churn_clientes_de_banco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise e classificação de churn de clientes de banco\n",
    "\n",
    "Projeto de machine learning para análise de dados e classificação de clientes que potencialmente poderão encerrar suas contas em um banco, os dados estão disponíveis na plataforma [Kaggle](https://www.kaggle.com/datasets/shrutimechlearn/churn-modelling).\n",
    "\n",
    "O problema a ser solucionado consiste em utilizar os dados bancários dos clientes que efetuaram ou não o encerramento de suas contas para criar um modelo preditivo que irá identificar possíveis clientes que poderão ou não deixar a instituição bancária, podendo assim determinar a taxa de churn (rotatividade/perda de clientes) e com base nos perfis mapeados tomar ações para retenção dos mesmos, evitando impactos como, por exemplo, aumento de custos, queda de lucro, reputação da marca entre outros fatores sensíveis a experiência do cliente com o produto da empresa.\n",
    "\n",
    "## Demanda da análise\n",
    "\n",
    "- Analisar a correlação dos dados dos clientes.\n",
    "- Gerar um modelo preditivo de churn por parte de um cliente.\n",
    "\n",
    "## Tópicos da análise\n",
    "\n",
    "1. Análise das medidas estatísticas dos dados\n",
    "2. Distribuição dos valores da váriavel dependente (target)\n",
    "3. Distribuição dos dados entre as variáveis independentes (features) e dependente (target)\n",
    "4. Correlação entre as variáveis\n",
    "5. Preparação dos dados para aplicação no modelo preditivo\n",
    "6. Separação (treino e teste), padronização e balanceamento dos dados\n",
    "7. Modelo preditivo: Regressão Logística\n",
    "   1. Busca pelos melhores parâmetros e treinamento do modelo\n",
    "   2. Treinamento e predições do modelo\n",
    "   3. Métricas de avaliação do modelo\n",
    "8. Modelo preditivo: Decision Tree\n",
    "   1. Busca pelos melhores parâmetros e treinamento do modelo\n",
    "   2. Treinamento e predições do modelo\n",
    "   3. Métricas de avaliação do modelo\n",
    "9. Modelo preditivo: Random Forest\n",
    "   1. Busca pelos melhores parâmetros e treinamento do modelo\n",
    "   2. Treinamento e predições do modelo\n",
    "   3. Métricas de avaliação do modelo\n",
    "10. Modelo preditivo: SVM (Support Vector Machine)\n",
    "    1. Busca pelos melhores parâmetros e treinamento do modelo\n",
    "    2. Treinamento e predições do modelo\n",
    "    3. Métricas de avaliação do modelo\n",
    "11.  Comparativo gráfico e de métricas\n",
    "12.  Conclusão\n",
    "\n",
    "## Dicionário de dados\n",
    "\n",
    "**RowNumber:** Números de linha de 1 a 10.000<br>\n",
    "**CustomerId:** Ids exclusivos para identificação do cliente do banco<br>\n",
    "**Surname:** Sobrenome do cliente<br>\n",
    "**CreditScore:** Score do cliente<br>\n",
    "**Geography:** País onde o cliente reside<br>\n",
    "**Gender:** Gênero, masculino ou feminino<br>\n",
    "**Age:** Idade do cliente<br>\n",
    "**Tenure:** Número de anos que o cliente está no banco<br>\n",
    "**Balance:** Saldo bancário do cliente<br>\n",
    "**NumOfProducts:** Número de produtos bancários que o cliente está utilizando<br>\n",
    "**HasCrCard:** Flag para saber se o cliente possui um cartão de crédito do banco ou não<br>\n",
    "**IsActiveMember:** Flag para saber se o cliente é um membro ativo do banco ou não<br>\n",
    "**EstimatedSalary:** Salário estimado do cliente em dólares<br>\n",
    "**Exited:** Flag, 1 se o cliente encerrou a conta no banco e 0 se o cliente permaneceu<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importação de bibliotecas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação de pacotes e definição de parâmetros globais\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import gc\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
    "from sklearn.metrics import (accuracy_score,\n",
    "                             precision_score,\n",
    "                             recall_score,\n",
    "                             f1_score,\n",
    "                             confusion_matrix,\n",
    "                             auc,\n",
    "                             roc_curve)\n",
    "\n",
    "from tabulate import tabulate\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set_style('darkgrid')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregamento dos dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Efetuando a limpeza de memória antes do carregamento dos dados\n",
    "gc.collect()\n",
    "\n",
    "# Caminho do arquivo de dados\n",
    "path = 'dados/Churn_Modelling.csv'\n",
    "\n",
    "# Criando um dataframe a partir do arquivo no diretório de dados\n",
    "df = pd.read_csv(path, encoding='utf-8', sep=',')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detalhes do dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 2.6 MB\n"
     ]
    }
   ],
   "source": [
    "# Informações do dataframe como nome das colunas, contagem de\n",
    "# de dados, tipo de dados e quantidade de memória utilizada\n",
    "\n",
    "df.info(memory_usage='deep')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de linhas (registros): 10000\n",
      "\n",
      "Quantidade de colunas (variáveis): 14\n"
     ]
    }
   ],
   "source": [
    "print(f'\\nQuantidade de linhas (registros): {df.shape[0]}' )\n",
    "print(f'\\nQuantidade de colunas (variáveis): { df.shape[1]}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>101348.88</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>15592531</td>\n",
       "      <td>Bartlett</td>\n",
       "      <td>822</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>15656148</td>\n",
       "      <td>Obinna</td>\n",
       "      <td>376</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>15792365</td>\n",
       "      <td>He</td>\n",
       "      <td>501</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>15592389</td>\n",
       "      <td>H?</td>\n",
       "      <td>684</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age   \n",
       "0          1    15634602  Hargrave          619    France  Female   42  \\\n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "5          6    15574012       Chu          645     Spain    Male   44   \n",
       "6          7    15592531  Bartlett          822    France    Male   50   \n",
       "7          8    15656148    Obinna          376   Germany  Female   29   \n",
       "8          9    15792365        He          501    France    Male   44   \n",
       "9         10    15592389        H?          684    France    Male   27   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember   \n",
       "0       2       0.00              1          1               1  \\\n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "5       8  113755.78              2          1               0   \n",
       "6       7       0.00              2          1               1   \n",
       "7       4  115046.74              4          1               0   \n",
       "8       4  142051.07              2          0               1   \n",
       "9       2  134603.88              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  \n",
       "5        149756.71       1  \n",
       "6         10062.80       0  \n",
       "7        119346.88       1  \n",
       "8         74940.50       0  \n",
       "9         71725.73       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primeiras linhas do dataframe\n",
    "\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removendo colunas que não serão utilizadas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['RowNumber', 'CustomerId', 'Surname'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário registros duplicados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count\n",
       "False  10000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(keep='first').value_counts().to_frame()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário valores únicos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>6382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantidade\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"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique().to_frame().rename(columns={0: 'Quantidade'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Geography  France  Germany  Spain\n",
      "count        5014     2509   2477\n",
      "\n",
      "Gender  Female  Male\n",
      "count     4543  5457\n",
      "\n",
      "Tenure   0     1     2     3    4     5    6     7     8    9    10\n",
      "count   413  1035  1048  1009  989  1012  967  1028  1025  984  490\n",
      "\n",
      "NumOfProducts     1     2    3   4\n",
      "count          5084  4590  266  60\n"
     ]
    }
   ],
   "source": [
    "# Verificando a quantidade de valores únicos de coluna(s) específica(s)\n",
    "\n",
    "def exibe_valores_unicos(coluna):\n",
    "    '''\n",
    "    Verifica a quantidade de valores únicos de coluna(s) específica(s)\n",
    "\n",
    "    :param coluna: Column Dataframe\n",
    "        Dataframe e coluna a ser analisado.\n",
    "    '''\n",
    "    coluna_qtde = pd.DataFrame(coluna.value_counts().sort_index())\n",
    "    coluna_qtde = coluna_qtde.T\n",
    "    print(f'\\n{coluna_qtde}')\n",
    "\n",
    "cols = ['Geography', 'Gender', 'Tenure', 'NumOfProducts']\n",
    "\n",
    "for col in cols:\n",
    "    exibe_valores_unicos(df[col])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário os tipos de valores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Tipo\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"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.to_frame().rename(columns={0: 'Tipo'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário valores nulos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantidade\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"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().to_frame().rename(columns={0: 'Quantidade'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amostra de dados do dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
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       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>553</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33877.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>631</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>138292.64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152422.91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>800</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>168190.33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68052.08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4879</th>\n",
       "      <td>783</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>81811.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164213.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8466</th>\n",
       "      <td>613</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
       "      <td>131307.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83343.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4710</th>\n",
       "      <td>585</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>162261.01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>137028.51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>581</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>101633.04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110431.51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7883</th>\n",
       "      <td>551</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51143.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6517</th>\n",
       "      <td>628</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>155903.82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71159.84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4641</th>\n",
       "      <td>485</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>133658.24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70209.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts   \n",
       "5172          553    France    Male   37       2       0.00              2  \\\n",
       "2404          631   Germany    Male   37       8  138292.64              2   \n",
       "438           800    France  Female   38       2  168190.33              2   \n",
       "4879          783     Spain  Female   44       3   81811.71              1   \n",
       "8466          613    France    Male   36       9  131307.11              1   \n",
       "4710          585   Germany  Female   40       3  162261.01              2   \n",
       "66            581   Germany  Female   34       1  101633.04              1   \n",
       "7883          551    France  Female   45       6       0.00              2   \n",
       "6517          628   Germany  Female   51       9  155903.82              2   \n",
       "4641          485   Germany    Male   34       3  133658.24              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "5172          1               0         33877.29       0  \n",
       "2404          0               0        152422.91       1  \n",
       "438           1               0         68052.08       0  \n",
       "4879          1               0        164213.53       1  \n",
       "8466          0               0         83343.73       0  \n",
       "4710          1               0        137028.51       0  \n",
       "66            1               0        110431.51       0  \n",
       "7883          1               1         51143.43       0  \n",
       "6517          1               1         71159.84       0  \n",
       "4641          1               0         70209.83       0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Análise das medidas estatísticas dos dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>350.00</td>\n",
       "      <td>584.00</td>\n",
       "      <td>652.000</td>\n",
       "      <td>718.0000</td>\n",
       "      <td>850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>18.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>37.000</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97198.540</td>\n",
       "      <td>127644.2400</td>\n",
       "      <td>250898.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>11.58</td>\n",
       "      <td>51002.11</td>\n",
       "      <td>100193.915</td>\n",
       "      <td>149388.2475</td>\n",
       "      <td>199992.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count           mean           std     min       25%   \n",
       "CreditScore      10000.0     650.528800     96.653299  350.00    584.00  \\\n",
       "Age              10000.0      38.921800     10.487806   18.00     32.00   \n",
       "Tenure           10000.0       5.012800      2.892174    0.00      3.00   \n",
       "Balance          10000.0   76485.889288  62397.405202    0.00      0.00   \n",
       "NumOfProducts    10000.0       1.530200      0.581654    1.00      1.00   \n",
       "EstimatedSalary  10000.0  100090.239881  57510.492818   11.58  51002.11   \n",
       "\n",
       "                        50%          75%        max  \n",
       "CreditScore         652.000     718.0000     850.00  \n",
       "Age                  37.000      44.0000      92.00  \n",
       "Tenure                5.000       7.0000      10.00  \n",
       "Balance           97198.540  127644.2400  250898.09  \n",
       "NumOfProducts         1.000       2.0000       4.00  \n",
       "EstimatedSalary  100193.915  149388.2475  199992.48  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrição dos dados estatísticos\n",
    "\n",
    "cols = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n",
    "df[cols].describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2rdB+eW3FNM1C23x8fFyup92hsnF1zztbL7lvP87WuzoX7QoVSWhoqJYuXaqGDRvKz89P999/v7766ivaEVAEx44d0913361hw4YVCITzlPZnOtoSKqL87SgyMlLLli3TFVdcIcMwNGrUKH311VeSaEdFQXBVTBEREYqPj3csx8XFKSIiwosVAd63c+fOApMF5v3SzN9W4uPjHW0lPDzc6bbw8HDFxcUVWB8eHk67Q6Vz4T2f10ZCQ0OVlpbmmPw5JydH6enpqlmzptN2FR4eXqhd5bUfV9cAKopDhw5p7dq1jmW73S5fX1/aEXARe/bs0R133KHbb79do0ePdnufl9ZnOtoSKpoL21Fez6s8ee9JEu2oKAiuiikyMlLffvutEhISlJ2drXXr1qlbt27eLgvwquzsbM2ePVtpaWmOb8y49dZbFRQUpO3bt0uSoqOjdeP/t3fnAVXV6R/HP4dVQEQR2UTELS23msqy0STNLdwqddRGJzX7ZTaZmZoZaWpuuWWmo+Uy2WKalTqpZWNOjpa2mUsupYIbyuIGqGz3/P4griAg4AD3AO/XP3LPOfee5977cDz3w/d8b5s2kqTw8HCtWbNGkrRz5055enoqMDBQ4eHhWrdundLT0xUfH6/vvvtO9913H793qHCaN2+uqKgoHTlyRFLm7094eLhcXV3VokULrV+/XpK0fv16tWjRQq6urjl+r44cOaLjx4+rWbNmatOmjb744gslJycrOTlZmzZtUps2bfLdB1BeuLq6atq0aTpz5oxM09R7772n9u3b83sE3MC5c+f0xBNPKDIyUv3795ckBQcHl/g53Y32AZQ1ef0eeXp66s0339SBAwckZX5Tbfv27SXxe1QYhpnX+DPc0MaNG7VgwQKlpqaqbdu2GjNmjKNLAhxu6dKlWr16tTIyMtSxY0eNHDlShw8fVmRkpBITExUSEqJZs2bJ29tbly5d0rhx43Ts2DG5ublpypQpatSokUzT1OzZs7VlyxZlZGRo2LBh6tq1qyR+71AxtG3bVu+++65CQkK0a9cuTZkyRVevXlWzZs00efJkubm5KSYmRmPHjlVcXJx8fHw0c+ZMBQcHKzU1Va+++qp2794twzA0btw4tWzZUlLmydHKlSuVnp6uPn36aODAgZKU7z6Asiz779G6deu0aNEipaen684779SECRP4PQJuYM6cOVq+fLnCwsLsy8LDwxUREVHi53T5nTcCZU1+v0ctWrTQjBkzlJKSorp162r69On8HhUSwRUAAAAAAAAsiUsFAQAAAAAAYEkEVwAAAAAAALAkgisAAAAAAABYEsEVAAAAAAAALIngCgAAAAAAAJZEcAUAAAAAAABLIrgCAAAAAACAJRFcAQAAAAAAwJIIrgAAAAAAAGBJLqW5s4SERJlmae6x5BiGVL26d7l6Tqg46F+UZfQvyjL6F2UZ/YuyjP5FWVfeejjr+RRGqQZXpqly8QJnVx6fEyoO+hdlGf2Lsoz+RVlG/6Iso39R1lXEHuZSQQAAAAAA4FCtWt2lCRPG5Vq+YcN69ezZNdfyX37ZrTZt7tG+fXtLo7xi8dNPP6hVq7scXUa+evbsqg0b1hdq21OnTurbb7eXcEWZCK4AAAAAAIDDffXVF/rxx+8Lte2qVe9r0KAn1aRJ0xKuCnmZNm2Sfv11X6nsi+AKAAAAAAA4XFBQsGbPnq60tLQCt33ppfEaMGBQKVSFvJileL0iwRUAAAAAAHC4IUOGKi4uTh988G6e62NiTqtVq7sUE3NaXl6VZRiGlixZpGeeeVJS5mWFzzzzpP75zyXq1OkBdevWUZs2fa6vv/5Kjz7aRZ06hWvBgnn2x0tNTdXcuTMVEdFOERHtNHFipC5duphjX8uXv6NOnR7Q7NnTJUnbt2/ToEGPqW3bP+uvf+2l//xnS77PJzk5SePHv6T27e9Xnz6P6ODBX3OsP3v2jMaMGaF27f6snj27aunSxcrIyMjzsZYsWaQRI0botddeVbt2f1bfvo/ov//9j319z55dtWDBPHXv3lEDB/aTaZqKijqm55//uzp0aKMePTpr2bK3ZbPZ7Pf57LM1euSRCHXo0EbLl7+TY3/PPPOklixZlOdr/9prE7R7909atuxt+2sfG3tWkZEvqnPntoqIaKe5c19XamqqJCk9PV3Tp09WREQ7tW/fWmPGjFBcXGy+r9v1CK4AAAAAAIDD+fnV0ODBT+rdd5fq9OlTN/UY+/fv1enTp/TOO+/qwQc7aObMqVq9eqWmT5+jZ54ZoQ8+eFeHDx+UJC1a9JYOHvxVr7/+hubNW6SkpCRFRr6Y4/H27PlFS5asUK9effXjj99r3LhR6tQpQsuXf6AuXbrrlVfG6uDBA3nW8vrrU3X8eJTmz1+sESNGaeXK9+3rTNPUuHGjVa2ar5Yte18vvTRemzdv0ooVy/J9bps3b5Zpmlqy5D1FRHTTuHGjdezY0WzrN2n27Lf00ksTdPHiRQ0b9oT8/Py0ePFyjRw5RmvWfKTVqz+UJO3c+a3mzZulJ598Wv/4x1IdPPirzpyJKdRrPHz4C2rSpJn69Pmrpkx5XWlpaXr22aG6evWK5s9frIkTp2nHjv/aQ8I1az7Szz//pNmz39I776zQ5cuXNW/e7ELtSyK4AgAAAAAAFtGzZx+FhIRq7tyZN3V/m82m554bpZCQWurW7RFdvXpVgwf/n+rXb6AuXbqrWjVfRUdH6erVq/rkk1UaNeol3XZbE9WrV1+RkRP1888/6siR3+2P17t3X9WsGaJatUK1Zs0qhYe3U+/e/RQaWlt9+vxV4eFt9eGHK3LVkZSUpK+//krPPTdKDRs20j33tNTjjz9hX//jj9/rzJkYjR49TqGhYfrTn+7SsGHPadWqD/N9bj4+Pho9+iWFhdXRX//6uJo2ba7PP19nX9+hQ2fVq1dfDRrcos2bN8ndvZJGjx6nsLA6at06XE888ZR9NNv69Z+pfftO6tQpQnXr1tPYsa/Izc29UK9x5cqV5eLiIg8PD1Wp4qOdO3coPj5WkZGTVK9efd155916/vkx+vTT1bp8+bJiYmLk7u6uoKAg1a4dpnHjJuivf328UPuSJJdCbwkAAAAAAFCCnJ2d9cILL+rpp5/QN99sLfL9fX2ry8PDQ5Lk7p4ZxAQGBtnXu7u7Ky0tTadPn1RaWpqeempgjvvbbDadOBGthg1vlZQ571aW6Ohj6t790RzbN2mSMzzKcuJEtDIyMtSgwS32ZbfeeluOx7p06aI6dmyTY98pKSm6ePGCfHyq5nrMJk2ayM3NTVnTSzVseKuio4/Z1wcFXXue0dHH1LDhrXJxccl2/+ZKSEhQYmKioqKOqUePR+zrfHyqKji4Zq59FkZU1DHVqhWqKlWq2Jc1bdpMGRkZOnXqhLp1e1hfffWFunXrqDvuuFP33/+AIiK6FPrxCa4AAAAAAIBlNG3aXBER3fTGGzPVr98A+3LDMHJte/2cUM7Ozrm2cXLKfbFZ1v0WLHhHHh6eOdb5+vrq4sXMua7c3Nzsy7P/nMVmy5DNlve8VFLOScxdXFxz7D80NEzTps3KdR8vr8p5Plb2ECpz3zYZxrXnln3EVH61Zv/3+vnVXV2vPf71r3V+c29dv99r29vs/zZoUE8ff7xeO3b8Vzt2bNOiRfO1efMmrVq1Ms/39HpcKggAAAAAACxl6NC/6+rVK1q58j37sqzg5/Lly/ZlNzsXVs2aIXJ2dtbFixcVElJLISG15OXlpXnzZuvcuXN53ic0tLb279+bY9m+fXsVGlo7z21dXFx04MC1Cdl/++2Q/edatWrr7Nkzqlq1mn3/MTGntGTJonzDnEOHDuWYXP3gwV9Vr179fGs9dOiA0tPTc9RatWo1Vanio7p16+ngwf32dZcvJ+vkyZP2266urjd8nbPXGBpaWydOHLdPbC9J+/fvkbOzs2rWDNHGjf/S9u3fqG3bB/Xyy69q5sw3tWfPbiUkJORZ+/UYcVWABQvm6sqVy3muCwjwV//+165R/eqrTfrll59KqzTgf+Li4qz09PxTc8DKXFyc9fjjT8rHp5qjSwEAAEAJ8PGpqqFD/65p0ybbL/Xz9fWVv3+APvjgXQ0a9KR++eVnffvtf9WgQcMiP76np5e6du2hmTOnafTol1Stmq/efHOOzp6NUVBQsGJjz+a6T+/ej+nppwdr1aoP1bLln7VjxzZ9883Xmj17fq5tvbwqq1OnCM2d+7rGjh2vlJSrWrp0sX19ixb3KjAwUBMnRur//m+YkpISNWPGFN11V4s8R41J0okTJ7RgwTx17dpDX3/9bx06dFAvv/xqntt26NBZS5Ys1owZr6lfvwE6cSJaS5cu0sMP95JhGHr00d567rmn1bz5n9S8+R1aunSxUlKu2u/fqNFt2rTpcz34YAdJ0jvv/CPH43t4eOjkyRM6f/6c7r77HgUH19SkSa/oqaf+rosXL2jOnNfVvn0neXt7Kzk5SQsXLrNfjrh580b5+weoWrXCncsTXBUgLS0tx8Rs2SUnJ+W4vXv3Tzp6NO9tAQDFK/tfjwAAAFD+RER01+efr1NcXJykzEv+xo6N1Jw5r6t//9668867NWDAIH377fabevxnnhmh+fPn6uWXxyg9PV23336HXn/9jXyDo8aNmygycqKWLl2shQvnKTS0tiZOnKo777w7z+1HjBilOXNe14gRw+Tt7a2ePfvorbfmSsq8pHHatNmaO/d1Pfnk3+Th4akHHnhQzzwzPN96mzdvrvPnz+vxxx9TrVqhmjnzDdWsGZLntp6eXpo1a57eeGOWBg16TFWrVlOvXn3Vv//APx7rDo0dO15vv71Qb745WxER3VW//rX5uPr0eUxHj/6uYcOeVI0aNTR8+AsaPfo5+/ouXXpo6tSJio4+pqVL39e0abM1Z84MPfnk3+Tp6aUOHTrpySeHSZIeeaS3YmNjNWnSK0pMvKSGDW/V9Omz8n2dr2eY5vVXNZac+PjEXNdQWt1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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Função para geração de gráficos boxplot\n",
    "\n",
    "def graf_boxplot(coluna, ax, title):\n",
    "    '''\n",
    "    Verifica a quantidade de valores únicos de coluna(s) específica(s)\n",
    "\n",
    "    :param coluna: Column Dataframe\n",
    "        Dataframe e coluna a ser analisado.\n",
    "    :param ax: Axis\n",
    "        Eixo, posição que o gráfico irá ocupar.\n",
    "    :param title: str\n",
    "        Título dado ao gráfico.\n",
    "    '''\n",
    "    sns.boxplot(data=coluna, ax=ax, notch=True,\n",
    "                orient='h', color='mediumturquoise')\n",
    "    ax.set(xlabel='', ylabel='')\n",
    "    ax.tick_params(labelsize=8)\n",
    "    ax.ticklabel_format(style='plain', axis='x')\n",
    "    ax.set_title(title, loc='right', fontsize=10, pad=-10)\n",
    "\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(\n",
    "    nrows=6, ncols=1, figsize=(15, 10))\n",
    "\n",
    "graf_boxplot(df['CreditScore'], ax1, 'Score de crédito')\n",
    "graf_boxplot(df['Age'], ax2, 'Idade')\n",
    "graf_boxplot(df['Tenure'], ax3, 'Anos como cliente')\n",
    "graf_boxplot(df['Balance'], ax4, 'Saldo bancário')\n",
    "graf_boxplot(df['NumOfProducts'], ax5, 'Número de produtos')\n",
    "graf_boxplot(df['EstimatedSalary'], ax6, 'Salário estimado')\n",
    "\n",
    "plt.subplots_adjust(top=1.9, bottom=0.95)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CREDITSCORE\n",
      "Limite inferior: 383.0\n",
      "Limite superior: 919.0\n",
      "\n",
      "AGE\n",
      "Limite inferior: 14.0\n",
      "Limite superior: 62.0\n",
      "\n",
      "NUMOFPRODUCTS\n",
      "Limite inferior: -0.5\n",
      "Limite superior: 3.5\n"
     ]
    }
   ],
   "source": [
    "# Geração dos limites inferior e superior das variáveis que apresentaram outliers\n",
    "\n",
    "cols = ['CreditScore', 'Age', 'NumOfProducts']\n",
    "\n",
    "for col in cols:\n",
    "    Q1 = df[col].quantile(.25)\n",
    "    Q3 = df[col].quantile(.75)\n",
    "    IIQ = Q3 - Q1\n",
    "    limite_inferior = Q1 - 1.5 * IIQ\n",
    "    limite_superior = Q3 + 1.5 * IIQ\n",
    "    print(f'\\n{col.upper()}')\n",
    "    print(f'Limite inferior: {limite_inferior}')\n",
    "    print(f'Limite superior: {limite_superior}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CREDITSCORE: [350 351 358 359 363 365 367 373 376 382]\n",
      "\n",
      "AGE: [63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 88\n",
      " 92]\n",
      "\n",
      "NUMOFPRODUCTS: [4]\n"
     ]
    }
   ],
   "source": [
    "# Função para exibição dos valores únicos dos outliers\n",
    "\n",
    "def valores_outliers (col, lim, sinal):\n",
    "    '''\n",
    "    Exibe os valores únicos dos outliers\n",
    "\n",
    "    :param col: Column Dataframe\n",
    "        Dataframe e coluna a ser analisado.\n",
    "    :param lim: int\n",
    "        Valor do limite inferior ou superior.\n",
    "    :param sinal: str\n",
    "        Símbolo de maior(>) ou menor(<).\n",
    "    '''\n",
    "    arr = np.array(df[col])\n",
    "    if sinal == '>':\n",
    "        val = arr[np.where(arr > lim)[0]]\n",
    "    else:\n",
    "        val = arr[np.where(arr < lim)[0]]\n",
    "    print(f'\\n{col.upper()}: {np.unique(val)}')\n",
    "    \n",
    "valores_outliers('CreditScore', 383, '<')\n",
    "valores_outliers('Age', 62, '>')\n",
    "valores_outliers('NumOfProducts', 3.5, '>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algumas variáveis independentes apresentaram alguns outliers como **CreditScore** (Score de crédito) onde há pontuações abaixo de 380, aproximadamente, **Age** (Idade) com casos entre 60 e pouco mais de 90 anos e **NumOfProducts** (Número de produtos) onde há um grupo de clientes com 4 produtos bancários.\n",
    "Estas situações não significam, necessariamente, que haja alguma inconsistência nos dados, mas sim que há casos que estão fora das orcorrências comuns identificadas no conjunto de dados, portanto seram mantidos.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Distribuição dos valores da váriavel dependente (target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_exited = df['Exited'].value_counts()\n",
    "df_exited = pd.DataFrame(df_exited)\n",
    "df_exited['%'] = df_exited['count'] / df_exited['count'].sum() * 100\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "plt.pie(df_exited['%'], labels=['A', 'E'], colors=[\n",
    "        '#008fd5', '#fc4f30'], autopct='%0.2f%%')\n",
    "plt.legend(loc='right', labels=['A - Conta ativa', 'E - Conta encerrada'],\n",
    "           labelspacing=1.5, bbox_to_anchor=(1.1, 0.1, 0.6, 0.9))\n",
    "plt.title('Distribuição de clientes com conta ativa e encerrada')\n",
    "\n",
    "cell_text = []\n",
    "for row in range(len(df_exited)):\n",
    "    cell_text.append(df_exited.iloc[row])\n",
    "table = plt.table(cellText=cell_text, colLabels=['Qtde', '%'],\n",
    "                  loc='bottom right', bbox=(1.2, 0.05, 0.5, 0.3))\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(10)\n",
    "for cell in table._cells:\n",
    "    if cell == (2, 1) or cell == (1, 1):\n",
    "        cell_text = table._cells[cell].get_text().get_text()\n",
    "        table._cells[cell].get_text().set_text(f'{float(cell_text):.3f}')\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embora seja comum clientes fecharem contas bancárias, o atual percentual de **20,37%** de perda de clientes (_2037 contas_) é um índice muito alto, observando o gráfico está se aproximando de 1/4 do total de contas que o banco já administrou.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Distribuição dos dados entre as variáveis independentes (features) e dependente (target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funções para gerar gráficos histplot e countplot\n",
    "\n",
    "saida = df['Exited'].replace({0: 'NÃO', 1: 'SIM'})\n",
    "hue_order = ['NÃO', 'SIM']\n",
    "\n",
    "\n",
    "def graf_countplot(df, x, title, ax):\n",
    "    '''\n",
    "    Função para gerar gráficos countplot\n",
    "\n",
    "    :param df: Dataframe\n",
    "        Nome do dataframe.\n",
    "    :param x: str\n",
    "        Nome da coluna do dataframe.\n",
    "    :param title: str\n",
    "        Título dado ao gráfico.\n",
    "    :param ax: Axis\n",
    "        Eixo, posição que o gráfico irá ocupar.\n",
    "    '''\n",
    "    ax = sns.countplot(data=df, x=x, hue=saida, ax=ax,\n",
    "                       hue_order=hue_order, palette='Set2')\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, size=8)\n",
    "    ax.set(xlabel='', ylabel='')\n",
    "    ax.set_title(title, loc='left')\n",
    "    ax.tick_params(labelsize=9)\n",
    "\n",
    "\n",
    "def graf_histplot(df, x, title, ax):\n",
    "    '''\n",
    "    Função para gerar gráficos histplot\n",
    "\n",
    "    :param df: Dataframe\n",
    "        Nome do dataframe.\n",
    "    :param x: str\n",
    "        Nome da coluna do dataframe.\n",
    "    :param title: str\n",
    "        Título dado ao gráfico.\n",
    "    :param ax: Axis\n",
    "        Eixo, posição que o gráfico irá ocupar.\n",
    "    '''\n",
    "    ax = sns.histplot(data=df, x=x, hue=saida, ax=ax,\n",
    "                      hue_order=hue_order,  palette='Set2', multiple='dodge')\n",
    "    ax.set(xlabel='', ylabel='')\n",
    "    ax.set_title(title, loc='left')\n",
    "    ax.tick_params(labelsize=9)\n",
    "\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6),\n",
    "      (ax7, ax8), (ax9, ax10)) = plt.subplots(5, 2, figsize=(20, 20))\n",
    "\n",
    "\n",
    "graf_countplot(df, 'Geography', '\\nLocalização', ax1)\n",
    "graf_countplot(df, 'Gender', '\\nGênero', ax2)\n",
    "graf_histplot(df, 'Age', '\\nIdade', ax3)\n",
    "graf_countplot(df, 'NumOfProducts', '\\nNúmero de produtos', ax4)\n",
    "graf_histplot(df, 'Balance', '\\nSaldo bancário', ax5)\n",
    "graf_countplot(df, 'IsActiveMember', '\\nCliente ativo', ax6)\n",
    "graf_histplot(df, 'CreditScore', '\\nScore de Crédito', ax7)\n",
    "graf_countplot(df, 'Tenure', '\\nAnos como cliente', ax8)\n",
    "graf_countplot(df, 'HasCrCard', '\\nPossui cartão de Crédito', ax9)\n",
    "graf_histplot(df, 'EstimatedSalary', '\\nSalário estimado', ax10)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A localização dos clientes que mais encerraram suas contas está mais concentrada na França (France) e Alemanha (Germany), sendo que visualmente há quase um empate na quantidade, destaque que se comparado aos clientes que mantiveram a conta a Alemanha é o país onde mais estão ocorrendo perdas de clientes.<br>\n",
    "- Os clientes do gênero feminino são os que mais fecharam suas contas, fica mais evidente quando comparado aos clientes que se mantiveram.<br>\n",
    "- Por idade, clientes com 40 e 50 anos aproximadamente são os que mais encerraram suas contas diferente dos que a mantiveram com idades de 28 e 40 anos aproximadamente.<br>\n",
    "- Geralmente clientes que possuem um único produto contratado são os que mais fecharam suas contas sendo que a maioria que manteve conta no banco possuem dois ou somente um produto contratado.<br>\n",
    "- Clientes sem saldo bancário possuem uma grande representação no volume de clientes, sendo estes os que mais encerraram suas contas.<br>\n",
    "- Clientes que não movimentaram suas contas, não ativos, são os que mais fecharam suas contas bancárias.<br>\n",
    "- Nos gráficos de Score de Crédito, Anos como cliente, Possui cartão de Crédito e Salário estimado, a quantidade de clientes que encerraram a conta, visualmente, segue em proporções semelhantes aos que mantiveram a conta.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Correlação entre as variáveis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
    "           'IsActiveMember', 'EstimatedSalary', 'Exited']].corr(method='pearson')\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plot = sns.heatmap(data=corr, cmap='seismic', vmin=-1, linewidths=.2,\n",
    "                   annot=True, annot_kws={'size': 8, 'fontweight': 'bold'})\n",
    "plot.tick_params(labelsize=9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A análise de correlação entre as **variáveis independentes** e a **variável target** (_Exited_) não identificou correlações fortemente significativas, positivas ou negativas. No entanto, é importante destacar que a falta de correlação forte não implica necessariamente que as variáveis não sejam importantes para a análise e elaboração de um modelo preditivo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Preparação dos dados para aplicação no modelo preditivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excellent</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": [
       "  CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts   \n",
       "0        good    France  Female   42       2       0.00              1  \\\n",
       "1        good     Spain  Female   41       1   83807.86              1   \n",
       "2        good    France  Female   42       8  159660.80              3   \n",
       "3        good    France  Female   39       1       0.00              2   \n",
       "4   excellent     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar os pontos de score em faixas de pontuação\n",
    "\n",
    "# very_low - de 0 a 300 pontos\n",
    "# low - de 301 a 500 pontos\n",
    "# good - de 501 a 700 pontos\n",
    "# excellent - de 701 a 1000 pontos\n",
    "\n",
    "faixa = [0, 301, 501, 701, df['CreditScore'].max()]\n",
    "rotulos = ['very_low',\n",
    "           'low',\n",
    "           'good',\n",
    "           'excellent']\n",
    "\n",
    "df['CreditScore'] = pd.cut(df['CreditScore'], bins=faixa, labels=rotulos)\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>good</td>\n",
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       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
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       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excellent</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</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": [
       "  CreditScore Geography  Gender    Age  Tenure    Balance  NumOfProducts   \n",
       "0        good    France  Female  35_44       2       0.00              1  \\\n",
       "1        good     Spain  Female  35_44       1   83807.86              1   \n",
       "2        good    France  Female  35_44       8  159660.80              3   \n",
       "3        good    France  Female  35_44       1       0.00              2   \n",
       "4   excellent     Spain  Female  35_44       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar idades numéricas em faixas etárias\n",
    "\n",
    "# 18_24 - de 18 a 24 anos\n",
    "# 25_34 - de 25 a 34 anos\n",
    "# 35_44 - de 35 a 44 anos\n",
    "# 45_54 - de 45 a 54 anos\n",
    "# 55_64 - de 55 a 64 anos\n",
    "# 65_More - maior ou igual a 65 anos\n",
    "\n",
    "faixa = [17, 25, 35, 45, 55, 65, df['Age'].max()]\n",
    "rotulos = ['18_24',\n",
    "           '25_34',\n",
    "           '35_44',\n",
    "           '45_54',\n",
    "           '55_64',\n",
    "           '65_More']\n",
    "\n",
    "df['Age'] = pd.cut(df['Age'], bins=faixa, labels=rotulos)\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
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       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>good</td>\n",
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       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excellent</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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": [
       "  CreditScore Geography  Gender    Age  Tenure  Balance  NumOfProducts   \n",
       "0        good    France  Female  35_44       2        0              1  \\\n",
       "1        good     Spain  Female  35_44       1        1              1   \n",
       "2        good    France  Female  35_44       8        1              3   \n",
       "3        good    France  Female  35_44       1        0              2   \n",
       "4   excellent     Spain  Female  35_44       2        1              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converter os saldos bancários em categorias\n",
    "\n",
    "# 1 - se há saldo bancário\n",
    "# 0 - se não há saldo bancário\n",
    "\n",
    "maior_que_zero = lambda k: 1 if k > 0 else 0\n",
    "\n",
    "df['Balance'] = df['Balance'].map(maior_que_zero)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_Product</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3_MoreProducts</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2_Products</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excellent</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_Product</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": [
       "  CreditScore Geography  Gender    Age  Tenure  Balance   NumOfProducts   \n",
       "0        good    France  Female  35_44       2        0       1_Product  \\\n",
       "1        good     Spain  Female  35_44       1        1       1_Product   \n",
       "2        good    France  Female  35_44       8        1  3_MoreProducts   \n",
       "3        good    France  Female  35_44       1        0      2_Products   \n",
       "4   excellent     Spain  Female  35_44       2        1       1_Product   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converter as quantidades de produtos em categorias\n",
    "\n",
    "# 1_Product - possui 1 produto\n",
    "# 2_Products - possui 2 produtos\n",
    "# 3_MoreProducts - possui 3 ou mais produtos\n",
    "\n",
    "dict = {'1': '1_Product', '2': '2_Products',\n",
    "        '3': '3_MoreProducts', '4': '3_MoreProducts'}\n",
    "\n",
    "for k in dict:\n",
    "    df['NumOfProducts'] = [str(num).replace(k, dict[k])\n",
    "                           for num in df['NumOfProducts']]\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 10))\n",
    "\n",
    "graf_countplot(df, 'CreditScore', '\\nScore (Pontuação) de crédito', ax1)\n",
    "graf_countplot(df, 'Age', '\\nFaixa Etária (Idade)', ax2)\n",
    "graf_countplot(df, 'Balance', '\\nSaldo Bancário', ax3)\n",
    "graf_countplot(df, 'NumOfProducts', '\\nNúmero de Produtos', ax4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variáveis como **CreditScore** (Pontuação de crédito), **Age** (Idade), **Balance** (Saldo bancário) e NumOfProducts (Número de produtos) apresentavam outliers, alguma ausência ou pouca variabilidade e representatividade nos seus dados, sendo assim, **CreditScore** que algumas pontuações eram consideradas outliers e **Age** que não contemplava todas as idades numéricas possíveis a partir dos 18 anos foram feitos agrupamentos por faixas de pontuação e faixas etárias respectivamente, **Balance** em que valores do saldo bancário foram convertidos em 0 (_zero_) que são contas vazias e 1 (_um_) que possuem algum saldo e **NumOfProducts** que inicialmente contia até 4 produtos e foram transformados em 3 categorias que representam 1 produto, 2 produtos e 3 ou mais produtos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Score_excellent</th>\n",
       "      <th>Score_good</th>\n",
       "      <th>Score_low</th>\n",
       "      <th>Geo_France</th>\n",
       "      <th>Geo_Germany</th>\n",
       "      <th>Geo_Spain</th>\n",
       "      <th>Num_1_Product</th>\n",
       "      <th>Num_2_Products</th>\n",
       "      <th>Num_3_MoreProducts</th>\n",
       "      <th>Age_18_24</th>\n",
       "      <th>Age_25_34</th>\n",
       "      <th>Age_35_44</th>\n",
       "      <th>Age_45_54</th>\n",
       "      <th>Age_55_64</th>\n",
       "      <th>Age_65_More</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1_Product</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_Product</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>35_44</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3_MoreProducts</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>35_44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2_Products</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excellent</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>35_44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_Product</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CreditScore Geography  Gender    Age  Tenure  Balance   NumOfProducts   \n",
       "0        good    France       0  35_44       2        0       1_Product  \\\n",
       "1        good     Spain       0  35_44       1        1       1_Product   \n",
       "2        good    France       0  35_44       8        1  3_MoreProducts   \n",
       "3        good    France       0  35_44       1        0      2_Products   \n",
       "4   excellent     Spain       0  35_44       2        1       1_Product   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  Score_excellent   \n",
       "0          1               1        101348.88       1              0.0  \\\n",
       "1          0               1        112542.58       0              0.0   \n",
       "2          1               0        113931.57       1              0.0   \n",
       "3          0               0         93826.63       0              0.0   \n",
       "4          1               1         79084.10       0              1.0   \n",
       "\n",
       "   Score_good  Score_low  Geo_France  Geo_Germany  Geo_Spain  Num_1_Product   \n",
       "0         1.0        0.0         1.0          0.0        0.0            1.0  \\\n",
       "1         1.0        0.0         0.0          0.0        1.0            1.0   \n",
       "2         1.0        0.0         1.0          0.0        0.0            0.0   \n",
       "3         1.0        0.0         1.0          0.0        0.0            0.0   \n",
       "4         0.0        0.0         0.0          0.0        1.0            1.0   \n",
       "\n",
       "   Num_2_Products  Num_3_MoreProducts  Age_18_24  Age_25_34  Age_35_44   \n",
       "0             0.0                 0.0        0.0        0.0        1.0  \\\n",
       "1             0.0                 0.0        0.0        0.0        1.0   \n",
       "2             0.0                 1.0        0.0        0.0        1.0   \n",
       "3             1.0                 0.0        0.0        0.0        1.0   \n",
       "4             0.0                 0.0        0.0        0.0        1.0   \n",
       "\n",
       "   Age_45_54  Age_55_64  Age_65_More  \n",
       "0        0.0        0.0          0.0  \n",
       "1        0.0        0.0          0.0  \n",
       "2        0.0        0.0          0.0  \n",
       "3        0.0        0.0          0.0  \n",
       "4        0.0        0.0          0.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando valores de colunas expecificas em novas e distintas categorias\n",
    "\n",
    "def transforme_onehotenconder(col, col_name, df):\n",
    "    '''\n",
    "    Função para transformando dos valores de colunas expecificas em categorias distintas\n",
    "\n",
    "    :param col: Column Dataframe\n",
    "        Dataframe e coluna a ser transformada.\n",
    "    :param col_name: str\n",
    "        Nome da coluna a ser transformada.        \n",
    "    :param names: list\n",
    "        Lista com nomes para nomeação das colunas transformadas.\n",
    "    :param df: Dataframe\n",
    "        Nome do dataframe.\n",
    "    return df: Dataframe\n",
    "        Dataframe com as colunas transformadas.\n",
    "    '''\n",
    "    ohe = OneHotEncoder().fit_transform(col.values.reshape(-1, 1)).toarray()\n",
    "    names = OneHotEncoder().fit(col.values.reshape(-1, 1)\n",
    "                                ).get_feature_names_out([col_name])\n",
    "    df_ohe = pd.DataFrame(ohe, columns=names)\n",
    "    df = pd.concat([df, df_ohe], axis=1)\n",
    "    return df\n",
    "\n",
    "\n",
    "def transforme_labelenconder(col, name):\n",
    "    '''\n",
    "    Função para transformando dos valores de colunas expecificas em categorias distintas\n",
    "\n",
    "    :param col: Column Dataframe\n",
    "        Dataframe e coluna a ser transformada.\n",
    "    :param name: str\n",
    "        Nome da nova coluna do dataframe.\n",
    "    return df: Dataframe\n",
    "        Dataframe com as colunas transformadas.\n",
    "    '''\n",
    "    df[name] = LabelEncoder().fit_transform(col)\n",
    "    return df\n",
    "\n",
    "\n",
    "df = transforme_onehotenconder(df['CreditScore'], 'Score', df)\n",
    "df = transforme_onehotenconder(df['Geography'], 'Geo', df)\n",
    "df = transforme_onehotenconder(df['NumOfProducts'], 'Num', df)\n",
    "df = transforme_onehotenconder(df['Age'], 'Age', df)\n",
    "df = transforme_labelenconder(df['Gender'], 'Gender')\n",
    "\n",
    "pd.set_option('display.max_columns', 26)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As variáveis categóricas **CreditScore** (Pontuação de crédito), **Geography** (Localização), **Age** (Faixa etária) e **NumOfProducts** (Número de produtos) foram utilizadas para a criação variáveis binárias, cada uma se tornou uma categoria distinta, no caso de **Gender** (Gênero) suas duas categorias foram convertidas em 0 e 1 passando a ser uma variável binária."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Padronização e separação (treino e teste) dos dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de dados divididos em treino e teste\n",
      "\n",
      "╒════════╤════════════╤═════════╕\n",
      "│        │ X          │ y       │\n",
      "╞════════╪════════════╪═════════╡\n",
      "│ Treino │ (8000, 21) │ (8000,) │\n",
      "├────────┼────────────┼─────────┤\n",
      "│ Teste  │ (2000, 21) │ (2000,) │\n",
      "╘════════╧════════════╧═════════╛\n"
     ]
    }
   ],
   "source": [
    "# Separação dos dados de treino e teste\n",
    "\n",
    "# Features, variáveis independentes\n",
    "X = df.drop(['CreditScore', 'Geography', 'Age',\n",
    "            'NumOfProducts', 'Exited'], axis=1)\n",
    "\n",
    "# Padronizando os dados das features\n",
    "std = StandardScaler().fit(X)\n",
    "X = std.transform(X)\n",
    "\n",
    "# Target, variável dependente\n",
    "y = df['Exited'].copy()\n",
    "\n",
    "# Definindo o percentual de dados para teste\n",
    "perc = 0.2\n",
    "\n",
    "# Definindo o random_state\n",
    "seed = 42\n",
    "\n",
    "# Separação dos dados de treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=perc, random_state=seed)\n",
    "\n",
    "print('\\nQuantidade de dados divididos em treino e teste\\n')\n",
    "dict = {\n",
    "    '': ['Treino', 'Teste'],\n",
    "    'X': [X_train.shape, X_test.shape],\n",
    "    'y': [y_train.shape, y_test.shape]\n",
    "}\n",
    "\n",
    "print(tabulate(dict, headers='keys', tablefmt='fancy_grid'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Modelo preditivo: Regressão Logística"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Busca pelos melhores parâmetros e treinamento do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "\n",
      "Melhores parâmetros\n",
      "\n",
      "solver : saga\n",
      "random_state : 42\n",
      "penalty : l1\n",
      "class_weight : balanced\n",
      "C : 5.17947467923121\n"
     ]
    }
   ],
   "source": [
    "def busca_parametros(estimator, scoring, params, cv=5, verbose=1):\n",
    "    '''\n",
    "    Função para efetuar a busca dos melhores parâmetros\n",
    "\n",
    "    :param estimator: str\n",
    "        Estimador que será usado para treinar o modelo.\n",
    "    :param scoring: str\n",
    "        Métrica utilizada para avaliação do modelo.\n",
    "    :param param_grid: dict\n",
    "        Dicionário com uma combinação de valores de hiperparâmetros a serem testados. \n",
    "    :param cv: int, default=5\n",
    "        Número de 'folds' de divisão dos dados na validação cruzada.\n",
    "    :param verbose: int, default=1\n",
    "        Detalhes das mensagens impressas pela função.\n",
    "    return:\n",
    "        Objeto com os melhores parâmetros.\n",
    "    '''\n",
    "    return RandomizedSearchCV(estimator=estimator, scoring=scoring,\n",
    "                              param_distributions=params, cv=cv, verbose=verbose)\n",
    "\n",
    "\n",
    "# Definindo o dicionário de parâmetros de regressão logística\n",
    "params = {\n",
    "    'C': np.logspace(-1, 1),\n",
    "    'class_weight': ['balanced'],\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'random_state': [42],\n",
    "    'solver': ['saga', 'lbfgs'],\n",
    "}\n",
    "\n",
    "# Efetuando a busca pelos melhores parâmetros\n",
    "search_cv_lr = busca_parametros(LogisticRegression(), 'recall', params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "search_cv_lr.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('\\nMelhores parâmetros\\n')\n",
    "\n",
    "for k, v in search_cv_lr.best_params_.items():\n",
    "    print(f'{k} : {v}')\n",
    "\n",
    "best_params = search_cv_lr.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Treinamento e predições do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definição do modelo de regressão logística com os melhores parâmetros\n",
    "logreg = LogisticRegression(**best_params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Predição com os dados de teste\n",
    "pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Métricas de avaliação do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matriz de confusão\n",
      "╒════╤══════╤═════╕\n",
      "│    │    0 │   1 │\n",
      "╞════╪══════╪═════╡\n",
      "│  0 │ 1243 │ 364 │\n",
      "├────┼──────┼─────┤\n",
      "│  1 │   88 │ 305 │\n",
      "╘════╧══════╧═════╛\n"
     ]
    }
   ],
   "source": [
    "def matriz_confusao(y_test, pred):\n",
    "    '''\n",
    "    Função para geração de visualização tabular da matriz de confusão\n",
    "\n",
    "    :param y_test: série - pandas\n",
    "        Série de dados de teste da variável target\n",
    "    :param pred: array - numpy\n",
    "        Array de dados previstos pelo modelo\n",
    "    '''\n",
    "    matrix = confusion_matrix(y_test, pred)\n",
    "    arr = matrix.T\n",
    "    dict = {\n",
    "        '': ['0', '1'],\n",
    "        '0': [i for i in arr[0]],\n",
    "        '1': [i for i in arr[1]]\n",
    "    }\n",
    "    print('\\nMatriz de confusão')\n",
    "    print(tabulate(dict, headers='keys', tablefmt='fancy_grid'))\n",
    "\n",
    "# Matriz de confusão, irá demonstrar visualmente as quantidades de\n",
    "# previsões verdadeiras e que o modelo também identificou dessa forma\n",
    "# os verdadeiros positivos (VP) e os verdadeiros negativos (VN) e\n",
    "# também as previsões erradas, falsos positivos (FP) e negativos (FN).\n",
    "# No caso de churn 1 = saída do banco e 0 = pemanência no banco\n",
    "\n",
    "# ╒════╤══════╤═════╕\n",
    "# │    │    0 │   1 │\n",
    "# ╞════╪══════╪═════╡\n",
    "# │  0 │  VP  │  FP │\n",
    "# ├────┼──────┼─────┤\n",
    "# │  1 │  FN  │  VN │\n",
    "# ╘════╧══════╧═════╛\n",
    "\n",
    "\n",
    "matriz_confusao(y_test, pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════╤═════════════╤══════════╤════════════╕\n",
      "│   Accuracy │   Precision │   Recall │   F1-score │\n",
      "╞════════════╪═════════════╪══════════╪════════════╡\n",
      "│       77.4 │       45.59 │   77.608 │     57.439 │\n",
      "╘════════════╧═════════════╧══════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "def pontuacao_modelo(y_test, pred):\n",
    "    '''\n",
    "    Função para geração da pontuação das métricas: Accuracy, Precision, Recall e F1-score\n",
    "\n",
    "    :param y_test: série - pandas\n",
    "        Série de dados de teste da variável target\n",
    "    :param pred: array - numpy\n",
    "        Array de dados previstos pelo modelo\n",
    "    :return: dict\n",
    "        Dicionário com a pontuação das métricas do modelo\n",
    "    '''    \n",
    "    dict = {\n",
    "        'Accuracy': [round(accuracy_score(y_test, pred)*100, 3)],\n",
    "        'Precision': [round(precision_score(y_test, pred)*100, 3)],\n",
    "        'Recall': [round(recall_score(y_test, pred)*100, 3)],\n",
    "        'F1-score': [round(f1_score(y_test, pred)*100, 3)],\n",
    "    }\n",
    "\n",
    "    return dict\n",
    "\n",
    "# - Accuracy (Acurácia): percentual de predições corretas em relação ao total de\n",
    "#   predições feitas pelo modelo.\n",
    "# - Precision (Precisão): percentual de acerto ao classificar uma predição em verdadeira\n",
    "#   positiva em relação as predições verdadeiras e falsas positivas.\n",
    "# - Recall (Sensibilidade ou Revocação): percentual de acerto ao classificar uma predição\n",
    "#   em verdadeira positiva em relação as predições verdadeiras positivas e falsas negativas.\n",
    "# - F1-score: média harmônica entre 'precision' e 'recall', fornecendo uma visão geral\n",
    "#   do desempenho do modelo.\n",
    "\n",
    "\n",
    "logreg_metrics = pontuacao_modelo(y_test, pred)\n",
    "\n",
    "print(tabulate(logreg_metrics, headers='keys', tablefmt='fancy_grid'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Modelo preditivo: Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Busca pelos melhores parâmetros e treinamento do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "\n",
      "Melhores parâmetros\n",
      "\n",
      "splitter : random\n",
      "random_state : 42\n",
      "max_features : None\n",
      "max_depth : 8\n",
      "criterion : entropy\n",
      "class_weight : balanced\n"
     ]
    }
   ],
   "source": [
    "# Definindo o dicionário de parâmetros de decision tree\n",
    "params = {\n",
    "    'class_weight': ['balanced'],\n",
    "    'criterion': ['entropy'],\n",
    "    'max_depth': np.arange(6, 9),\n",
    "    'max_features': ['auto', None],\n",
    "    'random_state': [42],\n",
    "    'splitter': ['best', 'random'],\n",
    "}\n",
    "\n",
    "# Efetuando a busca pelos melhores parâmetros\n",
    "search_cv_dt = busca_parametros(DecisionTreeClassifier(), 'recall', params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "search_cv_dt.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('\\nMelhores parâmetros\\n')\n",
    "\n",
    "for k, v in search_cv_dt.best_params_.items():\n",
    "    print(f'{k} : {v}')\n",
    "\n",
    "best_params = search_cv_dt.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Treinamento e predições do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definição do modelo de decision tree com os melhores parâmetros\n",
    "dtree = DecisionTreeClassifier(**best_params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "dtree.fit(X_train, y_train)\n",
    "\n",
    "# Predição com os dados de teste\n",
    "pred = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Métricas de avaliação do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matriz de confusão\n",
      "╒════╤══════╤═════╕\n",
      "│    │    0 │   1 │\n",
      "╞════╪══════╪═════╡\n",
      "│  0 │ 1169 │ 438 │\n",
      "├────┼──────┼─────┤\n",
      "│  1 │   89 │ 304 │\n",
      "╘════╧══════╧═════╛\n"
     ]
    }
   ],
   "source": [
    "# Matriz de confusão, irá demonstrar visualmente as quantidades de\n",
    "# previsões verdadeiras e que o modelo também identificou dessa forma\n",
    "# os verdadeiros positivos (VP) e os verdadeiros negativos (VN) e\n",
    "# também as previsões erradas, falsos positivos (FP) e negativos (FN).\n",
    "# No caso de churn 1 = saída do banco e 0 = pemanência no banco\n",
    "\n",
    "# ╒════╤══════╤═════╕\n",
    "# │    │    0 │   1 │\n",
    "# ╞════╪══════╪═════╡\n",
    "# │  0 │  VP  │  FP │\n",
    "# ├────┼──────┼─────┤\n",
    "# │  1 │  FN  │  VN │\n",
    "# ╘════╧══════╧═════╛\n",
    "\n",
    "matriz_confusao(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════╤═════════════╤══════════╤════════════╕\n",
      "│   Accuracy │   Precision │   Recall │   F1-score │\n",
      "╞════════════╪═════════════╪══════════╪════════════╡\n",
      "│      73.65 │       40.97 │   77.354 │     53.568 │\n",
      "╘════════════╧═════════════╧══════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "# - Accuracy (Acurácia): percentual de predições corretas em relação ao total de\n",
    "#   predições feitas pelo modelo.\n",
    "# - Precision (Precisão): percentual de acerto ao classificar uma predição em verdadeira\n",
    "#   positiva em relação as predições verdadeiras e falsas positivas.\n",
    "# - Recall (Sensibilidade ou Revocação): percentual de acerto ao classificar uma predição\n",
    "#   em verdadeira positiva em relação as predições verdadeiras positivas e falsas negativas.\n",
    "# - F1-score: média harmônica entre 'precision' e 'recall', fornecendo uma visão geral\n",
    "#   do desempenho do modelo.\n",
    "\n",
    "dt_metrics = pontuacao_modelo(y_test, pred)\n",
    "\n",
    "print(tabulate(dt_metrics, headers='keys', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Modelo preditivo: Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Busca pelos melhores parâmetros e treinamento do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "\n",
      "Melhores parâmetros\n",
      "\n",
      "random_state : 42\n",
      "n_estimators : 70\n",
      "max_features : auto\n",
      "max_depth : 5\n",
      "criterion : gini\n",
      "class_weight : balanced\n"
     ]
    }
   ],
   "source": [
    "# Definindo o dicionário de parâmetros de random forest\n",
    "params = {\n",
    "    'class_weight': ['balanced'],\n",
    "    'criterion': ['gini'],\n",
    "    'max_depth': np.arange(4, 9),\n",
    "    'max_features': ['auto'],\n",
    "    'n_estimators': np.arange(40, 91, 10),\n",
    "    'random_state': [42],\n",
    "}\n",
    "\n",
    "# Efetuando a busca pelos melhores parâmetros\n",
    "search_cv_rfc = busca_parametros(\n",
    "    RandomForestClassifier(), 'recall', params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "search_cv_rfc.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('\\nMelhores parâmetros\\n')\n",
    "\n",
    "for k, v in search_cv_rfc.best_params_.items():\n",
    "    print(f'{k} : {v}')\n",
    "\n",
    "best_params = search_cv_rfc.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Treinamento e predições do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definição do modelo de random forest com os melhores parâmetros\n",
    "randomforest = RandomForestClassifier(**best_params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "randomforest.fit(X_train, y_train)\n",
    "\n",
    "# Predição com os dados de teste\n",
    "pred = randomforest.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Métricas de avaliação do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matriz de confusão\n",
      "╒════╤══════╤═════╕\n",
      "│    │    0 │   1 │\n",
      "╞════╪══════╪═════╡\n",
      "│  0 │ 1220 │ 387 │\n",
      "├────┼──────┼─────┤\n",
      "│  1 │   88 │ 305 │\n",
      "╘════╧══════╧═════╛\n"
     ]
    }
   ],
   "source": [
    "# Matriz de confusão, irá demonstrar visualmente as quantidades de\n",
    "# previsões verdadeiras e que o modelo também identificou dessa forma\n",
    "# os verdadeiros positivos (VP) e os verdadeiros negativos (VN) e\n",
    "# também as previsões erradas, falsos positivos (FP) e negativos (FN).\n",
    "# No caso de churn 1 = saída do banco e 0 = pemanência no banco\n",
    "\n",
    "# ╒════╤══════╤═════╕\n",
    "# │    │    0 │   1 │\n",
    "# ╞════╪══════╪═════╡\n",
    "# │  0 │  VP  │  FP │\n",
    "# ├────┼──────┼─────┤\n",
    "# │  1 │  FN  │  VN │\n",
    "# ╘════╧══════╧═════╛\n",
    "\n",
    "matriz_confusao(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════╤═════════════╤══════════╤════════════╕\n",
      "│   Accuracy │   Precision │   Recall │   F1-score │\n",
      "╞════════════╪═════════════╪══════════╪════════════╡\n",
      "│      76.25 │      44.075 │   77.608 │     56.221 │\n",
      "╘════════════╧═════════════╧══════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "# - Accuracy (Acurácia): percentual de predições corretas em relação ao total de\n",
    "#   predições feitas pelo modelo.\n",
    "# - Precision (Precisão): percentual de acerto ao classificar uma predição em verdadeira\n",
    "#   positiva em relação as predições verdadeiras e falsas positivas.\n",
    "# - Recall (Sensibilidade ou Revocação): percentual de acerto ao classificar uma predição\n",
    "#   em verdadeira positiva em relação as predições verdadeiras positivas e falsas negativas.\n",
    "# - F1-score: média harmônica entre 'precision' e 'recall', fornecendo uma visão geral\n",
    "#   do desempenho do modelo.\n",
    "\n",
    "rf_metrics = pontuacao_modelo(y_test, pred)\n",
    "\n",
    "print(tabulate(rf_metrics, headers='keys', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Modelo preditivo: SVM (Support Vector Machine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Busca pelos melhores parâmetros e treinamento do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "\n",
      "Melhores parâmetros\n",
      "\n",
      "random_state : 42\n",
      "kernel : poly\n",
      "gamma : 0.20199999999999999\n",
      "degree : 1\n",
      "coef0 : 0.2012\n",
      "class_weight : balanced\n",
      "C : 4.239999999999999\n"
     ]
    }
   ],
   "source": [
    "# Definindo o dicionário de parâmetros de SVM\n",
    "params = {\n",
    "    'C': np.arange(4.2, 4.3, 0.02),\n",
    "    'coef0': np.arange(0.2012, 0.2013, 0.0002),\n",
    "    'degree': [1],\n",
    "    'gamma': np.arange(0.20198, 0.202, 0.00002),\n",
    "    'kernel': ['poly'],\n",
    "    'random_state': [42],\n",
    "    'class_weight': ['balanced'],\n",
    "}\n",
    "\n",
    "\n",
    "# Efetuando a busca pelos melhores parâmetros\n",
    "search_cv_svc = busca_parametros(SVC(), 'recall', params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "search_cv_svc.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('\\nMelhores parâmetros\\n')\n",
    "\n",
    "for k, v in search_cv_svc.best_params_.items():\n",
    "    print(f'{k} : {v}')\n",
    "\n",
    "best_params = search_cv_svc.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Treinamento e predições do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definição do modelo de random forest com os melhores parâmetros\n",
    "svmc = SVC(**best_params)\n",
    "\n",
    "# Treinamento do modelo\n",
    "svmc.fit(X_train, y_train)\n",
    "\n",
    "# Predição com os dados de teste\n",
    "pred = svmc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Métricas de avaliação do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matriz de confusão\n",
      "╒════╤══════╤═════╕\n",
      "│    │    0 │   1 │\n",
      "╞════╪══════╪═════╡\n",
      "│  0 │ 1219 │ 388 │\n",
      "├────┼──────┼─────┤\n",
      "│  1 │   85 │ 308 │\n",
      "╘════╧══════╧═════╛\n"
     ]
    }
   ],
   "source": [
    "# Matriz de confusão, irá demonstrar visualmente as quantidades de\n",
    "# previsões verdadeiras e que o modelo também identificou dessa forma\n",
    "# os verdadeiros positivos (VP) e os verdadeiros negativos (VN) e\n",
    "# também as previsões erradas, falsos positivos (FP) e negativos (FN).\n",
    "# No caso de churn 1 = saída do banco e 0 = pemanência no banco\n",
    "\n",
    "# ╒════╤══════╤═════╕\n",
    "# │    │    0 │   1 │\n",
    "# ╞════╪══════╪═════╡\n",
    "# │  0 │  VP  │  FP │\n",
    "# ├────┼──────┼─────┤\n",
    "# │  1 │  FN  │  VN │\n",
    "# ╘════╧══════╧═════╛\n",
    "\n",
    "matriz_confusao(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════╤═════════════╤══════════╤════════════╕\n",
      "│   Accuracy │   Precision │   Recall │   F1-score │\n",
      "╞════════════╪═════════════╪══════════╪════════════╡\n",
      "│      76.35 │      44.253 │   78.372 │     56.566 │\n",
      "╘════════════╧═════════════╧══════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "# - Accuracy (Acurácia): percentual de predições corretas em relação ao total de\n",
    "#   predições feitas pelo modelo.\n",
    "# - Precision (Precisão): percentual de acerto ao classificar uma predição em verdadeira\n",
    "#   positiva em relação as predições verdadeiras e falsas positivas.\n",
    "# - Recall (Sensibilidade ou Revocação): percentual de acerto ao classificar uma predição\n",
    "#   em verdadeira positiva em relação as predições verdadeiras positivas e falsas negativas.\n",
    "# - F1-score: média harmônica entre 'precision' e 'recall', fornecendo uma visão geral\n",
    "#   do desempenho do modelo.\n",
    "\n",
    "svm_metrics = pontuacao_modelo(y_test, pred)\n",
    "\n",
    "print(tabulate(svm_metrics, headers='keys', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Comparativo gráfico e de métricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando as probabilidades com dados de treino e teste\n",
    "pred_test_prob_lr = search_cv_lr.predict_proba(X_test)[:, 1]\n",
    "pred_train_prob_lr = search_cv_lr.predict_proba(X_train)[:, 1]\n",
    "pred_test_prob_dt = search_cv_dt.predict_proba(X_test)[:, 1]\n",
    "pred_train_prob_dt = search_cv_dt.predict_proba(X_train)[:, 1]\n",
    "pred_test_prob_rfc = search_cv_rfc.predict_proba(X_test)[:, 1]\n",
    "pred_train_prob_rfc = search_cv_rfc.predict_proba(X_train)[:, 1]\n",
    "pred_test_prob_svm = search_cv_svc.decision_function(X_test)\n",
    "pred_train_prob_svm = search_cv_svc.decision_function(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gera fpr (Taxa de falso positivo), tpr (Taxa de falso positivo) e thresholds\n",
    "fpr1, tpr1, thresholds1 = roc_curve(y_test, pred_test_prob_lr)\n",
    "fpr2, tpr2, thresholds2 = roc_curve(y_train, pred_train_prob_lr)\n",
    "fpr3, tpr3, thresholds3 = roc_curve(y_test, pred_test_prob_dt)\n",
    "fpr4, tpr4, thresholds4 = roc_curve(y_train, pred_train_prob_dt)\n",
    "fpr5, tpr5, thresholds5 = roc_curve(y_test, pred_test_prob_rfc)\n",
    "fpr6, tpr6, thresholds6 = roc_curve(y_train, pred_train_prob_rfc)\n",
    "fpr7, tpr7, thresholds5 = roc_curve(y_test, pred_test_prob_svm)\n",
    "fpr8, tpr8, thresholds6 = roc_curve(y_train, pred_train_prob_svm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def graf_roc_auc(fpr1, tpr1, fpr2, tpr2, auc1, auc2, title, ax):\n",
    "    '''\n",
    "    Função para geração de gráfico curva ROC AUC\n",
    "\n",
    "    :param fpr1, fpr2: array\n",
    "        Array de valores que representam a taxa de verdadeiros positivos.\n",
    "    :param tpr1, tpr2: array\n",
    "        Array de valores que representam a taxa de falsos positivos.\n",
    "    :param auc1, auc2: float\n",
    "        Valor que representa a área da curva.\n",
    "    :param title: str\n",
    "        Título dado ao gráfico.\n",
    "    :param ax: Axis\n",
    "        Eixo, posição que o gráfico irá ocupar.\n",
    "    '''\n",
    "    ax.plot([0, 1], [0, 1], '--')\n",
    "    ax.plot(fpr1, tpr1, label='Teste  - AUC = %0.2f' % auc1)\n",
    "    ax.plot(fpr2, tpr2, label='Treino - AUC = %0.2f' % auc2)\n",
    "    ax.tick_params(labelsize=10)\n",
    "    ax.set(xlabel='\\nTaxa de falso positivo (TPR)')\n",
    "    ax.set(ylabel='Taxa de verdadeiro positivo (FPR)')\n",
    "    ax.set_title(title, loc='left')\n",
    "    ax.legend(loc='center right', fontsize=10)\n",
    "\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 15))\n",
    "\n",
    "graf_roc_auc(fpr1, tpr1, fpr2, tpr2, auc(fpr1, tpr1)*100,\n",
    "             auc(fpr2, tpr2)*100, 'Curva ROC - Regressão Logística', ax1)\n",
    "graf_roc_auc(fpr3, tpr3, fpr4, tpr4, auc(fpr3, tpr3)*100,\n",
    "             auc(fpr4, tpr4)*100, 'Curva ROC - Decision Tree', ax2)\n",
    "graf_roc_auc(fpr5, tpr5, fpr6, tpr6, auc(fpr5, tpr5)*100,\n",
    "             auc(fpr6, tpr6)*100, 'Curva ROC - Random Forest', ax3)\n",
    "graf_roc_auc(fpr7, tpr7, fpr8, tpr8, auc(fpr7, tpr7)*100,\n",
    "             auc(fpr8, tpr8)*100, 'Curva ROC - Support Vector Machine', ax4)\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Regressão Logística</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Support Vector Machine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>77.400</td>\n",
       "      <td>73.650</td>\n",
       "      <td>76.250</td>\n",
       "      <td>76.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>45.590</td>\n",
       "      <td>40.970</td>\n",
       "      <td>44.075</td>\n",
       "      <td>44.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>77.608</td>\n",
       "      <td>77.354</td>\n",
       "      <td>77.608</td>\n",
       "      <td>78.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-score</th>\n",
       "      <td>57.439</td>\n",
       "      <td>53.568</td>\n",
       "      <td>56.221</td>\n",
       "      <td>56.566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Regressão Logística  Decision Tree  Random Forest   \n",
       "Accuracy                77.400         73.650         76.250  \\\n",
       "Precision               45.590         40.970         44.075   \n",
       "Recall                  77.608         77.354         77.608   \n",
       "F1-score                57.439         53.568         56.221   \n",
       "\n",
       "           Support Vector Machine  \n",
       "Accuracy                   76.350  \n",
       "Precision                  44.253  \n",
       "Recall                     78.372  \n",
       "F1-score                   56.566  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_logreg = pd.DataFrame(logreg_metrics)\n",
    "df_dt = pd.DataFrame(dt_metrics)\n",
    "df_rf = pd.DataFrame(rf_metrics)\n",
    "df_svm = pd.DataFrame(svm_metrics)\n",
    "df_metrics = pd.concat([df_logreg, df_dt, df_rf, df_svm])\n",
    "df_metrics.index = ['Regressão Logística', 'Decision Tree',\n",
    "                    'Random Forest', 'Support Vector Machine']\n",
    "df_metrics.T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Conclusão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Estes são os principais pontos destacados pela análise:**\n",
    "\n",
    "Da amostra de dados foi observado que aproximadamente 20,37% (taxa de churn) dos clientes decidiram deixar a instituição bancária.\n",
    "\n",
    "Entre as características que se destacam no grupo de churn, são clientes, em sua maioria, com score acima de 500 pontos, classificados como 'good'(bom) e 'excellent'(excelente). Embora em outras faixas etárias há perda clientes é entre 45 até 64 anos em que a situação é mais crítica, com perda aproximada de 50% dos clientes dentro dessa faixa. Os clientes que mais deixam a instituição possuem algum saldo bancário, mas têm baixa atividade em suas contas. Aqueles que têm contratado um ou, principalmente, três produtos bancários têm maior probabilidade de churn. \n",
    "\n",
    "Entre os três países europeus onde residem os clientes, a Alemanha (Germany) é o que está com a taxa de churn mais elevada, aproximadamente 33%.\n",
    "\n",
    "**Sobre os algoritmos testados no modelo:**\n",
    "\n",
    "Dado o contexto, o foco do problema é identificar os clientes que apresentam as características que precedem o encerramento da conta, foi adotado a métrica 'recall' como índice principal de avaliação de eficiência do modelo. Foram testados quatro algoritmos de machine learning: 'Regressão Logística', 'Decision Tree' (Árvore de decisão), 'Random Forest' (Floresta randômica) e 'Support Vector Machine (SVM)' (Máquina de vetores de suporte).\n",
    "\n",
    "Entre os algoritmos testados, o 'Support Vector Machine' se destacou com um recall de cerca de 78,37%. A métrica AUC (área sob a curva), que avalia de modo geral o desempenho do modelo de classificação, apresentou 84,69% nos dados de teste, ligeiramente superior aos 84,31% nos dados de treino.\n",
    "\n",
    "**Considerando as características identificadas dos clientes que saíram do banco, sugeri-se as seguintes estratégias de ação:**\n",
    "\n",
    "Analisar a oferta de produtos para limitar os clientes a, no máximo, dois produtos, reduzindo a complexidade e melhorando a satisfação.\n",
    "\n",
    "Reavaliar e redesenhar produtos com menor aceitação para torná-los mais atrativos e alinhados às necessidades dos clientes.\n",
    "\n",
    "Focar em estratégias de marketing direcionadas para clientes com idade acima de 45 anos, com saldo bancário e baixa atividade na conta bancária, oferecendo incentivos como bônus, prêmios, descontos e programas de fidelidade, estes podem auxiliar na retenção de clientes, lembrando que a comunicação deve adequada para este público específico.\n",
    "\n",
    "Realizar um mapeamento específico para entender as particularidades do mercado alemão, visando reverter a alta taxa de churn.\n",
    "\n",
    "**Em resumo, a análise revelou informações valiosas para aprimorar a eficácia das estratégias para retenção de clientes. O modelo SVM se mostrou a melhor ferramenta para a previsão de churn com um recall satisfatório, proporcionando uma base para as decisões informadas.**"
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