Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNHQzlzpMRo3k1e/ACsb9bG",
      "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/DeepthiTabithaBennet/Python_AppliedStatistics/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFbgls9myszh"
      },
      "source": [
        "# Written by Deepthi Tabitha Bennet\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vahdubCB0C3_",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "377a7f99-347b-402c-b1a5-75bd7d776d6e"
      },
      "source": [
        "# uplading data file from local drive in google colab\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d44dc1d2-821a-431f-b047-528de1e93fa6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d44dc1d2-821a-431f-b047-528de1e93fa6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving StatLab_HeightWeight - Height & Weight.csv to StatLab_HeightWeight - Height & Weight (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKb3TJYO0MMK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f0d7a60-6f3a-4f53-ab72-e2d553145d81"
      },
      "source": [
        "import io\n",
        "\n",
        "# dataset not included, please use your own dataset \n",
        "\n",
        "# reading csv file as pandas dataframe\n",
        "data = pd.read_csv(io.BytesIO(uploaded['StatLab_HeightWeight - Height & Weight.csv']))\n",
        "print(data.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Register Number         Name  Height  Weight\n",
            "0          20S001      Amritha     158      63\n",
            "1          20S002  Anita Janie     154      64\n",
            "2          20S003    Apprameyu     172      54\n",
            "3          20S004   Aravinthan     165      65\n",
            "4          20S005      Archana     167      55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjJYm21M0Mki",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd92c2ad-1db4-41cc-9ddf-3dee61cdbd07"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8cC2FXJ0YJU"
      },
      "source": [
        "# collecting x & y\n",
        "X = data['Height'].values\n",
        "Y = data['Weight'].values\n",
        "\n",
        "# calculate mean of x & y using an inbuilt numpy method mean()\n",
        "mean_x = np.mean(X)\n",
        "mean_y = np.mean(Y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyvdwfWa0YXN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baf3ee5f-6b21-481c-9dda-194fc86fb333"
      },
      "source": [
        "# total no.of input values\n",
        "val_count = len(X)\n",
        "\n",
        "# using the formula to calculate m & c\n",
        "numer = 0\n",
        "denom = 0\n",
        "for i in range(val_count):\n",
        "  numer += (X[i] - mean_x) * (Y[i] - mean_y)\n",
        "  denom += (X[i] - mean_x) ** 2\n",
        "m = numer / denom\n",
        "c = mean_y - (m * mean_x)\n",
        "\n",
        "print (f'm = {m} \\nc = {c}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "m = 0.7280689021144822 \n",
            "c = -60.329394687182486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_holrQd0j92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "0d9d7167-7e7a-4286-8d8f-a546d05036ae"
      },
      "source": [
        "# plotting values and regression line\n",
        "max_x = np.max(X) + 100\n",
        "min_x = np.min(Y) - 100\n",
        "\n",
        "# calculating line values x and y\n",
        "x = np.linspace (min_x, max_x, 100)\n",
        "y = c + m * x\n",
        "\n",
        "plt.plot(x, y, color='#58b970', label='Regression Line')\n",
        "plt.scatter(X,Y,c='#ef5423', label='data points')\n",
        "\n",
        "plt.xlabel('Height')\n",
        "plt.ylabel('Weight')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": {
        "id": "gC4kAXTP4vcr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b511829-ad3e-4f34-94e2-a123fd8fabee"
      },
      "source": [
        "# calculating R-squared value for measuring goodness of our model. \n",
        "ss_t = 0 #total sum of squares\n",
        "ss_r = 0 #total sum of square of residuals\n",
        "\n",
        "for i in range(int(val_count)):\n",
        "  y_pred = c + m * X[i]\n",
        "  ss_t += (Y[i] - mean_y) ** 2\n",
        "  ss_r += (Y[i] - y_pred) ** 2\n",
        "r2 = 1 - (ss_r/ss_t)\n",
        "\n",
        "print(r2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2403827773195496\n"
          ]
        }
      ]
    }
  ]
}