05-EDA-Bivariate-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Exploratory Data Analysis - Bivariate\n",
    "- Examine each independent (predictor) feature in relation to the dependent (response) feature\n",
    "- Form initial hypotheses regarding their level of correlation to the outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the cleaned source file into a DataFrame.\n",
    "data_dir = os.path.join(os.getcwd(), 'Data')\n",
    "source_file = os.path.join(data_dir, 'WA-Telco-Customer-Churn-EDA.xlsx')\n",
    "df = pd.read_excel(source_file, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID            object\n",
       "gender              category\n",
       "SeniorCitizen       category\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure                 int64\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float64\n",
       "TotalCharges         float64\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make appropriate data type assignments.\n",
    "df.gender = df.gender.astype('category')\n",
    "df.SeniorCitizen = df.SeniorCitizen.astype('category')\n",
    "df.Partner = df.Partner.astype('category')\n",
    "df.Dependents = df.Dependents.astype('category')\n",
    "df.PhoneService = df.PhoneService.astype('category')\n",
    "df.MultipleLines = df.MultipleLines.astype('category')\n",
    "df.InternetService = df.InternetService.astype('category')\n",
    "df.OnlineSecurity = df.OnlineSecurity.astype('category')\n",
    "df.OnlineBackup = df.OnlineBackup.astype('category')\n",
    "df.DeviceProtection = df.DeviceProtection.astype('category')\n",
    "df.TechSupport = df.TechSupport.astype('category')\n",
    "df.StreamingTV = df.StreamingTV.astype('category')\n",
    "df.StreamingMovies = df.StreamingMovies.astype('category')\n",
    "df.Contract = df.Contract.astype('category')\n",
    "df.PaperlessBilling = df.PaperlessBilling.astype('category')\n",
    "df.PaymentMethod = df.PaymentMethod.astype('category')\n",
    "df.TotalCharges = pd.to_numeric(df.TotalCharges, errors='coerce')\n",
    "df.Churn = df.Churn.astype('category')\n",
    "\n",
    "# Validate new data type assignments.\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</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>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2283.300441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2266.771362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>401.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1397.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3794.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tenure  MonthlyCharges  TotalCharges\n",
       "count  7043.000000     7043.000000   7032.000000\n",
       "mean     32.371149       64.761692   2283.300441\n",
       "std      24.559481       30.090047   2266.771362\n",
       "min       0.000000       18.250000     18.800000\n",
       "25%       9.000000       35.500000    401.450000\n",
       "50%      29.000000       70.350000   1397.475000\n",
       "75%      55.000000       89.850000   3794.737500\n",
       "max      72.000000      118.750000   8684.800000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Descriptive statistics regarding all numeric features.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Box-and-Whisker Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17f66500860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=\"tenure\", by=\"Churn\", figsize=(7,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.boxplot(column=\"MonthlyCharges\", by=\"Churn\", figsize=(7,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.boxplot(column=\"TotalCharges\", by=\"Churn\", figsize=(7,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Frequency Tables and Stacked Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_gender_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"gender\"])\n",
    "churn_gender_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_gender_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_senior_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"SeniorCitizen\"])\n",
    "churn_senior_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_senior_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_partner_df = pd.crosstab(index=df[\"Churn\"], columns=df['Partner'])\n",
    "churn_partner_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_partner_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_dependents_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"Dependents\"])\n",
    "churn_dependents_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_dependents_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_phonesvc_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"PhoneService\"])\n",
    "churn_phonesvc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_phonesvc_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_multilines_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"MultipleLines\"])\n",
    "churn_multilines_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_multilines_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_intservice_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"InternetService\"])\n",
    "churn_intservice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_intservice_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_onlinesec_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"OnlineSecurity\"])\n",
    "churn_onlinesec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_onlinesec_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_olbackup_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"OnlineBackup\"])\n",
    "churn_olbackup_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_olbackup_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_devprotect_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"DeviceProtection\"])\n",
    "churn_devprotect_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_devprotect_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tekspt_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"TechSupport\"])\n",
    "churn_tekspt_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tekspt_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tv_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"StreamingTV\"])\n",
    "churn_tv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tv_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_movies_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"StreamingMovies\"])\n",
    "churn_movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_movies_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_contract_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"Contract\"])\n",
    "churn_contract_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_contract_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_billing_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"PaperlessBilling\"])\n",
    "churn_billing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_billing_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_paymeth_df = pd.crosstab(index=df[\"Churn\"], columns=df[\"PaymentMethod\"])\n",
    "churn_paymeth_df  #/churn_paymeth_df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_paymeth_df.plot(kind=\"barh\", figsize=(6,4), stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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