Churn_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "#create string with the dataset filename\n",
    "datafile = 'WA_Fn-UseC_-Telco-Customer-Churn.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#open the file stream and store it\n",
    "openfile = open(datafile,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the vanilla DF from the CSV filedisplay(obj_df[col].display(obj_df[col].value_counts(normalize=True))value_counts(normalize=True))\n",
    "churn_df = pd.read_csv(openfile)"
   ]
  },
  {
   "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>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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data head\n",
    "churn_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data shape - (7043, 21)\n",
    "churn_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data types\n",
    "churn_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          7043\n",
       "gender                 2\n",
       "SeniorCitizen          2\n",
       "Partner                2\n",
       "Dependents             2\n",
       "tenure                73\n",
       "PhoneService           2\n",
       "MultipleLines          3\n",
       "InternetService        3\n",
       "OnlineSecurity         3\n",
       "OnlineBackup           3\n",
       "DeviceProtection       3\n",
       "TechSupport            3\n",
       "StreamingTV            3\n",
       "StreamingMovies        3\n",
       "Contract               3\n",
       "PaperlessBilling       2\n",
       "PaymentMethod          4\n",
       "MonthlyCharges      1585\n",
       "TotalCharges        6531\n",
       "Churn                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the number of unique values\n",
    "churn_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the numerical data, it seems that SeniorCitizen is also categorical but is already encoded as 0/1\n",
    "churn_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SeniorCitizen', 'tenure', 'MonthlyCharges'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter the columns index that are not OBJ\n",
    "drop_int = churn_df.columns[churn_df.dtypes != object]\n",
    "#churn_df.select_dtypes\n",
    "drop_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop int columns from DF and store in a new DF\n",
    "obj_df = churn_df.drop(columns=drop_int)\n",
    "#drop the customer ID\n",
    "obj_df = obj_df.drop(columns='customerID')\n",
    "churn_df = churn_df.drop(columns='customerID')\n",
    "#next step is to generate Pies to visualize the categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2160x2160 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display the features using pie chart\n",
    "#create pre df with data that is pure 'object' drop Totalcharges since it will be converted to numerical feature\n",
    "pie_df = obj_df.drop(columns='TotalCharges')\n",
    "n=0\n",
    "m=0\n",
    "pie_obj = [pie_df.columns]\n",
    "fig, pie_obj = plt.subplots(5,4,figsize=(30,30))\n",
    "for col in pie_df.columns:\n",
    "    if pie_df[col].dtype == 'object':\n",
    "        obj_plot = pie_df[col].value_counts()\n",
    "        pie_obj[n,m].set_title(col)\n",
    "        pie_obj[n,m].pie(obj_plot,radius=1,labels=obj_df[col].value_counts().index)\n",
    "        n+=1\n",
    "        if n%4 == 0:\n",
    "            m+=1\n",
    "            n=0\n",
    "#seems that \"totalCharges\" which should be numerical is an object and should be converted to numerical\n",
    "#need to see how to improve the display by grid - done\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#before converting TotalCharges from str to float - check for NA in TotalCharges\n",
    "(obj_df['TotalCharges'] == ' ').sum()\n",
    "#there are 11 empty string values in TotalCharges with the content of ' ' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7043.000000\n",
       "mean     2285.169963\n",
       "std      2267.544511\n",
       "min        18.800000\n",
       "25%       401.675000\n",
       "50%      1398.250000\n",
       "75%      3803.050000\n",
       "max      8684.800000\n",
       "Name: TotalCharges, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform the data from str to float in TotalCharges and replace blank with 'None'\n",
    "churn_df['TotalCharges'].replace(to_replace=' ',value=None,inplace=True)\n",
    "#convert the data type to float\n",
    "churn_df.TotalCharges = churn_df.TotalCharges.astype(float)\n",
    "churn_df['TotalCharges'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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        float64\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#review the Dtypes after conversion\n",
    "churn_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the nones with the Median of the feature\n",
    "obj_df.TotalCharges.fillna(1398.25,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the dist of the numerical features\n",
    "fig, dist_obj = plt.subplots(1,3,figsize=(20,5))\n",
    "\n",
    "dist_obj[0].hist(churn_df.MonthlyCharges)\n",
    "dist_obj[0].set_title('Histogram for monthly Charges per customer')\n",
    "dist_obj[0].set_xlabel('Number of customers')\n",
    "dist_obj[0].set_ylabel('monthly charges in USD')\n",
    "#we can see that the data is not normally dist\n",
    "\n",
    "dist_obj[1].hist(churn_df.tenure)\n",
    "dist_obj[1].set_title('Histogram for Tenure per customer')\n",
    "dist_obj[1].set_xlabel('Number of customers')\n",
    "dist_obj[1].set_ylabel('tenure')\n",
    "#we can see that the data is not normally dist\n",
    "\n",
    "dist_obj[2].hist(churn_df.TotalCharges)\n",
    "dist_obj[2].set_title('Histogram for Total Charges per customer')\n",
    "dist_obj[2].set_xlabel('Number of customers')\n",
    "dist_obj[2].set_ylabel('Total charges in USD')\n",
    "plt.show()\n",
    "#data is skewed to the left\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the quantile transformer from sklearn\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "\n",
    "#run the transformations for each of the above numerical features that are not distributed normally\n",
    "norm_trans_tc = QuantileTransformer(n_quantiles=churn_df['TotalCharges'].count(),output_distribution='normal')\n",
    "tc_norm = pd.DataFrame(churn_df['TotalCharges'])\n",
    "tc_norm = norm_trans_tc.fit_transform(tc_norm)\n",
    "\n",
    "norm_trans_mc = QuantileTransformer(n_quantiles=churn_df['MonthlyCharges'].count(),output_distribution='normal')\n",
    "mc_norm = pd.DataFrame(churn_df['MonthlyCharges'])\n",
    "mc_norm = norm_trans_mc.fit_transform(mc_norm)\n",
    "\n",
    "norm_trans = QuantileTransformer(n_quantiles=churn_df['tenure'].count(),output_distribution='normal')\n",
    "tenure_norm = pd.DataFrame(churn_df['tenure'])\n",
    "tenure_norm = norm_trans.fit_transform(tenure_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the distributions of the transformations\n",
    "f, norm_dist_obj = plt.subplots(1,3,figsize=(20,5))\n",
    "\n",
    "norm_dist_obj[0].set_title('normal transformation for MonthlyCharges')\n",
    "norm_dist_obj[0].hist(mc_norm)\n",
    "\n",
    "norm_dist_obj[1].hist(tenure_norm)\n",
    "norm_dist_obj[1].set_title('normal transformation for Tenure')\n",
    "\n",
    "norm_dist_obj[2].hist(tc_norm)\n",
    "norm_dist_obj[2].set_title('normal transformation for TotalCharges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BoxCox for TotalCharges'}, ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#perform normalization to the dist using boxcox for all numerical fetures\n",
    "tc_bc_fit, tc_lambda_fit = stats.boxcox(churn_df.TotalCharges)\n",
    "bc_tc = pd.DataFrame(tc_bc_fit)\n",
    "plt.title('BoxCox for TotalCharges')\n",
    "sns.distplot(bc_tc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BoxCox for MonthlyCharges'}, ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bc_mc_fit, mc_lambda_fit = stats.boxcox(churn_df.MonthlyCharges)\n",
    "bc_mc = pd.DataFrame(bc_mc_fit)\n",
    "plt.title('BoxCox for MonthlyCharges')\n",
    "sns.distplot(bc_mc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BoxCox for MonthlyCharges'}, ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ten_bc_fit, ten_lambda_fit = stats.boxcox(churn_df['tenure']+1)\n",
    "bc_ten = pd.DataFrame(ten_bc_fit)\n",
    "plt.title('BoxCox for MonthlyCharges')\n",
    "sns.distplot(bc_ten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#apply transformations to Dataframe\n",
    "churn_df['TotalCharges'] = tc_norm\n",
    "churn_df['MonthlyCharges'] = mc_norm\n",
    "churn_df['tenure'] = tenure_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\n",
       "Index: []"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df[churn_df.tenure == 0]\n",
    "#save to learn!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                int32\n",
       "SeniorCitizen         int64\n",
       "Partner               int32\n",
       "Dependents            int32\n",
       "tenure              float64\n",
       "PhoneService          int32\n",
       "MultipleLines         int32\n",
       "InternetService       int32\n",
       "OnlineSecurity        int32\n",
       "OnlineBackup          int32\n",
       "DeviceProtection      int32\n",
       "TechSupport           int32\n",
       "StreamingTV           int32\n",
       "StreamingMovies       int32\n",
       "Contract              int32\n",
       "PaperlessBilling      int32\n",
       "PaymentMethod         int32\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                 int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encode the objects , run a loop on all df objects\n",
    "import sklearn.preprocessing as pre\n",
    "\n",
    "#create the encode class variable\n",
    "encode_obj = pre.LabelEncoder()\n",
    "\n",
    "#run the loop on all objects\n",
    "for obj in churn_df.columns:\n",
    "    if churn_df[obj].dtype == object:\n",
    "        enc_obj = pd.DataFrame(encode_obj.fit_transform(churn_df[obj]))\n",
    "        enc_obj\n",
    "        churn_df[obj] = enc_obj\n",
    "#display new dtypes for the DataFrame\n",
    "\n",
    "churn_df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.504756</td>\n",
       "      <td>0.162147</td>\n",
       "      <td>0.483033</td>\n",
       "      <td>0.299588</td>\n",
       "      <td>0.166692</td>\n",
       "      <td>0.903166</td>\n",
       "      <td>0.940508</td>\n",
       "      <td>0.872923</td>\n",
       "      <td>0.790004</td>\n",
       "      <td>0.906432</td>\n",
       "      <td>0.904444</td>\n",
       "      <td>0.797104</td>\n",
       "      <td>0.985376</td>\n",
       "      <td>0.992475</td>\n",
       "      <td>0.690473</td>\n",
       "      <td>0.592219</td>\n",
       "      <td>1.574329</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.265370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.368612</td>\n",
       "      <td>0.499748</td>\n",
       "      <td>0.458110</td>\n",
       "      <td>1.457765</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.948554</td>\n",
       "      <td>0.737796</td>\n",
       "      <td>0.859848</td>\n",
       "      <td>0.880162</td>\n",
       "      <td>0.879949</td>\n",
       "      <td>0.861551</td>\n",
       "      <td>0.885002</td>\n",
       "      <td>0.885091</td>\n",
       "      <td>0.833755</td>\n",
       "      <td>0.491457</td>\n",
       "      <td>1.068104</td>\n",
       "      <td>1.002551</td>\n",
       "      <td>1.002619</td>\n",
       "      <td>0.441561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.199338</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>-5.199338</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.659373</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>1.000000</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004093</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.663138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.672927</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender  SeniorCitizen      Partner   Dependents       tenure  \\\n",
       "count  7043.000000    7043.000000  7043.000000  7043.000000  7043.000000   \n",
       "mean      0.504756       0.162147     0.483033     0.299588     0.166692   \n",
       "std       0.500013       0.368612     0.499748     0.458110     1.457765   \n",
       "min       0.000000       0.000000     0.000000     0.000000    -5.199338   \n",
       "25%       0.000000       0.000000     0.000000     0.000000    -0.659373   \n",
       "50%       1.000000       0.000000     0.000000     0.000000     0.004093   \n",
       "75%       1.000000       0.000000     1.000000     1.000000     0.663138   \n",
       "max       1.000000       1.000000     1.000000     1.000000     5.199338   \n",
       "\n",
       "       PhoneService  MultipleLines  InternetService  OnlineSecurity  \\\n",
       "count   7043.000000    7043.000000      7043.000000     7043.000000   \n",
       "mean       0.903166       0.940508         0.872923        0.790004   \n",
       "std        0.295752       0.948554         0.737796        0.859848   \n",
       "min        0.000000       0.000000         0.000000        0.000000   \n",
       "25%        1.000000       0.000000         0.000000        0.000000   \n",
       "50%        1.000000       1.000000         1.000000        1.000000   \n",
       "75%        1.000000       2.000000         1.000000        2.000000   \n",
       "max        1.000000       2.000000         2.000000        2.000000   \n",
       "\n",
       "       OnlineBackup  DeviceProtection  TechSupport  StreamingTV  \\\n",
       "count   7043.000000       7043.000000  7043.000000  7043.000000   \n",
       "mean       0.906432          0.904444     0.797104     0.985376   \n",
       "std        0.880162          0.879949     0.861551     0.885002   \n",
       "min        0.000000          0.000000     0.000000     0.000000   \n",
       "25%        0.000000          0.000000     0.000000     0.000000   \n",
       "50%        1.000000          1.000000     1.000000     1.000000   \n",
       "75%        2.000000          2.000000     2.000000     2.000000   \n",
       "max        2.000000          2.000000     2.000000     2.000000   \n",
       "\n",
       "       StreamingMovies     Contract  PaperlessBilling  PaymentMethod  \\\n",
       "count      7043.000000  7043.000000       7043.000000    7043.000000   \n",
       "mean          0.992475     0.690473          0.592219       1.574329   \n",
       "std           0.885091     0.833755          0.491457       1.068104   \n",
       "min           0.000000     0.000000          0.000000       0.000000   \n",
       "25%           0.000000     0.000000          0.000000       1.000000   \n",
       "50%           1.000000     0.000000          1.000000       2.000000   \n",
       "75%           2.000000     1.000000          1.000000       2.000000   \n",
       "max           2.000000     2.000000          1.000000       3.000000   \n",
       "\n",
       "       MonthlyCharges  TotalCharges        Churn  \n",
       "count     7043.000000   7043.000000  7043.000000  \n",
       "mean         0.000069      0.000012     0.265370  \n",
       "std          1.002551      1.002619     0.441561  \n",
       "min         -5.199338     -5.199338     0.000000  \n",
       "25%         -0.674490     -0.674490     0.000000  \n",
       "50%          0.000712      0.000000     0.000000  \n",
       "75%          0.672927      0.674490     1.000000  \n",
       "max          5.199338      5.199338     1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data after EDA and preprocessing - the data is scaled and noramlized\n",
    "churn_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Correlation with Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>0.396713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>0.289309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>0.282492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.228544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>0.195525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.191825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>0.178134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>0.164221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.163793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0.150889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>0.150448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>0.107062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>0.047291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>0.038492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>0.038037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>0.036581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>0.011942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.008612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Correlation with Churn\n",
       "Contract                        0.396713\n",
       "tenure                          0.305263\n",
       "OnlineSecurity                  0.289309\n",
       "TechSupport                     0.282492\n",
       "TotalCharges                    0.228544\n",
       "OnlineBackup                    0.195525\n",
       "PaperlessBilling                0.191825\n",
       "DeviceProtection                0.178134\n",
       "Dependents                      0.164221\n",
       "MonthlyCharges                  0.163793\n",
       "SeniorCitizen                   0.150889\n",
       "Partner                         0.150448\n",
       "PaymentMethod                   0.107062\n",
       "InternetService                 0.047291\n",
       "StreamingMovies                 0.038492\n",
       "MultipleLines                   0.038037\n",
       "StreamingTV                     0.036581\n",
       "PhoneService                    0.011942\n",
       "gender                          0.008612"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check corr with Churn - build data frame for corr with each feature\n",
    "corr_df_churn = pd.DataFrame(churn_df.corrwith(churn_df.Churn),columns={'Correlation with Churn'})\n",
    "corr_df_churn = corr_df_churn.abs()\n",
    "\n",
    "corr_df_churn.sort_values(by='Correlation with Churn',ascending=False,inplace=True)\n",
    "corr_df_churn.drop('Churn',inplace=True)\n",
    "corr_df_churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>gender</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.001808</td>\n",
       "      <td>0.010517</td>\n",
       "      <td>0.002496</td>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.006739</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.015017</td>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.006825</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.008743</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.011754</td>\n",
       "      <td>0.017352</td>\n",
       "      <td>0.014555</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>0.008612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0.001874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016479</td>\n",
       "      <td>0.211185</td>\n",
       "      <td>0.010750</td>\n",
       "      <td>0.008576</td>\n",
       "      <td>0.146185</td>\n",
       "      <td>0.032310</td>\n",
       "      <td>0.128221</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>0.021398</td>\n",
       "      <td>0.151268</td>\n",
       "      <td>0.030776</td>\n",
       "      <td>0.047266</td>\n",
       "      <td>0.142554</td>\n",
       "      <td>0.156530</td>\n",
       "      <td>0.038551</td>\n",
       "      <td>0.208226</td>\n",
       "      <td>0.103825</td>\n",
       "      <td>0.150889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>0.001808</td>\n",
       "      <td>0.016479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.452676</td>\n",
       "      <td>0.338406</td>\n",
       "      <td>0.017706</td>\n",
       "      <td>0.142410</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.150828</td>\n",
       "      <td>0.153130</td>\n",
       "      <td>0.166330</td>\n",
       "      <td>0.126733</td>\n",
       "      <td>0.137341</td>\n",
       "      <td>0.129574</td>\n",
       "      <td>0.294806</td>\n",
       "      <td>0.014877</td>\n",
       "      <td>0.154798</td>\n",
       "      <td>0.110354</td>\n",
       "      <td>0.341785</td>\n",
       "      <td>0.150448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>0.010517</td>\n",
       "      <td>0.211185</td>\n",
       "      <td>0.452676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140068</td>\n",
       "      <td>0.001762</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>0.044590</td>\n",
       "      <td>0.152166</td>\n",
       "      <td>0.091015</td>\n",
       "      <td>0.080537</td>\n",
       "      <td>0.133524</td>\n",
       "      <td>0.046885</td>\n",
       "      <td>0.021321</td>\n",
       "      <td>0.243187</td>\n",
       "      <td>0.111377</td>\n",
       "      <td>0.040292</td>\n",
       "      <td>0.097431</td>\n",
       "      <td>0.085570</td>\n",
       "      <td>0.164221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.002496</td>\n",
       "      <td>0.010750</td>\n",
       "      <td>0.338406</td>\n",
       "      <td>0.140068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011489</td>\n",
       "      <td>0.315338</td>\n",
       "      <td>0.030672</td>\n",
       "      <td>0.308411</td>\n",
       "      <td>0.328634</td>\n",
       "      <td>0.337331</td>\n",
       "      <td>0.296971</td>\n",
       "      <td>0.261923</td>\n",
       "      <td>0.271196</td>\n",
       "      <td>0.602054</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.338611</td>\n",
       "      <td>0.276798</td>\n",
       "      <td>0.738250</td>\n",
       "      <td>0.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.008576</td>\n",
       "      <td>0.017706</td>\n",
       "      <td>0.001762</td>\n",
       "      <td>0.011489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020538</td>\n",
       "      <td>0.387436</td>\n",
       "      <td>0.015198</td>\n",
       "      <td>0.024105</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>0.019158</td>\n",
       "      <td>0.055353</td>\n",
       "      <td>0.043870</td>\n",
       "      <td>0.002247</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.185490</td>\n",
       "      <td>0.090443</td>\n",
       "      <td>0.011942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>0.006739</td>\n",
       "      <td>0.146185</td>\n",
       "      <td>0.142410</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>0.315338</td>\n",
       "      <td>0.020538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109216</td>\n",
       "      <td>0.007141</td>\n",
       "      <td>0.117327</td>\n",
       "      <td>0.122318</td>\n",
       "      <td>0.011466</td>\n",
       "      <td>0.175059</td>\n",
       "      <td>0.180957</td>\n",
       "      <td>0.110842</td>\n",
       "      <td>0.165146</td>\n",
       "      <td>0.176793</td>\n",
       "      <td>0.480095</td>\n",
       "      <td>0.461214</td>\n",
       "      <td>0.038037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.032310</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.044590</td>\n",
       "      <td>0.030672</td>\n",
       "      <td>0.387436</td>\n",
       "      <td>0.109216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028416</td>\n",
       "      <td>0.036138</td>\n",
       "      <td>0.044944</td>\n",
       "      <td>0.026047</td>\n",
       "      <td>0.107417</td>\n",
       "      <td>0.098350</td>\n",
       "      <td>0.099721</td>\n",
       "      <td>0.138625</td>\n",
       "      <td>0.086140</td>\n",
       "      <td>0.288995</td>\n",
       "      <td>0.191851</td>\n",
       "      <td>0.047291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>0.015017</td>\n",
       "      <td>0.128221</td>\n",
       "      <td>0.150828</td>\n",
       "      <td>0.152166</td>\n",
       "      <td>0.308411</td>\n",
       "      <td>0.015198</td>\n",
       "      <td>0.007141</td>\n",
       "      <td>0.028416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185126</td>\n",
       "      <td>0.175985</td>\n",
       "      <td>0.285028</td>\n",
       "      <td>0.044669</td>\n",
       "      <td>0.055954</td>\n",
       "      <td>0.374416</td>\n",
       "      <td>0.157641</td>\n",
       "      <td>0.096726</td>\n",
       "      <td>0.022761</td>\n",
       "      <td>0.242369</td>\n",
       "      <td>0.289309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>0.153130</td>\n",
       "      <td>0.091015</td>\n",
       "      <td>0.328634</td>\n",
       "      <td>0.024105</td>\n",
       "      <td>0.117327</td>\n",
       "      <td>0.036138</td>\n",
       "      <td>0.185126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187757</td>\n",
       "      <td>0.195748</td>\n",
       "      <td>0.147186</td>\n",
       "      <td>0.136722</td>\n",
       "      <td>0.280980</td>\n",
       "      <td>0.013370</td>\n",
       "      <td>0.124847</td>\n",
       "      <td>0.150476</td>\n",
       "      <td>0.344426</td>\n",
       "      <td>0.195525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.021398</td>\n",
       "      <td>0.166330</td>\n",
       "      <td>0.080537</td>\n",
       "      <td>0.337331</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>0.122318</td>\n",
       "      <td>0.044944</td>\n",
       "      <td>0.175985</td>\n",
       "      <td>0.187757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240593</td>\n",
       "      <td>0.276652</td>\n",
       "      <td>0.288799</td>\n",
       "      <td>0.350277</td>\n",
       "      <td>0.038234</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.194977</td>\n",
       "      <td>0.362787</td>\n",
       "      <td>0.178134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>0.006825</td>\n",
       "      <td>0.151268</td>\n",
       "      <td>0.126733</td>\n",
       "      <td>0.133524</td>\n",
       "      <td>0.296971</td>\n",
       "      <td>0.019158</td>\n",
       "      <td>0.011466</td>\n",
       "      <td>0.026047</td>\n",
       "      <td>0.285028</td>\n",
       "      <td>0.195748</td>\n",
       "      <td>0.240593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161305</td>\n",
       "      <td>0.161316</td>\n",
       "      <td>0.425367</td>\n",
       "      <td>0.113600</td>\n",
       "      <td>0.104670</td>\n",
       "      <td>0.027965</td>\n",
       "      <td>0.261176</td>\n",
       "      <td>0.282492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.030776</td>\n",
       "      <td>0.137341</td>\n",
       "      <td>0.046885</td>\n",
       "      <td>0.261923</td>\n",
       "      <td>0.055353</td>\n",
       "      <td>0.175059</td>\n",
       "      <td>0.107417</td>\n",
       "      <td>0.044669</td>\n",
       "      <td>0.147186</td>\n",
       "      <td>0.276652</td>\n",
       "      <td>0.161305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434772</td>\n",
       "      <td>0.227116</td>\n",
       "      <td>0.096642</td>\n",
       "      <td>0.104234</td>\n",
       "      <td>0.360455</td>\n",
       "      <td>0.366224</td>\n",
       "      <td>0.036581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>0.008743</td>\n",
       "      <td>0.047266</td>\n",
       "      <td>0.129574</td>\n",
       "      <td>0.021321</td>\n",
       "      <td>0.271196</td>\n",
       "      <td>0.043870</td>\n",
       "      <td>0.180957</td>\n",
       "      <td>0.098350</td>\n",
       "      <td>0.055954</td>\n",
       "      <td>0.136722</td>\n",
       "      <td>0.288799</td>\n",
       "      <td>0.161316</td>\n",
       "      <td>0.434772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.231226</td>\n",
       "      <td>0.083700</td>\n",
       "      <td>0.111241</td>\n",
       "      <td>0.359244</td>\n",
       "      <td>0.369414</td>\n",
       "      <td>0.038492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.142554</td>\n",
       "      <td>0.294806</td>\n",
       "      <td>0.243187</td>\n",
       "      <td>0.602054</td>\n",
       "      <td>0.002247</td>\n",
       "      <td>0.110842</td>\n",
       "      <td>0.099721</td>\n",
       "      <td>0.374416</td>\n",
       "      <td>0.280980</td>\n",
       "      <td>0.350277</td>\n",
       "      <td>0.425367</td>\n",
       "      <td>0.227116</td>\n",
       "      <td>0.231226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.176733</td>\n",
       "      <td>0.227543</td>\n",
       "      <td>0.022933</td>\n",
       "      <td>0.468343</td>\n",
       "      <td>0.396713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.011754</td>\n",
       "      <td>0.156530</td>\n",
       "      <td>0.014877</td>\n",
       "      <td>0.111377</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.165146</td>\n",
       "      <td>0.138625</td>\n",
       "      <td>0.157641</td>\n",
       "      <td>0.013370</td>\n",
       "      <td>0.038234</td>\n",
       "      <td>0.113600</td>\n",
       "      <td>0.096642</td>\n",
       "      <td>0.083700</td>\n",
       "      <td>0.176733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062904</td>\n",
       "      <td>0.335136</td>\n",
       "      <td>0.157856</td>\n",
       "      <td>0.191825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>0.017352</td>\n",
       "      <td>0.038551</td>\n",
       "      <td>0.154798</td>\n",
       "      <td>0.040292</td>\n",
       "      <td>0.338611</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.176793</td>\n",
       "      <td>0.086140</td>\n",
       "      <td>0.096726</td>\n",
       "      <td>0.124847</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.104670</td>\n",
       "      <td>0.104234</td>\n",
       "      <td>0.111241</td>\n",
       "      <td>0.227543</td>\n",
       "      <td>0.062904</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199982</td>\n",
       "      <td>0.367126</td>\n",
       "      <td>0.107062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.014555</td>\n",
       "      <td>0.208226</td>\n",
       "      <td>0.110354</td>\n",
       "      <td>0.097431</td>\n",
       "      <td>0.276798</td>\n",
       "      <td>0.185490</td>\n",
       "      <td>0.480095</td>\n",
       "      <td>0.288995</td>\n",
       "      <td>0.022761</td>\n",
       "      <td>0.150476</td>\n",
       "      <td>0.194977</td>\n",
       "      <td>0.027965</td>\n",
       "      <td>0.360455</td>\n",
       "      <td>0.359244</td>\n",
       "      <td>0.022933</td>\n",
       "      <td>0.335136</td>\n",
       "      <td>0.199982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659506</td>\n",
       "      <td>0.163793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.006557</td>\n",
       "      <td>0.103825</td>\n",
       "      <td>0.341785</td>\n",
       "      <td>0.085570</td>\n",
       "      <td>0.738250</td>\n",
       "      <td>0.090443</td>\n",
       "      <td>0.461214</td>\n",
       "      <td>0.191851</td>\n",
       "      <td>0.242369</td>\n",
       "      <td>0.344426</td>\n",
       "      <td>0.362787</td>\n",
       "      <td>0.261176</td>\n",
       "      <td>0.366224</td>\n",
       "      <td>0.369414</td>\n",
       "      <td>0.468343</td>\n",
       "      <td>0.157856</td>\n",
       "      <td>0.367126</td>\n",
       "      <td>0.659506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>0.008612</td>\n",
       "      <td>0.150889</td>\n",
       "      <td>0.150448</td>\n",
       "      <td>0.164221</td>\n",
       "      <td>0.305263</td>\n",
       "      <td>0.011942</td>\n",
       "      <td>0.038037</td>\n",
       "      <td>0.047291</td>\n",
       "      <td>0.289309</td>\n",
       "      <td>0.195525</td>\n",
       "      <td>0.178134</td>\n",
       "      <td>0.282492</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.038492</td>\n",
       "      <td>0.396713</td>\n",
       "      <td>0.191825</td>\n",
       "      <td>0.107062</td>\n",
       "      <td>0.163793</td>\n",
       "      <td>0.228544</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    gender  SeniorCitizen   Partner  Dependents    tenure  \\\n",
       "gender            1.000000       0.001874  0.001808    0.010517  0.002496   \n",
       "SeniorCitizen     0.001874       1.000000  0.016479    0.211185  0.010750   \n",
       "Partner           0.001808       0.016479  1.000000    0.452676  0.338406   \n",
       "Dependents        0.010517       0.211185  0.452676    1.000000  0.140068   \n",
       "tenure            0.002496       0.010750  0.338406    0.140068  1.000000   \n",
       "PhoneService      0.006488       0.008576  0.017706    0.001762  0.011489   \n",
       "MultipleLines     0.006739       0.146185  0.142410    0.024991  0.315338   \n",
       "InternetService   0.000863       0.032310  0.000891    0.044590  0.030672   \n",
       "OnlineSecurity    0.015017       0.128221  0.150828    0.152166  0.308411   \n",
       "OnlineBackup      0.012057       0.013632  0.153130    0.091015  0.328634   \n",
       "DeviceProtection  0.000549       0.021398  0.166330    0.080537  0.337331   \n",
       "TechSupport       0.006825       0.151268  0.126733    0.133524  0.296971   \n",
       "StreamingTV       0.006421       0.030776  0.137341    0.046885  0.261923   \n",
       "StreamingMovies   0.008743       0.047266  0.129574    0.021321  0.271196   \n",
       "Contract          0.000126       0.142554  0.294806    0.243187  0.602054   \n",
       "PaperlessBilling  0.011754       0.156530  0.014877    0.111377  0.012154   \n",
       "PaymentMethod     0.017352       0.038551  0.154798    0.040292  0.338611   \n",
       "MonthlyCharges    0.014555       0.208226  0.110354    0.097431  0.276798   \n",
       "TotalCharges      0.006557       0.103825  0.341785    0.085570  0.738250   \n",
       "Churn             0.008612       0.150889  0.150448    0.164221  0.305263   \n",
       "\n",
       "                  PhoneService  MultipleLines  InternetService  \\\n",
       "gender                0.006488       0.006739         0.000863   \n",
       "SeniorCitizen         0.008576       0.146185         0.032310   \n",
       "Partner               0.017706       0.142410         0.000891   \n",
       "Dependents            0.001762       0.024991         0.044590   \n",
       "tenure                0.011489       0.315338         0.030672   \n",
       "PhoneService          1.000000       0.020538         0.387436   \n",
       "MultipleLines         0.020538       1.000000         0.109216   \n",
       "InternetService       0.387436       0.109216         1.000000   \n",
       "OnlineSecurity        0.015198       0.007141         0.028416   \n",
       "OnlineBackup          0.024105       0.117327         0.036138   \n",
       "DeviceProtection      0.003727       0.122318         0.044944   \n",
       "TechSupport           0.019158       0.011466         0.026047   \n",
       "StreamingTV           0.055353       0.175059         0.107417   \n",
       "StreamingMovies       0.043870       0.180957         0.098350   \n",
       "Contract              0.002247       0.110842         0.099721   \n",
       "PaperlessBilling      0.016505       0.165146         0.138625   \n",
       "PaymentMethod         0.004184       0.176793         0.086140   \n",
       "MonthlyCharges        0.185490       0.480095         0.288995   \n",
       "TotalCharges          0.090443       0.461214         0.191851   \n",
       "Churn                 0.011942       0.038037         0.047291   \n",
       "\n",
       "                  OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  \\\n",
       "gender                  0.015017      0.012057          0.000549     0.006825   \n",
       "SeniorCitizen           0.128221      0.013632          0.021398     0.151268   \n",
       "Partner                 0.150828      0.153130          0.166330     0.126733   \n",
       "Dependents              0.152166      0.091015          0.080537     0.133524   \n",
       "tenure                  0.308411      0.328634          0.337331     0.296971   \n",
       "PhoneService            0.015198      0.024105          0.003727     0.019158   \n",
       "MultipleLines           0.007141      0.117327          0.122318     0.011466   \n",
       "InternetService         0.028416      0.036138          0.044944     0.026047   \n",
       "OnlineSecurity          1.000000      0.185126          0.175985     0.285028   \n",
       "OnlineBackup            0.185126      1.000000          0.187757     0.195748   \n",
       "DeviceProtection        0.175985      0.187757          1.000000     0.240593   \n",
       "TechSupport             0.285028      0.195748          0.240593     1.000000   \n",
       "StreamingTV             0.044669      0.147186          0.276652     0.161305   \n",
       "StreamingMovies         0.055954      0.136722          0.288799     0.161316   \n",
       "Contract                0.374416      0.280980          0.350277     0.425367   \n",
       "PaperlessBilling        0.157641      0.013370          0.038234     0.113600   \n",
       "PaymentMethod           0.096726      0.124847          0.135750     0.104670   \n",
       "MonthlyCharges          0.022761      0.150476          0.194977     0.027965   \n",
       "TotalCharges            0.242369      0.344426          0.362787     0.261176   \n",
       "Churn                   0.289309      0.195525          0.178134     0.282492   \n",
       "\n",
       "                  StreamingTV  StreamingMovies  Contract  PaperlessBilling  \\\n",
       "gender               0.006421         0.008743  0.000126          0.011754   \n",
       "SeniorCitizen        0.030776         0.047266  0.142554          0.156530   \n",
       "Partner              0.137341         0.129574  0.294806          0.014877   \n",
       "Dependents           0.046885         0.021321  0.243187          0.111377   \n",
       "tenure               0.261923         0.271196  0.602054          0.012154   \n",
       "PhoneService         0.055353         0.043870  0.002247          0.016505   \n",
       "MultipleLines        0.175059         0.180957  0.110842          0.165146   \n",
       "InternetService      0.107417         0.098350  0.099721          0.138625   \n",
       "OnlineSecurity       0.044669         0.055954  0.374416          0.157641   \n",
       "OnlineBackup         0.147186         0.136722  0.280980          0.013370   \n",
       "DeviceProtection     0.276652         0.288799  0.350277          0.038234   \n",
       "TechSupport          0.161305         0.161316  0.425367          0.113600   \n",
       "StreamingTV          1.000000         0.434772  0.227116          0.096642   \n",
       "StreamingMovies      0.434772         1.000000  0.231226          0.083700   \n",
       "Contract             0.227116         0.231226  1.000000          0.176733   \n",
       "PaperlessBilling     0.096642         0.083700  0.176733          1.000000   \n",
       "PaymentMethod        0.104234         0.111241  0.227543          0.062904   \n",
       "MonthlyCharges       0.360455         0.359244  0.022933          0.335136   \n",
       "TotalCharges         0.366224         0.369414  0.468343          0.157856   \n",
       "Churn                0.036581         0.038492  0.396713          0.191825   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges  TotalCharges     Churn  \n",
       "gender                 0.017352        0.014555      0.006557  0.008612  \n",
       "SeniorCitizen          0.038551        0.208226      0.103825  0.150889  \n",
       "Partner                0.154798        0.110354      0.341785  0.150448  \n",
       "Dependents             0.040292        0.097431      0.085570  0.164221  \n",
       "tenure                 0.338611        0.276798      0.738250  0.305263  \n",
       "PhoneService           0.004184        0.185490      0.090443  0.011942  \n",
       "MultipleLines          0.176793        0.480095      0.461214  0.038037  \n",
       "InternetService        0.086140        0.288995      0.191851  0.047291  \n",
       "OnlineSecurity         0.096726        0.022761      0.242369  0.289309  \n",
       "OnlineBackup           0.124847        0.150476      0.344426  0.195525  \n",
       "DeviceProtection       0.135750        0.194977      0.362787  0.178134  \n",
       "TechSupport            0.104670        0.027965      0.261176  0.282492  \n",
       "StreamingTV            0.104234        0.360455      0.366224  0.036581  \n",
       "StreamingMovies        0.111241        0.359244      0.369414  0.038492  \n",
       "Contract               0.227543        0.022933      0.468343  0.396713  \n",
       "PaperlessBilling       0.062904        0.335136      0.157856  0.191825  \n",
       "PaymentMethod          1.000000        0.199982      0.367126  0.107062  \n",
       "MonthlyCharges         0.199982        1.000000      0.659506  0.163793  \n",
       "TotalCharges           0.367126        0.659506      1.000000  0.228544  \n",
       "Churn                  0.107062        0.163793      0.228544  1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the feature corr matrix in absolute values\n",
    "corr_mat = churn_df.corr().abs()\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display hitmap using SNS\n",
    "#f, corr_plot = plt.subplot(figsize=(10,10))\n",
    "plt.figure(figsize=(15,10))\n",
    "corr_plot = sns.heatmap(corr_mat,cmap=\"RdYlGn\",linewidths=.5)\n",
    "corr_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using lambda - example\n",
    "#churn_df['MonthlyCharges'].apply(lambda x:x*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4906: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
     "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>features</th>\n",
       "      <th>chi_score</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Contract</td>\n",
       "      <td>1115.780167</td>\n",
       "      <td>1.227941e-244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>551.611529</td>\n",
       "      <td>5.615882e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>523.303866</td>\n",
       "      <td>8.086236e-116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>230.086520</td>\n",
       "      <td>5.707869e-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>191.303140</td>\n",
       "      <td>1.651539e-43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>134.351545</td>\n",
       "      <td>4.576959e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>133.036443</td>\n",
       "      <td>8.876729e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>105.680863</td>\n",
       "      <td>8.661987e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Partner</td>\n",
       "      <td>82.412083</td>\n",
       "      <td>1.104764e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>58.492250</td>\n",
       "      <td>2.040933e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>9.821028</td>\n",
       "      <td>1.725279e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>9.746921</td>\n",
       "      <td>1.796233e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>8.235399</td>\n",
       "      <td>4.108113e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>7.490203</td>\n",
       "      <td>6.203556e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.258699</td>\n",
       "      <td>6.110156e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>0.097261</td>\n",
       "      <td>7.551421e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features    chi_score        p-value\n",
       "13          Contract  1115.780167  1.227941e-244\n",
       "7     OnlineSecurity   551.611529  5.615882e-122\n",
       "10       TechSupport   523.303866  8.086236e-116\n",
       "8       OnlineBackup   230.086520   5.707869e-52\n",
       "9   DeviceProtection   191.303140   1.651539e-43\n",
       "1      SeniorCitizen   134.351545   4.576959e-31\n",
       "3         Dependents   133.036443   8.876729e-31\n",
       "14  PaperlessBilling   105.680863   8.661987e-25\n",
       "2            Partner    82.412083   1.104764e-19\n",
       "15     PaymentMethod    58.492250   2.040933e-14\n",
       "6    InternetService     9.821028   1.725279e-03\n",
       "5      MultipleLines     9.746921   1.796233e-03\n",
       "12   StreamingMovies     8.235399   4.108113e-03\n",
       "11       StreamingTV     7.490203   6.203556e-03\n",
       "0             gender     0.258699   6.110156e-01\n",
       "4       PhoneService     0.097261   7.551421e-01"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run feature selection for the categorical features with the outcome of Churn(categorical as well) chi will run only on poisitive values, \n",
    "\n",
    "from sklearn.feature_selection import chi2, f_classif\n",
    "\n",
    "x_cat = churn_df.select_dtypes(int)\n",
    "y = churn_df['Churn']\n",
    "x_cat.drop(columns='Churn',inplace=True)\n",
    "\n",
    "chi_arr = chi2(x_cat,y)\n",
    "\n",
    "x_cat.columns\n",
    "\n",
    "chi2_matrix = pd.DataFrame({'features':x_cat.columns,'chi_score':chi_arr[0],'p-value':chi_arr[1]})\n",
    "\n",
    "chi2_matrix.sort_values(by='chi_score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#join the 2 matrices - \n",
    "corr_df_churn = corr_df_churn.join(chi2_matrix.set_index('features'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>chi_score</th>\n",
       "      <th>Correlation with Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  chi_score  Correlation with Churn\n",
       "Contract               16.0                    16.0\n",
       "OnlineSecurity         15.0                    15.0\n",
       "TechSupport            14.0                    14.0\n",
       "OnlineBackup           13.0                    13.0\n",
       "PaperlessBilling        9.0                    12.0\n",
       "DeviceProtection       12.0                    11.0\n",
       "Dependents             10.0                    10.0\n",
       "SeniorCitizen          11.0                     9.0\n",
       "Partner                 8.0                     8.0\n",
       "PaymentMethod           7.0                     7.0\n",
       "InternetService         6.0                     6.0\n",
       "StreamingMovies         4.0                     5.0\n",
       "MultipleLines           5.0                     4.0\n",
       "StreamingTV             3.0                     3.0\n",
       "PhoneService            1.0                     2.0\n",
       "gender                  2.0                     1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#display relevant columns\n",
    "corr_df_churn[{'Correlation with Churn','chi_score'}]\n",
    "\n",
    "#create new DF with relevant columns\n",
    "corr_rank = corr_df_churn[{'Correlation with Churn','chi_score'}]\n",
    "\n",
    "#rank the correlation\n",
    "corr_rank = corr_rank.dropna().rank()\n",
    "#display the ranks\n",
    "corr_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation rank - Chi vs Corr')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the correlation ranks\n",
    "_, axs = plt.subplots(figsize=(15,15))\n",
    "\n",
    "plt.barh(corr_rank.index,corr_rank['chi_score'],color='b',height=1)\n",
    "plt.barh(corr_rank.index,corr_rank['Correlation with Churn'],color='g',height=0.5)\n",
    "plt.legend(corr_rank.columns)\n",
    "plt.title('Correlation rank - Chi vs Corr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1732: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n",
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:723: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value, self.name)\n"
     ]
    },
    {
     "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>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>streaming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7021</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7025</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7028</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7034</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7035</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1559 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      StreamingTV  StreamingMovies  streaming\n",
       "6               2                0          2\n",
       "19              0                2          2\n",
       "20              0                2          2\n",
       "35              2                0          2\n",
       "41              2                0          2\n",
       "...           ...              ...        ...\n",
       "7021            2                0          2\n",
       "7025            0                2          2\n",
       "7028            2                0          2\n",
       "7034            2                0          2\n",
       "7035            2                0          2\n",
       "\n",
       "[1559 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create feature combination streaming tv + streaming movies\n",
    "#['No - 0' 'No internet service - 1' 'Yes - 2']\n",
    "comb_feat = churn_df[{'StreamingTV','StreamingMovies'}]\n",
    "\n",
    "comb_feat.insert(2,'streaming',churn_df['StreamingMovies'])\n",
    "\n",
    "#replace the values on Yes for each of the features to yes for \"streaming\"\n",
    "comb_feat.loc[(comb_feat[churn_df['StreamingMovies'] != churn_df['StreamingTV']]).index,'streaming'] = 2\n",
    "#check the results of the replace\n",
    "comb_feat[churn_df['StreamingMovies'] != churn_df['StreamingTV']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.010104058589715912"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check combined feature 'streaming' corr with predicted Churn\n",
    "comb_feat['streaming'].corr(y)\n",
    "#very low correlation - will not be considered in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop YearlyCharges since they correlate with monthly charges and had missing values that were added sintethically\n",
    "#run RFM for each customer - not feasible in this dataset\n",
    "#drop features with chi score less than 58 - since less then 58 are very low in comparisson to others\n",
    "#run logistic regrssion with confusion matrix and full metrics including AUC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.695233</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.726816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.124908</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.268742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.251156</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.371440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.381375</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.618121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.251156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.127958</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.478224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.301799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.563027</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.734253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.981249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.103590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.078968</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.521683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  Partner  Dependents    tenure  OnlineSecurity  \\\n",
       "0                 0        1           0 -1.695233               0   \n",
       "1                 0        0           0  0.124908               2   \n",
       "2                 0        0           0 -1.251156               2   \n",
       "3                 0        0           0  0.381375               2   \n",
       "4                 0        0           0 -1.251156               0   \n",
       "...             ...      ...         ...       ...             ...   \n",
       "7038              0        1           1 -0.127958               2   \n",
       "7039              0        1           1  5.199338               0   \n",
       "7040              0        1           1 -0.563027               2   \n",
       "7041              1        1           0 -0.981249               0   \n",
       "7042              0        0           0  1.078968               2   \n",
       "\n",
       "      OnlineBackup  DeviceProtection  TechSupport  Contract  PaperlessBilling  \\\n",
       "0                2                 0            0         0                 1   \n",
       "1                0                 2            0         1                 0   \n",
       "2                2                 0            0         0                 1   \n",
       "3                0                 2            2         1                 0   \n",
       "4                0                 0            0         0                 1   \n",
       "...            ...               ...          ...       ...               ...   \n",
       "7038             0                 2            2         1                 1   \n",
       "7039             2                 2            0         1                 1   \n",
       "7040             0                 0            0         0                 1   \n",
       "7041             0                 0            0         0                 1   \n",
       "7042             0                 2            2         2                 1   \n",
       "\n",
       "      PaymentMethod  MonthlyCharges  \n",
       "0                 2       -0.726816  \n",
       "1                 3       -0.268742  \n",
       "2                 3       -0.371440  \n",
       "3                 0       -0.618121  \n",
       "4                 2        0.018867  \n",
       "...             ...             ...  \n",
       "7038              3        0.478224  \n",
       "7039              1        1.301799  \n",
       "7040              2       -0.734253  \n",
       "7041              3        0.103590  \n",
       "7042              0        1.521683  \n",
       "\n",
       "[7043 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the Data frames for model training \n",
    "x_model = churn_df.drop(columns='Churn')\n",
    "y_model = pd.DataFrame(churn_df['Churn'])\n",
    "\n",
    "x_model.drop(columns={'InternetService','MultipleLines','StreamingMovies','StreamingTV','gender','PhoneService','TotalCharges'},inplace=True)\n",
    "x_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the train and hold out set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_model,y_model,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since the predicted variable is not uniform , remove samples using ClusterCentroids and feed them to the model\n",
    "#from imblearn.under_sampling import ClusterCentroids\n",
    "\n",
    "#cc = ClusterCentroids()\n",
    "\n",
    "#x_train_ada, y_train_ada = cc.fit_resample(X_train,y_train)\n",
    "\n",
    "#print(y_train.value_counts())\n",
    "#print(y_train_ada.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Pikuah\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "#train the model using logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "\n",
    "#create the logistic regression class with solver=liblinear - the data set is not so big. \n",
    "log_reg = LogisticRegression(solver='liblinear',C=100,penalty='l1',class_weight='balanced')\n",
    "#fit the model\n",
    "log_reg.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pd.DataFrame(log_reg.predict(X_test))\n",
    "\n",
    "#parameters = {\n",
    "#    'penalty' : ['l1','l2'], \n",
    "#    'C'       : np.logspace(-3,3,7),\n",
    "#    'solver'  : ['newton-cg', 'lbfgs', 'liblinear'],\n",
    "#}\n",
    "\n",
    "#cv = GridSearchCV(log_reg,param_grid = parameters, scoring='accuracy',cv=10)\n",
    "#cv.fit(X_train,y_train)\n",
    "#cv.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.71      0.80      1053\n",
      "           1       0.48      0.81      0.61       356\n",
      "\n",
      "    accuracy                           0.73      1409\n",
      "   macro avg       0.70      0.76      0.70      1409\n",
      "weighted avg       0.81      0.73      0.75      1409\n",
      "\n",
      "---------------------------------------------------\n",
      "F1 Score:  0.61\n",
      "---------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(\"---------------------------------------------------\")\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred), 2))\n",
    "print(\"---------------------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the coeff dataframe\n",
    "coef_df = pd.DataFrame(X_train.columns ,columns=['Features'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Features</th>\n",
       "      <th>coef value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tenure</td>\n",
       "      <td>0.817144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>0.803055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Contract</td>\n",
       "      <td>0.772417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>0.397832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>0.304413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>0.270852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>0.245324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>0.196663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>0.129499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partner</td>\n",
       "      <td>0.071887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>0.068777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>0.031695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features  coef value\n",
       "3             tenure    0.817144\n",
       "11    MonthlyCharges    0.803055\n",
       "8           Contract    0.772417\n",
       "9   PaperlessBilling    0.397832\n",
       "4     OnlineSecurity    0.304413\n",
       "0      SeniorCitizen    0.270852\n",
       "7        TechSupport    0.245324\n",
       "2         Dependents    0.196663\n",
       "5       OnlineBackup    0.129499\n",
       "1            Partner    0.071887\n",
       "6   DeviceProtection    0.068777\n",
       "10     PaymentMethod    0.031695"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the coef DFrame\n",
    "coef_df.insert(1,column='coef value',value=log_reg.coef_[0])\n",
    "coef_df['coef value'] = coef_df['coef value'].abs()\n",
    "coef_df.sort_values(by='coef value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 69.0, 'Ground Truth')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAJuCAYAAABhWwwrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/V0lEQVR4nO3debwcZZXw8d8h7PsOgbALCOiAgDCIKIKIiCyKzAszsigIMqDgxsi4O6Ko4+joiBBBJyiCqAgIbhgBEWVTQQ1rFIiBsA4BgbAk97x/VF3S3Nx7u2+6u+r27d/XT32qu+qpqtMd2pyceup5IjORJElS9y1RdwCSJEn9wsRLkiSpIiZekiRJFTHxkiRJqoiJlyRJUkVMvCRJkiqyZN0BjGfPPfxXx9qQavC5HT5SdwhS3/rQPedGlder8u/apdbctNLPNhwrXpIkSRWx4iVJkuozsKDuCCplxUuSJKkiVrwkSVJ9cqDuCCplxUuSJKkiJl6SJEkV8VajJEmqz4C3GiVJktQFVrwkSVJt0s71kiRJ6gYrXpIkqT728ZIkSVI3WPGSJEn1sY+XJEmSusGKlyRJqo+TZEuSJKkbrHhJkqT62MdLkiRJ3WDFS5Ik1cdxvCRJktQNVrwkSVJtnKtRkiRJXWHiJUmSVBFvNUqSpPrYuV6SJEndYMVLkiTVx871kiRJ6gYrXpIkqT5Oki1JkqRusOIlSZLqYx8vSZIkdYMVL0mSVB/H8ZIkSVI3WPGSJEn1sY+XJElSf4mILSPipobl8Yg4KSJWj4jLI+LOcr1awzGnRMTMiLg9IvZu5TomXpIkqT4DA9Uto8jM2zNzu8zcDtgBeAr4IfBBYHpmbg5ML98TEVsDhwDbAK8HTo+ISc0+romXJEnSC+0J/CUz7wEOAKaV26cBB5avDwDOz8xnMvMuYCawU7MT28dLkiTVJnNcjlx/CHBe+XqdzJwDkJlzImLtcvv6wLUNx8wut43KipckSeoLEXFMRNzYsBwzTJulgf2B7zU73TDbslkMVrwkSVJfyMypwNQmzfYBfp+ZD5TvH4iIyWW1azLwYLl9NrBBw3FTgPuaxWDFS5Ik1ScHqltacygLbzMCXAIcUb4+Ari4YfshEbFMRGwCbA5c3+zkVrwkSZKAiFge2As4tmHzacAFEXEUMAs4GCAzZ0TEBcAtwHzg+Gyhw5qJlyRJqs84mjIoM58C1hiy7RGKpxyHa38qcOpYruGtRkmSpIpY8ZIkSfVxyiBJkiR1gxUvSZJUn4FxOYBq11jxkiRJqogVL0mSVB/7eEmSJKkbrHhJkqT6jKNxvKpgxUuSJKkiVrwkSVJ97OMlSZKkbrDiJUmS6mMfL0mSJHWDiZckSVJFvNUoSZLq461GSZIkdYMVL0mSVJtMJ8mWJElSF1jxkiRJ9bGPlyRJkrrBipckSaqPUwZJkiSpG6x4SZKk+tjHS5IkSd1gxUuSJNXHPl6SJEnqBitekiSpPvbxkiRJUjdY8ZIkSfWxj5ckSZK6wcRLkiSpIt5qlCRJ9bFzvSRJkrrBipckSaqPFS9JkiR1gxUvSZJUH4eTkCRJUjdY8ZIkSfWxj5ckSZK6wYqXJEmqj328JEmS1A1WvCRJUn3s4yVJkqRusOIlSZLqYx8vSZIkdYMVL0mSVB/7eEmSJKkbTLwkSZIq4q1GSZJUH281SpIkqRuseEmSpPpk1h1Bpax4SZIkVcSKlyRJqo99vCRJktQNVrwkSVJ9rHhJkiSpG6x4SZKk+jhJtiRJkrrBipckSaqPfbwkSZLUDVa8JElSfRy5XpIkSd1gxUuSJNXHPl6SJEnqBitekiSpPla8JEmS1A0mXpIkSRXp+VuNERHAGsDymTmr7ngkSdIYOGVQb4iI7SPiQuAx4AHgr0P2rxYRZ0bEGRGxdC1BSpIkNejJildEHAacBSw1UpvMfDQiNgH2BH4EXFZReJIkqUU54ACq41pEbAV8nSLp+jKwI/DwCM3PAQI4oJroJEmSRtZziRfwXmBp4KuZeVJm/h5YMELbX5brXSqJTJIkjc3AQHVLExGxakR8PyJui4hbI2KXiFg9Ii6PiDvL9WoN7U+JiJkRcXtE7N3Kx+3FxGsPIIHPNmuYmfcBTwEbdjsoSZLU8/4b+GlmvhjYFrgV+CAwPTM3B6aX74mIrYFDgG2A1wOnR8SkZhfoxcRrPeDJzJzdYvt5wHJdjEeSJC2uHKhuGUVErAy8CjgbIDOfzcy5FN2VppXNpgEHlq8PAM7PzGcy8y5gJrBTs4/bi4nXM8DS5TASo4qI5YBVKZ58lCRJGsmmwEPANyPiDxFxVkSsAKyTmXMAyvXaZfv1gb81HD+73DaqXky87qboWL95C23fAEwCbulmQJIkaTENZGVLRBwTETc2LMc0RLIksD3wtcx8GfAk5W3FEQxXAGr6iGYvJl4/pfiwJ47WKCLWAD5H8SU4lIQkSX0uM6dm5o4Ny9SG3bOB2Zl5Xfn++xSJ2AMRMRmgXD/Y0H6DhuOnAPc1i6EXE68vAk8A74yIj0XESo07I2K5iPhn4EZgE+AR4Izqw5QkSU2Nk6caM/N+4G8RsWW5aU+KO2aXAEeU244ALi5fXwIcEhHLlOOGbg5c3+zj9twAqpn5QJlYfR/4KHAKZQIZETMo7tEuTVEVewY4NDMfrylcSZLUO94FnFvOePNX4G0UOcYFEXEUMAs4GCAzZ0TEBRTJ2Xzg+MwcaXir5/Vc4gWQmZdGxKuA/6EYQHXQVg2v/wC8MzNvqDQ4SZLUuhbG16pKZt7EC/OKQXuO0P5U4NSxXKMnEy+AzLwe2Cki/gF4JcUwE5OA+4FrMvPGOuOTJEkaqmcTr0GZ+Ufgj3XHIUmSFkM6V6MkSZK6wMRLkiSpIj13qzEiftm81SIyM4ftGCdJkmo0jjrXV6HnEi9g9xbbDd40DloYSVaSJKnbejHx+kST/asAOwO7UAye+jWg6bgakiSpBgP9VRvpucQrM5slXgBExB7AhcDWmfmW7kalqt11z2ze/9HPPP9+9n1zOOHowzjs/70JgG9+5/t84atnc/Vl57Paqqvw3HPP8YnPfYUZt91JLBF88MR3stP2/1BX+FLPWmny6uz/xeNYca1VyIHkD9/5JTd882esvdWG7PPpt7P08svy2OyHuOjE03n2iXlsc+Ar2OWYNz5//NpbbcDZ+36YB265p8ZPIdWn5xKvVmXmLyPiROAbEXF0Zp5Vd0zqnE02msIPpn0VgAULFrDHgYex56tfAcCcBx7itzf8gcnrrP18++9f8lMAfvitr/HIo3M57n0f4fyz/psllvD5EmkscsEA0z91Lvf/+W6WXmFZ3n7pp7jr139m388ezfRTv8Os625j2396Nbscuy9XfeH7zLjoN8y46DcArLXlBhx81ntNuvRC2V99vCb63zrfpbjNeHTdgah7rr3xJjZYfzLrrbsOAJ/78pm891+PIhrmjf/L3bPYecftAFhjtVVZacUVmHHbnTVEK/W2Jx6cy/1/vhuAZ598mkdm3sdK66zGGpuux6zrbgPgr1f/iS332WmRY7fZfxduueQ3VYYrjTsTOvHKzKeBJ3nhVEKaYH4y/Sre8NpXA3DF1dey9lpr8uLNN31Bmy1ftAlXXP1b5s9fwOz77ueW22dy/wMP1RGuNGGsMmVN1tlmI+696S88dMff2GKvHQDYat+dWXny6ou033q/f2TGxb+tOkyNdwNZ3TIOTOjEKyLWp+hsH83aqjc999xzXPnr63jdHrsx7+mnmXrO+Zxw9GGLtHvTvnuzzlpr8v+Oejef/e8z2e4lWzFpyUk1RCxNDEstvwwHnXESl3/yWzz7xDwu/cBUdjh8L95+6adYZoXlWPDc/Be0X2+7zXhu3rM8dMfsmiKWxocJ28crIpYDTi/f/mkMxx0DHANw+hc+xdGHH9qF6NQpV197I1ttsRlrrr4ad/zlLu69734OOuJfAXjgoYc5+O3v4vyvf4k111idfzvx2OeP+5dj38tGU9arK2yppy2x5CQOOuMk/nzRNdz+02Ja3Ef+MofzDjsNgNU3WZcX7bHdC47Zer9dmOFtRg0jHcdrfIuIjzZpsiywAbA3sAbFGF5fbfX8mTkVmArw3MN/HR91SY3ox5dfyRv22h2ALTbbhF9ddv7z+1530BF89+wvs9qqqzDv6afJhOWXW5bfXP97lpw0ic022aimqKXetu/n3sEjM+/l+rN+8vy25ddYmaceeRwi2PVdB/L7c6cvPCCCrfbdmW8d/MkaopXGl55LvICP09qAqAEMAKdm5ne6GpFqMe/pp/ntDX/gYye/u2nb/3v0MY59z4eIJZZgnbXW4DMffX8FEUoTz5Qdt+AfDtqNB26dxdE//jQAV3z+u6y+8brscPheANz+0xu4+YKrnj9mw51fzN/n/B9z/2a/Sg1jnPS9qkpkj80KHhFXMnriNR94FLgZuCAzF/vRNSteUj0+t8NH6g5B6lsfuufcSvtFP3nq4ZX9XbvCh86pvc93z1W8MnP3umOQJEkd4jhekiRJ6oaeq3hFxABF360XZ+bMuuORJElt6LM+Xj2XeAHzgOdMuiRJUq/pxVuNs4Gl6g5CkiRprHox8boMWDYiXl13IJIkqU0DA9Ut40AvJl6fAR4CvhYRk+sORpIkqVW92MdrK+BDwBeBWyLiW8A1wIPAgpEOysxfVROeJElqmZ3rx5eIOByYl5nfKzddyQsHUD2+XEaT9MBnlSRJE1svJCP/C8wBvtewbawjz9Y+Uq0kSRpGnw2g2guJFzQkTpnZi/3SJEmSeibxkiRJE1Gf9fGyeiRJklQRK16SJKk2OU7G16qKFS9JkqSK9ErFa52IGHGMrhZkZvbKZ5UkqX/0WR+vXkpGHBJCkiT1tF5JvJ4EvlB3EJIkqcOseI1LT2TmJ+oOQpIkqR29knhJkqSJqM9GrvepRkmSpIqYeEmSJFXEW42SJKk+fda53oqXJElSRcZ9xSszTQ4lSZqg0oqXJEmSumHcV7wkSdIEZsVLkiRJ3WDFS5Ik1WfAAVQlSZLUBVa8JElSfezjJUmSpG6w4iVJkupjxUuSJEndYMVLkiTVJtOKlyRJkrrAipckSaqPfbwkSZLUDSZekiRJFfFWoyRJqo+3GiVJktQNVrwkSVJt0oqXJEmSusGKlyRJqo8VL0mSJHWDFS9JklSfgboDqJYVL0mSpIpY8ZIkSbXxqUZJkiR1hRUvSZJUHytekiRJ6gYrXpIkqT4+1ShJkqRusOIlSZJq41ONkiRJ6goTL0mSJCAi7o6IP0XETRFxY7lt9Yi4PCLuLNerNbQ/JSJmRsTtEbF3K9cw8ZIkSfUZqHBpzWsyc7vM3LF8/0FgemZuDkwv3xMRWwOHANsArwdOj4hJzU5u4iVJkjSyA4Bp5etpwIEN28/PzGcy8y5gJrBTs5PZuV6SJNVmnHWuT+DnEZHAmZk5FVgnM+cAZOaciFi7bLs+cG3DsbPLbaMy8ZIkSX0hIo4BjmnYNLVMrgbtmpn3lcnV5RFx22inG2Zb0yzSxEuSJNWnwgFUyyRr6ij77yvXD0bEDyluHT4QEZPLatdk4MGy+Wxgg4bDpwD3NYvBPl6SJKnvRcQKEbHS4GvgdcCfgUuAI8pmRwAXl68vAQ6JiGUiYhNgc+D6Ztex4iVJkmqT42fKoHWAH0YEFPnRdzLzpxFxA3BBRBwFzAIOBsjMGRFxAXALMB84PjMXNLtIRxOviFgC2AxYDVi2lWMy81edjEGSJGmsMvOvwLbDbH8E2HOEY04FTh3LdTqSeEXE7sD7gT2AZcZwaHYqBkmS1IPGT8WrEm0nPRHxMeCjg2/bPZ8kSdJE1VbiVQ6P/7GGTbOAq4B7gWfaObckSZr4xlEfr0q0W/E6oVwPUNxq/O/MHFcjoUmSJI0X7SZeO1H00/puZn6p/XAkSVJf6bOKV7vjeK1Srn/abiCSJEkTXbsVr/spRm19tgOxSJKkPtNvfbzarXhdU663aTcQSZKkia7dxOsrFH28joiI5ToQjyRJ0oTVVuKVmddSjOG1AXBhRKzS5BBJkqTn5UB1y3jQ7jherwJ+DZwHHArcGRHnANcCD9PCswpOGSRJkvpFu53rr6S41Ui5XhN4zxiOd8ogSZL62HipRFWlE0nP0GmCnDZIkiRpGO0mXp/oSBSSJKk/ZX/Va9pKvDLTxEuSJKlF9q+SJEm16bc+Xu2O4yVJkqQWda3iFRFLAisBf8/M+d26jiRJ6l050F99vDpW8YqIpSPi7RFxaUQ8BDxDMZbXMxHxYLn9yIhYulPXlCRJ6iUdqXiVA6meQzGCPSw6pMSawD7l8rGIODwzr+7EtSVJUu+yj9cYRcRewM8pkq4olyeAmykm0b65fD+4byPg8oh4bbvXliRJ6iVtJV4RsTLwHWBpiqTqMmA3YJXMfFlm7lauVy63X1oeujRwXnm8JEnqU5lR2TIetFvx+ldgDYqpfz6Smftl5jWZmUMbltv3Bz5cblodOK7N60uSJPWMdhOvfcv1NZl5aisHZOanKSbWDmC/Nq8vSZJ6WA5Ut4wH7SZeW1BUuy4Y43GD7bdo8/qSJEk9o93Ea5Vyff8Yj3ugXNvHS5Ik9Y12h5N4FFgb2HCMx00p13PbvL4kSephDqA6NrdS9NX651YPiIgA/oXiFuUtbV5fkiSpZ7SbeP2oXL8sIr7Q4jGfBbYvX1/c5vUlSVIPy6xuGQ/aTbzOAOaUr0+KiF9HxAERsUJjo4hYISL2j4hfAe8rN88BprZ5fUmSpJ7RVh+vzJwXEQcB04FlgV2AC4GMiPuBJ4EVgHVZOI1QAPOAgzJzXjvXlyRJvc0+XmOUmdcCu7Kwv1eU510PeFG5XqJh3wxg18y8rt1rS5Ik9ZKOTJKdmTdFxEuBNwJvBnYCJgMrAX+nuK14PfAD4LLhRraXJEn9p98qXh1JvADKZOpHLOxwL0mSpAYdS7wkSZLGqt/ugbXdx0uSJEmtseIlSZJqYx+vYUTERwdfZ+Ynh9u+uBrPJ0mSNJG1WvH6OMUUPwCfHGH74jLxkiSpT2Va8RpJMHyS1c431mdd6iRJUj9rNfF6zRi3S5IkNZUDdUdQrZYSr8y8aizbJUmStCiHk5AkSapIW8NJRMSG5csHM/PpMRy3DLAOQGbOaicGSZLUuwb6rHN9uxWvu4G/Aq8b43G7NxwrSZLUFzoxgGo7qWp/pbmSJOkF+m04Cft4SZIkVaSuKYNWLddP1XR9SZI0DvTblEF1VbzeXK7/VtP1JUmSKtdyxSsiDgAOGGH3uyPiwGanAFYAtgM2oxi1/upWry9Jkiae7LM5bMZyq3E74EgWneYnGPsI9kFxm/G/xnicJElSz1qcPl7D3Ywdyw3ax4BfAp/MzNsX4/qSJGmC6Lc+XmNJvL4E/G/D+6AYhyuBY4HLmxw/ADyRmY+O4ZqSJEkTRsuJV2Y+RlGtel5EQJGAPZiZ93Q2NEmSNNH128j17Q4nsUm5frDdQCRJkia6thIvq1ySJKkd/TZyfdsDqEbEbhS3G+/KzKbjcpUTa28MDGTmr9u9viRJUq9oK/GKiD2AX1B0sN+R1gZEXQ24EsiIeFVmXtNODJIkqXf12zhe7Y5cPzgC/U2Z+YdWDsjMm4HflW8PbvP6kiRJPaPdxGsXimrXz8Z43M8obk++os3rS5Ik9Yx2+3htVq5vHeNxgwOnbjZqK0mSNKH123AS7Va8li/XT43xuHnleqU2ry9JktQz2q14zQXWANYe43GD7f/e5vUlSVIP67fhJNqteN1drsc6Sfbu5bqVpyAlSZImhHYTrysoOskfGBFbt3JARLwEeBNFp/xftnl9SZLUwzKrW8aDdhOvs4AFwCTgsmbJV0RsA1xSth8oj5ckSeoL7U4ZdGdE/A9wIrAh8LuIOBf4EcWTjk8AKwJbAfsD/wwsTVHtOiMzb2nn+pIkqbf121ONbU8ZBLwf2BTYD1gGeFu5DGfw270EOKkD15YkSeoZbSdembkAOCAiTgZOBlYfpfn/Aadl5n+2e90qLLfebnWHIPWlj03eve4QJFWk355q7ETFC4DM/FxEfBXYB3glMAVYGXgcmA1cDfwkM8c65pckSdKE0LHECyAznwS+Xy6SJEmj6rc+Xu0+1ShJkjRhRMSkiPhDRFxavl89Ii6PiDvL9WoNbU+JiJkRcXtE7N3K+U28JElSbbLCpUUn8sI5qD8ITM/MzYHp5XvKIbQOAbYBXg+cHhGTmp3cxEuSJAmIiCnAvrxwnNEDgGnl62nAgQ3bz8/MZzLzLmAmsFOza7TUxysiBkeYz8zcc5jti+sF55MkSf1lnPXx+hLFCA0rNWxbJzPnAGTmnIgYnG96feDahnazy22jarVz/e4MX6UbaXsroo1jJUmSxiQijgGOadg0NTOnlvveCDyYmb+LiN1bOd0w25rmNWN5qnGkRGlcpaqSJKl3VDmOV5lkTR1h967A/hHxBmBZYOWI+DbwQERMLqtdk4EHy/azgQ0ajp8C3Ncshpb6eGXmEuUyaYTti7s07YQmSZLUbZl5SmZOycyNKTrN/zIz30ox284RZbMjgIvL15cAh0TEMhGxCbA5cH2z63R0HC9JkqQJ5jTggog4CpgFHAyQmTMi4gLgFmA+cHw5m8+oTLwkSVJtBuoOYBiZeSVwZfn6EWDYBwEz81Tg1LGc2+EkJEmSKmLFS5Ik1Sb77Bm9VsfxOrxbAWTmOd06tyRJ0njSasXrf+nOmFsJmHhJktSnBvpsRM+xjuMlSZKkxdRq4vW2JvsPYOHcRbdSTCI5E3gSWAF4EbAHsDVFlesiivEvJElSHxvos7pOS4lXZk4baV9EfIAi6bofODozfzxK230oJp48ELgmM/9rLMFKkiT1sraGk4iIfwQ+Q1HZevVoSRdAZv6EYn7Hp4DPRkTTWbwlSdLElURly3jQ7jheJ5TnODsz72zlgLLdN4BJwLvavL4kSVLPaDfx2pWiz1bTuYmGuK5cv7LN60uSpB42UOEyHrSbeK27mOcZbL9Om9eXJEnqGe0mXnPL9a5jPG6w0vV4m9eXJEk9zD5eY3MDxfheR0bEdq0cEBHbAkeweLcoJUmSela7idfUcr00MD0i3hoRw6aUUfgXijG+lik3n9Hm9SVJUg/rtz5ebU2SnZmXRsQ5wOHAqsA04PMRcSXFAKpPActTDKD6aoo+XYOJ2TnNhp+QJEmaSNpKvEpvpxjH650USdXawD+N0HYw6TodeHcHri1JktQz2r3VSGYOZObxFAOjXgI8R5FgDV2eo5gqaLfMPCEzx0vVT5Ik1cRbjYspM68Gro6IpYFtgfWAFYEngPuAmzPz2U5dT5Ikqdd0LPEaVCZXN3T6vJIkaeIZL8M8VKXtW42SJElqTccrXhGxIbAVsBqwdGae0+lrSJKkiWGgvwpenUu8IuIY4L3A5kN2nTOk3b9TdMSfnZlv79T1JUmSxru2E6+IWBH4IbDH4KaG3TnMIdcCnwIyIr6QmTPajUGSJPWmAft4jdl5wJ4UCdddwGcYZUT6zPwlxVOOAG/swPUlSZJ6QluJV0S8AdiXorI1DXhxZn4I+FmTQy+nSNRe2aSdJEmawLLCZTxo91bj4eX6duDozFzQ4nE3l+ut2ry+JElSz2g38dqFIok8ZwxJF8AD5XqdNq8vSZJ62HgZUb4q7fbxWrtczxzjcc+V66XbvL4kSVLPaLfi9TRF8rTMGI9bt1w/2ub1JUlSDxsIn2oci8GnE7cZ43GvKtd/bfP6kiRJPaPdxOsqiqcTD42IlqpnEbEZcABF37Ar2ry+JEnqYf32VGO7idfgqPQbAl9q1jgi1gZ+ACwFLADObvP6kiRJPaOtxCszrwW+S1H1Oi4ifhYRewOrDLaJwosj4hTgz8BLKRLPr2WmtxolSVLf6MRcjUcBGwM7A68tF1hY1Xu64TqDPeh+AbyvA9eWJEk9zOEkxigznwJeDfw3xTAR0bBAcVtx8P2zwBeAN2Tm/HavLUmS1Es6UfEiM58F3hMRnwX+CdiNogq2CvAEcC9FR/zzM3N2J64pSZJ630B/jSbRmcRrUGbeD3y5XCRJktSg3UmyByJiQUR8s1MBSZKk/jFAVLaMB+328Rqc+ueqdgORJEma6NpNvO4v10+1G4gkSeo/DqA6NjeV6y3bPI8kSdKE127iNY1imIi3tjplkCRJ0qCBqG4ZD9oduf5C4CJgc+BbEbFcJ4KSJEmaiNqqUkXEhsAHgWUoxu96RUR8A7iaYuyuec3OkZmz2olBkiT1rn4bub7d24N388L+ahsAHx3D8dmBGCRJknpCJ5KeoXdNx8ldVEmSNN6Nl6cNq9Ju4jWtI1FIkiT1gbYSr8x8W6cCkSRJ/We8PG1YlXaHk5AkSVKL2p2rcdmIWDcilu9UQJIkSRPVmBOviFg1Ij4TEXcCT1IMG/H3iPhLRJwWEWt0PEpJkjQhDVS4jAdjSrwiYnPgD8DJwKYUTzAOLhsDHwD+EBEv7myYkiRJva/lxKucEuj7wEaDm4Y2KZcpwPciYqmORChJkiYsK14jOwh4KcWQG48AxwDrA0uX62OBh8q2WwMHdy5MSZKk3jeW4STeXK7nAa/OzFsb9s0Bvh4RVwM3AssBbwK+05EoJUnShJQOJzGi7SmqXecOSbqel5m3AedS3HJ8WfvhSZIkTRxjSbzWKde/adJucP/aYw9HkiT1E/t4jWzFcv1ok3Zzy/UKY45GkiRpAuvEJNmSJEmLZbxUoqrilEGSJEkVWZyKV3Y8CkmS1Jf6LalYnMTrooiWnv2MiFjQpE1mprc7JUlSX1jcpGe0zCtZmMD22egckiRpLAb6LFMYa+LVytfTZ1+hJElSa1pOvDLTjviSJKmjfKpRkiRJXWHiJUmSVBGfKJQkSbXxVqMkSZK6woqXJEmqTb8NoGrFS5IkqSImXpIkqTYDUd0ymohYNiKuj4ibI2JGRHyi3L56RFweEXeW69UajjklImZGxO0RsXcrn9fES5IkCZ4B9sjMbYHtgNdHxD8CHwSmZ+bmwPTyPRGxNXAIsA3weuD0iJjU7CImXpIkqTYDFS6jycIT5dulyiWBA4Bp5fZpwIHl6wOA8zPzmcy8C5gJ7NTs85p4SZIkARExKSJuAh4ELs/M64B1MnMOQLleu2y+PvC3hsNnl9tGZeIlSZJqkxUuEXFMRNzYsBzzglgyF2TmdsAUYKeIeMkooQ/Xa6zpQ5oOJyFJkvpCZk4FprbQbm5EXEnRd+uBiJicmXMiYjJFNQyKCtcGDYdNAe5rdm4rXpIkqTYDZGXLaCJirYhYtXy9HPBa4DbgEuCIstkRwMXl60uAQyJimYjYBNgcuL7Z57XiJUmSBJOBaeWTiUsAF2TmpRHxW+CCiDgKmAUcDJCZMyLiAuAWYD5wfGYuaHYREy9JklSb8TJXY2b+EXjZMNsfAfYc4ZhTgVPHch1vNUqSJFXEipckSaqNczVKkiSpK0y8JEmSKuKtRkmSVJvx0rm+Kla8JEmSKmLFS5Ik1WZguIl3JjArXpIkSRWx4iVJkmrTbCqficaKlyRJUkWseEmSpNr0V73LipckSVJlrHhJkqTaOI6XJEmSusKKlyRJqo1PNUqSJKkrrHhJkqTa9Fe9awIlXhGxP7A3sBGwXGbu2bBvBWBbIDPztzWFKEmS+lzPJ14RsQFwIbD94CYWTaCfAc4DpkTEdpn5pwpDlCRJI/Cpxh4SEcsDPwd2AO4Fvgo8ObRdZs4HzqJIyg6oMkZJkqRBPZ14AccDWwK/B7bKzHcDT4zQ9uJy/boqApMkSRqq1281voXituJ7M3ORStcQfwbmA1t0PSpJktQSh5PoLVsCC4BrmjXMzAHgMWC1bgclSZI0nF6veC0DzMvMBS22X4Gio70kSRoH+qve1fsVrweBFSNi1WYNI2JbYFlgdreDkiRJGk6vJ16/Kdf/1ELbD1Ek1ld1LxxJkjQWAxUu40GvJ15nUAwR8fGI2Hq4BhGxfER8laIj/uAxkiRJlevpPl6ZeVVEnA0cBVwXEZdR9OMiIj4AvBTYF1i1PORLmXlzHbFKkqRFZZ/18urpxKv0TopBU9/FwluOCZxWvh4cyf6/gA9UHp0kSVKp5xOv8onGkyLi68DRwK7AesAk4H6KoSa+bqVLkqTxZ7z0vapKzydegzJzBvCeuuOQJEkayYRJvCRJUu/pt5HrJ3TiFRFrAa+kGN3+V5k5t96IJElSP+vp4SQiYseI+EZEvG+YfYcAdwPfB34IzIqIN1UcoiRJGkVWuIwHPZ14Af8MHMGQvnkRsR5wNrAcxVONAawIfCciNqs6SEmSJOj9xOtV5fqSIduPoUi6/ghsDmxAMWL90sC7K4tOkiSNaoCsbBkPej3xmkxRPbxnyPZ9y+0fzsy/ZOa9wIkUla89qg1RkiSp0OuJ1xrA3MycP7ghIpYDtgOeAX4+uD0z/wg8C2xcbYiSJEmFXn+qcT6w8pBtL6cYPPW3mfnskH1PUE4pJEmS6ucAqr3lbmCriHh5Zt5Qbtuf4jbjNY0NI2ISsApwX6URqhKrrLIyU8/8T7bZZksyk3e84308NW8ep//Paayw4vLcc89sDjv8BP7+9yfqDlXqaStPXp0DvngcK661CjmQ/P47v+T6b/6MdbbakDd8+u0svfyyzJ39ED888XSefWLewuPWW4PjfvE5rvrSD7h26o9r/ARSvXr9VuPlFP22vhoRO0fEgRQd6wF+NKTtSykqYbOrC09V+eJ/fZKf/ewKXvLSV7P9Dntx6213cuYZn+ffP/RpXrb9a7noop/w/vcdV3eYUs8bWDDA5Z86l6/teTLfOPBj7Hj4Xqy5+fq88bNHM/208zlz7w9y289u5BXH7vuC41730bcy80pnbtOissL/jQe9nnj9JzAX2AH4DfADimEjrsjM3wxpO9jh/rdVBqjuW2mlFdntlTvzjW+eB8Bzzz3HY489zpZbbMavrr4WgF9Mv5o3vekNdYYpTQhPPDiX+/98NwDPPvk0D8+8j5XWWY01Nl2PWdfdBsBdV/+JF++z0/PHbPm6HXh01oM8dIf/7pV6OvEqn1Z8DXAF8DTFpNhfBw5qbBcRAbyNojp2RcVhqss23XQjHn74Ec4+64vccP3POPOMz7P88ssxY8bt7Lff6wB4y0FvZIMp69UcqTSxrDJlTdbdZiPuvekvPHjH39hirx0A2GrfnVl58uoALLXcMrziuP341ZcurDNUjWMDFS7jQU8nXgCZeXNmvjYzV8jM9TPz2Mx8bEizJYA9gU2An1UfpbppyUmTeNnLXsqZZ57Dy3famyeffIp/O/kEjj7mvfzrO4/kumt/wkorrcCzzz5Xd6jShLHU8stw8Bkn8fNPfotnn5jHjz4wlR0P34ujL/0Uy6ywHAueKx42f/V7D+K6s37Cc089U3PE0vjQ653rW5KZC1h0rK9hRcQxlP3EYtIqLLGED0GOd7PvncPs2XO4/oY/AHDhhZdx8gdO4GMf/zz77PvPAGy++aa8YZ896wxTmjCWWHISB59xEn+66Bpu++mNADzylzl857DTAFh9k3V50R7bAbD+dpux1T47secph7LsysuTmcx/5jlunHZ5XeFrnBkvfa+q0heJ11hk5lRgKsCSS6/fX/819KgHHniI2bPvY4stNuOOO/7CHnu8kltvvYO11lqDhx56hIjg3085kTOnfqvuUKUJYb/PvYOHZ97LdWf95Plty6+xMk898jhEsNu7DuR3504HYNrB//F8m1ed9Gaefeppky71tZ5JvCJiw06dKzNndepcGh9OfM9HOGfaV1h66aW4665ZHHX0eznsrW/huOOOBOCii37M/077br1BShPABjtuwT8ctBsP3DqLd/z40wBc8fnvsvrG67Lj4XsBcNtPb+DmC66qM0z1kPHS96oqkdkbRZ2IWNChU2VmtpRwWvGS6vGxybvXHYLUtz5yz7lR5fWO2Pigyv6unXb3Dyr9bMPpmYoXxROJ4+k8kiSpTQM9UgDqlF5KvDapOwBJkqR29EzilZktPZUoSZJ6R3/VuybAOF6SJEm9omcqXpIkaeIZ6LOa14RKvCJiHWB9YAVG6USfmb+qLChJkqTShEi8IuIE4N3AZi00TybI55YkSb2l5xOQiDgfOJjWh4lwOAlJksaJfpsyqKc710fEIcA/AY8Db6G4xQhwP0VSOQV4GzATeBjYMzN7+jNLkqTe1etJyJEUtw4/kpkXZua8wR2ZOZCZ92XmNGB74G/AxRHxonpClSRJQw1UuIwHvZ54vaxcf3vI9hd8rsx8AjgBWBH4twrikiRJWkSv9/FaFXgiM+c2bHuOhbccn5eZv42Ip4DXVhOaJElqpt+Gk+j1itcjwFJDts0Flo+IVUc4Zt1uBiRJkjSSXk+87gWWiYi1GrbdWq5f09gwIrYHlgeeqig2SZLURFb4v/Gg1xOv35br7Ru2XUYxZMR/RsTLI2KpiNgRmEbREf+aimOUJEkCej/xuoQiyXprw7avUVTCNgGuBZ4GrgO2AeYDp1YcoyRJGoFPNfaWKyluKX5lcEP5BOMeFNWwaFhmAW/OzOuqD1OSJKnHnmqMiAFgTmauD5CZ84Gryn1bAUtl5h8z805g14iYAmwAPJaZt9QVtyRJGl7m+Oh7VZWeSrxKI03580tgLRo+U2bOBmZXEZQkSVIzvZh4jcZ5GCVJ6iGO4yVJkqSumGgVL0mS1EPGy9OGVbHiJUmSVBETL0mS1PciYoOIuCIibo2IGRFxYrl99Yi4PCLuLNerNRxzSkTMjIjbI2LvVq5j4iVJkmozjqYMmg+8LzO3Av4ROD4itgY+CEzPzM2B6eV7yn2HUAzQ/nrg9IiY1OwivZh4rRMRC4YuwNoAw+0bssyvOX5JkjTOZOaczPx9+frvFHM/rw8cQDHtIOX6wPL1AcD5mflMZt4FzAR2anadXuxc75ARkiRNEONxOImI2Bh4GcWUg+tk5hwokrOIWLtstj7F1ISDZpfbRtVridcn6g5AkiT1pog4BjimYdPUzJw6pM2KwA+AkzLz8YgR6z3D7WiaRfZU4pWZJl6SJE0gVU4ZVCZZU0faHxFLUSRd52bmheXmByJiclntmgw8WG6fTTEt4aApwH3NYujFPl6SJEkdFUVp62zg1sz8r4ZdlwBHlK+PAC5u2H5IRCwTEZsAmwPXN7tOT1W8JEnSxDKOBlDdFTgM+FNE3FRu+3fgNOCCiDgKmAUcDJCZMyLiAuAWiicij8/MBc0uYuIlSZL6Xmb+mpEf4NtzhGNOBU4dy3VMvCRJUm1aGF9rQrGPlyRJUkWseEmSpNqMx3G8usmKlyRJUkWseEmSpNpUOY7XeGDFS5IkqSJWvCRJUm3s4yVJkqSusOIlSZJq4zhekiRJ6goTL0mSpIp4q1GSJNVmwOEkJEmS1A1WvCRJUm36q95lxUuSJKkyVrwkSVJtHEBVkiRJXWHFS5Ik1caKlyRJkrrCipckSapNOo6XJEmSusGKlyRJqo19vCRJktQVVrwkSVJt0oqXJEmSusGKlyRJqo1PNUqSJKkrTLwkSZIq4q1GSZJUG4eTkCRJUldY8ZIkSbWxc70kSZK6woqXJEmqjX28JEmS1BVWvCRJUm2cMkiSJEldYcVLkiTVZsCnGiVJktQNVrwkSVJt7OMlSZKkrrDiJUmSamMfL0mSJHWFFS9JklQb+3hJkiSpK0y8JEmSKuKtRkmSVBs710uSJKkrrHhJkqTa2LlekiRJXWHFS5Ik1cY+XpIkSeoKK16SJKk29vGSJElSV1jxkiRJtckcqDuESlnxkiRJqogVL0mSVJsB+3hJkiSpG6x4SZKk2qTjeEmSJKkbrHhJkqTa2MdLkiRJXWHiJUmSVBFvNUqSpNrYuV6SJEldYcVLkiTVZsCKlyRJkrrBipckSapNOpyEJEmSusGKlyRJqo1PNUqSJKkrrHhJkqTaOGWQJElSH4qIb0TEgxHx54Ztq0fE5RFxZ7lerWHfKRExMyJuj4i9W7mGiZckSapNZla2tOB/gdcP2fZBYHpmbg5ML98TEVsDhwDblMecHhGTml3AxEuSJAnIzF8B/zdk8wHAtPL1NODAhu3nZ+YzmXkXMBPYqdk17OMlSZJq0wMj16+TmXMAMnNORKxdbl8fuLah3exy26iseEmSpL4QEcdExI0NyzHtnG6YbU2zSCtekiSpNlWO45WZU4GpYzzsgYiYXFa7JgMPlttnAxs0tJsC3NfsZFa8JEmSRnYJcET5+gjg4obth0TEMhGxCbA5cH2zk1nxkiRJAiLiPGB3YM2ImA18DDgNuCAijgJmAQcDZOaMiLgAuAWYDxyfmQuaXcPES5Ik1WY8DaCamYeOsGvPEdqfCpw6lmt4q1GSJKkiVrwkSVJtnCRbkiRJXWHFS5Ik1aYHBlDtKCtekiRJFbHiJUmSapPj6KnGKljxkiRJqogVL0mSVBv7eEmSJKkrrHhJkqTaOI6XJEmSusKKlyRJqo1PNUqSJKkrrHhJkqTa2MdLkiRJXWHiJUmSVBFvNUqSpNp4q1GSJEldYcVLkiTVpr/qXVa8JEmSKhP9dm9V/SMijsnMqXXHIfUbf3vSyKx4aSI7pu4ApD7lb08agYmXJElSRUy8JEmSKmLipYnMPiZSPfztSSOwc70kSVJFrHhJkiRVxMRLktS2iNg4IrJcNq47Hmm8MvFSpRr+j3lxliPrjl/qNRHx8VZ/Y3XHKvUDpwxS1R4YYfuKwApN2szrfDhSXxnptyWpIiZeqlRmrjvc9oj4OPCx0dpIao+/Lal+3mqUJEmqiImXekJDP5TdI2LtiPiviLgjIp5q7JsSEVeW7T4+yrkG+7xcOUqbdSPitIi4OSIei4inI+KvEXFWRGzd2U8njQ8RsVRE7BURX46IGyNiTkQ8GxEPRsTPIuLQiIjFPPeUiPhiRMyIiCcj4pmIuC8iflduf/kox+4eEedFxKzyt/hYRFwfESdHxAojHSeNR95qVK95EXA+sA7wNPBcpy8QEW8EzqPod0Z5jWeBTYCjgMMi4h2ZeU6nry3VbFfg5w3vnymXtYDXlcubIuKQzBxo9aQRsS1wBbBauWkB8DiwLjAZ2L7cd+SQ45YEvgYc3bD5CYr+oC8vl7dHxN6ZeU+r8Uh1suKlXvNFYC6wJ7BCZq4MbNmpk0fETsAPKJKuM4GtgOUyc0VgI+B0YGng7IjYsVPXlcaJecB3gH0pkqLlMnMlYA3gRIpk6WDghDGe9wsUidXvgV2ApTJzdWBZYAvg/cCMYY77T4qk6wHgX4E1yniWA14D/IHi939hRPj3mXqCFS/1mgHgtZk5e3BDZt7RwfP/D0Vi9R+Z+dHGHZk5Czg+IuYD7wY+DBzYwWtLXRUR94+ye8/MvA74l6E7MvP/gC9HxH3A9yj++//yGC79inJ9QmZe23DeZ4E7KRKzobG+pLzOU8BemfmnhuOeA66MiFcDt1BUzPYHLhpDTFIt/BeCes23GpOuTipvh7yc4tbiIn8RNBi8xfjaiJjUjVikLllnlGWpFo6/rFxvFhGTx3DdueV6LMccBQRwWWPS1Sgz/87CZGvvMZxbqo0VL/Waa7p47leW6yWA20fpQzyYbK1AcQvmwS7GJHVMZjbtGB8RKwHvBN5Icat9VYZPytYH5rR46UuBdwDTImJX4BLghsx8apRjBn+P+zSp1A32xdyoxVikWpl4qdd0M8lZr1xPoqgAtGL5LsUiVS4itgCmA1MaNj9FUbEa7Ew/+NsYy9OEJ1M8GPMa4L3lsiAibqKook3NzHuHHDP4e1yRhcnVaPwtqid4q1G9ZkEXzz1YybotM6PF5e4uxiNV7ZsUSdfdFJ3o18jMFTJz7XLw1fUb2rY8rERmzs3MPYDdgM9RVK7nAzsAHwXujIhDhxw2+Hv8YIu/xd0X4/NKlTPx0kQzv1wvO0qbVUbYPng7Y1PHBlK/iYgNWNgJ/tDM/H7Zqb5RWyPfZ+avM/PfMvOVFLcwDwD+RPGU4jciorHSPPh7fGk715TGGxMvTTSPlusNRmmz8wjbB/uPLQ28qWMRSb2h8TfzhxHavLZTF8vMpzPzEuDN5aZlWdivCxb+HveNiFZuNUo9wcRLE83N5Xrv4apWEbEHxThCw7mRhX/hnBoRa412oYhYfbGjlMafxxpebzt0Z9np/sNjPWlELNlkjK15Da8buxJ8HUiKytjnm1xjKZMz9QoTL000F1B0Al4DOC8ipgBExHIRcQTwQ2Do7RMAMjMpnuZ6BtgQuC4i3hIRz3fajYj1I+KtEXE58NnufhSpUrcAs8rX34iIHQZ3RMQuwJUsHHl+LKZQ9OH6cES8rByNfvC8/wB8u3z7JPCrwX2ZeRPwpfLtOyPiexGx3eCURRExKSK2jYiPAH8BtluM2KTK+VSjJpTMvCMiTgU+AuwH7BcRj1E8gbUkxZg/f2aEf7ln5vURsR/FlEGbUAwWuSAi5lL0Q2l8cuqsLn0MqXKZmRFxPMU/TrYBboyIweEelqd4unF/4BeLcfpNgf8olwXlb3JFitv6UEzJdeQwfco+QNGJ/yTgLeXydEQ8SdFXs/HvsETqAVa8NOGUI84fBlxL8a/oScBNFNWsN9PkycjMvJzi0fdTgF9T3IJZlaKSdgtwNsVfQO/qRvxSXTLzUuBVFEM8zKVIbB6meNpx+8ycvhinvZfi9/JFit/kHIqkaz7F7+mrwEsy8/vDxLMgM99DMTL9VOB2it/vKhT9Oa8BPg5sl5ndHONP6pgo7q5IkiSp26x4SZIkVcTES5IkqSImXpIkSRUx8ZIkSaqIiZckSVJFTLwkSZIqYuIlSZJUERMvSZKkiph4SeopEXF3RGRE3F13LONd+T1lRFxZdyySCs7VKLUoItaimHJod4oJedekmEroKYqJt28FfgdcnJk31hKkRhURnZqq4xOZ+fEOnatlEXEksDFAHdeX1D4TL6mJiFgR+ATFXI/LD9Nk5XLZGNgH+HBE3AF8CvhOZo46N6Q0BkcCry5ff7y+MCQtLhMvaRQR8SLgImCbhs1/BKYDdwKPAMsCawM7UFTD1gW2AM4B/kQxQbfGhzeNsu8lwH+Ur2cAHx6l7W0di0hSXzHxkkYQEWsAvwA2Kjf9GTghM68a5ZglgAOAk4F/7HqQGpPMvGikfRExt+Htw6O1laTFZeIljewcFiZd1wD7ZObfRzsgMweAH0bERcDxwLyuRihJ6ik+1SgNIyJeAbyhfPs4cGizpKtRFv4nM28f4fwveNosIlaLiFMi4oaIeLjc97/DHLdBRJwWEb+PiP+LiGci4t6I+FFEHBkRk5p8rpafcmvWNiKuHGzTsO3/RcTlEXF/Gds9EfHNiHhxs+uVx68ZEZ+JiFsi4snyM94QEe+PiOH611UqInZv+F4+Xm7bMiK+FBG3RsTj5b4jG45p+zsf/K5Z2L+rsW0OjWmU8y9ffpc3RsSj5Xc8o/zOVxvLdyFp8VjxkoZ3UsPrszPzb926UERsT9GPbIMm7Y4FvggsN2TXeuXyRuC9EbF/Zt7d+UhHjW1Z4DzgwCG7NqToEH5oRLwpM38yyjl2AS6heFp00PLAjuVyZETs28Gw2xYRhwNnsOifybgTEZsCPwK2HrJr63I5NCJ2r/q/HanfmHhJQ0REAHs2bPp2Fy+3BnAxMAX4MXAZ8DCwPtBYSTqW4i/4QT8q286l6Mj/NmAT4KXAryPiZZn5UBfjHuobFEnX74DzgVkUCdS/AK8AlgG+HRFbZubDQw+OiM2An1I8HQrFQwnnAH8DJgOHAjsBFwBLdfODjMGuwIeABcDZFLejnwa2BO7v8LU+TPF9foqFD3oM96DASJ3+V6b47+XFFMntTyiGQNkUOI4iQd6I4jt/VceilrSozHRxcWlYKP71n+XyJLBkF66RDct84OBR2m5MMVbYYNt/GqbNcsClDef8XpPrXjmGGIdtC1w55HN8CoghbZYAftjQ5uQRzvWLhjbfGPqdAwF8Ycj17u7wn8nuLXzm3YfEMAfYup3vcXG/88X47+wZ4I3DtFkD+GtDu506/d+7i4vLwsU+XtKi1mt4PSsz53f5el/OzO+Nsv/dLLyV9YXMvGBog8ycB/wzRSIAcFBEbNHZMEf1y8z8cGa+YIDSLB42+EDDpr2HHhgR27KwwngH8M6h33l53vcD13c06vYdm5m31B1Eiz6VmZcO3ZiZjwCfbti0yJ+RpM4x8ZIWtUbD67nNGkfE/BE6Orc6VctXmux/c7meT1H1GVZmPg6cPhgWi/a36qb/HmlHZs6kuGUIi/YvgoWfD+ArmfnsCOdJRvn8NbiH4pZvL1gA/M8o+3/Z8Hq4PyNJHWLiJdXr3sy8a6SdEbE2C4e0uDkzH2xyvp83vN653eDG4Nom++8t18M9OffyhtfTm5yn2f4q/XpohW8cuyMzHx1l/70Nr326UeoiO9dLi3qk4fWqLbR/My/8R8zawJktXuveJvsnN7y+o4XzNbaZPGKrzlukw/wQz5TrZYbZ13hr9y+jnSQzHykHOl215ci6p9mf3Xgy6p9PZj5TPFMCFDMxSOoSEy9pUfc1vN4wIpYcrZ9XZl7S+D4iNh7DtZoNsLpSw+snWzjfEyMc21VlX67FtWK5nj/SbcYhnmR8JF69NDhuO38+kjrIW43Som6leNQeinGkXlpjLI2Dtq7QQvsVG163PODrUOXUR1UZTBaXjIilW2jfyvfQcyr+ziXVxB+6NETZb6exL9G/1BULC59SBNi8hfaNbe4bZv9gRalZgrNmk/2d1BjnZqM1LOfPXLWr0XTeePzOJdXExEsa3pcaXh8dEevXEUTZmf6e8u12EbFWk0Ne1/B6uKEX5pbr9YbZ16jKjvmNce7RpO2eTfaPR3PLdSe+8+dvGUZDpyxJvcPESxpGZv6GYiR5gFWA8yJixVEO6aYflOsleeFURi8QESsB/1q+TYqBS4caHHNqo3IKmZG8e4wxtqMxzhMiYtiR6ctE4z3VhNRRnfzOG/vwTchbrtJEZ+IljexwFlabdgN+ExGtTKeya4fj+AoLO3KfHBEHDW1QzpX4bRZWVX6QmXcOc66fNrz+7HBVk4j4JPDa9kJuXWbeTDFyPRRT2pw+dLLvMs7PAv9YVVwd1MnvvHHoke3bDUxS9XyqURpBOXTBaynmUtyaopP9VRFxM8WAk3dQdMIPikFXt6YY9ftFDadpe8iBzLw7It5DMVfjksD3I+JiiorcXIp+XW+nmHdv8JrHj3C6bwAnA6sDbwGujohzKYYb2BA4hGJC6vPL11U5jmKex5WBo4GdImJwrsZ1KUbl35nituQUmt+2G086+Z1PZ2Fl7OyI+CLFPw4WlNtmlgPWShqnTLykUWTmzIjYGfgkcCzFU47blstobgE+A5zboTjOLCslX6QYZ+mAchnqz8B+Iw20mpkPRcRbgQvL8+zKohW6S4GjqDDxKr/nfSiS3DWBfwD+c0izGcDBwK+qiqsTOvydXwb8GnglRYL/1SH7PwF8vM2QJXWRtxqlJjLzicx8L8Vk1ccB36Oodj1CMY3PYxS3gH5C8RffTpm5TWZ+u5Mjm2fmGcAWFLfcbqKodj1L8eTjj4G3Adtl5t1NzvMTYDvgm8Cs8hwPAVcAhwH7Z+ZTnYq7VWW/uq2A04DbKG6vzqWohJ1M8b3OqjquTujUd56ZC4C9gA8CvwUeZWG1S1IPiN6Z8UKSJKm3WfGSJEmqiImXJElSRUy8JEmSKmLiJUmSVBETL0mSpIqYeEmSJFXExEuSJKkiJl6SJEkVMfGSJEmqiImXJElSRUy8JEmSKmLiJUmSVJH/D0e4fAwAFEw2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the confusion matrix\n",
    "ax = plt.subplots(figsize=(10,10))\n",
    "ax = sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d')\n",
    "labels = ['False', 'True']\n",
    "ax.set_xticklabels(labels[::-1], fontsize=25)\n",
    "ax.set_yticklabels(labels[::-1], fontsize=25)\n",
    "ax.set_ylabel('Prediction', fontsize=30)\n",
    "ax.set_xlabel('Ground Truth', fontsize=30)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "62b40c985150cf47f964d193cdaf79411cf71bf68bab6f140373317a30443a2d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}