2. Multivariate_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Multivariate Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN+GUKfFzYeXqvnutE/jw6z",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/ML-Algorithms/blob/master/Multivariate_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": "0i3ojNd8Ba6R"
      },
      "source": [
        "# MULTIVARIATE REGRESSION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vW-yOXe7BcfJ"
      },
      "source": [
        "## Multi-variate regression or multiple linear regression is a Machine Learning model, which is made to find relation between multiple independent variables and thus, determine the values of a dependent variable. In linear regression, there was only one coefficient, as there was a single independent variable. But in multiple linear regression, there are infact, multiple coefficients for multiple variables as the equation below will demonstrate.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "do_rS-6QBcnx"
      },
      "source": [
        "## Mathematical Notation :\n",
        "<img src=\"https://miro.medium.com/max/1662/1*39FkA9sgT6E_txFT_KANjw.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDzu3tQBBcyL"
      },
      "source": [
        "## Unlike simple linear regression, a multiple linear regression or multivariate regression has more than one slopes, depending upon the number of independent parameters. But for all of them, we use only y axis when all xs depend on y individually"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80Gr2yDBBc7n"
      },
      "source": [
        "<img src=\"https://www.researchgate.net/profile/Mark_Vandam/publication/270961898/figure/fig1/AS:267470841839624@1440781426132/Multiple-linear-regression-MLR-model-predictions-for-individual-observations-ie.png\" width=60%>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgPFd62mCSXR"
      },
      "source": [
        "## Building model through codes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GG9cjLLlCYkU"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPdg86g5CN-2",
        "outputId": "0beb6f41-47d0-41af-8e80-203b9ed917b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "print(\"All packages installed successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All packages installed successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFXCDVA9CmZ3"
      },
      "source": [
        "## 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1z7F70j8CeAl",
        "outputId": "b1e0c75d-38e7-4aeb-9ec8-a69e985e9218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/House%20rent.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head(10)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Age</th>\n",
              "      <th>Rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1024</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15</td>\n",
              "      <td>11200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1200</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10</td>\n",
              "      <td>33650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>800</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>674</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10</td>\n",
              "      <td>6120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>766</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>680</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36</td>\n",
              "      <td>3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>3500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>900</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20</td>\n",
              "      <td>3345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>967</td>\n",
              "      <td>3.0</td>\n",
              "      <td>23</td>\n",
              "      <td>3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>720</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Area  Bedrooms  Age   Rent\n",
              "0  1024       3.0   15  11200\n",
              "1  1200       4.0   10  33650\n",
              "2   800       2.0    5   8000\n",
              "3   674       2.0   10   6120\n",
              "4   766       2.0   11   6000\n",
              "5   680       NaN   36   3200\n",
              "6   750       NaN   10   3500\n",
              "7   900       3.0   20   3345\n",
              "8   967       3.0   23   3100\n",
              "9   720       3.0    7   6200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYAGicfXCu-d"
      },
      "source": [
        "## 3. Exploring data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuXoKNi6Cygg"
      },
      "source": [
        "### Shape of the dataframe in the form of (rows,columns)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2VJ7hv8Cpjm",
        "outputId": "30f3b954-1e4d-43ae-8924-ba1ec7898d4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDlepRyEC4ad"
      },
      "source": [
        "### Check for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkQKWwD8C1de",
        "outputId": "ecddc20b-427c-443d-8908-a5e4f4e2064d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Are there null values ?\",df.isnull().values.any())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are there null values ? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Y9ybERjC6JM"
      },
      "source": [
        "### Column-wise count of missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51bk4KXoDBw9",
        "outputId": "9ffa3315-6748-4443-f587-29ab92436313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area        0\n",
              "Bedrooms    4\n",
              "Age         0\n",
              "Rent        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9uTd1hRDHfB"
      },
      "source": [
        "### Replacing null values with median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geGxQKI7DDuy",
        "outputId": "ec70f5d6-a59a-43aa-9376-4674ac57c520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "median = df.Bedrooms.median()\n",
        "median"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GfDlJh5DKwp",
        "outputId": "1c4463db-9c17-4c4b-9901-ac0ef0f2d3e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df.Bedrooms = df.Bedrooms.fillna(int(median))\n",
        "print(\"Dataframe after replacing the null values :\\n\")\n",
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataframe after replacing the null values :\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        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>Area</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Age</th>\n",
              "      <th>Rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1024</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15</td>\n",
              "      <td>11200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1200</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10</td>\n",
              "      <td>33650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>800</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>674</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10</td>\n",
              "      <td>6120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>766</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>680</td>\n",
              "      <td>3.0</td>\n",
              "      <td>36</td>\n",
              "      <td>3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>750</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10</td>\n",
              "      <td>3500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>900</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20</td>\n",
              "      <td>3345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>967</td>\n",
              "      <td>3.0</td>\n",
              "      <td>23</td>\n",
              "      <td>3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>720</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>850</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16</td>\n",
              "      <td>7820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1033</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>37020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1050</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6</td>\n",
              "      <td>33260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1120</td>\n",
              "      <td>4.0</td>\n",
              "      <td>30</td>\n",
              "      <td>10500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>839</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24</td>\n",
              "      <td>3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>921</td>\n",
              "      <td>3.0</td>\n",
              "      <td>18</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12</td>\n",
              "      <td>2400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>953</td>\n",
              "      <td>3.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>871</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12</td>\n",
              "      <td>3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>933</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1066</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>29820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1080</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10</td>\n",
              "      <td>9890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>825</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>6300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>795</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13</td>\n",
              "      <td>5960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>930</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11</td>\n",
              "      <td>4890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1128</td>\n",
              "      <td>4.0</td>\n",
              "      <td>21</td>\n",
              "      <td>9500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1154</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27</td>\n",
              "      <td>9230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1047</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50</td>\n",
              "      <td>6322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>877</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40</td>\n",
              "      <td>1020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>890</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>857</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17</td>\n",
              "      <td>3680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>956</td>\n",
              "      <td>2.0</td>\n",
              "      <td>25</td>\n",
              "      <td>7500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>970</td>\n",
              "      <td>3.0</td>\n",
              "      <td>31</td>\n",
              "      <td>7450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>928</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14</td>\n",
              "      <td>8900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>781</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8</td>\n",
              "      <td>4920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>760</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>5120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>658</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>993</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>10900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>890</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9</td>\n",
              "      <td>4980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1025</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>19000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Area  Bedrooms  Age   Rent\n",
              "0   1024       3.0   15  11200\n",
              "1   1200       4.0   10  33650\n",
              "2    800       2.0    5   8000\n",
              "3    674       2.0   10   6120\n",
              "4    766       2.0   11   6000\n",
              "5    680       3.0   36   3200\n",
              "6    750       3.0   10   3500\n",
              "7    900       3.0   20   3345\n",
              "8    967       3.0   23   3100\n",
              "9    720       3.0    7   6200\n",
              "10   850       4.0   16   7820\n",
              "11  1033       4.0    2  37020\n",
              "12  1050       3.0    6  33260\n",
              "13  1120       4.0   30  10500\n",
              "14   839       2.0   24   3400\n",
              "15   921       3.0   18   5000\n",
              "16   671       2.0   12   2400\n",
              "17   953       3.0   36   2720\n",
              "18   871       3.0   12   3100\n",
              "19   933       3.0   10   2950\n",
              "20  1066       4.0    3  29820\n",
              "21  1080       3.0   10   9890\n",
              "22   825       3.0    5   6300\n",
              "23   795       3.0   13   5960\n",
              "24   930       2.0   11   4890\n",
              "25  1128       4.0   21   9500\n",
              "26  1154       3.0   27   9230\n",
              "27  1047       3.0   50   6322\n",
              "28   877       2.0   40   1020\n",
              "29   890       3.0   28   2300\n",
              "30   857       2.0   17   3680\n",
              "31   956       2.0   25   7500\n",
              "32   970       3.0   31   7450\n",
              "33   928       2.0   14   8900\n",
              "34   781       3.0    8   4920\n",
              "35   760       3.0    4   5120\n",
              "36   658       2.0    1   3990\n",
              "37   993       4.0    2  10900\n",
              "38   890       2.0    9   4980\n",
              "39  1025       3.0    5  19000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUQO2NZyDRZN"
      },
      "source": [
        "## 4. Building the machine learning model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HMWZQ3iDNDQ"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "reg = LinearRegression()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIcM6YDCDUBM",
        "outputId": "4739e96a-b079-4b7a-b40e-d42a229aa3f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.fit(df[['Area','Bedrooms','Age']],df.Rent)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_LZuHiCEy8a",
        "outputId": "412aa0d3-a11b-41c5-c1d4-3d75c7f33079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  34.66745591, 2440.61024394, -354.75983519])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ff-2zVfE1N7",
        "outputId": "9bd91228-ffe7-4dcc-dd45-8aab3f42c27e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.intercept_"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-24001.729279468836"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aXeEpBzJwLu"
      },
      "source": [
        "### Predicted result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvXKBHu9E7Zp",
        "outputId": "19536f15-2c4d-4bb7-da74-3dff46aab04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.predict([[920,3,10]])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11666.56253816])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-zyv_I-J3V9"
      },
      "source": [
        "### Actual result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-bMqIY-J0TY",
        "outputId": "92e1f922-55d6-4181-fac3-d5c3903bd3f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y = 34.66745591*920+2440.61024394*3+-354.75983519*10+-24001.729279468844\n",
        "y"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11666.562537651156"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E52b1cUXJ60W",
        "outputId": "febafe09-0079-4d67-fb8b-126845c83bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r = 100 - (y-reg.predict([[920,3,10]]))\n",
        "print(\"Accuracy of model = \",int(r),\"%\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of model =  100 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3uCaIUGJ-KU"
      },
      "source": [
        "x = df.iloc[:,0].values.reshape(-1,1)\n",
        "x1 = df.iloc[:,1].values.reshape(-1,1)\n",
        "x2 = df.iloc[:,2].values.reshape(-1,1)\n",
        "y = df.iloc[:,-1].values"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISFK_xgDKAiG"
      },
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=1/4,random_state=0)\n",
        "x1_train,x1_test,y_train,y_test = train_test_split(x1,y,test_size=1/4,random_state=0)\n",
        "x2_train,x2_test,y_train,y_test = train_test_split(x2,y,test_size=1/4,random_state=0)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoZYZ-7bKE6d",
        "outputId": "c8a3602d-cb4b-48c8-ed44-e38a816957d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.fit(x_train,y_train)\n",
        "reg.fit(x1_train,y_train)\n",
        "reg.fit(x2_train,y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ltj-PWuKHwp"
      },
      "source": [
        "y_pred = reg.predict(x_test)\n",
        "y_pred = reg.predict(x1_test)\n",
        "y_pred = reg.predict(x2_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AccRMMIZKKK-",
        "outputId": "e0607ad1-7c30-412d-afa8-41a7deea4822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plt.figure(figsize=(25,20))\n",
        "plt.subplot(3,2,1)\n",
        "plt.scatter(x_train,y_train,c='k')\n",
        "plt.plot(x_train,reg.predict(x_train),color='blue')\n",
        "plt.xlabel('Area of house',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "plt.title('House Rent Prediction (Training set) \\n',size=24)\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "plt.scatter(x_test,y_test,c='k')\n",
        "plt.plot(x_train,reg.predict(x_train),color='blue')\n",
        "plt.xlabel('Area of house',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "plt.title('House Rent Prediction (Test set) \\n',size=24)\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "plt.scatter(x1_train,y_train,c='k')\n",
        "plt.plot(x1_train,reg.predict(x1_train),color='green')\n",
        "plt.xlabel('Bedrooms',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "plt.scatter(x1_test,y_test,c='k')\n",
        "plt.plot(x1_train,reg.predict(x1_train),color='green')\n",
        "plt.xlabel('Bedrooms',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "\n",
        "plt.subplot(3,2,5)\n",
        "plt.scatter(x2_train,y_train,c='k')\n",
        "plt.plot(x2_train,reg.predict(x2_train),color='red')\n",
        "plt.xlabel('Age of house',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "\n",
        "plt.subplot(3,2,6)\n",
        "plt.scatter(x2_test,y_test,c='k')\n",
        "plt.plot(x2_train,reg.predict(x2_train),color='red')\n",
        "plt.xlabel('Age of house',size=18)\n",
        "plt.ylabel('Rent of house',size=18)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1440 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GrhbR30KVtc"
      },
      "source": [
        "## In this exercise, we have got a large negative intercept. As the coefficient of house area is quite small, hence the slope is tending towards the negative direction, resulting in production of a negative scale of values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzikZnGoKVir"
      },
      "source": [
        "### From the graphs, we can also say that the house rent mostly depends on the age of the house and has almost linearly decreasing relation with age of house. This suggests as the age of house increases, its cost decreases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCczyxHXKggs"
      },
      "source": [
        "## Pros and Cons of Multivariate Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1t5RKBaKjWH"
      },
      "source": [
        "## Pros or Advantages\n",
        "### 1.Multiple factors can be fed into the model and analysed in one go\n",
        "### 2.Works quite fast with most datasets\n",
        "### 3.More realistic than linear regression\n",
        "<hr>\n",
        "\n",
        "\n",
        "## Cons or Disadvantages\n",
        "### 1.If there is a heirarchical dependence for more than 2 independent parameters, like x depends on y and y depends on z, then it would be difficult to plot such values and demonstrate regressions in a single plot.\n",
        "### 2.Not suitable for very big datasets of size 10 million or more\n",
        "### 3.Like linear regression, it cannot predict special mathematical patterns\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_a9kCl9QLKdn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}