From 836ef94946ded456f21c2ff91463bc49fd66f6c6 Mon Sep 17 00:00:00 2001
From: parthvijay27 <104063672+parthvijay27@users.noreply.github.com>
Date: Mon, 10 Jul 2023 16:04:12 +0530
Subject: [PATCH] Add files via upload

---
 Graded Assignment 2/ass_2_final.ipynb | 998 ++++++++++++++++++++++++++
 1 file changed, 998 insertions(+)
 create mode 100644 Graded Assignment 2/ass_2_final.ipynb

diff --git a/Graded Assignment 2/ass_2_final.ipynb b/Graded Assignment 2/ass_2_final.ipynb
new file mode 100644
index 0000000..70ae503
--- /dev/null
+++ b/Graded Assignment 2/ass_2_final.ipynb	
@@ -0,0 +1,998 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a639aedd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "653a3482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp      value\n",
+       "0      02-12-2013 21:15  73.967322\n",
+       "1      02-12-2013 21:20  74.935882\n",
+       "2      02-12-2013 21:25  76.124162\n",
+       "3      02-12-2013 21:30  78.140707\n",
+       "4      02-12-2013 21:35  79.329836\n",
+       "...                 ...        ...\n",
+       "22690  19-02-2014 15:05  98.185415\n",
+       "22691  19-02-2014 15:10  97.804168\n",
+       "22692  19-02-2014 15:15  97.135468\n",
+       "22693  19-02-2014 15:20  98.056852\n",
+       "22694  19-02-2014 15:25  96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_path = Path(\"/Users/vivekvijay/Downloads/machine_temp_failure_missing.csv\")\n",
+    "df = pd.read_csv(file_path)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7d332df3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 22695 entries, 0 to 22694\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   timestamp  22695 non-null  object \n",
+      " 1   value      21410 non-null  float64\n",
+      "dtypes: float64(1), object(1)\n",
+      "memory usage: 354.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b044233b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21410.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>85.968188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13.553070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.084721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>83.082867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>89.251290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>93.910748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>108.510543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              value\n",
+       "count  21410.000000\n",
+       "mean      85.968188\n",
+       "std       13.553070\n",
+       "min        2.084721\n",
+       "25%       83.082867\n",
+       "50%       89.251290\n",
+       "75%       93.910748\n",
+       "max      108.510543"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "425bd488",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp  temperature\n",
+       "0      02-12-2013 21:15    73.967322\n",
+       "1      02-12-2013 21:20    74.935882\n",
+       "2      02-12-2013 21:25    76.124162\n",
+       "3      02-12-2013 21:30    78.140707\n",
+       "4      02-12-2013 21:35    79.329836\n",
+       "...                 ...          ...\n",
+       "22690  19-02-2014 15:05    98.185415\n",
+       "22691  19-02-2014 15:10    97.804168\n",
+       "22692  19-02-2014 15:15    97.135468\n",
+       "22693  19-02-2014 15:20    98.056852\n",
+       "22694  19-02-2014 15:25    96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3f02950a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['timestamp', 'temperature'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bfee7da3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>delta_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "      <td>0.968560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "      <td>1.188280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "      <td>2.016546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "      <td>1.189128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "      <td>-0.619417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22689</th>\n",
+       "      <td>19-02-2014 15:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "      <td>-0.381246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "      <td>-0.668700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "      <td>0.921384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "      <td>-1.152991</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22694 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp  temperature  delta_temperature\n",
+       "0      02-12-2013 21:15    73.967322           0.968560\n",
+       "1      02-12-2013 21:20    74.935882           1.188280\n",
+       "2      02-12-2013 21:25    76.124162           2.016546\n",
+       "3      02-12-2013 21:30    78.140707           1.189128\n",
+       "4      02-12-2013 21:35    79.329836          -0.619417\n",
+       "...                 ...          ...                ...\n",
+       "22689  19-02-2014 15:00          NaN                NaN\n",
+       "22690  19-02-2014 15:05    98.185415          -0.381246\n",
+       "22691  19-02-2014 15:10    97.804168          -0.668700\n",
+       "22692  19-02-2014 15:15    97.135468           0.921384\n",
+       "22693  19-02-2014 15:20    98.056852          -1.152991\n",
+       "\n",
+       "[22694 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['delta_temperature'] = df['temperature'].shift(-1) - df['temperature']\n",
+    "df = df[:-1]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0863f03d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = df['timestamp']\n",
+    "y = df['temperature']\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"TimeStamps\")\n",
+    "plt.ylabel(\"Temperature\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c8599495",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = df['timestamp']\n",
+    "y = df['delta_temperature']\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"TimeStamps\")\n",
+    "plt.ylabel(\"Temperature\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3e8419a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "above_threshold = df[df['temperature'] > 70.00]\n",
+    "below_threshold = df[df['temperature'] <= 70.00]\n",
+    "\n",
+    "\n",
+    "plt.scatter(above_threshold['timestamp'], above_threshold['temperature'], color='red', label='Above 70.00')\n",
+    "\n",
+    "\n",
+    "plt.scatter(below_threshold['timestamp'], below_threshold['temperature'], color='blue', label='Below 70.00')\n",
+    "\n",
+    "\n",
+    "plt.xlabel('TimeStamps')\n",
+    "plt.ylabel('Temperature')\n",
+    "plt.legend()\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c0df8dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_dataset(file, date_col=None):\n",
+    "    '''\n",
+    "    folder: is a Path object\n",
+    "    file: the CSV filename in that Path object. \n",
+    "    date_col: specify a date_col to use as index_col \n",
+    "    \n",
+    "    returns: a pandas DataFrame with a DatetimeIndex\n",
+    "    '''\n",
+    "    df = pd.read_csv(file, \n",
+    "                     index_col=date_col, \n",
+    "                     parse_dates=[date_col])\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57ef107e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_dfs(df1, df2, col, title=None, xlabel=None, ylabel=None):\n",
+    "    '''\t\n",
+    "    df1: original dataframe without missing data\n",
+    "    df2: dataframe with missing data\n",
+    "    col: column name that contains missing data\n",
+    "    '''    \n",
+    "    df_missing = df2.rename(columns={col: 'missing'})\n",
+    "    \n",
+    "    columns = df_missing.loc[:, 'missing':].columns.tolist()\n",
+    "    subplots_size = len(columns)\n",
+    "    \n",
+    "    # subplots_size = df2.shape[1]\n",
+    "    fig, ax = plt.subplots(subplots_size+1, 1, sharex=True)\n",
+    "    plt.subplots_adjust(hspace=0.25)\n",
+    "    fig.suptitle = title \n",
+    "    \n",
+    "    df1[col].plot(ax=ax[0], figsize=(10, 16))\n",
+    "    ax[0].set_title('Original Dataset')\n",
+    "    ax[0].set_xlabel(xlabel)\n",
+    "    ax[0].set_ylabel(ylabel)    \n",
+    "    \n",
+    "    for i, colname in enumerate(columns):\n",
+    "        df_missing[colname].plot(ax=ax[i+1])\n",
+    "        ax[i+1].set_title(colname)\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6105403",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rmse_score(df1, df2, col=None):\n",
+    "    '''\n",
+    "    df1: original dataframe without missing data\n",
+    "    df2: dataframe with missing data\n",
+    "    col: column name that contains missing data\n",
+    "\n",
+    "    returns: a list of scores\n",
+    "    '''\n",
+    "    df_missing = df2.rename(columns={col: 'missing'})\n",
+    "    columns = df_missing.loc[:, 'missing':].columns.tolist()\n",
+    "    scores = []\n",
+    "    for comp_col in columns[1:]:\n",
+    "        rmse = np.sqrt(np.mean((df1[col] - df_missing[comp_col])**2))\n",
+    "        scores.append(rmse)\n",
+    "        print(f'RMSE for {comp_col}: {rmse}')\n",
+    "    return scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac4e3d1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4596cb9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file1 = \"/Users/vivekvijay/Downloads/machine_temp_failure_missing.csv\"\n",
+    "file2 =  \"/Users/vivekvijay/Downloads/machine_temp_failure_original.csv\"\n",
+    "df_missing = read_dataset(file1, 'timestamp')\n",
+    "df_original = read_dataset(file2, 'timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6deb878",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df_original.rename(columns ={\"value\":\"temperature\"},inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bee6bdfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfb38112",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a1bd3b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0ec6158",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dfs(df_original, \n",
+    "         df_missing, \n",
+    "         'temperature',\n",
+    "         title=\"Temp vs Time\",\n",
+    "         xlabel=\"Timestamp\",\n",
+    "         ylabel=\"Temperature\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41469ff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing['ffill'] = df_missing['temperature'].fillna(method='ffill')\n",
+    "df_missing['bfill'] = df_missing['temperature'].fillna(method='bfill')\n",
+    "df_missing['mean'] = df_missing['temperature'].fillna(df_missing['temperature'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c77f88d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = rmse_score(df_original, \n",
+    "                    df_missing, \n",
+    "                    'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11402c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dfs(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b130295f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "df_missing = read_dataset(file1, 'timestamp')\n",
+    "df_original = read_dataset(file2, 'timestamp')\n",
+    "df_missing.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df_original.rename(columns ={\"value\":\"temperature\"},inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5e220f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strategy = [\n",
+    "    ('Mean Strategy', 'mean'),\n",
+    "    ('Median Strategy', 'median'),\n",
+    "    ('Most Frequent Strategy', 'most_frequent')]\n",
+    "temp_vals = df_missing['temperature'].values.reshape(-1,1)\n",
+    "\n",
+    "for s_name, s in strategy:\n",
+    "    df_missing[s_name] = (\n",
+    "        SimpleImputer(strategy=s).fit_transform(temp_vals))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a71db380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8550b180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = rmse_score(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d9d2cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dfs(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b06e0626",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7de54d50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import scipy.stats as stats\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=df_missing['temperature'])\n",
+    "plt.title('Box Plot - Temperature')\n",
+    "plt.xlabel('Temperature')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95127734",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxenplot(data=df_missing['temperature'])\n",
+    "plt.title('Boxen Plot - Temperature')\n",
+    "plt.xlabel('Temperature')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f72ce78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "pd.plotting.lag_plot(df_missing['temperature'])\n",
+    "plt.title('Lag Plot - Temperature')\n",
+    "plt.xlabel('Temperature (t)')\n",
+    "plt.ylabel('Temperature (t+1)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89fe6a3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iqr_outliers(data):\n",
+    "    q1 = data.quantile(0.25)\n",
+    "    q3 = data.quantile(0.75)\n",
+    "    iqr = q3 - q1\n",
+    "    lower_bound = q1 - 1.5 * iqr\n",
+    "    upper_bound = q3 + 1.5 * iqr\n",
+    "    outliers = data[(data < lower_bound) | (data > upper_bound)]\n",
+    "    return outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c42ed264",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outliers = iqr_outliers(df_missing['temperature'])\n",
+    "print('Outliers:')\n",
+    "print(outliers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d50668e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = stats.zscore(df_missing['temperature'])\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(z_scores)\n",
+    "plt.title('Z-Score Plot - Temperature')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Z-Score')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}