diff --git a/03_lab_supervised_learning.ipynb b/03_lab_supervised_learning.ipynb new file mode 100644 index 0000000..b51a1bc --- /dev/null +++ b/03_lab_supervised_learning.ipynb @@ -0,0 +1,1706 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "p-4m5sUo5i62" + }, + "source": [ + "# Before your start:\n", + "- Read the README.md file\n", + "- Comment as much as you can and use the resources in the README.md file\n", + "- Happy learning!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "1kDYEX7g5i66" + }, + "outputs": [], + "source": [ + "# Import your libraries:\n", + "\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9w5TheqF5i68" + }, + "source": [ + "In this lab, we will explore a dataset that describes websites with different features and labels them either benign or malicious . We will use supervised learning algorithms to figure out what feature patterns malicious websites are likely to have and use our model to predict malicious websites.\n", + "\n", + "# Challenge 1 - Explore The Dataset\n", + "\n", + "Let's start by exploring the dataset. First load the data file:" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "id": "vo6RWOBX5i68" + }, + "outputs": [], + "source": [ + "websites = pd.read_csv(\"/content/website.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y_CCDrh_5i69" + }, + "source": [ + "#### Explore the data from an bird's-eye view.\n", + "\n", + "You should already been very familiar with the procedures now so we won't provide the instructions step by step. Reflect on what you did in the previous labs and explore the dataset.\n", + "\n", + "Things you'll be looking for:\n", + "\n", + "* What the dataset looks like?\n", + "* What are the data types?\n", + "* Which columns contain the features of the websites?\n", + "* Which column contains the feature we will predict? What is the code standing for benign vs malicious websites?\n", + "* Do we need to transform any of the columns from categorical to ordinal values? If so what are these columns?\n", + "\n", + "Feel free to add additional cells for your explorations. Make sure to comment what you find out." + ] + }, + { + "cell_type": "code", + "source": [ + "websites.head()" + ], + "metadata": { + "id": "FHCqChcE6s4B" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tMoQbBu25i69", + "outputId": "48be6639-e6a7-4e2c-da19-879c17ddf354" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The actual shape of my data is 1781 rows and 21 columns\n" + ] + } + ], + "source": [ + "print(\"The actual shape of my data is\",websites.shape[0],\"rows and\",websites.shape[1],\"columns\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "r9WQ-K_V5i69", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f9927544-005f-4407-cb54-a02c3ba0d10c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Our data has the following data types:\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "URL object\n", + "URL_LENGTH int64\n", + "NUMBER_SPECIAL_CHARACTERS int64\n", + "CHARSET object\n", + "SERVER object\n", + "CONTENT_LENGTH float64\n", + "WHOIS_COUNTRY object\n", + "WHOIS_STATEPRO object\n", + "WHOIS_REGDATE object\n", + "WHOIS_UPDATED_DATE object\n", + "TCP_CONVERSATION_EXCHANGE int64\n", + "DIST_REMOTE_TCP_PORT int64\n", + "REMOTE_IPS int64\n", + "APP_BYTES int64\n", + "SOURCE_APP_PACKETS int64\n", + "REMOTE_APP_PACKETS int64\n", + "SOURCE_APP_BYTES int64\n", + "REMOTE_APP_BYTES int64\n", + "APP_PACKETS int64\n", + "DNS_QUERY_TIMES float64\n", + "Type int64\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "print(\"Our data has the following data types:\\n\")\n", + "websites.dtypes" + ] + }, + { + "cell_type": "code", + "source": [ + "# features of the websites could be NUMBER_SPECIAL_CHARACTERS, CHARSET, CONTENT_LENGTH, SERVER ... and almost all of them or all (?)\n", + "websites.columns" + ], + "metadata": { + "id": "AZqdCGpK7Ltf", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ea0396c7-4140-4b54-9fdc-dd2b6d0850ff" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['URL', 'URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CHARSET', 'SERVER',\n", + " 'CONTENT_LENGTH', 'WHOIS_COUNTRY', 'WHOIS_STATEPRO', 'WHOIS_REGDATE',\n", + " 'WHOIS_UPDATED_DATE', 'TCP_CONVERSATION_EXCHANGE',\n", + " 'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n", + " 'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n", + " 'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Which column contains the feature we will predict? I would say the \"Type\", because it's an \"1\" or \"0 \" classification. It's not somenthing that\n", + "# it's intrisic of the website, but rather a \"result\" of the caractheristics.\n", + "websites[\"Type\"].unique()" + ], + "metadata": { + "id": "A0xlVp0w7LrE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9bb4814f-a2ed-4f2c-a70d-892ebecc4d3c" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 0])" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Do we need to transform any of the columns from categorical to ordinal values? If so what are these columns?\n", + "# I would probably change the \"WHOIS_UPDATED_DATE \" columns(but first need to analyze if it's a column to use further).\n", + "\n", + "# URL object --- > ok\n", + "# WHOIS_REGDATE object --- > let's see\n", + "# WHOIS_UPDATED_DATE object --- > let's see\n", + "\n", + "websites[\"CHARSET\"].unique()\n", + "websites[\"SERVER\"].unique() # djisus, let it be object\n", + "websites[\"WHOIS_COUNTRY\"].unique() # let it be object, but clean the United Kingdom, Cyprus and \"[u'GB'; u'UK']\"\n", + "websites[\"WHOIS_STATEPRO\"].unique() # let it be object, but is this a problem: 'widestep@mail.ru' ???" + ], + "metadata": { + "id": "kCvCcq5V7Lma" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EgcRmxhl5i6-" + }, + "source": [ + "#### Next, evaluate if the columns in this dataset are strongly correlated.\n", + "\n", + "If our dataset has strongly correlated columns, we need to choose certain ML algorithms instead of others. We need to evaluate this for our dataset now.\n", + "\n", + "Luckily, most of the columns in this dataset are ordinal which makes things a lot easier for us. In the next cells below, evaluate the level of collinearity of the data.\n", + "\n", + "We provide some general directions for you to consult in order to complete this step:\n", + "\n", + "1. You will create a correlation matrix using the numeric columns in the dataset.\n", + "\n", + "1. Create a heatmap using `seaborn` to visualize which columns have high collinearity.\n", + "\n", + "1. Comment on which columns you might need to remove due to high collinearity." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Q8Cpaihy5i6-" + }, + "outputs": [], + "source": [ + "numeric_columns = ['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH', 'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n", + " 'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "3vyJl0ev5i6_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "8897ba7b-47b7-48d2-b0a5-dda9a8056926" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# example in class with salaries dataset\n", + "\n", + "corr=np.abs(websites[numeric_columns].corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()\n", + "\n", + "## I think high collinearity is when you have variables that are highly correlated between themselves, so it means thar we can drop\n", + "## one (or more, actually) because they will add noise, repeated info to the target." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-6xm_7cd5i6_" + }, + "source": [ + "# Challenge 2 - Remove Column Collinearity.\n", + "\n", + "From the heatmap you created, you should have seen at least 3 columns that can be removed due to high collinearity. Remove these columns from the dataset.\n", + "\n", + "Note that you should remove as few columns as you can. You don't have to remove all the columns at once. But instead, try removing one column, then produce the heatmap again to determine if additional columns should be removed. As long as the dataset no longer contains columns that are correlated for over 90%, you can stop. Also, keep in mind when two columns have high collinearity, you only need to remove one of them but not both.\n", + "\n", + "In the cells below, remove as few columns as you can to eliminate the high collinearity in the dataset. Make sure to comment on your way so that the instructional team can learn about your thinking process which allows them to give feedback. At the end, print the heatmap again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4gIyopKV5i7A" + }, + "outputs": [], + "source": [ + "# Removing 'SOURCE_APP_PACKETS'\n", + "numeric_columns = ['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH', 'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS', 'APP_BYTES', 'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n", + " 'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type']\n", + "\n", + "corr=np.abs(websites[numeric_columns].corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "leJCw7S_5i7A" + }, + "outputs": [], + "source": [ + "# Removing 'SOURCE_APP_PACKETS' plus 'APP_BYTES'\n", + "numeric_columns = ['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH', 'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS', 'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n", + " 'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type']\n", + "\n", + "corr=np.abs(websites[numeric_columns].corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "# Removing 'SOURCE_APP_PACKETS' plus 'APP_BYTES' plus 'APP_PACKETS'\n", + "numeric_columns = ['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH', 'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS', 'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES','DNS_QUERY_TIMES', 'Type']\n", + "\n", + "corr=np.abs(websites[numeric_columns].corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "JLpSXILhIou3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Removing 'SOURCE_APP_PACKETS' plus 'APP_BYTES' plus 'APP_PACKETS' plus 'REMOTE_APP_PACKETS'\n", + "numeric_columns = ['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH', 'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES','DNS_QUERY_TIMES', 'Type']\n", + "\n", + "corr=np.abs(websites[numeric_columns].corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "JdjrmMDVJAwx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dY9P5UjH5i7B" + }, + "source": [ + "# Challenge 3 - Handle Missing Values\n", + "\n", + "The next step would be handling missing values. **We start by examining the number of missing values in each column, which you will do in the next cell.**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "pXV6sGFM5i7B", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "df1a60dc-0290-4ed5-f3eb-cf7b42e1f9f6" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "URL 0\n", + "URL_LENGTH 0\n", + "NUMBER_SPECIAL_CHARACTERS 0\n", + "CHARSET 0\n", + "SERVER 1\n", + "CONTENT_LENGTH 812\n", + "WHOIS_COUNTRY 0\n", + "WHOIS_STATEPRO 0\n", + "WHOIS_REGDATE 0\n", + "WHOIS_UPDATED_DATE 0\n", + "TCP_CONVERSATION_EXCHANGE 0\n", + "DIST_REMOTE_TCP_PORT 0\n", + "REMOTE_IPS 0\n", + "APP_BYTES 0\n", + "SOURCE_APP_PACKETS 0\n", + "REMOTE_APP_PACKETS 0\n", + "SOURCE_APP_BYTES 0\n", + "REMOTE_APP_BYTES 0\n", + "APP_PACKETS 0\n", + "DNS_QUERY_TIMES 1\n", + "Type 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "websites.isnull().sum()\n", + "# not bad -column wise -- basically need to handle \"just\" one column" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lhomxjxo5i7C" + }, + "source": [ + "If you remember in the previous labs, we drop a column if the column contains a high proportion of missing values. After dropping those problematic columns, we drop the rows with missing values.\n", + "\n", + "#### In the cells below, handle the missing values from the dataset. Remember to comment the rationale of your decisions." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lunupnKv5i7C", + "outputId": "a756d809-a042-4673-d305-baa3cc80f817" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "812" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ], + "source": [ + "content_nulls = websites[\"CONTENT_LENGTH\"].isnull().sum() # 812 nulls in CONTENT_LENGTH\n", + "content_nulls" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MsXohn8N5i7C", + "outputId": "9cebdda5-1687-40ab-b926-ba09ed2a685f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1781" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "websites.shape[0]" + ] + }, + { + "cell_type": "code", + "source": [ + "ratio_nulls = (content_nulls/websites.shape[0])*100\n", + "print(\"The ratio of nulls in our dataset is\", ratio_nulls,\"%\\n\")\n", + "print(\"Djisus Christ!!!\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VezV_aYjL6F2", + "outputId": "66eb30da-deff-4bb1-f6f0-29476f467fd4" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The ratio of nulls in our dataset is 45.59236384053902 %\n", + "\n", + "Djisus Christ!!!\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# I am gonna drop this, righ??\n", + "websites.drop(\"CONTENT_LENGTH\", axis = 1, inplace = True)" + ], + "metadata": { + "id": "H3o8OuZkNOuY" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vLXCvE3G5i7C" + }, + "source": [ + "#### Again, examine the number of missing values in each column.\n", + "\n", + "If all cleaned, proceed. Otherwise, go back and do more cleaning." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6cMaFKlt5i7D", + "outputId": "1c649d9a-4f19-4017-a84b-03fa8d5c4fee" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "number of nulls in server column: 1\n", + "number of nulls in dns query times column: 1\n" + ] + } + ], + "source": [ + "websites.isnull().sum()\n", + "print(\"number of nulls in server column:\", websites[\"SERVER\"].isnull().sum())\n", + "print(\"number of nulls in dns query times column:\", websites[\"DNS_QUERY_TIMES\"].isnull().sum())" + ] + }, + { + "cell_type": "code", + "source": [ + "# using this now because i will lose only 2 rows\n", + "websites.dropna(inplace = True)" + ], + "metadata": { + "id": "UsNc1uprSBJ0" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "websites.isnull().sum()\n", + "# Done here" + ], + "metadata": { + "id": "-3nksrllG3tM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f4a5a094-3112-4ab9-8040-488f9f4d3ca5" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "URL 0\n", + "URL_LENGTH 0\n", + "NUMBER_SPECIAL_CHARACTERS 0\n", + "CHARSET 0\n", + "SERVER 0\n", + "WHOIS_COUNTRY 0\n", + "WHOIS_STATEPRO 0\n", + "WHOIS_REGDATE 0\n", + "WHOIS_UPDATED_DATE 0\n", + "TCP_CONVERSATION_EXCHANGE 0\n", + "DIST_REMOTE_TCP_PORT 0\n", + "REMOTE_IPS 0\n", + "APP_BYTES 0\n", + "SOURCE_APP_PACKETS 0\n", + "REMOTE_APP_PACKETS 0\n", + "SOURCE_APP_BYTES 0\n", + "REMOTE_APP_BYTES 0\n", + "APP_PACKETS 0\n", + "DNS_QUERY_TIMES 0\n", + "Type 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqS4Olr45i7D" + }, + "source": [ + "# Challenge 4 - Handle `WHOIS_*` Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JeldMXKU5i7D" + }, + "source": [ + "There are several categorical columns we need to handle. These columns are:\n", + "\n", + "* `URL`\n", + "* `CHARSET`\n", + "* `SERVER`\n", + "* `WHOIS_COUNTRY`\n", + "* `WHOIS_STATEPRO`\n", + "* `WHOIS_REGDATE`\n", + "* `WHOIS_UPDATED_DATE`\n", + "\n", + "How to handle string columns is always case by case. Let's start by working on `WHOIS_COUNTRY`. Your steps are:\n", + "\n", + "1. List out the unique values of `WHOIS_COUNTRY`.\n", + "1. Consolidate the country values with consistent country codes. For example, the following values refer to the same country and should use consistent country code:\n", + " * `CY` and `Cyprus`\n", + " * `US` and `us`\n", + " * `SE` and `se`\n", + " * `GB`, `United Kingdom`, and `[u'GB'; u'UK']`\n", + "\n", + "#### In the cells below, fix the country values as intructed above." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rtMbP7T95i7E", + "outputId": "18270895-4a62-4ad1-c795-a17339afd4ef" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['None', 'US', 'SC', 'GB', 'UK', 'RU', 'AU', 'CA', 'PA', 'se', 'IN',\n", + " 'LU', 'TH', \"[u'GB'; u'UK']\", 'FR', 'NL', 'UG', 'JP', 'CN', 'SE',\n", + " 'SI', 'IL', 'ru', 'KY', 'AT', 'CZ', 'PH', 'BE', 'NO', 'TR', 'LV',\n", + " 'DE', 'ES', 'BR', 'us', 'KR', 'HK', 'UA', 'CH', 'United Kingdom',\n", + " 'BS', 'PK', 'IT', 'Cyprus', 'BY', 'AE', 'IE', 'UY', 'KG'],\n", + " dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "websites[\"WHOIS_COUNTRY\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FVSoysa15i7E" + }, + "source": [ + "Since we have fixed the country values, can we convert this column to ordinal now?\n", + "\n", + "Not yet. If you reflect on the previous labs how we handle categorical columns, you probably remember we ended up dropping a lot of those columns because there are too many unique values. Too many unique values in a column is not desirable in machine learning because it makes prediction inaccurate. But there are workarounds under certain conditions. One of the fixable conditions is:\n", + "\n", + "#### If a limited number of values account for the majority of data, we can retain these top values and re-label all other rare values.\n", + "\n", + "The `WHOIS_COUNTRY` column happens to be this case. You can verify it by print a bar chart of the `value_counts` in the next cell to verify:" + ] + }, + { + "cell_type": "code", + "source": [ + "websites[\"WHOIS_COUNTRY\"].value_counts()" + ], + "metadata": { + "id": "Wbk-LK6I0Fdp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# TA tip\n", + "websites[\"WHOIS_COUNTRY\"].value_counts().plot(kind ='bar')" + ], + "metadata": { + "id": "FiVqm5g6q6gV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 544 + }, + "outputId": "1b80d510-79df-473d-8cf1-8c22a59112b6" + }, + "execution_count": 86, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 86 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c79t9-8B5i7F" + }, + "source": [ + "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": true, + "id": "WedXO7b95i7F" + }, + "outputs": [], + "source": [ + "# syntax: df['Your Column'].value_counts().nlargest(\"N\")\n", + "top_10 = websites[\"WHOIS_COUNTRY\"].value_counts().nlargest(10).index" + ] + }, + { + "cell_type": "code", + "source": [ + "# went for positive function from previous lab\n", + "def country_function(x):\n", + " if x in top_10:\n", + " return x\n", + " else:\n", + " return \"Other\"\n", + "\n", + "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].apply(country_function)\n", + "websites.head(10)" + ], + "metadata": { + "id": "me9bdXJQIsj1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "websites[\"WHOIS_COUNTRY\"].unique()" + ], + "metadata": { + "id": "IeWE4EQaMuxD", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c330a172-ad51-4fe2-990f-c1f4bb79f95c" + }, + "execution_count": 80, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['None', 'US', 'Other', 'GB', 'AU', 'CA', 'PA', 'IN', 'JP', 'ES'],\n", + " dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 80 + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "kzv1K7_9PsVi" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5J3K9MN5i7G" + }, + "source": [ + "Now since `WHOIS_COUNTRY` has been re-labelled, we don't need `WHOIS_STATEPRO` any more because the values of the states or provinces may not be relevant any more. We'll drop this column.\n", + "\n", + "In addition, we will also drop `WHOIS_REGDATE` and `WHOIS_UPDATED_DATE`. These are the registration and update dates of the website domains. Not of our concerns.\n", + "\n", + "#### In the next cell, drop `['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "I8fGDm865i7G" + }, + "outputs": [], + "source": [ + "# websites.drop(\"WHOIS_STATEPRO\", axis = 1, inplace = True)\n", + "# websites.drop(\"WHOIS_REGDATE\", axis = 1, inplace = True)\n", + "# websites.drop(\"WHOIS_UPDATED_DATE\", axis = 1, inplace = True)\n", + "\n", + "# was not sure if i could do all at the same time :/\n", + "exercise_says_to_drop = ['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']\n", + "websites.drop(columns = exercise_says_to_drop, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "45Lmh1sM5i7G" + }, + "source": [ + "# Challenge 5 - Handle Remaining Categorical Data & Convert to Ordinal\n", + "\n", + "Now print the `dtypes` of the data again. Besides `WHOIS_COUNTRY` which we already fixed, there should be 3 categorical columns left: `URL`, `CHARSET`, and `SERVER`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DZ2q7MlQ5i7G" + }, + "outputs": [], + "source": [ + "websites.dtypes # right" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c-pk2u005i7H" + }, + "source": [ + "#### `URL` is easy. We'll simply drop it because it has too many unique values that there's no way for us to consolidate." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "g5sAx4-b5i7H" + }, + "outputs": [], + "source": [ + "websites.drop(\"URL\", axis = 1, inplace = True) # nice" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VeIspaAI5i7I" + }, + "source": [ + "#### Print the unique value counts of `CHARSET`. You see there are only a few unique values. So we can keep it as it is." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LCteHSuc5i7I", + "outputId": "892a96b9-5d06-4d64-dc31-a6ab9d61d42e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['iso-8859-1' 'UTF-8' 'us-ascii' 'ISO-8859-1' 'utf-8' 'None'\n", + " 'windows-1251' 'ISO-8859' 'windows-1252']\n" + ] + } + ], + "source": [ + "print(websites[\"CHARSET\"].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2lyGZKrj5i7I" + }, + "source": [ + "`SERVER` is a little more complicated. Print its unique values and think about how you can consolidate those values.\n", + "\n", + "#### Before you think of your own solution, don't read the instructions that come next." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kK6gTTpq5i7J" + }, + "outputs": [], + "source": [ + "print(websites[\"SERVER\"].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RwC3i1MB5i7J" + }, + "source": [ + "![Think Hard](../images/think-hard.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": { + "id": "k1wyDWJb5i7Z" + }, + "outputs": [], + "source": [ + "# maybe we can \"categorize\"/group by microsoft, apache, nginx something of that sort, like in top 10 countries." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MHWoms385i7Z" + }, + "source": [ + "Although there are so many unique values in the `SERVER` column, there are actually only 3 main server types: `Microsoft`, `Apache`, and `nginx`. Just check if each `SERVER` value contains any of those server types and re-label them. For `SERVER` values that don't contain any of those substrings, label with `Other`.\n", + "\n", + "At the end, your `SERVER` column should only contain 4 unique values: `Microsoft`, `Apache`, `nginx`, and `Other`." + ] + }, + { + "cell_type": "code", + "source": [ + "# gonna try\n", + "\n", + "def server_function(x):\n", + " for server in [\"Microsoft\", \"Apache\", \"nginx\"]:\n", + " if server in x:\n", + " return server\n", + " return \"Other\"\n", + "\n", + "websites[\"SERVER\"] = websites[\"SERVER\"].apply(server_function)" + ], + "metadata": { + "id": "W9GXUr9xFzSd" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Server is now categorized in:\\n\")\n", + "websites[\"SERVER\"].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wsQcv1o6IHh6", + "outputId": "4f611386-663e-4f26-c298-2f293c588657" + }, + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Server is now categorized in:\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Apache 642\n", + "Other 504\n", + "nginx 435\n", + "Microsoft 198\n", + "Name: SERVER, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 37 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nuZO3SVP5i7a" + }, + "source": [ + "OK, all our categorical data are fixed now. **Let's convert them to ordinal data using Pandas' `get_dummies` function ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)). Also, assign the data with dummy values to a new variable `website_dummy`.**" + ] + }, + { + "cell_type": "code", + "source": [ + "websites.dtypes" + ], + "metadata": { + "id": "G9Wtto3yU5YA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "scrolled": false, + "id": "_nuK1iYo5i7Z" + }, + "outputs": [], + "source": [ + "categorical_columns = [\"CHARSET\", \"SERVER\", \"WHOIS_COUNTRY\"]" + ] + }, + { + "cell_type": "code", + "source": [ + "# (columns = categorical_columns, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)[source]\n", + "# Probably a more python way to do this, but it is what it is:\n", + "\n", + "website_dummy_C = pd.get_dummies(websites[\"CHARSET\"])\n", + "website_dummy_S = pd.get_dummies(websites[\"SERVER\"])\n", + "website_dummy_W = pd.get_dummies(websites[\"WHOIS_COUNTRY\"])" + ], + "metadata": { + "id": "AMPoXJpFI4lt" + }, + "execution_count": 40, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xn0BGBin5i7a" + }, + "source": [ + "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R06wAyKo5i7a" + }, + "outputs": [], + "source": [ + "website_dummy = pd.concat([website_dummy_C, website_dummy_S, website_dummy_W], axis=1)\n", + "website_dummy" + ] + }, + { + "cell_type": "code", + "source": [ + "website_dummy.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VkoQKKx-RM7x", + "outputId": "f1a45fa7-31b4-467d-8d39-2586e45724d6" + }, + "execution_count": 90, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['ISO-8859', 'ISO-8859-1', 'None', 'UTF-8', 'iso-8859-1', 'us-ascii',\n", + " 'utf-8', 'windows-1251', 'windows-1252', 'Apache', 'Microsoft', 'Other',\n", + " 'nginx', 'AU', 'CA', 'CN', 'ES', 'GB', 'IN', 'JP', 'None', 'Other',\n", + " 'PA', 'US'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 90 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pAUnpxBk5i7b" + }, + "source": [ + "# Challenge 6 - Modeling, Prediction, and Evaluation\n", + "\n", + "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**" + ] + }, + { + "cell_type": "code", + "source": [ + "# now that all features are numeric, lets being training our model\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "features = website_dummy #selecting all columns from website_dummy\n", + "target = websites[\"Type\"] #selecting \"Type\" as our target variable\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.20, random_state = 0)" + ], + "metadata": { + "id": "lUjLOvt1TnWb" + }, + "execution_count": 43, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fwvCs1M35i7b" + }, + "source": [ + "#### In this lab, we will try two different models and compare our results.\n", + "\n", + "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "wrGd8qwQ5i7b" + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "logistic = LogisticRegression (max_iter = 4000) #used in class the same." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qjIa6sOI5i7c" + }, + "source": [ + "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "84vMMfsa5i7c" + }, + "outputs": [], + "source": [ + "logistic.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "94TC-n3m5i7c" + }, + "source": [ + "finally, import `confusion_matrix` and `accuracy_score` from `sklearn.metrics` and fit our testing data. Assign the fitted data to `y_pred` and print the confusion matrix as well as the accuracy score" + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"accuracy_score is:\", logistic.score(X_test, y_test))" + ], + "metadata": { + "id": "Qs775qsBpocg", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d6187e42-5a23-45ac-9488-e990f8b77300" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accuracy_score is: 0.9157303370786517\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "id": "88BVygRy5i7d", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ce685f4f-bd38-4397-c5fd-c8c1a91ec853" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 1.00 0.95 317\n", + " 1 0.91 0.26 0.40 39\n", + "\n", + " accuracy 0.92 356\n", + " macro avg 0.91 0.63 0.68 356\n", + "weighted avg 0.92 0.92 0.89 356\n", + "\n" + ] + } + ], + "source": [ + "# confusion_matrix, classification_report\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "pred = logistic.predict(X_test)\n", + "print(classification_report(y_test, pred)) #### important this order. true/predict" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5ogiIiWf5i7d" + }, + "source": [ + "What are your thoughts on the performance of the model? Write your conclusions below." + ] + }, + { + "cell_type": "code", + "source": [ + "confusion_matrix(y_test, pred) #### bad recall." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m3dPCwb-zC-v", + "outputId": "936a9a97-f50f-4acd-e19d-2a364cca001d" + }, + "execution_count": 52, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[316, 1],\n", + " [ 29, 10]])" + ] + }, + "metadata": {}, + "execution_count": 52 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import recall_score\n", + "recall_score(y_test, pred) #### bad recall. something wrong here!!!" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dreRZc7vzXiU", + "outputId": "8f6a0ee2-3769-4e0b-86a8-44987d1b732f" + }, + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.2564102564102564" + ] + }, + "metadata": {}, + "execution_count": 53 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J-A5UIgM5i7d" + }, + "source": [ + "#### Our second algorithm is is K-Nearest Neighbors.\n", + "\n", + "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "id": "IAL0eSi95i7e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "outputId": "939b2f69-17e3-4ab9-9b6a-ba542436cd48" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "KNeighborsClassifier(n_neighbors=3)" + ], + "text/html": [ + "
KNeighborsClassifier(n_neighbors=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 93 + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "#create an instance of our model\n", + "knn_3 = KNeighborsClassifier(n_neighbors = 3)\n", + "\n", + "#training the model to our data\n", + "knn_3.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R97jiK5-5i7e" + }, + "source": [ + "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "id": "FvOaVsqu5i7e", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c5b57de9-48f1-4315-cf18-c14b609c7db0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accuracy score is 0.8595505617977528\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[316, 1],\n", + " [ 29, 10]])" + ] + }, + "metadata": {}, + "execution_count": 94 + } + ], + "source": [ + "#evaluate it\n", + "print(\"accuracy score is\", knn_3.score(X_test, y_test))\n", + "# confusion matrix knn\n", + "confusion_matrix(y_test, pred)" + ] + }, + { + "cell_type": "code", + "source": [ + "# true negative false positive\n", + "# false negative true positive" + ], + "metadata": { + "id": "2QApX8Mt62ej" + }, + "execution_count": 64, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ly6W4PiF5i7e" + }, + "source": [ + "#### We'll create another K-Nearest Neighbors model with k=5.\n", + "\n", + "Initialize and fit the model below and print the confusion matrix and the accuracy score." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "id": "wT_druEw5i7f", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c9b3e182-0317-4233-d262-54c0b435d32a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accuracy score is 0.9101123595505618\n", + "[[316 1]\n", + " [ 29 10]]\n" + ] + } + ], + "source": [ + "#create an instance of our model\n", + "knn_5 = KNeighborsClassifier(n_neighbors = 5)\n", + "\n", + "#training the model to our data\n", + "knn_5.fit(X_train, y_train)\n", + "print(\"accuracy score is\", knn_5.score(X_test, y_test))\n", + "# confusion matrix knn = 5 n\n", + "print(confusion_matrix(y_test, pred))" + ] + }, + { + "cell_type": "code", + "source": [ + "# true negative false positive\n", + "# false negative true positive" + ], + "metadata": { + "id": "s3Q2zzixb_V-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mPay2G8Q5i7f" + }, + "source": [ + "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "id": "0NXYrytS5i7f", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6bd914e8-f09f-4be8-ba52-d613f81b96a4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accuracy score for knn = 3 is 0.8595505617977528 \n", + "while confusion matrix is:\n", + "[[316 1]\n", + " [ 29 10]] \n", + "\n", + "accuracy score for knn = 5 is 0.9101123595505618 \n", + "while confusion matrix is:\n", + "[[316 1]\n", + " [ 29 10]]\n" + ] + } + ], + "source": [ + "print(\"accuracy score for knn = 3 is\", knn_3.score(X_test, y_test), \"\\nwhile confusion matrix is:\")\n", + "print(confusion_matrix(y_test, pred),\"\\n\")\n", + "print(\"accuracy score for knn = 5 is\", knn_5.score(X_test, y_test),\"\\nwhile confusion matrix is:\")\n", + "print(confusion_matrix(y_test, pred))\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# so it's funny how the accucary score got a little better, but the confusion matrix remains the same." + ], + "metadata": { + "id": "vzfplagxd2dO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EORJ15IZ5i7f" + }, + "source": [ + "# Bonus Challenge - Feature Scaling\n", + "\n", + "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n", + "\n", + "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n", + "\n", + "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pakhj8c25i7f" + }, + "outputs": [], + "source": [ + "# Your code here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ironhack-3.7", + "language": "python", + "name": "ironhack-3.7" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/.gitignore b/data/.gitignore similarity index 100% rename from .gitignore rename to data/.gitignore diff --git a/README.md b/data/README.md old mode 100755 new mode 100644 similarity index 100% rename from README.md rename to data/README.md