9_churn_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "316cbc71-4ee5-4a9d-aec9-57de0c57ab15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784a850f-f4e5-42d1-b57c-e0e9054e8b76",
   "metadata": {},
   "source": [
    "# Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7560dac3-4466-4170-99c5-8ee45b521988",
   "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>261</th>\n",
       "      <td>3606-TWKGI</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>106.90</td>\n",
       "      <td>1364.3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>1904-WAJAA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>64.35</td>\n",
       "      <td>1558.65</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5637</th>\n",
       "      <td>4423-JWZJN</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>64</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>90.25</td>\n",
       "      <td>5629.15</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5912</th>\n",
       "      <td>7248-VZQLC</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>47</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</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>Bank transfer (automatic)</td>\n",
       "      <td>85.20</td>\n",
       "      <td>3969.35</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6638</th>\n",
       "      <td>4990-ALDGW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>57</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.75</td>\n",
       "      <td>1118.8</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "261   3606-TWKGI    Male              1      No         No      13   \n",
       "6975  1904-WAJAA  Female              0     Yes        Yes      24   \n",
       "5637  4423-JWZJN    Male              0     Yes        Yes      64   \n",
       "5912  7248-VZQLC  Female              1      No         No      47   \n",
       "6638  4990-ALDGW    Male              0      No         No      57   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "261           Yes           Yes     Fiber optic                   No  ...   \n",
       "6975          Yes           Yes             DSL                  Yes  ...   \n",
       "5637          Yes           Yes     Fiber optic                   No  ...   \n",
       "5912          Yes           Yes     Fiber optic                   No  ...   \n",
       "6638          Yes            No              No  No internet service  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "261                   Yes                   No                  Yes   \n",
       "6975                  Yes                   No                   No   \n",
       "5637                  Yes                   No                   No   \n",
       "5912                  Yes                   No                   No   \n",
       "6638  No internet service  No internet service  No internet service   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "261                   Yes  Month-to-month              Yes   \n",
       "6975                   No        Two year               No   \n",
       "5637                  Yes        One year               No   \n",
       "5912                   No  Month-to-month              Yes   \n",
       "6638  No internet service        Two year               No   \n",
       "\n",
       "                  PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "261            Electronic check         106.90        1364.3   Yes  \n",
       "6975           Electronic check          64.35       1558.65    No  \n",
       "5637    Credit card (automatic)          90.25       5629.15    No  \n",
       "5912  Bank transfer (automatic)          85.20       3969.35   Yes  \n",
       "6638               Mailed check          20.75        1118.8    No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"customer_churn.csv\")\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0028f67-3fb6-4e20-a84d-16daf3b43d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop customerID since it is not needed\n",
    "\n",
    "df.drop(\"customerID\", axis=\"columns\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04323407-2506-4ad7-a4f0-5d67e4be01a5",
   "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         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "132f0649-8576-4dd6-a8ba-13ab658168fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalCharges.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1d9f5d2-da35-403b-b987-f275153b4b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.85,  56.95,  53.85, ...,  29.6 ,  74.4 , 105.65])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MonthlyCharges.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "008a6dac-9a1b-4965-90fd-4cb6a3fb7adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "7038    False\n",
       "7039    False\n",
       "7040    False\n",
       "7041    False\n",
       "7042    False\n",
       "Name: TotalCharges, Length: 7043, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df.TotalCharges, errors=\"coerce\").isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bfb331b-aa77-489b-b663-524a6f36b8ee",
   "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>488</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.25</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>80.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.75</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>56.05</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.00</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>73.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "488   Female              0     Yes        Yes       0           No   \n",
       "753     Male              0      No        Yes       0          Yes   \n",
       "936   Female              0     Yes        Yes       0          Yes   \n",
       "1082    Male              0     Yes        Yes       0          Yes   \n",
       "1340  Female              0     Yes        Yes       0           No   \n",
       "3331    Male              0     Yes        Yes       0          Yes   \n",
       "3826    Male              0     Yes        Yes       0          Yes   \n",
       "4380  Female              0     Yes        Yes       0          Yes   \n",
       "5218    Male              0     Yes        Yes       0          Yes   \n",
       "6670  Female              0     Yes        Yes       0          Yes   \n",
       "6754    Male              0      No        Yes       0          Yes   \n",
       "\n",
       "         MultipleLines InternetService       OnlineSecurity  \\\n",
       "488   No phone service             DSL                  Yes   \n",
       "753                 No              No  No internet service   \n",
       "936                 No             DSL                  Yes   \n",
       "1082               Yes              No  No internet service   \n",
       "1340  No phone service             DSL                  Yes   \n",
       "3331                No              No  No internet service   \n",
       "3826               Yes              No  No internet service   \n",
       "4380                No              No  No internet service   \n",
       "5218                No              No  No internet service   \n",
       "6670               Yes             DSL                   No   \n",
       "6754               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55                 No  \n",
       "753                Mailed check           20.25                 No  \n",
       "936                Mailed check           80.85                 No  \n",
       "1082               Mailed check           25.75                 No  \n",
       "1340    Credit card (automatic)           56.05                 No  \n",
       "3331               Mailed check           19.85                 No  \n",
       "3826               Mailed check           25.35                 No  \n",
       "4380               Mailed check           20.00                 No  \n",
       "5218               Mailed check           19.70                 No  \n",
       "6670               Mailed check           73.35                 No  \n",
       "6754  Bank transfer (automatic)           61.90                 No  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pd.to_numeric(df.TotalCharges, errors=\"coerce\").isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d038f36-bbc2-4c6c-ab52-6dc4ae74b37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51cb74e8-dc39-4695-8055-7b86b400dd21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df.TotalCharges!=\" \"]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea257e7d-ad45-4a23-bef9-82a6647615ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kx/sh62mr7x1wqcyp7f9f_glpvh0000gn/T/ipykernel_3136/973151263.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1.TotalCharges = pd.to_numeric(df1.TotalCharges)\n"
     ]
    }
   ],
   "source": [
    "df1.TotalCharges = pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11e19f56-0a6a-46dd-b649-a957a1c5bacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.TotalCharges.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "997e47f6-70ca-40cb-8696-42f3375c8b57",
   "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>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>89.10</td>\n",
       "      <td>1949.40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>29.75</td>\n",
       "      <td>301.90</td>\n",
       "      <td>No</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",
       "      <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>7037</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>21.15</td>\n",
       "      <td>1419.40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>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>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5163 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0     Female              0     Yes         No       1           No   \n",
       "1       Male              0      No         No      34          Yes   \n",
       "3       Male              0      No         No      45           No   \n",
       "6       Male              0      No        Yes      22          Yes   \n",
       "7     Female              0      No         No      10           No   \n",
       "...      ...            ...     ...        ...     ...          ...   \n",
       "7037  Female              0      No         No      72          Yes   \n",
       "7038    Male              0     Yes        Yes      24          Yes   \n",
       "7039  Female              0     Yes        Yes      72          Yes   \n",
       "7040  Female              0     Yes        Yes      11           No   \n",
       "7042    Male              0      No         No      66          Yes   \n",
       "\n",
       "         MultipleLines InternetService       OnlineSecurity  \\\n",
       "0     No phone service             DSL                   No   \n",
       "1                   No             DSL                  Yes   \n",
       "3     No phone service             DSL                  Yes   \n",
       "6                  Yes     Fiber optic                   No   \n",
       "7     No phone service             DSL                  Yes   \n",
       "...                ...             ...                  ...   \n",
       "7037                No              No  No internet service   \n",
       "7038               Yes             DSL                  Yes   \n",
       "7039               Yes     Fiber optic                   No   \n",
       "7040  No phone service             DSL                  Yes   \n",
       "7042                No     Fiber optic                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "0                     Yes                   No                   No   \n",
       "1                      No                  Yes                   No   \n",
       "3                      No                  Yes                  Yes   \n",
       "6                     Yes                   No                   No   \n",
       "7                      No                   No                   No   \n",
       "...                   ...                  ...                  ...   \n",
       "7037  No internet service  No internet service  No internet service   \n",
       "7038                   No                  Yes                  Yes   \n",
       "7039                  Yes                  Yes                   No   \n",
       "7040                   No                   No                   No   \n",
       "7042                   No                  Yes                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies        Contract  \\\n",
       "0                      No                   No  Month-to-month   \n",
       "1                      No                   No        One year   \n",
       "3                      No                   No        One year   \n",
       "6                     Yes                   No  Month-to-month   \n",
       "7                      No                   No  Month-to-month   \n",
       "...                   ...                  ...             ...   \n",
       "7037  No internet service  No internet service        Two year   \n",
       "7038                  Yes                  Yes        One year   \n",
       "7039                  Yes                  Yes        One year   \n",
       "7040                   No                   No  Month-to-month   \n",
       "7042                  Yes                  Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "0                 Yes           Electronic check           29.85   \n",
       "1                  No               Mailed check           56.95   \n",
       "3                  No  Bank transfer (automatic)           42.30   \n",
       "6                 Yes    Credit card (automatic)           89.10   \n",
       "7                  No               Mailed check           29.75   \n",
       "...               ...                        ...             ...   \n",
       "7037              Yes  Bank transfer (automatic)           21.15   \n",
       "7038              Yes               Mailed check           84.80   \n",
       "7039              Yes    Credit card (automatic)          103.20   \n",
       "7040              Yes           Electronic check           29.60   \n",
       "7042              Yes  Bank transfer (automatic)          105.65   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "1          1889.50    No  \n",
       "3          1840.75    No  \n",
       "6          1949.40    No  \n",
       "7           301.90    No  \n",
       "...            ...   ...  \n",
       "7037       1419.40    No  \n",
       "7038       1990.50    No  \n",
       "7039       7362.90    No  \n",
       "7040        346.45    No  \n",
       "7042       6844.50    No  \n",
       "\n",
       "[5163 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.Churn==\"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1ea7615-112e-499a-8a01-2f2539c52c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e902fa0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tenure_churn_no = df1[df1.Churn==\"No\"].tenure\n",
    "tenure_churn_yes = df1[df1.Churn==\"Yes\"].tenure\n",
    "\n",
    "plt.xlabel(\"tenure\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.title(\"Customer Churn Prediction Visualization\")\n",
    "\n",
    "plt.hist([tenure_churn_yes, tenure_churn_no], color=[\"green\",\"red\"], label=[\"Churn=Yes\",\"Churn=No\"])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7eb978a-8eb8-42a5-bab5-c4f768926e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZmWHSpEmYNGkSHj58iH379mHGjBnw8fHBjRs3pB/7wv4cP38ebd++HRkZGdi6davad1XYc5u0debMGfTq1Qtt2rQp9G7E9evXo23btli+fLla+aNHj155n89+d88zxHdH+sERIipWISEhEEJgxIgRyMnJ0Vifm5uLnTt3Sp9r1qyJM2fOqNU5cOAAHj9+XOQ+nJ2dMWTIEPTv3x8XL15EZmYmAEgTo5//V1/BnVDr169XK9+yZQsyMjKk9a+rbt26cHNzw+nTp9G0adNCl2cvKekiICAASqUSU6dOxa1btwqts3XrVp3bLbgz6PnvYMeOHTq3VVJ8fHxgZmaGK1euFNnvwD/fT5UqVbBx40YIIaTtr127hqNHj750P127dsXBgwc1Lns+q6hzsDDt27cHoHlexsbGIjExUW/nZQGVSoUuXbpg48aNWLp0KUxMTDB48GCd2qhYsSL69OmDMWPG4MGDB9JdVzVr1kRKSoo0Wgf8MwF/z549atsXJF/P3sQghCjysQovc/36dXTt2hW1atXCli1bCr0pQSaTadzxeubMGY2Hfury3bVs2RJWVlYa393Nmzdx4MABvX93pB8cIaJi1bJlSyxfvhyBgYHw9vbG6NGj0bBhQ+Tm5uLUqVNYsWIF3N3dpevpAwcOxKxZszB79my0adMGFy5cwJIlS6BQKNTabdGiBXx9feHp6YlKlSohMTER69atQ8uWLaV5PR4eHgCAefPmoWvXrjA1NYWnpyc6deoEHx8fTJs2Denp6WjdurV0l5mXlxcGDhyot+P/+uuv0bVrV/j4+GDIkCGoWrUqHjx4gMTERMTHx+N///vfK7WrUCjw008/wdfXF15eXmoPZvzzzz+xfv16nD59usiRqaIolUp07NgRERERqFSpEmrUqIH9+/e/UnJVUmrWrIm5c+di5syZ+Ouvv9ClSxdUqlQJd+/eRUxMjDS6aGJigo8//hjDhw/Hu+++ixEjRuDhw4cIDQ3V6pLZ3LlzsXv3brz99tuYMWMGPDw88PDhQ0RFRWHSpEmoV68e3njjDVhZWWHDhg2oX78+KlSoAJVKBZVKpdFe3bp1MXLkSCxevBgmJibo2rWrdJeZi4sLJk6cqPe+CggIwC+//IJvvvkGPj4+Wt0N2qNHD7i7u6Np06ZwdHTEtWvXsHDhQtSoUUO6k6pv376YPXs2+vXrhylTpuDJkydYtGgR8vLy1Nrq1KkTzM3N0b9/f0ydOhVPnjzB8uXLX/mBol27dsXDhw+xZMkSnD9/Xm3dG2+8AUdHR/j6+uLjjz/GnDlz0KZNG1y8eBFz586Fq6ur2l2Stra2qFGjBn766Sd06NAB9vb2qFy5cqGPE6hYsSJmzZqFGTNmYNCgQejfvz/u37+PsLAwWFpaYs6cOa90PGRgJTunm4xVQkKCGDx4sKhevbowNzcXNjY2wsvLS8yePVukpKRI9bKzs8XUqVOFi4uLsLKyEm3atBEJCQkad5NMnz5dNG3aVFSqVElYWFiIWrVqiYkTJ4q///5bra3hw4cLR0dHIZPJ1O4KycrKEtOmTRM1atQQcrlcVKlSRYwePVqkpqaqxV2jRg3RvXt3jeMBIMaMGaNWVnB31GeffaZWfvr0aeHn5yecnJyEXC4XSqVStG/fXnz11VdSnYK7VmJjY3Xq1+TkZDFt2jTRsGFDYW1tLSwsLETt2rXFqFGjxNmzZ6V6gwcPFjY2NhrbF3bXz507d0SfPn2Evb29UCgU4oMPPhAnT54s9C6zwtoUovD+EeLldwUJUXQ/Pu9lfbZ9+3bRrl07YWdnJywsLESNGjVEnz59xL59+9TqffPNN8LNzU2Ym5uLOnXqiFWrVhV6RxMKuePoxo0bYtiwYUKpVAq5XC5UKpXw8/MTd+/eleps3LhR1KtXT8jlcrU2Cuv7vLw8MW/ePFGnTh0hl8tF5cqVxQcffCBu3LihVq9NmzaiYcOGGsdc1N1dRcnJyRHOzs6F3vVU4PnvbP78+aJVq1aicuXKwtzcXFSvXl0EBASIq1evqm23a9cu0bhxY2FlZSVq1aollixZUugx79y5UzRq1EhYWlqKqlWriilTpojdu3dr3O2ozV1mAIpcCs7d7OxsMXnyZFG1alVhaWkpmjRpIrZv315o+/v27RNeXl7CwsJCAJD29fxdZgW++eYb4enpKczNzYVCoRDvvPOOOH/+vFodXf4skmHJhHhmbJiIiIjICHEOERERERk9JkRERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0+GBGLeXn5+P27duwtbU16OsJiIiISH+EEHj06BFUKhVMTIoeB2JCpKXbt29r9dRWIiIiKn1u3LjxwndoMiHSUsE7pm7cuFHk27yJiIiodElPT4eLi8tL3xXJhEhLBZfJ7OzsmBARERGVMS+b7sJJ1URERGT0mBARERGR0WNCREREREaPc4iIiKhcysvLQ25ubkmHQQYml8thamr62u0wISIionJFCIHk5GQ8fPiwpEOhYlKxYkUolcrXek4gEyIiIipXCpIhJycnWFtb82G65ZgQApmZmUhJSQEAVKlS5ZXbYkJERETlRl5enpQMOTg4lHQ4VAysrKwAACkpKXBycnrly2ecVE1EROVGwZwha2vrEo6EilPB9/06c8aYEBERUbnDy2TGRR/fNxMiIiIiMnpMiIiIiMoQmUyG7du3l3QY5Q4nVRMRkVGQhRXvZTQxR7zSdsnJyfjkk0/wyy+/4NatW3ByckLjxo0RFBSEDh066DlKw/n444+xbNkynD9/Hvb29lL56dOn0axZM/zvf//DO++8U4IRquMIERERUSlx9epVeHt748CBA4iMjMTZs2cRFRWFdu3aYcyYMQbbryEeYBkSEgIXFxe1uHNzczFkyBD4+/uXqmQIYEJERERUagQGBkImkyEmJgZ9+vRBnTp10LBhQ0yaNAnHjx+X6v3999949913YW1tDTc3N+zYsUNat2bNGlSsWFGt3e3bt6tNPA4NDUXjxo2xatUq1KpVCxYWFhBCQCaT4ZtvvimybV2YmZnhu+++w08//YTNmzcDAD755BM8ePAAixYtQlpaGkaOHAknJyfY2dmhffv2OH36tLT96dOn0a5dO9ja2sLOzg7e3t44efLkK8WiDSZEREREpcCDBw8QFRWFMWPGwMbGRmP9s0lOWFgY/Pz8cObMGXTr1g0DBgzAgwcPdNrf5cuX8eOPP2LLli1ISEjQuu0KFSq8cOnatatUt169eggPD8fo0aOxZ88eREREYPXq1bC1tUX37t2RnJyMXbt2IS4uDk2aNEGHDh2kfQ0YMADVqlVDbGws4uLiMH36dMjlcp2OURecQ1SeGeK2U/Fq18SJiOjFLl++DCEE6tWr99K6Q4YMQf/+/QEA4eHhWLx4MWJiYtClSxet95eTk4N169bB0dFRp7afTZ4KU/CgxAITJkzATz/9hG7dumHcuHFo3749Dhw4gLNnzyIlJQUWFhYAgM8//xzbt2/H5s2bMXLkSFy/fh1TpkyR+sPNzU3rY3sVTIiIiIhKAfHvPzi1eaaOp6en9P82NjawtbWVXl+hrRo1amgkQ9q0Xbt2bZ32I5PJMHPmTBw6dAgfffQRACAuLg6PHz/WeJp4VlYWrly5AgCYNGkShg8fjnXr1qFjx454//338cYbb+i0b13wkhkREVEp4ObmBplMhsTExJfWff7SkUwmQ35+PgDAxMRESq4KFDZpurDLci9rG9DtklkBMzMztf/m5+ejSpUqSEhIUFsuXryIKVOmAPhnntP58+fRvXt3HDhwAA0aNMC2bdte2C+vgyNEREREpYC9vT18fHywdOlSjB8/XiNhefjwocZk6cI4Ojri0aNHyMjIkNp42WUuXeh6yawwTZo0QXJyMszMzFCzZs0i69WpUwd16tTBxIkT0b9/f6xevRrvvvuujhFrhwkRERFRKbFs2TK0atUKzZs3x9y5c+Hp6YmnT58iOjoay5cv12r0qEWLFrC2tsaMGTMwbtw4xMTEYM2aNXqLUddLZoXp2LEjWrZsiV69emHevHmoW7cubt++jV27dqFXr15o2LAhpkyZgj59+sDV1RU3b95EbGws3nvvPT0cQeF4yYyIiKiUcHV1RXx8PNq1a4fg4GC4u7ujU6dO2L9/P5YvX65VG/b29li/fj127doFDw8PbNy4EaGhoYYNXEcymQy7du3C22+/jWHDhqFOnTro168frl69CmdnZ5iamuL+/fsYNGgQ6tSpAz8/P3Tt2hVhYWGGi0k8f6GRCpWeng6FQoG0tDTY2dmVdDja4V1mRGRknjx5gqSkJLi6usLS0rKkw6Fi8qLvXdvfb44QERERkdEr0YTo119/RY8ePaBSqV76srpRo0ZBJpNh4cKFauXZ2dkYN24cKleuDBsbG/Ts2RM3b95Uq5OamoqBAwdCoVBAoVBg4MCBePjwof4PiIiIiMqkEk2IMjIy0KhRIyxZsuSF9bZv344TJ05ApVJprAsKCsK2bduwadMmHDlyBI8fP4avry/y8vKkOv7+/khISEBUVBSioqKQkJCAgQMH6v14iIiIqGwq0bvMunbtWujzCp5169YtjB07Fnv27EH37t3V1qWlpeHbb7+VHtoEAOvXr4eLiwv27dsHHx8fJCYmIioqCsePH0eLFi0AACtXrkTLli1x8eJF1K1b1zAHR0RERGVGqZ5DlJ+fj4EDB2LKlClo2LChxvq4uDjk5uaic+fOUplKpYK7uzuOHj0KADh27BgUCoWUDAHAm2++CYVCIdUpTHZ2NtLT09UWIiIiKp9KdUI0b948mJmZYfz48YWuT05Ohrm5OSpVqqRW7uzsjOTkZKmOk5OTxrZOTk5SncJERERIc44UCgVcXFxe40iIiIioNCu1CVFcXBy+/PJLrFmzRqv3ujxLCKG2TWHbP1/neSEhIUhLS5OWGzdu6BQDERERlR2lNiH67bffkJKSgurVq8PMzAxmZma4du0agoODpcd8K5VK5OTkIDU1VW3blJQUODs7S3Xu3r2r0f69e/ekOoWxsLCAnZ2d2kJERETlU6lNiAYOHIgzZ86ovfRNpVJhypQp2LNnDwDA29sbcrkc0dHR0nZ37tzBuXPn0KpVKwBAy5YtkZaWhpiYGKnOiRMnkJaWJtUhIiIi41aid5k9fvwYly9flj4nJSUhISEB9vb2qF69OhwcHNTqy+VyKJVK6c4whUKBgIAABAcHw8HBAfb29pg8eTI8PDyku87q16+PLl26YMSIEfj6668BACNHjoSvry/vMCMiojJHJpNh27Zt6NWrV0mHUq6U6AjRyZMn4eXlBS8vLwDApEmT4OXlhdmzZ2vdxhdffIFevXrBz88PrVu3hrW1NXbu3AlTU1OpzoYNG+Dh4YHOnTujc+fO8PT0xLp16/R+PEREVIrJZMW7vKLk5GSMGzcOtWrVgoWFBVxcXNCjRw/s379fj51RPArmAXfp0kWt/OHDh5DJZDh06FDJBFaIEh0hatu2LXR5ldrVq1c1yiwtLbF48WIsXry4yO0KXnRHRERUml29ehWtW7dGxYoVERkZCU9PT+Tm5mLPnj0YM2YM/vjjD4PsNzc3F3K53CBtm5mZYf/+/Th48CDatWtnkH3oQ6mdQ0RERGRsAgMDIZPJEBMTgz59+qBOnTpo2LAhJk2ahOPHj0v1/v77b7z77ruwtraGm5sbduzYIa1bs2YNKlasqNbu9u3b1e6sDg0NRePGjbFq1SppJKrg7utvvvmmyLZfhY2NDYYOHYrp06e/sN7Zs2fRvn17WFlZwcHBASNHjsTjx49fa9+6YEJERERUCjx48ABRUVEYM2YMbGxsNNY/m+SEhYXBz88PZ86cQbdu3TBgwAA8ePBAp/1dvnwZP/74I7Zs2YKEhASt265QocILl8LeQBEaGoqzZ89i8+bNhcaSmZmJLl26oFKlSoiNjcX//vc/7Nu3D2PHjtXpmF5HiV4yIyIion9cvnwZQgjUq1fvpXWHDBmC/v37AwDCw8OxePFixMTEaMzVeZGcnBysW7cOjo6OOrX9bPJUGCsrK40ylUqFCRMmYObMmYVOBt+wYQOysrLw3XffScngkiVL0KNHD8ybN++Fj8nRFyZEREREpUDBnFptHkbs6ekp/b+NjQ1sbW2RkpKi0/5q1KihkQxp03bt2rV12k+BadOm4euvv8aqVavg5+enti4xMRGNGjVSGxlr3bo18vPzcfHixWJJiHjJjIiIqBRwc3ODTCZDYmLiS+s+PwFaJpMhPz8fAGBiYqJxw1Jubq5GG4VdlntZ28CrXTID/rnkFxISgrCwMGRmZqqte9HbI3R9W8Wr4ggRERFRKWBvbw8fHx8sXboU48eP10hYHj58qDFZujCOjo549OgRMjIypDZedplLF69yyazAuHHjsGjRInz55Zdq5Q0aNMDatWvVYv79999hYmKCOnXqvHbM2uAIERERUSmxbNky5OXloXnz5tiyZQv+/PNPJCYmYtGiRWjZsqVWbbRo0QLW1taYMWMGLl++jO+//x5r1qzRW4y1a9d+4VK1atUit7W0tERYWBgWLVqkVj5gwABYWlpi8ODBOHfuHA4ePIhx48Zh4MCBxXK5DGBCREREVGq4uroiPj4e7dq1Q3BwMNzd3dGpUyfs378fy5cv16qNgmfv7dq1Cx4eHti4cSNCQ0MNG7gOBg8ejFq1aqmVWVtbY8+ePXjw4AGaNWuGPn36oEOHDliyZEmxxSUTujwZ0Yilp6dDoVAgLS2t7Lzo1RDXXXm6EFEp9uTJEyQlJcHV1RWWlpYlHQ4Vkxd979r+fnOEiIiIiIweEyIiIiIyekyIiIiIyOgxISIiIiKjx4SIiIjKHd4vZFz08X0zISIionKj4CnLzz8Jmcq3gu/7+ads64JPqiYionLD1NQUFStWlN69ZW1tXWyvfqDiJ4RAZmYmUlJSULFiRZiamr5yW0yIiIioXFEqlQCg88tOqeyqWLGi9L2/KiZERERUrshkMlSpUgVOTk6FvtSUyhe5XP5aI0MFmBAREVG5ZGpqqpcfSjIOnFRNRERERo8JERERERk9JkRERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0mBARERGR0WNCREREREaPCREREREZPSZEREREZPSYEBEREZHRY0JERERERo8JERERERk9JkRERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0SjQh+vXXX9GjRw+oVCrIZDJs375dWpebm4tp06bBw8MDNjY2UKlUGDRoEG7fvq3WRnZ2NsaNG4fKlSvDxsYGPXv2xM2bN9XqpKamYuDAgVAoFFAoFBg4cCAePnxYDEdIREREZUGJJkQZGRlo1KgRlixZorEuMzMT8fHxmDVrFuLj47F161ZcunQJPXv2VKsXFBSEbdu2YdOmTThy5AgeP34MX19f5OXlSXX8/f2RkJCAqKgoREVFISEhAQMHDjT48REREVHZIBNCiJIOAgBkMhm2bduGXr16FVknNjYWzZs3x7Vr11C9enWkpaXB0dER69atQ9++fQEAt2/fhouLC3bt2gUfHx8kJiaiQYMGOH78OFq0aAEAOH78OFq2bIk//vgDdevW1Sq+9PR0KBQKpKWlwc7O7rWPt1jIZPpvs3ScLkRERFrR9ve7TM0hSktLg0wmQ8WKFQEAcXFxyM3NRefOnaU6KpUK7u7uOHr0KADg2LFjUCgUUjIEAG+++SYUCoVUpzDZ2dlIT09XW4iIiKh8KjMJ0ZMnTzB9+nT4+/tLGV5ycjLMzc1RqVIltbrOzs5ITk6W6jg5OWm05+TkJNUpTEREhDTnSKFQwMXFRY9HQ0RERKVJmUiIcnNz0a9fP+Tn52PZsmUvrS+EgOyZy0WyQi4dPV/neSEhIUhLS5OWGzduvFrwREREVOqV+oQoNzcXfn5+SEpKQnR0tNr1P6VSiZycHKSmpqptk5KSAmdnZ6nO3bt3Ndq9d++eVKcwFhYWsLOzU1uIiIiofCrVCVFBMvTnn39i3759cHBwUFvv7e0NuVyO6OhoqezOnTs4d+4cWrVqBQBo2bIl0tLSEBMTI9U5ceIE0tLSpDpERERk3MxKcuePHz/G5cuXpc9JSUlISEiAvb09VCoV+vTpg/j4ePz888/Iy8uT5vzY29vD3NwcCoUCAQEBCA4OhoODA+zt7TF58mR4eHigY8eOAID69eujS5cuGDFiBL7++msAwMiRI+Hr66v1HWZERERUvpXobfeHDh1Cu3btNMoHDx6M0NBQuLq6FrrdwYMH0bZtWwD/TLaeMmUKvv/+e2RlZaFDhw5YtmyZ2iToBw8eYPz48dixYwcAoGfPnliyZIl0t5o2eNv9v3jbPRERlSHa/n6XmucQlXZMiP7F04WIiMqQcvkcIiIiIiJDYEJERERERo8JERERERk9JkRERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0mBARERGR0WNCREREREaPCREREREZPSZEREREZPSYEBEREZHRY0JERERERo8JERERERk9JkRERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0mBARERGR0WNCREREREaPCREREREZPSZEREREZPR0ToiioqJw5MgR6fPSpUvRuHFj+Pv7IzU1Va/BERERERUHnROiKVOmID09HQBw9uxZBAcHo1u3bvjrr78wadIkvQdIREREZGhmum6QlJSEBg0aAAC2bNkCX19fhIeHIz4+Ht26ddN7gERERESGpvMIkbm5OTIzMwEA+/btQ+fOnQEA9vb20sgRERERUVmi8whR69atMWnSJLRu3RoxMTH44YcfAACXLl1CtWrV9B4gERERkaHpPEK0dOlSyOVybN68GcuXL0fVqlUBALt370aXLl30HiARERGRocmEEELbyk+fPsWGDRvQuXNnVKlSxZBxlTrp6elQKBRIS0uDnZ1dSYejHZlM/21qf7oQERGVOG1/v3UaITIzM8Po0aORk5Pz2gESERERlRY6XzJr0aIFTp06ZYhYiIiIiEqEzpOqAwMDERwcjJs3b8Lb2xs2NjZq6z09PfUWHBEREVFx0GkOEQCYmGgOKslkMgghIJPJkJeXp7fgShPOIfoX5xAREVEZou3v9ys9mJGIiIioPNE5IapRo4Yh4iAiIiIqMa/0tvt169ahdevWUKlUuHbtGgBg4cKF+Omnn3Rq59dff0WPHj2gUqkgk8mwfft2tfVCCISGhkKlUsHKygpt27bF+fPn1epkZ2dj3LhxqFy5MmxsbNCzZ0/cvHlTrU5qaioGDhwIhUIBhUKBgQMH4uHDhzofNxEREZVPOidEy5cvx6RJk9CtWzc8fPhQmjNUsWJFLFy4UKe2MjIy0KhRIyxZsqTQ9ZGRkViwYAGWLFmC2NhYKJVKdOrUCY8ePZLqBAUFYdu2bdi0aROOHDmCx48fw9fXV20uk7+/PxISEhAVFYWoqCgkJCRg4MCBuh46ERERlVdCR/Xr1xfbtm0TQghRoUIFceXKFSGEEGfPnhUODg66NicBILUrhBD5+flCqVSKTz/9VCp78uSJUCgU4quvvhJCCPHw4UMhl8vFpk2bpDq3bt0SJiYmIioqSgghxIULFwQAcfz4canOsWPHBADxxx9/aB1fWlqaACDS0tJe9RCL3z9ToPW7EBERlSHa/n7rPEKUlJQELy8vjXILCwtkZGS8bn6mtp/k5GTp5bEF+2jTpg2OHj0KAIiLi0Nubq5aHZVKBXd3d6nOsWPHoFAo0KJFC6nOm2++CYVCIdUpTHZ2NtLT09UWIiIiKp90TohcXV2RkJCgUb579240aNBAHzEBAJKTkwEAzs7OauXOzs7SuuTkZJibm6NSpUovrOPk5KTRvpOTk1SnMBEREdKcI4VCARcXl9c6HiIiIiq9dL7LbMqUKRgzZgyePHkCIQRiYmKwceNGRERE4JtvvtF7gLLnnqUj/n3e0Ys8X6ew+i9rJyQkBJMmTZI+p6enMykiIiIqp3ROiIYOHYqnT59i6tSpyMzMhL+/P6pWrYovv/wS/fr101tgSqUSwD8jPM++SDYlJUUaNVIqlcjJyUFqaqraKFFKSgpatWol1bl7965G+/fu3dMYfXqWhYUFLCws9HIsREREVLq90m33I0aMwLVr15CSkoLk5GTcuHEDAQEBeg3M1dUVSqUS0dHRUllOTg4OHz4sJTve3t6Qy+Vqde7cuYNz585JdVq2bIm0tDTExMRIdU6cOIG0tDSpDhERERk3nUeInlW5cuXX2vnjx49x+fJl6XNSUhISEhJgb2+P6tWrIygoCOHh4XBzc4ObmxvCw8NhbW0Nf39/AIBCoUBAQACCg4Ph4OAAe3t7TJ48GR4eHujYsSMAoH79+ujSpQtGjBiBr7/+GgAwcuRI+Pr6om7duq8VPxEREZUPOidE9+/fx+zZs3Hw4EGkpKQgPz9fbf2DBw+0buvkyZNo166d9Llgzs7gwYOxZs0aTJ06FVlZWQgMDERqaipatGiBvXv3wtbWVtrmiy++gJmZGfz8/JCVlYUOHTpgzZo1MDU1leps2LAB48ePl+5G69mzZ5HPPiIiIiLjo/PLXbt27YorV64gICAAzs7OGhOTBw8erNcASwu+3PVffLkrERGVIQZ7ueuRI0dw5MgRNGrU6LUCJCIiIiotdJ5UXa9ePWRlZRkiFiIiIqISoXNCtGzZMsycOROHDx/G/fv3+TRnIiIiKvN0vmRWsWJFpKWloX379mrlBQ86fPalqkRERERlgc4J0YABA2Bubo7vv/++0EnVRERERGWNzgnRuXPncOrUKT7Dh4iIiMoNnecQNW3aFDdu3DBELEREREQlQucRonHjxmHChAmYMmUKPDw8IJfL1dZ7enrqLTgiIiKi4qDzgxlNTDQHlWQyWbmfVM0HM/6LD2YkIqIyxGAPZkxKSnqtwIiIiIhKG50Toho1ahgiDiIiIqIS80pvu79y5QoWLlyIxMREyGQy1K9fHxMmTMAbb7yh7/iIiIiIDE7nu8z27NmDBg0aICYmBp6ennB3d8eJEyfQsGFDREdHGyJGIiIiIoPSeVK1l5cXfHx88Omnn6qVT58+HXv37kV8fLxeAywtOKn6X5xUTUREZYi2v986jxAlJiYiICBAo3zYsGG4cOGCrs0RERERlTidEyJHR0ckJCRolCckJMDJyUkfMREREREVK50nVY8YMQIjR47EX3/9hVatWkEmk+HIkSOYN28egoODDREjERERkUHpPIdICIGFCxdi/vz5uH37NgBApVJhypQpGD9+fLl92SvnEP2Lc4iIiKgM0fb3W+eE6FmPHj0CANja2r5qE2UGE6J/MSEiIqIyxGCTqtu3b4+HDx8C+CcRKkiG0tPT0b59+1eLloiIiKgE6ZwQHTp0CDk5ORrlT548wW+//aaXoIiIiIiKk9aTqs+cOSP9/4ULF5CcnCx9zsvLQ1RUFKpWrarf6IiIiIiKgdYJUePGjSGTySCTyQq9NGZlZYXFixfrNTgiIiKi4qB1QpSUlAQhBGrVqoWYmBg4OjpK68zNzeHk5ARTU1ODBElERERkSFonRAVvuc/PzzdYMEREREQlQedJ1WvXrsUvv/wifZ46dSoqVqyIVq1a4dq1a3oNjoiIiKg46JwQhYeHw8rKCgBw7NgxLFmyBJGRkahcuTImTpyo9wCJiIiIDE3nV3fcuHEDtWvXBgBs374dffr0wciRI9G6dWu0bdtW3/ERERERGZzOI0QVKlTA/fv3AQB79+5Fx44dAQCWlpbIysrSb3RERERExUDnEaJOnTph+PDh8PLywqVLl9C9e3cAwPnz51GzZk19x0dERERkcDqPEC1duhQtW7bEvXv3sGXLFjg4OAAA4uLi0L9/f70HSERERGRor/VyV2PCl7v+i6cLERGVIdr+fut8yezXX3994fq3335b1yaJiIiISpTOCVFhd5LJnhmJyMvLe62AiIiIiIqbznOIUlNT1ZaUlBRERUWhWbNm2Lt3ryFiJCIiIjIonUeIFAqFRlmnTp1gYWGBiRMnIi4uTi+BERERERUXnUeIiuLo6IiLFy/qqzkiIiKiYqNzQnTmzBm15fTp04iKisLo0aPRqFEjvQb39OlTfPTRR3B1dYWVlRVq1aqFuXPnqr1gVgiB0NBQqFQqWFlZoW3btjh//rxaO9nZ2Rg3bhwqV64MGxsb9OzZEzdv3tRrrERERFR26XzJrHHjxpDJZHj+bv0333wTq1at0ltgADBv3jx89dVXWLt2LRo2bIiTJ09i6NChUCgUmDBhAgAgMjISCxYswJo1a1CnTh3897//RadOnXDx4kXY2toCAIKCgrBz505s2rQJDg4OCA4Ohq+vL+Li4mBqaqrXmImIiKjs0fk5RM+/0d7ExASOjo6wtLTUa2AA4OvrC2dnZ3z77bdS2XvvvQdra2usW7cOQgioVCoEBQVh2rRpAP4ZDXJ2dsa8efMwatQopKWlwdHREevWrUPfvn0BALdv34aLiwt27doFHx8frWLhc4j+xecQERFRGaLt77fOl8xq1Kihtri4uBgkGQKAt956C/v378elS5cAAKdPn8aRI0fQrVs3AEBSUhKSk5PRuXNnaRsLCwu0adMGR48eBfDPE7Rzc3PV6qhUKri7u0t1iIiIyLhpnRAdOHAADRo0QHp6usa6tLQ0NGzYEL/99pteg5s2bRr69++PevXqQS6Xw8vLC0FBQdIrQpKTkwEAzs7Oats5OztL65KTk2Fubo5KlSoVWacw2dnZSE9PV1uIiIiofNI6IVq4cCFGjBhR6HCTQqHAqFGjsGDBAr0G98MPP2D9+vX4/vvvER8fj7Vr1+Lzzz/H2rVr1erJnrs0JITQKHvey+pERERAoVBIi4uLy6sfCBEREZVqWidEp0+fRpcuXYpc37lzZ70/g2jKlCmYPn06+vXrBw8PDwwcOBATJ05EREQEAECpVAKAxkhPSkqKNGqkVCqRk5OD1NTUIusUJiQkBGlpadJy48YNfR4aERERlSJaJ0R3796FXC4vcr2ZmRnu3bunl6AKZGZmwsREPURTU1PptntXV1colUpER0dL63NycnD48GG0atUKAODt7Q25XK5W586dOzh37pxUpzAWFhaws7NTW4iIiKh80vq2+6pVq+Ls2bOoXbt2oevPnDmDKlWq6C0wAOjRowc++eQTVK9eHQ0bNsSpU6ewYMECDBs2DMA/l8qCgoIQHh4ONzc3uLm5ITw8HNbW1vD39wfwz+W8gIAABAcHw8HBAfb29pg8eTI8PDzQsWNHvcZLREREZZPWCVG3bt0we/ZsdO3aVeOusqysLMyZMwe+vr56DW7x4sWYNWsWAgMDkZKSApVKhVGjRmH27NlSnalTpyIrKwuBgYFITU1FixYtsHfvXukZRADwxRdfwMzMDH5+fsjKykKHDh2wZs0aPoOIiIiIAOjwHKK7d++iSZMmMDU1xdixY1G3bl3IZDIkJiZi6dKlyMvLQ3x8/Avn5ZRlfA7Rv/gcIiIiKkO0/f3WeoTI2dkZR48exejRoxESEiI9qVomk8HHxwfLli0rt8kQERERlW86vbqjRo0a2LVrF1JTU3H58mUIIeDm5qbxjB8iIiKiskTnd5kBQKVKldCsWTN9x0JERERUInR+dQcRERFRecOEiIiIiIweEyIiIiIyelolRE2aNJFefTF37lxkZmYaNCgiIiKi4qRVQpSYmIiMjAwAQFhYGB4/fmzQoIiIiIiKk1Z3mTVu3BhDhw7FW2+9BSEEPv/8c1SoUKHQus8+RZqIiIioLNDqSdUXL17EnDlzcOXKFcTHx6NBgwYwM9PMpWQyGeLj4w0SaEnjk6r/xSdVExFRGaLt77fWr+4oYGJiguTkZDg5Ob12kGUJE6J/MSEiIqIyRO+v7iiQn5//WoERERERlTav9KTqK1euYOHChUhMTIRMJkP9+vUxYcIEvPHGG/qOj4iIiMjgdH4O0Z49e9CgQQPExMTA09MT7u7uOHHiBBo2bIjo6GhDxEhERERkUDrPIfLy8oKPjw8+/fRTtfLp06dj7969nFRdmnAOERERGTltf791HiFKTExEQECARvmwYcNw4cIFXZsjIiIiKnE6J0SOjo5ISEjQKE9ISDC6O8+IiIiofNB5UvWIESMwcuRI/PXXX2jVqhVkMhmOHDmCefPmITg42BAxEhERERmUznOIhBBYuHAh5s+fj9u3bwMAVCoVpkyZgvHjx0NmiHkrpQDnEP2Lc4iIiKgMMdiDGZ/16NEjAICtre2rNlFmMCH6FxMiIiIqQwz2YMZnGUMiREREROWfzpOqiYiIiMobJkRERERk9JgQERERkdHTKSHKzc1Fu3btcOnSJUPFQ0RERFTsdEqI5HI5zp07V25vrSciIiLjpPMls0GDBuHbb781RCxEREREJULn2+5zcnLwzTffIDo6Gk2bNoWNjY3a+gULFugtOCIiIqLioHNCdO7cOTRp0gQANOYS8VIaERERlUU6J0QHDx40RBxEREREJeaVb7u/fPky9uzZg6ysLAD/vOOMiIiIqCzSOSG6f/8+OnTogDp16qBbt264c+cOAGD48OF82z0RERGVSTonRBMnToRcLsf169dhbW0tlfft2xdRUVF6DY6IiIioOOg8h2jv3r3Ys2cPqlWrplbu5uaGa9eu6S0wIiIiouKi8whRRkaG2shQgb///hsWFhZ6CYqIiIioOOmcEL399tv47rvvpM8ymQz5+fn47LPP0K5dO70GR0RERFQcdL5k9tlnn6Ft27Y4efIkcnJyMHXqVJw/fx4PHjzA77//bogYiYiIiAxK5xGiBg0a4MyZM2jevDk6deqEjIwM9O7dG6dOncIbb7xhiBiJiIiIDOqVnkOkVCoRFhaGn3/+Gbt27cJ///tfVKlSRd+xAQBu3bqFDz74AA4ODrC2tkbjxo0RFxcnrRdCIDQ0FCqVClZWVmjbti3Onz+v1kZ2djbGjRuHypUrw8bGBj179sTNmzcNEi8RERGVPa+UEKWmpuLzzz9HQEAAhg8fjvnz5+PBgwf6jg2pqalo3bo15HI5du/ejQsXLmD+/PmoWLGiVCcyMhILFizAkiVLEBsbC6VSiU6dOuHRo0dSnaCgIGzbtg2bNm3CkSNH8PjxY/j6+iIvL0/vMRMREVHZIxM6PmL68OHDeOedd2BnZ4emTZsCAOLi4vDw4UPs2LEDbdq00Vtw06dPx++//47ffvut0PVCCKhUKgQFBWHatGkA/hkNcnZ2xrx58zBq1CikpaXB0dER69atQ9++fQEAt2/fhouLC3bt2gUfHx+tYklPT4dCoUBaWhrs7Oz0c4CGZoh3y/GJ5EREVIZo+/ut8wjRmDFj4Ofnh6SkJGzduhVbt27FX3/9hX79+mHMmDGvFfTzduzYgaZNm+L999+Hk5MTvLy8sHLlSml9UlISkpOT0blzZ6nMwsICbdq0wdGjRwH8k6zl5uaq1VGpVHB3d5fqFCY7Oxvp6elqCxEREZVPOidEV65cQXBwMExNTaUyU1NTTJo0CVeuXNFrcH/99ReWL18ONzc37NmzBx9++CHGjx8v3fafnJwMAHB2dlbbztnZWVqXnJwMc3NzVKpUqcg6hYmIiIBCoZAWFxcXfR4aERERlSI6J0RNmjRBYmKiRnliYiIaN26sj5gk+fn5aNKkCcLDw+Hl5YVRo0ZhxIgRWL58uVo92XOXhoQQGmXPe1mdkJAQpKWlScuNGzde/UCIiMoamUz/C1EpptVziM6cOSP9//jx4zFhwgRcvnwZb775JgDg+PHjWLp0KT799FO9BlelShU0aNBArax+/frYsmULgH/udgP+GQV69i63lJQUadRIqVQiJycHqampaqNEKSkpaNWqVZH7trCw4JO3iYiIjIRWCVHjxo0hk8nw7PzrqVOnatTz9/eXJi7rQ+vWrXHx4kW1skuXLqFGjRoAAFdXVyiVSkRHR8PLywsAkJOTg8OHD2PevHkAAG9vb8jlckRHR8PPzw8AcOfOHZw7dw6RkZF6i5WIiIjKLq0SoqSkJEPHUaiJEyeiVatWCA8Ph5+fH2JiYrBixQqsWLECwD+XyoKCghAeHg43Nze4ubkhPDwc1tbW8Pf3BwAoFAoEBAQgODgYDg4OsLe3x+TJk+Hh4YGOHTuWyHERERFR6aJVQlQwIlPcmjVrhm3btiEkJARz586Fq6srFi5ciAEDBkh1pk6diqysLAQGBiI1NRUtWrTA3r17YWtrK9X54osvYGZmBj8/P2RlZaFDhw5Ys2aN2sRwIiIiMl46P4cI+Ofp0b///jtSUlKQn5+vtm78+PF6C6404XOI/sXnEBEZB/79QeWEtr/fOr/cdfXq1fjwww9hbm4OBwcHtTu1ZDJZuU2IiIiIqPzSOSGaPXs2Zs+ejZCQEJiYvNKbP4iIiIhKFZ0zmszMTPTr14/JEBEREZUbOmc1AQEB+N///meIWIiIiIhKhM6TqvPy8uDr64usrCx4eHhALperrV+wYIFeAywtOKn6X5wUSWQc+PcHlRMGm1QdHh6OPXv2oG7dugCgMamaiIioRBjqN4iJnFHQOSFasGABVq1ahSFDhhggHCIiIqLip3NCZGFhgdatWxsiFiKi8oejFkRlgs6TqidMmIDFixcbIhYiIiKiEqHzCFFMTAwOHDiAn3/+GQ0bNtSYVL1161a9BUdERERUHHROiCpWrIjevXsbIhYiIiKiEvFKr+4gIiIiKk/4uGkiIiIyejqPELm6ur7weUN//fXXawVEREREVNx0ToiCgoLUPufm5uLUqVOIiorClClT9BUXERERUbHROSGaMGFCoeVLly7FyZMnXzsgIiIiouKmtzlEXbt2xZYtW/TVHBEREVGx0VtCtHnzZtjb2+urOSIiIqJio/MlMy8vL7VJ1UIIJCcn4969e1i2bJlegyMiIiIqDjonRL169VL7bGJiAkdHR7Rt2xb16tXTV1xERERExUYmBN8QqI309HQoFAqkpaXBzs6upMPRjiFeKsnThUg3ZfXlrmXx74+y2tdkUNr+fus8QkRE5URZ/MEjKm+YxJUaWidEJiYmL3wgIwDIZDI8ffr0tYMiIiIiKk5aJ0Tbtm0rct3Ro0exePFi8OobERERlUVaJ0TvvPOORtkff/yBkJAQ7Ny5EwMGDMDHH3+s1+CIiIiIisMrPYfo9u3bGDFiBDw9PfH06VMkJCRg7dq1qF69ur7jIyIiIjI4nRKitLQ0TJs2DbVr18b58+exf/9+7Ny5E+7u7oaKj4iIiMjgtL5kFhkZiXnz5kGpVGLjxo2FXkIjIiIiKou0fg6RiYkJrKys0LFjR5iamhZZb+vWrXoLrjThc4j+xYnz5QfPj+JRVm+rLovnR1ns67IYcxmj9+cQDRo06KW33RMRERGVRVonRGvWrDFgGEREREQlR29vuyciIiIqq5gQERERkdFjQkRERERGjwkRERERGT0mRERERGT0mBARERGR0StTCVFERARkMhmCgoKkMiEEQkNDoVKpYGVlhbZt2+L8+fNq22VnZ2PcuHGoXLkybGxs0LNnT9y8ebOYoyei1yaTGWYhIqNXZhKi2NhYrFixAp6enmrlkZGRWLBgAZYsWYLY2FgolUp06tQJjx49kuoEBQVh27Zt2LRpE44cOYLHjx/D19cXeXl5xX0YREREVAqViYTo8ePHGDBgAFauXIlKlSpJ5UIILFy4EDNnzkTv3r3h7u6OtWvXIjMzE99//z2Af15I++2332L+/Pno2LEjvLy8sH79epw9exb79u0rqUMiIiKiUqRMJERjxoxB9+7d0bFjR7XypKQkJCcno3PnzlKZhYUF2rRpg6NHjwIA4uLikJubq1ZHpVLB3d1dqkNERETGTetXd5SUTZs2IT4+HrGxsRrrkpOTAQDOzs5q5c7Ozrh27ZpUx9zcXG1kqaBOwfaFyc7ORnZ2tvQ5PT39lY+BiIiISrdSPUJ048YNTJgwAevXr4elpWWR9Z5/6awQ4qUvon1ZnYiICCgUCmlxcXHRLXgiIiIqM0p1QhQXF4eUlBR4e3vDzMwMZmZmOHz4MBYtWgQzMzNpZOj5kZ6UlBRpnVKpRE5ODlJTU4usU5iQkBCkpaVJy40bN/R8dERERFRalOqEqEOHDjh79iwSEhKkpWnTphgwYAASEhJQq1YtKJVKREdHS9vk5OTg8OHDaNWqFQDA29sbcrlcrc6dO3dw7tw5qU5hLCwsYGdnp7YQERFR+VSq5xDZ2trC3d1drczGxgYODg5SeVBQEMLDw+Hm5gY3NzeEh4fD2toa/v7+AACFQoGAgAAEBwfDwcEB9vb2mDx5Mjw8PDQmaRMREZFxKtUJkTamTp2KrKwsBAYGIjU1FS1atMDevXtha2sr1fniiy9gZmYGPz8/ZGVloUOHDlizZg1MTU1LMHIiIiIqLWRCCFHSQZQF6enpUCgUSEtLKzuXzwzxBF6eLuVHWTw/DPVUaUPGXRZjBnh+PIvnR5mm7e93qZ5DRERERFQcmBARERGR0WNCREREREaPCREREREZPSZEREREZPTK/G33RETGTBZmmLuUeI8SGRsmRERE/zJEcsHEgqhs4CUzIiIiMnpMiIiIiMjoMSEiIiIio8eEiIiIiIweEyIiIiIyekyIiIiIyOjxtnsiIip2fMQBlTYcISIiIiKjx4SIiIiIjB4TIiIiIjJ6TIiIiIjI6DEhIiIiIqPHhIiIiIiMHhMiIiIiMnpMiIiIiMjoMSEiIiIio8eEiIiIiIweEyIiIiIyenyXGRERkRb4/rXyjSNEREREZPSYEBEREZHRY0JERERERo8JERERERk9TqomIiIi3cj0P8EcomSnmDMhInpdhviLASjxvxyIiIwJL5kRERGR0WNCREREREaPCREREREZPSZEREREZPSYEBEREZHRY0JERERERq9UJ0QRERFo1qwZbG1t4eTkhF69euHixYtqdYQQCA0NhUqlgpWVFdq2bYvz58+r1cnOzsa4ceNQuXJl2NjYoGfPnrh582ZxHgoRERGVYqU6ITp8+DDGjBmD48ePIzo6Gk+fPkXnzp2RkZEh1YmMjMSCBQuwZMkSxMbGQqlUolOnTnj06JFUJygoCNu2bcOmTZtw5MgRPH78GL6+vsjLyyuJwyIiIqJSplQ/mDEqKkrt8+rVq+Hk5IS4uDi8/fbbEEJg4cKFmDlzJnr37g0AWLt2LZydnfH9999j1KhRSEtLw7fffot169ahY8eOAID169fDxcUF+/btg4+PT7EfFxEREZUupXqE6HlpaWkAAHt7ewBAUlISkpOT0blzZ6mOhYUF2rRpg6NHjwIA4uLikJubq1ZHpVLB3d1dqlOY7OxspKenqy1ERERUPpWZhEgIgUmTJuGtt96Cu7s7ACA5ORkA4OzsrFbX2dlZWpecnAxzc3NUqlSpyDqFiYiIgEKhkBYXFxd9Hg4RERGVImUmIRo7dizOnDmDjRs3aqyTPfcuKSGERtnzXlYnJCQEaWlp0nLjxo1XC5yIiIhKvTKREI0bNw47duzAwYMHUa1aNalcqVQCgMZIT0pKijRqpFQqkZOTg9TU1CLrFMbCwgJ2dnZqCxEREZVPpTohEkJg7Nix2Lp1Kw4cOABXV1e19a6urlAqlYiOjpbKcnJycPjwYbRq1QoA4O3tDblcrlbnzp07OHfunFSHiIiIjFupvstszJgx+P777/HTTz/B1tZWGglSKBSwsrKCTCZDUFAQwsPD4ebmBjc3N4SHh8Pa2hr+/v5S3YCAAAQHB8PBwQH29vaYPHkyPDw8pLvOiIiIyLiV6oRo+fLlAIC2bduqla9evRpDhgwBAEydOhVZWVkIDAxEamoqWrRogb1798LW1laq/8UXX8DMzAx+fn7IyspChw4dsGbNGpiamhbXoRAREVEpJhNCiJIOoixIT0+HQqFAWlpa2ZlP9JKJ5a+Ep4smQ/QzYPi+Lovnh4H7Wham//ZFqN6b/Ldhw8UMGCjuZ84P9vW/TYfqvcl/Gy6DfxYNFLO2v9+leg4RERERUXFgQkRERERGjwkRERERGT0mRERERGT0mBARERGR0SvVt90TkQHvIjJIq0REZRMTolKAP3hEREQli5fMiIiIyOgxISIiIiKjx0tmVLqU1ac+ExFRmcYRIiIiIjJ6TIiIiIjI6DEhIiIiIqPHOURkVAzytmq9t0hERMWNI0RERERk9JgQERERkdFjQkRERERGjwkRERERGT0mRERERGT0mBARERGR0WNCREREREaPCREREREZPSZEREREZPSYEBEREZHRY0JERERERo8JERERERk9JkRERERk9JgQERERkdEzK+kAqOyShcn03qbQe4tEREQvx4SIiAyCCTMRlSW8ZEZERERGjyNERERE5ZQhRmqB8jlayxEiIiIiMnpMiIiIiMjoMSEiIiIio8eEiIiIiIweEyIiIiIyekaVEC1btgyurq6wtLSEt7c3fvvtt5IOiYiIiEoBo0mIfvjhBwQFBWHmzJk4deoU/vOf/6Br1664fv16SYdGREREJcxoEqIFCxYgICAAw4cPR/369bFw4UK4uLhg+fLlJR0aERERlTCjSIhycnIQFxeHzp07q5V37twZR48eLaGoiIiIqLQwiidV//3338jLy4Ozs7NaubOzM5KTkwvdJjs7G9nZ2dLntLQ0AEB6err+A3yi/yYBwACRAs8evwHiNkjMwP/HzZj/v3mDNMrzQ6Np/Tf5b8M8PzSa13+T/zbM80OjeYM0apjeKPjdFuLFz9c2ioSogEym/ghzIYRGWYGIiAiEhYVplLu4uBgkNkNQGKRRg7T6/80brGHDxV0WYwZ4fqg3zPNDo3mDNFoGYwZ4fhTWvEEaNWzMjx49guIF+zCKhKhy5cowNTXVGA1KSUnRGDUqEBISgkmTJkmf8/Pz8eDBAzg4OBSZROlbeno6XFxccOPGDdjZ2RXLPo0R+7l4sJ+LD/u6eLCfi8fr9rMQAo8ePYJKpXphPaNIiMzNzeHt7Y3o6Gi8++67Unl0dDTeeeedQrexsLCAhYWFWlnFihUNGWaR7Ozs+IetGLCfiwf7ufiwr4sH+7l4vE4/v2hkqIBRJEQAMGnSJAwcOBBNmzZFy5YtsWLFCly/fh0ffvhhSYdGREREJcxoEqK+ffvi/v37mDt3Lu7cuQN3d3fs2rULNWrUKOnQiIiIqIQZTUIEAIGBgQgMDCzpMLRmYWGBOXPmaFy6I/1iPxcP9nPxYV8XD/Zz8SiufpaJl92HRkRERFTOGcWDGYmIiIhehAkRERERGT0mRERERGT0mBARERGR0WNCVMIiIiLQrFkz2NrawsnJCb169cLFixfV6gghEBoaCpVKBSsrK7Rt2xbnz58voYjLh4iICMhkMgQFBUll7Gf9uXXrFj744AM4ODjA2toajRs3RlxcnLSeff36nj59io8++giurq6wsrJCrVq1MHfuXOTn50t12M+6+/XXX9GjRw+oVCrIZDJs375dbb02fZqdnY1x48ahcuXKsLGxQc+ePXHz5s1iPIrS70X9nJubi2nTpsHDwwM2NjZQqVQYNGgQbt++rdaGvvuZCVEJO3z4MMaMGYPjx48jOjoaT58+RefOnZGRkSHViYyMxIIFC7BkyRLExsZCqVSiU6dOePToUQlGXnbFxsZixYoV8PT0VCtnP+tHamoqWrduDblcjt27d+PChQuYP3++2pPe2devb968efjqq6+wZMkSJCYmIjIyEp999hkWL14s1WE/6y4jIwONGjXCkiVLCl2vTZ8GBQVh27Zt2LRpE44cOYLHjx/D19cXeXl5xXUYpd6L+jkzMxPx8fGYNWsW4uPjsXXrVly6dAk9e/ZUq6f3fhZUqqSkpAgA4vDhw0IIIfLz84VSqRSffvqpVOfJkydCoVCIr776qqTCLLMePXok3NzcRHR0tGjTpo2YMGGCEIL9rE/Tpk0Tb731VpHr2df60b17dzFs2DC1st69e4sPPvhACMF+1gcAYtu2bdJnbfr04cOHQi6Xi02bNkl1bt26JUxMTERUVFSxxV6WPN/PhYmJiREAxLVr14QQhulnjhCVMmlpaQAAe3t7AEBSUhKSk5PRuXNnqY6FhQXatGmDo0ePlkiMZdmYMWPQvXt3dOzYUa2c/aw/O3bsQNOmTfH+++/DyckJXl5eWLlypbSefa0fb731Fvbv349Lly4BAE6fPo0jR46gW7duANjPhqBNn8bFxSE3N1etjkqlgru7O/v9NaSlpUEmk0kjzYboZ6N6UnVpJ4TApEmT8NZbb8Hd3R0AkJycDABwdnZWq+vs7Ixr164Ve4xl2aZNmxAfH4/Y2FiNdexn/fnrr7+wfPlyTJo0CTNmzEBMTAzGjx8PCwsLDBo0iH2tJ9OmTUNaWhrq1asHU1NT5OXl4ZNPPkH//v0B8Jw2BG36NDk5Gebm5qhUqZJGnYLtSTdPnjzB9OnT4e/vL73c1RD9zISoFBk7dizOnDmDI0eOaKyTyWRqn4UQGmVUtBs3bmDChAnYu3cvLC0ti6zHfn59+fn5aNq0KcLDwwEAXl5eOH/+PJYvX45BgwZJ9djXr+eHH37A+vXr8f3336Nhw4ZISEhAUFAQVCoVBg8eLNVjP+vfq/Qp+/3V5Obmol+/fsjPz8eyZcteWv91+pmXzEqJcePGYceOHTh48CCqVasmlSuVSgDQyHhTUlI0/pVCRYuLi0NKSgq8vb1hZmYGMzMzHD58GIsWLYKZmZnUl+zn11elShU0aNBArax+/fq4fv06AJ7T+jJlyhRMnz4d/fr1g4eHBwYOHIiJEyciIiICAPvZELTpU6VSiZycHKSmphZZh7STm5sLPz8/JCUlITo6WhodAgzTz0yISpgQAmPHjsXWrVtx4MABuLq6qq13dXWFUqlEdHS0VJaTk4PDhw+jVatWxR1umdWhQwecPXsWCQkJ0tK0aVMMGDAACQkJqFWrFvtZT1q3bq3x6IhLly6hRo0aAHhO60tmZiZMTNT/Cjc1NZVuu2c/6582fert7Q25XK5W586dOzh37hz7XQcFydCff/6Jffv2wcHBQW29Qfr5laZik96MHj1aKBQKcejQIXHnzh1pyczMlOp8+umnQqFQiK1bt4qzZ8+K/v37iypVqoj09PQSjLzse/YuMyHYz/oSExMjzMzMxCeffCL+/PNPsWHDBmFtbS3Wr18v1WFfv77BgweLqlWrip9//lkkJSWJrVu3isqVK4upU6dKddjPunv06JE4deqUOHXqlAAgFixYIE6dOiXd3aRNn3744YeiWrVqYt++fSI+Pl60b99eNGrUSDx9+rSkDqvUeVE/5+bmip49e4pq1aqJhIQEtd/G7OxsqQ199zMTohIGoNBl9erVUp38/HwxZ84coVQqhYWFhXj77bfF2bNnSy7ocuL5hIj9rD87d+4U7u7uwsLCQtSrV0+sWLFCbT37+vWlp6eLCRMmiOrVqwtLS0tRq1YtMXPmTLUfDPaz7g4ePFjo38mDBw8WQmjXp1lZWWLs2LHC3t5eWFlZCV9fX3H9+vUSOJrS60X9nJSUVORv48GDB6U29N3PMiGEeLWxJSIiIqLygXOIiIiIyOgxISIiIiKjx4SIiIiIjB4TIiIiIjJ6TIiIiIjI6DEhIiIiIqPHhIiIiIiMHhMiIiozZDIZtm/fXuT6Q4cOQSaT4eHDhyUeCxGVLUyIiOiFhgwZAplMhg8//FBjXWBgIGQyGYYMGaLXfYaGhqJx48Z6bVMXycnJGDduHGrVqgULCwu4uLigR48e2L9/f4nFRESGxYSIiF7KxcUFmzZtQlZWllT25MkTbNy4EdWrVy/ByPTv6tWr8Pb2xoEDBxAZGYmzZ88iKioK7dq1w5gxYwy675ycHIO2T0RFY0JERC/VpEkTVK9eHVu3bpXKtm7dChcXF3h5eanVzc7Oxvjx4+Hk5ARLS0u89dZbiI2NldYXXNbav38/mjZtCmtra7Rq1QoXL14EAKxZswZhYWE4ffo0ZDIZZDIZ1qxZI23/999/491334W1tTXc3NywY8eOQmPOyMiAnZ0dNm/erFa+c+dO2NjY4NGjR4VuVzDqFRMTgz59+qBOnTpo2LAhJk2ahOPHj6vVfVEseXl5CAgIgKurK6ysrFC3bl18+eWXatsPGTIEvXr1QkREBFQqFerUqQMAOHr0KBo3bgxLS0s0bdoU27dvh0wmQ0JCgrTthQsX0K1bN1SoUAHOzs4YOHAg/v77b2n95s2b4eHhASsrKzg4OKBjx47IyMgo9JiJiAkREWlp6NChWL16tfR51apVGDZsmEa9qVOnYsuWLVi7di3i4+NRu3Zt+Pj44MGDB2r1Zs6cifnz5+PkyZMwMzOT2urbty+Cg4PRsGFD3LlzB3fu3EHfvn2l7cLCwuDn54czZ86gW7duGDBggEbbAGBjY4N+/fqpxQwAq1evRp8+fWBra6uxzYMHDxAVFYUxY8bAxsZGY33FihXVPr8olvz8fFSrVg0//vgjLly4gNmzZ2PGjBn48ccf1drYv38/EhMTER0djZ9//hmPHj1Cjx494OHhgfj4eHz88ceYNm2a2jZ37txBmzZt0LhxY5w8eRJRUVG4e/cu/Pz8pPX9+/fHsGHDkJiYiEOHDqF3797gqyuJXuB131hLROXb4MGDxTvvvCPu3bsnLCwsRFJSkrh69aqwtLQU9+7dE++88470JvDHjx8LuVwuNmzYIG2fk5MjVCqViIyMFEL8/1uu9+3bJ9X55ZdfBACRlZUlhBBizpw5olGjRhqxABAfffSR9Pnx48dCJpOJ3bt3q7WdmpoqhBDixIkTwtTUVNy6dUsIIcS9e/eEXC4Xhw4dKvRYT5w4IQCIrVu3vrRfXhZLYQIDA8V7770nfR48eLBwdnZWe0P98uXLhYODg9QXQgixcuVKAUCcOnVKCCHErFmzROfOndXavnHjhgAgLl68KOLi4gQAcfXq1ZceBxH9gyNERKSVypUro3v37li7di1Wr16N7t27o3Llymp1rly5gtzcXLRu3Voqk8vlaN68ORITE9Xqenp6Sv9fpUoVAEBKSspL43h2OxsbG9ja2ha5XfPmzdGwYUN89913AIB169ahevXqePvttwutL/4dQZHJZC+NQ5tYvvrqKzRt2hSOjo6oUKECVq5cievXr6u14eHhAXNzc+nzxYsX4enpCUtLS7XjeFZcXBwOHjyIChUqSEu9evUA/PMdNGrUCB06dICHhwfef/99rFy5EqmpqVodE5GxYkJERFobNmwY1qxZg7Vr1xZ6uayohEIIoVEml8ul/y9Yl5+f/9IYnt2uYNsXbTd8+HDpstnq1asxdOjQIhMeNzc3yGQyjeTtVWL58ccfMXHiRAwbNgx79+5FQkIChg4dqjFx+vlLc4X1lXjuUld+fj569OiBhIQEteXPP//E22+/DVNTU0RHR2P37t1o0KABFi9ejLp16yIpKUmr4yIyRkyIiEhrXbp0QU5ODnJycuDj46Oxvnbt2jA3N8eRI0ekstzcXJw8eRL169fXej/m5ubIy8vTS8wffPABrl+/jkWLFuH8+fMYPHhwkXXt7e3h4+ODpUuXFjoBWZfnG/32229o1aoVAgMD4eXlhdq1a+PKlSsv3a5evXo4c+YMsrOzpbKTJ0+q1WnSpAnOnz+PmjVronbt2mpLQYIlk8nQunVrhIWF4dSpUzA3N8e2bdu0jp/I2DAhIiKtmZqaIjExEYmJiTA1NdVYb2Njg9GjR2PKlCmIiorChQsXMGLECGRmZiIgIEDr/dSsWRNJSUlISEjA33//rZYc6KpSpUro3bs3pkyZgs6dO6NatWovrL9s2TLk5eWhefPm2LJlC/78808kJiZi0aJFaNmypdb7rV27Nk6ePIk9e/bg0qVLmDVrltrddkXx9/dHfn4+Ro4cicTEROzZsweff/45gP8fSRszZgwePHiA/v37IyYmBn/99Rf27t2LYcOGIS8vDydOnEB4eDhOnjyJ69evY+vWrbh3755OSSmRsWFCREQ6sbOzg52dXZHrP/30U7z33nsYOHAgmjRpgsuXL2PPnj2oVKmS1vt477330KVLF7Rr1w6Ojo7YuHHja8UcEBCAnJycQi/zPc/V1RXx8fFo164dgoOD4e7ujk6dOmH//v1Yvny51vv88MMP0bt3b/Tt2xctWrTA/fv3ERgY+NLt7OzssHPnTiQkJKBx48aYOXMmZs+eDQDSvCKVSoXff/8deXl58PHxgbu7OyZMmACFQgETExPY2dnh119/Rbdu3VCnTh189NFHmD9/Prp27ap1/ETGRiaevzhNRFTObNiwARMmTMDt27fVJjCXFRs2bMDQoUORlpYGKyurkg6HqFwyK+kAiIgMJTMzE0lJSYiIiMCoUaPKTDL03XffoVatWqhatSpOnz6NadOmwc/Pj8kQkQHxkhkRlVuRkZFo3LgxnJ2dERISUtLhaC05ORkffPAB6tevj4kTJ+L999/HihUrSjosonKNl8yIiIjI6HGEiIiIiIweEyIiIiIyekyIiIiIyOgxISIiIiKjx4SIiIiIjB4TIiIiIjJ6TIiIiIjI6DEhIiIiIqPHhIiIiIiM3v8BVfTDfZF2sUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc_churn_no = df1[df1.Churn==\"No\"].MonthlyCharges\n",
    "mc_churn_yes = df1[df1.Churn==\"Yes\"].MonthlyCharges\n",
    "\n",
    "plt.xlabel(\"Monthly Charges\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.title(\"Customer Churn Prediction Visualization\")\n",
    "\n",
    "plt.hist([mc_churn_yes, mc_churn_no], rwidth=0.95, color=[\"green\",\"red\"], label=[\"Churn=Yes\",\"Churn=No\"])\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7f107ee-b892-4487-87ad-1518283b796b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# most of the columns are yes, no etc. let's print unique values in object columns to see data values\n",
    "\n",
    "def print_unique_col_values(df):\n",
    "    for column in df:\n",
    "        if df[column].dtypes == \"object\":\n",
    "            print(f\"{column}: {df[column].unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61390a89-fb93-43f9-9de3-c8d324ebb75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No phone service' 'No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup: ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection: ['No' 'Yes' 'No internet service']\n",
      "TechSupport: ['No' 'Yes' 'No internet service']\n",
      "StreamingTV: ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies: ['No' 'Yes' 'No internet service']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "TotalCharges: ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_col_values(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf4087b3-7e93-4317-b9f3-ab5c50b69a8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kx/sh62mr7x1wqcyp7f9f_glpvh0000gn/T/ipykernel_3136/2715025249.py:2: 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",
      "  df1.replace(\"No internet service\", \"No\", inplace=True)\n",
      "/var/folders/kx/sh62mr7x1wqcyp7f9f_glpvh0000gn/T/ipykernel_3136/2715025249.py:3: 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",
      "  df1.replace(\"No phone service\", \"No\", inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Some of the columns have no internet service or no phone service, that can be replaced with a simple No\n",
    "df1.replace(\"No internet service\", \"No\", inplace=True)\n",
    "df1.replace(\"No phone service\", \"No\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5a827df-056f-41db-9184-73113491004e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes']\n",
      "OnlineBackup: ['Yes' 'No']\n",
      "DeviceProtection: ['No' 'Yes']\n",
      "TechSupport: ['No' 'Yes']\n",
      "StreamingTV: ['No' 'Yes']\n",
      "StreamingMovies: ['No' 'Yes']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_col_values(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21264c3a-e4b8-4e97-b9cc-b443acd353c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kx/sh62mr7x1wqcyp7f9f_glpvh0000gn/T/ipykernel_3136/1443351738.py:7: 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",
      "  df1[col].replace({\"Yes\":1, \"No\":0}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# convert yes and no to 1 and 0\n",
    "\n",
    "yes_no_columns = ['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
    "                  'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
    "\n",
    "for col in yes_no_columns:\n",
    "    df1[col].replace({\"Yes\":1, \"No\":0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5eed4c56-893d-478e-8e5f-3963d3734f64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: [1 0]\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "Churn: [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "    print(f\"{col}: {df1[col].unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34e0b5ba-1b9a-4e88-a6cd-f81829d04a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kx/sh62mr7x1wqcyp7f9f_glpvh0000gn/T/ipykernel_3136/3198163853.py:1: 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",
      "  df1[\"gender\"].replace({\"Female\":1, \"Male\":0}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df1[\"gender\"].replace({\"Female\":1, \"Male\":0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "076d4aa1-3892-4e3b-a1b1-fddbba978743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       1\n",
       "       ..\n",
       "7038    0\n",
       "7039    1\n",
       "7040    1\n",
       "7041    0\n",
       "7042    0\n",
       "Name: gender, Length: 7032, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"gender\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d77793-a84d-47a5-a1aa-73b4d2e9880e",
   "metadata": {},
   "source": [
    "### One Hot Encoding for Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c427112-84e0-49b6-b286-fbc816245bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
       "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.get_dummies(data=df1, columns=[\"InternetService\",\"Contract\",\"PaymentMethod\"])\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31b6086b-70d9-42d6-9795-07f7fac21a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2390</th>\n",
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       "      <th>494</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5672</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "1765       1              0        1           0      58             1   \n",
       "2390       1              0        1           1       8             1   \n",
       "494        0              0        0           0       1             1   \n",
       "5672       1              0        1           0      49             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "1765              1               1             1                 1  ...   \n",
       "2390              0               0             0                 0  ...   \n",
       "494               0               0             0                 0  ...   \n",
       "5672              1               1             1                 1  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "1765                    0                            1                   0   \n",
       "2390                    0                            0                   1   \n",
       "494                     0                            0                   1   \n",
       "5672                    1                            0                   0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "1765                        0                  1                  0   \n",
       "2390                        0                  1                  0   \n",
       "494                         1                  0                  0   \n",
       "5672                        0                  1                  0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "1765                                        0   \n",
       "2390                                        0   \n",
       "494                                         0   \n",
       "5672                                        0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "1765                                      1                               0   \n",
       "2390                                      0                               1   \n",
       "494                                       0                               0   \n",
       "5672                                      0                               1   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "1765                           0  \n",
       "2390                           0  \n",
       "494                            1  \n",
       "5672                           0  \n",
       "\n",
       "[4 rows x 27 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f712f57-d049-4ff4-bcd1-4273409ad847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                       int64\n",
       "SeniorCitizen                                int64\n",
       "Partner                                      int64\n",
       "Dependents                                   int64\n",
       "tenure                                       int64\n",
       "PhoneService                                 int64\n",
       "MultipleLines                                int64\n",
       "OnlineSecurity                               int64\n",
       "OnlineBackup                                 int64\n",
       "DeviceProtection                             int64\n",
       "TechSupport                                  int64\n",
       "StreamingTV                                  int64\n",
       "StreamingMovies                              int64\n",
       "PaperlessBilling                             int64\n",
       "MonthlyCharges                             float64\n",
       "TotalCharges                               float64\n",
       "Churn                                        int64\n",
       "InternetService_DSL                          uint8\n",
       "InternetService_Fiber optic                  uint8\n",
       "InternetService_No                           uint8\n",
       "Contract_Month-to-month                      uint8\n",
       "Contract_One year                            uint8\n",
       "Contract_Two year                            uint8\n",
       "PaymentMethod_Bank transfer (automatic)      uint8\n",
       "PaymentMethod_Credit card (automatic)        uint8\n",
       "PaymentMethod_Electronic check               uint8\n",
       "PaymentMethod_Mailed check                   uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3fa8ac5-fc2f-40eb-9759-41fda61f642b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_scale = ['tenure','MonthlyCharges','TotalCharges']\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df2[cols_to_scale] = scaler.fit_transform(df2[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c69b220d-4df0-4a9f-9b76-4cd3725a665a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: [1 0]\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [0.         0.46478873 0.01408451 0.61971831 0.09859155 0.29577465\n",
      " 0.12676056 0.38028169 0.85915493 0.16901408 0.21126761 0.8028169\n",
      " 0.67605634 0.33802817 0.95774648 0.71830986 0.98591549 0.28169014\n",
      " 0.15492958 0.4084507  0.64788732 1.         0.22535211 0.36619718\n",
      " 0.05633803 0.63380282 0.14084507 0.97183099 0.87323944 0.5915493\n",
      " 0.1971831  0.83098592 0.23943662 0.91549296 0.11267606 0.02816901\n",
      " 0.42253521 0.69014085 0.88732394 0.77464789 0.08450704 0.57746479\n",
      " 0.47887324 0.66197183 0.3943662  0.90140845 0.52112676 0.94366197\n",
      " 0.43661972 0.76056338 0.50704225 0.49295775 0.56338028 0.07042254\n",
      " 0.04225352 0.45070423 0.92957746 0.30985915 0.78873239 0.84507042\n",
      " 0.18309859 0.26760563 0.73239437 0.54929577 0.81690141 0.32394366\n",
      " 0.6056338  0.25352113 0.74647887 0.70422535 0.35211268 0.53521127]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\n",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "PaperlessBilling: [1 0]\n",
      "MonthlyCharges: [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
      "TotalCharges: [0.0012751  0.21586661 0.01031041 ... 0.03780868 0.03321025 0.78764136]\n",
      "Churn: [0 1]\n",
      "InternetService_DSL: [1 0]\n",
      "InternetService_Fiber optic: [0 1]\n",
      "InternetService_No: [0 1]\n",
      "Contract_Month-to-month: [1 0]\n",
      "Contract_One year: [0 1]\n",
      "Contract_Two year: [0 1]\n",
      "PaymentMethod_Bank transfer (automatic): [0 1]\n",
      "PaymentMethod_Credit card (automatic): [0 1]\n",
      "PaymentMethod_Electronic check: [1 0]\n",
      "PaymentMethod_Mailed check: [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df2:\n",
    "    print(f'{col}: {df2[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d195f2bb-81c7-4487-970d-1033f58473d3",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a884676-e0db-4430-9f6b-ec738d4b9e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop(\"Churn\", axis=\"columns\")\n",
    "y = df2[\"Churn\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b19a1813-cee9-4c7f-ac05-37105b316f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5625, 26)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b67b82b-a830-450c-bd1e-636db1d8161c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1407, 26)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d62861a-e903-4f9e-9aa8-a675e7ec8899",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.169014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2773</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.704225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5387</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.985915</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "5664       1              1        0           0  0.126761             1   \n",
       "101        1              0        1           1  0.000000             1   \n",
       "2621       0              0        1           0  0.985915             1   \n",
       "392        1              1        0           0  0.014085             1   \n",
       "1327       0              0        1           0  0.816901             1   \n",
       "3607       1              0        0           0  0.169014             1   \n",
       "2773       0              0        1           0  0.323944             0   \n",
       "1936       1              0        1           0  0.704225             1   \n",
       "5387       0              0        0           0  0.042254             0   \n",
       "4331       0              0        0           0  0.985915             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "5664              0               0             0                 1  ...   \n",
       "101               0               0             0                 0  ...   \n",
       "2621              0               0             1                 1  ...   \n",
       "392               0               0             0                 0  ...   \n",
       "1327              1               0             0                 1  ...   \n",
       "3607              0               1             0                 0  ...   \n",
       "2773              0               0             0                 1  ...   \n",
       "1936              0               1             1                 0  ...   \n",
       "5387              0               0             0                 0  ...   \n",
       "4331              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "5664                    0                            1                   0   \n",
       "101                     0                            0                   1   \n",
       "2621                    1                            0                   0   \n",
       "392                     1                            0                   0   \n",
       "1327                    0                            1                   0   \n",
       "3607                    1                            0                   0   \n",
       "2773                    1                            0                   0   \n",
       "1936                    1                            0                   0   \n",
       "5387                    1                            0                   0   \n",
       "4331                    0                            0                   1   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "5664                        1                  0                  0   \n",
       "101                         1                  0                  0   \n",
       "2621                        0                  0                  1   \n",
       "392                         1                  0                  0   \n",
       "1327                        0                  1                  0   \n",
       "3607                        0                  1                  0   \n",
       "2773                        1                  0                  0   \n",
       "1936                        0                  1                  0   \n",
       "5387                        1                  0                  0   \n",
       "4331                        0                  0                  1   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "5664                                        0   \n",
       "101                                         0   \n",
       "2621                                        0   \n",
       "392                                         0   \n",
       "1327                                        1   \n",
       "3607                                        0   \n",
       "2773                                        0   \n",
       "1936                                        1   \n",
       "5387                                        0   \n",
       "4331                                        1   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "5664                                      1                               0   \n",
       "101                                       0                               1   \n",
       "2621                                      1                               0   \n",
       "392                                       0                               1   \n",
       "1327                                      0                               0   \n",
       "3607                                      0                               0   \n",
       "2773                                      0                               1   \n",
       "1936                                      0                               0   \n",
       "5387                                      0                               1   \n",
       "4331                                      0                               0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "5664                           0  \n",
       "101                            0  \n",
       "2621                           0  \n",
       "392                            0  \n",
       "1327                           0  \n",
       "3607                           1  \n",
       "2773                           0  \n",
       "1936                           0  \n",
       "5387                           0  \n",
       "4331                           0  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "84a647c3-4b4b-4bae-8755-2569d6bbc7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27c4e02-c76b-455e-823d-9bff04e81515",
   "metadata": {},
   "source": [
    "### Build a Model (ANN) in Tensorflow/Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "409c4fd6-338e-4581-9ba7-afdf86c9bef9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-16 09:42:49.882210: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-01-16 09:42:49.882304: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-16 09:42:50.132523: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-01-16 09:42:50.371973: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176/176 [==============================] - 2s 6ms/step - loss: 0.5243 - accuracy: 0.7436\n",
      "Epoch 2/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4397 - accuracy: 0.7883\n",
      "Epoch 3/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4253 - accuracy: 0.7982\n",
      "Epoch 4/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.4199 - accuracy: 0.8009\n",
      "Epoch 5/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.4169 - accuracy: 0.8028\n",
      "Epoch 6/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4154 - accuracy: 0.8043\n",
      "Epoch 7/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4141 - accuracy: 0.8037\n",
      "Epoch 8/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4129 - accuracy: 0.8052\n",
      "Epoch 9/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4126 - accuracy: 0.8041\n",
      "Epoch 10/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4122 - accuracy: 0.8046\n",
      "Epoch 11/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4117 - accuracy: 0.8057\n",
      "Epoch 12/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4106 - accuracy: 0.8084\n",
      "Epoch 13/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4102 - accuracy: 0.8075\n",
      "Epoch 14/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4093 - accuracy: 0.8059\n",
      "Epoch 15/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.4100 - accuracy: 0.8076\n",
      "Epoch 16/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4083 - accuracy: 0.8044\n",
      "Epoch 17/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4072 - accuracy: 0.8059\n",
      "Epoch 18/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4067 - accuracy: 0.8100\n",
      "Epoch 19/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4061 - accuracy: 0.8103\n",
      "Epoch 20/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4046 - accuracy: 0.8126\n",
      "Epoch 21/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4046 - accuracy: 0.8087\n",
      "Epoch 22/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4039 - accuracy: 0.8091\n",
      "Epoch 23/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4036 - accuracy: 0.8107\n",
      "Epoch 24/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.4034 - accuracy: 0.8110\n",
      "Epoch 25/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.4028 - accuracy: 0.8123\n",
      "Epoch 26/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4022 - accuracy: 0.8126\n",
      "Epoch 27/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4011 - accuracy: 0.8121\n",
      "Epoch 28/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4002 - accuracy: 0.8114\n",
      "Epoch 29/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.4007 - accuracy: 0.8112\n",
      "Epoch 30/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3995 - accuracy: 0.8133\n",
      "Epoch 31/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3991 - accuracy: 0.8140\n",
      "Epoch 32/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3990 - accuracy: 0.8142\n",
      "Epoch 33/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3982 - accuracy: 0.8149\n",
      "Epoch 34/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3980 - accuracy: 0.8140\n",
      "Epoch 35/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3978 - accuracy: 0.8130\n",
      "Epoch 36/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3966 - accuracy: 0.8130\n",
      "Epoch 37/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3963 - accuracy: 0.8165\n",
      "Epoch 38/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3969 - accuracy: 0.8148\n",
      "Epoch 39/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3955 - accuracy: 0.8156\n",
      "Epoch 40/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3961 - accuracy: 0.8151\n",
      "Epoch 41/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3952 - accuracy: 0.8130\n",
      "Epoch 42/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3949 - accuracy: 0.8160\n",
      "Epoch 43/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3959 - accuracy: 0.8124\n",
      "Epoch 44/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3946 - accuracy: 0.8148\n",
      "Epoch 45/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3949 - accuracy: 0.8144\n",
      "Epoch 46/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3939 - accuracy: 0.8158\n",
      "Epoch 47/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3934 - accuracy: 0.8164\n",
      "Epoch 48/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3933 - accuracy: 0.8192\n",
      "Epoch 49/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3935 - accuracy: 0.8146\n",
      "Epoch 50/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3924 - accuracy: 0.8162\n",
      "Epoch 51/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3928 - accuracy: 0.8155\n",
      "Epoch 52/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3923 - accuracy: 0.8158\n",
      "Epoch 53/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3925 - accuracy: 0.8155\n",
      "Epoch 54/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3911 - accuracy: 0.8162\n",
      "Epoch 55/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3926 - accuracy: 0.8121\n",
      "Epoch 56/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3918 - accuracy: 0.8155\n",
      "Epoch 57/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3909 - accuracy: 0.8137\n",
      "Epoch 58/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3906 - accuracy: 0.8160\n",
      "Epoch 59/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3908 - accuracy: 0.8181\n",
      "Epoch 60/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3904 - accuracy: 0.8156\n",
      "Epoch 61/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3905 - accuracy: 0.8172\n",
      "Epoch 62/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3903 - accuracy: 0.8144\n",
      "Epoch 63/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3895 - accuracy: 0.8169\n",
      "Epoch 64/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3896 - accuracy: 0.8153\n",
      "Epoch 65/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3889 - accuracy: 0.8165\n",
      "Epoch 66/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3896 - accuracy: 0.8181\n",
      "Epoch 67/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3896 - accuracy: 0.8171\n",
      "Epoch 68/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3895 - accuracy: 0.8130\n",
      "Epoch 69/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3886 - accuracy: 0.8160\n",
      "Epoch 70/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3891 - accuracy: 0.8178\n",
      "Epoch 71/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3893 - accuracy: 0.8183\n",
      "Epoch 72/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3885 - accuracy: 0.8169\n",
      "Epoch 73/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3879 - accuracy: 0.8190\n",
      "Epoch 74/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3883 - accuracy: 0.8156\n",
      "Epoch 75/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3885 - accuracy: 0.8164\n",
      "Epoch 76/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3886 - accuracy: 0.8142\n",
      "Epoch 77/100\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3879 - accuracy: 0.8169\n",
      "Epoch 78/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3890 - accuracy: 0.8155\n",
      "Epoch 79/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3872 - accuracy: 0.8151\n",
      "Epoch 80/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3873 - accuracy: 0.8185\n",
      "Epoch 81/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3868 - accuracy: 0.8171\n",
      "Epoch 82/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3874 - accuracy: 0.8165\n",
      "Epoch 83/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3876 - accuracy: 0.8174\n",
      "Epoch 84/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3872 - accuracy: 0.8167\n",
      "Epoch 85/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3865 - accuracy: 0.8183\n",
      "Epoch 86/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3861 - accuracy: 0.8148\n",
      "Epoch 87/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3866 - accuracy: 0.8155\n",
      "Epoch 88/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3858 - accuracy: 0.8183\n",
      "Epoch 89/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3862 - accuracy: 0.8187\n",
      "Epoch 90/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3864 - accuracy: 0.8201\n",
      "Epoch 91/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3854 - accuracy: 0.8199\n",
      "Epoch 92/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3858 - accuracy: 0.8171\n",
      "Epoch 93/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3857 - accuracy: 0.8188\n",
      "Epoch 94/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3859 - accuracy: 0.8194\n",
      "Epoch 95/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3858 - accuracy: 0.8174\n",
      "Epoch 96/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3855 - accuracy: 0.8213\n",
      "Epoch 97/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3847 - accuracy: 0.8197\n",
      "Epoch 98/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3855 - accuracy: 0.8190\n",
      "Epoch 99/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3850 - accuracy: 0.8185\n",
      "Epoch 100/100\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3848 - accuracy: 0.8178\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x290b78400>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(20, input_shape=(26,), activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fa8b7e64-552e-41c7-84b0-67184c2204a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-16 09:44:22.611130: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.22520147],\n",
       "       [0.37053412],\n",
       "       [0.01249378],\n",
       "       [0.6939377 ],\n",
       "       [0.41018167]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = model.predict(X_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e25888ca-d40b-45a5-9903-160daa653926",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for element in yp:\n",
    "    if element > 0.5:\n",
    "        y_pred.append(1)\n",
    "    else:\n",
    "        y_pred.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a3b6eac9-1257-4330-99f1-8e65e67adc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 1, 0, 1, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1b90742-8d1c-43e4-9dbd-f64e48a9e3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.90      0.86       999\n",
      "           1       0.68      0.50      0.58       408\n",
      "\n",
      "    accuracy                           0.79      1407\n",
      "   macro avg       0.75      0.70      0.72      1407\n",
      "weighted avg       0.78      0.79      0.78      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "543da8ed-6c2b-4c61-8767-e32352458f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_pred)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Truth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "105776b4-9afb-401c-afa8-e39eac9c94e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1407,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e116a2b4-8b5b-4f9d-8ed3-4bf569ece054",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13d8ec16-fe87-4730-bbfd-ef32335a3237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# doğru tahmin oranı\n",
    "round((901+206)/(901+98+202+206),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d838d22-1c7f-4c37-bcbf-51056d151b5c",
   "metadata": {},
   "source": [
    "### Precision for 0 class. i.e. Precision for customers who did not churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "20e17217-4d3e-4d06-b837-5600578701f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 tahminleri arasında doğru tahmin oranı\n",
    "round(901/(901+202),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f36c78-59f6-43ed-bfa0-c12813134389",
   "metadata": {},
   "source": [
    "### Precision for 1 class. i.e. Precision for customers who actually churned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6c3621d7-813a-47d8-84cc-5fc1fbb433f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 tahminleri arasında doğru tahmin oranı\n",
    "round(206/(206+98),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cde03c4-1b87-4a2f-84f3-114e93879a94",
   "metadata": {},
   "source": [
    "### Recall for 0 class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "166004f7-1f1e-4ff5-ad9b-2c6e698bed76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gerçekte 0 olanlar arasında doğru tahmin oranı\n",
    "round(901/(901+98),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ab7905-fb27-4bc3-8f6e-06feffd9a72f",
   "metadata": {},
   "source": [
    "### Recall for 1 class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "22979594-c19d-4d89-b8e2-4bf1e883486f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gerçekte 1 olanlar arasında doğru tahmin oranı\n",
    "round(206/(206+202),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a85c448-0277-4d96-93ec-4674450cff94",
   "metadata": {},
   "source": [
    "# Bank Churn Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ac203dae-8764-41bf-88c2-80ae305988aa",
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Churn_Modelling.csv\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8e478a70-fc01-4a65-8d52-934d451a4a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
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       "      <th>0</th>\n",
       "      <td>619</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
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       "      <td>42</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
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      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"RowNumber\",\"CustomerId\",\"Surname\"], axis=1, inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbc826c-d5ed-4206-b577-872c9e906665",
   "metadata": {},
   "source": [
    "### Scaling --> CreditScore - Age - Tenure - Balance - EstimatedSalary - NumOfProducts\n",
    "### One-Hot Encoding (Dummy) --> Geography\n",
    "### 0-1 --> Gender - HasCrCard - IsActiveMember - Exited"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a603d9a3-9d1e-455a-98bb-056de8ac784e",
   "metadata": {},
   "source": [
    "## 0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "11658e3a-118d-4f04-a13e-7264814c97ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gender\"].replace({\"Female\":1, \"Male\":0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "888a77b3-59e5-4fb5-a89f-c71ca315d9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France       1   42       2       0.00              1   \n",
       "1          608     Spain       1   41       1   83807.86              1   \n",
       "2          502    France       1   42       8  159660.80              3   \n",
       "3          699    France       1   39       1       0.00              2   \n",
       "4          850     Spain       1   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7655f75d-7fc5-48e8-9a6b-1fd48198c1db",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e1a920fc-445a-43f6-ade3-12f7a145f347",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.get_dummies(data=df, columns=[\"Geography\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0d248a51-8215-4f8d-9c8f-2bf7020116ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619       1   42       2       0.00              1          1   \n",
       "1          608       1   41       1   83807.86              1          0   \n",
       "2          502       1   42       8  159660.80              3          1   \n",
       "3          699       1   39       1       0.00              2          0   \n",
       "4          850       1   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1        101348.88       1                 1   \n",
       "1               1        112542.58       0                 0   \n",
       "2               0        113931.57       1                 1   \n",
       "3               0         93826.63       0                 1   \n",
       "4               1         79084.10       0                 0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                  0                0  \n",
       "1                  0                1  \n",
       "2                  0                0  \n",
       "3                  0                0  \n",
       "4                  0                1  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)"
   ]
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  {
   "cell_type": "markdown",
   "id": "e9e34ddb-3669-4926-bc44-ad46b15403be",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "36736774-7ef3-421a-823d-9e151b22b510",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "cols_to_scale = [\"CreditScore\",\"Age\",\"Tenure\",\"Balance\",\"EstimatedSalary\",\"NumOfProducts\"]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df1[cols_to_scale] = scaler.fit_transform(df1[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d4a87a01-9cda-4ee0-adcd-d3ced8027276",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.538</td>\n",
       "      <td>1</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>0.334031</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.562709</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.304</td>\n",
       "      <td>1</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.636357</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.569654</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.283784</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.000</td>\n",
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       "      <td>0.337838</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.500246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.395400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender       Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "0        0.538       1  0.324324     0.2  0.000000       0.000000          1   \n",
       "1        0.516       1  0.310811     0.1  0.334031       0.000000          0   \n",
       "2        0.304       1  0.324324     0.8  0.636357       0.666667          1   \n",
       "3        0.698       1  0.283784     0.1  0.000000       0.333333          0   \n",
       "4        1.000       1  0.337838     0.2  0.500246       0.000000          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1         0.506735       1                 1   \n",
       "1               1         0.562709       0                 0   \n",
       "2               0         0.569654       1                 1   \n",
       "3               0         0.469120       0                 1   \n",
       "4               1         0.395400       0                 0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                  0                0  \n",
       "1                  0                1  \n",
       "2                  0                0  \n",
       "3                  0                0  \n",
       "4                  0                1  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc50622b-ef44-40b6-a593-903a2225b2a3",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e452b26e-6c61-4ba0-a45d-64b8d13e388f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop(\"Exited\", axis=1)\n",
    "y = df1[\"Exited\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "816ecd8d-75f2-4286-9785-77955fd4fea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 12)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "61349461-8eee-4fe2-a3b9-1431f11e4b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 12)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "335eaef3-401d-451a-92f8-6541c3393081",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Geography_France</th>\n",
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       "      <th>Geography_Spain</th>\n",
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       "  </thead>\n",
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       "      <th>612</th>\n",
       "      <td>0.510</td>\n",
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       "      <th>5622</th>\n",
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       "      <th>8622</th>\n",
       "      <td>0.712</td>\n",
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       "      <td>0.243243</td>\n",
       "      <td>0.9</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8314</th>\n",
       "      <td>0.586</td>\n",
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       "      <td>0.202703</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.764975</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8609</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0</td>\n",
       "      <td>0.297297</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.936749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>0.442</td>\n",
       "      <td>0</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.318653</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.564138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6389</th>\n",
       "      <td>0.510</td>\n",
       "      <td>1</td>\n",
       "      <td>0.162162</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.539750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.932125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6667</th>\n",
       "      <td>0.544</td>\n",
       "      <td>1</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.624826</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>0.790</td>\n",
       "      <td>0</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250197</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Gender       Age  Tenure   Balance  NumOfProducts  \\\n",
       "612         0.510       1  0.621622     0.2  0.516368       0.000000   \n",
       "5622        0.640       0  0.270270     1.0  0.356388       0.000000   \n",
       "8622        0.712       0  0.243243     0.9  0.233446       0.333333   \n",
       "8314        0.586       0  0.202703     0.4  0.000000       0.333333   \n",
       "8609        0.656       0  0.297297     0.1  0.000000       0.333333   \n",
       "3436        0.442       0  0.310811     0.8  0.000000       0.000000   \n",
       "890         0.876       0  0.256757     0.8  0.564138       0.000000   \n",
       "6389        0.510       1  0.162162     0.9  0.539750       0.000000   \n",
       "6667        0.544       1  0.108108     0.8  0.000000       0.333333   \n",
       "2279        0.790       0  0.216216     0.9  0.000000       0.333333   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Geography_France  \\\n",
       "612           1               1         0.067958                 1   \n",
       "5622          0               0         0.721388                 1   \n",
       "8622          1               0         0.203832                 0   \n",
       "8314          1               1         0.764975                 1   \n",
       "8609          1               1         0.936749                 1   \n",
       "3436          1               1         0.318653                 1   \n",
       "890           0               0         0.330040                 0   \n",
       "6389          0               1         0.932125                 1   \n",
       "6667          1               1         0.624826                 0   \n",
       "2279          1               0         0.250197                 0   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  \n",
       "612                   0                0  \n",
       "5622                  0                0  \n",
       "8622                  1                0  \n",
       "8314                  0                0  \n",
       "8609                  0                0  \n",
       "3436                  0                0  \n",
       "890                   0                1  \n",
       "6389                  0                0  \n",
       "6667                  0                1  \n",
       "2279                  0                1  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "101f3251-a382-46b5-94db-a8f1a314eb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac72aff-f584-43a1-a455-767825bef179",
   "metadata": {},
   "source": [
    "## Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c19fe906-abea-4a7c-bd81-e2b989dee8fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " 11/250 [>.............................] - ETA: 1s - loss: 0.7884 - accuracy: 0.4375 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-17 14:32:33.911688: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250/250 [==============================] - 1s 4ms/step - loss: 0.5162 - accuracy: 0.7723\n",
      "Epoch 2/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4646 - accuracy: 0.8028\n",
      "Epoch 3/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4487 - accuracy: 0.8084\n",
      "Epoch 4/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4382 - accuracy: 0.8148\n",
      "Epoch 5/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4330 - accuracy: 0.8140\n",
      "Epoch 6/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4280 - accuracy: 0.8141\n",
      "Epoch 7/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4260 - accuracy: 0.8150\n",
      "Epoch 8/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4241 - accuracy: 0.8173\n",
      "Epoch 9/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4226 - accuracy: 0.8165\n",
      "Epoch 10/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4196 - accuracy: 0.8173\n",
      "Epoch 11/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4172 - accuracy: 0.8163\n",
      "Epoch 12/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4154 - accuracy: 0.8183\n",
      "Epoch 13/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4127 - accuracy: 0.8188\n",
      "Epoch 14/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.4092 - accuracy: 0.8165\n",
      "Epoch 15/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4065 - accuracy: 0.8193\n",
      "Epoch 16/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.4034 - accuracy: 0.8240\n",
      "Epoch 17/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3999 - accuracy: 0.8241\n",
      "Epoch 18/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3959 - accuracy: 0.8255\n",
      "Epoch 19/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3918 - accuracy: 0.8276\n",
      "Epoch 20/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3877 - accuracy: 0.8304\n",
      "Epoch 21/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3833 - accuracy: 0.8309\n",
      "Epoch 22/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3796 - accuracy: 0.8344\n",
      "Epoch 23/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3755 - accuracy: 0.8374\n",
      "Epoch 24/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3721 - accuracy: 0.8405\n",
      "Epoch 25/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3681 - accuracy: 0.8434\n",
      "Epoch 26/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3649 - accuracy: 0.8453\n",
      "Epoch 27/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3630 - accuracy: 0.8493\n",
      "Epoch 28/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3612 - accuracy: 0.8485\n",
      "Epoch 29/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3594 - accuracy: 0.8486\n",
      "Epoch 30/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3576 - accuracy: 0.8506\n",
      "Epoch 31/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3557 - accuracy: 0.8505\n",
      "Epoch 32/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3551 - accuracy: 0.8524\n",
      "Epoch 33/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3537 - accuracy: 0.8535\n",
      "Epoch 34/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3529 - accuracy: 0.8546\n",
      "Epoch 35/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3522 - accuracy: 0.8551\n",
      "Epoch 36/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3512 - accuracy: 0.8548\n",
      "Epoch 37/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3516 - accuracy: 0.8533\n",
      "Epoch 38/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3510 - accuracy: 0.8523\n",
      "Epoch 39/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3497 - accuracy: 0.8574\n",
      "Epoch 40/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3492 - accuracy: 0.8573\n",
      "Epoch 41/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3496 - accuracy: 0.8563\n",
      "Epoch 42/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3493 - accuracy: 0.8574\n",
      "Epoch 43/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3483 - accuracy: 0.8575\n",
      "Epoch 44/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3472 - accuracy: 0.8585\n",
      "Epoch 45/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3473 - accuracy: 0.8584\n",
      "Epoch 46/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3468 - accuracy: 0.8561\n",
      "Epoch 47/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3468 - accuracy: 0.8563\n",
      "Epoch 48/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3476 - accuracy: 0.8585\n",
      "Epoch 49/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3469 - accuracy: 0.8573\n",
      "Epoch 50/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3468 - accuracy: 0.8575\n",
      "Epoch 51/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3459 - accuracy: 0.8583\n",
      "Epoch 52/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3452 - accuracy: 0.8583\n",
      "Epoch 53/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3462 - accuracy: 0.8568\n",
      "Epoch 54/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3446 - accuracy: 0.8581\n",
      "Epoch 55/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3443 - accuracy: 0.8594\n",
      "Epoch 56/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3439 - accuracy: 0.8598\n",
      "Epoch 57/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3445 - accuracy: 0.8593\n",
      "Epoch 58/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3434 - accuracy: 0.8590\n",
      "Epoch 59/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3437 - accuracy: 0.8586\n",
      "Epoch 60/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3439 - accuracy: 0.8613\n",
      "Epoch 61/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3436 - accuracy: 0.8598\n",
      "Epoch 62/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3432 - accuracy: 0.8614\n",
      "Epoch 63/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3425 - accuracy: 0.8604\n",
      "Epoch 64/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3431 - accuracy: 0.8599\n",
      "Epoch 65/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3424 - accuracy: 0.8603\n",
      "Epoch 66/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3426 - accuracy: 0.8620\n",
      "Epoch 67/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3415 - accuracy: 0.8595\n",
      "Epoch 68/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3414 - accuracy: 0.8595\n",
      "Epoch 69/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3421 - accuracy: 0.8619\n",
      "Epoch 70/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3417 - accuracy: 0.8610\n",
      "Epoch 71/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3408 - accuracy: 0.8620\n",
      "Epoch 72/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3403 - accuracy: 0.8613\n",
      "Epoch 73/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3408 - accuracy: 0.8620\n",
      "Epoch 74/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3406 - accuracy: 0.8613\n",
      "Epoch 75/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3405 - accuracy: 0.8603\n",
      "Epoch 76/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3403 - accuracy: 0.8591\n",
      "Epoch 77/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3399 - accuracy: 0.8621\n",
      "Epoch 78/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3397 - accuracy: 0.8608\n",
      "Epoch 79/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3392 - accuracy: 0.8620\n",
      "Epoch 80/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3392 - accuracy: 0.8620\n",
      "Epoch 81/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3403 - accuracy: 0.8590\n",
      "Epoch 82/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3392 - accuracy: 0.8611\n",
      "Epoch 83/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3386 - accuracy: 0.8599\n",
      "Epoch 84/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3394 - accuracy: 0.8626\n",
      "Epoch 85/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3389 - accuracy: 0.8634\n",
      "Epoch 86/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3398 - accuracy: 0.8631\n",
      "Epoch 87/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3382 - accuracy: 0.8635\n",
      "Epoch 88/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3379 - accuracy: 0.8626\n",
      "Epoch 89/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3385 - accuracy: 0.8615\n",
      "Epoch 90/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3384 - accuracy: 0.8615\n",
      "Epoch 91/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3388 - accuracy: 0.8620\n",
      "Epoch 92/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3380 - accuracy: 0.8629\n",
      "Epoch 93/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3387 - accuracy: 0.8613\n",
      "Epoch 94/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3379 - accuracy: 0.8624\n",
      "Epoch 95/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3378 - accuracy: 0.8620\n",
      "Epoch 96/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3375 - accuracy: 0.8619\n",
      "Epoch 97/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3378 - accuracy: 0.8609\n",
      "Epoch 98/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3384 - accuracy: 0.8609\n",
      "Epoch 99/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3378 - accuracy: 0.8648\n",
      "Epoch 100/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.3370 - accuracy: 0.8648\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2c6a3ebe0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape=(12,), activation=\"relu\"),\n",
    "    keras.layers.Dense(5, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(X_train,y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "2dbde20c-67f0-4e50-88a8-35d5ae953d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-17 14:35:14.607380: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.48850888],\n",
       "       [0.11226815],\n",
       "       [0.33870688],\n",
       "       [0.16190839],\n",
       "       [0.03855677]], dtype=float32)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = model.predict(X_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5b2ccf2b-01de-44fd-a40c-4e463ae30904",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "\n",
    "for element in yp:\n",
    "    if element > 0.5:\n",
    "        y_pred.append(1)\n",
    "    else:\n",
    "        y_pred.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6d275a3e-ade7-4da4-a00b-e698a706b210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0cacceb0-812b-4dd0-be38-9418715abaf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.95      0.91      1589\n",
      "           1       0.71      0.46      0.56       411\n",
      "\n",
      "    accuracy                           0.85      2000\n",
      "   macro avg       0.79      0.71      0.73      2000\n",
      "weighted avg       0.84      0.85      0.84      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7465f716-c178-4e90-8d0c-0e5c94cec2ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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