INDRO_CAR_PRICE_PRD_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Inderdev07/car-price-prediction/blob/main/INDRO_CAR_PRICE_PRD_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "4d324446",
      "metadata": {
        "id": "4d324446"
      },
      "outputs": [],
      "source": [
        "import  pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "8dd91982",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "8dd91982",
        "outputId": "dcfeb299-2c99-4321-8faf-e317d59eb90d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     car_ID  symboling                   CarName fueltype aspiration  \\\n",
              "0         1          3        alfa-romero giulia      gas        std   \n",
              "1         2          3       alfa-romero stelvio      gas        std   \n",
              "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
              "3         4          2               audi 100 ls      gas        std   \n",
              "4         5          2                audi 100ls      gas        std   \n",
              "..      ...        ...                       ...      ...        ...   \n",
              "200     201         -1           volvo 145e (sw)      gas        std   \n",
              "201     202         -1               volvo 144ea      gas      turbo   \n",
              "202     203         -1               volvo 244dl      gas        std   \n",
              "203     204         -1                 volvo 246   diesel      turbo   \n",
              "204     205         -1               volvo 264gl      gas      turbo   \n",
              "\n",
              "    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \\\n",
              "0          two  convertible        rwd          front       88.6  ...   \n",
              "1          two  convertible        rwd          front       88.6  ...   \n",
              "2          two    hatchback        rwd          front       94.5  ...   \n",
              "3         four        sedan        fwd          front       99.8  ...   \n",
              "4         four        sedan        4wd          front       99.4  ...   \n",
              "..         ...          ...        ...            ...        ...  ...   \n",
              "200       four        sedan        rwd          front      109.1  ...   \n",
              "201       four        sedan        rwd          front      109.1  ...   \n",
              "202       four        sedan        rwd          front      109.1  ...   \n",
              "203       four        sedan        rwd          front      109.1  ...   \n",
              "204       four        sedan        rwd          front      109.1  ...   \n",
              "\n",
              "     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \\\n",
              "0           130        mpfi       3.47    2.68              9.0        111   \n",
              "1           130        mpfi       3.47    2.68              9.0        111   \n",
              "2           152        mpfi       2.68    3.47              9.0        154   \n",
              "3           109        mpfi       3.19    3.40             10.0        102   \n",
              "4           136        mpfi       3.19    3.40              8.0        115   \n",
              "..          ...         ...        ...     ...              ...        ...   \n",
              "200         141        mpfi       3.78    3.15              9.5        114   \n",
              "201         141        mpfi       3.78    3.15              8.7        160   \n",
              "202         173        mpfi       3.58    2.87              8.8        134   \n",
              "203         145         idi       3.01    3.40             23.0        106   \n",
              "204         141        mpfi       3.78    3.15              9.5        114   \n",
              "\n",
              "     peakrpm citympg  highwaympg    price  \n",
              "0       5000      21          27  13495.0  \n",
              "1       5000      21          27  16500.0  \n",
              "2       5000      19          26  16500.0  \n",
              "3       5500      24          30  13950.0  \n",
              "4       5500      18          22  17450.0  \n",
              "..       ...     ...         ...      ...  \n",
              "200     5400      23          28  16845.0  \n",
              "201     5300      19          25  19045.0  \n",
              "202     5500      18          23  21485.0  \n",
              "203     4800      26          27  22470.0  \n",
              "204     5400      19          25  22625.0  \n",
              "\n",
              "[205 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d24b86b8-1bdc-4553-893f-5793754c761a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</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>...</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>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero stelvio</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>...</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>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</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>...</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>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</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>...</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>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</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>...</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>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>201</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 145e (sw)</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>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>141</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.5</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>16845.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>202</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 144ea</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>141</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>8.7</td>\n",
              "      <td>160</td>\n",
              "      <td>5300</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>19045.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>203</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 244dl</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>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>173</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.58</td>\n",
              "      <td>2.87</td>\n",
              "      <td>8.8</td>\n",
              "      <td>134</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "      <td>21485.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>204</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 246</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>145</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.01</td>\n",
              "      <td>3.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>106</td>\n",
              "      <td>4800</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>22470.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>205</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 264gl</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>141</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.5</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>22625.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d24b86b8-1bdc-4553-893f-5793754c761a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d24b86b8-1bdc-4553-893f-5793754c761a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d24b86b8-1bdc-4553-893f-5793754c761a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "cardf = pd.read_csv(r\"https://raw.githubusercontent.com/Inderdev07/car-price-prediction/main/CarPrice_Assignment.csv\")\n",
        "cardf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "0ece6a0a",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "0ece6a0a",
        "outputId": "8896f127-25de-46a0-b5f0-cfed3e56e329"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
              "0       1          3        alfa-romero giulia      gas        std        two   \n",
              "1       2          3       alfa-romero stelvio      gas        std        two   \n",
              "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
              "3       4          2               audi 100 ls      gas        std       four   \n",
              "4       5          2                audi 100ls      gas        std       four   \n",
              "\n",
              "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
              "0  convertible        rwd          front       88.6  ...         130   \n",
              "1  convertible        rwd          front       88.6  ...         130   \n",
              "2    hatchback        rwd          front       94.5  ...         152   \n",
              "3        sedan        fwd          front       99.8  ...         109   \n",
              "4        sedan        4wd          front       99.4  ...         136   \n",
              "\n",
              "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
              "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
              "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
              "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
              "\n",
              "   highwaympg    price  \n",
              "0          27  13495.0  \n",
              "1          27  16500.0  \n",
              "2          26  16500.0  \n",
              "3          30  13950.0  \n",
              "4          22  17450.0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a43fb90-39d7-4b5f-b92e-ca34a5d6e8cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</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>...</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>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero stelvio</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>...</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>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</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>...</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>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</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>...</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>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</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>...</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>17450.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a43fb90-39d7-4b5f-b92e-ca34a5d6e8cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5a43fb90-39d7-4b5f-b92e-ca34a5d6e8cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5a43fb90-39d7-4b5f-b92e-ca34a5d6e8cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "cardf.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "5b4e4617",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5b4e4617",
        "outputId": "fbff56aa-d307-43fd-e38d-764e3271de40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 3, 'alfa-romero giulia', ..., 21, 27, 13495.0],\n",
              "       [2, 3, 'alfa-romero stelvio', ..., 21, 27, 16500.0],\n",
              "       [3, 1, 'alfa-romero Quadrifoglio', ..., 19, 26, 16500.0],\n",
              "       ...,\n",
              "       [203, -1, 'volvo 244dl', ..., 18, 23, 21485.0],\n",
              "       [204, -1, 'volvo 246', ..., 26, 27, 22470.0],\n",
              "       [205, -1, 'volvo 264gl', ..., 19, 25, 22625.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "cardf.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "4fcfc786",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fcfc786",
        "outputId": "9235d86c-b4cb-4c68-d5f0-723c80d24a75"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "cardf.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "adb3e196",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "adb3e196",
        "outputId": "b78a0b99-18f2-4b4d-ed51-09e4dc8003d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           car_ID   symboling   wheelbase   carlength    carwidth   carheight  \\\n",
              "count  205.000000  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
              "mean   103.000000    0.834146   98.756585  174.049268   65.907805   53.724878   \n",
              "std     59.322565    1.245307    6.021776   12.337289    2.145204    2.443522   \n",
              "min      1.000000   -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
              "25%     52.000000    0.000000   94.500000  166.300000   64.100000   52.000000   \n",
              "50%    103.000000    1.000000   97.000000  173.200000   65.500000   54.100000   \n",
              "75%    154.000000    2.000000  102.400000  183.100000   66.900000   55.500000   \n",
              "max    205.000000    3.000000  120.900000  208.100000   72.300000   59.800000   \n",
              "\n",
              "        curbweight  enginesize   boreratio      stroke  compressionratio  \\\n",
              "count   205.000000  205.000000  205.000000  205.000000        205.000000   \n",
              "mean   2555.565854  126.907317    3.329756    3.255415         10.142537   \n",
              "std     520.680204   41.642693    0.270844    0.313597          3.972040   \n",
              "min    1488.000000   61.000000    2.540000    2.070000          7.000000   \n",
              "25%    2145.000000   97.000000    3.150000    3.110000          8.600000   \n",
              "50%    2414.000000  120.000000    3.310000    3.290000          9.000000   \n",
              "75%    2935.000000  141.000000    3.580000    3.410000          9.400000   \n",
              "max    4066.000000  326.000000    3.940000    4.170000         23.000000   \n",
              "\n",
              "       horsepower      peakrpm     citympg  highwaympg         price  \n",
              "count  205.000000   205.000000  205.000000  205.000000    205.000000  \n",
              "mean   104.117073  5125.121951   25.219512   30.751220  13276.710571  \n",
              "std     39.544167   476.985643    6.542142    6.886443   7988.852332  \n",
              "min     48.000000  4150.000000   13.000000   16.000000   5118.000000  \n",
              "25%     70.000000  4800.000000   19.000000   25.000000   7788.000000  \n",
              "50%     95.000000  5200.000000   24.000000   30.000000  10295.000000  \n",
              "75%    116.000000  5500.000000   30.000000   34.000000  16503.000000  \n",
              "max    288.000000  6600.000000   49.000000   54.000000  45400.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5215703b-0b35-4478-9f5a-afb42512760f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>carlength</th>\n",
              "      <th>carwidth</th>\n",
              "      <th>carheight</th>\n",
              "      <th>curbweight</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>0.834146</td>\n",
              "      <td>98.756585</td>\n",
              "      <td>174.049268</td>\n",
              "      <td>65.907805</td>\n",
              "      <td>53.724878</td>\n",
              "      <td>2555.565854</td>\n",
              "      <td>126.907317</td>\n",
              "      <td>3.329756</td>\n",
              "      <td>3.255415</td>\n",
              "      <td>10.142537</td>\n",
              "      <td>104.117073</td>\n",
              "      <td>5125.121951</td>\n",
              "      <td>25.219512</td>\n",
              "      <td>30.751220</td>\n",
              "      <td>13276.710571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>59.322565</td>\n",
              "      <td>1.245307</td>\n",
              "      <td>6.021776</td>\n",
              "      <td>12.337289</td>\n",
              "      <td>2.145204</td>\n",
              "      <td>2.443522</td>\n",
              "      <td>520.680204</td>\n",
              "      <td>41.642693</td>\n",
              "      <td>0.270844</td>\n",
              "      <td>0.313597</td>\n",
              "      <td>3.972040</td>\n",
              "      <td>39.544167</td>\n",
              "      <td>476.985643</td>\n",
              "      <td>6.542142</td>\n",
              "      <td>6.886443</td>\n",
              "      <td>7988.852332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>166.300000</td>\n",
              "      <td>64.100000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2145.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7788.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.200000</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>2414.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>3.290000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>5200.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>10295.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>154.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>183.100000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>2935.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>3.580000</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>16503.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>205.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.100000</td>\n",
              "      <td>72.300000</td>\n",
              "      <td>59.800000</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>4.170000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>45400.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5215703b-0b35-4478-9f5a-afb42512760f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5215703b-0b35-4478-9f5a-afb42512760f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5215703b-0b35-4478-9f5a-afb42512760f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "cardf.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "12ca1ea7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12ca1ea7",
        "outputId": "448b8384-d742-4cdd-d2c6-2290939fbae8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   car_ID            205 non-null    int64  \n",
            " 1   symboling         205 non-null    int64  \n",
            " 2   CarName           205 non-null    object \n",
            " 3   fueltype          205 non-null    object \n",
            " 4   aspiration        205 non-null    object \n",
            " 5   doornumber        205 non-null    object \n",
            " 6   carbody           205 non-null    object \n",
            " 7   drivewheel        205 non-null    object \n",
            " 8   enginelocation    205 non-null    object \n",
            " 9   wheelbase         205 non-null    float64\n",
            " 10  carlength         205 non-null    float64\n",
            " 11  carwidth          205 non-null    float64\n",
            " 12  carheight         205 non-null    float64\n",
            " 13  curbweight        205 non-null    int64  \n",
            " 14  enginetype        205 non-null    object \n",
            " 15  cylindernumber    205 non-null    object \n",
            " 16  enginesize        205 non-null    int64  \n",
            " 17  fuelsystem        205 non-null    object \n",
            " 18  boreratio         205 non-null    float64\n",
            " 19  stroke            205 non-null    float64\n",
            " 20  compressionratio  205 non-null    float64\n",
            " 21  horsepower        205 non-null    int64  \n",
            " 22  peakrpm           205 non-null    int64  \n",
            " 23  citympg           205 non-null    int64  \n",
            " 24  highwaympg        205 non-null    int64  \n",
            " 25  price             205 non-null    float64\n",
            "dtypes: float64(8), int64(8), object(10)\n",
            "memory usage: 41.8+ KB\n"
          ]
        }
      ],
      "source": [
        "cardf.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "e77fd5d3",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "e77fd5d3",
        "outputId": "e91f449f-b6b8-4ea6-d715-382f9d8fbce1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     car_ID  symboling  CarName  fueltype  aspiration  doornumber  carbody  \\\n",
              "0     False      False    False     False       False       False    False   \n",
              "1     False      False    False     False       False       False    False   \n",
              "2     False      False    False     False       False       False    False   \n",
              "3     False      False    False     False       False       False    False   \n",
              "4     False      False    False     False       False       False    False   \n",
              "..      ...        ...      ...       ...         ...         ...      ...   \n",
              "200   False      False    False     False       False       False    False   \n",
              "201   False      False    False     False       False       False    False   \n",
              "202   False      False    False     False       False       False    False   \n",
              "203   False      False    False     False       False       False    False   \n",
              "204   False      False    False     False       False       False    False   \n",
              "\n",
              "     drivewheel  enginelocation  wheelbase  ...  enginesize  fuelsystem  \\\n",
              "0         False           False      False  ...       False       False   \n",
              "1         False           False      False  ...       False       False   \n",
              "2         False           False      False  ...       False       False   \n",
              "3         False           False      False  ...       False       False   \n",
              "4         False           False      False  ...       False       False   \n",
              "..          ...             ...        ...  ...         ...         ...   \n",
              "200       False           False      False  ...       False       False   \n",
              "201       False           False      False  ...       False       False   \n",
              "202       False           False      False  ...       False       False   \n",
              "203       False           False      False  ...       False       False   \n",
              "204       False           False      False  ...       False       False   \n",
              "\n",
              "     boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
              "0        False   False             False       False    False    False   \n",
              "1        False   False             False       False    False    False   \n",
              "2        False   False             False       False    False    False   \n",
              "3        False   False             False       False    False    False   \n",
              "4        False   False             False       False    False    False   \n",
              "..         ...     ...               ...         ...      ...      ...   \n",
              "200      False   False             False       False    False    False   \n",
              "201      False   False             False       False    False    False   \n",
              "202      False   False             False       False    False    False   \n",
              "203      False   False             False       False    False    False   \n",
              "204      False   False             False       False    False    False   \n",
              "\n",
              "     highwaympg  price  \n",
              "0         False  False  \n",
              "1         False  False  \n",
              "2         False  False  \n",
              "3         False  False  \n",
              "4         False  False  \n",
              "..          ...    ...  \n",
              "200       False  False  \n",
              "201       False  False  \n",
              "202       False  False  \n",
              "203       False  False  \n",
              "204       False  False  \n",
              "\n",
              "[205 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d244bbe3-bdba-47d0-99e2-6e62e14f7313\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d244bbe3-bdba-47d0-99e2-6e62e14f7313')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d244bbe3-bdba-47d0-99e2-6e62e14f7313 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d244bbe3-bdba-47d0-99e2-6e62e14f7313');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "cardf.isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "90633936",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90633936",
        "outputId": "ae17b1fe-4b09-45d9-c770-6d5051bf09c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "car_ID              0\n",
              "symboling           0\n",
              "CarName             0\n",
              "fueltype            0\n",
              "aspiration          0\n",
              "doornumber          0\n",
              "carbody             0\n",
              "drivewheel          0\n",
              "enginelocation      0\n",
              "wheelbase           0\n",
              "carlength           0\n",
              "carwidth            0\n",
              "carheight           0\n",
              "curbweight          0\n",
              "enginetype          0\n",
              "cylindernumber      0\n",
              "enginesize          0\n",
              "fuelsystem          0\n",
              "boreratio           0\n",
              "stroke              0\n",
              "compressionratio    0\n",
              "horsepower          0\n",
              "peakrpm             0\n",
              "citympg             0\n",
              "highwaympg          0\n",
              "price               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "cardf.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "ef20e4c0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ef20e4c0",
        "outputId": "0ad2671e-9a86-449d-f4a8-d65a441f7bfe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['gas', 'diesel'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "cardf.fueltype.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "8f4b98bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f4b98bb",
        "outputId": "0855faae-ab93-430a-d9ea-6de4ec06a020"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      dohc\n",
              "1      dohc\n",
              "2      ohcv\n",
              "3       ohc\n",
              "4       ohc\n",
              "       ... \n",
              "200     ohc\n",
              "201     ohc\n",
              "202    ohcv\n",
              "203     ohc\n",
              "204     ohc\n",
              "Name: enginetype, Length: 205, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "cardf.enginetype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "52db96fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52db96fa",
        "outputId": "9ebd191f-4e61-4ff8-bc0e-846aa5ec9bff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      130\n",
              "1      130\n",
              "2      152\n",
              "3      109\n",
              "4      136\n",
              "      ... \n",
              "200    141\n",
              "201    141\n",
              "202    173\n",
              "203    145\n",
              "204    141\n",
              "Name: enginesize, Length: 205, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "cardf.enginesize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "fac850d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fac850d6",
        "outputId": "12d6c853-56d9-498b-f59c-cc78e1845e4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      111\n",
              "1      111\n",
              "2      154\n",
              "3      102\n",
              "4      115\n",
              "      ... \n",
              "200    114\n",
              "201    160\n",
              "202    134\n",
              "203    106\n",
              "204    114\n",
              "Name: horsepower, Length: 205, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "cardf.horsepower"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "56044d81",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56044d81",
        "outputId": "325bf1b7-299d-4d67-8366-b698db680dfe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         gas\n",
              "1         gas\n",
              "2         gas\n",
              "3         gas\n",
              "4         gas\n",
              "        ...  \n",
              "200       gas\n",
              "201       gas\n",
              "202       gas\n",
              "203    diesel\n",
              "204       gas\n",
              "Name: fueltype, Length: 205, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "cardf.fueltype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "755cbbe4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "755cbbe4",
        "outputId": "dfdc50f2-388a-4931-db58-cd4e844e6ef4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "cardf.fueltype.replace(['gas','diesel'],[1,2],inplace = True)\n",
        "cardf['fueltype'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "ab4a35d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ab4a35d2",
        "outputId": "fea37846-52c9-40e3-e497-fbee2041cb21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "cardf.enginetype.replace( ['dohc', 'ohcv', 'ohc', 'l', 'rotor', 'ohcf', 'dohcv'] , [1,2,3,4,5,6,7],inplace =True)\n",
        "cardf[\"enginetype\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "04c69431",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04c69431",
        "outputId": "7b012645-3525-49a3-d2cc-0c349acea6bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "cardf.enginesize.unique()\n",
        "cardf.horsepower.unique()\n",
        "cardf.price.unique()\n",
        "cardf.fueltype.unique()\n",
        "\n",
        "cardf.enginetype.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "b8eab14e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "b8eab14e",
        "outputId": "90e65185-60d7-42b0-a81f-a2f57cc7e99f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     fueltype  enginetype  enginesize  horsepower    price\n",
              "0           1           1         130         111  13495.0\n",
              "1           1           1         130         111  16500.0\n",
              "2           1           2         152         154  16500.0\n",
              "3           1           3         109         102  13950.0\n",
              "4           1           3         136         115  17450.0\n",
              "..        ...         ...         ...         ...      ...\n",
              "200         1           3         141         114  16845.0\n",
              "201         1           3         141         160  19045.0\n",
              "202         1           2         173         134  21485.0\n",
              "203         2           3         145         106  22470.0\n",
              "204         1           3         141         114  22625.0\n",
              "\n",
              "[205 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15223108-4e8c-4d20-b8b3-eb8d98b48ca2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>fueltype</th>\n",
              "      <th>enginetype</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>111</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>111</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>152</td>\n",
              "      <td>154</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>109</td>\n",
              "      <td>102</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>136</td>\n",
              "      <td>115</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>141</td>\n",
              "      <td>114</td>\n",
              "      <td>16845.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>141</td>\n",
              "      <td>160</td>\n",
              "      <td>19045.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>173</td>\n",
              "      <td>134</td>\n",
              "      <td>21485.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>106</td>\n",
              "      <td>22470.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>141</td>\n",
              "      <td>114</td>\n",
              "      <td>22625.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15223108-4e8c-4d20-b8b3-eb8d98b48ca2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-15223108-4e8c-4d20-b8b3-eb8d98b48ca2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-15223108-4e8c-4d20-b8b3-eb8d98b48ca2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "car=cardf [['fueltype', 'enginetype','enginesize','horsepower','price']]\n",
        "#car.drop(car.columns[0],axis=1,inplace=True)\n",
        "car"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "2086be77",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2086be77",
        "outputId": "2ab3acc2-204f-4ed4-fdf2-aa02a9bc45c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      13495.0\n",
              "1      16500.0\n",
              "2      16500.0\n",
              "3      13950.0\n",
              "4      17450.0\n",
              "        ...   \n",
              "200    16845.0\n",
              "201    19045.0\n",
              "202    21485.0\n",
              "203    22470.0\n",
              "204    22625.0\n",
              "Name: price, Length: 205, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "car[\"price\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "db2bb73b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "db2bb73b",
        "outputId": "f9d7e26d-b199-40fb-b234-9a168500fbff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "car[\"fueltype\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "f133757f",
      "metadata": {
        "id": "f133757f"
      },
      "outputs": [],
      "source": [
        "car.to_csv('car.csv',header=None)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "c70a4ac3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c70a4ac3",
        "outputId": "93b31a97-100e-4697-8795-5445117de7d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   fueltype    205 non-null    int64  \n",
            " 1   enginetype  205 non-null    int64  \n",
            " 2   enginesize  205 non-null    int64  \n",
            " 3   horsepower  205 non-null    int64  \n",
            " 4   price       205 non-null    float64\n",
            "dtypes: float64(1), int64(4)\n",
            "memory usage: 8.1 KB\n"
          ]
        }
      ],
      "source": [
        "car.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "15050c30",
      "metadata": {
        "id": "15050c30"
      },
      "outputs": [],
      "source": [
        "car=car.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "28270a2b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28270a2b",
        "outputId": "ee5b908f-2107-41d2-b26f-33f8019556cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0000e+00, 1.0000e+00, 1.3000e+02, 1.1100e+02, 1.3495e+04],\n",
              "       [1.0000e+00, 1.0000e+00, 1.3000e+02, 1.1100e+02, 1.6500e+04],\n",
              "       [1.0000e+00, 2.0000e+00, 1.5200e+02, 1.5400e+02, 1.6500e+04],\n",
              "       ...,\n",
              "       [1.0000e+00, 2.0000e+00, 1.7300e+02, 1.3400e+02, 2.1485e+04],\n",
              "       [2.0000e+00, 3.0000e+00, 1.4500e+02, 1.0600e+02, 2.2470e+04],\n",
              "       [1.0000e+00, 3.0000e+00, 1.4100e+02, 1.1400e+02, 2.2625e+04]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "car"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "ad1bc150",
      "metadata": {
        "id": "ad1bc150"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "418d7539",
      "metadata": {
        "id": "418d7539"
      },
      "source": [
        "# Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "86613488",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "86613488",
        "outputId": "1d484cc7-1062-4985-dc89-d541e3cc39b1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 20 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cardf.hist(figsize=(10,20))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "5a1120f8",
      "metadata": {
        "id": "5a1120f8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "5b1d63df",
      "metadata": {
        "id": "5b1d63df"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "ebbfc4de",
      "metadata": {
        "id": "ebbfc4de"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "3c9d4cf7",
      "metadata": {
        "id": "3c9d4cf7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "e6797ffe",
      "metadata": {
        "id": "e6797ffe"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "c9736323",
      "metadata": {
        "id": "c9736323"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "cf0ae2e4",
      "metadata": {
        "id": "cf0ae2e4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "9a20c2e2",
      "metadata": {
        "id": "9a20c2e2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "4e1132c6",
      "metadata": {
        "id": "4e1132c6"
      },
      "source": [
        "# Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "f8e71849",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8e71849",
        "outputId": "292ddfee-b836-437e-fa15-1e4f1329e98f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "X = cardf [['fueltype', 'enginetype','enginesize','horsepower']]\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "dfe8ef90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfe8ef90",
        "outputId": "ec4b40f2-207f-4ec6-b2fd-67bb0a0678bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "Y = cardf[['price']]\n",
        "Y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "36e4849a",
      "metadata": {
        "id": "36e4849a"
      },
      "outputs": [],
      "source": [
        "X=X.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "60847b43",
      "metadata": {
        "id": "60847b43"
      },
      "outputs": [],
      "source": [
        "Y=Y.values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6f0f10f",
      "metadata": {
        "id": "a6f0f10f"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "9a01a617",
      "metadata": {
        "id": "9a01a617"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "1e7195df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e7195df",
        "outputId": "d24e0239-8653-43cf-958b-2ac334ad5d18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train: (164, 4)\n",
            "y_train: (164, 1)\n",
            "x_test: (41, 4)\n",
            "y_test: (41, 1)\n"
          ]
        }
      ],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X,Y, test_size=0.20,shuffle=False,random_state=42)\n",
        "print(\"x_train:\",x_train.shape)\n",
        "print(\"y_train:\",y_train.shape)\n",
        "print(\"x_test:\",x_test.shape)\n",
        "print(\"y_test:\",y_test.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "db6fcf20",
      "metadata": {
        "id": "db6fcf20"
      },
      "outputs": [],
      "source": [
        "Model1  = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "1bee59cc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "1bee59cc",
        "outputId": "3a44c795-19fd-46d4-afdc-194841e6fe0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "Model1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "b934efaf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "b934efaf",
        "outputId": "07c69f4d-2fde-4366-ceec-ca4875b620a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "Model1.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "5d43dab8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d43dab8",
        "outputId": "508da76a-9ea2-403b-a0ea-8d81f1c97ed9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7135.44384793],\n",
              "       [ 8913.13454963],\n",
              "       [ 8913.13454963],\n",
              "       [15916.01162096],\n",
              "       [15916.01162096],\n",
              "       [15916.01162096],\n",
              "       [15916.01162096],\n",
              "       [15916.01162096],\n",
              "       [15916.01162096],\n",
              "       [11455.41972232],\n",
              "       [11916.58295533],\n",
              "       [11455.41972232],\n",
              "       [11455.41972232],\n",
              "       [11455.41972232],\n",
              "       [20793.37595044],\n",
              "       [20793.37595044],\n",
              "       [20441.83588984],\n",
              "       [19286.33605327],\n",
              "       [ 8937.96491328],\n",
              "       [ 9461.11384994],\n",
              "       [ 8937.96491328],\n",
              "       [ 9461.11384994],\n",
              "       [ 9461.11384994],\n",
              "       [10062.8931072 ],\n",
              "       [10515.73403174],\n",
              "       [ 9812.65391054],\n",
              "       [ 9812.65391054],\n",
              "       [14338.66371167],\n",
              "       [10062.8931072 ],\n",
              "       [ 9672.0378863 ],\n",
              "       [15197.64567843],\n",
              "       [15197.64567843],\n",
              "       [15197.64567843],\n",
              "       [15197.64567843],\n",
              "       [17301.38043994],\n",
              "       [17301.38043994],\n",
              "       [15197.64567843],\n",
              "       [18431.81423593],\n",
              "       [19713.78249418],\n",
              "       [18280.99678327],\n",
              "       [15197.64567843]])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "prediction1=Model1.predict(x_test)\n",
        "prediction1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "7062d698",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7062d698",
        "outputId": "cc9b4d65-50ed-445c-d3dc-f7bbe4ea2c81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47.232450710924354 % errors\n"
          ]
        }
      ],
      "source": [
        "error_score1=(r2_score(y_test,prediction1))*100\n",
        "print(error_score1,\"%\", \"errors\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "32656b38",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32656b38",
        "outputId": "2df7fe58-6c40-41e7-86ae-093f8f149572"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12540.42601453]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "Model1.predict([[1,1,130,111]])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9c2c3a8",
      "metadata": {
        "id": "b9c2c3a8"
      },
      "source": [
        "## Decision Tree Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "80e2eb93",
      "metadata": {
        "id": "80e2eb93"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "a1022283",
      "metadata": {
        "id": "a1022283"
      },
      "outputs": [],
      "source": [
        "Model2  = DecisionTreeRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "17f4e432",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "17f4e432",
        "outputId": "279a692b-db83-40e3-f94e-32947aed8d0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "Model2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "e58ae521",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "e58ae521",
        "outputId": "b7188766-92e3-4c58-c26a-2d93178d5612"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "Model2.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "0e8c5b67",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e8c5b67",
        "outputId": "626b1a14-f54e-4977-83f4-5252127c3077"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7969.42857143,  9418.        ,  9418.        , 11415.66666667,\n",
              "       11415.66666667, 11415.66666667, 11415.66666667, 11415.66666667,\n",
              "       11415.66666667, 10270.5       , 10698.        , 10270.5       ,\n",
              "       10270.5       , 10270.5       , 16278.        , 16278.        ,\n",
              "       15690.        , 15750.        ,  7885.        ,  8221.66666667,\n",
              "        7885.        ,  8221.66666667,  8221.66666667, 11670.        ,\n",
              "        9995.        , 10787.5       , 10787.5       , 16293.75      ,\n",
              "       11670.        , 12290.        , 16387.5       , 16387.5       ,\n",
              "       16387.5       , 16387.5       , 18685.        , 18685.        ,\n",
              "       16387.5       , 19045.        , 21485.        , 22470.        ,\n",
              "       16387.5       ])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "prediction2=Model2.predict(x_test)\n",
        "prediction2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "4edfc700",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4edfc700",
        "outputId": "874bd73e-5513-43e9-9dc5-eff04d109261"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "81.47517130118844 % errors\n"
          ]
        }
      ],
      "source": [
        "error_score2=(r2_score(y_test,prediction2))*100\n",
        "print(error_score2,\"%\", \"errors\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acf5f9ad",
      "metadata": {
        "id": "acf5f9ad"
      },
      "source": [
        "## Kneighbor Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "a68d0f95",
      "metadata": {
        "id": "a68d0f95"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "3f4a3ed0",
      "metadata": {
        "id": "3f4a3ed0"
      },
      "outputs": [],
      "source": [
        "Model3=KNeighborsRegressor(n_neighbors=5,p=2, metric='minkowski')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "fe862269",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "fe862269",
        "outputId": "dc72379f-b8fa-4779-ff38-b872afbde2e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "Model3.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "fc5b63c9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc5b63c9",
        "outputId": "de53b705-1951-498c-bba3-d7f8f200b095"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7898.    ],\n",
              "       [ 8661.6   ],\n",
              "       [ 8661.6   ],\n",
              "       [10165.    ],\n",
              "       [10165.    ],\n",
              "       [10165.    ],\n",
              "       [10165.    ],\n",
              "       [10165.    ],\n",
              "       [10165.    ],\n",
              "       [10426.    ],\n",
              "       [ 7982.8   ],\n",
              "       [10426.    ],\n",
              "       [10426.    ],\n",
              "       [10426.    ],\n",
              "       [16768.8   ],\n",
              "       [16768.8   ],\n",
              "       [16239.    ],\n",
              "       [16099.2   ],\n",
              "       [ 6939.2   ],\n",
              "       [ 8571.    ],\n",
              "       [ 6939.2   ],\n",
              "       [ 8571.    ],\n",
              "       [ 8571.    ],\n",
              "       [ 8749.4   ],\n",
              "       [13328.    ],\n",
              "       [10411.    ],\n",
              "       [10411.    ],\n",
              "       [16525.    ],\n",
              "       [ 8749.4   ],\n",
              "       [10411.    ],\n",
              "       [16982.    ],\n",
              "       [16982.    ],\n",
              "       [16982.    ],\n",
              "       [16982.    ],\n",
              "       [18399.8334],\n",
              "       [18399.8334],\n",
              "       [16982.    ],\n",
              "       [18154.8334],\n",
              "       [27012.2   ],\n",
              "       [18107.    ],\n",
              "       [16982.    ]])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "prediction3=Model3.predict(x_test)\n",
        "prediction3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "1372e835",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1372e835",
        "outputId": "f64625fa-4872-4a1d-c15b-37c8a70d92b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66.94680160102679 % errors\n"
          ]
        }
      ],
      "source": [
        "error_score3=(r2_score(y_test,prediction3))*100\n",
        "print(error_score3,\"%\", \"errors\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "fcd683fd",
      "metadata": {
        "id": "fcd683fd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "ae39d34f",
      "metadata": {
        "id": "ae39d34f"
      },
      "outputs": [],
      "source": [
        "Model4=KNeighborsRegressor(n_neighbors=10,p=2, metric='minkowski')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "c0d4de67",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "c0d4de67",
        "outputId": "08e86178-793d-49b9-c7a7-0d975086b3fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "Model4.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "48ccd949",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48ccd949",
        "outputId": "a1dccdc7-9466-41c5-f276-9e26c8e678f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7868.3   ],\n",
              "       [ 9318.8   ],\n",
              "       [ 9318.8   ],\n",
              "       [13077.9   ],\n",
              "       [13077.9   ],\n",
              "       [13077.9   ],\n",
              "       [13077.9   ],\n",
              "       [13077.9   ],\n",
              "       [13077.9   ],\n",
              "       [10881.4   ],\n",
              "       [ 7994.4   ],\n",
              "       [10881.4   ],\n",
              "       [10881.4   ],\n",
              "       [10881.4   ],\n",
              "       [15749.1   ],\n",
              "       [15749.1   ],\n",
              "       [15362.    ],\n",
              "       [14821.1   ],\n",
              "       [ 7111.6   ],\n",
              "       [ 8856.1   ],\n",
              "       [ 7111.6   ],\n",
              "       [ 8856.1   ],\n",
              "       [ 8856.1   ],\n",
              "       [ 8034.2   ],\n",
              "       [12232.3   ],\n",
              "       [ 9466.    ],\n",
              "       [ 9466.    ],\n",
              "       [15892.    ],\n",
              "       [ 8034.2   ],\n",
              "       [ 9466.    ],\n",
              "       [14385.2   ],\n",
              "       [14385.2   ],\n",
              "       [14385.2   ],\n",
              "       [14385.2   ],\n",
              "       [18607.2167],\n",
              "       [18607.2167],\n",
              "       [14385.2   ],\n",
              "       [18421.5167],\n",
              "       [22959.9   ],\n",
              "       [17267.5   ],\n",
              "       [14385.2   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "prediction4=Model4.predict(x_test)\n",
        "prediction4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "19a2c566",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19a2c566",
        "outputId": "1e05e081-492b-43c0-8c20-d472f50f691d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64.70055000608576 % errors\n"
          ]
        }
      ],
      "source": [
        "error_score4=(r2_score(y_test,prediction4))*100\n",
        "print(error_score4,\"%\", \"errors\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "b48a3ff6",
      "metadata": {
        "id": "b48a3ff6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "b27ba0bb",
      "metadata": {
        "id": "b27ba0bb"
      },
      "outputs": [],
      "source": [
        "Model5=KNeighborsRegressor(n_neighbors=50,p=1, metric='euclidean')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "4a80893e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4a80893e",
        "outputId": "1a0153c4-2135-4f13-ee36-da8e1a1abf50"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(metric='euclidean', n_neighbors=50, p=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(metric=&#x27;euclidean&#x27;, n_neighbors=50, p=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(metric=&#x27;euclidean&#x27;, n_neighbors=50, p=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "Model5.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "bcc3dfef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcc3dfef",
        "outputId": "a245ea96-381a-4d8b-e676-4c2c1967b4b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7193.48   ],\n",
              "       [11128.22   ],\n",
              "       [11128.22   ],\n",
              "       [15357.3    ],\n",
              "       [15357.3    ],\n",
              "       [15357.3    ],\n",
              "       [15357.3    ],\n",
              "       [15357.3    ],\n",
              "       [15357.3    ],\n",
              "       [10469.8    ],\n",
              "       [ 8412.76   ],\n",
              "       [10469.8    ],\n",
              "       [10469.8    ],\n",
              "       [10469.8    ],\n",
              "       [19791.78334],\n",
              "       [19791.78334],\n",
              "       [18645.00334],\n",
              "       [17474.10334],\n",
              "       [ 7162.2    ],\n",
              "       [10183.44   ],\n",
              "       [ 7162.2    ],\n",
              "       [10183.44   ],\n",
              "       [10183.44   ],\n",
              "       [ 7174.34   ],\n",
              "       [10756.74   ],\n",
              "       [10175.62   ],\n",
              "       [10175.62   ],\n",
              "       [13352.1    ],\n",
              "       [ 7174.34   ],\n",
              "       [10193.26   ],\n",
              "       [14914.42   ],\n",
              "       [14914.42   ],\n",
              "       [14914.42   ],\n",
              "       [14914.42   ],\n",
              "       [15591.14334],\n",
              "       [15591.14334],\n",
              "       [14914.42   ],\n",
              "       [16238.38334],\n",
              "       [17319.48   ],\n",
              "       [14099.94   ],\n",
              "       [14914.42   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "prediction5=Model5.predict(x_test)\n",
        "prediction5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "df1066f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df1066f6",
        "outputId": "90c8993a-19bf-4946-e075-52ad2c91af4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38.22817855813159 % errors\n"
          ]
        }
      ],
      "source": [
        "error_score5=(r2_score(y_test,prediction5))*100\n",
        "print(error_score5,\"%\", \"errors\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "c57499e1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c57499e1",
        "outputId": "21fc4a33-b507-486f-b5a1-f35a4bd55299"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[17370.22334]])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "#'fueltype', 'enginetype','enginesize','horsepower'\n",
        "Model5.predict([[1,2,152,154]])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fee3e6e3",
      "metadata": {
        "id": "fee3e6e3"
      },
      "source": [
        "# Comparison of different models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "0907d736",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "0907d736",
        "outputId": "d5a3b618-0979-4169-b910-2a6aee33e41c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "error=[error_score1,error_score2,error_score3,error_score4,error_score5]\n",
        "num=[1,2,3,4,5]\n",
        "models=[\"Linear Regression\",\"Decision Tree Regressor\",\"KNeighbor Regresor_1\",\"KNeighbor Regresor_2\",\"KNeighbor Regresor_3\"]\n",
        "a=sns.barplot(x=num,y=error,hue=models)\n",
        "a.set(xlabel=\"Models\",ylabel=\"Error\")\n",
        "plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58f90fd5",
      "metadata": {
        "id": "58f90fd5"
      },
      "source": [
        "# Result"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "428cdf70",
      "metadata": {
        "id": "428cdf70"
      },
      "source": [
        "when we use Model5 i.e, \"Model5=KNeighborsRegressor(n_neighbors=50,p=1, metric='euclidean')\" then we get less errors i.e, \"38.3277370472583 % errors\". So we use this model for prediction the price of Car."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a1b6855c",
      "metadata": {
        "id": "a1b6855c"
      },
      "source": [
        "# Joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "4f3b6163",
      "metadata": {
        "id": "4f3b6163"
      },
      "outputs": [],
      "source": [
        "import joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "cc95a446",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc95a446",
        "outputId": "0e4e3a07-38ae-4e12-a7a5-91da7939e6c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['car.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "joblib.dump(Model5 , 'car.joblib' )   #Kneighbor_5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "a88924ee",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a88924ee",
        "outputId": "b267b0cc-e51a-417a-9e5b-154e2d0483fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[7448.74]]\n",
            "[[12317.72334]]\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "import numpy as np\n",
        "\n",
        "newModel = joblib.load(\"car.joblib\")\n",
        "d1 = np.array([[4,78,2,100]])\n",
        "print( newModel.predict(d1))\n",
        "d1 = np.array([[3,100,1,200]])\n",
        "print(newModel.predict(d1) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "2c0fbe7f",
      "metadata": {
        "id": "2c0fbe7f"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "fe646079",
      "metadata": {
        "id": "fe646079"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}