Multiple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPo1/jVmtDE9X+OTsJBXsvz",
      "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/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": "RmGFoV8P22AE"
      },
      "source": [
        "# **Multiple Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75W-ukM9p6LC"
      },
      "source": [
        "# Written by Deepthi Tabitha Bennet\n",
        "\n",
        "!pip install matplotlib\n",
        "!pip install sklearn\n",
        "!pip install LinearRegression\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWyZDKvUvt94"
      },
      "source": [
        "raw_data = pd.read_csv('Housing_Data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kQumzvYsv1Y"
      },
      "source": [
        "raw_data.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGmvmAJusv6J"
      },
      "source": [
        "sns.pairplot(raw_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-RE0_pmsv95"
      },
      "source": [
        "x = raw_data[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms', 'Avg. Area Number of Bedrooms', 'Area Population']]\n",
        "y = raw_data['Price']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCTLqMejvtlA"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtlnUPffvtt-"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCbrRU6hvt3O"
      },
      "source": [
        "print(model.coef_)\n",
        "print(model.intercept_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ngs8G_5vt6e"
      },
      "source": [
        "pd.DataFrame(model.coef_, x.columns, columns = ['Coeff'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVw4lndpC45e"
      },
      "source": [
        "predictions = model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PblNR_9BC5BQ"
      },
      "source": [
        "plt.scatter(y_test, predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3fxeEceC5Tf"
      },
      "source": [
        "plt.hist(y_test - predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EI_gS2JfDFHO"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "metrics.mean_absolute_error(y_test, predictions)\n",
        "metrics.mean_squared_error(y_test, predictions)\n",
        "np.sqrt(metrics.mean_squared_error(y_test, predictions))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}