Multiple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Linear Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOkJ0B30qqaIueOaAIs76ds",
      "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/Multiple_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 and the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2iRX35_B1ib"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po3bju8yEGAT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "a158bdb9-59e1-4519-81e3-9a4184638222"
      },
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8utqFe50FRF",
        "outputId": "92cf7cf7-dda0-425b-84b7-01696a4d8693"
      },
      "source": [
        "feature_cols=['TV','radio']\n",
        "X = df[feature_cols]\n",
        "y = df['sales']\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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9058130981062116\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8437592354846349"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5kEY8mD0p37",
        "outputId": "06a70672-1568-4870-c3f3-619ef9bbb272"
      },
      "source": [
        "y_pred=reg.predict(X_test)\n",
        "reg.intercept_"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.8011494144977664"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMvrQAsY2qW8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8841d0d-ca4e-4d45-fda1-c769c25e5e92"
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04730327, 0.18558002])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OogoKlz404TB",
        "outputId": "a043d779-3930-4af1-b755-135bfe4c7de1"
      },
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.mean_squared_error(y_test,y_pred))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.4487951631015834\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5yRUGXZ1KnF",
        "outputId": "51ee5116-64b4-47c7-95db-e98bd1cded64"
      },
      "source": [
        "print(metrics.mean_absolute_error(y_test,y_pred))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.4756896161120967\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7h1uTJA21QVv",
        "outputId": "e71a96a7-2593-4d36-9112-b9aa5016e3a9"
      },
      "source": [
        "print(np.sqrt(metrics.mean_squared_error(y_test,y_pred)))#rmse"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.8570932025888156\n"
          ]
        }
      ]
    },
    {
      "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": "f23330bd-a53f-4861-8a17-645677b0341c"
      },
      "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": 11,
      "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": "40b8b68c-8fc3-45be-da7d-da2f1674ce46"
      },
      "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": 12,
      "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": 13,
      "outputs": []
    }
  ]
}