Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Linear Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNq4+sXFw1a9GcuYnYvFR6d",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/philsaurabh/Tutorials/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2mzBgA8B-2B"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2iRX35_B1ib",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2823d18-d080-4be8-8354-67fc6176695d"
      },
      "source": [
        "import pandas as pd\n",
        "import scipy.stats as st\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po3bju8yEGAT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "7a751e90-3bae-466c-ee2b-ca4954a8b8bc"
      },
      "source": [
        "# To read data as dataframe\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/philsaurabh/Tutorials/main/Advertising.csv')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0     TV  radio  newspaper  sales\n",
              "0           1  230.1   37.8       69.2   22.1\n",
              "1           2   44.5   39.3       45.1   10.4\n",
              "2           3   17.2   45.9       69.3    9.3\n",
              "3           4  151.5   41.3       58.5   18.5\n",
              "4           5  180.8   10.8       58.4   12.9"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLWYrUDvyu_E"
      },
      "source": [
        "# Check Null Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zw6GzbJRyyBE",
        "outputId": "377238e6-6876-480f-bd11-becc884a8873"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "TV            0\n",
              "radio         0\n",
              "newspaper     0\n",
              "sales         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtWnAVzsz06x"
      },
      "source": [
        "# Model Building\n",
        "For Simple Linear Regression,\n",
        "\n",
        "Equation of linear regression\n",
        "\n",
        "*y=c+m<sub>1</sub>x<sub>1</sub>+m<sub>2</sub>x<sub>2</sub>+...+m<sub>n</sub>x<sub>n</sub>*\n",
        "\n",
        "*y*  is the response\n",
        "*c*  is the intercept\n",
        "*m<sub>1</sub>*  is the coefficient for the first feature\n",
        "*m<sub>n</sub>*  is the coefficient for the nth feature\n",
        "In our case:\n",
        "\n",
        "*y=c+m<sub>1</sub>×TV*\n",
        "\n",
        "The  *m*  values are called the model coefficients or model parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTek6buP0xCJ"
      },
      "source": [
        "X = df['TV']\n",
        "y = df['sales']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLEHEaKq03C9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2)# random_state = 100"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tA7Cfwf-04Bn",
        "outputId": "ed459a58-dd1d-4368-e5b0-a5aa9095889c"
      },
      "source": [
        "# The training dataset\n",
        "print(X_train.head())\n",
        "print(y_train.head())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "147    243.2\n",
            "11     214.7\n",
            "141    193.7\n",
            "44      25.1\n",
            "166     17.9\n",
            "Name: TV, dtype: float64\n",
            "147    25.4\n",
            "11     17.4\n",
            "141    19.2\n",
            "44      8.5\n",
            "166     8.0\n",
            "Name: sales, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOm6xnG_1OLA"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "# Add a constant to get an intercept\n",
        "X_train_sm = sm.add_constant(X_train)\n",
        "\n",
        "# Fit the resgression line using 'OLS'\n",
        "lr = sm.OLS(y_train, X_train_sm).fit()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnlG6vBn1R3F",
        "outputId": "9e472bd9-b91c-4450-e094-cb97861c114f"
      },
      "source": [
        "# Print the parameters, i.e. the intercept and the slope of the regression line fitted\n",
        "lr.params"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const    7.117657\n",
              "TV       0.048274\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5ch4kjK1VdZ",
        "outputId": "5d2e821b-5bd4-403a-c76a-92b933c7653e"
      },
      "source": [
        "# Performing a summary operation lists out all the different parameters of the regression line fitted\n",
        "print(lr.summary())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  sales   R-squared:                       0.634\n",
            "Model:                            OLS   Adj. R-squared:                  0.631\n",
            "Method:                 Least Squares   F-statistic:                     273.4\n",
            "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           2.75e-36\n",
            "Time:                        11:08:17   Log-Likelihood:                -408.87\n",
            "No. Observations:                 160   AIC:                             821.7\n",
            "Df Residuals:                     158   BIC:                             827.9\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          7.1177      0.493     14.448      0.000       6.145       8.091\n",
            "TV             0.0483      0.003     16.534      0.000       0.043       0.054\n",
            "==============================================================================\n",
            "Omnibus:                        0.559   Durbin-Watson:                   2.029\n",
            "Prob(Omnibus):                  0.756   Jarque-Bera (JB):                0.583\n",
            "Skew:                          -0.139   Prob(JB):                        0.747\n",
            "Kurtosis:                       2.902   Cond. No.                         335.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiUtWl8C1y3i"
      },
      "source": [
        "# Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFe1c96k1yOv"
      },
      "source": [
        "y_train_pred = lr.predict(X_train_sm)\n",
        "res = (y_train - y_train_pred)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NB8eBmIv12d8"
      },
      "source": [
        "# Add a constant to X_test\n",
        "X_test_sm = sm.add_constant(X_test)\n",
        "\n",
        "# Predict the y values corresponding to X_test_sm\n",
        "y_pred = lr.predict(X_test_sm)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joYCnGwZ18vs",
        "outputId": "6effd9db-80c8-4e92-f7a9-484716d8c35c"
      },
      "source": [
        "y_pred.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35     21.150837\n",
              "62     18.669566\n",
              "43     17.105496\n",
              "157    14.349065\n",
              "104    18.616465\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTf6sGRD1-wk",
        "outputId": "306f6297-9860-4469-b8b5-8632442ef627"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "import numpy as np\n",
        "#Returns the mean squared error; we'll take a square root\n",
        "np.sqrt(mean_squared_error(y_test, y_pred))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.732923528444344"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntH4tqb_2H9P",
        "outputId": "8844b1ca-8df5-4ed3-a7b7-b8bad00b166c"
      },
      "source": [
        "# Checking the R-squared on the test set\n",
        "r_squared = r2_score(y_test, y_pred)\n",
        "r_squared"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5200927781124346"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLj5zaLiyu5A"
      },
      "source": [
        "# Additional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bqXWFpdzEqN"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "tssAT4Hryj7J",
        "outputId": "59d5b6f6-f639-4b60-a7a4-ed1412f743de"
      },
      "source": [
        "# Data Visualisation\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "# Let's see the correlation between different variables.\n",
        "sns.heatmap(df.corr(),annot= True)# , cmap=\"YlGnBu\", annot = True\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "dzr4K1-UzKQ7",
        "outputId": "8c9895e7-8440-49c4-dd0e-9cc54bb020a8"
      },
      "source": [
        "# Outlier Analysis\n",
        "fig, axs = plt.subplots(3, figsize = (5,5))\n",
        "plt1 = sns.boxplot(df['TV'], ax = axs[0])\n",
        "plt2 = sns.boxplot(df['newspaper'], ax = axs[1])\n",
        "plt3 = sns.boxplot(df['radio'], ax = axs[2])\n",
        "plt.tight_layout()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDxNKgkBzldN"
      },
      "source": [
        "# There are no considerable outliers present in the data."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1kTfuLpSzdxG",
        "outputId": "a1c8f5cf-9469-4c04-d137-2efc767cc673"
      },
      "source": [
        "plt.scatter(X_train, y_train)\n",
        "plt.plot(X_train, 7.1177 + 0.0483*X_train, 'r')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZncFSng5TbE"
      },
      "source": [
        "# Using Sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZeSt-k12Rc1",
        "outputId": "d1e0062e-4604-4b15-a703-13598230514a"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "# To read data as dataframe\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/philsaurabh/Tutorials/main/Advertising.csv')\n",
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, 1].values\n",
        "print(X)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2)# random_state = 100\n",
        "reg = LinearRegression().fit(X_train, y_train)\n",
        "print(reg.score(X_train, y_train))\n",
        "reg.score(X_test, y_test)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  1.  230.1  37.8  69.2]\n",
            " [  2.   44.5  39.3  45.1]\n",
            " [  3.   17.2  45.9  69.3]\n",
            " [  4.  151.5  41.3  58.5]\n",
            " [  5.  180.8  10.8  58.4]\n",
            " [  6.    8.7  48.9  75. ]\n",
            " [  7.   57.5  32.8  23.5]\n",
            " [  8.  120.2  19.6  11.6]\n",
            " [  9.    8.6   2.1   1. ]\n",
            " [ 10.  199.8   2.6  21.2]\n",
            " [ 11.   66.1   5.8  24.2]\n",
            " [ 12.  214.7  24.    4. ]\n",
            " [ 13.   23.8  35.1  65.9]\n",
            " [ 14.   97.5   7.6   7.2]\n",
            " [ 15.  204.1  32.9  46. ]\n",
            " [ 16.  195.4  47.7  52.9]\n",
            " [ 17.   67.8  36.6 114. ]\n",
            " [ 18.  281.4  39.6  55.8]\n",
            " [ 19.   69.2  20.5  18.3]\n",
            " [ 20.  147.3  23.9  19.1]\n",
            " [ 21.  218.4  27.7  53.4]\n",
            " [ 22.  237.4   5.1  23.5]\n",
            " [ 23.   13.2  15.9  49.6]\n",
            " [ 24.  228.3  16.9  26.2]\n",
            " [ 25.   62.3  12.6  18.3]\n",
            " [ 26.  262.9   3.5  19.5]\n",
            " [ 27.  142.9  29.3  12.6]\n",
            " [ 28.  240.1  16.7  22.9]\n",
            " [ 29.  248.8  27.1  22.9]\n",
            " [ 30.   70.6  16.   40.8]\n",
            " [ 31.  292.9  28.3  43.2]\n",
            " [ 32.  112.9  17.4  38.6]\n",
            " [ 33.   97.2   1.5  30. ]\n",
            " [ 34.  265.6  20.    0.3]\n",
            " [ 35.   95.7   1.4   7.4]\n",
            " [ 36.  290.7   4.1   8.5]\n",
            " [ 37.  266.9  43.8   5. ]\n",
            " [ 38.   74.7  49.4  45.7]\n",
            " [ 39.   43.1  26.7  35.1]\n",
            " [ 40.  228.   37.7  32. ]\n",
            " [ 41.  202.5  22.3  31.6]\n",
            " [ 42.  177.   33.4  38.7]\n",
            " [ 43.  293.6  27.7   1.8]\n",
            " [ 44.  206.9   8.4  26.4]\n",
            " [ 45.   25.1  25.7  43.3]\n",
            " [ 46.  175.1  22.5  31.5]\n",
            " [ 47.   89.7   9.9  35.7]\n",
            " [ 48.  239.9  41.5  18.5]\n",
            " [ 49.  227.2  15.8  49.9]\n",
            " [ 50.   66.9  11.7  36.8]\n",
            " [ 51.  199.8   3.1  34.6]\n",
            " [ 52.  100.4   9.6   3.6]\n",
            " [ 53.  216.4  41.7  39.6]\n",
            " [ 54.  182.6  46.2  58.7]\n",
            " [ 55.  262.7  28.8  15.9]\n",
            " [ 56.  198.9  49.4  60. ]\n",
            " [ 57.    7.3  28.1  41.4]\n",
            " [ 58.  136.2  19.2  16.6]\n",
            " [ 59.  210.8  49.6  37.7]\n",
            " [ 60.  210.7  29.5   9.3]\n",
            " [ 61.   53.5   2.   21.4]\n",
            " [ 62.  261.3  42.7  54.7]\n",
            " [ 63.  239.3  15.5  27.3]\n",
            " [ 64.  102.7  29.6   8.4]\n",
            " [ 65.  131.1  42.8  28.9]\n",
            " [ 66.   69.    9.3   0.9]\n",
            " [ 67.   31.5  24.6   2.2]\n",
            " [ 68.  139.3  14.5  10.2]\n",
            " [ 69.  237.4  27.5  11. ]\n",
            " [ 70.  216.8  43.9  27.2]\n",
            " [ 71.  199.1  30.6  38.7]\n",
            " [ 72.  109.8  14.3  31.7]\n",
            " [ 73.   26.8  33.   19.3]\n",
            " [ 74.  129.4   5.7  31.3]\n",
            " [ 75.  213.4  24.6  13.1]\n",
            " [ 76.   16.9  43.7  89.4]\n",
            " [ 77.   27.5   1.6  20.7]\n",
            " [ 78.  120.5  28.5  14.2]\n",
            " [ 79.    5.4  29.9   9.4]\n",
            " [ 80.  116.    7.7  23.1]\n",
            " [ 81.   76.4  26.7  22.3]\n",
            " [ 82.  239.8   4.1  36.9]\n",
            " [ 83.   75.3  20.3  32.5]\n",
            " [ 84.   68.4  44.5  35.6]\n",
            " [ 85.  213.5  43.   33.8]\n",
            " [ 86.  193.2  18.4  65.7]\n",
            " [ 87.   76.3  27.5  16. ]\n",
            " [ 88.  110.7  40.6  63.2]\n",
            " [ 89.   88.3  25.5  73.4]\n",
            " [ 90.  109.8  47.8  51.4]\n",
            " [ 91.  134.3   4.9   9.3]\n",
            " [ 92.   28.6   1.5  33. ]\n",
            " [ 93.  217.7  33.5  59. ]\n",
            " [ 94.  250.9  36.5  72.3]\n",
            " [ 95.  107.4  14.   10.9]\n",
            " [ 96.  163.3  31.6  52.9]\n",
            " [ 97.  197.6   3.5   5.9]\n",
            " [ 98.  184.9  21.   22. ]\n",
            " [ 99.  289.7  42.3  51.2]\n",
            " [100.  135.2  41.7  45.9]\n",
            " [101.  222.4   4.3  49.8]\n",
            " [102.  296.4  36.3 100.9]\n",
            " [103.  280.2  10.1  21.4]\n",
            " [104.  187.9  17.2  17.9]\n",
            " [105.  238.2  34.3   5.3]\n",
            " [106.  137.9  46.4  59. ]\n",
            " [107.   25.   11.   29.7]\n",
            " [108.   90.4   0.3  23.2]\n",
            " [109.   13.1   0.4  25.6]\n",
            " [110.  255.4  26.9   5.5]\n",
            " [111.  225.8   8.2  56.5]\n",
            " [112.  241.7  38.   23.2]\n",
            " [113.  175.7  15.4   2.4]\n",
            " [114.  209.6  20.6  10.7]\n",
            " [115.   78.2  46.8  34.5]\n",
            " [116.   75.1  35.   52.7]\n",
            " [117.  139.2  14.3  25.6]\n",
            " [118.   76.4   0.8  14.8]\n",
            " [119.  125.7  36.9  79.2]\n",
            " [120.   19.4  16.   22.3]\n",
            " [121.  141.3  26.8  46.2]\n",
            " [122.   18.8  21.7  50.4]\n",
            " [123.  224.    2.4  15.6]\n",
            " [124.  123.1  34.6  12.4]\n",
            " [125.  229.5  32.3  74.2]\n",
            " [126.   87.2  11.8  25.9]\n",
            " [127.    7.8  38.9  50.6]\n",
            " [128.   80.2   0.    9.2]\n",
            " [129.  220.3  49.    3.2]\n",
            " [130.   59.6  12.   43.1]\n",
            " [131.    0.7  39.6   8.7]\n",
            " [132.  265.2   2.9  43. ]\n",
            " [133.    8.4  27.2   2.1]\n",
            " [134.  219.8  33.5  45.1]\n",
            " [135.   36.9  38.6  65.6]\n",
            " [136.   48.3  47.    8.5]\n",
            " [137.   25.6  39.    9.3]\n",
            " [138.  273.7  28.9  59.7]\n",
            " [139.   43.   25.9  20.5]\n",
            " [140.  184.9  43.9   1.7]\n",
            " [141.   73.4  17.   12.9]\n",
            " [142.  193.7  35.4  75.6]\n",
            " [143.  220.5  33.2  37.9]\n",
            " [144.  104.6   5.7  34.4]\n",
            " [145.   96.2  14.8  38.9]\n",
            " [146.  140.3   1.9   9. ]\n",
            " [147.  240.1   7.3   8.7]\n",
            " [148.  243.2  49.   44.3]\n",
            " [149.   38.   40.3  11.9]\n",
            " [150.   44.7  25.8  20.6]\n",
            " [151.  280.7  13.9  37. ]\n",
            " [152.  121.    8.4  48.7]\n",
            " [153.  197.6  23.3  14.2]\n",
            " [154.  171.3  39.7  37.7]\n",
            " [155.  187.8  21.1   9.5]\n",
            " [156.    4.1  11.6   5.7]\n",
            " [157.   93.9  43.5  50.5]\n",
            " [158.  149.8   1.3  24.3]\n",
            " [159.   11.7  36.9  45.2]\n",
            " [160.  131.7  18.4  34.6]\n",
            " [161.  172.5  18.1  30.7]\n",
            " [162.   85.7  35.8  49.3]\n",
            " [163.  188.4  18.1  25.6]\n",
            " [164.  163.5  36.8   7.4]\n",
            " [165.  117.2  14.7   5.4]\n",
            " [166.  234.5   3.4  84.8]\n",
            " [167.   17.9  37.6  21.6]\n",
            " [168.  206.8   5.2  19.4]\n",
            " [169.  215.4  23.6  57.6]\n",
            " [170.  284.3  10.6   6.4]\n",
            " [171.   50.   11.6  18.4]\n",
            " [172.  164.5  20.9  47.4]\n",
            " [173.   19.6  20.1  17. ]\n",
            " [174.  168.4   7.1  12.8]\n",
            " [175.  222.4   3.4  13.1]\n",
            " [176.  276.9  48.9  41.8]\n",
            " [177.  248.4  30.2  20.3]\n",
            " [178.  170.2   7.8  35.2]\n",
            " [179.  276.7   2.3  23.7]\n",
            " [180.  165.6  10.   17.6]\n",
            " [181.  156.6   2.6   8.3]\n",
            " [182.  218.5   5.4  27.4]\n",
            " [183.   56.2   5.7  29.7]\n",
            " [184.  287.6  43.   71.8]\n",
            " [185.  253.8  21.3  30. ]\n",
            " [186.  205.   45.1  19.6]\n",
            " [187.  139.5   2.1  26.6]\n",
            " [188.  191.1  28.7  18.2]\n",
            " [189.  286.   13.9   3.7]\n",
            " [190.   18.7  12.1  23.4]\n",
            " [191.   39.5  41.1   5.8]\n",
            " [192.   75.5  10.8   6. ]\n",
            " [193.   17.2   4.1  31.6]\n",
            " [194.  166.8  42.    3.6]\n",
            " [195.  149.7  35.6   6. ]\n",
            " [196.   38.2   3.7  13.8]\n",
            " [197.   94.2   4.9   8.1]\n",
            " [198.  177.    9.3   6.4]\n",
            " [199.  283.6  42.   66.2]\n",
            " [200.  232.1   8.6   8.7]]\n",
            "1.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CG1Ikik3t6t",
        "outputId": "a8aa2fad-72cc-4f8e-893c-067f6bff9098"
      },
      "source": [
        "print(reg.intercept_)\n",
        "print(reg.coef_)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2.842170943040401e-14\n",
            "[2.28022226e-17 1.00000000e+00]\n"
          ]
        }
      ]
    }
  ]
}