churn_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer Churn, also known as customer attrition, or customer turnover, is the loss of customers and it is an important and challenging problem for ecomerce and online businesses. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For any business in a designated period of time, customers can fall into 3 main categories:\n",
    "   \n",
    "1. **Newly Acquired Customers**  \n",
    "2. **Existing Customers**   \n",
    "3. **Churned Customers** \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Churned Customers are those who have decided to end their relationship a company. Because they can translate in a direct loss of revenue, predicting possible customers who can churn beforehand can help company save this loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can classify customer churn by grouping cases into different categories:  \n",
    "\n",
    "1. **Contractual Churn**: This type of churn is  applicable to business that provide different services such as cable companies. It happens when customers decide not to continue with their expired contracts.\n",
    "\n",
    "2. **Voluntary Churn**: It refers to the situation in which customer decides to cancel their existing service and can be applicable for companies that provides a service not based in a fixed-term contract, such as prepaid cellphones or streaming subscriptions.\n",
    "\n",
    "\n",
    "3. **Non-contractual Churn**: This type of churn, which is applicable to businesses that depend on retail locations or online stores as an example, can be associated to consumers leaving a possible purchasee without completing the transaction.\n",
    "\n",
    "4. **Involuntary Churn**: This happens when a customer can not longer stay with the credit card company or can not pay their credit card bill."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several and completely different reasons of custumer churn. Among them, we can find lack of usage of the product, poor service, or better pricing in other similar services.    \n",
    "\n",
    "Nevertheless, one thing holds `true` for all industries: \n",
    "        \n",
    "> **It cost more to adquire new customers than it is to retain existing ones**\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because it costs so much to adquire them, it is wise to word hard towards retaining them. A company avoid customer churn by knowing its customers, and one of the best way to achieve this is through the analysis of historical and new customer data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the metrics to keep track of customer churn is **Retention Rate**, an indication of to what degree the products satisfie a strong market demand, known as product-market fit. If a product-market fit is not satisfactory, a company will likely experience customers churning. \n",
    "\n",
    "A powerful tool to analyse and improve Retention Rate is Churn Prediction; a technique that helps to find out which customer is more likely to churn in the given period of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the current churn analysis project, we'll try to achieve the following aims:  \n",
    "\n",
    "1. Build a classification model to predict which customers will churn\n",
    "2. Analyze how the different features affect retention or more specifically, customer churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always let's import the required libraries. We'll start by importing `pandas` to handle the Data and `numpy` to perform some numerical calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll import the libraries for making plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to preprocess and perform feature engineering, we need the `LabelEncoder` from `scikit-learn` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And last, we import the libraries for splitting the set, finding the best model and evaluating the final models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, roc_auc_score, roc_curve, precision_recall_curve, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [dataset](https://www.kaggle.com/adammaus/predicting-churn-for-bank-customers) used for this project was obtained from Kaggle. It contains data about customers who are withdrawing their account from a bank.\n",
    "\n",
    "Let's first load the data and understand its content."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 10000 observations, from which we have 14 attributes\n"
     ]
    }
   ],
   "source": [
    "print(f\"The dataset contains {df.shape[0]} observations, from which we have {df.shape[1]} attributes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among their attributes, we observe ['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Among their attributes, we observe {df.columns.tolist()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if we have any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column          Missing values\n",
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Column          Missing values\")\n",
    "print(\"------------------------------\")\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And which type of data we have in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column               types\n",
      "--------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RowNumber            int64\n",
       "CustomerId           int64\n",
       "Surname             object\n",
       "CreditScore          int64\n",
       "Geography           object\n",
       "Gender              object\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "Exited               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Column               types\")\n",
    "print(\"--------------------------\")\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset contains both categorical and continuous variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many unique values do we have for each attribute?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column         Unique Values\n",
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RowNumber          10000\n",
       "CustomerId         10000\n",
       "Surname             2932\n",
       "CreditScore          460\n",
       "Geography              3\n",
       "Gender                 2\n",
       "Age                   70\n",
       "Tenure                11\n",
       "Balance             6382\n",
       "NumOfProducts          4\n",
       "HasCrCard              2\n",
       "IsActiveMember         2\n",
       "EstimatedSalary     9999\n",
       "Exited                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Column         Unique Values\")\n",
    "print(\"----------------------------\")\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our target variable is Exited. We can observe that it has only two possible variables: [1, 0]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Our target variable is Exited. We can observe that it has only two possible variables: {df['Exited'].unique().tolist()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the name of the attributes, we can distinguish that three columns are spectific for identifying each customer. We need to drop them from the dataset as they are not going to be useful for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also can rename ethe columns to avoid having Capital letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_names = {\n",
    "    'CreditScore': 'credit_score',\n",
    "    'Geography': 'country',\n",
    "    'Gender': 'gender',\n",
    "    'Age': 'age',\n",
    "    'Tenure': 'tenure',\n",
    "    'Balance': 'balance',\n",
    "    'NumOfProducts': 'number_products',\n",
    "    'HasCrCard': 'owns_credit_card',\n",
    "    'IsActiveMember': 'is_active_member',\n",
    "    'EstimatedSalary': 'estimated_salary',\n",
    "    'Exited': 'exited'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns=new_names, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>credit_score</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>tenure</th>\n",
       "      <th>balance</th>\n",
       "      <th>number_products</th>\n",
       "      <th>owns_credit_card</th>\n",
       "      <th>is_active_member</th>\n",
       "      <th>estimated_salary</th>\n",
       "      <th>exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit_score country  gender  age  tenure    balance  number_products  \\\n",
       "0           619  France  Female   42       2       0.00                1   \n",
       "1           608   Spain  Female   41       1   83807.86                1   \n",
       "2           502  France  Female   42       8  159660.80                3   \n",
       "3           699  France  Female   39       1       0.00                2   \n",
       "4           850   Spain  Female   43       2  125510.82                1   \n",
       "\n",
       "   owns_credit_card  is_active_member  estimated_salary  exited  \n",
       "0                 1                 1         101348.88       1  \n",
       "1                 0                 1         112542.58       0  \n",
       "2                 1                 0         113931.57       1  \n",
       "3                 0                 0          93826.63       0  \n",
       "4                 1                 1          79084.10       0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our target variable is `exited`, we should analyse the data focusing on how the different features are related to this variable. Let's start by getting how many customers have been lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "amount_retained = df[df['exited'] == 0]['exited'].count() / df.shape[0] * 100\n",
    "amount_lost = df[df['exited'] == 1]['exited'].count() / df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEzCAYAAAAM4BV4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZgU1fX/8fdhEXFlERFZBBUVl5/biJoFt4i4BUMiYlzQ8A2aaIxxN6hFuUSMGg2JMaKgYEKQYFSiBiUoGk1EcImKBkUFBZFFFlEQBM7vj3t7aJqemR7onmGGz+t5+umuW7erTvV2uu69VWXujoiISLE1qO0ARESkflKCERGRklCCERGRklCCERGRklCCERGRklCCERGRklCC2QBmNtDMPOv2iZk9bGa71XZshTKzLeJ2HJBT3jFu00m1FVs+ZtbGzJ40syUxviNrMZY94mvXrLZiqAlmdk58rbcp4jLzfu5qg5ltE7fvnBKuo/eGLj++TguKHFJV61xgZgOLtTwlmA23BDg83i4DDgAmmNnWtRpV4bYAEkLc2eYQtumFGo+ocgOA/YHTCfG9Woux7EF47ep1gimRij539VVv4JwNfO59wHHFC6XmNartAOqwVe7+Unz8kpl9BPwLOAH4a25lM2sINHT3lTUYY15m1rSiee6+Anipovm1aC9gkrs/WduB1DVm1tTdl9d2HPVFTb2e7j4LmFXq9ZSUu+tWzRswEFiQU9YUcODyOP0AMAU4BZgKfA18O847AJgALAMWAX8GWmctq2Nc1g+BB4GlwDwgyRPL0cAk4CtgLvAHYJus+UfGZR0HjAW+AIbGstxbx6x1n5S1jIZxmz8CVsTt+WFOHJntPRZ4A/iSsBe0TwGvZyfgUeDzuK1/B3bPmp8b54wqlvdj4M2s12QMsH2cNxEYk1M/8xrtm1V2NTA9axnjgJ2y6uaNpxrvbR/g/rjNs4Az4/wrgE+A+cAtQIOcWPcFnoiv01LCn5mdqnq/47x+wNvAcmAB8Fxl7w/hn7fnfJ52AIYDn8VtnAiU5Tzvu8Ar8TOwiPD5PKKC99KBjhWsvw0wDPggxvwucCOwRZ7XszdwD6FlYRaQ5nntvh+XsRx4HiiLzz2nktcgs/wzgBHAYuCfWfP/j/B9WAHMBK7I+U7kbuvAOO9EYDzhe/054U9d98p+Z7Le2yPj+/5FfG1+mifub8f3d1l8r+4Fts2p0w34L+Ez/grwjfi5GFi038piLWhzuuW+8bGsS3zzz8r6cC2IH+gzge8A7YBW8UP6H0LyOTN+Id7IfHGyPtSz45fmOOAmYA1wQdY69wFWEn5wTgTOj8sel+dDOQu4gZCQvgEcFctvAA6LtybkTzA3ERLkNTGWIbHO6TlfpnnA68BphB+Zd4G3AKvktWwSvyTT4vO+H58zG2gR6xxGaBJ7Jj4+sJLlXRNfp98DPYBehITaNs6fSBUJBjib8OP9U+CIuIzfA7sB2wGXxvrfy46nmu/tTOBXhIT8F2A1cDshGfYgNAk60Ccrzt0JP6ATgJ7xtXobmJx5jSt5v7vF9/DqWOe7wM3ANyp5Lc9h/QTzAvApcC5wMuGHeinxD0F8jVYCt8Z1nwBcC3wvzs/7uatg/fsBt8XX8gjCH4fZwD1ZdTKv54z4+h0LDIplvbPqHQSsIvwwHw9cTvjcFZpg5gB3xeUfHeddHl/Tm2L5VYREc2HWa/EM4bOb2dZ2cd6FwEWE79OxwG/iZ+CbFf3OZL237xE+58cSErADXbPqfTPG8VB8/c+Kr9uYrDo7E/4APAucBPQHPiQkpIFF+62s7R/runjLvPGEJsZGhDb5Zwn/RNrEOg/EN/6AnOcOIvwIbZdVdihZP9hZH+qnc557b/ygNIjTo+KHrWFWnd7xuYfnfCjvyFnWNvm+XOQkGKBF/CAmOfWeBKZlTT9A+AJ3zio7JS5rr0pey/Pj83bNKmtH+JG6OqtsIjmJIc+ymsUvyG8qqbPeclg/wfweeLiSZZxEnn/e1Xxv78+qsx3hhyr3vXwZeChr+kFCIs7+B9+Z8MN0YhXv92XAK9X8nJ9DVoIhJD4n7o3Esq0Je1v3xOkfAJ9Vssy8n7sC42lE2Kv/ivUT9oicuq8Do7KmRxOSsWWVDagqlqzlP5JTvh1hDyL3e3E9IQE3jNNjgIlVbFeDuG1PAcOyygeSP8Fcn1XWOL7+g7LK/gU8m7OOo3M+478m7NlslVXnDLL2sopxUyf/hmtJ+FH4mvCl3xU4zd3nZNWZ7e6v5zyvKyFxfJ4pcPdJhH9g38qp+0jO9N8I/zzaZS3rEXdfnVXnYcIPdu6ynihgm/LZF9iK9fuVHgL2MLNWWWUz3P29rOm34307KtYVeNXdP8gUeGh7fpH1t6EqhxOaKu+v5vNyvQ6cYGapmXWN/WeFqM57OyGrzueEH4nnct7L6UDbrOnvED4Ta8yskZk1IvzrnEFo7smW+36/DhxoZneYWTcz26LAbcrWFZjn7s9lxf4l8Dhrt+9NYHszG25m3Tdm0IsFF5vZ22a2nPBd+zNhr7dDTvWnc6bfZt3PXVdgrMdf0uhv1Qgn9/U8nJBc/5p5L+L78QzQmso/85hZu/gazSZ8X78GuhP+rFalfFvdPfPHpF1c7lYxttE5cb0Q13FwfGpXYLy7L8tabu7vzUZTgtlwS4BDCF/sdoR/s//IqTM3z/PaVFA+l7C3kG1eBdNtKlpW/IH6LM+y8q2zEJl15T4/M529nsU5dTIDGrasYvmFvh5VaRnv51Raq2rDgF8S9gYnAXPN7MYCEk11tiXfa5WvLPu12wG4krV/bDK3XYH2edZZzt3/SWjW6kbYi1tgZndVMwG0Yf3PZGZdLeJ6phGa73Yl7OUuMLOROX9ECnUxoYnskbjMrsAFcV7uZ6qq126nPLHn25aK5L6vO8T7TP9q5vZsLM99P8qZWQNC/9g3gOsIzYaHAP+g8u9KRmXb2pzQZ/qHnLhWEPZ2MnGt93rEZPNFAesvmEaRbbhV7j6lijqep2wOsGOe8taEjrZsufUy03Oy7tepE38EWwILC4ilEJl17UhIXBmt433uejZk+fvkKW+9AcvOxNeG0ISZz1eEobLZmmdPuPsa4A7gDjNrT2g6uInQr/HHStZfnfd2Qywk/Njel2de7vau9367+3BgePyx70XYxqWEvoNCVLZ95e+Vuz8BPGFm2xP6Bu8EfkcY2FAdpxKaMwdkCsxs72ouI+NTKv4+FSL39cxs70nk/1MxrZJl7Q4cCBzv7uMyhZWN7qyGxcRmLkKCz/VJvF/v9Yh7P0U75gm0B1MbJgHHmdm2mQIzO4TQ1pt77Mn3cqZ7Eb7kmaGLk4Dv5fyz7kX441DVcSyF7F1A6HBfRviyZ+sNvOvu86t4flUmAQebWadMgZm1Jfy7q+6xOP8hjBDqW0mdWYQhz9m6V1TZ3T9290GE5qrMj1tFr1113tsNMYGQjF9x9yk5txmFLsTd57v7PYS2+ur8YE8CdjSzbpmC+KN0Inm2z92XuPtIQlKs6rXLpynhn3e2M6oRb7bJwHfNzLLKem3gsmDtZ23nPO/FFHdfGuvl7klB2C7I2jYz24XQOb9RYpPlS8CeFcSVSTCTgWPj+5eR+3uz0bQHU/N+A/wEeMrMbiH8YxhEaLt+OKfuPmZ2TyzvRhhm+vP4DxvCkM3XgEfN7G5CU90twFPu/p/KgnD3lWb2IdDbzN4i/LN/I0+9hWZ2J3CNma0iDEXuRRidcnq1t359DxCaff5hZtcROqwTwj/ye6qzIHdfbGY3ADfFPoYnCe31JwKpu88m/Nj1M7M7CO3qRxE6r8vF13wh4Yu6JNbpHOOEtf9OzzOzUcAyd3+T6r23G2IgoeP/CTMbRniN2hJGEz3g7hMreqKZpYRmrInxeQcSRmYVuveCuz9lZv8GHjKzqwh7jJcRfjBvjes5j9AHMI7wb7kz4c/JiLiMvJ87z3982HjgIjObBLxPSC67FxpvjlsICXK0mQ0l9C3228BlZT5rA4HfxuTwPOEP+x7AUe6e+bH+H9DTzE4h/Ln5JJbNAm43s2uBbQnDqmdvaDw5riAc9L2GMMhgKaHP6kRggLu/S9irvAB43Mx+Q+jbvZqQNIunWKMFNqcbeYYp56nzADClgnkHEjoDlxF2aUeS/1iJMwhDWJcSOoFTcob8Asew9jiYeVR8HMy+eeLoTkgqX1H1cTAp8DHhH9nbwBlVbW++ZVXweuxKOA5mKaEN+HGyRqPFOhOpYhRZVt3zYowrCE0Bo1l3ZNfVcVuWAn8iDNnNHmFzDmGQwcL4Hr0B9MtZx6WEocarWPc4mELf25NyljcDuK2A13Qvwo/GQsKPwXRCIs4Mf837fhOacibEz9FXhCR5Ve7nKec557D+MOVWhGSxKK7/OeCQrPmHExL3J3E9HxJ+3Jtk1Vnvc1fB+rchDNhYGG/3sXYE375VvJ75XrtTWXts0wuEfo9CR5Hl/QwThqK/El+LzDE/l2TN34Hwp2Yh6x4Hcwjhz8JyQif9ObkxU/Eostz3diLrj4w8lJDkPyeMAn2b8Ado+5zlvUH4nrxO2IMq6nEwmbHzsgkxs46EL+bJ7v547UYjIrJh1AcjIiIloQQjIiIloSYyEREpCe3BiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISWxyCcbMfm5mb5nZVDO7OJa1MLPxZvZevG8ey83MBpvZdDN7w8wOylpO31j/PTOr7BK6IiJSAptUgjGzfYEfA12B/YGTzGx3wpX3Jrh7Z8JV+TKXeT2ecEnWzkB/4O64nBaEy+4eGpeVZJKSiIjUjE0qwQBdgEnuvszdVxEux9oL6AkMj3WGA6fExz2BER68BDQzszbAccB4d1/o7osI1/Ze57rrIiJSWo1qO4AcbwE3mVlLwrWqTwCmEK5pPifW+RRoHR+3JVxbPWNWLKuofB1m1p+w58Pee+998NSpU4u3JSIimweraMYmtQfj7u8AtwBPA+OA14HVOXUcKMpV0tx9iLuXuXtZ06ZNi7FIERGJNqkEA+DuQ939YHfvBiwC3gXmxqYv4v28WH020D7r6e1iWUXlIiJSQza5BGNmO8b7DoT+l5HAWCAzEqwv8Fh8PBY4O44mOwxYEpvSngK6m1nz2LnfPZaJiEgN2dT6YAAejn0wXwMXuPtiMxsEjDazfsBMoHes+yShn2Y6sAw4F8DdF5rZDcDkWO96d19YkxshIrK5s9ClIWVlZT5lypTaDkNEpK6pG538IiJSfyjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISWxyCcbMfmFmU83sLTP7i5ltaWadzGySmU03s4fMbItYt0mcnh7nd8xaztWxfJqZHVdb2yMisrnapBKMmbUFLgLK3H1foCHQB7gFuMPddwcWAf3iU/oBi2L5HbEeZrZ3fN4+QA/gD2bWsCa3RURkc7dJJZioEdDUzBoBWwFzgKOBMXH+cOCU+LhnnCbOP8bMLJaPcvcV7v4h4ZLKXWsofhERYRNLMO4+G7gN+IiQWJYArwCL3X1VrDYLaBsftwU+js9dFeu3zC7P8xwREakBjWo7gGxm1pyw99EJWAz8ldDEVar19Qf6A3To0GGjl3f35Oc3ehlS//zkkG61HYJIrdik9mCA7wAfuvt8d/8a+BvwTaBZbDIDaAfMjo9nA+0B4vztgc+yy/M8p5y7D3H3Mncva9WqVSm2R0Rks7WpJZiPgMPMbKvYl3IM8DbwLPCDWKcv8Fh8PDZOE+c/4+4ey/vEUWadgM7AyzW0DSIiwibWRObuk8xsDPAqsAp4DRgCPAGMMrMbY9nQ+JShwINmNh1YSBg5hrtPNbPRhOS0CrjA3VfX6MaIiGzmNqkEA+DuCZDkFH9AnlFg7v4VcGoFy7kJuKnoAYqISEE2OMGkadoc2AV4J0mSFcULSURE6oOC+mDSYFDW9NGE/pJXgPfTNN2nRPGJiEgdVWgn/xnA/7KmbwdeIIzwmgbcXOS4RESkjis0wexM6AchTdP2wP5AkiTJS8BvgMNKE56IiNRVhSaYpYRjTCCctmVRkiSZYb9fEU7pIiIiUq7QTv7ngKvSNF0DXMba41AA9mDd07KIiIgUvAfzC2AFMIpwCpcBWfPOBnSOFBERWUeVezBpmmaSUM8kSZbmqXIcoZlMRESkXCFNZA2AGcDJwLjcmUmSfF7kmEREpB6osoksSZJVwEzUkS8iItVQaB/MLcCANE13KGUwIiJSfxQ6iqw70AaYmabpK8BcwLPme5IkpxU7OCmdXxyx7mV2Vq5YQbfv9+S0yy8G4MVHH+fpESP5/LOF7Lb/fpx57ZU0a1Xx/4spT0/gifseYNGn89iuZQvOvu4qdj9wf+Z8MIPhA3/F/Nnhagkd9tqT3pdeRJtdOwIwedx4Hv7tH2i0xRacde2V7Fl2EADzZ83mgeQmLh3yOxo01NWuReqiQhPMDoQj9rOnpQ6747m13WlfLVvG1cf34qBjjgLg3Vde47G77+XiP9zJjh3a8dfbBzPsmuu55J7BeZf1zqTJPPr7e+h3U8Iu+3Th8wWflc/bvlVLfjwopUWbnfA1a3huzCMMvSblmpH3s3rVKh69awhXP3gfH70zjdG3DebaUQ8AMPr2wfzgFxcquYjUYQUlmCRJjip1IFJ7Xn/mebZp3ozdD/x/ALz5wn846Jgj2Xm3TgAc368vvzzx+8yfNZtW7da/8vTjQ+7n+H596bRfOCVdsx3XXrxtq223ZatttwVgjTsNGjRk/sdhb+bLJZ/TrNUObL9DS/bqejALfvkJAK9OmEizVjvQad+9S7fRIlJy1T6bcpqmRmgumxcHAEgd99IT4zj0hOMI13gLwnXbyicA+OT9D9dLMGtWr+ajd6bx/7p9k6TXD/l65Ur2P+JbfO9nP2GLLZuU17v06BNZsXw5vmYNJ/X/EQDbNG/Gl0s+Z9HceXz87nu02bUjX325jHHDHuSiu35Twi0WkZpQcIJJ0/QEwnVaDojPOwR4NU3Te4HnkiT5U2lClFL6bM6nvPfafznzmivLy/Y5vCtDB1zPt3v1ZMf27Xhy6HDMjJVfrX+40+cLF7F61Spem/Aclwz5HQ0bNeSPlw3gH8NG0POnPy6vd/szT7Bi+XJeemIcLXbaCYAGDRrQ58pLuPfqhEaNG3PGgMt5fMgwjujdi9nT3+fJocNp1Kgx37/4p+y8266lfzFEpKgKPV3/2YTLEP8P6A9Y1ux3gX7FCMbM9jSz17Nun5vZxWbWwszGm9l78b55rG9mNtjMppvZG2Z2UNay+sb675lZ34rXunl7+cmn2W3//dihbZvysr26lnFS/3O596prufaU02jRZieabLUVzbOavjK2aBL2Uo7s3Yvtd2jJNs2accwPezP13y+tV7dJ06Z8u1dPRgz8FUsXLorrOpgrht3NJfcMxjA+emcah5/Ug+EDf8XZ113N8f3O5k833VqirReRUip0mPIA4NYkSfoCuXsqU4GiNJa7+zR3P8DdDwAOBpYBjwBXARPcvTMwIU4DHA90jrf+wN0AZtaCsLd1KOFKmEkmKcm6Jj35FIedeNx65Uec+j3Sh0dyy7hHOfCoI1izenXevYittts29Llk/+UwW69ehq9Zw8oVX7F4/vx1y9156NY7OfWyi/hi8RJ8zRpattmJXfbei9nvvb/B2ycitafQBLMLML6CeV8B2xUnnHUcA7zv7jOBnsDwWD4cOCU+7gmM8OAloJmZtSGcvma8uy9090Ux9nXH5Qrvv/EWi+cvKB89lvH1ihV88v4HuDsLP53LyJtv46g+32er7bbNu5zDTz6eiaP/xtKFi1j2+VKeGTma/b71DSCMMPt42rusWb2a5V98ycN33sVW227LTh13WWcZLz72OO332oP2e3Rm6+23Y+WKFcz5YAbvvvIaO7TduTQvgIiUVKF9MB8DBwLP5JlXBkwvWkRr9QH+Eh+3dvc58fGnQOv4uC3rnsl5ViyrqHwdZtafsOdDhw4dihZ4XTHpiXEccNS32XLrdU/S8PXKlQy79gYWzPqELbfaisNOPp6Tz1vbCjru/geZ/vobXPjb0HR1Qr++fLF4CQN/cCaNt9iCg75zJD3OPROA5Uu/YPRtg1k8bz6Nm2zBLvt04YLf3krjJmsHAHyxeDHPjnqYy+67C4CGjRpx2uUX89uf/oJGTcLxMSJS99g6o4UqkKbpVcAvgZ8CjwKfEzr5mwEPAdcnSZL/IIkNCcpsC+ATYB93n2tmi929Wdb8Re7e3MweBwa5+wuxfAJwJXAksKW73xjLrwWWu/ttFa2zrKzMp0yZslFx3z1ZJ5WW9f3kkG61HYJIKVXYJl7oHswtQHtC89TqWPZvoCFwTzGTS3Q88Kq7z43Tc82sjbvPiU1g82L57BhXRrtYNpuQZLLLJxY5RhERqURBfTBJkniSJBcQLi72M+Aa4OfA3rG82E5nbfMYhBFsmZFgfVl7wbOxwNlxNNlhwJLYlPYU0N3MmsfO/e6xTEREaki1DrRMkuR9oKRDesxsa+BY4Lys4kHAaDPrRzizc+9Y/iRwAqEPaBlwLoC7LzSzG4DJsd717r6wlHGLiMi6qpVg0jTdk9BZvmXuvCRJnixGQO7+JdAyp+wzwqiy3LoO5N2DcvdhwLBixCQiItVXUIJJ03Q/QpNVF/J36DihP0ZERAQofA9mGPA1cBKhOWplySISEZF6odAE0wX4fpIk6igXEZGCFHok/8vA5nckooiIbLBC92D6A39J03QZ8CywOLdCkiTLihmYiIjUbYUmmAXADGBEJXXUyS8iIuUKTTB/Ag4HbkOd/CIiUoBCE8xRwI+TJBlZymBERKT+KLSTfwbhSHkREZGCFJpgLgcGpGnasXShiIhIfVJoE1lKGKb8bpqmM8g/iqxrEeMSEZE6rtAE81a8iYiIFKSgBJMkybmlDkREROqXap1NGSBN05ZAC2BhkiSfFT8kERGpDwpOMGmangYMJFx0LFP2LnBdkiR/LX5oIiJSlxU0iixN08wVJj8gXNTrhHj/ATAqTdM+xQrIzJqZ2Rgz+5+ZvWNmh5tZCzMbb2bvxfvmsa6Z2WAzm25mb5jZQVnL6Rvrv2dmfSteo4iIlEKhezADgCFJkpyfUz4iTdM/Ei6hPKpIMf0WGOfuPzCzLYCtgF8CE9x9kJldBVwFXAkcD3SOt0OBu4FDzawFkABlhGvVvGJmY919UZFiFBGRKhR6HMzuwMMVzHs4zt9oZrY90A0YCuDuK919MdATGB6rDQdOiY97AiM8eAloZmZtgOOA8e6+MCaV8UCPYsQoIiKFKTTBzCXsDeRTFucXQydgPnC/mb1mZveZ2dZAa3efE+t8CrSOj9sCH2c9f1Ysq6h8HWbW38ymmNmU+fPnF2kTREQECm8iux8YmKZpQ2AMIaHsCJxKaB67uYjxHAT8zN0nmdlvCc1h5dzdzcyLsTJ3HwIMASgrKyvKMkVEJCg0wVwPNCb82KdZ5csJZ1i+oUjxzAJmufukOD0mrnOumbVx9zmxCWxenD8baJ/1/HaxbDZwZE75xCLFKCIiBSj0QMs1hHOR3QbsC7QB5gBvJUlStI5zd//UzD42sz3dfRpwDPB2vPUFBsX7x+JTxgIXmtkoQif/kpiEngJ+lRltBnQHri5WnCIiUrWCEkyapmcDT8QDK/+VM68FcFKSJJVdjKw6fgb8OY4gywyLbgCMNrN+wEygd6z7JGHI9HTC2Z7PBXD3hWZ2AzA51rve3RcWKT4RESlAdfpgDgfyHbnfKc4vSoJx99fJP6DgmDx1HbigguUMA4YVIyYREam+QkeRWSXzWgKfFyEWERGpRyrcg0nTtCfhOJOMa9M0zR3LuyXwbdY2RYmIiACVN5HtCOyXNb0bsFNOnZXA08CNRY5LRETquAoTTJIk9wL3AqRp+izw0yRJ3qmpwEREpG4rdJjyUfnK0zRtliTJele3FBERKXSY8k+AbZMk+XWcPgB4HGiTpunrQM8kSWaVLkwREalrCh1F9jPWHSk2GPgEOCMuY1CR4xIRkTqu0ATTAZgGkKZpK+CbwBVJkowinCbm6NKEJyIidVWhCWYFsEV8fBThqPnMEf0LgWZFjktEROq4Qo/kfxm4IE3TWcBFwLgkSVbHebsSmstERETKFboHcymwD/Am4ezFA7LmnQa8WOS4RESkjit0mPLbwG5pmrYEFiZJkn3tlMsIFwETEREpZ+F8kVJWVuZTpkzZqGXcPfn5IkUj9clPDulW2yGIlFKF56os9DiY0VXVSZKkd1V1RERk81FoJ3+rPGXNgb0Ip/CfVrSIRESkXtjYU8W0Bx4B7ihWQGY2A1gKrAZWuXuZmbUAHgI6AjOA3u6+yMwM+C3homPLgHPc/dW4nL7ANXGxN7r78GLFKCIiVSt0FFleSZJ8DNwM/Lo44ZQ7yt0PcPfMhceuAia4e2dgQpwGOB7oHG/9gbsBYkJKCJdR7gokWZdPFhGRGrBRCSZaDbQrwnIq0xPI7IEMB07JKh/hwUtAMzNrAxwHjHf3he6+CBgP9ChxjCIikqXQTv698xRvAXQhnCqmmBccc+BpM3PgHncfArR29zlx/qdA6/i4LfBx1nNnxbKKytdhZv0Jez506NChiJsgIiKFdvK/Rfjhz2XAFOD/ihYRfMvdZ5vZjsB4M/tf9kx395h8NlpMXkMgDFMuxjJFRCQoNMHk6+T/CpiVJMnsIsaDu8+O9/PM7BFCH8pcM2vj7nNiE9i8WH024cwCGe1i2WzgyJzyicWMU0REKlfoKLLnSh0IgJltDTRw96XxcXfgemAs0JdwWYC+wGPxKWOBC81sFKFDf0lMQk8Bv8rq2O8OXF0T2yAiIkGhfTB9gPZJktyaZ97lwMwkSao8GLMArYFHwuhjGgEj3X2cmU0GRptZP2AmkDmo80nCEOXphGHK5wK4+0Izy+4but7dFxYhPhERKVChTWRXA/dVMBRuvsIAABmxSURBVO/LOH+jE4y7fwDsn6f8M+CYPOUOXFDBsoYBwzY2JhER2TCFDlPendDRn887hONQREREyhWaYJZR8bEu7QkXJBMRESlXaIL5J3BtmqY7ZhfGyycPAJ4udmAiIlK3FdoHcyXwEvB+mqbjgDlA5oj5xcAVpQlPRETqqoL2YJIk+YjQ+f57QpPY8fH+d8BB8ZxkIiIi5QrdgyFJkvnoWBIRESlQMU52KSIish4lGBERKQklGBERKQklGBERKYkKE0yaps+kabpXfHx2mqYtay4sERGp6yrbg/k20Cw+vh/YrfThiIhIfVHZMOWPgVPTNP2CcGGxTvFxXkmSvF3s4EREpO6qLMHcDPwBuJhwNcuRFdSzOL9hcUMTEZG6rMImsiRJ7iWc4PIIQhK5EDg6z+2oeC8iIlKu0iP5kySZC8xN0zQFHkuS5JOaCMrMGgJTgNnufpKZdQJGAS2BV4Cz3H2lmTUBRgAHA58Bp7n7jLiMq4F+wGrgInd/qiZiFxGRoNBLJqcAaZpuAewHtAAWAm8mSbKyBHH9nHCdme3i9C3AHe4+ysz+SEgcd8f7Re6+u5n1ifVOM7O9gT7APsDOwD/NbA93X12CWEVEJI+Cj4NJ0/QKYC7wMvBUvJ8bL5lcNGbWDjiReAVNC9dPPhoYE6sMB06Jj3vGaeL8Y2L9nsAod1/h7h8SLqnctZhxiohI5QpKMGmaXkzo9B9J6HPpEu9HAjenaXpREWO6k3D6/zVxuiWw2N1XxelZQNv4uC1htBtx/pJYv7w8z3PKmVl/M5tiZlPmz59fxE0QEZFCz6Z8ATAoSZIBWWXTgOfTNF0MXAQM3thgzOwkYJ67v2JmR27s8qri7kOAIQBlZWVe6vWJiGxOCm0iaw88W8G8iVR8OeXq+ibwXTObQejUPxr4LdDMzDLJsB0wOz6eHWMjzt+e0NlfXp7nOSIiUgMKTTAfAd0rmHdsnL/R3P1qd2/n7h0JnfTPuPsZhOT2g1itL/BYfDw2ThPnP+PuHsv7mFmTOAKtM6HPSEREakihTWSDgcFpmrYgdKbPBXYETgXOIYz6KqUrgVFmdiPwGjA0lg8FHjSz6YRRbX0A3H2qmY0G3gZWARdoBJmISM2y8Ie/amma/hhICMN+nXDw5SfAwCRJ7itZhDWkrKzMp0yZslHLuHvy80WKRuqTnxzSrbZDECklq2hGwcOU45H97YFdgMPjffv6kFxERKT4Cm0iAyBJEicM//24qroiIrJ50wXHRESkJJRgRESkJJRgRESkJJRgRESkJAo9F9kzaZruVcG8PdI0faa4YYmISF1X6B7Mkaw9dX6u7QAN9BcRkXVUp4lsvSMy4/VhjgY+LVpEIiJSL1R4HEyapglwXZx04KVwYcu8bi1yXCIiUsdVdqDlk8ACwmkABgO3AzNy6qwE/pckyb9KEp2IiNRZFSaYJEkmA5MB0jRdCjyRJMmCmgpMRETqtoJOFZMkyfCqa4mIiKxVUIJJ07Qx4ZT8vQgX79oyt06SJDsWNzQREanLCj3Z5R3AecDjhIt/rSxZRCIiUi8UmmBOBa5KkuT2UgZjZlsCzwNNCLGNcfckXpVyFNASeAU4y91XmlkTYARwMOFSyae5+4y4rKuBfsBq4CJ3f6qUsYuIyLoKPQ7GgDdKGUi0Ajja3fcHDgB6mNlhwC3AHe6+O7CIkDiI94ti+R2xHma2N+HqlvsAPYA/mFnDGohfRESiQhPMvcDppQwEwIMv4mTjeHPCwZxjYvlw4JT4uGecJs4/xswslo9y9xXu/iEwHeha6vhFRGStQpvI5gJnpGn6LDAeWJwz35MkubsYAcU9jVeA3YG7gPeBxe6+KlaZBbSNj9sSL37m7qvMbAmhGa0t8FLWYrOfk72u/kB/gA4dOhQjfBERiQpNMHfG+w7AEXnmO1CUBOPuq4EDzKwZ8AiQ9ySbRVrXEGAIQFlZ2XqnwhERkQ1X6HEwNX5af3dfbGbPAocDzcysUdyLaQfMjtVmA+2BWWbWCNie0NmfKc/Ifo6IiNSATep6MGbWKu65YGZNgWOBdwhDo38Qq/UFHouPx8Zp4vxn3N1jeR8zaxJHoHUGXq6ZrRARESj8QMsTqqqTJMmTGx8ObYDhsR+mATDa3R83s7eBUWZ2I/AaMDTWHwo8aGbTgYWEkWO4+1QzGw28DawCLohNbyIiUkMK7YN5nNDPYjnl2f0WGz0M2N3fAA7MU/4BeUaBuftXhGN08i3rJuCmjY1JREQ2TKEJplOesubAccC5wDnFCkhEROqHQjv5Z+Ypngm8nqbpauCXwHeLGZiIiNRtxejkf41wIKSIiEi5jUow8ZLJ5wBzihKNiIjUG4WOIpvMuh36AFsAHYFtCf0wIiIi5Qrt5J/K+gnmK+CvwKNJkkwtalQiIlLnFdrJf06J4xARkXqm0D0YoLzPZT+gBeHAxjeTJNHFx0REZD0Fd/KnaXoF4azKLwNPAZOBuWmaXl6i2EREpA4rKMGkaXoxcDMwEjgK6AIcGadvTtP0olIFKCIidVOhTWQXAIOSJBmQVTYNeD5N08XARcDgYgcnIiJ1V6FNZO0JZzTOZyLhdPgiIiLlCk0wHwHdK5h3bJwvIiJSrtAmssHA4DRNWwBjCJ39OxLOZHwO8POSRCciInVWQXswSZL8HjgP6AE8CUwB/hGnz0+S5K6SRSgiInVSwcOUkyS5l9AXswvhMsa7AO2TJLmvWMGYWXsze9bM3jazqWb281jewszGm9l78b55LDczG2xm083sDTM7KGtZfWP998ysb0XrFBGR0qjWgZZJkjjwcbyVwirgUnd/1cy2BV4xs/GEZrgJ7j7IzK4CrgKuBI4nXA65M3AocDdwqJm1ABKgjHCKm1fMbKy7LypR3CIikqPgBJOm6c7AyUBbYMuc2Z4kyZUbG4y7zyGemdndl5rZO3F9PQnH3QAMJ4xcuzKWj3B3B14ys2Zm1ibWHe/uCwFikuoB/GVjYxQRkcIUejblPoQfdgPmA7mnh3HCD37RmFlHwuWTJwGtY/IB+BRoHR+3Zd29qVmxrKLy3HX0B/oDdOjQoXjBi2xili+fUNshyCaoadNjSrr8QvdgbgIeJnTof17CeAAws23i+i5298/NrHyeu7uZ5Z7ZeYO4+xBgCEBZWVlRlikiIkGhnfwtgaE1lFwaE5LLn939b7F4bmz6It7Pi+WzCQMPMtrFsorKRUSkhhSaYP7G2j6QkrGwqzIUeMfdf5M1ayyQGQnWF3gsq/zsOJrsMGBJbEp7CuhuZs3jiLPusUxERGpIoU1kFwJD0zS9D3gGWJxbIUmSJ4sQzzeBs4A3zez1WPZLYBAw2sz6ATOB3nHek8AJwHRgGfHKmu6+0MxuIJzxGeD6TIe/iIjUjEITzB5AV6AT8KM88x1ouLHBuPsLhIEE+azXGxVHj11QwbKGAcM2NiYREdkwhSaY+4HPgRMJewu6yJiIiFSqOnswvZIkUT+GiIgUpNBO/pcBHSgiIiIFK3QP5hLggTRNl1NxJ/+yYgYmIiJ1W6EJ5pV4P7ySOhvdyS8iIvVHoQnmR4SRYiIiIgUpKMEkSfJAZfPTNG1clGhERKTeqNbp+rOlaWrA0cDpQC+gRbGCEhGRuq/aCSZN08MISeVUwlmNF6LT4IuISI5CT9e/HyGp9CFcyXIlsAVhdNldSZKsKlmEIiJSJ1V4HEyaprumaTogTdO3gNeBS4GpwNmEK0ga8JqSi4gU04oVKzn//NvYc88fsuOOJ3Pooefx1FMvl89/9tlXOeCAc2nZ8kR69LiUjz6aW+GyevS4lA4dvk/r1t/l0EP78/e/v1g+79e/HkmrVieV31q0OIGttz6WBQuWAHDHHQ/Rvn0vDj64H2+99UH58/7zn7fo3fu6Emx5/VPZgZbTgesJp4g5D9gpSZKTkyT5M7C0JoITkc3PqlWradeuFU8//Rs+/fQxkuRczjrrBmbO/JQFC5Zw+ukDue66c5g9+xEOOmgPzjrrhgqXdeutF/DBB6OZO3csv/vdL+jXbxBz5nwGwBVX/JD58x8vv11yyWl067Y/O+ywPXPmfMbw4eOYOvVB/u//Tua664aWx3bVVX/k1lt/WiOvRV1XWYKZSdhL2Zdwqv5vpGm6wYMCREQKsfXWTbnmmr7ssstONGjQgBNOOIyOHXfitdfe5bHH/kWXLh3p1esIttxyCwYMOJs33/yAadM+yrus/fbblUaNwiF6ZsbXX69i1qz569Vzd0aOHM8ZZ3QH4OOP57H//ruz3XZbc/TRBzFjRrig7u9//zAnnvgNdtllpxJtff1SYYJJkqQT8A3gAcKZjP8OzE3T9N44reNiRKTk5s5dxHvvzaJLl468885M9ttv1/J5W2/dlF133Zl33plR4fN79RpA8+bHc8QRF/Ltb+/PwQfvsV6dF198k/nzF3PKKd8GYLfddmbq1A9ZvPgLnnnmVbp06cisWfP4618ncvHFpxZ9G+urSs9FliTJS0mSXES4nn134FHg+8CYWOXHaZqWlTZEEdlcff31Kn70o19xxhnd2XPPDnzxxXK2337rdepst93WLF26vMJl/O1vNzFv3t955JFf8Z3vlNGgwfo/e3/609N873vd2GabpgC0bLk9V1zxQ44//jLGjZvEzTefx2WX/YEbb/w/xo59ge7dL+HUU6/NuzckaxV0ssskSdYkSfLPJEn6EYYmfw8YHe8npWn6TjGCMbNhZjbPzN7KKmthZuPN7L143zyWm5kNNrPpZvaGmR2U9Zy+sf57ZtY337pEZNO2Zs0a+vUbROPGjbnjjp8BsM02Tfn883VPe7h06Zdsu23TSpfVuHEjjjuuK//85xQef/zf68xbtuwrHnnk+fLmsYzevY/mP//5I489djNTp35IkyaN2X//3bn66iGMGXMDvXodwS9/eU8RtrT+KvRsyuWSJPk6SZLHkiQ5HdiRcAXK94oUzwNAj5yyq4AJ7t4ZmBCnAY4njGbrDPQH7oaQkIAEOJRwkbQkk5REpG5wd84//3bmzVvEX/6S0Lhx6P7t0mUX3nxz7YiuL79czgcfzKFLl44FLXf16tV8+OEn65SNHfsizZtvS7du++d9zvLlKxg4cBg333we06fPpl27Vmy33dYcfPCe64wuk/VtVKd9PIPyyHjbaO7+vJl1zCnuSRhkAOFkmxOBK2P5iHhVy5fMrJmZtYl1x2cukWxm4wlJSweDitQRF130W6ZNm8kTT9xK06ZNysu/+91vMWDAEB599Hl69DiMm2/+E/vu24k991z/aiLTpn3EjBmf0q3b/jRq1JAxYybywgtvcuON/dep9+c/P80Pf3gsZvkvpjto0J8588zu7LzzDpgZ7733MXPnLuK5516nY8c2xd3weqYujApr7e5z4uNPCU10EPqFPs6qNyuWVVQuInXARx/NZejQx2nSpDGdOq3tUP/d735Bnz7HMHJkwiWX/J4f/WgQhxyyFyNGXFNe52c/uzPWvRh356abRnDWWTNp2LABu+3WlhEjruHAAzuX1589ewETJ77GnXdelDeWadM+YsKEKTz33O8AaNOmJZde2oeysn60atVsnXXL+izsAGw64h7M4+6+b5xe7O7NsuYvcvfmZvY4MMjdX4jlEwh7NkcCW7r7jbH8WmC5u9+WZ139Cc1rdOjQ4eCZM2duVOx3T35+o54v9dNPDulW2yGwfPmE2g5BNkFNmx5TjMXk3/VjA/pgasHc2PRFvJ8Xy2cD7bPqtYtlFZWvx92HuHuZu5e1atWq6IGLiGzO6kKCGQtkRoL1BR7LKj87jiY7DFgSm9KeArqbWfPYud89lomISA3apPpgzOwvhCauHcxsFmE02CBgtJn1I5xdoHes/iRwAuGUNsuAcwHcfaGZ3QBMjvWuz3T4i4hIzdmkEoy7n17BrPUaCuPosQsqWM4wYFgRQxMRkWqqC01kIiJSBynBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISSjBiIhISdTrBGNmPcxsmplNN7OrajseEZHNSb1NMGbWELgLOB7YGzjdzPau3ahERDYf9TbBAF2B6e7+gbuvBEYBPWs5JhGRzUaj2g6ghNoCH2dNzwIOza5gZv2B/nHyCzObVkOxbQ52ABbUdhCbgp/WdgCSS5/N4hrn7j3yzajPCaZK7j4EGFLbcdRHZjbF3ctqOw6RXPps1pz63EQ2G2ifNd0ulomISA2ozwlmMtDZzDqZ2RZAH2BsLcckIrLZqLdNZO6+yswuBJ4CGgLD3H1qLYe1OVHTo2yq9NmsIebutR2DiIjUQ/W5iUxERGqREoyIiJSEEoxgZqvN7HUze8vM/m5mzaqo38zMCjq8w8z+XaQYO5rZW8VYltRfZvbFRj7/ADM7oVjxbO6UYARgubsf4O77AguBC6qo34wCjx90929sbHAiNegAQAmmSJRgJNd/CGdBAMDMLjezyWb2hpmlsXgQsFvc67nVzLYxswlm9qqZvWlmPbOe/0W8P9LMJprZGDP7n5n92cwszjvYzJ4zs1fM7Ckza5NV/l8z+y9VJz2RvOLe7zPxMzzBzDrE8lPjXvt/zez5eDjD9cBp8bN9Wu1GXg+4u26b+Q34It43BP4K9IjT3QlDOo3wZ+RxoBvQEXgr6/mNgO3i4x2A6awdoZhZ9pHAEsIBrw0IiexbQGPg30CrWO80wpBygDeAbvHxrdnr1E23fLfM5y2n7O9A3/j4R8Cj8fGbQNv4uFm8Pwf4fW1vR3251dvjYKRamprZ64Q9l3eA8bG8e7y9Fqe3AToDH+U834BfmVk3YE1cTmvg05x6L7v7LIC4vo7AYmBfYHzcoWkIzIn9QM3c/fn43AcJZ8YWqa7DgV7x8YPAr+PjF4EHzGw08LfaCKy+U4IRiH0wZrYV4cDUC4DBhMRxs7vfk13ZzDrmPP8MoBVwsLt/bWYzgC3zrGdF1uPVhM+fAVPd/fCcdVQ60EBkY7n7+WZ2KHAi8IqZHVzbMdU36oORcu6+DLgIuNTMGhGSzY/MbBsAM2trZjsCS4Fts566PTAvJpejgF2qsdppQCszOzyuo7GZ7ePui4HFZvatWO+Mjdo42Zz9m3CqKAifo38BmNlu7j7J3a8D5hPOXZj72ZaNoD0YWYe7v2ZmbwCnu/uDZtYF+E9svvoCONPd3zezF+Ow4X8AtwB/N7M3gSnA/6qxvpVm9gNgsJltT/hM3glMBc4FhpmZA08XcTOl/trKzGZlTf8G+Blwv5ldTkgk58Z5t5pZZ8Je9ATgv4Tm36tiE+7N7v5QzYVe/+hUMSIiUhJqIhMRkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZJQghERkZLQ6fpFqpCm6fcJF2E7CGgKzCRcPvq2JEk+qYH1TwHeSpLknDj9ALBvkiRlcborcEKSJANznjcQuDBJkh1KHaNIPtqDEalEmqa3A6OBD4CzCJeQvgM4BrirlsK6gXDt+IyuQJKn3n3AcTURkEg+2oMRqUCapicDlwD9kiQZljXruTRNhxCSTY1LkuT9AuvNAmZVWVGkRJRgRCr2C+DVnOQCQJIkqwlX8yRN0x2A24GTCE1oLwOXJUkyJVM/TdMZwBhgNnApsDXhktTnJ0myOKvevsA9wMHAh8CVuevObiJL0/Qc4HexPHP1wOeSJDkyXxNZmqadCHtgRxOu5DgR+EWSJNOz6jhwMdAa+DHgwF+BS5IkWVHlqyYSqYlMJI80TRsD3wDGFVD9UUJT1GXAaYTv1bNpmu6eU683oWmtPyFxnAT8KmudTQlJZxvgh8CNhMtHd6hk3U8QkhvA4fH20wq2qQnh0sBdCInjHKATYY+sRU71S4GdgTOBW4HzgJ9XEofIerQHI5JfS6AJ4RrtFUrTtAfwTeDIJEmei2XPADOAywk/zBlfA6ckSbIq1tsb6MPahHAusCNwaGzeyuz5vFDR+pMkmR/rkCTJS1Vs07mEZLVHkiQfxOVPIvQvnQfcnFV3RmZQAfBUmqbfBHoBv65iHSLltAcjUjmvYn5XYF4muQAkSfIlYZTZt3LqPptJLtHbwI5xbymzrFcyySUu60Vg3oYGnyfWVzPJJS5/FvBinlifzpl+G2hXpDhkM6EEI5LfZ8AKKm+eAmhD/gQwF8htdlqcM72S0A/SJE7vVMGyipVg2sS4chUa65ZFikM2E0owInkkSfI14Z99VcN85xCatXK1BhZWc7WfVrCsfGUbopixilRJCUakYncCZWma9s2dkaZpg9j/MonQzNUta95WwIlU0ndSgcnAwWmaljdFxb6PqhLMyli3qj2MSXH5nbKW35YwmKG6sYpUSZ38IhVIkuTvaZr+Bhgaf+gfA74A9gLOJ3SEfy9N038DD6VpehWhae0ywnDlW6u5yvuBa4An4hDjpoSDKhdU8bz/xfufxwEGnydJMi1PvQcIo9f+kabpdcBqwgGaCwhDo0WKSnswIpVIkuRSwtDjzsBIYDxhCO8E4Cex2imx/E7C8SIGHJ19bEmB61pGaJL7EhhF+PG/lHBqmsr8i5DMfk7YS8mbLOIxLN8hJKShwHDCKLkjkyRRE5kUnblXNUhGRESk+rQHIyIiJaEEIyIiJaEEIyIiJaEEIyIiJaEEIyIiJaEEIyIiJaEEIyIiJaEEIyIiJfH/AYTPlHR3b9NNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.countplot(x='exited', palette=\"Set3\", data=df)\n",
    "plt.xticks([0, 1], ['Retained', 'Lost'])\n",
    "plt.xlabel('Condition', size=15, labelpad=12, color='grey')\n",
    "plt.ylabel('Amount of customers', size=15, labelpad=12, color='grey')\n",
    "plt.title(\"Proportion of customers lost and retained\", size=15, pad=20)\n",
    "plt.ylim(0, 9000)\n",
    "plt.text(-0.15, 7000, f\"{round(amount_retained, 2)}%\", fontsize=12)\n",
    "plt.text(0.85, 1000, f\"{round(amount_lost, 2)}%\", fontsize=12)\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that **20.37%** of the customers have churned. \n",
    "\n",
    "This information is valuable because for classification models we need to confirm that our dataset does not suffer from data imbalance, which usually reflects an unequal distribution of classes within a dataset. Even though the class is not equally distributed, we can say that it does not suffer from high-class imbalance.\n",
    "\n",
    "After that, we will analyse the relationship between categorical variables and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_labels = [['gender', 'country'], ['owns_credit_card', 'is_active_member']]\n",
    "colors = [['Set1', 'Set2'], ['Set3', 'PuRd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(15, 10))\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        feature = categorical_labels[i][j]\n",
    "        color = colors[i][j]\n",
    "        ax1 = sns.countplot(x=feature, hue='exited', palette=color, data=df, ax=ax[i][j])\n",
    "        ax1.set_xlabel(feature, labelpad=10)\n",
    "        ax1.set_ylim(0, 6000)\n",
    "        ax1.legend(title='Exited', labels= ['No', 'Yes'])\n",
    "        if i == 1:\n",
    "            ax1.set_xticklabels(['No', 'Yes'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of female customers churning is 25.07% while the percetage of male customers churning is 16.46%\n"
     ]
    }
   ],
   "source": [
    "female_churn = round(df[(df['exited'] == 1) & (df['gender'] == 'Female')]['exited'].count() / df[df['gender'] == 'Female']['exited'].count()*100, 2)\n",
    "male_churn = round(df[(df['exited'] == 1) & (df['gender'] == 'Male')]['exited'].count() / df[df['gender'] == 'Male']['exited'].count() * 100, 2)\n",
    "\n",
    "print(f\"The percentage of female customers churning is {female_churn}% while the percetage of male customers churning is {male_churn}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of active members churning is 14.27% while the percetage of inactive members churning is 26.85%\n"
     ]
    }
   ],
   "source": [
    "active_churn = round(df[(df['exited'] == 1) & (df['is_active_member'] == 1)]['exited'].count() / df[df['is_active_member'] == 1]['exited'].count()*100, 2)\n",
    "inactive_churn = round(df[(df['exited'] == 1) & (df['is_active_member'] == 0)]['exited'].count() / df[df['is_active_member'] == 0]['exited'].count() * 100, 2)\n",
    "\n",
    "print(f\"The percentage of active members churning is {active_churn}% while the percetage of inactive members churning is {inactive_churn}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of custumers with credit card churning is 20.18% while the percetage that do not have credit cards and\n",
      "churn is 20.81%\n"
     ]
    }
   ],
   "source": [
    "credit_churn = round(df[(df['exited'] == 1) & (df['owns_credit_card'] == 1)]['exited'].count() / df[df['owns_credit_card'] == 1]['exited'].count()*100, 2)\n",
    "no_credit_churn = round(df[(df['exited'] == 1) & (df['owns_credit_card'] == 0)]['exited'].count() / df[df['owns_credit_card'] == 0]['exited'].count() * 100, 2)\n",
    "\n",
    "print(f\"The percentage of custumers with credit card churning is {credit_churn}% while the percetage that do not have credit cards and\")\n",
    "print(f\"churn is {no_credit_churn}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots and the calculations, we learn several things:\n",
    "\n",
    "- The proportion of female customers that churn is greater than the proportion of male customers. \n",
    "\n",
    "- Most of the customers come from France. Nevertheless, Germany and Spain have a greater proportion of customers churning.\n",
    "\n",
    "- The proportion of inactive members that churn is higher than the proportion of active members."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will focus on the relationship between continuous variables and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_labels = [['age', 'credit_score'], \n",
    "                    ['tenure', 'balance'],\n",
    "                   ['number_products', 'estimated_salary']]\n",
    "num_colors = [['Set1', 'Set2'], \n",
    "              ['Set3', 'PuRd'],\n",
    "              ['Spectral', 'Wistia']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2, figsize=(12, 12))\n",
    "for i in range(3):\n",
    "    for j in range(2):\n",
    "        feature = numerical_labels[i][j]\n",
    "        color = num_colors[i][j]\n",
    "        ax1 = sns.boxplot(x='exited', y=feature, palette=color, data=df, ax=ax[i][j])\n",
    "        ax1.set_xlabel('Exited', labelpad=10)\n",
    "        ax1.set_xticklabels(['No', 'Yes'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>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",
       "    <tr>\n",
       "      <th>exited</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>37.408389</td>\n",
       "      <td>10.125363</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>44.837997</td>\n",
       "      <td>9.761562</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count       mean        std   min   25%   50%   75%   max\n",
       "exited                                                            \n",
       "0       7963.0  37.408389  10.125363  18.0  31.0  36.0  41.0  92.0\n",
       "1       2037.0  44.837997   9.761562  18.0  38.0  45.0  51.0  84.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check median age for churning and no churning customers\n",
    "pd.DataFrame(df.groupby('exited')['age'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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",
       "    <tr>\n",
       "      <th>exited</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>72745.296779</td>\n",
       "      <td>62848.040701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>92072.68</td>\n",
       "      <td>126410.28</td>\n",
       "      <td>221532.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>91108.539337</td>\n",
       "      <td>58360.794816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38340.02</td>\n",
       "      <td>109349.29</td>\n",
       "      <td>131433.33</td>\n",
       "      <td>250898.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count          mean           std  min       25%        50%  \\\n",
       "exited                                                                 \n",
       "0       7963.0  72745.296779  62848.040701  0.0      0.00   92072.68   \n",
       "1       2037.0  91108.539337  58360.794816  0.0  38340.02  109349.29   \n",
       "\n",
       "              75%        max  \n",
       "exited                        \n",
       "0       126410.28  221532.80  \n",
       "1       131433.33  250898.09  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check median age for churning and no churning customers\n",
    "pd.DataFrame(df.groupby('exited')['balance'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots and the calculations, we learn several things:\n",
    "\n",
    "- Customers that churn are older than those who are retained.\n",
    "\n",
    "- There is no difference in the median credit score or tenure between lost and retained customers.\n",
    "\n",
    "- Among the customers that churn, most of them seem to still have a significant balance in their bank account.\n",
    "\n",
    "- Neither estimated salary nor the number of products seems to have an effect on customer churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last, we can examine the correlation between the different continuos variables to discover if there is any codependency between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 762.375x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, vars=['age', 'credit_score', 'balance', 'estimated_salary'], \n",
    "             hue=\"exited\", palette='husl')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No relationship can be inferred between continuous variables from the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we dive into finding a model to predict customer churn, we need to do some feature engineering. First, we'll create new features. After that, we'll encode categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Variable Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the variables that we have, can be combined in new features that describe better the customers. \n",
    "\n",
    "We saw before that credit score had no effect on churning because credit score frequently increases with time (and consequently, with age), we'll create a new feature to account for credit score behavior by age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['creditscore_age_ratio'] = df['credit_score'] / df['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.boxplot(y='creditscore_age_ratio', x='exited', palette='summer', data=df)\n",
    "ax.set_xticklabels(['No', 'Yes'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The customers who are churning appear to have a smaller credit score by age ratio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen that the estimated salary has no effect on the likelihood of a customer churning. However, a feature that could be interesting to explore is the ratio between balance and the salary because this can be an estimation of which percentage of their salary a customer spend , and could be a probable indicator of churning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['balance_salary_ratio'] = df['balance'] / df['estimated_salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.boxplot(y='balance_salary_ratio', x='exited', palette='winter', data=df)\n",
    "ax.set_xticklabels(['No', 'Yes'])\n",
    "ax.set_ylim(-1, 6)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, we can learn that customers with a higher balance salary ratio churn more than those with a lower ratio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Categorical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are going to select all the independant variables that we are going to use to predict the class of a customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_drop = ['exited', 'estimated_salary', 'balance', 'age', 'credit_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(x_drop, axis=1)\n",
    "y = df['exited']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the model require numerical values, we need to encode those categorical values that are strings (`gender` and `country`) into numerical values. For that aim, we'll use the `LabelEncoder` from `scikit-learn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in ['gender', 'country']:\n",
    "    le = LabelEncoder()\n",
    "    le.fit(x[label])\n",
    "    print(le.classes_)\n",
    "    x.loc[:, label] = le.transform(x[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to train and test our model, we need to split our dataset into to subdatasets, the training and the test dataset.\n",
    "\n",
    "It is common to use the rule of 80%-20% to split the original dataset. It is important to use a reliable method to split the dataset to avoid data leakage; this is the presence in the test set of examples that were also in the training set and can cause overfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, \n",
    "                                                    shuffle=True, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to build different models looking for the best fit. Predicting customer churn is a **binary classification problem**: Customers are either lost or retained in a given period of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this aim, we'll test: \n",
    "\n",
    "1. [Logistic regression classifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
    "2. [Support Vector Machine](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) with Radial basis function kernel\n",
    "3. [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=random%20forest#sklearn.ensemble.RandomForestClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create two functions that will help getting the results easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_best_model(model):\n",
    "    \"\"\"\n",
    "    Prints the best parameteres, score and estimator for the model given.\n",
    "    \"\"\"\n",
    "    print(f\"The best parameters are: {model.best_params_}\")\n",
    "    print(f\"The best model score is: {model.best_score_}\")    \n",
    "    print(f\"The best estimator is: {model.best_estimator_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores(y, predicted, predicted_proba):\n",
    "    \"\"\"\n",
    "    returns the roc auc scores for the given predictions\n",
    "    \"\"\"\n",
    "    auc_score = roc_auc_score(y, predicted)\n",
    "    fpr_df, tpr_df, _ = roc_curve(y, predicted_proba) \n",
    "    return auc_score, fpr_df, tpr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_predicted):\n",
    "    plt.figure()\n",
    "    random_confusion = confusion_matrix(y_test, y_predicted)\n",
    "    ax = sns.heatmap(random_confusion, annot=True, cmap=\"YlGnBu\");\n",
    "    ax.set_ylim([0,2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Parameter Searching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will determine the parameters and values that we want to search in each of our models. Then, we will perform the `GridSearchCV` and set the best parameters obtained in our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_log = {\n",
    "    'C': [0.1, 1, 10, 50, 100, 200],\n",
    "    'max_iter': [200, 300],\n",
    "    'penalty': ['l2'],\n",
    "    'tol':[0.00001, 0.0001],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_first = LogisticRegression(solver='lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_grid = GridSearchCV(log_first, param_grid=param_grid_log, cv=10, verbose=1, n_jobs=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 24 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-2)]: Using backend LokyBackend with 11 concurrent workers.\n",
      "[Parallel(n_jobs=-2)]: Done  28 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=-2)]: Done 178 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=-2)]: Done 240 out of 240 | elapsed:    4.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='warn',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='warn', n_jobs=-2,\n",
       "             param_grid={'C': [0.1, 1, 10, 50, 100, 200],\n",
       "                         'max_iter': [200, 300], 'penalty': ['l2'],\n",
       "                         'tol': [1e-05, 0.0001]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_grid.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are: {'C': 100, 'max_iter': 200, 'penalty': 'l2', 'tol': 1e-05}\n",
      "The best model score is: 0.8118\n",
      "The best estimator is: LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=200,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=1e-05, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print_best_model(log_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_svm = {\n",
    "    'C': [0.5, 100, 150],\n",
    "    'kernel': ['rbf'],\n",
    "    'gamma': [0.1, 0.01, 0.001],\n",
    "    'probability': [True]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_first = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_grid = GridSearchCV(svm_first, param_grid=param_grid_svm, cv=3, verbose=3, n_jobs=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-2)]: Using backend LokyBackend with 11 concurrent workers.\n",
      "[Parallel(n_jobs=-2)]: Done  16 out of  27 | elapsed:   32.6s remaining:   22.4s\n",
      "[Parallel(n_jobs=-2)]: Done  27 out of  27 | elapsed:   52.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=-2,\n",
       "             param_grid={'C': [0.5, 100, 150], 'gamma': [0.1, 0.01, 0.001],\n",
       "                         'kernel': ['rbf'], 'probability': [True]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are: {'C': 150, 'gamma': 0.01, 'kernel': 'rbf', 'probability': True}\n",
      "The best model score is: 0.8343\n",
      "The best estimator is: SVC(C=150, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
      "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
      "    verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print_best_model(svm_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'max_depth': [3, 5, 6], \n",
    "              'max_features': [2, 4, 6],\n",
    "              'n_estimators':[50, 100],\n",
    "              'min_samples_split': [3, 5, 7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_grid = GridSearchCV(random_forest, param_grid, cv=5, refit=True, verbose=3, n_jobs=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-2)]: Using backend LokyBackend with 11 concurrent workers.\n",
      "[Parallel(n_jobs=-2)]: Done  10 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=-2)]: Done 106 tasks      | elapsed:    5.5s\n",
      "[Parallel(n_jobs=-2)]: Done 270 out of 270 | elapsed:   15.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=-2,\n",
       "             param_grid={'max_depth': [3, 5, 6], 'max_features': [2, 4, 6],\n",
       "                         'min_samples_split': [3, 5, 7],\n",
       "                         'n_estimators': [50, 100]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_grid.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are: {'max_depth': 6, 'max_features': 6, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "The best model score is: 0.8449\n",
      "The best estimator is: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=6, max_features=6, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=3,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print_best_model(random_forest_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Fitting Best Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have the best models and we can train them using the training dataset and test their performance using the testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_log_estimator = LogisticRegression(C=100, max_iter=200, penalty='l2', tol=1e-05, solver='lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=200,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=1e-05, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_log_estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_svm_estimator = SVC(C=100, gamma=0.01, kernel='rbf', probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_svm_estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf_estimator = RandomForestClassifier(max_depth=6, max_features=6, min_samples_split=3, n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=6, max_features=6, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=3,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_rf_estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Metrics of Best Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training the best models with our training dataset, we are going to see how well they perform using our test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predict_train = best_log_estimator.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predict_test = best_log_estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.813625"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, log_predict_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.99      0.89      6370\n",
      "           1       0.72      0.14      0.24      1630\n",
      "\n",
      "    accuracy                           0.81      8000\n",
      "   macro avg       0.77      0.56      0.56      8000\n",
      "weighted avg       0.80      0.81      0.76      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, log_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, log_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.98      0.89      1593\n",
      "           1       0.65      0.14      0.23       407\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.73      0.56      0.56      2000\n",
      "weighted avg       0.78      0.81      0.76      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, log_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(y_test, log_predict_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a. **Radial Kernel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_predict_train = best_svm_estimator.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_predict_test = best_svm_estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84575"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, svm_predict_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.98      0.91      6370\n",
      "           1       0.80      0.33      0.46      1630\n",
      "\n",
      "    accuracy                           0.85      8000\n",
      "   macro avg       0.82      0.65      0.69      8000\n",
      "weighted avg       0.84      0.85      0.82      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, svm_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8345"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, svm_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.96      0.90      1593\n",
      "           1       0.70      0.32      0.44       407\n",
      "\n",
      "    accuracy                           0.83      2000\n",
      "   macro avg       0.78      0.64      0.67      2000\n",
      "weighted avg       0.82      0.83      0.81      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svm_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(y_test, svm_predict_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predict_train = best_rf_estimator.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predict_test = best_rf_estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8525"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, rf_predict_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91      6370\n",
      "           1       0.79      0.38      0.51      1630\n",
      "\n",
      "    accuracy                           0.85      8000\n",
      "   macro avg       0.82      0.68      0.71      8000\n",
      "weighted avg       0.84      0.85      0.83      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, rf_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.838"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, rf_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.96      0.90      1593\n",
      "           1       0.70      0.35      0.47       407\n",
      "\n",
      "    accuracy                           0.84      2000\n",
      "   macro avg       0.78      0.66      0.69      2000\n",
      "weighted avg       0.82      0.84      0.82      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rf_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(y_test, rf_predict_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison of ROC Curves for the three models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further investigate the False positive rates and true positive rates using ROC Curve and calculating the area under the curve that it is also a metric of the prediction power of our model (if the value is closer to 1 means that our model does a good job in differentiating a random sample into the two classes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc_log, fpr_log, tpr_log = get_scores(y, best_log_estimator.predict(x), best_log_estimator.predict_proba(x)[:,1])\n",
    "auc_svm, fpr_svm, tpr_svm = get_scores(y, best_svm_estimator.predict(x), best_svm_estimator.predict_proba(x)[:,1])\n",
    "auc_rf, fpr_rf, tpr_rf = get_scores(y, best_rf_estimator.predict(x), best_rf_estimator.predict_proba(x)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6), linewidth= 1)\n",
    "plt.plot(fpr_log, tpr_log, label = f'Logistic Regression Score: {str(round(auc_log, 3))}', color='#FA8072')\n",
    "plt.plot(fpr_svm, tpr_svm, label = f'SVM RBF Score: {str(round(auc_svm, 3))}', color='#82E0AA')\n",
    "plt.plot(fpr_rf, tpr_rf, label = f'Random Forest Score: {str(round(auc_rf, 3))}', color='#A569BD')\n",
    "plt.plot([0,1], [0,1], '--', label = 'score 0.5', color='#34495E')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the Area Under the Curve value for the three models, we see that the Random Forest performs better than the logistic regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to analyze how the different features affect customer churn. For that, we are going to check the variable importance. That is to quantify how useful every variable is for our model.  \n",
    "\n",
    "This is an important analysis. We can identify what are the features that make a customer more likely to churn and plan targeted strategies.  \n",
    "\n",
    "For SVM is not possible to get the feature importance. As the algorithm works like a black box. For Logistic Regression, we can use the function SelectFromModelfrom sklearn.feature_selection. This function will select the features based on importance weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_most_important_features(model, num_features, model_type):\n",
    "    selector = SelectFromModel(model, threshold=-np.inf, max_features=num_features)\n",
    "    selector.fit(X_train, y_train)\n",
    "    if model_type == \"logistic\":\n",
    "        features_idx = selector.get_support()\n",
    "        features_name = features[features_idx]\n",
    "    else:\n",
    "        return \"It is not possible to get attributes\"\n",
    "    return features_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'number_products', 'is_active_member',\n",
       "       'creditscore_age_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_most_important_features(model=best_log_estimator, num_features=4, model_type=\"logistic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that gender, number of products, member status, and the credit score by age ratio are the four most important features to predict if a customer will churn.  \n",
    "\n",
    "On the other hand, getting the feature importance from RandomForest is easy. The scikit-learn implementation has the method .feature_importances_ which will inform us of the importance of each feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_importance(model, feature_list):\n",
    "    \"\"\"\n",
    "    Function that gets and plots the feature importance\n",
    "    for the given model\n",
    "    :param model: the model to evaluaate\n",
    "    :param feature_list: a list of features contained in the model\n",
    "\n",
    "    :returns a plot with feature importance\n",
    "    \"\"\"\n",
    "    #Get the list of feaature importance from the model\n",
    "    importances = list(model.feature_importances_)\n",
    "    #zip together feature names and importance\n",
    "    feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "    #sort the feature importance by importance\n",
    "    feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "    #Print the list of feature importance\n",
    "    [print('Variable: {} Importance: {}'.format(*pair)) for pair in feature_importances];\n",
    "    #set colors for the plot\n",
    "    colors = cm.rainbow(np.linspace(0, 1, len(feature_list)))\n",
    "    \n",
    "    #get the list of features sorted\n",
    "    characteristics = [x[0] for x in feature_importances]\n",
    "    #get the list of importance sorted\n",
    "    importances_plot = [x[1] for x in feature_importances]\n",
    "    #plot in a bar plot\n",
    "    plt.bar(characteristics, importances_plot, color=colors)\n",
    "    #adjust characteristics of the plot\n",
    "    plt.xticks(list(range(len(characteristics))), characteristics, rotation = 90)\n",
    "    plt.gca().spines['top'].set_visible(False)\n",
    "    plt.gca().spines['bottom'].set_visible(False)\n",
    "    plt.gca().spines['left'].set_visible(False)\n",
    "    plt.gca().spines['right'].set_visible(False)\n",
    "    plt.gcf().subplots_adjust(bottom=0.3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: number_products Importance: 0.44\n",
      "Variable: creditscore_age_ratio Importance: 0.31\n",
      "Variable: is_active_member Importance: 0.11\n",
      "Variable: balance_salary_ratio Importance: 0.07\n",
      "Variable: country Importance: 0.03\n",
      "Variable: gender Importance: 0.02\n",
      "Variable: tenure Importance: 0.01\n",
      "Variable: owns_credit_card Importance: 0.0\n"
     ]
    },
    {
     "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": [
    "feature_importance(best_rf_estimator, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see thatnumber of products, member status, and the credit score by age ratio are again the most important features to predict if a customer will churn. Also, the balance salario ratio is important in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}