Regression_02_Multiple_Linear_Backward_Elimination_Method.ipynb

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  "metadata": {
    "colab": {
      "name": "Regression-02-Multiple Linear - Backward Elimination Method.ipynb",
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      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO6xW9pICC6BwmZtLcAvR8T",
      "include_colab_link": true
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    "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/jihadkamil/MLearning/blob/master/Regression_02_Multiple_Linear_Backward_Elimination_Method.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gi6ytmTCO_u-",
        "colab_type": "text"
      },
      "source": [
        "#Multiple Linear Regression (Backward Elimintation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lU5GhD8OQEy",
        "colab_type": "text"
      },
      "source": [
        "# Importing the libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcbWh_N5OXrq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I9aLYDlObld",
        "colab_type": "text"
      },
      "source": [
        "\n",
        " \n",
        "# Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLDRj5GqOd7w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('50_Startups.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "print(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VCLxsAoOhnq",
        "colab_type": "text"
      },
      "source": [
        "# Encoding categorical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl-eg0-9OjaK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))\n",
        "print(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UixiW3G8OmUt",
        "colab_type": "text"
      },
      "source": [
        "# Avoiding the Dummy Variable Trap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNu7HPILOpfm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = X[:, 1:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1FXbMQrOuF_",
        "colab_type": "text"
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vu1bL-GNOwaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "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 = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLulFQ5tOz2u",
        "colab_type": "text"
      },
      "source": [
        "# Training the Multiple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYO5yPf9O2Jg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9qUZQbJO31g",
        "colab_type": "text"
      },
      "source": [
        "# Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SB_ZKBneO5js",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor.predict(X_test)\n",
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "plR8bGD1O7s3",
        "colab_type": "text"
      },
      "source": [
        "# Building the optimal model using Backward Elimination"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BttBi6GO9qb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import statsmodels.formula.api as sm\n",
        "X = np.append(arr = np.ones((50, 1)).astype(int), values = X, axis = 1)\n",
        "X_opt = X[:, [0, 1, 2, 3, 4, 5]]\n",
        "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "regressor_OLS.summary()\n",
        "X_opt = X[:, [0, 1, 3, 4, 5]]\n",
        "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "regressor_OLS.summary()\n",
        "X_opt = X[:, [0, 3, 4, 5]]\n",
        "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "regressor_OLS.summary()\n",
        "X_opt = X[:, [0, 3, 5]]\n",
        "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "regressor_OLS.summary()\n",
        "X_opt = X[:, [0, 3]]\n",
        "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "regressor_OLS.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gt__kwElOUV5",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}