Logistic_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMAifyqXLSmNRmbrzpV6W9I",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amit-raj-repo/ML-Logistic-Regression-Python/blob/master/Logistic_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": "xBK32ZbWfDux",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7LzrMT0gOx9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgPLBnikglr7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the dataset\n",
        "\n",
        "df = pd.read_csv(\"/content/gdrive/My Drive/Data/data.csv\").dropna(axis = 0)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAhNqBEwg4Qg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Seprating dependent and independent variables\n",
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:,-1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Yqzggqug9PJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef7f97c7-21ab-47e9-e0d3-fadbd75611a7"
      },
      "source": [
        "print(X.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(169559, 25)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wq9xElWBhAGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Splitting into test and train\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,\n",
        "                                                    random_state=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mkwtm-AhC6c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#===============================================================#\n",
        "# Logistic Regression Model\n",
        "#===============================================================#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Szn9WcIZhJpz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = LogisticRegression(penalty='l2', random_state=3, max_iter= 500,\n",
        "                            warm_start=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kN4YYOQHiV-G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "ae6f4450-3e92-4f72-fb48-cc2ce31da03a"
      },
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=3, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYRgDPM1ioD7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "d53deef1-802f-4d33-a8d8-1842a773c4e6"
      },
      "source": [
        "#Getting all the model coefficients\n",
        "model.coef_"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.76704230e-02,  2.26789014e-03,  2.60337475e-02,\n",
              "        -6.78084339e-02,  2.63834735e-02,  2.58283722e-02,\n",
              "        -1.26264989e-02, -4.90159060e-02, -1.91578841e-01,\n",
              "        -8.49623751e-02, -4.10931524e-01,  2.96591603e-01,\n",
              "        -1.31284177e-01, -3.89736711e-01,  2.47506821e-02,\n",
              "        -2.14167556e-01, -4.46304666e-02,  3.76507113e-02,\n",
              "        -9.07717583e-02,  1.00535133e-02,  4.49173364e-02,\n",
              "        -8.73036619e-03,  8.20668150e+00, -5.55617800e+00,\n",
              "         1.56363965e-02]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSUxrPHIrHV3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Getting the predictions\n",
        "pred = model.predict(X_test)\n",
        "predProb = model.predict_proba(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2dkqehDrSLU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6709cb88-a4f8-486d-a031-cc58aba3c825"
      },
      "source": [
        "#Checking the accuracy\n",
        "print(metrics.accuracy_score(y_test, pred))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8100967209247464\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrxa1ijUrU-j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b64dc1d8-517c-4eaf-cb93-c1496964f3bd"
      },
      "source": [
        "#Checking the confusion metrics\n",
        "print(confusion_matrix(y_test, pred))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[25416   494]\n",
            " [ 5946  2056]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jif05cmMrY6M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Getting values for FPR and TPR we take the probability of 1 here thats why predProb[:,1] \n",
        "fpr, tpr, threshold = metrics.roc_curve(y_test, predProb[:,1])\n",
        "roc_auc = metrics.auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBzUPWS-rdTt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "51038024-81b2-40c8-c3ef-2012b250ac20"
      },
      "source": [
        "# ROC Curve\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(roc_auc), marker='.')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}