Project_Sales_Forecasting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/John-Peter-R/ML/blob/main/Project_Sales_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXb9_nH8dfDl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c7df9f2-f181-47e0-93e5-5649bcd8b85a"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "import statsmodels.formula.api as smf"
      ],
      "execution_count": null,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsoU2gSEeLIY",
        "outputId": "a1b2b9e2-7ef0-416d-cd22-7beb85408be1"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAqDMQ-7eSsn"
      },
      "source": [
        "dataset = pd.read_csv('/content/advertising.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAePeMMcTjuW"
      },
      "source": [
        "dftv=dataset.TV\n",
        "dfr=dataset.Radio\n",
        "dfn=dataset.Newspaper\n",
        "dfs=dataset.Sales"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JV_-b84wT6AW",
        "outputId": "5ac7c874-dd05-47b5-aa47-4fc2ed5a3367"
      },
      "source": [
        "fig=plt.figure(figsize=(5,5)) \n",
        "ax=fig.add_axes([0,0,1,1])\n",
        "bp=ax.boxplot(dftv)\n",
        "plt.title('Box plot of TV')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oHymi__LUOx4",
        "outputId": "82b788cb-1068-4f21-fd89-ba528d73a097"
      },
      "source": [
        "fig=plt.figure(figsize=(5,5)) \n",
        "ax=fig.add_axes([0,0,1,1])\n",
        "bp=ax.boxplot(dfr)\n",
        "plt.title('Box plot of FM')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MHzo_6r9Uysf",
        "outputId": "f5cc4a50-a48e-471b-83ba-e0f0bc145f0e"
      },
      "source": [
        "fig=plt.figure(figsize=(5,5)) \n",
        "ax=fig.add_axes([0,0,1,1])\n",
        "bp=ax.boxplot(dfn)\n",
        "plt.title('Box plot of Newspaper')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "q2UERURLUzyf",
        "outputId": "3ce36257-3de1-405a-f9bd-11c8c251b1c8"
      },
      "source": [
        "fig=plt.figure(figsize=(5,5)) \n",
        "ax=fig.add_axes([0,0,1,1])\n",
        "bp=ax.boxplot(dfs)\n",
        "plt.title('Box plot of Sales')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "zbrF62_FF1An",
        "outputId": "fc9a3f59-1458-439b-8674-528a569fb2a3"
      },
      "source": [
        "df3=dataset.copy(deep=True)\n",
        "df3.drop('Newspaper',axis=1,inplace=True)\n",
        "df3"
      ],
      "execution_count": null,
      "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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>16.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>14.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>18.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        TV  Radio  Sales\n",
              "0    230.1   37.8   22.1\n",
              "1     44.5   39.3   10.4\n",
              "2     17.2   45.9   12.0\n",
              "3    151.5   41.3   16.5\n",
              "4    180.8   10.8   17.9\n",
              "..     ...    ...    ...\n",
              "195   38.2    3.7    7.6\n",
              "196   94.2    4.9   14.0\n",
              "197  177.0    9.3   14.8\n",
              "198  283.6   42.0   25.5\n",
              "199  232.1    8.6   18.4\n",
              "\n",
              "[200 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBb8jzTuF6iz"
      },
      "source": [
        "X1 = df3.iloc[:, :-1].values\n",
        "y1 = df3.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "7C90dFoWGBku",
        "outputId": "da13b553-c565-4069-a96c-1211046a1053"
      },
      "source": [
        "df4=df3.copy(deep=True)\n",
        "df4.drop('Radio',axis=1,inplace=True)\n",
        "df4"
      ],
      "execution_count": null,
      "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>TV</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>16.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>14.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>18.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        TV  Sales\n",
              "0    230.1   22.1\n",
              "1     44.5   10.4\n",
              "2     17.2   12.0\n",
              "3    151.5   16.5\n",
              "4    180.8   17.9\n",
              "..     ...    ...\n",
              "195   38.2    7.6\n",
              "196   94.2   14.0\n",
              "197  177.0   14.8\n",
              "198  283.6   25.5\n",
              "199  232.1   18.4\n",
              "\n",
              "[200 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPWUlJWtGImE"
      },
      "source": [
        "X3 = dataset.iloc[:, :-1].values\n",
        "y3 = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "RME0I-VCD_iD",
        "outputId": "ee93dfd0-3596-46ef-c96d-0d499e67f9c7"
      },
      "source": [
        "xax=X[:,0]\n",
        "yax=y\n",
        "plt.scatter(xax,yax)\n",
        "plt.xlabel(\"TV\")\n",
        "plt.ylabel(\"Sales\")\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "IXkuAN5sEDFU",
        "outputId": "ffd3116b-a5d8-43df-a5d5-f4f63a100390"
      },
      "source": [
        "xax=X[:,1]\n",
        "yax=y\n",
        "plt.scatter(xax,yax)\n",
        "plt.xlabel(\"Radio\")\n",
        "plt.ylabel(\"Sales\")\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "yLoeFoQhEGAz",
        "outputId": "ddcbd064-6472-4dc1-ceb4-96441e74bdfc"
      },
      "source": [
        "xax=X[:,2]\n",
        "yax=y\n",
        "plt.scatter(xax,yax)\n",
        "plt.xlabel(\"Newspaper\")\n",
        "plt.ylabel(\"Sales\")\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": "v1xh1DEhHPCr"
      },
      "source": [
        "#One independent variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8h1xs1RSEKwc"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X3_train, X3_test, y3_train, y3_test = train_test_split(X3, y3, test_size = 1/3, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGqOpVPpGpgQ",
        "outputId": "444d2e8e-25a5-413b-dd15-3f2da9aac9f3"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor3 = LinearRegression()\n",
        "regressor3.fit(X3_train, y3_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3xfQlgLGzr4",
        "outputId": "cee52a48-9a2d-498e-dc6e-3b8df9730725"
      },
      "source": [
        "y4_pred = regressor3.predict(X3_test)\n",
        "y4_pred\n",
        "r_sq2 = regressor3.score(X3, y3)\n",
        "r_sq2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9017760223584486"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FW4N8D4-HVCJ"
      },
      "source": [
        "#Two independent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF_oxTx1HCi5"
      },
      "source": [
        "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_ga8f-kHe9S",
        "outputId": "2b14164d-90e4-47e5-bffd-7dd18c19c8d7"
      },
      "source": [
        "regressor1 = LinearRegression()\n",
        "regressor1.fit(X1_train, y1_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7E0Id3n8HpAo",
        "outputId": "5eb6f2a0-8ccd-44e6-f722-a0d84499afa5"
      },
      "source": [
        "y1_pred = regressor1.predict(X1_test)\n",
        "r_sq1=regressor1.score(X1,y1)\n",
        "r_sq1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9023271513637827"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToZFIUhPIPwJ"
      },
      "source": [
        "#Three independent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-doEIlzIUVP"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaQvhhLsIdsL",
        "outputId": "7818b7b7-9522-40db-f296-e539c29f8708"
      },
      "source": [
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amF9a0ETIk0c",
        "outputId": "e0b46d19-fa11-42fe-b72a-721749302d52"
      },
      "source": [
        "y_pred = regressor.predict(X_test)\n",
        "r_sq=regressor.score(X,y)\n",
        "r_sq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9021417667568035"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcMcJh4PI0Gw",
        "outputId": "754ad4d3-1f0b-4ae8-d6c3-c45fe30d6490"
      },
      "source": [
        "results = smf.ols('Sales~TV+Radio+Newspaper ', data=dataset).fit()\n",
        "print(results.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  Sales   R-squared:                       0.903\n",
            "Model:                            OLS   Adj. R-squared:                  0.901\n",
            "Method:                 Least Squares   F-statistic:                     605.4\n",
            "Date:                Tue, 02 Nov 2021   Prob (F-statistic):           8.13e-99\n",
            "Time:                        14:52:52   Log-Likelihood:                -383.34\n",
            "No. Observations:                 200   AIC:                             774.7\n",
            "Df Residuals:                     196   BIC:                             787.9\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      4.6251      0.308     15.041      0.000       4.019       5.232\n",
            "TV             0.0544      0.001     39.592      0.000       0.052       0.057\n",
            "Radio          0.1070      0.008     12.604      0.000       0.090       0.124\n",
            "Newspaper      0.0003      0.006      0.058      0.954      -0.011       0.012\n",
            "==============================================================================\n",
            "Omnibus:                       16.081   Durbin-Watson:                   2.251\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.655\n",
            "Skew:                          -0.431   Prob(JB):                     9.88e-07\n",
            "Kurtosis:                       4.605   Cond. No.                         454.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrbEqrbjI9aB",
        "outputId": "d176ef06-7227-4d7a-d61a-67f75f87e6ad"
      },
      "source": [
        "df2=dataset.copy(deep=True)\n",
        "df2['tv_radio']=df2['TV']*df2['Radio']\n",
        "results = smf.ols('Sales~TV+Radio+Newspaper+tv_radio ', data=df2).fit()\n",
        "print(results.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  Sales   R-squared:                       0.914\n",
            "Model:                            OLS   Adj. R-squared:                  0.912\n",
            "Method:                 Least Squares   F-statistic:                     518.6\n",
            "Date:                Tue, 02 Nov 2021   Prob (F-statistic):          1.08e-102\n",
            "Time:                        14:55:46   Log-Likelihood:                -370.80\n",
            "No. Observations:                 200   AIC:                             751.6\n",
            "Df Residuals:                     195   BIC:                             768.1\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      6.1723      0.419     14.722      0.000       5.345       6.999\n",
            "TV             0.0435      0.002     17.433      0.000       0.039       0.048\n",
            "Radio          0.0415      0.015      2.740      0.007       0.012       0.071\n",
            "Newspaper      0.0013      0.005      0.247      0.805      -0.009       0.012\n",
            "tv_radio       0.0004    8.7e-05      5.103      0.000       0.000       0.001\n",
            "==============================================================================\n",
            "Omnibus:                        7.535   Durbin-Watson:                   2.288\n",
            "Prob(Omnibus):                  0.023   Jarque-Bera (JB):               10.029\n",
            "Skew:                          -0.246   Prob(JB):                      0.00664\n",
            "Kurtosis:                       3.980   Cond. No.                     1.84e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.84e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fm_WhfX1JYij",
        "outputId": "c67f5874-2db5-4a22-c1d5-02846fa9157e"
      },
      "source": [
        "df2=dataset.copy(deep=True)\n",
        "df2['tv_radio']=df2['TV']*df2['Radio']\n",
        "df2['tv_np']=df2['TV']*df2['Newspaper']\n",
        "df2['radio_np']=df2['Radio']*df2['Newspaper']\n",
        "results = smf.ols('Sales~TV+Radio+Newspaper+tv_radio+tv_np+radio_np ', data=df2).fit()\n",
        "print(results.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  Sales   R-squared:                       0.915\n",
            "Model:                            OLS   Adj. R-squared:                  0.912\n",
            "Method:                 Least Squares   F-statistic:                     344.4\n",
            "Date:                Tue, 02 Nov 2021   Prob (F-statistic):          3.16e-100\n",
            "Time:                        14:56:57   Log-Likelihood:                -370.20\n",
            "No. Observations:                 200   AIC:                             754.4\n",
            "Df Residuals:                     193   BIC:                             777.5\n",
            "Df Model:                           6                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      6.2650      0.531     11.805      0.000       5.218       7.312\n",
            "TV             0.0444      0.003     16.506      0.000       0.039       0.050\n",
            "Radio          0.0295      0.019      1.548      0.123      -0.008       0.067\n",
            "Newspaper     -0.0006      0.017     -0.035      0.972      -0.034       0.033\n",
            "tv_radio       0.0005   9.55e-05      4.876      0.000       0.000       0.001\n",
            "tv_np      -4.112e-05   5.98e-05     -0.688      0.493      -0.000    7.68e-05\n",
            "radio_np       0.0003      0.000      0.717      0.474      -0.000       0.001\n",
            "==============================================================================\n",
            "Omnibus:                        5.751   Durbin-Watson:                   2.320\n",
            "Prob(Omnibus):                  0.056   Jarque-Bera (JB):                6.668\n",
            "Skew:                          -0.227   Prob(JB):                       0.0357\n",
            "Kurtosis:                       3.771   Cond. No.                     3.80e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.8e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    }
  ]
}