Multiple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **[YBI Foundation](https://www.ybifoundation.org/)**"
      ],
      "metadata": {
        "id": "cV3D0DjkIEXJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[Join Telegram to Get Updates of all Future FREE Bootcamps and Courses](https://telegram.me/ybif_ybifoundation)**"
      ],
      "metadata": {
        "id": "5-TOzRfWIEXJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Multiple Linear Regression**"
      ],
      "metadata": {
        "id": "W6xdAv0dIEXJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[Watch Video Tutorial](https://www.youtube.com/c/YBIFoundation?sub_confirmation=1)**"
      ],
      "metadata": {
        "id": "OHcw9SXRIEXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1 : import library\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "HFawugBVIEXK"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2 : import data\n",
        "house = pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/Boston.csv')"
      ],
      "metadata": {
        "id": "HEKleXPwIEXK"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CJMYHPNGMIYb",
        "outputId": "60e6178d-0604-4378-9f2b-33e15b0d050b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS     NX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c450e626-b399-4aa2-8d7c-b4f8270ff3f5\">\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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c450e626-b399-4aa2-8d7c-b4f8270ff3f5')\"\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-c450e626-b399-4aa2-8d7c-b4f8270ff3f5 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-c450e626-b399-4aa2-8d7c-b4f8270ff3f5');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHHkcDmwMIcK",
        "outputId": "44efaff8-bb4a-4437-aed7-18d3fe28690e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   CRIM     506 non-null    float64\n",
            " 1   ZN       506 non-null    float64\n",
            " 2   INDUS    506 non-null    float64\n",
            " 3   CHAS     506 non-null    int64  \n",
            " 4   NX       506 non-null    float64\n",
            " 5   RM       506 non-null    float64\n",
            " 6   AGE      506 non-null    float64\n",
            " 7   DIS      506 non-null    float64\n",
            " 8   RAD      506 non-null    int64  \n",
            " 9   TAX      506 non-null    float64\n",
            " 10  PTRATIO  506 non-null    float64\n",
            " 11  B        506 non-null    float64\n",
            " 12  LSTAT    506 non-null    float64\n",
            " 13  MEDV     506 non-null    float64\n",
            "dtypes: float64(12), int64(2)\n",
            "memory usage: 55.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LTcvLFtuMIfu",
        "outputId": "9fec658d-59f6-455a-d258-88f4a2767cc4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             CRIM          ZN       INDUS        CHAS          NX          RM  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
              "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
              "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
              "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
              "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
              "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
              "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
              "\n",
              "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
              "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
              "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
              "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
              "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
              "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
              "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
              "\n",
              "            LSTAT        MEDV  \n",
              "count  506.000000  506.000000  \n",
              "mean    12.653063   22.532806  \n",
              "std      7.141062    9.197104  \n",
              "min      1.730000    5.000000  \n",
              "25%      6.950000   17.025000  \n",
              "50%     11.360000   21.200000  \n",
              "75%     16.955000   25.000000  \n",
              "max     37.970000   50.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19d4a419-5a6c-4b10-88ab-1b62a6b0f7f0\">\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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19d4a419-5a6c-4b10-88ab-1b62a6b0f7f0')\"\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-19d4a419-5a6c-4b10-88ab-1b62a6b0f7f0 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-19d4a419-5a6c-4b10-88ab-1b62a6b0f7f0');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3 : define target (y) and features (X)"
      ],
      "metadata": {
        "id": "Uj8st72NIEXK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2e57ab1-66e9-4dac-a64f-d92323d55b31",
        "id": "LhttRpxLIEXK"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
              "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = house['MEDV']"
      ],
      "metadata": {
        "id": "VYGUVC2dIEXK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = house.drop(['MEDV'],axis=1)"
      ],
      "metadata": {
        "id": "r7eF9atZIEXL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4 : train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)"
      ],
      "metadata": {
        "id": "Pm4Mq56eIEXL"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shape of train and test sample\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80e3eb71-bfcb-4e7f-bb5f-aa8542392a7c",
        "id": "SkaYtiMaIEXL"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((354, 13), (152, 13), (354,), (152,))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5 : select model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "wPb7H1QwIEXL"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6 : train or fit model\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "c0d5440a-95cd-459b-8b99-e325ae24d430",
        "id": "OEQQCVGVIEXL"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35447555-5870-4cea-ac4d-e0ca0a9a56e3",
        "id": "9-qn0tAVIEXL"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34.21916368862993"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f08ad1a0-8fb8-4291-b668-45967b8e4755",
        "id": "RmrR-seXIEXM"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.29412069e-01,  3.65184937e-02,  1.54418944e-02,  2.35486887e+00,\n",
              "       -2.04171489e+01,  4.41356565e+00,  4.61075512e-03, -1.58626723e+00,\n",
              "        2.51478665e-01, -9.59591213e-03, -9.64169204e-01,  1.00972679e-02,\n",
              "       -5.43198745e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7 : predict model\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "8drDi3yAIEXM"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fa0ea04-0465-41c7-f5d1-8d45f9f5d702",
        "id": "0WCIXVocIEXM"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([31.71733828, 22.02143302, 21.16613197, 39.77837246, 20.10258512,\n",
              "       22.86056216, 18.35574643, 14.7902735 , 22.55778646, 21.34594953,\n",
              "       18.38491085, 27.9664665 , 29.85929012,  6.44680773, 10.68297311,\n",
              "       26.24809521, 21.89368671, 25.22692365,  3.62385942, 36.21920372,\n",
              "       24.07812335, 22.94103934, 14.27095261, 20.79013279, 24.22725035,\n",
              "       16.7379611 , 18.74856986, 20.96709658, 28.513571  , 20.86346628,\n",
              "        9.23450577, 17.06754852, 22.06953886, 22.23121875, 39.25875323,\n",
              "       26.16769924, 42.50354003, 19.34517962, 34.51869058, 14.07023676,\n",
              "       13.81055358, 23.27727535, 11.79100403,  9.01040731, 21.64587594,\n",
              "       25.55339317, 18.16941728, 16.81991401, 14.66170215, 14.86477172,\n",
              "       33.78924259, 33.26959074, 15.49208778, 24.08269034, 27.63531226,\n",
              "       19.58288727, 45.02488529, 20.96959671, 20.07202649, 27.67146866,\n",
              "       34.59154418, 12.71353064, 23.66247812, 31.65792337, 28.97459925,\n",
              "       32.45963484, 13.93494747, 35.491924  , 19.35871482, 19.60341885,\n",
              "        1.43927038, 24.10206738, 33.67200257, 20.62160583, 26.89383792,\n",
              "       21.28629335, 31.94640391, 29.73908623, 13.93454775, 13.81678383,\n",
              "       19.75873615, 21.54069878, 20.86933991, 23.62698265, 28.79508068,\n",
              "       23.64118169,  6.95157816, 22.19831966, -6.82270042, 16.96842453,\n",
              "       16.76859897, 25.43664303, 14.95151023,  3.71667789, 15.02525824,\n",
              "       16.90607726, 21.45897878, 31.65915538, 30.72068155, 23.72584448,\n",
              "       22.18882729, 13.76042247, 18.47384318, 18.1524094 , 36.60119404,\n",
              "       27.49121167, 11.00093835, 17.26407285, 22.49004463, 16.52993633,\n",
              "       29.49279312, 22.89418353, 24.67840473, 20.37710587, 19.68603018,\n",
              "       22.55437435, 27.31673957, 24.86003524, 20.2018396 , 29.14358757,\n",
              "        7.42840113,  5.85287912, 25.34843348, 38.73123659, 23.94325177,\n",
              "       25.28198173, 20.11046586, 19.75220882, 25.06978342, 35.15909482,\n",
              "       27.31951047, 27.2616268 , 31.39965843, 16.55315203, 14.29555368,\n",
              "       23.76937723,  7.64840244, 23.34914332, 21.36612339, 26.12068678,\n",
              "       25.31847859, 13.1171793 , 17.66685837, 36.19968161, 20.50074493,\n",
              "       27.94813333, 22.45926502, 18.14585016, 31.24201417, 20.85014715,\n",
              "       27.35824971, 30.53239318])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8 : model accuracy\n",
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error"
      ],
      "metadata": {
        "id": "ufx9nzgqIEXM"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5ac4b07-49eb-4d54-b3f0-b4bfc49ea1f8",
        "id": "KU2B-K1HIEXM"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.155030927602485"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b794c599-c271-4063-87f2-81f02619e21a",
        "id": "i9TGy1bWIEXN"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1635593588221789"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ac129cc-7bc0-4427-d4f8-98a14effceb6",
        "id": "S30N2JqxIEXN"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.718012877838433"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Star and Watch on GitHub to Receive Updates**\n",
        "**Action 1: ⭐Star Repository as it make easy for you to find it again. You can see all the repositories and topics you have starred by going to your stars page.**\n",
        "\n",
        "**Action 2: 👁 Watch Repository and get notified of all future updates and activities in this repository.**\n",
        "\n",
        "**[Click Here to Visit Fundamental Repository on GitHub](https://github.com/YBIFoundation/Fundamentals)**"
      ],
      "metadata": {
        "id": "W6x2msrfIEXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "z7bJb7ylIEXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "yolqpA6VIEXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Upvote NoteBook on Kaggle and Receive Updates** \n",
        "**[Click Here to Visit Kaggle](https://www.kaggle.com/ybifoundation)**"
      ],
      "metadata": {
        "id": "RwfrkluGIEXO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "M-lgRjWOIEXO"
      }
    }
  ]
}