Pandas_EDA_Example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pandas_EDA_Example.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOD8fMLXqR48oF5vQlrxGoc",
      "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/tawfiqam/MI564/blob/main/Pandas_EDA_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "om330C0HWO3H"
      },
      "source": [
        "#Another Pandas Example - EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmeaxfF3UJpL"
      },
      "source": [
        "Source:https://www.kaggle.com/iabhishekbhardwaj/telco-customer-churn\n",
        "\n",
        "File: https://drive.google.com/file/d/14r061-e4yHhtPF6X241leQeRA22VLMfz/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Gbaa6nTTRxb"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns # For creating plots\n",
        "import matplotlib.ticker as mtick # For specifying the axes tick format \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set(style = 'white')"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9iX8mNLWTMw"
      },
      "source": [
        "##Reading csv files uploaded to colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3SQhWzvWHWG"
      },
      "source": [
        "import pandas as pd\n",
        "import zipfile\n",
        "\n",
        "zf = zipfile.ZipFile('archive.zip') \n",
        "telecom_cust = pd.read_csv(zf.open('WA_Fn-UseC_-Telco-Customer-Churn.csv'))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "RaPp_XZsiahL",
        "outputId": "79bd1d48-fa11-463f-9c37-02c9c4ba4434"
      },
      "source": [
        "telecom_cust.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "0  7590-VHVEG  Female              0  ...          29.85        29.85     No\n",
              "1  5575-GNVDE    Male              0  ...          56.95       1889.5     No\n",
              "2  3668-QPYBK    Male              0  ...          53.85       108.15    Yes\n",
              "3  7795-CFOCW    Male              0  ...          42.30      1840.75     No\n",
              "4  9237-HQITU  Female              0  ...          70.70       151.65    Yes\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hh_KJLSUWZjn"
      },
      "source": [
        "## Reading from Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjfUc7eRTeUK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8fd62ad-865b-4fe3-9a68-da061c42729a"
      },
      "source": [
        "#alternatively, we can read directly from Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "telecom_cust = pd.read_csv('/content/drive/MyDrive/MachineLearning:17:610:564:90/Examples/WA_Fn-UseC_-Telco-Customer-Churn.csv')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWEKMiVcTzXs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "4e77a5f3-047b-49a5-93b8-8681f9c6fe85"
      },
      "source": [
        "telecom_cust.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "0  7590-VHVEG  Female              0  ...          29.85        29.85     No\n",
              "1  5575-GNVDE    Male              0  ...          56.95       1889.5     No\n",
              "2  3668-QPYBK    Male              0  ...          53.85       108.15    Yes\n",
              "3  7795-CFOCW    Male              0  ...          42.30      1840.75     No\n",
              "4  9237-HQITU  Female              0  ...          70.70       151.65    Yes\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Xj_TQDaMSYBH",
        "outputId": "3d69feb7-67c7-48c0-c7bd-7582b2398e31"
      },
      "source": [
        "telecom_cust.describe()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.162147</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>64.761692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.368612</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>30.090047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>35.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>70.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>89.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>118.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SeniorCitizen       tenure  MonthlyCharges\n",
              "count    7043.000000  7043.000000     7043.000000\n",
              "mean        0.162147    32.371149       64.761692\n",
              "std         0.368612    24.559481       30.090047\n",
              "min         0.000000     0.000000       18.250000\n",
              "25%         0.000000     9.000000       35.500000\n",
              "50%         0.000000    29.000000       70.350000\n",
              "75%         0.000000    55.000000       89.850000\n",
              "max         1.000000    72.000000      118.750000"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "q9MsOh4pSlUQ",
        "outputId": "e7a86a12-b195-4117-bcd4-ffc80e72d529"
      },
      "source": [
        "df_dummies.describe()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>Partner_No</th>\n",
              "      <th>Partner_Yes</th>\n",
              "      <th>Dependents_No</th>\n",
              "      <th>Dependents_Yes</th>\n",
              "      <th>PhoneService_No</th>\n",
              "      <th>PhoneService_Yes</th>\n",
              "      <th>MultipleLines_No</th>\n",
              "      <th>MultipleLines_No phone service</th>\n",
              "      <th>MultipleLines_Yes</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>OnlineSecurity_No</th>\n",
              "      <th>OnlineSecurity_No internet service</th>\n",
              "      <th>OnlineSecurity_Yes</th>\n",
              "      <th>OnlineBackup_No</th>\n",
              "      <th>OnlineBackup_No internet service</th>\n",
              "      <th>OnlineBackup_Yes</th>\n",
              "      <th>DeviceProtection_No</th>\n",
              "      <th>DeviceProtection_No internet service</th>\n",
              "      <th>DeviceProtection_Yes</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</th>\n",
              "      <th>StreamingTV_No</th>\n",
              "      <th>StreamingTV_No internet service</th>\n",
              "      <th>StreamingTV_Yes</th>\n",
              "      <th>StreamingMovies_No</th>\n",
              "      <th>StreamingMovies_No internet service</th>\n",
              "      <th>StreamingMovies_Yes</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>...</th>\n",
              "      <th>TotalCharges_973.35</th>\n",
              "      <th>TotalCharges_973.45</th>\n",
              "      <th>TotalCharges_973.55</th>\n",
              "      <th>TotalCharges_973.65</th>\n",
              "      <th>TotalCharges_973.95</th>\n",
              "      <th>TotalCharges_978</th>\n",
              "      <th>TotalCharges_978.6</th>\n",
              "      <th>TotalCharges_979.05</th>\n",
              "      <th>TotalCharges_979.5</th>\n",
              "      <th>TotalCharges_98.35</th>\n",
              "      <th>TotalCharges_98.5</th>\n",
              "      <th>TotalCharges_980.35</th>\n",
              "      <th>TotalCharges_981.45</th>\n",
              "      <th>TotalCharges_982.95</th>\n",
              "      <th>TotalCharges_985.05</th>\n",
              "      <th>TotalCharges_987.95</th>\n",
              "      <th>TotalCharges_989.05</th>\n",
              "      <th>TotalCharges_989.45</th>\n",
              "      <th>TotalCharges_99.45</th>\n",
              "      <th>TotalCharges_99.6</th>\n",
              "      <th>TotalCharges_99.75</th>\n",
              "      <th>TotalCharges_99.95</th>\n",
              "      <th>TotalCharges_990.3</th>\n",
              "      <th>TotalCharges_990.45</th>\n",
              "      <th>TotalCharges_990.85</th>\n",
              "      <th>TotalCharges_990.9</th>\n",
              "      <th>TotalCharges_992.7</th>\n",
              "      <th>TotalCharges_993.15</th>\n",
              "      <th>TotalCharges_994.55</th>\n",
              "      <th>TotalCharges_994.8</th>\n",
              "      <th>TotalCharges_995.35</th>\n",
              "      <th>TotalCharges_996.45</th>\n",
              "      <th>TotalCharges_996.85</th>\n",
              "      <th>TotalCharges_996.95</th>\n",
              "      <th>TotalCharges_997.65</th>\n",
              "      <th>TotalCharges_997.75</th>\n",
              "      <th>TotalCharges_998.1</th>\n",
              "      <th>TotalCharges_999.45</th>\n",
              "      <th>TotalCharges_999.8</th>\n",
              "      <th>TotalCharges_999.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.162147</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>64.761692</td>\n",
              "      <td>0.265370</td>\n",
              "      <td>0.495244</td>\n",
              "      <td>0.504756</td>\n",
              "      <td>0.516967</td>\n",
              "      <td>0.483033</td>\n",
              "      <td>0.700412</td>\n",
              "      <td>0.299588</td>\n",
              "      <td>0.096834</td>\n",
              "      <td>0.903166</td>\n",
              "      <td>0.481329</td>\n",
              "      <td>0.096834</td>\n",
              "      <td>0.421837</td>\n",
              "      <td>0.343746</td>\n",
              "      <td>0.439585</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.496663</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.286668</td>\n",
              "      <td>0.438450</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.344881</td>\n",
              "      <td>0.439443</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.343888</td>\n",
              "      <td>0.493114</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.290217</td>\n",
              "      <td>0.398978</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.384353</td>\n",
              "      <td>0.395428</td>\n",
              "      <td>0.216669</td>\n",
              "      <td>0.387903</td>\n",
              "      <td>0.550192</td>\n",
              "      <td>0.209144</td>\n",
              "      <td>0.240664</td>\n",
              "      <td>0.407781</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "      <td>0.000142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.368612</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>30.090047</td>\n",
              "      <td>0.441561</td>\n",
              "      <td>0.500013</td>\n",
              "      <td>0.500013</td>\n",
              "      <td>0.499748</td>\n",
              "      <td>0.499748</td>\n",
              "      <td>0.458110</td>\n",
              "      <td>0.458110</td>\n",
              "      <td>0.295752</td>\n",
              "      <td>0.295752</td>\n",
              "      <td>0.499687</td>\n",
              "      <td>0.295752</td>\n",
              "      <td>0.493888</td>\n",
              "      <td>0.474991</td>\n",
              "      <td>0.496372</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.500024</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.452237</td>\n",
              "      <td>0.496232</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.475363</td>\n",
              "      <td>0.496355</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.475038</td>\n",
              "      <td>0.499988</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.453895</td>\n",
              "      <td>0.489723</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.486477</td>\n",
              "      <td>0.488977</td>\n",
              "      <td>0.412004</td>\n",
              "      <td>0.487307</td>\n",
              "      <td>0.497510</td>\n",
              "      <td>0.406726</td>\n",
              "      <td>0.427517</td>\n",
              "      <td>0.491457</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.016850</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.016850</td>\n",
              "      <td>0.016850</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.016850</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "      <td>0.011916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>70.350000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>89.850000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>118.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 6576 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       SeniorCitizen       tenure  ...  TotalCharges_999.8  TotalCharges_999.9\n",
              "count    7043.000000  7043.000000  ...         7043.000000         7043.000000\n",
              "mean        0.162147    32.371149  ...            0.000142            0.000142\n",
              "std         0.368612    24.559481  ...            0.011916            0.011916\n",
              "min         0.000000     0.000000  ...            0.000000            0.000000\n",
              "25%         0.000000     9.000000  ...            0.000000            0.000000\n",
              "50%         0.000000    29.000000  ...            0.000000            0.000000\n",
              "75%         0.000000    55.000000  ...            0.000000            0.000000\n",
              "max         1.000000    72.000000  ...            1.000000            1.000000\n",
              "\n",
              "[8 rows x 6576 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdlrMFQMTzcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1895f337-1805-4c45-9792-2c38e86a4e57"
      },
      "source": [
        "telecom_cust.columns.values"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
              "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
              "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
              "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
              "       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',\n",
              "       'TotalCharges', 'Churn'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-cI7l_cTze9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "823226f2-0b61-4b66-8988-557c42a54b55"
      },
      "source": [
        "# Checking the data types of all the columns\n",
        "telecom_cust.dtypes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FZEtdTDTzhe"
      },
      "source": [
        "#Removing missing values \n",
        "telecom_cust.dropna(inplace = True)\n",
        "#Removing missing values in a different way\n",
        "telecom_cust = telecom_cust.dropna()\n",
        "#Remove customer IDs from the data set\n",
        "df2 = telecom_cust.iloc[2:,2:]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "UlKnCFZXig8t",
        "outputId": "31bd8b13-9c73-47aa-8d5c-e1d7631ff6d7"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>22</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>89.10</td>\n",
              "      <td>1949.4</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SeniorCitizen Partner Dependents  ...  MonthlyCharges TotalCharges Churn\n",
              "2              0      No         No  ...           53.85       108.15   Yes\n",
              "3              0      No         No  ...           42.30      1840.75    No\n",
              "4              0      No         No  ...           70.70       151.65   Yes\n",
              "5              0      No         No  ...           99.65        820.5   Yes\n",
              "6              0      No        Yes  ...           89.10       1949.4    No\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yLyvwz3TzkH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "25150a4b-c63c-42ae-b392-fd90d00045bf"
      },
      "source": [
        "#Convertin the predictor variable in a binary numeric variable\n",
        "df2['Churn'].replace(to_replace='Yes', value=1, inplace=True)\n",
        "df2['Churn'].replace(to_replace='No',  value=0, inplace=True)\n",
        "\n",
        "df2.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85         29.85     0\n",
              "1    Male              0      No  ...          56.95        1889.5     0\n",
              "2    Male              0      No  ...          53.85        108.15     1\n",
              "3    Male              0      No  ...          42.30       1840.75     0\n",
              "4  Female              0      No  ...          70.70        151.65     1\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "GyJ_V5-2irZB",
        "outputId": "afdba06e-373d-489f-e582-263afe0f8861"
      },
      "source": [
        "#Let's convert all the categorical variables into dummy variables\n",
        "df_dummies = pd.get_dummies(df2)\n",
        "df_dummies.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>Partner_No</th>\n",
              "      <th>Partner_Yes</th>\n",
              "      <th>Dependents_No</th>\n",
              "      <th>Dependents_Yes</th>\n",
              "      <th>PhoneService_No</th>\n",
              "      <th>PhoneService_Yes</th>\n",
              "      <th>MultipleLines_No</th>\n",
              "      <th>MultipleLines_No phone service</th>\n",
              "      <th>MultipleLines_Yes</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>OnlineSecurity_No</th>\n",
              "      <th>OnlineSecurity_No internet service</th>\n",
              "      <th>OnlineSecurity_Yes</th>\n",
              "      <th>OnlineBackup_No</th>\n",
              "      <th>OnlineBackup_No internet service</th>\n",
              "      <th>OnlineBackup_Yes</th>\n",
              "      <th>DeviceProtection_No</th>\n",
              "      <th>DeviceProtection_No internet service</th>\n",
              "      <th>DeviceProtection_Yes</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</th>\n",
              "      <th>StreamingTV_No</th>\n",
              "      <th>StreamingTV_No internet service</th>\n",
              "      <th>StreamingTV_Yes</th>\n",
              "      <th>StreamingMovies_No</th>\n",
              "      <th>StreamingMovies_No internet service</th>\n",
              "      <th>StreamingMovies_Yes</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>...</th>\n",
              "      <th>TotalCharges_973.35</th>\n",
              "      <th>TotalCharges_973.45</th>\n",
              "      <th>TotalCharges_973.55</th>\n",
              "      <th>TotalCharges_973.65</th>\n",
              "      <th>TotalCharges_973.95</th>\n",
              "      <th>TotalCharges_978</th>\n",
              "      <th>TotalCharges_978.6</th>\n",
              "      <th>TotalCharges_979.05</th>\n",
              "      <th>TotalCharges_979.5</th>\n",
              "      <th>TotalCharges_98.35</th>\n",
              "      <th>TotalCharges_98.5</th>\n",
              "      <th>TotalCharges_980.35</th>\n",
              "      <th>TotalCharges_981.45</th>\n",
              "      <th>TotalCharges_982.95</th>\n",
              "      <th>TotalCharges_985.05</th>\n",
              "      <th>TotalCharges_987.95</th>\n",
              "      <th>TotalCharges_989.05</th>\n",
              "      <th>TotalCharges_989.45</th>\n",
              "      <th>TotalCharges_99.45</th>\n",
              "      <th>TotalCharges_99.6</th>\n",
              "      <th>TotalCharges_99.75</th>\n",
              "      <th>TotalCharges_99.95</th>\n",
              "      <th>TotalCharges_990.3</th>\n",
              "      <th>TotalCharges_990.45</th>\n",
              "      <th>TotalCharges_990.85</th>\n",
              "      <th>TotalCharges_990.9</th>\n",
              "      <th>TotalCharges_992.7</th>\n",
              "      <th>TotalCharges_993.15</th>\n",
              "      <th>TotalCharges_994.55</th>\n",
              "      <th>TotalCharges_994.8</th>\n",
              "      <th>TotalCharges_995.35</th>\n",
              "      <th>TotalCharges_996.45</th>\n",
              "      <th>TotalCharges_996.85</th>\n",
              "      <th>TotalCharges_996.95</th>\n",
              "      <th>TotalCharges_997.65</th>\n",
              "      <th>TotalCharges_997.75</th>\n",
              "      <th>TotalCharges_998.1</th>\n",
              "      <th>TotalCharges_999.45</th>\n",
              "      <th>TotalCharges_999.8</th>\n",
              "      <th>TotalCharges_999.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 6576 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   SeniorCitizen  tenure  ...  TotalCharges_999.8  TotalCharges_999.9\n",
              "0              0       1  ...                   0                   0\n",
              "1              0      34  ...                   0                   0\n",
              "2              0       2  ...                   0                   0\n",
              "3              0      45  ...                   0                   0\n",
              "4              0       2  ...                   0                   0\n",
              "\n",
              "[5 rows x 6576 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv9Pi5DgWa-H"
      },
      "source": [
        "##EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoTWQzTShlXT"
      },
      "source": [
        "corr = df_dummies.corr()['Churn']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wab-c0jerhTR",
        "outputId": "2f73f1ee-1fb0-4fc1-e247-c27bfa1ded13"
      },
      "source": [
        "corr.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6576,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYdpth8iUVnk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "9372075a-3330-4935-bf3a-89971b0f9ad7"
      },
      "source": [
        "ax = sns.distplot(telecom_cust['tenure'], hist=True, kde=False, \n",
        "             bins=int(180/5), color = 'darkblue', \n",
        "             hist_kws={'edgecolor':'black'},\n",
        "             kde_kws={'linewidth': 4})\n",
        "ax.set_ylabel('# of Customers')\n",
        "ax.set_xlabel('Tenure (months)')\n",
        "ax.set_title('# of Customers by their tenure')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '# of Customers by their tenure')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": "Nf66nmNSUkzB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "c77b50b7-ef7f-4b93-d0f9-38288f2fabcb"
      },
      "source": [
        "ax = sns.kdeplot(telecom_cust.MonthlyCharges[(telecom_cust[\"Churn\"] == 'No') ],\n",
        "                color=\"Red\", shade = True)\n",
        "ax = sns.kdeplot(telecom_cust.MonthlyCharges[(telecom_cust[\"Churn\"] == 'Yes') ],\n",
        "                ax =ax, color=\"Blue\", shade= True)\n",
        "ax.legend([\"Not Churn\",\"Churn\"],loc='upper right')\n",
        "ax.set_ylabel('Density')\n",
        "ax.set_xlabel('Monthly Charges')\n",
        "ax.set_title('Distribution of monthly charges by churn')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of monthly charges by churn')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "zMmMedQPUpFI"
      },
      "source": [
        "#Logistic Regression Classifier (predictive model)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCJ5o1szUrKY"
      },
      "source": [
        "# We will use the data frame where we had created dummy variables\n",
        "y = df_dummies['Churn'].values\n",
        "X = df_dummies.drop(columns = ['Churn'])\n",
        "\n",
        "# Scaling all the variables to a range of 0 to 1\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "features = X.columns.values\n",
        "scaler = MinMaxScaler(feature_range = (0,1))\n",
        "scaler.fit(X)\n",
        "X = pd.DataFrame(scaler.transform(X))\n",
        "X.columns = features"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxiQ5dXQUzlf"
      },
      "source": [
        "# Create Train & Test Data\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=0.3, random_state=101)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5c1YBhaU1wf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13a867cb-f8a2-48f6-e828-5ff1baf2b584"
      },
      "source": [
        "# Running logistic regression model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()\n",
        "result = model.fit(X_train, y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0WTacJRU3aF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e06ffd4-8866-4071-c4bd-04a9e7e793ab"
      },
      "source": [
        "from sklearn import metrics\n",
        "prediction_test = model.predict(X_test)\n",
        "# Print the prediction accuracy\n",
        "print (metrics.accuracy_score(y_test, prediction_test))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8045433033601515\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISuArrnGU5GD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "506597f6-1d4c-4155-db5a-cf056864adc9"
      },
      "source": [
        "# To get the weights of all the variables\n",
        "weights = pd.Series(model.coef_[0],\n",
        "                 index=X.columns.values)\n",
        "print (weights.sort_values(ascending = False)[:10].plot(kind='bar'))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ]
        },
        {
          "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": "1vgJ-UDOU9LB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "25313533-705b-4b64-db1b-70e3aff9bba5"
      },
      "source": [
        "print(weights.sort_values(ascending = False)[-10:].plot(kind='bar'))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}