Auto.ipynb

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  "metadata": {
    "colab": {
      "name": "Session Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jChAOlvNBV2"
      },
      "source": [
        "# Introdction\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuE5ZNg6RifG"
      },
      "source": [
        "## The data belongs to car insurance company. <br>`symboling` refers to the risk associated with this car relative to its set.<br> `normalized-losses` is the average loss per car per year. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eO2ptQRSU4l"
      },
      "source": [
        "## Questions:\r\n",
        "\r\n",
        "\r\n",
        "---\r\n",
        "\r\n",
        "\r\n",
        "What questions we wish to ask?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoBF_uwEcNQr"
      },
      "source": [
        "> What are the features affecting the price of the car?\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zq184eWT8eZA"
      },
      "source": [
        "# import packages\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ihIUUNPT8mYv",
        "outputId": "77981f3c-74ef-451d-b0d7-898cde6858ae"
      },
      "source": [
        "# read dataset\r\n",
        "df = pd.read_csv('Automobile_data.csv')\r\n",
        "df.head()"
      ],
      "execution_count": 2,
      "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-of-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-of-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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164</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>3.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164</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>3.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling normalized-losses         make  ... city-mpg highway-mpg  price\n",
              "0          3                 ?  alfa-romero  ...       21          27  13495\n",
              "1          3                 ?  alfa-romero  ...       21          27  16500\n",
              "2          1                 ?  alfa-romero  ...       19          26  16500\n",
              "3          2               164         audi  ...       24          30  13950\n",
              "4          2               164         audi  ...       18          22  17450\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CicpdtHl9ugh",
        "outputId": "c332e681-99d6-44f1-ea90-55f08c0cfac7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x59dt81r9v-Z",
        "outputId": "657b90de-4b65-48b8-ad94-bbbd41cf46d4"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 4,
      "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  205 non-null    object \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-of-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-of-cylinders   205 non-null    object \n",
            " 16  engine-size        205 non-null    int64  \n",
            " 17  fuel-system        205 non-null    object \n",
            " 18  bore               205 non-null    object \n",
            " 19  stroke             205 non-null    object \n",
            " 20  compression-ratio  205 non-null    float64\n",
            " 21  horsepower         205 non-null    object \n",
            " 22  peak-rpm           205 non-null    object \n",
            " 23  city-mpg           205 non-null    int64  \n",
            " 24  highway-mpg        205 non-null    int64  \n",
            " 25  price              205 non-null    object \n",
            "dtypes: float64(5), int64(5), object(16)\n",
            "memory usage: 41.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAiEWLLMxbLn"
      },
      "source": [
        "Adding a correlation heat map to check if there are relations between features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6y3ynNF_c7k3",
        "outputId": "8e6c5151-c9e9-4054-f65c-af230c083f8c"
      },
      "source": [
        "corr = df.corr() # correlation map\r\n",
        "fig, ax = plt.subplots(figsize=(20,20))\r\n",
        "ax = sns.heatmap(corr, annot=True, cmap= 'vlag_r', center = 0)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLyBzQw3eSbH"
      },
      "source": [
        "**From the previous heat map it is shown that there are correlation between some features like :**\r\n",
        ">1. wheel-base &\r\n",
        ">> a. length\\\r\n",
        ">> b. width\\\r\n",
        ">> c. curb-weight\r\n",
        "> 2. length &\r\n",
        ">> a. width\\\r\n",
        ">> b. curb-weight\r\n",
        "> 3. width & \r\n",
        ">> a. curb-weight  \r\n",
        ">> b. engine-size\r\n",
        "> 4. engine-size &\r\n",
        ">> a. curb-weight  \r\n",
        "> 5. highway-mpg &\r\n",
        ">> a. city-mpg\\\r\n",
        ">> b. curb-weight  \r\n",
        ">> c. length\r\n",
        "> 6. Price &\r\n",
        ">> Width\\\r\n",
        ">> Curb-weight\\\r\n",
        ">> Engine-size\\\r\n",
        ">> Horsepower"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIROVWVTMyQd"
      },
      "source": [
        "#Data Cleaning \r\n",
        "\r\n",
        "---\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsNz6eOBMhJu"
      },
      "source": [
        "## Looking for missing data in normalized-losses column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9D-Rl_Q94Ll",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47735133-47b2-4511-bc3a-53350e35b5b9"
      },
      "source": [
        "pd.to_numeric(df['normalized-losses'], errors='coerce').isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IzyWrrMAU5YC",
        "outputId": "04490d62-0325-42b0-f886-74c73f05874c"
      },
      "source": [
        "df[df['normalized-losses'] == '?']"
      ],
      "execution_count": 7,
      "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-of-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-of-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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>?</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>?</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>3.4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110</td>\n",
              "      <td>5500</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>15250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>?</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>3.4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110</td>\n",
              "      <td>5500</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>18920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>?</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>3.4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>160</td>\n",
              "      <td>5500</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>?</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>9.0</td>\n",
              "      <td>121</td>\n",
              "      <td>4250</td>\n",
              "      <td>20</td>\n",
              "      <td>25</td>\n",
              "      <td>24565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>?</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>8.0</td>\n",
              "      <td>182</td>\n",
              "      <td>5400</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>30760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>?</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>8.0</td>\n",
              "      <td>182</td>\n",
              "      <td>5400</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>41315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>?</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>8.0</td>\n",
              "      <td>182</td>\n",
              "      <td>5400</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>36880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>isuzu</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>94.3</td>\n",
              "      <td>170.7</td>\n",
              "      <td>61.8</td>\n",
              "      <td>53.5</td>\n",
              "      <td>2337</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>111</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.23</td>\n",
              "      <td>8.5</td>\n",
              "      <td>78</td>\n",
              "      <td>4800</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>6785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>isuzu</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>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>9.6</td>\n",
              "      <td>70</td>\n",
              "      <td>5400</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>isuzu</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>155.9</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>9.6</td>\n",
              "      <td>70</td>\n",
              "      <td>5400</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>isuzu</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>96.0</td>\n",
              "      <td>172.6</td>\n",
              "      <td>65.2</td>\n",
              "      <td>51.4</td>\n",
              "      <td>2734</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>119</td>\n",
              "      <td>spfi</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.23</td>\n",
              "      <td>9.2</td>\n",
              "      <td>90</td>\n",
              "      <td>5000</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>11048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>jaguar</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>113.0</td>\n",
              "      <td>199.6</td>\n",
              "      <td>69.6</td>\n",
              "      <td>52.8</td>\n",
              "      <td>4066</td>\n",
              "      <td>dohc</td>\n",
              "      <td>six</td>\n",
              "      <td>258</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.63</td>\n",
              "      <td>4.17</td>\n",
              "      <td>8.1</td>\n",
              "      <td>176</td>\n",
              "      <td>4750</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>35550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>jaguar</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>102.0</td>\n",
              "      <td>191.7</td>\n",
              "      <td>70.6</td>\n",
              "      <td>47.8</td>\n",
              "      <td>3950</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>twelve</td>\n",
              "      <td>326</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.54</td>\n",
              "      <td>2.76</td>\n",
              "      <td>11.5</td>\n",
              "      <td>262</td>\n",
              "      <td>5000</td>\n",
              "      <td>13</td>\n",
              "      <td>17</td>\n",
              "      <td>36000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>mazda</td>\n",
              "      <td>diesel</td>\n",
              "      <td>std</td>\n",
              "      <td>?</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>98.8</td>\n",
              "      <td>177.8</td>\n",
              "      <td>66.5</td>\n",
              "      <td>55.5</td>\n",
              "      <td>2443</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>122</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.39</td>\n",
              "      <td>3.39</td>\n",
              "      <td>22.7</td>\n",
              "      <td>64</td>\n",
              "      <td>4650</td>\n",
              "      <td>36</td>\n",
              "      <td>42</td>\n",
              "      <td>10795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>mazda</td>\n",
              "      <td>diesel</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>104.9</td>\n",
              "      <td>175.0</td>\n",
              "      <td>66.1</td>\n",
              "      <td>54.4</td>\n",
              "      <td>2700</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>134</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.64</td>\n",
              "      <td>22.0</td>\n",
              "      <td>72</td>\n",
              "      <td>4200</td>\n",
              "      <td>31</td>\n",
              "      <td>39</td>\n",
              "      <td>18344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>-1</td>\n",
              "      <td>?</td>\n",
              "      <td>mercedes-benz</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>115.6</td>\n",
              "      <td>202.6</td>\n",
              "      <td>71.7</td>\n",
              "      <td>56.5</td>\n",
              "      <td>3740</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>eight</td>\n",
              "      <td>234</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.46</td>\n",
              "      <td>3.1</td>\n",
              "      <td>8.3</td>\n",
              "      <td>155</td>\n",
              "      <td>4750</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>34184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>mercedes-benz</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>120.9</td>\n",
              "      <td>208.1</td>\n",
              "      <td>71.7</td>\n",
              "      <td>56.7</td>\n",
              "      <td>3900</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>eight</td>\n",
              "      <td>308</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.8</td>\n",
              "      <td>3.35</td>\n",
              "      <td>8.0</td>\n",
              "      <td>184</td>\n",
              "      <td>4500</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>40960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>mercedes-benz</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hardtop</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>112.0</td>\n",
              "      <td>199.2</td>\n",
              "      <td>72.0</td>\n",
              "      <td>55.4</td>\n",
              "      <td>3715</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>eight</td>\n",
              "      <td>304</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.8</td>\n",
              "      <td>3.35</td>\n",
              "      <td>8.0</td>\n",
              "      <td>184</td>\n",
              "      <td>4500</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>45400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>mercury</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>102.7</td>\n",
              "      <td>178.4</td>\n",
              "      <td>68.0</td>\n",
              "      <td>54.8</td>\n",
              "      <td>2910</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>140</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.12</td>\n",
              "      <td>8.0</td>\n",
              "      <td>175</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>16503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>mitsubishi</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>2833</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>156</td>\n",
              "      <td>spdi</td>\n",
              "      <td>3.58</td>\n",
              "      <td>3.86</td>\n",
              "      <td>7.0</td>\n",
              "      <td>145</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>12629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>mitsubishi</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>2921</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>156</td>\n",
              "      <td>spdi</td>\n",
              "      <td>3.59</td>\n",
              "      <td>3.86</td>\n",
              "      <td>7.0</td>\n",
              "      <td>145</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>14869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>mitsubishi</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>2926</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>156</td>\n",
              "      <td>spdi</td>\n",
              "      <td>3.59</td>\n",
              "      <td>3.86</td>\n",
              "      <td>7.0</td>\n",
              "      <td>145</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>14489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>peugot</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>114.2</td>\n",
              "      <td>198.9</td>\n",
              "      <td>68.4</td>\n",
              "      <td>58.7</td>\n",
              "      <td>3230</td>\n",
              "      <td>l</td>\n",
              "      <td>four</td>\n",
              "      <td>120</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.46</td>\n",
              "      <td>3.19</td>\n",
              "      <td>8.4</td>\n",
              "      <td>97</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>12440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>peugot</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>114.2</td>\n",
              "      <td>198.9</td>\n",
              "      <td>68.4</td>\n",
              "      <td>58.7</td>\n",
              "      <td>3430</td>\n",
              "      <td>l</td>\n",
              "      <td>four</td>\n",
              "      <td>152</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3.52</td>\n",
              "      <td>21.0</td>\n",
              "      <td>95</td>\n",
              "      <td>4150</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>13860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>peugot</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>114.2</td>\n",
              "      <td>198.9</td>\n",
              "      <td>68.4</td>\n",
              "      <td>56.7</td>\n",
              "      <td>3285</td>\n",
              "      <td>l</td>\n",
              "      <td>four</td>\n",
              "      <td>120</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.46</td>\n",
              "      <td>2.19</td>\n",
              "      <td>8.4</td>\n",
              "      <td>95</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>16695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>peugot</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>114.2</td>\n",
              "      <td>198.9</td>\n",
              "      <td>68.4</td>\n",
              "      <td>58.7</td>\n",
              "      <td>3485</td>\n",
              "      <td>l</td>\n",
              "      <td>four</td>\n",
              "      <td>152</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3.52</td>\n",
              "      <td>21.0</td>\n",
              "      <td>95</td>\n",
              "      <td>4150</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>17075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>plymouth</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</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>2818</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>156</td>\n",
              "      <td>spdi</td>\n",
              "      <td>3.59</td>\n",
              "      <td>3.86</td>\n",
              "      <td>7.0</td>\n",
              "      <td>145</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>12764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>porsche</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hardtop</td>\n",
              "      <td>rwd</td>\n",
              "      <td>rear</td>\n",
              "      <td>89.5</td>\n",
              "      <td>168.9</td>\n",
              "      <td>65.0</td>\n",
              "      <td>51.6</td>\n",
              "      <td>2756</td>\n",
              "      <td>ohcf</td>\n",
              "      <td>six</td>\n",
              "      <td>194</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.74</td>\n",
              "      <td>2.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>207</td>\n",
              "      <td>5900</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>32528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>porsche</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hardtop</td>\n",
              "      <td>rwd</td>\n",
              "      <td>rear</td>\n",
              "      <td>89.5</td>\n",
              "      <td>168.9</td>\n",
              "      <td>65.0</td>\n",
              "      <td>51.6</td>\n",
              "      <td>2756</td>\n",
              "      <td>ohcf</td>\n",
              "      <td>six</td>\n",
              "      <td>194</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.74</td>\n",
              "      <td>2.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>207</td>\n",
              "      <td>5900</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>34028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>porsche</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>rear</td>\n",
              "      <td>89.5</td>\n",
              "      <td>168.9</td>\n",
              "      <td>65.0</td>\n",
              "      <td>51.6</td>\n",
              "      <td>2800</td>\n",
              "      <td>ohcf</td>\n",
              "      <td>six</td>\n",
              "      <td>194</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.74</td>\n",
              "      <td>2.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>207</td>\n",
              "      <td>5900</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>37028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>porsche</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>98.4</td>\n",
              "      <td>175.7</td>\n",
              "      <td>72.3</td>\n",
              "      <td>50.5</td>\n",
              "      <td>3366</td>\n",
              "      <td>dohcv</td>\n",
              "      <td>eight</td>\n",
              "      <td>203</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.94</td>\n",
              "      <td>3.11</td>\n",
              "      <td>10.0</td>\n",
              "      <td>288</td>\n",
              "      <td>5750</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>renault</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>96.1</td>\n",
              "      <td>181.5</td>\n",
              "      <td>66.5</td>\n",
              "      <td>55.2</td>\n",
              "      <td>2579</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>132</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.46</td>\n",
              "      <td>3.9</td>\n",
              "      <td>8.7</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "      <td>9295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>renault</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>96.1</td>\n",
              "      <td>176.8</td>\n",
              "      <td>66.6</td>\n",
              "      <td>50.5</td>\n",
              "      <td>2460</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>132</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.46</td>\n",
              "      <td>3.9</td>\n",
              "      <td>8.7</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "      <td>9895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>-1</td>\n",
              "      <td>?</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>104.5</td>\n",
              "      <td>187.8</td>\n",
              "      <td>66.5</td>\n",
              "      <td>54.1</td>\n",
              "      <td>3151</td>\n",
              "      <td>dohc</td>\n",
              "      <td>six</td>\n",
              "      <td>161</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.35</td>\n",
              "      <td>9.2</td>\n",
              "      <td>156</td>\n",
              "      <td>5200</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>15750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>volkswagen</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>159.3</td>\n",
              "      <td>64.2</td>\n",
              "      <td>55.6</td>\n",
              "      <td>2254</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>90</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>11595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>volkswagen</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>100.4</td>\n",
              "      <td>180.2</td>\n",
              "      <td>66.9</td>\n",
              "      <td>55.1</td>\n",
              "      <td>2661</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>110</td>\n",
              "      <td>5500</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>13295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>volkswagen</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>100.4</td>\n",
              "      <td>180.2</td>\n",
              "      <td>66.9</td>\n",
              "      <td>55.1</td>\n",
              "      <td>2579</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>97</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.01</td>\n",
              "      <td>3.4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>68</td>\n",
              "      <td>4500</td>\n",
              "      <td>33</td>\n",
              "      <td>38</td>\n",
              "      <td>13845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>volkswagen</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>100.4</td>\n",
              "      <td>183.1</td>\n",
              "      <td>66.9</td>\n",
              "      <td>55.1</td>\n",
              "      <td>2563</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.4</td>\n",
              "      <td>9.0</td>\n",
              "      <td>88</td>\n",
              "      <td>5500</td>\n",
              "      <td>25</td>\n",
              "      <td>31</td>\n",
              "      <td>12290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     symboling normalized-losses           make  ... city-mpg highway-mpg  price\n",
              "0            3                 ?    alfa-romero  ...       21          27  13495\n",
              "1            3                 ?    alfa-romero  ...       21          27  16500\n",
              "2            1                 ?    alfa-romero  ...       19          26  16500\n",
              "5            2                 ?           audi  ...       19          25  15250\n",
              "7            1                 ?           audi  ...       19          25  18920\n",
              "9            0                 ?           audi  ...       16          22      ?\n",
              "14           1                 ?            bmw  ...       20          25  24565\n",
              "15           0                 ?            bmw  ...       16          22  30760\n",
              "16           0                 ?            bmw  ...       16          22  41315\n",
              "17           0                 ?            bmw  ...       15          20  36880\n",
              "43           0                 ?          isuzu  ...       24          29   6785\n",
              "44           1                 ?          isuzu  ...       38          43      ?\n",
              "45           0                 ?          isuzu  ...       38          43      ?\n",
              "46           2                 ?          isuzu  ...       24          29  11048\n",
              "48           0                 ?         jaguar  ...       15          19  35550\n",
              "49           0                 ?         jaguar  ...       13          17  36000\n",
              "63           0                 ?          mazda  ...       36          42  10795\n",
              "66           0                 ?          mazda  ...       31          39  18344\n",
              "71          -1                 ?  mercedes-benz  ...       16          18  34184\n",
              "73           0                 ?  mercedes-benz  ...       14          16  40960\n",
              "74           1                 ?  mercedes-benz  ...       14          16  45400\n",
              "75           1                 ?        mercury  ...       19          24  16503\n",
              "82           3                 ?     mitsubishi  ...       19          24  12629\n",
              "83           3                 ?     mitsubishi  ...       19          24  14869\n",
              "84           3                 ?     mitsubishi  ...       19          24  14489\n",
              "109          0                 ?         peugot  ...       19          24  12440\n",
              "110          0                 ?         peugot  ...       25          25  13860\n",
              "113          0                 ?         peugot  ...       19          24  16695\n",
              "114          0                 ?         peugot  ...       25          25  17075\n",
              "124          3                 ?       plymouth  ...       19          24  12764\n",
              "126          3                 ?        porsche  ...       17          25  32528\n",
              "127          3                 ?        porsche  ...       17          25  34028\n",
              "128          3                 ?        porsche  ...       17          25  37028\n",
              "129          1                 ?        porsche  ...       17          28      ?\n",
              "130          0                 ?        renault  ...       23          31   9295\n",
              "131          2                 ?        renault  ...       23          31   9895\n",
              "181         -1                 ?         toyota  ...       19          24  15750\n",
              "189          3                 ?     volkswagen  ...       24          29  11595\n",
              "191          0                 ?     volkswagen  ...       19          24  13295\n",
              "192          0                 ?     volkswagen  ...       33          38  13845\n",
              "193          0                 ?     volkswagen  ...       25          31  12290\n",
              "\n",
              "[41 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUoMK-50MbTB"
      },
      "source": [
        "df['normalized-losses'] = pd.to_numeric(df['normalized-losses'], errors='coerce')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWKvfwCf-7Aj",
        "outputId": "41a7424e-f465-4516-96b5-dc6c0cc43252"
      },
      "source": [
        "df['normalized-losses'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "161.0    11\n",
              "91.0      8\n",
              "150.0     7\n",
              "104.0     6\n",
              "128.0     6\n",
              "134.0     6\n",
              "102.0     5\n",
              "85.0      5\n",
              "103.0     5\n",
              "168.0     5\n",
              "74.0      5\n",
              "95.0      5\n",
              "94.0      5\n",
              "65.0      5\n",
              "122.0     4\n",
              "148.0     4\n",
              "118.0     4\n",
              "93.0      4\n",
              "106.0     4\n",
              "115.0     3\n",
              "125.0     3\n",
              "83.0      3\n",
              "101.0     3\n",
              "137.0     3\n",
              "154.0     3\n",
              "188.0     2\n",
              "81.0      2\n",
              "192.0     2\n",
              "158.0     2\n",
              "145.0     2\n",
              "113.0     2\n",
              "110.0     2\n",
              "153.0     2\n",
              "129.0     2\n",
              "194.0     2\n",
              "197.0     2\n",
              "87.0      2\n",
              "89.0      2\n",
              "119.0     2\n",
              "164.0     2\n",
              "108.0     2\n",
              "231.0     1\n",
              "107.0     1\n",
              "78.0      1\n",
              "256.0     1\n",
              "98.0      1\n",
              "121.0     1\n",
              "77.0      1\n",
              "142.0     1\n",
              "90.0      1\n",
              "186.0     1\n",
              "Name: normalized-losses, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqv6GDl1xmI6"
      },
      "source": [
        "We Could fill the null values with the column mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDPUwsWr-cxt",
        "outputId": "a79a5e2f-abb8-45fd-a2c7-8da57b2965e3"
      },
      "source": [
        "normalized_losses_mean = df['normalized-losses'].mean()\r\n",
        "normalized_losses_mean"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "122.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rShq8xAXO_mx"
      },
      "source": [
        "df['normalized-losses'].fillna(normalized_losses_mean, inplace = True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zl7DzelVPzfc",
        "outputId": "f367754f-dde5-4eaf-a5ce-1c338296ea7e"
      },
      "source": [
        "df['normalized-losses'].isnull().sum() # Check"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9auIg-ukUdkf"
      },
      "source": [
        "## Cleaning num-of-doors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2tSOn87QLDc",
        "outputId": "2c8f1b90-f30c-465b-b7f7-2e42435703ab"
      },
      "source": [
        "df['num-of-doors'].value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "four    114\n",
              "two      89\n",
              "?         2\n",
              "Name: num-of-doors, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fVz7c18QXSL",
        "outputId": "2c994d66-28ea-43a2-8a61-1fc00fded7f2"
      },
      "source": [
        "df['num-of-doors'].replace('four', 4, inplace = True)\r\n",
        "df['num-of-doors'].replace('two', 2, inplace = True)\r\n",
        "df['num-of-doors'] = pd.to_numeric(df['num-of-doors'], errors='coerce')\r\n",
        "\r\n",
        "df['num-of-doors'].value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    114\n",
              "2.0     89\n",
              "Name: num-of-doors, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5rrd9AtTIBs",
        "outputId": "33cd5d4d-41c0-4a11-ad07-f5f7417f96b1"
      },
      "source": [
        "df['num-of-doors'].isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEsXqkvfTPqj"
      },
      "source": [
        "# Dropping the 2 null values\r\n",
        "df.dropna(inplace = True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNwkMl1oTcH4",
        "outputId": "416fb9b3-12b6-4ed7-c8e6-c90785dfe64f"
      },
      "source": [
        "df['num-of-doors'].isnull().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrAu2NxpVlbs"
      },
      "source": [
        "##Other numerical columns that should be treated `peak-rpm`, `horsepower`, `bore`, `stroke` and `price`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOvp8yvzVB15",
        "outputId": "ab123604-6103-485a-f374-b7ccc7ddda9c"
      },
      "source": [
        "df['peak-rpm'].unique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['5000', '5500', '5800', '4250', '5400', '5100', '4800', '6000',\n",
              "       '4750', '4200', '4350', '4500', '5200', '4150', '5600', '5900',\n",
              "       '5750', '?', '5250', '4900', '4400', '6600', '5300'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SFv7ufxWHlF",
        "outputId": "ec3be5a8-fe44-4304-9977-80cae79d4ac9"
      },
      "source": [
        "pd.to_numeric(df['peak-rpm'], errors='coerce').isnull().sum()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsPCrDGPXFMW"
      },
      "source": [
        "Dropping the non numerical"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38K7TXUQW1yS"
      },
      "source": [
        "df.drop(df[pd.to_numeric(df['peak-rpm'], errors='coerce').isnull()].index, inplace= True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_M9k4zqOcR7g",
        "outputId": "7fbf056c-67b8-480a-8d1f-9eca8e06d70a"
      },
      "source": [
        "pd.to_numeric(df['price'], errors= 'coerce').isnull().sum()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuKzjDLacrdO"
      },
      "source": [
        "Dropping the records with missing prices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f43yeK_VcX2g"
      },
      "source": [
        "df.drop(df[pd.to_numeric(df['price'], errors='coerce').isnull()].index, inplace= True)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxHy-WRvepr3",
        "outputId": "4aa60571-f473-48a0-8d18-3fd4b78643d1"
      },
      "source": [
        "pd.to_numeric(df['bore'], errors= 'coerce').isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqkPGISReyKK"
      },
      "source": [
        "df.drop(df[pd.to_numeric(df['bore'], errors='coerce').isnull()].index, inplace= True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw48D2nPe1bu",
        "outputId": "196dc83c-f1dd-4f7c-b3b8-a6634e7d26e7"
      },
      "source": [
        "pd.to_numeric(df['stroke'], errors= 'coerce').isnull().sum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sd2AkD_QdA4P"
      },
      "source": [
        "Changing data type for qualitative columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paU0MlfBc8yC"
      },
      "source": [
        "df[['price', 'horsepower', 'peak-rpm', 'num-of-doors']] = df[['price', 'horsepower', 'peak-rpm', 'num-of-doors']].astype(int)\r\n",
        "df[['bore', 'stroke', 'normalized-losses']] = df[['bore', 'stroke', 'normalized-losses']].astype(float)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGStUanUdftc",
        "outputId": "90d42e2e-08a1-4fd8-b883-c536f700bd86"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 193 entries, 0 to 204\n",
            "Data columns (total 26 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   symboling          193 non-null    int64  \n",
            " 1   normalized-losses  193 non-null    float64\n",
            " 2   make               193 non-null    object \n",
            " 3   fuel-type          193 non-null    object \n",
            " 4   aspiration         193 non-null    object \n",
            " 5   num-of-doors       193 non-null    int64  \n",
            " 6   body-style         193 non-null    object \n",
            " 7   drive-wheels       193 non-null    object \n",
            " 8   engine-location    193 non-null    object \n",
            " 9   wheel-base         193 non-null    float64\n",
            " 10  length             193 non-null    float64\n",
            " 11  width              193 non-null    float64\n",
            " 12  height             193 non-null    float64\n",
            " 13  curb-weight        193 non-null    int64  \n",
            " 14  engine-type        193 non-null    object \n",
            " 15  num-of-cylinders   193 non-null    object \n",
            " 16  engine-size        193 non-null    int64  \n",
            " 17  fuel-system        193 non-null    object \n",
            " 18  bore               193 non-null    float64\n",
            " 19  stroke             193 non-null    float64\n",
            " 20  compression-ratio  193 non-null    float64\n",
            " 21  horsepower         193 non-null    int64  \n",
            " 22  peak-rpm           193 non-null    int64  \n",
            " 23  city-mpg           193 non-null    int64  \n",
            " 24  highway-mpg        193 non-null    int64  \n",
            " 25  price              193 non-null    int64  \n",
            "dtypes: float64(8), int64(9), object(9)\n",
            "memory usage: 40.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmKYSbYkkszP"
      },
      "source": [
        "##Checking outliers for numerical variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "lqLJXwbwfnP_",
        "outputId": "b2390cdb-0a3d-4fff-dfe7-314c570aee49"
      },
      "source": [
        "plt.figure(figsize=[10,10])\r\n",
        "plt.subplot(3,2,1)\r\n",
        "df.boxplot(column=['city-mpg'])\r\n",
        "plt.subplot(3,2,2)\r\n",
        "df.boxplot(column=['curb-weight'])\r\n",
        "plt.subplot(3,2,3)\r\n",
        "df.boxplot(column=['symboling'])\r\n",
        "plt.subplot(3,2,4)\r\n",
        "df.boxplot(column=['price'])\r\n",
        "plt.subplot(3,2,5)\r\n",
        "df.boxplot(column=['bore'])\r\n",
        "plt.subplot(3,2,6)\r\n",
        "df.boxplot(column=['wheel-base'])\r\n",
        ";"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "''"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "ZIkXdRkRhjnb",
        "outputId": "7145d6f6-7b4f-463f-8b38-c3176115d73f"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 29,
      "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>num-of-doors</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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>193.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.797927</td>\n",
              "      <td>121.284974</td>\n",
              "      <td>3.160622</td>\n",
              "      <td>98.923834</td>\n",
              "      <td>174.326425</td>\n",
              "      <td>65.893782</td>\n",
              "      <td>53.869948</td>\n",
              "      <td>2561.507772</td>\n",
              "      <td>128.124352</td>\n",
              "      <td>3.330622</td>\n",
              "      <td>3.248860</td>\n",
              "      <td>10.143627</td>\n",
              "      <td>103.481865</td>\n",
              "      <td>5099.740933</td>\n",
              "      <td>25.326425</td>\n",
              "      <td>30.787565</td>\n",
              "      <td>13285.025907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.235582</td>\n",
              "      <td>32.342674</td>\n",
              "      <td>0.989583</td>\n",
              "      <td>6.152409</td>\n",
              "      <td>12.478593</td>\n",
              "      <td>2.137795</td>\n",
              "      <td>2.394770</td>\n",
              "      <td>526.700026</td>\n",
              "      <td>41.590452</td>\n",
              "      <td>0.272385</td>\n",
              "      <td>0.315421</td>\n",
              "      <td>3.977491</td>\n",
              "      <td>37.960107</td>\n",
              "      <td>468.694369</td>\n",
              "      <td>6.387828</td>\n",
              "      <td>6.816910</td>\n",
              "      <td>8089.082886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>2.000000</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>95.000000</td>\n",
              "      <td>2.000000</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>98.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7738.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.200000</td>\n",
              "      <td>65.400000</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>5100.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10245.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>184.600000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.700000</td>\n",
              "      <td>2952.000000</td>\n",
              "      <td>146.000000</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>16515.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.100000</td>\n",
              "      <td>72.000000</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>262.000000</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  ...  highway-mpg         price\n",
              "count  193.000000         193.000000  ...   193.000000    193.000000\n",
              "mean     0.797927         121.284974  ...    30.787565  13285.025907\n",
              "std      1.235582          32.342674  ...     6.816910   8089.082886\n",
              "min     -2.000000          65.000000  ...    16.000000   5118.000000\n",
              "25%      0.000000          95.000000  ...    25.000000   7738.000000\n",
              "50%      1.000000         122.000000  ...    30.000000  10245.000000\n",
              "75%      2.000000         134.000000  ...    34.000000  16515.000000\n",
              "max      3.000000         256.000000  ...    54.000000  45400.000000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2czQ4UcblNbA"
      },
      "source": [
        "# Exploring Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "IajMSPP5lLHi",
        "outputId": "8f4271a2-c715-4706-8234-4cd4aeee6491"
      },
      "source": [
        "plt.figure(figsize=[12,8])\r\n",
        "plt.subplot(2,2,1)\r\n",
        "bin_edges = np.arange(5000, 50000+1000, 1000)\r\n",
        "plt.hist(data=df, x='price', bins = bin_edges)\r\n",
        "plt.xlabel('price')\r\n",
        "plt.subplot(2,2,2)\r\n",
        "bin_edges = np.arange(5, 60+3, 3)\r\n",
        "plt.hist(data=df, x='city-mpg', bins = bin_edges)\r\n",
        "plt.xlabel('city-mpg')\r\n",
        "plt.subplot(2,2,3)\r\n",
        "bin_edges = np.arange(5, 60+3, 3)\r\n",
        "plt.hist(data=df, x='highway-mpg', bins = bin_edges)\r\n",
        "plt.xlabel('highway-mpg')\r\n",
        "plt.subplot(2,2,4)\r\n",
        "bin_edges = np.arange(30, 300+10, 10)\r\n",
        "plt.hist(data=df, x='horsepower', bins = bin_edges)\r\n",
        "plt.xlabel('horsepower');"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kl4QuvPR1-GX"
      },
      "source": [
        "Price seems to be right skewed with too many outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "Cd-eah1om30m",
        "outputId": "4a0da864-91cd-4561-c3ff-10be8975dbd6"
      },
      "source": [
        "base_color = sns.color_palette()[0]\r\n",
        "plt.figure(figsize=[20,5])\r\n",
        "order = df.groupby('make').price.mean().sort_values(ascending = False).index\r\n",
        "sns.barplot(data= df , x='make', y='price', color=base_color, order= order)\r\n",
        "plt.xticks(rotation = 30)\r\n",
        ";"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "''"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "zUaGw0L_rUOg",
        "outputId": "8db5485c-0811-4616-ddbc-636bb936168e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 32,
      "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-of-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-of-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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses         make  ... city-mpg highway-mpg  price\n",
              "0          3              122.0  alfa-romero  ...       21          27  13495\n",
              "1          3              122.0  alfa-romero  ...       21          27  16500\n",
              "2          1              122.0  alfa-romero  ...       19          26  16500\n",
              "3          2              164.0         audi  ...       24          30  13950\n",
              "4          2              164.0         audi  ...       18          22  17450\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcOuXhJ12Utx"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "KLvRVaT-s9vP",
        "outputId": "6134c2d7-d48f-4a45-9778-3a28c5b1bd87"
      },
      "source": [
        "plt.figure(figsize=[20,15])\r\n",
        "plt.subplot(2,2,1)\r\n",
        "sns.regplot(data=df, x='horsepower', y='price')\r\n",
        "plt.subplot(2,2,2)\r\n",
        "sns.regplot(data=df, x='engine-size', y='price')\r\n",
        "plt.subplot(2,2,3)\r\n",
        "sns.regplot(data=df, x='curb-weight', y='price')\r\n",
        "plt.subplot(2,2,4)\r\n",
        "sns.regplot(data=df, x='width', y='price');"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9xMUK-J4c-M"
      },
      "source": [
        "# Encoding some categorical Features\r\n",
        "\r\n",
        "--------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7ZE1KToX68y"
      },
      "source": [
        "## Encoding the body-style feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acyb1RCJ4__M"
      },
      "source": [
        "df_new = df"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLXQPX9r4cF7",
        "outputId": "f7d2a548-6a8a-4609-edb4-bdce6ac67a76"
      },
      "source": [
        "df_new['body-style'].value_counts()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sedan          92\n",
              "hatchback      63\n",
              "wagon          24\n",
              "hardtop         8\n",
              "convertible     6\n",
              "Name: body-style, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8UeuH8u5j81"
      },
      "source": [
        "# We can select more or less\r\n",
        "dummies_body = pd.get_dummies(df_new['body-style'], drop_first=True)\r\n",
        "dummies_make = pd.get_dummies(df_new['make'], drop_first=True)\r\n",
        "dummies_wheels = pd.get_dummies(df_new['drive-wheels'], drop_first=True) \r\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "jotkKuuZ400U",
        "outputId": "724aafeb-e4d4-4d17-82e5-2336a647de92"
      },
      "source": [
        "df_new = df_new.join(dummies_body).join(dummies_make).join(dummies_wheels)\r\n",
        "df_new.head()"
      ],
      "execution_count": 37,
      "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",
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              "        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-of-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-of-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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "      <th>hardtop</th>\n",
              "      <th>hatchback</th>\n",
              "      <th>sedan</th>\n",
              "      <th>wagon</th>\n",
              "      <th>audi</th>\n",
              "      <th>bmw</th>\n",
              "      <th>chevrolet</th>\n",
              "      <th>dodge</th>\n",
              "      <th>honda</th>\n",
              "      <th>isuzu</th>\n",
              "      <th>jaguar</th>\n",
              "      <th>mazda</th>\n",
              "      <th>mercedes-benz</th>\n",
              "      <th>mercury</th>\n",
              "      <th>mitsubishi</th>\n",
              "      <th>nissan</th>\n",
              "      <th>peugot</th>\n",
              "      <th>plymouth</th>\n",
              "      <th>porsche</th>\n",
              "      <th>saab</th>\n",
              "      <th>subaru</th>\n",
              "      <th>toyota</th>\n",
              "      <th>volkswagen</th>\n",
              "      <th>volvo</th>\n",
              "      <th>fwd</th>\n",
              "      <th>rwd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses         make  ... volvo fwd  rwd\n",
              "0          3              122.0  alfa-romero  ...     0   0    1\n",
              "1          3              122.0  alfa-romero  ...     0   0    1\n",
              "2          1              122.0  alfa-romero  ...     0   0    1\n",
              "3          2              164.0         audi  ...     0   1    0\n",
              "4          2              164.0         audi  ...     0   0    0\n",
              "\n",
              "[5 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vENhYQeryVn-"
      },
      "source": [
        "## Question#1 What are the features affecting the price of the car?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Grk2AYGNy1BH"
      },
      "source": [
        "From exploratory part of the analysis we found the price is most afftect with the following variables:\r\n",
        "> Width\\\r\n",
        "> Curb-weight\\\r\n",
        "> Engine-size\\\r\n",
        "> Horsepower  \r\n",
        "> city-mpg  \r\n",
        "> highway-mpg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MtfNqXnz3HY"
      },
      "source": [
        "### Building Linear Regression Model between the above models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "LjC2-uZ5z2hj",
        "outputId": "b0854057-3ccf-4404-826b-31b15ea933a8"
      },
      "source": [
        "import statsmodels.api as sm\r\n",
        "from patsy import dmatrices\r\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\r\n",
        "df['intercept'] = 1\r\n",
        "df.rename(columns={'curb-weight' : 'curb_weight', 'engine-size' : 'engine_size', 'city-mpg':'city_mpg',\r\n",
        "                    'highway-mpg':'highway_mpg'}, inplace = True) # for some reason VIF check doesn't work on columns with `-` inbetween\r\n",
        "df.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
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              "      <th>compression-ratio</th>\n",
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              "      <th>peak-rpm</th>\n",
              "      <th>city_mpg</th>\n",
              "      <th>highway_mpg</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
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              "      <td>std</td>\n",
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              "      <th>1</th>\n",
              "      <td>3</td>\n",
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              "      <td>111</td>\n",
              "      <td>5000</td>\n",
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              "      <td>16500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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",
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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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "      <td>1</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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses         make  ... highway_mpg  price  intercept\n",
              "0          3              122.0  alfa-romero  ...          27  13495          1\n",
              "1          3              122.0  alfa-romero  ...          27  16500          1\n",
              "2          1              122.0  alfa-romero  ...          26  16500          1\n",
              "3          2              164.0         audi  ...          30  13950          1\n",
              "4          2              164.0         audi  ...          22  17450          1\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoW4XBHV7SrI"
      },
      "source": [
        "#### Multicolinearity Check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "L2sgJoOL2vqI",
        "outputId": "380b6fe3-4aad-4741-c592-e8ef792fb154"
      },
      "source": [
        "y, X = dmatrices('price~width+curb_weight+engine_size+horsepower+city_mpg+highway_mpg', df, return_type = 'dataframe')\r\n",
        "vif = pd.DataFrame()\r\n",
        "vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\r\n",
        "vif[\"features\"] = X.columns\r\n",
        "vif.round(1)"
      ],
      "execution_count": 39,
      "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>VIF Factor</th>\n",
              "      <th>features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3376.8</td>\n",
              "      <td>Intercept</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.1</td>\n",
              "      <td>width</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.6</td>\n",
              "      <td>curb_weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.2</td>\n",
              "      <td>engine_size</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.1</td>\n",
              "      <td>horsepower</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>22.1</td>\n",
              "      <td>city_mpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>22.5</td>\n",
              "      <td>highway_mpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   VIF Factor     features\n",
              "0      3376.8    Intercept\n",
              "1         4.1        width\n",
              "2         8.6  curb_weight\n",
              "3         6.2  engine_size\n",
              "4         6.1   horsepower\n",
              "5        22.1     city_mpg\n",
              "6        22.5  highway_mpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YVkE8NhKKbb"
      },
      "source": [
        "As excepted city_mpg and highway_mpg are dependant varaibles. Deleting `highway_mpg` and try the check again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "O_fMFBMmKeYz",
        "outputId": "d2d2d265-a6a9-4a07-fb87-006f4a7fbce6"
      },
      "source": [
        "y, X = dmatrices('price~width+curb_weight+engine_size+horsepower+city_mpg', df, return_type = 'dataframe')\r\n",
        "vif = pd.DataFrame()\r\n",
        "vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\r\n",
        "vif[\"features\"] = X.columns\r\n",
        "vif.round(1)"
      ],
      "execution_count": 40,
      "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>VIF Factor</th>\n",
              "      <th>features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3266.0</td>\n",
              "      <td>Intercept</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.1</td>\n",
              "      <td>width</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>curb_weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.2</td>\n",
              "      <td>engine_size</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>horsepower</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.2</td>\n",
              "      <td>city_mpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   VIF Factor     features\n",
              "0      3266.0    Intercept\n",
              "1         4.1        width\n",
              "2         8.1  curb_weight\n",
              "3         6.2  engine_size\n",
              "4         5.9   horsepower\n",
              "5         4.2     city_mpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hVsLl8i7Bo2"
      },
      "source": [
        "All VIFs are below 10, which means that those variables are not correlated to each other and we can use them all."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "bPuW_vwAy0Li",
        "outputId": "9d337d79-e56c-40bf-d05e-1ac4bff98b3d"
      },
      "source": [
        "reg_m = sm.OLS(df['price'], df[['intercept', 'width', 'curb_weight', 'engine_size', 'horsepower', 'city_mpg']])\r\n",
        "res = reg_m.fit()\r\n",
        "res.summary()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.827</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.822</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   178.3</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sat, 13 Mar 2021</td> <th>  Prob (F-statistic):</th> <td>3.63e-69</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>10:49:17</td>     <th>  Log-Likelihood:    </th> <td> -1840.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   193</td>      <th>  AIC:               </th> <td>   3694.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   187</td>      <th>  BIC:               </th> <td>   3713.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>intercept</th>   <td>-5.234e+04</td> <td>  1.4e+04</td> <td>   -3.728</td> <td> 0.000</td> <td>   -8e+04</td> <td>-2.46e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>width</th>       <td>  619.2442</td> <td>  234.275</td> <td>    2.643</td> <td> 0.009</td> <td>  157.083</td> <td> 1081.405</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>curb_weight</th> <td>    2.0797</td> <td>    1.334</td> <td>    1.559</td> <td> 0.121</td> <td>   -0.552</td> <td>    4.711</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>engine_size</th> <td>   94.5528</td> <td>   14.716</td> <td>    6.425</td> <td> 0.000</td> <td>   65.522</td> <td>  123.584</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>horsepower</th>  <td>   52.5937</td> <td>   15.823</td> <td>    3.324</td> <td> 0.001</td> <td>   21.379</td> <td>   83.808</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>city_mpg</th>    <td>   76.3065</td> <td>   79.448</td> <td>    0.960</td> <td> 0.338</td> <td>  -80.424</td> <td>  233.037</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>23.751</td> <th>  Durbin-Watson:     </th> <td>   0.757</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  56.054</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.527</td> <th>  Prob(JB):          </th> <td>6.73e-13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 5.420</td> <th>  Cond. No.          </th> <td>1.50e+05</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.5e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                  price   R-squared:                       0.827\n",
              "Model:                            OLS   Adj. R-squared:                  0.822\n",
              "Method:                 Least Squares   F-statistic:                     178.3\n",
              "Date:                Sat, 13 Mar 2021   Prob (F-statistic):           3.63e-69\n",
              "Time:                        10:49:17   Log-Likelihood:                -1840.9\n",
              "No. Observations:                 193   AIC:                             3694.\n",
              "Df Residuals:                     187   BIC:                             3713.\n",
              "Df Model:                           5                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===============================================================================\n",
              "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------\n",
              "intercept   -5.234e+04    1.4e+04     -3.728      0.000      -8e+04   -2.46e+04\n",
              "width         619.2442    234.275      2.643      0.009     157.083    1081.405\n",
              "curb_weight     2.0797      1.334      1.559      0.121      -0.552       4.711\n",
              "engine_size    94.5528     14.716      6.425      0.000      65.522     123.584\n",
              "horsepower     52.5937     15.823      3.324      0.001      21.379      83.808\n",
              "city_mpg       76.3065     79.448      0.960      0.338     -80.424     233.037\n",
              "==============================================================================\n",
              "Omnibus:                       23.751   Durbin-Watson:                   0.757\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.054\n",
              "Skew:                           0.527   Prob(JB):                     6.73e-13\n",
              "Kurtosis:                       5.420   Cond. No.                     1.50e+05\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.5e+05. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPBXj3Md7tA3"
      },
      "source": [
        "We got a good model with all variables statistically significant except the `curb_weight` and `city_mpg`.  \r\n",
        "Trying again without the `curb_weight` nor `city_mpg` variabale:  \r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "aj-HDRocIclY",
        "outputId": "a78413dd-391f-4697-bcf9-b496d451033c"
      },
      "source": [
        "reg_m = sm.OLS(df['price'], df[['intercept', 'width', 'engine_size', 'horsepower']])\r\n",
        "res = reg_m.fit()\r\n",
        "res.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.824</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.821</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   295.4</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sat, 13 Mar 2021</td> <th>  Prob (F-statistic):</th> <td>4.52e-71</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>10:49:17</td>     <th>  Log-Likelihood:    </th> <td> -1842.3</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   193</td>      <th>  AIC:               </th> <td>   3693.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   189</td>      <th>  BIC:               </th> <td>   3706.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>intercept</th>   <td>-5.902e+04</td> <td> 1.05e+04</td> <td>   -5.621</td> <td> 0.000</td> <td>-7.97e+04</td> <td>-3.83e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>width</th>       <td>  818.5003</td> <td>  171.557</td> <td>    4.771</td> <td> 0.000</td> <td>  480.088</td> <td> 1156.913</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>engine_size</th> <td>  106.0220</td> <td>   12.930</td> <td>    8.199</td> <td> 0.000</td> <td>   80.516</td> <td>  131.528</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>horsepower</th>  <td>   46.2852</td> <td>   12.166</td> <td>    3.805</td> <td> 0.000</td> <td>   22.288</td> <td>   70.283</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>23.511</td> <th>  Durbin-Watson:     </th> <td>   0.790</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  52.315</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.544</td> <th>  Prob(JB):          </th> <td>4.37e-12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 5.307</td> <th>  Cond. No.          </th> <td>7.88e+03</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 7.88e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                  price   R-squared:                       0.824\n",
              "Model:                            OLS   Adj. R-squared:                  0.821\n",
              "Method:                 Least Squares   F-statistic:                     295.4\n",
              "Date:                Sat, 13 Mar 2021   Prob (F-statistic):           4.52e-71\n",
              "Time:                        10:49:17   Log-Likelihood:                -1842.3\n",
              "No. Observations:                 193   AIC:                             3693.\n",
              "Df Residuals:                     189   BIC:                             3706.\n",
              "Df Model:                           3                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===============================================================================\n",
              "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------\n",
              "intercept   -5.902e+04   1.05e+04     -5.621      0.000   -7.97e+04   -3.83e+04\n",
              "width         818.5003    171.557      4.771      0.000     480.088    1156.913\n",
              "engine_size   106.0220     12.930      8.199      0.000      80.516     131.528\n",
              "horsepower     46.2852     12.166      3.805      0.000      22.288      70.283\n",
              "==============================================================================\n",
              "Omnibus:                       23.511   Durbin-Watson:                   0.790\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.315\n",
              "Skew:                           0.544   Prob(JB):                     4.37e-12\n",
              "Kurtosis:                       5.307   Cond. No.                     7.88e+03\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 7.88e+03. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dHcaE34yTaT"
      },
      "source": [
        "pred_price = -59020 + (df.width * 818.5) + (df.engine_size * 106) + (df.horsepower * 46.3)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ny2JcyAymBVT"
      },
      "source": [
        "### Trying out with modeling with Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "SCg0-JVDqQqN",
        "outputId": "7b37a6f0-a873-44bb-d13b-b950b14f403d"
      },
      "source": [
        "df_new.head()"
      ],
      "execution_count": 44,
      "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-of-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-of-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>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "      <th>hardtop</th>\n",
              "      <th>hatchback</th>\n",
              "      <th>sedan</th>\n",
              "      <th>wagon</th>\n",
              "      <th>audi</th>\n",
              "      <th>bmw</th>\n",
              "      <th>chevrolet</th>\n",
              "      <th>dodge</th>\n",
              "      <th>honda</th>\n",
              "      <th>isuzu</th>\n",
              "      <th>jaguar</th>\n",
              "      <th>mazda</th>\n",
              "      <th>mercedes-benz</th>\n",
              "      <th>mercury</th>\n",
              "      <th>mitsubishi</th>\n",
              "      <th>nissan</th>\n",
              "      <th>peugot</th>\n",
              "      <th>plymouth</th>\n",
              "      <th>porsche</th>\n",
              "      <th>saab</th>\n",
              "      <th>subaru</th>\n",
              "      <th>toyota</th>\n",
              "      <th>volkswagen</th>\n",
              "      <th>volvo</th>\n",
              "      <th>fwd</th>\n",
              "      <th>rwd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>2</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</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>4</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</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   symboling  normalized-losses         make  ... volvo fwd  rwd\n",
              "0          3              122.0  alfa-romero  ...     0   0    1\n",
              "1          3              122.0  alfa-romero  ...     0   0    1\n",
              "2          1              122.0  alfa-romero  ...     0   0    1\n",
              "3          2              164.0         audi  ...     0   1    0\n",
              "4          2              164.0         audi  ...     0   0    0\n",
              "\n",
              "[5 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsS8kaTh3Qhz"
      },
      "source": [
        "# Sci-Kit Learn Linear Regression using the previous numerical features + categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icQyahGnlobn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34048487-db52-4169-9a7d-e0a9ecabac25"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\r\n",
        "#features = df_new['width', 'engine-size', 'horsepower']\r\n",
        "X = df_new[['width', 'engine-size', 'horsepower', 'wheel-base','curb-weight','bore',\r\n",
        "            'hardtop',\r\n",
        "            'hatchback',\t'sedan',\t'wagon',\t'audi',\t'bmw',\t'chevrolet',\t'dodge',\t'honda',\r\n",
        "            'isuzu',\t'jaguar',\t'mazda',\t'mercedes-benz',\t'mercury',\t'mitsubishi',\t'nissan',\r\n",
        "            'peugot',\t'plymouth',\t'porsche',\t'saab',\t'subaru',\t'toyota',\t'volkswagen',\t'volvo',\r\n",
        "            'fwd',\t'rwd',]]\r\n",
        "y = df_new['price']\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\r\n",
        "regr = LinearRegression()\r\n",
        "regr.fit(X_train, y_train)\r\n",
        "y_pred = regr.predict(X_test)\r\n",
        "print('Mean Root squared error: %.2f'% np.sqrt(mean_squared_error(y_test, y_pred)))\r\n",
        "print('R2: %.2f'% r2_score(y_test, y_pred))\r\n",
        "print('Mean Absolute Error: %.2f' % mean_absolute_error(y_test, y_pred))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Root squared error: 2218.88\n",
            "R2: 0.93\n",
            "Mean Absolute Error: 1686.54\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFjz67vn3r2-"
      },
      "source": [
        "Using RandomForestRessor with same features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXA8k6u8mYsD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba4e9c07-96e7-4c9d-ebb7-5c5cbb3fc949"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "from sklearn.ensemble import RandomForestRegressor\r\n",
        "regressor = RandomForestRegressor(n_estimators=300, random_state=0)\r\n",
        "regressor.fit(X_train, y_train)\r\n",
        "y_pred = regressor.predict(X_test)\r\n",
        "print('Mean Root squared error: %.2f'% np.sqrt(mean_squared_error(y_test, y_pred)))\r\n",
        "print('R2: %.2f'% r2_score(y_test, y_pred))\r\n",
        "print('Mean Absolute Error: %.2f' % mean_absolute_error(y_test, y_pred))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Root squared error: 2123.40\n",
            "R2: 0.93\n",
            "Mean Absolute Error: 1401.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mBUXplX38bx"
      },
      "source": [
        "# Limitation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ws_yJskd4AAu"
      },
      "source": [
        "> Dataset was limited in number of rows, thus the outliers were kept.  \r\n",
        "> We dropped 12 records total.  \r\n",
        "> We wished to estimate the normalized losses but it had many NAN values\r\n"
      ]
    }
  ]
}