM,9,4,3:55:21 PM,15,25.6,,-1.3486163,36.732494,-1.300921,36.828195,Rider_Id_636,1151
-Order_No_7028,User_Id_2330,Bike,3,Business,2,4,5:25:22 PM,2,4,5:25:31 PM,2,4,5:26:23 PM,2,4,5:42:31 PM,2,4,6:05:17 PM,9,,,-1.2536275,36.798793200000006,-1.2687407,36.7373043,Rider_Id_205,1366
-Order_No_3082,User_Id_2956,Bike,3,Business,13,3,3:59:39 PM,13,3,4:19:06 PM,13,3,4:19:18 PM,13,3,5:24:34 PM,13,3,6:36:12 PM,6,26.1,,-1.290894,36.822971,-1.2584143,36.8048002,Rider_Id_86,4298
-Order_No_18021,User_Id_2457,Bike,3,Business,30,2,11:58:26 AM,30,2,12:00:57 PM,30,2,12:09:14 PM,30,2,12:12:19 PM,30,2,12:57:34 PM,8,22.5,,-1.2551895,36.7822034,-1.3025131,36.817387,Rider_Id_290,2715
-Order_No_18435,User_Id_2423,Bike,3,Business,22,5,1:18:29 PM,22,5,1:20:12 PM,22,5,1:23:19 PM,22,5,1:32:45 PM,22,5,1:55:01 PM,9,31.1,,-1.2551895,36.7822034,-1.2940713,36.7890758,Rider_Id_177,1336
-Order_No_10889,User_Id_238,Bike,3,Business,14,2,11:52:29 AM,14,2,11:52:44 AM,14,2,11:53:17 AM,14,2,12:05:14 PM,14,2,12:05:32 PM,3,,,-1.3077869,36.844320700000004,-1.308366,36.823613,Rider_Id_349,18
-Order_No_12627,User_Id_3711,Bike,3,Business,28,3,1:48:13 PM,28,3,1:48:55 PM,28,3,1:52:54 PM,28,3,1:58:41 PM,28,3,2:28:36 PM,17,,,-1.3014461000000002,36.7661381,-1.369429,36.762751,Rider_Id_609,1795
-Order_No_8535,User_Id_1721,Bike,3,Business,30,2,1:13:54 PM,30,2,1:19:02 PM,30,2,1:24:52 PM,30,2,1:30:09 PM,30,2,1:58:59 PM,21,,,-1.2551895,36.7822034,-1.332624,36.913187,Rider_Id_438,1730
-Order_No_21063,User_Id_1004,Bike,3,Business,4,2,2:21:55 PM,4,2,2:23:00 PM,4,2,3:19:37 PM,4,2,3:28:33 PM,4,2,4:05:07 PM,12,22.4,,-1.2662728,36.8151265,-1.3250048,36.869718799999994,Rider_Id_856,2194
-Order_No_19704,User_Id_2823,Bike,3,Business,21,4,3:20:17 PM,21,4,3:20:56 PM,21,4,3:26:05 PM,21,4,3:48:19 PM,21,4,4:08:11 PM,8,22.4,,-1.2615888000000002,36.7928732,-1.3014461000000002,36.7661381,Rider_Id_116,1192
-Order_No_6872,User_Id_3300,Bike,3,Business,12,3,11:32:01 AM,12,3,11:38:49 AM,12,3,11:54:51 AM,12,3,11:58:18 AM,12,3,12:19:53 PM,7,19.4,,-1.2628473,36.781805,-1.303596,36.778378000000004,Rider_Id_582,1295
-Order_No_23495,User_Id_2532,Bike,3,Business,17,5,12:48:08 PM,17,5,12:48:34 PM,17,5,12:57:56 PM,17,5,1:02:04 PM,17,5,1:15:53 PM,8,,,-1.2551895,36.7822034,-1.2824911,36.8218042,Rider_Id_953,829
-Order_No_163,User_Id_3557,Bike,3,Business,8,5,12:32:19 PM,8,5,12:34:36 PM,8,5,12:42:55 PM,8,5,12:47:44 PM,8,5,12:56:54 PM,2,28.2,,-1.2546732,36.808679600000005,-1.2724096,36.8135361,Rider_Id_932,550
-Order_No_20672,User_Id_1333,Bike,2,Personal,18,5,12:36:59 PM,18,5,12:38:43 PM,18,5,12:53:33 PM,18,5,12:59:10 PM,18,5,1:47:09 PM,21,21.2,,-1.2223457,36.8904633,-1.2927531,36.7651386,Rider_Id_105,2879
-Order_No_13771,User_Id_1856,Bike,3,Business,9,5,9:58:10 AM,9,5,10:03:15 AM,9,5,10:36:43 AM,9,5,10:39:29 AM,9,5,11:33:47 AM,5,25.5,,-1.273009,36.802807,-1.2890893,36.8268633,Rider_Id_660,3258
-Order_No_24508,User_Id_1687,Bike,3,Business,29,5,11:30:01 AM,29,5,11:30:56 AM,29,5,11:35:11 AM,29,5,11:43:23 AM,29,5,11:50:31 AM,3,26.8,,-1.2626574,36.8014034,-1.2732803,36.794037700000004,Rider_Id_564,428
-Order_No_12240,User_Id_11,Bike,1,Personal,4,4,2:50:58 PM,4,4,2:53:08 PM,4,4,3:13:36 PM,4,4,3:19:51 PM,4,4,3:44:51 PM,8,24.6,,-1.2604472,36.8365287,-1.2962731,36.811186299999996,Rider_Id_795,1500
-Order_No_17560,User_Id_1882,Bike,3,Business,22,4,7:22:19 AM,22,4,8:05:23 AM,22,4,8:30:12 AM,22,4,8:44:27 AM,22,4,9:02:56 AM,5,19.4,3.1,-1.2958783,36.7928125,-1.2877809999999998,36.817865999999995,Rider_Id_612,1109
-Order_No_18328,User_Id_2112,Bike,3,Business,27,3,12:07:57 PM,27,3,12:08:56 PM,27,3,12:24:22 PM,27,3,12:53:15 PM,27,3,12:53:17 PM,14,26.2,,-1.2571472,36.795063299999995,-1.2156006000000001,36.8916865,Rider_Id_953,2
-Order_No_22548,User_Id_2399,Bike,1,Personal,24,5,1:12:17 PM,24,5,1:12:27 PM,24,5,1:16:32 PM,24,5,1:18:21 PM,24,5,2:04:45 PM,12,20.8,,-1.2551895,36.7822034,-1.3097713,36.849494,Rider_Id_668,2784
-Order_No_27501,User_Id_2436,Bike,3,Business,10,1,1:47:47 PM,10,1,2:33:27 PM,10,1,2:42:32 PM,10,1,3:38:57 PM,10,1,3:58:17 PM,9,18.7,,-1.2571472,36.795063299999995,-1.2295205,36.812042600000005,Rider_Id_685,1160
-Order_No_13252,User_Id_1500,Bike,3,Business,2,4,12:55:53 PM,2,4,12:56:49 PM,2,4,1:02:54 PM,2,4,1:05:28 PM,2,4,1:34:30 PM,13,,,-1.2638185,36.7930057,-1.3228003000000002,36.8306435,Rider_Id_155,1742
-Order_No_9102,User_Id_412,Bike,3,Business,16,3,12:17:27 PM,16,3,12:17:47 PM,16,3,12:33:41 PM,16,3,12:46:03 PM,16,3,1:12:34 PM,3,23.0,,-1.272828,36.816608,-1.266837,36.799248999999996,Rider_Id_611,1591
-Order_No_11595,User_Id_1642,Bike,3,Business,10,1,8:34:46 AM,10,1,8:35:15 AM,10,1,8:35:33 AM,10,1,8:50:32 AM,10,1,9:14:56 AM,10,18.0,,-1.2728069,36.7962362,-1.2421591,36.8153854,Rider_Id_387,1464
-Order_No_15358,User_Id_2383,Bike,3,Business,27,3,3:24:53 PM,27,3,3:25:26 PM,27,3,3:27:57 PM,27,3,3:30:50 PM,27,3,3:34:04 PM,3,29.0,,-1.2608993,36.799246000000004,-1.2696819,36.8028694,Rider_Id_424,194
-Order_No_11972,User_Id_1402,Bike,3,Business,7,1,12:52:16 PM,7,1,12:52:57 PM,7,1,12:58:07 PM,7,1,1:11:28 PM,7,1,1:21:38 PM,2,24.8,,-1.279395,36.825364,-1.281754,36.8324229,Rider_Id_330,610
-Order_No_13663,User_Id_3337,Bike,1,Personal,4,3,10:35:11 AM,4,3,11:04:55 AM,4,3,11:14:36 AM,4,3,11:17:16 AM,4,3,11:26:09 AM,3,16.9,,-1.2661263999999999,36.8030359,-1.2781938999999998,36.8191922,Rider_Id_698,533
-Order_No_9196,User_Id_2946,Bike,2,Personal,19,2,9:11:30 AM,19,2,9:21:33 AM,19,2,9:33:59 AM,19,2,9:36:27 AM,19,2,10:13:09 AM,9,15.6,1.1,-1.2749662,36.7658781,-1.2611578,36.814530700000006,Rider_Id_89,2202
-Order_No_2809,User_Id_402,Bike,3,Business,27,1,12:47:25 PM,27,1,12:48:04 PM,27,1,12:56:22 PM,27,1,12:57:30 PM,27,1,1:12:37 PM,3,21.0,,-1.2527958000000001,36.8003131,-1.2551895,36.7822034,Rider_Id_341,907
-Order_No_13111,User_Id_3621,Bike,3,Business,24,4,5:15:31 PM,24,4,5:15:47 PM,24,4,5:36:00 PM,24,4,5:37:10 PM,24,4,6:00:20 PM,5,26.2,,-1.2836583000000001,36.7886014,-1.2859576000000001,36.8197604,Rider_Id_462,1390
-Order_No_26089,User_Id_2987,Bike,3,Business,22,3,1:38:32 PM,22,3,1:41:48 PM,22,3,1:59:31 PM,22,3,2:05:16 PM,22,3,2:18:36 PM,11,25.8,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_100,800
-Order_No_734,User_Id_3630,Bike,2,Personal,14,1,12:32:06 PM,14,1,12:37:10 PM,14,1,12:55:23 PM,14,1,1:16:49 PM,14,1,1:49:39 PM,17,22.8,,-1.2141875,36.791039000000005,-1.3038156,36.812273299999994,Rider_Id_633,1970
-Order_No_6584,User_Id_2190,Bike,3,Business,20,1,7:30:09 AM,20,1,7:30:39 AM,20,1,7:54:16 AM,20,1,8:08:11 AM,20,1,8:49:29 AM,16,14.3,,-1.3019530000000001,36.788458,-1.206696,36.847709,Rider_Id_478,2478
-Order_No_21067,User_Id_1245,Bike,3,Business,14,4,8:35:04 AM,14,4,8:36:10 AM,14,4,8:42:35 AM,14,4,8:55:16 AM,14,4,9:27:37 AM,16,18.0,,-1.2602337,36.79905479999999,-1.3198615,36.7110323,Rider_Id_427,1941
-Order_No_14552,User_Id_812,Bike,1,Personal,11,5,2:16:53 PM,11,5,2:29:51 PM,11,5,2:48:28 PM,11,5,3:03:41 PM,11,5,3:12:19 PM,5,22.5,,-1.2855798999999999,36.817620899999994,-1.2681867,36.814832200000005,Rider_Id_813,518
-Order_No_19246,User_Id_2271,Bike,3,Business,7,6,12:31:07 PM,7,6,1:12:19 PM,7,6,1:15:44 PM,7,6,1:52:44 PM,7,6,2:13:42 PM,12,21.2,,-1.228911,36.881876,-1.293515,36.897607,Rider_Id_192,1258
-Order_No_18274,User_Id_868,Bike,3,Business,8,3,2:47:50 PM,8,3,2:48:15 PM,8,3,2:50:36 PM,8,3,3:04:00 PM,8,3,3:15:06 PM,4,,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_178,666
-Order_No_23527,User_Id_3326,Bike,3,Business,14,4,1:54:02 PM,14,4,1:55:33 PM,14,4,2:04:15 PM,14,4,2:11:23 PM,14,4,2:29:03 PM,4,27.7,,-1.2551895,36.7822034,-1.2738521999999999,36.799281799999996,Rider_Id_432,1060
-Order_No_27990,User_Id_2532,Bike,3,Business,10,5,8:44:50 AM,10,5,8:46:28 AM,10,5,9:03:05 AM,10,5,9:18:11 AM,10,5,9:39:23 AM,6,,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_491,1272
-Order_No_18359,User_Id_1642,Bike,3,Business,15,5,1:01:26 PM,15,5,1:14:06 PM,15,5,1:43:14 PM,15,5,1:44:31 PM,15,5,1:59:07 PM,4,28.2,,-1.278191,36.8143267,-1.2726506000000002,36.7960153,Rider_Id_411,876
-Order_No_1887,User_Id_1231,Bike,3,Business,21,5,12:03:52 PM,21,5,12:10:32 PM,21,5,12:34:51 PM,21,5,12:42:34 PM,21,5,1:29:45 PM,8,22.8,,-1.2821421999999998,36.8161565,-1.3014461000000002,36.7661381,Rider_Id_831,2831
-Order_No_8257,User_Id_718,Bike,3,Business,2,2,10:19:50 AM,2,2,10:22:32 AM,2,2,10:26:24 AM,2,2,10:29:01 AM,2,2,10:51:41 AM,8,20.7,,-1.2551895,36.7822034,-1.3010058999999998,36.8216816,Rider_Id_85,1360
-Order_No_25909,User_Id_3647,Bike,3,Business,4,1,4:05:54 PM,4,1,4:06:00 PM,4,1,4:07:06 PM,4,1,4:08:38 PM,4,1,4:43:32 PM,25,28.5,,-1.1846355,36.948572799999994,-1.2726389999999999,36.794723,Rider_Id_131,2094
-Order_No_2905,User_Id_788,Bike,3,Business,22,4,8:35:31 AM,22,4,9:11:36 AM,22,4,9:28:26 AM,22,4,9:50:21 AM,22,4,10:08:18 AM,12,19.4,3.0,-1.2494503000000001,36.8531852,-1.225322,36.80855,Rider_Id_878,1077
-Order_No_24005,User_Id_306,Bike,1,Personal,5,7,3:24:48 PM,5,7,3:32:51 PM,5,7,3:48:38 PM,5,7,3:52:33 PM,5,7,4:11:25 PM,8,21.1,,-1.3159197,36.8516724,-1.3086326000000001,36.8080886,Rider_Id_890,1132
-Order_No_8376,User_Id_187,Bike,3,Business,22,6,3:17:26 PM,22,6,3:21:19 PM,22,6,3:36:16 PM,22,6,3:38:13 PM,22,6,4:53:03 PM,27,26.0,,-1.2859912,36.8756811,-1.3165542,36.700067,Rider_Id_48,4490
-Order_No_24368,User_Id_2423,Bike,3,Business,14,5,2:40:10 PM,14,5,2:41:39 PM,14,5,2:51:13 PM,14,5,2:58:32 PM,14,5,3:49:05 PM,8,27.3,,-1.2551895,36.7822034,-1.3033012,36.8170864,Rider_Id_795,3033
-Order_No_9836,User_Id_718,Bike,3,Business,26,2,2:19:47 PM,26,2,2:20:01 PM,26,2,2:24:29 PM,26,2,2:41:55 PM,26,2,3:17:23 PM,12,30.9,,-1.2551895,36.7822034,-1.320157,36.830887,Rider_Id_177,2128
-Order_No_27812,User_Id_265,Bike,3,Business,4,1,11:29:31 AM,4,1,11:31:23 AM,4,1,11:58:17 AM,4,1,12:05:06 PM,4,1,12:28:19 PM,12,26.4,,-1.329285,36.88678529999999,-1.300921,36.828195,Rider_Id_0,1393
-Order_No_9247,User_Id_1687,Bike,3,Business,13,4,9:06:53 AM,13,4,9:07:18 AM,13,4,9:51:42 AM,13,4,10:02:48 AM,13,4,10:13:17 AM,2,18.6,,-1.2626574,36.8014034,-1.269571,36.807668,Rider_Id_92,629
-Order_No_9273,User_Id_3621,Bike,3,Business,13,7,1:34:46 PM,13,7,1:35:18 PM,13,7,1:57:21 PM,13,7,2:01:17 PM,13,7,2:57:49 PM,19,24.7,,-1.2836583000000001,36.7886014,-1.2000932,36.7754458,Rider_Id_246,3392
-Order_No_644,User_Id_3530,Bike,1,Personal,25,2,12:33:51 PM,25,2,12:34:52 PM,25,2,1:26:13 PM,25,2,1:29:40 PM,25,2,1:48:31 PM,4,23.8,,-1.2813497999999999,36.7930729,-1.2804664,36.7701111,Rider_Id_565,1131
-Order_No_17525,User_Id_867,Bike,3,Business,25,3,7:35:17 AM,25,3,8:33:31 AM,25,3,8:47:36 AM,25,3,9:12:43 AM,25,3,9:38:36 AM,10,13.5,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_663,1553
-Order_No_16096,User_Id_2987,Bike,3,Business,2,2,1:14:23 PM,2,2,1:15:11 PM,2,2,1:42:00 PM,2,2,1:48:52 PM,2,2,2:16:34 PM,10,26.0,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_268,1662
-Order_No_21545,User_Id_2029,Bike,1,Personal,21,5,2:56:31 PM,21,5,2:57:04 PM,21,5,3:02:36 PM,21,5,3:10:28 PM,21,5,3:27:45 PM,8,26.9,,-1.238874,36.807889,-1.2566888999999999,36.7930784,Rider_Id_882,1037
-Order_No_22825,User_Id_324,Bike,3,Personal,18,2,12:06:15 PM,18,2,12:06:43 PM,18,2,12:10:40 PM,18,2,12:14:12 PM,18,2,1:10:05 PM,15,,,-1.2653676,36.79110120000001,-1.332942,36.714065000000005,Rider_Id_167,3353
-Order_No_1289,User_Id_1329,Bike,3,Business,3,4,11:23:43 AM,3,4,11:24:09 AM,3,4,11:30:09 AM,3,4,11:36:04 AM,3,4,11:57:14 AM,4,23.4,,-1.273056,36.811298,-1.2580565000000001,36.808482500000004,Rider_Id_407,1270
-Order_No_1816,User_Id_3331,Bike,3,Business,5,3,2:10:43 PM,5,3,2:17:54 PM,5,3,2:23:57 PM,5,3,2:26:36 PM,5,3,2:44:10 PM,10,25.0,,-1.2551895,36.7822034,-1.3025666999999999,36.7803495,Rider_Id_878,1054
-Order_No_7876,User_Id_1484,Bike,3,Business,2,5,11:06:27 AM,2,5,11:07:48 AM,2,5,11:14:15 AM,2,5,11:25:15 AM,2,5,12:07:59 PM,12,23.3,,-1.2571472,36.795063299999995,-1.3083777,36.85900770000001,Rider_Id_199,2564
-Order_No_26797,User_Id_3054,Bike,3,Personal,7,4,9:12:03 AM,7,4,9:20:18 AM,7,4,9:38:53 AM,7,4,9:47:40 AM,7,4,10:02:49 AM,6,,,-1.2546732,36.7923244,-1.2934656999999998,36.8212737,Rider_Id_378,909
-Order_No_15784,User_Id_2330,Bike,3,Business,16,3,10:48:59 AM,16,3,10:49:50 AM,16,3,10:51:25 AM,16,3,10:58:18 AM,16,3,11:12:58 AM,6,22.9,,-1.2921463,36.8029626,-1.3015197,36.7658456,Rider_Id_85,880
-Order_No_22960,User_Id_246,Bike,3,Business,15,6,12:43:36 PM,15,6,12:46:27 PM,15,6,1:10:11 PM,15,6,1:13:08 PM,15,6,1:56:37 PM,15,22.2,,-1.2584143,36.8048002,-1.3146822,36.8900627,Rider_Id_300,2609
-Order_No_12046,User_Id_1340,Bike,3,Business,13,3,3:29:39 PM,13,3,3:29:50 PM,13,3,3:38:03 PM,13,3,3:44:42 PM,13,3,4:01:59 PM,6,29.0,,-1.3007714,36.8001722,-1.2707178999999997,36.7979713,Rider_Id_25,1037
-Order_No_23170,User_Id_3404,Bike,3,Business,1,5,5:28:34 PM,1,5,5:55:32 PM,1,5,6:17:06 PM,1,5,6:25:45 PM,1,5,6:49:59 PM,6,26.3,,-1.279395,36.825364,-1.282937,36.781698399999996,Rider_Id_540,1454
-Order_No_9461,User_Id_718,Bike,3,Business,8,5,11:49:10 AM,8,5,11:54:54 AM,8,5,12:15:29 PM,8,5,12:22:05 PM,8,5,1:12:41 PM,9,28.2,,-1.2551895,36.7822034,-1.2786703000000001,36.826044,Rider_Id_103,3036
-Order_No_5391,User_Id_3647,Bike,3,Business,26,2,10:11:17 AM,26,2,10:12:44 AM,26,2,10:19:04 AM,26,2,10:29:53 AM,26,2,10:35:42 AM,2,,,-1.2726389999999999,36.794723,-1.2746436,36.803682,Rider_Id_100,349
-Order_No_3031,User_Id_457,Bike,3,Personal,10,3,4:40:23 PM,10,3,4:42:10 PM,10,3,4:51:07 PM,10,3,4:58:57 PM,10,3,5:25:16 PM,8,27.4,,-1.2697893,36.8124106,-1.3095666000000001,36.7698746,Rider_Id_461,1579
-Order_No_9383,User_Id_2711,Bike,1,Personal,30,4,10:07:15 AM,30,4,10:13:48 AM,30,4,10:24:24 AM,30,4,10:25:51 AM,30,4,10:45:03 AM,5,16.3,,-1.3047132,36.808955299999994,-1.2924639,36.77957979999999,Rider_Id_343,1152
-Order_No_23288,User_Id_2509,Bike,3,Business,18,2,10:33:27 AM,18,2,10:34:22 AM,18,2,10:48:31 AM,18,2,11:05:44 AM,18,2,11:45:15 AM,20,,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_931,2371
-Order_No_22329,User_Id_868,Bike,3,Business,13,3,2:57:22 PM,13,3,2:57:44 PM,13,3,3:01:23 PM,13,3,3:09:12 PM,13,3,3:09:15 PM,3,27.2,,-1.2783848999999998,36.8163445,-1.265715,36.823815,Rider_Id_79,3
-Order_No_1402,User_Id_1482,Bike,3,Business,2,2,4:09:03 PM,2,2,4:12:42 PM,2,2,4:20:49 PM,2,2,4:36:09 PM,2,2,4:55:53 PM,7,28.3,,-1.2664135,36.8322682,-1.2822773,36.795312100000004,Rider_Id_383,1184
-Order_No_9425,User_Id_113,Bike,1,Business,9,2,5:17:05 PM,9,2,6:12:52 PM,9,2,6:21:38 PM,9,2,6:25:43 PM,9,2,6:47:40 PM,7,18.8,,-1.2618802,36.8068534,-1.2963097,36.7688221,Rider_Id_674,1317
-Order_No_13323,User_Id_496,Bike,3,Business,30,2,3:22:49 PM,30,2,4:08:48 PM,30,2,4:45:04 PM,30,2,4:54:57 PM,30,2,5:21:26 PM,11,,,-1.2551895,36.7822034,-1.3080793,36.7505396,Rider_Id_318,1589
-Order_No_727,User_Id_1046,Bike,1,Personal,12,5,3:45:43 PM,12,5,3:58:07 PM,12,5,4:16:18 PM,12,5,4:18:05 PM,12,5,4:30:51 PM,7,27.0,,-1.3054537,36.8284768,-1.3321614,36.782028100000005,Rider_Id_299,766
-Order_No_17240,User_Id_2358,Bike,4,Business,18,1,9:50:44 AM,18,1,9:54:38 AM,18,1,10:21:27 AM,18,1,10:24:36 AM,18,1,11:08:32 AM,7,15.0,0.2,-1.3256903999999998,36.8747943,-1.290678,36.8829787,Rider_Id_192,2636
-Order_No_21159,User_Id_635,Bike,3,Business,26,1,3:10:12 PM,26,1,3:10:37 PM,26,1,3:33:05 PM,26,1,3:40:16 PM,26,1,3:46:16 PM,3,22.3,0.7,-1.2611642,36.7949932,-1.2599563,36.799343900000004,Rider_Id_107,360
-Order_No_5845,User_Id_3517,Bike,3,Business,2,5,4:13:52 PM,2,5,4:14:17 PM,2,5,4:15:55 PM,2,5,4:26:42 PM,2,5,5:00:56 PM,14,25.1,,-1.319613,36.711055,-1.3882008,36.7699445,Rider_Id_532,2054
-Order_No_8047,User_Id_3295,Bike,3,Business,28,2,3:06:45 PM,28,2,3:07:03 PM,28,2,3:07:31 PM,28,2,3:21:01 PM,28,2,3:50:56 PM,4,19.5,,-1.2615048,36.8116724,-1.2571472,36.795063299999995,Rider_Id_875,1795
-Order_No_6108,User_Id_302,Bike,3,Business,1,3,12:21:32 PM,1,3,12:22:13 PM,1,3,12:28:57 PM,1,3,12:43:39 PM,1,3,12:58:27 PM,6,,,-1.2649605,36.798177700000004,-1.2909633999999999,36.7813995,Rider_Id_72,888
-Order_No_9948,User_Id_2858,Bike,3,Business,31,3,10:53:45 AM,31,3,10:54:07 AM,31,3,11:53:12 AM,31,3,11:57:41 AM,31,3,12:55:29 PM,18,24.7,,-1.3164106000000002,36.6952197,-1.2605635,36.7985638,Rider_Id_783,3468
-Order_No_16326,User_Id_2803,Bike,3,Business,2,5,10:03:01 AM,2,5,10:04:59 AM,2,5,10:21:17 AM,2,5,10:22:28 AM,2,5,10:39:30 AM,3,18.2,16.9,-1.2765736,36.851364600000004,-1.2689598999999998,36.8398401,Rider_Id_237,1022
-Order_No_15162,User_Id_3517,Bike,3,Business,26,5,9:23:44 AM,26,5,9:33:13 AM,26,5,10:07:22 AM,26,5,10:25:35 AM,26,5,11:10:28 AM,18,,,-1.2867454,36.8259017,-1.3882008,36.7699445,Rider_Id_359,2693
-Order_No_26873,User_Id_1523,Bike,1,Personal,2,3,7:17:35 AM,2,3,7:33:21 AM,2,3,8:14:04 AM,2,3,8:16:28 AM,2,3,8:36:07 AM,3,18.1,1.1,-1.2676569,36.80181279999999,-1.273854,36.8003368,Rider_Id_432,1179
-Order_No_19808,User_Id_552,Bike,3,Business,16,1,12:20:44 PM,16,1,12:21:03 PM,16,1,12:22:19 PM,16,1,12:38:54 PM,16,1,1:03:49 PM,23,19.4,,-1.3404187,36.7614685,-1.3562374,36.904295399999995,Rider_Id_735,1495
-Order_No_16255,User_Id_1594,Bike,3,Business,10,5,12:18:32 PM,10,5,12:18:56 PM,10,5,12:30:07 PM,10,5,12:32:29 PM,10,5,2:10:59 PM,13,,,-1.225322,36.80855,-1.2876803999999997,36.7802778,Rider_Id_689,5910
-Order_No_13067,User_Id_3517,Bike,3,Business,10,4,10:13:15 AM,10,4,10:13:21 AM,10,4,10:27:03 AM,10,4,10:54:03 AM,10,4,11:23:31 AM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_297,1768
-Order_No_17724,User_Id_788,Bike,3,Business,27,6,8:45:17 AM,27,6,8:49:04 AM,27,6,8:49:25 AM,27,6,9:37:19 AM,27,6,10:12:30 AM,16,,,-1.3052491000000002,36.8223899,-1.225322,36.80855,Rider_Id_593,2111
-Order_No_15283,User_Id_2763,Bike,3,Business,14,3,10:52:03 AM,14,3,10:52:39 AM,14,3,11:00:56 AM,14,3,11:12:28 AM,14,3,11:42:42 AM,16,24.7,,-1.326825,36.864345,-1.2551895,36.7822034,Rider_Id_550,1814
-Order_No_7433,User_Id_2956,Bike,3,Business,22,5,9:06:10 AM,22,5,9:06:37 AM,22,5,9:16:52 AM,22,5,9:18:40 AM,22,5,9:25:42 AM,2,,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_427,422
-Order_No_20247,User_Id_1473,Bike,3,Business,9,5,11:49:04 AM,9,5,11:49:46 AM,9,5,12:19:34 PM,9,5,12:44:20 PM,9,5,1:19:24 PM,21,25.6,,-1.3700383,36.9190174,-1.3025666999999999,36.7803495,Rider_Id_699,2104
-Order_No_22490,User_Id_2987,Bike,3,Business,5,1,2:06:15 PM,5,1,2:20:00 PM,5,1,2:27:17 PM,5,1,2:29:23 PM,5,1,2:45:19 PM,7,26.1,,-1.2702120000000001,36.82108770000001,-1.3043821000000002,36.7840586,Rider_Id_551,956
-Order_No_26999,User_Id_2630,Bike,2,Personal,30,3,12:18:16 PM,30,3,12:32:32 PM,30,3,12:45:08 PM,30,3,12:50:09 PM,30,3,1:06:05 PM,5,22.6,,-1.2782498,36.8251128,-1.301796,36.8012024,Rider_Id_480,956
-Order_No_16971,User_Id_2112,Bike,3,Business,21,5,6:25:42 PM,21,5,7:09:35 PM,21,5,7:13:46 PM,21,5,7:19:45 PM,21,5,7:47:40 PM,13,23.0,,-1.2156006000000001,36.8916865,-1.279395,36.825364,Rider_Id_72,1675
-Order_No_19331,User_Id_2457,Bike,3,Business,15,2,10:42:48 AM,15,2,10:44:00 AM,15,2,10:56:35 AM,15,2,10:57:49 AM,15,2,11:13:40 AM,8,24.4,,-1.2551895,36.7822034,-1.3047132,36.808955299999994,Rider_Id_932,951
-Order_No_12980,User_Id_1931,Bike,3,Personal,13,3,12:33:05 PM,13,3,1:09:11 PM,13,3,1:19:41 PM,13,3,1:21:06 PM,13,3,1:41:52 PM,6,20.1,,-1.2998129999999999,36.787139200000006,-1.2833620000000001,36.8260149,Rider_Id_698,1246
-Order_No_17709,User_Id_1801,Bike,1,Personal,3,3,12:00:55 PM,3,3,12:04:48 PM,3,3,12:58:59 PM,3,3,1:05:46 PM,3,3,1:23:20 PM,5,23.8,,-1.2902869,36.8085396,-1.2951297,36.8335521,Rider_Id_844,1054
-Order_No_145,User_Id_3291,Bike,3,Business,23,2,4:01:18 PM,23,2,4:32:35 PM,23,2,5:00:05 PM,23,2,5:30:03 PM,23,2,5:30:07 PM,2,,,-1.266837,36.799248999999996,-1.2600926000000001,36.8088685,Rider_Id_76,4
-Order_No_18038,User_Id_393,Bike,3,Business,24,3,1:30:36 PM,24,3,1:32:04 PM,24,3,1:39:11 PM,24,3,2:06:50 PM,24,3,2:40:05 PM,14,,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_206,1995
-Order_No_19427,User_Id_84,Bike,3,Business,2,2,4:34:29 PM,2,2,4:36:37 PM,2,2,4:47:48 PM,2,2,5:02:56 PM,2,2,5:12:43 PM,3,28.3,,-1.2572951,36.79886,-1.2551895,36.7822034,Rider_Id_279,587
-Order_No_25060,User_Id_1774,Bike,1,Personal,27,4,10:57:47 AM,27,4,10:58:21 AM,27,4,11:09:47 AM,27,4,11:19:56 AM,27,4,11:44:46 AM,17,23.0,,-1.2890635,36.83185220000001,-1.3700383,36.9190174,Rider_Id_336,1490
-Order_No_23603,User_Id_2804,Bike,3,Business,25,4,11:22:48 AM,25,4,11:23:25 AM,25,4,11:26:02 AM,25,4,11:37:54 AM,25,4,12:44:23 PM,29,20.0,,-1.2551895,36.7822034,-1.1270934,36.648315999999994,Rider_Id_536,3989
-Order_No_15568,User_Id_635,Bike,3,Business,4,1,12:53:46 PM,4,1,2:02:14 PM,4,1,2:24:13 PM,4,1,2:27:27 PM,4,1,2:48:04 PM,7,,,-1.2602337,36.79905479999999,-1.3081376,36.83297720000001,Rider_Id_516,1237
-Order_No_8192,User_Id_393,Bike,3,Business,1,5,8:29:31 AM,1,5,8:29:57 AM,1,5,8:39:21 AM,1,5,8:57:04 AM,1,5,9:16:56 AM,11,23.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_858,1192
-Order_No_8527,User_Id_3264,Bike,3,Business,17,3,2:41:30 PM,17,3,2:44:46 PM,17,3,2:46:02 PM,17,3,2:51:41 PM,17,3,3:02:50 PM,6,,,-1.2551895,36.7822034,-1.2882462,36.81645379999999,Rider_Id_291,669
-Order_No_24742,User_Id_2986,Bike,3,Personal,18,5,10:21:21 AM,18,5,10:22:15 AM,18,5,10:32:11 AM,18,5,10:50:31 AM,18,5,11:18:38 AM,13,20.3,,-1.338484,36.883377,-1.2903886,36.8063847,Rider_Id_418,1687
-Order_No_18746,User_Id_1500,Bike,3,Business,23,4,12:46:27 PM,23,4,12:51:25 PM,23,4,1:35:21 PM,23,4,2:02:52 PM,23,4,2:03:46 PM,5,24.7,,-1.2638185,36.7930057,-1.282937,36.781698399999996,Rider_Id_2,54
-Order_No_13092,User_Id_1346,Bike,3,Business,17,3,3:41:25 PM,17,3,4:03:16 PM,17,3,4:23:04 PM,17,3,4:30:31 PM,17,3,4:45:59 PM,5,,,-1.2917868000000001,36.7872675,-1.2802618000000001,36.8191684,Rider_Id_855,928
-Order_No_14175,User_Id_20,Bike,3,Business,9,4,9:49:49 AM,9,4,9:50:36 AM,9,4,9:53:09 AM,9,4,10:00:32 AM,9,4,10:18:39 AM,8,,,-1.2629666000000002,36.8068369,-1.2295205,36.812042600000005,Rider_Id_227,1087
-Order_No_618,User_Id_2701,Bike,3,Business,6,3,1:34:33 PM,6,3,1:35:53 PM,6,3,1:49:56 PM,6,3,1:57:00 PM,6,3,2:17:49 PM,8,,,-1.2584143,36.8048002,-1.2765736,36.851364600000004,Rider_Id_25,1249
-Order_No_25309,User_Id_640,Bike,3,Business,10,1,3:13:20 PM,10,1,3:46:05 PM,10,1,3:55:29 PM,10,1,4:17:36 PM,10,1,4:52:12 PM,14,18.8,,-1.3246455,36.8754415,-1.304033,36.7848695,Rider_Id_449,2076
-Order_No_10534,User_Id_2330,Bike,3,Business,8,5,1:18:15 PM,8,5,1:25:35 PM,8,5,1:46:13 PM,8,5,1:52:12 PM,8,5,2:37:28 PM,11,27.3,,-1.2921463,36.8029626,-1.2324923,36.836377500000005,Rider_Id_660,2716
-Order_No_4011,User_Id_66,Bike,2,Personal,6,1,10:39:54 AM,6,1,10:40:13 AM,6,1,11:01:41 AM,6,1,11:03:25 AM,6,1,11:33:18 AM,7,20.4,,-1.2739008,36.813977200000004,-1.3121789,36.82986210000001,Rider_Id_402,1793
-Order_No_13818,User_Id_2330,Bike,3,Business,30,4,3:18:31 PM,30,4,3:24:01 PM,30,4,3:51:26 PM,30,4,3:53:16 PM,30,4,4:17:55 PM,13,,,-1.2124582,36.802894,-1.2284323,36.8757058,Rider_Id_7,1479
-Order_No_6701,User_Id_718,Bike,3,Business,14,4,3:15:35 PM,14,4,4:27:11 PM,14,4,4:32:16 PM,14,4,4:37:24 PM,14,4,5:08:07 PM,10,26.4,,-1.2551895,36.7822034,-1.308217,36.77413,Rider_Id_328,1843
-Order_No_24594,User_Id_3599,Bike,3,Business,28,3,1:09:53 PM,28,3,1:26:40 PM,28,3,1:38:21 PM,28,3,1:59:46 PM,28,3,2:23:14 PM,8,,,-1.3302996,36.8707935,-1.2998789,36.816896299999996,Rider_Id_521,1408
-Order_No_6939,User_Id_1363,Bike,3,Business,25,1,9:16:31 AM,25,1,9:17:31 AM,25,1,9:17:53 AM,25,1,9:20:52 AM,25,1,9:42:11 AM,5,21.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_206,1279
-Order_No_21092,User_Id_384,Bike,1,Personal,24,4,2:50:22 PM,24,4,2:53:46 PM,24,4,2:58:07 PM,24,4,3:05:05 PM,24,4,3:17:24 PM,4,22.8,,-1.299029,36.782493,-1.2765492,36.7669809,Rider_Id_761,739
-Order_No_15068,User_Id_496,Bike,3,Business,15,5,11:54:50 AM,15,5,11:55:52 AM,15,5,12:51:39 PM,15,5,12:57:28 PM,15,5,1:57:47 PM,11,26.1,,-1.3217077,36.8170671,-1.2571472,36.795063299999995,Rider_Id_304,3619
-Order_No_6269,User_Id_393,Bike,3,Business,2,2,10:58:20 AM,2,2,11:02:01 AM,2,2,11:14:38 AM,2,2,11:24:12 AM,2,2,12:04:35 PM,6,26.4,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_343,2423
-Order_No_19495,User_Id_3689,Bike,3,Business,20,4,8:54:39 AM,20,4,8:54:50 AM,20,4,9:02:19 AM,20,4,9:40:00 AM,20,4,9:53:31 AM,6,18.7,0.2,-1.290894,36.822971,-1.2527958000000001,36.8003131,Rider_Id_404,811
-Order_No_11426,User_Id_496,Bike,3,Business,24,4,4:16:57 PM,24,4,4:17:13 PM,24,4,4:28:39 PM,24,4,4:35:57 PM,24,4,4:51:35 PM,7,21.4,,-1.2553615,36.789537599999996,-1.2843046,36.774613200000005,Rider_Id_103,938
-Order_No_11264,User_Id_3291,Bike,3,Business,20,2,12:28:36 PM,20,2,12:39:17 PM,20,2,12:49:26 PM,20,2,12:58:50 PM,20,2,1:21:16 PM,12,19.6,,-1.228911,36.881876,-1.2600926000000001,36.8088685,Rider_Id_31,1346
-Order_No_20733,User_Id_3283,Bike,3,Business,25,1,4:24:31 PM,25,1,4:25:37 PM,25,1,4:39:08 PM,25,1,5:06:00 PM,25,1,5:06:02 PM,7,28.5,,-1.2783848999999998,36.8163445,-1.2628473,36.781805,Rider_Id_953,2
-Order_No_4995,User_Id_1745,Bike,2,Personal,11,2,9:59:23 AM,11,2,10:15:39 AM,11,2,10:37:56 AM,11,2,10:41:27 AM,11,2,11:09:17 AM,11,24.0,,-1.3169591,36.7000029,-1.304033,36.7848695,Rider_Id_844,1670
-Order_No_876,User_Id_3186,Bike,3,Business,19,2,2:28:07 PM,19,2,2:29:42 PM,19,2,2:38:41 PM,19,2,2:41:03 PM,19,2,3:05:31 PM,5,29.5,,-1.2980228999999999,36.78865,-1.2949455,36.8195238,Rider_Id_906,1468
-Order_No_14704,User_Id_2186,Bike,1,Personal,28,1,3:03:10 PM,28,1,3:04:38 PM,28,1,3:20:45 PM,28,1,3:32:26 PM,28,1,4:00:44 PM,11,24.3,,-1.2795197,36.8168287,-1.328128,36.8719,Rider_Id_432,1698
-Order_No_684,User_Id_136,Bike,3,Business,13,2,3:19:39 PM,13,2,3:27:20 PM,13,2,3:39:13 PM,13,2,3:46:35 PM,13,2,4:10:48 PM,8,26.1,,-1.3014461000000002,36.7661381,-1.3326173000000001,36.71462329999999,Rider_Id_612,1453
-Order_No_15354,User_Id_3686,Bike,3,Business,8,5,2:40:18 PM,8,5,2:40:46 PM,8,5,2:55:29 PM,8,5,3:03:10 PM,8,5,3:48:43 PM,23,27.2,,-1.2551895,36.7822034,-1.3447079,36.91105,Rider_Id_880,2733
-Order_No_19568,User_Id_3651,Bike,3,Business,28,1,4:09:29 PM,28,1,4:12:10 PM,28,1,4:28:30 PM,28,1,4:38:28 PM,28,1,5:21:25 PM,15,22.6,,-1.2579303999999998,36.8019667,-1.3310785,36.8933109,Rider_Id_216,2577
-Order_No_11214,User_Id_2658,Bike,3,Business,12,5,2:34:18 PM,12,5,2:38:06 PM,12,5,2:47:38 PM,12,5,2:50:28 PM,12,5,3:46:28 PM,8,,,-1.2813012,36.832396200000005,-1.2963097,36.7688221,Rider_Id_787,3360
-Order_No_21039,User_Id_2212,Bike,3,Business,28,3,9:51:44 AM,28,3,10:16:00 AM,28,3,10:29:25 AM,28,3,10:39:27 AM,28,3,11:02:26 AM,7,,,-1.2544652,36.759919399999994,-1.2571472,36.795063299999995,Rider_Id_268,1379
-Order_No_12142,User_Id_2683,Bike,2,Personal,23,3,5:24:11 PM,23,3,5:43:23 PM,23,3,5:51:21 PM,23,3,5:55:06 PM,23,3,6:10:29 PM,5,20.7,,-1.2977386999999998,36.803144700000004,-1.3071725,36.7742592,Rider_Id_749,923
-Order_No_25419,User_Id_1207,Bike,3,Business,28,1,2:35:38 PM,28,1,2:35:46 PM,28,1,2:39:41 PM,28,1,2:49:31 PM,28,1,3:05:47 PM,8,26.1,,-1.2629666000000002,36.8068369,-1.2745321000000003,36.765088,Rider_Id_45,976
-Order_No_20431,User_Id_496,Bike,3,Business,30,4,12:54:06 PM,30,4,12:54:22 PM,30,4,1:02:28 PM,30,4,1:28:00 PM,30,4,2:20:08 PM,21,,,-1.2571472,36.795063299999995,-1.191363,36.92197120000001,Rider_Id_424,3128
-Order_No_15642,User_Id_2212,Bike,3,Business,7,3,12:45:40 PM,7,3,12:46:32 PM,7,3,12:54:47 PM,7,3,1:04:00 PM,7,3,1:14:44 PM,5,25.2,,-1.2571472,36.795063299999995,-1.2559559999999999,36.772901,Rider_Id_952,644
-Order_No_2393,User_Id_393,Bike,3,Business,8,4,10:58:27 AM,8,4,10:58:43 AM,8,4,10:58:48 AM,8,4,11:22:52 AM,8,4,12:11:30 PM,14,26.2,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_239,2918
-Order_No_17572,User_Id_1724,Bike,3,Business,23,2,10:21:31 AM,23,2,10:37:01 AM,23,2,10:56:20 AM,23,2,11:01:52 AM,23,2,11:28:38 AM,9,18.1,,-1.303976,36.815921100000004,-1.2551895,36.7822034,Rider_Id_662,1606
-Order_No_1371,User_Id_2587,Bike,3,Business,28,5,2:07:33 PM,28,5,2:16:49 PM,28,5,2:25:03 PM,28,5,2:27:22 PM,28,5,2:47:06 PM,5,25.3,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_844,1184
-Order_No_18485,User_Id_3496,Bike,3,Business,21,5,11:17:22 AM,21,5,11:33:18 AM,21,5,11:43:47 AM,21,5,12:00:50 PM,21,5,12:50:28 PM,13,22.9,,-1.3569069999999999,36.906808000000005,-1.300921,36.828195,Rider_Id_19,2978
-Order_No_23032,User_Id_393,Bike,3,Business,26,3,1:37:08 PM,26,3,1:37:33 PM,26,3,1:40:17 PM,26,3,1:51:43 PM,26,3,2:33:08 PM,14,27.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_731,2485
-Order_No_12081,User_Id_3201,Bike,3,Business,31,3,10:53:15 AM,31,3,10:53:39 AM,31,3,11:05:03 AM,31,3,11:10:32 AM,31,3,11:19:20 AM,3,24.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_49,528
-Order_No_14529,User_Id_1721,Bike,3,Business,30,2,12:14:58 PM,30,2,1:07:44 PM,30,2,1:12:46 PM,30,2,1:17:46 PM,30,2,1:30:32 PM,4,,,-1.2551895,36.7822034,-1.2744463,36.8132178,Rider_Id_709,766
-Order_No_26068,User_Id_2956,Bike,3,Business,9,5,4:32:08 PM,9,5,4:34:42 PM,9,5,4:37:27 PM,9,5,4:40:37 PM,9,5,4:51:50 PM,3,24.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_353,673
-Order_No_24597,User_Id_1133,Bike,3,Business,27,1,12:11:27 PM,27,1,12:11:40 PM,27,1,12:20:34 PM,27,1,12:27:50 PM,27,1,12:48:47 PM,11,23.8,,-1.3332749,36.870814700000004,-1.2778465,36.8180077,Rider_Id_648,1257
-Order_No_5956,User_Id_3260,Bike,1,Personal,15,3,3:20:23 PM,15,3,3:21:21 PM,15,3,3:25:01 PM,15,3,3:27:37 PM,15,3,3:39:42 PM,7,21.4,,-1.2560455,36.780086700000005,-1.2939882,36.759119899999995,Rider_Id_217,725
-Order_No_5987,User_Id_2415,Bike,1,Personal,7,6,2:33:27 PM,7,6,2:34:56 PM,7,6,2:46:31 PM,7,6,2:48:59 PM,7,6,3:29:47 PM,8,21.6,,-1.2766473999999999,36.8155764,-1.292188,36.764855,Rider_Id_208,2448
-Order_No_13624,User_Id_1243,Bike,1,Business,4,2,2:23:14 PM,4,2,2:23:53 PM,4,2,2:28:05 PM,4,2,2:53:46 PM,4,2,3:14:54 PM,10,20.2,,-1.304524,36.775371,-1.2551895,36.7822034,Rider_Id_350,1268
-Order_No_27228,User_Id_2423,Bike,3,Business,2,2,3:41:53 PM,2,2,3:43:17 PM,2,2,3:55:57 PM,2,2,4:40:31 PM,2,2,4:58:16 PM,3,23.7,,-1.2571472,36.795063299999995,-1.2753108999999998,36.8130185,Rider_Id_190,1065
-Order_No_20467,User_Id_2271,Bike,3,Business,26,5,10:38:40 AM,26,5,10:38:58 AM,26,5,10:49:47 AM,26,5,11:06:29 AM,26,5,11:45:00 AM,20,,,-1.2628473,36.781805,-1.293515,36.897607,Rider_Id_427,2311
-Order_No_3531,User_Id_2330,Bike,3,Business,14,3,1:29:03 PM,14,3,1:29:30 PM,14,3,1:44:54 PM,14,3,1:49:45 PM,14,3,2:35:35 PM,16,25.5,,-1.2267055,36.8013889,-1.3015197,36.7658456,Rider_Id_38,2750
-Order_No_4132,User_Id_391,Bike,3,Business,26,5,12:54:53 PM,26,5,1:08:58 PM,26,5,2:12:13 PM,26,5,2:37:54 PM,26,5,3:01:08 PM,5,,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_237,1394
-Order_No_1979,User_Id_510,Bike,3,Business,7,5,11:23:20 AM,7,5,11:29:32 AM,7,5,11:53:00 AM,7,5,11:59:50 AM,7,5,12:25:22 PM,13,25.3,,-1.2860183,36.8975338,-1.3189252,36.861726899999994,Rider_Id_408,1532
-Order_No_7668,User_Id_681,Bike,1,Personal,19,3,9:47:08 AM,19,3,9:47:20 AM,19,3,9:50:23 AM,19,3,9:54:28 AM,19,3,10:53:13 AM,19,,,-1.2857315,36.7953424,-1.39598,36.776394,Rider_Id_105,3525
-Order_No_2099,User_Id_1534,Bike,2,Personal,19,2,3:28:49 PM,19,2,3:29:20 PM,19,2,3:38:54 PM,19,2,3:46:23 PM,19,2,4:42:56 PM,11,18.4,,-1.2583017,36.8023526,-1.33013,36.83102,Rider_Id_755,3393
-Order_No_3084,User_Id_1527,Bike,3,Business,10,1,10:45:59 AM,10,1,10:46:57 AM,10,1,11:06:38 AM,10,1,11:25:10 AM,10,1,12:01:06 PM,16,18.5,,-1.2991441000000001,36.7528804,-1.3004062,36.829741,Rider_Id_643,2156
-Order_No_8033,User_Id_2834,Bike,3,Business,12,3,11:21:38 AM,12,3,11:28:38 AM,12,3,11:43:59 AM,12,3,11:47:47 AM,12,3,11:56:11 AM,2,19.4,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_336,504
-Order_No_22150,User_Id_393,Bike,3,Business,25,5,12:23:28 PM,25,5,1:28:54 PM,25,5,1:39:06 PM,25,5,1:46:10 PM,25,5,2:25:23 PM,14,23.3,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_432,2353
-Order_No_21958,User_Id_867,Bike,3,Business,25,1,2:10:54 PM,25,1,2:13:08 PM,25,1,2:21:42 PM,25,1,2:33:24 PM,25,1,2:48:39 PM,9,30.1,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_484,915
-Order_No_27473,User_Id_478,Bike,3,Business,7,1,7:35:28 AM,7,1,7:39:40 AM,7,1,7:59:42 AM,7,1,8:12:12 AM,7,1,9:13:09 AM,10,21.6,,-1.2551895,36.7822034,-1.2973682,36.785987799999994,Rider_Id_829,3657
-Order_No_20254,User_Id_3644,Bike,1,Personal,29,5,12:07:31 PM,29,5,12:14:11 PM,29,5,12:19:32 PM,29,5,12:32:11 PM,29,5,12:58:54 PM,5,19.8,,-1.3205506999999999,36.8426247,-1.317411,36.818222,Rider_Id_212,1603
-Order_No_18892,User_Id_1287,Bike,3,Business,28,4,10:23:28 AM,28,4,10:23:38 AM,28,4,10:25:52 AM,28,4,10:31:31 AM,28,4,11:16:34 AM,16,22.4,,-1.2551895,36.7822034,-1.2227899,36.8851953,Rider_Id_523,2703
-Order_No_20822,User_Id_3291,Bike,3,Business,17,5,9:52:38 AM,17,5,9:53:18 AM,17,5,10:03:11 AM,17,5,10:13:35 AM,17,5,10:24:07 AM,4,,,-1.2663631000000002,36.8163329,-1.2600926000000001,36.8088685,Rider_Id_27,632
-Order_No_9321,User_Id_2779,Bike,1,Personal,3,3,8:00:14 AM,3,3,8:00:26 AM,3,3,8:15:59 AM,3,3,8:17:30 AM,3,3,9:25:18 AM,24,18.3,,-1.2183760000000001,36.8821822,-1.331936,36.8840499,Rider_Id_260,4068
-Order_No_23055,User_Id_3203,Bike,3,Business,26,4,11:45:45 AM,26,4,11:45:58 AM,26,4,11:46:12 AM,26,4,12:13:56 PM,26,4,12:22:02 PM,9,18.9,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_174,486
-Order_No_12167,User_Id_1527,Bike,3,Business,28,4,11:13:53 AM,28,4,11:14:07 AM,28,4,11:25:32 AM,28,4,11:53:20 AM,28,4,11:53:23 AM,9,23.8,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_762,3
-Order_No_7365,User_Id_3434,Bike,3,Business,8,5,12:48:11 PM,8,5,12:49:46 PM,8,5,1:04:16 PM,8,5,1:05:51 PM,8,5,2:33:58 PM,17,,,-1.2963097,36.7688221,-1.2482465,36.8889062,Rider_Id_844,5287
-Order_No_13352,User_Id_1209,Bike,3,Business,29,3,11:49:32 AM,29,3,11:55:22 AM,29,3,12:12:47 PM,29,3,12:34:52 PM,29,3,12:43:50 PM,4,,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_19,538
-Order_No_15788,User_Id_2330,Bike,3,Business,30,3,2:36:10 PM,30,3,2:39:21 PM,30,3,2:57:22 PM,30,3,3:08:39 PM,30,3,3:45:10 PM,12,29.1,,-1.2326041,36.8009029,-1.2915246999999999,36.805575700000006,Rider_Id_513,2191
-Order_No_27972,User_Id_2523,Bike,3,Business,14,3,2:34:49 PM,14,3,3:10:58 PM,14,3,3:33:04 PM,14,3,3:35:51 PM,14,3,3:56:06 PM,9,25.5,,-1.2551895,36.7822034,-1.306431,36.812042,Rider_Id_383,1215
-Order_No_3019,User_Id_1368,Bike,3,Business,10,4,10:04:11 AM,10,4,10:05:05 AM,10,4,10:10:30 AM,10,4,10:18:19 AM,10,4,10:41:51 AM,15,19.1,,-1.2571472,36.795063299999995,-1.2190317,36.8825092,Rider_Id_477,1412
-Order_No_3753,User_Id_1500,Bike,3,Business,8,3,8:01:28 AM,8,3,8:02:55 AM,8,3,8:14:18 AM,8,3,8:37:27 AM,8,3,9:04:36 AM,11,,,-1.3077869,36.844320700000004,-1.2638185,36.7930057,Rider_Id_956,1629
-Order_No_6482,User_Id_393,Bike,3,Business,20,3,2:26:10 PM,20,3,2:36:28 PM,20,3,2:36:33 PM,20,3,2:58:23 PM,20,3,3:14:24 PM,4,28.5,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_111,961
-Order_No_24386,User_Id_3689,Bike,3,Business,6,3,6:34:46 PM,6,3,7:09:11 PM,6,3,7:12:05 PM,6,3,7:18:07 PM,6,3,7:24:18 PM,1,19.8,,-1.2649605,36.798177700000004,-1.2601708999999999,36.8002238,Rider_Id_103,371
-Order_No_9655,User_Id_53,Bike,3,Business,30,4,11:41:46 AM,30,4,11:42:51 AM,30,4,12:21:05 PM,30,4,12:40:49 PM,30,4,12:41:54 PM,13,,,-1.2770708,36.8231093,-1.2257343,36.89335429999999,Rider_Id_921,65
-Order_No_23892,User_Id_2994,Bike,1,Personal,26,2,3:08:53 PM,26,2,3:09:58 PM,26,2,3:18:16 PM,26,2,3:24:27 PM,26,2,3:42:37 PM,7,,,-1.268027,36.8037005,-1.2913246,36.8019522,Rider_Id_832,1090
-Order_No_16000,User_Id_3647,Bike,3,Business,15,4,10:54:52 AM,15,4,10:58:28 AM,15,4,11:12:22 AM,15,4,11:15:38 AM,15,4,11:40:12 AM,8,24.2,,-1.2726389999999999,36.794723,-1.306245,36.819297799999994,Rider_Id_685,1474
-Order_No_19191,User_Id_868,Bike,3,Business,12,2,2:26:56 PM,12,2,2:29:42 PM,12,2,2:59:40 PM,12,2,3:04:48 PM,12,2,3:17:38 PM,7,26.9,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_79,770
-Order_No_1003,User_Id_1928,Bike,1,Business,27,3,12:51:03 PM,27,3,12:52:58 PM,27,3,1:13:43 PM,27,3,1:28:14 PM,27,3,1:46:06 PM,8,,,-1.289187,36.794845,-1.2579303999999998,36.8019667,Rider_Id_846,1072
-Order_No_8454,User_Id_2100,Bike,3,Business,12,1,11:26:51 AM,12,1,11:35:43 AM,12,1,11:46:40 AM,12,1,11:48:21 AM,12,1,12:15:34 PM,8,23.6,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_744,1633
-Order_No_25254,User_Id_3727,Bike,3,Business,13,3,4:16:30 PM,13,3,4:16:45 PM,13,3,4:17:37 PM,13,3,4:22:24 PM,13,3,4:47:07 PM,10,27.3,,-1.2963097,36.7688221,-1.2659148999999998,36.805432200000006,Rider_Id_354,1483
-Order_No_16422,User_Id_3017,Bike,3,Business,31,4,8:37:09 PM,31,4,8:37:28 PM,31,4,8:44:04 PM,31,4,8:47:51 PM,31,4,9:21:51 PM,11,18.2,,-1.299313,36.78314879999999,-1.2664227,36.7453671,Rider_Id_116,2040
-Order_No_7759,User_Id_432,Bike,3,Business,27,1,2:02:39 PM,27,1,2:17:40 PM,27,1,2:47:14 PM,27,1,2:49:19 PM,27,1,3:33:10 PM,9,23.2,,-1.3012008,36.764868,-1.290894,36.822971,Rider_Id_544,2631
-Order_No_26616,User_Id_2523,Bike,3,Business,13,1,9:13:15 AM,13,1,9:13:53 AM,13,1,9:18:28 AM,13,1,9:19:53 AM,13,1,10:03:34 AM,20,,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_190,2621
-Order_No_1455,User_Id_575,Bike,1,Personal,4,4,4:19:56 PM,4,4,4:20:23 PM,4,4,4:31:40 PM,4,4,4:56:27 PM,4,4,5:21:06 PM,7,,,-1.2684962,36.8079433,-1.2857690000000002,36.760073,Rider_Id_178,1479
-Order_No_20018,User_Id_2271,Bike,3,Business,19,2,12:52:32 PM,19,2,12:53:18 PM,19,2,12:59:26 PM,19,2,1:02:45 PM,19,2,1:29:16 PM,14,27.5,,-1.265715,36.823815,-1.293515,36.897607,Rider_Id_110,1591
-Order_No_5477,User_Id_3272,Bike,1,Personal,15,1,9:59:23 AM,15,1,9:59:56 AM,15,1,10:07:42 AM,15,1,10:12:47 AM,15,1,10:44:45 AM,13,,,-1.2696341,36.8042045,-1.317151,36.865734,Rider_Id_268,1918
-Order_No_27253,User_Id_2330,Bike,3,Business,19,2,4:49:00 PM,19,2,4:49:31 PM,19,2,5:10:42 PM,19,2,5:30:15 PM,19,2,5:30:25 PM,5,28.5,,-1.3040812,36.7924217,-1.2890715,36.8157588,Rider_Id_314,10
-Order_No_10203,User_Id_2674,Bike,3,Personal,7,1,10:41:34 AM,7,1,10:42:53 AM,7,1,10:53:01 AM,7,1,10:56:49 AM,7,1,11:15:58 AM,15,24.6,,-1.2611916,36.8444489,-1.1919469999999999,36.934836,Rider_Id_932,1149
-Order_No_14536,User_Id_1329,Bike,3,Business,2,4,12:57:54 PM,2,4,12:58:44 PM,2,4,12:59:02 PM,2,4,1:03:55 PM,2,4,1:16:40 PM,5,,,-1.273056,36.811298,-1.2964347999999999,36.8182757,Rider_Id_119,765
-Order_No_3569,User_Id_2838,Bike,1,Personal,7,4,12:02:28 PM,7,4,12:04:11 PM,7,4,12:27:49 PM,7,4,12:32:27 PM,7,4,1:02:33 PM,11,17.2,,-1.2248308,36.801257299999996,-1.2795261,36.826018499999996,Rider_Id_808,1806
-Order_No_9083,User_Id_3194,Bike,3,Business,22,4,3:40:14 PM,22,4,3:41:44 PM,22,4,3:49:00 PM,22,4,3:52:06 PM,22,4,4:08:41 PM,4,26.2,,-1.2551895,36.7822034,-1.2618631999999999,36.8100226,Rider_Id_190,995
-Order_No_23457,User_Id_2987,Bike,3,Business,26,3,2:56:37 PM,26,3,2:59:52 PM,26,3,3:15:14 PM,26,3,3:22:27 PM,26,3,3:57:31 PM,4,26.9,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_383,2104
-Order_No_22152,User_Id_970,Bike,3,Business,21,4,9:22:12 AM,21,4,9:25:01 AM,21,4,9:52:27 AM,21,4,10:02:39 AM,21,4,10:23:12 AM,8,19.2,,-1.2935508,36.794894899999996,-1.2551895,36.7822034,Rider_Id_774,1233
-Order_No_393,User_Id_2829,Bike,3,Business,9,4,1:10:21 PM,9,4,1:13:01 PM,9,4,1:20:04 PM,9,4,1:24:13 PM,9,4,2:13:51 PM,21,,,-1.2571472,36.795063299999995,-1.3562374,36.904295399999995,Rider_Id_205,2978
-Order_No_11702,User_Id_1500,Bike,3,Business,16,4,11:01:47 AM,16,4,11:03:05 AM,16,4,11:10:11 AM,16,4,11:21:26 AM,16,4,11:42:07 AM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_92,1241
-Order_No_8842,User_Id_1661,Bike,1,Personal,12,5,2:00:09 PM,12,5,2:00:18 PM,12,5,2:20:17 PM,12,5,2:22:00 PM,12,5,2:52:21 PM,18,27.3,,-1.2825223,36.901456599999996,-1.2611642,36.7949932,Rider_Id_622,1821
-Order_No_949,User_Id_1500,Bike,3,Business,7,5,9:53:19 AM,7,5,10:24:42 AM,7,5,10:28:44 AM,7,5,10:45:28 AM,7,5,11:04:06 AM,10,25.3,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_926,1118
-Order_No_7047,User_Id_393,Bike,3,Business,24,3,2:37:42 PM,24,3,2:38:26 PM,24,3,2:38:33 PM,24,3,2:46:14 PM,24,3,3:14:10 PM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_942,1676
-Order_No_22102,User_Id_1075,Bike,3,Business,21,4,12:14:55 PM,21,4,12:15:58 PM,21,4,12:40:36 PM,21,4,12:44:00 PM,21,4,1:44:33 PM,17,25.0,,-1.2527958000000001,36.8003131,-1.3277669,36.8975169,Rider_Id_846,3633
-Order_No_321,User_Id_635,Bike,3,Business,4,2,8:49:34 AM,4,2,8:49:51 AM,4,2,9:03:56 AM,4,2,9:09:04 AM,4,2,9:20:01 AM,6,16.7,,-1.2890253999999999,36.7990753,-1.2599563,36.799343900000004,Rider_Id_726,657
-Order_No_23007,User_Id_2016,Bike,3,Personal,19,2,3:09:38 PM,19,2,3:11:35 PM,19,2,3:26:45 PM,19,2,3:50:10 PM,19,2,4:02:18 PM,5,29.9,,-1.2821928999999999,36.8281227,-1.2871136,36.785507,Rider_Id_845,728
-Order_No_5762,User_Id_2150,Bike,3,Business,2,3,12:38:22 PM,2,3,12:38:47 PM,2,3,12:51:18 PM,2,3,12:56:37 PM,2,3,1:45:16 PM,23,24.0,,-1.228911,36.881876,-1.3444459999999998,36.756597,Rider_Id_75,2919
-Order_No_7403,User_Id_3557,Bike,3,Business,5,2,10:36:55 AM,5,2,10:37:09 AM,5,2,10:48:45 AM,5,2,10:50:01 AM,5,2,10:56:21 AM,1,23.4,,-1.2673786,36.8081593,-1.2546732,36.808679600000005,Rider_Id_650,380
-Order_No_1902,User_Id_186,Bike,3,Business,25,1,2:08:54 PM,25,1,2:09:35 PM,25,1,2:14:35 PM,25,1,2:35:58 PM,25,1,2:36:01 PM,7,30.1,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_404,3
-Order_No_2636,User_Id_552,Bike,3,Business,12,6,1:14:09 PM,12,6,1:14:36 PM,12,6,1:22:02 PM,12,6,1:26:10 PM,12,6,1:58:59 PM,14,,,-1.2852569,36.8976121,-1.3562374,36.904295399999995,Rider_Id_547,1969
-Order_No_1051,User_Id_3091,Bike,3,Business,28,1,1:22:07 PM,28,1,1:36:33 PM,28,1,1:51:23 PM,28,1,2:04:41 PM,28,1,2:40:39 PM,19,24.7,,-1.2927792,36.9053492,-1.3018758,36.75908629999999,Rider_Id_888,2158
-Order_No_6328,User_Id_1116,Bike,2,Personal,10,5,1:29:19 PM,10,5,1:30:04 PM,10,5,1:36:33 PM,10,5,1:50:58 PM,10,5,1:51:09 PM,3,21.8,,-1.26017,36.8097563,-1.2732317,36.799558700000006,Rider_Id_832,11
-Order_No_14311,User_Id_2828,Bike,1,Business,18,6,4:59:05 PM,18,6,4:59:14 PM,18,6,5:12:01 PM,18,6,5:46:43 PM,18,6,6:32:34 PM,25,19.2,,-1.2642719999999998,36.7978791,-1.3592118,36.6717545,Rider_Id_427,2751
-Order_No_3587,User_Id_3621,Bike,3,Business,7,5,12:44:00 PM,7,5,12:50:46 PM,7,5,1:07:06 PM,7,5,1:17:25 PM,7,5,1:56:58 PM,20,,,-1.2836583000000001,36.7886014,-1.1774212,36.830552399999995,Rider_Id_269,2373
-Order_No_22729,User_Id_2030,Bike,3,Business,19,2,11:34:41 AM,19,2,11:35:54 AM,19,2,11:50:48 AM,19,2,11:57:23 AM,19,2,12:08:01 PM,3,27.4,,-1.265715,36.823815,-1.2601197,36.7985271,Rider_Id_484,638
-Order_No_20624,User_Id_2978,Bike,3,Business,11,2,9:39:09 AM,11,2,9:42:46 AM,11,2,9:48:29 AM,11,2,10:15:27 AM,11,2,10:15:45 AM,6,18.2,1.9,-1.2551895,36.7822034,-1.2817758,36.798025200000005,Rider_Id_295,18
-Order_No_25819,User_Id_552,Bike,3,Business,12,2,10:30:36 AM,12,2,10:30:49 AM,12,2,11:12:37 AM,12,2,11:16:37 AM,12,2,12:19:00 PM,15,24.7,,-1.2859912,36.8756811,-1.3632162,36.9108253,Rider_Id_513,3743
-Order_No_9715,User_Id_3222,Bike,3,Business,8,5,2:21:14 PM,8,5,2:22:11 PM,8,5,2:32:04 PM,8,5,2:38:52 PM,8,5,2:58:02 PM,6,27.4,,-1.2551895,36.7822034,-1.2357748,36.7900524,Rider_Id_882,1150
-Order_No_14061,User_Id_3201,Bike,3,Business,29,6,1:45:19 PM,29,6,1:46:25 PM,29,6,1:59:47 PM,29,6,2:18:19 PM,29,6,2:18:24 PM,5,26.5,,-1.263709,36.8293799,-1.2584143,36.8048002,Rider_Id_845,5
-Order_No_5881,User_Id_302,Bike,3,Business,3,6,2:24:11 PM,3,6,2:24:57 PM,3,6,2:52:50 PM,3,6,2:54:01 PM,3,6,3:09:03 PM,1,22.4,,-1.2649605,36.798177700000004,-1.25841,36.804026,Rider_Id_112,902
-Order_No_2290,User_Id_393,Bike,3,Business,7,3,10:30:23 AM,7,3,10:30:33 AM,7,3,10:30:37 AM,7,3,10:47:20 AM,7,3,11:16:15 AM,14,25.0,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_62,1735
-Order_No_11396,User_Id_1207,Bike,1,Business,8,6,9:17:44 AM,8,6,9:17:58 AM,8,6,9:25:33 AM,8,6,9:40:54 AM,8,6,9:58:44 AM,13,18.4,,-1.2628638,36.8070399,-1.2156006000000001,36.8916865,Rider_Id_882,1070
-Order_No_24725,User_Id_970,Bike,3,Business,18,4,1:49:14 PM,18,4,1:49:25 PM,18,4,1:50:49 PM,18,4,1:55:33 PM,18,4,2:06:05 PM,4,,,-1.2551895,36.7822034,-1.2566688999999998,36.779695200000006,Rider_Id_349,632
-Order_No_28059,User_Id_2869,Bike,3,Business,4,2,9:05:46 AM,4,2,9:22:19 AM,4,2,9:47:51 AM,4,2,10:02:25 AM,4,2,10:40:04 AM,14,16.4,99.1,-1.3025826,36.7670807,-1.3083777,36.85900770000001,Rider_Id_429,2259
-Order_No_10246,User_Id_3283,Bike,3,Business,5,6,10:26:36 AM,5,6,10:27:39 AM,5,6,11:01:58 AM,5,6,11:35:15 AM,5,6,11:53:59 AM,13,22.3,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_48,1124
-Order_No_4201,User_Id_3332,Bike,3,Personal,8,2,3:00:46 PM,8,2,3:09:19 PM,8,2,3:23:30 PM,8,2,4:09:54 PM,8,2,4:21:56 PM,5,25.2,,-1.2508229,36.7895263,-1.2463396,36.7547983,Rider_Id_205,722
-Order_No_24833,User_Id_1754,Bike,3,Business,27,3,2:07:20 PM,27,3,2:10:41 PM,27,3,2:10:46 PM,27,3,2:29:53 PM,27,3,2:54:14 PM,12,,,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_900,1461
-Order_No_23414,User_Id_3647,Bike,3,Business,28,3,4:50:33 PM,28,3,4:50:55 PM,28,3,5:02:25 PM,28,3,5:06:18 PM,28,3,6:26:25 PM,17,23.2,,-1.2726389999999999,36.794723,-1.207546,36.7913976,Rider_Id_56,4807
-Order_No_14117,User_Id_2532,Bike,3,Business,21,4,2:43:29 PM,21,4,2:49:20 PM,21,4,3:08:00 PM,21,4,3:11:55 PM,21,4,3:44:49 PM,7,26.2,,-1.2551895,36.7822034,-1.2898178999999999,36.827084899999996,Rider_Id_831,1974
-Order_No_22684,User_Id_2523,Bike,3,Business,19,2,10:16:36 AM,19,2,10:20:09 AM,19,2,10:31:39 AM,19,2,10:41:17 AM,19,2,10:55:26 AM,9,27.3,,-1.2551895,36.7822034,-1.2724387,36.8212279,Rider_Id_365,849
-Order_No_11057,User_Id_3647,Bike,3,Business,27,4,3:55:38 PM,27,4,4:00:02 PM,27,4,4:10:30 PM,27,4,4:13:14 PM,27,4,4:27:36 PM,7,23.8,,-1.2726389999999999,36.794723,-1.2950041,36.814358299999995,Rider_Id_386,862
-Order_No_18080,User_Id_3491,Bike,3,Business,21,1,11:02:23 AM,21,1,11:02:56 AM,21,1,11:12:17 AM,21,1,11:16:54 AM,21,1,11:24:06 AM,5,20.2,,-1.2156006000000001,36.8916865,-1.2297202,36.874550799999994,Rider_Id_735,432
-Order_No_2617,User_Id_1033,Bike,3,Personal,28,4,10:54:55 AM,28,4,10:56:06 AM,28,4,11:11:07 AM,28,4,11:14:19 AM,28,4,11:36:50 AM,9,23.7,,-1.233018,36.8004477,-1.2736771,36.8123141,Rider_Id_634,1351
-Order_No_7023,User_Id_3560,Bike,3,Business,23,6,11:57:35 AM,23,6,11:57:56 AM,23,6,11:58:54 AM,23,6,12:07:44 PM,23,6,12:07:59 PM,1,26.4,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_101,15
-Order_No_26831,User_Id_633,Bike,3,Business,18,5,11:28:00 AM,18,5,11:30:06 AM,18,5,11:30:14 AM,18,5,11:40:06 AM,18,5,11:53:33 AM,5,20.2,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_725,807
-Order_No_13471,User_Id_2978,Bike,3,Business,25,5,10:52:10 AM,25,5,10:53:02 AM,25,5,10:55:06 AM,25,5,10:58:36 AM,25,5,11:26:39 AM,12,26.6,,-1.2551895,36.7822034,-1.3256809999999999,36.7959072,Rider_Id_518,1683
-Order_No_16226,User_Id_2956,Bike,3,Business,11,1,9:54:58 AM,11,1,9:57:22 AM,11,1,10:25:13 AM,11,1,10:32:52 AM,11,1,10:51:34 AM,14,26.4,,-1.207546,36.7913976,-1.2584143,36.8048002,Rider_Id_764,1122
-Order_No_4267,User_Id_3080,Bike,3,Business,21,4,2:06:19 PM,21,4,2:06:30 PM,21,4,2:06:55 PM,21,4,2:37:23 PM,21,4,3:41:02 PM,19,26.1,,-1.2571472,36.795063299999995,-1.3389278999999998,36.7092201,Rider_Id_26,3819
-Order_No_21652,User_Id_3647,Bike,3,Business,16,4,11:57:05 AM,16,4,11:58:11 AM,16,4,12:08:22 PM,16,4,12:13:39 PM,16,4,12:26:00 PM,5,,,-1.2726389999999999,36.794723,-1.2538731,36.7850223,Rider_Id_147,741
-Order_No_7426,User_Id_2982,Bike,3,Business,1,4,10:42:06 AM,1,4,10:42:11 AM,1,4,10:56:36 AM,1,4,11:08:13 AM,1,4,11:53:59 AM,29,,,-1.300921,36.828195,-1.1355095,36.9689533,Rider_Id_516,2746
-Order_No_19628,User_Id_2200,Bike,3,Business,24,6,11:08:39 AM,24,6,11:37:00 AM,24,6,11:50:49 AM,24,6,11:57:59 AM,24,6,12:35:05 PM,9,23.9,,-1.290894,36.822971,-1.2765492,36.7669809,Rider_Id_477,2226
-Order_No_17620,User_Id_1880,Bike,3,Business,28,3,1:23:18 PM,28,3,1:23:59 PM,28,3,1:57:31 PM,28,3,2:04:46 PM,28,3,2:30:15 PM,11,,,-1.3004997999999999,36.8041558,-1.2571472,36.795063299999995,Rider_Id_612,1529
-Order_No_28017,User_Id_1796,Bike,3,Business,30,2,9:40:44 AM,30,2,9:41:10 AM,30,2,9:52:56 AM,30,2,10:07:35 AM,30,2,10:20:46 AM,9,,,-1.3033561000000002,36.8163631,-1.331619,36.847976,Rider_Id_709,791
-Order_No_2504,User_Id_3017,Bike,3,Business,27,5,3:21:17 PM,27,5,3:22:10 PM,27,5,3:26:38 PM,27,5,3:28:52 PM,27,5,3:34:38 PM,2,24.0,,-1.299313,36.78314879999999,-1.2946558000000001,36.7908316,Rider_Id_312,346
-Order_No_12881,User_Id_390,Bike,1,Personal,25,5,9:06:00 AM,25,5,9:08:24 AM,25,5,9:23:52 AM,25,5,9:32:36 AM,25,5,9:47:22 AM,5,16.8,,-1.2828362,36.818923600000005,-1.2601,36.796528,Rider_Id_338,886
-Order_No_21302,User_Id_3108,Bike,3,Business,17,3,8:54:39 AM,17,3,8:54:50 AM,17,3,8:57:15 AM,17,3,9:03:33 AM,17,3,9:20:17 AM,10,,,-1.2551895,36.7822034,-1.2903067,36.83756339999999,Rider_Id_291,1004
-Order_No_2004,User_Id_3470,Bike,3,Business,26,1,4:21:42 PM,26,1,4:34:19 PM,26,1,4:47:03 PM,26,1,4:48:49 PM,26,1,5:09:01 PM,2,20.0,,-1.28878,36.8168312,-1.2905033000000001,36.8096462,Rider_Id_362,1212
-Order_No_6790,User_Id_498,Bike,1,Personal,27,1,10:54:21 AM,27,1,10:54:52 AM,27,1,11:03:55 AM,27,1,11:08:59 AM,27,1,11:49:02 AM,21,24.2,,-1.3046965,36.8016456,-1.16697,36.826728,Rider_Id_397,2403
-Order_No_24310,User_Id_1626,Bike,1,Personal,4,2,8:23:18 AM,4,2,8:23:40 AM,4,2,8:46:41 AM,4,2,8:51:36 AM,4,2,9:22:54 AM,13,16.3,,-1.2365885,36.8898127,-1.2036664,36.819262200000004,Rider_Id_216,1878
-Order_No_8179,User_Id_246,Bike,3,Business,29,1,9:03:15 AM,29,1,9:03:26 AM,29,1,9:04:11 AM,29,1,9:09:50 AM,29,1,9:34:52 AM,13,19.1,,-1.2584143,36.8048002,-1.2811642,36.88075,Rider_Id_630,1502
-Order_No_20170,User_Id_3647,Bike,3,Business,15,2,3:21:15 PM,15,2,3:21:41 PM,15,2,3:37:56 PM,15,2,3:40:55 PM,15,2,4:07:32 PM,13,24.4,,-1.2726389999999999,36.794723,-1.2309704,36.8688593,Rider_Id_932,1597
-Order_No_2315,User_Id_2896,Bike,3,Business,26,1,10:08:00 AM,26,1,10:08:13 AM,26,1,10:24:56 AM,26,1,10:29:46 AM,26,1,11:20:55 AM,13,17.8,4.9,-1.3338005,36.863026,-1.2776707,36.820777,Rider_Id_114,3069
-Order_No_14294,User_Id_517,Bike,3,Business,29,3,3:00:18 PM,29,3,3:02:52 PM,29,3,3:03:08 PM,29,3,3:25:12 PM,29,3,3:48:02 PM,11,21.0,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_349,1370
-Order_No_354,User_Id_1258,Bike,2,Personal,27,4,1:40:33 PM,27,4,2:12:49 PM,27,4,2:43:16 PM,27,4,2:50:06 PM,27,4,4:10:33 PM,21,25.1,,-1.3646468,36.7499829,-1.303924,36.8758792,Rider_Id_226,4827
-Order_No_26215,User_Id_2098,Bike,2,Personal,4,4,3:39:58 PM,4,4,3:42:32 PM,4,4,3:52:45 PM,4,4,3:58:32 PM,4,4,4:32:45 PM,10,25.0,,-1.3207146,36.8305771,-1.2810586000000002,36.883829399999996,Rider_Id_254,2053
-Order_No_11327,User_Id_325,Bike,3,Business,23,3,11:49:04 AM,23,3,11:50:06 AM,23,3,11:51:23 AM,23,3,12:05:04 PM,23,3,12:26:12 PM,8,,,-1.2551895,36.7822034,-1.2950408000000002,36.809817200000005,Rider_Id_648,1268
-Order_No_14810,User_Id_3698,Bike,3,Business,22,3,10:42:19 AM,22,3,10:42:39 AM,22,3,11:14:29 AM,22,3,11:15:38 AM,22,3,11:51:02 AM,24,23.3,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_856,2124
-Order_No_11772,User_Id_3145,Bike,3,Business,7,1,9:28:15 AM,7,1,9:29:11 AM,7,1,9:41:05 AM,7,1,9:45:55 AM,7,1,10:06:01 AM,5,21.2,,-1.2726389999999999,36.794723,-1.2862436000000002,36.791017,Rider_Id_434,1206
-Order_No_8063,User_Id_718,Bike,3,Business,22,5,10:07:39 AM,22,5,10:09:21 AM,22,5,10:11:16 AM,22,5,10:17:43 AM,22,5,10:38:43 AM,11,22.1,,-1.2551895,36.7822034,-1.3034451,36.752312599999996,Rider_Id_854,1260
-Order_No_9492,User_Id_3426,Bike,3,Business,11,1,11:20:41 AM,11,1,11:21:09 AM,11,1,11:28:56 AM,11,1,11:36:11 AM,11,1,11:52:48 AM,9,26.6,,-1.2584143,36.8048002,-1.3012008,36.764868,Rider_Id_205,997
-Order_No_267,User_Id_641,Bike,3,Personal,2,2,12:28:05 PM,2,2,12:29:27 PM,2,2,12:51:17 PM,2,2,12:53:33 PM,2,2,1:23:46 PM,14,20.8,,-1.2848681,36.7904515,-1.2297202,36.881950700000004,Rider_Id_432,1813
-Order_No_17869,User_Id_2066,Bike,3,Business,26,4,2:03:12 PM,26,4,2:03:17 PM,26,4,2:09:27 PM,26,4,2:23:24 PM,26,4,2:46:16 PM,16,22.2,,-1.316961,36.820337,-1.2343236000000002,36.8861,Rider_Id_74,1372
-Order_No_12966,User_Id_2843,Bike,3,Personal,18,3,2:14:52 PM,18,3,2:15:18 PM,18,3,2:17:37 PM,18,3,2:33:02 PM,18,3,2:43:01 PM,8,20.9,,-1.2567464,36.7915273,-1.2698167999999999,36.7421898,Rider_Id_805,599
-Order_No_7029,User_Id_970,Bike,3,Business,11,4,1:03:18 PM,11,4,1:03:39 PM,11,4,1:04:31 PM,11,4,1:12:53 PM,11,4,1:25:17 PM,4,25.4,,-1.2551895,36.7822034,-1.2654228,36.8033218,Rider_Id_709,744
-Order_No_23563,User_Id_3291,Bike,3,Business,25,5,8:58:15 AM,25,5,9:00:07 AM,25,5,9:13:59 AM,25,5,9:35:18 AM,25,5,10:06:38 AM,12,20.9,,-1.318062,36.849326,-1.2600926000000001,36.8088685,Rider_Id_956,1880
-Order_No_26362,User_Id_2200,Bike,3,Business,9,6,11:55:08 AM,9,6,1:11:33 PM,9,6,1:57:25 PM,9,6,1:59:07 PM,9,6,2:35:43 PM,9,30.2,,-1.3025826,36.7670807,-1.290894,36.822971,Rider_Id_118,2196
-Order_No_1161,User_Id_3517,Bike,3,Business,14,2,3:09:26 PM,14,2,3:16:02 PM,14,2,3:47:25 PM,14,2,4:15:42 PM,14,2,4:38:24 PM,6,,,-1.300921,36.828195,-1.3015998,36.8764939,Rider_Id_20,1362
-Order_No_23881,User_Id_432,Bike,3,Business,4,4,4:56:53 PM,4,4,4:57:12 PM,4,4,4:57:43 PM,4,4,5:03:38 PM,4,4,5:48:03 PM,28,25.9,,-1.3012008,36.764868,-1.3916458999999999,36.93058920000001,Rider_Id_871,2665
-Order_No_22246,User_Id_552,Bike,3,Business,3,1,12:20:04 PM,3,1,12:20:45 PM,3,1,12:20:57 PM,3,1,12:33:35 PM,3,1,1:28:44 PM,18,21.7,,-1.279395,36.825364,-1.3562374,36.904295399999995,Rider_Id_906,3309
-Order_No_9333,User_Id_549,Bike,2,Personal,19,3,12:55:08 PM,19,3,12:56:02 PM,19,3,12:57:34 PM,19,3,1:03:17 PM,19,3,1:15:53 PM,5,,,-1.2857524,36.819680600000005,-1.2684239,36.79524620000001,Rider_Id_456,756
-Order_No_7469,User_Id_186,Bike,3,Business,4,3,1:35:39 PM,4,3,1:35:50 PM,4,3,1:37:24 PM,4,3,1:44:49 PM,4,3,2:21:18 PM,24,18.2,,-1.3700383,36.9190174,-1.2579303999999998,36.8019667,Rider_Id_791,2189
-Order_No_22330,User_Id_3218,Bike,3,Business,5,6,10:26:28 AM,5,6,10:27:31 AM,5,6,10:34:25 AM,5,6,10:40:57 AM,5,6,11:00:29 AM,8,22.5,,-1.2765736,36.851364600000004,-1.2413316,36.878552500000005,Rider_Id_100,1172
-Order_No_5129,User_Id_1469,Bike,3,Business,16,4,9:52:18 AM,16,4,9:52:32 AM,16,4,9:59:23 AM,16,4,10:05:37 AM,16,4,10:37:40 AM,11,,,-1.2551895,36.7822034,-1.2642197,36.716824700000004,Rider_Id_649,1923
-Order_No_15473,User_Id_867,Bike,3,Business,21,4,2:39:49 PM,21,4,2:40:01 PM,21,4,2:40:49 PM,21,4,2:49:11 PM,21,4,3:06:03 PM,9,26.5,,-1.2638185,36.7930057,-1.2991441000000001,36.7528804,Rider_Id_545,1012
-Order_No_2598,User_Id_733,Bike,3,Business,2,4,1:58:29 PM,2,4,2:00:00 PM,2,4,2:00:46 PM,2,4,2:08:50 PM,2,4,2:34:16 PM,8,,,-1.2551895,36.7822034,-1.2962832,36.784968299999996,Rider_Id_361,1526
-Order_No_25123,User_Id_3424,Bike,1,Personal,4,2,3:18:56 PM,4,2,3:30:29 PM,4,2,4:04:55 PM,4,2,4:06:43 PM,4,2,4:42:45 PM,7,20.3,,-1.2940713,36.7890758,-1.2891615,36.730566499999995,Rider_Id_350,2162
-Order_No_15738,User_Id_3710,Bike,3,Business,23,5,4:41:57 PM,23,5,5:24:10 PM,23,5,5:24:57 PM,23,5,5:59:16 PM,23,5,6:14:52 PM,7,24.4,,-1.233433,36.800420200000005,-1.2654878999999999,36.7984789,Rider_Id_925,936
-Order_No_27333,User_Id_2509,Bike,3,Business,11,2,3:06:26 PM,11,2,3:08:04 PM,11,2,3:18:00 PM,11,2,3:21:59 PM,11,2,3:48:16 PM,14,25.7,,-1.2571472,36.795063299999995,-1.2156006000000001,36.8916865,Rider_Id_415,1577
-Order_No_4419,User_Id_517,Bike,3,Business,9,6,8:55:42 AM,9,6,8:55:59 AM,9,6,8:56:13 AM,9,6,9:34:43 AM,9,6,9:34:47 AM,11,20.0,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,4
-Order_No_17967,User_Id_190,Bike,3,Business,27,1,3:32:23 PM,27,1,3:34:44 PM,27,1,3:40:12 PM,27,1,3:51:34 PM,27,1,4:04:19 PM,5,23.0,,-1.3270347,36.866736200000005,-1.3390024,36.8746657,Rider_Id_218,765
-Order_No_11547,User_Id_2956,Bike,3,Business,18,1,12:41:59 PM,18,1,12:42:24 PM,18,1,12:52:48 PM,18,1,12:57:06 PM,18,1,1:05:43 PM,2,28.1,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_399,517
-Order_No_4335,User_Id_3641,Bike,3,Business,14,4,9:24:43 AM,14,4,10:10:30 AM,14,4,10:11:14 AM,14,4,10:13:11 AM,14,4,10:42:15 AM,9,18.0,,-1.2551895,36.7822034,-1.303105,36.819134999999996,Rider_Id_660,1744
-Order_No_19075,User_Id_1527,Bike,3,Business,8,3,9:37:14 AM,8,3,9:40:45 AM,8,3,9:48:13 AM,8,3,9:59:04 AM,8,3,10:09:02 AM,5,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_843,598
-Order_No_5043,User_Id_1273,Bike,2,Personal,3,2,3:22:26 PM,3,2,3:22:52 PM,3,2,3:39:36 PM,3,2,3:45:48 PM,3,2,3:52:32 PM,4,20.0,,-1.2933466999999998,36.7810385,-1.2687973999999997,36.7960339,Rider_Id_312,404
-Order_No_579,User_Id_302,Bike,3,Business,16,5,2:03:21 PM,16,5,2:04:30 PM,16,5,2:08:25 PM,16,5,2:20:51 PM,16,5,2:50:13 PM,2,,,-1.2649605,36.798177700000004,-1.25896,36.8016691,Rider_Id_125,1762
-Order_No_6488,User_Id_3222,Bike,3,Business,18,2,3:02:20 PM,18,2,3:02:51 PM,18,2,3:05:39 PM,18,2,3:09:33 PM,18,2,3:18:57 PM,4,25.7,,-1.2551895,36.7822034,-1.2617028000000001,36.7983849,Rider_Id_575,564
-Order_No_5206,User_Id_490,Bike,3,Personal,25,3,10:57:12 AM,25,3,10:57:17 AM,25,3,11:05:36 AM,25,3,11:12:11 AM,25,3,11:34:39 AM,10,,,-1.2607156000000002,36.814536700000005,-1.2793831,36.7617573,Rider_Id_616,1348
-Order_No_8235,User_Id_2532,Bike,3,Business,31,3,9:06:30 AM,31,3,10:29:47 AM,31,3,10:38:20 AM,31,3,10:46:01 AM,31,3,11:04:41 AM,7,24.9,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_237,1120
-Order_No_2538,User_Id_3295,Bike,3,Business,16,2,12:15:41 PM,16,2,12:17:20 PM,16,2,12:32:05 PM,16,2,12:35:39 PM,16,2,1:02:44 PM,12,,,-1.2571472,36.795063299999995,-1.2409346,36.874305299999996,Rider_Id_660,1625
-Order_No_6797,User_Id_749,Bike,1,Personal,21,4,3:41:22 PM,21,4,3:42:48 PM,21,4,4:10:01 PM,21,4,4:31:01 PM,21,4,4:55:03 PM,8,22.5,,-1.2635089,36.78873370000001,-1.304033,36.7848695,Rider_Id_425,1442
-Order_No_1255,User_Id_3491,Bike,3,Business,25,1,4:38:38 PM,25,1,4:49:53 PM,25,1,5:02:54 PM,25,1,5:12:27 PM,25,1,5:56:05 PM,13,27.7,,-1.2326041,36.8009029,-1.2283403,36.8822756,Rider_Id_953,2618
-Order_No_27858,User_Id_2436,Bike,3,Business,15,5,3:16:11 PM,15,5,3:17:50 PM,15,5,3:23:19 PM,15,5,3:38:07 PM,15,5,3:43:35 PM,4,18.0,,-1.2551895,36.7822034,-1.2627937,36.815695700000006,Rider_Id_124,328
-Order_No_7150,User_Id_412,Bike,3,Business,2,2,2:24:27 PM,2,2,2:25:43 PM,2,2,2:41:50 PM,2,2,2:58:24 PM,2,2,2:58:37 PM,4,29.4,,-1.2628473,36.781805,-1.266837,36.799248999999996,Rider_Id_850,13
-Order_No_5403,User_Id_633,Bike,3,Business,20,3,4:17:13 PM,20,3,4:17:45 PM,20,3,4:29:08 PM,20,3,4:31:50 PM,20,3,4:39:27 PM,4,29.2,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_127,457
-Order_No_10899,User_Id_2518,Bike,3,Personal,20,4,2:17:04 PM,20,4,2:17:53 PM,20,4,2:41:15 PM,20,4,2:47:35 PM,20,4,3:00:06 PM,5,25.4,,-1.2990731000000002,36.848805799999994,-1.3246455,36.8754415,Rider_Id_26,751
-Order_No_17627,User_Id_2987,Bike,3,Business,18,1,1:57:23 PM,18,1,1:58:55 PM,18,1,2:23:42 PM,18,1,2:30:16 PM,18,1,2:48:54 PM,9,17.4,,-1.2702120000000001,36.82108770000001,-1.3014461000000002,36.7661381,Rider_Id_886,1118
-Order_No_18873,User_Id_2436,Bike,3,Business,22,5,2:34:59 PM,22,5,2:37:00 PM,22,5,2:43:59 PM,22,5,2:57:38 PM,22,5,3:28:14 PM,9,27.1,,-1.2551895,36.7822034,-1.2315181,36.814054299999995,Rider_Id_434,1836
-Order_No_22426,User_Id_2423,Bike,3,Business,4,2,10:51:06 AM,4,2,10:53:57 AM,4,2,11:08:55 AM,4,2,11:11:45 AM,4,2,11:29:22 AM,9,19.4,,-1.2967701999999999,36.8040381,-1.2551895,36.7822034,Rider_Id_205,1057
-Order_No_14909,User_Id_2330,Bike,3,Business,26,2,12:30:48 PM,26,2,12:32:14 PM,26,2,12:48:03 PM,26,2,12:55:19 PM,26,2,1:10:57 PM,7,27.5,,-1.2325706,36.808392299999994,-1.2536275,36.798793200000006,Rider_Id_318,938
-Order_No_24049,User_Id_393,Bike,3,Business,30,5,10:42:35 AM,30,5,11:12:59 AM,30,5,11:13:06 AM,30,5,11:40:09 AM,30,5,12:00:13 PM,10,23.5,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_39,1204
-Order_No_19781,User_Id_1500,Bike,3,Business,29,4,4:30:30 PM,29,4,5:05:06 PM,29,4,5:21:33 PM,29,4,5:22:43 PM,29,4,5:55:35 PM,10,21.0,,-1.2991441000000001,36.7528804,-1.2574219,36.7927073,Rider_Id_523,1972
-Order_No_26834,User_Id_1601,Bike,1,Personal,28,4,9:27:16 AM,28,4,9:27:52 AM,28,4,9:36:29 AM,28,4,9:40:19 AM,28,4,9:51:37 AM,5,,,-1.25537,36.7756,-1.2613754,36.795771,Rider_Id_795,678
-Order_No_13541,User_Id_1276,Bike,3,Business,31,2,12:10:54 PM,31,2,12:32:42 PM,31,2,12:47:01 PM,31,2,1:00:18 PM,31,2,1:38:56 PM,17,18.4,,-1.3103985,36.9114173,-1.2745107,36.797450299999994,Rider_Id_345,2318
-Order_No_21342,User_Id_3426,Bike,3,Business,12,6,2:08:31 PM,12,6,2:08:46 PM,12,6,2:20:23 PM,12,6,2:22:47 PM,12,6,2:46:29 PM,9,24.9,,-1.3012008,36.764868,-1.2594538999999998,36.8067841,Rider_Id_817,1422
-Order_No_13150,User_Id_3283,Bike,3,Business,10,1,4:32:21 PM,10,1,4:32:39 PM,10,1,4:46:00 PM,10,1,5:01:06 PM,10,1,5:12:04 PM,5,18.9,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_864,658
-Order_No_16030,User_Id_1500,Bike,3,Business,21,4,2:03:57 PM,21,4,2:05:32 PM,21,4,2:10:11 PM,21,4,2:14:16 PM,21,4,2:36:04 PM,10,26.2,,-1.2991441000000001,36.7528804,-1.2638185,36.7930057,Rider_Id_545,1308
-Order_No_24582,User_Id_868,Bike,3,Business,20,4,10:51:06 AM,20,4,10:51:12 AM,20,4,10:54:55 AM,20,4,11:17:02 AM,20,4,11:41:05 AM,10,22.2,,-1.228911,36.881876,-1.265715,36.823815,Rider_Id_685,1443
-Order_No_25798,User_Id_2532,Bike,3,Business,6,1,4:19:23 PM,6,1,4:19:46 PM,6,1,4:23:22 PM,6,1,4:57:00 PM,6,1,5:02:58 PM,3,22.5,,-1.2571472,36.795063299999995,-1.2745107,36.797450299999994,Rider_Id_116,358
-Order_No_21810,User_Id_3492,Bike,3,Business,4,2,1:11:59 PM,4,2,1:13:24 PM,4,2,1:18:39 PM,4,2,1:23:07 PM,4,2,1:57:25 PM,7,20.6,,-1.299207,36.8414337,-1.2764851000000002,36.8007126,Rider_Id_2,2058
-Order_No_21925,User_Id_1882,Bike,3,Business,2,2,11:23:25 AM,2,2,1:12:45 PM,2,2,1:24:01 PM,2,2,1:38:18 PM,2,2,2:15:28 PM,7,29.4,,-1.2958783,36.7928125,-1.2588774999999999,36.8079744,Rider_Id_306,2230
-Order_No_25612,User_Id_540,Bike,3,Business,15,2,10:50:55 AM,15,2,10:53:56 AM,15,2,11:05:21 AM,15,2,11:09:41 AM,15,2,11:34:54 AM,7,24.1,,-1.2551895,36.7822034,-1.2878180000000001,36.828623,Rider_Id_576,1513
-Order_No_587,User_Id_1637,Bike,3,Business,23,5,1:06:09 PM,23,5,2:44:50 PM,23,5,3:08:20 PM,23,5,3:13:36 PM,23,5,3:23:41 PM,2,26.2,,-1.2615888000000002,36.7928732,-1.2527958000000001,36.8003131,Rider_Id_434,605
-Order_No_19206,User_Id_3487,Bike,3,Business,13,1,3:07:43 PM,13,1,3:15:15 PM,13,1,3:15:36 PM,13,1,3:31:09 PM,13,1,3:48:07 PM,5,,,-1.2527958000000001,36.8003131,-1.279395,36.825364,Rider_Id_89,1018
-Order_No_21883,User_Id_2200,Bike,3,Business,30,4,9:59:08 AM,30,4,10:00:36 AM,30,4,10:32:04 AM,30,4,10:52:00 AM,30,4,11:13:03 AM,9,,,-1.2859912,36.8756811,-1.290894,36.822971,Rider_Id_183,1263
-Order_No_26766,User_Id_186,Bike,3,Business,10,1,1:01:26 PM,10,1,1:05:19 PM,10,1,1:21:57 PM,10,1,1:33:27 PM,10,1,1:52:35 PM,5,18.3,,-1.2571472,36.795063299999995,-1.2883158000000001,36.8199114,Rider_Id_139,1148
-Order_No_8135,User_Id_302,Bike,3,Business,3,6,9:02:17 PM,3,6,9:07:47 PM,3,6,9:17:24 PM,3,6,9:18:48 PM,3,6,9:40:36 PM,8,18.3,0.6,-1.2649605,36.798177700000004,-1.2930663999999998,36.78495,Rider_Id_40,1308
-Order_No_1691,User_Id_3560,Bike,3,Business,13,6,11:14:13 AM,13,6,11:15:15 AM,13,6,11:30:37 AM,13,6,11:43:22 AM,13,6,11:57:08 AM,5,25.9,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_394,826
-Order_No_1005,User_Id_2956,Bike,3,Business,15,3,2:33:02 PM,15,3,2:34:03 PM,15,3,2:37:15 PM,15,3,2:41:56 PM,15,3,2:59:06 PM,4,25.9,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_930,1030
-Order_No_27958,User_Id_496,Bike,3,Business,15,1,2:41:44 PM,15,1,2:41:52 PM,15,1,2:48:36 PM,15,1,2:52:24 PM,15,1,3:13:50 PM,13,,,-1.2551895,36.7822034,-1.3233955,36.856294399999996,Rider_Id_96,1286
-Order_No_25788,User_Id_1133,Bike,3,Business,27,4,2:39:35 PM,27,4,2:43:29 PM,27,4,2:58:28 PM,27,4,3:05:17 PM,27,4,3:36:08 PM,13,25.1,,-1.3332749,36.870814700000004,-1.2734192,36.8072379,Rider_Id_623,1851
-Order_No_20036,User_Id_2528,Bike,3,Business,8,1,9:39:25 AM,8,1,9:39:35 AM,8,1,9:55:06 AM,8,1,9:59:05 AM,8,1,10:11:05 AM,3,21.7,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_520,720
-Order_No_25150,User_Id_788,Bike,3,Business,23,5,2:24:53 PM,23,5,3:10:39 PM,23,5,3:54:18 PM,23,5,3:56:33 PM,23,5,4:17:15 PM,9,26.2,,-1.2551895,36.7822034,-1.225322,36.80855,Rider_Id_878,1242
-Order_No_27827,User_Id_393,Bike,3,Business,10,3,8:40:03 AM,10,3,8:41:13 AM,10,3,8:51:30 AM,10,3,9:05:11 AM,10,3,9:29:30 AM,11,21.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_843,1459
-Order_No_9957,User_Id_3222,Bike,3,Business,6,1,3:20:51 PM,6,1,3:24:35 PM,6,1,3:34:25 PM,6,1,3:37:04 PM,6,1,4:03:15 PM,8,,,-1.2551895,36.7822034,-1.295156,36.809430600000006,Rider_Id_853,1571
-Order_No_4899,User_Id_2164,Bike,3,Business,15,5,3:16:05 PM,15,5,5:24:53 PM,15,5,5:30:40 PM,15,5,5:33:55 PM,15,5,5:52:00 PM,6,27.6,,-1.2551895,36.7822034,-1.285382,36.8162878,Rider_Id_411,1085
-Order_No_22782,User_Id_3247,Bike,3,Business,23,4,8:56:18 AM,23,4,8:56:45 AM,23,4,8:57:08 AM,23,4,9:10:26 AM,23,4,9:42:39 AM,18,,,-1.2571472,36.795063299999995,-1.3244885,36.897791999999995,Rider_Id_576,1933
-Order_No_11756,User_Id_3772,Bike,1,Personal,3,3,1:58:32 PM,3,3,1:59:24 PM,3,3,2:08:24 PM,3,3,2:15:21 PM,3,3,2:28:38 PM,5,26.8,,-1.3054537,36.8284768,-1.3125593,36.8430941,Rider_Id_848,797
-Order_No_363,User_Id_302,Bike,3,Business,13,3,3:53:05 PM,13,3,3:53:18 PM,13,3,4:09:25 PM,13,3,4:23:34 PM,13,3,4:38:57 PM,8,29.0,,-1.2976313999999998,36.777256,-1.2649605,36.798177700000004,Rider_Id_205,923
-Order_No_23067,User_Id_510,Bike,3,Business,5,5,5:07:54 PM,5,5,5:08:11 PM,5,5,5:17:33 PM,5,5,5:20:36 PM,5,5,5:55:33 PM,13,24.7,,-1.2860183,36.8975338,-1.3632162,36.9108253,Rider_Id_890,2097
-Order_No_4987,User_Id_893,Bike,3,Business,23,4,12:53:50 PM,23,4,12:54:02 PM,23,4,1:00:26 PM,23,4,1:11:49 PM,23,4,1:36:59 PM,17,23.1,,-1.2663194,36.809901,-1.3373344999999999,36.7118861,Rider_Id_660,1510
-Order_No_16479,User_Id_1637,Bike,3,Business,23,2,10:38:25 AM,23,2,10:39:03 AM,23,2,10:44:39 AM,23,2,10:51:04 AM,23,2,10:56:15 AM,3,17.8,,-1.2696183,36.8021778,-1.2615888000000002,36.7928732,Rider_Id_578,311
-Order_No_20931,User_Id_1667,Bike,3,Business,22,5,3:00:52 PM,22,5,3:05:43 PM,22,5,3:22:19 PM,22,5,3:46:00 PM,22,5,4:09:52 PM,6,27.4,,-1.2624531,36.8228476,-1.2908945,36.82465139999999,Rider_Id_21,1432
-Order_No_26916,User_Id_3718,Bike,3,Business,29,4,11:12:45 AM,29,4,11:15:04 AM,29,4,11:20:08 AM,29,4,11:23:30 AM,29,4,12:42:57 PM,5,21.3,,-1.2650027,36.8126242,-1.2906618,36.8131222,Rider_Id_268,4767
-Order_No_15597,User_Id_1207,Bike,3,Business,10,6,10:28:25 AM,10,6,10:28:50 AM,10,6,10:35:09 AM,10,6,10:37:49 AM,10,6,10:53:37 AM,8,23.7,,-1.2628638,36.8070399,-1.2745321000000003,36.765088,Rider_Id_453,948
-Order_No_689,User_Id_1304,Bike,3,Business,18,3,11:47:07 AM,18,3,11:47:13 AM,18,3,12:16:57 PM,18,3,12:19:36 PM,18,3,12:43:07 PM,9,19.8,,-1.2551895,36.7822034,-1.2315181,36.814054299999995,Rider_Id_616,1411
-Order_No_5546,User_Id_3599,Bike,3,Business,5,3,12:53:19 PM,5,3,1:19:11 PM,5,3,1:19:15 PM,5,3,1:50:15 PM,5,3,2:02:10 PM,11,25.0,,-1.3302996,36.8707935,-1.2985749,36.8088,Rider_Id_406,715
-Order_No_19061,User_Id_3291,Bike,3,Business,31,5,10:52:19 AM,31,5,10:53:27 AM,31,5,11:31:49 AM,31,5,12:02:50 PM,31,5,12:42:05 PM,18,21.5,,-1.3393547,36.8910035,-1.2600926000000001,36.8088685,Rider_Id_126,2355
-Order_No_13610,User_Id_1075,Bike,3,Business,14,4,11:34:51 AM,14,4,11:55:47 AM,14,4,12:02:53 PM,14,4,12:07:28 PM,14,4,12:46:16 PM,18,23.9,,-1.2527958000000001,36.8003131,-1.2422942,36.6669445,Rider_Id_351,2328
-Order_No_23598,User_Id_3674,Bike,3,Business,20,5,9:16:43 AM,20,5,9:17:16 AM,20,5,9:20:01 AM,20,5,9:26:21 AM,20,5,9:54:46 AM,8,14.7,,-1.2551895,36.7822034,-1.2910973999999997,36.790268700000006,Rider_Id_726,1705
-Order_No_24650,User_Id_2112,Bike,3,Business,21,4,5:22:05 PM,21,4,5:23:16 PM,21,4,5:28:44 PM,21,4,5:51:29 PM,21,4,6:21:04 PM,12,,,-1.279395,36.825364,-1.2156006000000001,36.8916865,Rider_Id_248,1775
-Order_No_28138,User_Id_2174,Bike,3,Business,28,3,11:27:26 AM,28,3,11:27:31 AM,28,3,11:30:09 AM,28,3,11:45:04 AM,28,3,12:49:40 PM,23,,,-1.2482781,36.884091999999995,-1.3446058,36.760517799999995,Rider_Id_946,3876
-Order_No_110,User_Id_3517,Bike,3,Business,22,4,9:41:57 AM,22,4,9:57:19 AM,22,4,10:41:48 AM,22,4,10:50:32 AM,22,4,11:51:58 AM,34,22.8,,-1.1518113,36.6654506,-1.300921,36.828195,Rider_Id_804,3686
-Order_No_7059,User_Id_799,Bike,1,Personal,28,1,12:33:37 PM,28,1,12:34:03 PM,28,1,12:40:27 PM,28,1,12:48:33 PM,28,1,1:08:08 PM,10,22.6,,-1.304033,36.7848695,-1.2655306,36.828148,Rider_Id_919,1175
-Order_No_13733,User_Id_2869,Bike,3,Business,24,3,8:28:01 AM,24,3,8:33:31 AM,24,3,9:44:49 AM,24,3,9:55:28 AM,24,3,10:46:38 AM,8,,,-1.3025826,36.7670807,-1.2688224,36.794406200000005,Rider_Id_931,3070
-Order_No_6904,User_Id_633,Bike,3,Business,5,3,1:14:56 PM,5,3,2:49:22 PM,5,3,3:04:29 PM,5,3,3:34:11 PM,5,3,3:53:41 PM,9,24.9,,-1.2574219,36.7927073,-1.3004062,36.829741,Rider_Id_523,1170
-Order_No_15983,User_Id_2987,Bike,3,Business,30,1,11:54:49 AM,30,1,11:54:59 AM,30,1,11:55:51 AM,30,1,12:14:45 PM,30,1,12:24:46 PM,4,22.8,,-1.2702120000000001,36.82108770000001,-1.260232,36.807334000000004,Rider_Id_408,601
-Order_No_16677,User_Id_3621,Bike,3,Business,14,1,11:49:20 AM,14,1,11:52:01 AM,14,1,12:02:33 PM,14,1,12:06:08 PM,14,1,12:29:47 PM,10,23.1,,-1.2836583000000001,36.7886014,-1.2698167999999999,36.7421898,Rider_Id_919,1419
-Order_No_25179,User_Id_1500,Bike,3,Business,12,5,10:29:02 AM,12,5,10:30:43 AM,12,5,10:36:08 AM,12,5,10:58:56 AM,12,5,11:12:29 AM,8,26.7,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_401,813
-Order_No_17806,User_Id_3091,Bike,3,Business,5,3,10:49:06 AM,5,3,10:50:02 AM,5,3,11:09:15 AM,5,3,11:11:38 AM,5,3,12:08:03 PM,9,22.3,,-1.3025826,36.7670807,-1.2591018999999999,36.800577000000004,Rider_Id_762,3385
-Order_No_19947,User_Id_1576,Bike,3,Business,18,5,3:20:56 PM,18,5,3:29:41 PM,18,5,3:44:04 PM,18,5,3:45:48 PM,18,5,4:00:38 PM,8,24.2,,-1.3100608999999999,36.820536499999996,-1.3332749,36.870814700000004,Rider_Id_192,890
-Order_No_18677,User_Id_635,Bike,3,Business,27,6,12:13:59 PM,27,6,12:18:26 PM,27,6,12:37:23 PM,27,6,12:58:25 PM,27,6,1:15:53 PM,8,,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_167,1048
-Order_No_23808,User_Id_2725,Bike,1,Personal,8,1,2:54:21 PM,8,1,3:23:08 PM,8,1,3:33:24 PM,8,1,3:49:50 PM,8,1,4:11:08 PM,5,24.8,,-1.3060459999999998,36.836705,-1.2851847,36.8314086,Rider_Id_662,1278
-Order_No_23295,User_Id_2587,Bike,3,Business,26,3,1:18:27 PM,26,3,1:18:34 PM,26,3,1:21:08 PM,26,3,1:30:44 PM,26,3,1:41:58 PM,6,23.9,,-1.28878,36.8168312,-1.2646084,36.8028645,Rider_Id_726,674
-Order_No_27041,User_Id_246,Bike,3,Business,25,2,5:47:00 PM,25,2,5:46:34 PM,25,2,5:47:02 PM,25,2,5:56:10 PM,25,2,6:02:02 PM,3,24.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_875,352
-Order_No_19436,User_Id_3347,Bike,1,Personal,1,3,1:23:30 PM,1,3,1:23:40 PM,1,3,1:34:48 PM,1,3,1:40:04 PM,1,3,1:53:11 PM,3,23.9,,-1.2854406,36.789409,-1.280467,36.7859296,Rider_Id_465,787
-Order_No_22893,User_Id_1404,Bike,2,Personal,15,3,1:38:17 PM,15,3,1:39:15 PM,15,3,1:56:21 PM,15,3,2:09:28 PM,15,3,2:28:06 PM,9,21.7,,-1.2973827,36.794287100000005,-1.258348,36.844901799999995,Rider_Id_234,1118
-Order_No_15416,User_Id_2112,Bike,3,Business,22,5,2:10:50 PM,22,5,2:11:19 PM,22,5,2:23:49 PM,22,5,2:41:11 PM,22,5,3:22:34 PM,15,27.3,,-1.2156006000000001,36.8916865,-1.2594538999999998,36.8067841,Rider_Id_436,2483
-Order_No_6435,User_Id_3517,Bike,3,Business,19,2,2:00:34 PM,19,2,2:28:01 PM,19,2,3:14:28 PM,19,2,3:16:47 PM,19,2,3:43:21 PM,16,29.8,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_448,1594
-Order_No_10817,User_Id_3321,Bike,3,Personal,23,2,8:57:38 AM,23,2,9:05:48 AM,23,2,9:37:46 AM,23,2,9:49:14 AM,23,2,10:45:11 AM,15,,,-1.3339943,36.7263409,-1.2723552,36.7881306,Rider_Id_572,3357
-Order_No_24294,User_Id_2330,Bike,3,Business,3,5,5:09:33 PM,3,5,5:13:44 PM,3,5,5:20:42 PM,3,5,5:24:15 PM,3,5,5:56:11 PM,7,,,-1.2536275,36.798793200000006,-1.2915246999999999,36.805575700000006,Rider_Id_607,1916
-Order_No_4846,User_Id_393,Bike,3,Business,15,5,3:18:39 PM,15,5,3:23:21 PM,15,5,3:23:26 PM,15,5,3:53:59 PM,15,5,3:54:02 PM,5,28.2,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_188,3
-Order_No_20507,User_Id_1013,Bike,1,Personal,21,5,10:32:31 AM,21,5,10:34:51 AM,21,5,10:51:18 AM,21,5,10:57:20 AM,21,5,11:11:53 AM,6,20.0,,-1.2922888000000001,36.7755707,-1.2731645,36.7900343,Rider_Id_731,873
-Order_No_25105,User_Id_3247,Bike,3,Business,22,4,2:29:20 PM,22,4,2:34:18 PM,22,4,2:40:51 PM,22,4,2:45:45 PM,22,4,3:00:52 PM,5,26.4,,-1.2571472,36.795063299999995,-1.2655306,36.828148,Rider_Id_407,907
-Order_No_18456,User_Id_478,Bike,3,Business,13,4,3:01:38 PM,13,4,3:14:49 PM,13,4,3:18:44 PM,13,4,3:22:06 PM,13,4,3:46:25 PM,9,23.0,,-1.2551895,36.7822034,-1.2991116000000003,36.7960658,Rider_Id_50,1459
-Order_No_11403,User_Id_909,Bike,3,Business,5,5,1:13:48 PM,5,5,1:15:45 PM,5,5,1:34:14 PM,5,5,1:49:06 PM,5,5,1:56:58 PM,7,26.7,,-1.207546,36.7913976,-1.225322,36.80855,Rider_Id_882,472
-Order_No_8457,User_Id_2199,Bike,1,Personal,9,2,1:33:32 PM,9,2,1:41:51 PM,9,2,1:48:11 PM,9,2,1:55:16 PM,9,2,2:59:47 PM,7,22.4,,-1.2914795000000001,36.824897299999996,-1.2568563999999998,36.8050742,Rider_Id_268,3871
-Order_No_8574,User_Id_703,Bike,1,Personal,2,4,10:57:01 AM,2,4,10:57:08 AM,2,4,11:09:55 AM,2,4,11:16:16 AM,2,4,11:56:33 AM,22,22.6,,-1.2628638,36.8070399,-1.3466861,36.6695761,Rider_Id_379,2417
-Order_No_26544,User_Id_2145,Bike,3,Personal,12,3,12:13:10 PM,12,3,12:17:23 PM,12,3,12:30:27 PM,12,3,12:37:00 PM,12,3,12:57:35 PM,6,19.3,,-1.2752846000000002,36.802702200000006,-1.2926563999999998,36.78731810000001,Rider_Id_868,1235
-Order_No_1920,User_Id_3689,Bike,3,Business,6,6,10:31:28 AM,6,6,10:32:39 AM,6,6,10:53:59 AM,6,6,11:00:11 AM,6,6,11:31:24 AM,7,24.5,,-1.2527958000000001,36.8003131,-1.2521881000000001,36.7537237,Rider_Id_268,1873
-Order_No_21163,User_Id_393,Bike,3,Business,10,1,2:49:04 PM,10,1,2:49:51 PM,10,1,2:50:12 PM,10,1,3:30:34 PM,10,1,3:30:40 PM,13,18.1,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_97,6
-Order_No_26430,User_Id_1075,Bike,3,Business,7,2,10:52:38 AM,7,2,10:53:20 AM,7,2,10:56:07 AM,7,2,11:05:22 AM,7,2,11:32:55 AM,7,,,-1.2527958000000001,36.8003131,-1.3012173,36.8093987,Rider_Id_575,1653
-Order_No_1298,User_Id_393,Bike,3,Business,10,5,4:39:14 PM,10,5,4:41:13 PM,10,5,4:50:06 PM,10,5,4:51:31 PM,10,5,5:29:36 PM,3,,,-1.3167113000000001,36.8301563,-1.3210541,36.8169608,Rider_Id_910,2285
-Order_No_15006,User_Id_20,Bike,3,Business,1,1,9:42:01 AM,1,1,9:42:23 AM,1,1,10:20:00 AM,1,1,10:21:19 AM,1,1,10:57:33 AM,27,21.4,,-1.4135469999999999,36.955076399999996,-1.2628638,36.8070399,Rider_Id_863,2174
-Order_No_20201,User_Id_1499,Bike,2,Personal,24,4,9:46:09 AM,24,4,9:46:35 AM,24,4,9:51:24 AM,24,4,9:57:37 AM,24,4,10:17:17 AM,7,16.8,,-1.2606445,36.8185586,-1.294904,36.814658,Rider_Id_103,1180
-Order_No_20626,User_Id_1819,Bike,1,Personal,30,4,11:28:43 AM,30,4,11:36:03 AM,30,4,11:47:32 AM,30,4,11:52:20 AM,30,4,12:10:18 PM,7,20.1,,-1.3108368000000001,36.8226308,-1.3231598,36.7821562,Rider_Id_224,1078
-Order_No_23136,User_Id_1075,Bike,3,Business,18,1,4:40:35 PM,18,1,4:50:48 PM,18,1,5:07:09 PM,18,1,5:10:00 PM,18,1,5:49:50 PM,13,29.4,,-1.2285834999999998,36.882646799999996,-1.2527958000000001,36.8003131,Rider_Id_452,2390
-Order_No_23979,User_Id_3415,Bike,1,Personal,7,5,8:26:53 AM,7,5,8:42:50 AM,7,5,9:11:04 AM,7,5,10:18:10 AM,7,5,11:09:46 AM,36,18.4,,-1.4383017,36.9806911,-1.2697662,36.7595857,Rider_Id_938,3096
-Order_No_7815,User_Id_868,Bike,3,Business,31,1,10:19:09 AM,31,1,10:19:26 AM,31,1,10:19:51 AM,31,1,10:50:04 AM,31,1,11:01:59 AM,4,21.0,,-1.2600926000000001,36.8088685,-1.265715,36.823815,Rider_Id_427,715
-Order_No_360,User_Id_1687,Bike,3,Business,27,2,1:18:41 PM,27,2,1:21:34 PM,27,2,1:23:55 PM,27,2,1:38:44 PM,27,2,2:27:53 PM,19,25.4,,-1.2626574,36.8014034,-1.370436,36.7536015,Rider_Id_40,2949
-Order_No_10171,User_Id_1927,Bike,3,Business,28,5,8:48:51 AM,28,5,8:50:59 AM,28,5,8:51:05 AM,28,5,9:20:49 AM,28,5,9:26:29 AM,10,20.7,,-1.305399,36.822184899999996,-1.3472066,36.7692638,Rider_Id_179,340
-Order_No_236,User_Id_496,Bike,3,Business,13,4,11:22:20 AM,13,4,11:26:00 AM,13,4,11:39:09 AM,13,4,11:44:18 AM,13,4,12:26:46 PM,9,21.9,,-1.2551895,36.7822034,-1.2346243000000001,36.8109003,Rider_Id_787,2548
-Order_No_18888,User_Id_635,Bike,3,Business,5,6,12:04:48 PM,5,6,12:06:17 PM,5,6,12:16:19 PM,5,6,12:49:26 PM,5,6,12:59:58 PM,7,22.6,,-1.3007481,36.8341778,-1.2599563,36.799343900000004,Rider_Id_406,632
-Order_No_14380,User_Id_1724,Bike,3,Business,18,1,2:14:40 PM,18,1,2:18:02 PM,18,1,2:25:06 PM,18,1,2:28:10 PM,18,1,2:38:39 PM,4,29.3,,-1.2672588,36.8000252,-1.2551895,36.7822034,Rider_Id_877,629
-Order_No_17973,User_Id_246,Bike,3,Business,5,3,3:37:10 PM,5,3,3:37:58 PM,5,3,3:46:47 PM,5,3,3:54:14 PM,5,3,4:01:21 PM,2,25.1,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_259,427
-Order_No_27234,User_Id_1081,Bike,3,Business,7,4,2:11:49 PM,7,4,2:12:35 PM,7,4,2:13:57 PM,7,4,2:44:38 PM,7,4,3:34:28 PM,15,27.6,,-1.2571472,36.795063299999995,-1.3472066,36.7692638,Rider_Id_669,2990
-Order_No_24760,User_Id_3688,Bike,3,Business,4,2,11:45:45 AM,4,2,11:46:17 AM,4,2,11:52:43 AM,4,2,11:54:17 AM,4,2,12:08:51 PM,8,20.7,,-1.2551895,36.7822034,-1.2904118999999998,36.7898312,Rider_Id_7,874
-Order_No_12983,User_Id_3264,Bike,3,Business,15,4,4:09:01 PM,15,4,5:50:01 PM,15,4,5:57:23 PM,15,4,6:01:40 PM,15,4,6:12:42 PM,3,25.4,,-1.2551895,36.7822034,-1.2642665000000002,36.778828600000004,Rider_Id_958,662
-Order_No_9490,User_Id_3201,Bike,3,Business,24,5,6:17:10 PM,24,5,6:17:28 PM,24,5,6:27:30 PM,24,5,6:30:41 PM,24,5,6:39:05 PM,1,,,-1.2527958000000001,36.8003131,-1.2642719999999998,36.7978791,Rider_Id_622,504
-Order_No_21488,User_Id_1489,Bike,2,Personal,14,7,3:11:09 PM,14,7,3:11:19 PM,14,7,3:23:26 PM,14,7,3:32:36 PM,14,7,4:07:12 PM,17,28.6,,-1.2902161,36.893618100000005,-1.1891303,36.896427,Rider_Id_532,2076
-Order_No_11526,User_Id_3647,Bike,3,Business,7,4,3:07:25 PM,7,4,3:08:52 PM,7,4,3:18:50 PM,7,4,3:24:51 PM,7,4,3:47:37 PM,9,27.9,,-1.2726389999999999,36.794723,-1.2486795,36.81568239999999,Rider_Id_89,1366
-Order_No_15733,User_Id_3407,Bike,3,Business,18,4,12:37:13 PM,18,4,12:51:59 PM,18,4,1:06:57 PM,18,4,1:08:02 PM,18,4,1:32:27 PM,4,,,-1.2810888999999999,36.8247135,-1.3089235000000001,36.8372446,Rider_Id_747,1465
-Order_No_421,User_Id_2330,Bike,3,Business,11,2,1:45:22 PM,11,2,1:48:42 PM,11,2,2:00:17 PM,11,2,2:01:52 PM,11,2,2:19:04 PM,6,21.1,,-1.2328823,36.8436797,-1.2355732,36.878606299999994,Rider_Id_158,1032
-Order_No_14897,User_Id_517,Bike,3,Business,3,4,1:34:24 PM,3,4,1:34:32 PM,3,4,1:44:22 PM,3,4,1:53:07 PM,3,4,2:16:54 PM,11,26.1,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,1427
-Order_No_24359,User_Id_718,Bike,3,Business,5,2,3:22:19 PM,5,2,3:35:34 PM,5,2,3:55:35 PM,5,2,4:06:54 PM,5,2,4:33:15 PM,8,27.3,,-1.2551895,36.7822034,-1.2996363000000002,36.8186864,Rider_Id_513,1581
-Order_No_2357,User_Id_2987,Bike,3,Business,18,1,3:22:10 PM,18,1,3:22:14 PM,18,1,3:22:18 PM,18,1,3:32:01 PM,18,1,3:45:55 PM,12,17.4,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_886,834
-Order_No_5652,User_Id_547,Bike,1,Personal,16,1,2:20:44 PM,16,1,3:03:40 PM,16,1,3:19:21 PM,16,1,3:24:28 PM,16,1,3:44:47 PM,5,22.4,,-1.2795985,36.823300399999994,-1.2903421,36.7885282,Rider_Id_616,1219
-Order_No_15754,User_Id_2921,Bike,1,Personal,18,2,12:54:46 PM,18,2,12:55:36 PM,18,2,1:05:19 PM,18,2,1:08:16 PM,18,2,1:24:42 PM,4,22.8,,-1.2766663999999999,36.818441,-1.3012173,36.8093987,Rider_Id_21,986
-Order_No_11020,User_Id_1724,Bike,3,Business,24,5,9:57:09 AM,24,5,9:58:50 AM,24,5,10:13:41 AM,24,5,10:20:16 AM,24,5,10:32:19 AM,8,18.2,,-1.2843061000000002,36.82294,-1.2551895,36.7822034,Rider_Id_204,723
-Order_No_10226,User_Id_909,Bike,3,Business,27,1,3:28:46 PM,27,1,3:29:57 PM,27,1,3:47:09 PM,27,1,3:49:46 PM,27,1,4:17:16 PM,14,,,-1.225322,36.80855,-1.2413316,36.878552500000005,Rider_Id_882,1650
-Order_No_14990,User_Id_496,Bike,3,Business,12,2,11:05:11 AM,12,2,11:05:56 AM,12,2,11:16:35 AM,12,2,11:20:34 AM,12,2,11:59:15 AM,11,24.3,,-1.2551895,36.7822034,-1.3004371000000001,36.743036,Rider_Id_328,2321
-Order_No_3043,User_Id_351,Bike,3,Personal,6,4,11:09:35 AM,6,4,11:11:29 AM,6,4,11:32:54 AM,6,4,11:42:44 AM,6,4,11:55:28 AM,5,21.8,,-1.3052285,36.79390121,-1.2929576999999999,36.7955434,Rider_Id_447,764
-Order_No_16777,User_Id_718,Bike,3,Business,26,5,2:39:19 PM,26,5,2:49:25 PM,26,5,3:12:24 PM,26,5,3:14:01 PM,26,5,4:06:12 PM,9,,,-1.2551895,36.7822034,-1.2956171,36.7888097,Rider_Id_247,3131
-Order_No_19345,User_Id_1329,Bike,3,Business,2,4,2:01:46 PM,2,4,2:02:34 PM,2,4,2:11:02 PM,2,4,2:14:19 PM,2,4,2:43:55 PM,12,,,-1.273056,36.811298,-1.2672045,36.717206899999994,Rider_Id_329,1776
-Order_No_14165,User_Id_1205,Bike,3,Business,7,5,11:41:19 AM,7,5,11:45:23 AM,7,5,12:07:14 PM,7,5,12:26:23 PM,7,5,1:06:18 PM,5,25.6,,-1.2810807,36.8144229,-1.2998459999999998,36.7940155,Rider_Id_268,2395
-Order_No_19136,User_Id_733,Bike,3,Business,28,4,11:47:03 AM,28,4,11:47:18 AM,28,4,11:49:56 AM,28,4,11:56:32 AM,28,4,1:03:57 PM,15,23.8,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_579,4045
-Order_No_23537,User_Id_1966,Bike,3,Business,5,5,9:08:08 AM,5,5,9:08:21 AM,5,5,9:28:07 AM,5,5,9:45:32 AM,5,5,10:24:01 AM,21,19.6,,-1.3700383,36.9190174,-1.2917868000000001,36.7872675,Rider_Id_177,2309
-Order_No_412,User_Id_2041,Bike,1,Business,5,5,3:19:44 PM,5,5,3:21:10 PM,5,5,3:53:18 PM,5,5,4:30:00 PM,5,5,4:53:10 PM,3,26.7,,-1.2995132,36.8043032,-1.290894,36.822971,Rider_Id_532,1390
-Order_No_22511,User_Id_3517,Bike,3,Business,19,5,7:58:13 AM,19,5,7:58:17 AM,19,5,8:18:08 AM,19,5,9:11:38 AM,19,5,9:33:35 AM,15,20.4,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_208,1317
-Order_No_7812,User_Id_2509,Bike,3,Business,20,2,3:29:33 PM,20,2,3:32:03 PM,20,2,3:35:49 PM,20,2,3:43:58 PM,20,2,4:17:48 PM,20,23.8,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_931,2030
-Order_No_21613,User_Id_2112,Bike,3,Business,14,4,3:12:01 PM,14,4,3:13:08 PM,14,4,3:20:43 PM,14,4,3:25:25 PM,14,4,3:38:32 PM,5,27.4,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_691,787
-Order_No_25730,User_Id_661,Bike,3,Business,29,2,2:50:26 PM,29,2,3:01:39 PM,29,2,3:17:00 PM,29,2,3:39:15 PM,29,2,3:58:49 PM,4,23.6,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_312,1174
-Order_No_18150,User_Id_1642,Bike,3,Business,25,4,9:17:24 AM,25,4,9:18:53 AM,25,4,9:28:09 AM,25,4,9:33:15 AM,25,4,9:48:07 AM,5,,,-1.2728069,36.7962362,-1.288269,36.818275299999996,Rider_Id_291,892
-Order_No_20599,User_Id_2216,Bike,3,Business,21,4,10:59:17 AM,21,4,10:59:40 AM,21,4,11:11:15 AM,21,4,11:21:42 AM,21,4,11:41:09 AM,10,27.9,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_177,1167
-Order_No_6636,User_Id_1363,Bike,3,Business,16,4,8:44:38 AM,16,4,8:45:04 AM,16,4,8:45:06 AM,16,4,8:47:15 AM,16,4,8:47:18 AM,1,,,-1.3083268999999997,36.825346200000006,-1.3004062,36.829741,Rider_Id_120,3
-Order_No_9061,User_Id_1724,Bike,3,Business,23,2,4:02:45 PM,23,2,4:07:33 PM,23,2,4:13:13 PM,23,2,4:28:24 PM,23,2,4:50:35 PM,7,18.8,,-1.2551895,36.7822034,-1.2756120000000002,36.76379779999999,Rider_Id_518,1331
-Order_No_10307,User_Id_868,Bike,3,Business,21,4,9:36:09 AM,21,4,9:39:57 AM,21,4,9:52:04 AM,21,4,10:06:18 AM,21,4,10:28:30 AM,8,21.6,,-1.303596,36.778378000000004,-1.265715,36.823815,Rider_Id_306,1332
-Order_No_7379,User_Id_1976,Bike,3,Business,6,1,9:47:05 AM,6,1,9:47:43 AM,6,1,9:52:48 AM,6,1,9:55:50 AM,6,1,10:07:15 AM,7,15.0,,-1.257028,36.7810435,-1.2857671,36.7875614,Rider_Id_726,685
-Order_No_9275,User_Id_2801,Bike,3,Business,29,1,3:59:39 PM,29,1,4:00:10 PM,29,1,4:02:24 PM,29,1,4:22:48 PM,29,1,4:51:03 PM,15,,,-1.2889373,36.8604464,-1.2976940000000001,36.749788,Rider_Id_564,1695
-Order_No_7494,User_Id_633,Bike,3,Business,21,4,8:43:42 AM,21,4,8:44:35 AM,21,4,9:08:41 AM,21,4,9:25:38 AM,21,4,9:36:56 AM,4,21.9,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_728,678
-Order_No_4734,User_Id_598,Bike,1,Business,16,3,3:30:21 PM,16,3,3:31:04 PM,16,3,3:43:42 PM,16,3,3:47:17 PM,16,3,4:40:28 PM,18,25.4,,-1.2536797,36.7995085,-1.3157001,36.7075432,Rider_Id_520,3191
-Order_No_3020,User_Id_2658,Bike,3,Business,1,1,9:28:03 PM,1,1,9:28:34 PM,1,1,9:29:54 PM,1,1,9:43:03 PM,1,1,10:19:00 PM,11,23.4,,-1.2963097,36.7688221,-1.25843,36.69036,Rider_Id_202,2157
-Order_No_24099,User_Id_1961,Bike,3,Business,20,1,10:29:46 AM,20,1,10:30:28 AM,20,1,10:32:07 AM,20,1,10:39:38 AM,20,1,10:54:58 AM,6,,,-1.3444459999999998,36.756597,-1.3188358999999998,36.703852600000005,Rider_Id_942,920
-Order_No_24676,User_Id_206,Bike,2,Personal,13,1,1:52:27 PM,13,1,1:52:58 PM,13,1,1:58:52 PM,13,1,2:08:14 PM,13,1,3:13:58 PM,28,19.7,,-1.3233955,36.856294399999996,-1.1806988999999999,36.93328579999999,Rider_Id_519,3944
-Order_No_1555,User_Id_3496,Bike,3,Business,15,3,10:01:50 AM,15,3,10:59:02 AM,15,3,11:13:11 AM,15,3,11:49:10 AM,15,3,12:04:53 PM,6,18.1,,-1.3127723,36.87580560000001,-1.300921,36.828195,Rider_Id_670,943
-Order_No_17681,User_Id_393,Bike,3,Business,14,3,8:54:33 AM,14,3,8:56:14 AM,14,3,8:59:13 AM,14,3,9:11:23 AM,14,3,9:45:13 AM,14,18.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_663,2030
-Order_No_13297,User_Id_540,Bike,3,Business,15,5,3:05:57 PM,15,5,3:06:47 PM,15,5,3:24:20 PM,15,5,3:25:21 PM,15,5,3:42:29 PM,4,17.8,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_147,1028
-Order_No_11899,User_Id_1976,Bike,3,Business,5,1,10:35:32 AM,5,1,10:36:01 AM,5,1,10:41:29 AM,5,1,10:48:41 AM,5,1,11:04:09 AM,15,24.3,,-1.257028,36.7810435,-1.2461448999999998,36.8640088,Rider_Id_45,928
-Order_No_20468,User_Id_852,Bike,2,Personal,11,3,5:23:31 PM,11,3,5:27:19 PM,11,3,5:31:16 PM,11,3,5:41:19 PM,11,3,5:56:40 PM,8,19.9,,-1.298066,36.776709499999996,-1.263593,36.7882903,Rider_Id_431,921
-Order_No_14915,User_Id_1955,Bike,3,Business,27,3,1:53:18 PM,27,3,1:56:43 PM,27,3,2:03:23 PM,27,3,2:12:44 PM,27,3,2:36:31 PM,7,29.5,,-1.2551895,36.7822034,-1.2878961000000002,36.8305648,Rider_Id_910,1427
-Order_No_514,User_Id_136,Bike,3,Business,14,5,12:40:45 PM,14,5,12:41:05 PM,14,5,12:51:20 PM,14,5,12:54:37 PM,14,5,1:24:38 PM,9,24.4,,-1.2248308,36.801257299999996,-1.2560874,36.7931031,Rider_Id_847,1801
-Order_No_10076,User_Id_2987,Bike,3,Business,28,2,12:42:56 PM,28,2,12:43:06 PM,28,2,12:43:12 PM,28,2,12:53:53 PM,28,2,1:06:47 PM,10,17.6,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_397,774
-Order_No_4459,User_Id_3593,Bike,1,Personal,2,4,10:09:06 AM,2,4,10:10:49 AM,2,4,10:23:07 AM,2,4,10:38:33 AM,2,4,11:19:02 AM,7,22.3,,-1.2913246,36.8019522,-1.2619756000000002,36.800139,Rider_Id_700,2429
-Order_No_19020,User_Id_909,Bike,3,Business,3,3,2:00:36 PM,3,3,2:00:43 PM,3,3,2:03:59 PM,3,3,2:07:40 PM,3,3,2:31:14 PM,8,28.4,,-1.225322,36.80855,-1.225441,36.763899,Rider_Id_843,1414
-Order_No_11290,User_Id_1215,Bike,2,Personal,26,3,12:16:39 PM,26,3,12:17:44 PM,26,3,12:18:02 PM,26,3,12:24:51 PM,26,3,12:52:49 PM,8,24.0,,-1.2655953000000002,36.791546399999994,-1.2460004,36.8247116,Rider_Id_747,1678
-Order_No_10037,User_Id_970,Bike,3,Business,25,4,3:30:20 PM,25,4,3:31:24 PM,25,4,3:32:31 PM,25,4,3:38:56 PM,25,4,3:39:09 PM,1,,,-1.2551895,36.7822034,-1.2538731,36.7850223,Rider_Id_258,13
-Order_No_24752,User_Id_3404,Bike,3,Business,6,4,11:23:34 AM,6,4,11:23:49 AM,6,4,12:17:52 PM,6,4,12:37:24 PM,6,4,1:13:37 PM,16,24.2,,-1.207546,36.7913976,-1.279395,36.825364,Rider_Id_743,2173
-Order_No_13074,User_Id_2066,Bike,3,Business,2,4,2:02:48 PM,2,4,2:03:09 PM,2,4,2:09:22 PM,2,4,2:12:50 PM,2,4,2:37:21 PM,12,,,-1.3210188999999999,36.841055100000005,-1.2551895,36.7822034,Rider_Id_68,1471
-Order_No_25117,User_Id_2956,Bike,3,Business,9,4,12:44:27 PM,9,4,12:47:40 PM,9,4,1:40:00 PM,9,4,1:52:36 PM,9,4,2:06:09 PM,2,,,-1.2527958000000001,36.8003131,-1.2584143,36.8048002,Rider_Id_435,813
-Order_No_15118,User_Id_3556,Bike,1,Personal,26,6,9:24:54 AM,26,6,9:25:02 AM,26,6,9:30:28 AM,26,6,9:40:30 AM,26,6,9:48:47 AM,2,18.6,,-1.2792875,36.8228711,-1.286198,36.8193847,Rider_Id_89,497
-Order_No_15660,User_Id_2159,Bike,3,Business,19,5,10:14:37 AM,19,5,10:16:33 AM,19,5,10:38:16 AM,19,5,10:47:49 AM,19,5,11:04:59 AM,7,25.0,,-1.304735,36.784605400000004,-1.306245,36.819297799999994,Rider_Id_708,1030
-Order_No_8163,User_Id_1882,Bike,3,Business,22,4,5:05:13 PM,22,4,5:06:33 PM,22,4,5:16:42 PM,22,4,5:26:17 PM,22,4,6:01:53 PM,13,24.1,,-1.2958783,36.7928125,-1.316704,36.699771999999996,Rider_Id_685,2136
-Order_No_12685,User_Id_2701,Bike,3,Business,7,1,1:46:34 PM,7,1,1:46:51 PM,7,1,2:20:02 PM,7,1,2:43:21 PM,7,1,3:02:26 PM,5,20.5,,-1.2601708999999999,36.8002238,-1.279395,36.825364,Rider_Id_100,1145
-Order_No_3006,User_Id_1880,Bike,3,Business,7,5,1:08:31 PM,7,5,1:10:43 PM,7,5,1:30:14 PM,7,5,1:40:12 PM,7,5,2:59:28 PM,12,24.9,,-1.2571472,36.795063299999995,-1.3155299999999999,36.863732,Rider_Id_88,4756
-Order_No_2187,User_Id_1271,Bike,3,Business,8,2,10:28:04 AM,8,2,10:28:37 AM,8,2,10:36:29 AM,8,2,10:38:11 AM,8,2,11:14:12 AM,14,23.5,,-1.3244885,36.897791999999995,-1.4136642,36.957045,Rider_Id_418,2161
-Order_No_16179,User_Id_2330,Bike,3,Business,30,4,4:05:48 PM,30,4,4:07:36 PM,30,4,4:26:10 PM,30,4,4:29:08 PM,30,4,4:59:42 PM,8,,,-1.3015197,36.7658456,-1.3192788999999998,36.711299100000005,Rider_Id_602,1834
-Order_No_12061,User_Id_510,Bike,3,Business,18,2,1:19:58 PM,18,2,1:27:54 PM,18,2,1:32:22 PM,18,2,1:37:04 PM,18,2,2:05:47 PM,16,25.7,,-1.2584143,36.8048002,-1.2860183,36.8975338,Rider_Id_932,1723
-Order_No_11888,User_Id_1124,Bike,1,Business,31,5,5:23:25 PM,31,5,5:24:37 PM,31,5,6:04:23 PM,31,5,6:10:03 PM,31,5,7:01:28 PM,12,19.9,,-1.2871085,36.8303905,-1.2812601000000001,36.73402720000001,Rider_Id_465,3085
-Order_No_4014,User_Id_776,Bike,1,Personal,14,1,11:27:40 AM,14,1,12:03:06 PM,14,1,12:05:27 PM,14,1,12:25:54 PM,14,1,1:27:05 PM,4,22.8,,-1.278967,36.8236745,-1.2753757,36.8026592,Rider_Id_100,3671
-Order_No_22808,User_Id_1500,Bike,3,Business,21,1,1:01:10 PM,21,1,1:38:08 PM,21,1,1:38:20 PM,21,1,1:53:36 PM,21,1,2:04:31 PM,9,21.6,,-1.2991441000000001,36.7528804,-1.2569006,36.7925141,Rider_Id_687,655
-Order_No_9094,User_Id_2216,Bike,3,Business,3,1,2:35:12 PM,3,1,3:01:38 PM,3,1,3:22:36 PM,3,1,3:31:54 PM,3,1,3:58:25 PM,11,23.2,,-1.2831602,36.880967,-1.306431,36.812042,Rider_Id_237,1591
-Order_No_9173,User_Id_1075,Bike,3,Business,8,5,3:30:36 PM,8,5,3:39:11 PM,8,5,3:51:41 PM,8,5,3:55:59 PM,8,5,4:26:32 PM,2,30.5,,-1.2527958000000001,36.8003131,-1.2679657,36.8081617,Rider_Id_849,1833
-Order_No_8208,User_Id_3549,Bike,3,Business,20,3,2:21:05 PM,20,3,2:22:07 PM,20,3,2:43:14 PM,20,3,2:47:01 PM,20,3,3:07:50 PM,18,29.2,,-1.280897,36.8151821,-1.1901614,36.9346522,Rider_Id_377,1249
-Order_No_20233,User_Id_1245,Bike,3,Business,22,5,2:22:04 PM,22,5,2:47:42 PM,22,5,3:22:49 PM,22,5,3:30:24 PM,22,5,4:02:22 PM,18,31.1,,-1.3198615,36.7110323,-1.300921,36.828195,Rider_Id_628,1918
-Order_No_6157,User_Id_3599,Bike,3,Business,30,4,12:33:57 PM,30,4,2:03:00 PM,30,4,2:32:04 PM,30,4,2:40:57 PM,30,4,3:08:57 PM,9,,,-1.3302996,36.8707935,-1.2872899,36.820434000000006,Rider_Id_944,1680
-Order_No_9807,User_Id_3169,Bike,1,Personal,8,3,8:26:40 PM,8,3,8:42:42 PM,8,3,9:07:53 PM,8,3,9:25:03 PM,8,3,10:03:36 PM,11,19.2,,-1.2273128,36.809039899999995,-1.2629291999999999,36.8032916,Rider_Id_541,2313
-Order_No_745,User_Id_265,Bike,3,Business,19,2,11:45:43 AM,19,2,11:46:19 AM,19,2,11:53:55 AM,19,2,11:55:44 AM,19,2,12:44:00 PM,10,27.3,,-1.2960899,36.7606947,-1.300921,36.828195,Rider_Id_865,2896
-Order_No_19859,User_Id_3203,Bike,3,Business,21,4,9:33:46 AM,21,4,9:34:27 AM,21,4,9:45:08 AM,21,4,9:57:44 AM,21,4,10:11:12 AM,9,17.0,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_427,808
-Order_No_23500,User_Id_393,Bike,3,Business,10,4,11:33:18 AM,10,4,11:41:09 AM,10,4,12:01:55 PM,10,4,12:03:35 PM,10,4,12:50:34 PM,14,24.8,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_725,2819
-Order_No_23560,User_Id_3147,Bike,3,Business,20,2,3:15:25 PM,20,2,4:08:32 PM,20,2,4:15:41 PM,20,2,4:19:11 PM,20,2,5:08:18 PM,17,23.8,,-1.3472066,36.7692638,-1.2551895,36.7822034,Rider_Id_875,2947
-Order_No_23105,User_Id_1244,Bike,3,Business,3,4,8:53:06 AM,3,4,8:55:01 AM,3,4,8:58:06 AM,3,4,9:15:10 AM,3,4,9:32:45 AM,11,20.2,,-1.331619,36.847976,-1.303596,36.778378000000004,Rider_Id_404,1055
-Order_No_4891,User_Id_1934,Bike,2,Personal,16,4,6:59:11 AM,16,4,7:02:49 AM,16,4,7:22:35 AM,16,4,7:26:51 AM,16,4,7:43:42 AM,10,13.4,,-1.2502923000000001,36.788756799999994,-1.304033,36.7848695,Rider_Id_598,1011
-Order_No_16305,User_Id_3283,Bike,3,Business,20,2,3:19:05 PM,20,2,3:38:47 PM,20,2,3:38:49 PM,20,2,3:50:56 PM,20,2,4:08:54 PM,13,24.1,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_345,1078
-Order_No_2069,User_Id_3560,Bike,3,Business,31,1,9:35:51 AM,31,1,10:46:19 AM,31,1,10:55:16 AM,31,1,10:58:44 AM,31,1,11:22:07 AM,18,20.8,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_404,1403
-Order_No_16205,User_Id_3647,Bike,3,Business,30,5,1:45:28 PM,30,5,1:47:10 PM,30,5,2:01:44 PM,30,5,2:08:10 PM,30,5,2:47:43 PM,11,24.7,,-1.2726389999999999,36.794723,-1.234958,36.8104235,Rider_Id_40,2373
-Order_No_27628,User_Id_1075,Bike,3,Business,18,1,1:42:42 PM,18,1,1:50:17 PM,18,1,2:02:40 PM,18,1,2:05:43 PM,18,1,2:21:33 PM,5,30.8,,-1.279395,36.825364,-1.2527958000000001,36.8003131,Rider_Id_733,950
-Order_No_9483,User_Id_238,Bike,3,Business,24,4,2:20:43 PM,24,4,2:31:17 PM,24,4,2:31:47 PM,24,4,3:29:16 PM,24,4,3:29:27 PM,5,28.4,,-1.32671,36.8600138,-1.3077869,36.844320700000004,Rider_Id_86,11
-Order_No_5560,User_Id_3283,Bike,3,Business,2,6,12:56:44 PM,2,6,12:56:55 PM,2,6,1:06:42 PM,2,6,1:15:59 PM,2,6,1:30:48 PM,5,27.3,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_427,889
-Order_No_10785,User_Id_393,Bike,3,Business,20,1,10:49:00 AM,20,1,10:49:13 AM,20,1,10:50:52 AM,20,1,10:59:25 AM,20,1,11:14:23 AM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_591,898
-Order_No_3826,User_Id_136,Bike,3,Business,3,1,12:16:50 PM,3,1,12:17:40 PM,3,1,12:38:43 PM,3,1,12:58:25 PM,3,1,1:39:28 PM,20,20.3,,-1.3326173000000001,36.71462329999999,-1.3330427,36.8635157,Rider_Id_350,2463
-Order_No_11460,User_Id_3611,Bike,1,Personal,3,1,3:13:51 PM,3,1,3:14:53 PM,3,1,3:25:26 PM,3,1,3:29:58 PM,3,1,3:57:40 PM,5,23.2,,-1.2824911,36.8218042,-1.2740813999999998,36.795312100000004,Rider_Id_447,1662
-Order_No_15902,User_Id_1245,Bike,3,Business,6,6,10:25:41 AM,6,6,10:25:51 AM,6,6,10:35:47 AM,6,6,10:52:38 AM,6,6,11:23:46 AM,17,26.9,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_728,1868
-Order_No_14240,User_Id_2358,Bike,4,Business,18,4,10:02:34 AM,18,4,10:15:42 AM,18,4,10:16:32 AM,18,4,11:05:42 AM,18,4,11:15:55 AM,5,25.7,,-1.288641,36.816646,-1.2979559999999999,36.787462,Rider_Id_266,613
-Order_No_12218,User_Id_1920,Bike,3,Business,23,5,12:10:20 PM,23,5,12:47:30 PM,23,5,12:49:37 PM,23,5,12:53:10 PM,23,5,1:23:17 PM,10,23.9,,-1.2551895,36.7822034,-1.321627,36.835193,Rider_Id_237,1807
-Order_No_8693,User_Id_1502,Bike,3,Business,16,3,5:57:29 PM,16,3,6:05:01 PM,16,3,6:17:10 PM,16,3,6:26:40 PM,16,3,6:57:01 PM,6,23.2,,-1.2232123,36.8932547,-1.1898857,36.9103054,Rider_Id_75,1821
-Order_No_7017,User_Id_1450,Bike,1,Personal,25,1,8:22:00 AM,25,1,8:54:49 AM,25,1,9:44:06 AM,25,1,9:46:52 AM,25,1,10:16:37 AM,5,14.9,,-1.3211235000000001,36.7765164,-1.3108386,36.7555239,Rider_Id_752,1785
-Order_No_5352,User_Id_3283,Bike,3,Business,13,4,3:54:22 PM,13,4,3:54:54 PM,13,4,4:07:02 PM,13,4,4:17:51 PM,13,4,4:51:33 PM,14,22.9,,-1.228911,36.881876,-1.2628473,36.781805,Rider_Id_60,2022
-Order_No_4753,User_Id_186,Bike,3,Business,30,5,4:08:18 PM,30,5,4:11:28 PM,30,5,4:42:49 PM,30,5,4:54:56 PM,30,5,5:22:13 PM,17,22.1,,-1.2902869,36.8085396,-1.3700383,36.9190174,Rider_Id_932,1637
-Order_No_12397,User_Id_2763,Bike,3,Business,8,4,3:11:52 PM,8,4,3:52:50 PM,8,4,4:01:01 PM,8,4,4:03:30 PM,8,4,5:13:19 PM,16,27.5,,-1.2551895,36.7822034,-1.3321944,36.889232899999996,Rider_Id_85,4189
-Order_No_7896,User_Id_1754,Bike,3,Business,9,3,9:05:52 AM,9,3,9:06:01 AM,9,3,9:06:09 AM,9,3,9:09:40 AM,9,3,9:54:16 AM,19,17.7,0.6,-1.2860183,36.8975338,-1.2559559999999999,36.789867,Rider_Id_589,2676
-Order_No_6921,User_Id_2174,Bike,3,Business,17,5,11:48:33 AM,17,5,11:48:38 AM,17,5,11:51:53 AM,17,5,1:07:08 PM,17,5,1:37:08 PM,14,17.7,,-1.2482781,36.884091999999995,-1.25537,36.7756,Rider_Id_946,1800
-Order_No_2104,User_Id_1075,Bike,3,Business,23,2,10:11:35 AM,23,2,10:11:48 AM,23,2,10:43:03 AM,23,2,11:02:10 AM,23,2,11:33:52 AM,15,,,-1.2156006000000001,36.8916865,-1.2527958000000001,36.8003131,Rider_Id_266,1902
-Order_No_28168,User_Id_3599,Bike,3,Business,2,4,12:41:08 PM,2,4,1:04:18 PM,2,4,1:15:52 PM,2,4,1:26:58 PM,2,4,1:49:17 PM,13,,,-1.3302996,36.8707935,-1.2760462,36.807469700000006,Rider_Id_675,1339
-Order_No_16051,User_Id_1719,Bike,3,Business,20,2,10:57:43 AM,20,2,10:58:54 AM,20,2,11:13:15 AM,20,2,11:17:42 AM,20,2,12:01:50 PM,15,19.7,,-1.3100608999999999,36.820536499999996,-1.2330694,36.745451,Rider_Id_65,2648
-Order_No_26603,User_Id_2254,Bike,3,Business,17,3,2:07:57 PM,17,3,3:02:11 PM,17,3,3:30:36 PM,17,3,3:44:05 PM,17,3,4:29:56 PM,6,28.2,,-1.2564799,36.8112138,-1.2900209999999999,36.828206,Rider_Id_268,2751
-Order_No_4628,User_Id_714,Bike,3,Business,18,5,3:09:46 PM,18,5,3:12:13 PM,18,5,3:26:36 PM,18,5,3:35:26 PM,18,5,4:06:31 PM,9,27.0,,-1.2628638,36.8070399,-1.2326041,36.8009029,Rider_Id_518,1865
-Order_No_26416,User_Id_2521,Bike,3,Business,25,5,12:32:01 PM,25,5,12:32:16 PM,25,5,12:35:02 PM,25,5,12:40:40 PM,25,5,1:15:50 PM,14,26.3,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,2110
-Order_No_22185,User_Id_635,Bike,3,Business,4,1,1:57:49 PM,4,1,3:10:25 PM,4,1,3:22:15 PM,4,1,3:56:17 PM,4,1,4:49:24 PM,7,,,-1.300921,36.828195,-1.2599563,36.799343900000004,Rider_Id_61,3187
-Order_No_12353,User_Id_3080,Bike,3,Business,13,3,3:00:43 PM,13,3,3:06:57 PM,13,3,3:08:46 PM,13,3,3:12:56 PM,13,3,3:27:49 PM,4,27.2,,-1.2551895,36.7822034,-1.2668454,36.79406420000001,Rider_Id_436,893
-Order_No_11470,User_Id_1036,Bike,1,Personal,11,4,12:30:17 PM,11,4,12:30:28 PM,11,4,12:36:37 PM,11,4,12:45:39 PM,11,4,12:55:27 PM,4,23.9,,-1.2658334999999998,36.804286700000006,-1.2655306,36.828148,Rider_Id_795,588
-Order_No_10261,User_Id_3147,Bike,3,Business,26,3,1:56:21 PM,26,3,1:58:09 PM,26,3,2:02:57 PM,26,3,2:07:11 PM,26,3,2:47:51 PM,11,27.0,,-1.3472066,36.7692638,-1.2852142,36.79402929999999,Rider_Id_623,2440
-Order_No_15532,User_Id_3222,Bike,3,Business,21,2,8:45:13 AM,21,2,8:45:25 AM,21,2,8:46:07 AM,21,2,8:53:07 AM,21,2,9:15:18 AM,9,,,-1.2551895,36.7822034,-1.3034286,36.826553000000004,Rider_Id_155,1331
-Order_No_4663,User_Id_2436,Bike,3,Business,5,1,4:31:36 PM,5,1,4:32:36 PM,5,1,4:46:33 PM,5,1,5:01:20 PM,5,1,5:10:54 PM,3,24.3,,-1.262073,36.7986087,-1.2551895,36.7822034,Rider_Id_858,574
-Order_No_12300,User_Id_1346,Bike,3,Business,12,2,2:47:26 PM,12,2,2:48:06 PM,12,2,2:57:12 PM,12,2,3:01:12 PM,12,2,3:20:47 PM,6,26.9,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_559,1175
-Order_No_5907,User_Id_2828,Bike,3,Business,27,3,3:52:54 PM,27,3,3:54:04 PM,27,3,4:01:56 PM,27,3,4:08:36 PM,27,3,4:52:17 PM,12,29.5,,-1.2615888000000002,36.7928732,-1.3193822,36.7949906,Rider_Id_168,2621
-Order_No_4711,User_Id_3540,Bike,3,Personal,31,4,9:17:26 AM,31,4,9:17:37 AM,31,4,9:20:31 AM,31,4,9:26:40 AM,31,4,9:36:39 AM,6,20.4,,-1.2724792,36.7807386,-1.2950707000000001,36.7917289,Rider_Id_379,599
-Order_No_20967,User_Id_265,Bike,3,Business,16,2,11:22:24 AM,16,2,11:23:28 AM,16,2,11:34:27 AM,16,2,11:37:03 AM,16,2,11:58:49 AM,8,23.7,,-1.2927600000000001,36.783799,-1.300921,36.828195,Rider_Id_62,1306
-Order_No_5860,User_Id_1500,Bike,3,Business,30,2,3:43:37 PM,30,2,4:49:15 PM,30,2,5:15:55 PM,30,2,5:29:31 PM,30,2,5:34:05 PM,2,,,-1.2638185,36.7930057,-1.2567464,36.7915273,Rider_Id_132,274
-Order_No_25163,User_Id_598,Bike,1,Business,25,5,5:18:21 PM,25,5,5:19:13 PM,25,5,5:34:36 PM,25,5,5:38:04 PM,25,5,6:28:02 PM,20,21.1,,-1.2536797,36.7995085,-1.2516072,36.6665983,Rider_Id_103,2998
-Order_No_9651,User_Id_393,Bike,3,Business,2,4,1:54:31 PM,2,4,1:57:58 PM,2,4,2:16:17 PM,2,4,2:22:17 PM,2,4,2:22:20 PM,6,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_931,3
-Order_No_7004,User_Id_2627,Bike,3,Business,12,2,1:34:41 PM,12,2,1:35:15 PM,12,2,1:37:22 PM,12,2,1:53:35 PM,12,2,2:15:45 PM,12,,,-1.2571472,36.795063299999995,-1.2475028000000001,36.877469700000006,Rider_Id_770,1330
-Order_No_21764,User_Id_2041,Bike,1,Business,15,3,12:40:22 PM,15,3,12:40:40 PM,15,3,12:49:41 PM,15,3,12:56:58 PM,15,3,1:22:57 PM,10,18.2,,-1.290894,36.822971,-1.249526,36.7711579,Rider_Id_449,1559
-Order_No_26052,User_Id_1594,Bike,3,Business,25,1,12:13:56 PM,25,1,12:42:14 PM,25,1,12:47:56 PM,25,1,1:05:20 PM,25,1,1:55:51 PM,24,27.3,,-1.3562374,36.904295399999995,-1.2156006000000001,36.8916865,Rider_Id_206,3031
-Order_No_2592,User_Id_2330,Bike,3,Business,30,2,4:33:01 PM,30,2,5:04:48 PM,30,2,5:06:27 PM,30,2,5:09:30 PM,30,2,5:27:18 PM,9,,,-1.2536275,36.798793200000006,-1.2687407,36.7373043,Rider_Id_245,1068
-Order_No_4980,User_Id_2008,Bike,3,Business,31,1,11:05:12 AM,31,1,11:05:22 AM,31,1,11:17:46 AM,31,1,11:24:27 AM,31,1,11:58:22 AM,19,20.9,,-1.2508229,36.7895263,-1.311025,36.9001319,Rider_Id_446,2035
-Order_No_1410,User_Id_218,Bike,1,Personal,9,4,11:44:49 AM,9,4,11:45:59 AM,9,4,11:57:06 AM,9,4,12:03:11 PM,9,4,12:21:12 PM,4,23.6,,-1.2914681000000001,36.80128870000001,-1.2795261,36.826018499999996,Rider_Id_882,1081
-Order_No_18075,User_Id_3578,Bike,3,Business,1,5,11:16:59 AM,1,5,11:17:11 AM,1,5,11:24:49 AM,1,5,11:26:12 AM,1,5,11:47:35 AM,7,26.3,,-1.2527958000000001,36.8003131,-1.2814878,36.77948320000001,Rider_Id_547,1283
-Order_No_10352,User_Id_2454,Bike,3,Business,21,1,1:43:31 PM,21,1,1:43:48 PM,21,1,1:50:26 PM,21,1,1:54:54 PM,21,1,2:12:44 PM,7,27.6,,-1.2670714,36.801027399999995,-1.2993231,36.7753372,Rider_Id_531,1070
-Order_No_14995,User_Id_2587,Bike,3,Business,30,1,4:34:55 PM,30,1,4:36:13 PM,30,1,5:01:37 PM,30,1,5:16:32 PM,30,1,5:55:12 PM,11,15.9,,-1.2859912,36.8756811,-1.2885528999999998,36.816014100000004,Rider_Id_281,2320
-Order_No_14069,User_Id_540,Bike,3,Business,25,5,11:13:09 AM,25,5,11:14:38 AM,25,5,11:21:38 AM,25,5,11:27:31 AM,25,5,11:45:29 AM,8,26.5,,-1.2551895,36.7822034,-1.2940713,36.7890758,Rider_Id_663,1078
-Order_No_7343,User_Id_3557,Bike,3,Business,3,3,4:28:32 PM,3,3,4:29:03 PM,3,3,4:48:47 PM,3,3,4:56:07 PM,3,3,4:56:15 PM,2,27.5,,-1.2546732,36.808679600000005,-1.2695287,36.798177700000004,Rider_Id_850,8
-Order_No_26365,User_Id_2523,Bike,3,Business,15,3,5:00:43 PM,15,3,5:00:53 PM,15,3,5:02:03 PM,15,3,5:06:54 PM,15,3,5:58:52 PM,28,25.1,,-1.2551895,36.7822034,-1.4073185,36.9556349,Rider_Id_709,3118
-Order_No_7897,User_Id_2454,Bike,3,Business,9,2,11:04:50 AM,9,2,12:07:10 PM,9,2,12:19:21 PM,9,2,12:29:13 PM,9,2,12:52:37 PM,12,27.1,,-1.3264333000000001,36.71071,-1.2941413,36.7958937,Rider_Id_800,1404
-Order_No_15740,User_Id_1961,Bike,3,Business,30,4,11:41:50 AM,30,4,11:47:13 AM,30,4,11:51:53 AM,30,4,12:08:22 PM,30,4,12:42:58 PM,25,,,-1.3393547,36.8910035,-1.3188358999999998,36.703852600000005,Rider_Id_675,2076
-Order_No_26145,User_Id_1402,Bike,3,Business,11,2,2:27:50 PM,11,2,2:48:54 PM,11,2,3:05:05 PM,11,2,3:35:43 PM,11,2,3:58:28 PM,6,21.4,,-1.2571472,36.795063299999995,-1.2813012,36.832396200000005,Rider_Id_109,1365
-Order_No_2151,User_Id_1500,Bike,3,Business,2,2,10:04:48 AM,2,2,10:06:10 AM,2,2,10:08:13 AM,2,2,10:25:48 AM,2,2,10:44:13 AM,8,20.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_177,1105
-Order_No_25097,User_Id_1976,Bike,3,Business,31,5,12:45:02 PM,31,5,12:53:45 PM,31,5,1:07:22 PM,31,5,1:11:53 PM,31,5,1:22:39 PM,5,21.8,,-1.257028,36.7810435,-1.2580991000000001,36.794151899999996,Rider_Id_553,646
-Order_No_17779,User_Id_2532,Bike,3,Business,10,5,1:54:46 PM,10,5,2:11:41 PM,10,5,2:32:02 PM,10,5,3:00:57 PM,10,5,3:37:17 PM,11,,,-1.3352545,36.8659639,-1.2854406,36.789409,Rider_Id_567,2180
-Order_No_9640,User_Id_1146,Bike,3,Business,28,4,1:46:54 PM,28,4,1:48:34 PM,28,4,1:55:42 PM,28,4,2:23:18 PM,28,4,2:37:32 PM,5,29.9,,-1.3004062,36.829741,-1.3083266000000002,36.8494756,Rider_Id_85,854
-Order_No_3796,User_Id_3168,Bike,3,Personal,19,2,8:33:22 AM,19,2,9:02:37 AM,19,2,9:35:24 AM,19,2,9:38:02 AM,19,2,10:07:42 AM,12,15.3,0.9,-1.2895718,36.7844688,-1.3255736,36.7275277,Rider_Id_752,1780
-Order_No_12308,User_Id_2986,Bike,3,Personal,11,5,3:54:24 PM,11,5,3:55:19 PM,11,5,4:05:59 PM,11,5,4:32:00 PM,11,5,5:10:50 PM,4,24.4,,-1.2906604,36.8318242,-1.2903886,36.8063847,Rider_Id_237,2330
-Order_No_18354,User_Id_978,Bike,1,Personal,18,2,2:31:25 PM,18,2,2:52:44 PM,18,2,2:56:53 PM,18,2,3:15:30 PM,18,2,3:36:23 PM,5,27.3,,-1.3084067,36.83450139999999,-1.2805312,36.832668700000006,Rider_Id_731,1253
-Order_No_12166,User_Id_1527,Bike,3,Business,8,1,11:51:00 AM,8,1,11:51:18 AM,8,1,12:04:09 PM,8,1,12:10:57 PM,8,1,12:49:16 PM,5,28.0,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_155,2299
-Order_No_1333,User_Id_1390,Bike,1,Personal,30,4,11:22:27 AM,30,4,11:22:34 AM,30,4,11:34:29 AM,30,4,11:42:31 AM,30,4,12:05:58 PM,9,20.1,,-1.3080783999999999,36.776235,-1.3092311,36.8328403,Rider_Id_447,1407
-Order_No_1419,User_Id_2339,Bike,3,Personal,4,3,1:55:25 PM,4,3,2:07:58 PM,4,3,2:33:41 PM,4,3,2:46:21 PM,4,3,2:58:31 PM,2,18.2,,-1.2864298,36.8224345,-1.2854988999999999,36.8218183,Rider_Id_531,730
-Order_No_25053,User_Id_3283,Bike,3,Business,30,6,11:25:57 AM,30,6,11:40:27 AM,30,6,11:52:04 AM,30,6,12:02:11 PM,30,6,12:14:28 PM,6,26.8,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_372,737
-Order_No_10084,User_Id_393,Bike,3,Business,12,2,8:53:35 AM,12,2,8:53:43 AM,12,2,8:54:07 AM,12,2,9:20:08 AM,12,2,9:51:20 AM,14,18.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1872
-Order_No_26439,User_Id_1471,Bike,3,Business,30,2,11:26:33 AM,30,2,11:26:48 AM,30,2,11:49:27 AM,30,2,11:50:37 AM,30,2,1:06:48 PM,27,,,-1.2551895,36.7822034,-1.3554977,36.9574664,Rider_Id_198,4571
-Order_No_10192,User_Id_136,Bike,3,Business,11,1,12:54:36 PM,11,1,12:56:31 PM,11,1,1:03:17 PM,11,1,1:11:40 PM,11,1,1:23:25 PM,5,26.6,,-1.2654716000000001,36.7983635,-1.2633232,36.830506799999995,Rider_Id_178,705
-Order_No_23138,User_Id_2949,Bike,3,Personal,24,1,2:22:04 PM,24,1,2:22:15 PM,24,1,2:27:20 PM,24,1,2:31:13 PM,24,1,2:52:51 PM,10,,,-1.3080793,36.7505396,-1.2882073,36.82376729999999,Rider_Id_85,1298
-Order_No_8400,User_Id_3404,Bike,3,Business,1,5,1:50:27 PM,1,5,1:52:06 PM,1,5,2:08:49 PM,1,5,2:11:33 PM,1,5,2:41:49 PM,11,28.3,,-1.279395,36.825364,-1.3291155000000001,36.8861,Rider_Id_548,1816
-Order_No_27238,User_Id_1687,Bike,3,Business,8,3,10:12:12 AM,8,3,10:14:41 AM,8,3,10:23:11 AM,8,3,10:36:25 AM,8,3,10:51:15 AM,6,,,-1.2626574,36.8014034,-1.2384517,36.7711438,Rider_Id_956,890
-Order_No_15138,User_Id_2865,Bike,1,Personal,23,6,4:06:52 PM,23,6,4:09:06 PM,23,6,4:31:27 PM,23,6,5:20:58 PM,23,6,5:41:39 PM,8,,,-1.2789683,36.8284768,-1.3275191000000002,36.7992817,Rider_Id_584,1241
-Order_No_11391,User_Id_2810,Bike,1,Personal,30,1,9:55:03 AM,30,1,9:55:32 AM,30,1,10:10:17 AM,30,1,10:23:32 AM,30,1,10:47:24 AM,8,16.3,2.0,-1.2549476000000002,36.8269576,-1.2105983999999999,36.8109774,Rider_Id_427,1432
-Order_No_23946,User_Id_3213,Bike,1,Personal,16,4,12:53:09 PM,16,4,12:58:23 PM,16,4,1:34:22 PM,16,4,1:40:58 PM,16,4,2:37:00 PM,14,19.1,,-1.3519945,36.747136700000006,-1.3007534,36.7964576,Rider_Id_327,3362
-Order_No_3713,User_Id_778,Bike,3,Business,30,3,9:57:37 AM,30,3,10:03:00 AM,30,3,10:14:22 AM,30,3,10:31:20 AM,30,3,10:55:28 AM,7,19.5,,-1.2839186000000002,36.8222431,-1.2535486,36.7933541,Rider_Id_101,1448
-Order_No_6945,User_Id_2278,Bike,1,Personal,26,3,12:14:43 PM,26,3,12:16:27 PM,26,3,12:31:31 PM,26,3,12:35:19 PM,26,3,12:58:09 PM,6,23.8,,-1.2857117,36.7968296,-1.3012237,36.8261656,Rider_Id_548,1370
-Order_No_21041,User_Id_914,Bike,3,Business,3,1,11:22:18 AM,3,1,11:22:43 AM,3,1,11:33:01 AM,3,1,11:59:02 AM,3,1,12:11:29 PM,6,20.4,,-1.2995481000000002,36.8190561,-1.2670213,36.7994482,Rider_Id_857,747
-Order_No_28259,User_Id_432,Bike,3,Business,27,1,11:31:33 AM,27,1,11:33:38 AM,27,1,12:24:31 PM,27,1,12:41:25 PM,27,1,1:11:52 PM,7,20.9,,-1.3012008,36.764868,-1.2980815,36.8085686,Rider_Id_706,1827
-Order_No_2312,User_Id_1209,Bike,3,Business,9,3,9:41:40 AM,9,3,9:46:39 AM,9,3,9:55:04 AM,9,3,9:59:01 AM,9,3,10:05:11 AM,4,20.0,,-1.3077869,36.844320700000004,-1.3004062,36.829741,Rider_Id_358,370
-Order_No_3785,User_Id_869,Bike,3,Personal,7,1,4:29:38 PM,7,1,4:33:51 PM,7,1,4:40:15 PM,7,1,4:48:02 PM,7,1,5:11:40 PM,5,19.9,,-1.2775276000000002,36.8140021,-1.2624803999999998,36.797476700000004,Rider_Id_478,1418
-Order_No_18504,User_Id_100,Bike,1,Personal,31,5,9:30:27 AM,31,5,9:31:20 AM,31,5,9:40:29 AM,31,5,9:49:03 AM,31,5,10:20:21 AM,10,15.0,,-1.2472823,36.772846799999996,-1.2962832,36.784968299999996,Rider_Id_882,1878
-Order_No_12591,User_Id_2174,Bike,3,Business,6,4,10:44:46 AM,6,4,10:44:54 AM,6,4,10:51:10 AM,6,4,11:09:35 AM,6,4,11:20:36 AM,5,22.2,,-1.2482781,36.884091999999995,-1.2297202,36.881950700000004,Rider_Id_946,661
-Order_No_23517,User_Id_3064,Bike,3,Business,8,4,3:54:14 PM,8,4,3:56:31 PM,8,4,4:09:08 PM,8,4,4:27:15 PM,8,4,4:45:28 PM,12,27.1,,-1.2571472,36.795063299999995,-1.2427734,36.8807215,Rider_Id_812,1093
-Order_No_17410,User_Id_788,Bike,3,Business,25,1,11:14:40 AM,25,1,11:15:08 AM,25,1,11:28:35 AM,25,1,11:32:39 AM,25,1,11:51:01 AM,8,25.7,,-1.225322,36.80855,-1.2596969999999998,36.800802000000004,Rider_Id_399,1102
-Order_No_9525,User_Id_2801,Bike,3,Business,10,1,6:02:51 PM,10,1,6:03:09 PM,10,1,6:07:13 PM,10,1,6:13:19 PM,10,1,6:21:28 PM,4,19.1,,-1.2671974,36.804959000000004,-1.2674273999999999,36.787082700000006,Rider_Id_344,489
-Order_No_17100,User_Id_1927,Bike,3,Business,22,5,12:40:54 PM,22,5,12:41:02 PM,22,5,12:41:21 PM,22,5,12:56:38 PM,22,5,1:38:21 PM,23,,,-1.2285834999999998,36.882646799999996,-1.3472066,36.7692638,Rider_Id_539,2503
-Order_No_12871,User_Id_733,Bike,3,Business,25,4,12:10:38 PM,25,4,12:11:03 PM,25,4,12:11:16 PM,25,4,12:26:59 PM,25,4,12:27:52 PM,5,,,-1.2551895,36.7822034,-1.2629461000000002,36.76531370000001,Rider_Id_576,53
-Order_No_22450,User_Id_3372,Bike,3,Business,10,5,10:41:30 AM,10,5,10:41:47 AM,10,5,10:48:16 AM,10,5,10:51:37 AM,10,5,11:17:08 AM,12,19.3,,-1.2297202,36.881950700000004,-1.2143386999999999,36.7914548,Rider_Id_648,1531
-Order_No_712,User_Id_2174,Bike,3,Business,3,4,10:33:12 AM,3,4,10:33:15 AM,3,4,10:35:22 AM,3,4,11:15:38 AM,3,4,1:00:02 PM,28,23.7,,-1.2482781,36.884091999999995,-1.391222,36.760676000000004,Rider_Id_307,6264
-Order_No_3392,User_Id_3686,Bike,3,Business,13,2,3:30:03 PM,13,2,4:22:50 PM,13,2,4:31:07 PM,13,2,4:35:53 PM,13,2,5:36:42 PM,10,23.7,,-1.2551895,36.7822034,-1.2287282,36.8115304,Rider_Id_829,3649
-Order_No_26055,User_Id_2982,Bike,3,Business,3,1,9:11:55 AM,3,1,9:30:56 AM,3,1,9:49:38 AM,3,1,10:26:33 AM,3,1,11:00:04 AM,18,18.2,4.9,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_25,2011
-Order_No_1853,User_Id_3647,Bike,3,Business,2,3,5:03:34 PM,2,3,5:04:08 PM,2,3,5:15:14 PM,2,3,5:34:03 PM,2,3,5:34:05 PM,4,25.2,,-1.2726389999999999,36.794723,-1.2776075,36.769247299999996,Rider_Id_576,2
-Order_No_11863,User_Id_1363,Bike,3,Business,29,5,9:15:04 AM,29,5,9:16:23 AM,29,5,9:30:22 AM,29,5,9:34:58 AM,29,5,9:57:27 AM,5,20.9,,-1.3004062,36.829741,-1.2806799999999998,36.831154999999995,Rider_Id_907,1349
-Order_No_22236,User_Id_3234,Bike,3,Business,11,3,4:02:34 PM,11,3,4:04:23 PM,11,3,4:11:08 PM,11,3,4:18:41 PM,11,3,4:55:38 PM,6,20.7,,-1.2664428,36.809453000000005,-1.2859331,36.7865464,Rider_Id_312,2217
-Order_No_17037,User_Id_269,Bike,3,Business,13,4,9:58:03 AM,13,4,9:58:58 AM,13,4,10:22:07 AM,13,4,10:29:50 AM,13,4,10:48:42 AM,9,18.0,1.9,-1.3226301999999999,36.8719901,-1.3632162,36.9108253,Rider_Id_48,1132
-Order_No_15031,User_Id_2423,Bike,3,Business,16,1,2:46:06 PM,16,1,2:46:46 PM,16,1,2:50:41 PM,16,1,2:55:40 PM,16,1,3:18:59 PM,8,22.5,,-1.2551895,36.7822034,-1.3012173,36.8093987,Rider_Id_147,1399
-Order_No_1080,User_Id_1342,Bike,3,Business,4,4,4:11:06 PM,4,4,4:11:13 PM,4,4,4:11:37 PM,4,4,4:18:56 PM,4,4,4:29:25 PM,6,24.6,,-1.3025826,36.7670807,-1.3043951999999999,36.766413799999995,Rider_Id_882,629
-Order_No_11425,User_Id_2330,Bike,3,Business,19,2,3:11:59 PM,19,2,3:12:56 PM,19,2,3:36:19 PM,19,2,3:41:18 PM,19,2,4:08:13 PM,7,29.9,,-1.2291477,36.8440295,-1.2326041,36.8009029,Rider_Id_339,1615
-Order_No_22676,User_Id_1384,Bike,3,Business,9,4,1:48:40 PM,9,4,1:49:25 PM,9,4,1:59:27 PM,9,4,2:14:01 PM,9,4,2:46:45 PM,13,,,-1.2517448,36.7924971,-1.2985023999999998,36.866930100000005,Rider_Id_484,1964
-Order_No_1195,User_Id_3091,Bike,3,Business,9,4,1:38:28 PM,9,4,1:43:26 PM,9,4,2:10:28 PM,9,4,2:19:16 PM,9,4,2:27:28 PM,2,,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_522,492
-Order_No_22438,User_Id_1926,Bike,3,Business,16,4,3:11:36 PM,16,4,3:12:12 PM,16,4,3:31:41 PM,16,4,3:51:43 PM,16,4,4:53:18 PM,13,,,-1.34364,36.8925345,-1.3050321000000003,36.88432460000001,Rider_Id_787,3695
-Order_No_25503,User_Id_893,Bike,3,Business,6,4,10:24:00 AM,6,4,10:24:15 AM,6,4,10:35:11 AM,6,4,10:39:24 AM,6,4,11:03:29 AM,12,22.2,,-1.2152204,36.8563184,-1.2932780000000001,36.827789,Rider_Id_456,1445
-Order_No_16811,User_Id_393,Bike,3,Business,13,3,2:25:45 PM,13,3,2:26:14 PM,13,3,2:32:41 PM,13,3,2:42:15 PM,13,3,3:02:25 PM,4,29.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_145,1210
-Order_No_7745,User_Id_3201,Bike,3,Business,29,3,3:44:38 PM,29,3,3:48:58 PM,29,3,4:11:16 PM,29,3,4:32:25 PM,29,3,4:44:12 PM,7,21.3,,-1.2836183,36.8297208,-1.2584143,36.8048002,Rider_Id_812,707
-Order_No_7251,User_Id_867,Bike,3,Business,29,1,9:44:18 AM,29,1,9:44:29 AM,29,1,9:46:50 AM,29,1,10:13:22 AM,29,1,10:13:26 AM,11,,,-1.2770708,36.8231093,-1.2991441000000001,36.7528804,Rider_Id_613,4
-Order_No_14713,User_Id_2457,Bike,3,Business,13,5,1:24:48 PM,13,5,1:29:26 PM,13,5,1:44:42 PM,13,5,1:52:34 PM,13,5,2:26:30 PM,18,19.2,,-1.3165542,36.700067,-1.269534,36.80697,Rider_Id_116,2036
-Order_No_6238,User_Id_1346,Bike,3,Business,25,1,11:07:38 AM,25,1,11:08:03 AM,25,1,11:13:21 AM,25,1,11:20:03 AM,25,1,11:34:17 AM,6,25.3,,-1.2917868000000001,36.7872675,-1.2695287,36.798177700000004,Rider_Id_322,854
-Order_No_16392,User_Id_2932,Bike,1,Personal,12,5,10:03:21 AM,12,5,10:04:14 AM,12,5,10:10:25 AM,12,5,10:13:31 AM,12,5,10:24:29 AM,6,18.4,,-1.263894,36.77163,-1.2658315,36.809049,Rider_Id_536,658
-Order_No_16183,User_Id_2457,Bike,1,Business,26,4,1:40:48 PM,26,4,1:41:58 PM,26,4,1:53:03 PM,26,4,2:16:39 PM,26,4,3:00:39 PM,7,22.0,,-1.2571472,36.795063299999995,-1.3047132,36.808955299999994,Rider_Id_147,2640
-Order_No_9308,User_Id_2271,Bike,3,Business,11,1,12:17:29 PM,11,1,12:18:04 PM,11,1,1:00:53 PM,11,1,1:28:10 PM,11,1,2:01:53 PM,20,26.7,,-1.2628473,36.781805,-1.293515,36.897607,Rider_Id_326,2023
-Order_No_21760,User_Id_1719,Bike,3,Business,23,2,10:18:01 AM,23,2,10:18:18 AM,23,2,10:22:25 AM,23,2,10:24:46 AM,23,2,11:13:22 AM,16,,,-1.3039706,36.7688174,-1.23966,36.75258,Rider_Id_831,2916
-Order_No_10467,User_Id_970,Bike,3,Business,8,3,12:44:28 PM,8,3,12:44:51 PM,8,3,12:52:09 PM,8,3,12:55:44 PM,8,3,1:17:16 PM,5,,,-1.2551895,36.7822034,-1.2728069,36.7962362,Rider_Id_216,1292
-Order_No_21802,User_Id_391,Bike,3,Business,2,2,11:52:22 AM,2,2,11:58:04 AM,2,2,12:22:23 PM,2,2,12:46:13 PM,2,2,1:05:51 PM,5,26.0,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_77,1178
-Order_No_19044,User_Id_635,Bike,3,Business,24,5,8:23:24 AM,24,5,8:23:37 AM,24,5,8:28:32 AM,24,5,8:36:29 AM,24,5,9:04:44 AM,10,15.9,,-1.2599563,36.799343900000004,-1.3000862,36.7512967,Rider_Id_116,1695
-Order_No_3930,User_Id_3635,Bike,1,Business,3,3,4:52:56 PM,3,3,4:53:57 PM,3,3,4:54:19 PM,3,3,5:04:34 PM,3,3,5:11:37 PM,3,23.7,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_536,423
-Order_No_3558,User_Id_1244,Bike,3,Business,13,6,1:41:27 PM,13,6,1:46:10 PM,13,6,2:22:39 PM,13,6,2:42:37 PM,13,6,2:57:53 PM,8,27.7,,-1.272828,36.816608,-1.303596,36.778378000000004,Rider_Id_363,916
-Order_No_41,User_Id_3109,Bike,2,Personal,11,3,5:32:05 PM,11,3,5:33:41 PM,11,3,5:42:37 PM,11,3,5:49:25 PM,11,3,6:08:33 PM,6,19.9,,-1.2884403999999998,36.7736747,-1.2693522,36.797399299999995,Rider_Id_148,1148
-Order_No_27565,User_Id_1471,Bike,3,Business,21,5,9:12:00 AM,21,5,9:12:13 AM,21,5,9:13:54 AM,21,5,9:22:51 AM,21,5,10:23:36 AM,18,19.3,,-1.2571472,36.795063299999995,-1.3206433000000002,36.9117533,Rider_Id_401,3645
-Order_No_13373,User_Id_393,Bike,3,Business,12,2,12:49:34 PM,12,2,12:49:41 PM,12,2,12:49:50 PM,12,2,1:38:50 PM,12,2,1:38:53 PM,14,26.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_239,3
-Order_No_8183,User_Id_928,Bike,3,Business,30,2,2:42:42 PM,30,2,4:50:16 PM,30,2,5:03:13 PM,30,2,5:40:36 PM,30,2,5:40:38 PM,8,,,-1.2551895,36.7822034,-1.2990401000000003,36.8116114,Rider_Id_953,2
-Order_No_8817,User_Id_393,Bike,3,Business,14,2,10:53:38 AM,14,2,10:54:13 AM,14,2,11:10:56 AM,14,2,11:37:39 AM,14,2,11:37:40 AM,4,,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_910,1
-Order_No_15956,User_Id_2987,Bike,3,Business,24,1,3:41:34 PM,24,1,3:42:16 PM,24,1,3:46:56 PM,24,1,3:51:10 PM,24,1,4:00:46 PM,3,,,-1.2805757,36.8223948,-1.2702120000000001,36.82108770000001,Rider_Id_857,576
-Order_No_23227,User_Id_2174,Bike,3,Business,27,2,12:27:02 PM,27,2,12:27:08 PM,27,2,12:29:39 PM,27,2,12:51:49 PM,27,2,1:13:41 PM,15,24.0,,-1.2482781,36.884091999999995,-1.214071,36.7985052,Rider_Id_946,1312
-Order_No_4972,User_Id_2995,Bike,3,Business,3,3,1:44:34 PM,3,3,1:44:44 PM,3,3,1:46:05 PM,3,3,1:55:03 PM,3,3,3:19:27 PM,23,28.4,,-1.207546,36.7913976,-1.3025826,36.7670807,Rider_Id_409,5064
-Order_No_14702,User_Id_1500,Bike,3,Business,22,5,12:15:17 PM,22,5,12:18:18 PM,22,5,12:18:30 PM,22,5,1:20:49 PM,22,5,1:21:35 PM,8,28.3,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_239,46
-Order_No_16287,User_Id_3149,Bike,3,Business,22,2,12:46:28 PM,22,2,12:47:50 PM,22,2,12:51:11 PM,22,2,1:28:37 PM,22,2,1:37:57 PM,6,19.3,,-1.3004062,36.829741,-1.3177546999999998,36.8303703,Rider_Id_297,560
-Order_No_2209,User_Id_2412,Bike,3,Business,16,4,11:39:53 AM,16,4,11:40:40 AM,16,4,12:04:15 PM,16,4,12:15:48 PM,16,4,12:37:53 PM,9,14.7,,-1.2810586000000002,36.883829399999996,-1.2297202,36.881950700000004,Rider_Id_249,1325
-Order_No_25853,User_Id_391,Bike,3,Business,31,1,1:12:02 PM,31,1,1:12:46 PM,31,1,1:21:22 PM,31,1,1:27:04 PM,31,1,1:36:58 PM,6,21.1,,-1.2599563,36.799343900000004,-1.2867454,36.8259017,Rider_Id_576,594
-Order_No_26280,User_Id_2523,Bike,3,Business,7,4,10:16:18 AM,7,4,10:16:41 AM,7,4,10:18:39 AM,7,4,10:28:35 AM,7,4,11:04:41 AM,7,19.2,,-1.2551895,36.7822034,-1.2545163000000001,36.8243717,Rider_Id_854,2166
-Order_No_11920,User_Id_2030,Bike,3,Business,9,3,10:42:18 AM,9,3,10:42:50 AM,9,3,10:52:47 AM,9,3,10:54:57 AM,9,3,11:17:33 AM,3,25.2,,-1.2963097,36.7688221,-1.295908,36.791433000000005,Rider_Id_548,1356
-Order_No_20264,User_Id_928,Bike,3,Business,7,5,1:38:36 PM,7,5,2:11:21 PM,7,5,2:39:00 PM,7,5,2:45:46 PM,7,5,3:10:01 PM,8,26.6,,-1.2551895,36.7822034,-1.2995132,36.8043032,Rider_Id_425,1455
-Order_No_25499,User_Id_3650,Bike,3,Business,7,5,2:09:57 PM,7,5,2:45:42 PM,7,5,2:50:39 PM,7,5,2:56:00 PM,7,5,3:12:57 PM,6,26.8,,-1.2848376,36.802762200000004,-1.2905113000000001,36.759113799999994,Rider_Id_920,1017
-Order_No_26634,User_Id_2112,Bike,3,Business,8,6,1:06:01 PM,8,6,1:06:22 PM,8,6,1:23:09 PM,8,6,1:26:32 PM,8,6,1:44:14 PM,5,19.9,,-1.2285834999999998,36.882646799999996,-1.2156006000000001,36.8916865,Rider_Id_152,1062
-Order_No_19823,User_Id_3554,Bike,3,Personal,17,3,10:36:01 AM,17,3,10:36:09 AM,17,3,10:37:04 AM,17,3,11:13:54 AM,17,3,11:52:00 AM,18,25.3,,-1.2861764999999998,36.8209086,-1.335477,36.930829700000004,Rider_Id_942,2286
-Order_No_22197,User_Id_304,Bike,1,Personal,16,2,8:20:58 AM,16,2,8:21:15 AM,16,2,8:29:42 AM,16,2,8:40:44 AM,16,2,8:55:33 AM,7,,,-1.2854173999999998,36.7951152,-1.2580991000000001,36.794151899999996,Rider_Id_875,889
-Order_No_19833,User_Id_1527,Bike,3,Business,10,1,9:20:33 AM,10,1,9:21:00 AM,10,1,9:28:11 AM,10,1,9:30:53 AM,10,1,9:50:40 AM,9,16.6,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_139,1187
-Order_No_21961,User_Id_2070,Bike,3,Business,22,2,11:45:45 AM,22,2,11:46:34 AM,22,2,11:49:31 AM,22,2,11:58:25 AM,22,2,12:46:21 PM,17,24.1,,-1.29237,36.8241297,-1.3361484,36.91611279999999,Rider_Id_101,2876
-Order_No_9940,User_Id_346,Bike,3,Business,13,3,2:07:31 PM,13,3,2:21:32 PM,13,3,2:28:47 PM,13,3,2:58:50 PM,13,3,3:12:08 PM,6,23.7,,-1.2551895,36.7822034,-1.2885528999999998,36.816014100000004,Rider_Id_268,798
-Order_No_405,User_Id_1287,Bike,3,Business,20,3,2:46:02 PM,20,3,2:46:12 PM,20,3,3:17:02 PM,20,3,3:23:35 PM,20,3,4:13:41 PM,29,29.5,,-1.2551895,36.7822034,-1.417211,36.959410399999996,Rider_Id_770,3006
-Order_No_15892,User_Id_2289,Bike,3,Business,18,2,9:03:36 AM,18,2,9:03:45 AM,18,2,9:04:27 AM,18,2,9:11:15 AM,18,2,9:19:11 AM,1,19.9,,-1.2551895,36.7822034,-1.2615888000000002,36.7928732,Rider_Id_708,476
-Order_No_9337,User_Id_3080,Bike,1,Business,12,2,4:24:57 PM,12,2,4:44:27 PM,12,2,4:51:49 PM,12,2,5:00:15 PM,12,2,5:47:05 PM,12,21.2,,-1.2861764999999998,36.8209086,-1.2388687,36.7529026,Rider_Id_616,2810
-Order_No_1556,User_Id_868,Bike,3,Business,13,6,9:55:00 AM,13,6,9:58:23 AM,13,6,10:15:43 AM,13,6,10:34:14 AM,13,6,11:18:43 AM,14,25.0,,-1.293515,36.897607,-1.265715,36.823815,Rider_Id_519,2669
-Order_No_27105,User_Id_570,Bike,3,Business,16,2,9:33:36 AM,16,2,9:34:14 AM,16,2,9:42:33 AM,16,2,9:45:23 AM,16,2,10:22:42 AM,11,,,-1.2649605,36.798177700000004,-1.3322618,36.8045412,Rider_Id_245,2239
-Order_No_4796,User_Id_1280,Bike,3,Business,8,4,5:24:37 PM,8,4,5:25:27 PM,8,4,5:41:27 PM,8,4,5:44:16 PM,8,4,5:54:52 PM,3,25.6,,-1.2551895,36.7822034,-1.2654348,36.8022464,Rider_Id_578,636
-Order_No_20409,User_Id_2207,Bike,3,Business,20,4,9:06:06 AM,20,4,9:07:02 AM,20,4,9:19:57 AM,20,4,9:31:06 AM,20,4,9:39:50 AM,2,18.6,0.2,-1.2686289,36.8073469,-1.2768326,36.8073804,Rider_Id_50,524
-Order_No_24116,User_Id_1363,Bike,3,Business,14,5,11:02:02 AM,14,5,11:03:09 AM,14,5,11:17:06 AM,14,5,11:36:23 AM,14,5,11:41:53 AM,8,22.0,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_567,330
-Order_No_18996,User_Id_2000,Bike,3,Business,4,1,10:47:08 AM,4,1,11:15:59 AM,4,1,11:24:30 AM,4,1,11:28:58 AM,4,1,11:43:11 AM,7,26.5,,-1.2551895,36.7822034,-1.2896398999999998,36.812449799999996,Rider_Id_71,853
-Order_No_20689,User_Id_517,Bike,3,Business,18,6,11:29:25 AM,18,6,11:29:41 AM,18,6,11:31:27 AM,18,6,11:49:48 AM,18,6,11:49:50 AM,4,21.1,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_349,2
-Order_No_15801,User_Id_3647,Bike,3,Business,25,4,3:35:59 PM,25,4,3:39:09 PM,25,4,4:03:50 PM,25,4,4:05:07 PM,25,4,4:46:58 PM,13,21.0,,-1.2726389999999999,36.794723,-1.3228877,36.8460277,Rider_Id_731,2511
-Order_No_22239,User_Id_3491,Bike,3,Business,4,5,3:27:18 PM,4,5,3:58:59 PM,4,5,4:02:32 PM,4,5,4:15:20 PM,4,5,4:58:24 PM,11,,,-1.279395,36.825364,-1.2283403,36.8822756,Rider_Id_178,2584
-Order_No_16986,User_Id_1578,Bike,3,Business,25,4,4:10:11 PM,25,4,5:15:11 PM,25,4,5:21:53 PM,25,4,5:25:50 PM,25,4,5:43:28 PM,5,,,-1.28878,36.8168312,-1.2591018999999999,36.800577000000004,Rider_Id_878,1058
-Order_No_9802,User_Id_635,Bike,3,Business,12,5,3:55:33 PM,12,5,3:58:01 PM,12,5,4:07:19 PM,12,5,4:31:04 PM,12,5,4:55:35 PM,10,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_25,1471
-Order_No_26472,User_Id_1329,Bike,3,Business,24,3,4:35:06 PM,24,3,4:36:00 PM,24,3,4:36:34 PM,24,3,4:42:46 PM,24,3,4:58:30 PM,7,,,-1.273056,36.811298,-1.2596241000000001,36.762407,Rider_Id_96,944
-Order_No_2934,User_Id_393,Bike,3,Business,12,1,3:54:34 PM,12,1,3:58:10 PM,12,1,4:11:55 PM,12,1,4:15:21 PM,12,1,4:24:30 PM,6,26.7,,-1.3177546999999998,36.8303703,-1.3077869,36.844320700000004,Rider_Id_436,549
-Order_No_6756,User_Id_2358,Bike,4,Business,25,3,12:51:01 PM,25,3,12:52:05 PM,25,3,1:10:34 PM,25,3,2:01:21 PM,25,3,2:39:07 PM,11,17.9,,-1.3256903999999998,36.8747943,-1.2658527,36.801269299999994,Rider_Id_6,2266
-Order_No_25100,User_Id_1245,Bike,3,Business,14,3,9:59:56 AM,14,3,10:03:15 AM,14,3,10:27:24 AM,14,3,11:22:54 AM,14,3,11:49:33 AM,17,24.7,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_69,1599
-Order_No_26007,User_Id_760,Bike,3,Business,19,2,3:36:46 PM,19,2,3:37:23 PM,19,2,3:50:28 PM,19,2,3:52:28 PM,19,2,4:12:38 PM,4,29.3,,-1.2886303,36.8026947,-1.282937,36.781698399999996,Rider_Id_362,1210
-Order_No_17656,User_Id_53,Bike,3,Business,2,2,9:43:44 AM,2,2,9:45:32 AM,2,2,9:46:11 AM,2,2,9:47:21 AM,2,2,10:25:22 AM,5,20.3,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_306,2281
-Order_No_13731,User_Id_1502,Bike,3,Business,7,5,5:16:13 PM,7,5,5:31:28 PM,7,5,5:38:57 PM,7,5,5:52:49 PM,7,5,6:11:29 PM,17,23.1,,-1.2232123,36.8932547,-1.2551895,36.7822034,Rider_Id_222,1120
-Order_No_6099,User_Id_2801,Bike,3,Business,19,3,10:32:58 AM,19,3,10:41:32 AM,19,3,11:35:56 AM,19,3,12:08:17 PM,19,3,12:51:19 PM,7,22.1,,-1.289081,36.8300409,-1.2650181999999999,36.7926717,Rider_Id_268,2582
-Order_No_10747,User_Id_186,Bike,3,Business,14,5,2:47:29 PM,14,5,2:48:16 PM,14,5,2:55:55 PM,14,5,3:05:09 PM,14,5,3:17:30 PM,7,22.9,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_788,741
-Order_No_25827,User_Id_2776,Bike,3,Personal,15,6,1:13:10 PM,15,6,1:14:15 PM,15,6,1:43:06 PM,15,6,1:53:13 PM,15,6,2:05:16 PM,5,26.1,,-1.2918688,36.7603541,-1.2762513,36.7964048,Rider_Id_722,723
-Order_No_7148,User_Id_111,Bike,3,Business,4,2,2:31:29 PM,4,2,2:32:18 PM,4,2,2:43:55 PM,4,2,2:54:36 PM,4,2,3:17:18 PM,14,20.3,,-1.2551895,36.7822034,-1.2297202,36.881950700000004,Rider_Id_882,1362
-Order_No_15677,User_Id_393,Bike,3,Business,26,5,1:05:18 PM,26,5,1:20:51 PM,26,5,1:29:34 PM,26,5,1:56:48 PM,26,5,2:19:39 PM,10,,,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_731,1371
-Order_No_15055,User_Id_3565,Bike,3,Business,8,5,8:08:50 AM,8,5,8:09:28 AM,8,5,8:39:21 AM,8,5,8:43:58 AM,8,5,9:05:00 AM,18,20.4,,-1.2844665000000002,36.7866113,-1.207546,36.7913976,Rider_Id_932,1262
-Order_No_24261,User_Id_970,Bike,3,Business,10,3,3:21:09 PM,10,3,3:22:11 PM,10,3,3:22:33 PM,10,3,3:40:52 PM,10,3,4:10:18 PM,8,30.5,,-1.2857241000000001,36.820348100000004,-1.2551895,36.7822034,Rider_Id_338,1766
-Order_No_8581,User_Id_3283,Bike,3,Business,9,3,9:17:19 AM,9,3,9:20:20 AM,9,3,9:29:12 AM,9,3,9:44:31 AM,9,3,10:00:12 AM,5,20.2,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_319,941
-Order_No_15619,User_Id_2066,Bike,3,Business,16,2,6:07:46 PM,16,2,6:25:19 PM,16,2,6:35:40 PM,16,2,6:47:12 PM,16,2,7:08:00 PM,8,25.1,,-1.316961,36.820337,-1.2724885,36.785699900000004,Rider_Id_449,1248
-Order_No_11078,User_Id_1410,Bike,3,Business,3,4,12:07:01 PM,3,4,12:07:07 PM,3,4,12:07:26 PM,3,4,12:28:26 PM,3,4,1:00:42 PM,20,22.0,,-1.300921,36.828195,-1.2847613000000002,36.967084,Rider_Id_581,1936
-Order_No_6272,User_Id_1029,Bike,2,Personal,18,1,7:59:10 PM,18,1,8:02:35 PM,18,1,8:12:39 PM,18,1,8:20:25 PM,18,1,8:39:01 PM,9,,,-1.2872058000000002,36.7802742,-1.2586758,36.7386812,Rider_Id_956,1116
-Order_No_13973,User_Id_3222,Bike,3,Business,8,5,11:06:58 AM,8,5,11:07:13 AM,8,5,11:14:39 AM,8,5,11:16:07 AM,8,5,12:07:33 PM,17,28.3,,-1.2551895,36.7822034,-1.2977643,36.8844591,Rider_Id_25,3086
-Order_No_22126,User_Id_136,Bike,3,Business,19,2,12:38:58 PM,19,2,12:39:41 PM,19,2,12:43:18 PM,19,2,12:44:53 PM,19,2,1:32:40 PM,17,27.4,,-1.3254522,36.721658399999995,-1.2629838,36.8086926,Rider_Id_655,2867
-Order_No_17759,User_Id_136,Bike,3,Business,1,5,2:24:26 PM,1,5,2:27:33 PM,1,5,3:07:52 PM,1,5,3:23:33 PM,1,5,3:48:34 PM,20,31.9,,-1.2874648000000002,36.8271985,-1.3254522,36.721658399999995,Rider_Id_336,1501
-Order_No_8492,User_Id_3300,Bike,3,Business,17,3,10:09:35 AM,17,3,10:10:19 AM,17,3,10:18:41 AM,17,3,10:29:05 AM,17,3,10:48:51 AM,9,,,-1.2600926000000001,36.8088685,-1.303596,36.778378000000004,Rider_Id_96,1186
-Order_No_821,User_Id_540,Bike,3,Business,27,3,11:28:37 AM,27,3,11:29:20 AM,27,3,11:30:03 AM,27,3,11:41:44 AM,27,3,12:05:04 PM,12,25.9,,-1.2551895,36.7822034,-1.3076241000000002,36.787963299999994,Rider_Id_576,1400
-Order_No_11856,User_Id_200,Bike,3,Personal,9,6,2:34:27 PM,9,6,2:35:21 PM,9,6,2:49:11 PM,9,6,2:57:48 PM,9,6,3:06:20 PM,3,30.1,,-1.2971024,36.7836598,-1.2875012,36.7927159,Rider_Id_801,512
-Order_No_15278,User_Id_2393,Bike,1,Personal,11,4,10:02:36 AM,11,4,10:02:59 AM,11,4,10:28:55 AM,11,4,10:34:54 AM,11,4,11:14:19 AM,19,23.8,,-1.2518056999999998,36.7997135,-1.3337086,36.765843100000005,Rider_Id_446,2365
-Order_No_5548,User_Id_3283,Bike,3,Business,17,4,1:13:13 PM,17,4,1:13:32 PM,17,4,1:20:29 PM,17,4,1:28:01 PM,17,4,1:41:55 PM,9,25.3,,-1.2999309,36.830799799999994,-1.2628473,36.781805,Rider_Id_842,834
-Order_No_2956,User_Id_3434,Bike,3,Business,3,1,2:49:23 PM,3,1,3:04:34 PM,3,1,3:55:15 PM,3,1,5:11:15 PM,3,1,6:34:31 PM,30,22.7,,-1.1612808000000001,36.9563278,-1.2963097,36.7688221,Rider_Id_700,4996
-Order_No_17669,User_Id_1724,Bike,3,Business,5,3,10:42:10 AM,5,3,11:25:29 AM,5,3,11:37:04 AM,5,3,11:39:12 AM,5,3,12:17:13 PM,9,22.4,,-1.2551895,36.7822034,-1.303105,36.819134999999996,Rider_Id_382,2281
-Order_No_11419,User_Id_1588,Bike,3,Business,29,1,12:49:50 PM,29,1,12:57:28 PM,29,1,1:05:18 PM,29,1,1:20:30 PM,29,1,2:36:07 PM,9,,,-1.2551895,36.7822034,-1.306245,36.819297799999994,Rider_Id_498,4537
-Order_No_3541,User_Id_2887,Bike,3,Business,16,4,2:38:38 PM,16,4,3:39:58 PM,16,4,3:46:49 PM,16,4,3:57:07 PM,16,4,4:23:46 PM,9,,,-1.3352545,36.8659639,-1.2813012,36.832396200000005,Rider_Id_411,1599
-Order_No_22039,User_Id_3560,Bike,3,Business,14,3,11:27:16 AM,14,3,11:28:20 AM,14,3,11:36:59 AM,14,3,11:52:08 AM,14,3,12:28:37 PM,5,24.4,,-1.2584143,36.8048002,-1.279395,36.825364,Rider_Id_935,2189
-Order_No_18889,User_Id_3186,Bike,3,Business,25,2,10:24:03 AM,25,2,10:24:50 AM,25,2,10:37:49 AM,25,2,10:51:29 AM,25,2,11:05:15 AM,6,24.0,,-1.271672,36.783604,-1.2980228999999999,36.78865,Rider_Id_754,826
-Order_No_24055,User_Id_1516,Bike,2,Personal,19,5,2:22:21 PM,19,5,4:12:31 PM,19,5,4:12:55 PM,19,5,4:30:51 PM,19,5,4:57:30 PM,9,27.5,,-1.2795261,36.826018499999996,-1.2972892,36.7499229,Rider_Id_857,1599
-Order_No_19684,User_Id_3718,Bike,3,Business,27,4,11:14:45 AM,27,4,11:14:57 AM,27,4,11:20:00 AM,27,4,11:25:17 AM,27,4,11:39:52 AM,4,22.6,,-1.2650027,36.8126242,-1.2699091,36.788555200000005,Rider_Id_795,875
-Order_No_16285,User_Id_1882,Bike,3,Business,26,6,10:16:34 AM,26,6,10:32:25 AM,26,6,11:09:14 AM,26,6,11:15:44 AM,26,6,12:01:55 PM,15,25.8,,-1.2273427,36.799468299999994,-1.2160906999999999,36.885194899999995,Rider_Id_772,2771
-Order_No_17530,User_Id_928,Bike,3,Business,9,4,4:05:53 PM,9,4,4:27:03 PM,9,4,4:35:59 PM,9,4,4:49:04 PM,9,4,5:26:57 PM,11,,,-1.2571472,36.795063299999995,-1.3039222,36.8534707,Rider_Id_155,2273
-Order_No_134,User_Id_1204,Bike,1,Personal,6,4,12:11:04 PM,6,4,12:11:24 PM,6,4,12:55:46 PM,6,4,1:01:08 PM,6,4,1:35:23 PM,21,21.9,,-1.1937538,36.926865299999996,-1.2977386999999998,36.803144700000004,Rider_Id_561,2055
-Order_No_12358,User_Id_435,Bike,3,Business,15,3,3:17:50 PM,15,3,4:11:07 PM,15,3,4:58:45 PM,15,3,5:02:36 PM,15,3,5:43:28 PM,22,25.2,,-1.326545,36.667483000000004,-1.300921,36.828195,Rider_Id_831,2452
-Order_No_21512,User_Id_1341,Bike,3,Business,30,4,10:35:26 AM,30,4,10:36:00 AM,30,4,10:47:00 AM,30,4,11:08:21 AM,30,4,11:20:45 AM,7,,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_875,744
-Order_No_21110,User_Id_502,Bike,3,Personal,20,3,8:57:55 AM,20,3,8:58:14 AM,20,3,9:01:08 AM,20,3,9:03:55 AM,20,3,9:21:33 AM,6,16.2,0.2,-1.3019136999999998,36.7925105,-1.279085,36.819303000000005,Rider_Id_88,1058
-Order_No_4381,User_Id_3330,Bike,3,Business,20,3,12:48:43 PM,20,3,12:48:56 PM,20,3,12:49:00 PM,20,3,1:02:57 PM,20,3,1:15:57 PM,3,20.2,,-1.2652108,36.8056075,-1.2825816,36.8092346,Rider_Id_813,780
-Order_No_20544,User_Id_478,Bike,3,Business,21,4,12:17:02 PM,21,4,12:17:49 PM,21,4,12:28:26 PM,21,4,1:01:27 PM,21,4,1:19:11 PM,2,28.2,,-1.2715686000000002,36.8023526,-1.2571472,36.795063299999995,Rider_Id_462,1064
-Order_No_22154,User_Id_2075,Bike,1,Personal,23,1,12:23:02 PM,23,1,12:23:32 PM,23,1,12:37:09 PM,23,1,12:42:15 PM,23,1,12:57:34 PM,7,18.6,,-1.2927600000000001,36.783799,-1.2551895,36.7822034,Rider_Id_519,919
-Order_No_22966,User_Id_3647,Bike,3,Business,17,5,10:34:03 AM,17,5,10:34:52 AM,17,5,10:47:31 AM,17,5,11:03:48 AM,17,5,11:32:06 AM,6,,,-1.2985749,36.8088,-1.265432,36.795034,Rider_Id_158,1698
-Order_No_20299,User_Id_2521,Bike,3,Business,5,5,8:56:47 AM,5,5,9:02:41 AM,5,5,9:16:27 AM,5,5,9:40:49 AM,5,5,10:04:51 AM,8,19.9,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_819,1442
-Order_No_10268,User_Id_3362,Bike,1,Personal,16,1,1:40:41 PM,16,1,1:41:23 PM,16,1,1:49:36 PM,16,1,1:53:47 PM,16,1,2:57:56 PM,14,22.4,,-1.2765492,36.7669809,-1.3083777,36.85900770000001,Rider_Id_228,3849
-Order_No_13278,User_Id_2258,Bike,3,Business,11,1,11:27:34 AM,11,1,11:27:59 AM,11,1,11:38:25 AM,11,1,12:07:29 PM,11,1,12:33:37 PM,21,18.3,,-1.2579303999999998,36.8019667,-1.3417811000000002,36.914606,Rider_Id_541,1568
-Order_No_21662,User_Id_2649,Bike,3,Business,3,1,3:55:24 PM,3,1,3:56:44 PM,3,1,3:58:47 PM,3,1,4:13:51 PM,3,1,4:39:40 PM,12,23.0,,-1.2752846000000002,36.802702200000006,-1.2049641000000002,36.8173887,Rider_Id_25,1549
-Order_No_22689,User_Id_733,Bike,3,Business,5,2,10:43:45 AM,5,2,10:44:14 AM,5,2,10:53:19 AM,5,2,10:56:00 AM,5,2,11:22:13 AM,15,23.4,,-1.2551895,36.7822034,-1.207546,36.7913976,Rider_Id_40,1573
-Order_No_14737,User_Id_2250,Bike,1,Personal,31,5,6:06:26 PM,31,5,6:07:55 PM,31,5,6:19:51 PM,31,5,6:23:36 PM,31,5,6:30:52 PM,2,19.7,,-1.2712385,36.8072852,-1.260232,36.807334000000004,Rider_Id_427,436
-Order_No_14861,User_Id_1187,Bike,1,Personal,17,1,10:40:49 AM,17,1,10:53:09 AM,17,1,11:11:00 AM,17,1,11:19:50 AM,17,1,11:48:04 AM,4,25.2,,-1.3025958,36.8614441,-1.3302867,36.8542859,Rider_Id_810,1694
-Order_No_567,User_Id_635,Bike,3,Business,4,6,9:30:11 AM,4,6,9:30:39 AM,4,6,9:42:25 AM,4,6,10:45:11 AM,4,6,11:07:56 AM,8,,,-1.3081376,36.83297720000001,-1.2602337,36.79905479999999,Rider_Id_712,1365
-Order_No_17243,User_Id_2122,Bike,3,Business,18,1,10:26:04 AM,18,1,10:28:37 AM,18,1,10:56:20 AM,18,1,11:08:09 AM,18,1,11:41:33 AM,22,27.9,,-1.207546,36.7913976,-1.2975066000000002,36.76894229999999,Rider_Id_764,2004
-Order_No_21372,User_Id_816,Bike,3,Business,22,5,1:35:56 PM,22,5,1:39:30 PM,22,5,1:47:43 PM,22,5,1:51:55 PM,22,5,2:32:41 PM,6,27.3,,-1.2649605,36.798177700000004,-1.290894,36.822971,Rider_Id_260,2446
-Order_No_7456,User_Id_1081,Bike,3,Business,13,3,11:00:34 AM,13,3,11:01:02 AM,13,3,11:04:00 AM,13,3,11:08:56 AM,13,3,11:38:23 AM,11,25.0,,-1.2551895,36.7822034,-1.3173868999999998,36.8112044,Rider_Id_523,1767
-Order_No_307,User_Id_1271,Bike,3,Business,9,3,12:17:01 PM,9,3,12:26:11 PM,9,3,12:53:41 PM,9,3,12:56:25 PM,9,3,2:04:03 PM,22,25.5,,-1.3244885,36.897791999999995,-1.3107918,36.761794,Rider_Id_420,4058
-Order_No_22159,User_Id_1329,Bike,3,Business,29,5,5:40:04 PM,29,5,5:47:59 PM,29,5,5:55:54 PM,29,5,6:02:23 PM,29,5,6:12:37 PM,4,28.0,,-1.273056,36.811298,-1.2829591999999999,36.8046174,Rider_Id_291,614
-Order_No_26184,User_Id_862,Bike,1,Personal,20,1,4:32:06 PM,20,1,4:35:58 PM,20,1,4:49:10 PM,20,1,4:54:16 PM,20,1,5:15:12 PM,10,20.3,,-1.2455165000000001,36.7909394,-1.2962832,36.784968299999996,Rider_Id_74,1256
-Order_No_25248,User_Id_248,Bike,3,Business,29,1,4:21:11 PM,29,1,4:21:49 PM,29,1,4:24:50 PM,29,1,4:33:17 PM,29,1,4:44:07 PM,3,23.5,,-1.304735,36.784605400000004,-1.3036464,36.79505770000001,Rider_Id_282,650
-Order_No_26577,User_Id_1927,Bike,3,Business,8,6,12:01:42 PM,8,6,12:01:45 PM,8,6,12:03:07 PM,8,6,12:17:05 PM,8,6,12:45:55 PM,10,18.3,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_477,1730
-Order_No_16695,User_Id_2100,Bike,3,Business,25,2,3:30:51 PM,25,2,3:32:52 PM,25,2,3:33:04 PM,25,2,4:53:28 PM,25,2,4:53:32 PM,4,23.8,,-1.2765736,36.851364600000004,-1.3083777,36.85900770000001,Rider_Id_179,4
-Order_No_23702,User_Id_2066,Bike,3,Business,1,6,11:20:41 AM,1,6,11:20:59 AM,1,6,11:24:01 AM,1,6,11:38:15 AM,1,6,11:46:37 AM,5,18.4,,-1.316961,36.820337,-1.3230485,36.839476,Rider_Id_179,502
-Order_No_21853,User_Id_1899,Bike,3,Business,15,2,10:24:03 AM,15,2,11:08:17 AM,15,2,11:16:21 AM,15,2,11:23:44 AM,15,2,11:23:58 AM,6,22.6,,-1.318062,36.849326,-1.331619,36.847976,Rider_Id_408,14
-Order_No_12555,User_Id_3291,Bike,3,Business,20,1,10:00:20 AM,20,1,10:00:34 AM,20,1,10:08:20 AM,20,1,10:24:09 AM,20,1,10:53:57 AM,13,14.2,,-1.3313643,36.857742200000004,-1.2600926000000001,36.8088685,Rider_Id_213,1788
-Order_No_21565,User_Id_2982,Bike,3,Business,17,1,12:53:49 PM,17,1,12:56:39 PM,17,1,1:00:33 PM,17,1,1:57:13 PM,17,1,2:39:40 PM,18,24.8,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_69,2547
-Order_No_3094,User_Id_3491,Bike,3,Business,4,2,1:44:52 PM,4,2,2:24:17 PM,4,2,2:24:22 PM,4,2,2:35:59 PM,4,2,2:56:27 PM,9,22.5,,-1.2283403,36.8822756,-1.2662524,36.824479499999995,Rider_Id_75,1228
-Order_No_28013,User_Id_2150,Bike,3,Business,13,2,9:25:59 AM,13,2,9:26:10 AM,13,2,9:29:03 AM,13,2,9:39:19 AM,13,2,9:57:02 AM,12,19.8,8.9,-1.331619,36.847976,-1.3444459999999998,36.756597,Rider_Id_439,1063
-Order_No_6893,User_Id_1734,Bike,3,Personal,7,1,1:32:46 PM,7,1,1:38:17 PM,7,1,1:38:23 PM,7,1,1:50:54 PM,7,1,1:58:38 PM,3,20.6,,-1.2821883,36.826961100000005,-1.298529,36.816482,Rider_Id_923,464
-Order_No_26303,User_Id_3091,Bike,3,Business,26,1,4:02:45 PM,26,1,4:06:11 PM,26,1,4:17:33 PM,26,1,4:20:11 PM,26,1,4:41:33 PM,8,22.0,0.8,-1.3012008,36.764868,-1.2551895,36.7822034,Rider_Id_698,1282
-Order_No_11402,User_Id_1250,Bike,1,Personal,20,3,12:58:33 PM,20,3,12:59:06 PM,20,3,1:49:12 PM,20,3,1:57:25 PM,20,3,2:35:40 PM,9,23.4,,-1.2620741000000002,36.815814700000004,-1.2743743,36.7657321,Rider_Id_906,2295
-Order_No_21162,User_Id_136,Bike,3,Business,12,4,3:59:48 PM,12,4,4:00:41 PM,12,4,4:18:11 PM,12,4,4:21:11 PM,12,4,5:05:46 PM,21,21.7,,-1.2914795000000001,36.824897299999996,-1.3326173000000001,36.71462329999999,Rider_Id_105,2675
-Order_No_12171,User_Id_635,Bike,3,Business,17,4,3:24:12 PM,17,4,3:46:39 PM,17,4,4:08:11 PM,17,4,4:22:44 PM,17,4,4:55:22 PM,17,27.8,,-1.177202,36.8351183,-1.2599563,36.799343900000004,Rider_Id_445,1958
-Order_No_2013,User_Id_2195,Bike,3,Business,15,3,9:53:21 AM,15,3,9:53:45 AM,15,3,9:57:21 AM,15,3,10:17:11 AM,15,3,10:37:41 AM,12,,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_383,1230
-Order_No_7197,User_Id_2989,Bike,3,Business,18,4,3:46:52 PM,18,4,3:48:27 PM,18,4,3:48:37 PM,18,4,3:51:46 PM,18,4,4:18:12 PM,12,26.7,,-1.3472066,36.7692638,-1.2793858999999999,36.821800200000006,Rider_Id_79,1586
-Order_No_655,User_Id_2523,Bike,3,Business,19,1,12:06:27 PM,19,1,12:26:16 PM,19,1,12:32:05 PM,19,1,12:37:32 PM,19,1,12:54:46 PM,8,24.7,,-1.2551895,36.7822034,-1.2995738000000001,36.8199719,Rider_Id_660,1034
-Order_No_23877,User_Id_302,Bike,3,Business,19,3,6:39:47 PM,19,3,7:05:54 PM,19,3,7:14:53 PM,19,3,7:19:49 PM,19,3,7:44:52 PM,7,23.3,,-1.2649605,36.798177700000004,-1.2696795,36.8640088,Rider_Id_38,1503
-Order_No_12278,User_Id_2523,Bike,3,Business,25,1,11:40:38 AM,25,1,11:41:18 AM,25,1,11:51:21 AM,25,1,11:52:27 AM,25,1,12:14:18 PM,11,26.9,,-1.2551895,36.7822034,-1.2310607,36.803577600000004,Rider_Id_598,1311
-Order_No_10840,User_Id_3032,Bike,3,Business,12,1,11:25:43 AM,12,1,11:27:08 AM,12,1,11:52:30 AM,12,1,11:59:26 AM,12,1,12:48:11 PM,17,23.7,,-1.2571472,36.795063299999995,-1.228394,36.911267200000005,Rider_Id_108,2925
-Order_No_20623,User_Id_3295,Bike,3,Business,30,2,3:54:38 PM,30,2,4:53:41 PM,30,2,5:04:29 PM,30,2,5:10:11 PM,30,2,5:10:37 PM,2,,,-1.2571472,36.795063299999995,-1.2739424,36.815535700000005,Rider_Id_903,26
-Order_No_19740,User_Id_1724,Bike,3,Business,4,4,2:22:06 PM,4,4,2:22:52 PM,4,4,2:35:13 PM,4,4,3:12:48 PM,4,4,3:39:50 PM,11,24.7,,-1.2892959,36.837867100000004,-1.2551895,36.7822034,Rider_Id_845,1622
-Order_No_4075,User_Id_3239,Bike,3,Personal,31,2,9:08:00 AM,31,2,9:08:13 AM,31,2,9:14:46 AM,31,2,9:20:59 AM,31,2,9:49:18 AM,14,16.8,0.9,-1.2836832,36.8306719,-1.3094179,36.71859179999999,Rider_Id_414,1699
-Order_No_23077,User_Id_3331,Bike,3,Business,12,3,3:05:17 PM,12,3,3:07:34 PM,12,3,3:11:41 PM,12,3,3:14:10 PM,12,3,3:18:44 PM,2,20.8,,-1.2551895,36.7822034,-1.2566586000000002,36.792968200000004,Rider_Id_894,274
-Order_No_3947,User_Id_1209,Bike,3,Business,9,6,8:34:02 AM,9,6,8:34:14 AM,9,6,8:36:20 AM,9,6,8:56:18 AM,9,6,9:03:13 AM,4,19.9,,-1.3228003000000002,36.8306435,-1.3004062,36.829741,Rider_Id_523,415
-Order_No_26276,User_Id_1209,Bike,3,Business,30,2,2:36:53 PM,30,2,3:20:42 PM,30,2,3:20:47 PM,30,2,3:40:01 PM,30,2,4:18:34 PM,5,,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_61,2313
-Order_No_4996,User_Id_3127,Bike,3,Business,21,5,2:53:51 PM,21,5,4:21:28 PM,21,5,4:36:00 PM,21,5,4:37:51 PM,21,5,4:59:22 PM,6,25.1,,-1.2918246,36.801584999999996,-1.269534,36.80697,Rider_Id_38,1291
-Order_No_6632,User_Id_2303,Bike,2,Business,2,2,10:49:16 AM,2,2,10:49:32 AM,2,2,10:55:03 AM,2,2,11:08:35 AM,2,2,11:23:56 AM,7,20.8,,-1.2562823,36.7741174,-1.249986,36.786390600000004,Rider_Id_38,921
-Order_No_5710,User_Id_20,Bike,3,Business,12,3,8:33:26 AM,12,3,8:34:11 AM,12,3,8:49:19 AM,12,3,8:53:37 AM,12,3,9:15:55 AM,5,,,-1.2566688999999998,36.779695200000006,-1.2629666000000002,36.8068369,Rider_Id_139,1338
-Order_No_6311,User_Id_1578,Bike,3,Business,5,1,10:44:13 AM,5,1,10:44:24 AM,5,1,10:58:41 AM,5,1,11:13:55 AM,5,1,11:50:24 AM,13,24.7,,-1.2232123,36.8932547,-1.28878,36.8168312,Rider_Id_89,2189
-Order_No_3969,User_Id_2954,Bike,1,Personal,22,6,12:05:01 PM,22,6,12:05:36 PM,22,6,12:07:51 PM,22,6,12:36:50 PM,22,6,1:14:10 PM,22,21.1,,-1.3257893,36.8940194,-1.2224213,36.816525299999995,Rider_Id_248,2240
-Order_No_22359,User_Id_3214,Bike,3,Business,8,5,9:59:05 AM,8,5,10:00:16 AM,8,5,10:14:46 AM,8,5,10:19:52 AM,8,5,10:43:40 AM,10,20.4,,-1.3102256,36.84323679999999,-1.289795,36.7957132,Rider_Id_148,1428
-Order_No_383,User_Id_633,Bike,3,Business,10,4,2:07:07 PM,10,4,2:07:28 PM,10,4,2:07:52 PM,10,4,2:12:57 PM,10,4,2:24:49 PM,5,25.4,,-1.2770708,36.8231093,-1.3004062,36.829741,Rider_Id_812,712
-Order_No_8818,User_Id_346,Bike,3,Business,7,4,4:35:03 PM,7,4,4:36:03 PM,7,4,4:44:10 PM,7,4,4:50:03 PM,7,4,4:59:19 PM,1,29.6,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_653,556
-Order_No_1250,User_Id_3621,Bike,3,Business,13,6,2:58:03 PM,13,6,2:58:39 PM,13,6,3:21:42 PM,13,6,3:36:56 PM,13,6,4:03:07 PM,18,27.6,,-1.2836583000000001,36.7886014,-1.3123348999999997,36.9152001,Rider_Id_119,1571
-Order_No_18930,User_Id_46,Bike,3,Business,19,2,10:37:59 AM,19,2,10:39:06 AM,19,2,11:05:39 AM,19,2,11:18:11 AM,19,2,11:49:24 AM,7,27.7,,-1.2836832,36.8306719,-1.3056581,36.8445565,Rider_Id_849,1873
-Order_No_12013,User_Id_1856,Bike,3,Business,5,2,4:13:51 PM,5,2,4:17:58 PM,5,2,4:27:36 PM,5,2,4:37:08 PM,5,2,4:45:04 PM,3,,,-1.2738936,36.8035805,-1.2727979999999999,36.8124195,Rider_Id_763,476
-Order_No_20572,User_Id_393,Bike,3,Business,8,5,3:42:33 PM,8,5,3:57:28 PM,8,5,3:57:31 PM,8,5,4:33:08 PM,8,5,4:33:11 PM,14,28.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_10,3
-Order_No_20619,User_Id_1484,Bike,3,Business,7,4,1:38:57 PM,7,4,1:41:41 PM,7,4,1:46:42 PM,7,4,2:16:53 PM,7,4,2:24:50 PM,6,27.9,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_26,477
-Order_No_1878,User_Id_3404,Bike,3,Business,18,2,4:11:30 PM,18,2,5:20:25 PM,18,2,5:31:44 PM,18,2,5:33:51 PM,18,2,5:57:53 PM,5,23.5,,-1.2765736,36.851364600000004,-1.279395,36.825364,Rider_Id_178,1442
-Order_No_15345,User_Id_1152,Bike,3,Personal,3,5,5:35:02 PM,3,5,5:37:53 PM,3,5,5:39:03 PM,3,5,5:49:56 PM,3,5,6:13:25 PM,6,,,-1.2990813,36.8034213,-1.2905113000000001,36.759113799999994,Rider_Id_564,1409
-Order_No_1423,User_Id_1368,Bike,3,Business,13,3,2:47:27 PM,13,3,2:48:07 PM,13,3,2:53:01 PM,13,3,2:58:08 PM,13,3,3:12:08 PM,4,29.1,,-1.2571472,36.795063299999995,-1.26071,36.8206332,Rider_Id_40,840
-Order_No_27639,User_Id_2982,Bike,3,Business,24,6,9:40:32 AM,24,6,9:42:16 AM,24,6,9:42:19 AM,24,6,10:11:06 AM,24,6,10:40:16 AM,18,18.7,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_120,1750
-Order_No_4168,User_Id_3149,Bike,3,Business,6,3,3:23:29 PM,6,3,3:24:34 PM,6,3,3:33:00 PM,6,3,3:43:18 PM,6,3,3:56:09 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_72,771
-Order_No_10252,User_Id_1642,Bike,3,Business,28,4,2:38:28 PM,28,4,2:40:23 PM,28,4,2:47:52 PM,28,4,3:01:16 PM,28,4,3:06:13 PM,5,27.5,,-1.2817058000000001,36.819105799999996,-1.2726506000000002,36.7960153,Rider_Id_365,297
-Order_No_16516,User_Id_2423,Bike,3,Business,3,2,10:06:22 AM,3,2,10:15:57 AM,3,2,10:31:17 AM,3,2,10:33:08 AM,3,2,11:44:54 AM,7,18.5,,-1.2551895,36.7822034,-1.2934656999999998,36.8212737,Rider_Id_844,4306
-Order_No_10378,User_Id_2195,Bike,1,Business,1,3,10:30:05 AM,1,3,10:30:20 AM,1,3,10:43:53 AM,1,3,10:56:04 AM,1,3,10:56:18 AM,3,19.7,,-1.331619,36.847976,-1.3313643,36.857742200000004,Rider_Id_890,14
-Order_No_22241,User_Id_1250,Bike,1,Personal,17,2,11:42:02 AM,17,2,11:42:32 AM,17,2,11:54:57 AM,17,2,12:11:49 PM,17,2,12:29:53 PM,9,19.1,,-1.2620741000000002,36.815814700000004,-1.2743743,36.7657321,Rider_Id_648,1084
-Order_No_3574,User_Id_412,Bike,3,Business,1,5,10:19:23 AM,1,5,10:19:31 AM,1,5,10:19:49 AM,1,5,11:19:15 AM,1,5,11:34:56 AM,12,28.7,,-1.228911,36.881876,-1.266837,36.799248999999996,Rider_Id_539,941
-Order_No_16634,User_Id_1838,Bike,3,Business,17,5,1:22:49 PM,17,5,1:23:18 PM,17,5,1:30:54 PM,17,5,1:36:59 PM,17,5,1:45:20 PM,2,,,-1.2649605,36.798177700000004,-1.2617028000000001,36.7983849,Rider_Id_660,501
-Order_No_4074,User_Id_265,Bike,3,Business,23,4,9:43:13 AM,23,4,9:43:29 AM,23,4,9:44:26 AM,23,4,11:14:34 AM,23,4,11:17:36 AM,16,18.6,,-1.300921,36.828195,-1.3711733,36.7319722,Rider_Id_589,182
-Order_No_14481,User_Id_3565,Bike,3,Business,27,6,11:11:07 AM,27,6,11:11:27 AM,27,6,11:22:14 AM,27,6,12:52:19 PM,27,6,1:37:55 PM,23,,,-1.2844665000000002,36.7866113,-1.2843118999999998,36.9079841,Rider_Id_648,2736
-Order_No_27308,User_Id_540,Bike,3,Business,15,1,2:18:32 PM,15,1,4:15:07 PM,15,1,4:25:42 PM,15,1,4:27:34 PM,15,1,4:45:13 PM,7,,,-1.2551895,36.7822034,-1.2814031000000001,36.82016470000001,Rider_Id_208,1059
-Order_No_22267,User_Id_302,Bike,3,Business,10,1,7:06:14 PM,10,1,7:06:58 PM,10,1,7:07:30 PM,10,1,7:29:24 PM,10,1,7:43:32 PM,7,18.6,,-1.2649605,36.798177700000004,-1.287428,36.7800821,Rider_Id_427,848
-Order_No_9485,User_Id_2069,Bike,3,Business,15,5,3:09:16 PM,15,5,3:11:41 PM,15,5,3:39:48 PM,15,5,4:26:11 PM,15,5,4:58:33 PM,12,27.9,,-1.2600926000000001,36.8088685,-1.228911,36.881876,Rider_Id_210,1942
-Order_No_10000,User_Id_2829,Bike,3,Business,15,3,10:55:19 AM,15,3,10:56:38 AM,15,3,10:58:56 AM,15,3,11:10:28 AM,15,3,11:36:51 AM,6,,,-1.2571472,36.795063299999995,-1.2925919,36.81705770000001,Rider_Id_404,1583
-Order_No_11732,User_Id_3718,Bike,3,Business,12,1,9:15:04 AM,12,1,9:18:24 AM,12,1,9:32:00 AM,12,1,9:47:17 AM,12,1,10:00:07 AM,5,19.2,0.3,-1.2906618,36.8131222,-1.2650027,36.8126242,Rider_Id_85,770
-Order_No_14974,User_Id_3689,Bike,3,Business,27,1,2:26:45 PM,27,1,2:27:10 PM,27,1,2:46:36 PM,27,1,3:05:24 PM,27,1,3:20:09 PM,6,,,-1.2904016,36.82263029999999,-1.2527958000000001,36.8003131,Rider_Id_88,885
-Order_No_26304,User_Id_2523,Bike,3,Business,14,1,3:07:56 PM,14,1,3:10:51 PM,14,1,3:28:54 PM,14,1,3:33:38 PM,14,1,4:01:11 PM,9,25.7,,-1.2516167,36.8678844,-1.2544663999999999,36.8224273,Rider_Id_447,1653
-Order_No_16244,User_Id_393,Bike,3,Business,7,2,8:50:59 AM,7,2,8:51:11 AM,7,2,8:51:14 AM,7,2,9:40:54 AM,7,2,9:40:57 AM,11,,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_619,3
-Order_No_26954,User_Id_621,Bike,1,Personal,5,5,4:19:50 PM,5,5,4:30:01 PM,5,5,4:49:41 PM,5,5,5:18:02 PM,5,5,5:31:29 PM,2,25.0,,-1.3090663,36.813686499999996,-1.3114875,36.8200312,Rider_Id_751,807
-Order_No_6882,User_Id_517,Bike,3,Business,29,2,9:41:12 AM,29,2,9:41:39 AM,29,2,9:41:47 AM,29,2,9:52:03 AM,29,2,10:12:40 AM,12,18.1,,-1.3167113000000001,36.8301563,-1.2551895,36.7822034,Rider_Id_188,1237
-Order_No_25541,User_Id_510,Bike,3,Business,17,4,1:53:58 PM,17,4,1:54:17 PM,17,4,1:54:27 PM,17,4,1:59:43 PM,17,4,2:38:45 PM,12,23.7,,-1.2860183,36.8975338,-1.325469,36.862403,Rider_Id_900,2342
-Order_No_4398,User_Id_1746,Bike,3,Business,20,1,12:38:29 PM,20,1,12:38:46 PM,20,1,1:07:55 PM,20,1,1:10:44 PM,20,1,1:41:57 PM,12,20.1,,-1.3069686000000003,36.8596454,-1.296797,36.776452500000005,Rider_Id_402,1873
-Order_No_7703,User_Id_346,Bike,3,Business,27,3,8:40:39 AM,27,3,8:41:12 AM,27,3,8:41:13 AM,27,3,9:15:39 AM,27,3,9:40:20 AM,16,,,-1.207546,36.7913976,-1.28878,36.8168312,Rider_Id_101,1481
-Order_No_6989,User_Id_346,Bike,3,Business,2,2,12:53:55 PM,2,2,12:57:33 PM,2,2,1:11:26 PM,2,2,1:14:09 PM,2,2,1:26:22 PM,7,26.0,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_953,733
-Order_No_13004,User_Id_2677,Bike,3,Business,16,2,12:50:25 PM,16,2,12:52:07 PM,16,2,1:05:50 PM,16,2,1:12:47 PM,16,2,1:45:27 PM,14,,,-1.3077869,36.844320700000004,-1.2991441000000001,36.7528804,Rider_Id_712,1960
-Order_No_8050,User_Id_1363,Bike,3,Business,23,2,12:09:59 PM,23,2,12:17:41 PM,23,2,12:30:36 PM,23,2,12:32:27 PM,23,2,1:16:16 PM,7,18.2,,-1.3004062,36.829741,-1.2593834,36.8045189,Rider_Id_477,2629
-Order_No_4948,User_Id_1500,Bike,3,Business,20,2,1:54:39 PM,20,2,1:54:59 PM,20,2,2:12:28 PM,20,2,2:46:14 PM,20,2,3:07:37 PM,8,24.2,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_631,1283
-Order_No_13182,User_Id_3647,Bike,3,Business,25,1,11:41:20 AM,25,1,11:42:15 AM,25,1,12:15:30 PM,25,1,12:19:55 PM,25,1,12:40:27 PM,5,27.3,,-1.2726389999999999,36.794723,-1.2935921000000001,36.7876166,Rider_Id_652,1232
-Order_No_20838,User_Id_2066,Bike,3,Business,6,4,11:56:56 AM,6,4,11:57:23 AM,6,4,11:59:54 AM,6,4,12:03:52 PM,6,4,12:35:04 PM,10,22.0,,-1.316961,36.820337,-1.2970278999999998,36.761356,Rider_Id_676,1872
-Order_No_25698,User_Id_2066,Bike,3,Business,26,4,1:25:58 PM,26,4,1:56:05 PM,26,4,2:05:03 PM,26,4,2:08:10 PM,26,4,2:28:25 PM,9,21.8,,-1.316961,36.820337,-1.3014461000000002,36.7661381,Rider_Id_844,1215
-Order_No_23065,User_Id_510,Bike,3,Business,27,3,5:13:51 PM,27,3,5:14:21 PM,27,3,5:23:33 PM,27,3,5:27:26 PM,27,3,5:44:16 PM,7,27.5,,-1.2755887,36.8742523,-1.2860183,36.8975338,Rider_Id_933,1010
-Order_No_20908,User_Id_1500,Bike,3,Business,21,3,4:37:49 PM,21,3,4:42:36 PM,21,3,4:42:52 PM,21,3,4:53:58 PM,21,3,5:16:59 PM,8,,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_515,1381
-Order_No_2245,User_Id_2956,Bike,3,Business,25,5,10:22:46 AM,25,5,10:24:36 AM,25,5,10:30:42 AM,25,5,10:32:50 AM,25,5,10:47:46 AM,2,21.0,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_676,896
-Order_No_14348,User_Id_238,Bike,3,Business,6,3,11:10:19 AM,6,3,11:10:52 AM,6,3,11:14:23 AM,6,3,11:26:31 AM,6,3,11:40:13 AM,4,,,-1.3004062,36.829741,-1.3077869,36.844320700000004,Rider_Id_686,822
-Order_No_2037,User_Id_3291,Bike,3,Business,17,3,1:26:35 PM,17,3,1:27:14 PM,17,3,1:38:43 PM,17,3,1:54:00 PM,17,3,2:05:14 PM,5,,,-1.2566688999999998,36.779695200000006,-1.2600926000000001,36.8088685,Rider_Id_217,674
-Order_No_26305,User_Id_1797,Bike,3,Personal,18,4,11:57:23 AM,18,4,11:57:53 AM,18,4,12:27:47 PM,18,4,12:32:26 PM,18,4,1:23:29 PM,19,25.5,,-1.3295831,36.7388987,-1.2630521000000001,36.806801899999996,Rider_Id_893,3063
-Order_No_9676,User_Id_393,Bike,3,Business,28,1,11:47:23 AM,28,1,1:01:54 PM,28,1,1:02:08 PM,28,1,1:35:48 PM,28,1,1:35:50 PM,4,26.3,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_619,2
-Order_No_19252,User_Id_3517,Bike,3,Business,6,1,4:15:59 PM,6,1,4:26:33 PM,6,1,4:40:57 PM,6,1,5:00:11 PM,6,1,5:37:37 PM,16,,,-1.300921,36.828195,-1.3882008,36.7699445,Rider_Id_325,2246
-Order_No_2841,User_Id_1410,Bike,3,Business,9,3,8:36:37 AM,9,3,8:53:09 AM,9,3,9:05:34 AM,9,3,9:30:12 AM,9,3,9:46:33 AM,12,20.1,,-1.300921,36.828195,-1.2742942,36.9194976,Rider_Id_813,981
-Order_No_19860,User_Id_2801,Bike,3,Business,2,4,10:10:01 AM,2,4,10:10:35 AM,2,4,10:16:17 AM,2,4,10:18:28 AM,2,4,10:37:14 AM,9,,,-1.2674273999999999,36.787082700000006,-1.2575690000000002,36.842164000000004,Rider_Id_245,1126
-Order_No_22800,User_Id_733,Bike,3,Business,5,2,11:43:37 AM,5,2,11:43:47 AM,5,2,11:44:38 AM,5,2,11:58:58 AM,5,2,1:05:31 PM,20,23.6,,-1.2551895,36.7822034,-1.3185307,36.9001089,Rider_Id_831,3993
-Order_No_24553,User_Id_540,Bike,3,Business,2,5,1:18:28 PM,2,5,1:18:37 PM,2,5,1:19:22 PM,2,5,1:21:25 PM,2,5,1:50:22 PM,11,23.7,,-1.2551895,36.7822034,-1.312469,36.815177,Rider_Id_70,1737
-Order_No_15033,User_Id_391,Bike,3,Business,10,1,11:48:29 AM,10,1,11:59:40 AM,10,1,12:33:25 PM,10,1,12:45:13 PM,10,1,1:01:59 PM,5,18.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_410,1006
-Order_No_17383,User_Id_2587,Bike,3,Business,18,4,1:49:04 PM,18,4,1:53:09 PM,18,4,2:02:41 PM,18,4,2:11:51 PM,18,4,3:17:41 PM,18,,,-1.28878,36.8168312,-1.3562374,36.904295399999995,Rider_Id_299,3950
-Order_No_13593,User_Id_2634,Bike,3,Business,27,1,12:28:54 PM,27,1,12:28:54 PM,27,1,12:33:00 PM,27,1,12:37:02 PM,27,1,1:13:50 PM,21,20.9,,-1.2963097,36.7688221,-1.3160976999999998,36.913164200000004,Rider_Id_425,2208
-Order_No_4243,User_Id_868,Bike,3,Business,15,1,4:06:06 PM,15,1,4:10:45 PM,15,1,4:24:38 PM,15,1,4:33:01 PM,15,1,4:48:14 PM,6,,,-1.3033561000000002,36.8163631,-1.265715,36.823815,Rider_Id_126,913
-Order_No_16807,User_Id_2150,Bike,3,Business,4,6,3:25:38 PM,4,6,3:26:09 PM,4,6,3:42:51 PM,4,6,3:44:06 PM,4,6,4:33:11 PM,23,,,-1.228911,36.881876,-1.3444459999999998,36.756597,Rider_Id_260,2945
-Order_No_6710,User_Id_1168,Bike,3,Personal,28,2,1:10:25 PM,28,2,1:11:50 PM,28,2,1:24:27 PM,28,2,1:26:05 PM,28,2,1:44:05 PM,7,21.5,,-1.2813012,36.832396200000005,-1.3159197,36.8516724,Rider_Id_812,1080
-Order_No_22487,User_Id_209,Bike,1,Personal,15,1,11:21:22 AM,15,1,11:21:32 AM,15,1,11:29:42 AM,15,1,11:32:55 AM,15,1,11:42:56 AM,3,,,-1.2528485,36.7953722,-1.2674273999999999,36.787082700000006,Rider_Id_720,601
-Order_No_24128,User_Id_640,Bike,3,Business,18,4,12:38:05 PM,18,4,12:38:42 PM,18,4,12:49:24 PM,18,4,1:03:13 PM,18,4,2:02:37 PM,23,,,-1.2969741000000001,36.7856612,-1.3724413,36.9284922,Rider_Id_155,3564
-Order_No_15931,User_Id_684,Bike,3,Personal,15,5,5:36:23 PM,15,5,5:37:37 PM,15,5,5:45:05 PM,15,5,5:51:29 PM,15,5,6:09:07 PM,8,27.8,,-1.265715,36.808525,-1.3086326000000001,36.8080886,Rider_Id_383,1058
-Order_No_21118,User_Id_2190,Bike,3,Business,15,3,5:11:50 PM,15,3,5:12:04 PM,15,3,5:14:20 PM,15,3,5:20:42 PM,15,3,6:21:34 PM,11,20.8,,-1.3019530000000001,36.788458,-1.3298486999999999,36.8248917,Rider_Id_334,3652
-Order_No_27953,User_Id_868,Bike,3,Business,16,3,10:33:33 AM,16,3,10:33:46 AM,16,3,10:39:08 AM,16,3,10:56:51 AM,16,3,11:21:21 AM,7,22.9,,-1.2628473,36.781805,-1.265715,36.823815,Rider_Id_932,1470
-Order_No_27934,User_Id_2956,Bike,3,Business,26,1,4:27:02 PM,26,1,4:27:15 PM,26,1,4:31:38 PM,26,1,4:39:29 PM,26,1,4:53:45 PM,3,20.0,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_50,856
-Order_No_1831,User_Id_3647,Bike,3,Business,31,3,10:34:36 AM,31,3,10:35:39 AM,31,3,10:43:31 AM,31,3,10:47:10 AM,31,3,11:01:57 AM,4,24.9,,-1.2726389999999999,36.794723,-1.2609186000000001,36.7938342,Rider_Id_620,887
-Order_No_9421,User_Id_335,Bike,2,Personal,20,3,11:04:50 AM,20,3,11:07:42 AM,20,3,11:18:01 AM,20,3,11:23:35 AM,20,3,11:42:23 AM,6,20.2,,-1.2685950000000001,36.810189,-1.2924173,36.779781400000005,Rider_Id_858,1128
-Order_No_27685,User_Id_3283,Bike,3,Business,4,5,10:51:12 AM,4,5,10:56:07 AM,4,5,11:02:43 AM,4,5,11:07:52 AM,4,5,11:26:44 AM,6,17.5,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_489,1132
-Order_No_19228,User_Id_3496,Bike,3,Business,13,4,12:13:11 PM,13,4,1:10:35 PM,13,4,1:10:57 PM,13,4,1:31:22 PM,13,4,1:46:40 PM,5,21.8,,-1.300921,36.828195,-1.2780878,36.846943599999996,Rider_Id_340,918
-Order_No_6791,User_Id_3465,Bike,3,Business,5,5,12:22:06 PM,5,5,12:22:21 PM,5,5,12:24:07 PM,5,5,12:29:09 PM,5,5,1:00:46 PM,9,24.4,,-1.2551895,36.7822034,-1.312406,36.827447,Rider_Id_349,1897
-Order_No_24836,User_Id_2685,Bike,1,Personal,5,2,1:45:37 PM,5,2,1:50:27 PM,5,2,1:57:53 PM,5,2,2:05:32 PM,5,2,2:30:04 PM,9,22.5,,-1.29362,36.78745,-1.2646973999999997,36.80517879999999,Rider_Id_474,1472
-Order_No_1811,User_Id_1471,Bike,3,Business,28,2,12:27:44 PM,28,2,12:29:19 PM,28,2,12:41:30 PM,28,2,12:47:34 PM,28,2,12:59:50 PM,4,21.3,,-1.2551895,36.7822034,-1.2642978,36.819261,Rider_Id_541,736
-Order_No_10695,User_Id_3194,Bike,3,Business,25,1,3:26:58 PM,25,1,3:27:17 PM,25,1,3:45:27 PM,25,1,3:49:08 PM,25,1,4:13:32 PM,8,29.9,,-1.2551895,36.7822034,-1.2597436000000002,36.8282526,Rider_Id_828,1464
-Order_No_20740,User_Id_136,Bike,3,Business,28,5,4:49:10 PM,28,5,4:55:34 PM,28,5,5:08:15 PM,28,5,5:11:08 PM,28,5,5:28:28 PM,4,,,-1.3025826,36.7670807,-1.2962832,36.784968299999996,Rider_Id_747,1040
-Order_No_23498,User_Id_1642,Bike,3,Business,3,4,9:30:07 AM,3,4,9:30:41 AM,3,4,10:04:43 AM,3,4,10:13:42 AM,3,4,10:21:36 AM,3,20.5,,-1.2837018,36.7860445,-1.2728069,36.7962362,Rider_Id_829,474
-Order_No_1990,User_Id_478,Bike,3,Business,3,5,12:59:27 PM,3,5,1:00:02 PM,3,5,1:38:03 PM,3,5,1:50:49 PM,3,5,2:43:27 PM,8,,,-1.2854173999999998,36.7951152,-1.2551895,36.7822034,Rider_Id_92,3158
-Order_No_20362,User_Id_733,Bike,3,Business,3,3,3:06:51 PM,3,3,3:07:20 PM,3,3,3:21:45 PM,3,3,3:51:34 PM,3,3,3:56:39 PM,11,28.3,,-1.2571472,36.795063299999995,-1.3257473,36.859206799999995,Rider_Id_349,305
-Order_No_55,User_Id_2066,Bike,3,Business,5,1,10:52:19 AM,5,1,10:56:17 AM,5,1,11:13:29 AM,5,1,11:17:10 AM,5,1,11:43:01 AM,10,24.6,,-1.316961,36.820337,-1.3662774,36.760781099999996,Rider_Id_532,1551
-Order_No_26839,User_Id_1578,Bike,3,Business,17,1,9:02:51 AM,17,1,9:04:12 AM,17,1,9:10:46 AM,17,1,9:42:56 AM,17,1,9:59:45 AM,5,19.2,,-1.2584143,36.8048002,-1.28878,36.8168312,Rider_Id_150,1009
-Order_No_18507,User_Id_2383,Bike,3,Business,27,3,2:57:02 PM,27,3,2:57:57 PM,27,3,2:59:03 PM,27,3,3:03:05 PM,27,3,3:35:10 PM,14,28.9,,-1.2608993,36.799246000000004,-1.3176268,36.8741735,Rider_Id_205,1925
-Order_No_26981,User_Id_3557,Bike,3,Business,14,2,11:58:30 AM,14,2,11:59:31 AM,14,2,12:00:59 PM,14,2,12:06:17 PM,14,2,12:30:02 PM,7,,,-1.2546732,36.808679600000005,-1.292462,36.7972911,Rider_Id_930,1425
-Order_No_25087,User_Id_540,Bike,3,Business,18,4,12:44:48 PM,18,4,1:42:19 PM,18,4,1:42:42 PM,18,4,1:49:32 PM,18,4,2:14:06 PM,4,,,-1.2551895,36.7822034,-1.2753108999999998,36.8130185,Rider_Id_576,1474
-Order_No_10594,User_Id_3677,Bike,3,Personal,1,1,4:21:09 PM,1,1,4:23:14 PM,1,1,4:33:14 PM,1,1,4:50:42 PM,1,1,4:56:51 PM,6,27.6,,-1.2921983,36.7965431,-1.2728069,36.7962362,Rider_Id_362,369
-Order_No_12658,User_Id_914,Bike,3,Business,24,3,1:55:53 PM,24,3,1:57:16 PM,24,3,2:05:22 PM,24,3,2:13:03 PM,24,3,2:26:50 PM,3,18.2,,-1.2670213,36.7994482,-1.2630521000000001,36.806801899999996,Rider_Id_722,827
-Order_No_13222,User_Id_718,Bike,3,Business,21,4,4:15:37 PM,21,4,4:16:11 PM,21,4,4:20:33 PM,21,4,4:32:30 PM,21,4,5:24:18 PM,11,20.0,,-1.2551895,36.7822034,-1.303558,36.775613,Rider_Id_574,3108
-Order_No_19457,User_Id_2183,Bike,3,Business,8,2,10:40:28 AM,8,2,10:40:34 AM,8,2,10:49:47 AM,8,2,10:54:31 AM,8,2,10:54:38 AM,2,23.5,,-1.2551895,36.7822034,-1.2650042,36.790392700000005,Rider_Id_49,7
-Order_No_18054,User_Id_1271,Bike,3,Business,5,2,3:30:30 PM,5,2,3:56:08 PM,5,2,4:17:35 PM,5,2,4:36:11 PM,5,2,4:48:55 PM,9,25.1,,-1.3244885,36.897791999999995,-1.3323107,36.8692616,Rider_Id_377,764
-Order_No_27967,User_Id_3201,Bike,3,Business,19,3,7:06:20 PM,19,3,7:19:53 PM,19,3,7:33:11 PM,19,3,7:35:27 PM,19,3,7:43:23 PM,3,20.1,,-1.2584143,36.8048002,-1.2752846000000002,36.802702200000006,Rider_Id_383,476
-Order_No_25253,User_Id_1500,Bike,3,Business,10,4,11:27:57 AM,10,4,11:59:45 AM,10,4,12:15:27 PM,10,4,1:01:21 PM,10,4,1:23:17 PM,8,24.7,,-1.3004062,36.829741,-1.2574219,36.7927073,Rider_Id_436,1316
-Order_No_15973,User_Id_2767,Bike,3,Business,30,2,2:04:19 PM,30,2,2:04:34 PM,30,2,2:16:37 PM,30,2,2:29:34 PM,30,2,2:50:28 PM,10,27.6,,-1.3039056999999998,36.7928391,-1.257773,36.795486,Rider_Id_655,1254
-Order_No_7799,User_Id_517,Bike,3,Business,29,2,2:24:26 PM,29,2,2:24:37 PM,29,2,2:24:53 PM,29,2,2:26:31 PM,29,2,2:55:29 PM,14,26.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1738
-Order_No_4856,User_Id_517,Bike,3,Business,31,1,12:05:16 PM,31,1,1:10:12 PM,31,1,1:10:20 PM,31,1,1:20:19 PM,31,1,1:32:58 PM,11,21.2,,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_39,759
-Order_No_21485,User_Id_2247,Bike,2,Personal,29,2,1:26:07 PM,29,2,1:26:20 PM,29,2,1:27:36 PM,29,2,1:56:34 PM,29,2,2:29:48 PM,12,23.6,,-1.324282,36.86625,-1.2846,36.7873609,Rider_Id_379,1994
-Order_No_8750,User_Id_627,Bike,3,Business,29,5,1:15:46 PM,29,5,1:20:21 PM,29,5,1:45:42 PM,29,5,1:56:03 PM,29,5,2:33:33 PM,6,21.1,,-1.2579303999999998,36.8019667,-1.3001813,36.811122,Rider_Id_326,2250
-Order_No_177,User_Id_1082,Bike,1,Personal,7,7,4:27:34 PM,7,7,4:44:36 PM,7,7,5:10:55 PM,7,7,5:12:27 PM,7,7,5:33:31 PM,12,23.1,,-1.2606138,36.6963627,-1.3071979999999999,36.7502243,Rider_Id_731,1264
-Order_No_568,User_Id_2154,Bike,3,Business,3,3,2:38:59 PM,3,3,2:54:50 PM,3,3,3:04:03 PM,3,3,4:00:41 PM,3,3,4:37:39 PM,12,28.4,,-1.3014461000000002,36.7661381,-1.2765736,36.851364600000004,Rider_Id_96,2218
-Order_No_13659,User_Id_2587,Bike,3,Business,31,4,3:29:09 PM,31,4,3:32:24 PM,31,4,3:39:29 PM,31,4,3:47:19 PM,31,4,4:06:36 PM,5,28.8,,-1.28878,36.8168312,-1.2922364,36.78955379999999,Rider_Id_88,1157
-Order_No_27674,User_Id_2509,Bike,3,Business,29,5,9:01:15 AM,29,5,9:01:34 AM,29,5,9:04:15 AM,29,5,9:13:40 AM,29,5,9:44:37 AM,17,20.7,,-1.2571472,36.795063299999995,-1.2852569,36.8976121,Rider_Id_770,1857
-Order_No_15458,User_Id_1073,Bike,3,Business,9,3,9:18:23 AM,9,3,9:19:32 AM,9,3,9:25:07 AM,9,3,9:30:14 AM,9,3,9:58:37 AM,17,20.0,,-1.2563576,36.7881585,-1.22386,36.8887256,Rider_Id_45,1703
-Order_No_23216,User_Id_1148,Bike,3,Business,17,3,3:22:55 PM,17,3,3:23:18 PM,17,3,4:07:57 PM,17,3,4:44:36 PM,17,3,4:50:58 PM,9,,,-1.3104867,36.8495593,-1.2664428,36.809453000000005,Rider_Id_436,382
-Order_No_16882,User_Id_2069,Bike,3,Business,29,3,10:24:50 AM,29,3,11:09:49 AM,29,3,11:18:55 AM,29,3,11:24:08 AM,29,3,11:44:24 AM,9,,,-1.265715,36.823815,-1.228911,36.881876,Rider_Id_27,1216
-Order_No_3368,User_Id_1804,Bike,3,Personal,19,3,11:10:08 AM,19,3,11:10:33 AM,19,3,11:22:53 AM,19,3,11:28:35 AM,19,3,11:32:14 AM,1,,,-1.2839993,36.791851,-1.2859206,36.78882720000001,Rider_Id_612,219
-Order_No_13907,User_Id_1329,Bike,3,Business,18,1,1:13:31 PM,18,1,1:13:56 PM,18,1,1:23:12 PM,18,1,1:24:44 PM,18,1,1:44:36 PM,9,26.8,,-1.273056,36.811298,-1.2486688999999997,36.874896899999996,Rider_Id_206,1192
-Order_No_27233,User_Id_2587,Bike,3,Business,12,3,2:55:34 PM,12,3,3:25:05 PM,12,3,3:25:34 PM,12,3,3:31:41 PM,12,3,3:43:07 PM,7,26.4,,-1.28878,36.8168312,-1.2551895,36.7822034,Rider_Id_648,686
-Order_No_22251,User_Id_540,Bike,3,Business,9,4,8:36:11 AM,9,4,8:40:20 AM,9,4,8:40:25 AM,9,4,8:45:49 AM,9,4,9:00:09 AM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_576,860
-Order_No_21422,User_Id_2987,Bike,3,Business,24,1,5:02:38 PM,24,1,5:03:12 PM,24,1,5:15:30 PM,24,1,5:20:41 PM,24,1,5:40:35 PM,10,24.7,,-1.2702120000000001,36.82108770000001,-1.2297202,36.881950700000004,Rider_Id_427,1194
-Order_No_26610,User_Id_2828,Bike,1,Business,22,6,1:55:59 PM,22,6,1:56:12 PM,22,6,2:04:51 PM,22,6,2:35:43 PM,22,6,3:00:39 PM,15,,,-1.2615888000000002,36.7928732,-1.3291155000000001,36.8861,Rider_Id_65,1496
-Order_No_7780,User_Id_797,Bike,3,Business,11,5,2:46:19 PM,11,5,3:04:21 PM,11,5,3:10:43 PM,11,5,3:16:59 PM,11,5,3:34:20 PM,7,22.6,,-1.2559559999999999,36.789867,-1.2823851000000002,36.8186472,Rider_Id_101,1041
-Order_No_4905,User_Id_2801,Bike,3,Business,15,4,9:10:48 AM,15,4,9:12:14 AM,15,4,9:30:51 AM,15,4,9:33:10 AM,15,4,9:45:20 AM,7,19.8,,-1.2595421999999998,36.7871178,-1.2684319,36.81658529999999,Rider_Id_103,730
-Order_No_11266,User_Id_1130,Bike,3,Personal,27,5,12:16:28 PM,27,5,12:16:35 PM,27,5,12:34:48 PM,27,5,12:43:22 PM,27,5,12:59:40 PM,12,22.6,,-1.259488,36.800101,-1.2482465,36.8889062,Rider_Id_857,978
-Order_No_24013,User_Id_2008,Bike,3,Business,7,1,2:40:44 PM,7,1,3:02:05 PM,7,1,3:13:30 PM,7,1,3:19:20 PM,7,1,3:40:00 PM,6,20.7,,-1.285244,36.832159000000004,-1.2508229,36.7895263,Rider_Id_280,1240
-Order_No_19385,User_Id_1271,Bike,3,Business,17,1,5:24:23 PM,17,1,5:26:59 PM,17,1,5:30:36 PM,17,1,5:44:13 PM,17,1,6:14:56 PM,20,22.8,,-1.3244885,36.897791999999995,-1.2637056999999998,36.805170000000004,Rider_Id_871,1843
-Order_No_15828,User_Id_2894,Bike,3,Business,23,5,4:46:40 PM,23,5,4:47:07 PM,23,5,4:49:51 PM,23,5,4:54:39 PM,23,5,5:18:41 PM,3,24.5,,-1.2906618,36.8131222,-1.2892358999999998,36.788121200000006,Rider_Id_74,1442
-Order_No_26507,User_Id_1556,Bike,3,Business,26,1,9:15:49 AM,26,1,9:17:30 AM,26,1,9:18:05 AM,26,1,9:32:58 AM,26,1,10:22:42 AM,14,18.0,5.0,-1.2551895,36.7822034,-1.2297202,36.881950700000004,Rider_Id_387,2984
-Order_No_26315,User_Id_3057,Bike,2,Personal,2,4,9:22:38 AM,2,4,9:23:03 AM,2,4,9:29:15 AM,2,4,10:08:30 AM,2,4,10:17:43 AM,6,13.3,,-1.2543163999999998,36.831860999999996,-1.258984,36.835837,Rider_Id_427,553
-Order_No_23028,User_Id_496,Bike,3,Business,8,2,1:22:48 PM,8,2,1:23:21 PM,8,2,1:28:56 PM,8,2,1:34:56 PM,8,2,2:04:36 PM,22,25.4,,-1.2571472,36.795063299999995,-1.374163,36.913444,Rider_Id_178,1780
-Order_No_3199,User_Id_1473,Bike,3,Business,4,1,12:51:52 PM,4,1,12:52:48 PM,4,1,1:14:59 PM,4,1,1:23:03 PM,4,1,2:16:11 PM,18,26.6,,-1.2825783000000002,36.817162100000004,-1.3700383,36.9190174,Rider_Id_404,3188
-Order_No_4002,User_Id_1469,Bike,3,Business,8,3,3:03:06 PM,8,3,3:04:00 PM,8,3,3:05:41 PM,8,3,3:29:13 PM,8,3,3:50:48 PM,15,,,-1.2551895,36.7822034,-1.2343236000000002,36.8861,Rider_Id_411,1295
-Order_No_20080,User_Id_845,Bike,3,Business,29,5,4:20:40 PM,29,5,4:20:51 PM,29,5,4:36:37 PM,29,5,4:40:54 PM,29,5,5:02:43 PM,3,20.3,,-1.2694073000000001,36.7989138,-1.2697141,36.798177700000004,Rider_Id_281,1309
-Order_No_15869,User_Id_1500,Bike,3,Business,22,5,10:20:00 AM,22,5,10:20:53 AM,22,5,10:23:43 AM,22,5,10:44:02 AM,22,5,10:59:30 AM,5,28.7,,-1.2638185,36.7930057,-1.2614075,36.81404439999999,Rider_Id_343,928
-Order_No_25508,User_Id_3149,Bike,3,Business,13,3,12:38:39 PM,13,3,12:39:00 PM,13,3,12:39:04 PM,13,3,1:12:22 PM,13,3,1:19:26 PM,5,,,-1.3004062,36.829741,-1.3167113000000001,36.8301563,Rider_Id_842,424
-Order_No_2339,User_Id_2733,Bike,3,Business,7,5,3:33:42 PM,7,5,3:39:23 PM,7,5,3:44:52 PM,7,5,3:48:17 PM,7,5,4:26:35 PM,7,,,-1.30484,36.8300956,-1.2963097,36.7688221,Rider_Id_863,2298
-Order_No_3841,User_Id_685,Bike,3,Business,10,1,11:38:29 AM,10,1,11:40:26 AM,10,1,11:48:15 AM,10,1,12:05:18 PM,10,1,12:14:11 PM,3,18.5,,-1.2672782,36.804143599999996,-1.265432,36.795034,Rider_Id_205,533
-Order_No_14149,User_Id_2030,Bike,3,Business,4,5,2:05:53 PM,4,5,2:06:56 PM,4,5,2:13:56 PM,4,5,2:20:18 PM,4,5,2:30:09 PM,2,,,-1.2628638,36.8070399,-1.2753108999999998,36.8130185,Rider_Id_322,591
-Order_No_7248,User_Id_3599,Bike,3,Business,25,2,11:28:51 AM,25,2,11:44:49 AM,25,2,12:45:01 PM,25,2,1:15:12 PM,25,2,2:07:01 PM,12,24.0,,-1.3302996,36.8707935,-1.2668071,36.8068157,Rider_Id_519,3109
-Order_No_6991,User_Id_3201,Bike,3,Business,27,4,4:04:10 PM,27,4,4:04:21 PM,27,4,4:14:20 PM,27,4,4:20:22 PM,27,4,4:22:04 PM,2,24.9,,-1.2561334,36.7990458,-1.2649605,36.798177700000004,Rider_Id_623,102
-Order_No_2979,User_Id_2330,Bike,3,Business,5,2,1:46:27 PM,5,2,2:54:24 PM,5,2,3:13:03 PM,5,2,4:19:44 PM,5,2,4:19:55 PM,11,27.3,,-1.2326041,36.8009029,-1.293082,36.8278578,Rider_Id_953,11
-Order_No_15967,User_Id_1085,Bike,3,Business,4,1,12:29:35 PM,4,1,12:30:01 PM,4,1,12:56:56 PM,4,1,12:59:02 PM,4,1,1:27:38 PM,7,25.5,,-1.232864,36.808470799999995,-1.2536275,36.798793200000006,Rider_Id_131,1716
-Order_No_15722,User_Id_2956,Bike,3,Business,7,2,12:23:35 PM,7,2,12:26:40 PM,7,2,12:52:59 PM,7,2,12:57:41 PM,7,2,1:31:44 PM,21,,,-1.3562374,36.904295399999995,-1.2584143,36.8048002,Rider_Id_883,2043
-Order_No_21910,User_Id_2587,Bike,3,Business,10,5,12:35:56 PM,10,5,12:55:17 PM,10,5,1:22:02 PM,10,5,1:31:10 PM,10,5,1:53:24 PM,10,,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_709,1334
-Order_No_6486,User_Id_3486,Bike,3,Business,19,3,2:44:24 PM,19,3,2:44:42 PM,19,3,2:58:40 PM,19,3,3:00:19 PM,19,3,3:09:11 PM,4,24.6,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_915,532
-Order_No_16889,User_Id_393,Bike,3,Business,15,3,3:52:22 PM,15,3,3:53:20 PM,15,3,4:03:09 PM,15,3,4:13:14 PM,15,3,4:40:07 PM,11,25.9,,-1.3167113000000001,36.8301563,-1.2638185,36.7930057,Rider_Id_709,1613
-Order_No_2479,User_Id_633,Bike,3,Business,26,2,11:40:33 AM,26,2,11:40:54 AM,26,2,11:50:43 AM,26,2,12:10:06 PM,26,2,12:30:49 PM,9,27.4,,-1.2638185,36.7930057,-1.3004062,36.829741,Rider_Id_349,1243
-Order_No_10385,User_Id_248,Bike,3,Business,12,2,1:10:26 PM,12,2,1:11:54 PM,12,2,1:20:28 PM,12,2,1:27:12 PM,12,2,1:38:08 PM,3,24.3,,-1.2857117,36.7968296,-1.304735,36.784605400000004,Rider_Id_559,656
-Order_No_12434,User_Id_1195,Bike,3,Business,18,6,11:55:25 AM,18,6,11:56:18 AM,18,6,11:56:39 AM,18,6,12:10:43 PM,18,6,12:44:46 PM,16,21.2,,-1.3228003000000002,36.8306435,-1.2811975,36.7446573,Rider_Id_206,2043
-Order_No_7682,User_Id_3355,Bike,3,Business,29,4,1:50:05 PM,29,4,1:52:23 PM,29,4,2:08:37 PM,29,4,2:22:26 PM,29,4,2:45:37 PM,13,24.8,,-1.3562374,36.904295399999995,-1.3052491000000002,36.8223899,Rider_Id_587,1391
-Order_No_20565,User_Id_393,Bike,3,Business,13,4,12:04:30 PM,13,4,12:04:34 PM,13,4,12:04:41 PM,13,4,12:09:57 PM,13,4,12:42:50 PM,14,22.2,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1973
-Order_No_1179,User_Id_393,Bike,3,Business,9,3,10:49:00 AM,9,3,10:49:17 AM,9,3,10:52:09 AM,9,3,10:55:00 AM,9,3,11:23:38 AM,14,25.1,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1718
-Order_No_5492,User_Id_20,Bike,3,Business,28,3,9:03:15 AM,28,3,9:03:56 AM,28,3,9:07:06 AM,28,3,9:13:00 AM,28,3,9:29:51 AM,8,20.5,,-1.2628638,36.8070399,-1.2749662,36.7658781,Rider_Id_551,1011
-Order_No_22594,User_Id_142,Bike,3,Business,14,4,7:36:11 AM,14,4,7:37:00 AM,14,4,7:54:57 AM,14,4,8:10:36 AM,14,4,8:31:53 AM,9,18.0,,-1.2679056999999998,36.8180272,-1.2963097,36.7688221,Rider_Id_452,1277
-Order_No_5922,User_Id_53,Bike,3,Business,4,4,11:49:28 AM,4,4,11:51:43 AM,4,4,12:01:41 PM,4,4,12:04:58 PM,4,4,12:34:43 PM,11,25.6,,-1.2991441000000001,36.7528804,-1.2770708,36.8231093,Rider_Id_420,1785
-Order_No_15703,User_Id_3232,Bike,3,Business,2,3,12:01:53 PM,2,3,12:02:07 PM,2,3,12:04:00 PM,2,3,12:09:43 PM,2,3,1:09:22 PM,4,23.9,,-1.2551895,36.7822034,-1.27267,36.8124492,Rider_Id_191,3579
-Order_No_16668,User_Id_909,Bike,3,Business,10,3,9:53:17 AM,10,3,9:53:30 AM,10,3,9:57:20 AM,10,3,10:02:48 AM,10,3,11:11:26 AM,14,21.8,,-1.225322,36.80855,-1.2486688999999997,36.874896899999996,Rider_Id_863,4118
-Order_No_24773,User_Id_1675,Bike,1,Personal,15,3,9:58:33 AM,15,3,10:00:47 AM,15,3,10:11:31 AM,15,3,10:16:14 AM,15,3,10:55:20 AM,8,16.3,,-1.296797,36.776452500000005,-1.2850639,36.8179454,Rider_Id_674,2346
-Order_No_7983,User_Id_706,Bike,3,Business,18,1,10:27:38 AM,18,1,10:27:52 AM,18,1,10:40:42 AM,18,1,10:44:17 AM,18,1,11:18:29 AM,13,,,-1.3077869,36.844320700000004,-1.2758254,36.8944551,Rider_Id_858,2052
-Order_No_25777,User_Id_136,Bike,3,Business,22,5,5:00:30 PM,22,5,5:00:52 PM,22,5,5:05:06 PM,22,5,5:16:13 PM,22,5,5:27:00 PM,4,25.7,,-1.2654716000000001,36.7983635,-1.2902869,36.8085396,Rider_Id_156,647
-Order_No_12797,User_Id_3426,Bike,3,Business,16,5,5:03:21 PM,16,5,5:04:47 PM,16,5,5:12:44 PM,16,5,5:20:55 PM,16,5,5:38:07 PM,5,26.1,,-1.3014461000000002,36.7661381,-1.2835228,36.7437619,Rider_Id_276,1032
-Order_No_8867,User_Id_1637,Bike,3,Business,1,4,4:02:46 PM,1,4,4:03:12 PM,1,4,4:08:14 PM,1,4,4:10:25 PM,1,4,4:20:38 PM,6,,,-1.2765492,36.7669809,-1.2615888000000002,36.7928732,Rider_Id_240,613
-Order_No_12339,User_Id_3686,Bike,3,Business,13,4,11:34:40 AM,13,4,12:57:18 PM,13,4,1:05:37 PM,13,4,1:21:08 PM,13,4,1:58:46 PM,6,21.8,,-1.2551895,36.7822034,-1.2805757,36.8223948,Rider_Id_124,2258
-Order_No_17747,User_Id_9,Bike,1,Personal,23,4,8:17:44 AM,23,4,8:17:59 AM,23,4,8:26:38 AM,23,4,8:31:28 AM,23,4,8:50:17 AM,7,15.8,,-1.2761126,36.7668191,-1.2697141,36.798177700000004,Rider_Id_735,1129
-Order_No_27205,User_Id_552,Bike,3,Business,2,4,3:25:27 PM,2,4,3:26:10 PM,2,4,3:58:39 PM,2,4,4:10:21 PM,2,4,4:32:53 PM,16,,,-1.2859912,36.8756811,-1.3562374,36.904295399999995,Rider_Id_126,1352
-Order_No_5292,User_Id_517,Bike,3,Business,29,3,8:39:15 AM,29,3,8:39:33 AM,29,3,8:40:05 AM,29,3,9:22:02 AM,29,3,9:22:05 AM,11,17.9,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_515,3
-Order_No_11141,User_Id_20,Bike,3,Business,30,4,10:23:34 AM,30,4,10:24:02 AM,30,4,10:28:36 AM,30,4,10:30:02 AM,30,4,10:46:11 AM,7,19.5,,-1.2680478000000002,36.8013518,-1.2909699,36.7826542,Rider_Id_96,969
-Order_No_11171,User_Id_1578,Bike,3,Business,9,1,11:51:35 AM,9,1,11:51:47 AM,9,1,12:09:32 PM,9,1,12:16:16 PM,9,1,12:23:58 PM,2,20.1,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_24,462
-Order_No_22881,User_Id_3669,Bike,3,Business,8,2,2:34:37 PM,8,2,2:39:23 PM,8,2,2:46:28 PM,8,2,2:53:19 PM,8,2,3:26:22 PM,14,25.1,,-1.2615888000000002,36.7928732,-1.3246122999999999,36.790226700000005,Rider_Id_539,1983
-Order_No_8820,User_Id_2030,Bike,3,Business,11,2,3:33:48 PM,11,2,3:39:59 PM,11,2,3:58:07 PM,11,2,4:44:40 PM,11,2,4:51:58 PM,1,21.3,,-1.2733047,36.8109744,-1.2753108999999998,36.8130185,Rider_Id_940,438
-Order_No_12137,User_Id_1961,Bike,3,Business,10,1,2:27:05 PM,10,1,2:27:39 PM,10,1,2:42:35 PM,10,1,2:51:18 PM,10,1,3:16:20 PM,19,18.2,,-1.331619,36.847976,-1.3188358999999998,36.703852600000005,Rider_Id_524,1502
-Order_No_15015,User_Id_391,Bike,3,Business,31,5,12:42:35 PM,31,5,12:58:58 PM,31,5,1:00:23 PM,31,5,1:36:09 PM,31,5,1:50:01 PM,5,24.7,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_158,832
-Order_No_12265,User_Id_540,Bike,3,Business,26,2,10:17:53 AM,26,2,10:18:08 AM,26,2,10:24:47 AM,26,2,10:29:39 AM,26,2,10:44:51 AM,5,21.9,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_817,912
-Order_No_5080,User_Id_2330,Bike,3,Business,26,2,6:37:17 PM,26,2,6:39:09 PM,26,2,6:48:48 PM,26,2,6:55:09 PM,26,2,7:05:43 PM,8,29.7,,-1.3270197,36.7037303,-1.3015197,36.7658456,Rider_Id_628,634
-Order_No_639,User_Id_1329,Bike,3,Business,3,5,10:36:21 AM,3,5,10:36:38 AM,3,5,10:37:12 AM,3,5,10:49:43 AM,3,5,10:58:15 AM,2,,,-1.273056,36.811298,-1.262073,36.798550299999995,Rider_Id_119,512
-Order_No_4828,User_Id_868,Bike,3,Business,30,4,9:20:24 AM,30,4,9:29:16 AM,30,4,9:39:25 AM,30,4,10:03:02 AM,30,4,10:03:04 AM,9,19.3,,-1.2296618000000001,36.84382720000001,-1.265715,36.823815,Rider_Id_740,2
-Order_No_20186,User_Id_3486,Bike,3,Business,5,2,10:21:15 AM,5,2,10:21:46 AM,5,2,10:24:52 AM,5,2,10:26:42 AM,5,2,11:00:16 AM,16,18.2,,-1.2551895,36.7822034,-1.3165542,36.700067,Rider_Id_854,2014
-Order_No_15259,User_Id_1329,Bike,3,Business,30,6,1:40:24 PM,30,6,1:41:04 PM,30,6,1:41:41 PM,30,6,1:47:25 PM,30,6,2:01:49 PM,7,28.4,,-1.273056,36.811298,-1.3096379999999999,36.805289,Rider_Id_119,864
-Order_No_19646,User_Id_1500,Bike,3,Business,13,3,12:43:15 PM,13,3,12:43:25 PM,13,3,12:46:51 PM,13,3,1:07:16 PM,13,3,1:29:00 PM,8,,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_763,1304
-Order_No_12744,User_Id_2691,Bike,3,Business,19,1,12:27:29 PM,19,1,12:50:53 PM,19,1,1:02:21 PM,19,1,1:04:39 PM,19,1,1:22:04 PM,6,24.6,,-1.2857671,36.7875614,-1.2810205,36.8248105,Rider_Id_194,1045
-Order_No_6860,User_Id_3352,Bike,3,Personal,18,5,9:35:32 AM,18,5,9:35:51 AM,18,5,9:39:40 AM,18,5,9:41:27 AM,18,5,10:15:21 AM,20,20.3,,-1.3127303999999997,36.8189817,-1.1855066,36.825464600000004,Rider_Id_551,2034
-Order_No_21258,User_Id_909,Bike,3,Business,4,2,3:00:28 PM,4,2,3:14:48 PM,4,2,3:19:46 PM,4,2,3:23:11 PM,4,2,3:56:39 PM,7,23.9,11.0,-1.2584143,36.8048002,-1.225322,36.80855,Rider_Id_40,2008
-Order_No_4266,User_Id_2801,Bike,3,Business,8,3,12:53:20 PM,8,3,12:53:33 PM,8,3,1:01:04 PM,8,3,1:08:38 PM,8,3,1:20:33 PM,3,,,-1.2674273999999999,36.787082700000006,-1.2566586000000002,36.792968200000004,Rider_Id_815,715
-Order_No_7246,User_Id_265,Bike,3,Business,22,5,11:21:22 AM,22,5,11:21:32 AM,22,5,11:22:05 AM,22,5,12:34:16 PM,22,5,12:41:13 PM,13,,,-1.300921,36.828195,-1.2353665,36.8128044,Rider_Id_137,417
-Order_No_26997,User_Id_3201,Bike,3,Business,12,2,2:27:41 PM,12,2,2:27:59 PM,12,2,2:34:48 PM,12,2,2:48:08 PM,12,2,2:53:50 PM,2,26.9,,-1.2551895,36.7822034,-1.2584143,36.8048002,Rider_Id_647,342
-Order_No_3805,User_Id_1153,Bike,2,Personal,27,1,10:06:17 AM,27,1,10:06:34 AM,27,1,10:20:54 AM,27,1,10:23:02 AM,27,1,10:53:01 AM,15,16.5,,-1.3178263000000001,36.7141818,-1.2620159,36.7956487,Rider_Id_412,1799
-Order_No_14402,User_Id_2587,Bike,3,Business,15,5,1:59:04 PM,15,5,2:01:47 PM,15,5,2:32:58 PM,15,5,2:39:17 PM,15,5,2:44:14 PM,1,27.9,,-1.279395,36.825364,-1.28878,36.8168312,Rider_Id_863,297
-Order_No_17137,User_Id_1207,Bike,3,Business,7,1,1:46:14 PM,7,1,1:49:01 PM,7,1,1:52:46 PM,7,1,2:19:06 PM,7,1,2:19:16 PM,7,26.7,,-1.2629666000000002,36.8068369,-1.284382,36.774604499999995,Rider_Id_517,10
-Order_No_26384,User_Id_1336,Bike,3,Business,19,2,12:58:22 PM,19,2,1:06:43 PM,19,2,1:17:18 PM,19,2,1:21:37 PM,19,2,2:26:25 PM,8,26.8,,-1.2885029,36.825604999999996,-1.2615888000000002,36.7928732,Rider_Id_548,3888
-Order_No_4863,User_Id_2154,Bike,3,Business,17,4,11:44:43 AM,17,4,11:48:37 AM,17,4,11:56:01 AM,17,4,12:10:01 PM,17,4,12:32:39 PM,5,25.6,,-1.290894,36.822971,-1.2765736,36.851364600000004,Rider_Id_812,1358
-Order_No_14706,User_Id_1874,Bike,3,Business,5,3,9:45:05 AM,5,3,9:45:10 AM,5,3,9:50:32 AM,5,3,9:57:25 AM,5,3,10:55:29 AM,20,18.2,,-1.2571472,36.795063299999995,-1.3700383,36.9190174,Rider_Id_620,3484
-Order_No_24414,User_Id_3452,Bike,2,Personal,24,1,5:31:48 PM,24,1,5:31:56 PM,24,1,5:42:32 PM,24,1,5:51:55 PM,24,1,6:28:04 PM,11,24.7,,-1.3196808999999998,36.8410572,-1.308217,36.77413,Rider_Id_857,2169
-Order_No_28127,User_Id_3698,Bike,3,Business,29,3,9:59:12 AM,29,3,10:00:09 AM,29,3,10:12:02 AM,29,3,10:21:49 AM,29,3,10:55:29 AM,20,18.0,,-1.34364,36.8925345,-1.3107918,36.761794,Rider_Id_690,2020
-Order_No_4457,User_Id_1416,Bike,1,Personal,25,2,11:50:19 AM,25,2,11:50:52 AM,25,2,11:54:52 AM,25,2,11:56:26 AM,25,2,12:12:23 PM,10,23.7,,-1.2849389999999998,36.829984,-1.3241819,36.882342799999996,Rider_Id_409,957
-Order_No_4228,User_Id_868,Bike,3,Business,13,4,10:42:29 AM,13,4,10:42:29 AM,13,4,10:51:33 AM,13,4,11:14:07 AM,13,4,11:17:54 AM,6,22.1,,-1.307143,36.825009,-1.2663631000000002,36.8163329,Rider_Id_864,227
-Order_No_18226,User_Id_1882,Bike,3,Business,12,6,2:23:41 PM,12,6,2:51:14 PM,12,6,3:48:14 PM,12,6,3:54:50 PM,12,6,4:23:31 PM,9,24.9,,-1.2475028000000001,36.877469700000006,-1.277512,36.820013,Rider_Id_332,1721
-Order_No_24357,User_Id_1245,Bike,3,Business,29,4,1:21:46 PM,29,4,1:22:06 PM,29,4,1:31:29 PM,29,4,1:38:29 PM,29,4,2:18:06 PM,17,24.6,,-1.2599563,36.799343900000004,-1.3198615,36.7110323,Rider_Id_167,2377
-Order_No_24897,User_Id_635,Bike,3,Business,27,2,12:22:35 PM,27,2,12:27:24 PM,27,2,12:30:39 PM,27,2,12:36:59 PM,27,2,12:53:15 PM,7,23.9,,-1.2599563,36.799343900000004,-1.300921,36.828195,Rider_Id_19,976
-Order_No_25240,User_Id_517,Bike,3,Business,4,5,10:08:16 AM,4,5,10:08:43 AM,4,5,10:11:56 AM,4,5,10:17:54 AM,4,5,10:39:30 AM,11,17.3,0.4,-1.3167113000000001,36.8301563,-1.2574219,36.7927073,Rider_Id_523,1296
-Order_No_10244,User_Id_3525,Bike,3,Business,28,2,12:48:27 PM,28,2,12:53:45 PM,28,2,1:26:42 PM,28,2,1:31:35 PM,28,2,2:43:24 PM,16,21.5,,-1.3700383,36.9190174,-1.27915,36.89929,Rider_Id_876,4309
-Order_No_24083,User_Id_733,Bike,3,Business,21,5,10:42:27 AM,21,5,10:42:46 AM,21,5,10:49:52 AM,21,5,10:53:02 AM,21,5,11:28:48 AM,20,22.6,,-1.2551895,36.7822034,-1.3251247,36.899592600000005,Rider_Id_685,2146
-Order_No_16701,User_Id_3326,Bike,3,Business,23,5,1:10:14 PM,23,5,3:07:16 PM,23,5,3:19:53 PM,23,5,3:28:58 PM,23,5,3:41:02 PM,4,26.3,,-1.2551895,36.7822034,-1.2681518,36.809816399999995,Rider_Id_649,724
-Order_No_21135,User_Id_1329,Bike,3,Business,14,4,9:25:31 AM,14,4,9:26:56 AM,14,4,9:37:24 AM,14,4,9:38:39 AM,14,4,10:33:21 AM,16,17.7,,-1.273056,36.811298,-1.361923,36.770045,Rider_Id_523,3282
-Order_No_10816,User_Id_1889,Bike,3,Personal,30,3,9:49:40 AM,30,3,9:50:10 AM,30,3,9:56:49 AM,30,3,10:06:47 AM,30,3,10:26:40 AM,10,17.2,,-1.2767916000000001,36.820096500000005,-1.2937613000000001,36.7579401,Rider_Id_786,1193
-Order_No_3684,User_Id_2587,Bike,3,Business,5,3,10:59:11 AM,5,3,11:00:05 AM,5,3,11:23:19 AM,5,3,11:50:22 AM,5,3,12:08:30 PM,10,22.5,,-1.225322,36.80855,-1.28878,36.8168312,Rider_Id_101,1088
-Order_No_13625,User_Id_3706,Bike,2,Personal,11,2,8:22:06 AM,11,2,8:32:28 AM,11,2,8:53:32 AM,11,2,8:57:55 AM,11,2,9:23:53 AM,9,18.6,2.1,-1.2518056999999998,36.7997135,-1.2846195,36.7958316,Rider_Id_882,1558
-Order_No_20607,User_Id_2654,Bike,3,Personal,15,1,11:39:29 AM,15,1,11:42:45 AM,15,1,12:37:50 PM,15,1,12:49:05 PM,15,1,1:31:36 PM,11,,,-1.2800391000000002,36.831202000000005,-1.2927792,36.9053492,Rider_Id_155,2551
-Order_No_17244,User_Id_1917,Bike,3,Personal,24,4,1:47:34 PM,24,4,1:48:52 PM,24,4,2:14:09 PM,24,4,2:22:02 PM,24,4,2:48:21 PM,9,28.3,,-1.2767423,36.82007670000001,-1.3265668,36.8022026,Rider_Id_758,1579
-Order_No_27394,User_Id_3599,Bike,3,Business,11,2,11:06:40 AM,11,2,11:22:34 AM,11,2,11:56:01 AM,11,2,12:13:03 PM,11,2,12:58:32 PM,12,19.5,,-1.3302996,36.8707935,-1.3043821000000002,36.7840586,Rider_Id_661,2729
-Order_No_7773,User_Id_2020,Bike,1,Personal,21,7,12:40:20 PM,21,7,12:56:30 PM,21,7,1:43:34 PM,21,7,1:48:19 PM,21,7,2:47:00 PM,21,27.2,,-1.392306,36.734044,-1.3014816999999999,36.7907294,Rider_Id_647,3521
-Order_No_14104,User_Id_1651,Bike,3,Business,7,2,8:33:44 AM,7,2,8:35:47 AM,7,2,8:42:41 AM,7,2,8:43:52 AM,7,2,9:19:55 AM,21,,,-1.3562374,36.904295399999995,-1.2551895,36.7822034,Rider_Id_863,2163
-Order_No_4069,User_Id_1356,Bike,3,Business,26,2,1:26:54 PM,26,2,1:27:57 PM,26,2,1:49:59 PM,26,2,1:52:30 PM,26,2,2:29:01 PM,17,29.5,,-1.2000097,36.7670515,-1.2949473999999999,36.8270407,Rider_Id_959,2191
-Order_No_27491,User_Id_3077,Bike,3,Business,7,4,9:58:28 AM,7,4,10:24:13 AM,7,4,10:48:40 AM,7,4,10:56:26 AM,7,4,11:25:29 AM,14,16.9,,-1.2241193000000001,36.88199198,-1.2672588,36.8000252,Rider_Id_900,1743
-Order_No_4229,User_Id_1073,Bike,3,Business,3,1,3:34:10 PM,3,1,3:35:36 PM,3,1,3:45:59 PM,3,1,3:55:04 PM,3,1,4:18:49 PM,9,23.1,,-1.2562361000000002,36.78464038,-1.2906104999999999,36.805327899999995,Rider_Id_770,1425
-Order_No_7927,User_Id_3560,Bike,3,Business,27,1,4:34:23 PM,27,1,4:35:57 PM,27,1,5:04:14 PM,27,1,5:27:13 PM,27,1,5:27:32 PM,1,,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_819,19
-Order_No_11983,User_Id_3291,Bike,3,Business,25,1,4:14:21 PM,25,1,4:15:19 PM,25,1,4:15:27 PM,25,1,4:35:31 PM,25,1,4:51:58 PM,4,28.3,,-1.272828,36.816608,-1.2600926000000001,36.8088685,Rider_Id_828,987
-Order_No_21242,User_Id_3560,Bike,3,Business,4,5,10:24:15 AM,4,5,10:41:05 AM,4,5,10:48:53 AM,4,5,10:57:07 AM,4,5,11:11:08 AM,1,17.1,,-1.290894,36.822971,-1.279395,36.825364,Rider_Id_569,841
-Order_No_667,User_Id_1151,Bike,2,Personal,4,2,10:19:35 AM,4,2,10:20:28 AM,4,2,10:34:24 AM,4,2,10:54:44 AM,4,2,11:21:19 AM,10,18.7,,-1.311121,36.900472,-1.3233955,36.856294399999996,Rider_Id_753,1595
-Order_No_1363,User_Id_1329,Bike,3,Business,16,6,2:35:31 PM,16,6,2:35:44 PM,16,6,2:37:12 PM,16,6,2:38:32 PM,16,6,2:48:32 PM,3,,,-1.273056,36.811298,-1.2649605,36.798177700000004,Rider_Id_622,600
-Order_No_14765,User_Id_2532,Bike,3,Business,10,5,4:00:27 PM,10,5,4:28:49 PM,10,5,4:43:16 PM,10,5,4:50:05 PM,10,5,5:14:17 PM,6,,,-1.2571472,36.795063299999995,-1.2824911,36.8218042,Rider_Id_491,1452
-Order_No_19311,User_Id_517,Bike,3,Business,27,4,2:21:34 PM,27,4,2:21:44 PM,27,4,2:24:19 PM,27,4,2:26:36 PM,27,4,2:35:22 PM,4,23.6,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_523,526
-Order_No_23057,User_Id_391,Bike,3,Business,10,5,11:09:08 AM,10,5,11:17:59 AM,10,5,11:29:29 AM,10,5,12:13:31 PM,10,5,12:15:32 PM,5,,,-1.300921,36.828195,-1.2784063,36.8222933,Rider_Id_436,121
-Order_No_17117,User_Id_2511,Bike,1,Personal,14,5,12:36:57 PM,14,5,12:38:05 PM,14,5,12:53:16 PM,14,5,12:55:29 PM,14,5,1:16:39 PM,5,24.6,,-1.2622055,36.776342799999995,-1.2661639999999998,36.800460799999996,Rider_Id_453,1270
-Order_No_1796,User_Id_3283,Bike,3,Business,11,2,9:42:41 AM,11,2,9:43:24 AM,11,2,10:02:06 AM,11,2,10:10:08 AM,11,2,10:32:45 AM,5,16.6,,-1.2600926000000001,36.8088685,-1.2628473,36.781805,Rider_Id_27,1357
-Order_No_16410,User_Id_2457,Bike,3,Business,31,3,10:36:44 AM,31,3,10:37:28 AM,31,3,10:39:27 AM,31,3,10:45:45 AM,31,3,11:59:33 AM,4,24.7,,-1.2551895,36.7822034,-1.269534,36.80697,Rider_Id_478,4428
-Order_No_7513,User_Id_2174,Bike,3,Business,12,2,10:31:36 AM,12,2,10:31:44 AM,12,2,10:34:50 AM,12,2,10:51:47 AM,12,2,12:23:03 PM,15,28.2,,-1.2482781,36.884091999999995,-1.214071,36.7985052,Rider_Id_12,5476
-Order_No_52,User_Id_391,Bike,3,Business,1,5,11:06:07 AM,1,5,11:08:12 AM,1,5,11:27:46 AM,1,5,11:41:47 AM,1,5,12:08:03 PM,14,26.4,,-1.3380736,36.8769529,-1.2784063,36.8222933,Rider_Id_404,1576
-Order_No_19489,User_Id_393,Bike,3,Business,8,2,10:17:48 AM,8,2,10:18:34 AM,8,2,10:21:33 AM,8,2,10:27:33 AM,8,2,10:56:20 AM,14,19.0,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1727
-Order_No_5792,User_Id_1484,Bike,3,Business,29,2,9:39:09 AM,29,2,9:40:07 AM,29,2,9:40:13 AM,29,2,9:49:19 AM,29,2,10:12:22 AM,8,18.4,,-1.2551895,36.7822034,-1.3032,36.817333000000005,Rider_Id_228,1383
-Order_No_14621,User_Id_2212,Bike,3,Business,27,3,12:27:52 PM,27,3,12:28:49 PM,27,3,12:37:11 PM,27,3,12:46:09 PM,27,3,1:16:05 PM,14,25.9,,-1.2571472,36.795063299999995,-1.3291155000000001,36.8861,Rider_Id_922,1796
-Order_No_27542,User_Id_954,Bike,1,Personal,28,4,11:24:59 AM,28,4,11:25:19 AM,28,4,11:48:54 AM,28,4,11:50:13 AM,28,4,12:19:32 PM,16,,,-1.2763087,36.8170342,-1.3298444999999999,36.7601244,Rider_Id_874,1759
-Order_No_14276,User_Id_2869,Bike,3,Business,15,5,1:28:55 PM,15,5,1:29:30 PM,15,5,1:44:24 PM,15,5,1:54:50 PM,15,5,2:14:13 PM,9,17.6,,-1.2551895,36.7822034,-1.3025826,36.7670807,Rider_Id_825,1163
-Order_No_8707,User_Id_265,Bike,3,Business,29,1,10:17:03 AM,29,1,10:30:19 AM,29,1,10:47:21 AM,29,1,11:22:29 AM,29,1,12:08:47 PM,26,,,-1.2543158,36.99104620000001,-1.300921,36.828195,Rider_Id_340,2778
-Order_No_9784,User_Id_3222,Bike,3,Business,29,2,3:10:15 PM,29,2,3:10:36 PM,29,2,3:10:58 PM,29,2,3:19:06 PM,29,2,3:46:00 PM,11,27.2,,-1.2551895,36.7822034,-1.2217966999999998,36.810990000000004,Rider_Id_579,1614
-Order_No_3977,User_Id_1491,Bike,3,Personal,10,5,12:29:46 PM,10,5,12:30:04 PM,10,5,12:37:51 PM,10,5,12:42:38 PM,10,5,1:09:48 PM,12,19.1,,-1.326825,36.864345,-1.2838207,36.8121326,Rider_Id_650,1630
-Order_No_1382,User_Id_2869,Bike,3,Business,8,6,11:47:16 AM,8,6,11:47:59 AM,8,6,12:00:48 PM,8,6,12:20:58 PM,8,6,1:38:01 PM,12,20.1,,-1.2765736,36.851364600000004,-1.3025826,36.7670807,Rider_Id_404,4623
-Order_No_16439,User_Id_909,Bike,3,Business,2,6,12:46:26 PM,2,6,1:22:40 PM,2,6,1:23:02 PM,2,6,1:39:26 PM,2,6,2:02:51 PM,10,30.8,,-1.2765736,36.851364600000004,-1.225322,36.80855,Rider_Id_143,1405
-Order_No_23627,User_Id_136,Bike,3,Business,8,5,9:56:59 AM,8,5,10:13:16 AM,8,5,10:35:47 AM,8,5,10:37:19 AM,8,5,11:29:35 AM,17,,,-1.3326173000000001,36.71462329999999,-1.2590031000000002,36.8078168,Rider_Id_735,3136
-Order_No_15819,User_Id_3647,Bike,3,Business,18,1,9:13:06 AM,18,1,9:14:45 AM,18,1,9:22:50 AM,18,1,9:30:20 AM,18,1,9:47:46 AM,9,21.8,,-1.2726389999999999,36.794723,-1.302088,36.831162,Rider_Id_247,1046
-Order_No_27495,User_Id_1075,Bike,3,Business,30,3,5:12:00 PM,30,3,5:13:50 PM,30,3,5:22:02 PM,30,3,5:30:28 PM,30,3,5:55:59 PM,5,27.2,,-1.2527958000000001,36.8003131,-1.2811503,36.8235884,Rider_Id_407,1531
-Order_No_679,User_Id_2688,Bike,2,Personal,14,1,5:38:31 PM,14,1,5:46:33 PM,14,1,6:02:18 PM,14,1,6:04:02 PM,14,1,6:09:34 PM,4,23.2,,-1.2914606000000002,36.771210700000005,-1.271983,36.796063000000004,Rider_Id_919,332
-Order_No_11876,User_Id_478,Bike,3,Business,20,4,10:04:22 AM,20,4,10:04:48 AM,20,4,10:09:09 AM,20,4,10:19:24 AM,20,4,10:54:16 AM,15,18.7,0.2,-1.2551895,36.7822034,-1.3226301999999999,36.8719901,Rider_Id_870,2092
-Order_No_10657,User_Id_2174,Bike,3,Business,21,2,12:35:49 PM,21,2,12:36:16 PM,21,2,12:36:24 PM,21,2,1:37:08 PM,21,2,1:51:14 PM,23,24.9,,-1.2482781,36.884091999999995,-1.2759525,36.7082022,Rider_Id_86,846
-Order_No_18291,User_Id_2545,Bike,2,Personal,2,6,12:23:09 PM,2,6,12:24:01 PM,2,6,12:52:47 PM,2,6,1:01:07 PM,2,6,1:34:03 PM,15,23.8,,-1.2297202,36.874550799999994,-1.2797184,36.7811448,Rider_Id_549,1976
-Order_No_26195,User_Id_3058,Bike,3,Personal,29,5,12:37:01 PM,29,5,12:37:51 PM,29,5,12:44:49 PM,29,5,12:46:46 PM,29,5,1:04:02 PM,7,19.5,,-1.2841726,36.8179589,-1.3043649,36.7816132,Rider_Id_476,1036
-Order_No_8460,User_Id_3516,Bike,3,Business,3,1,9:37:54 AM,3,1,9:38:41 AM,3,1,9:45:36 AM,3,1,9:48:58 AM,3,1,10:09:40 AM,3,18.2,5.0,-1.3039706,36.7688174,-1.295908,36.791433000000005,Rider_Id_362,1242
-Order_No_22504,User_Id_393,Bike,3,Business,14,1,2:08:18 PM,14,1,2:10:16 PM,14,1,2:12:29 PM,14,1,2:16:18 PM,14,1,2:47:19 PM,14,25.9,,-1.3167113000000001,36.8301563,-1.306378,36.7519845,Rider_Id_523,1861
-Order_No_16620,User_Id_928,Bike,3,Business,10,5,12:33:47 PM,10,5,12:41:53 PM,10,5,1:25:28 PM,10,5,1:45:15 PM,10,5,1:59:30 PM,6,,,-1.2551895,36.7822034,-1.2897368,36.8226783,Rider_Id_436,855
-Order_No_19019,User_Id_2763,Bike,3,Business,24,3,2:02:51 PM,24,3,2:03:06 PM,24,3,2:04:39 PM,24,3,2:08:35 PM,24,3,2:23:53 PM,8,,,-1.2551895,36.7822034,-1.2934638,36.7668028,Rider_Id_422,918
-Order_No_17947,User_Id_3560,Bike,3,Business,6,1,3:59:57 PM,6,1,4:00:04 PM,6,1,4:11:27 PM,6,1,4:18:00 PM,6,1,4:42:37 PM,19,24.4,,-1.3705766000000001,36.9120742,-1.279395,36.825364,Rider_Id_406,1477
-Order_No_27073,User_Id_393,Bike,3,Business,1,1,8:52:22 AM,1,1,8:53:19 AM,1,1,9:16:49 AM,1,1,9:29:58 AM,1,1,10:04:09 AM,8,21.5,,-1.3167113000000001,36.8301563,-1.2770708,36.8231093,Rider_Id_843,2051
-Order_No_27137,User_Id_3647,Bike,3,Business,24,5,12:21:43 PM,24,5,12:22:33 PM,24,5,12:25:36 PM,24,5,12:38:20 PM,24,5,1:00:54 PM,5,21.6,,-1.2726389999999999,36.794723,-1.2608573,36.809985499999996,Rider_Id_882,1354
-Order_No_8224,User_Id_3517,Bike,3,Business,4,1,8:27:00 AM,4,1,8:43:30 AM,4,1,9:17:09 AM,4,1,9:29:14 AM,4,1,10:05:19 AM,16,22.0,,-1.3882008,36.7699445,-1.300921,36.828195,Rider_Id_559,2165
-Order_No_1644,User_Id_1683,Bike,3,Business,4,3,4:21:55 PM,4,3,4:36:25 PM,4,3,4:43:07 PM,4,3,4:47:19 PM,4,3,5:04:22 PM,4,,,-1.2765334,36.80150510000001,-1.2675332000000001,36.808561,Rider_Id_103,1023
-Order_No_24497,User_Id_3300,Bike,3,Business,16,2,3:35:13 PM,16,2,3:35:52 PM,16,2,3:49:55 PM,16,2,3:56:45 PM,16,2,4:15:52 PM,11,,,-1.318062,36.849326,-1.303596,36.778378000000004,Rider_Id_768,1147
-Order_No_21527,User_Id_816,Bike,3,Business,22,2,3:41:20 PM,22,2,3:41:34 PM,22,2,3:58:20 PM,22,2,4:06:26 PM,22,2,4:16:43 PM,2,25.6,,-1.279395,36.825364,-1.290894,36.822971,Rider_Id_462,617
-Order_No_24593,User_Id_2193,Bike,3,Personal,6,3,3:11:02 PM,6,3,3:12:18 PM,6,3,3:19:21 PM,6,3,3:22:23 PM,6,3,3:35:24 PM,7,,,-1.317337,36.82698,-1.2768326,36.8073804,Rider_Id_195,781
-Order_No_11658,User_Id_1482,Bike,3,Business,1,4,9:41:43 AM,1,4,9:42:06 AM,1,4,9:47:47 AM,1,4,9:49:00 AM,1,4,10:18:18 AM,11,,,-1.3385790000000002,36.89630379999999,-1.2966600000000001,36.828668,Rider_Id_402,1758
-Order_No_12109,User_Id_2350,Bike,1,Personal,18,3,11:19:03 AM,18,3,11:19:21 AM,18,3,11:23:20 AM,18,3,11:33:36 AM,18,3,11:41:02 AM,3,19.8,,-1.2901786,36.824294,-1.2922257,36.802759,Rider_Id_583,446
-Order_No_16120,User_Id_1469,Bike,3,Business,25,5,12:01:13 PM,25,5,12:01:45 PM,25,5,12:01:54 PM,25,5,12:11:41 PM,25,5,12:41:45 PM,8,26.3,,-1.2551895,36.7822034,-1.2954581,36.810997,Rider_Id_268,1804
-Order_No_14137,User_Id_2123,Bike,1,Personal,2,1,1:27:12 PM,2,1,1:30:09 PM,2,1,2:00:35 PM,2,1,2:06:46 PM,2,1,2:30:57 PM,14,20.2,,-1.2846475,36.8275903,-1.3382857,36.887071500000005,Rider_Id_752,1451
-Order_No_21321,User_Id_640,Bike,3,Business,4,4,9:10:32 AM,4,4,9:10:47 AM,4,4,9:12:40 AM,4,4,9:15:08 AM,4,4,11:03:29 AM,14,21.9,,-1.304033,36.7848695,-1.3246455,36.8754415,Rider_Id_202,6501
-Order_No_4775,User_Id_2869,Bike,3,Business,21,2,10:10:21 AM,21,2,10:10:45 AM,21,2,10:16:17 AM,21,2,10:23:56 AM,21,2,10:35:34 AM,3,,,-1.2763494,36.7671783,-1.3025826,36.7670807,Rider_Id_678,698
-Order_No_26884,User_Id_302,Bike,3,Business,5,6,3:33:36 PM,5,6,3:33:53 PM,5,6,3:39:10 PM,5,6,3:52:59 PM,5,6,4:00:44 PM,2,21.9,,-1.2649605,36.798177700000004,-1.2517571,36.81702670000001,Rider_Id_881,465
-Order_No_25440,User_Id_1502,Bike,3,Business,1,1,10:01:08 AM,1,1,10:01:19 AM,1,1,10:08:10 AM,1,1,10:23:42 AM,1,1,10:45:01 AM,13,18.3,,-1.260232,36.807334000000004,-1.2156006000000001,36.8916865,Rider_Id_882,1279
-Order_No_14655,User_Id_1724,Bike,3,Business,18,2,3:56:46 PM,18,2,3:57:16 PM,18,2,4:01:04 PM,18,2,4:34:17 PM,18,2,4:43:14 PM,2,,,-1.2584143,36.8048002,-1.2690013,36.8092872,Rider_Id_875,537
-Order_No_26008,User_Id_53,Bike,3,Business,27,3,12:30:08 PM,27,3,12:31:54 PM,27,3,12:49:20 PM,27,3,1:00:47 PM,27,3,1:21:13 PM,5,26.1,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_603,1226
-Order_No_8462,User_Id_136,Bike,3,Business,11,2,12:18:28 PM,11,2,12:19:50 PM,11,2,12:56:12 PM,11,2,1:10:11 PM,11,2,2:40:33 PM,15,19.3,,-1.2949455,36.8195238,-1.3254522,36.721658399999995,Rider_Id_168,5422
-Order_No_14683,User_Id_265,Bike,3,Business,19,2,2:34:23 PM,19,2,3:22:52 PM,19,2,3:46:03 PM,19,2,3:54:15 PM,19,2,4:25:20 PM,10,30.2,,-1.3472066,36.7692638,-1.300921,36.828195,Rider_Id_605,1865
-Order_No_17743,User_Id_2931,Bike,3,Business,16,3,5:18:14 PM,16,3,5:18:29 PM,16,3,5:28:08 PM,16,3,5:30:37 PM,16,3,6:02:20 PM,13,23.2,,-1.2452664,36.802854100000005,-1.2981858,36.747492,Rider_Id_205,1903
-Order_No_17192,User_Id_496,Bike,3,Business,3,5,12:06:31 PM,3,5,12:08:00 PM,3,5,12:18:52 PM,3,5,12:20:58 PM,3,5,12:44:00 PM,6,,,-1.282937,36.781698399999996,-1.2551895,36.7822034,Rider_Id_741,1382
-Order_No_2519,User_Id_393,Bike,3,Business,26,1,1:03:25 PM,26,1,1:52:05 PM,26,1,2:08:18 PM,26,1,2:16:35 PM,26,1,2:47:48 PM,10,22.0,0.8,-1.3177546999999998,36.8303703,-1.2574219,36.7927073,Rider_Id_649,1873
-Order_No_2781,User_Id_3291,Bike,3,Business,9,4,10:07:05 AM,9,4,10:07:22 AM,9,4,10:38:06 AM,9,4,10:44:26 AM,9,4,11:09:10 AM,12,23.8,,-1.331619,36.847976,-1.2600926000000001,36.8088685,Rider_Id_415,1484
-Order_No_27992,User_Id_186,Bike,3,Business,24,5,3:38:40 PM,24,5,3:39:23 PM,24,5,3:54:32 PM,24,5,4:00:09 PM,24,5,4:07:53 PM,7,22.9,,-1.2825816,36.8092346,-1.2571472,36.795063299999995,Rider_Id_800,464
-Order_No_2904,User_Id_1141,Bike,3,Business,25,1,2:15:41 PM,25,1,2:49:04 PM,25,1,3:20:38 PM,25,1,3:38:59 PM,25,1,4:08:28 PM,7,,,-1.2877645,36.837853499999994,-1.303149,36.864154,Rider_Id_752,1769
-Order_No_11670,User_Id_1146,Bike,3,Business,23,2,12:41:17 PM,23,2,12:41:52 PM,23,2,12:44:19 PM,23,2,1:05:11 PM,23,2,1:21:22 PM,7,,,-1.3167113000000001,36.8301563,-1.3083266000000002,36.8494756,Rider_Id_571,971
-Order_No_24741,User_Id_3346,Bike,2,Personal,8,2,7:37:07 PM,8,2,7:44:10 PM,8,2,7:44:38 PM,8,2,8:03:15 PM,8,2,8:37:19 PM,11,18.0,0.8,-1.2857241000000001,36.820348100000004,-1.3404187,36.7614685,Rider_Id_805,2044
-Order_No_3230,User_Id_3780,Bike,1,Personal,18,1,10:25:19 AM,18,1,12:00:22 PM,18,1,12:03:16 PM,18,1,12:13:50 PM,18,1,12:27:04 PM,4,,,-1.2868973,36.8232984,-1.2784495,36.7981729,Rider_Id_444,794
-Order_No_22263,User_Id_640,Bike,3,Business,29,5,3:35:19 PM,29,5,3:43:28 PM,29,5,4:14:11 PM,29,5,4:19:04 PM,29,5,4:41:39 PM,7,21.0,,-1.304033,36.7848695,-1.2641723999999999,36.8014198,Rider_Id_208,1355
-Order_No_20934,User_Id_914,Bike,3,Business,2,2,11:19:59 AM,2,2,11:22:29 AM,2,2,11:29:45 AM,2,2,11:46:07 AM,2,2,11:59:27 AM,7,26.3,,-1.2670213,36.7994482,-1.3023305,36.826051899999996,Rider_Id_119,800
-Order_No_7069,User_Id_2987,Bike,3,Business,1,3,1:32:16 PM,1,3,1:32:23 PM,1,3,1:32:48 PM,1,3,2:07:19 PM,1,3,2:25:28 PM,12,23.7,,-1.2702120000000001,36.82108770000001,-1.2156006000000001,36.8916865,Rider_Id_406,1089
-Order_No_20510,User_Id_3080,Bike,3,Business,10,3,4:56:07 PM,10,3,4:56:19 PM,10,3,4:59:21 PM,10,3,5:01:06 PM,10,3,5:41:47 PM,13,27.5,,-1.2551895,36.7822034,-1.3261306000000002,36.850596,Rider_Id_709,2441
-Order_No_22400,User_Id_265,Bike,3,Business,6,1,3:13:11 PM,6,1,3:16:29 PM,6,1,3:31:31 PM,6,1,4:00:54 PM,6,1,4:22:42 PM,8,,,-1.3154845,36.7912475,-1.300921,36.828195,Rider_Id_411,1308
-Order_No_4112,User_Id_517,Bike,3,Business,3,1,10:48:15 AM,3,1,10:48:29 AM,3,1,10:48:55 AM,3,1,10:54:51 AM,3,1,11:13:14 AM,11,21.7,,-1.3228003000000002,36.8306435,-1.2638185,36.7930057,Rider_Id_168,1103
-Order_No_16318,User_Id_2956,Bike,3,Business,10,1,2:32:56 PM,10,1,2:34:12 PM,10,1,2:42:03 PM,10,1,2:47:26 PM,10,1,2:54:02 PM,3,18.1,,-1.2584143,36.8048002,-1.2551895,36.7822034,Rider_Id_937,396
-Order_No_18751,User_Id_136,Bike,3,Business,23,3,3:21:11 PM,23,3,3:36:30 PM,23,3,3:51:32 PM,23,3,4:13:56 PM,23,3,5:33:26 PM,24,22.4,,-1.3326173000000001,36.71462329999999,-1.240248,36.8230472,Rider_Id_226,4770
-Order_No_1313,User_Id_1209,Bike,3,Business,24,3,12:37:29 PM,24,3,12:38:06 PM,24,3,12:45:12 PM,24,3,12:46:28 PM,24,3,12:55:36 PM,6,,,-1.3004062,36.829741,-1.3228003000000002,36.8306435,Rider_Id_206,548
-Order_No_24230,User_Id_1996,Bike,3,Business,18,6,11:25:10 AM,18,6,11:25:28 AM,18,6,11:32:02 AM,18,6,11:34:45 AM,18,6,11:51:25 AM,6,21.2,,-1.2946358999999998,36.8352718,-1.3272104,36.85284179999999,Rider_Id_211,1000
-Order_No_4015,User_Id_3149,Bike,3,Business,8,1,1:30:09 PM,8,1,1:31:12 PM,8,1,1:38:20 PM,8,1,1:47:20 PM,8,1,2:02:34 PM,7,29.8,,-1.3077869,36.844320700000004,-1.3167113000000001,36.8301563,Rider_Id_349,914
-Order_No_28071,User_Id_393,Bike,3,Business,1,5,8:36:15 AM,1,5,8:41:55 AM,1,5,8:52:43 AM,1,5,9:06:42 AM,1,5,9:18:10 AM,4,23.4,,-1.3167113000000001,36.8301563,-1.3004062,36.829741,Rider_Id_328,688
-Order_No_17519,User_Id_1245,Bike,3,Business,18,1,12:49:17 PM,18,1,12:49:30 PM,18,1,12:49:34 PM,18,1,1:33:37 PM,18,1,2:12:56 PM,17,31.0,,-1.300921,36.828195,-1.3198615,36.7110323,Rider_Id_519,2359
-Order_No_13577,User_Id_246,Bike,3,Business,29,1,3:02:26 PM,29,1,3:02:31 PM,29,1,3:10:56 PM,29,1,3:14:13 PM,29,1,3:21:54 PM,1,24.6,,-1.2584143,36.8048002,-1.2597528999999998,36.806629,Rider_Id_620,461
-Order_No_21635,User_Id_968,Bike,2,Personal,26,2,3:43:02 PM,26,2,3:43:38 PM,26,2,4:05:52 PM,26,2,4:20:09 PM,26,2,4:29:30 PM,3,,,-1.2584143,36.8048002,-1.2615888000000002,36.7928732,Rider_Id_840,561
-Order_No_23872,User_Id_136,Bike,3,Business,28,1,1:43:12 PM,28,1,2:04:39 PM,28,1,2:27:35 PM,28,1,2:29:56 PM,28,1,3:18:18 PM,16,26.2,,-1.3254522,36.721658399999995,-1.281614,36.8275424,Rider_Id_332,2902
-Order_No_22934,User_Id_1122,Bike,3,Personal,21,2,11:03:21 AM,21,2,11:21:00 AM,21,2,11:46:40 AM,21,2,12:07:50 PM,21,2,12:19:30 PM,12,22.7,,-1.3244885,36.897791999999995,-1.2882278999999999,36.9682691,Rider_Id_75,700
-Order_No_3931,User_Id_3083,Bike,1,Personal,12,5,12:57:58 PM,12,5,1:32:05 PM,12,5,1:38:45 PM,12,5,1:41:36 PM,12,5,2:09:13 PM,5,27.1,,-1.2524976,36.8062935,-1.280665,36.8154325,Rider_Id_844,1657
-Order_No_19534,User_Id_868,Bike,3,Business,14,2,3:40:05 PM,14,2,3:40:50 PM,14,2,3:51:36 PM,14,2,3:55:24 PM,14,2,4:17:45 PM,6,,,-1.307143,36.825009,-1.265715,36.823815,Rider_Id_351,1341
-Order_No_14098,User_Id_1927,Bike,3,Business,10,4,2:34:51 PM,10,4,2:44:39 PM,10,4,2:55:47 PM,10,4,3:13:22 PM,10,4,3:28:50 PM,10,21.9,1.1,-1.3051608000000001,36.828891399999996,-1.3404187,36.7614685,Rider_Id_742,928
-Order_No_5003,User_Id_3148,Bike,2,Personal,19,3,2:33:33 PM,19,3,2:33:37 PM,19,3,2:48:31 PM,19,3,2:52:12 PM,19,3,3:14:05 PM,5,25.7,,-1.2788027,36.7939088,-1.2868973,36.8232984,Rider_Id_785,1313
-Order_No_13567,User_Id_868,Bike,3,Business,7,2,9:12:47 AM,7,2,9:13:03 AM,7,2,9:15:43 AM,7,2,9:26:35 AM,7,2,9:51:42 AM,9,,,-1.331619,36.847976,-1.265715,36.823815,Rider_Id_709,1507
-Order_No_21008,User_Id_346,Bike,3,Business,15,3,4:18:00 PM,15,3,4:19:56 PM,15,3,4:27:05 PM,15,3,5:06:03 PM,15,3,5:06:12 PM,3,24.9,,-1.28878,36.8168312,-1.2909536,36.8407162,Rider_Id_411,9
-Order_No_17085,User_Id_393,Bike,3,Business,4,6,8:29:30 AM,4,6,8:45:06 AM,4,6,8:56:35 AM,4,6,9:07:32 AM,4,6,9:36:26 AM,14,15.6,,-1.3177546999999998,36.8303703,-1.306378,36.7519845,Rider_Id_345,1734
-Order_No_19210,User_Id_2423,Bike,3,Business,13,3,1:01:30 PM,13,3,1:04:15 PM,13,3,1:11:55 PM,13,3,1:19:34 PM,13,3,1:51:33 PM,5,24.7,,-1.2551895,36.7822034,-1.270054,36.815164,Rider_Id_658,1919
-Order_No_21700,User_Id_3283,Bike,3,Business,29,5,4:15:21 PM,29,5,4:16:12 PM,29,5,4:33:51 PM,29,5,4:48:19 PM,29,5,5:16:31 PM,13,27.9,,-1.331619,36.847976,-1.2628473,36.781805,Rider_Id_61,1692
-Order_No_21775,User_Id_3621,Bike,3,Business,16,6,5:27:07 PM,16,6,5:28:09 PM,16,6,5:36:16 PM,16,6,5:41:47 PM,16,6,5:59:51 PM,12,,,-1.2836583000000001,36.7886014,-1.2533491,36.861591100000005,Rider_Id_427,1084
-Order_No_531,User_Id_1271,Bike,3,Business,27,3,4:20:07 PM,27,3,4:20:41 PM,27,3,4:40:05 PM,27,3,4:46:16 PM,27,3,5:17:54 PM,12,27.5,,-1.3107918,36.761794,-1.3287648,36.744573200000005,Rider_Id_682,1898
-Order_No_20500,User_Id_797,Bike,3,Business,29,3,11:32:38 AM,29,3,11:34:28 AM,29,3,11:41:51 AM,29,3,11:45:40 AM,29,3,12:02:14 PM,7,18.4,,-1.2551895,36.7822034,-1.2949473999999999,36.8270407,Rider_Id_79,994
-Order_No_1306,User_Id_1853,Bike,3,Personal,11,1,10:54:18 AM,11,1,10:54:55 AM,11,1,11:00:39 AM,11,1,11:06:28 AM,11,1,11:18:21 AM,6,24.0,,-1.2983604,36.8074766,-1.274089,36.7997508,Rider_Id_283,713
-Order_No_5645,User_Id_2771,Bike,1,Personal,12,2,12:12:19 PM,12,2,12:12:56 PM,12,2,12:16:34 PM,12,2,12:25:21 PM,12,2,12:45:11 PM,13,20.8,,-1.2811975,36.8109382,-1.2763120000000001,36.882742,Rider_Id_345,1190
-Order_No_27165,User_Id_718,Bike,3,Business,6,4,4:02:37 PM,6,4,4:03:13 PM,6,4,4:12:58 PM,6,4,4:15:26 PM,6,4,5:22:49 PM,19,,,-1.2551895,36.7822034,-1.3325582,36.740669700000005,Rider_Id_925,4043
-Order_No_2446,User_Id_2174,Bike,3,Business,12,5,3:32:13 PM,12,5,3:32:35 PM,12,5,3:32:44 PM,12,5,4:37:48 PM,12,5,4:38:40 PM,15,26.5,,-1.2482781,36.884091999999995,-1.214071,36.7985052,Rider_Id_86,52
-Order_No_15579,User_Id_661,Bike,3,Business,25,3,2:51:22 PM,25,3,4:00:53 PM,25,3,4:09:45 PM,25,3,4:12:51 PM,25,3,4:27:19 PM,4,17.9,,-1.2950044,36.7512209,-1.2963097,36.7688221,Rider_Id_140,868
-Order_No_9378,User_Id_1927,Bike,3,Business,13,3,2:16:52 PM,13,3,2:18:56 PM,13,3,2:24:06 PM,13,3,2:33:13 PM,13,3,3:31:42 PM,12,29.0,,-1.3472066,36.7692638,-1.2811503,36.8235884,Rider_Id_317,3509
-Order_No_16617,User_Id_2974,Bike,3,Business,19,2,12:59:46 PM,19,2,1:00:29 PM,19,2,1:05:45 PM,19,2,1:08:06 PM,19,2,1:51:43 PM,19,27.3,,-1.2944423999999999,36.77979379999999,-1.2038216999999998,36.87945070000001,Rider_Id_129,2617
-Order_No_7397,User_Id_3647,Bike,3,Business,2,5,3:19:04 PM,2,5,3:21:13 PM,2,5,3:27:54 PM,2,5,3:33:38 PM,2,5,4:22:26 PM,8,25.2,,-1.2726389999999999,36.794723,-1.308217,36.77413,Rider_Id_726,2928
-Order_No_20829,User_Id_114,Bike,1,Personal,3,3,1:37:25 PM,3,3,1:39:11 PM,3,3,2:14:29 PM,3,3,3:05:43 PM,3,3,3:46:18 PM,10,26.9,,-1.3025826,36.7670807,-1.3072409999999999,36.830236799999994,Rider_Id_565,2435
-Order_No_24939,User_Id_2279,Bike,3,Business,28,5,12:37:51 PM,28,5,1:02:59 PM,28,5,1:36:29 PM,28,5,2:03:23 PM,28,5,2:55:08 PM,20,25.3,,-1.1708105,36.8336102,-1.2571472,36.795063299999995,Rider_Id_882,3105
-Order_No_19104,User_Id_1721,Bike,3,Business,25,5,8:16:22 AM,25,5,8:17:09 AM,25,5,8:37:24 AM,25,5,9:40:47 AM,25,5,9:50:05 AM,2,20.6,,-1.2571472,36.795063299999995,-1.2644693999999999,36.8107125,Rider_Id_290,558
-Order_No_16769,User_Id_2350,Bike,1,Personal,24,1,10:20:36 AM,24,1,10:22:04 AM,24,1,10:40:02 AM,24,1,11:01:01 AM,24,1,11:13:26 AM,4,22.5,,-1.293886,36.7596951,-1.2977713,36.7841682,Rider_Id_565,745
-Order_No_7045,User_Id_412,Bike,3,Business,10,5,12:41:34 PM,10,5,12:42:09 PM,10,5,12:48:40 PM,10,5,1:09:41 PM,10,5,1:10:03 PM,3,,,-1.265715,36.823815,-1.266837,36.799248999999996,Rider_Id_436,22
-Order_No_10434,User_Id_2330,Bike,3,Business,31,4,2:41:13 PM,31,4,2:41:42 PM,31,4,2:42:05 PM,31,4,2:50:30 PM,31,4,2:58:21 PM,2,28.8,,-1.2642903,36.80021370000001,-1.2695287,36.798177700000004,Rider_Id_219,471
-Order_No_2229,User_Id_2956,Bike,3,Business,16,4,1:57:51 PM,16,4,1:58:10 PM,16,4,2:04:41 PM,16,4,2:13:20 PM,16,4,2:40:16 PM,9,,,-1.3014461000000002,36.7661381,-1.2584143,36.8048002,Rider_Id_535,1616
-Order_No_582,User_Id_3727,Bike,3,Business,10,4,4:15:11 PM,10,4,4:15:28 PM,10,4,4:30:32 PM,10,4,4:32:47 PM,10,4,4:42:14 PM,3,23.3,,-1.2963097,36.7688221,-1.293393,36.790237,Rider_Id_862,567
-Order_No_17292,User_Id_3355,Bike,3,Business,15,2,3:38:15 PM,15,2,3:39:09 PM,15,2,3:48:07 PM,15,2,3:53:45 PM,15,2,4:19:17 PM,7,24.4,,-1.2571472,36.795063299999995,-1.3052491000000002,36.8223899,Rider_Id_817,1532
-Order_No_23918,User_Id_2174,Bike,3,Business,7,1,10:54:35 AM,7,1,10:54:38 AM,7,1,10:54:51 AM,7,1,11:32:44 AM,7,1,12:19:21 PM,33,24.8,,-1.2482781,36.884091999999995,-1.324878,36.7070285,Rider_Id_12,2797
-Order_No_11576,User_Id_1217,Bike,3,Personal,14,1,8:21:04 AM,14,1,8:21:35 AM,14,1,8:26:29 AM,14,1,8:35:33 AM,14,1,8:53:41 AM,12,18.8,0.3,-1.2886685,36.8297138,-1.2251345,36.8835216,Rider_Id_940,1088
-Order_No_12315,User_Id_3050,Bike,3,Business,3,3,4:55:44 PM,3,3,4:56:38 PM,3,3,4:58:03 PM,3,3,6:30:03 PM,3,3,7:00:06 PM,16,27.2,,-1.2945073999999999,36.8083195,-1.343062,36.709497,Rider_Id_539,1803
-Order_No_18378,User_Id_2200,Bike,3,Business,14,4,4:00:00 PM,14,4,4:02:35 PM,14,4,4:11:50 PM,14,4,4:13:58 PM,14,4,5:02:52 PM,4,27.8,,-1.3041128999999998,36.810493900000004,-1.290894,36.822971,Rider_Id_336,2934
-Order_No_17149,User_Id_3262,Bike,3,Personal,12,2,5:12:58 PM,12,2,5:13:54 PM,12,2,5:28:26 PM,12,2,5:40:40 PM,12,2,6:27:40 PM,36,28.6,,-1.306219,36.869912400000004,-1.0921598999999997,37.005025,Rider_Id_675,2820
-Order_No_4716,User_Id_3765,Bike,1,Personal,17,1,10:53:16 AM,17,1,10:53:33 AM,17,1,11:38:36 AM,17,1,11:56:32 AM,17,1,12:34:32 PM,18,25.2,,-1.3124921999999999,36.880497399999996,-1.2800648000000001,36.762267,Rider_Id_620,2280
-Order_No_9310,User_Id_2789,Bike,3,Business,20,3,12:10:28 PM,20,3,12:11:08 PM,20,3,12:38:37 PM,20,3,12:43:48 PM,20,3,2:40:19 PM,14,26.3,,-1.304735,36.784605400000004,-1.2333023,36.779419399999995,Rider_Id_362,6991
-Order_No_5083,User_Id_2330,Bike,3,Business,12,2,9:07:28 AM,12,2,9:07:51 AM,12,2,9:14:15 AM,12,2,9:15:20 AM,12,2,9:36:57 AM,11,17.9,,-1.3015197,36.7658456,-1.2687407,36.7373043,Rider_Id_847,1297
-Order_No_19169,User_Id_1932,Bike,1,Business,28,6,12:41:33 PM,28,6,12:41:52 PM,28,6,1:02:55 PM,28,6,1:21:36 PM,28,6,1:40:34 PM,13,24.1,,-1.2498583,36.775496600000004,-1.2922857,36.781327399999995,Rider_Id_180,1138
-Order_No_16913,User_Id_2154,Bike,3,Business,29,5,1:16:42 PM,29,5,1:18:58 PM,29,5,1:20:22 PM,29,5,1:26:04 PM,29,5,1:54:14 PM,16,26.6,,-1.339046,36.762596,-1.2765736,36.851364600000004,Rider_Id_900,1690
-Order_No_17573,User_Id_3222,Bike,3,Business,16,4,11:43:42 AM,16,4,11:44:35 AM,16,4,11:50:37 AM,16,4,11:56:35 AM,16,4,12:25:26 PM,9,,,-1.2551895,36.7822034,-1.2604472,36.8365287,Rider_Id_155,1731
-Order_No_23248,User_Id_2869,Bike,3,Business,5,3,1:55:02 PM,5,3,1:55:16 PM,5,3,2:04:02 PM,5,3,2:08:40 PM,5,3,2:28:39 PM,9,18.8,0.2,-1.2649605,36.798177700000004,-1.3025826,36.7670807,Rider_Id_96,1199
-Order_No_7648,User_Id_2149,Bike,1,Personal,19,5,3:39:50 PM,19,5,3:43:46 PM,19,5,3:54:00 PM,19,5,3:56:42 PM,19,5,4:21:46 PM,9,27.6,,-1.2675332000000001,36.808561,-1.3086326000000001,36.8080886,Rider_Id_45,1504
-Order_No_13825,User_Id_867,Bike,3,Business,22,4,12:19:45 PM,22,4,1:03:25 PM,22,4,1:14:59 PM,22,4,1:26:09 PM,22,4,1:49:54 PM,10,22.4,,-1.2996533000000001,36.829262899999996,-1.306378,36.7519845,Rider_Id_878,1425
-Order_No_3210,User_Id_1304,Bike,3,Business,7,3,1:37:02 PM,7,3,1:37:33 PM,7,3,1:54:39 PM,7,3,2:01:28 PM,7,3,2:55:33 PM,18,28.3,,-1.3332749,36.870814700000004,-1.2343236000000002,36.8861,Rider_Id_114,3245
-Order_No_14373,User_Id_907,Bike,2,Personal,31,4,8:48:00 AM,31,4,8:48:33 AM,31,4,9:02:53 AM,31,4,9:04:44 AM,31,4,9:13:09 AM,3,17.6,,-1.2913246,36.8019522,-1.2998459999999998,36.7940155,Rider_Id_698,505
-Order_No_2373,User_Id_393,Bike,3,Business,20,3,2:05:12 PM,20,3,4:02:22 PM,20,3,4:11:41 PM,20,3,4:28:17 PM,20,3,4:47:58 PM,7,23.4,,-1.3122473999999997,36.8728544,-1.3177546999999998,36.8303703,Rider_Id_445,1181
-Order_No_19183,User_Id_2457,Bike,3,Business,8,3,10:37:33 AM,8,3,10:37:56 AM,8,3,10:38:05 AM,8,3,10:56:00 AM,8,3,12:04:42 PM,8,22.4,,-1.2551895,36.7822034,-1.3016151000000002,36.811735399999996,Rider_Id_101,4122
-Order_No_3218,User_Id_1642,Bike,3,Business,24,3,2:14:44 PM,24,3,2:15:01 PM,24,3,2:23:34 PM,24,3,2:50:28 PM,24,3,3:22:05 PM,12,18.5,,-1.2308211000000002,36.867904100000004,-1.2728069,36.7962362,Rider_Id_96,1897
-Order_No_13607,User_Id_3201,Bike,3,Business,16,2,10:21:18 AM,16,2,10:23:01 AM,16,2,10:31:39 AM,16,2,10:34:49 AM,16,2,10:50:02 AM,9,,,-1.2584143,36.8048002,-1.2912540000000001,36.765724,Rider_Id_72,913
-Order_No_165,User_Id_1010,Bike,3,Personal,9,4,11:15:04 AM,9,4,11:16:48 AM,9,4,11:22:03 AM,9,4,11:33:20 AM,9,4,11:43:18 AM,3,24.0,,-1.254191,36.809913200000004,-1.2463211,36.789487,Rider_Id_415,598
-Order_No_18790,User_Id_2174,Bike,3,Business,22,4,10:25:54 AM,22,4,10:26:12 AM,22,4,10:41:48 AM,22,4,11:05:44 AM,22,4,11:43:49 AM,21,22.5,,-1.2482781,36.884091999999995,-1.3205111,36.7801016,Rider_Id_12,2285
-Order_No_748,User_Id_540,Bike,3,Business,19,3,2:24:53 PM,19,3,2:42:06 PM,19,3,2:51:20 PM,19,3,3:06:30 PM,19,3,4:20:05 PM,6,24.3,,-1.2649605,36.798177700000004,-1.2940713,36.7890758,Rider_Id_554,4415
-Order_No_18745,User_Id_2841,Bike,1,Personal,25,1,12:46:11 PM,25,1,12:49:08 PM,25,1,12:59:08 PM,25,1,1:01:58 PM,25,1,1:23:18 PM,9,,,-1.2639254,36.81269520000001,-1.3044625,36.8543915,Rider_Id_88,1280
-Order_No_2686,User_Id_53,Bike,3,Business,22,5,10:04:47 AM,22,5,10:06:26 AM,22,5,10:19:02 AM,22,5,10:52:36 AM,22,5,11:10:12 AM,5,28.7,,-1.3004062,36.829741,-1.2770708,36.8231093,Rider_Id_812,1056
-Order_No_6889,User_Id_391,Bike,3,Business,19,3,12:30:21 PM,19,3,1:00:50 PM,19,3,1:07:52 PM,19,3,1:25:02 PM,19,3,1:58:48 PM,5,22.0,,-1.300921,36.828195,-1.2867454,36.8259017,Rider_Id_276,2026
-Order_No_19720,User_Id_496,Bike,3,Business,30,2,11:51:31 AM,30,2,12:33:06 PM,30,2,12:36:49 PM,30,2,1:12:48 PM,30,2,1:55:06 PM,8,,,-1.2551895,36.7822034,-1.2958261,36.8304671,Rider_Id_119,2538
-Order_No_4563,User_Id_3355,Bike,3,Business,31,2,11:49:05 AM,31,2,11:55:19 AM,31,2,12:24:50 PM,31,2,12:55:22 PM,31,2,1:10:47 PM,10,18.3,,-1.3472066,36.7692638,-1.3052491000000002,36.8223899,Rider_Id_414,925
-Order_No_19207,User_Id_1688,Bike,3,Business,7,3,1:21:54 PM,7,3,1:22:12 PM,7,3,1:29:01 PM,7,3,2:06:13 PM,7,3,2:44:48 PM,19,28.3,,-1.300921,36.828195,-1.3916458999999999,36.93058920000001,Rider_Id_208,2315
-Order_No_12940,User_Id_2066,Bike,3,Business,4,5,2:36:10 PM,4,5,2:39:19 PM,4,5,2:53:16 PM,4,5,3:03:02 PM,4,5,3:26:16 PM,12,24.1,,-1.316961,36.820337,-1.3270790000000001,36.881469,Rider_Id_101,1394
-Order_No_14214,User_Id_2412,Bike,3,Business,2,2,12:03:05 PM,2,2,12:03:25 PM,2,2,12:03:41 PM,2,2,12:08:29 PM,2,2,12:30:59 PM,13,21.0,,-1.2571472,36.795063299999995,-1.2297202,36.881950700000004,Rider_Id_31,1350
-Order_No_13995,User_Id_248,Bike,3,Business,9,5,11:25:56 AM,9,5,11:27:17 AM,9,5,11:34:58 AM,9,5,11:48:02 AM,9,5,12:04:00 PM,8,25.9,,-1.304735,36.784605400000004,-1.2729652,36.8064396,Rider_Id_726,958
-Order_No_24651,User_Id_2870,Bike,1,Personal,7,2,9:01:27 AM,7,2,9:01:42 AM,7,2,9:11:21 AM,7,2,9:14:12 AM,7,2,9:36:46 AM,13,16.8,,-1.2592428,36.7894541,-1.32751,36.828812,Rider_Id_754,1354
-Order_No_8792,User_Id_2829,Bike,3,Business,12,5,1:24:14 PM,12,5,1:24:52 PM,12,5,1:31:55 PM,12,5,1:36:12 PM,12,5,2:18:47 PM,20,27.3,,-1.2571472,36.795063299999995,-1.3632162,36.9108253,Rider_Id_532,2555
-Order_No_9318,User_Id_2587,Bike,3,Business,7,4,12:29:48 PM,7,4,12:32:09 PM,7,4,12:45:28 PM,7,4,12:49:06 PM,7,4,1:10:12 PM,5,24.2,,-1.2765736,36.851364600000004,-1.28878,36.8168312,Rider_Id_513,1266
-Order_No_18762,User_Id_3701,Bike,3,Business,8,3,3:33:31 PM,8,3,3:33:54 PM,8,3,3:39:57 PM,8,3,3:40:57 PM,8,3,4:03:07 PM,8,,,-1.2612573999999999,36.7879622,-1.300722,36.816865,Rider_Id_520,1330
-Order_No_24065,User_Id_3692,Bike,2,Personal,13,5,5:28:28 PM,13,5,5:29:49 PM,13,5,5:40:46 PM,13,5,5:43:33 PM,13,5,5:58:45 PM,9,19.1,,-1.2738936,36.8035805,-1.240218,36.766816,Rider_Id_379,912
-Order_No_23471,User_Id_2350,Bike,1,Personal,24,4,10:25:04 AM,24,4,10:27:29 AM,24,4,10:33:15 AM,24,4,10:39:42 AM,24,4,10:55:18 AM,9,21.4,,-1.3114711000000001,36.8088708,-1.2745107,36.797450299999994,Rider_Id_735,936
-Order_No_21684,User_Id_346,Bike,3,Business,18,5,4:30:58 PM,18,5,4:31:59 PM,18,5,4:33:42 PM,18,5,4:44:07 PM,18,5,4:47:06 PM,1,25.3,,-1.28878,36.8168312,-1.279395,36.825364,Rider_Id_237,179
-Order_No_2012,User_Id_3149,Bike,3,Business,21,5,9:52:33 AM,21,5,9:58:36 AM,21,5,10:42:38 AM,21,5,10:48:51 AM,21,5,12:14:33 PM,9,23.0,,-1.2770708,36.8231093,-1.3228003000000002,36.8306435,Rider_Id_26,5142
-Order_No_884,User_Id_3291,Bike,3,Business,2,6,1:13:33 PM,2,6,1:27:34 PM,2,6,1:31:16 PM,2,6,1:38:02 PM,2,6,1:47:40 PM,5,31.1,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_291,578
-Order_No_18598,User_Id_2415,Bike,1,Personal,23,6,8:54:24 AM,23,6,8:59:07 AM,23,6,9:08:45 AM,23,6,9:10:29 AM,23,6,9:26:55 AM,9,,,-1.2864025,36.8175763,-1.3272104,36.85284179999999,Rider_Id_752,986
-Order_No_12305,User_Id_1192,Bike,1,Personal,13,5,1:20:57 PM,13,5,1:23:47 PM,13,5,2:04:38 PM,13,5,2:11:52 PM,13,5,2:51:17 PM,6,19.1,,-1.2857521,36.82774570000001,-1.2941023999999999,36.79578,Rider_Id_313,2365
-Order_No_20195,User_Id_246,Bike,3,Business,1,7,1:51:32 PM,1,7,1:51:47 PM,1,7,2:18:09 PM,1,7,2:27:26 PM,1,7,2:40:08 PM,15,23.2,,-1.2297202,36.874550799999994,-1.2584143,36.8048002,Rider_Id_265,762
-Order_No_4785,User_Id_393,Bike,3,Business,23,3,10:49:47 AM,23,3,10:49:55 AM,23,3,10:52:56 AM,23,3,11:18:30 AM,23,3,11:18:31 AM,5,,,-1.3167113000000001,36.8301563,-1.3077869,36.844320700000004,Rider_Id_523,1
-Order_No_13985,User_Id_3686,Bike,3,Business,8,5,3:11:05 PM,8,5,3:11:37 PM,8,5,3:27:26 PM,8,5,3:31:36 PM,8,5,4:39:31 PM,20,27.0,,-1.2551895,36.7822034,-1.3450912,36.897266200000004,Rider_Id_205,4075
-Order_No_12918,User_Id_2200,Bike,3,Business,18,2,1:32:46 PM,18,2,1:33:09 PM,18,2,1:44:02 PM,18,2,1:51:06 PM,18,2,2:38:59 PM,11,25.8,,-1.2927792,36.9053492,-1.290894,36.822971,Rider_Id_231,2873
-Order_No_26909,User_Id_3291,Bike,3,Business,17,3,4:21:38 PM,17,3,4:21:55 PM,17,3,4:24:38 PM,17,3,4:31:08 PM,17,3,4:43:18 PM,5,,,-1.2628473,36.781805,-1.2600926000000001,36.8088685,Rider_Id_161,730
-Order_No_7481,User_Id_3283,Bike,3,Business,17,5,10:43:19 AM,17,5,10:53:07 AM,17,5,11:07:38 AM,17,5,11:16:41 AM,17,5,11:38:51 AM,6,17.4,,-1.272828,36.816608,-1.2628473,36.781805,Rider_Id_322,1330
-Order_No_6741,User_Id_3641,Bike,3,Business,15,2,3:49:14 PM,15,2,3:53:00 PM,15,2,3:57:14 PM,15,2,4:01:29 PM,15,2,4:38:35 PM,12,24.6,,-1.2551895,36.7822034,-1.3276043,36.836921999999994,Rider_Id_131,2226
-Order_No_5640,User_Id_1500,Bike,3,Business,4,4,8:23:25 AM,4,4,8:23:35 AM,4,4,8:23:42 AM,4,4,8:39:15 AM,4,4,8:55:03 AM,8,22.0,,-1.3004062,36.829741,-1.2638185,36.7930057,Rider_Id_843,948
-Order_No_6895,User_Id_1424,Bike,3,Business,18,4,2:19:44 PM,18,4,2:20:13 PM,18,4,2:22:05 PM,18,4,2:26:52 PM,18,4,2:33:10 PM,2,,,-1.3015700000000001,36.7871,-1.2990348999999999,36.8030545,Rider_Id_132,378
-Order_No_2707,User_Id_3560,Bike,3,Business,12,2,2:53:28 PM,12,2,3:01:52 PM,12,2,3:13:45 PM,12,2,3:51:45 PM,12,2,4:49:25 PM,5,,,-1.2571472,36.795063299999995,-1.279395,36.825364,Rider_Id_763,3460
-Order_No_12042,User_Id_2982,Bike,3,Business,24,3,9:01:46 AM,24,3,9:10:05 AM,24,3,9:35:30 AM,24,3,10:05:36 AM,24,3,10:39:57 AM,18,,,-1.300921,36.828195,-1.177202,36.8351183,Rider_Id_487,2061
-Order_No_15747,User_Id_2383,Bike,3,Business,3,3,9:28:58 AM,3,3,9:30:40 AM,3,3,9:50:50 AM,3,3,9:52:25 AM,3,3,10:00:35 AM,2,21.5,,-1.2608993,36.799246000000004,-1.2516391000000002,36.8114886,Rider_Id_487,490
-Order_No_6702,User_Id_157,Bike,3,Business,9,2,3:27:18 PM,9,2,3:37:03 PM,9,2,4:12:56 PM,9,2,4:16:15 PM,9,2,4:37:45 PM,8,30.7,,-1.225322,36.80855,-1.2615888000000002,36.7928732,Rider_Id_668,1290
-Order_No_9033,User_Id_2777,Bike,3,Business,18,5,4:08:41 PM,18,5,4:09:07 PM,18,5,4:12:57 PM,18,5,4:22:31 PM,18,5,5:39:34 PM,15,27.0,,-1.3024388,36.859699,-1.2551895,36.7822034,Rider_Id_456,4623
-Order_No_23667,User_Id_1796,Bike,3,Business,24,3,9:36:29 AM,24,3,9:43:25 AM,24,3,9:55:11 AM,24,3,10:02:08 AM,24,3,10:59:31 AM,11,,,-1.265715,36.823815,-1.331619,36.847976,Rider_Id_13,3443
-Order_No_18451,User_Id_467,Bike,3,Business,8,3,9:21:39 AM,8,3,9:22:12 AM,8,3,9:31:43 AM,8,3,9:34:59 AM,8,3,9:57:06 AM,7,,,-1.304735,36.784605400000004,-1.2619185,36.759662,Rider_Id_548,1327
-Order_No_3391,User_Id_2828,Bike,3,Business,3,2,4:12:20 PM,3,2,4:16:02 PM,3,2,4:21:59 PM,3,2,4:33:01 PM,3,2,5:46:46 PM,15,18.9,,-1.2615888000000002,36.7928732,-1.3248812,36.859699,Rider_Id_279,4425
-Order_No_3761,User_Id_2607,Bike,2,Personal,30,6,3:09:05 PM,30,6,4:07:15 PM,30,6,4:20:48 PM,30,6,4:30:39 PM,30,6,4:50:30 PM,8,23.8,,-1.2917966,36.7730221,-1.304119,36.819649,Rider_Id_844,1191
-Order_No_26033,User_Id_2956,Bike,3,Business,14,2,12:47:40 PM,14,2,12:48:29 PM,14,2,1:40:51 PM,14,2,1:50:40 PM,14,2,2:02:05 PM,6,,,-1.279395,36.825364,-1.2584143,36.8048002,Rider_Id_448,685
-Order_No_18585,User_Id_867,Bike,3,Business,14,4,10:23:23 AM,14,4,10:23:32 AM,14,4,10:23:45 AM,14,4,10:38:56 AM,14,4,11:11:38 AM,15,23.7,,-1.2991441000000001,36.7528804,-1.3228003000000002,36.8306435,Rider_Id_39,1962
-Order_No_2543,User_Id_2660,Bike,2,Personal,7,1,5:31:14 PM,7,1,5:32:19 PM,7,1,5:42:55 PM,7,1,5:47:32 PM,7,1,6:28:00 PM,15,19.8,,-1.2596351000000001,36.8063205,-1.326825,36.864345,Rider_Id_789,2428
-Order_No_471,User_Id_517,Bike,3,Business,31,5,11:19:01 AM,31,5,11:19:36 AM,31,5,11:22:05 AM,31,5,11:25:06 AM,31,5,11:53:46 AM,14,21.6,,-1.3228003000000002,36.8306435,-1.2991441000000001,36.7528804,Rider_Id_211,1720
diff --git a/utils/request.py b/utils/request.py
deleted file mode 100644
index 5048604..0000000
--- a/utils/request.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""
-
-    Simple Script to test the API once deployed
-
-    Author: Explore Data Science Academy.
-
-    Note:
-    ---------------------------------------------------------------------
-    Plase follow the instructions provided within the README.md file
-    located at the root of this repo for guidance on how to use this
-    script correctly.
-    ----------------------------------------------------------------------
-
-    Description: This file contains code used to formulate a POST request
-    which can be used to develop/debug the Model API once it has been
-    deployed.
-
-"""
-
-# Import dependencies
-import requests
-import pandas as pd
-import numpy as np
-
-# Load data from file to send as an API POST request.
-# We prepare a DataFrame with the public test set + riders data
-# from the Zindi challenge.
-test = pd.read_csv('data/test_data.csv')
-riders = pd.read_csv('data/riders.csv')
-test = test.merge(riders, how='left', on='Rider Id')
-
-# Convert our DataFrame to a JSON string.
-# This step is necessary in order to transmit our data via HTTP/S
-feature_vector_json = test.iloc[1].to_json()
-
-# Specify the URL at which the API will be hosted.
-# NOTE: When testing your instance of the API on a remote machine
-# replace the URL below with its public IP:
-
-# url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-#url = 'http://34.240.251.83:5000/api_v0.1'
-url = 'http://0.0.0.0:5000/api_v0.1'
-
-# Perform the POST request.
-print(f"Sending POST request to web server API at: {url}")
-print("")
-print(f"Querying API with the following data: \n {test.iloc[1].to_list()}")
-print("")
-# Here `api_response` represents the response we get from our API
-api_response = requests.post(url, json=feature_vector_json)
-
-# Display the prediction result
-print("Received POST response:")
-print("*"*50)
-print(f"API prediction result: {api_response.json()}")
-print(f"The response took: {api_response.elapsed.total_seconds()} seconds")
-print("*"*50)
diff --git a/utils/train_model.py b/utils/train_model.py
deleted file mode 100644
index d9638fb..0000000
--- a/utils/train_model.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""
-    Simple file to create a Sklearn model for deployment in our API
-
-    Author: Explore Data Science Academy
-
-    Description: This script is responsible for training a simple linear
-    regression model which is used within the API for initial demonstration
-    purposes.
-
-"""
-
-# Dependencies
-import pandas as pd
-import pickle
-from sklearn.linear_model import LinearRegression
-
-# Fetch training data and preprocess for modeling
-train = pd.read_csv('data/train_data.csv')
-riders = pd.read_csv('data/riders.csv')
-train = train.merge(riders, how='left', on='Rider Id')
-
-y_train = train[['Time from Pickup to Arrival']]
-X_train = train[['Pickup Lat','Pickup Long',
-                 'Destination Lat','Destination Long']]
-
-# Fit model
-lm_regression = LinearRegression(normalize=True)
-print ("Training Model...")
-lm_regression.fit(X_train, y_train)
-
-# Pickle model for use within our API
-save_path = '../trained-models/sendy_simple_lm_regression.pkl'
-print (f"Training completed. Saving model to: {save_path}")
-pickle.dump(lm_regression, open(save_path,'wb'))