Linear_Regression_Basic_model.ipynb

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    "colab": {
      "name": "Linear Regression Basic model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMnkmiSxReZndBVsIOKZrGY",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shuaib11-Github/CodeHub/blob/main/Linear_Regression_Basic_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XeR89c9t-FkB"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "id": "KyzTfFp3LQoI",
        "outputId": "2a2c4424-b2d4-4d67-820e-12617cb5f204"
      },
      "source": [
        "df = pd.read_csv('StudentHoursScores.csv')\n",
        "df"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>7.7</td>\n",
              "      <td>79</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.9</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.5</td>\n",
              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.3</td>\n",
              "      <td>33</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.1</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8.9</td>\n",
              "      <td>87</td>\n",
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              "      <th>6</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
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              "      <th>7</th>\n",
              "      <td>1.9</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.7</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.3</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.5</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>9.2</td>\n",
              "      <td>88</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.5</td>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3.5</td>\n",
              "      <td>34</td>\n",
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              "      <th>14</th>\n",
              "      <td>8.5</td>\n",
              "      <td>85</td>\n",
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              "      <th>15</th>\n",
              "      <td>3.2</td>\n",
              "      <td>32</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>6.5</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>9.6</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4.3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>4.1</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2.6</td>\n",
              "      <td>25</td>\n",
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            ],
            "text/plain": [
              "    Hours  Scores\n",
              "0     7.7      79\n",
              "1     5.9      60\n",
              "2     4.5      45\n",
              "3     3.3      33\n",
              "4     1.1      12\n",
              "5     8.9      87\n",
              "6     2.5      21\n",
              "7     1.9      19\n",
              "8     2.7      29\n",
              "9     8.3      81\n",
              "10    5.5      58\n",
              "11    9.2      88\n",
              "12    1.5      14\n",
              "13    3.5      34\n",
              "14    8.5      85\n",
              "15    3.2      32\n",
              "16    6.5      66\n",
              "17    2.5      21\n",
              "18    9.6      96\n",
              "19    4.3      42\n",
              "20    4.1      40\n",
              "21    3.0      30\n",
              "22    2.6      25"
            ]
          },
          "metadata": {
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          },
          "execution_count": 49
        }
      ]
    },
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      },
      "source": [
        "df.info()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23 entries, 0 to 22\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Hours   23 non-null     float64\n",
            " 1   Scores  23 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 496.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 297
        },
        "id": "qseXXQa-L45L",
        "outputId": "818d0901-ec3b-42e7-a68c-9ae629f3efce"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.817391</td>\n",
              "      <td>47.695652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.709688</td>\n",
              "      <td>27.103228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.650000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.100000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>72.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.600000</td>\n",
              "      <td>96.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "           Hours     Scores\n",
              "count  23.000000  23.000000\n",
              "mean    4.817391  47.695652\n",
              "std     2.709688  27.103228\n",
              "min     1.100000  12.000000\n",
              "25%     2.650000  27.000000\n",
              "50%     4.100000  40.000000\n",
              "75%     7.100000  72.500000\n",
              "max     9.600000  96.000000"
            ]
          },
          "metadata": {
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          "execution_count": 32
        }
      ]
    },
    {
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        },
        "id": "Atl5FXYML7f6",
        "outputId": "0638269d-cd95-4efc-911b-e8536db3aea7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 2)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 33
        }
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      "source": [
        "df.head()"
      ],
      "execution_count": 34,
      "outputs": [
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          "output_type": "execute_result",
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              "      <td>5.9</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.5</td>\n",
              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.3</td>\n",
              "      <td>33</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.1</td>\n",
              "      <td>12</td>\n",
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            "text/plain": [
              "   Hours  Scores\n",
              "0    7.7      79\n",
              "1    5.9      60\n",
              "2    4.5      45\n",
              "3    3.3      33\n",
              "4    1.1      12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "TB-7FiumMFLR",
        "outputId": "3933793b-4649-4956-9d24-d08455175e05"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>9.6</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4.3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>4.1</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2.6</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "    Hours  Scores\n",
              "18    9.6      96\n",
              "19    4.3      42\n",
              "20    4.1      40\n",
              "21    3.0      30\n",
              "22    2.6      25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzObtBz1MGSM",
        "outputId": "79df8c23-77fc-4985-83a3-2665defa13ee"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hours     float64\n",
              "Scores      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzXRJIK5MIUG",
        "outputId": "74eb0799-d27a-4d6d-8531-9d2ea2055586"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hours', 'Scores'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "-ppiHTbIMJZS",
        "outputId": "168fa235-26bf-41fe-fbea-8583abf234dd"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Hours</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.997656</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.997656\n",
              "Scores  0.997656  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5EGVJOfMLZq"
      },
      "source": [
        "X = df.iloc[:,0:-1]\n",
        "y = df.iloc[:,-1]"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_I3VAejXMZnS"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iaVbX25MqZG"
      },
      "source": [
        "lr = LinearRegression()\n",
        "model = lr.fit(X_train, y_train)\n",
        "#t_pred = model.predict(X_train)\n",
        "pred = model.predict(X_test)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ibp9fTZM0om",
        "outputId": "4bf4d379-096e-4b2b-a415-b94982472a16"
      },
      "source": [
        "print(model.intercept_)\n",
        "print(model.coef_)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2569567372371395\n",
            "[9.9073553]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuRM4g3zNCkl",
        "outputId": "8a40c79a-340c-4cfd-cc0d-82915e9f92fa"
      },
      "source": [
        "print('accuracy: ', r2_score(y_test, pred))\n",
        "print('mean_square_error: ', mean_squared_error(y_test, pred))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy:  0.9311770139374417\n",
            "mean_square_error:  3.7164412473781487\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNmtHeb5NjeB",
        "outputId": "74c26721-b78a-4b9d-ba3f-591ec2e02ab5"
      },
      "source": [
        "vif = 1/ 1-(r2_score(y_test, pred))\n",
        "vif"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06882298606255832"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8iCOLrtNuY9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBeBRQebOE4a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhfbCjYbOE_N"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gt7ymE0POFDS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}