Telco Churn Analysis Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "Churn is a one of the biggest problem in the telecom industry. Research has shown that the average monthly churn rate among the top 4 wireless carriers in the US is 1.9% - 2%.\n",
    "\n",
    "\"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.\" [IBM Sample Data Sets]\n",
    "## Data information\n",
    "\n",
    "Each row represents a customer, each column contains customer’s attributes described on the column Metadata.\n",
    "\n",
    "### The data set includes information about:\n",
    "- Customers who left within the last month – the column is called Churn\n",
    "- Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device   protection, tech support, and streaming TV and movies\n",
    "- Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges\n",
    "- Demographic info about customers – gender, age range, and if they have partners and dependents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, Import the data stored in SQL Database.\n",
    "- make a odbc connection\n",
    "- read as a pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyodbc \n",
    "conn = pyodbc.connect('Driver={SQL Server};'\n",
    "                      'Server=DESKTOP-FV1I7CQ;'\n",
    "                      \"uid=SanketDatabase;pwd=patil\")\n",
    "\n",
    "cursor = conn.cursor()\n",
    "\n",
    "df = pd.read_sql_query('Select * from [Telco Churn ].dbo.[Telco-Customer-Churn]',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's Look at the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender SeniorCitizen Partner Dependents tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female             0     Yes         No      1           No   \n",
       "1  5575-GNVDE    Male             0      No         No     34          Yes   \n",
       "2  3668-QPYBK    Male             0      No         No      2          Yes   \n",
       "3  7795-CFOCW    Male             0      No         No     45           No   \n",
       "4  9237-HQITU  Female             0      No         No      2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
       "0           Electronic check          29.85         29.85     No  \n",
       "1               Mailed check          56.95       1889.50     No  \n",
       "2               Mailed check          53.85        108.15    Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75     No  \n",
       "4           Electronic check          70.70        151.65    Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial plan for data exploration\n",
    "- Overview the data\n",
    "- check datatypes of the features\n",
    "- check null values and take action\n",
    "- Feature Engineering (One-hot-encoding, skewness check)\n",
    "- Data Exploration (Visual Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # For creating plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 21)\n",
      "['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have 7043 customer data and 21 columns including target variable Churn\n",
    "- Now we should look at datatype for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customerID           object\n",
      "gender               object\n",
      "SeniorCitizen        object\n",
      "Partner              object\n",
      "Dependents           object\n",
      "tenure               object\n",
      "PhoneService         object\n",
      "MultipleLines        object\n",
      "InternetService      object\n",
      "OnlineSecurity       object\n",
      "OnlineBackup         object\n",
      "DeviceProtection     object\n",
      "TechSupport          object\n",
      "StreamingTV          object\n",
      "StreamingMovies      object\n",
      "Contract             object\n",
      "PaperlessBilling     object\n",
      "PaymentMethod        object\n",
      "MonthlyCharges      float64\n",
      "TotalCharges        float64\n",
      "Churn                object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- change the data type of tenure and seniorCitizen column\n",
    "- check null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           0\n",
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure = pd.to_numeric(df.tenure, errors='coerce')\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "\n",
    "- Drop missing values\n",
    "- Customer ID is not going to help ML model; we must drop that coulmn\n",
    "- replace the churn values i.e Yes, No to 1,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female            No     Yes         No       1           No   \n",
       "1    Male            No      No         No      34          Yes   \n",
       "2    Male            No      No         No       2          Yes   \n",
       "3    Male            No      No         No      45           No   \n",
       "4  Female            No      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85         29.85   \n",
       "1               No               Mailed check           56.95       1889.50   \n",
       "2              Yes               Mailed check           53.85        108.15   \n",
       "3               No  Bank transfer (automatic)           42.30       1840.75   \n",
       "4              Yes           Electronic check           70.70        151.65   \n",
       "\n",
       "   Churn  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      0  \n",
       "4      1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace = True)\n",
    "df2 = df.iloc[:,1:]\n",
    "\n",
    "df2['Churn'] = df2['Churn'].replace(['Yes', 'No'], [1, 0])\n",
    "df2['SeniorCitizen'] = df2['SeniorCitizen'].replace(['1','0'], ['Yes', 'No'])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>3549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>5890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>3639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>4933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>3096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>7032</td>\n",
       "      <td>3</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>3875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>7032</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>7032</td>\n",
       "      <td>4</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>2365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique               top  freq\n",
       "gender            7032      2              Male  3549\n",
       "SeniorCitizen     7032      2                No  5890\n",
       "Partner           7032      2                No  3639\n",
       "Dependents        7032      2                No  4933\n",
       "PhoneService      7032      2               Yes  6352\n",
       "MultipleLines     7032      3                No  3385\n",
       "InternetService   7032      3       Fiber optic  3096\n",
       "OnlineSecurity    7032      3                No  3497\n",
       "OnlineBackup      7032      3                No  3087\n",
       "DeviceProtection  7032      3                No  3094\n",
       "TechSupport       7032      3                No  3472\n",
       "StreamingTV       7032      3                No  2809\n",
       "StreamingMovies   7032      3                No  2781\n",
       "Contract          7032      3    Month-to-month  3875\n",
       "PaperlessBilling  7032      2               Yes  4168\n",
       "PaymentMethod     7032      4  Electronic check  2365"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe(include='object').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Identify which variables are binary, categorical and not ordinal, categorical and ordinal, and numeric. The non-numeric features will need to be encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Unique Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>6530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Unique Values\n",
       "Variable                       \n",
       "gender                        2\n",
       "SeniorCitizen                 2\n",
       "Partner                       2\n",
       "Dependents                    2\n",
       "tenure                       72\n",
       "PhoneService                  2\n",
       "MultipleLines                 3\n",
       "InternetService               3\n",
       "OnlineSecurity                3\n",
       "OnlineBackup                  3\n",
       "DeviceProtection              3\n",
       "TechSupport                   3\n",
       "StreamingTV                   3\n",
       "StreamingMovies               3\n",
       "Contract                      3\n",
       "PaperlessBilling              2\n",
       "PaymentMethod                 4\n",
       "MonthlyCharges             1584\n",
       "TotalCharges               6530\n",
       "Churn                         2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uniques = pd.DataFrame([[i, len(df2[i].unique())] for i in df2.columns], columns=['Variable', 'Unique Values']).set_index('Variable')\n",
    "df_uniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of binary freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'SeniorCitizen',\n",
       " 'Partner',\n",
       " 'Dependents',\n",
       " 'PhoneService',\n",
       " 'PaperlessBilling',\n",
       " 'Churn']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_variables = list(df_uniques[df_uniques['Unique Values'] == 2].index)\n",
    "binary_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of categorical freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MultipleLines',\n",
       " 'InternetService',\n",
       " 'OnlineSecurity',\n",
       " 'OnlineBackup',\n",
       " 'DeviceProtection',\n",
       " 'TechSupport',\n",
       " 'StreamingTV',\n",
       " 'StreamingMovies',\n",
       " 'Contract',\n",
       " 'PaymentMethod']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_variables = list(df_uniques[(4 >= df_uniques['Unique Values']) & (df_uniques['Unique Values'] > 2)].index)\n",
    "categorical_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- check the unique levels in categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['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",
       " ['PaymentMethod',\n",
       "  ['Electronic check',\n",
       "   'Mailed check',\n",
       "   'Bank transfer (automatic)',\n",
       "   'Credit card (automatic)']]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[i, list(df[i].unique())] for i in categorical_variables]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- create a list of ordinal catagorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_variables = ['Contract']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of Numeric freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MonthlyCharges', 'TotalCharges', 'tenure']\n"
     ]
    }
   ],
   "source": [
    "numeric_variables = list(set(df2.columns) - set(ordinal_variables) - set(categorical_variables) - set(binary_variables))\n",
    "print(numeric_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- check the skewness of the Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'MonthlyCharges'}>,\n",
       "        <AxesSubplot:title={'center':'TotalCharges'}>],\n",
       "       [<AxesSubplot:title={'center':'tenure'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2[numeric_variables].hist(figsize=(12, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyCharges = -0.22205554798978874\n",
      "TotalCharges = 0.9614373590490068\n",
      "tenure = 0.23768011839839964\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew \n",
    "for i in numeric_variables:\n",
    "    print(i + \" =\", skew(df2[i])) # log1p,sqrt,cbrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.cbrt(df2['TotalCharges']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['TotalCharges']= np.cbrt(df2['TotalCharges'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- convert ordinal features in numeric levels respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer, LabelEncoder, OrdinalEncoder\n",
    "lb, le = LabelBinarizer(), LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Contract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Contract\n",
       "count       7032\n",
       "unique         3\n",
       "top            0\n",
       "freq        3875"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in ordinal_variables:\n",
    "    df2[column] = le.fit_transform(df2[column])\n",
    "\n",
    "df2[ordinal_variables].astype('category').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>PhoneService</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.504693</td>\n",
       "      <td>0.162400</td>\n",
       "      <td>0.482509</td>\n",
       "      <td>0.298493</td>\n",
       "      <td>0.903299</td>\n",
       "      <td>0.592719</td>\n",
       "      <td>0.265785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500014</td>\n",
       "      <td>0.368844</td>\n",
       "      <td>0.499729</td>\n",
       "      <td>0.457629</td>\n",
       "      <td>0.295571</td>\n",
       "      <td>0.491363</td>\n",
       "      <td>0.441782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender  SeniorCitizen      Partner   Dependents  PhoneService  \\\n",
       "count  7032.000000    7032.000000  7032.000000  7032.000000   7032.000000   \n",
       "mean      0.504693       0.162400     0.482509     0.298493      0.903299   \n",
       "std       0.500014       0.368844     0.499729     0.457629      0.295571   \n",
       "min       0.000000       0.000000     0.000000     0.000000      0.000000   \n",
       "25%       0.000000       0.000000     0.000000     0.000000      1.000000   \n",
       "50%       1.000000       0.000000     0.000000     0.000000      1.000000   \n",
       "75%       1.000000       0.000000     1.000000     1.000000      1.000000   \n",
       "max       1.000000       1.000000     1.000000     1.000000      1.000000   \n",
       "\n",
       "       PaperlessBilling        Churn  \n",
       "count       7032.000000  7032.000000  \n",
       "mean           0.592719     0.265785  \n",
       "std            0.491363     0.441782  \n",
       "min            0.000000     0.000000  \n",
       "25%            0.000000     0.000000  \n",
       "50%            1.000000     0.000000  \n",
       "75%            1.000000     1.000000  \n",
       "max            1.000000     1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in binary_variables:\n",
    "    df2[column] = lb.fit_transform(df2[column])\n",
    "\n",
    "df2[binary_variables].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_variables = list(set(categorical_variables) - set(ordinal_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df2, columns = categorical_variables, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.504693</td>\n",
       "      <td>0.500014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.162400</td>\n",
       "      <td>0.368844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.482509</td>\n",
       "      <td>0.499729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.298493</td>\n",
       "      <td>0.457629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>32.421786</td>\n",
       "      <td>24.545260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.903299</td>\n",
       "      <td>0.295571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.688567</td>\n",
       "      <td>0.832934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.592719</td>\n",
       "      <td>0.491363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>64.798208</td>\n",
       "      <td>30.085974</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>35.587500</td>\n",
       "      <td>70.35000</td>\n",
       "      <td>89.862500</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>11.355248</td>\n",
       "      <td>4.899482</td>\n",
       "      <td>2.659006</td>\n",
       "      <td>7.376955</td>\n",
       "      <td>11.18016</td>\n",
       "      <td>15.597701</td>\n",
       "      <td>20.555116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.265785</td>\n",
       "      <td>0.441782</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.286547</td>\n",
       "      <td>0.452180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.096701</td>\n",
       "      <td>0.295571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.421928</td>\n",
       "      <td>0.493902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.440273</td>\n",
       "      <td>0.496455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_No</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.388367</td>\n",
       "      <td>0.487414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.290102</td>\n",
       "      <td>0.453842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216297</td>\n",
       "      <td>0.411748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.336320</td>\n",
       "      <td>0.472483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.228100</td>\n",
       "      <td>0.419637</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.344852</td>\n",
       "      <td>0.475354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.384386</td>\n",
       "      <td>0.486484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.411650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.343857</td>\n",
       "      <td>0.475028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        count       mean        std  \\\n",
       "gender                                 7032.0   0.504693   0.500014   \n",
       "SeniorCitizen                          7032.0   0.162400   0.368844   \n",
       "Partner                                7032.0   0.482509   0.499729   \n",
       "Dependents                             7032.0   0.298493   0.457629   \n",
       "tenure                                 7032.0  32.421786  24.545260   \n",
       "PhoneService                           7032.0   0.903299   0.295571   \n",
       "Contract                               7032.0   0.688567   0.832934   \n",
       "PaperlessBilling                       7032.0   0.592719   0.491363   \n",
       "MonthlyCharges                         7032.0  64.798208  30.085974   \n",
       "TotalCharges                           7032.0  11.355248   4.899482   \n",
       "Churn                                  7032.0   0.265785   0.441782   \n",
       "OnlineSecurity_No internet service     7032.0   0.216155   0.411650   \n",
       "OnlineSecurity_Yes                     7032.0   0.286547   0.452180   \n",
       "MultipleLines_No phone service         7032.0   0.096701   0.295571   \n",
       "MultipleLines_Yes                      7032.0   0.421928   0.493902   \n",
       "InternetService_Fiber optic            7032.0   0.440273   0.496455   \n",
       "InternetService_No                     7032.0   0.216155   0.411650   \n",
       "StreamingMovies_No internet service    7032.0   0.216155   0.411650   \n",
       "StreamingMovies_Yes                    7032.0   0.388367   0.487414   \n",
       "TechSupport_No internet service        7032.0   0.216155   0.411650   \n",
       "TechSupport_Yes                        7032.0   0.290102   0.453842   \n",
       "PaymentMethod_Credit card (automatic)  7032.0   0.216297   0.411748   \n",
       "PaymentMethod_Electronic check         7032.0   0.336320   0.472483   \n",
       "PaymentMethod_Mailed check             7032.0   0.228100   0.419637   \n",
       "OnlineBackup_No internet service       7032.0   0.216155   0.411650   \n",
       "OnlineBackup_Yes                       7032.0   0.344852   0.475354   \n",
       "StreamingTV_No internet service        7032.0   0.216155   0.411650   \n",
       "StreamingTV_Yes                        7032.0   0.384386   0.486484   \n",
       "DeviceProtection_No internet service   7032.0   0.216155   0.411650   \n",
       "DeviceProtection_Yes                   7032.0   0.343857   0.475028   \n",
       "\n",
       "                                             min        25%       50%  \\\n",
       "gender                                  0.000000   0.000000   1.00000   \n",
       "SeniorCitizen                           0.000000   0.000000   0.00000   \n",
       "Partner                                 0.000000   0.000000   0.00000   \n",
       "Dependents                              0.000000   0.000000   0.00000   \n",
       "tenure                                  1.000000   9.000000  29.00000   \n",
       "PhoneService                            0.000000   1.000000   1.00000   \n",
       "Contract                                0.000000   0.000000   0.00000   \n",
       "PaperlessBilling                        0.000000   0.000000   1.00000   \n",
       "MonthlyCharges                         18.250000  35.587500  70.35000   \n",
       "TotalCharges                            2.659006   7.376955  11.18016   \n",
       "Churn                                   0.000000   0.000000   0.00000   \n",
       "OnlineSecurity_No internet service      0.000000   0.000000   0.00000   \n",
       "OnlineSecurity_Yes                      0.000000   0.000000   0.00000   \n",
       "MultipleLines_No phone service          0.000000   0.000000   0.00000   \n",
       "MultipleLines_Yes                       0.000000   0.000000   0.00000   \n",
       "InternetService_Fiber optic             0.000000   0.000000   0.00000   \n",
       "InternetService_No                      0.000000   0.000000   0.00000   \n",
       "StreamingMovies_No internet service     0.000000   0.000000   0.00000   \n",
       "StreamingMovies_Yes                     0.000000   0.000000   0.00000   \n",
       "TechSupport_No internet service         0.000000   0.000000   0.00000   \n",
       "TechSupport_Yes                         0.000000   0.000000   0.00000   \n",
       "PaymentMethod_Credit card (automatic)   0.000000   0.000000   0.00000   \n",
       "PaymentMethod_Electronic check          0.000000   0.000000   0.00000   \n",
       "PaymentMethod_Mailed check              0.000000   0.000000   0.00000   \n",
       "OnlineBackup_No internet service        0.000000   0.000000   0.00000   \n",
       "OnlineBackup_Yes                        0.000000   0.000000   0.00000   \n",
       "StreamingTV_No internet service         0.000000   0.000000   0.00000   \n",
       "StreamingTV_Yes                         0.000000   0.000000   0.00000   \n",
       "DeviceProtection_No internet service    0.000000   0.000000   0.00000   \n",
       "DeviceProtection_Yes                    0.000000   0.000000   0.00000   \n",
       "\n",
       "                                             75%         max  \n",
       "gender                                  1.000000    1.000000  \n",
       "SeniorCitizen                           0.000000    1.000000  \n",
       "Partner                                 1.000000    1.000000  \n",
       "Dependents                              1.000000    1.000000  \n",
       "tenure                                 55.000000   72.000000  \n",
       "PhoneService                            1.000000    1.000000  \n",
       "Contract                                1.000000    2.000000  \n",
       "PaperlessBilling                        1.000000    1.000000  \n",
       "MonthlyCharges                         89.862500  118.750000  \n",
       "TotalCharges                           15.597701   20.555116  \n",
       "Churn                                   1.000000    1.000000  \n",
       "OnlineSecurity_No internet service      0.000000    1.000000  \n",
       "OnlineSecurity_Yes                      1.000000    1.000000  \n",
       "MultipleLines_No phone service          0.000000    1.000000  \n",
       "MultipleLines_Yes                       1.000000    1.000000  \n",
       "InternetService_Fiber optic             1.000000    1.000000  \n",
       "InternetService_No                      0.000000    1.000000  \n",
       "StreamingMovies_No internet service     0.000000    1.000000  \n",
       "StreamingMovies_Yes                     1.000000    1.000000  \n",
       "TechSupport_No internet service         0.000000    1.000000  \n",
       "TechSupport_Yes                         1.000000    1.000000  \n",
       "PaymentMethod_Credit card (automatic)   0.000000    1.000000  \n",
       "PaymentMethod_Electronic check          1.000000    1.000000  \n",
       "PaymentMethod_Mailed check              0.000000    1.000000  \n",
       "OnlineBackup_No internet service        0.000000    1.000000  \n",
       "OnlineBackup_Yes                        1.000000    1.000000  \n",
       "StreamingTV_No internet service         0.000000    1.000000  \n",
       "StreamingTV_Yes                         1.000000    1.000000  \n",
       "DeviceProtection_No internet service    0.000000    1.000000  \n",
       "DeviceProtection_Yes                    1.000000    1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.761</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.723</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_No</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <td>7032.0</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        count   mean    std  min    25%  \\\n",
       "gender                                 7032.0  0.505  0.500  0.0  0.000   \n",
       "SeniorCitizen                          7032.0  0.162  0.369  0.0  0.000   \n",
       "Partner                                7032.0  0.483  0.500  0.0  0.000   \n",
       "Dependents                             7032.0  0.298  0.458  0.0  0.000   \n",
       "tenure                                 7032.0  0.443  0.346  0.0  0.113   \n",
       "PhoneService                           7032.0  0.903  0.296  0.0  1.000   \n",
       "Contract                               7032.0  0.344  0.416  0.0  0.000   \n",
       "PaperlessBilling                       7032.0  0.593  0.491  0.0  0.000   \n",
       "MonthlyCharges                         7032.0  0.463  0.299  0.0  0.173   \n",
       "TotalCharges                           7032.0  0.486  0.274  0.0  0.264   \n",
       "Churn                                  7032.0  0.266  0.442  0.0  0.000   \n",
       "OnlineSecurity_No internet service     7032.0  0.216  0.412  0.0  0.000   \n",
       "OnlineSecurity_Yes                     7032.0  0.287  0.452  0.0  0.000   \n",
       "MultipleLines_No phone service         7032.0  0.097  0.296  0.0  0.000   \n",
       "MultipleLines_Yes                      7032.0  0.422  0.494  0.0  0.000   \n",
       "InternetService_Fiber optic            7032.0  0.440  0.496  0.0  0.000   \n",
       "InternetService_No                     7032.0  0.216  0.412  0.0  0.000   \n",
       "StreamingMovies_No internet service    7032.0  0.216  0.412  0.0  0.000   \n",
       "StreamingMovies_Yes                    7032.0  0.388  0.487  0.0  0.000   \n",
       "TechSupport_No internet service        7032.0  0.216  0.412  0.0  0.000   \n",
       "TechSupport_Yes                        7032.0  0.290  0.454  0.0  0.000   \n",
       "PaymentMethod_Credit card (automatic)  7032.0  0.216  0.412  0.0  0.000   \n",
       "PaymentMethod_Electronic check         7032.0  0.336  0.472  0.0  0.000   \n",
       "PaymentMethod_Mailed check             7032.0  0.228  0.420  0.0  0.000   \n",
       "OnlineBackup_No internet service       7032.0  0.216  0.412  0.0  0.000   \n",
       "OnlineBackup_Yes                       7032.0  0.345  0.475  0.0  0.000   \n",
       "StreamingTV_No internet service        7032.0  0.216  0.412  0.0  0.000   \n",
       "StreamingTV_Yes                        7032.0  0.384  0.486  0.0  0.000   \n",
       "DeviceProtection_No internet service   7032.0  0.216  0.412  0.0  0.000   \n",
       "DeviceProtection_Yes                   7032.0  0.344  0.475  0.0  0.000   \n",
       "\n",
       "                                         50%    75%  max  \n",
       "gender                                 1.000  1.000  1.0  \n",
       "SeniorCitizen                          0.000  0.000  1.0  \n",
       "Partner                                0.000  1.000  1.0  \n",
       "Dependents                             0.000  1.000  1.0  \n",
       "tenure                                 0.394  0.761  1.0  \n",
       "PhoneService                           1.000  1.000  1.0  \n",
       "Contract                               0.000  0.500  1.0  \n",
       "PaperlessBilling                       1.000  1.000  1.0  \n",
       "MonthlyCharges                         0.518  0.713  1.0  \n",
       "TotalCharges                           0.476  0.723  1.0  \n",
       "Churn                                  0.000  1.000  1.0  \n",
       "OnlineSecurity_No internet service     0.000  0.000  1.0  \n",
       "OnlineSecurity_Yes                     0.000  1.000  1.0  \n",
       "MultipleLines_No phone service         0.000  0.000  1.0  \n",
       "MultipleLines_Yes                      0.000  1.000  1.0  \n",
       "InternetService_Fiber optic            0.000  1.000  1.0  \n",
       "InternetService_No                     0.000  0.000  1.0  \n",
       "StreamingMovies_No internet service    0.000  0.000  1.0  \n",
       "StreamingMovies_Yes                    0.000  1.000  1.0  \n",
       "TechSupport_No internet service        0.000  0.000  1.0  \n",
       "TechSupport_Yes                        0.000  1.000  1.0  \n",
       "PaymentMethod_Credit card (automatic)  0.000  0.000  1.0  \n",
       "PaymentMethod_Electronic check         0.000  1.000  1.0  \n",
       "PaymentMethod_Mailed check             0.000  0.000  1.0  \n",
       "OnlineBackup_No internet service       0.000  0.000  1.0  \n",
       "OnlineBackup_Yes                       0.000  1.000  1.0  \n",
       "StreamingTV_No internet service        0.000  0.000  1.0  \n",
       "StreamingTV_Yes                        0.000  1.000  1.0  \n",
       "DeviceProtection_No internet service   0.000  0.000  1.0  \n",
       "DeviceProtection_Yes                   0.000  1.000  1.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mm = MinMaxScaler()\n",
    "\n",
    "for column in [ordinal_variables + numeric_variables]:\n",
    "    df[column] = mm.fit_transform(df[column])\n",
    "    \n",
    "round(df.describe().T, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Analysis\n",
    "1. see gender distribution in the dataset\n",
    "  \n",
    "  It is equal in distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.countplot( df['gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. see SeniorCitizen distribution in the dataset\n",
    "   \n",
    "   About 16% customer are Senior Citizen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '% of Senior Citizens')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (df['SeniorCitizen'].value_counts()*100.0 /len(df))\\\n",
    ".plot.pie(autopct='%.1f%%', labels = ['No', 'Yes'],figsize =(5,5), fontsize = 12 )  \n",
    "ax.set_ylabel('Senior Citizens',fontsize = 12)\n",
    "ax.set_title('% of Senior Citizens', fontsize = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot dependent and non dependent customers to gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1606519e190>,\n",
       "  <matplotlib.axis.XTick at 0x1606519e160>],\n",
       " [Text(0, 0, 'Not a Dependents'), Text(1, 0, 'Dependents')])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.countplot( df['Dependents'], hue= df.Partner)\n",
    "plt.xticks([0,1], ['Not a Dependents', 'Dependents'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- plot contract type counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '# of Customers by Contract Type')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['Contract'].value_counts().plot(kind = 'bar',rot = 0, width = 0.3)\n",
    "ax.set_ylabel('# of Customers')\n",
    "ax.set_title('# of Customers by Contract Type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How many custmer in this dataset churned ?\n",
    "   \n",
    "   26.578498 percent customer in this dataset churned;\n",
    "   this is unbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    73.421502\n",
       "1    26.578498\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.countplot( df['Churn'])\n",
    "((df['Churn']).value_counts() / len(df))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- see the correlation of churn with other variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "df2.corr()['Churn'].sort_values(ascending = False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with imbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y, X = df['Churn'], df.drop(columns='Churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from imblearn.under_sampling import RandomUnderSampler\n",
    "#undersample = RandomUnderSampler(sampling_strategy='majority')\n",
    "#X_under, y_under = undersample.fit_resample(X, y)\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_under, y_under, test_size=0.3, random_state=42,stratify=y_under)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Under smapling using TomekLinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from imblearn.under_sampling import TomekLinks\n",
    "#undersample = TomekLinks(sampling_strategy='majority')\n",
    "#X_under, y_under = undersample.fit_resample(X, y)\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_under, y_under, test_size=0.3, random_state=42,stratify=y_under)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Over sampling using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from imblearn.over_sampling import SMOTE\n",
    "#oversample = SMOTE(sampling_strategy='minority')\n",
    "#X_over, y_over = oversample.fit_resample(X, y)\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_over, y_over, test_size=0.3, random_state=42,stratify=y_over)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Over-sampling SMOTE followed by under-sampling Tomek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek\n",
    "bothsample = SMOTETomek(sampling_strategy='auto')\n",
    "X_both, y_both = bothsample.fit_resample(X, y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_both, y_both, test_size=0.30, random_state=42,stratify=y_both)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Implementation\n",
    "1. Hyperparameter tuning\n",
    "2. ML algorithm fitting, prediction\n",
    "3. Result assessment - confusition Matrix, ROC & Precesion/recall curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. KNN ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Split training and testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Find and plot the k - F1 result for KNN ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_k = 66\n",
    "f1_scores = list()\n",
    "error_rates = list() # 1-accuracy\n",
    "\n",
    "for k in range(1, max_k):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
    "    knn = knn.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = knn.predict(X_test)\n",
    "    f1 = f1_score(y_pred, y_test)\n",
    "    f1_scores.append((k, round(f1_score(y_test, y_pred), 4)))\n",
    "    error = 1-round(accuracy_score(y_test, y_pred), 4)\n",
    "    error_rates.append((k, error))\n",
    "    \n",
    "f1_results = pd.DataFrame(f1_scores, columns=['K', 'F1 Score'])\n",
    "error_results = pd.DataFrame(error_rates, columns=['K', 'Error Rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot F1 results\n",
    "sns.set_context('talk')\n",
    "sns.set_style('ticks')\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "ax = f1_results.set_index('K').plot( figsize=(15, 12), linewidth=6)\n",
    "ax.set(xlabel='K', ylabel='F1 Score')\n",
    "ax.set_xticks(range(1, max_k, 2));\n",
    "plt.title('KNN F1 Score')\n",
    "plt.savefig('knn_f1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- plot k-error rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "sns.set_style('ticks')\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "ax = error_results.set_index('K').plot( figsize=(15, 12), linewidth=6)\n",
    "ax.set(xlabel='K', ylabel='Error Rate')\n",
    "ax.set_xticks(range(1, max_k, 2))\n",
    "plt.title('KNN Elbow Curve')\n",
    "plt.savefig('knn_elbow.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- train KNN model, estimate and report outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.77      0.82      1468\n",
      "           1       0.80      0.90      0.84      1468\n",
      "\n",
      "    accuracy                           0.83      2936\n",
      "   macro avg       0.84      0.83      0.83      2936\n",
      "weighted avg       0.84      0.83      0.83      2936\n",
      "\n",
      "Accuracy score:  0.83\n",
      "F1 Score:  0.84\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn = knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "# Preciision, recall, f-score from the multi-class support function\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('Accuracy score: ', round(accuracy_score(y_test, y_pred), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 76.5, 'Ground Truth')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, precision_recall_curve, confusion_matrix\n",
    "\n",
    "sns.set_context('talk')\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "_, ax = plt.subplots(figsize=(12,12))\n",
    "ax = sns.heatmap(cm, annot=True, fmt='d', annot_kws={\"size\": 40, \"weight\": \"bold\"})\n",
    "\n",
    "labels = ['False', 'True']\n",
    "ax.set_xticklabels(labels, fontsize=25);\n",
    "ax.set_yticklabels(labels, fontsize=25);\n",
    "ax.set_ylabel('Prediction', fontsize=30);\n",
    "ax.set_xlabel('Ground Truth', fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot ROC and Precision-Recall curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "\n",
    "fig, axList = plt.subplots(ncols=2)\n",
    "fig.set_size_inches(16, 8)\n",
    "\n",
    "# Get the probabilities for each of the two categories\n",
    "y_prob = knn.predict_proba(X_test)\n",
    "\n",
    "# Plot the ROC-AUC curve\n",
    "ax = axList[0]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob[:,1])\n",
    "ax.plot(fpr, tpr, linewidth=5)\n",
    "# It is customary to draw a diagonal dotted line in ROC plots.\n",
    "# This is to indicate completely random prediction. Deviation from this\n",
    "# dotted line towards the upper left corner signifies the power of the model.\n",
    "ax.plot([0, 1], [0, 1], ls='--', lw=.3)\n",
    "ax.set(xlabel='False Positive Rate',\n",
    "       ylabel='True Positive Rate',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='ROC curve')\n",
    "ax.grid(True)\n",
    "\n",
    "# Plot the precision-recall curve\n",
    "ax = axList[1]\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_prob[:,1])\n",
    "ax.plot(recall, precision, linewidth=5)\n",
    "ax.set(xlabel='Recall', ylabel='Precision',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='Precision-Recall curve')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Logistic regression ML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- train logistic regression ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82      1468\n",
      "           1       0.82      0.82      0.82      1468\n",
      "\n",
      "    accuracy                           0.82      2936\n",
      "   macro avg       0.82      0.82      0.82      2936\n",
      "weighted avg       0.82      0.82      0.82      2936\n",
      "\n",
      "Accuracy score:  0.82\n",
      "F1 Score:  0.82\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty='l2', solver='liblinear').fit(X_train, y_train)\n",
    "y_pred = lr_l1.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('Accuracy score: ', round(accuracy_score(y_test, y_pred), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 76.5, 'Ground Truth')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, precision_recall_curve, confusion_matrix\n",
    "\n",
    "sns.set_context('talk')\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "_, ax = plt.subplots(figsize=(12,12))\n",
    "ax = sns.heatmap(cm, annot=True, fmt='d', annot_kws={\"size\": 40, \"weight\": \"bold\"})\n",
    "\n",
    "labels = ['False', 'True']\n",
    "ax.set_xticklabels(labels, fontsize=25);\n",
    "ax.set_yticklabels(labels, fontsize=25);\n",
    "ax.set_ylabel('Prediction', fontsize=30);\n",
    "ax.set_xlabel('Ground Truth', fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot ROC and Precision-Recall curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "\n",
    "fig, axList = plt.subplots(ncols=2)\n",
    "fig.set_size_inches(16, 8)\n",
    "\n",
    "# Get the probabilities for each of the two categories\n",
    "y_prob = lr_l1.predict_proba(X_test)\n",
    "\n",
    "# Plot the ROC-AUC curve\n",
    "ax = axList[0]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob[:,1])\n",
    "ax.plot(fpr, tpr, linewidth=5)\n",
    "# It is customary to draw a diagonal dotted line in ROC plots.\n",
    "# This is to indicate completely random prediction. Deviation from this\n",
    "# dotted line towards the upper left corner signifies the power of the model.\n",
    "ax.plot([0, 1], [0, 1], ls='--', lw=.3)\n",
    "ax.set(xlabel='False Positive Rate',\n",
    "       ylabel='True Positive Rate',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='ROC curve')\n",
    "ax.grid(True)\n",
    "\n",
    "# Plot the precision-recall curve\n",
    "ax = axList[1]\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_prob[:,1])\n",
    "ax.plot(recall, precision, linewidth=5)\n",
    "ax.set(xlabel='Recall', ylabel='Precision',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='Precision-Recall curve')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Random Forest ML model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- train random forest model with hyperparameter, estimate and report outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.84      0.86      1468\n",
      "           1       0.85      0.88      0.86      1468\n",
      "\n",
      "    accuracy                           0.86      2936\n",
      "   macro avg       0.86      0.86      0.86      2936\n",
      "weighted avg       0.86      0.86      0.86      2936\n",
      "\n",
      "Accuracy score:  0.86\n",
      "F1 Score:  0.86\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 76.5, 'Ground Truth')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/bQ+xG3Bv+nOPTZt/bl8t+OhtlShS0NL2Srf2+nj2t5q/bI1V35PnLujfy+EqmD+vzTiAO+rR6wWbtvj4ePXrNUTLl/1q1f5kvcf1w7I58vKyXmfRuWsHq4C7Veun5ZfbumY7NjZWz3fop82b/itF/GDsFI2f8I4GvGQdQPv6+qhx43r6aekqm7kNeLGHzfMTEhLU5fmBWr/uv2/ruvfsqCnTxln18w/Io27dn9NnU2fbjAvgwaKkBJnWtYgbmv39SrXq/5bDwbYkbbYTvBYOyqfXez9vFWxLUqECefVG7+ftjhN64KjV+z0Hj9ksfHykVDGbYFuSGtcOtgqUk/xz8bLdZ50Ls12g1bF5A5sxTCaTXuneweZzSNLF8Ot2xwbcjbe3txo0rGPT/sN3K2yCbUnaumWH5s5eYtNepGghFStexPK+WvXHFB8fb9VnzszFVsF2kimT7S9mLF6yqE2byWRSuw4tbNo3rNtqFWxL0vx53+nI4eM2fe+nzAVuymx27iuLI8ONTGvAqEk6fOKs3WueHh5KSGfN19FT52zanqpZRZ6e9n/uTG5XkivXIqzet23ypNo0rquL4dd0PuySzv97WfkD/ZOdh72g2M/H/qJJTzvbEebxs7/lX3ZvL+XI7q2omJtW7TlzpL4DA+AOypV/yCZbLEmrVq5L9p6//tyj/gO727QHBeXXubP/SJJee/ltvTF4tIqXKKKSJYupZKni2r71T7vjxd0TmCeJjLAtJ6lStZLy5g20aV/202q7Y/y87FcNHf6y7Rj5AhV+5ardewA8GATcyLTMdk698vAw6eWu7fV76H6F7D+S5rESEhI1YegAXb4WoStXr1v+b/VKjyR7T55ktvXKmSO7TZvJZFLBfIEqmC9QwY+WS3bM30P368T//yN9t1qVK9rtX7xwkI7c84PC5j/3qmubp222/Pvz70M2wbZ/bl+VLlY42fkA7uTcuQvq1vklBQXlv/MqmF9BBQvowP7Dyd5z/dp1u+3R96wliY+P18kTZ3TyxJkU5/DSy71s2hITE7Vls2023F69t6Rk9wTfvXuf3faq1R7VurWbU5wX4A4LF52JgBsuo2jB/Br9Sk/VqlxBv4fuT9e9np4eerKG/X+8knPklP3sesF8thmnlNy8dVtnLlzUmq1/auHytTbXSxcrrNaNatu9t2WDJ/Tb9hCrtj//PqRhE79Qn+daqkThIN2IjtH2Xfs05esfbO7v3rap3Yw64I6uXb2ulSt+S9c9FR8tb9OWmJioCxf+TfMYefxzq1KlcurZu5M6PGdb4rFk0VKdsLPouewj9r9lO3fO9od2STp7xn77I4+UIeAGnIyAG5lebl8fdW/XVN3aPG03u2yUH9fY/gOVzdMzxQz2vZb9tlWjPp2T7PXCQfk0bdRr8rbzNbckPVWrqh57pIzNosw1W//Smq1/pfjs1o3qqPeztvWfANLGw8NDnbu2t2nfs3t/mnf++PyrCerU2XaMJBvWbdWbQ0bbvVa8hG1dd1xcXLInVF4Nv2a3/e56cwDOQcCNTKvCwyX1bPOn9MxTT6R4MIwRdh88puXrttm016n+qHL75krzOP9cumK33WQy6ZmnHtewfp3lnzv5E+lMJpM+ffdVvfb+VLs7odgT6J9bL3Zuo+eaPZWuI+kBWHtlcF+VLWubZV76wy9pHqN4cdugWbpTkvLJpC/16SczlJCQYLdP3rwBNm03biR/Om5UVHQy46TvWzm4KTtlnMg4Wepf4+ho+3/ZwDW992pvPd+i4QMPtk+dD9OQDz5Top2/fF7s3CZdY927rWAS31w55Zsrp65F3Eh1jLz+ufX1xJF6q3/nNJWH5PbNpcA8uQm2AQe0btNMb787xKb94sXLdncuSU7xZLLLF/+9LA8PD+XO45fsvfaOh7+dwqFfsbFx9sdJ4Yd6AA+GS/+LHB4ero8//lgtW7ZUpUqVVKNGDUnS5cuX1a1bN23fvt3JM4SrOXU+TP1GTtRVO18XP9usvio8VDJd4124FG63/UZ0jL5ZuUHtB72jmd+lni1btXmHZn73i+KTyYTd7fT5f/XG+M/17pTZaeoPwFrL1k9rxpyPlS2b7ZfAbw//QDdv3krTOJ6enipUOMjutdJlSmjku4P11+61apzMQTz2dlRJiE/+v+l7tyZM4u1tv2QNsGJOdO4ri3PZkpLQ0FC9/PLLunbtmuXkvqRdGy5cuKC//vpLu3bt0siRI9W1a1dnThUu4sjJsxo46mOF2wm2SxYtqKF9O6V7zCJB+VSlXBkF5MmtsMvhWrXpD127q/4yITFR0+b/KEnq19H+frmTZn1jc6BNrpw51PTJGipdtLBuRMdow45QHb9nwdTyddvk7eWldwfZbmkGwL7nnm+tz7740G6w+83in9JVTpI3b4BWr9qgA/sOKTo6RhUrlVP7Z5+Rt/d/W3UGBgZowZIv9GzbXtq+zf5Wgne796TauyWyywSQablkwH358mW9+OKLioiIUOXKldW6dWstWbJEJ07cqXENCgrSE088oT/++EPjxo1T+fLlVb16dSfPGplZ6IGjeuV/U3Qj+qbNtYA8fpr67mv3tWBz7JC+Vu/7Pd9S/d+epKOnrbf6+2LRMjWoVVUPlbD++nn5um02wXa+gDz6euJIq9MuX+rSVhNnLtHiFdb7CX+/eqOa16+l4BS2PwRwR/+B3fXBhLftlmP99eduvTF4VLrGu3Tpinp0GWTVNvOrBfrp56+tSkmyZ/fW519OUI2qTysu7r+ykLt/ncQzW/JlZfZ+SJCkuGRKTQA8OC5ZUjJ79mxFRETo2Wef1bfffqsuXbood+7clusFCxbU3Llz9cILL8hsNmvhwoVOnC0yu61/7dXAUR8nG2zPHDtUJe2cDnk/AvPk1odDB9jsoR2fkKAl9wTLcXHxmjLve5sxXu3ewSrYlu7spjCsXyeVL1PCpv/CZbZbEQKwNnzkq/rwo3ftBtu7/tqrZ9v1TnMpSUp2h+7Te6Mn2bQXL1FUrVo/bdVmbxHk3dnxe2XPbv9aZDK7mgBWEs3OfWVxLhlwb9q0STly5NDw4cNT7Dd06FDlzJlTu3btSnXM4ODgVF/Ien7dslOvjZumW3YWIgXlC9DcD4erbKliGfrMh0oUUYWHbAPjP/8+ZPV+e+h+m/KWXDlzqJWdo6mlO0F328Z1bdp37j3IV81ACsZPeEfDRrxi99rWLTvUrnWPZLfiux8/fPez3Z1Jnqz/uNV7e9v8+fraP2lWknLntr8AMzycUyYBZ3PJgDssLExlypSRr2/KK699fHxUqlQpXb3KXzawtWrzDg2f9JXi7SxCKlW0kOZPfNuwUxqLFbJdSPXvPUcv7z183KZPySIFkz2KXrpzMuW9om/e0vUUthID3NnEj0drwEs97F5b8fNaPdeuT7Lb7d2vGzeidPmy7YLqIkUKWb23d7hOjhzZlSuZnZsC7WwjKMlyBD0A53HJGm4vLy9FRESkqe/NmzeVM2fq28qFhISk2uf2Mdujd+Ga1v0eoncmz7K79d9jj5TRZ6MHp7g/9t3MZrPCr0fqwqUrCrsUrguXwlW1wkOqUv7hZO+xF+Tfu+VfWrYMvFdCMpnsFNZZAW5r7PgR6tvf/qL6ubOX6M0ho1NcpJjE09NThYsUVLFiRVSseGEVK1ZEX8/9xm5QncTLy/af37h7dhk5fuyU3XuLFSuiI0dsfyAvVtx+guBIGvfwh3szZ5FvQg8dOqRnn31W69evV8GC/5WDbtu2TZ988omOHz+uvHnzqmvXrurdu7fVvfv27dPEiRO1f/9++fj4qH379nrllVes1kecPn1aH374oUJCQuTp6almzZpp6NChqSaBXTLgLlOmjPbt26czZ86oRAnbr+aTHD9+XKdOnVKVKlUe3OSQ6e05dEwjJs2wu2Xe41UqaMo7rypXOhZItuw/XOfCLlm1tW1cN8WA+9iZ8zZt+fP6W7339bH9QfHU+TDFxcfLy852ZZJ06lyYTZuHh0n+dvbzBdzZoFd666WXe9u9NnnSFxr73uQ0jVO0WGHt3rdBnvf8wHzu3D/6dskyu/cEBeW3exjNv/f8PbJn936791euWtFuwF25ckW7/XeH7rPbDmQ1J0+e1IABA2y2yAwNDdXAgQPVvHlzvfbaa9q1a5cmTpwos9msPn36SJLOnDmjnj17qmrVqpoyZYpOnDihTz75RFFRURo16s6C6YiICPXo0UP58+fXhAkTFB4ero8++kj//vuvvvrqqxTn5pIlJS1atFBiYqLeeecd3b59226fq1evaujQoTKZTGratOkDniEyq0vh1/Ta+1N1286q/eqVHtG0UYPTFWxLUtmStifJbfpzj65G2D/6+a+/D+vMP7ZfFdd4tLzV+8IF8tn0uXnrtn7dYn/rsMTERK3c9IdN+0PFi6ZYhgK4m0aNn9SY94fZvfbpJzPSHGxL0vlzFxRh57/1Lt2eTfaenr1fsNu+bcsOq/d//blbMTG2i7lbt21m9/427ZrbtO3ZvV/hVyirRBq48KLJ+Ph4LVq0SM8++6zduHDq1KmqUKGCPvroI9WrV09DhgxRnz599OWXXyo29s4arhkzZsjPz0/Tp09X/fr11bt3b40YMULffPONLl68KElatGiRIiMjNXPmTDVq1EgdO3bUpEmTtGnTJu3duzfFObrkv8KdOnVSuXLl9Ndff6lp06Z6//33FRZ2J7O3aNEivffee2revLkOHTqkkiVLqlOn9O+fjKzp3SmzrfbBTpI/0F+T3npR2e/jgIin69a0abseGaV3P5ltE9ifvXBR73wyy+44zevXsnpfr8ZjdvtNmLFY+4+etGozm82aPOc7HT551qZ/3eBHU5w/4E4CAv017YsPbTLSkrRpw3b9z84OIqlZ/tOvNm11n6ylN4cNsml/ulkDDX5joE379WsRWvfbFqu227djtWb1Bpu+TZs1UJ17/t7p0et5PVLuIZu+6dk3HHBVu3bt0qRJk9S7d2+9+eabVtdu376tkJAQPf209S5ATZs2VWRkpEJDQyVJ27dvV4MGDax2AmrWrJkSEhK0bds2S58aNWooIOC/9RJ169aVj4+PNm/enOIcXbKkxNvbWzNnztQrr7yiPXv2aPHixZZrY8eOlXQnAClbtqw+//xz5ciRw1lTRSay/vdd+mP3AbvXLl+9rgbdBqdpnIGd2uilLm0t7xvXrq7ihQro7D1fB28N+VttBo5Qs3q1FJgnt06eu6BVm3fY3RGlXo3KNntlFy1YQHWqP6rtu6y/Do6Milb3oR+ocZ1gVXiohG7euq0Nf4TqyCnrvb0lKbu3lzq3apymzwW4g2FvvayCBQvYvfZUwzoKjzyapnFaNe9iOajmi8/nqnPXDjbb8o18d7CebvaU1qzeqNjYWD1eO1hNmzWwu/Xg5Elf6Iadxc3TP5urdh2esWrz9PTUD8vm6vtvl+vYsVOqWrWS3ax3xPVILVzwQ5o+D+DKypQpo3Xr1ilv3rxaunSp1bVz584pLi5OpUqVsmpPKkk+deqUKleurLCwMJs+gYGB8vX11alTd9ZTnDx5Uq1bt7bq4+npqaJFi1r6JMclA25Jyp8/v7755hutX79e69at09GjRxUVFaWcOXOqVKlSatCggVq0aGH3aF64p1nfG5Pp8fLKpjGv9Vb/tz+yqQu/cClcc35YleL9hfLn1ZhXe9m99vaL3fTcK6MUfc/+v/EJCfp1y079umVnimO/3LW9CiSzcwHgbgIC/dW91/MZPu7xY6c0YfxUjRrzps214BpVFFyjSor3/7Zmk6Z/NtfutV0he7Xg6+/UrUdHq/bs2b3VtftzKY477v1PdP1a2jYYAJx9vHpatl9OboOLfPlsSzCT3LhxZwOCexc1+vjc2WIzKioq2T5J/aKioixjpdYnOS4fjTZq1EiNGjVy9jSQyR07fV4Hjp02bPzgSo/ow6EDkt1mMDlFgvLr8zGDlS8gj93rRQvm14yxQ/Xi6MmKTOfWZM8/01A92tuv9QTcUYdnWypnTmO+8Zzy8VfKny+vXnzZ/g/Pydm0Ybt693gtxb3y3xwyRnnzBarFM2n/turrud9o1gwOfQOSdhq698C5JB4eHin2MZvNVt9KpaWPPS4fcNtz5MgRJSYm6uGHHybDDUnStl3Gr9J/um4NFS2YX6OmzLE5uv1eJpNJTZ+sqZEDu6a6/eCjj5TWd1PH6IMvFmrLXykvypCkPH4+erV7Bz3XvEF6pg9keY2frm/o+G+P+EChofv0wYS3lT9/3hT7RkVF6/Ops/XRhM9TPZgqLi5OPbu+ohHvvKZBr/RO8bTJmzdvafJHX+jjj6bf12eAG3PyaY9p2Z75fvj53TkQ6t4MdNJ7Pz8/S9baXpY6JibGMoavr6/dPtHR0SpSpEiK83DZaDQmJkaLFi2Sv7+/nnvuztdqFy9e1MCBA3X48GFJUqFChTRhwgTVqFHDmVNFJnD45JkH8pwKD5XU99Pe0/bQ/Vq3PUR7Dx/XpfDrunnrtnL7+ahw/ryqVaWCWtR/XA/b2d0kOYUL5NNnowfr6OlzWrVph3YfPKazFy4qMipGnp4eCszjp7Kliql21Upq1bC2fJI5GANwZ48+Vj71Tg768fsV+uXnNXq2Y2s1blJPlatUUt58AfLy8tLV8Gs6euSENqzfqm8W/6Qr6dg9JD4+Xu+P+VgL53+v519oq0ZN6qlY8SLy98+ta9cidPbMea1bu0VLFi/V+XMXDPyEgGspXry4PD09dfas9aYCSe9LlSolHx8fBQUF6cwZ61ghPDxcUVFRltruUqVK2fRJSEjQ+fPnU90RzyUD7ujoaL3wwgs6fvy4GjdubAm4R48erUOH/jse+8KFC+rfv79Wr15ttfk5XN+cD4enq/+EoQM1YajtzgBGMJlMqlv9UdWtnvG7g5QtWUxle2bsUfOAu6hYtu4Dec7t27FatOAHLTJgweKpk2f14QdT9eEHUzN8bCAryp49u4KDg7V27Vr16NHDUhKyZs0a+fn5qVKlSpKkOnXqaOPGjRo2bJjlW6Q1a9bI09NTNWvWtPSZM2eOrl+/Ln9/f0l3DtSJiYlR7dq1U5yHS24LuGjRIh07dkwBAQGqV6+epDvZ7c2bN8tkMmny5MnasWOH2rZtq5s3b2rOnDlOnjEAAEAmlpjo3JeBXnzxRYWGhmrIkCHavHmzpkyZotmzZ2vAgAGW08j79u2ry5cvq3///tq4caPmzp2r8ePHq2PHjipc+M4prp07d5a3t7d69uyp3377Td9//72GDh2qevXqqVq1ainOwSUD7vXr18vDw0OzZ8+2ZLc3bdoks9msihUrqkWLFvL399eoUaOUM2dOy/6JAAAAcC9PPPGEpk2bphMnTmjQoEFasWKFhg0bpn79+ln6lClTRnPmzFFMTIxeffVVzZ07V7169dLbb79t6RMYGKj58+fL399fb775pj755BM1a9ZMn3zySapzcMmSklOnTql48eIqX/6/erzt27ff+Sq/7n9fGebKlUvFixe3qdsBAADAXZy8aDKjtG/fXu3bt7dpb9KkiZo0aZLivcHBwfruu+9S7FO2bFnNmzcv3fNyyQz37du3rfZBNJvN2rnzzn7ESXU2SRITE5WQkPZt2gAAAICM5JIBd6FChXThwgXLvol79+5VRESEpTA+SUREhM6ePaugoCBnTRUAAABuziUD7vLly+vatWuaN2+eoqKi9MUXX8hkMql27dqWlaVxcXF67733FBsbq+rVqzt5xgAAAJmYOdG5ryzOJQPunj17ytPTUxMnTlSNGjW0ZcsWSVKvXndO+Pr777/15JNPavXq1fLy8lLPnj2dOFsAAAC4M5cMuCtXrqzJkycrb968MpvNyp07t/73v/9ZDrjx8fGx7JE4a9YslStXzskzBgAAyMQSzc59ZXEuuUuJJD399NNq0qSJrl69qoCAAKsz7IsXL67PP/9c9erVk5eXlxNnCQAAAHfnsgG3dOdEv7x589q0e3l5qVGjRk6YEQAAAGAt0wfciRl0+tDdGXAAAAD8x2zwaY/uLtMH3BUrVnR4DJPJpIMHD2bAbAAAAID0yfQBd9Je2wAAADCIGyxcdKZMH3DPnz/f2VMAAAAA7lumD7jvPaodAAAAcCWZPuAGAACAwSgpMZRLB9wnTpzQkSNHdOvWLZvdTBISEnTz5k1dvHhRmzdv1qpVq5w0SwAAALgzlwy4ExMT9dZbb+mXX35Jta/ZbJbJZHoAswIAAABsuWTA/eOPP2rFihWS7hxykydPHl25ckV58uRR9uzZde3aNcXFxclkMql8+fLq1q2bk2cMAACQiZnZh9tILnkazC+//CKTyaTu3btrz549WrNmjbJly6b69etry5Yt2rVrl/73v//J29tbly5d0lNPPeXsKQMAAMBNuWTAfeTIEeXMmVOvv/66PD095ePjo7Jly2rnzp2SJG9vb3Xs2FFDhw5VeHi4FixY4OQZAwAAZGKJZue+sjiXDLijoqJUtGhR5ciRw9L20EMP6dKlS7p69aqlrWPHjsqVK5c2bdrkhFkCAAAALhpw58yZUx4e1lMvXry4JOnkyZOWNm9vb5UoUULnzp17oPMDAAAAkrhkwF24cGGdO3dOt2/ftrQVLVpUZrNZR44cseobGxur2NjYBz1FAAAAl2FONDv1ldW5ZMBds2ZNxcTEaOLEiZb9t8uXLy/pzoJKs/nOH9yJEyd0+vRpFSxY0GlzBQAAgHtzyYC7W7du8vLy0uLFi/XUU08pNjZWjzzyiMqXL689e/aoT58+mjBhgnr27KnExERVrVrV2VMGAADIvFg0aSiXDLiLFy+uyZMny8/PT9HR0fL29pYkDR48WCaTSX/88YfmzZuny5cvK0eOHBo0aJCTZwwAAAB35ZIH30hS48aNVbNmTYWEhFja6tevrzlz5mjWrFk6f/68SpcurUGDBlkWVAIAAAAPmssG3JKUO3duNWzY0Krt8ccf1+OPP+6kGQEAALigRE6aNFKmLymJiorSzZs3nT0NAAAA4L5k+oA7ODhY/fr1c/Y0AAAAsi4WTRoq0wfckizb/NnTvXt3jRs37gHOBgAAAEg7l67hlqQ///xTCQkJzp4GAAAAYJfLB9wAAABwkBuUdTiTS5SUAAAAAK6KDDcAAICbS2m9HBxHhhsAAAAwEAE3AAAAYCBKSgAAANwdiyYN5RIBd3h4uJYtW3bf1yWpbdu2GTonAAAAIC1M5kxeJV+uXDmZTCaHxjCZTDp48KDDc7l97HeHxwCQ9RWq1sPZUwDgIq7eOObsKUiSIvs97dTn55651qnPN5pLZLgd/Zkgk/9MAQAA4FyUlBgq0wfchw8fdvYUAAAAgPuW6QNuAAAAGMtMhttQbAsIAAAAGIiAGwAAADAQJSUAAADujpISQ5HhBgAAAAxEhhsAAMDdJTp7AlkbGW4AAADAQATcAAAAgIEoKQEAAHBz7MNtLDLcAAAAgIHIcAMAALg7MtyGIsMNAAAAGIiAGwAAADAQJSUAAADujn24DUWGGwAAADAQGW4AAAA3x7aAxiLDDQAAABiIgBsAAAAwECUlAAAA7o5Fk4Yiww0AAAAYiAw3AACAm2PRpLHIcAMAAAAGIuAGAAAADERJCQAAgLtj0aShyHADAAAABiLgBgAAAAxESQkAAICbM1NSYigy3AAAAICByHADAAC4OzLchiLDDQAAABiIgBsAAAAwECUlAAAAbo5Fk8Yiww0AAAAYiAw3AACAuyPDbSgy3AAAAICBCLgBAAAAA1FSAgAA4OZYNGksMtwAAACAgchwAwAAuDky3MYiww0AAAAYiIAbAAAAMBAlJQAAAG6OkhJjkeEGAAAADESGGwAAwN2ZTc6eQZZGhhsAAAAwEAE3AAAAYCBKSgAAANwciyaNRYYbAAAAMFCGZLj//vtv/fTTTzp8+LCio6OVkJAgs9mc4j0mk0krV67MiMcDAADAAeZEFk0ayeGAe+rUqfriiy+s2lIKtk0mk8xms0wm/mABAACQ9TkUcO/cuVPTp0+3CqJz584tHx+fjJofAAAA4NIcCrgXL14s6U7W+o033lDHjh2VO3fuDJkYAAAAHgwWTRrLoYB7165dMplM6tSpk/r27ZtRcwIAAACyDId2KYmIiJAkNWnSJEMmAwAAAGQ1DmW4AwICdPnyZeXMmTOj5gMAAIAHzMzR7oZyKMNdpUoVSXe2BQQAAABgy6GAu1OnTjKbzZo7d66ioqIyak4AAAB4gMyJzn1ldQ4F3E888YT69u2rCxcuqHPnztq6datiY2Mzam4AAACAy3Oohnv8+PGSpHz58uno0aPq37+/PD09lT9//lTrujlpEgAAAO7AoYD766+/tpwYmXT4TXx8vMLCwpK9h5MmAQAAMheOdjeWQwF34cKFM2oeAAAAQJbkUMC9YcOGjJoHAAAAnMRsdvYMsjaHFk0CAAAASBkBNwAAAGAgh0pK7nb79m399NNP2rJli44dO6br16/Lw8NDefLkUenSpVW7dm21a9dOfn5+GfVIAAAAZAAWTRorQzLcf/zxhxo1aqT33ntPGzdu1Llz53Tjxg1FRETo7Nmz2rx5s8aPH69mzZpp+/btGfFIAAAAQJK0ZMkSNW/eXFWqVFGrVq30888/W13ftm2bOnTooMqVK6thw4aaM2eOzRj79u1Tt27dVLVqVdWtW1eTJ09WXFxchszP4YB769at6tevn8LDw2U2m2U2m1WsWDFVqVJFjz32mAoXLmxpDw8PV79+/fT7779nxNwBAACQAcyJJqe+HPHtt99qzJgxeuqppzR9+nTVrl1bQ4cO1erVqyVJoaGhGjhwoEqXLq1p06apVatWmjhxombPnm0Z48yZM+rZs6eyZ8+uKVOmqHfv3po7d67lzBlHmczm+1+XGhkZqSZNmigiIkJeXl4aMGCAOnfurMDAQKt+ly9f1pIlSzRz5kzFxcUpb968+vXXX12uvOT2MX5QAJC6QtV6OHsKAFzE1RvHnD0FSdLpKk2c+vySe36773tfeOEFeXt7a/78+Za2Ll26yMPDQwsWLFDPnj0VExOj7777znL9o48+0nfffaft27fL29tbb7/9trZv3661a9fK29tbkrR48WKNHTtWGzduVFBQ0P1/ODmY4V60aJEiIiKULVs2ffXVV3r55Zdtgm1Jyp8/v1599VXNmDFD2bJl09WrV21S/QAAAEB63b59Wz4+PlZt/v7+un79um7fvq2QkBA9/fTTVtebNm2qyMhIhYaGSpK2b9+uBg0aWIJtSWrWrJkSEhK0bds2h+fo0KLJzZs3y2QyqX379qpdu3aq/Z944gm1b99e3333nVavXq0uXbo48ngAAABkAGfvwx0cHJxqn5CQELvt3bt317vvvqvVq1frySef1LZt27Rp0yYNGTJE586dU1xcnEqVKmV1T4kSJSRJp06dUuXKlRUWFmbTJzAwUL6+vjp16tR9fqr/OBRwJ02gSZO0fw3RpEkTfffddzpz5owjjwYAAAD0zDPPaMeOHRo8eLClrV27durbt692794tSfL19bW6JykjHhUVpRs3btjtk9QvKirK4Tk6FHDHxMRIkvLkyZPme5L6RkREOPJoAAAAZBBnbwuYXPY6LV588UXt3r1bI0aMUIUKFbR3715Nnz5dvr6+atGihSTJZLL/+Tw8PJS0nNFeH7PZLA8Pxzf1cyjg9vf315UrV3TmzBk99thjabonKbPt7+/vyKMBAADg5kJDQ7Vt2zaNHz9e7du3lyTVrFlTuXPn1qhRo/Tss89Kkk2WOum9n5+fJbNtL5MdExOTIZt8OBSyP/roozKbzVarPlPz7bffymQyqWLFio48GgAAAG7uwoULkqRq1apZtSfVhB86dEienp46e/as1fWk96VKlZKPj4+CgoJsyp3Dw8MVFRVlU9t9PxwKuFu2bCnpztcA48ePV2o7DE6YMMHylUFSih8AAADOZTabnPq6X0nB8F9//WXVvmfPHklS6dKlFRwcrLVr11rFqWvWrJGfn58qVaokSapTp442btyo2NhYqz6enp6qWbPmfc8viUMlJc2aNdO8efP0999/a/78+dq5c6eee+45Va5cWXnz5pXJZNKVK1e0d+9e/fDDDzp8+LBMJpPKly9vCdYBAACA+1GxYkU1btxYH3zwgaKjo1W+fHnt379fn3/+uerVq6fKlSvrxRdfVK9evTRkyBC1a9dOu3fv1uzZs/XGG28oZ86ckqS+fftq5cqV6t+/v3r06KHTp09r8uTJ6tixowoXLuzwPB06+Ea6k8rv1auXzpw5k2xBehKz2awiRYpo0aJFKliwoCOPdQoOvgGQFhx8AyCtMsvBNycqNXXq88vsX3Pf98bGxuqzzz7Tzz//rPDwcBUpUkQtW7ZU//79Lftq//bbb5o6dapOnTqloKAgdenSRb1797YaJyQkRBMnTtShQ4cUEBCgtm3b6pVXXpGXl5dDn03KgIBbkq5evarJkydr2bJlio+Pt9snW7ZsatWqlYYPH56uXU0yEwJuAGlBwA0grTJLwH28gnMD7ocO3n/A7QoyJOBOcvXqVe3YsUPHjh3T9evXlZiYKH9/fz3yyCN6/PHH7Z5C6UoIuAGkBQE3gLQi4L4jqwfcDtVw3yswMJDFkAAAAC4m0YGFi0id4zt5AwAAAEhWmjLcq1atsvz67gz23e33g2w4AAAAsro0Bdyvv/66TCaTTCaTVZCc1H4/7h0LAAAAzuHIXthIXZpruJNbW5mBay4BAACALCdNAff48ePT1Q4AAADXYU4kw22kNAXc7dq1S1c7AAAAgDucskvJP//8o5CQEGc8GgAAAHigHAq4y5UrpwoVKujw4cNpvickJESNGzfWsGHDHHk0AAAAMojZ7NxXVudwhju9iyY9PT1lNpt15coVRx8NAAAAZHppquG+fPmyTp8+nez1/fv368aNG6mOExMTozlz5kiScuXKlbYZAgAAwFAsmjRWmgJuT09Pvfzyy4qMjLS5Zjab9e6776broSaTSdWrV0/XPQAAAIArSlNJSWBgoF599VWZzWarV5J721N7BQUFaejQoYZ9KAAAACCzSPPBN507d5avr68SExMtbSNGjJDJZNLLL7+swoULp3i/yWSSt7e3ChQooEcffVTZs2e//1kDAAAgwyRy0qSh0hxwm0wmtWnTxqptxIgRkqRGjRqpXLlyGTszAAAAIAtIc8Btz/z58yVJxYsXz5DJAAAA4MEzk+E2lEMBd82aNS2/3rVrl7Zt26bXXnvNpt+YMWN08+ZNPffccwoODnbkkQAAAIBLcXgf7qioKA0cOFBdu3bVl19+qejoaJs+u3bt0s8//6xu3bpp+PDhiouLc/SxAAAAgEtwKOA2m83q37+/Nm/ebNmB5Ny5czb9/Pz8LP2XL1+ut956y5HHAgAAIANx0qSxHAq4ly1bptDQUElS7dq19fPPP9tdPLl48WJt2rRJTz31lMxms1avXq3Nmzc78mgAAADAJTgUcP/888+SpMcee0yzZs1S2bJlk+0bFBSkL774QhUrVpQkffvtt448GgAAABkk0Wxy6iurcyjgPnz4sEwmk3r06CEPj9SHMplM6t69u8xms/7++29HHg0AAAC4BIcC7qioKElS0aJF03xPyZIlJUnXr1935NEAAACAS3BoW8D8+fMrLCxMFy9eTPM9165dk/TfQkoAAAA4F/twG8uhDHfp0qUlScuXL0/zPStXrpQkPfzww448GgAAAHAJDgXcrVu3ltls1vr16zVv3rxU+//444/65ZdfZDKZ1LRpU0ceDQAAALgEk9l8/7sfxsbGqkOHDjp27JhMJpOCg4PVtm1bVaxYUf7+/pLu1GofPnxYK1as0O+//y6z2axixYpp5cqV8vb2zqjP8UDcPva7s6cAwAUUqtbD2VMA4CKu3jjm7ClIkkKLtXHq86udS3u1hCtyqIbb29tb06ZNU6dOnXTt2jWFhIQoJCQk2f5ms1kBAQH68ssvXS7YBgAAAO6Hw0e7lyxZUitXrlTLli3l6elpOXHy3peHh4eaNm2qZcuWqUyZMhkxdwAAAGQA9uE2lkMlJfeKiorSli1bdPr0aYWHhys+Pl7+/v4qU6aMatWqpaCgoIx6lFNQUgIgLSgpAZBWmaWkJKRoW6c+P/j8Mqc+32gOlZTcy9fXVy1atMjIITMVn4rPOXsKAFzAzQtbnT0FAEAmkqEBNwAAAFwP+3AbK00B993HsD/22GN22+/H3WMBAAAAWVGaAu6OHTvKZDLJZDLp4MGDNu33496xAAAA4BzusHDRmdJcUpLc2soMXHMJAAAAZDlpCrhffvnldLUDAAAAuCNDtwXM6rJ5F3H2FAC4AHYpAZBWXvlKO3sKkqQdhds79fmPX1jq1OcbzeGDbwAAAAAkj20BAQAA3ByLJo2VpoB72bJlhjy8bdu2howLAAAAZBZpCriHDx9+39v/JcdkMhFwAwAAIMtzeFvA+8VaTQAAgMyBkyaNlaaAe/78+cle+/vvvzV58mQlJiaqRo0a6tChgx599FHly5dP2bJlU0REhA4dOqTly5dr7dq1ypUrl8aNG6fHH388wz4EAAAAkFk5tC3gpUuX1KpVK0VGRuqtt95Sz549U+y/YsUKvfXWW8qePbuWLl2qUqVK3e+jnYJtAQGkBdsCAkirzLIt4NaCzzr1+U/++4NTn280h7YFnDFjhiIiItS8efNUg21JatWqldq3b6+bN2/qyy+/dOTRAAAAgEtwKODesGGDTCaT2rRpk+Z7WrRoIUn6448/HHk0AAAA4BIc2of7ypUrkqSAgIA035MrVy5JUmRkpCOPBgAAQAYxi0WTRnIow12gQAFJ0pEjR9J8T2hoqCSpYMGCjjwaAAAAcAkOBdwVK1aU2WzWV199paioqFT7h4WFaebMmTKZTKpZs6YjjwYAAEAGSTQ795XVORRwd+vWTZL0zz//qEuXLtqzZ0+yfTdv3qwuXbro2rVr8vDwSNMiSwAAAMDVOVTDHRwcrE6dOmnJkiU6evSoOnXqpEKFCql8+fLy9/eXJIWHh+vAgQO6cuWK5bCbkSNHqnTpzLENDgAAAGAkhwJuSRo1apRy5MihBQsWKD4+XhcuXFBYWJhVn6RA29fXV8OGDVPHjh0dfSwAAAAySCKLJg3lcMBtMpn01ltv6dlnn9UPP/ygLVu26PTp00pISLjzgGzZ9NBDD+npp59Whw4dFBQU5PCkAQAAAFfh0EmTyTGbzbp27ZpMJlO6tgzM7DhpEkBacNIkgLTKLCdNbghybvVBw4vfOfX5RnM4w22PyWRSYGCgEUMDAAAgg7EPt7EyNOC+fPmy/vzzT507d04RERHq1auXChQooIsXL+rcuXMKDg7OyMcBAAAAmV6GBNxXrlzR+PHj9euvvyoxMdHS3qZNGxUoUEC7du3SG2+8ofLly+v9999XxYoVM+KxAAAAyACJqXeBAxzah1uSTp06pfbt22vVqlVKSEiQ2WzWvWXh58+fl9ls1qFDh9SpUydt377d0ccCAAAALsGhgDsuLk6DBg3SpUuXZDKZ9Nxzz+mzzz6z6VenTh3VqVNHZrNZsbGxev3113X16lVHHg0AAAC4BIcC7qVLl+rkyZPKli2bvvrqK73//vtq3LixTb+KFStq9uzZGj16tEwmkyIjI7V48WJHHg0AAIAMYpbJqa+szqGAe82aNTKZTGrbtq2efPLJVPt36tRJzzzzjMxmszZt2uTIowEAAACX4FDAffjwYUlSkyZN0nxP8+bNJUmnT5925NEAAADIIIlOfmV1DgXckZGRkqS8efOm+Z78+fNLkm7fvu3IowEAAACX4FDA7e/vL0kKDw9P8z1Jme08efI48mgAAADAJTgUcD/yyCOSpA0bNqT5niVLlshkMlnuBQAAgHNRUmIshwLupk2bymw268cff1RoaGiKfRMTEzV27Fjt3r1bkuzuZgIAAABkNQ6dNNm+fXvNmzdPJ0+eVJ8+fdSnTx898cQTluvx8fE6e/asdu7cqYULF+ro0aOSpCJFiqhDhw6OzRwAAAAZwh225nMmk/neYyHT6cyZM+rSpYuuXLkikynlPyyz2SxfX18tXLhQ5cqVc+SxTpHNu4izpwDABdy8sNXZUwDgIrzylXb2FCRJK4M6OfX5z1xc4tTnG83ho91LlCihZcuWqWHDhpJkOdrd3qtGjRr68ccfXTLYBgAAAO6HQyUlSfLly6fp06fr9OnT2rx5sw4dOqRr164pPj5e/v7+evjhh1W3bl1VqlQpIx4HAACADJRIRYmhHAq4//jjDxUvXlxFitwptShZsqRKliyZEfMCAAAAsgSHSkomTZqkJk2a6NNPP82o+QAAAOABS5TJqa+szqGA++zZszKbzSpfvnxGzQcAAADIUhwKuOPj4yXdqeEGAAAAYMuhgLtatWqSpE2bNmXEXAAAAOAEZie/sjqHAu5Ro0YpICBAs2bN0qeffqqLFy9m1LwAAACALMGhg2/mz5+vsLAwff3110oaJigoSAULFpSvr2+KB+GYTCbNmDHjfh/tFBx8AyAtOPgGQFplloNvlhXs7NTnt/13sVOfbzSHtgX84IMPrIJqs9msixcvkukGAABwIYnOnkAW5/DBN/cmyNOaME/tGHgAAAAgK3Ao4D58+HBGzQMAAABOkkgi1FAOLZoEAAAAkLJ0Z7jPnTunlStX6ujRo4qMjFRAQICqVKmili1bKk+ePEbMEQAAAHBZaQ64ExMTNWHCBC1atEgJCQlW13755Rd9/PHHeuONN9SlS5cMnyQAAACM4w57YTtTmgPud955Rz/99FOyiyJjYmI0duxYRUVFacCAARk2QQAAAMCVpSngDg0N1dKlS2UymeTn56fOnTurXr16yps3r8LDw7Vp0yYtXLhQN2/e1LRp09SqVSsVLlzY6LkDAAAgA7AtoLHSFHCvWLFCkuTv76+FCxeqTJkylmslS5ZU9erV1bhxY3Xt2lXx8fH64Ycf9OqrrxozYwAAAMCFpGmXkl27dslkMql3795WwfbdKleurNatW8tsNis0NDRDJwkAAAC4qjQF3EknR1auXDnFfnXr1pUknTp1ysFpAQAA4EFJNDn3ldWlKeCOjo6WJPn4+KTYr1ChQpKkyMhIB6cFAAAAZA1pquGOj4+XyWSSp6dniv1y5MghSbp165bjMwMAAMADkSg3SDM7ESdNAgAAAAYi4AYAAAAMlO6j3QEAAJC1cNKkschwAwAAAAZKV4Z7//79unHjRrLXz5w5Y/l1SEhIssfAJ6lRo0Z6Hg8AAAADuMPWfM6UroD73XffTbWPyXTnT6xbt26p9jt48GB6Hg8AAAC4nDQH3KllqwEAAADYSlPA3a5dO6PnAQAAACdJdPYEsrg0Bdzjx483eh4AAABAlsS2gAAAAG6OwmFjsS0gAAAAYCACbgAAAMBAlJQAAAC4OfbhNhYZbgAAALi0v/76S506dVLlypVVt25dvf/++4qOjrZc37Ztmzp06KDKlSurYcOGmjNnjs0Y+/btU7du3VS1alXVrVtXkydPVlxcXIbMj4AbAAAALmvPnj3q1auX8ufPry+++EKDBg3Szz//rHfeeUeSFBoaqoEDB6p06dKaNm2aWrVqpYkTJ2r27NmWMc6cOaOePXsqe/bsmjJlinr37q25c+dm2E59lJQAAAC4OVfeh3vSpEmqUqWKPv30U5lMJtWuXVuJiYmaO3eubt68qalTp6pChQr66KOPJEn16tVTfHy8vvzyS3Xr1k3e3t6aMWOG/Pz8NH36dHl7e6t+/frKkSOHxo4dqwEDBigoKMihOZLhBgAAgEu6evWqQkJC1KlTJ5lM/xWid+nSRevWrZOHh4dCQkL09NNPW93XtGlTRUZGKjQ0VJK0fft2NWjQQN7e3pY+zZo1U0JCgrZt2+bwPAm4AQAA3Fyik1/36+jRozKbzcqTJ48GDx6sKlWqqHr16ho9erRu3bqlc+fOKS4uTqVKlbK6r0SJEpKkU6dO6ebNmwoLC7PpExgYKF9fX506dcqBGd5BSQkAAACcKjg4ONU+ISEhNm1Xr16VJA0fPlxNmjTRF198oSNHjmjKlCm6ffu2nn/+eUmSr6+v1X0+Pj6SpKioKN24ccNun6R+UVFR6fswdhBwAwAAwCUl7SJSrVo1jR49WpL0xBNPyGw2a8KECerYsaMkWZWb3M3Dw0NmsznZPmazWR4ejheEEHADAAC4ObOT9+G2l71Oi6RMdb169aza69atqw8//FD79u2TJJssddJ7Pz8/S2bbXiY7JiZGfn5+9zW3u1HDDQAAAJdUsmRJSVJsbKxVe1Lmu2jRovL09NTZs2etrie9L1WqlHx8fBQUFKQzZ85Y9QkPD1dUVJRNbff9IOAGAABwc666aLJMmTIqUqSIVq1aZdW+ceNGZcuWTVWrVlVwcLDWrl1rKR2RpDVr1sjPz0+VKlWSJNWpU0cbN260CtzXrFkjT09P1axZ04EZ3kHADQAAAJdkMpn05ptvKiQkRG+++aZ+//13zZgxQ1988YW6deumwMBAvfjiiwoNDdWQIUO0efNmTZkyRbNnz9aAAQOUM2dOSVLfvn11+fJl9e/fXxs3brQcetOxY0cVLlzY8Xma7w73kaJs3kWcPQUALuDmha3OngIAF+GVr7SzpyBJml6sq1Of/9K5hQ7dv27dOn3++ec6fvy48ubNq+eff14DBgywLHj87bffNHXqVJ06dUpBQUHq0qWLevfubTVGSEiIJk6cqEOHDikgIEBt27bVK6+8Ii8vL4fmJhFwpwsBN4C0IOAGkFaZJeD+zMkB98sOBtyZHSUlAAAAgIHYFhAAAMDNUe5gLDLcAAAAgIEIuAEAAAADUVICAADg5hKdfNJkVkeGGwAAADAQGW4AAAA358hpj0gdGW4AAADAQATcAAAAgIEoKQEAAHBzlJQYiww3AAAAYCAy3AAAAG6OkyaNRYYbAAAAMBABNwAAAGAgSkoAAADcHCdNGosMNwAAAGAgAm4AAADAQJSUAAAAuDn24TYWGW4AAADAQGS4AQAA3Bz7cBuLDDcAAABgIAJuAAAAwECUlAAAALi5RIpKDEWGGwAAADAQGW4AAAA3x7aAxiLDDQAAABiIgBsAAAAwECUlAAAAbo4lk8Yiww0AAAAYiAw3AACAm2PRpLHIcAMAAAAGIuAGAAAADERJCQAAgJtLNDl7BlkbGW4AAADAQGS4AQAA3FwiGwMaigw3AAAAYCACbgAAAMBAlJQAAAC4OQpKjEWGGwAAADAQGW4AAAA3x0mTxiLDDQAAABiIgBsAAAAwECUlAAAAbo59uI1FhhsAAAAwEAE3AAAAYCBKSgAAANwcBSXGIsMNAAAAGIgMNwAAgJtjH25jkeEGAAAADETADQAAABiIkhIAAAA3xz7cxiLDDQAAABiIDDcAAICbI79tLDLcAAAAgIEIuAEAAAADUVICAADg5tiH21hkuAEAAAADkeEGAABwc2aWTRqKDDcAAABgIDLcyPKyZ8+uk8d3Kigov6Vt8+bf1ajJc8neEx/7T4Y8O7XneHt7q2PH1mrWtIGqVXtMBfLnlY9PLt24EaV/L15WaOg+/bLyNy1btlrx8fEZMifA3dy+HasmHXro6rXrlrbgqo9q3mcT0zzGrdu3tWbDVm3fuUsHDx9T+LXrunnzlnx9fZQ/b6Aeq1hOT9Wppfp1asrDI+25rOjoGP3863pt37lLR0+c0tVrEcqWzVP58waqYvmyavJUHT1Vp5Y8PT3T85ElSVevXdfy1eu05fe/dPb8P7p6PUI+uXIpf75A1apWWc883UCPVngk3eMCSD8CbmR5r7zc2yrYziyaN2uor778SIULF7S5FhgYoMDAAFUoX1Zdu3TQ6dPn1H/Am9qwcZsTZgq4tkU/LLcKttPDbDbrxxVrNOXLuboeEWlz/XpEpK5HROrYydP6ccWvKlW8qMa+/boqVyqf6tjfL1+lKV/OU0TkDesLt6Wo6BidOntev6zZoEceKq1RQ19O05hJfvh5tSZPn6PIG1H253vitBZ+v1wtmzbUqDdfVq5cOdM8NrImFk0ai5ISZGlVqlTUqHffcPY0bHTp0kHLl31tN9i2p2TJYlq1cpGefbaVwTMDspZDR49r+uxF93VvYmKiRn/4qcZM+NRusG3PqbPn1eOlofp1/ZYU+703caremzjNNti248jxk+r58jD9tml7mubw1ddLNGbCVJtg255f1mxQv8EjdSMqOk1jA7g/BNzIssqVe0grli9wauYmMdF2EcrDD5fWjC8/StfXzpKULVs2zZn1iUqWLJZR0wOytBOnz+rFN0fp1u3b93X/nEU/aOkva9J9X3xCgkaOnaSDR47bvf7VvCX6fvnqdI0ZFxevN0d9oO07d6XYb9P2nZo2Y366xt574LDGTPg0Xfcg60mU2amvrI6AG1lS82YNtXXzchUqFOS0OcTE3NSY9z6yaR854jVlz57dpv3ffy9p6rRZemv4+5oyZYbOnw+z6ZMrV069PmSgIfMFspItv/+pbgPf0JXwa/d1/+UrV/Xl3MV2rz1UqoR6vNBegwf2Uttnmihnzhw2fWJj4zTh069s2s+ev6Av5thm3L28sql1s0Ya+ko/9er8rAraKYNLSEjU2+M+VkzMTbvziouP14dTvrR7rU6t6ho8sJc6tW9ld75rNmzVn7v22r0XgOOo4UaWkidPbo0bO0IDB3R3aJxs3kXS3DdXrpzauWO1ypd72Kq9Z+/XtP33v2z6t3ymsU3bH3+EqGnzF6z+IX1n1AStWb1EderUtOrb9Omn0jw3wN1E3ojSlC/n6rtlqxwaZ8WaDXYz492fb6fXX+qjbNn+W8Q4qHdX9Xx5mP4Ju2jVd9fe/Tp7/oKKFy1saZuz6HvFJyRY9cuZI7vmfDZBj5b/bwHjS326aOioD7Vp+06rvlfCr2nJ0hXq07Wjzdw2bPlD5y/8a9Pet9vzGjywp+X9c22bq1PfwbodG2vVb943S1WzemWb+wE4jgw3soS8eQM0bOggHT64zeFgO70+/+xDm2B78uQvtXTpSpu+gYEBCgjwt2kfPeYjm6zVrVu39N7/PrbpW6RI2uq+AXdy7XqEZi34Ts+80MfhYFuSNt8T6EpSkUJBemOQdbAtSYUKFtCbg/raHSd074F7xv3Tpk+X59paBduSlDNHDk18b7jdTPcPP/9q91krf9to05bbz1cv9ups1Va2TCm1aWH7g//2nSFpqilH1mR28iurI+BGlrB61RJ9MG6k8ufPa3PNyO30unZ9Vt26PmvVtm//Ib397od2+yfck9lKcvWa/a+9r9nZWSE62v7XyYA76z/kbU35cq6uXbdd3Ojpmf5/6o4cP2nTVj+F7fmS20HkytX//tsOu3hZl8Ov2vSpXbOa3Xtz5cyhds88bdN+7p8wnTh1xqotMTFRO0P22PRtUPdxZc/ubdPetOGTNm0JCYnauct2DACOI+BGlmBvAWJCQoLefme8ttvJKGWEvHkDNGniaKu2+Ph49e49WHFxcXbviYiI1OXL4TbtrVra/qMqSa1bN7Vp22Yn8wa4O3sLlD08PPTagJ6q+ljFdI2VkJCgiWPe0nvDB+uVft31QvuWalSvtmpUeTTZe/Lk9rPbnjPHf+s1ktuaMMhOoiBJ+bJl7Lbv3nfQ6v2J02cVbae2u/wjD9m9v+I938ol2XfwaLJzAXD/qOFGlnTixGkNfHGYNm7arqeb1DfkGZM+GqN8+QKt2r6asUC79+xP8b7Fi5fqtdf6WbWNHPGaPDw8NH/B97pw4aKKFCmoHt076q1hL1v1S0xM1Mcff5ExHwDIwooWLqj3hr+mWtWraPufKe/scS9PT0/Vq10z9Y53sZcRl2RVEpLct20JicnvgJzDzgJrSTp20jrDffL0Obv9ChcsYLfd18dHuf18bbYOPHnmbLJzQdbmDjuFOBMBN7KUq1ev6ZMpMzTl0xm6efOWYc95vFZ1m1KS8PBrGj3GdleSe338yZfq1u1ZBQYGWNq8vLz07juv6913Xk/2vsTERA18cZh+/yPk/icOZHG5/XzVo1N7dX++nXLmsN2Nwyj26qqzeXqqRtXHLO8D/PPYvff8P2EqXcL+dp8XL1+xf88F612M7l2wmcQ/T2677UnzuTfgvhB2Kdn+AO4fATeyhF279mrmzIVavGSpbqThsAdHvf+/t2zaJn08XdevR6R674UL/6plq2764ftZaT74Zs/eA3rrrfe1fsPWdM8VcAcVyz2s59o2V8unG8jXx+eBPjv07wNatuo3m/a6jwcrt5+v5X3hgkHyyZXTpvTjt83bk82or9/8u932a9es/665lszfPb4+uZKdt4+dMwquRaT+dxiyJk6aNJZL13AnJiZq3759Wr58uRYuXChJiouL07lz9r9aQ9Y1YOBQfTVj/gMJths2qKsGDepYtV28eFmffT4nzWP8+dduPVq5gebMXZKm/pcvXVH4VdvFVgDu+N+IwXqhXcsHHmyfOnNeg0eOVaKdspAXe3exep8tm6dqBVex6bd81Tqt3Wj7w/Q3P/1isy1gkqiYGKv30fe8T+Lt5ZXc1OWVzTbnFh1tfxwAjnHZDPePP/6oadOm6eLF/75G69q1qy5cuKAWLVqoefPmGjt2rHI8wK8U4R7urauWpC+/+jpdJSze3t4aNvQlde7ULk39mzSpr6eeqq3BQ0bpq3SeIgfAGKfOnFefV4fbXQz5XJvmdhcm9ni+vTZs+cOqLTExUa+/84HqPh6sGlUfVWKiWb//Gaq/dv+d7LNjY60XZsclUx+e3K4qyV2LTWbBNwDHuGTA/fHHH2vWrFkym83y8PCQh4eHZbu1f//9VwkJCVq5cqX+/fdfzZs3T9ns/BQP3I9y5R5So0bW22ndunVLX36V9iA4V66cWrlioZ588nGr9rNn/9EPP6zQhbCLKlasiF54vo2C7lpw5eXlpc8/G69r16/ru+9+duyDAHDI4WMnNeD1dxR+1XZLz1LFi2rYq/3t3le9SiW1bNpQv6zZYHNt244QbduRtjUa9nZmssdkMqUwRvLX4H7MLJo0lMuVlOzYsUMzZ85Ujhw5NGbMGP3555967LH/FqXUqlVLEydOVM6cObVr1y59++23TpwtspqXXuxl07bil9/sbvWXnGlTP7AJtjdt+l0VH62nYcPf15RPZ+iNN0erQqV62rbN9uvkaZ9+oDwpLIQCYKzQvfvV6+VhdoPtQP88mjZhdIoLNse89aqqV66Upmf558mt/HkDbdqze1vvrW2vPERKfu9/SYqLt73mlS35EhQA98/lAu4FCxbIZDLpgw8+0AsvvCBfX1+bPq1bt9bEiRNlNpu1YsUKJ8wSWZGXl5c6vdDWpn3hwh/SPEblyhXVo7v1kcxxcXEa+NIwm5KUiIhIvdB5oM0JlHnzBqh7t+fSPnEAGWbL73+q/5B3dCMq2uZaoH8ezfx0vEoWL5riGDmyZ9fMKeP0XJsWKWaqCwUV0IxPxsnLyzaYzpPb+t++XDltF0BKUlxc8gd/2TsvwNc3+UWWyNoSnfzK6lyu1mLPnj3Kly+fmjdvnmK/xo0bq0CBAjp+/Hiaxg0ODs6I6SELe7pJfZtj2a9fj9CatZvSPEbXzh1s2tat26Ljx0/Z7f/vv5e0+tcN6tD+Gav2xo3qadpns9P8XACOW71us0a8P8nuftpBBfJpxifjVKZk8TSN5e3trdHDXtEL7Vvqx59X64+QPbp46c4WgMWKFNLTDeuqy7Ot5ZMrl8KvXre5P2+gddY7wN/+t17JLaaUZPeHhoA89rcuBOAYlwu4IyIiVK5cuTT1DQoK0qFDhwyeEdzFc8+1tmlbs3ZTuo6Of/zx6jZthw4dS/Ge43YO1ChZyv6evQCMseq3TRr+v4/s7kZSqkQxzZg8VoWSOWQmJY88VEojX38p2evn/gnT7dhYm/YSxQpbvQ/Kn8/u/dcjbyQ79vWISJu2woXS/xkApM7lAm5/f/80bftnNpt1/vx5BQQEpNpXkkJCUl+oks27SJrGQtbU9OmnbNpW/2q78Ckl+fIlf4RzcrJ52v5nmtJCKAAZ67dN2zXy/Ul2g+3KFcvp84/eS/GAGUccPmb/BMvyZa2PbE+ujCXsX/sH2dyIirab4S5dIm0ZemQ9LJo0lsvVcFerVk2RkZFauXJliv1++uknXbt2TVWrVn1AM0NWVqFCWeXPbxssb926I13jRETaZpQqVUr5G5tHyj1k03YlHYs0Ady/3fsOavh7ExVvZ/Hh48FVNfPT8ekKtuPjExR28bL27D+kNRu2av43P2nl2o3J9re3a4mHh4eqV65o1Va2TEm7td4Hjtj/Bu3QEfvllo9WKJvS9AHcJ5fLcHfr1k1r167V//73P+XIkUONGjWyup6YmKgff/xR48aNk8lk0gsvvOCkmSIrebLu4zZt//wTpjNnzqdrnLNnzyu4emWrtgYN6qhUqeI6deqsTf+CBQuo8T3bEEp3Tp4EYKxLl8P16vD37JZ0BFeppM8njlH27N527kze2I8/szkGvmjhgnrm6QY2fcOvXdev6zfbtFevXEmB96wn8fb2VpVKFWz27t68badiY2Plfc+uJr9u2GIzrqenh2pVr5LGT4Ksxh0WLjqTy2W4a9Soob59+yoiIkIvv/yygoODdeDAneDj2WefVc2aNTVq1CjdunVLzz33nGrXru3kGSMrqFrVdguv3Xv2p3ucVavW27R5eXnpmyVf2WTQfX19tHjhdLuHN61Zk3xGDEDGeHvcx7p23fZbqfx5AzXp/ZHpDrYlKbjKozZt5y/8q2+W/mLVFhNzU8NGf2hzDLwkderQ0u7YTe38cH71eoRmL/zequ3w0RNa8avt30V1agUrT26/FOcP4P64XIZbkt58800VLVpU06ZNU3j4f1+t799/JwDy8/NT//791a9fP2dNEVlM2YdL27Tt25f+Bbk/Ll2pse8PV8F7FldVr/aYDuzbrCXfLNPJk2dUpHBBvfBCWxUuXNBmjD17D6RrZxQA6bdu83b98dduu9cuh1/VU606p2mcF3t30aA+XS3vG9arLV+fXIq65wj1sR9/ru07d6lypfKKio7RyrUbFXbRtv760QqPqMlTde0+q3XTRpo+a6GuXo+wav989kL9feCwgqs+qouXruinVb/p5q3bNvf3eCFtJ98CSD+XDLgl6YUXXlCHDh20e/duHTt2TDdu3FDOnDlVqlQp1ahRQzmT2ZMUuB8P2wm4jx2zv5VfSm7ciNKrg9/Rd9/MsLkWGBigQS/ZHqxzt9u3b+uVV0am+7kA0mfmfGMOTcuVM4d6dX5W02bank67cdsObdyW/LoQ/zy5NWH0sGQXTefKlVNDX+2vEf/7yOba1h0h2prCKZZNnqpDOYmbSzSzaNJILhtwS3e+iq9Zs6Zq1qzp7KkgCzOZTFZHrCc5f/7CfY23dOlKDXxxmD7/bLw8PT3TfF9sbKz69n9Df6Tx6GcA9+fYydM6cDjl7Tod0bvLs9oRssem3jolBfLl1cwp41S8aOEU+7Vq2lD/XrysT7+al+axK5Uvq/eGD05zfwDp53I13MCD5uvrY/c0uCvhV+97zFmzF6lxk+d04OCRNPU/fOS4mjzdUUuW/HTfzwSQNlv/MPaHWi8vL30+cYxaNm2Ypv4tmjylb2Z9qjKlSqSpf7/uz2v8qKEK9E/9EJvmjetr1qcfKLef7anNcC9mJ7+yOpfLcN+7K0lqTCaT1q1bZ9Bs4A58fX3stt975Hp6bd22U5WrNFSTxvXUtm0L1apVTcWKFlbu3L6Kjo7RxUtXtGPHLv2y8jctW7ZaZr7uAx6Iw8dOGP6MXLly6sNRQ9X1uTb6aeVahe49oLCLl3TrdqwC8uRWwaD8ql2jmpo0qKtydkraUtOqaUPVe6KGVq7dqA1b/9Cps+d19dp15cyRQ0H586lGtcfU8ukGeqxi2g6SA+AYk9nF/hVP6ymTJpNJZrNZJpMpw06b5OAbAGlx88JWZ08BgIvwypf+H6iM0LVEe6c+f+GZpU59vtFcLsM9fvz4ZK/FxMTo0qVL2rBhg44fP65XX31VLVva3z4JAAAAdyS6RWGH87hcwN2uXerbFr322msaMWKEpk+frieftN2XFAAAAHhQsuSiSQ8PD7399tvKli2bvvzyS2dPBwAAIFMzO/l/WV2WDLglKXfu3CpdurR27drl7KkAAADAjWXZgFuSrl27pps3HdtJAgAAAHCEy9Vwp9WCBQsUFhamhx9+2NlTAQAAyNQSnT2BLM7lAu5hw4Yle81sNis2NlYnT57U8ePHZTKZ2KUEAAAATuVyAffPP/9s2WM7NcHBwerVq9cDmBUAAABgn8sF3G3btpXJZEr2uqenpwICAlS9enXVr18/xb4AAABgH26juVzA/cEHH8jDI0uv9QQAAEAW4nKRa+/evTV06FBFRkY6eyoAAABZAvtwG8vlMtz79+9Xzpw5lTt3bmdPBQAAAEiVy2W4ExISlDdvXmdPAwAAAEgTlwu4GzVqpKNHj3KCJAAAQAZJdPIrq3O5kpIRI0bowoUL6t27t5o3b67q1asrf/78yp49e7L3PPHEEw9whgAAAMB/XC7grlu3ruXXy5cv1/Lly1PsbzKZdPDgQaOnBQAA4LLScr4J7p/LlZSYzeZ0vRIT3eGLCgAAALz88stq0qSJVdu2bdvUoUMHVa5cWQ0bNtScOXNs7tu3b5+6deumqlWrqm7dupo8ebLi4uIybF6ZOsPdvXt3PfLII3r77bctbYcPH3bijAAAAJAZLV++XL/99puKFy9uaQsNDdXAgQPVvHlzvfbaa9q1a5cmTpwos9msPn36SJLOnDmjnj17qmrVqpoyZYpOnDihTz75RFFRURo1alSGzC1TB9x//vmnEhISnD0NAACALM3VT5q8ePGixo0bp4IFC1q1T506VRUqVNBHH30kSapXr57i4+P15Zdfqlu3bvL29taMGTPk5+en6dOny9vbW/Xr11eOHDk0duxYDRgwQEFBQQ7Pz+VKSgAAAIC7vfPOO6pTp47VRhm3b99WSEiInn76aau+TZs2VWRkpEJDQyVJ27dvV4MGDeTt7W3p06xZMyUkJGjbtm0ZMj8CbgAAADfnytsCfv/99zpw4IDeffddq/Zz584pLi5OpUqVsmovUaKEJOnUqVO6efOmwsLCbPoEBgbK19dXp06dcnB2d2TqkhIAAABkfcHBwan2CQkJsWn7559/NH78eI0fP16BgYFW127cuCFJ8vX1tWr38fGRJEVFRSXbJ6lfVFRU2j5AKshwAwAAwOWYzWaNHDlS9evXV9OmTe1el+5sEW2Ph4dHin3MZrM8PDImVM70Ge79+/erUaNG932/yWTSunXrMnBGAAAAWYvZyYsm7WWvU7No0SIdOXJEK1asUHx8vKT/guz4+Hj5+flJkk2WOum9n5+fJbNtL5MdExNjGcNRmT7gjo2N1T///HPf9yf3Uw0AAABc15o1a3Tt2jWrQxGTVKxYUWPGjJGnp6fOnj1rdS3pfalSpeTj46OgoCCdOXPGqk94eLiioqJsarvvV6YPuAsVKqT27ds7exoAAABZlituC/jee+8pOjraqu3zzz/XoUOH9Nlnn6lo0aJavXq11q5dqx49eliSsGvWrJGfn58qVaokSapTp442btyoYcOGWXYqWbNmjTw9PVWzZs0MmatLBNwvv/yys6cBAACATKR06dI2bf7+/vL29tajjz4qSXrxxRfVq1cvDRkyRO3atdPu3bs1e/ZsvfHGG8qZM6ckqW/fvlq5cqX69++vHj166PTp05o8ebI6duyowoULZ8hcWTQJAACALOmJJ57QtGnTdOLECQ0aNEgrVqzQsGHD1K9fP0ufMmXKaM6cOYqJidGrr76quXPnqlevXlYnnTvKZE6qLs+EypUrp+rVq2vRokXOnookKZt3EWdPAYALuHlhq7OnAMBFeOWzzdI6Q/NizZ36/NXnVjv1+UYjww0AAAAYKNPXcAMAAMBYjp72iJRl6oB7/Pjxyps3r7OnAQAAANy3TB1wt2vXztlTAAAAABySqQNuAAAAGM/ZJ01mdSyaBAAAAAxEwA0AAAAYiJISAAAAN+eKR7u7EjLcAAAAgIHIcAMAALi5THzweJZAhhsAAAAwEAE3AAAAYCBKSgAAANwciyaNRYYbAAAAMBAZbgAAADfHSZPGIsMNAAAAGIiAGwAAADAQJSUAAABuLpF9uA1FhhsAAAAwEBluAAAAN0d+21hkuAEAAAADEXADAAAABqKkBAAAwM1x0qSxyHADAAAABiLDDQAA4ObIcBuLDDcAAABgIAJuAAAAwECUlAAAALg5MydNGooMNwAAAGAgAm4AAADAQJSUAAAAuDl2KTEWGW4AAADAQGS4AQAA3JyZDLehyHADAAAABiLgBgAAAAxESQkAAICbYx9uY5HhBgAAAAxEhhsAAMDNsS2gschwAwAAAAYi4AYAAAAMREkJAACAm2PRpLHIcAMAAAAGIsMNAADg5lg0aSwy3AAAAICBCLgBAAAAA1FSAgAA4ObMlJQYigw3AAAAYCAy3AAAAG4ukW0BDUWGGwAAADAQATcAAABgIEpKAAAA3ByLJo1FhhsAAAAwEBluAAAAN8eiSWOR4QYAAAAMRMANAAAAGIiSEgAAADfHokljkeEGAAAADETADQAAABiIkhIAAAA3xy4lxiLDDQAAABiIDDcAAICbY9GkschwAwAAAAYi4AYAAAAMREkJAACAm2PRpLHIcAMAAAAGIsMNAADg5lg0aSwy3AAAAICBCLgBAAAAA1FSAgAA4ObM5kRnTyFLI8MNAAAAGIgMNwAAgJtLZNGkochwAwAAAAYi4AYAAAAMREkJAACAmzNz0qShyHADAAAABiLDDQAA4OZYNGksMtwAAACAgQi4AQAAAANRUgIAAODmWDRpLDLcAAAAgIHIcAMAALi5RDLchiLDDQAAABiIgBsAAAAwECUlAAAAbs7MPtyGIsMNAAAAGIiAGwAAADAQJSUAAABujn24jUWGGwAAADAQGW4AAAA3l8iiSUOR4QYAAAAMRMANAAAAGIiSEgAAADfHokljkeEGAAAADESGGwAAwM0lkuE2FBluAAAAwEAE3AAAAICBKCkBAABwcyyaNBYZbgAAAMBAZLgBAADcHCdNGosMNwAAAGAgAm4AAADAQJSUAAAAuDkWTRqLDDcAAABgIDLcAAAAbo6TJo1FhhsAAAAwEAE3AAAAXFZiYqKWLFmiVq1aqWrVqmrcuLHGjx+vqKgoS59t27apQ4cOqly5sho2bKg5c+bYjLNv3z5169ZNVatWVd26dTV58mTFxcVlyBwpKQEAAHBzZhfeh3vWrFmaMmWK+vTpoyeeeEKnTp3S1KlTdfz4cc2ePVuhoaEaOHCgmjdvrtdee027du3SxIkTZTab1adPH0nSmTNn1LNnT1WtWlVTpkzRiRMn9MknnygqKkqjRo1yeI4E3AAAAHBJZrNZs2bN0vPPP6833nhDklS7dm0FBARoyJAhOnTokKZOnaoKFSroo48+kiTVq1dP8fHx+vLLL9WtWzd5e3trxowZ8vPz0/Tp0+Xt7a369esrR44cGjt2rAYMGKCgoCCH5klJCQAAAFxSdHS0WrdurZYtW1q1ly5dWpJ07NgxhYSE6Omnn7a63rRpU0VGRio0NFSStH37djVo0EDe3t6WPs2aNVNCQoK2bdvm8DzJcAMAALg5Z+9SEhwcnGqfkJAQmzZfX1+98847Nu3r1q2TJFWoUEFxcXEqVaqU1fUSJUpIkk6dOqXKlSsrLCzMpk9gYKB8fX116tSpNH+O5JDhBgAAQJaxd+9ezZgxQ40bN9aNGzck3QnM7+bj4yNJioqKSrZPUr+7F1/eLzLcAAAAbs7ZJ03ay17fj127dmngwIEqWrSoxo4da8lOm0wmu/09PDwsn91eH7PZLA8Px/PTZLgBAADg8latWqVevXqpUKFCmjdvngICAuTn5ydJNlnqpPd+fn6WzLa9THZMTIxlDEcQcAMAAMClzZ07V6+//rqqVKmiRYsWqUCBApKk4sWLy9PTU2fPnrXqn/S+VKlS8vHxUVBQkM6cOWPVJzw8XFFRUTa13feDgBsAAMDNmZ38P0d8//33+vDDD9W8eXPNmjXLKiOdPXt2BQcHa+3atVZlM2vWrJGfn58qVaokSapTp442btyo2NhYqz6enp6qWbOmQ/OTJJPZ2UU7LiSbdxFnTwGAC7h5YauzpwDARXjlK+3sKUiSsuco5tTn37517r7uCw8PV6NGjRQYGKiJEycqWzbr5YnFixfXkSNH1KtXLzVr1kzt2rXT7t279eWXX+qNN95Qv379JEknTpxQu3btVK1aNfXo0UOnT5/W5MmT1aFDB40ZM8bRj0fAnR4E3ADSgoAbQFplloDbO3tRpz4/9vb5+7pv2bJleuutt5K9PnHiRLVp00a//fabpk6dqlOnTikoKEhdunRR7969rfqGhIRo4sSJOnTokAICAtS2bVu98sor8vLyuq+53Y2AOx0IuAGkBQE3gLQi4L7jfgNuV0ENNwAAAGAg9uEGAABwcxQ8GIsMNwAAAGAgMtwAAABujvy2schwAwAAAAZilxLAAcHBwZLubCUEAMnh7wrAvZHhBgAAAAxEwA0AAAAYiIAbAAAAMBABNwAAAGAgAm4AAADAQATcAAAAgIEIuAEAAAADEXADAAAABuLgGwAAAMBAZLgBAAAAAxFwAwAAAAYi4AYAAAAMlM3ZEwAy0iOPPJKu/n/99Zdy585938/buXOnunfvLkk6cOCAsmXjPynAVS1dulQjRoxI9301a9bUggULDJgRgKyC6ABZUsmSJRUYGJhqP09PzwcwGwCuIG/evKpWrZpNe1hYmMLCwuTt7a1KlSrZXC9btuyDmB4AF0bAjSxpwIABat++vbOnAcCF1K9fX/Xr17dpnzZtmj777DPlz59fS5YsccLMALg6argBAAAAAxFwAwAAAAaipAS4y5kzZ7Rw4ULt3LlTFy5c0M2bN+Xr66tHHnlELVu2VIcOHdJc9713717NmzdPBw8eVFhYmLJnz65SpUqpcePG6ty5s3x9fW3uiY2N1ZIlS7Rq1SodP35ccXFxKlSokJ566in16dNHBQoUyOiPDCADJC3Y3r59uz788EOtX79eHh4eqlixoubMmaN33nlHP/30k1q1aqVJkybZ3J+0YLNIkSLasGGDzfW//vpLCxYsUGhoqK5fv67cuXOrSpUq6tatm5544gnDPx8AxxBwA/9v3bp1GjJkiGJjY5UrVy4VK1ZMZrNZ58+f186dOy2vjz/+ONWx1q5dqyFDhig+Pl4BAQF66KGHFB0drb///lt79+7Vzz//rG+++cYq6L506ZL69++vQ4cOyWQyqXDhwvL399fx48c1b948LVu2TNOnT1f16tWN/G0A4IBXXnlFu3fvVtmyZXX16lXlz5/f4d2LJk2apJkzZ0qS8uTJo7Jly+rSpUtav3691q9fr759+2ro0KEZMX0ABiHgBiRFRERo5MiRio2NVadOnfTWW28pZ86ckqSYmBhNnjxZCxYs0C+//KKBAwfq4YcfTnasxMRE/e9//1N8fLyGDh2qXr16WbLiBw4cUN++fXXs2DEtXrxY/fv3lySZzWa9+uqrOnTokKpXr66xY8eqdOnSkqQbN25o/Pjx+vHHHzVo0CCtWLFC+fPnN/h3BMD92L9/vxYsWKAaNWooMTFRkZGRDo33zTffaObMmcqdO7feffddtW7dWtKdvzNWr16tt99+W7NmzVLJkiX13HPPZcRHAGAAariRJY0YMUKPPPJIsq9u3bpZ9Q8JCVFcXJzy58+vd955xxJsS1KuXLk0fPhweXl5SZKOHj2a4rOvXr2qy5cvS5I6duxoVYJSsWJFDRkyRI0bN5a/v7+lff369dq9e7cKFCigWbNmWYJtSfLz89O4ceNUuXJlXbt2TfPmzbvf3xYABmvevLlq1KghSfLw8LD67zy9YmNjNW3aNEnSBx98YAm2JclkMqlFixaWzPa0adMUHx9//xMHYCgy3MiSUtuH+959cxs1aqTdu3fr1q1bdr/+vX37tvz9/XX58mXdvHkzxWcHBAQoT548ioiI0JtvvqkXX3xRlStXlofHnZ9vO3bsqI4dO1rds27dOklS48aNlStXLpsxTSaTWrdurb1792rjxo18fQxkUhlZ8rV7925duXJFPj4+atSokd0+rVu31vvvv6+LFy/q4MGDeuyxxzLs+QAyDgE3sqT73Yc7R44cOnz4sA4fPqxz587p7NmzOn78uI4dO6a4uDhJd77KTYmnp6fefPNNvfvuu9q8ebM2b96sPHnyqFatWqpTp46eeuopFSxY0OqepKz5xo0bdfjwYbvjJn01ffr0aZnNZplMpnR/PgDGyshyr2PHjkmS4uLi1KVLl2T7eXp6KjExUSdPniTgBjIpAm7g/23evFlTpkzRwYMHrdoLFCigZs2aacuWLYqIiEjTWB07dlSJEiU0d+5c/f7774qIiNDatWu1du1amUwmPfXUUxozZowl8I6KipL034l2KUlISFB0dLTdXU4AOFeOHDkybKwbN25IulNaEhoammp/R+vFARiHgBuQtGPHDg0cOFCJiYmqUqWKWrVqpbJly6pMmTLKmzevJOnJJ59M15i1atVSrVq1dOvWLYWEhOivv/7S1q1bdeDAAW3cuFFhYWFatmyZTCaTpWb83XffVdeuXTP88wHIHJL7hsxeqVrS3wsVK1bU0qVLDZ0XAGOxaBKQNHPmTCUmJurxxx/X4sWL1bVrV9WsWdMSbMfGxuratWtpGis2NlYnTpzQ3r17Jd3JeNWtW1dDhgzR0qVLNXnyZEnS4cOHdeTIEUlSqVKlJP33FbI9YWFh2rNnjy5dunTfnxOAcyQtnk4qTbuXvf+uk/5eOH36dLILIs1ms3bs2KHTp08rNjY2g2YLIKMRcAOSzp8/L0kqV66c3YNtli1bZvmHMrWdALZs2aIWLVqof//+dv8BrF27tuXXCQkJkqQGDRpIklatWqXw8HC7444cOVLPP/+8Xn/99TR8IgCZSUBAgCTp5MmTNtcSEhLsHnZTo0YN+fn5KTo6OtkM94oVK9SjRw81b95c//77b8ZOGkCGIeAGJMs2fCtXrtSJEycs7bdv39bChQs1duxYS9utW7dSHKtevXoKCAjQ9evX9dZbb+n69euWa9HR0ZowYYIkqVChQpb9vFu0aKGyZcsqMjJSffr0scp0R0VFacyYMfr9999lMpkse3cDcB1Ju5ccO3ZM8+fPt5SWJJ0BYG+70Vy5cln+ex83bpx+/PFHJSYmWq6vW7dOo0ePlnRnO8LixYsb/TEA3CdquAFJgwYN0u+//67Lly+rVatWKlmypLy9vXXmzBnFxMQoMDBQpUqV0uHDh1PNInl7e+vTTz9Vnz59tGrVKq1fv17FixeXh4eHzp07p5iYGOXMmVMffvihvL29JUleXl6aPn26+vbtq0OHDqlly5YqVaqUcubMqdOnTysmJkbSnf3F69WrZ/jvB4CMVb9+fQUHByskJETjxo3TnDlzFBAQoJMnTyouLk6vvPKKZc/tu/Xr10/nzp3Td999p5EjR+qjjz5S0aJFdfHiRUsZSrVq1aySAgAyHzLcgKRKlSpp+fLlat26tQoXLqyzZ8/q7NmzKl68uAYOHKhffvlF3bt3lyRt2rQp1a0Ba9Wqpe+//15t2rRR/vz5dfr0aZ09e1ZBQUHq1q2bVq1apccff9zqnmLFiumnn37SsGHDVLlyZV2+fFlHjx6Vj4+PmjZtqoULF6pHjx6G/R4AMI6Hh4dmz56twYMH6+GHH1Z4eLguXLigJ554QkuWLFGrVq3s3mcymfT+++9r9uzZatKkibJly6ZDhw4pOjpaVapU0TvvvKOvv/7a7v79ADIPkzm1yAEAAADAfSPDDQAAABiIgBsAAAAwEAE3AAAAYCACbgAAAMBABNwAAACAgQi4AQAAAAMRcAMAAAAGIuAGAAAADMTR7gCsXL16VatXr9b27dt19OhRhYeHKy4uTv7+/ipQoIBq1Kihp556Sk888YSzp+qSpk2bps8++0yStH79ehUtWjTVe86fP69GjRo59NwiRYpow4YNDo1xv86fPy9/f3/5+vpatd/9e/Htt9+qSpUqTpgdABiPgBuAJCkmJkbTpk3T4sWLdevWLZvrly9f1uXLl3XgwAHNmzdPVatW1ejRo1W+fHknzBauIDY2VjNnztSMGTO0cuVKm4AbANwFATcAhYWFqW/fvjp+/LgkKUeOHGrUqJHq1KmjwoULy8fHR9euXdPff/+tVatW6eTJk9q9e7e6dOmiL7/8UjVr1nTyJ8jaChQooGXLltm9dunSJfXv31+SVLFiRY0bN85uPy8vL6Oml6yZM2dq6tSpD/y5AJDZEHADbi4qKkq9e/fWyZMnJUmNGzfWqFGjFBQUZNO3fv36eumllzR//nxNmDBB0dHRev3117V69Wr5+fk96Km7DW9v72S/Sbj7993HxydTfeOQmJjo7CkAQKbAoknAzU2cONESbLdr106fffaZ3WA7iaenp3r16qUhQ4ZIulNqMmvWrAcyVwAAXBEBN+DGTp8+rR9++EGSVLx4cY0ZM0YmkylN9/bu3VuFCxeWJC1fvlxms9mweQIA4MooKQHc2DfffKOEhARJ0oABA5QjR4403+vl5aVOnTpp3759qlevnuLi4uTt7W25Pnz4cP30008qV66cfvjhB3366adatmyZIiIiVKBAATVr1kxDhw61GnPLli1aunSp9u7dqytXrih79uwqWrSonnzySXXt2jXZzHvDhg31zz//qFSpUvr111+TnXPVqlUVExOjmjVrasGCBZb2u3cB+fzzz9WwYUMtXbpUy5cv17FjxxQdHa2goCDVrVtXvXv3VvHixZN9Rnx8vFavXq3vv/9eJ0+eVFRUlIoVK6ZWrVqpZ8+eafmtNVRa/1yS+knStm3blD9/frvjDRgwQJs2bZIkHTlyRJK0dOlSjRgxwqpf0u9vSrulXL16VbNnz9bGjRv1zz//KHv27CpZsqSeeeYZderUyer/vwDAlRBwA25s7dq1ku6UiTRu3Djd9yct1kvNW2+9pZUrV1renz9/Xjlz5rS8v379uoYNG6bNmzdb3RcbG6tDhw7p0KFDmj9/vsaMGaN27dqle57pcfPmTfXs2VM7d+60aj937pyWLFmiH3/8UZ999pnq169vc+/169c1cOBA7d6926r96NGj+vjjj/XLL7+oRo0ahs4/PVL7c3mQdu3apf79+ysiIsLSduvWLe3du1d79+7Vzz//rK+//pqdTgC4JAJuwE2FhYXpn3/+kSQ99thj8vf3N+Q5R48e1eHDh1WtWjVLFn3Tpk1q3769pDtB1d1BatmyZdWtWzeVLVtWMTEx2rp1q2WrwuHDhysxMVEdOnQwZK6SNGHCBF2+fFlVqlRR586dVapUKV26dEmLFy/W9u3bFRsbq+HDh2vdunXy8fGx3JeYmKh+/frp77//liTVqFFDXbp0UeHChXXmzBnNnz9f+/bt07Fjxwybe3qk9ufiiIYNG2rZsmVasmSJvv32W0nSjBkzVKBAgWR3S5k4caJMJpPatGmj5s2by8/PTwcPHtT06dN17do17d+/X5MmTdKYMWMcnh8APGgE3ICbSlooKUklSpQw7DmJiYkqXry45s6daylZefzxxy3X586dawm2mzZtqo8//tgqKKtdu7Zat26tHj16KCIiQu+9957q1q2b4sJOR1y+fFlt2rTRhx9+KA+P/5a5NGrUSAMGDNDmzZt19epVbd68WS1atLBc/+GHHyzBdps2bTRhwgRLPXzlypXVokULDR48WL/99psh806v1P5cHOHv7y9/f3+rMpQyZcqkesjPlClT1KxZM8v74OBgNWjQQC1bttStW7f0888/a9SoUVZ/LgDgCvhbC3BT169ft/w6b968KfYNCwuzlHYk9woPD0/2/nbt2tmtD09ISNC8efMkSfny5dP48ePtZkDLly9vqQm+ffu25s+fn4ZPeH+yZ8+ukSNH2gR1JpNJHTt2tLw/fPiw1fVvvvlGkpQnTx6NHj3aZvFptmzZ9MEHH2Sq7ROT+3NxhqZNm1oF20mKFSumevXqSZKio6Mt38oAgCshww24qbv3SE5th5HPPvvMsptJct54441ka7qTO7J7//79lsC/VatWViUa92rZsqXGjRunGzduaMuWLTYLLjNKxYoVky2vKVasmOXX0dHRll9fuXJFBw8elCQ1aNAg2c+RO3duNWnSREuXLs24CTsgMx2lnhRU23P3NzCRkZEPYjoAkKHIcANuKnfu3JZfp5SdzggFCxa023706FHLrytXrpziGF5eXqpYsaIk6fjx44ZtQ5hS2UOuXLksv46Pj7f8+tSpU5b5pHbwzKOPPurgDDNOcn8uzpDSXLJnz2759d2/7wDgKshwA27q7qzhpUuXUuw7btw4u0eG79y5U927d0/1WcntLJGeshbpTtmJdCc7HxkZqTx58qR6T3qltEvH3WUidwf8V65csfw6tcWnSZ8hM8hMO36k9O3G3djvHYArIsMNuKnixYsrMDBQkrR7926rEomMltxhOncHT2k5cCdpz3BJ97VwzqijxtN6WJB0p5Y7s0jPvJOTUb+nGTEXAMisCLgBN+Xh4WE5jOTWrVuWw0sepLuzwXdniZOTVPri5eVlNyOaUvYzLi5Ot27dSv8k0+Du3ThSK8+5e5/pzCy5bP69bty48SCmAwAujYAbcGNdu3a1BFZTp05VTEzMA33+I488Yvl10pZ6yYmNjbUsTCxRooRVhjspa5xSQB0WFubIVFNUunRpeXp6SpL27duXYt9Dhw4ZNo+MlPR5JOf9vgJAVkHADbixcuXKWQ46OX36tMaMGWNVtpGae09jTK8KFSpYstwrVqxIMeBfsWKFoqKiJN3Zm/tuSVvthYeHJxsc3nuKZUYKCAiwnCC5adOmZLPcsbGxWr16tWHzyEh3b1+Y3FZ8R44c0b///pvsGJSJAMAdBNyAmxs5cqQl07x8+XJ16dJFZ8+eTfGeAwcOqE+fPvr8888deraXl5e6desm6c6BMyNHjrS7C8Xhw4c1YcIESVKOHDnUpUsXq+tJ84+Li7Psh32306dPa/r06Q7NNTU9evSQdOdo+GHDhik2Ntbqutls1tixY1NdoJpZ3P3tw8KFC22uR0dH67333ktxDG9vb8uvH/S3JwCQmWSe1TsAnMLX11fz5s3ToEGDFBoaqt27d6tp06aqVauWGjdurKJFiyowMFDh4eE6fvy4fvvtN+3du9dyv6enp7p27WoTBKdV//79tXHjRu3fv1+rV6/WyZMnbY52X7RokSVzPXLkSJUsWdJqjDZt2ujHH3+UJH300Ue6ePGi6tevL0n6888/tWDBAsXExKhIkSKGHZzSsGFDtWzZUr/88ou2bdumDh06qE+fPipdurTCwsK0ePFi7dixQ7ly5XKJ4LNhw4bKnTu3IiMjtW7dOr300kvq2LGjcufOrUOHDmn+/Pk6ffq0ihcvnuwPaAUKFLD8evr06erdu7cSExMz1f7fAPAgEHADUGBgoBYuXKgFCxboq6++0tWrV/XHH3/ojz/+SPYeLy8vNW3aVP369VO5cuXu+9ne3t6aPXu2Xn/9dW3fvl1HjhzRO++8Y9MvV65cGj16tNq2bWtzrVatWurbt69mzZql+Ph4zZkzR3PmzLF6xrhx47RhwwZDTyocP368TCaTVqxYoaNHj+qtt96yul6kSBH17NnT7haLmU3u3Lk1fvx4DR48WHFxcVq/fr3Wr19v1adz584qU6aM3n//fbtj1K5dWz4+PoqOjtbq1au1evVqeXl5KTQ01Cr7DQBZHQE3AEl3MtU9e/ZUp06dtG7dOm3evFkHDx5UWFiYbt68KT8/PwUGBqp8+fKqVauWGjRoYJXBdIS/v7/mzJmjDRs2aNmyZdq7d6/Cw8Pl6+urokWLqlGjRurQoUOKzxs6dKjq1q2rxYsXKzQ0VBEREcqfP78ef/xx9e7dWw8//LA2bNiQIfNNjre3tyZNmqS2bdtq4cKFOnTokK5evaqCBQuqcePGevHFF/X7778bOoeM1LhxY61cuVKzZ8/W9u3bdenSJfn5+enRRx9V586dVb9+fbvlJkkKFCiguXPn6pNPPtH+/ft1+/Zt5c+fX2FhYVb7wANAVmcyc4oAAAAAYBgWTQIAAAAGIuAGAAAADETADQAAABiIgBsAAAAwEAE3AAAAYCACbgAAAMBABNwAAACAgQi4AQAAAAMRcAMAAAAGIuAGAAAADETADQAAABiIgBsAAAAw0P8Byymx2c2vhzMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf= RandomForestClassifier(n_estimators = 600, random_state=42,warm_start= True, n_jobs= -1, max_features = \"sqrt\", max_depth= 140 ).fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('Accuracy score: ', round(accuracy_score(y_test, y_pred), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred), 2))\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve, confusion_matrix\n",
    "\n",
    "sns.set_context('talk')\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "_, ax = plt.subplots(figsize=(12,12))\n",
    "ax = sns.heatmap(cm, annot=True, fmt='d', annot_kws={\"size\": 40, \"weight\": \"bold\"})\n",
    "\n",
    "labels = ['False', 'True']\n",
    "ax.set_xticklabels(labels, fontsize=25);\n",
    "ax.set_yticklabels(labels, fontsize=25);\n",
    "ax.set_ylabel('Prediction', fontsize=30);\n",
    "ax.set_xlabel('Ground Truth', fontsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   34.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  8.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 600, 'max_features': 'sqrt', 'max_depth': 140}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# max depth\n",
    "max_depth = [int(x) for x in np.linspace(100, 500, num = 11)]\n",
    "max_depth.append(None)\n",
    "max_features = [\"auto\", \"sqrt\",\"log1\"]\n",
    "# create random grid\n",
    "random_grid = {\n",
    " 'n_estimators': n_estimators, #   max_features = [‘auto’, ‘sqrt’,\"log1\"] /n\n",
    " 'max_features': max_features,\n",
    " 'max_depth': max_depth\n",
    " }\n",
    "# Random search of parameters\n",
    "rfc_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "# Fit the model\n",
    "rfc_random.fit(X_train, y_train)\n",
    "# print results\n",
    "print(rfc_random.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Model positive recall is 88 percent which is important in this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "\n",
    "fig, axList = plt.subplots(ncols=2)\n",
    "fig.set_size_inches(16, 8)\n",
    "\n",
    "# Get the probabilities for each of the two categories\n",
    "y_prob = rf.predict_proba(X_test)\n",
    "\n",
    "# Plot the ROC-AUC curve\n",
    "ax = axList[0]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob[:,1])\n",
    "ax.plot(fpr, tpr, linewidth=5)\n",
    "# It is customary to draw a diagonal dotted line in ROC plots.\n",
    "# This is to indicate completely random prediction. Deviation from this\n",
    "# dotted line towards the upper left corner signifies the power of the model.\n",
    "ax.plot([0, 1], [0, 1], ls='--', lw=.3)\n",
    "ax.set(xlabel='False Positive Rate',\n",
    "       ylabel='True Positive Rate',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='ROC curve')\n",
    "ax.grid(True)\n",
    "\n",
    "# Plot the precision-recall curve\n",
    "ax = axList[1]\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_prob[:,1])\n",
    "ax.plot(recall, precision, linewidth=5)\n",
    "ax.set(xlabel='Recall', ylabel='Precision',\n",
    "       xlim=[-.01, 1.01], ylim=[-.01, 1.01],\n",
    "       title='Precision-Recall curve')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Key Findings\n",
    "\n",
    "so far, Random Forest model performed well.\n",
    "\n",
    "- KNN and Randomforest model predicted similar results\n",
    "- RandomForest model preformed good in positive class prediction which is the main focus for this problem with very high recall score (specificity)\n",
    "- our model has a 88% recall. In such problems, a good recall value is expected.\n",
    "- Precision and Recall follows a trade-off, and you need to find a point where your recall, as well as your precision, is more than good but both can't increase simultaneously.\n",
    "#### overall  86%  is a very good result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suggestions for next steps\n",
    " \n",
    "- some feature could be more importanat and ignoring some will make better model\n",
    "- For this backward feature selection would give better result."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}