Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Linear Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOjhW1H2DwHrqE6A4wSZN+B",
      "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": "5e2c1507-b841-4a75-972d-5e5554979297"
      },
      "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": "0128bbf1-89e9-45af-9a49-31546bc2ccfc"
      },
      "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": "10bb131b-9345-424a-adfc-8ba2efcd6200"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 3,
      "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": 3
        }
      ]
    },
    {
      "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']#df.iloc[: , 1:3]\n",
        "y = df['sales']"
      ],
      "execution_count": 24,
      "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tA7Cfwf-04Bn",
        "outputId": "911fac85-0ebd-41ef-96ab-0bb1f221b83b"
      },
      "source": [
        "# The training dataset\n",
        "print(X_train.head())\n",
        "print(y_train.head())"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        TV  radio\n",
            "58   210.8   49.6\n",
            "120  141.3   26.8\n",
            "97   184.9   21.0\n",
            "85   193.2   18.4\n",
            "125   87.2   11.8\n",
            "58     23.8\n",
            "120    15.5\n",
            "97     15.5\n",
            "85     15.2\n",
            "125    10.6\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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnlG6vBn1R3F",
        "outputId": "0a1acadc-208d-4e6f-b91f-5aaa64efe594"
      },
      "source": [
        "# Print the parameters, i.e. the intercept and the slope of the regression line fitted\n",
        "lr.params"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const    2.888290\n",
              "TV       0.045227\n",
              "radio    0.193908\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5ch4kjK1VdZ",
        "outputId": "114ea5ba-5642-4079-e179-96fe0009caa0"
      },
      "source": [
        "# Performing a summary operation lists out all the different parameters of the regression line fitted\n",
        "print(lr.summary())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  sales   R-squared:                       0.909\n",
            "Model:                            OLS   Adj. R-squared:                  0.908\n",
            "Method:                 Least Squares   F-statistic:                     785.9\n",
            "Date:                Sat, 13 Nov 2021   Prob (F-statistic):           1.64e-82\n",
            "Time:                        04:49:16   Log-Likelihood:                -298.60\n",
            "No. Observations:                 160   AIC:                             603.2\n",
            "Df Residuals:                     157   BIC:                             612.4\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          2.8883      0.308      9.392      0.000       2.281       3.496\n",
            "TV             0.0452      0.001     30.738      0.000       0.042       0.048\n",
            "radio          0.1939      0.008     23.496      0.000       0.178       0.210\n",
            "==============================================================================\n",
            "Omnibus:                       20.339   Durbin-Watson:                   1.722\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.808\n",
            "Skew:                          -0.898   Prob(JB):                     6.76e-06\n",
            "Kurtosis:                       3.589   Cond. No.                         419.\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": 30,
      "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": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joYCnGwZ18vs",
        "outputId": "4ae8af4f-ae7b-40ba-8b5e-68bfb291d4f6"
      },
      "source": [
        "y_pred.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43     13.874574\n",
              "165    14.153300\n",
              "112    13.820846\n",
              "195     5.333418\n",
              "39     20.510357\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTf6sGRD1-wk",
        "outputId": "a53bc20b-c2da-4d27-ab3c-2a33032c503d"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.046257188670841"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntH4tqb_2H9P",
        "outputId": "052f384a-2063-4520-ec06-4e59761e596b"
      },
      "source": [
        "# Checking the R-squared on the test set\n",
        "r_squared = r2_score(y_test, y_pred)\n",
        "r_squared"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8486348249368486"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "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": "556edde4-cacf-47dc-d186-fca52499db86"
      },
      "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": 15,
      "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": "1b1a4891-96db-4d8e-c300-a5b6a3ef9288"
      },
      "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": 16,
      "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": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1kTfuLpSzdxG",
        "outputId": "bbb81966-93c3-4b6f-e561-6c3839cb666e"
      },
      "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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kpMvv4/Meu1sNDuff7qYCUFFexrhzDo+tAJqOCLgQGKXOACi1p1Ws11+0ra2tMGgQrFrFlIxNF1w4gdf6H2H7NrvzL6VbzUQEXAiMqE0195vQRG/iRPjZz7Ka/3T02dx88uiO9SMdcDr/cQ41xBURcCEw7DxUBQw+tE94RvlMyURv+XI4wsaj3m03+OgjGtdt47bZyyDlXJd1UaCgbWfHypKmx7OFdETAhcCoG1RJ09otTHt1XfvitBqYtbiZmgP3Fm8vH9u3w/HHw+LF2dueeSZtFfe6r1j/Zz4N2LVF5bybXKMkKrYrre3WfQ6Gmpoa3dTUVLLjCeFT27DANoxSWVHOovohIVhkAPfcA1ddld0+ahRMmZI3RGICmRlKYD0dmJDiF4btSqnFWuuazHbJAxcCJe5TmX3j3Xc7FkfIFO8NG6yc7T/9KRbiDTBuzopILYbhhSgt5CEhFCFQ4j6QWRQ7dsApp8BLL2Vteu32+7im7SDrEf3Btxg7dGekPVMvIYXGJc20tLbZbjPhxh4lp0Q8cCFQCp3lGGseesjypMvK0sV75EjYuZPG1z9iVMv+kVlrMx9e1wbN5amacGOPUn0VEXAhUOI+ldk1a9d2hEhGjUrftm6dFSKZORO6dInUI7obvNqby1M14cYeJadEQihC4BSSaheVUf6i2LUL6upg7tzsbVOnOq7SHqVHdDd4tdcprNa7Z5kR1zhKk57yCrhSagowDNiktf5Gom0ccBmwObHbz7XWTwdlpNC5CKqGSsluCo8/nr1KO8Bpp8HTT+ddpd20cQOv9jrNYL3p7MMDs9EvMr9Dd10wMNSbjpsQyoPA6Tbtd2mtByb+iXgLvhFECMFrnNYzn37aESLJFO/Vq60QyXPP0fjmxrzVGe0e0VXC5iArOhaK15CCqWG1wL9DBZDXA9dav6yUqgreFEGwCCKEEEhhLa1h0iT4ic1iY5Mnw2WXpTW5fbJIfURvbmlFQftEqKiu6bhbWZf2frkpgGXKtP1Uj7uLUuzMmDcT9vJsxQxi/kgp9aZSaopSqrfTTkqp0UqpJqVU0+bNm512EwKiVPW4/SSIUX4n8W9uafV+bhYvhn32gS5d0sV71Chr9qTWWeIN3p4s6gZVsqh+CJUV5WROtYvSgGbypvTZfzrSAr/csStEi/wj0+POFO8kYY5NFCrgfwAOBgYCG4A7nHbUWk/WWtdorWv69IlPDQwTiOIjnxuCGOXPJf6uzs3WrXDBBVaIpKbGCpkAHHcczz7zD2onvEh13/OoveOvjp9RyJOFU43uXKvnlBLTMma8YNc3O8IcmyhIwLXWH2utd2qtdwH3Acf4a5bgB6b+uIKIkdrdFDLJOjdaW6EQpWCvvWDGjI5tTz0FWtN4z0zGLPo0500y+RTkVLQilwB0dZh56dReakzLmPGCmz6EPaehoDRCpVQ/rfWGxJ8jgOX+mST4hck/Lr9jpJmpX05iur6lFZYtgzPOgOYMT/raa2HCBGsCToJ8sXW7uhmp5BMAp8d2p/ZSY1rGjBec+tZVKXZpHYn0VjdphI8BJwH7KKU+Am4CTlJKDcR6+lwDXB6gjUKBmPbjCjrNL/WmkFlkq3z7NsY/fw8jly+AW1PeNHAgPPEEVFXZfma+m2Sux/CuSjHy6Nw3qkqHa1gZkWsY50WDnfoWpYwZN1koF9o0PxCALYLPlOrH5YfwlnL9TOg4N2csmc+d8+7K3mHWLDj33Lyfk+8mmetpZ6fWeUvrRl0gozSpxW9M6JuUk405QXu1fpXWLGnZ2bffhmHD4L330prf/z8Xc9DD91qLJLgkX/+d+pVKvj7GYlaqUBRO5WRlKn3MCTrf1q/86sDj9du2WSl/kyentw8YYE11P+QQDirgY/N5aU6LBaeSr4+m5EwLpUcE3FCi4pX5JbyBxetnzLDS/zLJUYvEK7kENnNSjh1RHZMQoo9UIzSQKOV3+zXpxtfc7/fft9aPVCpdvC+5BP79bys90CfxdkNyUs7dFwyMTBU7IR6IB24ggUwLLxC/BtmKHjDavh2uuw7uvju9/cADYd48ODz8QkkmDIp5xelJMCpPiHFHBjENpLp+nm0eswI+aDjL02f5lUES2o917lw455zs9vvvtzzuiEx4iSNOA7gjj65k1uLmSKffmYYMYsYIv+LFfqXu+TXI5vpG8OGH1uo1//xnevuFF/LUlTcx4ZVm1r/byn63LhTPL0CcngQfe+3DyBV9iisSAzcQv+LFUZpqnzeuv2MHXH+95VH3798h3l/9Krz+ujWtfexExj73QSTGBjoDTgPVUSz6FFfEAzcQv2KpuSr0NS5pLqm35HQzeWnSNOr+dF32G37/exq/NZyJ899h/fT17PfcZ/xn+47IjA14pVRhKD+Pk2uquZ2IS7aN/4iAG4ofYQunHyBQ8prTqTeTPl9s4fdP3sqxH61I32nECJgyBSoqbMM/bj47KqQKaUXPMr7YtoO2XZboBTUL1e/Zrk4D2E4x8Lhn24QxFiQC3onJNcmk1J7r/nt259ynH2TMokfT2r/osTu9XnoRjj02rd1tqU+InueXKaSptbSTBHH+/c5eyvUkWHPg3pHIQinlk00pS0EkEQE3kHxfSrdf2mTb1dOX2h6nJJ7rSy/BkCH8dVf6IgDjB1/CtOPPY8LIb9ra7ta2Unl+XoTC7c3H7/MfxGxXpydBp/ZSeqmlFNWwUntFwA0j35fS65e2blCl4yxBPzxX2x/s/t2tiTTPP5+278bawVwy+P+xsq07+1WUMyHHj9sp/FNRXsbuPbqV1PPzes7dCqbfTw5hV6cstZdaSlENq3SzCLhh5PtSFvKlDariXeoPVuldnP3sI9Rd/2D6Tt27wwsvwIknsi/gdnVsJ5vzrcUYBF7Pea6xhyRBPDmEXdmw1F5qKUU1rJujCLhh5PtSFvKlDWqG4MTnVnHYmuVMnf4LerZ9mb5x/HgrLbBLYZmsbmwu1eO613NuJ6RlXRW7d+/Gv1rbArO1kOvs5zkstZdaSlEN6+YoAm4Y+b6UTtv3Ki/LakvF14p3n30Go0axaM6ctOa/9z+CH5/9Mz7p1ZsPbvA2Y9SOXDaX8nHdq1CEOaXey3X2+xyW2kstpaiGdU1lKr1h5Ks/3bikmbGPv9GekpakrKti4nlHBveF0homTUpfpT3B986/hVeqB7X/XYolqUpZX9yvmuhRw69zmPTim1taUZBWBiLo8xSXmiwyld4A3HzZ8t3p6wZVcvPcFVmpaW07dTCxxtdfh6FD4ZNP0ttvuIEnh19G/ZyVWXHP5CSPIL3iUj6ux7FIFfhzDjNvbhraRbyyBOfJ65OlaYIvAh4RvDyu5vtSttjkFYOP4rV1K4weDdOnp7cfeyzMnAn77w/AcEB369b+g+hiM0MvqEGsUj+up16TpAhcPX1p+6zEUoiV3+LjdA67KEV1/TxXx7AbuEyKt+8rLRVJWLncxSC1UEpA45JmahsWUF0/j9qGBba1OfysS+JXje40tIb77rNqkey1V7p4z51rbX/11XbxTpKshf1Bw1nsclEjw825coOv9cU9kFrTBbKfNoKqyxJEjXi7cwhWn5LHGDvzjZzHCCu9rhCiVBvILSLgAeP2h+XnF91X8Vq+HA44wMoWGT26vXn190bDl19awj1smKuPyndj8VOE6gZVMuHcI6isKEdheXyliEnnmqQTpBg4ic+1M3ILbC4yz2EXm8q8bTs1N89dkb0hQSDORECYdLNJIiGUgHGb+1rII3+uR+Ye3bq0H7d3zzJuOttDfvS//w1XXQUPPZTW/NZXqxk94gY+qtjXGnxasdmTIObLCghiqnepH33z/diDEoNclQGLCQOknsOq+nm2+9iVAkgSdu65F8Ke6FQIIuAB4/au7vWL7hSva1q7JauQ0La2XbafkcUjj8APfpDVfMNFNzFt//9KaytEWPMN9hXjAUVl8CnfJJ2gxCDXccOsyGjSAK9JN5skIuAB4/au7vWL7lsx/VWrrBDI6tXp7VdeCXfcAbvtxqMOnlch3qTdYN+Y6UvZr6Kcip5ltt5c5rnKFOvBh/ZJu2mFOfiUq0BYkGKQ67jgj+dfUV5GS2v29ako5RyDADHpZpNEBLwI3Hh9Xu7qXr7oRRXT37YNrr4a/vjH9J0GDIA5c+BrX0trDuLR0u4JoqyLoqyrom1nRx8yz5Xd+6a9ui5ribmwvM5UEWhuaS1ZFkryc6+d8UZgtbjHnXN41hyDsi6KceeEv96oX5hys0kiAl4AjUuas3Ktm1taGTN9KU1rtzC+7oj29qDu6m7qaWTuz+OPw/nnZ2+cOjXnKu1BPFraPUG07dJ5i1E5paXZEdbgU1gikDxmUGEAEz3UuCMC7hG7WXdJNDDt1XXUHLh32pc6iB90vkfmJAe0bOSB2eP52uY16RsuucSaOdmzZ95jOf1wwZqtV8iP2Ulc/9XaxtKbTvP8PjuiPPgUFEGLrGkeatwRAfdIvlrOOrFP0F/y5OePm7MiKy5ZtrON+r88yKVNT6a/6cADYd48ONz5kdcpLJT5wy120kOhYRmn99lN0Y7y4JMg+IHkgXvEjQfY3NLqy2QUN3y5oyPD5OTVr7Hm1mG8e/uIdPG+/37YtQvWrMkr3m7zsIud9FBorrrT+y46rn/Jc76jSBATeoToIh64R9zEnhUdazQGvQpIxacbeazxNwzc8G7aticP+w6/GvZjfnnhsa6P6yUPu9hJD4U+6nt5X1RSC0tJWCvDCOEgAu6RfLHnzEd5COAHtGMH3HgjiyZMSGve3LOCUeffzIq+B1NZUc4vPQqWF1H2IzOl0Hiqm/eZWNfCD0ycTSgUTl4BV0pNAYYBm7TW30i07Q1MB6qANcD5WuvPgjMzOmR6gBU9y9Ca9kL8Tt65Lz+g+fOtyn8Z/PLUH/LIoLOsOiUUXijIiyhHfdKDV080Lt66ibMJhcJx44E/CPweeDilrR54UWvdoJSqT/x9nf/mRRMnD7BxSbPjAsH5FlRwZONGuOACePnl9PYRI5h39Xh++vy6LBEdfGifgrJDvOasQ3RTyrx4onHy1qN+YxX8Ja+Aa61fVkpVZTQPB05KvH4I+AudSMCdyDWAp2wKATmycyf8+tdw003p7XvuaXnhxx4LwFlA2x57+TYr0asoRzmlzIsnGqe4cdRvrIK/FBoD76u13pB4vRHo67SjUmo0MBqgf//+BR7ODHKFSZxqdKfx0ktw8smWgKdw52mXMWngOezXuyeDN/RkYYZ3nRouqW1YUJQY2aULpnrzgw/tw8K3N0deHLx4onGLG0f5xir4S9GDmFprrZRyXJdNaz0ZmAzWkmrFHi8o/IiB5oqBO8YgP/nEmgU5f35a81+qj2bMsGv4rOde7W3NLa1MfXVd2t+Z3rWfYmQXWsh3/KjgxROVuLFgKoUK+MdKqX5a6w1KqX7AJj+NKjV+xUAHH9onTeCSdO2i0j2/Xbvg9tvhuoyoU1kZV/73bTzd+xDXx8z0rgtd1NiOfJOW7I4fJdx6ohI3Fkyl0Ik8c4CLE68vBp7MsW/o5Fvlxa+VOBa+vdm2fY8e3Swh+fvfoVcv6No1XbxvucUKm2zfzjMexDtJc0tre5/GDh1AmU3l/X9v3+F5Modbr93UUEOSsBZ/EIRicZNG+BjWgOU+SqmPgJuABmCGUupSYC1gUyEpGuTzrhuXNPuW+me3/57bvmDirLtgXEb630knwWOPwb77pjV7LVKVJLVPfi1q7NaWOIQaJG4smIibLJQLHTad7LMtgZDPu04Knx1ehald8LRm1OK5jHtxcvZO8+fDqac6fobbIlWZpIYy/FrU2I0tKrGfIAilJ/YzMXMN6uWK8RYSAx3ffztH3vJ/2fs/W9PaV136Ywbcewd0y3+6Mwff9iovQykriyWZBWIXZ0/2CfwblMtVMCvJRcf1F89VEEIi9gKeS8xyeaQ9urkcHti61Vrsd/p0Bqc0L+k3gJsvvplR55/gWeDyPc4vfHtzToH2c1AuaUtmDfSK8jLGneNhnU1BEHwn9gKeS8ySq6bY0dLa5pyJorVV4S9llfZ25s6FYcMYBDT61YkM8gl0EJM5JEYsCNEj9gKeT8xyxXizUuSWL4czz4QPP0zf8ZprYKu16IQAAAp6SURBVMIE6N49sH5Aeq56Rc8yenTr0l6DJVOgRXAFIf4o7bCGYhDU1NTopqamkh3PDUlRdPLEy9u2sXLLU/Dgg+kbjjwSnngCqqs9H6sQr9huJaDysq6S7iYInQCl1GKtdU1me+w98HwkPdXahgVpIl63YiF3P3VH9htmzoSRIz0fp9jJQnGq1yEIgj/IijwJxg4dwGFbN7Bg8mjW3DosXbyvvBJarfTAQsQbip8sFLd6HYIgFE+n8cAdwxfbtsGYMdTdey91Kfuv7XMA70yexql1J/pyfCehbW5ppbp+Xt6QitTrEAQhk07hgdutE7jw1/dYNV7Ly+Heezt2njoVtObATet8E2/ILbRu1i4sdA1JQRDiS6fwwJPhiwNaNjJ59ngO27wmfYdLLoFJk6Bnz8BscDOrMVdMW+o8C4KQSfwFfPt2Lpn1u/RV2oGP9vwql5x3E/MfuLIkZmQKsFPuT66YtqQGCoKQSnwFfO5cOOccAC5Naf7Z6T9mxjdPBaXoqhSNS5oLFkWvaYGpApyZ9ZJEYtqCILglXgL+4YdWlsg//5nefPpw6o74AZ926ZHWvlPrghckKDYtUGpQC4JQLOYPYu7YAT//uTUg2b9/u3hv6VXBwkefBa054JlGfnnhsXS1WZjSLpUvX/1wKD4tUGpQC4JQLOZ64PPnw9ChWc03nnI5Dx81DJSi/C3NhESIpG5QJWMcVoxPjTu79az9yMuWmLYgCMVglge+cSN85zuWt50i3s8dchzf/MmfqbruKR4++uz2JeAzPWKn+HJqu1vP2s1nCYIgBIkZHvjatVBVld62xx6M/t5vmL9n7lokqR6xm7izW89aYtiCIISNGR740093vJ440Vo/cutWns8j3pDuEbuJO7v1rCWGLQhC2JjhgV9xBfzwh+2hkST51mzM9IjdpP158awlhi0IQpiY4YFDlngDDD60D5mtyb8zPWK76fR2U9fFsxYEwRTM8MBtaFzSzKzFzWkzGhXWGo3j647I2t9LOVbxrAVBMAFzPPAM7ARZY60XaYeUYxUEIW4YK+BeBVnS/gRBiBvGCrhXQZZyrIIgxA1jBdyLICezT1rbdrZPp5fBSUEQTMfYQUy39bEzp8bv1Lpd6EW8BUEwGWM9cLBEfOzQAexXUc76llYmPrcqKy2w2KJTgiAIUcVYDxycC081rd3Cwrc3F7xwgiAIggkYLeA3z11h611Pe3Wdo3AnkewTQRBMx9gQSuOSZj77T5vttnziLdkngiDEAWM98EJi2ApkMWBBEGJDUQKulFoDfA7sBHZorWv8MMoNXmPYlRXlLKofEpA1giAIpccPD3yw1voTHz7HE06VCMvLugBK6nQLghB7jI2B203kUcDIo/eXaoKCIHQKivXANTBfKaWBP2qtJ/tgkyvqBlXStHZLWsaJBmYtbqbmwL0lXCIIQuwp1gM/QWt9FHAGcJVS6tuZOyilRiulmpRSTZs321cKLJSFb2/OyjiRSTqCIHQWihJwrXVz4v9NwBPAMTb7TNZa12ita/r06VPM4bKQErGCIHRmChZwpdTuSqk9kq+B04DlfhnmBikRKwhCZ6YYD7wv8IpS6g3gH8A8rfWz/pjlDikRKwhCZ6bgQUyt9fvAkT7a4hm3FQkFQRDiiLEzMZPI+pWCIHRWjM0DFwRB6OyIgAuCIBiKCLggCIKhiIALgiAYigi4IAiCoRiXhZJcYV7SBgVB6OwYJeBOa2ACIuKCIHQ6jAqhyArzgiAIHRgl4FK8ShAEoQOjBFyKVwmCIHRglIBL8SpBEIQOjBrElOJVgiAIHRgl4CDFqwRBEJIYFUIRBEEQOhABFwRBMBQRcEEQBEMRARcEQTAUEXBBEARDiXwWihSvEgRBsCfSAi7FqwRBEJyJdAhFilcJgiA4E2kBl+JVgiAIzkRawKV4lSAIgjORFnApXiUIguBMpAcxpXiVIAiCM5EWcJDiVYIgCE5EOoQiCIIgOCMCLgiCYCgi4IIgCIYiAi4IgmAoIuCCIAiGorTWpTuYUpuBtQW8dR/gE5/NCZM49SdOfYF49SdOfYF49cdrXw7UWvfJbCypgBeKUqpJa10Tth1+Eaf+xKkvEK/+xKkvEK/++NUXCaEIgiAYigi4IAiCoZgi4JPDNsBn4tSfOPUF4tWfOPUF4tUfX/piRAxcEARByMYUD1wQBEHIQARcEATBUCIv4Eqp05VSq5RSq5VS9WHb4xWl1Bql1DKl1FKlVFOibW+l1PNKqXcT//cO204nlFJTlFKblFLLU9ps7VcWv0tcqzeVUkeFZ3k2Dn0Zp5RqTlyfpUqpM1O2XZ/oyyql1NBwrHZGKXWAUmqhUuotpdQKpdRPEu3GXZ8cfTHy+iildlNK/UMp9UaiPzcn2quVUq8l7J6ulOqeaO+R+Ht1YnuVqwNprSP7D+gKvAccBHQH3gC+HrZdHvuwBtgno+02oD7xuh64NWw7c9j/beAoYHk++4EzgWcABRwHvBa2/S76Mg74qc2+X09833oA1YnvYdew+5BhYz/gqMTrPYB3EnYbd31y9MXI65M4x70Sr8uA1xLnfAbw3UT7vcAViddXAvcmXn8XmO7mOFH3wI8BVmut39dabwf+DAwP2SY/GA48lHj9EFAXoi050Vq/DGzJaHayfzjwsLZ4FahQSvUrjaX5ceiLE8OBP2utv9RafwCsxvo+Rgat9Qat9euJ158DK4FKDLw+OfriRKSvT+Icf5H4syzxTwNDgJmJ9sxrk7xmM4GTlVIq33GiLuCVwIcpf39E7osaRTQwXym1WCk1OtHWV2u9IfF6I9A3HNMKxsl+U6/XjxIhhSkp4Syj+pJ45B6E5ekZfX0y+gKGXh+lVFel1FJgE/A81lNCi9Z6R2KXVJvb+5PY/i/gK/mOEXUBjwMnaK2PAs4ArlJKfTt1o7aemYzN5TTdfuAPwMHAQGADcEe45nhHKdULmAVcrbXemrrNtOtj0xdjr4/WeqfWeiCwP9bTwaF+HyPqAt4MHJDy9/6JNmPQWjcn/t8EPIF1IT9OProm/t8UnoUF4WS/cddLa/1x4oe2C7iPjsdwI/qilCrDErxpWuvZiWYjr49dX0y/PgBa6xZgIfAtrLBVcinLVJvb+5PYvhfwab7PjrqA/xM4JDFy2x0ruD8nZJtco5TaXSm1R/I1cBqwHKsPFyd2uxh4MhwLC8bJ/jnADxLZDscB/0p5lI8kGTHgEVjXB6y+fDeRHVANHAL8o9T25SIRI30AWKm1vjNlk3HXx6kvpl4fpVQfpVRF4nU5cCpWXH8hcF5it8xrk7xm5wELEk9PuQl7tNbFaO6ZWCPS7wE3hG2PR9sPwhopfwNYkbQfK7b1IvAu8AKwd9i25ujDY1iPrm1YMbtLnezHGnn/38S1WgbUhG2/i748krD1zcSPqF/K/jck+rIKOCNs+236cwJWeORNYGni35kmXp8cfTHy+gDfBJYk7F4O3JhoPwjrRrMaeBzokWjfLfH36sT2g9wcR6bSC4IgGErUQyiCIAiCAyLggiAIhiICLgiCYCgi4IIgCIYiAi4IgmAoIuCCIAiGIgIuCIJgKP8fXTF3ES8kxkcAAAAASUVORK5CYII=\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": "a7f75339-9437-404e-d594-db1384748b09"
      },
      "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": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CG1Ikik3t6t",
        "outputId": "9b1f29b4-ae91-46e7-a2e5-28e285f38ce7"
      },
      "source": [
        "print(reg.intercept_)\n",
        "print(reg.coef_)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.9895196601282805e-13\n",
            "[2.30127134e-18 1.00000000e+00 8.32667268e-16 0.00000000e+00]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_1hhQBVokdl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}