multiple_linear_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CazISR8X_HUG"
      },
      "source": [
        "# Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOyqYHTk_Q57"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "cv45D37-0r--"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgC61-ah_WIz"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('50_Startups.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "1Ew0ToWc0wSC"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVkxxMlI4WF2",
        "outputId": "44e6a7f4-1845-432f-b9c8-90b5a1b1e6e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
            "0   165349.20       136897.80        471784.10    New York  192261.83\n",
            "1   162597.70       151377.59        443898.53  California  191792.06\n",
            "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
            "3   144372.41       118671.85        383199.62    New York  182901.99\n",
            "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
            "5   131876.90        99814.71        362861.36    New York  156991.12\n",
            "6   134615.46       147198.87        127716.82  California  156122.51\n",
            "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
            "8   120542.52       148718.95        311613.29    New York  152211.77\n",
            "9   123334.88       108679.17        304981.62  California  149759.96\n",
            "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
            "11  100671.96        91790.61        249744.55  California  144259.40\n",
            "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
            "13   91992.39       135495.07        252664.93  California  134307.35\n",
            "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
            "15  114523.61       122616.84        261776.23    New York  129917.04\n",
            "16   78013.11       121597.55        264346.06  California  126992.93\n",
            "17   94657.16       145077.58        282574.31    New York  125370.37\n",
            "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
            "19   86419.70       153514.11             0.00    New York  122776.86\n",
            "20   76253.86       113867.30        298664.47  California  118474.03\n",
            "21   78389.47       153773.43        299737.29    New York  111313.02\n",
            "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
            "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
            "24   77044.01        99281.34        140574.81    New York  108552.04\n",
            "25   64664.71       139553.16        137962.62  California  107404.34\n",
            "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
            "27   72107.60       127864.55        353183.81    New York  105008.31\n",
            "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
            "29   65605.48       153032.06        107138.38    New York  101004.64\n",
            "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
            "31   61136.38       152701.92         88218.23    New York   97483.56\n",
            "32   63408.86       129219.61         46085.25  California   97427.84\n",
            "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
            "34   46426.07       157693.92        210797.67  California   96712.80\n",
            "35   46014.02        85047.44        205517.64    New York   96479.51\n",
            "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
            "37   44069.95        51283.14        197029.42  California   89949.14\n",
            "38   20229.59        65947.93        185265.10    New York   81229.06\n",
            "39   38558.51        82982.09        174999.30  California   81005.76\n",
            "40   28754.33       118546.05        172795.67  California   78239.91\n",
            "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
            "42   23640.93        96189.63        148001.11  California   71498.49\n",
            "43   15505.73       127382.30         35534.17    New York   69758.98\n",
            "44   22177.74       154806.14         28334.72  California   65200.33\n",
            "45    1000.23       124153.04          1903.93    New York   64926.08\n",
            "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
            "47       0.00       135426.92             0.00  California   42559.73\n",
            "48     542.05        51743.15             0.00    New York   35673.41\n",
            "49       0.00       116983.80         45173.06  California   14681.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pz32JYsK4bOb",
        "outputId": "79688b3c-438a-42f6-b07a-3319fcfb4c54"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[192261.83 191792.06 191050.39 182901.99 166187.94 156991.12 156122.51\n",
            " 155752.6  152211.77 149759.96 146121.95 144259.4  141585.52 134307.35\n",
            " 132602.65 129917.04 126992.93 125370.37 124266.9  122776.86 118474.03\n",
            " 111313.02 110352.25 108733.99 108552.04 107404.34 105733.54 105008.31\n",
            " 103282.38 101004.64  99937.59  97483.56  97427.84  96778.92  96712.8\n",
            "  96479.51  90708.19  89949.14  81229.06  81005.76  78239.91  77798.83\n",
            "  71498.49  69758.98  65200.33  64926.08  49490.75  42559.73  35673.41\n",
            "  14681.4 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82PF9-Tt4hdr",
        "outputId": "728ec324-684c-4002-9021-a9c5ab28925e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[165349.2 136897.8 471784.1 'New York']\n",
            " [162597.7 151377.59 443898.53 'California']\n",
            " [153441.51 101145.55 407934.54 'Florida']\n",
            " [144372.41 118671.85 383199.62 'New York']\n",
            " [142107.34 91391.77 366168.42 'Florida']\n",
            " [131876.9 99814.71 362861.36 'New York']\n",
            " [134615.46 147198.87 127716.82 'California']\n",
            " [130298.13 145530.06 323876.68 'Florida']\n",
            " [120542.52 148718.95 311613.29 'New York']\n",
            " [123334.88 108679.17 304981.62 'California']\n",
            " [101913.08 110594.11 229160.95 'Florida']\n",
            " [100671.96 91790.61 249744.55 'California']\n",
            " [93863.75 127320.38 249839.44 'Florida']\n",
            " [91992.39 135495.07 252664.93 'California']\n",
            " [119943.24 156547.42 256512.92 'Florida']\n",
            " [114523.61 122616.84 261776.23 'New York']\n",
            " [78013.11 121597.55 264346.06 'California']\n",
            " [94657.16 145077.58 282574.31 'New York']\n",
            " [91749.16 114175.79 294919.57 'Florida']\n",
            " [86419.7 153514.11 0.0 'New York']\n",
            " [76253.86 113867.3 298664.47 'California']\n",
            " [78389.47 153773.43 299737.29 'New York']\n",
            " [73994.56 122782.75 303319.26 'Florida']\n",
            " [67532.53 105751.03 304768.73 'Florida']\n",
            " [77044.01 99281.34 140574.81 'New York']\n",
            " [64664.71 139553.16 137962.62 'California']\n",
            " [75328.87 144135.98 134050.07 'Florida']\n",
            " [72107.6 127864.55 353183.81 'New York']\n",
            " [66051.52 182645.56 118148.2 'Florida']\n",
            " [65605.48 153032.06 107138.38 'New York']\n",
            " [61994.48 115641.28 91131.24 'Florida']\n",
            " [61136.38 152701.92 88218.23 'New York']\n",
            " [63408.86 129219.61 46085.25 'California']\n",
            " [55493.95 103057.49 214634.81 'Florida']\n",
            " [46426.07 157693.92 210797.67 'California']\n",
            " [46014.02 85047.44 205517.64 'New York']\n",
            " [28663.76 127056.21 201126.82 'Florida']\n",
            " [44069.95 51283.14 197029.42 'California']\n",
            " [20229.59 65947.93 185265.1 'New York']\n",
            " [38558.51 82982.09 174999.3 'California']\n",
            " [28754.33 118546.05 172795.67 'California']\n",
            " [27892.92 84710.77 164470.71 'Florida']\n",
            " [23640.93 96189.63 148001.11 'California']\n",
            " [15505.73 127382.3 35534.17 'New York']\n",
            " [22177.74 154806.14 28334.72 'California']\n",
            " [1000.23 124153.04 1903.93 'New York']\n",
            " [1315.46 115816.21 297114.46 'Florida']\n",
            " [0.0 135426.92 0.0 'California']\n",
            " [542.05 51743.15 0.0 'New York']\n",
            " [0.0 116983.8 45173.06 'California']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VadrvE7s_lS9"
      },
      "source": [
        "## Encoding categorical data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "metadata": {
        "id": "5Cmv6LAo1lZ3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBB3ZvcJ4GxV",
        "outputId": "e295334a-3f11-467d-f058-a3fbd1cf95e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 0.0 1.0 165349.2 136897.8 471784.1]\n",
            " [1.0 0.0 0.0 162597.7 151377.59 443898.53]\n",
            " [0.0 1.0 0.0 153441.51 101145.55 407934.54]\n",
            " [0.0 0.0 1.0 144372.41 118671.85 383199.62]\n",
            " [0.0 1.0 0.0 142107.34 91391.77 366168.42]\n",
            " [0.0 0.0 1.0 131876.9 99814.71 362861.36]\n",
            " [1.0 0.0 0.0 134615.46 147198.87 127716.82]\n",
            " [0.0 1.0 0.0 130298.13 145530.06 323876.68]\n",
            " [0.0 0.0 1.0 120542.52 148718.95 311613.29]\n",
            " [1.0 0.0 0.0 123334.88 108679.17 304981.62]\n",
            " [0.0 1.0 0.0 101913.08 110594.11 229160.95]\n",
            " [1.0 0.0 0.0 100671.96 91790.61 249744.55]\n",
            " [0.0 1.0 0.0 93863.75 127320.38 249839.44]\n",
            " [1.0 0.0 0.0 91992.39 135495.07 252664.93]\n",
            " [0.0 1.0 0.0 119943.24 156547.42 256512.92]\n",
            " [0.0 0.0 1.0 114523.61 122616.84 261776.23]\n",
            " [1.0 0.0 0.0 78013.11 121597.55 264346.06]\n",
            " [0.0 0.0 1.0 94657.16 145077.58 282574.31]\n",
            " [0.0 1.0 0.0 91749.16 114175.79 294919.57]\n",
            " [0.0 0.0 1.0 86419.7 153514.11 0.0]\n",
            " [1.0 0.0 0.0 76253.86 113867.3 298664.47]\n",
            " [0.0 0.0 1.0 78389.47 153773.43 299737.29]\n",
            " [0.0 1.0 0.0 73994.56 122782.75 303319.26]\n",
            " [0.0 1.0 0.0 67532.53 105751.03 304768.73]\n",
            " [0.0 0.0 1.0 77044.01 99281.34 140574.81]\n",
            " [1.0 0.0 0.0 64664.71 139553.16 137962.62]\n",
            " [0.0 1.0 0.0 75328.87 144135.98 134050.07]\n",
            " [0.0 0.0 1.0 72107.6 127864.55 353183.81]\n",
            " [0.0 1.0 0.0 66051.52 182645.56 118148.2]\n",
            " [0.0 0.0 1.0 65605.48 153032.06 107138.38]\n",
            " [0.0 1.0 0.0 61994.48 115641.28 91131.24]\n",
            " [0.0 0.0 1.0 61136.38 152701.92 88218.23]\n",
            " [1.0 0.0 0.0 63408.86 129219.61 46085.25]\n",
            " [0.0 1.0 0.0 55493.95 103057.49 214634.81]\n",
            " [1.0 0.0 0.0 46426.07 157693.92 210797.67]\n",
            " [0.0 0.0 1.0 46014.02 85047.44 205517.64]\n",
            " [0.0 1.0 0.0 28663.76 127056.21 201126.82]\n",
            " [1.0 0.0 0.0 44069.95 51283.14 197029.42]\n",
            " [0.0 0.0 1.0 20229.59 65947.93 185265.1]\n",
            " [1.0 0.0 0.0 38558.51 82982.09 174999.3]\n",
            " [1.0 0.0 0.0 28754.33 118546.05 172795.67]\n",
            " [0.0 1.0 0.0 27892.92 84710.77 164470.71]\n",
            " [1.0 0.0 0.0 23640.93 96189.63 148001.11]\n",
            " [0.0 0.0 1.0 15505.73 127382.3 35534.17]\n",
            " [1.0 0.0 0.0 22177.74 154806.14 28334.72]\n",
            " [0.0 0.0 1.0 1000.23 124153.04 1903.93]\n",
            " [0.0 1.0 0.0 1315.46 115816.21 297114.46]\n",
            " [1.0 0.0 0.0 0.0 135426.92 0.0]\n",
            " [0.0 0.0 1.0 542.05 51743.15 0.0]\n",
            " [1.0 0.0 0.0 0.0 116983.8 45173.06]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-McZVsQBINc"
      },
      "source": [
        "## Training the Multiple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "VpXK2lH31ev8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the Multiple Linear Regression model in the training set"
      ],
      "metadata": {
        "id": "ll9DPBLy7umL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "3QutMRMx74Py",
        "outputId": "b72c5b28-349f-47ed-b01f-9579f5f17e16"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting the test set Result"
      ],
      "metadata": {
        "id": "XpedqklRwyoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)\n",
        "np.set_printoptions(precision = 2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)), 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBQb8NCWw8CO",
        "outputId": "255bd53f-aa35-46bb-f6ba-5ad4275d6642"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103015.2  103282.38]\n",
            " [132582.28 144259.4 ]\n",
            " [132447.74 146121.95]\n",
            " [ 71976.1   77798.83]\n",
            " [178537.48 191050.39]\n",
            " [116161.24 105008.31]\n",
            " [ 67851.69  81229.06]\n",
            " [ 98791.73  97483.56]\n",
            " [113969.44 110352.25]\n",
            " [167921.07 166187.94]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6C-DpEvpxkiR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}