cars_regression_overfit_example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cars_regression_overfit_example.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMC5d4BugCjUc7TBeBsqyv2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/christophergaughan/ChristopherGaughan.io/blob/master/cars_regression_overfit_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "kWxYhp-jvQjt",
        "outputId": "3ae3aca2-32ab-4b1c-ac81-ca1740215d91"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/gdrive/My Drive/Colab Notebooks'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wr5DHmDvSZS",
        "outputId": "8388340d-02cc-4116-e803-a5a108753d63"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive/MyDrive/Colab\\ Notebooks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n",
            "/gdrive/MyDrive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgb6wtkYww0Q"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T89n-XUqxCYi"
      },
      "source": [
        "df = pd.read_excel('car_data_fixed_final.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DBQzw525xXlt",
        "outputId": "0ea173c4-b3e5-4553-92b5-1c9c1c9ff619"
      },
      "source": [
        "df.head(40)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>body_style</th>\n",
              "      <th>drive_wheels</th>\n",
              "      <th>engine_location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
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              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_type</th>\n",
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              "      <th>engine_size</th>\n",
              "      <th>fuel_system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
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              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
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              "      <td>130</td>\n",
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              "      <td>3.47</td>\n",
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              "      <td>5000.0</td>\n",
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              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
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              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
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              "      <td>48.8</td>\n",
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              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
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              "      <td>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
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              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.00</td>\n",
              "      <td>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.00</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.00</td>\n",
              "      <td>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>177.3</td>\n",
              "      <td>66.3</td>\n",
              "      <td>53.1</td>\n",
              "      <td>2507</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.50</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>15250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2844</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.50</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>17710.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2954</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.50</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>18920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.9</td>\n",
              "      <td>3086</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>131</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.30</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>23875.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.5</td>\n",
              "      <td>178.2</td>\n",
              "      <td>67.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3053</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>131</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>7.00</td>\n",
              "      <td>160.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>192.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2395</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>108</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.50</td>\n",
              "      <td>2.80</td>\n",
              "      <td>8.80</td>\n",
              "      <td>101.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>16430.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2395</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>108</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.50</td>\n",
              "      <td>2.80</td>\n",
              "      <td>8.80</td>\n",
              "      <td>101.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>16925.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2710</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>164</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.00</td>\n",
              "      <td>121.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>20970.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2765</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>164</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.00</td>\n",
              "      <td>121.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>21105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>103.5</td>\n",
              "      <td>189.0</td>\n",
              "      <td>66.9</td>\n",
              "      <td>55.7</td>\n",
              "      <td>3055</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>164</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.00</td>\n",
              "      <td>121.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>20</td>\n",
              "      <td>25</td>\n",
              "      <td>24565.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>103.5</td>\n",
              "      <td>189.0</td>\n",
              "      <td>66.9</td>\n",
              "      <td>55.7</td>\n",
              "      <td>3230</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>209</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.39</td>\n",
              "      <td>8.00</td>\n",
              "      <td>182.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>30760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>103.5</td>\n",
              "      <td>193.8</td>\n",
              "      <td>67.9</td>\n",
              "      <td>53.7</td>\n",
              "      <td>3380</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>209</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.39</td>\n",
              "      <td>8.00</td>\n",
              "      <td>182.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>41315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bmw</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>110.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>70.9</td>\n",
              "      <td>56.3</td>\n",
              "      <td>3505</td>\n",
              "      <td>ohc</td>\n",
              "      <td>six</td>\n",
              "      <td>209</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.39</td>\n",
              "      <td>8.00</td>\n",
              "      <td>182.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>36880.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2</td>\n",
              "      <td>121.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.4</td>\n",
              "      <td>141.1</td>\n",
              "      <td>60.3</td>\n",
              "      <td>53.2</td>\n",
              "      <td>1488</td>\n",
              "      <td>l</td>\n",
              "      <td>three</td>\n",
              "      <td>61</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.03</td>\n",
              "      <td>9.50</td>\n",
              "      <td>48.0</td>\n",
              "      <td>5100.0</td>\n",
              "      <td>47</td>\n",
              "      <td>53</td>\n",
              "      <td>5151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>98.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>155.9</td>\n",
              "      <td>63.6</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1874</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.11</td>\n",
              "      <td>9.60</td>\n",
              "      <td>70.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>6295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>158.8</td>\n",
              "      <td>63.6</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1909</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.11</td>\n",
              "      <td>9.60</td>\n",
              "      <td>70.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>6575.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>118.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.8</td>\n",
              "      <td>1876</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.97</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.41</td>\n",
              "      <td>68.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>37</td>\n",
              "      <td>41</td>\n",
              "      <td>5572.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>118.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.8</td>\n",
              "      <td>1876</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.97</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.40</td>\n",
              "      <td>68.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>6377.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>118.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.8</td>\n",
              "      <td>2128</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>98</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.39</td>\n",
              "      <td>7.60</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>7957.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>148.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.6</td>\n",
              "      <td>1967</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.97</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.40</td>\n",
              "      <td>68.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>6229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>148.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.6</td>\n",
              "      <td>1989</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.97</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.40</td>\n",
              "      <td>68.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>6692.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>148.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.6</td>\n",
              "      <td>1989</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>90</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>2.97</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.40</td>\n",
              "      <td>68.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>7609.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>148.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>?</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>157.3</td>\n",
              "      <td>63.8</td>\n",
              "      <td>50.6</td>\n",
              "      <td>2191</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>98</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.39</td>\n",
              "      <td>7.60</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>8558.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>-1</td>\n",
              "      <td>110.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>103.3</td>\n",
              "      <td>174.6</td>\n",
              "      <td>64.6</td>\n",
              "      <td>59.8</td>\n",
              "      <td>2535</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>122</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>3.34</td>\n",
              "      <td>3.46</td>\n",
              "      <td>8.50</td>\n",
              "      <td>88.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>8921.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>3</td>\n",
              "      <td>145.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>95.9</td>\n",
              "      <td>173.2</td>\n",
              "      <td>66.3</td>\n",
              "      <td>50.2</td>\n",
              "      <td>2811</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>156</td>\n",
              "      <td>mfi</td>\n",
              "      <td>3.60</td>\n",
              "      <td>3.90</td>\n",
              "      <td>7.00</td>\n",
              "      <td>145.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>12964.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2</td>\n",
              "      <td>137.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>86.6</td>\n",
              "      <td>144.6</td>\n",
              "      <td>63.9</td>\n",
              "      <td>50.8</td>\n",
              "      <td>1713</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.60</td>\n",
              "      <td>58.0</td>\n",
              "      <td>4800.0</td>\n",
              "      <td>49</td>\n",
              "      <td>54</td>\n",
              "      <td>6479.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2</td>\n",
              "      <td>137.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>86.6</td>\n",
              "      <td>144.6</td>\n",
              "      <td>63.9</td>\n",
              "      <td>50.8</td>\n",
              "      <td>1819</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>6855.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1</td>\n",
              "      <td>101.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>150.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>1837</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>79</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.07</td>\n",
              "      <td>10.10</td>\n",
              "      <td>60.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>38</td>\n",
              "      <td>42</td>\n",
              "      <td>5399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "      <td>101.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>150.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>1940</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30</td>\n",
              "      <td>34</td>\n",
              "      <td>6529.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1</td>\n",
              "      <td>101.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>93.7</td>\n",
              "      <td>150.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>52.6</td>\n",
              "      <td>1956</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30</td>\n",
              "      <td>34</td>\n",
              "      <td>7129.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.5</td>\n",
              "      <td>163.4</td>\n",
              "      <td>64.0</td>\n",
              "      <td>54.5</td>\n",
              "      <td>2010</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30</td>\n",
              "      <td>34</td>\n",
              "      <td>7295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.5</td>\n",
              "      <td>157.1</td>\n",
              "      <td>63.9</td>\n",
              "      <td>58.3</td>\n",
              "      <td>2024</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>92</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>2.92</td>\n",
              "      <td>3.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30</td>\n",
              "      <td>34</td>\n",
              "      <td>7295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.5</td>\n",
              "      <td>167.5</td>\n",
              "      <td>65.2</td>\n",
              "      <td>53.3</td>\n",
              "      <td>2236</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>110</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.58</td>\n",
              "      <td>9.00</td>\n",
              "      <td>86.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>7895.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.5</td>\n",
              "      <td>167.5</td>\n",
              "      <td>65.2</td>\n",
              "      <td>53.3</td>\n",
              "      <td>2289</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>110</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.58</td>\n",
              "      <td>9.00</td>\n",
              "      <td>86.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>9095.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.5</td>\n",
              "      <td>175.4</td>\n",
              "      <td>65.2</td>\n",
              "      <td>54.1</td>\n",
              "      <td>2304</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>110</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.58</td>\n",
              "      <td>9.00</td>\n",
              "      <td>86.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>8845.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    symboling  normalized_losses         make  ... city_mpg hw_mpg    price\n",
              "0           3                NaN  alfa-romero  ...       21     27  13495.0\n",
              "1           3                NaN  alfa-romero  ...       21     27  16500.0\n",
              "2           1                NaN  alfa-romero  ...       19     26  16500.0\n",
              "3           2              164.0         audi  ...       24     30  13950.0\n",
              "4           2              164.0         audi  ...       18     22  17450.0\n",
              "5           2                NaN         audi  ...       19     25  15250.0\n",
              "6           1              158.0         audi  ...       19     25  17710.0\n",
              "7           1                NaN         audi  ...       19     25  18920.0\n",
              "8           1              158.0         audi  ...       17     20  23875.0\n",
              "9           0                NaN         audi  ...       16     22      NaN\n",
              "10          2              192.0          bmw  ...       23     29  16430.0\n",
              "11          0              192.0          bmw  ...       23     29  16925.0\n",
              "12          0              188.0          bmw  ...       21     28  20970.0\n",
              "13          0              188.0          bmw  ...       21     28  21105.0\n",
              "14          1                NaN          bmw  ...       20     25  24565.0\n",
              "15          0                NaN          bmw  ...       16     22  30760.0\n",
              "16          0                NaN          bmw  ...       16     22  41315.0\n",
              "17          0                NaN          bmw  ...       15     20  36880.0\n",
              "18          2              121.0    chevrolet  ...       47     53   5151.0\n",
              "19          1               98.0    chevrolet  ...       38     43   6295.0\n",
              "20          0               81.0    chevrolet  ...       38     43   6575.0\n",
              "21          1              118.0        dodge  ...       37     41   5572.0\n",
              "22          1              118.0        dodge  ...       31     38   6377.0\n",
              "23          1              118.0        dodge  ...       24     30   7957.0\n",
              "24          1              148.0        dodge  ...       31     38   6229.0\n",
              "25          1              148.0        dodge  ...       31     38   6692.0\n",
              "26          1              148.0        dodge  ...       31     38   7609.0\n",
              "27          1              148.0        dodge  ...       24     30   8558.0\n",
              "28         -1              110.0        dodge  ...       24     30   8921.0\n",
              "29          3              145.0        dodge  ...       19     24  12964.0\n",
              "30          2              137.0        honda  ...       49     54   6479.0\n",
              "31          2              137.0        honda  ...       31     38   6855.0\n",
              "32          1              101.0        honda  ...       38     42   5399.0\n",
              "33          1              101.0        honda  ...       30     34   6529.0\n",
              "34          1              101.0        honda  ...       30     34   7129.0\n",
              "35          0              110.0        honda  ...       30     34   7295.0\n",
              "36          0               78.0        honda  ...       30     34   7295.0\n",
              "37          0              106.0        honda  ...       27     33   7895.0\n",
              "38          0              106.0        honda  ...       27     33   9095.0\n",
              "39          0               85.0        honda  ...       27     33   8845.0\n",
              "\n",
              "[40 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOc81HsPxaRw",
        "outputId": "1ab55d3b-385f-4088-abb1-f5cafaea711a"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 26 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   symboling          205 non-null    int64  \n",
            " 1   normalized_losses  164 non-null    float64\n",
            " 2   make               205 non-null    object \n",
            " 3   fuel_type          205 non-null    object \n",
            " 4   aspiration         205 non-null    object \n",
            " 5   num_doors          205 non-null    object \n",
            " 6   body_style         205 non-null    object \n",
            " 7   drive_wheels       205 non-null    object \n",
            " 8   engine_location    205 non-null    object \n",
            " 9   wheel-base         205 non-null    float64\n",
            " 10  length             205 non-null    float64\n",
            " 11  width              205 non-null    float64\n",
            " 12  height             205 non-null    float64\n",
            " 13  curb_weight        205 non-null    int64  \n",
            " 14  engine_type        205 non-null    object \n",
            " 15  num_cylinders      205 non-null    object \n",
            " 16  engine_size        205 non-null    int64  \n",
            " 17  fuel_system        205 non-null    object \n",
            " 18  bore               201 non-null    float64\n",
            " 19  stroke             201 non-null    float64\n",
            " 20  compression_ratio  205 non-null    float64\n",
            " 21  horsepower         203 non-null    float64\n",
            " 22  peak_rpm           203 non-null    float64\n",
            " 23  city_mpg           205 non-null    int64  \n",
            " 24  hw_mpg             205 non-null    int64  \n",
            " 25  price              201 non-null    float64\n",
            "dtypes: float64(11), int64(5), object(10)\n",
            "memory usage: 41.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybwkGwcN-UDU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "9jqpMGrz6mmv",
        "outputId": "5a8ddec3-25b4-4a4f-beb8-8062ba1e5e09"
      },
      "source": [
        "df.applymap(lambda x: type(x).__name__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
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              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    symboling normalized_losses make fuel_type  ... peak_rpm city_mpg hw_mpg  price\n",
              "0         int             float  str       str  ...    float      int    int  float\n",
              "1         int             float  str       str  ...    float      int    int  float\n",
              "2         int             float  str       str  ...    float      int    int  float\n",
              "3         int             float  str       str  ...    float      int    int  float\n",
              "4         int             float  str       str  ...    float      int    int  float\n",
              "..        ...               ...  ...       ...  ...      ...      ...    ...    ...\n",
              "200       int             float  str       str  ...    float      int    int  float\n",
              "201       int             float  str       str  ...    float      int    int  float\n",
              "202       int             float  str       str  ...    float      int    int  float\n",
              "203       int             float  str       str  ...    float      int    int  float\n",
              "204       int             float  str       str  ...    float      int    int  float\n",
              "\n",
              "[205 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OD8HtW0H8OJQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxYZLbHC7fLD",
        "outputId": "a7f64ac7-8254-4b61-aa5c-794c80205c9f"
      },
      "source": [
        "df[\"make\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "toyota           32\n",
              "nissan           18\n",
              "mazda            17\n",
              "honda            13\n",
              "mitsubishi       13\n",
              "subaru           12\n",
              "volkswagen       12\n",
              "volvo            11\n",
              "peugot           11\n",
              "dodge             9\n",
              "mercedes-benz     8\n",
              "bmw               8\n",
              "plymouth          7\n",
              "audi              7\n",
              "saab              6\n",
              "porsche           5\n",
              "isuzu             4\n",
              "alfa-romero       3\n",
              "jaguar            3\n",
              "chevrolet         3\n",
              "renault           2\n",
              "mercury           1\n",
              "Name: make, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxVwWAii7pmO",
        "outputId": "5fa92a16-9ddf-4145-d3c0-41abb20015f6"
      },
      "source": [
        "df[\"fuel_type\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gas       185\n",
              "diesel     20\n",
              "Name: fuel_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHxrkouR9E3O",
        "outputId": "5fa7b14a-8784-4c99-87ea-cb8383068431"
      },
      "source": [
        "df[\"price\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8495.0     2\n",
              "18150.0    2\n",
              "7295.0     2\n",
              "6229.0     2\n",
              "8845.0     2\n",
              "          ..\n",
              "15580.0    1\n",
              "6377.0     1\n",
              "30760.0    1\n",
              "16925.0    1\n",
              "18920.0    1\n",
              "Name: price, Length: 186, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "nsXkUoxEy4zl",
        "outputId": "7fd38b21-1b9a-4ad6-f48f-9feab500ceec"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_size</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>205.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>201.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.834146</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>98.756585</td>\n",
              "      <td>174.049268</td>\n",
              "      <td>65.907805</td>\n",
              "      <td>53.724878</td>\n",
              "      <td>2555.565854</td>\n",
              "      <td>126.907317</td>\n",
              "      <td>3.329751</td>\n",
              "      <td>3.255423</td>\n",
              "      <td>10.142537</td>\n",
              "      <td>104.256158</td>\n",
              "      <td>5125.369458</td>\n",
              "      <td>25.219512</td>\n",
              "      <td>30.751220</td>\n",
              "      <td>13207.129353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.245307</td>\n",
              "      <td>35.442168</td>\n",
              "      <td>6.021776</td>\n",
              "      <td>12.337289</td>\n",
              "      <td>2.145204</td>\n",
              "      <td>2.443522</td>\n",
              "      <td>520.680204</td>\n",
              "      <td>41.642693</td>\n",
              "      <td>0.273539</td>\n",
              "      <td>0.316717</td>\n",
              "      <td>3.972040</td>\n",
              "      <td>39.714369</td>\n",
              "      <td>479.334560</td>\n",
              "      <td>6.542142</td>\n",
              "      <td>6.886443</td>\n",
              "      <td>7947.066342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>86.600000</td>\n",
              "      <td>141.100000</td>\n",
              "      <td>60.300000</td>\n",
              "      <td>47.800000</td>\n",
              "      <td>1488.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>2.540000</td>\n",
              "      <td>2.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>4150.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>5118.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>166.300000</td>\n",
              "      <td>64.100000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2145.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7775.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.200000</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>2414.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>3.290000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>5200.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10295.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>183.100000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>2935.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>3.590000</td>\n",
              "      <td>3.410000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>5500.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>16500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.100000</td>\n",
              "      <td>72.300000</td>\n",
              "      <td>59.800000</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>4.170000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>45400.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        symboling  normalized_losses  ...      hw_mpg         price\n",
              "count  205.000000         164.000000  ...  205.000000    201.000000\n",
              "mean     0.834146         122.000000  ...   30.751220  13207.129353\n",
              "std      1.245307          35.442168  ...    6.886443   7947.066342\n",
              "min     -2.000000          65.000000  ...   16.000000   5118.000000\n",
              "25%      0.000000          94.000000  ...   25.000000   7775.000000\n",
              "50%      1.000000         115.000000  ...   30.000000  10295.000000\n",
              "75%      2.000000         150.000000  ...   34.000000  16500.000000\n",
              "max      3.000000         256.000000  ...   54.000000  45400.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "ax343O1KzLLl",
        "outputId": "8d9e60fe-5c60-437e-eda2-c09f026e63f8"
      },
      "source": [
        "%matplotlib inline   \n",
        "import matplotlib.pyplot as plt\n",
        "df.hist(bins=15, figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZikHS3dqthRh"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_set = train_test_split(df, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNc7UZxUs9g8"
      },
      "source": [
        "median = df[\"price\"].median()\n",
        "df[\"price\"].fillna(median, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxbmniN7omBE",
        "outputId": "e1aa0357-a13a-4cf7-c693-a9f6310e78db"
      },
      "source": [
        "df['price'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10295.0    5\n",
              "13499.0    2\n",
              "6692.0     2\n",
              "6229.0     2\n",
              "8495.0     2\n",
              "          ..\n",
              "15580.0    1\n",
              "6377.0     1\n",
              "30760.0    1\n",
              "16925.0    1\n",
              "18920.0    1\n",
              "Name: price, Length: 186, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_G1oqEGpsaz"
      },
      "source": [
        "df[\"pricing_cat\"] = pd.cut(df[\"price\"],\n",
        "                               bins=[0., 6000.1, 10000.1, 20000.1, 40000., np.inf], \n",
        "                               labels=[1, 2, 3, 4, 5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ZALH-M49vqxT",
        "outputId": "4830b875-ab30-42c0-f101-d956b425a485"
      },
      "source": [
        "df[\"pricing_cat\"].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f075670ada0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBoFhIdJ0uiM"
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(df, df[\"pricing_cat\"]):\n",
        "    strat_train_set = df.loc[train_index]\n",
        "    strat_test_set = df.loc[test_index]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVVyXpQjnN3m",
        "outputId": "454bfda8-ee5e-48f8-cc37-51255a35d7d3"
      },
      "source": [
        "strat_test_set[\"pricing_cat\"].value_counts() / len(strat_test_set)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    0.439024\n",
              "3    0.390244\n",
              "4    0.097561\n",
              "1    0.048780\n",
              "5    0.024390\n",
              "Name: pricing_cat, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGEo_oPdrnob"
      },
      "source": [
        "for set_ in (strat_train_set, strat_test_set):\n",
        "    set_.drop(\"pricing_cat\", axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlEPBlDwMDQG"
      },
      "source": [
        "housing = strat_train_set.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "gWYcku4DMX-S",
        "outputId": "bc5b0e87-86f0-4442-eac3-af89e0aebd8e"
      },
      "source": [
        "df.plot(kind=\"scatter\", x=\"engine_size\", y=\"price\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0756e14be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "3_LGOHhlM8nn",
        "outputId": "3081c3b6-7ba6-4cfe-fe3a-5abb0cf0618b"
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"engine_size\", y=\"price\", alpha=0.4,\n",
        "    s=housing[\"compression_ratio\"], label=\"compression_raio\", figsize=(10,7),\n",
        "    c=\"hw_mpg\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
        ")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f075631f978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-ocRhwwOapp"
      },
      "source": [
        "corr_matrix = df.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snShpG2rO78g",
        "outputId": "660dfb6c-9d3a-47cf-d140-c29ec80f05ae"
      },
      "source": [
        "corr_matrix[\"price\"].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                1.000000\n",
              "engine_size          0.860343\n",
              "curb_weight          0.819817\n",
              "horsepower           0.749844\n",
              "width                0.724558\n",
              "length               0.686567\n",
              "wheel-base           0.584847\n",
              "bore                 0.533139\n",
              "normalized_losses    0.203254\n",
              "height               0.140439\n",
              "stroke               0.083716\n",
              "compression_ratio    0.072890\n",
              "symboling           -0.080149\n",
              "peak_rpm            -0.106712\n",
              "city_mpg            -0.668822\n",
              "hw_mpg              -0.693037\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QVAS-prHPPJl",
        "outputId": "0727e569-c751-49f5-ef42-5abb3d63c378"
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "attributes = [\"price\", \"engine_size\", \"curb_weight\", \"width\", \"length\",\n",
        "                                \"city_mpg\", \"hw_mpg\", \"bore\", \"stroke\"]\n",
        "\n",
        "scatter_matrix(df[attributes], figsize=(15, 12))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f0758061240>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0758291ef0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07562af6a0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756497ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07564212b0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075646ee80>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075670a2b0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07566b3cc0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07566b3dd8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0756876278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07564ab198>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0757b24320>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07568d5518>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075694bb38>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07585ce748>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07568ddc88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07567baef0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756880198>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0756712400>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756781668>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075654b8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07568b9b38>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756502da0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07567effd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07565aa2b0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756938518>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07565e3780>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f07566659e8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075664dc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075696d8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07569f1c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07569bbfd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756a4e390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756a2d710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756a5da90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756b66e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0756b0a1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756ad1550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756c118d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756bddc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756bc4fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756b41390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756273710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756226a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07561d7e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f07561971d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07561c8550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075617a8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075612bc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07560e0fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f075609d390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f07560d1710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756082a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0756034e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0755ff31d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755fa5550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755f578d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755f89c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755f3cfd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755ef9390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755eab710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755e5ca90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755e90e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0755e501d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755e00550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755db28d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755d64c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755d18fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755cd6390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755d08710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755cbaa90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755c6ce10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0755c2b1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755bdc550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755c0f8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755bc1c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755b74fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755b32390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755ae3710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755b14a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0755ac8e10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 81 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "3e664aZCQ50t",
        "outputId": "f01536b5-b50f-4ff6-871e-350ea51590f2"
      },
      "source": [
        "df.plot(kind=\"scatter\", x=\"price\", y=\"engine_size\", \n",
        "             alpha=0.5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f07554f9d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvngmFtwTAWh"
      },
      "source": [
        "df[\"city_hw_mpg_ratio\"] = df[\"city_mpg\"]/df[\"hw_mpg\"]\n",
        "df[\"curb_wt_engine_size_ratio\"]= df[\"curb_weight\"]/df[\"engine_size\"]\n",
        "df['engine_size_curb_wt_ratio'] = df['engine_size']/df['curb_weight']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEnljSW8Xvk5",
        "outputId": "7aa39de3-b5c9-49b7-a9a3-5bab2c907da8"
      },
      "source": [
        "corr_matrix = df.corr()\n",
        "corr_matrix[\"price\"].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                        1.000000\n",
              "engine_size                  0.860343\n",
              "curb_weight                  0.819817\n",
              "horsepower                   0.749844\n",
              "width                        0.724558\n",
              "length                       0.686567\n",
              "engine_size_curb_wt_ratio    0.587916\n",
              "wheel-base                   0.584847\n",
              "bore                         0.533139\n",
              "normalized_losses            0.203254\n",
              "height                       0.140439\n",
              "stroke                       0.083716\n",
              "compression_ratio            0.072890\n",
              "symboling                   -0.080149\n",
              "peak_rpm                    -0.106712\n",
              "city_hw_mpg_ratio           -0.247039\n",
              "curb_wt_engine_size_ratio   -0.431969\n",
              "city_mpg                    -0.668822\n",
              "hw_mpg                      -0.693037\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qLLNBYKYO_k"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(strategy=\"median\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbvmE8FPZ2ik"
      },
      "source": [
        "price_point = df.drop([\"normalized_losses\", \"make\", \"aspiration\", \"num_doors\", \"fuel_type\", \"body_style\", \"drive_wheels\", \"engine_location\", \"engine_type\", \"num_cylinders\", \"fuel_system\"], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ys6nSBUQbWEa",
        "outputId": "9a9a5483-ac49-4ac8-ed32-f1a673ea7e23"
      },
      "source": [
        "imputer.fit(price_point)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
              "              missing_values=nan, strategy='median', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "Eiw_leMJbmnD",
        "outputId": "c6869ef8-e6a2-449b-fdbe-deb3a392e538"
      },
      "source": [
        "df.applymap(lambda x: type(x).__name__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>body_style</th>\n",
              "      <th>drive_wheels</th>\n",
              "      <th>engine_location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>num_cylinders</th>\n",
              "      <th>engine_size</th>\n",
              "      <th>fuel_system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
              "      <th>pricing_cat</th>\n",
              "      <th>city_hw_mpg_ratio</th>\n",
              "      <th>curb_wt_engine_size_ratio</th>\n",
              "      <th>engine_size_curb_wt_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
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              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>str</td>\n",
              "      <td>int</td>\n",
              "      <td>str</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>int</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "      <td>float</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    symboling  ... engine_size_curb_wt_ratio\n",
              "0         int  ...                     float\n",
              "1         int  ...                     float\n",
              "2         int  ...                     float\n",
              "3         int  ...                     float\n",
              "4         int  ...                     float\n",
              "..        ...  ...                       ...\n",
              "200       int  ...                     float\n",
              "201       int  ...                     float\n",
              "202       int  ...                     float\n",
              "203       int  ...                     float\n",
              "204       int  ...                     float\n",
              "\n",
              "[205 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lBRVKficIaF",
        "outputId": "4135ecd6-003c-49ec-f469-41f6b50a970c"
      },
      "source": [
        "imputer.statistics_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.00000000e+00, 9.70000000e+01, 1.73200000e+02, 6.55000000e+01,\n",
              "       5.41000000e+01, 2.41400000e+03, 1.20000000e+02, 3.31000000e+00,\n",
              "       3.29000000e+00, 9.00000000e+00, 9.50000000e+01, 5.20000000e+03,\n",
              "       2.40000000e+01, 3.00000000e+01, 1.02950000e+04, 3.00000000e+00,\n",
              "       8.12500000e-01, 2.08659794e+01, 4.79249012e-02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kyYqVL-vjf5j",
        "outputId": "021cd017-abe5-47de-af0b-2324920c108c"
      },
      "source": [
        "price_point.median().values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.00000000e+00, 9.70000000e+01, 1.73200000e+02, 6.55000000e+01,\n",
              "       5.41000000e+01, 2.41400000e+03, 1.20000000e+02, 3.31000000e+00,\n",
              "       3.29000000e+00, 9.00000000e+00, 9.50000000e+01, 5.20000000e+03,\n",
              "       2.40000000e+01, 3.00000000e+01, 1.02950000e+04, 8.12500000e-01,\n",
              "       2.08659794e+01, 4.79249012e-02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BY-epuu1j4ha"
      },
      "source": [
        "X = imputer.transform(price_point)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHxKnYZzkgrk"
      },
      "source": [
        "pricing_tr = pd.DataFrame(X, columns=price_point.columns,\n",
        "                          index=price_point.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KH5QNB3qg04L"
      },
      "source": [
        "# Handling Text and Categorical Attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93mewyMmlDTP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "c6b5d54b-1958-4628-b909-a50f04a21001"
      },
      "source": [
        "pricing_cat = df[[\"make\"]]\n",
        "pricing_cat.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>make</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alfa-romero</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>alfa-romero</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alfa-romero</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>audi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          make\n",
              "0  alfa-romero\n",
              "1  alfa-romero\n",
              "2  alfa-romero\n",
              "3         audi\n",
              "4         audi\n",
              "5         audi\n",
              "6         audi\n",
              "7         audi\n",
              "8         audi\n",
              "9         audi"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYWTI5DEhkUH",
        "outputId": "f160955b-20c4-427a-d301-dfbaf6ba7108"
      },
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "ordinal_encoder = OrdinalEncoder()\n",
        "pricing_cat_encoded = ordinal_encoder.fit_transform(pricing_cat)\n",
        "pricing_cat_encoded[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmh_B9zNjO6p",
        "outputId": "daef7b75-bb2f-4f36-ce67-13d4559cee45"
      },
      "source": [
        "ordinal_encoder.categories_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
              "        'isuzu', 'jaguar', 'mazda', 'mercedes-benz', 'mercury',\n",
              "        'mitsubishi', 'nissan', 'peugot', 'plymouth', 'porsche', 'renault',\n",
              "        'saab', 'subaru', 'toyota', 'volkswagen', 'volvo'], dtype=object)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T2VUDRAjwrM",
        "outputId": "4b09abd8-5a46-45ca-d158-082ae426e0a7"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "cat_encoder = OneHotEncoder()\n",
        "pricing_cat_1hot = cat_encoder.fit_transform(pricing_cat)\n",
        "pricing_cat_1hot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<205x22 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 205 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJ5T0RHAvGTt",
        "outputId": "6bc9fd9f-8edb-4f3e-e6c1-3b081d088767"
      },
      "source": [
        "pricing_cat_1hot.toarray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CrK-IOgvegC",
        "outputId": "36f08bec-6fe5-4d04-dbbc-efa1ca62dc0b"
      },
      "source": [
        "cat_encoder.categories_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
              "        'isuzu', 'jaguar', 'mazda', 'mercedes-benz', 'mercury',\n",
              "        'mitsubishi', 'nissan', 'peugot', 'plymouth', 'porsche', 'renault',\n",
              "        'saab', 'subaru', 'toyota', 'volkswagen', 'volvo'], dtype=object)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2Pp3tb0zPtw"
      },
      "source": [
        "col_names = \"curb_weight\", \"engine_size\", \"city_mpg\", \"hw_mpg\"\n",
        "curb_weight_ix, engine_size_ix, city_mpg_ix, hw_mpg_ix = [\n",
        "    df.columns.get_loc(c) for c in col_names] # get the column indices \n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "_ZTdiHvuvx8r",
        "outputId": "b1e3a274-03b9-4e16-db37-41e7e6631ae3"
      },
      "source": [
        "# from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "\n",
        "# curb_weight_ix, engine_size_ix, city_mpg_ix, hw_mpg_ix = 3, 4, 5, 6\n",
        "\n",
        "# class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
        "#     def __init__(self, add_engine_size_curb_wt_ratio=True): # no *args or **kargs\n",
        "#         self.add_engine_size_curb_wt_ratio = add_engine_size_curb_wt_ratio\n",
        "#     def fit(self, X, y=None):\n",
        "#         return self  # nothing else to do\n",
        "#     def transform(self, X):\n",
        "#         curb_wt_engine_size_ratio = X[:, curb_weight_ix] / X[:, engine_size_ix]\n",
        "#         city_hw_mpg_ratio = X[:, city_mpg_ix] / X[:, hw_mpg_ix]\n",
        "#         if self.add_engine_size_curb_wt_ratio:\n",
        "#             engine_size_curb_wt_ratio = X[:, engine_size_ix] / X[:, curb_weight_ix]\n",
        "#             return np.c_[X,engine_size_curb_wt_ratio , city_hw_mpg_ratio,\n",
        "#                 curb_wt_engine_size_ratio]\n",
        "\n",
        "#         else:\n",
        "#             return np.c_[X, curb_wt_engine_size_ratio, city_hw_mpg_ratio,\n",
        "#                          engine_size_curb_wt_ratio]\n",
        "\n",
        "# attr_adder = CombinedAttributesAdder(add_engine_size_curb_wt_ratio=False)\n",
        "# price_point_extra_attribs = attr_adder.transform(df.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-252-ac098eb17bbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mattr_adder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCombinedAttributesAdder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_engine_size_curb_wt_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mprice_point_extra_attribs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattr_adder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-252-ac098eb17bbd>\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m  \u001b[0;31m# nothing else to do\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0mcurb_wt_engine_size_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurb_weight_ix\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine_size_ix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m         \u001b[0mcity_hw_mpg_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcity_mpg_ix\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhw_mpg_ix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_engine_size_curb_wt_ratio\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kBTZ6EJ1e8G"
      },
      "source": [
        "# How can we convert categorical columns into the numerical counterpart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCAiw8ZK4y0O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "25dcf69b-9ed6-44f8-fae7-6a4df3c85c35"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>body_style</th>\n",
              "      <th>drive_wheels</th>\n",
              "      <th>engine_location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>num_cylinders</th>\n",
              "      <th>engine_size</th>\n",
              "      <th>fuel_system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>177.3</td>\n",
              "      <td>66.3</td>\n",
              "      <td>53.1</td>\n",
              "      <td>2507</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>15250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2844</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>17710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2954</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>18920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.9</td>\n",
              "      <td>3086</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>131</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.3</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>23875.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.5</td>\n",
              "      <td>178.2</td>\n",
              "      <td>67.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3053</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>131</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>7.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized_losses         make  ... city_mpg hw_mpg    price\n",
              "0          3                NaN  alfa-romero  ...       21     27  13495.0\n",
              "1          3                NaN  alfa-romero  ...       21     27  16500.0\n",
              "2          1                NaN  alfa-romero  ...       19     26  16500.0\n",
              "3          2              164.0         audi  ...       24     30  13950.0\n",
              "4          2              164.0         audi  ...       18     22  17450.0\n",
              "5          2                NaN         audi  ...       19     25  15250.0\n",
              "6          1              158.0         audi  ...       19     25  17710.0\n",
              "7          1                NaN         audi  ...       19     25  18920.0\n",
              "8          1              158.0         audi  ...       17     20  23875.0\n",
              "9          0                NaN         audi  ...       16     22      NaN\n",
              "\n",
              "[10 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgXiIk0q-LDN"
      },
      "source": [
        "df = pd.read_excel('car_data_fixed_final.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "kmnuMa6C1vok",
        "outputId": "9a2e285f-15e0-42d7-9c87-78b16927adcf"
      },
      "source": [
        "# df.body_style = pd.Categorical(df.body_style) #covert columntype from x to y\n",
        "# df[\"body_style\"] = df.body_style.cat.codes # replace every value in column with their categorical codes\n",
        "# df.head(10)\n",
        "catregorical_columns = [\"make\", \"fuel_type\", \"aspiration\", \"num_doors\", \"body_style\", \"drive_wheels\", \"engine_location\", \"engine_type\", \"num_cylinders\", \"fuel_system\"]\n",
        "for col in catregorical_columns:\n",
        "    df[col] = pd.Categorical(df[col])\n",
        "    df[col] = df[col].cat.codes\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>body_style</th>\n",
              "      <th>drive_wheels</th>\n",
              "      <th>engine_location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>num_cylinders</th>\n",
              "      <th>engine_size</th>\n",
              "      <th>fuel_system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>5</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>5</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>152</td>\n",
              "      <td>5</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>109</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99.8</td>\n",
              "      <td>177.3</td>\n",
              "      <td>66.3</td>\n",
              "      <td>53.1</td>\n",
              "      <td>2507</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>15250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2844</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>17710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
              "      <td>2954</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>18920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.9</td>\n",
              "      <td>3086</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>131</td>\n",
              "      <td>5</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.3</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>23875.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>99.5</td>\n",
              "      <td>178.2</td>\n",
              "      <td>67.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3053</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>131</td>\n",
              "      <td>5</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>7.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized_losses  make  ...  city_mpg  hw_mpg    price\n",
              "0          3                NaN     0  ...        21      27  13495.0\n",
              "1          3                NaN     0  ...        21      27  16500.0\n",
              "2          1                NaN     0  ...        19      26  16500.0\n",
              "3          2              164.0     1  ...        24      30  13950.0\n",
              "4          2              164.0     1  ...        18      22  17450.0\n",
              "5          2                NaN     1  ...        19      25  15250.0\n",
              "6          1              158.0     1  ...        19      25  17710.0\n",
              "7          1                NaN     1  ...        19      25  18920.0\n",
              "8          1              158.0     1  ...        17      20  23875.0\n",
              "9          0                NaN     1  ...        16      22      NaN\n",
              "\n",
              "[10 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "Jc64c5Hr2Ff8",
        "outputId": "d0debb61-c2a6-4194-b3cf-cd6b824fc671"
      },
      "source": [
        "df= df.dropna()\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>symboling</th>\n",
              "      <th>normalized_losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>body_style</th>\n",
              "      <th>drive_wheels</th>\n",
              "      <th>engine_location</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb_weight</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>num_cylinders</th>\n",
              "      <th>engine_size</th>\n",
              "      <th>fuel_system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression_ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak_rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>hw_mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>109</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.7</td>\n",
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              "      <td>5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>17710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>105.8</td>\n",
              "      <td>192.7</td>\n",
              "      <td>71.4</td>\n",
              "      <td>55.9</td>\n",
              "      <td>3086</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>131</td>\n",
              "      <td>5</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.3</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>23875.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>192.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2395</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>5</td>\n",
              "      <td>3.50</td>\n",
              "      <td>2.80</td>\n",
              "      <td>8.8</td>\n",
              "      <td>101.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>16430.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2395</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>5</td>\n",
              "      <td>3.50</td>\n",
              "      <td>2.80</td>\n",
              "      <td>8.8</td>\n",
              "      <td>101.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>16925.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2710</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>164</td>\n",
              "      <td>5</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>20970.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>101.2</td>\n",
              "      <td>176.8</td>\n",
              "      <td>64.8</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2765</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>164</td>\n",
              "      <td>5</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>21105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2</td>\n",
              "      <td>121.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>88.4</td>\n",
              "      <td>141.1</td>\n",
              "      <td>60.3</td>\n",
              "      <td>53.2</td>\n",
              "      <td>1488</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2.91</td>\n",
              "      <td>3.03</td>\n",
              "      <td>9.5</td>\n",
              "      <td>48.0</td>\n",
              "      <td>5100.0</td>\n",
              "      <td>47</td>\n",
              "      <td>53</td>\n",
              "      <td>5151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>98.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>94.5</td>\n",
              "      <td>155.9</td>\n",
              "      <td>63.6</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1874</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.11</td>\n",
              "      <td>9.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>6295.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    symboling  normalized_losses  make  ...  city_mpg  hw_mpg    price\n",
              "3           2              164.0     1  ...        24      30  13950.0\n",
              "4           2              164.0     1  ...        18      22  17450.0\n",
              "6           1              158.0     1  ...        19      25  17710.0\n",
              "8           1              158.0     1  ...        17      20  23875.0\n",
              "10          2              192.0     2  ...        23      29  16430.0\n",
              "11          0              192.0     2  ...        23      29  16925.0\n",
              "12          0              188.0     2  ...        21      28  20970.0\n",
              "13          0              188.0     2  ...        21      28  21105.0\n",
              "18          2              121.0     3  ...        47      53   5151.0\n",
              "19          1               98.0     3  ...        38      43   6295.0\n",
              "\n",
              "[10 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrPgXj0k6Zhe",
        "outputId": "ea0b64c9-0e93-43fe-c55b-204e2b76453f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 160 entries, 3 to 204\n",
            "Data columns (total 26 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   symboling          160 non-null    int64  \n",
            " 1   normalized_losses  160 non-null    float64\n",
            " 2   make               160 non-null    int8   \n",
            " 3   fuel_type          160 non-null    int8   \n",
            " 4   aspiration         160 non-null    int8   \n",
            " 5   num_doors          160 non-null    int8   \n",
            " 6   body_style         160 non-null    int8   \n",
            " 7   drive_wheels       160 non-null    int8   \n",
            " 8   engine_location    160 non-null    int8   \n",
            " 9   wheel-base         160 non-null    float64\n",
            " 10  length             160 non-null    float64\n",
            " 11  width              160 non-null    float64\n",
            " 12  height             160 non-null    float64\n",
            " 13  curb_weight        160 non-null    int64  \n",
            " 14  engine_type        160 non-null    int8   \n",
            " 15  num_cylinders      160 non-null    int8   \n",
            " 16  engine_size        160 non-null    int64  \n",
            " 17  fuel_system        160 non-null    int8   \n",
            " 18  bore               160 non-null    float64\n",
            " 19  stroke             160 non-null    float64\n",
            " 20  compression_ratio  160 non-null    float64\n",
            " 21  horsepower         160 non-null    float64\n",
            " 22  peak_rpm           160 non-null    float64\n",
            " 23  city_mpg           160 non-null    int64  \n",
            " 24  hw_mpg             160 non-null    int64  \n",
            " 25  price              160 non-null    float64\n",
            "dtypes: float64(11), int64(5), int8(10)\n",
            "memory usage: 22.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XU2NG92C6-uk",
        "outputId": "65576938-79c5-4ebe-e63f-c2d2daee4031"
      },
      "source": [
        "from sklearn.preprocessing import Normalizer\n",
        "data = df.to_numpy()\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.0000e+00,  1.6400e+02,  1.0000e+00, ...,  3.0000e+01,\n",
              "         1.3950e+04,  3.0000e+00],\n",
              "       [ 2.0000e+00,  1.6400e+02,  1.0000e+00, ...,  2.2000e+01,\n",
              "         1.7450e+04,  3.0000e+00],\n",
              "       [ 1.0000e+00,  1.5800e+02,  1.0000e+00, ...,  2.5000e+01,\n",
              "         1.7710e+04,  3.0000e+00],\n",
              "       ...,\n",
              "       [-1.0000e+00,  9.5000e+01,  2.1000e+01, ...,  2.3000e+01,\n",
              "         2.1485e+04,  3.0000e+00],\n",
              "       [-1.0000e+00,  9.5000e+01,  2.1000e+01, ...,  2.7000e+01,\n",
              "         2.2470e+04,  3.0000e+00],\n",
              "       [-1.0000e+00,  9.5000e+01,  2.1000e+01, ...,  2.5000e+01,\n",
              "         2.2625e+04,  3.0000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QS5SZV758U1J",
        "outputId": "b215c7f8-7dc6-4e97-b509-7f26f83feb11"
      },
      "source": [
        "# Now we will normalize the above array\n",
        "data = Normalizer().fit_transform(data)\n",
        "data\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.31757741e-04,  1.08041347e-02,  6.58788703e-05, ...,\n",
              "         1.97636611e-03,  9.19010240e-01,  1.97636611e-04],\n",
              "       [ 1.08015759e-04,  8.85729221e-03,  5.40078793e-05, ...,\n",
              "         1.18817335e-03,  9.42437494e-01,  1.62023638e-04],\n",
              "       [ 5.32929380e-05,  8.42028420e-03,  5.32929380e-05, ...,\n",
              "         1.33232345e-03,  9.43817932e-01,  1.59878814e-04],\n",
              "       ...,\n",
              "       [-4.46748713e-05,  4.24411278e-03,  9.38172298e-04, ...,\n",
              "         1.02752204e-03,  9.59839611e-01,  1.34024614e-04],\n",
              "       [-4.30974795e-05,  4.09426055e-03,  9.05047069e-04, ...,\n",
              "         1.16363195e-03,  9.68400363e-01,  1.29292438e-04],\n",
              "       [-4.26197932e-05,  4.04888036e-03,  8.95015658e-04, ...,\n",
              "         1.06549483e-03,  9.64272822e-01,  1.27859380e-04]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1ozlXJX-iXm",
        "outputId": "6025585f-d378-4705-a7de-f8c80068b97e"
      },
      "source": [
        "X, y = np.delete(data, -4, axis = 1), data[:,-4]\n",
        "print(X.shape,y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(160, 26) (160,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sdoKfBw9Tb7",
        "outputId": "b814f398-4420-48ab-f09c-71138144a800"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .85)\n",
        "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(24, 26) (24,) (136, 26) (136,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2Tv1giFAH3h",
        "outputId": "abe3f72a-a3ed-4aee-e447-ff4b870fda49"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(\"accuracy = {}\".format(accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy = 0.6537567884105903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYcxyct3BpGH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "be494a5d-9c33-41bd-d660-82bd5f19cf53"
      },
      "source": [
        "test_sizes = []\n",
        "accuracies = []\n",
        "for test_size in np.arange(0.90, 0.10, -0.01):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size)\n",
        "    test_sizes.append(test_size)\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "    accuracy = model.score(X_test, y_test)\n",
        "    accuracies.append(accuracy)\n",
        "    print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, test_size, accuracy)\n",
        "\n",
        "plt.scatter(test_sizes, accuracies, s = 4.00)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16, 26) (16,) (144, 26) (144,) 0.9 0.9054312145857011\n",
            "(17, 26) (17,) (143, 26) (143,) 0.89 0.8860948247949052\n",
            "(19, 26) (19,) (141, 26) (141,) 0.88 0.9707541174748897\n",
            "(20, 26) (20,) (140, 26) (140,) 0.87 0.9214334842982969\n",
            "(22, 26) (22,) (138, 26) (138,) 0.86 0.6624540023059214\n",
            "(24, 26) (24,) (136, 26) (136,) 0.85 0.4354472854532089\n",
            "(25, 26) (25,) (135, 26) (135,) 0.84 -29.55547812536829\n",
            "(27, 26) (27,) (133, 26) (133,) 0.83 0.8877992454844327\n",
            "(28, 26) (28,) (132, 26) (132,) 0.82 0.7240915253858935\n",
            "(30, 26) (30,) (130, 26) (130,) 0.8099999999999999 0.9455984156482903\n",
            "(32, 26) (32,) (128, 26) (128,) 0.7999999999999999 0.7302864048424127\n",
            "(33, 26) (33,) (127, 26) (127,) 0.7899999999999999 0.9649763593713994\n",
            "(35, 26) (35,) (125, 26) (125,) 0.7799999999999999 0.9643479658904279\n",
            "(36, 26) (36,) (124, 26) (124,) 0.7699999999999999 0.9529788991601147\n",
            "(38, 26) (38,) (122, 26) (122,) 0.7599999999999999 0.9742539862607612\n",
            "(40, 26) (40,) (120, 26) (120,) 0.7499999999999999 0.9715743772007489\n",
            "(41, 26) (41,) (119, 26) (119,) 0.7399999999999999 0.9686846271169752\n",
            "(43, 26) (43,) (117, 26) (117,) 0.7299999999999999 0.9749837390665533\n",
            "(44, 26) (44,) (116, 26) (116,) 0.7199999999999999 0.9802036679778848\n",
            "(46, 26) (46,) (114, 26) (114,) 0.7099999999999999 0.8929183314227112\n",
            "(48, 26) (48,) (112, 26) (112,) 0.6999999999999998 0.9704218995426777\n",
            "(49, 26) (49,) (111, 26) (111,) 0.6899999999999998 0.9747471159872836\n",
            "(51, 26) (51,) (109, 26) (109,) 0.6799999999999998 0.970867217542249\n",
            "(52, 26) (52,) (108, 26) (108,) 0.6699999999999998 0.9782368051918201\n",
            "(54, 26) (54,) (106, 26) (106,) 0.6599999999999998 0.9755933731602897\n",
            "(56, 26) (56,) (104, 26) (104,) 0.6499999999999998 0.9797998744174264\n",
            "(57, 26) (57,) (103, 26) (103,) 0.6399999999999998 0.9821854097129888\n",
            "(59, 26) (59,) (101, 26) (101,) 0.6299999999999998 0.98087969301173\n",
            "(60, 26) (60,) (100, 26) (100,) 0.6199999999999998 0.9811043084736535\n",
            "(62, 26) (62,) (98, 26) (98,) 0.6099999999999998 0.9850455890373963\n",
            "(64, 26) (64,) (96, 26) (96,) 0.5999999999999998 0.9828016863086594\n",
            "(65, 26) (65,) (95, 26) (95,) 0.5899999999999997 0.985856741303214\n",
            "(67, 26) (67,) (93, 26) (93,) 0.5799999999999997 0.9800642023217099\n",
            "(68, 26) (68,) (92, 26) (92,) 0.5699999999999997 0.9761158691740082\n",
            "(70, 26) (70,) (90, 26) (90,) 0.5599999999999997 0.9875897472835632\n",
            "(72, 26) (72,) (88, 26) (88,) 0.5499999999999997 0.9791359369669265\n",
            "(73, 26) (73,) (87, 26) (87,) 0.5399999999999997 0.9870677484728517\n",
            "(75, 26) (75,) (85, 26) (85,) 0.5299999999999997 0.9853540184369323\n",
            "(76, 26) (76,) (84, 26) (84,) 0.5199999999999997 0.989178818644704\n",
            "(78, 26) (78,) (82, 26) (82,) 0.5099999999999997 0.9818554376400399\n",
            "(80, 26) (80,) (80, 26) (80,) 0.49999999999999967 0.9788957240108944\n",
            "(81, 26) (81,) (79, 26) (79,) 0.48999999999999966 0.9784408911866755\n",
            "(83, 26) (83,) (77, 26) (77,) 0.47999999999999965 0.9850589611882754\n",
            "(84, 26) (84,) (76, 26) (76,) 0.46999999999999964 0.9779850114241658\n",
            "(86, 26) (86,) (74, 26) (74,) 0.45999999999999963 0.9857491244797137\n",
            "(88, 26) (88,) (72, 26) (72,) 0.4499999999999996 0.9756356591039125\n",
            "(89, 26) (89,) (71, 26) (71,) 0.4399999999999996 0.9861669168664169\n",
            "(91, 26) (91,) (69, 26) (69,) 0.4299999999999996 0.9870828182701783\n",
            "(92, 26) (92,) (68, 26) (68,) 0.4199999999999996 0.9873591456700375\n",
            "(94, 26) (94,) (66, 26) (66,) 0.4099999999999996 0.9891690851478626\n",
            "(96, 26) (96,) (64, 26) (64,) 0.3999999999999996 0.9843998816730642\n",
            "(97, 26) (97,) (63, 26) (63,) 0.38999999999999957 0.9879161931337297\n",
            "(99, 26) (99,) (61, 26) (61,) 0.37999999999999956 0.9785147782506135\n",
            "(100, 26) (100,) (60, 26) (60,) 0.36999999999999955 0.9862392969773317\n",
            "(102, 26) (102,) (58, 26) (58,) 0.35999999999999954 0.9891759834203626\n",
            "(104, 26) (104,) (56, 26) (56,) 0.34999999999999953 0.9854703934061548\n",
            "(105, 26) (105,) (55, 26) (55,) 0.3399999999999995 0.9811294816346181\n",
            "(107, 26) (107,) (53, 26) (53,) 0.3299999999999995 0.9784281323722382\n",
            "(108, 26) (108,) (52, 26) (52,) 0.3199999999999995 0.9832379986513404\n",
            "(110, 26) (110,) (50, 26) (50,) 0.3099999999999995 0.9786402454232906\n",
            "(112, 26) (112,) (48, 26) (48,) 0.2999999999999995 0.9882035015274137\n",
            "(113, 26) (113,) (47, 26) (47,) 0.2899999999999995 0.9875274484509219\n",
            "(115, 26) (115,) (45, 26) (45,) 0.27999999999999947 0.9889312802591496\n",
            "(116, 26) (116,) (44, 26) (44,) 0.26999999999999946 0.9935672632012158\n",
            "(118, 26) (118,) (42, 26) (42,) 0.25999999999999945 0.9836597958683205\n",
            "(120, 26) (120,) (40, 26) (40,) 0.24999999999999944 0.9801332137101793\n",
            "(121, 26) (121,) (39, 26) (39,) 0.23999999999999944 0.9880185737559163\n",
            "(123, 26) (123,) (37, 26) (37,) 0.22999999999999943 0.9691257024167792\n",
            "(124, 26) (124,) (36, 26) (36,) 0.21999999999999942 0.9838108196596925\n",
            "(126, 26) (126,) (34, 26) (34,) 0.2099999999999994 0.9869608562062488\n",
            "(128, 26) (128,) (32, 26) (32,) 0.1999999999999994 0.9852445161291029\n",
            "(129, 26) (129,) (31, 26) (31,) 0.1899999999999994 0.9878925185514996\n",
            "(131, 26) (131,) (29, 26) (29,) 0.17999999999999938 0.9778274981625117\n",
            "(132, 26) (132,) (28, 26) (28,) 0.16999999999999937 0.98392154150755\n",
            "(134, 26) (134,) (26, 26) (26,) 0.15999999999999936 0.981545137737622\n",
            "(136, 26) (136,) (24, 26) (24,) 0.14999999999999936 0.9845616952344628\n",
            "(137, 26) (137,) (23, 26) (23,) 0.13999999999999935 0.9591883762350496\n",
            "(139, 26) (139,) (21, 26) (21,) 0.12999999999999934 0.9866911275432384\n",
            "(140, 26) (140,) (20, 26) (20,) 0.11999999999999933 0.9910290978785922\n",
            "(142, 26) (142,) (18, 26) (18,) 0.10999999999999932 0.9876924638112873\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f33ca13a5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EakVBzYqEp2q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}